DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL

Transcription

1 DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL

2 Non-experimental Methods Arndt Reichert October 14, 2014 DIME, World Bank

3 What we know so far We want to isolate the causal effect ( impact ) of our interventions on outcomes of interest Randomizing the assignment to treatment is the gold standard methodology (simple, precise, cheap) What if we really cannot use it? Key problem is the search for a counterfactual There are other methods (difference-in-differences, matching, discontinuity design) Each of these relies on key assumptions

4 Compare before & after? 14 Annual income per capita (in 100 USD) Is this the impact of the program? 2 0 Before project After project

5 Compare treatment & control? Annual income per capita (in 100 USD) Is this the impact of the program? Treatment Control 2 0 After project

6 Combine the two differences! Annual income per capita (in 100 USD) Treatment Control 2 0 Before project After project

7 Combine the two differences! Annual income per capita (in 100 USD) Treatment Control 2 0 Before project After project

8 Difference-in-differences Annual income per capita (in 100 USD) Before project After project Difference in treated group Difference in control group = Treatment effect

9 This talk 1. Difference-in-differences 2. Matching (+ diff-in-diff) 3. Discontinuity design

10 Key assumption: parallel trends Treatment Effect

11 To make diff-in-diff work Can we find a plausible counterfactual? This can be difficult Try to find control group that mimics randomization!

12 Example: Efficient Lighting and Appliances Project in Mexico Principal objective increase the use of energy-efficient technologies at the residential level & reduce GHG emissions Intervention subsidy for replacement of old and inefficient air conditioners in Target households with 10 years old, fully functional appliance only households in a warm climate zone eligible Assignment non-random

13 What s a good counterfactual? How about: 1. Non-participating households 2. Participating households which have not yet replaced appliance But why did they not participate? Not eligible or eligible but not interested Geographic location No or newer air conditioner Low level of energy consumption No money for replacement

14 This talk 1. Difference-in-differences 2. Matching (+ diff-in-diff) 3. Discontinuity design

15 Matching (+ diff-in-diff) Counterfactual: Non-participating households Each participating household is matched with a similar non-participating household based on observable characteristics On average, matched participants and nonparticipants share the same observable characteristics (by construction!) Estimate the effect of program by using difference-in-differences But what about unobservables??!

16 Davis, Fuchs, Gertler (forthcoming). Cash for Coolers, AEJ: Economic Policy Design a control group by establishing close matches on observable characteristics same location similar pre-treatment electricity consumption Compare only observations that have a good match Treatment group: Participants that could find a match Comparison group: Non-participants similar enough to the participants

17 Implications In most cases, we cannot match everyone Bigger sample better matches (Costly!) Can t say much about the sample trimmed out Matched participant Portion of treatment group trimmed out Nonparticipants Participants Pre-treatment electricity consumption

18 Parallel trends? Before program participation! Looks like common trend assumption holds only with matching

19 Matching (+ diff-in-diff): Results by Month Air conditioner replacement did actually increase electricity consumption in summer

20 Implications How does matching help us with our original quest for a counterfactual? Why did comparison households not participate? Could participation decision be correlated with important unobservables? Geographic location weather differences? Level of energy consumption pre-treatment differences difference in expected energy consumption?

21 Conclusion Advantage of the matching method Yet Can help find a counterfactual where observable characteristics Hard to ignore the role of unobservable characteristics We can only measure the impact for those participants that could be matched to similar non-participants requires a lot of data hard to predict how efficient the matching exercise will be

22 This talk 1. Difference-in-differences 2. Matching (+ diff-in-diff) 3. Discontinuity design

23 Regression discontinuity designs RDD is more similar to randomization Based on the selection process Need a clear & enforced eligibility rule A simple, quantifiable score Assignment to treatment is based on this rule A threshold is established Compare individuals just above the threshold to individuals just below the threshold

24 RDD logic Assignment to the treatment depends on continuous score or ranking potential beneficiaries are ordered by looking at the score there is a cut-off point for eligibility clearly defined criterion determined ex ante cut-off determines the assignment to treatment This usually results from administrative decisions resource constraints limit coverage very targeted intervention transparent rules

25 RDD in practice Conditional Cash Transfer program with education component in Colombia Poverty index score determines program eligibility Idea: compare program take up and school completion between Treatment group: individuals below poverty threshold Comparison group: individuals above poverty threshold Around the threshold, assignment to treatment is (nearly) random The only difference is program participation

26 RDD example 1: Poverty index score as forcing variable Impact of conditional cash transfers on education in Colombia [Baez & Camacho (2011), World Bank Policy Research Working Paper 5681]

27 Validity assessment of RDD approach No discontinuity when using other household-level variables

28 RDD drawbacks But how generalizable are the results? They only tell us about how the program affects education of children in household around the poverty threshold! The program is likely to have different effects on the poorest

29 What can we learn from a very local estimate?

30 RDD example 2: Distance to geographic border as forcing variable Impact of sustained exposure to air pollution on life expectancy from China s Huai River policy Provision of free coal for heating boilers in cities north of the Huai River Combustion of coal in boilers is associated with the release of air pollutants (particularly particulate matter) [Chen, Ebenstein, Greenstone, and Li (2013), PNAS 110 (32): ]

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32 Impact of policy on particulate matter Much higher pollution North of the river

33 Impact of policy on life expectancy Lower life expectancy North of the river

34 Validity assessment of RDD approach Effects on cardiorespiratory deaths but no effects on noncardiorespiratory deaths

35 When can we use RDDs? To design a prospective evaluation when randomization is not feasible But need a clear allocation rule with a cut-off! Poverty index and geographic borders are two examples for such an allocation rule To evaluate ex-post interventions using discontinuities as natural experiments

36 Summary Randomized controlled trials require minimal assumptions and provide intuitive estimates Non-experimental methods require assumptions that must be carefully tested More data-intensive Not always testable Get creative: Mix-and-match types of methods! Address relevant questions with relevant techniques