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/

Daco, G. (2025, May). LinkedIn. https://www.linkedin.com/posts/gregorydaco_inflation-fed-fomc-activity-7323335677599793152-c–l/

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.

Lemieux, P. (2018, September 6). The St. Louis Fed on Imports and GDP. Econlib. https://www.econlib.org/imports-as-a-drag-on-the-economy/

Mankiw, N. G. (2001). Principles of Economics. Harcourt College Publishers.

Saunders, P.J. (2010). A Time Series Analysis of the Role of Imports in India’s Phenomenal Economic Growth. Indian Journal of Economics and Business, 91, 101-109.

Schonberger, J. (2025, April 30). Shrinking GDP and elevated inflation put Fed in tough spot. Yahoo Finance. https://finance.yahoo.com/news/shrinking-gdp-and-elevated-inflation-put-fed-in-tough-spot-142211609.html

Sekara, D., Dzuibinski, S., & Caldwell, P. (2025, July 16). Morningstar’s Q3 2025 US Market Outlook: Has the Storm Passed, or Are We in the Eye of a Hurricane? Morningstar. https://www.morningstar.com/markets/morningstars-q3-2025-us-market-outlook-has-storm-passed-or-are-we-eye-hurricane

Srikant, K. (2025, May 14). Fact Check: Does an increase in imports directly reduce GDP? Econofact.https://econofact.org/factbrief/fact-check-does-an-increase-in-imports-directly-reduce-gdp

Wiseman, P., & Rugaber, C. (2025, April 29). U.S. economy shrinks 0.3% in first quarter as Trump tradewars disrupt businesses. AP News.https://www.ap.org/news-highlights/spotlights/2025/u-s-economy-shrinks-0-3-in-first-quarter-as-trump-trade-wars-disrupt-businesses/

Wolla, S. A. (2018, September 4). How Do Imports Affect GDP? | St. Louis Fed . Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/publications/page-one-economics/2018/09/04/how-do-imports-affect-gdp

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.

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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.

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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

Chan, Xiang Ying Estelle. The Impact of Affordable Housing on Housing Markets and Affordability. Massachusetts Institute of Technology, 2016. DSpace@MIT, https://dspace.mit.edu/handle/1721.1/107862

DeSilver, Drew. “A Look at the State of Affordable Housing in the U.S.” Pew Research Center, 25 Oct. 2024, https://www.pewresearch.org/short-reads/2024/10/25/a-look-at-the-state-of-affordable-housing-in-the-us/

Hamilton, Emily. Inclusionary Zoning Hurts More Than It Helps. Mercatus Center at George Mason University, Sept. 2019, www.mercatus.org/research/policy-briefs/inclusionary-zoning-hurts-more-it-helps

Office of the Comptroller of the Currency. Low-Income Housing Tax Credits: Affordable Housing Investment Opportunities for Banks. Community Developments Insights, Mar. 2014. U.S. Department of the Treasury, https://www.occ.gov/publications-and-resources/publications/community-affairs/community-developments-insights/pub-insights-mar-2014.pdf

U.S. Department of Housing and Urban Development. Federal Tools for Production and Preservation of Affordable Rental Housing. HUD User, 2023, https://www.huduser.gov/portal//portal/sites/default/files/pdf/Federal-Tools-for-Production-and-Preservation-of-Affordable-Rental-Housing.pdf

Wang, Ruoniu. Inclusionary Housing in the United States: Prevalence, Practices, and Production in Local Jurisdictions as of 2019. Grounded Solutions Network, Jan. 2021, https://groundedsolutions.org/wp-content/uploads/2021-01/Inclusionary_Housing_US_v1_0.pdf

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.

The post How Fair Are Fair Market Rents? appeared first on Exploratio Journal.

Abstract

I evaluate a technical trading strategy that combines the Relative Strength Index (RSI) with a moving‑average (MA) trend filter. Using daily data for the 30 Dow Jones Industrial Average (DJIA) constituents over July 18, 2015–July 18, 2025, I test RSI deviation thresholds of 10, 15, and 20 points from neutral and apply a quarterly walk‑forward selection of RSI and SMA lookbacks. Returns are computed with next‑day execution and later adjusted for transaction and borrow costs. Across all specifications, Sharpe ratios are not statistically different from zero at the 5% level, and realistic costs eliminate small gross gains. Return distributions display high excess kurtosis and low skewness, consistent with exposure to higher‑order risks. I interpret the RSI and SMA combination less as a price‑forecasting tool and more as a risk filter that concentrates tail exposure. The strategy underperforms unconditionally, but the profile suggests it could perform better in specific market regimes in which such risks are compensated. I discuss implications for market efficiency and for using technical rules to target or avoid priced co‑moment exposures.

1. Introduction

Can a double-signal technical trading strategy generate consistent, risk-adjusted returns over the past decade?

To answer this, I run a 10-year backtest on three RSI deviation thresholds (10, 15, and 20 points from neutral), each paired with MA-based trend confirmation. Performance is measured gross of transaction costs and then adjusted using basis-point cost scenarios. I compute risk-adjusted metrics, including Sharpe ratios with Newey-West standard errors, Calmar ratios, and Sortino ratios, for each variation.

Multiple trading influencers promote variations of the RSI and MA strategy as a ‘magic bullet’. These influencers and channels have large followings and often promote their strategy in tandem with a course or funded trading partnership. Some examples are The Moving Average (YouTube, 1.07M+ subscribers), Trading Lab (YouTube, 1.74M+ subscribers), LuxAlgo (TikTok, Instagram, YouTube, 1.2M+ total followers), and many others. They create videos with bright, attention-grabbing colors and enticing thumbnail covers that draw inexperienced audiences in, promising to teach the “right way” to use these indicators and generate unrealistic returns. While these videos focus on anecdotal success and visual appeal, this study tests the strategy in a systematic, data-driven way.

This paper finds that none of the tested variations deliver statistically significant Sharpe ratios, with 95% confidence intervals spanning zero across all strategies. Some configurations yield small positive gross returns, but realistic transaction costs erase these gains. Our results suggest that this RSI–SMA strategy does not provide persistent predictive power and that any observed profitability likely stems from random variation rather than structural inefficiency.

Many investors employ technical trading strategies, such as the RSI and MA, despite decades of academic skepticism. If markets are efficient and past prices contain no useful information, such strategies should not be effective; yet, they persist in practice. We contribute to the literature by testing a simple yet popular combination of RSI and MA to evaluate risk-adjusted performance and exposure to certain risk profiles.

We focus not only on whether this strategy performs well, but also on whether any consistent performance challenges the Efficient Market Hypothesis (EMH), which states that past price data should not offer predictive advantages. McInish & Puglisi (1980) find that markets are efficient through runs tests of utility-preferred stocks. Brock, Lakonishok, and LeBaron (1992), however, find profitability in technical trading rules through nearly a century of DJIA data and contradict the EMH. This conflict shows how empirical results vary based on methodology, asset class, and time horizon, creating a rift between academic theory and real-world trading behavior. Understanding whether this tension reflects outdated testing methods, behavioral inefficiencies, or changing market dynamics is important for both researchers and investors.

The Relative Strength Index identifies overbought and oversold conditions, while the moving average smooths price action to show trend direction. Online trading communities often promote these indicators as part of a ‘signal confluence’ approach before entering trades. As retail trading grows with the rise of zero-commission trading and options contracts, underexperienced traders often rely on signals as primary strategies. These indicators, widely circulated in trading literature and online forums, influence trading decisions despite limited evidence of predictive power, making them a useful case study for examining whether such strategies expose traders to specific market conditions.

In addition to evaluating strategy performance, we reframe technical analysis as a potential filter for hidden risk exposures. This approach considers whether consistent performance could arise from taking on unattractive risks that most investors avoid, and therefore the most rewarding. We account for tail risk and other exposures not well captured in metrics like beta and volatility. In that case, the strategy would not forecast price action but would filter for market conditions where the trade-off between risk and return is most favorable.

The next section reviews the literature on technical analysis, while section three discusses the theoretical context behind risk exposure in asset pricing. Section four describes our methodology, and section five presents the results. Section six discusses the implications, section seven concludes the study, followed by references.

2. Literature Review

2.1 Early Academic Skepticism

Historically, there has been skepticism towards technical analysis in academia. Technical analysis was originally conceptualized around observed patterns rather than formal theory. Some studies, such as Tabell and Tabell’s (1964), emphasize Dow Theory and the Elliott Wave Principle, highlighting cycles and human behavior, themes still relevant today. Levy (1966) offers a more neutral perspective, critiquing both technical and fundamental approaches to trading. While he finds more empirical support for fundamental analysis, his openness suggests that even early researchers saw potential for technical signals to reflect meaningful information. Other studies go further by testing technical analysis and find no edge, concluding that price behaves randomly. Van Horne and Parker (1968) test a moving average and do not find excess returns, and are often cited as one of the earliest academic rejections of technical analysis. Their paper, however, is methodologically limited by a small sample size and lacks out-of-sample testing, limitations that this paper will address with modern tools and risk framing.

