Bribery cost is proxied by the cost needed to smooth business operation ... The burden of high wage cost is more felt in many small and medium scale firms,.
The Impact of Licensing Decentralization on Firm Location Choice: the Case of Indonesia Ari Kuncoro1 I. Introduction Spatial centralization of resources and spatial concentration of manufacturing in a country’ s largest metropolitan areas are issues that many developing countries have been struggling with for two or more decades. The typical problems are unbalanced urban hierarchies and congestion, crime and social inequality in very large metro areas. We focus on one issue. In certain situation, will decentralization of some power to regulate from the central government to local governments lower significantly the degree of centralization and concentration. In earlier work (Henderson and Kuncoro (1996)), it was found that the early economic liberalization in Indonesia which started with the 1983 Banking Deregulation was associated with increased centralization of unincorporated manufacturing firms. Although the liberalization gave unincorporated manufacturing firms better access to government and other centralized services, firms needed to centralize to take advantage of these opportunities because the bureaucratic process is centralized. The second economic liberalization was launched in 1986. Some part of the program was an attempt by the central government to revert the centralization process by decentralizing some bureaucratic process to regional government (province and municipalities). We examine this issue in the context of Indonesia. Among several important reforms in the 1986 liberalization package was the decision of central government to give provincial and district (kabupaten) planning agency (BAPPEDA) more power in several aspect of administrative licensing system such as the choice of local site, environmental feasibility, property tax etc. Admittedly, the central government still held considerable power in licensing system. From the point of view of a firm there is not so much interest in such decentralization. What most important is whether such
1
Faculty of Economics, University of Indonesia
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decentralization to regional government reduce or even increase the transaction cost. In Indonesian context, cynics said that transferring the power to give license to local governments only transferred corruption from center to provinces. In this paper we try to assess the impact of decentralizing administrative licensing system to local government. Such decentralization itself may reduce or increase transaction cost on firm. A firm chooses a location, which gives a highest level of (potential) profit. If a firm has to pay fee (legal or in the form of bribe) to local government they will choose a location, which given the level of bribe still yields the highest profit. II. Data 1. Manufacturing Survey Due to the data availability, we only examine the impact of decentralization of licensing regime in Indonesia on the location decision of firms in manufacturing sector. There are two major categories of manufacturing firms in the Indonesian census: government firm and nongovernment firms namely incorporated and unincorporated firms. In this study we focus on nongovernment firms.
2. Other Data Base For the 1986 firm sample we use the 1980 base information to construct independent variable at each location (kabupaten=district), while for the 1991 sample we use the 1986 base information. In particular we need data on spatial distribution of infrastructure and industrial environment. All of these information comes from the “past” i.e. the 1980 and the 1986 survey of large and medium manufacturing firms, aggregated into the district level data. Besides manufacturing surveys we also used the 1980 Population Census of Indonesia and the 1985 Intercensal Survey to provide information on population size.
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III. The Model In this section, we present a model of locational choices by non-government firms for new manufacturing plants. A new firm is defined as the one, which is about to enter market in 1986 (for the 1986 sample), and in 1991 (for the 1991 sample). A firm is defined as a new thus a new entrant if its age its 1 year old in 1986 or for the case of the 1991 sample, it is 1 year old in 1991. For the firm choice of location, firm j chooses location k from among M possible locations, if (1)
∏ jk* = max [∏ j1, ∏ j2,….., ∏ jM] j= 1, ….N
where ∏ jk are the log of long-run profits associated with location k for plant j. In turn, (2)
∏ jk = βXk + εjk
where Xk are locational attributes in linier, log or dummy variable form at location k such as infrastructure reliability, population size, wage level and so on, and εjk is an error drawing. In other words, the optimal profit in location k is a function of the prices of variable inputs prevailing in location k and other non-priced location characteristics. Firm j chooses location k if (3)
βXk + εjk ≥ βXl + εjl for all l ≠ k.
If the error terms are independent and identically distributed (iid) and followed the extreme value distribution, then the probability Pjk that plant j locates in k is given by
Pjk =
exp( βX k ) M
∑ exp( βX s =1
s
)
(4) In the estimation, parameters of the model are only identified by imposing the normalization that the constant term to be zero.
