Demographics

Error message

Deprecated function: The each() function is deprecated. This message will be suppressed on further calls in _menu_load_objects() (line 579 of /var/www/drupal-7.x/includes/menu.inc).

Credit, Income, and Inequality

Published by Anonymous (not verified) on Mon, 12/07/2021 - 1:07am in

Credit, Income, and Inequality

Access to credit plays a central role in shaping economic opportunities of households and businesses. Access to credit also plays a crucial role in helping an economy successfully exit from the pandemic doldrums. The ability to get a loan may allow individuals to purchase a home, invest in education and training, or start and then expand a business. Hence access to credit has important implications for upward mobility and potentially also for inequality. Adverse selection and moral hazard problems due to asymmetric information between lenders and borrowers affect credit availability. Because of these information issues, lenders may limit credit or post higher lending rates and often require borrowers to pledge collateral. Consequently, relatively poor individuals with limited capital endowment may experience credit denial, irrespective of the quality of their investment ideas. As a result, their exclusion from credit access can hinder economic mobility and entrench income inequality. In this post, we describe the results of our recent paper which contributes to the understanding of this mechanism.

Access to Credit Has a Positive Effect on the Income of Small Business Owners
In our paper, we study how banks’ credit decisions (credit acceptance or rejection) affect applicants’ income and its distribution in a developed economy. We identify this effect using a unique data set of business loan applications to a single large European bank during the time period 2002-16. Our focus is on loan applications from small and micro enterprises (with total assets less than EUR 10 million) that are majority-owned by individuals and do not have a credit relationship with another regulated bank. For these applicants, the bank decides whether to grant loans based on a credit score cutoff rule. Specifically, each applicant is assigned a credit score at the time of the loan application, which consists of an internal rating constructed by the bank. Then, credit is granted to applicants with credit scores above the cutoff, and denied otherwise. We exploit the cutoff rule as a source of variation in the bank’s credit decision. Our approach builds on the idea that applicants whose credit scores are around the cutoff share approximately the same traits, including similar incomes. Thus, we quantify the effect of a loan origination on individual income by comparing changes in the income of accepted and denied applicants, who prior to the bank’s credit decision have similar credit scores.

Our main finding is summarized by the chart below, which shows applicants’ income five years after the loan decision against the credit score. The chart reveals a clear upward shift in applicants’ income around the cutoff, with marginally accepted applicants experiencing higher incomes than marginally rejected applicants. In general, our analysis shows that access to credit has a positive effect on the income of small business owners. Specifically, being approved for a loan implies an increase in the recipient’s income of approximately 6 percent one to three years after the loan decision, and an increase of 11 percent five years after.

Credit, Income, and Inequality

We next investigate the economic channels behind the observed positive impact of a loan origination on individual income. We document that firms of accepted applicants use the borrowed funds to make investments and expand their business, ultimately experiencing higher profitability and growth rates compared to firms of rejected applicants. We also show that the effect of credit access on income is more pronounced when we compare marginally accepted and rejected applicants whose credit score is positively affected by the soft information held by the bank (for example, on the quality of the investment opportunities of the firm). Overall, these results suggest that credit provision to small businesses is pivotal to foster entrepreneurship and economic mobility.

Our Findings Support a Negative Finance-Inequality Nexus
A natural implication of our key findings is that the income distribution of applicants around the cutoff changes in response to the bank’s credit decisions. Using two standard aggregate measures of income inequality (the Gini coefficient and the Theil index), we document a tighter income distribution among accepted applicants and a wider income distribution among rejected applicants.

We next examine how credit origination affects the income distribution in the economy more generally. First, we show that loan approval has a stronger effect on applicants’ future income in low-income regions compared to high-income regions, thus potentially affecting the income distribution within and across geographical areas. Second, we exploit the Great Recession to analyze how an economic crisis and associated credit crunch affect the credit-income relationship. We show that the positive effect of credit access on individual income is somewhat stronger during the crisis period, when small business owners are more credit-constrained. Overall, these results are in line with the theory pointing to a negative relationship between credit availability and income inequality.

Our findings have two key and interrelated economic implications. First, credit decisions strongly affect applicants’ future income and its subsequent dynamics, altering lifetime income expectations and realizations. Second, credit decisions exert substantial effects on the income distribution. In general, our study supports the idea that extending credit to individuals with good investment ideas improves economic mobility and reduces income inequality.

Manthos Delis is a professor of financial economics at Montpellier Business School.

Fulvia FringuellottiFulvia Fringuellotti is an economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Steven Ongena is a professor of banking at the University of Zurich.

Additional Posts in this Series

Banking the Unbanked: The Past and Future of the Free Checking Account
Hold the Check: Overdrafts, Fee Caps, and Financial Inclusion

Related Reading
Economic Inequality: A Research Series

How to cite this post:
Manthos Delis, Fulvia Fringuellotti, and Steven Ongena, “Credit, Income, and Inequality,” Federal Reserve Bank of New York Liberty Street Economics, July 1, 2021, https://libertystreeteconomics.newyorkfed.org/2021/07/credit-income-and-....

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Credit, Income, and Inequality

Published by Anonymous (not verified) on Thu, 01/07/2021 - 9:00pm in

Manthos Delis, Fulvia Fringuellotti, and Steven Ongena

Access to credit plays a central role in shaping economic opportunities of households and businesses. Access to credit also plays a crucial role in helping an economy successfully exit from the pandemic doldrums. The ability to get a loan may allow individuals to purchase a home, invest in education and training, or start and then expand a business. Hence access to credit has important implications for upward mobility and potentially also for inequality. Adverse selection and moral hazard problems due to asymmetric information between lenders and borrowers affect credit availability. Because of these information issues, lenders may limit credit or post higher lending rates and often require borrowers to pledge collateral. Consequently, relatively poor individuals with limited capital endowment may experience credit denial, irrespective of the quality of their investment ideas. As a result, their exclusion from credit access can hinder economic mobility and entrench income inequality. In this post, we describe the results of our recent paper which contributes to the understanding of this mechanism.

Access to Credit Has a Positive Effect on the Income of Small Business Owners

In our paper, we study how banks’ credit decisions (credit acceptance or rejection) affect applicants’ income and its distribution in a developed economy. We identify this effect using a unique data set of business loan applications to a single large European bank during the time period 2002-16. Our focus is on loan applications from small and micro enterprises (with total assets less than EUR 10 million) that are majority-owned by individuals and do not have a credit relationship with another regulated bank. For these applicants, the bank decides whether to grant loans based on a credit score cutoff rule. Specifically, each applicant is assigned a credit score at the time of the loan application, which consists of an internal rating constructed by the bank. Then, credit is granted to applicants with credit scores above the cutoff, and denied otherwise. We exploit the cutoff rule as a source of variation in the bank’s credit decision. Our approach builds on the idea that applicants whose credit scores are around the cutoff share approximately the same traits, including similar incomes. Thus, we quantify the effect of a loan origination on individual income by comparing changes in the income of accepted and denied applicants, who prior to the bank’s credit decision have similar credit scores.

Our main finding is summarized by the chart below, which shows applicants’ income five years after the loan decision against the credit score. The chart reveals a clear upward shift in applicants’ income around the cutoff, with marginally accepted applicants experiencing higher incomes than marginally rejected applicants. In general, our analysis shows that access to credit has a positive effect on the income of small business owners. Specifically, being approved for a loan implies an increase in the recipient’s income of approximately 6 percent one to three years after the loan decision, and an increase of 11 percent five years after.

Credit, Income, and Inequality

We next investigate the economic channels behind the observed positive impact of a loan origination on individual income. We document that firms of accepted applicants use the borrowed funds to make investments and expand their business, ultimately experiencing higher profitability and growth rates compared to firms of rejected applicants. We also show that the effect of credit access on income is more pronounced when we compare marginally accepted and rejected applicants whose credit score is positively affected by the soft information held by the bank (for example, on the quality of the investment opportunities of the firm). Overall, these results suggest that credit provision to small businesses is pivotal to foster entrepreneurship and economic mobility.

