EXPORTING CHRISTIANITY: GOVERNANCE AND DOCTRINE
IN THE GLOBALIZATION OF US DENOMINATIONS
Gordon H. Hanson
UC San Diego and NBER

Chong Xiang
Purdue University and NBER

June 2013

Abstract
In this paper we build a model of market competition among religious denominations, using aframework that involves incomplete contracts and the production of club goods. We treat
denominations akin to multinational enterprises, which decide which countries to enter based on
local market conditions and their own “productivity.” The model guides us in estimating how adenomination’s religious doctrine and governance structure affect its ability to attract adherents.
Using data on the foreign operations of US Protestant denominations in 2005 from the World
Christian Database, we find that (1) denominations with stricter religious doctrine attract more
adherents in countries in which the risk of natural disaster or disease outbreak is greater and inwhich government provision of health services is weaker, and (2) denominations with adecentralized governance structure attract more adherents in countries in which the pastor cost ofconnecting with congregants is lower. These findings illuminate factors shaping the compositionof religion within countries, helping account for the rise of new Protestant groups. They also
provide empirical evidence for the recent theoretical developments in organization and trade.

We thank Eli Berman, Bob Gibbons, Avner Greif, Murat Iyigun, Larry Iannaccone, and Bob Staiger for
helpful comments; Anna Allen, Yuri Bots, Sohini Chowdhury, Oana Hirakawa, Kanako Hotta, Bryan
Lofland, Lindsay Oldenski, Lindsay Rickey, and Chase Stafford for research assistance; and theTempleton Foundation and the Center on Pacific Economies at UCSD for financial support.

1.

INTRODUCTION

Rivalry between religions is a constant feature of human society. Historically,
competition was often resolved by military conflict (Iyigun, 2008), with victors imposing theirreligious beliefs on the vanquished. Today, it is common for religious groups to compete foradherents through the market. Over the last 50 years, options for religious practice have
expanded globally, as the end of colonialism, the demise of communist regimes, and the spreadof democracy have weakened the control of state-sponsored churches and reduced the prevalenceof governments based on anti-clericalism (Mickelthwait and Wooldridge, 2009). Religious
groups, be they Christian, Jewish, or Muslim, are increasingly international in outlook, with thegoal to build global bodies of believers, rather than simply national ones (Thomas, 2010).
Christian groups have a long tradition of market-based competition, dating back to theProtestant Reformation (Ekelund, Hebert and Tollison, 2006). Over time, Protestantdenominations have grown at the expense of Catholics and the Orthodox. 1 Within Protestantism,
there is competition between mainline denominations, which trace their origins to the
Reformation, and groups arising in the last two centuries, including Pentecostals, Mormons,
Jehovah’s Witnesses, and Seventh Day Adventists. In 2010, Pentecostalism and related groups
claimed 400-600 million adherents worldwide, accounting for a quarter of all Christians. While
competition within Christianity has attracted considerable interest from other social sciences(Meyer, 2004, and Robbins, 2004), it has received little attention from economists.
In this paper, we build a model of competition among religious groups and apply it to theforeign operations of Protestant denominations headquartered in the US. We treat adenomination akin to a multinational enterprise, which differentiates its own “brand” along thedimensions of religious doctrine and organizational structure. A denomination’s organization anddoctrine are typically established early in its history and thereafter stable over time (Melton,
1989; Chaves, 1993b). We can therefore examine how organization and doctrine affectdenomination performance, distinct from the usual context in which multinational organizational
structure is endogenous (e.g., Nunn, 2007; Rajan and Wulf, 2006; Marin and Verdier, 2008).
In our theoretical model, each country consists of many local religious markets. A
denomination chooses which markets to enter based on the combined objectives of attracting
1

Between 1970 and 2005, the share of Protestants among Christians rose from 26% to 35%, while the share of
Christians in the global population remained stable at 33%.

1

members and generating revenues. 2 It enters a market by choosing to recognize a local
congregation as a member of its global organization, which is analogous to international
licensing or franchising, and by agreeing to have a local pastor manage the congregation. We
assume denominations vary in their attractiveness to believers, which we treat as a fixedcharacteristic similar to firm productivity (Melitz, 2003). Entry into a local market is subject to a
fixed cost, which keeps low productivity groups from entering unattractive locations.
Organizational structure takes one of two broad forms. Centralized denominations placeauthority over pastors and doctrine in the hands of international bodies, whereas decentralized
denominations give individual congregations control (Chaves, 1993a). We assume thattransactions between a pastor (manager) and a denomination (headquarters) are subject toincomplete contracts (Grossman and Hart, 1986). 3 In decentralized denominations, the pastor has
greater authority, which increases his incentive to invest in serving his congregation; incentralized denominations, the denominational headquarters has greater authority, which gives it
more control over how its member congregations operate.
Following Iannaccone (1992), we assume that the doctrinal strictness of a denomination
affects free riding among its members. The functions of a congregation include organizing
worship, educational activities, charitable undertakings, and other social services, in whichcongregants are both consumers and producers. These services have the quality of club goods,
giving congregants an incentive to free ride on the efforts of others. Having a stricter religious
doctrine limits the access of church members to secular goods, thereby increasing their incentive
to participate in church life. When the cost of secular goods increases, participation intensity(and the attendant production of club goods) increases more among strict groups than among lessstrict ones, raising the marginal utility in the former group relative to the latter.
Our model has two predictions. First, in countries where the cost of relevant secular
goods is higher, stricter denominations will have larger membership, due to their being more
efficient in producing club goods. Second, in countries where the cost to pastors of serving
congregants is lower, decentralized denominations will have larger membership, due to their
pastors’ stronger investment incentives. These predictions guide our empirical estimation, in
which we use the World Christian Database to examine the number of adherents that US-

2
3

See Iannaccone (1998a) for a discussion of literature on treating churches as payoff maximizing firms.
See Allen (1995) for an incomplete contracts view of church organization.

2

headquartered denominations attract in foreign markets in 2005.
To empirically implement our first prediction on how the cost of secular goods affects
religious adherence, we draw on recent literature testing Iannaccone (1992). Hungerman (2005)
and Gruber and Hungerman (2007) show that government social services are substitutes for theclub goods that churches provide. Historically, when the US government has expanded(contracted) social spending, donations to and participation in Christian churches has decreased(increased). Berman (2000) and Chen (2010) obtain related findings when examining ultraorthodox Judaism in Israel and Islamic worship in Indonesia, respectively. 4
The supply of social services that we can measure reliably across countries at all income
levels relate to health care. We also observe variation in the demand for health services
associated with incidence of natural disasters and disease outbreaks. Weaker supply of publichealth services or stronger demand for health services drives up the cost of obtaining these
services – either by increasing queuing for government-provided health care or the price ofprivately supplied health care – which according to our model means stronger demand for strict
denominations over non-strict ones. For instance, prior to the 2010 earthquake in Haiti, the mostactive Protestant groups in the country were US denominations with strict religious doctrine(including Adventist and Pentecostal churches). In the aftermath of the earthquake, these groups
saw sharp increases in church activity. 5 In the estimation, we find that, controlling fordenomination and country fixed effects, stricter denominations attract more members incountries with a weaker provision of health services and with higher incidence of disasters or
disease. Placebo tests show no such interaction effect between these country characteristics andtheoretically irrelevant features of religious doctrine, such as the frequency of communion. Ourcontribution to the literature is to show how demand for and supply of public services affect therelative success of strict religious groups across a large number of countries. 6
To implement our second prediction, we need measures of costs that pastors face inserving their congregants. Beyond the worship service, pastors connect with congregants through4

See also Dehejia, DeLeirec, and Luttmer (2007) and Scheve and Stasavage (2006).
See Tom Phillips, “Religion Fills Void Left by Aid Agencies,” The Guardian, Jan. 25, 2010; Anne Barnard,“Suffering, Haitians Turn to Charismatic Prayer”, New York Times, November 24, 2010; Kwame Dawes, “AmidDisasters, A Preacher Holds Fast to His Faith in Haiti,” USA Today, Jan. 5, 2011; and Christophe Wargny, “Haiti inthe Hands of the NGOs,” Counterpunch, Jan. 7, 2011.
6
Recent literature examines religiosity across countries (Barro and McCleary, 2005; McCleary and Barro, 2006;
Barro and Hwang, 2007), but does not consider within-country competition among religious groups.
53home visits, participating in community events, and other forms of outreach. The ease ofoutreach is likely to depend on local communication and transportation costs. Existing literature
documents that expanding transportation networks is associated with increased local trade(Donaldson, 2010) and more frequent transactions between buyers and suppliers (Datta, 2012),
with these effects being stronger in service industries (Chandra and Thompson, 2000).
Improvements in land transportation may thus benefit activities requiring face-to-faceinteraction, such as religion. Similarly, improved communication services are associated withstronger connections between local buyers and sellers, particularly in developing-country
markets (Jensen, 2007; Aker, 2010; Aker and Mbiti, 2010). Consistent with our model, we findthat decentralized denominations attract more members in countries with better communications
and land transportation. 7 Placebo tests show that no such interaction effect exists for airtransportation, which is unlikely to affect pastor costs of connecting with members.
Our findings on denominational organizational structure are relevant for recent literature
on organizations in international trade (Antràs and Rossi-Hansberg, 2009). Antràs and Helpman(2004) examine theoretically the entrepreneurs’ choice between a centralized structure ofvertically integrating with suppliers versus a decentralized structure of contracting with
suppliers. Bloom, Sadun, and Van Reenen (2009) examine empirically what makes multinationalfirms more decentralized in their internal organization. We complement this work by showing
where decentralized organizations are more likely to succeed.
In section 2, we present our data on the size, religious doctrine, and governance structureof denominations. In section 3, we present a model of entry and competition among religious
groups. In section 4, we derive the empirical specification and discuss estimation issues. In
section 5, we present our estimation results. And in section 6, we offer final discussion.

2. DATA AND EMPIRICAL SETTING
2.1 Christian denominations
The data for our analysis are from the World Christian Database (WCD). The WCD
tracks religious affiliation for Christian denominations across countries, giving numbers of

7

This finding is consistent with recent literature in public finance and macroeconomics (e.g., Fernald, 1999; Roller
and Waverman, 2001) that links labor productivity and infrastructure.

4

affiliated members and congregations in 1970 and 2005. 8 Each denomination is identified by its
name, religious tradition (e.g., Baptist, Holiness-Pentecostal, Reformed-Presbyterian), andmegabloc, and is accompanied by information on its international affiliation. Megablocs include
Roman Catholics, Orthodox, and Anglicans, which constitute the historic or traditional church;
conventional Protestants; Independents, which includes churches that have split from Protestantdenominations or that are unaffiliated with international church bodies; and Marginals, which aregroups considered outside the Christian mainstream, the largest of which are the Mormons andthe Jehovah’s Witnesses. With some abuse of terminology, we use the term Protestant to refer tofour WCD blocs: Anglicans, traditional Protestants, Independents, and Marginals.
In the raw WCD, there are over 6,300 individual denominations, which represent a muchsmaller number of denominational aggregates that share an international governing body,internal organizational structure, and religious doctrine. We form denominational aggregates bycombining sub-denominations that have a common megabloc, religious tradition, and name orassociation with an international governing organization. 9
We focus on Protestant denominations headquartered in the US, the country for which wehave by far the most complete data. Between 1970 and 2005, the share of Protestants belonging
either to the UK-headquartered Anglican Church or to other non-US-headquartereddenominations each fell, the former from 14% to 10% and the latter from 17% to 15%.
Denominations headquartered in the United States saw their market share rise from 13% to 23%.
Sources beyond the WCD, including The Handbook of Denominations in the United States,(Mead, Hill, and Atwood, 2001), help us link US religious groups across countries, permitting us
to identify the global operations of US denominations. The absence of comparable data makes
similar matching problematic for non-US based groups. Our sample consists of 130 US

8The WCD is maintained by Gordon-Conwell Theological Seminary and builds on material originally published in
the World Christian Encyclopedia (WCE) by Barret, Kurian, and Johnson (2001). The WCE compiled periodic
censuses that individual Christian denominations conduct of their membership and produced initial estimates of thenumber of adherents by denomination in 1970. These estimates were cross-checked against information obtained
from national censuses, national church bodies, and interviews of church leaders. The WCD further updates
denomination counts in the WCE to 2005, based on cross-correlated information from 5,000 questionnaires ofnational church bodies, field surveys in 200 countries, a large body of published and unpublished contemporary
material provided by individual churches, and interviews of bishops, church leaders, and theologians.
9
In some countries, owing to government regulation of religion, the WCD groups denominations into “union of
bodies of different traditions.” We drop a country from the sample if more than 20% of its affiliated Christians fall
into this category. These include six large nations (Australia, Canada, China, Congo, Germany, and Pakistan) and 10
small ones, which in 2005 represented 13% of Christians in the WCD.

