DP2010-35

Industrial relocation pol icy andheterogeneous plants sorted byproductiv ity: Ev idence from Japan
Toshihiro OKUBO
E i ichi T OMIURA
December 22, 2010

Industrial relocation policy and heterogeneous plants sorted by productivity:
Evidence from Japan
December 14, 2010
Toshihiro Okubo* and Eiichi Tomiura**

Abstract
In an economic geography model with firm heterogeneity, Baldwin and Okubo (2006) show that
regional policies for promoting periphery development attract low-productivity firms andadversely affect the productivity gap within a country. This paper empirically examines their
theoretical prediction by using plant-level data during active relocation policies in Japan. Ourestimation results from plant-level regressions and propensity-score matching are generally
consistent with the theory. As compared to other regions, those targeted by policies, especiallyby industrial relocation subsidy programs, tend to have low-productivity plants.

*Research Institute of Economics and Business Administration, Kobe University** Corresponding author: Department of Economics, Yokohama National University,

79-4 Tokiwa-dai, Hodogaya-ku, Yokohama City, 240-8501, Japan. Phone: +81-(0)45-339-3563. Fax:
+81-(0)45-339-3574, E-mail: tomiura@ynu.ac.jp.
11. Introduction
Uneven development of regions within a country has been a serious social concern. To
alleviate this problem, governments often adopt policies that promote the relocation of firms
from industrial centers to undeveloped areas. By constructing a new economic geography modelwith firm heterogeneity, however, Baldwin and Okubo (2006) show that relocation subsidies forincreasing the share of industry in periphery regions “attract the least productive firms since
they have the lowest opportunity cost of leaving the agglomerated region” (p.2). If their
theoretical prediction is true, regional policies should unintentionally widen the productivity gap
between the core and the periphery.1 This paper, the first empirical investigation of the sorting
effect of regional policy, exploits plant-level data derived from manufacturing censuses during
the period of active relocation policies in Japan.
Japanese industrial relocation policy can be regarded as a rare, suitable case for testing
this theoretical prediction. First, all the regional policy programs considered in this paper aredesigned to relocate manufacturing plants from congested industrial core regions to
undeveloped rural regions within a country, as formalized by Baldwin and Okubo (2006). In
contrast, regional policies in other countries or other periods have been carried out for different
objectives (e.g., the four Structural Funds and the Cohesion Fund to fight unemployment andstimulate economic growth in the poor regions in EU member countries and to support thetransition of Eastern Europe into the EU). Second, Japan is not a federated country; instead,regional and local administrative units (prefecture, city, town, or village) possess only limited
fiscal and judicial autonomy. This institutional fact implies that we can safely study the effects

1

In another example of theoretical work on the unintended effects of regional policy, Depont andMartin (2006) show that regional policies, especially subsidies to poor regions proportional to firms’
profits financed by national taxes, increase cross-regional income inequality within a country. Theirargument rests on the observation that the effect of subsidy spills over to rich regions, where many
owners of capital (beneficiary of subsidized profits) reside.
2

of relocation policy on plants in the country by investigating the programs undertaken by thenational government alone. In other countries, various layers of public authority (e.g., the states
and the federal government in the U.S.A., and the nations and the European Commission ofEurope) undertake their respective regional policies. Finally, Japan has accumulated experiencein many regional subsidy programs, and the country has maintained rich regional data sets at theplant level. As discussed in the next section, the Japanese government actively tried to relocate
plants from crowded industrial centers to peripheral regions particularly in the 1970s and 1980s.
This makes Japan a good case by which to study regional policy impacts. In addition, theJapanese government has been consistently collecting comprehensive information on all
manufacturing plants across all regions of the country. By making use of plant-level data
derived from consecutive waves of manufacturing censuses, we can trace the effects of these
economic policies in Japan.
While the list of global policy experiences over time is long, this issue remains vital inmany countries. In Europe, the EU Structural Funds have had a substantial impact on therelocation of industries to peripheral countries in the last decade. However, as discussed byMidelfart-Knarvik and Overman (2002), regional policies result in attracting R&D intensive
sectors to periphery regions with less skilled labor endowments. This tendency acts as a counterto comparative advantage and consequently fails to stimulate peripheral economic
development.2 More generally, the recent issue of the World Bank’s influential World
Development Report (2009), titled “Reshaping Economic Geography,” argues that the side
effects of regional development policy are becoming more serious in the age of globalization.
When a country becomes more open to international trade “without changing the level of2Midelfart-Knarvik and Overman (2002) found that Ireland is the only successful case; it meets the
EU average by attracting R&D intensive sectors. Boldrin and Canova (2001) and Dall’erba andGallo (2007) found no positive impacts of EU regional policies on productivity and economic
growth. Various results on EU structural funds are summarized in Mohl and Hagen (2010).
3

permissible subsidies to firms in remote regions, the subsidies will lead to an increasing
distortion of the spatial allocation for industry” (Baldwin et al., 2003, p. 478). In this sense,
while we examine only the case of Japan during the 1970s and 1980s, this investigation of a
traditional relocation policy has far broader significance for our age.
The main results of our study are as follows. All regional policy programs are able toattract plants to targeted regions. However, plants located in these regions have below-average
productivity. The comparison of regression results across years and the investigation of plant
productivity distributions basically suggest no significant impact of relocation policy programson plant productivity. By using a matching technique and comparing pairs of plants in targeted
and non-targeted regions, we also confirm that all regional policies always attract significantly
lower productivity plants.
The remainder of this paper is divided as follows. Section 2 offers a brief overview of thehistory of relocation policies in Japan. Section 3 describes the plant-level data acquired for ourstudy. Section 4 explains our empirical methods. Section 5 reports the estimation results from
regressions and propensity score matching. Section 6 presents the conclusions.

2. Overview of the relocation policy in Japan
This section, a historical summary of Japan’s industrial relocation policy after World War
II, is not presented as a comprehensive history but as a brief and sketchy backgroundexplanation for our estimations. It centers on the policy experience of Japan during the 1970s
and 1980s, which provides us with a valuable opportunity to test the theoretical prediction of
Baldwin and Okubo (2006).
Before discussing relocation policies, it would be informative to quickly scan theevolution of Japan’s economic geography after World War II. As described by Fujita and4

Tabuchi (1997), the Japanese economic landscape has experienced two major transformations:first, the shift from the traditional Tokyo-Osaka bipolar system to the Pacific Industrial Belt
during the historic high-growth period of the 1960s, and second, the move to a regional system,
dominated by monopolar Tokyo, after the oil crisis in the mid-1970s. Labor reallocation was
substantial from the agricultural sector in the periphery toward manufacturing industries on the
Pacific Ocean coast during the first transformation period, whereas Tokyo attracted central
management functions and service industries during the second period. Behind this geographic
conversion, Japan also experienced changes in its industrial structure, which transitioned fromone dominated by heavy industries dependent on imported materials to one led byknowledge-intensive, high-tech industries.
During this period, the Japanese government was involved in a series of active initiatives
designed to encourage the relocation of firms from heaving manufacturing centers to
undeveloped periphery regions. While it gradually shrank during the high economic growth of
the 1960s and early 1970s, the wide income gap between the core (the Pacific Industrial Belt,
especially Tokyo and Osaka) and the periphery has remained one of the top priorities in theeconomic policy package. Social concerns, such as air pollution, commuting congestion, andsoaring housing prices in core regions, promoted public supports for nationwide industrial
repositioning. The government’s long-term regional policy plan emphasized “balanced
development” of regions. As Japan is not a federated nation, the central national government has
a strong authority in many policy arenas, including regional development policy. Under theleadership of the central government, transport infrastructure systems, such as highways andrailroads, have been developed to facilitate the relocation from high-density center regions. The
industrial relocation policy programs considered in this paper are among these grand strategy
packages.
5

