School Inputs, Household Substitution, and Test Scores

Abstract

Empirical studies of the relationship between school inputs and test scores typically do not account for household responses to changes in school inputs. Evidence from India and Zambia shows that student test scores are higher when schools receive unanticipated grants, but there is no impact of grants that are anticipated. We show that the most likely mechanism for this result is that households offset their own spending in response to anticipated grants. Our results confirm the importance of optimal household responses and suggest caution when interpreting estimates of school inputs on learning outcomes as parameters of an education production function. (JEL D12, H52, I21, O15) T he relationship between school inputs and education outcomes is of fundamental importance for education policy and has been the subject of hundreds of empirical studies around the world (see Hanushek 2002, and Hanushek and Luque 2003 for reviews of US and international evidence, respectively). However, while the empirical public finance literature has traditionally paid careful attention to the behavioral responses of agents to public programs, 1 the empirical literature estimating education production functions has typically not accounted for household 1

School Grants and Education Quality: Experimental Evidence from Senegal

“…To shed light on these questions, Table 6 reports the effect of the grant programme on household characteristics (panel A), teacher characteristics (panel B), and school characteristics (panel C). While other studies in developing countries have shown that households may act as either substitutes or complements to school investments (Das et al 2013), we find virtually no effect of school grants on parental expenditure on schooling (tuition, uniforms, supplies) for either grade or either region, nor any effect on whether the student has a tutor. While other studies in developing countries have shown that households may act as either substitutes or complements to school investments (Das et al 2013), we find virtually no effect of school grants on parental expenditure on schooling (tuition, uniforms, supplies) for either grade or either region, nor any effect on whether the student has a tutor.…”

“…13 We present separate estimates of programme impacts for grade 3 (column (1)), grade 5 (column (2)) and the difference (column (3)), as well as impacts in the south (column (4)), north (column (5)) and the difference (column (6)). It is noteworthy that unlike Das et al (2013), we do not find strong evidence of a parental response, even in the short run. Students also do not appear to substitute away from or towards labour as measured by whether they work after school or miss school (though there is a small marginally significant effect on missing school in the south).…”

“…Furthermore, both in Das et al (2013) and in Blimpo et al (2015), the grant amounts are between $500 and $700 per school, which is much lower than the amount provided by the school grants programme in Senegal (and even lower for Beasley and Huillery (2017)). While most other grant programmes restrict somewhat the use of grant funding, they are not usually tied to a successful grant application of this type.…”

Channeling Remittances to Education: A Field Experiment among Migrants from El Salvador

“…Shi (2012) documents a flypaper effect in the context of a change in school fees in rural China. Conversely, Das et al (2013) find crowd-out of household educational expenditures in response to anticipated public grants to schools. The Angrist et al (2002) study of Colombian private school vouchers comes closest to finding crowd-in in response to a subsidy, but that paper does not provide a formal statistical test of the hypothesis that household educational expenditures rose by more than the value of the subsidy.…”

Test score gaps between private and government sector students at school entry age in India

“…A related but distinct literature has also emerged in the past decade evaluating various interventions for the low productivity of Indian schools, for example through incentives paid to teachers for greater attendance (Duflo, Hanna, & Ryan, 2012), computer-assisted learning (Banerjee, Cole, Duflo, & Linden, 2007), para-teachers (Atherton & Kingdon, 2010, Banerjee et al, 2007Muralidharan & Sundararaman, 2012), performance-based incentives to teachers (Muralidharan & Sundararaman, 2011), diagnostic feedback to teachers (Muralidharan & Sundararaman, 2010) and block grants to schools (Das et al, 2013).…”