American Economic Journal: Microeconomics 2012, 4(3): 152–170
http://dx.doi.org/10.1257/mic.4.3.152
Competition and the Strategic Choices of Churches†
By Adam D. Rennhoff and Mark F. Owens*
We examine how the decisions of churches are impacted by the decisions of rival churches. Using a novel dataset, we estimate a model
of strategic interaction, which accounts for the location and denomination of churches. We focus on a church’s decision of whether to
provide a weekday child care program. Empirical evidence indicates that churches compete more strongly with same-denomination
churches than with different-denomination churches. These effects
diminish with distance. (JEL J13, L31, Z12)
R
eligious congregations provide a wide variety of services to local communities.
They provide aid for those in need, coordinate social activities, and provide
child care, along with many other service activities. The wide variety of services
that churches offer has the ability to address community needs and benefit a large
number of individuals.1 While the benefit of these services is difficult to quantify,
prior studies find that religious involvement is correlated with greater life expectancy (McCullough et al. 2000) and higher incomes (Gruber 2005). Though causality cannot be proven, this at least suggests that churches generate social benefits,
which could be related to the services they provide. Access to many church-provided services is not limited strictly to church members, and the benefits extend to
nonmembers in the community at-large, particularly when churches address community needs not met by other organizations.
Given the large (and varied) roles that churches play in local communities, it is
somewhat surprising how infrequently economists have studied the behaviors and
choices of churches themselves.2 The relatively small amount of research on church
behavior is at least partially data driven, since many forms of charitable giving are
informal and unobservable, making the benefits resulting from church-sponsored services difficult to quantify. In addition, churches may have different motivations and
considerations in entering the market for services than do other organizations. It is
often the case that local governments and for-profit firms offer what appear to be close
substitutes for the services offered by churches. However, it is difficult to determine
* Rennhoff: Middle Tennessee State University, Department of Economics and Finance, P.O. Box 27,
Murfreesboro, TN 37132 (e-mail: [email protected]). Owens: Middle Tennessee State University,
Department of Economics and Finance, P.O. Box 27, Murfreesboro, TN 37132 (e-mail: [email protected]).
We thank J. Laron Kirby for research assistance and seminar participants at Middle Tennessee State University and
the University of Georgia for comments.
†
To comment on this article in the online discussion forum, or to view additional materials, visit the article page
at http://dx.doi.org/10.1257/mic.4.3.152
1 We recognize that not all religious congregations refer to themselves as a “church.” However, this is the common convention for our sample of Christian churches. We refer to religious congregations as “churches” for brevity.
2 A recent EconLit keyword search for “church” yielded 310 results, while a keyword search for “airline”
revealed 826 results in peer-reviewed journals.
152
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
153
if individuals regard these alternatives as substitutes for church-sponsored services.
Further, it is unclear whether churches are providing a service to meet an unserved
need in the community, to generate profits, or instead to match services with other
churches in order to attract members. Together these unique features make the decisions of churches to offer services an interesting area for economic research.
In this paper, we set out to formally model the behavior of Christian churches
by examining the decision of whether to provide a weekday child care program
(either a full-time or “mother’s day out” part-time day care program).3 We specify a
model, based on empirical models of discrete games, that allows church payoffs to
depend on the decisions of rival churches.4 The model incorporates unique features
of church decisions by allowing the degree of interdependence between any two
churches to depend on the denomination of each church and the distance between
the churches’ locations. We seek to measure whether there is any evidence of strategic interaction between churches.
We choose to consider the decision to offer a child care program for several reasons. First, church-sponsored child care plays a large role in the market and the benefits of the service extend well beyond the members of a church. A National Council
of Churches (NCC) study in the early 1980s found that Christian churches were the
single-largest provider of child care services in the US (Lindner, Mattis, and Rogers
1983). Second, the decision to offer child care is easier to observe and quantify than
other services that churches may provide. Third, the choice to offer child care involves
substantial time costs of planning, as well as explicit costs for labor, capital, and physical space making this decision a good candidate for the study of strategic interactions.
To conduct our study, we construct a unique dataset of all Christian churches in
two suburban Nashville, Tennessee counties. We collect a rich set of church characteristics using published works, online resources, and telephone calls. In addition,
we conduct physical inspections of each church and record numerous observable
characteristics about the features of the church buildings and property. Together
these sources allow us to compile a detailed set of characteristics for each church,
which is unavailable through other sources.
Our results support the basic hypothesis that a church’s decisions are affected
by the decisions of other churches. Churches are strategic substitutes, meaning
that, holding all else constant, a church is less likely to provide child care if a
nearby church offers it. Further, we find strong evidence that a church’s response
to the decision of another nearby church to offer day care service depends crucially on the denomination of the rival. If the nearby rival shares the same denomination as the potential entrant, the impact is substantially larger than if the nearby
rival is a different-denomination church. Interestingly, for-profit centers appear to
have minimal impact on church child care decisions. All strategic effects diminish
as geographic distance increases.
3 The decision to focus on Christian churches, as opposed to including all types of religious congregations, is
empirically-motivated. More on this will be discussed in Section III.
4 The term “rival” should not be read to imply hostility. We simply use the term to acknowledge that, for example, the desire to increase the congregation size is, to a large degree, a zero-sum game.
154
American Economic Journal: Microeconomicsaugust 2012
Our estimates also indicate that the number of denomination-specific adherents
in a location, defined as a count of the number of individuals identifying themselves
as followers of a specific denomination, has a large impact on the profitability of a
church-run day care.5 Although we do not estimate a model of household child care
decisions, the importance (in the child care provision decision) of a church’s adherent population size provides some evidence that, perhaps, families have a preference
for sending their children to a day care run by their favored religious denomination. This also provides a plausible interpretation for the strong same-denomination
strategic effects. Interestingly, the overall population size is far less important in a
church’s child care decision than the number of adherents to a church’s denomination.
We also conduct counterfactual simulations to characterize the impact that an
increase in the number of preschool aged children, an increase in the number of
congregation adherents, and the impact of the absence of for-profit providers in the
market have on the equilibrium number of day care providers. Each of these appear
to increase the number of day care providers as expected. Increased church adherents have a notably larger impact than increased preschool aged children. The exclusion of for-profit providers has a negligible effect on the total number of nonprofit
centers, but appears to impact which churches choose to offer care.
The remainder of the paper is organized as follows. The next section contains
a brief review of relevant research in the economic literature relating to churches
and the empirical models of discrete games. The empirical model is presented in
Section II and the data used in this study are described in Section III. We present
our estimation strategy in Section IV. Estimates and counterfactual simulations are
presented in Section V and Section VI concludes.
