Cities' characteristics that influence industrial park location: Evidence from Northern cities/provinces in Vietnam

Chi Pham(Nov, 2015)

AbstractThe Vietnamese government has been putting a lot of effort into designing policies that help industrialize and modernize the Vietnamese economy. One of the methods to industrialize the economy is to attract industrial parks to certain key economic areas to further develop the industry. In order to help guide policymakers into making effective policies, understanding of local/regional economic conditions which affect an industrial park location decision should be cultivated. This research paper will serve the purpose of gaining more knowledge on this issue. The paper strives to answer one question: What regional economic conditions affect the likeliness of an industrial park locating in a certain area? Using cross sectional and time series data, I investigated the characteristics of 25 cities and provinces in North Vietnam and the number of industrial parks in each city/province during the period of 2008-2014. From these empirical data, simple econometrics regressions are ran to find out if there are relationships between the probability of locating an industrial park in a city/province and certain characteristics of that city/province. The paper concludes by giving out results from the empirical model.

IntroductionIt cannot be denied that industrial parks bring benefits to regional economic development: they help attract capital, create more jobs, reduce costs through positive agglomeration externalities, etc. However, studies on the industry development in Vietnam have put little focus on regional research. Researches on the topic of industrial park distribution in general are also rare and industrial parks are usually studied individually. This paper aims to explore general patterns of industrial park spatial distribution in Vietnam by investigating if certain characteristics of an area affect the probability of an industrial park locating there. It is argued that various factors affect industrial location. The major factors are: capital, government policy, raw material inputs, labor force, markets and transport. Putting government policy aside and focusing on other regional factors of the location, an econometric model is used to explore the relationship between the variables. The yearly data of 25 cities and provinces and of over 100 industrial parks in North Vietnam across the time period of 2008-2014 are gathered mainly from the Vietnam General Statistical Office and the Vietnam Invest Network Corporation. Apart from this brief introduction, the paper is organized in the following way. Section (I) looks at previous literatures that research the topic of industrial location patterns and gives theoretical background about the factors that may affect industrial distribution. Section (II) lays out the estimation strategy and build a model to research the issue which helps investigate the relationship between the city/province characteristics and the probability of locating an industrial park in that city. In section (III), the exploratory analysis is carried out, running regressions with empirical data, and showing the relationship results. In the last section, some conclusions are drawn.

(I) BackgroundThere has been little research on the topic of industrial park location patterns in general and in Vietnam specifically. Previous literatures have looked at the industrial location in general. Two papers that stood out in this topic are Hildebrandt, A. and J. Worz (2004) and Hansen, E. R. (1986). Hildebrandts paper looks into the factors causing concentration tendencies in industrial manufacturing industries in Central and Eastern European countries and concludes that local concentration of demand, human capital intensity and labor intensity are the main factors for regional concentration of manufacturing firms. Hansens paper discusses the location choice of manufacturing plants in the State of Sao Paulo and finds that local agglomeration economies have strong influence on location and transport costs to primary market and access to agglomeration economies of the metro area matter to firms. On the other hand, empirical research on factors that affect industrial park location patterns has not been paid much attention. Theoretically speaking, according to Badri, M. (2007), critical factors which can affect industrial location can be divided into two categories: geographical and non-geographical factors. Geographical factors include raw materials, utilities, labor, transportation, markets, industrial site and climate. Non-geographical factors are capital, government attitude, community, tax structure and economic factors. Part (II) below breaks down these categories and tries to come up with an estimation strategy with empirical measures for the factors.

(II) Estimation StrategyIn order to further determine if the factors of geographical and non-geographical categories affect the probability of locating an industrial park in a certain area, a sample of 25 cities/provinces in the North region of Vietnam across the time span of 2008 to 2014 are retrieved. Due to the limitation of data source, the paper will only look at a few factors that are critical to the industry park location patterns. The probability of locating an industrial park in a certain city is measured by the ratio of the number of industrial parks in that city to the total number of industrial parks in the North region. Since the purpose of the paper is to induce outlook into industrialization policy, factors regarding to government involvement are not included in the model. Climate factor also will not be included in the model because the climate in the Northern region of Vietnam is relatively similar across cities (a range of 68 to 76 degree Fahrenheit in average temperature across regions in the North). With regard to raw material, a dummy for each city is generated where it equals 1 if the city has a coal or metal mine and equals 0 otherwise. Another dummy variable which measures if that city has an electricity plant or not will give insight to the utilities factor. In terms of labor, the data of labor force of the city/province and the number of skilled labor measured in thousands of people are retrieved from the Vietnam General Statistical Office. Transportation factor is measured by three variables: average distance of the city to a major airport (which is the Noi Bai International Airport), average distance of a city to a major seaport (the Hai Phong sea port or the Cai Lan sea port whichever is closer) and average distance from industrial parks in one province to major markets (which are cities with the biggest population in the region, in this case Ha Noi or Hai Phong whichever is closer). Markets factor is measured by the population of each city and the retail sales in each city. A dummy variable of the topographic of a city/province is generated, which equals 1 if it is in a plain and 0 if it is in a mountain. This variable suggests information about the industrial site factor. Capital factor is measured by the million dollar amount of foreign direct investment project licensed in each province (accumulation of projects having effect as of the end of each year).Table 1: Dependent variable and independent variables of the regression modelDependent variable nameDescription

probThe ratio of the number of industrial parks in a city/province to the total number of industrial parks in Vietnam (%) 1)re positive relation between the probabily of hat there is dasticity.e accross e distance of a city to a major seaport (w

