Religious adherence and county economic growth in the US

Journal of Economic Behavior & Organization 72 (2009) 438–450

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Religious adherence and county economic growth in the US Anil Rupasingha a,∗ , John b. Chilton b,1 a b

Dept. of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces, NM 88003-8003, USA Department of Economics, American University of Sharjah, Sharjah 26666, United Arab Emirates

a r t i c l e

i n f o

Article history: Received 4 October 2007 Received in revised form 16 May 2009 Accepted 18 May 2009 Available online 3 June 2009 JEL classification: R11 Z12 Keywords: Religion Income growth USA counties

a b s t r a c t We estimate a Barro-type conditional convergence model using religious adherence data from the American Religious Data Archive to analyze independent effects of church adherence rates on economic growth in the United States at the county-level. Per capita income growth is modeled as a function of initial per capita income, initial human capital stock, and a set of control and related variables including religious adherence, religious diversity, and regional indicator variables. We also investigate the independent effects of three main denominations, namely Catholics, Evangelical Christians, and Mainline Christians, on county economic growth. Our results indicate that the religious adherence in general is significantly greater than zero and not beneficial for US county income growth. We find mixed results for effects of various denominations. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The growing literature on institutional and cultural factors has added a new dimension to studies of economic growth as mainstream economists start to analyze the roles of culture, social ties, and institutions as factors of production. Among the cultural factors, religion has been receiving increased attention in literature. The interest in the concept of religion and economic outcomes was heightened with the publication of Barro and McCleary (2003) and with policy initiatives such as White House Faith-Based and Community Initiative. Our objective in this paper is to investigate the effects of religion on economic growth in the US. We use county-level church adherence data from American Religious Data Archive (ARDA) and study the effects of church adherence on economic growth at the county-level, consisting of over 3000 observations. The economic significance of religion has a long history in economics. Most studies claim that Weber (2002) was the first to identify the significant role that religion plays in economic performance in a society. Anderson (1988) and Iannaccone (1998) claim that Adam Smith laid the foundation for the topic of religion and economics. Arguing that “successful explanations of economic performance must go beyond narrow measures of economic variables to encompass political social forces,” (p. 760), Barro and McCleary (2003) examine the effects of religion on economic growth using data from World Value Survey (WVS) and measure religiosity using survey questions on religious service attendance and beliefs in God, hell, heaven, and afterlife. They find that some aspects of beliefs are positively associated with economic growth while religious service attendance is negatively associated with growth. Guiso et al. (2003) using cross-country data from WVS, find religious beliefs are conducive to higher income and income growth and Christian religions are more positively associated with economic growth. Sala-

∗ Corresponding author. Tel.: +1 575 646 3215; fax: +1 575 646 3808. E-mail addresses: [email protected] (A. Rupasingha), [email protected] (J.b. Chilton). 1 Tel.: +971 6 5152526. 0167-2681/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2009.05.020

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i-Martin et al. (2004) find that Islam, and in some specifications, Confucianism, are positively associated with per capita income growth. Noland (2005), using cross-country and within-country data, rejects the null hypothesis that religion is uncorrelated with economic performance in a country. Several studies investigate the effects of religion on other aspects of human behavior. Stulza and Williamson (2003) show that a country’s principal religion predicts the cross-sectional variation in creditor rights better than other conventional determinants and Catholic countries protect the rights of creditors less well than Protestant countries. Brown and Taylor (2007), using individual level data from the British National Child Development Study, find a positive association between education and church attendance. Berggren (1997) studies effects of religion on such activities as divorce, abortion, nonpayment of debt, and children born out of wedlock using data from Sweden. A parallel literature has documented the importance of religion on economic and social outcomes in the US. Lipford et al. (1993) study the impact of church membership (using US state level data) on abortion, divorce, murder, illegitimate births and crime and find that church has a negative effect on most of these variables. Heath et al. (1995) study the effects of religion on the level of income using US state level data. More importantly, they analyze the effects of various denominations categorized as Jewish, Catholic, liberal and fundamentalist Protestant and find that Jewish membership is positively associated with state per capita income, liberal Protestantism is not associated with state per capita income, and Catholicism and fundamentalist Protestantism are inversely associated with state per capita income. Crain and Lee (1999), in a comprehensive study of determinants of income growth at state-level, incorporate church membership but find no significance of it on per capita income growth. Lipford and Tollison (2003) argue that while religious participation reduces participants’ incomes, high incomes discourage religious participation. They find empirical support for their claim using US state-level data on per capita personal income and church membership. Several papers investigate the effect of religion on human capital and earnings (Fan, 2008; Sander, 2002; Steen, 1996; Tomes, 1984). Fan (2008) formulates a theoretical model of linking religion and education. His model attempts to combine sociological and economics literature and shows that people’s religious participation is determined by the concern for their children’s human capital accumulation as well as their religious beliefs. Sander (2002) using General Social Survey (GSS) data investigates the endogeneity between human capital and religious activities. He finds that education is not an exogenous determinant of attendance at weekly religious services and religious contributions and there is no causal effect of education on religious activity when education is treated as endogenous. Steen (1996) examines earnings differentials and rates of return to human capital for men from different religious backgrounds using data from the National Longitudinal Survey. He finds that men raised as Catholics have significantly higher earnings than men raised as Protestants and Jewish men have significantly higher earnings than men raised in all other religious traditions. Tomes (1984) examines the effects of religious and denominational backgrounds on earnings and the returns to human capital and finds no evidence to support that religious or denominational background affect earnings except Jewish tradition. Our primary contribution of this paper is the incorporation of religious adherence as a determinant of economic growth in the United States at the county-level. Use of county level data with more than 3000 observations provides us with a very rich cross-sectional variation in the US that a state-level data set lacks. We also investigate the individual effects of various denominations, namely Catholics, Mainline Protestants, and Evangelical Protestants (the excluded group is made up of all other church groups), on county economic growth. Another aspect of present study is that, following cross-country studies, it incorporates religious diversity in a county as another determinant of county income growth. In terms of religion data, unlike previous studies that use ARDA data, we use adherence rates as opposed to membership data. Compared to membership data, adherent data seem to reflect a more accurate count of religiosity in a locality.2 We control for potential problems arising from the possibility that the religious adherence is influenced by county income growth. We also test the robustness of our results using newly developed spatial econometric techniques which are more appropriate when using smaller observations that have spillover effects. The remainder of this paper is organized as follows. Section 2 sheds some light on the relationship between religion and economic performance. Section 3 describes the data, the analytical framework and methodology. Section 4 presents and discusses main results. Section 5 contains the summary and the conclusions. 2. Religion and economic performance According to previous literature, religion can affect economic performance through various channels. Anderson (1988), quoting from Adam Smith’s Theory of Moral Sentiments, points out that religious beliefs provide strong incentives to follow moral restraints such as trust, honesty, benevolence, and restraint from violence that have an effect on civil society. Iannaccone (1995) points out that all religions work to instill certain values, morals, and behaviors in their followers and these values, beliefs, and morals are seen in most aspects of human behavior. Another argument is that the belief in or the fear of God leads adherents to abide by “a kind of internal moral enforcement mechanism” (Anderson, 1988, p. 1069). All religions emphasize supernatural monitoring (Iannaccone, 1995) and this leads adherents to be trustworthy, truthful, honest, and

