Journal of Rural and Community Development
ISSN: 1712-8277 © Journal of Rural and Community Development
www.jrcd.ca
Small Business Lending and Economic Well-Being in
Texas Counties: A Test with Community
Reinvestment Act Data
F. CarsonMencken
Baylor University
Waco, Texas, USA
[email protected]
Charles M. Tolbert
Baylor University
Waco, Texas, USA
[email protected]
Abstract
Utilizing the Community Capitals framework we examine the impact of Community
Reinvestment Act (CRA) reported small business lending on the economic wellbeing
of Texas counties in 1999–2000. We combine data from multiple data sources,
including the County Federal Financial Institutions Examination Council (FFIEC)
annual county Aggregate and Disclosure data—collected under directive of the 1977
Community Reinvestment Act—and use GeoDa to model the impact of small
business lending in each Texas county from 1996–1999 on the 1999 county poverty
rate, median family income, Gini income inequality coefficient, 2000 per capita
income and 2000 nonfarm earnings per worker. Controlling for other dimensions of
the Community Capitals Framework, the results show positive effects of small
business lending on two income measures—per worker nonfarm earnings, and per
capita income. Furthermore, we find the small business lending from 1996–1999
reduced poverty and income inequality in the most rural Texas counties.
Implications for theory, policy, and research are discussed.
Keywords: community development; small business; financing; rural
1.0 Introduction
Over the last 40 years the financial sector in the United States has gone through a
major transformation from local and regional banks to multistate firms. The
restructuring is a result of technological improvements in banking, changes in
interstate banking laws, and increased competition for market concentration (Berger,
Demsetz, & Strahan, 1999; Cetorelli & Strahan 2006; Hughes, Lang, Mester, &
Moon, 1999; Wheelock & Wilson, 2000). One concerned raised by this
transformation is that small businesses in rural locations may lose access to lending
(Elyasiani & Goldberg, 2004; DeYoung, Glennon, & Nigro, 2008; Shaffer &
Collender, 2008). In this paper we examine the implications this financial sector
transformation presents for local economies and their small businesses. More
specifically, we test the impact of small business lending on county level measures
of economic development, with a focus on the impact in rural counties. We use the
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Journal of Rural and Community Development, 13, 1(2018) 47–66 48
254 counties in the state of Texas (USA) as a case study. We begin with a review of
the Community Capitals framework, which informs our analysis.
2.0 Community Capitals
According to the Community Capitals framework (Flora & Flora, 2013), rural
communities with healthy levels of built capital, human capital, social capital,
financial capital, natural amenities and cultural capital are able to identify and
resolve local problems that resulted from the decades-long macro-level changes in
farming (Ginder, Stone, & Otto, 1985; Lobao & Meyer, 2001) agriculture
commodification (Guptill & Welsh, 2014; Lyson, 2004), manufacturing (Fitchen,
1991; Slack, 2014) and retail trade (Vias, 2004). Community-based research has
documented the impact of many of the various forms of community capital, such as
the built environment, local amenities, and social capital (c.f. Agnitsch, Flora, &
Ryan, 2006; Besser & Miller, 2013a, 2013b; Besser, Miller, & Malik, 2012; Coffè,
2009; Lyson & Tolbert, 1996; Portes, 1998; Tolbert, Irwin, Lyson, & Nucci, 2002).
Research on banking and finance is critical to understanding the effects of global
and national processes on nonmetropolitan communities. Sociologists have not done
as much research on this form of capital (Flora & Flora 2013).1 That which is
emerging indicates a positive relationship between local bank ownership and
conventional business loans to small businesses in nonmetropolitan economies
(Mencken & Tolbert, In Press; Mencken & Tolbert, 2016). Other research has
concluded that the consolidation of the banking industry has meant, on average,
fewer locally owned banks in nonmetropolitan America (Tolbert, Mencken, Riggs,
& Li, 2014), and a greater reliance on multi-market (i.e. absentee-owned) banks for
business lending (see Collender & Shaffer 2009).
We focus on one aspect of community capital- small business financial lending. In
1977, the 95th United States Congress enacted the Community Reinvestment Act to
combat ‘redlining,’ or the practice of bank discrimination against lending in lower
income neighborhoods (Fishbein, 1992; Friedman & Squires, 2005; Squires, 2011).
