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

1. Introduction

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

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

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

2. Literature Review

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

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

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

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

 3. Data and Methodology

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

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

A. Quantitative framework

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

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

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

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

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

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

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

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

where y_t is the observed stablecoin price.

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

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

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

B. Event Observation Framework

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

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

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

4.  Analysis Results

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

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

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

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

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

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

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

 TABLE 1—MAJOR STABLECOIN DEPEGGING EPISODES, 2018–2024

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

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

Figure 5. Ethereum Average Gas Price in March 2020

5. Conclusion

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

References

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

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

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

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

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

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

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

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

About the author

Abhiram Kode

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Birth Of Nike

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

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

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

Brand Positioning in Popular Culture

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

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

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

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

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

Esports and Digital Marketing Strategies

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

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

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

Market Dominance in Collector Communities

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

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

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

• Iconic athlete partnerships and limited edition drops
• Cultural relevance

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

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

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

Comparative Strategic Analysis

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

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

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

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

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

Conclusion

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

About the author

Divyansh Garg

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

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

Abstract

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

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

I. Introduction

A. Background Information

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

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

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

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

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

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

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

B. Thesis Statement

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

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

II. Methodology

A. Growth of Startups

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

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

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

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

B. Growth of Literacy

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

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

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

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

C. Regression Results

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

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

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

i. Startups in the AI Sector vs Literacy Rate

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

ii. Startups in the Green Technology Sector vs Literacy Rate

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

iii. Startups in the IT Sector vs Literacy Rate

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

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

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

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

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

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

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

D. Discussion

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

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

III. Conclusion

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

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

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

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

IV. References

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

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

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

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

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

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

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

About the author

Aryan Bajoria

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

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

Introduction

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

Positive Impacts

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

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

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

Negative Impacts

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

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

Conditions

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

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

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

Counterarguments

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

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

Conclusion

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

Bibliography

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

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

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

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

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

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

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

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

About the author

Youngwoo Nam

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

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

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.

References

Bessembinder, H., & Chan, K. (1998). Market Efficiency and the Returns to Technical Analysis. Financial Management, 27(2), 5–17. https://doi.org/10.2307/3666289

Blume, L., Easley, D., & O’Hara, M. (1994). Market Statistics and Technical Analysis: The Role of V olume. The Journal of Finance, 49(1), 153–181. https://doi.org/10.2307/2329139

Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731–1764. https://doi.org/10.2307/2328994

Brown, J. H., Crocker, D. K., & Foerster, S. R. (2009). Trading V olume and Stock Investments. Financial Analysts Journal, 65(2), 67–84. http://www.jstor.org/stable/40390354

Conrad, J., Dittmar, R. F., & Ghysels, E. (2013). Ex Ante Skewness and Expected Stock Returns. The Journal of Finance, 68(1), 85–124. http://www.jstor.org/stable/23324392

Elkamhi, R., & Stefanova, D. (2015). Dynamic Hedging and Extreme Asset Co-movements. The Review of Financial Studies, 28(3), 743–790. http://www.jstor.org/stable/24465726

Frugier, A. (2014). Higher-order Moments and Investor Sentiment (Alles’ Model Revisited). Quarterly Journal of Finance and Accounting, 51(3–4), 45–70. http://www.jstor.org/stable/qjfinacct.51.3-4.45

Han, Y ., Yang, K., & Zhou, G. (2013). A New Anomaly: The Cross-Sectional Profitability of Technical Analysis. The Journal of Financial and Quantitative Analysis, 48(5), 1433–1461. http://www.jstor.org/stable/43303847

Kavajecz, K. A., & Odders-White, E. R. (2004). Technical Analysis and Liquidity Provision. The Review of Financial Studies, 17(4), 1043–1071. http://www.jstor.org/stable/3598058 5

Levy, R. A. (1966). Conceptual Foundations of Technical Analysis. Financial Analysts Journal, 22(4), 83–89. http://www.jstor.org/stable/4470026

Malkiel, B. G., & Saha, A. (2005). Hedge Funds: Risk and Return. Financial Analysts Journal, 61(6), 80–88. http://www.jstor.org/stable/4480718

McInish, T., & Puglisi, D. J. (1980). Technical Analysis and Utility Preferred Stocks. Nebraska Journal of Economics and Business, 19(3), 55–63. http://www.jstor.org/stable/40472670

Moosa, I., & Li, L. (2011). Technical and Fundamental Trading in the Chinese Stock Market: Evidence Based on Time-Series and Panel Data. Emerging Markets Finance & Trade, 47, 23–31. http://www.jstor.org/stable/27917672

Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the Equity Risk Premium: The Role of Technical Indicators. Management Science, 60(7), 1772–1791. http://www.jstor.org/stable/42919633

Qi, M., & Wu, Y . (2006). Technical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market. Journal of Money, Credit and Banking, 38(8), 2135–2158. http://www.jstor.org/stable/4123046

Smith, D. M., Wang, N., Wang, Y ., & Zychowicz, E. J. (2016). Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry. The Journal of Financial and Quantitative Analysis, 51(6), 1991–2013. http://www.jstor.org/stable/44157641

Tabell, Edmund W., & Tabell, Anthony W. (1964). The Case for Technical Analysis. Financial Analysts Journal, 20(2), 67–76. http://www.jstor.org/stable/4469619

Van Horne, J. C., & George G. C. Parker. (1968). Technical Trading Rules: A Comment. Financial Analysts Journal, 24(4), 128–132. http://www.jstor.org/stable/4470382

Yahoo Finance. (2025). Dow Jones Industrial Average historical data [Data set]. Yahoo. Retrieved July 18, 2025, from https://finance.yahoo.com/

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.  

References

1. Olano, M. V. (2024, April 12). Chart: Which US states generate the most solar and wind energy? Canary Media. https://www.canarymedia.com/articles/clean-energy/chart-which-us-states-generate-the-most-solar-and-wind-energy
2. Discover the sunniest cities in California. (2025, June 23). Our Guide to SLO CAL and Beyond. https://www.slocal.com/blog/post/discover-the-sunniest-cities-in-california/
3. Walker, E., & Aggarwal, V. (2025, July 31). Solar panel cost in 2025: It may be lower than you think. EnergySage. https://www.energysage.com/local-data/solar-panel-cost/
4. Solar achievements timeline. (n.d.). Energy.gov. https://www.energy.gov/eere/solar/solar-achievements-timeline
5. Koski, A. (2024, November 6). California solar Industry [Facts you might not know]. Current Home. https://www.currenthome.com/blog/california-solar-industry-facts-you-might-not-know/
6. California Solar Initiative. (n.d.). https://www.cpuc.ca.gov/-/media/cpuc-website/files/legacyfiles/j/4215-jan09.pdf
7. SEIA. (2025, June 26). Solar Market Insight Report – SEIA. https://seia.org/research-resources/us-solar-market-insight/
8. Solar Photovoltaic Technology Basics | NREL (n.d.). https://www.nrel.gov/research/re-photovoltaics
9. Solarnplus. (2024, June 25). How PV cells harness the sun to generate electricity. Solar N Plus. https://www.solarnplus.com/how-pv-cells-harness-the-sun-to-generate-electricity/
10. Xipeng et al. Monocrystalline Silicon Cell – an overview | ScienceDirect Topics. (n.d.). https://www.sciencedirect.com/topics/engineering/monocrystalline-silicon-cell 
11. How efficient are polysilicon solar cells? – BLOG – Tongwei Co., Ltd.,. (n.d.). https://en.tongwei.cn/blog/8.html
12. U.S. Department of Energy. (2022). Solar Photovoltaics Supply Chain Deep Dive assessment. https://www.energy.gov/sites/default/files/2022-02/Solar%20Energy%20Supply%20Chain%20Report%20-%20Final.pdf
13. Solar thermal energy: what it is and its benefits. (2024, May 22). REPSOL. https://www.repsol.com/en/energy-and-the-future/future-of-the-world/solar-thermal-energy/index.cshtml
14. Solarnplus. (2024, November 26). What is Concentrated Solar Power? Solar N Plus. https://www.solarnplus.com/what-is-concentrated-solar-power/
15. Wikipedia contributors. (2024, August 3). Parabolic trough. Wikipedia. https://en.wikipedia.org/wiki/Parabolic_trough
16. Thoubboron, K. (2024, February 28). Concentrated Solar Power (CSP): What you need to know. EnergySage. https://www.energysage.com/about-clean-energy/solar/contentrated-solar-power-overview/
17. Wikipedia contributors. (2025, July 20). Concentrated solar power. Wikipedia. https://en.wikipedia.org/wiki/Concentrated_solar_power
18. Wikipedia contributors. (2025, August 1). Solar power in California. Wikipedia. https://en.wikipedia.org/wiki/Solar_power_in_California
19. Annual Days of Sunshine in California – Current results. (n.d.-a). https://www.currentresults.com/Weather/California/annual-days-of-sunshine.php
20. Statistics and Charts. (n.d.). CaliforniaDGStats. https://www.californiadgstats.ca.gov/charts/nem/
21. Which states have the most Solar-Powered schools? (2023, February 15). GovTech. https://www.govtech.com/education/k-12/which-states-have-the-most-solar-powered-schools
22. FACT SHEET: Obama Administration Announces Clean Energy Savings for. (2016, July 19). whitehouse.gov. https://obamawhitehouse.archives.gov/the-press-office/2016/07/19/fact-sheet-obama-administration-announces-clean-energy-savings-all
23. Biden administration approves expansion of solar power on U.S. land. (2021, December 21). PBS News. https://www.pbs.org/newshour/nation/biden-administration-approves-expansion-of-solar-power-on-u-s-land
24. Biden-Harris administration announces $7 billion solar for all grants to deliver residential solar, saving Low-Income Americans $350 million annually and advancing environmental justice across America | US EPA. (2024, April 22). US EPA. https://www.epa.gov/newsreleases/biden-harris-administration-announces-7-billion-solar-all-grants-deliver-residential
25. Butler, P. (2025, April 25). California Solar panel incentives: tax credits, rebates, financing and more. CNET. https://www.cnet.com/home/solar/california-solar-panel-incentives-tax-credits-rebates-financing-and-more/
26. EHN Curators. (2025, August 7). California stalls on community solar as Trump moves to pull federal funds. EHN. https://www.ehn.org/california-stalls-on-community-solar-as-trump-moves-to-pull-federal-funds
27. Walker, E. (2025, July 24). President Trump Signs Bill Killing The solar Tax Credit—Here’s What it Means for Homeowners. EnergySage. https://www.energysage.com/news/congress-passes-bill-ending-residential-solar-tax-credit/
28. Thoubboron, K. (2023, February 27). California Solar mandate: What you need to know. EnergySage. https://www.energysage.com/blog/an-overview-of-the-california-solar-mandate/
29. New Day Solar (2025, April 16). Understanding California’s solar mandates for new builds in 2025. New Day Solar. https://www.newdaysolar.com/understanding-californias-solar-mandates-for-new-builds-in-2025/
30. Koski, A. (2024, November 29). California Solar incentives, tax credits, rebates, and financing options for 2025. Current Home. https://www.currenthome.com/blog/california-solar-incentives-tax-credits-and-rebates-2025/
31. Solar vs Grid Power: Cost Comparison for SF Homeowners. (n.d.). Earthwise Energy Solutions | Local Solar Power Installers. https://earthwiseenergy.com/solar-vs-grid-power-cost-comparison-for-sf-homeowners/
32. Richard, & Richard. (2025, February 27). The real cost of solar panels: from purchase to payoff (And everything between). Residential Solar Panels. https://www.residentialsolarpanels.org/financial-aspects/cost-analysis-assessment/the-real-cost-of-solar-panels-from-purchase-to-payoff-and-everything-between/
33. Langone, A., & Walker, E. (2025, August 4). New study: Solar panels can add up to $79K to your home’s value. EnergySage. https://www.energysage.com/news/solar-power-as-a-home-improvement-strategy/
34. Sun Solar Electric.  (n.d.). Everything you need to know about solar financing. Sun Solar Electric. https://www.sunsolarelectric.org/blog/207-everything-you-need-to-know-about-solar-financing
35. Solar Photovoltaic System cost benchmarks. (n.d). Energy.gov. https://www.energy.gov/eere/solar/solar-photovoltaic-system-cost-benchmarks
36. Ivanpah Solar Electric Generating System | Concentrating Solar Power Projects | NREL. (2022, October 21). https://solarpaces.nrel.gov/project/ivanpah-solar-electric-generating-system
37. Mojave Solar Project | Concentrating Solar Power Projects | NREL. (2023, October 25). https://solarpaces.nrel.gov/project/mojave-solar-project
38. U.S. Energy Information Administration. (2022). Levelized costs of new generation resources in the Annual Energy Outlook 2022. In U.S. Energy Information Administration. https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf
39. Inflation calculator. (n.d.). https://www.calculator.net/inflation-calculator.html
40. Khan et al. (2024). The economics of concentrating solar power (CSP): Assessing cost competitiveness and deployment potential. Renewable and Sustainable Energy Reviews, 200, 114551. https://www.sciencedirect.com/science/article/abs/pii/S1364032124002740
41. SEIA. (2024, October 2). A new reality: the path forward for California’s solar and storage industry – SEIA. https://seia.org/blog/new-reality-path-forward-californias-solar-and-storage-industry/
42. Richardson, M. (2025, July 31). Solar farm land lease rates: Average rent per acre. US Light Energy. https://uslightenergy.com/solar-land-lease-rates-how-much-do-solar-companies-pay-to-lease-land/
43. Farmer, T. (2023, November 14). How much does a solar farm cost? HomeGuide. https://homeguide.com/costs/solar-farm-cost
44. Southwestern states have better solar resources and higher solar PV capacity factors – U.S. Energy Information Administration (EIA). (n.d.). https://www.eia.gov/todayinenergy/detail.php?id=39832
45. Bohrium | AI for Science with Global Scientists. (n.d.). https://www.bohrium.com/paper-details/capacity-factors-of-solar-photovoltaic-energy-facilities-in-california-annual-mean-and-variability/812585412068376577-31943
46. VCIB. (2025, August 6). VCIB Commercial Solar Equipment Loans. https://vancitycommunityinvestmentbank.ca/commercial-lending/clean-energy-financing/solar-financing/
47. Agopian, A. (2024, May 20). For Solar Developers – Solar Property Insurance is Priced. Solar Insure. https://www.solarinsure.com/for-solar-developers-how-solar-property-insurance-is-priced
48. Penrod, E. (2024, October 22). Renewable PPA prices continue to rise — and may do so through 2030, say LevelTen, Ascend analysts. Utility Dive. https://www.utilitydive.com/news/ppa-power-purchase-prices-wind-solar-levelten-ascend-analytics/730245/
49. Sunrun Revenue 2014-2025 | RUN. (2025). Macrotrends.net. https://macrotrends.net/stocks/charts/RUN/sunrun/revenue
50. ‌Sunrun Gross Profit 2014-2025 | RUN. (2025). Macrotrends.net. https://macrotrends.net/stocks/charts/RUN/sunrun/gross-profit
51. Sunlux. (2025, January 14). Solar Power vs. Rising Cost of Electricity in California – Sunlux. Sunlux. https://sunlux.com/blog/solar-power-vs-rising-cost-of-electricity-in-california/
52. Rayzon Solar. (2025, May 21). Latest advancements in solar PV module technology (2025). Rayzon Solar. https://rayzonsolar.com/blog/solar-pv-module-innovations-2025

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.

