short-term changes in welfare, such as ownership of consumer durables and housing conditions were added to the model, the projected poverty changes were far more accurate.

For estates, the survey design effects do not appear to cause any bias and adding consumer durables and housing conditions makes the imputations somewhat accurate. However, the fall in poverty in this sector was very rapid and the variables included in the model could not adequately capture it.

Although this analysis clarifies that using additional variables substantially improves the accuracy of the imputation in rural areas, these additional variables are not currently available in LFS. Therefore, we conclude that it is not possible to accurately predict poverty using the existing LFS. If the LFS were used to create a frequent welfare tracking system in Sri Lanka, either the LFS should expand its questionnaire while modifying its questionnaire and sampling strategy to become comparable to the HIES, or a new survey containing the full set of welfare proxies in the HIES should be established.

# _Imputing poverty directly using a Probit model_

The imputation approach developed by Elbers et al. (2003) has become one of the most popular approaches in projecting poverty statistics. It has been utilized widely by researchers and practitioners and continues to evolve in response to technical issues raised by the community of users. However, this is by no means the only methodology that can project poverty statistics. Here, we use a simpler and more direct method by projecting poverty using a probit model, as suggested by Tarozzi and Deaton (2007), and Tarozzi (2011).

Table 16 shows the results of backward and forward imputations, respectively. In general, the results are similar to those generated using ELL, but slightly less accurate. For example, when imputing poverty in 2006, adding wages to the probit model only changes estimated poverty from 4.0 to 4.2 percent, while in ELL estimated poverty rises from 4.6 to 5.1. When imputing 2009 poverty in rural areas, the probit estimator projects a poverty rate of 9.8 percent for 2009/10, which is still 1.2 percentage points higher than the direct estimation. This compares unfavorably with the imputation based on the ELL method, projects a poverty rate of 9.0 percent, which is only 0.4 percentage point larger than the direct estimation. The forward imputation also shows qualitatively similar results. For estates, even after ownership of consumer durables and housing conditions are added into the consumption models, both forward and backward projections fail to produce accurate estimates of poverty rates. As in rural areas, the probit based imputations produce less accurate projections of poverty rates than the ELL based imputations.

**Table 16. The HIES to HIES imputations using Probit as imputation formula**

||Estimation<br>Forward I|ofpovert<br>mputation|yrates(%)f<br>(probit)|or 2009/10<br>|Estimation<br>Backward|ofpoverty<br>imputation|rates(%)f<br>(probit)|or 2006/07<br>|
|---|---|---|---|---|---|---|---|---|
||Variables<br>only from<br>LFS|With<br>wage<br>data|With<br>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|12.3|11.0|9.7|8.2|8.8|9.6|11.1|13.5|
|Urban|6.1|4.7|3.8|4.4|4.0|4.2|5.3|6.2|
|Rural|12.5|11.1|9.8|8.6|9.3|10.1|11.7|13.8|
|Estate|25.1|24.9|22.0|11.4|14.0|16.7|17.1|27.7|

Source: Authors’ estimations using HIES 2006/07 and 2009/10 data 23