5

denominations that have adherents abroad. 10
The denominations in our sample have distinct origins. Mainline traditions include
movements brought from Europe to the United States in the 17th and 18th centuries (e.g.,
Congregationalists, Lutherans, Methodists, Presbyterians, and Quakers). In 2005, they accounted
for 12% of adherents outside of the United States that belong to US denominations, down from
23% in 1970. Evangelical and fundamentalist denominations, such as Southern Baptists, include
groups that split off from mainline denominations in the 19th and early 20th centuries, typicallyover doctrinal disputes. From 1970 to 2005, their share of foreign adherents in US denominations
fell somewhat, from 29% to 25%. 11 Pentecostalism, 12 now a century old, espouses speaking intongues and other ecstatic practices including healing and prophesying. Between 1970 and 2005,
the share of foreign adherents in US denominations belonging to Pentecostal or other so-called“renewalist” groups rose from 22% to 37%. Mormons and Jehovah’s Witnesses are consideredoutside the Christian mainstream because they accept religious texts other than the Christian
Bible. Between 1970 and 2005, these two groups, plus Seventh Day Adventists, saw their shareof foreign adherents belonging to US denominations rise from 16% to 21%. 13
Table 1 lists the 35 largest Protestant denominations worldwide (based on their adherents
outside the US), of which 24 are headquartered in the United States. The size of denominations
varies immensely, with the largest US denomination (Assemblies of God) having 42.4 million
adherents outside of the United States in 2005, the 10th largest (United Methodist Church USA)
having 3.7 million, the 30th largest (Pentecostal Church of God) having 0.6 million, and all
denominations below the 80th rank having fewer than 0.1 million. Figure 1a plots for US
denominations the number of adherents abroad against the number of adherents in the UnitedStates. The strong positive relationship indicates that denominations successful in the United
10

We begin with 204 Protestant denominations headquartered in the United States. Of these, 16 are not found in the
WCD (including some older groups that have been subsumed into newer denominations and some very small groups
not captured by the WCD), 42 have no congregations in the WCD outside of the US (including old denominations in
the process of dying out and a few recently created entities with minimal foreign presence), 8 have congregations
outside the US but only in countries excluded from the sample owing to aggregation problems (see note 9), and 8 arevery small denominations on which we could find no information on their organization or doctrine.
11
Another schismatic tradition, the Holiness Movement, split off from mainline Methodists in the late 19th century.
It emphasizes the restrictive doctrine of sanctification, in which believers purify themselves of sin (and are then tosin no more). Between 1970 and 2005, their share of foreign adherents of US denominations fell from 12% to 6%.
12
A related group, the Charismatic Movement, emerged in the United States in the 1960s and includes individuals
who left mainline denominations and embrace speaking in tongues.
13
Additionally, Mormons maintain a strict dietary regimen, rigid guidelines on charitable giving, and a requirementthat young men provide two years of missionary service; Jehovah’s Witnesses have an elaborate theology
surrounding the end of the world and are required to go door-to-door to convert non-believers.

6

States are also successful globally, consistent with a Melitz (2003) style framework.
There is also variation in the number of countries in which denominations operate. Most
denominations are present in fewer than a dozen countries. Table 2 shows the number ofcountries in the sample in which US denominations have a presence in 1970 and in 2005. In
1970, only 10.9% of country-denomination cells have positive entries; by 2005, the share rises to
13.5%. As seen in Figure 1b, US denominations that attract more adherents in the United States(and presumably are more “productive”) are present in more countries abroad. In the empiricalanalysis, we address the entry of denominations into countries in order to control for possible
selection bias in estimating determinants of denomination size.

2.2 Denominational doctrine and governanceIn the production of religious services, one can think of the church as the factory, thepastor as the manager, and, given that worship is a collective activity, the congregants as both
workers and consumers (Iannaccone, 1998a). The denomination provides the intellectual
property used in production, which includes religious doctrine and a system of governance(Chaves, 1993a). Denominations range in form from loose membership associations to rigidly
hierarchical bodies. We code denominations according to the their governance structure and thestrictness of their religious doctrine, using information from Melton (1989), Barrett, Kurian, andJohnson (2001), the World Christian Database, and denomination websites. In many cases, thecore elements of a denomination’s doctrine and governance were established by a religious
entrepreneur who founded the organization. 14 In the early years of a movement, some elementsof doctrine and structure are malleable but once codified tend to change only slowly over time
(Melton, 1989; Chaves and Sutton, 2004; Finke and Stark, 2008).
Congregations that belong to a denomination share a defined religious doctrine, given in
the denomination’s statement of faith. The doctrine is a system of belief that is in part what
attracts adherents to church. Christianity is organized around the life and teachings of Jesus
Christ, as described in the New Testament of the Christian Bible. Previous literature has
attempted to define doctrinal strictness of Christian groups in order to apply the Iannaccone(1992) model. Following Hoge (1979), Iannaccone (1998b), and Ekelund, et al. (2006), we
14

For example, Aimee Semple McPherson founded the Foursquare Church, Joseph Smith founded the Mormons,and Charles Taze Russell founded the Jehovah’s Witnesses. See Allen (1995) for case studies of church founding.

7define strict religious doctrine to include the beliefs listed in Table 3, which also gives the shareof US denominations that abide by these beliefs: (a) the Bible is the infallible word of God, (b) to
become a Christian one must openly repent and convert (be “born again”), (c) one should try
actively to convert others to Christianity, (d) Christ’s return to earth (second coming) isimminent, (e) non-believers are damned to eternal suffering, (f) one should live by a strict moral
code (modest dress, no smoking or drinking, no sexual activity outside marriage, etc.), (g)believers should be “sanctified” (see note 11), (h) speaking in tongues is evidence of one’s
religiosity, and (i) divine healing is possible.
Some beliefs – evangelism, repentance and conversion, infallibility of the Bible, anddamnation of non-believers – are common to most groups, with at least 70% of US
denominations adopting one of these. Others – the imminence of Christ’s second coming,
sanctification, speaking in tongues, behavioral restrictions, and divine healing – are lesscommon. The second set of beliefs includes the dimensions along which strict groups
differentiate themselves. For instance, the Assemblies of God, a Pentecostal denomination,
endorses speaking in tongues but not sanctification, whereas the Church of the Nazarene, aconservative ‘Holiness’ denomination, endorses sanctification but not speaking in tongues. Bothendorse divine healing, though they differ on the imminence of Christ’s second coming. Each ofthese beliefs imposes time costs, lifestyle constraints, and impediments to maintaining
relationships with non-Christians or Christians in other denominations. In the language of
Iannaccone (1992), they stigmatize believers, raising the cost of participating in secular activities
and helping other adherents identify those willing to be active in congregation life.
To measure strictness in an environment in which denominations are differentiated, we
define a denomination to be strict if the row mean of the nine doctrinal dummy variables isgreater than or equal to 0.5, which applies to 45% of the denominations in the sample. Figure 2
shows the distribution for the row mean of the nine doctrine variables for US denominations.
The cutoff of 0.5 captures a break in the distribution between the top two quintiles ofdenominations and the rest; the cutoff divides denominations between those above the medianand those at the median or below. Empirical results presented in section 4 are robust to raising
the strictness cutoff to 0.6, which reduces the share of strict denominations to 32%, and to
dropping any individual dimension of strictness from the calculation of the row mean.
In Iannaccone’s (1992) club good model of religious organizations, which we incorporate
8into our model of denominations in section 3, stricter groups elicit greater participation fromtheir members by raising the perceived cost of secular goods and services. To examine how
strictness relates to religious participation among the denominations in our data, we use theGeneral Social Surveys in 2000, 2002, and 2004, which sample the US population on religion,
politics, and social behavior. The GSS contains detailed data on the denominational affiliation ofindividuals who self-identify as Protestant, where the GSS denominations span two thirds ofthose in our data. 15 In unreported results, we find that, controlling for age, gender, andeducational degree, individuals in the GSS belonging to strict denominations (according to ourdefinition) are 25% more likely than individuals in non-strict denominations to attend religious
services at least once a week. These findings are consistent with survey evidence from othersources (Iannaccone, 1998b), which show that the strictness of the religious group to which
individuals belong correlates positively with the time and money that individuals contribute tochurch organizations. In the US, strict denominations thus succeed in attracting individuals witha relatively strong willingness to participate actively in church life.
In terms of organization, belonging to a denomination means a congregation agrees togovern itself according to a pre-specified structure. There is well-recognized variation in the
degree of centralization among denominational governance systems. 16 The most decentralized
system is a congregational polity, in which the congregation retains control over the hiring andfiring of pastors and religious doctrine (Chaves, 1993b). The denomination, through national orinternational bodies, operates at arms’ length. It provides congregations with a range of services,
including recommending pastoral candidates, providing guidance on theology, publishingeducational material, training pastors and lay leaders, extending loans for church construction,organizing national ministries to reach new converts, raising funds to support global operations,and organizing relief in response to disasters (as in Haiti following the 2010 earthquake). In
return for these services, congregations pay fees to the denomination. 17 Local churches, in effect,
use the denomination as a consulting service.
In centralized denominations, control rights reside not in the congregation but higher up
15

For one-third of the individuals in the GSS, the denomination affiliation given (e.g., Other Baptist, Other
Presbyterian, Church of God) is too aggregate to match to the denominations in our sample.
16
Bloom, Sadun, and Van Reenen (2009) suggest that regional variation in the organization of religion may affectorganizational choices by private firms. They find that multinational firms are more prone to centralized decision
making in regions in which hierarchical religions (Catholicism, Eastern Orthodoxy, and Islam) are more prevalent.
17
In the United States, individual congregations on average keep 79% of the revenues they generate, a share that hasremained stable over time (Chaves, 1998).

9

in the denominational hierarchy. Denominational bodies above the congregation screen
applicants to the ministry, assign pastors to churches, discipline pastors, and set religious
doctrine for member churches. The denomination, in effect, has the power to license its brand –including the denomination name, religious doctrine, and governance structure – to individual
congregations and decide who will manage each church. There are two common forms ofcentralized governance. In an episcopal (or connectional) structure, power resides in thebishopric, as in the United Methodist Church, the Evangelical Lutheran Church, and the Mormon
Church. The chief authority over congregations within a region is a bishop, who ordains pastors,
assigns pastors to churches, adjudicates disputes, and performs administrative duties. A councilof bishops controls church doctrine. A second hierarchical system is the presbyterian structure,
as in the Presbyterian Church USA and the Reformed Church. Power resides in a regional
governing body known as the presbytery, which consists of a pastor and an elder from eachcongregation, and other church leaders. The presbytery ordains, installs, and removes pastors andestablishes and dissolves congregations. Above the presbytery is a general assembly, which
resolves disputes at the presbytery level and settles issues of religious doctrine.
We define a denomination to be decentralized if it has a congregational polity, which as
seen in Table 3 applies to 55% of denominations. Among the denominations, the correlation
between having a congregational polity and being strict is -0.16 (and insignificant), implying that
decentralized denominations are weakly less likely to have a strict religious doctrine.
Denominations tend to enter a country by supporting missionaries or organizing mass
revival meetings, as documented in an expanding literature (e.g., Brouwer, Gifford, and Rose,
1996). Once it has established itself in a market, a denomination may grow by attracting newmembers to existing congregations or by adding congregations. Figure 3 plots the log number ofaffiliated Christians against the log number of congregations, where each data point represents
the worldwide total for a denomination. The linear relationship between adherents andcongregations suggests that denominational expansion occurs more on the extensive margin(adding congregations) than on the intensive one (adding members to existing congregations).

3. THEORYTo briefly outline our framework, we present a model of competition between religious
denominations. There are many countries and each country is segmented by geography into10

many regional markets. First, each denomination decides whether to enter each regional market.
If entry takes place a denomination establishes one congregation in a region. Second, to manage
the congregation the denomination (the principal) works with a pastor (the agent). The
denomination and the pastor have the same objective of maximizing a weighted sum ofcongregation membership and monetary income. However, the pastor exerts effort, which raises
congregation members’ utility, and bears the cost of this effort, where effort levels are subject tocontractual incompleteness. The pastor chooses his effort level and – depending on thegovernance structure – either the pastor or the denomination sets the membership fee to charge
each congregation member. Third, within each regional market the denominations that haveentered compete to attract congregation members, who choose which denomination to attend.
(there is no competition between regional markets). Fourth, competition among denominations
determines congregation membership in each regional market and each denomination-pastor pairbargain over the monetary surplus that they jointly generate. Finally, aggregating across regional
markets within a country determines the numbers of congregations and congregation membersthat each denomination has in each national market.
In what follows, we proceed by deriving the demand for participation in denominations,characterizing the interaction between denominations and pastors in supplying religious services,solving for the market equilibrium, and deriving comparative statics for relative denomination
size, where we distinguish denominations by their strictness and governance structure.

3.1 Properties of the indirect utility function
In this subsection we explicitly derive the congregation members’ indirect utility. To doso we model the congregation members’ choice of participation intensity given their choices ofdenomination, drawing on the club-good framework of Iannaccone (1992). In this framework,
congregants derive utility from religious participation and the consumption of a secular good.
One example of the secular good is government-provided social services, which are substitutes
for the club goods and other church-provided services, as documented by Berman (2000),
Hungerman (2005), Gruber and Hungerman (2007), and Chen (2010). To be specific, thecongregants are identical and each obtains utility,u=(.) ( S d + K d )1/ d ,(1)where K = RαQ1-α, Q = R , and α and d are constants between 0 and 1. Equation (1) says that11

utility is CES between the secular good, whose quantity is S, and that of religious participation,
whose sub-utility is K. K, in turn, depends on the congregant’s own participation intensity, R,
whose expenditure share is α, and the average participation intensity of the other members of thesame congregation, Q = R . Each congregant has income I, and pays the shadow prices πS and πR
for S and R, respectively. πS captures both the explicit and implicit prices for obtaining S. When S
is allocated via a price mechanism, πS will be an explicit price; when S is quantity rationed, as
under government provision of services, πS will be the implicit queuing cost (e.g., delays inscheduling, waiting in line) to obtain the good. Each congregant pays a per-person membershipfee, p, which may take the form of volunteer work or donations. 18 The congregant thenmaximizes utility (1) subject to the budget constraint and taking other members’ choices ofparticipation as given. At the Nash-equilibrium the choice of R is the same for all congregants,
and the indirect utility function is
V (.)
=

ρ
I − p 1/ d
D ,=
Π π S− ρ + π R− ρα d ,=D π S− ρ + π R− ρα ρ ,Π(2)where ρ = d/(1 – d) and ρ/d > 1 is the substitution-elasticity between S and K in (1).
It is straightforward to show, using (2), that –β ≡ ∂V/∂p < 0; i.e., an increase in themembership fee decreases members’ disposable income and reduces utility. We show in the
Appendix that (see also Iannaccone 1992):∂V> 0 if∂π S
d > α, and
1πS
1− d ρ 1.
)>(πR
d −α αLemma 1.

