INDUSTRY CLUSTERS IN THE TVA REGION: DO THEY AFFECT DEVELOPMENT OF RURAL AREAS?

Transcription

1 INDUSTRY CLUSTERS IN THE TVA REGION: DO THEY AFFECT DEVELOPMENT OF RURAL AREAS? Mark S. Henry, David L. Barkley, and Yibin Zhang December 1997 Contractor Paper 98-9 About the Authors Mark Henry is a Professor of Agricultural and Applied Economics at Clemson University. David L. Barkley is a Professor and Economic Development Specialist in the Department of Agricultural and Applied Economics at Clemson University. Contractor papers are distributed by TVA Rural Studies as part of its effort to improve the information available to rural decision makers. Each contractor paper reflects the research and opinions of the authors. Research papers are published without going through a formal review process and TVA Rural Studies neither endorses nor disavows any opinions in these papers.

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3 3 I. Introduction As the twenty-first century approaches, businesses in the rural South face a challenging economic environment: intensification of global competition, expansion of computer-aided production and adoption of more flexible production systems. In this scenario, regional economic policy-makers, concerned with local economic development problems, are paying more attention to lagging rural areas and the formulation of new rural development strategies. As a result, policy analysis has expanded from a focus on small business development and retention and expansion programs to include the promotion of districts or clusters based on targeted industrial activities. The principal goal of this strategy is to establish industry clusters geographically bounded collections of similar and/or related firms that together provide competitive advantages for member firms and their host economies (Rosenfeld 1995). Benefits attributed to industry clusters include the availability of localization economies, conducive environments for industrial restructuring, enhanced interfirm networking, and the ability to focus institutional support. The availability of these benefits is believed to improve the productivity and growth prospects of cluster firms, and thus, regions successful in establishing clusters are expected to realize greater income and employment growth than areas following a less focused approach to industrial development. Support for targeting industry clusters as an economic development strategy is widespread. The new economic geography of Krugman (1991) and others demonstrates that a spatial concentration of economic activity stimulates a process of cumulative causation that encourages further growth of the cluster and region. Recent empirical research finds a positive correlation between the level of an industry s activity in an area and the industry s employment growth and labor productivity in the area. Since income earned in an industry derives mainly from employment and productivity growth, there may be an association between industry clusters and industry income as well. And the literature is replete with fascinating case studies of the evolution of industry clusters and their impacts on local economic development. The purpose of this study is to investigate the importance of industry clusters on industry income change in nonmetropolitan areas of the states in the Tennessee Valley Authority (TVA) region of the United States. The existence of an industry cluster will result in relatively large income growth at a nonmetropolitan location if, as with metropolitan areas, the cluster location realizes cost advantages over other nonmetro areas. Alternatively, the income growth associated with nonmetro industry clusters may be relatively small if: rural clusters are relatively undeveloped and localization economies are weak, rural areas attract the types of manufacturing plants (e.g., branch plants, standardized production, labor intensive) that neither significantly contribute to nor rely on external economies, or existing agglomerations retard the attraction of new firms because of perceived shortages in the local labor or input markets. The report is organized as follows. First, we review the advantages of an industry cluster strategy and summarize the findings of earlier research. Second, we estimate equations that explain changes in industry income (growth and decline) for TVA nonmetropolitan areas of Component Economic Areas identified by the U.S. Department

