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International relocation: firm and industry determinants
a,b ,
*
a,bEnrico Pennings
, Leo Sleuwaegen
a
Catholic University of Leuven, Naamsestraat 69, 3000 Leuven, Belgium b
Erasmus University Rotterdam, Rotterdam, The Netherlands
Received 16 November 1998; received in revised form 12 October 1999; accepted 21 October 1999
Abstract
This article is the first to explore the determinants of international relocation of a firm. It is found that labour intensive firms in a highly industrialized and open economy such as Belgium tend to relocate more to other countries than their highly productive capital intensive counterparts. Access to a global network, firm size, and the rate of innovation have a positive effect on the probability of relocation. Uncertainty has a negative impact on the probability of relocation. The positive effect of firm size and profitability on the relocation decision is clearly distinct from its effect on the exit decision of a firm. 2000 Elsevier Science S.A. All rights reserved.
Keywords: Relocation; Multinationals; Investment; Sunk costs
JEL classification: F21; F23
1. Introduction
Relocation of activities from one country to another is of immediate policy interest as business relocations are claimed to be job exporting with firms moving to low-cost labor-abundant locations (Arthuis, 1993; OECD, 1995; Brainard and Riker, 1997; European Parliament, 1998). Especially small countries with an open economy are vulnerable to relocation. In Belgium, for example, the number of people employed in the diamond industry sharply decreased from 19,010 in 1961 to 3831 in 1992 after massive relocation to — mainly — Thailand.
Thus far, most studies have looked at the determinants of investment abroad without making a distinction between expansion investments and relocation decisions that involve ‘the move of a manufacturing process from one place to another’ (Mucchielli and Saucier, 1997). Few studies have looked at the motives for relocation in such a strict sense. From a sample of business relocation
*Corresponding author. Tel.: 132-16-326-774; fax: 132-16-326-732.
E-mail address: [email protected] (E. Pennings)
announcements in the U.S., Chan et al. (1995) find that main reasons for the business relocation are cost savings and business expansions, regardless of whether the firm is a plant or a headquarter. Within a European context, Mucchielli and Saucier (1997) speculate that restructuring, and flexible responses to new market conditions for innovative products are equally, if not more, important motives. Belderbos and Sleuwaegen (1996) and Belderbos (1997) report an important number of Japanese investments in Europe and the U.S. over the period 1986–1989 replacing Japanese production as a response to trade measures targeting electronics exports from Japan.
Using a novel data set, this paper sets out to systematically investigate relocation decisions of firms located in Belgium. The paper is structured as follows. In Section 2 we explain the data. Subsequently, we build a logit model that explains relocation. Section 4 discusses the results and Section 5 summarizes the findings.
2. Data
The sample of firms to which a questionnaire was sent out was obtained by merging two data sets. First, a sample of all firms that reported a collective layoff in the period from 1990 to 1996. Each firm in Belgium that: (i) has at least 20 employees; and (ii) lays off more than 10% of its workforce needs to report such a dismissal at the government. This data set, maintained by the Federal Planning Bureau (FPB) in Belgium, consists of 827 firms. The second sample was obtained by a draw from firms in a data set maintained by the National Institute of Statistics (NIS) in Belgium, which contains all firms with a VAT-number. From this large sample 2172 firms with more than 20 employees were randomly selected. So the total number of questionnaires amounts to 2999.
The response rate to the questionnaire was 15.5% resulting in a sample of 466 observations. Tables 1 and 2 respectively show the sectors and the firm size of the initial sample and the sample of responding firms. The tables illustrate that the distribution of the respondents well corresponds with the distribution of firms in the initial sample. Subsequently, the data from the responding firms were linked to data from the balance sheet of these companies. This link resulted in a final sample of 372 firms.
Within the final sample 14.0% of the firms responded that they relocated activities between 1990
Table 1
Distribution of sectors among initial sample of firms and responding sample
No. of firms (%) in No. of firms (%) initial sample responding
1. Primary 38 (1.3) 6 (1.3)
2. Basic industry 1487 (49.6) 247 (53.0)
3. Construction 177 (5.9) 28 (6.0)
4. Trade 681 (22.7) 112 (24.0)
5. Services 294 (9.8) 35 (7.5)
6. Transport and communication 204 (6.8) 23 (4.9) 7. Financial corporations 64 (2.1) 10 (2.1)
8. Other services 54 (1.8) 5 (1.1)
Table 2
Distribution of firm size among initial sample of firms and responding sample
Firm size No. of firms No. of firms (No. of employees) (%) in initial sample (%) responding
20–49 897 (29.9) 112 (24.0)
50–99 745 (24.8) 111 (23.8)
100–199 628 (20.9) 103 (22.1)
200–499 463 (15.4) 85 (18.2)
500–999 149 (5.0) 23 (4.9)
.1000 117 (3.9) 32 (6.9)
Total 2999 (100) 466 (100)
and 1996. Table 3 shows the countries to which activities were relocated. The table illustrates that neighbouring countries are the most preferred destination regions for relocation.
