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 importance of measuring dispersion in firm-level outcomes

    Ignoring the large variation in firm-level outcomes can create misunderstandings about the consequences of many policies

    Chad Syverson, May 2014
    Recent research has revealed enormous variation in performance and growth among firms, which both drives and is driven by large reallocations of inputs and outputs across firms (churning) within industries and markets. These differences in firm-level outcomes and the associated turnover of firms affect many economic policies (both labor- and non-labor-oriented), on both a microeconomic and a macroeconomic scale, and are affected by them. Properly evaluating these policies requires familiarity with the sources and consequences of firm-level variation and within-industry reallocation.
  • 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.
  • Randomized control trials in an imperfect world

    How can we assess the policy effectiveness of randomized control trials when people don’t comply?

    Zahra Siddique, December 2014
    Randomized control trials (RCTs) have become increasingly important as an evidence-based method to evaluate interventions such as government programs and policy initiatives. Frequently, however, RCTs are characterized by “imperfect compliance,” in that not all the subjects who are randomly assigned to take a treatment choose to do so. This could result in a failure to identify the treatment effect, or the impact of the treatment on the population. However, useful information on treatment effectiveness can still be recovered by estimating “bounds,” or a range of values in which treatment effectiveness can lie.
  • 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.
  • 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.
  • 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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