4 4 of Commerce for the period 1981 to Separate equations are estimated for fortyeight two-digit SIC industries. Area characteristics used to explain income change for each industry include variables that reflect cluster attributes, labor availability and quality, local market conditions, quality of life, public expenditure and taxation levels, and urbanization economies. Next, we summarize the empirical results. The general conclusion is that industry clustering is positively associated with nonmetropolitan income change both income gains and losses. Suggested implications of our findings for nonmetro development policy are presented in Section V. II. Industry Clusters and Economic Change Earlier empirical research and case studies find that the clustering of similar and related firms provides benefits to members of these clusters, and the availability of these benefits enhances the employment growth potential of the industry agglomerations. These benefits may be grouped into four general areas: stronger localization economies, enhanced opportunities for industrial restructuring, favorable environments for interfirm networking, and more focused institutional support (Barkley and Henry, 1997). Clustering Strengthens Localization Economies. The concentration of an industry at a particular location may result in significant cost savings or economies to firms in the cluster. These economies external to the firms but internal to the cluster are referred to as localization economies. Sources of potential cost savings include a greater availability of specialized intermediate input suppliers and business services; a larger pool of trained, specialized workers; public infrastructure investments (energy, water, waste treatment, transportation) geared to the needs of a particular industrial sector; financial markets familiar with the industry s product markets and production processes; and an enhanced likelihood of firms sharing information on markets, research and development programs, and production methods. In addition, investments (and growth) by cluster members may benefit others in the cluster by providing higher quality or lower cost inputs, attracting new customers to the area, or encouraging public investments in infrastructure that benefit all area businesses (Harrison, 1992). The presence of these external economies encourages a concentration of industrial activity at a limited number of locations, thus stimulating additional locational cost savings and encouraging further growth of the cluster. Clustering Facilitates Industrial Restructuring. Well documented in the literature is the transition in industrial organization from large, vertically integrated firms engaged in mass production to relatively small, vertically disintegrated firms focused on specialty or batch production. Vertical disintegration and the adoption of more flexible manufacturing organizations and production technologies appear to be more prominent and easily attained among firms in industry clusters (Holmes, 1995). Proximity between the more specialized firms and their input suppliers and product markets enhances the flow of goods through the production system, an especially important consideration for firms using just-in-time inventory replacement procedures (Smith and Florida, 1994). Ready access to product and input markets also is beneficial to firm survival since shortened product life cycles mandate quicker adaptability to market changes. And a spatial concentration of industry activity provides the pool of skilled labor required by the computer-aided technologies and flexible manufacturing organizations (Rauch,

5 5 1993b; Berman, Bound, and Griliches, 1994). Thus Scott (1986) concludes that vertical disintegration encourages clustering, and clustering encourages vertical disintegration. That is, the larger clusters become, the more attractive these clusters are as potential locations for the small, vertically disintegrated firms focused on small batch production. Clustering Stimulates Networking Among Firms. Networking is cooperation among firms to take advantage of complementarities, exploit new markets, integrate activities, or pool resources or knowledge to achieve economies of scale or address common problems (Rosenfeld, 1995). Networks are categorized as horizontal if they link firms that use similar specialized services or technologies or vertical if they link firms that perform different functions in the value-added chain (Rosenfeld, 1992). Horizontal networks include interfirm arrangements to conduct research and product development, share the cost of specialized services or equipment, collect marketing information, and supply markets. Examples of vertical networking are shared information and expertise among buyers and suppliers; and collaboration on product design, engineering, and marketing. Both vertical and horizontal networks provide smaller firms with the scale economies and access to information and markets normally available only to larger firms. The collaboration and cooperation associated with networks occur more naturally and frequently within industry clusters (Harrison, 1992; Rosenfeld, 1995). For example, a survey of U.S. rural manufacturing networks by Malecki and Tootle (1996) finds that firms in networks perceive significant advantages from cooperation with their counterparts. Networking firms were more likely than non-networking firms to engage in collaborating and information sharing in marketing, new product development, and technological upgrading. The networking firms also reported that their competitiveness, profitability, and locational stability were enhanced by interfirm cooperation and collaboration. Clustering Permits Greater Focusing of Public Resources. The focusing of industry development efforts on specific industry clusters permits regions to use their limited economic development resources more efficiently (Carlson and Mattoon, 1994). First, a cluster approach enables regions to focus their recruitment, retention and expansion, and small business development programs rather than attempting to provide assistance for every existing and potential type of business. This tailoring of development initiatives permits clearer identification of specific industry needs and enables (for a given budget expenditure) the provision of fewer but more highly valued program offerings. Second, because of linkages between firms in a cluster, programs supporting specific businesses will have relatively large multiplier effects for the area economy. The total employment and income gains from recruiting (or retaining) cluster members will likely exceed those associated with non-cluster firms of similar size. Third, firms in a cluster may be integrated more strongly into the local economy than a more random collection of branch plants. This intensified local dependence should contribute to a greater interest in local affairs and a higher level of community involvement by cluster members (Mair 1993). Limits to Agglomerative Forces. Krugman (1991) demonstrates that the advantages associated with industry concentrations ( centripetal forces ) will lead to expansion of the concentrations at the expense of alternative locations if countervailing locational disadvantages are not present. The principal disadvantages attributed to concentrating

