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Julide Y1ld1r1ma* and Nadir Ocalb
aGazi University, Department of Econometrics, AnkaraTurkey; b Middle East Technical University, Department of Economics, AnkaraTurkey
Abstract. Even though the convergence of regional per capita incomes has been a highly debated issue internationally, empirical evidence regarding Turkey is limited as well as contradictory. This paper is an attempt to investigate regional income inequality and convergence dynamics in Turkish GDP. First, Theil coefficient of concentration index has been employed in order to analyze the dispersion aspects of convergence process which shows a procyclical character. Then, the paper investigates the convergence dynamics, taking regional interdependencies into account. Empirical results indicate that there is convergence at the national level. Moreover, spatial error model is preferred by the model selection criteria, indicating that typical least squares regional convergence model is misspecified.
Keywords: Income inequality, convergence, spatial analysis, Turkey.
Introduction
There have been traditionally two opposing views about the expected longrun trajectories of regional development. First, it has been argued that interregional mobility of capital and labour and sufficient time for tuning would eventually selfcorrect the regional inequalities. However, the existence of significant adjustment costs of flowing inputs among spatially distinct regions contributes to the second view that regional divergence is more likely. In particular, economies of scale, agglomeration of human capital, institutional framework and geographical structures of certain regions accrue economic rents to be more local (Martin and Sunley, 1998). Recent studies revealed that there are economic disparities within each country which are generally higher than those observed at the country level (Barro and Salai Martin, 1991; Neven and Gouyette, 1995; Fagerberg and Verspagen, 1996; Quah, 1996; Pekkala, 1999; Terrasi, 1999; Azzoni, 2001 and Akita, 2003). Empirical studies provide evidence concerning convergence of regional economies, which provide assistance in planning and evaluating regional policy measures. So, the challenge for the national governments would be to provide sufficient incentives to amend unequal regional development.
The persistent disparities in aggregate growth and the large differences in wealth of Eastern and Western regions has been the main concern of the policy makers in Turkey. Since 1963, there has been eight Five Year Development Plans launched to achieve regional convergence. Although the wealth disparities across Turkish regions and provinces have been a debated issue, there is limited empirical evidence concerning the regional economic convergence in Turkey. Filiztekin (1997) investigates convergence across Turkish provinces between 19751995, applying single crosssection methodology and finds divergence of per capital output in all periods except 199095. He reports that the dominant sector in Turkey is still the low productive agricultural sector, even though there has been massive flow of labor from that sector to others, especially services. However, Tansel and Gungor (1998) repeat the single crosssection studies for the same time period but come up with contradictory results to those of Filiztekin (1997). The difference between the two studies can be due to the fact that Filiztekin (1997) concerns with per capita incomes whereas Tansel and Gungor (1998) concern with convergence in labor productivity. Using again the province level labor productivities, Temel et al. (1999) provide an evidence of polarization around highly industrialized regions. In their nonparametric regression analysis, they show that besides the significant labor mobility from eastern agricultural provinces to western industrialized provinces, there is significant concentration around mainly the three western provinces of Turkey. Yet, the previous studies present a regional inequality analysis for Turkey at a disaggregated level and ignore the spatial dimension to the pattern of growth across regions. More recent paper by Gezici and Hewings (2003) explore the regional inequalities considering the spatial patterns and found similar indication of disparities between East and West of Turkey during the period of 198097. Although the existing intraregional inequalities were found to be declining, they argue that spatial dependence to few wealthier provinces would be persistent in Turkey. The aim of this study is to provide a new look to the existing regional economic differences in Turkey and emphasize the fact that regional convergence needs to be properly spatialized. In this paper, the regional inequality issue has been investigated employing Theil coefficient of concentration using spatially disaggregated data for Turkey for the period 19792001. Then, convergence analysis has been incorporating the spillover effects between the provinces.
Empirical analysis using disaggregated NUTS (Nomenclature of Units for Territorial Statistics) level 3 areas GDP data, gives evidence in favor of regional convergence at the national level. However, there is a significant income inequality between the regions; even though within region inequality is relatively small, justifying the regional classification that has been made. It appears that Theil coefficient exhibits a procyclical character, such that it has a tendency to increase in periods of economic expansion and to decrease in periods of recession. In the second part of the paper, the beta convergence analysis for 67 provinces of Turkey, taking regional interdependencies into account has been presented. The empirical analysis reveals that there is beta convergence. However, when spatial autocorrelations are taken into account convergence rate increases where the spatial error model outperforms the other models, indicating that there are interdependencies in growth rates of provinces. The paper is organized as follows: The methodological issues and results of empirical analysis are presented in Section 2. Final section concludes the paper.
