Measuring the Effect of Small Business Employment on the ...
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Measuring the Effect of Small Business Employment on the Growth in
Mississippi and Louisiana Communities
INTRODUCTION
Statement of the problem
United States is one of the most developed countries in the world. However, poverty
continues to be a problem in several parts of the country. Some of the affected states, counties,
and cities have implemented policies to try and solve the poverty issues. The objective of this
study is to investigate the economic effects of pro-economic development small business
ownership across Mississippi and Louisiana counties. Mississippi and Louisiana are among the
poorest states in the U.S. Mississippi ranks as the poorest state with a median household income
of $39,680 and a poverty rate of 21.5%. Louisiana ranks as the third poorest state with a median
household income of $44,555 and a poverty rate of 19.8%. On the other hand, Maryland ranks as
the richest state in the U.S., with a median household income of $73,971 and a poverty rate of
10.1% in 2014 (Baron, 2014). There is a significant gap between Maryland and these two poor
states.
Several policies exist both at the federal and local government level designed to solve the
poverty problem in Mississippi and Louisiana. The Small Business Administration (SBA) is a
U.S. government agency that provides support to entrepreneurs and small businesses. At a
regional level, there are several small business development centers directed by the SBA, and
also local organizations and educational institutions in Mississippi and Louisiana. According to
the Mississippi and the Louisiana SBA district websites, there are 29 small business counseling
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stations and workshops in Mississippi and 11 counseling stations and workshops in Louisiana,
respectively. The focus on small firms is driven by evidence in economic growth literature
showing that microfinance tailored for small business formation helps to alleviate poverty. Small
business ownership by the poor will provide owners and employees with an income, provide
goods and opportunities that may not be provided to the poor by large firms, and improve the
economic and social well-being of the poor. Small businesses have attracted a lot of attention
from local governments and academic researchers. Entrepreneurship not only promotes
economic growth, but also promotes economic development and reduces poverty. In economic
growth literature, employment also has been found to be positively related to economic growth.
Several studies have attempted to measure the impacts of firm size on cross country economic
growth in the U.S. with mixed results. Some suggested the importance of studying smaller
regions. Small businesses have been found to play a role in employment growth, employment
also has been found to be positively related to economic growth. However, their contribution to
our understanding of the impact of small business employment on local communities’ economic
growth is limited. After the burst of housing bubble, and the collapse of Lehman Brothers, 8.7
jobs were lost in the Great Recession, lasted from December 2007 to June 2009. The U.S.
economy started to recover at a slow pace after the 2009 trough. The impact of small business
employment on the economic growth of Mississippi and Louisiana communities beyond the
Great Recession remains an unanswered empirical question.
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Objectives and Hypotheses
We want to take a closer look at how these jobs provided by small businesses to the local
communities affecting economic growth of Mississippi and Louisiana counties. The objective of
this study is to measure the effect of small business employment on the economic growth of
Mississippi and Louisiana communities, where economic growth is measured by the growth rate
of per capita personal income between 2010 and 2012 after adjusting for inflation. Empirical
tests involving a Solow-type growth model will be estimated. Small business employment is
measured by the total employment of enterprise with 0 to 19, and 20 to 99 employees. Small
business ownership is measured by the number of firms and establishments that employ 0 to 19,
and 20 to 99employees. These variables are designed to measure the economic impacts of small
business employment, and combing small business employment with small business ownership
on economic growth in Mississippi and Louisiana counties. A positive effect of small business
employment factors on economic growth shows that pro- economic development small business
policies are associated with an improvement in the economic well-being of Mississippi and
Louisiana communities. This result would suggest that policies tailored to promote small
business formation, which provide jobs to local communities have been beneficial to these
communities. On the other hand, a negative impact would suggest changes in the policies
tailored in enhancing small business formation as these may not be benefiting the poor by
providing jobs. The findings of this study will provide much needed input to policy makers and
community leaders from an economic perspective which may help them make timely
adjustments to their current policies to achieve the desired economic development objectives.
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LITERATURE REVIEW
Since the focus of this paper is to examine the effect of small business ownership factors (which
includes small business employment and small business ownership) on economic growth at the
county level, we document prior studies which examined the economic impacts of small
businesses in the U.S. and studies related economic growth at the U.S. local community level.
Most of the existing studies are either at the city or county level.
The work by Shaffer (2002) is among the first studies to examine the economic impacts
of different firm sizes at the U.S. local community level. Specifically, Shaffer (2002) examined
the linkage between different firm types, retail, manufacturing and wholesale and economic
growth where economic growth is measured using the growth rate in median household income.
Shaffer (2002) adopted an extended Solow-type growth model in a sample of more than 700 U.S.
cities over 1979 to 1989. Findings show that manufacturing and retail firms are negatively
associated with economic growth. Results also show that wholesale and service firms did not
significantly impact economic growth during the study period. Shaffer suggested the possibility
that economic development might be facilitated by good strategy and smaller firm size, but
remains as an unanswered question.