Our Findings Support a Negative Finance-Inequality Nexus

A natural implication of our key findings is that the income distribution of applicants around the cutoff changes in response to the bank’s credit decisions. Using two standard aggregate measures of income inequality (the Gini coefficient and the Theil index), we document a tighter income distribution among accepted applicants and a wider income distribution among rejected applicants.

We next examine how credit origination affects the income distribution in the economy more generally. First, we show that loan approval has a stronger effect on applicants’ future income in low-income regions compared to high-income regions, thus potentially affecting the income distribution within and across geographical areas. Second, we exploit the Great Recession to analyze how an economic crisis and associated credit crunch affect the credit-income relationship. We show that the positive effect of credit access on individual income is somewhat stronger during the crisis period, when small business owners are more credit-constrained. Overall, these results are in line with the theory pointing to a negative relationship between credit availability and income inequality.

Our findings have two key and interrelated economic implications. First, credit decisions strongly affect applicants’ future income and its subsequent dynamics, altering lifetime income expectations and realizations. Second, credit decisions exert substantial effects on the income distribution. In general, our study supports the idea that extending credit to individuals with good investment ideas improves economic mobility and reduces income inequality.

Manthos Delis is a professor of financial economics at Montpellier Business School.

Fulvia FringuellottiFulvia Fringuellotti is an economist in the Federal Reserve Bank of New York’s Research and Statistics Group.



Steven Ongena is a professor of banking at the University of Zurich.

Additional Posts in this Series

Banking the Unbanked: The Past and Future of the Free Checking Account

Hold the Check: Overdrafts, Fee Caps, and Financial Inclusion



Related Reading

Economic Inequality: A Research Series

How to cite this post:

Manthos Delis, Fulvia Fringuellotti, and Steven Ongena, “Credit, Income, and Inequality,” Federal Reserve Bank of New York Liberty Street Economics, July 1, 2021, https://libertystreeteconomics.newyorkfed.org/2021/07/credit-income-and-....




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Racial and Income Gaps in Consumer Spending following COVID-19

Published by Anonymous (not verified) on Wed, 23/06/2021 - 4:51am in

Ruchi Avtar, Rajashri Chakrabarti, Maxim Pinkovskiy, and Giorgio Topa

LSE_2021_covid-inequality_seriesVII_chakrabarti_460

This post is the first in a two-part series that seeks to understand whether consumer spending patterns during the COVID-19 pandemic evolved differentially across counties by race and income. As the pandemic hit and social distancing restrictions were put into place in March 2020, consumer spending plummeted. Subsequently, as social distancing restrictions began to be relaxed later in spring 2020, consumer spending started to rebound. We find that higher-income counties had a considerably steeper decline and a shallower recovery than low-income counties did. The differences by race were also sizeable as the pandemic struck but became considerably more muted after summer of 2020. The decline and the recovery until the end of summer were sharper for majority-minority (MM) than majority nonminority (MNM) counties, while both sets of counties showed similar growth in spending after that. The second post in this series highlights the goods and services that were most adversely affected (or “constrained”) by the pandemic. Then, differentiating households by income, that post explores which households were more exposed to these pandemic-constrained expenditure categories.

Data and Background

We capture consumer spending by using detailed county-level card transaction data provided by Commerce Signals, a Verisk Analytics business. Commerce Signals captures spending by a permissioned panel of around 40 million U.S. households, which means that it includes data on spending at large businesses as well. The aggregate trends from Commerce Signals align well with national retail sales numbers. We use county-level Commerce Signals data to identify the differences in consumer spending between residents of low-income and higher-income counties and between residents in MM and MNM counties. Following earlier posts in our ongoing Economic Inequality series, we use data on race and income composition at the county level from the 2014-18 waves of the American Community Survey to differentiate between low-income and higher-income counties, and between MM and other counties. We define low-income counties as those that fall in the lowest quartile of the population-weighted distribution of median household income. Recognizing that the term “minority” does not fully capture the racial and ethnic diversity present, we note that in our definition MM counties are those in which at least half the population is Hispanic, and/or non-Hispanic Black, Asian American, Pacific Islander, or Native American. We seasonally adjust the data by dividing weekly data for each group by the corresponding data in 2019. Next, we index each series to January 2020 and rescale the series to present percent changes relative to January 2020. In other words, each spending series represents the year-on-year growth in spending relative to the year-on-year growth obtaining in January 2020.

Differences in Consumer Spending by Income

Here we consider two measures of spending: total spending and spending in restaurants and bars, looking first at the difference in total spending by median county income. We find that there is practically no difference in the pre-COVID period in consumer spending growth between low-income counties and other counties. Spending declines drastically across all counties in March 2020. Notably, though, the percentage decline in spending in low-income counties (relative to January 2020) was perceptibly smaller than that for higher-income counties. In other words, households in higher-income counties reduced their total consumption much more than households in low-income counties. The rebound in spending between April and June 2020 is also more rapid in low-income counties, which almost returns to pre-pandemic levels by July 2020. While the gap between both groups of counties began closing towards the last quarter of 2020, it widened around the holidays. As of the end of March 2021, seasonally adjusted consumption in both low-income and higher-income counties surpassed their pre-pandemic levels.

What might be the factors behind these differences? The composition of consumption differs considerably between high- and low-income households. Low-income households spend a greater proportion of their income on necessities, the demand for which is inelastic, and there is not much room for cutbacks even in cases of financial losses. Such households are also more likely to have lost jobs and received unemployment assistance and income support, which may have prevented deeper cuts. In contrast, a larger share of the consumption basket of higher income households pertain to goods and services that were hit the hardest by social distancing, for example: travel, hotels, and recreation. The decline in these industries during the pandemic constrained the consumption basket of the richer households relatively more, leading to a sharper decline in spending. Higher-income households may have also resorted to precautionary saving facing the uncertainties of the pandemic. Together, these factors help explain the smaller percentage decline in spending in low-income counties as the pandemic struck as well as a sharper return during recovery (see the chart below).


Racial and Income Gaps in Consumer Spending following COVID-19

The story remains very similar when we only consider spending at restaurants and bars. The food service industry as a whole was greatly affected by the stay-at-home orders that most U.S. states announced at the onset of the pandemic. Consumer spending at restaurants and bars declined by an even greater magnitude than total spending. And when distinguishing by income, we see that just like total spending, spending in low-income counties declined less than in high-income counties and recovered noticeably faster. One explanation may be that restaurant types predominating in low-income counties (quick service or drive-throughs at fast food restaurants) would have been less affected by social distancing than those in high-income counties (sit-down dining). Spending in the higher-income group had not yet returned to pre-pandemic levels of seasonally adjusted consumption by the end of March 2021 (see the next chart).

More generally, these patterns should not be interpreted to mean that the loss of well-being for low-income households was more limited than for higher-income ones. Notably, low-income counties suffered from larger job losses. Rather, they suggest that low-income households had less latitude to adjust their spending, as the higher share of necessities in their consumption basket would make it difficult to cut spending by more even in the face of higher job losses.


Racial and Income Gaps in Consumer Spending following COVID-19

Differences in Consumer Spending by Race

We now turn to differences in aggregate consumer spending by race. As we see in the chart below, there is a gap between MM and MNM counties, with MNM counties experiencing smaller declines than MM counties after the pandemic begins. The gap arises at the beginning of the pandemic, but essentially disappears by the late summer of 2020. This disparity between both groups of counties remains small throughout, and we see the gap closing completely more than once in 2021.


Racial and Income Gaps in Consumer Spending following COVID-19

This analysis may mask the differential experience of smaller versus larger businesses since the consumer spending data we analyze here capture spending at both small and large businesses. Stayed tuned for parallel analysis in an upcoming blog. Coupled with the analysis in this post, that forthcoming analysis will allow us to distinguish between spending at small versus large businesses and unearth how these differed across race and income.

Conclusion

In this post, we have looked at the trends in spending at all businesses as captured by credit and debit card transactions during the course of the pandemic. Although all counties saw an immediate decline in consumer spending as the pandemic hit, differences have emerged in both the magnitude and subsequent recovery patterns. The analysis suggests that low-income counties experienced a shallower recession and a more robust recovery in consumption than higher-income counties did. This may be a consequence of government policies supporting income, as well as of individuals in lower-income counties having lower consumption of the kinds of discretionary purchases—restaurant meals, travel, and entertainment—that were hardest hit by the pandemic. However, heterogeneity in economic activity by race is both more limited and more nuanced. We will revisit this topic on heterogeneity in economic activity in several weeks in a forthcoming post on small business experience, where we will investigate whether activity at small businesses varied across counties that differed by race and income. In the companion post in this series, we will focus on goods and services that were cut back the most during the pandemic and investigate which households cut spending on these goods the most.

