Business,Owner,and Regional Characteristics in Latino-owned Business Growth: An Empirical Analysis Using Confidential Census Microdata

Abstract

This article uses over 100,000 observations from limited-access and nationally representative US Census Bureau microdata sets to test determinants of employment growth among Latino-owned businesses (LOBs) in the Unites States. We draw variables from prior studies on determinants business growth in the general population and uniquely apply them to LOB using the robust data. Specifically, we examine the impact of numerous business owner, business, and regional characteristics on employment growth. We include industry and state-level fixed effects and test the robustness of results to various employment growth timespans. Some findings include (1) Latina-owned businesses grow faster than LOB, (2) formal education has a positive effect on employment growth and this effect is larger with education level and time, (3) Puerto Rican-owned businesses grow 2 percent slower than Mexican-owned establishments, (4) having multiple establishments reduces employment growth, (5) relying on personal savings for start-up capital impedes growth, and (6) nonmetro adjacency has a significant and negative effect, while population density does not. Our findings show that LOB may grow differently than other businesses and help advance the understanding of factors related to success of LOB. Implementing straightforward and low-cost policies aimed at better support for LOB could help bolster regional growth.

Keywords

entrepreneurship microdata employment Latino

While firm growth is the focus of much research, a consensus on factors associated with firm growth has yet to emerge (Audretsch, Coad, and Segarra 2014). This lack of consensus extends to how the importance of these factors differs across various subgroups of small business owners. Subgroup variation is of particular importance in the United States, where immigrants and business start-ups seem to go hand in hand. Although small businesses do not account for the majority of jobs, they produce disproportionately large job growth relative to their share of employment (Haltiwanger 2009; Carree et al. 2015). The finding that the determinants of fast growth need not be the same as the determinants of average or normal growth (Lopez-Garcia and Puente 2012) combined with the finding that high-growth firms have a low probability of maintaining that high growth (Daunfeldt and Halvarsson 2015) highlight the need for a more comprehensive examination into factors impacting employment growth in general rather than only examining high-growth firms. Latino-owned businesses (LOBs) are a particularly important group for study because they are smaller on average and have significant capacity for growth (Rivers and Porras 2015). How regional attributes interact with growth of these businesses is therefore an important policy question.

The number of LOBs continues to grow relatively quickly in the United States. Specifically, from 2007 to 2012, LOBs grew by more than 46 percent to 3.3 million firms, outpacing the total number of all US firms, which increased by just 2.0 percent to 27.6 million (Bernstein 2016).¹ This article thus focuses on a comprehensive examination of growth in businesses owned by the fastest-growing ethnic group in the United States: Latinos. As mostly small businesses, their growth may be more consistent over time, like the race of the tortoise, and contribute substantially to overall regional job growth. Additionally, because we use a cross section of LOB types, some of our findings may inform future research on businesses owned by other groups.

Researchers explored immigrant and ethnic business ownership extensively over the past 20 years but were constrained by small sample size and a lack of microdata on immigrant and ethnic business ownership. Robles and Cordero-Guzman (2007) assess the literature and write that one challenge in Latino business ownership research is the lack of sufficiently large data sets for country-of-origin analysis. Findings indicating that recent immigrant entrepreneurs may outperform other young firms only emphasizes the importance of such research (Neville et al. 2014). Prior studies using microdata (e.g., Lofstrom and Bates 2009) faced limitations in their examination of comprehensive demographic and geographic differences with respect to the success of immigrant business owners in the United States. Our study takes advantage of the Federal Statistical Research Data Center network to lift the sample size limitations faced by earlier work, while maintaining information about business location as part of the analysis.

The ways in which immigrant business owners influence and are influenced by their environment is a subject of interest in the literature. For example, Light (1979) suggests that limited opportunities in the labor market (e.g., language barriers, discrimination, or unemployment) cause people to seek self-employment. Waldinger and colleagues argue that immigrant entrepreneurship experience cannot result entirely from culture but rather also from the interaction of the group characteristics (e.g., human capital) of different immigrants and the opportunity structures they experience (Waldinger 1993; Waldinger, Ward, and Aldrich 2000). Researchers interested in immigrant business ownership have examined the impact of acculturation, that is, the process of adapting to a new culture (Calo 1995), the impact of financial resources (or lack thereof; Cavalluzzo and Wolken 2005; Robb and Fairlie 2007), human and social capital (Hansen 1995; Peters and Brush 1996), networks (Walton-Roberts and Hiebert 1997), and motivation, business strategy, and community resources (Liu 2012). Together this work provides ample theoretical background for an empirical examination on the impact of business owner, business, and regional characteristics on outcomes.

With the importance of local characteristics as a determinant of firm entries, more complete data than what was available for prior works allow for more detailed analysis of the impact of establishment location, agglomeration in terms of employment, the role of geography, and specific industry effects (Arauzo Carod, Liviano Solís, and Martín Bofarull 2007; Duboz, Kroichvili, and Le Gallo 2016). We use limited-access Census microdata from the Survey of Business Owners (SBO), Longitudinal Business Database (LBD), and Integrated Longitudinal Business Database (ILBD) to estimate how individual, business, and regional factors affect the employment growth of LOBs over time, using pooled ordinary least squares (OLS). This examination helps paint a more complete picture of how business owner, business, and regional factors affect establishment LOB employment growth in particular and may also inform future studies about how similar non-LOB enterprises might grow.

We explore factors associated with employment growth among LOB including the importance of start-up capital to future employment growth, while also investigating findings that LOB in particular tend to concentrate in relatively vulnerable sectors (Bates, Lofstrom, and Servon 2011) and are more likely to go out of business than white-owned businesses (Ahn 2011). So what are the main barriers to LOB growth? The environment that facilitates entrepreneurship in general and LOB in particular is largely dependent on these factors and on activities of local economic development decision makers and practitioners to promote entrepreneurship. Efforts to promote LOB at various levels of government and nongovernmental entities are dependent upon decision makers and practitioners understanding the determinants of growth of LOB, as well as the characteristics of business owners, businesses, and the region.

