Research Analysis Lib Data

In the current research the herndahl index was

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Unformatted text preview: within a market area (Herfindahl 1950). The index is calculated by squaring the individual shares, that is, the share of each firm competing in a market, and summing the results. In the current research, the Herfindahl Index was adopted to measure the concentration of persons within professed religious groups using U.S. counties as the basic units of analysis. The general formula for the Herfindahl Index is: Hj = ∑ S2 ij S represents the number in each denomination within a county divided by the total number of church adherents in the county, i represents the index of summation over all religious denominations in county j. H represents the probability that any two persons, selected at random, within a county will be adherents of the same organized religious group (Iannacone 1991). For example, in the simplest case assume one county has five discrete religious group affiliations, each with an equal 20 percent market share of adherents. The indexed concentration for the county is H = .202 + .202 + .202 + .202 + .202, or .20. Thus, if two persons were selected at random from that county the odds are one in five, or 20 percent, that they will be adherents of the same recognized denominational group. A second county has three discrete religious affiliations with 50 percent, 30 percent, and 20 percent market shares, respectively. The index then would be H = .502 + .302 + .202, or .38. The odds are 38 percent that two people selected at random will have the same religious affiliation. Still a third county has 25 discrete religious denominational affiliations each with the following percentages in each denominational affiliation: one with THE IMPACT OF RELIGIOUS HOMOGENEITY 345 40 percent, one with 20 percent, one with 10 percent, and 22 with 3.15 percent each. The index score is H = .402 + .202 + .102 + 22 (.03152) = .23. In this case, the odds of selecting two persons at random with the same denominational affiliations are 23 percent. The index shows the probability that two persons selected at random would share the same denominational affiliation, relative to the concentration of adherents in a fewer or greater number of religious groups. When more adherents are in fewer religious affiliations the index score is higher. Conversely, when adherents are spread over a greater number of affiliations the index score is lower. The index theoretically could range from 0.00, when there are no adherents with any affiliations, to 1.00, when all adherents have a single affiliation. Covariates In order to examine more completely the effects of religious homogeneity on divorce, the effects of other potential influences on the divorce rate must be held constant. Divorce rates have been found to be associated with higher levels of geographic mobility and, by extension, lower levels of community involvement and integration (Breault and Kposowa 1987; Glenn and Shelton 1985). Hence, percent population change from 1980 to 1990 is used as an indicator of population instability. It also has been generally established that a higher concentration of young adults contributes to a higher divorce rate (Martin and Bumpass 1989); thus we include the percentage of the population 15 to 34 years of age. Likewise, race and ethnicity have been shown to have an impact on the divorce rate (U.S. Bureau of the Census 1992b). Hence, we use percentage of whites, percentage of Native Americans, percentage of Asian/Pacific Islanders, and percentage of Hispanics/Latinos as measures of race and ethnicity. The percentage of African Americans is not included because of its high correlation, r = .82, with percentage white. Previous research has shown that the relative concentration of males and females within a population impacts the divorce rate (Guttentag and Secord 1983; Trent and South 1989). Here, we use the percentage of females in the population as an indicator of gender concentration. Also, given evidence that the level of economic instability is positively associated with the divorce rate (South 1985), we include four census-based measures that are assumed to represent different dimensions of this variable: percentage of the civilian labor force employed in manufacturing, percentage unemployed, percentage urban, and the median family income. Because general area of residence apparently has an effect on the likelihood of divorce in the United States, we also include measures of region. Thus, the four major regions that have been historically utilized by the Census Bureau in reporting population data were selected for input: Northeast, South, West, and Midwest, each coded as 1 = Yes and 0 = No. 346 LARRY C. MULLINS ET AL. Descriptive Results Descriptive statistics (means and standard deviations) are shown in Table 1. An examination of the intercorrelations indicates that these v...
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This note was uploaded on 01/27/2014 for the course SOCI 3040 taught by Professor Lauramckinney during the Fall '13 term at Tulane.

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