Figure 1 shows the Gini coefficients of two-digit manufacturing sectors for 47 prefecturesfrom the 1970s to the present.3 Cross-regional variations are measured in terms of the numberof plants, employees, and value added. All three Gini coefficients decline over time. In
particular, the Gini coefficients in terms of plants and employees steadily decline until the
mid-1990s. This indicates that manufacturing geographically became diversified acrossJapanese regions because of factors such as improvement of the infrastructure and highway
network as well as regional development policies.
(Figure 1)
In this paper, we study several regional development policy initiatives. First, we examine
the Industrial Relocation Subsidy, which subsidizes plants relocated from “the moving-out
promotion areas”4 to “the inducement areas”5 (both areas explicitly defined by national
government orders). Since almost all the regions in Japan are categorized under one or the otherof these categories, this policy program should have a wide impact on Japanese industrial
relocations. This subsidy program has a long history, dating back to 1972, and was stimulated
by serious congestions in industrial centers; these congestions were aggravated during thehigh-growth years of the 1960s.
Second, we examine the program Technopolis, started in 1983 and designed to formhigh-technology industrial complexes (e.g., machinery sectors). During the 1960s, Japanencouraged heavy industries, including petrochemicals, which depended on cheap imported oil.
However, the national government, after the oil price hikes in the 1970s, tried to shift thecountry’s industrial configuration from heavy to high-technology industries. The Technopolis3

The data are taken from Manufacturing Census (Report by Industries) for each year (METI).
The areas from which plants are encouraged to leave (iten-sokushin chi-iki in Japanese) are mainly
in and around Tokyo, Osaka, and Nagoya.
5 The areas to which plants are encouraged to relocate (yudo chi-iki in Japanese) include almost allareas outside the three major industrial centers.
4

6

plan was expected to play a pivotal role in transforming the countrywide industrial structure,
while, at the same time, encouraging more balanced growth across regions through theestablishment of new industry complexes in undeveloped areas.
In addition to these two governmental programs, we also include the following three
initiatives, all of which were designed to support rural regions with declining manufacturing.
While Technopolis emphasized high technology, manufacturing industries (firms developing
high technology or manufacturing high-technology products, for example, machinery) remain
the policy target. The additional three programs focus, instead, on non-manufacturing activities,
such as software programming or scientific research that, although closely related to andintended to support manufacturing industries in the same region, reflect the historical shift inJapanese industrial structures.
Started in 1988, Intelligent Location (zuno ritti in Japanese)6 is designed to attract
software and information service industries, which critically support the progression of hightechnology-intensive manufacturing.7 Although expanding its scope to information-related
non-manufacturing activities, this program shares the fundamental policy objective with thepreceding Technopolis in stimulating regional manufacturing.
Regional Hub City (chiho kyoten toshi in Japanese) began in 1992 and was created to
attract regional headquarters. While the repositioning programs of earlier periods often tried torelocate manufacturing plants to alleviate congestion and pollution in urban areas, this plan
focuses on regional headquarter offices. Its changed perspective is again in line with the generaltrend of the declining share of manufacturing in the Japanese economy. This program wasinitially started as part of a regional infrastructure development plan. However, by changing the6

As the original Japanese names of policy programs (shown in parentheses) have been translated by
the authors, some of the English names are not officially authorized.
7 As this program started relatively late in our sample period, our analysis is limited to OLS
estimations.
7

office locations, the Regional Hub City program is likely to have an indirect effect on thelocations of manufacturing plants owned by the same firm and those of others competing in thesame markets.
Science City (gakuen toshi in Japanese) is designed to attract academic and researchfacilities. A prime example of this program is Tsukuba City, in which a national university was
relocated from urban Tokyo as part of a comprehensive development plan for a new city.
Integral to a national research policy, this program started in the early 1970s. However, since
Japanese economic planning in the 1980s began to focus more on R&D, science, andtechnology than on production per se, the Science City program should be examined together
with other industrial relocation programs.8
Finally, in addition to the above industrial relocation programs, we also consider Coal
Mining Areas (san tan chi-iki in Japanese). Started in 1961, this policy program is intended tohelp industries in coal-mining regions. Although this initiative began much earlier than any of
the others already mentioned, the political commitment to it has been strong and sustained.
Japan had traditionally depended on domestic coal as the primary source of energy, until cheap
oil imports started to expand in the late 1950s. Following this fundamental shift in demand,called the Energy Revolution, coal-mining areas, once populous and rich, entered a declining
phase. The Japanese government supported these declining regions by providing funds topromote other industries in them. As this program clearly identifies the targeted regions, as thefunds devoted to this program were far greater than those allocated to others and as
manufacturing industries were often expected to develop in these regions, we decided to include
the Coal Mining Areas program in our analysis.

8

For example, Technopolis requires each targeted regions to have at least one university with anengineering department.
8

As we examine the above policies, all mandated by national law, the targeted regions are
explicitly defined by public ordinance documents. Whether or not the region is selected by eachpolicy is identified at the most basic geographical unit (village, town, or city). Any region in
Japan belongs to one village, town, or city exclusively. No more meticulous official
geographical unit exists in the Japanese system. While most of the previous studies depend on
broader prefecture or state-level data, our identification of target regions at the city/town/village
level is far more complete and precise.9

3. Description of plant-level data
This section describes the data used in our study. We derive plant-level data fromJapan’s Census of Manufacturers. Basic plant characteristics, such as output (shipment),
employment (number of regular workers), and expenditures on materials, are included in thiscensus for virtually all plants across all manufacturing industries.

3.1. Coverage of plants
Although the annual survey covers plants above the given size threshold, small-sizedplants are only included in the “census years” (year with 0, 3, 5, or 8 as its last digit). We
concentrate on the census years to avoid truncations due to the sampling of plants. As
plant-level data are maintained only for the plants with no less than five employees in theoriginal micro-data files of the central government, even for the most recent census, our sample
excludes plants with less than five employees. Since these extremely small-sized plants produce

9

In some exceptional cases, targeted regions are defined by chome addresses (similar to street
numbers) within the same city/town/village. As our plant data cannot identify a plant’s chomeaddress, we assume that all firms located in a city/town/village are subsidized if some parts of thecity/town/village are targeted by a policy.
9

negligible volumes of output, their omission is unlikely to affect our conclusion on economic
geography.
Among the available plant-level data, this paper focuses on plants owned by multi-plantfirms. The census captures whether or not each plant is a part of a multi-plant firm, although noidentifier is available for linking plants under the same ownership. Hence, the aggregation of
our plant-level data to the firm level is impossible. We look at multi-plant firms because they arelikely to relocate their plants more responsively to variations in economic geography conditions
and in the government’s subsidy. By focusing on these relatively “footloose” plants, this paper
sheds light on the possible effects of the relocation policy on plant productivity. Our sample ofplants owned by multi-plant firms includes 48,000 plants in 1978 and then steadily grows to
66,000 by 1990 (Table 1).

