Quasi-Experimental Methods. Technical Track
|
|
- Bethany Dawson
- 5 years ago
- Views:
Transcription
1 Quasi-Experimental Methods Technical Track East Asia Regional Impact Evaluation Workshop Seoul, South Korea Joost de Laat, World Bank
2 Randomized Assignment IE Methods Toolbox Discontinuity Design Difference-in- Differences Matching
3 Anti-poverty Programs Pensions Education Agriculture Discontinuity Design Many social programs select beneficiaries using an index or score: Targeted to households below a given poverty index/income Targeted to population above a certain age Scholarships targeted to students with high scores on standarized text Fertilizer program targeted to small farms less than given number of hectares)
4 Example: Effect of scholarship program on school attendance Goal Improve school attendance for poor students Method o Households with a score (Pa) of assets 50 are poor o Households with a score (Pa) of assets >50 are not poor Intervention Poor households receive scholarships to send children to school
5 Enrollment enrolled POOR NON POOR score
6 Enrollment enrolled POOR NON POOR score
7 Regression Discontinuity Design-Baseline Eligible Not eligible
8 Regression Discontinuity Design-Post Intervention IMPACT
9 For a Discontinuity Design you need 1) Continuous eligibility index (e.g. income) 2) Clearly defined cut-off. Households with a score cutoff are eligible Households with a score > cutoff are not-eligible Or vice-versa
10 Example Progresa CCT Eligibility for Progresa is based on national poverty index Household is poor if score 750 Eligibility for Progresa: o Eligible if score 750 o Not eligible if score > 750
11 Example of Progresa Score vs. consumption at Baseline-No treatment Consumption Fitted values Fitted values puntaje estimado en focalizacion Poverty Index
12 Example of Progresa Score vs. consumption post-intervention period-treatment Consumption Fitted values Fitted values 30.58** Estimated impact on consumption (Y) puntaje estimado en focalizacion Poverty Index (**) Significant at 1%
13 Example Cambodia CCT Eligibility is based on an index of the likelihood of dropping out of school. 2 cutoff points within each school: Applicants with the highest dropout risk offered US $60 per year scholarship Applicants with intermediate dropout risk offered US $45 per year scholarship Applicants with low dropout risk were not offered scholarship by the program No Scholarship US$ 45 scholarship US$ 60 scholarship Cutoff 1 Cutoff 2 Likelihood of dropping out of school
14 Large impact on US $45 scholarship No scholarship versus $45 $60 versus $45 scholarship Estimate of impact Probability Estimate of impact Probability Relative ranking Relative ranking Recipients Non-recipients Recipients Non-recipients Source: Filmer, and Schady Does More Cash in Conditional Cash Transfer Programs Always Lead to Larger Impacts on School Attendance?, Journal of Development Economics
15 Sharp and Fuzzy Discontinuity Sharp discontinuity The discontinuity precisely determines treatment Equivalent to random assignment in a neighborhood Fuzzy discontinuity Discontinuity is highly correlated with treatment. Use the assignment to estimate the probability of enrollment for the program
16 Identification for sharp discontinuity y i = β 0 + β 1 D i + δ(score i ) + ε i D i = 1 If household i receives transfer 0 If household i does not receive transfer δ(score i ) = Function that is continuous around the cut-off point Assignment rule under sharp discontinuity: D i = 1 D i = 0 score i 50 score i > 50
17 Identification for fuzzy discontinuity y i = β 0 + β 1 D i + δ(score i ) + ε i D i = 1 If household receives transfer 0 If household does not receive transfer However, some who are not ineligible take up the program. Some who are eligible do not. The reason why they do this could be correlated with the outcome of interest.
18 Estimation for fuzzy discontinuity In a first regression, use the score to predict whether individual takes up program or not. D i = γ 0 + γ 1 I(score i > 50) + η i Dummy variable In the second equation, use this predicted value for enrollment rather than actual enrollment. ^ y i = β 0 + β 1 D i + δ(score i ) + ε i Continuous function
19 Example Social assistance to the unemployed: o Low social assistance payments to individuals under 30 o Higher payments for individuals over 30 What is the effect of increased social assistance on employment? Lemieux & Milligan, 2008
20
21 Advantages of RDD for evaluation Yields an unbiased estimate of treatment effect at the discontinuity Can take advantage of a known rule for assigning the benefit o o This is common in the design of social interventions No need to exclude a group of eligible households/ individuals from treatment
22 Potential disadvantages of RDD Local average treatment effects: We estimate the effect of the program around the cut-off point This is not always generalizable Power. The effect is estimated at the discontinuity, so we generally have fewer observations than in a randomized experiment with the same sample size. Specification can be sensitive to functional form. Make sure the relationship between the assignment variable and the outcome variable is correctly modeled, including: (1) Nonlinear Relationships and (2) Interactions.
23 False RDD
24 Keep in Mind Discontinuity Design Requires continuous eligibility criteria with clear cut-off. Gives unbiased estimate of the treatment effect: Observations just across the cut-off are good comparisons. No need to exclude a group of eligible households/ individuals from treatment. Can sometimes use it for programs that already ongoing.
25 IE Methods Randomized Assignment Toolbox Discontinuity Design Difference-in- Differences Matching
26 Differences-in-Difference- Outline 1. What is Differences-in-Differences (diff-in-diff)? 2. Weaknesses 3. Test for strength of internal validity 4. When to use
27 What is Differences-differences? (diff-in-diff) Compare the change in outcomes for those that enrolled in the program with the change in outcomes for those that did not enroll in the program. If we can not randomize, can we try to mimic randomization? Natural Experiments: unexpected change in policy, natural disasters. Exploit variation of policies in time and space
28 Group affected by the policy change (treatment) Group that is not affected by the policy change (comparison) After the program start Before the program start Difference Y 1 D i =1 Y 1 D i =0 Y 0 D i =1 Y 0 D i =0 (Y 1 D=1)-(Y 0 D=1) (Y 1 D=0)-(Y 0 D=0) DD=[(Y 1 D=1)-(Y 0 D=1)] - [(Y 1 D=0)-(Y 0 D=0)]
29 Difference-in-differences (Diff-in-diff) Y=School attendance P=Girls scholarship program Enrolled (T) After (1) Before (0) Not Enrolled (C) - = Difference Diff-in-Diff: Impact=(Y T1 -Y T0 )-(Y C1 -Y C0 )
30 Impact =(A-B)-(C-D)=(A-C)-(B-D) School Attendance Not enrolled Enrolled D=0.78 C=0.81 A=0.74 Impact=0.11 B=0.60 Similar trends before the program t=0 t=1 Time
31 Example of Progresa Follow-up (t=1) Consumption (Y) Baseline (t=0) Consumption (Y) Enrolled Not Enrolled Difference Difference Estimated Impact on Consumption (Y) Linear Regression 27.06** Multivariate Linear Regression 25.53** Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).
