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The impact of the coronavirus on African American unemployment: lessons from history

The impact of the coronavirus on African American unemployment: lessons from history In this article, our fundamental research question is to investigate the effect of the Coronavirus (named COVID-19) on the African American labor market. More specifically, we attempt to examine the potential economic impact of COVID-19 on the state of racial disparities among the African American labor market by examining two effects, namely, employment and income differentials, using national, state, and city level data (using data for all 77 neigh- borhood areas of the City of Chicago). Our central finding is that the labor market does not appear to treat black and white laborers as homogeneous, as attested by the finding that African American workers suffer from higher unem- ployment rates with higher volatility, lower median incomes, and they are more likely to work in the service sector, compared to their white counterparts, and we find this condition to be even larger in the City of Chicago. These find- ings have important policy implications. Keywords: Labor markets, Unemployment, Financial crisis JEL Classification: E2, E3, J4 impact is not likely to be equal across different racial 1 Introduction groups among U.S. workers who will experience these The coronavirus is a rapidly evolving health pandemic disruptions differently. The existing literature empha - that will have repercussions beyond individual health and sizes unemployment differentials (Hellerstein et al., 2008; the U.S. healthcare system. It has become clear that the Boustan and Margo, 2009; Bond and Lehmann, 2018; Yu outbreak of COVID-19 has disrupted the U.S. economy and Sun, 2019; Button and Walker, 2020; Couch et  al., in general, and its economic impact on the labor market 2020; Kim et al., 2021; Macartney et al., 2021; Mandel and is unprecedented and highly uncertain making it more Semyonov, 2021) as well as income differentials (Tangen - difficult for policymakers to formulate an appropriate tially, Ileanu, and Tanasoiu, 2008; Raymond, 2018; Abdul policy response. Over decades, we find no other infec - Khalid and Yang, 2021; Chantreuil et al., 2021; Contreras tious disease outbreak that had more than a tiny effect et  al., 2021; Ren, 2022) among different racial groups of on the U.S. labor market. It is notable that there will be a workers. significant household and macroeconomic impact as this In this article, we are motivated by such research on virus have generated large reductions in employment and racial disparities and our goal is to examine the effect of earnings in the U.S. labor market and thus triggering an the Coronavirus may have had on the state of racial dis- economic recession. parities among the African American labor community. This, however, is only a partial effect on the labor mar - To better understand the possible racial disparities, we ket. While the virus shock will affect household employ - attempt to quantify the potential economic impact of ment and income, we anticipate that the economic COVID-19 on the African American labor market by controlling for two effects, namely, employment differen - *Correspondence: ehabyamani@csu.edu tials and income differentials. Department of Accounting and Finance, Chicago State University, 9501 S. King Drive, Chicago, IL 60628, USA © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. 3 Page 2 of 18 E. Coupet , E. Yamani First, employment differentials effect. Under this analy - emerge from our unemployment differentials and income sis, we classify COVID-19 as an external shock (i.e., an differentials analyses. unplanned and unexpected event) that can have a sub- First, our unemployment differentials results show that stantial impact on the labor market. Using monthly and the level of national African American unemployment quarterly U.S. unemployment data defined by race, we is nearly twice that of the white unemployment over the analyze the short-run effect of an exogenous shock by entire full sample period. Furthermore, while the two testing for Granger causality using cointegration and examined recession episodes (i.e., the September 11 ter- error-correction models. For comparative measures, we rorist attack and the 2007–2008 recession) experienced predict the unemployment differentials effect by draw - exogenous shocks to the labor market and led to signifi - ing comparisons to the two most recent economic reces- cant increases in the unemployment rates in all sectors, sions: the terrorist attack on September 11, 2001  (9/11) the increase in unemployment rate in the white sector and the 2008 global financial crisis. The rationale for paled to that of the African American sector. examining these two historic recessions is to learn how Along the same lines, we also find that white unemploy - different racial groups might be impacted by exogenous ment Granger-causes African American unemployment, shocks in two different scenarios, and therefore, we can indicating a long-run association between white unem- predict how different racial groups might fare from a ployment and African American unemployment, in the recession that may follow the COVID-19 pandemic. sense that unemployment is first decreased in the white Second, income differentials effect. We extend our sector, followed by a lagged unemployment decrease in analysis further and present a comparative income dif- the African American labor market. This finding suggest ferential analysis across various racial groups in the City that most of the unemployment in the white sector are of Chicago. To this end, we use data for all 77 neighbor- of the structural and frictional forms, while the African hood areas of the City of Chicago, to better quantify the American unemployment is largely cyclical in nature. Put effect of the virus may have had on the black employment differently, the African American labor market appears to in the south and southeast sides of the city of Chicago, serve as a secondary labor market to the white sector that which are mostly populated by African Americans. We fills in during expansionary times but suffers great losses apply a traditional earnings function model to under- during economic downturns. stand the net effect of the COVID-19 on the South and Second, our income differentials analysis results show Southeast sides of the of City which are populated mostly that such observed racial disparities are even larger by African Americans. among the African American labor community in the Our main finding is that firms in the labor market City of Chicago. We find that African American workers appear to prefer white employees to African American in the Southeast and South sides of Chicago suffer from and Hispanics, suggesting that firms do not treat these higher unemployment rates with higher volatility, lower laborers from the two markets as homogeneous. This median incomes, and they are more likely to work in the conclusion is attested by several interesting findings that service sector, compared to their counterparts in other parts of the City. Our findings have important policy implications. While it is uncertain to know for sure what will be the It is important to analyze the impact of both recessions separately, because effect of this purely healthy-related exogenous shock to this gives us a more clear-cut explanation of how two crises with different rea - the economy, the effect of the COVID-19 virus is certain sons may have different employment impact on the same market. While both exogenous shocks to the economy have had deleterious effects on the unem - to be deep and broad for the African Americans who suf- ployment rates in general, their duration, and obviously causes, are differ - fer from higher unemployment rates and lower median ent. On one hand, the September 11, 2001 attack was political in nature and incomes. To alleviate this expected hardship, targeted unexpected by the population at large. Its aim was to place fear in the hearts of the American people, and the political reaction was swift as the Federal public policy should be introduced so that we must government moved to restructure the political system to ensure safety to the allocate funding and resources to where they are most American people. Although the memories may be everlasting, the economy needed, and policy recommendations must be reflective rebounded relatively quickly as the unemployment data suggest, given that the average unemployment rates took approximately 45  months to return to of this reality. A uniform policy approach will not address its pre-9/11 levels. This was in all account, a purely exogenous shock to an the varied needs of groups and communities. economy that was humming along. Hence, the uncertainty of coronavirus cri- Hence, we propose two targeted policy recommenda- sis is larger comparing to the September 11, 2001 recession that was caused by shorter analysis time. On the other hand, the 2007–2008 recession was an tions. First, we recommend stimulating private fixed endogenous event that began in the real estate market and manifested into the capital formation in African American communities by global economy. This relatively painful shock took 92 months for the economy providing guaranteed heavily subsidized loans to those to return to its pre-shock level of unemployment. In comparison to the 2008 market downturn, COVID-19 crisis led consumers and firms all around the investing in African American communities. Our second world to put off spending; they are in wait-and-see mode. The impact of the coronavirus on African American unemployment: lessons from history Page 3 of 18 3 recommendation is to enforce fair wages to ensure equi- unemployment in the African American. Kaplan (1999), table wages across the labor markets. There is an abun - for example, examines the number of job opportunities dance of evidence suggesting that the marginal product within very small neighborhoods, and finds that they of labor is not compensated equitably across various sec- do not vary much from neighborhood to neighborhood tors of the labor market. among white neighborhoods, but African American The remainder of the paper is organized as follows. communities fall short of their white counterpart. Section  2 reviews the literature. Section  3 outlines the econometric methodology which we employ. Our data 2.2 Income differentials are presented in Sect. 4. Sections 5 reports and discusses Our research is related to another strand of the literature our empirical results. Section 6 concludes. which examines income inequality among various racial groups (Tangentially, Ileanu, and Tanasoiu, 2008; Ray- 2 Literature review mond, 2018; Abdul Khalid and Yang, 2021; Chantreuil 2.1 Employment differentials et al., 2021; Contreras et al. 2021; Ren, 2022). Our work builds on the recent research on racial employ- Broadly presented, there are two strands of litera- ment differentials among different groups of workers ture that explain employment and income differentials defined by race. An incomplete list includes Hellerstein between African Americans and other sectors of the et  al. (2008), Boustan and Margo (2009), Bond and labor market, namely the white sector. The first strand Lehmann (2018), Yu and Sun (2019), Button and Walker of literature takes a micro approach and postulates that a (2020), Couch et al. (2020), Kim et al. (2021), Macartney laborer’s potential earnings are a function of investments et al. (2021), and Mandel and Semyonov (2021). in human capital (Becker, 1958; Mincer, 1958; Chiswick, Differences in unemployment rates between African 2003; Ileanu and Tanasoiu, 2008; Aali-Bujari et al., 2019). American and whites have been an ongoing discussion This body of literature evolved from the seminal works and research topic. Lynch and Hyclak (1984) analyze the of Becker (1962) and Mincer (1958) who contributed to disparities among various groups in the labor market, the study of labor economics by developing what is now and they find that the level of the natural rate of unem - known as the earnings function. Further, Aali-Bujari et al. ployment has changed over time with a rising labor force (2019) use Mincer’s (1958) earnings function to conclude participation among non-traditional groups in the labor that the level of education among Mexicans magnifies the market. Robinson (2010) explains differences in the levels increase in income levels and enlarges the human capital. of unemployment between Blacks and Whites from a cul- The second strand of the literature, very deep and tural perspective, in the sense that employers engage in broad in scope, takes a macro approach to analyze the employment discrimination based on tastes derived from income differentials between African Americans and “infotainment” to bias their hiring practices and con- other sectors of the labor market. Raymond (2018) finds tribute to the wage gap between the two groups. Mouw that race is the strongest predictor of persistent negative (2000) uses a fixed effects model to explain the increase equity in the southeast of the U.S., even after control- in unemployment gap between minority groups using the ling for factors relating to the 2008 crisis. Mouw (2000) spatial mismatch hypothesis. This theory hypothesizes analyzes unemployment rates in Chicago and Detroit by that both residential segregation and job decentralization targeting spatial employment opportunities and residen- adversely affect employment opportunities of minorities. tial housing. Using panel data and a fixed-effect model, Realizing that the unemployment gap is only one facet Mouw (2000) finds that decentralization of employment of the overall inequities that occur between racial com- and the loss of manufacturing jobs resulted in spatial dis- munities, researchers have incorporated many factors in tribution of employment in the two cities. attempt to explain overall inequities. Raymond (2018), Relatedly, Immergluck (1998) looks at proximity of job for example, utilizes simple regression models to control opportunities in urban areas to explain unemployment for various factors and find that race remains the strong - rates among urban dwellers, and he finds that race and est predictor of persistent negative equity in the south- educational attainment have the largest effects on unem - eastern U.S. Further, Nkomo and Ariss (2014) show that ployment rates. Further, Hoynes et al. (2012) find that the the historical origins of white privilege explain persis- net effect of the 2007–2008 recession on unemployment tence in the racial divide in organizations and the Ameri- was not homogeneous across the various sectors of the can labor market. Prior research has also focused on the labor market. Specifically, African Americans and His - lack of job opportunities in African American commu- panics suffered higher levels of unemployment during nities that contribute to increased levels of long-term this crisis. 