In Shaffer (2006), he presented the empirical association between average establishment
size and subsequent growth rates of employment by sector at the county level. Shaffer (2006)
used a cross-sectional sample of more than 2000 U.S. counties over 1982 to 1987. His findings
indicated a negative relationship in which small establishments are associated with faster
subsequent job growth.
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In a study of Komarek and Loveridge (2015) on firm size and economic development,
they investigated the role of business size distribution on income and employment growth in U.S.
counties from 1990 to 2000. Employment shares in small firms (1 to 4 employees) increase
employment growth, but decrease income growth. In addition, as employment in large-scale
manufacturing sectors declined (recessions), counties with a stronger entrepreneurial base could
recover employment and income more quickly than counties whose employment base was so
tied to big companies.
Rupasingha (2013) examined the effects of small and local businesses on local economic
well-being in U.S. counties for the period 2000 to 2008. The methodology used in this paper is
based on Mankiw, et al. (1992), and in addition to an ad hoc regression equation. Results showed
that local business matters for local economies and that smaller local businesses played a more
important role in boosting local economic performance than larger local businesses during the
study period. In general, local entrepreneurship has a positive effect on per capita income growth
and employment growth and a negative effect on change in poverty in counties.
Gebremariam (2004) showed the existence of a positive relationship between small
businesses and economic growth in West Virginia using time series data for the period 1980-
2001. Gebremariam (2004) claimed a strong inverse relationship between the relative size of
small business and the incidence of poverty, and a strong inverse relationship between the per
capita real gross state product growth and the incidence of poverty, which supported the idea of
anti-poverty impacts of small business development.
Rupasingha, Goetz, and Freshwater (2002) used a Barro-type empirical growth model to
study the effects of social capital on economic growth across 3040 U.S. counties for the period
1990 to 1996. Results showed that social capital or civic engagement is an important
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independent determinant of economic growth in the U.S, where social capital or civic
engagement is measured by using the density of membership organizations, crime rate, charitable
giving and voter participation. This study also suggested per capita income grows more rapidly
in counties with high levels of social capital. Similar result also shown in Rupasingha, Goetz,
and Freshwater (2000), they used a conditional convergence growth model to examined the
effect of social and institutional variables on economic growth in 3040 U.S. counties through
1990 to 1996.
Nene, et al. (2013) used a Solow-type growth model to examine the effect of Walmart
(which consider to be a large enterprise) on the growth rate of personal per capita incomes in
Nebraska counties during 1980 to 1995. The empirical results showed that counties with a
Walmart experienced lower economic growth when compared to counties without a Walmart
store during the study period.
Ranjith (2015) empirically tested the effectiveness of small business as a development
strategy for the alleviation in 1066 urban counties in the U.S. during 2000. The empirical results
showed that non-employer microenterprise is less effective in reducing poverty, but
microenterprise with 1 to 4 employees is an effective option in reducing urban poverty.
According to a report released by the U.S. Journal of Economic Perspectives (2006), it
claimed that, overall, small business benefits not only the business owners themselves, but also
our national economy and local society. Entrepreneurship creates jobs for the society and create
opportunities for women and minorities, which has formed a virtuous cycle. Above ideas also
suggested by Skuras et al (2005), which used data of 513 firms selected from five European
countries during 1999 to 2000. It showed that area in the European Union which have a higher
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employment growth rate is associate with the large proportion of employees who work in smaller
firms.
Kritikos (2014), who supported the idea of small businesses introduce innovations which
open up new markets, and increase competition which resulted in lowering price, pushing firms
to have better performance, and encourage structural changes which could promote future
economic growth. In Acs (2003), he also stated that new firm start-ups play a very important
role in economic growth. Besides job creation, higher rates of entrepreneurial activity in a county
imply lower barriers to enter and greater local competition, which benefit consumers and
promote growth.
Larochelle (2009) used a Carlino-Mills model of simultaneous equations to find the
relationship between job creation, peoples’ migration decisions, and microbusiness. By using
2405 U.S. counties’ data, Larochelle found that small business variables significantly influenced
population growth and job creation.
In Bridenstine-Brooks (2006), she conducted a survey for Oklahoma counties and
gathered data of the communities included for 2006. Overall, the econometric results suggested
that entrepreneurship has a positive effect in rural Oklahoma counties.
According to a presentation held by the Federal Reserve Banks of St. Louis and Kansas
City, Macke and Gines (2013) presented that local entrepreneurs create jobs and increase local
incomes and wealth. This idea was also supported by Henderson (2002). Macke and Gines also
claimed entrepreneurship allows senior populations to continue to be productive and add
economic value to local economies.
