Asian entrepreneurship in the coronavirus era

Vol.:(0123456789) Journal of Population Economics (2024) 37:22 https://doi.org/10.1007/s00148-024-00985-1 1 3 ORIGINAL PAPER Asian entrepreneurship inthecoronavirus era CatalinaAmuedo‑Dorantes1· CristinaBorra2 · ChunbeiWang3 Received: 1 September 2022 / Accepted: 5 January 2024 / Published online: 15 February 2024 © The Author(s) 2024 Abstract The COVID-19 pandemic has had a deleterious impact on the world economy. Studies have documented the disproportional impact of the pandemic on minorities, immigrants, and business owners in the USA. In this study, we use Current Population Survey monthly data spanning from January 2014 through December 2021 to examine how the COVID-19 pandemic affected Asian entrepreneurship. We show that the pandemic disproportionally hurt Asian entrepreneurship, particularly among immigrants, up until the end of 2020. A detailed analysis of Asian business dynamics reveals a substantial increase in self-employment exits during the first year of the pandemic. We fail to find convincing evidence of differential industry/job-type concentration, individual preferences, majority-minority disparities, narrower clientele, or differential access to government support as primary drivers for such patterns. Instead, we find suggestive evidence of discrimination playing a non-negligible role that subsided in 2021, coinciding with the rollout of vaccines. Keywords Asian· COVID· Self-employment· Business dynamics· Entry· Exit· Discrimination JEL Classification J15· J61· J71· J78 Catalina Amuedo-Dorantes, Cristina Borra, and Chunbei Wang have contributed equally. Responsible editor: Alfonso Flores-Lagunes. * Cristina Borra cbor[email protected] Catalina Amuedo-Dorantes [email protected] Chunbei Wang chunbeiw[email protected] 1 University ofCalifornia, Merced, CA, USA 2 Universidad de Sevilla, Seville, Spain 3 Virginia Tech, Blacksburg, VA, USA C.Amuedo-Dorantes et al. 1 3 22 Page 2 of 34 1 Introduction The COVID-19 pandemic has had far-reaching effects on the global economy, prompting numerous studies to investigate its impact on various societal outcomes, including job loss, business ownership, sales, health, and inequality, among others (e.g., Adams-Prassl etal. 2020; Alon etal. 2020; Borjas and Cassidy 2020; Brodeur etal. 2021; Couch etal. 2020; Fairlie 2020; Fairlie and Fossen 2022b; Forsythe et al. 2020). Notably, studies have uncovered a disproportionately large negative impact of the pandemic on the employment outcomes of Asian Americans. Couch etal. (2020) show that Asians’ unemployment rate increased much more than that of Whites in the USA. Even more surprisingly, the gap continued to widen during the recovery in stark contrast to the narrowing unemployment gaps between Whites and other disadvantaged minority groups, such as Blacks and Hispanics. This pattern is unprecedented. Unlike Blacks and Hispanics, who tend to be severely hit by economic downturns, Asians typically fare better during recessions, resembling the performance of Whites partially due to their higher educational attainment (Cunningham 2018; Xu 2018). The pandemic’s disproportionate impact on Asians extended beyond the wage and salary sector to include Asian-owned businesses. Fairlie (2020) documented a 26% reduction in the number of Asian active business owners in the first 3months of the pandemic, a figure much larger than the 17% reduction among Whites. Even after the economy started to rebound in May and June of 2020, the number of Asian active business owners was 10% lower than in February of that year, compared to 5% in the case of Whites. Similarly, the Bureau of Labor Statistics (2021) documented how Asian self-employment dropped almost three times as much as overall self-employment during the 12-month period following the onset of the pandemic relative to the 12-month period preceding it. This is in addition to the pandemic’s already significant impact on the self-employment sector. As Graeber etal. (2021) show, self-employed individuals were more likely to experience income loss during the pandemic than those in the wage sector. While previous studies revealed a disparate impact on Asian-owned businesses in the first few months of the pandemic, the message was overshadowed by the discussions of other typically disadvantaged minority groups, such as Blacks and Hispanics. Hence, many questions remain unanswered. Our research contributes to the literature by offering the first comprehensive analysis of the impact of the COVID-19 pandemic on business ownership among Asians in the USA, delving into the underlying dynamics of formation and survival to provide a thorough understanding of this phenomenon.1 Additionally, we investigate likely mechanisms responsible for the disproportionate negative 1 At this point, it is worth noticing that the analysis focuses on self-employment as defined in by the Bureau of Labor Statistics. Many self-employed individuals may be in business by themselves, whereas others may manage employees. When the business is created by the person is question, s/he is referred as an entrepreneur. This is also the person who would usually bear most of the risks and rewards from the business. Given the close linkages among self-employment, business ownership, and entrepreneurship, we use these three terms interchangeably in the paper. 1 3 Asian entrepreneurship inthecoronavirus era Page 3 of 34 22 effects experienced by Asians, including industry concentration, limited access to government resources, clientele constraints, supply chain disruptions, risk preferences, and discrimination. To conclude, we examine how Asian entrepreneurship evolved as employment fully recovered, shedding some light on the dynamic nature of the pandemic’s effects. Gaining a better understanding of how Asian entrepreneurship was impacted during the recent pandemic is of relevance for various reasons. First, Asians are the third largest race group in the USA, following Whites and Blacks, with approximately 24 million people comprising 7.2% of the population in 2020 (Jones etal. 2021). Asians are known to have high business ownership rates and remarkable business performance (Bates and Dunham 1993; Borjas 1986). Studies repeatedly find that, compared to whites, Asian-owned businesses have higher annual sales, are more likely to have high profit levels, and more likely to hire employees (Fairlie and Robb 2008; Robb and Fairlie 2009; Pew Research Center 2015). Asian immigrant entrepreneurs have also been known to play a crucial role in contributing to hightech sectors, such as the Silicon Valley and technology and engineering industries (Azoulay et al. 2022; Saxenian 1999, 2000; Wadhwa et al. 2007). Second, given the hiring rates (Kochhar 2015), understanding how Asian entrepreneurship was impacted by the pandemic can shed light on the unprecedented unemployment patterns among Asians during this period. Third, the pandemic might have impacted minority-owned businesses through mechanisms that may have differed from those affecting minority wage and salary workers. Identifying the mechanisms at play could inform policies that help alleviate the unduly negative impacts of similar events on minority groups (Bowler and Harris 2022). We explore various mechanisms that could be potentially contributing to the disparate impact Asian-owned businesses. First, during the pandemic, Asians, especially Asian immigrants, as a minority group, may have encountered a range of additional challenges that threatened their businesses’ performance. One such constraint was their reliance on a narrower customer base, often composed of co-ethnics, which can result in a smaller customer pool compared relative to White-owned businesses. Relatedly, Asian-owned businesses may have experienced a more restrictive access to government support due to their immigration status. Both aspects might have hampered these minority-owned businesses’ survival during the pandemic. Additionally, Asian businesses often rely on sourcing inputs from Asia. Because the pandemic interrupted international trade of goods and people from countries like China, it might have interrupted Asian-owned businesses due to the lack of much needed inputs. Second, the concentration of Asian businesses in certain industries could have contributed to the disparate impact that the