PurposeThis paper examines how oil supply shocks and monetary policy actions jointly affect wage inequality in the US, with a particular focus on the role of education. The study addresses three key questions: (1) how exogenous oil supply shocks influence wage inequality, (2) how exogenous monetary policy shocks shape income distribution, and (3) whether these effects differ within and between education groups, revealing the role of human capital in amplifying or mitigating inequality.Design/methodology/approachThe analysis uses quarterly US data from 2000 to 2021. Wage inequality is measured using the Theil index constructed from CPS/BLS weekly earnings of full-time workers aged 25 and above, allowing additive decomposition into within- and between-education components (high school, bachelor's and advanced degree). Identification relies on exogenous oil supply news shocks and exogenous monetary policy shocks. A vector autoregression (VAR) framework is estimated, and impulse response functions trace the dynamic effects of both shocks on inequality over a 10-quarter horizon.FindingsThe results show that overall wage inequality increased by about 15% over the sample period, with roughly 75% driven by within-education dispersion rather than differences across education levels. Oil supply shocks significantly raise wage inequality, increasing dispersion within high-school and advanced-degree groups and widening inequality between education groups. In contrast, contractionary monetary policy shocks compress wage inequality, with the strongest effects observed among advanced-degree earners and a reduction in between-education wage gaps.Research limitations/implicationsThis study focuses exclusively on the US due to data availability at a quarterly frequency, which limits the generalizability of the findings to other economies with different labor market institutions and energy dependence. Wage inequality is measured using CPS/BLS data for full-time workers, excluding self-employed and part-time workers, who may experience different distributional effects. The analysis is confined to education-based groupings and does not account for other dimensions of inequality, such as race, gender or industry. Finally, while exogenous shock measures are used, the VAR framework captures average dynamic responses and may not fully reflect nonlinearities or structural changes across different economic regimes.Practical implicationsThe findings show that macroeconomic policies have important distributional effects. Oil supply shocks significantly increase wage inequality, especially within high-school and advanced-degree groups, implying that energy price volatility can worsen income dispersion. Policies that reduce exposure to oil shocks-such as energy diversification and strategic reserves-may therefore also help limit inequality. Monetary policy, while aimed at stabilizing inflation and output, affects income distribution as well: contractionary shocks compress wage inequality, particularly among highly educated workers. Since most inequality arises within education groups, education alone is insufficient; complementary labor market and earnings-stabilization policies are needed to mitigate the unequal effects of macroeconomic shocks.Originality/valueThis study is among the first to jointly analyze oil supply shocks and monetary policy shocks using exogenous identification while decomposing wage inequality by education. By highlighting heterogeneous distributional responses across education groups, the paper provides new insights into how energy shocks and stabilization policies interact with human capital to shape income inequality.

We hypothesize that the role of education in driving wage inequality is not the same across three racial groups in the United States. Using the Current Population Survey (CPS) data for the period 2000-2021, we show that education weighs at most $32\%$ in explaining wage inequality. Further, we observe a decrease in the role of education in explaining wage inequality among Black and White Americans.

PurposeThe primary focus of this study is to examine the distributional consequences of the widespread increase in prices. The fundamental question the study aims to address is whether the dynamics of income distribution due to higher inflation differ in the short term compared to the long run.Design/methodology/approachThe authors estimated a panel-data model (fixed effects) using inequality and inflation data available at a high frequency, i.e. on a quarterly basis for over 30 years, and found evidence that inflation causes rapid swings in income distribution.Findings The authors' contribution to the literature lies in providing evidence that inflation rapidly causes swings in income distribution, even after controlling for the state of the economy. The authors also demonstrate that the magnitude and direction of the effect of inflation on income inequality depend on whether the initial inflation rate is below or above the Federal Reserve's target of 2%.Originality/valueTo the best of the authors' knowledge, the authors are the first to emphasize that the targets set by central banks can drive the strength and direction of the relationship between inflation and income inequality.

Purpose Existing empirical evidence suggests that episodes of financial stress (crises) can act as driver of growth of inequality. Consequently, in this study, the authors explore the time-varying predictive power of an index of financial stress for growth in income (and consumption) inequality in the UK. The authors focus on the UK since income (and consumption) inequality data are available at a high frequency, i.e. on a quarterly basis for over 40 years (June, 1975 to March, 2016). Design/methodology/approach The authors use Wang and Rossi's approach to analyze the time-varying impact of financial stress on inequality. Hence, the method provides a more appropriate inference of the effect rather than a constant parameter Granger causality method. Besides, understandably, the time-varying approach helps to depict the time-variation in the strength of predictability of financial stress on inequality. Findings This study’s findings point that financial distress correspond to subsequent increases in inequality, with the index of financial stress containing important information in predicting growth in income inequality for both in and out-of-sample periods. Interestingly, the strength of the in-sample predictive power is high post the period of the global financial crisis, as was observed in the early part of the sample. The authors believe these findings highlight an important role of financial stress for inequality – an area of investigation that has in general remained untouched. Originality/value Accurate prediction of inequality at a higher frequency should be more relevant to policymakers in designing appropriate policies to circumvent the wide-ranging negative impacts of inequality, compared to when predictions are only available at the lower annual frequency.