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Blanchard (2011) evaluated the effect of small business on population health and welfare
in 3060 counties in the contiguous U.S. through 1994 to 2006. Results suggested that all the
entrepreneurship factors have inverse relationship with mortality rate, rate of obesity, and
percentage of diabetes. Blanchard concluded that entrepreneurship facilitates collective efficacy
for a community and provides a problem-solving capacity for addressing local public health
problems.
The above literature showed that small business and entrepreneurship have contributed a lot to
U.S. economic growth, yet there are still a lot of different voices suggested different ideas.
Based on data released by the U.S. Census Bureau, firms that employ fewer 20
employees created most jobs from 1990 to 2003. Edmitston (2007) claimed that the cost of job
creation is much lower than the benefit from small business, but big-firm jobs usually have better
quality than small-firm jobs. Based on Edmitston (2007)’s analytical result, there is no
significant evidence shows that small businesses are more innovative than large firms. The idea
of small businesses are lack of entrenched bureaucracy, more competitive markets, and stronger
incentives to innovate do not stand. However, attempting to recruit large enterprises to a specific
community are unlikely to be successful, and they are not likely to be cost-effective even if they
are successful.
Fleming and Goetz (2010) compared locally-owned businesses and non-local
ownership’s effects to local economic growth. Fleming and Goetz (2010) adopted a
parsimonious standard equilibrium growth model in the sample of U.S. counties during 2000 to
2007. Regression results provided evidence that local ownership matters for economic growth.
However, only for locally-owned firms with size of 10 to 99 employees, results are robust across
rural and urban counties.
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Shaffer (2002) stated that negative linkages were found between economic growth and
manufacturing and retail firms. Aranoff (2010) also pointed out that even small and medium size
businesses accounted for the majority of firms and almost half of the GDP which was generated
by nonagricultural sectors, it accounted for only about 30 percent of merchandise exports
between 1997 and 2007.
Moreover, Henderson (2002) claimed that small businesses in rural areas find it harder to
access venture capital. Access to technology can also be more difficult. Bridenstine-Brooks
(2006) used some case studies which showed that recourses that may help small business were
provided at local levels, but they were underutilized in many circumstances.
In another instance, Van Stel et al. (2005) suggested a negative relationship between
entrepreneurial activities and GDP growth for developing countries, which could be explained by
a lower level of human capital. In contrast, another possible explanation is due to lack of
investment from larger firms. Now that we have documented the existing literature in this area,
our empirical strategy is presented next.
Model Specification, Data and Empirical Analysis
Our study uses a Solow-type neoclassical economic growth model to examine the economic
impacts of small businesses on economic growth. The Solow-type growth model is specified as
follows:
Growthi = Constant + β [Small Business Ownership]i + α [Conditioning Set]i + Errori
where the subscript i indicates the ith county in Mississippi and Louisiana, β and α are parameters
to be estimated.
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Table 1 Definition of variables
Variable Definition
Counties Counties in Mississippi and Louisiana
Growth Growth rate of per capita personal income between 2010 and 2012
Firm 0 - 19 Number of firm that employ 0 to 19 employees
Firm 20 - 99 Number of firm that employ 20 to 99 employees
Est 0 - 19 Number of establishment that employ 0 to 19 employees
Est 20 - 99 Number of establishment that employ 20 to 99 employees
Emp 0 - 19 Number of employees work in enterprise that employ 0 to 19 employees
Emp 20 - 99 Number of employees work in enterprise that employ 20 to 99 employees
POPDEN Population density per mile square in 2010
UNEMP Unemployment population/Labor force in 2010
EDU Population aged 25 and over who have attained at least 4 years of college education
GOV Total government expenditure in 2010
WHITE Caucasian population in 2010
PCPI Per capita personal income in 2010
RURAL Dummy for rural county = 1, 0, otherwise
FARM Dummy for farm dependent county = 1, 0, otherwise
MS Dummy for Mississippi counties = 1, 0, otherwise
Growth is the growth rate of personal per capita income between 2010 and 2012 in each
county. Personal per capita income measures the average income earned per person in each
county in a specified year. We use this variable in this study in the same manner as in
Rupasingha, Goetz and Freshwater (2000, 2002), Komarek and Loveridge (2015), Nene et al
(2013), and Rupasingha (2013).
Small Business Ownership is a vector of small business ownership specific variables
designed to measure the impact of different small business sizes on economic growth. The
number of employees employed by a firm is used to determine firm size. As of 2010, data on
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firm size was broken down to include firms that employ: less than 20 employees, 20 to 99
employees, 100-499 employees and 500 and more employees. In this study we focus on firms
that employ less than 20 employees and 20 to 99 employees. From here on, firms that hire less
than 20 employees will be referred to as Very Small Firms and those that employ 20-99
employees are referred to as simply Small Firms. For each firm size (Very Small and Small), the
vector of small business specific variables includes; the number of firms, the number of
establishments and total employment. The number of firms and the number of establishments
are designed to capture the volume of business activity within a county and market size. It is
therefore important to distinguish between the number of firms and the number of establishments
since some firms have more than one establishment within a county and this needs to be
accounted for.1 The total employment variable is designed to capture human capital. In
economic growth literature, employment has been found to be positively related to economic
growth. Seyfried (2005) concluded that the real GDP growth is positively related to employment
growth in ten largest U.S. states. The same result is confirmed by Padalino and Vivarelli (1997).
