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)YoungGak Kim(JSPS, Hitotsubashi University)

HyeogUg Kwon
(Nihon University, RIETI)Paper prepared for SESSION 5b: Location Choice and Firm Performance (WG2)Comparative Analysis of Enterprise Data ConferenceTokyo
October 2009

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 effect ismore important than the location effect. We also find a statistically significant negative correlation
between firm effect and location effect, 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.

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’
TFP levels can be explained by firm effects and how much by location effects. The TFP level in year
t of factory 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/town/village level), andindustry specific effects (measured by a dummy for the industry to which the factory belongs):3

TFPi ,t = Gt ( Agei ,t , Scalei ,t , f i ,t , li , ji ,t )(1)

Specifically, we estimate the following econometric model:

ln TFPi ,t = β a1,t Agei ,t + β a 2,t Agei2,t + β s1,t Scalei ,t + β s 2,t Scalei2,t+ ∑ β l ′,t DLl ′ (li ) + ∑ β f ,t DF f ′ ( f i ,t ) + ∑ β j ′,t DI j ′ ( ji ,t ) + Ri ,t
l′

f′

j′

(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 is the location of factory i, and fi,t and ji, t denote the firm and the industry to which factory ibelongs in year t. DLl’ is a dummy variable for location l’ which takes value one if the location offactory i, li, is equal to l’, otherwise this dummy variable takes value zero. Similarly, DFf’ is adummy variable for firm f’ which takes value one if the firm to which factory i belongs, fi, t, is equal3

We use the industry classification of the Japan Industrial Productivity Database (JIP), which subdivides
the manufacturing sector into 52 industries.

2

to f’, otherwise this dummy variable takes value zero, and DIj’ is a dummy variable for industry j’which takes value one if the industry to which factory i belongs, ji, t is equal to j’, otherwise thisdummy variable takes value zero. Ri,t is the residual term. The coefficient βl’,t shows the location
effect of location l’ on factories’ TFP level, while the coefficient βf’,t shows the firm effect of firm f’
on factories’ TFP level.

We classify factories into six subsectors based on factories’ main output and calculate therelative 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 a certain industry incomparison with the TFP level of a hypothetical representative factory in year t in that 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) since 1909. This census covers
all manufacturing factories with four or more employees.4 Since 1996, the data include information
on factories’ affiliation, so that we can group factories according to their parent firms, although thedata do not include detailed information on parent firms. We used data for the period of 1996-2003.
Input and output deflators and rates of capital depreciation are taken from the Japan Industrial
Productivity Database (JIP).
Typically, studies using micro data of the Census of Manufactures measure labor input in termsof man-hours, calculated as the product of the number of employees and the industry average ofannual working hours. The underlying assumption is that the labor service provided by one hour ofwork is the same for all workers in a particular industry. However, if the labor service provided by
one hour of work differs across regions, our estimates of location effects will be biased. For example,
factories in a certain ward of the Tokyo metropolitan area might employ more skilled workers thanfactories in other areas. If we do not take account of this difference and measure labor input by
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

man-hours, we will overestimate the TFP level of factories in this ward.
We deal with this problem by measuring labor input using the total wage bill of a factory andadjusting this for regional wage premiums. Assuming that wage differentials between workers withdifferent characteristics reflect differences in their marginal productivity, the wage bill should
provide a good measure of labor input. At the same time, however, given that labor mobility in Japanis not perfect and there exist substantial regional wage premiums, it is important to take suchregional wage premiums into account to avoid underestimating labor input in regions where thewage premium is high. Therefore, we adjust the wage bill data using estimation results for 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 andKambayashi, 2009).5 Thus, we measure labor input as the total wage bill divided by the wage
premium for the city where the factory is located.6
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. Intermediate input iscalculated as the sum of the cost of manufacturing and selling, and general and administrative
expenses, minus wages and capital depreciation.
We assume that firm- and location-specific effects on productivity are persistent, and use athree-year rolling panel estimation to estimate equation (2). We will refer to estimated coefficients
based on observations from 1996 to 1998 as estimates for 1998, those based on observations from1997 to 1999 as estimates for 1999, etc. Overall, we have sets of estimates for 1998, 1999, 2000,
2001, 2002, and 2003.
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, we cannot decompose
the TFP of this factory into the firm effect and the location effect. In the case of locations with only asmall number of factories, or in the case of firms that only have a small number of factories, ourestimates 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 two factories or firms without
multiple locations (that is, firms whose factories all fall within the same location) in each rolling
panel. We also excluded observations of factories falling in locations with fewer than two factories
5

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
To check the robustness of our results, we also estimate factories’ TFP level measuring labor input
using man hours. However, the results remain largely unchanged.

4

in each rolling panel.7 We should note that our approach is not free from sample selection bias
problems.
We estimate the TFP level of each factory for each year using equation (3). The original data set
for this paper consists of 2,590,769 (factory times year) observations. We can calculate the TFP levelfor 736,947 observations. We cannot derive the TFP level for the other 1,853,822 observations (mostof them are for small factories) mainly because of the absence of information on capital stocks.8
Table 1 shows summary statistics for the measured TFP levels of factories.