3. The empirical model
The decision to relocate activities is modeled within a logit model relating the probability to
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relocate to a set of explanatory variables x . In the logit model the probability of relocation is F xi s ibd
where F .s d5exp . / 1s d f 1exp . , ands dg b is the vector of coefficients.
International relocation is defined as either the decision to move part of the production to another country, or to replace part of the production by a combination of an investment abroad and subcontracting during the period 1990–1996. It thus excludes the cases of complete disinvestment with a relocation of all the activities to another country. Descriptive statistics and precise definitions of the explanatory variables are provided in Appendix A.
Among the explanatory variables, capital investment and the degree of sunkness of investment are proxied by two firm-specific and two industry wide variables. The firm-specific variables are (i) C / L:
Table 3
Host countries of relocation
Host country No. of Host country No. of
relocations relocations
France 12 Russia 2
Netherlands 7 Austria 1
UK 5 Canada 1
Germany 4 China 1
Tunisia 3 Czech Republic 1
USA 3 Hungary 1
Italy 2 Malaysia 1
Luxembourg 2 South Africa 1
Poland 2 Spain 1
1
ratio of fixed capital to the number of employees, and (ii) S / T: the ratio of the sunk tangible assets
2
(plant, machinery and equipment) to the total tangible assets. Other sunk costs are given by industry wide variable IT1, an industry dummy variable that takes the value 1 if the company belongs to an R&D or advertising intensive industry. Industries displaying high R&D and / or advertising intensities are typically endogenous sunk cost industries (cf. Sutton, 1991; Davies and Lyons, 1996). Remaining manufacturing industries take a value equal to one for the dummy variable IT2. The services industries are taken as the reference sector in the estimating model. Services sectors are primarily market oriented and need a close connection with customers, and hence, will show a smaller probability to relocate.
Uncertainty is measured by the interaction term UNC, denoting the product of S / T and the variation coefficient of the de-trended sales. Profitability is measured by P/ S, the profit to sales ratio, while firm size is measured by FS, the logarithm of the average turnover. The complexity of the operations and the degree of vertical integration of a firm is proxied by V/ S, i.e. the value added to sales ratio. Innovation is measured by INN, a dummy variable that equals one if the firm has successfully accomplished a combined product and process innovation. MUL is a dummy variable that equals 1 if the company belongs to a multinational group with more than one foreign subsidiary. The variable, CP, represents a dummy variable that is 1 if the company faces increased competitive
3
pressure and 0 otherwise.
4. Results
Table 4 shows the results of the logit regressions.
The results can be explained by a blend of two streams within literature, (i) a cost minimizing literature which basically states that firms produce at locations where production is least costly, and (ii) a multinational investment literature claiming the importance of transferable technological advantages and the impact of operational flexibility within a multinational framework especially in the presence of high uncertainty. Current theoretical research on relocation has mainly focused on the first stream of literature. Cordella and Grilo (1998) and Collie and Vandenbussche (1999) emphasize the importance of labour cost. Considering the second stream, Motta and Thisse (1994) show that sunk cost is an important impediment to investments in relocation.
Consistent with the cost-minimizing literature, the results of the logit model indicate that labour intensive firms, displaying a low capital to labour ratio, are more likely to relocate activities from Belgium. From a location point of view, Belgian comparative advantage is strongly based on the use of capital-intensive production technologies. Firms and industries using labour intensive technologies are at a relative disadvantage, unable to follow the high labour productivity gains made in the other sectors of the economy. High unit labour costs force them to either close down or relocate activities to
1
Fixed capital consists of formation expenses, intangible assets, tangible assets, and financial assets. 2
Apart from plant, machinery and equipment, the tangible assets include land and buildings, furniture and vehicles, leasing and other similar rights, assets under construction, and other tangible assets.
3
Table 4
Results of the logistic regression: maximum likelihood estimation of international relocation
Variable Coefficient Coefficient Coefficient (st. dev.) (st. dev.) (st. dev.)
Intercept 25.452*** 25.471*** 25.290***
(1.947) (1.951) (1.963)
UNC 214.609** 214.408** 218.874**
(7.372) (7.324) (9.642)
Loglikelihood 2123.61 2120.59 2120.27
No. of obs. 371 371 371
* p,0.1; ** p,0.05; *** p,0.01 (one-tailed test).
low labour cost countries, especially when they are facing strong competitive pressure from imports. However, the competitive pressure variable is found to be not significantly different from zero.
