Due to the nature of the within-between model, it is easier to interpret the coefficients, since the within effect is clearly separated from the between effect. Therefore, if agricultural value, increases by one percent within a province, everything else constant, we can expect to see a decrease in the share of illiterate people by 4 percent. Similarly, a percent change in Industry and Services, ceteris paribus, is associated with a decline in the share of Illiterate people by 2.8 and 2.4 percent, respectfully. Also, if the unemployment rate increases by 1 percent in a province, ceteris paribus, we can expect to see an increase in the share of illiterate people by 0.05 percent. Therefore, within a province, changes in the economic indicators have a positive effect on the decline in illiteracy levels. The model explains 78.3 % of the variance within a province.

Individual means of time-varying variables capture the between entity effects. Lower education attainment indicators, illiterate, literate without a diploma, and primary education, are positively correlated with the share of the Kurdish population. Hence, if a share of Kurdish population increases by one percent in a province, holding other variables constant, we can expect to see an increase in illiteracy rate by 5.3 percent. This result implies that there is a negative effect of Kurdishness on the illiteracy rates. One explanation for this might be because Kurds are not allowed to use Kurdish language in public, and are not being taught in their mother language. Benson (2005) compared the education in a language that students don't understand to “holding learners under water without teaching them how to swim.” In the report on the discrimination in Turkey's education system, Kaya (2015) emphasized the important role of providing the education in a mother language, to the quality of education, where teaching in a language students do not understand leads to higher drop-out rates and repeated years of schooling. Another explanation for a positive correlation between low educational attainment and the Kurdishness of a province, is the there is a practice of the lower provision of funds for education from central government to Kurdish provinces.

Among the economic factors in the between model, only industrial value has a significant relationship with illiteracy rates. If the industrial value increases by one percent, holding other variables constant, we can expect to see a decline in the share of illiterate people by 0.78 percent.

In the second regression, I tested the effects of ethnic characteristics and economic performance on the share of the population with an undergraduate diploma. Within a province, a one percent increase in agriculture, ceteris paribus, is associated with a decline of 0.39 percent in the share of people with a university degree. On the other hand, industry and service positively correlate with the proportion of the population with a university degree, where a percent increase in these two sectors, ceteris paribus, is associated with an increase in Bachelor's diploma holders by 3.9 and 6.9 percent. This very high increase implies that the economic outcomes largely affect higher education. Between effects are following: A percent increase in agriculture, ceteris paribus, is associated with a decrease in the share of the population with an undergraduate diploma by 0.8 percent. However, higher negative coefficient has a share of Kurdish population variable, where a percent increase in Kurdishness, leads to a decrease in the proportion of the population with the undergraduate diploma by 3.1 percent. Moreover, services are positively associated with the undergraduate diploma shares, with a coefficient of 1.4. The overall R^2 of the regression with Bachelor diploma is 74.5.

Results of the regression with Y variable infant mortality rate are as follows: Within a province, a percent increase in industry and services, ceteris paribus, is associated with the decline in the infant mortality rates by 3.12 and 4.47 per thousand live births. A percent increase in the unemployment rate, ceteris paribus, is associated with an increase in the IMR by 0.8 per thousand live births. Across provinces, a one percent increase in Kurdishness, ceteris paribus, is associated with an increase in the infant mortality rates by 4.9. Additionally, a percent change in the average agricultural product value and the mean services value, ceteris paribus, is associated with an increase by 1.6, and a decrease by 1.4 infant deaths per thousand live births. Again, Kurdishness had the highest coefficient in the regression meaning that Kurdish provinces have the highest effect on the IMR, which is a proxy for low public health provision scores. Additionally, we can see that the higher agricultural activity in a province might lead to more infant deaths per thousand live births.