Methodology and Empirical Results
Regional Inequalities
This section presents an inequality analysis where inequality indicators are calculated and their evolution over time is investigated. The regional convergence studies claim that growth process of regions is similar to that of national states, mainly due to free capital and labour mobility compared to international level. However, Terassi (1999) for Italy, Petrakos and Saratsis (2000) for Greece and Azzoni (2001) for Brazil indicate that there are serious income inequalities among regions, which may show oscillations in time. Whereas Fagerberg and Verspagen (1995), Funke (1995) and Chatterji and Dewhurst (1996) report existence of selective tendencies, convergence clubs and asymmetric shocks within economies which result in spatial inequalities.
In this study Theil coefficient of concentration index (Theil (1967)) has been employed in order to analyze the dispersion aspects of convergence process, using NUTS level 3 data, relating to 67 provinces of Turkey for the time period 19792001, by forming 4 subregions namely Marmara; Ege, Akdeniz & West Anatolia; East & South East Anatolia; and Karadeniz & MidCentral Anatolia, considering the spatial dimension of the post1980 development. The population data has been obtained from State Institute of Statistics. Population data that is based on 1980, 1985, 1990 and 1997 official census have been interpolated for the years that do not coincide with the census. Real GDP data on the other hand comes from two sources: State Institute of Statistics publishes provincial real GDP data since 1987. GDP data for earlier years are obtained from ztn (1988). Even though the price deflators for all provinces do not exist, price deflators for one major province in each region are available, which are used to construct price indices for each province. Then they have been used to deflate the GDP data from ztn (1988).
Theil coefficient of concentration index has been a very popular index for analyzing spatial distributions, for it is independent of the number of regions and thus compares inequalities of different regional systems. Additionally it is decomposable in betweenand within group inequalities. The following formulas are used to calculate the index:
EMBED Equation.3 (1)
EMBED Equation.3 (2)
EMBED Equation.3 (3)
Where T denotes the total inequality, Tbr between region inequality and Twr within region inequality. yi and xi are regional shares of national income and population respectively and Yr and Xr are the same shares for regions.
Table 1. Theil Coefficient: Total, regional, between regions and within regions
Years T T1 T2 T3 T4 TWR %TWR TBR %TBR
1979
19800.0430.0780.0140.0270.0220.01228.0990.03171.9010.0240.0530.0060.0080.0270.00939.3040.01460.69619810.0410.0740.0180.0270.0240.01024.9300.03175.07019820.0330.0640.0120.0160.0270.01133.9400.02266.06019830.0500.0820.0200.0270.0250.01121.9530.03978.04719840.0520.0820.0220.0270.0240.01121.2820.04178.71819850.0460.0830.0150.0270.0250.01225.7580.03474.24219860.0470.0850.0140.0280.0250.01122.8790.03677.12119870.0490.0730.0280.0260.0260.01327.2700.03672.73019880.0430.0700.0250.0260.0260.00614.9750.03785.02519890.0440.0740.0220.0270.0250.00511.0550.03988.94519900.0340.0660.0200.0270.0250.0038.2870.03191.71319910.0330.0670.0170.0270.0240.0039.5450.03090.45519920.0340.0670.0180.0270.0240.0039.3950.03190.60519930.0360.0690.0190.0270.0240.00410.0200.03289.98019940.0330.0600.0230.0280.0230.0038.5570.03091.44319950.0360.0650.0220.0280.0230.0038.5170.03391.48319960.0360.0670.0190.0280.0210.0049.9680.03390.03219970.0370.0710.0170.0280.0220.00410.0610.03489.93919980.0360.0680.0180.0280.0210.0049.9290.03290.07119990.0340.0660.0170.0290.0200.0038.5850.03191.41520000.0360.0670.0180.0290.0200.0039.5350.03290.46520010.0310.0620.0160.0280.0190.0039.9910.02890.009The subscripts 1 to 4 refer to regions considered. % refers to percentage share of Tbr (Twr) in total.
