43 E. Funkhouser Economics of Education Review 14 1999 31–50
in which the head has higher mean education, and live in households with higher income.
4. Estimation results
In this section, I estimate the determinants of school attendance in Eq. 5 using pooled data for 1980–1985.
These results are reported in Table 2. In the first two columns, means of each variable are reported for rural
and urban areas.
13
In columns 3 and 5 of Table 2, the results of the logit estimation for school attendance are
shown for rural and urban areas. The marginal prob- abilities — calculated holding other characteristics fixed
to actual values for each individual — are shown in Col- umns 4 and 6.
4.1. Household characteristics The first set of variables describe household character-
istics. These variables are organized into three groups. The first group — head employed, logarithm of head
hourly income, presence of other workers, number of other workers, logarithm of second earner hourly
income, presence of other income, logarithm of other income — describes the employment and earnings out-
comes of household members in which the teenager resides. The second group — number of non-workers
and presence of children under 12 — describes the types of non-workers that are in the household. The third group
describes two other characteristics of the head of the household: education of the head and a dummy variable
for female head.
Taken together, the coefficients on the household labor market variables present an interesting picture of the
relationship between work and school for teenagers in Costa Rica. It is useful to note that the magnitude of the
effects seen in columns 4 and 6 are quite similar in rural and urban areas.
Better labor market outcomes of the head are associa- ted with a higher probability of school attendance,
though the pattern is weak. For employment, the increase in attendance associated with the head being employed
is 3.8 percentage points in rural areas and 1.7 percentage points in urban areas. Of more statistical importance is
the pattern between school attendance and the labor mar- ket outcomes of household members other than the head.
Both the presence and number of non-head members
13
For logarithm of head hourly wage, the logarithm of the next earner’s hourly wage, and logarithm of non-wage income,
zero was assigned for zero values in levels and separate vari- ables for head working, number of non-head workers, and non-
wage income missing were included. For this reason, the mean value of each of the logarithm variables includes zero values.
who are working is associated with lower probability of current school attendance and the magnitude of the mar-
ginal changes are larger than those found for the head. Given the number of non-heads working, though, a
higher hourly wage earned by the second earner — and higher income in the household — is associated with a
higher probability of school attendance. One possibility for explaining the former pattern is that the costs of
search in the labor market are reduced with more house- hold members in the labor market.
The presence of non-working members in the house- hold are also important. The presence of other non-work-
ing adults in the household is associated with higher school attendance — by 3.6 percentage points in rural
areas and 3.7 percentage points in urban areas. In con- trast, the presence of younger children is a deterrent to
school attendance — by 2.0 percentage points in rural areas and 1.0 percentage points in urban areas.
4.2. Teenager characteristics The next rows of Table 2 report the coefficients on
the variables related to the teenager characteristics, with two labor market variables predicted own-wage and
return to the next year of education and several demo- graphic characteristics female, age, birth order, and
education. The effect of the predicted own-wage effect is positive and significant, with a 50 percent increase
associated with a higher probability of school attendance by 4.7 percentage points in rural areas and 2.0 percentage
points in urban areas. These coefficients are not consist- ent with the predicted wage measuring the opportunity
cost of school attendance and may reflect lifetime wealth as a proxy for future income.
14
The effect of the return to education is small and less conclusive. The net effect
of a 50 percent increase in education is zero in rural areas and negative in urban areas.
15
Demographic factors are important determinants of school attendance. Females are approximately three per-
centage points more likely to attend school in both rural and rural areas. There are large effects of age on school
attendance, even controlling for completed education. Each of the entries in the column for marginal prob-
abilities measures the change in probability of attendance for teenagers of the given age compared with otherwise
14
When the age dummy variables are not included, the coef- ficient on the predicted wage is negative and significant. Since
the variation in predicted wage comes from regional labor mar- ket characteristics, those areas with worse labor market out-
comes have lower attendance rates.