2 According to Bradley et al. (1992), in cases where denominations reported only communicant, confirmed or full members, total adherents were estimated (see Bradley et al. for details). Total adherents include all members (communicant, confirmed or full members), their children and number of other participants who may not be considered members.

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ethical. Religious people are discouraged from engaging in activities such as divorce, abortion, non-payment of debt, and children born out of wedlock, treating these activities as sinful (Berggren, 1997). Higher religious adherence also leads to higher social capital in a community or locality. Putnam (1993) argues that trust is higher in societies that have dense networks of civic engagement. These networks include neighborhood associations, sports clubs, choral societies, and political parties. Religious institutions and religious service attendance are often cited as sources of social capital (Putnam, 2000; Smidt, 1999; Greeley, 1997; Wuthnow, 1997; Tolbert et al., 1998). This form of capital has been viewed as a vehicle for improving individuals’ well-being and for discouraging free riding and shirking.3 Religiosity connects an individual to a group of individuals (community) and this connection promotes the well-being of that particular individual and the group is also influenced by the well-being of the individual. Forms of religious social capital with direct economic implications include volunteer labor for community activities, donations to public causes, subsidized educational facilities, and economic enterprises belong to certain religious groups. Religion can promote social activities thereby increasing social interaction, and that in turn will promote social capital. On the whole, religiosity may help reduce information and transaction costs (Torgler, 2006) by promoting trust, information sharing, and voluntary labor. When transaction costs and the costs of gathering and disseminating information are decreased, more exchange will take place, thus enlarging the scope of transactions and interactions. Conversely the lack of religious qualities results in demands for more external controls, such as, tougher law enforcement and security systems, monitoring and enforcement. Religious values can be a substitute for physical inputs such as policing and legal service. Religious institutions are known to monitor the behavior of their members and sometimes reward high levels of loyalty, and punish inappropriate behavior. Members of these institutions monitor, give advice, and encourage fellow members to abide by institutions. Mainstream religions emphasize supernatural monitoring such as “God is watching you” (Iannaccone, 1995) and enforce certain behaviors to a certain extent (Anderson and Tollison, 1992). Adam Smith describes an individual’s moral reputation as a capital asset because religions tend to produce and distribute moral information about individual members, and therefore a wealth-maximizing individual has an economic incentive to participate in religious activities (as found in Anderson, 1988). An alternative analysis to the above is that religious adherence or participation requires additional resources in terms time and good (Barro and McCleary, 2003) and this leads to a negative relationship between religious adherence and economic growth. Some other studies put this as opportunity cost of time, indicating that higher opportunity cost of time leads to lower adherence (Azzi and Ehrenberg, 1975; Lipford and Tollison, 2003). Religious diversity or polarization has also received attention in literature (Montalvoa and Reynal-Querol, 2005; Barro and McCleary, 2003; Lipford and Tollison, 2003). The original argument dating back to Adam Smith is that established (a state-funded and protected monopoly) churches tend to be sagging in enforcing the moral virtues of followers (Hull and Bold, 1998). In other words, greater diversity of religion in a country or region promotes higher competition resulting in higher quality religion (Barro and McCleary, 2003). Iannaccone (1998) cites empirical evidence to support Smith’s claim that concentrated religious markets result in lower levels of religious participation. An underlying point related to present research is that a monopolized religious market may contribute to negative economic growth. Hull and Bold (1998) argue that empirical findings to support Smith’s claim may not be applicable to the US since competition between established and non-established churches is not comparable to competition among non-established churches, even if concentration is high in a market of non-established churches. A related argument to Smith’s argument is that religious fractionalization, similar to ethnic fractionalization, has a negative effect on economic development. From a social capital perspective, religiously fragmented societies have less social capital, leading to less-trusting societies. An alternative point of view is that greater diversity in the form of a “melting pot” (Florida, 2002) can enhance economic well-being in a society.

3. Data, methods and estimation issues Different studies have used different variables to measure religiosity. This is not surprising given the multi-dimensional nature of religiosity. The most cross-country studies utilize data from the WVS and use answers to survey questions on frequency of religious attendance, belief in God and hell, and religious denominations in order to measure religiosity. Most US studies use data from the ARDA and they measure religiosity using percent of church memberships. Several other studies use GSS data and use questions similar to those of the WVS. We use ARDA data and the percent of adherents as our religiosity measure. We also group adherents into various denominations (see Appendix A) and use these categories in a separate growth model. This classification is based on one of the authors’ personal experiences and closely follows the groupings found in Steensland et al. (2000). For our empirical application, we examine data for all counties in the contiguous US over the period from 1990 to 2000, using the 10-year interval of the US decennial Census.4

3

It should also be noted that extremist religious organizations and groups such as KKK and Mafia may not contribute to the overall well-being of a society. Although ARDA data are available for several decades (1952, 1971, 1980, 1990, and 2000) allowing one to conduct a panel study as would be preferable, we decided against this because constructing a panel using ARDA data seems to be problematic. The coverage was uneven across survey years: the 1971 data contain 53 denominations, representing an estimated 81 percent of church membership in the United States. The 1980 data contain information on 111 denominations representing an estimated 91 percent of U.S. church membership. The1990 data contain information on 133 Judeo-Christian church bodies. 4

