and housing conditions improves the poverty estimate, it remains far below the lower bound of the direct estimate.

||Estimat<br>Fo<br>|**Ta**<br>ion ofpoverty <br>rward imputat|**ble 15. The H**<br>rates(%)for<br>ion|**IES to HIES**<br>2009/10<br>|**imputations**<br>Estimation<br>Backw<br>|ofpoverty<br>ard imputa<br>|rates(%)fo<br>tion<br>|r 2006/07<br>|
|---|---|---|---|---|---|---|---|---|
||Variables<br>only from<br>LFS|With wage<br>data|With wage<br>& durables|Direct<br>estimation|Variables<br>only from<br>LFS|With<br>wage<br>data|With<br>wage &<br>durables|Direct<br>estimation|
|National|11.6|10.2|9.0|8.2|10.3|11.7|12.5|<br>13.5|
||(0.51)|(0.53)|(0.47)|(0.39)|(0.55)|(0.56)|(0.55)|(0.46)|
|Urban|5.0|4.7|4.1|4.4|4.6|5.1|5.2|6.2|
||(0.99)|(0.99)|(0.99)|(0.66)|(0.95)|(0.92)|(0.87)|(0.75)|
|Rural|11.7|10.0|9.0|8.6|11.1|12.6|13.4|13.8|
||(0.67)|(0.61)|(0.61)|(0.45)|(0.64)|(0.66)|(0.64)|(0.53)|
|Estate|26.4|26.8|21.6|11.4|13.2|15.4|19.0|27.7|
||(2.71)|(2.23)|(2.23)|(1.63)|(2.54)|(1.44)|(2.57)|(2.24)|

Source: Authors' estimations using the corresponding rounds of HIES. Notes: Tables A1-1 to A1-3 show the consumption models with means of these variables included in the final models for urban areas, rural areas and estates. The figures in parentheses are cluster-robust standard errors for the direct estimations and the standard errors that include the modeling and sampling errors for imputation.

At the national level, the backward imputation estimates a poverty rate of 12.5 percent for 2006/7 once both wage data and ownership of durables are included. This is a 2.2 percentage point increase compared to the prediction based on variables that are common to the LFS. It still underestimates the poverty rate compared with the direct estimate of 13.5 percent. The imputed poverty rate is now within the 95 percent confidence interval of the direct estimation, and the difference between the imputed and direct estimates is no longer significant at the five percent level. The result of forward imputation is also qualitatively the same – expanding the variable set by adding ownership of consumer durables improves the precision of imputation substantially.

In sum, the HIES to LFS imputation fails to accurately project poverty estimates in the context of Sri Lanka between 2006/7 and 2009/10. The above analyses clarified likely causes of the biases in the imputations for urban areas and rural areas, but still failed to find causes of the biases in the imputations for estates. The main reason for the drop for estate sector in direct estimation may be due to wage increase in the plantation sector employees in late 2009, which was not captured in either the wage index or the common variables used in the model.

For urban areas, the LFS fails to predict well because of differences in the design of the LFS and HIES. In particular, the reference period for employment differs between the HIES and the LFS, and the two surveys also follow slightly different sampling schemes. But, the subsequent analyses using the HIES to HIES imputation suggest that if the LFS questionnaire and sampling scheme were made consistent with the HIES, the survey-to-survey imputation would work well.

For rural areas, the biases are caused by the limited availability of variables that change over time. As a result, the variables in the model explained little of the actual change in consumption, which were instead captured in changes in the model’s intercept or other coefficients. When other variables that better reflect 22