Insert Table 1

Next, 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: materials, chemicals, general machinery,electric machinery, transportation machinery, and miscellaneous products. Table 2 shows the numberof observations used for the estimation and the number of locations and firms for which factories areincluded in the observations for each year. Because of the data-screening processes explained above,
the number of observations used for the estimation is substantially smaller than the total number offactories for which it was possible to calculate TFP levels. Table 3 shows the summary statistics ofthis estimation. 59,357 firm effects and 36,874 location effects were estimated, and the standard
deviations of firm effects and of location effects are almost of the same size.

Insert Tables 2 and 3

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 4 and 5. In Table 4, correlation coefficients arecalculated across firms, while in Table 5, correlation coefficients are calculated across locations.

Insert Tables 4 and 5

Starting with the correlation between firm effects and other firm characteristics (Table 4), we
7

We looked at each rolling panel separately and then determined which observations to eliminate.
This problem is more serious for years after 2000. The reason is that from 2000, the Census of
Manufactures stopped collecting capital stock data for factories with 29 or fewer employees in
non-benchmark years.

85

find a positive correlation between firm effects and the aggregated TFP level of the firm.9 Firm
effects are also positively correlated with factories’ gross output as well as the number of factories afirm owns. 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-levelproductivity and the 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 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 andActivities. Again we find a positive correlation between these two variables.
Turning to the correlations between location-specific effects and other location characteristics(Table 5), we find that location-specific effect are positively correlated with the average TFP level of
all factories in the same location.10 In addition, using micro-data from the Census of Manufactures
to calculate average wage rates and taking land prices from the Chiiki-keizai-deta CD-ROM(Regional Economy Data CD-ROM) published by Toyo Keizai, we find that location effects are also
positively correlated with the average wage, the regional wage premium, and the average land priceof 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 the agglomeration of
economic activities, we calculate the “manufacturing gravity” of each location, where this is definedas the distance-discounted aggregated sum of manufacturing activities for each location. To obtainthis, we first sum up the manufacturing gross output for each location and year using the micro dataof the Census of Manufactures, and then calculate the manufacturing gravity of location l byaggregating these values of all the locations weighted by the inverses of squares of the distances
between location l and all the other locations.11 That is, the manufacturing gravity of location l at
We calculate the aggregated log value of the TFP of firm f’ in year t, lnTFPf’,t, as a weighted average ofthe log value of the TFP level of all the establishments which belong to this firm:

9

ln TFPf ′,t =∑salesi ,tf (i ) = f ′∑ salesln TFPi ,ti ′, tf (i′) = f ′We calculate the aggregated TFP of location l’, lnTFPl’,t, as a weighted average of the log values of the
TFP level of all the factories located in this location:

10

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.
11
Probably it would be more appropriate to measure agglomeration effects taking into account all
economic activities, not just manufacturing activities. However, unfortunately, information on the output

6

time t, GDP gravityl,t, is calculated by the following equation:

1Y
2 ∑ i , l ′, tl ′ Distl ′, liGDP gravityl ,t = ∑(4)where Yi,l’,t is the gross output of factory i in location l’ at time t, and Distl’,l is the distance between
locations l’ and l. In the case where l’ is equal to l, the distance is calculated as two thirds of theradian of the hypothetical circular area whose square measure is the same as that of location l. We
find a positive correlation between location effects and manufacturing gravity in many industries. It
seems that the agglomeration of manufacturing activity 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 more manufacturing activity (manufacturing
gravity).
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 6. 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 6of all economic activities at the local level is not available.

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 positive
correlation with the average TFP level of all the factories in a particular location and the location’s
manufacturing gravity. This raises the question whether “self-sorting” occurs in that more productive
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 7 andindicate that there is a statistically significant negative correlation between firm and location effects
for all six manufacturing subsectors.

Insert Table 7

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 three variables for location
characteristics: manufacturing gravity, wage premiums, and land prices.12 Table 8 shows the results,
which indicate that there is no clear pattern suggesting that more productive firms tend to place theirfactories in locations with low wage premiums or low land prices. While a negative and significant
correlation between firm effects and wage premiums can be observed in the materials industry, thecorrelations between firm effects and wage premiums or land prices in all other subsectors (as wellas that between firm effects and land prices in the materials industry) are either insignificant or
actually significantly positive.

Insert Table 8

12

Examining the correlation between these variables across locations, we find a high positive correlation
between wage premiums and land prices, a low but statistically significant positive correlations between
manufacturing gravity and land prices, and a low but statistically significant negative correlation between
manufacturing gravity and wage premiums. The last result may be due to the fact that manufacturing
activities in general tend to be located in regions with low wage premiums.

