Market thickness and outsourcing services

Regional Science and Urban Economics 37 (2007) 220 – 238 www.elsevier.com/locate/regec

Market thickness and outsourcing services Yukako Ono ⁎ Federal Reserve Bank of Chicago, 230 S. LaSalle, Chicago, IL, 60604, USA Accepted 28 August 2006 Available online 7 November 2006

Abstract This paper examines whether the outsourcing of business services is supported more in thicker local markets. In particular, I examine manufacturing plants' practices of outsourcing business services. Using plant-level data from the 1992 Annual Survey of Manufactures (ASM), I find evidence that supports the view that a plant's likelihood of outsourcing is greater in thicker local markets, while the extent of the effect varies across services. © 2006 Elsevier B.V. All rights reserved. JEL classification: R3; L0 Keywords: Outsourcing; In-house production; Business service

1. Introduction Over the past few decades, many empirical studies have found evidence of a productivity advantage for firms located in larger cities.1 The existing literature explains the agglomeration economies by various factors, including knowledge spillovers, greater search opportunities for workers and firms, and greater opportunities for specialization in larger cities (Marshall, 1890).2 However, few empirical studies are performed to investigate the relationship between specialization and market thickness. Holmes (1999) uses industry-level data, and shows that there is a positive correlation between local demand for a specific input and local outsourcing of that ⁎ Tel.: +1 312 322 5942. E-mail address: [email protected]. 1 For empirical evidence of agglomeration economies, see Ciccone and Hall (1996), Glaeser et al. (1992), Henderson (1986), and Moomaw (1981). 2 Abdel-Rahman and Fujita (1990) provide a theoretical model in which a greater variety of intermediate inputs in a larger city increases a final producer's efficiency. Jaffe et al. (1993) provide empirical evidence that knowledge spillovers are geographically localized, using patent citation data. 0166-0462/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.regsciurbeco.2006.08.007

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input. Hubbard (2001) examines the contractual form of trucking and market thickness and find that spot arrangement is more common when local trucking markets are thicker, suggesting that more vertically disintegrated practice is facilitated in thicker markets.3 In this paper, I examine the decision of manufacturing plants to outsource business services, which I argue provides additional evidence that more specialization is possible in larger cities. Outsourcing of clerical and administrative services has been a big concern for many business firms (Johnson, 1997) during the last few decades. The corresponding high growth of service industries is also an important economic phenomenon in recent decades. Part of this growth might be related to increased outsourcing (Abraham and Taylor, 1996). Reflecting these factors, in 1992, the U.S. Census Bureau started collecting the cost information of several white-collar services such as advertising,4 bookkeeping and accounting, legal services, and software and data-processing services — services studied in this paper.5 According to the Statistical Abstract of the United States (U.S. Census Bureau, 1997), of all manufacturing employees in the U.S. in 1992, as many as 32% were non-production workers who presumably engaged in in-house performance of administrative and clerical tasks, some of which could have been provided through the market. The choice between outsourcing and performing services in-house therefore should have a significant impact on the labor productivity measure of manufacturers. I use plant-level data from the ASM, a portion of the Longitudinal Research Database (LRD) compiled by the U.S. Census Bureau. This dataset allows me to relate each plant's decision to outsource a particular service to the degree of local market thickness, controlling for plant characteristics such as size, age, and industry. I investigate the outsourcing of four white-collar services. Section 2 describes in more detail the data used in this paper. In Section 3, I present the empirical implementation. In Section 4, I provide the results of industry-level analysis, where I examine how an industry's outsourcing propensity varies across cities and how the outsourcing propensity is associated with the variation of market thickness. I then perform a plant-level analysis, where I examine how the outsourcing propensity of a plant in a given industry varies across cities depending on the market thickness. 2. Data I use plant-level data from the ASM compiled in 1992, the first year when data on the outsourcing of white-collar services were collected — the addition resulted from the high growth of business service industries and the increasing trend of outsourcing. While the 1992 Census of Manufactures canvases every manufacturing plant with a limited set of questions, the ASM is a 3 Goodfriend and McDermott (1995) and Stigler (1951) provide theoretical models in which vertical disintegration becomes possible as the market size grows. 4 According to the 1992 ASM questionnaire, advertising includes printing, media coverage, and other services and materials. 5 The ASM also provides the cost data of building repair, machinery repair, and refuse removal services, analyses of which are beyond the scope of this paper. Outsourcing of blue-collar service sectors may be influenced by unionization and/or regulation rather than to achieve the benefit of specialization. Building and machinery repairs are occasional events. Thus, the demand for these services in a particular year comes from the incidence of a breakdown or demolition, as well as any outsourcing decisions. I also exclude communication services from the analyses, since its definition given in the questionnaire does not suit an examination of the choice between outsourcing and in-housing; the definition includes “telephone service,” and it is unlikely that some plants can produce telephone service in-house.

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Table 1 Fraction of plants outsourcing 31,994 plants Advertising Bookkeeping and accounting Legal services Software and data-processing

.535 .479 .546 .483

Source: Author's calculations based on data from LRD.

