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.

  • 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 2019
    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.
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  • The importance and challenges of measuring work hours Updated

    Measuring work hours correctly is important, but different surveys can tell different stories

    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.
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  • 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.
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  • 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 2019
    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.
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  • 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.
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  • 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.
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  • 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.
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  • Defining informality vs mitigating its negative effects

    More important than defining and measuring informality is focusing on reducing its detrimental consequences

    There are more informal workers than formal workers across the globe, and yet there remains confusion as to what makes workers or firms informal and how to measure the extent of it. Informal work and informal economic activities imply large efficiency and welfare losses, in terms of low productivity, low earnings, sub-standard working conditions, and lack of social insurance coverage. Rather than quibbling over definitions and measures of informality, it is crucial for policymakers to address these correlates of informality in order to mitigate the negative efficiency and welfare effects.
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  • 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.
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  • The labor market in Israel, 2000–2016

    Unlike most OECD countries, Israel experienced a major increase in both employment and participation rates over the last 15 years

    Tali LaromOsnat Lifshitz, January 2018
    Following a decline in employment and participation rates during the 1980s and 1990s, Israel managed to reverse these trends during the last 15 years. This was accompanied by a substantial decrease in unemployment. New labor force participants are mostly from the low end of the education distribution, and many are relatively old. They entered the labor force in response to cuts in welfare payments and increases in the mandatory retirement age. Net household income for all population groups has increased due to growth in labor income; however, inequality between households has increased.
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