3 Page 4 of 18 E. Coupet , E. Yamani AA 3 Methodology ¨ ˙ L 1 Y 3.1 Emplo yment differentials analysis L (1 − γ − α − β) Y Quantifying the Impact of COVID-19 on Labor Market: γ A Our goal is to examine the economic impact of COVID- (1 − γ − α − β) A 19 by drawing comparisons to the recent recessions. (6) α K L We consider the impact of Coronavirus on the African − − American labor markets nationally (as well as in the state (1 − γ − α − β) K L of Illinois) and compare it to the those during the two L o ˙ ˙ ˙ L L β H − − − most recent economic recessions: the terrorist attack on L L (1 − γ − α − β) H the U.S. on September 11, 2001  and the global financial crisis in 2008. As Eq. (6) indicates, except for output growth, the coef- Labor Model: We begin with a typical firm’s Cobb– ficients of all the right-hand-side variables are negative. Douglas production function with constant returns to Holding all other factors constant, an increase in output scale of a firm at any given time can be expressed as: causes an increase in the growth of African American employment. Because the level of employed labor is fixed γ α β 1−γ−α−β Y = A K H L (1) t t t t t any point in time, an increase in the employment rate of African Americans can only come from a reduction of where Y is each firm’s temporal output; A is the level of employment in the other sectors, holding output constant. multifactor productivity; H is the level of human capital The purpose of the labor market study is two-fold. First, embodied and L is the level of employment. Each factor we analyze the differences in unemployment rates among exhibits diminishing returns. That is: γ , α, andβare < 1. three sectors of the labor market: African Americans, Except for their racial makeup, workers are homogene- Whites, and Latin. In addition to differences in the levels of ous. The firm’s labor force is diverse and consists of a vec - unemployment among the three sectors of the labor mar- tor of races and nationalities: ket, we will test for differential effects on unemployment AA W L O L = L + L + L + L (2) rates resulting from exogenous shocks in the economy. To t t t t t accomplish this, we will decompose the time into three where AA , W , L , and O refer to the employment rates periods around two monumental crises in contemporary among African Americans, Whites, Latin, and others, American history. We will look at unemployment levels respectively. To analyze the production function’s short- surrounding the September 11 attacks terrorist act and run dynamics, we take logs and differentiate Eq. (1) w.r.t. the Great Financial Recession. We will test for changes in dY to time (for example, Y = ) . This yield: dt the mean unemployment rates before and after exogenous shocks from the two crises. ˙ ˙ ˙ ˙ ˙ Y A K L H = γ + α + (1 − γ − α − β) + β . (3) Unemployment Rate Levels Analysis: Let µ = aver- t−j,t Y A K L H age unemployment rate for the ith sector of the labor mar- Taking time derivatives of Eq.  (2) and dividing by L ket from time t-j to t; µ = average unemployment t t ,t+k yields: rate for the ith sector from the time of event, t, to time t + k, a later date; and µ is defined as logitu , where t−j,t ˙ ˙ ˙ ˙ AA W L O ˙ ˙ ˙ ˙ ˙ L L L L L logitu = ln(u/(1 − u)) given that unemployment rates are (4) = + + + . positive. If the fiscal and monetary stimuli work well to L L L L L restore the labor market sector equilibrium from an exog- Substituting Eq. (4) into Eq. (3) yields Eq. (5): i i enous shock, then µ �= µ . For example, suppose t−j,t t ,t+k the unemployment rate in a labor market is a%. As a result ˙ ˙ ˙ Y A K = γ + α + (1 − γ − α − β) of an exogenous shock, the unemployment rate rises above Y A K a% to b%. ˙ ˙ ˙ ˙ (5) AA W L O ˙ ˙ ˙ ˙ ˙ L L L L H If the government and central bank prescribe the exact + + + + β . L L L L H amount of intervention in the financial and capital mar - kets, the average unemployment rate will be restored to Rearranging Eq. (5) for the employment growth of Afri- a%. If workers are homogeneous, then the net effect on this can American employment leaves: sector should be the same for all other sectors of the labor The impact of the coronavirus on African American unemployment: lessons from history Page 5 of 18 3 i i o o exogenous variables that are not related to investments in market—that is, µ − µ = µ − µ . If t−j,t t ,t+k t−j,t t ,t+k human capital, as defined in Eq.  (1). A structural equa - the market values one sector of the market over the other tion that is typically used to estimate earnings in Eq.  (9) for any reason, then the differences in each unemployment is: level for the sectors will not converge. In this case, it may be i i o o 2 that µ − µ > µ − µ . t−j,t t ,t+k t−j,t t ,t+k α β F y = S H e (10) The dynamics of the labor market will be analyzed with a system of equations. Two non-stationary variables are where H refers to the number of years of experience and cointegrated of order 1, CI (1,1), if their levels are non- F is a vector of variables that are not related to human stationary and stationary in their first difference. If so, capital such as race, language, gender. Taking logs of we use the Johansen method to test for the rank of the Eq. (10), we get, system of equation to determine long-run relationships. lny = αlnS + βlnH + F (11) If there is a long-run relationship, then we use a Vector Error-Correction Model (VECM) to establish the long- Equation  (11), known as the earnings function, is used run and short-run causality between the variables. If the to estimate an individual’s post investment earnings. We system is cointegrated, we use an error correction model will estimate the coefficients of Eq. (11) for neighborhood of the form: area households in the City of Chicago with regression �µ = α + β ec + β �µ + β �µ + e Eq. (12) below: i,t 0 1 i,t−1 2 j,t−1 i,t i,t−1 (7) lny = α + βlnS + F + e (12) i 0 i i i where µ is the unemployment rate at time t of one race, i,t µ is the unemployment rate of another race at the same j,t Essential Workers Sector: The likelihood of working as time, ec is the error correction term from the previous i,t an essential worker in the City of Chicago, denoted as period, and e is the white noise error term in the cur- i,t Ess , is assumed to be a function of the educational level rent period. If the variables are not cointegrated, then we and other exogenous variables such as race, gender, and can establish a vector autoregression (VAR) model to test income, as follows for short-run causality Prob(Ess) = f (Schooling , income, X) (13) �µ = γ + β �µ + β �µ + e i,t 3 i,t−1 4 j,t−1 i,t (8) where we assume the following relationships ex ante: 2 2 This will be followed by the impulse response function, ∂(Prob(Ess)) ∂Schooling < 0; ∂ (Prob(Ess)) ∂Schooling > 0; establishing in the time domain the effect of an exog - ∂(Prob(Ess)) ∂Income < 0. < 0 . Essential service work- enous variable on the other variables. ers are deemed necessary functions for society. This includes emergency room healthcare providers in hos- 3.2 I ncome differentials analysis pitals, customer service representatives in retail outlets, The Earnings Function: In the second part of our analysis, and emergency service providers such as firefighters, we proceed with the development of the earnings func- police, etc. We assume that the likelihood of working in tion, followed by a labor market segment model. Mincer the service sector decreases with the number of years of (1958) and Ileanu (2008) model the earnings function of schooling. However, with increases in schooling beyond an individual using the stylized general function as: college, this likelihood increases. The nonlinearity incor - porates emergency room healthcare providers. We also y = h(S, x, F ) + ε (9) assume, a priori, the likelihood of being an essential ser- vice provider is a decreasing function of income—how- where y is net earnings; S is the years of schooling; and ever, in an increasing rate. x represents the years of experience; and F is a vector of Note that it is also expected that the dynamics within the labor market 4 Data may not be contemporaneous. If the shock is a negative, then unemployment We extract data from two databases: The Bureau of Labor will increase in the non-preferred sector of the labor market followed by an statistics (BLS) and the Environmental Systems Research increase in the preferred sector. Because negative exogenous shocks are typi- cally followed by fiscal and monetary policies, this will lead to an immediate Institute (ESRI) databases. We use BLS to collect reduction in the preferred sector of the labor market followed by a reduction monthly data on the national unemployment rates (as in the non-preferred sector. Therefore, exogenous negative shocks and subse - well as quarterly data for the state of Illinois), while we quent positive fiscal treatments affect both sectors in magnitude and speed of adjustments. Negative shocks begin with an increase in unemployment rates use ESRI data to collect household level market-related of the non-preferred leading to an increase in the unemployment rates of the information for all 77 neighborhood areas of the City preferred sector. Positive treatments affect the market in the opposite direc - of Chicago. Our entire annual sample period begins in tion. This is known as feedback effect between the two sectors of the labor market. 3 Page 6 of 18 E. Coupet , E. Yamani January 1989 and covers the period until February 2020. Table 1 Descriptive statistics on the monthly National U.S. Unemployment Rates Although we examine the unemployment rates over the full sample period spanning the period from January 1st, African White Latin Total 1989 to February 1st, 2020, we focus our analysis on the American periods before and after the terrorist attack on Septem- Panel A. Full Sample Period—Jan/1/1989 to 2/1/2020 ber 11, 2001 and the 2007–2008 global financial crisis, as N 374 374 374 374 the key events. For this, we examine two separate sub- Mean 10.61 5.11 7.92 5.81 periods around each crisis. These sub-periods are: (1) Median 10.50 4.70 7.50 5.40 the pre-9/11 crisis period covers the period from Janu- S.D 2.62 1.46 2.30 1.58 ary 1st, 1989 to September 11th, 2001; (2) the post-9/11 Max. 16.8 9.20 13.00 10.0 crisis period spans the period from September 11th, 2001 Min. 5.4 3.10 3.90 3.5 to February 1st, 2008; (3) the pre-2008 crisis period cov- Panel B. 9/11 Subsample Period ers the period from January 1st, 2008 to November 1st, B.1. Pre-9/11 period—Jan/1/1989 to 9/11/2001 2010; (4) the post-2008 crisis period spans the period N 143 143 143 143 from November 1st, 2010 to February 1st, 2020. Refer to Mean 10.81 4.85 8.66 5.58 Tables  1, 70, 120 and 130 for descriptive statistics of the Median 10.80 4.70 8.80 5.40 data. S.D 1.95 0.98 1.75 1.08 Max. 14.70 6.90 12.10 7.80 5 Empirical results Min. 7.00 3.40 5.10 3.80 5.1 Emplo yment differentials results B.2. Post-9/11 Sample—9/11/2001 to 2/1/2008 5.1.1 Level shock analysis—the case of the United States N 77 77 77 77 To set the stage, Table  1 provides the descriptive statistics Mean 9.75 4.62 6.54 5.27 of historical unemployment for the full sample and by race. Median 9.80 4.60 6.60 5.40 From January 1989 to February 2020, the average monthly S.D 0.97 0.48 0.98 0.55 unemployment rate for African Americans is 10.61%, com- Max. 11.50 5.50 8.30 6.30 pared to 5.11% for the White Americans. This is more than Min. 7.60 3.80 4.80 4.40 twice the unemployment rate of White Americans and Panel C. 2008 Global Financial Crisis Subsample Period exceeds that of the Latino sector by approximately 34%. The C.1. Crisis Period—2/1/2008 to 11/1/2010 standard deviation of 2.62% for the African American unem- N 35 35 35 35 ployment rate also significantly higher than that of the White Mean 13.56 7.44 10.67 8.20 American sector as well. This is an indication of the volatility Median 14.80 8.50 12.00 9.40 of those unemployed. A higher level would be an indication S.D 2.75 1.75 2.37 1.86 that household employment levels are inconsistent, an indi- Max. 16.80 9.20 13.00 10.0 cation that household income is volatile as well. Min. 8.40 4.40 6.20 4.90 To get an understanding on the net effect of crisis on each C.2.Post Crisis Period—11/1/2010 to 2/1/2020 sector of the labor market, Table 2 reports the mean differen - N 111 111 111 111 tial for unemployment rates across various racial groups in Mean 10.20 5.14 7.20 5.83 the U.S. before and after each economic recession. Monthly Median 9.40 4.50 6.60 5.20 African American unemployment rates for the 143 months S.D 3.36 1.65 2.53 1.85 prior to the 9/11 crisis was 13.56%, with a standard devia- Max. 16.5 8.50 12.90 9.30 tion of 2.75%. For the 77  months after the crisis, the aver- Min. 5.40 3.10 3.90 3.50 age African American unemployment rate fell to 9.75%, a decrease of 1.06% which is statistically significant at the 1% level. In comparison, over the same months leading to the in unemployment to a high of 7.44%. All the unemployment 9/11 crisis, White Americans averaged an unemployment differential shocks are significant at the 1% level. It is notable rate of 4.85%. For the 77 months after the crisis, the unem- that African Americans not only experience higher long-run ployment rate fell to 4.62%, a 0.23% (1% p-value) decline. equilibrium unemployment rates, but that exogenous shocks The 9/11 shock paled against the financial crisis of 2008. The affect the African American labor market at a larger scale. exogenous shock of the financial crisis caused an increase of To provide a visual illustration of Tables 1 and 2, Fig. 1 plots 3.81% in unemployment to a high of 13.56% in the African the time series fluctuations of national unemployment rates American sector. This is much higher than the effect on the defined by race over the full sample period as well as the sub - White American sector which experienced a 2.82% increase sample periods. The impact of the coronavirus on African American unemployment: lessons from history Page 7 of 18 3 Table 2 Mean differential analysis for unemployment rates in the U.S Panel A. Pre-September 11—Post September 11 attacks means differential analysis A.1. African Americans Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Mean 10.81 9.75 -1.06 -5.38 (0.000) S.D 1.95 0.97 N 143 77 A.2. White Americans Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Mean 4.85 4.62 − 0.23 − 2.33 (0.01) S.D 0.98 .48 N 143 77 Panel B. Pre-2008- Post 2008 means differential analysis B.1 African Americans Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Mean 13.56 10.2 − 3.36 − 5.96 (0.000) S.D 2.75 3.36 N 35 111 B.2. White Americans Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Mean 7.44 5.14 − 2.3 − 6.87 (0.000) S.D 1.75 1.65 N 35 111 Panel C. 2018 crisis means differential analysis C.1. African Americans Post 9/11 UER Max 2018 Crisis UER Mean Differential t-statistic (p-value) Mean 9.75 13.56 3.81 7.97 (0.000) S.D 0.97 2.75 N 77 35 C.2. White Americans Post 9/11 UER Max 2018 Crisis UER Mean Differential t-statistic (p-value) Mean 4.62 7.44 2.82 9.37 (0.000) S.D 0.48 1.75 N 77 35 5.2 M arket dynamics—the case of the United States among the two series. In Table  4, we report the results To analyze the dynamics of the labor markets, we exam- of the Johansen maximum likelihood test, and the Trace ine whether markets are cointegrated. Cointegration statistic suggests that the null hypothesis of no cointegra- requires that both series are non-stationary in their lev- tion cannot be rejected at the 5% level for the full sam- els and stationary in their first difference. In Table  3, we ple. Therefore, the two series are not cointegrated. run the Augmented Dickey-Fuller and Phillips-Perron We also use the impulse response function to quantify Test with optimal lag length of 4 which was determined the responsiveness of employment variables to struc- using the AIC (Information Criterion). Both tests show tural changes in the system. Figure 2 depicts the response that we cannot reject the null hypothesis of a unit-root of different racial groups (white, African American, (non-stationarity) for the unemployment levels of the full and Latin) to a shock in unemployment and per capita sample. However, we reject the null hypothesis of unit income. Figure  2 suggests that a one-standard deviation root in their first difference at the 1% level. This criterion The subsample for the post-911 sample suggests cointegration with a meets the minimum standard to test for cointegration rank = 2. However, the sample size has only 9 observations. This is too small to perform any meaningful time series analysis. 