1. Introduction

Environmental, social and governance (ESG) ratings are a framework to score companies based on three broad criteria. Firstly, the environmental dimension considers a firm’s impact on the natural ecosystem. Secondly, the social dimension evaluates the firm’s relationship with its stakeholders: customers, suppliers and communities. Lastly, governance focuses or the board structure and diversity.

Rating the ethics of a company isn’t a new practice. Before ESG, there was corporate social responsibility (CSR) in the 1990s and even prior to that there was socially responsible investing (SRI) in the 1970s ((Krantz, 2024). But neither CSR nor SRI ever gained the recognition ESG has in the past decade. This can be accounted for due to a key difference in their foundations. Measures like CSR and SRI believed and conveyed that businesses should behave ethically because it is morally the correct thing to do. In contrast, ESG ratings aim to score a company’s sustainability and vulnerability to external factors – variables that link directly to monetary returns. As a result, the rigorous set of variables that ESG evaluates makes it an excellent metric to assess a company’s performance through factors that don’t appear on the traditional financial balance sheets. Hence, more and more investors are starting to incorporate these ratings, along with the financial data, to determine a company’s financial return on investment (ROI).

However, the author’s evaluation of rating methodology of Crisil ESG Ratings & Analytics Ltd, a central ESG provider in India, reveals a crucial flaw in current ESG calculation that significantly hinders ESG’s ability to indicate financial performance. Currently a uniform ESG pillar weightage framework of 40% governance 35% environment and 25% social is used across all sectors. While this may be apt for sustainability indication it hinders financial performance indication as the uniform weightage scheme doesn’t account for sector-specific materiality differences. For example, the environment pillar may be far more important for an energy company then for an information technology, for whom governance may be more important. To address this limitation, this section empirically investigates whether sector-specific ESG pillar weighting systems improves the strength of the relationship between ESG scores, and return on capital Employed (ROCE) and aims to find the most optimum ESG weighting systems for 12 sectors. Thus, the research paper aims to answer the question:

What is the optimal sector-specific weighting system of environmental, social, and governance pillars for explaining variation in ROI, and do these optimized models outperform equal-weight ESG frameworks in the Indian market?

The research paper is structured as follows. It begins with a literature review on the importance of considering a company’s qualitative aspects while investing and studies regarding correlation between ESG and ROI. Followed by this is the development of a sector specific ESG weightage scheme through correlations between ESG and return on capital employed (ROCE). The paper concludes by outlining limitations and identifying avenues for future research.

2. Literature Review

2.1 The Efficiency of Traditional Financial Metrics

Investors have relied on and further developed financial analysis for nearly a century. As a result, most indicators of future growth have already been discovered and are heavily used by investors. The Efficient Market Hypothesis, as developed by Fama (1970) states that in a semi-strong efficient market publicly available financial information is rapidly priced in. Consistent with this, Green, Hand, and Zhang (2013) show that most well-documented return predictors lose statistical significance once widely known, as arbitrage activity erodes whatever informational edge they carried.  

These findings collectively suggest that due to most forms of analysis of traditional financial metrics being well known company stocks tend to be priced accordingly. Due to this, it has become increasingly difficult to generate excess returns through these forms of financial analysis and investors must look for other sources of information.

2.2 Unexplained Volatility and the Case for Qualitative Metrics

Traditional financial variables explain less of stock price behavior than is commonly assumed. Roll (1988) documented that financial and industry factors only accounted for a small part of individual stock variation; the remainder being determined by firm specific qualitative factors. This is because financial analysis can’t predict regulatory exposure, reputational events, stakeholder dynamics, and governance failures.

More recent empirical work further proves this trend. Edmans (2011) finds that firms with strong employee satisfaction earn returns of roughly two to three percent annually above market benchmarks and these results persist over time. Similarly, Hartzmark and Sussman (2019) indicate that investor invest more money in response to sustainability signals even in underlying financial variables remain constant. This indicates qualitative firm characteristics influence share prices through channels that conventional financial metrics miss. 

This pattern along with the previous section show that quantitative financial data is priced quickly and efficiently, while qualitative signals are under processed. That gap is where investors can earn excess returns. ESG frameworks are an excellent cumulation of these qualitative indicators. The question is whether it reports this information in a way that is useful for predicting financial performance or does its goal of reporting sustainability cause it to stray from being an indicator of future financial performance.

2.3 Correlation Between ESG and Financial Performance

There is extensive literature on ESG and financial performance. The broad findings can be summarized by Friede, Busch, and Bassen (2015) review of over 2,000 empirical studies, finding that 90% reported a non-negative relationship between ESG scores and financial performance, with the majority showing a positive one. While this isn’t perfect it shows potential.

The study by Khan, Serafeim, and Yoon (2016) distinguish between ESG factor that are financially material to a given industry and those that are not. Firms outperform their competitors when they score well on the material ESG factors while the immaterial ones show no correlation at all.

These two studies, taken together show that currently ESG scores have not reached their financial performance indication potential, primarily due to composite scores not accounting for differing ESG factor materiality across different firms. This is a result of the uniform ESG weightage scheme across all sectors. Hence, this literature review shows that developing a new sector- specific ESG weightage system, so that the final ESG scores give more importance to the more material factors based on the sector, can make ESG an indicator of future financial performance. Furthermore, this would allow investors to identify companies not valued for their qualitative factors allowing them to earn excess returns.  

3. Proposed Sector-Specific Optimized ESG Weighting scheme and Correlation with ROCE

3.1 Methodology

3.11 Variables

Table 3.11  Variables Note. Author made

3.12 Data Sourcing and Sector Classification 

The study uses ESG scores published by Crisil for the financial years 2023 and 2024. The sample comprises 748 listed Indian companies spanning 12 sectors. The 12 sectors were decided to ensure simplicity as well as homogeneity in regard to main pillar importance in each sector.

The following sector classification has been used:

Table 3.12  Sector classification Note. Author made

Firm-level ROCE data was collected from two independent, reliable financial databases: “Screener” and “Equitymaster”. This ensured consistency and cross-verification of financial performance data. The ESG data for composite scores and main pillar scores was obtained from Crisil’s archives. Companies with incomplete ESG pillar scores or missing ROCE data were excluded.

3.13 Framework of Quantitative Analysis

The strength of the relationship between ESG scores and ROCE was measured using Spearman’s rank correlation coefficient ( . This is beneficial as outliers are extremely common in sectoral ROCE data, making it important to reduce their impact of empirical results as done by this metric. All correlations were computed on a sector-by-sector basis to account for structural heterogeneity across industries and correlations were reported to an accuracy of two decimal places to enforce accuracy.

A python program was developed to generate sector specific main pillar weightage systems that were more optimized ROCE indicators based on Crisil’s individual main pillar scores, environmental, social and governance. For each firm i in sector s, the composite ESG score was defined as:

subject to:

Two sets of weightage systems were generated:

1. Unconstrained Model:

The objective of this model was to generate weightage systems that maximized ESG’s correlation with ROCE. No lower bound for a main pillars weightage was set, which means the model could deliver a weightage scheme in which the optimum weightage for a main pillar for a sector is 0%.

1. Constrained Model:

The objective of this model remains the same as the unconstrained model but a lower bound for a main pillars optimum weight was set at 15%. By setting this minimum at 15% the conceptual integrity of ESG evaluation is preserved.

For each sector, three correlations were calculated to draw comparisons:

1. Crisil’s composite ESG score and ROCE
2. Unconstrained optimized ESG score and ROCE
3. Constrained optimized ESG score and ROCE

3.2 Baseline: Correlation Under Crisil’s Fixed Weightage Scheme

Crisil’s ESG framework calculates composite scores through a uniform weighting structure across sectors. Governance with the highest weightage (40%), followed by environmental (35%), and lastly social (25%). This allows comparability but assumes that environmental, social, and governance risks affect all industries equally. To test this assumption, sector-wise correlations between ESG scores (under Crisil’s weighting scheme) and ROCE were computed.

Table 3.2 Sector-Wise Correlation Between ESG and ROCE (Crisil Weightage Scheme) Note. Author’s calculations based on raw data from souces:18, 19, 20 and 21

The results demonstrate substantial heterogeneity not only in the magnitude but also in the economic interpretation of the ESG–ROCE relationship across sectors. 

Industries such as communication services, informational technology demonstrated strong positive correlations due to the structural importance of governance quality and regulatory compliance (governance and environment have high weightages in Crisil’s weightage scheme). In contrast, financials consumer discretionary – auto (-0.02) show near-zero correlations. This can be accounted to the fact that in financials, ROCE may be influenced more heavily by macroeconomic cycles driven through context rather than ESG scoring differentials. As for auto manufacturing, majority of the companies aren’t automobile brands themselves but brand that produce automobile parts. Due to the massive information asymmetries in India and more lenient production rules these companies tend to a low correlation with ESG in present. Additionally, cyclical demand fluctuations, technological transitions (e.g., EV shifts), commodity price volatility, and high fixed-cost structures materially shape ROCE. For this very reason, it was separated from the consumer discretionary sector to not obscure the correlation of the rest of the companies in that sector.

Relatively moderate positive correlations in energy, industrials, healthcare, consumer staples, consumer discretionary – durables and services, materials, real estate and utilities) suggest partial alignment but also potential miss weighting of pillars. For instance, environmental factors may be economically dominant in energy, but Crisil’s model still gives the environmental pillar the lowest weightage.

Collectively, these patterns indicate Crisil’s weightage scheme is able to predict financial performance more strongly for companies that whose ESG factor materiality aligns with Crisil’s weightage scheme. The findings therefore provide substantive economic reasoning for investigating sector-optimized weighting models rather than relying on a single standardized ESG structure.

3.3 Optimized ESG Weightage Scheme 

To test whether sectoral customization improves explanatory power, ESG weights were optimized to maximize correlation with ROCE. A python program was used to come up with unconstrained and constrained weightage schemes to maximize ESG’s correlation with ROCE.

Table 3.31  Sector-Wise Unconstrained Optimization System’s Correlation with ROCE Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

As the results indicate, the unconstrained weighting system results in theory significant jumps in ESG’s correlation with ROCE. However, in practice these could vary significantly based on the data set used and since a lot of weightages for environmental, social or governance pillars are near zero in some sectors this weighting scheme pulls away from the very concept of ESG. Thus, it is of more relevance to determine and the optimized weighting system that is constrained at a minimum of 15% weightage for each main pillar as this keeps the core, multidimensional concept of ESG intact while allowing for a generalization as the most appropriate sector-specific weightage scheme in the Indian Market.

Table 3.32  Sector-Wise Constrained Optimization System (Minimum 15%) Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

Under the constrained optimization system, communication services record the strongest correlation under the constrained framework, reflecting a very tight alignment between ESG performance and capital efficiency. Information technology and energy also demonstrate relatively strong positive associations due to the government-dominant allocations (G ≈ 0.68) that influence oversight, capital discipline, and regulatory management. Moderate correlations are observed in consumer staples, real estate, industrials, and utilities as while the optimization of weightages accounts for sector materiality ESG factors there are sector specific aspect in each that are not accounted by ESG factors.

However, financials and consumer discretionary – auto continue to exhibit weak correlations despite governance-heavy weightings (0.70 in both sectors). This suggests that even when pillar emphasis is adjusted to reflect sector logic, ESG scores may not translate strongly into ROCE variation due to reasons explained in detail in 3.2 which ESG factors simply can’t account for. ESG performance may influence long-term strategic positioning or risk mitigation in these sectors.

These findings indicate that while sector-specific optimization improves statistical alignment in several industries with most demonstration a strong or moderate correlation it cannot uniformly overcome structural characteristics that weaken the ESG–ROCE transmission mechanism. The constrained framework proposed therefore demonstrates the strengths and limits of ESG factor’s ability to indicate financial performance: its predictive power is considerable when firms are affected by the qualitative factors ESG reports but gets weaker when financial outcomes are driven primarily by macroeconomic or capital-cycle forces.

3.4 Comparative Performance: Crisil vs Proposed Sector-Specific Weighting Scheme

Table 3.4 Comparative Performance: Crisi’s Weightage scheme vs Author’s Proposed Weightage Scheme  Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

As indicated by the data, applying sector specific weightages drastically improves the correlation between ESG and ROCE across most sectors. The most significant improvement is seen in real estate (consumer discretionary – auto and financials have high percentage improvement but correlation is still near-zero) as the proposed weightage scheme gives allows for governance to drive the composite score as in India it is the most material to financial performance. Energy Consumer Staples and Utilities have also shown strong improvements due to improved sector-specific materiality considerations by the proposed weightage scheme. Moderate improvements can be seen in Consumer Discretionary – Durables & Services, Information Technology, Healthcare, Materials. Only two sectors Communication Services and Industrials showed small improvements as for both Crisil’s weightage scheme fairly rated these companies through their weightage system leaving little room for improvement.  

Taken together the table demonstrates the need for sector-specific weightage schemes for ESG calculation. The proposed weightage scheme gives importance to ESG pillars based on their sector specific materiality to financial performance allowing for ESG to become a substantial indicator of financial performance.

3.5 How to Calculate ESG Scores for Multi-Sectorial Companies.

The weightage schemes this research paper has presented makes it clear as to the method used to calculate the most reliable ESG ratings for most companies. However, there are certain companies who don’t entirely fit into one sector. An example of this is reliance limited.

For a multi-sector company like Reliance Industries Limited, the ESG score should be a weighted sum of the scores calculated using each sector’s optimal weightage.

The General Formula:

Where:

• ​W_sector is the percentage of revenue from that sector
• ESG_sector is the ESG score calculated using the optimal weights for that specific sector.

4. Strengths and Limitations of the Quantitative Analysis

This study has several methodological strengths. Data used is from reputable sources: Screener, Equitymaster and Crisil Ratings and Analytics. Furthermore, the study only uses data available to everyone publicly which makes this highly relevant for retail investors and smaller firms, not just larger corporations. The clear segregation into 12 sectors makes using the proposed weightage scheme straightforward and the system also accounts for multi-sectoral companies. Additionally, the use of ROCE as the dependent variable strengthens the analysis by focusing on operational capital efficiency instead of market-based performance. This aligns ESG evaluation with fundamental financial productivity. Statistically, the Spearman’s correlation was used to reduce weightage of outliers as when data is limited outliers could significantly harm reliability of the conclusion. Lastly, the implementation of both unconstrained and constrained optimization frameworks allows for differentiation between purely statistical maximization and theoretically grounded calibration. 

However, certain limitations must be acknowledged. The study is conducted over a limited period, due to lack of publicly available ESG data in India. This potentially restricts insights into the stability of optimized weightages overtime and across economic cycles. Secondly, ESG scores are published annually as an aggregate which doesn’t account for intra-year variation in sustainability performance that could have affected ROCE. Sizes of sector-level datasets may have also affected overall results. Most importantly, the dataset is exclusively composed of Indian companies. As a results, the proposed weightage scheme is only directly applicable to India and potentially other economies at similar stages of development. This is because the sector-specific weightage system gives importance to the pillars based on their materiality due to the Indian context such as local information asymmetries and the tendency of Indian government to impose regulation.  For example, it is likely that a highly developed country has low information asymmetry regarding business practices (between businesses and consumers), whereas in an underdeveloped or develop this asymmetry may be higher. Therefore, the optimized weightage schemes identified in this study are most appropriate for India or countries with comparable institutional and developmental characteristics. Due to this same concept, these ESG weightage systems are also only relevant in India while it remains in its current broad development stage.

5. Concluding Remarks

This research has examined the evolving role of ESG as an indicator of corporate financial performance within the Indian context. While frameworks like those provided by Crisil have significantly improved the accessibility of sustainability data, this study identifies a critical gap in the application of uniform, non-sector-specific weighting systems. Such models fail to capture the unique operational parameters and types of risks inherent to different industries, hence diluting the correlation between ESG performance and financial returns.

Through empirical analysis and quantitative modeling, this paper produces a sector-specific weighting scheme, tailored to the environmental, social, and governance priorities of specific industries, that serves as a far more robust indicator of ROCE. By improving the framework of existing ESG practices as financial performance indicators this paper aims for ESG ratings to be more widely used alongside financial metrics while predicting the future share performance of a firm. 

The proposed framework also carries broader implications for markets and society. By providing a stronger and more consistent correlation between ESG performance and financial returns, this framework increases the likelihood that sustainability considerations will be integrated into investment decision-making. This could lead to qualitative factors regarding companies’ sustainability and ethics also being priced in (as financial metrics are now) in the long run. Once this has happened more companies are likely to make most decisions keeping consequences regarding sustainability and ethics in mind with the profit-maximizing goal of improving their stock price. This will result in huge positive externalities as more firms demonstrate responsible environmental stewardship, ethical governance structures, and socially sustainable practices. In the long run, such alignment between financial incentives and sustainability outcomes has the potential to reinforce ethical corporate conduct and hence have considerable benefits to society from an economic and social perspective. 

6. Future Scope for Research

The author intends to pursue further research and publication in the areas outlined below, building upon the analytical framework developed in this study.

1) Cross-Country Optimal Weightage Comparative Studies: Applying this optimization framework across multiple countries at varying stages of development would determine whether sectoral ESG relevance is development dependent. It would provide insights into how differing governments’ regulation tendencies and information asymmetries across the development spectrum affect the ideal ESG weightages for financial performance indication. Moreover, conclusions from such a study could be significant as it could lead to a significant alteration in the methodology of major multinational ESG providers like MSCI transforming the ESG landscape completely and leading to much higher correlations between ESG and ROI globally. 

2) Multi-Year Longitudinal Analysis Across Economic Cycles: Extending this study over a larger time frame once there is Indian ESG data available over more years would make this system far more reliable and would also allow for insights into how the optimum ESG weightages change across sectors through economic cycles. 

3) High-Frequency or Quarterly ESG Data: If quarterly ESG metrics become available in India, studies on how ESG shocks influence ROCE within shorter intervals. This would be useful to determine the speed of ESG-financial transmission mechanisms which would be useful for investors as it will indicate the average time they have to act on information reported by ESG.

4) ESG in Stress Periods: Since ESG is primarily an indicator of sustainability, such a study would indicate whether firms with higher ESG ratings are able to outperform competitors during stress periods.  

References

“Crisil ESG Ratings & Analytics Limited.” CRISIL, 2021, www.crisilesg.com/en/home.html.

“Crisil ESG Ratings & Analytics Ltd Gets SEBI Nod to Offer ESG Ratings.” CRISIL, 2024, www.crisilratings.com/en/home/newsroom/press-releases/2024/04/crisil-esg-ratings-and-analytics-limited-gets-sebi-nod-to-offer-esg-ratings.html.

Edmans, Alex. “Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices.” Journal of Financial Economics, vol. 101, no. 3, 2011, pp. 621-640.

Equitymaster – Get Unbiased Equity Research on Indian Stocks and Share Market Updates for Value Investing in India. Equitymaster, 2025, www.equitymaster.com.

“ESG Ratings 2023.” CRISIL, 2023, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2023.html.

“ESG Ratings Criteria and Methodology.” CRISIL, 2025, www.crisilratings.com/content/dam/crisilesg/criteria-and-methodology-for-crisil-esg-ratings/archives/criteria-and-methodology-for-esg-ratings-feb-2025.pdf.

“ESG Ratings List 2024.” CRISIL, 2024, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2024.html.

Fama, Eugene F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance, vol. 25, no. 2, 1970, pp. 383-417.

Friede, Gunnar, et al. “ESG and Financial Performance: Aggregated Evidence from More than 2000 Empirical Studies.” Journal of Sustainable Finance & Investment, vol. 5, no. 4, 2015, pp. 210-233.

Green, Jeremiah, et al. “The Supraview of Return Predictive Signals.” Review of Accounting Studies, vol. 18, no. 3, 2013, pp. 692-730.

Hartzmark, Samuel M., and Abigail B. Sussman. “Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows.” The Journal of Finance, vol. 74, no. 6, 2019, pp. 2789-2837.

Khan, Mozaffar, et al. “Corporate Sustainability: First Evidence on Materiality.” The Accounting Review, vol. 91, no. 6, 2016, pp. 1697-1724.

Krantz, Tim. “The History of Environmental Social and Governance (ESG).” IBM, 8 Feb. 2024, www.ibm.com/think/topics/environmental-social-and-governance-history.

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“Stock Screener and Fundamental Analysis Tool for Indian Stocks – Screener.” Screener, www.screener.in.

About the author

Nimay Shah

Nimay is an IB Diploma student with a keen interest in finance, economics, and quantitative research. His work focuses on applying mathematical and data-driven methods to improve investment analysis, particularly in sustainable finance and ESG.

Through this research, Nimay aims to bridge the gap between academic theory and practical investing by developing more effective approaches to evaluating corporate performance. He hopes to pursue business, finance, or economics at university and continue exploring the intersection of data, markets, and sustainability.

The post ESG–ROI Linkages in India: Developing Sector-Specific Weightage Frameworks for Optimized Financial Performance Indication appeared first on Exploratio Journal.

Abstract

India’s Unified Payments Interface (UPI) has reshaped the country’s financial landscape, establishing India as the global leader in real-time digital payment adoption. However, this rapid expansion has been accompanied by a corresponding rise in digital payment fraud, raising concerns for consumer protection and the effectiveness of structural systems. Applying Routine Activity Theory, the paper analyzes literature to explain this outcome as the product of a convergence between motivated offenders, suitable targets, and absence of capable guardians. Recent studies show that victimization remains consistent despite awareness of digital payment frauds. Educated and technologically aware users continue to fall victim to complex psychological manipulation. Once targeted, victimization largely depends on the exploitation of cognitive biases. Institutional guardianship remains India’s most persistent weak point. Victims recover a fraction of the stolen funds, redressal mechanisms are largely unsatisfactory, and legal loopholes leave a majority of victims outside of the protection system. The paper concludes with recommendations to put a stop to legal loopholes, modernize legal frameworks, and add an additional layer of protection at the point of transaction rather than relying solely on user vigilance.

Introduction

India’s creation and adoption of the Unified Payment Interface (UPI) is an example of one of the most rapid financial digitisation initiatives in recent history, expanding from 20 million transactions in  FY 2016-17 to over 241.62 billion in FY 2025-26 (Ministry of Finance, 2026). This represents a 12,000-fold increase.  In 2012, over 87% of all transactions in India were conducted in cash, and fewer than 10% of the population had ever used any non-cash payment instrument (Iyer, 2017). Today, India accounts for 48.5% of worldwide real-time digital payment transactions, making it the global leader in digital payments (Singh and Agarwal, 2025). Furthermore, of the total retail digital payment transactions, UPI accounted for over 81% of them, emerging as the largest real-time retail payment system globally (Ministry of Finance, 2025). Between April 2016 (at launch) and March 2026, the number of banks live on UPI increased from 21 to 703. These statistics prove that UPI has evolved from merely being a means of payment to a financial infrastructure critical to India’s economy.

However, the same expansion that produced this success also generated a fraud crisis of proportionate scale. According to the Association of Certified Fraud Examiners, fraud is any activity that relies on deception in order to achieve a gain. Fraud becomes a crime when it is a knowing misrepresentation of the truth or concealment of a material fact to induce another to act to his or her detriment. Within the UPI ecosystem, fraud commonly involves social engineering and other deceptive practices that manipulate users into authorising transactions or disclosing sensitive credentials. The number of reported UPI fraud cases rose from 195,000 in 2020-21 to 7,25,000 in 2022-23, while the amount lost increased from ₹111 crore (approximately $15 million as per the exchange rates for FY 2020-21) to ₹573 crore (approximately $72.4 million as per the exchange rates for FY 2022-23) in the same period (Agrawal, 2026). Across all digital payment modes, fraud cases increased from 1,19,699 in 2020-21 to 14,57,000 in 2023-24, recording a compound annual growth rate (CAGR) of 130.03%, which outpaces the 40-105% annual growth in UPI transactions (Kumar and Dharshan, 2026). Iyer (2017) documented that card fraud in India increased in direct proportion to card adoption throughout 2011–2016, with losses doubling from ₹4 billion ($89.58 million as per the 2011 exchange rates) to ₹7.68 billion ($113.56 million as per the 2016 exchange rates) as card usage expanded. Moreover,  according to Wadkar et al. (2025), UPI fraud accounts for 47.25% of all cybercrime cases reported in India since 2020.Understanding why fraud has scaled at this rate requires examining not only the architecture of UPI itself, but the behavioral and structural conditions that enable exploitation. Statistics alone cannot explain why UPI fraud has scaled at this rate or why users remain vulnerable despite high awareness. This paper applies Routine Activity Theory (Cohen and Felson, 1979) to examine the structural and behavioral conditions that enable this crisis.

Theoretical Framework:

According to Routine Activity Theory (Cohen and Felson, 1979), a criminal event occurs when three elements converge in both time and space: a motivated offender who perceives an opportunity for gain, a suitable target, and the absence of a capable guardian who can prevent the crime. Additionally, Cohen and Felson argue that changes in routine activities, such as shifts in work patterns, leisure activities, and household arrangements, impact the convergence of motivated offenders, suitable targets, and the lack of capable guardians. This, in turn, affects crime rates. Scholars have since extended RAT beyond street crime to fraud contexts. Holtfreter et al. (2008) conducted a study that extended the Routine Activity Theory (RAT) to the context of consumer fraud victimization,  using a sample of 922 participants. Their study found that a one-unit increase in the buying routine index (a measure of how frequently respondents engaged in consumer activities such as online shopping) was associated with a 61% increase in the odds of being targeted for fraud.

Holtfreter et al. (2008) also tested Self-Control Theory alongside RAT, finding that while low self-control significantly increased the probability that a targeted individual would become a victim (302% per unit increase), it had no effect on whether the individual was targeted in the first place. This targeting/victimization distinction is directly relevant to the UPI fraud paradox examined later in this paper: why do 52.5% of users become victims despite 96.2% awareness? Once targeted, a user’s capacity for deliberate, cautious decision-making (rather than prior knowledge alone) determines victimization outcomes.