Ruchi Avtar is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Rajashri ChakrabartiRajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

Maxim PinkovskiyMaxim Pinkovskiy is a senior economist in the Research and Statistics Group.

Giorgio TopaGiorgio Topa is a vice president in the Research and Statistics Group.

How to cite this post:

Ruchi Avtar, Rajashri Chakrabarti, Maxim Pinkovskiy, and Giorgio Topa, “Racial and Income Gaps in Consumer Spending following COVID-19,” Federal Reserve Bank of New York Liberty Street Economics, May 12, 2021, https://libertystreeteconomics.newyorkfed.org/2021/05/racial-and-income-....

Related Reading

Who’s Ready to Spend? Constrained Consumption across the Income Distribution

Discretionary and Nondiscretionary Services Expenditures during the COVID-19 Recession

Economic Inequality Research Series

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Understanding the Racial and Income Gap in Covid-19: Health Insurance, Comorbidities, and Medical Facilities

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:42am in

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy

 Health Insurance, Comorbidities, and Medical Facilities

Our previous work documents that low-income and majority-minority areas were considerably more affected by COVID-19, as captured by markedly higher case and death rates. In a four-part series starting with this post, we seek to understand the reasons behind these income and racial disparities. Do disparities in health status translate into disparities in COVID-19 intensity? Does the health system play a role through health insurance and hospital capacity? Can disparities in COVID-19 intensity be explained by high-density, crowded environments? Does social distancing, pollution, or the age composition of the county matter? Does the prevalence of essential service jobs make a difference? This post will focus on the first two questions. The next three posts in this series will focus on the remaining questions. The posts will follow a similar structure. In each post, we will aim to understand whether the factors considered in that post affect overall COVID-19 intensity, whether the racial and income gaps can be further explained when we additionally include the factors in consideration in that post, and whether and to what extent the factors under consideration in that post independently affect racial and income gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).

Data and Definitions
Since our COVID-19 data are available at the county level, we use data on race and income composition at the county level to differentiate between low-income and other counties, and majority-minority (MM) and other counties (see earlier Liberty Street Economics post for details about these data). We define low-income counties as those that fall in the lowest quartile of the population-weighted distribution of median household income. We define MM counties as those in which at least half the population is Hispanic and/or non-Hispanic Black.

In this post, we additionally leverage pre-COVID county-level data on comorbidities, health insurance and hospital beds. Our data on comorbidities include data on obesity, hypertension, diabetes, heart disease, cancer and respiratory diseases. We use two measures from the supply side of the health market—total number of beds and number of ICU beds—to construct a county-level measure of hospital capacity in the immediate pre-COVID period: share of hospital beds that are in the ICU. Throughout this series, we capture COVID-19 intensity by case rates as of December 15. Results for death rates are qualitatively similar and are not reported here.

The CDC advises that individuals with comorbidities are at increased risk of severe complications of COVID-19. Uninsured individuals have worse self-reported health and may be more vulnerable to COVID-19. In addition, lack of insurance limits access to health care and other hospital facilities, and can increase exposure and vulnerability to this disease. Finally, ICU resources are essential for treating severe cases of COVID-19. Timely access to ICU services and medical facilities can reduce death rates. Better treatment and cures due to access to health resources can also control the spread of the disease and hence reduce case rates. At issue is whether the incidence of comorbidities and access to health insurance and health resources vary along income and racial lines. If they do, they could potentially affect some of the racial and income disparities we see in the data.

Health Factors and COVID-19 Race and Income Gap

For existing comorbidities, access to health facilities and health insurance to affect racial and income disparities, they should be correlated with low-income and MM status. Looking at the correlations, we find that both low-income and MM counties are also counties that have higher uninsurance rates, a lower share of hospital resources in the ICU (for low-income counties, but not MM counties) and a higher rate of comorbidities. An important question then is whether the higher exposures to COVID-19 in MM and low-income counties are partly explained by some of these health factors. 

To better understand the role of these various health factors in explaining the racial and income gap of COVID-19 incidence we use a multivariate regression analysis. All regressions control for time-invariant characteristics of the states and exploit within-state across-county variation to understand patterns. For better comprehensibility, throughout this series, we will present the regression coefficients of interest as bar charts, as shown below. The left-hand panel shows the differences in cases per 1,000 for low-income counties compared to others (the income gap), while the right-hand panel shows the differences for MM counties compared to others (the minority gap). The baseline bars in blue show that low-income counties have 4.2 more cases per 1,000 people than other counties and MM counties have 14 more cases per 1,000 people than other counties after controlling for population density and urban status. We define counties that fall within a metropolitan statistical area (MSA) as urban counties. 

Building on the baseline bars for both panels, the second bars in gold account for differences in comorbidities between counties. Comparing the first and second bars in the right panel, we find that inclusion of comorbidities reduces the minority gap by more than a quarter, while the left panel shows that the income gap remains similar. These imply that differences in prevalence of comorbidities in MM versus other counties are associated with differences in COVID-19 case rates in these counties. 

In each panel, the third bar in light gray includes additional health variables: health insurance and share of ICU beds (as a proportion of all hospital beds) in a county. Comparing the minority and income gaps between the light gray bars and the gold bars we find that the inclusion of these health variables leads to a decline in both gaps. The income gap falls a quarter of its value and the minority gap shrinks to less than two-thirds. Thus, in addition to the role of comorbidities (the gold bars), the results in the light gray bars suggest that some of the racial and income inequalities in COVID-19 exposures (the blue bars) are contributed by inequalities in other health factors, such as, ICU bed availability, and access to health insurance.

Separately, we also estimate regressions where we add the uninsurance rate and proportion of ICU beds individually and then taken together, but without adding comorbidities, to the baseline regressions. The fourth bars in both panels, in dark gray, show results when we include uninsurance rates to our baseline specification (without controlling for comorbidities or share of ICU beds). We find the inclusion of uninsurance reduces the minority gap by close to a quarter and the income gap by more than a third (compared to the baseline). In results not reported, we find a very similar reduction in the gaps when both uninsurance rates and proportion of ICU beds are added together to the baseline specification, but only a small bridging of the gaps when only the proportion of ICU beds is added. Therefore, both comorbidities and access to health insurance separately explain important components of the greater COVID-19 intensity in low-income and MM counties.

LSE_2021_heterogeneity_racial-gap1_chakrabarti_ch1-01

Looking more closely at the regression results that include baseline variables, comorbidities and the health variables (results corresponding to the light gray bars), we find that obesity, heart disease, hypertension and diabetes, and lack of health insurance are associated with higher COVID-19 incidence, controlling for the low-income and MM variables. In the chart below we depict the partial associations with heart disease rate, uninsurance rates and share of beds in ICU (the coefficients on all comorbidities are not shown to save space). We find that a one percentage point increase in the heart disease rate per 1,000 people in a county is associated with 3.44 more COVID cases per 1,000. A one percentage point increase in a county’s uninsurance rate is associated with 0.99 more cases per 1,000 people in a county. The increase associated with a greater fraction of beds in the ICU is not statistically distinguishable from zero.

LSE_2021_heterogeneity_racial-gap1_chakrabarti_ch2-02

Conclusion
This post takes a deep dive to understand the disparities in COVID-19 case rates by race and income. We find that a quarter of the income gap and more than a third of the racial gap in case rates are contributed by health status and system factors. These results suggest that policy can play a role in reducing the disparities in COVID-19 incidence. Increased access to health insurance and greater effort by public health professionals to battle chronic diseases may play an important role in reducing the impact of COVID-19 and bridging the income and racial gap of COVID-19 intensity. In our next post, we will look at the role of crowding and whether it can explain more of the gap in COVID-19 occurrence.


