Stumbling Blocks to Entrepreneurship in Low- and …...Stumbling Blocks to Entrepreneurship in Low- and Moderate-Income Communities 91 those policies that best support entrepreneurial

Introduction

There is growing and widespread interest among policymakers at all levelsof government in recent years to promote entrepreneurial-friendly environ-ments and entrepreneurship in terms of both self-employment and businessownership. The interest is motivated by a substantial body of research indi-cating that entrepreneurs spur the diffusion and implementation ofinnovative ideas, thereby creating new products, services, and markets. Inaddition, and perhaps more importantly, entrepreneurs contribute, whetherthrough self-employment or the establishment of small businesses, to jobformation and economic growth and development. Some consider self-employment, moreover, a route out of poverty or off the unemploymentrolls for some individuals, especially those encountering discrimination inthe labor market. Self-employment may even be a way to increase one’searnings, as compared to working for someone else. All these factors play arole in governmental efforts to foster entrepreneurship, in terms of bothself-employment and business ownership, particularly in low- and moder-ate-income (LMI) communities.

While there may be convincing evidence demonstrating the importanceof entrepreneurship for increasing social welfare, there is some uncertaintyabout the most important determinants of entrepreneurship and, hence,

James R. Barth, Glenn Yago, and Betsy Zeidman

Stumbling Blocks to Entrepreneurship inLow- and Moderate-Income Communities

91

those policies that best support entrepreneurial activity. There are at leasttwo levels at which this uncertainty arises. The first is at the level of indi-vidual entrepreneurship itself. It is not fully known why some individualsbecome entrepreneurs, while others become wage or salary workers. Thesecond level of uncertainty pertains to the factors most responsible forenabling or preventing a would-be entrepreneur from becoming self-employed or establishing a business. Both of these issues have been studied,but researchers, so far, have failed to reach enough of a consensus to providea true road map to meaningful policy actions.

The purpose of this paper is to explore several aspects of the variousefforts that have been made in recent years to understand better what worksbest at promoting entrepreneurship throughout the United States, espe-cially in the LMI communities. This is obviously an extremely importantissue but, nonetheless, a difficult challenge for any researcher. Our approachto addressing the topic is to rely mainly on the work of other researchers,but also to make a modest attempt to contribute to the research in the area.

The plan of the remainder of the paper is as follows. In the next section, webriefly discuss the importance of entrepreneurial activity in contribution toeconomic growth and development, and social welfare more broadly. The thirdsection focuses more narrowly on the commitment of financial institutions tochannel loans to businesses in LMI communities. The fourth section focuses ona newly constructed measure indicating the degree of “loan bias” that exists inthese communities. The fifth section focuses on selected databases that are avail-able to study entrepreneurship, various empirical studies that have examinedseveral determinants of different measures of entrepreneurial activity, andpotential stumbling blocks or barriers to entrepreneurship considered by thesestudies. It also identifies inconsistencies in findings and the lack of commondata sources that limit the confidence that one can have in any proposed policyactions to foster greater entrepreneurship. The sixth section discusses the typesof regulatory stumbling blocks that may impede the development of greaterentrepreneurial activity.

The seventh section changes pace and discusses an alternative approachto trying to identify those factors that help explain the differences in

92 James R. Barth, Glenn Yago, and Betsy Zeidman

entrepreneurial activity across geographical regions. The approach is tofocus on the size distribution of businesses in different regions of thecountry, based upon the notion that relatively smaller firms, in contrastto relatively bigger firms, contain the breeding grounds for the initialexpression of the entrepreneurial spirit, no matter where those firms arelocated. The eighth and last section contains a summary and conclusions.

Overview of the importance of entrepreneurship

Economic theory does not provide clear guidance on providing an opera-tional way in which to classify entrepreneurs from nonentrepreneurs. Forexample, Kihlstrom and Laffont (1979, p. 720) develop a theoretical model andfind “that less-risk-averse individuals become entrepreneurs, while the more-risk-averse work as laborers.” As another example, Lucas (1978, p. 510)constructs a model in which “each member of the workforce is endowed witha ‘talent for managing’ which varies across workers.” Thus, either innate differ-ences in attitudes toward risk or talents for managing are used to explain whysome individuals are entrepreneurs and others are paid workers. More generally,as Holtz-Eakin and Rosen (1994a, p. 338) state, “in the nonstatistical litera-ture…entrepreneurs are characterized in terms of their daring, risk-taking,animal spirits, and so on….” Those who study entrepreneurship empirically,however, require a more concrete way in which to identify entrepreneurs that ismore amenable to measurement in order to examine various factors that mayhelp explain differences in the degree of entrepreneurship that exists over timeand across geographical regions.

Thomas Hoenig (2005, p. 2), president of the Federal Reserve Bank ofKansas City, in this regard, suggests that the entrepreneur is someone whorecognizes “the potential of new ideas, designs applications, develops newproducts, and successfully brings these products to markets.” Based on thisdefinition, individuals who are self-employed or own relatively small busi-nesses could be considered entrepreneurs. At the very least, they areentrepreneurial enough to bring products and services to the marketplace.Indeed, among the empirical studies of entrepreneurship, Evans andLeighton (1989), Blanchflower and Oswald (1998), and Fairlie (1999) useself-employment to define entrepreneurs; Gentry and Hubbard (2004) use

Stumbling Blocks to Entrepreneurship in Low- and Moderate-Income Communities 93

business ownership; Meyer (1990) uses both self-employment and businessownership status; and Holtz-Eakin, Joulfaian, and Rosen (1994a, b) usefilers of IRS form 1040 schedule C to define entrepreneurs.

Tables 1 and 2 provide data on the economic impact of entrepreneurship.Table 1 shows that small businesses are extremely important for employ-ment and economic growth. Specifically, it shows that small businesses(that is to say, those with fewer than 500 employees) account for 99 percentof all firms in the United States, 86 percent of all establishments, 50 percentof total employment, 45 percent of annual payroll, and 39 percent of totalreceipts. Enterprises with zero to five employees, moreover, account for 47percent of all firms and 37 percent of all establishments.1 These enterprises,not surprisingly, account for only 5 percent of employment and 4 percentof annual payroll and receipts. But from these small firms, through theprocess of “creative destruction,” come the far bigger firms that help sustainthe dynamic process of job creation and economic development. Indeed,“over the past decade, small firms [in other words, firms with fewer than500 employees] have provided 60 to 80 percent of the net new jobs in theeconomy, and…almost all of these net new jobs stem from startups in thefirst two years of operation” (U.S. Small Business Administration Office ofAdvocacy and the Ewing Marion Kauffman Foundation, 2004). Further-more, Acs and Armington (2004) empirically find that a higher ratio ofentrepreneurial activity is associated strongly with faster growth of localeconomies. It, therefore, is incumbent upon policymakers concerned withgrowth and employment not to erect stumbling blocks or, worse yet, barri-ers to the establishment and operation of small businesses.

Table 2 provides a somewhat broader view of the role of small business inthe economy because it includes the self-employed. But, it also provides anote of caution insofar as it provides information on the race/ethnicity andgender of owners of firms. It is important for social welfare that all races,ethnicities, and genders are provided the opportunity to become self-employed or small business owners. Recent demographic data showing thegrowing importance of different social and ethnic groups in the total popu-lation only underscore this fact. Yet, Table 2 raises the issue as to whetherthis opportunity is indeed available to individuals in all these groups. It



Table 1Number of Firms, Number of Establishments, Employment, Annual

Payroll, and Receipts by Employment Size of the Enterprise

Table 2Number of Firms, Receipts, Employment, and Annual Payroll by

Race/Ethnicity and Gender

Data Type Total Employment Size of the Enterprise

0 % 1-4 % 5-9 % 10-19 % 20-99 % 100-499 % 500+ %Firms (thousands) 5,698 770 14% 2,696 47% 1,011 18% 614 11% 508 9% 82 1% 17 0%Establishments (thousands) 7,201 771 11% 2,699 37% 1,024 14% 653 9% 693 10% 333 5% 1,028 14%Employment (thousands) 112,401 0 0% 5,698 5% 6,640 6% 8,246 7% 19,874 18% 15,909 14% 56,034 50%Annual payroll (US$ billions) 3,943 38 1% 156 4% 182 5% 241 6% 624 16% 536 14% 2,166 55%Receipts (US$ billions) 22,063 215 1% 938 4% 888 4% 1,086 5% 2,885 13% 2,547 12% 13,504 61%

Source: 2002 County Business Patterns

Population Number of Firms Receipts Number of Employees Annual Payroll(millions) (thousands) (billions) (thousands) (billions)

All U.S. 281 22,977 22,635 110,833 3,815Male 49% 57% 31% 39% 35%Female 51% 28% 4% 7% 5%

Black 35 1,198 93 771 18Male 48% 48% 70% 65% 70%Female 52% 46% 23% 23% 22%

White 211 19,895 8,304 52,209 1,549Male 49% 60% 81% 78% 82%Female 51% 28% 10% 13% 10%

Asian 10 1,105 343 2,294 59Male 48% 58% 73% 68% 73%Female 52% 31% 16% 19% 17%

Hispanic 35 1,574 226 1,546 37Male 51% 59% 76% 72% 76%Female 49% 34% 16% 18% 17%

Source: 2002 Survey of Business OwnersNote: Minimum 50 percent ownership required for gender designation. Percentages may not add to 100 because of firms with equal male-female ownership.

shows the ownership distribution of firms based upon different demographiccharacteristics.2 More importantly, however, is the fact that it compares thedistribution of the ownership of businesses in terms of the population amongthe different races/ethnicities and genders to the distribution of the numberof firms, receipts, employees, and payroll among these same demographicgroups. One finds a striking imbalance in the different distributions.

The percentage of the population accounted for by females, for instance, indi-cates that they are significantly underrepresented as majority owners of firmsand especially so with respect to receipts and employment of firms. Femalesrepresent 51 percent of the population but just 12 percent of receipts and 14percent of employment of firms that are majority-controlled by a single gender.African-Americans and Hispanics also are underrepresented in terms of self-employment or ownership along the various dimensions indicated incomparison to their respective percentages of the total population. African-Americans comprise some 12 percent of the population but a mere 5 percent ofthe number of firms that are majority-controlled by a single race. This disparityis even more striking if one considers receipts and employment, where African-American-owned firms account for just 1 percent of receipts and employment.Hispanics account for 13 percent of the population but just 7 percent of firmsand 3 percent of receipts and employment. Conversely, Asian-Americanscomprise shares of firm employment and receipts in approximate parity to theirshare of the population and comprise a share of firm numbers higher than theirshare of population.

This type of information raises important questions about potentialstumbling blocks, if not downright barriers, to individuals in several differ-ent demographic groups for becoming self-employed or business owners. Italso suggests, however, that these potential hurdles may be less important,if not unimportant, for at least one minority group. The fact that manyindividuals from these demographic groups are also in LMI communitiesonly intensifies the importance of such questions. The reason, of course, isthat to limit the opportunities of these individuals to become self-employedor establish small businesses is to limit the opportunities of a large andincreasing potential portion of the U.S. population to grow and prosperthrough entrepreneurship. One of the limiting factors most frequently


mentioned is lack of access to capital. We explore this issue further in thenext section.

Some potential stumbling blocks to entrepreneurial activity inLMI communities

Many studies of the determinants of entrepreneurship frequentlymention that a major barrier to entrepreneurship (that is to say, self-employment or establishing a small business) is lack of access to funds, orwhat is referred to as “liquidity constraints.” The enactment of theCommunity Reinvestment Act of 1977 (CRA), however, requires thatbanks channel a portion of their funds to the communities in which theyare located. Appendix 1 shows the percentage of the population in each ofthe 280 metropolitan statistical areas (MSAs) that is accounted for by LMI3

individuals and the percentage of the total amount of loans made to busi-nesses in these communities by banks under CRA4 in 2000. It is importantto note, however, that only banks with assets of greater than $250 millionwere required to report under CRA in 2000 (this minimum was increasedto $1 billion in 2005). Reporting banks, moreover, are required to reportdata only on loans of $1 million in size or less. For these reasons, CRA datacan be viewed as data on the small business lending of banks that accountedfor more than 90 percent of total bank assets and business loans in 2000.5

Additionally, for our analysis in this section, we assume that LMI individ-uals live mainly in LMI census tracts, rather than being located randomlythroughout the census tracts in MSAs. This assumption seems plausiblegiven that the U.S. Census Bureau notes that census tracts are “designed tobe relatively homogeneous units with respect to population characteristics,economic status, and living conditions.”

It is quite clear that the distribution of the LMI population as a percent-age of an MSA’s total population varies substantially across the MSAs—themean is 40 percent, and the standard deviation is 0.03 percent. The sharesrange from a high of 49 percent in Yolo, Calif., to a low of 34 percent inJacksonville, N.C. Bank loans made by reporting banks to businesses in theLMI communities as a percentage of the total amount of bank loans made


in these MSAs varies even more widely—the mean is 23 percent, and thestandard deviation is 0.10 percent. These figures range from a high of 54percent in Des Moines, Iowa, to a low of 0.2 percent in Dover, Del. Inter-estingly, the mean share of loans made to businesses in LMI communitiesis substantially lower than the mean of the LMI population as a share of thetotal population. Indeed, it is more than 40 percent lower.

When one focuses on just low-income (LI) communities, one finds thatthe share of the total population in MSAs accounted for by the LI segmentranges from a high of 32 percent in Yolo, Calif., to a low of 16 percent inJacksonville, N.C. The mean is 23 percent, and the standard deviation is0.03 percent. At the same time, the share of the total amount of loans madeby reporting banks to businesses in LI communities ranges from a high of28 percent in Sioux City, Iowa-Neb. to a low of 0 percent in each of 45MSAs. The mean share of loans made to businesses in LI communities is amere 6 percent (in other words, 75 percent lower than the mean of the LIpopulation as a share of the total population), and the standard deviation is0.05 percent—the latter figure quite lower than the variation of the share ofloans made to businesses in LMI communities.

The idea of parity between share of population and share of business activityis one which seems to hold appeal for some commentators on entrepreneurship,particularly with respect to entrepreneurship in minority populations. This canbe seen in The State of Minority Business (Minority Business DevelopmentAgency, 2001), which notes that “minority-owned business activity…continuesto be significantly smaller than minority representation of the nation’s popula-tion.” It describes this deviation from parity, moreover, as an “opportunity gap.”In the next section, we construct a measure that reflects this view—perhapsnaïve to an economist—of the relationship between share of population andshare of business activity, the latter taken here to be business loans from banksreporting under CRA.

In this naïve view, the ideal world is one in which the shares of the totalamount of loans made to businesses in the LMI communities in each andevery MSA would match one-for-one the shares of the total population inthe MSAs accounted for by LMI individuals. The world is far from perfect


based upon such a view. The difference between the LMI share of popula-tion and share of loans made to businesses in LMI communities varies froma high of 42 percentage points for Dover, Del. (an MSA with 42 percentLMI individuals and 0 percent of total loans going to businesses in LMIcommunities), to a low of -18 percentage points for Des Moines, Iowa (anMSA with 36 percent LMI individuals and 54 percent of total loans goingto businesses in LMI communities). Moreover, there are only 19 MSAs outof a total of 280 for which we have data (only 7 percent) where the differ-ence is zero or less. The difference between the LI share of population andshare of loans to businesses in LI communities varies from a high of 31percentage points for Yolo, Calif. (an MSA with 31 percent LI individualsand 0 percent of total loans going to businesses in LMI communities), to alow of -5 percentage points for Sioux City, Iowa-Neb. (an MSA with 22percent LI individuals and 27 percent of total loans going to businesses inLI communities).

Table 3 shows the pairwise correlations between the LI and LMI sharesof population and the LI and LMI shares of loans, both in terms of numbersand amounts. The LI share of the population is correlated significantly andpositively with the LI shares of numbers and amounts of loans. The corre-lations, however, are quite low. With regard to the LMI share of populationand the LMI share of loans, either in terms of numbers or amounts, thecorrelation is also positive and significant, although quite low. According tothe naïve view described previously, all the correlations would have beenpositive and one.

A measure of LMI ‘loan bias’

Another way to view the data in the previous section is in terms of an LIand LMI community “loan bias” based on the naïve view of an ideal worldnoted previously, where the share of total loans to businesses in LI/LMIcommunities would be equal to the share of the total population that iscomprised by LI/LMI individuals for each and every MSA. We choose to usethe term “loan bias” not in a pejorative sense but, rather, to demonstrate thatwhat initially may appear to be a bias to some may require closer inspectionto determine whether this is indeed the case. Appendix 1 presents a measure


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of the LI and LMI loan bias for each of the MSAs. It is calculated as 1 minusthe ratio of the LI/LMI share of the total amount of loans in an MSA to theLI/LMI share of the total population in that MSA. A value of 0 would indi-cate no loan bias, while a value of 1 would indicate maximum loan bias. TheLI loan bias ranges from 1 to -0.26. Forty-three MSAs have an LI score of 1,meaning businesses in the LI communities received none of the loans banksmade under CRA in these MSAs. Four MSAs, on the other hand, havescores less than 0, meaning businesses in the LI communities received alarger percentage of the total amount of loans made in these MSAs than theLI share of total population. The degree of LMI loan bias is only marginallybetter. While no MSA has a score of 1, many, nonetheless, have relativelyhigh scores. Twenty MSAs have LMI loan bias scores of 0.75 or more. Thismeans that for businesses in the LMI communities, their share of the totalloans to all businesses in these MSAs is less than one-fourth of the LMIcommunities’ share of the total population.

Table 3 indicates that the LI share of the population of an MSA is notcorrelated with LI loan bias, whereas LI share of the amount of loans iscorrelated significantly and negatively with LI loan bias. The same resultshold for the correlations involving LMI shares. This means that loan bias isless with a greater share of loans to businesses in LI/LMI communities butis not related to the share of the population comprised by LI/LMI individ-uals. Interestingly, the table also shows that the number of branches perfinancial institution is correlated significantly and negatively with LMI loanbias but not correlated with LI loan bias. Thus, for LMI loan bias, thedegree of financial development in the MSA, as measured by branches perinstitution, matters.

The measure of LMI loan bias obviously is based upon a naïve view of theworld and, thus, simply a statistical construct. Yet, as seen previously, sucha naïve view may not be uninfluential, and our construct, therefore, may beuseful as a sort of benchmark by which to try to understand the reasons forthe substantial variation in the distributions of LMI loans and LMI popu-lations across MSAs. Since the measure reflects smaller loans to businessesby reporting banks, it is clear that businesses in LMI communities in some


MSAs receive a substantial portion of the total smaller loans to all businessesin these MSAs compared to the LMI communities’ share of the total popu-lation. Businesses located in still other LMI communities fare far worse inthis respect, however. Whether these differences in loan bias across thevarious MSAs can be explained fully by focusing on the world of econom-ics is the issue to which we turn next. For in free and competitive markets,one would expect differences in loan bias across regions, but differences thatreflect economic factors, like the creditworthiness of businesses.

We now consider a slightly less naïve view of the world in which dispro-portionately fewer funds may flow to businesses in LMI communities inpart because the incomes in those areas also are disproportionately lowerthan in other areas of MSAs. We, therefore, recalculated our measure ofloan bias, but this time, we based it on income rather than population.Specifically, this measure of loan bias is calculated as 1 minus the ratio ofthe LI/LMI share of the total amount of loans to businesses in an MSA tothe LI/LMI community share of the total income of that MSA. Interest-ingly, the LI and LMI loan bias measures based upon population arecorrelated positively and significantly with the same two respective loan biasmeasures when based upon income, with correlations of 0.98 and 0.84,respectively. However, the average LI/LMI loan bias figure based uponpopulation is 0.75/0.41, whereas the average LI/LMI loan bias figure basedupon income is -0.22/-0.85. This means that when one calculates the loanbias based upon population, the share of total loans made to businesses inLI/LMI communities, on average, is less than the LI/LMI community shareof total population in the 275 MSAs. But, when one calculates loan biasbased upon income, the share of the total amount of loans made to busi-nesses, on average, is greater than the LI/LMI community share of totalincome in the MSAs. Yet, beyond the averages, one still finds that 51percent of the LI communities and 13 percent of the LMI communitieshave positive loan bias figures based upon income. This exercise suggeststhat, to the extent that income of an area correlates with the amount ofloans to businesses one might expect to be made, economic factors indeeddo help explain the reason more funds flow to some areas as compared toothers within MSAs.