Literature Review

Our theoretical grounding is drawn from the neoclassical literature on output as a function of land, capital, and technology (Solow 1956; Sakashita 1968), with additional theoretical support from the economics of agglomeration literature, where firm profitability and growth is influenced by the broader environment, especially the markets and skills available in that environment (Porter 1996; Krugman 1997). The nature of firm growth is a heterogeneous and dynamic process that involves economic, social, and cultural factors (Delmar, Davidsson, and Gartner 2003; Wong, Ho, and Autio 2005; Audretsch, Coad, and Segarra 2014). Different conceptions of firm growth increase the complexity of discussions of business growth. Delmar, Davidsson, and Gartner (2003) classify firm growth into four types: organic growth, creation of new firms, concentration of existing firms (mergers, acquisitions), and growth through innovation and diffusion of new products and processes. Additionally, analysis of firm growth may have two faces: quantitative/empirical (i.e., “how much”) and qualitative/theoretical (i.e., “how”; Coad 2009; McKelvie and Wiklund 2010; Audretsch, Coad, and Segarra 2014). While the former may usually be investigated by econometric analyses of databases, the latter is more complex, requiring detailed information on many dimensions of a firm’s activity. This article’s analysis follows the former strands of literature, specifically enhancing the quantitative/empirical (i.e., “how much”) understanding of LOB employment growth in the United States.

Audretsch, Coad, and Segarra (2014) summarize four main justifications of the complexity of growth: (1) the indicators used to measure growth are not neutral with respect to empirical results; (2) firm growth is not only explained with traditional observable variables such as location, industry, size, age, or capital, but it is also associated with specific unobservable factors such as a firm’s managerial capital or the skills of its workforce; (3) innovation performance differs between firms and its impact on firm growth varies in time and space; and (4) the differences in firms’ profiles—location, market position, public support, and so on—impact with varying intensities on firm growth.

Although factors associated with small business survival at the establishment level are well studied, less is known about factors associated with business employment growth at the establishment level. Further, few studies attempt comprehensive investigations into the impact of business owner characteristics, business characteristics, and local and geographic characteristics on establishment-level outcomes, or employment growth, especially within LOB. Although some studies examine employment growth, many use aggregated data (often to the state, county, city, or metropolitan statistical area), which necessarily precludes the use of individual establishment control variables (e.g., Robbins et al. 2000; Shaffer 2006). This is primarily because of difficulties obtaining microdata. Fairlie and Robb (2009) conducted studies on gender differences in business ownership and on minority-owned business using Federal Statistical Research Data Center (FSRDC) data but did not focus on LOB or growth. Although heterogeneity and local factors have a significant impact on firm growth (Acs and Mueller 2007), there is limited empirical consensus on the drivers of firm growth. Challenges are not only related to unobservable heterogeneity at the firm level but are also associated with the low persistence of growth rates over time (Audretsch, Coad, and Segarra 2014; Daunfeldt and Halvarsson 2015; Lopez-Garcia and Puente 2012). Despite these challenges, this section reviews literature on employment growth by dividing past work into three broad categories: (1) business owner characteristics, (2) business characteristics, and (3) regional characteristics.

Business Owner Characteristics

A major factor impacting Latino business employment growth is the lack of financial resources combined with the fact that Latinos primarily draw capital from personal savings, informal loans from friends or family, and moneylenders (Haynes, Onochie, and Lee 2008; Raijman and Tienda 2000). This is especially important given the finding that access to credit is important for growth (Lopez-Garcia and Puente 2012). Given that many Latinos are recent arrivals, their mechanisms for accessing credit for business ownership may be different than the general population.

Human capital is the theoretical mechanism providing the foundation for business owner characteristics often found to have a significant impact on business growth. These characteristics include business owner education, age, and prior experience. Education, though sometimes found to be insignificant (Schutjens and Wever 2005; Almus 2002), often has a significant and positive impact (Honjo 2004; Kangasharju and Pekkala 2002; Motoyama 2014; Colombo and Grilli 2010; Unger et al. 2011). Some studies have found that owner age, despite the expectation of increasing human capital with experience, has a significant and negative impact (Honjo 2004; Kangasharju and Pekkala 2002). We test whether these findings apply to LOB; for example, it may be that education serves as more of a signaling mechanism to establish credibility in the general population than it does for LOB, while age may also play a different role or run on a different trajectory with LOB, given the time it may take for an immigrant to get to the point where a business start is feasible.

Finally, gender is an important consideration. Although research indicates that women business owners have lower employment levels in general (Fairlie and Robb 2009; Coleman and Robb 2012), in terms of employment growth, results have been mixed. There is evidence that women-owned business grow less quickly (Alsos, Isaksen, and Ljunggren 2006; Bosma et al. 2004; Orser, Riding, and Manley 2006), but there are notable exceptions (Chaganti and Parasuraman 1996; Fischer, Reuber, and Dyke 1993; Robb and Watson 2012). Further, there is limited research into the validity of these results when discussing Latinas in particular. It is possible that cultural differences mean Latinas face higher barriers to entry than other females, hindering growth, or perhaps that those who do start businesses are more determined to grow.

Business Characteristics

Although larger businesses are more likely to survive (Brüderl, Preisendorfer, and Ziegler 1992; Gimeno et al. 1997), some past work indicates that smaller establishment and firm size is associated with faster employment growth rates (Shaffer 2002, 2006), while other evidence points towards medium-sized (20–500 employees) employers (Acs and Mueller 2007), or no relationship at all (Haltiwanger, Jarmin, and Miranda 2013) if properly controlling for business age. It appears that young business age, more than firm size, is most important in growth (Henrekson and Johansson 2010; Lawless 2014; Haltiwanger, Jarmin, and Miranda 2013; Anderson and Eshima 2013). The fact that many Latinos are immigrants translates into a younger average proprietor age for LOB, which in turn may mean that the businesses themselves are younger, resulting in business-age-related growth instead of growth associated with LOB status. It is thus important to test business age as a determinant of growth in exploring LOB growth.

Past studies on the impact of industry/sector on growth are varied (Schutjens and Wever 2005; Almus 2002; Kangasharju and Pekkala 2002; Honjo 2004; Lööf and Nabavi 2014). More recent studies examine why Latinos tend to concentrate in sectors perceived as relatively vulnerable, such as the services (Puryear et al. 2008; Robles and Cordero-Guzman 2007), construction, wholesale trade, and retail trade sectors (US Census Bureau 2010). While this concentration is observed, we are interested in learning the impact of the sector on LOB growth.

Regional Characteristics

Probably due to data limitations, few studies examine the impact of business location characteristics on LOB employment growth (Trettin and Welter 2011), despite the fact that the literature on the inverse concept—the impact of immigrants on regional growth—is well-developed (Kemeny 2017). This study includes local and geographic controls based in part on factors found to be significant in past studies such as local industrial mix (Almus 2002; Honjo 2004; Li et al. 2016). Other received findings include the share of a population that is highly educated having a positive impact on business success due to both demand and supply-side factors (Millán et al. 2014; Peer and Penker 2016), that population density has a positive effect on employment growth (Shaffer 2002), and that social capital plays a significant and positive role on business growth (Brüderl and Preisendörfer 1998; Bosma et al. 2004; Fazio and Lavecchia 2013). Ager and Brückner (2013) find that places with a large native majority and a large immigrant population of a single ethnicity reduce growth. Missing from the literature is how these regional characteristics affect minority-owned businesses.