pandemic had on them. For example, Asian immigrants often concentrate in industries that were more negatively affected by the pandemic, such as restaurants, nail salons, or other businesses that involve inperson services. Additionally, Asian businesses are less prone to qualify as essential and, in turn, less likely to have been sheltered from forced shutdowns. Third, Asians may have had a more cautious attitude toward the virus. If that was the case, it is hypothetically possible for some to have exited self-employment and closed their business to avoid getting sick. C.Amuedo-Dorantes et al. 1 3 22 Page 4 of 34 Fourth, the violence and harassment against Asians during the pandemic could have negatively impacted their business performance. Anti-Asian violence, harassment, and hate crimes increased markedly after the onset of the pandemic (Human Rights Watch 2020; CBS News 2020; The Guardian 2020; NPR 2021; Stop AAPI Hate Press Release 2020; The City, NYC 2022). This pattern was in stark contrast to the low levels of harassment experienced by Asians prior to the pandemic (Ramakrishnan etal. 2017). Several academic studies have also confirmed a significant increase in prejudice against Asians among the general population (Nguyen etal. 2020; Darling-Hammond etal. 2020; Hswen etal. 2021; Lu etal. 2021). In the selfemployment context, prejudice against Asian-owned businesses could stem from customers, banks, and suppliers, impacting business survival rates (Becker 1957; Borjas and Bronars 1989; Blanchflower et al. 2003; Alden and Hammaarstedt, 2016; Cavalluzzo and Cavalluzzo 1998; Alden and Hammarstedt, 2016). In addition to taste discrimination, statistical discrimination may have occurred.2 Customers, banks, and suppliers might believe they were at a higher risk of contracting the virus in Asian communities given its origin in China. As a result, they might avoid interactions with Asian-owned businesses altogether. Regardless of whether discrimination was taste- or statistically based, it could have significantly risen operational costs, lower revenues, and threaten business survival. Using monthly data from the Current Population Survey (CPS) (January 2014 to December 2021) and a difference-in-differences approach, we first identify the additional impact of the initial pandemic shock on Asian-owned businesses, vis-à-vis businesses owned by non-Hispanic Whites – an impact that lasted until December 2020, when the first COVID vaccine started to be administered. Specifically, selfemployment dropped by 13% more among Asian immigrants, when compared to non-Hispanic Whites, following the onset of the pandemic. Next, we exploit the possibility to link the monthly CPS to examine transitions into and out of self-employment to provide insights into the underlying dynamics. We document a twofold increase (by 129%) in self-employment exits among Asian immigrants — a finding that proves robust to numerous identification tests and model specifications. Subsequently, we delve into explanations for our findings. As noted earlier, we consider the four sets of explanations that encompass most rationalizations for the distinct business performance; although the list may not be exhaustive. We fail to find consistent empirical evidence for most explanations, except for discrimination, which appeared greater for Asian immigrant subgroups more prone to discrimination based on their recent arrival and, therefore, partial integration, immigrant subgroups originating from East Asia, where the virus was first detected, and Asian immigrants in states with higher internet search volumes on terms associated to anti- Asian sentiments (Hswen etal. 2021). 2 Phelps’ (1972) model of statistical discrimination is based on stereotyping in a limited-information rational-optimization framework in which agents minimize information costs by typecasting an individual based on her/his group features. Becker’s (1957) model of taste-based discrimination considers instead that agents are prejudiced against minorities and are willing to incur in a cost that reflects their dislike. The same outcome can often be supported by both frameworks (Guryan and Charles 2013). 1 3 Asian entrepreneurship inthecoronavirus era Page 5 of 34 22 Finally, we extend our data period to December 2021 to examine if the negative impacts of the pandemic on Asian-owned businesses persisted in the longer run, as employment fully recovered. We find that the differential impact of the pandemic on Asian entrepreneurship dissipated by the beginning of 2021, coinciding with the rollout of vaccines. Overall, the study makes several contributions to the literature. First, it adds to a growing literature examining the impact of the COVID-19 pandemic on the labor market outcomes of minority or disadvantaged groups compared to Whites (e.g., Couch etal. 2020; Crossley etal. 2021; Graeber etal. 2021; Honoré and Hu 2022; Mar and Ong 2020). Specifically, we provide a thorough documentation of the additional impact of the COVID-19 pandemic on Asian-owned businesses, their formation, and their survival dynamics up to two years since the onset of the pandemic. Second, we contribute to the discrimination literature, which has examined the emergence of such practices against different demographic groups after sudden political, terrorist, or epidemic events, as in the case of Arab men after 9/11 (i.e., Davila and Mora 2005; Kaushal etal. 2007; Wang 2016), Germans after World War II (Ferrara and Fishback 2020), or gay men during the HIV/AIDS epidemic (Herek and Glunt 1988; Herek and Capitanio 1993). We add to this literature by providing suggestive evidence of the impact of discriminatory behaviors against Asians following the onset of the COVID-19 pandemic. Additionally, we inform the literature on customer and credit market discrimination and self-employment outcomes (e.g., Blanchflower etal. 2003; Borjas and Bronars 1989; Alden and Hammaarstedt, 2016; Cavalluzzo and Cavalluzzo 1998). Finally, we contribute to the literature of understanding self-employment trends (e.g., Robb and Fairlie 2009; Lofstrom and Wang 2009; Levine and Rubinstein 2017; Wang 2019; Wang and Lofstrom 2020) by delving into the underlying dynamics of transitions into and out of self-employment. This allows us to show how the decrease in the self-employment rate among Asians is mainly driven by increased exits rather than deterred entries. 2 Data We rely on various datasets to conduct the analysis. Data on self-employment outcomes and demographic characteristics of business owners are gathered from the January 2014 through December 2021 monthly Current Population Surveys (CPS) (Flood etal. 2021).3 We restrict our samples to individuals between the ages of 20 3 An important concern with the March 2020 CPS sample was its lower response rate. In that regard, the Bureau of Labor Statistic states: “Although the response rate was adversely affected by pandemicrelated issues, BLS was still able to obtain estimates that met our standards for accuracy and reliability” (https:// www. bls. gov/ cps/ emplo ymentsitua tioncovid 19- faqapril- 2020. pdf). More importantly, because of our difference-in-differences research design, the concern would be if Asians exhibit a disproportionate decline in their response rate when compared to the main control group –namely, Whites. We can test if that was the case by matching samples across months. When we do so, we find that, while overall response rates have declined, the reduction in Asians’ response rate is not significantly different from the reduction in the response rate among Whites. This is true for March 2020 (0.004; t = 0.45) and April 2020 (0.000; t = 0.02). Therefore, the research design should not be negatively affected. C.Amuedo-Dorantes et al. 1 3 22 Page 6 of 34 and 64, excluding those who live in group quarters or have missing labor force or employment status information. The monthly CPS is ideal for various reasons. First, because of its high frequency, it provides timely information on the impacts of the pandemic. Second, its rotational design allows for six out of eight rotation groups in each month’s sample to be longitudinally matched to the following month. In this manner, we can distinguish entries and exits,4 which are informative of self-employment dynamics masked under a static self-employment rate measure.5 