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Programme Evaluation for Policy Analysis Mike Brewer, 4 October 2011 www.pepa.ac.uk PEPA is based at the IFS and CEMMAP Institute for Fiscal Studies Outline Who we are Overview and aims The 5 projects Training and capacity building Institute for Fiscal Studies

Who we are: PI and co-Is Richard Blundell, UCL & IFS Mike Brewer, University of Essex & IFS Andrew Chesher, UCL & IFS Monica Costa Dias, IFS Thomas Crossley, Cambridge & IFS Lorraine Dearden, IoE & IFS Hamish Low, Cambridge & IFS Costas Meghir, Yale & IFS Imran Rasul, UCL & IFS Adam Rosen, UCL Barbara Sianesi, IFS DWP is a partner Institute for Fiscal Studies Programme Evaluation for Policy Analysis: overview

PEPA is about ways to do, and ways to get the most out of, programme evaluation estimating the government policies (although can often generalise) Institute for Fiscal Studies casual impact of Programme Evaluation for Policy Analysis: overview PEPA is about ways to do, and ways to get the most out of, programme evaluation

Aims To stimulate a step change in the conduct of programme evaluation in the United Kingdom (and around the world) To maximise the value of programme evaluation by improving the design of evaluations, and improving the way that such evaluations add to the knowledge base Beneficiaries those who do programme evaluation those who commission, design and make decisions based on the results of evaluations those interested in impact of labour market, education and health policies Institute for Fiscal Studies More on our aims: three challenges for programme evaluation

1. We know the outcomes for participants on a training programme. But what was the counterfactual? 2. Given the counter-factual, we can estimate the programmes impact. But how certain are we? 3. Given that the evaluation has been done, how can we get the most value from it? How can we generalize what we learn from this evaluation to other training programs? How should we synthesize the lessons learned from multiple studies of different training programs? Institute for Fiscal Studies PEPA: overview PEPA

0. Core programme evaluation skills 1. Are RCTs worth it? Barbara Sianesi, Jeremy Lise Institute for Fiscal Studies 2. Inference Thomas Crossley, Mike Brewer, Marcos Hernandez, John Ham 3. Control functions and 4. Structural dynamic models

evidence synthesis Richard Blundell, Adam Rosen, Monica Costa Dias, Andrew Chesher 5. Social networks Hamish Low, Monica Costa Imran Rasul, Marcos Dias, Costas Meghir Hernandez

1. Making the most of RCTs: reassessing ERA (Sianesi & Lise) The Employment, Retention and Advancement demonstration (2003-2007) first large-scale RCT in social policy in UK (over 16,000 people) has been evaluated experimentally (Hendra et al., 2011) Aim: maximise the value of the ERA experiment Improve the design of non-experimental evaluations Improve way such evaluations add to the knowledge base Institute for Fiscal Studies

Gold standard randomisation is still rare costly, impractical or politically infeasible Project 1A lack of external validity and ex ante analysis Project 1B 1a. Lessons for non-experimental methods (Sianesi) Non-experimental evaluation methods have been assessed against an experimental benchmark in a small number of US studies in the 1970s and 1980s Exploit a recent and UK-based random experiment to learn about and possibly improve upon the performance of non-experimental methods routinely used in UK evaluations pilot-control areas individual matching difference-in-differences

The experimental estimates will be compared against the best alternative that can be devised with the available data Institute for Fiscal Studies 1b. A reassessment of the ERA (Lise) Can experimental data be combined with behavioural models of labour market behaviour to lead to better ex ante evaluations? Methodology take a typical search and matching model, and calibrate it to match the data on ERA comparison group simulate ERA policy within the model check if simulated outcomes match observed data for ERA

participants Experimental variation allows testing of theoretical model If simulated outcomes match ERA participants outcomes, then: Institute for Fiscal Studies can use simulations to evaluate ex ante alternative ERA policies can see how estimate of policy impact changes once 2. Improving inference for policy evaluation (Crossley, Brewer, Hernandez, Ham) Critical to characterise uncertainty of estimates (and

thus perform inference correctly) This can be hard when data have a multi-level structure, and where there is serial correlation in the treatment and in group-level shocks when the estimated policy impacts are complex and discontinuous functions of estimated parameters Similarly, can be hard to perform power calculations in all but simplest RCT Aims Review, disseminate and (hopefully) develop techniques Provide resources Substantive applications: impact of labour market or welfare-to-work programmes Institute for Fiscal Studies 2a. Inference and power in Diff-in-Diff

(Crossley, Brewer, Hernandez) A common evaluation technique is to use diff-in-diff over areas and time Serially-correlated errors and group-level structure of data mean nave inference often incorrect (standard errors too small; Bertrand et al. 2004) But most solutions work only for large number of groups, and literature evolving much faster than practice Aims Demonstrate the problems for inference caused by seriallycorrelated and multi-level data, and the practicality and relevance of a range of suggested solutions, providing resources where appropriate Develop new tools for inference randomisation/permutation tests serial correlation in the non-linear DiD Institute for Fiscal Studies

2a. Inference and power in Diff-in-Diff (Crossley, Brewer, Hernandez) Flip side to inference is a power calculation Will produce resources to carry out power calculations for non-experimental designs. difference-in-differences instrumental variables regression discontinuity Power calculations will reflect: Cluster effects: observations from different agents are not independent from each other Monte Carlo methods to deal with a reduced number of clusters Different patterns of time-series correlation

