Income inequality in BiH: Combining survey and tax record data Dzelila Kramer and Nermin oruc Conference “inequalities in see”, eizg, Zagreb, 14 sep 2015

Outline Motivation: Data issues in measuring inequality Approaches: Combining survey and tax data Methodology Results: Original vs corrected data Conclusions Further steps

Introduction Recent increase in interest of inequality literature in combining data sources (Piketty, 2003; Atkinson et al, 2011; Burkhauser et al, 2012; Alvaredo et al, 2013; Lakner and Milanovic, 2013). The studies have shown that inequality measures are sensitive to misreporting problems at the top of the income distribution, even if high-income groups represent by definition a very small fraction of the population (Leigh, 2007; Alvaredo, 2011).

Motivation Specific issues in both survey and tax record data can severely affect estimates of inequality E.g. Unit non-response in survey data: Under-reporting or non-reporting at the top of distribution in surveys (also at the bottom – Bollinger et al, 2014; Brewer et al, 2015). Results in under- estimation of inequality. Also, possible under-reporting of non-wage income (Susin, 2015). Item non-response in tax record data: Non-taxable income not observed in tax record data. Results in (possible) over-estimation of inequality.

Approaches Approach A Estimating Gini for 99% of population from survey data -> estimating Gini for top 1% from tax data -> estimating implied Gini by fitting Pareto distribution to the top incomes Atkinson, Piketty and Saez (2011), Alvaredo (2011), Lakner and Milanovic, 2013 Approach B Imputing tax record data for the top of distribution to survey data -> estimating Gini using combined data Bach et al. (2009), HBAI-SPI adjustment in the UK

Survey data EHBS 2011 7,000 households, 20,000 individuals Includes all different income sources: Wage, capital gains, pensions, social transfers, remittances, … Data are not top-coded, although the incomes at the top are lower than in tax record data Top 1% share in income: 3.1%

Tax record data Around 500,000 observations Data are not bottom-coded Tax data do not contain information about individual who receive income from non-taxable sources

Income Net post-tax annual income Includes wages, capital gains, pensions, social transfers and remittances Estimated share of taxable income recipients in tax record data is 27%. Estimated share of taxable income recipients in survey data is 30%. Difference possibly due to tax evasion (informal economy). Direct matching of individuals not possible.

Methodology Based on Atkinson (2007) and Alvaredo (2011) Individual observations, for comparative purpose. Incomes from tax record data for top 1% added to survey data. Inverted Pareto coefficient 1.6. Incomes from survey data for recipients of non-taxable income added to tax record data, using expansion factor. Non-taxable income received by individuals who have taxable income (and are therefore included in the tax record dataset) not imputed from survey data. May results in (still some) over-estimation of inequality.

Results (1) Tax record data Survey data Original Gini: 0.4478 Corrected Gini: 0.3348 Survey data Original Gini: 0.3765 Corrected Gini: 0.4045

Results (2) – Decomposition by income source Population share Gini tax Gini survey Taxable income 0.304 0.44783 0.40992 Employment allowances 0.001 0.06618 0.2468 Meal allowances 0.220 0.23176 0.23116 Remittances from abroad 0.029 0.87689 0.82264 Remittances within acountry 0.012 0.84574 0.79785 Pensions and social benefits 0.434 0.30953 0.3185 Total 1.000 0.3348 0.3765

Conclusions Is income inequality increasing? (Jenkins, 2015) Using tax record data only will result in over-estimation of inequality. Non- taxable income sources (pensions, social transfers, remittances) reduce inequality. Using survey data only will result in under-estimation of inequality. Top of the distribution has a significant effect on inequality (trends in particular), and should not be ignored. Still some more work to be done before ultimate conclusions.

Further steps Using Diaz-Bazan (2015) approach to determine threshold(s) b Determining threshold b for the bottom of distribution and using data from tax record to improve coverage at the bottom as well (Brewer et al, 2015) Using data for more years Testing alternative methods of data imputation Estimating more inequality measures

Income inequality in BiH: Combining survey and tax record data Dzelila Kramer and Nermin oruc Conference “inequalities in see”, eizg, Zagreb, 14 sep 2015