Clearly, the naïve view of the world reflected in the ideal of parity and of“opportunity gaps” is not compelling insofar as when one considers justincome (ignoring other factors, such as the presence of collateral, that mayaffect lending decisions), much of the loan bias we noted above disappears.Yet, there still remains substantial variation in the income-based measure ofloan bias, and an assessment of the reasons for this variation may be a fruit-ful subject for future work. In any event, whether these measures of loan biashave any explanatory power in explaining the number and size compositionof establishments in LMI communities within MSAs is assessed below.

Selected databases and studies of entrepreneurship

There is a rapidly growing literature pertaining to entrepreneurship. Thefocus here is on selected empirical studies that examine why some individ-uals become entrepreneurs, while others do not. The focus is also on studiesthat examine factors that explain the entrepreneurial process of starting orowning a small business or becoming self-employed. Given our interest inmainly empirical studies rather than theoretical studies, it seems useful tobegin with a brief overview of the different datasets that are typically usedby researchers when studying entrepreneurship.

Table 4 provides information on what seem to be the most widely used data-bases for studies focusing on the United States. The table shows that there aresubstantial differences in the datasets in terms of the issues that can be exam-ined. Some are longitudinal datasets that allow researchers to study the sameindividuals or cohorts of the same individuals over time to determine the factorsthat help explain why some individuals choose self-employment over paidemployment. Others allow researchers to focus on examining new businessstartups over time, or small businesses over time or across geographical regionsrather than the choice individuals make between self-employment and wageand salary work. Despite the different types of studies, all of these studies usuallytry to include as much information as available on the characteristics of the self-employed, the characteristics of business owners, the characteristics of thebusiness, and the sources of funding for becoming self-employed or establishingor owning a business.

Tabl

e 4

Sele

cted

Inf

orm

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pStumbling Blocks to Entrepreneurship in Low- and Moderate-Income Communities 105

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Table 4 shows that it is quite difficult to compare the results of studiesusing these different datasets. Apart from trying to explain different meas-ures of entrepreneurship, the various factors that one can control for in anysingle study clearly are constrained by the dataset that is chosen. Thus,different studies using different datasets necessarily cannot control for acommon and broad set of factors that might better enable one to explainwhatever measure of entrepreneurship that is employed. Yet, the omissionof any important factors may bias whatever results one obtains from a singledataset. This is not to disparage the considerable and costly efforts tocompile all these datasets. Instead, the point is that more effort should bemade to reach a consensus on what information contained in the differentdatasets can be combined and what additional information is needed toanswer the pressing issues about how policymakers can decide upon the bestactions to take to promote entrepreneurship.

To illustrate the importance of the use of different datasets, in trying to reacha discussion on what works best to promote entrepreneurship, Table 5provides information about several studies that have employed some of thedatasets in Table 4. There are several comments based upon these studies. First,Blanchflower and Oswald (1998) and Holtz-Eakin, Joulfarian, and Rosan(1994a, b) find that liquidity constraints are a barrier to entrepreneurship,whereas Vos, Yeh, Carter, and Tagg (2005); Hurst and Lusardi (2003); andMoore (2004) do not. Second, Mitchell and Pearce (2005) find there is prej-udicial loan discrimination against African-American and Hispanic owners ofsmall businesses, whereas Bostic and Lampani (1999) find loan racial disparityfor African-American-owned but not Hispanic-owned businesses, and Meyer(1990) finds that liquidity constraints do not seem to explain the low African-American self-employment rate. Third, Puri and Robinson (2004) find thatentrepreneurs differ from nonentrepreneurs insofar as being innately moreoptimistic and risk-loving, whereas Guiso and Schivardi (2004) argue thatentrepreneurship can be acquired through learning, irrespective of such differ-ences in attitudes. Fourth, Black and Strahan (2002) find that more bankbranches and greater consolidation in the banking industry foster entrepre-neurship, whereas Mitchell and Pearce (2005) argue that the move by largerbanks to transactional lending through credit scores and “harder” informationmay lead to greater loan discrimination against small businesses. Fifth,



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's lo

cal

inap

porp

riate

ly fr

om a

naly

ses o

fan

d fir

ms o

wne

d by

Asia

ns, H

ispan

ics,

and

geog

raph

y sh

ould

be

cons

ider

ed in

diffe

renc

es in

the

cred

it m

arke

t w

omen

, but

diff

eren

ces e

xist

betw

een

orde

r to

unde

rsta

nd ra

cial

disp

ariti

esex

perie

nces

of w

hite

-ow

ned

whi

te-o

wne

d an

d bl

ack-

owne

d fir

ms.

of sm

all b

usin

ess f

inan

ce.

and

min

ority

-ow

ned

firm

s.

Tabl

e 5

Stud

ies

Exam

inin

g D

iffer

ent L

evel

s of

Ent

repr

eneu

rial

Act

ivit

y ov

er T

ime

and

Geo

grap

hica

l Are

as


Aut

hor(

s)Pu

rpos

eEn

trep

rene

uria

l Foc

usD

ata

Res

ults

Polic

y Im

plic

atio

ns

Bre

voor

t an

d E

xam

ine

the

rela

tion

ship

bet

wee

n Sm

all b

usin

esse

s.C

RA

ann

ual d

ata

from

199

7 to

200

1.D

ista

nce

is a

ssoc

iate

d ne

gati

vely

wit

h th

eD

ista

nce

may

be

of in

crea

sing

Han

nan

(200

4)di

stan

ce a

nd c

omm

erci

al le

ndin

g lik

elih

ood

of a

loca

l com

mer

cial

loan

bei

ngim

port

ance

in lo

cal m

arke

t le

ndin

g.an

d ho

w it

has

evo

lved

ove

r ti

me.

mad

e an

d th

at t

he d

eter

rent

eff

ect

of

dist

ance

is c

onsi

sten

tly m

ore

impo

rtan

t th

e sm

alle

r th

e ba

nk.

Cav

allu

zzo

and

Exa

min

e th

e im

pact

of

pers

onal

Bus

ines

ses

wit

h fe

wer

19

98 S

urve

y of

Sm

all B

usin

ess

Subs

tant

ial u

nexp

lain

ed d

iffer

ence

s in

den

ial

Rac

ial d

ispa

riti

es e

xist

eve

n af

ter

Wol

ken

(200

2)w

ealth

on

smal

l bus

ines

s lo

an

than

500

em

ploy

ees.

Fina

nces

, Dun

and

Bra

dstr

eet,

rate

s be

twee

n A

fric

an-A

mer

ican

-,co

ntro

lling

for

var

ious

con

stra

ints

.tu

rndo

wns

acr

oss

and

Fede

ral R

eser

ve S

yste

m d

ata

His

pani

c-, A

sian

-, a

nd w

hite

-ow

ned

firm

s.de

mog

raph

ic g

roup

s.G

reat

er p

erso

nal w

ealth

is a

ssoc

iate

d w

ith

low

er p

roba

bilty

of

loan

den

ial.

DeY

oung

, E

xam

ine

how

incr

ease

dSm

all b

usin

ess

loan

s m

ade

toR

ando

m s

ampl

e of

35,

999

On

aver

age,

lend

ers

that

use

cre

dit

scor

ing

Mor

e ge

nero

us g

over

nmen

t lo

anG

lenn

on, a

nd

borr

ower

-len

der

dist

ance

firm

s un

der

SBA

7(a

)SB

A 7

(a)

guar

ante

edm

odel

s ex

peri

ence

hig

her

defa

ult

rate

sgu

aran

tees

may

not

gen

erat

e N

igro

(20

04)

wor

sens

the

per

form

ance

of

loan

pro

gram

.lo

ans

orig

inat

ed b

y 5,

552

than

tho

se t

hat

do n

ot. L

oan

defa

ults

de

sire

d re

sults

.sm

all b

usin

ess

loan

s, a

nd h

owqu

alifi

ed S

BA

pro

gram

incr

ease

wit

h bo

rrow

er-l

ende

r di

stan

ce,

new

lend

ing

tech

nolo

gies

and

le

nder

s be

twee

n 19

83 a

nd 2

001.

and

high

er lo

an g

uara

ntee

s ar

e as

soci

ated

exis

ting

gov

ernm

ent

subs

idie

s w

ith

high

er d

efau

lt ra

tes.

may

mit

igat

e or

exa

cerb

ate

thes

e ef

fect

s.

Dun

n an

d E

xam

ine

the

impa

cts

of in

divi

dual

'sSe

lf-em

ploy

ed.

Nat

iona

l Lon

gitu

dina

l Sur

veys

.T

he f

inan

cial

ass

ets

of y

oung

men

exe

rt a

T

hese

dat

a su

gges

t st

rong

rol

es f

orH

oltz

-Eak

in (

2000

)ow

n w

ealth

and

hum

an c

apit

al, a

nd

Spec

ifica

lly, s

ampl

es o

f yo

ung

men

stat

isti

cally

sig

nific

ant

but

quan

tita

tive

ly

fam

ily-s

peci

fic c

apit

al a

nd

pare

ntal

wea

lth, a

nd s

elf-

empl

oym

ent

who

wer

e ag

e 14

-24

in 1

966,

mat

ure

mod

est

effe

ct o

n th

e tr

ansi

tion

into

tran

smis

sion

of

thes

e sk

ills

wit

hin

expe

rien

ce o

n th

e pr

obab

ility

tha

t an

w

omen

who

wer

e ag

e 30

-44

in 1

967,

self-

empl

oym

ent.

Usi

ng t

his

as o

ur m

etic

,fa

mili

es in

enh

anci

ng t

he p

roba

bilit

yin

divi

dual

tra

nsit

s fr

om w

age/

and

olde

r m

en w

ho w

ere

age

45-5

9w

e fin

d a

rela

tive

ly s

mal

l im

pact

of

capi

tal

of m

akin

g a

trans

ition

to e

ntre

pren

eurs

hip.

sala

ry t

o se

lf-em

ploy

men

t.in

196

6.m

arke

t co

nstr

aint

s in

the

NLS

.

Evan

s an

d E

xam

ine

the

proc

ess

of s

elec

tion

into

Self-

empl

oyed

.N

atio

nal L

ongi

tudi

nal S

urve

y, s

ampl

ePr

obab

ility

is h

ighe

r of

bei

ng s

elf-

empl

oyed

U

nem

ploy

ed w

orke

rs w

ith

the

poor

est

Leig

hton

(19

89)

self-

empl

oym

ent

over

the

life

cyc

le

of m

en f

ollo

wed

fro

m 1

966

to 1

981.

for

unem

ploy

ed a

nd h

ighl

y ed

ucat

ed, b

utop

port

unit

ies

in t

he w

age

sect

or s

wit

ch

and

the

dete

rmin

ants

of

not

rela

ted

to a

ge o

r ex

peri

ence

for

fir

st

to a

nd r

emai

n in

sel

f-em

ploy

men

t.se

lf-em

ploy

men

t ea

rnin

gs.

20 y

ears

of

expe

rien

ce. A

lso,

ret

urn

to w

age

expe

rien

ce in

sel

f-em

ploy

men

t is

low

er t

han

the

retu

rn t

o w

age

expe

rien

ce in

wag

e w

ork.

Tabl

e 5

(con

t.)

Aut

hor(

s)Pu

rpos

eEn

trep

rene

uria

l Foc

usD

ata

Res

ults

Polic

y Im

plic

atio

ns

Fair

lie (

1999

)E

xam

ine

raci

al p

atte

rns

in

Self-

empl

oyed

.22

yea

rs o

f da

ta f

rom

the

Pan

el S

tudy

Rac

ial d

iffer

ence

s in

ass

et le

vels

and

Exi

stin

g po

licie

s th

at p

rom

ote

min

orit

y tr

ansi

tion

s be

twee

n se

lf-em

ploy

men

tof

Inc

ome

Dyn

amic

s (P

SID

).pr

obab

ilitie

s of

hav

ing

self-

empl

oyed

bu

sine

ss o

wne

rshi

p ne

ed t

o be

mod

ified

an

d w

age/

sala

ry w

ork

amon

g pr

ime-

fath

ers

expl

ain

a la

rge

part

of

the

gap

inor

red

esig

ned

to r

efle

ct t

he r

acia

lag

e m

en.

blac

k/w

hite

ent

ry r

ate,

but

non

e of

the

di

ffer

ence

s in

tra

nsit

ion

rate

s in

to a

nd

gap

in t

he e

xit

rate

.ou

t of

sel

f-em

ploy

men

t.

Fair

lie a

nd

Exa

min

e th

e ca

uses

of

Smal

l bus

ines

ses

base

d on

fili

ng19

92 C

hara

cter

isti

cs o

f Pr

ior

wor

k ex

peri

ence

in a

fam

ily

Mos

t di

sadv

anta

ged

busi

ness

Rob

b (2

003)

inte

rgen

erat

iona

l lin

ks in

bus

ines

s IR

S fo

rm 1

040

sche

dule

C.

Bus

ines

s O

wne

rs.

busi

ness

has

a p

osit

ive

effe

ct o

n bu

sine

ss

deve

lopm

ent

polic

ies

curr

ently

in p

lace

, ow

ners

hip

and

the

rela

ted

issu

e of

ou

tcom

es. A

lso,

inhe

rite

d bu

sine

sses

are

such

as

set-

asid

es a

nd lo

an a

ssis

tanc

e ho

w h

avin

g a

fam

ily b

usin

ess

mor

e su

cces

sful

than

non

inhe

rited

bus

ines

ses.

prog

ram

s, ar

e ta

rget

ed to

war

d al

levi

atin

gba

ckgr

ound

aff

ects

sm

all

finan

cial

con

stra

ints

, not

tow

ard

busi

ness

out

com

es.

prov

idin

g op

port

unit

ies

for

wor

k ex

peri

ence

in s

mal

l bus

ines

s.

Gen

try

and

Exa

min

e th

e im

port

ance

of

savi

ng b

y H

ouse

hold

s re

port

ing

owni

ng

1983

and

198

9 Su

rvey

s of

Ent

repr

eneu

rial

hou

seho

lds

own

a St

udie

s of

the

sav

ing

deci

sion

sH

ubba

rd (

2004

)en

trep

rene

uria

l hou

seho

lds

and

the

one

or m

ore

busi

ness

es w

ith

a C

onsu

mer

Fin

ance

s.su

bsta

ntia

l sha

re o

f ho

useh

old

of w

ealth

y ho

useh

olds

sho

uld

pay

mor

epo

ssib

le in

terd

epen

denc

e be

twee

n to

tal m

arke

t va

lue

of >

$5,0

00.

wea

lth a

nd in

com

e, a

nd t

his

atte

ntio

n to

the

rol

e of

ent

repr

eneu

rial

entr

epre

neur

s' in

vest

men

t an

d sh

are

incr

ease

s th

roug

hout

the

deci

sion

s an

d th

eir

role

in w

ealth

savi

ng d

ecis

ions

.w

ealth

/inc

ome

dist

ribu

tion

; ac

cum

ulat

ion.

thei

r po

rtfo

lios

are

very

und

iver

sifie

d;

thei

r in

com

e ra

tios

and

sav

ing

rate

s ar

e hi

gher

.

Gom

pers

, Ler

ner,

Exa

min

e fa

ctor

s th

at le

ad t

o cr

eati

onE

ntre

pren

eurs

are

em

ploy

ees

who

1986

-199

9, u

sing

Fi

ndin

gs in

dica

te t

he b

reed

ing

Polic

ies

that

see

k to

fost

er e

ntre

pren

euria

lan

d Sc

harf

stei

n (2

003)

of v

entu

re c

apit

al-b

acke

d st

artu

p le

ave

publ

ic c

ompa

nies

to

star

tV

entu

reO

ne d

atab

ase.

grou

nds

for

entr

epre

neur

ial

vent

ure

capi

tal a

ctiv

ity

by p

rovi

ding

fir

ms,

a pr

oces

s ca

lled

“ent

repr

eneu

rial

new

ven

ture

cap

ital

-bac

ked

firm

s.fir

ms

are

othe

r en

trep

rene

uria

l fir

ms.

capi

tal o

r in

vest

men

t in

cent

ives

may

not

spaw

ning

”(th

at is

to sa

y, th

e pr

open

sity

be e

noug

h. I

nste

ad, r

egio

ns m

ay n

eed

publ

icly

tra

ded

com

pani

es t

o sp

awn

to a

ttra

ct f

irm

s w

ith

exis

ting

poo

ls o

f ne

w v

entu

re c

apit

al-b

acke

d fir

ms)

.w

orke

rs w

ho h

ave

the

“tra

inin

g an

d co

nditi

onin

g” to

bec

ome

entr

epre

neur

s. St

imul

atin

g en

trep

rene

ursh

ip in

a r

egio

n w

ith fe

w e

xist

ing

entr

epre

neur

s is

diffi

cult.

Gui

so a

nd

Test

whe

ther

the

tal

ent

to b

ecom

e an

Ent

repr

eneu

rs a

re a

ssum

ed t

o ge

tIt

alia

n fir

m d

ata

from

198

2 to

199

0.G

eogr

aphi

cal a

gglo

mer

atio

n of

Po

licy

acti

ons

shou

ld p

rom

ote

the

Schi

vard

i (20

04)

entr

epre

neur

is le

arna

ble.

mor

e ou

tput

from

any

com

bina

tion

Num

ber

of f

irm

s in

a g

iven

indu

stry

firm

s is

due

to

diff

eren

ces

in le

arni

ng

lear

ning

pro

cess

to

incr

ease

of

inpu

ts s

o th

at e

ntre

pren

euri

al

in a

giv

en a

rea

is a

pro

xy f

or le

arni

ngop

port

unit

ies,

not

diff

eren

ces

in

entr

epre

neur

ial a

ctiv

ity.

abili

ty is

equ

ival

ent

to a

fir

m’s

exte

rnal

itie

s an

d kn

owle

dge

spill

over

s.st

artu

p co

sts.

tota

l fac

tor

prod

ucti

vity

.


Tabl

e 5

(con

t.)


Aut

hor(

s)Pu

rpos

eEn

trep

rene

uria

l Foc

usD

ata

Res

ults

Polic

y Im

plic

atio

ns

Gui

so, S

upie

nza,

Te

st w

heth

er lo

cal f

inan

cial

Pr

obab

ility

a p

erso

n be

com

es19

92-1

998,

Ita

lian

data

on

Fina

ncia

l dev

elop

men

t in

crea

ses

the

prob

abili

tyLo

cal f

inan

cial

dev

elop

men

t is

and

Zin

gale

s de

velo

pmen

t m

atte

rs f

or

self-

empl

oyed

. Als

o us

es t

heho

useh

olds

, fir

ms,

and

fin

anci

ala

pers

on b

ecom

es s

elf-

empl

oyed

, and

dec

reas

esim

port

ant

for

self-

empl

oyed

and

(200

4)va

riou

s ou

tcom

es.

aver

age

age

of t

he s

elf-

empl

oyed

.in

stit

utio

ns. C

reat

e a

mea

sure

of

the

aver

age

age

of e

ntre

pren

eurs

. It a

lso in

crea

ses

smal

l firm

s.fin

anci

al u

nder

deve

lopm

ent

that

th

e ra

tio

of n

ew f

irm

s to

the

pop

ulat

ion.

is t

he p

roba

bilit

y a

hous

ehol

d is

sh

ut o

ff f

rom

the

cre

dit

mar

ket.