Within province results for the fourth dependent variable, nursing staff per thousand people, are as follows: A percent change in industry and service value, ceteris paribus, might be associated with an increase in the number of nurses per thousand population by 0.5 and 0.8, respectfully. Thus, positive changes in health provision from year to year, are due to higher economic performances in secondary and tertiary sectors. Across provinces, a percent change in mean agriculture and mean unemployment rate, ceteris paribus, is associated with a decline of 0.2 and 0.05 nurses per thousand population. Additionally, a percent increase in mean Services, ceteris paribus, is associated with an increase of 0.3 nurses per thousand population. Therefore, it seems that provinces with the large tertiary sector, on average, have more nursing staff per capita. Share of the Kurdish population had no significant relationship with the number of nurses in each province. Overall R^2 is 48.9.

Lastly, regression results on the direct provision of public schools, number of general high schools per capita, show a positive relationship between the agricultural value and the number of secondary schools on both levels. Additionally, services and industry are negatively correlated with the dependent variable. Share of Kurdish population has no significant relationship with the number of secondary schools. But, R^2 of both within and between effects (0.18, and 0.3 respectfully), is low meaning that these results are not adequate for further discussion.

Since regressions results with different proxies for public goods provision vary, I decided to create a Public goods provision index using the EFA to capture the effects of the common variable which affects all dependent variables. In this case, that variable is the public provision in education and health.

3.2 Exploratory Factor Analysis

There has been a growing trend in the empirical research of the effects of race or ethnicity or cultural disparities between groups in various countries, and on the cross-country level. To estimate the relationship between ethnic and economic characteristics, and the delegation of those goods, I decided to create a public goods provision index. If the ethnic structure really does affect lower public goods provision in Turkish provinces, then it will help in redesigning the policies to stimulate the take-up in the future, or to design specific projects made only for underdeveloped areas Kurdish areas. Again, all variables used for the creation of index measure only health and education dimension of public goods provision. All variables were standardized before conducting the analysis.

To construct the index, I used the Exploratory Factor Analysis, whose goal is to identify the underlying relationships between measured variables. Specifically, factor analysis groups sub-indicators that are collinear to form a composite indicator that captures as much of common information among variables as possible. (Nardo, et al., 2015) There are two popular techniques used in EFA, maximum likelihood and principal axis factoring. Maximum likelihood method is widely used on the data that is normally distributed, which is not the case in the data used for this research. Therefore, I used the principal axis factoring, which is similar to the Principal Components Analysis, because they both produce results where the first factor/component explains the highest possible common variance, the second factor next highest common variance, and so on. The first advantage of the Principal axis factoring is that it works when the normality assumption is violated (Fabrigar, Wegener, MacCallum, & Strahan, 1999). Secondly, it is less likely to produce incorrect information, than the maximum likelihood method (Finch & West, 1997). However, the major limitations are that it does not allow for the computation of confidence intervals and significance tests.

Since several treatment variables that are trying to explain the public goods provision in Turkey are highly correlated with each other, I used the Cronbach's alpha method, which is a measure of internal consistency, or reliability of the data, or how closely related a set of items are as a group. Cronbach's alpha ranges from 0 to 1, and it shows the ratio of two variances. It also shows the overall alpha scores if each variable was excluded from the dataset. If all the scale items included in the dataset are highly correlated or have high covariances, then alpha is going to be high, which means that the items measure the same underlying effect.

Kaiser-Meyer-Olkin's test (KMO), indicates whether the variables can be grouped into a smaller set of factors. KMO ranges from 0 to 1, where the rule of thumb is to have the score higher than 0.6. Keiser and Rice (1974), classify the scores from unacceptable (> .5), to marvelous (<= .9).

3.2.1 Indices

Firstly, I decided to create one index capturing overall provision of public goods. Overall means it runs on variables that both, directly and indirectly, measure public goods in health and education. Using the EFA method, we are trying to measure the latent variable, Public goods provision. This latent variable cannot be directly measured with a single variable. Instead, it is seen through the relationships it causes in a set of dependent variables. Therefore, first EFA has been conducted on 16 variables:

· shares of illiterate, literate without a diploma, primary education, high school, Bachelor, Masters, and Doctorate diploma population,

· number of Preprimary, primary, general secondary, and vocational high schools,

· number of hospitals, beds, physicians, and nurses per thousand population, and

· the infant mortality rates.