DeLiema (2018) reinforces this by applying RAT to study financial fraud against older adults, examining 53 cases of financial exploitation. The study investigated interpersonal financial exploitation amongst adults aged 65 years or older. The research found that social isolation, which means a lack of capable guardians like trusted friends, family, or oversight from institutions, was the main factor allowing these crimes to happen. The study also highlighted that perpetrators actively worked to create this isolation. They limited the victims’ social interactions to increase dependence before taking advantage of them.

Scale and Nature of the Problem:

Before examining specific fraud techniques, it is important to establish why the architecture of the Unified Payments Interface (UPI) itself creates a structurally suitable target for fraud. Understanding these design characteristics provides the foundation for explaining how offenders exploit the system. The simple and easy-to-use nature of UPI makes it an appealing option for online banking: the user’s phone number is linked to their bank accounts, debit cards, and credit cards through a platform, which allows for instant transactions at no cost. Singh and Agarwal (2025) state that while this attribute provides convenience, it also means that a single phone number becomes the single point of compromise that gives the attacker access to all of the user’s financial information simultaneously.

Sharma, Matharu and Sinha (2026) surveyed 183 UPI users in the Shimla district. As the study was confined to a single district and employed a relatively small sample, its findings have limited generalisability to the wider Indian population. The study found that although 96.2% of respondents reported being aware of UPI fraud, 52.5% had nevertheless become victims. The authors concluded that awareness alone may not translate into safe behaviour, highlighting behavioural factors such as overconfidence and susceptibility to social engineering. By contrast, Singh and Agarwal (2025) surveyed 280 respondents in Varanasi, excluded 30 inconsistent responses, and analysed 250 valid cases. Using the Friedman ranking test, the study identified awareness (mean rank = 6.72) and education (mean rank = 6.42) as the most effective countermeasures against UPI fraud. The two studies are not necessarily incompatible. Rather, when taken together, they suggest that awareness and education are seen as the best defences, but they do not always prevent fraud in practice. The difference in district, sample size, and research design may also contribute to the contrast.

In a comparative study of India, Sweden, and the United States of America, it was found that losses due to card fraud in India grew at an average annual rate of 12.19% between 2011 and 2016 (Iyer, 2017). Sweden, however, despite having the highest percentage of card transactions among the countries studied, demonstrated the lowest average annual fraud growth rate of 4.83%, which is nearly a third of the growth rate in India (Iyer, 2017). According to the study, India’s higher growth rate could be a result of card payment systems being introduced relatively recently, when compared to Sweden and the USA.

Furthermore, Sweden’s digital payment ecosystem functions within a complex regulatory framework managed by Finansinspektionen, the Swedish Financial Supervisory Authority. This agency sets security rules, checks compliance, and inspects payment platforms. Digital payment providers must follow the Payment Services Act, anti-money laundering laws, counter-terrorist financing regulations, the General Data Protection Regulation, and the Payment Card Industry Data Security Standard. They are also required to implement strong authentication, encryption, tokenization, and transaction monitoring systems as necessary security measures (Abraham et al., 2023).

According to Mukhopadhyay and Mukhopadhyay (2024), despite becoming victims, a large proportion of people were unwilling to leave UPI as a means of payment. Only 7.4% of fraud victims switched entirely to cash, 8.8% reduced their use of digital payments, and 51.5% reported no reduction in UPI usage. The Pearson correlation coefficient between fraud victimisation and reduced UPI usage is -0.408, a moderate negative relationship that confirms victims are largely unwilling to exit the platform. Holtfreter et al. (2008) demonstrated that a 1-unit increase in routine consumer activity, such as purchasing something online, translates into a 61% increase in the odds of being targeted for fraud. The data indicates that since victims themselves are substantially reluctant to use different means of payment, people will almost always be exposed to and within the environment in which motivated offenders operate.

It is difficult to determine the true scale of the problem due to issues regarding under-reporting. Mukhopadhyay and Mukhopadhyay (2024) found that 20.6% of respondents who had experienced UPI fraud did not report the incident to the concerned authorities. This finding is based on a survey of 70 valid respondents conducted over a five-day period (20–25 April 2023). Sirajutheen and Abirami (2026) found that, during the “Golden Hour” (the critical period immediately following a cyber fraud during which prompt reporting maximizes the likelihood of freezing fraudulent transactions and recovering funds) of a fraud incident, 40% of fraud victims would incorrectly call their bank’s IVR (Interactive Voice Response) helpline instead of the National Cyber Crime Helpline (1930). Meanwhile, post-fraud grievance redressal mechanisms prove to be largely ineffective, with 33% of people finding them unsatisfactory and only 6% describing the bank’s response as very satisfactory (Sirajutheen and Abirami, 2026). While under-reporting is an issue in and of itself, the low recovery rate of assets further highlights the shortcomings of guardianship. According to Sharma and Singh (2024), ₹2294 crore ($274.14 million as per the 2024 exchange rates) was lost in cyber fraud, but only ₹57 crore ($6.81 million as per the 2024 exchange rates) was successfully recovered, showing a recovery rate of 2.5%. The low recovery rate indicates that the existing institutional mechanisms for recovering funds after UPI fraud are objectively ineffective. Such limited recovery could reduce the effectiveness of formal reporting systems and suggests that current guardianship mechanisms provide insufficient protection to victims.

How Fraudsters Operate and Why People Become Victims

One of the most prominent modalities of UPI fraud is social engineering (the psychological manipulation of the target’s behavior). Sharma and Singh (2024) present social engineering attacks in which fraudsters impersonate trusted authorities and create a sense of urgency regarding issues such as KYC compliance, blocked accounts, or failed transactions. Similarly, Wadkar et al. (2025) studied two UPI fraud cases involving educated and technologically aware victims who were deceived by messages warning of imminent electricity disconnection and directing them to malicious links. These studies demonstrate that digital payment fraud takes advantage of psychological pressure and trust rather than being a result of mere technological ignorance.

Other prevalent techniques include phishing and vishing (voice phishing), fake QR code scams, fraudulent payment request schemes in which fraudsters send payment requests disguised as payment receipts, and SIM swap attacks that redirect incoming OTPs to the fraudster’s device (Sharma and Singh, 2024; Singh and Agarwal, 2025). Sirajutheen and Abirami (2026) report that while awareness of phishing (67%) and vishing (70%) is relatively high among UPI users, awareness of more complex schemes (such as mule account operations, in which victims unknowingly launder money by renting their bank accounts to fraudsters) is relatively low, with only 6% of respondents being familiar with the term.

Singh and Agarwal (2025) point out that the platform connects a debit card, credit card, and bank account to one phone number. This means that one successful manipulation gives the offender access to several financial tools at once. Sharma and Singh (2024) also mention that digitization has not only widened the market for fraud but has also made criminals more skilled. Offenders keep adapting their techniques to keep up with the changes in the platforms they target.

Survey data from Sharma, Matharu, and Sinha (2026), drawn from 183 UPI users in Shimla district, indicates that 68.3% of fraud victims are graduates or postgraduates, challenging the assumption that higher educational attainment inherently protects individuals from digital financial fraud. As established above, 96.2% of the same respondents reported being aware of UPI fraud, yet 52.5% had nevertheless experienced victimization (Sharma et al., 2026). Singh and Agarwal (2025), meanwhile, identified fraud awareness as the highest-ranked preventive measure among surveyed stakeholders, assigning it a mean rank of 6.72. Taken together, these findings expose a structural gap: awareness and education are the most widely recommended individual-level defences against digital payment fraud, yet their presence has failed to prevent victimization at scale. Sirajutheen and Abirami (2026) conducted a correlation analysis that revealed a strong positive relationship between the frequency of digital usage and victimization (r = .825). This finding aligns with the Routine Activity Theory, which suggests that increased routine exposure creates greater opportunities for motivated offenders. Holtfreter et al. (2008), in a study of 922 adults, established a crucial distinction between the determinants of fraud targeting and the determinants of fraud victimization. Their logistic regression models demonstrated that routine consumer activity predicted whether individuals were selected as targets, producing a 61% increase in targeting odds per unit increase in activity. Individual traits, however, did not affect targeting at all.

Holtfreter et al. (2008) established that while RAT explains targeting, individual factors determine whether a targeted person becomes a victim. In the UPI context, fraudsters systematically exploit cognitive biases.

Several cognitive and social biases contribute to vulnerability to fraud. Overconfidence bias leads users to underestimate fraud risk and disregard security protocols and updates. FOMO (Fear of Missing Out) is exploited through fabricated urgency, such as claims of limited offers or imminent account suspension, to push users toward hasty decisions. Reciprocity bias, along with sympathy exploitation, is described as common in romance and distress scams. Confirmation bias, anchoring, authority bias, and groupthink are identified as social influences that interact with these factors to further increase susceptibility to manipulation and fraud (Sharma, Matharu, & Sinha, 2026).[1]  This reveals that exploiting the architecture of human decision-making has a vital role in achieving UPI fraud.

The comparison with DeLiema’s study is instructive here. In elder fraud, cognitive vulnerability arose from neurological decline, which is a form of diminished capacity that reduced the victim’s ability to evaluate threats accurately (DeLiema, 2018). In UPI fraud, cognitive vulnerability is manufactured when fraudsters design scenarios that activate the biases listed above, effectively inducing a temporary incapacity in otherwise competent individuals. The complex nature of the attack is what converts the targeted individual into a victim, regardless of education and awareness.

Victims and non-victims attribute fraud causation differently. Victims emphasize financial illiteracy and technological complexity, while non-victims emphasize cybersecurity vulnerabilities and slow investigation. This divergence suggests that victimization is associated with a shift in how individuals understand the threat (Singh and Agarwal, 2025).

Systematic Failure

The institutional response to UPI fraud victimization has largely been a refusal to accept responsibility. Wadkar et al. (2025) document that when two fraud victims reached out to a pseudonymous Bank ABC, the bank maintained that the transactions were authorized because the victims had shared their UPI PIN or one-time password (OTP). This was the case regardless of whether the victims did so under deceptive circumstances, leading the bank to conclude that the transactions were non-refundable. The RBI’s own framework reinforces this position, specifying that if a transaction occurs as a result of the customer sharing their credentials, the loss rests with the customer until the bank is formally notified (Wadkar et al., 2025).

Sharma and Singh (2024) state that social engineering attacks trick customers into disclosing information by impersonating legitimate authorities and manufacturing crises. However, the victim does not share credentials negligently. Rather, they are deceived into doing so. Yet the legal framework treats engineered deception and careless disclosure as equivalent, and holds the victim equally liable in both cases.

One of the most prominent legal gaps in India’s UPI consumer protection framework is the APP (Authorized Push Payment) loophole. According to Agrawal (2026), as per RBI[1] ’s 2017 Consumer Protection Circular, banks are obligated to cover losses from unauthorized transactions if reported within three days. APP fraud, however, operates by deceiving the victim into authorizing the transfer themselves, so the transaction is technically classified as authorized and falls entirely outside the protection framework.

The failure to provide an appropriate institutional response is evident in the recovery statistics. In 2022, the recovery rate was determined to be 2.5% (Sharma and Singh, 2024). This statistic highlights a significant failure of systemic guardianship. Furthermore, it was revealed that only 6% of respondents rated their bank’s grievance redressal process as “very satisfactory,” while 33% found it “unsatisfactory.” These perceptions align with the recovery outcomes, indicating that the current system is not effectively providing reliable protection  Sirajutheen and Abirami, 2026, p.301).

The 20.6% non-reporting rate among UPI fraud victims is not clear evidence of a lack of action. The authors suggest two possible explanations for non-reporting: that the grievance redressal mechanism may be difficult for victims to access, or that victims may lack sufficient awareness of how and where to report UPI fraud (Mukhopadhyay and Mukhopadhyay, 2024).

Furthermore, in elder fraud cases, capable guardians (financial institutions, social services, family members) consistently failed to identify victimization until losses had already occurred, at which point intervention was too late (DeLiema, 2018). When we look at UPI, the transactions are much more rapid, and the window for intervention is narrower. Therefore, the convenience of quick transactions also brings forth the issue of timely prevention by guardians.

Recommendations

The most significant reform that Indian policymakers can make is to close the APP liability loophole. ​​The United Kingdom introduced a mandatory reimbursement of up to £85,000 (approximately $113,000 as per the 2026 exchange rates) for APP fraud, requiring payment service providers to compensate eligible victims and strengthening incentives for fraud prevention (Agrarwal, 2026). A comparable reform in the Indian context would require banks and payment platforms to compensate victims of social engineering fraud, removing the current presumption that authorized transfer equals voluntary consent.

Legal frameworks governing digital payments must evolve continuously to match the pace of technological change (Ballaji, 2024). Amending the Information Technology Act (2000) and the Payment Settlement Systems Act (2007) is recommended to address evolving forms of digital payment fraud, support a fraud management framework, and implement AI for fraud detection (Banerjee et. al, 2025).

It has been established that a majority of users aware of digital payment fraud have become victims (Sharma, Matharu, and Sinha, 2026). Therefore, policymakers should design interventions that reduce the probability of a person falling victim to fraud at the moment of the transaction. An example of such could be a form of QR authentication before the approval of a transaction.

Furthermore, since 40% of users were unaware of the National Cybercrime Helpline (1930) during a fraud incident (Sirajutheen and Abirami, 2026), a simple display of the helpline in any UPI platform (such as Google Pay) would allow for timely action and early intervention.

Additionally, while many studies support using AI in fraud detection, there is little guidance on how to implement these systems effectively. Future research should focus on building and testing practical AI-based fraud detection frameworks that can be integrated into digital payment systems.

Several methodological limitations in the current literature also need to be addressed. Future research should use nationally representative samples rather than regional ones, as this would allow the findings to be generalized across India. Furthermore, future work should test which specific cognitive biases result in relatively greater victimization, so that prevention efforts can be prioritized. Finally, comparative research across a wider set of countries would help identify which regulatory and infrastructural conditions most effectively curb fraud growth.

Conclusion

This paper explains why India’s digital payment revolution driven by UPI has produced a growing class of fraud victims. The observations suggest that UPI fraud stems from structural factors that create opportunities for fraud, rather than solely from individual negligence. UPI’s architecture, in which a single phone number links a user’s bank accounts, debit cards, and credit cards, creates a single point of vulnerability, while its convenience ensures routine digital activity that places users in the realm of motivated offenders.

Routine activity governs who is targeted, but awareness and education alone do not determine who becomes a victim. Victimization depends on whether fraudsters succeed in manufacturing the cognitive conditions, such as urgency, authority, and overconfidence, that override otherwise informed judgment.

One of the most critical issues is the absence of effective guardianship. Recovery rates for stolen funds remain minimal, and redressal mechanisms are widely regarded as unsatisfactory. Additionally, the Authorized Push Payment loophole allows banks to treat engineered deception and careless disclosure as equivalent, leaving a large category of victims without reimbursement.

Therefore, closing the gap between UPI’s convenience and its vulnerability will require structural changes, not merely individual reforms. Tackling the APP loophole, updating the legal frameworks governing digital payments, addressing methodological limitations, and introducing safeguards at the point of transaction are of utmost importance. As UPI continues to expand as the backbone of India’s digital economy, the strength of its guardianship mechanisms is likely to determine whether this expansion will occur without a proportionate rise in victimization.

References

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About the Author

Aadhavan Singh

Aadhavan is currently a Grade 12 student at La Martiniere College, India. He is a national-level shooter and passionate trekker.

The post Exploring How India’s Digital Payment Revolution Created a New Class of Fraud Victims: An Analysis of UPI Scams appeared first on Exploratio Journal.

Introduction

“87% of neighborhoods in San Francisco undergoing gentrification were once redlined as hazardous” (“From Redlining to Gentrification: The Policy of the Past that Affects Health Outcomes Today”). Redlining was a discriminatory practice in which banks and government agencies denied services such as insurance and mortgages to residents of neighborhoods with large African American and other minority populations. Over time, this practice caused significant disinvestment and economic deterioration of neighborhoods it affected, leaving many people in a fixed place of poverty that prevented upward socioeconomic mobility.

Established in 1933 as a part of Franklin Delano Roosevelt’s “New Deal” program to lift the country out of economic depression, the Home Owners’ Loan Corporation (HOLC) provided mortgage relief to homeowners at risk of losing their homes through foreclosure, in order to stabilize the housing market. As a part of this process, the HOLC created numerous residential security maps, grading different neighborhoods A-D (A being a highly secure and desirable neighborhood & D being a hazardous neighborhood) in about 200 different U.S. cities. Though aiming to create stability in the housing market, these letter grades were primarily based upon the socioeconomic, racial, and ethnic makeup of the neighborhoods’ residents, which facilitated the development of discrimination in the U.S. housing market. With the Johnson administration’s passage of the Fair Housing Act in 1968, any means of housing discrimination on the basis of race, sex, familial status, nationality, and disability were prohibited, thereby marking the closure of the HOLC’s rampant redlining practices of the mid-20th century. However, despite the practice’s prohibition, redlining had already caused a large amount of socioeconomic damage on the communities it had impacted, through a ripple effect of discrimination prevalent in many of society’s pillar institutions (ie schooling, employment, healthy food access)

In this paper I aim to answer the following question: What are the long-term economic impacts of 1930s-era HOLC redlining in the San Francisco Bay Area? By focusing on the quantitative relationships between redlining tract coverage and census data of racial demographics, household income, educational access, and employment, this paper aims to quantify the extent of redlining’s association with economic disparity throughout the late 20th and early 21st centuries through distributional impacts, categorical comparisons, and regression analysis.

From conducting distributional, categorical, and regression analyses, there are several patterns that can be concluded about the long-term economic impacts of New Deal-era HOLC redlining in the Bay Area. Firstly, the distributions holistically showed minimal change in shape over time indicating that the economic indicators measured stayed consistent over the course of several decades. This consistency goes to show that the economic impacts caused by redlining are solid and aren’t weak enough to change over time. Secondly, the regression analysis showed a negative relationship between income and redlining tract coverage with an increasing slope magnitude over time and a statistically significant relationship between the two variables indicated by the very low p-values.  

Section 1: Distributional Impacts

To examine the persistent long-term economic impacts of redlining across different HOLC-graded neighborhoods (graded on desirability), I first generated histograms of median household income (1980-2000), unemployment (1980-2000), and white-occupied housing (1980-2020) across HOLC grades A-D, which reveal several long term distributional economic trends.

Histograms were chosen because they allow one to see the persistence or change of these economic patterns over time by noticing a persistence of change in the spread and shape of the census data across HOLC grades. The incorporation of histogram series by time period with the four different grades per serie allows one to see variation within each grade and how the socioeconomic indicator of one grade changed over time relative to another. Each histogram plots the relative frequency of a given census variable across HOLC tracts A-D as defined by the 1930s HOLC redlining maps. By using relative frequency histograms rather than raw count histograms, the distributions are normalized which allows for fair and accurate comparison across grades that have a different number of census tracts. To ensure accurate comparability over time, the relative frequency histogram that plots median household income (1980-200) across HOLC grades A-D were converted from nominal US dollars to real 2020 US dollars using the Bureau of Labor Statistics’ annual average CPI values.

Subsection 1A: Median Household Income

The first socioeconomic indicator is real median household income. This variable measures the typical earning level of all households in a neighborhood, which is a strong indicator of overall economic opportunity in that neighborhood. Because nominal incomes change with inflation, the median household incomes were converted from nominal to real 2020 U.S. dollars using annual average CPI values from the U.S. Bureau of Labor Statistics. 

The first observation that can be drawn from Figure 1.1 is that the shape of the distributions of median household income for each HOLC grade remains approximately the same consistently across all three dates of measure. This indicates that aggregate household income for each grade persisted over the period of measure.

The second observation that can be drawn from Figure 1.1 has to do with the differences in intergrade clustering and spread of the data. Grade A’s distribution has a slight right-skew with a moderately-large income range that persists across the three dates of measure. Moving from the Grade A distribution to the Grade D distribution, the data gradually shifts left with each grade closer to D. Grade D’s distribution shows one, a high level of clustering at the left side of the left side of the histogram and two, an income range that is significantly smaller than that of Grade A’s distribution. These two observations continue throughout the three dates of measure.

From these observations, it can be concluded that Grade A Bay Area neighborhoods have a larger range of median household income values with fewer observations of lower median household income values. This conclusion is in accordance with the inference that those in a grade with greater security and desirability tend to have higher job opportunity, educational access, and healthcare access. The subtle right-skew in Grade A’s distribution indicates that a smaller proportion of tracts have exceptionally high incomes which increases the mean and pulls the distribution’s tail rightward. The gradual leftward shift of the distributions from Grade A to Grade D indicates that as the magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the median household income for the tracts. Moving to Grade D, it can be concluded that tracts judged to have lower security and desirability by the HOLC have a smaller range of median household income values and a high proportion of observations of lower median household income. This conclusion is in agreement with the inference that individuals in this grade tend to have lesser job opportunity, lesser educational access, and lesser healthcare access, traits which undermine ability to achieve high household income.

In addition, the moderately-large range of income denoted in Grade A’s distribution hints at a more discrete but key principle of 1930s HOLC redlining: racial prejudice. As observed in Grade D’s distribution, there was a visible clustering of tracts toward the left of the histogram around the lower income values. Intuitively, this logic should apply to Grade A as well but in an opposite fashion: clustering of tracts toward the right of the histogram around the higher income values. However, in Grade A’s distribution there is a significantly-sized range and spread of income, much larger than that of Grade D’s distribution, indicating that Grade A neighborhoods contained households with a variety of median household income values. From this distributional observation, it can be understood that HOLC redlining grading was deeply influenced by racial prejudice, more so than objective economic data for a lot of the time.

Subsection 1B: Unemployment

The second socioeconomic indicator is percent of civilians unemployed (16+). This variable measures the share of the working-class population per grade that are actively seeking employment but cannot find work. Redlined neighborhoods often see disinvestment and lower amounts of socioeconomic mobility so distributional unemployment is a strong indicator of the severity of redlining experienced by grade.

Similar to the distribution of median household income across HOLC grades, the first observation drawn from Figure 1.2 is that consistently across all three dates of measure, the shape of the distribution for each respective HOLC grade remains approximately the same, with only a few minor shifts, which indicates that unemployment shares for each grade persisted over time. This persistence in unemployment suggests that the socioeconomic division caused by 1930s HOLC redlining has stayed relatively intact in the latter half of the 20th century, with census level unemployment shares continuing to mirror structural inequalities imposed by HOLC redlining maps.

The second observation drawn from Figure 1.2 is concerned with the differences in intergrade clustering and spread of the data as well. Generally, these differences are the same as in Figure 1.1 but a mirror flip. Grade A’s distribution shows high left-clustering, a small right-skew, and a very high proportion of observations on that side of the histogram. Moving from the Grade A distribution to the Grade D distribution, the data gradually gains overall spread/range and magnitude of right-skew while also losing peak height. Grade D’s distribution shows a range and median greater than that of Grade A. This pattern of inter-grade shifting from A to D remains approximately the same over the period of measure.

From these observations, it can generally be concluded that the magnitude of risk assessed by the HOLC for the tracts is directly proportional to the percent of civilians (16+) unemployed for the tracts. The association between these two variables is consistent with the inference that redlined neighborhoods tend to have lower economic opportunity, in this case job access, resulting in a higher unemployment rate in these tracts. While the median percent of civilians (16+) unemployed increases moving from Grade A to Grade D, the magnitude of right skew also increases which means that there is a higher degree of variability in tracts deemed less secure and desirable by the HOLC. Economically, this increase in variability in percent of civilians (16+) unemployed from Grade A to Grade D means that the magnitude of risk assessed by the HOLC for the tracts is associated with a higher degree of economic volatility in the tracts. These tracts that have experienced a high amount of redlining have also seen uneven patterns of disinvestment, reinvestment, demographic/population change, gentrification, industrial restructuring, factors which are greatly responsible for a high variation in unemployment in these tracts. In addition to an increase in magnitude of right skew from Grade A to Grade D, a decrease in peak height is also observed. This means the magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the proportion of observations made. Similar to an increase in spread, a decrease in peak height also denotes a proportional relationship between degree of redlining and degree of economic volatility observed. From a statistical standpoint, the decrease in peak height, or flattening, of the distribution means that a fewer proportion of tracts share a common unemployment rate for civilians (16+). This flattening represents a growing internal heterogeneity suggesting that redlining produced a fragmented and nonuniform economic landscape in the areas it greatly impacted.

Subsection 1.C: White Share

The third socioeconomic indicator is percent of white-occupied houses. This variable measures a specific racial composition of occupied housing units. Historically, redlined neighborhoods in the mid 20th century saw a large amount of urban flight in which primarily caucasian residents departed urban areas for suburban neighborhoods. As a result of this exodus, over time, redlined neighborhoods saw a change in white residents so by measuring this variable, we will see how the magnitude and direction of this change across grades and the fluctuations in change over time. 

Similar to the previous two distributions, the first observation that can be drawn from Figure 1.3 is that across the five dates of measure, the shape of the distribution for HOLC grades A, B, and C remain approximately the same, with only a few minor shifts. This indicates that for these grades, the distribution of percent of white occupied homes persisted over the course of 4 decades. Likewise to the other socioeconomic indicators measured in Figures 1.1 and 1.2 respectively, the persistence of shares of white occupied homes suggests that the census-level outcomes of the racial bias  ingrained into HOLC redlining maps has seen very minimal change in the highly to moderately secure and desirable tracts (A-C), indicating that housing segregation on the basis of race has persisted over time in the Bay Area.

The second set of observations that can be drawn from Figure 1.3 concerns the shape of each grade’s distribution over the period of measure: what the shape means and for Grade D, what a shift in the distribution’s shape means. Firstly, moving left to right from Grade A to Grade D, there is a gradual flattening of the distribution, most significantly moving from Grade A to Grade B, and there is a shift from a right-skew to a left-skew. Moving vertically down the figure from 1980 to 2020, the distributions of Grades B, C, and D see a subtle and gradual increase in aggregate peak height over time. In Grade D, there is a sharp increase in proportion of observations over time at the 7-20% range of the 2020 distribution.

