Inequality Trends and Determinants in Sri Lanka 1980-2002: A Shapley Approach to Decomposition

Sri Lanka liberalised its economy in 1977, paving the way for more rapid economic growth and higher rates of job creation. But tensions over distributional issues still plague the body politic. This paper investigates the evolution of Sri Lanka's income distribution in the period 1980-2002 and uses the Shapley value decomposition methodology to determine underlying causes. The study finds that while average incomes rose across strata, the rich experienced more rapid income growth leading to greater inequality. Inequality change was driven by differential access to infrastructure, education, and occupation status. Demographic factors including ethnicity, and spatial factors, contributed very little. The study recommends policies that ensure more equitable access to income earning assets such as education and infrastructure services and make sure that increases in inequality do not take place along sectoral, regional and ethnic fault lines.income inequality; Sri Lanka; Shapley value decomposition

Averaging Lorenz Curves

A large number of functional forms have been suggested in the literature for estimating Lorenz curves that describe the relationship between income and population shares. One way of choosing a particular functional form is to pick the one that best fits the data in some sense. Another approach, and the one followed here, is to use Bayesian model averaging to average the alternative functional forms. In this averaging process, the different Lorenz curves are weighted by their posterior probabilities of being correct. Unlike a strategy of picking the best-fitting function, Bayesian model averaging gives posterior standard deviations that reflect the functional form uncertainty. Building on our earlier work (Chotikapanich and Griffiths 2002), we construct likelihood functions using the Dirichlet distribution and estimate a number of Lorenz functions for Australian income units. Prior information is formulated in terms of the Gini coefficient and the income shares of the poorest 10% and poorest 90% of the population. Posterior density functions for these quantities are derived for each Lorenz function and are averaged over all the Lorenz functions.Gini coefficient; Bayesian inference; Dirichlet distribution.

Estimating Income Distributions Using a Mixture of Gamma Densities

The estimation of income distributions is important for assessing income inequality and poverty and for making comparisons of inequality and poverty over time, countries and regions, as well as before and after changes in taxation and transfer policies. Distributions have been estimated both parametrically and nonparametrically. Parametric estimation is convenient because it facilitates subsequent inferences about inequality and poverty measures and lends itself to further analysis such as the combining of regional distributions into a national distribution. Nonparametric estimation makes inferences more difficult, but it does not place what are sometimes unreasonable restrictions on the nature of the distribution. By estimating a mixture of gamma distributions, in this paper we attempt to benefit from the advantages of parametric estimation without suffering the disadvantage of inflexibility. Using a sample of Canadian income data, we use Bayesian inference to estimate gamma mixtures with two and three components. We describe how to obtain a predictive density and distribution function for income and illustrate the flexibility of the mixture. Posterior densities for Lorenz curve ordinates and the Gini coefficient are obtained

Estimating Lorenz Curves Using a Dirichlet Distribution

The Lorenz curve relates the cumulative proportion of income to the cumulative proportion of population. When a particular functional form of the Lorenz curve is specified it is typically estimated by linear or nonlinear least squares assuming that the error terms are independently and normally distributed. Observations on cumulative proportions are clearly neither independent nor normally distributed. This paper proposes and applies a new methodology which recognizes the cumulative proportional nature of the Lorenz curve data by assuming that the proportion of income is distributed as a Dirichlet distribution. Five Lorenz-curve specifications were used to demonstrate the technique. Once a likelihood function and the posterior probability density function for each specification are derived we can use maximum likelihood or Bayesian estimation to estimate the parameters. Maximum likelihood estimates and Bayesian posterior probability density functions for the Gini coefficient are also obtained for each Lorenz-curve specification.