(3.1)
(3.2)

Condition (3.1) requires that the secular good and religious participation are close substitutes,
which is consistent with Hungerman’s (2005) and Gruber and Hungerman’s (2007) findings that
increases in social spending by the US government are correlated with decreases in charitable
activities by US Christian churches. Condition (3.2) requires that πS is not too low (i.e., secular
goods are not too cheap relative to religious goods). To see the intuition for Lemma 1, consider

18

We leave unspecified whether the membership fee is paid in donations of time or money owing to Gruber’s(2004) empirical finding that religious giving and religious attendance are substitutes.

12

an increase in πS, holding πR constant. Real income falls, 19 which tends to reduce utility, but it
also raises individual and average participation intensity in a congregation, as long as (3.1) holds,
consistent with our findings from the GSS discussed in section 2. 20 Under (3.2), the value of
religious participation, R, is sufficiently high such that the utility gains from higher participation
more than offset the loss in real income. We also show in the Appendix that
∂ 2V
πS
π
Lemma 2. Given (3.1) and (3.2), then as long as
is not too high,> 0 . If S
2
∂ (π S )πR
πR∂ 2V
is too high, then< 0.
∂ (π S ) 2
To see the intuition of Lemma 2, note that the Nash equilibrium of the club-good model isinefficient, as a congregant does not internalize the benefits of his/her own participation on othermembers and so under-participate. When∂V
> 0 , the increase in πS alleviates the efficiency∂π Sloss and moves indirect utility V closer to the efficient level. If πS is not too high, the efficiency
gain is sufficiently large that V rises at an increasing rate with πS. However, if πS is too high, V isalready close to the efficient utility level, leaving little room for further efficiency gain andcausing V to increase with πS at a decreasing rate. We provide a numerical example to show how
V changes with πS in Appendix Figure A1. Hereafter, we assume parameter values are such that

∂V
∂ 2V
> 0.
> 0 and2∂π S∂ (π S )(4)

3.2 Model setup and the demand for religion
In this subsection we lay out the market structure of our model and examine thecongregation members’ choice of denominations. Each country k has many regional markets,mindexed by m. In market m of country k, a total number of Ok individuals choose among
religious organizations indexed by j. To be present in a regional market, a denomination must
establish a congregation, incurring fixed cost, fk, which implies that each denomination j will

19

Consistent with this conception of the costs of religious participation, Lipford and Tollison (2003) find that
increased religious participation is associated with lower individual income.
20
We follow Iannaccone (1992) and model strictness as a way to alleviate the free-rider problem in producing
worship services. Strictness may also help deal with adverse selection.

13

establish no more than one congregation in each regional market m. 21 Denomination j provides
its congregations with access to its credence goods, whose additional value in utility (relative to acongregation with no denominational affiliation) is δjk. Each congregation is managed by apastor, who oversees worship services and outreach to its members. The pastor exerts effort e mjk .
As e mjk increases so does the quality of the congregation’s services.
Members of the congregation enjoy services provided by the church, including a personal
relationship with the pastor, and contribute time and money to help provide worship services,
outreach to new members, charitable activities, and other club goods. We adopt a discrete choiceframework and specify that the utility for person i in country k from participating in
denomination j in local market m, U ijkm , is given by,

U ijkm = (δ jk + e mjk ) + V jkm (π Sjk , π Rjk , I mjk − p mjk ) + ε ijkm ,(5)where e mjk is the pastor’s effort level, which captures the pastor’s contribution to the religious
services perceived by congregants. The indirect utility V(.) is given by equation (2), and weassume that the shadow prices πSjk and πRjk are constant across regions within a country. ε ijkm is an
iid extreme value error term, observable to person i. Implicit in (5) is our assumption that there isno competition between regional markets (since they are geographically segmented); i.e. theindividuals in market m only derive utility from the denominations that have chosen to enter m.
To match our data we focus on individual choices among Protestant denominations, with otherreligious groups implicitly remaining in the background. 22 To derive the total number of21

Implicitly, we assume that to serve a market a denomination must engage in foreign direct investment, implyingthat there is no “export” option. In practice, some denominations do reach members through radio and television
programming, which are forms of exporting religious services. Since we lack data on the extent to which individual
denominations use radio and TV ministries, such activities are outside of the scope of our analysis.
22

Our assumption in equation (5) that ε ijk is iid extreme value implies independence of irrelevant alternatives (IIA).
m

IIA allows us to estimate the model with data on a subset of the religious choices available to individuals in acountry. This approach applies to the following decision structures: (a) a nested structure in which individuals firstchoose among religious aggregates (e.g., Protestantism, Catholicism, Judaism, Islam, etc.), before choosing amongindividual denominations or groups within these aggregates, in which case our analysis would apply to the subbranch of the decision tree that applies to choice among Protestant denominations; or (b) a non-nested structure inwhich individuals choose among all religious groups simultaneously, in which case we would be applying IIA andestimating outcomes among the Protestant subset of choices available. Under either structure, we control forunobserved religious options using country fixed effects, which capture market competitiveness, as seen in (6).
Finally, to keep the discussions focused we exclude the possibility of an outside good, as in Anderson, de Palma andThisse (1992). Adding an outside good does not affect our analyses except to change the expression for Pkm.

14

individuals who participate in denomination j, X mjk , we apply Anderson, de Palma and Thisse(1992) and Feenstra (2004) to obtain:
=
X
m
jk

exp[δ jk + e mjk + V jkm ]
Pkm

m
m
=
O
k , Pk∑ exp[δ + e + V ],
jk

j

m
jk

m
jk

(6)

m

where Okm is the population in market m and Pk the competitiveness of market m, akin to a priceindex for religious services. While we focus on Protestant denominations, other religious groupsm

are implicitly captured in Pk , which we control for in the estimation using country fixed effects.
Following a long tradition in the trade literature (Helpman and Krugman, 1986), we assume thatthe religious market is monopolistically competitive, such that a given denomination does notinternalize the effects of its actions on Pk . By equation (6), ∂ln X mjk /∂ p mjk = – β, where themparameter β > 0 measures the elasticity of demand for church membership; a high β indicateselastic demand. Equation (6) also implies that ∂ln X mjk /∂ e mjk = 1.

3.3 Organizational structure and pastor incentives
In this subsection we lay out the timing of our model, explain the objective andorganizational structure of the denomination, and examine the denomination and the pastor’s
choice for effort level and membership fee. Timing is as follows. (i) The denomination decides
whether or not to enter country k and market m. (ii) Price and effort levels, p mjk and e mjk , arechosen, which determine congregation membership. (iii) The denomination and pastor bargain
over the monetary surplus generated in market m. Bargaining results from incomplete contracts(Grossman and Hart, 1986): we assume no contract written at stage (ii) can govern trade at stage(iii). We assume parties’ outside options are 0 and that each gets half the monetary surplus. 23
Following Besley and Ghatak’s (2005) formulation of objectives in non-profit
organizations, we assume that the pastor values both the number of congregation members, withweight γ, and monetary income from serving the congregation. Likewise, a denomination values
the number of believers it attracts, also with weight γ, as well as the monetary income its

23

Both assumptions are innocuous and are made to simplify the exposition. Our results hold up when the outside
option is non-zero or when the Nash-bargaining share is a constant between 0 and 1.

15

congregations generate. 24 In addition, we assume that the pastor serves members at marginal
cost, gk, and that the cost of his effort, e mjk , is given by,c(e mjk ) = exp(hk e mjk ),(7)

per congregation member, where hk > 0 affects effort costs. When hk is low, the pastor can easily
remain connected with congregation members (e.g., by visiting members on week days). 25
We classify the organizational structure of a denomination as decentralized (D) or
centralized (C), which is chosen by the denomination at an earlier time and taken as given. Under
the D structure, the pastor has control rights, which gives him the right to choose the membershipfee, p mjk . 26 The joint monetary surplus for the congregation is X mjk ( p mjk – gk), where gk is the
pastor’s variable cost for serving the congregation, meaning the pastor’s payoff is 27

X mjk γ + 0.5 X mjk ( p mjk – gk) – fk – X mjk c( e mjk ).

(8)

The first order conditions for effort and the membership fee are, respectively,
1c '( ekD ) + c( ekD ) =γ + ( pkD − g k ) ,
2
pkD =

1

β

(9)

+ g k + 2[c(ekD ) − γ ] .

(10)m

Due to logit demand in (6), indirect utility, V jkm , and market competitiveness, Pk , do not affect24

As compared with Besley and Ghatak (2005), we (i) assume that the principal and the agent have the same
mission objective (i.e., congregation membership) and so abstract away from the matching between principals andagents, and (ii) consider an incomplete-contract environment. The weighting parameter, γ, affects the price level (seeequations (10) and (12)) and so denomination size, but does not affect how denomination size varies with the key
parameters of our model, hk, and πSjk. Therefore, Propositions 1 and 2, below, still hold even if the denomination andthe pastor place different utility weights on the number of believers.
25
We assume that each congregation has a single pastor. In practice, the organizational structure of a congregation
may be more complex and may depend on the characteristics of the congregants (e.g., congregations containing
wealthier members may have more pastors per congregant and may choose to donate money over time). We abstract
away from these considerations owing to the fact that we lack data on the employment structure of congregations.
26
Consider the following extension to incorporate elements of the framework describing the delegation of authority
in Aghion and Tirole (1997). Suppose the project of congregation-building is the vector (connection, membershipfee), where a successful connection between the pastor and members of the congregation causes the utility ofmembers to rise. There is uncertainty about the correct way to connect with members, with a pastor-preferredconnection option and a denomination-preferred connection option. The D structure allocates formal authority overthe vector (connection, membership fee) to the pastor. Compared with our current framework, the pastor has evenstronger effort incentives under the D structure since he can choose not only his preferred price but also his preferredconnection option. Such a setting would strengthen results derived below.
27
In our setting the allocation of control rights has no impact on bargaining power. To relax this assumption,
suppose control rights increase bargaining power. Then under the D structure the pastor has even stronger incentives
since he receives more than half the surplus. This setting would strengthen our results. In other words, our results
hold up as long as control rights do not decrease bargaining power too much.

16

fees or effort, though they do affect the number of adherents; in the rest of this subsection, we
drop the index for region (m) and denomination (j). Equation (9) says that a higher fee provides
the pastor with stronger incentives to exert effort, as the left-hand side of (9) is increasing ineffort. Equation (9) also says that the pastor has stronger incentives to exert effort when his
Dmarginal effort cost, hk, is lower. Equation (10) says that an increase in effort cost, c( ek ) , leads
to a more than one-for-one increase in membership fees. This is due to the hold-up problem
under incomplete contracts. Since the pastor captures only half of the monetary surplus, he is not
fully compensated for his effort. To alleviate hold-up, he over-compensates his effort in pricing.
In (10), the pastor internalizes only his own non-monetary benefit to believers and not thedenomination’s. The other terms in (10) say that the membership fee is high if variable cost, gk,
is high, or demand is inelastic (β is low). From (9), (10) and (7),
ekD =

1
1
,
ln
hk 2 β hk(11)where hk measures the pastor’s effort cost, as defined in equation (7). Equation (11) says that
pastor effort is high when the effort cost, hk, is low.
Under the C structure, the control rights over the congregation rest with thedenomination, such that the denomination chooses the membership fee. Since thedenomination’s payoff is γXjk + Xjk(pjk – gk)/2 – fk, 28 it selects the fee,
pkC =

1

β

+ g k − 2γ .

(12)Equations (10) and (12) imply that the membership fee is lower under the C structure than underDC
the D structure: pk < pk . This is because the cost of pastor effort does not enter into thedenomination’s payoff, leading the denomination to ignore the effort cost in pricing. Equations(7) and (6) still hold under the C structure, and the first order condition for pastor effort is stillC

equation (9), except that fee is pk . Plugging (12) into (9) we obtain
ekC =

1
1
.
ln
hk 2 β ( hk + 1)

(13)

Equations (11) and (13) say that the effort level is lower under the C structure than the D28

We choose to have the denomination bear the church fixed cost, fk, in order to simplify the expressions for theentry threshold and the number of churches and believers. Who bears fk has no effect on the first order conditions.

17

C

D

structure: ek < ek . Under the C structure, the denomination sets prices and ignores the pastor
effort cost in its pricing decision. For the pastor, lack of control rights under the C structure
aggravates the hold-up problem, creating weak incentives to invest in effort.
To summarize, the denomination and the pastor disagree about pricing; the denomination
CD

prefers the lower price, pk , as defined in (12), but the pastor prefers the higher price, pk , asdefined in (10). Relative to the C structure, the D structure provides stronger incentives to thepastor by allocating control rights to him, which results in high effort but also a high membershipfee. 29 Since we do not observe effort levels or membership fees in our data, we next examine
predictions about the numbers of believers and congregations under the C and D structures.

3.4 Market equilibrium and denomination size
Since our data has the numbers of congregations and adherents in country k fordenomination j, we aggregate across religious markets m within country k to derive thesevariables in this subsection. We first derive results under the D structure; results for the C
structure are analogous. Under the D structure, the denomination is active in markets where
pastor utility is non-negative. Plugging (10) into pastor’s payoff (8), the denomination enters
local market m if and only if

exp[δ jk + ekD + V jk (., pkD )] Okm
2β

Pk

≥ fk,

(14)

where Pk and Vjk are invariant across markets because price and effort level are invariant acrossmarkets. Intuitively, the denomination enters larger markets and markets with lower entry costs.
m

We rewrite the population in local market m as Ok = Oksm, where sm is the size of local market
m, with cdf Gk(.), and Ok is a shifter reflecting the total population of country k. Equation (14)then gives the threshold market size for entry:s =Dk2 β f k Pk exp[ −δ jk − ekD − V jk (., pkD )]
Ok

,

(15)

Equation (15) says that a denomination enters more markets the lower is the fixed cost or thelarger is country k. Following literature on city size (Gabaix, 2009), we assume the distribution
29

Consistent with these predictions, in US congregations pastor salaries rise more quickly with church attendance indenominations with congregational polities than in hierarchical denominations (McMillan and Price, 2003).