3.2. Sample period
Our sample period consists of the following six consecutive census years: 1978, 1980,
1983, 1985, 1988, and 1990. We selected these years for the following reasons.
First, as mentioned in Section 2, the 1970s and 1980s were the heyday of Japan’s active
relocation policies. In later years, the focus of Japanese regional initiatives shifted frommanufacturing to non-manufacturing activities. Although this shift naturally reflects changing
shares in the national economy, the government’s efforts on gathering data on
non-manufacturing (service) sectors are generally insufficient when compared to the extensive
censuses carried out on the manufacturing sector. Public support programs have also begun toconcentrate more on functions, such as R&D, rather than on specific locations. Furthermore,
greater emphasis was placed on strengthening the roles of Tokyo as a global financial center,
especially during the Bubble boom period around 1990. After the bubble burst, public
10

construction projects were actively financed in rural areas as part of macroeconomic stimulus
during the 1990s but without the policy objective of industrial relocation. Consequently, toavoid these various contaminations in recent years when testing the productivity-sorting effectby Baldwin and Okubo (2006), we focus on the earlier period when policy programs forrelocating manufacturing plants from crowded core areas to undeveloped periphery areas wereactively undertaken.
Second, the 1970s and 1980s are suitable periods to measure the impact of regional policy
on firm relocation across Japan. From the 1990s onward, unprecedented exchange rate
appreciation led factories to relocate to lower wage Asian countries through foreign direct
investment. More recently, production processes and firm organization have become much
more complex. Many Japanese firms build production networks linked to domestic andoverseas production by intermediate inputs purchase, foreign direct investment, andoutsourcing. Since complete data sets covering global location information of individual plants
are not available, we cannot appropriately examine regional policies in such a globalized era.
To circumvent the statistical difficulties that arise from the complexity of firm organization andoversea production/relocation, we focus on the 1970s and 1980s. Finally, the Census of
Manufacturers does not maintain plant-level data before the mid-1970s, even in the original
government data files.
As no plant identifier tracing micro-data over time is available, our data set isunfortunately in the format of repeated cross-sections. As a result, we cannot discuss causality
direction or dynamic effects on entry/exit or on productivity growth. In addition, without any
longitudinal identifier in our repeated cross-section data, we cannot estimate the total factor
productivity of each firm. Furthermore, previous research in related fields shows that the choice11

of productivity measure is unlikely to affect results.10 Therefore, we measure productivity byvalue added per worker.
(Table 1)
During this period, the regions targeted by policies attracted plants. As the upper panel ofTable 2 shows, the number of plants located in targeted regions increased substantially.
Furthermore, this growth in the targeted regions is higher than the national average. As a result,the share of target regions in Japan rose during the sample period.
(Table 2)
The above table demonstrates that the share of targeted regions has increased during aseries of relocation policy programs. This finding, reported in Table 2, is robust, even if weconcentrate on the regions actually subsidized among the inducement areas listed by the law.11
However, the mere rise in the number of plants does not imply the success of these relocation
policies. We will examine their impacts on productivity, especially at the plant level.
While this paper focuses on the mean comparisons in what follows, a brief overview ofthe distributional information at the plant level will be informative. Table 3 summarizes thefrequency distribution of plants over productivity intervals (an empirical counterpart ofcumulative distribution function), comparing plants in targeted regions with those in
non-targeted regions in the cases of Technopolis and Intelligent Locations.
Several points are noteworthy. First, the comparison across years 1980-85-90 shows thatthe distribution patterns have barely evolved over time. The relocation policy programs appear
to have no noticeable impacts on productivity distributions after the programs started. Our
10

For example, Bernard and Jones (1996) report that the difference between TFP and laborproductivity is relatively small when countries are compared. Syverson (2004) also confirms that his
result from TFP on the plant-level relation between productivity and spatial competition is robust
even if labor productivity is used.
11 Staffs at Regional Economy Section in METI rearranged the historical data on individual subsidy
provision after deleting firm identifiers and provided them to the authors exclusively for thisresearch project.
12

finding of no policy impact in this inter-year comparison is also in line with previous research.
For example, Martin et al. (2010) find that the policy did not succeed in reversing the relative
productivity decline of firms in French clusters. We will later reexamine this policy impact after
its implementation by the difference-in-difference regressions.
Second, the plants located in targeted regions tend to be distributed relatively heavily over
low-productivity ranges, compared with the plants located outside of targeted regions. While wemust note that the distributions of these two groups of plants overlap over a wide range of
productivity intervals, this difference is consistent with our assumption of lower average
productivity in targeted regions.
Finally, as the dispersion among plants in non-targeted regions is substantially larger thanthat in targeted regions, it will be more informative to focus on selected plants among widely
heterogeneous plants when we compare with plants in targeted regions. The matching results
reported later will partly respond to this last concern.
(Table 3)

4. Empirical methods
This section explains the empirical methods used to estimate the impact of relocation
policies on plant productivity. We take two different approaches. First, we estimate theregressions with the policy dummy variable. Second, we select comparable plants on the basisof the propensity score matching method.

4.1. Policy Premium
In the first approach, to estimate the policy premium on plant productivity, we
estimate the following plant-level reduced-form regression:
13

PROD j  const  1SIZE j   2 MAT j   3 LABOR j   k POLICYk    l INDl    m PREFm   j.

(1)

The plant is indexed by j. As no longitudinal plant identifier linking plants over time is available,
all the regressions are in a cross-sectional format. The dependent variable is the productivity
PROD, which is defined by the value added per worker.12 Included on the right-hand side of theregression are plant characteristics, industry dummies (IND), prefecture dummies (PREF), andrelocation policy dummies (POLICY).13 Plant-level variables included in (1) are the plant size,
SIZE (the number of workers), the intensity of material used, MAT (expenditures on materials
divided by output, both in yen), and the labor intensity LABOR (labor inputs [total wage paid]
divided by output, both in yen). All variables are in logarithmic form.14 No other plant-levelvariables related to productivity, such as capital, are available in our census data that include
small-sized plants.15 Industries are defined at a two-digit level. The dummies for 47 prefectures
are included to control for region-specific determinants of plant productivity, such as
cross-regional differences in human capital. The error term is expressed by  in (1). POLICY is avector of dummy variables, which takes the value of 1 when the plant is located in the region(city, town, or village) targeted by each relocation policy program.16 If the coefficient on apolicy dummy is positive/negative, the plants located in the regions targeted by the policy are,on average, more/less productive than those located in other regions after controlling for plantcharacteristics, industry effects, and prefecture-specific factors. We must note, however, that we

12

The denominator is the number of regular employees. The numerator is output (shipment) minus
the consumption tax, depreciation, and raw material costs.
13 The policy dummy takes the value of 1 for the regions always targeted through the sample period.
As a robustness check, however, the dummy for the regions targeted at each year (not necessarily
targeted in other years) is also used in the cases of Technolopolis and Intelligent Location.
14 For MAT and LABOR, the value of 1 is added before taking the logarithm.
15 The manufacturing census contains data on tangible fixed assets only for large-sized plants.
16 The policy dummy is defined time-invariant, irrespective of the start and finish of each policy.
14

should not interpret equation (1) as indicating the direction of causality. As our sample isconstrained as repeated cross-sections, we cannot discuss dynamics or causality appropriately.
While all the regressions reported above are based on plant-level repeated cross-section
data, we estimate the following difference-in-difference regression at city/town/village level as arobustness check to investigate the impact on average productivity after the policy isimplemented:

PROD rt  1 POLICY 1r   2 POLICY 2 rt    t YEARt   rt

(2)t

The dependent variable is the average productivity at the regional level r (city, town or village)
calculated by firm level productivity data. Included on the right-hand side are the targeted
region dummy POLICY1 (taking one or zero for each region r consistently for all years), thepolicy dummy POLICY2 (taking the value one only after targeted), and year dummies YEAR.
The regions over all years are pooled. The error term is expressed by . If 1 is significantly
positive but 2 is not, then, the policy picks up regions where productive plants had locatedeven before the implementation of the policy. The policy per se has no significant impact. On
the other hand, if 2 is significantly positive, the region becomes productive after the region
was targeted by the policy. This indicates that the policy contributes to boost average
productivity in the region. While we cannot perfectly examine the dynamics of plant
productivity in our repeated cross-section plant-level data, this additional regression will be
informative as a robustness check purpose in discussing the timing of policy effects.