The results for income inequality analysis are presented in Table 1 and Figures 1 and 2. The analysis indicates a convergence throughout the period under consideration. However, it has a procyclical character, such that it has a tendency to increase in periods of economic expansion and to decrease in periods of recession. Turkey has experienced one of the most severe economic crises in the Republic era, which contributed to the military intervention, in 1980. In that year inequality index shrunk almost by 45 per cent. During the expansion period, from 1983 till 1988, Theil index exhibited an increasing trend. However, from 1989 onwards Theil index decreased, hitting bottom in 1994 after another major economic crisis. A similar behaviour can be observed for 1999 and 2001 crisis years, supporting the hypothesis that in expansion periods richer regions receive more benefits than poorer regions, thus increasing the inequality. However, in recession periods, the richer areas would be affected more quickly and seriously compared to the poorer regions. This could be due to the fact that recessions are generally more severe compared to expansions. This finding is in line with Petrakos and Saratsis (2000) and Gezici and Hewings (2003) who report that regional inequalities have a procyclical nature in Greece and Turkey, respectively.
Moreover, withinregion inequality indices for regions 2 and 3 indicate an increase in inequality within the respective regions, implying divergence, whereas for regions 1 and 4 the opposite is observed. Additionally, region 1, which is the richest region, has the highest level of withinregion inequality, especially in expansion years, among all regions, thus contributing the nationwide inequality considerably. Decomposition of the overall regional inequality into between region and withinregion components reveals the procyclical nature of the inequality once again. Even though until 1987 within region inequality accounts almost a quarter of total inequality, after 1987 it is relatively stable around ten per cent of total inequality. Thus, the decomposition analysis indicates a meaningful partition of 67 provinces into four regions, in that income disparity within the regions is smaller compared to the between region inequality, so that these four regions can be assumed to be homogeneous in nature.
Overall the inequality analysis indicated that income inequality has a procyclical nature in Turkey in the time period under consideration, which raises an important question concerning the relationship between regional inequalities and economic performance. Moreover, even though the overall income inequality decreased, regional disparities are observed.
Figure 1. Theil Coefficients
SHAPE \* MERGEFORMAT
Figure 2 . Inequality Decomposition by Theil Index
SHAPE \* MERGEFORMAT
Spatial Analysis
The issue of economic convergence at subnational level has attracted a lot of attention in recent years. With the seminal work of Romer (1986) and Barro and SalaiMartin (1991) a large number of studies has been devoted to investigating variations in economic performance of countries. These studies reported huge economic disparities within each country. Beta convergence analysis has generally been employed in order to investigate convergence across economies or regions using crosssectional data, implementing the following equation:
EMBED Equation.3 (4)
where yit denotes real income or real GDP of region i, at time t(=1, 2, , N), yi0 denotes income or GDP per capita at some initial time 0; ( is the intercept term, which may incorporate any rate of technological progress; u is random error term distributed iid(0,(2), which may represent random shocks to technology or tastes. A negative value of ( signifies the beta convergence and convergence rate is calculated using the following formula:
EMBED Equation.3 (5)
However, this approach assumes that all regions or economies under consideration have the same steady state income path. But this is a highly restrictive assumption and may induce significant heterogeneity bias in estimates of convergence coefficient. Moreover as Quah (1993b) points out traditional crosssection approach does not reveal the dynamics of the growth processes.
In empirical literature two alternative approaches have been introduced to correct the heterogeneity bias associated with the traditional crosssection analysis. The first is to employ time series analysis to investigate the rates of convergence by looking for common stochastic trends in the individual regional time series data. But this approach is applicable only if long time series data is available at the regional level as well as national level. Alternatively, control variables that can proxy or capture the differences in the paths of steady state incomes of regions, such as rates of accumulation of physical capital, net migration rates, differences in industrial structure, can be included in the traditional crosssection estimates. However, to obtain long time series data as well as reliable proxy data is a difficult task especially for a developing country such as Turkey.
Another dimension of the convergence analysis is that the regional economic growth may follow a spatial pattern. It is important to investigate the spatial patterns that may indicate the spillover effects among regions. Gezici and Hewings (2003) points out that if the growth rates of the poor regions are higher than the growth rates of the rich regions, the spatial inequality may decrease over time, which may result in convergence. Even though the neoclassical model assumes perfect mobility of factors of production between economies, there may be significant adjustment costs or barriers to mobility for labour and possibly for capital. In cases where regions pursue their own growth promoting policies, there may be spillover effects from that regions to the adjacent regions. Cheshire and Gordon (1998) points that economic rents from research and development and other sources may more likely to accrue locally, where regions are more selfcontained. Moreover, Fagerberg et al. (1996) claim that rates of technological diffusion may follow a spatial pattern as regions may have different capacities to create or absorb new technologies. Thus, incorporating spatial effects into the analysis may impact significantly on any estimated convergence effects.