From these observations, it can be concluded that magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the percent of white-occupied houses for the tracts over time. As the HOLC grade declines in security and desirability, the distribution becomes increasingly concentrated toward lower percentage values of white-occupied houses. This inverse association, shown by the shift of a right-skew to a left-skew from Grade A to Grade D in each date of measure, confirms our understanding of HOLC redlining having a basis of racial prejudice, as lower-tier grades and areas with higher levels of redlining tend to have a greater minority demographic and a lower majority, in this case caucasian, demographic. The flattening of the distributions in the earlier dates of measure (1980 and 1990 primarily) indicate an increase in demographic variability as tracts become more “hazardous.” While secure and desirable areas remain racially homogenous with a high proportion of caucasians, areas with lower security and desirability are more racially heterogenous reflecting both racial segregation’s influence in HOLC redlining as well as subsequent population shifts driven by disinvestment and suburbanization. The increase in overall peak height for Grades B, C, and D but retainment of a roughly flat shape relative to Grade A’s distribution indicate that there is a greater amount of census data concerning white-occupied housing moving forward in time, but the demographic trends revealed by this data remain persistent as mentioned in detail above. The sharp increase in peak height in 2020 at the 7-20% range of the Grade D histogram indicates that there is a significantly higher proportion of observations concentrated around the lower percent of white-occupied houses in recent times, a trend that can be explained by “white-flight” and suburbanization. This migratory occurrence is defined as the migration of primarily caucasian residents from urban to suburban areas, which results in the economic degradation of urban areas through decreased funding and overall maintenance.

Section 2: Categorical / Group-Level Comparisons

To further explore the economic impacts of 1930s HOLC redlining in the Bay Area, I used categorical plots to visualize redlining’s impact through median household income and educational attainment across HOLC grades A-D.

Definitionally, a categorical plot allows for comparison of a single numerical variable across four levels of categorical variables, in this case, each level corresponding to a HOLC grade A-D. The important distinction that needs to be made in order to understand what the differences are in the displaying of categorical variables (HOLC grades) is the difference in interpretation over time for the histograms vs. the categorical plots. The histogram focuses on distributional change within a singular HOLC grade over time, which provides insight into intra-grade socioeconomic change over the period of measure. On the other hand, the categorical plot focuses on the change in inter-grade variation over time for a given socioeconomic indicator.  Due to this difference, I chose to incorporate categorical plots to supplement the distributions in order to visualize comparative disparities between HOLC grades.

Furthermore, in connecting the two models, a single bar within a categorical plot can be interpreted like a distribution for the HOLC grade that the bar represents, through the spread of data points within the bar.

Subsection 2A: Median Household Income

The first socioeconomic indicator to be analyzed categorically is median household income (real 2020 USD) across HOLC grades A-D. In the figure that assesses this variable, there are three categorical plots, one plot for each period of measure, 1980, 1990, and 2000 respectively.

The first observation that can be drawn from Figure 2.1 is the clear downward trend from Grade A to D that is consistent across all three dates of measure, with Grade A having the highest peak and Grade D having the lowest peak. The second observation that can be drawn from Figure 2.1 concerns intra-grade variability indicated by the distribution of data points within each HOLC Grade’s bar. Consistently across all three dates of measure, Grade A tracts show the greatest intra-grade variability, indicated by a greater spread of data points within the bar. As the HOLC grade declines however, there is a decrease in intra-grade variability, indicated by the increasing clustering of data points within each bar. The third observation that can be drawn from Figure 2.1 is an increase in the peak height for each HOLC Grade’s bar over time, while the vertical separation between each bar remains approximately equivalent over time. 

From the first observation, it can be roughly concluded that magnitude of risk and insecurity determined by the HOLC and the median household income are inversely proportional for the tracts. This observation is consistent with the basis of redlining, in which redlined neighborhoods saw high levels of disinvestment and economic deterioration, preventing upward socioeconomic mobility for residents of these tracts, therefore explaining why residents of redlined tracts tended to have lower median incomes than those of non-redlined tracts. From the second observation concerning intra-grade variability, the higher levels of variability in tracts deemed less risky and more secure by the HOLC indicate that in these tracts, there is a greater degree of economic opportunity and mobility, providing an explanation for a wider spread in Grade A and a tighter clustering in lower grades, where many residents of redlined tracts are stuck in a lower socioeconomic position and unable to advance upwards. From the third observation concerning the increase in relative heights of each HOLC Grade’s bar over time, we can conclude that across all tracts, redlined or not, general development over the period of measure through technological innovation and globalization promoted the median household incomes for each HOLC Grade. Despite this overall increase, the vertical separation for each catplot between each HOLC Grade remains approximately equivalent suggesting that relative disparities between grades persisted despite overall income growth over the period of measure.

Subsection 2B: Educational Attainment

The second socioeconomic indicator to be analyzed categorically is educational attainment across HOLC grades A-D. This variable will be measured by the percent of adults 25 and older with 4 or more years of college education. In the figure below, there are three categorical plots, one plot for each period of measure, 1980, 1990, and 2000 respectively.

The first observation that can be drawn from Figure 2.2 is the clear downward trend from Grade A to Grade D across all three dates of measure, with Grade A having the highest peak and Grade A having the lowest peak. The second observation that can be drawn is the gradual upward shift in each grade over time, shown by the increasing peak height for each HOLC Grade’s bar. In connecting these two observations, we see that the difference in peak heights between the HOLC Grades decreases over the period of measure, with the bars for Grades B-D increasing by a greater amount than the bar for Grade A, revealing a flattening of the downward trend from Grade A to D.

From the first observation, it can be concluded that magnitude of risk and insecurity determined by the HOLC is inversely proportional to the percent of adults 25 years of age and older with 4 or more years of college education for the tracts. This conclusion aligns with the economics of redlining in which neighborhoods that experienced the negative implications of the practice saw less economic opportunity as well as overall disinvestment in their communities. Tracking back to Figure 2.1, we see that neighborhoods in lower tier HOLC grades have generally a lower aggregate median household income. Due to the aggregate financial status of these neighborhoods, we can infer that one of the reasons that households in redlined neighborhoods saw lower degrees of educational attainment was they comprehensively had less disposable income to spend on privileges like textbooks, tutoring services, or in this case, a college education.

From the second observation, it can be concluded that the moderate to lower tier grades saw upward educational mobility over the period of measure. This pattern can be primarily attributed to neighborhood redevelopment and gentrification throughout the Bay Area, particularly in tracts close in proximity to university centers, like UC Berkeley, or booming industries that have attracted more college-educated residents in search of work on a domestic and international level, like Silicon Valley and the South Bay. Coinciding with these inferences, the Bay Area saw several urban redevelopment projects in parts of Oakland, Emeryville, and San Francisco’s Mission District in which communities experienced infrastructural improvement in housing and schools. In a research report titled Engaging Schools in Urban Revitalization: The Y-PLAN (Youth – Plan, Learn, Act, Now!), authors Deborah L. McKoy and Jeffrey M. Vincent define the Y-PLAN initiative in the context of the urban Bay Area: 

“West Oakland, California, is an industrial area suffering the abandonment and blight common to other neighborhoods after the loss of manufacturing employers, a process that began in the 1950s…Stepping into this environment in 2000 was the Y-PLAN (Youth—Plan, Learn, Act, Now!), a model for youth civic engagement in city planning that uses urban space slated for redevelopment as a catalyst for community revitalization and education reform. Sponsored by the Center for Cities & Schools at the University of California (UC), Berkeley, Y-PLAN facilitates positive community outcomes by partnering graduate student “mentors,” local high school students, government agencies, private interests, and other community parties to work on a real-world planning issue. The Y-PLAN is both a pedagogical tool and a planning studio that addresses specific issues in local communities” (McKoy & Vincent, 2007, p. 1).

Due to the implementation of programs such as the Y-PLAN that educationally mobilized the Bay Area’s youth in impoverished areas, many of these neighborhood areas saw educational improvement reflected by the increasing peak heights for tracts in HOLC Grades B-D in the categorical plot. As a result of the flattening of the downward trend, it can be concluded that the educational disparities within the Bay Area were ameliorated over the period of measure, in part due to youth initiatives and other programs seeking educational improvement.

Section 3: Regression Analysis of Raw Median Household Income

To determine the impact of redlining tract coverage on median household income, I also conducted ordinary least squares (OLS) linear regressions for both the raw and logarithm of median household income as a function of redlining tract coverage from a 1930s HOLC map. While the histograms and catplots provide distributional insight and categorical comparison, respectively, into disparities across HOLC grades, the regression allows for high quantitative precision in determining the extent to which redlining coverage can accurately predict income for a Bay Area neighborhood. More specifically, the categorical plots allowed us to see a general slope trend based on the peaks of each bar, but the regression expands upon this observation by making it more specific numerically.

Because nominal incomes change with inflation, the median household incomes were converted from nominal to real 2020 U.S. dollars using annual average CPI values from the U.S. Bureau of Labor Statistics. The purpose of conducting a log-transformed regression was to linearize the relationship and allow for the slope coefficient to be interpreted as a percent change in income for a one-unit increase in tract coverage.

The two key regression outputs that will be analyzed in this section are slope coefficient and p-values, which together, can assess the magnitude and certainty, respectively, of the relationship between redlining tract coverage and median household income over time. For the raw income model, the slope coefficient is interpreted as the predicted change in real median household income by the OLS regression line per one-unit (or 100%) increase in redlining tract coverage. For the log-transformed income model, the slope coefficient is interpreted as the predicted percent change in real median household income by the OLS regression line per 1.0 (or 100%) increase in redlining tract coverage. Given that the distribution of income is skewed, taking the logarithm of income compresses high-income and low-income outliers, which creates a more symmetric and homoscedastic residual distribution. The patterns observed in the logarithm-transformed regression are largely identical to those observed in the untransformed regression, so I decided to include logarithm-transformed regression analysis in the first appendix of the paper. The p-values for each model (raw income and log-transformed income) represent the probability of observing each respective slope by random chance. A low p-value (less than the alpha level of 5%) indicates that the observed relationship is unlikely due to random chance.

Several neighborhood wealth-related trends can be concluded from OLS regression analysis of raw and log-transformed real median household income (2020 dollars) as a function of redlining tract coverage from 1980-2000. While quantitative, these patterns share similarities with the distributional trends derived from the histograms showing real median household income (2020 dollars) across HOLC grades A-D described in detail earlier in the paper.

Here, our parameter beta represents the true slope of the population regression line relating the explanatory and response variables of redlining tract coverage and raw income respectively. The null hypothesis is that beta is equal to zero and the alternative hypothesis is that beta is less than zero.

Figure 3.1 Raw Income as a function of Redlining Tract Coverage

Figure 3.1 confirms a negative association between income and redlining tract coverage based on the slope coefficients. Firstly, the slope coefficient for the 1980 OLS regression is -15,205.29. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $15,205.29 decrease in real median household income (2020 dollars) in 1980. Secondly, the slope coefficient for the 1990 OLS regression is -29,742.12. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $29,742.12 decrease in real median household income (2020 dollars) in 1990. Thirdly, the slope coefficient for the 2000 OLS regression is -46,819.90. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $46,819.90 decrease in real median household income (2020 dollars) in 2000.

While I noted that the association between income and redlining tract coverage remains negative, from the slope coefficients in Figure 3.1, it can also be observed that the magnitude of the decrease in income increases with each subsequent date of measure. From a graphical standpoint, this can be understood as an initially negative regression line that gets steeper and steeper in the negative direction over time. So what does this mean in the context of redlining? From this observation, it can be concluded that the negative impact of redlining coverage on household income worsens overtime, as the same increase in coverage is met with a greater magnitude of decrease in income over the period of measure. This trend can be best explained by the extensive impact of disinvestment in higher redlined areas. Following the HOLC’s classification of neighborhoods as “hazardous” via their redlining maps, banks and financial institutions chose not to lend money to businesses and individuals, insure mortgages, or fund development in these areas due to the high-risk attributed to these areas by the HOLC. As a result of this lack of financial and infrastructural support from banking institutions, these areas deteriorated over time through an aggregate decrease in property values as well as less socioeconomic mobility and in this case, access to high-paying jobs. Due to the extensive decline of the economic makeup of these areas caused by disinvestment, it makes sense why the slope coefficient of the income vs. redlining tract coverage regression decrease over time.

Figure 3.1 also displays very low p-values that all fall significantly below the general alpha level of 5%. Therefore, we can ascertain a relationship between redlining tract coverage and income. Firstly, the p-value for the 1980 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 1980, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $15,205.29 per percentage point of redlining tract coverage by random chance is less than approximately   (rounded 0.0002%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income in 1980 for this sample. Secondly, the p-value for the 1990 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 1990, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $29,742.12 per percentage point of redlining tract coverage by random chance is less than   (rounded 0.0008%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income 1990. Thirdly, the p-value for the 2000 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 2000, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $46,819.90 per percentage point of redlining tract coverage by random chance is less than   (rounded 0.0002%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income 2000.

The consistently low p-values across all three dates of measure (less than the alpha level of 5%) confirms the high statistical strength between redlining tract coverage as an independent variable and median household income as a dependent variable. Furthermore, this aspect of the regression corroborates the trends observed from the slope coefficients of the decreasing negative association between these two variables, indicating that it is extremely unlikely this relationship occurred by random chance.

Conclusion

From distributional impacts, to categorical plots, to regression analysis, we can conclude several long term economic impacts that vary by extent to which a given census tract was redlined and how it changed over time. With the histograms, we saw that a lot of the economic patterns surrounding income and unemployment maintained shape for a given grade distribution over time indicating that a lot of the economic impacts stayed stagnant over the course of a few decades. With the regression analysis, we saw that the magnitude of negative slope for raw income as a function of redlining tract coverage greatly decreased in the 20 year period from around $30,000 to $42,000 in 1980 and 2000 respectively. With the log transformed regression, the slope coefficients were approximately equivalent for each year indicating that income dropped by the same percentage each year which shows relative stability in the influence of redlining, not due to external factors.

Looking forward, I would love to dive deeper into how gentrification interacts with these socioeconomic patterns of redlining. By definition, gentrification is the process in which a poorer area is infrastructurally improved as a result of a wealthier demographic of people moving in. This economic improvement is seen over time through improved housing, healthcare, and new business. By exploring gentrification in the context of redlining, I would ask whether modern reinvestment in previously redlined areas resulted in economic growth, ameliorating the negative effects of 1930s-era HOLC redlining.

While in this paper, I primarily studied solely socioeconomic indicators measuring the effects of redlining (unemployment, income, racial demographics, etc.), I would be curious to explore more health-related effects of this same era of redlining. By conducting research on food deserts, urban areas with limited access to good-quality fresh food, through measuring, for example, the number of fast food restaurants in a given census tract as well as illnesses through measuring, for example, the number of diabetes occurrences or hospital visits in a given census tract, I would bring in a new dimension of human health and biology in association with the redlining I explored in this paper. 

Bibliography

De los Santos, H., Jiang, K., Bernardi, J., & Okechukwu, C. (2021, May 26). From redlining to gentrification: The policy of the past that affects health outcomes today. Harvard Medical Journal. Retrieved October 28, 2025, from https://info.primarycare.hms.harvard.edu/perspectives/articles/redlining-gentrification-health-outcomes

Jonathan Schroeder, David Van Riper, Steven Manson, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 20.0 [dataset]. Minneapolis, MN: IPUMS. 2025. http://doi.org/10.18128/D050.V20.0

McKoy, D. L., & Vincent, J. M. (2007, June). Engaging schools in urban revitalization: The y-PLAN. Association of Collegiate Schools of Planning. https://doi.org/10.1177/0739456×06298817

Nelson, R. K., Winling, L, et al. (2023). Mapping Inequality: Redlining in New Deal America. Digital Scholarship Lab. https://dsl.richmond.edu/panorama/redlininghttps://dsl.richmond.edu/panorama/redlining.

Appendix A: Regression Analysis of the Logarithm of Median Household Income

While the raw-income regression shows the dollar change in income as a function of redlining tract coverage, by taking the logarithm of income and performing the same regression function, we see the percent change in income as a function of redlining tract coverage for the same three dates of measure. Though not visible from the regression outputs, the log-transformed regression mitigates the effect of outliers and influential points that undermined the strength of the relationship between raw-income and redlining tract coverage, providing us slope coefficients and p-values that are optimal for ensuring an accurate relationship between the two variables.

Here, our parameter beta represents the true slope of the population regression line relating the explanatory and response variables of redlining tract coverage and logarithm of income respectively. The null hypothesis is that beta is equal to zero and the alternative hypothesis is that beta is less than zero.

Figure A1 Logarithm of Income as a function of Redlining Tract Coverage

Similar to the raw income regression, the results of the log-transformed regression shown in Figure A1 confirm a negative association between redlining tract coverage and income as well. The slope coefficient for the 1980 OLS regression is -0.30. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 30% decrease in real median household income (2020 dollars) in 1980. The slope coefficient for the 1990 OLS regression is -0.28. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 28% decrease in real median household income (2020 dollars) in 1980. The slope coefficient for the 2000 OLS regression is -0.32. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 32% decrease in real median household income (2020 dollars) in 1980.

Similar to the raw income regression, the results of the log-transformed income regression evidenced in Figure A1 show very low p-values. Therefore, we can ascertain a relationship between redlining tract coverage and income. The p-value for the 1980 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 1980, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.30 or less by random chance alone is less than  %. Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 1980. The p-value for the 1990 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 1990, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.28 or less by random chance alone is less than  . Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 1990. Lastly, the p-value for the 2000 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 2000, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.32 or less by random chance alone is less than  . Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 2000.

For the most part, these two outputs of the regression reveal largely similar or identical patterns as the raw-income regression. Firstly, the slope coefficients reveal a negative relationship between redlining tract coverage and median household income that steepens over time. This steepening means that the percent decrease in median household income per 100% increase in redlining tract coverage increases with each subsequent date of measure.

Again, the p-values for this regression are far below the alpha level of 5% meaning that a relationship between redlining tract coverage and the logarithm of income is statistically significant across all three dates of measure, and not due to random chance.

About the author

Rishi Haldar

Rishi is a senior at Miramonte High School with interests in economics, mathematics, statistics, and history. He plans to attend Cornell University in the fall, where he will be studying economics in the College of Arts and Sciences. Apart from academics, Rishi is a guitarist in a band that plays local gigs (restaurants, fundraisers, etc.) and plays soccer for a club team and his high school team.

The post Redlining: Quantifying the Economic History of the San Francisco Bay Area appeared first on Exploratio Journal.

Abstract

This paper explores the complex relationship between tariffs on Gross Domestic Product (GDP) and the U.S. trade balances with its major trading partners. It investigates if imports and greater trade balances changed between the U.S and its top trading partners after tariffs were placed. The conclusion is no significant change in trade balances since the implementation of the Trump administration’s tariffs.

The evidence shows that in the months of April to July, the tariffs have not significantly changed U.S. net trade. The effects studied in this paper are a result of new trade policies by the Trump Administration which put retaliatory tariffs on most of the world. As a result, many firms and businesses frontloaded the tariffs which caused a 40% increase in imports in Q1 2025. Also in Q1, real GDP decreased by 0.5% (U.S. Bureau of Economic Analysis, 2025) according to the most recent estimate. However, imports do not reduce GDP and are only included in the calculation to support accounting principles. As a result of this misconception, many news articles written by journalists who are not economists have had misleading claims with regards to the GDP decrease. The current results of the findings could potentially be attributed to the uncertainty in the administration’s tariff policy or simply that not enough time has passed for significant changes to be observable in the data.

Introduction & Literature Review

The role of imports in shaping a nation’s economy has become increasingly significant following President Trump’s trade wars and tariffs on countries across the world. Many of these tariffs were put during the first quarter of 2025 while the others were placed on April 2, 2025, also known as Liberation Day. However, news of President Trump’s intention of using tariffs has been clear before his Inauguration and use of tariffs on foreign countries was common in his first term as well. Since his election, many businesses and firms have increased inventories and the amount of imported goods in anticipation of high tariff rates to go into effect soon.

As noted above, the heart of GDP measurement is the widely cited expenditure formula: GDP = C + I + G + (X-M) where C denotes consumption, I investment, G government expenditures, X exports, and M imports. The superficial glance at this equation shows imports as a direct drag on GDP. However, economists consistently clarify that this superficial glance is quite misleading as the negative sign simply represents an accounting principle to prevent double counting.

Bill Conerly (2025), a writer at Forbes, clarifies that “U.S. imports are neither added nor subtracted conceptually” (para. 3) for GDP. He explains that with perfect data available to statisticians, imports wouldn’t be included in a GDP calculation (Conerly, 2025).

Looking at the subtraction, Greg Mankiw notes, “this subtraction is made because imports of goods and services are included in other components of GDP,” (Mankiw, 2001, p. 499) Mankiw also notes how a purchase of an imported good raises consumption, investment or government expenditures.

The St. Louis Fed adds that “imports (foreign production) should have no impact on GDP,”(Wolla, 2018, para. 9). They explain the variable M as an accounting variable rather than an expenditure variable. It is also important to note that the imported goods will have an effect on the GDP of the country that produces them. Since it isn’t the United States, they don’t affect U.S. GDP. However, it can take into account if the goods are intermediate or partially produced in the U.S. Since the expenditure variables of C, I, and G only take final goods into account, GDP will be affected based on the amount of the goods that was domestically produced.

Keshav Srikant, a writer with Econofact, supports the net-zero effect on imports on GDP. However, he also notes how imports can potentially indirectly reduce GDP if they replace domestic consumption or if domestic government expenditures are reduced as a result of higher purchases of foreign goods (Srikant 2025). Further study of macroeconomic trends are required to make an argument for this situation as these latent variables could drive import growth and GDP declines when those two variables are not correlated.

Ultimately, imports do not directly reduce GDP and their inclusion in the components of GDP is a measure to prevent double counting.

There are many researchers who have explored the growth of imports and its, relationship with the overall economy. In fact, many specific case studies have found that an increase in imports often leads to an increase in real GDP .

A study by Peter Saunders, focused on a time series analysis of the role of imports in the economic rise of India from 1970 to 2005, analyzes the long term relationship between imports and India’s real GDP. Saunders establishes that both variables, imports and real GDP, are cointegrated using Johansen’s test of cointegration (Saunders 2010). This test proves if two variables have a long term equilibrium relationship, meaning that despite short term deviations or outliers, the variables have a long term observed relationship. A Vector Error Correction Model (VECM) examines the relationship between cointegrated variables. In the VECM used by Saunders, the results indicated that imports have positively impacted India’s economic growth in the short-term. Saunders highlights how this result defies traditional expectations that imports could be a drag on the economy (Saunders 2010).

In another study by M.Y Khan et al, about the relationship between imports and economic growth in Pakistan, a similar conclusion was reached. This study used data from 1975 to 2014 with the methodology of a Granger Causality Test. This test focuses on proving directional relationships between time series variables. The results showed that there was a bi-directional relationship between imports and economic growth in Pakistan, meaning that both time series variables mutually supported one another (Khan et al, 2019).

Research focusing on the relationship Rwandan economic growth with imports and exports showed a positive long run relationship (Al Hemzawi & Umutoni 2021) . The study concluded that a one percent increase in imports led to a 0.32% rise in Rwandan GDP. To get that correlation, the authors used a multivariate Ordinary Least Squares regression which is a way to minimize variance between variables. They also used quarterly time series data in the regression.

Immediately after the Q1 GDP contraction was announced, many news publications released misleading or false articles regarding the cause behind this result. They accredited the cause to be the tariff jumping effect and the dramatic import surge that occurred because firms and businesses rushed to purchase foreign goods before tariff prices were assigned to goods. The underlying demand was quite consistent to previous levels while business investment surged as an offset to the imports. Despite the fact that imports do not directly reduce GDP, news outlets continued to push that narrative.

For example, an AP News article stated “First-quarter growth was weighed down by a surge of imports, ” (Wiseman 2025, para. 2) while The Hill said “GDP shrank in the first quarter mostly because of lower consumer spending and a pull-forward in imports ahead of President Trump’s tariffs, ” (Burns 2025,para. 4). Many other outlets made misleading claims regarding the import surge. Although journalistic misconceptions are not uncommon, even the Federal Open Market Committee has made mistakes with regards to the effect of imports on GDP (Lemieux 2018).

On the other hand, many top economists have had different opinions. Many economists have attributed the contraction to the economic activity as a result of the imports, not by the imports directly. For example, Paul Gruenwald, a global chief economist for S&P Global Ratings, mentioned that Q1 GDP data was “distorted by the front-running of tariffs,” (2025). Gregory Daco, a chief economist at EY , added “the contraction was largely a function of economic activity being pulled forward as importers, business, and consumers rushed to get ahead of tariff implementation,” (2025). Economist Preston Caldwell ofMorningstar added that imported goods could be stored in inventories but “it just didn’t show up in the data because of measurement error,” (2025).

Some top economists also challenged the fear that this GDP result was the first domino in a potential recession. Caldwell added that this result “doesn’t mark the beginning of a recession,” (2025). Others mentioned potential for economic uncertainty further down the line as more policy was unveiled. “Demand in the first quarter looks to be driven by businesses battening down the hatches before the storm,” Chief economist Luke Tiley of the Wilmington Trust said (2025).

One potential explanation for the GDP decrease is a phenomenon called the substitution effect, a phenomenon that suggests that the tariff induced frontloading substituted for domestic purchases. If this is the case, GDP would decrease since less money would be spent toward domestic production. This has been prevalent in the past as well.

In the 1990s, the Northern American Free Trade Agreement (NAFTA) contemplated potential tariff reductions. 96% such reductions were announced far in advance, giving consumers and firms the chance to act on this information ( Khan & Khederlarian 2021). A study found that in anticipation of an upcoming tariff reduction of 1%, imports dropped by a sizable 6% in the months before the tariff implementation when compared to regular months. The study used an Herfindahl-Hirschman Index, a method to measure market concentration, and applied it to the spread of imports. Their final result articulated that firms shift their purchases to periods when lower costs can be attainable and that these anticipatory dynamics are true (Khan & Khederlarian 2021).

