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    Journals Career Network Click here to contact the Editorial Office Editorial Office Trina Ott Assistant Editor 1805 Cambridge Street Cambridge MA 02138 617 496 3293 qje admin editorialexpress com Published on behalf of President and Fellows of Harvard University Impact Factor 6 654 5 Yr impact factor 9 794 Editors Pol Antràs Robert J Barro Lawrence F Katz Andrei Shleifer View full editorial board Assistant Editor Trina Ott Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed For Authors Services for authors Instructions to authors Submit now Self archiving policy for authors P56qQ0myhZIZ9qtHtIIeI0jcYDo8lVt6 true Looking for your next opportunity Looking for jobs Corporate Services What we offer Advertising sales Reprints Supplements Most Most Read The Impact of Jury Race in Criminal Trials The High Frequency Trading Arms Race Frequent Batch Auctions as a Market Design Response Where is the land of Opportunity The Geography of Intergenerational Mobility in the United States The Employment Effects of Credit Market Disruptions Firm level Evidence from the 2008 9 Financial Crisis The Real Costs of Credit Access Evidence from the Payday Lending Market View all Most Read articles Most Cited The Market for Lemons Quality Uncertainty and the Market Mechanism

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  • The Aggregate Effect of School Choice: Evidence from a Two-Stage Experiment in India
    which is not taught by the public schools and lottery winners have much higher test scores in Hindi Furthermore the mean cost per student in the private schools in our sample was less than a third of the cost in public schools Thus private schools in this setting deliver slightly better test score gains than their public counterparts better on Hindi and same in other subjects and do so at a substantially lower cost per student Finally we find no evidence of spillovers on public school students who do not apply for the voucher or on private school students suggesting that the positive effects on voucher winners did not come at the expense of other students JEL Codes C93 H44 H52 I21 O15 The Author s 2015 Published by Oxford University Press on behalf of President and Fellows of Harvard College All rights reserved For Permissions please email journals permissions oup com Previous Next Article Table of Contents This Article The Quarterly Journal of Economics 2015 130 3 1011 1066 doi 10 1093 qje qjv013 First published online February 27 2015 Abstract Free Full Text HTML Free Full Text PDF Free Supplementary Data All Versions of this Article qjv013v1 130 3 1011 most recent Classifications Series Editor s Choice Article Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Add to my archive Download citation Request Permissions Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Muralidharan K Articles by Sundararaman V Search for related content Related Content C93 Field Experiments H44 Publicly Provided Goods Mixed Markets H52 Government Expenditures and Education I21 Analysis of Education O15 Human Resources Human Development Income Distribution Migration Load related web page information Share Email this article CiteULike Delicious Facebook Google Mendeley Twitter What s this Search this journal Advanced Current Issue November 2015 130 4 Alert me to new issues The Journal About the journal Rights permissions We are mobile find out more Journals Career Network Click here to contact the Editorial Office Editorial Office Trina Ott Assistant Editor 1805 Cambridge Street Cambridge MA 02138 617 496 3293 qje admin editorialexpress com Published on behalf of President and Fellows of Harvard University Impact Factor 6 654 5 Yr impact factor 9 794 Editors Pol Antràs Robert J Barro Lawrence F Katz Andrei Shleifer View full editorial board Assistant Editor Trina Ott Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed For Authors Services for authors Instructions to authors Submit now Self archiving policy for authors P56qQ0myhZIZ9qtHtIIeI0jcYDo8lVt6 true Looking for your next opportunity Looking for jobs Corporate Services What we offer Advertising sales Reprints Supplements Most Most Read The Impact of Jury Race in Criminal Trials The High Frequency Trading Arms Race Frequent Batch Auctions as a Market Design Response Where is the land of Opportunity The Geography of

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  • The Aggregate Effect of School Choice: Evidence from a Two-Stage Experiment in India
    mo mml msub mml mi ε mml mi mml mrow mml mi i mml mi mml mi s mml mi mml mi v mml mi mml mrow mml msub mml mo mml mo mml mrow mml math Tisv Yn β0 β1 Tisv Y0 β2 Voucheri βZi Zi βXi Xi εisv 1 where mml math display inline mml mrow mml msub mml mi T mml mi mml mrow mml mi i mml mi mml mi s mml mi mml mi v mml mi mml mrow mml msub mml mrow mml mo stretchy true mml mo mml mrow mml msub mml mi Y mml mi mml mi n mml mi mml msub mml mrow mml mo stretchy true mml mo mml mrow mml mrow mml math Tisv Yn represents normalized test scores for student i in subject s in village v at the end of n years of the experiment Since test scores are highly correlated over time we control for baseline test scores to increase the precision of our estimates 24 We also include a set of district fixed effects mml math display inline mml mrow mml msub mml mi Z mml mi mml mi i mml mi mml