Private School Response

Finally, I turn to the effects of DPEP on the private school response. As I argued earlier, the gap between state territoriality and state functionality should open a space for a private school response to cater to under- served populations. In Figure4.11I repeat the plot from Figure4.7for private schools by looking at the percentage of villages with at least one private school. As the Census of India only began collecting in- formation on the number of private schools in the 2011 census, I only have data on private schools at the village level from the economic census in 1998 and 2005, as well as the population census in 2011.

As expected, there is a negative relationship at the discontinuity between receiving DPEP, suggesting that districts that received DPEP funding saw a greater increase in the percent of villages with at least one private schools.

When testing this more formally in Table4.12, confirming the results from the plot, I find that the effect of DPEP is strongest in 1998, and wanes a little after this point. However, none of the specifications are significant, although all three have the same signs.

% of Villages in District with a Private School

1998 2005 2011

Literacy Cutoff -4.34 -0.83 -3.13

(5.92) (5.57) (8.93)

Observations 369 366 365

∗_p<0.1,∗∗_p<0.05,∗∗∗ _{p<0.01. Robust standard errors in parentheses.}

Table 4.12:Reggresion Discontinuity Estimates of DPEP on Percent of Villages with Private Schools

DPEP districts show a similar response with regards to private schools. Figure4.12plots the effects of the DPEP literacy threshold on the number ofprivateschools per 10,000 school-aged children after DPEP.

0 25 50 75 100

Villages with Private School (%) (1998)

0 20 40 60 80 1991 Female Literacy (%) 0 25 50 75 100

Villages with Private School (%) (2005)

0 20 40 60 80 1991 Female Literacy (%) 0 25 50 75 100

Villages with Private School (%) (2011)

0 20 40 60 80 1991 Female Literacy (%)

% of Villages in District with a Private School

Figure 4.11:Villages with Private Schools Around DPEP Literacy Cutoff

The effect of DPEP funding on the number of villages with at least one private schools in a district. The literacy cutoff for receiving DPEP funds is 39.3 percent. The dots show the percentage of villages in a district with at least one private school within a small bin of the literacy margin. The lines are the second-order local polynomial best-fit lines fit separately on each side of the cutoff. The plot is based on the procedure developed byCalonico, Cattaneo and Titiunik(2014b).

In all time periods and data sources, we see a positive effect of receiving DPEP funds on a growth in private schools: receiving DPEP funds results inmoreprivate schools after DPEP was introduced.

0 50 100 150 200

Private Schools per Capita

0 20 40 60 80 1991 Female Literacy (%) Source: EC. Year: 1998.

0 50 100 150 0 20 40 60 80 1991 Female Literacy (%) Source: DISE. Year: 1998.

0 100 200 300 0 20 40 60 80 1991 Female Literacy (%) Source: EC. Year: 2005.

0 100 200 300 400

Private Schools per Capita

0 20 40 60 80 1991 Female Literacy (%) Source: DISE. Year: 2005.

10 15 20 25 30 0 20 40 60 80 1991 Female Literacy (%) Source: Population Census. Year: 2011.

Private Schools per 10,000 School-Aged Children

Figure 4.12:Private Schools Around DPEP Literacy Cutoff

The effect of DPEP funding on the number of private schools per 10,000 school-aged children in a district. The literacy cutoff for receiving DPEP funds is 39.3 percent. The dots show the number of private schools in a district within a small bin of the literacy margin. The lines are the second-order local polynomial best-fit lines fit separately on each side of the cutoff. The plot is based on the procedure developed byCalonico, Cattaneo and Titiunik(2014b).

I present these results more formally in Table4.13where the columns progress chronologically, rely- ing on three different datasets to test the relationship between DPEP and private school growth. As the regression discontinuity plots suggest, there is a consistently positive relationship between receiving DPEP funds and private school growth, resulting in between 1 and 35 more private schools per 10,000 school-aged children in districts that received DPEP funding. None of these results are significant, however, so although the point estimates are large, I hesitate to read too much into them.

Finally, I turn to the more general case that uses a difference-in-difference estimator to look at receiving DPEP funding on the number of private schools in a district. This set-up is identical to that presented in Table4.8with the dependent variable changed. Again, we see an increase in the number of private schools in districts that received DPEP funding. From Table4.14the coefficient on DPEP District x Post-DPEP

Private Schools per 10,000 School-Aged Children Literacy Cutoff -17.02 -14.00 -1.49 -34.98 -0.54

(11.14) (13.15) (7.98) (31.12) (9.70)

Observations 372 371 372 371 371

Year 1998 1998 2005 2005 2011

Data Source EC DISE EC DISE Population Census

∗ _p<0.1,∗∗_p<0.05,∗∗∗_{p<0.01. Robust standard errors in parentheses.}

Table 4.13:Reggresion Discontinuity Estimates of DPEP on Private Schools

show that private schools were far more likely to locate in districts in which DPEP was implemented (and by association where there was greatergovernmentschool construction). The coefficient on all specifications is significantly different to zero and the effect is also large, as receiving DPEP funding led to between a 0 school per year increase in the most demanding specification with district and year fixed effects and district time trends in column 5, and 3 private schools per year increase in the remaining columns.

Private Schools per 10,000 School-Aged Children DPEP District x 0.142∗∗ _0.142∗∗∗ _0.142∗∗ _0.142∗∗∗ _0.001 Post-DPEP (0.072) (0.021) (0.072) (0.017) (0.009) DPEP District -0.712∗∗∗ _-1.480∗∗∗ _-0.712∗∗∗ _-1.480∗∗∗ _-2.296∗∗∗ (0.052) (0.108) (0.052) (0.079) (0.091) Post-DPEP 0.513∗∗∗ _0.513∗∗∗ _1.029∗∗∗ _1.029∗∗∗ _0.190∗∗∗ (0.063) (0.016) (0.134) (0.043) (0.016) Constant 2.913∗∗∗ _2.668∗∗∗ _2.645∗∗∗ _2.401∗∗∗ _2.730∗∗∗ (0.046) (0.043) (0.104) (0.060) (0.015) Observations 7480 7480 7480 7480 7480 Districts 478 478 478 478 478

District FE No Yes No Yes Yes

Year FE No No Yes Yes Yes

District Trends No No No No Yes

∗_p<0.1,∗∗_p<0.05,∗∗∗_{p<0.01. Robust standard errors in parentheses.}

Table 4.14:Difference-in-Difference: Private Schools

In this section I have shown that there was a small private sector response to the introduction of DPEP. Depending on the estimator used, the introduction of DPEP financing led to a greater number of villages with at least one private school and a small increase in the total number of private schools per school-aged children.

struction, greater financial resources did result in greater numbers of government schools built after DPEP was introduced. Unfortunately, these increased financial resources did not lead to any noticeable improve- ment in educational outcomes, whether measured as literacy or independently measured test scores. In the third stage, we also observe a private school response. Greater public resources “crowded-in” private investment through the construction of a greater number of private schools.

Given the local nature of some of the tests presented above, in the next I use the full range of observations to explore the effects of DPEP more generally.