Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions Across Countries

This paper develops a micro-econometric method to account for differences across distributions of household income. Going beyond the determination of earnings in labor markets, we also estimate statistical models for occupational choice and for the conditional distributions of education, fertility and non-labor incomes. We import combinations of estimated parameters from these models to simulate counterfactual income distributions. This allows us to decompose differences between functionals of two income distributions (such as inequality or poverty measures) into shares due to differences in the structure of labor market returns (price effects); differences in the occupational structure; and differences in the underlying distribution of assets (endowment effects). We apply the method to the differences between the Brazilian income distribution and those of the United States and Mexico, and find that most of Brazil's excess income inequality is due to underlying inequalities in the distribution of two key endowments: access to education and to sources of non-labor income, mainly pensions.http://deepblue.lib.umich.edu/bitstream/2027.42/39863/3/wp478.pd

Income Distributions, Inequality, and Poverty in Asia, 1992–2010

Income distributions for developing countries in Asia are modeled using beta-2 distributions, which are estimated by a method of moments procedure applied to grouped data. Estimated parameters of these distributions are used to calculate measures of inequality, poverty, and pro-poor growth in four time periods over 1992–2010. Changes in these measures are examined for 11 countries, with a major focus on the People’s Republic of China (PRC), India, and Indonesia, which are separated into rural and urban regions. We find that the PRC has grown rapidly with increasing inequality accompanying this growth. India has been relatively stagnant. Indonesia has grown rapidly after suffering an initial set back from the Asian financial crisis in 1997

Normal background concentrations (NBCs) of contaminants in English soils : final project report

The British Geological Survey (BGS) has been commissioned by the Department for Environment, Food and Rural Affairs (Defra) to give guidance on what are normal levels of contaminants in English soils in support of the Part 2A Contaminated Land Statutory Guidance. This has initially been done by studying the distribution of four contaminants – arsenic, lead, benzo[a]pyrene (BaP) and asbestos – in topsoils from England. This work was extended to a further four contaminants (cadmium, copper, nickel and mercury) which enabled methodologies developed to be tested on a larger range of contaminants. The first phase of the Project gathered data sets that were: nationally extensive; systematically collected so a broad range of land uses were represented; and collected and analysed to demonstrably and acceptable levels of quality. Information on the soil contaminant concentrations in urban areas was of particular importance as the normal background is considered to be a combination of both natural and diffuse anthropogenic contributions to the soil. Issues of soil quality are most important in areas where these affect most people, namely, the urban environment. The two principal data sets used in this work are the BGS Geochemical Baseline Survey of the Environment (G-BASE) rural and urban topsoils (37,269 samples) and the English NSI (National Soil Inventory) topsoils (4,864 samples) reanalysed at the BGS laboratories by X-ray fluorescence spectrometry (XRFS) so both data sets were highly compatible. These two data sets provide results for most inorganic element contaminants, though results explored for mercury and BaP are drawn from a variety of different and much less extensive data sets

Supersampling and network reconstruction of urban mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to human mobility such as GPS traces, mobile phone data, etc., it is still true that such data sets represent a subsample of the population of interest, and then might give an incomplete picture of the entire population in question. Notwithstanding the abundant usage of such inherently limited data sets, the impact of sampling biases on mobility patterns is unclear -- we do not have methods available to reliably infer mobility information from a limited data set. Here, we investigate the effects of sampling using a data set of millions of taxi movements in New York City. On the one hand, we show that mobility patterns are highly stable once an appropriate simple rescaling is applied to the data, implying negligible loss of information due to subsampling over long time scales. On the other hand, contrasting an appropriate null model on the weighted network of vehicle flows reveals distinctive features which need to be accounted for. Accordingly, we formulate a "supersampling" methodology which allows us to reliably extrapolate mobility data from a reduced sample and propose a number of network-based metrics to reliably assess its quality (and that of other human mobility models). Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is needed.Comment: 14 pages, 4 figure

A stochastic sub-national population projection methodology with an application to the Waikato region of New Zealand