6 6 activity ( centrifugal forces ) are higher transportation costs, increased competition for local inputs, higher public service costs, and increased external costs associated with congestion. Thus, prospects for growth of existing industrial concentrations relative to alternative locations depend on the relative strengths of the centripetal and centrifugal forces. Industry agglomerations may have little economic growth potential if the centripetal forces contributing to earlier industry clustering are weak relative to the centrifugal forces associated with current cluster size. Norton (1992) also suggests that the growth of industry clusters may be restricted by life cycle forces. Clusters may evolve along an industrial life cycle much like individual firms, and the later phases of this cycle may be characterized by decline. Specifically, Norton (p. 166) proposes that, over time, industry agglomerations may experience a loss of regenerative potential due to both rising costs and a tendency in the key industry for management to shrink from the unknown. Communities with clusters and firms in the clusters may cling to the old ways because the traditional methods of doing business are associated with past successes. Yet the tried-and-tested may not be most appropriate for new competitive environments. Norton cites Pittsburgh, Detroit, and Boston as examples of areas with clusters that suffered competitive declines. Prior Research Findings. Previous empirical studies provide mixed support for the contribution of regional industry agglomerations to industry labor productivity and employment growth in the region. Ciccone and Hall (1996) and Gibbs and Bernat (1997) find that labor productivity and wages are higher in areas with concentrations of an industry s activity. And studies using comparisons among states, metropolitan areas, and nonmetropolitan areas find that industry employment growth in a region is positively correlated with base year industry employment in the region (Henderson, 1986; Sveikaukas, Gowdy, and Funk, 1988; O hullacháin and Satterthwaite, 1992; and Henry and Drabenstott, 1996). McDonald notes, however, that many of these earlier findings reflect weak localization effects, that is, employment growth was highest for clusters but employment growth rates were higher in areas with smaller base year employment (indicating a decentralization of employment in that industry). And other research concludes that the importance of industry localization effects is limited, varies significantly among cluster types, and declines as city size increases. Case studies of industry clusters, both metro and nonmetro agglomerations, are more positive regarding the role of clusters in industry employment growth and regional economic development. Yet some regional scientists remain skeptical about the ability to replicate such successes in many rural areas. For example, Camagni (1995) finds that conducive environments for new industry clusters exist in lagging European regions, but they are rare and not fully developed. And Hansen (1993) suggests that many nonmetro areas are unpromising locations for clusters because of their legacy of large corporate farms, tenant farms, reliance on government subsidies, low levels of education, and discrimination. III. Research Design This study is a cross-sectional and time series analysis of income change in forty-eight two-digit SIC industries for the 1981 to 1994 period in rural counties of the TVA region. The purpose of our empirical analysis is to determine if there is a statistical association between industry income change in nonmetropolitan areas and the level of industry

