Economics 172 Issues in African Economic Development Professor Ted Miguel Department of Economics University of California, Berkeley
Economics 172 Issues in African Economic Development Lecture 10 February 15, 2007
Economics 172: Lecture 10 3
Economics 172: Lecture 10 4
Counting HIV+ people in Kenya Based on antenatal clinic survey data, the official UNAIDS estimate of HIV+ adults in Kenya by late 2001 was 15.0% The 2003 Kenya Demographic and Health Survey (DHS) tried to survey a representative subsample of population. 73.4% agreed to be tested This data indicates that only 6.7% of Kenyan 15-49 year olds tested are HIV+! Which of the two numbers is closer to the truth? Economics 172: Lecture 10 5
Economics 172: Lecture 10 6
Economics 172: Lecture 10 7
Economics 172: Lecture 10 8
Economics 172: Lecture 10 9
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) Economics 172: Lecture 10 10
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? Economics 172: Lecture 10 11
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? -- Labor productivity / labor turnover Economics 172: Lecture 10 12
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? -- Labor productivity / labor turnover -- Human capital accumulation (orphans) -- Investment and savings (as time horizons change) Economics 172: Lecture 10 13
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? (2) Why does HIV/AIDS continue to spread in Africa? Economics 172: Lecture 10 14
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? (2) Why does HIV/AIDS continue to spread in Africa? (3) What can / should public policy do about HIV/AIDS? Economics 172: Lecture 10 15
Econometric method: difference-in-differences How do we evaluate the impact of a program or event if we do not have a randomized experimental design? Economics 172: Lecture 10 16
Econometric method: difference-in-differences How do we evaluate the impact of a program or event if we do not have a randomized experimental design? A common and powerful approach utilizes data on the same individual (or household, firm, village, etc.) over time. This is called panel data (or longitudinal data ) Economics 172: Lecture 10 17
Econometric method: difference-in-differences How do we evaluate the impact of a program or event if we do not have a randomized experimental design? A common and powerful approach utilizes data on the same individual (or household, firm, village, etc.) over time. This is called panel data (or longitudinal data ) What assumptions allow us to estimate causal impacts of a program/event using panel data? Economics 172: Lecture 10 18
Treatment effects and omitted variable bias (1) Y i = a + bt i + cx i + e i (2) E(Y i T i =1) E(Y i T i =0) = [a + b + ce(x i T i =1) + E(e i T i =1)] [a + 0 + ce(x i T i =0) + E(e i T i =0)] = b + c [E(X i T i =1) E(X i T i =0)] True effect Omitted variable/selection bias term Economics 172: Lecture 10 19
Extend the data to two time periods (pre, post) (1) Y it = a + bt it + cx it + e it t = 0: Pre-program/pre-event period t = 1: Post-program/post-event period Economics 172: Lecture 10 20
Extend the data to two time periods (pre, post) (1) Y it = a + bt it + cx it + e it t = 0: Pre-program/pre-event period t = 1: Post-program/post-event period For concreteness, let the event here be an individual falling ill with AIDS, and the outcome be their productivity at work (e.g., how many kilos of tea they pick per day) Economics 172: Lecture 10 21
Extend the data to two time periods (pre, post) (1) Y it = a + bt it + cx it + e it t = 0: Pre-program/pre-event period t = 1: Post-program/post-event period In period t = 1, the treatment effect estimate is (as before): (2) E(Y i1 T i1 =1) E(Y i1 T i1 =0) = [a + b + ce(x i1 T i1 =1) + E(e i1 T i1 =1)] [a + 0 + ce(x i1 T i1 =0) + E(e i1 T i1 =0)] = b + c [E(X i1 T i1 =1) E(X i1 T i1 =0)] True effect Omitted variable bias term for t=1 Economics 172: Lecture 10 22
Extend the data to two time periods (pre, post) In period t = 0, before the event, the difference between the two groups is the following, where we know who will later become ill in period t = 1 (T i1 =1): (3) E(Y i0 T i1 =1) E(Y i0 T i1 =0) Economics 172: Lecture 10 23
Extend the data to two time periods (pre, post) In period t = 0, before the event, the difference between the two groups is the following, where we know who will later become ill in period t = 1 (T i1 =1): (3) E(Y i0 T i1 =1) E(Y i0 T i1 =0) = [a + 0 + ce(x i0 T i1 =1) + E(e i0 T i1 =1)] [a + 0 + ce(x i0 T i1 =0) + E(e i0 T i1 =0)] = c [E(X i0 T i1 =1) E(X i0 T i1 =0)] Omitted variable bias term for t=0 Economics 172: Lecture 10 24
Extend the data to two time periods (pre, post) In period t = 0, before the event, the difference between the two groups is the following, where we know who will later become ill in period t = 1 (T i1 =1): (3) E(Y i0 T i1 =1) E(Y i0 T i1 =0) = [a + 0 + ce(x i0 T i1 =1) + E(e i0 T i1 =1)] [a + 0 + ce(x i0 T i1 =0) + E(e i0 T i1 =0)] = c [E(X i0 T i1 =1) E(X i0 T i1 =0)] Omitted variable bias term for t=0 Economics 172: Lecture 10 25
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} Economics 172: Lecture 10 26
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} = b + c [E(X i1 T i1 =1) E(X i1 T i1 =0)] c [E(X i0 T i1 =1) E(X i0 T i1 =0)] Economics 172: Lecture 10 27
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} = b + c [E(X i1 T i1 =1) E(X i1 T i1 =0)] c [E(X i0 T i1 =1) E(X i0 T i1 =0)] = True effect + {(OVB term for t=1) (OVB term for t=0)} Economics 172: Lecture 10 28
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} = b + c [E(X i1 T i1 =1) E(X i1 T i1 =0)] c [E(X i0 T i1 =1) E(X i0 T i1 =0)] = True effect =0? + {(OVB term for t=1) (OVB term for t=0)} Economics 172: Lecture 10 29