2.2 Conditional Effectiveness and Market Context

More modern studies suggest that technical analysis may work in certain market conditions. Han, Yang, and Zhou (2013) and Brown, Crocker, and Foerster (2009) both find that using moving average and buy-and-hold strategies, assets with higher risk components like volatility and turnover earn higher returns. This supports the idea that investor behavior and market dynamics may create price movements that technical analysis is better suited to capture than fundamental analysis. This may imply success in specific market conditions, particularly conditions that are traditionally avoided.

Building on this conditionality, other studies explore when and why technical analysis might work. Bessembinder and Chan (1998) similarly show that technical trading rules may have statistical predictive power, but lack economic significance due to trading costs and nonsynchronous trading effects. This counters the notion of technical analysis as a magic bullet for market context. Likewise, Qi and Wu (2006) test thousands of trading rules and find conditional effectiveness in the earlier half of their sample period, indicating that technical analysis may exploit inefficient markets. Together, these studies suggest that technical analysis can be used as a lens for inefficiency, a concept that will be explored further in this paper.

2.3 Behavioral and Structural Explanations

Many papers support the idea of technical analysis as a filter for behavior and structure. Smith et al. (2016) and Moosa and Li (2011) argue that technical levels correspond with real behavioral structures in different markets. The former shows that hedge funds using technical analysis have higher returns during periods of high sentiment, highlighting the connection to irrational investor behavior. The latter investigates the disproportionate success of technical strategies in Chinese markets, examining the distortion of markets by government intervention and high retail participation. These behavioral patterns may create a unique environment suitable for technical analysis where rational models fail. Other studies, such as Blume et al. (1994) and Kavajecz and Odders-White (2004), find correlations between specific signals and market information. Blume et al. argue that volume reflects trader confidence, while Kavajecz and Odders-White find an alignment between technical indicators and changes in liquidity. These findings imply that, in addition to behavior, structural market components may help inform the conditions where technical analysis could be successful.

2.4 Agreements and Differences

Across the literature, two general camps emerge. Early research, represented by Van Horne and Parker (1968), contends that technical analysis lacks predictive ability, even after accounting for randomness and naïve benchmarks. More recent research, however, finds pockets of conditional efficacy, whether due to market regimes (Han, Yang, & Zhou, 2013; Qi & Wu, 2006) or behavioral/structural patterns (Smith et al., 2016; Moosa & Li, 2011). Where the camps converge is in emphasizing the role of context: even the most ardent supporters recognize that strategy efficacy relies on particular circumstances, whereas skeptics admit anomalies may appear in particular settings. The disagreement is in whether those circumstances are exploitable after costs, and whether findings represent persistent inefficiencies or fleeting artifacts of market design.

This divide directly shapes our methodological choices. First, to address concerns over sample bias and parameter overfitting, we evaluate a multi-signal strategy rather than isolated rules, mirroring how traders filter entries. Second, we incorporate transaction cost adjustments to test whether any gross profitability survives realistic frictions, a point stressed by cost-sensitive studies like Bessembinder and Chan (1998). Finally, inspired by the state-dependent findings in both behavioral and structural work, we adopt a rolling quarterly parameter optimization to account for changing market conditions. This design allows us to test not only whether the strategy performs overall, but also whether it behaves differently across varying environments, bridging the empirical gap between one-size-fits-all tests and purely context-specific studies.

3. Theoretical Context

3.1 Strategy Risk Profile and Testable Claim

We test whether a dual-signal RSI+MA strategy inherently loads on tail risk, marked by high kurtosis and near-zero or negative skewness, because it systematically buys into short-term weakness and sells into short-term strength relative to prevailing trends. In its mean-reversion phases, the strategy takes long positions when RSI signals oversold conditions in uptrends and short positions when RSI signals overbought conditions in downtrends. This pattern can produce many small gains interrupted by rare but significant losses (a “knife-catching” profile), yielding elevated kurtosis and potentially negative skewness. The SMA trend filter mitigates some of this tail risk but does not fully eliminate it. If these return characteristics align with risks that most investors avoid, the strategy may command a risk premium in expected returns.

3.2 Why Higher-Order Risks Matter

In finance, investors are not only concerned with the magnitude of returns, but also with the distribution of gains and losses. Traditional models like the Capital Asset Pricing Model (CAPM) focus on volatility and market sensitivity as the primary sources of risk. However, this framework often overlooks more complex behaviors in return distributions that describe the shape and extremity of gains and losses. For example, two assets might have the same average return and volatility, but one may suddenly crash while the other steadily gains, with investors generally preferring the latter. By intentionally taking on the types of risk that most investors avoid, a strategy may earn higher expected returns as compensation for bearing that discomfort. This paper explores the idea that technical analysis, though often dismissed as unscientific, can function as a filter for the risk exposures that investors implicitly price. This could allow strategies to profit by taking on return profiles that are mispriced or systematically avoided. Central to identifying these patterns are two higher-order risk moments, skewness and kurtosis.

3.3 Definitions and Investor Preferences

A growing body of research suggests that co-skewness and co-kurtosis are crucial in asset pricing. Skewness captures asymmetry in the return distributions of a portfolio. Positive skewness represents large gains with frequent small losses, while negative skewness represents frequent small gains with the potential for large losses. Kurtosis measures the tailedness of the 0 return distribution, where high kurtosis represents a higher probability of extreme values, while low kurtosis represents a lower probability of extreme values.

Co-skewness and co-kurtosis, then, are the relationships between these metrics when comparing one variable to another, generally an asset compared to the market portfolio. Co-skewness describes how the asymmetry of one variable’s distribution is connected to movements in another variable. Co-kurtosis describes how the tendency for extreme values in one variable is connected to movements in another variable. A portfolio with negative co-skewness and high co-kurtosis, for example, tends to suffer its worst losses at the same time as the market, and experiences extreme returns in the same period, amplifying drawdown. However, CAPM suggests that assets performing poorly when investors most value consumption must offer higher expected returns as compensation. Because bad co-skewness and co-kurtosis are so unattractive to investors, the risk may be compensated for.

3.4 Pricing of Higher-Order Risks

Some studies explain this correlation between investor behavior and higher-order risk moments. Conrad, Dittmar, and Ghysels (2013) argue that skewness isn’t only measurable in hindsight, and that investors price ex ante skewness through the options market. They find that stocks with more expected negative skewness earn higher average returns, consistent with the theory that investments with more inherent risk require more compensation. The authors also find that stocks with positive expected skewness earn lower average returns, but the potential for large positive price changes remains attractive to investors. Frugier (2014) adds to this conclusion, arguing that these higher-order moments are embedded signals of trader psychology. He examines past stock data and finds that negative skewness and high kurtosis arise in times of 1 unresolved uncertainty and fear, indicating these psychological factors. These studies show that skewness and kurtosis must be priced in because investors prefer certain shapes of risk, like the frequency and size of gains and losses. These findings suggest that technical analysis could succeed if it accurately filters undesirable risk exposures that demand higher returns.

3.5 Institutional and Practical Evidence

Similar filters seem to be used in institutional trading. A study by Malkiel and Saha (2005) investigates risk exposure in hedge funds. They find that an overwhelming majority of hedge funds in the TASS database exhibit returns with low skewness and high kurtosis, generally avoided by traditional investors. This is supported by Elkamhi and Stefanova (2015), who show that in volatility-based portfolios, even small exposures to skewness and kurtosis can lead to outsized losses. Because of this, portfolios would benefit from explicit identification of these moments to hedge against them. If technical analysis can isolate exposure to higher-order risks, it should be viewed as a way to expand the investor’s span, to broaden the set of priced risks a portfolio can intentionally target or avoid. Technical strategies can be evaluated not just on signal accuracy, but on whether their return profiles reflect exposure to hidden risks. This perspective will be tested in the analysis that follows.

4. Strategy and Methodology

This strategy will test multiple combinations of the Relative Strength Index (RSI) and moving average (MA). The RSI(14) and MA(200) are the most standard values, and these period lengths are commonly referenced in social media posts. Other lengths modify the quantity, and 2 potentially ‘quality’ of each signal as trends become smoother with greater lengths. However, market conditions may affect this ‘quality’, so we perform a quarterly walk-forward selection of RSI and MA combinations to optimize for shifting regimes.

4.1 Universe & Data

We retrieved daily price data for all 30 constituents of the Dow Jones Industrial Average (DJIA) over ten years (July 18, 2015, to July 18, 2025) from the Yahoo Finance Historical Data API (see Appendix for the full list of tickers). Results are non-point-in-time, backfilled from the most recent members. We used close prices adjusted for dividends and splits.

