Do More Productive Firms Locate New Factories in More Productive Locations?
An Empirical Analysis Based on Panel Data of Japan’s Census of Manufactures

Kyoji Fukao(Hitotsubashi University, RIETI)
Kenta Ikeuchi(NISTEP)
YoungGak Kim(Senshu University)
Hyeog Ug Kwon
(Nihon University, RIETI)

Abstract
Using a Melitz-style model of heterogeneous firms, Baldwin and Okubo (2006) recently
presented a theoretical model in which self-sorting occurs and more productive factories choose to
locate in more productive areas. The model suggests that firm-specific factors and regional factors
affect each other through the endogeneity of location decisions. However, to date there are few
studies empirically testing this issue. Against this background, our aim is to examine the relationshipbetween firm and location-specific factors in location decisions using factory-level panel data fromJapan’s Census of Manufactures. We begin by estimating how much of the differences in factories’
TFP levels can be explained by firm and by location effects. The estimation results show that both
effects have a significant impact on the productivity level of a factory, and that the firm effects are
more important than the location effects. We also find a statistically significant negative correlation
between firm effects and location effects, and investigate what causes this relationship. One potential
explanation is that more productive firms may tend to set up new factories in less productive
locations such as rural areas, where factor prices such as land prices and wage rates are usually low,in order to benefit from low factor prices. To examine this, we estimate a mixed logit model oflocation choice. The results indicate that more productive firms indeed tend to set up new factories
in low-productivity locations, which is consistent with our hypothesis.
Key words: TFP, firm effects, location effects
JEL classification: R30, D24, O53

1. Introduction
The literature on productivity shows that there are large differences in the productivity offactories even in a narrowly defined, highly homogeneous industry (see Bartelsman and Doms,
2000). Many researchers on productivity have looked for firm- or factory-specific factors which may
be responsible for such productivity differences, such as human capital, capital vintage, and thecharacteristics of the firm (in terms of R&D, IT, FDI, exports, etc.) to which the factory belongs. On
the other hand, many researchers on regional economics have looked for regional factors which may
explain the differences in productivity among different areas, such as agglomeration effects due tolocal industry-specific knowledge spillovers and natural cost advantages (e.g., Ciccone and Hall,
1996, and Henderson, 2003). Both groups of researchers, however, usually fail to incorporate theperspective of the other group. The former does not take into account location factors, while thelatter usually does not fully take account of the characteristics of the firm to which a factory belongs.
Using a Melitz-style model of heterogeneous firms, Baldwin and Okubo (2006) recently
presented a theoretical model in which self-sorting occurs and more productive factories choose to
locate in more productive areas. Their result suggests that firm-specific factors and regional factors
affect each other through the endogeneity of location decisions.
Despite the importance of this issue, there are few empirical studies on this topic, probably
because of a lack of appropriate data.1 Against this background, our aim is to examine this issue
using factory-level panel data of Japan’s Census of Manufactures.2
First, we decompose factories’ TFP levels into firm effects, location effects, and factory-specificcharacteristics, such as size and age. Next, we investigate the characteristics of our estimated firm
effects and location effects by calculating the coefficients of correlation between these effects andseveral firm- and location-specific characteristics. Based on the estimated firm and location effects,
we examine how much of the total variation in TFP levels across factories can be explained by firm
effects and by location effects and test whether more productive firms tend to have factories in moreproductive locations. We also estimate location choice models and test whether more productive
firms tend to open up new factories in more productive locations.
The structure of the paper is as follows. The next section introduces our methodology for
estimating firm and location effects on TFP, explains data sources and the construction of variables,and presents our estimation results of firm and location effects. In Section 3, we examine thecharacteristics of the estimated firm and location effects by calculating the coefficients of correlation
1

Using start-up data of foreign-owned factories in the United States, Shaver and Flyer (2000) haveshown that productive foreign-owned firms tend to locate their activities in less productive regions.
Shaver and Flyer suggest that the reason is that they receive fewer net benefits from agglomeration andtechnology spillovers from other firms.
2
We were able to gain access to the micro data of the Census of Manufactures and the Basic Survey ofJapanese Business Structure and Activities as part of our research project on the “Firm and Industry Level
Analysis of Productivity” at the Research Institute of Economy, Trade and Industry (RIETI).

1

between these effects and various firm and location characteristics. In addition, we conduct ananalysis of variance in order to examine the relative importance of the two effects. In Section 4, wethen test whether firm and location effects are positively correlated. We also estimate models offirms’ location choices. Section 5 summarizes our results and discusses remaining issues to beinvestigated in future research.

2. Estimation of Firm and Location Effects on Factories’ TFP
We begin our examination of the role of location and firm effects on factory-level TFP bypresenting our methodology for measuring TFP and isolating the two effects from other,factory-specific effects. This is followed by an explanation of our data sources and the variables used.
Finally, this section presents our estimation results of firm and location effects.
Let us start with our methodology for estimating how much of the differences in factories’ TFPlevels can be explained by firm effects and how much by location effects. The TFP level in year t offactory i, which belongs to firm f and is located in location l, is assumed to be determined by thefollowing factors: factory age and size, firm effects (measured by a dummy for the firm to which thefactory belongs), location effects (measured by a location dummy at the city/ward/town/village
level), industry specific effects (measured by a dummy for the industry to which the factory belongs),
and year effects:

TFPi ,t  GAgei ,t , Scalei ,t , f i ,t , li ,t , t (1)Specifically, we estimate the following econometric model:

ln TFPi ,t   a1,t Agei ,t   s1,t Scalei ,t  l ,t DLl  li ,t     f ',t DFf   fi ,t     j ',t DI j '  ji ,t    t ' DYt ' t   Ri ,t
l

f

j

t'

