included in each survey are less comparable, compared to a situation in which each survey uses the same sampling strategy. This in turn makes SSI based on the LFS appear to be less accurate when benchmarking it against actual expenditures from the HIES, reducing the apparent accuracy of the SSI results. In other words, the different sampling design likely increases the discrepancy between the HIES and the LFS. This design difference could be partially corrected by adjusting the weights on the LFS to make the sample more comparable to the HIES, but whether this would substantially improve the accuracy of the validation is an open question left for future work.<sup>10</sup> Given the differences in the sampling strategies used to collect the HIES and LFS, it is important to check whether the surveys are comparable, both in terms of the geographic distribution of households and the types of household interviewed. To get a better handle on this, we compare the population shares and estimated poverty rates for the seven provinces within each sector, to check if the LFS underrepresents poor provinces in urban areas (see Table 9). In rural areas, both the poverty rates at the province level and the population shares across provinces differ little. In the estate sector, the province level poverty estimates are also very similar across surveys, except for Western Province. However, Western province makes up a small share of the estate sector, and as a result, the national average poverty rate for estates differ little between the HIES to HIES and the HIES to LFS imputations.

**Table 9. Comparisons in population shares (%) and poverty rates (%) between the HIES to HIES and the HIES to LFS imputations – HIES “2009”**

|||**Urb**<br>|**an**|||**Ru**<br>|**ral**|||**Est**<br>|**ate**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Province**|**Popul**<br>**sha**|**ation**<br>**re**|**Povert**|**y rate**|**Popul**<br>**sha**|**ation**<br>**re**|**Povert**|**y rate**|**Popul**<br>**sha**|**ation**<br>**re**|**Povert**|**y rate**|
||**HIES**|**LFS**|**HIES**|**LFS**|**HIES**|**LFS**|**HIES**|**LFS**|**HIES**|**LFS**|**HIES**|**LFS**|
|All|||6.6|4.4|||12.7|12.3|||30.0|31.5|
|Western|71.5|78.2|7.2|4.4|28.0|29.4|9.5|9.7|5.8|3.6|27.6|15.9|
|Central|8.8|8.2|5.2|3.2|13.4|11.4|12.3|13.0|51.5|54.1|30.0|32.3|
|Southern|8.6|8.2|4.3|4.8|16.7|15.6|15.6|13.4|3.8|1.0|27.0|33.1|
|North Western|3.8|2.3|8.1|6.5|14.9|16.0|13.9|12.6|1.1|0.2|25.5|27.7|
|North Central|2.1|1.2|2.9|3.3|8.1|7.8|13.2|12.5|0|0|||
|Uva|2.6|0.7|4.1|1.2|7.7|7.8|14|13.8|15.9|23.4|30.1|32.2|
|Sabragamuwa|2.8|1.3|3.5|7|11.2|12.1|13.8|14.9|22|17.6|31.1|31.2|

Source: Authors' estimations using the corresponding rounds of HIES and LFS Note: Poverty rates are predicted using the same periods, geographic coverage and consumption models of the HIES to HIES imputation for both HIES to HIES and HIES to LFS imputations.

There are more discrepancies between the two surveys in urban areas, where we see the largest differences in mean household characteristics, For example, the poverty rates for Western provinces differ by 3 percentage points. In addition, Western province’s share of the population is 71 percent in the HIES and 78 percent in the LFS. Given that the LFS underestimates poverty within the urban areas of Western

> 10 One potential strategy for balancing the two samples on observables would be estimate the probability that a particular household is in the LFS as opposed to the HIES, given its observable characteristics in 2006 or 2009. The estimated probabilities could then be used to reweight the LFS sample in order to balance the two samples on observables.

15