Independent variables nameDescription

mineDummy = 1 if there is coal or metal mine in the city/province, = 0 if otherwise

elecDummy = 1 if there is an electricity plant in the city/province, =0 if otherwise

laborLabor force of the city/province (thousand people)

skilledNumber of skilled labor in the city/province (thousand people)

dseaAverage distance of a city/province to a major seaport (mile)

dairAverage distance of a city/province to a major airport (mile)

dcityDistance to a major city (mile)

densityPopulation density of the city/province (person/mile square)

popPopulation of the province (thousand people)

retailTotal retail sales of the city/province ($million)

DplainDummy = 1 if the city is in the plains, = 0 if it is in the mountains

FDIForeign Direct Investment (FDI) accumulated in the city/town ($million)

The regression equation is:

Where i indicates the city/province (i=1.25)t indicates the year (t=2008.2014)The hypothesis for this regression is:H0: Ha: H0 is not trueThe unit of analysis is the single city/province in North Vietnam. The type of model used to test the hypotheses is a simple linear econometrics model, running regression against 175 observations from 25 cities and provinces in North Vietnam from 2008 until 2014.

(III) Exploratory analysis Using the econometric software STATA 14 to run a robust regression, the following results are obtained: There are positive relations between the dependent variable and mine, elec, labor, skilled, density, Dplain and fdi variables; there are negative relations between the dependent variable and dsea, dair, dcity, pop and retail variables. With a 10% confidence, the parameters of mine, labor, dsea and pop are not significant (Appendix table 2).Table 2: Regression resultVariableCoefficientRobust Sts. Err.p-value

mine0.81930.25410.457

elec0.60140.34330.082

labor0.00530.00380.166

skilled0.02000.00670.004

dsea-0.00020.00300.944

dair-0.00470.00270.067

dcity-0.00110.00030.000

density0.00570.00180.002

pop-0.00330.00240.169

retail-0.00210.00060.000

Dplain1.28510.51260.013

fdi0.47630.10110.000

constant1.24670.60900.042

Going into more detail, the fact that the city has a coal or metal mine does not have a significant effect on the probability of locating an industrial park there. Further research on the main products of firms in the Northern industrial parks is suggested to look at whether other inputs other than coal or metal have an effect on industrial park location pattern. On the other hand, we can say that there is a 0.6% increase in the probability of locating an industrial park in a city/province that has an electricity plant, this suggests the significance of utility factor in industrial location pattern. For every 1000 people increase in skilled labor of a city, there is an increase of 0.02% in the concerned probability, the labor variable is positively related to the dependent variable but not significant. This fact indicates that the labor input does not have a large impact on the industrial location pattern. A recent phenomenon of firms having labor from nearby cities to commute to the industrial park by the company bus everyday could be one of the explanation to this relation. For every 1 mile increase in the average distance of the city to a major airport, there is a 0.005% decrease in the probability of locating an industrial park in that city, the distance to a major seaport is also negatively related but not significant. This could mean that the major transportation method from supplier to the industrial park is not by sea or by air but mainly by roadway or railway. For every 1 mile increase in the average distance of the city to a major market, there is a 0.001% decrease in the probability of locating an industrial park in that city. For every increase of 100 people in a mile square of a city averagely, there is a 0.57% increase in the probability of locating an industrial park in that city. By locating near a dense market, firms are more likely to save transportation cost. Retail sale is negatively related to the dependent variable which is against normal theory of market consumption factor in location pattern. Further research on retail and wholesale should give a deeper understanding of whether wholesale would be a more appropriate measure for market factor. A plain region would have 1.285% more chance than a mountain one of having an industrial park located there. This is understandable since it is easier for construction and transport activities to take place in plain regions. For every $1 million increase in the FDI in a city, there is a 0.476% increase in the probability of locating an industrial park in that city. Capital is an important factor in investing in an industrial park. Moreover, investors are more likely to invest in a city that has a large FDI amount because it suggests big investment potential.

(IV) ConclusionAll in all, it cannot be denied that certain characteristics of a city/province affect the probability of investors constructing industrial parks in a city/province. Specifically in this research, both geographical and non-geographical factors have certain effects on the industrial park location patterns in the North region of Vietnam. The major factors that determine the concerned probability are utilities, industrial site and capital. There is more chance of an industrial park locating in a plain areas that are near an electricity plant and has large FDI amount than other areas. Other factors such as labor, market and transportation need further research on a broader scale with more accurate measures and data to find whether they have significant relationships with the industrial park location pattern.

ApendixSTATA regression results:Table 1. Normal OLS regression

Heteroskedacity is found in this regression after performing the Breusch-Pagan test.

P-value is 0.0367