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.

  • 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.
  • Replication in labor economics

    Is there a reproducibility crisis in labor economics?

    W. Robert Reed, December 2017
    There is growing concern that much of the empirical research in labor economics and other applied areas may not be reproducible. Correspondingly, recent years have seen an increase in replication studies published in economics journals. Despite this increase, there are many unresolved issues about how replications should be done, and how to interpret their results. Replications have demonstrated a potential for clarifying the reliability and robustness of previous research. Much can be done to encourage more replication research, and to exploit the scientific value of existing replication studies.
  • What is the role for molecular genetic data in public policy?

    There is potential value from incorporating genetic data in the design of effective public policy, but also some risks

    Both the availability and sheer volume of data sets containing individual molecular genetic information are growing at a rapid pace. Many argue that these data can facilitate the identification of genes underlying important socio-economic outcomes, such as educational attainment and fertility. Opponents often counter that the benefits are as yet unclear, and that the threat to individual privacy is a serious one. The initial exploration presented herein suggests that significant benefits to the understanding of socio-economic outcomes and the design of both social and education policy may be gained by effectively and safely utilizing genetic data.
  • 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.
  • Gross domestic product: Are other measures needed?

    GDP summarizes only one aspect of a country’s condition; other measures in addition to GDP would be valuable

    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. It does not measure happiness, so residents can be dissatisfied even when GDP is rising. GDP does not consider environmental factors or reflect what individuals do outside paid employment. It might increase in times of military conflicts and after natural disasters or terrorist acts, as the loss of property is not counted. Hence, complementary measures may help to show a more comprehensive picture of an economy.
  • The need for and use of panel data

    Panel data provide an efficient and cost-effective means to measure changing behaviors and attitudes over time

    Hans-Jürgen Andreß, April 2017
    Stability and change are essential elements of social reality and economic progress. Cross-sectional surveys are a means of providing information on specific issues at a particular point in time, though without providing any information about the prevailing stability. Limited information on change can be obtained by retrospective questioning, but this is often impaired by “recall bias.” However, valid information on change is essential for assessing whether phenomena such as poverty are permanent or only temporary. Panel data analyses can address these problems as well as provide an essential tool for effective policy design.
  • Measuring flows of international migration

    Consistent measures of migration are needed to understand patterns and impacts on labor market outcomes

    James Raymer, April 2017
    International migration alters the socio-economic conditions of the individuals and families migrating as well as the host and sending countries. The data to study and to track these movements, however, are largely inadequate or missing. Understanding the reasons for these data limitations and recently developed methods for overcoming them is crucial for implementing effective policies. Improving the available information on global migration patterns will result in numerous and wide-ranging benefits, including improved population estimations and providing a clearer picture of why certain migrants choose certain destinations.
  • Identifying and measuring economic discrimination

    Using decomposition methods helps measure both the amount and source of economic discrimination between groups

    Sergio Pinheiro Firpo, March 2017
    Differences in wages between men and women, white and black workers, or any two distinct groups are a controversial feature of the labor market, raising concern about discrimination by employers. Decomposition methods shed light on those differences by separating them into: (i) composition effects, which are explained by differences in the distribution of observable variables, e.g. education level; and (ii) structural effects, which are explained by differences in the returns to observable and unobservable variables. Often, a significant structural effect, such as different returns to education, can be indicative of discrimination.
  • Using linear regression to establish empirical relationships

    Linear regression is a powerful tool for estimating the relationship between one variable and a set of other variables

    Marno Verbeek, February 2017
    Linear regression is a powerful tool for investigating the relationships between multiple variables by relating one variable to a set of variables. It can identify the effect of one variable while adjusting for other observable differences. For example, it can analyze how wages relate to gender, after controlling for differences in background characteristics such as education and experience. A linear regression model is typically estimated by ordinary least squares, which minimizes the differences between the observed sample values and the fitted values from the model. Multiple tools are available to evaluate the model.
  • Measuring entrepreneurship: Type, motivation, and growth

    Effective measurement can help policymakers harness a wide variety of gains from entrepreneurship

    Sameeksha Desai, January 2017
    Policymakers rely on entrepreneurs to create jobs, provide incomes, innovate, pay taxes to support public revenues, create competition in industries, and much more. Due to its highly heterogeneous nature, the choice of entrepreneurship measures is critically important, impacting the diagnosis, analysis, projection, and understanding of potential and existing policy. Some key aspects to measure include the how (self-employment, new firm formation), why (necessity, opportunity), and what (growth). As such, gaining better insight into the challenges of measuring entrepreneurship is a necessary and productive investment for policymakers.
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