msub mml mo stretchy false mml mo mml mrow mml math Zi to absorb geographic variation and increase efficiency and account for the stratification of the village level lottery at the district level The main estimate of interest is mml math display inline mml mrow mml msub mml mi β mml mi mml mn 2 mml mn mml msub mml mrow mml math β2 which provides an unbiased estimate of the impact of winning a voucher on test scores the ITT estimate since the voucher was assigned by lottery We estimate mml math display inline mml mrow mml msub mml mi β mml mi mml mn 2 mml mn mml msub mml mrow mml math β2 both with and without controlling for household socioeconomic characteristics mml math display inline mml mrow mml mo stretchy false mml mo mml msub mml mi X mml mi mml mi i mml mi mml msub mml mo stretchy false mml mo mml mrow mml math Xi shown in Table I As described in Section II a key feature of our design is the ability to estimate the impact of winning the voucher relative to the control group in control villages The estimation sample therefore includes the applicants who won the voucher lottery and applicants whose villages were not selected by lottery to receive the voucher The estimation sample does not include the applicants who lost the lottery but were in treatment villages we use this sample later when analyzing spillover effects Test scores are normalized relative to the distribution of the public school students in the control villages on each test since these students represent the business as usual distribution of test scores Standard errors are clustered at the village level to account for common shocks to test scores that may occur at the village level While we focus our discussion on the ITT estimate we also present the ATT impact of attending a private school by scaling up the ITT estimate above by the inverse of the voucher take up rate defined as the fraction of voucher recipients who accepted the voucher and stayed in a private school for two and four years respectively 25 These results are presented in Table VI for test scores at the end of two and four years of the program with Panel A showing the impact of being awarded a voucher and Panel B showing the average causal impact of attending a private school for those who accepted the voucher and enrolled in a private school for two and four years Results in Table VI includes the controls shown in Table I for greater precision but are unchanged without the controls View this table In this window In a new window Table VI Test Score Impacts At the end of two and four years we find that voucher lottery winners had slightly lower scores on Telugu and math than lottery losers not significant columns 1 2 5 and 6 and higher scores in English 0 19σ after two years p 02 and 0 12σ after four years p 098 columns 3 and 7 The average impact across the three subjects that were assessed at the end of two years was close to zero Table VI column 4 These results suggest that the large cross sectional differences in math and Telugu test scores of 0 65σ shown in Table II are mostly driven by omitted variables and not by differential effectiveness of public and private schools However a key determinant of education outcomes by subject is the allocation of instructional time across different subjects 26 We present data from school timetables in Table VII and see that private schools have sharply different patterns of time allocation than public schools In particular they allocate a lot less time per week to Telugu and math which are the two main subjects taught in the public schools accounting for over 500 minutes week and around 28 of total instructional time each Private schools spend around 200 minutes less on Telugu and 160 minutes less on math a week 40 and 32 less instructional time respectively On the other hand they spend significantly more time on other subjects such as English 90 minutes week social studies 65 minutes week science 100 minutes week Hindi 215 minutes week and computer use 45 minutes week They also spend an hour week more on other periods which include arts crafts sports and study hall Overall we see that the three subjects that were tested at the end of two years of the program account for 70 of the instruction time in public schools but for less than 50 of that in the private schools View this table In this window In a new window Table VII School Time Use Instructional Time by Subject Minutes per Week Thus limiting our analysis to these subjects may provide an incomplete picture of the impact of the voucher Based on the time table data we also conducted tests in EVS and Hindi after four years of the voucher program 27 Although this still does not account for all the subjects computer use for instance the tested subjects now account for over 80 of instructional time in both types of subjects and are also closer to being equal across school types 81 for private and 85 for public schools The full set of test score results are presented in Table VI columns 5 8 and 10 Voucher winning students score slightly better in EVS 0 08σ p 16 and much better in Hindi 0 55σ p 001 Since the test score gains are mainly found in Hindi and since public schools do not teach Hindi we analyze the Hindi results in more detail at the individual question level by skill to better understand what the program impact means in terms of actual ability to use Hindi We present