This paper studies the secular increase in US income inequality and its relation to growing house prices over the past three decades. We explore income inequality’s effect on house prices based on a high-frequency (quarterly) data-set for all US states, including the District of Columbia. The analysis shows that higher income inequality decreases the growth rate of house prices. However, the relationship differs for the Northeast region. We find higher income inequality corresponds with higher house prices across the states within the Northeast region.

Given the complexity of interaction between inflation and inequality, we examine whether the impact of inflation on inequality differs among distinct levels of income inequality across the US states. Results reveal that there is a negative contemporaneous effect of inflation on the inequality which becomes stronger with higher levels of income inequality. However, over a one year period, we find higher inflation rate to further increase income inequality only when income inequality is initially relatively low.

This paper aims to clarify the relationship between monetary policy shocks and wage inequality. We emphasize the relevance of within and between wage group inequalities in explaining total wage inequality in the United States. Relying on the quarterly data for the period 2000-2020, our analysis shows that racial disparities explain 12% of observed total wage inequality. Subsequently, we examine the role of monetary policy in wage inequality. We do not find compelling evidence that shows that monetary policy plays a role in exacerbating the racial wage gap. However, there is evidence that accommodative monetary policy plays a role in magnifying between group wage inequalities but the impact occurs after 2008.

Using weekly earnings data from Current Population Survey for Black and White Americans employed full time, we examine how disparities based on race stand relative to disparities between wage groups. We find that wage disparity between wage groups prevails significantly as compared to race wage gap on explaining overall wage inequality among Blacks and Whites in the United States.

A common tool in forecasting literature used in predicting future economic conditions is the term spread, which tends to contract near peaks and rise near troughs. Building on this known relationship, this paper explores the predictive power of the yield spread on the distribution of income in the United Kingdom (UK). The results reveal that income inequality responds negatively to increases in the yield spread over the medium-term. Specifically, we show that the term spread can help to predict UK's income inequality growth both in- and out-of-sample. Our empirical findings show that it is the expected component of the term spread that has predictive power for lower income inequality in the UK.

The resource curse is sometimes associated with poor resource-rich countries. However, using panel evidence from the United States, we find that the resource curse is also prevalent in a wealthy resource-rich country. This study investigates the impact of oil resources on income inequality, with a particular focus on distinguishing between the effects from oil abundance (i.e. production) versus oil dependency (i.e. consumption). We observe contrasting non-monotonic outcomes from oil abundance in comparison to oil dependency. For oil abundance, states with low oil production will have less inequality if they increase oil production, and states with high oil production will have increased income inequality if they increase production. The opposite holds true for oil dependency. The findings suggest several channels of concern. For example, oil-rich states are more vulnerable to rent-seeking behaviour as oil production and oil revenues increase, which can adversely affect the income distribution gap. On the other hand, oil-dependent states are more likely to be affected by commodity price shocks which can increase income inequality.

In this paper, we analyze time-varying predictability of financial stress due to growth in income inequality of the United States (US) over the annual period of 1913 to 2016. In order to ensure that we remove the asset price effects on income inequality, and provide incorrect inferences regarding the impact on financial stress, we work with capital-gains excluded six alternative measures of top shares of pretax income and wages. We find that the top 10%, the top 10% to 5%, and the top 5% to 1% inequality growth rates tend to predict financial stress relatively better than the corresponding inequality growth rates associated with the top 1%, top 0.1%, and the top 0.01% of the income distribution. Moreover, all the six metrics of inequality growth is capable of predicting the heightened financial stress observed during the onset of the Great Depression and the same associated with the recent global financial crisis. Finally, our in-sample evidence of predictability tends to carry over to an out-of-sample forecasting exercise under four out of the six measures of inequality considered, and in particular for the broader measures of inequality – a result consistent with our in-sample analysis.