Institute for Fiscal Studies 2b. Inference in duration analysis (Brewer, Ham) Duration/survivor or transition models are natural tools for programme evaluation when outcomes of interest are spells or transitions Estimated policy impacts often complex, discontinuous functions of the estimated parameters of a statistical model Will establish how best to use event history models to provide policy-makers with estimates of the impact of a policy on the hazard rate expected time spent in various states correct confidence intervals around these both Will build on Eberwein, Ham and Lalonde (2002), Ham

and Woutersen (2009) and Ham, Li and Sheppard (2010) Institute for Fiscal Studies 3. Control functions in policy evaluation (Blundell, Costa Dias, Rosen, Chesher, Kitagawa) Choice among alternative evaluation methods is driven by three concerns Question to be answered Type and quality of data available Assignment rule (the mechanism that allocates individuals to the programme) This project focuses on the last Idea The ideal assignment rule comes from an RCT But if we know something about the assignment rule, then

the control function approach allows us to account for/correct for the endogenous selection into treatment 3. The control function approach: example Interested in the impact of university education on subsequent labour market earnings (the returns to university education) Unobservable determinants of earnings, e.g. underlying ability, will be correlated with the decision to attend university, so a simple regression will provide a biased view of the returns to university By modelling key features of the decision to attend university the assignment rule to university the control function approach can correctly recover the average return to university among those who took up a place

3. The control function approach: example (continued) These key features will ideally be factors that determine assignment to university but do not determine directly final earnings in the labour market Family socio-economic background, level of university fees, distance to university, availability of university places (if rationed) If can write down an equation modelling the way these factors determine university attendance, we can construct an index (or control function) that can then be included in the earnings regression along with the indicator for attending university. Extension of the Heckman selection approach that controls for the endogenous selection into treatment

3. The control function approach: our research Research questions: Under what circumstances does the use of a control function compare favourably to matching and instrumental variables? What are the key trade-offs? How does a control function approach map into a behavioural model? What can a control function approach tell us about structural parameters of interest? Can we weaken the control function approach by incorporating partial knowledge of the assignment rule to produce bounds? Will study various education and labour market policies 4. Dynamic behavioural models for policy evaluation (Low, Dias, Shaw, Meghir, Pistaferri) Classical ex post empirical evaluation methods

often fail to explain the nature of the estimated effect Cannot disentangle impact of programme on incentives from how incentives affect individual decisions Cannot account for dynamic responses (anticipation or changes now affect decisions in future) Studies often rely on different sets of behavioural assumptions Difficult to understand, as not explicitly stated Complicates task of synthetising information from different studies Cannot be used for counterfactual analysis Results are specific to the policy, time and environment 4. Dynamic behavioural models for policy

evaluation Aim: to address these weaknesses using a structural (dynamic behavioural) approach Explicitly formalises incentives and decisions But relies on heavy set of (explicit) behavioural assumptions Will study ways to make minimal and transparent assumptions Use quasi-experimental data to estimate and validate models of behaviour Explore the use of optimality conditions independent of the full structure of the model - to estimate some parameters Use robust estimates of bounds on treatment effects to bound structural parameters Institute for Fiscal Studies

4. Dynamic behavioural models for policy evaluation: applications Impact of welfare time-limits Develop dynamic model to study how time-limits in welfare eligibility may affect claiming decisions at different stages of life Use the US programme, Targeted Help to Needy Families, as the empirical application Our model will replicate, and then generalise, previous empirical results Impact of welfare-to-work on education Institute for Fiscal Studies Use structural behavioural model of education and labour supply choices to evaluate how future welfare-towork programmes affects the ex ante value of education

Use evaluation studies to validate the behavioural assumptions Use partial identification to provide bounds for structural parameters 5. Social networks and program evaluation (Rasul, Fitzsimons, Hernandez, Malde) To understand individuals or householdss behaviour, must recognize that individuals are embedded within social networks In developing countries, networks play various roles: substitute for missing markets key source of insurance and other resources to their members Will seek to understand how networks interplay

with policy interventions Will combine developments in theories of network formation and behavior within networks with empirical methods for program evaluation with social interactions Institute for Fiscal Studies 5. Social networks and program evaluation: example of Progresa Progresa is village-level intervention in rural Mexico. Previous research has shown that: 1 in 5 households are isolated (none of their extended family resides within the same village) On some margins, only non-isolated households responded to Progresa

Was it because poor families needed assistance and encouragement to join the programme? Or was it because of nature of Progresa intervention, part of which was to encourage teenage girls to stay in school? 5. Social networks and program evaluation Substantive research questions How are the benefits of program interventions dissipated within communities once social networks are accounted for? How do such spillovers (from beneficiary to non-beneficiary households) affect the cost-benefit analysis of programs, and how we think about targeting? Why and how are social networks formed (can investigate this by studying particular interventions)

Methodological research questions How best to measuring whether and how households are socially tied (blood ties , resource flows)? PEPA: research questions Institute for Fiscal Studies Training and capacity building Mixture of courses, masterclasses, workshops and resources (how-to manuals, software) All projects have their own TCB programme Plus core TCB offering in general programme evaluation skills 4 standard courses/year and 1 advanced course/year 1 course/year for those designing or commissioning evaluations

Institute for Fiscal Studies PEPA: training and capacity building Institute for Fiscal Studies PEPA management and administration team Director Now until October 2012: Mike Brewer April 2012 thereafter: Lorraine Dearden Co-director: Monica Costa Dias Administrator: Kylie Groves IT: Andrew Reynolds DWP are partner organisation, with hope that this eases

access to their data. In practice, very reliant on key contact (Mike Daly) Institute for Fiscal Studies

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