Over time the CRA was recognized as an effective tool to bring economic
development through an expansion of CRA scoring to include loans to small
businesses and small business government loan programs (Abromowitz, 1993). It is
the primary federal regulatory action to bring needed credit to underserved
communities, urban and rural. Two responsibilities of the CRA regulators are to
score lending institutions on their practices of extending credit to small businesses
and to government business loan programs—such as the Small Business
Association. Over the last 25 years the CRA has been used to increase financial
capital in local communities, especially those in historically underserved areas. We
use the CRA data on small business lending in Texas counties in an attempt to
understand how small business lending affects traditional measures of place wellbeing
from the Community Capitals framework.
Previous findings from research informed by the Community Capital framework—
broadly defined—indicate that communities with a greater proportion of locallyowned
and locally-oriented businesses are more civically engaged and have higher
levels of economic well-being—less poverty, inequality, crime, chronic
unemployment, community health issues—than those communities in which
1 For exceptions see Green (1987; 1986; 1984) Bird and Sapp (2004), and in Australia, see Shaffer
and Collender (2008).
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Journal of Rural and Community Development, 13, 1(2018) 47–66 49
employment is concentrated in a few absentee-owned firms (see Blanchard, Tolbert,
& Mencken 2012; Irwin, Tolbert, & Lyson, 1999; Lee & Berthelot, 2010; Lee &
Thomas, 2010; Lyson, Torres, & Welsh, 2001; Mencken, Bader, & Polson, 2006;
Tolbert et al. 2002; Tolbert, Lyson, & Irwin, 1998;). The local entrepreneur and
community leader are central agents of development in their communities. Local
business owners who depend on local clients–customers for their livelihood will take
a greater interest in the civic welfare of their communities. By working with local
leaders to plan carefully the use of space—shops, cafes, services—, local
entrepreneurs help to save downtowns of rural communities, thus keeping them from
becoming a blight of shuttered buildings, replaced by big-box retailers along the
highway bypass (Hall & Porterfield, 2001).
Local business owners are the foundation of the civically-engaged, independent
middle class. Their entrepreneurial nature means that they are likely to be effective
leaders and facilitators of community integration. The local entrepreneurs become
important agents for organizing and managing local civic engagement (Blanchard et
al., 2012; Blanchard & Matthews, 2006). In contrast, managers and professionals
who are employed locally by absentee-owned firms are more likely to advocate for
corporate interests over local interests. Those corporate workers who rotate
geographical assignments every three to five years are not likely to invest in local
community issues when they will be moving on in a few years—unless these
issues directly affect their employer. Yet the Community Capitals framework
provides that lack of financial capital for local entrepreneurs will limit the
abilities of communities to provide self-direction.
3.0 Small Business Financing
According to the Survey of Business Owners, seventy-eight percent of all business
start-ups in the United States are supported by the assets of the individual(s) starting
the company (Mencken & Tolbert, 2016). Many small-business ventures also
involve significant asset investment from family and friends (Avery, Bostic, &
Samolyk, 1998; Loscocco & Robinson, 1991). Qualitative interviews with small
business owners in rural Texas (see Tolbert et al., 2014) reveal cases of self-financed
businesses that did not survive more than a year. One young woman interviewed in
fall 2012 had convinced her father to cash in part of his 401K plan to finance her
specialty cake baking business after being turned down by banks. Another person
interviewed with extensive experience owning small businesses opened a
restaurant–events venue with personal savings. It closed in 12 months.
This raises an important question: does the source of small business finance have
long-term implications for businesses and the communities in which they are
embedded? The answer appears to be ‘yes.’ Small business start-ups that receive
bank loans are more likely to be ‘fully capitalized.’ Full capitalization allows
businesses to be more flexible, to weather downturns in sales, and to diversify
(Bolton & Rosenthal, 2005; Fairlie & Robb, 2007; Robb & Fairlie, 2007). Those
small-businesses that are financed through bank loans have been found to have, on
average, higher gross sales (Bird & Sapp, 2004). Bank loans are essentially required
for businesses to be successful in more capital-intensive industries, such as
construction, transportation, or light manufacturing (Loscocco & Robinson, 1991: p. 524).
Those industries which lack ‘hard assets’ such as plant equipment lack the physical
collateral banks prefer to back business loans. Those start-ups that are undercapitalized, on
the other hand, have much higher rates of failure (Avery et al., 1998; Bates, 2005).
