Data

  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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