8There is also no clear evidence that more productive firms tend to place their factories inlocations with a low manufacturing gravity in order to escape some kind of negative congestion
effect, such as spillovers of their technology to rival firms. In the case of the transportation
machinery and miscellaneous products industries, the correlations between firm effects andmanufacturing gravity are negative and significant, although the correlation coefficients are not large.
Moreover, in the other four subsectors, the correlation is either positive and significant (in the caseof electric machinery) or very close to zero and not significant.
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:exp( β ′ z f ,l ,t )Pr( y f ,t = l ) = L∑ exp(β ′ 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 in our data set and their distribution is shown in Table 9,with panel (a) showing the number of observations in the Census of Manufactures overall and panel(b) showing the number for which it is possible to calculate firm and location effects. The numbers
are not very largely, partly because Japan’s manufacturing sector has shrunk rapidly during thisperiod and, as can be seen in panel (a), the number of newly opened factories has declined intandem; and partly because, as indicated in panel (b) we can calculate both firm and location effects
only for a fraction of newly established factories.

Insert Table 9

The results of our estimation of the mixed logit model of location decisions are presented inTable 10.13 Specification 1 represents the baseline estimation and as independent variables only
13

The number of potential locations, that is, the number of choices in location decisions is greater than9

includes the location effect and the cross-term of location and firm effects. The estimatedcoefficients on the location effect are positive in most cases and significantly so in two subsectors(chemicals and transportation machinery). This result provides weak evidence suggesting that firms
tend to prefer more productive locations. The estimated coefficients on the cross term of firm andlocation effects are negative and significant in five of the six subsectors. This result implies that
more productive firms tend to be less attracted to more productive locations. This finding is
consistent with the results of our analysis of the correlation between firm and location effects acrossfactories.

Insert Table 10

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 thebaseline specification. The results are reported as specifications 2 and 3. Against expectation, we do
not find any statistically significant negative effects of factor prices on location decisions.14
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 agglomeration of economic
activities. Above, we suggested that there was a positive correlation between our location effect and“manufacturing gravity (Table 5). To test the importance of agglomeration effects for location
decisions, we replaced the location effect variable with manufacturing gravity in our analysis oflocation choice. If more productive firms are less attracted by manufacturing gravity, a negative
coefficient for the cross term of firm effects and manufacturing gravity is expected. The results arereported as specifications 4 and 5 in Table 10. We find that manufacturing gravity has a positive andstatistically significant effect on location decision in all industries. But we do not find negative andsignificant coefficient for the cross term of firm effects and manufacturing gravity.
As we already pointed out, the number of newly established factories during our observation
period is not very large and in some sectors, such as chemicals and transportation machinery,

1,500 in each subsector.
14
In some industries, the estimated coefficient for the local wage premium was positive and significant,against our conjecture. One possible explanation for this result is that, because the equation does notinclude sufficient variables to control for the positive effects of agglomeration, the estimate for the local
wage premium picks up these effects.

10available observations are less than one hundred (Panel b of Table 9). Therefore, in order to checkthe robustness of our results, we also estimated our location choice model at a more aggregatedindustry level, dividing manufacturing industry into only two large sectors, machinery (generalmachinery, electric machinery, and transportation machinery) and non-machinery (materials,
chemicals, and miscellaneous products). Estimating our mixed logit model for these two sectors, wefind that the results remain largely unchanged (Table 11). As another robustness check of our results,
we calculate factories’ TFP level using man-hour data to represent labor input instead of the wage
bill divided by the local wage premium. The results of our estimation of location choices based onthese TFP values are shown in Table 12 and are similar to the estimation results in Table 10.

Insert Table 12

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 manufacturing gravity.
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.
11

We found a positive and significant correlation between location effects and manufacturing gravity,
but the correlation coefficient is rather small. When we replaced the location effect variable in our
location choice analysis with manufacturing gravity, 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 manufacturing gravity. These results indicate that we still do
not understand well what local characteristics cause positive location effects.
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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Establish-Firm Data,” RAND Journal of Economics, 27, pp.700-721.
Baldwin, R. E. and T. Okubo (2006) “Heterogeneous Firms, Agglomeration and Economic
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Bartelsman, E. J. and M. Doms (2000) “Understanding Productivity: Lessons from Longitudinal
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Ciccone, A. and R. E. Hall (1996) “Productivity and the Density of Economic Activity,” American
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13

Table 1. Summary statistics of the productivity of factories
Obs.
Mean
SD
Materials
218,509-0.1200.256
Chemicals
24,891-0.135
0.355
General machinery
96,965-0.0940.240
Electric machinery
83,199-0.162
0.252
Transportation machinery
37,591-0.0980.227
Miscellaneous products
275,792-0.145
0.263
Total
736,947-0.130
0.259

14

Min.
-1.078-1.459-0.990-1.146-1.015-1.130-1.459Median-0.142-0.173-0.111-0.181-0.118-0.167-0.151

Max.
0.843
1.261
0.790
0.833
0.827
0.837
1.261

Table 2. Number of observations used for the estimation and number of locations and firms forwhich factories are included in the observations
Year

