# **3. Estimating the Drivers and Determinants of Regional Poverty**

The estimation strategy to understand regional determinants of poverty is based on an existing literature associating growth composition, labor, and other regional variation to poverty reduction. Explanatory factors of the evolution of regional poverty has generally be classified as either “patterns of growth” or “initial conditions”. The methodology in this paper fallows closely previous literature (Ferreira, Leite, Ravallion, 2010; Suryahadi, Suryadarma, and Sumarto, 2009; Montalvo and Ravallion, 2010; Loayza and Raddatz, 2010).

Our data set comprises of 26 NUTS2 regions from 2006 to 2013. Descriptive variables at the regional level are not available for all years and depending on the specification, results will have fewer observations. In terms of patterns of growth, we consider regional and spatial variations in economic variables, shifts in sectoral growth, the composition of growth, fiscal spending. In terms of initial conditions, we control for demographic and infrastructure variables.

## **<u>Patterns of Growth</u>**

The starting point estimation is a first difference equation (Eq 1). The dependent variable is the headcount ratio based on the Foster-Greer-Thorbecke family of poverty indices. The headcount ratio or poverty rate is based on imputed annual household income per capita and international poverty lines. The change in a poverty index is regressed onto changes in the value added of sector _s_ in region _r_ and at time _t_ . The primary sector is agriculture ( _s_ =1), the secondary sector is industry ( _s_ =2), and the tertiary sector is services ( _s_ =3). This is the most basic specification for estimating the impact of the “pattern of growth”.

Since the equation is a first difference, any time-invariant regional level fixed effects are removed. From the poverty convergence results earlier, it is evident that the impact of growth patterns by sector will be uneven regionally. Therefore, it is also necessary to interact the regressors with a regional dummy. There are two regional groupings that are tested in this paper for robustness; East and West, and a 3-region grouping proposed by the OECD. Table 17 lists these groupings in detail. The West region excludes the NUTS1 regions of TRA and TRB. TRA and TRB are mountainous regions and geographically harsher than the rest of the country; they can also be referred to as the “roof” of Turkey. The OECD (2014) groups Turkey’s regions into three groups: the Developed West, Anatolian Tigers, and the rest of the country.

Overall, western and coastal regions are more developed with more modern industrial and agricultural production along with professional services compared to the eastern regions. TR1-Istanbul is the only region that has negligible agricultural production in terms of its sectoral gross value added (GVA) breakdown (Figure 10). Only 0.2 percent of Istanbul’s production comes from agriculture compared to the national average of 9 percent. Most of Istanbul’s economy consists of professional services such as finance, communication and information along with manufacturing. TR4-Dogu Marmara is the most industrialized region in the country with large-scale industrial production and manufacturing firms. TR5-Bati Anadolu, includes the capital and second largest city Ankara. It has the second lowest agricultural share in terms of its GVA after Istanbul. Government jobs and other professional services along with sizable manufacturing and constructing industries make TR5-Bati Anadolu one of the more developed regions in Turkey.

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