Exploratory factor analysis starts with producing eigenvalues which show the proportion of the total variance explained by each factor. Each factor is a specific combination of variables whose factor loadings represent the weight of each variable in the given factor. Any factor with an eigenvalue ?1 explains more variance than a single observed variable. However, there is no specific rule when choosing the right number of factors. The Kaiser's stopping rule (Factor Analysis: A Short Introduction, Part 1 , n.d.) says that we should include the components whose eigenvalues are above 1. The rule of thumb is to include all components which have a steep slope in the scree plot. Finally, it is the choice of the researcher to decide on how many factors he/she should continue. Since the factor one explains 59% of the total variance, and loads on all variables, I decided to use only one factor. Cronbach's alpha score was 0.90 Cronbach's alpha scores table is in the Appendix 2, which implies that the dataset is reliable and that it could be used for the applied scenarios research. Lance et.al. (2006), and Nunally (1978).

Factor loadings for the Overall Provision Index are shown in the first column of the Table 1. It loads negatively on low performances and low infant mortality rates, and positively on higher education and the health indicators. The KMO score of .7681 is classified as Meritorious, and the variables are considered adequate for the exploratory factor analysis (EFL). Ankara province has the highest index score, followed by the Eskisehir province. Both provinces are considered developed, urban areas. The lowest Overall Provision Index scores are for the provinces Mus, Agri, Sanliurfa, followed by Mardin, Bitlis, and Sirnak, which are the least developed provinces, and provinces populated by Kurdish ethnic group.

Table 2 Factor loadings for the Overall Provision, Direct Provision, and Indirect Provision Index

Variables	Overall provision loadings	Direct provision loadings	Indirect provision loadings
% Illiterate	-0.77		-0.80
% Literate without diploma	-0.70		-0.72
% Primary education diploma	-0.30
% High school diploma	0.81		0.81
% Bachelor diploma	0.89		0.92
% Masters diploma	0.82		0.87
% Doctorate diploma	0.80		0.77
Infant Mortality Rate	-0.56		-0.58
No. Pre-prim schools	-0.60
No. Primary schools	-0.73
No. General secondary schools	0.11	0.51
No. Vocational secondary schools	0.21	0.56
No. Pre-prim teachers
No. General secondary teachers		0.62
No. Vocational secondary teachers		0.67
No. Hospitals	0.21	0.58
No. Hospital beds	0.64	0.73
No. Physicians	0.77	0.57
No. Nurses	0.77	0.88

Secondly, I grouped dependent variables in 2 types: Variables capturing direct effects of the funding from the central government such as public institutions (schools and hospitals per capita), hospital beds per thousand people, and staff in both sectors (teachers and medical staff per capita). The second, indirect type, are the effects of government projects, and funding, such as share of illiterate people, share of the population with Master's degree, and the infant mortality rates. All variables loaded strongly on the first factor in both EFA's.

Hospital beds, Nurses, and the high school level teachers have the highest loadings, and therefore will have the highest weight in the Direct Provision Index variable. In the Indirect Provision Index, all variables have very high loadings. Variables that have lower loadings are still important since they still have a strong relationship with the underlying factor. The Cronbach's alpha for Direct Provision Index was 0.79, and for the Indirect Provision 0.88, with KMO's of 0.65, and 0.77, respectfully. Direct Index explains 57% of the variation in variables loading in the Index and has an eigenvalue of 3.3. The explained variation of variables loading in the Indirect Index is higher, 82%, with an eigenvalue of 4.4. These two indices were used as dependent variables in the regressions with the same treatment variables as in the previous chapter.