Introduction

The global aviation industry is dominated by two companies—US based Boeing and European based Airbus—together accounting for over 93% of the single-aisle aircraft segment. This duopoly has set the standards for innovation, safety, and service infrastructure. China’s Commercial Aircraft Corporation (COMAC) is a newcomer to this segment of aviation with the C919 which is to compete against the Airbus A320 and Boeing 737 Max series of jets. The C919 made its debut with China Eastern Airlines in 2023 marking a new step in China’s goal of technological self-reliance. This paper explores whether the C919 can be a serious competitor in the large airliner market.

COMAC’s order backlog of C919s is estimated to be between 713-1,000 aircraft, largely from Chinese carriers: Air China, China Eastern, and China Southern. In 2024 alone, the C919 recorded approximately 300 new orders, representing nearly a quarter of global single-aisle orders that year. For a newcomer that’s very impressive. However, most of the orders are Chinese and it is unclear whether they can deliver on their current order book. While it is strong in the domestic market it has challenges it must overcome the current duopoly. An example being regulatory hurdles like the European Aviation Safety Agency anticipating a 3–6 year process before C919 can fly in EU airspace.

The Chinese government’s other goal is to make this aircraft entirely in China with Chinese components. In order to achieve this goal of self-reliance Chinese manufacturers are making Chinese made engines and other components for the C919. China wishes to be self-reliant in the manufacturing of this new aircraft but still relies on Western made parts critical to its operation, including General Electric engines and Honeywell made flight controls.

At the same time Boeing and Airbus are constrained by production capabilities, issues, and full order books. Airbus is reportedly sold out its A320neo slots for the rest of the decade and Boeing is swamped with quality and delivery setbacks. This could create openings for COMAC, especially if they can offer competitive pricing, and attract international buyers.

This paper will test whether the COMAC C919 can expand past its domestic foothold to compete in the global market. It will analyze the aircraft’s capabilities, market position, regulatory barriers, geopolitical hurdles, and lastly short-term and long-term disruptive potential.

Overview of the C919 and its Development Cost

The C919 project began in January 2009 a year after China established COMAC to compete against the dominance of Boeing and Airbus. The program targeted a maiden flight by 2014 but the first prototype flew on May 5, 2017, followed by several flight-test aircraft verifying systems through 2019. By September 2022 COMAC received CAAC (Civil Aviation Administration of China) certification and delivered the first production C919 to China Eastern Airlines on December 9, 2022. COMAC has ramped up production in its pre mass production phase with plans aiming for 30 deliveries in 2025. It will then proceed to increase capacity to 50 aircraft per year. COMAC’s long-term ambition is to reach 150 C919 deliveries annually within five years as it would rival Boeing and Airbus narrow-body output.

Technical Specifications

The C919 is a conventional twin-engine narrow-body airliner seating 156–174 passengers, depending on configurations of the aircraft, with a range of 2200 nautical miles at maximum payload shown on COMAC’s website. The C919 uses Western made equipment like the CFM International LEAP‑1C engines, from GE (US) and Safran (France), and avionics supplied through joint ventures with firms like Honeywell. COMAC is developing a domestic engine called the CJ-1000A, with first engine test runs in 2017 and flight testing expected soon . These new engines will reduce dependency on Western engines and other equipment. This can also change the specifications of the aircraft in the future.

Development Costs

Official figures for the C919’s program are not regularly available as Comac does not publish program costs or a list price for the C919. A 2017 report by NBC News said the C919 jet was developed at a cost of $8.6 billion. An estimate by PilotPassion priced the individual aircraft cost at between $90-100 million. It has also been reported that a state-subsidized deal saw COMAC charging $108 million per jet to Air China, raising the original selling price from the $50–60 million per plane initially proposed as market competitive by COMAC; at this price the C919 is more expensive than a newly manufactured 737 Max 7 aircraft which has a 2025 list price of $99.7 million [AXON, 2025].

A fair comparison to the development of the C919 is the C-series development costs which highlights the financial burden and strategic risks involved in entering the commercial aircraft market. COMAC allocated around $8.6 billion USD for the C919’s development, though the true cost, when you include delays, may approach $15–20 billion [Walsh, 2023]. Backed by the Chinese government as a national industrial priority, the C919 was developed over 15 years. In contrast, the Bombardier C-Series program, initially budgeted at $2.1 billion, saw its costs balloon to over $5.4 billion due to delays and low early sales [Trimble, 2013][Lu, 2015]. Unlike COMAC, Bombardier did not have state backing and ceded control of the program to Airbus in 2018 to avoid bankruptcy. The COMAC case shows that while they had state backing the development costs are way out of proportion compared to Bombardier, and later Airbus, so the breakeven point for this program is likely to be much higher.

The C919 as an Import Substitution Strategy?

An import substitution is when a country will make a political choice to make things which substitute for international market goods when they want to promote domestic production or cut reliance on foreign powers. This has historically been proven to be devastating to some economies like in LatinAmerica where goods being produced in their native countries would be sold at a loss, subsidized by governments, to promote domestic economic growth.

China may be doing this within the framework of the “Made in China 2025” industrial policy. This initiative aims to reduce reliance on foreign technologies in critical sectors by cultivating domestic innovation and production capabilities. In the case of the C919, while the initial versions of the aircraft heavily depend on Western components such as CFM International LEAP-1C engines, Honeywell avionics, and Rockwell Collins communication systems, China has made it clear their intention to gradually replace these with indigenous alternatives. This is shown with the development of the CJ-1000A engine by the Aero Engine Corporation of China (AECC), designed to eventually replace the LEAP-1C and make the C919 fully domestically powered.

This push reflects a strategy which ensures technological independence from geopolitical risks, like U.S. export controls, which recently have been turbulent. At the moment China is selling their C919 at a loss to promote the aircraft as a success. Import substitution is being used to push these efforts to produce a competitive aircraft and build a self-sufficient aviation industry capable of challenging Western dominance at every level of the supply chain. The political choice and unanswered question is for how long China will be willing to subsidize this domestic aircraft.

The Boeing-Airbus Duopoly

Boeing

Founded in 1916 in Seattle, Washington, Boeing is one of the oldest and one of the most influential aerospace companies in the world. Its commercial aircraft division gained global prominence with the launch of the 707 jetliner in the 1950s and became the standard of civil aviation with models like the 737, 747, 777, and 787 Dreamliner. The 737 series, in particular, became the best-selling commercial aircraft family in history, cementing its dominance in the narrow-body market.

Boeing’s competitive edge has historically included its global support infrastructure, long-standing relationships with airlines, and U.S. government influence in international trade negotiations. However, recent setbacks, like the 737 MAX grounding in 2019 after two fatal crashes and ongoing quality control issues, have damaged its reputation and disrupted its delivery schedules. It has provided an opening for them to lose customers to the other large aircraft company, Airbus, and potential new entrants like COMAC.

Airbus

Airbus was founded in 1970 as a European consortium to compete with U.S. manufacturers like Boeing. Headquartered in Toulouse, France, Airbus rose to prominence in the 1980s and 1990s with successful aircraft like the A320, which introduced fly-by-wire technology, becoming a standard in many aircraft today. Airbus solidified its presence in the wide-body market with the A330 and later challenged Boeing’s long-haul dominance with the A350 and superjumbo A380.

Airbus has matched or exceeded Boeing in annual deliveries for several years, particularly in the wake of the 737 MAX crisis. Its A320neo family, launched in 2016, quickly became the standard in fuel-efficient, single-aisle aircraft, and has outsold the 737 MAX in recent years. With strong support from the European Union it is now considered to be an equal to Boeing creating the Boeing-Airbus duopoly in the commercial airliner market.

Timeline

This monopoly could be broken in time with indigenous projects like China’s CJ-1000A engine and domestic avionics development, which could mature into more competitive alternatives over time. If successful this might pressure Western firms to invest more heavily in innovation to stay ahead of a rising competitor. COMAC may create price competition by offering aircraft at lower or subsidized prices. Especially in emerging markets or within the Global South if COMAC gains momentum. It would force Boeing and Airbus to innovate in how aircraft are marketed and supported.

Airbus may face more immediate competitive pressure due to its deeper integration with the Chinese aviation market. Since 2008, Airbus has operated a final assembly line for A320 aircraft in Tianjin, China. This facility reflects Airbus’s pivot toward the Chinese market while placing it in the proximity of COMAC’s industrial and political sphere. The C919’s flight control systems and overall cockpit are more closely modeled on Airbus’s designs which make the aircraft more familiar for Airbus-trained pilots and maintenance crews. Chinese airlines familiar with Airbus systems may see the C919 as an easy transition, especially for domestic use. This could severely threaten Airbus’ position within China and more widely in Asia. But any change to global innovation between the three players will only happen in the long-term if COMAC is able to certify internationally, improve supply-chain independence, and scale production competitively.

Market Analysis

Previous new entrants and challenges to the Boeing-Airbus duopoly have not fared particularly well. The A220’s development began with Bombardier as the C‑Series, with initial budget projections at around US $2.1 billion, equally funded by Bombardier and the Canadian government. Over time program delays, rising complexity, and increased testing costs raised total expenditure to around US $3.5 billion.Accounting write‑downs and financial restructuring later brought the all‑in development cost to roughly US $5.4 billion. An important part in this story is also the engine costs. The PW1500G geared‑turbofan engine was developed specifically for the A220 at an estimated 10 billion dollars. The engine itself will retail at US $12 million per engine depending on maintenance commitments and airline negotiations (Hartley, 2025). Early manufacturing costs for the A220 were estimated at about US $33 million per airframe, including general and administrative overhead and supply chain inefficiencies.[Memon, 2023]. Bombardier originally sold some aircraft at below cost. Some examples were at the low price of $24 million in aggressive pricing strategies. This prompted a U.S. trade petition alleging the practice of dumping. [‌ Ogechi, 2025]

The A220 program shows how an aircraft can succeed but still struggle commercially due to high development costs and component issues. Pricing individual units below cost early on, Bombardier couldn’t absorb losses long-term without a strategic partnership. This eventually led to Airbus’s involvement and then to majority ownership of the Bombardier C-Series, later renamed the A220 programme.

The C919, backed by long-term state investment, reflects an even larger financial commitment, but benefits from sheer scale expectation in China’s domestic aviation market. While its unit price exceeds Western competitors, COMAC subsidizes production heavily to cover initial losses which is similar to the C-Series’s early years, but on a larger scale. The question is how long is COMAC and the Chinese government willing to subsidize the production and sale of the C919.

C919 costs

COMAC officially stated a development budget of around US $8.6 billion, but independent assessments suggest real costs likely exceed US $20 billion. Unit cost estimates for the C919 are not certain as COMAC does not announce actual figures but estimates are around $90-$100 million per airframe as previously suggested. Higher from earlier estimates of about $50–60 million per airframe. This is higher than the Boeing 737 Max and A320 family of aircraft. Now while that might be discouraging to the aircraft’s success the state government has been seen subsidizing the sale of the aircraft to domestic carriers at lower prices per airframe. This represents a failure to compete on price unless subsidized by the government. To which customers and for how long the Chinese government might be willing to provide such subsidies remains unclear.

Strategic and Geopolitical Dimensions

Boeing’s Political Vulnerability and Export Disruptions

Political entanglements, particularly in the Second Trump administration, introduces long-lasting vulnerabilities to Boeing. In 2025 trade tensions led to Chinese authorities banning domestic airlines from accepting new Boeing aircraft and American aircraft parts as retaliation from measures of US imposed tariffs as high as 145% on Chinese goods. China returned several Boeing 737 MAX jets to U.S. facilities which strand millions of dollars worth of inventory. Boeing CEO Kelly Ortberg confirmed that many airline clients had stopped taking delivery of ordered aircraft. This exposes Boeing’s operations and its vulnerabilities to do business internationally, specifically in China, weakening the duopoly between Airbus and Boeing by adding political tensions. It allows COMAC to also add itself to the competition as Boeing is in a weakened state.