I. Literature Review
There are two strands of literature to discuss in relation to this study: the literature
on the economic behavior of religious organizations and churches and the empirical
literature on estimating models of discrete games.
A. Literature on Religion and Economics
Most of the economic studies of religion focus on the behavior of individual
church members. McCullough et al. (2000) present a meta-analysis of studies that
investigate whether religious people live longer than their non-religious counterparts. They find that religious involvement is associated with lower mortality rates.
Gruber (2005) looks at the impact religiosity has on economic outcomes for church
members and finds, for example, that religious participation is positively related
to a higher level of income. Gruber and Hungerman (2008) look at the effect of
repealing laws that prohibited retail activity on Sunday had on the behavior of
individuals. They find that the likelihood of drinking and drug use increased in
5 Note that “adherents” is different than church members. The number of “adherents” captures the “pool” of
individuals in a given area following a particular branch of Christianity (Southern Baptist, Methodist, etc.). This is
a location characteristic, similar to Census population estimates, rather than a church characteristic.
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
155
churchgoers following the repeal of these so-called “blue laws.” Another branch of
the religion literature has focused on external factors relating to church charitable
giving. Gruber and Hungerman (2007) and Hungerman (2009) examine whether
government expansion has crowded out faith-based charity and find evidence of
crowding-out particularly in homogenous communities.
Relative to the number of studies relating individuals’ outcomes to their church
attendance, very few economic studies focus on the behavior of the church itself or
how such behavior is influenced by the actions of other churches. Two studies suggest a link between charitable giving and competition between churches. Zaleski
and Zech (1995) investigate how competition between churches affects the amount
of money members contribute to their church. They find that Protestants give more
when their congregation faces substantial competition whereas Catholics give more
when their church represents a minority in a heavily concentrated market. Zaleski
and Zech conjecture that this finding is likely due to each church improving their
product due to competition but, they are unable to formally test this hypothesis.
Pepall et al. (2006) look at church spending on charitable services (soup kitchens,
etc.) and find that churches give more to these charitable services when faced with
increased inter- and intra-denominational competition. They conclude that charitable service spending is one of the mechanisms by which churches compete.
Walrath (2010), to our knowledge, is the only other paper to use empirical discrete game techniques to examine church behavior. Walrath looks at the entry decisions of churches with the goal of determining whether there is “over entry” of
Protestant churches in different geographic areas, due to the (relative) lack of a
strong hierarchical structure (such as with the Catholic Church).
B. Empirical Models of Discrete Games
The empirical strategy used in this paper to model church behavior originates
with Bresnahan and Reiss (1991) and Berry (1992). In Berry, a reduced-form profit
function is used in a latent variable approach to identify the impact of demographic
characteristics and the actions of rival firms on an airline’s decision of which markets to enter. We extend this basic framework by incorporating elements from Seim
(2006) and Dai (2007). Seim, in examining the entry and location choices of video
rental stores, allowed each agent’s profit to depend not just on the entry decisions
of others, but also on their chosen location. From a modeling standpoint, this was
done by allowing the profit of agent i (conditional upon entry) to depend on the
number of other entrants within x miles from agent i’s location. In this manner, the
estimated impact of a competitor two miles away, for example, may differ from the
impact of a competitor eight miles away. As shown in Section II, we will utilize a
similar strategy.
In Dai’s (2007) study on cell phone entry choices, the number of other competitors/
entrants matters, but so too does their identity. In this context, Dai allows the profit of
Cingular, for example, to be affected differently by Sprint or Verizon’s entry into the
market. Dai is able to estimate these firm-specific parameters because she observes
each firm’s entry decision in over 19,000 cities. We, on the other hand, observe only
one equivalent “entry” decision per church, which makes following Dai’s approach
156
American Economic Journal: Microeconomicsaugust 2012
infeasible. In order to incorporate the basic premise behind Dai’s modeling assumption, however, we allow the impact of competitor churches to differ depending on
whether they belong to the same denomination or not. In this manner, we are able to
incorporate information regarding spatial distances, as in Seim (2006), and allow for
at least a small degree of heterogeneity in the effect of others’ decisions.
In models of this type, it is likely that games may result in multiple equilibria.
This necessitates either the use of a selection rule or the adoption of an estimation strategy, such as Ciliberto and Tamer (2009), that involves finding the set of
parameters that satisfy the specified equilibrium conditions rather than defining an
equilibrium selection rule. It is not feasible to adopt Ciliberto and Tamer’s strategy
for our single-market dataset.6 We, therefore, rely on an equilibrium selection rule,
which is discussed in greater detail in Section IIB.
II. Empirical Model
A. Model Setup
Each church i faces a decision of whether to offer a weekday child care program.
We assume that churches make their decisions in order to maximize a value function. In following the common convention in the literature on empirical models of
discrete games (Berry 1992; Mazzeo 2002; Cohen, Freeborn, and McManus 2011),
we assume a reduced-form value function for each church. The proper objective
function for nonprofits is the subject of some prior research, including Harrison
and Lybecker (2005). The reduced-form value function is appealing for studying
church decisions because it allows us to be agnostic about what the value function
actually is (profit or revenue maximization, membership size maximization, etc.).
Our assumption does, however, require that all churches are optimizing the same
objective function.7 For convenience, we interchangeably use the terms “objective
function” and “profit function.” We now consider the functional form of church i’s
profit function.
The value that church i receives from offering child care is a function of observed
and unobserved (to the econometrician) profit-shifters, as well as the child care
decisions of rival churches. Church i’s general profit function can be written:
​ ​ , γ) + ​ui​​ ,
(1)​
V​i​ = ​Xi​​ β + f (​D−i
where ​X​i​ is a vector of observable characteristics and profit-shifters for church i,
f () is a function that relates the child care decisions of other churches (​D−
​ i​) to the
profitability of church i’s child care decision, and u​ ​i​captures unobserved factors that
affect church i. The vectors β and γ are parameters to be estimated. For notational
simplicity, we refer to θ = {β, γ} as the set of parameters to estimate.
6 We provide a greater explanation for this infeasibility in Section IIB.
A potential solution to this constraint would be to allow the effect of “profit-shifter” model parameters to vary
across denominations. This would allow Catholic churches, for example, to have one objective function, while
Baptists have another. Unfortunately, allowing for such an approach is infeasible given our relatively small sample
size.