sample survey from that plant population asking a longer set of questions. Out of the plants in the ASM sample, I include those whose data are not subject to imputation.6 I also focus on plants in urban areas – those located in the primary metropolitan statistical areas (PMSA) – in the contiguous U.S.A.; this leaves 31,994 plants. In the 1992 ASM questionnaire sent to plants, the Census Bureau inquires about their expenditures on outsourced services. I examine empirically a plant's decision to outsource any amount of a given service, using a discrete variable rather than a continuous variable (expenditure of a service). The continuous variable analysis has to incorporate a plant's decision whether or not to outsource, since we observe expenditure only when a plant decides to outsource. While a typical Heckman two-stage process requires variables which influence the decision-making but does not enter the continuous equation for identification, it is not obvious what variables play such a role. The discrete analysis in this paper provides insight to the first part of the process.7 Table 1 shows the fraction of plants outsourcing any amount of each service in my sample; about half outsource a given service. To show how the outsourcing propensities vary across geographical areas, I also show how the fraction of plants outsourcing in each PMSA varies across locations. Summary statistics are shown in Table 2. A point of this paper is to examine whether these spatial variations in outsourcing propensities can be explained by differences in the degree of market thickness across space. 3. Empirical implementation 3.1. Basic specification I consider a manufacturing plant as a final producer that requires service as an input for its production. A final producer can either outsource the service at market price or perform the service in-house. I assume that final producers' in-house production of a service does not require any fixed cost; it occurs in a facility that has already been set up for their final production. The marginal cost varies among final producers depending on their characteristics, such as age, size, and so forth. In addition, under the assumption that the service is traded only within a local market, the market price varies across local markets depending on the level of competition among 6

Most of the plants excluded due to the imputation are small plants. Since plant sizes are controlled for in the empirical analyses, the exclusion of such plants does not cause a qualitative change in the results. 7 I have performed some experiments in which I use non-linearity with the assumption of normal distribution for identification. However, non-linearity from the normal distribution does not seem to be enough for identification. Given that the cost of each service is a small portion of manufacturing costs, it might be difficult to quantify the cost reduction benefit of outsourcing.

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Table 2 Fraction of plants outsourcing: PMSA level 271 PMSAs

Advertising Bookkeeping and accounting Legal services Software and data-processing

Mean

S.D.

.503 .401 .512 .451

.122 .116 .101 .098

Source: Author's calculations based on data from LRD.

local service suppliers.8 The final producer will outsource the service if market price for the service is lower than its in-house marginal costs. Note that the data for various services' prices are not readily available across geographic areas. However, the price of a service (given its quality) should be lower in a larger local market with more service suppliers. Using a model in which the service is produced by intermediate suppliers who play 2-stage Cournot oligopolistic entry game, Okuno-Fujiwara (1988) shows that, with some conditions, an upward shift of the demand curve decreases the equilibrium market price. Thus, in this framework, outsourcing will be favored more in larger local markets. In this paper, I test whether there is any evidence of a positive relationship between a manufacturing plant's outsourcing probability and the degree of market thickness. More specifically, let us denote the market thickness measure for city k as Sk, and the characteristics of plant i by a vector Ai. I specify the net benefit for plant i in PMSA k of outsourcing service s, as Ykis⁎ ¼ ðSk ; AiVÞbs þ uski :

ð1Þ

While Ai controls for in-house marginal costs for plant i to perform service s, Sk controls for the costs to outsource the service in city k where the plant is located. Plant i in city k outsources service s if Ykis⁎ ≥ 0. Based on Eq. (1), I use a probit model to examine a plant's decision to outsource a given service in relation to the plant's characteristics and the degree of thickness in the local market where the plant is located. 3.2. Measure for market thickness I now discuss what measure to use to capture the market thickness represented by Sk. One might think that the number of intermediate suppliers of each service can be used. However, this strategy is dubious, since it could easily pick up a spurious correlation between outsourcing and entry into the market for service suppliers due to city specific effects. For example, consider the state of the local transportation system: a better local transportation system might enhance communication between demanders and suppliers and encourage outsourcing, which would attract more service suppliers. In this paper, I use PMSA employment as a variable to represent the market thickness, taking the PMSA as the unit for the local market. A greater economic base in the local market represents a greater demand for a service, which then induces more service suppliers to enter and operate in 8

Service transaction often requires face-to-face communication (Kolko, 2000). Therefore the distance to the client would be reflected in price and/or quality of service.

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Table 3 PMSA with specialized manufacturing industries: industries' shares in PMSA manufacturing {σkl} 2-Digit SIC manufacturing industries

PMSA

σkl

20: Food products 21: Tobacco products 22: Textiles 23: Apparel made from fabrics 24: Lumber and wood products, except furniture 25: Furniture and fixtures 26: Paper products 27: Printing and publishing 28: Chemicals 29: Petroleum refining 30: Rubber and plastic products 31: Leather products 32: Stone, clay, glass and concrete products 33: Primary metal products 34: Fabricated metal products 35: Industrial and commercial machinery and computer 36: Electronic and other electrical equipment 37: Transportation equipment 38: Measuring, analyzing, and controlling instruments 39: Miscellaneous manufacturing industries

Merced, CA Richmond, VA Burlington, NC Brownsville, TX Medford, OR Hickory, NC Green Bay, WI Washington, DC Richland, WA Galveston, TX Lawton, OK Lewiston, ME Vineland, NJ Steubenville, OH Texarkana, TX Rochester, MN Anderson, IN Flint, MI San Angelo, TX Providence, RI

0.595 0.193 0.553 0.420 0.551 0.332 0.347 0.422 0.645 0.371 0.655 0.236 0.480 0.646 0.288 0.563 0.648 0.658 0.458 0.251

Source: Author's calculations based on 1992 CBP.

