• 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 2022
    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.
  • 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 2019
    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.
  • Transparency in empirical economic research

    Open science can enhance research credibility, but only with the correct incentives

    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.
  • 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 2019
    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.
  • Do workers work more when earnings are high?

    Studies of independent contractors suggest that workers’ effort may be more responsive to wage incentives than previously thought

    Tess M. Stafford, November 2018
    A fundamental question in economic policy is how labor supply responds to changes in remuneration. The responsiveness of labor supply determines the size of the employment impact and efficiency loss of progressive income taxation. It also affects predictions about the impacts of policies ranging from fiscal responses to business cycles to government transfer programs. The characteristics of jobs held by independent contractors provide an opportunity to overcome problems faced by earlier studies and help answer this fundamental question.
  • Big Data in economics

    New sources of data create challenges that may require new skills

    Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.
  • Measuring employment and unemployment

    Should statistical criteria for measuring employment and unemployment be re-examined?

    Measuring employment and unemployment is essential for economic policy. Internationally agreed measures (e.g. headcount employment and unemployment rates based on standard definitions) enhance comparability across time and space, but changes in real labor markets and policy agendas challenge these traditional conventions. Boundaries between different labor market states are blurred, complicating identification. Individual experiences in each state may vary considerably, highlighting the importance of how each employed or unemployed person is weighted in statistical indices.
  • The usefulness of experiments

    Are experiments the gold standard or just over-hyped?

    Jeffrey A. Smith, May 2018
    Non-experimental evaluations of programs compare individuals who choose to participate in a program to individuals who do not. Such comparisons run the risk of conflating non-random selection into the program with its causal effects. By randomly assigning individuals to participate in the program or not, experimental evaluations remove the potential for non-random selection to bias comparisons of participants and non-participants. In so doing, they provide compelling causal evidence of program effects. At the same time, experiments are not a panacea, and require careful design and interpretation.
  • Relative deprivation in the labor market

    The choice of reference group crucially determines subjective deprivation and thus affects labor market behavior

    Paolo Verme, June 2017
    Why do different population groups (e.g. rural vs. urban, youth vs. elderly and men vs. women) experience the same objective labor status differently? One hypothesis is that people are more concerned with relative deprivation than objective deprivation and they value their own status relative to the status of their peers—the reference group. One way to test this hypothesis in the labor market is to measure individual differences in labor status while controlling for characteristics that define population groups. This measure is called “relative labor deprivation” and can help policymakers to better understand how labor claims are generated.
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