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Randomized control trials in an imperfect world

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

10.15185/izawol.110 110 Siddique, Z

by Zahra Siddique

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

10.15185/izawol.132 132 Donni, O

by Olivier Donni

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

10.15185/izawol.148 148 Winkelmann, R

by Rainer Winkelmann

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.

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

10.15185/izawol.191 191 Tumen, S

by Semih Tumen

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.

Evaluating the efficiency of public services

Differences in efficiency in public services can offer clues about good practice

10.15185/izawol.196 196 Johnes, G

by Geraint Johnes

Efficiency is an important consideration for those who manage public services. Costs vary with output and with a variety of other factors. In the case of higher education, for example, factors include quality, student demographics, the scale and scope of the higher education provider, and the size and character of the real estate. But even when taking all these factors into account, costs vary across providers because of differences in efficiency. Such differences offer clues about good practice that can lead to improvements in the system as a whole. The role of efficiency is illustrated by reference to higher education institutions in England.

Gravity models: A tool for migration analysis

Availability of bilateral data on migratory flows has renewed interest in using gravity models to identify migration determinants

10.15185/izawol.239 239 Ramos, R

by Raul Ramos

Gravity models have long been popular for analyzing economic phenomena related to the movement of goods and services, capital, or even people; however, data limitations regarding migration flows have hindered their use in this context. With access to improved bilateral (country to country) data, researchers can now use gravity models to better assess the impacts of migration policy, for instance, the effects of visa restriction policies on migration flows. The specification, estimation, and interpretation of gravity models are illustrated in different contexts and limitations of current practices are described to enable policymakers to make better informed decisions.

Using instrumental variables to establish causality

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

10.15185/izawol.250 250 Becker, S

by Sascha O. Becker

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.

Disentangling policy effects into causal channels

Splitting a policy intervention’s effect into its causal channels can improve the quality of policy analysis

10.15185/izawol.259 259 Huber, M

by Martin Huber

Policy evaluation aims at assessing the causal effect of an intervention (for example job-seeker counseling) on a specific outcome (for example employment). Frequently, the causal channels through which an effect materializes can be important when forming policy advice. For instance, it is essential to know whether counseling affects employment through training programs, sanctions, job search assistance, or other dimensions, in order to design an optimal counseling process. So-called “mediation analysis” is concerned with disentangling causal effects into various causal channels to assess their respective importance.

Estimating the return to schooling using the Mincer equation

The Mincer equation gives comparable estimates of the average monetary returns of one additional year of education

10.15185/izawol.278 278 Patrinos, H

by Harry Anthony Patrinos

The Mincer equation—arguably the most widely used in empirical work—can be used to explain a host of economic, and even non-economic, phenomena. One such application involves explaining (and estimating) employment earnings as a function of schooling and labor market experience. The Mincer equation provides estimates of the average monetary returns of one additional year of education. This information is important for policymakers who must decide on education spending, prioritization of schooling levels, and education financing programs such as student loans.

Can lab experiments help design personnel policies?

Employers can use laboratory experiments to structure payment policies and incentive schemes

10.15185/izawol.318 318 Villeval, M

by Marie Claire Villeval

Can a company attract a different type of employee by changing its compensation scheme? Is it sufficient to pay more to increase employees’ motivation? Should a firm provide evaluation feedback to employees based on their absolute or their relative performance? Laboratory experiments can help address these questions by identifying the causal impact of variations in personnel policy on employees’ productivity and mobility. Although they are collected in an artificial environment, the qualitative external validity of findings from the lab is now well recognized.

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

10.15185/izawol.320 320 Doucouliagos, C

by Chris Doucouliagos

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.

Maximum likelihood and economic modeling

Maximum likelihood is a general and flexible method to estimate the parameters of models in labor economics

10.15185/izawol.326 326 Lanot, G

by Gauthier Lanot

Most of the data available to economists is observational rather than the outcome of natural or quasi experiments. This complicates analysis because it is common for observationally distinct individuals to exhibit similar responses to a given environment and for observationally identical individuals to respond differently to similar incentives. In such situations, using maximum likelihood methods to fit an economic model can provide a general approach to describing the observed data, whatever its nature. The predictions obtained from a fitted model provide crucial information about the distributional outcomes of economic policies.

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

10.15185/izawol.336 336 Verbeek, M

by Marno Verbeek

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.