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 50
4.0 Restructuring of the Finance Industry
Complicating matters for small businesses and creating significant challenges for
local nonmetropolitan communities is the ongoing restructuring of the financial
services sector, which has eliminated many locally owned banks. Current estimates
put the percentage of locally owned financial services at no greater than 25% for any
given county in the nation (see Tolbert et al., 2014). The banking industry has been
one of the most regulated industries in the United States. The McFadden Act of 1927
and the 1956 Bank Holding Company Act put severe restrictions on interstate banking
(Omarova & Tahyar, 2011).2 The latter was intended to limit the spatial expansion of large
banking groups and their monopolization of local credit markets. Despite these
regulations, since 1976 the financial sector has consolidated into fewer and fewer firms.
This process was accelerated by the passage and implementation of the 1994 Riegle-
Neal Interstate Banking Act, which led to a flurry of mergers and acquisitions
(Berger et al., 1999; Cetorelli & Strahan, 2006). This consolidation was followed by
a proliferation of establishments at the local level, many of which were former
independent and regional banks that serviced local businesses (Berger & Black,
2011; Berger & Udell, 1996; Boot, 2011; Collender & Shafer, 2003; 2009; Devaney
& Weber, 1995) 3 In 2014, over half of all branch establishments in the United States
were owned by a bank or bank holding company in another state. The consolidation
is also reflected in the deposits controlled by the largest national banks. In Texas the
top three banks in 1994 controlled 30.4% of total big bank deposits. In 2014, the top
three banks held 47.8% of total big bank deposits.4
This consolidation raises the fear of the emergence of ‘credit deserts,’ particularly
in rural economies. Historically, in small towns throughout rural America local
businesses and community banks formed symbiotic relationships. Moreover there
were three interrelated routes through which entrepreneurs could secure loans: good
credit, good collateral, and community reputation (Elyasiani and Goldberg 2004;
Flora and Flora 2013; Kilkenny 2002). Small businesses, and particularly those in
rural economies, have tended to rely upon small, locally owned depository
institutions (vs. larger, non-local institutions) and their practices of relational (aka.
judgment or ‘soft’ data) lending for financing (Berger, Miller, Petersen, Rajan, &
Stein, 2005; Berger & Udell, 1995, 1996, 2002; Collender & Frizell, 2002).
Restructuring of the financial sector, however, has increased the social and spatial
distance between borrower and lender (Brevoort & Hannan, 2004; DeYoung et al.,
2008; Kilkenny, 2002; Shaffer & Collender, 2008). The headquarters of financial
services where lending policies and practices are determined, which increasingly
rely on hard-data lending, are set in locales far removed from the small businesses
that need access to the capital. This distance threatens to undermine the well-being
2 The Bank Holding Company Act allowed for some flexibility in interstate banking through BHCs,
each state had, and significantly enforced, their own regulations which set very strict rules on out-ofstate
acquisitions. Only a handful of multi-state bank holding companies were in existence in the
early 1980s.
3 Between 1984 and 2011, the number of FDIC reported bank firms declined from 14,496 to 6,291,
while the number of banking establishments increased from 42,717 to 83,209 (see Tolbert et al,
2014).
4 In 1994 the top three Texas banks were Nationsbank of Texas, NA; Texas Commerce Bank, NA;
and Bank One, Texas, NA. In 2014 the top three banks were Bank of America, NA; Wells Fargo,
NA/Wells Fargo Southcentral, NA; and JP Morgan Chase, NA (www2.fdic.gov). Big bank deposits
refers to total deposits in the 50 largest banks in the state for any given year.