18

of regional market size, Gk(.), is Pareto with lower bound b and shape parameter a > 1, such that
Gk(s) = 1 – (b/s)a. For expositional ease we assume that there is a unit measure of regional
markets in country k, which implies that the number of markets denomination j enters, or thenumber of congregations j has, is n Djk = 1 − G ( skD ) . Using (15) and Gk(.), we show that,
D
a ln
ln n=
jk

B1
O
b
+ a[ln k + ekD + δ jk + V jk (., pkD )], =
B1
fk
Pk
2β

ln X Djk = ( a − 1) ln

B2Oaba a1−1 1+ a[ln k + ekD + δ jk + V jk (., pkD )], B2 = (
)
,
fk
Pk
a −1
2β

(16)

∞

where X Djk = ∫ D X mjk dG ( skm ) is the aggregate number of adherents in country k for denominationsk

j. Equation (16) implies that the intensive margin, X Djk / n Djk , does not depend on market size andthat all the adjustment of X Djk is through the extensive margin, n Djk , consistent with Figure 3.
Such predictions are typical of models with firm heterogeneity (Melitz, 2003).
Under the C structure, the total number of congregations and adherents are:
C
a ln
ln n=
jk

B1
O
+ a[ln k + ekC + δ jk + V jk (., pkC )],fk
Pk

ln X Cjk = ( a − 1) ln

B2O+ a[ln k + ekC + δ jk + V jk (., pkC )],
fk
Pk

(17)

where the coefficients B1 and B2 are as defined in equation (16). With these results in hand, we
derive comparative statics. We first examine the effect of religious doctrine on denomination
size. We consider how shocks to the market for secular goods affect the relative size of strict
groups. From club-good utility, changes in the price of the secular good change the demand forstrictness. For denomination j in country k, we assume that the shadow price for the secular goodcan be decomposed as, πSjk = πSj + πSk, for all local markets m. πSj is high for the adherents ofdenomination j if denomination j is strict; πSk is high for the population of country k if country k
has a weak supply of social services (implying higher queuing costs and therefore higher shadow
prices for these services) or if country k has strong demand for social services. The first property
follows Iannaccone’s (1992) conception that membership in a strict religious group raises thecost of consuming secular goods. The second property follows from the standard notion thatprice is negatively (positively) correlated with supply (demand) shocks.
What happens to participation in denominations when the shadow price of the secular
19

good increases? Suppose, for instance, that the availability of social services in country k falls.
Then πSk increases, and utility increases for all denominations in country k, strict or not, since∂V/∂πS > 0, by equation (4). However, there is an additional effect, as well. The increase inindirect utility is higher for strict denominations, for which πSjk is higher, since ∂2V/∂(πS)2 > 0 byequation (4). In other words, strict denominations face a relatively large increase in demand if
government provision of social services falls. Summarizing,
Proposition 1. Weaker government provision of (and/or stronger demand for) social
services raise the size of strict denominations by more than less strict ones:∂ 2 ln G Ojk∂ (π Sk )∂ (π Sj )> 0, where G = X, n and O = D, C.

The Appendix contains the proof.
Turning to the effect of organizational structure on size, by equations (16) and (17), thesize of a decentralized denomination relative to a centralized one is,

ln

n Djk
n

C
jk

= ln

X Djk
X

C
jk

= a[( ekD − ekC ) + (V jkD − V jkC )] + B3,(18)

where B3 is a constant. Equation (18) implies that there is an ambiguous ranking of absolute sizefor centralized and decentralized denominations, as a comparison depends on the elements of theindirect utility function Vjk(.). However, we can examine the differential impacts that a change inthe marginal cost of pastor effort has on the size of decentralized and centralized denominations.
Suppose hk decreases. Then pastor effort increases and size increases for both C and D structures.
Intuitively, given that the D structure provides the pastor with stronger incentives, the decrease in
hk should have a larger impact under the D structure. We show in the appendix that 30
30

It is possible to extend the analysis to incorporate ownership of church property. Following Grossman and Hart
(1986), we assume that ownership and control rights rest with the same party. Under the D structure the local
congregation owns the church, but under the C structure the denomination owns the church. In practice, under acongregational polity, the congregation tends to own church buildings, while under episcopal or presbyterian
polities, the denomination typically controls church property. Ownership affects the pastor’s incentives by changing
his outside option, should bargaining fail. Under the D structure, the denomination’s outside option remains 0, but
the pastor controls the church and should bargaining fail the pastor converts the church into an independent entity, inwhich case the denomination input no longer affects demand. We assume that the monetary surplus shrinks to theDDfraction d k < 1 of the size when bargaining is successful. The denomination then gets fraction (1 – d k )/2 of theDmonetary surplus in bargaining, while the pastor receives fraction (1+ d k )/2.

Under the C structure, thedenomination owns the church. Should bargaining fail, pastor effort no longer affects demand and the monetary20

Proposition 2. A change in the cost of pastor effort has a larger impact on the size ofdecentralized (D) denominations than centralized (C) denominations:∂ ln(G Djk / G Cjk )∂hk< 0, where G = X, n.

To summarize, our model has two predictions: (1) Doctrine: Weaker supply of orstronger demand for social services raises the number of adherents and congregations more forstrict denominations than less strict ones (Proposition 1); and (2) Organization: A decrease in the
pastor’s cost of serving congregants raises the numbers of adherents and congregations more fora decentralized denomination than a centralized one (Proposition 2). 31 Although we have focusedon Protestant denominations in deriving these predictions, our framework can be applied tocompetition among other religious groups, as well.

4. EMPIRICAL SPECIFICATION
4.1 Empirical Specifications
The predictions of our model guide the empirical specification. We approximate indirect
utility, Vjk(.), using its second-order Taylor expansion; i.e. Vjk(.) is a second-order polynomial
involving the variables πSjk, πRjk, and Ik – pk. We assume that πRjk is country-k specific and let thevector Zk represent the variables Ik, pk and πRk. We also assume that the general quality ofdenomination j’s credence good in country k is δjk = ςj – c1tjk, where tjk captures communication
costs for denomination j in country k (e.g., distance to denomination headquarters) and includes
an iid random-cost component (which allows the ranking of denominations across countries todiffer). We measure πSk using the vector Hk, which includes the supply of and demand for social
services in country k, and πSj by the strictness of denomination j, STRj (recall that πSjk = πSj +πSk). The empirical specification for V jkO + δ jk is then:

V jkO + δ jk = γj + γk + c2ZkSTRj + η1HkSTRj – c1tjk,

O = D, C,(19)

C

surplus collected by the denomination shrinks to the fraction d k < 1 of the size under successful bargaining. The
C

pastor’s outside option is 0. The denomination receives the share (1+ d k )/2 of the monetary surplus in bargainingCand the pastor receives the share (1 – d k )/2. Other derivations go through and Propositions 1 and 2 hold.
31

Both predictions are for the countries where a given denomination is present. In the Appendix we derive theconditions under which denomination j enters country k. In our empirical exercises we explicitly control for entry.

21

where the c’s are constants, and O = {Decentralized, Centralized}. γj and γk are denominationOand country fixed effects, which absorb the variables with j- and k-specific subscripts (e.g. pk ,Zk, and ςj). By Proposition 1, we have that η1 < 0.
Plugging equation (19) into (16) and (17), it follows that,ln X Ojk = f O ( hk ) + γj + γk + c2ZkSTRj + η1HkSTRj – c1tjk,
ln n Ojk = f O ( hk ) + γj + γk + c2ZkSTRj + η1HkSTRj – c1tjk,(20)

In equation (20), X Ojk and n Ojk are, respectively, the numbers of adherents and congregations that
denomination j has in country k, and hk is the marginal cost of pastor effort in country k.
Proposition 2 implies that ∂fD/ ∂hk < ∂f C / ∂hk . We approximate f O (.) by λ1Rk + η2RkDECj,

where Rk measures the pastor cost of serving congregants in country k and DECj measures thedecentralization of denomination j, where by Proposition 2, η2 > 0. Equation (20) implies thefollowing regressions:ln X jk = γj + γk + η2RkDECj + η1HkSTRj + λ3ZkSTRj + λ4tjk + ujk,

ln n jk = γj + γk + η2RkDECj + η1HkSTRj + λ3ZkSTRj + λ4tjk + vjk.

(21)

In equation (21), ujk and vjk are error terms capturing unobserved costs (assumed uncorrelatedwith the regressors), and Zk is expanded to include the competitiveness of the country’s religious
market (Pk) and country size (Ok). 32 By Proposition 1, η1 < 0: stronger supply of (or weaker
demand for) social services reduces the size of strict denominations by more than non-strict ones;by Proposition 2, η2 > 0: a lower cost of pastor effort increases the size of decentralized
denominations by more than centralized ones.

4.2 Regression variables and estimation method
To see how the variables in (16) and (17) are captured in (21), by assumption, δjk (denomination j’s additional
value in k) is in the terms γj and tjk. By approximation, fk (fixed entry cost), Ok (population), Pk (market
competitiveness), Ik (congregants’ income), gk (pastor variable cost) and πRk (shadow price of religious participation)
are in γk and Zk; for πSjk (shadow price of secular good), its country-k component is in γk and Hk and its
denomination-j component in γj and STRj; for hk (pastor effort cost) its level effect is in γk and its interaction withorganizational structure (working through price pk and effort level ek) captured by the term RkDECj.
32

22

Following equation (21), we regress the log size of a denomination in a country,
measured as the number of adherents or congregations in 2005, on country fixed effects, γk,denomination fixed effects, γj, and interactions between country and denomination
characteristics. 33 Denomination fixed effects absorb doctrinal strictness and governancestructure, the quality of a denomination’s credence goods, other determinants of denomination
productivity, and entry barriers specific to a denomination and constant across countries. Country
fixed effects absorb national market size (population, urbanization, average income, education,etc.), barriers to the import of religion common across denominations, variable costs in providing
services to congregants common across denominations, and the competitiveness of the religious
market in a country (represented by the variety of religious options available in a country). The
key regressors are interactions between a denomination’s governance structure (DECj =1 if adenomination has a congregational polity and is decentralized) or religious doctrine (STRj =1 if
the denomination is above the median in terms of doctrinal strictness) and country characteristics
that capture the cost to pastors of connecting with congregants or the supply of and demand forsocial services. Table 4 provides summary statistics on regression variables.
Consider first measures of social service demand and supply, Hk. 34 Following theliterature (Berman, 2000; Hungerman, 2005; Gruber and Hungerman, 2007), we assume that theclub goods produced by congregations substitute for social services provided by the state. In
terms of state-provided social services, we focus on health care. In many countries, governments
are the primary providers of health services, especially for the poor or middle class. 35 These
services are among the best measured social services provided by the state; data on the provisionof other social services, such as welfare programs, aid to children in poor families, disability
insurance, etc., are unavailable for most low and middle-income countries. We assume that theprice of health services responds to the supply of health-care personnel and infrastructure and to
demand shocks for health care. In many high-income countries, the government provides health33

Although the WCD counts adherents in 1970 and 2005, we lack data for most regressors prior to 1970, limiting
our analysis to the 2005 cross section. However, we do use the 1970 WCD data in our treatment of selection bias.
34
Implicitly, we assume that the supply of social services in a country does not simply adjust to demand in a mannerthat keeps prices constant across national markets (i.e., we presuppose the existence of idiosyncratic national factorsthat affect either supply – such as the historical legacy of training for health care workers – or demand – such as
cultural attitudes or preferences – that create differences in the shadow prices of social services across countries).
35
For the subsample of 35 OECD countries, there are data on social spending, overall and by type. For thesecountries in 2007 (close to our sample year of 2005), the correlation between total social spending per capita andhealth spending per capita is 0.77, suggesting that health related spending correlates well with government transfers.

23

care at a set price, using quantity rationing to allocate supply. In developing countries, it isincreasingly common for the government to impose user fees for health services (Leive and Xu,
2008; Peters et al., 2008); where fees are set below market levels, the providers of these services
often demand side payments to deliver care (Kruk et al., 2008). Whether allocated via quantityrationing or a price mechanism, the effective cost of obtaining health services is likely to berelated to the supply of personnel who deliver these services and the resources they have at theirdisposal (Whitehead, Dahlgren and Evans, 2001). We measure the supply of health services
using medical personnel (nurses and midwives) per capita, 36 log hospital beds per capita, andhealth expenditure as a share of GDP, from World Development Indicators averaged over 19702004. While religious organizations do not provide medical services per se, they offer spiritual(e.g. prayer, belief in an afterlife, faith healing) and material (e.g., meals, home visits for thesick) substitutes for medical services. We examine whether more expansive health services
means weaker demand for services from religious groups.
The demand for social services provided by the state tends to be greater where theincidence of adverse shocks is higher. Following natural disasters or disease outbreaks, healthcare systems are severely taxed (Noji, 1997; Warring and Brown, 2005), restricting theavailability of health care to the population. We measure aggregate shocks using the incidencesof natural disasters and disease outbreaks based on data from the International Emergency Event
Database (www.emdat.be). While such shocks are temporary, they are often severe in nature,
leading to large disruptions in consumption. Anthropological evidence from traditional societies
shows that the number of religions present in a region is positively correlated with disease load
(Fincher and Thornbill, 2008), consistent with the idea that religious organizations provide
insurance against the risk of contagion. We measure shock incidence as the number of events
that occurred in a country over 1970 to 2004 (for disasters) or 1995-2004 (for disease
outbreaks), 37 divided by the number of years in the period, which is the annualized shockincidence. Following Yang (2008), we define a serious natural disaster as an earthquake over36

Physicians are an obvious additional type of medical personnel. However, in many poor countries physicians playa relatively small role in the delivery of health care, with nurses being more prevalent (Clemens, 2007). Among thepoorest 30 countries in the sample, the median nation has 0.5 physicians and 5 nurses per 10,000 people, such thatthe ratio of nurses to physicians is 10 to one. In the rest of the sample, the ratio of nurses to physicians is 3 to one.
Below, we discuss results including physicians per capita interacted with strictness as a regressor but we do notinclude the variable in estimating the principal components of health services.
37
We use a shorter window for disease outbreaks than for natural disasters because country coverage of disease
outbreaks broken by type of disease is incomplete in the1970s and 1980s.