American and European governments might not play fair as the exposure felt by a new competitor might push them to impose tariffs on COMAC’s planes, making them uneconomical. This happened to Airbus and Bombardier with the A220 when this line was subjected to American tariffs; this was due in part to Boeing feeing the need to cut out another potential competitor to their 737 MAX jets. The fact that it is also a Chinese made product also puts into question if the U.S. or EU will approve the use of it in their airspace. So China will need to overcome these hurdles in order to improve their odds at being feasible on the international market.

China’s Export Strategy

China’s use of aircraft exports as a tool of diplomacy and influence is consistent with its broader economic strategy. Under the Belt and Road Initiative, China has funded infrastructure projects across Pakistan, Southeast Asia, Africa, and Latin America. These are done through outright subsidies of the Chinese government or loans. COMAC can use this to their advantage and leverage these relationships. COMAC has done promotional campaigns in early 2024, with promotional flights designed to establish market trust and set attractive terms to the table.

The development of the C919 is deeply interwoven with China’s industrial strategy of advanced technology and products being made in China using state subsidies, infrastructure investments, and direct financial support to maintain production momentum. Analysts at CSIS say COMAC had received between $49 billion and $72 billion USD in Chinese government support. [8] These injections allow COMAC to price the C919 competitively, and give benefits to those who buy the jet, to build market share. In target regions Chinese firms will often sell products at a loss to maintain employment, future influence, and deepen trade ties. These tactics are shown across Chinese businesses. Selling aircraft cheaply in developing markets enables COMAC to establish an operational footprint for Chinese made products and foster brand familiarity before profitability becomes a possibility.

What now?

This introduces new competition dynamics for Boeing and Airbus, Boeing struggles with export bans and political backlash while Airbus, with an A320 final assembly line in Tianjin, has deeper ties within the Chinese system. China’s ability to redirect domestic airlines from Boeing or Airbus to COMAC reflects strategic maneuvering to weaken American dominance. If COMAC scales effectively it doesn’t just compete technologically but will reshape and reorient the framework of commercial aviation.

The emergence of the C919 reflects a convergence of economic policy and geopolitics: state-subsidized aviation supporting a broader Chinese strategy. Through loss-leading exports, import substitution, infrastructure partnerships, and strategic targeting of developing markets, the C919 could serve as a lever. This stands in contrast to Boeing’s more conventional, market-driven model which can make the duopoly unstable and potentially prompting both Airbus and Boeing to rethink their global positioning.

Long-Term Outlook and Challenges

One of COMAC’s most significant hurdles is overcoming skepticism toward Chinese made commercial aircraft, especially in markets where aviation safety is closely tied to brand trust. While the domestic market may become accustomed to flying on the C919 due to the strong presence of state-owned airlines, international passengers, mostly in North America and Europe, may be more hesitant. For airlines, the decision to buy a C919 depends on cost competitiveness, operational reliability, parts availability, and fuel efficiency compared to Boeing and Airbus offerings. However, for passengers the primary drivers are safety and comfort. It is not clear how they might feel about a new entrant. COMAC also needs to establish a maintenance and parts network to support the aircrafts they wish to sell and also create a good safety record. This can be done through transparent operations and regulatory oversight by reliable aviation organizations. Public perception can also be influenced by geopolitics and media perception between China and certain countries could amplify hesitancy to fly on a Chinese-made jet, regardless of the aircraft’s approval and safety.

The C919 holds certification only from the CAAC in China. Without approvals from the European Union Aviation Safety Agency (EASA) or the U.S. FAA the C919 cannot operate with most major Western airlines or fly in certain markets. The EASA certification process is estimated to take three to six years, with no delays or needing to change the aircraft characteristics. The FAA might take longer. This can delay the C919’s short term market potential outside China. Securing certifications will require compliance with international safety standards and the testing of Chinese systems internationally.

China has been heavily subsidizing domestic sales to sell aircrafts to Chinese airlines, offering favorable financing terms and reduced prices. Once the C919 secures foreign certification and builds a safety record, China could strategically deploy subsidies internationally like developing markets where cost competitiveness can override brand concerns. This would align with China’s export strategy in which pricing is used as a tool of geopolitical influence.

If COMAC can overcome certification hurdles and prove operational reliability the C919 could slowly chip away at Boeing and Airbus’s dominance in select markets. However, reputational trust will likely be the decisive factor. The first decade of service will be critical as strong performance, absence of major incidents, competitive pricing, and effective global maintenance support could take the C919 from a domestically favored jet into a competitive product internationally.

Conclusion

The COMAC C919 represents China’s most ambitious attempt to achieve domestic self-reliance and challenge the duopoly held by Boeing and Airbus in the global commercial aviation market. Backed by state investment, industrial policy, and a growing domestic aviation market, the C919 is a geopolitical instrument and a symbol of China’s push toward technological self-reliance. While its technicals and some design aspects mirror some of Airbus’s aircraft, its long-term goals and potential lies in its ability to scale production, reduce dependence on foreign components, and secure sales internationally. However, significant challenges remain. From certification delays and limited global trust to the hurdles of building a global support system and winning over airline customers and passengers COMAC has an uphill battle to fight. The public and airlines continue to favor the established aircraft manufactures and the lack of FAA or EASA certification restricts the C919’s international reach. Also political instability, especially involving America and Chinese relations, have already disrupted many of the traditional aspects of the duopoly. This offers COMAC a rare opening to expand into cost sensitive markets in Southeast Asia, Africa, and Latin America.

It is also worth noting Airbus was born in a similar story to COMAC in the 1970s and 1980s, when it was founded as a new player in the civil aviation market. Before Airbus, American giants like Boeing dominated the world of civil aviation. It seemed unlikely that Airbus could unseat Boeing and America’s monopoly on the industry but through sustained government support, multinational European cooperation, and a focus on technological innovation, like the introduction of fly-by-wire controls in the A320, Airbus gained market share, slowly but it reshaped the landscape of civil aviation. COMAC appears to be following a similar playbook with investment in domestic manufacturing, support from the government, and achieving strategic trade goals rather than initial profitability. It could replicate Airbus’s path which can transform it from a purely domestic competitor into a global competitor.

The C919’s commercial viability will depend on how effectively COMAC can navigate the next decade and the international markets, building partnerships, and establishing long-term operational reliability. While it is unlikely to unseat Boeing or Airbus in the near term, the C919 could reshape the landscape, even promote innovation, especially if China begins to use aircraft exports as a tool for economic diplomacy. The C919 is not disrupting the Airbus and Boeing duopoly at the moment but its recent progress and potential progress signals that the era of an unchallenged duopoly may be approaching its end.

Bibliography

AXON Aviation Group. “Aircraft Pricing. ” AXON Aviation, 2024, www.axonaviation.com/commercial-aircraft/aircraft-data/aircraft-pricing.

Baculinao, Eric. “China’s C919 Passenger Jet Makes Maiden Flight in Challenge to Boeing, Airbus.” NBC News, 4 May 2017, www.nbcnews.com/news/china/china-s-c919-passenger-jet-set-maiden-flight-challenge-boeing-n754171.

Bjorn Fehrm. “How Much Did the CSeries Cost Bombardier? – Leeham News and Analysis.” Leeham News and Analysis, 20 Feb. 2020, leehamnews.com/2020/02/20/what-did-the-cseries-cost-bombardier/.

Grant, Eli. “The Long Road to Certification: China’s C919 Faces European Delays, Raising Stakes for Global Ambitions.” Ainvest, 29 Apr. 2025,www.ainvest.com/news/long-road-certification-china-c919-faces-european-delays-raising-stakes-global-ambitions-2504/.

Hartley, Paul. “How Much New Commercial Jet Engines Cost.” Simple Flying, 9 Apr. 2025,simpleflying.com/how-much-new-commercial-jet-engines-cost/.

Lee, Amanda.“South China Morning Post.” South China Morning Post, 29 May 2023,www.scmp.com/economy/china-economy/article/3222192/chinas-c919-timeline-2008-23-first-commercial-flight-15-years-making.

Lu, Vanessa.“The Toronto Star.” Toronto Star, 18 Dec. 2015, www.thestar.com/business/bombardier-s-cseries-jet-certified-for-commercial-service/article_8b-58d9-bb8c-b0e9adddbb5f.html.dad0d7a3-11

Memon, Omar.“How the Bombardier C-Series Program Transitioned to the Airbus A220 Family.”Simple Flying, 14 Mar. 2023, simpleflying.com/bombardier-c-series-airbus-a220-transition-story/.

Ogechi.L. “The Story of Airbus A220. An Aircraft Boeing Didn’t Like.” Medium, 18 June 2025,medium.com/%40OgechiL/the-story-of-airbus-a220-an-aircraft-boeing-didnt-like-3f607d4978d9.

Ostrower, Jon.“Airbus Quietly Cultivates ‘Building Block’ Tech for A320 Successor.” The Air Current, 9Dec. 2022, theaircurrent.com/technology/airbus-eaction-xwing-a320-successor/.

Ostrower, Jon.“U.S. Engine and Component Ban Poised to Cripple China’s Commercial Aircraft Manufacturing.” The Air Current, 7 June 2025,theaircurrent.com/china/us-china-jetliner-restrictions-ge-rtx-honeywell-comac/.

Shah, Aditi, and Tim Hepher.“Aircraft Lessor DAE Sees China’s COMAC Breaking Airbus, Boeing Duopoly.” Reuters, 21 June 2024,www.reuters.com/business/aerospace-defense/aircraft-lessor-dae-sees-chinas-comac-breaking-airbus-boeing-duopoly-2024-06-21/.

“South China Morning Post.” South China Morning Post, 25 Dec. 2023,www.scmp.com/economy/china-economy/article/3246205/chinas-c919-jet-scores-higher-price-latest-deal-boeing-returns-market.

Spray, Aaron.“Airbus vs Boeing vs COMAC: How the Plane Makers’ Market Share Compares in Asia.”Simple Flying, 3 Feb. 2025, simpleflying.com/airbus-boeing-comac-market-share-asia/.

Spray, Aaron.“How Much Does a Boeing 737 MAX Cost in 2025?” Simple Flying, 8 Apr. 2025,simpleflying.com/boeing-737-max-cost-2025/.

Terlep, Sharon.“Boeing Hit from All Sides in Trump’s Trade War.” WSJ, The Wall Street Journal, 15 Apr. 2025, www.wsj.com/business/airlines/boeing-hit-from-all-sides-in-trumps-trade-war-cdc616d6.

Toure, Seydou. “Comac Breaks through against Airbus and Boeing.” Sneci, 10 Mar. 2025,www.sneci.com/en/comac-breaks-through-against-airbus-and-boeing.

Trimble, Stephen.“Bombardier Acknowledges CSeries Cost Pressure.” Flight Global, 16 Sept. 2013,Bombardier acknowledges CSeries cost pressure | News | Flight Global

Walsh, Sean. “How Much Does a Comac C919 Cost? (2024 Price).” Pilot Passion, 30 May 2023,pilotpassion.com/comac-c919-cost/.

‌

About the author

Aidan Ko

Aidan is a Senior at Success Academy High School in Manhattan. He has always been interested in economics and its impact on the world around us and political conversations. Aidan participates in Model UN, as it perfectly incorporates his interests, and he follows the stock market during the day to track trends.

The post How commercially viable is the C919 in the duopoly between Boeing and Airbus? appeared first on Exploratio Journal.

Abstract

Previous research has demonstrated that NBA payroll size positively impacts team championship contention and team success. However, the 2025 NBA Finals, which featured the Indiana Pacers and the Oklahoma City Thunder, provided the necessity to re-examine this correlation because neither teams’ total payroll was near the top of the league in the 2024–25 season. This paper investigates the impacts of NBA team payrolls, relative to the league salary cap, on team success as measured by regular season wins. Utilizing 11 years of financial and performance-related data from the 2014–15 to the 2024–25 season (inclusive), the study quantifies the correlation between spending and on-court results, and measures how well each team outperforms or underperforms their expected seasonal win totals based on annual spending. The study also investigates how the impacts of payroll efficiency on expected wins differ across various markets. Simulation results on different market sizes reveal that although higher payrolls remain generally associated with more wins, efficient resource management and strategic innovation are becoming increasingly more important.

1. Introduction

Over the past 11 years, the financial environment of the National Basketball Association (NBA) has undergone a dramatic transformation. This has altered the framework in which teams operate both on and off the court. The league, once characterized by predictable hierarchies based on spending, now reflects a more dynamic and uncertain battleground where new rules, economic incentives and roster strategies are changing. Not only has the salary cap steadily risen, but evolving collective bargaining agreements have also introduced mechanisms to promote parity and fiscal responsibility, such as luxury tax penalties and cap-smoothing regulations. In turn, team-building philosophies are continually adapting in response.

At the core of NBA team strategy lies a tension between financial investment and competitive success. Ownership groups have long discussed the merits of exceeding salary cap thresholds and incurring luxury tax payments in pursuit of a championship. Historically, empirical research has supported this perspective: a strong positive correlation between payroll spending and regular-season wins suggested that money could indeed provide an advantage in the standings (Gao, 2017), as exemplified by the Golden State Warriors’ championship success from the 2014–15 to the 2017–18 NBA season. The prevailing wisdom supported a team’s decision to stack rosters with elite talent in order to maximize the probability of postseason success.