7 7 activity in the areas in the prior period (using annual data). Evidence of such an association is consistent with the existence of localization economies and provides support for the promotion of industry clusters as a nonmetropolitan industrialization strategy. This approach differs from earlier research by not imposing a priori restrictions on the beginning and end periods, e.g., change across regions over a ten year period. Alternatively, one could impose alternative lag structures on the data (e.g., two years, five years or 10 years) and reestimate the regressions. However, missing data problems severely limit the number of observations even at the two SIC digit level as the lag length is increased. Accordingly, one-year lags are maintained in this research. Industry clusters or agglomerations are measured by income at the two-digit SIC level. This level of income disaggregation was selected to insure sufficient spatial dispersion of activity across nonmetropolitan areas and permit statistical analysis. The two-digit level of industry classification also captures many of the forward and backward linked industries of more finely defined three and four-digit sectors. The principal shortcomings of defining clusters with two-digit income are that members of these industry categories will, on average, have fewer commonalities than members of more specific industry classifications, and linkages to cluster industries outside the SIC are not represented. Industry income and income change are measured for the nonmetropolitan areas of the BEA s multi-county Component Economic Areas (CEAs). County-level income data for two-digit SIC industries were obtained from the Regional Economic Information Service (REIS), U.S. Department of Commerce. Multi-county regions were selected as the geographical scale because nonmetro industry agglomerations generally are not confined to a single nonmetro county (see, for example, Rosenfeld s descriptions of the hosiery cluster in North Carolina and the furniture cluster in Mississippi). The 100 CEAs in the TVA region selected for analysis contained both metropolitan and nonmetropolitan counties in order to test for the influence of industry activity in the metro areas on the proximate nonmetro counties Model Specification. Of interest to this study is the relationship between nonmetropolitan income change in an industry and the initial level of industry activity in the CEA s nonmetro counties. The initial level is proxied by the prior year s income in that two digit SIC industry. Preliminary Model Specification. We conduct the research on Component Economic Areas (CEAs) that are aggregations of nonmetropolitan counties with a metropolitan county(s) core. Defined by the Bureau of Economic Analysis, the CEAs are delineated to reflect regions with substantial commuter interaction (Johnson, 1995). Because CEAs are rather independent economic regions, they are appropriate for analysis of cross-section data. Maps of the CEAs in the TVA region are provided in the Appendix to this report. We expand the model of O Uhuallachain and Satterthwaite (1992) as follows: Ln (E n t+1 - E n t )= a + b Ln (E n t ) + c Ln (E m t ) + d Ln (POP t ) + X e + f Ln (E nb t ) + g Ln (E nf t ) + h Ln (E mb t ) + q Ln (E mf t ) + U (1)

8 8 In growth rate form, Ln [(E n t+1 - E n t )/ E n t ] = a + (b-1) Ln (E n t ) + c Ln (E m t ) + d Ln (POP t ) + X e + f Ln (E nb t ) + g Ln (E nf t ) + h Ln (E mb t ) + q Ln (E mf t ) + U (2) Where n, stands for the nonmetro part of a CEA, m for the corresponding metro area. Superscript t+1 and t represent beginning and ending time periods. E is industry income in the CEA. POP is the total labor force in the metro part of the CEA. X is a vector of 1980 socioeconomic conditions in the nonmetro part of the CEA, which include a poverty level index, average wage rate level and government expense indices. The subscripts b and f stand for backward and forward linkages to an industry; and U is the random error term. In work regarding localization economies, the choice of the dependent variable is important and affects the interpretation of the model estimates. Two concepts are relevant here: absolute growth and the growth rate. Wasylenko and McGuire (1985) used state level data to test the hypothesis of localization economies with employment change as the dependent variable. They found no support for the hypothesis which McDonald (1989) considers to result from misspecification of the dependent variable: the absolute change measurement is likely to be larger for larger regions even if the percentage change is small. However, applying percentage changes may also be problematic. For small regions, the measure of growth will be large due to the small base though no substantial income growth took place. A log-log form of the model avoids this base year problem. For instance, the b parameter estimate for Ln (E n t ), is the elasticity of the absolute growth in income with respect to the initial scale of income, E n t. If b > 1 then increasing E n t will increase the growth rate. If 0 < b < 1, the growth rate will decline while absolute growth increases with larger E n t. If b < 0 then economic decline is associated with a larger beginning period income in the industry. (McDonald, 1989). Table 3.1 Interpretation of the b parameter in regions that are growing:(e n t < E n t+1 ) Situation I (b<0 ) II (0<b<1) III (b>1) Absolute Growth Decrease Growing Fast Growing Growth Rate Decrease Decrease and Dispersion across CEAs Increase and Concentration Fixed Effects and the Covariance Model The behavior of the error term, U, is likely to be different from the usual crosssection model because we employ panel data. There are several kinds of model specifications for dealing with panel data under different assumptions with respect to the