A potential alternate explanation is that the small decrease in government expenditures was the key factor in the GDP decrease.

There are some potential gaps in data which limit the study of GDP accounting. For example, these accounting principles say nothing about potential causality with latent variables or economic impacts. There is also difficulty in GDP data collections since it can be difficult to only count final goods. Finally, GDP data could be fixed-weighted calculations that can add error as the economy changes and price structures evolve. However, when calculated in a chain-weighted approach to account for economic evolution, there are still struggles with new goods being added.

Methodology

This analysis uses monthly trade data and monthly effective tariff rates for the United States with its largest trading partners. It also uses the same data for the European Union to use as a control. The source for monthly trade data values were the U.S. Census Bureau and Eurostat. The effective tariff rate values were gathered from trusted sources and reflect prior US tariffs and changes as newer tariffs went into effect. This analysis employs a linear regression test with net trade balances and effective tariff rates to analyze the potential correlation between the two.

Results

The data shows a minimal negative relationship between tariff rate and change in trade balances. This means that since the tariffs went into effect, there hasn’t been a significant increase or decrease in U.S. trade balances with main trading partners. The correlation coefficient was 0.0806 which confirms that in the three months since the tariffs went into effect, there weren’t any significant changes in trade balances that were caused by the changes in effective tariff rates. The coefficient of determination is 0.0065, or approximately 0, which meant that any changes that did occur in trade balances were not from the changes in effective tariff rates. Finally, the t-score value equals 0.0977 and indicates that the observed result aligns with the null hypothesis and the difference between the sample data and the population data is not statistically significant.

Discussion

Ultimately, the analysis proves that there is no correlation between the vast increases in effective tariff rates and changes in net trade balances. By contrast, as shown in Table 2, the U.S. trade deficits actually became larger for some countries such as Vietnam or the United Kingdom despite increases in effective tariff rates. Other countries such as China or Italy faced similar decreases in trade deficits despite having large differences in net trade and change in effective tariff rates. Furthermore, some countries were levied with larger tariffs than others, making predicting the change in trade balances harder to anticipate.

A potential explanation for the results of the analysis is the extreme volatility in tariff policy during the study period. Following the implementation of the “Liberation Day” reciprocal tariffs in early April, several countries experienced rapid and significant changes in their tariff rates. For example, China briefly faced tariff levels exceeding 140%, while Brazil was subjected to a 50% tariff following political disputes with the U.S. administration. In addition to these enacted measures, frequent public threats of new tariffs introduced further uncertainty into global trade markets. Simultaneously, reports of partial or full trade agreements with major partners like the EU, China, Japan, and South Korea, led to subsequent reductions in effective tariff rates. This pattern of escalation followed by negotiated de-escalation likely diluted the measurable macroeconomic impact of tariffs, complicating attempts to identify stable relationships between tariff levels and trade or GDP outcomes.

Another potential reason can be shown through the pressures of the markets. Financial Times commentator Robert Armstrong coined the current administration’s trade policies as “TACO Trade”. The acronym refers to some of the administration’s sudden reversals of tariffs. Armstrong coined the term when describing the pattern of placing large tariffs on countries which led to economic panic, shock, and stock market hits. He then explained how later reversals of these tariff policies have led to market comebacks. Additionally, the market uncertainty can be explained as how stocks look like they are trending upward and then stop due to a social media post or claim by the government. It’s possible that many firms and businesses believed that the tariff rate changes wouldn’t be in place long term and thus, no changes were found in the U.S. trade balances.

Implications for Policy and Future Research

Investigating the nuanced economic effects on GDP is key for future policy regarding tariff measures and potential trade deals. As occurred in Q1, there are potential short term distortions in GDP measurement so it’s important to keep these in mind. An area for future research is on the study of tariffs-driven import behavior and with the substitution effect’s prominence in the short and long term. This would provide key insights into how firms react to the government policy and how both parties can better facilitate economic policy.Finally, it’s important to continue to analyze changes in trade balances to see if significant changes will be present with the passing of time and more recent data.

Works Cited

Al Hemzawi, B., & Umutoni, N. (2021). Impact of Exports and Imports on the Economic Growth. MSc. Thesis, Jönköping University. Buckling up for a long ride: chief economists add detail to a downbeat outlook. (2025, May 28). World Economic Forum. https://www.weforum.org/stories/2025/05/wef-chief-economists-uncertainty-global-outlook

Burns, T. (2025, June 26). US economy shrank faster than expected, new data shows. The Hill. https://thehill.com/business/5371005-us-gdp-revised-lower-consumer-spending

Conerly, B. (2025, March 11). Understanding GDP: Why Imports Don’t Actually Reduce Economic Growth. Forbes.https://www.forbes.com/sites/billconerly/2025/03/11/understanding-gdp-why-imports-dont-actually-reduce-economic-growth/

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Freund, C., Pierola, M. D., & Rocha, N. (2021). How Does Trade Respond to Anticipated Tariff Changes? Evidence from NAFTA (Policy Research Working Paper No. 9561). World Bank. Gross Domestic Product, 1st Quarter 2025 (Third Estimate) | U.S. (2025, June 25). Bureau of Economic Analysis. https://www.bea.gov/news/2025/gross-domestic-product-1st-quarter-2025-third-estimate-gdp-industry-and-corporate-profits

Khan, M. Y ., Akhtar, S., & Riaz, S. (2019). Dynamic Relationship Between Imports and Economic Growth in Pakistan. Journal of Economics and Sustainable Development, 10(10), 70–77.

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About the author

Ishaan Bafna

Ishaan Bafna is a 12th grade student at Kingswood Oxford School with strong academic and research interests in economics and mathematics. Ishaan actively pursues opportunities that integrates analytical thinking with critical reasoning and problem-solving. Known for his intellectual curiosity and work ethic, Ishaan wishes to pursue a career at the intersection of economics, mathematics and technology.

Outside of the classroom, Ishaan is a leader of his schools Math Team and Mock Trial Team, a lead peer tutor, and a varsity golf athlete. Ishaan has interned with The Hartford Insurance as a Lean Portfolio Management Intern. He is also a National Merit Commended Scholar and a recipient of various awards at his school.

The post The macroeconomic effects of tariffs on GDP and trade balances, through the lens of Q1 2025 GDP change appeared first on Exploratio Journal.

This paper examines the stability of five leading stablecoins USDT, USDC, BUSD, TUSD, and DAI using a nonlinear machine learning model combined with an event based analysis of major depegging episodes. Fiat backed stablecoins show muted and short lived deviations from their pegs during external shocks, reflecting liquid reserves, arbitrage and institutional support, and often trade at small premiums. By contrast, the crypto collateralized DAI comoves strongly with systemic risk, embedding mark to market leverage, on chain frictions and liquidation dynamics that mirror contagion effects in the banking literature. Our approach validates and extends recent work on stablecoin fragility and shows how design choices translate into distinct patterns of resilience or vulnerability under stress, with implications for regulation and digital asset market structure.

1. Introduction

When Silicon Valley Bank collapsed in March 2023 it sent a shockwave through the stablecoin market. The news that Circle held part of USDC’s reserves at the failed bank drove its price down to about $0.87. DAI, which is backed by crypto collateral, also slipped below its peg. This episode, together with earlier events such as the 2018 USDT reserve rumor discount and the 2020 Black Thursday crisis in DAI, highlights a fundamental divide in how different types of stablecoins behave under stress.

Fiat backed stablecoins such as USDC, USDT, BUSD and TUSD mainly face redemption bottlenecks during moments of panic. Because their backing sits in cash or liquid assets, arbitrage and institutional support usually close the gap quickly, and these coins often trade at a small premium rather than a discount during calm periods. By contrast, crypto collateralized coins such as DAI embed mark to market leverage, liquidation risk and on chain frictions directly into their design. When the underlying collateral becomes volatile or gas fees spike, liquidations cascade, arbitrage slows, and prices can swing both below and above the peg. This reflects the panic-driven withdrawals and cascading effects described in Diamond and Dybvig’s model of bank runs.

In Section 1, the analysis introduces the contrasting behavior of fiat-backed and crypto-collateralized stablecoins under stress, using the USDC–SVB banking shock, the 2018 USDT reserve-rumor episode, and Black Thursday (2020) to illustrate why collateral design and on-chain frictions matter. Section 2 reviews the existing literature on stablecoin stability, systemic risk transmission, and nonlinear modeling, drawing on the work of Lyons and Viswanath-Natraj, Grobys et al., and Klages-Mundt et al. Section 3 outlines the data and methodology, combining a Gaussian Ridge Neural Network estimation of daily stablecoin prices against four macro-financial risk indexes with a structured event analysis of major depegging episodes between 2018 and 2024. Section 4 reports the main results, showing that fiat-backed stablecoins exhibit low SSE and weak correlations with systemic risk, whereas DAI shows high correlation and mixed-sign coefficients. Model predictions are compared with real-world events to demonstrate that macro shocks affect fiat-backed coins briefly, while on-chain shocks cause deeper, asymmetric deviations in DAI. Finally, Section 5 discusses the implications for stablecoin design, financial stability, and the regulation of crypto-dollar instruments.

2. Literature Review

  Stablecoins resemble fixed exchange rate regimes because they promise convertibility at par, yet their credibility depends on collateral, redemption, and confidence. Lyons and Viswanath-Natraj (2023) show that fiat backed designs such as USDT and USDC remain close to par through arbitrage and redemption and often trade at small premiums. Grobys et al. (2021) document that crypto collateralized tokens such as DAI display nonlinear dependence on systemic risk indexes. These findings echo Diamond and Dybvig (1983), where stability is sustainable in good states but fragile when coordination failures and run dynamics emerge.

  A second group of studies focuses on how stablecoin designs embed different risk channels. Klages-Mundt et al. (2020) classify stablecoins into fiat backed, crypto collateralized, and algorithmic types and show that risk profiles vary sharply across designs. Crypto collateralized coins encode mark to market leverage, on chain liquidation risk, and settlement frictions. Algorithmic designs attempt to engineer stability reflexively but can amplify feedback loops. The Terra Luna collapse in 2022 confirmed these theoretical warnings, while the USDC–SVB banking shock showed that even fiat backed coins can temporarily lose their peg. Liquidity concentration on venues such as Curve 3pool and Binance has also shown that market microstructure can transmit stress (Briola and coauthors, 2023).

  A third group of studies applies advances in financial econometrics and machine learning. Mallqui and Fernandes (2019) and Shen et al. (2020) show that radial basis function neural networks outperform linear benchmarks in predicting asset prices and volatility. Corbet et al. (2021) survey machine learning applications in crypto markets and find that neural and recurrent architectures can identify volatility patterns that GARCH style methods may miss.

  This paper extends the literature by applying a radial basis function neural network to five leading stablecoins (USDC, USDT, BUSD, TUSD, and DAI) and linking daily prices to four macro financial risk factors. By evaluating both the sum of squared errors and the correlation between predicted and observed series, the analysis captures predictive accuracy and structural co movement with systemic risk. Combining model-based results with event-based evidence from major depegging episodes shows that fiat backed coins mostly experience short lived redemption pressures, while crypto collateralized coins encode collateral volatility and on chain frictions.

 3. Data and Methodology

This study employs a dual-method approach to examine stablecoin stability. On the quantitative side, the analysis constructs and trains a Gaussian Ridge Neural Network (GRNN) to model nonlinear sensitivity of stablecoin prices to macro-financial risk indexes. On the qualitative side, structured event observation complements the quantitative modeling.

It is important to note that the sample ranges differ across stablecoins, reflecting their varied launch dates. As a result, correlation estimates are not strictly apples-to-apples. For example, USDT and DAI have longer and more volatile histories than newer entrants such as BUSD and TUSD. This difference in data coverage should be taken into account when interpreting the comparative strength of correlations across stablecoins.

A. Quantitative framework

The quantitative analysis uses a Gaussian Ridge Neural Network (GRNN) to link stablecoin price with systemic financial risk indexes. Gaussian ridge functions capture the nonlinear behavior typical of stablecoins, which remain close to their peg under normal conditions but deviate sharply during systemic or crypto-specific stress. This design enables the model to detect nonlinear fragility that linear regressions fail to capture. The model uses daily values of four macro-financial indexes—NFCIRISK, KCFSI, STLFSI4, and the 10-Year Expected Inflation Risk Premium—from the Federal Reserve’s FRED database and use them as the feature vector (the model’s inputs).

The model estimates relationships between daily stablecoin prices to four macro-financial risk factors. Let

Each stablecoin (USDT, USDC, DAI, BUSD, and TUSD) is estimated independently using identical macro financial inputs and two hidden nodes. The model optimizes weights, centers, spreads, and biases by minimizing the sum of squared errors (SSE) between observed and predicted prices.

Step 1: Input Layer to Hidden Nodes (Linear Stage). For each hidden node j ∈ {1, 2}, the model computes a separate weighted sum of the four macro- financial risk factors plus a bias term:

Step 2: Hidden Node Activation (Nonlinear Stage). ach hidden node transforms its input through a Gaussian ridge function:

Step 3: Output Layer (Linear Stage). The hidden node activations are combined to generate the predicted price (or deviation from par) of the stablecoin:

where β₀is the output bias and v₁, v₂ are weights from hidden nodes to the output node.

Step 4: Error. The model’s error term for each observation is the difference between the predicted and actual price:

where y_t is the observed stablecoin price.

Step 5: Model Fit. The model measures overall fit using the Sum of Squared Errors (SSE):

and by the Pearson correlation coefficient between actual and predicted prices:

As shown in Figure 1, the network links four macro-financial risk factors to two hidden nodes and then to an output node representing the predicted stablecoin price or deviation from par.

B. Event Observation Framework

The analysis compiles a structured dataset of major stablecoin depegging episodes between 2018 and 2024 to complement the model-based analysis. For each event, the event window and the date of maximum deviation, the affected stablecoin or coins, the lowest observed price recorded on CoinMarketCap during the episode, and a classification of the primary trigger are documented. The analysis codes triggers as either macro-financial such as external banking shocks, market stress, or regulatory actions or on-chain/DeFi, including protocol-specific failures, liquidity imbalances, or infrastructure stress. This classification enables us to distinguish between stress transmitted through traditional financial channels and stress that originates within digital-asset markets.

The analysis draws events from multiple sources including industry reports, regulatory filings, market data providers such as DeFiLlama and Kaiko, and commentary from central banks. The framework organizes each episode into six thematic drivers of fragility: market stress and panic events, regulatory and legal drivers, blockchain and infrastructure dependence, issuer behavior and transparency, liquidity concentration and market microstructure, and adoption or utility shocks.

  This event-based framework captures dimensions of fragility, confidence, governance, and infrastructure bottlenecks that lie outside the scope of purely statistical modeling. Together with the GRNN estimation, it provides a more holistic view of stablecoin stability, linking sensitivity to systemic risk factors with the historical record of crises and structural vulnerabilities.

4.  Analysis Results

A. Quantitative Results: Stablecoin Sensitivity to Financial Risk Indexes (GRNN)

After minimizing SSE, the estimated input-to-hidden weights show a sharp contrast between fiat backed and crypto collateralized stablecoins. Fiat backed coinsload near one across systemic risk indexes, consistent with a muted and proportional response to macro conditions. By contrast, DAI exhibits large mixed-sign coefficients and a high bias term.

  Figure 2 plots SSE against correlation for the five stablecoins, illustrating how DAI diverges from the fiat backed group. Figure 3 shows the average absolute input-to-hidden weights by risk factor and stablecoin, highlighting the near-unit values of fiat coins and the much larger magnitudes of DAI.

The large mixed sign coefficients for DAI provide evidence that leverage and on chain frictions transmit macro shocks directly into the stablecoin’s peg. Fiat backed stablecoins work much like a currency board or a hard peg regime: they hold reserves in cash or short term government securities and can meet redemptions quickly, which limits how far prices move when stress hits. Crypto collateralized coins such as DAI are closer to a soft peg backed by volatile assets.

Because DAI’s collateral is marked to market on chain, any rise in systemic risk immediately cuts collateral values and pushes collateral ratios toward liquidation. This sets off margin calls, liquidations, and delays in arbitrage that make price swings larger and longer. On chain bottlenecks such as gas fee spikes or thin liquidity slow down adjustment further and create the kind of liquidity spirals seen in past financial crises. The large mixed sign coefficients estimated for DAI are not random noise but evidence that leverage and on chain frictions transmit macro shocks directly into the stablecoin’s peg.

B. Event Observations: Linking Model Predictions to Real World Stress Episodes

A structured dataset of major depegging episodes between 2018 and 2024 was compiled, recording for each episode the event window, affected stablecoins, lowest observed price on CoinMarketCap, and the primary trigger categorized as either macro-financial or on-chain/DeFi. Table 1 summarizes these events. Macro events such as the SVB banking shock and BUSD’s regulatory action caused temporary but pronounced deviations in fiat backed coins. On-chain events such as Black Thursday (2020) and Curve 3pool imbalances (2023) produced sharper and more asymmetric deviations in DAI and USDT. Comparing GRNN predictions with observed prices shows that the model captures macro-financial sensitivity but underestimates DeFi-specific shocks, consistent with its input structure based on four systemic risk indexes.

 TABLE 1—MAJOR STABLECOIN DEPEGGING EPISODES, 2018–2024

Figure 4, Figure 5 and Figure 6 compare DAI price behavior with Ethereum market conditions in March 2020.

 The first figure plots daily DAI deviations from one dollar together with the Ethereum average gas price. Deviations rise when gas fees are elevated, which suggests that network congestion makes it harder to execute arbitrage or liquidations that would normally stabilize the peg. The second figure contrasts DAI daily highs and lows with the Ethereum low price over the same dates. Around mid-March, when Ethereum volatility jumps, the DAI high low spread widens at the same time, which points to stress in collateral mechanics and liquidity. Importantly, DAI moved both above and below one dollar. It fell below par when confidence weakened after collateral auctions failed to clear, and it rose above par when liquidators and arbitrageurs needed DAI to repay vault debt, which created temporary scarcity. Taken together, the patterns indicate that DAI instability during stress reflects not only broader market shocks but also on chain frictions such as high gas costs and collateral volatility.

Figure 5. Ethereum Average Gas Price in March 2020

5. Conclusion

This paper analyzes the stability of leading stablecoins using a nonlinear machine learning model combined with a review of major depegging episodes. The results reveal a clear divide between fiat backed and crypto collateralized designs. Fiat backed stablecoins show muted and short lived deviations from their pegs during external shocks, reflecting liquid reserves, arbitrage and institutional support. In contrast, the crypto collateralized DAI moves more strongly with systemic risk, embedding mark to market leverage, on chain frictions and liquidation dynamics similar to run effects in traditional finance. Linking model based evidence to historical stress events validates and extends recent work on stablecoin fragility, showing that fiat backed coins behave like tightly managed exchange rate regimes, while crypto collateralized coins resemble leveraged intermediaries whose stability depends on collateral valuation, market infrastructure and the speed of on chain adjustments. Future research could integrate real time indicators of liquidity and collateral quality, examine the market microstructure of trading venues and bridges, and test policy or design interventions such as redemption limits or insurance funds to better assess stablecoin resilience under stress.

References

Briola, Riccardo, and coauthors. 2023. “Anatomy of a Stablecoin Run: Evidence from the Terra-Luna Collapse.” Finance Research Letters, 51: 1544–6123.

Corbet, Shaen, Brian Lucey, Larisa Yarovaya, et al. 2021. “Machine Learning in Cryptocurrency Markets: A Survey.” Finance Research Letters.

Diamond, Douglas W., and Philip H. Dybvig. 1983. “Bank Runs, Deposit Insurance, and Liquidity.” Journal of Political Economy, 91(3): 401–419.

Grobys, Klaus, et al. 2021. “Stablecoins and Systemic Risk: Nonlinear Dependence and Stress Episodes.” Finance Research Letters.

Klages-Mundt, Ariah, Dominik Harz, Lewis Gudgeon, Jun-You Liu, and Andreea Minca. 2020. “Stablecoins 2.0: Economic Foundations and Risk-based Models.” AFT ’20, 59–79. ACM.

Lyons, Richard K., and Ganesh Viswanath-Natraj. 2023. “What Keeps Stablecoins Stable?” Journal of International Money and Finance, 131: 102838.

Mallqui, Daniela C., and Ricardo A. Fernandes. 2019. “Predicting the Direction, Maximum, Minimum, and Close Values of Daily Bitcoin Price Using Machine Learning Techniques.” IEEE Access, 7: 148551–148563.

Shen, Dawei, et al. 2020. “Nonlinear and Deep Learning Approaches to Crypto Asset Volatility Forecasting.” Applied Soft Computing.

About the author

Abhiram Kode

Abhiram is a rising 11th-grade student at Rock Hill High School in Frisco, Texas, with strong academic and research interests at the intersection of finance, technology, and engineering. He is deeply passionate about investment banking, fintech, cryptocurrency markets, and applied robotics, and actively pursues opportunities that blend analytical thinking with real-world problem solving.

Beyond research, Abhiram tutors mathematics at Kumon, plays varsity tennis, and participates in competitive chess and speed cubing. He has earned multiple national and international awards in mathematics and science Olympiads. Known for his curiosity, discipline, and self-driven learning, Abhiram aspires to pursue a future career that combines finance, technology, and innovation.

The post Stablecoin Stability Under Stress appeared first on Exploratio Journal.

The purpose of this paper is to examine the competitive dynamics and cultural significance of the three leading footwear brands of the world; Nike, Adidas and Puma. This research compares the brands through a detailed analysis of their origins, their strategies and market influences. Based on their financial reports, origin stories, and the market surveys, the study explores how each brand’s approach has shaped the global sportswear culture.

Key findings reveal that Nike leads the global athletic footwear market. A major cause being their storytelling through every shoe strategy, their extensive market campaigns and its cultural integration that connects the Nike shoes with every generation. Adidas, though strong in heritage and design innovation, focuses majorly on sustainability and fashion collaborations, keeping it the loop with newer generations. Puma, by its celebrity and motorsports collaborations, has solidified its position as a culturally lifestyle brand. These three brands together, have revolutionized the athletic footwear market and have transcended into being more than just footwear labels, and have become an expression of creativity and identity for generations today. The study concludes that even though Nike clearly dominated in revenue and influence, Adidas and Puma still continue to diversify and expand with their partnerships, innovations and style, ensuring that in the end the global footwear market remains dynamic and competitive.

Swoosh, Stripes, and the Pouncing Cat: The Shoe Showdown

The footwear industry, which was valued at over $138 billion globally in 2024, is one of the most competitive and culturally influential markets in today’s economy. At the heart of the industry lies a fascinating tale of sibling rivalry that fundamentally gave birth to not only two of the most widely recognized brands but also the entire landscape of sports marketing and consumer culture. The story of Adidas and Puma starts with a family feud between two brothers Adolf Dassler and Rudolf Dassler, whose personal conflicts in a small Bavarian town gave birth to two sporting goods’ giants that continue to battle for market dominance even today. Along with that, the emergence of another American powerhouse, Nike, which revolutionised athletic marketing through strategic celebrity endorsements and cultural positioning, fundamentally transforming how sports brands connect with consumers, is discussed in this paper.

Together, these three companies have not only dominated the athletic footwear market but have also played crucial roles in shaping pop culture and its sub-tiers such as sneakerhead culture and the integration of sports brands into fashion, music, and esports.

A Story of Two Brothers: The Origin of Adidas and Puma

Adolf “Adi” Dassler and Rudolf “Rudi” Dassler grew up in the small Bavarian town, Herzogenaurach in Germany in a shoemaking family. They grew up learning about the trade in a town long known for footwear. In 1924, operating from their mother’s laundry, they founded Gebrüder Dassler Schuhfabrik (The Dassler Brothers’ Shoe Factory). By 1925, they were handcrafting leather soccer boots with nailed studs and track shoes with forged metal spikes for runners. Adi Dassler’s constant experimentation and his urge to innovate laid the foundation for their future successes. From the 1928 Amsterdam Olympics they gained quick successes and built up a reputation from the German champion Lina Radke winning gold wearing Dassler track shoes; and in 1932 a German runner taking bronze wearing Adidas football boots. Their major turning point came in the 1936 Berlin Olympics. The brothers had developed a close relationship with coaches of the German Olympic team, and also assisted as volunteer track coaches. Most famously, American sprinter Jesse Owens won four gold medals in Berlin wearing the Dasslers’ spiked running shoes. This victory in Hitler’s showcase Games gave the tiny factory global exposure.

In 1933 with Hitler’s rise, both the brothers joined the Nazi party (like many German businessmen of the time) and became local members of the Nazi athletic programs.This affiliation actually helped their business; the regime emphasized physical fitness and athletic competition, and the Dassler firm secured large orders. Sales grew rapidly, and by the mid-1930s almost all German Olympians were wearing Dassler spikes. When World War II began, the Dassler factory was converted to war production. Wartime shortages strained the family business, and family tensions festered.

According to later accounts, a critical incident occurred in 1933 when during an Allied air raid, Adi and his family took shelter in a bunker. He reportedly exclaimed “The bastards are back again, ” referring to the enemy planes, just as Rudi and his family entered the bunker. Rudi apparently interpreted the comment as being directed at himself and his family, deepening his mistrust of Adi.

When Rudi was captured by American forces near the end of the war, he was accused of being a member of the SS. He suspected that Adi had betrayed him, possibly to remove him from the company. Rudi’s subsequent time in a U.S. prisoner-of-war camp only hardened that belief. Later, during the denazification process, Adi too was accused, but he claimed Rudi was the one sabotaging him behind the scenes. Neither could prove their accusations, but all trust between them vanished.

In April 1948, they formalised their split. They divided everything: the staff, the machines, even the family. Adi retained the original factory on the northern bank of the Aurach River and launched his own company, naming it Adidas. On the southern side of the river, Rudi too started his own venture which he first named Ruda but later changed it to Puma. By 1949, two companies were born from the ashes of one broken relationship.

