1. Module 5 Inference with Panel Data
Measuring poverty
Multidimensional poverty
Poverty Dynamics
Panel Data
Inference with Panel Data
International Poverty Comparisons
Vulnerability
Tackling Poverty
Module 5 Inference with Panel Data
Jonathan Haughton
June 2017

2. JH: Poverty Measurement Course
Objectives
Explain the essential ideas and vocabulary of regression analysis.
List and explain the main regression problems, including
Measurement error
Omitted variable bias
Simultaneity bias
Sample selectivity bias
Multicolinearity
Heteroskedasticity
Outliers
Show how panel data can help address some of these problems
Outline the key ideas underlying impact evaluation
Explain how impact evaluation can be more powerful with panel data, and illustrate this with an example from Thailand
June 2017
JH: Poverty Measurement Course

3. JH: Poverty Measurement Course
Regression 1
Fit a line to data
As here, Vietnam 1998
Engel curves
Linear
Food spending/cap = (spending/capita)
Quadratic
F/cap = (spend/cap) – (spend/cap)^2
H&H chap 2, Fig 1.9
June 2017
JH: Poverty Measurement Course

4. JH: Poverty Measurement Course
Vocabulary
Intercept/constant
Coefficients
Dependent variable
Independent variables/regressors/covariates
Error (unobserved)
Mean zero; identically & independently distributed
True equation; estimated equation
Residual (observed):
June 2017
JH: Poverty Measurement Course

5. JH: Poverty Measurement Course
Example
R2 = Goodness of fit.
Coefficient signs as expected
Consumption/capita: in logs.
t-statistics shown. [>2 ≈ “significant at 5%”]
Phnom Penh variable is binary
Dependency ratio: (young + old)/(prime age)
Femaleness: % aged who are female. Positive coefficient
June 2017
JH: Poverty Measurement Course

6. JH: Poverty Measurement Course
Regression problems:
Measurement error: y = a + bX + ε
In Y: no bias, but poor fit
In X: estimate of b biased toward 0
Omitted variable bias
True: Child health = a + b MumEd + c MumAbility
Estimated: Child health = a + b MumEd
If ed, ability, correlated, estimate of b is too high
June 2017
JH: Poverty Measurement Course

7. More regression problems
Simultaneity bias
E.g. Child health = a + b Nutrient intake
But which comes first? Feed the weak or the healthy?
Sample selectivity bias
E.g. Only observe wages for those who work
Solution: Two steps (Heckman)
Multicolinearity
Some X variables are intercorrelated
Hard to get accurate coefficient estimates
June 2017
JH: Poverty Measurement Course

8. Yet more regression problems
Heteroskedasticity
Errors do not have constant variance
Coefficients not biased; but standard errors inefficient
June 2017
JH: Poverty Measurement Course

9. Regression Problems Again
Outliers
Typo or truth?
June 2017
JH: Poverty Measurement Course

10. Panel data can be helpful
Rice output = a + b fertilizer + c ability + ε
Ability unobservable, so estimate
Rice output = a + b fertilizer + w
But estimate of b is likely overstated.
Use panel data:
Differencing washes out “ability”, allows an unbiased estimate of b
Very useful, but only works if ability is time-invariant.
June 2017
JH: Poverty Measurement Course

11. JH: Poverty Measurement Course
Impact Evaluation
Purpose: quantify the impact of a project or policy
E.g. Does microcredit raise incomes
Complex; requires construction of a counterfactual
Gold standard: Randomized Controlled Trial
Randomly select beneficiaries (and controls)
Collect baseline data
After intervention, collect data again; compare outcomes between treated and controls
June 2017
JH: Poverty Measurement Course

12. Powerful: Double Differences
Panel data with pre- and post-treatment
E.g. (30 – 10) – (21 – 14) = 13
June 2017
JH: Poverty Measurement Course

13. Can combine with regression
Error: time-invariant and innovation components
Sweeps away effects of variables, including unobservables, that do not vary over time
Jalan and Ravallion (1998): Poverty alleviation program in China
Double differencing biased; keep variables for initial conditions that influence program placement
June 2017
JH: Poverty Measurement Course

14. Example: Thailand Village Fund
Village-run microcredit; any impact on expenditure or income per capita?
Rural panel, Socio-Economic Surveys, 2002 and 2004
T = treatment = borrow from Village Fund
X variables include household variables (e.g. age of head, education of head, no. of adults).
Y is log of expenditure (or income) per capita
Estimates of γ:
Expenditure: 3.5% (s.e. 1.5%; p-value 0.02)
Income: 1.4% (s.e. 1.8%; p-value 0.44)
Boonperm et al. 2013
June 2017
JH: Poverty Measurement Course

15. JH: Poverty Measurement Course
Extensions
There may be time-varying unobserved heterogeneity, biasing the coefficient on T
E.g. Local conditions may change over time
Solution 1: Interact treatment with time-varying variables
Khandker (2006): Microcredit in Bangladesh
Solution 2: Arellano-Bond dynamic lagged-dependent-variable approach
See Jalan and Ravallion (1998), mentioned above
June 2017
JH: Poverty Measurement Course

16. JH: Poverty Measurement Course
Reading
Haughton & Khandker (2009).
Handbook on Poverty and Inequality. Chapter 14; 13 (pp ). World Bank, Washington DC.
Khandker, Koolwal, & Samad (2010)
Handbook on Impact Evaluation, World Bank, Washington DC.
Haughton & Haughton (2011).
Living Standards Analytics, Springer, New York. Chapters 2 (Regression) and 12 (Impact Evaluation)
Boonperm et al. (2013)
Does the Village Fund matter in Thailand? Evaluating the impact on incomes and spending. Journal of Asian Economics 25: 3-16.
Cameron & Trivedi (2009)
Colin Cameron & Pravin Trivedi. Microeconometrics Using Stata. Stata Press.
June 2017
JH: Poverty Measurement Course