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IV. The List of Independent Variables 1. Bribery Cost The main interest of this paper is to investigate the impact of some license decentralization to local governments on firm location. The decentralization itself may reduce or increase transaction costs depending on the honesty of local official. Transaction cost might be more conveniently termed as bribery cost. Bribery cost is proxied by the cost needed to smooth business operation not necessarily related to production cost. Regarding manufacturing firms, Central Bureau of Statistics officials off the record hinted that considerable amount of bribery cost is hidden under “other costs”. Included in this category is management fee and royalty fee. Admittedly there is a problem with this proxy. The data may not include the initial sum of bribe that has to be paid to various government agencies to set up a new business. The data only cover rather crudely bribery cost that needs to be paid to government officials at province and district level.
Other Independent Variables 2. Cost Variables There are variables that directly affect firm cost. Given the same amenities, a firm will choose a location with a lower wage. A firm however, may accept higher wage in return for a better quality of labor skill, infrastructure service and access to market. From manufacturing firms’point of view, the successive yearly increase of the regional minimum wages in Indonesia is only recent phenomenon. The burden of high wage cost is more felt in many small and medium scale firms, which employ labor intensive production method. For these firms the degree of substitutability between capital and labor may be very low simply because they could not afford the cost of capital investment. Since the level of minimum wage is imposed differently from one province to another, a firm will choose location where the combination of wage cost and other factor that is an optimal for a firm. We employ nominal wage instead of real wage since in the estimation we use structural profit function. In other words we assume that an individual firm in choosing location try to maximize nominal profit.
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3. Distance to the nearest business center Another important consideration to reduce operating cost is the availability of business facilities. Business services are most likely to be better in the old industrial centers, so the distance to the nearest business center will affect the location decision. In this study there are two business centers; Jakarta and Surabaya. The coefficient of distance is expected to be negative. The distance variable also captures the need to cut transportation costs, particularly if most customers are in urban areas. The same consideration also applies if a reliable supply of direct inputs can only be obtained in the business center.
4. Infrastructure One thing that deters a new firm from choosing a particular location is the lack of infrastructure service. In the modern world it is almost impossible for a firm to operate without electricity. The lack of electricity provision may force a firm to generate electricity by itself, which is an additional cost. Even if a public or private provision is available, the question is still how reliable it is. The impact of infrastructure provision on the location decision is represented by the reliability of the public provision of electricity. To capture the reliability of electricity we use the average percentage of electricity consumption in kwh originated from the state power company (PLN). The lower the number means the higher percentage of power generated by firm itself, which indicates the low degree of reliability.
5. Demand Variable Local population could be regarded as one measure of the size of local market. A bigger population size is always associated wit urban areas. New firms are likely to locate in urban areas because local demand is high so they can sell some their products without incurring transport cost.
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6. Industrial Environment Romer (1986) and Jacobs (1984) have argued that a concentration of individual firm sinto cities will generate a type of externalities, which comes from the knowledge spillovers. For a new firm facing a market uncertainty, it is beneficial to choose location around other firms particularly because of the benefits of information spillovers. Overtime, the interaction among many firms in a particular location will lead to the development of a location specific knowledge which is unique to that location. The existence of such a network will enable firm to avoid the high cost of creating it. With regard to knowledge spillovers that emerge from such information network, there are two opposing views on how ideas and innovation are transmitted from firm to firm. Romer (1986) argued that knowledge spillovers take place between firms within a specific industry. Accordingly, a less diverse or a more specialized region is good for growth in the location. On the contrary, Jacobs (1984) suggests that technological spillovers come not from within the industry but from other industries. Consequently, diversity in industrial environment is very important for learning process and innovation. To measure industrial diversity we use Hirschman-Herfindahl index, where for any two digit manufacturing industry we sum the employment shares of other two-digit manufacturing industries. For the manufacturing diversity in industry j in location k, the index is
dim jk =
∑ ( Emp
i =1,i ≠ j
ik
)2 (
∑ Emp
i =1,i ≠ j
ik
)2
(5) where Emp refers to employment. An increase in index reflects higher concentration or less diversity in a particular location.