The Birth Of Nike

Nike was originally founded as Blue Ribbon Sports (BRS) on 25 January, 1964 by a University of Oregon track athlete Phil Knight and his coach, Bill Bowerman, each contributing $500 to the startup. Initially BRS was a sole American distributor for the Japanese shoe company Onitsuka Tiger (now known as Asics) with Knight selling shoes out of his car trunk at track meets. By 1966, BRS had successfully opened their first retail store in Santa Monica.

By 1970, tensions arose between BRS and Onitsuka over design rights when Onitsuka secretly began lining up other distributors. Onitsuka even proposed taking a 51% stake in BRS, trying to take control over the business built by Bowerman and Knight. This gave Bowerman and Knight the idea to create their own brand in 1971. They designed shoes with prior proven designs like the Cortez (that was designed by Bowerman but was previously being sold under the brand Onitsuka Tiger) and arranged to start their own manufacturing in a Mexican factory that was already producing goods for big western companies like Adidas to ensure professional-grade quality oriented towards the U.S market.

Now all BRS needed was a new brand name and a logo; multiple names were considered like the “Dimension Six, ” “Bengal, ” “Falcon” , but none resonated until Jeff Johnson suggested “Nike” , after the Greek goddess of victory. Knight admitted he felt it was the strongest option among them all, being short, memorable, and culturally resonant. To create a logo that embodied movement and differentiation from earlier brands like Adidas and Puma, Nike enlisted Carolyn Davidson who was a Portland State University design student working part-time for BRS. She presented six different ideas and sketches out of which the “Swoosh” , that was a clean, dynamic checkmark was chosen, even though Knight admitted, “I don’t love it, but it will grow on me. ” They paid Davidson $35 for her work in 1971 and hence Nike became as we know it today.

Brand Positioning in Popular Culture

Adidas, Puma and Nike all have set the stage on fire with their pop culture collaborations that have brought out a new world of fashion.

In 2018, Adidas relaunched their Samba’s which were earlier soccer shoes in the 1950s, that were featured in major films and series like “That ‘70s Show” and “Beverly Hills Cop” . Adidas has also relaunched the Superstars, which were popular among basketball players in the 1970s, and was worn by 75% of NBA stars in 1973. Adidas has also partnered with designers and pop stars like Pharrel Williams and Jeremy Scott, releasing new sneaker designs and apparel.

Puma has collaborated majorly with the global pop star Rihanna, making her the creative designer in 2014 and launching the Fenty Creeper in 2015, which instantly became a trend setter, being sold out in just a few hours. Puma also relaunched the Speedcat OG. This was originally a street item, but would now be worn by F1 icons, because of Puma’s role as the official provider for F1 apparel.

Puma also collaborated with music stars and fashion icons like Jay-Z, Big Sean, Karl Lagerfeld, Trapstar, The Weeknd, BTS, J. Cole, and Alexander McQueen, each bringing something unique to their apparel, making the company more culturally positioned. These ongoing partnerships span apparel lines and endorsement deals, contributing to Puma’s culturally positioned brand image.

Nike has successfully merged pop‑culture and gaming through high‑profile collaborations and strategic esports partnerships. Its collaborations with artists like Travis Scott and Virgil Abloh have created sneaker releases that sell out instantly. Shoes like “Cactus Jack” and “The Ten” have defined sneakerhead culture, and have generated massive resale premiums.

Esports and Digital Marketing Strategies

Adidas became the Official Merchandise Sponsor of the Esports World Cup 2024 that was held in Riyadh, and provided the full apparel ranges for players and staff, highlighting their ambition to embed the brand into high-profile esports tournaments. Adidas also collaborated with the group 100 Thieves releasing co-branded merchandise which included jerseys, tracksuits, Rivalry sneakers, and also accessories. The brand collaborated closely with the organisation’s founder, Nadeshot. Apart from this, Adidas also partnered with the Gaming Icon, Tyler “Ninja” Blevins, making him the first pro sponsor.

Puma became Gen.G’s (previously known as KSV Esports) official global provider for jerseys and apparel. Aside from that Puma has also collaborated with major esports groups like Cloud9, RKDO apparel, and has thus released new apparel, jerseys, and much more.

Nike on the esports front, has entered into major partnerships, including a multi-year jersey and footwear deal with China’s LPL (League of Legends Pro League), an agreement with the prominent Korean team T1, and a co-branded sneaker drop with the Faze Clan. These collaborations are all meant to legitimize the gamers as athletes and to integrate fitness into the gaming culture with the help of merchandise. Nike by the help of the partnerships has reinforced its identity in all the markets whether it be athletic performance, music culture, or digital enforcement, by aligning both sneaker-obsessed collectors and the emerging gaming influencers.

Market Dominance in Collector Communities

Sneakerhead Culture started in the late 1970s-80s, and revolved around collecting, showcasing, and obsessing over rare, limited-edition sneakers. The sneakerhead movement engaged popular demand and elevated shoes and sneakers like Adidas Superstars, Puma Suede/Clyde, and particularly Nike Air Jordans which released new, rarer-than-ever sneakers, which defined the collector peak among sneaker enthusiasts. Nike’s strategic rarity, like the “Banned” Air Jordan 1, turned sneakers into status symbols and cultural statements rather than mere athletic gear.

Nike didn’t just start the sneaker game, it wrote the playbook, and Adidas and Puma are still trying to read it. According to the Colorful Socks’ 2025 report, “In 2020, Nike and Air Jordan combined held 71.3% of the sneaker resale market, while Adidas accounted for 27.9%. ” Similarly on StockX in 2020, Nike constituted 50% of all sneakers resold on the website. As Forbes observes, Nike has historically “ensured supply never quite meets demand, ” transforming its limited‑edition releases into a $1 billion+ secondary market driven by scarcity and hype.

Nike’s dominance in the sneakerhead culture relies on two pillars:

• Iconic athlete partnerships and limited edition drops
• Cultural relevance

The 1985 launch of the Air Jordan I, tied to Michael Jordan’s rookie season and the NBA “banned” controversy, generated $126 million in sales by season’s end and elevated the sneaker to a symbol of rebellion and aspiration. High-Profile Collabs like Travis Scott’s Cactus Jack Jordans and Virgil Abloh’s Off‑White “The Ten” project dominate the collectors’ collections.These partnerships routinely spark immediate sell‑outs and massive resale markups, further cementing Nike’s cultural cache. In addition, Nike’s limited drops keeps collectors on high alert leading to a strategic outburst of resale.

Nike today is more culturally relevant than its German counterparts not because of luck, but due to strategy. Nike turns every sneaker into a story of triumph, whether it’s the “Just Do It” ethos, the “Banned” Jordan ads, or athlete origin tales; so wearing its shoes feels like carrying a piece of that winning narrative.

Adidas and Puma both lag behind Nike. Although Adidas’s sneaker lineup of Superstar and Yeezy have a strong following, its resale market peaks at around 30%, due to the lack of consistent scarcity tactics. Puma isn’t a go‑to for serious collectors because its shoes don’t sell much on resale sites and it hasn’t teamed up with as many big‑name athletes, so most people think of it more as a fashion brand than a collector’s favorite.

Comparative Strategic Analysis

To answer this question, a number of different aspects have to be taken into consideration.

Nike has always dominated the internet as compared to the other footwear giants. Nike’s digital dominance represents perhaps the clearest indicator of its cultural supremacy. Nike with over 300 maintained social media profiles and a staggering follower count of more than 300 million followers, clearly dwarfs Adidas and Puma, who maintain significantly smaller social media presences of approximately 50-60 million and 20-30 million followers respectively. Nike’s fan following shows how the brand creates targeted content that resonates with diverse audiences and how the fans wait for new drops and apparel.

The revenues of the three companies depend on a very important factor that has not yet been discussed: generational preference. According to the latest (2024) Piper Sandler survey, 61% of teenagers prefer Nike as their footwear brand, a commanding lead that has persisted for over 12 consecutive years. This dominance becomes more striking when compared to competitors. Adidas has fallen to just 6% teen preference, while Puma remains below 5%. Nike has continued to maintain this lead over the years representing just how culturally connected it is with youngsters. Apart from the teen preference, Nike leads in being preferred amongst young adults (18-34), adults ages 35-49, and even consumers over 50, maintaining the highest share across all age groups, though its dominance is most pronounced among younger adults, as newer, more comfort oriented brands like Sketchers and New Balance are gaining more preference among older age groups.

Nike’s celebrity collaborations represent a wave of cultural influence. The brand invested $4.29 billion in marketing alone during 2024, with a significant portion dedicated to athlete and celebrity endorsements. Adidas and Puma too, invested heavily in marketing, with Adidas investing around $3.04 billion in 2024 and Puma investing around $1.86 billion in 2024. In 2024 Nike had higher revenue than its competitors due to its heavily invested marketing campaigns. In 2025 Nike is likely to invest more than $4.60 billion on marketing. Adidas and Puma on the other foot, make a significant jump with Adidas likely to invest $8.07 billion in 2025 and Puma investing somewhere around $2 billion.

Nike has used various out of the box strategies for marketing and for remaining the customer’s first preference, and still continues to do so. One of these was Nike’s “Banned” Controversy, as many call it. In 1985 Michael Jordan wore black and red sneakers, later famously known as the Air Jordan 1s, which violated the NBA’s strict uniform color policies. Despite this fact, Nike acknowledged this issue by continuing to pay the $5000 fine that was charged every time Jordan wore these shoes in the NBA game. Nike capitalized on the “forbidden” status of the shoes by launching an ad campaign proclaiming the shoes were so bold they’d been “banned, ” but the NBA “can’t stop you from wearing them. ” This strategy was a success. As a result, fans flocked to own the “Banned” sneakers, and Nike sold $70 million worth of Air Jordans just months after release, with over $100 million by the end of 1985. Hence, Nike invented and used a “kick-start” strategy to market the shoes and to create one of the most memorable brand legends in sports history; proving sometimes, breaking the rules is the perfect fit for success.

Conclusion

After looking at the tales of Adidas, Puma and Nike, it is clear that all three companies have made a huge impact on sports, fashion, and even the way people express themselves. Each brand started from extremely humble beginnings, especially Adidas and Puma, that was created due to a family split. Nike which was founded a bit later, brought its own culture and started trends that even today are seen being followed. Yet, Adidas and Puma can’t be left unnoticed as they too have loyal fans who are always looking to stand out, with creative designs and unique partnerships that keep them and the company in the spotlight. What really stands out about all brands is how they have changed with the course of time. From early days focused on athletes, to now working with musicians, gamers, and artists, they have helped shape what’s cool in fashion and entertainment. Even today, new generations find something exciting in their stories, their symbols, and their styles.

In the end, whether someone prefers the classic three stripes, the pouncing cat, or the Nike swoosh, it shows how these companies have become more than just brands. They’re part of the way people show who they are, stay active, and feel connected to something bigger. And no matter which one is the most popular, it’s clear all three have played a big part in shaping the culture around us.

References

(2023). Sneaker Sale Statistics. Retrieved September 29, 2025, from https://runrepeat.com/sneaker-resale-statistics https://www.forbes.com/sites/deborahweinswig/2016/03/18/sneaker-cult ure-fuels-1-billion-secondary-market/

(2024, April). Sneakers Market future insights. Retrieved September 28, 2025, from https://www.futuremarketinsights.com/reports/sneakers-market

(2025). Sneaker Resale Market Statistics. Retrieved September 28, 2025, from https://bestcolorfulsocks.com/blogs/news/sneaker-flipping-market-statistics

Carlson, D. (n.d.). Nike, Inc. | History, Logo, Headquarters, & Facts. Britannica. Retrieved September 28, 2025, from https://www.britannica.com/money/Nike-Inc

DECA & Piper Sandler. (2024). DECA and Piper Sandler complete 48th semi-annual survey. Piper Sandler Teen Survey. Retrieved September 29, 2025, from https://www.decadirect.org/articles/deca-and-piper-sandler-complete-48t h-semi-annual-survey

Income Statement – adidas Annual Report 2024. (2025, March 5). adidas Annual Report 2024. Retrieved September 28, 2025, from https://report.adidas-group.com/2024/en/group-management-report-fina ncial-review/business-performance/income-statement.html

NIKE, Inc. Reports Fiscal 2024 Fourth Quarter and Full Year Results. (2024, June 27). Nike Investor Relations. Retrieved September 28, 2025, from https://investors.nike.com/investors/news-events-and-reports/investor-n ews/investor-news-details/2024/NIKE-Inc. -Reports-Fiscal-2024-Fourth- Quarter-and-Full-Year-Results/default.aspx

Nike, Inc. – Wikipedia. (n.d.). Wikipedia, the free encyclopedia. Retrieved September 28, 2025, from https://en.wikipedia.org/wiki/Nike,_ Inc

Puma SE Annual Report 2024: Combined Management Report Overview. (2024). Puma Annual Report. Retrieved September 28, 2025, from https://annual-report.puma.com/2024/en/combined-management-report/ overview-2024/index.html

SankeyArt.com. (2024). Nike 2024 Income Statement Sankey Diagram. Nike 2024 Income Statement Sankey Art. Retrieved September 29, 2025, from https://www.sankeyart.com/sankeys/public/21614/

SGB Online. (2025). Piper Sandler Teen preferences in shoes. Retrieved September 29, 2025, from https://sgbonline.com/exec-piper-sandlers-fall-survey-finds-continued-shifts-in-brands-winning-with-teens/

Sports History Weekly. (2025). Story of Adolf and Rudolf Dassler. Story of Adolf and Rudolf Dassler. Retrieved 09 28, 2025, from https://www.sportshistoryweekly.com/stories/adidas-puma-sneakers-sho es-germany-adolf-rudolf-dassler,1245

Wikipedia. (2025). Dassler brothers feud. Dassler brothers feud – Wikipedia. Retrieved September 28, 2025, from https://en.wikipedia.org/wiki/Dassler brothers _ _

About the author

Divyansh Garg

Divyansh is a 10th grade student from India. He is passionate about economics and fascinated by the world of business, brands, and marketing, hence he was naturally inclined to write a paper on something related. Divyansh enjoys researching and writing on such topics.

The post Athletic Footwear Market Dynamics: A Comparative Analysis of Nike, Adidas, and Puma appeared first on Exploratio Journal.

Abstract

Indian startup ecosystem has exploded in the past decade, calling for a deeper understanding in the factors associated with this growth. Previous research on literacy rates in India have found a strong correlation with economic growth (Desai, 2012). This led me to the hypothesis that literacy might also affect startup activity in a region. In this study, I will compare the growth in the number of startups in India and literacy separately, then conduct a regression analysis on the literacy rates and startup counts across four major sectors (AI, Green Technology, Healthcare and Lifesciences, and IT Services) in multiple Indian states to determine whether there is a relationship between the two. Contrary to what I initially hypothesized, I did not find a strong association between the number of startups in a region to the literacy rate. These results might help us guide government policies and resources more effectively and it challenges the assumption that entrepreneurial growth is linked with literacy.

Keywords: Literacy Rates, Startup Growth, Regional Development, Entrepreneurship

I. Introduction

A. Background Information

India has seen rapid growth in startup activity since the mid-2010s, driven by digital adoption, funding flows, and sectoral innovations (especially in technology and AI). The number of new startups in India in the year 2016, identified by the Department for Promotion of Industrial and Internal Trade (DPIIT), was 502, while in the year 2023 it was 34842 (Department for Promotion of Industry and Internal Trade [DPIIT], n.d.-b).

This growth in startup activity has been associated with growth in a vast number of other fields. This includes technology, innovation, job creation, economic development, and overall societal progress. Startups lead to the disruption of pre-existing industries and form the path for advancement, along with acting as major job creators. Thus, promoting startups is essential for the overall economic and social development of a country (Kumar & Yadav, 2024).

To boost startup growth, the Government of India has undertaken multiple initiatives, which include the Startup India Initiative (Department for Promotion of Industry and Internal Trade [DPIIT], n.d.-a), which involves several programs to support entrepreneurs and provide benefits like startup recognition, tax exemptions, easier regulatory compliance and funding support to entrepreneurs. Apart from this, it has also launched programs like Make in India (2014) and Digital India (2015), encouraging domestic manufacturing and boosting digital infrastructure.

On the other hand, Indian literacy rates have had a consistent increase since the 1980s, rising from 43.6% in 1981 to 63.82% in 2011 (Registrar General & Census Commissioner, India, n.d.). In India, the literacy rate is calculated as the percentage of people aged 7 and above who can both read and write with understanding in any language. Literacy rates have been associated with economic growth and rural development in the country.

While the relationship between literacy as a factor of human capital and economic participation has been thoroughly explored, its direct relationship with startup activity is still underexplored.

Determining the factors that could be associated with the growth in startup activity could help us boost startup growth in regions with comparatively lower growth. It would also help us determine how to direct government funds more effectively, promoting maximum growth in both startup activity and literacy targeted across various industries and sectors.

In this study, I aim to investigate the extent to which state-level literacy rates in India correlate with the density and sectoral distribution of startups.

B. Thesis Statement

This study explores the relationship between literacy rates and the density and distribution of startups based on state and sector in India.

This study treats the two phenomena separately: first documenting the growth of startups, then documenting literacy trends. While both literacy and the number of startups in India have risen over time, regression analysis shows that there isn’t a strong relationship between the two, suggesting that literacy isn’t a suitable predictor of the number of startups.

II. Methodology

A. Growth of Startups

In India, the surge in the number of startups started in the 2010s. This was promoted by new government policies and the IT boom in the 2000s.

Figure 1 is a graph of the total number of new startups identified every year by DPIIT from 2016-2023. We can see that the number of startups grew rapidly in the period, with around 502 new startups in 2016 to 34842 new startups in 2023. For the purpose of comparing the growth, I have assumed that the number of startups in any state and industry in any year is approximately equal to the cumulative sum of the new startups from the year 2016. In our data, the number of startups in the year 2023 is 123412, while the number of startups reported by DPIIT is 117254. This slight difference might arise due to the regular updates in the list of currently active startups, which might have caused the delisting of startups which were shut down, merged or lost eligibility. In the time considered by us (2016-2023), most of the startups are new, so we can assume that most of them have not lost their eligibility yet.

These startups were split into multiple sectors, with many of them being in emerging industries and well-established ones like IT Services, Healthcare & Lifesciences, Construction, Agriculture, Food & Beverages, and Education. Figure 2 shows a rough split between these industries in the year 2023.

This study will focus on four large sectors, namely AI, Green Technology, Healthcare and Lifesciences, and IT Services. AI is an emerging sector, which has been growing rapidly for the past few years, while the other three (Green Technology, Healthcare and Lifesciences, and IT Services) are sectors which have grown consistently over a long period of time, thus allowing us to explore both recent and well-established sectors.

B. Growth of Literacy

In India, literacy rates have risen consistently since the country’s independence in 1947. On comparing the literacy percentages in India from the year 1951, we find that there is a linear growth in the same, as shown in Figure 4. Since the 2021 all-India census was delayed, we can assume that the growth till the year 2021 would have remained consistent and can thus project the value of the same. Here, the projected value for the year 2021 can be calculated by the average growth rate of 9.1% points per 10-year period. So, the graph projects an 82.1% literacy rate in the year 2021.

It is important to note that these values may differ from the actual literacy rates, and do not account for the changing policies or other external factors which may affect the literacy rate. For example, the Ministry of Statistics & Programme Implementation’s annual report for 2023 projects an overall literacy rate of 80.9%, suggesting a slightly lower growth during this time span. However, within the scope of this study, we can assume that the growth has remained consistent in the 10-year period of 2001-2011.

In an ideal scenario, the data for literacy would be available for the same period as the startup data from 2016. However, since the 2021 census in India was postponed, I will be comparing the percentage change in literacy rates across different states from the year 2001 to the year 2011.

In Figure 5, we can see that there has been a growth in the literacy rate in every state in the decade 2001-2011. For my analysis, I have considered the growth in these states in this period and compared them with the growth in the number of startups from 2017-2023.

C. Regression Results

To determine the relationship between the literacy rate and the number of startups in a region, we have considered the percentage change in the number of new startups identified by DPIIT from 017 across four major sectors (AI, Green Technology, Healthcare and Lifesciences, and IT Services) to 2023 and run a linear regression analysis with the percentage change in literacy from 2001 to 2011 across multiple Indian States. Since we do not have accurate values for literacy during the period of startup growth, my analysis here is exploratory. Additionally, while the literacy rates consider a 10-year period and the startup data considers a 6-year period, during the selected timeframes, both exhibit a near-linear growth, allowing a meaningful comparison. The collected data was cleaned and missing/null values were dropped for every single sector.

It is worth noting that since the literacy rates were rising consistently both before and during the sudden rise in startup growth, as confirmed by projected results from DPIIT and MOSPI, it is unlikely that there is a reverse causal relationship between the number of startups and literacy rates, i.e., the number of startups does not strongly influence the literacy rate of a region.

A broad overview of the data used in our analysis is listed in Table 1. In the analysis, I dropped the values from different states for each of the four industries separately for which the percentage change could not be calculated, due to null values in the starting year (2017).

i. Startups in the AI Sector vs Literacy Rate

I have used the OLS (Ordinary Least Squares) regression for quantifying the linear effect of literacy on the number of AI startups in a region. From Table 1, we can see that we have considered a total of 13 states in this analysis. The p-value in this table is quite high (0.353), which makes this result statistically insignificant, i.e., there is no strong evidence of a linear association between the two variables.

ii. Startups in the Green Technology Sector vs Literacy Rate

In Table 3 and Figure 7, we are trying to determine if a linear relationship exists between the Literacy Rate and the number of Green Technology Startups in a region. Here, I have considered 16 states for the regression. In this case too, the p value is extremely high (0.656), thus making the result statistically insignificant.

iii. Startups in the IT Sector vs Literacy Rate

Table 4: Regression Results between the Literacy Rate and the Number of IT Services
Startups in a region

Figure 8: Linear regression comparing the Literacy Rate to the Number of IT Services Startups in a region

In Table 4 and Figure 8, we are trying to determine if a linear relationship exists between the percentage change in literacy rate and the number of IT Services startups in a region. Here, I have considered 23 states for the analysis. In this case, the p-value is much lower than the previous cases (0.041), which suggests that this result may be statistically significant. The slope here is approximately 22.03, i.e., a 1%-point increase in the literacy rate is associated with ~ 22 new IT startups per year. Additionally, literacy accounts for ~ 19% of variance in annual new IT Services startup counts (R² = 0.185).

iv. Startups in the Healthcare and Lifesciences Sector vs Literacy Rate

Table 5: Regression Results between Literacy Rate and the number of Healthcare and
Lifesciences Startups in a region

Figure 9: Linear regression comparing the Literacy Rate to the number of Healthcare and
Lifesciences Startups in a region

Finally, through Table 5 and Figure 9, the results of a regression analysis between the percentage change in literacy rate of a region and the percentage change in number of Healthcare and Lifesciences Startups identified in a year are presented. Here, we have considered 20 states for the analysis. This result is also statistically insignificant (p = 0.444).

D. Discussion

From the given regression tables, we can infer that the relationship between literacy and the number of startups varies across different sectors. Since the p-values of the regressions of literacy with the number of startups in the sectors Healthcare and Lifesciences, Green Technology and AI are large, we can say that any relationship in them is not meaningful. The relationship between literacy rate and IT Services startups might require further research, as the data considered shows a meaningful statistical relationship. This might indicate that literacy might be associated differently across sectors.

The findings aligned closely with those predicted by the log-normalized regression model as well. Given the small sample size and limited model, this relationship might not be extremely meaningful and requires further analyses. Additionally, it is important to note that since literacy data may not grow at the same rate as I predicted, this study only provides an exploratory insight.

III. Conclusion

Through this exploratory study, I compared the growth patterns for startups and literacy separately, with the initial growth in startups starting much after literacy. By analyzing the growth of startups in India, we realized that while their distribution is highly uneven across sectors and regions, the growth across them is fairly consistent. Additionally, this growth only began recently, unlike literacy, which has had consistent growth for a long time.

Comparing linear regression results of the number of startups in a region across four major sectors (AI, IT Services, Green Technology, Healthcare and Lifesciences) and the literacy rates, we did not find a strong association between the two, except in the IT Services sector.

This study was limited due the absence of recent literacy data, after the initiation of startup growth in the country. Additionally, since the growth of startups began only recently, we are unable to identify larger patterns in its growth.

These results could be further explored at a larger scale for identifying which industries will receive a boost from literacy. Future research identifying the reason for the variance of this relationship across sectors and accounting for tertiary variables would help us ascertain whether this association exists across a larger range of sectors. This might allow for a more effective allocation of Government funds, since the growth in literacy might also affect startup growth in certain sectors. Additionally, the increase in startups in a certain sector (for example Edtech) could possibly boost literacy rates as well. Eliminating confounders like the overall economic development of a region and demographic composition would provide stronger results.

IV. References

DPIIT. (n.d.-a). About startup India initiative. Initiative. https://www.startupindia.gov.in/content/sih/en/about-startup-india-initiative.html 9 Ministry of Commerce and Industry, Department for Promotion of Industry and Internal Trade (DPIIT). (n.d.-b). Industry, state and year wise startups recognized by DPIIT till last week [Data set]. Open Government Data (OGD) Platform India. Retrieved 4 July, 2025, from https://www.data.gov.in/resource/industry-state-and-year-wise- startups-recognized-dpiit-till-last-week

Desai, V. S. (2012). IMPORTANCE OF LITERACY IN INDIA’S ECONOMIC GROWTH.

Katiyar, S. P. (2015). Growth of Literacy in India – A Trend Analysis.

Kumar, D. D., & Yadav, D. A. K. (2024). The role of startups in driving technological advancement in the Indian economy. Journal of Social Review and Development, 3(Special 1), 15–19.