Boltho and Glyn (1995) found the elasticity of employment with respect to economic growth is
also positive for a set of Organization for Economic Cooperation and Development (OECD)
countries. Blanchflower and Posen (2013) suggested that underemployed, inactive worker, and
discouraged workers caused downward pressure on wage inflation. We therefore expect a
positive sign on this variable based on the arguments in prior studies.
The Conditioning set is a vector of standard variables that have been found to be
important in explaining economic growth in economic growth literature. The condition set
consists of initial personal per capita income, population density, unemployment, education,
1 The U.S. Bureau of Census counts all establishments owned by one firm as a single firm in the number of firms data set.
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rural counties, the proportion of Caucasians, farm dependent counties, Mississippi counties and
government expenditure.
PCPI variable is the initial personal per capita income (2010 personal per capita income).
Income represents wages and salaries, investment income, government transfer payments, and
employee insurance. The use of initial personal per capita income is to capture conditional
convergence noted by Rupasingha, Goetz and Freshwater (2000, 2002), Shaffer (2002),
Komarek and Loveridge (2015), and Rupasingha (2013). Gebremariam (2004) adopted one-
period lagged poverty rate and real gross state product per capita which are similar to initial per
capita income. The sign of the coefficient on this variable can be positive or negative depending
on whether there is convergence or divergence across counties. A negative sign on this variable
suggests that poor counties are catching up with the rich ones and a positive sign will suggest
that the gap between poor and rich counties is widening.
Population density (POPDEN) measures the number of people per square mile. The
POPDEN of a county, as of 2010, is used to control agglomeration effects. This variable was
used by Shaffer (2002), Rupasingha (2013), Komarek and Loveridge (2015), and Nene et al
(2013) to capture agglomeration economies. The agglomeration effect is the benefits from cities
and industrial cluster. Using Silicon Valley as an example, agglomeration promotes growth
through spillover effects. The sign of the coefficient on this variable is expected to be positive.
Unemployment (UNEMP) refers to the number of people unemployed divided by total
labor force in each county. The UNEMP variable captures the economic health of a county. High
unemployment rates impose restrictions on economic growth and development. Komarek and
Loveridge (2015), and Nene et al. (2013) used unemployment. Gebremariam (2004) used nature
log of unemployment rate. The usage of log normalized the distribution of the data. The sign on
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this variable is expected to be negative.
The education variable (EDU) reflects the stock of human capital available in a county.
It measures population aged 25 and over who have attained at least 4 years of college education
divided by the total population in a county. Skuras et al. (2005) suggested that human capital
contributes to higher levels of knowledge and assign the entrepreneur with competitive
advantages. Initial education was used in Rupasingha, and Goetz and Freshwater (2000, 2002),
Komarek and Loveridge (2015), Larochelle (2009), Ranjith (2015), Nene et al (2013), and
Rupasingha (2013) to capture human capital. A positive sign on the education variable
coefficient is expected.
The rural counties variable (RURAL) is a dummy variable that takes 1 for completely
rural counties or urban populations of less than 2,500 classified under the rural-urban continuum
codes 8 and 9, and 0 otherwise. We use the 2013 Rural-Urban Continuum Codes due to
unavailability of codes specific to 2010, the initial year. According to, Small business in rural
areas find it harder to access capital and technology (Henderson, 2002). Rural areas also lack of
intense communication, and the clusters of large numbers of potential customers for the creation
of small businesses (Acs and Malecki, 2003). Rupasingha, and Goetz and Freshwater (2000,
2002), The RURAL variable has been widely used in county level studies (Larochelle, 2009;
Rupasingha ,2013) (These studies showed a negative relationship between economic growth and
rural counties. Other studies used the urban dummy variable to capture the importance of
location characteristics (Blanchard, 2011; Komarek and Loveridge, 2015). According to authors
who used the urban dummy variable, urban counties were associated with higher growth when
compared to their rural counterparts. We therefore expect a negative sign on RURAL variable
coefficient.
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The proportion of Caucasians in a county (WHITE) captures the total non-Hispanic
Caucasian population divided by the total population in a county. Komarek and Loveridge
(2015), Alesina et al. (1997), Rupasingha, and Goetz and Freshwater (2000), and Shaffer (2002)
adopted similar variables to capture the demographical effects on economic growth. Komarek
and Loveridge (2015) found counties with a higher percentage of non-Hispanic Caucasian
residents tend to have higher growth rates of employment and per-capita income. Shaffer (2002)
claimed that Caucasian population also positively correlate with wage rate and production cost.