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 51
of small town economies, as access to necessary financial capital is becoming more
difficult to secure. Most importantly, a small business sector in rural communities
needs access to traditional and affordable sources of financial capital to launch new
businesses, and to sustain and expand existing enterprises (Black & Strahan, 2002;
Davis, Haltiwanger, & Jarmin, 2008; Mencken & Tolbert, 2016). Flora and Flora
(2013) summarize the situation thusly:
for rural communities and businesses alike, there is a crisis of
capital availability. As savers and investors are lured by higher
profits outside the local area and are facilitated by new laws making
it easier to move from one place to another, financial capital is
becoming more and more mobile. As capital becomes more mobile,
rural communities lose control of it. (p. 175)
Mencken and Tolbert (2016) find that bank loans have historically been a more
prevalent source of business start-up and business expansion capital, and that
nonmetropolitan business owners were more likely to report using conventional
loans to start and/or expand their business than metropolitan business owners. They
also find that the rate of loan use has declined over time, while home equity loans
and personal credit cards have increased as a source of start-ups and expansions in
both metropolitan and nonmetropolitan businesses. The shift from traditional bank
loans to credit cards and home equity loans is attributed to the decline in small
business lending. This decline can be further verified in Figure 1, which shows that,
at the national level, the proportion of small business lending to nonmetropolitan
counties has declined since 1996.5 However, these data also show very little change
across time in the proportion of small business loans going to Texas nonmetropolitan
counties. In fact, these Texas data show that nonmetropolitan small business lending
received a greater share of all Texas lending between 1996 and 1999. Moreover, in
1996 Texas nonmetropolitan counties received 20,110 small business loans. In 1999,
Texas nonmetropolitan counties received 52,635 small business loans, an increase
of 161%. Texas nonmetropolitan counties were counter to national nonmetropolitan
counties for the time period in question.
In order to better understand what the potential changes in financial restructuring
mean for the effectiveness of community capital, we propose to examine the impact
of small business lending, as reported to the federal regulator under the guidelines
of the Community Reinvestment Act, on measures of local development in each
Texas county for the 1999–2000 timeframe. The analysis is informed by the
Community Capitals framework. We predict that counties with a greater volume of
small business loans, representing more financial capital flowing to small businesses
in the community, will have higher levels of well-being. We also test for differences
in the effects of small business lending across the urban-rural continuum.
5 County Federal Financial Institutions Examination Council (FFIEC) annual county Aggregate and
Disclosure data. The data were downloaded from https://www.ffiec.gov/cra/ on 7/7/2015.
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Journal of Rural and Community Development, 13, 1(2018) 47–66 52
Figure 1: Percent of All Small Business Loans Going to Nonmetropolitan US and
Nonmetropolitan Texas Counties.
5.0 Data and Methods
In the analysis we examine the effects of per capita small business lending on five
measures of economic well-being for Texas counties. The analysis is limited to the
end of the 1990s because of the damage done to the financial industry during the
Great Recession, 2007–2009. During the Great Recession small business and farm
loans were only 55% of their inflation adjusted value for the 1996–1999 time period
(see Mencken & Tolbert, 2016). We fear that trying to benchmark the impact of
small business lending would grossly misrepresent historical trends by focusing on
this Great Recession time period. We also limit the analysis to the counties in Texas,
for four reasons.6 First, Riegle-Neal was not implemented uniformly in each state
following its passage in 1994. Some states delayed implementation until 1997, the
impact of which would not be relevant until 1998–at the earliest. Texas was an early
adapter. Second, Texas is the second largest economy in the United States, and 12th
largest in the world. It has five of the top 10 largest cities in the United States, and
it also has extremely rural areas (e.g. Loving County, population 82). Third, Texas
has a history of large bank presence. As reported above, in 1994 30% of total
deposits in Texas were concentrated in three banks. That rate of concentration is
twice the national rate for the same time period. The diverse mixture of counties
allows us to assess the impact of CRA small business lending on county
development while controlling for state-level differences in Riegle-Neal adaptation
6 Three counties are removed due to their very small populations. These are Kenedy County
(population 461), Loving County (population 82), and Borden County (population 641).
0
5
10
15
20
25
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
U.S. Nonmetro Total Texas Nonmetro Total
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 53
and idiosyncratic banking structures. Fourth, the data in Figure 1 show that lending
to Texas nonmetropolitan counties does not fit the same pattern as national
nonmetropolitan trends during this time frame.
5.1 Dependent Variables
The dependent variables in the analysis are taken from the Decennial Census of
Housing and Population. There are four measures we use, three of which are
standard in the volume of research on community capital and civic society (see
Lyson et al., 2001; Tolbert, Mencken, Blanchard, & Li, 2016). The standard
measures include 1999 Gini income coefficient; 1999 county poverty rate; 2000 per
capita income and 1999 median family income. Because median family and
personal income measures include all source income—earnings, transfer
payments, and so forth—we also include a measure of per worker nonfarm
earnings (2000), which gives us an understanding of how small business lending
trends affect earnings across Texas counties.
