1. Using Survey Data to Improve Registry Data The Case of Syrian Refugees Paolo Verme World Bank

2. Motivation UNHCR keeps records of all refugees registered with the proGres database UNHCR collects a rich set of additional information via home visits’ surveys This information is little exploited for analysis UNHCR needs to target refugees with its cash assistance program due to funding shortages How good is the UNHCR targeting capacity? Can UNHCR improve its targeting capacity by making better use of available information and reduce the cost of surveys?

3. Background A WB-UNHCR partnership: WB analytical expertise on poverty and welfare and UNHCR expertise on refugees Pilot study in March 2014 Two countries (Jordan and Lebanon) 6 Data sets (3 Jordan and 3 Lebanon): UNHCR registry for Jordan and Lebanon (PG: 650,000 records Jordan; 1.2 m records Lebanon); UNHCR and WFP home visits (45,000 cases in Jordan, 2 rounds; 30,000 cases Lebanon, 1 round) UNHCR and WFP survey (1,700 cases Lebanon, 1 round) Focus on refugees living outside camps  How Poor Are Refugees? A Welfare Assessment of Syrian Refugees Living in Jordan and Lebanon (16 December, 2015)

4. Basic Idea 1.Construct welfare aggregates 2.Construct a welfare and poverty model with PG+HV data 3.Tests best proxies for welfare and poverty 4.Tests composite indexes for welfare 5.Derive the best welfare and poverty models (PG+HV data) 6.Find the best variables to add to the proGres data 7.Predict welfare and poverty using PG data 8.Use the new PG model for targeting assistance programs

5. 1) Construct Welfare Aggregate

7. 2) Construct Welfare Models

8. 3) Test best proxies of Welfare Basic tabulations Univariate regressions on 185 variables Ranking by R2 Multivariate tests: Backward and forward regressions Manual tests of key explanatory variables Tests repeated for composite welfare indicators Maximizing R squared

9. 3) Test best proxies of Welfare

10. VariableDescriptionObsW R2 lin csize_hvIndividuals in case (HV)159750.470121638 dem_p_childProportion of children159750.312118415 edu_p_attendProportion of children in school159750.152996621 pov_inc_unhcrUNHCR Monthly Financial Assistance157870.150577538 cash_large Large Family &/or Family with Babies, Toddlers or Children Attending School50000.135079263 prot_bail_date 7080.100568917 pov_cop_aidHumanitarian assistance157880.092286375 cash_smotherSingle Women50000.058084132 prot_aggrScore for work and residence permit, MOI and bail out doc38630.050228105 cash_elderlyElderly 60 and Above50000.048847739 prot_moi_diff_apoliceNot comfortable approaching a police station1870.041721867 pov_inc_aggrTotal number of kinds of income157870.03765949 prot_bailBailedOutFromCamp132120.035152565 cash_decisionDecision_for_cash_Assistance158110.034241792 pov_cop_hostLiving together with host family157880.029711534 ref_elderlyElderly alone11110.028308115 prot_know_schoolSchool149490.022848084 edu_p_notattendProportion of children not in school159750.02232405 house_sanitarySanitaryFacilitiesStatus156020.021637243 dem_pafemaleFemale Principal Applicant158350.021228419

11. 4) Test Composite Indexes of Welfare VariableObsMeanStd. Dev.MinMaxR2% i_rent159750.915180.278622010.0174271.742672 i_latrine159750.7730830.418852010.0143871.438695 i_good_liv~d159750.4765570.499466010.0135781.357762 i_housecon~n159750.867230.339337010.0071250.712506 i_pipewater159750.8786850.326503010.0070030.700316 i_good_san~y159750.1387170.345662010.0067620.676154 i_good_ven~n159750.286260.452026010.0060470.604679 i_waste159750.7467920.434863010.0058520.585157 i_water159750.7976840.401739010.0051250.512468 i_good_ele~y159750.2811890.449594010.0050170.501678

12. 4) Test Composite Indexes of Welfare VariableObsMeanStd. Dev.MinMaxR2 ind_house_crowd 159751.7818871.3645090160.267 ind_house_crowd1159752.5515061.6975710580.022 ind_wash_water 159753.1962441.168634040.014 ind_nfi159750.161690.381374070.011 ind_house_subjective 159751.7365881.619757060.009 ind_house_assets159758.36823.0066310130.008 ind_cope_index159752.4480131.727429050.007 ind_wash_hygiene 159754.1923631.150333050.007 ind_cope_wfp159751.6654771.481963080.006 ind_food_wfp1597542.5523616.6338201120.003 ind_house_quality 159751.6853830.57311020.003 ind_food_score 1597522.134598.498840560.002 ind_food_variety159757.1016591.576538080.001

13. 5) Derive the optimal Welfare Model (PG+HV )

14. Welfare Model (Cont.)

15. 6) Select the Best Variables to Add to PG Data The poverty model predicts poverty correctly 90.1% of the times PG-HV predictors: Case size Rent Place of destination (country and region) Official entry and point of entry Place of origin (Damascus vs other regions) HV predictors (h) Principal applicant characteristics (age and marital status) Selected assets (latrine, piped water, kitchen) Education and former occupation are less important than expected

16. 7) Predict Welfare using new PG model Wexp_unhcr_lncap Coeft Individuals in case (HV) -0.212***-69.811 Proportion of children -0.611***-25.245 Concrete House 0.195***8.017 Santitation average or above 0.109***7.244 Ventilation average or above 0.100***6.194 Free Housing -0.705***-24.419 Proportion school-aged children 0.113***8.919 Proportion of children in school -0.207***-15.267 Sharing costs with host family -0.095***-6.628 Living together with host family 0.114***8.987 IsCertificateValid 0.124***9.190 _cons 4.715***162.779 Number of observations 14,150 R2 0.555 Adjusted/Pseudo R2 0.554

17. 8) Use New PG Model to Target Assistance Income ExpenditureNon poorPoorTotal Non poor 8.537.245.6 Poor 4.649.854.4 Total 13.186.9100.0

18. Conclusion World Bank and UNHCR have complementary skills and resources Existing UNHCR data can be used to improve targeting: Shift from income to expenditure Reduce leaking and costs Improve coverage and targeting The analysis highlighted areas to improve: Data collection (survey design, sampling, questionnaires) Data management (PG management system, survey administration) Data analysis (economists and social protection officers)