New business formation has also been found to contribute to employment growth (Josep Maria, Liviano Solís, and Martín Bofarull 2007; Andersson and Noseleit 2011), though impacts vary by industry (Andersson and Noseleit 2011). Local industrial structure also has local employment benefits when businesses locate to regions with more agglomerated industries (Hoogstra and van Dijk 2004; Baptista and Preto 2011; Bogas and Barbosa 2014; Li et al. 2016), though there is some evidence of diseconomies of agglomeration under some circumstances (Lee 2016). Local labor market structure also appears to be important with evidence that high-tech employment, and the population of science and engineering graduates in the area is associated with faster-growing firms (Motoyama 2014). We suspect a dynamic market will open new business opportunities and help growth of LOB.

The reviewed past results in employment growth literature do not examine LOB and point to numerous testable hypotheses with respect to LOB employment growth. We divide these hypotheses into the aforementioned three topic areas in Table 1.

Table 1.

Latino-owned Business Growth Hypotheses and Supporting Literature.

Hypotheses	Supporting Literature
1. Business owner characteristics
Hypothesis 1a. Older owners experience lower growth rates	Honjo (2004); Kangasharju and Pekkala (2002)
Hypothesis 1b. Women owners have lower growth rates	Alsos, Isaksen, and Ljunggren (2006); Bosma et al. (2004); Chaganti and Parasuraman (1996); Fischer, Reuber, and Dyke (1993); Orser, Riding, and Manley (2006); Robb and Watson (2012)
Hypothesis 1c. Higher education increases growth rates	Almus (2002); Colombo and Grilli (2010); Honjo (2004); Kangasharju and Pekkala (2002); Motoyama (2014); Schutjens and Wever (2005); Unger et al. (2011)
2. Business characteristics
Hypothesis 2a. Business age decreases growth rates	Anderson and Eshima (2013); Haltiwanger, Jarmin, and Miranda (2013); Henrekson and Johansson (2010); Lawless (2014)
Hypothesis 2b. Business size does not impact growth rates	Acs and Mueller (2007); Haltiwanger, Jarmin, and Miranda (2013); Shaffer (2002); Shaffer (2006)
Hypothesis 2c. Activity in a high-barrier industry increases growth rates	Almus (2002); Honjo (2004); Lööf and Nabavi (2014); Kangasharju and Pekkala (2002); Schutjens and Wever (2005)
Hypothesis 2d. Activity in a low-barrier industry decreases growth rates	Puryear et al. (2008); Robles and Cordero-Guzman (2007)
3. Regional characteristics
Hypothesis 3a. Higher education of local population increases growth rates	Millán et al. (2014); Motoyama (2014); Peer and Penker (2016)
Hypothesis 3b. Ethnic enclaves are associated with higher growth^a	Ager and Brückner (2013); Bosma et al. (2004); Brüderl and Preisendörfer (1998); Fazio and Lavecchia (2013)
Hypothesis 3c. Same industry employment (as a measure of agglomeration) increases growth rates	Baptista and Preto (2011); Bogas and Barbosa (2014); Hoogstra and van Dijk (2004); Lee (2016); Li et al. (2016)
Hypothesis 3d. Higher population density increases employment growth	Shaffer (2002)

^aFor the purposes of this study, we define “ethnic enclave” as a county with many nonwhite people of the same ancestral origin.

In sum, we are interested in learning whether received findings for all business owner types also hold in the case of LOB. For example, we note that LOB tend to be smaller than the average business; do observed differences in business characteristics mean that factors associated with growth are different than the general-populations findings available from the literature? Our hypotheses thus explore whether past general population results hold for a population of specific policy interest. We test our hypotheses with a data set not previously available to researchers interested in these questions.

Method

Factors associated with LOB ownership and dynamics include the characteristics of business, business owner, and region. The standard economic model predicts that these factors are associated with the firm’s production process. As such, the empirical approach to test these hypotheses is grounded in the theory of firm entry, growth, and exit, which is well-developed in the small business and industrial organization literature (Borjas 1986; Evans 1987; Evans and Jovanovic 1989; Evans and Leighton 1989; T. Dunne, Roberts, and Samuelson 1989; P. Dunne and Hughes 1994).

We test our hypotheses using pooled OLS regression.² The explanatory variables that represent business owner characteristics refer to the traits of the owner or majority shareholder of the firm and include variables such as gender, education, and age, and ancestral origin, all of which have been shown to affect general population business growth in past research, but are untested for LOB (Coleman and Robb 2012; Motoyama 2014; Colombo and Grilli 2010; Unger et al. 2011).³ We also include business characteristics such as business age, and sources of capital, size, and industry, as well as public information about the county in which the business is located, which have similarly been found to be important covariates when modeling business growth (Anderson and Eshima 2013; Haltiwanger, Jarmin, and Miranda 2013; Honjo 2004; Lööf and Nabavi 2014; Millán et al. 2014; Peer and Penker 2016).

The definitions and methods provided by the Bureau of the Census Business Dynamics Statistics (see http://www.census.gov/ces/dataproducts/bds/overview.html for more details) provide the basis for the formulation of employment change variables. This growth rate measure is standard in analysis of establishment-level dynamics because it shares useful properties of log differences while also accommodating entry and exit (Haltiwanger, Jarmin, and Miranda 2013; Törnqvist, Vartia, and Vartia 1985). To ameliorate concerns of endogeneity, the model regresses future growth on current explanatory variable levels. Thus, the establishment-level employment growth rate becomes:

g_{i t} = \frac{(E_{i t + τ} - E_{i t})}{A_{i t}} w h e r e A_{i t} = \frac{1}{2} (E_{i t + τ} + E_{i t}) .