Ham

ilton

(20

00)

Exa

min

e th

e ea

rnin

gs d

iffer

enti

als

in

Self-

empl

oyed

.19

84 S

urve

y of

Inc

ome

and

Ent

repr

eneu

rs h

ave

not

only

low

er in

itia

lLi

ttle

evi

denc

e is

fou

nd t

hat

the

earn

ings

self-

empl

oym

ent a

nd p

aid

empl

oym

ent.

Prog

ram

Par

tici

pati

on. S

ampl

e of

ea

rnin

gs t

han

empl

oyee

s w

ith

the

sam

edi

ffer

enti

al r

efle

cts

the

sele

ctio

n of

8,77

1 m

ale

scho

ol le

aver

s age

d 18

-65

char

acte

rist

ics

but

also

low

er e

arni

ngs

grow

th.

low

-abi

lity

paid

em

ploy

ees

into

wor

king

in t

he n

onfa

rm s

ecto

r.se

lf-em

ploy

men

t.

Hol

tz-E

akin

, E

xam

ine

to w

hat

exte

nt li

quid

ity

Indi

vidu

als

who

file

d IR

S fo

rmFe

dera

l tax

dat

a fo

r 19

81 a

nd 1

985.

Liqu

idit

y co

nstr

aint

s ex

ert

a no

tice

able

Sole

pro

prie

tors

hips

are

und

erca

pita

lized

.Jo

ulfa

ian,

and

co

nstr

aint

s in

crea

se t

he li

kelih

ood

of10

40 s

ched

ule

C in

198

1 an

din

fluen

ce o

n th

e vi

abili

ty o

f en

trep

rene

uria

lR

osen

(19

94a)

entr

epre

neur

ial f

ailu

re.

1985

, and

hav

e a

cash

flo

w g

reat

er

ente

rpri

ses.

than

$5,

000.

Hol

tz-E

akin

, E

xam

ine

how

the

rec

eipt

of

Tran

siti

on t

o fil

ing

IRS

form

104

0Fe

dera

l tax

dat

a fo

r 19

81 a

nd 1

985.

The

siz

e of

the

inhe

rita

nce

has

aLi

quid

ity

cons

trai

nts

can

Joul

faia

n, a

nd

an in

heri

tanc

e af

fect

ssc

hedu

le C

(fr

om 1

981

to 1

985)

.su

bsta

ntia

l eff

ect

on b

oth

the

prob

abili

tyha

ve a

sub

stan

tial

influ

ence

on

Ros

en(1

994b

)en

trep

rene

ursh

ip.

of b

ecom

ing

an e

ntre

pren

eur

and

the

entr

epre

neur

ship

dec

isio

n.

amou

nt o

f ca

pita

l em

ploy

ed in

the

ne

w e

nter

pris

e.

Hur

st a

nd

Exa

min

e w

heth

er t

he

Bus

ines

s ow

ners

(w

ith

resu

lts t

hePa

nel S

tudy

of

Inco

me

Dyn

amic

s T

hrou

ghou

t m

ost

of t

he w

ealth

dis

trib

utio

nLi

quid

ity

cons

trai

nts

do n

ot p

reve

nt

Lusa

rdi (

2003

)in

abili

ty o

f w

ould

-be

sam

e fo

r se

lf-em

ploy

ed).

and

Nat

iona

l Sur

vey

of S

mal

l (u

p th

roug

h $2

00,0

00 in

hou

seho

ld w

ealth

),

entr

epre

neur

s fr

om s

tart

ing

a bu

sine

ss.

entr

epre

neur

s to

acq

uire

the

B

usin

ess

Fina

nces

.th

ere

is n

o di

scer

nabl

e re

lati

onsh

ip b

etw

een

capi

tal n

eces

sary

to

star

t a

hous

ehol

d w

ealth

and

the

prob

abili

ty o

f sta

rtin

gbu

sines

s is

an o

bsta

cle

to n

ewa

busi

ness

. Onl

y fo

r ho

useh

olds

at

the

top

ofbu

sine

ss f

orm

atio

n.th

e w

ealth

dis

trib

utio

n is

a p

osit

ive

rela

tion

ship

fou

nd.

Mey

er (

1990

)E

xam

ine

expl

anat

ions

for

Se

lf-em

ploy

ed.

1984

Sur

vey

of I

ncom

e an

d Pr

ogra

mT

he e

vide

nce

does

not

sup

port

liqu

idit

y-C

ultu

ral d

iffer

ence

s m

ay e

xpla

in

diffe

renc

es in

sel

f-em

ploy

men

t, Pa

rtic

ipat

ion;

198

2 C

hara

cter

isti

csco

nstr

aint

/low

-ass

ets

expl

anat

ion

for

the

low

blac

k/w

hite

diff

eren

ces i

n se

lf-em

ploy

men

t.ne

t in

com

e, n

umbe

r of

of

Bus

ines

s O

wne

rs.

blac

k se

lf-em

ploy

men

t ra

te; c

ultu

ral d

iffer

ence

sem

ploy

ees,

and

for

m o

f m

ay e

xpla

in b

lack

/whi

te d

iffer

ence

s in

orga

niza

tion

bet

wee

n bl

acks

se

lf-em

ploy

men

t.an

d w

hite

s, w

ith

spec

ial f

ocus

on li

quid

ity

cons

trai

nts

and

cons

umer

dis

crim

inat

ion.

Tabl

e 5

(con

t.)


Aut

hor(

s)Pu

rpos

eEn

trep

rene

uria

l Foc

usD

ata

Res

ults

Polic

y Im

plic

atio

ns

Mit

chel

l and

Te

st o

f disc

rimin

atio

n in

lend

ing

Bus

ines

ses w

ith fe

wer

than

500

em

ploy

ees.

1998

Sur

vey

of S

mal

l Bus

ines

s Fin

ance

s,T

he p

repo

nder

ance

of

evid

ence

is c

onsi

sten

tD

iscr

imin

atio

n is

a p

robl

em in

acc

ess

toPe

arce

(20

05)

to s

mal

l bus

ines

ses.

uses

mod

els

of t

he p

roba

bilit

y th

atw

ith

prej

udic

ial d

iscr

imin

atio

n ag

ains

tcr

edit

for

som

e m

inor

ity-

owne

d fir

ms,

sm

all b

usin

ess

owne

rs h

ave

Afr

ican

-Am

eric

an a

nd H

ispa

nic

firm

ow

ners

.an

d th

e m

ove

by la

rger

ban

ks t

oou

tstan

ding

loan

s and

hav

e ap

plic

atio

nsA

lso,

pre

fere

ntia

l pra

ctic

es c

hara

cter

ize

the

tran

sact

iona

l len

ding

thr

ough

cre

dit

for

new

rel

atio

nshi

p an

d tr

ansa

ctio

nal

gran

ting

of

tran

sact

ion

loan

s to

a s

igni

fican

tlysc

ores

and

oth

er “

hard

er”

info

rmat

ion

loan

s de

nied

by

bank

s an

d no

nban

ks.

grea

ter

degr

ee t

han

the

gran

ting

of

may

lead

to

grea

ter

disc

rim

inat

ion

than

rela

tion

ship

loan

s.w

ith

rela

tion

ship

lend

ing.

Moo

re (

2004

)Te

st w

heth

er w

ealth

aff

ects

“N

ew”

entr

epre

neur

s ar

e ho

useh

olds

1995

, 199

8, a

nd 2

001

Surv

ey o

f A

pos

itiv

e re

lati

onsh

ip b

etw

een

wea

lth a

ndFo

r the

maj

ority

of p

oten

tial e

ntre

pren

eurs

, th

e de

cisi

on t

o be

th

at s

tart

ed a

bus

ines

s in

the

last

thr

eeC

onsu

mer

Fin

ance

s. H

ome

equi

tyst

arti

ng a

bus

ines

s is

onl

y si

gnifi

cant

for

liqui

dity

con

stra

ints

are

not

bin

ding

.an

ent

repr

eneu

r.ye

ars

and

have

no

prio

r bu

sine

sses

.va

lue

is u

sed

as a

pro

xy f

or w

ealth

.ho

useh

olds

in t

he t

op q

uart

ile o

f th

e ho

me

equi

ty d

istr

ibut

ion.

Pete

rsen

and

E

xam

ine

whe

ther

the

dis

tanc

eB

usin

esse

s with

few

er th

an 5

00 e

mpl

oyee

s.19

93 N

atio

nal S

urve

y of

Sm

all B

usin

ess

Find

tha

t in

form

atio

nally

opa

que

firm

s ha

veG

reat

er in

form

atio

n av

aila

bilit

y an

dR

ajan

(20

02)

of f

irm

s fr

om t

heir

lend

er is

a

Fina

nce.

Inf

orm

atio

n on

dis

tanc

e of

clos

er le

nder

s, a

nd t

hat

bank

s ar

e cl

oser

tha

nre

duce

d co

sts

of p

roce

ssin

g m

eans

go

od p

redi

ctor

of

cred

it q

ualit

y,

firm

fro

m le

nder

and

met

hod

of

othe

r le

nder

s. A

lso,

ban

k tr

ansa

ctio

ns a

reac

cess

of

smal

l fir

ms

to c

redi

t ca

n be

and

whe

ther

dis

tanc

e ha

s co

mm

unic

atio

n (p

erso

n, p

hone

, or

mor

e lik

ely

to b

e co

nduc

ted

in p

erso

n th

anpr

ovid

ed b

y fin

anci

al in

stit

utio

ns a

t be

com

e a

less

-use

ful p

redi

ctor

m

ail)

use

d.tr

ansa

ctio

ns w

ith

othe

r le

nder

s.gr

eate

r di

stan

ce.

of c

redi

t qu

alit

y.

Puri

and

Ex

amin

e w

heth

er e

ntre

pren

eurs

An

entr

epre

neur

is a

res

pond

ent

who

Surv

ey o

f Con

sum

er F

inan

ces,

mai

nly

Ent

repr

eneu

rs a

re s

igni

fican

tly m

ore

likel

y to

Ent

repr

eneu

rs a

re o

ptim

istic

and

risk

-lovi

ng,

Rob

inso

n (2

004)

diff

er f

rom

non

entr

epre

neur

s m

ust

own

som

e or

all

of a

t le

ast

one

1995

, 199

8, a

nd 2

001,

but

som

eth

ink

they

will

live

long

er, s

ugge

stin

g th

ey a

rebu

t th

e w

illin

gnes

s to

tak

e ri

sk is

in t

erm

s of

fun

dam

enta

l pr

ivat

ely

owne

d bu

sine

ss, a

nd t

heda

ta g

oing

bac

k to

199

2.m

ore

opti

mis

tic

abou

t lif

e pr

ospe

cts.

Als

o,te

mpe

red

by s

tron

g fa

mily

tie

s, g

ood

atti

tude

s, s

uch

as o

ptim

ism

re

spon

dent

mus

t be

ful

l-ti

me

they

are

mor

e ri

sk-l

ovin

g th

an t

hehe

alth

pra

ctic

es, a

nd lo

ng p

lann

ing

and

risk

-tak

ing.

self-

empl

oyed

.no

nent

repr

eneu

r po

pula

tion

.ho

rizo

ns.

Vos

, Yeh

, Car

ter,

Test

whe

ther

sm

all b

usin

esse

sB

usin

esse

s w

ith

few

er t

han

500

U.K

. and

U.S

., 19

98 S

urve

y of

Sm

all

Smal

l bus

ines

ses

that

see

k ex

tern

al f

undi

ngSm

all b

usin

esse

s ar

e no

t su

bjec

t to

fia

ncin

g an

d Ta

gg (

2005

)ar

e co

nstr

aine

d in

the

ir a

cces

s em

ploy

ees.

Bus

ines

s Fi

nanc

es f

or U

.S. a

nd 2

004

usua

lly g

et w

hat

they

wan

t.co

nstr

aint

s.to

fin

anci

ng.

Fede

ratio

n of

Sm

all B

usin

esse

s for

U.K

.

Tabl

e 5

(con

t.)

Peterson and Rajan (2002) find that small businesses that are distant fromlenders no longer have to be the highest-quality credits, indicating they havegreater access to credit, whereas Brevoort and Hannan (2004) find noevidence that distance is becoming less important, but, instead, find thatdistance is associated negatively with the likelihood of a local commercial loanbeing made. Sixth, and last, DeYoung, Glennon, and Nigro (2004) find thatlenders making loans made to small businesses under the SBA 7(a) loan guar-antee program experience higher default rates with greater borrower-lenderdistance and higher loan guarantees. The question that immediately arisesbased upon these findings is whether one confidently can suggest ways toimprove entrepreneurship, especially in LMI communities. Table 6 providesa slightly different view of the same issue. In this table, different potentialstumbling blocks to entrepreneurship are listed across the top, while differentmeasures of entrepreneurship are listed down the side. In the middle of thetable are various studies of entrepreneurship that are linked to both the differ-ent stumbling blocks and the different entrepreneurship measure. For each ofthe studies, moreover, we indicate whether stumbling blocks to entrepreneur-ial activity indeed exist.

It is clear that there are differences among the studies as to whether or notstumbling blocks or barriers to entrepreneurship actually exist or, even ifsome do, whether they are significant. Unfortunately, differences in entre-preneurship measures and differences in datasets make it difficult to decideupon which results should be the best guide to policy wherever there arecontrary findings. This is certainly the case with respect to the existence ofliquidity constraints. However, there does appear to be agreement amongthe studies reviewed that discrimination, particularly involving African-Americans, is a barrier to entrepreneurship. Also, there seems to beagreement that the existence of entrepreneurial firms in a region helps spurthe establishment of still more such firms. Furthermore, as the next sectionshows, there appears to be a consensus that governmental regulations can bestumbling blocks to entrepreneurship. Finally, there appears to be agree-ment that individuals can learn or be taught to become entrepreneurs. Atthe very least, agreement that there are indeed these types of stumblingblocks or barriers to entrepreneurship should provide better guidance as to



Mea

sure

of

Not

Am

enab

le to

Pol

icy

Amen

able

to P

olic

yEn

trep

rene

ursh

ipEx

ogen

ous C

hara

cter

istic

s of E

ntre

pren

eurs

Tale

nt o

r Abi

lity

Agg

lom

erat

ion

Fina

ncin

g or

D

iscri

min

atio

nR

egul

atio

nis

Lear

nabl

eof

Ent

repr

eneu

rsLi

quid

ity C

onst

rain

tsO

ptim

istic

Low

Risk

Ave

rsio

nTa

lent

or

Abi

lity

Indi

vidu

al Ye

s: Pu

ri Ye

s: K

nigh

t Ye

s: Lu

cas

Yes:

Dun

n an

dYe

s: G

ompe

rs,N

o: H

urst

and

Lusa

rdi

Yes:

Bate

s (19

91);

Yes:

Barth

, Se

lf-Em

ploy

men

tan

d Ro

bins

on

(192

1); Y

es: P

uri a

nd

(197

8);

Hol

tz-Ea

kin

Lern

er, a

nd S

char

fstein

(200

3); N

o: M

oore

(200

4);

Yes:

Blan

chflo

wer,

Levi

ne,

Cor

des,

and

(200

4)Ro

bins

on (2

004)

;Ye

s: Sc

hum

pete

r(2

000)

; Yes

:(2

003)

; Yes

: Gui

soYe

s: G

entry

and

Hub

bard

(200

4);

and

Zim

mer

man

(199

8);

Yeze

r (19

86);

Yes:

Kih

lstro

m an

d (1

911)

Gui

so an

dan

d Sc

hiva

rdi (

2004

)Ye

s: G

uiso

, Sup

ienza

, and

Ye

s: M

itche

ll an

d Pe

arce

Yes:

Berk

owitz

La

ffont

(197

9)Sc

hiva

rdi (

2004

)Zi

ngale

s (20

04);

Yes:

(200

5)an

d W

hite

(200

1);

Blan

chflo

wer a

nd O

swald

(199

8);

Yes:

Persa

d (2

004)

Yes:

Fairl

ie (1

999)

; Yes

: Blac

k an

d St

raha

n (2

002)

; Yes

: Im

mer

gluc

kan

d Sm

ith (2

001)

Indi

vidu

al Ye

s: Pu

riYe

s: K

nigh

t (19

21);

Yes:

Luca

sYe

s: D

unn

and

Yes:

Gom

pers,

Ler

ner,

No:

Hur

st an

d Lu

sard

i (20

03);

Yes:

Bate

s (19

91);

Yes:

Barth

, Cor

des,

Busin

ess

and

Robi

nson

Ye

s: Pu

ri an

d (1

978)

; Yes

: H

oltz-

Eaki

nan

d Sc

harfs

tein

(200

3);

No:

Moo

re (2

004)

; Yes

: Gen

tryYe

s: Bl

anch

flowe

r, an

d Ye

zer (

1986

); O

wner

ship

(200

4)Ro

bins

on (2

004)

;Sc

hum

pete

r (2

000)

; Yes

:Ye

s: G

uiso

and

and

Hub

bard

(200

4); Y

es: G

uiso

, Le

vine

, and

Zim

mer

man

Yes:

Berk

owitz

and

Yes:

Kih

lstro

m an

d (1

911)

Gui

so an

d Sc

hiva

rdi (

2004

)Su

pien

za, a

nd Z

inga

les (2

004)

; (1

998)

; Yes

: Mitc

hell

Whi

te (2

001)

; Yes

: La

ffont

(197

9)Sc

hiva

rdi (

2004

)Ye

s: Bl

anch

flowe

r and

Osw

ald

and

Pear

ce (2

005)

Persa

d (2

004)

(199

8); Y

es: F

airlie

(199

9);

Yes:

Blac

k an

d St

raha

n (2

002)

; Ye

s: Im

mer

gluc

k an

d Sm

ith (2

001)

New

Firm

Ye

s: Pu

ri an

d Ye

s: K

nigh

t (19

21);

Yes:

Luca

s (19

78);

Yes:

Dun

n an

dYe

s: G

ompe

rs, L

erne

r, N

o: H

urst

and

Lusa

rdi (

2003

); Ye

s: Ba

tes (

1991

);Ye

s: Ba

rth, C

orde

s,St

artu

psRo

bins

on

Yes:

Puri

and

Robi

nson

Yes:

Schu

mpe

ter

Hol

tz-Ea

kin

and

Scha

rfste

in (2

003)

;N

o: M

oore

(200

4); Y

es: G

entry

Yes:

Blan

chflo

wer,

and

Yeze

r (19

86);

(200

4)(2

004)

; Yes

: Kih

lstro

m

(191

1)(2

000)

; Yes

:Ye

s: G

uiso

and

and

Hub

bard

(200

4); Y

es: G

uiso

,Le

vine

, and

Zim

mer

man

Yes:

Berk

owitz

and

and

Laffo

nt (1

979)

Gui

so an

d Sc

hiva

rdi (

2004

)Su

pien

za, a

nd Z

inga

les (2

004)

; (1

998)

; Yes

: Mitc

hell

Whi

te (2

001)

; Yes

: Sc

hiva

rdi (

2004

)Ye

s: Bl

anch

flowe

r and

Osw

ald

and

Pear

ce (2

005)

Persa

d (2

004)

(199

8); Y

es: F

airlie

(199

9); Y

es:

Blac

k an

d St

raha

n (2

002)

; Yes

: Im

mer

gluc

k an

d Sm

ith (2

001)

Firm

s by

Yes:

Puri

and

Yes:

Kni

ght (

1921

); Ye

s: Lu

cas (

1978

); Ye

s: D

unn

and

No

Stud

iesN

o: V

os, Y

eh, C

arte

r, an

d Ta

gg

Yes:

Bate

s (19

91);

Yes:

Barth

, Cor

des,

Num

ber o

f Ro

bins

on

Yes:

Puri

and

Ye

s: Sc

hum

pete

rH

oltz-

Eaki

n(2

005)

; No:

Pet

erse

n an

d Ra

janYe

s: Bl

anch

flowe

r, an

d Ye

zer (

1986

);Em

ploy

ees

(200

4)Ro

bins

on (2

004)

; (1

911)

(200

0); Y

es:

(200

2); Y

es: I

mm

ergl

uck

and

Levi

ne, a

nd Z

imm

erm

an

Yes:

Berk

owitz

and

Yes:

Kih

lstro

m an

d G

uiso

and

Smith

(200

1)

(199

8); Y

es: M

itche

llW

hite

(200

1); Y

es:

Laffo

nt (1

979)

Schi

vard

i (20

04)

and

Pear

ce (2

005)

; Pe

rsad

(200

4)O

nly

betw

een

white

an

d bl

ack

busin

ess

owne

rs: B

ostic

and

Lam

pani

(199

9)

Tabl

e 6

Stum

blin

g B

lock

s to

Ent

repr

eneu

rshi

p

how to allocate available resources to the benefit of all communities, butespecially LMI communities.