7 Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
157
The function f (​D​−i​, γ) captures the impact that rival churches have on church
i’s payoff. In order to allow for some heterogeneity in the manner in which rival
churches affect church i, we estimate separate γ ’s based on each church’s denomination. More specifically, let ​Δ−
​ i​denote the set of churches of the same denomination
as i, but excluding i, and let Ω
​ i​​ denote the set of churches belonging to a different
denomination. In our estimation, we consider the following specification for f () :
3
[
 
 
j∈​Δ−
​ i​
k∈​Ωi​​
]
∑   ​  ​​ ​γ​1b​  ​∑   ​  ​​​ Djb​ ​ + ​γ2b
​ ​ ​∑   ​  ​​​ Dkb
​ ​.
(2)f (​D−
​ i​, γ) = ​
b=1
In equation (2) we adopt a distance band approach. D
​ j​b​ indexes the decisions of
churches of the same denomination as church i located within distance band b
from church i and ​D​kb​indexes the decisions of churches of different denominations
located within distance band b from church i. We include three different distance
bands: (i) between 0 and 4 miles, (ii) between 4 miles and 8 miles, and (iii) between
8 miles and 12 miles.8 By estimating separate ​γb​​ coefficients, we are again able to
distinguish between the impact of same- and different-denomination churches.9
​u​i​is included in equation (1) to account for unobserved factors and characteristics
that affect the profitability of church i’s child care decision. Given the spatial and
organizational nature of the data, it seems inappropriate to assume (or require) that
the unobservables are independent across churches. In order to allow for correlation,
we decompose u​ ​i​as follows:
​ ​ + ​εi​​ ,
(3)​u​i​ = ​ω​id​ + ​ηic​​ + ​μidc
where ​
ω​id​ is a denomination-specific unobservable, η​ic​ ​ is a city-specific
​ ​is a denomination-city specific unobservable, and ε​ i​​represents
unobservable,10 ​μidc
i ’s idiosyncratic benefit from providing child care. We assume that each of the four
unobservables are distributed i.i.d. standard normal. Although these unobservables
are assumed to be independent, our specification allows the unobserved profitability
between any two churches to be correlated based on a common denomination, a
common location, or a combination of both.
8 While the bands we have selected may appear to be quite “wide,” the counties used in this research contain a
proportion of rural areas. Anecdotally, it does not seem to be uncommon for individuals to attend a church six or
more miles from their house.
9 We also considered a second specification for f () in which each rival church’s decision is weighted according
to the inverse distance (in miles) between their location and the location of church i. By construction, any given
church’s influence on church i declines with geographic distance. Results for this second specification were virtually identical to the specification in equation (2) and are, therefore, omitted for brevity. Estimates are available from
the authors by request.
10 In the two counties, we categorize all locations into one of eight cities (Brentwood, Fairview, Franklin,
LaVergne, Murfreesboro, Smyrna, rural Rutherford County, and rural Williamson county). The first six are cities in
the legal and practical sense. We include the rural categories to capture those locations outside the city boundaries.
We want to emphasize that our use of the term “city” should not be interpreted to imply that separate markets are
present in our sample. A twenty minute drive could easily allow one to pass through three or four of these cities.
158
American Economic Journal: Microeconomicsaugust 2012
B. Equilibrium
The equilibrium condition in our model is that, conditional upon the decisions
of all other churches, church i will only offer child care if the value of doing so is
greater than zero. More formally, the equilibrium (binary) decision for church i may
be written:
​ ​ | θ, X, ω, η, μ, ε) ≥ 0} ,
(4)​D​i​ = I {V (​D−i
where I () is an indicator function taking the value one if the value of the church’s
objective function is positive, given the strategies of the other churches. It is wellknown that multiple equilibria are probable in games of this type (see, for example,
Hartmann 2010). One possible solution to this problem is to utilize an equilibrium
selection rule that will identify a single equilibrium. Bajari, Hong, and Ryan (2004)
argue that the equilibrium that maximizes total joint profits is the one most likely
to occur, whereas Berry (1992) adopts a timing assumption that ensures a unique
equilibrium prediction.
We consider four plausible selections rules for our data and let the fit of the model
determine which is our preferred rule. The first selection rule assumes churches make
their child care decision sequentially based on the age of each church. The order is
determined by the number of years each church has been in existence with the oldest
church moving first. Our second rule selects a unique equilibrium based on maximizing total denomination profits. More specifically, we use an iterative procedure
to search for a stable equilibrium in which each denomination selects the child care
configuration that maximizes total denomination profits, subject to the strategies
of the other denominations. The third selection rule utilizes a sequential selection
mechanism based on the likely need for child care in a given location. More specifically, we construct an index for each church based on the local demographics (population, preschool aged children, married couples, and dual-income households). We
posit that these characteristics should positively influence a church’s decision to
offer child care, giving our index the interpretation of reflecting the churches we
might expect to be located in the areas where demand for child care services is
highest. The fourth selection rule is motivated by Bajari, Hong, and Ryan’s (2004)
finding that the most likely equilibrium to occur is that which maximizes total joint
profits. We, therefore, consider a selection rule in which the equilibrium maximizing
total church profits is chosen.
While these selection rules may simplify the estimation and discussion of results,
one downside of such an approach is that the resulting parameter estimates are
dependent on the precise equilibrium selection rule chosen. A potential solution to
the assumption of a specific equilibrium selection rule is to estimate sets of parameters—as opposed to point estimates—that are consistent with specified equilibrium
conditions. Ciliberto and Tamer (2009) propose a method to estimate these parameter sets. In this manner, estimation does not depend on an equilibrium selection
rule, although point estimates of θ are not possible.
Unfortunately, implementing Ciliberto and Tamer’s (2009) estimation strategy is
not feasible in our case. The first stage in Ciliberto and Tamer’s (2009) estimation
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
159
strategy involves obtaining a consistent estimate of the probability of observing any
possible market configuration. Whether done parametrically, using the multinomial
logit (Cohen, Freeborn, and McManus 2011), or nonparametrically (Ciliberto and
Tamer 2009) this essentially requires using variation in local market characteristics
(demographics) to explain observed variation in local market structure. This necessitates the observation of multiple geographic markets. It is difficult with our data,
in which churches in certain locations certainly “compete” against churches which
may be located in a different city or county, to think of defining numerous distinct geographic markets. Realistically, all churches in our sample belong to a single
market,11 making Ciliberto-Tamer ill suited for our data.