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To determine the effects of church adherence on income growth we specify an income growth model using growth in county-level per capita personal income across the US as the dependent variable. Conventional growth regressions (Barro and Sala-i-Martin, 1991, 1992) are obtained by fitting the equations of the form gn = a + ˇyn0 +   xn + n

(1)

where gn is the average growth rate of per capita income for region/country n between years 0 and T, yn0 is the initial logarithm of per capita income, xn is a vector of variables that control for cross-region/country heterogeneity, ˛ and ˇ are parameters,  is a vector of parameters, and n is an error term. This growth model assumes that the per capita income growth rate in a country or region to be inversely related to the starting levels of output or income level (ˇ < 0) and suggests that poorer economies should grow faster than richer economies, and poor economies should eventually “catch up” based on the assumption of decreasing returns to capital, which should cause more advanced economies to grow more slowly than less advanced ones. The accuracy of the speed of convergence from various cross-section studies by Barro and Sala-i-Martin (1991, 1992) and Mankiw et al. (1992) has been questioned by Bernard and Durlauf (1995) and others. Evans (1997) notes that the OLS is unlikely to be consistent and instead develops a particular 3SLS-IV estimator. Higgins et al. (2006) use the methodology suggested by Evans (1997) for a sample of over 3000 US counties, and report convergence rates of 6–8 percent from 3SLS regressions, and 2 percent when the OLS estimator is used. We follow Evans (1997) procedure as detailed in Higgins et al. (2006) in our empirical application. The first and second stages of this procedure involve using instrumental variables (IVs) to estimate regression gn = ω + ˇyn0 + n

(2)

where gn = [(yn,T − yn,0 )/T] − [(yn,T−1 − yn,−1 )/T], yn0 = yn,0 − yn,−1 , ω and ˇ are parameters, and n is the error term. We use 1980 values of conditioning variables with the exception of interstate highway access, state right-to-work laws, amenity scale, regional and rural/urban indicator variables, as instruments. Based on our sample period (1990–2000), we define gn = [(yn,2000 − yn,1990 )/T] − [(yn,1999 − yn,1989 )/T] and use estimated coefficient of ˇ* from Eq. (2) to construct the variable n = gn − ˇ*yn,0 . Then the third stage regression takes the form n =  + xn + εn

(3)

where  and  are parameters and εn is the error. Our variable of interest here is the church adherence and effects of it on county economic growth. Religion-related variables included in the growth model are per capita church adherence that includes all the denominations in the ARDA data set, per capita adherents for Catholics, Evangelical Christians, and Mainline Christians, and a religious diversity variable. The religious diversity or plurality variable is calculated as follows: Reldiv = 1 −



i

(Denomi )2

(4)

where Denomi denotes the share of population identified as of denominations i ∈ I = {133 Judeo-Christian church bodies in 1990 ARDA data set}. Due to various perspectives discussed in Section 2, we are not able to decide ex ante the sign of religious adherence and religious diversity on economic growth. We include standard variables based on previous literature (see Goetz and Hu, 1996) as other control variables in the growth model. These variables are drawn from several government agencies including the Census Bureau, the USDA, and the Bureau of Economic Analysis. Initial per capita income is included to capture the convergence effect noted by Barro and Sala-i-Martin (1992). The schooling attainment level serves as a proxy for human capital stocks. We use the percent of the population 25 years or older in 1990 with a bachelor’s, graduate or a professional degree to capture human capital effect on growth. Per capita government highway and education spending and local taxes per capita are included as measures of local public spending activities and taxes, respectively. Highway and education spending is a proxy for infrastructure expenditure and higher spending can be attractive to new businesses and people who are looking to migrate. Common belief is that higher local taxes lower economic growth by discouraging firms from starting or expanding their businesses and high-skilled labor from migrating into these high-tax localities. We include the level of natural amenities in a county, as measured by climate and a number of related variables (McGranahan, 1999), and expect higher levels of amenities to be associated with faster economic growth, all else equal. The percentage of the population who are nonwhite is a demographic variable that many labor studies have found to be associated with income and earning rates and hence with overall costs of production. The presence of an interstate highway interchange in a county is used to measure the accessibility of a county. The variable right-to-work laws is a state level indicator that is included in the model and the hypothesis is that these laws discourage businesses from locating in a locality and hence has a negative effect on economic growth. Population density in a county is included as a measure of agglomeration. Another related variable but uncorrelated with population density is a metro dummy variable (rural–urban continuum codes 0–3) which is included to measure the attraction of economic activities to metro areas and hence for higher economic growth. We also group rural-urban continuum codes 5,7 and 9 together to create a dummy variable so that we can test whether the excluded group of metro-adjacent rural counties (rural-urban continuum codes 4, 6 and 8) grows more rapidly than the group not adjacent to metro areas (Rupasingha et al., 2002). Seven other