the local market, enhancing competition among them. The greater competition will be reflected in lower price, given quality. In addition to PMSA employment, I use local industry composition to capture market thickness. Industry composition should be correlated with market thickness. Even in an area with large employment, if the city consists of industries that do not require a service intensively, the market thickness for the service in the city would be small. An intensity measure will be calculated for each of the four services examined in this paper. Note however that, as I mention later, due to the limitation of the data, the intensity measure can be calculated only for the manufacturing sector. Therefore, in practice, I examine whether a plant's likelihood of outsourcing a given service is greater if the plant is located in a city consisting of manufacturing industries that require the service more intensively. To illustrate how industrial composition might influence the extent of the market, consider the example of Washington, DC, and Denver, CO, two PMSAs that are very similar in terms of manufacturing employment: manufacturing employment is 80,565 in Washington, DC, and 83,059 in Denver. However, their industrial composition is quite different. The printing and publishing industry constitutes 42% of the manufacturing sector in Washington, DC, whereas it represents only 17% in Denver. On the other hand, the fabricated metal industry constitutes only 3.8% of manufacturing in Washington, DC, compared with 12% in Denver (the 1992 County Business Pattern (CBP) (U.S. Census Bureau, 1994)). If a service's percentage of total input is intrinsically different between the printing and publishing industry and the fabricated metal industry, then the demand for that service is different in each PMSA even though they are the same size in terms of their employment. To demonstrate how industrial composition differs across geographical areas, in Table 3, using the 1992 CBP, I list the PMSAs within which the degree that a given industry (2-digit SIC) dominates in the manufacturing sector is greater than any other PMSA. Each PMSA specializes in

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a different manufacturing sector, indicating the significant difference in industrial composition across PMSAs. For example, the manufacturing sector in the Merced, CA, PMSA is dominated by the food processing industry, and its share in the Merced, CA, PMSA is greater than that of any other PMSA. However, Merced PMSA's manufacturing sector does not include Tobacco, Chemical, and Leather industries, each of which dominates the manufacturing sectors in Richmond, VA, Richland, WA, and Lewiston, ME, respectively.9 3.3. Index for the service usage intensity In this section, I describe a framework that captures the effects of industry composition and develop an index for the service usage intensity. For simplicity, I assume there is only one kind of service. Using a Cobb–Douglas specification, I write a final producer's production function as x = qγz1-γ, where x is output level, q, service input, z, material inputs, and γ a share parameter for service input. I assume that the service input is traded within a local market, whereas the material input is traded in international and/or national markets; the price of the material input is given to a city. Treating the price of the material input as the numéraire and solving the cost
minimization 1−g
1−g g 1 problem with the level of output given at ¯x , a firm's demand for service is q ¼ 1−g ¯x; p p is the market price if a firm decides to outsource the service but it can be considered a marginal cost if the firm performs the service in-house. By approximating this expression using a Maclaurin series, we obtain q ¼ gp¯x. Note that the share parameter γ is likely to be different across industries. Let γl represent γ for industry l, σkl, the share of industry l in the total output of final producers in city k, and X¯k, the aggregate output in city k. Then, the demand for a service by industry l in city k can be written as ¯ qkl ¼ gl rkl Xpkk . Thus, if all final producers were to outsource the service, the aggregate demand for the service in city k, Qk, is written as Qk ¼ hk

X¯ k pk

ð2Þ

where θk = Σlγlσkl, which represents the average intensity of the service usage by industries in city k. θk varies across cities, because industrial composition varies across cities, and because the cost share parameters for a given service are different across industries, while they are assumed to be the same across cities for a given industry. In reality, a firm uses various kinds of services, and the intensity of the firm's use of services would vary across services. Introducing multiple services and labeling each of them by s, I write θk for service s as X hsk ¼ gsl rkl ; ð3Þ l

where

γls

is the cost share parameter for service s of industry l.

3.3.1. Calculating the index First, I calculate cost share parameters {γls}, which tell us how intensively each industry requires service s, regardless of location and how the service is procured. A typical approach to estimate γls may be to use Input–Output (IO) coefficients. However, in my framework where 9

Note that the CBP publication was used for Table 3 to ensure that no confidential data was revealed.

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some plants perform services in-house, IO coefficients are not plausible, since they are calculated by ignoring the fact that some plants perform services in-house. Thus, the IO coefficient is in general smaller than a true share parameter. In addition, the degree of the downward bias would vary across industries depending on the fractions of the plants that are not outsourcing. In this paper, I use the cost information from the ASM, which allows me to estimate the share parameters using only the plants outsourcing a given service. Note that this lets me calculate {θks} based on information only from the manufacturing sector. As I describe in Appendix A, the exclusion of other industries does not influence the qualitative result of the empirical estimation, as long as a service's cost share outside of the manufacturing sector is not systematically different from that of the manufacturers. In the estimation, however, I also control for the share of nonmanufacturing sector in a PMSA economy, which would proxy for the service usage intensity that comes from the non-manufacturing sector. As I mentioned earlier, in estimation, the coefficient for θs captures how a plant's decision to outsource any amount of given service is associated with how intensively the service is used by the whole manufacturing industry at the plant's locality. Note that there might also be a situation where it is better to restrict ourselves to the manufacturing sector in capturing the degree of market thickness. For example, within each service category, service is further differentiated. The type of, for example, software data-processing services that manufacturing industries require may be different from that of other industries. It is also possible that service suppliers find it advantageous to specialize in servicing a specific industry. In such cases, the market base that influences the entry of suppliers servicing manufacturing plants is the demand size based on the manufacturing sector. To calculate γls, I first calculate the share of the cost of service s in the total production cost for each manufacturing plant, using the ASM.10 I then take the average of such shares for the plants in each 3-digit SIC industry. Note that this calculation is based on the data of plants outsourcing the service; the data of the in-house cost of a given service are not available.11 In my framework, I assume that both outsourced and in-house services are identical in terms of productivity, and thus a service's cost share of a plant is the same whether that plant outsources or produces the service in-house.12 Table 4 shows summary statistics for the estimated cost share parameters, which differ substantially across industries. Next, I calculate each of the 3-digit SIC manufacturing industry's share in local manufacturing production {σkl} and using the industries' shares in local manufacturing output as weights based on Eq. (3), I calculate θks by taking the weighted average of cost share parameters {γls}.13 Table 5 shows summary statistics of θks calculated at PMSA level.14 10 The total cost is calculated based on costs for labor, capital, materials, and other peripheral costs, which are provided in the 1992 ASM. Note again that if some services are produced in-house, it must be the case that the plant uses its employees to perform these services; since these costs are already accounted for in the labor costs, one would not underestimate the total cost in this case. 11 When a plant produces a service in-house, the cost should be included in labor costs. However, the data provide neither the breakdown nor the number of employees engaged in the in-house production of a particular service. 12 Note that in the data, when a plant outsources a service, it is possible that the plant also performs some amount of that service in-house. Thus γsl may be understated. However, under the assumption that the ratio of the in-housed amount and the outsourced amount is exogenously determine, γsl can still be used as a measure of relative greatness of the service's cost share across industries, and thus it will not influence the relative greatness of the service usage intensity measured by θsk. 13 Ono (2001) shows an examination of the correlation between θs and the number of suppliers by regressing the number of suppliers on θs and population for each of the four services. θs obtained positive and significant coefficients, which justifies my use of {θs} in capturing the effect of local market scale. 14 The 1990 definition of PMSAs is used, while a CMSA is used if a PMSA is a subset of the CMSA.