5.2 Independent Variables
The primary variable of interest is financial capital, and in particular small business
lending. The Community Capitals framework identifies financial capital for local
and small businesses as a needed asset for community well-being. We utilize the
publicly available County Federal Financial Institutions Examination Council
(FFIEC) annual county Aggregate and Disclosure data. The 1977 Community
Reinvestment Act requires that lending institutions report the number of loans and
the value of all loans to small businesses, delineated by assets. We examine the
effects of the amount of small business lending with a measure of total small
business loan amounts to businesses with less than $250,000 in assets per capita for
the 1996–1999 time period in each county. We do not know if the loans for these
small businesses come from locally owned banks, a weakness of the study’s ability
to directly test the Community Capitals model. To our knowledge, no such data are
available for all counties over time. We do have data on how much county x received
in total small business lending for each year between 1996 and 2015.
We calculate this measure for the 1996–1999 time period. Measures of central
tendency are presented in Table 1. The mean amount of small business lending per
capita in Texas was $1,074.77 ($975 standard deviation), with a median value of
$778.2. The variable has a skewness score of 2.62, and a kurtosis factor of 9.35—
indicating significant skewness in the distribution. Due to this we take the natural
log of the measure for the analysis.
5.3 Community Capital Controls
There are a variety of other county-level measures of community capital that we
control in this analysis. We first utilized several measures from previous research
(Lyson & Tolbert, 1996; Mencken & Tolbert, 2005; Tolbert et al. 2002; Tolbert et
al., 1998). These include percent of total manufacturing that is ‘small
manufacturing’ (i.e. less than 20 employees), per capita third places—such as pubs,
barber shops, coffee houses—, per capita national civic associations, proportion of
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 54
the adult population with at least a high school diploma, and proportion of the voting
age population who voted in the most recent presidential election. We also included
a measure of what percentage of religious adherents attend civically engaged
denominations from the 2000 Census of Churches.7
Table 1. Descriptive Statistics
Mean Std. Dev.
Total Small Business Lending 1996–1999
dollars per Capita
$1,074.78 $975
Dependent Variables
Per Capita Income 2000
$22,645 $5,840
Gini Income Inequality 1999
0.45663 0.03186
Median Family Income 1999
$38,608 $8,479
County Poverty Rate 1999
16.537 6.534
Nonfarm Earnings per Worker 2000
$17,996.75 $7,068
Community Capitals
Percent High School Grads 2000
62.87 8.65
Percent Small Manufacturing 2000
0.65801 0.28331
Percent in Civically Engaged Congregations
23.06521 15.79705
Percent Total Population Voting in Presidential
2000
20.05 58.89
Third Places Per Capita 2000
11.67582 39.19078
National Associations Per Capita 2000
1.6181 4.76707
Demographic Controls
Percent Foreign Born 2000
7.17515 6.37291
Percent White 2000
65.67771 21.30656
Percent Retail 2000
32.7544 7.4786
Population Density 2000
86.17047 254.5064
Metro Counties
30.3 46.1
7 These denominations include African Methodist Episcopal Zion, American Baptist, Church of
Christ, Congregational Christian, Disciples of Christ, Episcopal, Jewish, Latter-Day Saints, Lutheran,
Methodist, Presbyterian, and Unitarian.
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Journal of Rural and Community Development, 13, 1(2018) 47–66 55
5.4 Demographics
Past research indicates that the well-being of counties is also affected by the
demographic composition of the county. Poverty rates are not uniform across the
spatial landscape. Counties with a greater proportion of foreign migrants, and racial–
ethnic minorities tend, on average, to have lower levels of well-being (Sherman,
2014; Tickamyer, White, Tadlock, & Henderson, 2007). We control for percent of
the county white, non-Hispanic in 1999; percent of the county foreign born in 1999;
population density 1999; and percent employed in retail trade. In order to control for
the possibility that higher income counties are more likely to attract higher levels of
small business lending, we add a time lagged dependent variable to each model. The
effects of small business lending are net of previous levels of the five income,
poverty and inequality measures.
The Community Capitals framework is a rural community-based model. We test to
what extent small business lending has the same impact in urban and rural settings.