24

seven on the Richter scale, a windstorm (i.e., hurricane) lasting five days or more, or a landslide
or volcanic eruption that affects more than 1000 people. Disease measures cover cholera, dengue
fever, influenza/SARS, and meningococcal outbreaks that affect more than 1000 people. Serious
natural disasters are surprisingly common events, with mean annual probabilities of occurrenceof 1% (hurricanes) to 3% (earthquakes, volcanic eruptions). Mass disease outbreaks are also
common, with mean annual occurrence probabilities of 0.3% (influenza/SARS) to 2% (cholera).
We also examine how strictness interacts with financial development, which may capture thepotential to hedge against environmental risk through financial markets.
We measure the cost to pastors of communicating with congregants, Rk, using
communications and transportation infrastructure, including telephone mainlines per capita,
cellular subscriptions per capita, road network (total length and fraction of roads paved), andpassenger cars per capita. 38 A pastor’s responsibilities include communicating with congregants(to encourage participation in church events), reaching out to new converts, conferring withchurch leaders on managing the congregation, and attending local events in the community.
Pastor costs for outreach are likely to be lower where a larger fraction of the population haveaccess to mainline and cellular phones (because it is easy for the pastor to call his congregants
during week days), and/or where there is a larger network of paved roads and passenger vehicles
are readily available (because it is easy for the pastor to visit his congregants during week days).
The literature suggests that improved land transportation infrastructure increases economic
activity disproportionately for the service sector (Chandra and Thompson, 2000), perhaps due tolower costs of connecting consumers to service hubs. Our infrastructure data are from the WDI,
averaged over 1991-2005, 39 and show substantial variation across countries. For example, thenumber of cars per 100 people has a mean of 13.4 but a standard deviation of 16.2.
Although we control for country fixed effects, additional country characteristics (e.g.,
education, average income) may have different effects on strict versus non-strict or centralized
versus decentralized denominations. If these characteristics are correlated with our main
regressors, the estimation may be subject to omitted-variable bias. To address this concern, we
include, as additional controls, interactions between decentralization and strictness and country38

As a matter of convention, we measure usage or access rates (e.g., cellular subscriptions per capita) in levels andfactor quantities (e.g., hospital beds or passenger cars) as log values (which implicitly are in log per capita terms,
given that log population interacted with strictness and decentralization appears as a regressor).
39
Measures of communications infrastructure are unavailable for many countries before 1990, which accounts forwhy we average their values over a shorter time span than for health services.

25

characteristics that past work has shown to correlate with religiosity (Barro and McCleary, 2005;
McCleary and Barro, 2006; Sacerdote and Glaeser, 2001). 40 Our results are robust to theinclusion or exclusion of these additional variables. Additionally, we employ placebo tests andexamine individual types of natural disasters (e.g., earthquakes) and disease outbreaks (e.g.,
influenza/SARS), as we explain in subsections 5.2 and 5.3.
Another estimation issue is that most denominations are not present in most countries(see Table 2), creating potential problems with sample selection. That is, in the log specificationin (21) observations with zero adherents are dropped. Under OLS, the regressors may becorrelated with the expectation of the error term conditional on a denomination being present in
the country. To address the issue, we compare results using three estimation methods: OLS, a
Heckman (1979) correction for sample selection, and a nonparametric correction for sample
selection, motivated by Das, Newey, and Vella, (2003). In practice, we find similar estimatesacross the three estimators, suggesting sample selection is not important for the estimation.
In either Heckman or nonparametric approaches, we need variables that are correlatedwith a denomination’s presence in a country in 2005 but not independently correlated with theerror terms in equation (21). We use two instruments for denomination presence in 2005. One(available for all denominations) is an indicator for whether the denomination was present in the
country in 1970, with the identifying assumption being that presence in 1970 is correlated withthe error for denomination size in 2005 only through denomination and country fixed effects. A
second instrument (missing for some denominations) is the entry decisions in 1970 ofdenominations with a similar number of adherents in the United States. 41 The reasoning behind
this instrument is that size in the United States, the home country for all denominations in our
sample, captures a denomination’s average attraction to adherents, similar to firm sales being asufficient statistic for firm productivity in the Melitz (2003) model. Since denomination
productivity affects entry, the entry decisions of denominations with similar numbers of US
40

The additional controls are the percentages of the adult population with primary, secondary, and tertiary education(Barro and Lee, 2010), log per capita GDP, log population, the urbanization rate, log life expectancy, the total
fertility rate, log distance from the United States, whether a country’s official language is English, an index for thegovernment regulation of religion (Grim and Finke, 2006), whether the country had a state religion in the past, theFreedom House rule of law index, an index for ethnic fractionalization (Alesina and La Ferrara, 2005), the fractionof the population that had migrated to the US as of 1970, and indicators for whether Catholicism, Islam, Orthodoxy,
Hinduism or Buddhism, or Judaism is the dominant religion in a country.
41
We define similar sized denominations using a size window of log 3 (matching to each denomination, otherdenominations with log US size plus or minus 1.5). Because the window for neighboring denominations is truncatedfor the largest and smallest groups, the instrument is not defined for these denominations.

26

adherents are likely to be correlated. The identifying assumption is that the unobserved barriers
to entry of similarly US-sized denominations are uncorrelated. In nonparametric estimation, we
use a linear probability, probit, or logit model to estimate the probability a denomination ispresent in a country in 2005 and then construct dummy variables that capture the value of thepredicted probability of presence based on dividing predicted values into 50 equal-sized bins. 42
We include these dummies in the second-stage regression for log denomination size.

5. EMPIRICAL RESULTS
5.1 Main estimation results
Table 5 presents baseline OLS results. 43 We show coefficient estimates for four sets ofinteractions: between denomination strictness and (a) country incidence of natural disasters, (b)country incidence of disease outbreaks, and (c) country health infrastructure; and between adenomination being decentralized and country communication and transportation infrastructure.
The data contain multiple measures of a country’s infrastructure, which are likely to becorrelated with each other. Because including these measures together introduces collinearity
into the regression, we begin by using the first principal component of the infrastructure
variables and later examine the results for the individual measures. We treat disasters, disease
outbreaks and health services similarly. 44 By construction, the first principal components havemean zero and a standard deviation of one.
The first column of Table 5 shows that the interactions between strictness and theincidences of natural disasters and disease outbreaks are positive and precisely estimated. To
understand the results, recall that the regressions include country and denomination fixed effects.
Interpreting the within-denomination variation, the results indicate that a strict denomination willtend to attract more adherents in countries with a higher incidence of disasters, be they42

Results are similar when we use bins of 100 instead of 50 in the second-stage estimation. We also experimented
with using the polynomials of estimated propensity scores and obtained similar results.
43
The results here and in Table 6 include the additional control variables listed in note 36. To save space we do notreport their coefficient estimates, which are available from us upon request. The results without these controls are
similar, and also available from us upon request.
44
For disasters, we use the first principal component of four incidence variables: earthquakes, landslides, volcanic
eruptions, and windstorms; for disease outbreaks, we use the first principal component for four events: cholera,
dengue fever, influenza/SARS, and meningococcal outbreaks; for health services, we use the first principal
component of three variables: medical personnel per capita, log hospital beds, and health expenditure as a share of
GDP; and for communications and transportation infrastructure, we use the first principal component of fivevariables: telephone mainlines per capita, cellular subscribers per capita, log road network per square kilometer,
percent roads paved, and log passenger cars.

27

geological, meteorological, or biological in origin. Equivalently, the within-country variation
indicates that a country that has a higher incidence of disasters will tend to have its Christianbelievers more concentrated in strict denominations. The coefficient estimates suggest that the
impact of disasters on denomination size is large. A one standard deviation increase in theincidence index is associated with an increase in the relative size of strict denominations of 22
log points for either natural disasters (e.g., going from Austria to Nicaragua) or disease outbreaks(e.g. going from Italy to Nepal). In column (1), we also see that the interaction between strictness
and the provision of health services is negative and precisely estimated. A strict denomination
will have more adherents in countries with worse provision of health services (or, equivalently, acountry with worse health services will tend to have more of its believers in strict groups).
These results are consistent with Proposition 1, which says that the relative size of strict
denominations is larger in countries in which the shadow price of secular goods and services isgreater. Strict denominations do better in countries in which the net demand for social services
is stronger, where higher net demand may come from weaker government supply of social
services (measured here in terms of health infrastructure) or stronger public demand for services
associated with the risk of shocks to income or health. In our model, stronger demand for or
weaker availability of social services increases queuing (or user fees) and therefore the price ofobtaining services, making membership in a strict religious organization (with its superior
capacity for producing club goods) more attractive.
In the second column of Table 5, the interaction between denomination decentralizationand country provision of communication and transportation infrastructure is positive andprecisely estimated. Decentralized denominations attract more adherents in countries in which
the supply of communications and transportation infrastructure is greater. This finding is
consistent with Proposition 2, which states that the relative size of decentralized denominations
is larger in countries in which the marginal cost to pastors of connecting with congregants islower. From our model, lower cost to pastors of connecting with congregants has a larger effecton the size of decentralized denominations, owing to stronger pastor effort incentives created bytheir having control rights. Our finding of positive effects of infrastructure on relative
denomination size is broadly consistent with findings in the literature on how public
infrastructure affects industrial productivity, and provides empirical evidence that governancestructure affects performance for international organizations.
28

In column (3), we combine the strictness and decentralization interactions in a single
regression, which leaves results unchanged. In columns (4)-(5), we measure denomination size
using the log number of congregations, instead of the log number of adherents. The results are
qualitatively the same. Because results for adherents and congregations are similar, we limit thepresentation of further results to regressions with log adherents as the dependent variable. Later
results are robust to using log congregations as the dependent variable, instead.
Table 6 contains results for alternative estimators (see Appendix Table A1 for the firststage results). As Table 2 shows, many denominations have not entered certain countries. To
address sample selection, we estimate two alternative models. Columns (1) and (2) show results
for a Heckman estimator, in which we use as instruments either denomination presence in the
country in 1970 (first column) or lagged presence and average presence in 1970 ofdenominations that attract a similar number of adherents in the United States (second column).
Columns (3)-(9) present results for a nonparametric correction for sample selection, in which we
first estimate the probability of presence in 2005 using a linear probability, probit, or logit modeland then use dummy variables for the predicted probability of presence in 2005 as regressors inthe second stage estimation of log adherents in 2005. The instruments for presence in 2005 are
the same as the Heckman model. Both the Heckman and nonparametric results are similar tocolumn (3) of Table 5, suggesting that self-selection into entry does not matter much for thecoefficient estimates. To streamline the exposition, we present further results for two estimators:
OLS and a nonparametric correction for sample section based on a first stage probit model,
which corresponds to column (6) in Table 6.

5.2 Placebo tests
In the regressions in Tables 5 and 6, we assume that we have included the relevant
denomination and country characteristics that capture the logic of Propositions 1 and 2. To
examine whether our results may simply be artifacts of the data, and unrelated to the workings ofour model, we conduct placebo tests by interacting country variables with theoretically irrelevant
denomination characteristics, and by interacting denomination strictness and decentralization
with theoretically irrelevant country characteristics.
In Table 7, we consider placebos for health services and transportation infrastructure.
The public health services that we examine – medical personnel per capita, the supply of hospital29

beds, and public health expenditure as a share of GDP – are labor, capital, or spending inputs thatare likely to help individuals weather health shocks that affect their productivity and life
expectancy. One concern is that these health services may be correlated with unobserved
components of country income, in which case the negative strictness-health service interactionthat we find may be a byproduct of richer countries having weaker preferences for strict religious
doctrine. As a placebo for health services, we consider the supply of dentists per capita, which isincreasing in average income, suggesting that dental care is a normal good. While greater
availability of dental care may enhance the quality of life, it does not represent the type of healthservice that theory suggests substitutes for the club goods provided by religious groups. In
columns (1) and (2), we replace the strictness-health service interaction with the interaction
between strictness and dentists per capita. The coefficients on the interaction are small and very
imprecisely estimated, implying there is no relation between the availability of dental services
and the success of strict denominations. These results help allay concerns that we may havemisinterpreted the strictness-health service interaction in Tables 5 and 6.
A related issue applies to the interaction between denomination decentralization andcommunication and land transportation infrastructure. Infrastructure is meant to capture the costto pastors of connecting with congregants. However, the supply of transportation infrastructure
might be correlated with unobserved country characteristics. For example, suppose decentralized
governance structures were more likely to be chosen in higher income countries, then thepositive interaction that we find between decentralization and infrastructure might simply bepicking up a positive correlation between decentralization and unobserved components ofcountry income. To examine this possibility, we use air transportation services, measured either
as log passenger departures or log air carrier departures, as placebos. Air transportation is not an
input in the provision of pastor services because church members are drawn overwhelmingly
from the local area. Thus, according to our model, air transportation should have no effect on
pastor effort cost and we should find no result for air transportation. Columns (3)-(5) of Table 7
show results for interactions between decentralization and air transport. Coefficients arenegative, rather than positive, and imprecisely estimated in all cases. These results suggest thatthe positive interaction between decentralization and infrastructure is not driven by omittedvariable bias. To threaten our identification, the omitted country variables must be correlatedwith land transportation but uncorrelated with air transportation.
30

One might be concerned that what matters for congregation size is not the cost to pastorsof reaching out to congregants but the alternative options that potential congregants have tointeract with others. To gauge the importance of social interactions for our results, in Appendix
Table A2 we present regressions in which we split the sample of countries into above or below
the median level of population density (similar findings obtain for splitting the sample according
to above or below the median urbanization rate). Comparing columns (1) and (2) of Table A2
with column (3) of Table 5 or columns (3)-(5) of Table A2 with columns (3) and (5) of Table 6,
the results for the two sub-samples are similar to each other and also similar to the full sample.
This helps allay concerns that opportunities for social interaction undermine our exercise.
Turning to theoretically irrelevant denomination characteristics, in Table 8 we replace
denomination strictness with the frequency of Holy Communion. Communion is a practiceconducted during worship services that symbolizes Christ’s last supper prior to his crucifixion, at
which he shared bread and wine with his disciples. Nearly all Christian denominations include
some form of communion in their liturgy, which centers on simulating the taking of bread andwine, but they vary greatly in the frequency with which they practice the ritual. Some groups
share communion weekly, others monthly, and still others quarterly or annually. The Bible does
not dictate the frequency of communion. The frequency of its practice reflects, in part, thepreferences of believers for pomp and circumstance in worship. We use communion frequency
as a placebo for strict religious doctrine, as the ritual captures ceremonial features of worship but
is not related to the stigmatizing features of doctrine that matters in theory for the denomination
capacity to product club goods. We identify a denomination as practicing frequent communion if
it does so at least monthly, which applies to 39% of the sample; results are similar when we
define frequent communion to be either weekly or quarterly. Monthly communion has acorrelation of -0.22 with strictness and -0.09 with decentralization.
The first two columns of Table 8 are based on OLS; the second two columns are based ona nonparametric correction for sample selection. Relative to Table 5, we replace the interaction
between strictness and disasters, disease, and health services with the interaction between
communion frequency and these country variables. All communion frequency interactions are
small quantitatively and imprecisely estimated. Thus, we find no significant interaction between
country characteristics and theoretically unimportant features of religious practice.