3.3 Regression results on the Public goods provision indices

To test whether the public goods provision was correlated with the ethnic characteristics and economic performances of provinces, I ran two models, one with GRP values (in TL), and another with the sectoral shares of each GRP component. Additionally, I generated individual mean, or the higher-level entity mean, for each independent variable to exclude the between province effect from the within effect. In the model explained previously, that is the , or the between province effects. Also, to get the within province effects , or time varying variables, I subtracted the individual means from actual values of each variable. Therefore, the regression results on three indices are presented in Table 3.

Table 3 Regression results with Indices

Regression Table 2
	(1)	(2)	(3)	(4)	(5)	(6)
	Public Goods Provision Index	Direct Provision Index	Indirect Provision Index
Kurdishness	-1.529***	-1.513***	-1.080**	-0.886*	-1.593***	-1.655***
	(-5.51)	(-5.74)	(-3.06)	(-2.35)	(-5.81)	(-5.95)
W Agriculture		0.135***		0.275***		0.126**
		(3.32)		(3.88)		(2.80)
W Services		1.424***		1.091***		1.505***
		(13.65)		(6.17)		(14.89)
W Industry		0.829***		0.624***		0.841***
		(7.33)		(4.90)		(7.81)
W % Agri. product	-1.958*		-0.835		-1.888*
	(-2.13)		(-1.09)		(-2.10)
W % Serv. Product	-0.236		-0.202		-0.476
	(-0.24)		(-0.22)		(-0.49)
W % Ind. Product	-0.566		0.744		-1.133
	(-0.52)		(0.68)		(-1.14)
W GRP	1.265***		1.033***		1.358***
	(11.71)		(5.67)		(16.49)
W UR	0.00265	-0.000990	0.00176	0.0000855	0.00373	-0.00121
	(0.53)	(-0.17)	(0.34)	(0.02)	(1.05)	(-0.27)
M Agriculture		-0.417***		-0.422*		-0.305**
		(-3.45)		(-2.04)		(-2.76)
M Service		0.640***		0.311		0.412***
		(4.18)		(1.37)		(3.66)
M Industry		0.167		-0.160		0.115
		(1.64)		(-0.97)		(1.53)
M % Agri. product	-2.760***		-0.834		-2.891**
	(-3.49)		(-0.74)		(-3.23)
M % Serv. product	2.252*		5.121***		0.511
	(2.56)		(3.93)		(0.51)
M % Ind. product	-2.242*		-5.128***		-0.486
	(-2.52)		(-3.95)		(-0.47)
M GRP	0.160*		-0.176		0.0467
	(2.11)		(-1.56)		(0.59)
M UR	-0.0491	-0.0459	-0.120***	-0.122***	-0.0293	-0.0231
	(-1.77)	(-1.74)	(-4.15)	(-3.60)	(-0.98)	(-0.79)
Income levels	0.347	1.021	-1.946	-0.829	0.894	1.417
	(0.35)	(0.95)	(-1.04)	(-0.43)	(1.55)	(1.89)
Population	-5.15e-08	-0.000000152**	-0.000000164***	-0.000000237**	0.000000103**	1.62e-08
	(-1.95)	(-3.14)	(-3.88)	(-2.91)	(2.62)	(0.38)
Urban pop.	0.0211***	0.0193***	0.0266***	0.0178**	0.0139*	0.0151**
	(3.44)	(3.70)	(4.37)	(3.19)	(2.48)	(2.91)
constant	-3.815**	-4.515***	0.714	1.804	-1.152	-2.802**
	(-3.05)	(-4.02)	(0.42)	(1.03)	(-0.84)	(-2.93)
Note: t statistics in parentheses; * p<0.05, **p<0.01, ***p<0.001

First two regressions in the Table 1 I ran on the dependent variable Public goods provision index. Overall R^2 for both models was high, explaining 73, and 76 percent of variation between variables. Within a province, a percent increase in the share of agricultural product in total GRP, ceteris paribus, is associated with a decline in the Overall Provision Index by 0.019. However, a percent increase in the total GRP, ceteris paribus, is associated with an increase in the Overall Provision Index by 1.26. The coefficient for the overall GRP is in line with the coefficients from the second model, where within a province, a percent increase in the agriculture, industry, and services values, ceteris paribus, are associated with the increase in the Index score by 0.13, 0.8, and 1.4 respectively.

