ECOM30003/ECOM90003: Applied Microeconometric Modelling - Report Writing Economics Assignment Help

Assignment Task:

''Can Electronic Procurement Improve Infrastructure Provision? Evidence from Public Works in India and Indonesia" by Lewis?Faupel, Neggers, Olken and Pande conducts an evaluation of the impact of e-procurement in India and Indonesia on outcomes related to the process, as well as outcomes related to cost and quality of projects undertaken under government procurement. The following questions use an extract of the data used by the authors of this paper and asks you to conduct analysis related to results found in Table 4 of the paper. 

Your submission should include your written responses to all questions, and then an appendix including all requested tables, and your stata code. Tables should be numbered sequentially. The table containing results for Questions 4?10 should have column headings indicating the question they correspond to. Please keep your written answers brief and to the point, referring to the tables by table number and column heading where appropriate. 

Questions 1 and 2 asks you to look at variation in the data that will be used to identify the policy impact. 

1. [5 marks] In order to demonstrate the variation the authors use to identify their model, code the year that e?procurement is introduced as zero if it occurs after the years for which there is information on packets (tab package_year to see what years there is information on procurement packets). Collapse the data set to create a table that crosstabulates the year that e?procurement is introduced and state. Show year e?procurement was introduced in columns and states in the rows of the table and label the table Table 1. How many states are there in the data? How many states introduced e?procurement during the years for which we have data on procurement? Describe the timing and number of states introducing e?procurement. [Hint: use the stata command collapse.] 

2. [5 marks] Using the original data set on packets (of contracts) and the sample used in Table 4 column 6 of Lewis?Faupel et al., create a table cross tabulating e?procurement status (ie whether the policy is in effect) and state. Show e?procurement status in columns and states in the rows of the table and label the table Table 2. Comment on the percent of observations for which the policy is in effect. Are all states that you identified as introducing e?procurement in the years for which packet data are available in Table 1 represented in Table 2? If not, why is this the case? [Hint: use stata’s help to learn about the post?estimation command e(sample) to define a sample used in estimating the model contained on Table 4 column 6.] 

3. [10 marks] What is the empirical challenge in identifying the causal impact of the e?procurement program. Briefly explain the strategy that the authors employ to overcome the identification problem and the empirical specification used to implement this strategy. What parameter measures the causal impact of e?procurement? Be explicit about the key identifying assumptions. How should the standard errors be modelled? 

Questions 4—10 asks you to estimate various models. Please report (all) results in a single table labelled Table 3, and use the same format as Table 4 of Lewis?Faupel et al. Also, to rule out the potential for different sample sizes driving different estimates, make sure you use the sample used in Table 4, panel A, column 6 of Lewis?Faupel et al. The only specification for which the sample size should be different is the specification estimated in Question 9. The standard errors reported for all specifications should account for any issues identified in Question 3. [Hint: See outreg2 or esttab to output stata results into excel.] 

4. [3 marks] Regress the outcome reported in Table 4 panel A column 6 on the indicator for e-procurement (without any other controls). What is the interpretation (sign, size and significance)? of the estimated coefficients? 

5. [5 marks] Add indicators for year of package to the above specification. What happens to the estimated coefficient on e?procurement (size and significance)? What does this tell you about whether the estimates have a causal interpretation? 

6. [5 marks] Add state fixed effects to the specification estimated in Question 5. What happens to the estimated coefficient on e?procurement (sign, size and significance)? What does this tell you about whether the estimates have a causal interpretation? 

7. [5 marks] Add log road length and log estimated costs to Question 6’s specification. What happens to the estimated coefficient on e?procurement (sign, size and significance)? What does this tell you about whether the estimates have a causal interpretation? 

8. [6 marks] The specification reported in Table 4, panel A column 6 includes the year of first inspection and monitor fixed effects. Explain why they are included. What happens to the estimated coefficient on e?procurement (sign, size and significance) when you add them to the model estimated in Question 7? 

9. [6 marks] Re?estimate the specification estimated in Question 8, but exclude observations from the state Andhra Pradesh. What happens to the estimated coefficient on e?procurement (sign, size and significance) when you drop these observations from the estimation sample? 

10. [5 marks] Although not reported in the paper, the common trend assumption is typically investigated by allowing for different time paths for treated and non?treated units in the pre? treatment period. For the purpose of this assignment, we will take a simplified approach to this by asking whether there is a significant difference in the outcome variable between treatment and non? treatment states one period before treatment is introduced. This will involve including an additional explanatory variable to the specification reported in Table 4, panel A column 6. This additional variable is an indicator variable equal to one in the year before e?procurement is introduced in a state and zero otherwise. Here is the code to create this variable: 

gen eprocL=(package_year==(eproc_start_year?1)) 

Create this additional variable and add it to the model reported Table 4, panel A column 6. What do the results from estimating this model suggest about the common trends assumption? What does this tell you about whether the estimates have a causal interpretation?

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