Materials

Chemicals

Generalmachinery

Electric
machineryTransportatio Miscellaneoun machinerys products

1996 Observations

18,483

3,333

4,818

6,805

2,984

18,256

Locations
Firms

1,416
2,975

365
497

706
1,050

910
1,273

387
521

1,615
3,037

1997 Observations

21,830

3,700

6,004

8,182

3,441

22,130Locations
Firms

1,463
3,251

387
527

748
1,175

950
1,378

403
560

1,668
3,357

1998 Observations

22,601

3,789

6,308

8,423

3,596

23,108Locations
Firms

1,489
3,390

404
545

765
1,218

960
1,427

420
595

1,686
3,498

1999 Observations

22,571

3,803

6,306

8,275

3,647

23,059Locations
Firms

1,484
3,465

408
558

762
1,252

971
1,447

424
605

1,693
3,559

2000 Observations

18,607

3,401

5,352

7,137

3,316

19,794

Locations
Firms

1,467
3,342

402
551

747
1,204

946
1,388

421
584

1,677
3,457

2001 Observations

14,543

2,978

4,367

5,961

2,950

16,427

Locations
Firms

1,435
3,164

387
540

709
1,111

903
1,326

407
566

1,598
3,248

2002 Observations

9,810

2,388

3,139

4,545

2,490

12,521

Locations
Firms

1,155
1,921

324
423

577
743

774
976

363
466

1,364
2,365

2003 Observations

8,549

2,160

2,695

3,921

2,338

11,222

Locations
Firms

1,110
1,777

308
414

548
697

732
886

341
446

1,304
2,203

15

Table 3. Summary statistics of the estimation of firm and location effects(1) Firm effects
Obs.
Mean
SD
Min.
MedianMaterials
17,946-0.0410.247-1.494-0.049Chemicals
3,436
0.248
0.419
-2.3630.251
General machinery
6,504-0.1540.418-2.825-0.146
Electric machinery
7,992-0.1180.315-1.900-0.115
Transportation machinery
3,665
0.091
0.242
-1.4600.073
Miscellaneous products
19,814-0.2190.288-2.315-0.238
Total
59,357-0.0980.329-2.825-0.101

Max.
1.469
2.269
1.890
1.591
1.229
1.224
2.269

(2) Location effects
Materials
Chemicals
General machinery
Electric machineryTransportation machinery
Miscellaneous products
Total

Obs.
9,568
2,602
4,668
6,104
2,790
11,14236,874Mean-0.032
0.092
0.195-0.0470.044-0.010
0.016

16

SD
0.238
0.381
0.677
0.314
0.259
0.257
0.361

Min.
-1.674-1.620-2.092-1.961-0.969-1.884-2.092

Median-0.0140.089
0.000-0.045
0.036
0.000
0.000

Max.
1.248
2.172
3.243
2.418
1.455
1.704
3.243

Table 4. Correlation between estimated firm effects and characteristics of firms
Manufacturing
1
2
3
4
1
1 Estimated firm effects
0.48 ***
1
2 Average TFP
0.05 ***
0.03 ***
1
3 Total output
0.04 ***
0.02 ***
0.25 ***
1
4 Number of factories
0.10 ***
0.16 ***
0.12 ***
0.09 ***
5 TFP (Basic Survey)

5

1

Materials
1 Estimated firm effects
2 Average TFP
3 Total output
4 Number of factories
5 TFP (Basic Survey)

1
1
0.65 ***
0.02 ***
0.07 ***
0.10 ***

2

3

4

5

1
0.02 ***
0.04 ***
0.12 ***

1
0.32 ***
0.18 ***

1
0.11 ***

1

2

3

4

5

1
0.16 ***
0.02
0.19 ***

1
0.12 ***
0.17 ***

1
0.05 ***

1

2

3

4

5

1
0.02 ***
0.04 ***
0.14 ***

1
0.37 ***
0.17 ***

1
0.13 ***

1

2

3

4

5

1
0.04 ***
0.01
0.15 ***

1
0.59 ***
0.17 ***

1
0.07 ***

1

2

3

4

5

1
0.02 **-0.01
0.09 ***

1
0.39 ***
0.17 ***

1
0.23 ***

1

Chemicals
1 Estimated firm effects
2 Average TFP
3 Total output
4 Number of factories
5 TFP (Basic Survey)

1
1
0.43 ***
0.09 ***
0.01
0.12 ***

General machinery
1 Estimated firm effects
2 Average TFP
3 Total output
4 Number of factories
5 TFP (Basic Survey)

1
1
0.40 ***
0.02-0.01
0.03 **

Electric machinery
1 Estimated firm effects
2 Average TFP
3 Total output
4 Number of factories
5 TFP (Basic Survey)

1
1
0.39 ***
0.03 ***
0.01
0.06 ***

Transportation machinery
1 Estimated firm effects
2 Average TFP
3 Total output
4 Number of factories
5 TFP (Basic Survey)

1
1
0.42 ***
-0.02
-0.01
0.10 ***

Miscellaneous products
12
3
4
1 Estimated firm effects
1
0.54 ***
1
2 Average TFP
0.04 ***
0.04 ***
1
3 Total output
0.01
0.02 ***
0.46 ***
1
4 Number of factories
0.10 ***
0.21 ***
0.13 ***
0.08 ***
5 TFP (Basic Survey)
Note: "Average TFP" is the average TFP level of all factories of a particular firm.

