ECON2900: Development, Poverty, and Famine - Economics Assignment Help
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Instructions: This assignment is due at 5pm on Friday May 15th, and is worth 25% of your final mark for this course. Hand in your work through Turnitin. No extensions will be given, and late assignments will receive no credit. If you have a university approved excuse for not handing in this assignment, then your marks for your final exam will be weighted up accordingly to compensate for the missed work.
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Answers should be in sentence form (i.e. single word or single number answers with out explanation will be considered incomplete). However, simplicity of presentation is important, so try to make your comments/discussion brief and to the point.
Question 1. (50 marks) Use the data in migration.dta to answer this question. The data can be downloaded from Wattle. (The data are a 50% random sample of the Rural sample of the Rural-Urban Migration in China Project surveyed in 2008. In this sample only individuals who are currently working and are aged 16 to 60 are included).
(i) (6 marks) Estimate the following migration model by OLS for the total sample. Note that you will need to generate the relevant variables as described below before you can run the regression. (Write down the command you used to generate each of the relevant variables in your answers and then present the stata regression results).
migrate = β0+β1age+β2male+β3healthy+β4married+δEdu+γP rov+u
X healthy is a dummy variable which equals 1 if the individual self assessed health condition is very good or good, zero otherwise;
X married is a dummy variable indicating whether the individual is married (in cluding married, re-married, or cohabiting);
X Edu is a set of 3 dummy variables indicating whether the individual has junior high school or less, senior high school, or a 3-year college and above education;
X P rov is a set of dummy variables indicating individual’s home province.
(ii) (14 marks) Provide an intuitive explanation of the meaning of the sign and size of the coefficients. Test whether the coefficients are statistically significant. (For the vector of education dummy variables you need to discuss which ones are statistically significantly different from the omitted category and which ones are not. For the vector of provincial dummy variables you only need to discuss whether there are significant variations across different provinces. Do not forget to discuss the constant term (who does it capture and what does it mean).
(iii) (3 marks) Interpret R2.
(iv) (18 marks) The Chinese government restricts rural migrants from obtaining social services (including health care and children’s schooling) in cities. As a result, married women may be less likely than married men to migrate. How should we test statistically if this is indeed the case? Re-estimate the model above and include an additional variable to test this hypothesis and interpret the sign, the magnitude and the significance level of the coefficients on the migration probability of married men, married women, single men and single women. Contrast and compare the four groups in their migration probabilities. (Again, write down the command you used to generate the relevant variable for this test, present the stata regression results, and then interpret the relevant results).
(v) (3 marks) Based on economic theory, migrants on average are more “driven”. Such “drive” may also be correlated with the years of schooling individuals possess. As “drive” is an unobservable characteristic we are unable to control for it in the model. Please explain how omitting this variable would bias the estimated effect of education on migration probability.
(vi) (6 marks) If we do have an omitted variable problem, think of a cheap way to resolve this problem. Provide a short explanation (no more than 100 words) of how this approach can resolve the problem.
Question 2. (50 marks) Schooling typically raises future earnings. However, in a developing country called Nambia, rural children’s school enrolment is quite low. One explanation is that schooling competes with labour-intensive jobs for children. To reduce child labour and ensure children go to school, Nambia’s government in 2001-02 implemented a food-for-education program (FFE). Participating households received monthly food rations as long as they sent their children to primary school. The program implemented a two-stage targeting method. First, economically back ward areas were chosen by the central government. Second, within the chosen areas, economically backward households were chosen to participate by communities based on local knowledge. To receive food rations, children must have attended at least 85% of all classes each month. Using a representative Household Expenditure Survey for the country, a program evaluation was conducted. The evaluation took the form of an instrumental variable estimation:
Yi = β0 + β1F F Ei + β2Xi + i (1)
F F Ei = α0 + α1F F EVj + α2Xi + υi (2)
where Yi refers to whether child i participates in the labour market or attends school (two separate equations), F F Eiindicates the amount of grain received per child from
the food-for-education program; F F EVjis an indicator (dummy variable) for whether the village, where the child lives, is located in the program area or not, which is used as the instrumental variable. Xiis other control variables, including all area level variables which were used to determine whether the areas were chosen to participate the program or not. The selected estimated results for the main variable(s) are listed in the table below. Please read the question and the table carefully and answer the following questions:
(i) (15 marks) Why did the program evaluators decide to use an instrumental vari able approach? What are the possible endogeneity problems preventing them from using a simple OLS regression (Equation 1 only) to estimate the causal effect of the program?
(ii) (15 marks) Discuss the instrumental variable they used and assess the validity of the instrument.
(iii) (20 marks) The outcome variables used are dummy variables. For labour force participation, 1 indicates the child was working while zero indicates that the child was not working. Similarly, for school participation, 1 indicates the child went to school while zero indicates the child did not go to school. The main independent variable F F Eiis the amount of grain per child received by the household (in 100 kg). Interpret the main results presented in the table below (the sign, the magnitude of the coefficients and their significance levels). Note that the top row under each variable name is the estimated coefficient, while the second row presented in the bracket is the standard error of the coefficient. Also, other control variables, (Xi), are included but not reported in the table. Therefore you do not need to comment on them.
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