3 Page 8 of 18 E. Coupet , E. Yamani Fig. 1 National unemployment rates by race The impact of the coronavirus on African American unemployment: lessons from history Page 9 of 18 3 Table 3 Unit root tests for unemployment rates for the U.S Table 4 Johansen cointegration tests for unemployment rates in the U.S Variable ADF Phillips-Perron Max Rank Parameters LL Trace 5% Critical Panel A. Full Sample Period Panel A. Full sample period Total − 1.574 − 0.908 0 30 − 113.10 27.685* 29.68 ΔTotal − 4.701*** − 18.178*** 1 35 − 101.92 5.32 15.41 AA − 0.847 − 0.918 2 38 − 100.16 1.81 3.76 ΔAA − 7.600*** − 25.686*** Panel B. Pre 9/11 Subsample Period Whites − 1.604 − 1.000 0 30 − 48.565 27.437* 29.68 ΔWhites − 4.986*** − 19.515*** 1 35 − 39.951 10.208 15.41 Latin − 1.104 − 1.125 2 38 − 34.847 0.442 3.76 ΔLatin − 7.081 − 25.284*** Panel C. Post 9/11 Subsample Period Panel B. Post 911 Subsample Period 0 30 4.507 44.521 29.68 Total − 1.396 − 1.096 1 35 18.468 16.598 15.41 ΔTotal − 2.872*** − 8.914*** 2 38 24.983 3.567* 3.76 AA − 1.578 − 1.967 3 39 26.767 ΔAA − 3.798*** − 13.088*** Panel D. Pre 2008 Crisis Subsample Period Whites − 1.322 − 1.231 0 30 − 6.583 28.523* 29.68 ΔWhites − 3.550*** − 9.485*** 1 35 2.209 10.944 15.41 Latin − 0.957 − 1.280 2 38 7054 1.254 3.76 ΔLatin − 3.879*** − 12.414*** 3 39 7.681 Panel C. Financial Crisis—Inception to Peak Panel E. Post 2008 Crisis Subsample Period Total − 1.273 − 1.499 0 30 − 6.583 28.523* 29.68 ΔTotal − 1.218 − 3.356** 1 35 2.209 10.944 15.41 AA − 1.246 − 1.293 2 38 7054 1.254 3.76 ΔAA − 2.123** − 6.702*** 3 39 7.681 Whites − 1.404 − 1.547 ΔWhites − 1.133 − 3.774*** Latin − 1.435 − 1.597 ΔLatin − 1.591 − 6.546*** unemployment to African unemployment. This find - Panel D. Post Financial Recession ing is corroborated by the Granger Causality test results Total − 3.451** − 2.969** in Table  6. In a nutshell, White unemployment Granger ΔTotal − 6.148*** − 14.607*** causes unemployment in the Latin and African American AA − 1.058 − 1.077 communities and Latino unemployment ganger cause ΔAA − 5.719*** − 17.790*** African American Unemployment. We can also see cau- Whites − 3.560*** − 3.125** sality running from the African American sector to the ΔWhites − 5.919*** − 14.992*** white sector. Latin − 2.802 − 2.883 ΔLatin − 5.574*** − 13.985 5.3 Level shock analysis—the case of Illinois Moving on to our analysis for the State of Illinois, Table 7 provides a summary of descriptive statistics for the unemployment rates for the State of Illinois, and Fig.  3 shock to the White unemployment sector causes a posi- plots the time series fluctuations of Illinois unemploy - tive effect in the African American unemployment for 4 ment rates defined by race. Unambiguously, the unem - subsequent months. The same effect occurs for shocks ployment rates in Illinois are higher than the national emanating from the Latino sector as well, albeit not to averages for all racial groups. The mean unemployment the same magnitude. rate for the African American sector is 15.2%, compared To test for short-run causality, Table  5 reports the to 5.60% for the White sector, representing a multiple Vector Autoregressive (VAR) results which suggest of 2.71 of African American to white unemployment. that there is short-run causality running from White African Americans performed far worse on same-sector We use the first-differenced data in this VAR model. We use a hybrid of 5 lag length tests (LR, FPE, and three Information criteria tests) to determine the optimal lag lengths, as they differ according to the sample size. 3 Page 10 of 18 E. Coupet , E. Yamani IMPULSE RESPONSE FUNCTIONS Full Sample varbasic, D.UER_AA, D.UER_AA varbasic, D.UER_AA, D.UER_L varbasic, D.UER_AA, D.UER_W -.2 varbasic, D.UER_L, D.UER_AA varbasic, D.UER_L, D.UER_L varbasic, D.UER_L, D.UER_W -.2 varbasic, D.UER_W, D.UER_AA varbasic, D.UER_W, D.UER_L varbasic, D.UER_W, D.UER_W -.2 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable Fig. 2 Impulse response functions 5.4 Labor market dynamics—the case of Illinois comparison of national to Illinois. The mean unemploy - To test the hypothesis that the demand for labor starts in ment rate for African Americans in Illinois is higher by the white sector in the state of Illinois, as it is believed a multiple of 1.43, compared to 1.10 for the white sec- to exist nationally, we look for cointegration among the tor. The Hispanic sector has a mean unemployment rate African American and white unemployment rates. In of 8.50%. Similarly, it was much higher than the national unreported results, the results of the Augmented Dickey- unemployment rate by a multiple of 1.07. The standard Fuller and Phillips-Perron unit root tests confirm that all deviation of the unemployment rates for the full sample unemployment rate and GDP series are non-stationary in Illinois is higher than they are for the national unem- in their levels and stationary in their first-differences. ployment rates. The standard deviation of the unemploy - Also, the results of the Johansen Cointegration test sug- ment rates for the African American sector is 4.68%, gest a maximum rank of order 2. Two series are said to compared to only 1.82% for the White sector. Again, be cointegrated if they are non-stationary in their levels, this was more than twice as volatile as the white sector, but stationary in their first differences. Using this out - and higher than the Hispanic laborers who experienced come, we run a vector error-correction model in Table 9. a standard deviation of 3.19%. Clearly, the white sector’s The error-correction coefficients are statistically sig - market is more stable than the other two markets. nificant and negative at the 5% level. This suggest that Table  8 reports the mean differential for unemploy - white, Hispanic, and real GDP Granger-cause African ment rates across various racial groups in Illinois before American unemployment in the long run. Long run equi- and after each recession. The figures show that Illinois librium is controlled by two error correction functions. benefitted well post 9/11 and 2008 crises. After the 9/11 Results show that 112% of the deviation from the long- crisis, African Americans saw a drop of 2.25% in their run equilibrium in the African American unemployment mean unemployment rates. This is much higher than the is restored in the first in the first month after experienc - white sector that experienced 0.98% decrease in mean ing a shock by unemployment in the Latin community unemployment rates. However, during the 2008 crisis, and GDP. This is followed by another correction of 320% African Americans experienced a 2.27% increase in the of the disequilibrium from long-run equilibrium by the mean unemployment rates, compared to 1.34% by the other error-correction equation. white sector. The impact of the coronavirus on African American unemployment: lessons from history Page 11 of 18 3 Table 5 Vector Autoregressive Regression for Unemployment Rates by Race Full Sample Period Pre-2008 Sample Period ΔUER_AA ΔUER_W ΔUER_L ΔUER_AA ΔUER_W ΔUER_L t t t t t t ΔUER_AA .− 453*** .056*** .079 .− 439*** .055*** .126** t-1 (.053) (.016) (.042) (.067) (.019) (.057) ΔUER_AA − .253*** .037** − .007 − .239*** .044** .000 t-2 (.059) (.018) (.047) (.074) (.021) (.064) ΔUER_AA − .123** .024 .025 − .161** .030 .001 t-3 (.060) (.019) (.048) (.076) (.019) (.065) ΔUER_AA − .116 .003 − .063 − .170** .000 − .062 t-4 (.060) (.019) (.048) (.076) (.021) (.065) ΔUER_AA − .105 .025 .009 − .113 .011 − .028 t-5 (.060) (.019) (.048) (.076) (.021) (.065) ΔUER_AA − .100 .022 − .008 − .141* .011 − .039 t-6 (.058) (.018) (.046) (.073) (.021) (.063) ΔUER_AA − .032 .037 .063 − .035 .031 .097* t-7 (.052) (.016) (.042) (.066) (.019) (.057) ΔUER_W .670*** − .116** .503*** .620** − .175** .381* t-1 (.188) (.058) (.151) (.262) (.073) (.225) ΔUER_W .583*** .133** .697*** .312 .121 .517** t-2 (.196) (.061) (.158) (.270) (.076) (.232) ΔUER_W .021 .072 .787*** .307 .073 .889*** t-3 (.200) (.062) (.161) (.272) (.076) (.234) ΔUER_W .417** .059 .614*** .544* − .019 .664*** t-4 (.205) (.063) (.165) (.280) (.078) (.240) ΔUER_W .594*** .097 .232 .279 .035 .115 t-5 (.206) (.064) (.166) (.281) (.079) (.241) ΔUER_W .197 .149** .424*** .101 .217*** .820*** t-6 (.185) (.063) (.163) (.274) (.077) (.235) ΔUER_W − .367 .036 .251 − .346 .077 .290 t-7 (.194) (.060) (.156) (.272) (.076) (.234) ΔUER_L − .031 − .016 − 564*** − .036 − .022 − .602*** t-1 (.071) (.022) (.057) (.084) (.024) (.072) ΔUER_L .068 − .001 − .442*** .131 − .023 − .461*** t-2 (.080) (.025) (.065) (.094) (.026) (.081) ΔUER_L .134 .026 − .287*** .187 .018 − .318*** t-3 (.085) (.026) (.069) (.099) (.029) (.085) ΔUER_L .132 .002 − .176** .209** .008 − .150** t-4 (.086) (.027) (.070) (.101) (.028) (.086) ΔUER_L .090 − .010 − .101 .206** .001 − .087 t-5 (.083) (.026) (.067) (.096) (.027) (.083) ΔUER_L .007 − .035 − .132 .006 − .029 − .168** t-6 (.078) (.024) (.063) (.091) (.025) (.078) ΔUER_L .131 − .003 − .166*** .135 − .008 − .176*** t-7 (.069) (.021) (.055) (.080) (.022) (.069) Constant − .021 .000 − .019 − .018 .002 − .027 (.023) (.007) (.018) (.029) (.008) (.025) N = 36; Standard error in parentheses; **5% sig level; ***1% sig level JBera Test .778 .010 .032 0.576 .327 .381 Lagrange Multiplier Test (H : Noautocorrelationatlagorder) 2 2 4.64 1.74 (.995) Lag 1 χ Lag 1 χ (.864) 2 2 9.77 10.11 (.341) Lag 2 χ Lag 2 χ (.369) 2 2 2.68 1.97 (.991) Lag 3 χ Lag 3 χ (.978) 2 2 3.69 2.20 (.988) Lag 4 χ Lag 4 χ (.931) 3 Page 12 of 18 E. Coupet , E. Yamani Table 6 Granger Causality for Unemployment Rates by Race in Table 7 Descriptive statistics on the unemployment rates for the US the State of Illinois 2 2 Equation Excluded df African White Latin Total χ Prob > χ American Panel A. Granger Causality Tests—Full Sample Panel A. State of Illinois full sample period—Jan/1/1989 to 2/1/2020 ΔUER_AA ΔUER_W 31.17 7 0.000 N 39 39 39 39 ΔUER_L 9.110 7 0.245 Mean 15.2 5.60 8.50 6.82 ALL 70.77 14 0.000 Median 14.0 5.10 7.60 6.50 ΔUER_W ΔUER_AA 17.54 7 0.014 S.D 4.68 1.82 3.19 2.04 ΔUER_L 6.10 7 0.528 Max. 26.2 9.6 18.5 11.4 ALL 22.89 14 0.062 Min. 8.7 3.2 3.60 3.9 ΔUER_L ΔUER_AA 11.19 7 0.131 Panel B. 9/11 subsample period ΔUER_W 55.74 7 0.000 Pre-9/11 period—1989 to 2001 ALL 88.42 14 0.000 N 13 13 13 13 Panel B. Granger Causality Test—Pre 2008 Sample Mean 13.88 4.39 7.12 5.68 2 2 Equation Excluded df χ Prob > χ Median 13.40 4.30 7.00 5.40 ΔUER_AA ΔUER_W 12.12 7 0.097 S.D 3.21 0.92 1.66 1.17 ΔUER_L 13.79 7 0.055 Max. 18.30 6.00 10.60 7.60 ALL 39.52 14 0.000 Min. 9.40 3.20 4.70 4.30 ΔUER_W ΔUER_AA 12.35 7 0.014 Post-9/11 sample—2001 to 2008 ΔUER_L 5.823 7 0.528 N 8 8 8 8 ALL 17.69 14 0.062 Mean 11.63 4.94 7.05 5.81 ΔUER_L ΔUER_AA 11.19 7 0.141 Median 11.85 4.95 6.80 5.85 ΔUER_W 55.74 7 0.000 S.D 1.16 0.72 1.29 .79 ALL 88.42 14 0.000 Max. 13.10 5.70 9.10 6.70 Panel C. Granger Causality Test—Post 2008 Sample Min. 10.00 7.60 5.50 4.5 2 2 Equation Excluded df χ Prob > χ Panel C. 2008 global financial crisis subsample period ΔUER_AA ΔUER_W 9.22 2 0.010 Crisis period—2009 to 2010 ΔUER_L 2.31 2 0.314 N 11 11 11 11 ALL 9.27 4 0.055 Mean 13.90 6.28 8.40 7.22 ΔUER_W ΔUER_AA 1.78 2 0.412 Median 14.40 5.90 8.10 7.00 ΔUER_L 0.55 2 0.758 S.D 3.92 2.20 3.32 2.40 ALL 2.13 4 0.713 Max. 19.40 9.10 12.70 10.20 ΔUER_L ΔUER_AA 0.916 2 0.632 Min. 8.70 3.30 3.60 3.90 ΔUER_W 5.657 2 0.059 ALL 6.749 4 0.050 Southeast/South sides of the city earn 56% of the typical household across the city. Note also that the Southeast/ 5.5 I ncome differentials results South side of the city report the lowest median income We now turn our attention to examine the unemploy- ($15,030) in the city. As a further evidence, the aver- ment and income differentials in the City of Chicago. age housing values (which are proxy of wealth) equal Tables 10 and 11 provide descriptive statistics for house- $254,850 in the city compared to $197,104 in the South/ holds in 77 community areas in the city of Chicago, and Southeast sides of Chicago. Again, note that the neigh- for the 24 Community areas that makeup the city’s South borhood area with the lowest housing values is also and Southeast sides, respectively. The median income located in the South/Southeast sides of Chicago. Further, household across all 77 community areas is $53,392 com- the Southeast/ South side of the city corresponds to the pared to only $37,477 in the South and Southeast sides highest percentage of renters in the city. Over 50% of the of Chicago. The disparity in income is exacerbated when Southeast/South side residences are renter occupied, comparing the maximum median income levels. The compared with 47.2 across the city. maximum median income for the entire city in 2019 was When it comes to educational attainment (schooling), $111,962, compared to only $62,824 in the south/South- 15.1% of the households within the South/Southeast east sides of the city. At the surface, households in the The impact of the coronavirus on African American unemployment: lessons from history Page 13 of 18 3 Fig. 3 Illinois unemployment rates by race Table 8 Mean differential analysis for the unemployment rates for the State of Illinois Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Panel A. Pre-911- Illinois Post 911 Means Differential analysis A.1. African Americans Mean 13.88 11.63 − 2.25 − 2.29 (.02) S.D 3.21 1.16 N 13 8 A.2. White Americans Mean 5.60 4.62 − 0.98 − 1.84 (0.04) S.D 1.82 .48 N 13 8 Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Panel B. Pre-2008- Post 2008 Means Differential analysis B.1 African Americans Mean 11.63 13.9 2.27 1.81 (0.04) S.D 1.16 3.92 N 8 11 B.2. White Americans Mean 4.94 6.28 1.34 1.88 (0.04) S.D 0.72 2.20 N 8 11 3 Page 14 of 18 E. Coupet , E. Yamani Table 9 Illinois vector error-correction model African American Unemployment Whites Unemployment Latin GDP ΔUER_AA ΔUER_W Unemployment ΔGDP_L t t t ΔUER_L ce1 − 1.12** .029 − .675 − 3989 t-1 (.521) (.270) (.433) (2941) ce2 3.20** − .000 − .000** − .093 t-1 (1.41) (.000) (.000) (.079) ΔUER_AA .117 .027 .471 228 t-1 (.337) (.175) (.280) (1903) ΔUER_AA − .04 .126 .398 − 363 t-2 (.252) (.131) (.209) (1420) ΔUER_W − 2.34 − .320 − 1.60 − 2913 t-1 (1.255) (.650) (1.042) (7083) ΔUER_W − .453 − .066 − .953 − 5102 t-2 (1.11) (.577) (.924) (6280) ΔUER_L 1.085** .371 .545 649 t-1 (.430) (.223) (.357) (2428) ΔUER_L .173 0.050 .160 649 t-2 (.359) (.186) (.298) (2428) ΔGDP_L − .000 − .000 − .000 .407 t-1 (.000) (.000) (.000) (.286) ΔGDP_L − .000 .000 .000 − .068 t-2 .000 (0.000) (.000) (.297) Constant − .954 .502 .453 .002 (.959) (.497) (.796) (5413) Normality Test .719 .960 1.280 1.617 Jarque–Bera (.697) (.619) (.527) (.446) (p-value) Autocorrelation Lag(1) 9.3375 X (.899) (p-value) Lag(2) 13.576 (.630) N = 36; Standard error in parentheses; **5% sig level; ***1% sig level sides have less than a high school diploma. In compari- significant at the 1% level. A one unit increase in son, 16.2% of households within the city has attained less the percentage of households with at least a college than a high school diploma. Households obtaining a high increases the median income by 146%. A College degree school diploma and some college, the South/Southeast explains 50% of the variation in median income. Speci- sides report 58.4%, compared to 51.2% of households fication (2) adds the dummy variable for households in across the city. However, when it comes to obtaining a the South/Southeast sides of the city. The coefficient college degree or higher, the Southeast/Side sides reports is negative and statistically significant at the 1% level. only 26.4% of households, compared to 32.7% of the This supports the common belief of wage and earnings entire city. The mean unemployment rate in the City of suppression of African Americans (Nkomo and Ariss, Chicago was 8.5% in 2019, with a standard deviation of 2014; Raymond, 2018; Mouw, 2000; Lynch and Hyclak, 5.5%. The maximum unemployment rate in the city was 2001; Immergluck, 1998). Controlling for educational 3.2%. Compared to the city, the South/Southeast sides attainment, households in the south/southeast sides of the city had an average unemployment rate of 12.6%, of the city will have their median income reduced by almost 50% higher. 