these results in Online Appendix Table A 3 and see that attending a private school more than doubles the probability of students reading letters correctly and more than triples the probability of being able to read words sentences and paragraphs If we weight all subjects equally as in Kling Liebman and Katz 2007 and exclude Hindi which is not taught in public schools the mean test score impact of winning a voucher is not significantly different from zero Table VI column 9 If we include Hindi we find that students who won a voucher scored 0 13σ higher and the average student who accepted the voucher to attend a private school scored 0 26σ higher column 11 Whereas views on optimal weights across subjects and on whether Hindi should be treated equally with other subjects given that it is not taught in public schools may vary we can still unambiguously infer that private schools are more productive because they deliver similar outcomes in Telugu and math with less instructional time and use the extra time to improve test scores in Hindi IV B Robustness to Attrition The main threat to the results is from the differential attrition noted in Online Appendix Table A 2 We verify that our results are robust to this concern using two different procedures First we report the ITT effects of winning a voucher using inverse probability reweighting to account for the differential probability of attrition based on observables and see that doing so barely changes the estimated effects presented in Table VI Panel A Online Appendix Table A 4 Panel A Second we present conservative confidence intervals using the bounding procedure suggested by Lee 2009 and find that the overall results are unchanged Online Appendix Table A 4 Panel B IV C Spillover Effects An important concern in the global school choice literature is that positive estimated effects of vouchers from experimental studies may be overstating the benefits of private schools because these estimates do not account for potential negative spillovers to students in the public schools who do not apply for the voucher or for potential negative spillovers on the students who start in the private schools and who are exposed to lower scoring peers from public schools as a result of the voucher program Hsieh and Urquiola 2006 Our two stage design allows us to estimate these spillovers We calculate three different sets of spillovers as described in Section II B and the estimating equations all take the same form as equation 1 but the right hand side variable of interest is now an indicator for being in a voucher village The estimation samples comprise the concerned group for whom we want to estimate the spillovers lottery losers nonapplicants and students attending private schools before the school choice program from both treatment and control villages The village level lottery ensures that we obtain unbiased reduced form estimates of these three spillovers Table VIII Panel A compares the within village control group to the across village control group Note that the former is the traditional control group used in typical experimental studies of school choice the lottery losers in the treatment villages and that this sample has not been used so far in any of the analysis due to the possibility of spillovers as discussed in Section II B We find no difference between the groups and the combined effects across subjects are not only insignificant but close to zero 28 Panel B estimates if there were any spillovers on nonapplicants and we again find no significant effects on either individual subjects or on the aggregate test scores across subjects Thus even though the literature has often worried about the possibility of negative spillovers on students who are left behind in public schools in response to voucher programs these spillovers were not empirically salient in our setting even though a large fraction 23 of public school students moved out to private schools 29 View this table In this window In a new window Table VIII Spillovers ITT Estimates In the Indian context a greater concern has been the possibility that the RtE Act clause on quotas in private schools would lead to negative spillovers on the students who start out in the private schools see Shah 2012 for an example We estimate these spillovers in Table VIII Panel C and find that there are no significant negative spillovers on the students who were in private schools to begin with In addition to these average spillover effects on private school students in the exposed cohorts we also estimate the extent of spillovers as a function of the number of voucher winning students who join a particular private school Since this is endogenous we first construct a measure of potential exposure to voucher students for each private school in both treatment and control villages as the number of voucher applicants for whom it is the nearest private school We then construct an instrumental variable for the number of voucher winning students who join any given private school by interacting the potential exposure with the randomly determined fraction of these students who win a voucher The instrument will take a value of 0 for all private schools in control villages where no vouchers were awarded and can vary across private schools in treatment villages We present the IV estimates of spillovers on private school students as a