32 Progresa Policy Recommendation? Impact of Progresa on Consumption (Y) Case 1: Before & After 34.28** Case 2: Enrolled & Not Enrolled Case 3: Randomized Assignment 29.75** Case 4: Randomized Promotion 30.4** Case 5: Discontinuity Design 30.58** Case 6: Difference-in-Differences 25.53** Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).
33 Regression (for 2 time periods) Y ii = α + β tttt + γ D i + δ tttt D i + ε ii y: outcome D i : treatment dummy tttt D i : if treatment in second period DD=[(Y 1 D=1)-(Y 0 D=1)] - [(Y 1 D=0)-(Y 0 D=0)]
34 Conditional Expectation Y ii = α + β tttt + γ D i + δ tttt D i + ε ii E(Y ii D i = 1) = +β + γ + δ E Y ii D i = 1 = +γ E Y ii D i = 0 = +β E Y ii D i = 0 = Treatment Group Control Group
35 Conditional Expectation Y ii = α + β tttt + γ D i + δ tttt D i + ε ii E Y ii Y ii D i = 1 E Y ii Y ii D i = 0 =(change in Y for treatment ) (change in Y for control) β + δ (β) = δ
36 If we have more than 2 time periods/groups Regression with fixed effects for time and group Y ii = +λ t + θ i + δ D ii + ε ii D ii indicates treatment in group i and period t λ t : year dummies θ t : group dummies
37 Differences-in-Difference- Outline 1. What is Differences-in-Differences (diffin-diff)? 2. Weaknesses 3. Test for strength of internal validity 4. When to use
38 Problem I: Common trends or shocks across groups Diff-in-Diff only valid if both groups would have had similar trends without the program. Then the change in observed outcomes for those not enrolled is a good counterfactual What if attendance for those enrolled would have increased by more than those not enrolled in any case? VIOLATION OF EQUAL TRENDS!
39 Same Trend School Attendance D=0.78 C=0.81 A=0.74 B=0.60 Similar trends before the program T=0 T=1 Time
40 Different Trend School Attendance Different trends before the program D=0.78 B=0.60 C=0.81 A=0.74 Diff-in-Diff cannot measure the impact of the program T=0 T=1 Time
41 What if an event affects only one group? Case 1: Training program Only highly motivated people participated in the program A new company is opened in the village and only the more motivated people apply for a job. Job prospects for those in the treatment would have improved even in the absence of the training program. DD overestimate the effect of the program Case 2: Subsidies on fertilizer (weather shocks) Treatment group: subsidized farmers. Control group: Unsubsidized farmers. Drought severely affects farmers that use fertilizer. DD underestimates the effect of the program
42 . Problem 2: Changes in group composition over time Diff-in-Diff requires that we follow the same types of people over time. For example, all the healthy people drop out of a healthcare program, because they don t need the treatment. So average health outcomes for those in the program is lower at the end of the program DD underestimates the effect of the program For example, all the sick people drop out of a health-care program, because they cannot walk to the clinic. DD overestimates the effect of the program
43 Considerations If program impact is heterogeneous across individual characteristics, pre-treatment differences in observed characteristics can create non-parallel outcome dynamics (Abadie, 2005). Similarly, bias would occur when the size of the response depends in a non-linear way on the size of the intervention, and we compare a group with high treatment intensity, with a group with low treatment intensity When outcomes within the unit of time/group are correlated, OLS standard errors understate the st. dev. of the DD estimator (Bertrand et al., 2004).
44 Differences-in-Difference- Outline 1. What is Differences-in-Differences (diffin-diff)? 2. Weaknesses 3. Test for strength of internal validity 4. When to use
45 Test for Trend School Attendance To test this, at least 3 observations in time are needed: o 2 observations before o 1 observation after. Before treatment t=-1 Before treatment t=0 After treatment t=1 Time
46 Sensitivity Analysis Placebo Test: Use a fake treatment group Should have no impact Use a different comparison group. Should still have an impact. Use a different outcome which should not be affected by the program. Should have no impact
47 Example Schooling and labor market consequences of school construction in Indonesia: evidence from an unusual policy experiment Esther Duflo, MIT American Economic Review, Sept 2001
48 School infrastructure Research questions Educational achievement? Educational achievement Salary level? What is the economic return on schooling?
49 Program description : The Indonesian government built 61,000 schools equivalent to one school per 500 children between 5 and 14 years old The enrollment rate increased from 69% to 85% between 1973 and 1978 Assignment rule -> The number of schools built in each region depended on the number of children out of school in those regions in 1972, before the start of the program.
50 Identification of the treatment effect There are 2 sources of variations in the intensity of the program for a given individual: By region There is variation in the number of schools received in each region. By age o o Children who were older than 12 years in 1972 did not benefit from the program. The younger a child was 1972, the more it benefited from the program because she spent more time in the new schools.
51 Sources of data 1995 population census. Individual-level data on: o birth date o 1995 salary level o 1995 level of education The intensity of the building program in the birth region of each person in the sample. Sample: men born between 1950 and 1972.
