Urbanization and productivity : evidence from Turkish provinces over the period 1980-2000

WPS4327 Policy ReseaRch WoRking PaPeR 4327 Urbanization and Productivity: Evidence from Turkish Provinces over the Period 1980-2000 Souleymane Coulibaly Uwe Deichmann Somik Lall The World Bank Sustainable Development Network Urban Unit August 2007  Policy ReseaRch WoRking PaPeR 4327 Abstract Since the early 1980s, Turkey has been going through a The sector-by-sector estimation confirms this result, rapid urbanization process at a pace beyond the World although the localization economies effect is negative average. This paper aims at assessing the impact of for the non-oil mineral sector, and the urbanization this rapid urbanization process on the country's sector economies effect is weak for natural-resource-based productivity. The authors built a database combining sectors such as the wood and metal industry. two-digit manufacturing data and some geographical, Although the data cover the period up to 2000 and infrastructural, and socio-economic data collected at the thus ignore the financial crisis that hit Turkey in 2001, provincial level by the Turkish State Institute of Statistics. the current structural transformation of the country away The paper develops a parsimonious econometric relation from the agricultural sector gives room to use the insights linking sector productivity to accessibility, localization, of these results as a preliminary step to understand the and urbanization economies, proxying variables in the new challenges faced by the Turkish manufacturing tradition of the New Economic Geography literature. sector. The results provide a discussion base to revisit the The estimation results suggest that both localization policy agenda on the improvement of the accessibility to and urbanization economies, as well as market markets, the improvement of the business environment accessibility, are productivity-enhancing factors in Turkey, to ease the creation and development of new firms, although the causation link between productivity and and a well-managed urbanization process to tap in the these agglomeration measures is not clearly established. economic potential of cities. This paper--a product of the Urban Unit, Sustainable Development Network in the Europe and Central Asia region--is part of a larger effort in the department to assess the impact of the growing urbanization on productivity in the Europe and Central Asia region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at scoulibaly2@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team  Urbanization and Productivity: Evidence from Turkish Provinces over the Period 1980-2000 Souleymane Coulibaly1, Uwe Deichmann, Somik Lall PF FP The World Bank2 PF FP J.E.L. classifications: C4, R1, R3 Keywords: localization, urbanization, sectoral productivity, factor analysis Acknowledgements: We are indebted to Mohammed Dalil Essakali, Banu Demir, Marianne Fay, Zafer Mustafaoglu, Christine Kessides, Ulrich Zachau (World Bank), and Ismael Tuncer (Mersin University, Turkey) for their valuable comments throughout this work. Ismael Tuncer provided us with the relevant data. Financial support from ECA Chief Economist is greatly acknowledged. 1 Corresponding author: scoulibaly2@worldbank.org P P 2 The findings reported in this paper are those of the authors alone, and should not be attributed to the P P T World Bank, its executive director, or the countries they represent. T  Table of content Pages 1) Introduction .................................................................................................................... 3 TU UT 2) Theoretical framework and empirical approach............................................................. 5 TU UT Theoretical framework.................................................................................................... 5 TU UT Empirical approach ........................................................................................................ 6 TU UT 3) Descriptive analysis........................................................................................................ 6 TU UT Data sources and econometric issues............................................................................. 6 TU UT Spatial concentration of sectors...................................................................................... 8 TU UT Sectoral concentration and infrastructure endowment of the provinces...................... 10 TU UT 4) Empirical estimation..................................................................................................... 11 TU UT Estimation results.......................................................................................................... 12 TU UT Correlation vs causality................................................................................................ 19 TU UT 4) Policy implications ....................................................................................................... 20 TU UT Improving accessibility to market................................................................................. 21 TU UT Improving business environment .................................................................................. 21 TU UT Accompanying the urbanization process ...................................................................... 22 TU UT 5) Conclusion.................................................................................................................... 23 TU UT Reference ......................................................................................................................... 25 TU UT Appendix 1: Province adjustments................................................................................ 27 TU UT Appendix 2: Variables included in the Factor Analysis.............................................. 29 TU UT Appendix 3: Specifications using the database adjusted with a simple average and TU provincial population and per capita GDP to proxy market capacity....................... 