17

5

1

Table 5. Correlation between estimated location effects and characteristics of locations
Manufacturing
1
2
3
4
5
1 Estimated location effects
1
2 Average TFP
0.29 ***
1.00
0.09 ***
0.06 ***
1.00
3 Average wage
0.15 ***
0.23 ***
0.56 ***
1.00
4 Wage premium
0.08 ***
0.08 ***
0.55 ***
0.46 ***
1.00
5 Manufacturing gravity
0.02 ***
0.08 ***
0.58 ***
0.53 ***
0.69 ***
6 Land prices

6

1.00

Materials
1 Estimated location effects
2 Average TFP
3 Average wage
4 Wage premium
5 Manufacturing gravity
6 Land prices

1
1
0.28 ***
0.09 ***
0.17 ***
0.06 ***
0.08 ***

2

3

4

5

6

1.00
0.02 ***
0.24 ***
0.05 ***
0.04 ***

1.00
0.54 ***
0.52 ***
0.58 ***

1.00
0.44 ***
0.52 ***

1.00
0.69 ***

1.00

2

3

4

5

6

1.00
0.04 ***
0.09 ***
0.04 ***
0.03 **

1.00
0.56 ***
0.50 ***
0.52 ***

1.00
0.42 ***
0.54 ***

1.00
0.65 ***

1.00

2

3

4

5

6

1.00
0.05 ***
0.23 ***
0.07 ***
0.09 ***

1.00
0.56 ***
0.55 ***
0.56 ***

1.00
0.46 ***
0.54 ***

1.00
0.68 ***

1.00

2

3

4

5

6

1.00
0.09 ***
0.28 ***
0.14 ***
0.14 ***

1.00
0.57 ***
0.59 ***
0.58 ***

1.00
0.50 ***
0.54 ***

1.00
0.69 ***

1.00

2

3

4

5

6

1.00
-0.02 **
0.13 ***
0.02 **
0.04 ***

1.00
0.58 ***
0.58 ***
0.55 ***

1.00
0.46 ***
0.51 ***

1.00
0.66 ***

1.00

5

6

1.00
0.69 ***

1.00

Chemicals
1 Estimated location effects
2 Average TFP
3 Average wage
4 Wage premium
5 Manufacturing gravity
6 Land prices

1
1
0.31 ***
0.00
0.05 **
0.04-0.11 ***

General machinery
1 Estimated location effects
2 Average TFP
3 Average wage
4 Wage premium
5 Manufacturing gravity
6 Land prices

1
1
0.18 ***
0.02
0.01
0.01
-0.08 ***

Electric machinery
1 Estimated location effects
2 Average TFP
3 Average wage
4 Wage premium
5 Manufacturing gravity
6 Land prices

1
1
0.32 ***
0.08 ***
0.11 ***
0.05 ***
0.02

Transportation machinery
1 Estimated location effects
2 Average TFP
3 Average wage
4 Wage premium
5 Manufacturing gravity
6 Land prices

1
1
0.31 ***-0.11 ***
-0.01
-0.02
-0.15 ***

Miscellaneous products
12
3
4
1 Estimated location effects
1
2 Average TFP
0.35 ***
1.00
0.20 ***
0.13 ***
1.00
3 Average wage
0.31 ***
0.35 ***
0.53 ***
1.00
4 Wage premium
0.19 ***
0.13 ***
0.50 ***
0.44 ***
5 Manufacturing gravity
0.15 ***
0.15 ***
0.57 ***
0.52 ***
6 Land prices
Note: "Average TFP" is the average TFP level of all factories in a particular location.

18

Table 6. Results of the analysis of variance
Materials

Chemicals

Partial sum of#squared deviations
402.8 ( 11% ) 1750

Transportation machinery

Electric machinery

641

628

Partial sum of#squared deviations
6.8
548.0 ( 12% ) 1941

Firm

1144.4 ( 32% )

3553

8.1

413.5 ( 28% )

601

12.2

363.2 ( 31% )

1329

7.3

394.6 ( 25% )

1569

5.9

133.1 ( 24% )

644

7.4

1476.3 ( 33% )

3740

9.8

Age

2.4 ( 0% )

1

58.8

1.2 ( 0% )

1

22.1

0.9 ( 0% )

1

22.9

0.6 ( 0% )

1

14.7

0.0 ( 0% )

11.7

2.0 ( 0% )

1

49.7

2

2.0 ( 0% )

1

50.9

0.9 ( 0% )

1

16.3

0.7 ( 0% )

1

19.2

0.8 ( 0% )

1

18.1

0.0 ( 0% )

11.5

1.5 ( 0% )

1

36.2

Size

0.1 ( 0% )

12.5

1.0 ( 0% )

1

18.1

0.3 ( 0% )18.8

0.1 ( 0% )12.0

0.1 ( 0% )

14.4

0.3 ( 0% )

1

7.6

2

0.0 ( 0% )10.0

1.2 ( 0% )

1

20.9

0.5 ( 0% )

1

13.0

0.0 ( 0% )11.0

0.0 ( 0% )

11.6

0.6 ( 0% )

1

14.3

6.2 ( 0% )

15

10.3

6.3 ( 0% )

8

13.9

0.2 ( 0% )

41.1

3.3 ( 0% )

7

11.0

1.5 ( 0% )

2

26.3

6.5 ( 0% )

10

16.1

Age

Size

Industry
Total

3537.6F

1462.2#

F

1170.4

Partial sum of#squared deviations
5.8
281.9 ( 18% ) 1337

Miscellaneous products

Partial sum of#squared deviations
9.5
231.1 ( 20% ) 1052

Location

Partial sum ofsquared deviations
5.8
342.3 ( 23% )General machinery
F

1590.8

Partial sum ofsquared deviations
5.0
119.2 ( 22% )

F

550.5

#

F

4426.3Observations

48,08810,447

15,984

21,28910,21852,932

2

0.516

0.646

0.563

0.509

0.544

0.571

0.456

0.597

0.486

0.431

0.478

0.520

R

2

Adj. R

Note: The industry classification is based on the JIP (Japan Industrial Productivity) Database.

19

F
7.0

Table 7. Correlation between firm effects andlocation effects
Industry
Corr. Coef.
Materials-0.341
Chemicals-0.548General machinery-0.685
Electric machinery-0.603
Transportation machinery-0.554
Miscellaneous products-0.361
Total-0.355
Note: All coefficients are statistically significant at the 0.1% level.

20

Table 8. Correlation between estimated firm effects and characteristics of locations
Manufacturing
12
3
4
1 Estimated firm effects
10.00
12 Manufacturing gravity
0.07 *** -0.02 ***1
3 Wage premium
0.00
0.03 ***
0.41 ***
1
4 Land prices
Materials
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1
0.00
-0.04 ***
-0.01

2

3

4

1
-0.03 ***
0.04 ***

1
0.40 ***

1

2

3

4

1
-0.08 ***
0.01

1
0.36 ***

1

2

3

4

1
-0.05 ***
0.02 **

1
0.46 ***

1

2

3

4

1
0.05 ***
0.10 ***

1
0.48 ***

1

2

3

4

1
-0.04 ***
0.50 ***

1
0.32 ***

1

2

3

4

1
-0.01
0.02 **

1
0.44 ***

1

Chemicals
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1
0.00
0.05 ***
0.11 ***

General machinery
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1
0.01
0.06 ***
0.05 ***

Electric machinery
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1
0.04 ***
0.06 ***-0.01

Transportation machinery
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1-0.05 ***
0.02 **
0.03 **

Miscellaneous products
1 Estimated firm effects
2 Manufacturing gravity
3 Wage premium
4 Land prices

1
1-0.03 ***
0.00
0.02 ***

21

Table 9. Number of newly established factories in our dataa. Number of new factories in the micro data of the Census of Manufactures
1997

1998

1999

2000

2001

2002

2003

Total

Materials

1,300

445

467

533

383

385

379

3,892

Chemicals

163

66

58

79

61

80

56

563

General machinery

473

172

198

210

164

189

165

1,571

Electric machinery

457

197

210

221

201

178

148

1,612

Transportation machinery

194

91

80

103

102

93

105

768

Miscellaneous products

1,631

506

543

574

453

460

383

4,550

Total

4,218

1,477

1,556

1,720

1,364

1,385

1,236

12,956b. Number of new factories for which both firm and location effects can be calculated
1997

1998

1999

2000

2001

2002

2003

Materials

169

89

87

118

43

49

44

599

Chemicals

27

18

5

20

10

9

5

94

General machinery

29

15

20

15

7

15

7

108

Electric machinery

66

24

18

32

19

22

23

204

Transportation machinery

18

6

8

14

11

9

21

87

Miscellaneous products

234

67

87

106

73

78

40

685

Total

543

219

225

305

163

182

140

1,777

22

Total

Table 10. Location choice: Estimation results of mixed logit models
Materials

Chemicals

Generalmachinery

Electric
machineryTransportation
machineryMiscellaneous
products

0.143(0.186)-2.07 ***
(0.781)0.001-4.30E+03
798,635

0.579 *
(0.334)-0.935(0.793)0.003-5.50E+02
33,372

0.032(0.262)-2.11 ***
(0.611)
0.009-6.92E+02
69,991-0.161(0.244)-3.78 ***
(0.770)0.009-1.35E+03
167,3221.36 ***(0.478)-7.38 ***
(1.731)
0.017-5.00E+02
30,4500.214(0.175)-1.55 ***
(0.401)
0.003-5.00E+03
1,036,334-0.259(0.201)-2.13 ***
(0.809)2.360 ***
(0.293)0.009-4.06E+03
736,467

0.643 *
(0.346)-1.2(0.815)-0.190(1.037)0.003-5.06E+02
30,0200.0438(0.269)-2.14 ***
(0.627)1.090(0.769)0.01-6.47E+02
62,820-0.245(0.251)-3.84 ***
(0.790)1.590 ***
(0.487)0.013-1.29E+03
154,471

1.4 ***(0.480)-7.63 ***
(1.790)2.130 **
(0.952)
0.022-4.83E+02
28,664-0.211(0.189)-1.85 ***
(0.427)2.470 ***
(0.260)0.012-4.85E+03
982,522-0.0196(0.267)-2.04 *
(1.160)0.218 ***
(0.055)0.004-2.12E+03
208,0540.744 *
(0.415)-0.913(0.913)0.019(0.164)0.005-2.67E+02
10,8000.0524(0.343)-3.14 ***
(0.851)0.074(0.131)0.018-3.87E+02
24,389-0.371(0.363)-4.65 ***
(1.414)0.374 ***
(0.103)0.021-5.51E+02
36,0611.71 **(0.677)-8.89 ***
(2.326)0.038(0.186)0.027-2.55E+02
9,669

0.141
(0.276)-1.49 *
(0.777)0.395 ***
(0.046)0.016-2.58E+03
268,647

0.159 ***
(0.010)-0.00375(0.044)0.013-4.62E+03
1,670,404

0.16 ***(0.028)-0.0138(0.074)0.012-1.03E+03
371,5160.166 ***
(0.023)0.00903(0.064)0.015-9.71E+02
351,664

0.155 ***
(0.018)0.031(0.063)0.011-1.95E+03
703,3280.188 ***
(0.025)0.0125(0.087)0.027-7.96E+02
292,108

0.165 ***
(0.012)0.0161(0.035)0.014-5.27E+03
1,905,792

0.134 ***
(0.022)0.0356(0.076)1.400 ***
(0.377)0.013-1.88E+03
591,700

0.166 ***
(0.032)0.0198(0.109)3.810 ***
(0.577)0.052-7.61E+02
249,775

0.143 ***
(0.015)0.0251(0.044)2.080 ***
(0.232)0.02-4.99E+03
1,585,950

Specification 1
Location effectFirm effect × Location effectR-squared
Log-likelihood
Observations

Specification 2
Location effectFirm effect × Location effectRegional wage premium
R-squared
Log-likelihood
Observations

Specification 3
Location effectFirm effect × Location effectLand prices
R-squared
Log-likelihood
Observations

Specification 4
Manufacturing gravity
Firm effect × Manufacturing gravity
R-squared
Log-likelihood
Observations

Specification 5
Manufacturing gravity

0.131 ***
0.129 ***
0.144 ***
(0.014)(0.038)(0.029)Firm effect × Manufacturing gravity
0.0000144
0.002620.00948(0.058)(0.097)(0.080)Regional wage premium
2.360 ***
2.590 ***
1.970 ***
(0.247)(0.526)(0.536)R-squared
0.022
0.024
0.02
Log-likelihood-4.41E+03-9.74E+02-9.32E+02
Observations
1,401,650
310,400
295,850
Notes: 1. Figures in parentheses are standard errors.
2. * p<.1, ** p<.05, *** and p<.01
3. Labor input is adjusted by the regional wage premium when calculating the TFP index of each establishment

23

Table 11. Location choice: Estimation results of mixed logit models
General machinery,electric machinery, andtransportation machinery

Materials, chemicals, andmiscellaneous products

Specification 6
Location effectFirm effect × Location effectR-squared
Log-likelihood
Observations

0.0796(0.219)-3.3 ***(0.933)0.002-4.20E+03
763,367

0.884 ***
(0.135)-1.38 **
(0.559)0.002-1.25E+04
3,226,893-0.15(0.232)-3.45 ***
(0.996)2.120 ***
(0.294)0.009-4.00E+03
708,127

0.263 *
(0.151)-1.57 ***
(0.593)2.650 ***
(0.160)0.013-1.19E+04
3,017,627

0.154(0.328)-4.38 ***
(1.586)0.284 ***
(0.056)0.009-2.04E+03
198,050

0.633 ***
(0.220)-1.08(0.902)0.359 ***
(0.030)0.013-6.10E+03
761,9790.169 ***
(0.012)0.0304(0.051)
0.015-4.50E+03
1,630,700