III. Data
A. Overview
This study uses an original dataset, collected over the summer of 2008, of Christian
churches in Rutherford County, Tennessee and Williamson County, Tennessee.12
The two counties are part of the Nashville, Tennessee metropolitan statistical area
(MSA) and have a combined population of over 400,000 residents inhabiting both
urban and rural areas. We record the names, locations, denominations, membership
sizes, day care service, and observable physical characteristics for a total of 424
churches: 135 in Williamson County and 289 in Rutherford County. The collection
of church characteristics presents a difficult challenge as there is no comprehensive
database of information on local churches. We obtained the listing of churches and
information on each church’s characteristics via Internet searches, telephone calls,
and finally physical visual inspections of each church in our sample.13
In order to determine the membership size of each church, we first contacted
the local governing body of each denomination and then attempted to contact each
church by telephone to confirm the membership size.14 We were successfully able
to gather self-reported membership data for 256 (60 percent) of the churches.15
11 In order to be thorough, even ignoring our belief that the data constitute a single geographic market, we
nevertheless used a GPS graph to divide the counties into distinct markets. We were able to construct 22 different
“markets.” To see why this exercise is insufficient, consider a simplified world in which each market contained a
church from the five most popular denominations in our data. This would imply there are 32 (= ​2​5​) different possible market configurations. In looking at the data, we observe only 11 of these configurations. Such lack of coverage nullifies any attempt to use parametric or nonparametric techniques to estimate the probability of observing
these 32 configurations.
12 We collected information about all religious congregations in the two counties. We exclude some of these
from the analysis due to the lack of realistic substitutes. We exclude a Jewish synagogue, a Buddhist temple, an
Orthodox Church, Jehovah’s Witnesses, and the Church of Jesus Christ of Latter Day Saints from the data.
13 We began using telephone book/yellow page listings. We supplemented this list using the listing of churches
available through the HomeTownLocator Gazetteer (http://www.hometownlocator.com), a website specializing in
providing “local information” and through Google searches. We made physical inspection of all churches on our
list, which lead us to encounter a number of churches that were not on our initial list.
14 The local associations of Southern Baptist and United Methodist churches graciously provided us with access
to the membership numbers for their churches. A local minister of a Church of Christ provided us with Royster
(2009) which contains the membership sizes.
15 Most of the churches that we were unable to contact are very small congregations and are unlikely to offer
services such as day care. Approximately 70 percent of these were categorized in our visual inspections as having
small church buildings. In addition, only 8 percent of the churches that provide child care programs had missing
membership information.
160
American Economic Journal: Microeconomicsaugust 2012
We used these reported membership figures, along with the other observed characteristics (described in greater detail below), to impute congregation sizes for the
churches with missing values. We then used these figures for congregation sizes to
categorize each church as being small (fewer than 75 families), medium (between
75 and 300 families), or large (more than 300 families).16
We were able to construct our child care measure using a variety of different
sources.17 We consulted church websites and state child-care licensing lists, where
applicable. We looked for signage indicating child care programs during our physical inspections and, when the results were inconclusive, we followed up with telephone inquiries to the church.
An important element in our model is the notion of same and different denominations. Denominations and affiliations are often part of a church’s name, in other
cases these were determined over the telephone. If a church did not claim to be
affiliated with a denomination or if the denomination could not be determined, the
church is classified as part of our “Other Christian” category.18
Records from the Williamson County Assessor of Property and the Rutherford
County Property Assessor allowed us to determine the age of the church, as well as
the dates of church building renovations or additions. We used this data to create a
variable indicating whether the church was built or remodeled in the past 10 years.
Remaining church characteristics were compiled from our visual inspections. While
some of these measures, such as the presence of a playground or athletic field on
church property, are objective, others are, admittedly, more subjective in nature.
We also categorized the physical size of the church building into one of three size
categories. These detailed descriptions provide an accurate picture of the overall
impression projected by each church to the community that is impossible to quantify
with a single variable. We acknowledge the obvious downside of using subjective
measures, but the lack of quantitative data on these churches forces us to improvise
as best we can.
Summary statistics for the church data appear in Table 1.19
As reported in Table 1, almost one-quarter of the churches in our sample are some
form of Baptist, with Other Christian and Church of Christ being the second and third
most common church types, respectively. Fifty-nine of the churches (14 percent)
16 We transform membership numbers into three general size categories for three main reasons. First, because
a child care program may attract new members to the church, there is a concern that membership size may be an
endogenous characteristic. By transforming membership numbers into categories (meant to account for the number of families potentially utilizing child care, as well as the level of resources available to begin the child care
program), we believe that this minimizes the potential endogeneity. Second, not all churches record membership
in the same manner. Some record the total number of registered individuals, others count the number of registered
families, and others record only the actual attendance. Since all three measures are transformed into a single count
of families, we believe the broad categories also reduce measurement error to some extent. Finally, using general
size categories minimizes any likely measurement error due to the imputation of some size values.
17 We consider offering day care service to mean offering either full-time or part-time care during the normal
business week. We do not count churches that only offer child care during church services or short term “Vacation
Bible School” programs.
18 This category includes all churches that cannot be categorized in our other denominations. We establish a
unique category for a denomination if there are at least four churches in the denomination. Denominations which
have fewer than four churches are categorized in this “other” category.
19 We collected data on numerous other church features such as the presence of landscaping, church signs, bus
or van service, stained glass, and paved parking lots among others. We suppress these from the tables for space
considerations.
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
161
Table 1—Summary Statistics
Total
Child
care
Southern Baptist
Church of Christ
Methodist
Missionary Baptist
Prim. Baptist
Presbyterian
Cumberland Presby
Church of God
Holy
Episcopal
Assemblies of God
Freewill Baptist
Catholic
Lutheran
African Methodist
Other Christian
Other Pentecostal
104
72
46
20
16
16
12
10
9
7
7
6
5
5
4
78
7
22
9
10
1
0
5
1
0
0
1
0
0
3
0
0
7
0
214.9
156.3
208.7
56.0
59.9
104.9
85.1
59.6
98.1
204.2
67.2
76.0
1,476.1
126.6
64.3
189.6
78.2
6
10
8
10
14
9
33
26
26
18
20
40
250
22
24
12
22
Totals
424
59
116
—
Southern Baptist
Church of Christ
Methodist
Missionary Baptist
Prim. Baptist
Presbyterian
Cumberland Presby
Church of God
Holy
Episcopal
Assemblies of God
Freewill Baptist
Catholic
Lutheran
African Methodist
Other Christian
Other Pentecostal
Totals
Avg. church
size (# fam.)
Min. church
size (# fam.)
Max. church
size (# fam.)
