Topics in Development Economics and Policy Assignment 1 Due: January 25th, 2016, 2:10pm (beginning of class) Location: Lecture room

Econ 403: Topics in Development Economics and Policy Assignment 1 Due: January 25th, 2016, 2:10pm (beginning of class) Location: Lecture room
[Note: Please hand in your own solutions] I. Robustness of the Cross-Country Income – Poverty Relationship
The objective of this question is to assess whether modifications to the regression model in Besley & Burgess (2003) affect the conclusions regarding the sufficiency
of promoting national income per capita growth to achieve the MDG poverty goal.
• In order to carry this exercise, you will use the Stata dataset titled “povertygoals7_bis.dta”, made available by the authors. it is available on the course’s
Blackboard webpage.
• This dataset is composed of repeated observations for a sample of countries. We will employ the sample of countries used for the analysis by the authors, those that
include extreme poverty measures (which use a poverty of approximately $1.09 per person/day).
Instructions
(a) Estimate the bivariate regression: ln Pit = θ + ηln µit + εit where ln Pit = the natural logarithm of the poverty headcount ratio (headcoun) ln µit = the
natural logarithm of the GNP per capita, PPP (gnppc) Hints: Construct variables in natural logs using ‘generate’ command. For the regression analysis, use ‘regress’
command, with option ‘if povline < 50’. Also, include the option ‘cluster(ccode)’ to allow the error terms to be correlated for all observations for each country. The
command should have the following structure: regress lny lnx if povline < 50, cluster(ccode) [2 points] (b) Estimate the multivariate regression allowing for country fixed effects (which control for unobserved characteristics that are fixed at the country level). ln Pit
= Σi θi1(ccode = i) + ηln µit + εit where 1(ccode = i) are indicator variables for each one of all countries i
2
Hints: The country fixed effects specification can be estimated in Stata using the ‘areg’ command, with the option ‘absorb(ccode)’ The command should have the
following structure: areg lny lnx if povline < 50, absorb(ccode) cluster(ccode) [2 points] (c) Estimate the two following multivariate regressions that gradually add the following controls:
(1) year fixed effects: ln Pit = Σi θi1(ccode = i) + ηln µit + Σtδt1(year = t) + εit where 1(year = t) are indicator variables for each one of the years (denoted
t) in the dataset.
(2) the natural logarithm of population size ln Pit = Σi θi1(ccode = i) + ηln µit + Σtδt1(year = t) + β ln POPit + εit where ln POPit = the natural logarithm of
the country population (pop) Hint: The year fixed effects should be added manually to the country fixed effects specification, by creating year dummy variables. The
other options mentioned above should remain the same.) [3 points] (d) Test for the statistical significance of each estimate of the η (simple/partial) correlation in each model. Interpret the magnitudes of the coefficients in each
model, and relate these, in the context of the discussion of omitted variables (or unobserved heterogeneity) bias. [5 points] (e) Calculate the predicted annual growth rate of national income per capita necessary to halve world poverty by 2015 (from 1990). Interpret the results. What
conclusions can we reach regarding the main analysis conducted by the authors? [5 points] (f) Repeat steps (a) – (e) using as dependent variable the natural logarithm of the poverty gap. What conclusions can we reach in this case? [3 points]

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