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 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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  • 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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  • Using instrumental variables to establish causality

    Even with observational data, causality can be recovered with the help of instrumental variables estimation

    Sascha O. Becker, April 2016
    Randomized control trials are often considered the gold standard to establish causality. However, in many policy-relevant situations, these trials are not possible. Instrumental variables affect the outcome only via a specific treatment; as such, they allow for the estimation of a causal effect. However, finding valid instruments is difficult. Moreover, instrumental variables estimates recover a causal effect only for a specific part of the population. While those limitations are important, the objective of establishing causality remains; and instrumental variables are an important econometric tool to achieve this objective.
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  • Poverty persistence and poverty dynamics

    Snapshots of who is poor in one period provide an incomplete picture of poverty

    Martin Biewen, November 2014
    A considerable part of the poverty that is measured in a single period is transitory rather than persistent. In most countries, only a portion of people who are currently poor are persistently poor. People who are persistently poor or who cycle into and out of poverty should be the main focus of anti-poverty policies. Understanding the characteristics of the persistently poor, and the circumstances and mechanisms associated with entry into and exit from poverty, can help to inform governments about options to reduce persistent poverty. Differences in poverty persistence across countries can shed additional light on possible sources of poverty persistence.
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  • Matching as a regression estimator

    Matching avoids making assumptions about the functional form of the regression equation, making analysis more reliable

    Dan A. Black, September 2015
    “Matching” is a statistical technique used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study. Matching provides an alternative to older estimation methods, such as ordinary least squares (OLS), which involves strong assumptions that are usually without much justification from economic theory. While the use of simple OLS models may have been appropriate in the early days of computing during the 1970s and 1980s, the remarkable increase in computing power since then has made other methods, in particular matching, very easy to implement.
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  • Intergenerational income persistence

    Measures of intergenerational persistence can be indicative of equality of opportunity, but the relationship is not clear cut

    Jo Blanden, August 2015
    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, are concerned with measuring the strength of the relationship between parents’ socio-economic status and that of their children as adults. However, reliable measurement requires overcoming important data and methodological difficulties. Moreover, the association between equality of opportunity and common measures of intergenerational persistence is not as clear-cut as is often assumed.
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  • What makes a good job? Job quality and job satisfaction

    Job satisfaction is important to well-being, but intervention may be needed only if markets are impeded from improving job quality

    Andrew E. Clark, December 2015
    Many measures of job satisfaction have been trending downward. Because jobs are a key part of most people’s lives, knowing what makes a good job (job quality) is vital to knowing how well society is doing. Integral to worker well-being, job quality also affects the labor market through related decisions on whether to work, whether to quit, and how much effort to put into a job. Empirical work on what constitutes a good job finds that workers value more than wages; they also value job security and interest in their work. Policy to affect job quality requires information on the cost of the different aspects of job quality and how much workers value them.
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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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  • Measuring the cost of children

    Knowing the real cost of children is important for crafting better
 economic policy

    Olivier Donni, March 2015
    The cost of children is a critical parameter used in determining many economic policies. For instance, correctly setting the tax deduction for families with children requires assessing the true household cost of children. Evaluating child poverty at the individual level requires making a clear distinction between the share of family resources received by children and that received by parents. The standard ad hoc measures (equivalence scales) used in official publications to measure the cost of children are arbitrary and are not informed by any economic theory. However, economists have developed methods that are grounded in economic theory and can replace ad hoc measures.
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  • Meta-regression analysis: Producing credible estimates from diverse evidence

    Meta-regression methods can be used to develop evidence-based policies when the evidence base lacks credibility

    Chris Doucouliagos, November 2016
    Good policy requires reliable scientific knowledge, but there are many obstacles. Most econometric estimates lack adequate statistical power; some estimates cannot be replicated; publication selection bias (the selective reporting of results) is common; and there is wide variation in the evidence base on most policy issues. Meta-regression analysis offers a way to increase statistical power, correct the evidence base for a range of biases, and make sense of the unceasing flow of contradictory econometric estimates. It enables policymakers to develop evidence-based policies even when the initial evidence base lacks credibility.
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