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© Institute for Fiscal Studies

Programme Evaluation for Policy Analysis

Mike Brewer, 4 October 2011www.pepa.ac.uk

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Outline

Who we areOverview and aimsThe 5 projects

Training and capacity building© Institute for Fiscal Studies

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Who we are: PI and co-Is

Richard Blundell, UCL & IFSMike Brewer, University of Essex & IFSAndrew Chesher, UCL & IFS

Monica Costa Dias, IFSThomas Crossley, Cambridge & IFSLorraine Dearden

,

IoE

& IFS

Hamish Low, Cambridge & IFS

Costas

Meghir, Yale & IFSImran Rasul, UCL & IFSAdam Rosen, UCLBarbara Sianesi, IFSDWP is a “partner”

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Programme Evaluation for Policy Analysis: overview

PEPA is about ways to do, and ways to get the most out of, “programme evaluation”

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“estimating the casual impact of”

“government policies” (although can often generalise)

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Programme Evaluation for Policy Analysis: overview

PEPA is about ways to do, and ways to get the most out of, “programme evaluation”AimsTo 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

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More on our aims: three challenges for programme evaluation

We know the outcomes for participants on a training programme. But what was the counterfactual?

Given the counter-factual, we can estimate the programme’s impact. But how certain are we?

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?

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PEPA: overview

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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 experimentImprove the design of non-experimental evaluations Improve way such evaluations add to the knowledge base “Gold standard” randomisation is still rare costly, impractical or politically infeasible → Project 1A

lack of external validity and

ex ante

analysis → Project 1B

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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

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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?Methodologytake a typical search and matching model, and calibrate it to match the data on ERA comparison groupsimulate 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:

can use simulations to evaluate

ex ante alternative ERA policiescan see how estimate of policy impact changes once interactions with wider labour market are taken into account© Institute for Fiscal Studies

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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 whendata have a multi-level structure, and where there is serial correlation in the treatment and in group-level shockswhen 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

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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 naïve 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 practiceAims

Demonstrate the problems for inference caused by serially-correlated 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

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2a. Inference and power in Diff-in-Diff (Crossley, Brewer, Hernandez)

Flip side to inference is a

power calculationWill produce resources to carry out power calculations for non-experimental designs.difference-in-differencesinstrumental 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

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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 transitionsEstimated 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 withestimates of the impact of a policy on the hazard rateexpected 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

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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 answeredType and quality of data availableAssignment 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

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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

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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 marketFamily 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

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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

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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 analysisResults are specific to the policy, time and environment

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4. Dynamic behavioural models for policy evaluation

Aim: to address these weaknesses using a structural (dynamic behavioural) approachExplicitly formalises incentives and decisionsBut relies on heavy set of (explicit) behavioural assumptions

Will study ways to make minimal and transparent assumptionsUse quasi-experimental data to estimate and validate models of behaviourExplore 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

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4. Dynamic behavioural models for policy evaluation: applications

Impact of welfare time-limitsDevelop 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 applicationOur model will replicate, and then generalise, previous empirical resultsImpact of welfare-to-work on educationUse structural behavioural model of education and labour supply choices to evaluate how future welfare-to-work 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

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5. Social networks and program evaluation(Rasul, Fitzsimons, Hernandez,

Malde)

To understand individuals’ or households’s behaviour, must recognize that individuals are embedded within social networksIn 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

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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?

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5. Social networks and program evaluation

Substantive research questionsHow 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)?

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PEPA: research questions

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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/year1 course/year for those designing or commissioning evaluations

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PEPA: training and capacity building

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PEPA management and administration team

DirectorNow until October 2012: Mike BrewerApril 2012 thereafter: Lorraine Dearden

Co-director: Monica Costa DiasAdministrator: Kylie GrovesIT: Andrew ReynoldsDWP are partner organisation, with hope that this eases access to their data. In practice, very reliant on key contact

(Mike Daly)

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