The relationship between household wealth and educational enrollment of children can be estimated without expenditure data. A method for doing so - which uses an index based on household asset ownership indicators - is proposed and defended in this paper. In India, children from the wealthiest households are over 30 percentage points more likely to be in school than those from the poorest households, although this gap varies considerably across states. To estimate the relationship between household wealth and the probability that a child (aged 6 to 14) is enrolled in school, Filmer and Pritchett use National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993. In developing their estimate Filmer and Pritchett had to overcome a methodological difficulty: The NFHS, modeled closely on the Demographic and Health Surveys, measures neither household income nor consumption expenditures. As a proxy for long-run household wealth, they constructed a linear asset index from a set of asset indicators, using principal components analysis to derive the weights. This asset index is robust, produces internally coherent results, and provides a close correspondence with data on state domestic product and on state level poverty rates. They validate the asset index using data on consumption spending and asset ownership from Indonesia, Nepal, and Pakistan. The asset index has reasonable coherence with current consumption expenditures and, more importantly, works as well as - or better than - traditional expenditure-based measures in predicting enrollment status. The authors find that on average a child from a wealthy household (in the top 20 percent on the asset index developed for this analysis) is 31 percent more likely to be enrolled in school than a child from a poor household (in the bottom 40 percent). This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to inform educational policy. The study was funded by the Bank`s Research Support Budget under the research project Educational Enrollment and Dropout (RPO 682-11).

Estimating Wealth Effects without Expenditure Data or Tears: With an Application to Educational Enrollments in States of India

This introduction to the R package rgenoud is a modied version of Mebane and Sekhon (2011), published in the Journal of Statistical Software. That version of the introduction contains higher resolution gures. genoud is an R function that combines evolutionary algorithm methods with a derivativebased (quasi-Newton) method to solve dicult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model’s parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to nd any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

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Genetic Optimization Using Derivatives: The rgenoud Package for R

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model.

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Should I Use Fixed or Random Effects

Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance

Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China

1. The Nature of Econometrics and Economic Data 2. The Simple Regression Model 3. Multiple Regression Analysis: Estimation 4. Multiple Regression Analysis: Inference 5. Multiple Regression Analysis: OLS Asymptotics 6. Multiple Regression Analysis: Further Issues 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 8. Heteroskedasticity 9. More on Specification and Data Issues 10. Basic Regression Analysis with Time Series Data 11. Further Issues in Using OLS with Time Series Data 12. Serial Correlation and Heteroskedasticity 13. Pooling Cross Sections Across Time: Simple Panel Data Methods 14. Advanced Panel Data Methods 15. Instrumental Variables Estimation and Two Stage Least Squares 16. Simultaneous Equations Models 17. Limited Dependant Variable Models and Sample Selection Corrections 18. Advanced Time Series Topics 19. Carrying Out an Empirical Project

Jeffrey M. Wooldridge

Papers

Introduction to Econometrics