Using administrative data to make development policy more effective

Transcription

1 Using administrative data to make development policy more effective Javier Baez and Julieta Trias The World Bank

2 What is impact evaluation? 2 Remember the defining characteristics of IE: Attribution o A method to identify the impact attributable to an intervention emphasis on causal effects Counterfactual o Outcomes are compared with a counterfactual situation what would have happened without the intervention

3 IEs are data intensive 3 IEs often rely on primary data collection Rich baseline & follow up information Suitable to answer the what, why and how questions of program impacts But brings challenges: Timing: Designing and collecting surveys takes a lot of time Costs: Around two thirds of the evaluation budget (300K-400K)

4 But sometimes IEs can be carried out with administrative data 4 Administrative data (AD) is an alternative to address key policy questions of attribution Program effectiveness: will the nutritional program reduce malnutrition? Program design: what is the marginal value added of different program alternatives (e.g. nutrition supplements vs. nutrition supplements + training on good nutrition practices)? And often at a much lower cost and more quickly

5 What is administrative data? 5 Data originally collected for three main purposes: monitoring of government programs and interventions, targeting government interventions, enabling regulation and auditing Derived from an administrative source, usually a government unit (sectoral ministries, program implementation and administration units, etc.) Often of high frequency and with large coverage of target group (e.g. children enrolled in school, migration records, vital records, social security records, etc.)

6 Could be very rich in information 6 For instance, administrative data in the health sector could include: Supply side: health centers, type and quantity of services provided, location, staff, etc. Demand side: number of users, patient profile, clinical history, location, use of services, health status of patients, health insurance, out of pocket expenditures, etc. Costs: budget, cost data and payments

7 Can be complemented with secondary data to broaden the scope of the analysis 7 Several sources of micro-data available: Population based surveys (LSMS, DHS, MIC) Group-specific surveys (labor force, student performance surveys Census: population, housing, agriculture Geo-referenced data Weather data: rainfall, temperature, etc. Enterprise surveys

8 Developing administrative data for IE purposes (1) 8 Ability to link datasets accurately is critical Based on personal information: national ID, name, date of birth, gender, location, phone number, etc. Unique identifiers have to be carefully recorded across ADs Need to employ different data merging algorithms to assess the robustness of the findings Based on age cohorts: date and place of birth Based on geographic information: locality, detailed geo-reference data

9 Developing administrative data for IE purposes (2) 9 AD have to be relevant for the IE Need to capture information on treatment and control group (e.g. program allocation mechanism) Need to include information for a period of time that is relevant for the program/intervention evaluated and research questions asked Include information on response and control variables Very useful if it includes data on program processes, implementation and operation (often built in M&E systems developed to manage programs)

10 Developing administrative data for IE purposes (3) 10 Ensuring that the data are of high quality is challenging AD usually developed and managed by different agencies with varied: Protocols to gather and enter the data Data management systems Quality control checks Meta-data, documentation Technical capacity

11 Developing administrative data for IE purposes (4) 11 Data availability Most times AD is not publically available If available, access to some part of the data often restricted (e.g. personal information, earnings, etc.) Confidentiality: rigorous protocols to protect confidentiality of the data are key to develop trust between agencies and policy analysts/researchers Meta-data not always available or fully documented Information recorded in different data platforms and not organized for statistical analysis

12 Using AD to assess and enhance the effectiveness of programs (1) 12 Assessing the effectiveness of program alternatives to improve design Testing the marginal benefit of program alternatives goes beyond learning whether program works or not (remember Impact Evaluation 2.0) Measuring the cost-effectiveness of program alternatives

13 Case 1: Which treatment is more effective? 13 Objective: address short-term poverty and increasing households productive potential MIS with data of all people enrolled in the program CCT MIS data for participants assigned to each treatment arm (1/3, 1/3, 1/3) CCT CCT + scholarship for an occupational training CCT + grant for productive investments MIS data for participants assigned to each cash treatment arm (1/6, 1/6) $X $X + $Y For instance, program manager interested in learning if the scholarship has an additional impact: E(Y CCT+S ) - E(Y CCT ) Or if more cash ($Y) has extra benefits: E(Y CCT(X+Y) ) - E(Y CCT(X) )