4.2 Signal Construction

The RSI is computed on adjusted closes using Wilder’s smoothing as:

Where AU_t and AD_t are the exponentially smoothed average gains and losses over n days. The SMA is calculated as:

4.3 Position Sizing and Execution Convention

Signals are generated and executed at the close on day t. Each stock may trigger at most one entry per day, and each position is allocated 1/10 of the total portfolio value to avoid concentration risk. Position weight per stock is fixed-weight per trade. Buy and sell signals are processed independently for each stock.

4.4 Walk-Forward Selection

To adapt the strategy to evolving market conditions, we partitioned the 10-year sample into 40 quarterly segments. For each RSI deviation threshold (10, 15, and 20), an initial quarter runs with default parameters of RSI(50) and SMA(200). At the end of each quarter, we evaluate RSI and SMA combinations based on their Sharpe ratios. We apply the combination with the highest Sharpe ratio to the next quarter. This process repeats in a walk-forward fashion across all 40 quarters, producing a series of returns in which each quarter’s parameters reflect the most profitable configuration from the preceding quarter.

4.5 Shorting

This backtest assumes that short sales are permitted for all DJIA constituents at all times, with full borrow availability. A borrowing cost is applied to all open short positions, representing the annualized interest rate charged for borrowing shares. This cost, denoted c_b , is assumed to fall within the range of 30-50 basis points per year (0.30%-0.50%), with the base case using = c_b 0.50%.

This daily borrow charge is calculated as:

Where ShortNotional_t is the dollar value of the short position on day t . Borrow costs t are deducted from portfolio equity each day the short position remains open. Dividend payments owed on short positions are already incorporated in the adjusted price series used for return calculations, so no separate adjustment is required.

4.6 Transaction Costs

Transaction costs were incorporated by first calculating daily portfolio turnover as the average absolute position change across the 30 DJIA stocks.

We multiplied this turnover by a per-dollar transaction cost rate c to estimate daily cost drag.

We then calculated the Compound Annual Growth Rate (CAGR) from the net return series, with results reported for costs of 0, 2, 5, and 10 basis points per side.

All other performance metrics are based on gross returns, with the impact of transaction costs summarized separately in the CAGR table.

4.7 Risk and Return Metrics

We evaluate portfolio performance using a set of risk-adjusted and distributional metrics. The Sharpe ratio is computed using Newey-West adjusted standard errors to account for serial correlation in daily returns.

r o represents the daily average return of our portfolio, while NW represents the standard deviation of returns that has been adjusted for autocorrelation (the similarity between a variable’s values at different points in time) and heteroskedasticity (the spread of standard deviations of a variable).

We use a Calmar ratio to measure the trade-off between annualized return and maximum drawdown.

Where CAGR is the Compound Annual Growth Rate of our portfolio over the testing period, and the denominator is the absolute value of the maximum drawdown of the portfolio.

The Sortino ratio isolates downside volatility as opposed to both sides.

Where T^— is the number of days with negative returns, and r_t the return on day t. Wholly, the denominator represents downside deviation, or the standard deviation of negative returns.

Skewness and kurtosis are computed to assess asymmetry and tail heaviness in the return distribution.

Where T o represents the total number of days in the sample and represents the standard deviation of all returns. Skewness represents asymmetry of the return distribution (whether it is more tilted to gains or losses). Kurtosis represents the “tailedness” of the distribution (the likelihood of extreme gains or losses). All ratios are computed using gross daily returns unless otherwise noted, with transaction-cost-adjusted returns reported separately.

5. Results

This section presents the performance of an RSI and MA strategy under three different threshold configurations: DEV 10, DEV 15, and DEV 20. These values represent the entry condition deviation in RSI. Results are based on a portfolio spanning July 2015 to July 2025.

5.1 Cumulative Returns

The cumulative return chart shows diverging performance across the three RSI deviation tests, all ending with negative returns. Differences in performance begin to emerge in mid-2017, early in the testing period. DEV 10 (blue) showed brief periods of positive returns and was the most volatile, ending with the highest return of the three. DEV 15 and 20, although considerably more stable in return trajectory, finished with even lower returns. Returns tended to move in the same direction for most of the period, suggesting positive covariance between strategies.

5.2 Annualized Metrics

Annual returns were negative across all three configurations. Annual excess returns versus SHY and SPY were also negative, the lowest being DEV 20 (-2.129%) and the highest being DEV 10 (-1.514%). V olatility followed the same pattern, from 2.162% (DEV 20) to 3.154% (DEV 10). The Sharpe ratio also decreased with higher deviation thresholds, ranging from -0.480 (DEV 10) to -0.985 (DEV 20). However, 95% Newey-West adjusted confidence intervals for the Sharpe ratios include 0 in all three strategies, implying that the Sharpe ratios are statistically insignificant. Small, negative Sortino and Calmar ratios suggest poor performance compared to risk-free assets and drawdown, indicating greater downside risk.

5.3 Transaction Costs

To address the effects of transaction costs, we recalculated CAGR for each RSI Dev strategy after applying per-dollar transaction costs of 0, 2, 5, and 10 basis points per side. Across all three strategies, higher transaction costs reduced CAGR approximately linearly with cost magnitude. For further context, we calculated total transaction costs as a percentage of portfolio value. Dev 10 exhibited the highest turnover (5.25% at 10 bps), confirming that smaller deviation thresholds are more sensitive to cost assumptions.

The above graphs show trade logs for each strategy, blue representing long trades and orange representing short trades. This log contextualizes higher transaction costs for the lower threshold strategy, Dev 10, which had the highest trade volume.

5.4 Return Distributions

Returns for all three strategies were density-adjusted and compared to normal distributions. Return distribution plots for each test show a sharper central peak and heavier tails than Gaussian (high excess kurtosis). All three strategies display high kurtosis with values ranging from 18.006 (DEV 15) to 21.788 (DEV 20). Although most returns are clustered around the mean, this shows that these strategies experienced a significantly higher frequency of extreme return days than a normal distribution.

Skewness values ranged from low (DEV 10 and DEV 15) to low-moderate (DEV 20). DEV 10 and DEV 20 had positive skew, implying a higher probability of large positive gains, while DEV 15 had a negative skew and a tendency towards large negative gains.

6. Discussion

This section interprets the performance of the RSI and MA strategy beyond raw returns. While the strategy underperformed, its results reveal insights into market efficiency, hidden risk exposures, and the conditional role of technical analysis.

6.1 Underperformance and Market Efficiency

The RSI and MA strategy tested in this paper underperformed across all annualized metrics in the ten-year testing period, but its implications suggest a broader role of technical analysis in risk exposure. All three strategy variants show negative risk-adjusted values when compared to SHY and display negative Sharpe, Calmar, and Sortino ratios. These outcomes are consistent with the weak form of the Efficient Market Hypothesis, which holds that past price data contains no predictive or actionable information. However, this underperformance does not imply that the signals were entirely random or meaningless. The return paths of DEV 10, 15, and 20 were similar for most of the ten years. Positive covariance in the three strategy variations suggests exposure to similar structural patterns in the market. The strategy may not unconditionally generate alpha, but it might expose investors to certain risks hidden in asset prices.

6.2 Hidden Risk Exposure and Conditional Opportunity

The most compelling pattern in the results is the consistently high kurtosis. This indicates frequent extreme returns, which traditional investors tend to avoid due to financial and psychological discomfort. Relatively low skewness values indicate minor asymmetry in returns, but the heavy-tailed return distributions suggest that the RSI and MA strategy filters for hidden, 2 non-diversifiable risk. This aligns with research by Conrad et al. (2013) and Frugier (2014), who argue that investors price these risks because they reflect the large, abrupt losses during ‘bad times’.

Consistency across all three thresholds suggests that the strategy systematically targets certain classes of risk that are not rewarded over long periods, but may be under unique conditions. This helps explain the underperformance, as strategies with undesirable risk (high kurtosis and low skewness) should not be expected to outperform in all periods, especially in a mostly efficient market. However, it might work over certain regimes where these risks are mispriced, such as volatility clusters and trend reversals.

6.3 Reframing Technical Analysis

Instead of interpreting the negative returns as evidence against technical analysis, these results suggest a more nuanced framing. The value of the RSI and MA strategy is likely not in forecasting price but in allowing investors to bear discomfort in exchange for higher compensation. This is only possible, of course, with research into exactly when and where these strategies conditionally succeed. This can be tested across periods segmented by different macroeconomic factors, and doing so might reveal how technical analysis’s payoffs align with inefficiency-prone conditions.

Ultimately, although this study does not contradict market efficiency, it reveals how technical analysis could account for shortcomings of traditional investment strategies. It might add value by identifying risks that investors expect compensation for. In this way, it can be used as a tool for portfolio construction to access unconventional sources of returns, or simply to hedge against risks in traditional portfolios.