E_it is employment in year t for establishment i and $E_{i t}$ is employment in year t for establishment i. Known as the DHS (Davis, Haltiwanger, and Schuh 1996) measure, this measure is a monotonic transformation of relative growth and can be defined at any level of aggregation including establishment, firm, local area, and industry (Haltiwanger, Jarmin, and Miranda 2009, 2013). We use the same average size approach for establishment births, existing establishments, and establishment deaths. It is a second-order approximation of the log difference for growth rates around zero. As Decker et al. (2014) point out, the use of a symmetric growth rate does not remove the potential for regression to the mean effects. In our analysis, however, we use the average firm size measure since, as discussed in Haltiwanger, Jarmin, and Miranda (2013) and Decker et al. (2014), this helps take into account bias from the regression to the mean. Of course, it does not completely remove the potential for regression to the mean effects, which is always a potential weakness in these types of analysis. To formally summarize the model, we use the establishment-level growth rate measures and the estimation equation takes the following form:

g_{i t} = β X_{i t} + γ Z_{i t} + δ L_{i t} + ρ_{t} + ∊_{i t},

where $g_{i t}$ refers to employment growth for the ith establishment between years t and $t + τ$ , X is a vector of business owner variables, Z is a vector of business characteristics, L is a vector of business location variables, and $ρ_{t}$ is a year fixed effect.⁴ We estimate this model using linear relationships, and although the presented coefficients result from a single regression, we separate the coefficients into the three variable types outlined in the literature review section (business owner, business, and regional). First, we use a one-year time horizon ( $τ = 1$ ) to calculate the employment growth under various levels of fixed effects. Then, we repeat the model with two- and three-year time intervals ( $τ = 2, 3$ ) to test the stability of the initial specification over time and with various fixed effects. To control for the nonlinear nature of the effect of business age on employment growth, we include indicator variables for if the business is younger than three years or older than three years (with the reference category being three years).

Data

Our restricted access data come from the FSRDC program, specifically: the 2002–2007 ILBD (n = approximately 18 million observations annually), the 2002–2007 LBD (n = approximately 8 million observations annually), and the 2002 SBO (n = approximately 2.3 million observations in 2002). The SBO is cross-sectional, not a panel. We merge the three data sets to determine longitudinal changes of the matched businesses.

All FSRDC results must go through disclosure analysis to verify that they contain no risk of improper disclosure of any identifiable information. In the analysis that follows, certain estimated coefficients are suppressed to protect confidentiality of individuals and businesses. Under Census guidelines, the reader can be shown that a nondisclosed variable was included in estimated equation, but not the coefficient value, sign, or significance. As is required with FSRDC research, we only provide summary statistics or regression results for variables that pass disclosure requirements and may not provide the minimum or maximum. Tables 2 –4 summarize the variables covered by our sample of LOB for our regressions, with the exception of the publicly available variables (and state, year, and business organization type⁵ fixed effects), which concerned the disclosure review.

Table 2.

Business Owner Variable Summary Statistics.

Business Owner Variables	Mean	Std. Dev.	Source
Woman	.190	.392	SBO
Primary source of income	.612	.487	SBO
Owner ancestry (base: Mexican)			SBO
Mexican	.418	.493
Cuban	.135	.341
Puerto Rican	.066	.249
Other Hispanic	.382	.486
Owner education (base: <HS)			SBO
Less than high school	.081	.273
High school	.181	.385
Some college	.142	.349
Tech or trade school	.047	.211
Associate degree	.037	.188
Bachelor’s degree	.153	.360
Graduate degree	.137	.344
Weekly hours (base: 40)			SBO
Zero	.023	.150
Less than 20	.064	.245
20–39	.083	.276
40	.131	.337
41–59	.274	.446
Greater than 59	.201	.400
Primary duties (multiple response)			SBO
Produce goods/services	.354	.478
Management	.542	.498
Finances	.435	.496
Other function	.273	.446
Owner age (base: less than 25)			SBO
Less than 25	.004	.059
25–34	.075	.263
35–44	.235	.424
45–54	.239	.426
55–64	.155	.362
Older than 64	.069	.253

Note: SBO = Survey of Business Owners.

Table 3.

Business Variable Summary Statistics.

Business Variables	Mean	Std. Dev.	Source
Employment growth (dependent)			LBD/ILBD
One year	.050	.685
Two year	.042	.725
Three year	.042	.781
Business age (base: three years)			LBD
Less than three years	.175	.380
Three years	.080	.272
More than three years	.745	.436
Multiple establishments	.165	.371	ILBD/LBD
Start-up capital source (base: savings)			SBO
Savings	.563	.496
Assets	.119	.324
Credit	.091	.287
Government loan or guarantee	.025	.156
Bank loan	.146	.353
Venture capital	.033	.178
None needed	.058	.234
Other	.235	.424
Expansion capital source (base: savings)			SBO
Savings	.211	.408
Assets	.056	.229
Credit	.110	.312
Government loan or guarantee	.016	.127
Bank loan	.205	.403
Venture capital	.016	.127
None needed	.338	.473
Other	.237	.425
Customer type (>10 percent of sales)			SBO
Federal government	.052	.221
State or local government	.078	.268
Other businesses	.279	.448
Individuals	.385	.487
NAICS industry (base: retail trade)			LBD/ILBD
11: Ag., forestry, fishing, and hunting	.006	.078
21: Mining, quarrying, oil, and gas	.003	.051
22: Utilities	.000	.017
23: Construction	.113	.316
31–33: Manufacturing	.076	.265
42: Wholesale trade	.061	.239
44 and 45: Retail trade	.153	.360
48 and 49: Transportation and warehousing	.047	.211
51: Information	.016	.125
52: Finance and insurance	.043	.204
53: Real estate, rental, and leasing	.024	.154
54: Prof., scientific, and tech. service	.116	.321
55: Management of companies	.005	.069
56: Admin. and waste mng. services	.059	.237
61: Educational services	.012	.110
62: Health care and social assistance	.081	.273
71: Arts, entertainment, and recreation	.010	.101
72: Accommodation and food services	.104	.305
81: Other services	.070	.256

Note: SBO = Survey of Business Owners; NAICS = North American Industrial Classification System; ILBD = Integrated Longitudinal Business Database; LBD = Longitudinal Business Database.

Table 4.

Regional Variable Summary Statistics.

Regional (County-level) Variables	Mean	Std. Dev.	Source
Population			Census/ACS
% Population black			Census/ACS
% Population Asian			Census/ACS
% Population Hispanic			Census/ACS
% With bachelor’s degree			Census/ACS
Per capita income			BEA
Unemployment			BEA
Natural Amenities Scale			USDA
Rural–urban continuum code			USDA
1: Metro area, ≥1 million pop.	.671	.470
2: Metro area, 0.25–1 million pop.	.175	.380
3: Metro area, <250,000	.072	.258
4: Adjacent to metro, ≥20,000	.028	.165
5: Not adjacent to metro, ≥20,000	.012	.109
6: Adjacent to metro, 2,500–19,999	.023	.151
7: Not adjacent to metro, 2,500–19,999	.016	.125
8: Adjacent to a metro, <2,500	.002	.039
9: not adjacent to a metro, <2,500	.003	.050
% Households headed by single female			Census/ACS
Number of building permits issued			Census

Note: USDA = US Department of Agriculture; HS = high school; ACS = American Community Survey; BEA = Bureau of Economic Analysis; FE = fixed effects.

