JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Theory: Cultural variables are incorporated into a baseline endogenous economic growth model. Hypotheses: Cultural attitudes toward achievement and thrift have a positive effect on economic growth. Cultural attitudes concerning postmaterialism have a negative effect on economic growth. Methods: Ordinary least squares regression is used to test economic and cultural models of growth on a cross section of 25 countries. The encompassing principle is used to resolve competing theoretical specifications and to generate a final parsimonious model. A variant of Leamer's Extreme Bounds Analysis (EBA) is used to evaluate the sensitivity of parameter estimates. The conclusions are further supported by nonparametric methods including robust regression and bootstrap resampling. The data for the analysis are from the World Values Survey (1990) and from Levine and Renelt (1992).Results: An empirical model that incorporates both cultural and economic variables is superior to an explanation emphasizing one set of these variables. The final model is robust to: (1) alterations in the conditioning set of variables; (2) elimination of influential cases; and (3) variations in estimation procedures.

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Testing theories about political change requires analysts to make assumptions about the memory of their time series. Applied analyses are often based on inferences that time series are integrated and cointegrated. Typically analyses rest on Dickey—Fuller pretests for unit roots and a test for cointegration based on the Engle—Granger two-step method. We argue that this approach is not a good one and use Monte Carlo analysis to show that these tests can lead analysts to conclude falsely that the data are cointegrated (or nearly cointegrated) when the data are near-integrated and not cointegrating. Further, analysts are likely to conclude falsely that the relationship is not cointegrated when it is. We show how inferences are highly sensitive to sample size and the signal-to-noise ratio in the data. We suggest three things. First, analysts should use the single equation error correction test for cointegrating relationships; second, caution is in order in all cases where near-integration is a reasonable alternative to unit roots; and third, analysts should drop the language of cointegration in many cases and adopt single-equation error correction models when the theory of error correction is relevant.

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