52 A first estimation of the impact Step 1: Let s simplify the problem and estimate the impact of the program. We simplify the intensity of the program: high or low We simplify the groups of children affected by the program o Young cohort of children who benefitted o Older cohort of children who did not benefit
53 Let s look at the average of the outcome variable years of schooling Intensity of the Building Program Age in 1974 High Low 2-6 (young cohort) (older cohort) Difference DD (0.089)
54 Let s look at the average of the outcome variable years of schooling Intensity of the Building program Age in 1974 High Low Difference 2-6 (young cohort) (older cohort) DD (0.089)
55 Idea: o o Placebo DD (Cf. p.798, Table 3, panel B) Look for 2 groups whom you know did not benefit, compute a DD, and check whether the estimated effect is 0. If it is NOT 0, we re in trouble Intensity of the Building Program Age in 1974 High Low Difference DD (0.098)
56 Step 2: Let s estimate this with a regression S = c+ α + β + γ.( PT. ) + δ.( C. T) + ε i ijk j k j i j i ijk with S P T ijk j j ijk = education level of person i in region j in cohort k = 1 if the person was born in a region with a high program intensity = 1 if the person belongs to the "young" cohort C j = dummy variable for region j βk = cohort fixed-effect α = district of birth fixed-effect ε = error term for person i in region j in cohort k
57 Step 3: Let s use additional information We will use the intensity of the program in each region: S = c+ α + β + γ.( PT. ) + δ.( C. T) + ε ijk j k j i j i ijk where P C j j = the intensity of building activity in region j = a vector of regional characteristics We estimate the effect of the program for each cohort separately: S c α β γ.( P. d) δ CT ε ijk j k l j i l j i ijk l= 2 l= 2 where = d i = a dummy variable for belonging to cohort i
58 Program effect per cohort γ l Age in 1974
59 For y = Dependent variable = Salary
60 Conclusion Results: For each school built per 1000 students; o The average educational achievement increase by years o The average salaries increased by % Making sure the DD estimation is accurate: o A placebo DD gave 0 estimated effect o Use various alternative specifications o Check that the impact estimates for each age cohort make sense.
61 Keep in Mind! Difference-in-Differences Combines Enrolled & Not Enrolled with Before & After. Slope: Generate counterfactual for change in outcome FUNDAMENTAL ASSUMPTION Trends slopes- are the same in treatments and comparisons To test this, at least 3 observations in time are needed: o 2 observations before o 1 observation after.
62 IE Methods Randomized Assignment Toolbox Discontinuity Design Difference-in- Differences Matching
63 Matching The group that enrolled is, on average, different the group that did not enroll However, some individuals are similar. So, can match similar individuals with each other
64 ENROLLED NOT ENROLLED VERY POOR POOR RICH VERY RICH
65 Compare Outcomes for Similar People ENROLLED Y NOT ENROLLED VERY POOR POOR RICH VERY RICH 5 4
66 More Complicated in Practice Match on all observable characteristics (e.g. income, gender, education ) Comparison group: non-participants with similar characteristics Create one aggregate Propensity Score to match: Compute everyone s probability of participating, based on their observable characteristics. Choose matches that have the same probability of participation as the treatments.
67 Density of propensity scores Density Non-Participants Participants Common Support 0 Propensity Score 1
68 Estimation strategy Predict the propensity scores for participants and nonparticipants. If participation status is binary, run a limited dependent variable regression and predict participation status for all units. Common support: Restrict the analysis to participants with P(X) s which are identical P(X) s to nonparticipants.
69 Estimation strategy Estimate the treatment effect for participant by finding the set of nonparticipants with P(X) s similar to that of the participant Take the difference between the outcome for the participant and the mean outcome for the similar nonparticipants. Repeat the exercise for all participants. Take the weighted average of the outcome differences across all matched participants to obtain: The average treatment effect on the matched treated. Estimate the standard error around the treatment effect for statistical inference.
70 Finding similar nonparticipants Different weighting functions to match nonparticipants with P(X) s similar to the P(X) of the participant: Stratification Nearest neighbor Radius Kernel
71 Main Problems
72 Problem One: Need Similar People ENROLLED NOT ENROLLED VERY POOR POOR RICH VERY RICH
73 Problem Two: Can Only Match on Observables MATCHING DOES NOT OVERCOME SELECTION PROBLEM! What if we can t collect data on people characteristics that are relevant for program participation and outcomes?
74 Summary Requirements for successful matching implementation: Data on variables that matter for participation. Common support No selection on based on unobservables. Matching can be combined with DID. Matching performed on baseline X s. DD controls for time-invariant unobservables.
75 Looking for a Volunteer
76 Case 7: Progresa Matching (P-Score) Baseline Characteristics Estimated Coefficient Probit Regression, Prob Enrolled=1 Head s age (years) ** Spouse s age (years) ** Head s education (years) ** Spouse s education (years) -0.03** Head is female= Indigenous= ** Number of household members 0.216** Dirt floor= ** Bathroom= ** Hectares of Land ** Distance to Hospital (km) 0.001* Constant 0.664** Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).
77 Case 7: Progresa Common Support Density: Pr (Enrolled) Density: Pr (Enrolled) Density: Pr (Enrolled) Pr (Enrolled)
78 Case 7: Progresa Matching (P-Score) Estimated Impact on Consumption (Y) Multivariate Linear Regression Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**). If significant at 10% level, we label impact with +
79 When to use Use when selection into program participation status is based on observable variables. Requirements Understand which variables matter for participation in the program (e.g., program rules) Data available on these variables that matter prior to units becoming participants or nonparticipants (baseline data). Common Support Best when combined with diff-diff Key assumption: There are no remaining unobservable differences between participants and nonparticipants
80 Keep in Mind! Matching Requires large samples and good quality data. Matching at baseline can be very useful: o o Know the assignment rule and match based on it combine with other techniques (i.e. diff-in-diff) Ex-post matching is risky: o o If there is no baseline, be careful! matching on endogenous expost variables gives bad results.
81 Progresa Policy Recommendation? Impact of Progresa on Consumption (Y) Case 1: Before & After 34.28** Case 2: Enrolled & Not Enrolled Case 3: Randomized Assignment 29.75** Case 4: Randomized Promotion 30.4** Case 5: Discontinuity Design 30.58** Case 6: Differences in Differences 25.53** Case 7: Matching Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**). If significant at 10% level, we label impact with +
82 Progresa Policy Recommendation? Impact of Progresa on Consumption (Y) Case 1: Before & After 34.28** Case 2: Enrolled & Not Enrolled Case 3: Randomized Assignment 29.75** Case 4: Randomized Promotion 30.4** Case 5: Discontinuity Design 30.58** Case 6: Differences in Differences 25.53** Case 7: Matching Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**). If significant at 10% level, we label impact with +
83 IE Methods Randomized Assignment Discontinuity Design Difference-in- Differences Toolbox Choose Your Method Matching + Diff-in- Diff
84 Where Do Comparison Groups come from? The rules of program operation determine the evaluation strategy. We can almost always find a valid comparison group if: the operational rules for selecting beneficiaries are equitable, transparent and accountable; the evaluation is designed prospectively.
