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How Data Quality Affects our Understanding of the Earnings Distribution (Hardcover, 1st ed. 2022)
Loot Price: R1,602
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How Data Quality Affects our Understanding of the Earnings Distribution (Hardcover, 1st ed. 2022)
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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.
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