Third, the monthly CPS offers a large sample size. Alongside its nationally representative nature, this allows for the study of low frequency labor market outcomes, such as self-employment of minority groups, as would be the case with Asians. Table A.1 provides a glance of the labor market statuses of individuals in the primary control group (non-Hispanic native-born Whites) and treatment groups (Asians and Asian immigrants) from well before the onset of the COVID-19 pandemic (i.e., from January 2014) until December 2020, when vaccinations became available in the USA. During that period, the self-employment rate of non-Hispanic Whites remained stable, whereas that of Asians and Asian immigrants dropped, respectively, by 10.6% and 12.9% after the onset of the pandemic. Asians’ unemployment rate increased threefold from 2.5 to 7.4%,6 almost twice as much as the rise among non-Hispanic Whites.7 Panel B of TableA.1 informs about self-employ- ment entries and exits. Self-employment entry rates changed similarly for non- Hispanic Whites and Asians from before to after the onset of the pandemic. However, self-employment exit rates rose by 67% among Asians (and 78% among Asian immigrants) — practically four times the 18% increase in exists experienced by non- Hispanic Whites. We conduct further analyses using information from four additional datasets, which we merge to the CPS data: (1) administrative state and county level data on daily COVID-19 cases and deaths from USAFacts (2020), which we use as an alternative measure of the COVID treatment variable; (2) data from the COVID-19 U.S. State Policy (CUSP) Database on the adoption of various non-pharmaceutical interventions (NPIs) by states and counties, which we also employ as yet another measure of the COVID treatment variable8; (3) data on the seasonally adjusted state trends in 4 We use the link variable provided in IPUMS CPS to match the data across months and follow Madrian and Lefgren (2000) to check the sex, age, and race of the observations to ensure a correct match. Longitudinal weights are then used throughout the dynamic analyses to account for the loss of observations during the matching process. 5 Fairlie (2014) uses the same approach to develop the Kauffman Index of Entrepreneurship. 6 Asian immigrants experienced a similar increase in unemployment, from 2.4% to 7.5%. 7 The unemployment rates reported here seem lower than those published by the Bureau of Labor Statistics. This is because of our sample restrictions to the ages between 20 and 64. 8 CUSP data are compiled by the Boston School of Public Health, providing information on the exact date of the declaration of the state of emergency, school and non-essential business closures, mask wearing mandates, and safe-at-home/shelter-at-home policies more directly associated with work stoppages. 1 3 Asian entrepreneurship inthecoronavirus era Page 7 of 34 22 outpatient care visits to healthcare providers from the Outpatient Influenza-like Illness Surveillance Network (ILINet), as an additional measure of COVID intensity;9 and (4) following Stephens-Davidowitz (2014), Google Trends data on search queries on the following terms: “Yellow People”, as well as various anti-China phrases, including “Hate China”, “Back to China”, “China die”, and “No China” in each state during our sample period.10 We choose terms related to anti-Asian sentiments, while trying to optimize the number of states for which information is available.11 Google Trends reports a normalized index of search activity computed by geography and time period that ranges from 0 to 100, allowing for comparisons across locations and over time. TableA.2 in Appendix A provides summary statistics of these measures. As shown therein, there was a drastic increase in Google searches of anti-Asian terms following the onset of the pandemic. FiguresA.1A through A.1C in Appendix A also show substantial increase in the use of these Google search terms in 2020. In sum, summary statistics uncover a differential response of non-Hispanic Whites and Asian self-employment rates to the pandemic. In what follows, we use a quasi-experimental approach to better assess the impact of the pandemic on Asian entrepreneurship.12 3 Methodology To explore whether and how the onset of the COVID-19 pandemic excessively impacted Asians’ self-employment rates, as well as their transitions into and out of self-employment compared to Whites, we estimate a difference-in-differences (DD) model. Since everyone, including the control group we use here, was affected by the pandemic, we show in Appendix B.1 that the DD model identifies the additional causal impact of the pandemic on Asians.13 9 We follow Chatterji and Li (2020) and compute the seasonally adjusted reduction in the number of non-influenza visits to healthcare providers by state each month. 10 Prior research using Google search intensity data to measure racial animus include the works by Chetty etal. (2020) and Anderson etal. (2020). Prior work has also used Google trends data to predict consumer behavior (Choi and Varian 2012), fertility (Kearney and Levine 2015), wellbeing (Askitas and Zimmerman 2015), and different impacts of the COVID-19 pandemic (Goldsmith-Pinkham and Sojourner 2020, Brodeur etal. 2021). 11 Google has an unreported privacy threshold and reports a zero if total searches fall below that threshold. 12 Appendix TablesA.3 through A.5 show the sample means for demographic characteristics for Whites and Asians before and after COVID onset separately for the full sample, the entry sample, and the exit sample. The largest differences between non-Hispanic Whites and Asians are not with regards to their gender, age, or educational attainment but, rather, with regards to their marital status, the inclusion of both immigrants and natives in our original Asian sample, and their residential choices. We control for all these variables in our main specification. 13 The same approach is used in Kaushal etal. (2007), Orrenius and Zavodny (2009), and Wang (2019) to examine how 9/11, an event with a national impact, differentially affected Arab and Muslim immigrants or Latin American immigrants’ labor market outcomes when compared to natives. C.Amuedo-Dorantes et al. 1 3 22 Page 8 of 34 The DD model is as follows: where Yi,j,s,t is one of three dependent variables we examine: a static measure of selfemployment — a dummy equal to 1 if individual i in industry j and state s is selfemployed at time t; and two dynamic measures of self-employment — entry and exit. Self-employment information is based on the class of worker question, which indicates if respondents worked for their own enterprise(s) or for someone else as employees.14 Entry equals 1 if the respondent is not self-employed in time t, but becomes self-employed at time t + 1, and 0 otherwise. Hence, the sample used to examine entries is composed of those not currently self-employed, including those unemployed, not in the labor force, or employed in the wage sector. The dependent variable Exit equals 1 if individual i is self-employed at time t, but not any more in t + 1; otherwise, it equals 0. Therefore, the sample used to examine exits is composed of those currently self-employed. The variable Asiani is a dichotomous variable that equals 1 if an individual reports being Asian (native or foreign-born), and 0 otherwise. To evaluate any differential impact of the pandemic on Asian entrepreneurship, we use non-Hispanic native-born Whites as the main comparison group. This group satisfies two important conditions. First, while everyone was affected by the pandemic, Whites did not experience the extra hurdles or discrimination that Asians faced, allowing us to evaluate any excess impacts. Second, as we shall show through placebo tests and event study analyses, Whites exhibited self-employment trends parallel to those of Asians for a very long period prior to the onset of the pandemic, allowing us to evaluate how much the trends have diverged due to the pandemic. COVIDt is a dummy variable equal to 1 for observations recorded from March 2020 onwards, after the Center for Disease Control and Prevention announced the first confirmed death from COVID-19 in the USA.15 Note its coefficient is not reported in the results as it is collinear with the year-month fixed effects. In posterior robustness checks, we show that our results are robust to using, instead, February 2020 or April 2020 as the pandemic’s onset dates. The coefficient 𝛽1 on the interaction term (Asiani∗COVIDt) is the estimate of main interest. In addition, Eq. (1) controls for demographic characteristics, Xi,s,t , such as gender, age, age squared, years of education, marital status, foreign-born status and, if so, years since migration and whether living in an urban area — all of which can affect entrepreneurial engagement. We also control for aggregate time-varying MSA traits potentially influencing the self-employment rate of Asians included in the vector Zm,t , such as the size of ethnic enclaves as measured by the proportion of Asians in the MSA and the proportion of immigrants in the MSA. Ethnic enclaves play a crucial role in an ethnic minority group’s decision to become (1) Yi,j,s,t = 𝛼 + 𝛽1Asiani ∗ COVIDt + 𝛽2Asiani + 𝛽3COVIDt + Xi,s,t𝛾 + Zm,t𝛿 + Bs,t𝜌 + 𝜃j + 𝜃s + 𝜃t + 𝜀i,j,s,t 14 If a worker has multiple sources of employment, the type of employment in which s/he spent the most time during the reference week is used. This definition includes all types of businesses: incorporated and unincorporated, as well as businesses with or without employees. 