Income and Working in Agriculture in Adulthood

This section of the chapter examine how the change in the number of years of schooling completed by children who dropped out during the crisis affects their incomes and their probability of working in the agricultural sector in adulthood. We look into the difference in the impact of predicted complete years of schooling on labour outcomes between dropouts and non-dropouts. We also look at how ignoring food expenditure shock and education shock during the crisis may have underestimated the impact of an additional year of schooling on the incomes and probability of working in the agricultural sector.

Table 4.6shows that there is no long-term impact of education shock during the crisis on income and agricultural work in adulthood. We ran seemingly unrelated regression with a restriction on the sample of dropouts and non-dropouts using the same model specification for both. Then, we performed a Chow test of predicted years of education and work experience. The Chow test shows that the coefficients of predicted years of schooling have no significant differences between the dropouts and non-dropouts, either for income or for work in agriculture. However, in the job experience variable, an additional year of job experience for the dropouts contributes 16% more income than for non-dropouts, although it makes no difference to the probability of working in agriculture.

4.6. Findings 91

These findings may occur due to the difference in the completed years of schooling being only 1.2 years less for dropouts than for non-dropouts. The small magnitude of the effect of dropping-out on complete years of schooling indicates a negligible effect in the long term on children’s income and employment.

TABLE4.6: Income and Working in Agriculture in 2014: Dropouts and Non-dropouts

Exp14² -0.008** -0.001 -0.010 0.008*

(0.003) (0.002) (0.016) (0.005)

Age_14 -1.609** 0.103 -5.822 -0.177

(0.681) (0.190) (4.002) (0.692)

Age²_14 0.028** -0.002 0.097 0.003

(0.012) (0.003) (0.066) (0.012)

Provincial differences Yes Yes Yes Yes

Observations 201 1,806 201 1,806

Chow test Chow test

Income Agrwrk

Edupred F(1,1552) =0.25 F(1,1432)=0.01

Prob > F =0.6142 Prob > F =0.9316

Experience14 F(1,1552) = 4.12 F(1,1432) =0.81

Prob > F = 0.0426 Prob > F = 0.3679 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

All estimation standard errors are clustered at the household level. All samples are weighted Income is a natural log of permanent income

Agrwrk is a binomial variable of working in agriculture = 1 and not working in agriculture = 0 Variables definition refer to Table4.1

TABLE4.7: Income and Working in Agriculture in 2014: Predicted and

Age²_14 -0.001 0.001 0.001 0.000

(0.003) (0.002) (0.001) (0.001)

Provincial differences Yes Yes Yes Yes

Observations 3,159 3,159 2,911 2,911

All estimation standard errors are clustered at the household level. All samples are weighted.

Income is natural log of permanent income

Agrwrk is binomial of working in agriculture = 1 and not working in agriculture = 0 Variables definition refer to Table4.1

4.7. Heterogeneous Effects 93

We ran a seemingly unrelated estimation comparing the same model with a different education variable, using predicted years of education from our main endogenous treatment regression and reported years of education. The seemingly unrelated estimation uses a stacking method that enables us to test whether the effect of predicted years of education on the children’s income as adults is similar to the reported years of education. We use a similar method to see differences between predicted and reported years of schooling in determining whether the children are working in the agricultural sector or not in 2014. As suggested by Mood (2010), we use the linear probability model for working in agriculture to enable us to compare the years of schooling variable, before stacking them using the seemingly unrelated estimation.

Table4.7shows that an additional year of the predicted years of schooling results in 4% more income than the reported years of schooling. The adjusted Wald test is testing the difference between the predicted and reported years of schooling, showing that the coefficients are statistically different. Hence, we can say that an additional year of schooling with respect to dropping out during the financial crisis increased income by 4% more than without considering the crisis.

Similarly, the probability of working in the agricultural sector is much less for the predicted years of schooling than the probability associated with the reported one. For each additional predicted year of schooling, there is a 1% smaller probability of working in the agricultural sector compared to the reported years of schooling.

Although both differences between reported and predicted years of schooling are small, they are significant, as they indicate that without considering dropping out during the financial crisis, we will underestimate the return to schooling.

4.7 Heterogeneous Effects

In this section, we look at the different effects of pre-crisis conditions on the complete years of education of dropouts and non-dropouts. We interact with the household expenditure quintile position in 1997, age category in 1997, household location in 1997, and gender of the children. Table 4.8 shows the drop-out variables during the crisis, the respective heterogeneous variables that we want to see and their interaction. The drop-out variables are significant, except for the birth year effect, but the magnitude is slightly different.

Children who lived outside Java island in 1997 lost more years of schooling than those who lived in Java pre-crisis. Also, the interaction of those dropping out during the crisis and living in Java pre-crisis is significantly positive, indicating a lower likelihood of being a dropout than if they lived outside the island in 1997. In contrast, although living in an urban area pre-crisis led to more years of schooling, the interaction with dropouts is not significant. The sex of the children and the wealth status of their households in 1997 do not explain the different effects of dropping out on complete years of schooling.

The age of the children pre-crisis shows the different effects of dropping out on completed years of schooling, with the highest impact being on the children who were ten years old in 1997. It also shows that it barely affected the oldest and the youngest children in our cohort in the long run.

TABLE4.8: Heterogeneous Effects

(1) (2) (3) (4) (5)

Dependent variable: years of schooling (YoS) in 2014 Quintile Age Male Java Urban

Dropouts9798 -1.802** -0.394 -1.410** -2.170*** -1.236**

(0.785) (1.024) (0.650) (0.828) (0.613)

Male97 -0.191* -0.230** -0.235* -0.214* -0.228**

(0.111) (0.112) (0.121) (0.112) (0.110) 2. Expcap_quint97 (base 1. Expcap_quint97) 0.299

(0.256) Interaction: dropouts9798×2. Expcap_quint97 0.490

(0.452) Interaction: dropouts9798×3. Expcap_quint97 0.497

(0.430) Interaction: dropouts9798×4. Expcap_quint97 0.454

(0.495) Interaction: dropouts9798×5. Expcap_quint97 0.303

(0.498)

7. Age97 (base 6. Age97) -0.324

(0.290)