V. Econometric Results We estimate the model for every two digit (ISIC code) manufacturing industry (textile=32, wood=33, paper=34, chemical=35, non-metallic mineral=36, machinery (38) and miscellaneous (39). Industries excluded are food (31) and basic metal (37). The reason for food is that it is too
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ubiquitous, while for basic there are only very few observation. Table 1 and table 2 present the results of econometric estimation. Generally, variables that are more likely to be significant are distance to the nearest business center and population. As expected, the coefficient of distance is negative, while for population is positive. The infrastructure variable is rather disappointing since it always has wrong sign.
Wage Cost Since the information on the regional minimum wages is only available from 1985 onward we can use regional minimum wage only in the 1991 regression. For the 1986 sample, we experiment with the actual wage. The result is not too encouraging since. The wage coefficients for some industries are positive, while for wood products, paper products and miscellaneous the coefficients are negative as expected but they are insignificant. Obviously, there is a problem of endogeneity in using the actual wage. The use of regional minimum wage (RMW) as independent variable in the 1991 sample gives more plausible result. The coefficient of RMW is negative and significant at 5 percent level for wood products (ISIC 33), chemical (ISIC 35), and machinery (ISIC38). The coefficients are negative and significant at 10 percent level for non-metallic mineral and miscellaneous industry (ISIC 39). For these industries, the negative effect of regional minimum wage in discouraging firms from locating in one particular location is quite apparent. In several instances in Indonesia, the imposition of minimum wage policy will increase workers’expectation. Many small and medium size enterprises have complained a lot that they are less able to meet demand for higher wages since it is often proceeds without regard to workers’productivity. Refusal to meet demand for higher pay, as proved in many industrial areas in Java is also dangerous. Since it may end up with worker strike that is often combined with the act of violence that destroy considerable part of plant and equipment. The above regression result suggest that at lest for some industries are not only looking for a region with lower minimum wage but also perhaps a region where the imposition of minimum wage is less strict.
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Distance to Nearest Business Center Unlike in the previous works for Industrialized countries where much longer distances are involved (Brazil and USA) and the impact of access to major national metro areas or regional capital is very little, this study finds that access to major business centers is very important. Judging from the coefficient of distance from the Indonesian sample, firms in textiles (ISIC 32), wood products (ISIC33), paper products (ISIC34), non-metallic mineral (ISIC36) and machinery (ISIC38) are more clustered near major business centers in the 1991 sample compared to the 1986 sample. This suggests that after the 1986 economic liberalization, some trend of industrial centralization take place in Indonesia. This happened despite some efforts on the part of the central government to decentralize some licensing power to local governments. The trend of industrial centralization after the 1986 economic liberalization however is understandable given the fact that most of power to grant license is still at the hand of the central government particularly for medium and large scale projects. Even for smaller projects there is also tendency to locate close to the center. One plausible explanation is the centralization of banking service. Since mid 1983, the most dramatic reforms were in the banking sector. Public sector loans were made competitively available at market interest rates instead of being rationed and subsidized. In addition, in 1988 restrictions on the private part of the banking industry were lifted, permitting rapid growth in this sector. The banking liberalization however has not removed the administrative and spatial hierarchy in the loan process of or the interpersonal nature of granting larger loans. To take advantage of liberalization in terms of obtaining significant size loans for capital expansion, firms would need to pursue loan applications and personal contract in a few large metro areas. It seems that for a single firm effective access to large loans in the capital markets would be greatly enhanced by centralized location.
Industrial Diversity
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There is no uniformity regarding the impact of industrial diversity on firm location. In the 1991 sample for example, the coefficients for textiles, wood products, chemical, and non-metallic mineral are positive and significant. For these industries knowledge spillovers from within industry is more important than from outside industry. New firms tend to concentrate close to the location of older firms from the same industry. Thus it reinforces the concentration trend. Only, the coefficient of wood products switches sign from negative in the 1986 sample to positive in the 1991 sample suggesting also the trend of concentration.