Government of India, Ministry of Statistics and Programme Implementation, & National Sample Survey Office. (n.d.). Annual Report, plfs, 2023-24. Annual Report, Periodic Labour Force Survey (PLFS), 2023-24. https://dge.gov.in/dge/sites/default/files/2024- 10/Annual_Report_Periodic_Labour_Force_Survey_23_24.pdf

Open Government Data (OGD) Platform India / Government of India. (2016, August 5). Literacy rate from 1951 to 2011 | open government data (OGD) platform India. Literacy Rate from 1951 to 2011. https://www.data.gov.in/resource/literacy-rate-1951-2011

Registrar General & Census Commissioner, India. (n.d.). https://www.data.gov.in/resource/literates-and-literacy-rates-sex-census-2001-and-2011.

About the author

Aryan Bajoria

Aryan is a Class 12 student at Lakshmipat Singhania Academy, India. His academic interests lie in data science, artificial intelligence, computer science, and entrepreneurship. Outside academics, Aryan likes to build tech projects and research startup ecosystems and AI.

The post Literacy Rates and Startup Growth in Indian States appeared first on Exploratio Journal.

Introduction

Today jobs are very competitive. Many people try to find good jobs, and it is not easy. For this reason, teenagers’ first job is very important. It is like the first step for their future. Some research from OECD and BLS shows that jobs can change the future. Teenagers with jobs sometimes get better results later in life. But not every job is good. Some jobs make problems for school. In this essay I will write that part-time jobs can help teenagers’ careers, but it depends on how many hours they work and what kind of job they do.

Positive Impacts

There are some good aspects of part-time jobs. First, teenagers can earn money. They can use this money for school expenses, to support their family, or to save for the future. This point can be divided into two categories. Some teenagers need money to pay for necessary things or school supplies because their families cannot afford them. For example, the OECD (2025) reports that in less wealthy countries such as India or Brazil, students who work during school earn 5–10% more money in the future. In that case, their income also makes a small but important part of their family budget while they are still in school. In richer countries like Korea or the US, families may already have enough, but teenagers still get psychological benefits. They feel proud to use their own money, they understand the value of hard work, and they become more ambitious (Mortimer 2010).

Second, jobs teach skills. Teenagers learn to use time better, to be responsible, and work in a team. For example, if you work in a shop you must be on time, you must listen to your boss, and you must talk to customers. These things are not always taught in class. Later, they can get better jobs because they already have valuable work skills and experience. Research shows that teenagers who work part-time often develop soft skills like teamwork, problem solving, and leadership (Kroupova 2024). These skills are useful in college and their future career.

Third, part-time jobs let teenagers try different work. Maybe one student works in the library and sees if he likes that job. Another student works in a restaurant and sees if he doesn’t like it. Also, they can meet new people and learn from them. This can help them think about careers. Ballo (2022) writes that job experience is especially important for students from weaker family backgrounds because it gives them a better chance in the labor market. Safrul Muluk (2017) also found that students with average or high GPA can balance work and study, and this experience can help them finish school with experience that is useful for the future.

Negative Impacts

But jobs also have bad points. If a student spends a lot of time working, his grades may drop. Research shows that the exact number of hours is important. For example, the University of Washington (2011) found that students who work more than 20 hours per week often see lower grades and less school engagement. The University of Virginia (2012) also reports that students working over 20 hours show more stress and even higher risk of problem behavior. Sometimes they miss homework or feel too tired in class. The Monitoring the Future project (Staff et al., 2010) also explains that long hours reduce academic engagement and focus. Students may not attend class fully or may sleep less, which hurts their learning.

Also, jobs take away time from other things. In addition to having less time for schoolwork, students also have less time to take care of their health, and less time for friends. This can mean they skip meals, sleep fewer hours, or have little time to exercise, which makes them more likely to get sick. They may also lose important social connections with classmates because they cannot join after-school activities. As a result, they can feel very tired or stressed. Verulava (2022) showed that heavy part-time work can cause health problems like lack of sleep and high stress, and other studies also connect long work hours with depression and lower life satisfaction. Experts say that 10 to 15 hours per week is usually safe, but more than 20 hours is harmful (Kroupova 2024; OECD 2025). If the balance is broken, it is not good for their life.

Conditions

The results of part-time jobs depend on the conditions. Ten to fifteen hours per week is usually safe, but more than twenty hours is risky. “Safe” means that 10–15 hours normally does not harm school grades or health and sometimes even helps students gain skills. “Risky” means that when students work more than 20 hours, many studies show negative effects. For example, the University of Washington (2011) studied U.S. high school students and found that those working above 20 hours often had lower GPA and missed homework. Kroupova (2024) explained that high-intensity work reduces academic achievement, while the OECD (2025) said moderate hours are safe.

The quality of the job also matters. A job related to the student’s future career can give more useful experience than a simple job in fast food. For example, a library job can help a student interested in education, while a restaurant job may not connect to their goals.

Gender and family background also make differences. Mortimer (2010) found that female students in the U.S. often gain soft skills like responsibility and teamwork, while male students sometimes use jobs more for independence. Family support is also important. Ballo (2022) showed that students from poorer or single-parent families can benefit more from job experience, because it helps them enter the labor market faster. But Verulava (2022) found that students without strong family support feel more stress and health problems when they work too much. So the results are not the same for everyone, because gender and family support can change the outcome.

Counterarguments

Some students should work less. If a student wants to go to college, then he should focus on studying and not spend too much time at work. But working “less” does not mean “not working.” Research shows that working about 10–15 hours per week is usually safe, while working more than 20 hours often causes problems. Students who plan to go to college may still need to work some hours to help pay for tuition and other expenses. Students with a low GPA also need more time for school. If they reduce work hours, they can study harder, raise their GPA, and later get into a better college. This can give them better jobs and higher salaries in the future, even more than the money they earn from a part-time job now (OECD 2025). Freshmen and sophomores are young and should focus on classes, because early academic success is more important.

Some students should work more. If a student does not plan to go to college, then a job is more useful. Working “more” can also mean starting earlier. For example, students who want technical jobs can learn by practice, and those who want to begin their career at 17 or 18 instead of going to college could benefit from starting to work in high school, even as early as 14 or 15. Juniors and seniors are older, and they can handle more work hours than younger students.

Conclusion

Part-time jobs can help teenagers, but only with limits. Too much work is not good. The best way is to have a good job, the right number of hours, and support from schools and families. If these three things are together, then part-time jobs can really help for the future.

Bibliography

Bachman, Jerald G., Jeremy Staff, Patrick M. O’Malley, and John E. Schulenberg. 2011. “Adolescent Work Intensity and Substance Use: The Mediational Role of School Engagement.” Prevention Science 12 (2): 173–183. https://pmc.ncbi.nlm.nih.gov/articles/PMC2926992

Warren, John Robert, Paul C. LePore, and Robert D. Mare. 2012. “Adolescent Employment and Psychosocial Outcomes.” Research in Social Stratification and Mobility 30 (2): 135–149. https://www.researchgate.net/publication/258127684_Adolescent_Employment_and_Psychosocial_Outcomes?

Staff, Jeremy, John E. Schulenberg, and Jerald G. Bachman. 2010. “Adolescent Work and Academic Achievement.” In The Benefits and Risks of Adolescent Employment, edited by Jeylan T. Mortimer, 119–138. Washington, DC: National Academies Press. https://pmc.ncbi.nlm.nih.gov/articles/PMC2936460

Verulava, Tengiz, and Revaz Jorbenadze. 2022. “The Impact of Part-Time Employment on Students’ Health: A Georgian Case.” Malta Medical Journal 34 (1): 36–43. https://www.um.edu.mt/library/oar/bitstream/123456789/91260/1/MMJ34%281%29A6.pdf?

Kroupova, Zuzana. 2024. “Part-Time Employment and Educational Outcomes among Adolescents.” Journal of Youth Studies 27 (3): 295–312. https://pmc.ncbi.nlm.nih.gov/articles/PMC11315806

OECD. 2025. Teenage Part-Time Working: How Schools Can Optimise Benefits and Reduce Risks. Paris: OECD Publishing. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/teenage-part-time-working_75275b29/0dd35152-en.pdf?

Mortimer, Jeylan T. 2010. The Benefits and Risks of Adolescent Employment. Washington, DC: National Academies Press. https://pmc.ncbi.nlm.nih.gov/articles/PMC2936460

Ballo, Jari. 2022. “The Role of Student Employment in Higher Education and Its Impact on Students.” International Journal of Educational Research 115: 101–120. https://www.researchgate.net/publication/362764071_The_Role_of_Student_
Employment_in_Higher_Education_and_its_Impact_on_Students?

About the author

Youngwoo Nam

Born and raised in South Korea, Youngwoo is currently a student at Avon Old Farms School, where he is an active member of both the Business Club and the Math Club. He has a strong interest in pursuing a future career in business, particularly in the fields of economics and finance.

The post Balancing Work and Study: The Effects of Part-Time Employment on Teenagers appeared first on Exploratio Journal.

1. Introduction

Payment systems are fundamental organizational frameworks that allow economic entities to exchange economic value, such as goods, services, and financial assets. Payment systems have developed over time, both physically and technologically, from ancient barter to today’s digital economic infrastructure, acting as a unit of account, medium of exchange and store of value in various ways. See McLeay, Radia, and Thomas (2014), Boel (2019), and Peneder (2021).

In today’s economies, payment systems are more than means of transferring money; they represent institutional frameworks, create financial credibility, and rely on digital protocols. Modern bank deposits are essentially electronic forms of money; however, the mechanics of creating, clearing, and settling payments have undergone profound changes over the centuries. The evolution of payment systems includes commodity-based exchanges involving cattle, salt, and shells, to paper-based financial instruments including cheques and giro systems, and finally to today’s systems, such as ACH networks, payments cards, and mobile apps. See Chakravorti and McHugh (2002), Amith Donald Menezes (2017), and Anbukarasi and P.J (2024).

Network effects often support and ultimately influence the uptake and use of payment sys- tems because of the extent to which their increased use increases the value and usefulness of the payment system itself. Data-driven use has helped to create global payment standards such as SWIFT. Today, these systems not only perform a technical function, but also act as geo-economic levers in the politically sensitive global finance architecture. See Scott and Zachariadis (2012) and BIS (2020).

The shift to non-cash payments represents a major social change, with an emphasis on con- venience, speed, and efficiency. However, it raises questions about data protection, financial supervision, and the exclusion of marginalized users (Celestin and Sujatha, 2024).

2. From barter to early money: The origins of exchange

2.1 Limitations of the barter system

Barter is the direct exchange of goods and services. There were a number of restrictions on transactions that occurred with barter. Most notably, there had to be a double coincidence of wants – both trading parties had to possess what the other wanted. It is thought that this inef- ficiency limited the scope and complexity of the early trading networks and economic systems. Anthropologists study barter in order to understand the conditions under which it was prac- ticed. When it was present, barter was often incorporated into systems of gift exchange and informal lending, which suggests that it was not a primary mode of trade in most societies. See Hann (2006), García (2018), and Fauvelle (2025).

2.2 Emergence of commodity money

To reduce the vulnerability and restrictions of barter, many ancient societies began to adopt commodity money – objects that were widely used as a precursor of money. Commodity types vary by geography and culture. These items commonly exhibited key properties such as divis- ibility, durability, portability, and fungibility.

Historical evidence shows a wide range of commodity choices. Cowry shells were used as currency in Asia and Africa; salt was used as money in many Mediterranean and sub-Saharan African areas; cocoa beans circulated as money in Mesoamerica; and wampum beads circulated in early colonial America. Even in isolated settings, such as prisoner of war camps, cigarettes were used as money, as there was consistent demand for them from other prisoners. These early forms of currency were fundamental in developing the social conventions surrounding money and its use as a medium of exchange. See Radford (1945), Şaul (2004), Agha (2017), McKillop (2021), and Fauvelle (2024).

2.3. Transition to metallic coinage

Coinage metal originated in Lydia (modern-day Turkey) in the 7th century BCE. The first step of the process involved putting an electrum coinage, which is an alloy of gold and silver, into circulation. These pieces had markings of state symbols and standardized weights, helping develop trust and verifiability in exchanges while allowing the governing body to control and enforce money standards.

Metallic coinage stimulated long-distance and large-scale commerce, as it became a com- monly used money in imperial expansion. Gold and silver, being scarce, durable, and divisible, were widely accepted and symbolically reinforced the authority of states, contributing to the emergence of monetary sovereignty as part of a broader state-building project. See Mundell (2002), MacDonald (2017), Curta (2021), and Raza, Syed, Rizwan, and Ahmed (2025).

2.4 Theoretical frameworks in money’s origins

Metallist theory states that money developed spontaneously from market exchanges, as indi- viduals eventually started to accept the highly saleable or wanted commodity, such as gold or silver, not for its intrinsic usefulness, but because others accepted it in exchange. After a while, the general acceptance of commodity money began to take hold, as it gained momen- tum through its permanence, fungibility, security, and trust, reinforced by state protection. See Menger (1989) and Penchev (2014). The use of commodity money not only signified practi- cal use, but it also represented the initial stages of money as a socially constructed institution, arising from community exchange and trust, rather than from top-down imposition. Through bank developments and active states, money developed its institutional nature (Davis, 2020).

By contrast, chartalist theory argues that money derives its value essentially from the state, and not from the intrinsic value of the commodity. Money is accepted as payment because the state has decreed its use, particularly for paying taxes, which means that money is a legal instrument of public policy. See Wray (1997) and Ehnts (2019).

Search-theoretical models show how money can evolve or be created in decentralized mar- kets. Individuals start to accept certain goods, rather than trading them for their value or bar- tering them for their return, on the assumption that others will accept them, which reduces transaction costs and trade barriers. This makes the money, when interpreted or accepted, a social means of coordinating transactions that evolves from the constraints of barter logistics. See Kiyotaki and Wright (1990) and Iwai (1996).

3. Credit through time: Trust and financial exchange

3.1 Trust and reciprocity in early credit systems

In the first human societies, economic exchange was often informal and could take place with- out the use of formal currency. Delayed reciprocity, an informal and conditional form of credit based on social and personal trust rather than financial institutions, was the most common form of economic exchange (Sengupta and De, 2020).

This informal credit functioned without intermediaries or formal financial instruments such as banknotes and bills of exchange. Instead, informal credit is a collective social phenomenon based on mutual consent from shared social memory and reputational risk of default. An in- dividual’s engagement in exchange was therefore highly dependent on the predictable social norms of obligation (Hann, 2006).

These types of exchange practices exemplify that credit is an economic concept that precedes coinage and paper money. Credit emerges from styles of deferred exchange with social norms of enforcement and not via legal means or institutional norms.

3.2 Temples, palaces, and early record-keeping

In the ancient Mesopotamian city-states of Sumer and Babylonia, temples and palaces acted as proto-financial institutions that lent and distributed commodities at a level never seen before

the advent of banks. They lent grain and silver and charged a nominal rate of 33 percent on grain loans and about 20 percent for silver loans. See Hallo (1996), Roth (1997), and Hudson (2019).

They maintained cuneiform tablets that precisely detailed their loans along with the names of the borrowers, collateral, time to repay, and any goods that were exchanged. One method of accounting was using barley as a unit of account, and then they could convert values of wool, metals, and labor into barley equivalents to create a consistent price or debt that could be repaid in kind or with commodities that were exchangeable. Other records outlined labor assignments, food rations, and provisioning, suggesting temples were comprehensive admin- istrative agencies that also tracked the workers’ expenditures. See Podany (2003) and Cripps (2017).

They tracked relative prices to keep the value of goods and interest calculations consistent. For example, 400 sila of barley equaled one shekel of silver – a value that fluctuated seasonally – illustrating how price setting evolved within early regulatory frameworks (Powell, 1996).

They provided both monetary and insurance functions to cash-strapped families by lending money at low interest to support social cohesion and a sense of state legitimacy. They fostered centralized credit allocation and standardized weights and measures and initiated the develop- ment of written records. Moreover, they delayed the development of financial trust long before banking practice was born (Hudson, 2002).

In ancient Greece, certain cities became notable temple sanctuaries with their own financial management, such as the treasuries at Delphi and also the ones at Delos. These treasuries functioned as custodians of civic wealth, holding public funds for city-states and institutions, and serving as venues for sanctioned international lending. The Athenian state also ran its public revenues and wartime finances through central accounts in repositories that included the treasuries of the Delian League and temples that had deposits and loaned out money at typically lower interest rates than found in private lending. This utilization of the model of financial administration also represents an early way of citizens getting in place civic accountability over finance and social movements to record the capital they were entrusting to the future public. Both rulers and citizens kept records of how much they were worth and how much they owed on stones that served as stelae, records that publicly documented loans and repayments. See Economou and Kyriazis (2024) and Hudson (2024).

3.3 War, state power, and the rise of public finance

The increase in the scope and duration of wars in early modern Europe had a major impact on the subsequent institutionalization of public credit systems. As warfare shifted from a series of discrete and risky campaigns to a continuous military campaign, sovereigns increasingly em- braced debt financing as a way to finance long-term military expenditures. The Hundred Years’ War (1337-1453), among other conflicts, demonstrated the inability of private credit, even from the wealthiest elites, to support the financial needs of a prolonged war. In response, the new states centralized taxes, helping to increase the credibility of public debt. They also formalized debt instruments and developed legal frameworks for debt forgiveness, thus embedding credit instruments in the public administration structure. See Levy (2016) and Hendrickson (2024).

In the Italian Wars (1494–1559), the Italian city-states of Florence and Venice developed aspects of public borrowing through luoghiand prestiti– early state bonds backed by anticipated tax revenues. These fiscal mechanisms displaced the personal basis of credit with institutional obligations, channelled through merchant bankers who acted as intermediaries in the creation of sovereign debt contracts. See Fratianni and Spinelli (2006) and Pezzolo (2007).

In 17th-century England, an epochal change took place, as years of wars, especially the Nine Years’ War (1688–1697) and the War of the Spanish Succession (1701–1714), created a major transformation in public finance. The Bank of England was established in 1694 and allowed the government to issue funded debt with long maturities, funded by the power of parliament to impose taxation. This established borrowing as formalized system with predictable risk for the creditor and allowed the institutional state a substantial increase in the amount and duration of bonds that functioned as public credit. Public credit had formally transferred from a simple personal trust to an explicit legal institutional framework with tax, parliamentary powers, and central banking marked by fiscal sovereignty. See Bell, Brooks, and Moore (2009) and Brandon (2018).

3.4 Merchant networks and financial instruments

In the late Middle Ages, when long-distance trade reached a zenith, the networks of traders be- came a key channel for extending credit and developing new financial instruments. Merchants extended credit on the basis of myriad forms of trust, reputational leverage, and social enforce- ment mechanisms that relied heavily on networks, which is surprising in the absence of central banking and common legal institutions. See Levitin (2006) and Wechsberg (2014).

The bill of exchange – an essential financial innovation – was a written order enabling a mer- chant to initiate payment in one location to be settled in another. The bill of exchange allowed for value to be transferred across borders. Apart from serving as instruments of deferred pay- ment, bills of exchange gave rise to a phenomenon we now recognize as unique modern credit instruments, including promissory notes and letters of credit (Bolton and Guidi-Bruscoli, 2021). Merchant families such as the Medici, Fugger, and Rothschild established transnational fi- nancial empires based on complex systems of trust, documentation, and legal contracting, in which the language of credit and contractual obligation increasingly began to intersect. These merchant houses collaborated with correspondent banks in other cities, which honored bills of exchange either through personal trust networks or institutional guarantees. As a result, reputation emerged as a central mechanism for contract enforcement (Hoggson, 2007).

The Hanseatic League flourished between the 13th and 17th centuries as a decentralized trading union, bringing together cities from the north of Europe such as Lübeck, Hamburg, and Bruges. It operated in an independent commercial context, free of central government con- trol, and developed sophisticated business lending systems. Merchants used instruments like bills of responsibilities, letters of reprisal, and sealed ledgers to manage debts and accomplish payments across jurisdictional boundaries without an actual physical transfer. The League’s

Kontore (trading outposts) optioned trade documentation, weights, and measurements, and organized coordination of dispute settlements in merchant courts and quasi-legal legitimiza- tion. At this level of scale, economic agents developed complex payment and credit systems within a vast geographic reach. This established early conditions for transnational finance and embedded credit and trust into enforcement mechanisms independent of sovereign legal sys- tems (Kirby and Kirby, 2023).

As these practices matured, they helped to standardize credit instruments, develop mer- chant law, and establish commercial courts, all of which were foundational institutions that underpinned trade by lowering uncertainty and dispute resolution costs. Thus, credit evolved from a system grounded in personal trust to one increasingly institutionalized through com- merce and legal infrastructure (Trimble, 1948).

3.5 From personal trust to institutional legitimacy

In the past, social credit was based on trust, social memory, and informal standards embedded in institutions such as family, religious communities, and business networks. The repayment was made possible by moral suasion, through the application of social and communal penalties rather than legal ones.

One of the consequences of the expansion of trade and the increasing complexity of the economy in the late medieval and early modern periods was the slow institutionalization of credit practices. These practices have gradually moved to institutionalized forms, such as writ- ten contracts, accounting, litigation before a judge to obtain a decision that is legally enforced by the losing party, and reliance on state fiat institutions to enforce contractual obligations. Credit has become primarily governed by a formal set of rules and obligations that shift trust from the individual to the institution.

4. Paper money and banking foundations

4.1 Origins of paper currency: From imperial China to early Europe

The oldest known use of paper money was during the Tang Dynasty (7th century CE), then formalized in the state-issued paper currency of the Song Dynasty (11th century). Merchants initially used jiaozi – private promissory notes – to avoid the inconvenience of transporting bulky coinage. As trade expanded, the Song government centralized issuance by establishing a monopoly and introducing jiaochao, official state-backed notes redeemable in coin and usable for tax payments. See Von Glahn (2016) and Von Glahn (2018).

This practice was institutionalized through a central state monopoly on the fiat printing process, legal enforcement, sanctions by public officials, and guaranteed redemption in copper coin.

In Europe, by contrast, the use of paper money has taken many centuries and various forms of experimentation. The initial European banknotes were issued in Sweden from the Stock- holms Banco in 1661, followed by the Bank of England in 1694. The first forms of banknotes emerged from the merchant deposits of coins, and were used immediately by merchants and governments to issue loans (Ferguson and Srinivasan, 2013).

4.2 Instruments of exchange: Bills, notes, and merchant law

As trade became more centralized and long-distance by the late medieval and early modern periods, merchants devised instruments by which value could be traded over long distances without the physical transfer of currency, such as bills of exchange, promissory notes, and letters of credit. Financial instruments transferred value through space and time, allowed for later payment, and minimized travel risks and theft (De Roover, 1944).

A bill of exchange was a written, transferable order from one person to another, instruct- ing them to pay a certain sum of money at a specified time and place. The bill of exchange originated among 13th to 14th century Italian merchant-bankers, eventually becoming com- monplace in pan-European trade. As they became more popular, bills of exchange became a tradable instrument with the ability to be backed and resold. Bills of exchange laid the foun- dation for modern banking instruments and established the basic principles of liquidity, risk management, and interbank payments in modern banking systems. See (Usher, 1914) and (Kadens, 2004).

The Lyon fairs that emerged during the 15th and 16th centuries in France were significant international financial centers to which people and money from all over Europe would come to settle debts, endorse the bills of exchange, and normalize cross-border dealings for credit transactions – effectively functioning as a clearinghouse that later authored commercial legal institutions and expedited the establishment of a pan-European financial architecture (Braudel, 2025).

As these instruments became more widespread, commercial law, known as the LexMerca- toria, developed to regulate and enforce commercial obligations. Merchant courts and notaries were the judges and certifiers of such instruments. The growth of commercial law and insti- tutions to back credit enforcement played a crucial role in the standardization of these instru- ments. See Aigler (1923) and Benson (2002).

Eventually, states took merchant law and added it to their national legal systems. This cre- ated formal laws around instruments like promissory notes – which were first accepted legally by France and then codified for formal use through England’s Promissory Notes Act of 1704. The recording of this process demonstrated the institutionalization of the commercial credit and incorporated informal credit networks and lending into the formal legal system (Munro, 2003).

4.3 Banking institutions and the emergence of central trust

Moving from regionally organized banks to central institutions demonstrated a substantial evo- lution of formalization around credit and monetary trust. Before centralized banking became

popular, money exchanges were much more constricted and tied to the exchange of commodi- ties, either gold or silver, or bilaterally between any two banks or public lending institutions. And though the emergence of banking geared toward deposit institutions was concerned with the monetary stability of payments, successful transactions, counterparty risk, and managing state debt, as exemplified in the Banco di San Giorgio (Genoa 1407) or the Bank of Amster- dam (1609), wherein public banks furthered the business cycle for transacting by employing deposit, clearing, and commodities trade across regions, they presented greater efficiency and uniformity through transacting. See Fratianni (2006) and Bolt, Frost, Shin, and Wierts (2024). In the early 14th century, the Peruzzi family in Florence operated an extensive banking house, lending long-term credit to monarchs such as Edward III of England and transferring large sums of money through bills of exchange and double-entry bookkeeping. Banks were also established in Venice, where there were a variety of institutional forms primarily support- ing maritime trade, which aided giro banking and the establishment of financial institutions by establishing a central clearing and deposit function behind an institution like the Banco di Rialto (1587), institutionalizing a centralized model of monetary settlement for public banking systems. Together, they contributed to the underpinnings of sovereign finance and commer- cial credit-based systems. Ultimately, they also developed a proto-central banking system in Renaissance Europe. See Lane (1937) and Fryde (1951).

The accumulation of involvements in increasingly complex trading and the enlargement of sovereign borrowing resulted in a movement of trust away from private goldsmiths and merchant banks to state-backed financial institutions. The establishment of the Bank of England in 1694 was momentous: it enabled the state to legally issue debt financed by the state and created the institutions that would form the foundation of modern central banking.

As a public bank subject to parliamentary governance, the Bank of England conferred le- gitimacy to currency issuance by anchoring it in specific rights to state authority and taxation (Desan, 2014).

These institutions developed the infrastructures that enabled liquidity, public borrowing, and regulation of currency circulation. By enabling these emergent systems of public borrow- ing, they moved money away from the world of commodity or contractual assets and firmly into the world of government-backed systems. This allowed for economies of scale and the format needed to develop a stable financial architecture, which created the base for modern monetary systems (Ugolini, 2017).