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4. Empirical results I proceed with the empirical analysis, using data at industry-level and estimating how the outsourcing propensity of a given manufacturing industry varies across PMSAs with the measures of market thickness. I then use plant-level data, and controlling for plant characteristics, I examine how a plant's likelihood of outsourcing is associated with the measures of market thickness. 4.1. Industry-level analysis For a given 2-digit SIC manufacturing industry, I calculate the share of plants in the sample that outsource a given service in each city. Using the share as a dependent variable, I perform a weighted least square regression with the service usage intensity index θs and log PMSA employment. I assume that the coefficients of these variables are common across industries. Thus I pool the data of different manufacturing industries and perform a regression, adding the 2-digit SIC industry dummies to the regressors. For each industry, the number of plants in each PMSA is used to form the weights. More specifically, we estimate the following equation: Probslk ¼ bs1 hsk þ bs2 Nk þ hsl þ uslk ;

ð4Þ

s represents the probability that industry l in city k outsources service s and is where Problk measured by the share of plants outsourcing. Nk is log PMSA employment in city k, and hls is industry fixed effects. Note that here I use the θks that is based on {γls} at the 2-digit SIC level, while I use θks based on the 3-digit SIC industry data for the probit analysis. As I mentioned before, I run the regression with and without controls for the share of the non-manufacturing sector in the PMSA economy; the results are qualitatively the same. In Table 6, I present the result of the regressions in which I control for the share of the non-manufacturing sector in PMSA employment. Consistent with my hypothesis, the coefficients for PMSA employment are positive and significant for all services, suggesting that plants in bigger cities are more likely to outsource services. However the relationship between the service usage intensity index θs and an industry's outsourcing likelihood varies across services. Though coefficients for θs are not significant for all services, they are positive and significant for bookkeeping and accounting and for legal services. For these services, a given manufacturing industry's outsourcing propensity tends to be greater in a city that consists of a group of manufacturing industries that require these services more intensively. Lastly, let me note that the coefficient for the share of non-manufacturing sector is negative for all services. For a given industry, the share of plants' outsourcing is greater in a city with a greater

Table 4 Cost share parameters: γsl Estimated for each 3-digit manufacturing industry

Advertising Bookkeeping and accounting Legal services Software and data-processing Source: Author's calculations based on data from LRD.

Mean

S.D.

.0127 .0048 .0045 .0028

.0126 .0031 .0024 .0019

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Table 5 Service usage intensity index: θs 271 PMSAs

Advertising Bookkeeping and accounting Legal services Software and data-processing

Mean

S.D.

.0108 .00415 .00412 .00253

.00376 .000850 .000940 .000749

Source: Author's calculations based on data from LRD.

manufacturing share. It is possible that the intensity of service usage is greater for the manufacturing sector than non-manufacturing sectors for which the intensity measure was not calculated due to the lack of data. In this case, the greater share of non-manufacturing sector would lower the service usage intensity for a city as a whole. It is also possible that within each of the service categories, services are differentiated further. A high concentration of the manufacturing sector in a city might increase the demand for a service specific to manufacturing. I will discuss this point in more detail when I present the probit analysis controlling for plant characteristics. 4.2. Plant level analysis Here I control for plant characteristics, utilizing plant-level data. I perform probit analysis, controlling for size, age, industry, and affiliation type of each plant. For plant size, I use a measure of Asset — the sum of building and machinery assets estimated at the beginning of year. Age is based on the first year a plant started the current business.15 Affiliation type is controlled for by a dummy variable indicating plants that are affiliated with multi-plant firms. Lastly, here I control for industry by including 3-digit SIC industry dummies. The summary statistics for these variables are shown in Table 7. The results from the probit are again qualitatively the same with and without controlling for the share of the non-manufacturing sector. Table 8 shows the results of the probit analysis, in which I control for the non-manufacturing share. Consistent with my hypothesis, the coefficients for PMSA employment are again all positive and significant; manufacturing plants in bigger cities are more likely to outsource. Market thickness, represented by PMSA employment, might be positively associated with the number of service suppliers and thus the degree of competition among them. Greater competition among intermediate suppliers, in turn, would make outsourcing less costly as compared to in-housing. Now, let us examine whether the service usage intensity of the local manufacturing sector surrounding a plant has any additional effect on the plant's outsourcing likelihood. The results are again different across services. The coefficients for θs are positive for all services, but not highly significant except for legal services. (For advertising, bookkeeping and accounting, and software data-processing services, p-values are .62, .14, and .21, respectively.) Note that such effects of θs are estimated by controlling for PMSA employment as well as non-manufacturing sector share. For legal services, the positive and significant coefficient for θs suggests that given city size and the share of non-manufacturing sector, a manufacturing plant is more likely to outsource legal services when the plant is located in the same city with other manufacturing plants intensively using the legal services. It is possible that the geographical concentration of manufacturing “Age” is defined as the years since a plant started the business classified in the same SIC-code (2-digit) as the plant's current business. The first year they started the current business is identified every 5 years by the Census of Manufactures. 15

