# **2. POVERTY TRENDS**

## **_Imputation_**<sup>**_11_**</sup>

Household-level poverty is calculated using imputed income from the LFS. The LFS is ideal due to the availability of regional and migrant information, but only has wage data which is insufficient to capture total income for welfare measurement. Thus total income needs to be imputed. The SILC data set can also be used to study regional poverty rates, but only at the NUTS1 level. Another disadvantage of the SILC is that spatial deflation in Turkey is only available at the NUTS2 level (LFS data), and not at the NUTS1 level since TUIK computes regional CPIs only at the NUTS2 level. Income and household characteristics from the SILC are used to produce a model to impute total income into the LFS.

The imputation model is an OLS with regionally deflated incomes at the NUTS2 level and time-invariant characteristics. Regressions are conducted using household-level data. For each year, the model specified from the SILC may vary. For every household, total household income is imputed 100 times. At the regional and comparison group level, poverty rate estimates and standard errors are computed from these 100 imputations. The international $5/day per capita 2005PPP poverty line is used. One way to interpret the poverty rates from imputed income is simply the conditional welfare based on observables.

Observed income poverty can be computed using the SILC but only at the NUTS1 level and without differentiation between the host and foreign-born populations. As a validation exercise, observed poverty computed using the SILC and imputed poverty using the LFS can be compared.

Figure 7 illustrates this comparison and shows that, at the NUTS1 level, imputed poverty rates using the LFS are very similar to observed poverty in the SILC. One caveat is that 2012 and 2013 LFS uses models computed in the 2011 SILC due to a lag in the SILC data availability and changes in the LFS survey methodology.

## **_Estimates of Poverty_**

Table 4 lists results of imputed poverty rates by geographic location, comparison group, and year. Results are shown using the OLS imputation model with time-invariant characteristics and regionally deflated incomes. Recall that in the LFS, the number of recent migrant households in the sample is small when broken down to geographical groups. An implication of small sample size is high standard errors in our poverty estimates for this group, especially in 2009. However, in 2013, the number of foreign-born households increased significantly which allows for more precise estimates of migrant poverty rates.

In principle, the migration and assimilation literature sets expectations that migrant households are generally worse off than host community households upon arrival. The rate of assimilation of migrants depend on their education, language, and opportunities. The children of migrant households also do better than their parents. However, in Turkey, migrant households traditionally have not been worse off than host community households. This is due to the homogeneity of migrants which tend to be of Turkish heritage.

> 11 See the Annex for further details on the survey-to-survey imputation methodology.

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