What Is Women’s Work? Reflections from Four North Indian Urban Clusters

The actual extent of the female employment challenge in India is much debated. Data on female labour force participation (FLFP) in India is hampered by shortcomings in data validity and data accuracy. The objective of this article is to explore challenges to data accuracy through two potential sources of error: measurement error and reporting error. Drawing on a unique source of granular survey data from households in four north Indian urban clusters, we demonstrate that the precise nature of the survey employed has meaningful impacts on the reporting of FLFP. Furthermore, the gender composition of respondents also seems to matter although, after controlling for gender, self-reporting is indistinguishable from proxy reporting.

The Misreporting Trade-Off Between List Experiments and Direct Questions in Practice: Partition Validation Evidence from Two Countries

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase nonstrategic misreporting. We provide the first empirical assessment of this trade-off between strategic and nonstrategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents’ true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of nonstrategic reporting errors. For both list experiments, partition validation reveals nonstrategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.

How Prevalent Is Downward Rigidity in Nominal Wages? International Evidence from Payroll Records and Pay Slips

For more than 80 years, many macroeconomic analyses have been premised on the assumption that workers’ nominal wage rates cannot be cut. Contrary evidence from household surveys reasonably has been discounted on the grounds that the measurement of frequent wage cuts might be an artifact of reporting error. This article summarizes a more recent wave of studies based on more accurate wage data from payroll records and pay slips. By and large, these studies indicate that, except in extreme circumstances (when nominal wage cuts are either legally prohibited or rendered beside the point by very high inflation), nominal wage cuts from one year to the next appear quite common, typically affecting 15–25 percent of job stayers in periods of low inflation.

Asking Survey Questions About Criminal Justice Involvement

Criminal justice involvement is a multifaceted construct encompassing various forms of contact with the criminal justice system. It is a sensitive topic to ask about in surveys and also a sensitive topic for respondents to answer. This article provides guidance for writing survey questions on criminal justice involvement, starting with a review of potential causes for reporting error and nonresponse error associated with survey questions on criminal justice involvement. Questions about criminal justice involvement are subject to errors that are common to any survey (eg, misunderstanding questions, recall bias, telescoping). Reponses to these questions are also subject to underreporting because of social desirability concerns. We also address strategies to reduce error for questions pertaining to criminal justice involvement (eg, self-administered data collection, wording of forgiving questions, indirect methods). We then discuss common design decisions associated with writing survey questions on criminal justice involvement (eg, type and frequency of criminal justice involvement, reference period,) and provide examples of questions from current surveys.