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Table 6 Linear probability regression 3,538 PMSA-industry units θ

s

log PMSA employment Share of non-mfg. 2-digit SIC dummies

Advertising

Bookkeeping and accounting

Legal services

Software data-processing

2.085 (1.42) .0108⁎⁎⁎ (3.53) − .0895 (−1.16) Yes

20.290⁎ (1.93) .0395⁎⁎⁎ (10.55) − .148⁎ (− 1.89) Yes

15.095⁎⁎ (2.01) .0119⁎⁎⁎ (3.31) −.168⁎⁎ (− 2.04) Yes

− 3.804 (−.38) .0175⁎⁎⁎ (3.82) − .205⁎⁎⁎ (−2.79) Yes

Source: Author's calculations based on data from LRD. ( ): t-statistics calculated based on White-corrected standard errors with clustering over industries in the same PMSA. ⁎: Significant at 10% Level. ⁎⁎: Significant at 5% Level. ⁎⁎⁎: Significant at 1% Level.

industries that require intensive use of legal services attracts more law firms to the city, especially those which specialize in servicing the manufacturing sector; this increases the competition among law firms and makes outsourcing more cost efficient than having an in-house legal department. Note that in the stylized framework that I use to derive the index for the service usage intensity, it is shown in Eq. (2) that θks X¯k represents the demand base for service s in city k. Therefore, I also performed probit analyses with the product of θks and PMSA employment, instead of estimating the coefficients separately. The coefficients for the product of θks and log PMSA employment are positive and significant for all services; dF / dX is .178 for advertising, 3.21 for bookkeeping and accounting, 1.668 for legal services, and 2.719 for software data-processing services; these coefficients were all significant at 1% level. Let us turn to the results regarding other variables. The coefficient for the share of nonmanufacturing sector is negative for all services and is highly significant for software dataprocessing services. For software data-processing services, a plant is more likely to outsource if the plant is in a city with a greater manufacturing share. Again, it is possible that the negative coefficient might be indicating that the intensity of the use of software data-processing is greater for the manufacturing sector than non-manufacturing sectors for which the intensity measure was not calculated due to the limitation of data. However, as mentioned earlier, it is also possible that within each of the service categories, services are differentiated further. It is possible that high Table 7 Summary statistics of regressors Mean PMSA level variables: 271 PMSAs log PMSA employment Non-mfg. share in PMSA employment Plant characteristics: 31,944 plants log assets (thou.) Plant age Fraction of plants in multi-estab. firms: .66 Source: Author's calculations based on data from LRD.

11.6 .807

7.8 13.94

S.D. 1.14 .0889

2.09 11.56

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Table 8 Probit anlysis dF / dX Advertising θ

s

log PMSA employment Share of non-mfg. Size Age D: Multi 3-Digit SIC dummies

.720 (.49) .00835⁎⁎⁎ (2.99) − .0273 (−.36) .0284⁎⁎⁎ (9.42) .00410⁎⁎⁎ (11.54) − .181⁎⁎⁎ (−15.05) Yes

Bookkeeping and accounting

Legal services

Software data-processing

14.43 (1.46) .0261⁎⁎⁎ (6.48) − .109 (− 1.53) .0131⁎⁎⁎ (4.38) .00340⁎⁎⁎ (9.51) − .435⁎⁎⁎ (− 54.61) Yes

14.59⁎⁎ (2.09) .0118⁎⁎⁎ (3.72) −.098 (− 1.42) .0647⁎⁎⁎ (18.63) .00399⁎⁎⁎ (11.26) −.255⁎⁎⁎ (− 22.12) Yes

11.025 (1.26) .0230⁎⁎⁎ (6.14) − .121⁎⁎ (−1.90) .0858⁎⁎⁎ (34.67) .00274⁎⁎⁎ (8.28) − .110⁎⁎⁎ (−11.92) Yes

Source: Author's calculations based on data from LRD. ( ): z-statistics calculated based on White-corrected standard errors with clustering over plants in the same PMSA. ⁎⁎: Significant at 5% Level. ⁎⁎⁎: Significant at 1% Level.

concentration of the manufacturing sector in a city increases the demand for software dataprocessing services specific to manufacturing, say a CAD system. The greater demand then increases the entry and therefore the competition of specialized suppliers, and in turn makes it more favorable for manufacturing plants to outsource the service. Turning to plant characteristics, both plant size and age are positively associated with a plant's outsourcing probability; bigger and/or older plants are more likely to outsource services. It is possible that outsourcing requires some fixed costs incurred in service transaction and the searching process for compatible suppliers, which gives bigger firms a greater advantage from outsourcing.16 Similarly, it is possible that plants that just started their business face difficulties in finding compatible suppliers, which would lower their likelihood of outsourcing. The coefficients for the dummy variable indicating plants affiliated with multi-establishment firms are all negative and significant, showing that the role of outsourcing might be shared within a firm. (For more detailed analysis regarding the intra-firm sharing of outsourcing role, see Ono (2003).) In Table 9, I show the effect of a one standard deviation increase in each variable on a plant's outsourcing likelihood. First, one standard deviation increase in city size (log PMSA employment) increases a plant's outsourcing likelihood by .01, .03, .014, and .026 for advertising, bookkeeping and accounting, legal, and software data-processing services, respectively. These magnitude are equivalent of 8% to 28% of the average deviation of PMSA level outsourcing propensities from their mean shown on Table 2. Now, let us compare the effect of city size with that of plant characteristics. The effect of PMSA size is generally smaller than that of plant characteristics. However, for bookkeeping and accounting, the effect of one standard deviation increase in PMSA employment is compatible and 16 Note that given that the dependent variable for each service indicates whether a plant outsources any amount of the service, the positive coefficient could be interpreted as a result of a larger plant requiring a greater variety of services, which increases the probability that at least one type of service is outsourced.

