The Effects of User Fee Reductions on Enrollment: Evidence from a quasi-experiment Felipe Barrera-Osorio (HDNED World Bank) Leigh L. Linden (Columbia University) Miguel Urquiola (Columbia University)

The objective of the study What are the effects of user fee reductions on enrollment? It is possible to see differential responses to the program from different groups of populations Specifically, we can analyze the different responses between females and males

Gratuidad program Each year the government issues a resolution that stipulates which items schools may charge for and the maximum fee for each. These expenses are equivalent to between 7 and 29 monthly dollars, (between 6 and 25 percent of the minimum wage) The Gratuidad program subsidizes some of these fees. The program is targeted using the Sisben index that identify the most vulnerable households in Colombia. The system is implemented with a survey about households’ infrastructure, demographics and human capital characteristics. Based on these, each household receives an score between 0 and 100 Based on the score, it places households into six different levels, 1 being the most poor, and 6 the richest. Households with scores below a cutoff score of 11 are given a Level of 1; those between 11 and a cutoff of 22 receive a Level 2; Level 3—22 and 43, and so on The extent to which students benefit from these reductions is a function of their Sisben level.

Gratuidad For basic education (grades 1-9) Sisben 1 children enjoy a 100 percent reduction of complementary service charges, while those in Sisben levels 2 and above receive no reduction. For high school (grades 10-11), Sisben 1 children benefit from the elimination of both academic and complementary services fees, while Sisben 2 households receive roughly a 50 percent reduction Households in levels 3 and higher receive no benefit.

Gratuidad: items of charges

Direct cost of education

Distribution of cost across items

Identification and Estimation Whether or not students benefit from the program is a discrete function of their score. Characteristics of the household (observable and unobservable) are similar for students just above and below the cutoff scores. Under this assumption, discrete differences in attendance rates between treated and untreated students close to the cutoff can be attributed to the fee reductions. Students with scores of 21.5 might provide an adequate control group for students with scores of 22.5 In short, close to the cutoffs the RD design resembles a (localized) randomized experiment

Data The information comes from two sources. First, data collected (in 2004 and 2005) directly through the Sisben survey. Demographic characteristics (gender, age, household composition, pregnancy, and marital status). Educational attainment (grades completed), type of enrollment (public/private), labor force participation, and income. Second, we use administrative enrollment records (October 2006) kept by the District Education Department (SED)

Descriptive statistics

Validation of the exercise First, properties of the assignment variable: We verified that the students’ Sisben score is a good predictor of their level And we verified that households were not able to influence their Sisben score Second, we investigated that the characteristics of individuals are smoothly related to the Sisben score at the cutoff points (e.g., that the control and treatment groups are similar) The treatment and control groups are similar. For example:

Example, Validation of similarity between treatment and control groups: Income

Results We present regressions’ coefficients between enrollment and level of Sisben (controlling by cube on the score) We present results for several subpopulations (e.g., gender, public / private students, etc) We discuss results with a band of 1 point

Results

Results

Results Results suggest that the program had a significant impact. The program raises the probability of enrollment for basic-aged Sisben 1 students by about 3 percent, and for high school-aged Sisben 2 students by about 6 percent. These positive effects seem to be larger for at-risk students. The program also seems to display a substantial degree of heterogeneous impacts for different populations. The program seems to increase the probability of attendance for males in the first grades and for girls in the higher grades