9 9 U (Kmenta, 1986). In this research, we applied a covariance model. The supposition behind this is that we assume each state and each year have unique features that are represented by their own intercepts. In examining sources of change in wages in the apparel industry in northern regions of Mexico, Hanson (1996) employed the same error structure to control for region and year effects. Since we have approximately one hundred CEAs, if each CEA is assigned a dummy, the regressions will inevitably incur collinearity problem. Hence we used a state dummy. This assumes that area characteristics across states are possibly associated with fixed effects in the error term but variations across CEAs in a given state are not important in the error term. Likely sources of variation in area characteristics across states are state taxes and subsidies that affect the local business climate. After introducing the fixed effect dummies, our model has the form: Ln (E n t+1 - E n t ) = a + b Ln (E n t ) + c Ln (E m t ) + d Ln (POP t ) + X e + f Ln (E nb t ) + g Ln (E nf t ) + h Ln (E mb t ) + q Ln (E mf t ) + Σ kt Yt + Σ lj Dj + U 1 (3) Yt =1 for the t th year, = 0 for the base year, 1981 Dj = 1 for the j th state, = 0 for the base state (Arkansas). and kt and lj are vectors of regression parameters that represent intercept shifters for the year and state effects, respectively. Pooled model for growing and declining Final model specification Since we have negative growth in certain years and CEAs, the logarithm operation on the dependent variable for those observations is not possible. To adjust for the negative observations on the change in income, we take absolute values of the income differences. In theory, O Uhuallachain and Satterthwaite (1992) argued that industrial employment change between areas is not a symmetric process for growing and declining regions. One alternative is to divide the data into growing and declining observations and regress them individually. The absolute values on the declining income change observations simply means that the estimated regression parameters are interpreted a bit differently. A positive parameter means that a larger value of the independent variable will yield a larger decline in income. However, it is also convenient to introduce dummies to represent negative observations and pool the data in one regression model. Letting growing regions be the base and dummies to allow for intercept and slope changes for declining observations, we have the final model estimated:

10 10 Ln E n t+1 - E n t = a + b Ln (E n t ) + c Ln (E m t ) + d Ln (POP t ) + X e + f Ln (E nb t ) + g Ln (E nf t ) + h Ln (E mb t ) + q Ln (E mf t ) + Σ kt Yt +Σ lj Dj + a + Σ b Z + U 2 (4) where, a is the intercept dummy variable, a = 0 for the i observations that have a positive E n t+1 - E n t = 1 for the i observations that have a negative E n t+1 - E n t ; b is a vector of slope dummy variables, b = 0 for the i observations that have a positive E n t+1 - E n t, = 1 for the i observations that have a negative E n t+1 - E n t ; and Z is the same vector of independent variables as on the right hand side of (3). In growth rate form, Ln [ E t+1 t n - E n / E t n ]= a + (b-1) Ln (E t n ) + c Ln (E t m ) + d Ln (POP t ) + X e + f Ln (E t nb ) + g Ln (E t nf ) + h Ln (E t mb ) + q Ln (E t mf ) +Σ kt Yt +Σ lj Dj + a + Σ b Z + U 2 (5) Besides the simplification of the regression, this approach also suggests a test for differences between growing and declining regions with respect to the size of the beginning period industrial cluster in the rural area. Following the discussion of Table 3.1, in declining regions, the interpretation of estimates of localization economies are as follows (Table 3.2): Table 3.2 Interpretation of the b parameter in regions that are declining: E n t+1 < E n t Situation I (b<0) II (0<b<1) III ( b>1) Absolute Decline Decrease Increase Increase Declining Rate Decrease Decrease Increase For declining regions, the localization economies parameter, b, has interpretations that are the reverse of those of the growing regions. For instance in situation II, with larger initial income size, the absolute decline would increase, which means the gap between the end year income and beginning year income will broaden. However, the rate of decline is smaller. Like the interpretation for growing regions, Case II is also indicates a converging process across regions. Agglomeration Economies. As noted earlier, firms in clusters may benefit, relative to non-clustered firms, in three ways: Localization Economies, Urbanization Economies, and Industrial Spillover Effects. Localization economies result from a smoothly functioning labor market specialized to that industry, as well as infrastructure and an entrepreneurial environment that reduce the start-up costs of late comers. Thus, Localization economies stem from the size of the industry cluster and are associated with