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Table 9 Effect of 1 S.D. change θs log PMSA employment Non-mfg. share Size Age

Advertising

Bookkeeping and accounting

Legal services

Software data-processing

.00271 .00952 − .00252 .0594 .0474

.0123 .0298 − .00969 .0274 .0393

.0137 .0135 −.00871 .129 .0459

.00826 .0262 − .0108 .179 .0317

Source: Author's calculations based on data from LRD.

even slightly greater than that of plant size. The degree of competition may change vastly with city size for accounting firms and related services such as processing bookkeeping, billing, or preparing financial statements and/or tax returns. Finally, note that the level effect of city size is quite small for advertising. It is possible that for the advertising industry, factors other than local market price, such as the reputation of suppliers, is more important in deciding with which supplier to contract, and results in the smaller effect of local market size. Next, consider the effect of the service usage intensity measure based on manufacturing sector. The effect of a one standard deviation increase in θs ranges from .0027 to .0137 and is smaller than effects of other variables. However, for legal service for which the coefficient for θs is most significant, the effect of a one standard deviation change in θs is equivalent to that of PMSA employment. Note that if I calculate the effect based on the results of probits that do not control for non-manufacturing sectors share, the effects of θs are much greater, while the effect of other variables stay almost the same. The magnitude of the effects of a one standard deviation increase in θs are .00907 for advertising, .0447 for bookkeeping and accounting, .035 for legal services, and .0209 for software data-processing services. For bookkeeping and accounting and legal services, the effects of a one standard deviation increase in θs are greater than those of PMSA employment. 5. Robustness analysis 5.1. An issue of service usage intensity In the previous section, one of my measures for market thickness was the service usage intensity index θs. The measure for a given service is calculated based on the service's cost shares computed for each manufacturing industry. The service's cost shares are derived based on the expenditure data of plants that outsource the service based on the framework in which the service's cost share is assumed to be independent regardless of the way the service is procured (see Section 3.3). Note that there is a possibility that a greater need for a service increases the probability that a plant outsources a part of the service, and therefore my left-hand side variable.17 However, for each industry, a service's cost share is calculated at national level. Therefore if there is positive correlation between a service's cost shares and outsourcing propensities, the greater use of the service by a particular industry will shift the industry's propensity to outsource regardless of the

17

On the other hand, it is also possible that a greater need for a service might increase the scale economy of performing the service in-house (even after controlling for overall plant size) and may reduce the plant's likelihood of outsourcing.

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Table 10 Summary statistics for θ˜ s: 31,944 plants

Advertising Bookkeeping and accounting Legal services Software and data-processing

Mean

S.D.

.00310 .0132 .00501 .00502

.00123 .00719 .00185 .00193

Source: Author's calculations based on data from LRD.

location of the industry. Such shift of the outsourcing propensity is captured by industry dummy variables. In the case of the linear probability model, with the presence of such a positive correlation, based on Eqs. (3) and (4), an industry's propensity to outsource service s in city k is written as X Probslk ¼ bs1 gsl rlk þ bs2 Nk þ bs3l gsl þ hsl þ uslk ; ð5Þ l s s γl β 3l

will be subsumed by industry fixed effects hls. By demeaning, the equation is where rewritten as X Probslk −Prob ¯ sl ¼ bs1 gsl ðrlk −¯ r l Þ þ bs2 ðNk −N¯ Þ þ uslk −¯usl ; ð6Þ l

where Prob ¯ ls, σ¯l, and ūls are the national averages of each of the variables for industry l, and N¯ is s the average city size. The cost share parameter for industry l, γls, influences Problk − Prob ¯ ls as a weight for the industry's relative degree of geographical concentration represented by σlk − σ ¯l.18 To eliminate any effect of the own-industry cost share in examining a plant's outsourcing propensity, here, I perform an experiment using a new index, which measures the weighted average of a service usage intensity of industries other than the industry of the plant I consider. Thus, I ask whether a plant benefits from the more intense use of a service by other industries. For plants in industry j in city k, the new index will represent the average service usage intensity for industries in city k other than the industry j. More specifically, the new index is formulated as; s h˜ kj ¼

X

gsl rjKk;l ;

ð7Þ

lpj

where γls stands for intensity of using service s for industry l, and σj⊄k,l stands for industry l's share in total output of manufacturing industries in city k excluding industry j. Table 10 shows the summary statistics for the new index, and Table 11 shows the results of probit analysis with the new index. Again, the coefficients for PMSA employment are all positive and significant, and coefficients for θ˜ s are now positive and significant for both bookkeeping and accounting services and legal services. Again, I also performed the probit analyses with the product of θ˜ s and log PMSA employment. The coefficients are positive and significant for all services.

