COURSE ID: D-EF19 LANGUAGE:

PROGRAM EVALUATION IN STATA

Providing effective evaluation of economic, social and medical programs has become an increasingly important requirement for both public and private institutions. This workshop seeks therefore, to provide participants with the requisite tools, both theoretical and applied, for the correct implementation of modern micro-econometric methods for implementing program evaluation using Stata. As such, the program has been developed to encompass both: standard statistical methods of program evaluation: regression-adjustment, matching, selection-models and difference-in-differences methodologies; and the more advanced econometric techniques: for example, instrumental variables, endogenous regression-adjustment and regression discontinuity design.

At the end of the workshop participants are expected to be able to master complex evaluation design by: identifying the type of data required in their specific policy framework; evaluating which specific econometric method is more appropriate for the analysis in hand; and finally extracting policy recommendations from the obtained results. Participants should leave the course being in a position to autonomously implement, with the aid of the Stata routines utilized during the sessions, the theories and methodologies discussed during the course of the workshop.

In common with TStat’s course philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the workshop, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques.

Researchers and professionals working in public and private institutions needing to undertake econometric program evaluation analysis using micro data. Although these methodologies are commonly used to evaluate policy interventions in, for example, the labour market, investment activities of enterprises, education policy, regional development, etc., they can in fact be used across a variety of studies, such as public health sector evaluation, which aim to estimate the ex-post impact of a given intervention or project on specific targets.

Participants should have an introductory knowledge of econometrics and the statistical software Stata.

DAY 1: ECONOMETRICS OF PROGRAM EVALUATION

 

SESSION I: INTRODUCTION TO THE ECONOMETRICS OF PROGRAM EVALUATION

Concept of counterfactual causality

Experimental and quasi-experimental settings

Non-random sampling: selection on observables and selection on unobservables

Definition of treatment effects: types of effects and potential outcome

Notation and working hypotheses: SUTVA, CIA and CMI

 

SESSION II: LINEAR AND NON-LINEAR REGRESSION ADJUSTMENT

The control function regression approach

Non-linear models

Stata implementation with the commands teffects ra and ivtreatreg

 

DAY 2: MATCHING AND REWEIGHTING

 

SESSION III: MATCHING AND REWEIGHTING

The selection on observable setting

Identification conditions for Matching

Matching in practice: tests and sensitivity analysis

Reweighting on the propensity score

 

SESSION IV: STATA IMPLEMENTATION OF MATCHING AND REWEIGHTING

Presentation of real datasets for program evalution

Matching with the commands teffects, pscore, psmatch2

Reweighting with the commands teffects and treatrew

 

DAY 3: INSTRUMENTAL-VARIABLES AND SELECTION MODELS

 

SESSION V: INSTRUMETAL-VARIABLES AND SELECTION MODELS

Selection-on-unobservables and the logic of IV

Endogeneity and consistent estimation

Types of IV methods

Stata implementation with the commands ivregress and ivtreatreg

Heckman selection model (heckit)

Stata implementation with the commnads treatreg and ivtreareg

 

SESSION VI: DIFFERENCE-IN-DIFFERENCES (DID)

DID statistical setting

DID with longitudinal data

DID with repeated cross-section

Pre-post treatment dynamic effect

Implementation in Stata

 

DAY 4: REGRESSION DISCONTINUITY DESIGN

 

SESSION VII: LOCAL AVERAGE TREATMENT EFFECT (LATE)

The LATE setting

The relationship between LATE and IV

Implementation in Stata

 

SESSION VIII: REGRESSION DISCONTINUITY DESIGN (RDD)

RDD as a local approximation of a natural experiment

Sharp RDD  setting and estimation

Fuzzy RDD setting and estimation

Implementation in Stata

The workshop will be held in Frankfurt am Main, from the 9th to 12th April 2018.

 

FLEMING’S DELUXE HOTEL FRANKFURT-CITY ♦ Eschenheimer Tor 2, 60318 Frankfurt am Main

Students*: € 1050.00

Academic: € 1750.00

Government / Nonprofit: € 2075.00

Commercial: € 2400.00

 

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.

All fees are subject to VAT (applied at the current Italian rate of 22%).

The number of participants is limited to 15. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 8 individuals are enrolled.

Course fees cover: course materials (handouts, Stata do files and datasets to be used during the course), a temporary licence of Stata (valid for 30 days from the beginning of the course), lunch and coffee breaks.

In order to maximize the usefulness of this workshop, we recommend that participants bring their own laptops with them, to be able to actively participate in the empirical sessions.

Individuals interested in attending this workshop, must return their completed registration forms to TStat by the 26th of March 2018.


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Providing effective evaluation of economic, social and medical programs has become an increasingly important requirement for both public and private institutions. This workshop seeks therefore, to provide participants with the requisite tools, both theoretical and applied, for the correct implementation of modern micro-econometric methods for implementing program evaluation using Stata. As such, the program has been developed to encompass both: standard statistical methods of program evaluation: regression-adjustment, matching, selection-models and difference-in-differences methodologies; and the more advanced econometric techniques: for example, instrumental variables, endogenous regression-adjustment and regression discontinuity design.