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Testing economic hypotheses with state-level data : a comment on Donohue and Levitt (2001) / Christopher L. Foote and Christopher F. Goetz

State-level data are often used in the empirical research of both macroeconomists and microeconomists. Using data that follows states over time allows economists to hold constant a host of potentially confounding factors that might contaminate an assignment of cause and effect. A good example is a f... Full description

PPN (Catalogue-ID): 505088797
Personen: Foote, Christopher L.
Goetz, Christopher F.
Format: eBook eBook
Language: English
Published: 2005
Series: Working paper series / Federal Reserve Bank of Boston (05,15)
Physical Description: Online-Ressource, 19 S., Text, graph. Darst.
Technische Details: Systemvoraussetzungen: Acrobat reader.

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520 |a State-level data are often used in the empirical research of both macroeconomists and microeconomists. Using data that follows states over time allows economists to hold constant a host of potentially confounding factors that might contaminate an assignment of cause and effect. A good example is a fascinating paper by Donohue and Levitt (2001, henceforth DL), which purports to show that hypothetical individuals resulting from aborted fetuses, had they been born and developed into youths, would have been more likely to commit crimes than youths resulting from fetuses carried to term. We revisit that paper, showing that the actual implementation of DL's statistical test in their paper differed from what was described. (Specifically, controls for state-year effects were left out of their regression model. ) We show that when DL's key test is run as described and augmented with state-level population data, evidence for higher per capita criminal propensities among the youths who would have developed, had they not been aborted as fetuses, vanishes. Two lessons for empirical researchers are, first, that controls may impact results in ways that are hard to predict, and second, that these controls are probably not powerful enough to compensate for the omission of a key variable in the regression model. (Data and programs to support this comment are available on the web site of the Federal Reserve Bank of Boston.) 
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