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Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-indifferences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.

Openness for trade can have positive welfare effects in terms of higher growth. But increased openness may also increase uncertainty through a higher volatility of employment. We use regional data from Germany to test whether openness for trade has an impact on volatility. We find a downward trend in the unconditional volatility of employment, which has been interrupted by the re-unification period. Patterns are similar to those for output volatility. The conditional volatility of employment, measuring idiosyncratic developments across states, in contrast, has remained fairly unchanged. In contrast to evidence for the US, we do not find evidence for a significant link between employment volatility and trade openness.

Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods in order to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and underlying hypothesis behind leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-in-differences, and panel-data techniques. Illustrative applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for the disadvantaged, and compulsory-school and tracking reforms. (DIPF/Orig.).

Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-in-differences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.