11 11 the characteristics of that industry production methods, input networks and industrial organizations. However, there are also significant economies external to the industry that industrial clusters could enjoy. First, there are possible Urbanization economies associated with larger urban complexes. The urban node can provide several advantages to the firms including skilled labor, transportation services, other public infrastructure and sophisticated business services. All of these will facilitate the growth of firms, not only in urban area but also in peripheral suburbs. However, the effect may fade with distance from the urban center. Second, industrial clusters in an urban center could benefit nearby rural areas by seeking lower land or labor costs at the urban fringe as the cluster expands. We isolate this urban spillover effect from urbanization economies because, as appears likely to us, industry clusters in the urban core may work through regional labor and land markets to promote industrial clusters in nearby rural areas in addition to general urbanization economies. Of course, the opposite or urban backwash effects are also possible; if the flows are reversed, growth of that industry in an urban center dampens the development of neighboring rural localities. Based on the above discussion of agglomeration effects, we classified the factors that would, conceivably, stimulate the growth of industrial clusters into two branches the effects originating from that industry or related industries, and the effects emanating from the environment, such as business climate, quality of life, etc. Each of these two effects could be divided into two geographical categories urban and rural. Table 3.3 shows this taxonomy. Table 3.3 Potential Growth Effects From Rural Industrial Clusters From that Industry From the Environment Rural Localization Effect Cost of doing business + Quality of Life Urban Industrial Spillover Effect Urbanization Effect The cost of doing business/quality of life effect reflects the idiosyncratic nature of localities, which might include government taxes, educational quality of the labor force, percentage of households in poverty, etc. Hansen (1995) argues that rural areas that make efforts to provide quality labor supply and public service can promote the growth of industrial clusters. Besides the four effects above, we also test for influences from linked industries (backward or forward) on the core industry. We call these Linkage spillover effects. From a forward linkage perspective, we note that larger industrial markets in the CEA for the products of the core industry may stimulate income growth in the core industry. A larger presence of Backward linked or supplier industries in the CEA can be expected to reduce costs of the core industry and thus stimulate growth of the core industry.

12 12 Working Hypotheses: Throughout the analysis, five principal hypotheses are examined: 1. Higher levels of industry concentration in a rural area provide advantages to late coming firms in the same industry (Localization effect). 2. The growth of rural industrial clusters may benefit from larger proximate urban complexes that provide larger labor pools, a wider range of urban services and larger consumer markets for rural industry sales than would be found in small urban places. (Urbanization economies). 3. Urban areas may enhance rural industry income as a given industry spreads to the urban fringe and then to nearby rural areas in search of lower land and labor costs. Nevertheless, these effects are likely to fade with distance to the urban complex; therefore, it may be less significant than the localization effect. (Industrial spillover hypothesis). 4. A larger presence of backward and forward linked businesses near a rural area may facilitate the growth of a given industrial cluster in a rural community. This linkage spillover effect may reinforce the general urbanization economies. (Industry linkage effects) 5. Finally, the economic and social environment may affect the development of clusters. The quality of the labor force, public infrastructure in communities, taxation level and general income levels may influence growth of the clusters. (Socioeconomic milieu hypothesis) Data and Variables Our research covers the twelve states in the TVA region which contain 1,035 counties. These counties are aggregated into 107 CEAs. The states covered in our analysis include: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. Some CEAs extending beyond our geographical area were dropped. Also, some CEAs lie across two or three states. In that situation, the state dummy is given the value of the state that contains the most nonmetro-counties because the fixed effects for state dummies were evaluated by identifying the location of the nonmetro counties The time span is 14 years, The targeted industries are the 48 two digit groups of industries defined by Standard Industrial Classification (SIC). Data sources include the Regional Economic Information Service (REIS), Bureau of Economic Analysis, U.S. Department of Commerce, 1996) for income at the two digit SIC level (note that employment data are available only at the one digit SIC level); CEA designations for counties are also from the Bureau of Economic Analysis, U.S. Department of Commerce (see Johnson, 1995 for methods used to group counties by CEA). The City and County Data Book, U.S. Department of Commerce is the source for the remaining variables. The US input-output model from IMPLAN (1994) was used to identify backward and forward linked industries at the two digit level by construction of an IO model at the two digit SIC level of aggregation. Generally, the top three to five 2 digit SIC industries with the largest dollar backward links and forward links were

