The document summarizes the regression discontinuity method used to evaluate the impact of Morocco's National Human Development Initiative (INDH) poverty reduction program. Key points: - INDH targeted communities with poverty rates over 30% for additional funding. This threshold was used to compare outcomes just above and below the cutoff in a regression discontinuity design. - Panel survey data from 2008, 2011, and 2013 was used to analyze economic outcomes like income, consumption, and assets at the household level around the threshold. - Regression models found INDH caused a 12.5% increase in consumption in 2008 and 20.7% in 2011, but no significant effects on income or assets. - The analysis is

1. Regression
Discontinuity Method
Day 3, Lecture 2
By Ragui Assaad
Training on Applied Micro-Econometrics and
Public Policy Evaluation
July 25-27, 2016
Economic Research Forum

2. Eligibility Rules as Instruments for
Participation
• Many programs have eligibility rules that specify that
individuals must be above or below a given threshold of
some variable to participate in the program
• This variable can be something like income or expenditure or a
constructed index, such as a poverty index, or marginaliy index
• To establish the effect of the program, one can compare
participants and non-participants in the “neighborhood” of
that threshold
• They should be fairly similar to each other, except for being on
either side of the threshold
2

3. Sharp vs. Fuzzy Regression
Discontinuity
• Sharp regression discontinuity (SRD) is when eligibiity
rules are strictly enforced and the probability of
partcipation is zero on one side of the threshold and one
on the other
• Fuzzy regression discontinuity is when other factos affect
participation or eligibility rules are not strictly enforced, so
there is some probability of participation below the
threshold and not 100% participation above the threshold,
but still a discontinuous jump in participation at the
threshold.
3

4. SRD vs FRD
4

5. RD and IV Approaches: what is the
connection?
• RD approach introduces introduces an exogenous
variable (a dummy for being below or above the
threshold) that strongly affects participation but that does
not directly affect the outcome of interest, conditional on
controlling for the continuous effects of the eligibility
variable itself.
• This variable can essentially serve as an IV for
participation
5

6. SRD Estimation
• Let Si be the continuous variables that determines
eligibility for participation, with s* the cutoff for eligibility
• Let Yi be the outcome variable of interest
•
• with Si <s* participating and Si>s* not participating
• Identifying assumption is that the expected value of the
unobservables of individuals at either side of the threshold
are equal, that is without the program they would achieve
similar outcomes
• Thus
• Taking the limit as
6
Yi
= bSi
+ui
E(Yi
s*
-e)-E(Yi
s*
+e)= E(bXi
s*
-e)-E(bXi
s*
+e)
e ®0

7. 7
Outcome without program Outcome with program

8. SRD Estimation
8
e ®0Taking the limit as , the effect of the program can be
measured as
tSRD
= lim
e®0
E(Yi
s*
-e)-lim
e®0
E(Yi
s*
+e)=Y-
-Y+
In practice, this is obtained through kernel-weighted
local polynomial regressions of the outcome variable
as a function of the eligibility variable Si. The Kernel
function gives the maximum weight to observations
near the cutoff with declining weight for further
observations up to a pre-specified bandwidth. The
bandwidth specifies the width of the window around
the cutoff to use. Any observations outside that
“window” get a weight of zero..

9. SRD Estimations
• Use these regressions to estimate the mean outcome
below and above the threshold in a narrow window
around the threshold
• Use these means to construct the test statistic
• Use bootstrapping to calculate SE’s
9

10. FRD Estimation
• Since eligibility rules are often not strictly enforced, the
probability of treatment is often increased at the threshold,
but does not go from zero to one.
• We estimate a probability of treatment at both sides of the
threshold
10
lim
e®0
Pr(T =1 S -e)-lim
e®0
Pr(T =1 S +e)
The effect of the program is therefore given by:
tFRD
=
lim
e®0
E(Yi
Si
= s*
-e)-lim
e®0
E(Yi
Si
= s*
+e)
lim
e®0
Pr(T =1 Si
= s*
-e)-lim
e®0
Pr(T =1 Si
= s*
+e)