7. Conclusion

In conclusion, this study tested a widely used technical trading strategy, a combination of the Relative Strength Index and moving average. The analysis spanned ten years and evaluated three different RSI deviation thresholds. While the strategy underperformed across all key return metrics, its characteristics offer important insights into the academic and practical discourse around technical analysis. The strategy’s exposure to extreme returns, seen in its high kurtosis, suggests that it filters for hidden, non-diversifiable risk. This finding is relevant in today’s markets, where signal-based strategies are frequently promoted across social media platforms and adopted by traders in search of an edge. Understanding what these strategies are actually capturing, even when they fail to generate alpha, is essential to evaluating their role in constructing portfolios and risk management.

For researchers, these findings support a reframing of technical analysis as a mechanism for identifying priced risk exposures. Further study should test performance in different market conditions where these risks might be mispriced. For traders and portfolio managers, these findings emphasize the risk structure behind technical analysis. While signals like the RSI and MA might not yield consistent profits, they can help in designing portfolios that take advantage of risks that are compensated under certain conditions. This understanding can also help retail traders avoid misusing signals as forecasters of price action. In this way, the lasting contribution of technical analysis is not its forecasts, but its ability to expose the risks that markets reward.

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About the author

Roshan Shah

Roshan is currently a senior at Glenbrook South High School in Glenview, Illinois. He intends to complete an undergraduate degree in Applied Mathematics. He is particularly interested in the intersection of mathematics, computer science, and finance, and looks forward to pursuing a career in financial technology. In his free time, Roshan enjoys a wide variety of extracurricular activities, including Model United Nations, digital music production, working out at the gym, and spending time with friends. He also enjoys helping his community by providing food and clothing to the homeless in Chicago.

The post Technical Trading and Higher-Order Risks: An Empirical Evaluation of Relative Strength Index and Moving Average Strategies appeared first on Exploratio Journal.

I: Introduction

As the world grapples with a transition to renewable energy, solar energy has emerged as the leading contender in the journey toward a cleaner and sustainable future. While solar energy is seen as relatively environmentally friendly, the financial viability of solar energy remains a key factor in its widespread adoption and integration. This paper is going to explore the understudied area of the financial viability of solar renewable energy to further understand the financial and economic nuances of the complex and intricate field of solar energy.  

To narrow the scope of my research and provide a detailed analysis on the topic at stake, I have decided to focus on the state of California as my region of focus. California is the USA’s leading state in the generation of solar energy, producing over 68,800 GWh of electricity in 2023, which is over double any other state’s output [1]. Additionally, California boasts a high average of 263-300+ sunny days per year, making solar energy a form of energy that can be easily harnessed.  [2] . Not only this, but the average cost of a solar system in California ranks the cheapest out of all states at USD$20,363 before incentives [3]. As a result, California provides a prime example of how solar energy can be harnessed effectively and a suitable region for this case study. 

With this information in mind, why is the topic of ‘The Financial Viability of Solar Energy In California’ important in today’s world and our future? As California leads the nation in solar energy adoption, understanding the intricate nature of the costs, payback period, and regulatory situation can help provide various stakeholders such as consumers, investors, and policymakers with detailed insights into the present-day situation and future growth of the solar sector. Ultimately, this study is essential to understand how solar energy can continue to be scaled upwards in California, and place further emphasis on the implementation of renewable technologies in our rapidly developing world. 

To conduct this study, there are several key areas that need to be considered and analyzed. The next section will explore the historical background of solar energy production in California. Following this, section III will dive into the difference between solar photovoltaics and solar thermal. In section IV, an investigation into the status of solar energy in California will be conducted. In section V the federal and state regulatory framework for solar energy will be explored. After, section VI will contain an analysis of the financial viability of solar energy, including the customer side and the production and distribution side. Finally, in section VII, major findings will be highlighted, concluding remarks will be given, and future research opportunities will be noted. 

II: Historical Background of Solar Energy Production in CA

The salient developments in the solar energy sector are noted below  in a chronological order. 

• In 1955, PV tech was born in the USA, with Bell Laboratories researchers creating a 6% efficient cell.  [4]
• California solar energy industry formed 1970  with it predominantly being used by people who live in off-grid societies  [5]
• U.S. Department of Energy was formed in 1977 [4]
• 1979, when ARCO Solar opened the world’s biggest solar photovoltaic facility in Camarillo [5]
• In 1985, researchers at Stanford University created 25%-efficient cells [4]
• In 1986, a solar thermal electric facility was commissioned in Kramer Junction, CA, which was the world’s largest solar thermal facility at the time. [4]
• In 2007, the California Solar Initiative was launched by the California Public Utilities Commission (CPUC) as part of the broader “Go Solar California” campaign. It was a USD $3.3 billion initiative funded by ratepayers aiming to install over 3000MW of solar capacity in the form of photovoltaic and thermal systems [4]. 
• In 2014, the Ivanpah Solar Electric Generating System opened in the Mojave Desert , becoming the world’s largest concentrated solar power (CSP) plant.
• In February 2016, the U.S. reached 1 million solar installations, driven by a dramatic decrease in solar prices [4]
• In the present (June 9 2025), across the USA, the current solar capacity is 248GW large, with a total of 278,447 jobs in the industry.  [7]

III: Types of Solar Energy

Solar energy in California is generated in two forms, solar photovoltaic (PV) and solar thermal. This section will highlight the differences between the two. 

III a. Solar Photovoltaic 

Photovoltaics gets its name from the process of turning light (photons) energy from the sun into electricity (voltage) using solar cells made of semiconducting materials in a phenomenon called the photovoltaic effect [8]. When the energy from photons hits a semiconductor, it transfers its energy to electrons, allowing them to break free from their bonds and become free electrons. This free electron creates an electron-hole pair with the corresponding hole it leaves behind after it has been freed. The semiconductors in solar cells usually consist of a built-in electric field that separates the electron hole pairs, forcing the electrons to flow one way and the holes the other way. This electric field is created by the junction of p-type and n-type semiconductor materials. Electrodes are attached to both the p-type and n-type regions which allow the electrons to flow through an external circuit. This flow creates an electric current, which can be harnessed and utilized as electricity. After being harnessed as electricity by an appliance such as a lightbulb for example, the electrons recombine with their corresponding holes to complete the cycle that goes on indefinitely as long as there is sunlight hitting the solar cell [9]. This technology was first exploited in 1954 by scientists at Bell Laboratories who created a working solar cell that harnessed the sun’s light energy and transferred it to electrical energy. 

So, what materials of semiconductors are typically used in PV systems? Silicon is the main material used as semiconductors in a solar cell, but there are two types: monocrystalline and polycrystalline. Monocrystalline silicon cells are made from a single continuous silicon crystal structure, which makes it more efficient than polycrystalline cells but more expensive as a result [9]. Their efficiency ranges from 16 to 24 % [10]. Polycrystalline silicon cells are made from multiple smaller interconnected silicon crystals which makes them less efficient but more affordable [9]. Their efficiency ranges 15% to 20% [11]. 

In California, solar PV panels are typically made of either monocrystalline silicon cells, or polycrystalline silicon cells. Approximately 97% of global silicon wafer production needed for solar cells occurs in China. They are then shipped from China and made into solar cells. About 75% of the silicon solar cell production occurs in Vietnam, Malaysia, and Thailand by Chinese subsidiaries [12]. 

III b. Solar Thermal

Solar thermal is a form of renewable energy in which sunlight is transferred and converted into thermal energy (heat) instead of directly into electricity like photovoltaics. [13]. In California, a technology called concentrated solar power (CSP) is used, which is a specific type of solar thermal technology that uses mirrors or receivers to concentrate large amounts of sunlight onto a small receiver. Inside the receiver is a heat-transfer fluid that is heated by the energy from the concentrated sunlight [14]. The thermal energy of this fluid is used to produce steam, which transfers the energy to mechanical energy through the turning of turbines, which power a generator to produce electricity [14]. 

In California, there are two main types of CSP technology that are used to generate electricity: Parabolic Troughs and Solar Power Towers. While both use mirrors to concentrate sunlight to generate electricity, there is a difference in the way these systems are set up. 

Parabolic Trough systems are the most common type of CSP technology. They consist of long, parabolic shaped mirrors that reflect concentrated sunlight toward a tube that runs parallel across the mirrors’ center [16]. These trough shaped mirrors are aligned on a north-south horizontal axis, allowing the continuous tracking of the sun throughout the whole day from it rising in the east to setting in the west [14]. Inside the tube that runs along the mirrors’ center is the heat transfer fluid that is heated to produce steam, which turns turbines that generate electricity [16]. While the thermal efficiency when the transfer fluid is used to heat steam to drive a standard turbine generator ranges from 50% to 80%, the overall conversion efficiency from solar power to electrical output power is just 15% [15]. 