30 UT 2  1) Introduction U Since the early 1980s, Turkey has been going through a rapid urbanization process at a pace beyond the world average. A recent World Bank study qualified this process as "the strongest socio-economic force that has changed peoples' lives since the foundation of the Turkish Republic in 1923" (World Bank 2004). More than 20% of the country's urban population lives in Istanbul's various district municipalities, and half of the urban population lives in the seven largest urban settlements (each of which includes several municipalities). About 75% of the total urban population lives in the 352 largest municipalities with an average of 104,000 residents per municipality. The remaining 2,848 municipalities have an average population size of about 4,000 (World Bank 2004). Figure 1: Turkey vs World urbanization rate 80% 70 60 50 40 30 20 10 0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 World Turkey Sources: WDI 2006. Given this backdrop, Turkey appears to be a good laboratory for assessing the impact of agglomeration economies on productivity. However, we first need to clearly define what we mean by agglomeration economies. Indeed, a contrasted point in the agglomeration literature is whether agglomeration economies are related to the concentration of an industry or to the size of a location itself. Rosenthal and Strange (2004) nicely summarize this debate by revisiting the seminal contributions of Marshall (1920) and Jacobs (1969) on this topic. According to Marshall and the Marshallian-externality-based papers (Henderson 1974 and 1988, Carlino 1978, Stelting and al. 1994 among others), the micro-foundations of agglomeration stem from urban specialization through localized spillovers induced by firms operating in the same sector, while Jacobs and Jacobian-externality-based papers (Shefer 1973, Sveikaukas 1975, Segal 1976, Fogarty and Garofalo 1978, Moomaw 1981, and Tabuchi 1986 among others) emphasize on urban diversity fostering cross- fertilization of ideas from various sectors. 3  The new economic geography literature encompasses these two ideas: firms located in an agglomerated area can take advantage from a larger market and the proximity of intermediate products' suppliers (localization and urbanization effects), but this positive externality can be counterbalanced by high congestion costs and increased competition from other firms located in the same place. Urban agglomeration can thus reinforce or reduce firms' productivity depending on which of these forces prevail, and these forces depends on the characteristics of the place where the firm is located (population density, accessibility to other places, congestion effects, industrial specialization of the location, access to financial and other professional services...). For instance, using two-digit Japanese manufacturing data, Nakamura (1985) estimates that a doubling of industry scale leads to a 4.5% increase in productivity, while a doubling of city population leads to 3.4% increase. Henderson (1986) finds almost no evidence of urbanization effects, while a 10% increase in own industry employment induces a 1% increase in output. Moonmaw (1983) finds evidence of both, while Rosenthal and Strange (2003) and Henderson (2003) find stronger evidence of localization effects. Taken together, all these papers are more favorable to the existence of localization economies than urbanization economies. More recently, Lall and others (2004) used a genuine plant-level database to examine the impact of improved market access, intra-industry localization economies and inter- industry urbanization economies on Indian's manufacturing firms' productivity. They found that access to market through improved interregional infrastructure is an important determinant of plant-level productivity, whereas the benefits of locating in dense urban areas do not appear to offset the associated costs. In this paper, we use Turkish two-digit manufacturing data and geographical, infrastructural and socio-economic data to assess the impact of the increasing urbanization of the country on sectoral productivity. A parsimonious model linking sectors' productivity to accessibility, localization and urbanization economies proxy variables, and controlling for sector and sector-time specific effects is estimated using various adjustments of the initial database. The specifications pooling all the sectors indicate a positive correlation between sectoral productivity and: (i) a better accessibility to local, national and international markets; (ii) the number of firms operating in the same sector; (iii) the total number of firms operating in the same province. This suggests that both localization and urbanization economies, as well as market accessibility are some productivity-enhancer factors in Turkey, although the causation link between productivity and these agglomeration measures is not clearly established. The sector-by-sector estimation confirms this result, although the localization economies effect is negative for non-oil mineral sector, and the urbanization economies effect is weak for natural-resource-based sectors such as Wood and Metal industry. Although the data used cover the period up to 2000 and thus ignoring the financial crisis that hit Turkey in 2001, the current structural transformation of country away from the agricultural sector gives room to use the insights of these results as a preliminary step to understand the new challenges faced by the Turkish manufacturing sector. The results 4  provide a discussion base to revisit the policy agenda on the improvement of the accessibility to markets, the improvement of the business environment to ease the creation and development of new firms, and a well-managed urbanization process to tap in the economic potentiality of cities. The remaining of the paper is organized as follows. Section 2 presents a simple theoretical framework and the empirical approach chosen to assess the impact of accessibility, localization and urbanization economies on Turkish sectors' productivity. Section 3 presents and comments the empirical results, while Section 4 explores some policy implications of these results. Section 5 concludes the paper. 