Yet, the NBA is not static, and recent seasons have supported this idea. The 2024–25 campaign, specifically, delivers a striking counter-example: for the first time in the modern era, both NBA Finals teams, the Indiana Pacers and the Oklahoma City Thunder (OKC), reached the championship without paying any luxury tax charges. This development signals a broader shift: careful roster construction, player development and organizational discipline can offer a viable path to contention despite financial limitations. The 2025 NBA Finals raise questions about the key factors of success: has the relationship between payroll and success weakened, or is this season nothing more than an anomaly?

By tracing the relationship between team payroll and regular-season wins across eleven seasons, this research dives into whether spending is still a predictive factor for competitiveness. Each season is analyzed as a unique competitive environment, accounting for contextual changes that affect both individual team behavior and overall league economics.

The analysis compares the recent triumphs of non-luxury-tax teams within this historical and strategic context, illuminating larger trends in NBA parity and fiscal management. By looking into both the enduring and changing aspects of the payroll-to-wins paradigm, this study provides insights to help understand the future of basketball competition.

2. Related Works

Previous research on NBA payroll and performance has primarily focused on the overall relationship between team payroll size and winning percentage, generally confirming a positive correlation where teams with higher payrolls tend to win more. Several studies examine how salaries differ between teams, supporting the theory that salary inequality can reflect strategic investments in star players to maximize outcomes (Leon, 2025). Other research uses methods such as data envelopment analysis to evaluate how well teams convert financial and human resources into organizational success, including both on-court performance and franchise value (HarvardSports, 2023). These investigations highlight how intelligent roster construction, player development, and management practices significantly influence results beyond raw payroll numbers. Additionally, changes in Collective Bargaining Agreements (CBA) and luxury tax regulations have been studied for their impact on spending behaviors and competitive balance.

Most prior work, however, treated the league as a relatively homogenous entity or only broadly controlled for market effects without segmenting teams by detailed market tiers. Despite market size being a key variable in NBA economics and sports analytics, explicit analysis of payroll efficiency differences across large, medium, and small-market tiers remains limited.

This paper first studies the general relationship between payroll efficiency and wins, then further explores the effect of market sizes by introducing market tier as a moderating factor, offering a more detailed perspective on how spending effectiveness varies across different market sizes. It also demonstrates the analytical challenges posed by unequal team distributions among tiers and suggests more nuanced comparisons near market boundaries. These contributions provide new insights into the role of market size in NBA financial strategy and team performance.

3. Methodology

In this section, we discuss data collection and simulation methodologies.

3.1 Data Collection

Relevant data was scraped from two primary sources: Spotrac for financial data, including team payroll and salary cap figures, and Basketball-Reference for team win-loss records and additional season info (2025–26 NBA team salary cap tracker, 2025; Basketball statistics and history, n.d.). Python was used for web scraping owing to its flexible libraries and compatibility with structured data collection. Python libraries, such as requests and BeautifulSoup, were used for data scraping and cleaning of payroll, salary cap and win/loss data. Focusing on the most recent CBA and modern markets, without loss of generality, our data traces back to the history of each NBA team from the 2014–15 season to the 2024–25 season. We chose to start from the 2014–15 season to allow the immediate effects of the 2011 CBA to settle. Moreover, data sanity checks were applied using pandas to ensure year-over-year consistency and match teams across sources, resolving naming discrepancies, and verifying that payroll values aligned with reported league cap figures. Once compiled into a structured dataset, the information was imported into R for the analysis phase. Combined with R tidyverse packages, scatterplots were created to visually explore the relationship between each team’s payroll, as a share of the NBA salary cap, and regular season wins. These graphs revealed visible trends and helped identify potential correlations or outliers, making it easier to observe how spending efficiency and team success varied across different market sizes and eras.

3.2 Variable Construction

To investigate the potential effect of market size, teams were classified based on NBA market valuations and metropolitan statistical areas, according to HoopSocial (Burns, 2025). Specifically, large markets reach over 2 million homes, medium NBA markets reach between 1.5 to 2 million homes and small NBA markets reach less than 1.5 million homes. Market size categorizations were assigned to each franchise and included as categorical variables in the final analysis. Top-market (e.g., New York Knicks, Golden State Warriors, Los Angeles Lakers) and small-market franchises (e.g., New Orleans Pelicans, Indiana Pacers, OKC) were explicitly tagged based on local economic market size.

4. Experiment Results and Discussions

This section analyzes the findings on the relationship between payroll efficiency, market tier and overall cap efficiency in the NBA from 2014–15 through 2024–25. It examines the connection between payroll efficiency and wins, then compares results across market tiers, and finally evaluates advanced cap efficiency metrics to assess how teams convert spending into competitive success.

Cap efficiency is how effectively an NBA team utilizes its financial resources to achieve on-court success. It’s quantified by the residuals from a regression model that predicts team wins based on the percentage of the salary cap used. Specifically, it’s formulated as

Payroll Efficiency = team payroll / Salary Cap

4.1 Payroll Efficiency vs. Wins

Figure 1 is a scatterplot that shows NBA teams from the 2014–15 through 2014–25 seasons. Overall, teams that allot a large portion of their available salary cap to payroll generally achieve more wins during the regular season. With a correlation coefficient of r=0.52, Figure 110 illustrates a stronger relationship between payroll as a proportion of the salary cap and the number of regular season wins compared to previous studies. The figure also illustrates the growing advantage of increased payroll spending in order to maximize wins in a season. For example, in 2024, the Boston Celtics won the NBA championship with the fourth-highest season payroll at a value of $184,845,028. By handing out large contracts to star players such as Jaylen Brown and Kristaps Porzingis, the Celtics invested in impactful contributors, helping elevate the team’s overall potential.

Despite this moderate correlation, the spread of data points is significant. Teams with average payrolls have, at times, recorded high win totals through smart trades, effective coaching strategies, and strong player development. This is most clearly seen in the 2024–25 season, when neither NBA Finals teams paid the luxury tax, as shown in Figure 2.11

Figure 2 highlights the OKC and Indiana Pacers (IND) as outliers. Each team’s payroll efficiency value was situated close to league average, indicating that their payrolls relative to the salary cap weren’t among the highest in the league. Nevertheless, OKC achieved the most wins across the NBA, a surprising feat given their average payroll efficiency. This suggests that OKC was able to optimize its roster and performance well beyond spending expectations. The Pacers also secured a playoff spot by recording more wins than most teams with similar payrolls. Collectively, these two teams demonstrated exceptional efficiency and management, helping them convert average payroll investment into winning seasons and playoff appearances. As both teams adapt to changes in payroll regulation, the strength of each team’s development system also increases.

The graphs show that while increasing payroll can translate into potentially more wins, it doesn’t guarantee elite performance. Occasionally, teams that spend a lot underperform, and some mid-payroll teams overachieve. Hence, payroll is important, but not determinative.

Figure 1 and 2 underscore the relationship between payroll efficiency and wins while identifying notable outliers like the Thunder and Pacers. However, league-wide trends may differ depending on a team’s market size. To account for these structural differences, the next section explores results by market tier.

4.2 Market Tier Distribution

Categorizing teams based on market tiers allows for fairer comparisons among similar units. This enables more meaningful interpretations of trends such as payroll efficiency vs wins.

Figure 3 displays how payroll increases across different market tiers affect expected wins. Large- and medium-market teams experience greater gains in wins compared to smaller-market franchises when they increase spending. Notably, medium-market teams are projected to achieve bigger improvements when increasing payroll by 1% compared to large-market teams. Medium-market teams earn 0.334 additional wins compared to large-market teams that earn an additional 0.321 wins, a 0.013 win difference. This may be because large-market teams already operate near the limits of payroll efficiency, which means less room for further gains. Medium-market teams, on the other hand, often have more flexibility and potential to capitalize on increased spending. Small-market teams show smaller increases compared to the other two, reflecting a lower sensitivity to payroll changes. These differences could be due to factors such as small market teams’ limited access to top-tier talent, and limited flexibility for large- and small-market teams to grow because the former already maximizes payroll efficiency and the latter continues to face financial constraints. While spending more on players generally benefits teams, the exact extent varies based on market size and underlying competitive dynamics.

Figure 4 indicates that, in general, teams increasing their payroll spending by 1% can expect to win roughly 0.303 additional wins in the same season. This quantifies the practical impact of efficient payroll spending, showing how even a small increase in payroll can translate into tangible competitive benefits over the course of a full season.

Although market tier explains some variation in payroll efficiency’s effect on wins, it doesn’t fully capture a team’s performance relative to payroll expectations. For this, we examine the Cap Efficiency metric, quantifying value gained per payroll dollar against expected wins.

4.3 Cap Efficiency Metrics

Cap Efficiency represents the percent difference between expected wins and actual wins relative to payroll. A team with high Cap-Efficiency wins more games than expected for its spending, suggesting they are getting more value per dollar spent in terms of team regular season success and roster construction. Conversely, low Cap-Efficiency signals that a team is underperforming relative to the amount they spend. This metric sheds light on financial management’s strategic role in competitive outcomes.

Figure 5 visualizes the Cap-Efficiency Index for NBA teams across different seasons and Nielsen market tiers. Each heatmap block corresponds to a market tier, with teams listed along the y-axis and seasons on the x-axis. The color scale represents cap efficiency, where 100 is the league mean. Blocks that are lighter in color represent values above 100, indicating that a team is getting more wins per payroll dollar than the league average. Dark-colored blocks illustrate values below 100, suggesting less efficiency. Teams are sorted from high to low mean cap-efficiency within each market tier to highlight patterns and identify consistently efficient or inefficient franchises.

Based on Figure 5, clear outliers stand out in each market tier. For example, the Washington Wizards, despite being a large market team, win significantly less than expected. Meanwhile, teams like the Boston Celtics have won much more than payroll spending would predict compared to other large market teams. This further illustrates that wins are affected by more factors than just payroll efficiency. Additionally, among small-market teams, the OKC wins a lot more than expected in a given season, making them the most consistently successful team in the classification. In contrast, the Charlotte Hornets have underperformed relative to their spending. This highlights that teams within each market size show significant variability in how effectively their spending correlates to wins. It also allows us to conclude that additional factors beyond payroll spending play a huge role in a team’s success in any given season.

Teams across different market tiers gain varying numbers of expected wins for each 1% increase in payroll spending. The linear regression in graph 6 indicates that teams with payroll efficiency values near the league average tend to record win totals close to the league average of 41 wins. As payroll efficiency rises, so does the trend line: around every 10% increase in payroll ratio corresponds to about 2 to 3 additional regular season wins from the 2014–15 season to the 2024–25 season (inclusive). For example, teams that spend 10% above the average relative to the salary cap typically achieve a win percentage of around 0.540, or 44 to 45 wins.

5. Conclusions

This research illustrates that, historically, higher payrolls relative to the NBA salary cap are correlated with greater regular season success. However, when considering the success of non-luxury tax teams such as the Indiana Pacers and the OKC, this relationship is in flux. The evolution of the CBA has introduced stricter spending controls and harsher penalties for teams that exceed cap and tax thresholds. Now, success requires creative navigation of the CBA, emphasis on clever roster construction, internal talent growth and careful financial planning.

The insights from this analysis are valuable as the NBA’s financial environment becomes increasingly restrictive. Teams across all market sizes benefit from understanding that efficient cap management can still produce competitive and even championship-level squads. As the new CBA encourages parity and places increasing constraints on heavy-spending teams, franchises willing to innovate and maximize value are in a better position to thrive. Ultimately, the findings emphasize that while payroll remains an important ingredient, resourcefulness and a deep understanding of league rules has become even more critical for sustained NBA success.

Future research can benefit from more granular data on roster construction beyond payroll, such as player age, length of contract, injury history and performance metrics. Examining strategic adjustments to the tax penalties and stricter tax escalators would also clarify how teams optimize beyond salary allocation. Moreover, it’s important to note that the distribution of teams across market tiers is uneven, leading to skewed comparisons. More refined analyses between borderline-tier teams or pairwise comparisons would better capture market size effects on payroll efficiency.

Furthermore, while this paper considers market size as a moderator of payroll efficiency, other factors such as ownership philosophy, coaching impact and player development likely influence success. Advanced econometric models or machine learning could help better understand the complex, nonlinear relationships between financial decisions and performance. Monitoring the relationship between spending limits and competitive results will be increasingly important as the CBA tightens financial constraints. Teams able to innovate and maximize spending will be in the best position to succeed.

Acknowledgements

I would like to thank Professor Paramveer Dhillon of the University of Michigan for his inspiring guidance and encouragement throughout this research endeavor. Dr. Dhillon’s support is indispensable for the study to be completed the way it is.

References

Basketball Reference. (n.d.). Basketball statistics and history. Basketball-Reference.com. https://www.basketball-reference.com

Burns, M. (2025, June 5). 2025 NBA Team Market Size Rankings. HoopSocial. https://hoop-social.com/nba-team-market-size-rankings

CBA – National Basketball Players Association. (2023) Collective Bargaining Agreement (CBA). Nbpa.com. https://nbpa.com/cba

Gao, J. (2017). Exploring the impacts of salary allocation on team performance. Academia.edu. https://www.academia.edu/96573049/Exploring_the_Impacts_of_Salary_Allocation_on_Team_Performance

Harvardsports. (2023) Pay to Play: An Analysis of Payroll and Performance in the MLB and NBA. The Harvard Sports Analysis Collective. https://harvardsportsanalysis.org/2023/02/pay-to-play-an-analysis-of-payroll-and-performance-in-the-mlb-and-nba/

Leon, S. (2025, March 4). NBA payrolls: Spending big doesn’t always mean winning big. The Sports Cast https://thesportscast.net/2025/03/04/nba-payrolls-spending-big-doesnt-always-mean-winning-big/

Spotrac. (2025). 2025–26 NBA team salary cap tracker. Spotrac.com. https://www.spotrac.com/nba/cap

About the author

Arik Zhang

Arik is a senior at Millburn High School from Millburn, New Jersey. He is deeply passionate about statistics and its applications in business modeling and sports analysis. He also enjoys pursuing a variety of scientific research inspired by his classroom studies. He is a scholarship-holding member of the American Chemical Society and presented his work on molecular synthesis at ACS Fall 2025 in Washington, D.C.