In contrast, results from Rupasingha, and Goetz and Freshwater (2000, 2002) fail to support
ethnic diversity have negative effect on growth rate. However, these articles neither confirmed
that ethnic diversity would promote economic growth in U.S. counties. The sign on this variable
can either be positive or negative.
Farm dependent counties (FARM) is a dummy variable that takes 1 for counties where
faming accounted for at 25% or more of its earnings or 16% or more of its overall employment
averaged over 2010 to 2012, or 0 otherwise. Similarly, Ranjith (2015), and Larochelle (2009)
used the percentage of employment in agricultural sectors and the percentage of agricultural
establishments, respectively to capture farm dependent counties. According to them, agricultural
sectors has negative effects on economic development. The FARM variable is designed to
capture the impact of the agricultural sector on economic growth. The sign on this variable can
either be positive or negative.
Government expenditure (GOV) is federal government expenditures on grants,
procurement, retirement and disability, salaries and wages, and other direct payments. This
variable is also used by Shaffer (2002), Gebremariam (2004), Rupasingha, Goetz, and
Freshwater (2000), Komarek and Loveridge (2015), Nene et al. (2013), and Rupasingha (2013).
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Government expenditure stimulates economic growth when spent on aid and local development.
However, Taylor (2011) found that government expenditure may have negative effects when it is
from tax generated or temporary payment. The sign on this variable can either be positive or
negative.
Mississippi counties (MS) is a dummy variable that takes 1 for counties locate in
Mississippi, or 0 in Louisiana. As stated early in the introduction, Mississippi rank as the poorest
state in the U.S. Wooldridge (2009) suggested that the coefficients attached to the dummy
variables are called differential intercept coefficients. In our case, it can be depicted graphically
as an intercept shift between counties in Mississippi and Louisiana.
Data Analysis
Table 2 Descriptive statistics Variable Mean Std. Dev. Maximum Minimum
GROWTH 4.005093 5.335747 27.52902 -11.78368
Firm 0 - 19 743.9452 1262.241 7946 5
Firm 20 - 99 98.08904 178.072 1245 2
Est 0 - 19 747.3493 1268.741 7981 5
Est 20 - 99 110.8425 201.0016 1377 2
Emp 0 - 19 3266.521 5524.671 35469 0
Emp 20 - 99 3142.151 6105.601 42697 0
EDU 10546.9 19786.22 150216 109
GOV 553000000 114000000 925000000 28003000
PCPI 30829.65 5524.399 48308 21338
POPDEN 103.4705 230.2481 2029.4 3.4
UNEMP 0.107281 0.028871 0.196 0.061
WHITE 31839.42 43439.43 288079.6 489.288
RURAL
1 0
FARM
1 0
MS 1 0
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The average growth rate of per capita personal income was 4.01% between 2010 and 2012, while
the highest growth happened in Issaquena county, Mississippi with a growth rate of 27.53%, and
the lowest growth happened in St. Bernard Parish, Louisiana with growth rate of -11.78%.
The data set used in this analysis consists of 82 Mississippi counties and 64 Louisiana parishes.
The sources of the data used in this analysis released by the U.S. Census Bureau, U.S. Bureau of
Labor Statistics, U.S. Bureau of Economic Analysis and U.S. Department of Agriculture. The
descriptive statistics on the variables of interest show that the data on most of the regressors are
highly skewed.
Each county had 743.95 firms that employ 0 to 19 employees on average in 2010. Jefferson
Parish, Louisiana had 7981 firms ranked as the county that had the largest number of firms that
employ 0 to 19 employees, and Issaquena county, Mississippi ranked as the lowest with 5 firms.
For firms that employ 20 to 99 employees, the average number of firms in each county in 2010
was 110.84. East Baton Rouge Parish, Louisiana had 1245 firms employ 20 to 99 employees
ranked as the largest, and Issaquena county, Mississippi ranked as the lowest with 2 firms.
For variables related to number of establishments, they follow the exact same pattern with
variables related to number of firms. On average, each county had 747.35 establishments that
employ 0 to 19 employees in 2010. Jefferson Parish, Louisiana had 7946 establishments ranked
as the county which had the largest number of establishments that employ 0 to 19 employees,
and Issaquena, Mississippi ranked as the lowest with 5 establishments. The average number of
establishments that employ 20 to 99 employees in each county in 2010 is 110.84. East Baton
Rouge Parish, Louisiana had 1377 establishments that employ 20 to 99 employees ranked as the
largest, and Issaquena county, Mississippi ranked as the lowest with 2 firms.
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For variables related to number of people employed by different sizes of enterprise, East Baton
Rouge Parish, Louisiana had the largest number of people employed among enterprise with 0 to
19, 20 to 99 and 0 to 99 employees. In contrast, Issaquena county, Mississippi ranked as the
lowest which had 0 people employed among enterprise with 0 to 19, 20 to 99 and 0 to 99
employees. On average, there were 3622.52 employees work in enterprise that employ 0 to 19
employees. At maximum, East Baton Rouge Parish had total of 35469 employees work in
enterprise that employ 0 to 19 employees. There were 3142.15 employees work in enterprise that
employ 20 to 99 employees on average in each county. East Baton Rouge Parish had a total of
42697 people employees work in enterprise that employ 0 to 19 employees.