The evolution of wealth inequality over the long run depends on income growth, inflation, and interest rates. In this paper, we examine, in a dynamic setting, the effect of these three macroeconomic variables on wealth inequality in the United States over the periods 1929–2009 and 1962–2009. The results show that these macroeconomic factors explain a significant amount of the changes in wealth inequality. The results indicate that increases in inflation and income growth contribute positively to net wealth shares of adults in the bottom 50% and middle 40% of the wealth distribution, leading to decreases in overall wealth inequality. Interestingly, the results show increases in interest rates contribute to lower wealth inequality in the U.S. although this result does not hold across all the inequality measures.

The United Kingdom (UK) in terms of income inequality is ranked among the highest in Europe. Likewise, within the last four decades, UK is characterized with drastic increases in household debt. In this paper, we analyze time-varying predictability of growth in household debt for growth in income (and consumption) inequality based on a high-frequency (quarterly) data set over 1975:Q2 to 2016:Q1. Results indicate that the growth in household debt has a strong predictive power, both for within and out-of-samples, on growth rate of income (and consumption) inequality in the UK. Interestingly, the strength of the predictive power is found to have increased after 2008. Based on time-varying impulse response functions, we also find that higher growth rate in household debt corresponds with subsequent increases in income inequality.

Using industry-level data for all U.S. states obtained from the U.S. Bureau of Economic Analysis, this paper examines the impact that unconventional monetary policy has had on six U.S. industries. The results indicate that the impact varies across the industries analysed. The results show that total wages and salaries increased the most in the finance sector. However, the strongest impact on total real output was in the wholesale sector.

Using Piketty and Zucman’s (Q J Econ 129(3):1255-1310, 2014) recently published capital share data, this paper uses structural VARs to understand the relationship between long-term interest rates, capital shares, and the distribution of income in the United States. The results indicate that increases in capital shares increase income inequality. Moreover, the relationship between the interest rate and capital shares is found to be negative and statistically significant. The results suggest that low long-term rates, through an equity and business investment channel, further increase the unequal distribution of income in the U.S. The results further illuminate the channels through which monetary policy can potentially affect the distribution of income.

Using quarterly real GDP data from 2005 to 2019 for all U. S. states from the Bureau of Economic Analysis, we construct an economic inequality measure which is additively decomposable into within and between-region inequality. We find increases in economic disparity in terms of total real GDP across the states. The results show that states belonging to the South and West regions are growing apart, contributing significantly toward the level of total economic disparity in the country. However, in terms of per-capita real GDP, economic disparity across states is much smaller. The results emphasize the role of population dynamics in mitigating economic disparity across U. S. states.

In this paper we empirically investigate how the evolution of the three Fisher Variables (income growth, interest rates, and the price level) have driven income inequality across a variety of countries, with particular focus on Brazil, Russia, India, China, and South Africa (known as the BRICS economies), during the period 2001 to 2015. The results suggest that increases in inflation and real income growth contribute to increases in income inequality. We find some evidence that increases in real interest rates correspond with higher income inequality. The results also reveal that the relationship between the three Fisher Variables and income inequality for the BRICS economies is stronger compared to the full sample. Interestingly, for these five economies, the relationship between real interest rates and income inequality is negative.

Due to the Great Recession, the Federal Reserve engaged in unconventional monetary policy (QE) to fight the effects of the economic downturn. Literature asserts that QE did have impacts on economic growth and helped alleviate the effects of the recession. Recently, critics have asserted that the benefits of QE may not have been equally distributed across households. In this paper, we build a state-level dataset to investigate the dynamics of QE measures and median income across the U.S states. The findings indicate that, for the period 2008 to 2014, there is statistical evidence that increases in the Federal Reserve's balance sheet correspond with higher nominal median income. However, once we adjust for inflation, the results become statistically insignificant and the impact of QE on median income becomes almost zero.

In this paper we investigate how the evolution of income growth, real interest rates, and inflation have driven income inequality across a variety of countries with particular focus on the BRICS economies (Brazil, Russia, India, China, and South Africa) during the period 2001 to 2015. Our work suggests that, when central banks of the BRICS economies use monetary policy for macroeconomic stabilization, they need to consider the impact monetary policy changes have on the distribution of income in their nations. Our estimates reveal that the unintended consequence of policies that induce economic growth and higher prices is higher income inequality. We find that the positive relationship between the three macroeconomic variables and income inequality for the BRICS economies is stronger during the post-2008 period.

Understanding within and between group inequality is fundamental in understanding the evolution of income inequality in any country. Using a century of data for France, and the Theil measure of income inequality, which is decomposable, we show that income inequality within the bottom 90\% accounts for over sixty percent of overall income inequality today. These findings indicate that income within the middle class has gotten substantially more unequal in France. We also document distributional heterogeneity of interest rate changes. We find that higher interest rate contributes to higher within group’s income inequalities and lower between group’s income inequalities.