Data and methods

Gross 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.

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.

Anti-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.

When 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.

In 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.

The 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.

Economists 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.

Work 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.

Risk 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.

A 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.