Between provinces, a percent increase in the share of Kurdish population, ceteris paribus, is associated with a decline in the Overall Provision Index score by 1.53. Additionally, higher shares of agricultural and industrial product seem to correlate negatively with the public provision index, with the coefficients of -2.76, and -2.42 respectively. Total GRP value and the share of services product in total GRP, have positive relationship with the index scores. However, when we look at the results with sectoral values, agriculture has a significant negative relationship with the index, where a percent increase in the agricultural value, ceteris paribus, is associated with the decline in the index score of 0.42. Results from the regression table with the Overall provision index imply that the provision of public goods in education and health is negatively associated with the share of Kurdish population, and the higher shares of the primary and secondary sector in total GRP. Therefore, if a province has a high share of Kurdish population and high share of agricultural production in total provincial production, it will most likely have lower provision of public goods. However, if in general, economic activity in a province is increasing, public goods provision will be higher. Regression produced insignificant coefficient for unemployment rates using both, values, and sectoral shares.

Above-mentioned results lead to a conclusion that across provinces, the biggest determinant of a public goods provision in health and education is the ethnic structure of a province. However, it is important to highlight the limitations of the process in construction of the Index and the usage of indirect variables. Limitations of the Index are that it explains 59% of the total variance of 16 variables included in the Exploratory factor analysis. Additionally, out of 59% of the variation, index loads highly on the variables such as illiterate, literate without a diploma, and other educational levels, which have higher correlation with the shares of the Kurdish population. On the other side, it also loads highly on hospital beds, physicians, and nurses, which show the direct and the fastest change in the public goods provision. Therefore, I ran two regressions both for Direct and Indirect Provision Index to see if the clustering variables would affect coefficients in the model.

Direct Provision Index, as previously mentioned, runs on the number of public institutions per capita, and on the teachers and medical staff in hospitals, as well as hospital beds per thousand people. Within province effects imply that a one percent change in the agriculture, industry, and service value, ceteris paribus, is associated with the increase in the Direct Provision Index of 0.27, 1.09, and 0.62, respectfully. Additionally, none of the three sectoral shares in total GRP were significant on the within level. On the between province level, share of Kurdish population negatively correlates with the Direct Provision Index. Therefore, a percent change in the share of the Kurdish population, ceteris paribus, leads to the decline in the Direct Index score by 1.08, and 0.9, depending on the economic dimension variables used. Additionally, agricultural value negatively correlates with the Direct Provision Index, meaning that a province experiencing higher agricultural activity might be receiving proportionally less than other provinces. Share of services sector in total GRP positively correlates with the index, where a percent increase in the share of tertiary sector, ceteris paribus, is associated with the increase in the Direct Provision Index by 5.12. on the contrary, share of industrial product has negative relationship with the Index with the coefficient of -5.12. That said, a province focusing the economic activity on the industry instead of on the services sector, might on average have lower scores in the Direct Provision Index. Similarly, unemployment rates are negatively correlated with the index scores, where a percent increase in the unemployment rate, ceteris paribus, is associated with the decline in the Index scores by 0.12. Therefore, we can argue that provinces with the higher agricultural and industrial product in total GRP have less public goods provision, measured by the number of public institutions and staff employed in the health and education sectors. However, Kurdishness is significantly correlated with the Direct Index which is contrary to the findings from the individual regression models on the size of the nursing staff per capita, and the number of general secondary schools. These results, also imply that there has been a practice of lower provision in health and education to Kurdish areas, which is in line with the findings of the Overall Provision Index.