In his capacity as a student leader, Arik is committed to fostering interdisciplinary work. As the head editor of the Sports section of his school newspaper, he integrates statistics and analysis into reporting to expand the depth of complexities of stories . As co-president of the Science Olympiad team, Arik combines rigorous scientific thinking with team-building to cultivate a collaborative, high-achieving culture.

Arik is also a strong believer in community service and education. As captain of the Millburn Limited Prep speech team, he coordinates and coaches year-round speech and debate classes at local schools. He also directed a summer speech camp for elementary- and middle-school students during the summer of 2025, the proceeds from which were donated to organizations serving local communities in-need.

The post Maximizing Wins per Dollar: A Systematic Analysis of Payroll Efficiency in the NBA appeared first on Exploratio Journal.

Abstract

This paper highlights the critical role of fan engagement in shaping the business success and asset management strategies of two of the world’s most successful football clubs Real Madrid and FC Barcelona.Through a comparative analysis, the study explorers how fan loyalty, cultural significance, and global outreach influence the financial performance and long-term strategic decisions of each club.By inspecting key areas such as revenue generation, brand value, merchandise sales, and media rights, the research highlights how fan-driven factors contribute to the club’s financial sustainability and their approach to managing valuable assets and players.the research also identifies differences in the club’s business models, particularly in terms of leveraging fan loyalty for global expansion and asset optimization.

History of Real Madrid & FC Barcelona

Real Madrid and FC Barcelona are one of the biggest and fiercest sports rivalries in the world. The rich history and heritage of both clubs influence their fan bases. The “El Clásico” averages a viewership of more than 660 million people worldwide, and as of November  2024, Real Madrid has won 105 of the matches compared to FC Barcelona’s 101 (with 52 draws; Gifford, 2024). 

Real Madrid was founded in 1902 as The Madrid Football club were granted the honorary title “real” by King Alfonso XII in 1920 in addition to the crown being added to their crest. Their history consists of gifted players like Ferenc Puskás, Alfredo Di Stéfano, Paco Gento, Hector Rial, Miguel Muñoz, and Cristiano Ronaldo. Real Madrid also holds the record for winning the most number of champions league titles (15). Their rival, FC Barcelona, was founded in 1899 by businessman Joan Gamper. The club’s first trophy was the Copa Macaya (which they won in 1902), and they won the Copa del Rey in 1910. Barcelona have won 31 Copa del Rey, the highest number for any team. Some of the legendary players that played for FC Barcelona are Johan Cruyff, Diego Maradona, Luis figo, Rivaldo, Ronaldinho, Samuel Eto’o, Xavi and most notably, Lionel Messi (Gifford, 2024). 

The rivalry between Real Madrid and FC Barcelona transcends the football pitch, illustrating how fan involvement significantly influences business organization and asset management strategies, as both clubs leverage their passionate supporter bases to drive revenue, enhance brand loyalty, and navigate the complexities of modern sports economics in a competitive landscape.

Fandom & Rivalry in Sport

The International Fan

In the context of Pu and James’s (2016) study, a fan is defined as an individual who shows a strong emotional attachment and identification with a sports team, having a sense of belonging and community with other supporters. Fans connect with their teams through multiple means, including following games, participating in discussions, and consuming related media, regardless of their geographical proximity to the team. Emotion and psychology link the fan to a felt reality, signifying devotion and pride. A local fan is one who resides within a proximal distance to the location of the team, and often, he has direct access to games, events, and other activities associated with the team. It makes it more real to relate to the team.

An international fan, on the other hand, is one who supports a team coming from a very distant place, often outside of one’s country. International fans communicate with their team more through media, social networks, and merchandise by relying on digital means, though not in the same direct ways as local fans. This study presents distinct motivations and experiences of fans.

Fans are the most significant predictor of whether a team can be successful, as huge revenues will be collected at the turnstiles selling tickets, in merchandise such as clothing and souvenirs, and in food and other concession sales from the additional people who have attended games. The franchise will always have more supporters to pay for the higher revenue for the short and long-term stability if it can attract more fans. Loyal fans also help in the creation of brand strength within the team. This is because their loyalty makes the team image even more attractive for sponsors and partners, increasing their appeal. Such loyalty can be seen as stability and continuity. 

Fans also impact a team’s success by the support they provide; a noisy, boisterous crowd can create a charged game-day atmosphere that may affect player performance. Home court is improved with a home crowd for psychological advantage to players and as an intimidating setting to visiting opponents. Fans also help create a shared community and identity that might revolve around the team.

In this context, identification can create an ethos of support where the fans mobilize both when the going is good as well as when it’s tough to rally around the team to motivate both players and staff. Social media is a phenomenon of today’s world, though it is beyond the confines of the arena for contemporary fan engagement. By the power of social media, it enables fans to voice their support and content, thus connecting with other fans worldwide, multiplying their reach and influence of the team. This online presence can attract new fans as well as sustain fans’ interest from a distance. Fans provide the team with necessary feedback, which can be used to improve and become better. It could be performance critiques or even marketing strategy feedback. The possibility of loyalty and satisfaction is much higher when a team listens to their fans and adapts

The study shows light on how international NBA fans are greatly affected and influenced by media coverage, especially through television broadcasts,social media and online platforms. This detection is also relevant for Real Madrid, who have one the largest and diverse international fan bases in the world. The club heavily invests in digital media which consists of streaming matches,and constantly creating content across platforms like YouTube and other official social media apps. The club’s online presence much like the NBA’s allows international fans to access exclusive player content, engage in fan communities and connect with other international fans despite geographical barriers through social media.

One of the key findings from Pu and James (2016) is that international NBA fans create strong psychological connections with players and teams, despite the geographical distance. Likewise, Real Madrid fans around the globe share an emotional bond with the club, to a great extent driven by the history, its legendary players (Alfredo Di stefano, David Beckam, Zinedine Zidane ,Cristiano Ronaldo, Sergio Ramos, Ferenc Puskas) and iconic status of the club. Real Madrid’s brand is constructed around a sense of prestige, success,and glamor, which appeals to fans beyond the boundaries of Spain. Similar to the NBA, the clubs exploit the global presence of their players which allows fans from different cultures and countries to emotionally connect with the team regardless of where they are located.

The study  also highlights how international fans often connect with the league or team due to their cultural or personal views. With respect to Real Madrid, this is especially relevant when considering the club’s ability to attract diverse fan bases from every part of the world. For example, fans from Latin America, the Middle East, and Asia in many instances feel a sense of cultural affinity with Real Madrid, through shared language, similar values of ambition and excellence, or admiration for the club’s international players. The club’s outreach strategy also aligns with this by not only focusing on performance on the pitch but also by leveraging cultural touchpoint,which also includes its association with well known brands like Adidas, tailoring marketing content to various regional tastes and preferences.

For international NBA fans, entertainment and social connection are key motivations as mentioned in the study. Real Madrid’s engagement with their international fans is based on creating entertainment beyond the pitch,which includes behind-the-scenes content,interviews with the players,and fan events worldwide. The club’s social media campaigns encourage the audience to interact with the team and other fans through virtual engagement, fan clubs, and events, which creates a sense of community. This idea matches directly with the motivations of distant NBA fans, who engage with the teams not only for the thrill of the sport but also want to feel included in a global social network of fans.

The NBA has used its image as a global sporting brand to build a fan base through global branding, Real Madrid has paved the way for them to be internationally recognized as a symbol of football excellence. The club reinforces their global brand by entering into strategic global partnerships building their audience from around the world. The club’s ‘Los Blancos’ brand, combined with the club’s success in both domestic and international competitions, makes it an attractive option for fans worldwide who perceive the club as not just a football team but as a cultural and global icon.

International NBA fans form strong psychological bonds because they feel a sense of belonging to a larger community. Real Madrid have created a similar community for their international fans through fan interaction and participation on their global platforms, by organizing international tours and engaging in local and international events, they foster a sense of belonging for fans no matter where they live.

Rivalry

Luís Figo’s transfer from F.C. Barcelona to Real Madrid in 2000 was perhaps among the most controversial deals ever. The Portuguese winger was adored in Barcelona, having joined in 1995 and quickly won the hearts of fans. However, in a shocking turn of events, he joined their arch-rivals Real Madrid for an astounding release clause of €60 million, arguably making him the most expensive player in the market at that moment. This move invoked a brutal backlash from the Barcelona fans, who ultimately termed it betrayal; sporadically giving rise to violent confrontations, one in which Figo was hit by a barrage of items and had a pig’s head tossed at him during his first return to Camp Nou. The transfer was not only an intensifying drill for this historic rivalry but also initiated the period of “Galácticos” for Real Madrid, fusing with Figo’s legacy of a player who rose from the allegiances of football for good or for evil.

Real Madrid is very famous for its “Galácticos” policy. Galactico is the spanish word for ‘galactic’ and is used to describe footballers who are deemed to be superstars or have levels of talent that equal the galaxies or are out of the world. The club targets marquee players to create a star-studded lineup. This approach led to high-profile signings, like Cristiano Ronaldo in 2009 and Eden Hazard in 2019; all of whom at crucial points in the competitive cycle as well- for instance immediately after having major success. The club tends to ‘let go’ players when they fail to meet its present needs or whose game becomes poor with significant churn. As an example, they sold Cristiano Ronaldo in 2018; they were then able to restock their team and regroup from the shock loss of superstar status (Kelly, 2023). Whereas FC Barcelona have mainly purchased young prospects who have potential, such as Frenkie de Jong in 2019. That has been the philosophy of building stars out of the youth  reports that Barcelona is slow to sell its players, mainly showing loyalty to the veterans, as was the case with Lionel Messi, until financial issues made it impossible to continue to do so in 2021. 

This shift was massive because they were not able to design alternatives for the well-known names (attacking football, 2024). Real Madrid has followed the policy of effective signing. Through this process, it reschedules and rejuvenates the team in the playing field to emerge victorious in prominent titles in the short-term cycle, such as the two UEFA Champion League titles earned in 2014 and 2018. It facilitates its transition very rapidly and allows it to attain short-term victories. Barcelona has followed a sustainable model. It spends in creating human assets in the long-term. However, their recent performances, especially after losing the services of Messi, seem to have exposed some of its weaknesses in the strategy devised. They were also beset by severe financial challenges and did not have instantaneous star power, making their recent performances not that brilliant.

Real Madrid’s acquisitions did not stop their string of successes in both domestic as well as European competitions whereas their releases like Ronaldo helped them rebuild effectively. The first place Barcelona succeeded was through emphasis on youth development. Success after that has been tainted with a failure to transition fast when players are lost, for instance, in La Liga and the Champions League. Real Madrid and FC Barcelona can have vastly contrasting philosophies about gaining and releasing their players that may reflect wider strategic ambitions. Real Madrid is bent on winning now through power houses whereas Barcelona would go for gradual development. That has largely dictated the recent few years’ course, achievements, and difficulties.

The organizational structure of both the teams are unique and not common in sports teams around the world.  In the case of Real Madrid, they are owned by the members or ‘socios’ as they call them, all the business decisions and ownership is in the hands of the members. The members vote for the President and the Board of Directors. According to their 2014 annual report, there are approximately 91000 ‘socios’ who pay on an average 123 euros per year as membership fees. To make the presidential election process or the Board of Directors election process easier the ‘socios’ hold an election to select and form a Member Assembly, which consists of 2000 members and run a four-year term. The main responsibilities of the Membership Assembly consist of framing and approving the club’s budget for the season, as well as authorizing the club to borrow money if needed. The Membership Assembly also has the authority to discipline the president of the club (Bonn, 2022).

With FC Barcelona their members are called ‘soci’. There are reportedly around 140,000 ‘soci’ who pay an annual fee of 150 euros. They are represented by a group of members who come together with the board to vote on major decisions. Adult members are allowed to vote in the presidential elections and each president serves a four-year term. The ‘soci’ must approve all the club’s financial and sporting decisions before they can pass into the club statute (Pettigrove, 2015).

Business Outcomes & Organization

Revenue & Sponsorship

Real Madrid has three streams of revenue mainly the commercial revenues, broadcasting revenues and ticketing revenues. The total revenue of the ‘los blancos’ in 23/24 shows a 27% jump compared to the 22/23 figures and 42% increase from their 18/19 figures which was their previous highest. The match day and commercial revenue streams have contributed mainly. With reference to broadcasting the revenues received from La Liga in 23/24 are lower than the revenues received in 22/23 season. With the recent renovations in the Santiago Bernabéu, the completely revamped stadium will generate around 400 million euros from over 200 events per year, this also includes the NFL’s inaugural game in Spain(Garcia, 2024). Likewise, the commercial revenue which includes sponsorship deals, the most notable ones being kit partners Adidas, sleeve sponsors HP, and telecom partnership with Orange including 5G integration at the stadium (Leveridge, 2024).