32.8%. 5.5.1 Earnings function5.5.2 Essential workers Specification 1 of Table  12 is a stylized estimate of Table  13 presents the results of our analysis of the likeli- Eq.  (4). Grad, the percentage of households with a col- hood of being an essential worker. Specification 1 is the lege degree, is the proxy for level of schooling. The baseline equation. A one-unit increase in the percentage coefficient of this variable is positive and statistically of households with high school diploma or less, increase The impact of the coronavirus on African American unemployment: lessons from history Page 15 of 18 3 Table 10 Descriptive statistics for the 77 community areas in the City of Chicago N Mean Median S.D Max. Min. Household size 77 2.69 2.68 .59 4.3 1.53 Median income 77 $53,392 $50,178 $24,081 $111,962 $15,030 Unemployment rates_2019 77 8.5% 7% 5.5% 23.2% 1.9% Employed in 2019 77 17,717 12,876 14,668 74,135 758 Population growth 77 -.03% − .13% .47% 2.04% − .81% House value 77 254,850 227,477 110,828 594,571 62,083 % Owner occupied 77 40.2% 36.4% 18.1% 79.8% 12.4% % Renter occupied 77 47.2% 50.6% 15.9% 74.6% 13.8% % vacancy 77 12.6% 10.1% 5.9% 32.4% 6.3% % < HS Dip 77 16.2% 13.6% 10.0% 47.3% 1.4% %w/HS Dip 77 25.3% 26.0% 9.9% 46.7% 4.4% % W/Some college 77 25.9% 25.8% 8.4% 45.1% 8.2% % w/Grad 77 32.7% 26.2% 21.9% 84.9% 5.4% % w/White collar jobs 77 55.8% 52.8% 15.1% 89.1% 29.7% % w/ service jobs 77 24.1% 24.8% 7.2% 39.8% 7.6% % w/blue collar jobs 77 20.1% 19.6% 10.5% 45.5% 3.3% Table 11 Descriptive Statistics for the 24 Community Areas in South and Southeast Areas of the City of Chicago N Mean Median S.D Max. Min. Household size 24 2.5 2.5 .39 3.34 1.8 Median income 24 $37,477 $34,518 $12,245 $62,824 $15,030 Unemployment Rates_2019 24 12.6% 12.8% 4.7% 22.3% 4.4% Employed in 2019 24 8159 8439 5215 20,223 758 Population growth 24 − .1% − .14% .36% .73% − .81% House Value 24 197,104 174,356 79,882 343,120 62,083 % Owner occupied 24 34.0% 29.6% 17.3% 66.8% 12.4% % Renter occupied 24 51.0% 54.1% 16.2% 74.6% 23.7% % vacancy 24 15.1% 15.8% 5.2% 24.8% 8.1% % < HS Dip 24 15.2% 13.5% 6.7% 32.3% 3% %w/HS Dip 24 26.6% 27.1% 7.4% 37.1% 6.4% % W/Some College 24 31.8% 33.9% 8.3% 45.1% 13.5% % w/Grad 24 26.4% 24.4% 15.3% 76.7% 6.7% % w/White Collar Jobs 24 53.9% 52.7% 10.9% 83% 38.3% % w/ Service Jobs 24 28.2% 29.0% 6.3% 39.8% 11.1% % w/Blue Collar Jobs 24 17.9% 17.0% 7.5% 35.8% 5.8% Community Areas: Chatham, Avalon Park, South Chicago, Burnside, Calumet Heights, Roseland, Pullman, South Deering, East Side, West Pullman, Riverdale, Hegewisch, Armour Square, Douglas, Oakland, Fuller Park, Grand Boulevard, Kenwood, Washington Park, Hyde Park, Woodlawn, South Shore, Bridgeport, Greater Grand Crossing the percentage of workers in the service sector. This level coefficient is negative and statistically significant at the 1% of schooling explains approximately 70% of the varia- level. A one percent increase in median income reduces tion in percentage of workers in the service sector. Hold- the percentage of households working in the services sec- ing schooling constant, if a head of household is from the tor by 6.1%. Again, if the households are in the South/ South/Southeast side of Chicago, there is an additional Southeast sides of the City, they face a marginally higher 3.5% likelihood of working as an essential worker. Speci- likelihood of working as an essential worker, while control- fication 3 brings household income into the equation. Its ling for schooling and income. 3 Page 16 of 18 E. Coupet , E. Yamani Table 12 Earnings function analysis Table 13 Essential workers in the city of Chicago Dependent Variable: Log of Median Income Dependent % of variable: percentage of workers in services (1) (2) (1) (2) (3) (4) Grad 1.46*** 1.33*** No college degree .28*** .26*** .19*** .20*** (.144) (.127) (.016) (.016) (.028) (.029) Southside – − .328*** LN of Median Income – – − .061*** − .048*** (.076) (.016) (.017) Constant 10.31*** 10.46*** Southside – .035*** – .019** (.065) (.067) (.010) (.010) R .50 .61 Constant .10*** .053*** .768*** .616*** .013) (.010) (.190) (.199) N 77 77 Heteroskedasticity-Robust Errors in parenthesis AIC 45 27.5 R .70 .75 .78 .79 RMSE .320 .284 N 77 77 77 77 Normality Chi-Square test 1.96 0.33 P-values in parentheses (.38) (.85) AIC − 272 − 281 − 289 − 289 RMSE .039 .036 .035 .034 Heteroskedasticity-Robust Errors in parenthesis Chi-Square 4.73 2.21 13.65 .68 (P-values) (.09) (.33) (.00) (.71) Normality Test 6 Conclusion In the research reported in the present study, our central finding is that firms in the labor market appear to pre - the white sector that fills in during expansionary times but fer white employees to African American and Hispanics. suffers great losses during economic downturns. The state This finding is attested by several interesting findings that of Illinois exhibits the same phenomenon, but to a greater emerge from our employment and income differential level. analyses. Moving onto our income differential analysis, we show Our employment differential analysis reveals that there that the African Americans in the south part of Chicago is racial employment disparity which is first evident from are more likely to have lower median incomes and they the persistent near two-fold level of the national unem- tend to work in the service sector of the economy, com- ployment rates in the African American labor market. pared to their counterparts in other parts of the City. Over the full sample period, the unemployment in the Until the COVID-19 pandemic, the service sector did not African American sector is nearly twice that of the white carry the “essential worker” moniker it has come to be sector, and we find this condition to be even larger in the known as. In fact, it was the sector that was considered City of Chicago, particularly the Southeast and South sides low-skilled and was paid less in earnings. That sector of of the City. A similar pattern is observed in the two sub- the labor force is typically female and non-unionized— sample periods surrounding the 9/11 terrorist attack and particularly women of color. They now find themselves the 2007–2008 recession. While these two episodes expe- on the front line of the health battlefield without ade - rienced exogenous shocks to the labor market and led to quate personal protection equipment. This is now a sec - significant increases in the unemployment rates in all sec - tor of the labor market that arguably deserves hazard pay. tors, the increase in unemployment rate in the white sec- These findings corroborate the narrative in the main - tor paled to that of the African American sector. stream media that African Americans and women of The major takeaway from our analysis is that there is a color are paid less than white workers for doing the same long-run association between white unemployment and jobs. Simply stated, African Americans are not paid the African American unemployment, in the sense that white marginal product of their labor. unemployment Granger-causes African American unem- Our findings have important policy implications. ployment. That is, white unemployment experiences “nat - While it is uncertain to know for sure what will be the ural-rate” even within aggregate demand gaps when the effect of this purely healthy-related exogenous shock macro economy is not experiencing cyclical downturn. In to the economy, the effect of the COVID-19 is certain contrast, African American unemployment is largely cycli- to be deep for the African Americans who suffer from cal in nature, in the sense that the African American labor higher unemployment rates and lower median incomes. market appears to serve as a secondary labor market to There is a great opportunity for local, state, and national The impact of the coronavirus on African American unemployment: lessons from history Page 17 of 18 3 Funding leadership to alleviate the burden that the African Ameri- The authors received no financial support for the research of this article. can Community carries. To alleviate this expected hard- ship, targeted public policy should be introduced so that Availability of data and materials All data will be available upon request. we must allocate funding and resources to where they are most needed, and policy recommendations must be Declarations reflective of this reality. A uniform policy approach will not address the varied needs of groups and communities Competing interests given that people will differentially experience the ini - The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. tial and longer-term consequences of the viral pandemic social distancing protocols. Received: 10 August 2021 Accepted: 17 March 2022 Hence, we propose two targeted policy recommenda- tions. First, we recommend stimulating private fixed cap - ital formation in African American communities. More specifically, we recommend providing guaranteed heav - References Aali-Bujari, A., Venegas-Martinez, F., Garcia-Santillan, A.: Schooling levels and ily subsidized loans to those investing in African Ameri- wage gains in Mexico. Econ. Sociol. 12(4), 74–83 (2019) can communities. An increase in capital expenditures Abdul Khalid, M., Yang, L.: Income inequality and ethnic cleavages in Malaysia: in largely African American communities will increase Evidence from distributional national accounts (1984–2014). J. Asian Econ. 72, 101252 (2021) economic output, increase and stabilize employment Becker, G.S.: Investments in human capital: a theoretical analysis. J. Polit. Econ. (decrease unemployment), increase household income, 70(5), 9–49 (1962) and increase local tax revenues. For maximum effective - Bond, T.N., Lehmann, J.K.: Prejudice and racial matches in employment. Labour Econ. 51, 271–293 (2018) ness, target industries that have the greatest leakages Boustan, L.P., Margo, R.A.: Race, segregation, and postal employment: new from those communities. evidence on spatial mismatch. J. Urban Econ. 65(1), 1–10 (2009) Our second recommendation is to enforce fair wages Button, P., Walker, B.: Employment discrimination against Indigenous Peoples in the United States: Evidence from a field experiment. Labour Econ. 65, to ensure equitable wages across the labor markets. 101851 (2020) There is an abundance of evidence suggesting that the Chantreuil, F., Fourrey, K., Lebon, I., Rebiere, T.: Magnitude and evolution of gen- marginal product of labor is not compensated equitably der and race contributions to earnings inequality across US regions. Res. Econ. 75(1), 45–59 (2021) across various sectors of the labor market. Unfair, below- Chiswick, B.R.: Jacob Mincer, Experience and the Distribution of Earnings. Rev. market, wages to African Americans leads to a reduc- Econ. Household 1(4), 343–361 (2003) tion in income, expenditures and savings in the African Contreras, S., Ghosh, A., Hasan, I.: Income inequality and minority labor market dynamics: Medium term effects from the Great Recession. Econ. Lett. American community, which in turn reduces expected 199, 109717 (2021) free cash flows to potential investors in the community, Couch, K.A., Fairlie, R., Xu, H.: Early evidence of the impacts of COVID-19 on making investments less attractive. This contributes to minority unemployment. J. Public Econ. 192, 104287 (2020) Hellerstein, J.K., Neumark, D., Mclnerney, M.: Spatial mismatch or racial mis- an increase in unemployment that further decreases to match? J. Labour Econ. 64(2), 464–479 (2008) household income—a vicious cycle. Reduced wage also Hoynes, H., Miller, D.L., Schaller, J.: Who suffers during recessions. J. Econ. Persp. reduces that individual’s propensity to repay interest on 26(3), 27–48 (2012) Ileanu, B.V., Tanasoiu, O.E.: Factors of the earning functions and their influence capital. This makes home ownership less likely and access capital of an organization. J. Appl. Quant. Methods 3(4), 366–374 (2008) to liquidity less likely. During economic downturn, a lack Immergluck, D.: Job proximity and the urban employment problem: do suit- of liquidity increases hardship for the individual and for able nearby jobs improve neighborhood employment rates?: a reply. Urban Stud. J. 35(12), 2359–2368 (1998) the community. Kim, A.T., Kim, C., Tuttle, S.E., Zhang, Y.: COVID-19 and the decline in Asian American employment. Res. Soc. Stratif. Mobility 71, 1000563 (2021) Acknowledgements Lynch, G.J., Hyclak, T.: Cyclical and noncyclical unemployment differences We would like to express our gratitude to Robin Ficke and Alex Singer from among demographic groups. Growth Chang. 15(1), 9–17 (1984) World Business Chicago for their help in providing the relevant date required Macartney, H., Nielsen, E., Rodriguez, V.: Unequal worker exposure to establish- to perform our empirical analysis in this report. We must also acknowledge ment deaths. Lab. Econ. 73, 102073 (2021) Stephanie Bechteler from Chicago Urban League for her general support and Mandel, H., Semyonov, M.: The gender-race intersection and the ‘sheltering- feedback. The following authors have affiliations with organizations with direct effect’ of public-sector employment. Res. Soc. Stratif. Mob. 71, 1000563 or indirect financial interest in the subject matter discussed in the manuscript: (2021) Mincer, J.: Investment in human capital and personal income distribution. J. Authors’ contributions Polit. Econ. 66(4), 281–302 (1958) All authors have participated in (a) conception and design, or analysis and Mouw, T.: Job Relocation and the Racial Gap in Unemployment in Detroit and interpretation of the data; (b) drafting the article or revising it critically for Chicago, 1980 to 1990. Am. Sociol. Rev. 65(5), 730–753 (2000) important intellectual content; and (c) Both authors read and approved the Nkomo, S.M., Ariss, A.: The historical Origins of Ethnic (white) Privilege in US final manuscript. Organizations. J. Manag. Psychol. 29(4), 389–404 (2014) 3 Page 18 of 18 E. Coupet , E. Yamani Raymond, E.L.: Race, uneven recovery and persistent negative equity in the southeastern United States. J. Urban A. ff 40(6), 824–837 (2018) Ren, C.: Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality. Soc. Sci. Res. 1, 102710 (2022) Yu, W., Sun, S.: Race-ethnicity, class, and unemployment dynamics: do macro- economic shifts alter existing disadvantages? Mobility 63, 100422 (2019) Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal for Labour Market Research Springer Journals

