Using three worldwide databases, we investigate how average similarity between pairs of languages and cultures are influenced by geographic distance and time of common ancestry. Generally, the similarity between languages or cultures decreases as the geographic distance increases. This occurs even for languages and cultures without a known common ancestor, suggesting the influence of diffusion. At any given distance, related languages are more similar than unrelated languages. However, remotely related cultures are no more similar than entirely unrelated ones, indicating that inherited cultural features tend to be lost more readily over time than inherited linguistic features.

Purpose Mayan towns in the Guatemalan highlands hold periodic markets on specific days of the week. A market is attended by local townspeople, by peasants residing in the town's hinterland, and by vendors bringing wares from other towns. This study aims to determine the effects of physical, environmental, and cultural differences on the number of vendors that are sent from one Guatemalan town to a periodic market in another.

Multiple proposed determinants of the long-term historical shift in marriage preference from polygynous to monogamous unions are tested simultaneously using data on a worldwide sample of 186 pre-industrial societies. Since the diffusion of monogamy though conquest and population migration is well documented, we employ network autocorrelation regression models that include the cultural transmission of monogamy as an endogenous predictor variable. Linguistic and spatial transmission processes are found to be significant factors that jointly affect the world-wide variance of monogamy, while religious transmission processes are not significant, suggesting genomic variation may play a role in shaping the incidence of monogamy. Other significant factors are reduction of extrinsic risks due to pathogen stress and endemic violence, a highly articulated extrahousehold division of labor, and a beneficent environment, results which are consistent with female choice as a binding constraint in marriage decisions.

Which U.S. institutions of higher education offer the best value to consumers? To answer this question, we evaluate U.S. institutions relative to a data envelopment analysis (DEA) multi-factor frontier based on 2000–2001 data for 1,179 4-year institutions. The resulting DEA “best buy” scores allow the ranking of institutions by a weighted sum of institutional characteristics per dollar of average net price. The net price is calculated as tuition, fees, room, and board less per student financial aid. Institutional characteristics include SAT score, athletic expenditures, instructional expenditures, value of buildings, dorm capacity, and student body characteristics. The DEA scores indicate the distance of each institution from the “best buy” frontier for the chosen characteristics, providing an objective means of ranking institutions as the best values in higher education.

Since the days of Walter Bagehot, a number of economists have described how financial development facilitates economic growth. An extensive empirical literature has subsequently established that the relationship between financial development and economic growth is conditioned by the cultural and legal environment, so that the positive effects of financial development on economic growth might not exist within any given national context. This study investigates whether financial growth has any role in the economic development of Bangladesh, using district-level data to estimate a spatial model. We find that both too little and too much financial development harms growth. We explain this pattern by arguing that the theoretical literature is indeed correct, that financial development facilitates growth, but that Bangladesh exhibits a pattern previous studies have found in Turkey and China, of widespread political interference with the financial sector, so that financial resources are not allocated to investments with the highest rate of return.

Changes in R necessitate updated R scripts for Eff, E. Anthon, & Dow, Malcolm M. (2009). How to Deal with Missing Data and Galton’s Problem in Cross-Cultural Survey Research: A Primer for R. Structure and Dynamics, 3(3). Retrieved from: http://www.escholarship.org/uc/item/7cm1f10b

Explanations of the causes of war fall roughly into two schools: those arguing for the primacy of environment and technology, and those arguing for the primacy of sociopolitical factors. We re-examine two hypotheses from the former school, viz, societies are more likely to engage in war when they have: 1) more productive subsistence technology; and 2) higher population density. Using data from the Standard Cross-Cultural Sample, and up-to-date multivariate modeling methods, we find only qualified support for the first hypothesis and find the reverse relationship for the second: higher population densities lead to less war, not more. We show that omitted variable bias can explain the failure of previous studies to discover this relationship. Finally, we show that the two schools seem to be equally correct, in that each explains about the same proportion of the variation in frequency of external war.

Cross-cultural researchers often combine several component variables into a composite index or "scale." The value of a scale for a particular observation is sensitive not only to the values of its component variables, but also to the values of the weights used to combine the components. This sensitivity to weight values is unfortunate, given that the choice of weighting scheme is in some ways arbitrary. A method is presented here, based on linear programming, which reduces the sensitivity of a scale to the component weights. An example scale is produced, for the prevalence of markets and property rights in the societies of the Standard Cross-Cultural Sample. A program, written for R, is included.

The results of a Becker–Peltzman–Stigler model of local school district decision-making yields biased or inconsistent efficiency measures when some school outputs are not measured. Empirical investigation of data for 95 Tennessee counties in the 1999–2000 academic year finds that Data Envelopment Analysis (DEA) efficiency measures, and efficiency rankings based on those measures, are highly sensitive to changes in the number of output measures used. An artifact of the DEA process causes increasing correlation of efficiency scores with the inverse of per pupil expenditures as outputs increase. Hence, high-stakes policy initiatives should not be based on such scores.