The total revenue of FC Barcelona grew by around 25% in 22/23 which is reported to be around 859 million euros. Most of its income generated is from commercial sales during the 22/23 season (Sim, 2023). Sponsorship deals include Nike as their kit partner, Japanese e-commerce company Rakuten, and the Turkish home appliances company Reko. The revenue from sponsorship deals amount to a combined 247 million euros (Dalleres, 2021).  The revenue expected from the stadium after renovations is projected to be around 350 million euros. Spotify is also one of the main sponsors, with a sponsorship deal worth around 280 million euros. From LaLiga reports it is confirmed that FC Barcelona also received 800 million euro from sale of their broadcasting rights to Sixth Street and from the proposed sale of shares in the club’s media subsidiary (Dixon, 2022).

Both the Spanish giants have had a huge impact on the Spanish economy. As of 2019 both of their combined revenue stands at 0.12% of Spain’s GDP, together reaching a combined revenue of 1.6 billion euros as of 2019. If player transfers revenues were also considered the economic impact on the economy would be 0.14% which is 1.8 billion euros. Real Madrid ended the 2018-2019 season with their operating revenues totaling 757 million euros. The growth which the club has projected is impressive. In 2003, it had a turnover of 193 million euros, in 2008 the turnover was over 351 million euros and a decade later they generated 514 million euros. After 2018, their turnover has exceeded 700 million euros in revenue. In 2019, the club’s wages expenditure exceeded 355 million euros or 47% of their operating revenues. Their highest salary level was in 2017 which totaled 377 million euros or 56% of their revenues. Over the past decade, Real Madrid have invested 911 million euros in player acquisition. Despite having heavy investment and high wage spending, Real Madrid closed every year with net profits. In the past few years, they have amassed over 347 million euros in net profits. 

In the case of FC Barcelona, their operating revenues in the 2018-19 season was a total 837 million euros, compared to the 690 million euros they made from the previous season with a revenue growth of 21% in 2019. FC Barcelona grew more compared to Real Madrid. In 2003, they made 123 million euros. Five years later it surpassed 290 million and after a decade it reached 483 million euros. Barça’s wages expenditure in 2019 was 426 million euros or 51% of their operating revenue. Over the last decade Barça have invested 868 million in player acquisition. Although on the other hand they accumulated 113 million euros in net profits, which is significantly lower than their rivals (Somoggi, 2019).

Real Madrid’s Valdebebas academy complex consists of 11 football pitches including the Alfredo Di Stefano stadium and the Santiago Bernabeu. The club’s youth development academy also known as ‘La Fabrica’ hosts 364 players in total across female and male sections. There are a total of 55 players in the La Liga that have passed through the ‘La Fabrica’. They generate an income of 39 million euros from the academy. The income from youth player sales for the club has generated 395 million euros since 2009 (de Juan,Quaile, 2023). During the summer transfer window of 2022, Real Madrid made 15 million euros with the departure of Borja Mayoral, Miguel Gutierrez, and Victor Chust. Although only a few players make it to the first team like Dani Carvajal, Nacho Fernandez, and Lucas Vazquez other team players like Vinicius Junior, Rodrygo, Federico Valverde and Mariano Diaz all joined as youngsters and appeared for the Real Madrid Castilla the club’s reserve team. La Fabrica’s origins date back to the 1950s, started by the club’s most famous president Santiago Bernabeu. The academy aims to create what they call a ‘global’ player. The academy encourages their players to adapt a flexible style of football and to not develop a style that is too fixed (Rai,2023). The club made 95 million euros in the 2020 summer transfer window from home grown academy player sales (Matt, 2020).

FC Barcelona’s ‘La Masia’ which is Catalan for ‘the farmhouse’. The original building was an ancient farmstead which was built in 1702 and the club’s Camp Nou stadium was built in 1957 next to the ‘La Masia’. Some of the greatest players who came out of the academy are Pep Guardiola, Carles Puyol, Victor Valdes, Andres Inieta, Xavi, and most notably Lionel Messi (Kelly, 2023). In 2024 FC Barcelona generated an income of 189 million euros from home grown player sales. The market value of the starting eleven is worth approximately around 304 million euros and more than 220 million euros or 72% of that value comes from La Masia players (Gabriele, 2022). Unlike Real Madrid (who are focused on acquiring young talented players), La Masia’ and FC Barcelona are dedicated to developing in-house talent. 

The Financial Situation of Barcelona

For more than a decade, FC Barcelona was one of the most formidable and revered football clubs in the world. The famous blue and maroon stripes conveyed a sense of sporting excellence and supreme domination. The iconic organization is in major debts; an ever increasing wage bill and an underperforming playing 11. Between 2003 and 2010 Barcelona’s revenue grew from 123 million euros to 387 million euros, an increase of 215%. This growth continued reaching an all-time high of 814 million euros in 2019 (Gabriele, 2022). The COVID-19 pandemic was also a problem for the club with players continuing to draw salaries and matches being suspended. In 2020 Barcelona’s total revenue fell by 14% to 729 million euros. Throughout 2018-2020 the club reported a net loss of more than 430 million euros (Jakeman, 2024). While it is tough to pinpoint the moment that FC Barcelona’s decisions landed them in debt, Neymar Jr departure from the club could be named as the most agreeable moment. The Brazilian superstar was sold for a record breaking fee of 222 million euros to PSG. Barca tried replacing Neymar with two players (Philippe Coutinho and Ousmane Dembélé) for an expensive amount of 135 million euro each and failed to live up to the expectations. Coutinho was released three and a half years later for just 20 million euros and Dembélé was sold for a loss of 85 million to PSG. Barcelona’s wage bill increased significantly when Antonie Griezmann arrived for 120 million euros, the Frenchman lived up to be an unsuccessful addition and was sold to Atlético Madrid for 20 million euros (Transfermarkt, 2024).

During the summers of 2019 and 2022, Barcelona’s debt increased from 217 million euros to more than 1.3 billion euros, most of the debt as much as 60% was considered short-term loans. Much of these loans seems to have accumulated due to the redevelopment of Barcelona’s grounds and to finance high-profile transfers. Goldman Sachs have loaned more than 815 million to the club. From 2012 to 2022, Barcelona have spent 1.63 billion euros on players, which when calculated on a net basis led to a loss of 650 million euros. In comparison, Real Madrid over the same period have spent 1.16 billion euros and a spend of just 179 million euros. Simultaneously, it was expensive for the club to retain Lionel Messi, because his last four-year contract was rumored to be worth 555 million euros. From 2016 to 2020, Barcelona’s salaries grew 61% approximately coming up to 350 million euros per year. To counter the two main issues of making the club more effective on the pitch and getting the club’s finances under control, Barcelona’s president Joan Laporta spent 168.9 million euros for player acquisition. To get the club’s finances under control, Laporta worked in four Levers. One Sixth Street purchased 10% of Barcelona’s La Liga rights for 25 years worth around 200 million euros. In Lever two Sixth Street purchased 15% of the club’s La Liga rights for 25 years for  around 300 million euros. In Lever three the Socios purchased 24.5% of Barca studios for 100 million euros. And in Lever four Orpheus Media purchased 24.5% of Barca studios for 100 million (Gabriele,2022). In 2023 the club’s total income rose to 806 million euros coming close to their 2019 record of 852 million euros. A new kit deal with Nike worth around 120 million euros per year also helps increase revenue. To further increase income the club is renovating their iconic Camp Nou stadium and are hoping it will bring a major increase in matchday revenue (Jakeman, 2024).

Stadiums

When it comes to the topic of iconic stadiums, the two stadiums that cross every football fan’s mind are Real Madrid’s Santiago Bernabéu and FC Barcelona’s Camp Nou (Spotify camp Nou). Real Madrid’s Santiago Bernabéu has surpassed 350 million euros in income compared to the 150 million euros it generated pre-pandemic despite the stadium’s renovations still underway. The club hopes to increase the revenues generated by the iconic stadium by tapping into its fullest potential. The club’s financial forecasts predict an income of 400 million euros annually from the stadium alone. 

One of the major reasons for this surge in income ss the introduction of more VIP sections. These sections for their fans and guests have generated higher income per seat than that of regular seating. The club creates a one-of-a-kind and exceptional experience for their customers. The club also plans to host events year-round in the stadium hoping to host events ranging from games to concerts and corporate gatherings. Real Madrid have also collaborated with entities like Legends to further optimize the stadium’s revenue potential. The addition of restaurants and paid parking areas will also increase the club’s revenue. The club also plans to host sports games beyond football with the NFL’s opening game being played in the Santiago Bernabéu in 2025 and they further hope to host future NBA games at the stadium (Kowalski, 2023).

On the other hand, FC Barcelona’s Camp Nou has been estimated to finish renovations and be fully functional by the end of 2025-26 season. The club hopes to reach 85 million euros by the first of June 2025. The club is confident that they will surpass expectations of exceeding 340 million euros by the 2025-26 season. The predicted income from different activities at stadium predicted by the club are approximately 80 million euros from museum tours, approximately 74 million euros from ticketing, approximately 77 million euros from hospitality boxes VIP, approximately 50.2 million euros from season tickets, 47.2 million euros from sponsors, 8.5 million euros from meetings and events, 8.1 million euros from food and drinks, 2.2 million euros from parking per season (Frieros, 2023). 

Relevance for La Liga

La Liga has replaced the English Premier League (EPL) as Europe’s best league, with two out of three of the richest football clubs in the world located in Spain. Both Real Madrid and FC Barcelona compete with each other to sign the biggest and the best players in the world. In 2013, Real Madrid signed Gareth bale, Isco and Asher Illarramendi for 100 million euros while Brazilian superstar Neymar Jr signed with FC Barcelona for 50 million euros, where he had an incredible run with the Catalans (Marsden, 2013). La Liga also hosts two of the best players in the world in Cristiano Ronaldo and Lionel Messi who together combined have won 13 Ballon d’ors combined.In fact since 2000 16 Ballon d’ors have been won by players in La Liga, and the award returned to the English Premier League after 16 years having won by Rodri of Manchester City. Real Marid and FC Barcelona combined have won 20 champions league, they have also won 8 of the 20 FIFA club World Cup.

Real Madrid affects La Liga in various ways. They are one of the most money-making clubs in football is Real Madrid, who rakes in above €700 million in annual revenue lately. This brings them quite a lot for the pocket as they bring in many ticket sales and merchandise into their books, including broadcasting rights. They come with significant sponsorship and develop the economic balance of La Liga. For instance in 2020 they accounted for 18% of La Liga’s total revenue (Bridge, Dhillon, Tantam, 2022). Real Madrid’s popularity and exposure also boosted and increased La Liga’s appeal for broadcasters, contributing to a broadcasting revenue pool that exceeds 2 billion euros annually. It is estimated that La Liga would garner around €1.9 billion from broadcasting rights for the 2021-2022 season. Such an amount would have been severely impacted by the matches of Real Madrid. Super matches are now able to bring in bigger sums of TV rights. 

Consequently, even lesser clubs will have to harvest since top clubs like Real Madrid are sure to exert a majority of the league’s domestic and international rights deals (European club association reports, 2022). Real Madrid plays a key role in promoting La Liga, which boasts an estimated global fanbase of over 450 million. This vast audience boosts the league’s visibility and draws in international supporters, resulting in higher revenue from merchandise sales. For example, in recent years, Real Madrid’s merchandise sales exceeded €100 million, making a substantial contribution to La Liga’s overall commercial income (UEFA, FIFA 2021). La Liga’s reputation gets a lift as their player wage bill surpasses €300 million. For the 2022-2023 season, the club’s wage bill reached almost €353 million. This amount is among the highest globally, enabling the team to draw in top talent and enhance the overall quality of the league. Additionally, the club’s focus on youth development plays a significant role in its international standing (transfermarkt, 2023).

 FC Barcelona happens to be one of the most cash-rich football clubs on earth.In recent seasons, their revenues have even exceeded €700 million. All these factors directly or indirectly impact the overall revenues of La Liga through tickets sold, merchandising, and broadcasting rights. Barcelona contributed to a large percentage of the total revenue in La Liga’s 2021 numbers, ensuring stability in the league’s economy (Deloitte Sports Business Group, 2022). Barcelona’s popularity makes the league more attractive to broadcasters, whose revenue from broadcast exceeds €2 billion annually. This highly publicized match interest leads to more television revenue. Higher television rights contracts could favor all teams from this league (European Club Association, 2021). Because Barcelona has a record of achieving several La Liga titles throughout its history, its successes influence the overall league competitiveness. 

That success creates imbalances which influence competition; first, they have the ability to attract more significant sponsorships and investments so that supremacy just keeps snowballing (Szymanski, 2019). Being among the most known football teams worldwide, FC Barcelona makes La Liga known globally. Having subscribed by more than 300 million fans, their cross-border tours as well as a media presence boosts the prominence of the league and provides more international subscriptions and sponsors (Koller, 2020). Barcelona invested much money and time in its youth departments, especially via the prominent La Masia academy-which makes the league synonymous with developing skills. At the same time, as major player wage earners and sharp strategic investors, the club maintains the best in the fields, which propels its league’s collective level (Drayer, 2021).

Conclusion

The comparative study of Real Madrid and FC Barcelona highlights the impact of fan engagement on the business success and asset management of these two elite football clubs. On one hand, both the clubs share a similar level of on-field success and global recognition and brand value, and on the other, their approaches and practices to managing assets and fan loyalty are very different. Real Madrid’s emphasis on global brand expansion and diversifying their revenue streams, teamed with their effective use of media rights and merchandising has enabled the club to maximize its fan base as a key driver of financial success. In contrast, FC Barcelona has a more community-focused model of fan engagement which fosters deeper, regional fan loyalty, in turn determining their approach to brand management and long term asset optimisation. This difference underscores the different ways in which fan engagement can shape not only the revenue models but also the broader business strategies of leading sports organizations.

