*The Socio-Economic Factors of the Covid-19 Pandemic in Turkey: A Spatial Perspective DOI: http://dx.doi.org/10.5772/intechopen.106048*

aspects of life are created. These indicators include income, work life, safety, housing, environment, social life, access to infrastructure services, education, life satisfaction, and civic engagement dimensions. Based on the previous literature, we select explanatory variables among the 41 sub–indicators. The dimensions that can affect the spread of Covid-19 but are not captured by the Life Index in Provinces, such as median age, percentage of individuals 65 years old and above, or population density are also added to the analysis.

Besides the socio–economic factors, the vaccine uptake decision of societies is a crucial weapon against the spread of Covid-19. Therefore, we use the vaccination rates for individuals 18 years old and above for each province as a control variable in the models.



**Table 1.**

*Socioeconomic and Covid–19 related variables and the data sources.*

A summary of explanatory variables that are employed in this analysis and the data sources are reported in **Table 1**.

The data period is determined by the announcement periods of the Turkish Ministry of Health. Vaccination rates started to be announced at the province level on 04.07.2021 on a daily basis. The total number of cases per 100,000 population is announced weekly. Therefore, we consider the average total number of cases and vaccination rates for July 2021 in this analysis.

### **2.2 Methodology and model selection process**

A standard OLS model is often estimated as a reference for the following spatial models. This study employs the same starting point. To understand the effect of location on the Covid-19 cases, many studies employ SAR and SEM specifications. You et al. [2] note that the SAR model will show how the infection burden in a location is affected by the infection burden in the neighboring locations. SEM is used to understand whether the OLS residuals are correlated to residuals of the neighboring locations. In the lines of [2, 3] also consider a SAC model. They argue that since the SAC model contains a spatial lag and a spatial error term, it can be seen as a combination of these two.

In fact, the spatial model family has a large set of approaches<sup>1</sup> , and model selection is a crucial part of its applications. Baum and Henry [4] argues that this selection must be based on the spillover type that the economic theory points out. Unlike [2]'s suggestion, [4] stresses that the SAC model is not the linear combination of SAR and SEM approaches. Not considering the spillover types in the selection of appropriate spatial models leads us to the identification problem noted in [5].

<sup>1</sup> For a detailed discussion, see [8, 24].

*The Socio-Economic Factors of the Covid-19 Pandemic in Turkey: A Spatial Perspective DOI: http://dx.doi.org/10.5772/intechopen.106048*

Jamison et al. [6] state that the locations that are closer to the center of the pandemic are affected more quickly than the distant ones. However, besides geographical proximity, Covid-19 can spread easily when the locations are connected on a network, such as traveling. It means that both global and local spillovers exist in the diffusion of infectious diseases. We argue in this paper that this nature of Covid-19 can be best captured with an SDM approach. Aydin and Yurdakul [7] also recommends using SDM as a departure point, when the true data generating process, as in the case of Covid-19, is unknown. SDM will also give the linear combination of SAR and SEM specifications [4], as intended by [2, 3].

The OLS model that is used as a benchmark is presented in Eq. (1).

$$\mathbf{y}\_i = \beta\_0 + \beta \mathbf{X}\_i + \mathbf{e}\_i \tag{1}$$

where *yi* is the total number of Covid-19 per 100,000 people in a given Turkish province. *β*<sup>0</sup> reflects the intercept term and β is the vector of coefficients for the explanatory variables. *Xi* is the socioeconomic variables that are shown in **Table 1** and *ε<sup>i</sup>* is the error term with iid. We check the OLS assumptions. No multicollinearity problem is detected. The insignificant variables (p < 0.10) are excluded from the model in order to refine.

The SDM specification that is used in this paper is shown in Eq. (2).

$$\mathbf{y}\_{i} = \mathbf{y}\_{0} + \rho \mathbf{W} \mathbf{y}\_{i} + \eta \mathbf{X}\_{i} + \mathbf{W} \mathbf{X}\_{i} \boldsymbol{\Theta} + \boldsymbol{\mu} \tag{2}$$

In Eq. (2), the dependent and the explanatory variables are the same as the OLS model defined in Eq. (1). However, here, we scale both the dependent and explanatory variables with a spatial weight matrix (W). The coefficient ρ reflects the global interactions in the spread of Covid-19 in Turkey, while θ demonstrates the local interactions. u is the error term.

Our model selection process follows [7] and we also compare our results with SAR and SLX specifications. The SAR and SLX models are shown in Eq. (3) and Eq. (4) respectively.

$$\mathbf{y}\_i = a\_0 + \rho \mathbf{W} \mathbf{y}\_i + a \mathbf{X}\_i + \lambda \tag{3}$$

$$\mathbf{y}\_i = \delta\_0 + \delta \mathbf{X}\_i + \mathbf{W} \mathbf{X}\_i \boldsymbol{\Theta} + \boldsymbol{\tau} \tag{4}$$

The spatial weight matrix used throughout all these models is the same. The elements of W take the value of 1 if two Turkish provinces are neighbors, and zero otherwise.