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indicator variables are included to capture regional effects. They are New England, Mideast, Great Lakes, Plains, Southeast, Southwest, and Rocky Mountain (and Far West being the excluded category). It has been observed that the income growth could not be exogenous with religiosity. In other words, the causality might run in the opposite direction and religiosity could be directly influenced by the income growth. One well-known theory in economics and religion literature is the secularization hypothesis, by which higher economic development makes people less religious (Barro, 2004). In other words, religious adherence affects county income growth, but income growth, in turn, influences religious adherence. The presence of endogeneity could create bias estimations. To test this possibility we employ the Davidson–MacKinnon auxiliary regression test for exogeneity. The null hypothesis of this test assumes that the model is correctly specified with a set of exogenous variables. The suspected endogenous variables are expressed as a linear function of a set of instruments (religious adherence equation), and the residuals from the first stage regressions are added to the model. These residuals should have no explanatory power under the null hypothesis. The estimated coefficient of the residuals for per capita adherence rates of total adherence and adherence rates for Evangelical and Mainline Protestants were significantly different from zero and therefore, the null hypothesis of exogenous variable was rejected.5 Hence, in order to overcome this endogeneity bias, we followed the two-stage procedure. The choice of variables for religious adherence equation is based on previous literature on determinants of religiosity (Azzi and Ehrenberg, 1975; Lipford and Tollison, 2003; Barro, 2004; Gruber, 2005) and also subject to availability of these variables at the county level and for the time period considered. These variables include the percent of the nonwhite population, the percent of the population below 30 years old, percent of the population over 65 years old, the percent of females in the population, the percent of family households, the percent of people who are 25 years old and have a college degree or higher, the percent of people who are 25 years old and are high school drop outs, unemployment rate, ethnic diversity, religious diversity, income inequality, and separate variables for the percent of people in a county who are Italian, Hispanics, Polish, Germans, and Swedish in a county. Estimated predicted values of religious adherence are included in the growth equation in order to account for endogeneity bias in total adherence rate, and Mainline and Evangelical Protestant adherence rates. The use of a relatively long time horizon (10 years) for measuring ex post income growth helps mitigate the possibility of endogeneity of other right hand side variables. 4. Spillover effects Although counties (the unit of analysis here) are well-demarcated political and administrative units, the underlying spatial relationships among these units can be difficult to distinguish using these boundaries, particularly with respect to religious adherence and income growth. These overlapping spatial relationships could cause problems with the framework used to collect information from these localities. For example, our primary variable of interest, the church adherence, may not line up with the borders of county units. It is likely that some of the adherents in a particular church live in a different county. The data on membership and adherent figures were collected by the county in which the congregation itself is located, rather than by the county in which individuals actually reside. Also the income growth (the dependent variable) can have spillover effects. One county’s income growth can be dependent on the economic growth of its neighboring counties. Therefore, any empirical analysis of issues in regions or localities should take these overlapping relationships into consideration. In the presence of spatial effects, the estimated parameters without spatial correction can be inefficient and/or biased. Previous studies on income growth using US county- and state-level data have confirmed that these regional crosssectional data display spatial dependence6 (Rey and Montouri, 1999; Rupasingha et al., 2002), implying that the growth is not independently distributed over space. Spatial dependence in a model can be due to two main reasons. On one hand the dependent variable in one locality can be influenced by the same in neighboring counties. If this is the case, the spatial lag model is estimated. On the other hand, spatial dependence could be present in the residuals when there are omitted unobservable variables that can be spatially correlated. Rey and Montouri (1999) suggest that a random shock in a locality will not only affect the growth rate in that locality but also the growth rates of other neighboring localities because of the presence of the spatial error dependence. If this is the case, the spatial error model is estimated to correct the spatial bias. If both types of spatial effects are evidenced, the suitable model is the general spatial model (LeSage, 1999). The general spatial model includes both the spatial lag term as well as the spatial error structure:  = W () + Xˇ + u u = Wu + ε ε∼N(0, 2 In )

(5)

where  denotes an nx1 vector of the dependent variable (income growth), X represents an nxk matrix containing the determinants of growth, and W is a spatial weight matrix. Scalar denotes a spatial lag parameter,  denotes scalar spatial

5

The estimated coefficient of the residuals for per capita adherence rates for Catholics was not significantly different from zero. Spatial dependence refers to the fact that one observation in a sample of cross-sectional observations depends on other cross-sectional observations. Anselin (1988) describes this as the “. . . existence of a functional relationship between what happens at one point in space and what happens elsewhere” / i. (p. 11). More formally, assume i and j are two cross-sectional observations and that spatial dependence is represented as: yi = f(yj ), where i = 1, . . ., n, j = 6

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Table 1 Descriptive statistics and variable explanations. Variable

Description

Mean

Std. dev.

LGRWTH90 PCADH90 A CATH90 A EVAN90 A MAIN90 RELFRC90 LINC90 COLLEG90 PCTAX92 GOVEXP92 NONWHITE HWY DUM RTW POPDEN90 AMNSCALE URBAN RURAL NENG MEST GLAK PLNS SEST SWST RKMT

Per capita income growth between 1990 and 2000 Per capita adherents 1990 Per capita Catholics adherents 1990 Per capita Evangelical Protestants adherents 1990 Per capita Mainline Protestants adherents 1990 Religious diversity index 1990 Log of per capita income 1990 Percent of population (25 years and older) who have college degree 1990 Per capita local taxes 1992 Per capita total highway and education expenditure 1992 Percent of population who are nonwhite 1990 Interstate highway access (0,1) Right to work laws (0,1) Population density 1990 Natural amenities index Metro counties (0,1) Rural counties (0,1) New England (0,1) Mideast (0,1) Great Lakes (0,1) Plains (0,1) Southeast (0,1) Southwest (0,1) Rocky Mountain (0,1)

0.393 59.4 12.916 27.260 16.553 0.491 9.611 13.479 660.7 1074.17 12.553 0.428 0.535 221.0 0.055 0.268 0.410 0.022 0.057 0.140 0.199 0.342 0.122 0.069

0.100 19.4 15.207 21.298 12.505 0.158 0.216 6.575 462.5 388.18 15.377 0.495 0.499 1438.4 2.279 0.443 0.492 0.145 0.232 0.348 0.399 0.475 0.327 0.254

error parameter and ˇ denotes the k parameters to be estimated for the explanatory variables. We estimate a general spatial model for our data to address the spillover issues discussed above and as a robustness check for our 2SLS results. 5. Results and discussion Variables used in the analysis and their descriptive statistics are provided in Table 1. Results of our empirical estimates are presented in Tables 2–4. As a test for multi-collinearity in the data, we estimate the variance inflation factor (VIF) and Table 2 OLS estimation results. Variable

Model 1

Model 2

Coefficient Constant LINC90 PRDADH90 A CATH90 PRDEVAN PRDMAIN RELFRC90 COLLEG90 PCTAX92 GOVEXP92 NONWHITE HWY DUM RTW POPDEN90 AMNSCALE URBAN RURAL NENG MEST GLAK PLNS SEST SWST RKMT Adj. R2 N

t-Stat

0.1312 −0.0092 −0.0002

8.76 5.50 6.31

−0.0078 0.0005 −0.000002 −0.000004 −0.000039 0.0015 −0.0008 0.0000001 −0.0005 0.0004 −0.0003 0.0018 0.0015 0.0065 0.0053 0.0039 0.0042 0.0021

3.00 10.47 1.54 4.30 2.33 3.86 1.41 0.44 3.56 0.90 0.71 1.31 1.16 4.84 3.51 2.99 2.94 1.57 0.17 3038

