Academic literature on the topic 'The Standardized World Income Inequality Database (SWIID)'

This paper uses Solt’s Standardized World Income Inequality Database and attempts to explain variations in Gini coefficients for net household income across 142 developing, transitional and developed societies. The causal model contains three sets of explanatory variables: (1) economic dualism, (2) educational attainment and educational inequality, and (3) political and state influences on income inequality. The most important cause of inequality is still the Kuznets effect: societies at low and high levels of development have less inequality than those at intermediate levels. Population growth increases inequality. Rising labor productivity in the agricultural relative to that in the non-agricultural sector, and being a former Soviet society reduce inequality. Educational attainment has less effect than educational inequality on income inequality. Government income transfers sometimes reduce inequality, but have no effect when all variables are in the model. Finally, liberal democracy has no net effect on inequality

Abstract Missing data are the most common problem in many research areas. For cross-section and time-series data, imputation can be a challenging problem. The most widely used method for filling missing observations is the multiple imputation which increase the number of the available data and thereby reducing biases that may occur when observations with missing values are simply deleted. The main purpose of this paper is to employ a bootstrapping expectation–maximization (EM) algorithm in order to impute missing values mainly to economic data. In the application we use a dataset that is consisted by annual panel data for the 27 countries of the European Union covering the period 2000-2017. The data were obtained from the databases of World Bank and Eurostat namely the Global Financial Development Database, The Standardized World Income Inequality Database by Solt (2019) and the World Development Indicators. Different indicators were chosen representing the development of banking system and stock markets, economic growth, economic inequality, innovation, fiscal policy, physical and human capital, and trade openness. Finally, diagnostic tools are used inspecting the imputations that are created. Keywords: Multiple imputation, Amelia II, Economic data, Financial development, Inequality.

This paper examines the effect of governance on inequality, specifically if improvements in the World Bank’s Worldwide Governance Indicators affect inequality as measured by two Gini coefficients: Market Gini, before taxes and redistribution, and Net Gini, after taxes and redistribution. The data for the Gini measurements was taken from the Standardized World Income Inequality Database (SWIID) and the data for the Worldwide Governance Indicators was taken from the World Bank. Data for fifteen (15) years, from the start of the Worldwide Governance Indicators until 2013, was combined with data from SWIID for the same years. In all, data from one hundred fifty-six (156) countries with a full set of six (6) indicators for the years that had at least one corresponding Gini measurements were used in this study: in total one thousand seven hundred and forty-seven (1747) observations. In a pooled OLS regression, controlling for growth with the variable GDP per Capita expressed as a per cent (%) change on an annual basis, the individual indicators gave the following results, where a positive sign indicates increased inequality and vice versa: Control of Corruption and Regulatory Quality showed a positive sign for both Gini measurements. Rule of Law, Government Effectiveness, Political Stability and the Absence of Violence/Terrorism, gave a negative sign for both Gini measurements. Voice and Accountability showed a positive sign for Market Gini and a negative sign for Net Gini. The fact that an improvement in Control of Corruption increased inequality both before and after taxes and redistribution was unexpected and should be further researched.

This document is part of a series of technical notes describing the compilation of a new companion database that complements the World Income Inequality Database. It aims at facilitating the analysis of inequality as well as progress in achieving the global goal of reducing inequality within and across countries. This new dataset also includes an annual series reporting the income distribution at the percentile level for all citizens in the world, regardless of where they live, since 1950 to present. A previous note described the selection of income distribution series. Since these series may differ across welfare concepts and other methods used, this technical note describes the second stage, constructing integrated and standardized country series. It discusses all the necessary adjustments conducted to construct the final series for each country, with consistent estimates of the distribution of net income per capita over the entire period for which information is available. This is mainly divided into two stages. First, integrating country series by interlinking series that overlap over time, then using a more general regression-based approach.