15
With the inclusion of dummy variables for each year of education, the returns to education variable measures within-
education level variation in returns across years expressed rela- tive to the yearly mean with the inclusion of year dummy
variables.
44 E. Funkhouser Economics of Education Review 14 1999 31–50
Table 2 Determinants of school attendance 1980–1985
Means Logit estimation
Rural Urban
Rural Urban
Coeff. One-unit change
Coeff. One-unit change
1 2
3 4
5 6
Household characteristics Head of household working
0.789 0.800
0.304 0.097 0.038
0.153 0.121 0.017
Log of head hourly income 1.984
2.281 0.025 0.026
0.001 0.080 0.030
0.003 Any other members working
0.733 0.618
20.700 0.093 20.090
20.833 0.128 20.091
Number of non-head members 1.649
1.165 20.081 0.028
20.010 20.047 0.039
20.005 working
Log of 2nd earner’s hourly income 1.572
1.647 0.175 0.029
0.009 0.212 0.037
0.009 Log of other income
1.867 1.482
0.025 0.057 0.001
20.033 0.072 20.002
Other income missing 0.755
0.808 0.209 0.426
0.026 0.014 0.543
0.002 Number non-workers
2.151 2.136
0.287 0.022 0.036
0.362 0.030 0.037
Presence of children , 12 0.745
0.653 20.156 0.059
20.020 20.092 0.068
20.010 Head education
3.523 6.304
0.105 0.010 0.013
0.102 0.010 0.011
Head female 0.140
0.228 0.283 0.080
0.035 0.252 0.085
0.027 Teenager characteristics
Predicted own hourly wage 2.006
2.152 0.932 0.225
0.047 0.466 0.277
0.020 Return to next year of education
0.099 0.110
20.430 1.467 2.814 1.669
20.001 2.015
Return to education squared10 0.014
0.019 0.094 0.691
21.283 0.684 Female
0.509 0.504
0.254 0.123 0.032
0.252 0.152 0.028
Age 13 0.167
0.159 20.912 0.085
20.074 20.655 0.149
20.036 Age 14
0.175 0.172
22.044 0.099 20.171
21.853 0.152 20.108
Age 15 0.179
0.178 23.151 0.122
20.144 22.798 0.167
20.121 Age 16
0.171 0.181
23.625 0.145 20.052
23.654 0.191 20.126
Age 17 0.169
0.183 24.216 0.169
2.058 24.267 0.212
20.093 Second oldest
0.297 0.267
0.161 0.063 0.020
0.160 0.082 0.017
Third oldest 0.127
0.091 0.238 0.084
0.010 0.215 0.127
0.006 Fourth oldest
0.034 0.020
0.554 0.140 0.040
0.072 0.238 20.015
Fifth 1 oldest 0.007
0.003 1.267 0.316
20.070 1.116 0.766
20.008 One year education
0.014 0.012
2.561 0.383 0.288
2.862 0.444 0.402
Two years education 0.026
0.011 2.608 0.356
0.008 2.432 0.450
20.067 Three years education
0.042 0.021
2.553 0.347 20.009
2.338 0.415 2.015
Four years education 0.068
0.032 3.007 0.340
0.072 2.985 0.409
0.100 Five years education
0.125 0.089
3.560 0.345 0.091
3.547 0.400 0.084
Six years education 0.437
0.258 1.377 0.338
20.325 2.395 0.385
20.175 Seven years education
0.096 0.172
4.141 0.348 0.420
4.188 0.394 0.262
Eight years education 0.080
0.155 4.805 0.352
0.123 4.494 0.398
0.037 Nine years education
0.053 0.120
5.676 0.358 0.111
6.000 0.410 0.126
Ten years education 0.032
0.084 6.917 0.538
0.095 7.229 0.568
0.043 Eleven years education
0.012 0.035
5.086 0.411 20.167
5.072 0.442 20.110
Year of survey 1981 data
0.207 0.207
0.437 0.105 0.055
0.135 0.127 0.015
1982 data 0.190
0.195 0.485 0.158
0.006 0.054 0.182
20.009 1983 data
0.199 0.201
20.031 0.109 20.065
20.057 0.148 20.012
1985 data 0.196
0.186 20.037 0.094
20.001 0.021 0.113
0.009 Six region dummies
Yes Yes
Constant 23.878 0.711
22.389 0.891 Log-likelihood
25166.5 23319.2
Mean dependent variable 0.444
0.752 N
13 032 9340
Note: One-unit changes are calculated by increasing indicated variable by unit while holding all other variables constant at their actual values. For sequential dummy variables i.e. age, years of education, year, changes are between categories. Changes are 50
percent increases for variables indicated by an asterisk. Source: Estimations by author.
