Figure 3 shows that a higher share of the food and beverage sector (Z31) is positively correlated with some variables characteristic of a rural environment: the average household size (v32), the number of primary school students per teacher (v38) and the distance to nearest major seaport (v44). The share of the textile (Z32) and wood products (Z33) sectors are positively correlated with industrial share in GDP (v14), electricity consumption per capita (v30 and v31), and number of counters with local, long distance and international calls per capita (v33). The share of paper products (Z34), chemicals (Z35), basic metal (Z37) and fabricated metal (Z38) sectors are positively correlated with population density (v20), state highways and provincial roads in provinces areas (v34), ratio of asphalt roads in villages (v35), ratio of villages with sufficient freshwater (v36), and the literacy rate (v40). The non-metallic mineral products sector (Z36) does not depict a specific correlation with the socio-economic and infrastructure variables.

Figure 3 suggests that the first factor is a rural-urban split, while the second factor highlights provinces’ endowments in infrastructure. The textile and wood sectors seem to interact more with energy infrastructures, while the paper, chemicals and metallic sectors seem to interact more with transportation infrastructures. The non-metallic mineral products sector is particularly concentrated in two provinces (Sinop and Corum) where it represents more than two-third of the manufacturing sector.

# <u>4) Empirical estimation</u>

For the sake of comparison, we use the four databases built in th _e data source and econometric issues_ Section (the initial panel, the two moving average panels, and the simple average panel) to assess the econometric relation derived in Section 2. Since we use different urbanization proxies, the ultimate equations to be estimated are: _Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _N r_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _URBANr_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _LOANr_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _ELECTr_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _ROADr_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t Ln_ ( _ys_ , _r_ , _t_ ) = α + α _S Ln_ ( _ACCESSr_ , _t_ )+ α _L Ln_ ( _PN s_ , _r_ , _t_ )+ α _U Ln_ ( _UNIVr_ , _t_ )+ α _T t_ + _FEs_ + _FEst_ + ε _s_ , _r_ , _t_ The proxies included in these specifications directly reflect the parameters of the shift factor introduced in the theoretical framework, i.e. _g(accessibility, localization, urbanization)._ Appendix 3 departs from this approach by including provincial population and per capita as market capacity proxies. The specifications using per capita GDP as market capacity proxy provide qualitatively identical results whereas the specifications using population tend to suggest that a larger population alter the productivity of the 11