(2)

where Agei,t is the age of factory i in year t, Scalei,t is the number of employees of this factory in yeart, li,t is the location of factory i, and fi,t denotes the firm to which factory i belongs in year t. DLl’ is adummy variable for location l’ which takes value one if the location of factory i, li, is equal to l’,otherwise this dummy variable takes value zero. Similarly, DFf ’ is a dummy variable for firm f’which takes value one if the firm to which factory i belongs, fi, t, is equal to f’, otherwise this dummy
variable takes value zero, DIj’ is a dummy variable for industry j’ which takes value one if theindustry to which factory i belongs, ji, t is equal to j’, otherwise this dummy variable takes value
2

zero,3 and DYt’ is a dummy variable for year t’ which takes value one if the observation year t isequal to t’, otherwise this dummy variable takes value zero. Ri,t is the residual term. The coefficient
βl’,t shows the location effect of location l’ in year t on factories’ TFP level, while the coefficient βf ’,tshows the firm effect of firm f’ in year t on factories’ TFP level.
When we estimate firm and location effects, we ran three-year rolling panel regression. For
example, we estimate values of year t using data of t-2, t-1, and t. We take this approach in order toavoid endogeneity problems in our estimation of a location choice model. We do not include
productivity data of newly set-up factories in our estimation of the firm and the location effects, bothof which are used in the estimation of a location choice model.
We estimate firm and location effects using equation (2). In order to take account of thepossibility that firm and location effects may differ across industries, we estimated equation (2)separately for the following six manufacturing subsectors, as shown Table 1: materials, chemicals,
general machinery, electric machinery, transportation machinery, and miscellaneous products.

Insert Table 1

We calculate the relative TFP level of each factory vis-à-vis the industry average TFP level.
Following Good, Nadiri, and Sickles (1997), we measure the TFP level of factory i in year t in acertain industry in comparison with the TFP level of a hypothetical representative factory in year t inthat industry using the following equation:N1
ln TFPi ,t  (ln Qi ,t  ln Qt )   ( sn,i ,t  sn,t )(ln X n,i ,t  ln X n,t )
n1 2

(3)

where Qi,t is the gross output of factory i in year t, sn,i,t is the cost share of the n-th input, and Xn,i,t isthe amount of the n-th input at factory i in year t. Variables with upper bars denote the arithmetic
mean of each variable over all factories in that industry in year t. Three inputs, labor, capital, andintermediate input, are taken into account in our analysis.
The main data source for this paper is the longitudinal data of the Census of Manufactures
conducted by the Ministry of Economy, Trade and Industry (METI). This census covers all
manufacturing factories with four or more employees.4 Since 1997, the data include information on
factories’ affiliation, so that we can group factories according to their parent firms, although the data
3

Industry dummy variables are based on JIP industry classification which divides the whole
manufacturing sector into 52 subsectors.
4
Factories with three or fewer employees are included in specific years, starting with the 1981 survey
and then in years ending with 0, 3, 5, and 8.

3

do not include detailed information on parent firms. We used data for the period of 1997-2007.
Gross output is measured as the sum of shipments, revenues from repairing and fixing services,and revenues from performing subcontracted work. Intermediate inputs are defined as the sum of
raw materials, fuel, electricity and subcontracting expenses for consigned production used by theplant. Using industry level price deflators taken from the Japan Industrial Productivity Database(JIP) 2010, Gross output and intermediate inputs are converted to values in constant prices of 2000.
Following preceding studies using micro data of the Census of Manufactures, we measure labor
input in terms of man-hours, calculated as the product of the number of employees and the industry
average of annual working hours. The underlying assumption is that the labor service provided by
one hour of work is the same for all workers in a particular industry.5
Capital input is measured as real capital stock, which is defined as the product of the nominal
book value of tangible fixed assets (taken from the Census of Manufactures) and the book-to-market
value ratio for each industry, which is calculated using industry level investment data and the bookvalue of industry-level capital stock from the Census of Manufactures.
Labor costs are defined as total salaries and intermediate costs are defined as the sum of raw
materials, fuel, electricity and subcontracting expenses for consigned production provided in the
Census of Manufactures, respectively. Capital costs are calculated by multiplying the real net capitalstock with the user cost of capital. The latter is estimated as follows:

ck 

p
1 z
p k {(1  u )i   j  ( k )}1 u
pk

where pk, i, δ, u and z are the price of investment goods, the interest rate, the depreciation rate, thecorporate tax rate, and the present value of depreciation deduction on a unit of nominal investment,
respectively. Data on investment goods prices, interest rates, and corporate tax rates are taken fromthe JIP 2010, the Bank of Japan and the Ministry of Finance Statistics Monthly. The depreciation rate
for each sector is taken from the JIP 2010. We measure the cost share of each factor by dividing thecosts of each factor by total costs, which is the sum of labor costs, intermediate input costs, andcapital costs. Figure 1 plots sectoral average values of TFP level over the 11-year period 1997-2007.
It shows that the largest increases in relative TFP level occurred in electrical machinery.

Insert Figure 1

5

We should note that if the labor service provided by one hour of work differs across regions, ourestimates of location effects will be biased. For example, factories in a certain ward of the Tokyo
metropolitan area might employ more skilled workers than factories in other areas. If we do not takeaccount of this difference and measure labor input by man-hours, we will overestimate the TFP level offactories in this ward.

4In the case of independent factories, we cannot decompose the TFP level into the firm effectand the location effect. Similarly, in the case of locations with just one factory for the wholeobservation period, we cannot decompose the TFP of this factory into the firm effect and the location
effect. In the case of locations with only a small number of factories, or in the case of firms that only
have a small number of factories, our estimates of firm and location effects are likely to be unreliable.
To obtain reliable results, we excluded observations of factories belonging to firms with fewer than
three observations or observations of factories located in cities/wards/towns/villages with fewer than
three observations. We should note that our approach is not free from sample selection bias
problems.
Table 2 shows total number of factories, number of factories, of which TFP were measured, andnumber of factories, of which TFP data used for measuring firm effects and location effects for eachyear. The original data set for this paper consists of 3,786,975 (factory times year) observations. We
can calculate the TFP level for 909,005 observations. We cannot derive the TFP level for the other
2,877,970 observations (most of them are for small factories) mainly because of the absence of
information on capital stocks.6 The number of observations used for the estimation is 189,270. Table
3 shows total number of cities/wards/towns/villages and number of cities/wards/towns/villages, ofwhich location effects are estimated.

