IZA World of Labor

Data

  • 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.
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  • 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.
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  • 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 (self-employment, new firm formation), (necessity, opportunity), and (growth). As such, gaining better insight into the challenges of measuring entrepreneurship is a necessary and productive investment for policymakers.
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  • Why do we need longitudinal survey data?

    Knowing people’s history helps in understanding their present state and where they are heading

    Heather Joshi, November 2016
    Information from longitudinal surveys transforms snapshots of a given moment into something with a time dimension. It illuminates patterns of events within an individual’s life and records mobility and immobility between older and younger generations. It can track the different pathways of men and women and people of diverse socio-economic background through the life course. It can join up data on aspects of a person’s life, health, education, family, and employment and show how these domains affect one another. It is ideal for bridging the different silos of policies that affect people’s lives.
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  • Performance measures and worker productivity

    Choosing the right performance measures can inform and improve decision-making in policy and management

    Jan Sauermann, May 2016
    Measuring workers’ productivity is important for public policy and private-sector decision-making. Due to a lack of reliable methods to determine workers’ productivity, firms often use specific performance measures, such as how different incentives affect employees’ behavior. The public sector also uses these measures to monitor and evaluate personnel, such as teachers. To select the right performance measures, and as a result design better employment contracts and improve productivity, policymakers and managers need to understand the advantages and disadvantages of the available metrics.
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  • Can “happiness data” help evaluate economic policies?

    “Happiness data” may help assess the welfare effects of a new labor market policy, like a change in benefit generosity

    Robert MacCulloch, January 2016
    Imagine a government confronted with a controversial policy question, like whether it should cut the level of unemployment benefits. Will social welfare rise as a result? Will some groups be winners and other groups be losers? Will the welfare gap between the employed and unemployed increase? “Happiness data” offer a new way to make these kinds of evaluations. These data allow us to track the well-being of the whole population, and also sub-groups like the employed and unemployed people, and correlate the results with relevant policy changes.
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  • The challenges of linking survey and administrative data

    Combining survey and administrative data is growing in popularity, even though data access is still highly restricted

    Steffen Künn, December 2015
    Using administrative records data and survey data to enhance each other offers huge potential for scientific and policy-related research. Two recent changes have expanded the potential for creating such linked data: the improved availability of data sources and progress in data-matching technology. These developments are reflected, among other ways, in the growing number of academic papers in labor economics that use linked survey and administrative data. While the number of studies using linked data is still small, the trend is clearly upward. Slowing the growth, however, are concerns about data security and privacy, which impede data access.
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  • Google search activity data and breaking trends

    Google search activity data are an unconventional survey full of unbiased, revealed answers in need of the right question

    Nikolaos Askitas, November 2015
    Using Google search activity data can help detect, in real time and at high frequency, a wide spectrum of breaking socio-economic trends around the world. This wealth of data is the result of an ongoing and ever more pervasive digitization of information. Search activity data stand in contrast to more traditional economic measurement approaches, which are still tailored to an earlier era of scarce computing power. Search activity data can be used for more timely, informed, and effective policy making for the benefit of society, particularly in times of crisis. Indeed, having such data shifts the relation between theory and the data to support it.
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  • The use of natural experiments in migration research

    Data on rapid, unexpected refugee flows can credibly identify the impact of migration on native workers’ labor market outcomes

    Semih Tumen, October 2015
    Estimating the causal effect of immigration on the labor market outcomes of native workers has been a major concern in the literature. Because immigrants decide whether and where to migrate, immigrant populations generally consist of individuals with characteristics that differ from those of a randomly selected sample. One solution is to focus on events such as civil wars and natural catastrophes that generate rapid and unexpected flows of refugees into a country unrelated to their personal characteristics, location, and employment preferences. These “natural experiments” yield estimates that find small negative effects on native workers’ employment but not on wages.
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  • Counting on count data models

    Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data

    Rainer Winkelmann, May 2015
    Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples.
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