31

5.3 Extended regression results
So far, we have focused on the interactions between key denomination characteristics andthe principal components of relevant country characteristics. Next, we examine interactions
between the denomination variables and the individual elements of disaster incidence, disease
outbreaks, health services, and communications and transport infrastructure.
In Table 9, we present interactions between denomination strictness and the incidence ofspecific types of disasters. Other regressors are the same as in Table 5. For disasters, there is apositive and significant interaction between strictness and three of four measures: earthquakes,
landslides, and volcanic eruptions, which in Table 4 are the disasters with a relatively highannual incidence. The impact of disaster incidence on denomination size is large quantitatively.
Using results in panel (b), increasing the incidence of disasters by one standard deviation isassociated with an increase in the relative size of strict denominations by 19 log points forearthquakes (going from Italy to the Philippines) and 23 log points for either landslides (goingfrom Austria to Colombia) or volcanic eruptions (going from Argentina to Papua New Guinea).
Turning next to biological events, Table 10 presents interactions between strictness andthe outbreak incidences of cholera, dengue fever, influenza/SARS, and meningococcal illness.
Strictness interacts positively with all but meningococcal outbreaks, with the strongest results for
influenza/SARS. A one standard deviation increase in the incidence of influenza outbreaks is
associated with an increase in the relative size of strict denominations of 17 log points.
In Table 11 we present the interactions between strictness and health services. There is anegative and significant interaction between strictness and medical personnel per capita and log
hospital beds; for health expenditure as a share of GDP the interaction is negative but
imprecisely estimated. The results in panel (b) imply that increasing medical personnel by one
standard deviation is associated with a reduction in the relative size of strict denominations by 32
log points (e.g., going from Ghana to Poland), with a one standard deviation increase in hospitalbeds associated with a 43 log point decrease in the relative size of strict groups (e.g., going from
Ghana to Uruguay). Two additional results in Table 11 are for physicians per capita and financial
development. The interaction of the former with strictness is imprecisely estimated, whereas thelatter is negative and precisely estimated. The absence of a significant interaction between
strictness and the supply of physicians may reflect the relatively small role that physicians, as
opposed to nurses, play in the delivery of health care in many poor countries (see note 32). The
32

negative interaction between strictness and financial development suggests that in countries withwell-organized financial markets individuals are less reliant on assistance from religiousorganizations and more able to insure themselves against risk through private means.
In Table 12, we consider the interaction between a denomination being decentralized andindividual communications and transportation infrastructure variables. Decentralization interacts
positively with telephone mainlines per capita, cellular subscribers per capita, log passenger cars,
and the size of a country’s road network, though coefficients are not precisely estimated in allcases. Whether intended or not, improvements in communications and transportation
infrastructure appears to result in larger market shares for less hierarchical religious groups.

5.4 Discussion
What do our coefficient estimates imply about the relative performance of Protestantdenominations in sample countries? Consider the primary country characteristics that affect therelative size of strict versus non-strict denominations: natural disasters, disease outbreaks, anddelivery of health services. 45 Within sample countries, strict denominations are on average 18.2
log points larger than non-strict denominations. Based on coefficients from column (3) of Table
5, cross-country differences in the incidence of natural disasters explain 11.2% of this size
difference, cross-country differences in the provision of health services explain 8.2%, and crosscountry differences in the incidence of disease outbreaks explains less than one percent.
To further illustrate the quantitative significance of our results, we present two examples,
one involving Guatemala and Honduras, the other Guinea and Senegal. Both pairs consist ofneighboring countries that have similar per capita GDPs, and we examine which factors matterfor explaining within-pair differences in the relative size of strict denominations. The exercise
amounts to a double differencing as we consider the average log relative size of strict
denominations in one country minus that in another.
First, consider Guatemala and Honduras. In Guatemala, the average size advantage ofstrict denominations is 41.7 log points, versus 23.8 in Honduras, for a double difference of 17.9
log points. The two countries have relatively similar per capita GDPs ($4675 in Guatemala,$3028 in Honduras), identical patterns of disease outbreaks, and very similar delivery of health
45

The effects of communication and transportation infrastructure on the relative size of decentralized denominations
are similar, and we do not discuss them to save space; e.g., in the Guinea-Senegal example, the difference ininfrastructure explains 13.3% of the relative size difference in decentralized denominations.

33

services. Where they differ is in the incidence of natural disasters. Owing to its distinct
geography, Guatemala has active volcanoes, whereas Honduras does not, resulting in Guatemala
having an over eight percent higher annual frequency of either volcanic eruptions or of severe
earthquakes. Using coefficient estimates from column (3) of Table 5, the difference in theincidence of natural disasters can account for 17.7% of the relative size difference in strict
denominations between the two countries.
Our next example is Guinea and Senegal, two neighboring East African countries thathave similar per capita GDPs ($1012 in Guinea, $1373 in Senegal). The average relative size
advantage of strict denominations is 148.8 log points in Guinea and 59.4 log points in Senegal,
for a double difference of 89.5 log points. While the two countries have nearly identicalincidences of natural disasters, Guinea has a higher incidence of disease outbreaks and poorerdelivery of health services. Again using coefficients from column (3) of Table 5, these two
factors together can account for 11.0% of the relative size advantage of strict denominations in
Guinea versus Senegal. These examples illustrate the manner in which disasters, disease, andhealth services affect the relative performance of strict denominations across countries.

6. FINAL DISCUSSION
In the last four decades, religious groups headquartered in the United States have
expanded rapidly across borders (e.g. Woodberry and Shah, 2004), much as US multinational
enterprises have sought new markets abroad. In both cases, globalization has been made possibleby the dismantling of government barriers. Much as US multinationals rely on their advantages
in organization and intellectual property to compete in foreign markets (Bloom, Sadun, and Van
Reenen, 2009), US Christian denominations succeed on the basis of their governance structure
and religious doctrine. Along these dimensions, denominations are heterogeneous.
The literature provides explanations for why strict religious groups have prospered in
Israel (Berman, 2000) and Indonesia (Chen, 2010), based on Iannaccone’s (1992) model ofreligious groups as clubs. Our contribution is to show theoretically and empirically how
denominations compete for believers and how country characteristics affect the market value of adenomination’s attributes, which is helpful for understanding the global expansion of Protestant
Christianity. Strictness is more desirable in countries in which individuals are more exposed toshocks associated with natural disasters and disease outbreaks, and less desirable in countries34with better health services. Weak governments and weak institutions may thus favor strict
religious organizations. What may in part account for the recent globalization of Protestant
Christianity is the lowering of state barriers to religion (Barro and Hwang, 2007), coupled withthe slow expansion of state capacity in many developing countries, leaving individuals without
adequate means to insulate themselves against risk.
The traditional Christian church, encompassing Catholics, Eastern Orthodox, andAnglicans, is strongly hierarchical, with the bishopric controlling church doctrine and the hiringand placement of pastors and other religious personnel. The Protestant Reformation introduced
greater diversity in organization form into Christianity (Ekelund, Hebert, and Tollison, 2006),
but the advent of Protestantism did not mean a complete break with centralized control. Some
Protestant denominations maintain the hierarchical features of the traditional church, whereas asothers endow local congregations with considerable power.
In theory, congregational control implies stronger incentives to pastors to invest inbuilding churches. Our finding that decentralized denominations attract more adherents incountries with better communication and transportation infrastructure is consistent with thisreasoning. Our results also help account for the attraction of the non-traditional church incountries as they develop. Development typically brings with it improvements in infrastructure,creating advantages for more decentralized religious groups. The process of economic
development thus appears to change the composition of religious organizations, leading to lessstrictness (to the extent that development brings greater insulation from risk, through either state
programs or private markets) and more decentralization (to the extent that improved
infrastructure strengthens managerial investment incentives).
There is variation in doctrine and organization within non-Christian religions, for which
our framework is relevant. For example, the radical form of Sunni Islam practiced by al-Qaeda
and the Taliban is stricter than the main branches of Sunni Islam. Shia Islam (e.g. Iran, Yemen)
has a hierarchy of Imams and tends to be more centrally organized than Sunni Islam (e.g. Egypt,Indonesia). Among the countries in which Shia and Sunni Islam are present, there is variation in
the government provisions of public services. Our finding that weak governments andinstitutions favor strict religious groups suggests that future research could explore the link
between weak states or weak local and regional institutions and radical Islam.

35

APPENDIX
1. Lemmas 1 and 2
To prove Lemma 1, equation (2) implies that∂V∂Π
I − p ∂D1/ d=
Π − D1/ d
(
)
2
∂π S
Π
∂π S
∂π S

1
1
I−p
D1/ d −1Π− ρ −1),(A1)ρπ−+ D1/ d ) = ρV π S− ρ −1 ( −
(
S
2
Π dD
Π
d
ρ
1
1
−
After some algebra,> 0 ⇔ π R− ρ ( dα ρ − α d ) − π S− ρ (1 − d ) > 0 , which holds if
Π dD
=

ρ

dα − α d > 0 (recall that ρ = d/(1 – d) > d), or d > α (this is equation (3.1) in the text) and
π Sρ
1− d
, which implies equation (3.2) in the text.
>ρπ R ( d − α )α ρρTo prove Lemma 2, equation (A1) above implies that
∂ 2V
∂V
1
1
( − ρπ S− ρ −1 ) − ρπ S− ρ −1
− ρ −1 1− ρ −2 1− ρ −1
=
ρπ
(
−
)
+
ρ
V
(
−
ρ
−
1)
π
(
−
)
+
ρ
V
π
[
−
+
]
S
S
S
∂ (π S ) 2 ∂π S
Π dD
Π dD
Π2
dD 2
1
1 2
1
1
1
1
) − ( ρ + 1)π Sρ ( −) + ρ( 2 −)]= V π S−2( ρ +1) [ ρ ( −
Π dD
Π dD
Π dD 211 2
1
1) −( 2 −) B ], B =1 + ( ρ + 1)π Sρπ R− ρα ρ
= V π S−2( ρ +1) [ ρ ( −
2
dD
Π dD
Π
1
1
1
1
1
1
1− d
)[ ρ ( −) − B( +)] + B 2 2 }= V π S−2( ρ +1) {( −
Π dD
Π dD
Π dD
d D
1
1
1− d
ρ d − Bd − Π ( ρ + 1)π Sρ
= V π S−2( ρ +1) [( −)C + B 2 2 ], C =
Π dD
d D
dΠ
ρ +1
Cπ
∂V
1− d
= V π S−2( ρ +1) ( S(A2)+ B 2 2 ).
ρV ∂π Sd D
In equation (A2) the variable B > 0, and C < 0, because the numerator of C is∂V
∂ 2V
≤ 0 . In addition,( ρ − 1)( d − 1) − 2 − ( ρ + 1)( d + 1)π Sρπ R− ρα ρ . Therefore,> 0 if2∂π S∂ (π S )∂ 2V∂V
B(1 − d ) ( −Cπ Sρ +1 )∂ 2V
> 0 as long as 0 <</. Finally,< 0 if π S is large because∂ (π S ) 2
∂π S
d 2D2
ρV
∂ (π S ) 2

1− d
Cπ Sρ +1 ∂Vthe term
has π Sρ +1 but the term B 2 2 has only π Sρ .
d D
ρV ∂π STo illustrate the intuition for Lemmas 1 and 2, let I = 1, πR = 1, α = 0.3 < d = 0.8. Then equation

36

[(π S ) d ( ρ −1) + (δ R ) − ρ d ]1/ d, where δR = α1/d. Figure A1 plots V(π S ) ρ + (δ R ) − ρagainst πS, and it shows that: (a) V is an increasing function of πS when πS is not too low (πS ≥2.6); (b) V is a convex function of πS when πS is not too high (πS ≤ 4.2); and (c) V is an increasing
concave function of πS when πS ∈ [2.6, 4.2]. (a) illustrates Lemma 1 and corresponds to the casewhen equation (3.2) in the text holds. (b) and (c) illustrate Lemma 2. Figure A1 is similar to
Figure 1 in Iannaccone (1992), except that the latter has log(V) and log(πS) on the axes.
(2) in the text simplifies to V =2. Entry into country k
We assume that to enter a country, a denomination incurs a separate fixed cost, Fk, which
captures the cost of sending missionaries abroad or organizing a ministry. Under the D structure,
to determine the condition under which a denomination enters country k, note that thedenomination receives variable profits 0.5 X mjk ( p mjk – gk) from regional market m and total
variable profit

∞

1

1

∫ 2 ( p − g ) X dG(s ) = 2 ( pk – gk) X
D
k

skD

k

m
jk

m
k

D

D
jk

from country k.