Regulatory stumbling blocks to entrepreneurship

An additional way of identifying stumbling blocks to entrepreneurship isto ask entrepreneurs directly what they perceive to be barriers to startingand operating a business. Every four years, the National Federation of Inde-pendent Business conducts a nationwide survey of small business ownersknown as Small Business Problems and Priorities. Table 7 includes selectedproblems identified as critical by respondents to the 2004 survey. Interest-ingly, few of the barriers studied in the empirical literature are identified ascritical by survey respondents. For instance, liquidity constraints are thetopic of numerous studies, as noted earlier—many of which find them tobe binding—yet the difficulty of obtaining long-term loans is ranked 68th,and the difficulty of obtaining short-term loans is ranked 70th of 75 prob-lems. Additionally, just 7 percent of respondents rated these two problemsas being of “critical” importance. Instead, business owners tended to stressthree broad groups of problems: those that are not amenable to policyactions (such as earnings), those that typically are beyond the scope of smallbusiness policy (such as health care costs) and those problems that are asso-ciated with governmental tax or regulatory policies. Indeed, as Table 7shows, the cost of workers’ compensation insurance is ranked the third-most important problem; business taxes are ranked fifth (see Table 8 fordifference in sales taxes across states); property taxes are ranked sixth; and“unreasonable” government regulation is ranked the ninth-most importantstumbling block.

Anecdotal evidence for the importance of governmental regulations asstumbling blocks to entrepreneurship is substantial. For instance, Cleve-land, Ohio, requires any new taxicab company to have a fleet of at least 25cars—all of which must be three years old or newer. Akron, Canton, andDayton, Ohio, all require potential taxicab operators to convince govern-ment officials that their firms will meet so-called public convenience andnecessity requirements before they can begin operation. Licensing is also apotential stumbling block to entrepreneurship. The state of California



Problem Rank Percent of RespondentsIdentifying as “Critical”

EmployeesCost of health insurance 1 65.6Workers’ compensation costs 3 32.8Locating qualified employees 11 14FICA (Social Security) taxes 13 14.3Unemployment compensation (UC) 19 14.4Keeping skilled employees 28 12.4Health/safety regulations 30 10.4

FinanceCash flow 7 21.6Poor earnings (profits) 12 18.6Highly variable earnings (profits) 23 10.6Obtaining long-term (5 years or more)

business loans 68 6.7Obtaining short-term

(12 months or revolving) business loans 70 6.7

RegulationUnreasonable government regulation 9 19.5Frequent changes in federal

tax laws and rules 15 12.7State/local paperwork 17 11.6Federal paperwork 18 12.2Health/safety regulations 30 10.4

TaxesFederal taxes on business income 5 23.2Property taxes (real, personal, or inventory) 6 22.7State taxes on business income 8 20.2FICA (Social Security) taxes 13 14.3Estate (death) taxes 36 17.3

Source: “Small Business Problems and Priorities,” National Federation of Independent Business

requires professions, such as landscape architects and interior decorators, tobe licensed. Furthermore, nationally, there are some 500 occupations(including fence installers and courtroom shorthand reporters) that havelicensing requirements. Table 8 shows that there are still other stumblingblocks, such as fees to incorporate and fees to establish limited liabilitycompanies, which exist and vary widely across states. All these stumblingblocks undoubtedly contribute to variation in entrepreneurial activity acrossgeographical regions.

Table 7Selected Problems Identified by Small Business Owners


State Name State Sales Tax (Percent) Fees to Incorporate Fees to Establish LimitedLiability Companies

Alabama 4 $40 $40 Alaska none $250 $250 Arizona 5.6 $60 $50 Arkansas 6 $40 $40 California 7.25 $100 $70 Colorado 2.9 $50 $50 Connecticut 6 $50 $60 Delaware none $15 min $90 Florida 6 $35 $100 Georgia 4 $100 $100 Hawaii 4 $100 $100 Idaho 6 $100 $100 Illinois 6.25 $150 $500 Indiana 6 $90 $90 Iowa 5 $50 $50 Kansas 5.3 $90 $165 Kentucky 6 $40 $40 Louisiana 4 $60 $60 Maine 5 $125 $125 Maryland 5 $100 $100 Massachusetts 5 $275 $500 Michigan 6 $10 $50 Minnesota 6.5 $135 $135 Mississippi 7 $50 $50 Missouri 4.225 $25 $105 Montana none $70 $70 Nebraska 5.5 $25 $135 Nevada 6.5 $175 $75 New Hampshire none $35 $35 New Jersey 6 $125 $125 New Mexico 5 $100 $50 New York 4.25 $125 $200 North Carolina 4.5 $125 $125 North Dakota 5 $30 $125 Ohio 6 $125 $125 Oklahoma 4.5 $50 $100 Oregon none $50 $50 Pennsylvania 6 $125 $125 Rhode Island 7 $230 $150 South Carolina 5 $135 $110 South Dakota 4 $125 $125 Tennessee 7 $100 $300 Texas 6.25 $300 $200 Utah 4.75 $52 $52 Vermont 6 $75 $75 Virginia 5 $25 $100 Washington 6.5 $175 $175 Washington, D.C. 5.75 $150 $150 West Virginia 6 $50 $100 Wisconsin 5 $100 $170 Wyoming 4 $100 $100

Source: AT&T Small Business Resources

Table 8State Sales Tax, Initial Fees to Establish Domestic Corporations

and Limited Liability Companies

Other regulations that may act as stumbling blocks are those that applyto lending. While intended to benefit borrowers, these regulations can havethe perverse effect of decreasing the availability of loans to businesses. Bank-ruptcy exemption regulations are one such group of regulations that maypresent a barrier to entrepreneurship. The liabilities of unincorporated firmsare personal liabilities of the firms’ owners, and, thus, an increase inpersonal bankruptcy exemptions decreases the recovery value of defaultedloans and, therefore, may increase the cost of loans and decrease their avail-ability. Berkowitz and White (2000) study the impact of personalbankruptcy exemption levels on the probability of small firms being deniedcredit, using data from the 1993 “Survey of Small Business Finances,” andthey find that high exemption levels “are associated with an increase in theprobability of noncorporate firms being denied credit” (p. 446). Persad(2004), using SBA 7(a) data, finds that personal bankruptcy exemptionlevels are associated positively with default rates and also with interest rateson loans. In addition, Barth, Cordes, and Yezer (1983 and 1986) find thatrestrictions on creditor remedies (such as wage garnishment, wage assign-ment, and deficiency judgments) have net costs to borrowers in the personalloan market, a result that is applicable to small business finance, as the“Survey of Small Business Finances” suggests that many small businessowners fund their operations with personal liabilities.

An indirect approach to assessing determinants of entrepreneurship

The initial approach taken here to examining factors that may help toexplain cross-sectional variation in entrepreneurship across geographicregions is based on the size of businesses as measured by number of employ-ees. The analysis is based on the total number and size composition ofestablishments in 204 MSAs. We examine establishments grouped into fourdifferent size categories (zero, one to 10, 11-100, and more than 100employees), as well as all establishments combined. To the extent that theintensity of entrepreneurial activity is greater in smaller than bigger busi-nesses, an examination of the determinants of the relative importance ofsmaller versus bigger businesses represents an indirect approach to studyingdifferences in entrepreneurship across geographical regions.6


The basic model is as follows:

(1) ESTij = α + β1’Dij + β2’ Pj + β3’ Fij + β4’ Bij + εij ,

where EST is either all establishments or the share of establishments asrepresented by one of the four different size categories. D includes the race,ethnic, gender, age, and educational level (four-year college degree or higherand high school diploma or lower) composition of the population, as wellas average household income, homeownership rate, the poverty level,unemployment rate, and number of establishments per square mile (exceptin the total number of establishment regressions, in which case, only theland area is used). P is the state sales tax rate. F is the measures of availablefinancial resources, about which more will be said momentarily. B is themeasures of loan bias discussed earlier (that is to say, BLMIPB = loan biasfor LMI communities based on income; BLIPB = loan bias for LI commu-nities based on population; BLMIIB = loan bias for LMI communitiesbased on income; BLIIB = loan bias for LI communities based on income).ε is a random error term, and i is a subscript for MSA, while j is a subscriptfor state.

The variables included in F are the total number of financial institutions; thenumber of branches per institution; the total deposits per institution; the totalnumber and average size of loans to businesses made by banks under CRA; theproportion of the number and amount of loans to businesses in LI and moder-ate-income (MI) communities to the total number and amount of loans to allbusinesses in an MSA; the proportion of the number and amount of loans madeto businesses in LMI communities to all businesses in an MSA; and the propor-tion of the number and amount of loans made to those businesses with receiptsless than $1 million to the total number and amount of loans to all businesses.A list of all the variables, their definitions, data sources, and summary of statis-tics are provided in Table 9a, while Table 9b contains the pairwise correlationsfor the variables. There are five basic models, one for total establishments andfour representing each of the establishment size categories (zero, one to 10, 11-100, and more than 100 employees), with six specifications provided for eachmodel, with the different specifications reflecting mainly the inclusion or


Vari

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Def

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Sum

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M

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Max

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sus,

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POP

Tot

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sus,

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0.67

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CG

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Popu

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HG

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pop

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U.S

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sus,

2000

48.1

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DW

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a sh

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of to

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BLAC

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DH

ISP

Hisp

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tota

l pop

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U.S

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sus,

2000

8.8%

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93.1

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DPO

VPo

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pula

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as a

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tota

l pop

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12.1

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UN

MP

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2000

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AREA

Land

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sU

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FIN

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0.20

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4.72

0.02

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0.24

0.21

0.56

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FDEP

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dep

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per i

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)FD

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38.7

934

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16.6

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ALA/

FALN

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RA,

200

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ses i

n LM

I com

mun

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, am

ount

, as a

shar

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all

loan

sC

RA,

200

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260.

224.

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32FA

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ses i

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I com

mun

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, num

ber,

as a

shar

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all

loan

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200

10.

230.

195.

390.

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LIA/

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Loan

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busin

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s in

LI c

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mou

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ll lo

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CR

A, 2

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0.06

0.05

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FALI

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ALN

Loan

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0.04

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FAM

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Loan

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s in

MI c

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mou

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ll lo

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CR

A, 2

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0.20

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4.85

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Loan

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1.18

122 James R. Barth, Glenn Yago, and Betsy ZeidmanTa

ble

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Cor

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0.36

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0.03

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0.34

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0.53

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CG

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0.39

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0.04

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0.22

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0.34

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0.41

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0.31

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DH

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0.23

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0.15

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**-0

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**-0

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0.19

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0.3*

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FIN

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0.11

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0.17

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0.07

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0.36

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0.29

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0.31

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-0.1

3**

0.17

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-0.1

5***

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2***

FALN

0.31

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0.09

-0.3

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0.76

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0.42

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0.36

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0.25

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FALA

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0.46

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0.61

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0.37

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0.23

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0.01

0.04

0.36

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IA/F

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-0.0

40.

02-0

.05

-0.0

7-0

.01

0-0

.08

FALM

IN/F

ALN

-0.0

6-0

.08

00.

010.

11**

-0.0

40.

02-0

.05

-0.0

6-0

.02

0.01

-0.0

7FA

LIA/

FALA

-0.2

***

-0.1

8***

0.04

0.19

***

0.21

***

-0.0

10

-0.0

40

-0.0

20.

040.

03FA

LIN

/FAL

N-0

.24*

**-0

.24*

**0.

11**

0.23

***

0.25

***

-0.0

1-0

.01

-0.0

50.

02-0

.01

0.03

0.04

FAM

IA/F

ALA

-0.0

2-0

.04

-0.0

1-0

.02

0.06

-0.0

40.

02-0

.05

-0.0

8-0

.01

-0.0

1-0

.09

FAM

IN/F

ALN

-0.0

3-0

.06

-0.0

1-0

.01

0.08

-0.0

40.

02-0

.05

-0.0

6-0

.01

0-0

.08

FLSM

A/FA

LA-0

.19*

**-0

.05

0.11

**-0

.01

0.13

**-0

.36*

**-0

.2**

*-0

.37*

**0.

06-0

.13*

*0.

11*

0.05

FLSM

N/F

ALN

-0.2

1***

-0.1

7***

0.31

***

0.27

***

0.1*

-0.2

6***

-0.1

*-0

.23*

**0.

04-0

.03

00.

16**

*BL

MIP

B0.

030.

1-0

.09

-0.0

7-0

.06

0.02

-0.1

3**

-0.0

40.

21**

*-0

.16*

**0.

23**

*0.

18**

*BL

MII

B0.

18**

*0.

22**

*-0

.04

-0.2

***

-0.2

2***

-0.0

2-0

.04

-0.0

20

-0.0

20

-0.0

4BL

IPB

0.02

-0.0

2-0

.15*

*-0

.03

0.06

0.01

-0.0

7-0

.01

0.27

***

-0.1

7***

0.24

***

0.23

***

BLII

B0.

18**

*0.

19**

*-0

.02

-0.1

8***

-0.2

1***

-0.0

7-0

.06

-0.0

10.

06-0

.03

00.

03


Tabl

e 9b

(co

nt.)

DAS

IAN

DH

ISP

DPO

VD

UN

MP

DAL

LEST

/DAR

EAPT

AXFI

NST

IFB

RAN

CH

/FIN

STI

FDEP

O/F

INST

IFA

LNFA

LAV

EFA

LMIA

/FAL

AL1

0ESH

L20E

SHL1

00ES

HL1

00PE

SHD

POP

DAG

E254

4D

HIN

CD

HO

MEO

DC

GR

ADD

HG

RAD

DW

HIT

ED

BLAC

K1

DAS

IAN

-0.0

71

DH

ISP

-0.1

9***

0.17

***

1D

POV

0.16

***

-0.0

80.

51**

*1

DU

NM

P0.

050.

030.

51**

*0.

77**

*1

DAL

LEST

/DAR

EA0.

060.

37**

*0.

14**

-0.2

1***

-0.1

1*1

PTAX

-0.0

50.

14**

*0.

24**

*0.

090.

2***

0.05

1FI

NST

I0.

16**

*0.

27**

*0.

1*-0

.15*

**-0

.09

0.56

***

0.08

1FB

RAN

CH

/FIN

STI

-0.2

1***

-0.1

7***

0.02

0.07

0.03

-0.2

1***

-0.1

1*-0

.39*

**1

FDEP

O/F

INST

I-0

.04

0.39

***

0.29

***

-0.0

20

0.3*

**0.

12**

0.47

***

-0.2

2***

1FA

LN0.

16**

*0.

34**

*0.

17**

*-0

.11*

*-0

.06

0.47

***

0.13

**0.

89**

*-0

.39*

**0.

48**

*1

FALA

VE

-0.0

3-0

.21*

**-0

.32*

**-0

.19*

**-0

.22*

**-0

.12*

*-0

.07

-0.1

*0.

15**

*-0

.09

-0.1

7***

1FA

LMIA

/FAL

A0.

11*

0.01

0.01

0.09

0.05

-0.0

4-0

.02

-0.0

40.

030.

01-0

.03

-0.0

1FA

LMIN

/FAL

N0.

12**

-0.0

10

0.08

0.05

-0.0

4-0

.03

-0.0

30.

020

-0.0

30.

01FA

LIA/

FALA

0.12

**-0

.03

-0.1

*0

0-0

.01

0.01

0.01

0.03

0.09

-0.0

10.

12**

FALI

N/F

ALN

0.15

***

-0.0

5-0

.13*

*0

0-0

.02

-0.0

30.

010.

020.

1*-0

.03

0.17

***

FAM

IA/F

ALA

0.09

0.02

0.03

0.09

0.06

-0.0

4-0

.03

-0.0

40.

03-0

.01

-0.0

3-0

.03

FAM

IN/F

ALN

0.11

*0

0.02

0.09

0.05

-0.0

3-0

.03

-0.0

30.

02-0

.02

-0.0

3-0

.01

FLSM

A/FA

LA0.

24**

*-0

.23*

**-0

.15*

**0.

23**

*0.

04-0

.24*

**-0

.12*

*-0

.32*

**0.

24**

*-0

.22*

**-0

.36*

**0.

07FL

SMN

/FAL

N0.

05-0

.11*

*-0

.18*

**0.

11**

-0.0

2-0

.15*

**-0

.13*

*-0

.2**

*0.

32**

*-0

.16*

**-0

.26*

**0.

5***

BLM

IPB

0.04

-0.1

4**

-0.1

8***

-0.0

8-0

.06

-0.0

50.

010

-0.1

5**

-0.1

4**

-0.0

20.

02BL

MII

B-0

.11*

0.03

0.1

0.07

0.05

-0.0

90

-0.0

3-0

.04

-0.0

8-0

.01

-0.1

7***

BLIP

B0.

01-0

.15*

*-0

.21*

**-0

.16*

**-0

.1*

-0.0

6-0

.06

-0.0

1-0

.11*

-0.1

4**

-0.0

30.

11*

BLII

B-0

.16*

**-0

.02

0.06

0.01

0.01

-0.1

3**

-0.0

2-0

.08

-0.0

2-0

.1*

-0.0

7-0

.12*


FALM

IN/F

ALN

FALI

A/FA

LAFA

LIN

/FAL

NFA

MIA

/FAL

AFA

MIN

/FAL

NFL

SMA/

FALA

FLSM

N/F

ALN

BLM

IPB

BLM

IIB

BLIP

BBL

IIB

L10E

SHL2

0ESH

L100

ESH

L100

PESH

DPO

PD

AGE2

544

DH

INC

DH

OM

EOD

CG

RAD

DH

GR

ADD

WH

ITE

DBL

ACK

DAS

IAN

DH

ISP

DPO

VD

UN

MP

DAL

LEST

/DAR

EAPT

AXFI

NST

IFB

RAN

CH

/FIN

STI

FDEP

O/F

INST

IFA

LNFA

LAV

EFA

LMIA

/FAL

A1

FALM

IN/F

ALN

0.99

***

1FA

LIA/

FALA

0.25

***

0.2*

**1

FALI

N/F

ALN

0.19

***

0.17

***

0.92

***

1FA

MIA

/FAL

A0.

98**

*0.

98**

*0.

080.

031

FAM

IN/F

ALN

0.98

***

0.99

***

0.1*

0.05

0.99

***

1FL

SMA/

FALA

0.06

0.07

-0.0

40.

010.

070.

071

FLSM

N/F

ALN

0.01

0.02

0.04

0.08

00.

010.

61**

*1

BLM

IPB

-0.4

1***

-0.3

3***

-0.3

7***

-0.3

3***

-0.3

5***

-0.2

9***

0.08

-0.0

11

BLM

IIB

-0.2

1***

-0.1

7***

-0.9

8***

-0.9

***

-0.0

4-0

.08

0.1

-0.0

30.

37**

*1

BLIP

B-0

.35*

**-0

.29*

**-0

.29*

**-0

.25*

**-0

.31*

**-0

.26*

**0.

03-0

.01

0.84

***

0.28

***

1BL

IIB

-0.2

***

-0.1

7***

-0.9

6***

-0.8

7***

-0.0

4-0

.07

0.08

-0.0

20.

