AERC Workshop - Week 2

Transcription

1 AERC Wrkshp - Week 2 5th March - 9th March 2018 Training n Impact Evaluatin AERC and Faculty f Ecnmics and Business, University f Grningen, Netherlands Curse bjectives: The bjective f this part f the curse is t prvide participants with better knwledge abut the thery and practice f impact analyses in develping cuntries. Special attentin will be given t the impact f financial inclusin prjects. Curse Descriptin: Until recently, empirical testing f the impact f financial interventins, such as micrfinance, was extremely weak, and cntrversial. Mst research n the impact f micrfinance in the bradest sense suffered frm severe methdlgical prblems: almst nne f the available empirical studies apprpriately addressed prblems related t self-selectin bias and/r prgram placement bias. Frtunately, in the last few years, we have seen several new empirical analyses using rigrus methdlgies. These new analyses are ften based n s-called randmized cntrlled trials. In a randmized cntrlled trial the impact f an interventin is studied by randmly assigning different husehlds t treatment and cntrl grups. In this shrt curse, we will discuss the majr aspects f experimental design in the cntext f financial interventins in develping cuntries. Special attentin will be given t financial interventins. The aim is t prvide a better understanding f the thery and practice f field experiments in develping cuntries. Fr example, participants will e.g. learn hw t design randmized experiments t measure impacts f financial inclusin interventins. STATA: during the afternn sessins f the curse we will use STATA t cnduct practical examples. Backgrund Literature: Glennerster and Takavarasha (2013), Running Randmized Evaluatins: A Practical Guide, Princetn University Press. Reader f recent articles Lecturers: Rbert Lensink (University f Grningen/ Wageningen University) Adriana Garcia (University f Grningen)

2 Prgram (subject t minr changes) Mnday 5 March : Intr t the curse; Welcme; The relevance f Financial Inclusin: thery and empirics : Cffee break : Impact evaluatin methds 1: Intrductin t impact evaluatin and the use f experiments. Why impact evaluatin and Why experiments? Different types f evaluatins Initial steps in setting up an evaluatin : Impact evaluatin methds 2: classificatin and nn-experimental methds Classificatin f impact evaluatin methds Nn-experimental methds Quasi-experimental methds : Cffee/tea : Stata sessin 1: Intrductin t stata Data management with stata

3 Tuesday 6 March : Examples f nn-experimental Micrfinance studies Pitt and Khandker (1998) and critique Cffee/tea : Examples f nn-experimental Micrfinance studies Cleman Insurance example : Impact evaluatin methds 3: experimental methds Experimental methds Randm assignment: basics Different randmizatin designs Steps in simple randm assignment Stratified randmizatin : Cffee : Stata sessin 2 Nn-experimental methds Prpensity Scre Marching (PSM) Duble Difference (DD) DD Cmbined with PSM

4 Wednesday 7 March : Impact evaluatin methds 4: Implementatin prblems Prblems with cmpliance Attritin prblems Cffee/tea : Impact evaluatin methds 5: Pwer Pwer Prblems : Finishing pwer lecture : Cffee/tea : Stata sessin 3 Lecturer Adriana Garcia Experimental methds Randm sampling and randm assignment Balancing test Pst-treatment estimatin Randmized inference

5 Thursday 8 March : Examples f RCTs Financial Literacy in Rwanda BDS in Vietnam : Cffee/Tea : examples f RCTs The impacts f Micrcredit: Evidence frm Bsnia and Herzegvina (Ausburg, et al., 2015) : Examples f RCTs Insurance example : Cffee/ tea : Stata Sessin Experimental methds: Implementatin prblems Nn-cmpliance Attritin Regressin discntinuity

6 Friday 9 March Case study presentatins by participants (25 min per grup: 15 min presentatin, 10 min discussin) : Grup 1 Thery f change : Grup 2 Hw t randmize? : Grup 3 Why t randmize? : Cffee/Tea : Grup 4 Why t randmize? : Grup 5 Implementatin prblems : Feedback sessin