The Impact of Bribery Cost The regression result suggests that the negative impact of bribery cost is more apparent in the 1991 sample compared to the 1986 sample. In the 1991 sample the coefficient of bribery cost is negative and significant for textiles, wood products, paper products and machinery. While in the 1986 sample it is only negative and significant for textiles. This suggests that the decentralization of some power to administrate license, to local governments has created locational preference on the part of individual firms to choose to choose region with lower or optimal bribe. In other words, firms after controlling for other factors, prefer to choose a region with less corrupt local government. The decentralization of some administrative power to grant license to local officials has actually make one region competes with one another in attracting manufacturing firms or business firms in general. This pattern of choosing location with a lower bribe or less corruption is less apparent in the 1986 sample, when the license system was very centralized. This result points to the situation where after some period of rigid centralization, decentralizing administrative license system may increase transaction cost in some local areas due to various reasons such as the temptation of short-sighted local officials to extract excessive rent (legal or illegal) from the existing and entering firms.2 In doing so, they may not maximize their revenues.
2
Businessmen in Indonesia often complain at the behavior of local officials. What they complain the most is the lack of transparency and guarantee that their business will not be bothered by various incursion of local government into their business once the business start. Businessmen do not mind to pay bribe provided that it is reasonable and no further delay imposed on them after they pay bribe.
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Some learning periods are needed before they are aware that they need to compete with other locations to attract new firms as potential tax base. It is also interesting to observe that the coefficient of distance variable is in general more negative in the 1991 sample compared to the 1986 sample. Thus, firms are more clustered near major business centers. This indicates that manufacturing firms are more centralized after the 1986 economic liberalization. This is exactly the opposite of what the central government intended to do, namely, to revert the centralization of manufacturing industry by decentralizing licensing regime. One possible explanation is that after controlling for other factors, local officials in locations close the business centers may be smarter in determining the optimal level of bribe thus are able to attract more new firms to locate. Another explanation is also possible. Perhaps there was not enough decentralization. It is true that after the 1986 liberalization the central government still holds a considerable power in approving new investments including their locations, particularly if the scale of the projects are “big”enough. In this respect other centralizing factors are too strong. Several centralizing factors can be named for example the banking system and the existence of agglomeration of old industry. As mentioned above, the spatial hierarchy of the national banking system in granting loans still exists, thus attract new firms to locate near major business centers, where most bank headquarters located. Also the sign of the coefficients of industrial diversity suggests that firms in most industries prefer regional specialization and tend to locate close to older firms from the same industry. It is not clear whether in the most recent industrial surveys, the pattern of choosing location with low or optimal bribe still exist. There is a need to estimate the model for more recent data set. It would be possible, after allowing local officials some learning periods, they may more aware that in fact they compete with other locations in attracting business, thus induces them to determine more reasonable bribe level with more security guarantee to businessmen. There is no guarantee that the process of industrial centralization will be reversed
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since other agglomeration factors such as population size and cluster of mature industry will still exert strong influence on the trend of centralization. Conclusion In certain situations, economic liberalization policies can increase the degree of spatial centralization. In the case of Indonesia the 1983 Banking Deregulation was associated with increased centralization of unincorporated manufacturing firms. Although the liberalization gave unincorporated manufacturing firms better access to government and other centralized services, firms needed to centralize to take advantage of these opportunities because the bureaucratic process is centralized. The Indonesian government was intent to reverse this process or at least to slow it down. The second package of economic liberalization in 1986 contained steps in this direction. One important step was the decision of central government to give provincial and district (kabupaten) planning agency (BAPPEDA) more power in several aspect of administrative licensing system such as the choice of local site, environmental feasibility, property tax etc. We then examine, whether regional government abused their new power. The immediate impact of license decentralization was that manufacturing firms started to show locational preference for region with lower bribe. From the point of view of decentralization, more and more new firms are clustered in the old business centers. In this respect other centralizing factors are too strong. Several centralizing factors can be named for example the banking system and the existence of agglomeration of old industry.
