**Table 14. HIES** **<u>to HIES imputation from 2006/07 to 2009/10 for the estate sector</u>**

||||Mea|n of X in|
|---|---|---|---|---|
|Description|Coefficient|Std. Err.|2006/7|2009/10|
|Intercept|8.1312|0.0496|||
|Motor car van|0.3413|0.188|0.011|0.011|
|Computers|0.3564|0.0856|0.014|0.022|
|Cookers|0.1071|0.0293|0.106|0.115|
|Dependency ratio (age -14, 64+ to HH size)|-0.1315|0.039|0.328|0.324|
|Head’s major industry: Inactive|-0.0654|0.0198|0.271|0.248|
|Household size|-0.2078|0.0127|4.2|4.2|
|Household size squared|0.0088|0.001|21.4|20.6|
|Head’s education passed AL|0.3811|0.0825|0.018|0.023|
|Telephone|0.1465|0.0294|0.097|0.389|
|Floor>750 sqft|0.1207|0.0388|0.060|0.059|
|Line room/Row house|-0.0612|0.0179|0.693|0.640|
|Maximum household education completed primary|0.0826|0.0207|0.602|0.620|
|Maximumhousehold education passed OL|0.1427|0.0344|0.075|0.090|
|Maximumhousehold education passed AL|0.18|0.0519|0.051|0.062|
|Per capita wage index|0.0028|0.0015|11.8|14.3|
|Radio|0.0378|0.0186|0.693|0.694|
|Three wheeler|0.2134|0.0843|0.011|0.023|
|TV|0.0969|0.0192|0.617|0.705|
|VCD|0.0694|0.0217|0.230|0.399|
|Washingmachine|0.2095|0.0826|0.013|0.016|
|Source: Authors’ calculations usingHIES 2006/07 and 2009/10|||||

Table 15 summarizes the results of the HIES to HIES imputations with additional variables. For each sector, the left-most column shows the estimates using only variables common to the LFS and the HIES. The next column adds wage data, and the final column adds the consumer durable and housing data available in the HIES. The direct estimates of poverty differs slightly from those reported previously (Table 7) because it reflects the full HIES sample.<sup>13</sup> For urban areas, adding the additional variables on consumer durables has little effect. This is because, as discussed above, imputing from the HIES to the HIES based on variables common to the LFS works well in urban areas, so the benefit of including additional information is small. This reconfirms that the main source of biases in the imputations for urban areas is the difference in survey designs between the HIES and the LFS. However, there is noticeable improvement when wage data are added to the model.

For rural areas and estates, adding ownership of consumer durables and other variables to the model greatly improves the accuracy of the predictions. This is particularly noticeable for the backward imputations. For example, for rural areas, backward imputation with the common variables only estimates a poverty rate of 11.1 percent for 2006/7. This is about midway between the direct estimates of 2006/7 and 2009/10, which suggests that common variables of LFS and HIES are not fully tracking the increase in consumption between 2006/7 and 2009/10. Adding wage data improves the imputed poverty rate slightly to 12.6 percent. However, adding further ownership of consumer durables and housing conditions, which are available only in HIES, improves the imputed poverty rates significantly. The imputed poverty rate of 2006/7 is now 13.4 percent, which is just 0.4 percentage point lower than the direct estimate and inside its 95 percent confidence interval. For estates, although including durable goods

> 13 Table 14 is based on the full HIES sample from July to June, whereas table 7 is based on a subsample from July to December covering a subset of districts.

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