Although the location of the businesses is available from these merged data sets, county and county-equivalent characteristics such as agglomeration, racial makeup, amenities, and industry size are taken from published Bureau of the Census, Bureau of Economic Analysis and US Department of Agriculture (USDA) sources.

The distribution of employment growth, the dependent variable in our regression, deserves special attention. First, Table 3 shows that mean employment growth is fairly stable over time (ranging from 0.042 to 0.050), with the variance increasing with $τ$ (ranging from 0.685 to 0.781). Figure 1 displays a kernel density function of the one-year employment growth variable. The smoothness of this density highlights the advantage and comprehensive nature of our large sample. By our definition of employment growth, $g_{i t} \in (- 2, 2)$ . Nevertheless, the FSRDC requires sufficiently large “bins” and that researchers trim the upper and lower bounds of the density.

Figure 1.

Kernel density of one-year employment growth. Figure 1 uses an Epanechnikov kernel, which is optimal in the mean square error sense, with a bin bandwidth of .090. FSRDC disclosure guideline precludes the release of densities for the two- and three-year employment growth variables, though their distributions behave similarly (with the higher variance shown in Table 3 translating into “flatter” distributions).

Results and Discussion

Tables 5 –7 contain the results of the analysis into factors affecting LOB employment growth. Although the same-column results in each table occur in a single regression, we separate the variable coefficient estimates in the order of the aforementioned topic areas: (1) business owner characteristics, (2) business characteristics, and (3) regional characteristics. To conserve space, we leave some statistically insignificant local controls to the Appendix, including the county-level population, number of building permits issued, percent of households headed by a single female, and employment by the same two-digit industry (in county and state). As noted above, although we include control variables for state, year, and business organization type, we cannot provide their regression coefficients due to FSRDC disclosure limitations.

Table 5.

Pooled Ordinary Least Squares Results: Latino-owned Business Employment Growth (Business Owner Variables).

Business Owner Variables	(1)	(2)	(3)	(4)	(5)
	Employment Growth
	One Year	One Year	One Year	Two Year	Three Year
Woman	.02*** (.01)	.02*** (.01)	.02*** (.01)	.01 (.01)	.01 (.02)
Primary source of income	−.02** (.01)	−.02** (.01)	−.02** (.01)	−.02 (.02)	−.02 (.02)
Owner ancestry (base: Mexican)
Cuban	.02* (.01)	.01 (.01)	.01 (.01)	.02 (.02)	−.00 (.02)
Puerto Rican	−.02*** (.01)	−.02*** (.01)	−.02*** (.01)	−.02 (.02)	−.04 (.03)
Other Hispanic	.00 (.01)	.00 (.01)	.00 (.01)	.01 (.01)	−.01 (.02)
Owner education (base: <HS)
High school	.01 (.01)	.01 (.01)	.01 (.01)	.03* (.02)	.03 (.03)
Some college	.01 (.01)	.02 (.01)	.02 (.01)	.03 (.02)	.05* (.03)
Tech or trade school	.01 (.01)	.02 (.01)	.02 (.01)	.03 (.03)	.08** (.04)
Associate degree	.02 (.02)	.02 (.02)	.02 (.02)	.03 (.03)	.05 (.04)
Bachelor’s degree	.04*** (.01)	.04*** (.01)	.04*** (.01)	.05*** (.02)	.07** (.03)
Graduate degree	.03*** (.01)	.03*** (.01)	.04*** (.01)	.05** (.02)	.07** (.03)
Weekly hours (base: 40)
Zero	−.02 (.02)	−.02 (.02)	−.02 (.02)	−.03 (.04)	−.04 (.05)
Less than 20	−.02 (.01)	−.02 (.01)	−.02 (.01)	−.05 (.03)	−.06 (.04)
20–39	−.01 (.01)	−.01 (.01)	−.01 (.01)	−.02 (.02)	−.01 (.03)
41–59	−.00 (.01)	−.00 (.01)	−.00 (.01)	.01 (.02)	.00 (.02)
Greater than 59	.00 (.01)	.00 (.01)	.00 (.01)	.01 (.02)	.01 (.03)
Primary duties (multiple response)
Produce goods/services	.00 (.01)	.00 (.01)	.00 (.01)	−.00 (.01)	−.00 (.02)
Management	−.01 (.01)	−.01 (.01)	−.01 (.01)	−.02 (.01)	−.03* (.02)
Finances	−.01* (.01)	−.01* (.01)	−.01** (.01)	−.00 (.01)	−.01 (.02)
Other function	−.01 (.02)	−.01 (.02)	−.01 (.02)	.00 (.03)	.00 (.04)
Owner age (base: less than 25)
25–34	−.00 (.04)	−.00 (.04)	−.00 (.04)	.04 (.07)	.03 (.10)
35–44	−.03 (.03)	−.03 (.03)	−.03 (.03)	.01 (.07)	−.04 (.09)
45–54	−.04 (.03)	−.04 (.03)	−.04 (.03)	−.02 (.07)	−.07 (.09)
55–64	−.05 (.03)	−.05 (.03)	−.05 (.03)	−.03 (.07)	−.10 (.09)
Older than 64	−.05 (.03)	−.05 (.03)	−.05 (.03)	−.04 (.07)	−.10 (.09)

Note: Cluster-robust standard errors are given in parentheses.

*p < .1.

**p < .05.

***p < .01.

Table 6.

Pooled Ordinary Least Squares Results: Latino-owned Business Employment Growth (Business Variables).

