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Even with observational data, causality can be recovered with the help of instrumental variables estimation
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 (IV) 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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Even with observational data, causality can be
recovered with the help of instrumental variables estimation
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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Natural resource shocks can help studying how
low-skilled men respond to changes in labor market conditions
In the context of growing worldwide inequality,
it is important to know what happens when the demand for low-skilled workers
changes. Because natural resource shocks are global in nature, but have
highly localized impacts on labor prospects in resource extraction areas,
they offer a unique opportunity to evaluate low-skilled men's behavior when
faced with extreme variations in local labor market conditions. This
situation can be utilized to evaluate a broad range of outcomes, from
education and income, to marital and fertility status, to voting
behavior.
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Open science can enhance research credibility,
but only with the correct incentives
The open science and research transparency
movement aims to make the research process more visible and to strengthen
the credibility of results. Examples of open research practices include open
data, pre-registration, and replication. Open science proponents argue that
making data and codes publicly available enables researchers to evaluate the
truth of a claim and improve its credibility. Opponents often counter that
replications are costly and that open science efforts are not always
rewarded with publication of results.
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Should statistical criteria for measuring employment and
unemployment be re-examined?
Measuring employment and unemployment is essential for economic
policy. Internationally agreed measures (e.g. headcount employment and unemployment rates
based on standard definitions) enhance comparability across time and space, but changes in
real labor markets and policy agendas challenge these traditional conventions. Boundaries
between different labor market states are blurred, complicating identification. Individual
experiences in each state may vary considerably, highlighting the importance of how each
employed or unemployed person is weighted in statistical indices.
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Knowing the real cost of children is important for crafting better economic policy
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 methods can be used to develop
evidence-based policies when the evidence base lacks credibility
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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Incentivized measures are considered to be the
gold standard in measuring individuals’ risk preferences, but is that
correct?
Risk aversion is an important factor in many
settings, including individual decisions about investment or occupational
choice, and government choices about policies affecting environmental,
industrial, or health risks. Risk preferences are measured using surveys or
incentivized games with real consequences. Reviewing the different
approaches to measuring individual risk aversion shows that the best
approach will depend on the question being asked and the study's target
population. In particular, economists’ gold standard of incentivized games
may not be superior to surveys in all settings.
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