36**

*0.

98**

*0.

29**

*

Tabl

e 9b

(co

nt.)

exclusion of different combinations of the LI and MI loan variables, as willbe discussed in the next paragraph.

Tables 10 through 14 present the empirical results of our exercise. Theseresults may be summarized as follows:

Population

• Total population is not a significant factor in explaining the total levelor the shares of those establishments with either zero or 11-100 employ-ees in MSAs. However, population is related positively with a largershare of establishments with more than 100 employees, while associatednegatively with the share of establishments with one to 10 employees.

• MSAs with larger shares of the population in the 25-44 age grouptend to have more establishments. At the same time, this segment ofthe population is correlated negatively with a larger share of smallestablishments (in other words, those with zero and one to 10employees), while correlated positively with the share of establish-ments with more than 10 employees.

Household income

• There is no evidence of any relationship between household incomeand the total number of establishments in MSAs. However, the levelof household income is associated positively with the share of smallestablishments (zero to 10 employees) with the coefficients in 11 ofthe 12 regressions being significant at the 10 percent level or better.It also is correlated negatively with the share of establishments with100 or more employees.

Homeownership

• Homeownership is correlated positively but marginally with totalestablishments in five of the six regressions. The results do not indi-cate any relationship between homeownership percentage and the sizecomposition of establishments.

Education

• We do not find any significant relationships between either totalestablishments or the share of establishments with at least one



Table 10Determinants of the Total Number of Establishments in MSAs

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)C -52.35 -17.4971 -18.169 -19.3771 -17.8355 -15.104 -58.0395* -57.4187* -58.2001* -57.5618*

(0.13) (0.35) (0.33) (0.32) (0.34) (0.42) (0.07) (0.07) (0.07) (0.07)DPOP 6.9919*** 0.7933 0.8025 0.8182 0.7941 0.7341 4.7605* 4.7646* 4.7656* 4.7647*

(0.00) (0.21) (0.2) (0.2) (0.21) (0.25) (0.05) (0.05) (0.05) (0.05)DAGE2544 89.4948*** 36.5161** 36.887** 36.2153** 36.6461** 37.2844*** 56.6351** 56.194** 56.3769** 56.2983**

(0.00) (0.01) (0.01) (0.01) (0.01) (0.01) (0.03) (0.03) (0.03) (0.03)DHINC -0.2693 0.0307 0.0354 0.0292 0.0318 0.0238 0.0678 0.0713 0.0687 0.0721

(0.2) (0.72) (0.68) (0.74) (0.71) (0.78) (0.64) (0.62) (0.63) (0.62)DHOMEO 25.1614 15.0137* 15.4675* 15.716* 15.1018* 14.9274* 32.5734** 32.7334** 32.641** 32.884**

(0.2) (0.07) (0.06) (0.06) (0.07) (0.07) (0.02) (0.02) (0.02) (0.02)DCGRAD -17.8901 -1.6203 -0.9965 -2.2475 -1.3086 -1.9156 -19.1231 -19.3292 -18.9967 -19.5014

(0.35) (0.86) (0.92) (0.8) (0.89) (0.83) (0.13) (0.13) (0.13) (0.13)DHGRAD -30.979*** -8.2244 -7.5181 -9.3567 -7.8851 -8.6132 -27.6072*** -27.6173*** -27.3728*** -27.7399***

(0.01) (0.2) (0.25) (0.14) (0.22) (0.17) (0.00) (0.00) (0.00) (0.00)DWHITE 37.3751 -1.8691 -2.709 0.4799 -2.0379 -2.0295 26.8032 26.6357 26.8973 26.3373

(0.18) (0.89) (0.84) (0.97) (0.88) (0.88) (0.21) (0.21) (0.21) (0.22)DBLACK 35.5407 -7.6439 -8.2912 -4.6843 -7.7413 -7.1919 25.1725 24.8345 25.2457 24.3809

(0.19) (0.56) (0.51) (0.73) (0.55) (0.59) (0.21) (0.22) (0.21) (0.22)DASIAN 57.9889* -2.1364 -3.6306 0.2821 -2.5474 -1.6941 44.9694 44.9436 45.0031 44.6178

(0.09) (0.88) (0.8) (0.98) (0.86) (0.91) (0.1) (0.11) (0.1) (0.11)DHISP 40.2164 -5.3202 -6.307 -2.9899 -5.5578 -5.8122 26.7493 26.5794 26.7759 26.3059

(0.15) (0.7) (0.63) (0.83) (0.68) (0.67) (0.18) (0.18) (0.18) (0.19)DPOV -2.2796 11.2356 11.723 10.5979 11.3069 13.996 35.5476 36.418 35.5795 36.4716

(0.94) (0.44) (0.43) (0.47) (0.44) (0.33) (0.23) (0.23) (0.23) (0.23)DUNMP -38.6361 -0.0459 0.8192 4.1101 0.2486 -3.487 -21.7382 -22.6779 -21.7235 -22.7742

(0.26) (1) (0.97) (0.83) (0.99) (0.86) (0.46) (0.44) (0.46) (0.44)DAREA 0.0005* 0.0001 0.0001 0.0001 0.0001 0.0001 0.0004** 0.0004** 0.0004** 0.0004**

(0.05) (0.22) (0.23) (0.21) (0.22) (0.24) (0.03) (0.03) (0.03) (0.03)PTAX 0.4053 0.064 0.0598 0.0125 0.0659 0.0509 0.1419 0.1398 0.1416 0.138

(0.24) (0.76) (0.77) (0.94) (0.75) (0.81) (0.64) (0.64) (0.65) (0.65)FINSTI 54.2215*** 19.7425*** 19.706*** 19.8558*** 19.7332*** 19.7511*** 71.0229*** 71.0204*** 71.0165*** 70.9787***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)FBRANCH/ -1.5591 -0.0292 -0.154 -0.3516 -0.0387 0.6351 7.1136 6.9617 7.0515 6.8936FINSTI (0.77) (1) (0.98) (0.95) (0.99) (0.91) (0.24) (0.24) (0.24) (0.25)

FDEPO/FINSTI -1.5591 0.0177 6.9617 7.0515 6.8936(0.77) (0.58) (0.24) (0.24) (0.25)

FALN 0.9722*** 0.9726*** 0.9679*** 0.9725*** 0.9717***(0.00) (0.00) (0.00) (0.00) (0.00)

FALAVE 17.5219 19.1437 22.6161 18.2856 25.3088(0.4) (0.38) (0.34) (0.39) (0.37)

FALMIA/FALA 7.0293(0.27)

FALMIN/FALN -6.6582(0.27)

FALIA/FALA 28.6861(0.26)

FALIN/FALN -41.2395(0.25)

FAMIA/FALA 2.4595(0.49)

FAMIN/FALN -2.3066(0.48)

FLSMA/FALA -2.197(0.51)

FLSMN/FALN -2.9871(0.56)

BLMIPB 0.1719(0.87)

BLMIIB 0.0162(0.6)

BLIPB 0.0175(0.58)

BLIIB 0.0157(0.61)

Adjusted R2 0.91 0.96 0.96 0.96 0.96 0.96 0.88 0.88 0.88 0.88F-statistic 187.24 448.94 402.63 406.22 401.34 402.44 108.85 108.89 108.85 108.93Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Number of

Observations 304 304 304 304 304 304 263 263 263 263

Note: White heteroskedasticity-consistent standard errors and covariance, and p-values in parentheses. “*,”“**,” and “***” denote significance at 10, 5, and 1 percent level, respectively.


Table 11Determinants of the Proportion of All Establishments

with Zero Employees in MSAs

Adjusted R2 0.45 0.51 0.51 0.52 0.51 0.51 0.52 0.51 0.52 0.51F-statistic 15.52 18.38 16.64 17.66 16.47 16.69 16.80 15.94 17.05 15.95Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Number of 304 304 304 304 304 304 263 263 263 263

ObservationsNote: White heteroskedasticity-consistent standard errors and covariance, and p-values in parentheses. “*,”“**,” and “***” denote significance at 10, 5, and 1 percent level, respectively.

(11) (12) (13) (14) (15) (16) (17) (18) (19) (20)C 30.8829*** 29.2656*** 29.43*** 27.8883*** 29.4023*** 28.6697*** 33.1876*** 31.6319*** 34.5476***32.3549***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)DPOP 0.1408* -0.136 -0.1407 -0.1378 -0.1364 -0.1109 0.0407 0.0732 0.0229 0.0744

(0.09) (0.12) (0.11) (0.11) (0.12) (0.2) (0.74) (0.56) (0.85) (0.56)DAGE2544 -19.7088*** -17.9906*** -18.0376***-18.0403***-18.0329*** -18.2297*** -18.8713*** -20.3811*** -20.8687***-20.6081***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)DHINC 0.0591** 0.0638** 0.0624** 0.0603** 0.0633** 0.067** 0.0352 0.0364 0.0375 0.0372

(0.04) (0.02) (0.02) (0.03) (0.02) (0.02) (0.18) (0.19) (0.15) (0.18)DHOMEO 0.798 0.8918 0.7458 0.8654 0.8547 0.8727 0.6195 0.7023 0.1854 0.5789

(0.6) (0.53) (0.6) (0.54) (0.54) (0.54) (0.69) (0.65) (0.91) (0.71)DCGRAD -6.8144*** -4.1744* -4.369* -4.0379* -4.3048* -4.2408* -8.4136*** -7.2291*** -8.4427*** -7.2441***

(0.01) (0.09) (0.07) (0.1) (0.08) (0.09) (0.00) (0.01) (0.00) (0.01)DHGRAD -14.8809*** -12.1151*** -12.3388***-12.2407***-12.2593*** -12.0993*** -17.5918*** -15.9514*** -17.7986***-15.9402***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)DWHITE -8.2744** -8.576** -8.2787** -6.8336* -8.4957** -8.7224** -9.2084** -8.5682** -8.8726** -8.4037**

(0.03) (0.02) (0.03) (0.06) (0.02) (0.02) (0.02) (0.04) (0.03) (0.04)DBLACK -9.955*** -10.8093*** -10.5301***-8.6809*** -10.7582*** -11.2594*** -10.992*** -10.1465*** -10.5447***-9.8809***

(0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.01) (0.00) (0.01)DASIAN -10.0449** -12.9886*** -12.47*** -11.2739*** -12.7854*** -13.2606*** -11.6905*** -11.9891*** -11.401***-11.7154***

(0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01)DHISP -3.723 -5.4441 -5.0977 -3.7956 -5.3289 -5.464 -4.9681 -4.835 -4.651 -4.6695

(0.32) (0.12) (0.15) (0.26) (0.13) (0.12) (0.21) (0.21) (0.24) (0.23)DPOV -1.6707 -2.0844 -2.2035 -2.8781 -2.1236 -3.0137 -1.475 -2.5819 -0.3912 -2.1022

(0.72) (0.65) (0.63) (0.54) (0.65) (0.53) (0.76) (0.61) (0.94) (0.68)DUNMP 1.8673 5.3184 5.0948 7.4732 5.1864 6.6717 6.9156 8.6337 5.237 8.2132

(0.83) (0.52) (0.54) (0.36) (0.53) (0.42) (0.42) (0.34) (0.54) (0.36)DALLEST/ 3.7939 5.8303** 5.6273** 5.6892** 5.6909** 5.6316** 17.7425 21.3137 16.2277 22.493DAREA (0.18) (0.03) (0.04) (0.04) (0.03) (0.03) (0.31) (0.21) (0.35) (0.2)PTAX -0.19*** -0.1982*** -0.1976*** -0.2069*** -0.1992*** -0.1941*** -0.2221*** -0.2156*** -0.2056*** -0.215***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)FINSTI 0.3346 -1.4087*** -1.3929*** -1.3381*** -1.399*** -1.3822*** 1.3681* 1.2787* 1.4472* 1.3137*

(0.11) (0.00) (0.00) (0.00) (0.00) (0.00) (0.07) (0.1) (0.06) (0.09)FBRANCH/ -3.0738*** -2.255*** -2.2069*** -2.2312*** -2.2485*** -2.4762*** -2.6224*** -2.8274*** -2.7862*** -2.7961***FINSTI (0.00) (0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00)FDEPO/FINSTI -3.0738*** -0.0036 -2.8274*** -2.7862*** -2.7961***

(0.00) (0.37) (0.00) (0.00) (0.00)FALN 0.0425*** 0.0423*** 0.0413*** 0.0423*** 0.0426***

(0.00) (0.00) (0.00) (0.00) (0.00)FALAVE -29.752*** -30.2232***-26.0908***-30.1226*** -30.6803***

(0.00) (0.00) (0.00) (0.00) (0.00)FALMIA/FALA -2.1754*

(0.08)FALMIN/FALN 1.9719*

(0.09)FALIA/FALA 1.6983

(0.55)FALIN/FALN -8.9101**

(0.03)FAMIA/FALA -1.1306

(0.43)FAMIN/FALN 1.0553

(0.42)FLSMA/FALA 1.2357

(0.34)FLSMN/FALN 0.4453

(0.78)BLMIPB 1.3474***

(0.00)BLMIIB -0.0027

(0.52)BLIPB -0.0034

(0.41)BLIIB -0.003

(0.47)


Table 12Determinants of the Proportion of All Establishments

with One to 10 Employees in MSAs

Adjusted R2 0.22 0.45 0.45 0.47 0.46 0.48 0.21 0.24 0.20 0.23F-statistic 6.12 14.95 13.55 14.42 13.73 15.17 4.92 5.66 4.53 5.43Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Number of 304 304 304 304 304 304 263 263 263 263

ObservationsNote: White heteroskedasticity-consistent standard errors and covariance, and p-values in parentheses. “*,”“**,” and “***” denote significance at 10, 5, and 1 percent level, respectively.

(21) (22) (23) (24) (25) (26) (27) (28) (29) (30)C 93.1138*** 77.6937*** 77.3634***75.3165*** 76.9685*** 74.1037*** 98.0181*** 94.4051*** 98.2193*** 96.409***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)DPOP -0.2336** -0.2445 -0.2489 -0.2459 -0.2462 -0.186 -0.4361** -0.3977** -0.4131** -0.3949**

(0.04) (0.13) (0.13) (0.12) (0.13) (0.25) (0.03) (0.04) (0.04) (0.04)DAGE2544 -64.0189*** -41.7806*** -41.4877***-41.871*** -41.471*** -42.361*** -62.8361*** -64.0091*** -65.2091***-64.6551***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)DHINC 0.0837* 0.0685* 0.0705* 0.0626 0.0711* 0.0726* -0.0052 -0.011 0.0013 -0.0079

(0.06) (0.09) (0.08) (0.12) (0.07) (0.06) (0.92) (0.83) (0.98) (0.88)DHOMEO -5.1658 -1.2687 -1.1446 -1.2697 -1.0955 -0.4928 -7.7863** -8.0406** -7.6305** -8.2559**

(0.14) (0.67) (0.7) (0.66) (0.71) (0.87) (0.04) (0.03) (0.05) (0.03)DCGRAD 1.0725 7.3529 7.4891 7.5467 7.9867* 9.7572** -4.6631 -2.6662 -4.0077 -2.8131

(0.84) (0.11) (0.1) (0.1) (0.08) (0.04) (0.44) (0.64) (0.5) (0.63)DHGRAD 0.5533 6.6116 6.7724* 6.3432 7.3187* 8.6924** -4.2323 -1.9095 -3.0972 -1.9531

(0.91) (0.1) (0.09) (0.12) (0.06) (0.03) (0.43) (0.69) (0.55) (0.69)DWHITE -23.0425* -15.6057 -15.7591 -12.6056 -15.9846 -16.336 -20.0117* -18.6537* -19.7203* -18.3901*

(0.06) (0.23) (0.21) (0.29) (0.19) (0.13) (0.09) (0.06) (0.07) (0.07)DBLACK -25.1313** -19.9986 -19.9302 -16.3247 -20.1597* -20.663** -21.687* -19.6609** -21.4489** -19.2468**

(0.04) (0.11) (0.11) (0.16) (0.09) (0.05) (0.05) (0.04) (0.04) (0.04)DASIAN -20.4272 -16.7767 -17.1874 -13.8139 -17.7875 -19.211* -16.1942 -16.7962 -16.2046 -16.1189

(0.11) (0.19) (0.17) (0.25) (0.15) (0.08) (0.19) (0.12) (0.17) (0.14)DHISP -20.1849 -16.4755 -16.6959 -13.6295 -17.0359 -16.6043 -16.8538 -16.249 -16.9178 -15.9548

(0.11) (0.2) (0.18) (0.25) (0.16) (0.12) (0.15) (0.1) (0.12) (0.11)DPOV -2.0024 -2.515 -2.1387 -3.8674 -2.2049 -7.4542 -3.9417 -7.3362 -3.2361 -5.8301

(0.85) (0.78) (0.82) (0.68) (0.81) (0.42) (0.71) (0.47) (0.75) (0.57)DUNMP 5.8645 9.1675 9.6465 12.9711 9.8801 13.2707 -1.5469 3.1452 -2.2737 1.6654

(0.71) (0.5) (0.47) (0.34) (0.47) (0.31) (0.92) (0.84) (0.88) (0.91)DALLEST/ 37.0554*** 31.8127*** 32.0151*** 31.515*** 32.5208*** 29.8309*** 61.002* 72.7755** 58.8491* 74.0312**DAREA (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.06) (0.03) (0.08) (0.03)

PTAX -0.1909* -0.1514* -0.1556* -0.1686* -0.1476* -0.1169 -0.2106* -0.1992* -0.2018* -0.1984*(0.05) (0.08) (0.08) (0.06) (0.1) (0.15) (0.06) (0.07) (0.06) (0.07)

FINSTI 0.5794 0.3086 0.2737 0.4338 0.2509 0.0146 1.7491 1.5085 1.7819 1.615(0.22) (0.74) (0.77) (0.65) (0.79) (0.99) (0.24) (0.31) (0.23) (0.27)

FBRANCH/ -1.9647 1.1295 1.1058 1.1507 1.1079 -0.0222 -1.5584 -1.4913 -1.9898 -1.4802FINSTI (0.24) (0.41) (0.42) (0.39) (0.42) (0.99) (0.38) (0.37) (0.27) (0.38)

FDEPO/FINSTI -1.9647 0.0075 -1.4913 -1.9898 -1.4802(0.24) (0.47) (0.37) (0.27) (0.38)

FALN 0.0055 0.006 0.0033 0.0066 0.0093(0.76) (0.74) (0.86) (0.72) (0.58)

FALAVE -137.4752***-136.5849***-131.1443***-135.5213***-170.9063***(0.00) (0.00) (0.00) (0.00) (0.00)



FALIA/FALA 4.473(0.53)

FALIN/FALN -17.1149(0.12)

FAMIA/FALA 5.8771**(0.04)

FAMIN/FALN -5.6563**(0.03)

FLSMA/FALA -1.8321(0.32)

FLSMN/FALN 11.3279***(0.00)

BLMIPB 1.6144**(0.02)

BLMIIB 0.0116(0.32)

BLIPB 0.0063(0.55)

BLIIB 0.0103(0.36)


Table 13Determinants of the Proportion of All Establishments with

11-100 Employees in MSAs(31) (32) (33) (34) (35) (36) (37) (38) (39) (40)

C -7.1981 2.0116 1.9568 3.4213 2.0365 2.9359 -12.287** -10.5271* -13.3226** -11.4774**(0.19) (0.71) (0.72) (0.5) (0.71) (0.58) (0.03) (0.05) (0.02) (0.03)

DPOP -0.1168 -0.0183 -0.0197 -0.0202 -0.022 -0.0545 0.0057 -0.0112 0.0249 -0.0126(0.12) (0.89) (0.88) (0.87) (0.86) (0.67) (0.96) (0.92) (0.83) (0.91)