15 See: https:// www. cdc. gov/ media/ relea ses/ 2020/ s0229- COVID- 19- firstdeath. html. 1 3 Asian entrepreneurship inthecoronavirus era Page 9 of 34 22 self-employed, as their businesses often cater to a similar demographic. We also account for the unemployment rate in state s at time t as a control for local business cycles, Bs,t . Importantly, we include a set of industry fixed effects to control for differences in industry concentration between the two groups.16 In addition, state and temporal (year-month) fixed-effects are included to capture national trends common to all localities (such as nationwide changes in business startup policies or economic conditions), as well as time-invariant state level heterogeneity (such as the degree to which an area is business friendly). Finally, we include interaction terms of COVIDt with all control variables to allow for temporal changes in their impact. Standard errors are heteroskedasticity-robust and clustered at the state level to allow for arbitrary correlation within states in the error structure.17 4 The impact oftheCOVID pandemic onAsian self‑employment andits dynamics 4.1 Main findings Our main goal is to first establish the excess impact of the COVID pandemic on Asian self-employment compared to that of Whites, as well as on its underlying entry and exit dynamics to understand the mechanisms likely at play. Asians are known to be the fastest growing race groups in recent years, largely driven by increase in the number of Asian immigrants (Budiman and Ruiz 2021). Because immigrants and natives may face different obstacles when running their businesses, Table1 displays the estimated coefficients from the difference-in-differences model in Eq.(1) for all Asians, as well as by nativity. We estimate various model specifications that progressively add controls to help us gauge the impact of additional regressors on the estimated impact of the pandemic and address concerns regarding the potential endogenous nature of some controls. All the models include yearmonth and state fixed effects. Overall, the estimates in Table 1 show that Asian immigrant businesses were particularly hard hit by the pandemic. Regardless of the controls included in the model, the estimates in Table1 provide empirical evidence of a significant negative impact of the pandemic on Asian immigrants’ self-employment propensity relative to non-Hispanic Whites (Panel A). The estimated impact is rather stable across the 16 There are a total of 15 industry categories: Agriculture, Utilities, Construction, Manufacturing, Wholesale Trade, Retail Trade, Transportation and Warehousing, Information, Finance/Insurance and Real Estate, Professional/Scientific Management, Education/Health/Social Services, Entertainment and Recreational Services, Repair and Personal Services, Public Administration, and Other (including armed forces and missing industry information). The CPS provides industry for those who have worked in the past 5years, even if they are currently unemployed or not in the labor force. Everyone lacking information on industry is flagged accordingly. 17 Due to the outgoing rotation group design of the CPS data, some individuals are observed more than once. As recommended by Cameron and Miller (2015), in the presence of individual and a higher level of correlation, we cluster standard errors at the higher level of correlation, i.e., state level. Nevertheless, in the Appendix, we show that the results prove robust to clustering at the individual level. C.Amuedo-Dorantes et al. 1 3 22 Page 16 of 34 Table 3 Mechanisms #1 for increased exits: are Asian immigrants facing extra hurdles? Panel A: other immigrant or minority groups experiencing the same effects? Other treatment groups: Immigrants Racial/Ethnic groups Subgroup: All non-Asian immigrants Hispanic immigrants Blacks Hispanics Group*COVID onset − 0.025 − 0.026 − 0.001 − 0.003 (0.017) (0.017) (0.019) (0.011) N252,412 236,851 230,429 249,697 Panel B: Asians suffer more from supply chains disruptions during COVID? Model specification: High vs. low state imports from China High vs. low supply chain disruptions in state Retail trade and entertainment industries All other industries Asian*COVID onset 0.087** 0.082** 0.124** 0.096** (0.038) (0.033) (0.050) (0.038) Asian*High imp disrupt*COVID onset 0.022 0.035 (0.033) (0.029) Asian*High imports disruptions 0.015* 0.002 (0.008) (0.010) High imports disruptions*COVID onset 0.020*** − 0.016*** (0.003) (0.004) N227,206 227,206 34,371 192,835 Panel C: Asian or immigrant customers reducing consumption? Model specification: Heterogeneous effects in Asian enclaves Heterogeneous effects in Asian immigrant enclaves Asian*COVID onset 0.049*** Asian*COVID onset 0.049*** (0.017) (0.017) Asian*% Asian in MSA*COVID onset − 0.119 Asian*% Asian imm. in MSA *COVID onset − 0.184 (0.088) (0.140) 1 3 Asian entrepreneurship inthecoronavirus era Page 17 of 34 22 Table 3 (continued) Asian*% Asian in MSA 0.054** Asian*% Asian imm. MSA 0.071** (0.022) (0.030) % Asian in MSA*COVID onset 0.162*** % Asian imm. in MSA *COVID onset 0.245*** (0.047) (0.081) N227,206 227,206 The dependent variable is self-employment exit for all models. All models use Asian immigrants as the treatment group and native non-Hispanic Whites as the control group unless otherwise specified in the model. All models include the set of controls in the most complete model specification in Table1. “% Asian in MSA” and “% Asian Immigrant in MSA” variables are de-meaned. Standard errors are clustered at the state level. * p < 0.10; ** p < 0.05; *** p < 0.01 C.Amuedo-Dorantes et al. 1 3 22 Page 18 of 34 customers. The pandemic interrupted international trade of goods and services from China, as well as people flows due to imposed travel bans. The inability to access much-needed inputs may have placed Asian businesses at a particular disadvantage, as in the case of a Chinese grocery store selling specialized goods from China, or Chinese restaurants sourcing their supplies from China. To evaluate if supply chain issues were the main drivers for the found impacts, we first gather information on Chinese imports at the state level. Then, we create a dummy indicative of the states having a ‘high’ level of Chinese imports, which we interact in the model to gauge if the exit rate of Asian immigrant businesses after the COVID onset significantly differed across states based on their Chinese imports.23 In addition, we gather statelevel data on the share of firms indicating their exposure to supply chain disruptions.24 As shown in Columns 1 and 2 of Panel B in Table3, we find no evidence of Asian businesses exiting at a differential rate following the onset of the pandemic based on either the state’s imports from China or the extent of supply chain disruptions reported by businesses in the state, suggesting that, while relevant, trade disruptions may not have been the primary reason for the differential rate of Asian business exits. Finally, we evaluate the impact of the pandemic on Asian businesses across industries that may heavily rely on imported materials, including the retail trade industry and the arts, entertainment, recreation, accommodations, and food services industry (that encompass businesses such as Asian grocery stores and Asian restaurants). In Columns 3 and 4 of Panel B, Table3, we show that the effect on Asian businesses is slightly larger in the industries in Column 3 when compared to the impact on businesses in other industries. However, the effect remains large even after the exclusion of the industries in Column 3, suggesting supply chain issues alone are unlikely to be responsible for excess exits among Asian businesses. 