In an extreme case where γsl is very high for a particular industry and is very small for others, β1s captures how much more likely the industry is to outsource in a city with high concentration of the own industry. 18

Y. Ono / Regional Science and Urban Economics 37 (2007) 220–238

233

Table 11 Probit anlysis: With θ˜ s dF / dX θ˜ s log PMSA employment Share of non-mfg. Size Age Dummy: Multi 3-digit SIC dummies

Advertising

Bookkeeping and accounting

Legal services

Software data-processing

1.023 (1.49) .00857⁎⁎⁎ (3.23) − .0317 (−.42) .0285⁎⁎⁎ (9.48) .00410⁎⁎⁎ (11.51) − .181⁎⁎⁎ (−15.01) Yes

7.128⁎⁎ (2.23) .0278⁎⁎⁎ (7.07) − .103 (− 1.47) .0133⁎⁎⁎ (4.45) .00339⁎⁎⁎ (9.48) − .435 (− 54.78) Yes

10.457⁎⁎ (3.14) .0137⁎⁎⁎ (4.73) −.0960⁎⁎ (− 1.41) .0650⁎⁎⁎ (18.77) .00399⁎⁎⁎ (11.25) −.255⁎⁎⁎ (− 22.12) Yes

7.627 (1.63) .024⁎⁎⁎ (7.75) − .120⁎⁎⁎ (−1.89) .0858⁎⁎⁎ (34.67) .00273⁎⁎⁎ (8.25) − .110⁎⁎⁎ (−11.92) Yes

Source: Author's calculations based on data from LRD. ( ): z-statistics calculated based on White-corrected standard errors with clustering over plants in the same PMSA. ⁎⁎: Significant at 5% Level. ⁎⁎⁎: Significant at 1% Level.

5.2. Endogenous location choice: Fixed-effect logit As mentioned before, manufacturing plant locations are likely determined by exogenous factors, such as proximity to the source of raw materials, ports, and so forth.19 Returning to the example of the transportation system, even a good transportation system might not be an adequate reason for a manufacturing plant to move to that area, while a specialized supplier might prefer to operate there (which is the reason we do not use the number of service suppliers as a regressor). Therefore it is unlikely that the positive relationship between a plant's outsourcing probability and market size, which are found from the probit analyses, is driven by the possibility that a plant with intrinsically greater outsourcing probability will move to a greater local market. However suppose that a plant's location choice could be adjusted at the margin to locationspecific factors that influence local outsourcing environments. Such plants' location choice would influence our results when there are omitted variables. For example, a plant that has an inefficient technology for the in-house production of a service might be attracted to a city with greater market thickness for that service. If some of the plant characteristics that determine the efficiency of inhouse technology are not controlled for, the coefficient of θs would partially result from the correlation between the omitted variables and θs. There might also be unmeasured/uncontrolled city-specific factors that influence local price of services. An especially low price for a service in a PMSA attracts industries that intrinsically use that service more, increasing θs in the PMSA. Not controlling for the city fixed effects will lead to positive correlation between the likelihood of outsourcing and θs, even if there is no causal effect. As a solution for above-mentioned problem, I conduct an experiment, in which I perform a fixed-effect logit analysis to control for unobservable plant characteristics; plant fixed effects 19

In addition, the cost of each of the service inputs is a very small portion of the total costs for a manufacturing plant.

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Table 12 Fixed-effect logit 17,510 plants included θ

s

Coefficient

Z-stat.

25.756⁎⁎⁎

12.944

Source: Author's calculations based on data from LRD. ⁎⁎⁎: Significant at 1% Level.

would subsume PMSA fixed effects as shown below.20 Note that since I have only 1 year of data, I cannot use the variation in a plant's outsourcing decision over a number of years to control for plant/PMSA fixed effects. However, to the extent that plant/PMSA fixed effects are common across services, using the variation of a plant's outsourcing decision across different services, we can control for plant/PMSA fixed effects. Denoting plant specific effects by { fi} and PMSA specific effects by {gk}, and imposing the same structure across services, I rewrite Eq. (1) as Ykis⁎ ¼ ðhsk ; Nk ; AiVÞb þ fi þ gk þ uski ;

ð8Þ

where θks is the average service usage intensity measure as defined by Eq. (3), and Nk is log PMSA employment, and Ai is a vector of plant characteristics. Based on Eq. (8), I perform a fixed-effect logit analysis as in Chamberlain (1980). Again, note that fi subsumes the PMSA effects gk, and that fi is common among different services. To the extent that this assumption is true, we can remove the effects of unobservable plant/PMSA characteristics from the conditional likelihood function. Note also that since Nk and Ai do not vary across services, the coefficients for these variables are not identified in this experiment. Let ni stand for the number of services that plant i outsources, where ni is a positive integer from s 1 to 4. Let also yki stand for the realized value for Ykis which takes 0 or 1 depending on which choice is made. Following Chamberlain (1980), I use the likelihood that a particular set of services are selected to be outsourced conditional on ni services being outsourced (see Appendix B for derivation), such that   P s s exp h by k ki XX s  ; ln L¼ ð9Þ P s s P k iaIk exp hk bdi di aBni

s

where Ik is a set of plants in PMSA k. Bni is the set of all possible combinations of outsourcing decisions made for the four services where the total number of services outourced by plant i is ni; Bni ≡ {di = (di1,,,di4)|dis = 0 or 1,Σsdis = ni}. We utilize the variation in a plant's outsourcing decisions across services to identify the parameter. After eliminating plants that outsource all four of the services and those that outsource none of them, which do not contribute to the estimation, 17,510

20

As another solution, I restrict the sample to plants whose location choices are considered more likely to be exogenous. In particular, I restrict the sample to older plants that 1) started their business before the significant growth in the service industry during the 1970s, and 2) have not changed their location since that time. The probit analyses obtained based on such a sample supports the results from the unrestricted samples.