Using the Standard Cross-Cultural Sample, Roes and Raymond (2003) find that large societies are more likely to be located in resource-rich environments, engage in warfare, and hold beliefs in gods actively supporting human morality (“moralizing gods”). We revisit the Roes and Raymond study, using the methods presented in a series of papers by Dow and Eff. Our findings suggest that moralizing gods are less likely to be found in resource-rich environments or amongst societies frequently engaged in external warfare. We find that cultural transmission over geographic space is the most significant force in conditioning belief in moralizing gods; that moralizing gods are more likely to be found in pastoral societies; and that the relationship between society size and moralizing gods is non-linear, with both very large and very small societies less likely to have moralizing gods. We explain this non-linearity by arguing that the functions of moralizing gods can also be performed by the state, and we also argue that moralizing gods play an important role in stabilizing property rights.

Microfinance provides saving and lending services to the poor. It is not conceptually different from banking in the USA. People are able to save money at and borrow money from microfinance institutions. They are compensated for saving and charged for borrowing by an interest rate that compounds on savings or loans. It is different primarily in the magnitude of the financial transactions. Loans to the poor are much smaller on average than loans traditionally given by banks; these loans can be as small as $71. Also, because of their poverty, borrowers tend to have little or no collateral to secure loans (Murdoch, Dec. 1999).

Listwise deletion of cases with missing data prior to statistical analysis, the approach overwhelmingly used by cross-cultural survey researchers, requires the assumption that the missing data are missing completely at random. This assumption is not often likely to hold for cross-cultural sample data, and when it fails statistical analysis based only on complete-case subsamples introduces the possibility of biased estimates and standard errors. Over the past 20 or so years statisticians have made major advances in specifying the conditions under which missing data can be ignored when making inferences based on incomplete data. We review these conditions since they have a direct bearing on when the usual approaches to dealing with missing cross-cultural survey data are invalid.

Ember, Ember, and Low (2007) recently reported male mortality in warfare and environmental pathogen stress as statistically significant predictors of nonsororal polygyny. Two sources of bias can be identified in their data analysis: 1) omitted variable bias due to not including a variable for cultural trait transmission, that is, Galton's Problem; and 2) bias caused by extensive deletion of cases when the basic assumption required by listwise deletion, that the missing data are missing completely at random, does not hold. We first re-estimated Ember et al.'s model after adding a trait transmission variable using the listwise deletion subsample, and then again after using contemporary multiple imputation procedures to deal with missing data. Our findings indicate that the significant effects reported for male mortality and pathogen stress are the result of these two sources of bias. The only significant predictor of the world-wide distribution of nonsororal polygyny in the current analyses is cultural trait transmission.

Multiple imputation (MI) has become the preferred method for dealing with missing data in survey research. MI involves three steps: creating m multiply imputed complete datasets; estimating models on each of the m datasets using any standard statistical procedure; combining the resulting multiple estimates of each statistic of interest. This paper provides R programs for MI, and offers some advice for employing MI with data drawn from the Standard Cross-Cultural Sample (SCCS). A second set of R programs combines estimates from the m imputed data sets, and also deals with the problem of network autocorrelation effects, i.e., Galton’s Problem or the non-independence of cases, using two-stage instrumental variables (IV) regression. The objective of the paper is to provide programs, advice and explanations that will help researchers employing cross-cultural survey data, especially the SCCS, to deal with the twin problems of missing data and network autocorrelation effects, using the open source statistical package R. The paper is intended to complement a recent suite of publications by Dow and Eff where both theoretical and empirical issues underlying these two problems are discussed in detail.

Previous research on intergenerational mobility in income, occupation, or social class as a Markov process typically uses regression models to analyze cross-sectional data. In this paper we draw data from the National Longitudinal Survey of Youth (NLSY) to build Markov transition states, producing a set of stylized facts from these longitudinal data. We derive the probabilities that children will repeat the occupational, educational, or child-raising choices of their parents. This gives us insight into how such lifestyle choices are vertically transmitted from parents to children, and the degree of persistence of these choices over the generations.

Galton’s Problem is one of identifying functional relationships in a set of observations where the observations may be related through borrowing or descent. The problem has long been recognized in the fields of anthropology and cross-cultural research, but has been neglected in economics, even in cases where it is likely to be most acute, such as in regression models where each observation is a nation. The appropriate treatment for Galton’s Problem was developed in the 1980s by Malcolm M. Dow, Douglas R. White, and Michael L. Burton. Their solution is to estimate spatial models, where weight matrices for physical distance control for relationships of borrowing, and weight matrices for linguistic similarity control for relationships of descent. This paper documents a method for estimating a weight matrix based on language phylogenetic relationships. The method is applied to the 186 cultures of the Standard Cross-Cultural Sample, and applied to a set of 216 nations. The resulting weight matrices are made available for any researchers wishing to control for Galton’s Problem.