The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.
Comment mesurer le plus finement possible l'accélération ou la décélération d'une épidémie ?
This paper estimates trade barriers in government procurement, a market that accounts for 12% of world GDP. Using data from inter-country input-output tables in a gravity model, we find that home bias in government procurement is significantly higher than in trade between firms. However, this difference has been shrinking over time. Results also show that trade agreements with provisions on government procurement increase cross-border flows of services, whereas the effect on goods is small and not different from that in private markets. Provisions containing transparency and procedural requirements drive the liberalizing effect of trade agreements.
This chapter discusses whether the Middle East and North African (MENA) countries are prone to be cursed or blessed by their natural resources endowments. It thus reviews the literature on the resource curse theory. The existence of a resource curse is discussed and arguments against advocates of the resource curse are presented. Then, the resource curse transmission channels are presented. Finally, we present to what extent MENA countries are affected by the curse, drawing on existing literature as well as empirical data. The (scarce) literature shows that a resource curse may be underway in MENA economies. Broadly speaking, this literature often argues that the curse could be turned into a blessing through institutional improvements. The empirical data presented in this chapter tend to confirm this view. They show that the economic development of resource-rich MENAs has not been translated into human progress and has been largely non-inclusive. These results are stronger when the resource rent per capita is larger. Finally, the average institutional quality in resources-rich MENA countries appears to be lower than the average institutional quality in resources-poor MENA economies, suggesting some room for an institutional resource curse.
In the case of ordered categorical data, the concepts of minimum and maximum inequality are not straightforward. In this chapter, the authors consider the Cowell and Flachaire (2017) indices of inequality. The authors show that the minimum and maximum inequality depend on preliminary choices made before using these indices, on status and the sensitivity parameter. Specifically, maximum inequality can be given by the distribution which is the most concentrated in the top or bottom category, or by the uniform distribution.
This chapter presents a survey of some recent methods used in economics and finance to account for cyclical dependence and account for their multifaced dynamics: nonlinearities, extreme events, asymmetries, non-stationarity, time-varying moments. To circumvent the caveats of the standard spectral analysis, new tools are now used based on copula spectrum, quantile spectrum and Laplace periodogram in both non-parametric and parametric contexts. The chapter presents a comprehensive overview of both theoretical and empirical issues as well as a computational approach to explain how the methods can be implemented using the R Package.