Carnarvon Gorge: a comment on the sensitivity of consumer surplus estimation

Beal’s (1995) method of estimating the value of Carnarvon Gorge for recreational use is re‐examined. When an inconsistency in her estimation procedure is corrected, the estimated value of Carnarvon Gorge for camping is found to be six times higher. The sensitivity of the estimate to the choice of functional form is examined, and standard errors and interval estimates for consumer surplus are provided. Comments are made about functional form choice and prediction in log‐log models.Resource /Energy Economics and Policy,

Bayesian Assessment of Lorenz and Stochastic Dominance in Income Distributions

Hypothesis tests for dominance in income distributions has received considerable attention in recent literature. See, for example, Barrett and Donald (2003), Davidson and Duclos (2000) and references therein. Such tests are useful for assessing progress towards eliminating poverty and for evaluating the effectiveness of various policy initiatives directed towards welfare improvement. To date the focus in the literature has been on sampling theory tests. Such tests can be set up in various ways, with dominance as the null or alternative hypothesis, and with dominance in either direction (X dominates Y or Y dominates X). The result of a test is expressed as rejection of, or failure to reject, a null hypothesis. In this paper we develop and apply Bayesian methods of inference to problems of Lorenz and stochastic dominance. The result from a comparison of two income distributions is reported in terms of the posterior probabilities for each of the three possible outcomes: (a) X dominates Y, (b) Y dominates X, and (c) neither X nor Y is dominant. Reporting results about uncertain outcomes in terms of probabilities has the advantage of being more informative than a simple reject / do-not-reject outcome. Whether a probability is sufficiently high or low for a policy maker to take a particular action is then a decision for that policy maker. The methodology is applied to data for Canada from the Family Expenditure Survey for the years 1978 and 1986. We assess the likelihood of dominance from one time period to the next. Two alternative assumptions are made about the income distributions –Dagum and Singh-Maddala – and in each case the posterior probability of dominance is given by the proportion of times a relevant parameter inequality is satisfied by the posterior observations generated by Markov chain Monte Carlo.Bayesian, Income Distributions, Lorenz

Impact of Structural Change in Education, Industry and Infrastructure on Income Distribution in Sri Lanka

Income inequality increased in Sri Lanka following trade liberalization in 1977. This study applies a semi-parametric method to investigate whether structural changes in education, industry and infrastructure access underlay the change in the distribution. The study finds that while the concentration of people shifted towards higher income ranges at every stage in the distribution between 1985 and 2002, changes in access to infrastructure triggered much of the shift. Higher levels of educational attainment also had an impact. But the middle classes appear to have benefited disproportionately more from the provision of education and infrastructure services than did the poor. The analysis recommends that such services are targeted more effectively towards those in the poorest income deciles to enable them to move out of poverty to higher income ranges.Income inequality; Sri Lanka; education; infrastructure; kernel density decomposition

Estimating and Combining National Income Distributions using Limited Data

Recently, there has been a resurgence of studies on the distribution of income and inequality at regional and global levels, largely driven by the concerns of economists, international development organisations and the general public about the overall effects of globalisation on growth and inequality. A major data problem encountered in these studies is the nature of income distribution data that are available mainly in a summary form that includes mean (average) income and income shares of quintile or decile groups of the population. Past studies have either ignored distributional characteristics within each population sub-group, implying that all individuals in a quintile or decile group have the same income, or used simple distributions like the lognormal or Pareto to model income distribution within each country. The aim of the paper is to estimate national and regional income distributions within a more general framework that relaxes the assumption of constant-income-within-groups and is based on a general and versatile class of income distributions. A technique to estimate parameters of a class of generalised Beta distributions using grouped data is proposed. Regional income distribution is modelled using a mixture of country-specific distributions and its properties are examined. The techniques are used to analyse national and regional inequality trends for eight East Asian countries and three benchmark years 1988, 1993 and 2000.Income Distribution; Generalized Beta; Mixture of Distributions; Inequality

Measuring Poverty and Inequality from Highly Aggregated Small Area Data: The Changing Fortunes of Latrobe Valley Households

The Latrobe Valley generates 85% of Victoria's electricity. The progressive privatisation of the electricity industry between 1989 and 1997, had a lasting effect on income distribution in the region. This paper investigates the change in income level, inequality and poverty for this region between 1986 and 2006. To circumvent data availability issues, we propose a general method of using aggregated data to obtain regional income distributions. We find that in 1986 Latrobe Valley incomes were well above other non-metropolitan areas while inequality measures were relatively low. Mean income subsequently dropped below comparable locations while inequality rose. Although income levels had partially recovered by 2006, inequality measures continued to rise.Poverty, inequality, restructure, privatization, small-area income distribution.

Estimating Income Inequality in China Using Grouped Data and the Generalized Beta Distribution

Gini coefficient, generalized beta distribution, urban and rural inequality