Ruchi Avtar is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Chakrabarti_rajashri
Rajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

Pinkovskiy_maxim
Maxim Pinkovskiy is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:
Ruchi Avtar,
Rajashri Chakrabarti, and
Maxim Pinkovskiy, “Understanding the Racial and Income Gap in Covid-19: Health Insurance, Comorbidities, and Medical Facilities,” Federal Reserve Bank of New York Liberty Street Economics, January 12, 2021, https://libertystreeteconomics.newyorkfed.org/2021/01/understanding-the-....

Additional heterogeneity posts on Liberty Street Economics
Heterogeneity: A Multi-Part Research Series

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Understanding the Racial and Income Gap in COVID-19: Public Transportation and Home Crowding

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:42am in

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy

LSE_2021_heterogeneity_racial-gap2_chakrabarti_icon_460

This is the second post in a series that aims to understand the gap in COVID-19 intensity by race and income. In our first post, we looked at how comorbidities, uninsurance rates, and health resources may help to explain the race and income gap observed in COVID-19 intensity. We found that a quarter of the income gap and more than a third of the racial gap in case rates are explained by health status and system factors. In this post, we look at two factors related to indoor density—namely public transportation use and home crowding. Here, we will aim to understand whether these two factors affect overall COVID-19 intensity, whether the income and racial gaps of COVID-19 can be further explained when we additionally include these factors, and whether and to what extent these factors independently account for income and racial gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).

Background
Interpersonal interactions are a primary mechanism for spread of COVID-19, with poorly ventilated, crowded, and shared indoor environments exacerbating the spread of the virus. Many indoor interactions of this kind take place in public transit networks and in overcrowded apartments, which is why we focus our attention on these factors now.

Many researchers have cited public transportation as a possible major source of spread of COVID-19. Public transit is typically characterized by high density, low ventilation, and frequent turnover, leading to a high probability of an infected person entering and passing the virus to others. It is noteworthy that while most counties do not have public transportation networks, the central urban counties in major cities that were on the front lines of the pandemic in March 2020 are reliant on them. On the other hand, recent research has suggested that public transit is not a major source of transmission for other respiratory diseases, such as the flu. We conduct our analysis using county-level data on public transit use from the 2014-2018 five-year American Community Survey (ACS), which interviews approximately one out of twenty Americans and has reasonable sample sizes even at the county level.

The second density indicator that we consider is home crowding. When multiple individuals share the same living quarters, the possibility of spreading the infection if one member contracts the virus is very high. We measure home crowding by the number of persons per room, which we obtain at a county level from the detailed housing questionnaire of the 2014-2018 ACS.

Density and COVID-19 Race and Income Gap

For these density factors to help explain the COVID-19 racial and income gap, they should be correlated with the low-income and majority-minority (MM) status of counties. For the correlations, we find that residents in MM counties tend to use more public transit and to also have a significantly higher average number of persons per room. In contrast, low-income counties see less public transit ridership and have a lower number of persons per room. This is likely explained by low-income counties typically being relatively rural.

As in our previous post, we perform multivariate regression analysis to determine the extent to which density factors such as public transportation use and crowded living quarters can explain the observed racial and income gap in COVID-19 incidence and the extent to which these factors can explain overall COVID-19 intensity. The left panel of the chart below shows the difference in cases per 1,000 for low-income counties compared to others (the income gap), while the right panel shows the difference for MM counties compared to others (the minority gap). The first bars in blue in each panel show results from our original model estimated with data through December 15, where we regress cases per thousand on the baseline variables, namely population density, and indicators for urbanicity, low-income, and MM counties. The Post 1 bars in gold show the results from our previous post, where we include comorbidities and health factors in addition to baseline variables. The third set of bars, in light gray, represent the regression of cases per thousand on all the variables so far, augmented by the two density determinants discussed here. Lastly, the bars in dark gray depict the basic variables and the density determinants, but do not include the health variables introduced in the first post of this series.

LSE_2021_heterogeneity_racial-gap2_chakrabarti_ch1-01

Our baseline specification (which includes population density and MM, low-income, and urbanicity indicators) shows about 4.2 more cases per thousand in low-income counties, and 14 more cases per thousand for counties where minorities are in majority. When adding controls for comorbidities, uninsurance, and health resources measured by proportion of ICU beds, as depicted in the second bars in gold, we see a decrease in magnitude of these differentials (see the previous post in this series).

We next turn to the bars in light gray shown above, where we add the two mediating variables considered in this post to examine associations with COVID-19 cases. We find that the inclusion of the density factors—in addition to comorbidities, uninsurance rates, and health resources introduced in the first post in this series—further reduces the low-income and racial differentials for COVID-19 cases per thousand. The coefficient for the income gap is now less than two-thirds of the original baseline estimate, but remains statistically significant. The MM differential is still significant, at less than half of the baseline estimate.

To investigate the contributions of the density factors separately, the last bar in each panel (in dark gray) presents results from the regression of cases per 1,000 on the baseline variables (MM, low-income, urbanicity indicator, and population density) and the density indicators introduced in this post. We find that inclusion of these variables leads to a narrowing of the racial and income gaps of COVID-19 intensity. Comparing these results to the baseline results, shown in the bars in blue, we find that inclusion of public transit and persons per room reduces the MM gap from 14 cases per thousand to 7.2 per thousand, and the low-income gap from 4.2 cases per thousand to 3.9 per thousand. Thus, when considered separately, measures of crowding (as captured by persons per room and use of public transit) explain significant portions of the racial gap of COVID-19—almost half of the racial gap seen in our baseline estimates.

Lastly, we consider the associations between COVID-19 case rates and the density indicators, conditional on all the variables analyzed in our previous post (that is, from the multivariate regression corresponding to the light gray bars above). From the chart below, it is clear that having more persons per room is significantly associated with higher cases per thousand, where a unit increase in persons per room in a county is associated with 135 more COVID-19 cases per thousand. In results not reported, we find that this strong association between more persons per room and higher COVID-19 cases also remains when we exclude comorbidities and health variables. In contrast, the association between the percentage of persons commuting via public transit and cases per thousand is not statistically significant. However, this effect is largely driven by big cities where public transit is more common. In results not reported here where we account for this skewed distribution of the percentage commuting by public transit, we find that this variable is positively associated with COVID-19 case rates and the effect is statistically different from zero. In other words, not just home crowding, but crowding in public transit systems is also associated with higher COVID-19 incidence.

LSE_2021_heterogeneity_racial-gap2_chakrabarti_ch2-02

Conclusion
We conclude that density determinants like crowding play an important role in creating income and racial gaps in the incidence of COVID-19. Indeed, the combination of comorbidities, health insurance status, and density reduces the income gap by 42 percent and the racial gap by 60 percent. However, there is an unexplained portion of COVID-19 incidence that remains associated with both income and race. The analysis here and in our previous post implies that policy may play a significant role in reducing the disparities of COVID-19. For example, reducing crowding in public transportation systems, expanding health insurance coverage, battling disproportionate incidence of chronic diseases among minorities, and decreasing residential crowding may decrease the impacts of COVID-19 on low-income and minority individuals. In our next post, we will look at the role of three additional channels—namely social distancing, pollution, and demographics—to see whether they explain more of the income and racial gap in COVID-19 occurrence.

Ruchi Avtar is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Chakrabarti_rajashri
Rajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

Pinkovskiy_maxim
Maxim Pinkovskiy is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:
Ruchi Avtar,
Rajashri Chakrabarti, and
Maxim Pinkovskiy, “Understanding the Racial and Income Gap in COVID-19: Public Transportation and Home Crowding,” Federal Reserve Bank of New York Liberty Street Economics, January 12, 2021, https://libertystreeteconomics.newyorkfed.org/2021/01/understanding-the-....