We interact the measure of small business lending by the 2003 OMB metropolitannonmetropolitan
continuum. This will show whether or not the impact of small
business lending has the same effect in different types of metropolitan and
nonmetropolitan Texas counties. We estimate a least squares solution, weighting the
analysis by 2000 county population.
6.0 Results
Table 2 presents simple zero-order correlations for the primary independent
variable, total small business lending in the county for the 1996–99 time period and
each dependent variable. These results show significant correlations with each
county income measure. The strongest correlation is with 2000 nonfarm earnings.
These two variables share 17.9% variance. The correlations with 2000 per capita
income (r=0.402) and 1999 median family income (r=0.325) are also highly
significant (p<=0.001). Small business spending has a modest negative correlation
with the county 1999 poverty rate (r=-0.166) and no correlation with the county
1999 Gini coefficient of income inequality.
Table 2: Correlations Between Small Business Lending and Dependent Variable,
Texas Counties
Small Business Lending
1996–1999
r r2 p
Per Worker Nonfarm Earnings 2000 0.42326 0.179 ***
Per Capita Income 2000 0.40225 0.162 ***
Median family income 1999 0.32514 0.106 ***
Gini Coefficient 1999 -0.11528 0
Poverty Rate 1999 -0.1663 0.028 **
*p<.05; **p<.01; ***p<.001 n=251
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 56
Table 3 shows the least squares solution regression results for each dependent
variable. The models show that small business lending is an important predictor of
each county’s well-being measure, with one inconsistency. For each percent increase
in total small business lending for the 1996–1999 period, 2000 per capita income is
predicted to increase by $460 dollars. A similar effect is found for nonfarm earnings.
For each percent increase in small business lending, 2000 nonfarm earnings per
worker are predicted to increase by $1,312 dollars. However, the interactions are not
significant. The effects of small business lending are the same for all county
types. The findings for 1999 median family income are inconsistent. The analysis
shows that small business lending actually has a negative effect on median family
income, net of other factors.8 This negative effect is the same in all county types
along the urban-rural continuum. See figures 2 and 3 for graph of interactions.
The models for 1999 income inequality and 1999 poverty rate show that CRA
reported small business lending has different effects across the urban-rural
continuum. In the most urban Texas counties, small business lending has a
significant positive effect on inequality and poverty (b=0.019; b=0.018
respectively). However, the interaction effect is negative. Among rural counties
there is a negative effect of small business lending on the 1999 poverty rate and the
1999 Gini coefficient of income inequality. Figures 2 and 3 show the logarithmic
impact of small business lending at different levels of rurality—among completely
rural counties, and nonmetropolitan counties with a metropolitan area of 20,000 or
more. In nonmetropolitan counties, small business lending is inversely correlated
with both income inequality and poverty, even after controlling for a variety of
county characteristics. The more rural the county, the stronger the effects. These
results are consistent with previous research on county-level measures of well-being
in rural America, which show that measures of political and social capital predict
lower levels of poverty and income inequality in nonmetropolitan counties (see
Blanchard et al., 2012; Tolbert et al. 2002; Tolbert et al., 1998).
8 In previous models in which a median family income time lag was not included, the interaction
between small business lending and urban-rural continuum was significant.
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 57
Table 3. Least Squares Regression: The Effects of Small Business Lending on Economic Well-Being Measures in Texas Counties (n=251)
1999 Median
Family Income
2000 Nonfarm
Earnings
2000 Per
Capital Income
1999 Gini
Coefficient
1999 Poverty
Rate
b se p b se p b se p b se p b Se p
Log Total
Loans
1996–1999
-1,490 666.9 ** 1.312 0.33 *** 460.38 228.1 * 0.019 0.01 *** 0.02 0.01 **
Community Capitals
% Small
Manuf.
2000
1,463 789.9 -1.31 0.91 991.5 709.4 -0.01 0.01 0.01 0.01
% in Civic
Eng.
Denom.