5.1.4 Was it driven byhaving anarrower Asian clientele? If Asian immigrant businesses catered primarily to co-ethnics, and Asian customers were more fearful of the virus and less likely to go out, then Asian immigrant businesses may have particularly suffered. To assess this hypothesis, we examine if exit rates differed based on the share of Asians residing in the MSA where the business 23 A state is defined as one with a ‘high’ level of imports from China if the weight Chinese imports on its GDP is above the sample median based on data from USA Trade Online (https:// usatr ade. census. gov). States with a ‘high’ level of Chinese imports include Arkansas, California, Georgia, Illinois, Indiana, Kentucky, Michigan, Minnesota, Mississippi, Nevada, New Jersey, New Mexico, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Washington, and Wisconsin. 24 We use the answers to the question: ‘In the last week, did this business have disruptions in its supply chain?’ in the Small Business Pulse Survey, week 04/26/2020–05/02/2020 (https:// portal. census. gov/ pulse/ data). A state is defined as one with a ‘high’ level of supply-chain disruptions if the proportion of businesses answering positively is above the sample median. States with a ‘high’ level of supply chain disruptions include Alabama, Arkansas, California, Idaho, Louisiana, Maine, Maryland, Michigan, Mississippi, Nebraska, New Hampshire, New Jersey, New Mexico, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, South Carolina, Washington, and Wisconsin. There are a few similarities with the prior list of states with high level of imports from China. 1 3 Asian entrepreneurship inthecoronavirus era Page 19 of 34 22 was located. Other things equal, we would expect Asian-owned businesses to suffer more in areas with a higher concentration of Asians if the attitudes of Asian customers play a major role in contributing to a higher exit rate. We evaluate the heterogeneity across MSAs by adding the triple interaction of Asian, COVID Onset, and percent of Asians in MSA. As shown in the first column of Panel C in Table3, the exit rate of Asian immigrant businesses following the onset of the pandemic did not significantly differ across MSAs based on the concentration of co-ethnics. Because one might be concerned that the relevant measure is the share of Asian immigrants in the MSA, we repeat the exercise using that concentration as an alternative measure of the co-ethnic clientele in the second column of Panel C in Table3. We still fail to find supporting evidence of a fearful co-ethnic clientele likely explaining the differential exit rate of Asian immigrant businesses following the onset of the pandemic. 5.2 The role ofindustrial andoccupational distribution Over the course of the pandemic, states adopted various non-pharmaceutical interventions to curb its spread. The pandemic and these follow-up interventions had a negative impact on industries and occupations involving in-person services, as well as on non-essential businesses. An alternative explanation for the disproportionate impact of the pandemic on Asian self-employment exits is the potentially greater concentration of Asian entrepreneurship in those industries and occupations. Our models already include industry fixed effects. Nevertheless, to capture differences more accurately in the type of businesses held by entrepreneurs, we first experiment with including two additional control variables: a dummy indicative of an essential business, and a dummy variable signaling an occupation that allows for remote work.25 Our finding, shown in column (1) of Panel A in Table4, proves robust to the inclusion of these additional controls. Next, we consider including more disaggregated 3-digit industry fixed effects in the estimation as well as their interactions with the COVID onset dummies. As shown in column (2) of that same panel, our finding remains unchanged. In sum, industry or occupational segregation does not seem to drive the disproportionate impact of the pandemic on the survival of Asian immigrant businesses — a finding consistent with Couch etal.’s (2020) analysis of minority unemployment. Next, we examine whether Asians in non-essential businesses or occupations that do not allow for remote work experienced larger negative impacts than their counterparts in essential businesses or occupations that allowed for remote work. To that end, we include pairwise interaction terms of these dummy variables with the Asian 25 We follow Fairlie (2020) and define essential industries using the classification provided by Delaware State for essential and nonessential businesses (https:// busin ess. delaw are. gov/ wpconte nt/ uploa ds/ sites/ 118/ 2020/ 04/ DE- Indus try- List-4. 8. pdf). We use Dingel and Neiman (2020, Table 3) estimates on the share of jobs that can be done at home by industry. An industry is classified as allowing for remote work if the share of jobs that can be done remotely exceeds 70 percent. This ad-hoc cutoff is the approximate mode of the distribution of jobs in our sample. C.Amuedo-Dorantes et al. 1 3 22 Page 20 of 34 and COVID onset dummies. Based on the results in Panel B of Table4, the pandemic did not have a differentiated impact on the Asian self-employment exit rate based on whether the business was considered essential. Similarly, we fail to find much evidence of a differential impact of the pandemic on Asian businesses based on whether they allowed for remote work. In both instances, the pandemic continued to disproportionally hurt the survival of Asian immigrant businesses regardless of the added controls. 5.3 The role ofvoluntary exits andfear ofcontagion As documented earlier, the disproportionate impact of the pandemic on Asian selfemployment rates was driven by its damaging impact on Asian immigrant entrepreneurship survival rates when compared to those of non-Hispanic Whites. One could speculate that the increased exit rate among Asian immigrant entrepreneurs was a voluntary choice. Asian countries, especially those in East Asia, had a differential attitude towards the pandemic, implementing strict public health measures from the beginning of the outbreak, including stringent mask requirements, rapid contact tracing, isolation testing of all cases, quarantines, and border closings (Navarro 2021). Even though the evidence on whether Asians differed from non-Hispanic Whites with respect to COVID-19-related perceived threats and risks is inconclusive (e.g., Niño etal. 2021; Kumar and Encinosa 2023), the possibility exists that Asian immigrants may been more vigilant and, potentially, more prone to closing their businesses to avoid exposure to the virus. To assess this hypothesis, we first look at both work hours and family income among those self-employed who are identified as exiting self-employment the following month. If fear of contagion was the main driver behind the differential exit rate of Asian immigrants’ businesses, we would expect to find a reduction in work hours and no differential impact on family income prior to exiting self-employment. Yet, the estimates in Panel A of Table5 reveal that work hours among those exiting self-employment were, if anything, slightly higher for Asian immigrants than for non-Hispanic Whites. In addition, self-employed Asian immigrants were experiencing large reductions in family income prior to exiting self-employment when compared to non-Hispanic Whites. While we can only interpret family income as a proxy for business income, the large drop in income suggests their business closure was probably involuntary. Next, we examine the transitions of self-employed Asian immigrants from self-employment into different labor force statuses following the onset of the pandemic. As noted earlier, if fear of contagion was the