4.2. Matching Technique (Treatment Effect)
While the regression discussed above is straightforward, we also choose the matching
technique to select a pair of comparable plants from our sample. The motivations for our use ofmatching technique are as follows.
15

First, we must note that the number of plants located within the targeted regions isseverely limited in most of the policy programs. For example, merely around 5 -10 % of theplants in our sample are located in Technopolis regions (see Table 2). This implies that thepolicy premium estimated from the regression is based on the comparison of limited numbers ofplants located in the narrowly targeted regions with far larger numbers of much moreheterogeneous plants in wide non-targeted regions. While the theoretical models often consider
two regions in a country, regions in the real world are actually tremendously heterogeneous inmany dimensions. The matching technique leads us to concentrate, among many plants locatedin non-targeted regions, on plants relatively similar with those in targeted regions.
Second, plants located in targeted regions and those in non-targeted regions may differ
due to factors unobservable for econometricians. While theoretical models assume that regional
policy is chosen based on economic conditions such as per-capital income, actual policy
decisions in the real world can be affected by other factors such as political influences. Then, it
is possible that some of the economically similar regions happen to be targeted but others arenot. To tackle this potential bias, the matching technique helps us compare similar plants locatedin and outside of targeted regions.
The causal effect of the treatment is estimated as the mean difference in productivity
between the treated and the untreated groups. The treated group is composed of the plants
located in the targeted regions (city, town, or village level) identified by regional development
policy programs. On the other hand, the non-treated group is a set of all plants outside the target
regions. The average effect of the treatment on the treated group, ATT, is given by

E ( y1  y 0 | D  1) , where D is the policy dummy variable (D = 1 if plants are treated, and 0
otherwise). y1 and y 0 denote treated and non-treated plant-level productivity respectively. We
assume that the non-treated outcomes are independent of treatment status, conditional on16observable plant characteristics X  ( Size, Mat , Labor ) . Then, we adopt the propensity scorematching as in Rosenbaum and Robin (1983). The probability of treatment, p(X), defined as thepropensity score (0 < p < 1), given the observables is specified as p( X )  p( D  1 | X ) . The
propensity score is estimated by probit regression using X.17 The expected difference between
matched pairs is given by E ( y | D  1, p ( X ))  E ( y D  0, p ( X ))  E ( y1  y 0 p ( X )) . By
iterated expectations, the average across the distribution of propensity scoresgives ATT  E ( y1  y 0 ) .

5. Estimation results
5.1. Results from regression with dummies
The OLS regression results are reported in Table 4a. This regression exercise isintended to examine whether targeted regions tend to have an average productivity that issignificantly higher than that of other regions. We estimate cross-sectional regressions for all
years in our sample period irrespective of the starting period of each policy, with the policy
dummy variable consistently taking the value of 1 for the targeted regions.18(Table 4a)
The results shown in Table 4a demonstrate that the plants located in the regions
targeted by most relocation policy programs tend to have significantly lower productivity or to
be insignificantly different from plants located in regions not promoted by the policy. Negative
coefficients are found especially in the cases of Regional Hub Cities and Science Cities. The
17

To match the sample, we use caliper matching at the level of 0.05 and involve one-to-one
matching with replacement with common support. As we impose the common support, the treatedunits whose propensity score is higher than the largest propensity score in the non-treated group canremain unmatched. When none of the non-treated units are within δ from treated plant i, the plantremains unmatched. To check whether a pair of sample has similar characteristics X, we use abalance test.
18 The targeted region is strictly defined as the regions that have always been targeted since the startof each policy program.
17

regions targeted by these policy programs appear to attract low-productivity plants.
Consequently, we find empirical evidence favorable for the sorting effect predicted by Baldwin
and Okubo (2006) in these policy programs. We must note, however, that this negative effect on
manufacturing productivity should not be regarded as a policy failure, since these two programs
are intended to attract regional headquarter offices and academic/research facilities respectively,
not productive manufacturing plants. In addition, we should not directly consider the OLS
regression results as showing the causal effects of policy. We will later discuss the treatment
effect on the basis of plant matching.
In contrast, in the cases of Technopolis and Intelligent Locations, the positive productivity
premium is found. However, we cannot assert simply on the basis of this regression result thatthese two policy schemes are exceptionally successful in attracting high-productivity firms. By
comparing the productivity premium of the targeted regions before and after the start of thepolicy program, we can infer the policy effect. As reported in the table, the magnitude remains
the same over time and the gap had been significant even before these policy programs started(Technopolis from 1983, Intelligent Locations from 1988). Thus, the results indicate that thesetwo programs have selected regions in which higher productivity plants were alreadyconcentrated and/or average productivity was higher than the total for Japan.19 In this sense, weshould be cautious in concluding that these two programs succeed in attracting productiveplants.
As a robustness check, for Technopolis and Intelligent Locations, we further introduce analternative definition of a targeted region. In the above regressions, we have defined targeted
regions as those targeted consistently through the sample period since the start of the policy

19

As a related finding, Midelfart-Knarvik and Overman (2002) report that the EU Structural Fundsupports are effective in Ireland, where the share of high-skilled workers is higher than othertargeted countries.
18

program. We now alternatively define them as the regions targeted at each year (not necessarily
targeted in other years). The former definition concentrates on the narrower groups of regions
common across years, but the latter definition includes newly targeted regions in regressions onlater years. The estimation results based on this alternative definition are shown in Table 4b. The
regressions based on the latter definition are naturally limited to the years after the start of eachpolicy program, though the regression based on the former can be estimated in any year during
the sample period, as shown in the previous table. In both Technopolis and Intelligent Locations,the productivity of plants in the targeted regions are, on average, significantly lower at the
beginning of policies, though the premium turns to be positive in later years. Thus, thisadditional regression result rather strengthens our previous conclusion.
(Table 4b)
Next, the estimation results for the region-level difference-in-difference regression (2) are
shown in Table 5. The time-invariant targeted region dummy (β1) is significantly positive inboth Technopolis and Intelligent Locations, confirming our previous finding that policies appear
to pick up high average productivity regions where productive plants had already located. While
the significantly positive coefficient is found in Intelligent Locations, the timing-dependent
policy dummy (β2) tends to be weakly negative and not statistically significant in Technopolis,suggesting no significant impact of policy on the productivity of targeted regions after thepolicy is implemented. This additional region-level result largely confirms the plant-level results
explained in previous paragraphs.
(Table 5)5.2. Matching results

19Our main interest is to estimate the impact of regional policy programs directly. Table 6
reports ATT in the matching result. To discuss the periods before and after policy impact, weestimate ATT before starting policies in some programs in which treated groups are firms inregions to be targeted in the future but not yet subsidized. We note that balance tests pass in all
independent variables used in the probit estimation in each policy program (See Appendix 1 fora representative result).
As a result, almost all the cases have significantly negative values. Among them, in the
Industrial Relocation program, the plants in moving-out regions are significantly moreproductive and the comparable plants in the inducement regions are significantly less productiveat any point in time. Even if we distinguish the inducement regions listed by law and the regions
actually subsidized, this clear result is confirmed. The signs remain unchanged over time. These
indicate that the policy program cannot effectively work for high-productive firms to relocatefrom moving-out to inducement regions, although the number of firms increases in the latter.
Concerning the Technopolis and Intelligent Location programs, values are all significantly
negative for entire periods. Even after starting programs, the negative values remain unchanged.
Thus, as in the case of the Industrial Relocation program, the policy impact of these programs isnil. Therefore, after controlling for the plant characteristics, we can conclude that all regional
policy programs fail to attract significantly high-productivity plants.20(Table 6)
Endogeneity might, however, affect the results given in Table 6. It is possible that the
government intentionally subsidizes lower wage regions as a policy response to cross-regional
income inequality. On the other hand, the plants in high-wage sectors are likely to locate their
As a robustness check, we concentrate on the machinery sector, which was targeted in theTechnopolis and Intelligent Location policy programs. The estimates from plants only in themachinery sectors confirm our results from all sectors. The estimation results are in Appendix 2.
2020

plants in these regions to save labor costs. The low-wage regions, which are likely to be
subsidized, attract high-wage sectors, but not necessarily because of the subsidy. To control forthis possible bias, the treatment probability by the probit estimation now takes into account not
only plant characteristics X  ( Size, Mat , Labor ) with a two-digit sector dummy, as in Table 6,
but also the average wage in the region (city/town/village) where the plant is located and theaverage wage in the four-digit industry to which the plant belongs. The probit estimation results
show that the average wage in the region is significantly negative and the average wage of theindustry is significantly positive, as expected, in all programs in all years.21(Table 7)
Using revised propensity scores, ATTs are newly estimated, as reported in Table 7. While
treatment effects are smaller, most ATTs remain significantly negative. Thus, we confirm thatthe plants in subsidized regions tend to have lower productivity, even after considering thispossible endogeneity.
As additional robustness check inspired by the regression discontinuity design, we also
compare the targeted regions and their adjacent (non-subsidized) regions. Since regions locatedadjacently are likely to share various unobservable geographical factors, the location decisionsof plants may be affected by common factors. To isolate the policy effect from theseunobservable, possibly geographic, factors common to nearby regions, we compare targeted
regions with their neighbors. Different from the treatment effect estimated in Table 6, thisrobustness check restricts the non-treated group only to the regions directly adjacent to thosethat are targeted.
(Table 8)