The rivalry between Real Madrid and FC Barcelona extends beyond the exciting stands of a football field, both clubs compete fiercely in terms of business strategy, financial performance, and fan loyalty. Real Madrid has built on their global reach, prioritizing revenue diversification through sponsorships, commercial partnerships, and strong digital presence. Whereas contrary to their rivals FC Barcelona have maintained a business model centered around community based fan engagement, emphasizing its deep historical ties to Catalonia and a membership-driven structure that fosters long-term loyalty. This rivalry highlights each club’s different and unique approach to asset management,marketing, and how fan relations directly influences their financial sustainability and competitive positioning. Conclusively, the clash between the two football giants reveals the growing significance of strategic business decisions in the modern sports industry,where fan loyalty and operational efficiency play a pivotal role in driving success both on and off the field. 

References

Callejo, M. B., & Forcadell, F. J. (2006). Real Madrid football club: A new model of business organization for sports clubs in Spain. Global Business and Organizational Excellence, 26(1), 51-64.

Larsen, R. (2024). The Business Model and Revenue Streams of Madrid Explained. Retrieved from: https://www.untaylored.com/post/the-business-model-and-revenue-streams-of-real-madrid-explained 

Pu, H., & James, J. (2017). The distant fan segment: Exploring motives and psychological connection of International National Basketball Association fans. International Journal of Sports Marketing and Sponsorship, 18(4), 418-438.

Somoggi (2019,December 17). Real Madrid and Barcelona economic impact.SportsValue.  Retrieved  from https://www.sportsvalue.com.br/en/real-madrid-and-barcelona-economic-impact/

Gifford . (2024,December 5). Real Madrid. Encyclopaedia Britannica. Retrieved from https://www.britannica.com/topic/Real-Madrid

Gifford (2024,December 5 ). FC Barcelona. Encyclopaedia Britannica. Retrieved from https://www.britannica.com/topic/FC-Barcelona

Bonn  (2022,August 10). Real Madrid: Florentino Pérez y the socios of Los Blancos. Sporting News. Retrieved from https://www.sportingnews.com/in/football/news/real-madrid-florentino-perez-socios-los-blancos/zrv8nwkjorh4n5w9w8jngugy

Pettigrove (2015,August 4). Explaining the structure of Barcelona: The club’s working, youth, and La Masia board. Sportskeeda. Retrieved from https://www.sportskeeda.com/football/explaining-structure-barcelona-club-working-youth-la-masia-board

Kowalski (2023, June 28). Spain: Sky-high profits from the new Bernabéu. StadiumDB. Retrieved from https://stadiumdb.com/news/2023/06/spain_skyhigh_profits_from_the_new_bernabeu

Frieros (2023, September 10) Sport.Breakdown of Barca’s stadium income: Aiming for bigger profits. Sport. Retrieved from https://www.sport.es/en/news/barca/breakdown-barca-stadium-income-aiming-91927989

UEFA. (2022). UEFA annual report 2021/22. UEFA. Retrieved from https://editorial.uefa.com/resources/027e-175fe9b18b8d-59b8b4264404-1000/uefa_annual_report_2021-22.pdf

Deloitte. (2024). Annual review of football finance 2024. Deloitte. Retrieved from https://www.deloitte.com/global/en/Industries/tmt/research/gx-annual-review-of-football-finance.html

Marsden . (2013, September 10). La Liga: Why the Spanish league has taken the lead as the world’s best. Bleacher Report. Retrieved from https://bleacherreport.com/articles/1767737-la-liga-why-the-spanish-league-has-taken-lead-as-worlds-best

Dunn, R. (2023, February 2). Real Madrid and La Fábrica: Spain’s football factory. The New York Times. Retrieved from https://www.nytimes.com/athletic/4142743/2023/02/02/real-madrid-la-fabrica-spain/

Wiltse (2020, September 16). The case for justifying La Fábrica as Real Madrid’s cash cow. Managing Madrid. Retrieved from https://www.managingmadrid.com/2020/9/16/21439716/the-case-for-justifying-la-fabrica-as-real-madrids-cash-cow

De Juan  (2023, November 23). Real Madrid’s La Fábrica academy produces more than Barcelona’s La Masia academy. AS. Retrieved from https://en.as.com/soccer/real-madrids-la-fabrica-academy-producing-more-than-barcelonas-la-masia-academy-n/

Kelly  (2023,October 11). What is La Masia? Barcelona’s famous youth academy and the star players it has produced. Goal. Retrieved from https://www.goal.com/en-in/news/what-is-la-masia-barcelonas-famous-youth-academy-star-players-produced/or0mpbswqb671voxzkzpt67j2

Robertson (2024, January 31). Barcelona: Huge debt, terrible transfer record, and Xavi leaving – what’s gone wrong? Transfermarkt. Retrieved from https://www.transfermarkt.com/barcelona-huge-debt-terrible-transfer-record-and-xavi-leaving-whats-went-wrong-/view/news/433210

Gabriele  (2022,August 28). Barcelona. Generalist. Retrieved from https://www.generalist.com/briefing/barcelona

Jakeman (2024,September 29) FC Barcelona emerging from the financial crisis. Front Office Sports. Retrieved from https://frontofficesports.com/fc-barcelona-emerging-from-financial-crisis/#:~:text=Throughout%202018%2C%202019%2C%20and%202020,of%20the%20club’s%20wage%20structure.

Kelly (2023,March 19) What is a Galáctico? Ronaldo, Beckham, and all of Real Madrid’s super signings. Goal. Retrieved from https://www.goal.com/en-in/news/what-galactico-ronaldo-beckham-all-real-madrids-super-signings/1lrrb6x9j0n9f1actzefqw47am

Attacking football  (2024,November 12)La Masia: The rise, fall, and revival of Barcelona’s famed youth academy. Yahoo Sports. Retrieved from https://sports.yahoo.com/la-masia-rise-fall-revival-023400844.html

Garcia (2024,July 24) .Real Madrid first club to reach $1bn in revenue, report says. ESPN. Retrieved from https://www.espn.in/football/story/_/id/40629331/real-madrid-first-club-world-report-1bn-revenue

Leveridge, S. (2024, February 1). Real Madrid to announce $757 million sponsorship deal with HP, reports. Forbes. Retrieved from https://www.forbes.com/sites/samleveridge/2024/02/01/real-madrid-to-announce-757-million-sponsorship-deal-with-hp-as-reports/

Sim (2023, October 23). Barcelona’s 2023-24 budget: Income, loss, and Joan Laporta’s strategy. SportsPro Media. Retrieved from https://www.sportspromedia.com/news/barcelona-budget-2023-24-season-income-loss-joan-laporta/

Dalleres (2021,September 9) Barcelona suffered another financial blow with Rakuten set to pull the plug on €30m-a-year shirt sponsorship. City A.M. Retrieved from https://www.cityam.com/barcelona-suffer-another-financial-blow-with-rakuten-set-to-pull-plug-on-e30m-a-year-shirt-sponsorship/

Ed Dixon (2022, March 30). Barcelona’s Spotify sponsor: Shirt, Camp Nou stadium, and the Daniel Ek-Joan Laporta saga. SportsPro. Retrieved from https://www.sportspro.com/insights/analysis/barcelona-spotify-sponsor-shirt-camp-nou-stadium-daniel-ek-joan-laporta-worth/

TRT World. (2020,). Real Madrid generates biggest income in COVID-hit season. TRT World. Retrieved from https://www.trtworld.com/sport/real-madrid-generate-biggest-income-in-covid-hit-season-43141

Football Benchmark. (2023,February 23). Financial El Clásico: The rivalry between Real Madrid and Barcelona. Football Benchmark. Retrieved from https://www.footballbenchmark.com/library/financial_el_clasico

About the author

Sanika Sham

Sanika has a strong interest in business studies and business administration, with a particular focus on sports management. She is passionate about football and Formula 1, and enjoys playing sports as they allow her to experience the value of teamwork and team spirit. While she may not be an expert in every sport, her enthusiasm for both sports and business drives her academic and personal pursuits.

The post Real Madrid vs FC Barcelona: A Comparative Analysis of Fan Impact on Business Success and Asset Management appeared first on Exploratio Journal.

Abstract

Although 73% of workers in the U.S. have access to retirement plans, participation is still substantially lower than ideal at 56% (Bryan et al., 2024). Some barriers are financial, including the rising cost of living, stagnant wages, and the fact that many Americans live paycheck to paycheck. However, many Americans have the means to save but do not. Even among those who participate, savings rates may be insufficient for a secure retirement. As many as 92% of working households do not save enough money relative to their income and age (Rhee, 2013). This paper aims to explore how behavioral economics strategies, such as choice architecture, can be utilized to increase participation in retirement plans among individuals with both access and the capacity to save. Additionally, it examines strategies to increase savings rates among those who currently participate in retirement plans but contribute less than optimally.

Introduction

While millions of Americans have access to employer-sponsored retirement plans, many have still not saved sufficiently for retirement. Psychological barriers to participation, including prioritizing current spending over long-term savings, exist for people who have enough income to save for retirement. Middle-income earners can benefit from programs that utilize techniques that help them overcome psychological barriers in order to increase their savings rate. The benefit of saving money early can make a 20-year-old who saves $400 a month with a 7% average return a millionaire by the age of 65 (The Entrust Group, 2024). Although these middle-income earners often have financial literacy, they lack the incentives to change their financial behaviors. Behavioral economics utilizes strategies that increase participation and, therefore, provide increased financial well-being in retirement.

I. Access and Participation in 401(k) Plans

Although a large portion of employers provide access to retirement plans, there are still a significant number of employees who do not participate due to employment type, income, and company size. The Worker Participation in Employer-Sponsored Pensions: Data in Brief and Recent Trends provides data on pension access and participation rates among U.S. workers. As of March 2023, 73% of all U.S. workers had access to employer-sponsored retirement plans, but only 56% participated (Bryan et al., 2024). Although access is widespread, participation lags significantly. This large gap has implications for retirement savings and preparedness for a large portion of the workforce. Disparities of access and participation rates occur across the workforce based on employment type and job classifications. For example, for civilian workers, only 44% of part-time employees have access to employer-sponsored retirement plans, versus 82% access for full-time workers. Furthermore, income level also affects access. Approximately 90% high-wage earners in the top 25% of wage distribution had access, whereas lower earners in the bottom 25% had 48% access (Bryan et al., 2024). Company size also plays a role in access, with 91% for businesses with over 500 employees versus 53% for companies with fewer than 50 employees. The type of occupation also plays a role in access and participation. Service workers had the lowest rates of access of 43% with only 25% participation, whereas workers in management positions had 86% access with a 74% participation rate (Bryan et al., 2024). These statistics demonstrate that employer-sponsored retirement plans are available to many people, but economic and occupational constraints limit participation. In addition, there is a gap between access and actual enrollment. Strategies need to be implemented to improve access and participation rates, especially among lower-wage and part-time workers, to increase retirement preparedness across all sectors.

II. How Can Access Be Increased

Strategies have increased access to employer-sponsored retirement plans through easier access for part-time workers and tax incentives for employers, but more can still be done. One strategy that has increased access to retirement plans is the SECURE Act 2.0, which offered tax incentives and increased accessibility for employees of small businesses. The SECURE (Setting Every Community Up for Retirement) Act of 2019, followed by the SECURE Act 2.0, incentivized businesses to offer retirement plans to employees by providing tax breaks for those businesses that participated. In 2023, for the first time in history, over 70 million workers had access to 401(k) plans, an increase from 62.3 million in 2021. One major tenet of the SECURE Act that helped to increase access was the section that decreased the number of hours and years part-time workers needed to work to be eligible for employer-sponsored retirement plans (Gappa, 2024). Although the SECURE Act increased access, more still needs to be done. For businesses with fewer than 100 employees, only 34% provided retirement plans. These small businesses cited lack of money (48%), lack of time (22%), and lack of knowledge (21%) as to why they did not offer retirement plans to their employees (Gappa, 2024). These statistics illustrate why small businesses continue to lag behind. Further tax incentives, education, and improving the simplicity of starting an employer-based retirement plan can encourage broader access and participation.

III. Barriers to Participation

Although the SECURE Act 2.0 has increased access to retirement savings, millions of Americans are still unable to contribute to retirement plans due to a mix of economic pressures, including an increase in the cost of living and stagnant wages. According to a MarketWatch survey, 57% of Americans state that they live paycheck to paycheck, which means they have almost no money left after paying for food, rent, and utilities. Furthermore, this financial issue affects younger people more, with 65% of Gen Zers living paycheck to paycheck as opposed to 44% of Baby boomers (Haverstic, 2025). One major barrier to contributing to retirement savings is the cost of living. For example, housing costs for middle-income families in 2018 increased to 34.5% of a family’s total income, up from 27% in 1950 (Bernard & Russell, 2019). Furthermore, the minimum wage in 2025 has remained stagnant since 2009 at $7.25/hour, with little increase from 2002 when the minimum wage was $5.15/hour. Meanwhile, the average cost of a single-family home in 2002 was $224,400 compared to $497,700 in 2025 (Haverstic, 2025). The impact is felt even more in large cities. According to a study by the financial company Smart Assets, an individual living in a large city such as New York City needs to earn a salary of $138,000/year to live comfortably. Living comfortably is defined as a household budget where 50% of income is spent on housing and utilities, 30% on discretionary spending, and 20% savings. This is an hourly wage of $66.62, far more than the average minimum wage (DeJohn, 2024). With the increased cost of living and stagnant wages, many individuals fall short of having disposable income for retirement savings. This is especially true of younger generations between the ages of 19-34 years old, of which 14% fall below the poverty line as opposed to 10.1% of those between the ages of 34-64 (Haverstic, 2025). Many Americans are just trying to survive, not save.