main driver behind the differential exit rate of Asian immigrants’ businesses, we would expect most business closures to result in transitions out of the workforce, as opposed to unemployment. We estimate a multinomial logit model of self-employment exits (to unemployment, to out of the labor force, and to wage-employment), where remaining self-employed is the base or reference category. Panel B of Table5 reports the relative risk ratios from that estimation. Self-employed Asian immigrants were twice as likely than non-Hispanic Whites to exit the workforce. These transitions 1 3 Asian entrepreneurship inthecoronavirus era Page 21 of 34 22 could reflect fear of contagion, but also a lack of search activity when becoming unemployed in the midst of the pandemic. Therefore, it is helpful to also look at transition patterns from self-employment into other labor force statuses to further uncover information on what such transitions may be masking. To that end, we first look at transitions from self-employment into unemployment. Self-employed Asian immigrants were 2.6 times more likely to become unemployed than selfemployed non-Hispanic Whites following the onset of the pandemic. Because one needs to be actively searching for work to be considered unemployed, this finding reveals that Asians exiting self-employment were more likely than their non-His- panic White counterparts to be actively searching for employment, as opposed to evading work for fear of contagion. In addition, self-employed Asian immigrants and non-Hispanic Whites did not differ in their exit patterns to other wage and salary work, suggesting once more that fear of contagion was not inhibiting them from working. To further investigate the potential voluntary nature of Asian immigrant selfemployment exits during the pandemic, we examine how those transitions varied based on whether the spouse was employed as a wage and salary worker (Panel C Table 4 Mechanisms #2 for increased exits: is it due to Asian immigrants’ occupations or industries? The dependent variable is self-employment exit for all models. All models use Asian immigrants as the treatment group and native non-Hispanic Whites as the control group unless otherwise specified in the model. All models include the set of controls in the most complete model specification in Table1. Standard errors are clustered at the state level. * p < 0.10; ** p < 0.05; *** p < 0.01 Panel A: effects driven by Asians’ concentration in different types of businesses? Model specification: Control for essential and remote occupations Control for (detailed industry FE x Covid onset) Asian*COVID onset 0.103*** 0.102*** (0.027) (0.029) N227,206 227,206 Panel B: Asians experience more negative effects in non-essential or non-remote businesses? Model specification: Heterogeneous effects in essential vs. non-essential Heterogeneous effects in remote vs. nonremote Asian*COVID onset 0.120*** Asian*COVID onset 0.110*** (0.026) (0.029) Asian*Essential bus. *COVID onset − 0.032 Asian*Remote bus. *COVID onset − 0.016 (0.028) (0.023) Asian*Essential bus 0.015*** Asian*Remote bus 0.020*** (0.005) (0.006) Essential bus.*COVID onset − 0.008 Remote bus.*COVID onset − 0.014 (0.010) (0.021) N227,206 227,206 C.Amuedo-Dorantes et al. 1 3 22 Page 22 of 34 Table 5 Mechanisms #3 for increased exits: are Asian immigrants exiting for fear of contagion? Panel A: Asian immigrants’ work hours and family income right before exiting self-employment Outcome: Work hours Family income Asian*COVID onset 4.237* − 1.362*** (2.457) (0.415) N13,135 14,834 Panel B: Asian immigrant-owned businesses exit voluntarily? MNL of exit choice Exit to unemployment Exit to NILF Exit to wage-employment Asian*COVID onset 2.602*** 2.165*** 1.249 (0.560) (0.425) (0.180) N227,206 Log-likelihood − 1.78E+08 Panel C: Differential exit patterns by spousal wage and salary employment status With wage/salary spouse: Exit to unemployment Exit to NILF Exit to wage-employment Asian*COVID onset 1.527 2.144** 1.182 (0.528) (0.794) (0.269) N 95,228 Log likelihood − 6.62E+07 Without wage/salary spouse: Exit to unemployment Exit to NILF Exit to wage-employment Asian*COVID onset 3.372*** 2.078*** 1.307 (0.826) (0.431) (0.248) N 131,978 Log-likelihood − 1.09E+08 Panel D: excluding those nearing retirement (i.e., 60 +) MNL of exit choice Exit to unemployment Exit to NILF Exit to wage-employment Asian*COVID onset 2.372*** 2.290*** 1.344* (0.507) (0.501) (0.208) 1 3 Asian entrepreneurship inthecoronavirus era Page 23 of 34 22 All models use Asian immigrants as the treatment group and native non-Hispanic Whites as the control group. All models include the set of controls in the most complete model specification in Table1. Standard errors are clustered at the state level. Workhours and family income in Panel A are transformed using inverse hyperbolic sine to deal with negative or zero values. MNL models include all control variables except the interaction terms of Covid onset and control variables, and the base group is those who remain self-employed in the next period. Reported MNL results are relative risk ratios (rrr, which is transformed using formula eβ). Standard errors (se) of rrr are calculated as eβ *se(β). Significance levels are based on values of β and se(β). * p < 0.10; ** p < 0.05; *** p < 0.01 Table 5 (continued) N198,786 Log likelihood − 1.55E+08 Panel E: Labor market transitions among Asian immigrants who are wage and salary workers Multinomial logit model of leaving wage-employment Job quitter Exit to unemployment Exit to NILF Exit to self-employment Sample: Wage-employed who exited to unemployment Asian*COVID onset 1.627*** 1.130 0.532*** − 0.113** (0.145) (0.089) (0.115) (0.048) N1,873,198 17,436 Log-likelihood − 7.88E+08 C.Amuedo-Dorantes et al. 1 3 22 Page 24 of 34 in Table5). Other things equal, we would expect self-employed respondents with a spouse employed as a wage and salary worker to be more likely to voluntarily exit self-employment since they would have another source of income to rely upon. Yet, we do not observe that to be the case. Respondents exhibited the same likelihood of exiting from self-employment to out of the workforce. Furthermore, as we would expect from involuntary exits, respondents without a wage and salary spouse (i.e., an alternative source of income) were significantly more likely to exit self-employment, especially to unemployment, suggesting such exits were not likely to be voluntary. Exits from self-employment could have been driven by older working-age individuals considering an early retirement. In Panel D of Table5, we evaluate this possibility. As shown therein, self-employed Asians who were not close to retirement age exited to unemployment and wage and salary work at higher rates than White non-Hispanics following the pandemic onset, signaling they were interested in working. These results contradict the notion that Asian immigrants were mainly exiting self-employment at higher rates than non-Hispanic Whites voluntarily to avoid contagion. In addition, removing those close to retirement, who would have been more willing to voluntarily quit their businesses, did not lower the higher transition rate from self-employment into out of the workforce of Asian immigrants after the pandemic started. Finally, in Panel E of Table5, we look at exits from wage and salary work into other labor force statuses, including self-employment. If we are willing to assume that: (1) Asians were, in general, more concerned about fear of contagion, and (2) contagion fear was intense enough for someone to give up their livelihood, we would expect to observe Asians employed as wage and salary workers quitting their jobs voluntarily. Yet, we observe that wage and salary Asian immigrant workers were more likely to become unemployed (column 1), but less likely to be job quitters (column 4),26 signaling they were more likely to be involuntarily unemployed. Furthermore, these employees were not more likely to leave the labor force (column 2) and, possibly related to how self-employed counterparts were doing, less likely to go into self-employment (column 3). Overall, the results in Table 5 do not support the notion that the excess Asian immigrant self-employment exits, when compared to non-Hispanic Whites, following the onset of the pandemic were primarily voluntarily driven by business owners’ fear of contagion. 