# **Table A.18. Predicted poverty rates (and SEs) based on LASSO, Elastic net and Random Forest from NPS 2020/21 to TA 1 using 2020/21 Poverty Line, Tanzania (percentage)**

||||||**2010/11**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Method**|**Model**<br>**M1**|**Model**<br>**M2**|**Model**<br>**M3**|**Model**<br>**M4**|**Model**<br>**M5**|**Model**<br>**M6**|**Model**<br>**M7**|**Model**<br>**M8**|**Model**<br>**M9**|
|1) Multiple imputation (PMM)|**17.6**<br>(2.0)|**17.7**<br>(1.9)|16.1<br>(1.6)|**17.4**<br>(1.8)|**16.7**<br>(2.0)|16.5<br>(2.0)|**17.8**<br>(1.8)|**18.3**<br>(1.9)|**19.1**<sup>**a**</sup><br>(2.1)|
|2) Mltil imtatin (linar)|**17.3**|**17.6**|15.9|**17.5**|**17.0**|16.6|**17.9**|**18.6**|**18.7**|
|upe puo e|(2.0)|(1.8)|(1.7)|(1.8)|(1.8)|(1.8)|(1.9)|(1.8)|(1.9)|
||4.5|10.5|**19.1**<sup>**a**</sup>|**19.6**<sup>**a**</sup>|11.0|10.1|10.0|13.6|11.7|
|3) Lasso|(1.4)|(1.8)|(2.1)|(2.3)|(1.9)|(1.8)|(1.8)|(2.2)|(2.1)|
|4 Elti Nt|3.8|10.6|**19.1**<sup>**a**</sup>|**19.6**<sup>**a**</sup>|11.0|10.1|10.1|13.5|11.7|
|) asc e|(1.3)|(1.8)|(2.1)|(2.3)|(1.9)|(1.8)|(1.8)|(2.2)|(2.1)|
||10.4|7.8|**20.0**<sup>**a**</sup>|**17.9**|8.5|8.8|8.9|9.8|10.0|
|5) Random Forest|(1.5)|(1.5)|(2.1)|(2.2)|(1.6)|(1.7)|(1.7)|(1.9)|(1.6)|
|_Control variables_||||||||||
|Food expenditures|||Y|||||||
|Non-food expenditures||||Y||||||
|Furnishings and household|||||Y|||||
|expenses||||||||||
|Health expenditures||||||Y||||
|Education expenditures|||||||Y|||
|Utilities: water, kerosene, lighting||||||||Y|Y|
|Household assets & house<br>characteristics||Y|Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|N2(target survey)|711|711|711|711|711|711|711|711|711|
|**True poverty rate**<br>Note: Estimates shown in boldface or wit<br>net linear models and Random Forest mo<br>(validation). The list of selected variables<br>net. The number of sub-trees is set at 1000<br>selected variables with the variable impor|h a “<sup>**a**</sup>” respec<br>dels are traine<br>with penalize<br>in Random F<br>tance scores i|tively fall wi<br>d in the first<br>d unstandardi<br>orest. Both o<br>s provided in|thin the 95%<br>round when t<br>zed coefficie<br>ut-of-bag err<br>Appendix A|confidence i<br>he data is spl<br>nts is provid<br>or and valida<br>, Table A.23|21.0<br>(2.2)<br>nterval or one<br>it into two sub<br>ed in Appendi<br>tion error are<br>. We use five|standard err<br>sets: 50% u<br>x A, Table A<br>used to deter<br>nearest neig|or of the true<br>sed for trainin<br>.21 for Lasso,<br>mine the best<br>hbors with the|poverty rate.<br>g and 50% us<br>and Table A.<br>possible mod<br>predictive m|Lasso,Elastic<br>ed for testing<br>22 for Elastic<br>el. The list of<br>ean matching|

Note: Estimates shown in boldface or with a “<sup>**a**</sup> ” respectively fall within the 95% confidence interval or one standard error of the true poverty rate. Lasso,Elastic net linear models and Random Forest models are trained in the first round when the data is split into two subsets: 50% used for training and 50% used for testing (validation). The list of selected variables with penalized unstandardized coefficients is provided in Appendix A, Table A.21 for Lasso, and Table A.22 for Elastic net. The number of sub-trees is set at 1000 in Random Forest. Both out-of-bag error and validation error are used to determine the best possible model. The list of selected variables with the variable importance scores is provided in Appendix A, Table A.23. We use five nearest neighbors with the predictive mean matching method. 50 simulations are implemented. True poverty rate is the estimate directly obtained from the survey data.

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