Explore the vast range of titles available.

Objective This article documents wide‐ranging revisions to the Standardized World Income Inequality Database (SWIID), which seeks to maximize the comparability of income inequality estimates for the broadest possible coverage of countries and years. Methods Two k‐fold cross‐validations, by observation and by country, are used to evaluate the SWIID's success in predicting the Luxembourg Income Study (LIS), recognized in the field as setting the standard for comparability. Results The cross‐validations indicate that the new SWIID's estimates and their uncertainty are even more accurate than previous versions, extending its advantage in comparability over alternate income inequality data sets. Conclusion Given its superior coverage and comparability, the SWIID remains the optimum source of data for broadly cross‐national research on income inequality.

Data collection in consumer research has progressively moved away from traditional samples (e.g., university undergraduates) and toward Internet samples. In the last complete volume of the Journal of Consumer Research (June 2015–April 2016), 43% of behavioral studies were conducted on the crowdsourcing website Amazon Mechanical Turk (MTurk). The option to crowdsource empirical investigations has great efficiency benefits for both individual researchers and the field, but it also poses new challenges and questions for how research should be designed, conducted, analyzed, and evaluated. We assess the evidence on the reliability of crowdsourced populations and the conditions under which crowdsourcing is a valid strategy for data collection. Based on this evidence, we propose specific guidelines for researchers to conduct high-quality research via crowdsourcing. We hope this tutorial will strengthen the community’s scrutiny on data collection practices and move the field toward better and more valid crowdsourcing of consumer research.

Human feelings measured in integers (my happiness is an 8 out of 10, my pain 2 out of 6) have no objective scientific basis. They are “made-up” numbers on a scale that does not exist. Yet such data are extensively collected—despite criticism from, especially, economists— by governments and international organizations. We examine this paradox. We draw upon longitudinal information on the feelings and decisions of tens of thousands of randomly sampled citizens followed through time over four decades in three countries (n = 700,000 approximately). First, we show that a single feelings integer has greater predictive power than does a combined set of economic and social variables. Second, there is a clear inverse relationship between feelings integers and subsequent get-me-out-of-here actions (in the domain of neighborhoods, partners, jobs, and hospital visits). Third, this feelings-to-actions relationship takes a generic form, is consistently replicable, and is fairly close to linear in structure. Therefore, it seems that human beings can successfully operationalize an integer scale for feelings even though there is no true scale. How individuals are able to achieve this is not currently known. The implied scientific puzzle—an inherently cross-disciplinary one—demands attention.

Objective. Since 2008, the Standardized World Income Inequality Database (SWIID) has provided income inequality data that seek to maximize comparability while providing the broadest possible coverage of countries and years. This article describes the current SWIID’s construction, highlighting differences from its original version, and reevaluates the SWIID’s utility to cross-national income inequality research in light of recently available alternatives. Methods. Coverage of inequality data sets is assessed across country-years; comparability is evaluated in terms of success in predicting the Luxembourg Income Study (LIS), recognized in the field as the gold standard in comparability, before those data are released. Results. The SWIID offers coverage double that of the next largest income inequality data set, and its record of comparability is three to eight times better than those of alternate data sets. Conclusions. As its coverage and comparability far exceed those of the alternatives, the SWIID remains better suited for broadly cross-national research on income inequality than other available sources.

The recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. We evaluate different automated methods for record linkage, performing a series of comparisons across methods and against hand linking. We have three main findings that lead us to conclude that automated methods perform well. First, a number of automated methods generate very low (less than 5 percent) false positive rates. The automated methods trace out a frontier illustrating the trade-off between the false positive rate and the (true) match rate. Relative to more conservative automated algorithms, humans tend to link more observations but at a cost of higher rates of false positives. Second, when human linkers and algorithms use the same linking variables, there is relatively little disagreement between them. Third, across a number of plausible analyses, coefficient estimates and parameters of interest are very similar when using linked samples based on each of the different automated methods. We provide code and Stata commands to implement the various automated methods.

The increasing availability of data derived from linked consumer financial accounts has the potential to dramatically expand the potential for research. Examining the most comprehensive existing set of linked-account data, consisting of transaction and balance sheet data for millions of Americans, I demonstrate the power and versatility of such sources. I discuss advantages and concerns arising from this type of data andmatch a range of distributionalmoments to external sources. As one application, I test consumption elasticities across households with varying levels, and types, of debt. I find that heterogeneity in consumption elasticity can be explained entirely by credit and liquidity.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has had unprecedented impacts on health systems, public health, societies and individuals globally (The Lancet Public Health, 2020). In response to outbreaks, physical distancing measures, national lockdowns and travel restrictions to control the spread of COVID-19 have been implemented in many countries (Chu et al., 2020). In response to these measures, many public health researchers are choosing to switch from standard face-to-face data collection methods to remote data collection in support of continued research. Remote data collection is defined here as the collection of data via the phone, online or other virtual platforms, with study participants and researchers physically distanced. The aim of this commentary is to summarize methods, key challenges and opportunities of remote qualitative and quantitative data collection for public health research in low- and middle-income countries (LMIC). The framework we use to structure our discussion is the research process, starting from sampling and culminating in analysis. Within this, we draw out the steps in research most likely to be affected by the pandemic and attendant need to cease face-to-face interactions with research participants. We identify which steps are most affected and what are potential alternatives based on interviews and discussions, held between May and June 2020, with ∼30 researchers from the London School of Hygiene and Tropical Medicine and collaborating partners, representing a range of disciplines. Interviewees were selected or volunteered themselves, based on their experience and expertise in designing and conducting remote data collection. These consultations identified the following as the steps in research most likely to require attention: sampling and recruitment; informed consent; response rates; rapport with participants; privacy and safety; and analysis. Whilst the focus of this commentary is on LMIC, many of the lessons learnt are relevant to remote data collection in high-income countries.

When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does.

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form “how many of your links have trait k ?” Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.

This paper reviews the literature in historical record linkage in the United States and examines the performance of widely used record-linking algorithms and common variations in their assumptions. We use two high-quality, hand-linked data sets and one synthetic ground truth to examine the direct effects of linking algorithms on data quality. We find that (i) no algorithm (including hand linking) consistently produces representative samples; (ii) 15 to 37 percent of links chosen by widely used algorithms are classified as errors by trained human reviewers; and (iii) false links are systematically related to baseline sample characteristics, showing that some algorithms may introduce systematic measurement error into analyses. A case study shows that the combined effects of (i)–(iii) attenuate estimates of the intergenerational income elasticity by up to 29 percent, and common variations in algorithm assumptions result in greater attenuation. As current practice moves to automate linking and increase link rates, these results highlight the important potential consequences of linking errors on inferences with linked data. We conclude with constructive suggestions for reducing linking errors and directions for future research.