4.4 Paper-based payment systems: Cheques and giros

Cheques made their appearance in England in the second half of the 1600s in written directives to banks requesting that the banks transfer funds on the writer’s behalf. Cheques reduced the reliance on and need for carrying specie or paper currency to conduct high-value transactions since the value of the transactions fell on the bank or counterparty risk when transacting. In the broad sense, cheques became widespread during the 19th century with the rise of commercial banking and legal reforms that allowed for enforceable and negotiable instruments (Quinn and Roberds, 2008).

Giro systems developed in various forms by the post offices of 19th century Austria and Germany represented account-to-account value transfers without the need for any physical cash. They enabled individuals and firms to coordinate payments through centralized clear- ing ledgers, acting as early forerunners of digital transfer systems. They institutionalized the concept of non-physical payment by offering a trusted, centralized mechanism for value transfer (Hein, 1959).

These systems improved accessibility and user experience with non-physical payment mech- anisms. They also set the stage for the mechanized, institutional payment systems of the indus- trial age, where value transfer increasingly relied on state-supported frameworks rather than physical tokens (Berger, De Haan, and Eijffinger, 2001).

5. Industrial foundations of structured payment mechanisms

5.1 Factory wages and the institutionalization of payroll systems

In the early stages of industrialization, from the mid-18th to the 19th centuries, wage systems changed from an output-based piecework to organized, time-based payment. With factories structured in a way that allowed supervision, workers would be offered hourly and daily wage structures, thus allowing workers more expected incomes from simple wages in what is now seen as wage work. These new wage systems also instilled a sense of temporal discipline, as well as standardized hours of work (Schwarz, 2007).

From the middle of the 19th century, larger factories assumed some systematic approach to wage distributions, and there were issues of documentation. A combination of time books, pay- roll records, and attendance records was viewed as indispensable for tracking workers’ status of labor work intake and wage entitlements. Timekeepers and clerks documented the number of hours worked and wage entitlements, thereby establishing internal payroll systems. These mechanisms helped minimize wage disputes and facilitated the standardization of compensa- tion across occupational roles (Hanes, 1993).

Large industrial businesses in the late 19th and early 20th centuries, particularly in Great Britain, Germany, and the U.S., developed in-house payroll offices. These departments figured wages, paid deductions, regulated payments, and audited internal spending. This would have been representative of the general bureaucratization process taking place in industrial capital- ism (Jacoby, 2004).

In the early 20th century, scientific management began to take hold. Time-motion studies, coupled with performance-based pay systems like premium plans or bonus systems, helped to grow a greater reliance on wages on measurable efficiencies. Standard productivity expec- tations began to sound normal as wages began to consider the eventual implications of labor. This also offered a justification for the pay distinctions between different general workers (Tay- lor, 2023).

Even while wage structures were formalized and payroll accounting became institutional- ized, the whole premise also started a wage-based undertaking for a demand for increasingly stabilized and standardized payment units. The model created in this era served as a frame- work for modern human resources, payroll systems, and labor laws that define and shape em- ployment. As industrial economies expanded, paper currency emerged as the dominant wage medium, prompting the need for standardized systems of printing, verification, and circula- tion. These developments represented the full-scale manufacture and commercialization of banknotes by the late 19th and early 20th centuries (Osterman, 1987).

5.2 The printing revolution and mass production of banknotes

In the early 1800s, banks embraced steel-plate printing, which is how engraving rolls made of steel firmly became the means of transferring engravings reliably to print banknotes and reproduce quality banknotes to complete transactions frequently. This innovation replaced soft copper plates, enabling high-speed reproduction of consistent impressions and significantly reducing plate degradation (Robertson, 2005).

Lathes began to produce complex guillochépatterns – complex, repetitive designs carved in plates that are very difficult and time-consuming to hand forge. In parallel, significant advance- ments in ink formulation introduced new marks and watermarks, enhancing anti-counterfeiting measures (De and Canadiens, 2006).

As flatbed intaglio presses transitioned to rotary-intaglio presses capable of high-pressure printing on dry paper, production became industrialized because these could process larger volumes of sheets with consistent results. This shift effectively delineated artisanal methods from the industrialized processes of banknote production (López-Bosch, 2015).

The central banks and government mints also formed dedicated printing works with indus- trial printing equipment and systems for quality inspection, automatic numbering, and pack- aging. Industrial printing facilities were in operation well into the 20th century, many with added security and custom-built and portable for wartime, and turned out completely printed banknote circuits professionally (Reddy, 1988).

The printing revolution facilitated currency issue in high volumes, at lower marginal costs, and with globally standardized issue with an emphasis on shoring up the security of printing with high production practices. It established structural formalism and architectural rigor in state-backed finance, contemplative of specialized bureaus dedicated only to printing, engrav- ing, and issuing money. Many of the quality assurance and security practices employed at this time are part of processes evidencing central banks today (Reina, 2024).

With increased amounts quoted on printed money during the 19th century, banknotes were interchangeable with each other and more widely accepted, and the growing presence of printed money expedited the settlement of trade and finance. Banks started acknowledging and man- aging the growing volumes of payments, often in cheque and banknote forms, through develop- ing Federal Central clearing practices, laying the groundwork for settlement in clearinghouses.

5.3 Accounting machines and the automation of transaction recording

As the number of financial transactions increased towards the end of the 19th century, so did the increased volume of transactions, which increased the demand for reliable methods of record- ing and settling those transactions. Initially, banknotes were counted to their coin equivalents, marked and folded by the teller to settle the sum. With the advent of mechanical adders and typewriters, counting and accounting became faster and more reliable, less prone to human error, and the automatic recording of transactions made it easier to settle transactions.

In the early 20th century, specialized machines arrived that not only computed but also automatically printed and posted into journals or ledgers – automating the recording of sales, payroll, and other transactions. See Keenoy (1958) and Wilson and Sangster (1992).

By the end of the 19th century, punch-card tabulators were used. These machines, which were originally actuated by Census data, would also be adopted by companies to process pay- rolls, inventories, and financial data and began creating batch processing of business data (Hol- lerith, Couffignal, Dreyer, and Walther, 1973).

Mechanization in accounting was able to speed up the work and lower costs; however, there was a deskilling of the traditional bookkeeping role, moving many tasks into the hands of fe- male clerical workers. This shift in accounting practice not only deskilled manual posting in analytical accounting but also shifted the profession more towards managerial roles (Jedlick- ova, 2020).

Over the decades, machines evolved into computerized and later electronic accounting sys- tems, evolving even more to include early computers that were able to automate cheque pro- cessing and some record-keeping functions. Hence, they became integrated into modern ac- counting with built-in efficiency, accuracy, traceability, and batch processing for transactions (Bendovschi, 2015).

Mechanical and electromechanical systems had been leveraged to achieve very substantial productivity increases in banking in the early 20th century, but they still suffered from the same spatial and temporal constraints. As the world gained greater connectivity and consequently, expectations for speed and security grew, the banking sector turned to new digital technologies. The shift from analog to digital technologies was not merely a shift of technological capabilities but a redefinition of financial infrastructure – characterized by integrated databases, high-speed communications, and the ultimate merger of the internet.

6. Digital infrastructure and the rise of platform-based payments

6.1 From magnetic tapes to electronic funds transfer

The move to electronic payments was initiated in the early 20th century with telegraphic trans- fers, which allowed banks to transmit funds, eliminating the need for the physical movement of currency. The U.S. Federal Reserve launched Fedwire in 1918, allowing interbank settlements in real time through telegraph and later teletype technology to replace the manual clearing that could take days to finalize. See Engel and Hammar (2006) and Leaders and People (2023).

Automated clearinghouse (ACH) systems were developed in the 1960s to handle regular lower-dollar-value transactions, like payroll and utility bill payments, using magnetic tape stor- age and batch processing technology. Also during the 1960s, the Clearing House Interbank Pay- ments System (CHIPS) was developed for interbank transactions with large dollar amounts and was operating globally in the 1970s for large dollar interbank transactions, particularly in the international financial area (Stevens, 1984).

Before the formation of SWIFT in 1973, global messaging between banks was facilitated through various networks, which meant messages between banks were both inconsistent, more prone to security breaches, and unsafe. SWIFT, with communication standards, allowed inter- bank messaging across countries using a centralized means that largely replaced telex with more secure and reliable standards and became the foundation of global interbank service in- frastructure (Scott and Zachariadis, 2012).

Collectively, these advances provided the framework for the modern electronic payments ecosystem as a centralized and accelerated means of infrastructure for domestic or international financial transactions (Panurach, 1996).

6.2 The ATM revolution and card network expansion

The first ATM in the modern sense, which utilized peripheral devices for offline dispensing in tandem with magnetic-stripe cards, was released in the late 1960s. The first ATMs were limited to simply dispensing cash or printing out transaction records. In the early 1970s, some first-generation ATMs began being located at terminals that were connected to a central-host system. This meant that customers had real-time access to their accounts and could complete transactions such as deposits or fund transfers. This shift marked a transition from isolated terminals to integrated, network-based banking infrastructure (Konheim, 2016).

Initially, ATM networks were proprietary, allowing access only to the issuing banks’ cus- tomers. Beginning in the early 1970s, shared networks of ATMs began appearing in various cities that allowed account or customer access to ATM facilities of participating banks nation-wide. By 1990, shared interbank networks were supporting more than ninety percent of all ATM networks, facilitating conveniences and access for consumers (Matutes and Padilla, 1992).

During the 1980s, Visa and Mastercard established branded ATM networks, Plus and Cirrus, respectively, to facilitate access to cash globally. Plus was created in the early 1980s as a coop- erative association of U.S. banks, only later acquired by Visa; it has operated an interconnected network of more than one million ATMs worldwide. Many other networks have emerged in the United States in recent decades, such as STAR and Pulse, which have networks that encompass thousands of separate institutions and millions of ATMs (Kauffman and Wang, 1993).

ATM cards gradually developed into debit cards that offered checkout payments in addi- tion to cash access. This interoperability was crucial to building a seamless consumer payment experience across banking institutions and geographies. Eventually, ATMs transitioned from in-branch installations to widespread, off-premise locations operated by third parties, signifi- cantly broadening access and financial inclusion (Bátiz-Lazo, 2009).

6.3 Rise of private payment platforms and fintech

A significant shift occurred in the late 1990s, when private technology companies began to offer scalable, internet-based financial services that utilized existing banks’ infrastructures without being associated with the banks themselves. One of the first platforms was PayPal, which pro- vided peer-to-peer transfers via email and soon became a pillar product of e-commerce transac- tions. The rapid adoption of consumer behavior indicated that digital wallets might eventually be able to displace traditional financial agents while allowing the transaction to happen with speed and scale and in a similar economic transaction context to existing traditional financial transactions (Soni, 2022).

In this instance of Alipay, China introduced its consumer payments development in 2004 by linking mobile wallets to e-commerce transactions before developing a complete financial ecosystem. They would then reinforce that financial ecosystem with WeChat Pay by connecting the payment transactions directly to social communication. Today, both Alipay and WeChatPay account for the majority of retail payment transactions in China and are an example of the method by which private networks can supplant not just a banking infrastructure but a bank-led infrastructure (Klein, 2020).

Meanwhile, in Kenya, M-Pesa launched in 2007, creating a breakthrough in mobile money transfer via rudimentary handsets and a network of agents. M-Pesa was transformational for financial inclusion, particularly where there was limited or a complete lack of access to formal banking services. Further afield, these models have been successful in various emerging mar- kets in Africa and Asia, on-boarding millions of previously unbanked users (Ndung’u, 2018). By the 2010s, the fintech ecosystem had rapidly blossomed across multiple regions glob- ally, with large tech firms and telecommunications firms entering payments with their giant platforms (Google Wallet, Apple Pay, Venmo, and Cash App), offering consumers peer-to-peer payments and retail-style connections, among their range of data-informed financial products. Countries such as India and Brazil pioneered state-backed, privately managed platforms like UPI and Pix, which facilitated the best banking and e-commerce opportunities and pioneered instant, account-to-account digital payments between payee and payer at scale, sometimes with private or front-end interfaces (Cumming, Johan, and Reardon, 2023).

Collectively, these experiences reduced reliance on state-led banking infrastructure, put competition into the equation, and enabled digital financial service provision outside the for- mal banking system that usually excludes most underserved populations. Most notably, in the Global South, mobile payments technology served to accelerate the pace and inclusion, show- ing how private sector innovation could alter national and cross-border payment systems.

7. From coordination to control: The globalization and geopolitics of payment infrastructure

7.1 International monetary arrangements: From metal standards to fiat

Global payment infrastructures have become increasingly integrated, achieving levels of cross- border technical precision unprecedented in earlier eras. Despite their inherent cross-border nature, modern payment systems remain fragmented because of national, legal and technical barriers which require coordinated solutions. The very structure and design of these platforms may be affected by the impact of national interests, legal regimes and geopolitical conflicts. To- day, the international monetary infrastructure operates not only through the currencies them- selves, but also through the networks that transmit them. Monetary power is increasingly ex- ercised by controlling these financial conduits.

Gresham’s Law, often stated as “bad money drives out good,” illustrates how people tend to hoard or melt down the good coins and spend the debased or clipped coins when coins with different intrinsic values circulate at the same legal tender value. This often occurred in the me- dieval and early modern economy when sovereigns frequently debased the currency to finance a war or to help pay a debt. The result of these policies was the full-weight coinage disappear- ing from the economy, with the steady erosion of the currency unit. In this sense, Gresham’s Law illustrates problems related to monetary regulation, coin standards, and sovereign valua- tion actions that require clear institutional frameworks to maintain confidence in the currency and trust in exchange (Selgin, 2020).

The Classical Gold Standard (1870–1914) marked a high point of a worldwide integrated monetary system, where most of the larger economies pegged their national currencies to gold with fixed exchange rates and an open and expanding space for global trade and investment. The fixed quantity of gold allowed for fixed exchange rates and increased global trade and in- vestments between nations; most importantly, it provided for long-run price stability. In addition, central banks held gold in reserve to back the amount of paper currency they could issue, and they were required to redeem denominations of paper currency into gold upon demand. While the gold standard was meant to provide an anchor of monetary discipline, it tended to impair countries from shaping their responses to domestic economic crises, often resulting in deflationary spirals. When a financial crisis struck, the inflexibility of the gold standard became a burden, and the gold standard was suspended with the outbreak of World War I to meet the large military expenses (Eichengreen and Flandreau, 1997).

The Gold Bullion Standard (adopted by Great Britain in 1925 under Prime Minister Winston Churchill) was a variation on the classical gold standard, where the general public could no longer redeem currency for gold coins, but holders could redeem it for gold bullion. The policy move was also designed to strengthen the role of the British pound as a global reserve cur- rency, as a symbol of Britain’s intention to regain its pre-war financial hegemony. The meaning of this change was to minimize the circulation of gold in the domestic economy while maintain- ing the convertibility of gold for international holders. This restored international confidence in the British pound, as part of Great Britain’s efforts to support its currency during the de- flationary conditions and severe economic instability that followed the First World War. The new gold standard lasted only a short time and was abandoned in 1931, owing to the massive economic pressures of the Great Depression. The most serious problem is the massive capital flight from gold reserves, the speculative attack on the pound, and the soaring level of unem- ployment. Great Britain’s refusal to devalue its excessively overvalued exchange rate or to ease monetary supply in the face of falling prices and incomes has only made domestic deflation worse. Ultimately, the insistence on maintaining gold convertibility proved unsustainable, and Great Britain suspended it to regain monetary-policy autonomy (Officer, 2010).

As WWII’s end was approaching, in 1944, the Bretton Woods system pegged global cur- rencies to the U.S. dollar, which was immutable to gold at $35 per ounce. This system was a fixed exchange rate system with the target of postwar stability. By the late 1960s, however, con- tinued trade deficits and inflation in the U.S. caused a loss of confidence in the dollar and the dollar’s convertibility into gold. Pressure escalated for Eurodollar markets – creating a parallel liquidity outside Federal Reserve authority and an easier capacity to challenge U.S. monetary discipline. During heightened monetary tensions over eurodollar creation, French president Charles De Gaulle called on gold for his excess dollars in 1965, denouncing the unreliability of dollar dominance in the international monetary system and explaining that the scenario of excess dollars resulted in disproportionate advantage to the U.S. The Swiss National Bank and dozens of others, including the International Monetary Fund, quickly began to bring dollars back to the U.S. in return for gold, and the U.S’. gold reserves were diminished. This crisis of confidence highlighted the inherent contradiction of the Triffin Paradox (1960): to supply global liquidity, the U.S. had to run ongoing deficits, but these deficits led to a drop in foreign confidence in the dollar’s gold backing. The more dollars the world held, the less credible the U.S. promise of convertibility. In 1971, President Nixon decided to suspend the convertibility of gold for the dollar; this effectively ended the Bretton Woods system and began an era of floating fiat currencies (Bordo, 1993).

After the Bretton Woods system fell apart and the world transitioned away from gold com- mitments, a new form of international monetary regime emerged: fiat currencies – currencies that were supported by the authority of their sovereign state to impose taxes and the public’s faith. The transition also reorganized the global payments system – not only through the adop- tion of fiat currencies, but also by replacing fixed exchange rates with floating regimes, which were perceived as more effective shock absorbers for national economies. With the world ex- periencing floating currencies, the 1980s exhibited escalating volatility in exchange rates and trade challenges, primarily from the already addressed issue of the U.S. dollar’s overvaluation. At this point, many economists were expressing concerns about global trade. The Plaza Accord of 1985, where the G5 nations (the United States, Japan, West Germany, France, and the U.K.) cooperatively intervened in the global monetary system with a request to depreciate the dollar to protect trade dynamics. With this came an acceptance of managed fiat diplomacy, where instead of anchoring in metal, macro-coordination and central bank intervention brought us into a new chapter of monetary intimacy based on collective political will and convergence of economic policy. See Durani (2015), Bergsten and Green (2016), and Dapp (2021).

As fiat systems displace metal-backed currencies, the need for reliable institutional clear- inghouses has opened the door for them to handle the increasing complexity of transactions.

7.2 Emergence of clearing houses and inter bank settlements

During the period of rapid growth in trade, merchants and bankers all began to settle payments in central locations rather than making slow, bilateral transactions with merchants physically carrying coins or personal cheques from one bank to another (Norman, Shaw, and Speight, 2011).

By the mid-18th century, London established the first formal clearinghouse, which provided a method of centralizing information from member banks to deposit the cheque or bill to one location, determine net positions, and settle the differences. Other municipalities soon imple- mented similar exchanges or clearinghouses, such as the Boston Suffolk System in 1818, fol- lowed by the establishment of the New York Clearing House in 1853, which immediately fa- cilitated the exchange of millions in payments every day. Those exchanges provided a local exchange for the transfer of the cheque and the bill. The member bank would simply drop the cheque or bill into specific boxes located at the clearinghouse. Clerks would count the cheques and bills that came in and out, and only the net balances would be transferred, significantly reducing the amount of specie or cash that would be required to settle among various banks. See Loader (2019) and Cannon (2024).

The clearinghouses began to act as quasi-regulatory institutions, establishing reserve re- quirements, auditing members, and issuing loan certificates during a crisis, which ultimately bolstered trust in the payment system (Johnson, 2011).

In the middle of the 20th century, as telegraphy and computer technology became more reliable, clearing procedures between banks changed with the involvement of central banks. These systems evolved into RTGS (Real Time Gross Settlement) and automatic netting systems.

This paved the way for today’s fast and secure transactions. Global financial infrastructures such as CLS (Continuous Linked Settlement) mitigate settlement risk by settling both sides of foreign exchange transactions simultaneously (Tompkins and Olivares, 2016).

As settlement systems between banks have become more complex, particularly with the emergence of centralised institutions and time-critical settlement protocols, there has been a growing demand for accurate, high volume accounting. The speed and reliability of manual bookkeeping and paper-based procedures could no longer keep up with the operations of ever more complex financial systems. The operational pressure of the new clearing system eventu- ally led to the digital transformation of government payment systems through the use of RTGS.

7.2 Digitization of central bank systems and RTGS

Initially, in the context of payment modernization, transactions of a high value were settled by means of deferred net settlement systems. This involved pooling of inter-bank payments and settlement of net positions at the end of each day, which, though effective, creates systemic risk, as a failure by one institution to meet its net final settlement obligations could disrupt the entire settlement chain (Allsopp, Summers, and Veale, 2009).

In order to manage this risk, central banks implemented Real-Time Gross Settlement (RTGS) systems from the 1980s onward. RTGS systems allowed for high-value payment settlement to occur immediately and individually, thus limiting counterparty risk and general systemic fi- nancial risk. In short, payments became final and irrevocable, leaving transacting banks with much more accurate confidence about inter-bank transactions and settlements (Bech and Ho- bijn, 2006).

By the 2000s, RTGS systems were fairly ubiquitous across advanced economies, and emerg- ing markets were beginning to implement RTGS systems in earnest. This was not only a tech- nological shift but also a structural shift for the management of liquidity, credit, and risk at the national economy level. Because they had better control over intraday liquidity, central banks were able to be more proactive in managing systemic risk (O’Hara, 2005).

The RTGS system also aided monetary policy transmission by allowing a more precise as- sessment of interest rate effects and minimizing delays by settling transactions promptly rather than waiting until the end of the day, particularly in government securities and central bank- executed transactions. Standardized messaging formats and the expanded adoption of RTGS technology for other non-depository institutions in the recovery phase of developing market so- lutions further integrated these major components into a broader financial ecosystem (Mañalac, Yap, and Torreja Jr, 2001).

In hindsight, the advancement of RTGS systems marked a watershed moment in banking: national payments infrastructures were more predictable, visible, and safer than before; this was the precursor for globally coordinated instantaneous payments through financial flows in the digital age (Bech, 2007).

As RTGS systems replaced traditional clearinghouses, they quickly turned the speed and finality of transfers and payments into a strategic advantage for banks. However, the fact that RTGS systems exist in a national context raised several questions about global coordination and emphasized the urgent need for interoperability between RTGS systems.

7.4 Interoperability, data, and systemic risk

With the expansion of digital payments, it became apparent that interoperability – the ability for multiple platforms, devices, and types of institutions to communicate and transact easily together – would be essential. As countries moved to bring together mobile apps, banks, and fintech to develop near real-time infrastructure, central banks and regulatory bodies advocated for interoperability protocols and harmonized standards. See Boar, Claessens, Kosse, Leckow, and Rice (2021) and Akoguz, Roukny, and Vadasz (2025).

To manage the fragmented nature of payments messaging and to transfer more data be- tween systems, international systems began to implement ISO 20022, a standardized financial messaging standard. Adoption in central bank RTGS systems (e.g., Fedwire, TARGET, and CHIPS) improves operational efficiencies, data quality, and regulatory oversight across domestic and cross-border payments (Major and Mangano, 2020).

Greater connectivity introduces new vulnerabilities into the system. A failure of one institu- tion or platform may create a ripple effect of liquidity stress and operational incidents through- out the network. Systemically important institutions are becoming too interconnected to fail, with network effects amplifying financial contagion. The transmission of more financial data also increases the risks to security and privacy associated with data interchange between sys- tems. In this new paradigm, the established regulations, reliable infrastructure, and monitoring capabilities are key controls that will provide and support resiliency (Wang, 2017).

Given that risks are ever-present, the BIS, G20, and other international organizations are working together to advance frameworks for international payments oversight, interoperability, and stability (Lentzos and Rose, 2009).

Thus, interoperability enabled rapid cross-border payments and exchanges between pay- ment systems, but it also harbored systemic and cyber risks and made the payment infrastruc- ture an area of vulnerability and geopolitical control.

7.5 Geopolitical control and the weaponization of payment systems

Once thought neutral, the global financial infrastructure has become an important tool of state- craft. For example, payment messaging systems such as SWIFT serve as strategic hubs for pay- ments by countries – especially the U.S. and its allies – that want to use them as leverage to gain access to the global financial system. This was clear when Iran left SWIFT in 2012. Then, when Russia invaded Ukraine in 2022, SWIFT restricted access to the main Russian banks that were under the umbrella of SWIFT. The provision of payments messaging triggered major economic disruption, capital flight, and currency collapse across the entire Russian economy. See Majd (2018) and Tulun (2022).

This weaponized interdependence is based on structural power in the network. If control over the infrastructure is power, controlling something that is the basis of global financial struc- turing should bring terrific leverage. With the U.S. dollar and its dominance of SWIFT messaging and correspondent banking, the concentrated control functions as a single-keyveto, empow- ering dominant states to unilaterally halt financial flows globally (Crawford, 2025).

Financial exclusion drives targeted states to construct parallel systems. Russia built a mes- saging platform (SPFS) and a domestic card network (Mir). Meanwhile, China built a Cross- Border Interbank Payment System (CIPS) to reduce its reliance on both Western financial sys- tems and the dollar (Fan and Voronkova, 2024).

Consequently, strategic exclusion through SWIFT and equivalent dollar-clearing has sped up de-dollarization measures. Countries such as China, India, and ASEAN countries are mov- ing quickly to promote trades in local currency-based settlement and create direct payment corridors to circumvent the dominated international payment infrastructure. Central banks are also diversifying reserves into gold, yuan, and other regional currencies. See Burke (2024) and Saaida (2024).

These developments show us that our payment systems today are no longer neutral but are instead strategic political tools that are changing the landscape of power globally by creating new connections that can later be excluded.

8. Cryptocurrencies, digital identity, and thebattle for financial control

8.1 The genesis of crypto: Historical distrust and the monetary counter-narrative

The introduction of Bitcoin in 2009 as a new form of technology, in the wake of the Global Financial Crisis of 2007–2008, was not only a technological advancement, but also a systemic critique of centralised financial management. The design was deliberately identified from a structural position as a decentralised alternative that removed reliance on traditional financial intermediaries such as banks. This decision was a deliberate response to documented failures of central banks and regulators – who introduced moral hazard, embraced unprecedented risks, and shaped policy through opaque inflation-management frameworks (Segendorf, 2014).

