Determinants Of Industrial Location Research Articles

Determinants of industrial location: Kingdom of Yugoslavia in the interwar period

What determines the location of industry? Using panel data econometrics and a new dataset on interwar Yugoslavia the predictions of three theories—Heckscher-Ohlin, New Economic Geography, and Path Dependence—are quantified and compared. Results show that all three theories mattered and that New Economic Geography forces played a dominant role. The consensus view that several theories can simultaneously explain the distribution of industrial activity is thus reinforced. The main novelty is that Path Dependence can affect the location of industry in addition to Heckscher-Ohlin and New Economic Geography forces.

The Spatial Distribution of Manufacturing in South Africa 1970-1996, its Determinants and Policy Implications

This paper researches the change in regional specialisation and industry concentration in South African (SA) manufacturing 1970-96, and evaluates possible determinants of industry location. No evident trend towards greater regional specialisation or despecialisation emerges over most of the period if we take the economic weight of the regions into account. However, between 1993 and 1996, the period of international reintegration, all provinces but one became more specialised. Industry concentration also does not show a clear trend if we account for industry size, although industries of the same rank were more concentrated in the early 1990s than the beginning of the 1970s and 1980s. Drawing on predictions from trade and economic geography models, we find that high plant-internal scale economies, intensity in the use of human capital and high industry-specific productivity gradients between locations are associated with greater geographical concentration of an industry. Scale economies are the most important pro-concentration force. A greater deviation of labour intensity of production from the mean, and strong interfirm linkages, are associated with low geographical concentration. The latter results can be explained within the economic geography framework. Linkages are the most important determinant of industry geography.

Environmental Regulation and Industry Location in Europe

This paper estimates the effect of environmental regulation on industry location and compares it with other determinants of location such as agricultural, education and R&D country characteristics. The analysis is based on a general empirical trade model that captures the interaction between country and industry characteristics in determining industry location. The Johnson–Neyman technique is used to fully explicate the nature of the conditional interactions. The model is applied to data on 16 manufacturing industries from 13 European countries. The empirical results indicate that the pollution haven effect is present and that the relative strength of such an effect is of about the same magnitude as other determinants of industry location. A significant negative effect on industry location is observed only at relatively high levels of industry pollution intensity.

SPATIAL EFFECTS OF TRADE POLICY: EVIDENCE FROM BRAZIL*

ABSTRACT Does trade policy shape a country's internal economic geography? Empirical evidence on the spatial effects of trade policy in developing countries is limited. This paper contributes to this literature by looking at the experience of Brazil over the 1990s. In particular, an econometric analysis of the determinants of industrial location using data on regional manufacturing employment as well as data on several region and industry characteristics over the period 1990–1998 is performed. Estimation results suggest that trade openness favored location in states closer to the largest neighbor trading partner and that this effect increased through the end of the 1990s.

Environmental Regulation and Industry Location

This paper estimates the effect of environmental regulation on industry location and compares it with other determinants of location such as agricultural, education and R&D country characteristics. The analysis is based on a general empirical trade model that captures the interaction between country and industry characteristics in determining industry location. The Johnson-Neyman technique is used to fully explicate the nature of the conditional interactions. The model is applied to data on 16 manufacturing industries from 13 European countries. The empirical results indicate that the pollution haven effect is present and that the relative strength of such an effect is of about the same magnitude as other determinants of industry location. A significant negative effect on industry location is observed only at relatively high levels of industry pollution intensity.

Industrial Location in Developing Countries

Despite a diminishing role in industrial countries, the manufacturing sector continues to be an engine of economic growth in most developing countries. This article surveys the evidence on the determinants of industry location in developing countries. It also employs micro data for India and Indonesia to illustrate recent spatial dynamics of manufacturing relocation within urban agglomerations. Both theory and empirical evidence suggest that agglomeration benefits, market access, and infrastructure endowments in large cities outweigh the costs of congestion, higher wages, and land prices. Despite this evidence, many countries have tried to encourage industrial firms to locate in secondary cities or other lagging areas. Cross-country evidence suggests that fiscal incentives to do so rarely succeed. They appear to influence business location decisions among comparable locations, but the result may be a negative-sum game between regions and inefficiently low tax rates, which prevent public goods from being funded at sufficiently high levels. Relocation tends to be within and between agglomerations rather than from large cities to smaller cities or lagging regions. Rather than provide subsidies and tax breaks, policymakers should focus on streamlining laws and regulations to make the business environment more attractive.