The impact of the coronavirus on African American unemployment: lessons from history

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Abstract

In this article, our fundamental research question is to investigate the effect of the Coronavirus (named COVID-19) on the African American labor market. More specifically, we attempt to examine the potential economic impact of COVID-19 on the state of racial disparities among the African American labor market by examining two effects, namely, employment and income differentials, using national, state, and city level data (using data for all 77 neigh- borhood areas of the City of Chicago). Our central finding is that the labor market does not appear to treat black and white laborers as homogeneous, as attested by the finding that African American workers suffer from higher unem- ployment rates with higher volatility, lower median incomes, and they are more likely to work in the service sector, compared to their white counterparts, and we find this condition to be even larger in the City of Chicago. These find- ings have important policy implications. Keywords: Labor markets, Unemployment, Financial crisis JEL Classification: E2, E3, J4 impact is not likely to be equal across different racial 1 Introduction groups among U.S. workers who will experience these The coronavirus is a rapidly evolving health pandemic disruptions differently. The existing literature empha - that will have repercussions beyond individual health and sizes unemployment differentials (Hellerstein et al., 2008; the U.S. healthcare system. It has become clear that the Boustan and Margo, 2009; Bond and Lehmann, 2018; Yu outbreak of COVID-19 has disrupted the U.S. economy and Sun, 2019; Button and Walker, 2020; Couch et  al., in general, and its economic impact on the labor market 2020; Kim et al., 2021; Macartney et al., 2021; Mandel and is unprecedented and highly uncertain making it more Semyonov, 2021) as well as income differentials (Tangen - difficult for policymakers to formulate an appropriate tially, Ileanu, and Tanasoiu, 2008; Raymond, 2018; Abdul policy response. Over decades, we find no other infec - Khalid and Yang, 2021; Chantreuil et al., 2021; Contreras tious disease outbreak that had more than a tiny effect et  al., 2021; Ren, 2022) among different racial groups of on the U.S. labor market. It is notable that there will be a workers. significant household and macroeconomic impact as this In this article, we are motivated by such research on virus have generated large reductions in employment and racial disparities and our goal is to examine the effect of earnings in the U.S. labor market and thus triggering an the Coronavirus may have had on the state of racial dis- economic recession. parities among the African American labor community. This, however, is only a partial effect on the labor mar - To better understand the possible racial disparities, we ket. While the virus shock will affect household employ - attempt to quantify the potential economic impact of ment and income, we anticipate that the economic COVID-19 on the African American labor market by controlling for two effects, namely, employment differen - *Correspondence: ehabyamani@csu.edu tials and income differentials. Department of Accounting and Finance, Chicago State University, 9501 S. King Drive, Chicago, IL 60628, USA © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. 3 Page 2 of 18 E. Coupet , E. Yamani First, employment differentials effect. Under this analy - emerge from our unemployment differentials and income sis, we classify COVID-19 as an external shock (i.e., an differentials analyses. unplanned and unexpected event) that can have a sub- First, our unemployment differentials results show that stantial impact on the labor market. Using monthly and the level of national African American unemployment quarterly U.S. unemployment data defined by race, we is nearly twice that of the white unemployment over the analyze the short-run effect of an exogenous shock by entire full sample period. Furthermore, while the two testing for Granger causality using cointegration and examined recession episodes (i.e., the September 11 ter- error-correction models. For comparative measures, we rorist attack and the 2007–2008 recession) experienced predict the unemployment differentials effect by draw - exogenous shocks to the labor market and led to signifi - ing comparisons to the two most recent economic reces- cant increases in the unemployment rates in all sectors, sions: the terrorist attack on September 11, 2001  (9/11) the increase in unemployment rate in the white sector and the 2008 global financial crisis. The rationale for paled to that of the African American sector. examining these two historic recessions is to learn how Along the same lines, we also find that white unemploy - different racial groups might be impacted by exogenous ment Granger-causes African American unemployment, shocks in two different scenarios, and therefore, we can indicating a long-run association between white unem- predict how different racial groups might fare from a ployment and African American unemployment, in the recession that may follow the COVID-19 pandemic. sense that unemployment is first decreased in the white Second, income differentials effect. We extend our sector, followed by a lagged unemployment decrease in analysis further and present a comparative income dif- the African American labor market. This finding suggest ferential analysis across various racial groups in the City that most of the unemployment in the white sector are of Chicago. To this end, we use data for all 77 neighbor- of the structural and frictional forms, while the African hood areas of the City of Chicago, to better quantify the American unemployment is largely cyclical in nature. Put effect of the virus may have had on the black employment differently, the African American labor market appears to in the south and southeast sides of the city of Chicago, serve as a secondary labor market to the white sector that which are mostly populated by African Americans. We fills in during expansionary times but suffers great losses apply a traditional earnings function model to under- during economic downturns. stand the net effect of the COVID-19 on the South and Second, our income differentials analysis results show Southeast sides of the of City which are populated mostly that such observed racial disparities are even larger by African Americans. among the African American labor community in the Our main finding is that firms in the labor market City of Chicago. We find that African American workers appear to prefer white employees to African American in the Southeast and South sides of Chicago suffer from and Hispanics, suggesting that firms do not treat these higher unemployment rates with higher volatility, lower laborers from the two markets as homogeneous. This median incomes, and they are more likely to work in the conclusion is attested by several interesting findings that service sector, compared to their counterparts in other parts of the City. Our findings have important policy implications. While it is uncertain to know for sure what will be the It is important to analyze the impact of both recessions separately, because effect of this purely healthy-related exogenous shock to this gives us a more clear-cut explanation of how two crises with different rea - the economy, the effect of the COVID-19 virus is certain sons may have different employment impact on the same market. While both exogenous shocks to the economy have had deleterious effects on the unem - to be deep and broad for the African Americans who suf- ployment rates in general, their duration, and obviously causes, are differ - fer from higher unemployment rates and lower median ent. On one hand, the September 11, 2001 attack was political in nature and incomes. To alleviate this expected hardship, targeted unexpected by the population at large. Its aim was to place fear in the hearts of the American people, and the political reaction was swift as the Federal public policy should be introduced so that we must government moved to restructure the political system to ensure safety to the allocate funding and resources to where they are most American people. Although the memories may be everlasting, the economy needed, and policy recommendations must be reflective rebounded relatively quickly as the unemployment data suggest, given that the average unemployment rates took approximately 45  months to return to of this reality. A uniform policy approach will not address its pre-9/11 levels. This was in all account, a purely exogenous shock to an the varied needs of groups and communities. economy that was humming along. Hence, the uncertainty of coronavirus cri- Hence, we propose two targeted policy recommenda- sis is larger comparing to the September 11, 2001 recession that was caused by shorter analysis time. On the other hand, the 2007–2008 recession was an tions. First, we recommend stimulating private fixed endogenous event that began in the real estate market and manifested into the capital formation in African American communities by global economy. This relatively painful shock took 92 months for the economy providing guaranteed heavily subsidized loans to those to return to its pre-shock level of unemployment. In comparison to the 2008 market downturn, COVID-19 crisis led consumers and firms all around the investing in African American communities. Our second world to put off spending; they are in wait-and-see mode. The impact of the coronavirus on African American unemployment: lessons from history Page 3 of 18 3 recommendation is to enforce fair wages to ensure equi- unemployment in the African American. Kaplan (1999), table wages across the labor markets. There is an abun - for example, examines the number of job opportunities dance of evidence suggesting that the marginal product within very small neighborhoods, and finds that they of labor is not compensated equitably across various sec- do not vary much from neighborhood to neighborhood tors of the labor market. among white neighborhoods, but African American The remainder of the paper is organized as follows. communities fall short of their white counterpart. Section  2 reviews the literature. Section  3 outlines the econometric methodology which we employ. Our data 2.2 Income differentials are presented in Sect. 4. Sections 5 reports and discusses Our research is related to another strand of the literature our empirical results. Section 6 concludes. which examines income inequality among various racial groups (Tangentially, Ileanu, and Tanasoiu, 2008; Ray- 2 Literature review mond, 2018; Abdul Khalid and Yang, 2021; Chantreuil 2.1 Employment differentials et al., 2021; Contreras et al. 2021; Ren, 2022). Our work builds on the recent research on racial employ- Broadly presented, there are two strands of litera- ment differentials among different groups of workers ture that explain employment and income differentials defined by race. An incomplete list includes Hellerstein between African Americans and other sectors of the et  al. (2008), Boustan and Margo (2009), Bond and labor market, namely the white sector. The first strand Lehmann (2018), Yu and Sun (2019), Button and Walker of literature takes a micro approach and postulates that a (2020), Couch et al. (2020), Kim et al. (2021), Macartney laborer’s potential earnings are a function of investments et al. (2021), and Mandel and Semyonov (2021). in human capital (Becker, 1958; Mincer, 1958; Chiswick, Differences in unemployment rates between African 2003; Ileanu and Tanasoiu, 2008; Aali-Bujari et al., 2019). American and whites have been an ongoing discussion This body of literature evolved from the seminal works and research topic. Lynch and Hyclak (1984) analyze the of Becker (1962) and Mincer (1958) who contributed to disparities among various groups in the labor market, the study of labor economics by developing what is now and they find that the level of the natural rate of unem - known as the earnings function. Further, Aali-Bujari et al. ployment has changed over time with a rising labor force (2019) use Mincer’s (1958) earnings function to conclude participation among non-traditional groups in the labor that the level of education among Mexicans magnifies the market. Robinson (2010) explains differences in the levels increase in income levels and enlarges the human capital. of unemployment between Blacks and Whites from a cul- The second strand of the literature, very deep and tural perspective, in the sense that employers engage in broad in scope, takes a macro approach to analyze the employment discrimination based on tastes derived from income differentials between African Americans and “infotainment” to bias their hiring practices and con- other sectors of the labor market. Raymond (2018) finds tribute to the wage gap between the two groups. Mouw that race is the strongest predictor of persistent negative (2000) uses a fixed effects model to explain the increase equity in the southeast of the U.S., even after control- in unemployment gap between minority groups using the ling for factors relating to the 2008 crisis. Mouw (2000) spatial mismatch hypothesis. This theory hypothesizes analyzes unemployment rates in Chicago and Detroit by that both residential segregation and job decentralization targeting spatial employment opportunities and residen- adversely affect employment opportunities of minorities. tial housing. Using panel data and a fixed-effect model, Realizing that the unemployment gap is only one facet Mouw (2000) finds that decentralization of employment of the overall inequities that occur between racial com- and the loss of manufacturing jobs resulted in spatial dis- munities, researchers have incorporated many factors in tribution of employment in the two cities. attempt to explain overall inequities. Raymond (2018), Relatedly, Immergluck (1998) looks at proximity of job for example, utilizes simple regression models to control opportunities in urban areas to explain unemployment for various factors and find that race remains the strong - rates among urban dwellers, and he finds that race and est predictor of persistent negative equity in the south- educational attainment have the largest effects on unem - eastern U.S. Further, Nkomo and Ariss (2014) show that ployment rates. Further, Hoynes et al. (2012) find that the the historical origins of white privilege explain persis- net effect of the 2007–2008 recession on unemployment tence in the racial divide in organizations and the Ameri- was not homogeneous across the various sectors of the can labor market. Prior research has also focused on the labor market. Specifically, African Americans and His - lack of job opportunities in African American commu- panics suffered higher levels of unemployment during nities that contribute to increased levels of long-term this crisis. 