Additional heterogeneity posts on Liberty Street Economics
>Heterogeneity: A Multi-Part Research Series

Disclaimer

The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Some Workers Have Been Hit Much Harder than Others by the Pandemic

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:40am in

Jaison R. Abel and Richard Deitz

LSE_2021_EI-series_workers-harder_able_460

As the COVID-19 pandemic took hold in the United States, in just two months—between February and April 2020—the nation saw well over 20 million workers lose their jobs, an unprecedented 15 percent decline. Since then, substantial progress has been made, but employment still remains 5 percent below its pre-pandemic level. However, not all workers have been affected equally. This post is the first in a three-part series exploring disparities in labor market outcomes during the pandemic—and represents an extension of ongoing research into heterogeneities and inequalities in people’s experience across large segments of the economy including access to credit, health, housing, and education. Here we find that some workers were much more likely to lose their jobs than others, particularly lower-wage workers and those without a college degree, as well as women, minorities, and younger workers. However, as jobs have returned during the recovery, many of these differences have narrowed considerably, though some gaps are widening again as the labor market has weakened due to a renewed surge in the coronavirus. The next post in the series examines differences in patterns of commuting during the pandemic, and finds that workers in low-income and Black- and Hispanic-majority communities were more likely to commute for work. The final post in the series analyzes unemployment dynamics during the pandemic, and finds that Black workers experienced a lower job-finding rate and a higher separation rate into unemployment than white workers during the recovery, though this trend has reversed to some extent recently.

Low-Wage Workers Hit the Hardest

Lower-wage workers have borne much more of the brunt of job losses during the pandemic than higher-wage workers. To illustrate, we separate jobs into four categories based on each occupation’s median wage. Low-wage workers work in jobs that typically pay less than $30,000 annually, and include jobs such as food servers, cashiers, home health aides, and childcare workers. Lower-middle-wage workers work in jobs that typically pay between $30,000 and $50,000, and include jobs like administrative assistants, hairdressers, carpenters, and truck drivers. Upper-middle-wage workers work in jobs that typically pay between $50,000 and $85,000, including jobs such as teachers, police officers, accountants, and financial managers. High-wage workers are employed in jobs that typically pay over $85,000 per year, including software developers, engineers, lawyers, and business executives. For perspective, our high-wage and low-wage categories represent roughly the top and bottom 10 percent of workers, while the two middle-wage categories each cover about 40 percent of workers. As the chart below shows, between February and April 2020, employment declined by more than a third for low-wage workers, compared to a decline of 18 percent for lower-middle wage workers, and nine percent for upper-middle wage workers. By contrast, employment for high-wage workers held steady.

LSE_2021_heterogeneityVI-some-workers-hit-harder_abel_ch1_v2-01

The economy returned a substantial number of jobs after bottoming out in April 2020, particularly for low-wage workers. This partial but strong recovery helped narrow the gap between low-wage workers and their higher-paid counterparts. However, employment for the two lower-wage groups began to decline again in October as the winter wave of the virus began, even as jobs for the two higher-wage groups grew, opening up the gap once more. All in all, employment among high-wage workers is now slightly above where it was before the pandemic hit, and employment among both middle-wage groups is just slightly below. By contrast, employment among low-wage workers remains 14 percent below pre-pandemic levels and is trending down again.

Why have lower-wage workers been hit so much harder during the pandemic? Much of it can be traced to differences in the types of jobs held among the groups. Due to a combination of government restrictions and behavioral changes people made to avoid exposure to the virus, the largest losses during the pandemic accrued to the leisure and hospitality industry—most notably, restaurants, bars, and hotels—as well as retail, both of which tend to employ large numbers of lower-paid workers. Further, lower-wage workers have much less ability to work remotely—think food servers and cashiers—compared to higher-wage workers, such as managers, accountants, and attorneys. In fact, according to new data collected by the Bureau of Labor Statistics after the pandemic began, an average of nearly 60 percent of workers in our high-wage group reported that they telecommuted during the pandemic, compared to less than 10 percent for low-wage workers, as shown in the chart below. This pattern is consistent with findings by our colleagues in a related post showing that workers in low-income areas are more likely to commute to work than workers in high-income areas, suggesting that such workers are more dependent on occupations that require in-person work.

LSE_2021_heterogeneityVI-some-workers-hit-harder_abel_ch2_v2-01

An Uneven Experience

More broadly, employment outcomes through the pandemic have been highly uneven among different types of workers, as shown in the chart below. We group workers into categories based on educational attainment, race and ethnicity, gender, and age. While we find big differences in initial job losses across groups of workers, many of the initial gaps that opened have narrowed considerably through the recovery.

LSE_2021_heterogeneityVI-some-workers-hit-harder_abel_ch3-01-01

The length of each bar in the chart represents the magnitude of initial job loss, while the solid portion represents the remaining job shortfall at the end of 2020. Overall, for the nation as a whole, initial job losses totaled 15 percent and the remaining job shortfall is 5 percent. The first set of bars corresponds to workers of different wage levels, summarizing trends presented earlier. The next set of bars considers differences by educational attainment, and shows a similar pattern given the high correlation between education and wages. The least-educated workers—those without a high school diploma—saw employment fall by 24 percent, compared with 7 percent for workers with a college degree—a gap of 17 percentage points. By the end of 2020, job shortfalls totaled 6 to 7 percent for those without a college degree, compared with just two percent for those with a college degree—a smaller but still substantial gap of around 4 percentage points.

Looking across demographic groups, it is clear the pandemic caused outsized job losses for women, minorities, and younger workers as the pandemic took hold. Initial job losses among women were 4 percentage points higher than for men, and initial job losses among Black and Hispanic workers were several percentage points higher than for white workers. Furthermore, the pandemic has been quite challenging for younger workers (those under 30), with initial job losses nearly twice as large as mid-career (those aged 30 to 49) and older workers (those 50 and over).

These differences in job losses early in the pandemic reflect a combination of factors. First, some groups may be overrepresented in the two industries hit hardest by the pandemic—leisure and hospitality and retail—including younger workers and those without a college degree. Further, some jobs have been easier to hold onto than others, particularly those that can be done from home, and different groups may be overrepresented in jobs that can or cannot be performed remotely. College graduates, for example, tend to have more flexibility in their jobs and a greater ability to work remotely. And, a factor that may help explain the outsized job loss among women is that women tend to bear more of the burden of childcare responsibilities, which have increased significantly during the pandemic due, in part, to schools teaching online and many students at home. This factor may have contributed to a disproportionate share of women not working in order to care for their children. There may also be differences in the willingness to work among different groups given the dangers of COVID-19. However, it is difficult to determine the nature and magnitude of these influences.

Interestingly, consistent with recent research, most of the gaps across demographic groups have narrowed considerably during the recovery, particularly as jobs have been added in the hardest-hit sectors. The shortfall between men and women has closed completely, while the gap between Black and Hispanic workers relative to white workers has closed to one percentage point. This is consistent with research by our colleagues which finds that the job finding rate among Black workers has risen above the corresponding rate for white workers. And, the remaining jobs shortfall among younger workers has narrowed to within a couple of percentage points of mid-career and older workers. Unfortunately, as the job market began to weaken in late 2020 due to a renewed surge in the virus, there are signs that some of these gaps have begun to widen once more, as many of the most vulnerable workers are yet again being hit hardest.

Chart data

Abel_jaison
Jaison R. Abel is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Deitz_richardRichard Deitz is an assistant vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Jaison R. Abel and Richard Deitz, “Some Workers Have Been Hit Much Harder than Others by the Pandemic,” Federal Reserve Bank of New York Liberty Street Economics, February 9, 2021, https://libertystreeteconomics.newyorkfed.org/2021/02/some-workers-have-....

Related Reading

Which Workers Bear the Burden of Social Distancing Policies?

Economic Inequality Research Series

Economic Inequality and Equitable Growth

COVID-19: Information, Research and Analysis and Resources

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Understanding the Racial and Income Gap in Commuting for Work Following COVID-19

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:40am in

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy

LSE_2021_Understanding the Racial and Income Gap in Commuting for Work Following COVID-19

The introduction of numerous social distancing policies across the United States, combined with voluntary pullbacks in activity as responses to the COVID-19 outbreak, resulted in differences emerging in the types of work that were done from home and those that were not. Workers at businesses more likely to require in-person work—for example, some, but not all, workers in healthcare, retail, agriculture and construction—continued to come in on a regular basis. In contrast, workers in many other businesses, such as IT and finance, were generally better able to switch to working from home rather than commuting daily to work. In this post, we aim to understand whether following the onset of the pandemic there was a wedge in the incidence of commuting for work across income and race. And how did this difference, if any, change as the economy slowly recovered? We take advantage of a unique data source, SafeGraph cell phone data, to identify workers who continued to commute to work in low income versus higher income and majority-minority (MM) versus other counties.