85 Choosing your IE method(s) Money Excess demand No Excess demand Targeting Timing Phased Roll-out Targeted Universal Targeted Universal 1 Randomized assignment 4 RDD 1 Randomized assignment 2 Randomized promotion 3 DD with 5 Matching 1 Random ized Assignment 4 RDD 1 Randomized assignment to phases 2 Randomized Promotion to early take-up 3 DD with 5 matching Immediate Roll-out 1 Randomized Assignment 4 RDD 1 Randomized Assignment 2 Randomized Promotion 3 DD with 5 Matching 4 RDD If less than full Take-up: 2 Randomized Promotion 3 DD with 5 Matching
86 Test
87 Q1: What is the short-coming(s) of difference-difference? A. Those enrolled in the program might have a different trend over time as those not enrolled. B. It does not have a counter-factual. C. Sample size might be too small. D. People who are different to comparison group might drop out of the program E. Both A and C F. Both A and D.
88 Q2 You are evaluating a school management reform program that targets poor school. You decide to perform a diff-diff, comparing target schools with schools that did not receive the program. Over the same period government deployed more teachers to poor areas. Would this overestimate or under-estimate the program? A. Over-estimate B. Under-estimate C. Neither
89 Q3: What is the biggest short-coming of propensity match scoring? A. Cannot match on observables characteristics B. Cannot match on unobservables characteristics C. Different trends between treatment and comparison groups.
90 When is it possible to do regression discontinuity design? A. When there is a continuous eligibility criteria with a clear cut-off. B. When there is a comparison group of people who do not receive the program. C. When government randomly assigns some to receive the program and some not.
DIFFERENCE DIFFERENCES
DIFFERENCE IN DIFFERENCES & PANEL DATA Technical Track Session III Céline Ferré The World Bank Structure of this session 1 When do we use Differences-in- Differences? (Diff-in-Diff or DD) 2 Estimation
More informationMeasuring Impact. Impact Evaluation Methods for Policymakers. Sebastian Martinez. The World Bank
Impact Evaluation Measuring Impact Impact Evaluation Methods for Policymakers Sebastian Martinez The World Bank Note: slides by Sebastian Martinez. The content of this presentation reflects the views of
More informationSession III Differences in Differences (Dif- and Panel Data
Session III Differences in Differences (Dif- in-dif) and Panel Data Christel Vermeersch March 2007 Human Development Network Middle East and North Africa Region Spanish Impact Evaluation Fund Structure
More informationApplied Impact Evaluation
Applied Impact Evaluation Causal Inference & Random Assignment Paul Gertler UC Berkeley Our Objective Estimate the causal effect (impact) of intervention (P) on outcome (Y). (P) = Program or Treatment
More informationMEASURING IMPACT Impact Evaluation Methods for Policy Makers
MEASURING IMPACT Impact Evaluation Methods for Policy Makers This material constitutes supporting material for the "Impact Evaluation in Practice" book. This additional material is made freely but please
More informationMeasuring Impact. Paul Gertler Chief Economist Human Development Network The World Bank. The Farm, South Africa June 2006
Measuring Impact Paul Gertler Chief Economist Human Development Network The World Bank The Farm, South Africa June 2006 Motivation Traditional M&E: Is the program being implemented as designed? Could the
More informationTechnical Track Title Session V Regression Discontinuity (RD)
Impact Evaluation Technical Track Title Session V Regression Discontinuity (RD) Presenter: XXX Plamen Place, Nikolov Date Sarajevo, Bosnia and Herzegovina, 2009 Human Development Human Network Development
More informationSession III The Regression Discontinuity Design (RD)
REPUBLIC OF SOUTH AFRICA GOVERNMENT-WIDE MONITORING & IMPACT EVALUATION SEMINAR Session III The Regression Discontinuity Design (RD) Sebastian Martinez June 2006 Slides by Sebastian Galiani, Paul Gertler
More informationSession V Regression Discontinuity (RD)
Session V Regression Discontinuity (RD) Christel Vermeersch January 2008 Human Development Network Middle East and North Africa Region Spanish Impact Evaluation Fund Reminder: main objective of an evaluation.
More informationApplied Economics. Quasi-experiments: Instrumental Variables and Regresion Discontinuity. Department of Economics Universidad Carlos III de Madrid
Applied Economics Quasi-experiments: Instrumental Variables and Regresion Discontinuity Department of Economics Universidad Carlos III de Madrid Policy evaluation with quasi-experiments In a quasi-experiment
More informationBakke & Whited [JF 2012] Threshold Events and Identification: A Study of Cash Shortfalls Discussion by Fabian Brunner & Nicolas Boob
Bakke & Whited [JF 2012] Threshold Events and Identification: A Study of Cash Shortfalls Discussion by Background and Motivation Rauh (2006): Financial constraints and real investment Endogeneity: Investment
More informationEmpirical Approaches in Public Finance. Hilary Hoynes EC230. Outline of Lecture:
Lecture: Empirical Approaches in Public Finance Hilary Hoynes hwhoynes@ucdavis.edu EC230 Outline of Lecture: 1. Statement of canonical problem a. Challenges for causal identification 2. Non-experimental
More informationThe Impact of a $15 Minimum Wage on Hunger in America
The Impact of a $15 Minimum Wage on Hunger in America Appendix A: Theoretical Model SEPTEMBER 1, 2016 WILLIAM M. RODGERS III Since I only observe the outcome of whether the household nutritional level
More informationDIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL
DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL Non-experimental Methods Arndt Reichert October 14, 2014 DIME, World Bank What we know so far We want to isolate the causal effect ( impact ) of our interventions
More informationEVALUATING INDONESIA S UNCONDITIONAL CASH TRANSFER PROGRAM(S) *
EVALUATING INDONESIA S UNCONDITIONAL CASH TRANSFER PROGRAM(S) * SUDARNO SUMARTO The SMERU Research Institute * Based on a research report Of safety nets and safety ropes? An Evaluation of Indonesia s compensatory
More informationYannan Hu 1, Frank J. van Lenthe 1, Rasmus Hoffmann 1,2, Karen van Hedel 1,3 and Johan P. Mackenbach 1*
Hu et al. BMC Medical Research Methodology (2017) 17:68 DOI 10.1186/s12874-017-0317-5 RESEARCH ARTICLE Open Access Assessing the impact of natural policy experiments on socioeconomic inequalities in health:
More informationPeer Effects in Retirement Decisions
Peer Effects in Retirement Decisions Mario Meier 1 & Andrea Weber 2 1 University of Mannheim 2 Vienna University of Economics and Business, CEPR, IZA Meier & Weber (2016) Peers in Retirement 1 / 35 Motivation
More informationLabor Economics Field Exam Spring 2014
Labor Economics Field Exam Spring 2014 Instructions You have 4 hours to complete this exam. This is a closed book examination. No written materials are allowed. You can use a calculator. THE EXAM IS COMPOSED
More informationLabour Force Participation in the Euro Area: A Cohort Based Analysis
Labour Force Participation in the Euro Area: A Cohort Based Analysis Almut Balleer (University of Bonn) Ramon Gomez Salvador (European Central Bank) Jarkko Turunen (European Central Bank) ECB/CEPR LM workshop,
More informationFor Online Publication Additional results
For Online Publication Additional results This appendix reports additional results that are briefly discussed but not reported in the published paper. We start by reporting results on the potential costs
More informationDepression Babies: Do Macroeconomic Experiences Affect Risk-Taking?
Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? October 19, 2009 Ulrike Malmendier, UC Berkeley (joint work with Stefan Nagel, Stanford) 1 The Tale of Depression Babies I don t know
More informationYour Name (Please print) Did you agree to take the optional portion of the final exam Yes No. Directions
Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No (Your online answer will be used to verify your response.) Directions There are two parts to the final exam.
More informationPrinciples Of Impact Evaluation And Randomized Trials Craig McIntosh UCSD. Bill & Melinda Gates Foundation, June
Principles Of Impact Evaluation And Randomized Trials Craig McIntosh UCSD Bill & Melinda Gates Foundation, June 12 2013. Why are we here? What is the impact of the intervention? o What is the impact of
More informationEconomics 270c. Development Economics Lecture 11 April 3, 2007
Economics 270c Development Economics Lecture 11 April 3, 2007 Lecture 1: Global patterns of economic growth and development (1/16) The political economy of development Lecture 2: Inequality and growth
More information1 Payroll Tax Legislation 2. 2 Severance Payments Legislation 3
Web Appendix Contents 1 Payroll Tax Legislation 2 2 Severance Payments Legislation 3 3 Difference-in-Difference Results 5 3.1 Senior Workers, 1997 Change............................... 5 3.2 Young Workers,
More information14.471: Fall 2012: Recitation 3: Labor Supply: Blundell, Duncan and Meghir EMA (1998)
14.471: Fall 2012: Recitation 3: Labor Supply: Blundell, Duncan and Meghir EMA (1998) Daan Struyven September 29, 2012 Questions: How big is the labor supply elasticitiy? How should estimation deal whith
More informationContents: Appendix 3: Parallel Trends. Appendix
Mohanan M, Babiarz KS, Goldhaber-Fiebert JD, Miller G, Vera-Hernandez M. Effect of a large-scale social franchising and telemedicine program on childhood diarrhea and pneumonia outcomes in India. Health
More informationEmpirical Methods for Corporate Finance. Regression Discontinuity Design
Empirical Methods for Corporate Finance Regression Discontinuity Design Basic Idea of RDD Observations (e.g. firms, individuals, ) are treated based on cutoff rules that are known ex ante For instance,
More informationLabor Economics Field Exam Spring 2011
Labor Economics Field Exam Spring 2011 Instructions You have 4 hours to complete this exam. This is a closed book examination. No written materials are allowed. You can use a calculator. THE EXAM IS COMPOSED
More informationThe model is estimated including a fixed effect for each family (u i ). The estimated model was:
1. In a 1996 article, Mark Wilhelm examined whether parents bequests are altruistic. 1 According to the altruistic model of bequests, a parent with several children would leave larger bequests to children
More informationThe impact of cash transfers on productive activities and labor supply. The case of LEAP program in Ghana
The impact of cash transfers on productive activities and labor supply. The case of LEAP program in Ghana Silvio Daidone and Benjamin Davis Food and Agriculture Organization of the United Nations Agricultural
More informationDYNAMICS OF URBAN INFORMAL
DYNAMICS OF URBAN INFORMAL EMPLOYMENT IN BANGLADESH Selim Raihan Professor of Economics, University of Dhaka and Executive Director, SANEM ICRIER Conference on Creating Jobs in South Asia 3-4 December
More informationLABOR SUPPLY RESPONSES TO TAXES AND TRANSFERS: PART I (BASIC APPROACHES) Henrik Jacobsen Kleven London School of Economics
LABOR SUPPLY RESPONSES TO TAXES AND TRANSFERS: PART I (BASIC APPROACHES) Henrik Jacobsen Kleven London School of Economics Lecture Notes for MSc Public Finance (EC426): Lent 2013 AGENDA Efficiency cost
More informationTHE ECONOMIC IMPACT OF RISING THE RETIREMENT AGE: LESSONS FROM THE SEPTEMBER 1993 LAW*
THE ECONOMIC IMPACT OF RISING THE RETIREMENT AGE: LESSONS FROM THE SEPTEMBER 1993 LAW* Pedro Martins** Álvaro Novo*** Pedro Portugal*** 1. INTRODUCTION In most developed countries, pension systems have
More informationRegression Discontinuity Design
Regression Discontinuity Design Aniceto Orbeta, Jr. Philippine Institute for Development Studies Stream 2 Impact Evaluation Methods (Intermediate) Making Impact Evaluation Matter Better Evidence for Effective
More informationRANDOMIZED TRIALS Technical Track Session II Sergio Urzua University of Maryland
RANDOMIZED TRIALS Technical Track Session II Sergio Urzua University of Maryland Randomized trials o Evidence about counterfactuals often generated by randomized trials or experiments o Medical trials
More informationEffects of working part-time and full-time on physical and mental health in old age in Europe
Effects of working part-time and full-time on physical and mental health in old age in Europe Tunga Kantarcı Ingo Kolodziej Tilburg University and Netspar RWI - Leibniz Institute for Economic Research
More informationCore methodology I: Sector analysis of MDG determinants
UNDP UN-DESA UN-ESCAP Core methodology I: Sector analysis of MDG determinants Rob Vos (UN-DESA/DPAD) Presentation prepared for the inception and training workshop of the project Assessing Development Strategies
More informationTAXES, TRANSFERS, AND LABOR SUPPLY. Henrik Jacobsen Kleven London School of Economics. Lecture Notes for PhD Public Finance (EC426): Lent Term 2012
TAXES, TRANSFERS, AND LABOR SUPPLY Henrik Jacobsen Kleven London School of Economics Lecture Notes for PhD Public Finance (EC426): Lent Term 2012 AGENDA Why care about labor supply responses to taxes and
More informationManufacturing Busts, Housing Booms, and Declining Employment
Manufacturing Busts, Housing Booms, and Declining Employment Kerwin Kofi Charles University of Chicago Harris School of Public Policy And NBER Erik Hurst University of Chicago Booth School of Business
More informationData and Methods in FMLA Research Evidence
Data and Methods in FMLA Research Evidence The Family and Medical Leave Act (FMLA) was passed in 1993 to provide job-protected unpaid leave to eligible workers who needed time off from work to care for
