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ECONOMIES of DENSITY and PRODUCTIVITY in SERVICE INDUSTRIES: an ANALYSIS of PERSONAL SERVICE INDUSTRIES BASED on ESTABLISHMENT-LEVEL DATA Masayuki Morikawa*

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ECONOMIES of DENSITY and PRODUCTIVITY in SERVICE INDUSTRIES: an ANALYSIS of PERSONAL SERVICE INDUSTRIES BASED on ESTABLISHMENT-LEVEL DATA Masayuki Morikawa* ECONOMIES OF DENSITY AND PRODUCTIVITY IN SERVICE INDUSTRIES: AN ANALYSIS OF PERSONAL SERVICE INDUSTRIES BASED ON ESTABLISHMENT-LEVEL DATA Masayuki Morikawa* Abstract—This study aims to empirically investigate the determinants of the policy as a means to increase service sector productivity service industry productivity, such as economies of scale and economies of density. By using establishment-level data related to personal service substantially depends on aggregate-level international com- industries in Japan, the study estimates the production functions for both parisons and a few case studies. value-added and physical output measures. In almost all the service indus- The simultaneous occurrence of production and con- tries examined, significant economies of scale and economies of density are observed, wherein productivity increases by 7% to 15% when the sumption is frequently identified as a characteristic of the municipality population density doubles. These findings are confirmed by service industry. This implies that most service industries an estimation in which the measures of physical output are considered cannot stockpile inventories to smooth production and that instead of value added. service industry productivity is affected by demand condi- tions such as time series fluctuations of demand and spatial Downloaded from http://direct.mit.edu/rest/article-pdf/93/1/179/1919143/rest_a_00065.pdf by guest on 28 September 2021 difference in demand density. I. Introduction With regard to the time series aspect, by analyzing HIS study empirically investigates the determinants of industry-level time series data and correcting it for unob- T service industry productivity, such as economies of served input utilization, Basu, Fernald, and Kimball (2006) scale and economies of demand density. By using establish- reveal a larger discrepancy between the observed total fac- ment-level data related to personal service industries tor productivity (TFP; standard Solow residual) and the wherein the simultaneity of production and consumption is ‘‘purified’’ technology change in the nonmanufacturing sec- particularly prominent, the study estimates the production tor than that in the durable manufacturing sector. In Japan, functions for 10 narrowly defined industries in Japan. I Kawamoto (2005) conducts a similar analysis by using data compare the results of the analyses for the service industries for 1973 to 1988 from the Japan Industrial Productivity with those for the manufacturing and retail industries in (JIP) database and demonstrates that the influence of the order to highlight the differences between them. input utilization rate on the output in the nonmanufacturing Recent studies emphasize the importance of service industry is three times greater than that in the durable man- industries with regard to a country’s economic growth per- ufacturing industry. formance. For example, van Ark, O’Mahony, and Timmer Concerning the spatial aspect, considerable research on (2008) analyze the industry-level productivity growth of agglomeration economies is related to the subjects covered Europe and the United States and conclude that the major in this paper. Rosenthal and Strange (2004), in their repre- factor causing the divergence of aggregate-level productiv- sentative survey in this area, conclude that productivity ity is the difference in service sector productivity. In Japan, increases by 3% to 8% if a city’s population density dou- the productivity of the service sector has become an impor- bles. However, most of the empirical research in this area tant policy agenda. In light of this, the Service Productivity uses municipal-level data1 or manufacturing industry plant- and Innovation for Growth (SPRING) organization and Ser- level data.2 For example, Nakamura (1985), in a representa- vice Engineering Research Center were established to pro- tive study on Japan’s agglomeration economies on produc- mote innovation and productivity in the service sector. tivity, estimates the translog production function of two- Numerous estimations of production and cost functions digit manufacturing industries by analyzing city-level for the manufacturing sector have been conducted and used aggregated data from 1979. The study shows that the value in the planning of industrial policy. However, in the case of of the elasticity of labor productivity with respect to the service industries, empirical studies on fundamental pro- population of cities is approximately 3.4%; however, the duction structure, such as those on scale economies, have results vary