2,652
1,000
2,674
170
106
347
152
104
202
500
151
142
2,183
240
82
3,000
160
—
Small
size
Large
size
40
29
28
14
11
7
5
7
3
2
5
4
0
2
2
40
4
14
9
8
0
0
1
0
0
0
3
0
0
4
0
0
8
0
203
47
New building
Buildable space
Playground
Athletic fields
Gym
37
21
10
3
1
2
4
3
4
5
3
3
3
2
1
29
2
79
47
38
9
5
12
7
6
8
2
3
4
4
4
2
39
5
59
12
25
2
1
9
6
1
3
1
3
3
3
2
0
23
1
18
6
4
0
0
0
3
1
1
0
0
0
2
1
0
7
1
21
13
9
1
1
1
0
1
2
0
1
0
4
0
0
8
1
133
274
154
44
63
offer a child-care program and 83 percent of the churches with a program offer only
part-time care. These programs are often marketed as part-time “Mother’s Day Out”
or “Preschool” programs. The minimum number of families reported for a church
in our sample is six and the maximum is 3,000.20 As mentioned above, these family membership size measures map into the size categories on the right-hand side
of Table 1. From Table 1 (continued), we see that approximately 30 percent of the
churches are either less than 10 years old or have been refurbished or remodeled in
the past 10 years (“New building”).21
20 For completeness we include all churches in the two counties. Churches that serve a small number of families
are unlikely to have the resources required to offer child care or other services. Their inclusion, if anything, would
weaken our ability to observe strategic interactions.
21 This seems to be a higher-than-anticipated percentage. This is probably due to the fact that the metropolitan
Nashville area is one of the faster-growing areas of the country.
162
American Economic Journal: Microeconomicsaugust 2012
B. Location Characteristics
From the addresses we were able to determine the geographic coordinates for
each church, which allows us to calculate the distances between churches and compile information about each church’s geographic area. These characteristics represent location-specific factors which influence the demand or payoff for day care.
From the 2000 Census, we gather (at the Census block group level) median household income, population, the percentage of the population under five, the percentage
of married adults, and the percentage of dual income households. Using the Census
block group data, we construct totals for the four mile radius circle surrounding
each church.22 It is important to note that, although these Census variables can be
considered “market” variables, the manner in which they were constructed allows
these characteristics to be considered church-specific.
In addition to these general demographic characteristics, we consider a number
of other church-specific profit-shifters. The Association of Statisticians of American
Religious Bodies (ASARB) conducts a decennial study on membership in 149 different religious denominations. This study produces, for each county in the US,
denomination-specific adherence rates that represent the number of adherents each
denomination has (per thousand in the population). We assume that the adherents
of any given denomination are distributed across each Census block group in proportion to the population of each block group. In this manner, as with the Census
demographic data discussed above, we are able to construct a church-specific measure that captures the total number of adherents church i has within four miles of
their location. The number of adherents should not be confused with the number
of church members. The number of adherents is measured as the number of people
identifying themselves as followers of a particular denomination. Two neighboring
churches, which may be identical in terms of observed local Census demographics,
may differ in their likelihood of offering child care because of differences in the
number of nearby adherents.
We also include a variable that is defined as the number of workers employed
within four miles of each church. This worker count, which is calculated using Traffic
Analysis Zone (TAZ) data compiled by the Census, is a measure of the nonresidential
population in a given area. If parents base child care decisions on their work location,
as opposed to their home location, then this may capture some of that effect.
Finally, because a church’s decision may be influenced by the availability of fulltime for-profit child care, we include two measures that address the availability and
substitutability of for-profit care: (i) the distance to the closest for-profit child care
center and (ii) the total capacity—measured by the number of children—of forprofit child care centers within four miles of a given church. Records of the locations
and capacities of all licensed for-profit day care centers in the two sample counties come from the Tennessee Department of Human Services child care locator
(http://www.ja.state.tn.us/accweb).
22 We do this by determining the geographic center of each Census block group and then including those block
groups for which the center coordinates fall within the four mile radius circle from a given church.
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
163
Table 2—Geographic Characteristics
Mean
Distance to closest same-denomination church (miles)
Distance to closest different-denomination church (miles)
Distance to closest for-profit day care centers (miles)
Four-mile radius statistics
Number of same-denomination churches
Number of different-denomination churches
Number of church child care centers
Number of for-profit day care centers
Capacity of for-profit day care centers
Population 
(1,000)
Percent of population under 5 years
Percent of married adults
Household income ($1,000)
Percent of dual-income households
Number of congregation adherents (1,000)
Number of workers
Min.
2.56
0.61
1.79
5.32
34.86
5.81
10.62
944.92
7.35
7.20
62.28
51.86
57.45
2.30
11,050
0.07
0.01
0.11
Max.
12.00
4.11
9.26
0
23
0
99
0
17
0
28
0
2,731
1.39
16.12
4.93
10.73
45.95
77.63
20.54
117.67
43.55
70.23
5.90E-04
14.63
85
26,870
By proposing these for-profit measures as suitable profit-shifters, we are implicitly making the argument that it is appropriate to treat them as exogenous. Our
assumption, therefore, is that church choices do not impact the decisions of for-profit
child care centers. We believe this assumption is reasonable for our data. For-profit
child care centers differ substantially from church-run child care centers in terms of
pricing and quantity (hours per day, days per week). In order for us to empirically
support such a claim, we collected information on the prices and hours of operation
for all church-run and for-profit child care centers in the sample counties. Summary
statistics for this information appears in an Appendix.
Summary statistics for geographic/location characteristics are found in Table 2.
The mean distance to the nearest different-denomination church is 0.61 miles and
the mean distance to a same-denomination church is 2.56 miles. The mean number
of for-profit day care centers within a four mile radius is approximately eleven.
IV. Estimation
We use a method of simulated moments (MSM) estimator, similar to that used by
Dai (2007). We depart in several ways, however, due primarily to the fact that we
observe decisions for only one geographic market. The key to any such estimation
procedure is that we must be able to solve for the predicted equilibrium for observed
and unobserved characteristics and parameter values. Our method of moments estimator, in the most general terms, searches for parameter values that make our predicted equilibrium as “close” as possible to the observed equilibrium.
Our model contains four unobservable terms (ω, η, μ, ε) and, for tractability, we
have chosen to use simulation methods. As the initial step in estimation, we take R
standard normal draws for each of our unobservables.23 Notice that there are 424
churches so our matrix of random draws for ε will be (424 × R) but we partition the
23 In our estimation, we set R = 400, although we will keep the notation general.
164
American Economic Journal: Microeconomicsaugust 2012
sample into eight cities which implies that the matrix of random draws for η will be
(8 × R). Once we have made the requisite set of draws, we construct the single unobserved index variable (​ui​​) for each church, as outlined in equation (3). The resulting
matrix ​uR​ ​is a (424 × R) set of random draws for each church.