11. FRD Estimation in Practice
• Again we use kernel-weighted local polynomial regression to
estimate the outcome as a function of the eligibility variable Si
at each side of the threshold for the numerator of the ratio
• We then estimate a kernel-weighted local polynomial
regression of the treatment variable as a function of the
eligibility variable Si at each side of the threshold for the
denominator.
• Use the outcome regressions to estimate the mean outcomes
below and above the threshold in a window around the
threshold
• Use the treatment probability regressions to estimate the
mean probabilities of treatment below and above the threshold
• Construct the test-statistic
• Use bootstrapping to obtain SE’s
11

12. Sensitivity Analysis
• Results are likely to depend on the width of the window
around the threshold
• Conduct sensitivity analysis around the bandwidth of the
kernel function and the width of the window used to
calculate the mean outcomes and mean probability of
treatment
12

13. Case Study: The Impact of a
Community Development and
Poverty Reduction Program in
Morocco
Caroline Krafft
Joint work with Safaa El-Kogali, Touhami Abdelkhalek,
Mohamed Benkassmi, Monica Chavez, Lucy Bassett
and Fouzia Ejjanoui
13

14. Background: INDH
• To combat poverty and inequality, in 2005 Morocco
launched the National Human Development Initiative
(INDH)
• Community driven development program
• First phase: 2005-2010
• US$1.7 billion of spending, 700 local plans, 22,000
activities, 5.2 million beneficiaries
• In rural areas, targeted communes with high poverty rates
• Additional communes targeted in 2011-2015 (second
phase of US$2.1 billion)
14

15. Data for Evaluating INDH
15
• Decision to evaluate INDH occurred after program rolled
out
• No data from before the program
• In rural areas, communities were targeted if poverty (map)
rates were 30% or higher
• Targeting allows for regression discontinuity design (RDD)
• National Human Development Observatory (ONDH) INDH
impact evaluation panel survey
• Panel survey on the household level
• Communes just above and below cutoff (27-32%)
• 12 households per commune, 124 rural communes
• Rounds in 2008, 2011, 2013 (71% of control in Phase II)

16. Outcomes
16
• Economic outcomes
• Income
• Consumption
• Assets
• All in 2013 dirham, annually, and per capita
• US$1=8.17 Moroccan dirham

17. Methods
17
• RDD
• “Jump” in outcome at treatment threshold (30% poverty rate) should
be due to program
• Assuming a continuous relationship in the absence of the program
• Regression models
• Controlling for poverty rate
• Also in log terms, quantile (median) regressions for economic
outcomes
• Commune fixed effects
• With interactions between treatment and 2011 or 2013
• To estimate any additional impacts in 2011 or 2013 as compared to
2008

18. Balance
18
• Checked for balance of characteristics in treatment and
control communes
• Could only look at characteristics not affected by program
• Only clear difference was in terms of household size
• Higher poverty (treatment) households were larger
• Estimate economic outcomes all in per capita terms

19. Economic outcomes: RDD
19
*p<0.1; **p<0.05; ***p<0.01
No significant asset effects

20. RDD: Economic outcomes graphs (50% bandwidth)
20

21. Economic outcomes: OLS
21
*p<0.1; **p<0.05; ***p<0.01
• Log models imply INDH caused approximately 12.5% increase
in consumption in 2008, 20.7% in 2011
• No significant income or asset effects

22. Limitations
22
• No baseline data from before the program
• Treatment not randomly assigned
• RDD method
• If communities are similar right around cutoff, as good as random
• Extremely sensitive to bandwidth
• Program expanded to control areas in 2011 onwards
• Generalizability
• Just around cutoff for inclusion
• Problems in implementing INDH, particularly coordination
challenges