Do Underlying Risk Preferences explain Individuals’ Cognitive Ability?⁕ Evidence from a Sample of Pakistani Students

Emerging research in empirical economics posits a question on the relation between underlying risk preferences and reflective cognitive ability. In an experimental setting, a preliminary sample of 260 participants undergo a series of incentivized choice experiments to elicit risk preferences and a Cognitive Reflection Test (CRT) to obtain estimates of their reflective ability. We sidestep potential biases by using a Fechner error specification along with a contextualized version of the utility function. Individuals who are more likely to avoid risky outcomes have significantly lower scores on the CRT. The analysis validates a prominent relationship spanning the economics and psychology literature and suggests a potential direction of causal inference for future research.

New Perspectives on Henry Ludwell Moore’s Use of Harmonic Analysis

This paper reconsiders the contribution of Henry Ludwell Moore to dynamic economics through the use of harmonic analysis. We show that Moore’s analysis is innovative in its use of the Fourier transformation for the identification of cycles with different periodicities. This enables Moore to identify cycles of longer length with more precision than would be the case for the standard methodology. We are able to replicate the main features of his results and confirm the existence of a rainfall cycle with a periodicity similar to that of the business cycle (eight years). However, we find that the evidence for a longer (thirty-three year) rainfall cycle is weaker than Moore indicates. We also argue that a central theme of Moore’s analysis, the relationship between rainfall, agricultural productivity and the business cycle, marks an early precursor of the ‘Real Business Cycle’ approach. Stigler’s (1962) dismissal of Moore’s work on cycles as ‘a complete failure’ is therefore, in our opinion rather unfair. Instead, we argue that, although his work is certainly flawed, it nevertheless deserves a place in both the history of business cycle theory and empirical economics.

NEW PERSPECTIVES ON HENRY LUDWELL MOORE’S USE OF HARMONIC ANALYSIS

This paper reconsiders the contribution of Henry Ludwell Moore to dynamic economics through the use of harmonic analysis. We show that Moore’s analysis is innovative in its use of the Fourier transformation for the identification of cycles with different periodicities. This enables Moore to identify cycles of longer length with more precision than would be the case for the standard methodology. We are able to replicate the main features of his results and confirm the existence of a rainfall cycle with a periodicity similar to that of the business cycle (eight years). However, we find that the evidence for a longer (thirty-three-year) rainfall cycle is weaker than Moore indicates. We also argue that a central theme of Moore’s analysis—the relationship among rainfall, agricultural productivity, and the business cycle—marks an early precursor of the “real business cycle” approach. George Stigler’s (1962) dismissal of Moore’s work on cycles as “a complete failure” is therefore, in our opinion, unfair. Instead, we argue that, although his work is certainly flawed, it nevertheless deserves a place in both the history of business cycle theory and empirical economics.

Statistical Nonsignificance in Empirical Economics

Statistical significance is often interpreted as providing greater information than nonsignificance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are common in economics, where data-sets are large and there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. Therefore, we advocate visible reporting and discussion of nonsignificant results. (JEL C12, C90)

Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning

Many settings in empirical economics involve estimation of a large number of parameters. In such settings, methods that combine regularized estimation and data-driven choices of regularization parameters are useful. We provide guidance to applied researchers on the choice between regularized estimators and data-driven selection of regularization parameters. We characterize the risk and relative performance of regularized estimators as a function of the data-generating process and show that data-driven choices of regularization parameters yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We illustrate using examples from empirical economics.

How Does Microfinance Empower Women in Nigeria?

That microfinance institutions empower women has become a heated debate at both theoretical and empirical economics. A large proportion of women in developing countries are characterized by segregation, relegation, poverty, vulnerability; majority of them engaged in agriculture and related economic activities, while a few others have menial jobs. The objective of this chapter is to determine how microfinance has empowered women in Nigeria. It employed propensity score matching and logit model to estimate the effect of microfinance on women empowerment and welfare. The results show that age of women, education, belonging to saving association, and operating an account are the determinants of women empowerment and welfare as they access finance from the microfinance banks. It was also observed that there is disparity among women who have access to liquidity. It is recommended that more microfinance banks be cited in the rural sector where the majority of the poor reside, policies like low interest rates, national awareness, and incentives for more women to access micro-credits.