The Economics of Clothing

Global revenue of the apparel market

After a temporary contraction in 2020 due to the pandemic, the market regained momentum, reaching $1.79 trillion in 2024. This rebound is fueled by multiple structural and behavioral shifts, including the rapid expansion of e-commerce, increased consumer spending on fashion in emerging economies, and a strong push toward digital retail transformation.

Key growth drivers include the rise of fast fashion and athleisure segments, both of which continue to attract younger, style-conscious demographics. In addition, the demand for personalized and sustainable fashion is rising, prompting brands to invest in eco-friendly materials and circular business models. Technological innovations such as virtual fitting rooms, AI-powered styling tools, and streamlined supply chains have also enabled apparel companies to meet evolving consumer expectations more efficiently, supporting long-term revenue growth across global markets.

Navigating Inflation: How the Apparel Industry Is Responding to Rising Costs and Shifting Consumer Priorities

Inflation has emerged as a significant challenge for the apparel industry, influencing both consumer behavior and operational strategies. While the broader economy grapples with rising prices, the apparel sector faces unique pressures due to its global supply chains, seasonal demand patterns, and price-sensitive customer base.​

In the U.S., clothing inflation has diverged significantly between producer and consumer levels. Since the 1980s, the Producer Price Index (PPI) for clothing has surged, particularly after 2020, reflecting rising costs in raw materials, labor, and supply chain logistics. In contrast, the Consumer Price Index (CPI) for apparel has remained relatively flat, highlighting intense pricing pressure at the retail level. This gap does not necessarily suggests that brands and retailers have absorbed much of the cost increases to remain competitive in a price-sensitive market, since the gap starts to mark a considerable difference in the 2000, which coincides with the enter of China and other APAC countries (textile low costs producers) in world`s economy and trade (More).

Historically, apparel prices have remained relatively stable, often lagging behind general inflation trends. However, recent data indicates a shift, with clothing prices experiencing notable increases. This change is attributed to factors such as rising raw material costs, increased labor expenses, and supply chain disruptions. For instance, cotton prices surged by 30% between January and May 2022, significantly impacting manufacturing costs. 

In response, apparel companies are adopting multifaceted strategies to mitigate inflationary effects. McKinsey, in its ADAPT framework, outlines five key approaches: 

1. Adjusting discounts and promotions.

2. Developing the art and science of price change.

3. Accelerating decision-making

4. Planning for different scenarios.

5. Tracking performance metrics. 

By refining pricing strategies and optimizing promotions, retailers aim to preserve margins without alienating cost-conscious consumers.​

Moreover, companies are reevaluating their supply chains to enhance resilience. This includes diversifying sourcing locations, investing in digital tools for better demand forecasting, and exploring nearshoring options to reduce transportation costs and lead times. Such measures not only address immediate inflationary pressures but also position firms for long-term agility in a volatile market.​

Consumer behavior is also evolving in this context. With reduced purchasing power, shoppers are becoming more selective, prioritizing value and quality. This shift necessitates that brands emphasize their value propositions, whether through sustainable practices, ethical sourcing, or enhanced product durability.​

In summary, inflation is constantly rewriting strategies across the apparel industry, compelling stakeholders to innovate and adapt. By implementing strategic pricing, optimizing operations, and aligning with changing consumer preferences, companies can navigate these challenges and sustain growth in an inflationary environment.​

December: The Undisputed Champion of U.S. Clothing Retail Sales

U.S. clothing and accessory stores follow a clear seasonal rhythm—and December reigns supreme. Every year, this month delivers the highest sales, driven by the intensity of the holiday shopping season. Key events like Black Friday, Cyber Monday, and Super Saturday (often responsible for as much as 60% of some retailers’ holiday revenue) fuel a dramatic retail surge.

The dark blue trend line in the chart represents the long-term trend in clothing and accessory store sales, estimated using the Hodrick-Prescott filter—a mathematical tool used to separate cyclical components from the underlying trend in time series data.

The HP filter minimizes the sum of the squared deviations of the actual series from its trend component, penalizing rapid changes in the growth rate of the trend itself.

t=1Tyt- t2 + λ t=2T-1t+1- t- t- t-12

The HP filter solves the following optimization problem:

• Yt: observed value at time t

• τt: estimated trend

• λ: smoothing parameter (set to 14,400 for monthly data)

The first term ensures the trend stays close to the actual data, while the second penalizes excessive fluctuations in the trend. A higher λ results in a smoother trend.

In December 2024, sales at clothing and accessory stores rose by 4.07% month-over-month, according to Yahoo Finance, continuing the historical trend where December consistently outpaces all other months in retail performance.