For each set of random draws, we then use equations (1) and (4), along with a
selection rule, to find the predicted equilibrium. We proceed to do this for each of
the R random draws and then compute the relevant moment conditions (which are
 as the column vector of residuals from
discussed in greater detail below). Defining ​ ν​
our moment conditions, we repeat the above steps in order to minimize our objective
 ′ W​ ν​
 , where W is a weighting matrix. As is commonly done in method
function: ​ ν​
of moments estimation, we estimate the model in a two-stage procedure. In the first
stage, we estimate the parameters θ​
​   using an initial weighting matrix, assumed to be
 ) to construct an estimate
an identity matrix. We then use these initial estimates (​ θ​
−1


 ​ ν​
 ′ ​]​ ​. The model is then reesof the optimal weighting matrix ​ W​
  , where ​ W​
   = E[​ ν​
timated using the estimated optimal weighting matrix. Reported coefficients reflect
the parameter estimates from the second-stage estimation.
In our MSM estimation, we use the following moment conditions: (1) the difference between the predicted probability of offering a service and the observed decision for each church; (2) the difference between the predicted number of churches
offering the service and the observed number of churches offering the service; (3)
the difference between the predicted number of churches of a given denomination
offering a service and the observed number of churches of a given denomination
offering the service; (4) the difference between the predicted number of churches
offering a service in each county and the observed number of churches offering
the service in each county; and (5) the difference between the predicted number
of churches offering a service in each Census tract and the observed number of
churches offering the service in each Census tract.
Given that all 424 churches in our sample make a binary decision, there are 2​ ​424​
candidate equilibria to consider. Even with the most advanced computing power,
evaluating each of these combinations is infeasible. We, therefore, restrict our analysis to outcomes in which the total number of churches providing child care falls
between 45 and 75.24
V. Results
A. Coefficient Estimates
The estimates of our model are presented in Table 3. Our primary focus is on
the competitive effects, though we also include the coefficients for variables which
shift the demand for church-sponsored day care. The top row of Table 3 displays the
function values which measure the goodness-of-fit of the various selection rules.
For ease of exposition, the columns in Table 3 (selection rules) have been sorted
24 Using our parameter estimates, we examine the frequency with which each total number of child care providers is predicted to be an equilibrium. A selection of these results is presented in Table 4. We find that combinations
of less than 47 churches or more than 70 churches are never identified as equilibria.
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
165
Table 3— Parameter Estimates for Church Profit Function
Equilibrium selection rule
Objective function value
Competitive effects
Distance band #1 (0 to 4 miles)
Same 
denomination
Different 
denomination
Distance band #2 (4 to 8 miles)
Same 
denomination
Different 
denomination
Distance band #3 (8 to 12 miles)
Same 
denomination
Different 
denomination
Small size church
Large size church
New church
Large lot size
Population (1,000)
Percent under 5 years
Percent married
Household income ($1,000)
Percent dual-income
Congregation adherents (1,000)
Number of workers
Distance to for-profit
For-profit capacity
Williamson County
Intercept
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
Sequential
based on age
Max. denom.
profits
Location
characteristics
Max. total
profits
121.072
122.955
126.872
128.159
−0.413***
(0.139)
−0.178**
(0.087)
−0.562***
(0.181)
−0.091
(0.151)
−0.366**
(0.169)
−0.119
(0.098)
−0.287**
(0.141)
−0.140*
(0.082)
−0.189*
(0.107)
−0.055
(0.050)
−0.231*
(0.137)
−0.067
(0.079)
−0.092
(0.083)
−0.073
(0.135)
−0.136*
(0.075)
0.052
(0.055)
−0.011
(0.048)
0.202
(0.450)
4E-04
(3E-03)
0.030
(0.169)
3E-05
(9E-09)
−0.008
(0.240)
4E-05
(0.009)
1E-05
(9E-06)
−0.049*
(0.029)
0.465***
(0.047)
0.203***
(0.034)
−0.176
(0.144)
0.043
(0.045)
1.254*
(0.661)
0.670
(0.514)
0.006
(0.011)
−0.392
(0.430)
0.201***
(0.044)
−0.002
(0.057)
0.024*
(0.014)
−0.475*
(0.274)
−0.068
(0.060)
0.103
(0.264)
−0.089*
(0.049)
0.566***
(0.058)
0.175***
(0.022)
0.082
(0.083)
0.129
(0.141)
1.222*
(0.625)
0.716
(0.512)
−0.119
(0.171)
−0.298
(0.457)
0.115**
(0.048)
0.016
(0.043)
0.076
(0.056)
−0.271
(0.291)
0.010
(0.049)
0.170
(0.265)
−0.092
(0.051)
0.230***
(0.057)
0.106***
(0.032)
0.040
(0.029)
−0.115
(0.093)
1.274**
(0.638)
0.316
(0.493)
−0.003
(0.009)
−0.263
(0.456)
0.148***
(0.055)
0.019
(0.086)
0.123
(0.080)
−0.324
(0.265)
0.018
(0.035)
0.237
(0.271)
−0.049*
(0.031)
0.497***
(0.046)
0.093***
(0.031)
−0.011
(0.030)
−6E-06
(0.002)
1.155
(0.635)
0.397
(0.506)
0.001
(0.002)
−0.196
(0.440)
0.131***
(0.042)
−5E-06
(5E-06)
−0.050
(0.055)
−0.365
(0.281)
−0.038
(0.049)
0.251
(0.265)
166
American Economic Journal: Microeconomicsaugust 2012
according to objective function value. The selection rule providing the best fit is the
rule of sequential choice based on age presented in column 1. Column 2 presents
results obtained using maximizing denomination-specific total profits as the selection rule, and column 3 displays parameter estimates for our selection rule based on
an index of location characteristics. The selection rule based on maximizing total
church profits presented in column 4 yields the worst fit.
The parameter estimates in Table 3 show clear evidence of strategic interaction
between churches. The strength of the effect depends crucially on both the denomination of, and the distance to the rival. We generally find that ​γ​1b​ < 0 and ​γ​2b​ < 0
under each selection rule. The impact on the entry decision is largest for nearby
churches and the magnitude of the impact is substantially larger for same denomination versus different denomination churches.
First, consider the effects in the narrowest distance band. Row 2 of Table 3 indicates that church-sponsored day care of the same-denomination within a distance
of 4 miles has a negative and statistically significant impact on entry under all four
selection rules at the 5 percent level or better. Row 3 indicates that day cares operated
by a different denomination within 4 miles also have a negative affect though the
magnitude and statistical significance are smaller. Different denomination churches
have a significant impact at the 5 percent level for the age-based selection rule and
at the 10 percent level in the total profits selection rule. The impact of the closest
different denomination churches is negative, but not statistically significant under
the other selection rules.