Business Variables	(1)	(2)	(3)	(4)	(5)
	Employment Growth
	One Year	One Year	One Year	Two Year	Three Year
Business age (base: three years)
Less than three years	.29*** (.02)	.29*** (.02)	.29*** (.02)	.29*** (.02)	.28*** (.03)
More than three years	−.06*** (.02)	−.06*** (.02)	−.06*** (.02)	−.06*** (.02)	−.06*** (.02)
Multiple establishments	−.08*** (.01)	−.08*** (.01)	−.08*** (.01)	−.13*** (.02)	−.17*** (.03)
Start-up capital source (base: savings)
Personal assets	.02* (.01)	.02* (.01)	.02* (.01)	.05*** (.02)	.07*** (.02)
Credit	−.00 (.01)	−.00 (.01)	−.00 (.01)	−.01 (.02)	−.02 (.03)
Government loan or guarantee	.02 (.01)	.02 (.01)	.02* (.01)	.01 (.02)	.02 (.04)
Bank loan	−.00 (.01)	−.00 (.01)	−.00 (.01)	.00 (.01)	.02 (.02)
Venture capital	.01 (.01)	.01 (.01)	.01 (.01)	.04 (.03)	.05 (.04)
None needed	.00 (.01)	.00 (.01)	.00 (.01)	−.01 (.02)	−.01 (.03)
Other	−.01 (.02)	−.01 (.02)	−.01 (.02)	−.00 (.04)	−.05 (.06)
Expansion capital source (base: savings)
Personal assets	.01 (.01)	.01 (.01)	.01 (.01)	.02 (.02)	.01 (.03)
Credit	.01 (.01)	.01 (.01)	.01 (.01)	.02 (.02)	.02 (.03)
Government loan or guarantee	.02 (.02)	.02 (.02)	.02 (.02)	−.00 (.04)	−.02 (.07)
Bank loan	.00 (.01)	.00 (.01)	.00 (.01)	−.00 (.02)	.01 (.02)
Venture capital	−.02 (.02)	−.02 (.03)	−.02 (.03)	.01 (.04)	.07 (.06)
None needed	−.01 (.01)	−.01 (.01)	−.01 (.01)	−.03* (.01)	−.03 (.02)
Other	−.01 (.02)	−.01 (.02)	−.01 (.02)	−.00 (.04)	−.05 (.06)
NAICS industry (base: retail trade)
11: Ag., forestry, fishing, and hunting			.03 (.05)	−.12 (.10)	−.18 (.15)
21: Mining, quarrying, oil, and gas			.04 (.03)	.10* (.05)	.18** (.08)
22: Utilities			.06 (.12)	.11 (.16)	.12 (.24)
23: Construction			.00 (.01)	.00 (.02)	−.00 (.03)
31–33: Manufacturing			−.00 (.01)	.00 (.02)	.02 (.03)
42: Wholesale trade			.01 (.01)	.04* (.02)	.08** (.03)
48–49: Transportation and warehousing			.01 (.02)	−.00 (.03)	.03 (.04)
51: Information			.02 (.03)	−.04 (.05)	−.08 (.07)
52: Finance and insurance			.01 (.01)	.02 (.02)	.03 (.03)
53: Real estate, rental, and leasing			.01 (.02)	−.02 (.03)	−.05 (.04)
54: Professional, scientific, and tech. service			−.01 (.01)	.00 (.02)	−.01 (.03)
55: Management of companies and enterprise			−.00 (.03)	−.01 (.06)	.01 (.09)
56: Administrative support and waste management services			.00 (.01)	−.01 (.02)	−.04 (.04)
61: Educational services			−.00 (.02)	.01 (.03)	.02 (.05)
62: Health care and social assistance			−.01 (.01)	.01 (.02)	−.03 (.03)
71: Arts, entertainment, and recreation			−.02 (.03)	−.05 (.04)	−.03 (.07)
72: Accommodation and food services			.01 (.01)	.02 (.02)	.02 (.03)
81: Other services			−.03** (.01)	−.04** (.02)	−.06** (.03)
Customer type (>10 percent of sales)
Federal government	−.01 (.01)	−.00 (.01)	−.00 (.01)	−.03 (.03)	−.06 (.04)
State or local government	.01 (.01)	.01 (.01)	.01 (.01)	.01 (.02)	.01 (.03)
Other businesses	.01 (.01)	.01 (.01)	.01 (.01)	−.01 (.01)	−.01 (.02)
Individuals	.01 (.01)	.01 (.01)	.01 (.01)	.00 (.01)	.01 (.02)
Establishment employment	−.00*** (.00)	−.00*** (.00)	−.00*** (.00)	−.00*** (.00)	−.00*** (.00)
Establishment payroll	.00*** (.00)	.00*** (.00)	.00*** (.00)	.00*** (.00)	.00*** (.00)
Firm employment	.00*** (.00)	.00*** (.00)	.00*** (.00)	.00*** (.00)	.00*** (.00)
Firm payroll	−.00*** (.00)	−.00*** (.00)	−.00*** (.00)	−.00*** (.00)	−.00* (.00)
SBO business organization FE	YES	YES	YES	YES	YES

Note: Cluster-robust standard errors are given in parentheses. SBO = Survey of Business Owners; NAICS = North American Industrial Classification System.

*p < .1.

**p < .05.

***p < .01.

Table 7.

Pooled Ordinary Least Squares Results: Latino-owned Business Employment Growth (Regional Variables).

Regional (County-level) Variables	(1)	(2)	(3)	(4)	(5)
	Employment Growth
	One Year	One Year	One Year	Two Year	Three Year
% Population black	−.00 (.00)	−.00** (.00)	−.00*** (.00)	−.00 (.00)	.00 (.00)
% Population Asian	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	.00 (.00)
% Population Hispanic	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	.00 (.00)
% With bachelor’s degree	.00 (.00)	.00 (.00)	.00 (.00)	.00 (.00)	−.00 (.00)
Per capita income	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	.00 (.00)
Unemployment rate	−.00 (.00)	.00 (.00)	.00 (.00)	.00 (.00)	−.00 (.01)
Rurality code (base: 1 > 1M pop.)
2: Metro area, 0.25–1 million pop.	−.01 (.01)	−.01 (.01)	−.01 (.01)	−.01 (.02)	−.01 (.02)
3: Metro area, <250,000	−.01 (.01)	−.02 (.01)	−.02 (.01)	−.00 (.02)	−.02 (.03)
4: Adjacent to metro, ≥20,000	−.01 (.02)	−.01 (.02)	−.01 (.02)	.01 (.03)	−.01 (.04)
5: Not adjacent to metro, ≥20,000	−.05** (.02)	−.06** (.02)	−.06** (.02)	−.05 (.04)	−.10 (.07)
6: Adjacent to metro, 2,500–19,999	.03 (.02)	.02 (.02)	.02 (.02)	.00 (.04)	−.04 (.05)
7: Not adjacent to metro, 2,500–19,999	−.04 (.03)	−.05* (.03)	−.05* (.03)	−.10*** (.04)	−.18*** (.07)
8: Adjacent to a metro, <2,500	.01 (.04)	−.00 (.04)	−.01 (.05)	.07 (.06)	.06 (.10)
9: Not adjacent to a metro, <2,500	.02 (.04)	.01 (.04)	.01 (.04)	.01 (.06)	.11 (.09)
Constant	.21*** (.05)	.15** (.06)	.15** (.07)	.10 (.10)	.19 (.14)
Observations	∼113,000	∼113,000	∼113,000	∼86,000	∼63,000
R ²	0.05	0.05	0.05	0.06	0.06
Year FE	YES	YES	YES	YES	YES
State FE		YES	YES	YES	YES
NAICS industry FE			YES	YES	YES

Note: Cluster-robust standard errors are given in parentheses. NAICS = North American Industrial Classification System.