Lastly, the third index runs on the direct effects of the public funds delegated to provinces, specifically, how did government decision of funding affect the outcomes in human capital. The model with the Indirect Provision Index confirmed the results from the previous two sets of regressions. On the within level, higher economic activity in all three sectors positively affects the Index scores. However, share of the agricultural product in the total GRP, again, has a negative relationship with the public goods provision. Therefore, a percent increase in the share of the agricultural product, ceteris paribus, is associated with a decline in the index score of 2.89. According to these findings, the effects of the public goods provision are most likely going to be lower in the provinces with higher shares of agricultural product in total GRP. Between level results show that again higher share of agricultural sector, and agricultural value, negatively associate with the Indirect Provision Index. Finally, share of Kurdish population has the most significant coefficient, where a percent increase in the share of Kurdish population, ceteris paribus, leads to the decline in the Indirect Index score by 1.6. R^2 for both indices was high, 73 for Indirect, and 63 for the Direct Provision Index.

Therefore, the overall conclusion is that the Kurdish provinces profited significantly less in terms of a public goods provision. More to it, provinces with the higher agricultural and industrial activity have most likely lower scores than the provinces with the higher shares of services sector in total GRP. Since Kurdishness doesn't correlate with the share of agricultural product in total GRP, we can conclude that these are two separate factors shaping public goods provision in Turkey. However, this research paper focuses on the AKP-run government, and the mechanisms of public provision in the period of higher economic growth. Using variables in the present without excluding the historical effects doesn't necessarily show the provision of goods now. Since Kurdish population has been marginalized for a whole century, illiteracy levels and other proxies for public goods provision are worse than in other areas of Turkey. Therefore, in the next chapter I will present the results on the changes in the public goods provision by ignoring the historical practice which affected the current amount of goods in provinces.

3.4 Changes in Illiteracy Rates

Changes in illiteracy rates have been calculated by subtracting the 2000 Illiteracy shares from the corresponding province in the period 2009-2014. If in Adana province, proportion of illiterate people in 2009 was 9.8, and in 2010 7.9, we subtract the 2000 share 13.11. New values for 2009 and 2010 are going to be -3.2, and -5.1, respectively. Using this method, we only account for the actual changes from each year. Therefore, we can analyze the trend in changes, specifically whether Kurdish provinces had a significantly different trend in these changes than other provinces, during the AKP mandate. Similarly, using this method, we can analyze the changes in the infant mortality rates, and nursing staff. Regression results on the changes in illiteracy rates are in the following table.

Table 4 Regression results with Delta illiteracy rates

Regression Table 3
	(1)	(2)
	Changes in Illiteracy rates
Kurdishness	0.0939***	0.0907***
	(4.49)	(4.57)
W Agriculture		0.0498***
		(6.31)
W Services		0.0473***
		(4.50)
W Industry		0.0378***
		(6.06)
w % Agriculture product	0.18**
	(2.73)
w % Services product	-0.227*
	(-2.34)
w % Industry product	0.209*
	(2.01)
w GRP	0.0602***
	(4.70)
w UR	-0.000161	-0.000400
	(-0.32)	(-0.98)
M Agriculture		0.00817
		(1.51)
M Services		-0.00827
		(-1.27)
M Industry		-0.00766
		(-1.71)
M % Agriculture product	0.0996
	(1.75)
M % Services product	0.0706
	(1.31)
M % Industry product	-0.0697
	(-1.31)
M GRP	0.00108
	(0.24)
M UR	0.00208	0.00198
	(1.37)	(1.30)
Income levels	-0.241	-0.150
	(-1.75)	(-1.23)
Population	-4.29e-09*	-3.37e-10
	(-2.26)	(-0.14)
Urban pop.	-0.000354	-0.000497
	(-1.03)...

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