Solar Power Tower systems, on the other hand,  feature numerous mirror reflectors at ground level, known as heliostats. These heliostats are computer controlled which allows them to continuously adjust and reflect sunlight toward a single point at the top of a tower where the receiver is situated for the entire day. Just like mentioned above, within the receiver is the heat transfer fluid —typically a molten salt— which is used to generate steam and turn turbines for electricity generation [16]. Real world applications claim that the peak efficiency for Solar Power Tower systems range from 20% to 35%, however, due to variations in weather conditions, in practice the net annual solar to electricity conversion rate ranges from 7% to 20% [17].

IV: Status of Solar Energy in CA

Currently, California is home to the three of the largest operational solar photovoltaic facilities in the USA: the 550MW Topaz Solar Farm, the 550 MW Desert Sunlight Solar Farm, and the 579 MW Solar Star facility [18]. It is also home to large CSP plants, such as the 392 MW Ivanpah Solar Power Facility, the 280 MW Genesis Solar Energy Project, and the 280 MW Mojave Solar Project [18]. 

IV a: Solar Facility Locations in California 

According to this map of utility scale solar farms in California, the regions in which there is a high density of solar farms and facilities can be seen. Three prominent locations are in Southern California and the Desert Regions. Southern California’s abundance of solar farms is a result of the cities of Los Angeles and San Diego being in this region. These regions favour solar farms highly with a large abundance of sunshine yearly, with LA boasting 263-290 sunny days per year [19] and San Diego boasting an average of 263 sunny days per year [19]. Meanwhile, the Desert Regions in California such as Mojave desert also host large solar farms such as SEGS stated above, as they have high solar irradiance and available land. 

IV b: Solar Capacity of Different Sectors

Now that the locations of plants have been covered in California, we can dive into an exploration of the split of solar energy use between the state’s different sectors. 

The chart above showcases the percentage capacity of solar power generation projects in California from August 1st, 2015, onwards. It was created by the  California Distributed Generation Statistics (DGStats), which is the California Public Utilities Commission’s official public reporting site for solar power generation projects. According to the chart, 71.53% of solar capacity in California from August 1st, 2015, onwards is for the residential sector, 21.03% is for the commercial sector, 3.78% for the educational sector etc. Educational purposes in this context refer to schools that have installed solar capacity. In California, this number was 2,819 schools as of 2023 [21].

When this stat is viewed with information of California’s total solar capacity, which was 46,874 MW at the end of 2023 — enough to power 13.9 million homes [18] — it reiterates the widespread adoption of solar energy in California while simultaneously providing further information on how the solar energy capacity is split throughout the state. 

V: Regulatory Policies regarding Solar Energy: Federal vs CA 

This section will dive into an exploration of the regulatory framework around solar energy. It will consider federal policies, as well as state (CA) policies. 

V a: Federal

The Trump Administration and Biden Administration have different views, policies, and actions toward the solar industry, as both parties possess different philosophies. Biden: supporting solar energy growth, Trump: not so much. 

Pre-Biden, under the Obama Administration, solar electricity generation increased “30-fold and solar jobs grew 12 times faster than the rest of the economy”, according to the Whitehouse archives [22]. One notable initiative the Obama administration announced was the ‘Clean Energy Savings for All Initiative’. This targeted solar access and energy efficiency in low- and moderate-income communities, aiming for 1 GW of solar by 2020. [22]

Biden Administration

The Biden administration was very notably pro-solar energy. 

Firstly, strong federal support for expanding solar projects across the country was given, particularly in California. An example of this was when the Bureau of Land Management approved solar projects in Riverside County, California. Combined, these projects would be enough to power approximately 132,000 homes [23].

Secondly, Biden’s Inflation Reduction Act included a $7 billion grant program called “Solar for All’ administered by the US Environmental Protection Agency (EPA). This program aimed to achieve solar installations for a staggering 900,000 low-income and disadvantaged households nationwide, with California’s share being equivalent of USD $250 million [24].

Lastly, the Biden Administration helped to strengthen an existing federal solar tax credit, which is formally called the Residential Clean Energy Credit. It was expected to expire in 2022, but the Inflation Reduction Act extended it to 2035. But what is a tax credit? A tax credit is a dollar-for-dollar amount taxpayers claim to reduce the income tax they owe. This credit offered 30% for any costs related to a solar system installation. Hence, essentially, the benefit to customers is that they receive 30% back for purchases related to installing solar panels and batteries in the form of a tax credit [25]. 

Trump Administration

The Trump Administration, on the other hand, acted essentially the opposite. 

Firstly, the EPA, under Trump’s administration, moved to cancel the $7 billion ‘Solar for All’ program, including California’s $250 million share. This severely impacted solar projects for lower income households and in lower income communities [26].

Additionally, President Trump’s ‘Big Beautiful Bill’ will completely cut the residential federal solar tax credit by December 31st, 2025. Homeowners now have until this date to purchase and install solar systems in their residences or pay the full price in 2026. The residential solar tax credit is the only tax credit which is cut regarding solar energy (Section 25D of the U.S. Tax Code). Commercial solar projects and third-party owned systems use a different tax credit (Section 48E), so they still receive the 30% tax credit [27]. However, this is only available for systems that start construction by July 4, 2026, or are fully operational by the end of 2027 [27].

V b: State (CA)

California Solar Mandate

Perhaps the most significant mandate California has in place to promote the generation and use of solar energy is the California Solar Mandate. This is a building code that requires all new single- and multi-family homes that are up to three stories high to have a solar PV system installed as an energy source from 1st January 2020 onwards [28]. These laws were recently updated in 2025. Under the 2025 code, single-family homes, multifamily units, and specific nonresidential properties must include a rooftop solar PV system, and incorporate battery storage in certain areas [29]. This update aims to emphasize solar systems and reduce reliance on fossil fuels in California. 

Solar System Property Tax Exemption

The Solar System Property Tax Exemption is California’s way of ensuring to customers that installing a solar power system in their living space will not raise their property taxes [30]. This form of incentive means that any increase in value to one’s living property because of solar energy system installations will not contribute to property tax when property tax assessments are carried out. The estimated value for this exemption is USD $240 per year, but it depends on the home price, installation, and local taxes [25].

Self-Generation Incentive Program (SGIP)

California’s Self-Generation Incentive Program (SGIP) offers rebates that can cover significant costs associated with solar energy system installation and use [30]. Specifically, the utility customers of four major investor-owned utility companies: San Diego Gas & Electric, SoCalGas, Southern California Edison and Pacific Gas & Electric, are eligible to receive these rebates [25]. The California Public Utilities Commission offers USD $150 rebate for each kilowatt-hour of solar storage system, with higher rebates ranging from USD $850 to USD $1000 for those who meet certain criteria regarding income and geographic location [25], for example areas with high fire risk, according to the Current Home blog [30]. 

Disadvantaged Communities Single-Family Solar Homes (DAC-SASH) Program

California’s Disadvantaged Communities Single-Family Solar Homes (DAC-SASH) program provides incentives for those who receive lower-incomes and live in disadvantaged communities [25]. Eligible customers can receive up to $3 per watt in incentives for solar installations which in many cases can cover the full cost of installation [30]. In total, this program offers Californians USD $8.5 million annually [25].  

VI: Financial Viability of Solar Energy in California

This section will focus on the financial viability of solar energy in California. First, an exploration will be conducted into the customer decision making aspect of solar investments, followed by an analysis of the production and distribution side, including costs and comparisons with other forms of energy. 

VI a: Customer Side

Now it is time to dive into the customer side of solar energy usage. For reference. commercial and residential solar energy panels are typically made of photovoltaic cells in California. 

Benefits For Customers

Customers of solar panels, residential or commercial can use the electricity generated for their homes or office buildings, instead of electricity from the grid. Grid electricity prices rise 5.9% annually, so solar energy can save tens of thousands over the lifetime of their solar panels [31]. In San Francisco, a 5.7kW residential solar system can save $108,200 over 25 years [31].

Customers can also sell energy to the grid network of the area around them, which can generate a steady supply of revenue as shown in the business plan above. However, even if they use electricity for their own purposes, they can sell excess electricity generated back to the grid, and receive net metering benefits. In California, consumers can receive a rate of $0.08/kWh sent back to the grid [25]. 

To reiterate, residential and commercial customers also benefit from the 30% federal solar tax credit. However, the residential solar tax credit is being cut on January 1 2026, so to benefit, construction must start before that date. However, commercial solar projects and third-party owned systems use a different tax credit (Section 48E), so they still receive the 30% tax credit [27]. 

Solar Panels are relatively easy to maintain. Consumers can expect a cost of $150-$300 annually for professional cleaning and maintenance. Additionally, annual inspections are recommended to ensure the system has no serious issues, which can cost $150-$200 [32].

Additionally, solar systems can boost home value by 5-10%, according to EnergySage. In context, “for the average $790,000 California home, that’s an eye-popping $39,500 to $79,000 boost in resale value.” [33] This boost doesn’t affect property tax however, due to the California Solar System Property Tax Exemption.  