21

Owing to space constraints, we only report the results for 1980, 1985, and 1990 and omit otheryears. The probit results have also been omitted. The results will be available upon request.
21

The results reported in Table 8 basically confirm the main findings of the previous section,although the statistical significance varies depending on the policy program. In general, even if
we compare neighboring areas, the plants located in targeted regions tend to be significantly lessproductive than or insignificantly different from the plants located in other regions. The
Industrial Relocation program shows significantly negative effects in the whole period, whereas
the effects of Coal Mining Areas and Science Cities are not significant. In comparison, theplants in Technopolis or in Intelligent Locations are significantly or insignificantly positive.
Here, we need to interpret carefully the results from the matching with only those of theadjacent regions. Interestingly, after starting policy programs, the treatment effect tends to be
insignificant. This indicates two possibilities: (1) targeted regions attract more low-productivity
firms, thus lowering the productivity gap with neighboring regions or (2) relocation to theseregions might induce some related production in adjacent regions (e.g., parts and components).
This spillover effect or backward and forward linkages might reduce the productivity gap
between targeted and its adjacent regions.

6. Concluding remarks
The results reported in this paper are generally supportive of the theoretical prediction by
Baldwin and Okubo (2006). All policy programs in Japan can successfully attract firms, both innumber and share of plants, to targeted regions (Table 1). This may contribute to diversification
across Japanese regions, as the Gini coefficient has declined over time (Figure 1). However, as
shown in Table 4, plants located in targeted regions have lower average productivity, althoughproductive plants were active in the areas subsidized in Technopolis and Intelligent Locations
even before the start of these programs. The theory predicts that lower productivity firms arelikely to relocate to the targeted regions. Once we use the matching technique and compare a22

pair of firms in targeted regions and non-targeted regions, all regional policies are found to
attract lower productivity firms. We have to be careful about the relationship between the resultsof OLS and matching. The OLS regression discusses average productivity without the causality
issue, while the propensity score-matching technique singles out pairs and directly compares
their productivity without using unmatched samples in non-targeted regions.
Although we have found negative effects of relocation policy on plant productivity, thispaper is not intended to deny the role of policy in shaping economic geography. The policy
programs examined in this paper actually result in the location of more plants in targeted
regions, though the productivity of relocated plants tends to be low. Since the highunemployment rate in undeveloped regions has been a serious policy issue in many mature
countries, the activated relocations of plants should not be negatively evaluated. The variety ofpolicy schemes is another issue left for future analyses. “The reality is that besides place-based
incentives, governments have far more potent instruments for integration. They can build
institutions that unify all places and put in place infrastructure that connects some places toothers” (World Bank, 2009, p.xxiii). Even within our sample, various policy measurers support
targeted regions, not necessarily by subsidy. A more detailed analysis that distinguishes
individual support schemes will enrich our results in the future.

Acknowledgement
The authors acknowledge valuable comments from the participants of seminars andconferences. This research is partly financed by Grant-in-Aid for Scientific Research andJapan’s Research Institute of Economy, Trade, and Industry (RIETI). The access to official
micro-data was arranged by RIETI. Officials at the Ministry of Economy, Trade and Industry(METI), especially Yuji Hosoya, helped us collect historical documents on relocation policies.
23Views expressed in this paper are not those of RIETI or METI.

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25

Figure 1: Gini Coefficient

0.56
0.54
0.52

plantsemployees
value added

0.48
0.46
0.44
0.42

Year

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

0.4
1979

Index

0.5

Table 1: Basic Statistic
Year

Variable1978 PROD
SIZE
LABOR
MAT
1980 PROD
SIZE
LABOR
MAT
1983 PROD
SIZE
LABOR
MAT
1985 PROD
SIZE
LABOR
MAT
1988 PROD
SIZE
LABOR
MAT
1990 PROD
SIZE
LABOR
MAT

Observation Mean
Std. Dev.
Min
Max
48040
5.844501
1.059940
10.59039
48040
3.316469
1.280155
1.386294
9.78120748040
0.2345804
0.1613151
03.793714
48040
0.3865969
0.17278
05.542666
48559
5.979857
1.116493
010.81911
48559
3.310036
1.272666
1.386294
9.795791
48559
0.2255405
0.1594183
02.939515
48559
0.3944476
0.1736848
03.026459
53655
6.051981
1.130118
011.66635
53655
3.2761941.264586
1.386294
10.05096
53655
0.238533
0.1669842
03.813525
53655
0.3843739
0.1739884
02.918972
57942
6.138449
1.120561
011.59249
57942
3.31352
1.244825
1.386294
10.109257942
0.2389843
0.165001
04.167595
57942
0.3820283
0.1736199
04.973279
61726
6.2372051.125369011.79964
61726
3.302714
1.230873
1.386294
9.922604
61726
0.2429936
0.1755346
08.3984161726
0.3713824
0.1767701
011.35772
66093
6.347178
1.155394012.44145
66093
3.299687
1.21973
1.386294
9.943429
66093
0.2383255
0.1720533
07.920083
66093
0.3680061
0.1729084
03.832807

Table 2: Number of Firms in Targeted Areas
Number of firms located in the targeted areas
Industrial Relocation moving-out promotion area
inducement areas
Subsidized inducement area

Coal Mining Areas
Technopolis
Intelligent Location
Science City
Total

1978
10723
16839
3076
1263

1980
10377
17302
3175
1258

1983
11059
18958
3845
1430

1985
11847
20935
3623
1339
3423

234
48040

222
48559

233
53655

277
57942

Share of firms located in the targeted area in total Japan1978
Industrial Relocation moving-out promotion area 0.22321
inducement areas
0.35052
Subsidized inducement area 0.06403
Coal Mining Areas
0.026291
Technopolis
Intelligent Location
Science City
0.004871

1980
0.213699
0.356309
0.065384
0.025907

1988
11662
22839
11077
1375
6210
536
285
61726

1990
12187
24746
8477
1455
4053
6159
295
66093

1983
1985
1988
1990
0.206113 0.204463 0.188932 0.184392
0.353331 0.36131 0.370006 0.374412
0.071662 0.062528 0.179454 0.128259
0.026652 0.023109 0.022276 0.022014
0.059076 0.100606 0.061323
0.008684 0.093187
0.004572 0.004343 0.004781 0.004617 0.004463

Table 3: Productivity Distribution
Technopolis
1980
Non-targeted regions
Targeted Regions
VA
Freq.
Percent Cum.
Freq.
Percent Cum.
Cum Gap
0
562
1.29
1.29
54
1.09
1.09
0.2
0.5
5
0.01
1.3
2
0.04
1.13
0.17
1
10
0.02
1.32
0
0
1.13
0.19
1.5
11
0.03
1.35
2
0.04
1.17
0.18
2
37
0.08
1.43
3
0.06
1.23
0.2
2.5
61
0.14
1.57
8
0.16
1.39
0.18
3
149
0.34
1.91
17
0.34
1.74
0.17
3.5
282
0.65
2.56
30
0.61
2.34
0.22
4
964
2.21
4.77
110
2.22
4.56
0.21
4.5
3,263
7.48
12.26
399
8.05
12.62
-0.36
5
5,770
13.23
25.49
751
15.16
27.78
-2.29
5.5
9,111
20.89
46.38
1,161
23.44
51.21
-4.83
6
10,754
24.66
71.04
1,138
22.97
74.18
-3.14
6.5
7,093
16.27
87.31
717
14.47
88.66
-1.35
7
3,340
7.66
94.97
322
6.5
95.16
-0.19
7.5
1,435
3.29
98.26
158
3.19
98.34
-0.08
8
503
1.15
99.42
55
1.11
99.45
-0.03
8.5
176
0.4
99.82
16
0.32
99.78
0.04
9
47
0.11
99.93
7
0.14
99.92
0.01
9.5
21
0.05
99.97
3
0.06
99.98
-0.01
10
10
0.02
100
0
0
99.98
0.02
10.5
1
0
100
1
0.02
100
0
11
11.5
12

Cumulative density of firm productivity distribution

1985
Non-targeted regions
Freq.
Percent Cum.
625
1.2
7
0.01
8
0.02
15
0.03
42
0.08
54
0.1
101
0.19
263
0.51
761
1.46
2,737
5.26
6,221
11.96
9,230
17.75
12,96524.93
10,303
19.81
5,164
9.93
2,209
4.25
869
1.67
289
0.56
92
0.18
32
0.06
12
0.02
3
0.01
2
0

Intelligent Location
1980
1985
Non-targeted regions
Targeted Regions
Non-targeted regions
VA
Freq.
Percent Cum.
Freq.
Percent Cum.
Cum Gap
Freq.
Percent Cum.
0
502
1.23
1.23
114
1.46
1.46
-0.23
575
1.18
0.5
6
0.01
1.25
1
0.01
1.48
-0.23
7
0.01
1
10
0.02
1.27
0
0
1.48
-0.21
9
0.02
1.5
9
0.02
1.29
4
0.05
1.53
-0.24
16
0.03
2
31
0.08
1.37
9
0.12
1.64
-0.27
39
0.08
2.5
58
0.14
1.51
11
0.14
1.79
-0.28
55
0.11
3
134
0.33
1.84
32
0.41
2.2
-0.36
95
0.2
3.5
260
0.64
2.48
52
0.67
2.86
-0.38
242
0.5
4
897
2.2
4.68
177
2.27
5.14
-0.46
713
1.47
4.5
3,029
7.43
12.11
633
8.13
13.27
-1.16
2,571
5.29
5
5,330
13.07
25.18
1,191
15.3
28.57
-3.39
5,775
11.88
5.5
8,537
20.94
46.11
1,735
22.29
50.86
-4.75
8,585
17.67
6
10,118
24.81
70.93
1,774
22.79
73.65
-2.72
12,076
24.85
6.5
6,686
16.4
87.33
1,124
14.44
88.09
-0.76
9,738
20.04
7
3,145
7.71
95.04
517
6.64
94.73
0.31
4,843
9.97
7.5
1,339
3.28
98.32
254
3.26
98
0.32
2,058
4.23
8
457
1.12
99.44
101
1.3
99.29
0.15
806
1.66
8.5
158
0.39
99.83
34
0.44
99.73
0.1
268
0.55
9
41
0.1
99.93
13
0.17
99.9
0.03
84
0.17
9.5
19
0.05
99.98
5
0.06
99.96
0.02
29
0.06
10
7
0.02
100
3
0.04
100
0
10
0.02
10.5
2
0
100
0
0
100
0
3
0.01
11
1
0
11.5
12
Note: "cum gap" denotes the gap of cumulative densities between non-targeted and targeted regions.
Note: freq denotes the number of firms

Targeted Regions
Freq.
Percent Cum.
Cum Gap
1.2
93
1.57
1.57
-0.37
1.22
0
0
1.57
-0.35
1.23
1
0.02
1.58
-0.35
1.26
2
0.03
1.62
-0.36
1.34
2
0.03
1.65
-0.31
1.44
11
0.19
1.84
-0.4
1.64
12
0.2
2.04
-0.4
2.14
30
0.51
2.54
-0.4
3.61
94
1.58
4.13
-0.52
8.87
348
5.86
9.99
-1.12
20.83
782
13.17
23.16
-2.33
38.58
1,228
20.68
43.84
-5.26
63.51
1,442
24.28
68.12
-4.61
83.32
1,038
17.48
85.6
-2.28
93.25
495
8.34
93.94
-0.69
97.5
215
3.62
97.56
-0.06
99.17
99
1.67
99.23
-0.06
99.73
33
0.56
99.78
-0.05
99.91
10
0.17
99.95
-0.04
99.97
3
0.05
100
-0.03
99.99
0
0
100
-0.01
100
0
0
100
0
100
0
0
100
0

Targeted Regions
Freq.
Percent Cum.
Cum Gap
1.18
143
1.53
1.53
-0.35
1.2
0
0
1.53
-0.33
1.22
0
0
1.53
-0.31
1.25
1
0.01
1.54
-0.29
1.33
5
0.05
1.59
-0.26
1.44
10
0.11
1.7
-0.26
1.64
18
0.19
1.89
-0.25
2.14
51
0.55
2.44
-0.3
3.6
142
1.52
3.96
-0.36
8.89
514
5.5
9.46
-0.57
20.78
1,228
13.14
22.6
-1.82
38.44
1,873
20.04
42.65
-4.21
63.29
2,331
24.95
67.59
-4.3
83.33
1,603
17.16
84.75
-1.42
93.29
816
8.73
93.48
-0.19
97.53
366
3.92
97.4
0.13
99.19
162
1.73
99.13
0.06
99.74
54
0.58
99.71
0.03
99.91
18
0.19
99.9
0.01
99.97
6
0.06
99.97
0
99.99
2
0.02
99.99
0
100
1
0.01
100
0
100
0
0
100
0

1990
Non-targeted regions
Targeted Regions
Freq.
Percent Cum.
Freq.
Percent Cum.
Cum Gap
751
1.27
1.27
93
1.32
1.32
-0.05
1
0
1.27
0
0
1.32
-0.05
7
0.01
1.29
2
0.03
1.35
-0.06
14
0.02
1.31
5
0.07
1.42
-0.11
31
0.05
1.36
11
0.16
1.58
-0.22
64
0.11
1.47
5
0.07
1.65
-0.18
117
0.2
1.67
21
0.3
1.95
-0.28
199
0.34
2
23
0.33
2.28
-0.28
478
0.81
2.81
76
1.08
3.36
-0.55
1,952
3.31
6.12
270
3.84
7.2
-1.08
5,569
9.43
15.55
749
10.65
17.85
-2.3
8,928
15.12
30.66
1,256
17.86
35.71
-5.05
12,603
21.34
52
1,444
20.53
56.24
-4.24
13,878
23.5
75.5
1,569
22.31
78.56
-3.06
8,421
14.26
89.76
855
12.16
90.71
-0.95
3,703
6.27
96.03
410
5.83
96.54
-0.51
1,511
2.56
98.59
144
2.05
98.59
0
537
0.91
99.5
63
0.9
99.49
0.01
203
0.34
99.84
25
0.36
99.84
0
65
0.11
99.95
8
0.11
99.96
-0.01
14
0.02
99.97
3
0.04
100
-0.03
9
0.02
99.99
0
0
100
-0.01
4
0.01
100
0
0
100
0
1
0
100
0
0
1
0
100
0
0