Coefficient

t-Stat

0.1373 −0.0114

8.72 6.51

0.00005 0.0001 −0.0002 0.0036 0.0006 −0.000002 −0.000004 0.0014 −0.0008 −0.0001 −0.0000001 −0.0004 0.0003 −0.0004 0.0010 0.0011 0.0055 0.0036 0.0027 −0.0010 0.0002

2.41 1.17 3.64 1.16 11.04 1.84 3.55 3.57 1.34 5.42 0.33 2.73 0.64 0.90 0.69 0.81 4.10 2.37 1.85 0.63 0.18 0.16 3038

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A. Rupasingha, J.b. Chilton / Journal of Economic Behavior & Organization 72 (2009) 438–450 Table 3 3SLS estimation results. Variable

Constant LINC90 PRDADH90 A CATH90 PRDEVAN PRDMAIN RELFRC90 COLLEG90 PCTAX92 GOVEXP92 NONWHITE HWY DUM RTW POPDEN90 AMNSCALE URBAN RURAL NENG MEST GLAK PLNS SEST SWST RKMT

Model 1

Model 2

Coefficient

t-Stat

Coefficient

t-Stat

0.1312 −0.1128 −0.0002

8.76 67.18 6.31

0.1373 −0.1150

8.72 65.82

0.00005 0.0001 −0.0002 0.0036 0.0006 −0.000002 −0.000004 0.0014 −0.0008 −0.0001 −0.0000001 −0.0004 0.0003 −0.0004 0.0010 0.0011 0.0055 0.0036 0.0027 −0.0010 0.0002

2.41 1.17 3.64 1.16 11.04 1.84 3.55 3.57 1.34 5.42 0.33 2.73 0.64 0.90 0.69 0.81 4.10 2.37 1.85 0.63 0.18

−0.0078 0.0005 −0.000002 −0.000004 −0.00004 0.0015 −0.0008 0.0000001 −0.0005 0.0004 −0.0003 0.0018 0.0015 0.0065 0.0053 0.0039 0.0042 0.0021

Adj. R2 N

3.00 10.47 1.54 4.30 2.33 3.86 1.41 0.44 3.56 0.90 0.71 1.31 1.16 4.84 3.51 2.99 2.94 1.57 0.88 3038

0.88 3038

Table 4 3SLS estimation results with spatial correction. Variable

Constant LINC90 PRDADH90 A CATH90 PRDEVAN PRDMAIN RELFRC90 COLLEG90 PCTAX92 GOVEXP92 NONWHITE HWY DUM RTW POPDEN90 AMNSCALE URBAN RURAL NENG MEST GLAK PLNS SEST SWST RKMT Rho Lambda Adj. R2 N

Model 1

Model 2

Coefficient

t-Stat

Coefficient

0.1959 −0.1144 −0.0001

23.28 220.49 5.38

0.2123 −0.1165

21.69 205.22

0.00004 0.0001 −0.0001 0.0038 0.0005 −0.000001 −0.000003 −0.0001 0.0003 −0.0002 0.0000001 −0.0001 0.0030 −0.0037 0.0029 0.0008 0.0049 0.0026 0.0006 −0.0028 0.0015 0.0650 0.1510

2.27 1.75 1.41 1.58 12.80 1.59 5.37 5.83 0.80 0.38 0.35 0.71 5.24 8.13 1.48 0.52 3.53 1.80 0.41 1.86 1.16 6.30 101.79

−0.0047 0.0005 −0.000001 −0.000003 −0.0001 0.0003 −0.0003 0.0000001 −0.0003 0.0028 −0.0034 0.0032 0.0013 0.0059 0.0050 0.0025 0.0021 0.0029 0.0560 0.1690

2.52 11.62 1.30 6.00 3.66 0.73 0.54 0.14 2.01 4.98 7.48 1.69 0.85 4.16 3.41 1.95 1.52 2.20 6.15 117.03 0.90 3038

t-Stat

0.90 3038

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results show that multi-collinearity is not excessive in our data set.7 A negative and significant coefficient on the initial level of per capita income (LINC90) indicates convergence, conditional on other variables. According to Table 2, the estimated conditional convergence rate using conventional OLS is 1 percent in model 1 and 1.14 percent in model 2. It is significant at less than 1 percent level in both models. The 3SLS model estimates using Evans (1997) are presented in Table 3. Compare to OLS estimate, the convergence rate using 3SLS is around 11 percent. The specification results presented in Table 4 use the same 3SLS procedure but corrected for spatial dependence bias. The convergence rate is unchanged from that of the first 3SLS specification. According to Evans (1997) the huge difference in the convergence rate between the OLS and 3SLS estimates suggests that OLS introduces considerable bias. Therefore, the following inference of results is based on 3SLS procedure. The main focus of this study is the role of religion as a determinant of income growth in the US at the county-level. Even though we are only interested in modeling economic growth and assume that the direction of causality only flows in one direction with respect to church adherence, a wider system was considered to insure that the estimation of the growth equation is unbiased. We approach this by hypothesizing that there might be a feedback between dependent variable and church adherence variable (two-way causality). To obtain consistent estimates we use instrumental variable analysis and first estimate first stage church adherence equations for total adherence, Mainline Protestants, and Evangelicals Protestants. Variables included in the total church adherence equation were highly statistically significant except the college education variable. In the Evangelical equation the percent of the population below 30 years old, the percent of the population over 65 years old, the percent of family households, and the percent of Hispanics are not significant. In the Mainline equation the percent of the population below 30 years old, the percent of the population over 65 years old, the percent of people who are 25 years old and have a college degree or higher, and the percent of people who are 25 years old and high school drop out are not statistically significant. Predicted values of these estimations were used as instruments for respective variables in the growth model except for the Catholic variable. In the income growth model 1 (Table 3), the estimated coefficient for per capita adherence is negative and highly significant. The estimated coefficient for total adherent rate, −0.0002, means that an increase in this variable by one standard deviation (19.4, as shown in Table 1) would reduce the economic growth rate by 0.4 percent per year. This result supports the hypothesis that religious adherence or participation requires additional resources in terms of time and good (Barro and McCleary, 2003) or the hypothesis of opportunity cost of time, indicating that higher opportunity cost of time leads to lower adherence (Azzi and Ehrenberg, 1975; Lipford and Tollison, 2003). In general results differ somewhat from previous results obtained using international data (Barro and McCleary, 2003; Sala-i-Martin et al., 2004) and US data (Heath et al., 1995; Gruber, 2005). These differences may be due to two main reasons: data (international vs. regional; state vs. counties; individual survey data vs. cross-section data) and modeling and estimation issues (income growth vs. income levels; endogeneity). Our results are generally in line with Noland (2005) who uses international data and growth models and Lipford and Tollison (2003) who use US data and income level models. The religious diversity index in this specification is negative and statistically significant at 1 percent. This supports the social capital hypothesis that religiously fragmented societies to have less social capital leading to less-trusting societies and rejects the notion that greater diversity in the form of a “melting pot” (Florida, 2002) enhances economic well-being in a community. This result does not support the argument that greater religious diversity, based on the Adam Smith’s claim that more competition among churches lead to better religious adherence, may lead to better economic outcomes. Lipford and Tollison (2003) find this kind of variable (using concentration as opposed to diversity and income level) of church membership plays no role in income formation. We test independent effects of different denominations in a separate regression (Model 2) and results are shown in Table 3. While adherent rates for Catholics and Evangelical Protestants (instrumented) have positive effects, Mainline Protestants rate (instrumented) has a negative effect on county income growth. The Evangelical Protestants variable is not statistically significant at any conventional level and the other two denominations are significant at 1 percent level. The estimated coefficient for the Catholic adherent rate, −0.00005, means that an increase in this variable by one standard deviation (15.2, as shown in Table 1) would increase the economic growth rate by 0.08 percent per year. The estimated coefficient for the Mainline adherent rate, −0.0002, means that an increase in this variable by one standard deviation (12.5) would reduce the economic growth rate by 0.3 percent per year.8 Religious diversity in this specification is positive but not statistically significant. One striking difference in these results compared to most existing evidence and arguments is the positive relationship of Catholics with income growth.9 For example, Putnam (1993) argues that Catholic tradition may lead to less-trusting societies (and therefore negatively associated with economic growth) because of their emphasis on the vertical relationship with the