considerably across industries. Tabuchi (1986) been quite limited in number because of the lack of relevant also uses a cross-section of city-level manufacturing data firm- or establishment-level data. Therefore, the planning of from 1980 and conducts regression to explain labor produc- tivity. The result shows that the value of elasticity of labor Received for publication August 7, 2008. Revision accepted for publica- productivity with respect to the population density of a tion September 3, 2009. city ranges from 4% to 8% depending on the specification. * Research Institute of Economy, Trade and Industry. I thank Masahisa Fujita, Kyoji Fukao, Yoko Konishi, Hyeog Ug Kwon, Toshiyuki Matsuura, Takanobu Nakajima, Yoshihiko Nishiyama, Masa- hiko Ozaki, and an anonymous referee for their helpful comments and 1 Ciccone and Hall (1996) is a representative study in the United States. suggestions. I am grateful to the Research and Statistics Department of Kanemoto, Ohkawara, and Suzuki (1996) and Davis and Weinstein the Ministry of Economy, Trade and Industry for providing access to the (2001) are Japanese studies that use municipal-level data. data used in this research. The views expressed in the paper are solely 2 Henderson, Kuncoro, and Turner (1995) and Henderson (2003) are the those of the author and are not necessarily those of the RIETI or the representative studies that use data on manufacturing plants in the United METI. States. The Review of Economics and Statistics, February 2011, 93(1): 179–192 Ó 2011 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology 180 THE REVIEW OF ECONOMICS AND STATISTICS Analyses that are focused on nonmanufacturing industries theoretical implications of the empirical results. Section V are rarely conducted, whereas analyses based on service presents the conclusions, including policy implications. industry establishment-level data are almost nonexistent.3 Although the data used in the study concern a specific manufacturing industry, Syverson (2004) investigates the II. Data and Methods effect of spatial substitutability on the productivity of plants. The hypotheses are that consumers in a densely clus- A. Data tered market can easily switch suppliers and that inefficient This paper uses data on personal service industries producers may find it difficult to survive. As a result, in obtained from the Survey of Selected Service Industries such markets, the average productivities should be higher conducted by the Ministry of Economy, Trade and Industry and the dispersion of productivities among plants should be (METI). Introduced in 1973, this survey is conducted to lower. Syverson (2004) tests these hypotheses using the clarify the actual conditions of service industries and obtain data concerning ready-mixed concrete plants in the United basic data for policies concerning them. The participants of States and shows that the results are consistent with the Downloaded from http://direct.mit.edu/rest/article-pdf/93/1/179/1919143/rest_a_00065.pdf by guest on 28 September 2021 the survey are all establishments operating in Japan. Initi- hypotheses. Further, using data for ready-mixed concrete ally the survey considered only five service industries, but plants, Syverson (2007) demonstrates that price dispersion the coverage has gradually been expanded and now covers decreases when spatial competition increases. This work more than twenty industries. Although the survey is con- also suggests the possibility that the same mechanism plays ducted annually for goods-leasing and information service a more significant role in service, retail, or wholesale indus- industries, most industries are surveyed every three to four tries. Although the data used in these analyses are not years. The survey compiles information related to establish- directly related to the service sector, the main concept ments or firms according to industry characteristics. The behind these findings is the most relevant element for my questionnaire items cover a variety of issues, and some analyses. items may differ depending on the industry being surveyed. On the basis of this discussion, this study investigates the In this paper, I use recent (2002–2005) establishment- factors influencing the TFP of service industries by analyz- level data concerning ten major personal service industries ing unique establishment-level data related to 10 personal in Japan: movie theaters, golf courses, tennis courts, bowl- service industries in Japan and focuses on the effects of spa- ing alleys, fitness clubs, golf driving ranges, culture centers, tial demand density. The major results can be summarized theaters (including rental halls), wedding ceremony halls, as follows: and aesthetic salons (for example, facial and body massage but not hair and nail salons). Table 1 shows the market sizes In almost all the service industries examined, econo- and numbers of employees of these industries. mies of scale in
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