2) Theoretical framework and empirical approach U Many recent papers in the flourishing New Economic Geography literature have revisited the interaction between agglomeration economies and productivity using various proxy and estimation techniques. For instance, Ciccone and Hall (1996) and Ciccone (2002) estimated the relation between labor productivity and employment density. Cingano and Shivardi (2004) use a panel of plant-level data across Italian cities to estimate the long- run impact of city employment on firms' productivity. Rice and al. (2006) use travel time within Britain NUTS3 regions as the measure of proximity and estimate the impact of the underlying spatial variation of earnings on firms' productivity. Ottaviano and Pinelli (2006) assess the impact of market potential on Finnish firms' productivity using a basic new economic geography model highlighting market accessibility and demand linkages. Theoretical framework Following Lall and al. (2004), this paper makes the prior assumption that agglomeration economies impact on firms' productivity through three channels: (i) market accessibility, (ii) intra-industry localization economies, and (iii) inter-industry urbanization economies. In the tradition of the early eighties papers presented in the introduction, we consider the following production function of a representative firm: Yi = g(S, L,U ).Y~(Ki ) (1) where Y~(Ki ) is the firms' own constant return to scale technology for a vector of inputs K, g() is a Hick's neutral external shift factor whose arguments are accessibility (S), . intra-industry localization economies (L), and inter-industry urbanization economies (U). Since Y () is a constant return to scale function, we can aggregate over firms and use ~ . sector-location observations. Equation (1) may be rewritten as ys = Ys / Ns = g(S, L,U ).Y~(ks ) where Ns is labor inputs B B in sector s and ks is the vector of ratios of remaining factors to Ns. It directly links sectoral B B B B 5  productivity to agglomeration economies variables on the one hand, and sector-specific characteristics on the other hand. Empirical approach For the sake of simplicity, we assume a multiplicative form of g() in equation (1). . Hence, including the time and spatial dimensions and taking the log of this relation yield: Ln ys ( )= ( ) ( )+ ( ) ,r,t +SLn Sr +LLn Ls ,t ,r,t ULnUr +Tt + FEs + FEst +s ,t ,r,t (2) where Sr is a proxy for market accessibility, Ls ,t ,r,t is a proxy for localization economies, Ur is a proxy for urbanization economies, t is the time trend, FEs and FEst are sector ,t B B B B and sector-time fixed effects included to control for sector-specific effects (production function and internal shocks over time), and s ,r,t is an error term. Using sector and sector-time fixed effects instead of an explicit sectoral production function presents the advantage of switching all the potential econometric problems to the agglomeration variables that are of interest in this paper. Furthermore, the sector and sector-time fixed effects will correct any potential omitted variable problem. We measure market accessibility by the distance between the province capital and the nearest airport (ACCESS). The urbanization economies is captured by various proxies included alternatively in various specifications: the total number of firms within the province (Nr,t), the urbanization rate (URBAN), total amount of loan in the province B B (LOAN), the total electricity consumption in the province (ELECT), the ratio of asphalt roads in villages (ROAD), and the rate of university graduates in the 25 years and over population (UNIV). The localization economies will be captured by the "potential" number of same sector firms within the province (PNr,s,t) computed as follows: B B PNr,s,t = Nr,s,t + Nl,s,t lr Distr,l (3) where r and l are province indices, and Distr,l is the distance between the capital of B B province r and province l. Note that the coefficient of correlation between Ns ,r,tand PNs ,r,t is 0.98, indicating the relevance of this variable as a proxy for localization economies. Furthermore, it is in line with the way a market potential is computed in the new economic geography literature. 3) Descriptive analysis U Data sources and econometric issues 6  Various sources have been mobilized to construct the database used for this study. The core database is the manufacturing data from the Turkish State Institute of Statistics covering all the provinces over the period 1980-2000. The industries included in the database are classified according to the two-digit ISIC Revision 2 nomenclature. This database is complemented with population, geographical, infrastructure and socio- economic variables at the provincial level. The manufacturing and population data cover the entire period 1980-2000, while the infrastructure and socio-economic data are available with some five-year gaps. Since we do not have sector price indices at the provincial level, we use the country two-digit wholesale price index as price deflator. Although Turkey counts 81 provinces, some of them were established by a series of Laws adopted during the period 1989-1996, out of the territory of one or two of the initial 65 provinces.3 In order to have a consistent database over time, we added these new PF FP provinces back to their parent provinces as described in Appendix 1. The final database is a panel of 65 provinces, 8 sectors and 21 years, but including many missing observations since most of the sectors are concentrated in a few number of provinces and the infrastructure and socio-economic variables are available with some five-year gaps. The total number of observation is 10'920, and the total number of observations with non-missing sector output observation is 5'218. This unbalanced structure may cause some heteroskedastic problems, which we deal with by computing the White/Hubber robust standard errors in all the regressions. In addition to the unbalanced panel feature, the variability of some key variables such as the number of operating firms and their total outputs is quite high from year to year. There are also many gaps in the panel patterns. To cope with this problem, we opt for two approaches: a five-year non-centered moving average and a simple five-year average. While the simple average yields a balanced panel, the moving average leads to an unbalanced panel. It also raises the issue of the treatment of missing observations in the panel patterns. We propose two alternatives: (i) exclude from the computation all the missing observations preceded by two missing observations and succeeded by two missing observations, or (ii) keep all the missing observations and simply rely on the moving average process. Table 1 presents for each sector the percentages of observations with complete patterns, that is, with 21 non-missing observations (corresponding to the number of years) within the panel's cells. Some clarifications need to be made before presenting the estimation results. Since the infrastructure and socio-economic variables are available with five-year gaps, only the database constructed from simple five-year averages is used to estimate the specifications including LOAN, ELECT, ROAD and UNIV. Furthermore, in the moving average panels, only the productivity ( ys ), and number of firms ( PNs ,r,t ,r,t and Nr ) variables are ,t 3 After 1990, new provinces were carved out of the old provinces. In 1989 (law No: 3578), Aksaray was P P formed out of Konya, Bayburt out of G