Data and Background

In line with an earlier Liberty Street Economics post, we use data on race and income composition at the county level from the 2014-18 waves of the American Community Survey to differentiate between low-income and higher income counties, and MM and other counties. We define low-income counties as those that fall in the lowest quartile of the population weighted distribution of median household income. We define MM counties as those in which at least half the population is Hispanic and/or non-Hispanic Black.

In order to capture differences in commuting to work behavior across counties, we make use of SafeGraph’s aggregated and anonymized cell phone mobility data. This data determines the typical nighttime location of each mobile device, which is referred to as the device’s “home.” The subsequent mobility that shows time spent away from home is then used to determine the full-time and part-time work behavior. For example, a device that leaves home at 8 a.m. on weekdays, goes to the same location each weekday and returns home at 6 p.m. is typically coded as belonging to a full-time worker, while a device that is seen to spend 3-4 hours during the workday at a location other than the home location is coded as belonging to a part-time worker. We look at variations in these measures across the different counties using data through January 9, 2021.

In the time leading up to the pandemic, we see that SafeGraph captures people going to work both full time and part time. This would include all types of work, both work that needed to be done in the workplace and work that could be done at home if needed. However, when COVID-19 struck, most U.S. states issued shelter-in-place and stay-at-home orders. Almost all occupations and industries that had the ability to work from home, switched immediately to such work-from-home postures. Industries that required in-person engagement, or had a very low work-from-home ability continued to have workers coming in for full-time or part-time work. SafeGraph’s cell phone data enables us to identify the work-from-home and commuting-to-work patterns across counties both before and after the onset of the pandemic. We leverage the differences of these patterns across low-income versus higher income and MM versus other counties in the analysis below.

Differences in Commuting to Work by Income

We now turn to look at the differences in commuting to work as captured by number of devices at work full time and part time, by income. In the graph below, we see no difference in number of devices at work full time between low-income and higher income counties in the pre-COVID period, and a drastic decline in all devices going to work starting in the week of March 15, 2020. However, we see that this decline is much higher for higher income counties, implying that more workers in such counties were able to shift to working from home. In contrast, lower income counties saw a perceptibly smaller decline in the devices at work, suggesting that these counties had a higher incidence of workers who could not transition to working from home and continued to travel to work.

Since our data draws on mobile data, workers voluntarily or involuntarily leaving jobs and staying at home are reflected in the dip in number of devices at home full time. It is noteworthy that high-wage employment declined considerably less than low-wage employment. So if this was the major factor driving the patterns we see below, we would see an opposite pattern. This suggests that the differences in the commuting-to-work patterns between low income and high income counties are more likely to be contributed by differences in abilities to transition to working from home.

Toward the end of April, as states started to reopen (beginning from April 24), we see a recovery in full-time workers who commuted to work, which is faster in the low-income counties. This suggests that workers residing in low-income counties are more dependent on occupations that required commuting to work. From late June onward, we see a slightly downward trend for both low income and higher income counties, before a sharp uptick near the holiday season. While we do not have definitive evidence, a potential explanation for this is that increased demand for retail and services during the holidays required more employees to be physically at work. Under this hypothesis, the larger uptick experienced in the higher income counties could have reflected a greater increase in demand in those counties. The subsequent drop corresponds to the return from the increased pace of activity during the holidays.

LSE_2021_essential-work-inequality_chakrabarti_ch1_v3

Looking at part-time work behavior, we see a similar trend. The differences between low-income counties and other counties only show up as the pandemic hits, with workers in higher income counties more likely to be able to work from home. It is interesting to note that there is a slight upward trend post recovery in both types of counties in part-time work, while the full-time work showed a slight downward trend from July onward. This may suggest a shift from commuting for full-time work to commuting for part-time jobs for some workers in the second half of the year. Relative to the full-time chart, the uptick and subsequent decline around the holiday season in part-time work are not as pronounced.

LSE_2021_essential-work-inequality_chakrabarti_ch2_v3

Difference in Commuting to Work by Race

Turning to the racial differences as shown below, we see less of an overall difference between both MM and other counties across the course of the pandemic. Even before the plunge in full-time work, we see a small gap between MM and other counties in the beginning of March, which is possibly attributable to the fact a lot of firms in majority-nonminority counties started transitioning into work from home even before the shutdowns were announced. MM counties are less likely to have seen such transitioning, and as the chart depicts, also show a smaller decline in the devices at work full-time. Both MM and other counties showed a similar pattern of recovery, although the return to work is higher for MM counties suggesting that jobs held by workers in these counties are more likely to be occupations that required commuting to work and less amenable to remote work. Similar to the chart earlier based on income, we see a sharp uptick and subsequent drop around the holidays that is starker for majority-nonminority counties. The holiday uptick temporarily reduces the gap between the two types of counties, with the gap reopening subsequently.

LSE_2021_essential-work-inequality_chakrabarti_ch3_v3

In comparison to the full-time results presented above, the part-time work shows much smaller differences between MM and other counties throughout the course of the pandemic.

LSE_2021_essential-work-inequality_chakrabarti_ch4_v3

Conclusion

This post aimed to provide a high-frequency analysis of differences in full-time and part-time work commuting behavior. We found important differences in these behaviors across counties that differ by income and demographics. Although all counties experienced a sharp decline in mobility consistent with a sharp decline in commuting to work at the onset of the pandemic, followed by a subsequent partial recovery, low-income and, to a smaller extent, MM counties experienced greater commuting for work in the pandemic period. The difference in commuting between these areas and the rest of the country temporarily narrowed during peak demand times of the year, such as the holiday season. Our results are consistent with low-income and Black and Hispanic-majority communities being less able to substitute work at home for work away from home, contributing to their very high levels of vulnerability to COVID 19.

Chart data

Ruchi Avtar is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Chakrabarti_rajashriRajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

Pinkovskiy_maximMaxim Pinkovskiy is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy, “Understanding the Racial and Income Gap in Commuting for Work Following COVID-19,” Federal Reserve Bank of New York Liberty Street Economics, February 9, 2021, https://libertystreeteconomics.newyorkfed.org/2021/02/understanding-the-....

Related Reading

Economic Inequality Research Series
Economic Inequality and Equitable Growth

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Black and White Differences in the Labor Market Recovery from COVID-19

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:40am in

David Dam, Meghana Gaur, Fatih Karahan, Laura Pilossoph, and Will Schirmer

LSE_2021_EI-series-covid-recession_karahan_460

The ongoing COVID-19 pandemic and the various measures put in place to contain it caused a rapid deterioration in labor market conditions for many workers and plunged the nation into recession. The unemployment rate increased dramatically during the COVID recession, rising from 3.5 percent in February to 14.8 percent in April, accompanied by an almost three percentage point decline in labor force participation. While the subsequent labor market recovery in the aggregate has exceeded even some of the most optimistic scenarios put forth soon after this dramatic rise, the recovery has been markedly weaker for the Black population. In this post, we document several striking differences in labor market outcomes by race and use Current Population Survey (CPS) data to better understand them.

Recessions tend to have disproportionately adverse effects on the labor market outcomes of Black workers. For example, in the years leading up to the Great Recession of 2007-09, the unemployment gap between Black and white workers reached as low as 3.4 percentage points, but it peaked at 8.5 percentage points during the aftermath of the Great Recession. The COVID recession has been no outlier in this regard, as shown in the chart below. The unemployment rate rose significantly more for the Black population, pushing the Black-white unemployment gap from 3 percentage points in February to 5.4 percentage points in August. Similarly, while the long expansion following the Great Recession had narrowed the long-standing Black-white participation gap, the pandemic erased these gains. Participation fell more severely for the Black population at the onset of the pandemic and has since recovered more slowly.

LSE_2021_COVID-recession_karahan_ch1-v2-01

The evolution of the unemployment and labor force participation rates is shaped by flows between employment, unemployment, and being “not in” the labor force. For example, the unemployment rate declines if more people find jobs or fewer workers are displaced. Given that a large share of the unemployed are currently classified as temporarily unemployed (namely, those who have been given a date to return to work or who expect to return to work within six months) and that temporarily and permanently unemployed workers tend to find jobs or drop out of the labor force at very different rates, we distinguish between these two groups in our analysis. We use data from the CPS on individuals age 16 and older, and we compute the rate at which Black and white workers transition between employment (E), temporary unemployment (TU), permanent unemployment (PU), and not in labor force (N).