Consistent with intuition, the impact of other churches diminishes with distance.
The estimates in Table 3, row 4, show a negative effect for same denomination
churches in the 4 to 8 mile distance band. The magnitude of each estimate in row 4
is less than half of the corresponding estimate for the closest churches in row 2. The
negative impact from same denomination churches is statistically significant at the
10 percent level in columns 1, 2, and 4 and not statistically significant at conventional levels in column 3. The estimates in row 4 indicate that the impact of same
denomination churches in the range of 4 to 8 miles from the church has roughly the
same impact as different denomination churches within a 4 mile radius in row 3. The
effect for different denomination churches within 4 to 8 miles is small in magnitude
and not statistically significant. None of the estimated impacts are statistically significant in the 8 to 12 mile distance band.
The fact that results in Table 3 differ by selection rule provides some interesting insights. The selection rule which maximizes denomination profits (column 2)
indicates the largest negative impact from same denomination entrants in the two
smallest distance bands and does not find a significant impact from different denominations. This is not surprising given that other denomination’s actions are taken as
exogenous. The selection rule based on maximizing total joint profits of all churches
(column 4) produces the smallest magnitude for the impact of same denomination
churches on entry. The estimated impact is half as large as the estimated impact for
maximized denomination profits. Maximizing denomination-specific total profits in
column 2 performs notably better than equilibrium selection based on maximization
of (overall) total profits. This is interesting because, with the exception of certain
denominations such as Catholic, most of the denominations in our sample have a
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
167
very weak or nonexistent hierarchical structure. Perhaps this is further evidence that
the “competition” is truly an intra-denominational one.
Table 3 also displays the impact of church characteristics on church entry decisions. As compared to medium-sized churches, small churches are less likely to
provide child care and large churches are more likely to provide child care. The
estimates for large churches are positive and statistically significant at the 1 percent
level across all selection rules. The estimates for small churches are negative and
are statistically significant at the 10 percent level for the age, denomination profits,
and total profits selection rules. This seems reasonable given that larger churches are
likely to have both more families interested in child care and a resource advantage
over smaller churches.
Newer churches are more likely to offer child care and the effect is statistically
significant at the 1 percent level under all selection rules. Similarly, churches in
areas with more congregation adherents are more likely to offer child care. This
effect is significant at the 1 percent level in columns 1, 3, and 4 and at the 5 percent
level in column 2. The percentage of population under 5 years of age has a positive impact on entry. The impact is statistically significant at the 5 percent level in
column 3, and at the 10 percent level in columns 1 and 2. In column 1, for-profit
capacity has a negative impact and distance to for-profit has a positive impact on
entry with both effects significant at the 10 percent level. None of the other church
or location parameter estimates are statistically significant.
B. Simulations
Using our parameter estimates, we consider the likely impact of a 20 percent
increase in the number of adherents (for each church) and, separately, a 20 percent
increase in the proportion of the preschool-aged population. These constitute two
main predictors of profitability that are independent of church decisions. In addition we also consider the impact of eliminating the for-profit centers from the market. The simulation is based on the equilibrium selection rule based on church age,
which had the best goodness-of-fit. These results are presented in Table 4.
Each column in Table 4 reports values averaged over 400 random draws from
the set of unobservables.25 We present our results in this manner since we only
have one market for analysis. The first column, which characterizes our observed
sample forms the baseline model. In columns 2 through 4, each of the three
demand shifters leads to an increase in the expected number of providers, relative
to the baseline prediction. This change is largest for the 20 percent increase in
congregation adherents.
The effect of removing for-profit centers from the market is of interest. The distance to the nearest for-profit center seems to have a significant impact on entry in the
estimates presented in Table 3 (at least for our preferred set of estimates). However,
when for-profit centers are removed (Table 4, column 4) the impact of their omission is slight. There is only a marginal impact on the expected number of church
25 These are the same set of random draws used in the estimation procedure.
168
American Economic Journal: Microeconomicsaugust 2012
Table 4—Characterization of Equilibria over Random Draws
“Expected” number of providers
Percentage of draws resulting in
equilibrium with less than 50 providers
Percentage of draws resulting in
equilibrium with 56 to 62 providers
Percentage of draws resulting in
equilibrium with more than 70 providers
Baselinea
Number of
Adherentsb
Percentage of
preschoolersc
No for-profit
providersd
58.280
1.75
60.110
1.25
58.423
1.50
58.608
1.75
77.75
79.00
78.25
77.75
0.00
0.25
0.00
0.00
Note: All cells in Table 4 represent values averaged over 400 random draws from the set of unobservables.
a
Characterization of the equilibria associated with the observed sample.
b
This column presents the impact of a 20 percent increase in the number of adherents at each church.
c
This column represents the impact of a 20 percent increase in the percentage of preschool aged children in
each church’s neighborhood.
d
This column presents the impact of eliminating all for-profit providers from the neighborhoods.
child care centers. We see that removing the for-profits does increase the likelihood
that nearby churches will offer child care. However, the fact that nearby nonprofits
are more likely to offer child care reduces the likelihood that churches far from the
for-profits will offer child care. In the baseline model, the average distance that
each church child care provider is from the closest for-profit is 2.053 miles. In the
No For-Profit Providers counterfactual, the average distance each church child care
provider is from the closest for-profit location is 1.798.26 To summarize, roughly the
same number of churches would offer child care, although the location mix would
change. Such changes clearly have an impact on family welfare, although calculating the change in family welfare is beyond the scope of this paper.
VI. Conclusions and Extensions
The prevalence of church-sponsored activities along with the unique features
underlying their decisions make the strategic behavior of churches an interesting
area of study. We use a unique, hand-collected dataset to formally model the decisions of churches in a discrete game structure to further our limited knowledge of
the strategic behavior of churches.
Our model incorporates both the identity (i.e., denomination) and the location
of our churches. We use the model to test for the existence of strategic interactions
between churches in the decision of whether to provide a weekday child care program. Our results find evidence of strategic interactions between churches. A church
is significantly less likely to offer child care services if a nearby same-denomination
church offers child care. The presence of a nearby different-denomination, or forprofit day care also has a negative impact on a church’s entry decision but the magnitude of their impact is much lower.
26 These results are not presented in the table. The counterfactual distance is based on the locations of the forprofits in the original model.