Madison county had the highest personal per capita income in 2010 of $ 48308 which was more
than two times greater than the initial personal per capita income of Greene county with the
lowest value, $21338. Both Madison county and Greene county were from Mississippi.
On average, each county had 10550 people who attained at least 4 years of college education in
2010. East Baton Rouge Parish, which was ranked as the most educated parish in Louisiana had
150216 people who had at least 4 years of college in 2010, which was about 34.1% of its
population. In Mississippi, Issaquena county, had the least number of educated people in the
sample. Only 7.7% of Issaquena county’s population attained at least a 4 years of college in 2010.
On the other hand, 46.3% Madison county located in Mississippi ranked the highest it terms of
education with 46.3% of its population having attained 4 years of college in 2010.
Orleans Parish, Louisiana had the highest population density in 2010 of 2029.4 people per square
mile. Issaquena county, Mississippi had the lowest population density of 3.4 people per square
mile.
In 2010, Holmes county, Mississippi had the highest unemployment rate of 19.6%. The
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county with the lowest unemployment rate was Bossier Parish, Louisiana with 6.1%. The
average unemployment rate was 10.7% which was above the U.S. natural rate of unemployment.
The average highest non-Hispanic Caucasian population in a county in 2010 was 31839.65.
Jefferson Davis Parish, Louisiana had the highest non-Hispanic Caucasian population of 288080.
Issaquena county, Mississippi had the lowest non-Hispanic Caucasian population of 489. These
two counties were not the extreme counties on non-Hispanic Caucasian population in percentage
terms. Cameron Parish, in Louisiana had the highest percentage of non-Hispanic Caucasian of
96.1%, and Jefferson county, in Mississippi ranked the lowest with 13.9% of its population
identified as non-Hispanic Caucasian.
On average, in 2010, federal government spent 553 million dollars in counties in Mississippi and
Louisiana. East Baton Rouge Parish, Louisianan had the highest government expenditures of
about 925 million dollars, and Issaquena county, Mississippi had the lowest government
expenditures which amounted to 280 million dollars in 2010.
In 2010, 18% of the counties/parishes in Mississippi and Louisiana were classified as
rural. In the same year 6% of counties/parishes in Mississippi and Louisiana were classified as
farm based. The data also showed that about 62.5% of farm based counties/parishes had rural
characteristics.
Data on all the continuous variables in this study are skewed.2 The variables which
showed significant skewness include: all the Small Business Ownership variables, POPDEN,
EDU, WHITE and GOV. One of the important assumption for OLS is normality, significant
skewness may mislead the results of the analysis. In our case, taking the natural log of the
2 Skewness measures the degree to which data values are evenly or unevenly distributed around its mean. Data skewed to the right is said to be positively skewed and has more extreme measurements in the right tail of the distribution than in the left tail.
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variables that exhibit right skewness is the best approach to normalize the extreme value. Small
Business Ownership variables that relate to employment cannot be log transformed since their
minimum value was 0, However, it would not affect the regression result, because most of the
skewed variables are solved.
Many empirical studies suggested that small business employment is expected to be
influenced by the growth rate of per capita personal income (Carree et al, 2002; Beck et al., 2003;
Gebremariam, 2004), which suggested that small business employment, as an explanatory
variable, appears endogenous to the growth rate of per capita personal income. This violates the
OLS assumption of error terms are uncorrelated with the dependent variables, which could lead
to biased and inconsistent OLS coefficients (Wooldridge, 2009). In this case, a Two-Stage least
squares (2SLS) regression analysis can be used to solve the problem of the correlation between
dependent variable's (Growth) error terms and independent variables (Emp 0 – 19 and Emp 20
– 99).
Empirical Results
Due to the significant skewness in the data, I took log of Growth, Firm 0 - 19, Firm 20 - 99,
Est 0 - 19, Est 20 - 99, POPDEN, EDU, WHITE, UNEMP, PCPI, and GOV.
Due to the correlation between Growth’s error terms and Emp 0 – 19 and Emp 20 – 99, we
adopt lagged Emp 0 – 19 (Number of employees work in enterprise that employ 0 to 19
employees in 2009) and Emp 20 – 99 (Number of employees work in enterprise that employ 20
to 99 employees in 2009) as Instrumental variables to estimate our models using 2SLS.
Results in Table 3 are based on an OLS estimation procedure for 6 models. Model 1 include the
conditioning set and Emp 0 - 19. Model 2 includes both Firm 0 - 19 and Emp 0 - 19. Model 3
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includes both Est 0 - 19 and Emp 0 - 19. Model 4 include the conditioning set and Emp 20 - 99.