5.4 The role ofprejudice Prejudice against Asians increased substantially since the onset of the pandemic and exacerbated over time — a fact supported by anecdotal and academic evidence. For instance, Nguyen etal. (2020) analyzed 3,377,295 US race-related tweets and showed that the proportion ofnegative tweets referring to Asians increased by 68.4 26 Job quitters are defined as those reporting quitting their jobs when asked about the reason for being unemployed. 1 3 Asian entrepreneurship inthecoronavirus era Page 25 of 34 22 percent from November 2019 to March 2020, whereas those referring to Blacks or Latinx remained stable during that period. Similarly, using data from 2007 through 2020, Darling-Hammond et al. (2020) show that the Implicit Americanness Bias index (a measure of the subconscious belief that European Americans are more “American” than Asian Americans) reversed its 13-year steady declining trend and began to increase on March 8, 2020, as the use of terms such as “Chinese virus” became widely adopted by media. Astoundingly, the authors show that, from March 8 to March 31, 2020, Implicit Americanness Bias increased enough to offset more than three years of prior reductions. In a different setting, Lu et al. (2021) use a nationally representative survey dataset collected in August 2020 with an embedded vignette experiment concerning roommate selection. They find that priming COVID-19 salience has an immediate and statistically significant impact on the treatment group — they exhibited increased prejudice and discriminatory intent against East Asian hypothetical room-seekers. The authors conclude that “incidents of anti-Asian hostility reported in the media are not isolated acts but signal-ampli- fied racism against East Asians.” Prejudice against Asian-owned business can originate from customers, banks, and suppliers, which can have profound impacts on their survival rate (Becker 1957; Borjas and Bronars 1989). Customers may reduce their interactions with these businesses, leading to a substantial decline in business revenues. In that vein, Huang etal. (2023) show a substantial decrease in foot traffic to Asian restaurants, the magnitude of which is larger in areas with higher levels of support for President Trump. In addition, customers may also disrupt business operations. For example, news media has reported that Asian-owned restaurants received an increasing number of prank calls and fake orders (PBS 2020), as well as vandalisms (NBC News 2020a, b). Additionally, Asianowned businesses may encounter bank discrimination during this period. An extensive literature has documented that minority groups are more likely to experience credit market discrimination, e.g., being denied a loan or receiving a loan with higher interest rates than Whites (Blanchflower etal. 2003; Alden and Hammaarstedt, 2016; Cavalluzzo and Cavalluzzo 1998). Lastly, Asian-owned businesses may have been discriminated against by suppliers, restricting their access to important production materials (Alden and Hammarstedt, 2016). In addition to taste-based discrimination, the initial detection of the virus in China could have led to increased statistically based discrimination against Asian-owned businesses for fear of contracting the virus. Measuring discrimination is challenging, as one must rely on unexplained and disparate outcomes. However, in addition to the unexplained and disparate selfemployment exits experienced by Asian immigrants vis-à-vis non-Hispanic Whites, we first examine if the onset of the pandemic proved particularly more harmful for Asian immigrant groups more likely to experience discrimination. Next, we evaluate if there is empirical evidence of a direct link between prejudice measures against Asians and Asian immigrants’ self-employment exit rates. 5.4.1 Differential impacts acrossAsian immigrant subgroups To evaluate if certain Asian immigrant subgroups more likely to experience discrimination were, accordingly, more severely hurt by the onset of the pandemic, we C.Amuedo-Dorantes et al. 1 3 22 Page 32 of 34 Azoulay P, Jones BF, Daniel Kim J, Miranda J (2022) Immigration and entrepreneurship in the United States. Am Econ Rev Insights 4(1):71–88 Bates T, Dunham CR (1993) Asian-American success in self-employment. Econ Dev Q 7(2):199–214 Becker G (1957) The economics of discrimination. University of Chicago Press, Chicago, IL Blanchflower DG, Levine PB, Zimmerman DJ (2003) Discrimination in the small-business credit market. Rev Econ Stat 85(4):930–943 Borjas G (1986) The Self-Employment Experience of Immigrants. J Hum Resour 21:487–506 Borjas GJ, Bronars SG (1989) Consumer discrimination and self-employment. J Polit Econ 97(3):581–605 Borjas GJ, Cassidy H (2020) The Adverse Effect of the COVID-19 Labor Market Shock on Immigrant Employment. NBER Working Papers 27243, National Bureau of Economic Research, Inc Bowler J, Harris B (2022) Racial Inequality in the United States, Featured Stories, U.S. Department of the Treasury at https:// home. treas ury. gov/ news/ featu redstori es/ racialinequ alityin- theunited- states. Accessed 21 Apr 2023 Brodeur A, Clark AE, Fleche S, Powdthavee N (2021Jan) COVID-19, lockdowns and well-being: Evidence from Google trends. J Public Econ193:104346. https:// doi. org/ 10. 1016/j. jpube co. 2020. 104346 Budiman A, Ruiz NG (2021) Key Facts about Asian Americans, a Diverse and Growing Population. Washington, DC: Pew Research Center. https:// www. pewre search. org/ facttank/ 2021/ 04/ 29/ keyfacts- aboutasian- ameri cans/.Accessed 21 Apr 2023 Bureau of Labor Statistics (2021) U.S. Department of Labor, The Economics Daily, Employment declined sharply during COVID-19 pandemic for self-employed Asians at https:// www. bls. gov/ opub/ ted/ 2021/ emplo ymentdecli nedshar p lyduring- covid- 19- pande micfor- selfemplo yedasians. htm. Accessed 05 Nov 2021 Cameron AC, Miller DL (2015) A practitioner’s guide to cluster-robust inference. J Hum Resour 50(2):317–372 Cavalluzzo, Ken S., and Linda C. Cavalluzzo. 1998. "Market structure and discrimination: The case of small businesses."Journal of Money, Credit and Banking, 771–792. CBS News (2020) 2,120 hate incidents against Asian Americans reported during coronavirus pandemic. July 2, 2020. Available at https:// www. cbsne ws. com/ news/ antiasian- ameri canhate- incid entsup- racism/.Accessed 26 June 2021 Chatterji P, and Li Y (2020) Effects of the COVID-19 pandemic on outpatient providers in the US. National Bureau of Economic Research, No. w27173. https:// www. nber. org/ papers/ w27173. Accessed 7 June 2021 Chetty R, Hendren N, Jones MR, Porter SR (2020) Race and economic opportunity in the United States: an intergenerational perspective. Q J Econ 135:711–783 Choi H, Varian H (2012) Predicting the present with Google trends. Econ Rec 88:2–9 Couch KA, Fairlie RW, Huanan Xu (2020) Early evidence of the impacts of COVID-19 on minority unemployment. J Public Econ 192:104287 Crossley TF, Fisher P, Low H (2021) The heterogeneous and regressive consequences of COVID-19: evidence from high quality panel data. J Public Econ 193:104334 Cunningham E (2018) Great recession, great recovery-trends from the current population survey. Monthly Labor. Review 141:1 Davila A, Mora MT (2005) Changes in the earnings of Arab men in the U.S. between 2000 and 2002. J Popul Econ 18:587–601 Darling-Hammond S, Michaels EK, Allen AM etal (2020) After The ‘China Virus’ went viral: racially charged coronavirus coverage and trends in bias against Asian Americans. Health Educ Behav 47:870–879 Dingel JI, Neiman B (2020) How many jobs can be done at home? J Public Econ 189:1–8 Fairlie R (2020) The impact of COVID-19 on small business owners: evidence from the first three months after widespread social-distancing restrictions. J Econ Manag Strategy 29(4):727–740 Fairlie RW (2014) Kauffman Index of Entrepreneurial Activity, 1996–2013. Available at: SSRN: https:// ssrn. com/ abstr act= 24248 34orhttps:// doi. org/ 10. 2139/ ssrn. 24248 34. Accessed 26 June 2021 Fairlie R, Fossen FM (2022a) Did the paycheck protection program and economic injury disaster loan program get disbursed to minority communities in the early stages of COVID-19? Small Bus Econ 58:829–842. https:// doi. org/ 10. 1007/ s11187- 021- 00501-9 Fairlie R, Fossen FM (2022b) The early impacts of the COVID-19 pandemic on business sales. Small Bus Econ 58:1853–1864. https:// doi. org/ 10. 