Programme Evaluation with Multiple Treatments

By Markus Frölich. Social Science Research Network, 2004

147 citations recorded by the source corpus.

This paper reviews the main identification and estimation strategies for microeconometric policy evaluation. Particular emphasis is laid on evaluating policies consisting of multiple programmes, which is of high relevance in practice. For example, active labour market policies may consist of different training programmes, employment programmes and wage subsidies. Similarly, sickness rehabilitation policies often offer different vocational as well as non-vocational rehabilitation measures. First, the main identification strategies (control-for-confounding-variables, difference-in-difference, instrumental-variable, and regression-discontinuity identification) are discussed in the multiple-programme setting. Thereafter, the different nonparametric matching and weighting estimators of the average treatment effects and their properties are examined.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/61d672b7244795579677e697a7b95099b3e4f0ed

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In Economics and Econometrics

1. Jeronimo Carballo
2. Christian Volpe Martincus
3. Feng Jiang
4. W. McComas

In Management Science and Operations Research

1. Christian Volpe Martincus
2. Feng Jiang
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In Statistics and Probability

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2. Feng Jiang
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Citations in the corpus

• Non-Cognitive Child Outcomes and Universal High Quality Child Care (2010, unknown): “Because we have excluded the group of parents choosing home care from our analysis, we need an extended version of monotonicity, see Froelich (2004) for intuition and Datta Gupta and Simonsen (2007) for a formal proof.”
• The Effects of Inquiry Teaching on Student Science Achievement and Attitudes: Evidence from Propensity Score Analysis of PISA Data (2015, background): “Combined suggestions appeared in the recent literature (Caliendo & Kopeinig, 2008; Frölich, 2004; Spreeuwenberg et al., 2010; Stuart & Rubin, 2008), the causal analysis consisted of following major steps: (1) Select the covariates.”
• The effectiveness of active labour market policies: A meta-analysis (2019, unknown): “Frölich (2004) provides an overview of the main identification strategies applied in microeconometric policy evaluation.”
• Propensity score matching and subclassification in observational studies with multi‐level treatments (2015, unknown): “Frolich (2004a) found that the weighting estimator was inferior to pairwise matching estimators in terms of root mean squared error.”
• Going green: does it depend on education, gender or income? (2014, unknown): “5 Imbens (2000) and Lechner (2001) extend the method of propensity score matching to multiple mutually exclusive programmes.”
• Do Fiscal Transfers Alleviate Business Tax Competition? Evidence from Germany (2010, unknown): “In small samples, this bias can be fairly large (see Frölich, 2004, Abadie and Imbens, 2006).”
• Beyond The Average Effects: The Distributional Impacts of Export Promotion Programs in Developing Countries (2010, background): “More precisely, potential outcomes of each firm are not affected by the allocation of other firms to programs (see Frölich, 2004). goods to the samemarkets, on the other hand.13 We find that spillover do not seem to have systematic patterns across quantiles.14 In sum, even though spillovers are not…”