Model 5 includes both Firm 20 - 99 and Emp 20 - 99. Model 6 includes both Est 20 - 99 and
Emp 20 - 99. Firm 0 - 19 and Est 0 - 19 are insignificant in Model 2 and Model 3, respectively.
Firm 20 – 99 and Est 20 – 99 in Model 5 and Model 6. Small Business Employment variables
are significant, robust and positively relate to Growth through Model 1 to Model 6. Full
conditioning information set is considered in all the model specifications.
21
Table 3 2SLS estimation results
Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 3.479 (0.076)
21.383 (0.432)
21.482 (0.434)
-2.202 (-0.048)
41.410 (0.866)
40.651 (0.388)
Emp 0 - 19 0.0004 (2.670) ***
0.0003 (1.983) **
0.0003 (1.981) **
lnFirm 0 - 19
1.885 (1.121)
lnEst 0 - 19
1.896 (1.128)
Emp 20 - 99
0.0003 (2.346) **
0.0002 (2.254) **
0.0002 (2.236) **
lnFirm 20 - 99
3.066 (3.132) ***
lnEst 20 - 99
3.0004 (3.358) ***
lnPOPDEN -2.168 (-2.700) ***
-2.594 (-3.256) ***
-2.594 (-3.254) ***
-2.209 (-2.560) **
-3.188 (-3.379) ***
-3.083 (-3.358) ***
lnUNEMP 4.654 (1.640)
4.902 (1.726) *
4.900 (1.726) *
4.524 (1.593)
5.006 (1.828) *
4.704 (1.701) *
lnEDU 2.034 (2.413) **
1.803 (2.270) **
1.802 (2.270) **
2.005 (2.377) **
1.328 (1.588)
1.450 (1.744) *
lnGOV -2.650 (-2.502) **
-3.302 (-2.922) ***
-3.308 (-2.927) ***
-2.526 (-2.403) **
-3.413 (-3.584) ***
-3.459 (-3.553) ***
lnWHITE -1.368 (-1.368)
-1.724 (-1.808) **
-1.727 (-1.812) *
-1.291 (-1.400)
-1.454 (-1.605)
-1.585 (-1.749) *
lnPCPI 6.520 (1.269)
5.705 (1.098)
5.704 (1.098)
6.784 (1.323)
4.308 (0.859)
4.371 (0.874)
RURAL -3.397 (-2.815) ***
-3.057 (-2.335) **
-3.055 (-2.337) **
-3.427 (-2.821) ***
-2.793 (-2.225) **
-2.709 (-2.178) **
FARM 8.709 (2.897) ***
9.187 (3.042) ***
9.188 (3.045) ***
8.791 (2.918) ***
9.363 (3.117) ***
9.383 (3.191) ***
MS -1.736 (-1.477)
-1.972 (-1.572)
-1.972 (-1.572)
-1.555 (-1.306)
-1.473 (-1.275)
-1.471 (-1.272)
R2 0.433 0.443 0.443 0.432 0.472 0.472
Note that the numbers in the parenthesis are the t-statistics of the coefficients above them.
P-values are indicated as *** p < 0.01, ** p < 0.01, * p < 0.1
22
Interpretation of Results
This section only contains interpretation of the empirical results if the variable is significant in
the estimated model.
POPDEN is negatively related to Growth. As initial population density increases by 1%,
the growth rate of per capita personal income would decrease by 0.542%, 0.649%,
0.649%,0.552%,0.797%, and 0.771%, ceteris paribus, in Model 1, 2, 3, 4, 5, and 6, respectively.
The cluster of lower income individuals could hurt the regional economy. In this case,
agglomeration effects happened to be negatively impacted the economic growth in these counties.
UNEMP also appears to be significantly and positively related to Growth in Model 2, 3,
5, and 6, which is opposite from what Okun’s Law suggested. Ceteris paribus, as the ratio of
unemployment population and labor force increases by one percent, the growth rate of per capita
personal income would also increase by 1.225%, 1.225%, 1.250%, and 1.175%, in Model 2, 3, 5,
and 6, respectively. Meyer and Tasci (2012) suggested that fluctuations in the macro economy
cannot be measured by linear model implied by most forms of Okun’s law. During 2010 to 2012,
where the U.S. economy was recovery from the Great Recession. Knotek (2007) claimed that
one critique of Okun's law is that it may not hold during and after recessions, as evidenced by the
"jobless recoveries" following the past three recessions. Jobless recovery indicates that economic
recovery following a recession, where the economy as a whole improves, but the unemployment
rate remains high or increase over a prolonged period of time.
EDU is significant in all the models except Model 5. Ceteris paribus, as the population
aged 25 and over who have attained at least 4 years of college education increases by 1%,
Growth would increase by 0.509%, 0.451%, 0.451%, 0.500%, and 0.363%, in Model 1, 2, 3, 4,
23
and 6, respectively.
