Labor-Income Growth And Equity Returns: A Human Capital–Augmented Four-Factor Asset Pricing Model For Punjab, Pakistan

Abstract

This study develops and empirically evaluates a human capital–augmented four-factor asset-pricing model for non-financial firms associated with Punjab, Pakistan. Traditional models such as the Capital Asset Pricing Model (CAPM) and the Fama–French three-factor model (FF3) have been widely applied in emerging markets; however, they typically overlook the role of human capital, a critical driver of firm productivity and long-term value. By incorporating labor-income growth as a proxy for human capital, this study extends the conventional asset-pricing framework and investigates whether human capital risk plays a significant role in explaining return variations within Punjab’s corporate sector. Using monthly data for 164 non-financial PSX-listed firms from July 2010 to June 2020, the study constructs eight portfolios categorized by firm size, book-to-market ratio, and labor-income growth. The Fama–MacBeth two-pass regression methodology is employed to estimate time-series factor sensitivities and cross-sectional risk premiums. The empirical findings show that size, value, and human capital factors significantly explain time-series variation in excess portfolio returns. Small firms, high book-to-market firms, and firms with low labor-income growth consistently generate higher average returns, indicating the existence of size, value, and human capital premiums within Punjab’s market environment. However, the cross-sectional analysis reveals that factor betas do not reliably predict future returns, suggesting that historical risk exposures do not necessarily translate into future performance—an outcome consistent with the Efficient Market Hypothesis. These results highlight the importance of incorporating human capital into asset-pricing models, particularly in labor-intensive regional economies. The study contributes to the literature by demonstrating that human capital is a meaningful and priced source of risk in Punjab and underscores the need for broader, regionally contextualized asset-pricing frameworks.