Several powerful forces converge to make December the retail MVP:

• Holiday Gift-Giving: Clothing is a popular gift category, and consumers flock to stores to buy for friends and family.

• Major Discounts and Promotions: Deep holiday discounts drive urgency and volume.

• Longer Store Hours: Extended hours accommodate a larger wave of shoppers, increasing the potential for sales.

• Gift Card Boom: A crucial factor—25% of all annual gift card redemptions occur in December alone (The Gift Card Cafe). These gift cards often result in apparel purchases, not just in December, but stretching the momentum into January.

Despite headwinds like inflation and supply chain issues, the U.S. clothing retail industry has shown strong resilience. In 2024, it reached an estimated market value of $359 billion, with growth led by the non-luxury segment, emphasizing the demand for affordable fashion.

In summary, the cyclical nature of U.S. apparel sales centers around one pivotal month—December. Thanks to cultural habits, heavy promotions, and a surge in gift card activity, it continues to deliver unmatched results, firmly holding its place as the peak of retail activity each year.

Testing Clothing Sensibility to Independent Variables Moves

To better understand what drives clothing and footwear consumption in the US, we developed a high-precision regression model using 227 observations and key macroeconomic indicators.

The dependent variable is personal expenditure on clothing and footwear, while the independent variables include personal income per capita (X1), personal saving rate (X2), and consumer sentiment (X3). 

The model explains 99.18% of the variance in clothing expenditures (R² = 0.9918), highlighting its strong predictive power, through the Regression Function:

Ŷ= 74.48903+ 0.0063239*X1 ​- 5.835249*X2​ + 0.3780967*X3​ + ​

Key Insights

• Personal income per capita (X1): LINK

With a coefficient of 0.0063239, income per capita is a highly significant and positively correlated driver of clothing expenditure. This aligns with consumption theory—higher disposable income increases the ability to spend on non-essentials like apparel. The p-value (practically 0) confirms this is not a random relationship, making income a foundational variable in forecasting clothing market demand.

• Personal Saving Rate (X2): LINK

The saving rate carries a negative coefficient of -5.835249, which strongly supports the Marginal Propensity to Consume (MPC) framework. As consumers choose to save more, they reduce spending on discretionary items like clothing. This variable serves as a key counterbalance in the model—one that signals when cautious consumer sentiment may dampen retail sales.

• University of Michigan`s Consumer Sentiment (X3): LINK

With a positive coefficient of 0.3780967, the University of Michigan Consumer Sentiment Index shows that how consumers feel about the economy has a measurable impact on how much they spend on clothing. Although the magnitude is smaller than other variables, its significance (p = 0.00087) reflects its value as a leading indicator of short-term consumer behavior shifts.

Strategic Use of the Model for Forecasting and Market Entry: or investors and corporate decision-makers in the consumer space, this model provides a valuable, empirically validated framework to predict shifts in clothing market demand. Whether you're allocating capital, structuring M&A deals, or planning market entries, tracking these four variables can guide tactical and strategic decision-making.

Our Call to Action: We encourage B2C and D2C consumer investors—especially those in private equity, banking, and family offices, as well as business development teams at consumer companies, to use this model as part of your forecasting dashboards. By monitoring movements in income levels, apparel pricing, saving behavior, and sentiment indices, you can better time your investment theses, pricing strategies, and promotional cycles.

Let data lead your next consumer move.

Conclusion

The apparel industry stands at a pivotal crossroads—defined by digital transformation, inflationary headwinds, and shifting consumer behavior. As outlined throughout this report, the sector is navigating a landscape shaped by rising input costs, evolving expectations around sustainability and value, and a pronounced seasonal sales cycle led by December’s holiday surge. Yet, despite economic volatility and global supply chain challenges, both global and U.S. markets are proving resilient, with strong forward-looking revenue growth and consumer engagement.

Our analysis—ranging from cyclical sales behavior to price inflation divergence and econometric modeling—demonstrates that clothing consumption is highly sensitive to macroeconomic variables such as income, prices, saving behavior, and sentiment. These findings equip stakeholders with actionable insights into consumer spending behavior, allowing for more informed forecasting, investment allocation, and strategic planning.

For investors, M&A leaders, and corporate strategy teams, the implications are clear: leverage data, monitor key economic indicators, and prepare for agile responses. The tools and models shared in this report are designed to help you stay ahead of market shifts, identify high-conviction opportunities, and drive smarter decisions in the ever-evolving world of consumer apparel.