CAN THE PRESIDENT REALLY AFFECT ECONOMIC GROWTH? PRESIDENTIAL EFFORT AND THE POLITICAL BUSINESS CYCLE

Presidential elections are often seen as referendums on the health of the economy; however, little evidence exists on the president's ability to influence gross domestic product (GDP). This study examines the effect of the incentive to be reelected and the resulting increase in presidential effort on GDP growth. Growth is found to rise in reelection years for first‐term presidents after 1932 and to fall in election years before 1932, when reelection was uncommon, and for second‐term presidents generally. This effect is largest for high‐quality presidents—who probably have the highest return to effort—and is spread across multiple sectors of the economy. (JEL D78, D72, E32, J24)     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

Statistical Methods and Graphical Displays for Analyzing How the Association Between Two Qualitative Variables Differs Among Countries, Among Groups, Or Over Time: A Modified Regression-Type Approach

Many interesting sociological questions pertain to how the association between two variables depends on a third variable. In sociological applications, the third variable often pertains to countries, to subgroups of a population, or to time periods. We propose a regression-type approach that specifies that the log-odds- ratios that describe the two-way association of interest are a linear function of latent scores for the third variable. Additive and multiplicative models currently in use by researchers are special cases of the regression-type model. To illustrate the utility of the regression-type approach, we apply this approach to analyze (1) data on occupational mobility in the United States, Britain, and Japan (comparing mobility in these countries) and (2) data on the association between religion and voting behavior in U.S. presidential elections from 1968 to 1992 (comparing this association in the different elections). We also introduce here graphical displays that can be used to obtain worthwhile information about goodness-of-fit and to aid in substantive interpretation.     

Explaining the Flight of Cupid’s Arrow: A Spatial Random Utility Model of Partner Choice

Spatial homogamy may be defined as follows: anyone may be attracted to anyone else, but near candidates are more attractive than distant candidates. In this article, we propose a model of partner choice, where homogamy is defined in terms of spatial, demographic, socioeconomic and cultural similarity. A spatial choice model using random utility theory is formulated, taking into account a relaxation of the independence from the irrelevant alternatives property, as spatial alternatives are not independent of one another. We model partner choice given the characteristics of the chosen partner and a choice set of alternatives, using unique micro data on all new cohabiters in the Netherlands, linked to other relevant data sets. The model takes the spatial locations of potential candidates within a choice set into account, including an indicator for the spatial similarity between alternatives. We find that spatial homogamy is a vital component of partner matching, aside from and adding to the spatial effects in demographic, socioeconomic and cultural homogamy. Given a choice set of partners, the highest likelihood of a match occurs with a person who is born and lives near by, who is close in age, is in the same life stage and has the same marital status, who has the same educational and income level and the same labour market status, who speaks the same dialect and lives in a culturally similar residential area. The distance effect is most pronounced for those individuals with lower levels of education and those living in rural areas.     

A theory of unanimous jury voting with an ambiguous likelihood

Theory and Decision - We examine collective decision-making in a jury voting game under the unanimity rule when voters have ambiguous beliefs. Unlike in existing studies (Ellis in Theoretical...