Economics of Religious Conversion1 A. W. Hogan2 This version: 03/15/09 Abstract In this essay I test religious capital models by investigating empirically the causes of religious conversion in a large cross section of American youths. I attempt to avoid the endogeneity bias that is prevalent in previous efforts to estimate religiosity in order to identify causal effects. I find support for rational choice theories of religious behavior and religious capital accumulation. Religious capital is shown to be significant in determining the likelihood of changing religions as well as the likelihood of changing back to the faith in which one was raised. Furthermore, I argue that this study shows evidence of bargaining with religious organizations - as shown by the significant and positive effect on the likelihood of conversion from being diagnosed with an incurable disease. Keywords: religion, conversion, health JEL Classification: D10, I19, Z12 1 Thanks to Charles L. Baum II, John M. Nunley, and Mark Owens for assistance and comments. All remaining errors are my own. 2 Middle Tennessee State University, Department of Economics and Finance, 1301 E. Main St., Murfreesboro, TN 37132. e-mail: [email protected] 1 Introduction Since Gary Becker paved the way with “New Home Economics,” microeconomic theory has been applied to many different fields traditionally thought to be outside of the realm of economics. Though held by many to be the epitome of irrationality,3 even the religious behavior of households can be explored through the lens of rational choice theory. This paper joins a branch of the research into the economics of religion that studies the demand for religious goods and services empirically.4 To that end I attempt to estimate religiosity and identify what influences it. An individual's religiosity must be inferred from preferences revealed through that person's choices and actions. Previous studies have looked at the frequency of religious service attendance, contributions made to religious organizations (e.g., (Azzi and Ehrenberg 1975), (Durkin and Greeley 1991), (Iannaccone 1990, 1994, 1997b)), total time spent on religious activities (Neuman 1986), or frequency of prayer (Garza and Neuman 2004) as a proxy for religiosity. Instead, I use whether a person has changed religions (including to or from a “no religion” option). 5 In addition to examining conversion as revealed religiosity, this paper expands the literature by focusing on causal relationships between the independent and dependent variables. It is common in papers estimating religiosity to include measures of religious activity, opinions on faith, and family structure on the right hand side of the estimated equation. But how often somebody goes to church, whether they believe particular doctrines, and their choice of a spouse could all be caused by religious conversion. There is no denying that one's marriage or belief in the afterlife are correlated with religiosity, but marriage frequently leads to religious conversion.6 I leave out endogenous variables in 3 See (Stark and Finke 2000) for an excellent discussion of the popularity of the secularization theory (and its flaws). 4 See (Iannaccone 1998) for a survey of the economics of religion. 5 (Kluegel 1980) shows the importance of including the no-religion option and trends in religious conversion. 6 More information on the relationship between marriage and religion can be found in, for example, (Becker et al. 1977) and (Lehrer and Chiswick 1993) . 2 order to focus on purely causal effects to conversion.7 The first investments in a person's religious capital are made by their parents. Parents decide what faith, if any, their children will be raised in and how to educate them to these beliefs. Parental characteristics present us with an opportunity to proxy for how much investment in religious capital a person likely received. Religious capital theory suggests that for more investment in religious capital the benefit to participating in that religion, and thus the cost of leaving it, increase. Therefore we can predict that those with larger investments in religious capital will be less likely to leave their religion. With data on who switches religions and suitable proxies for religious capital investments, I test this claim. I find that more investment from parents in their children's religious capital decreases the likelihood that they will leave the faith in which they were raised. Those rational choice theories of religion that view the purpose of participating in religion as a good consumed after the actor's lifetime (e.g., (Azzi and Ehrenberg 1975)) immediately suggest the importance of the actor's expected lifetime. Those nearing the end of their life will be more demand more religious consumption. Using a number of health variables I attempt to estimate the effect of changes in the expected lifetime on religious conversion. By estimating the effect of being diagnosed with a number of difference diseases I show that being afflicted with cancer or a psychiatric disorder greatly increase the likelihood of converting religions. This paper is organized as follows. The next section describes the data source and how some of the variables were constructed. Section 3 describes the estimation techniques and how the final models were chosen. Section 4 discusses the results obtained from estimation and their interpretation. Section 5 concludes and summarizes the study. 