More informationNutrition and productivity
Nutrition and productivity Abhijit Banerjee Department of Economics, M.I.T. 1 A simple theory of nutrition and productivity The capacity curve (fig 1) The capacity curve: It relates income and work capacity
More information1. (9; 3ea) The table lists the survey results of 100 non-senior students. Math major Art major Biology major
Math 54 Test #2(Chapter 4, 5, 6, 7) Name: Show all necessary work for full credit. You may use graphing calculators for your calculation, but you must show all detail and use the proper notations. Total
More informationGone with the Storm: Rainfall Shocks and Household Wellbeing in Guatemala
Gone with the Storm: Rainfall Shocks and Household Wellbeing in Guatemala Javier E. Baez (World Bank) Leonardo Lucchetti (World Bank) Mateo Salazar (World Bank) Maria E. Genoni (World Bank) Washington
More informationComment on Gary V. Englehardt and Jonathan Gruber Social Security and the Evolution of Elderly Poverty
Comment on Gary V. Englehardt and Jonathan Gruber Social Security and the Evolution of Elderly Poverty David Card Department of Economics, UC Berkeley June 2004 *Prepared for the Berkeley Symposium on
More informationPlanning Sample Size for Randomized Evaluations Esther Duflo J-PAL
Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL povertyactionlab.org Planning Sample Size for Randomized Evaluations General question: How large does the sample need to be to credibly
More informationThe Effect of a Longer Working Horizon on Individual and Family Labour Supply
The Effect of a Longer Working Horizon on Individual and Family Labour Supply Francesca Carta Marta De Philippis Bank of Italy December 1, 2017 Paris, ASME BdF Labour Market Conference Motivation: delaying
More informationCASE STUDY 2: EXPANDING CREDIT ACCESS
CASE STUDY 2: EXPANDING CREDIT ACCESS Why Randomize? This case study is based on Expanding Credit Access: Using Randomized Supply Decisions To Estimate the Impacts, by Dean Karlan (Yale) and Jonathan Zinman
More informationDoes Participation in Microfinance Programs Improve Household Incomes: Empirical Evidence From Makueni District, Kenya.
AAAE Conference proceedings (2007) 405-410 Does Participation in Microfinance Programs Improve Household Incomes: Empirical Evidence From Makueni District, Kenya. Joy M Kiiru, John Mburu, Klaus Flohberg
More informationMigration Responses to Household Income Shocks: Evidence from Kyrgyzstan
Migration Responses to Household Income Shocks: Evidence from Kyrgyzstan Katrina Kosec Senior Research Fellow International Food Policy Research Institute Development Strategy and Governance Division Joint
More informationLong Term Effects of Temporary Labor Demand: Free Trade Zones, Female Education and Marriage Market Outcomes in the Dominican Republic
Long Term Effects of Temporary Labor Demand: Free Trade Zones, Female Education and Marriage Market Outcomes in the Dominican Republic Maria Micaela Sviatschi Columbia University June 15, 2015 Introduction
More informationDavid Newhouse Daniel Suryadarma
David Newhouse Daniel Suryadarma Outline of presentation 1. Motivation Vocational education expansion 2. Data 3. Determinants of choice of type 4. Effects of high school type Entire sample Cohort vs. age
More informationClosing routes to retirement: how do people respond? Johannes Geyer, Clara Welteke
Closing routes to retirement: how do people respond? Johannes Geyer, Clara Welteke DIW Berlin & IZA Research Affiliate, cwelteke@diw.de NETSPAR Workshop, January 20, 2017 Motivation: decreasing labor force
More informationEffects of Tax-Based Saving Incentives on Contribution Behavior: Lessons from the Introduction of the Riester Scheme in Germany
Modern Economy, 2016, 7, 1198-1222 http://www.scirp.org/journal/me ISSN Online: 2152-7261 ISSN Print: 2152-7245 Effects of Tax-Based Saving Incentives on Contribution Behavior: Lessons from the Introduction
More informationLECTURE: MEDICAID HILARY HOYNES UC DAVIS EC230 OUTLINE OF LECTURE: 1. Overview of Medicaid. 2. Medicaid expansions
LECTURE: MEDICAID HILARY HOYNES UC DAVIS EC230 OUTLINE OF LECTURE: 1. Overview of Medicaid 2. Medicaid expansions 3. Economic outcomes with Medicaid expansions 4. Crowd-out: Cutler and Gruber QJE 1996
More informationMeasuring and Mapping the Welfare Effects of Natural Disasters A Pilot
Measuring and Mapping the Welfare Effects of Natural Disasters A Pilot Luc Christiaensen,, World Bank, presentation at the Managing Vulnerability in East Asia workshop, Bangkok, June 25-26, 26, 2008 Key
More informationEcon Spring 2016 Section 12
Econ 140 - Spring 2016 Section 12 GSI: Fenella Carpena April 28, 2016 1 Experiments and Quasi-Experiments Exercise 1.0. Consider the STAR Experiment discussed in lecture where students were randomly assigned
More informationDoes Female Empowerment Promote Economic Development? Matthias Doepke (Northwestern) Michèle Tertilt (Mannheim)
Does Female Empowerment Promote Economic Development? Matthias Doepke (Northwestern) Michèle Tertilt (Mannheim) Evidence Evidence : Evidence : Evidence : Evidence : : Evidence : : Evidence : : Evidence
More informationFINAL REPORT AN EVALUATION OF THE IMPACT OF PROGRESA CASH PAYMENTS ON PRIVATE INTER-HOUSEHOLD TRANSFERS. Graciela Teruel Benjamin Davis
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE FINAL REPORT AN EVALUATION OF THE IMPACT OF PROGRESA CASH PAYMENTS ON PRIVATE INTER-HOUSEHOLD TRANSFERS Graciela Teruel Benjamin Davis International Food Policy
More informationGender Differences in the Labor Market Effects of the Dollar
Gender Differences in the Labor Market Effects of the Dollar Linda Goldberg and Joseph Tracy Federal Reserve Bank of New York and NBER April 2001 Abstract Although the dollar has been shown to influence
More informationInput Tariffs, Speed of Contract Enforcement, and the Productivity of Firms in India
Input Tariffs, Speed of Contract Enforcement, and the Productivity of Firms in India Reshad N Ahsan University of Melbourne December, 2011 Reshad N Ahsan (University of Melbourne) December 2011 1 / 25