*p < .1.

**p < .05.

***p < .01.

Recall that the dependent variable under consideration is the establishment-level employment growth rate. Columns (1)–(3) of Tables 5 –7 present the results with $τ = 1$ and various level of fixed effects. Columns (4) and (5) present the results with $τ = 2$ and $τ = 3$ , respectively. As the tables show, the coefficients for one-year employment growth appear mostly robust to the various levels of fixed effects. As a result, this article’s discussion of the employment growth results focus on columns (3) through (5).

Although most of the results are remarkably robust across the three time intervals in terms of sign and significance, it may be that the two- and three-year time intervals are less likely to be endogenous. The coefficients on the owner’s ancestral origin variables help investigate the impact of origin on firm growth, though we are limited to the categories imposed by the SBO (Mexican, Cuban, Puerto Rican, and other) and we cannot control for immigrant status.

The omitted two-digit North American Industrial Classification System (NAICS) category is 44–45: retail trade, the most common category. Compared with the retail trade sector, NAICS 21: mining, quarrying, oil, and gas, and NAICS 42: wholesale trade have a positive effect on growth rates, while NAICS 81: other services has a negative effect. Both NAICS 21 and 42 could be considered relatively high-barrier industries, while NAICS 81 is a relatively low-barrier industry.⁶

Some coefficients are statistically significant but displayed as 0.00*** or −0.00***. The Bureau of the Census limits the specificity with which the regression results can be displayed to protect against improper disclosure, so only the sign of the coefficient is available. As these coefficients are near zero, while statistically significant, their practical impact is small. For example, the employment and payroll variables are statistically significant, but near zero.⁷

Somewhat surprisingly, most business regional factors are statistically insignificant, although code 5 (urban population of 20,000 or more, not adjacent to a metro area) and code 7 (urban population of 2,500–19,999, not adjacent to a metro area) are exceptions. Also somewhat surprisingly, the USDA Economic Research Service natural amenities scale does not have a significant effect. We also tested multiple specifications to find the effect of enclave status, as measured by density of Hispanics and Hispanic country of origin populations in the county, but most were not significant.

The results presented here highlight some important extensions and developments in contemporary research in entrepreneurship in general, but specifically Latino entrepreneurship. As in the review of literature, these conclusions and suggestions are divided into three topic areas: (1) business owner characteristics, (2) business characteristics, and (3) regional characteristics.

Business Owner Characteristics

Although our coefficients on owner age are increasingly negative with age, they are insignificant and thus we fail to find support for our first hypothesis (Hypothesis 1a: Older owners experience lower growth rates) or past results (Honjo 2004; Kangasharju and Pekkala 2002). This finding combined with our numerous business owner controls and the finding that establishments owned by older individuals are more likely to survive (van Praag 2003; Honjo 2004) provide support for the finding that younger business owners (Latino business owners in particular) are more likely to engage in risky business decisions (Wennberg, Delmar, and McKelvie 2016). Hence, average employment growth is increased at the cost of survival probability. As a result, local economic practitioners and policy makers may face the perverse decision of supporting overall employment growth or individual business survival, or they should perhaps tailor their business support services to more closely reflect owner propensities (risk mitigation strategies for risk-loving owners). Future research should further examine the impact of age or risk preferences and work to better measure owner risk preferences.

We show that Latina-owned businesses demonstrate faster employment growth than their male-owned counterparts, and hence, we can reject our second hypothesis (Hypothesis 1b). As noted, past research into the employment growth of women-owned business has not produced consistent findings. It may also be that employment growth interacts with gender and ethnicity, which produced the inconsistent results. Further, given the consistent finding the women-owned businesses are less likely to survive (Haapanen and Tervo 2009; Georgellis, Sessions, and Tsitsianis 2007; Fairlie and Robb 2009), following the same logic as above, one might conclude that Latina business owners have higher risk tolerances. However, given evidence that women business owners are more risk-averse (Croson and Gneezy 2009; Barber and Odean 2001; Jianakoplos and Bernasek 1998), it could be that Latina business owners are not solely maximizing profit but also consider employment size a goal or weight work–life balance issues differently than males; this is an area for future research.

Also consistent with past findings (Honjo 2004; Kangasharju and Pekkala 2002; Motoyama 2014; Colombo and Grilli 2010; Unger et al. 2011), we show a statistically significant impact of education level on LOB employment growth, confirming Hypothesis 1c. The high number of observations in this article allowed us to disaggregate education more than previous work, enabling us to show that the size of the positive impact of education increases with education level. Furthermore, this relationship appears to be nonlinear over time, increasing in size as the time horizon increases.

This article also examines the relationship between LOB ancestry (or country of origin) and employment growth. The SBO specifies whether the Latino business owner has Cuban, Puerto Rican, or other Latino, relative to Mexican ancestry. Only Puerto Rican ancestry was significant, lowering growth by 2 percent in one year. Note that this article excludes businesses in Puerto Rico. These results help provide empirical evidence to support recent discussions of the low barriers to entry in the United States for Puerto Rican migrants and the accompanying struggles (e.g., Abel and Deitz 2015) and are consistent with evidence that out-migrants from Puerto Rico are relatively low skill (Borjas 2008).

Business Characteristics

That business age has a negative effect on growth is consistent with past findings (Henrekson and Johansson 2010; Lawless 2014; Haltiwanger, Jarmin, and Miranda 2013; Anderson and Eshima 2013) and confirmed our fourth hypothesis (Hypothesis 2a). Our results demonstrate that these past findings are robust to growth time frame as well as to additional control variables. Once again, the effects appear to compound as we increase the time interval of the analysis. Although we find that both establishment and firm employment have a statistically significant and positive effect on employment growth, the coefficient is near zero and hence not economically significant, confirming our fifth hypothesis (Hypothesis 2b) that firm size does not affect growth. This result is expected given the inclusion of numerous controls particularly business age (Haltiwanger, Jarmin, and Miranda 2013). Conversely, we find that multiple establishments have a significant and negative relationship with employment growth in LOB. It may be that previous findings relating business size negatively with employment growth resulted from omitted variable bias not only from failing to account for firm age but also as a result of failing to account for whether the firm had multiple establishments. It may also be that some of these businesses are in enclaves and find it difficult to expand further through additional sites once they reach the limits of what the enclave can support.