3 Page 4 of 18 E. Coupet , E. Yamani AA 3 Methodology ¨ ˙ L 1 Y 3.1 Emplo yment differentials analysis L (1 − γ − α − β) Y Quantifying the Impact of COVID-19 on Labor Market: γ A Our goal is to examine the economic impact of COVID- (1 − γ − α − β) A 19 by drawing comparisons to the recent recessions. (6) α K L We consider the impact of Coronavirus on the African − − American labor markets nationally (as well as in the state (1 − γ − α − β) K L of Illinois) and compare it to the those during the two L o ˙ ˙ ˙ L L β H − − − most recent economic recessions: the terrorist attack on L L (1 − γ − α − β) H the U.S. on September 11, 2001  and the global financial crisis in 2008. As Eq. (6) indicates, except for output growth, the coef- Labor Model: We begin with a typical firm’s Cobb– ficients of all the right-hand-side variables are negative. Douglas production function with constant returns to Holding all other factors constant, an increase in output scale of a firm at any given time can be expressed as: causes an increase in the growth of African American employment. Because the level of employed labor is fixed γ α β 1−γ−α−β Y = A K H L (1) t t t t t any point in time, an increase in the employment rate of African Americans can only come from a reduction of where Y is each firm’s temporal output; A is the level of employment in the other sectors, holding output constant. multifactor productivity; H is the level of human capital The purpose of the labor market study is two-fold. First, embodied and L is the level of employment. Each factor we analyze the differences in unemployment rates among exhibits diminishing returns. That is: γ , α, andβare < 1. three sectors of the labor market: African Americans, Except for their racial makeup, workers are homogene- Whites, and Latin. In addition to differences in the levels of ous. The firm’s labor force is diverse and consists of a vec - unemployment among the three sectors of the labor mar- tor of races and nationalities: ket, we will test for differential effects on unemployment AA W L O L = L + L + L + L (2) rates resulting from exogenous shocks in the economy. To t t t t t accomplish this, we will decompose the time into three where AA , W , L , and O refer to the employment rates periods around two monumental crises in contemporary among African Americans, Whites, Latin, and others, American history. We will look at unemployment levels respectively. To analyze the production function’s short- surrounding the September 11 attacks terrorist act and run dynamics, we take logs and differentiate Eq. (1) w.r.t. the Great Financial Recession. We will test for changes in dY to time (for example, Y = ) . This yield: dt the mean unemployment rates before and after exogenous shocks from the two crises. ˙ ˙ ˙ ˙ ˙ Y A K L H = γ + α + (1 − γ − α − β) + β . (3) Unemployment Rate Levels Analysis: Let µ = aver- t−j,t Y A K L H age unemployment rate for the ith sector of the labor mar- Taking time derivatives of Eq.  (2) and dividing by L ket from time t-j to t; µ = average unemployment t t ,t+k yields: rate for the ith sector from the time of event, t, to time t + k, a later date; and µ is defined as logitu , where t−j,t ˙ ˙ ˙ ˙ AA W L O ˙ ˙ ˙ ˙ ˙ L L L L L logitu = ln(u/(1 − u)) given that unemployment rates are (4) = + + + . positive. If the fiscal and monetary stimuli work well to L L L L L restore the labor market sector equilibrium from an exog- Substituting Eq. (4) into Eq. (3) yields Eq. (5): i i enous shock, then µ �= µ . For example, suppose t−j,t t ,t+k the unemployment rate in a labor market is a%. As a result ˙ ˙ ˙ Y A K = γ + α + (1 − γ − α − β) of an exogenous shock, the unemployment rate rises above Y A K a% to b%. ˙ ˙ ˙ ˙ (5) AA W L O ˙ ˙ ˙ ˙ ˙ L L L L H If the government and central bank prescribe the exact + + + + β . L L L L H amount of intervention in the financial and capital mar - kets, the average unemployment rate will be restored to Rearranging Eq. (5) for the employment growth of Afri- a%. If workers are homogeneous, then the net effect on this can American employment leaves: sector should be the same for all other sectors of the labor The impact of the coronavirus on African American unemployment: lessons from history Page 5 of 18 3 i i o o exogenous variables that are not related to investments in market—that is, µ − µ = µ − µ . If t−j,t t ,t+k t−j,t t ,t+k human capital, as defined in Eq.  (1). A structural equa - the market values one sector of the market over the other tion that is typically used to estimate earnings in Eq.  (9) for any reason, then the differences in each unemployment is: level for the sectors will not converge. In this case, it may be i i o o 2 that µ − µ > µ − µ . t−j,t t ,t+k t−j,t t ,t+k α β F y = S H e (10) The dynamics of the labor market will be analyzed with a system of equations. Two non-stationary variables are where H refers to the number of years of experience and cointegrated of order 1, CI (1,1), if their levels are non- F is a vector of variables that are not related to human stationary and stationary in their first difference. If so, capital such as race, language, gender. Taking logs of we use the Johansen method to test for the rank of the Eq. (10), we get, system of equation to determine long-run relationships. lny = αlnS + βlnH + F (11) If there is a long-run relationship, then we use a Vector Error-Correction Model (VECM) to establish the long- Equation  (11), known as the earnings function, is used run and short-run causality between the variables. If the to estimate an individual’s post investment earnings. We system is cointegrated, we use an error correction model will estimate the coefficients of Eq. (11) for neighborhood of the form: area households in the City of Chicago with regression �µ = α + β ec + β �µ + β �µ + e Eq. (12) below: i,t 0 1 i,t−1 2 j,t−1 i,t i,t−1 (7) lny = α + βlnS + F + e (12) i 0 i i i where µ is the unemployment rate at time t of one race, i,t µ is the unemployment rate of another race at the same j,t Essential Workers Sector: The likelihood of working as time, ec is the error correction term from the previous i,t an essential worker in the City of Chicago, denoted as period, and e is the white noise error term in the cur- i,t Ess , is assumed to be a function of the educational level rent period. If the variables are not cointegrated, then we and other exogenous variables such as race, gender, and can establish a vector autoregression (VAR) model to test income, as follows for short-run causality Prob(Ess) = f (Schooling , income, X) (13) �µ = γ + β �µ + β �µ + e i,t 3 i,t−1 4 j,t−1 i,t (8) where we assume the following relationships ex ante: 2 2 This will be followed by the impulse response function, ∂(Prob(Ess)) ∂Schooling < 0; ∂ (Prob(Ess)) ∂Schooling > 0; establishing in the time domain the effect of an exog - ∂(Prob(Ess)) ∂Income < 0. < 0 . Essential service work- enous variable on the other variables. ers are deemed necessary functions for society. This includes emergency room healthcare providers in hos- 3.2 I ncome differentials analysis pitals, customer service representatives in retail outlets, The Earnings Function: In the second part of our analysis, and emergency service providers such as firefighters, we proceed with the development of the earnings func- police, etc. We assume that the likelihood of working in tion, followed by a labor market segment model. Mincer the service sector decreases with the number of years of (1958) and Ileanu (2008) model the earnings function of schooling. However, with increases in schooling beyond an individual using the stylized general function as: college, this likelihood increases. The nonlinearity incor - porates emergency room healthcare providers. We also y = h(S, x, F ) + ε (9) assume, a priori, the likelihood of being an essential ser- vice provider is a decreasing function of income—how- where y is net earnings; S is the years of schooling; and ever, in an increasing rate. x represents the years of experience; and F is a vector of Note that it is also expected that the dynamics within the labor market 4 Data may not be contemporaneous. If the shock is a negative, then unemployment We extract data from two databases: The Bureau of Labor will increase in the non-preferred sector of the labor market followed by an statistics (BLS) and the Environmental Systems Research increase in the preferred sector. Because negative exogenous shocks are typi- cally followed by fiscal and monetary policies, this will lead to an immediate Institute (ESRI) databases. We use BLS to collect reduction in the preferred sector of the labor market followed by a reduction monthly data on the national unemployment rates (as in the non-preferred sector. Therefore, exogenous negative shocks and subse - well as quarterly data for the state of Illinois), while we quent positive fiscal treatments affect both sectors in magnitude and speed of adjustments. Negative shocks begin with an increase in unemployment rates use ESRI data to collect household level market-related of the non-preferred leading to an increase in the unemployment rates of the information for all 77 neighborhood areas of the City preferred sector. Positive treatments affect the market in the opposite direc - of Chicago. Our entire annual sample period begins in tion. This is known as feedback effect between the two sectors of the labor market. 3 Page 6 of 18 E. Coupet , E. Yamani January 1989 and covers the period until February 2020. Table 1 Descriptive statistics on the monthly National U.S. Unemployment Rates Although we examine the unemployment rates over the full sample period spanning the period from January 1st, African White Latin Total 1989 to February 1st, 2020, we focus our analysis on the American periods before and after the terrorist attack on Septem- Panel A. Full Sample Period—Jan/1/1989 to 2/1/2020 ber 11, 2001 and the 2007–2008 global financial crisis, as N 374 374 374 374 the key events. For this, we examine two separate sub- Mean 10.61 5.11 7.92 5.81 periods around each crisis. These sub-periods are: (1) Median 10.50 4.70 7.50 5.40 the pre-9/11 crisis period covers the period from Janu- S.D 2.62 1.46 2.30 1.58 ary 1st, 1989 to September 11th, 2001; (2) the post-9/11 Max. 16.8 9.20 13.00 10.0 crisis period spans the period from September 11th, 2001 Min. 5.4 3.10 3.90 3.5 to February 1st, 2008; (3) the pre-2008 crisis period cov- Panel B. 9/11 Subsample Period ers the period from January 1st, 2008 to November 1st, B.1. Pre-9/11 period—Jan/1/1989 to 9/11/2001 2010; (4) the post-2008 crisis period spans the period N 143 143 143 143 from November 1st, 2010 to February 1st, 2020. Refer to Mean 10.81 4.85 8.66 5.58 Tables  1, 70, 120 and 130 for descriptive statistics of the Median 10.80 4.70 8.80 5.40 data. S.D 1.95 0.98 1.75 1.08 Max. 14.70 6.90 12.10 7.80 5 Empirical results Min. 7.00 3.40 5.10 3.80 5.1 Emplo yment differentials results B.2. Post-9/11 Sample—9/11/2001 to 2/1/2008 5.1.1 Level shock analysis—the case of the United States N 77 77 77 77 To set the stage, Table  1 provides the descriptive statistics Mean 9.75 4.62 6.54 5.27 of historical unemployment for the full sample and by race. Median 9.80 4.60 6.60 5.40 From January 1989 to February 2020, the average monthly S.D 0.97 0.48 0.98 0.55 unemployment rate for African Americans is 10.61%, com- Max. 11.50 5.50 8.30 6.30 pared to 5.11% for the White Americans. This is more than Min. 7.60 3.80 4.80 4.40 twice the unemployment rate of White Americans and Panel C. 2008 Global Financial Crisis Subsample Period exceeds that of the Latino sector by approximately 34%. The C.1. Crisis Period—2/1/2008 to 11/1/2010 standard deviation of 2.62% for the African American unem- N 35 35 35 35 ployment rate also significantly higher than that of the White Mean 13.56 7.44 10.67 8.20 American sector as well. This is an indication of the volatility Median 14.80 8.50 12.00 9.40 of those unemployed. A higher level would be an indication S.D 2.75 1.75 2.37 1.86 that household employment levels are inconsistent, an indi- Max. 16.80 9.20 13.00 10.0 cation that household income is volatile as well. Min. 8.40 4.40 6.20 4.90 To get an understanding on the net effect of crisis on each C.2.Post Crisis Period—11/1/2010 to 2/1/2020 sector of the labor market, Table 2 reports the mean differen - N 111 111 111 111 tial for unemployment rates across various racial groups in Mean 10.20 5.14 7.20 5.83 the U.S. before and after each economic recession. Monthly Median 9.40 4.50 6.60 5.20 African American unemployment rates for the 143 months S.D 3.36 1.65 2.53 1.85 prior to the 9/11 crisis was 13.56%, with a standard devia- Max. 16.5 8.50 12.90 9.30 tion of 2.75%. For the 77  months after the crisis, the aver- Min. 5.40 3.10 3.90 3.50 age African American unemployment rate fell to 9.75%, a decrease of 1.06% which is statistically significant at the 1% level. In comparison, over the same months leading to the in unemployment to a high of 7.44%. All the unemployment 9/11 crisis, White Americans averaged an unemployment differential shocks are significant at the 1% level. It is notable rate of 4.85%. For the 77 months after the crisis, the unem- that African Americans not only experience higher long-run ployment rate fell to 4.62%, a 0.23% (1% p-value) decline. equilibrium unemployment rates, but that exogenous shocks The 9/11 shock paled against the financial crisis of 2008. The affect the African American labor market at a larger scale. exogenous shock of the financial crisis caused an increase of To provide a visual illustration of Tables 1 and 2, Fig. 1 plots 3.81% in unemployment to a high of 13.56% in the African the time series fluctuations of national unemployment rates American sector. This is much higher than the effect on the defined by race over the full sample period as well as the sub - White American sector which experienced a 2.82% increase sample periods. The impact of the coronavirus on African American unemployment: lessons from history Page 7 of 18 3 Table 2 Mean differential analysis for unemployment rates in the U.S Panel A. Pre-September 11—Post September 11 attacks means differential analysis A.1. African Americans Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Mean 10.81 9.75 -1.06 -5.38 (0.000) S.D 1.95 0.97 N 143 77 A.2. White Americans Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Mean 4.85 4.62 − 0.23 − 2.33 (0.01) S.D 0.98 .48 N 143 77 Panel B. Pre-2008- Post 2008 means differential analysis B.1 African Americans Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Mean 13.56 10.2 − 3.36 − 5.96 (0.000) S.D 2.75 3.36 N 35 111 B.2. White Americans Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Mean 7.44 5.14 − 2.3 − 6.87 (0.000) S.D 1.75 1.65 N 35 111 Panel C. 2018 crisis means differential analysis C.1. African Americans Post 9/11 UER Max 2018 Crisis UER Mean Differential t-statistic (p-value) Mean 9.75 13.56 3.81 7.97 (0.000) S.D 0.97 2.75 N 77 35 C.2. White Americans Post 9/11 UER Max 2018 Crisis UER Mean Differential t-statistic (p-value) Mean 4.62 7.44 2.82 9.37 (0.000) S.D 0.48 1.75 N 77 35 5.2 M arket dynamics—the case of the United States among the two series. In Table  4, we report the results To analyze the dynamics of the labor markets, we exam- of the Johansen maximum likelihood test, and the Trace ine whether markets are cointegrated. Cointegration statistic suggests that the null hypothesis of no cointegra- requires that both series are non-stationary in their lev- tion cannot be rejected at the 5% level for the full sam- els and stationary in their first difference. In Table  3, we ple. Therefore, the two series are not cointegrated. run the Augmented Dickey-Fuller and Phillips-Perron We also use the impulse response function to quantify Test with optimal lag length of 4 which was determined the responsiveness of employment variables to struc- using the AIC (Information Criterion). Both tests show tural changes in the system. Figure 2 depicts the response that we cannot reject the null hypothesis of a unit-root of different racial groups (white, African American, (non-stationarity) for the unemployment levels of the full and Latin) to a shock in unemployment and per capita sample. However, we reject the null hypothesis of unit income. Figure  2 suggests that a one-standard deviation root in their first difference at the 1% level. This criterion The subsample for the post-911 sample suggests cointegration with a meets the minimum standard to test for cointegration rank = 2. However, the sample size has only 9 observations. This is too small to perform any meaningful time series analysis. 