More informationMobile Financial Services for Women in Indonesia: A Baseline Survey Analysis
Mobile Financial Services for Women in Indonesia: A Baseline Survey Analysis James C. Knowles Abstract This report presents analysis of baseline data on 4,828 business owners (2,852 females and 1.976 males)
More informationEvaluating Search Periods for Welfare Applicants: Evidence from a Social Experiment
Evaluating Search Periods for Welfare Applicants: Evidence from a Social Experiment Jonneke Bolhaar, Nadine Ketel, Bas van der Klaauw ===== FIRST DRAFT, PRELIMINARY ===== Abstract We investigate the implications
More informationMinistry of Health, Labour and Welfare Statistics and Information Department
Special Report on the Longitudinal Survey of Newborns in the 21st Century and the Longitudinal Survey of Adults in the 21st Century: Ten-Year Follow-up, 2001 2011 Ministry of Health, Labour and Welfare
More informationUsing Randomized Evaluations to Improve Policy
Daniel Stein (DIME) Using Randomized Evaluations to Improve Policy Development Impact Evaluation Initiative innovations & solutions in infrastructure, agriculture & environment naivasha, april 23-27, 2011
More informationEvaluation of the effects of the active labour measures on reducing unemployment in Romania
National Scientific Research Institute for Labor and Social Protection Evaluation of the effects of the active labour measures on reducing unemployment in Romania Speranta PIRCIOG, PhD Senior Researcher
More informationEmpirical Methods for Corporate Finance
Empirical Methods for Corporate Finance Difference in Differences Note: This set of slides is inspired by that of Michael R. Roberts at Wharton Basics (As said earlier) one of the most causes of endogeneity
More informationFiscal Policy and Long-Term Growth
Fiscal Policy and Long-Term Growth Sanjeev Gupta Deputy Director of Fiscal Affairs Department International Monetary Fund Tokyo Fiscal Forum June 10, 2015 Outline Motivation The Channels: How Can Fiscal
More informationAcemoglu, et al (2008) cast doubt on the robustness of the cross-country empirical relationship between income and democracy. They demonstrate that
Acemoglu, et al (2008) cast doubt on the robustness of the cross-country empirical relationship between income and democracy. They demonstrate that the strong positive correlation between income and democracy
More informationPopulation Economics Field Exam Spring This is a closed book examination. No written materials are allowed. You can use a calculator.
Population Economics Field Exam Spring 2011 Instructions You have 4 hours to complete this exam. This is a closed book examination. No written materials are allowed. You can use a calculator. YOU MUST
More informationOnline Appendix (Not For Publication)
A Online Appendix (Not For Publication) Contents of the Appendix 1. The Village Democracy Survey (VDS) sample Figure A1: A map of counties where sample villages are located 2. Robustness checks for the
More informationShale Gas Development and Housing Values Over a Decade: Evidence from the Barnett Shale
Shale Gas Development and Housing Values Over a Decade: Evidence from the Barnett Shale Jeremy G. Weber (USDA/Economic Research Service) Wesley Burnett (West Virginia University) Irene M. Xiarchos (USDA/Office
More informationHappy Voters. Exploring the Intersections between Economics and Psychology. Federica Liberini 1, Eugenio Proto 2 Michela Redoano 2.
Exploring the Intersections between Economics and Psychology Federica Liberini 1, Eugenio Proto 2 Michela Redoano 2 1 ETH Zurich, 2 Warwick University and IZA 3 Warwick University 29 January 2015 Overview
More informationThe Role of Exponential-Growth Bias and Present Bias in Retirment Saving Decisions
The Role of Exponential-Growth Bias and Present Bias in Retirment Saving Decisions Gopi Shah Goda Stanford University & NBER Matthew Levy London School of Economics Colleen Flaherty Manchester University
More informationI ll Have What She s Having : Identifying Social Influence in Household Mortgage Decisions
I ll Have What She s Having : Identifying Social Influence in Household Mortgage Decisions Ben McCartney & Avni Shah 2016 CFPB Research Conference Mortgage Decisions are Important and Complex Mortgage
More informationRESOURCE POOLING WITHIN FAMILY NETWORKS: INSURANCE AND INVESTMENT
RESOURCE POOLING WITHIN FAMILY NETWORKS: INSURANCE AND INVESTMENT Manuela Angelucci 1 Giacomo De Giorgi 2 Imran Rasul 3 1 University of Michigan 2 Stanford University 3 University College London June 20,
More informationHome Energy Reporting Program Evaluation Report. June 8, 2015
Home Energy Reporting Program Evaluation Report (1/1/2014 12/31/2014) Final Presented to Potomac Edison June 8, 2015 Prepared by: Kathleen Ward Dana Max Bill Provencher Brent Barkett Navigant Consulting
More informationThe Effects of Increasing the Early Retirement Age on Social Security Claims and Job Exits
The Effects of Increasing the Early Retirement Age on Social Security Claims and Job Exits Day Manoli UCLA Andrea Weber University of Mannheim February 29, 2012 Abstract This paper presents empirical evidence
More informationUnequal Burden of Retirement Reform: Evidence from Australia
Unequal Burden of Retirement Reform: Evidence from Australia Todd Morris The University of Melbourne April 17, 2018 Todd Morris (University of Melbourne) Unequal Burden of Retirement Reform April 17, 2018
More informationEvaluation of Public Policy
Università degli Studi di Ferrara a.a. 2017-2018 The main objective of this course is to evaluate the effect of Public Policy changes on the budget of public entities. Effect of changes in electoral rules
More informationAnalysis of Microdata
Rainer Winkelmann Stefan Boes Analysis of Microdata Second Edition 4u Springer 1 Introduction 1 1.1 What Are Microdata? 1 1.2 Types of Microdata 4 1.2.1 Qualitative Data 4 1.2.2 Quantitative Data 6 1.3
More informationDoes Investing in School Capital Infrastructure Improve Student Achievement?