2000
4.612 2.26 * 0.02 0.01 * 5.27 1.98 ** -0.001 0.001 -0.06 0.3
% of
Adults
Voting in
2000 Pres.
4,174 4,628 -7.52 5.38 8,494 4,162 * 0.07 0.04 0.10 0.04 *
Third
Places Per
Cap 2000
-310 335 1,489 390.9 *** 358 302 -1.66 2.51 -2.36 3.01
% HS
Grad+
2000
2.79 0.55 *** -0.25 0.6 1.68 0.5 *** -0.14 0.05 ** -0.02 0.005 ***
Associatio
ns Per Cap
2000
2,108, 1,140, -823.6 1,327 1,036
1,026 18.55 8.41 * 22.77 10.1 *
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 58
Table 3. Least Squares Regression: The Effects of Small Business Lending on Economic Well-Being Measures in Texas Counties (n=251)
(continued)
1999 Median
Family Income
2000 Nonfarm
Earnings
2000 Per Capital
Income
1999 Gini
Coefficient
1999 Poverty
Rate
b se p b se p b se p b Se p b Se p
Demographics
Population
Density
2000
-2.13 0.89 * 0.01 0.00 *** 1.59 0.79 * 0.01 0.006 * 0.02 0.7
% Retail
2000
-110 331 -0.73 0.38 -975.3 299.2 ** -0.01 0.01 0.01 0.00 *
% Foreign
Born 2000
83.4 43.1 0.02 0.06 108.2 38.8 ** -.01 0.00 ** 0.3 005 **
% White
2000
2.59 17.58 0.01 0.02 6.04 15.8 0.01 0.13 -0.01 0.02
Urban-
Rural 03
-508.1 132.8 *** 0.41 0.15 180.8 116.9 0.01 0.01 0.03 0.1
Time Lag 0.96 0.06 *** 0.17 0.07 * 0.33 0.05 *** 0.36 0.05 *** 0.45 0.04 ***
Interaction Not Significant Not Significant Not Significant -0.003 0.001 *** -0.002 0.001 *
Intercept 0.39 0.03 3.96 4.25 1730.7 2796.7 0.39 0.03 * 0.20 0.03 ***
N 251 251
R2 0.897 *** 0.641 *** 0.747 *** 0.505 *** 0.847 ***
*p< .05; **p< .01; ***p< .001
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 59
Figure 2: The Effect of Small Business Lending on 1999 Gini Income Inequality in
Texas Nonmetropolitan and Rural Counties.
Figure 3: The Effect of Small Business Lending on 1999 County Poverty Rates in
Texas Nonmetropolitan and Rural Counties.
The other Community Capitals variables have limited effects. Percent in civically
engaged denominations has positive effects on each measure of income–earnings.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
4200
4400
4600
4800
Small Business Lending 000$s
Completely Rural Counties Nonmetro Counties w/Metro areas
0
0.05
0.1
0.15
0.2
0.25
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
4200
4400
4600
4800
Proportion in Poverty
Small Business Lending 000$s
Completely Rural Counties Nonmetro Counties w/Metro area
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 60
The measure for human capital—percent of adults 25+ with at least a high school
education—has a positive effect on 1999 median family and 2000 personal income.
However, only human capital has the expected effect on 1999 Gini coefficient of
income inequality and county 1999 poverty rate. Counties with higher levels of
adults with a high school or greater education have net lower levels of income
inequality and poverty in Texas.
7.0 Discussion
The primary purpose of this paper is to examine the impact of CRA reported small
business lending on county level measures of economic well-being. We are
particularly interested in how this type of lending affects economic well-being in
rural communities. Small business lending has positive effects on per capita and per
worker earnings. Small business lending also helps to reduce poverty and inequality
in nonmetropolitan Texas counties with the greatest impact in the most rural areas.
Our research makes unique contributions to the Community Capital framework in
two important ways. First, it extends this tradition empirically by integrating
financial capital into a model which predicts county economic well-being, while
controlling for other forms of community capital. Empirical tests on the role of local
finances has been lacking from this framework (Flora & Flora, 2013). The overall
findings support the notion that financial capital is important for economic
development. This capital helps to create more earnings, personal income, and less
income inequality and lowered the poverty rate in rural Texas counties. These three
findings are consistent with other studies (Bird & Sapp, 2004; Mencken & Tolbert,
2016; Tolbert et al., 2014), which document the importance of small business financing.
