Were small businesses more likely to permanently close in the pandemic?

Previous estimates indicate that COVID-19 led to a large drop in the number of operating businesses operating early in the pandemic, but surprisingly little is known on whether these shutdowns turned into permanent closures and whether small businesses were disproportionately hit. This paper provides the first analysis of permanent business closures using confidential administrative firm-level panel data covering the universe of businesses filing sales taxes from the California Department of Tax and Fee Administration. We find large increases in closure rates in the first two quarters of 2020, but a strong reversal of this trend in the third quarter of 2020. The increase in closures rates in the first two quarters of the pandemic was substantially larger for small businesses than large businesses, but the rebound in the third quarter was also larger. The disproportionate closing of small businesses led to a sharp concentration of market share among larger businesses as indicated by the Herfindahl-Hirschman Index with only a partial reversal after the initial increase. The findings highlight the fragility of small businesses during a large adverse shock and the consequences for the competitiveness of markets.

The early impacts of the COVID-19 pandemic on business sales.

Abstract: COVID-19 led to a massive shutdown of businesses in the second quarter of 2020. Estimates from the Current Population Survey, for example, indicate that the number of active business owners dropped by 22% from February to April 2020. We provide the first analysis of losses in sales among the universe of businesses in California using administrative data from the California Department of Tax and Fee Administration. Losses in taxable sales average 17% in the second quarter of 2020 relative to the second quarter of 2019 even though year-over-year sales typically grow by 3-4%. We find that sales losses were largest in businesses affected by mandatory lockdowns such as accommodations, which lost 91%, whereas online sales grew by 180%. Placing business types into different categories based on whether they were considered essential or nonessential (and thus subject to early lockdowns) and whether they have a moderate or high level of person-to-person contact, we find interesting correlations between sales losses and COVID-19 cases per capita across counties in California. The results suggest that local implementation and enforcement of lockdown restrictions as safety measures for public health and voluntary behavioral responses as reactions to the perceived local COVID-19 spread both played a role. Plain English Summary: Business sales dropped by 17% on average due to the pandemic during the second quarter of 2020 in California. Accommodations lost 91% of sales, whereas online sales grew by 180%. Sales fell more steeply in counties with more COVID-19 cases. We examine how much businesses lost in sales using administrative sales tax data. The average losses of 17% in the second quarter of 2020 relative to the second quarter of 2019 occurred even though year-over-year sales typically grow by 3-4%. We find that sales losses were largest in businesses affected by mandatory lockdowns such as accommodations, drinking places, and arts, entertainment, and recreation. Distinguishing between essential and nonessential businesses, which were subject to early lockdowns, and by the level of person-to-person contact, we find that local implementation and enforcement of lockdown restrictions for public health safety and voluntary responses to the perceived local COVID-19 spread both played a role. The results suggest that small businesses may need more support from governments and consumers to mitigate the strong shift to online vendors, and that the pandemic must be brought under control as a prerequisite to a full recovery. Supplementary Information: The online version contains supplementary material available at 10.1007/s11187-021-00479-4.

Did the Paycheck Protection Program and Economic Injury Disaster Loan Program get disbursed to minority communities in the early stages of COVID-19?

Social distancing restrictions and health- and economic-driven demand shifts from COVID-19 shut down many small businesses with especially negative impacts on minority owners. Is there evidence that the unprecedented federal government response to help small businesses-the Paycheck Protection Program (PPP) and the related COVID-19 Economic Injury Disaster Loans (EIDL)-which had a stated goal of helping disadvantaged groups, was disbursed evenly to minority communities? In this descriptive research note, we provide the first detailed analysis of how the 2020 PPP and EIDL funds were disbursed across minority communities in the country. From our analysis of data on the universe of loans from these programs and administrative data on employer firms, we generally find a slightly positive relationship between PPP loan receipt per business and the minority share of the population or businesses, although funds flowed to minority communities later than to communities with lower minority shares. PPP loan amounts per employee, however, are negatively related to the minority share of the population. The EIDL program, in contrast, both in numbers per business and amounts per employee, was distributed positively to minority communities.

Self-employment over the business cycle in the USA: a decomposition.

Entry rates into self-employment increase during recessions and decrease during economic upswings. I show that this is mostly explained by the higher unemployment rate during a recession, together with the fact that at all times, unemployed persons have a relatively high propensity to become self-employed out of necessity. I use econometric decomposition techniques to quantify these effects based on the monthly matched US Current Population Survey before, during, and after the Great Recession. I also document that the entry rate into self-employment with unincorporated businesses strongly increased during the recession, but not into self-employment with incorporated businesses. This highlights the association of unincorporated and incorporated self-employment with necessity and opportunity entrepreneurship, respectively. The results are useful for policymakers and practitioners to understand, forecast and act on the different types of self-employment that can be expected over the business cycle. There are also important implications for theories of the cyclicality of unemployment and entrepreneurship. Plain English Summary Self-employment will increase during recessions when unemployment is high, but it may not boost innovation. During recessions, increased unemployment underlies the higher entry rate into self-employment. Our evidence is from representative survey data from the USA covering the Great Recession. The upside is that self-employment enables workers who lose their jobs to continue to work, which can speed up the subsequent economic recovery. Thus, public policy should enable people to start businesses. However, as during recessions the unemployed mostly start unincorporated businesses, one cannot expect them to boost innovation as much as start-ups during better economic times. These insights also speak to the 2020 recession triggered by COVID-19. If unemployment remains high after the relaxation of the lockdowns, a rise can be expected especially in non-innovative self-employment. Thus, the principal policy implication of this study is that policymakers should ensure that their expectations for new businesses started during deep recessions are realistic for the circumstances.

The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam.

AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers' skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (Am Econ Assoc Pap Proc 108:43-47, 2018) for the US and the World Bank's STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in urban Viet Nam, in comparison to Lao PDR, are more concentrated in occupations affected by AI, which requires them to adapt or puts them at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.

How Are Patented AI, Software and Robot Technologies Related to Wage Changes in the United States?

We analyze the relationships of three different types of patented technologies, namely artificial intelligence, software and industrial robots, with individual-level wage changes in the United States from 2011 to 2021. The aim of the study is to investigate if the availability of AI technologies is associated with increases or decreases in individual workers' wages and how this association compares to previous innovations related to software and industrial robots. Our analysis is based on available indicators extracted from the text of patents to measure the exposure of occupations to these three types of technologies. We combine data on individual wages for the United States with the new technology measures and regress individual annual wage changes on these measures controlling for a variety of other factors. Our results indicate that innovations in software and industrial robots are associated with wage decreases, possibly indicating a large displacement effect of these technologies on human labor. On the contrary, for innovations in AI, we find wage increases, which may indicate that productivity effects and effects coming from the creation of new human tasks are larger than displacement effects of AI. AI exposure is associated with positive wage changes in services, whereas exposure to robots is associated with negative wage changes in manufacturing. The relationship of the AI exposure measure with wage increases has become stronger in 2016-2021 in comparison to the 5 years before. JEL Classification: J24, J31, O33.