Insert Tables 2 and 3

Table 4 shows the descriptive statistics of this estimation. 51,130 location effects and 119,636
firm effects were estimated, and the standard deviations of firm effects and of location effects arealmost of the same size.

Insert Table 4

3. The Characteristics of Firm and Location Effects and Their Relative ImportanceIn order to examine the characteristics of the estimated firm and location effects, we calculated
the coefficients of correlation between these effects and several firm- and location-specificcharacteristics. The results are shown in Tables 5 and 6. In Table 5, correlation coefficients arecalculated across firms, while in Table 6, correlation coefficients are calculated across locations.

6This problem is more serious for years after 2001. The reason is that from 2001, the Census of
Manufactures stopped collecting capital stock data for factories with 29 or fewer employees in
non-benchmark years.

5Insert Tables 5 and 6

Starting with the correlation between firm effects and other firm characteristics (Table 5), we
find a positive correlation between firm effects and the average TFP level of the firm. 7 Firm effects
are also positively correlated with factories’ gross output as well as the number of factories a firmowns. That is, larger firms tend to generate larger positive firm effects. These results are consistent
with Adams and Jaffe (1996) which found a strong correlation between plant-level productivity andthe number of plants of a firm.
One caveat with regard to these results, however, is that by relying on the Census of
Manufactures, which only provides information on manufacturing establishments, the calculation offirms’ TFP level does not fully cover firms’ headquarter activities and non-manufacturing activities.
Thus, in order to examine the correlation with the TFP level of firms’ total activities, we also
calculated firms’ TFP level using micro-data from the Basic Survey of Japanese Business Structure
and Activities. Again we find a positive correlation between these two variables.
Turning to the correlations between location effects and other location characteristics (Table 6),
we find that location effects are positively correlated with weighted average of TFP level of all thefactories in the same location. 8
In order to measure congestion effects (that is, negative effects of economic agglomeration), we
prepared data of two variables; wage premiums and land prices. We get land prices from theChiiki-keizai-deta CD-ROM (Regional Economy Data CD-ROM) published by Toyo Keizai. We use
regional wage premiums obtained by Daiji Kawaguchi and Ryo Kambayashi based on micro data of
the Basic Survey on Wage Structure as part of their background analysis for a recent study(Kawaguchi and Kambayashi, 2009).9
We find that location effects are positively correlated with the regional wage premium, and the7

We calculate the average log value of the TFP of firm f’ in year t, lnTFPf’,t, as a weighted average of thelog value of the TFP level of all the establishments which belong to this firm:ln TFPf ,t salesi ,tf (i )  f 

 salesf (i )  f 

ln TFPi ,ti , t8

We calculate the average TFP of location l’, lnTFPl’,t, as a weighted average of the log values of the TFPlevel of all the factories located in this location:ln TFPl ,t  l (i ) l 

salesi ,t salesl (i) l 

ln TFPi ,ti , twhere salesi, t denotes the total sales of factory i in year t.
9
Kawaguchi and Kambayashi estimated regional wage premiums by estimating a Mincer-type wage
function with each worker’s educational attainment, work experience (defined as age minus years ofeducation minus 6), tenure, quadratic terms of work experience and tenure, factory size, city dummies,
and industry dummies as explanatory variables, using all the survey data for full-time male workers
across all industries and for all of Japan.

6average land price of that location. That is, location effects tend to be greater in areas with higher
wage rates and higher land prices.
Next, in order to examine the relationship between location effects and industry agglomeration,
we prepared three sets of indices of industry agglomeration for each subsector of each location; total
shipment of subsector j in location l, total number of factories in subsector j of location l, and densityof industry agglomeration ((total shipment of subsector j in location l)/(square kilometers of locationl)).
We find a positive correlation between location effects and the three indices of industry
agglomeration. It seems that industry agglomeration has a positive effect on factories’ productivity.
To sum up our results, both the estimated firm effects and the location effects have plausible
characteristics. For example, larger firms tend to generate larger positive firm effects, and firm
effects are positively correlated with the average TFP level of all the factories of this firm. Location
effects are positively correlated with the average TFP level of all factories in a particular location,and location effects tend to be greater for locations with higher industry agglomeration level.
The next question we address is how much of the total variation in TFP levels across factories
can be explained by firm effects and how much by location effects. In order to answer this question,
we conduct an analysis of variance (ANOVA). ANOVA provides a measure of the fit of theregression of the contribution of each variable by measuring how well the variation in eachindependent variable predicts the variation in the dependent variable.
The ANOVA results are shown in Table 7. In the table, “partial sum of squared deviations”
denotes how much the variation of each variable contributes to the total variation (total sum ofsquared deviations) of the dependent variable, that is, each factory’s TFP level. The results show that,in all six manufacturing subsectors, both location and firm effects are important in explaining
factories’ productivity level. About 40-50 percent of the total variation can be explained by these two
effects. The table also shows that in all the manufacturing subsectors, the partial sum of squared
deviations of the firm effects is greater than the partial sum of squared deviations of the location
effects. Thus, to which firm a factory belongs is a more important determinant of this factory’s TFPlevel than in which location this factory is located.

Insert Table 7

4. Do More Productive Firms Set Up Their Factories in More Productive Locations?
In this section, we examine the relationship between firm and location effects. Above, it wassuggested that firm effects on factory-level productivity have a positive correlation with firm size
and the average TFP level of all the factories of this firm. Similarly, location effects have a positive7

correlation with the average TFP level of all the factories in a particular location and the location’s
industry agglomeration. This raises the question whether “self-sorting” occurs in that moreproductive firms set up their factories in more productive locations.
However, before conducting our analysis of location decisions, we examine the static
correlation between firm and location effects across factories. The results are shown in Table 8 andin Figure 2, indicating that there is a statistically significant negative correlation between firm andlocation effects for all six manufacturing subsectors.

Insert Table 8 and Figure 2

What causes this negative relationship? One potential explanation is that more productive firms
may tend to set up new factories in less productive locations such as rural areas, where factor prices
such as land prices and wage rates are usually low, in order to benefit from low factor costs. On theother hand, less productive firms may be unable to locate new factories in rural areas because of
their inability to solve logistical problems, which are common in rural areas.
To determine the cause of the negative relationship, we calculate correlation coefficients acrossfactories between firm effects and location characteristics. We use two variables for location
characteristics: wage premiums, and land prices.10 Table 9 shows the results, which indicate that
there is no clear pattern suggesting that more productive firms tend to place their factories inlocations with low wage premiums or low land prices. While a negative and significant correlation
between firm effects and land price can be observed in the general machinery industry, thecorrelations between firm effects and wage premiums or land prices in all other subsectors are either
insignificant or actually significantly positive.

Insert Table 9

So far, our analysis has focused on factories of all ages. However, many of the factories in our
sample were set up a long time ago and the regional characteristics on which location decisions wereoriginally based may have changed since the establishment of these factories. In order to take this
into account, we also examine the correlation between firm effects for newly opened factories andregional characteristics in the year they were established.
To do so, we estimate the following mixed logit model of location choice:10

Examining the correlation between these variables across locations, we find a high positive correlation
between wage premiums and land prices.

8exp(   z f ,l ,t )Pr( y f ,t  l )  L exp(   z f ,l ,t )for l  1...L and t  1....Tl  1

where the left-hand side denotes the probability of observing the establishment of a factory in year t
by firm f in location l, zf,l,t denotes a vector including characteristics of location l and cross terms
between characteristics of firm f and characteristics of location l as its elements, and β is thecoefficient vector.
The number of newly opened factories, of which it is possible to calculate firm and location
effects, and their distribution over time are shown in Table 10. The numbers are not very large, partly
because Japan’s manufacturing sector has shrunk rapidly during this period and partly because wecan calculate both firm and location effects only for a fraction of newly established factories.

Insert Table 10

Correlation coefficients between the location variables used in the estimation are reported inTable 11. High correlation coefficients suggest that there is a risk of multi-collinearly problems.
Because of this risk, we do not use all the location variables within one equation.

Insert Table 11

The results of our estimation of the mixed logit model of location decision are presented inTable 12. Specification 1 represents the baseline estimation and as independent variables only
includes the location effect, one of the location variables and the number of factories of the same
firm in the same location. 11. The estimated coefficients on the location effect are positive in mostcases and significant. This result provides a strong evidence suggesting that firms tend to prefermore productive locations. Table 12.2 shows the same estimation results with the sample of themachinery industries (including General Machinery, Electric Machinery, and Transportation
Machinery), which is almost same to that of Table 12.1.

Insert Table 12.1 and Table 12.2

In the estimation in Table 13, we added a cross term of location effects and firm effects. The
estimated coefficients on the cross term of firm and location effects are negative and significant. This11

In all the estimation, we observe that the presence of factories of the same firm in the same prefecture
has very strong effects on the probability of the selection of that location.

9

result implies that more productive firms tend to be less attracted to more productive locations. Thisfinding is consistent with the results of our analysis of the correlation between firm and location
effects across factories. In Table 13 we also estimated a model with industry agglomeration and its
cross term with firm effect. The results show that industry agglomeration has positive impact on thelocation choice, and that it is more important for the productive firms in machinery industries.

Insert Table 13.1 and Table 13.2

As already mentioned, one possible explanation of the negative correlation between firm andlocation effects is that more productive firms tend to set up their factories in less productive
locations, such as rural areas, so as to exploit the lower factor prices there. Moreover, they can do so
because they are able to overcome the logistical problems that may be associated with locatingproduction in rural areas – something that typically smaller less productive firms cannot do. If thishypothesis is correct, high factor prices should have a negative effect on firms’ location decision. To
test this, we added factor price variables such as the regional wage premium and land prices to our
specification of Table 13.
The results are reported in Table 14. Against expectation, we do not find any statisticallysignificant negative effects of factor prices on location decisions in specifications 1 and 2 of Table 14.
One possible explanation for this result is that, because the equation does not include sufficientvariables to control for the positive effects of agglomeration, the estimate for the local wage
premium picks up these effects.
Another question of considerable interest is what causes location effects. As many studies in thefield of economic geography have argued, one potential factor is the industry agglomeration. To test
the importance of agglomeration effects for location decisions, we replaced the location effectvariable with industry agglomeration in our analysis of location choice. If more productive firms areless attracted by industry agglomeration, a negative coefficient for the cross term of firm effects andindustrial agglomeration is expected. The results are reported as specifications 3 and 4 in Table 14.
We find that industry agglomeration has a positive and statistically significant effect on location
decision in all industries. But we do not find negative and significant coefficient for the cross term offirm effects and industry agglomeration.
In specifications 5 and 6, we test another possibility that location effect and industry
agglomeration captures pretty different aspects of the attraction of locations by adding the industry
agglomeration to the location effects and its cross term with firm effects as an explanatory variable.
The table shows that controlling such regional merits as location effects estimated above, industry
agglomeration, and presence of factories of the same firm, factor prices such as wage premium andland price have negative effect on the choice. This result is consistent with our hypothesis.
10Insert Table 14.1 and 14.2

5. Conclusion
Using micro data of Japan’s Census of Manufactures, we decomposed the TFP level of eachfactory into firm effects, location effects, and other, factory specific factors such as size and age.
Both the estimated firm effects and the estimated location effects have plausible characteristics. Thatis, larger firms tend to generate larger positive firm effects. and firm effects are positively correlatedwith the average TFP level of all the factories of this firm. Location effects are positively correlatedwith the average TFP level of all factories in a particular location, and location effects tend to bepositively correlated with industrial agglomeration.
Based on the estimated firm and location effects, we also conducted an analysis of variance andfound that both location and firm effects play a role in explaining factories’ TFP levels. In addition,comparing the contribution of the two effects, it was found that both location and firm effects have astatistically significant and large influence on factories’ productivity, and firm effects were the more
important of the two.
Next, our attention turned to the correlation between firm and location effects, which was
negative. That is, more productive firms tended to have factories in cities with weaker location
effects. We hypothesized that the reason for this may be that more productive firms set up theirfactories in less productive locations such as rural areas to benefit from low factor prices there. To
test this hypothesis, we estimated a location choice model. Consistent with our hypothesis, theestimation results showed that location effects had a positive effect on location decisions and thecross term of firm and location effects a significant negative effect. However, against our expectation,
we did not find a negative and significant effect of factor prices, such as wage premiums and land
prices, on location choices.
These findings mean that we need to examine our estimates of firm and location effects further.
We found a positive and significant correlation between location effects and industry agglomeration,
but the correlation coefficient is not very large. When we replaced the location effect variable in our
location choice analysis with industry agglomeration, we found a positive and statistically significant
effect on location decisions. However, we did not find a negative and significant coefficient on the
cross term, that is, firm effects times industrial agglomeration. These results indicate that we still do
not understand well what local characteristics cause positive location effects.
We also tested another possibility that location effect and industry agglomeration captures
pretty different aspects of the attraction of locations by adding the industry agglomeration to thelocation effects and its cross term with firm effects as an explanatory variable. We found that11controlling such regional merits as location effects estimated above, industry agglomeration, andpresence of factories of the same firm, factor prices such as wage premium and land price havenegative effect on the choice. This result is consistent with our hypothesis.
In the case of firm effects, we found a positive and significant correlation between firm size andfirm effects, but we were unable to examine how other firm specific factors, such as research anddevelopment expenditures, information technology investment, or the accumulation of intangible
assets, affect firm effects, because of a lack of such data. We hope that we can investigate theseissues in the future by matching our micro data of the Census of Manufactures with other firm level
data.
There are also several additional issues that still need to be tackled. First, as we have alreadyhighlighted, our approach is not free from sample selection bias problems. And second, it is likelythat firms make decisions on the location of new factories in the context of their networks already inplace. These are factors that a more refined location choice model should address and we hope todevelop and test such a model in the future.

12References
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13

Table 1. Industry Classification
Industry classification of thispaper
8 Livestock products
6 Miscellaneous products
9 Seafood products
6 Miscellaneous products
10 Flour and grain mill products
6 Miscellaneous products
11 Miscellaneous foods and related products
6 Miscellaneous products
12 Prepared animal foods and organic fertilizers
6 Miscellaneous products
13 Beverages
6 Miscellaneous products
14 Tobacco
6 Miscellaneous products
15 Textile products
6 Miscellaneous products
16 Lumber and wood products
1 Material products
17 Furniture and fixtures
1 Material products
18 Pulp, paper, and coated and glazed paper
1 Material products
19 Paper products
1 Material products
20 Printing, plate making for printing and bookbinding
6 Miscellaneous products
21 Leather and leather products
1 Material products
22 Rubber products
1 Material products
23 Chemical fertilizers
2 Chemicals
24 Basic inorganic chemicals
2 Chemicals
25 Basic organic chemicals
2 Chemicals
26 Organic chemicals
2 Chemicals
27 Chemical fibers
2 Chemicals
28 Miscellaneous chemical products
2 Chemicals
29 Pharmaceutical products
2 Chemicals
30 Petroleum products
2 Chemicals
31 Coal products
2 Chemicals
32 Glass and its products
1 Material products
33 Cement and its products
1 Material products
34 Pottery
1 Material products
35 Miscellaneous ceramic, stone and clay products
1 Material products
36 Pig iron and crude steel
1 Material products
37 Miscellaneous iron and steel
1 Material products
38 Smelting and refining of non-ferrous metals
1 Material products
39 Non-ferrous metal products
1 Material products
40 Fabricated constructional and architectural metal products 1 Material products
41 Miscellaneous fabricated metal products
1 Material products
42 General industry machinery
3 General machinery
43 Special industry machinery
3 General machinery
44 Miscellaneous machinery
3 General machinery
45 Office and service industry machines
3 General machinery
46 Electrical generating, transmission, distribution and
4 Electric machineryindustrial apparatus
47 Household electric appliances
4 Electric machinery
48 Electronic data processing machines, digital and analog
4 Electric machinerycomputer equipment and accessories
49 Communication equipment
4 Electric machinery
50 Electronic equipment and electric measuring instruments 4 Electric machinery
51 Semiconductor devices and integrated circuits
4 Electric machinery
52 Electronic parts
4 Electric machinery
53 Miscellaneous electrical machinery equipment
4 Electric machinery
54 Motor vehicles
5 Transportation machinery
55 Motor vehicle parts and accessories
5 Transportation machinery
56 Other transportation equipment
5 Transportation machinery
57 Precision machinery & equipment
3 General machinery
58 Plastic products
6 Miscellaneous products
59 Miscellaneous manufacturing industries
6 Miscellaneous products
JIP industry classification

14

1.00

Figure 1. Industry average of lnTFP
0.80

0.60

Material products
Chemicals

General machinery
0.40

Electric machineryTransportation machinery
Miscellaneous products

0.20

0.00
1997

1999

2001

2003

-0.20
15

2005

2007

Table 2. Number of Observations

year

Total number offactories

1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total

358,246
373,713345,457
341,421316,267
290,848504,503
270,905
468,840
258,543
258,232
3,786,975

Number of factories, of
Number of factories, of which TFP data werewhich TFP wereused for measuring
measuredfirm effects andlocation effects
142,872
7,393
141,379
14,615
134,554
22,174130,432
22,094
43,597
17,592
41,657
16,81840,78016,52540,483
17,393
110,799
21,55340,837
15,16841,615
17,945909,005
189,270

16

Table 3. Number of Cities/Wards/Towns/Villages (Year=2005)

Prefecture
1 Hokkai-do
2 Aomori
3 Iwate4 Miyagi
5 Akita
6 Yamagata
7 Fukushima
8 Ibaragi
9 Tochigi
10 Gunma
11 Saitama
12 Chiba
13 Tokyo
14 Kanagawa
15 Niigata
16 Toyama
17 Ishikawa
18 Fukui
19 Yamanashi
20 Nagano
21 Gifu
22 Shizuoka
23 Aichi24 Mie25 Shiga
26 Kyoto
27 Osaka
28 Hyogo
29 Nara30 Wakayama
31 Tottori
32 Shimane
33 Okayama
34 Hiroshima
35 Yamaguchi
36 Tokushima
37 Kagawa
38 Ehime
39 Kouchi
40 Fukuoka
41 Saga
42 Nagasaki
43 Kumamoto
44 Oita
45 Miyazaki
46 Kagoshima
47 Okinawa
Total

Total number of cities,
Total number of cities,
wards, towns andwards, towns andvillages, of which TFPvillageswere measured
186
108
40
31
35
31
36
34
25
21
35
32
59
49
44
44
31
31
38
33
70
68
56
52
62
52
50
46
30
29
15
15
19
17
17
15
28
23
81
57
42
40
38
30
76
72
29
27
26
23
36
31
66
62
48
47
39
24
30
19
19
16
21
16
27
25
30
28
20
19
24
19
17
17
20
19
34
19
76
68
20
18
23
15
47
39
18
16
30
22
45
30
41
20
1,899
1,569
17

Table 4. Descriptive Statistics of Location Effects and Firm Effects（1）Location effects
Number ofobservation
Material products
11,588Chemicals
5,045
General machinery
8,476Electric machinery
8,827
Transportation machinery
5,090
Miscellaneous products
12,104
Industry

Total

51,130-0.005-0.0560.106-0.183-0.001-0.018

Standard
deviation
0.277
0.502
0.504
0.396
0.297
0.388

-0.025

0.404

Mean

Min

MedianMax-2.233-3.194-2.660-2.347-1.675-2.5290.000-0.0350.040-0.178
0.000
0.003

1.589
2.629
2.572
1.901
1.138
2.104

-3.194-0.012

2.629

MinMedianMax-1.917-2.509-2.789-2.536-1.326-3.257-0.0650.000
0.000-0.0840.000-0.069

2.208
3.404
3.754
2.032
1.445
2.573

-3.257-0.024

3.754（2）Firm effects

Number ofIndustry
observations
Material products
32,990Chemicals
8,263
General machinery
19,771
Electric machinery
17,737
Transportation machinery
8,752
Miscellaneous products
32,123
Total

119,636-0.0760.011-0.010-0.1350.001-0.080Standard
deviation
0.322
0.561
0.512
0.428
0.285
0.536

-0.063

0.454

Mean

18

Table 5. Correlation Coefficients between Firm Variables
Variables
12
3
1 Firm effects
1
0.134
1
2 ∑shipment of the firm
3 Number of employees
0.051
0.851
1
4 Number of factories of the firm
0.022
0.465
0.512
5 Weighted average of lnTFP of the firm
0.298
0.258
0.170
6 lnTFP derived from Basic Survey Data*
0.076
0.329
0.233
Note: 1. All the coefficients are statistically significant at 1% level.
2. Basic Survey is Basic Survey of Japanese Business Structure and Activities .
Table 6. Correlation Coefficients between Location Variables
Variables
12
3
1 Location effects (by location and by subsector)
12 ln(Total shipment of the same subsector in the location)
0.087
1
3 ln(Number of factories in the same subsector in the location)
0.081
0.637
1
4 Wage premium
0.059
0.265
0.158
5 ln(Land price of the city)
0.061
0.248
0.257
Weighted average of lnTFP of factories in the same subsector of
0.065
0.197
-0.0766the location
7 ln(Industry agglomeration)
0.088
0.855
0.579
Note: All the coefficients are statistically significant at 1% level.

19

4

5

6

1
0.040
-0.008

1
0.563

1

4

5

6

1
0.567

1

0.107

0.067

1

0.365

0.504

0.184

7

1

Table 7. Results of ANOVA
Partialusm ofModel
2,687.7
Age of the factory
2.8ln(number of employees)
3.8
Location
338.9
Firm
1,920.0
Industry (JIP)
7.6
Year
35.7
Residual
2,079.1
Total
4,766.8

F

Prob > F

Chemicals

0
0
0
0
0
0
0

Model
Age of the factoryln(number of employees)
Location
Firm
Industry (JIP)
Year
Residual
Total

1,626.9
1.8
0.2
363.9
940.5
10.3
11.1
696.3
2,323.2

1912
1
1
667
1225
8
10
14448
16360

0.851
1.781
0.208
0.546
0.768
1.285
1.108
0.048
0.142

17.7
37.0
4.3
11.3
15.9
26.7
23.0

0
0
0.037900
00General machinery

11.0
78.8
105.9
7.2
9.8
14.3
100.8

Model
Age of the factoryln(number of employees)
Location
Firm
Industry (JIP)
Year
Residual
Total

1,340.6
0.9
1.1
254.4
902.1
0.5
12.1
888.5
2,229.1

4169
1
1
1014
3139
4
10
28567
32736

0.322
0.872
1.115
0.251
0.287
0.114
1.208
0.031
0.068

10.3
28.0
35.9
8.1
9.2
3.7
38.8

0
0
0
0
0
0.00530Electric machinery

Meansquare
0.392
2.795
3.755
0.254
0.349
0.507
3.574
0.035
0.073

Model
Age of the factoryln(number of employees)
Location
Firm
Industry (JIP)
Year
Residual
Total

2,289.8
0.0
0.6
284.9
963.8
1.8
343.1
1,121.1
3,410.9

3935
1
1
1070
2846
7
10
29112
33047

0.582
0.017
0.564
0.266
0.339
0.257
34.306
0.039
0.103

15.1
0.4
14.7
6.9
8.8
6.7
890.8

0
0.51040.00010
0
0
0

Transportation
machinery

Degrees
of
6860
1
1
1335
5498
15
10
58610
65470

Model
Age of the factoryln(number of employees)
Location
Firm
Industry (JIP)
Year
Residual
Total

490.7
0.2
2.8
144.2
269.9
2.2
2.9
407.1
897.8

1906
1
1
655
1237
2
10
15268
17174

0.257
0.198
2.772
0.220
0.218
1.123
0.287
0.027
0.052

9.7
7.4
104.0
8.3
8.2
42.1
10.8

0
0.006400000

Miscellaneous
products

Material products

SourceModel
Age of the factoryln(number of employees)
Location
Firm
Industry (JIP)
Year
Residual
Total

4,134.1
6.1
4.4
415.3
2,605.0
2.9
4.6
1,912.1
6,046.1

6393
1
1
1380
4992
9
10
54904
61297

0.647
6.138
4.393
0.301
0.522
0.318
0.459
0.035
0.099

18.6
176.3
126.1
8.6
15.0
9.1
13.2

0
0
0
0
0
0
0

20

Table 8. Correlation Coefficients between Location Effects and Firm Effects
Industry

Correlation Coeffcients

Material products-0.438Chemicals-0.491

General machinery-0.644

Electric machinery-0.667

Transportation machinery-0.620Miscellaneous products-0.205

Note: All the coeffcients are statistically significant at 1% level.

21

Figure 2. Scatter Diagrams of Location Effects and Firm Effects

22

Table 9. Correlation Coefficients between Firm Effects and Location Variables
12

1 Firm effects

1

2 Wage premium

0.0189***3 Land price of the city

0.0120** 0.5709***

1 Firm effects

1

2 Wage premium

0.010

3 Land price of the city1

10.0171** 0.4963***

1 Firm effects

1

2 Wage premium

0.0111*3 Land price of the city

-0.0183***0.06145***

1

1

1 Firm effects

1

2 Wage premium

0.0483***3 Land price of the city

0.0316*** 0.6373***

1

Transportation
machinery1

1 Firm effects

1 Firm effects

1

2 Wage premium

0.0525***3 Land price of the city

0.1253*** 0.6517***

1

2 Wage premium

0.0156*3 Land price of the city

0.0585*** 0.5432***

23

3

1

Miscellaneous
products

Electric
machinery

Generalmachinery

Chemicals

Material
products

Variables

1

1
1

1
1

Table 10. Number of Observations of Factory Startups in Location Choice Estimation
Industry

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Total

Material products

113

44

44

34

20

36

36

62

50

87

526

Chemicals

23

11

15

10

13

10

9

10

6

24

131

General machinery

44

19

21

18

19

17

10

21

15

54

238

Electric machinery

50

26

12

21

31

30

35

21

39

29

294

Transportation machinery

23

6

11

2

11

13

25

17

28

36

172

Miscellaneous product

156

38

39

43

44

42

24

47

45

139

617

Total
409
144
142
128
138
148
139
178
183
Notes: In our estimation, we used data of new factories, for which both the location effect data
and the firm effect data are available. Therefore, the number of observations is much smaller than
the number of all the startups.

369

1,978

24

Table 11.1 Correlation Coefficient between Locatoin Variables: Manufacturing Sector
Variables

12

3

1 Location effects

1

2 Wage premium

0.075

1

3 ln(Land price of the city)

0.089

0.565

1

4 Indusrty Agglomeration

0.117

0.371

0.541

4

1

Table 11.2. Correlation Coefficient between Locatoin Variables: Machinery Sector(General Machinery, Electric Machinery, and Transportation Machinery)
1 Location effects

1

2 Wage premium

0.061

1

3 ln(Land price of the city)

0.058

0.582

1

4 Indusrty Agglomeration

0.073

0.391

0.470

Note．All the coeffcients are statistically significant at 1% level.

25

1Table 12.1 Results of Mixed Logit Estmation 1: Manufacturing Sector(1)
Location effect

(2)

(3)

0.146 **
(0.063)

Wage premium (t-1)

1.14 ***(0.153)ln(Land price of the city) (t-1)

Log-likelihood

Number of observations

(5)

(6)

0.122 *
(0.063)
1.13 ***(0.153)0.128 **
(0.064)0.169 ***
(0.025)

Indusrty agglomeration (t-1)
ln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared(4)

3.65 ***(0.068)0.105-1.26E+04
1,887,285

3.62 ***(0.068)0.107-1.25E+04
1,853,868

3.61 ***(0.069)0.107-1.22E+04
1,791,607(7)-0.0318(0.070)0.167 ***
(0.025)0.405 ***
(0.014)3.64 ***(0.071)0.14-1.12E+04
1,755,951

3.62 ***(0.068)0.107-1.25E+04
1,853,868

0.406 ***
(0.014)3.64 ***(0.071)
0.14

3.61 ***(0.069)0.107-1.22E+04
1,791,607-1.12E+04
1,755,951

Table12.2 Results of Mixed Logit Estmation 1: Machinery Sector (General Machinery, Electric Machinery,
and Transportation Machinery)(1)
Location effect

(2)

(3)

0.172 *
(0.098)Wage premium (t-1)

0.0844 *(0.045)

Indusrty agglomeration (t-1)

Log-likelihood

(5)
0.153
(0.098)0.988 ***
(0.273)

1 ***(0.272)ln(Land price of the city) (t-1)

ln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared(4)

4.04 ***(0.121)
0.132

4.03 ***(0.121)
0.133

3.97 ***(0.121)
0.131

(6)

(7)

0.156
(0.100)0.0968(0.106)0.0825 *(0.045)0.332 ***
(0.023)4.08 ***(0.129)0.163

4.03 ***(0.121)
0.134

3.97 ***(0.121)0.131

0.331 ***
(0.023)4.08 ***(0.129)0.163-4.17E+03-4.15E+03-4.05E+03-3.63E+03-4.15E+03-4.05E+03-3.63E+03

Number of observations
524,215
Note 1. Standard errors are in parentheses.
2. * p<.1, ** p<.05, *** and p<.01

520,626

499,568

470,761

520,626

499,568

470,76126

Table 13.1 Results of Mixed Logit Estmation 2: Manufacturing Sector

Location effect
Location effect*Firm effect

(1)

(2)

3.57 ***(0.230)-1.54 ***
(0.101)

3.44 ***(0.235)-1.49 ***
(0.103)

Indusrty agglomeration (t-1)
Indusrty agglomeration(t-1)*Firm effectln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared
Log-likelihood

Number of observations

0.008-1.39E+04
1,887,285

3.65 ***(0.068)0.113-1.25E+04
1,887,285

(3)

(4)

0.244 ***
(0.062)0.076 ***
(0.028)0.261 ***
(0.063)0.066 **
(0.028)3.64 ***(0.071)0.14

0.037-1.26E+04
1,755,951-1.12E+04
1,755,951

Table 13.2 Results of Mixed Logit Estmation 2: Machinery Sector (General Machinery,
Electric Machinery, and Transportation Machinery)

Location effect
Location effect*Firm effect

(1)

(2)

4.4 ***(0.346)-1.94 ***
(0.156)

4.26 ***(0.363)-1.9 ***(0.164)

Indusrty agglomeration (t-1)
Indusrty agglomeration(t-1)*Firm effectln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared

0.017

4.04 ***(0.122)
0.148

(3)

(4)

0.304 ***
(0.095)0.019(0.043)0.297 ***
(0.097)0.017(0.044)4.08 ***(0.129)
0.163

0.029

Log-likelihood-4.72E+03-4.09E+03-4.20E+03-3.63E+03

Number of observations
Note 1. Standard errors are in parentheses.
2. * p<.1, ** p<.05, *** and p<.01

524,215524,215

470,761

470,76127

Table 14.1 Results of Mixed Logit Estmation 3: Manufacturing Sector

Location effect
Location effect*Firm effect

(1)

(2)

3.41 ***(0.236)-1.49 ***
(0.104)3.42 ***(0.238)-1.49 ***
(0.104)

Indusrty agglomeration (t-1)

0.265 ***
(0.064)0.072 **
(0.028)-0.635 ***(0.183)

Indusrty agglomeration (t-1)・Firm effect
Wage premium (t-1)

1.12 ***(0.153)ln(Land price of the city)(t-1)
ln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared
Log-likelihood

Number of observations

3.62 ***(0.069)0.115-1.24E+04
1,853,868(3)0.166 ***
(0.025)
3.61 ***(0.069)0.115-1.21E+04
1,791,607

3.63 ***(0.071)0.141-1.12E+04
1,725,699(4)

0.326 ***
(0.064)0.063 **
(0.028)(5)(6)

3.66 ***(0.255)-1.66 ***
(0.111)0.427 ***
(0.015)

3.67 ***(0.259)-1.67 ***
(0.113)0.468 ***
(0.016)-0.656 ***(0.183)-0.247 ***(0.030)3.64 ***(0.071)0.144-1.09E+04
1,675,350

3.62 ***(0.071)0.148-1.11E+04
1,725,699-0.251 ***(0.030)3.63 ***(0.071)0.152-1.08E+04
1,675,350

Table 14.2 Results of Mixed Logit Estmation 3: Machinery Sector (General Machinery, Electric Machinery,
and Transportation Machinery)
Location effect
Location effect*Firm effect

(1)

(2)

4.24 ***(0.362)-1.89 ***
(0.164)4.28 ***(0.366)-1.9 ***(0.165)

Indusrty agglomeration (t-1)

0.316 ***
(0.097)0.016(0.044)-0.738 **(0.323)

Indusrty agglomeration (t-1)・Firm effect
Wage premium (t-1)

0.972 ***
(0.274)ln(Land price of the city)(t-1)
ln(1+Number of factory of the same firmin the same prefecture (t-1))
R-squared

4.02 ***(0.122)0.149(3)0.0827 *(0.045)
3.97 ***(0.122)0.147

4.07 ***(0.129)0.163(4)0.34 ***(0.098)0.017(0.044)(5)(6)

4.62 ***(0.393)-2.09 ***
(0.177)0.349 ***
(0.025)4.64 ***(0.401)-2.1 ***(0.180)0.375 ***
(0.025)-0.766 **(0.324)-0.245 ***(0.053)4.01 ***(0.128)0.164

4.04 ***(0.129)0.18-0.245 ***(0.054)3.98 ***(0.129)0.181

Log-likelihood-4.08E+03-3.98E+03-3.62E+03-3.52E+03-3.55E+03-3.44E+03

Number of observations
Note 1. Standard errors are in parentheses.
2. * p<.1, ** p<.05, *** and p<.01

520,626

499,568

467,610450,038

467,610450,03828