7 For details on endogeneity bias see (Greene 2003). 3 2 Data The data used for this paper come from the 1979 National Longitudinal Survey of Youth (NLSY). The NLSY questioned nearly thirteen thousand people between 14-22 years of age in 1979 and revisited these same people for further questioning every year or two after the initial survey.8 The variables used here are shown in Table 1. Those variables that may require addition explanation are discussed through out the rest of this section. The NLSY asks each respondent which religion they were raised in, and subsequently asks their current religion in 1979, 1982, and 2000. The religion categories studied here are Baptist, Episcopalian, Lutheran, Methodist, Roman Catholic, Jewish, Protestant, Other and None. “Protestant,” in this case, is mutually exclusive of the other protestant denominations enumerated above, and indicates other protestant sects as well as non-denominational Christians. These categories are the same for the variables addressing the respondent's spouse's religion. Table 2 shows the religious make up of the NLSY population, as well as the number of converts to and from each religious category. The main dependent variable analyzed here is religious conversion. This variable, Change, is defined as a 1 if the respondent reports being of a different religion in 2000 than the religion in which they were raised, and a 0 otherwise. Table 3 shows the conversions that are being captured by the Change variable. This matrix shows the sum of converts for each possible change. Additionally I examine the persistent long term effects of religious capital by looking at those who change religions but eventually return to the religion in which they were raised. The Change Back variable is a 1 if the respondent reports changing their religion before 2000, but also reports being the same religion in 2000 as they were raised. A change matrix like the one described above can been seen for Change Back in Table 4. 8 More information on the NLSY data can be found at http://www.bls.gov/nls/nlsy79.htm 4 The variable M is a dummy indicator set to 1 if the respondent is married to somebody who was raised in the same religion as they were, and a zero otherwise. This unusual specification is weakly endogenous insofar as a spouse's traits will only be observed for those who are married, and getting married could cause religious conversion. All of the health variables (H) are indicators set to 1 if the respondent has been diagnosed by 2000 with a particular ailment (arthritis, diabetes, stroke, cancer or a psychiatric disorder)9, and a zero otherwise. While the author strives not to dismiss supernatural factors surrounding religious conversion, it is assumed that changing religions could not cause these diseases. The religious attendance variables (A) are vectors of dummy variables for each choice the respondent was offered in answering the question “In the past year, about how often have you attended religious service?” The choices offered as a response were: 1) Not at all, 2) infrequently or once per month, 3) 2-3 times per month, 4) once per week, or 5) more than once per week. In the raw data there are six choices, but there is no statistical difference in the models shown here between those who answered “infrequently” and those who answered “once per month,” - so those answers have been collapsed here. 3 Estimation Whether a person changes religious affiliation is estimated using a probit procedure.10 How sensitive the data are to this estimation technique can be examined in Table 6, which shows the same model estimated with probit and linear probability models. Table 5 shows results for different sets of independent variables in the estimation of Change. Both Wald tests to drop sets of variables as well as Bayesian and Akaike information criteria support model 9 The model also included an indicator for diagnosis of heart disease, but it predicted perfectly and was dropped from the probit model. 10 For further information on probit estimation see (Greene 2003). 5 #1. This specification includes the variable for the religion in which the respondent's spouse was raised (M), which, as previously discussed, could be endogenous. With this in mind I will focus on model #2, which is shown in equation (1). That model is used in subsequent estimation of Change (i.e., Tables 6 and 7). C= 01 X 2 K 3 H (1) This equation shows Change as a function of general demographic variables (X), religious capital variables (K), and health variables (H).