Dealing with omitted variable bias When is omitted variable bias not a problem? 1) Collect information on X 2) The omitted variable does not affect the outcome 3) The omitted variables are not correlated with the explanatory variable of interest (here, T) randomization Economics 172: Lecture 10 30
Dealing with omitted variable bias When is omitted variable bias not a problem? 1) Collect information on X 2) The omitted variable does not affect the outcome 3) The omitted variables are not correlated with the explanatory variable of interest (here, T) randomization 4) If we have panel data and the extent of omitted variable bias is constant over time, then a difference-indifferences (DD) estimate delivers the true effect Economics 172: Lecture 10 31
When is DD is a sound approach? 4) If we have panel data and the extent of omitted variable bias is constant over time, then a difference-indifferences (DD) estimate delivers the true effect When is this likely to hold? Economics 172: Lecture 10 32
When is DD is a sound approach? 4) If we have panel data and the extent of omitted variable bias is constant over time, then a difference-indifferences (DD) estimate delivers the true effect When is this likely to hold? Basically, when unobserved factors that could affect the outcome or individuals are stable over the study period Economics 172: Lecture 10 33
When is DD is a sound approach? 4) If we have panel data and the extent of omitted variable bias is constant over time, then a difference-indifferences (DD) estimate delivers the true effect When is this likely to hold? Basically, when unobserved factors that could affect the outcome or individuals are stable over the study period For concreteness, let the event here be an individual falling ill with AIDS, and the outcome be their productivity at work (e.g., how many kilos of tea they pick per day) -- If labor conditions, rules, effort, etc. are stable over the study period, then DD seems a reasonable approach Economics 172: Lecture 10 34
Key questions in the study of HIV/AIDS (0) Measuring the extent of the problem (today) (1) What impact does HIV/AIDS have on economic development in Africa? -- Labor productivity / labor turnover -- Human capital accumulation (orphans) -- Investment and savings (as time horizons change) Economics 172: Lecture 10 35
HIV/AIDS and labor productivity in Kenya Fox et al (2004) study the impact of HIV/AIDS illness on labor productivity on tea plantations in Kenya, 1997-2002 Economics 172: Lecture 10 36
HIV/AIDS and labor productivity in Kenya Fox et al (2004) study the impact of HIV/AIDS illness on labor productivity on tea plantations in Kenya, 1997-2002 They compare the labor productivity the kilograms of tea leaves picked per day of workers who became sick with HIV/AIDS during the study period to workers who remained healthy The total sample is 271 workers. 54 died or retired due to HIV/AIDS Economics 172: Lecture 10 37
HIV/AIDS and labor productivity in Kenya Fox et al (2004) study the impact of HIV/AIDS illness on labor productivity on tea plantations in Kenya, 1997-2002 They compare the labor productivity the kilograms of tea leaves picked per day of workers who became sick with HIV/AIDS during the study period to workers who remained healthy The total sample is 271 workers. 54 died or retired due to HIV/AIDS. In the statistical analysis, these are the treated group (T i1 =1). Economics 172: Lecture 10 38
Economics 172: Lecture 10 39
T i1 =1 AIDS T i1 =0 No AIDS Economics 172: Lecture 10 40
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T i1 =0 T i1 =1 Economics 172: Lecture 10 42
Pre, t=0 T i1 =0 T i1 =1 Post, t=1 Economics 172: Lecture 10 43
Economics 172: Lecture 10 44
Pre t=0 Post t=1 Economics 172: Lecture 10 45
Pre t=0 Post t=1 Diff 0 = +1% Diff 1 = -17% Economics 172: Lecture 10 46
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} Economics 172: Lecture 10 47
Taking the difference-in-differences The difference-in-differences (DD) estimator takes the differences between equations (2) and (3) to eliminate omitted variable bias and deliver the true effect: Equation (2) Equation (3) = {E(Y i1 T i1 =1) E(Y i1 T i1 =0)} {E(Y i0 T i1 =1) E(Y i0 T i1 =0)} = Diff 1 Diff 0 = (-17%) (+1%) = -18% Economics 172: Lecture 10 48
Interpreting Fox et al. s (2004) results Including absenteeism, final year income drops 35% Economics 172: Lecture 10 49
Interpreting Fox et al. s (2004) results Including absenteeism, final year income drops 35% Some controls are also likely ill with HIV/AIDS, leading the estimated T C difference to be a lower bound. The decline over time in C productivity may be AIDS related Sick workers have family member helpers. So estimates are again likely to be lower bounds Economics 172: Lecture 10 50
Interpreting Fox et al. s (2004) results Including absenteeism, final year income drops 35% Some controls are also likely ill with HIV/AIDS, leading the estimated T C difference to be a lower bound. The decline over time in C productivity may be AIDS related Sick workers have family member helpers. So estimates are again likely to be lower bounds Is the assumption of no time-varying omitted variables reasonable? AIDS victims have higher absenteeism three years prior. What is the right counterfactual? Economics 172: Lecture 10 51
Pre, t=0 T i1 =0 T i1 =1 Post, t=1 Economics 172: Lecture 10 52
For next time: continue the HIV/AIDS readings Economics 172: Lecture 10 53
Whiteboard #1 Economics 172: Lecture 10 54
Whiteboard #2 Economics 172: Lecture 10 55
Whiteboard #3 Economics 172: Lecture 10 56
Whiteboard #4 Economics 172: Lecture 10 57
Whiteboard #5 Economics 172: Lecture 10 58
Map of Africa Economics 172: Lecture 10 59
Map of Africa Economics 172: Lecture 10 60