Spatial dependence can be handled in beta convergence in alternative ways: The first approach, spatial error model, assumes that the spatial dependence operates through the error process, where any random shock follow a spatial pattern, so that shocks are correlated across adjacent regional economies, such that the error term in equation (1) may reveal a significant degree of spatial covariance, which can be represented as follows:
EMBED Equation.3
EMBED Equation.3 (6)
Where ( is the spatial error coefficient, (i is a white noise error component and W is a spatial weighting matrix. W may be constructed using information on physical distance between pairwise combinations of economies in the sample or may be defined such that element wij = 1 if i and j are physically adjacent and 0 otherwise. In this paper the latter approach is preferred.
Alternatively, spatial lag model examines the extent to which regional growth rates depend on the growth rates of adjacent regions, conditioning on the level of initial income:
EMBED Equation.3 (7)
where denotes the spatial autoregressive parameter.
Moreover, the spatial crossregressive model allows any spatial spillovers to be reflected in the initial levels of income as follows:
EMBED Equation.3 (8)
Where represents the spatial spillovers.
Table 2. Beta Convergence Regressions
OLS Spatial Lag Spatial Error Spatial Cross
Regressive
Constant 0.5732 2.5197** 2.0911* 0.5676
(0.685) (0.043) (0.087) (0.698)
logy0 0.0047 0.1539*** 0.1743*** 0.0054
(0.964) (0.104) (0.078) (0.956)
0.2330* 0.2401*
(0.000) (0.000)
0.0002
(0.951)
EMBED Equation.3 0.34 0.61 0.61 0.34
Loglikelihood 22.885 41.018 41.115 22.887
AIC 106.4923 93.08844 92.97926 108.4912
Schwartz 106.1444 92.56667 92.45748 107.9694
0.0002 0.0072 0.0083 0.0002
Values in parentheses are the p  values and (*), (**), (***) denote significance at 1%, 5% and 10% respectively.
In this section beta convergence analysis is performed, taking spatial dimension into account, in order to overcome the heterogeneity bias associated with the traditional convergence analysis. In addition to estimating the degree of beta convergence using equation (4), estimates of each of the three spatial spillover models described in equations (6), (7) and (8) are also provided. Table 2 reports estimates of convergence equations for real GDP growth between 1979 and 2001 using the province level data. Conventional least squares estimates of the speed of convergence suggest a speed of convergence of close to 0.02 percent per annum. Model selection criteria indicate the selection of the spatial error model, which has the highest convergence rate, implying a convergence rate of 0.8 percent per annum. Additionally, the null hypothesis of zero spatial autocorrelation in the least squares regression residuals is rejected for spatial lag and spatial error models, indicating that typical least squares regional convergence model is misspecified.
Conclusion
The existence of wealth disparities across Turkish regions and provinces is a well known and debated issue. However, the limited empirical evidence concerning regional economic convergence in Turkey cannot reach an agreement on that issue. The aim of this study is twofold: First the regional inequality issue has been investigated employing Theil coefficient of concentration using spatially disaggregated data for Turkey for the time period 19792001. Then, convergence analysis has been performed incorporating the spillover effects between the provinces.
Theil coefficient of correlation analysis indicated that, within region inequality accounts a small portion of the overall inequality. Thus, these four regions can be assumed to be homogeneous in nature. Moreover, Theil coefficient exhibits a procyclical character, such that it has a tendency to increase in periods of economic expansion and to decrease in periods of recession. Next, the convergence dynamics are investigated taking spatial dimension into account. Empirical results indicate that there is convergence at the national level. Moreover, the spatial error model is preferred by the model selection criteria, indicating that typical least squares regional convergence model is misspecified.
References
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* Corresponding author. jl~* , r (
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From 1990 onwards the number of provinces has been increased from 67 to 81. But the original 67 provinces have been included in our analysis, as the data relating to new provinces do not cover the time period under consideration.
A second and traditional WestEast partition has also been investigated. But the analysis indicated that a greater within region inequality than between region inequality, thus the inequality aspects relating to regions cannot be clarified.
See for example SalaiMartin (1996) for a detailed description of estimation methods.
For a detailed analysis of spatial econometric techniques and methods please see Anselin (1988) and Henley (2003).
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