Government Responses indicates that an increase in government transfers leads to higher unemployment rates for both men and women. Female income inequality, however, decreases when government transfers rise. When the female-to-male income ratio increases, we observe a subsequent decline in Government Responses transfer expenditures over the forecast period. Variance decomposition shows that male unemployment has a greater influence on the growth of government transfers than female unemployment. These findings suggest that implementing gender-targeted government transfers could help reduce income inequality, which, in turn, may lead to a decrease in government transfer spending over time.

Over the past four decades, income inequality in the United States has been rising, with wealth concentrating among the richest individuals. Data from the World Inequality Database shows that the top 10% of earners saw their share of total income grow from 34% in 1970 to 46% in 2020. Such levels of disparity have not been observed since the period preceding the Great Depression (Saez, 2019). According to the Congressional Budget Office (2022), from 1979 to 2019, the average income of households in the highest quintile more than doubled before taxes and transfers, with growth concentrated among the very top earners within this group. In contrast, Government Responses income for low- and middle-income Americans has stagnated since the 1980s and, after adjusting for inflation, has declined since 2000 (Bor et al., 2017). The persistent rise in inequality since 1980 has raised concerns among the public, scholars, policymakers, and politicians alike. Income distribution in the U.S. remains heavily skewed toward the top, Government Responses even within the wealthiest 20% of households. Post-tax, the average income of the top 1% reaches $1.4 million, nearly five times higher than that of the next 4% (Congressional Budget Office, 2022).

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Factors often cited as driving this widening inequality include education, skills, occupations, international trade, unions, assortative marriages, Government Responses and technological advancement. While these elements are acknowledged in this research, the study examines income inequality from a different perspective. Government objectives include maximizing employment and reducing inequality, with stabilization policies playing a key role. One such policy variable is Government Responses transfers, which can fluctuate with the business cycle. Previous research has explored both the effects of transfer payments on income inequality and the connections between inequality and unemployment. From a macroeconomic standpoint, unemployment, government transfer growth, and income dispersion may interact contemporaneously or over time, shaping the redistributive effects of welfare programs. Introducing a gender-based perspective into this analysis may reveal additional differences in these dynamics.

This study aims to shed light on the interconnections among income inequality, unemployment rates, and Government Responses transfer growth, with a particular focus on gender differences. We analyze two measures of income inequality for men and women alongside gender-specific unemployment rates and transfer growth. Employing a time series approach using a structural vector autoregression (SVAR) framework, this study offers a novel perspective on these dynamics through the lens of gender.

Our analysis covers annual data from 1962 to 2019, including income ratios (top 10% versus bottom 50%), unemployment rates by gender, and government transfer growth. Results from the SVAR model highlight several important findings. Impulse response analysis indicates that shocks to government transfers increase unemployment rates for both men and women, aligning with previous studies suggesting that transfers may reduce labor market participation (Ahmed, 2022). Variance decomposition shows that male unemployment has a stronger effect on government transfer growth than female unemployment. Shocks to male unemployment temporarily raise income inequality, while shocks to female unemployment tend to lower it. Meanwhile, shocks to the female income ratio reduce inequality for both genders, possibly reflecting assortative marriage effects (Greenwood et al., 2014). Additionally, government transfers decrease income inequality among women, with the female income ratio initially reducing transfer growth but contributing to an increase over time. Variations in government transfer growth are largely explained by female income ratios and unemployment rates, highlighting gender-specific effects. These findings suggest that targeted transfers can reduce poverty and inequality among female-headed households, and higher unemployment drives increased transfers, implying that carefully designed policies could enhance social outcomes while potentially lowering future transfer expenditures.

In this section, we review the relevant literature. Existing research links the increase in income inequality to several key factors, including technological advancements and creative destruction, globalization and international trade, the weakening of labor unions, and demographic differences in the workforce such as education, experience, occupation, gender, and marital status. For instance, Lemieux (2006), Goldin and Katz (2007), and Autor (2014) highlight changes in the returns to education as a significant driver. Caines et al. (2017), Atkinson (1997), and Acemoglu and Autor (2011) emphasize the role of evolving skills, tasks, and technologies in shaping income inequality. Hoffmann et al. (2020), Acemoglu and Autor (2011), and Autor (2019) contribute to the extensive literature demonstrating that shifts in demand for different types of tasks have affected wage distribution over time. Fortin et al. (2021) argue that the erosion of labor market institutions, such as unions, has contributed to rising income inequality. Additionally, Esping-Andersen (2007), Schwartz (2010), and Greenwood et al. (2014) examine how assortative marriage patterns influence household income disparities. Most of these studies rely on micro-econometric methods for their analyses.