In this paper we use a stochastic population projection methodology at the sub-national level as an alternative to the conventional deterministic cohort-component method. We briefly evaluate the accuracy of previous deterministic projections and find that there is a tendency for these to be conservative: under-projecting fast growing populations and over-projecting slow growing ones. We generate probabilistic population projections for five demographically distinct administrative areas within the Waikato region of New Zealand, namely Hamilton City, Franklin District, Thames-Coromandel District, Otorohanga District and South Waikato District. Although spatial interaction between the areas is not taken into account in the current version of the methodology, a consistent set of cross-regional assumptions is used. The results are compared to official sub-national deterministic projections. The accuracy of sub-national population projections is in New Zealand strongly affected by the instability of migration as a component of population change. Unlike the standard cohort-component methodology, in which net migration levels are projected, the key parameters of our stochastic methodology are age-gender-area specific net migration rates. The projected range of rates of population growth is wider for smaller regions and/or regions more strongly affected by net migration. Generally, the identified and modelled uncertainty makes the traditional ‘mid range’ scenario of sub-national population projections of limited use for policy analysis or planning beyond a relatively short projection horizon. Directions for further development of a stochastic sub-national projection methodology are suggested

Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions Across Countries

This paper develops a micro-econometric method to account for differences across distributions of household income. Going beyond the determination of earnings in labor markets, we also estimate statistical models for occupational choice and for the conditional distributions of education, fertility and non-labor incomes. We import combinations of estimated parameters from these models to simulate counterfactual income distributions. This allows us to decompose differences between functionals of two income distributions (such as inequality or poverty measures) into shares due to differences in the structure of labor market returns (price effects); differences in the occupational structure; and differences in the underlying distribution of assets (endowment effects). We apply the method to the differences between the Brazilian income distribution and those of the United States and Mexico, and find that most of Brazil's excess income inequality is due to underlying inequalities in the distribution of two key endowments: access to education and to sources of non-labor income, mainly pensions.Inequality, Distribution, Micro-simulations

Beyond Oaxaca-Blinder : accounting for differences in household income distributions across countries

The authors develop a microeconometric method to account for differences across distributions of household income. Going beyond the determination of earnings in labor markets, they also estimate statistical models for occupational choice and for conditional distributions of education, fertility, and nonlabor incomes. The authors import combinations of estimated parameters from these models to simulate counterfactual income distributions. This allows them to decompose differences between functionals of two income distributions (such as inequality or poverty measures) into shares because of differences in the structure of labor market returns (price effects), differences in the occupational structure, and differences in the underlying distribution of assets (endowment effects). The authors apply the method to the differences between the Brazilian income distribution and those of Mexico and the United States, and find that most of Brazil's excess income inequality is due to underlying inequalities in the distribution of two key endowments: access to education and to sources of nonlabor income, mainly pensions.Services&Transfers to Poor,Environmental Economics&Policies,Poverty Impact Evaluation,Economic Theory&Research,Health Economics&Finance,Inequality,Economic Theory&Research,Rural Poverty Reduction,Safety Nets and Transfers,Services&Transfers to Poor

Zipf Law for Brazilian Cities

This work studies the Zipf Law for cities in Brazil. Data from censuses of 1970, 1980, 1991 and 2000 were used to select a sample containing only cities with 30,000 inhabitants or more. The results show that the population distribution in Brazilian cities does follow a power law similar to the ones found in other countries. Estimates of the power law exponent were found to be 2.22 +/- 0.34 for the 1970 and 1980 censuses, and 2.26 +/- 0.11 for censuses of 1991 and 2000. More accurate results were obtained with the maximum likelihood estimator, showing an exponent equal to 2.41 for 1970 and 2.36 for the other three years.Comment: 12 pages, 6 figures, 3 tables, Elsevier LaTeX, accepted for publication in "Physica A". Correction of minor mistyping (eq. 8

Beyond Oaxaca-Blinder: accounting for differences in household income distributions across countries

This paper develops a micro-econometric method to account for differences across distributions of household income. Going beyond the determination of earnings in labor markets, we also estimate statistical models for occupational choice and for the conditional distributions of education, fertility and non-labor incomes. We import combinations of estimated parameters from these models to simulate counterfactual income distributions. This allows us to decompose differences between functionals of two income distributions (such as inequality or poverty measures) into shares due to differences in the structure of labor market returns (price effects); differences in the occupational structure; and differences in the underlying distribution of assets (endowment effects). We apply the method to the differences between the Brazilian income distribution and those of the United States and Mexico, and find that most of Brazil's excess income inequality is due to underlying inequalities in the distribution of two key endowments: access to education and to sources of non-labor income, mainly pensions.