DAGE2544 32.0876*** 18.6097*** 18.6675*** 18.6708*** 18.6845*** 18.9548*** 35.3272*** 35.788*** 36.3909*** 36.0906***(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

DHINC 0.0078 0.0164 0.0167 0.0196 0.0165 0.012 0.0537* 0.057* 0.0533* 0.0558*(0.76) (0.45) (0.44) (0.38) (0.45) (0.58) (0.07) (0.05) (0.07) (0.06)

DHOMEO 1.9593 -0.2729 -0.2601 -0.3436 -0.3023 -0.27 2.7789 2.924 3.1579 3.0539(0.34) (0.88) (0.88) (0.85) (0.87) (0.88) (0.2) (0.17) (0.15) (0.15)

DCGRAD 4.8999 0.0983 0.1091 0.0502 0.0189 0.1172 6.7583* 5.8213 6.9256* 5.8679*(0.12) (0.97) (0.97) (0.99) (0.99) (0.97) (0.06) (0.1) (0.05) (0.1)

DHGRAD 7.2376*** 2.5526 2.566 2.7943 2.4711 2.4685 8.9938*** 7.9328*** 9.4078*** 7.9371***(0.01) (0.24) (0.24) (0.2) (0.25) (0.27) (0.00) (0.01) (0.00) (0.01)

DWHITE 8.4366* 4.6491 4.6441 2.8819 4.7088 4.8717 9.4899** 8.8345** 9.2874* 8.6674**(0.05) (0.35) (0.36) (0.52) (0.34) (0.3) (0.05) (0.04) (0.06) (0.04)

DBLACK 3.5434 1.2417 1.2821 -0.9368 1.357 1.8785 3.8717 2.8819 3.569 2.6151(0.39) (0.8) (0.79) (0.83) (0.78) (0.68) (0.38) (0.46) (0.43) (0.51)

DASIAN 9.1397** 8.692* 8.6531* 6.9299 8.7936* 9.138* 9.4123* 9.7012** 9.1802* 9.3622**(0.05) (0.09) (0.09) (0.14) (0.08) (0.06) (0.07) (0.04) (0.08) (0.05)

DHISP 3.4735 2.416 2.4014 0.7275 2.4858 2.4472 4.7315 4.4229 4.4656 4.2474(0.42) (0.62) (0.63) (0.87) (0.61) (0.6) (0.3) (0.28) (0.34) (0.3)

DPOV 9.5649* 10.3635** 10.4323** 11.1373** 10.4551** 11.7852** 13.7282** 15.4207*** 13.023** 14.7452***(0.08) (0.04) (0.04) (0.03) (0.04) (0.02) (0.02) (0.01) (0.02) (0.01)

DUNMP 10.3572 6.854 6.9349 4.4638 6.8419 4.8765 6.028 3.7092 7.2009 4.3421(0.33) (0.45) (0.45) (0.62) (0.46) (0.6) (0.57) (0.73) (0.5) (0.69)

DALLEST/DAREA -17.6569*** -15.3258*** -15.3017*** -15.0627*** -15.3812***-14.9941*** -6.8599 -12.7596 -6.1309 -13.7956

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.7) (0.47) (0.73) (0.44)PTAX 0.1365** 0.1157** 0.1149** 0.1296** 0.1143** 0.109** 0.1274** 0.1221** 0.1162* 0.1215**

(0.01) (0.04) (0.04) (0.03) (0.04) (0.05) (0.04) (0.04) (0.06) (0.04)FINSTI -0.0253 0.7539 0.7479 0.6742 0.7494 0.7264 -1.6041** -1.4853* -1.6596** -1.5337**

(0.91) (0.17) (0.18) (0.21) (0.18) (0.21) (0.04) (0.05) (0.03) (0.04)FBRANCH/ 5.5478*** 3.431*** 3.4304*** 3.4502*** 3.4448*** 3.7687*** 5.6606*** 5.6022*** 5.6958*** 5.5803***FINSTI (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

FDEPO/ 5.5478*** -0.0027 5.6022*** 5.6958*** 5.5803***FINSTI (0.00) (0.62) (0.00) (0.00) (0.00)

FALN -0.0172 -0.0171 -0.0156 -0.0171 -0.0174(0.22) (0.23) (0.25) (0.23) (0.24)

FALAVE 90.8097*** 90.9548*** 87.0376*** 90.7309*** 93.0853***(0.00) (0.00) (0.00) (0.00) (0.00)



FALIA/FALA -5.1177(0.12)

FALIN/FALN 12.9234**(0.02)

FAMIA/FALA -0.3203(0.84)

FAMIN/FALN 0.1343(0.92)

FLSMA/FALA -1.6383(0.2)

FLSMN/FALN -0.9503(0.59)

BLMIPB -0.7146(0.11)

BLMIIB -0.0048(0.42)

BLIPB -0.0031(0.56)

BLIIB -0.0043(0.46)

Adjusted R2 0.21 0.46 0.46 0.48 0.46 0.47 0.25 0.27 0.26 0.26F-statistic 5.77 15.61 13.96 14.88 13.99 14.37 5.80 6.29 6.02 6.21Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Number of

Observations 304 304 304 304 304 304 263 263 263 263


Table 14Determinants of the proportion of All Establishments with 100

or More Employees in MSAs


(41) (42) (43) (44) (45)C -16.7986* -8.9709 -8.7502 -6.6261 -8.4072

(0.1) (0.39) (0.4) (0.49) (0.4)DPOP 0.2096 0.3988*** 0.4093*** 0.4038*** 0.4047***

(0.18) (0.01) (0.01) (0.00) (0.01)DAGE2544 51.6401*** 41.1614*** 40.8578*** 41.2405*** 40.8194***

(0.00) (0.00) (0.00) (0.00) (0.00)DHINC -0.1506*** -0.1487*** -0.1497*** -0.1425*** -0.151***

(0.00) (0.00) (0.00) (0.00) (0.00)DHOMEO 2.4085 0.6499 0.659 0.7478 0.543

(0.46) (0.83) (0.83) (0.81) (0.86)DCGRAD 0.842 -3.2768 -3.2292 -3.5589 -3.7009

(0.86) (0.45) (0.46) (0.41) (0.4)DHGRAD 7.09* 2.9509 3.0004 3.1032 2.4695

(0.07) (0.42) (0.42) (0.4) (0.5)DWHITE 22.8803** 19.5326* 19.3937* 16.5573* 19.7715**

(0.02) (0.06) (0.06) (0.08) (0.05)DBLACK 31.5429*** 29.5662*** 29.1783*** 25.9424*** 29.5609***

(0.00) (0.00) (0.00) (0.00) (0.00)DASIAN 21.3324** 21.0732** 21.0044** 18.1579* 21.7793**

(0.04) (0.05) (0.05) (0.06) (0.03)DHISP 20.4344** 19.5036* 19.3922* 16.6976* 19.879**

(0.04) (0.06) (0.06) (0.07) (0.05)DPOV -5.8919 -5.7641 -6.0902 -4.3918 -6.1267

(0.56) (0.56) (0.54) (0.66) (0.54)DUNMP -18.089 -21.34 -21.6762 -24.9081* -21.9085

(0.25) (0.15) (0.14) (0.09) (0.14)DALLEST/DAREA -23.1924*** -22.3171*** -22.3407*** -22.1415** -22.8306***

(0.00) (0.01) (0.01) (0.01) (0.00)PTAX 0.2444** 0.234** 0.2383** 0.2459** 0.2325**

(0.02) (0.02) (0.02) (0.01) (0.02)FINSTI -0.8887** 0.3463 0.3712 0.2301 0.3987

(0.05) (0.72) (0.71) (0.82) (0.69)FBRANCH/FINSTI -0.5093 -2.3054 -2.3293 -2.3698* -2.3042

(0.74) (0.1) (0.1) (0.08) (0.11)FDEPO/FINSTI -0.5093

(0.74)FALN -0.0308 -0.0313 -0.029 -0.0318

(0.14) (0.13) (0.19) (0.13)FALAVE 76.4174*** 75.8533*** 70.1975*** 74.913***

(0.00) (0.00) (0.00) (0.00)FALMIA/FALA -0.8847

(0.78)FALMIN/FALN 1.349

(0.64)FALIA/FALA -1.0536

(0.88)FALIN/FALN 13.1016

(0.19)FAMIA/FALA -4.4263

(0.13)FAMIN/FALN 4.4668*

(0.1)FLSMA/FALA

FLSMN/FALN

BLMIPB

BLMIIB

BLIPB

BLIIB

Adjusted R2 0.36 0.44 0.44 0.46 0.44F-statistic 11.17 14.18 12.84 13.68 12.93Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00Number of Observations 304 304 304 304 304


Table 14 (cont.)(46) (47) (48) (49) (50)

C -5.7093 -18.9187* -15.5099* -19.4443** -17.2865*(0.53) (0.06) (0.08) (0.04) (0.05)

DPOP 0.3514** 0.3896* 0.3357 0.3653* 0.3331(0.02) (0.06) (0.12) (0.09) (0.12)

DAGE2544 41.6359*** 46.3802*** 48.6022*** 49.687*** 49.1726***(0.00) (0.00) (0.00) (0.00) (0.00)

DHINC -0.1515*** -0.0838 -0.0824 -0.0922* -0.085*(0.00) (0.11) (0.11) (0.08) (0.1)

DHOMEO -0.1098 4.3878 4.4143 4.2873 4.6232(0.97) (0.21) (0.2) (0.22) (0.18)

DCGRAD -5.6336 6.3184 4.074 5.5248 4.1893(0.22) (0.23) (0.43) (0.29) (0.42)

DHGRAD 0.9385 12.8303*** 9.9281** 11.488*** 9.9562**(0.8) (0.00) (0.01) (0.01) (0.01)

DWHITE 20.1868** 19.7302* 18.3874** 19.3055** 18.1264**(0.02) (0.05) (0.03) (0.04) (0.03)

DBLACK 30.0439*** 28.8072*** 26.9255*** 28.4246*** 26.5126***(0.00) (0.00) (0.00) (0.00) (0.00)

DASIAN 23.3336** 18.4724* 19.0841** 18.4255* 18.4721**(0.01) (0.09) (0.04) (0.07) (0.05)

DHISP 19.6211** 17.0904* 16.6611** 17.1032* 16.3769**(0.03) (0.09) (0.04) (0.07) (0.05)

DPOV -1.3173 -8.3115 -5.5027 -9.3957 -6.8129(0.9) (0.42) (0.58) (0.35) (0.49)

DUNMP -24.8188* -11.3967 -15.4881 -10.1642 -14.2208(0.09) (0.45) (0.33) (0.51) (0.37)

DALLEST/DAREA -20.4685** -71.8846* -81.3296** -68.9459* -82.7286**(0.01) (0.06) (0.03) (0.07) (0.03)

PTAX 0.202** 0.3053*** 0.2928*** 0.2912*** 0.292***(0.04) (0.01) (0.01) (0.01) (0.01)

FINSTI 0.6412 -1.5131 -1.3019 -1.5695 -1.395(0.49) (0.34) (0.42) (0.33) (0.39)

FBRANCH/FINSTI -1.2703 -1.4798 -1.2834 -0.9197 -1.304(0.38) (0.38) (0.43) (0.6) (0.43)

FDEPO/FINSTI -0.0012 -1.2834 -0.9197 -1.304(0.89) (0.43) (0.6) (0.43)

FALN -0.0345*(0.07)

FALAVE 108.5013***(0.00)

FALMIA/FALA

FALMIN/FALN

FALIA/FALA

FALN/FALN

FAMIA/FALA

FAMIN/FALN

FLSMA/FALA 2.2348(0.25)

FLSMN/FALN -10.823***(0.00)

BLMIPB -2.2472***(0.00)

BLMIIB -0.0041(0.67)

BLIPB 0.0002(0.99)

BLIIB -0.003(0.75)

Adjusted R2 0.46 0.38 0.38 0.35 0.38F-statistic 14.05 9.94 10.09 8.91 9.86Prob(F-statistic) 0.00 0.00 0.00 0.00 0.00Number of Observations 304 263 263 263 263



employee and educational level. But the share of the population withat least a college degree or no more than a high school diploma tendto be associated negatively with the share of establishments with noemployees. This negative relationship is particularly strong in the caseof the high school variable.

Race/ethnicity

• We generally do not find any consistent relationships between therace/ethnic variables and the total number of establishments. The relation-ships between these variables and the size composition of establishmentsare more interesting. The regression results indicate that MSAs with alarger share of the population being Hispanic are also those with a largershare of establishments with 100 or more employees. On the other hand,MSAs with a larger mix of white, African-American, and Asian-Americanpopulations tend to be associated positively with a larger share of big estab-lishments and associated negatively with a larger share of the smallestestablishments. There is also some modest evidence that MSAs with largerAsian-American populations tend to have a larger share of establishmentswith 11-100 employees.

Poverty

• We did not find any relationships between the degree of poverty andthe number of establishments. There is also no evidence suggesting arelationship between poverty and the share of either the smallest orbiggest establishments in MSAs. Instead, the data indicate that MSAswith lower poverty rates tend to have a smaller proportion ofmedium-sized (11-100 employees) establishments.

Unemployment

• We do not find a relationship between unemployment rates withinMSAs and the total number of establishments or the size compositionof establishments. Only in two of the six regressions for the share ofestablishments with more than 100 employees are the coefficients forthe unemployment rate negative and marginally significant.

Land area

• Although we find no relationship between the number of establish-ments and the land area in the MSAs, we do find a negativerelationship between establishments per square mile and the share of


establishments with more than 10 employees. This relationship,however, is positive in the regressions for the share of establishmentswith fewer than 10 employees.

Sales tax rate

• We find no relationship between the state sales tax rate and the numberof total establishments in MSAs. However, we do find that a higher taxrate tends to have a negative correlation with the share of zero-to-10-employee establishments, whereas it tends to have a positive associationwith the share of establishments with 10 or more employees.

Financial institutions

• After controlling for branches per institution, MSAs with more finan-cial institutions tend to have more total establishments, but a smallershare of zero-employee establishments.

• Though we do not find a relationship between branches per financialinstitution within an MSA and the total number of establishments,we find the number of branches per financial institution tends to havea negative relationship with the share of zero-employee establish-ments, a positive relationship with the share of 11-to-100-employeeestablishments and a negative but marginally significant relationshipwith the share of 100+-employee establishments.

• After controlling for the number of institutions, deposits per institu-tion is correlated negatively with the share of zero-employeeestablishments, positively associated with the proportion of 11-to-100-employee establishments, and has no relationship to the othersize or total establishment variables.

Loan activity in LMI communities

• MSAs with larger numbers of loans tend to have not only more estab-lishments, but also a larger share of zero-employee establishments.

• We do not find a relationship between the average size of loans withinan MSA and the total number of establishments. However, we dofind that MSAs with higher-average-size loans tend to have a smallershare of zero-to-10-employee establishments and a larger share of11+-employee establishments.


• We find marginal evidence that the higher the share of loan amountsto businesses in LMI communities, the smaller the share of zero-employee establishments. Yet, the marginal evidence indicates thatthe higher the share of number of loans to businesses in LMI commu-nities, the larger the share of zero-employee establishments. We donot find that the share of the amount or number of loans to LMIcommunities provides any explanatory power with respect to othersize categories of establishments.

• We do not find any significant relationship between the share of loanamounts to businesses in LI communities and the total number ofestablishments or the size composition of establishments. However,the share of loan numbers to businesses in LI communities has a nega-tive and significant relationship to the share of zero-employeeestablishments, a negative but not significant relationship with theshare of one-to-10-employee establishments, a positive and significantrelationship with the share of 11-to-100-employee establishments, anda positive but not significant relationship with the share of 100+-employee establishments.

• The share of the total amount of loans to MI communities is relatedpositively to only the share of establishments with one to 10 employ-ees, while the share of the number of loans to MI communities isrelated negatively to the same-size establishments. The share of thetotal number of loans to MI communities also is associated positivelywith a larger share of establishments with more than 100 employees.

• While the share of total amount of loans to establishments with lessthan $1 million of receipts does not have a significant relationshipwith the total number or size composition of establishments, theshare of the total number of loans to such establishments tends tohave a positive correlation with the share of establishments with oneto 10 employees. At the same time, the share of the total number ofloans to establishments with less than $1 million in receipts tends tohave a negative association with the share of establishments with 100or more employees.


Loan bias

• None of the four measures of loan bias has a significant association withthe total number of establishments. However, the loan bias measure forLMI communities based upon population is associated positively withestablishments with zero employees and those with one to 10 employ-ees, while associated negatively with establishments with 100 or moreemployees. But these significant results disappear when the loan biasmeasure is based on income rather than population.

In summary, the results indicate that several factors matter for entrepre-neurship, as measured indirectly by the size composition of establishmentsin MSAs throughout the United States. The way in which these factors arerelated to entrepreneurship, however, varies depending on the size measureused. It is useful, therefore, to summarize the findings by establishment size.

Establishments with zero employees

One finds in MSAs that the greater the share of total establishments thatare zero-employee establishments, the lower the share of the populationaged 25 to 44, the higher the household income, the smaller the percentageof the labor force with a college degree, and the smaller the share of thelabor force that has a high school diploma or less. In addition, one finds thatthe greater the share of establishments that are zero-employee establish-ments, the greater the race/ethnic mix of the population; the lower the statesales tax rate; the larger the number of financial institutions; the lower thenumber of branches per institution; the lower the deposits per institution;the greater the number of loans; the lower the average loan size; the lowerthe share of the total amount of loans to businesses in LMI communities inMSAs; the larger the share of the total number of loans to businesses in LMIcommunities in MSAs; and the lower the share of the total number of loansmade to businesses in LI communities.

Establishments with one to 10 employees

Our work suggests that the greater the share of total establishments thatare one-to-10-employee establishments, the lower the share of the popula-tion aged 25 to 44, the higher the household income, the higher the

percentage of the labor force with a college degree (in two of six regressions),and the higher the share of the labor force that has a high school diplomaor less (in three of six regressions). In addition, one finds that the greater theshare of establishments that are one-to-10-employee establishments, thelower the state sales tax rate; the lower the average loan size; the greater theshare of the total amount of loans to businesses in MI communities inMSAs; the lower the share of the total number of loans to businesses in MIcommunities in MSAs; and the greater the share of the total number ofloans made to establishments with receipts of less than $1 million.

Establishments with 11 to 100 employees

The greater the share of total establishments that are 11-to-100-employeeestablishments, the greater the share of the population aged 25 to 44; thehigher the poverty rate; the higher the state sales tax rate; the higher thenumber of branches per institution; the higher the amount of deposits perinstitution; the higher the average loan size; and the larger the share of thetotal number of loans to businesses in LI communities in MSAs.

Establishments with 100 or more employees

One finds in MSAs that the greater the share of total establishments thatare 100+-employee establishments, the higher the population of the MSA; thehigher the share of the population aged 25 to 44; the lower the householdincome; the lower the race/ethnic mix of the population; the lower the unem-ployment rate (this is a marginal result in two of six regressions); the higherthe state sales tax rate; the larger the average loan size; the higher the share ofthe total number of the loans to businesses in MI communities in MSAs; andthe lower the share of the total number of loans made to establishments withreceipts of less than $1 million.

All establishments

The findings for all establishments are important because, to the extent thata factor increases this variable, any tradeoff between that factor’s effect on thesize composition of establishments and the number of establishments


becomes less important. The reason, of course, is that with more establish-ments, a smaller share of the total for any size category can experience,nonetheless, an absolute increase in the number of establishments. In the caseof other factors that are not significant for total establishments, but are notsignificant in explaining the size composition of establishments, there arenecessarily tradeoffs (that is to say, an increase in the share of one size categorywithin an MSA at the expense of a decrease in both the number and the shareof one or more other size categories). In this respect, only four factors seem tomatter for the total number of establishments. These factors are the share ofpopulation in the 25-44 age group, the homeownership rate, the number offinancial institutions, and the total number of loans made in an MSA. All fourare correlated positively with the total number of establishments.