While there is broad agreement that income inequality has been rising in the United States, there is still debate among researchers and policymakers regarding the factors driving this trend and the most effective policy interventions. In theory, governments can mitigate rising inequality using welfare tools, such as taxes and transfer programs. However, in practice, the effectiveness of these policies in redistributing income depends on their size, composition, progressivity, and other economic conditions (see, for example, Betson and Haveman, 1984; Higgins and Lustig, 2016; Joumard et al., 2013). Governments worldwide implement a variety of cash and in-kind transfer programs under social safety net policies to support low-income or impoverished households. In the United States, such programs include Social Security, Medicare, Medicaid, the Affordable Care Act, Unemployment Insurance (UI), Supplemental Nutrition Assistance Program (SNAP), and other income support measures. While these programs benefit poorer households, eligibility requirements and coverage vary across states, and recipients are typically required to actively seek employment or maintain their job status.

Income inequality is significant because it affects economic growth and stability. Keynes (1936) argued that reducing inequality can stimulate economic growth and achieve full employment through appropriate policy interventions. Research underscores the importance of addressing inequality and highlights the role of welfare policies in income redistribution. Studies by Kumhof and Rencière (2010), Ostry (2015), and Stiglitz (2015) suggest that redistributive policies can help prevent severe economic crises. Empirical evidence from McCombie and Spreafico (2015) and Cynamon and Fazzari (2015) indicates that higher inequality can impede economic growth and employment in the United States. Arestis (2018) emphasizes the need for policies promoting more equitable income distribution through coordinated fiscal and monetary measures to boost economic activity. Ahn et al. (2018) demonstrate that income inequality and macroeconomic outcomes influence each other, affecting aggregate consumption and productivity shocks. Their findings show that inequality shapes consumption patterns, with low-income households responding more slowly to economic changes. Collectively, these studies underscore the macroeconomic relevance of income inequality and its implications for economic growth and redistribution.

Jäntti and Jenkins (2010) underscore the influence of economic policies and institutional frameworks on income inequality. They examine how factors such as unemployment, inflation, and economic growth shape the distribution of income. Similarly, Breen and García-Peñalosa (2005) show that higher levels of income inequality are associated with increased macroeconomic volatility, indicating a potential feedback mechanism between income distribution and economic instability. These studies underscore the wider macroeconomic consequences of income inequality and highlight the need for further investigation into the links between macroeconomic indicators, especially unemployment and inequality.

Aghion (2002), drawing on Schumpeterian Growth Theory, argues that while innovation and technological advances may initially widen wage disparities between groups, they can also foster upward mobility and reduce inequality over time, particularly when accompanied by investments in education and skill development. This perspective lays the groundwork for exploring income disparities, including those related to gender.

Despite these contributions, there remains limited understanding of the dynamic interplay among income inequality, unemployment, and government transfer programs, particularly from a gendered perspective. This study seeks to fill this gap by employing a time series methodology to examine these variables, with a focus on gender-specific differences using aggregate-level data. By applying SVAR analysis, the paper provides fresh insights into how government transfer policies influence unemployment and income inequality across genders. The results enrich the discussion on the labor market impacts of welfare policies and gender inequality, extending prior research and offering practical guidance for policymakers addressing income disparities.

This study investigates the interactions between measures of income inequality, gender-specific unemployment rates, and the growth of government transfers using annual U.S. data. Income inequality is assessed via the ratio of income shares between the top 10% and the bottom 50% of earners for both men and women, alongside unemployment rates and government transfer growth from 1962 to 2019. Unemployment and government transfer data are obtained from the Federal Reserve Bank of St. Louis’s FRED database, which provides monthly observations. Income inequality data are sourced from the World Inequality Database (WID), following the Distributional National Accounts methodology, but are only available on an annual basis. Therefore, our dataset for empirical analysis is constructed using annual observations.

For estimating our structural VAR model, we include five variables: income ratios for males and females, unemployment rates for each gender, and government transfer growth. We opt for income ratios rather than Gini coefficients due to their clearer interpretability. Government transfers are incorporated based on Ahmed (2022), who identifies a shared long-term trend with labor force participation. To the best of our knowledge, this is the first study to utilize this particular series.

We use impulse response functions and variance decompositions to explore the dynamics among these variables and derive meaningful insights [1]. The structural vector autoregression (SVAR) model provides a straightforward yet effective framework for generating impulse responses and variance decompositions, enabling an objective analysis of the dynamic relationships among the included variables. Our estimated SVAR consists of five variables: the top 10% to bottom 50% income ratios for males and females, male and female unemployment rates, and the growth rate of government transfers.

The use of structural vector autoregression models is widespread in macroeconometrics, particularly for analyzing aggregate time series data. Numerous studies have applied this approach, and for further discussion, one may refer to Breitung (2001). Our empirical strategy follows the methodology outlined in Breitung (2001), Johnston and Mas (2018), and Ahmed et al. (2022). As Sims (1980) highlights, the SVAR framework offers a systematic yet straightforward method for imposing restrictions, allowing researchers to uncover empirical regularities that may remain hidden using previously applied techniques. We employ the SVAR model to compute impulse responses and variance decompositions, thereby examining the dynamic interactions among the variables. The reduced-form vector autoregression model can be specified as follows: