Localization vs. Diversity Economies “Marshall’s Scale Economies” By J. Vernon Henderson Journal of Urban Economies (2002) “Agglomeration Economies and the High-Tech Computer” By Nancy Wallace and Donald Walls Fisher Center Working Paper (2004) Beia Spiller, June 3, 2008
“Marshall’s Scale Economies”: Introduction Localization/Marshall, Arrow Romer (MAR) Economies: -Externalities arising from other plants in the same industry Urbanization/Jacobs Economies: -Externalities arising from scale or diversity of local economic activity outside of own industry -Result is important for urban development planning: industrial concentration vs. diverse metro areas
Introduction Other issues: -Differing impacts for single or multi-plant firms -Different levels of externalities due to new or established firms -Lagged vs. current externality effects
Industries -5 major capital goods or machinery industries -construction, metal working, special industrial, general industrial, refrigeration machinery and equipment -4 major high-tech industries -computers, electronic components, aircraft, medical instruments
Data -Census of Manufacturers- 1963, and every five years between 1967-1992. -Most of this data covers multi-plant firms (corporate) -Annual Survey of Manufacturers- non-census years -This data is taken for only non-affiliate plants -Restrictions: -Eliminates entries with imputed factors (keeps only entries with surveyed inputs and outputs) -Keeps only those firms that appear at least twice (results in mostly large/established firms in sample)
Data
Measuring Effects General Production Function: 2 � = α + β − + δ + − ε ln y ( t ) ln X ( t ) ln E ( t s ) ( t ) f ( t ) k k s j kj kj s = 0 k , j : firm, location y k ( t ): output ln X k ( t ): vector of plant inputs ln E j ( t-s ):vector of industrial environmental variables in t-s � ( t ): time fixed effects f kj : plant location fixed effects
Estimation -High tech and machinery industries are pooled: - � ’s and � ’s constrained to be same within each industry. - � ( t ) are separate for each industry
Diversity Measures 1. The degree of MSA specialization in a set of activities: 2 � � E ( t ) E ( t ) � � � ij i = − S ( t ) � � j E ( t ) E ( t ) � � i j E ij ( t ): employment in industry i in city j E j ( t ): total employment in city j � � i ( ) E ij t E i ( t ): national employment in industry i E ( t ): total national employment � � i ( E i t ) S j ( t ): ranges from 0 (perfectly diverse) to 2 (perfectly specialized)
More Diversity Measures 2. Overall MSA scale or total employment in the relevant industries. 3. MSA scale by counts of plants. 4. County level effects.
Estimation Issues Fixed effects will influence both environmental variables and inputs- OLS is biased, fixed effects estimation valid. Exogeneity Assumptions: -plant inputs and industrial environmental variables are strictly exogenous for all t to the � kj ( t ) -capital stock and labor/materials are state variables, so are exogenous to the � kj ( t )
Results: Localization/MAR Economies (1): OLS, (2): Plant/Location FE
Results: Localization/MAR Economies (1): OLS, (2): Plant/Location FE
Endogeneity Issues: Some Experiments 1. Add MSA-time fixed effects: to control for shocks that affect RHS variables and error term a. Results are similar except for high-tech non- affiliates: appear to benefit more from externalities than corporate firms. b. Small sample and loss of efficiency 2. Instruments to control for endogeneity of X k and E j a. Predetermined values of X k and E j b. Suffers from small sample size and weak instruments No evidence of correlation between X k and � kj c.
Other Localization Specifications -Localization effects only impact high-tech companies
Other Localization Specifications
Dynamic Externalities -No lagged effects for machinery or high-tech corp. -For non-affiliate high-tech: only 5 year lagged effects present.
Jacobs-Urbanization Economies
Jacobs-Urbanization Economies
Jacobs-Urbanization Economies Why does diversity appear to affect corporate machinery firms? -Unmeasured business service inputs. -Corporate machinery firms outsource more materials. Thus: Urbanization economy may be illusionary.
Conclusions Localization externalities: -mainly for high-tech industries. -very localized within county. -non-affiliates benefit more from these externalities. Diversity externalities: -no evidence to support static or dynamic Jacobs economies.