0.159 ***
(0.007)0.0146(0.029)0.014-1.18E+04
4,279,524

0.148 ***
(0.014)0.0413(0.064)2.080 ***
(0.249)0.021-4.33E+03
1,377,400

0.132 ***
(0.010)0.0222(0.038)2.320 ***
(0.155)0.022-1.12E+04
3,574,450

Specification 7
Location effectFirm effect × Location effectRegional wage premium
R-squared
Log-likelihood
Observations

Specification 8
Location effectFirm effect × Location effectLand prices
R-squared
Log-likelihood
Observations

Specification 9
Manufacturing gravity
Firm effect × Manufacturing gravity
R-squared
Log-likelihood
Observations

Specification 10
Manufacturing gravity
Firm effects × Manufacturing gravity
Regional wage premium
R-squared
Log-likelihood
Observations
Notes: 1. Figures in parentheses are standard errors.
2. * p<.1, ** p<.05, *** and p<.0124

Table 12. Location choice: Estimation results of mixed logit models, TFP is based on man-hour labor input data
Materials

Chemicals

Generalmachinery

Electric
machineryTransportation
machineryMiscellaneous
products

0.0464(0.197)-1.46 **
(0.697)0.001-4.48E+03
867,274

0.285(0.295)-0.736(0.778)0.001-5.93E+02
36,353-0.197(0.250)-1.92 ***
(0.526)0.008-7.35E+02
76,993-0.157(0.279)-2.09 ***
(0.556)0.007-1.46E+03
188,0160.306(0.411)-5.35 ***
(1.660)0.009-5.01E+02
31,311

0.381 ***
(0.146)-0.883 **(0.442)0.001-5.27E+03
1,146,733-0.137(0.213)-1.36 *
(0.747)2.220 ***
(0.283)0.008-4.16E+03
756,434

0.273(0.313)-0.887(0.819)0.013(1.012)0.001-5.36E+02
31,822-0.153(0.261)-1.86 ***
(0.542)1.070(0.748)0.009-6.75E+02
66,707-0.215(0.291)-2.17 ***
(0.595)1.510 ***
(0.469)0.011-1.36E+03
165,193

0.406(0.423)-5.68 ***
(1.703)1.990 **
(0.960)0.015-4.82E+02
28,7780.259(0.163)-1.05 **
(0.489)2.450 ***
(0.250)0.011-5.02E+03
1,024,024

0.0644(0.290)-1.2(1.045)0.213 ***
(0.054)0.004-2.17E+03
218,175

0.358(0.386)-0.357(0.940)-0.024(0.157)0.002-2.89E+02
11,652-0.0522(0.337)-2.31 ***
(0.733)0.058(0.127)0.012-4.08E+02
26,128-0.157(0.422)-2.45 **
(1.020)0.339 ***
(0.101)0.017-5.72E+02
38,0880.753(0.600)-6.7 ***(2.142)
0.113(0.190)0.018-2.54E+02
9,649

0.257
(0.237)-0.327(0.718)0.408 ***
(0.045)0.016-2.67E+03
285,892

0.157 ***
(0.011)-0.0119(0.040)0.013-4.74E+03
1,712,944

0.157 ***
(0.022)-0.012(0.066)0.012-1.08E+03
388,532

0.167 ***
(0.022)0.0239(0.058)0.014-1.01E+03
365,844

0.155 ***
(0.022)0.0175(0.049)0.01-2.01E+03
726,0160.189 ***
(0.020)0.0195(0.083)0.027-7.89E+02
289,272

0.159 ***
(0.010)0.0318(0.030)0.014-5.46E+03
1,976,692

0.129 ***
0.128 ***
(0.015)(0.030)Firm effect × Manufacturing gravity-0.00887-0.0163(0.053)(0.086)
Regional wage premium
2.360 ***
2.620 ***
(0.245)(0.514)R-squared
0.022
0.024
Log-likelihood-4.50E+03-1.02E+03
Observations
1,430,750
324,950
Notes: 1. Figures in parentheses are standard errors.
2. * p<.1, ** p<.05, *** and p<.01
3. The standard Tornqvist index is used for calculating the TFP index of each factory.

0.144 ***
(0.028)0.0394(0.072)1.960 ***
(0.526)0.02-9.70E+02
307,9750.134 ***
(0.027)0.016(0.060)1.390 ***
(0.371)0.013-1.94E+03
611,100

0.166 ***
(0.026)0.0305(0.102)3.770 ***
(0.581)
0.051-7.54E+02
247,3500.135 ***
(0.013)0.0477(0.037)1.990 ***
(0.228)0.02-5.18E+03
1,644,150

Specification 11
Location effectFirm effect × Location effectR-squared
Log-likelihood
Observations

Specification 12
Location effectFirm effect × Location effectRegional wage premium
R-squared
Log-likelihood
Observations

Specification 13
Location effectFirm effect × Location effectLand prices
R-squared
Log-likelihood
Observations

Specification 14
Manufacturing gravity
Firm effect × Manufacturing gravity
R-squared
Log-likelihood
Observations

Specification 15
Manufacturing gravity

25