Financing Options for Consumers

Cash Purchase: Buying in cash means consumers can benefit from all the savings that the solar system generates. Additionally, they do not have to pay loans or interest, but that means that there is a relatively higher initial investment cost. [34]

Solar Loans: These allow customers to borrow money to finance the installation of solar systems. They can be made through credit unions, banks, or specialized lenders. While there are no upfront costs and the system is fully owned by the consumer, interest rates must be paid. [34]

Solar Leasing and Purchasing Power Agreements (PPAs)

Leasing: This is paying a third party a set monthly rent for a solar system installation. [34]

Power Purchase Agreements (PPAs): Like leasing, but instead of a set rent for the solar installation, customers pay a set rate for the electricity generated. [34]

For both these options, installing and maintaining the system falls under the provider’s responsibility, and this option saves money on utility expenses. However, the system isn’t owned by the customer, meaning no personal savings can be generated, and no tax incentives or rebates can be enjoyed by the customer. [34]

VI b: Production and Distribution

Cost of PV systems

This section will go into the costs of a residential 5kW solar PV system (RPV), the cost of a 3MW commercial solar PV system (APV), and the cost of a 100MW utility scale solar PV system (UPV) according to the United States Department of Energy. The department of energy has calculated the levelized cost of energy (LCOE), which measures the lifetime costs of an energy system – including building the facility and operating it – divided by total energy production it is expected to produce over its lifetime. This metric is commonly used to compare the cost effectiveness of different forms of energy generation which will occur later in the paper. [35]

It is important to note that these figures represent a national average before the 30% federal tax credits have been accounted for, rather than local prices in California. The department of energy explains that “LCOE is lower than the value listed in these tables in locations with more annual sunshine (up to 30% less in the desert southwest, which includes southern California), and higher in regions with less annual sunshine (up to 30% more in the Pacific northwest, which includes parts of northern California).” [35] Hence, we can calculate the extremes of the LCOE of each scale of solar PV system in different parts of California, albeit before tax credits. 

Table 1: 2024 Q1 PV Cost and LCOE. Source: U.S. Department of Energy [35]

As the figure above from the department of energy shows, the LCOE for UPV systems is $47/MWh, while for APV systems and RPV systems it is $75/MWh and $142/MWh respectively. $/MWh means the cost of producing one megawatt hour of electricity using solar energy. The table below showcases the adjusted LCOE values for southern regions of California and the LCOE for the northern regions of California as well. [35]

Table 2: 2024 Q1 PV LCOE California Regions. Source: U.S. Department of Energy [35], and Author’s Calculations

The respective LCOEs were calculated by multiplying the LCOE by 0.7 for southern California, and 1.3 for northern California. 

In summary, the LCOE for UPV systems in California ranges from $32.9/MWh to $61.1/MWh, $52.5/MWh to $97.5/MWh for an APV system, and $99.4/MWh to $184.6/MWh for an RPV system. 

Cost of Solar Thermal systems

Solar Thermal systems are only used to generate electricity on a utility scale in California. The cost of a utility-scale solar thermal system in California typically ranges widely depending on the specific technology, scale, and design. There are no published average figures by the department of energy on solar thermal plants, so instead we can dive into the LCOEs of the Ivanpah Solar Power Plant and the Mojave Solar Project.

The NREL – which is the U.S. The Department of Energy’s primary national laboratory for energy systems – has published the LCOEs of the Ivanpah Solar Power Plant and the Mojave Solar Project. The LCOEs of these plants are $190 per MWh and $240 per MWh respectively as of 2020. [36] [37]. 

LCOE of other forms of energy

The US Energy Information Administration (EIA) published a report titled “Levelized Costs of New Generation Resources in the Annual Energy Outlook 2022” in March 2022, which included national averages of LCOE estimates by 2027 in 2021 USD. All figures are in 2021USD$ per MWh. The LCOE of four of the most frequent energy sources in California: Wind, Hydroelectric, Nuclear, and Natural Gas will be determined. In California because of stringent environmental restrictions, complex licensing, additional fees, higher fuel and operational costs, and higher labor, the LCOEs will be higher than national average, with the maximum LCOE determined from the report [38].

Table 3: LCOE of Alternative Energy Sources to Solar. Source: US Energy Information Administration [38]

Comparisons Between Energy Forms

It is important to note that all the findings above are for utility scale projects, and they are estimates for the year 2027. Hence, it is only justified to compare the UPV and utility scale solar thermal projects LCOEs with the above figures. Also, it is important to note that over time, the LCOEs tend to decrease. This is due to technological advancements, economies of scale, and reduced financing costs. Hence, this must be kept in consideration when comparing LCOEs. 

In order to compare figures, we need to convert our UPV LCOE and solar thermal LCOE to 2021 USD per MWh. This can be done using a CPI inflation rate calculator [39]. The table below showcases the results.

Table 4: LCOE Utility Solar in 2021 USD $/MWh

They can now be viewed in a table with all the other energy forms from lowest to highest LCOE. 

Table 5: LCOE of Energy Sources in Ascending Order in 2021 USD $/MWh

We can see that solar PV systems have the lowest LCOE in California, but the solar thermal projects in California have the highest LCOE. This can be misleading because the LCOEs calculated are of systems of different capacity, and the solar thermal plants taken into consideration are the largest in California, and up to five times in capacity the size of UPVs considered. In reality, global solar thermal LCOEs are at around $100/MWh [40]. In addition to this, the LCOEs for solar PV and thermal do not reflect any system-level subsidies, while the other forms of energy’s LCOEs do. Nonetheless, the table above provides a strong indication that solar PV has the relatively lowest cost to operate over its lifespan, and an indication that solar energy in California is relatively inexpensive as compared to other renewable energy sources for utility scale electricity generation. 

VI c: Example: Business Plan for a 3MW Solar Farm in California 

Introduction

This project is a commercial-scale solar farm, 3MW in size, located in the outskirts near San Diego in Southern California. The energy generated is intended to be sold to utility companies in the region through a PPA. The aim is for the PPA to provide a steady source of revenue for the farm so costs such as the bank loan, land lease costs, insurance costs, and operations and maintenance (O&M) costs can be paid off. 

Possible Project Owner Description 

This business plan can be for an investor with large amounts of idle capital, or a limited liability corporation with idle equity, as the bank loan used only finances 75% of the solar system cost, not to be mistaken with the entire project cost.

Technology

The project uses polycrystalline solar PV cells which are considered less efficient than monocrystalline cells but will cause reductions in cost. Their efficiency ranges 15% to 20% [11]. 

Market Analysis

The market for solar energy in California is large, with solar energy providing over 30% of the state’s electricity as of 2024 [41]. As touched upon in an earlier section, while the residential sector leads in solar capacity, California’s commercial sector still has a large portion of capacity at 21.03% [20] 

The target market for the solar farm’s energy output is utility firms who finance our project with a PPA. Examples of large utility companies in South California are Southern California Edison (SCE) and Pacific Gas and Electric Company (PG&E). These firms, alongside many other large utility companies, are all known to have given out countless PPAs in the past, giving this project an extra layer of feasibility. 

Management Team

The management team consists of one solar technician, one solar operator, and 1 security officer. 

The solar operator will handle the daily operation and maintenance of a solar energy system, while the solar technician handles the installation, troubleshooting, and repair of solar equipment. The security officer guards the farm at night to prevent break-in. 

Financial Plan 

For this project, a worst, realistic, and best-case scenario will be explored, with a simple payback period calculated as well for each. The specifications for each case will be showcased in the table below.

Table 6: Cases for 3MW solar Farm 

Using these figures the Payback Period could be calculated:

Table 7: Payback Period of Each Case

Ultimately, the payback period of these 3 cases proves to be quite extensive. However, it is important to note that each case had 8 varying factors (e.g. lease rates, system costs etc.), with the worst case taking the worst figures of each of these factors, and vice versa for the best case. These extremities are unlikely to occur. There could be varying factors, such as a good lease rate, but a poor insurance rate, and that can influence the payback periods to different degrees. Therefore, there is scope for future research to be done into calculating the payback period and comparing cases if only one factor changed (e.g. interest rates). 

The calculations for this case study are shown in Appendix A to this paper.   

VII: Major findings, concluding remarks and future research

VII a: Major Findings & Concluding Remarks 

This paper provided three major pieces of information summarized below, following which they will be discussed in more detail.  

1. A comprehensive analysis into the historical background leading up to present day, of the solar sector in California
2. Comparisons of the LCOEs between solar energy and other forms of energy
3. Example Business Plan for a 3MW solar farm in California

But altogether, what did these analyses conclude?

Firstly, solar energy in California remains relatively financially viable considering its low LCOE. However, the structure and future trajectory of solar energy in California can be majorly impacted by the public subsidies landscape, which impacts different stakeholders. 

As seen above, utility scale solar PV has the lowest LCOE among major sources of energy, outperforming wind, hydroelectric power, nuclear and natural gas. While large utility scale solar thermal plants have a high LCOE, when the global average LCOE for solar thermal systems is compared to the other forms of energy, and considering the federal tax credit system, the LCOE seems much more competitive.