Using theexpression for X Djk in (16), denomination j enters country k iflnpkD − g kBOaba a1−1 1+ ln( 2 ) a −1 + a (ln k + ekD + δ jk + V jkD ) ≥ ln Fk, B2 = (
)
fk
Pk
2
a −1
2β

Similarly, under the C structure, denomination j enters country k ifln

pkC − g kBO
+ ln( 2 ) a −1 + a (ln k + ekC + δ jk + V jkC ) ≥ lnFk.
2
fk
Pk

3. Proposition 1∂ 2 ln G Ojk∂ 2 ln G Ojka∂ 2V jkO
= => 0 , G = X, n and O = D, C, where the∂ (π Sk )∂ (π Sj ) 2∂ (π Sjk ) 2 2∂ (π Sjk ) 2
last inequality is by equation (4).

Since πSjk = πSj + πSk,

4. Proposition 2
We show that ∂[ln

G Djk
G

C
jk

] / ∂hk < 0 if β > 0.22 or1> 0.77, where G = X, n. Let µ be such thathln µ = e + δ jk + V (., p ) − ln Pk , O = C and D. By equation (18), ∂[ln
O
jk

O
k

sign as ∂[ln

O
jk

O
k

G Djk
G Cjk

] / ∂hk has the sameµ Djk
] / ∂hk , and we drop the subscripts j and k in the derivations below. By equations
µ Cjk
37

µ Djk∂V D ∂p D ∂V C ∂p C
∂e D ∂eC−(16) and (17), ∂[ln C ] / ∂h = ()+( D). By equations (2), (10) and−µ jk∂p ∂h ∂p C ∂h
∂h
∂h
∂p C
∂[c(e D )]∂[c(e D )]
∂e D
∂p D
= 0, and= 2c’(eD)+2, whereshows how
∂h
∂h
∂h
∂h
∂h
much c(eD) shifts following a change in h, holding eD constant. Equations (9) and (10) imply that∂V D ∂p D∂e D∂[c(e D )]1c’(eD) =and so=––2β. Therefore,∂p D ∂h2β∂h∂h(12), ∂VD/∂pD = –β,

∂[ln

µ Djk
∂eC
∂[c( e D )]
]
/
∂
h
=
2
−
−
β
µ Cjk
∂h
∂h

(A5)

To see the intuition for equation (A5), suppose hk decreases. Then under the D structure, both
effort level and price increase, but the effect of effort level dominates, and the term∂[c( e D )]reflects the net effect of effort level on lnμD. Under the C structure, however,−2 β∂heffort level increases but price does not (since the denomination ignores pastor effort in pricing),∂eC
and the term −reflect the total effect of effort level on lnμC. Using equations (11) and (13)∂h1
we can show that the effects on lnμD and lnμC both increase with , but the effect on lnμDh11increases faster with. Therefore, whenis large the effect on lnμD dominates and
h
h

µ Djk
µ Djk
∂[ln C ] / ∂h < 0. To be rigorous, by equations (11), (13) and (A5), we have ∂[ln C ] / ∂h =
µ jk
µ jk
1
h
h
1
1
1
h
1
+
) , which is negative if and only if
+ ln
+
− ln
(ln
) = 2 (ln2hh +1 h +1
h2β
h +1 h +1
2β h
h +1
h
1
1
1
> 0. This holds if> 0.77. In addition, since eC = ln
> 0, we
ln
−
h
h +1
h
h 2 β ( h + 1)112β1
implicitly assume>, which implies that
> 0.77 if β > 0.22.
> 1 , orhh
2 β ( h + 1)
1 − 2βµ Djk
1Therefore, ∂[ln C ] / ∂h < 0 if > 0.77 or β > 0.22.
µ jk
h

38

1.2

1

0.6

0.4

0.2

Price of Secular Good (πS)

Figure A1: Numerical Example for V and πS

39

7.6

7.05

6.5

5.95

5.4

4.85

4.3

3.75

3.2

2.65

2.1

1.55

0
1

Indirect Utility (V)0.8Table A1: First stage results for Heckman and nonparametric estimation
Model

Linear Pr(1)
0.887***(0.005)

Probit(2)
25.60***(0.764)Logit(3)76.57***(1.718)Linear Pr(4)
0.878***(0.006)-0.131***(0.049)Probit(7)25.97***(0.866)-2.899*(1.582)Logit(5)73.84***(1.901)-6.070*(3.115)

Strict*Natural disasters(1st principal component)
Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)

0.00295(0.003)-0.00261(0.004)0.00107(0.006)-0.0146*(0.008)0.0882(0.090)-0.069(0.104)-0.184(0.193)-0.0866(0.243)0.153(0.179)-0.182(0.205)-0.36(0.388)-0.219(0.470)0.00148(0.003)-0.00348(0.004)0.00188(0.007)-0.0184**(0.009)0.0516(0.129)-0.145(0.124)-0.205(0.244)-0.232(0.296)0.094(0.239)-0.313(0.240)-0.431(0.471)-0.438(0.553)Adjusted R squared
Observations

0.812
9,660

-9,292-9,292

0.82
9,660-9,292-9,292Presence 1970
Ave. Presence 1970, Similar Denom.

The dependent variable is the dummy variable for a denomination being present in a country in 2005. Columns 1, 3, 4 and 6 are thefirst stage estimation for columns 3, 5, 6 and 8 of Table 6, respectively. Column 2 is the first stage for columns 1 and 4 of Table 6.
Column 5 is the first stage for columns 2 and 7 of Table 6.

Table A2: Splitting the Sample of Countries by Population Density

Estimation method

OLS

Non-parametric/Linear probability model

Median population density

Below
(1)

Above
(2)

Below
(3)

Above
(4)

Below
(7)

Above
(5)

Decentralized*Infrastructure(1st principal component)

0.778**(0.358)0.814*(0.411)0.874**(0.350)0.940**(0.442)

1.050**(0.502)0.865*(0.434)

Instrument set
Observations
Adjusted R squared

-730
0.521

-872
0.567

A
730
0.704

A
872
0.701

B
629
0.717

B
786
0.713

The dependent variable is the log number of adherents. In columns (1), (3), and (7), the sample is countries with below median
population density in 2000; in columns (2), (4), and (5) the sample is countries with above median population density in 2000.
Regressions in columns (1) and (2) replicate the specification in column (3) of Table (7); regressions in columns (2) and (3) replicate
the specification in column (3) of Table 6; and regressions in column (7) and (5) replicate the specification in column (5) of Table 6.
Coefficient estimates for other regression variables are suppressed for expositional convenience. Standard errors clustered by country.
*** p<0.01, ** p<0.05, * p<0.10.

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45

Table 1: The 35 largest Protestant denominations, 2005
Denomination
Anglicans
Assemblies of God
Seventh-day Adventist Church
Southern Baptist Convention
Jehovah's Witnesses
SIM Church
New Apostolic Church
Church of God (Cleveland)
Ch of Jesus Christ of Latter-day Saints
American Baptist Churches in the USA
SFM/NPY/FFFM
Church of the Foursquare Gospel
United Methodist Church (USA)Presbyterian Church (USA)
Africa Inland Church
Christian and Missionary Alliance
Methodist Church of Great Britain
Pentecostal Assemblies of God (Canada)
United Pentecostal Church
Christian Aviation Ministries
Christian Brethren (Open)
Baptist Unions/BWA
PEMS France
Pres Ch of East Africa (Ch of Scotland)
Evangelical Alliance Mission
Salvation Army
Apostolic Church Missionary Movement
OMS International
Church of the Nazarene
Evangelical Lutheran Ch in America
Church of God of Prophecy
Churches of Christ (Instrumental)
Zion Christian Church
Former AUCECBMoravian Church

Headquarters
country
Britain
US
US
US
US
US
Switzerland
US
US
US
Swed/Norw/Finl
US
US
US
Britain/US
US
Britain
CanadaUSUS
USUSFranceBritainUS
US
Britain
US
US
US
US
US
South Africa
RussiaUS

Globaladherents(millions)
74.4
42.4
16.7
11.9
11.1
11.0
7.4
7.0
6.7
5.7
4.0
3.7
3.7
3.4
3.2
3.2
2.9
2.9
2.4
2.1
1.9
1.9
1.9
1.8
1.6
1.6
1.5
1.4
1.2
1.1
1.0
1.0
0.9
0.9
0.9

Share ofglobalProtestants
0.103
0.059
0.023
0.017
0.015
0.015
0.010
0.010
0.009
0.008
0.006
0.005
0.005
0.005
0.004
0.004
0.004
0.004
0.003
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001

This table is based on the global membership of denominations outside of the US.

46

Number ofcountries
present
140
149
216
110
214
14
149
124
146
11
37
59
44
20
7
50
18
29
90
7
43
44
6
3
28
86
7
11
102
19
81
31
5
13
31

Table 2: Presence of US denominations abroad in 1970 and 2005

Denomination
present in 2005

No

Denomination present in
1970
No
9,891

Yes
2

Total
9,893
(0.865)

Yes

305

1,242

Total

10,196(0.891)

1,244(0.109)1,547(0.135)
11,440This table shows the number of cases in which a US denomination (N = 130) is present in acountry (N = 88) in 1970 and 2005.

Table 3: Governance structure and religious doctrine of US denominations
Variable
Denomination is decentralized (congregational polity)
Denomination is theologically strict (row mean > .5)

Mean
0.554
0.446

St. Dev.
0.499
0.499

Is evangelism an essential function of all churches and believers?
Is repentance and conversion essential for all believers?
Is bible considered inerrant or infallible?
Is the damnation of non-believers emphasized?
Is imminence of 2nd coming of Christ emphasized?
Is sanctification emphasized?
Is speaking in tongues emphasized?
Are drinking, smoking, cultural activities, or dress restricted?
Is ongoing practice of divine healing emphasized?

0.808
0.754
0.746
0.700
0.262
0.223
0.208
0.246
0.292

0.396
0.432
0.437
0.460
0.441
0.418
0.407
0.450
0.457

The sample is 130 US Protestant denominations.

47

Table 4: Summary statistics for country variables
Variablelog GDP per capita
log population
mean % primary education (Barro & Lee)mean % secondary education (Barro & Lee)mean % tertiary education (Barro & Lee)urban population/total population
log life expectancy
total fertility rate
log distance to US= 1 if English official language
Immigrants in US/total population= 1 if Islam dominant religion= 1 if Catholicism dominant religion= 1 if Orthodox dominant religion= 1 if Judaism dominant religion= 1 if Buddhism, Hinduism dominant religion
regulation of religion index (Grim & Finke)= 1 if country had state religion in 1970
rule of law index (Freedom House)
ethnic fractionalization index (Alesina et al.)annual incidence of landslides (>1000 affected)
annual incidence of eruptions (>1000 affected)
annual incidence of wind storms (5+ days)
annual incidence of earthquakes (>7 Richter)annual incidence of military conflict
annual incidence of cholera (>1000 affected)
annual incidence of dengue fever (>1000 affected)
annual incidence of flu/SARS (>1000 affected)
annual incidence of meningococcal (>1000 affected)
log hospital beds
medical personnel per 1,000
public health expenditure/GDP x 100
dentists per 1,000
fixed mainlines per 1,000
cellular subscriptions per 1,000
passenger cars per 100
roads paved (%)
log road network per square km
log annual air carrier departures
log annual air passengers carried
48

Mean
7.63
16.33
33.00
33.12
10.46
0.40
4.16
3.74
8.93
0.18
0.01
0.22
0.40
0.07
0.01
0.07
2.68
0.42
0.00
0.39
0.02
0.03
0.01
0.03
0.20
0.02
0.01
0.003
0.01
0.98
3.81
3.87
0.37
16.59
15.70
13.44
49.72
-1.30
10.14
14.10

St. Dev.
1.60
1.39
13.85
19.07
8.48
0.22
0.16
1.69
0.45
0.39
0.01
0.41
0.49
0.25
0.11
0.25
2.65
0.50
0.98
0.25
0.04
0.09
0.02
0.09
0.29
0.74
0.33
0.18
0.32
0.98
3.86
2.10
0.35
19.52
15.87
16.22
32.69
1.44
1.82
2.15

Table 5: Baseline results
Dependent variable(3)
0.228***(0.056)

Log congregations
(4)
(7)
(5)
0.126**
0.132***(0.050)(0.050)0.215***(0.073)0.217***(0.077)0.187**(0.079)0.188**(0.081)-0.400***(0.143)-0.367***(0.138)-0.313**(0.135)-0.282**(0.130)Regressors
Strict*Natural disasters(1st principal component)(1)0.218***(0.056)

Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)
Adjusted R squared
Observations

0.615
1,613

Log adherents(2)

0.531***(0.120)0.531***(0.192)

0.606
1,980

0.633
1,602

0.593
1,613

0.531***(0.120)0.420***(0.151)

0.606
1,980

0.612
1,602

The dependent variable is either the log number of adherents (columns 1-3) or the log number of congregations(columns 4-6). Regressions include country dummies, denomination dummies, and interactions between
Decentralized and Strict and the following country characteristics: percentages of the adult population withprimary, secondary, and tertiary education (Barro and Lee, 2010), log per capita GDP, log population, the urbanization
rate, log life expectancy, the total fertility rate, log distance of the country from the United States, a dummy for whether acountry’s official language is English, the Grim and Finke (2006) index of the government regulation of religion, a dummy
for whether the country had a state religion in the past, the Freedom House rule of law index, the Alesina and La Ferrara(2005) ethnic fractionalization index, the fraction of the population that had migrated to the US as of 1970, and dummy
variables for whether Catholicism, Islam, Orthodoxy, Hinduism or Buddhism, or Judaism is the dominant religion in acountry . Standard errors clustered by country. *** p<0.01, ** p<0.05, * p<0.10.