“Agglomeration Economies”: Introduction -Role of agglomeration on the production decisions of firms in the high-tech computer cluster. -Trade-off between labor market pooling and labor market poaching
Functional Definition of the Computer Cluster New definition: -Services SICs are included -High-tech firms may include establishments not normally considered “high-tech” Computer Cluster includes: -Manufacturing (hardware) -Services (software) -All establishments that meet this criteria or report to a headquarter that does. These are found from the SIC and NAICS codes
Data Information at firm level: -NETS database: 1989-2002. -Identifies geographic location of each firm: both its headquarters and its establishments. 401 8-digit SICs from several computer-related manufacturing and service industries -any firm that ever had this SIC or reported to one that did = 539,942 establishments tracked over the period.
Issues with Data 1. Several firms operate exclusively within high-tech manufacturing -Not representative of high-tech firms. 2. Most capital, R&D capital, and outsourcing decisions are made at headquarters -Can’t treat individual establishments in multi- establishment firms as individual decision makers. 3. Lack of controls for relationship between plants and HQ- can lead to underestimates of std.err.
Geography of high-tech Computer Cluster
Geography of high-tech Computer Cluster
Measures of Geographic Network of Each Firm 1. Inner Network Interactivity: �� × × w e e ij i j i j = 2 π × circ e i = employment in firm i 1 for HQ and outlying establishments w ij = .5 between separate establishments 0 for single establishment firms circ = 60 mile radius of influence
Measures of Geographic Network of Each Firm 2. Outer Network Interactivity: �� × × w e e ij i j i j = 2 dist ij e i = employment in firm i w ij = same as above, for establishments outside of 60 miles radius of influence. 2 = distance between firms i and j dist ij
Measures of Geographic Network of Each Firm 3. Firm’s total MSA-level labor market exposure to workers in computer manufacturing SICs. 4. Firm’s total MSA-level labor market exposure to workers in computer services SICs. Purpose of these measures: -Decisions made at HQ will likely affect dispersed production units -Location of establishments will likely be coordinated with proximity to other employees of similar type
Firm Structure and Estimation of Production Function -Cobb-Douglas production function, 4 inputs ( X i ): labor, purchased inputs, R&D capital, physical capital. � = α α + ε ln Q ln ln X + 0 i i i -Translog functional form: � �� = α + α + α + ε ln Q ln ln X (. 5 ) (ln X )(ln X ) 0 i 0 i ij i j i i j -Inputs and outputs are endogenous choices determined by exogenous market state variables.
Firm Production Data COMPUSTAT database: 177 publicly-traded firms operating from 1989-2002 Output: total sales Labor inputs: total employees Purchased inputs: “Cost of Goods Sold” Capital Stock: “Net Purchased Plant and Equipment” R&D Capital: research expenditures
Production Function Results Assumption : geographic dispersion and labor market structure are fixed ex ante to production decisions. -Implication : use lagged exogenous network structure as explicative variables. � � = α α + γ + ε ln Q ln ln X ln Z + i 0 − i i i k , t 1 i k Z: natural log of all four geographic network measures.
Accounting for Heterogeneity in Firm-Level Technology Method follows Mundlak and Hellinghausen (1982) Coefficients: functions of variables reflecting inter-firm heterogeneity. New Cobb-Douglas Specification: = α + β + ε X y j 0 Vj j j where → 2 ε 0 σ ~ N ( , I ) j nj j
Accounting for Heterogeneity in Firm-Level Technology Coefficients on variable factors are functions of exogenous state variables: β = δ + ω Z − 1 j j , t j and → � ω ~ N ( 0 , ) j This implies: � j are random coefficients, distributed normally, with mean δ Z . j , − t 1
Accounting for Heterogeneity in Firm-Level Technology Z j,t-1 : K x L matrix of ex-ante decisions concerning the spatial configuration of the firm’s establishments � � → → ~ � � z ' 0 0 j 1 � � → → � � � = Z 0 0 − j , t 1 � � → → ~ 0 0 z ' � � jK � � ~ z ' Where is an l k -element vector of variables that jk determine network technology effects on the firm’s variable input choices.
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