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References: Henderson, J.V., A. Kuncoro, M. Turner, 1995, “Industrial Development in Cities,” Journal of Political Economy, vol. 103(5):1067-85 _____________ and A. Kuncoro, 1996 “Industrial Centralization in Indonesia,” World Bank Economic Review, vol. 10(3) Jacobs, Jane, 1984, Cities and the Wealth of Nations, Vintage Book, New York. Kittiprapas, S., P. McCann, 1999, “Industrial Location Behavior and Regional Restructuring within the Fifth Tiger Economy: Evidence from the Thai Electronic Industry, Applied Economics, vol. 31:37-51 Lee, K.S., 1981, Location Jobs in Developing Countries, Oxford University Press, New York. Romer, Paul, 1986, “Increasing Return and Long-Run Growth,” Journal of Political Economy, vol. 94(October), p. 1002-1037.
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Textiles
Table 1: Conditional Logit Estimation Factors Affecting Firm Location 1986 Sample WAGES INFRAS. BRIBE LPOP 0.5130 0.0007 -0.0104 0.3890 [6.119] [0.428] [-2.074] [6.433]
DST -0.0096 [-9.878]
DIV 0.0171 [12.175]
Wood products
-0.0936 [0.397]
-0.0091 [-1.904]
-0.0077 [-0.777]
-0.1070 [-0.610]
-0.0077 [-4.120]
-0.0240 [-2.867]
Paper products
-0.0005 [-0.002]
-0.0106 [-1.535]
0.0116 [0.979]
0.2230 [1.138]
-0.0167 [-5.453]
-0.0130 [-0.797]
Chemical
0.6240 [4.813]
-0.0085 [-3.122]
-0.0041 [-0.696]
0.7790 [7.493]
-0.0119 [-9.002]
0.0118 [0.114]
Non-metallic Mineral
0.1030 [1.090]
0.0016 [0.946]
0.0133 [3.057]
0.6530 [6.659]
-0.0007 [-2.246]
0.0401 [12.615]
Machinery
0.2860 [2.126]
-0.0195 [-5.403]
-0.0131 [-1.695]
0.4470 [3.826]
-0.0141 [-8.497]
0.0405 [8.061]
-0.0032 [-0.795]
-0.0027 [-0.047]
-0.0673 -0.0090 -0.0267 0.2810 Miscellaneous F= [-1.194] [-0.922] [-0.922] [0.950] Notes: for the 1986 sample, wages are the actual wages paid on workers Figures between parentheses are t-ratio WAGES: log of wages INFRAS: percentage of electricity from the state power company BRIBE: proxy of bribery costs LPOP: log of population DST: distance DIV: locational diversity
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Table 2: Conditional Logit Estimation Factors Affecting Firm Location 1991 Sample WAGES INFRAS. BRIBE LPOP DST 0.3010 -0.0007 -0.0259 0.2820 -0.0157 [2.713] [-0.362] [-4.663] [4.655] [-14.676]
DIV 0.0196 [14.929]
Wood products
-0.8150 [-4.528]
-0.0116 [-4.428]
-0.0167 [-2.184]
0.3460 [3.362]
-0.0132 [-11.793]
0.0087 [6.550]
Paper products
-0.1950 [-0.562]
-0.0149 [-2.418]
-0.0285 [-1.781]
0.3680 [1.811]
-0.0092 [-3.132]
0.0018 [0.114]
Chemical
-0.8550 [-4.610]
-0.0311 [-8.537]
-0.0014 [-0.213]
0.3560 [3.523]
-0.0146 [-10.804]
0.0067 [2.301]
Non-metallic Mineral
-0.4100 [-1.391]
-0.0013 [-0.580]
0.0054 [0.973]
0.4960 [4.080]
-0.0003 [-0.323]
0.0401 [12.615]
Machinery
-0.9830 [-4.733]
-0.0416 [-9.154]
-0.0205 [-2.650]
-0.0618 [-0.411]
-0.0199 [-9.775]
0.0005 [0.349]
-0.5190 -0.0345 0.0044 -0.0060 -0.0072 [-1.146] [-3.844] [0.215] [-0.020] [-1.894] Notes: for the 1991 sample, wages are the regional minimum wages Figures between parentheses are t-ratio WAGES: log of wages INFRAS: percentage of electricity from the state power company BRIBE: proxy of bribery costs LPOP: log of population DST: distance DIV: locational diversity
-0.0044 [0.869]
Textiles
Miscellaneous
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