7 Our results indicate that all the variables in model 1 have VIF values less than 10. Most of these variables have VIF values of 2 or less. The model 2 has one variable that has a VIF value of 11 and most of the other variables have VIF values closer to 2 or less. According to Greene (2007), “there is no consensus on what values of the variance inflation factor merit attention, or on what one should do with the results. Some authors (Chatterjee and Price, 1991) suggest that values in excess of 10 are problematic. Others suggest 30 or 40 as a benchmark value” (p. E5–18). The results of VIF test are available from authors upon request. 8 One should not draw the conclusion from our results that the negative effect of total religious adherence on income growth may be due to the finding that only mainline Protestantism reduces income growth. Overall adherence rate includes other denominations that we do not investigate in this paper such as Jews, Orthodox Christians, and other minor denominations in addition to Catholics and Protestants. 9 A casual examination of our data we observe that there are 17 counties that have more than 75 percent of the population who are Catholics and there are 56 counties that have more than 60 percent of the population who are Catholics. To check the role of outliers, we ran regressions dropping these counties one set at a time respectively and observe that results are basically unchanged.

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Church rather than a horizontal association with fellow citizens. Several other studies argue along the same line or present evidence to support this claim (see Blum and Dudley, 2001; Guiso et al., 2003). One possible reason for our result may be that the belief argument (strong faith in afterlife) presented in Section 2 leading more Catholic counties to reduced transaction costs, hence higher income growth. What is more striking is that this result remains the same (positive and highly significant) when we use earlier Catholic adherent rates (1971 and 1980) in place of 1990 data in separate estimations for the growth period between 1990 and 2000. Noland (2005) using cross-country data finds that the Catholic denomination is negatively associated with short-run growth (1970–90) but positively associated with long-run growth (1913–98). Our results give mixed evidence to Weber’s claim that Protestants contribute positively in explaining the economic prosperity as our results indicate a negative and statistically significant association between Mainline Protestants and income growth. Noland (2005) finds Protestants having negative effect on short-run growth but positive effect on long-run growth. Heath et al. (1995), using US state data and modeling income levels rather than growth, find Catholic and fundamental Protestant variables to be negative and liberal Protestants to be positive. The difference between their results and ours may be due to several reasons: state level data vs. county level data; income levels vs. income growth; account for endogeneity; and mainline Protestants have higher incomes that grow at slower rates.10 This last reason may be supported by Tomes’ (1984) suggestion that earnings differences between Protestant denominations may be due to the sorting of these denominations according to schooling and income. We now turn to other explanatory variables included in the two specifications. The results show that the education variable has a positive and significant effect on subsequent per capita income growth. Per capita local tax variable is negative and only marginally significant implying that local taxes discourage local economic activities. We find that a local government expenditure on education and highways together is statistically significant at the 1 percent level but negative, providing no support for the hypotheses that state and local government expenditure on these public goods increase income growth. In contrast, counties that have access to an interstate highway grew more rapidly. The nonwhite variable is negative and significant at the 1 percent level indicating that minority dominated counties have lower income growth rates. State level right-to-work laws have a marginal negative impact on county income growth. Natural amenities index is statistically significant but has an unexpected negative sign. In order to deal with spillover effects of religious attendance and other variables and to test for robustness of our results, we use spatial econometric methods to estimate the two specifications described above. Results are presented in Table 4. The significant spatial parameters (rho and lambda) indicate that both types of spatial effects described above are present in our data. In the presence of statistically significant spatial lag and spatial error parameters, the above inference based on 3SLS may be biased and inefficient. Some differences exist in the results between the 3SLS and spatial models. The significant spatial parameters have interesting implications. A positive and significant spatial dependence in the dependent variable (growth rate) indicates that the growth rate in a particular county is associated with (not independent of) growth rates in surrounding counties. This is strong evidence that spillover effects exist between counties with respect to income growth. The highly significant spatial error coefficients suggest that a random shock in a spatially significant omitted variable (including religious adherence in neighboring counties) that affect growth in a particular county triggers a change in the growth rate not only in that county but also in its neighboring counties. Religious variables in model 1 are statistically significant at the 1 percent level and negative, implying that these variables have a significant impact on county income growth even after correcting for spatial dependence bias. The interstate highway access measure which is significant at the 1 percent level in the 3SLs specification is not significant in the spatial model 1. The amenity variable which was highly significant (at 1 percent) in the 3SLS estimation is now significant at the 5 percent level. The variables that measure the urban and the rural nature of a county were not statistically significant in the 3SLS specification but they are significant at the 1 percent level in the spatial model 1. These results now imply that rural counties grow slower and urban counties grow faster compared to the excluded category of metro-adjacent rural counties. Most of the regional variables improved their significance levels with the incorporation of spatial effects. Results of the religious variables in the spatial model 2 (Table 4) are somewhat different from those in 3SLS model 2 (Table 3). Effects of Catholic denomination, education, government expenditure, and nonwhite variables remain about the same. The evangelical Protestant variable is now marginally significant at the 8 percent level and positive but the mainline protestant variable loses its statistical significance. The religious diversity variable also becomes only marginally significant after incorporation of spatial effects. While interstate highway access and amenity scale variables are not statistically significant any more, as in the spatial model 1, the urban rural variables become highly significant in the spatial model 2. 6. Conclusions The purpose of this paper was to show that religious adherence along with other conventional determinants of growth can explain county economic growth in the US. Drawing on the theory of conditional convergence, we formulated a countylevel empirical growth model that relates local per capita income growth to religious adherent rates. We also accounted for reverse causality between income growth and church adherence and for spatial spillover effects. Our results support the view that religious adherent rates matter in economic growth in the United States. We found statistically significant negative

