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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.

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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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