Second, this research is also important to help frame the impact of long-term
restructuring in the financial services industries. The 1994 Riegle-Neal Interstate
Banking and Branching Efficiency Act sought to remove the inefficiencies and to
ease awkward interstate banking restrictions. Critics of the legislation, namely small,
local community banks, feared an oligopoly in the national banking market
(DeYoung et al., 2008). This was deemed particularly problematic for small
businesses and farms in rural communities which have relied on local banks and
relationship lending for access to credit (Tolbert et al., 2014). The financial sector
transformation joins a long list of barriers to development in rural America. Local
community leaders were concerned that local deposits would be transferred out of
the communities, creating rural ‘credit deserts,’ akin to what has historically been an innercity
problem. Proponents of restructuring point to industry safeguards, such as anti-trust
laws, state and federal regulatory oversight of all mergers, and most importantly, the
Community Reinvestment Act which directs banks to make funds available to the entire
community they serve (Friedman & Squires, 2005; Johnson & Sarkar, 1996).
In 1977 the Community Reinvestment Act was passed to encourage banks to meet
the credit needs of the communities in which they operated. Much of the focus of
the CRA was to eliminate the practice of ‘redlining’ in which geographic units
became ‘credit deserts’ due to a cluster of circumstances deemed ‘high risk.’ These
were typically inner-city poor neighborhoods with high concentrations of minority
populations (Ross & Tootell, 2004; Squires, 2011). Yet recent research on the
sources of funding for small business start-ups and expansions in rural economies
shows that, over time, the proportion of rural based enterprises that utilize traditional
bank financing has been declining, while the proportion that uses less conventional
services (e.g. home equity loans, credit cards) has grown (see Mencken & Tolbert 2016).
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 61
One reason cited for the decline is that multi-site bank operations use hard-data lending
practices, whereas local and community banks have a long history of relationship lending.
Our research indicates that bank lending to small businesses was a vital economic
practice in all Texas counties in the years following the implementation of Riegel-
Neal. Small business lending helps urban and rural communities to increase income
and earnings, and nonmetropolitan communities to reduce poverty and inequality. If
relationship lending in rural economies has become a ‘thing of the past’ following
the restructuring of the financial services industries, then it is vitally important that
government entities continue to ensure that credit deserts do not materialize in rural
America. Post-Riegel-Neal, critics of the Community Reinvestment Act have been
calling for its repeal. In response to the ongoing concern that bank consolidation
may take deposits from local communities and put them elsewhere, Lawrence J.
White (2009), Arthur E. Imperatore Professor of Economics at New York
University, testifying before the Financial Services Committee of the U.S. House of
Representatives asked “why should a bank have a special obligation to lend to a
specific local geographic area?…The local orientation of the CRA is an
anachronism…[we should] place more trust in competition” (p. 185). We, on the
other hand, are concerned that erosion of the CRA powers could lead to less money
being invested in small businesses in urban and rural communities, as greater profits
could be found for these banks by investing in large multinational corporations. One way
to prevent this is aggressive oversight of the Community Reinvestment Act to make sure
that the money continues to be available to small businesses in urban and rural America.
There are weaknesses to this study. First, the Community Capitals framework
identifies financial capital as local capital that is loaned by local banks to local
entrepreneurs. Publicly available CRA data do not allow us to determine the
geography of the banks making the loan, only that the loan was given to a small
business in county x. It is possible and likely that banks in Dallas are lending money
to small businesses 450 miles away in Del Rio, TX. A second weakness of these
data is business credit cards. The CRA allows credit cards issued by banks to small
businesses to satisfy part of the CRA lending guidelines. The publicly available
CRA data do not allow us to determine if the loan in question is a credit card or
conventional bank loan. Third, it is natural to assume that financial capital will flow
to places with the greatest potential for returns. The CRA is designed to prevent the
flow of investment capital strictly to wealthy places. We have added a time lag to
each of our models in an attempt to control for this possibility.
This paper is part of an emerging agenda that will examine the importance of
financial capital for small businesses and the rural communities in which they are
located. This analysis is limited, for good reason, in both time and space. In order to
understand how small business financing affects local economic development it was
important to examine this relationship in a time context that was not tainted by the
worst economic downturn since the Great Depression. Because until the mid to late
1990s banking laws were, to an extent, state specific, it was important to do this
analysis in a geographical context which was both large enough to perform a robust
analysis while not having to worry about state-specific idiosyncrasies affect the
comparability of the results. Future analyses will expand to include all rural counties, and
in the time frame (2007–2010) that allows us to examine the impact of the Great Recession
of small business lending, and the resultant relationship with economic development.
Mencken & Tolbert
Journal of Rural and Community Development, 13, 1(2018) 47–66 62
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