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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

8. Market Access, Agglomeration Economies, and Productive Efficiency (I)

verfasst von : Akihiro Otsuka

Erschienen in: A New Perspective on Agglomeration Economies in Japan

Verlag: Springer Singapore

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Abstract

This chapter examines whether agglomeration economies, market access, and public fiscal transfer have a positive or negative influence on the productive efficiency of Japanese regional industries. To accomplish the research objective, this chapter applies stochastic frontier analysis to a prefecture level Japanese data set that consists of estimated spatial and industrial economic activities. An empirical result obtained in this chapter indicates that both agglomeration economies and the improvement of market access have a positive influence on the productive efficiency of Japanese manufacturing and non-manufacturing industries. In contrast, public fiscal transfer has a negative impact on productive efficiency. These findings indicate that many prefectures that are characterized by weak market access and/or high dependence on public fiscal transfer are often associated with low productive efficiency.

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Vorheriges Kapitel A New Approach to Dynamic Externalities (II)
Nächstes Kapitel Market Access, Agglomeration Economies, and Productive Efficiency (II)
Fußnoten
1
The Nikkei (2010.02.11).
 
2
This Japanese problem is often discussed as soft budget constraints (Otsuka et al. 2014). Previous studies also demonstrated that the fiscal transfer to local governments significantly affected the technical efficiency of regional economies, reducing their level of efficiency. For example, De Borger and Kerstens (1996) examined the efficiency of municipality finance in Belgium, and found that financial dependency on an intergovernmental subsidy reduced the efficiency of municipality finance. Otsuka et al. (2014) demonstrated that fiscal transfer to local governments significantly affected the cost efficiency of Japanese local governments.
 
3
See Fried et al. (1993) for a detailed description of the measurement of productive or technical efficiency, empirical models, and their estimation techniques.
 
4
See e.g., Färe et al. (1994). They use a nonparametric programming technique (activity analysis) to measure efficiency, while this study uses a parametric technique for the same purpose as a decomposition of productivity growth.
 
5
In the CRIEPI regional economic database, capital stocks are constructed from gross investment by using the benchmark year method. The CRIEPI regional economic database provides fixed capital stock classified into manufacturing and non-manufacturing industries for each prefecture.
 
6
It is expected that variations in the logarithm of the inverse of the capital coefficient, \( \ln \left(\frac{Y}{K}\right) \), have a constant slope over time under the production system that employs capital intense technology for the long term. However, the value fluctuates every year in an observed data set. Hence, we assume the fluctuations in \( \ln \left(\frac{Y}{K}\right) \) can be attributed to a change in capital utilization as well as to a time trend. Based on the assumption, a proxy of the capital utilization rate can be measured by a residual error term (ε) in the regression ln(Y/K) = α + βT + ε, where T is a time trend and β is a time-invariant slope of \( \ln \left(\frac{Y}{K}\right) \).
 
7
According to “Net Freight Flow Census (census logistics) 2000,” 81.7% of total shipment cost is attributed to automobile transportation, while the shares of marine, air, and rail transportations are merely 13%, 4.2%, and 1.2%, respectively. These statistics suggest that using the travel time of automobile transportation is preferable for constructing measures of market access.
 
8
In Japan, the income of the local government is divided into fiscal resources that the local government can freely use without any purpose restrictions, and those constrained by a specific purpose.
 
9
Jacob and Los (2007) label it as “unexplained assimilation,” which could be translated in the current context as “unexplained efficiency change.”
 
10
An exception is the Greater Tokyo Area, which shows contributions of 0.03% for manufacturing industries; however, the total average is 0.01%. Hence, the effect of population density is negligible in this study.
 
11
Table 8.4 indicates that the “Other” factor is calculated from residuals as unexplained efficiency change. The value of the “Other” factor is not small enough to be negligible. Thus, it implies that there are some regional differences that cannot be identified by the proposed model. Since our main research concern is to examine the impact of agglomeration economies and fiscal transfer on TFP, we do not explore the residuals further in this study.
 
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Metadaten
Titel
Market Access, Agglomeration Economies, and Productive Efficiency (I)
verfasst von
Akihiro Otsuka
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
A New Perspective on Agglomeration Economies in Japan

Print ISBN: 978-981-10-6489-0

Electronic ISBN: 978-981-10-6490-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-6490-6_8