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Demand or Supply for Schooling in Rural India? Sripad Motiram Lars Osberg Department of Economics, Dalhousie University May 25, 2007 - Goa.

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Presentation on theme: "Demand or Supply for Schooling in Rural India? Sripad Motiram Lars Osberg Department of Economics, Dalhousie University May 25, 2007 - Goa."— Presentation transcript:

1 Demand or Supply for Schooling in Rural India? Sripad Motiram Lars Osberg Department of Economics, Dalhousie University May 25, 2007 - Goa

2 Human Capital Investment in Rural India  Supply of Schooling Local school system availability and quality - a collective choice  Demand for Schooling by households Individual choices must take local system as an exogenous constraint Time is Major Input in Human Capital Investment  Allocate to market or non-market production, to leisure or to education ?  Which matters more – supply or demand ? HK - crucial for speed and equity of development

3 How much of inequality in human capital investment in rural India is explained by variation in quality of local schooling? How much is explained by the choices of households?  Indian Time Use Survey 1999 (ITUS) Micro-data on school attendance & human capital investment time HK i of rural Indians 6-18  All India School Education Survey (AISES) State level data on school quality 2002  Matched by geo-code by state  Probit estimates of school attendance  Selection bias regression models of HK i  Micro-Simulation of implications of: No impact of Scheduled Caste / Tribe No household income less than median No household head less than high school + No household has all illiterate adult females School quality ≥ Tamil Nadu School quality & parental education crucial

4 State or Union Territory%PTR >50* % No Building or Kaccha* Andhra Pradesh13.97%12.63% Arunachal Pradesh14.59%30.88% Assam23.33%18.33% Bihar78.83%8.84% Chhattisgarh31.46%7.12% Goa0.19%1.21% Gujarat10.35%9.89% Haryana28.17%0.38% Madhya Pradesh23.62%9.00% Meghalaya6.92%23.00% Orissa28.47%4.44% Tamil Nadu12.01%2.96% Tripura12.27%26.85% Uttar Pradesh57.97%1.95% West Bengal44.04%9.06%

5 AttendanceEnrolment Ages 6-10Ages 11-14Ages 15-18Ages 6-18 BoysGirlsBoysGirlsBoysGirlsBoysGirlsBoysGirls %%%%% Total Rural71.166.266.55430.519.256.747.967.956.2 Rural SC/ST73.369.869.757.433.319.659.851.769.260.2 Other Castes74.570.968.358.333.121.558.950.671.459.8 Literate Adult Females Rural 066.455.659.137.6 >078.58275.370.7

6 HK i = S i + H i + T i Investment time = class + travel + homework Rural Children (ITUS – 1999)Ages 6-10Ages 11-14Ages 15-18 BoysGirlsBoysGirlsBoysGirls %%% % class time (711) >070.10%66.20%66.50%54.00%30.50%19.20% Median over positive class times330 Median over all homework (721) times 40060000 % of all children homework >055.40%49.90%56.50%45.10%26.50%15.70% Median over positive homework times 110 120 165150 Median over all travel (791) times201520000 % of all children travel>060.10%54.10%56.50%45.40%26.60%16.90% Median over positive travel times30 40 60 Median Total (711+721+791) Time 450 495 540

7 Micro-Simulations of the Impact of SC/ST, Parental Education, Income and Poor Quality Schools Ages 6-10Ages 11-14Ages 15-18 BoysGirlsBoysGirlsBoysGirls Simulation I (SC/ST) ∆ % Attendance 2.322.6 ∆ % Attendance SC/ST6.236.21 ∆ Median HKi56.28 ∆ Median HKi SC/ST9.316.84 II (Parental Education) ∆ % Attendance5.039.8413.6813.5710.3617.74 ∆ Median HKi0-2.0118.9619.437.4414.25 III (Income) ∆ % Attendance0.840.45-0.380.26 ∆ Median HKi-4.91-8.840-1.52 IV (Quality) ∆ % Attendance8.538.854.664.05 ∆ Median HKi55.8753.2536.5440

8 Human Capital Investment is crucial for pace and equity of economic growth  Caveats District level geo-codes not yet used  more accurate, detailed modeling of local schools Proof of causality? very hard in non-experimental life  cross-sectional correlations “are consistent with”  Inequality in HK i implies inequality in incomes – but not the reverse  Small aggregate impacts of caste on HK i  Major determinants of attendance and HK i Local school quality Parental education  Lagged implication of collective school quality choice

9 Time Use Data – an important new tool for development analysis  Poor people do not have money but they do have time Data on market income & spending cannot reveal behaviour of children or many women or poor  Hence often ignored in empirical analysis  But everyone has 24 hours of time, every day – so excluded groups can be modelled with time use data Crucial aspects of the development process largely occur outside the market economy – but do use time  Human Capital investment decisions  Social Capital formation & Basic Goods (Water) Motiram and Osberg (2007)  Environmental Degradation & Deforestration  Time use micro-data can be linked to local social, economic and environmental variables to provide a detailed analysis of many crucial issues


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