Clearly, the empirical results presented here emphasize the need todevelop a more general microeconomic model and to assemble better microdata (preferably panels) to understand more fully the key determinants ofentrepreneurial activity in different geographical regions. Unfortunately, asdiscussed earlier with respect to the existing literature, there is an insuffi-cient database and no widely accepted microeconomic model yet availableto accomplish this task. This situation, however, should provide the moti-vation for researchers and policymakers to remedy the deficiency so thatmore progress can be made in identifying what works best at eliminatingstumbling blocks to entrepreneurship in LMI communities.

Policy recommendations

This paper conducts a selected review of the economic literature on entrepre-neurship and provides some tentative empirical analyses of the determinants ofentrepreneurship across MSAs. We find that the conclusions of previousresearchers, and even our findings, are consistent in some instances with oneanother and, in other instances, contradictory. Nevertheless, based on the liter-ature review, other papers not reviewed directly here, and our empirical analysis,we find sufficient agreement to draw several conclusions and potential policyrecommendations aimed at increasing entrepreneurial activity, particularly inLMI communities. These findings are as follows.


First, researchers frequently use different measures of entrepreneurship anddifferent datasets, limiting the ability to compare the work of different scholars andhampering an understanding of the factors that influence entrepreneurship and,thus, the development of effective policies. As noted previously, the term “entre-preneur” means different things in different studies. For example, a recentpaper published by the Federal Reserve Bank of Kansas City (Low, Hender-son, and Weiler, 2005) identifies entrepreneurs as the self-employed, whereasthe Kauffman Foundation’s “Index of Entrepreneurial Activity” (Fairlie,2005) identifies entrepreneurs as business owners (as reported in the “CurrentPopulation Survey” performed by the U.S. Bureau of the Census for theBureau of Labor Statistics).

One approach to addressing the problem for policymakers when researchersuse different definitions and control variables would be the construction of asingle, multiuse dataset through the creation of a data consortium that poolsinformation from different public and private datasets. Consortium partici-pants might contribute data on a blind basis, with researchers and participantsgaining access to the full pool of contributed information. This wouldimprove upon the information that is needed to better understand the bestway to promote or to facilitate entrepreneurship.

Second, our calculations of LI and LMI “loan bias” suggest that the financingreceived by businesses in many LI and LMI communities diverges from what some,perhaps naïvely, might consider appropriate, even when accounting for incomedisparity. In general, our first measure of loan bias (based on population) indi-cates that businesses in LI and LMI communities receive a significantlysmaller share of the total amount of loans than some might expect, given theLI and LMI shares of population. Our second measure of loan bias (based onincome) suggests that, in a large number of MSAs, this same type of lendinggap holds. To the extent that this “bias” is not explainable by economicfactors or is the result of regulatory barriers, incentives provided throughcapital access programs (in which lenders, borrowers, and the governmenteach contribute to a reserve fund to cover loan losses), and other credit-enhancement programs may be appropriate to help decrease this loan bias.7


Third, we and others find that financial variables are important to entrepreneur-ship. We specifically find that those MSAs with fewer financial institutions andlarger average loan sizes tend to have a smaller share of total establishmentswith less than 10 employees (potentially the most entrepreneurial establish-ments or those most associated with new startups). One potential way toincrease small business loan origination across MSAs is to increase the securi-tization of such loans. This would allow small business lenders to sell portionsof their loan portfolios and use the proceeds to originate more loans, as well aslower their capital requirements.8

Fourth, discrimination appears to persist, particularly as it relates to capitalaccess by African-American entrepreneurs. The continued difficulty ofAfrican-American-owned firms to gain financing, the smaller size of thesefirms, and the higher concentration of larger firms in MSAs that have largeAfrican-American populations suggest the continuing need for efforts toextend capital and other forms of support to African-American entrepre-neurs. This is particularly important when one considers that the numberof these firms is growing faster than the rate of all firms, and the growth (orlack thereof) has an increasingly significant effect on local communities.

Fifth, taxes and government regulations clearly are potentially importantimpediments to entrepreneurship. Indeed, they rank very high among thebarriers to entrepreneurship that are cited by entrepreneurs themselves.Workers’ compensation costs, health insurance costs, taxes, and a numberof government regulations are all factors which state and federal govern-ments could modify to promote entrepreneurship. Often well-meaningregulations have unanticipated consequences. For instance, high bank-ruptcy exemptions are intended to benefit borrowers, but have been foundto actually hurt borrowers by decreasing lenders’ willingness to providecapital. The relationship found between lower sales taxes and greater entre-preneurship suggests one approach to mitigating this adverse effect wouldbe decreased taxes. Of course, one must take account of the fact that taxrevenues help finance the infrastructure that is essential for the successfuloperation of businesses.


Sixth, and last, there does appear to be consensus in the literature that individualscan learn to become entrepreneurs. Well-developed training programs, withtargeted outreach, particularly to LI and LMI communities, can extend entre-preneurship to a wider population. Unfortunately, data currently reflectsbusinesses located in LI and LMI communities, but tells us little about theactual entrepreneur. Yet, the existence of entrepreneurial firms in a region spursthe growth of more such firms in a cluster effect. Programs such as thosesupported by the Kauffman Foundation should produce greater understandingof entrepreneurship in communities and, hence, increase the likelihood of morenew businesses being established throughout the country.

In summary, more work needs to be done to understand better the deter-minants of entrepreneurial activity. This includes developing bettermicroeconomic models that capture the different tradeoffs associated withspecific policy actions affecting entrepreneurial activity and additional empir-ical analysis to help decide which actions work best to promote such activity.


Authors’ note: The authors are very grateful for the helpful comments and assistance provided by

Donald McCarthy, Triphon Phumiwasana, Tong Li, and Sangeetha Malaiyandi, and to constructive

comments from our discussant, Richard M. Todd.


MSA LMI Share LMI Share LMI Loan LI Share LI Share LI Loanof of Amount Bias: Amount of of Amount Bias:

Population of Loans of Loans Population of Loans Amount ofLoans

Yolo, Calif. 49.1% 41.8% 0.15 31.5% 0.1% 1.00Dutchess County, N.Y. 46.2% 18.8% 0.59 26.0% 8.8% 0.66Vallejo-Fairfield-Napa, Calif. 46.1% 34.8% 0.25 24.7% 1.5% 0.94New York, N.Y. 45.9% 19.2% 0.58 31.0% 4.8% 0.84Oakland, Calif. 45.3% 25.4% 0.44 28.3% 13.5% 0.52Bryan-College Station, Texas 45.1% 16.3% 0.64 29.5% 2.8% 0.90Sacramento, Calif. 45.1% 29.6% 0.34 26.4% 7.2% 0.73San Francisco, Calif. 44.9% 35.6% 0.21 27.2% 16.0% 0.41Albany, Ga. 44.6% 29.9% 0.33 23.0% 15.1% 0.34Tuscaloosa, Ala. 44.2% 18.8% 0.57 23.6% 2.6% 0.89Riverside-San Bernardino, Calif. 43.8% 24.0% 0.45 28.4% 5.7% 0.80Columbus, Ga.-Ala. 43.7% 37.7% 0.14 20.7% 12.0% 0.42Yuba City, Calif. 43.6% 18.0% 0.59 19.5% 0.0% 1.00Springfield, Mass. 43.5% 24.2% 0.44 24.6% 8.5% 0.66Florence, S.C. 43.5% 22.5% 0.48 29.0% 8.1% 0.72Muncie, Ind. 43.4% 21.8% 0.50 19.8% 2.7% 0.87Huntington-Ashland,

W. Va-Ky.-Ohio 43.4% 28.8% 0.34 26.5% 7.6% 0.71Rocky Mount, N.C. 43.4% 8.4% 0.81 21.5% 1.2% 0.94Missoula, Mont. 43.3% 37.2% 0.14 19.6% 0.0% 1.00Auburn-Opelika, Ala. 43.2% 21.0% 0.51 29.6% 7.4% 0.75Odessa-Midland, Texas 43.1% 46.5% -0.08 19.8% 5.6% 0.72New Orleans, La. 43.1% 26.5% 0.38 29.0% 6.3% 0.78Charleston, W. Va. 43.1% 31.6% 0.27 28.0% 15.2% 0.46Houma, La. 43.0% 39.7% 0.08 29.3% 0.0% 1.00Fresno, Calif. 42.9% 27.4% 0.36 19.8% 3.4% 0.83Utica-Rome, N.Y. 42.9% 19.1% 0.55 27.5% 2.9% 0.89Yakima, Wash. 42.9% 34.3% 0.20 18.4% 13.4% 0.27Nassau-Suffolk, N.Y. 42.9% 12.7% 0.70 24.2% 0.1% 1.00Bakersfield, Calif. 42.8% 21.7% 0.49 28.2% 6.5% 0.77Louisville, Ky.-Ind. 42.8% 29.0% 0.32 22.5% 7.0% 0.69Lake Charles, La. 42.7% 21.5% 0.50 29.1% 2.0% 0.93Lewiston-Auburn, Maine 42.7% 20.4% 0.52 26.9% 8.3% 0.69Kalamazoo-Battle Creek, Mich. 42.7% 23.1% 0.46 22.5% 7.3% 0.68Springfield, Mo. 42.6% 15.3% 0.64 17.6% 2.9% 0.84Las Cruces, N.M. 42.6% 29.8% 0.30 25.3% 0.1% 1.00Alexandria, La. 42.6% 22.0% 0.48 25.9% 5.1% 0.80Sarasota-Bradenton, Fla. 42.6% 19.8% 0.53 20.4% 0.6% 0.97Mobile, Ala. 42.5% 12.9% 0.70 28.5% 5.7% 0.80Beaumont-Port Arthur, Texas 42.5% 21.8% 0.49 28.3% 5.6% 0.80Sharon, Pa. 42.5% 15.6% 0.63 17.3% 12.5% 0.28Stockton-Lodi, Calif. 42.5% 33.6% 0.21 23.5% 6.5% 0.72Greensboro/Winston-Salem/

High Point, N.C. 42.5% 18.4% 0.57 21.5% 1.6% 0.93Greenville, N.C. 42.5% 24.2% 0.43 21.5% 4.5% 0.79Corvallis, Ore. 42.4% 23.3% 0.45 23.4% 0.0% 1.00Providence-Fall River-

Warwick, R.I.-Mass. 42.4% 21.7% 0.49 24.5% 4.7% 0.81San Diego, Calif. 42.4% 25.1% 0.41 24.3% 1.9% 0.92Corpus Christi, Texas 42.3% 43.1% -0.02 27.8% 6.1% 0.78Bloomington-Normal, Ill. 42.3% 24.3% 0.43 24.7% 8.5% 0.65

Appendix 1Loans to Businesses in Low- and Moderate-Income

Communities: LI and LMI Shares of Population, Businesses’Share of Loans, and LI and LMI Loan Biases


Bangor, Maine 42.2% 14.1% 0.67 27.5% 0.0% 1.00Tallahassee, Fla. 42.2% 26.9% 0.36 28.3% 3.4% 0.88Merced, Calif. 42.1% 24.5% 0.42 26.1% 0.0% 1.00Dover, Del. 42.1% 0.2% 0.99 21.5% 0.0% 1.00Topeka, Kan. 42.1% 37.9% 0.10 21.0% 13.8% 0.34Amarillo, Texas 42.0% 25.3% 0.40 26.0% 8.8% 0.66St. Joseph, Mo. 42.0% 25.9% 0.38 26.3% 19.0% 0.28Wheeling, W. Va.-Ohio 42.0% 23.9% 0.43 24.6% 2.3% 0.91Orange County, Calif. 41.9% 34.8% 0.17 22.1% 4.6% 0.79Los Angeles-Long Beach, Calif. 41.9% 31.5% 0.25 29.7% 12.5% 0.58Decatur, Ala. 41.8% 11.1% 0.73 26.9% 2.0% 0.92Dallas, Texas 41.7% 21.4% 0.49 23.0% 4.6% 0.80Lafayette, La. 41.6% 12.3% 0.71 26.8% 3.7% 0.86Dayton-Springfield, Ohio 41.6% 27.2% 0.35 21.2% 10.1% 0.52Pine Bluff, Ark. 41.5% 22.9% 0.45 24.7% 9.3% 0.62Columbia, S.C. 41.5% 30.6% 0.26 21.2% 8.7% 0.59Duluth-Superior, Minn.-Wis. 41.5% 34.4% 0.17 26.8% 21.3% 0.21Grand Forks, N.D.-Minn. 41.5% 7.6% 0.82 25.0% 0.0% 1.00Daytona Beach, Fla. 41.3% 20.5% 0.50 24.5% 4.6% 0.81Hattiesburg, Miss. 41.3% 12.1% 0.71 24.5% 10.7% 0.57Cumberland, Md.-W. Va. 41.2% 14.6% 0.65 23.8% 4.7% 0.80McAllen-Edinburg-

Mission, Texas 41.2% 20.6% 0.50 19.1% 0.0% 1.00State College, Pa. 41.2% 18.2% 0.56 27.6% 7.9% 0.71Binghamton, N.Y. 41.2% 25.7% 0.38 26.2% 6.2% 0.77Gainesville, Fla. 41.2% 35.1% 0.15 25.9% 3.6% 0.86Asheville, N.C. 41.1% 30.3% 0.26 25.2% 1.0% 0.96Youngstown-Warren, Ohio 41.1% 13.9% 0.66 26.0% 6.5% 0.75Lincoln, Neb. 41.1% 19.4% 0.53 19.7% 4.0% 0.80Cleveland-Lorain-Elyria, Ohio 41.1% 16.1% 0.61 21.5% 7.5% 0.65Panama City, Fla. 41.0% 18.6% 0.55 25.5% 9.3% 0.64Ventura, Calif. 41.0% 33.2% 0.19 26.6% 3.9% 0.85San Luis Obispo-Atasc.-

Paso Robles, Calif. 41.0% 12.2% 0.70 21.7% 0.0% 1.00Erie, Pa. 40.9% 22.2% 0.46 25.5% 13.2% 0.48Lakeland-Winter Haven, Fla. 40.8% 24.6% 0.40 24.4% 3.7% 0.85Raleigh-Durham-

Chapel Hill, N.C. 40.8% 17.2% 0.58 23.3% 1.5% 0.94Johnstown, Pa. 40.7% 7.8% 0.81 22.0% 0.2% 0.99Elmira, N.Y. 40.7% 30.3% 0.26 25.8% 8.6% 0.67Salinas, Calif. 40.7% 26.7% 0.34 22.5% 9.6% 0.57Knoxville, Tenn. 40.7% 24.3% 0.40 25.8% 5.9% 0.77Owensboro, Ky. 40.6% 39.4% 0.03 26.7% 18.3% 0.31Pocatello, Idaho 40.6% 27.3% 0.33 25.9% 0.0% 1.00Orlando, Fla. 40.6% 22.3% 0.45 19.4% 3.7% 0.81Grand Junction, Colo. 40.5% 19.7% 0.52 24.6% 0.0% 1.00Austin-San Marcos, Texas 40.5% 16.8% 0.59 22.9% 4.0% 0.83Peoria-Pekin, Ill. 40.5% 20.8% 0.49 20.5% 4.7% 0.77El Paso, Texas 40.4% 37.7% 0.07 22.9% 15.6% 0.32Danville, Va. 40.4% 20.3% 0.50 22.8% 7.0% 0.69Medford-Ashland, Ore. 40.4% 16.5% 0.59 25.0% 11.4% 0.54Billings, Mont. 40.4% 23.3% 0.42 25.9% 10.4% 0.60Huntsville, Ala. 40.4% 18.1% 0.55 21.8% 5.5% 0.75Clarksville-Hopkinsville,

Tenn.-Ky. 40.3% 27.1% 0.33 23.1% 4.1% 0.82

MSA LMI Share of LMI Share LMI Loan LI Share LI Share LI LoanPopulation of Amount Bias: Amount of of Bias:

of Loans of Loans Population Amount of Amount ofLoans Loans

Appendix 1 (cont.)


Jackson, Tenn. 40.3% 26.2% 0.35 26.5% 18.5% 0.30Gadsden, Ala 40.3% 26.4% 0.34 23.5% 0.0% 1.00Pittsburgh, Pa. 40.3% 19.7% 0.51 25.7% 2.0% 0.92Tucson, Ariz. 40.2% 30.9% 0.23 24.9% 5.0% 0.80Minneapolis-

St. Paul, Minn.-Wis. 40.2% 12.3% 0.69 18.4% 3.8% 0.79Naples, Fla. 40.2% 3.0% 0.93 21.6% 0.4% 0.98Oklahoma City, Okla. 40.2% 27.6% 0.31 25.1% 4.7% 0.81Albany-Schenectady-

Troy, N.Y. 40.2% 19.8% 0.51 20.7% 7.3% 0.65Jacksonville, Fla. 40.2% 22.0% 0.45 19.9% 4.7% 0.76Sherman-Denison, Texas 40.2% 35.1% 0.13 25.1% 16.2% 0.36Columbia, Mo. 40.1% 29.9% 0.26 25.1% 18.2% 0.28Appleton-Oshkosh-

Neenah, Wis. 40.1% 7.9% 0.80 21.1% 2.1% 0.90Wichita, Kan. 40.1% 29.0% 0.28 19.6% 7.4% 0.62Fort Collins-Loveland, Colo. 40.1% 28.3% 0.29 22.9% 13.0% 0.43Lawrence, Kan. 40.0% 15.5% 0.61 25.8% 0.7% 0.97Champaign-Urbana, Ill. 40.0% 33.3% 0.17 26.6% 9.3% 0.65St. Cloud, Minn. 40.0% 6.7% 0.83 19.6% 6.1% 0.69Eugene-Springfield, Ore. 39.9% 35.1% 0.12 25.6% 11.6% 0.55Madison, Wis. 39.9% 29.9% 0.25 21.9% 5.5% 0.75Anniston, Ala. 39.9% 18.4% 0.54 23.7% 1.6% 0.93Norfolk-Virginia Beach-

Newport News, Va.-N.C. 39.9% 18.4% 0.54 19.4% 4.5% 0.77Biloxi-Gulfport-

Pascagoula, Miss. 39.9% 10.8% 0.73 24.9% 4.2% 0.83Waterloo-Cedar Falls, Iowa 39.9% 27.9% 0.30 24.2% 9.0% 0.63Chattanooga, Tenn.-Ga. 39.9% 19.0% 0.52 25.0% 14.7% 0.41Texarkana, Texas-Ark. 39.8% 19.9% 0.50 23.7% 1.9% 0.92Allentown-Bethlehem-

Easton, Pa. 39.8% 10.4% 0.74 20.3% 2.3% 0.89Las Vegas, Nev.-Ariz. 39.8% 20.0% 0.50 19.1% 2.2% 0.89Pensacola, Fla. 39.8% 24.2% 0.39 24.4% 10.2% 0.58Tyler, Texas 39.7% 18.4% 0.54 25.3% 12.9% 0.49Boise City, Idaho 39.7% 23.2% 0.42 18.4% 11.8% 0.36Mansfield, Ohio 39.7% 25.7% 0.35 24.3% 4.9% 0.80Chico-Paradise, Calif. 39.6% 20.4% 0.48 22.4% 0.0% 1.00Spokane, Wash. 39.6% 49.1% -0.24 24.5% 20.2% 0.18Harrisburg-Lebanon-

Carlisle, Pa. 39.6% 11.8% 0.70 18.9% 1.4% 0.93Punta Gorda, Fla. 39.6% 6.3% 0.84 22.9% 0.0% 1.00New London-

Norwich, Conn.-R.I. 39.6% 15.4% 0.61 22.2% 3.1% 0.86Baton Rouge, La. 39.6% 13.6% 0.66 26.3% 4.5% 0.83Decatur, Ill. 39.6% 46.4% -0.17 24.8% 25.2% -0.02Lynchburg, Va. 39.6% 12.8% 0.68 24.7% 4.6% 0.81Buffalo-Niagara Falls, N.Y. 39.5% 23.3% 0.41 25.7% 6.6% 0.74Augusta-Aiken, Ga.-S.C. 39.5% 21.4% 0.46 25.6% 5.9% 0.77Myrtle Beach, S.C. 39.5% 12.1% 0.69 22.5% 0.7% 0.97Casper, Wyo. 39.5% 29.3% 0.26 24.5% 18.5% 0.24Springfield, Ill. 39.5% 25.1% 0.36 19.4% 10.1% 0.48Johnson City-Kingsport-

Bristol, Tenn.-Va. 39.5% 16.7% 0.58 22.6% 2.2% 0.90Montgomery, Ala. 39.4% 25.2% 0.36 25.4% 14.1% 0.45



Appendix 1 (cont.)