45 E. Funkhouser Economics of Education Review 14 1999 31–50
similar teenagers one year younger. Each of these entries is negative, large, and statistically significant.
In the next rows of the table, the birth order of the teenager within the group of teenagers aged 12–17
within the household is shown.
16
For example, 29.7 per- cent of the teenagers in the sample for rural areas had
an older sibling aged 12–17. These teenagers are 2.0 per- centage points more likely to attend school than their
older siblings. Continuing down the column, third-oldest teenagers in rural areas are 1.0 percentage points more
likely to attend school than the second-oldest and 3.0 percentage points more likely than the oldest sibling. The
data show that younger teenagers are more likely to attend school than their older school age siblings.
The next rows of Table 2 show the effect of previous education on current school attendance. These coef-
ficients show that teenagers in the sample tended to com- plete schooling in the curriculum cycles of the edu-
cational system in Costa Rica 1–6, 7–11.
17
There are large drops in the probabilities of current attendance for
those who had completed zero years of education, six years of education, and eleven years of education.
4.3. Year effects In Fig. 3, the pattern over the years of recession
showed a drop in school attendance in rural areas and little change in urban areas. The final coefficients in
Table 2 show these year effects in school attendance with controls for the other variables included. Though not
entirely attributable to regional or macroeconomic con- ditions because there may be time-varying household
characteristics not otherwise included, these coefficients provide a rough indicator of the importance of factors
outside the household in explaining school attendance.
The results indicate that there is not much variation in school attendance that is explained by changing con-
ditions over the early 1980s in urban areas, but that these factors are important in explaining the drop in school
attendance in rural areas. Though the general pattern for rural areas in the table is similar to that in the previous
figure — there is a one-time drop in attendance rates without much rebound — the timing of the change is
one-year later with controls than that without controls. In rural areas, there is a 7.1 percentage point drop in
attendance rates observed between 1981 and 1982 in Fig. 1. In contrast, between these two years there is no change
in the year effect with controls in Table 2. The drop in the year effect occurs instead between 1982 and 1983.
16
This is not necessarily the true birth order of the teenager since siblings older than 17 or siblings that do not live in the
household are not counted.
17
Note that the marginal probabilities are calculated relative to one fewer year of education.
This pattern is explained by changes in the mean values of other explanatory variables in directions associated
with lower school attendance between 1981 and 1982 and in directions associated with higher school attend-
ance between 1982 and 1982. In particular, a worsening of the predicted own wage and the changing age distri-
bution within the category of teenagers between 1981 and 1982 were the largest contributors to lower attend-
ance between these years.
Expressed in terms of changes in the standardized lat- ent variable in the attendance decision, changes in the
predicted own wage contribute 2 0.302 compared to 0.048 for the year effect between 1981 and 1982. Other
teenager characteristics, mainly age, contribute 2 0.180; household economic characteristics contribute 2 0.061;
and other household characteristics contribute 2 0.041. Between 1982 and 1983 the contribution of all of these
categories is towards higher school attendance. The year effect, though, is a reduction of 0.516 in the latent vari-
able — or 6.5 percentage points in attendance rates — between 1982 and 1983.
18
5. Extensions
