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School inputs, household substitution, and test scores
 
Author:Das, Jishnu; Dercon, Stefan; Habyarimana, James; Krishnan, Pramila; Muralidharan, Karthik; Sundararaman, Venkatesh; Collection Title:Policy Research working paper ; no. WPS 5629Impact Evaluation series ; no. IE 49Paper is funded by the Knowledge for Change Program (KCP)
Country:World; Date Stored:2011/04/08
Document Date:2011/04/01Document Type:Policy Research Working Paper
SubTopics:Access to Finance; Teaching and Learning; Tertiary Education; Education For All; DisabilityLanguage:English
Major Sector:Education; Public Administration, Law, and JusticeRel. Proj ID:1W-Qsds Service Delivery -- -- P070007;
Region:The World RegionReport Number:WPS5629
Sub Sectors:Primary education; General public administration sectorTF No/Name:TF051762-KCP-USER; TF050450-NTF; TF051764-KCP-INCENTIVES,; TF041078-WORLD- HEALTH AND SOCIAL PROTECTION POLICIES; TF040606-WORLD:; TF040917-RUSSIA:; TF052376-KCP-EVALUATING; TF050207-BNPP-CAPACITY/BUILD:DECENTRALIZATION-GOVERNANCE; TF026542-PHRD-PHILIPPINES:
Volume No:1 of 1  

Summary: Empirical studies of the relationship between school inputs and test scores typically do not account for the fact that households will respond to changes in school inputs. This paper presents a dynamic household optimization model relating test scores to school and household inputs, and tests its predictions in two very different low-income country settings -- Zambia and India. The authors measure household spending changes and student test score gains in response to unanticipated as well as anticipated changes in school funding. Consistent with the optimization model, they find in both settings that households offset anticipated grants more than unanticipated grants. They also find that unanticipated school grants lead to significant improvements in student test scores but anticipated grants have no impact on test scores. The results suggest that naïve estimates of public education spending on learning outcomes that do not account for optimal household responses are likely to be considerably biased if used to estimate parameters of an education production function.

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