Estimation of Treatment Effects Without an Exclusion Restriction: with an Application to the Analysis of the School Breakfast Program / Daniel L. Millimet, Rusty Tchernis.

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Millimet, Daniel L

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w15539.Publication details: Cambridge, Mass. National Bureau of Economic Research 2009.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: While the rise in childhood obesity is clear, the policy ramifications are not. School nutrition programs such as the School Breakfast Program (SBP) have come under much scrutiny. However, the lack of experimental evidence, combined with non-random selection into these programs, makes identification of the causal effects of such programs difficult. In the case of the SBP, this difficulty is exacerbated by the apparent lack of exclusion restrictions. Here, we compare via Monte Carlo study several existing estimators that do not rely on exclusion restrictions for identification. In addition, we propose two new estimation strategies. Simulations illustrate the usefulness of our new estimators, as well as provide applied researchers several practical guidelines when analyzing the causal effects of binary treatments. More importantly, we find consistent evidence of a beneficial causal effect of SBP participation on childhood obesity when applying estimators designed to circumvent selection on unobservables.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w15539 (Browse shelf(Opens below))	Not For Loan

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November 2009.

While the rise in childhood obesity is clear, the policy ramifications are not. School nutrition programs such as the School Breakfast Program (SBP) have come under much scrutiny. However, the lack of experimental evidence, combined with non-random selection into these programs, makes identification of the causal effects of such programs difficult. In the case of the SBP, this difficulty is exacerbated by the apparent lack of exclusion restrictions. Here, we compare via Monte Carlo study several existing estimators that do not rely on exclusion restrictions for identification. In addition, we propose two new estimation strategies. Simulations illustrate the usefulness of our new estimators, as well as provide applied researchers several practical guidelines when analyzing the causal effects of binary treatments. More importantly, we find consistent evidence of a beneficial causal effect of SBP participation on childhood obesity when applying estimators designed to circumvent selection on unobservables.