This paper proposes a framework to model welfare effects that are associated with a price change in a population of heterogeneous consumers. The framework is similar to that of Hausman and Newey ( Econometrica, 1995, 63, 1445–1476), but allows for more general forms of heterogeneity. Individual demands are characterized by a general model that is nonparametric in the regressors, as well as monotonic in unobserved heterogeneity, allowing us to identify the distribution of welfare effects. We first argue why a decision maker should care about this distribution. Then we establish constructive identification, propose a sample counterparts estimator, and analyze its large‐sample properties. Finally, we apply all concepts to measuring the heterogeneous effect of a change of gasoline price using US consumer data and find very substantial differences in individual effects across quantiles.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

Quantile Regression, the first book of Hao and Naiman′s two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.

Policy makers across the Middle East and North Africa have for many years articulated plans to integrate their people spatially and economically. Wishing to bring communities together and narrow economic gaps, governments have made large capital investments in transport corridors and “new cities.†? Hoping to provide jobs in places with little economic activity, governments have designated new industrial zones supported by spatially targeted business incentives. Yet the results of these place-based initiatives in MENA are limited. The disparities between capital cities and lagging areas, and between richer and poorer quarters of cities, remain stark. Across much of the region, a fortunate few are connected to opportunity, while many more people are marginal to the formal economy—or live outside it, seemingly forgotten. Why have place-based spatial initiatives in MENA countries largely underdelivered, not yielding more sustainable jobs and growth? Although the challenges are many and vary across the region, this book explains that many of these place-based policies get one thing wrong: they attempt to treat inequity‘s spatial and physical symptoms, not its causes. This book presents the five roots causes of spatial inequity in institutional inefficiencies across MENA—urban regulatory frictions, credentialist education systems, centralized control over local public services, barriers to the spatial mobility of goods and people, and barriers to market entry and lopsided business environments—within cities, within countries, and across national borders

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.

The process of rural-urban transformation presents both opportunities and challenges for development. If managed effectively, it can result in growth that benefits everyone; if managed poorly, it can lead to stark welfare disparities and entire regions cut off from the advantages of agglomeration economies. The importance of rural-urban transition has been confirmed by two consecutive World Development Reports: WDR 2008 Agriculture for Development; and WDR 2009 Reshaping Economic Geography. Focusing on Sub-Saharan Africa and South Asia, this book picks up where the WDRs left off, investigating the influence of country conditions and policies on the pace, pattern, and consequences of rural-urban transition and suggesting strategies to ensure that its benefits results in shared improvements in well-being. The book uncovers vast inequalities, whether between two regions of one country, between rural and urban areas, or within cities themselves. The authors find little evidence to suggest that these inequalities will automatically diminish as countries develop: empirical and qualitative analysis suggests that spatial divides are mainly a function of country conditions, policies and institutions. By implication, policymakers must take active steps to ensure that rural-urban transition results in shared growth. Spatially unbiased provision of health and education services is crucial to ensuring that the benefits of transition are shared by all. But connective infrastructure and targeted interventions also emerge as important considerations, even in countries with severely constrained fiscal and administrative capacity. The authors suggest steps for navigating the tricky political economy of land reforms. And they alert readers to potential spillover effects that mean that policies designed for one space can have unintended consequences on another.Policymakers and development experts, as well as anyone concerned with the impact of rural-urban transition on growth and equity, will find this book a thought-provoking and informative read.