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Race and the Economics Professoriate in the United States  

Gregory N. Price

In 1894, W. E. B. Dubois completed coursework for a doctorate in economics at the University of Berlin, and in 1921, Sadie Alexander was the first Black American to earn a doctorate in economics at the University of Pennsylvania. Notwithstanding these rare early accomplishments by Black Americans in economics, there seems to be a more than one century “color line” in the hiring of Black economists in the United States academic labor market. The persistence of Black economist underrepresentation in economics faculties in the United States suggests that a color line constraining the hiring of Black economics faculty endures. In general, and in particular among economics doctorate–granting institutions in the United States, when sorting them by the number of Black Americans on the economics faculty, the median economics department has no Black economics faculty. Findings from the extant literature on the hiring and representation of Black economists suggest that the underrepresentation of Black PhD economists in economics faculties is consistent with, and conforms to, a history of racially discriminatory employment exclusion. This color line could be constraining the production of economics knowledge that can inform public policies that would reduce racial inequality and improve the material living standards of Black Americans in the United States. Future research on the underrepresentation of Black PhD economists in economics faculties in the United States could potentially benefit from accounting for unobservables that may matter for the supply and demand of Black PhD economists. This includes, but is not limited to, what is not observed about individual PhD economist mentoring experiences and parental occupational backgrounds.


Racial Inequality Across Income Volatility and Employment  

Michael Carr and Bradley L. Hardy

Volatility is an under-explored facet of economic insecurity, and it further helps to characterize otherwise omitted nuance in the economic situation facing many socioeconomically disadvantaged groups. Defined as a measure of short-run intragenerational mobility, standard measures of volatility leverage panel data in order to estimate higher moments of the growth rate of earnings or income, most often as variance transformations. Broadly, volatility can arise from one of two sources: instability in earnings among the continuously employed due to variable hours, hourly earnings, or salary changes; and/or instability in employment. The current literature shows that while both sources play an important role in the level of volatility for both men and women, trends are similar whether or not employment instability is accounted for, with overall declines in volatility for women and a largely flat trend for men over the last 40 years. The overall flat trend in volatility for men does seem at odds with other evidence that shows falling labor force participation for working-age men, and for Black men in particular. The link between these two processes—earnings changes over short periods of time and weekly or monthly snapshots of employment and labor force participation—remains largely absent from the literature because the most commonly used panel data sets are unable to capture within-year fluctuations in employment instability. Whether declining labor force participation for men increases or decreases volatility depends on whether there is a bifurcation in employment where some men are consistently employed over longer time horizons and some are not employed at all, or if declines in labor force participation at a point in time reflect increasing instability in employment over time. If the latter is true, then volatility could increase and could result in notably different trends in volatility over time by both race and gender.


The Macroeconomics of Stratification  

Stephanie Seguino

Stratification economics, which has emerged as a new subfield of research on inequality, is distinguished by a system-level analysis. It explores the role of power in influencing the processes and institutions that produce hierarchical economic and social orderings based on ascriptive characteristics. Macroeconomic factors play a role in buttressing stratification, especially by race and gender. Among the macroeconomic policy levers that produce and perpetuate intergroup inequality are monetary policy, fiscal expenditures, exchange rate policy, industrial policy, and trade, investment, and financial policies. These policies interact with a stratification “infrastructure,” comprised of racial and gender ideologies, norms, and stereotypes that are internalized at the individual level and act as a “stealth” factor in reproducing hierarchies. In stratified societies, racial and gender norms and stereotypes act to justify various forms of exclusion from prized economic assets such as good jobs. For example, gendered and racial stereotypes contribute to job segregation, with subordinated groups largely sequestered in the secondary labor market where wages are low and jobs are insecure. The net effect is that subordinated groups serve as shock absorbers that insulate members of the dominant group from the impact of negative macroeconomic phenomena such as unemployment and economic volatility. Further, racial and gender inequality have economy-wide effects, and play a role in determining the rate of economic growth and overall performance of an economy. The impact of intergroup inequality on macro-level outcomes depends on a country’s economic structure. While under some conditions, intergroup inequality acts as a stimulus to economic growth, under other conditions, it undermines societal well-being. Countries are not locked into a path whereby inequality has a positive or negative effect on growth. Rather, through their policy decisions, countries can choose the low road (stratification) or the high road (intergroup inequality). Thus, even if intergroup inequality has been a stimulus to growth in the past, it is possible to choose an equity-led growth path.


Economic Penalties Based on Neighborhood, and Wealth Building  

Rowena Gray and Raymond Kim

Building wealth over lifetimes became possible for a broader span of the population in developed countries over the 20th century compared to any time in history. This was driven by more people having the capacity to save because of the expansion of middle-class jobs and education, access to highly developed financial markets, and government support for real estate investment. Housing wealth remains the dominant wealth-building vehicle for those outside the top decile of the income distribution. This, coupled with the high and growing level of residential segregation and local allocation of public goods in countries such as the United States, drives the unequal ability of individuals to build wealth depending on neighborhood of origin and residence. Segregated neighborhoods are drawn along racial and class lines, and while much progress has been made, historical and structural factors such as the legacy of slavery have contributed to the difficulty of fully closing the Black–White wealth gap. More generally, children who grow up in lower-status areas are significantly less likely to rise up the wealth and status ladder, and this is driven by a variety of disadvantages in those neighborhoods. These include higher levels of pollution; worse public services, especially education; and fewer prospects for jobs and training. Some of these can be changed by moving individuals and families to better neighborhoods, while the effects of a polluted environment on the development of 0- to 5-year-olds have long-lasting and often irreversible consequences. These factors have kept the “American Dream” of equality of opportunity and the ability to save and build wealth as individuals and households out of reach for significant portions of society. There is renewed interest in infrastructure investments and place-based policies to address this opportunity gap, which, due to its scale, is beginning to be recognized as having negative implications for the aggregate economy. Economists should maintain their focus on these important questions and continue to improve data sets as the range of assets in which people can build and store wealth grows.