Estimation of Engel Curves from Survey Data with Zero Expenditures*

The objective of this article is to propose a Bayesian method for estimating a system of Engel functions using survey data that include zero expenditures. We deal explicitly with the problem of zero expenditures in the model and estimate a system of Engel functions that satisfy the adding‐up condition. Furthermore, using Markov chain Monte Carlo method, we estimate unobservable parameters, including consumption of commodities, total consumption and equivalence scale, and use their posterior distributions to calculate inequality measures and total consumption elasticities.     

Estimation of collective household models with Engel curves

The structural consumer demand methods used to estimate the parameters of collective household models are typically either very restrictive and easy to implement or very general and difficult to estimate. In this paper, we provide a middle ground. We adapt the very general framework of [Browning, M., Chiappori, P.A., Lewbel, A., 2004. Estimating Consumption Economies of Scale, Adult Equivalence Scales, and Household Bargaining Power, Boston College Working Papers in Economics 588] by adding a simple restriction that recasts the empirical model from a highly nonlinear demand system with price variation to a slightly nonlinear Engel curve system. Our restriction has an interpretation in terms of the behaviour of household scale economies and is testable. Our method identifies the levels of (not just changes in) household resource shares, and a variant of equivalence scales called indifference scales. We apply our methodology to Canadian expenditure data.     

Calculating a Standard Error for the Gini Coefficient: Some Further Results*

Several authors have suggested using the jackknife technique to approximate a standard error for the Gini coefficient. It has also been shown that the Gini measure can be obtained simply from an artificial ordinary least square (OLS) regression based on the data and their ranks. We show that obtaining an exact analytical expression for the standard error is actually a trivial matter. Further, by extending the regression framework to one involving seemingly unrelated regressions (SUR), several interesting hypotheses regarding the sensitivity of the Gini coefficient to changes in the data are readily tested in a formal manner.     

Inequality for Wage Earners and Self‐Employed: Evidence from Panel Data*

In this paper we highlight the importance of analysing the evolution of income inequality separately for employees and self‐employed workers. Using Spanish panel data on income and consumption for the period 1987–96, we find noticeable differences across these groups in the evolution of income inequality, and in the relative importance of the transitory and permanent components of income variance. The evolution of inequality is mainly explained by movements in the transitory component for the self‐employed and by the permanent component for the employees. Our results suggest that different policies should be implemented for each group.     

Variance Estimation for Generalized Entropy and Atkinson Inequality Indices: the Complex Survey Data Case*

We derive the sampling variances of generalized entropy and Atkinson indices when estimated from complex survey data, and show how they can be calculated straightforwardly using widely available software. We also show that, when the same approach is used to derive variance formulae for the independent and identically distributed case, it leads to estimators that are simpler than those proposed before. Both cases are illustrated with a comparison of income inequality in Britain and Germany.     

Who Benefits from Marriage?*

The phenomenon that married men earn higher average wages than unmarried men – the marriage premium – is well known. However, the robustness of the premium across the wage distribution and the underlying causes of the marriage premium are unclear. Focusing on the entire wage distribution and employing recently developed semi‐non‐parametric tests for quantile treatment effects, our findings cast doubt on the robustness of the premium. We find that the premium is explained by selection above the median, whereas a positive premium is obtained only at very low wages. The causal effect at low wages may be attributable to employer discrimination.     

Precautionary and Entrepreneurial Savings: New Evidence from German Households*

Various studies interpret the positive correlation between income risk and wealth as evidence of significant precautionary savings. However, these high estimates emerge from pooling non‐entrepreneurs and entrepreneurs, without controlling for heterogeneity. This article provides evidence for Germany based on representative panel data that includes private wealth balance sheets. Entrepreneurs, who face high income risk, hold more wealth than employees, but this tendency is not because of precautionary motives. Instead, they appear to save more for their old age, because they are usually not covered by statutory pension insurance. The analysis accounts for endogeneity in entrepreneurial choice and heterogeneous risk attitudes.     

Refining Targeting against Poverty Evidence from Tunisia*

We introduce a new methodology to target direct transfers against poverty. Our method is based on estimation methods that focus on the poor. Using data from Tunisia, we estimate ‘focused’ transfer schemes that highly improve anti‐poverty targeting performances. Post‐transfer poverty can be substantially reduced with the new estimation method. For example, a one‐third reduction in poverty severity from proxy‐means test transfer schemes based on OLS method to focused transfer schemes requires only a few hours of computer work based on methods available on popular statistical packages. Finally, the obtained levels of undercoverage of the poor are particularly low.     

Examining the Interaction between Saving and Contributions to Personal Pension Plans: Evidence from the BHPS*

This paper analyses the effects of social security reforms on saving in Britain. We use the British Household Panel Survey to investigate the interactions between voluntary additional contributions to personal pension plans (PPP) and saving in conventional forms. In particular, we test whether contributions to the PPP crowd out saving or constitute additional saving. Results suggest that not only have private pension schemes not crowded out private saving, but actually they have increased it too.     