Large firms not only have more plants that can be relocated, they can also profit more from relocating business. Especially when variable costs are low, large firms will reap more benefits from relocation than small firms. The results indicate that it is indeed the larger firm that relocates activities. Large firms and more profitable firms have also a better capacity to finance and absorb the adjustment cost of the relocation investment (Caves, 1996).
shifting production within such a network can be substantial when uncertainty is relatively high. On the contrary, a uni-national company without a network always needs to incur the sunk cost, and is therefore less likely to relocate part of its activity. As indicated by the positive coefficient of the
4
interaction variable P/ S*MUL, multinational firms also have a higher propensity to shift profitable units to other countries.
In the presence of uncertainty a company can benefit from waiting with relocation. By postponing relocation the firm gains from possible favourable changes at their home plant, while it can simply further postpone relocation when changes are unfavourable (see Dixit, 1989, for the similar entry and exit decision of the firm). The value of postponing relocation increases with uncertainty in either the home or the host country. The significance of UNC mainly reflects the value of waiting with the exit part of the relocation decision, and not so much the value of waiting with the entry abroad. Multinational firms with a more extensive network have a higher propensity to relocate, suggested by the coefficient of the interaction variable UNC*MUL. Though uncertainty is a significant determinant for uni-national companies, relocation is not negatively affected by uncertainty in the case of
5
multinationals.
Though it is recognized within literature that sunk costs are known as a barrier to international relocation of a firm (Caves and Porter, 1976; Motta and Thisse, 1994), we find no significance for S / T.
Following the recent work on building global networks with spatial flexibility, innovative firms are more likely to relocate and shift production than their non-innovative counterparts (Mucchielli and Saucier, 1997). The innovation variable is significant suggesting the need of transferable technological advantages to operate successfully in other countries.
The signs of variables IT1 and IT2 indicate that manufacturing firms have a higher propensity to relocate, especially the ones in industries with relatively low endogenous sunk costs, whereas typically market-oriented industries such as services have a lower propensity to relocate. The complexity and extent of the operations, as suggested by the negative coefficient of the V/ S ratio, on the other hand acts as a deterrent to relocate activities from Belgium.
5. Conclusions
This paper tests the determinants of the decision to relocate activities from Belgium to a foreign country against a unique data set covering a large set of Belgian and foreign-based multinational firms. It is found that labour intensive firms which work against the comparative advantage of Belgium in large scale capital intensive activities tend to relocate more from Belgium to foreign countries. Contrary to the exit decision of a firm, it is found that firm size has a positive impact on relocation. Consistent with the exit decision of a firm, it is found that uncertainty has a significant negative impact on relocation. Firms belonging to a multinational group are more likely to relocate activities from Belgium than Belgian firms of which the relocation decision constitutes their first
4
For multinationals the parameter value of P/ S is 9497 and its standard deviation can be calculated as 4288. Thus, for multinationals P/ S is significant at the 95% level.
5
foreign investment decision. Especially large profitable multinationals move more easily part of their activities to another country.
Acknowledgements
We are grateful to an anonymous referee and Reinhilde Veugelers for helpful comments and to Bart Van den Cruyce and Hilde Spinnewyn from the Federal Planning Bureau for kindly providing us with the data.
Appendix A
Correlation matrix of independent variables
S / T C / L CP MUL FS INN UNC V/ S P/ S IT1 IT2
S / T 1
C / L 0.63 1
CP 0.01 0.01 1
MUL 0.03 0.03 0.07 1
FS 0.11 0.31 0.02 0.25 1
INN 0.13 0.19 20.02 0.12 0.22 1
UNC 0.62 0.35 0.03 20.04 20.03 0.06 1
V/ S 0.21 0.02 0.04 0.07 20.24 0.03 0.12 1
P/ S 20.05 20.12 0.07 0.06 0.08 20.09 20.13 20.08 1
IT1 0.22 0.33 20.01 0.02 0.22 0.18 0.15 0.04 20.10 1
IT2 0.25 0.17 0.09 0.04 20.09 0.12 0.14 0.10 0.04 20.40 1
Descriptive statistics and definitions of independent variables Mean Standard deviation Definition
S / T 0.38 0.25 The average ratio of the sunk tangible assets (plant, machinery and equipment) over the total tangible assets from the period of 1994 to 1996.
C / L 5.20 1.66 The logarithm of the average ratio of fixed capital to the number of employees in full time equivalents from 1994 to 1996.
CP 0.86 0.35 Dummy variable which is 1 if the company has answered to have faced an increase in competitive pressure during 1990 to 1996.
MUL 0.27 0.44 Dummy variable which is 1 if the company belongs to a group of multinationals with more than one foreign subsidiary.
FS 13.88 1.37 The logarithm of the average turnover between 1990 and 1996.
INN 0.38 0.49 Dummy variable which is 1 if the firm has successfully accomplished a combined product and process innovation between 1990 and 1996.
UNC 0.05 0.06 Interaction term denting the product of S / T and the variation coefficient of the de-trended sales from 1989 to 1996.
V/ S 0.34 0.19 The average ratio of value added to sales from 1990 to 1996. P/ S 20.01 0.47 The average ratio of profit to sales from 1990 to 1996.
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