For consumers, the federal solar tax credit alongside California specific rebates and incentives (e.g. SGIP or DAC-SASH) reduce the upfront and long-term costs of solar. However, Trump’s “Big Beautiful Bill” aims to cut the 30% tax credit for residential solar systems, which can impact consumers who have lesser incomes. Despite this, the California specific incentives will continue to persist, which can mitigate the effects of this cut in incentives, and ensure that the decrease in solar popularity isn’t maximized. 

Furthermore, solar installation firms are extremely profitable in California. One notable mention is Sunrun Solar (founded in 2007). From Q1 2024 to Q1 2025, Sunrun Solar’s revenue was USD $2.083 billion [49], and their gross profit was USD $394 million [50]. Initially, because of the federal tax credit cuts, solar firms may take a slight hit due to a possible decrease in residential solar installations. However, because there was no tax credit cut on residential solar that was leased from solar firms, solar firms can benefit as there may be an influx of customers willing to purchase their services. 

The government benefits the most in the short-term future of solar energy in California, as the tax credit cuts mean that there may be an increase in tax revenue. However, these cuts may result in significant job losses in the solar sector. Unemployment results in lower income taxes and may result in the government handing out more welfare benefits. 

Despite these considerations, solar projects still have the landscape to thrive in California, considering that California grid electricity prices rise every year by 5.9% [51]. Through the business plan, it is showcased that in a well thought out scenario (best case) with favoring interest rates and lease rates, a solar farm can be highly profitable. However, the future rate of solar adoption, particularly residential solar, remains more sensitive to how the policy changes influence the affordability of solar technology, especially the tax credit cuts. 

Ultimately, whether solar energy in California is financially viable depends on whether solar energy can maintain a low LCOE and cost effectiveness in the ever-changing political and subsidy landscape. Some people may have the perspective that solar energy is financially viable due to low costs and sunny weather, while others may think that solar energy may not be financially viable in the long run due to changes in the regulatory landscape. In the end, maintaining a thriving future economic landscape for solar energy rests upon balancing these factors to ensure accessible, cost-effective, and scalable solar adoption across the state. 

VII b: Future Research

Future research can tie into advancements in solar PV cell technology, how the efficiencies of solar energy affect its viability, and calculating the payback period and return on investment with an increasing variety of the factors stated above.

• Solar PV Technology: 

Recent advancements have been made into increasing the efficiency of solar PV cells. Examples of these are perovskite solar cells or tandem solar cells. These cells have pushed the efficiency of solar PV cells to above 30% [52]. This, coupled with California’s vibrant sunny weather, with a high average of 263-300+ sunny days per year, allows for maximizing solar exposure, which can improve energy output and efficiency. It would be interesting to find out how this affects the financial viability of solar panels in California, if at all. 

• How Efficiency Influences Financial Viability

By studying the efficiency of solar cells whether PV or thermal and comparing them with efficiencies of other forms of energy, we can dive into another lens through which we can determine whether solar energy is viable in California. This can be analyzed to determine how much the overall viability of solar energy in California depends on the financial versus the efficiency side.

• Payback Period and Return on Investment Calculations

Above, this paper had dove into the payback period of a 3MW solar farm in California using proforma calculations. However, the calculations entailed a large variety of varying factors, all of which play a different magnitude of a role in the final payback period calculation. A study that considers more ‘What If?’ scenarios and calculation simulations can be carried out to better grasp an in depth understanding of the financial status of solar energy in California.

• Subsidy and Regulatory Policy for the Future

Above, the current situation regarding subsidies and regulatory policies were explored and analysed. However, an interesting avenue of research could be to model the expected growth of the California solar sector in relation to the subsidy and regulatory policy landscape. Two main points to research here could be the predicted growth rate across President Trump’s term, and the possible political tug of war between federal and state policies.  

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Appendix A: Business Plan 

Using Proforma data, these calculations were carried out above in Section VI c, “Example: Business Plan for a 3MW Solar Farm in California”. 

Acknowledgements

I would like to acknowledge and thank Dr Tayyeb Shabbir – Professor of  Finance & Economics, California State University Dominguez Hills and former faculty member, Wharton School, University of Pennsylvania Philadelphia, PA – for mentoring me as I conducted research on this topic. 

About the author

Khrish Butani

Khrish is a senior at King George V School. His interest for finance, business, economics, and regions with successful solar sectors were what pushed him to pursue this research project. Outside of the classroom, Khrish is an avid cricketer, representing Hong Kong at Under 16 and Under 19 level. At collegiate level, he hopes to continue researching different industries across the globe in which the financials are sometimes overlooked.

The post The Financial Viability of Solar Energy In California appeared first on Exploratio Journal.

Abstract

The paper presents an overview of the outlook of baby boomers’ retirement savings for both present and soon-to-be retirees. As the entire baby boom generation will have reached retirement age within the next couple of years, concerns arise about the economic stability of the vast majority of baby boomers. A simulation is run to analyze the ratio of retirement income and spending, along with various other figures, to help estimate the economic situation for median baby boomers. The simulation utilizes fixed income via equity, fixed income via interest rates, and Social Security as the main sources of income in retirement. The paper also addresses the different types of retirement accounts and plans as well as are being used today.

Introduction

This paper aims to assess whether the cohort of median baby boomers has enough savings to ensure economic stability throughout their retirement. “Retirement income security in the United States has traditionally been based on the so-called three-legged stool: Social Security, private pensions, and other personal savings (Gale 1997).” These three pillars give an overview of how a retiree or aspiring retiree may save. Many baby boomers will be unable to maintain their standard of living throughout retirement and are likely to be reliant on Social Security. It was found that ⅓ of young baby boomers will rely on Social Security for 90% of their retirement income (Picchi & Sherter, 2024). Since Social Security is meant to typically account for 40% of retirement income, it is clear that many baby boomers are or will have some sort of financial difficulty in the future. Since baby boomers are one of the largest generations ever, their retirement deeply affects the economy. This paper hopes to explore the issues that baby boomers may face during retirement and provide insight into issues regarding fulfilling retirement income.

I chose the median net worth of baby boomers for the express reason that there are large outliers. According to the Federal Reserve Survey of Consumer Finances, the difference in median net worth between those in the 20th-40th percentile and those in the 60th-80th percentile is $245,000 (Survey of Consumer Finances (SCF), n.d.). As the gap between the wealthiest and poorest retirees is massive, using other statistical measures, such as mean, provides skewed data. As medians are resistant to outliers, they are a far better representation of the overall population.

The first leg of the three-legged stool is Social Security. Social Security is a government-funded program that provides a fixed monthly payment. As the number of people currently about to retire or already retired is larger than in previous generations, the question of whether Social Security could handle the extra weight has arisen. It is generally accepted that a replacement rate of around 70% is a sufficient amount of income for retirement. Social Security benefits typically account for a replacement rate of around 40% (Biggs & Springstead, 2008). Overall, Americans are now living longer than previous generations. “In the United States, for example, life expectancy at age 65 increased from 11.9 years in 1900– 1902 to 19.1 years in 2010, and for age 80 from 5.3 to 9.1 years during the same period” (He. Et al). This increase in life expectancy is likely not something baby boomers expected when planning for retirement. The second leg of the three-legged stool is defined benefit (DBs) plans. DBs are plans where the employer pays out a certain sum of money to the employee. Social Security is considered to be akin to DBs as they both provide a stable income for life. Though DBs are very favorable to the employee as there is little to no risk taken on their part, employers tend to see DBs in a different light. Since the employer assumes the risk, DBs are normally only offered by larger corporations that have the means to properly execute these plans. Employers are required to pay out large sums of money to many former employees who no longer produce revenue; therefore, DBs tend to be viewed in a negative light by employers. (What Are Defined Benefit Retirement Plans?, n.d.). For that reason, companies have been less inclined to offer them.

The third leg of the three-legged stool is defined contribution (DC) plans, which allow employees to contribute to specialized accounts such as 401 (k). The employer may offer to match the employee’s contribution as a means to incentivize this form of retirement planning. The accounts in which the contributions are made tend to be investment accounts where the employee can invest their money as they please. In general, contributions and earnings are not taxed until they are distributed. The value of the account will change according to how much is contributed and the value of the investments made within the account (Retirement Plans Definitions | Internal Revenue Service, 2025). Companies may take a certain portion of one’s paycheck and directly contribute it to the account. The employee primarily assumes the risk of DCs, as it is predominantly their money being invested. Employees who have DCs must be careful about how they store their money. If the money in these accounts does not grow at a rate that keeps up with inflation, the employee risks losing purchasing power. On the other hand, if the employee places their money in investment vehicles that are too risky, then there is the chance of them losing the money in those accounts. To make the most of DC, it takes both education and time. Many people are not properly trained to invest their money and lack the time to research and make the smartest financial investments.