function of the number of voucher students received by the school in Online Appendix Table A 5 and again find no impact on the test scores of students who started out in private schools 30 Although set in India these results are consistent with those reported in Angrist and Lang 2004 who similarly find negligible impacts on white students from the school desegregation conducted under the Boston Metco Program Taken together our results suggest that the small test score gains for voucher winners are not achieved at the cost of negative outcomes for any other group of students who may have been indirectly affected by the voucher program Of course our results do not imply that peer effects and sorting never matter for evaluations of school choice But they do suggest more broadly that although spillovers are an important theoretical concern in the school choice literature they do not appear to be empirically first order in our context and may not be so for lottery based studies of school choice in programs that do not allow private schools to select students 31 IV D Heterogeneous Effects 1 Heterogeneous Effects by Student Characteristics We test for heterogeneity of the impact of the voucher program along several student characteristics including baseline scores sex parental literacy and affluence using a standard linear interaction specification of the form 2 where the parameter of interest is mml math display inline mml mrow mml msub mml mi β mml mi mml mn 4 mml mn mml msub mml mrow mml math β4 which estimates the extent to which the impact of the vouchers is different for students with the concerned characteristic Table IX Panel A reports estimates of mml math display inline mml mrow mml msub mml mi β mml mi mml mn 4 mml mn mml msub mml mrow mml math β4 over two and four years The main result is the lack of any consistent evidence of heterogeneous effects along most student characteristics In particular the baseline score can be treated as a summary statistic of educational inputs that students had received up to the point when they enter the study and the lack of any differential treatment effects by baseline score suggests that the impacts of the program were broad based 32 We also estimate a similar specification to test for heterogeneity among the public school students who did not apply for the voucher group 1 in Figure I and find no evidence of differential spillover effects on non applicants Table IX Panel B Overall we find limited evidence of student level heterogeneity for either the main effects or spillovers this is also true when we test for heterogeneity nonparametrically as a function of baseline test scores View this table In this window In a new window Table IX Heterogeneous Test Score Impacts by Student Characteristics ITT Estimates 2 Heterogeneous Effects by School Characteristics Our experiment was not designed to identify heterogeneous effects by school characteristics 33 but we report some suggestive results that are likely to be important for future research designed explicitly to study such heterogeneity In particular a key feature of private school heterogeneity in India is the medium of instruction All public schools in our sample teach in Telugu whereas over half the private schools use English as the medium of instruction The high actual and perceived returns to English in India have led to growing demand for English medium private schools 34 At the same time it is possible that switching to being taught in English may be disruptive to the learning of voucher winning students many of whom are first generation learners with illiterate parents Thus studying heterogeneous effects of attending private schools as a function of the medium of instruction is especially important in this context Since the choice of school attended and its medium of instruction is endogenous we use the medium of instruction of the nearest private school to each applicant household and its interaction with the receipt of the randomly assigned voucher as instruments for the medium of instruction of the private school attended We define the following variables of interest mml math display inline mml mrow mml mi A mml mi mml mo mml mo mml mi E mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mrow mml math A EM PSi student i attends an English medium private school mml math display inline mml mrow mml mi A mml mi mml mo mml mo mml mi T mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mrow mml math A TM PSi student i attends a Telugu medium private school mml math display inline mml mrow mml mi N mml mi mml mo mml mo mml mi P mml mi mml mi S mml mi mml mo mml mo mml mi E mml mi mml msub mml mi M mml mi mml mi i mml mi mml msub mml mrow mml math N PS EMi nearest Private school to student i teaches in English medium mml math display inline mml mrow mml mi N mml mi mml mo mml mo mml mi P mml mi mml mi S mml mi mml mo mml mo mml mi T mml mi mml msub mml mi M mml mi mml mi i mml mi mml msub mml mrow mml math N PS TMi nearest Private school to student i teaches in Telugu medium and are interested in estimating mml math display inline mml mrow mml msub mml mi β mml mi mml mn 2 mml mn mml msub mml mrow mml math β2 and mml math display inline mml mrow mml msub mml mi β mml mi mml mn 3 mml mn mml msub mml mrow mml math β3 in the second stage equation mml math display block mml mtable mml mtr mml mtd mml mrow mml