Bates, Lofstrom, and Servnon (2011) also suggest business owners with large amounts of personal capital to spend on business start-up will be able to open more lucrative businesses in the high-technology, high-barrier industries, while others are more limited in their ability to expand. Our employment growth results provide tepid support for our related hypotheses (Hypotheses 2c and d). LOB in the high-barrier industries of wholesale trade and mining, quarrying, oil, and gas both have higher growth rates than the relatively low-barrier retail trade. The two-digit NAICS coding system does not perfectly separate high- and low-barrier industries, so we also argue that our results related to owner education support the finding that high-barrier industries tend to grow faster.

A limitation of these data results from Latino business owners tending to participate in small informal economic activities, such as street vending businesses and, consequently, formal sources of identification would not capture the full range of self-employment opportunities in which Latinos participate. Indeed, Raijman and Tienda (2000) suggest census data do not adequately cover some types of economic activities, such as part-time and irregular work or informal self-employment (likely in a low-barrier industry). Future research may want to examine this aspect of Latino business ownership with other nonfederal data procurement methods.

Regional Characteristics

Many of the business location factors are statistically insignificant including local education, age, gender, race/ethnicity populations; population and population density; employment and establishment counts under various ethnicities; the interaction terms between business owner ethnicity and local population of that same ethnicity; unemployment rate; building permits issued; and county- and state-level employment in the same two-digit NAICS in which the business is operating. We note that variance inflation factor estimates indicate that multicollinearity is not a concern. Hence, we fail to find support for our hypotheses related to the education of the local population (Hypothesis 3a) or the importance of enclaves (Hypothesis 3b). It may be that future research can find better ways to measure enclaves, by examining finer geographic areas than county boundaries. It could also be that future research will show that enclave status or the size of the enclave affects firm employment change differently depending on their growth stage.

The results indicating that non-metro-adjacency may have a negative effect provide some limited support to the importance of agglomeration (Hypothesis 3c), consistent with Latino business owner comments found in case studies assembled by (Munoz and Spain 2015). Hence, although population density is insignificant and do not find support for (Hypothesis 3d) directly, our results indicate that previous studies that find population density to be significant (Shaffer 2002) were likely biased due to the omission of rurality variables. Indeed, cities and other high amenity locations are particularly likely to see higher employment growth rates, but that this is not simply the result of population density. Population density is often used as a proxy for more detailed regional and location characteristics in employment growth literature (Shaffer 2002; Baptista and Preto 2011; Acs and Armington 2004; Acs and Mueller 2007), with arguments that that regional population density is highly correlated with a number of factors such as the wage level, real estate prices, quality of communication infrastructure, and labor market quality and diversity (Fritsch and Mueller 2007). The results here highlight the need for caution in using population density as a catchall control variable and demonstrate more detailed rural–urban data can effect significance.

Conclusions

We improve on numerous past studies of LOB that use aggregated data and as a result fail to account for location and firm-specific heterogeneity. Past studies often use data sets with only the fastest growing firms (“gazelles”), and as such exclude other firms. Evidence that the determinants of fast growth need not be the same as the determinants of average or normal growth (Lopez-Garcia and Puente 2012) combined with the finding that high-growth firms have a low probability of maintaining that high growth (Daunfeldt and Halvarsson 2015) imply the need for a more comprehensive examination into factors impacting employment growth among slower-growing but steady employers. Finally, despite the growing importance of LOB in the United States, there is a dearth of studies examining how LOB grow. This study examines LOB exclusively and examines the impact of business owner ancestral origin on employment growth. We find that characteristics associated with general population business growth are not always supported in our results for LOB, which is an important insight in an era of increased immigration and immigrant-owned businesses.

This research provides new insights into an important sector of the US economy. For many economic development practitioners and policy makers, the goal is not simply the survival of local businesses but also local employment growth. Some practitioners may assume that factors impacting survival also impact the employment growth of a business in the same direction. The results presented here highlight the importance of drawing a distinction between survival and employment growth with some important factors impacting employment growth in the opposite direction as survival. Furthermore, economic development practitioners do not necessarily focus their efforts on high-growth companies, and thus, research that focuses exclusively on those fast-growing firms may not be as useful to practitioners as the results presented here if the goal is consistent regional growth or understanding and mitigating disparities that may exist among ethnicities in the United States.

Some practical conclusions might be for policy makers to employ techniques to support growth of LOB in response to our results. Implementing straightforward and low-cost policies aimed at better support for LOB could help bolster overall regional growth. For example, many Latinos are not familiar with or fearful of the formal banking sector (Todd and Kokodoko 2014). Efforts to embed Spanish-speaking service providers into natural local networks of Hispanics might help overcome hesitancy to establish credit and the overreliance on personal savings that we show to impede growth. Similarly, immigrant Hispanics may lack language skills that would allow them to network effectively in traditional business organizations. Parallel organizations where business is conducted in Spanish may be beneficial during the acculturation period, although a caution in this is that Munoz and Spain’s (2015) cases generally were unaware of or did not use such services. Studies delving into successful use of such services might complement existing research.

Footnotes

Appendix

Table A1.

Omitted Pooled Ordinary Least Squares Results: Latino-owned Business Employment Growth (Regional Variables).

Regional (County-level) Variables	(1)	(2)	(3)	(4)	(5)
	Employment Growth
	1 Year	1 Year	1 Year	2 Year	3 Year
Population	.00 (.00)	.00 (.00)	.00 (.00)	.00 (.00)	.00 (.00)
% Households headed by single female	.00 (.00)	.00 (.00)	.00 (.00)	.00 (.00)	−.00 (.00)
Number of building permits	.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)
Same industry employment in county	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00* (.00)
Same industry employment in state	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	.00 (.00)
US Department of Agriculture Natural Amenity Scale	−.00 (.00)	−.00 (.00)	−.00 (.00)	−.00 (.00)	.00 (.01)

Note: Cluster-robust standard errors are given in parentheses.

*p < .1.

**p < .05.

***p < .01.

Authors’ Note

Any opinions and conclusions expressed herein are those of the author and do not necessarily represent the views of the US Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.

Acknowledgments

Thanks to Anil Rupasingha, Myriam Quispe-Agnoli, Julie L. Hotchkiss, Melissa Banzhaf, Margaret C. Leventsin, J. Clint Carter, Mark Fossett, and Bethany DeSalvo for their assistance with the Federal Statistical Research Data Center (FSRDC) process. Support for this research at the Michigan and Texas FSRDC from the US Department of Agriculture–supported North Central Regional Center for Rural Development, the Department of Agricultural, Food, and Resource Economics at Michigan State University, the Interuniversity Consortium for Political and Social Research, and the Texas Federal Statistical Research Data Center Consortium is also gratefully acknowledged.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the USDA National Institute of Food and Agriculture, Hatch project 1014691.

ORCID iD

Craig Wesley Carpenter

Notes

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