Does Investing in School Capital Infrastructure Improve Student Achievement? Kai Hong Ph.D. Student Department of Economics Vanderbilt University VU Station B#351819 2301 Vanderbilt Place Nashville, TN37235
More informationAbadie s Semiparametric Difference-in-Difference Estimator
The Stata Journal (yyyy) vv, Number ii, pp. 1 9 Abadie s Semiparametric Difference-in-Difference Estimator Kenneth Houngbedji, PhD Paris School of Economics Paris, France kenneth.houngbedji [at] psemail.eu
More informationObesity, Disability, and Movement onto the DI Rolls
Obesity, Disability, and Movement onto the DI Rolls John Cawley Cornell University Richard V. Burkhauser Cornell University Prepared for the Sixth Annual Conference of Retirement Research Consortium The
More informationUniversity of Mannheim
Threshold Events and Identication: A Study of Cash Shortfalls Bakke and Whited, published in the Journal of Finance in June 2012 Introduction The paper combines three objectives 1 Provide general guidelines
More informationDoes Expanding Health Insurance Beyond Formal-Sector Workers Encourage Informality? Measuring the Impact of Mexico s Seguro Popular
Does Expanding Health Insurance Beyond Formal-Sector Workers Encourage Informality? Measuring the Impact of Mexico s Seguro Popular Reyes Aterido (WB-DECMG) Mary Hallward-Driemeier (WB-FPDCE) Carmen Pagés
More information: Corruption Lecture 2
14.75 : Corruption Lecture 2 Ben Olken Olken () Corruption Lecture 2 1 / 3 Outline Do we care? Magnitude and effi ciency costs The corrupt offi cial s decision problem Balancing risks, rents, and incentives
More informationFemale Labour Supply, Human Capital and Tax Reform
Female Labour Supply, Human Capital and Welfare Reform Richard Blundell, Monica Costa-Dias, Costas Meghir and Jonathan Shaw October 2013 Motivation Issues to be addressed: 1 How should labour supply, work
More informationDoes Female Empowerment Promote Economic Development?
Does Female Empowerment Promote Economic Development? Matthias Doepke (Northwestern) Michèle Tertilt (Mannheim) April 2018, Wien Evidence Development Policy Based on this evidence, various development
More informationManufacturing Decline, Housing Booms, and Non-Employment Manufacturing Decline, Housing Booms, and Non-Employment
Manufacturing Decline, Housing Booms, and Non-Employment Manufacturing Decline, Housing Booms, and Non-Employment Kerwin Kofi Charles University of Chicago Harris School of Public Policy And NBER Erik
More informationStudent Loan Nudges: Experimental Evidence on Borrowing and. Educational Attainment. Online Appendix: Not for Publication
Student Loan Nudges: Experimental Evidence on Borrowing and Educational Attainment Online Appendix: Not for Publication June 2018 1 Appendix A: Additional Tables and Figures Figure A.1: Screen Shots From
More informationTrading and Enforcing Patent Rights. Carlos J. Serrano University of Toronto and NBER
Trading and Enforcing Patent Rights Alberto Galasso University of Toronto Mark Schankerman London School of Economics and CEPR Carlos J. Serrano University of Toronto and NBER OECD-KNOWINNO Workshop @
More informationa. Explain why the coefficients change in the observed direction when switching from OLS to Tobit estimation.
1. Using data from IRS Form 5500 filings by U.S. pension plans, I estimated a model of contributions to pension plans as ln(1 + c i ) = α 0 + U i α 1 + PD i α 2 + e i Where the subscript i indicates the
More informationReproductive health, female empowerment and economic prosperity. Elizabeth Frankenberg Duncan Thomas
Reproductive health, female empowerment and economic prosperity Elizabeth Frankenberg Duncan Thomas Studies suggest females with more resources under own control more likely to use prenatal care have healthier
More informationCall Your Leader: Does the Mobile Phone Affect Policymaking?
Call Your Leader: Does the Mobile Phone Affect Policymaking? Jahen F. Rezki University of York 2018 Nordic Conference on Development Economics 11 June 2018 1/27 Motivation The role of media and the rapid
More informationNBER WORKING PAPER SERIES THE EFFECTS OF CHANGES IN STATE SSI SUPPLEMENTS ON PRE-RETIREMENT LABOR SUPPLY. David Neumark Elizabeth T.
NBER WORKING PAPER SERIES THE EFFECTS OF CHANGES IN STATE SSI SUPPLEMENTS ON PRE-RETIREMENT LABOR SUPPLY David Neumark Elizabeth T. Powers Working Paper 9851 http://www.nber.org/papers/w9851 NATIONAL BUREAU
More informationTopic 11: Disability Insurance
Topic 11: Disability Insurance Nathaniel Hendren Harvard Spring, 2018 Nathaniel Hendren (Harvard) Disability Insurance Spring, 2018 1 / 63 Disability Insurance Disability insurance in the US is one of
More informationReview questions for Multinomial Logit/Probit, Tobit, Heckit, Quantile Regressions
1. I estimated a multinomial logit model of employment behavior using data from the 2006 Current Population Survey. The three possible outcomes for a person are employed (outcome=1), unemployed (outcome=2)
More informationCenter for Demography and Ecology
Center for Demography and Ecology University of Wisconsin-Madison Money Matters: Returns to School Quality Throughout a Career Craig A. Olson Deena Ackerman CDE Working Paper No. 2004-19 Money Matters:
More informationThe Effects of the Health Insurance Availability on the Demand-side: An. Impact Evaluation of China s New Cooperative Medical Scheme
The Effects of the Health Insurance Availability on the Demand-side: An Impact Evaluation of China s New Cooperative Medical Scheme Binzhen Wu School of Economics and Management, Tsinghua University 100084,
More information