msub mml mi T mml mi mml mrow mml mi i mml mi mml mi s mml mi mml mi v mml mi mml mrow mml msub mml mrow mml mo stretchy true mml mo mml mrow mml msub mml mi Y mml mi mml mi n mml mi mml msub mml mrow mml mo stretchy true mml mo mml mrow mml mo mml mo mml msub mml mi β mml mi mml mn 0 mml mn mml msub mml mo mml mo mml msub mml mi β mml mi mml mn 1 mml mn mml msub mml mo mml mo mml msub mml mi T mml mi mml mrow mml mi i mml mi mml mi s mml mi mml mi v mml mi mml mrow mml msub mml mrow mml mo stretchy true mml mo mml mrow mml msub mml mi Y mml mi mml mn 0 mml mn mml msub mml mrow mml mo stretchy true mml mo mml mrow mml mo mml mo mml msub mml mi β mml mi mml mn 2 mml mn mml msub mml mo mml mo mml mi A mml mi mml mo mml mo mml mi E mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mrow mml mtd mml mtr mml mtr mml mtd mml mrow mml mo mml mo mml mo mml mo mml msub mml mi β mml mi mml mn 3 mml mn mml msub mml mo mml mo mml mi A mml mi mml mo mml mo mml mi T mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mo mml mo mml msub mml mi β mml mi mml mrow mml msub mml mi X mml mi mml mi i mml mi mml msub mml mrow mml msub mml mo mml mo mml msub mml mi X mml mi mml mi i mml mi mml msub mml mo mml mo mml msub mml mi ε mml mi mml mrow mml mi i mml mi mml mi s mml mi mml mi v mml mi mml mrow mml msub mml mo mml mo mml mrow mml mtd mml mtr mml mtable mml math Tisv Yn β0 β1 Tisv Y0 β2 A EM PSi β3 A TM PSi βXi Xi εisv 3 where the endogenous variables are mml math display inline mml mrow mml mi A mml mi mml mo mml mo mml mi E mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mrow mml math A EM PSi and mml math display inline mml mrow mml mi A mml mi mml mo mml mo mml mi T mml mi mml mi M mml mi mml mo mml mo mml mi P mml mi mml msub mml mi S mml mi mml mi i mml mi mml msub mml mrow mml math A TM PSi and the first stage equations are 4a 4b We use equations 4a and 4b to instrument for the two endogenous variables in equation 3 and present the two first stage regressions in Online Appendix Table A 7 The main parameters of interest mml math display inline mml mrow mml msub mml mi β mml mi mml mn 2 mml mn mml msub mml mrow mml math β2 and mml math display inline mml mrow mml msub mml mi β mml mi mml mn 3 mml mn mml msub mml mo stretchy false mml mo mml mrow mml math β3 from the IV estimation of equation 3 are presented in Table X Panel A A more conservative approach is to use only the interactions as instruments and we present the results from this specification in Table X Panel B 35 The estimation sample is the same as that in Table VI and comprises the voucher lottery winners in the treatment villages and the lottery losers in the control villages 36 At the end of four years of the voucher program we find that the causal impact of attending an English medium private school varies sharply by subject with students doing worse than staying in the public school in Telugu math and EVS but much better in English and Hindi The mean impact across subjects is positive 0 22σ but not significant On the other hand the estimated impact of attending a Telugu medium private school is positive for every subject and the mean impact across subjects is positive 0 53σ and significant Table X Panel A The IV estimates in Table X have large standard errors and are much less precise than the main estimates in Table VI Nevertheless some suggestive patterns emerge in the results View this table In this window In a new window Table X Heterogeneous Test Score Impacts by Medium of Instruction IV Estimates The first is that the causal impact of attending a Telugu medium private school for students who start out in public schools which all teach in Telugu medium on test scores in Telugu math and EVS appears to be greater than that of attending an English medium private school p values of 15 13 and 06 respectively The second is that there appears to be a negative impact of switching the medium of instruction on the learning of content in nonlanguage subjects We see this most clearly in the last column of Table X Panel A where we present the mean treatment effects across math and EVS which are the two content subjects while the other three are language subjects The difference in mean test scores across medium of instruction is 0 77σ which is a very large effect p 07 The third is that private schools appear to be even more effective than suggested by the estimates in Table VI a mean treatment effect of 0 53σ across subjects as opposed to 0 26σ when their students are not also going through the disruption of switching their medium of instruction 37 These results are only suggestive and have several caveats First they are highly imprecise Using a more conservative IV strategy reported in Table X Panel B we find similar estimates but the standard errors are too large for meaningful inference Second even with a precise IV estimate the medium of instruction will be correlated with other school characteristics However we see that on average the English medium schools have superior indicators of school quality including facilities teacher experience qualifications and salary and annual fees charged per child Online Appendix Table A 9 Since our main result in Table X is that Telugu medium private schools appear to be more effective than English medium ones the superior input based quality indicators of the English medium schools suggest that the