Table 6: Heckman and nonparametric estimation
Estimation procedure
1st stage estimation
Strict*Natural disasters(1st principal component)

HeckmanNonparametricNonparametric
ProbitProbitLinear Pr
ProbitLogit Linear Pr
Probit
Logit
(1)
(2)
(3)
(4)
(7)
(5)
(6)
(9)
0.197*** 0.227*** 0.185*** 0.196*** 0.191*** 0.239*** 0.216*** 0.219***(0.076)(0.084)(0.050)(0.054)(0.052)(0.062)(0.063)(0.064)

Strict*Disease outbreaks(1st principal component)

0.189**(0.093)0.251**(0.098)0.204**(0.083)0.158*(0.084)0.164**(0.082)0.268*** 0.227**(0.092)(0.088)0.231**(0.089)Strict*Health services(1st principal component)-0.341**(0.167)-0.376**(0.177)-0.336**(0.129)-0.340***(0.126)-0.325**(0.124)-0.353**(0.155)-0.341**(0.151)-0.342**(0.151)

Decentralized*Infrastructure 0.586**(1st principal component)(0.233)0.621**(0.251)0.450**(0.185)0.373*(0.192)0.386**(0.188)0.466**(0.190)0.382**(0.185)0.394**(0.181)

Instrument set
Adjusted R squared
Observations

B-9,660

A
0.664
1,602

A
0.661
1,602

A
0.661
1,602

B
0.675
1,415

B
0.671
1,415

B
0.672
1,415

A
-11,960

The dependent variable is the log number of adherents. Regressions include country dummies, denomination dummies, andinteractions between Decentralized and Strict and other country characteristics (see notes to Table 5). Columns 1-2 use a Heckman
estimator, with a probit model used to estimate the first stage probability of a denomination being present in a country; columns 3-8
use a nonparametric estimator, with a linear probability, probit, or logit model used to estimate the first stage probability of adenomination being present. The instrument set refers to the additional variables used in the first stage estimation, with set A
including lagged denomination presence in 1970 and set B including set A plus the average of lagged presence in 1970 for
denominations with a similar size in the US. Standard errors are clustered by country. *** p<0.01, ** p<0.05, * p<0.10.

50

Table 7: Placebo tests for health services and transportation infrastructure
Estimation method
Regressors
Strict*Natural disasters(1st principal comp.)

OLS
Nonparam.
(1)(2)
Regressors
0.236*** 0.214*** Strict*Natural disasters(0.063)(0.070)(1st principal comp.)(3)0.206***(0.056)(4)0.179**(0.068)Strict*Disease outbreaks(1st principal comp.)

0.280*** 0.299*** Strict*Disease outbreaks(0.083)(0.093)(1st principal comp.)

0.226***(0.075)0.237*** 0.241***(0.087)(0.086)0.224***(0.075)

Strict*Dentists per capita

0.122(0.344)

Strict*Health services(1st principal comp.)-0.410***(0.138)-0.342**(0.161)-0.374**(0.157)-0.404***(0.140)Decentralized*Log
Air transport-0.145(0.110)-0.069(0.123)-0.125(0.125)-0.171(0.106)-0.037(0.384)

Decentralized*Infrastructure 0.659*** 0.472**(1st principal comp.)(0.201)(0.197)
Air transport measure
Adjusted R squared
Observations-0.6291,606-0.6691,418

OLS

Passengers Carriers
0.626
0.664
1,579
1,420

Nonparametric(7)(5)
0.179***
0.204***(0.067)(0.057)

Passengers Carriers
0.665
0.625
1,395
1,607

The dependent variable is the log number of adherents. Regressions include country dummies, denomination dummies, andinteractions between Decentralized and Strict and other country characteristics (see notes to Table 5). In columns 1 and 2, dentists per
capita serve as a placebo for health services; in columns 3-6, air transport serves as a placebo for land transportation andcommunication infrastructure. Except for the boldfaced regressors above, the specifications in columns 1, 3, and 4 correspond to thatin column 3 of Table 5 and the specifications in columns 2, 5 and 6 correspond to that in column 7 of Table 6. Standard errors are
clustered by country. *** p<0.01, ** p<0.05, * p<0.10.

51

Table 8 Placebo tests for doctrinal strictness of denominations
Estimation method
1st stage estimation

OLS

Nonparametric
ProbitProbit
(3)
(4)

-(1)

-(2)

Communion*Natural
disasters(1st principal component)-0.044(0.091)-0.094(0.094)-0.046(0.086)-0.085(0.088)Communion*Disease
outbreaks(1st principal component)

0.006(0.120)0.008(0.114)0.029(0.111)

0.043(0.109)Communion*Health services(1st principal component)-0.022(0.137)0.029(0.143)0.040(0.131)0.105(0.142)

Decentralized*Infrastructure(1st principal component)
Instrument set
Adjusted R squared
Observations

0.635***(0.206)

0.612
1,531

0.633
1,520

0.423*(0.216)
B
0.656
1,367

B
0.674
1,357

The dependent variable is the log number of adherents. Regressions include country dummies,
denomination dummies, and interactions between Decentralized and Strict and other country
characteristics (see notes to Table 5). The frequency of communion serves as a placebo for thestrictness of religious doctrine. Except for the boldfaced regressors above, the specifications incolumns 1 and 2 correspond to those in column 3 of Table 5 and the specifications in columns
3 and 4 correspond to those in column 7 of Table 6. See notes to Table 6 on the definition of
the instrument set. Standard errors are clustered by country. *** p<0.01, ** p<0.05, * p<0.10.

Table 9: Extended results for strictness interactions with natural disasters(a) OLS
Strict*Disaster incidence

Landslides
6.915***(2.401)log adherents
VolcanicWind
Eruptions
Storms2.509***-0.317(0.517)(4.016)

Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)

0.228***(0.079)-0.361***(0.136)
0.536***(0.195)0.211***(0.076)-0.396***(0.137)0.556***(0.195)0.183**(0.085)-0.375**(0.152)0.549***(0.202)0.199***(0.075)-0.379***(0.142)0.525***(0.198)

0.633
1,602

0.633
1,602

0.631
1,602

0.632
1,602

7.200***(2.310)2.615***(0.602)-3.864(4.162)

1.901**(0.935)0.283***(0.090)-0.358**(0.151)
0.481**(0.191)0.261***(0.093)-0.380**(0.155)0.498**(0.194)0.238**(0.091)-0.390**(0.154)0.454**(0.204)0.260***(0.087)-0.372**(0.157)0.440**(0.194)

0.675
1,415

0.674
1,415

0.673
1,415

0.673
1,415

Adjusted R squared
Observations(b) Nonparametric(Instr. Set B)
Strict*Disaster incidence

Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)
Adjusted R squared
Observations

Earthquakes
1.782*(0.921)

Regressions include country dummies, denomination dummies, and interactions between
Decentralized and Strict and other country characteristics (see notes to Table 5). Except for theboldfaced regressors above, the specifications in panel (a) correspond to those in column 3 ofTable 5 and the specifications in panel (b) correspond to those in column 7 of Table 6. See notes
to Table 6 on the instrument set. Standard errors are clustered by country. *** p<0.01, **p<0.05, * p<0.10.

53

Table 10: Extended results for strictness interactions with disease outbreaks(a) OLS
Strict*Disease incidence

Cholera0.224*(0.117)log adherents
DengueFever
Flu/SARS
0.036
0.860***(0.189)(0.299)Strict*Natural disasters(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)

0.207***(0.053)-0.445***(0.144)0.539***(0.187)0.200***(0.057)-0.410***(0.144)0.539***(0.185)0.204***(0.053)-0.323**(0.143)0.525***(0.192)0.205***(0.053)-0.413***(0.144)0.540***(0.185)

0.632
1,602

0.632
1,602

0.632
1,602

0.632
1,602

0.193
(0.138)-0.086(0.208)1.085***(0.318)0.021(0.610)0.217***(0.060)-0.422***(0.154)0.479**(0.190)0.228***(0.067)-0.392**(0.152)
0.467**(0.186)0.206***(0.060)-0.288*(0.156)
0.471**(0.185)0.225***(0.060)-0.395**(0.152)0.450**(0.193)

0.672
1,415

0.672
1,415

0.675
1,415

0.673
1,415

Adjusted R squared
Observations(b) Nonparametric(Instr. Set B)
Strict*Disease incidence

Strict*Natural disasters(1st principal component)
Strict*Health services(1st principal component)
Decentralized*Infrastructure(1st principal component)
Adjusted R squared
Observations

Meningococcal-0.096(0.420)Regressions include country dummies, denomination dummies, and interactions between
Decentralized and Strict and other country characteristics (see notes to Table 5). Except for theboldfaced regressors above, the specifications in panel (a) correspond to those in column 3 ofTable 5 and the specifications in panel (b) correspond to those in column 7 of Table 6. See notes
to Table 6 on the instrument set. Standard errors are clustered by country. *** p<0.01, **p<0.05, * p<0.10.

54

Table 11: Extended results for strictness interactions with health expenditures(a) OLS
Strict*Health services

Hospital
beds-0.475***(0.181)log adherents
Nurses,HealthPhysicia
Midwives Expend.
ns-0.089**-0.0500.010(0.039)(0.054)(0.151)

Strict*Natural disasters(1st principal component)
Strict*Disease outbreaks(1st principal component)
Decentralized*Infrastructure(1st principal component)

0.191***(0.059)0.192**(0.077)0.618***(0.200)0.247***(0.055)0.176**(0.085)0.548***(0.180)

0.627
1,635

0.633
1,602

0.626
1,635

0.626
1,635

0.625
1,631

-0.456**(0.197)-0.087**(0.041)-0.048(0.056)0.089(0.159)-1.052***(0.342)0.191***(0.066)0.246***(0.088)0.585***(0.198)0.262***(0.059)0.218**(0.105)0.477**(0.185)0.224*** 0.224***(0.062)(0.067)0.294*** 0.280***(0.088)(0.094)0.601*** 0.638***(0.198)(0.194)0.249***(0.062)0.303***(0.089)0.452**(0.196)

0.671
1,444

0.675
1,415

Adjusted R squared
Observations(b) Nonparametric(Instr. Set B)
Strict*Health services

Strict*Natural disasters(1st principal component)
Strict*Disease outbreaks(1st principal component)
Decentralized*Infrastructure(1st principal component)
Adjusted R squared
Observations

0.228*** 0.226***(0.057)(0.060)0.252*** 0.237***(0.077)(0.078)0.629*** 0.658***(0.197)(0.195)

0.670
1,444

0.670
1,444

Financial
Develop.
-0.737**(0.333)0.251***(0.056)
0.286***(0.077)0.611***(0.196)

0.667
1,441

Regressions include country dummies, denomination dummies, and interactions between
Decentralized and Strict and other country characteristics (see notes to Table 5). Except for theboldfaced regressors above, the specifications in panel (a) correspond to those in column 3 ofTable 5 and the specifications in panel (b) correspond to those in column 7 of Table 6. See notes
to Table 6 on the instrument set. Standard errors are clustered by country. *** p<0.01, **p<0.05, * p<0.10.

55

Table 12: Extended results for decentralization interactions with infrastructure
log adherents
Cell
Passenger
PhonesCars
0.031**
0.026**(0.014)(0.011)(a) OLS
Decentralized*Infrastructure

Mainline
Phones
0.021*(0.012)

Strict*Natural disasters(1st principal component)
Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)

0.217*** 0.216***(0.057)(0.056)0.220*** 0.223***(0.076)(0.075)-0.357** -0.381***(0.146)(0.142)Adjusted R squared
Observations(b) Nonparametric(Instr. Set B)
Decentralized*Infrastructure

Strict*Natural disasters(1st principal component)
Strict*Disease outbreaks(1st principal component)
Strict*Health services(1st principal component)
Adjusted R squared
Observations

Road
Network
0.279**(0.137)0.232***(0.055)0.218***(0.077)-0.347**(0.139)0.212***(0.056)0.221***(0.073)-0.396***(0.138)

0.625
1,613

0.625
1,613

0.633
1,602

0.625
1,613

0.016
(0.012)0.018(0.016)0.029**(0.011)

0.214(0.145)0.214***(0.067)0.292***(0.092)-0.314*(0.167)

0.198***(0.066)0.302***(0.091)-0.340**(0.164)0.233***(0.061)
0.237***(0.088)-0.325**(0.155)0.201***(0.067)0.268***(0.084)-0.329**(0.159)

0.668
1,425

0.669
1,425

0.675
1,415

0.666
1,425

Regressions include country dummies, denomination dummies, and interactions between
Decentralized and Strict and other country characteristics (see notes to Table 5). Except for theboldfaced regressors above, the specifications in panel (a) correspond to those in column 3 ofTable 5 and the specifications in panel (b) correspond to those in column 7 of Table 6. Standard
errors are clustered by country. *** p<0.01, ** p<0.05, * p<0.10.

56

14
12
8

10

log adherents globally

16

18

Figure 1a: Global membership and US membership, 2005

6

8

12
10
log adherents in US

14

16

4
3
2
1

log countries entered

5

Figure 1b: Numbers of countries entered and US membership, 2005

6

8

10
12
log adherents in US

14

16

0

10

Frequency

20

30

Figure 2: Distribution of doctrinal strictness across US denominations

.2

0

.4
.6
Row mean of nine doctrine variables

.8

1

14
12
10
8

log adherents

16

18

Figure 3: Global No. of adherents and congregations for US denominations, 2005

2

4

6
8
log number of congregations

58

1012