Methodology

Using a simulation on Excel based on various figures and formulas, I was able to determine whether retired baby boomers had enough savings to last their retirement. Using the 4% rule, I was able to get a rough estimate of how much a person should be spending during their retirement. The 4% rule states that retirees should withdraw 4% of their retirement savings to last the entirety of their retirement (Williams & Kawashima, 2025). Through the simulation, I evaluated whether the 4% rule is feasible and allows for enough wealth to be maintained throughout retirement. According to the Federal Reserve Survey of Consumer Finance, the median financial net worth for a family of retired baby boomers is $410,000, and 42.5 percent of the family’s net worth is financial. The financial net worth of the family can then be calculated to be $174,250 (Survey of Consumer Finances (SCF), n.d.). The figures used for the simulation assume the partners of baby boomers. For the sake of the simulation, the financial net worth will be split into two different types of investments: financial assets in equity (Stocks, shares, securities, etc.), as well as financial assets in fixed income (Savings accounts, Bonds, CDs, Munis, etc). Based on Figure 2, it is shown that Financial assets in fixed income tend to yield an interest rate that is generally in the range of .4-6%

National Rates and Rate Caps

As shown by Figure 2 below, inflation over the past two decades has remained between 2% and 3%; the simulation will assume a 2.5% annual inflation rate.

According to the Social Security Administration, the estimated monthly benefit at the beginning of 2025 was $1976 (What Is the Average Monthly Benefit for a Retired Worker?, 2025). The 2.5% COLA (Cost of Living Adjustment) has benefits to around 68 million Social Security beneficiaries in January 2025 (Cost-Of-Living Adjustment (COLA) Information | News, n.d.). This adjustment allows Social Security to keep up with inflation. This is how Social Security combats the effects of inflation on money that does not grow.

The simulation will split the median financial net worth 50-50 between financial assets in equity and financial assets in fixed income. As financial assets in equity tend to be riskier than fixed income, they offer significantly higher returns and, overall, can lead to continued wealth growth well into retirement. Retirees are able to utilize their retirement accounts, such as 401 (k) and Roth IRAs, to grow their money in equity. These accounts tend to offer subsidies to retirees, which make them very appealing and useful. 401 (k) accounts have pre-tax dollars originally contributed to them, and employers may also match contributions to these accounts as an extra incentive. Your tax is determined by the amount that you withdraw (401(k) Plan Overview | Internal Revenue Service, 2025). This is beneficial as it allows for your money to grow and only gets taxed when you withdraw it, allowing for the amount of taxes to be minimized. The amount of tax a retiree pays, based on the amount withdrawn, is beneficial to the retiree as it allows them to be strategic in their withdrawals and optimize the amount paid in taxes. In addition to 401 (k), IRAs are extremely beneficial in regard to retirement savings. There are several types of IRAs that retirees may take advantage of. Though their benefits differ, they all tend to use pre-tax dollars as initial contributions, and the earnings are then taxed. As retirees tend to be in far lower tax brackets during retirement, the tax they pay is heavily offset (Which IRA’s Are Right for You?, n.d.).

Using figures provided by Robert J Shiller, an economist for the Yale School of Management, the average stock return based on the S&P 500, accounting for taxes, over the past 20 years is approximately 6% (Shiller, n.d.). The 6% is the real amount that retirees are able to utilize. Even though the return from the S&P 500 is roughly 8% this number is a nominal amount which does not account for taxes and other fees that impact the amount withdrawn. 6% is the amount that baby boomers will be able to utilize during retirement. For the sake of the simulation, we will assume a 2% margin of error, so the simulation will account for financial assets in equity by averaging returns from 4% to 8%. To calculate the returns from fixed income via interest and financial assets in equity, the simulation uses the simple interest formula P*(1+interest rate). The principle would be $81,750 for each, since their financial net worth is being put equally into both types of vessels. The principle is how much money the retirees have after the cost of living is subtracted. The average monthly spend is calculated by using the 4% rule and adjusting it for inflation; however, since we are assessing the families of baby boomers, we will use 8% instead to account for two people. The average life span is currently 75.8 years for males and 81.1 years for females (FastStats – Life Expectancy, 2025). The simulation, however, assumes a 90-year life span to account for cases where retirees may live longer than the average life span, and the simulation will still provide results for persons who will pass away before this age. The average annual spending is calculated by averaging the median annual spending for persons aged 55-64 and 65-74, which comes out to roughly $74,000.

Conclusion/Future Use

Based on the simulation, it is shown that overall, retired families of baby boomers will be unable to bridge the gap between the ratio of retirement income and spending. As previously stated in the introduction, a 70% replacement rate is generally considered sufficient for retirement. It is essential to note that the simulation has several limitations, as outlined in the limitations section of the paper. Retired baby boomers should aim for the Ratio of retirement income and spending to be 100% as this illustrates that their spending needs in relation to income are fully met.

As shown by Figure 3, the simulation predicts that families of retired baby boomers have limited economic resources to sustain them for the remainder of their retirement. As previously stated, since the simulation accounts for families of baby boomers, the 4% rule is doubled, and instead the monthly withdrawal becomes 8%. The ratio of retirement income and spending is around 50% at the beginning of retirement, and by the age of 90, it drops to 36%. This shows a clear deficit, especially later in retirement, where the ratio of retirement income and spending progressively gets further from the prescribed 70%.

Though the 4% rule is what is the general recommendation for retirees, it is unrealistic to assume that all baby boomers are or will be able to abide by it. Especially later in life, when the cost of living may increase due to increasing costs for medicine and care. The simulation was run again, but instead of 8% the simulation assumed a withdrawal rate of 15%. 15% is an extreme circumstance where baby boomers are withdrawn far above the recommended amount. This can help encapsulate any situations where baby boomers have costs that are unforeseen and therefore would have otherwise been unaccounted for.

Based on Figure 4, running the simulation again with the increases in the cost of living proves that Baby Boomers will still face difficulties in bridging the gap of ratio of retirement income and spending. At the beginning, the ratio is 67% but then a steep drop to 33% occurs, further proving the economic strain baby boomers will likely undertake. At the beginning, the simulation predicted a scenario where baby boomers were able to roughly keep up with the prescribed ratio of retirement income and spending; however, as time went on, the drop in the ratio signaled that, in the long term, staying near that 70% was not feasible. A potential solution to help bridge this gap is to begin saving earlier. Starting to invest at a young age allows the principal to grow over a long period of time. That principle has the possibility to grow exponentially over time, and when the principle is increased, more growth is possible. This paper can be used as a reference and a general outline of the retirement prospects for current retirees and those who plan to retire in the near future. The simulation provides a rough outline of how one’s retirement savings could be broken up and the potential yields of these accounts. The paper could also be utilized in educating the reader on the different types of retirement plans and aiding future retirees in planning for themselves and their loved ones. The simulation provides various predictions which retirees may find interesting, such as the ratio of retirement income and spending, monthly spending increases with inflation, etc.

Limitations

It is important to note that there are several factors limiting the accuracy of the results from the simulation. Everyone has different financial situations and varying levels of financial literacy. The report from the Global Financial Literacy Center utilized the 2021 TIAA GFLEC Personal Finance Index to compare the literacy of Gen Z to other generations, such as the Silent Generation and Baby Boom Generation. Approximately 40% of Baby Boomers were unable to answer more than 50% of the questions provided by TIAA-GFLEC correctly. These results indicate that financial literacy levels remain low even throughout adulthood (Yakoboski et al., n.d.). Some families of Baby Boomers may be receiving assistance from family, friends, etc. This makes it impossible to generalize results to all boomers, as their financial situations and support systems can vary greatly from family to family. In addition, the simulation assumes that the families will invest their money in a specific way with specific accounts. However, this may not be the status quo for many Baby Boomers, and their perception of how to properly save can also vary greatly. Some families may be more risk-averse than others based on personality, environment, upbringing, etc., causing their mindset in how to invest to change. For instance, boomers born right after World War 2 and boomers born in the 60’s may have different mindsets based on how they grew up and the economic environment of the time. The simulation predicts retired Baby Boomers can maintain their standard of living, assuming several investing principles are used and that people follow the four percent rule. However, emergencies and surprise purchases are possible, and there are expenditures that the simulation is unable to predict and therefore account for. It is also impossible to perfectly predict how markets and interest rates will vary over time. Though in the long run things tend to even out, there is always the possibility that unforeseen crises will affect the markets. As many facets of the economy are related, a major change to one sector could have consequences for another, causing a domino effect. The simulation is unable to account for major shifts in markets and interest rates, meaning that the results are not certain but rather a rough estimate under proper conditions.

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About the author

Charles Agle

Miles is currently a senior at Dr. Ronald E McNair Academic High School interested in economics. He is currently the captain of the varsity soccer team and a soccer referee at the grassroots level. He also is a UN NGO representative for AccessabilityAtlas.

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