Cryptocurrency is a digital asset that trades on a decentralized network, typically using the technology of blockchains, where transactions are secured and verified by an encryption algorithm. Cryptographic algorithms verify the legitimacy of the currency in a decentralized way, without central management. Blockchain assets use a form of decentralized algorithmic trust that challenges the sovereign monopoly on currency creation by codifying financial rules and verifying their legitimacy.

This financial innovation echoes past moments in monetary history, such as the 19th century Free Banking Era in the United States and Scotland, when privately issued currencies prolifer- ated in some areas – a system marked by fragmented currencies and episodic financial instabil- ity – thereby generating little stability in the absence of a central monetary authority. In addi- tion, when countries experience sovereign default or hyperinflation, communities have found alternative stores of value or means of exchange outside of official means (Fessenden, 2018).

Historically speaking, cryptocurrencies are not exceptions to attempts to circumvent the ac- cepted regime of money arrangements. They represent only the latest innovation of market- based solutions to modify the nature of payments as a result of institutional failures. Thus, they are a digital retake of the historical counter-narrative on money, which emphasizes auton- omy, transparency, and resistance to centralized control (Afzal and Asif, 2019).

8.2 Central bank digital currencies: Digital reinvention of monetary authority

Central bank digital currencies (CBDCs) are digital forms of government currency that are is- sued and regulated by a country’s central bank. Technically, CBDCs are government-backed digital tokens or account-based systems based on either an approved Distributed Ledger Tech- nology (DLT) or centralized databases. Unlike decentralized cryptocurrencies, CBDCs are directly and fully backed by the issuing authority and are intended to serve as legal and accept- able payment instruments, including programmed functions for settlement, traceability, and monetary control (Ward and Rochemont, 2019).

CBDCs present a government response to the growing influence of the decentralized climate of cryptocurrencies. Where crypto denotes a grassroots distrust of central authorities, CBDCs demonstrate an institutional counter-response, reaffirming sovereign monetary authority and regulating currency (Yusifov, 2024).

This evolution of the monetary authority follows a streak of innovations that ranges from ancient coinage regimes to modern programmable currencies, to orderly central banks under fiat conditions, to programmable state currencies today. Each of these systems is a current adaptation to the mutating state of money (Auer, Branzoli, Ferrero, Ilari, Palazzo, and Rainone, 2024).

Local authorities are seeing CBDCs as one way to design control for states over the currency. Among the large economies, China’s e-CNY is the most developed CBDC, since it enables the central bank to track and control transactions in real-time. The currency is being managed in real time at the national bank regulating level, permitting not simply management of the cur- rency but top-down management of all economic activity in a jurisdiction. India’s digital rupee emanates from the directionality toward inclusion and immediate state transfers to individuals, producing efficiencies in state welfare disbursements, managing and streamlining cash transac- tions, and enhancing monitoring and tracing. The European Central Bank positions the digital euro as a potential state option to respond in some way to the personal data and innovation risks posed by faster-growing private and foreign-dominated digital payment systems, and it contributes to states’ monetary sovereignty. See Mooij (2021), Ozili (2023), and Li (2025).

Thus, central bank digital currencies (CBDCs) represent more than just a technical advance- ment – they are a mechanism for states to reclaim their monetary power within a digitally dom- inated global economy. Just as we have witnessed the historical evolution from commodity and asset-based systems to fiat systems, the provision of CBDCs signifies a concerted effort to respond to a moment of financial decentralization by offering legitimacy and state-coerced trust in the money supply.

8.3 Cryptocurrencies as parallel economies: Disintermediation, autonomy, and the rise of decentralized finance

Since the early days of Bitcoin, blockchain technology has evolved from a mechanism for the pure exchange of digital currencies to a foundation for a wide range of financial innovations and governance structures. The introduction of Ethereum and the establishment of smart con- tracts, i.e., self-executing contracts that execute transactions once predetermined conditions are met, was a significant change (these contracts eliminated the need for intermediation). This made decentralized autonomous organizations (DAOs), emergent, rule-based systems on the blockchain that allowed open collectives to operate outside the control of governments, a vi- able entity. This was a huge step forward beyond peer-to-peer exchanges to self-governing and fully autonomous systems for coordinating financial activity. See DuPont (2019) and De Vries (2023).

This is centralized disintermediation in action. Cryptocurrencies function autonomously without traditional financial infrastructures – they do not require SWIFT networks, correspon- dent banking arrangements, or capital controls, and they enable the transfer of assets and the execution of payments that are (at least partially) independent of traditional regulatory struc- tures. This autonomy is not only technical but also political and represents an attack on the centralized authority of traditional banking institutions (Gomaa, 2018).

The emergence of Decentralized Finance (DeFi) has taken this independence even further. DeFi protocols are able to replicate traditional financial services such as lending, borrowing, insurance, and trading, but they do so in a way that is free from institutions or other interme- diaries. They also utilize protocols and mechanisms such as liquidity pools, which let users deposit token pairs into smart contracts, enabling decentralized trading without intermediaries, with rewards tied to trading volume and usage. By using collateralized smart contracts, lenders can safely lend and be automatically liquidated when the price of their asset falls below a certain value, creating a self-governing financial ecosystem. In fact, in many cases, DeFi is more than a replacement for the traditional system; it seeks to replace it completely (Arslanian, 2022).

These bureaucratic mechanisms are particularly useful in crisis-hit countries like Venezuela and Zimbabwe, where communities often turn to crypto-assets or stablecoins to escape hyper- inflation and capital controls. Not only are these alternatives a store of value, but they are also more independent of official pathways, largely relying on the black market exchange rate and exposing their users to currency risk. Accessing global markets and financial services is usu- ally not realistic. Where traditional banking does not work or is unavailable, unbanked people often have only mobile crypto wallets as a lifeline, with the use of a secure mobile medium al- lowing users to save and transfer money and use their money as part of the unbanked economy (Mumford, Sampson, and Shires, 2024).

However, the harmful threats associated with decentralized power are not insignificant. For example, there are extreme consequences that arise from a largely unregulated space that can lead to various negative effects, with each type of systemic volatility, token fraud, and smart contract code errors, as well as errors in the creation and understanding of models, playing their own role. However, global regulatory responses to these innovations have largely been reactive rather than proactive, often lagging behind the speed of technological change. This delayed response has left gaps in oversight, increasing exposure to systemic vulnerabilities such as smart contract exploits, asset volatility, and speculative bubbles (Li and Huang, 2020).

8.4 Digital identity and the financial self: From ledgers to biometric control

The concept of financial identity has evolved from simple accounting-based record-keeping systems to a modern system of multi-layered authentication and verification. In the past, finan- cial transactions relied on the trust of the community and detailed records in written accounts.

With the formalization of banking came the bureaucratization of identity through documents, signatures, and requirements for Know Your Customer (KYC). Digital identity has evolved in an age of databases, electronic communications, biometric data and algorithms (Tabaku and Duci, 2024).

Modern digital identity infrastructures, such as the linking of biometric identities with ac- cess to public and financial services in India (Aadhaar), in Nigeria (NIN), or the social credit- linked financial ID systems in China – extend government control and reach by linking existing biometric data with new access points to public and financial services. When institutions pro- mote digital identity systems, they do so with the idea of greater inclusion and efficiency, while scholars have criticized such systems for their features that enable surveillance, coercion, and exclusion. This reveals an underlying tension between administrative efficiency and surveil- lance risks – while such infrastructures improve targeting and delivery, they may also erode individual privacy and autonomy. In the CBDCs’ pilot projects, digital identity is not only an entry point but also a programmable boundary and behavior control mechanism. The financial self, previously defined only by control over capital, is increasingly organized through algorith- mic scoring, data traces, and biometric identification. See Salmony (2018), Mir, Kar, Gupta, and Sharma (2019), Wang and De Filippi (2020), and Okunoye (2022).

The boundary between identity and money is moving away from the established conven- tions of mainstream banking. Banks used to be seen as an accounting mechanism for financial orientation. Today, the focus has shifted to forms of active control. Economic life for individ- uals, corporations, and nations is now differentiated by layers of identity that are coded and enforced by the state. This may lead to more far-reaching changes in governance in relation to civil liberties and digital rights (Feher, 2021).

8.5 The global currency race: Geopolitical realignment through digital means

The current “currency race” is an increasing monetary competition, i.e., the competition between countries or institutions for the world’s favorite currency for global trade and savings in the digital dimension. This competition is no longer about interest rates or reserves, but about a different race: it is about who can build a faster, more secure, and widely accepted financial infrastructure. The race to create a digital infrastructure in every market is now an important way to reposition geopolitically. Currency influence today no longer depends on banks sitting on physical gold or silver. Instead, it is about utilizing digital networks and the ability to control transaction data and create and hold secure assets – financial instruments (government bonds, trusted digital currencies, etc.) that are safe, low-risk, and widely accepted in uncertain times (Aliyeva, 2025).

Dollar trading relies on infrastructure, including export invoicing and correspondent bank- ing systems, as well as the expansive Eurodollar market. However, the tightening of U.S. sanc- tions and the de-dollarization campaigns have exposed strategic vulnerabilities for countries that rely on dollar-dependent payment corridors. Many countries are beginning to develop replacement financial infrastructures that include improvements to national and regional real- time gross settlement (RTGS) systems, linkages to facilitate instant cross-border payments, channeling renminbi payments through one of the cleared renminbi banks or messaging sys- tems, frameworks for regional settlements in Asia, Africa, and Latin America, and new state- based card or messaging systems in parallel with existing global networks such as SWIFT and Visa. See Novak (1979), Buckley, Arner, Zetzsche, Lammer, and Gazi (2022), Pistor (2022), and Taylor (2025).

Multi-CBDC and wholesale pilots are signs that states will no longer simply digitize do- mestic monetary units and move on. Rather, they appear to be redesigning the architecture of cross-border liquidity systems to enable atomic, cross-currency settlement across national borders. From a strategic perspective, these projects aim to reduce friction in foreign exchange trading, reduce reliance on dollar intermediaries, improve surveillance capabilities against il- licit financial flows, and re-energize sovereign preference for transactions in selected currency areas. At the same time, CBDCs in large economies want to localize domestic payments in a sovereign, programmable way to create the technical infrastructure for subsequent internationalization (Sanz Bayón, 2025).

Stablecoins and tokenized deposits typically reinforce the dollar’s reach by providing dollar liquidity to digital ecosystems. From another standpoint, local or commoditized tokens seek to excise regional commerce from the established reserve hierarchy. This establishes a two-sided competition – the state up against decentralized architectures and traditional reserve currencies up against extant digital blocs (Fantacci and Gobbi, 2024).

Digital identity and data management have proved to be game changers: control of authen- tication, custody metadata and transaction analytics could bring regulatory and intelligence benefits. Although this is good news, there are pitfalls to be avoided – fragmentation into com- peting standards, interoperability gaps, and regulatory arbitrage can lead to system complexity and liquidity silos (O’hara and Hall, 2018).

The transition to a global currency race is not done with the flip of a switch, but it is a gradual transition to a multipolar world in which no single currency has the upper hand. And money, as we know it, has become diffuse, existing only for a select number of strong economies supported by credible institutions and technological infrastructure. In this world, credibility, convertibility, and connectivity are more important than simple reserve strength. The ability to shape financial networks, set standards, and create trust will determine which currencies and systems will determine global value flows in the future.

9. Conclusion: What is next for money and payments?

Money and payments developments have progressed from barter and metal currencies to in- stitutionalized financial infrastructures and algorithmic digital assets. This long-term trend reflects the interaction of trust, state power, and technological progress in the design of mone- tary systems. Historically, innovative forms of currency transfer between empires and markets have emerged to address problems of economic coordination, institutional legitimacy, and so- cial integration. The rise of central banking, followed by the gold standard and eventually fiat money, redefined national sovereignty by centralizing monetary authority and institutionaliz- ing the control of the economy. In the 20th century, electronic payments became easier thanks to infrastructures like SWIFT, credit cards, and Internet banking, deepening financial global- ization and creating new institutional dependencies. In recent years, the emergence of central bank digital currencies (CBDCs) reflects a resurgence of effort among nation-states to main- tain control over changing monetary systems that have come to be influenced by digitization, diminishing physical cash, and pervasive data practices promoting surveillance in ways that could undermine individual privacy.

At the same time, cryptocurrencies have typified decentralized money as a class of assets that are conceptually and structurally distinct from state-backed forms. Bitcoin, Ethereum, DeFi (decentralized finance), and other technical innovations represent not only a new technology but also an alternative monetary imaginary that challenges traditional intermediaries to create programmable, border-less, and trust-minimized forms of financial engagement.

The trajectories will not be a one-way swap nor a single-direction transition; one could expect systems to overlap with one another. These emerging hybrid ecosystems, made up of state- backed digital currencies, decentralized payment networks, and banking services operating on privately owned financial platforms, will coalesce in complex inter-relational ways that raise new issues related to privacy, governance, access, and systemic risk.

As payments are mediated by software protocols, identity schemes, and geopolitical posi- tioning, the very structure of money will become a battleground. So the next iteration of finance will not be about efficiency or innovation, but rather about the struggle for monetary authority in a fragmented, increasingly digitized, and globalized space.

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About the author

Panini Rao

Panini Rao is a 12th-grade student specializing in Commerce with Mathematics and Economics. An inquisitive and motivated learner, Panini has a keen interest in exploring new ideas and perspectives, particularly within the fields of economics, finance, and societal change. Beyond academics, Panini enjoys sketching, playing badminton, and spending time in nature, pursuits that inspire creativity and balance.

The post A historical analysis of the payment system from early stages to digital currencies appeared first on Exploratio Journal.

Abstract

Housing markets often defy the textbook principles of supply and demand. Economists expect that more housing supply should lower rents, but underlying affordability trends may suggest a more complicated reality. Using county-level data from the U.S. Department of Housing and Urban Development (OCC, 2014) from 2012 to 2025, this paper examines the relationship between unemployment, the construction of low-income housing, and fair market rents (FMRs). The analysis regresses unemployment rates against FMRs across multiple unit sizes and finds that there is a negative correlation between the two for all unit sizes (0-bedrooms to 4-bedrooms). It also tests whether greater low-income housing supply is associated with a reduction in rents; the results show a positive correlation between low-income units per 1,000 residents and FMRs, suggesting that affordable housing construction often follows rising rents rather than causing them to fall. However, when examining the share of low-income units within affordable housing projects, the correlation turns negative, indicating that the composition of affordable housing projects matters. These findings challenge the assumption that increasing supply alone improves affordability as a result of supply-side economics. The next step is to determine whether these trends have causal relationships, which will require expanding past HUD data to account for factors such as county-level political decisions, policing strategies, and domestic migration.

I. Introduction

Buying a home has long been considered a cornerstone of the American Dream, yet for  millions of Americans, it still remains out of reach. Even during periods of mass construction, affordability struggles persist, which raises questions regarding the factors that actually contribute to fair market rents (PD&R, 2025). Do areas with higher unemployment actually experience lower rents? And when states and towns invest in building low-income housing, does that added supply truly reduce rental costs? To answer questions like these, this paper focuses on fair market rents, the U.S. Department of Housing and Urban Development’s benchmark for affordability across counties in a given year.

Existing literature highlights the efforts of federal programs like LIHTC, which has financed over 2.4 million affordable units in the past 40 years (HUD, 2023). Yet scholars disagree on whether such efforts significantly improve affordability. Some argue that programs like LIHTC and inclusionary zoning stabilize house prices by expanding supply, but others believe that these policies have little impact on rents and can even correlate with increasing prices (Hamilton, 2019). To advance this debate, this paper examines how unemployment, the supply of low-income housing, and the composition of LIHTC projects correlate with fair market rents across U.S. counties and states.

This paper will be split into two main sections. First, I will evaluate the correlation between unemployment rates and fair market rents to measure if higher unemployment is associated with more affordability, since downturns in the labor market may reduce households’ ability to pay and push rents downward. This analysis will be done using county-year level data from HUD and will control for population and year. The second half of the paper will evaluate whether increases in the supply of affordable units are correlated with a decrease in Fair Market Rents. This analysis is significant as it will test the theory that more affordable units are associated with a decrease in prices; this section will use state-level data. Additionally, this section will evaluate how the percentage of affordable units within LIHTC projects correlates with fair market rents using the same state-year data. 

II. Fair Market Rents and Unemployment

A central question in housing affordability is whether local labor market conditions shape rental costs. A reasonable assumption to make would be that higher unemployment is associated with lower rents as weaker labor markets reduce household income and therefore constrain what renters are able to pay. This analysis tests that hypothesis using county-level data for fair market rents and unemployment rates since 2012. The FMR data is broken up by the number of bedrooms in a given unit ranging from 0-4.  

Figure 1 shows that FMRs have risen steadily across all unit sizes since 2013, with the greatest increases occurring after 2020. Specifically, there seems to be a gap between the fair market rents for 2-bedroom and 3-bedroom units suggesting that larger family-sized rentals have become less affordable at a faster rate.

Regressing this FMR data on unemployment rates attempts to understand how labor market conditions and affordability within a county relate to each other. After running multiple different regressions on FMRs and unemployment, the following results were outputted

Table 1 shows that when both county and year fixed effects are included, the relationship between unemployment and rents is weak, with high p-values, especially for two- , three- and four-bedroom units. Most coefficients fall close to zero and do not show a strong trend, with the exception of small positive values for studios and one-bedroom units. This result suggests that once both geographic differences and national trends are accounted for, unemployment alone does not explain much of the variation in rents. This outcome partially reflects the extent of the dataset: once both geographic differences and year-to-year shifts are absorbed by the model, little variation remains to be explained by unemployment alone.

Table 1: Regressions of Fair Market Rents on Unemployment

Additionally, Appendix Table A.1 displays other regressions that were run on the data. Panel A of Appendix Table A.1 presents the results of a basic regression of rents on unemployment rates without any controls, the regressions show a consistently negative and highly significant correlation between unemployment and rents. A one percent increase in unemployment is associated with a drop of roughly $13.85 in the fair market rent for 0-bedroom apartments and $31.63 for 4-bedroom apartments. Additionally, FMRs decrease by greater intervals as the number of bedrooms increases. These results suggest that higher unemployment is linked with lower rents across all unit sizes.

Panel B of Appendix Table A.1: When county fixed effects are introduced, the negative relationship remains and grows stronger in magnitude. For example, a one percent increase in unemployment corresponds with declines of about $17.41 for 0-bedroom units and $36.21 for 4-bedroom units.The p-values remain at ~0.000, meaning that this is still a very strong relationship. This suggests that within the same county, periods of higher unemployment are consistently associated with lower rents.

Panel C of Appendix Table A.1: When year fixed effects are introduced, the correlation between unemployment and rents remains negative and statistically significant across all unit sizes. This indicates that even after accounting for national shocks, such as inflation or broad economic cycles, higher unemployment within counties is still correlated with lower rents.

Together, the regressions suggest a relatively consistent negative association between unemployment and fair market rents, specifically when looking at variation within counties or across years. When both county and year effects are controlled for, the relationship largely disappears, showing that some of the variation in rents is tied to structural differences across places and broad national trends. Overall, the results support the conclusion that unemployment and rent prices have a negative association.

III. Supply and Affordability

The concept that more supply leads to lower prices has guided federal and state investments in affordable housing for decades. Since the late 1980s, the Low Income Housing Tax Credit (LIHTC) program has been the central method for this effort, financing millions of units nationwide. By looking at annual LIHTC production, shown in Figure 2 below, I can see how policy and market conditions have shaped the pace of affordable housing construction over the past 40 years. 

Figure 2 shows how the production of LI units has trended since the late 1980s. LIHTC production peaked in the early 2000s and in response to the 2008 crisis before declining in the 2010s. These shifts demonstrate how affordable housing construction responds to broader market and policy cycles rather than simply growing at a steady pace.

If adding affordable units truly decreases rents, states that build more low-income units (LI units) per 1,000 residents should have lower Fair Market Rents on average, given that other factors remain constant. It is important to calculate LI units given the population in the state in order to have a more applicable measurement for comparability. To test the hypothesis, FMRs were regressed on LI units per 1,000 residents using state–year data. The results are shown below:

Table 2 displays that when both state and year fixed effects are included, the relationship for 1-bedroom and 4-bedroom units is positive and significant for all unit sizes. These results show that even after accounting for state-specific characteristics and nationwide trends over time, more LI units per 1,000 residents continue to be correlated with higher FMRs. The data suggests that affordable housing is often added in response to rising rents rather than as a driver of lower rents.

Table 2: Regressions of Fair Market Rents on Low Income Units per 1,000 Residents

Additionally, Appendix Table A.2 displays other regressions that were run on the data. Panel A of Appendix Table A.2: Without any fixed effects, the regressions show a consistently positive and significant correlation between LI units per 1,000 residents and rents. An additional unit per 1,000 residents is associated with increases of roughly $19.26 in the fair market rent for 0-bedroom apartments and $35.15 for 4-bedroom apartments These results are surprising because it shows that states with more LI units tend to have higher average rents. This result invalidates the hypothesis; however, this model does not control for any variables like rents increasing over time

Panel B of Appendix Table A.2: When state fixed effects are introduced, there is still a positive correlation and low p-values signifying a significant result. For example, an additional LI unit per 1,000 residents corresponds with increases of about $84.92 for 0-bedroom units and $157.80 for 4-bedroom units, both of which are over four times the coefficient of the simple model results. These results suggest that within the same state, years with greater low income housing production are also years with higher FMRs. This is an important measurement as there are many differences in affordable housing supply from state to state as represented in Figure 3 below.

Figure 3 shows the disparity in affordable housing availability across the United States. States like Washington and Mississippi have more than twice the number of LI units per 1,000 residents compared to states such as Connecticut and Pennsylvania.

Panel C of Appendix Table A.2: When year fixed effects are introduced, the correlation between LI units per 1,000 residents and rents stays positive and significant across all unit sizes. This suggests that even after accounting for trends over time such as inflation, shifts in construction costs, etc., higher LI units per 1,000 residents within states are still associated with higher fair market rents. 

Beyond how many LIHTC units get built, the composition within each project matters: some developments set aside a small share of homes as affordable, others nearly all. After running regressions of FMRs on the percent of affordable units within LIHTC projects, the following results were outputted:

Table 3 shows that when both state and year fixed effects are included, there is a positive association between FMRs and the percentage of affordable units within LIHTC projects across all unit sizes. However, because the p-values are consistently high, the regression indicates that the relationship between affordable units and rents is ultimately unclear.

Table 3: Regressions of Fair Market Rents on Low Income Unit % in LIHTC Projects

Additionally, Appendix Table A.3 displays other regressions that were run on the data. Panel A of Appendix Table A.3: Without any controls, the affordable unit percentage is negatively associated with FMRs. For example, a one percentage point increase in affordable units corresponds with a drop of about $171 for studios and $214 for two-bedroom units. These relationships are statistically significant for most unit sizes, however the p-value for three bedrooms and four-bedrooms make them insignificant. 

Panel B of Appendix Table A.3: When state fixed effects are introduced, the relationship flips direction. The coefficients flip, turning strongly positive and ranging from $1,130 for studios to $1,755 for four-bedroom units. This reversal suggests that within a given state, periods with more affordable unit concentration tend to coincide with higher rents. Again, the p-values for three and four bedroom apartments render them insignificant

Panel C of Appendix Table A.3: With year fixed effects, the relationship turns negative again. This pattern implies that after accounting for broad national shifts over time, higher affordable housing shares are associated with lower rents. This time, the relationship is significant for all fair market rent types.

As the coefficient fluctuates between positive and negative values and the p-values are higher for this data, I cannot conclude a statistically significant relationship between the percentage of affordable units  in LIHTC projects and FMRs.

IV. Conclusion

The results of this quantitative analysis highlight the true complexity of housing affordability in the United States. At the county level, unemployment and fair market rents have an inverse association: higher unemployment is consistently correlated with lower rents, and lower unemployment with higher rents. This negative correlation reflects the simple reality that when fewer households can afford rising prices, rents tend to fall. By contrast, state-level analysis of affordable housing supply produces a more surprising result. Instead of reducing rents, more low-income housing units per 1,000 residents are positively associated with higher FMRs, suggesting that construction often follows rising rents rather than driving them down. Lastly, when looking at the share of affordable units within LIHTC projects, the findings seem less clear. The coefficients change between positive and negative depending on which factors are controlled, and many large p-values weaken the statistical significance of the results.

While each state has pursued its own policies, the national patterns in this paper highlight the need to evaluate which approaches are most effective. Comparing state-level strategies and identifying the best models would be a logical next step toward solving the housing dilemma and creating real progress for real people. My hope for future research is to conduct high-quality, data-driven evaluations of state-level and county-level approaches to affordable housing, identifying patterns that reveal which methods are most effective in different contexts. This work could guide policymakers toward evidence-based solutions that expand access to affordable housing where it is needed most. The analysis would require extensive data collection, but the conclusions could provide strong evidence for policies that could actually improve affordability and restore the hope of the American Dream for millions of Americans.

V. Citations

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Appendix

Appendix Table A.1: More Regressions of Fair Market Rents on Unemployment

Panel A: Simple Model (No Fixed Effects)

Panel B: Fixed Effects (county only)

Panel C: Fixed Effects (year only)

Appendix Table A.2: More Regressions of Fair Market Rents on Low Income Units per 1,000 Residents

Panel A: No Fixed Effects

Panel B: Fixed Effects (state only)

Panel C: Fixed Effects (year only)

Appendix Table A.3: More Regressions of Fair Market Rents on Low Income Unit % in LIHTC Projects

Panel A: Simple model (no fixed effects)

About the author

Rohan Rao

Rohan is a senior at Millburn High School where he is president of the DECA, Economics and Entrepreneurship clubs. After working with Millburn’s Township Committee and doing extensive research through debate, he developed a strong interest in affordable housing. He has started conducting formal research in the field of housing with the goal of making conclusions that can contribute to driving real policy changes.

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