The rate at which workers find jobs out of unemployment has declined for both Blacks and whites this year, with the level of job-finding significantly lower for Blacks until a recent reversal. Breaking down the job-finding rate into transitions from permanent and temporary unemployment clarifies the disparate experiences of Black and white workers (see chart below). Blacks have lower job-finding rates from both permanent and temporary unemployment but have seen a more gradual decline in job‑finding as the recession has progressed. In recent months, the white job-finding rates from both permanent and temporary unemployment have dropped below the corresponding Black job-finding rates. If the current job-finding rates were to continue, all else the same, we would expect a somewhat faster decline in the Black unemployment rate.

LSE_2021_COVID-recession_karahan_ch2-v2_Artboard 2

Black and white job loss rates have exhibited a similar pattern. For both Black and white workers, job loss resulting in temporary unemployment peaked in June before declining in recent months, as shown in the chart below. Job loss resulting in permanent unemployment similarly peaked in June. However, for employment loss resulting in both permanent and temporary unemployment, Black workers have experienced significantly higher rates than whites. The Black-white gap in job loss resulting in temporary unemployment widened at the peak of job loss resulting in temporary unemployment, while the gap in job loss resulting in permanent unemployment has been relatively stable throughout the recession.

LSE_2021_COVID-recession_karahan_ch3-v2_Artboard 2

An important feature of the U.S. labor market is that flows out of employment are not always to unemployment; a nonnegligible share of workers drop out of labor force each month. These flows are important determinants of the unemployment and labor force participation rates. Indeed, labor force exit from employment varies significantly for Black and white workers. Until June, the two groups exhibited similar trends as labor force exit from employment dropped. However, in recent months the labor force exit rate for white workers has reverted to pre-pandemic levels, while the labor force exit rate for Black workers has increased dramatically (see chart below). The divergence in Black and white labor force exit rates from employment in recent months suggests that labor force participation for the Black population may remain significantly depressed in the coming months while white labor force participation may recover more quickly, with this combination erasing the gains achieved during the long expansion following the Great Recession.

LSE_2021_COVID-recession_karahan_ch4-v2_Artboard 2

The COVID recession, like most post-war recessions, has had disproportionate effects on the Black population. We trace the rising and persistent Black-white unemployment and labor force participation gaps to the underlying flows between labor market states. For Black workers, a lower job-finding rate and a higher separation rate into unemployment have contributed to the larger increase and subsequent slower recovery of the unemployment rate. While the job-finding and job-loss rates for Black and white workers have converged recently, resulting in a narrowing of the Black-white unemployment gap, the transition rate from employment into nonparticipation for Black workers remains elevated. This relatively high rate of labor force exit for Black workers may lead to a persistently elevated Black-white labor force participation gap and an uneven labor market recovery.

Chart data

Dam_david
David Dam is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.
Gaur_meaghan2
Meghana Gaur is a senior research analyst in the Research and Statistics Group.
Karahan_fatih
Fatih Karahan is a senior economist in the Research and Statistics Group
Pilossoph_laura
Laura Pilossoph is an economist in the Research and Statistics Group.
Schirmer_will_2
Will Schirmer is a senior research analyst in the Research and Statistics Group.

How to cite this post:

David Dam, Meghana Gaur, Fatih Karahan, Laura Pilossoph, and Will Schirmer, “Black and White Differences in the Labor Market Recovery from COVID-19,” Federal Reserve Bank of New York Liberty Street Economics, February 9, 2021, https://libertystreeteconomics.newyorkfed.org/2021/02/black-and-white-di....

Related Reading

Economic Inequality Research Series
Economic Inequality and Equitable Growth

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

An Update on How Households Are Using Stimulus Checks

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:36am in

Olivier Armantier, Leo Goldman, Gizem Koşar, and Wilbert van der Klaauw

An Update on How Households Are Using Stimulus Checks

In October, we reported evidence on how households used their first economic impact payments, which they started to receive in mid-April 2020 as part of the CARES Act, and how they expected to use a second stimulus payment. In this post, we exploit new survey data to examine how households used the second round of stimulus checks, issued starting at the end of December 2020 as part of the Coronavirus Response and Relief Supplemental Appropriations (CRRSA) Act, and we investigate how they plan to use the third round authorized in March under the American Rescue Plan Act. We find remarkable stability in how stimulus checks are used over the three rounds, with a slight decline in the share dedicated to consumption and a proportional increase in the share saved. The average share of stimulus payments that households set aside for consumption—what economists call the marginal propensity to consume (MPC)—declined from 29 percent in the first round to 26 percent in the second and to 25 percent in the third.

How Households Used the Second Stimulus Check

The CRRSA Act authorized lump-sum economic impact payments of $600 to each eligible adult and child. To examine how households used these payments we draw again on the New York Fed Survey of Consumer Expectations (SCE), a nationally representative, internet-based survey of about 1,300 U.S. households. Since June 2013, the monthly survey has been collecting information on household heads’ economic expectations and behavior. In the January 2021 SCE survey, we included several questions regarding the second round of stimulus checks, whether households received such a payment and how they are using or expecting to use it.

We find that 68 percent of households reported having received an average of $1,314 ($1,200 median) in stimulus funds in this second round at the time they were surveyed. Those who had not yet received anything reported an average 35 percent chance of receiving a second-round stimulus check in the future. We also asked what share of this payment the household has already or expects to 1) spend or donate, 2) save or invest, and 3) use to pay down debts. In a follow-up question, respondents were asked to divide the share reported for the first group into categories: spending on essential items (such as necessary daily living expenses), spending on non-essential items (such as hobbies, leisure, and vacations), and donations.

Combining all respondents, we find that in January, households reported using or planning to use an average 16 percent of the second-round stimulus funds for essential spending, an average 6 percent for non-essential spending, and to donate 3 percent, resulting in a total MPC of 26 percent. They also reported saving or planning to save an average 37 percent of their stimulus checks and use 37 percent to pay down debt. These shares are very similar to those we found for the first round of stimulus checks, where households reported spending 29 percent, saving 36 percent, and using 35 percent to pay down debt. (See the table below.) The reported allocations are also in line with those that households reported back in August for a potential future second round of stimulus checks. At that point in time, they expected to use a slightly lower share for consumption (24 percent) and debt paydown (31 percent), with more expected to be saved (45 percent).


An Update on How Households Are Using Stimulus Checks

Our data allow us to assess how use of the second round of stimulus checks varies with household characteristics. For example, households making less than $40,000 report using or expecting to use 44 percent of their stimulus checks to pay down debt, while those making more than $75,000 would use or expect to use only 32 percent. Further, lower-income households are spending or expect to spend 27 percent of their stimulus payments, while higher-income households making more than $75,000 would spend 24 percent. The difference in spending on essentials is larger for the lower- and higher-income groups, 20 percent versus 12 percent. Looking at differences by education level, households without a college degree reported a slightly lower average MPC (24 percent) and a higher portion used to pay down debt (42 percent). These patterns again mirror those from our initial study of the first round of stimulus checks.

Expected Use of Third-Round Relief Payments

In March, a third round of stimulus checks of $1,400 to each eligible adult and child was authorized under the American Rescue Plan Act. In the March SCE survey, we elicited similar information about actual and expected uses of the third round of federal transfer payments. Some 32 percent of households had already received a third-round payment of on average $3,162 ($2,800 median) by the time they took the survey in March (which took place across the entire month). Those who had not yet received anything reported an average 55 percent chance of receiving a third-round stimulus check in the future. Combining all respondents, we can see in the table that respondents are using or expecting to use 25 percent this third round of payments for consumption. In particular, an average 13 percent of the latest stimulus check is expected to be spent on essential items and an average 8 percent on non-essential items.