14 Example: Philadelphia Low-Intensity Community Supervision Experiment 14 Background: Program for criminal offenders on probation or parole identified as being at low risk of committing a serious crime Objective: Seeking ways to reduce the cost of supervision to Philadelphia County Low v. high-intensity supervision by a probation officer (2.4 visits with the officer per year v. 4.5 visits per year) Evaluation: randomly assigned 1,559 offenders on parole (from a short sentence in county jail) or probation in to either treatment with follow up one year after (Barnes et al 2010)

15 Key lessons for program design and effectiveness, 15 No differences in crime between a low and high dosage in probation supervision Prevalence of offending and incarceration for one year after RCT start Source: Barnes et al (2010)

16 16 that led to changes in the operation and cost-effectiveness of the program The county adopted the low intensity approach for all low-risk offenders The changes tested were found to be a viable way to reduce costs in the criminal justice system A low-cost evaluation: less than $100,000. Low cost achieved by using administrative data (e.g., arrest records) that the county already collects for other purposes

17 Using AD to assess and enhance the effectiveness of programs (2) 17 Examining different aspects of program impacts Average treatment effects (TOT, ITT) Intensity treatment effects Short, medium- and long-term effects Distribution of treatment effects (sub-group analysis) Mechanisms that explain program effects or the absence of them

18 Case 2: Do conditional cash transfers raise human capital in the long-term? 18 Background 2001: Government of Colombia (GoC) implemented a standard CCT program (FA) in response to a major economic crisis : A short-term IE showed that FA raised school participation among participants vs. nonparticipants 2009: policy makers start questioning whether the increase in school participation actually translated into more school attainment

19 How to answer the question in the absence of primary data? 19 Challenges No formal plan in place to systematically track participants and non-participants over time Very little time, narrow budget Opportunities Rich and linkable administrative data available to: (1) identify participants and comparable non-participants and (2) assess the effects on indicators of school attainment and learning Strong support, interest and technical capacity from key local stakeholders

20 Rich administrative data available 20 Three different sources of AD: 1. A census of the poor collected between 1994 and 2003 to design the targeting system [SISBEN] 2. Administrative records from the M&E system of FA a rich longitudinal census of all program beneficiaries [SIFA] 3. Administrative records on registration and results for Icfes -- a standardized national test that is administered prior to graduation from high school [ICFES]

21 Mapping the data landscape 21 Icfes tests for students in grade 11 (T & C) [ICFES] First round of panel survey (T & C ) [Base] Program M&E system (census of participants, only T) [SIFA] Sisben I (T & C) [SISBEN] Sisben II (T & C) [SISBEN]

22 Our research strategy: RDD 22 Exploits the discontinuity arising from the program eligibility rules (using SISBEN + SIFA) Probability Program Probability Distance to the Eligibility threshold Observations= means different quartic Source: Baez et al (2011)

23 Allowed us to answer the questions of the Government, 23 Participant children are between 2.5 and 5 percentage points more likely to finish high school 0.35 School completion Distance to the Eligibility threshold Observations= means different quartic Source: Baez et al (2011)

24 in a credible way, 24 Household head education Partner education level Distance to the Eligibility threshold Distance to the Eligibility threshold means different quartic means different quartic Observations= Observations= Married household Social Security Distance to the Eligibility threshold Distance to the Eligibility threshold means different quartic means different quartic Observations= Observations= Source: Baez et al (2011)

25 and in a timely manner and at a very low cost 25 Solid preliminary results available 6 months after team got access to all the four datasets Total costs around one fifth of traditional IEs that rely on primary data collection plans Results helped Inform public debate about the future of the program Inform program modifications Continue an analytical agenda on the effects on postsecondary outcomes building on the same data

26 Administrative data can be very useful but we aware of the trade-offs 26 Pros Large sample sizes High frequency Fewer problems with attrition, non-response and measurement error Lower cost Cons Fewer variables and therefore more limited scope for the analysis Less control to ensure high and consistent quality Often is difficult to accurately link AD Access still very limited

27 Final Remarks 27 Enhance the quality of AD to create opportunities for policy/program relevant research at a low cost Use AD for Impact Evaluation. Test - learn - adapt. Use AD to assess the overall effect of the program. Use AD to learn about the effectiveness of program alternatives and improve design.