Measurement Error in Access to Markets*

Microeconometric studies increasingly utilize travel times to markets as a determinant of economic behaviour. These studies typically use self‐reported measures from surveys, often characterized by measurement error. This paper is the first validation study of access to markets data. Unique data from Peru allow comparison of self‐reported variables with scientifically calculated variables. We investigate the determinants of the deviation between imputed and self‐reported data and show that it is non‐classical and dependent on observable socio‐economic variables. Our results suggest that studies using self‐reported measures of access may be estimating biased effects.     

A Micro‐Decomposition Analysis of Aggregate Human Development Outcomes*

We show how differences in aggregate human development outcomes over time and space can be additively decomposed into a pure mean income (growth) component, a component attributed to differences in the distribution of income, and components attributed to ‘non‐income’ factors and differences in the model linking outcomes to income and non‐income characteristics. The income effect at the micro level is modelled non‐parametrically, so as to flexibly reflect potentially complex distributional changes. Our proposed method is illustrated using data for Morocco and Vietnam, and the results offer some surprising insights into the observed aggregate gains in schooling attainments.     

The Impact of Training on Productivity and Wages: Evidence from British Panel Data*

It is standard in the literature on training to use wages as a sufficient statistic for productivity. This paper examines the effects of work‐related training on direct measures of productivity. Using a new panel of British industries 1983–96 and a variety of estimation techniques we find that work‐related training is associated with significantly higher productivity. A 1% point increase in training is associated with an increase in value added per hour of about 0.6% and an increase in hourly wages of about 0.3%. We also show evidence using individual‐level data sets that is suggestive of training externalities.     

Partial Horizontal Inequity Orderings: A Non‐parametric Approach*

We provide a partial ordering view of horizontal inequity (HI), based on the Lorenz criterion, associated with different post‐tax income distributions and a (bistochastic) non‐parametric estimated benchmark distribution. As a consequence, several measures consistent with the Lorenz criterion can be rationalized. In addition, we establish the so‐called HI transfer principle, which imposes a normative minimum requirement that any HI measure must satisfy. Our proposed HI ordering is consistent with this principle. Moreover, we adopt a cardinal view to decompose the total effect of a tax system into a welfare gain caused by HI‐free income redistribution and a welfare loss caused by HI, without any additive decomposable restriction on the indices. Hence, more robust tests can be applied. Other decompositions in the literature are seen as particular cases.     

On Bayesian analysis and computation for functions with monotonicity and curvature restrictions

Our goal is inference for shape-restricted functions. Our functional form consists of finite linear combinations of basis functions. Prior elicitation is difficult due to the irregular shape of the parameter space. We show how to elicit priors that are flexible, theoretically consistent, and proper. We demonstrate that uniform priors over coefficients imply priors over economically relevant quantities that are quite informative and give an example of a non-uniform prior that addresses this issue. We introduce simulation methods that meet challenges posed by the shape of the parameter space. We analyze data from a consumer demand experiment.     

HAC estimation in a spatial framework

We suggest a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance–covariance (VC) matrix for a vector of sample moments within a spatial context. We demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models. We allow for more than one measure of distance, each of which may be measured with error. Monte Carlo results suggest that our estimator is reasonable in finite samples. We then consider a spatial model containing various complexities and demonstrate that our HAC estimator can be applied in the context of that model.     

Calculating a Standard Error for the Gini Coefficient: Some Further Results: Reply

The potential shortcomings of the regression (or stochastic) approach for computing exact standard errors of the Gini coefficient using ordinary least squares or weighted least squares are pointed out.     

Estimation of technical and allocative inefficiency: A primal system approach

The estimation of technical and allocative inefficiencies using a flexible (translog) cost system is found to be quite difficult, especially when both the inefficiencies are random. In this paper we use the alternative primal system consisting of the production function (translog) and the first-order conditions of cost minimization. The estimation of the primal system is more straightforward and it enables us to estimate observation-specific technical and allocative inefficiencies, and their impact on input demand and cost. We use data on steam-electric generating plants from the U.S. to estimate the model using both Cobb–Douglas and translog production functions.     

EL inference for partially identified models: Large deviations optimality and bootstrap validity

This paper addresses the issue of optimal inference for parameters that are partially identified in models with moment inequalities. There currently exists a variety of inferential methods for use in this setting. However, the question of choosing optimally among contending procedures is unresolved. In this paper, I first consider a canonical large deviations criterion for optimality and show that inference based on the empirical likelihood ratio statistic is optimal. Second, I introduce a new empirical likelihood bootstrap that provides a valid resampling method for moment inequality models and overcomes the implementation challenges that arise as a result of non-pivotal limit distributions. Lastly, I analyze the finite sample properties of the proposed framework using Monte Carlo simulations. The simulation results are encouraging.     

Exogeneity in structural equation models

The practical relevance of several concepts of exogeneity of treatments for the estimation of causal parameters based on observational data are discussed. We show that the traditional concepts, such as strong ignorability and weak and super-exogeneity, are too restrictive if interest lies in average effects (i.e. not on distributional effects of the treatment). We suggest a new definition of exogeneity, KL-exogeneity. It does not rely on distributional assumptions and is not based on counterfactual random variables. As a consequence it can be empirically tested using a proposed test that is simple to implement and is distribution-free.