differences shown in Table X may be a lower bound on the relative advantage of native language versus English medium instruction for the population of public school students who applied for the voucher These results are consistent with the education psychology literature which suggests that first generation learners may be better off being taught in their native language which can be reinforced at home see Abadzi 2006 for a summary Qualitative interviews by enumerators with teachers and parents suggest that one plausible reason for adverse effects of shifting to an English medium private school is that these schools use textbooks written in English for teaching nonlanguage subjects such as math and EVS and that reading textbooks in English was much more difficult for students shifting from public schools In the development economics literature Ramachandran 2012 provides recent evidence from Ethiopia showing that a switch to mother tongue instruction for primary school led to a significant increase in education attainment Jain 2014 examines historical data from colonial India and finds that linguistically mismatched districts where the official language did not match the local language had lower rates of literacy and college graduation results Our results are consistent with these findings in aggregate data and highlight the importance of more well identified research to directly estimate the impact of the medium of instruction on test scores 3 Heterogeneous Effects by Market Characteristics The market level experimental design allows us to test whether students who have greater choice among schools benefit more from a voucher Hoxby 2000 We use the distance data described above to calculate the number of private schools within a 1 km radius of each voucher applicant Our measure of choice and competition is constructed separately for each student and can therefore generate variation at the student level even for students living in the same village We estimate the relationship between the number of schools in an applicant s choice set and test scores both parametrically and nonparametrically For the first we use a linear interaction of voucher receipt and the number of schools in the choice set in a specification similar to equation 2 For the second we estimate equation 2 with the characteristic being whether the number of schools a student has in her choice set is in the top 25 top 10 or top 5 of the distribution of the number of schools These results are presented in Table XI and we find no significant effect of choice and competition when estimated with a linear interaction between voucher receipt and the number of schools in a student s choice set within 1 km However while conducting the study in a rural sample allows us to study spillovers by randomizing across villages a limitation is that around 50 of voucher applicants have only 0 27 or 1 21 private school within a 1 km radius Thus the extent of choice and competition between private schools is quite limited for many of the voucher applicants View this table In this window In a new window Table XI Heterogeneous Test Score Impacts by Market Competition ITT Estimates The nonparametric estimates might therefore be more appropriate in this context and they provide some suggestive evidence of the benefits of greater choice and competition since we find that voucher winners do significantly better when they have six or more schools within a 1 km radius of where they live Table XI We find evidence of larger impacts in areas with more choice and competition in both the two year and the four year results suggesting that the heterogeneity is likely to be real and does not just reflect sampling variation Furthermore we find similar results when we consider heterogeneous impacts as a function of the number of private schools in a half kilometer radius and with the total number of schools in a half or 1 km radius and not just the number of private schools available on request However these results are suggestive because they are only significant in markets representing the top 5 of the distribution of the extent of choice and competition and the rural setting may not be the best one to study heterogeneous impacts of school choice as a function of choice and competition However urban India has much greater population and school density A recent geocoded school census in the city of Patna found between 9 and 93 private schools within a 1 km radius of every public school with the median being greater than 50 Rangaraju Tooley and Dixon 2012 Our results therefore suggest that the impact of a school voucher program may be considerably larger in high density urban settings This is an important area for future research IV E Cost Effectiveness The combination of test score results Table VI and school timetable data Table VII already show that private schools are more productive than public schools because they are able to produce similar levels of test scores in math and Telugu using substantially less instructional time and use the extra time to produce higher test scores in other subjects especially Hindi Furthermore the results in Table X suggest that private schools may be even more productive when students attending them are not experiencing the disruption of switching their medium of instruction Finally it is worth highlighting that the average cost per student in