Similar to the second round of stimulus, household heads without a college degree plan to use more of the stimulus for paying down debt and less for consumption. Those without a college degree expect allocating 37 percent toward debt while those with a college degree planned to use 27 percent for that purpose. Comparing respondents with household incomes below $40,000, between $40,000 and $75,000, and above $75,000, we find a monotonically decreasing share used toward paying down debt, and higher-income households, on average, tending to save more.

Conclusion

We find remarkable stability in the actual and expected uses of stimulus checks across successive rounds, with average MPCs of around 26 percent and with most of the funds going towards saving and debt payments. Our findings are comparable to the MPC estimate of 27 percent found by Baker et al. (2020) using high-frequency transaction data from a Fintech nonprofit, for the first round of stimulus checks. Coibion et al. (2020), using a survey of individuals participating in the Nielsen Homescan panel, find a higher MPC of 42 percent, with 27 percent of the payments going toward saving and 31 percent toward debt payments. They find that 52 percent of respondents use the check mostly to pay off debt with only 15 percent of check recipients reporting using it mostly to increase spending. Their higher MPC estimate may reflect differences in sample composition and question wording, as well a difference in time between survey completion and stimulus payment receipt.

There are reasons to think the first two rounds of stimulus payments to households have contributed to changes in aggregate spending and savings. The second round of payments coincided with a 3.0 percent increase in real aggregate consumer spending and a 9.7 percent increase in real personal income in January. Despite these increases, spending as measured by real personal consumption expenditures (PCE) remains below pre-pandemic levels of February 2020, while the personal savings rate stood at a striking 19.8 percent in January. Our findings indicate that in an environment that continues to be characterized by constraints on many activities and by high unemployment, as well as high uncertainty about the duration and continued economic impact of the pandemic (including elevated uncertainty about future inflation), fiscal support continues to impact predominantly savings instead of consumption, with households planning to use the third relief payments mostly to pay off debt and save. As the economy reopens and fear and uncertainty recede, the high levels of saving should facilitate more spending in the future. However, a great deal of uncertainty and discussion exists about the pace of this spending increase and the extent of pent-up demand.

Olivier ArmantierOliver Armantier is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Leo GoldmanLeo Goldman is a senior research analyst in the Bank’s Research and Statistics Group.

Gizem KoşarGizem Kosar is an economist in the Bank’s Research and Statistics Group.

Wilbert van der KlaauwWilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Olivier Armantier, Leo Goldman, Gizem Koşar, and Wilbert van der Klaauw, “An Update on How Households Are Using Stimulus Checks,” Federal Reserve Bank of New York Liberty Street Economics, April 7, 2021, https://libertystreeteconomics.newyorkfed.org/2021/04/an-update-on-how-h....

Related Reading

How Have Households Used Their Stimulus Payments and How Would They Spend the Next? (October 13, 2020)

“Excess Savings” Are Not Excessive (April 5, 2021)

Center for Microeconomic Data—Survey of Consumer Expectations

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Women’s Labor Force Participation Was Rising to Record Highs—Until the Pandemic Hit

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:35am in

Jaison R. Abel and Richard Deitz

LSE_2021_womensLFP_deitz_460

Women’s labor force participation grew precipitously in the latter half of the 20th century, but by around the year 2000, that progress had stalled. In fact, the labor force participation rate for prime-age women (those aged 25 to 54) fell four percentage points between 2000 and 2015, breaking a decades-long trend. However, as the labor market gained traction in the aftermath of the Great Recession, more women were drawn into the labor force. In less than five years, between 2015 and early 2020, women’s labor force participation had recovered nearly all of the ground lost over the prior fifteen years. Then the pandemic hit, erasing these gains. In recent months, as the economy has begun to heal, women’s labor force participation has increased again, but there is much ground to be made up, especially for Black and Hispanic women. A strong labor market with rising wages, as was the case in the years leading up to the pandemic, will be instrumental in bringing more women back into the labor force.

Participation Was Closing In on a Record High before the Pandemic Hit

Women’s labor force participation climbed steadily until around the year 2000, shown in the chart below for prime-age women, reaching a peak of 77.3 percent. Then, between 2000 and 2015, the labor force participation rate fell a steep four percentage points to 73.3 percent, slightly less than the corresponding decline for prime-age men during this period. This decline in women’s labor force participation has been well documented, with researchers attributing it to a combination of demand side factors, such as reduced job opportunities due to trade and technology, and supply side factors, such as demographic shifts and greater access to programs such as disability insurance and the Supplemental Nutrition Assistance Program (SNAP). What may be less well known—and certainly less studied—is that between 2015 and 2020, prime-age women’s labor force participation increased by 3.5 percentage points, nearly erasing the entire decline of the prior fifteen years. This increase was nearly three times greater than that seen for men.


Women’s Labor Force Participation Was Rising to Record Highs—Until the Pandemic Hit

What brought more women into the labor market? It is unlikely that many of the structural factors that contributed to the prior period’s decline—such as the displacement effects of trade and technology or demographic shifts—changed quickly enough to bring such a rapid change in trajectory. In fact, many of these economic forces continued to put downward pressure on labor force participation. Rather, a historically strong labor market with rising wages for a sustained period were a likely cause. After a period of sluggish growth and slack labor markets in the immediate aftermath of the Great Recession, real wages began picking up strongly in 2015, as shown in the chart below, which plots real average hourly earnings. This strong growth in wages suggests that labor markets tightened considerably during this period, pushing up wages and bringing more women (and men) into the labor market.


Women’s Labor Force Participation Was Rising to Record Highs—Until the Pandemic Hit

Then the pandemic hit. Women’s labor force participation fell by well over three percentage points in just two months, between February and April 2020, reversing nearly all of the gains made between 2015 and 2020. This sharp decline is partly attributable to an increase in childcare responsibilities due to in-person school and daycare closings during the pandemic, a responsibility that tends to fall disproportionately on women. Participation has since recovered as economic conditions have improved and schools have begun to reopen, though as of March, a year into the pandemic, the labor force participation rate for prime-age women remains almost two percentage points off its pre-pandemic level.

An Uneven Experience

These ups and downs in labor force participation have not occurred equally among all women, as the chart below shows. Black women saw a decline of around five percentage points between 2000 and 2015, compared to a decline of roughly three to four percentage points for Hispanic and white women. When participation began to increase again between 2015 and early 2020, both Black and white women came close to recovering all of the ground that was lost and were approaching record highs. Hispanic women—whose labor force participation tends to be relatively low—saw participation exceed its previous peak by a full percentage point by early 2020.


Women’s Labor Force Participation Was Rising to Record Highs—Until the Pandemic Hit

However, when the pandemic hit, Black and Hispanic women saw much more sizeable declines in participation than white women, 5.2 and 5.9 percentage points, respectively, compared to 3.3 percentage points. The sharper decline may be due at least in part to higher rates of COVID infections among these groups, school closings leaving students at home requiring care that occurred at higher rates in communities where people of color are in the majority, as well as these groups being less able to telecommute during the pandemic. And, as of March 2021, labor force participation rates for these two groups remains 3.2 and 3.1 percentage points below pre-pandemic peaks, compared to about 1.4 percentage points for white women. Thus, Black and Hispanic women have more than twice the ground to make up compared to white women.

Looking Ahead

Will women’s labor force participation reach the highs that were seen before the pandemic hit? As more people are vaccinated and the economy continues to recover, and, importantly, as in-person schools and daycare fully reopen, participation should continue to rise, possibly quite rapidly. As was the case before the pandemic, a strong labor market with rising wages for a sustained period will help set the stage for another comeback in women’s labor force participation, particularly if more flexible work arrangements brought on by adapting to work during the pandemic persist.

Jaison R. AbelJaison R. Abel is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Richard DeitzRichard Deitz is an assistant vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Jaison R. Abel and Richard Deitz, “Women’s Labor Force Participation Was Rising to Record Highs—Until the Pandemic Hit,” Federal Reserve Bank of New York Liberty Street Economics, May 10, 2021, https://libertystreeteconomics.newyorkfed.org/2021/04/womens-labor-force....

Related Reading

Some Workers Have Been Hit Much Harder than Others by the Pandemic (February 2021)

Understanding the Racial and Income Gap in Commuting for Work Following COVID-19 (February 2021)

Black and White Differences in the Labor Market Recovery from COVID-19 (February 2021)

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Pages