This open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity model for the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey.

This open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity model for the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey.

This paper uses administrative data on schooling and earnings from Texas to estimate the effect of college quality on the distribution of earnings. We proxy college quality using the college sector from which students graduate and focus on identifying how graduating from UT-Austin, Texas A\&M or a community college affects the distribution of earnings relative to graduating from a non-flagship university in Texas. Our methodological approach uses the rich set of observable student academic ability and background characteristics in the data to adjust the earnings distributions across college sectors for the fact that college sector quality is correlated with factors that also affect earnings. Although our mean earnings estimates are similar to previous work in this area, we find evidence of substantial heterogeneity in the returns to college quality. At UT-Austin, the returns increase across the earnings distribution, while at Texas A\&M they tend to decline with one's place in the distribution. For community college graduates, the returns relative to non-flagship four-year graduates are negative across most of the distribution of earnings, but they approach zero and become positive for higher earners. Our data also allow us to estimate effects separately by race and ethnicity, and we find that historically under-represented minorities experience the highest returns in the upper tails of the earnings distribution, particularly among UT-Austin and community college graduates. While we focus on graduates, we also show our estimates are robust to examining college attendees as well as to many other changes in the sample and to the estimation strategy. Overall, these estimates provide the first direct evidence of the extent of heterogeneity in the effect of college quality on subsequent earnings, and our estimates point to the need to consider such heterogeneity in human capital models that incorporate college quality.

This paper uses administrative data on schooling and earnings from Texas to estimate the effect of college quality on the distribution of earnings. We proxy college quality using the college sector from which students graduate and focus on identifying how graduating from UT-Austin, Texas A &M or a community college affects the distribution of earnings relative to graduating from a non-flagship university in Texas. Our methodological approach uses the rich set of observable student academic ability and background characteristics in the data to adjust the earnings distributions across college sectors for the fact that college sector quality is correlated with factors that also affect earnings. Although our mean earnings estimates are similar to previous work in this area, we find evidence of substantial heterogeneity in the returns to college quality. At UT-Austin, the returns increase across the earnings distribution, while at Texas A &M they tend to decline with one's place in the distribution. For community college graduates, the returns relative to non-flagship four-year graduates are negative across most of the distribution of earnings, but they approach zero and become positive for higher earners. Our data also allow us to estimate effects separately by race and ethnicity, and we find that historically under-represented minorities experience the highest returns in the upper tails of the earnings distribution, particularly among UT-Austin and community college graduates. While we focus on graduates, we also show our estimates are robust to examining college attendees as well as to many other changes in the sample and to the estimation strategy. Overall, these estimates provide the first direct evidence of the extent of heterogeneity in the effect of college quality on subsequent earnings, and our estimates point to the need to consider such heterogeneity in human capital models that incorporate college quality.

This book provides an understanding of the origins of earnings distributions.

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What new theories, evidence, explanations, and policies have shaped our studies of income distribution in the 21st century? Editors Tony Atkinson and Francois Bourguignon assemble the expertise of leading authorities in this survey of substantive issues. In two volumes they address subjects that were not covered in Volume 1 (2000), such as education, health and experimental economics; and subjects that were covered but where there have been substantial new developments, such as the historical study of income inequality and globalization. Some chapters discuss future growth areas, such as inheritance, the links between inequality and macro-economics and finance, and the distributional implications of climate change. They also update empirical advances and major changes in the policy environment. - The volumes define and organize key areas of income distribution studies - Contributors focus on identifying newly developing questions and opportunities for future research - The authoritative articles emphasize the ways that income mobility and inequality studies have recently gained greater political significance