GOV appears to be negatively related to Growth. Ceteris paribus, as total government
expenditure increases by 1%, the growth rate of per capita personal income would decrease by
0.663%, 0.826%, 0.827%, 0.632%, 0.853% and 0.865%, in Model 1, 2, 3, 4, 5, and 6,
respectively. As Taylor (2011) suggested, a significant fraction of the ARRA funding was used
by local governments to pay debt. Economic activity remained stagnant and unemployment
remained high for several years after the recession was over.
WHITE is significantly and negatively related to Growth. Ceteris paribus, as the
percentage of non-Hispanic Caucasian population increases by 1%, the growth rate of per capita
personal income would decrease by 0.431%, 0.432%, and 0.396%, in Model 2, 3, and 6,
respectively. Similar to the case of population density, the cluster of non-Hispanic Caucasian
population did not promote economic growth at county level in Mississippi and Louisiana.
Rural had a relatively lower Growth than counties located in urban and metro areas.
Ceteris paribus, as compared to a non-rural county, rural county had a growth rate of per capita
personal income that was 3.40%, 3.06%, 3.055%, 3.427%, 2.793%, and 2.709% lower on
average, in Model 1, 2, 3, 4, 5, and 6, respectively. In Acs and Malecki (2003), they reported the
differences in growth and firm creation between rural areas and urban areas in the U.S. Rural
areas mostly lack potential customers and business clusters. Small business is very needed in U.S.
rural areas’ development
During 2010 to 2012, Farm had higher Farm then other counties. Ceteris paribus, as
compare to a non-farm dependent county, counties in Mississippi and Louisiana which were
farm dependent had a growth rate of per capita personal income that was 8.709%, 9.187%,
24
9.188%, 8.791%, 9.363%, and 9.383% higher on average, in Model 1, 2, 3, 4, 5, and 6,
respectively. One of the possible explanation behind this phenomenon is the Great Recession
happened in 2007 and 2008. Agricultural businesses are usually family owned in the U.S. In the
last recession, most businesses got hurt, but small agricultural businesses did not hurt as much.
Food is essential to life and the size of most of the agricultural businesses in the U.S. are small.
Counties in Mississippi and Louisiana, where faming accounted for 25% or more of the county's
earnings or 16% or more of the employment experienced higher growth during 2010 to 2012.
Emp 0 - 19 is statistically significant, robust and positively related to the growth rate in
per capita personal income through Model 1 to Model 3. These coefficients indicate that as one
more person gets hired by enterprises that employ 0 to 19 workers, growth rate in per capita
personal income would increase by 0.0004%, 0.0003% and 0.0003%, ceteris paribus, in Model 1,
2, and 3, respectively.
Emp 20 - 99 is statistically significant, robust and positively related Growth through
Model 4 to Model 6. The coefficients indicate that as one more person gets hired by enterprise
that employ 20 to 99 workers, the growth rate of per capita personal income would increase by
0.0003%, 0.0002%, and 0.0002%, ceteris paribus, in Model 4, 5, and 6, respectively.
Firm 20 - 99 is statistically significant and positively related to Growth in Model 5. This
coefficient indicates that as the number of firms that employ 20 to 99 employees increases by
one percent, the growth rate of per capita personal income would increase by 0.767%, ceteris
paribus.
Est 20 - 99 is statistically significant and positively related to Growth in Model 6. This
coefficient indicates that as number of establishments that employ 20 to 99 employees increases
25
by one percent, the growth rate of per capita personal income would increase by 0.750%, ceteris
paribus.
Summary and Conclusions
The objective of this study is to measure the effect of small business employment on the
economic growth of Mississippi and Louisiana communities, while controlling for other factors
which have been found in growth literature to be important in explaining economic growth.
As Fleming and Goetz (2010) suggested, empirical results from Model 4 to Model 6
suggest that firms and establishments with 20 to 99 employees were promoting economic growth
of Mississippi and Louisiana communities during 2010 to 2012.
Small business employment variables are robust regardless of other variables included
in the model. It implies that jobs created by small businesses were the key contributor to
economics growth in the years of 2010 to 2012. During the same time period, there was no
evidence of convergence found, which indicates these counties’ economics conditions were
getting worse during recovery period following the Great Recession.
Given the high statistical significance and robustness of variables that are related to the
number of employees gets hired by small business and the consistent results from variables
related to firms and establishments with 20 to 99 employees, I conclude that small businesses
that employed 20 to 99 employees and jobs created by them had the most significant impact on
the growth rate of per capita personal income in Mississippi and Louisiana in the years of 2010
to 2012.
From the empirical results, policy makers should emphasize on the development of firms
and establishments that employ 20 to 99 employees as a solution of promoting growth in
26
Mississippi and Louisiana.
This study contains several limitations. Future studies could expand the data as time-
series data and panel data to capture the time effect and location effect of small business
ownership.
27
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