3 Page 8 of 18 E. Coupet , E. Yamani Fig. 1 National unemployment rates by race The impact of the coronavirus on African American unemployment: lessons from history Page 9 of 18 3 Table 3 Unit root tests for unemployment rates for the U.S Table 4 Johansen cointegration tests for unemployment rates in the U.S Variable ADF Phillips-Perron Max Rank Parameters LL Trace 5% Critical Panel A. Full Sample Period Panel A. Full sample period Total − 1.574 − 0.908 0 30 − 113.10 27.685* 29.68 ΔTotal − 4.701*** − 18.178*** 1 35 − 101.92 5.32 15.41 AA − 0.847 − 0.918 2 38 − 100.16 1.81 3.76 ΔAA − 7.600*** − 25.686*** Panel B. Pre 9/11 Subsample Period Whites − 1.604 − 1.000 0 30 − 48.565 27.437* 29.68 ΔWhites − 4.986*** − 19.515*** 1 35 − 39.951 10.208 15.41 Latin − 1.104 − 1.125 2 38 − 34.847 0.442 3.76 ΔLatin − 7.081 − 25.284*** Panel C. Post 9/11 Subsample Period Panel B. Post 911 Subsample Period 0 30 4.507 44.521 29.68 Total − 1.396 − 1.096 1 35 18.468 16.598 15.41 ΔTotal − 2.872*** − 8.914*** 2 38 24.983 3.567* 3.76 AA − 1.578 − 1.967 3 39 26.767 ΔAA − 3.798*** − 13.088*** Panel D. Pre 2008 Crisis Subsample Period Whites − 1.322 − 1.231 0 30 − 6.583 28.523* 29.68 ΔWhites − 3.550*** − 9.485*** 1 35 2.209 10.944 15.41 Latin − 0.957 − 1.280 2 38 7054 1.254 3.76 ΔLatin − 3.879*** − 12.414*** 3 39 7.681 Panel C. Financial Crisis—Inception to Peak Panel E. Post 2008 Crisis Subsample Period Total − 1.273 − 1.499 0 30 − 6.583 28.523* 29.68 ΔTotal − 1.218 − 3.356** 1 35 2.209 10.944 15.41 AA − 1.246 − 1.293 2 38 7054 1.254 3.76 ΔAA − 2.123** − 6.702*** 3 39 7.681 Whites − 1.404 − 1.547 ΔWhites − 1.133 − 3.774*** Latin − 1.435 − 1.597 ΔLatin − 1.591 − 6.546*** unemployment to African unemployment. This find - Panel D. Post Financial Recession ing is corroborated by the Granger Causality test results Total − 3.451** − 2.969** in Table  6. In a nutshell, White unemployment Granger ΔTotal − 6.148*** − 14.607*** causes unemployment in the Latin and African American AA − 1.058 − 1.077 communities and Latino unemployment ganger cause ΔAA − 5.719*** − 17.790*** African American Unemployment. We can also see cau- Whites − 3.560*** − 3.125** sality running from the African American sector to the ΔWhites − 5.919*** − 14.992*** white sector. Latin − 2.802 − 2.883 ΔLatin − 5.574*** − 13.985 5.3 Level shock analysis—the case of Illinois Moving on to our analysis for the State of Illinois, Table 7 provides a summary of descriptive statistics for the unemployment rates for the State of Illinois, and Fig.  3 shock to the White unemployment sector causes a posi- plots the time series fluctuations of Illinois unemploy - tive effect in the African American unemployment for 4 ment rates defined by race. Unambiguously, the unem - subsequent months. The same effect occurs for shocks ployment rates in Illinois are higher than the national emanating from the Latino sector as well, albeit not to averages for all racial groups. The mean unemployment the same magnitude. rate for the African American sector is 15.2%, compared To test for short-run causality, Table  5 reports the to 5.60% for the White sector, representing a multiple Vector Autoregressive (VAR) results which suggest of 2.71 of African American to white unemployment. that there is short-run causality running from White African Americans performed far worse on same-sector We use the first-differenced data in this VAR model. We use a hybrid of 5 lag length tests (LR, FPE, and three Information criteria tests) to determine the optimal lag lengths, as they differ according to the sample size. 3 Page 10 of 18 E. Coupet , E. Yamani IMPULSE RESPONSE FUNCTIONS Full Sample varbasic, D.UER_AA, D.UER_AA varbasic, D.UER_AA, D.UER_L varbasic, D.UER_AA, D.UER_W -.2 varbasic, D.UER_L, D.UER_AA varbasic, D.UER_L, D.UER_L varbasic, D.UER_L, D.UER_W -.2 varbasic, D.UER_W, D.UER_AA varbasic, D.UER_W, D.UER_L varbasic, D.UER_W, D.UER_W -.2 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable Fig. 2 Impulse response functions 5.4 Labor market dynamics—the case of Illinois comparison of national to Illinois. The mean unemploy - To test the hypothesis that the demand for labor starts in ment rate for African Americans in Illinois is higher by the white sector in the state of Illinois, as it is believed a multiple of 1.43, compared to 1.10 for the white sec- to exist nationally, we look for cointegration among the tor. The Hispanic sector has a mean unemployment rate African American and white unemployment rates. In of 8.50%. Similarly, it was much higher than the national unreported results, the results of the Augmented Dickey- unemployment rate by a multiple of 1.07. The standard Fuller and Phillips-Perron unit root tests confirm that all deviation of the unemployment rates for the full sample unemployment rate and GDP series are non-stationary in Illinois is higher than they are for the national unem- in their levels and stationary in their first-differences. ployment rates. The standard deviation of the unemploy - Also, the results of the Johansen Cointegration test sug- ment rates for the African American sector is 4.68%, gest a maximum rank of order 2. Two series are said to compared to only 1.82% for the White sector. Again, be cointegrated if they are non-stationary in their levels, this was more than twice as volatile as the white sector, but stationary in their first differences. Using this out - and higher than the Hispanic laborers who experienced come, we run a vector error-correction model in Table 9. a standard deviation of 3.19%. Clearly, the white sector’s The error-correction coefficients are statistically sig - market is more stable than the other two markets. nificant and negative at the 5% level. This suggest that Table  8 reports the mean differential for unemploy - white, Hispanic, and real GDP Granger-cause African ment rates across various racial groups in Illinois before American unemployment in the long run. Long run equi- and after each recession. The figures show that Illinois librium is controlled by two error correction functions. benefitted well post 9/11 and 2008 crises. After the 9/11 Results show that 112% of the deviation from the long- crisis, African Americans saw a drop of 2.25% in their run equilibrium in the African American unemployment mean unemployment rates. This is much higher than the is restored in the first in the first month after experienc - white sector that experienced 0.98% decrease in mean ing a shock by unemployment in the Latin community unemployment rates. However, during the 2008 crisis, and GDP. This is followed by another correction of 320% African Americans experienced a 2.27% increase in the of the disequilibrium from long-run equilibrium by the mean unemployment rates, compared to 1.34% by the other error-correction equation. white sector. The impact of the coronavirus on African American unemployment: lessons from history Page 11 of 18 3 Table 5 Vector Autoregressive Regression for Unemployment Rates by Race Full Sample Period Pre-2008 Sample Period ΔUER_AA ΔUER_W ΔUER_L ΔUER_AA ΔUER_W ΔUER_L t t t t t t ΔUER_AA .− 453*** .056*** .079 .− 439*** .055*** .126** t-1 (.053) (.016) (.042) (.067) (.019) (.057) ΔUER_AA − .253*** .037** − .007 − .239*** .044** .000 t-2 (.059) (.018) (.047) (.074) (.021) (.064) ΔUER_AA − .123** .024 .025 − .161** .030 .001 t-3 (.060) (.019) (.048) (.076) (.019) (.065) ΔUER_AA − .116 .003 − .063 − .170** .000 − .062 t-4 (.060) (.019) (.048) (.076) (.021) (.065) ΔUER_AA − .105 .025 .009 − .113 .011 − .028 t-5 (.060) (.019) (.048) (.076) (.021) (.065) ΔUER_AA − .100 .022 − .008 − .141* .011 − .039 t-6 (.058) (.018) (.046) (.073) (.021) (.063) ΔUER_AA − .032 .037 .063 − .035 .031 .097* t-7 (.052) (.016) (.042) (.066) (.019) (.057) ΔUER_W .670*** − .116** .503*** .620** − .175** .381* t-1 (.188) (.058) (.151) (.262) (.073) (.225) ΔUER_W .583*** .133** .697*** .312 .121 .517** t-2 (.196) (.061) (.158) (.270) (.076) (.232) ΔUER_W .021 .072 .787*** .307 .073 .889*** t-3 (.200) (.062) (.161) (.272) (.076) (.234) ΔUER_W .417** .059 .614*** .544* − .019 .664*** t-4 (.205) (.063) (.165) (.280) (.078) (.240) ΔUER_W .594*** .097 .232 .279 .035 .115 t-5 (.206) (.064) (.166) (.281) (.079) (.241) ΔUER_W .197 .149** .424*** .101 .217*** .820*** t-6 (.185) (.063) (.163) (.274) (.077) (.235) ΔUER_W − .367 .036 .251 − .346 .077 .290 t-7 (.194) (.060) (.156) (.272) (.076) (.234) ΔUER_L − .031 − .016 − 564*** − .036 − .022 − .602*** t-1 (.071) (.022) (.057) (.084) (.024) (.072) ΔUER_L .068 − .001 − .442*** .131 − .023 − .461*** t-2 (.080) (.025) (.065) (.094) (.026) (.081) ΔUER_L .134 .026 − .287*** .187 .018 − .318*** t-3 (.085) (.026) (.069) (.099) (.029) (.085) ΔUER_L .132 .002 − .176** .209** .008 − .150** t-4 (.086) (.027) (.070) (.101) (.028) (.086) ΔUER_L .090 − .010 − .101 .206** .001 − .087 t-5 (.083) (.026) (.067) (.096) (.027) (.083) ΔUER_L .007 − .035 − .132 .006 − .029 − .168** t-6 (.078) (.024) (.063) (.091) (.025) (.078) ΔUER_L .131 − .003 − .166*** .135 − .008 − .176*** t-7 (.069) (.021) (.055) (.080) (.022) (.069) Constant − .021 .000 − .019 − .018 .002 − .027 (.023) (.007) (.018) (.029) (.008) (.025) N = 36; Standard error in parentheses; **5% sig level; ***1% sig level JBera Test .778 .010 .032 0.576 .327 .381 Lagrange Multiplier Test (H : Noautocorrelationatlagorder) 2 2 4.64 1.74 (.995) Lag 1 χ Lag 1 χ (.864) 2 2 9.77 10.11 (.341) Lag 2 χ Lag 2 χ (.369) 2 2 2.68 1.97 (.991) Lag 3 χ Lag 3 χ (.978) 2 2 3.69 2.20 (.988) Lag 4 χ Lag 4 χ (.931) 3 Page 12 of 18 E. Coupet , E. Yamani Table 6 Granger Causality for Unemployment Rates by Race in Table 7 Descriptive statistics on the unemployment rates for the US the State of Illinois 2 2 Equation Excluded df African White Latin Total χ Prob > χ American Panel A. Granger Causality Tests—Full Sample Panel A. State of Illinois full sample period—Jan/1/1989 to 2/1/2020 ΔUER_AA ΔUER_W 31.17 7 0.000 N 39 39 39 39 ΔUER_L 9.110 7 0.245 Mean 15.2 5.60 8.50 6.82 ALL 70.77 14 0.000 Median 14.0 5.10 7.60 6.50 ΔUER_W ΔUER_AA 17.54 7 0.014 S.D 4.68 1.82 3.19 2.04 ΔUER_L 6.10 7 0.528 Max. 26.2 9.6 18.5 11.4 ALL 22.89 14 0.062 Min. 8.7 3.2 3.60 3.9 ΔUER_L ΔUER_AA 11.19 7 0.131 Panel B. 9/11 subsample period ΔUER_W 55.74 7 0.000 Pre-9/11 period—1989 to 2001 ALL 88.42 14 0.000 N 13 13 13 13 Panel B. Granger Causality Test—Pre 2008 Sample Mean 13.88 4.39 7.12 5.68 2 2 Equation Excluded df χ Prob > χ Median 13.40 4.30 7.00 5.40 ΔUER_AA ΔUER_W 12.12 7 0.097 S.D 3.21 0.92 1.66 1.17 ΔUER_L 13.79 7 0.055 Max. 18.30 6.00 10.60 7.60 ALL 39.52 14 0.000 Min. 9.40 3.20 4.70 4.30 ΔUER_W ΔUER_AA 12.35 7 0.014 Post-9/11 sample—2001 to 2008 ΔUER_L 5.823 7 0.528 N 8 8 8 8 ALL 17.69 14 0.062 Mean 11.63 4.94 7.05 5.81 ΔUER_L ΔUER_AA 11.19 7 0.141 Median 11.85 4.95 6.80 5.85 ΔUER_W 55.74 7 0.000 S.D 1.16 0.72 1.29 .79 ALL 88.42 14 0.000 Max. 13.10 5.70 9.10 6.70 Panel C. Granger Causality Test—Post 2008 Sample Min. 10.00 7.60 5.50 4.5 2 2 Equation Excluded df χ Prob > χ Panel C. 2008 global financial crisis subsample period ΔUER_AA ΔUER_W 9.22 2 0.010 Crisis period—2009 to 2010 ΔUER_L 2.31 2 0.314 N 11 11 11 11 ALL 9.27 4 0.055 Mean 13.90 6.28 8.40 7.22 ΔUER_W ΔUER_AA 1.78 2 0.412 Median 14.40 5.90 8.10 7.00 ΔUER_L 0.55 2 0.758 S.D 3.92 2.20 3.32 2.40 ALL 2.13 4 0.713 Max. 19.40 9.10 12.70 10.20 ΔUER_L ΔUER_AA 0.916 2 0.632 Min. 8.70 3.30 3.60 3.90 ΔUER_W 5.657 2 0.059 ALL 6.749 4 0.050 Southeast/South sides of the city earn 56% of the typical household across the city. Note also that the Southeast/ 5.5 I ncome differentials results South side of the city report the lowest median income We now turn our attention to examine the unemploy- ($15,030) in the city. As a further evidence, the aver- ment and income differentials in the City of Chicago. age housing values (which are proxy of wealth) equal Tables 10 and 11 provide descriptive statistics for house- $254,850 in the city compared to $197,104 in the South/ holds in 77 community areas in the city of Chicago, and Southeast sides of Chicago. Again, note that the neigh- for the 24 Community areas that makeup the city’s South borhood area with the lowest housing values is also and Southeast sides, respectively. The median income located in the South/Southeast sides of Chicago. Further, household across all 77 community areas is $53,392 com- the Southeast/ South side of the city corresponds to the pared to only $37,477 in the South and Southeast sides highest percentage of renters in the city. Over 50% of the of Chicago. The disparity in income is exacerbated when Southeast/South side residences are renter occupied, comparing the maximum median income levels. The compared with 47.2 across the city. maximum median income for the entire city in 2019 was When it comes to educational attainment (schooling), $111,962, compared to only $62,824 in the south/South- 15.1% of the households within the South/Southeast east sides of the city. At the surface, households in the The impact of the coronavirus on African American unemployment: lessons from history Page 13 of 18 3 Fig. 3 Illinois unemployment rates by race Table 8 Mean differential analysis for the unemployment rates for the State of Illinois Pre-911 UER Post-911 UER Mean Differential t-statistic (p-value) Panel A. Pre-911- Illinois Post 911 Means Differential analysis A.1. African Americans Mean 13.88 11.63 − 2.25 − 2.29 (.02) S.D 3.21 1.16 N 13 8 A.2. White Americans Mean 5.60 4.62 − 0.98 − 1.84 (0.04) S.D 1.82 .48 N 13 8 Max 2008 UER Post-2008 UER Mean Differential t-statistic (p-value) Panel B. Pre-2008- Post 2008 Means Differential analysis B.1 African Americans Mean 11.63 13.9 2.27 1.81 (0.04) S.D 1.16 3.92 N 8 11 B.2. White Americans Mean 4.94 6.28 1.34 1.88 (0.04) S.D 0.72 2.20 N 8 11 3 Page 14 of 18 E. Coupet , E. Yamani Table 9 Illinois vector error-correction model African American Unemployment Whites Unemployment Latin GDP ΔUER_AA ΔUER_W Unemployment ΔGDP_L t t t ΔUER_L ce1 − 1.12** .029 − .675 − 3989 t-1 (.521) (.270) (.433) (2941) ce2 3.20** − .000 − .000** − .093 t-1 (1.41) (.000) (.000) (.079) ΔUER_AA .117 .027 .471 228 t-1 (.337) (.175) (.280) (1903) ΔUER_AA − .04 .126 .398 − 363 t-2 (.252) (.131) (.209) (1420) ΔUER_W − 2.34 − .320 − 1.60 − 2913 t-1 (1.255) (.650) (1.042) (7083) ΔUER_W − .453 − .066 − .953 − 5102 t-2 (1.11) (.577) (.924) (6280) ΔUER_L 1.085** .371 .545 649 t-1 (.430) (.223) (.357) (2428) ΔUER_L .173 0.050 .160 649 t-2 (.359) (.186) (.298) (2428) ΔGDP_L − .000 − .000 − .000 .407 t-1 (.000) (.000) (.000) (.286) ΔGDP_L − .000 .000 .000 − .068 t-2 .000 (0.000) (.000) (.297) Constant − .954 .502 .453 .002 (.959) (.497) (.796) (5413) Normality Test .719 .960 1.280 1.617 Jarque–Bera (.697) (.619) (.527) (.446) (p-value) Autocorrelation Lag(1) 9.3375 X (.899) (p-value) Lag(2) 13.576 (.630) N = 36; Standard error in parentheses; **5% sig level; ***1% sig level sides have less than a high school diploma. In compari- significant at the 1% level. A one unit increase in son, 16.2% of households within the city has attained less the percentage of households with at least a college than a high school diploma. Households obtaining a high increases the median income by 146%. A College degree school diploma and some college, the South/Southeast explains 50% of the variation in median income. Speci- sides report 58.4%, compared to 51.2% of households fication (2) adds the dummy variable for households in across the city. However, when it comes to obtaining a the South/Southeast sides of the city. The coefficient college degree or higher, the Southeast/Side sides reports is negative and statistically significant at the 1% level. only 26.4% of households, compared to 32.7% of the This supports the common belief of wage and earnings entire city. The mean unemployment rate in the City of suppression of African Americans (Nkomo and Ariss, Chicago was 8.5% in 2019, with a standard deviation of 2014; Raymond, 2018; Mouw, 2000; Lynch and Hyclak, 5.5%. The maximum unemployment rate in the city was 2001; Immergluck, 1998). Controlling for educational 3.2%. Compared to the city, the South/Southeast sides attainment, households in the south/southeast sides of the city had an average unemployment rate of 12.6%, of the city will have their median income reduced by almost 50% higher. 