the private schools in our sample is less than a third of the per student costs in the public schools Table III Panel C and that the value of the voucher was only around 40 of the per student costs in the public schools Thus private schools produce slightly better academic outcomes at lower cost and are unambiguously both more productive and cost effective than public schools in India Previous Section Next Section V Discussion and Conclusion We present evidence from the first experimental evaluation of the impact of a school choice program and the first experimental evidence on the relative effectiveness of private and public schools in a low income country Furthermore the two stage experimental design also allows us to estimate spillovers on nonapplicants and students in private schools Our results on private school productivity suggest that it may be possible to substantially increase human capital formation in developing countries like India by making more use of private provision in the delivery of education The costs of low productivity in public education delivery may be especially high in low income settings where low levels of human capital are likely to be barriers to both economic growth and the inclusiveness of growth and where fiscal constraints limit the total spending on education Our results showing no significant spillovers on private school students from receiving voucher recipients from public schools suggest that it may be possible to achieve greater levels of social integration in private schools as envisaged by the RtE Act without the efficiency costs that opponents of integration are concerned about Finally our demonstration of the centrality of accounting for patterns of time use in evaluating the effectiveness of private schools are perhaps the most general result for the global literature on school choice On one hand studies of vouchers and school choice that find no effects on test scores may understate the benefits by not accounting for other subjects that the private or elite public schools may be teaching On the other hand studies of charter schools finding positive effects on test scores may overstate the benefits if charter schools focus more on scores on high stakes tests and divert instructional time away from other subjects In the absence of data on long term outcomes such as employment and wages it is important for education researchers to devise test and validate more content neutral measures of learning that may enable meaningful comparisons of outcomes across varying instructional programs The policy implications of our results for education in India are particularly timely given the provision in the RtE Act for 25 reservation in private schools for disadvantaged students with the government reimbursing private school fees Our results suggest that this provision is likely to not only reduce social stratification at limited cost to current students in private schools but also to increase average productivity in the education sector by increasing the share of private schooling This may thus be a rare example of a policy that improves equity and efficiency and does so at a lower cost than the status quo 38 Nevertheless there are important caveats to the broad implication that greater private sector participation in education production supported by public funding and featuring enhanced school choice would improve the productivity of human capital formation The first caveat is that the private schools in our sample did not on average improve outcomes in math and Telugu though they spent less time and money and were more productive as a result In particular private schools did not do better on math and Telugu in spite of having a longer school year and school day substantially lower pupil teacher ratios and higher levels of teacher attendance and effort We point to four possible explanations for this result First the total instructional time on math and Telugu was lower in private schools 39 Second private schools spend much less per student and hire teachers with lower levels of education training and experience paying them much lower salaries Thus higher teacher effort in private schools may be offset by lower teacher knowledge and experience Third we find suggestive evidence that private schools are more effective if students are not also experiencing a disruption in the medium of instruction Table X and thus the lack of an overall impact may reflect the negative impacts of switching to English medium schools for those who did so Fourth there is suggestive evidence that a first order binding constraint in Indian schools both public and private is that the pedagogy mechanically follows the textbook as opposed to the level of the students who are typically way behind grade level competencies Banerjee and Duflo 2011 Pritchett and Beatty 2012 Muralidharan 2013 In such a setting the additional effort of private school teachers in transacting the curriculum may not translate into much additional learning To the extent that this last explanation is true the productivity advantage of private schools may mainly reflect their ability to pay lower teacher salaries as opposed to superior effectiveness of instruction Thus although it is

    Original URL path: https://qje.oxfordjournals.org/content/130/3/1011.full (2016-02-18)
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