Desempeño industrial en las principales ciudades de México, 1980-2003 / Industrial Performance in Mexico’s Principal Cities, 1980-2003

En la era de la globalización la producción manufacturera ha evidenciado transformaciones en el qué se produce (cambio estructural), en el cómo se produce (cambio en el proceso de producción) y en el para quién se produce (cambio en el destino de las mercancías). Tales transformaciones han repercutido en una readecuación en los patrones y determinantes de la localización industrial (reestructuración territorial). En este documento se analizan la evolución de la estructura económica y la producción manufacturera de México en el periodo 1980-2003, y las transformaciones en el patrón de distribución territorial en las principales ciudades del país. El universo de estudio comprende 81 ciudades con una población de 100 mil y más habitantes. Entre los hallazgos del estudio sobresale el que la reestructuración económica e industrial en México se ha caracterizado más por una ampliación cuantitativa de la base económica y menos por un cambio estructural cualitativo, salvo en el destino de la producción manufacturera, en donde se advierte una mayor inclinación hacia la exportación. Asimismo la reestructuración territorial ha estado determinada por el nuevo patrón locacional de la industria automotriz, la escasa dispersión espacial de las empresas maquiladoras, y la desindustrialización que han presentado algunas ciudades, particularmente la Ciudad de México. AbstractDuring the era of globalization, manufacturing production has undergone transformations as regards what is produced (structural change), how this is produced (change in the production process) and whom one produces for (change in the destination of merchandise). These transformations have led to a modification of the patterns and determinants of industrial location (territorial restructuring). This document analyzes the evolution of the economic structure and manufacturing production of Mexico during the period from 1980 to 2003 and the transformations in the pattern of territorial distribution of the country’s main cities. The universe of study comprises 81 cities with a population of 100,000 or more. One of the findings of the study is that Mexico’s economic and industrial restructuring has been characterized more by a quantitative expansion of the economic base and less by a qualitative change, except in the destination of manufacturing production, where a greater emphasis on exports has been observed. Likewise, territorial restructuring has been determined by the new locational pattern of the automobile industry, the reduced spatial distribution of maquiladoras and the deindustrialization observed in certain cities, particularly Mexico City.

Factors Influencing the Choice of Location: Local and Foreign Firms in the Philippines

HERRIN A. N. and PERNIA E. M. (1987) Factors influencing the choice of location: local and foreign firms in the Philippines, Reg. Studies 21, 531–541. The paper addresses the following questions. Is the locational behaviour of foreign firms systematically different from that of local enterprises? What are the more important determinants of industrial location, in general, and by specific types of firms? How effective are direct government influences in the choice of location? This analysis is based on data from a survey of 100 firms drawn from the top 1,000 corporations in the Philippines. The overall finding is that out of thirty-four factors that are commonly thought to influence location decision, only seven are considered as crucial. These determinants can be distilled into four, namely, access and transport, power, information and communication, and physical plant requirements. On the whole, they apply more or less equally to local and foreign firms, implying that there is little difference in their ...

The Dynamics of Postwar Industrial Location

T HE location of industrial employment is a critical factor in virtually all models of urban spatial structure. However, most theoretical and empirical models of the urban economy say little about the determinants of industrial location. In the extreme, all employment is assumed to be centrally located (e.g., Muth, 1969, or Mills and Mackinnon, 1973), but, even when this assumption is relaxed as in transportation and land use studies (see Brown et al., 1972) or the recent vintage of urban simulation models (e.g., Ingram et al., 1972), employment location remains exogenously determined.' Theoretical analyses of industrial location have provided few specific insights into the underlying behavioral relationships, and existing empirical work has failed to identify much more than aggregate trends in employment dispersal. This paper develops a conceptual framework for analyzing the dynamic changes in industrial location and presents tests of several major hypotheses about the determinants of employment location. The empirical analysis models the spatial structure of employment for three selected industries in the Boston metropolitan area for the period 1947 through 1968.

Shift and share and the theory of industrial location

‘… the shift and share technique is not a useful framework for regional projection … the competitive component is not associated with the forces others have argued determine a region's competitive position,’ Brown (1969) Chalmers J. A. and Beckhelm T. L. (1976). Shift and share and the theory of industrial location, Reg. Studies, 10, 15–23. The paper challenges the contention that the differential shift is not a useful concept because it cannot be given behavioral content. A set of hypotheses about the determinants of industrial location is presented in the framework of space cost and space revenue surfaces. The model is tested for 23 industries for as many SMSA's as there are disclosed data in the 1963 and 1967 Census of Manufacturers. The results show that the differential shift in many industries in a cross-section of cities can be well-explained by variables that would be expected to affect spatial variations in profits.