1990
Non-targeted regions
Targeted Regions
Freq.
Percent Cum.
Freq.
Percent Cum.
Cum Gap
673
1.22
1.22
171
1.56
1.56
-0.34
1
0
1.22
0
0
1.56
-0.34
7
0.01
1.23
2
0.02
1.58
-0.35
16
0.03
1.26
3
0.03
1.61
-0.35
34
0.06
1.33
8
0.07
1.68
-0.35
58
0.11
1.43
11
0.1
1.78
-0.35
111
0.2
1.63
27
0.25
2.03
-0.4
180
0.33
1.96
42
0.38
2.41
-0.45
446
0.81
2.77
108
0.99
3.4
-0.63
1,866
3.38
6.15
356
3.25
6.65
-0.5
5,167
9.37
15.52
1,151
10.51
17.16
-1.64
8,268
14.99
30.51
1,916
17.5
34.67
-4.16
11,759
21.32
51.84
2,288
20.9
55.57
-3.73
13,067
23.7
75.53
2,380
21.74
77.31
-1.78
7,862
14.26
89.79
1,414
12.92
90.23
-0.44
3,458
6.27
96.06
655
5.98
96.21
-0.15
1,411
2.56
98.62
244
2.23
98.44
0.18
493
0.89
99.51
107
0.98
99.42
0.09
186
0.34
99.85
42
0.38
99.8
0.05
59
0.11
99.96
14
0.13
99.93
0.03
12
0.02
99.98
5
0.05
99.97
0.01
8
0.01
99.99
1
0.01
99.98
0.01
3
0.01
100
1
0.01
99.99
0.01
1
0
100
0
0
99.99
0.01
0
0
100
1
0.01
100
0

Table 4a: OLS Results 1

Note: **: 5 % significance, *: 10% significanceTechnopolis

1978
0.0984Size

0.0814Labor-4.6120

Mat-3.1119-128.24 **-135.46 **-125.26 **-101.35 **Number of Obs
R-squaredF

48040
0.48571647.2548559
0.48621674.2353655
0.48151827.2357942
0.45721707.8661726
0.41581589.9166093
0.46852034.14Intelligent

1978
0.1244

1980
0.09891983
0.0684

1985
0.0952

1988
0.08881990
0.0875Size

0.0810Labor-4.6084

Mat-3.11147.62 **
27.87 **
-172.64 **-125.9 **9.74 **
27.74 **
-172.57 **1980
0.0897
6.56 **

0.104733.74 **

-5.0068-173.51 **-3.3752

7.48 **

0.104433.64 **

-5.0050-173.45 **-3.3752

1983
0.02411.82 *

0.097932.58 **

-4.7411-182.07 **-3.36075.34 **

0.097732.5 **-4.7394-182.03 **-3.36041985
0.05434.23 **

0.086829.06 **

-4.4204-176.08 **-2.9659

7.58 **

0.086628.98 **

-4.4192-176.09 **-2.96681988
0.0505
3.94 **

0.095331.43 **

-3.6172-159.32 **-2.2558

7.1 **

0.095131.37 **

-3.6150-159.25 **-2.25631990
0.0507
4.2 **

0.100334.67 **

-4.2644-189.48 **-2.9953-134.5 **7.41 **

0.100134.59 **

-4.2637-189.49 **-2.9972-125.94 **-128.26 **-135.49 **-125.34 **-101.41 **-134.61 **Number of Obs
R-squaredF

48040
0.48531649.9848559
0.48641675.1953655
0.4817
1829.157942
0.45761710.5661726
0.41611592.266093
0.46882036.72

Moving-out

1978
0.12121980
0.13651983
0.1457

1985
0.09001988
0.11201990
0.0976Size

0.0827Labor-4.6146

Mat-3.1129

9.56 **
28.31 **
-172.78 **10.26 **

0.106134.17 **

-5.0127-173.81 **-3.3767

11.58 **

0.099433.09 **

-4.7440-182.39 **-3.3616

7.3 **

0.088229.47 **

-4.4242-176.26 **-2.9660

8.89 **

0.097131.99 **

-3.6197-159.51 **-2.25648.14 **

0.101835.14 **

-4.2680-189.68 **-2.9959-125.98 **-128.38 **-135.67 **-125.31 **-101.44 **-134.57 **Number of Obs
R-squared
F

48040
0.48611649.7248559
0.48691678.8553655
0.48281836.9257942
0.45761710.2761726
0.41641594.0966093
0.46892037.51

Inducement

1978-0.14871980-0.14011983-0.11701985-0.13141988-0.14101990-0.1170Size

0.0802Labor-4.6059Mat-3.1141-12.5 **
27.5 **-172.57 **Number of Obs
R-squaredF-11.23 **0.103633.41 **

-5.0024-173.48 **-3.3780-9.64 **

0.097332.38 **

-4.7389-182.13 **-3.3636-11.01 **0.086228.85 **

-4.4166-176.07 **-2.9673-11.88 **0.094831.28 **

-3.6156-159.41 **-2.2606-10.44 **0.099934.54 **

-4.2642-189.6 **-2.9988-126.12 **-128.45 **-135.69 **-125.43 **-101.66 **-134.73 **48040
0.48681654.5548559
0.48641680.4253655
0.4824
1833.8757942
0.4582
1714.8961726
0.41701598.1766093
0.46922040.46

Coal

1978
-0.0452
-1.31

0.23

Size

0.08140.1048Labor-4.6110

Mat-3.109627.87 **
-172.5 **1980
0.008533.75 **

-5.0082-173.48 **-3.37371983
0.10452.97 **

0.097932.57 **

-4.7412-182.08 **-3.35991985
0.06501.77 *

0.086929.09 **

-4.4213-176.08 **-2.96541988
0.0521
1.4

0.095431.47 **

-3.6176-159.32 **-2.25611990
0.0666
1.91 *

0.100534.72 **

-4.2649-189.48 **-2.9953-125.74 **-128.13 **-135.45 **-125.22 **-101.35 **-134.48 **Number of Obs
R-squaredF

48040
0.48511643.0648559
0.48581671.0253655
0.48151827.6457942
0.4571
1706.8661726
0.41571589.0266093
0.46842033.17

Science1978
0.02811980-0.09011983-0.0834

1985-0.1358

1988
0.00811990-0.0148

0.55

-1.63

-1.53

Size

0.08140.10480.0980Labor-4.6109Mat-3.1095

27.86 **
-172.49 **33.77 **

-5.0082-173.49 **-3.3734

32.61 **

-4.7413-182.07 **-3.3605-2.65 **

0.087129.13 **

-4.4207-176.08 **-2.9654

0.15

-0.29

0.09540.100531.47 **

-3.6174-159.31 **-2.2559

34.73 **

-4.2649-189.47 **-2.9954-125.74 **-128.12 **-135.46 **-125.23 **-101.35 **-134.48 **Number of Obs
R-squaredF

48040
0.48511642.9648559
0.48581671.2253655
0.48081827.1657942
0.45711707.1361726
0.41571588.8966093
0.46842032.93

Regional Hub

1978-0.01291980-0.00881983-0.00611985-0.01211988-0.0158

1990-0.0158

-1.37

-0.9

-0.65

-1.33

Size

0.08150.10480.09800.0870Labor-4.6110

Mat-3.109327.88 **
-172.5 **Number of Obs
R-squaredF

33.76 **

-5.0082-173.48 **-3.3733

32.59 **

-4.7413-182.07 **-3.3602-125.73 **-128.11 **-135.44 **

48040
0.48511643.0848559
0.48581671.0853655
0.48151827.0229.11 **

-4.4210-176.08 **-2.9649-125.2 **57942
0.4571
1706.77-1.74 **

0.095531.49 **

-3.6175-159.32 **-2.2557-1.84 **

0.100634.75 **

-4.2651-189.49 **-2.9954-101.34 **-134.49 **61726
0.41571589.0966093
0.46842033.15

Table 4b: OLS Results 2
1978

1980

Note: **: 5 % significance, *: 10% significanceTechnopolis (subsidized only)