Although many Americans have financial constraints, some surveys demonstrate that some people overreport these financial strains. Approximately 60% of Americans self-report that they live paycheck to paycheck (Srikant, 2025). According to the Federal Reserve, 54% have a three-month emergency savings fund (Federal Reserve, 2024), and 34% live paycheck to paycheck (Bankrate, 2025). This highlights the difference between perception and reality for over 25% of those polled. Strategies can be implemented for those who have the perception of living paycheck to paycheck, but in reality, they do have some income to invest into retirement. Strategies for these people include accurate budgeting, reducing expenses, and increasing income. Being strictly budget-conscious by reducing dining out, buying new clothes, and buying cheaper gas can save a family up to $18,000 per year. Opting for public school over private school can save $42,000 to $84,000 per year for two children (“Embrace Living”, 2025). Other tools for saving money are driving a car for five more years than initially planned, opting out of expensive social activities, and reducing the accumulation of more debt, such as high-interest credit cards (“Embrace Living”, 2025). All of the above strategies can assist those who perceive that they are living paycheck to paycheck to have money to invest in retirement savings.

IV . Why It’s Important to Save and How to Do It

Leveraging compound interest can be a powerful tool to retire early or to support greater financial health in retirement. This requires income to exceed expenses, which may not be uniform across income earning years. In addition, employees should take full advantage of “free money” from employer 401(k) matching. The above strategies, complemented by tax-efficient accounts like HSAs, Roth IRAs, and taxable brokerage accounts, can make financial independence more easily attainable. The time it takes to reach financial independence relies on only one thing-your savings rate, which is the percentage of your salary that you set aside per year. Furthermore, your savings rate is determined by how much money you make and how much you spend in order to have a comfortable life. The most important aspect about a person’s savings rate and the years it will take to retire is that it is an exponential relationship, not linear (“The Shockingly Simple”, 2012). This is the power of compounding. Assuming a 5% investment return after inflation and a 4% withdrawal rate, if a person has a savings rate of 5% they can retire in 66 years, as opposed to a savings rate of 25% a person could retire in 32 years (Networthify, n.d.).

One of the most powerful tools for retiring early is decreasing your spending rate, rather than increasing your income (“The Shockingly Simple”, 2012). If a family chooses to cut a few non-essentials, it could mean that retirement age can be years earlier. Additional strategies for those with disposable income to retire early include building retirement savings, managing taxable income, and utilizing different types of investment accounts. One of the most important steps to pave a path for retirement preparedness is to contribute to a 401(k), especially if it is employer-matched. For example, if an employee with a salary of $80,000 contributes $8000 to their 401(k) and their employer matches $4000 after 30 years at an 8% return, it would be $1.55 million compared to $ 1.06 million if there wasn’t a match (“Early Retirees Guide, 2025). Another important step for those with high deductible health insurance is to utilize a health savings plan to its maximum amount for medical expenses because it is the only account whose contributions are tax-deductible with tax-free growth and withdrawals, a triple tax benefit (“Early Retirees Guide, 2025). For those with a lower income (less than 24% income bracket), a Roth IRA is another account that is important to fund because it can help maximize income that is tax-free in retirement. Another important way to prepare for retirement is to fund a taxable brokerage account. Although it lacks tax advantages, a taxable brokerage account has tremendous flexibility with no income limits, contribution limits, or penalties for withdrawal, therefore making it a good source of dividend income and money that is easily accessible for retirees (“Early Retirees Guide, 2025). The above step-by-step guide utilizes compounding interest, taking advantage of 401(k) employer-matching, along with other tax advantage accounts to help Americans save more.

V . Behavioral Economics and Nudging Better Decisions

Many people struggle to save for retirement, not because they lack the desire, but because of psychological biases that lead them to prioritize current spending over long-term savings goals. Behavioral economics examines when, how, and why decisions, on occasion, systematically vary from purely economically rational choices (Samson, 2014). In some cases, behavior is influenced by the structure and presentation of choices; this is known as choice architecture (Thaler & Sunstein, 2009). One powerful example of choice architecture is the default choice, which is a predetermined option that is automatically set if an alternative is not actively selected. Most often, people select the default choice (Samson, 2014). In other cases, decisions depend on heuristics, shortcuts to speed up or reduce the informational burden involved in decision making (Thaler & Sunstein, 2009). An example of heuristics is anchoring, where there is initial exposure to a number that establishes a reference point which the user then makes a judgment, and then this gives perceived value of a product (Samson, 2014). For example, if a fully loaded computer is $2000 but the customer makes customizations that make the price $1500, it is then viewed as a bargain even though the base model is $1000 (Samson, 2014). And yet in other cases, decisions may reflect biases related to cognitive or emotional factors. An example of this is the prospect theory, where people do not make the most rational choices when an option is framed as a win or a loss. Since people hate to lose, they often select a less optimal choice as long as they perceive that they will not lose anything (Samson, 2014). Understanding these influences allows individuals to consider where they may be leaving money on the table. Also, allowing for those setting policies, such as in a corporate setting for employees or in a government setting for citizens, to introduce nudges (part of choice architecture that alters a person’s behavior without giving up individual choice) that leverage the systematic nature of behavioral economics in influencing decisions (Thaler & Sunstein, 2009). Thoughtfully designing choices using nudges can produce better results while still preserving individual freedom to make alternative selections.

VI. Utilizing Behavioral Economics to Increase 401(k) Participation

Behavioral economics is an important part of financial well-being because financial literacy only improves knowledge, but does not change financial behaviors. Behavioral economics is growing because research has proven that financial literacy does not equate to financial well-being, the financial landscape has become significantly more complex, and individuals are now tasked with the massive responsibility of managing their retirement well-being (Soman & Choe, 2023). The power of behavioral economics can be utilized to create strategies to aid in an increase in participation in retirement funding, particularly by leveraging inertia with default options and automatic enrollment. Inertia, where people tend to stay with their current situation even after receiving new information, can be used as a powerful tool in behavioral economics (Hreha, 2023). Researchers compiled data from a large corporation in the United States that changed its 401(k) participation options from opt-in to automatic enrollment as a default. With automatic enrollment, participation rose significantly to 86%, as opposed to 49% where employees needed to make an effort to opt in, thus demonstrating the power of inertia (Madrian & Shea, 2001). Another tool in behavioral economics is choice architecture, where the way or amount of choices presented can affect participation. A study of over 800,000 employees at one company demonstrated that 401(k) participation dropped by 1.5-2% for every ten funds added; furthermore, participation rates were at their highest at 75% when only two funds were offered (Iyengar et al., 2003). Choice architecture was an effective nudge in increasing 401(k) participation. One of the most well-known behavioral interventions is a program called Save More Tomorrow (SMarT). In this program, participants commit to saving now from future pay raises, and once the employee is enrolled, they need to opt out to leave the program. This program takes advantage of using the present time to decide the future, therefore taking advantage of present bias, where the participant does not feel like they lose or sacrifice anything in the present time (Benartzi, 2017). Also, the participant never has a base pay that decreases because their take-home salary does not decrease, therefore taking advantage of the fact that the employee does not feel a loss of money and avoids the feeling of loss aversion. Third, the program takes advantage of inertia, because the participant has to actively opt out if they no longer want to participate. The SMarT program has helped 15 million Americans save for retirement. Because it was so successful, it became a part of the Pension Protection Act of 2006 (Benartzi, 2017). This program demonstrated the successful utilization of behavioral economics to increase savings for retirement readiness.

Conclusion

Although many Americans have access to employer-sponsored retirement plans, there remains a large gap in participation due to economic and psychological barriers. However, access alone is not enough. Behavior plays an important role in financial planning. Behavioral economics can not only provide insight as to why people do not save for retirement, but the field can also help increase participation rates. Behavioral strategies that utilize choice architecture and inertia have proven valuable in raising participation rates through automatic enrollment and simplifying plan options. Ultimately, financial well-being in retirement can be significantly impacted by designing plans utilizing behavioral economics that align with human behavior.

Acknowledgements

Andrew Patel would like to thank Dr. Ethan Pew, Clinical Assistant Professor of Marketing at The University of Texas at Austin McCombs School of Business, for mentoring him throughout the process of writing this paper.

References

Benartzi, S. (2017). Save More Tomorrow. Save More Tomorrow. Retrieved August 3, 2025, from http://www.shlomobenartzi.com/save-more-tomorrow

Bernard, T. S., & Russell, K. (2019, October 3). The Middle-Class Crunch: A Look at 4 Family Budgets. New York Times. https://www.nytimes.com/interactive/2019/10/03/your-money/middle-class-income.html

Bryan, Sylvia, L., Myers, Elizabeth, A., Topeleski, & John, J. (2024, September 18). Worker Participation in Employer-Sponsored Pensions: Data in Brief and Recent Trends. Congress. Retrieved August 2, 2025, from https://www.congress.gov/crs-product/R43439

DeJohn, J. (2024, March 19). Salary Needed to Live Comfortably ? 2024 Study (A. Conde, Ed.). SmartAsset.Retrieved August 2, 2025, from https://smartasset.com/data-studies/salary-needed-live-comfortably-2024

The Early Retiree’s Guide to Funding Retirement Accounts. (2025, May 2). FinancialSamurai. Retrieved August 2, 2025, from https://www.financialsamurai.com/the-early-retirees-guide-to-funding-retirement-accounts/

Embrace Living Paycheck-To-Paycheck To One Day Be Free. (2025, May 28). FinancialSamurai. Retrieved August 2, 2025, from https://www.financialsamurai.com/embrace-living-paycheck-to-paycheck-then-get-out

Gappa, S. (2024, June 18). 401(k) Account Access Statistics in 2023 (R. Hartill, Ed.). Capitalize. Retrieved August 1, 2025, from https://www.hicapitalize.com/resources/401k-account-access-statistics

Haverstic, C. (2025, May 14). 57% of Americans Live Paycheck to Paycheck in 2025 (C. Hill, Ed.). MarketWatch. Retrieved August 2, 2025, from https://www.marketwatch.com/financial-guides/banking/paycheck-to-paycheck-statistics

Hreha, J. (2023). What is Inertia In Behavioral Economics? The Behavioral Scientist. Retrieved August 3, 2025, from https://www.thebehavioralscientist.com/glossary/inertia

Madrian, B., & Shea, D. (2001). THE POWER OF SUGGESTION: INERTIA IN 401(k) PARTICIPATION AND SAVINGS BEHAVIOR. Quarterly Journal of Economics, CXVI(4), 1149-1187.

Report on the Economic Well-Being of U.S. Households in 2023. (2024, May). Federal Reserve. Retrieved August 3, 2025, from https://www.federalreserve.gov/publications/2024-economic-well-being-of-us-household s-in-2023-expenses.htm?utm_source=chatgpt.com

Retirement Planning Tips for Every Age: 20s, 30s, 40s, 50s, and 60s. (2024, October 21). The Entrust Group. Retrieved August 10, 2025, from https://www.theentrustgroup.com/blog/retirement-planning-tips-every-age

Samson, A. (n.d.). An Introduction to Behavioral Economics. Behavioral Economics. Retrieved August 2, 2025, from https://www.behavioraleconomics.com/resources/introduction-behavioral-economics/

Sethi-Iyengar, S., Huberman, G., & Jiang, W. (2003, January 1). How Much Choice is Too Much?: Contributions to 401(k) Retirement Plans.

The Shockingly Simple Math Behind Early Retirement. (2012, January 13). Mr. Money Mustache. Retrieved August 2, 2025, from https://www.mrmoneymustache.com/2012/01/13/the-shockingly-simple-math-behind-early-retirement/

Soman, D., & Choe, Y . (2023). Research handbook on nudges and society (L. A. Reisch & C. R. Sunstein, Eds.). Edward Elgar. Chapter 8: Behavioural interventions to improve financial wellbeing: a focus on budgeting

Srikant, K. (2025, February 26). Fact Check: Is there a consensus that a majority of Americans are living paycheck to paycheck? Econofact. Retrieved August 2, 2025, from https://econofact.org/factbrief/is-there-a-consensus-that-a-majority-of-americans-are-livin g-paycheck-to-paycheck

Thaler, R. H., & Sunstein, C. R. (2009). Nudge : improving decisions about health, wealth, and happiness (Revised and expanded edition ed.). Penguin Books.

When can I retire? (n.d.). Networthify. Retrieved August 3, 2025, fromhttps://networthify.com/calculator/earlyretirement?income=70000&initialBalance=0&expenses=52500&annualPct=5&withdrawalRate=4

Rhee, N. (2013, June). The Retirement Savings Crisis: Is It Worse Than We Think? National Institute on Retirement Security. Retrieved August 14, 2025, from https://www.nirsonline.org/reports/the-retirement-savings-crisis-is-it-worse-than-we-think/

About the author

Andrew Patel

Andrew is a sophomore at St. Stephen’s Episcopal School in Austin, Texas. He enjoys exploring topics in business, finance, and investment strategies. He has participated in leadership roles in his school’s business and investment clubs. He hopes to promote financial literacy to help others make informed decisions about money management and achieve financial goals.

The post Behavioral Economics and the Path to Retirement Readiness Through 401(k) Participation appeared first on Exploratio Journal.