Rochester, N.Y. 39.4% 18.4% 0.53 20.5% 5.5% 0.73Lubbock, Texas 39.4% 35.6% 0.10 22.5% 16.0% 0.29Monroe, La. 39.3% 22.5% 0.43 24.6% 13.9% 0.44Flagstaff, Utah-Ariz. 39.3% 17.4% 0.56 24.3% 4.7% 0.81Rapid City, S.D. 39.3% 35.0% 0.11 22.5% 0.0% 1.00Macon, Ga. 39.3% 49.9% -0.27 25.8% 14.0% 0.46Tampa-St. Petersburg-

Clearwater, Fla. 39.3% 21.6% 0.45 23.6% 0.8% 0.96Kokomo, Ind. 39.3% 25.5% 0.35 19.9% 9.7% 0.51Jackson, Mich. 39.2% 24.2% 0.38 20.5% 6.1% 0.70Anchorage, Alaska 39.2% 29.6% 0.25 23.2% 7.4% 0.68Salt Lake City-Ogden, Utah 39.2% 37.2% 0.05 20.4% 8.0% 0.61Fort Wayne, Ind. 39.2% 17.1% 0.56 18.5% 7.0% 0.62Miami, Fla. 39.2% 28.4% 0.27 25.5% 9.3% 0.64Athens, Ga. 39.2% 22.3% 0.43 24.4% 12.3% 0.50San Jose, Calif. 39.2% 34.8% 0.11 24.0% 8.5% 0.65Savannah, Ga. 39.0% 17.7% 0.55 24.9% 4.9% 0.80Brownsville-Harlingen-

San Benito, Texas 39.0% 21.5% 0.45 29.6% 1.0% 0.97Fayetteville-Springdale-

Rogers, Ark. 38.9% 8.9% 0.77 23.4% 0.0% 1.00Nashville, Tenn. 38.9% 27.6% 0.29 19.2% 5.3% 0.72St. Louis, Mo.-Ill. 38.8% 15.1% 0.61 19.8% 4.8% 0.76Killeen-Temple, Texas 38.8% 21.4% 0.45 21.6% 0.7% 0.97Lafayette, Ind. 38.8% 40.6% -0.05 24.2% 8.3% 0.66Florence, Ala. 38.8% 16.0% 0.59 22.7% 2.2% 0.90Benton Harbor, Mich. 38.8% 17.1% 0.56 25.0% 8.3% 0.67Lansing-East Lansing, Mich. 38.7% 23.3% 0.40 19.9% 10.9% 0.45Shreveport-Bossier City, La. 38.7% 36.7% 0.05 22.8% 20.1% 0.12Victoria, Texas 38.7% 38.2% 0.01 23.7% 0.9% 0.96Greenville-Spartanburg-

Anderson, S.C. 38.7% 15.5% 0.60 24.2% 5.2% 0.79Jackson, Miss. 38.6% 22.1% 0.43 25.0% 13.0% 0.48Jonesboro, Ark. 38.6% 9.0% 0.77 22.0% 0.0% 1.00West Palm Beach-

Boca Raton, Fla. 38.6% 19.9% 0.48 25.9% 2.7% 0.90Pittsfield, Mass. 38.6% 31.0% 0.20 24.9% 13.8% 0.45Wilmington, N.C. 38.6% 15.3% 0.60 24.2% 8.0% 0.67Tulsa, Okla. 38.5% 24.0% 0.38 23.8% 2.0% 0.92Fargo-Moorhead, N.D.-Minn. 38.5% 46.6% -0.21 23.7% 0.0% 1.00Cincinnati, Ohio-Ky.-Ind. 38.5% 17.8% 0.54 19.8% 6.8% 0.66Charlottesville, Va. 38.5% 23.9% 0.38 20.3% 2.5% 0.87Reading, Pa. 38.5% 12.1% 0.69 19.3% 3.7% 0.81Sioux Falls, S.D. 38.4% 37.6% 0.02 17.0% 0.0% 1.00Bloomington, Ind. 38.4% 41.4% -0.08 22.8% 0.6% 0.98Columbus, Ohio 38.4% 22.9% 0.40 19.0% 10.0% 0.47Lexington, Ky. 38.3% 28.8% 0.25 24.3% 3.9% 0.84Hickory-Morganton, N.C. 38.2% 12.1% 0.68 22.6% 0.0% 1.00Seattle-Bellevue-Everett, Wash. 38.2% 26.5% 0.31 20.8% 2.3% 0.89Fayetteville, N.C. 38.2% 20.5% 0.46 23.1% 8.0% 0.65Birmingham, Ala. 38.1% 25.7% 0.33 24.7% 9.4% 0.62Richland-Kennewick-

Pasco, Wash. 38.1% 26.6% 0.30 19.3% 0.0% 1.00Phoenix-Mesa, Ariz. 38.0% 30.0% 0.21 18.3% 8.5% 0.53



Appendix 1 (cont.)


Hartford, Conn. 37.9% 15.6% 0.59 22.2% 4.0% 0.82Omaha, Neb.-Iowa 37.9% 16.3% 0.57 17.9% 3.6% 0.80Evansville-Henderson, Ind.-Ky. 37.9% 22.9% 0.40 23.7% 1.2% 0.95Fort Smith, Ark.-Okla. 37.9% 18.1% 0.52 21.2% 4.3% 0.80Dothan, Ala. 37.7% 28.4% 0.25 22.5% 2.2% 0.90Rockford, Ill. 37.7% 16.6% 0.56 18.8% 6.7% 0.65Portland, Maine 37.7% 25.2% 0.33 18.9% 5.3% 0.72Waco, Texas 37.7% 24.7% 0.34 22.6% 4.9% 0.78Honolulu, Hawaii 37.7% 29.7% 0.21 21.3% 2.5% 0.88Santa Fe, N.M. 37.6% 40.1% -0.06 25.5% 0.0% 1.00Sioux City, Iowa-Neb. 37.6% 38.6% -0.02 22.2% 27.5% -0.24Great Falls, Mont. 37.6% 32.3% 0.14 20.2% 6.6% 0.67Toledo, Ohio 37.6% 19.2% 0.49 23.9% 9.9% 0.58Albuquerque, N.M. 37.6% 30.1% 0.20 23.3% 2.5% 0.89Syracuse, N.Y. 37.6% 22.2% 0.41 24.0% 8.5% 0.65Charleston-

North Charleston, S.C. 37.6% 18.3% 0.51 23.7% 4.1% 0.83Charlotte-Gastonia-

Rock Hill, N.C.-S.C. 37.6% 24.5% 0.35 23.7% 8.5% 0.64Ocala, Fla. 37.6% 6.7% 0.82 19.1% 0.8% 0.96

Saginaw-Bay City-Midland, Mich. 37.6% 24.1% 0.36 23.2% 8.2% 0.65Glens Falls, N.Y. 37.5% 6.4% 0.83 22.2% 0.0% 1.00Indianapolis, Ind. 37.5% 20.5% 0.45 24.7% 6.2% 0.75Little Rock-North

Little Rock, Ark. 37.5% 20.9% 0.44 23.2% 1.6% 0.93Pueblo, Colo. 37.5% 24.6% 0.34 20.4% 10.8% 0.47Roanoke, Va. 37.4% 19.5% 0.48 22.7% 9.8% 0.57Elkhart-Goshen, Ind. 37.4% 8.9% 0.76 17.1% 2.9% 0.83Sumter, S.C. 37.4% 42.6% -0.14 21.1% 0.0% 1.00Altoona, Pa. 37.4% 15.2% 0.59 21.0% 10.1% 0.52Lima, Ohio 37.3% 15.3% 0.59 22.8% 7.3% 0.68Rochester, Minn. 37.3% 4.9% 0.87 19.9% 0.0% 1.00Memphis, Tenn.-Ark.-Miss. 37.3% 14.6% 0.61 24.1% 5.7% 0.76Milwaukee-Waukesha, Wis. 37.3% 11.7% 0.69 25.0% 5.0% 0.80San Angelo, Texas 37.3% 33.3% 0.11 20.6% 20.6% 0.00San Antonio, Texas 37.3% 21.4% 0.43 20.6% 4.2% 0.79Barnstable-Yarmouth, Mass. 37.2% 13.7% 0.63 24.5% 0.0% 1.00Yuma, Ariz. 37.1% 29.0% 0.22 18.8% 0.0% 1.00Joplin, Mo. 37.1% 16.7% 0.55 19.8% 0.0% 1.00Iowa City, Iowa 37.1% 25.4% 0.32 24.3% 9.9% 0.59Santa Barbara-Santa

Maria-Lompoc, Calif. 37.1% 45.3% -0.22 24.7% 0.1% 0.99Santa Cruz-

Watsonville, Calif. 37.1% 25.2% 0.32 24.7% 0.0% 1.00Santa Rosa, Calif. 37.1% 18.2% 0.51 24.7% 0.0% 1.00Bellingham, Wash. 37.1% 20.2% 0.45 23.2% 0.0% 1.00Jamestown, N.Y. 36.9% 18.8% 0.49 20.0% 8.2% 0.59Janesville-Beloit, Wis. 36.9% 14.5% 0.61 23.4% 2.9% 0.88Modesto, Calif. 36.8% 21.2% 0.42 23.2% 0.7% 0.97Atlanta, Ga. 36.8% 18.3% 0.50 20.1% 2.9% 0.86Bismarck, N.D. 36.8% 26.6% 0.28 22.2% 0.0% 1.00Reno, Nev. 36.7% 40.0% -0.09 24.1% 0.4% 0.99York, Pa. 36.7% 13.5% 0.63 23.4% 4.9% 0.79



Appendix 1 (cont.)




Appendix 1 (cont.)

Scranton-Wilkes-Barre-Hazleton, Pa. 36.7% 12.0% 0.67 20.7% 3.2% 0.85

Wausau, Wis. 36.7% 7.2% 0.80 23.6% 0.0% 1.00Redding, Calif. 36.7% 10.6% 0.71 20.1% 0.0% 1.00Enid, Okla. 36.7% 21.2% 0.42 20.1% 0.0% 1.00Eau Claire, Wis. 36.7% 9.3% 0.75 21.3% 0.0% 1.00Kansas City, Mo.-Kan. 36.6% 20.4% 0.44 23.6% 5.0% 0.79Visalia-Tulare-

Porterville, Calif. 36.6% 18.3% 0.50 20.1% 0.0% 1.00Cheyenne, Wyo. 36.6% 42.6% -0.17 21.2% 0.0% 1.00La Crosse, Wis.-Minn. 36.6% 53.2% -0.45 21.6% 16.3% 0.24Portland-

Vancouver, Ore.-Wash. 36.5% 20.7% 0.43 23.6% 1.3% 0.94Canton-Massillon, Ohio 36.5% 13.3% 0.64 22.1% 4.0% 0.82Richmond-Petersburg, Va. 36.5% 24.3% 0.33 23.8% 8.7% 0.63Longview-Marshall, Texas 36.4% 26.8% 0.26 20.9% 6.4% 0.69Terre Haute, Ind. 36.4% 29.6% 0.19 19.9% 4.7% 0.76Parkersburg-Marietta,

W. Va.-Ohio 36.4% 26.5% 0.27 19.3% 0.0% 1.00Laredo, Texas 36.3% 20.3% 0.44 27.0% 0.0% 1.00Davenport-Moline-

Rock Island, Iowa-Ill. 36.2% 27.8% 0.23 21.8% 6.7% 0.69Melbourne-Titusville-

Palm Bay, Fla. 36.2% 30.8% 0.15 21.4% 0.4% 0.98Grand Rapids-

Muskegon- Holland, Mich. 36.1% 21.8% 0.40 23.6% 7.9% 0.66South Bend, Ind. 36.1% 25.6% 0.29 21.7% 7.8% 0.64Green Bay, Wis. 36.0% 19.0% 0.47 23.2% 8.5% 0.63Lawton, Okla. 35.8% 47.4% -0.32 20.4% 3.5% 0.83Cedar Rapids, Iowa 35.8% 20.1% 0.44 22.9% 9.4% 0.59Des Moines, Iowa 35.8% 54.2% -0.51 22.9% 12.2% 0.47Abilene, Texas 35.8% 29.3% 0.18 19.1% 0.2% 0.99Williamsport, Pa. 35.8% 14.9% 0.58 18.7% 0.0% 1.00Lancaster, Pa. 35.7% 7.2% 0.80 22.6% 0.6% 0.97Dubuque, Iowa 35.7% 23.6% 0.34 20.1% 17.9% 0.11Fort Lauderdale, Fla. 35.5% 21.5% 0.39 20.0% 7.4% 0.63Fort Myers-Cape Coral, Fla. 35.5% 10.4% 0.71 20.0% 0.1% 0.99Fort Pierce-

Port St. Lucie, Fla. 35.5% 22.3% 0.37 20.0% 3.0% 0.85Fort Walton Beach, Fla. 35.5% 11.4% 0.68 20.0% 0.0% 1.00Provo-Orem, Utah 35.4% 7.7% 0.78 22.2% 1.7% 0.92Burlington, Vt. 35.4% 16.7% 0.53 23.1% 8.4% 0.64Sheboygan, Wis. 35.3% 7.5% 0.79 22.1% 0.0% 1.00Colorado Springs, Colo. 35.2% 39.8% -0.13 22.2% 3.0% 0.87Goldsboro, N.C. 35.1% 15.9% 0.55 19.2% 1.0% 0.95Wichita Falls, Texas 34.6% 36.5% -0.05 18.7% 23.6% -0.26Jacksonville, N.C. 33.6% 8.1% 0.76 15.8% 0.0% 1.00

Source: Milken Institute, based on U.S. Census 2000, CRA 2001, and FDIC 2001


Establishment: A single physical location where business is conducted orwhere services or industrial operations are performed.

Employment: Paid employment consists of full- and part-time employ-ees, including salaried officers and executives of corporations, who wereon the payroll in the pay period including March 12. Included areemployees on sick leave, holidays, and vacations; not included are propri-etors and partners of unincorporated businesses.

Annual payroll: Total annual payroll includes all forms of compensation,such as salaries, wages, commissions, bonuses, vacation allowances, sick-leave pay, and the value of payments in-kind (for example, free meals andlodgings) paid during the year to all employees.

Receipts: (Net taxes) the revenue for goods produced, goods distributed,or services provided, including revenue earned from premiums, commis-sions and fees, rents, interest, dividends, and royalties. Receipts excludeall revenue collected for local, state, and federal taxes. Receipts areacquired from the Economic Census data for establishments in industriesthat are in-scope to the Economic Census; receipts are acquired from IRStax data for single-establishment businesses in industries that are out-of-scope to the Economic Census; payroll-to-receipts ratios are used toestimate receipts for multiestablishment businesses in industries that areout-of-scope to the Economic Census. Statistics of U.S. Businesses hasreceipts for 1997 only.

Enterprise: A business organization consisting of one or more domes-tic establishments that were specified under common ownership orcontrol. The enterprise and the establishment are the same for single-establishment firms. Each multiestablishment company forms oneenterprise—the enterprise employment and annual payroll are summedfrom the associated establishments.

Appendix 2Explanation of Terms Used in Entrepreneurship Studies

(See http://www.censv/csd/susb/defterm.html )


Firm: A business organization consisting of one or more domestic estab-lishments in the same state and industry that were specified undercommon ownership or control. The firm and the establishment are thesame for single-establishment firms. For each multiestablishment firm,establishments in the same industry within a state will be counted as onefirm—the firm employment and annual payroll are summed from theassociated establishments.

Enterprise size: Enterprise size designations are determined by thesummed employment of all associated establishments. The enterprise sizegroup “zero” includes enterprises for which no associated establishmentsreported paid employees in the mid-March pay period but paid employ-ees at some time during the year.

Establishment births: Establishments that have zero employment in thefirst quarter of the initial year and positive employment in the firstquarter of the subsequent year.

Establishment deaths: Establishments that have positive employment inthe first quarter of the initial year and zero employment in the firstquarter of the subsequent year.

Establishment expansions: Establishments that have positive first-quarter employment in both the initial and subsequent years and increaseemployment during the time period between the first quarter of the initialyear and the first quarter of the subsequent year.

Establishment contractions: Establishments that have positive first-quarter employment in both the initial and subsequent years and decreaseemployment during the time period between the first quarter of the initialyear and the first quarter of the subsequent year.

Metropolitan statistical area (MSA): An integrated economic and socialunit with a large population nucleus. Each MSA consists of one or morecounties or statistically equivalent areas meeting published standards ofpopulation and metropolitan character; in the six New England states

Appendix 2 (cont.)


(Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island,and Vermont), cities and towns (rather than counties) are used as thecomponent geographic units.

Legal Form of Organization (LFO):a. Corporations: Enterprises legally incorporated under state laws.b. Partnerships: Unincorporated enterprises owned by two or more

persons having financial interest in the business. c. Sole proprietorships: Unincorporated enterprises owned by one person. d. Nonprofit organizations: Enterprises with nonprofit status (tax-exempt). e. Other (associations, trust, joint ventures, estates, etc.): Enterprises

that are formed by other legal form of organization. f. Unknown: Enterprises with unknown legal form of organization.

Appendix 2 (cont.)

Endnotes1Firms can contain multiple establishments that are defined by the U.S. Census

Bureau as a “single physical location at which business is conducted.” See Appendix 2for definitions of these and other terms frequently used in studies of entrepreneurship.

2However, unlike Table 1, this table goes beyond simply the number of firmswith paid employees. As Davis, Haltiwanger, Jarmin, Krizan, Miranda, and Nucci(2005) carefully explain, the data sources for the two tables are quite different.Table 2 includes those firms in Table 1, but also adds all sole proprietorshipswithout employees and other corporations, partnerships, and other nonemployerbusiness entities, of which there were more than 17,000 in 2002.

3According to the U.S. Census Bureau, LI individuals in a given MSA are indi-viduals with annual income of 50 percent or less of that MSA’s median income,and LMI individuals are those with incomes that are 80 percent or less of themedian income in that MSA.

4LI communities consist of census tracts where the median family income of thatcensus tract is less than 50 percent of the MSA median family income. The LMIcategory consists of census tracts where the median family income of the censustract is less than 80 percent of the MSA median family income.

5However, the CRA data are not without limitations, including the fact thatloans may be made to a firm with an address in an LMI community, but theproceeds are used to fund operations—in the case of a firm with multiple establish-ments—outside LMI communities.

6However, Lucas (1978) argues that smaller businesses have less managerialtalent, and, therefore, one would expect to find that smaller businesses likely are tobe located in regions with lower levels of income per capita.

7For further discussion of credit enhancement as a potential alleviator of the capitalaccess “gaps” facing LMI businesses, see Yago, Zeidman, and Schmidt (2003).

8For further discussion of the role of securitization, see Yago, Zeidman, andSchmidt (2003).


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Documents

Stumbling Blocks to Entrepreneurship in Low- and …...Stumbling Blocks to Entrepreneurship in Low- and Moderate-Income Communities 91 those policies that best support entrepreneurial