Vol. 4 No. 3
Rennhoff and Owens: the Strategic Choices of Churches
169
Table 1A—Summary Statistics: Child Care Centers
Average
Percent offering full time
Daily rate ($/day)
Average possible number of days per week
Average hours open per day
Hourly rate ($/hour)
Observations
Median
For-profit
98.9
$49.89
5
11:17
$4.42
Church-run
16.9
$19.26
2.69
5:09
$3.74
For-profit
98.9
$48.00
5
11:30
$4.17
Church-run
16.9
$17.30
2
5:00
$3.01
85
59
85
59
One of the primary predictors of profitability for a church day care is the number
of congregation adherents that live nearby. The number of adherents has a larger
impact than other demographic characteristics, suggesting that the religious affiliation of the provider represents an important consideration in this portion of the child
care market. This pattern also suggests a plausible explanation for our finding that
same-denomination churches have a greater impact on child care decisions. When
a nearby, same-denomination day care is present, it reduces the pool of potential
children for a day care perhaps because some families prefer all else equal to send
their children to a day care run by their own denomination.
This study is one of the first to explicitly model the choices and behaviors of
churches. The results highlight the existence of strategic interactions between
churches and demonstrate that a church’s response to a rival’s choice to offer a service depends crucially on whether the rival is of the same or of a different denomination. These findings indicate unique considerations underlying church decisions and
suggest plenty of opportunities for further study on church behavior.
Appendix
We obtained information about the cost and hours of operation for all the child
care centers in the two county area (for-profit and church-run). Price information
was typically not available through a center’s website, regardless of their nonprofit
status. Therefore, all information was collected by directly contacting the centers
and inquiring about hours of operation and costs. These calls were made during the
first two weeks of May 2010. Table 1A presents summary statistics for the characteristics of for-profit and church-run child care centers. The summary statistics demonstrate that each is operating in a very different market. All but one of the for-profit
centers offer full-time care, but only 17 percent of the church-run programs offer a
full-time option. On average for-profit centers operate five days a week for about 11
hours a day. Church-run programs generally operate two or three days a week for
only five hours a day. The cost is also quite different with for-profit centers charging almost $50 per day versus less than $20 for church sponsored programs. Even
after accounting for the large difference in hours of operation, the mean hourly rate
is $0.68 and the median hourly rate is $1.16 per hour higher at for-profit centers. It
does not appear that the two types of day care providers are particularly close substitutes for one another.
170
American Economic Journal: Microeconomicsaugust 2012
References
Anselin, Luc. 1988. Spatial Econometrics: Methods and Models (Studies in Operational Regional Sci-
ence). London: Kluwer Academic.
Bajari, Patrick, Han Hong, and Stephen Ryan. 2004. “Identification and Estimation of Discrete Games
of Complete Information.” National Bureau of Economic Research (NBER) Working Paper T0301.
Berry, Steven T. 1992. “Estimation of a Model of Entry in the Airline Industry.” Econometrica 60 (4):
889–917.
Bresnahan, Timothy F., and Peter C. Reiss. 1991. “Entry and Competition in Concentrated Markets.”
Journal of Political Economy 99 (5): 977–1009.
Ciliberto, Federico, and Elie Tamer. 2009. “Market Structure and Multiple Equilibria in Airline Mar-
kets.” Econometrica 77 (6): 1791–1828.
Cohen, Andrew, Beth Freeborn, and Brian McManus. 2011. “Competition and Crowding-Out in
the Market for Outpatient Substance-Abuse Treatment.” http://www.unc.edu~mcmanusb/OSAT_
Competition_Dec2011.pdf.
Dai, Mian. 2007. “Strategic Interactions and Market Structure in the US Mobile Telecommunications
Industry.” http://www.lebow.drexel.edu/Faculty/Departments/Economics/Seminars.php?action=vi
ew&SeminarID=276.
Elzinga, Kenneth G., and Colin T. Page. 2009. “Congregational Economies of Scale and the Megachurch: An Application of the Stigler Survivor Technique.” Paper presented at the Meeting of the
Association for the Study of Religion, Economics, and Culture, Washington, DC, April 2–5.
Gruber, Jonathan. 2005. “Religious Market Structure, Religious Participation, and Outcomes: Is Religion Good for You?” National Bureau of Economic Research (NBER) Working Paper 11377.
Gruber, Jonathan, and Daniel M. Hungerman. 2007. “Faith-Based Charity and Crowd-Out during the
Great Depression.” Journal of Public Economics 91 (5–6): 1043–69.
Gruber, Jonathan, and Daniel M. Hungerman. 2008. “The Church versus the Mall: What Happens
When Religion Faces Increased Secular Competition?” Quarterly Journal of Economics 123 (2):
831–62.
Harrison, Teresa D., and Kristina M. Lybecker. 2005. “The Effect of the Nonprofit Motive on Hospital
Competitive Behavior.” B.E. Journal of Economic Analysis and Policy: Contributions to Economic
Analysis and Policy 4 (1): 1–15.
Hartmann, Wesley R. 2010. “Demand Estimation with Social Interactions and the Implications for Targeted Marketing.” Marketing Science 29 (4): 585–601.
Hungerman, Daniel M. 2009. “Crowd-Out and Diversity.” Journal of Public Economics 93 (5–6):
729–40.
Lindner, Eileen W., Mary C. Mattis, and June R. Rogers. 1983. When Churches Mind the Children: A
Study of Day Care in Local Parishes. Ypsilanti: HighScope Research Foundation.
Mazzeo, Michael J. 2002. “Product Choice and Oligopoly Market Structure.” RAND Journal of Economics 33 (2): 221–42.
McCullough, Michael E., William T. Hoyt, David B. Larson, Harold G. Koenig, and Carl Thoresen.
2000. “Religious Involvement and Mortality: A Meta-Analytic Review.” Health Psychology 19 (3):
211–22.
Pepall, Lynne, Dan Richards, John Straub, and Michael DeBartolo. 2006. “Crowding Out and Competition in the Religious Marketplace.” Social Science Research Network (SSRN) Working Paper
910264.
Royster, Carl H. 2009. Churches of Christ in the United States. Nashville: 21st Century Christian.
Seim, Katja. 2006. “An Empirical Model of Firm Entry with Endogenous Product-Type Choices.”
RAND Journal of Economics 37 (3): 619–40.
Walrath, Michael W. 2010. “Religion as an Industry: Estimating a Strategic Entry Model for Churches.”
http://personal.stthomas.edu/walr1237/walrath_ChurchEntry.pdf.
Zaleski, Peter A., and Charles E. Zech. 1995. “The Effect of Religious Market Competition on Church
Giving.” Review of Social Economy 53 (3): 350–67.