32.8%. 5.5.1 Earnings function5.5.2 Essential workers Specification 1 of Table  12 is a stylized estimate of Table  13 presents the results of our analysis of the likeli- Eq.  (4). Grad, the percentage of households with a col- hood of being an essential worker. Specification 1 is the lege degree, is the proxy for level of schooling. The baseline equation. A one-unit increase in the percentage coefficient of this variable is positive and statistically of households with high school diploma or less, increase The impact of the coronavirus on African American unemployment: lessons from history Page 15 of 18 3 Table 10 Descriptive statistics for the 77 community areas in the City of Chicago N Mean Median S.D Max. Min. Household size 77 2.69 2.68 .59 4.3 1.53 Median income 77 $53,392 $50,178 $24,081 $111,962 $15,030 Unemployment rates_2019 77 8.5% 7% 5.5% 23.2% 1.9% Employed in 2019 77 17,717 12,876 14,668 74,135 758 Population growth 77 -.03% − .13% .47% 2.04% − .81% House value 77 254,850 227,477 110,828 594,571 62,083 % Owner occupied 77 40.2% 36.4% 18.1% 79.8% 12.4% % Renter occupied 77 47.2% 50.6% 15.9% 74.6% 13.8% % vacancy 77 12.6% 10.1% 5.9% 32.4% 6.3% % < HS Dip 77 16.2% 13.6% 10.0% 47.3% 1.4% %w/HS Dip 77 25.3% 26.0% 9.9% 46.7% 4.4% % W/Some college 77 25.9% 25.8% 8.4% 45.1% 8.2% % w/Grad 77 32.7% 26.2% 21.9% 84.9% 5.4% % w/White collar jobs 77 55.8% 52.8% 15.1% 89.1% 29.7% % w/ service jobs 77 24.1% 24.8% 7.2% 39.8% 7.6% % w/blue collar jobs 77 20.1% 19.6% 10.5% 45.5% 3.3% Table 11 Descriptive Statistics for the 24 Community Areas in South and Southeast Areas of the City of Chicago N Mean Median S.D Max. Min. Household size 24 2.5 2.5 .39 3.34 1.8 Median income 24 $37,477 $34,518 $12,245 $62,824 $15,030 Unemployment Rates_2019 24 12.6% 12.8% 4.7% 22.3% 4.4% Employed in 2019 24 8159 8439 5215 20,223 758 Population growth 24 − .1% − .14% .36% .73% − .81% House Value 24 197,104 174,356 79,882 343,120 62,083 % Owner occupied 24 34.0% 29.6% 17.3% 66.8% 12.4% % Renter occupied 24 51.0% 54.1% 16.2% 74.6% 23.7% % vacancy 24 15.1% 15.8% 5.2% 24.8% 8.1% % < HS Dip 24 15.2% 13.5% 6.7% 32.3% 3% %w/HS Dip 24 26.6% 27.1% 7.4% 37.1% 6.4% % W/Some College 24 31.8% 33.9% 8.3% 45.1% 13.5% % w/Grad 24 26.4% 24.4% 15.3% 76.7% 6.7% % w/White Collar Jobs 24 53.9% 52.7% 10.9% 83% 38.3% % w/ Service Jobs 24 28.2% 29.0% 6.3% 39.8% 11.1% % w/Blue Collar Jobs 24 17.9% 17.0% 7.5% 35.8% 5.8% Community Areas: Chatham, Avalon Park, South Chicago, Burnside, Calumet Heights, Roseland, Pullman, South Deering, East Side, West Pullman, Riverdale, Hegewisch, Armour Square, Douglas, Oakland, Fuller Park, Grand Boulevard, Kenwood, Washington Park, Hyde Park, Woodlawn, South Shore, Bridgeport, Greater Grand Crossing the percentage of workers in the service sector. This level coefficient is negative and statistically significant at the 1% of schooling explains approximately 70% of the varia- level. A one percent increase in median income reduces tion in percentage of workers in the service sector. Hold- the percentage of households working in the services sec- ing schooling constant, if a head of household is from the tor by 6.1%. Again, if the households are in the South/ South/Southeast side of Chicago, there is an additional Southeast sides of the City, they face a marginally higher 3.5% likelihood of working as an essential worker. Speci- likelihood of working as an essential worker, while control- fication 3 brings household income into the equation. Its ling for schooling and income. 3 Page 16 of 18 E. Coupet , E. Yamani Table 12 Earnings function analysis Table 13 Essential workers in the city of Chicago Dependent Variable: Log of Median Income Dependent % of variable: percentage of workers in services (1) (2) (1) (2) (3) (4) Grad 1.46*** 1.33*** No college degree .28*** .26*** .19*** .20*** (.144) (.127) (.016) (.016) (.028) (.029) Southside – − .328*** LN of Median Income – – − .061*** − .048*** (.076) (.016) (.017) Constant 10.31*** 10.46*** Southside – .035*** – .019** (.065) (.067) (.010) (.010) R .50 .61 Constant .10*** .053*** .768*** .616*** .013) (.010) (.190) (.199) N 77 77 Heteroskedasticity-Robust Errors in parenthesis AIC 45 27.5 R .70 .75 .78 .79 RMSE .320 .284 N 77 77 77 77 Normality Chi-Square test 1.96 0.33 P-values in parentheses (.38) (.85) AIC − 272 − 281 − 289 − 289 RMSE .039 .036 .035 .034 Heteroskedasticity-Robust Errors in parenthesis Chi-Square 4.73 2.21 13.65 .68 (P-values) (.09) (.33) (.00) (.71) Normality Test 6 Conclusion In the research reported in the present study, our central finding is that firms in the labor market appear to pre - the white sector that fills in during expansionary times but fer white employees to African American and Hispanics. suffers great losses during economic downturns. The state This finding is attested by several interesting findings that of Illinois exhibits the same phenomenon, but to a greater emerge from our employment and income differential level. analyses. Moving onto our income differential analysis, we show Our employment differential analysis reveals that there that the African Americans in the south part of Chicago is racial employment disparity which is first evident from are more likely to have lower median incomes and they the persistent near two-fold level of the national unem- tend to work in the service sector of the economy, com- ployment rates in the African American labor market. pared to their counterparts in other parts of the City. Over the full sample period, the unemployment in the Until the COVID-19 pandemic, the service sector did not African American sector is nearly twice that of the white carry the “essential worker” moniker it has come to be sector, and we find this condition to be even larger in the known as. In fact, it was the sector that was considered City of Chicago, particularly the Southeast and South sides low-skilled and was paid less in earnings. That sector of of the City. A similar pattern is observed in the two sub- the labor force is typically female and non-unionized— sample periods surrounding the 9/11 terrorist attack and particularly women of color. They now find themselves the 2007–2008 recession. While these two episodes expe- on the front line of the health battlefield without ade - rienced exogenous shocks to the labor market and led to quate personal protection equipment. This is now a sec - significant increases in the unemployment rates in all sec - tor of the labor market that arguably deserves hazard pay. tors, the increase in unemployment rate in the white sec- These findings corroborate the narrative in the main - tor paled to that of the African American sector. stream media that African Americans and women of The major takeaway from our analysis is that there is a color are paid less than white workers for doing the same long-run association between white unemployment and jobs. Simply stated, African Americans are not paid the African American unemployment, in the sense that white marginal product of their labor. unemployment Granger-causes African American unem- Our findings have important policy implications. ployment. That is, white unemployment experiences “nat - While it is uncertain to know for sure what will be the ural-rate” even within aggregate demand gaps when the effect of this purely healthy-related exogenous shock macro economy is not experiencing cyclical downturn. In to the economy, the effect of the COVID-19 is certain contrast, African American unemployment is largely cycli- to be deep for the African Americans who suffer from cal in nature, in the sense that the African American labor higher unemployment rates and lower median incomes. market appears to serve as a secondary labor market to There is a great opportunity for local, state, and national The impact of the coronavirus on African American unemployment: lessons from history Page 17 of 18 3 Funding leadership to alleviate the burden that the African Ameri- The authors received no financial support for the research of this article. can Community carries. To alleviate this expected hard- ship, targeted public policy should be introduced so that Availability of data and materials All data will be available upon request. we must allocate funding and resources to where they are most needed, and policy recommendations must be Declarations reflective of this reality. A uniform policy approach will not address the varied needs of groups and communities Competing interests given that people will differentially experience the ini - The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. tial and longer-term consequences of the viral pandemic social distancing protocols. Received: 10 August 2021 Accepted: 17 March 2022 Hence, we propose two targeted policy recommenda- tions. First, we recommend stimulating private fixed cap - ital formation in African American communities. More specifically, we recommend providing guaranteed heav - References Aali-Bujari, A., Venegas-Martinez, F., Garcia-Santillan, A.: Schooling levels and ily subsidized loans to those investing in African Ameri- wage gains in Mexico. Econ. Sociol. 12(4), 74–83 (2019) can communities. An increase in capital expenditures Abdul Khalid, M., Yang, L.: Income inequality and ethnic cleavages in Malaysia: in largely African American communities will increase Evidence from distributional national accounts (1984–2014). J. Asian Econ. 72, 101252 (2021) economic output, increase and stabilize employment Becker, G.S.: Investments in human capital: a theoretical analysis. J. Polit. Econ. (decrease unemployment), increase household income, 70(5), 9–49 (1962) and increase local tax revenues. For maximum effective - Bond, T.N., Lehmann, J.K.: Prejudice and racial matches in employment. Labour Econ. 51, 271–293 (2018) ness, target industries that have the greatest leakages Boustan, L.P., Margo, R.A.: Race, segregation, and postal employment: new from those communities. evidence on spatial mismatch. J. Urban Econ. 65(1), 1–10 (2009) Our second recommendation is to enforce fair wages Button, P., Walker, B.: Employment discrimination against Indigenous Peoples in the United States: Evidence from a field experiment. Labour Econ. 65, to ensure equitable wages across the labor markets. 101851 (2020) There is an abundance of evidence suggesting that the Chantreuil, F., Fourrey, K., Lebon, I., Rebiere, T.: Magnitude and evolution of gen- marginal product of labor is not compensated equitably der and race contributions to earnings inequality across US regions. Res. Econ. 75(1), 45–59 (2021) across various sectors of the labor market. Unfair, below- Chiswick, B.R.: Jacob Mincer, Experience and the Distribution of Earnings. Rev. market, wages to African Americans leads to a reduc- Econ. Household 1(4), 343–361 (2003) tion in income, expenditures and savings in the African Contreras, S., Ghosh, A., Hasan, I.: Income inequality and minority labor market dynamics: Medium term effects from the Great Recession. Econ. Lett. American community, which in turn reduces expected 199, 109717 (2021) free cash flows to potential investors in the community, Couch, K.A., Fairlie, R., Xu, H.: Early evidence of the impacts of COVID-19 on making investments less attractive. This contributes to minority unemployment. J. Public Econ. 192, 104287 (2020) Hellerstein, J.K., Neumark, D., Mclnerney, M.: Spatial mismatch or racial mis- an increase in unemployment that further decreases to match? J. Labour Econ. 64(2), 464–479 (2008) household income—a vicious cycle. Reduced wage also Hoynes, H., Miller, D.L., Schaller, J.: Who suffers during recessions. J. Econ. Persp. reduces that individual’s propensity to repay interest on 26(3), 27–48 (2012) Ileanu, B.V., Tanasoiu, O.E.: Factors of the earning functions and their influence capital. This makes home ownership less likely and access capital of an organization. J. Appl. Quant. Methods 3(4), 366–374 (2008) to liquidity less likely. During economic downturn, a lack Immergluck, D.: Job proximity and the urban employment problem: do suit- of liquidity increases hardship for the individual and for able nearby jobs improve neighborhood employment rates?: a reply. Urban Stud. J. 35(12), 2359–2368 (1998) the community. Kim, A.T., Kim, C., Tuttle, S.E., Zhang, Y.: COVID-19 and the decline in Asian American employment. Res. Soc. Stratif. Mobility 71, 1000563 (2021) Acknowledgements Lynch, G.J., Hyclak, T.: Cyclical and noncyclical unemployment differences We would like to express our gratitude to Robin Ficke and Alex Singer from among demographic groups. Growth Chang. 15(1), 9–17 (1984) World Business Chicago for their help in providing the relevant date required Macartney, H., Nielsen, E., Rodriguez, V.: Unequal worker exposure to establish- to perform our empirical analysis in this report. We must also acknowledge ment deaths. Lab. Econ. 73, 102073 (2021) Stephanie Bechteler from Chicago Urban League for her general support and Mandel, H., Semyonov, M.: The gender-race intersection and the ‘sheltering- feedback. The following authors have affiliations with organizations with direct effect’ of public-sector employment. Res. Soc. Stratif. Mob. 71, 1000563 or indirect financial interest in the subject matter discussed in the manuscript: (2021) Mincer, J.: Investment in human capital and personal income distribution. J. Authors’ contributions Polit. Econ. 66(4), 281–302 (1958) All authors have participated in (a) conception and design, or analysis and Mouw, T.: Job Relocation and the Racial Gap in Unemployment in Detroit and interpretation of the data; (b) drafting the article or revising it critically for Chicago, 1980 to 1990. Am. Sociol. Rev. 65(5), 730–753 (2000) important intellectual content; and (c) Both authors read and approved the Nkomo, S.M., Ariss, A.: The historical Origins of Ethnic (white) Privilege in US final manuscript. Organizations. J. Manag. Psychol. 29(4), 389–404 (2014) 3 Page 18 of 18 E. Coupet , E. Yamani Raymond, E.L.: Race, uneven recovery and persistent negative equity in the southeastern United States. J. Urban A. ff 40(6), 824–837 (2018) Ren, C.: Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality. Soc. Sci. Res. 1, 102710 (2022) Yu, W., Sun, S.: Race-ethnicity, class, and unemployment dynamics: do macro- economic shifts alter existing disadvantages? Mobility 63, 100422 (2019) Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations.

Journal

Journal for Labour Market ResearchSpringer Journals

Published: Dec 1, 2022

Keywords: Labor markets; Unemployment; Financial crisis; E2; E3; J4

References