1983-0.3406

Size

0.0980Labor-4.7415

Mat-3.3599-3.37 **
32.59 **
-182.1 **1985
0.0412
2.46 **

0.086929.09 **

-4.4207-176.08 **-2.9653

1988
0.04293.27 **

0.095331.45 **

-3.6173-159.32 **-2.2559

1990
0.0261
1.69 *

0.100434.7 **-4.2649-189.48 **-2.9954-135.45 **-125.22 **-101.36 **-134.49 **53655
0.4816
1827.8357942
0.45711707.06

61726
0.41581589.5966093
0.46842033.12

1983

1985

Inteligent (subsidized only)

1988-0.06941990
0.0768Size

0.0955Labor-3.6174Mat-2.2565

Number of Obs
R-squared
F

1978

1980

-1.43
31.49 **
-159.32 **Number of Obs
R-squaredF

Inducement(subsidized only)

1978-0.0192
-1.24

Size

0.0814Labor-4.6114Mat-3.109427.87 **
-172.5 **Number of Obs
R-squared
F

1980
0.0571
3.54 **

0.104733.73 **

-5.0075-173.48 **-3.37421983
0.01270.87

0.097932.58 **

-4.7409-182.03 **-3.36041985
0.03302.16 **

0.086929.08 **

-4.4203-176.05 **-2.9656

4.9 **

0.100434.68 **

-4.2644-189.49 **-2.9963-101.36 **-134.55 **61726
0.41571589.0266093
0.46862034.58

1988-0.0598

1990-0.0033-2.8 **

0.095531.49 **

-3.6176-159.33 **-2.2558-0.15

0.100534.73 **

-4.2649-189.46 **-2.9954-125.73 **-128.17 **-135.45 **-125.23 **-101.35 **-134.48 **48040
0.48431643.0548559
0.48591671.95

53655
0.48151827.0457942
0.4571
1706.9761726
0.41571589.466093
0.46842032.92

Table 5: Average Productivity Impact at Town/village Level
Technopolis

Inteligent

Coefficient t-value

Coefficient t-value

4.35 **

beta 10.0795beta 2-0.0019 -0.17

0.0839 6.35 **0.0283 2.95 **year dummies and constant are omitted
Dependent variables are average productivity at town and village level.
Sample: 1978, 1980, 1983, 1985, 1988, 1990
We regress with cluster option for town/village because firms within the same district might be correlated.

Table 6: ATT Results

Note: **: 5 % significance, *: 10% significanceIndustrial Relocation inducement areas

1978-0.30001

Subsidized areas-0.1946-20.86 **-6.86 **

1980
-0.2983-19.78 **-0.0649-2.22 **

-18.75 **-0.1616-5.91 **

-19.84 **-0.0972-3.32 **

-18.09 **-0.1946-11.79 **

0.26

0.46

-0.16

-2.75

-0.19

Technopolis-0.0878-0.081-0.1708-0.1197-0.0864

Intelligent Location-0.061-3.2 **

Science
Region-4.65 **

-7.28 **

-0.0927-4.77 **

-5.42 **

-0.0951-5.33 **

0.141

0.124

0.0486

0.0436

1.46

0.99

0.51

0.47

-0.163-0.1625-0.1521-0.1431-10.15 **moving-out-3.34 **

-0.1318

1988-0.24310.0128-0.0914-0.007

1985-0.2768

Coal Mining Area

-3.86 **

0.02151983-0.2694

0.309718.93 **

-9.46 **

0.298217.51 **

-9.53 **

0.303918.04 **

-9.34 **

0.254
15.46 **

-0.0094-4.14 **

-0.0904-5.29 **

0.2257
2.46 **-0.125-8.45 **

0.238614.64 **

1990
-0.2427-18.68 **-0.1558-8.05 **

-0.1425-2.88 **

-0.1058-5.1 **-0.0803-4.68 **

0.03090.35-0.1314-9 **0.240414.65 **

Note: "Inducement area" is defined as all regions specified in laws, while "Subsidized area" is defined as the regions which actually receive subsidy.

Table7: ATT Results and Endogeneity

Note: **: 5 % significance, *: 10% significanceIndustrial Relocation inducement areas

1980-0.10541

Subsidized areas

0.02262-0.0382

0.67

-1.09

-0.03529-0.15972-4.18 **

Coal Mining Area

-0.73

1985
-0.10702-4.49 **

-2.99 **

Technopolis-0.00904

Intelligent Location-0.05583

Science-0.07146-0.15111

-0.59

-1.42

-0.34
-2.4 **-0.05328-2.06 **

-0.06657-3.01 **

Table 8: ATT Results: Adjacent Regions
Industrial Relocationinducement

1978-0.2233

Subsidized-0.0886-10.68 **-3.74 **

1980
-0.1837-8.42 **

-0.1054-4.4 **-4.5 **-0.08904-3.66 **

-0.20684-3.94 **

-0.04749-1.88 *

-0.0411-1.91 **

-0.20742-2.05 **

Note: **: 5 % significance, *: 10% significance1983-0.1876-8.97 **

-0.0982-4.17 **

-10.36 **-0.0411-1.78 *

-10 **-0.051-1.7 *-10.55 **-0.0687-3.06 **

0.0915

Coal Mining Area

-0.0113
-0.15

-0.08

1.5

0.64

0.66

-1.08

Science0.1042-0.0915-0.2738-0.2134-0.1747-0.0889

0.81

-0.7

3.27 **

2 **-0.0058

2.3 **

0.02911.31

0.02670.13121990-0.1937

Intelligent Location

2.26 **

0.03971988-0.1865

0.10950.05820.08181985-0.205

Technopolis3.38 **

0.0783

1990-0.09908

3.51 **

0.03430.00790.29

0.0373

0.99

1.59

1.37

1.48

0.11980.04920.052-0.0842-2.87 **

-2.28 **

-2.07 **

-0.97

Appendix 1: Matching Results in a Representative Regression
Inducement subsidized area in 1990
Probit Regression
Coefficients z-valueSize
0.04438.01 **
Labor
0.1373
3.41 **
Mat-0.0649-1.56
sec12
0.71901.43
sec13
0 5936
0.5936
1 17
1.17
sec14
0.55391.1
sec15
0.60411.2
sec16
0.82411.63
sec17
0.30820.61
sec18
0.11260.22
sec19
0.07270.14
sec20
0.24790.49
sec21
0.75061.48
sec22
0.13320.26
sec23-0 0282-0.0282
-0 06
-0.06
sec24
0.37480.74
sec25
0.65131.29
sec26
0.28730.57
sec27
0.15560.31
sec28
0.23710.47
sec29
0.13110.26
sec30
0.35110.7
sec31-0.0433-0.09
sec32
0.2451
0.49
sec34
34
00.2382
2382
0.47
0 47
Const
-1.6737-3.32 **
R-squaredObs

0.0301
66093

Untreated Sample
Treated Sample

57616
8477

Balance test
Mean
Treated Control
t-testUnmatched
3.35283.29194.29 **
Matched3.35283.3524
0.02

Size
Labor

Unmatched
Matched

0.24455
024455
0.24455

0.23741023741
0.24494

3 57 **
3.57
-0.15

Mat

Unmatched
Matched

0.365310.365310.36840.36417

-1.54
0.41

Note: In the matched sample, each variables should have no significant
differences between treated and control groups.

Appendix 2: ATT Results in Machinery Sector
Technopolis

1978-0.1989

Intelligent Location-0.1365-4.35
4.35 **-3.99 **

Technopolis

1985-0.0398

Intelligent Location-0.1183-2.33 **
-3.64
3 64 **

1980-0.155-3.82
3.82 **-0.1821-5.5 **

1988
0.00570.11-0.0829-2.77
2 77 **

1983-0.2342-5.78
5.78 **-0.0973-2.84 **

1990
-0.1965-5.77 **

-0.0994-3.25
3 25 **