10

We are thankful to one of the reviewers for pointing out some of these reasons.

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association between church adherence in general and county income growth between 1990 and 2000. The regressions yield a robust pattern of coefficients with respect to Catholic denomination but results of Evangelical Protestants and Mainline Protestants differ, depending on specification. Mainline Protestants have a negative and significant impact on county income growth in the 3SLS specification but fail to remain robust with the incorporation of spatial dependence. The Evangelical Protestants variable is positive but not significant in the 3SLS model. This variable is positive and weakly significant in the spatial specification. Our results have some implications for future research on county economic growth. They show that religion is a significant determinant of county economic growth in the US and any future research on county economic growth should incorporate this aspect in growth models. Our results reinforce the view that it may be worthwhile to look beyond the conventional determinants of growth such as human capital, taxes, and government infrastructure expenditure when supporting county income growth initiatives. Our results also point to the fact that any future research that uses county-level ARDA data should be disaggregated to account for independent denominational effects as they seem to have different effects on potential dependent variables.

Appendix A. Code

Official name

Catholic 81

Catholic Church

Evangelical Protestant 1 Advent Christian Church 5 African Methodist Episcopal Zion Church 11 Allegheny Wesleyan Methodist Connection 17 American Baptist Association 27 American Evangelical Lutheran Church 32 Amish; Other Groups 39 Apostolic Christian Churches (Nazarean) 40 Apostolic Christian Church of America Inc. 45 Apostolic Lutheran Church of America 53 Assemblies of God 55 Associate Reformed Presbyterian Church 57 Baptist General Conference 59 Baptist Missionary Association of America 60 Barren River Missionary Baptists 61 Beachy Amish Mennonite Churches 63 Berean Fundamental Church 66 Bible Church of Christ Inc. 67 Fellowship of Fundamentalist Bible Churches 70 Bruderhof Communities Inc. 71 Brethren Church, The (Ashland, Ohio) 72 Brethren Church (Progressive) 75 Brethren in Christ Church 82 Central Baptist Association Ministries 83 Christ Catholic Church 84 Calvary Chapel Fellowship Churches 89 Christian and Missionary Alliance 91 Christian Catholic Church 97 Christian Churches and Churches of Christ 105 Christian Reformed Church in North America 107 Christian Union 108 Christian Unity Baptist Association 121 Church of God General Conference 123 Church of God (Anderson, Indiana) 125 Church of God (Apostolic) 127 Church of God (Cleveland, Tennessee) 133 Church of God (Seventh Day) 143 Church of God in Christ, Mennonite 145 Church of God of Prophecy 146 Church of God, Mountain Assembly Inc. 157 Church of the Brethren 163 Church of the Lutheran Brethren of America 164 Church of the Lutheran Confession 165 Church of the Nazarene 167 Churches of Christ 171 Churches of God, General Conference 173 Community of Christ

Code

Official name

250 251 257 258 259 263 264 265 266 269 273 283 284 285 286 287 288 289 291 296 297 304 306 320 322 323 324 325 329 335 336 339 347 349 351 353 356 359 360 361 362 363 365 367 369 370

Holiness Methodist Church Holy Orthodox Church in North America Hutterian Brethren Independent Free Will Baptists Associations Independent Fundamental Churches of America International Church of the Foursquare Gospel International Churches of Christ International Pentecostal Church of Christ Interstate & Foreign Landmark Missionary Baptists Association Jasper Baptist and Pleasant Valley Baptist Associations Landmark Missionary Baptists, Independent Associations, etc. Lutheran Church—Missouri Synod American Association of Lutheran Churches Mennonite Church Eastern Pennsylvania Mennonite Church Mennonite Church, The General Conference Mennonite Church USA New Hope Baptist Association Missionary Church Midwest Congregational Christian Fellowship Mennonite; Other Groups National Primitive Baptist Convention, USA New Testament Association of Independent Baptist Churches “Old” Missionary Baptists Associations Old Order Mennonite Old Order Amish Church Old Order River Brethren Old Regular Baptists Open Bible Standard Churches Inc. Orthodox Presbyterian Church Original Free Will Baptists Pentecostal Church of God Pentecostal Free Will Baptist Church Inc. International Pentecostal Holiness Church Pilgrim Holiness Church Christian Brethren Presbyterian Church in America Primitive Advent Christian Church Primitive Baptist Churches—Old Line Primitive Baptists Associations Primitive Baptists, Eastern District Association of Primitive Methodist Church in the USA Progressive Primitive Baptists The Protestant Conference (Lutheran) Protestant Reformed Churches in America Reformed Baptist Churches