1007/ s11187- 021- 00479-4 Fairlie RW, Robb AM (2008) Race and Entrepreneurial Success: Black-, Asian-, and White-Owned Businesses in the United States. MIT Press, Cambridge, Massachusetts; London, England. Accessed 18 May 2020 1 3 Asian entrepreneurship inthecoronavirus era Page 33 of 34 22 Flood S, King M, Rodgers R, Ruggles S, Warren JR, Westberry M (2021) Integrated Public Use Microdata Series, Current Population Survey: Version 9.0. IPUMS, Minneapolis, MN. https:// doi. org/ 10. 18128/ D030. V9.0 Forsythe E, Kahn LB, Lange F, Wiczer D (2020) Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims. J Public Econ189:104238. https:// doi. org/ 10. 1016/j. jpube co. 2020. 104238 Goldsmith-Pinkham P, Sojourner A (2020) Predicting initial unemployment insurance claims using Google Trends. Working Paper Graeber D, Kritikos AS, Seebauer J (2021) COVID-19: a crisis of the female self-employed. J Popul Econ 34:1141–1187 Guryan J, Charles KK (2013) Taste-based or statistical discrimination: the economics of discrimination returns to its roots. Econ J 123(572):417–432 Herek GM, Glunt EK (1988) An epidemic of stigma. Public reactions to AIDS. Am Psychol 43(11):886–891 Herek GM, Capitanio JP (1993) Public reactions to AIDS in the United States: a second decade of stigma. Am J Public Health 83(4):574–577 Honoré BE, Hu L (2023) The COVID-19 pandemic and Asian American employment. Empir Econ 64:2053–2083. https:// doi. org/ 10. 1007/ s00181- 022- 02306-5 Hswen Y, Xu X, Hing A, Hawkins JB, Brownstein JS, Gee GC (2021) Association of “#COVID19” versus “#Chinesevirus” with anti-Asian sentiments on Twitter: March 9–23, 2020. Am J Public Health 111(5):956–964 Huang JT, Krupenkin M, Rothschild D etal (2023) The cost of anti-Asian racism during the COVID-19 pandemic. Nat Hum Behav 7:682–695. https:// doi. org/ 10. 1038/ s41562- 022- 01493-6 Human Rights Watch (2020) Covid-19 fueling anti-Asian racism and xenophobia worldwide. https:// www. hrw. org/ news/ 2020/ 05/ 12/ covid- 19- fueli nganti- asian racism- and- xenop hobiaworld wide Jones N, Marks R, Ramirez R, Ríos‐Vargas M (2021) 2020 Census illuminates racial and ethnic composition of the country. United States Census Bureau. https:// www. census. gov/ libra ry/ stori es/ 2021/ 08/ impro vedrace- ethni citymeasu resreveal- unitedstates- popul ationmuch- moremulti racial. html. Accessed 4 Nov 2021 Kaushal N, Kaestner R, Reimers C (2007) Labor market effects of September 11th on Arab and Muslim residents of the United States. J Hum Resour 42:275–308 Kearney MS, Levine PB (2015) Media influences on social outcomes: the impact of MTV’s 16 and pregnant on teen childbearing. Am Econ Rev 105:3597–3632 Kochhar R (2015) Three-in-Ten U.S. jobs are held by the self-employed and the workers they hire: hiring more prevalent among self-employed Asians, Whites and Men. Washington, D.C.: Pew Research Center, October Kumar V, Encinosa W (2023) Racial disparities in the perceived risk of COVID-19 and in getting needed medical care. J Racial Ethn Health Disparities 10:4–13 Levine R, Rubinstein Y (2017) Smart and illicit: who becomes an entrepreneur and do they earn more? Q J Econ 132(2):963–1018 Lofstrom M, Wang C (2009) Mexican-American self-employment: a dynamic analysis of business ownership. In: Ethnicity and Labor market outcomes, research in labor economics. Emerald Group Publishing Limited Lu Y, Kaushal N, Huang X, Michael Gaddis S (2021) Priming COVID-19 salience increases prejudice and discriminatory intent against Asians and Hispanics. Proc Natl Acad Sci 118(36):1–7 Mar D, Ong P (2020) Covid-19’s employment disruptions to Asian Americans. AAPI Nexus: Policy, Practice and Community,17(1–2). Available at https:// www. aasc. ucla. edu/ resou rces/ polic yrepo rts/ COVID 19_ Emplo yment_ CNKAASC_ 072020. pdf.Accessed 4 Apr 2023 Montenovo L, Jiang X, Lozano-Rojas F, Schmutte I, Simon K, Weinberg BA, Wing C (2022) Determinants of disparities in early COVID-19 job losses. Demography 59(3):827–855 Navarro V (2021) Why Asian countries are controlling the pandemic better than the United States and Western Europe. Int J Health Serv 51(2):261–264 Niño M, Harris C, Drawve G, Fitzpatrick HK (2021) 2021 race and ethnicity, gender, and age on perceived threats and fear of COVID-19: evidence from two national data sources. SSM Popul Health 13:100717 NBC News (2020) Smashed Windows and Racist Graffiti: Vandals Target Asian Americans amid Coronavirus. Available at: https:// www. nbcne ws. com/ news/ asianameri ca/ smash edwindo wsracist- graff itivanda lstarget- asianameri cansamid- coron avirusn1180 556. Accessed 6 May 2023 C.Amuedo-Dorantes et al. 1 3 22 Page 34 of 34 Nguyen TT, Criss S, Dwivedi P, Huang D, Keralis J, Hsu E, Phan L, Nguyen LH, Yardi I, Glymour MM, Allen AM (2020) Exploring US shifts in anti-Asian sentiment with the emergence of COVID-19. Int J Environ Res Public Health 17(19):7032 NPR (2021) Anger and fear as Asian American seniors targeted in Bay Area attacks. Available at: https:// www. npr. org/ 2021/ 02/ 12/ 96694 0217/ anger- and- fearas- asianamer i cansenio rst arge tedin- bayarea- attac ks. Accessed 6 May 2023 Orrenius PM, Zavodny M (2009) The effects of tougher enforcement on the job prospects of recent Latin American immigrants. J Policy Anal Manage 28(2):239–257 PBS (2020) Racism targets Asian food, business during COVID-19 pandemic. Available at: https:// www. pbs. org/ newsh our/ nation/ racismtarge tsasian- foodbusin essduring- covid- 19- pande mic. Accessed 6 May 2023 Pew Research Center (2015) Three-in-ten U.S. jobs are held by the self-employed and the workers they hire. Available at: https:// www. pewso cialt rends. org/ 2015/ 10/ 22/ threein- ten-u- sjobs- areheld- bythe- selfemplo yed- and- theworke rsthey- hire/. Accessed 26 June 2021 Phelps ES (1972) The statistical theory of racism and sexism. Am Econ Rev 62(4):659–661 Ramakrishnan K, Wong J, Lee J, Lee T (2017) 2016 Post-Election National Asian American Survey. Riverside, Calif.: National Asian American Survey. Available at https:// naasu rvey. com/ wpconte nt/ uploa ds/ 2017/ 05/ NAAS16- postelect ionreport. pdf.Accessed 19 Sept 2021 Robb AM, Fairlie RW (2009) Determinants of business success: an examination of Asian-owned businesses in the USA. J Popul Econ 22:827–858 Saxenian A (1999) Silicon Valley’s new immigrant entrepreneurs. Public Policy Institute of California, San Francisco Saxenian A (2000) Networks of immigrant entrepreneurs. In The Silicon Valley edge: A habitat for innovation and entrepreneurship. In: Lee C-M, Miller WF, Rowen HS (eds) 248– 74. Stanford University Press, Stanford Stephens-Davidowitz S (2014) The cost of racial animus on a Black candidate: evidence using Google search data. J Public Econ 118:26–40 Stop AAPI Hate Press Release (2020) Available at: http:// www. asian pacifi cpol icyan dplan ningc ouncil. org/ wpconte nt/ uploa ds/ Press_ Relea se_4_ 23_ 20. pdf. Accessed 26 June 2021 The City, NYC (2022) 2021 Saw Another Surge of Anti-Asian Hate Crimes in Subway. Available at https:// www. theci ty. nyc/ 2022/1/ 4/ 22867 479/ 2021- sawanoth ersurge- ofanti- asianhate- crimesin- subway. Accessed 6 May 2023 The Guardian (2020) Turn back this wave of hate’: 100 writers call for an end to anti-Asian hostility. May 25, 2020. Available at https:// www. thegu ardian. com/ world/ 2020/ may/ 25/ asianameri canwrite rstrump- coron avirus.Accessed 10 Apr 2020 Wadhwa V, Saxenian A, Rissing B, Gereffi G (2007) America’s new immigrant entrepreneurs. Duke Science, Technology, and Innovation Paper no. 23. Duke University, Durham, NC Wang C (2016) The impact of 9/11 on the self-employment outcomes of Arab and Muslim immigrants. Int Migr Rev 52:430–457 Wang C (2019) Tightened immigration policies and the self-employment dynamics of Mexican immigrants. J Policy Anal Manage 38(4):944–977 Wang C, Lofstrom M (2020) September 11 and the rise of necessity self-employment among Mexican immigrants. East Econ J 46(1):5–33 World Health Organization. 2020. WHO Timeline - COVID-19. April 27, 2020. Available at: https:// www. who. int/ news/ item/ 27- 04- 2020- whotimel ine--- covid- 19.Accessed 20 June 2020 Xu H (2018) First fired, first hired? Business cycles and immigrant labor market transitions. IZA J Dev Migr 8(1):1–36 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.