Releasing Earnings Distributions using Differential Privacy

The U.S. Census Bureau recently released data on earnings percentiles of graduates from post-secondary institutions. This paper describes and evaluates the disclosure avoidance system developed for these statistics. We propose a differentially private algorithm for releasing these data based on standard differentially private building blocks, by constructing a histogram of earnings and the application of the Laplace mechanism to recover a differentially-private CDF of earnings. We demonstrate that our algorithm can release earnings distributions with low error, and our algorithm out-performs prior work based on the concept of smooth sensitivity from Nissim et al. (2007).

Within-Country Analysis of the Growth–Employment–Poverty Nexus

This chapter analyses the within-country growth–employment–poverty nexus. First, it calculates labour market indicators’ elasticities with respect to gross domestic product per capita growth. It finds that in the Latin America region and in most countries, labour market indicators improved with percentage increases in GDP per capita. Second, it estimates poverty elasticities with respect to employment and earnings indicators and finds that in the region and in most of the countries, poverty measures were related in the welfare-improving direction with percentage changes in some employment and earnings indicators. Finally, it analyses the patterns of earnings changes across deciles of the earnings distributions in each country and finds that 70 per cent of the country-decile cells exhibited positive earnings changes.