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Appendix A (Continued ) Code 177 178 179 181 183 185 189 191 196 197 199 201 203 205 206 208 209 211 213 215 216 217 218 219 220 221 223 230 237 241 244 247

Official name Congregational Holiness Church Conference of the Evangelical Mennonite Church Conservative Baptist Association of America Conservative Congregational Christian Conference Conservative Mennonite Conference Cumberland Presbyterian Church Duck River and Kindred Baptists Associations Enterprise Baptists Association Evangelical and Reformed Church Evangelical Church Evangelical Congregational Church Evangelical Covenant Church Evangelical Free Church of America Evangelical Lutheran Church in America (Eielsen Synod) Evangelical Lutheran Church Evangelical Lutheran Churches, Association of Evangelical Lutheran Synod Fellowship of Evangelical Bible Churches Evangelical Mennonite Church Evangelical Methodist Church Evangelical Presbyterian Church Fire Baptized Holiness Church, (Wesleyan) Evangelical United Brethren Church Finnish Evangelical Lutheran Church (Suomi Synod) Association of Free Lutheran Congregations Free Methodist Church of North America National Association of Free Will Baptists Fundamental Methodist Conference Inc. Mennonite Brethren Churches, US Conference of General Six Principle Baptists Grace Brethren Churches, Fellowship of Holiness Church of God Inc.

Mainline Protestant 19 American Baptist Churches in the USA 29 American Lutheran Church 193 Episcopal Church 195 Estonian Evangelical Lutheran Church 207 Evangelical Lutheran Church in America 226 Friends (Quakers) 262 International Council of Community Churches 274 Latvian Evangelical Lutheran Church in America 281 Lutheran Church in America 290 Universal Fellowship of Metropolitan Community Churches 292 Moravian Church in America—Alaska Province 293 Moravian Church in America—Northern Province 294 Moravian Church in America 295 Moravian Church in America—Southern Province Orthodox 7 22 34 78

Orthodox Church in America: Albanian Orthodox Archdiocese American Carpatho-Russian Orthodox Greek Catholic Church Antiochian Orthodox Christian Archdiocese of North America Bulgarian Orthodox Diocese of the USA

Code

Official name

373 375 379 386 388 403 405 409 412 413 414 415 418 419 420 426 430 432 436 438 441 444 447 455 463 466 467 469 497 498 499

Reformed Church in the United States Reformed Episcopal Church Reformed Mennonite Church Regular Baptists General Association of Regular Baptist Churches Salvation Army Schwenkfelder Church Separate Baptists in Christ Slovak Evangelical Lutheran Church Seventh-day Adventist Church Stauffer Mennonite Church Seventh-Day Baptist General Conference, USA and Canada Southwide Baptist Fellowship Southern Baptist Convention Strict Baptists Two-Seed-in-the-Spirit Predestinarian Baptists Truevine Baptists Association Unaffiliated Conservative Amish Mennonite Church United Baptists Church of the United Brethren in Christ United Christian Church United Evangelical Lutheran Church United Lutheran Church in America United Reformed Churches in North America Vineyard USA Wayne Trail Missionary Baptist Association Wesleyan Church Wisconsin Evangelical Lutheran Synod Black Baptists Estimate Independent, Charismatic Churches Independent, Non-Charismatic Churches

307 313 328 338 355 358 371 390 391 392 443 449 453

Netherlands Reformed Congregations North American Baptist Conference Oregon Yearly Meeting of Friends Church Pacific Yearly Meeting of Friends Presbyterian Church (U.S.A.) Presbyterian Church in the U.S.A. Reformed Church in America Religious Society of Friends (Conservative) Religious Society of Friends (General Conference) Religious Society of Friends (Philadelphia and Vicinity) United Church of Christ United Methodist Church United Presbyterian Church in the United States of America

333

Malankara Orthodox Syrian Church, American Diocese

334

Malankara Archdiocese of the Syrian Orthodox Church in North America Romanian Orthodox Archdiocese in America and Canada

395

400

186 245 246

Byelorussion Council of Orthodox Churches in North America Coptic Orthodox Church Greek Orthodox Archdiocese of Vasiloupulis Greek Orthodox Archdiocese of America

330 331 332

Macedonian Orthodox Church: American Diocese Orthodox Church in America: Territorial Dioceses Orthodox Church in America: Bulgarian Diocese

423 431

Orthodox Church in America: Romanian Orthodox Episcopate of America Patriarchal Parishes of the Russian Orthodox Church in the USA Russian Orthodox Church Outside of Russia Serbian Orthodox Church in the USA Serbian Orthodox Church in the USA (New Gracanica Metropolitanate) Syrian Orthodox Church of Antioch Ukrainian Orthodox Church of the USA

141

Church of God in Christ

80

Black Protestant 101 Christian Methodist Episcopal Church

397

401 410 411

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449

Appendix A (Continued ) Code 110 Jewish 270 271 Other faith 15 56 65 68 76 85 111 129 148 149 151 174 188 231 233 252 267 268 277 279

Official name

Code

Official name

Church of Christ (Holiness), U.S.A.

384

Reformed Zion Union Apostolic Church

Conservative Judaism Reform Judaism

496

Jewish Estimate

Amana Church Society Bahá’í Bethel Ministeral Association Inc. Bohemian and Moravian Brethren Buddhism Central Yearly Meeting of Friends Church of Christ, Scientist Church of God (New Dunkards) Church of Jesus Christ (Cutlerites) Church of Jesus Christ, The (Bickertonites) Church of Jesus Christ of Latter-day Saints Congregational Christian Churches Divine Science Church General Association of General Baptists General Church of the New Jerusalem Hindu Muslim Estimate Jain Life and Advent Union Lumber River Annual Conference of the Holiness Methodist Church

298 299 303 315 318 357 381 383 416 425 433 434 435 452 459 461 462 464 490

Missionary Church Association Missionary Bands of the World Inc. National Spiritualist Association of Churches North American Old Roman Catholic Church Old Catholic Church in America Presbyterian Church in the United States Reformed Presbyterian Church, Evangelical Synod Reformed Presbyterian Church of North America Sikh Tao Universalist Church in America Unitarian Churches Unitarian Universalist Association of Congregations United Presbyterian Church of North America United Zion Church Unity of the Brethren Vedanta Society Volunteers of America Zoroastrian

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