Data and methods
Data, and the methods used to analyze them, are the foundation for evidence-based research. Articles in this subject area discuss the value of different types of data collection, and explain important statistical and econometric methods that provide ways to summarize and present information, and to identify and quantify correlation or causality.
Subject Editor
Royal Holloway, University of London, UK, and IZA, Germany
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Gross domestic product: Are other measures needed? Updated
GDP summarizes only one aspect of a country’s condition; other measures in addition to GDP would be valuable
Barbara M. Fraumeni, April 2022Gross domestic product (GDP) is the key indicator of the health of an economy and can be easily compared across countries. But it has limitations. GDP tells what is going on today, but does not inform about sustainability of growth. The majority of time is spent in home production, yet the value of this time is not included in GDP. GDP does not measure happiness, so residents can be dissatisfied even when GDP is rising. In addition, GDP does not consider environmental factors, reflect what individuals do outside paid employment, or even measure the current or future potential human capital of a country. Hence, complementary measures may help to show a more comprehensive picture of an economy.MoreLess -
Statistical profiling of unemployed jobseekers
The increasing availability of big data allows for the profiling of unemployed jobseekers via statistical models
Statistical models can help public employment services to identify factors associated with long-term unemployment and to identify at-risk groups. Such profiling models will likely become more prominent as increasing availability of big data combined with new machine learning techniques improve their predictive power. However, to achieve the best results, a continuous dialogue between data analysts, policymakers, and case workers is key. Indeed, when developing and implementing such tools, normative decisions are required. Profiling practices can misclassify many individuals, and they can reinforce but also prevent existing patterns of discrimination.MoreLess -
Correspondence testing studies Updated
What is there to learn about discrimination in hiring?
Dan-Olof Rooth, January 2021Anti-discrimination policies play an important role in public discussions. However, identifying discriminatory practices in the labor market is not an easy task. Correspondence testing provides a credible way to reveal discrimination in hiring and provide hard facts for policies, and it has provided evidence of discrimination in hiring across almost all continents except Africa. The method involves sending matched pairs of identical job applications to employers posting jobs—the only difference being a characteristic that signals membership to a group.MoreLess -
Recruiting intensity Updated
Recruiting intensity is critical for understanding fluctuations in the labor market
R. Jason Faberman, July 2020When hiring new workers, employers use a wide variety of different recruiting methods in addition to posting a vacancy announcement, such as adjusting education, experience, or technical requirements, or offering higher wages. The intensity with which employers make use of these alternative methods can vary widely depending on a firm’s performance and with the business cycle. In fact, persistently low recruiting intensity partly helps to explain the sluggish pace of job growth in the US economy following the Great Recession, and the historically subpar wage growth during the subsequent expansion.MoreLess -
Using natural resource shocks to study economic behavior
Natural resource shocks can help studying how low-skilled men respond to changes in labor market conditions
Dan A. Black, December 2019In the context of growing worldwide inequality, it is important to know what happens when the demand for low-skilled workers changes. Because natural resource shocks are global in nature, but have highly localized impacts on labor prospects in resource extraction areas, they offer a unique opportunity to evaluate low-skilled men's behavior when faced with extreme variations in local labor market conditions. This situation can be utilized to evaluate a broad range of outcomes, from education and income, to marital and fertility status, to voting behavior.MoreLess -
Transparency in empirical economic research
Open science can enhance research credibility, but only with the correct incentives
Cristina Blanco-PerezAbel Brodeur, November 2019The open science and research transparency movement aims to make the research process more visible and to strengthen the credibility of results. Examples of open research practices include open data, pre-registration, and replication. Open science proponents argue that making data and codes publicly available enables researchers to evaluate the truth of a claim and improve its credibility. Opponents often counter that replications are costly and that open science efforts are not always rewarded with publication of results.MoreLess -
Measuring income inequality
Summary measures of inequality differ from one another and give different pictures of the evolution of economic inequality over time
Ija Trapeznikova, July 2019Economists use various metrics for measuring income inequality. Here, the most commonly used measures—the Lorenz curve, the Gini coefficient, decile ratios, the Palma ratio, and the Theil index—are discussed in relation to their benefits and limitations. Equally important is the choice of what to measure: pre-tax and after-tax income, consumption, and wealth are useful indicators; and different sources of income such as wages, capital gains, taxes, and benefits can be examined. Understanding the dimensions of economic inequality is a key first step toward choosing the right policies to address it.MoreLess -
The importance and challenges of measuring work hours Updated
Measuring work hours correctly is important, but different surveys can tell different stories
Jay StewartHarley Frazis, July 2019Work hours are key components in estimating productivity growth and hourly wages as well as being a useful cyclical indicator in their own right, so measuring them correctly is important. The US Bureau of Labor Statistics (BLS) collects data on work hours in several surveys and publishes four widely used series that measure average weekly hours. The series tell different stories about average weekly hours and trends in those hours but qualitatively similar stories about the cyclical behavior of work hours. The research summarized here explains the differences in levels, but only some of the differences in trends.MoreLess -
Measuring individual risk preferences
Incentivized measures are considered to be the gold standard in measuring individuals’ risk preferences, but is that correct?
Catherine C. Eckel, June 2019Risk aversion is an important factor in many settings, including individual decisions about investment or occupational choice, and government choices about policies affecting environmental, industrial, or health risks. Risk preferences are measured using surveys or incentivized games with real consequences. Reviewing the different approaches to measuring individual risk aversion shows that the best approach will depend on the question being asked and the study's target population. In particular, economists’ gold standard of incentivized games may not be superior to surveys in all settings.MoreLess -
Intergenerational income persistence Updated
Measures of intergenerational persistence can be indicative of equality of opportunity, but the relationship is not clear-cut
Jo Blanden, January 2019A strong association between incomes across generations—with children from poor families likely to be poor as adults—is frequently considered an indicator of insufficient equality of opportunity. Studies of such “intergenerational persistence,” or lack of intergenerational mobility, measure the strength of the relationship between parents’ socio-economic status and that of their children as adults. However, the association between equality of opportunity and common measures of intergenerational persistence is not as clear-cut as is often assumed. To aid interpretation researchers often compare measures across time and space but must recognize that reliable measurement requires overcoming important data and methodological difficulties.MoreLess