Y. Ono / Regional Science and Urban Economics 37 (2007) 220–238

235

plants remain in the sample. Table 12 shows the result. Again, it is shown that θs is positively and significantly associated with a plant's outsourcing decision. I also performed the conditional logit with θ˜ s, which represents the average service usage intensity for industries other than the plant's own industry. The result was qualitatively similar. A given plant is more likely to outsource services that local industries use intensively than services that local industries use less intensively.21 This mitigates concern that my results are driven by plant/city specific effects as opposed to the effect of market thickness. 6. Conclusion In this paper, I examine the decisions by manufacturing plants to outsource business services in relation to the degree of local market thickness in order to test whether there is any evidence that outsourcing is supported more in a thicker market. Using plant-level data from the 1992 ASM and using the market thickness measure, I found that even controlling for plant characteristics, a plant's use of outsourcing is positively and significantly associated with the degree of market thickness measured by PMSA employment. These empirical findings indicate a possibility that a firm in a larger city benefits from the greater scale of a local market by outsourcing. The results also indicate that, for legal services, in addition to greater size of a city, if the city has high share of industries that use the service intensively, a plant's outsourcing probability is greater. Moreover, the share of manufacturing sector is positively associated with a plant's outsourcing propensity. As I discussed, it is possible that the kind of service required by manufacturing sector differs from that of other sectors. The greater demand base from the manufacturing sector might attract suppliers specializing in servicing the manufacturing sector, which in turn enhances their competition and allows a manufacturing plant in the locality to outsource the service. Overall, the results presented in this paper support the view in Holmes (1999) that more vertical disintegration is facilitated when the demand for an intermediate input is geographically concentrated. The evidence presented here is even more direct in the sense that it is based on plantlevel data, which allows us to look directly at a plant's outsourcing decision and to connect the decision regarding outsourcing of a particular service to factors that would decide the local market thickness for that service. The findings in this paper are also consistent with those in Hubbard (2001), which provides evidence that simple spot arrangements are less common when local markets are thin based on the data on the contractual form of trucking. Hubbard (2001) also shows that the evidence is stronger for transactions which involve relation-specific investment. Such an element will be interesting to consider in the future to explain the different degree across services to which the use of outsourcing varies with the market size. Acknowledgements I am most grateful for advice provided by J. Vernon Henderson and Andrew Foster. I also thank Thomas Hubbard, Harumi Ito and Daniel McMillen, and the Editor (Antonio Ciccone) and two anonymous referees for helpful comments and suggestions. Support of the National Science Foundation (grant SBR9730142) for this research is gratefully acknowledged. All remaining 21 The coefficient was 23.467⁎⁎⁎ for θ˜ s. I also performed the analyses with θs × log PMSA employment, as well as θ˜ s × log PMSA employment. Again qualitative results remain the same. The coefficients are 1.761⁎⁎⁎ for θs × log PMSA employment, and 1.645⁎⁎⁎ for θ˜ s × log PMSA employment.

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errors are mine. The research in this paper was conducted while the author was a Census Bureau research associate at the Boston Research Data Center. Research results and conclusions expressed are those of the author and do not necessarily indicate concurrence by the Census Bureau of the Federal Reserve Bank of Chicago. This paper has been screened to insure that no confidential data are revealed. Appendix A. Effect of excluding other industries in calculating θs To simplify the notation, I omit the superscript s in what follows. For each service, the average service usage intensity for industries in city k, θk, can be written as θk=θ(θkM,θkS,θkO ), where θkM is the service usage intensity of the manufacturing sector, θkS is that of the service sector, θkO is that of other sectors, respectively. Then, denoting a plant's outsourcing probability by Probki, we can write dProbki AProbki dhk ¼ : Ahk dhM dhM k k As long as θkS and θkO are not systematically (negatively) correlated with θkM, by examining the dProb ki sign of dh , we could also infer the sign of AProb Ahk (how the probability of outsourcing changes with the service usage intensity based on all sectors). Note that there might be attenuation because of possible noise in the measure of the service usage intensity. However, this suggests that the coefficients could have been greater than estimated, strengthening my empirical results. ki

M k

Appendix B. Conditional likelihood Below, I explain how Eq. (9) is derived. For simplicity of notation, here I assume that Ai and Nk is included in fi and gk, respectively. First, based on Eq. (8), the probability that plant i outsources service s is written as follows: ProbðYkis ¼ 1Þ ¼

expðbhsk þ fi þ gk Þ : 1 þ expðbhsk þ fi þ gk Þ

ð10Þ

The probability that plant i performs the service in-house is, ProbðYkis ¼ 0Þ ¼

1 : 1 þ expðbhsk þ fi þ gk Þ

ð11Þ

Assuming that decisions of outsourcing are independent across services, the probability that particular decisions are made for four services are as follows: exp Probð y1ki ; y2ki ; y3ki ; y4ki Þ ¼ ¼

ð

P s

js ð1 þ

Þ

ðbhsk þ fi þ gk Þykis

þ fi þ gk ÞÞ P s s expðni ð fi þ gk ÞÞexp bhk yki expðbhsk

ð

s

js ð1 þ expðbhsk þ fi þ gk ÞÞ

Þ;

where ni = Σsykis , the total number of services outsourced by plant i.

ð12Þ

Y. Ono / Regional Science and Urban Economics 37 (2007) 220–238

237

Now, the probability that plant i outsources ni number of services is

Prob

X

! ykis ¼ ni

¼

X exp

 P s

 ðbhsk þ fi þ gk Þdis

js ð1 þ expðbhsk þ fi þ gk ÞÞ   P P s s expðni ð fi þ gk ÞÞ exp bhk di di aBni

s

¼

js ð1 þ

di aBni expðbhsk

s

þ fi þ gk ÞÞ

ð13Þ :

Using the above,

Prob

y1ki ; y2ki ; y3ki ; y4ki j

X s

! ykis

¼ ni

Probðy1ki ; y2ki ; y3ki ; y4ki Þ   P s yki ¼ ni Prob s   P s s exp bhk yki s  : ¼ P s s P exp bhk di ¼

di aBni

ð14Þ

ð15Þ

s

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