MODELING of SWIFT & Survey to Survey Imputation

SWIFT modeling Cross-validation to decide the optimal p-value Run the stepwise regression using the optimal p-value determined by the cross-validation Check the coefficients of the final model Simulate household expenditures using PovMap or MI Estimate poverty rates using the simulated expenditures and MI’s formula using “mi estimate” To check stability, conduct backward imputation analysis

Steps for SWIFT modeling Objective Program 1. Cross Validation Choose optimal p-value crossvalidation.do 2. Finalization of models Find a model with the optimal p-value Simulation and estimation.do or PovMap 3. Simulate household expenditure/income Simulate household expenditures based on the above estimation Or 4. Estimate poverty statistics Using the simulated expenditures or income, poverty rates are estimated using “mi estimate” mi estimate

Data preparation You need to have y0 and y1 y0: data for developing models Need both consumption and regression variables GLSS6 with lnrpcexp y1: data for estimating poverty rates using models Need only regression variables that have the same definitions as Y0 SWIFT data GLSS6 without lnrpcexp Both data need to have all variables used in cross-validation, simulation, and estimation stages

crossvaliation2.do Choose a location of the dataset properly If you want to change variable sets, you can change global macros for variable groups, such as location, etc.

What level do you want to do the modeling? If you want to create a model for only urban areas or only one region, you need to modify the program here. Note that you need to be consistent between cross-validation and simulation/estimation

Defining 10 folds Randomly define 10 folds (subsamples)

Loop Randomly define 10 folds (subsamples)

Result matrix A pe fold_ poor_ r2 mse pred_poor absdiff 0.01 1 0.348 0.426 0.382 0.302 0.046 2 0.316 0.427 0.243 0.248 0.067 3 0.225 0.434 0.245 0.310 0.085 4 0.106 0.442 0.228 0.233 0.126 5 0.238 0.439 0.274 0.281 0.043 6 0.344 0.460 0.273 0.249 0.095 7 0.358 0.271 0.307 0.131 8 0.154 0.452 0.264 0.244 0.090 9 0.456 0.279 0.318 0.010 10 0.304 0.436 0.252 0.052 0.02 0.386 0.321 0.027 0.437 0.240 0.294 0.022 0.466 0.250 0.293 0.068 0.450 0.221 0.236 0.129 0.445 0.270 0.303 0.065 0.392 0.286 0.284 0.155 0.462 0.278 0.098 0.467 0.299 0.009 0.232 0.300 0.004

Results of cross validation Average of absolute differences between actual and projected poverty rates (absdiff) Mean Squared Errors (mse) 2 percent is chosen since the massive increase in MSE after 2%

Simulation and estimation.do

Simulation and estimation.do

Simulation results -------------------------------------------------------------- Over | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ Actual | .2744967 .0458151 .1811929 .3678006 Simulation | .2817209 .0516824 .1675013 .3959405 Number of observation = 1016 Area = Region 8 Urban

Thank you! If you have any question, please let me know at nyoshida@worldbank.org