13 13 selected. Total income in the three to five backward linked and forward linked industries was used as the measure of local backward and forward links. Missing data. There are missing observations embedded in the REIS panel data because ranges are given for some observations rather than income. Even with the missing data, a sufficient number of observations is available to estimate most of models by SIC group. Therefore, the missing values were deleted. Table 3.4 summarizes the data available for the dependent variable, nonmetro income for each two digit SIC industry. The total number of possible observations is 1,147. Table3.4 Data Available for the Dependent Variable, Nonmetro Income Percentage of OBS. With E n t = 0 Percentage of OBS. With E n t+1 - E n t >0 Percentage of OBS. With E n t+1 - E n t <0 Percentage of OBS. With E n t MISSING SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC Percentage of OBS. With E n t+1 - E n t =0 & E n t <>0

14 14 SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC SIC * Numbers are in percentage. Total observations:1157. * SICs in gray were excluded from our analysis because of few observations. * E n t is the income in the nonmetro counties of a CEA; * E n t+1 - E n t is the difference in non-metro income between two years. There are five SICs (shaded in Table 3.4) where the number of missing values or zero observations is so large that reliable estimations are prohibited: SICs 31, 37, 38, 46, and 84. As expected given the national trends towards services, most of the services and trade sectors (SICs above 50) had large shares of growing observations industry income growth over a two-year period. However, only two manufacturing industries (SICs 24 and 28) had a majority of the observations exhibiting year to year growth in income. But after controlling for missing values and zero observations, it is also true that growing years in manufacturing income tend to dominate declining years by a two to one margin. This would be a surprising result if employment was the measure of economic activity since the manufacturing share of US employment declined over the period. However, in many manufacturing industries, conventional wisdom holds that the period saw increases in output per employee and thus manufacturing income growth was occurring even as employment fell. For these rural areas and industry groups, the ad hoc evidence in Table 3.4 tends to support the conventional wisdom. Income increases were more likely than declines in all two digit manufacturing industries usually by a wide margin.

15 15 Influential Observations Influential observations are identified by the DFITS statistic. Instead of deleting the outliers, we applied the Bounded Influence Estimation procedure to minimize their influence (Welsch, 1980). A weighted Least Square Model is utilized with weights of w I : W I = 1 if DFFITS 0.34 = 0.34/ DFFITS if DFFITS > 0.34 The advantage to this method is that it keeps observations with a high residual rather than deleting them arbitrarily. Variables The dependent variable is the log of annual income change in a two digit SIC industry in the rural part of the CEA. Three groups of independent variables are included on the right-hand side of the model. Agglomeration forces: Ln (E n t ): Income in that industry in the previous year in the rural part of the CEA a proxy for Localization Economies. Ln (E m t ): Income in that industry in the previous year in the metro part of the CEA a proxy for Urban Industry Spillover effects. Ln (POP t ): Total labor force in the metro part of the CEA in the previous year - a proxy for Urbanization Economies. A positive sign on the urbanization economies and urban industry spillovers coefficients is expected if proximity to larger cities provides marketing or production advantages to rural industry. For positive values of the dependent variable, strong cluster effects are associated with a coefficient on the localization variable that is positive and greater than one; weak cluster effects exist for industries with a coefficient on the localization variable that is greater than zero but less than one. Linkage spillovers: Ln (E nb t ), Backward Linkage Effects from Rural, the sum of income in the top 5 backward linkage industries (from input-output analysis) in the rural part of the CEA. Ln (E mb t ), Backward Linkage Effects from Urban, the sum of income in the top 5 backward linkage industries (from input-output analysis) in the urban part of the CEA. Ln (E mf t ), Forward Linkage Effects from Urban, the sum of income in the top 5 forward linkage industries (from input-output analysis) in the urban part of the CEA. Ln (E nf t ), Forward Linkage Effects from Rural, the sum of income in the top 5 forward linkage industries (from input-output analysis) in the rural part of the CEA.

16 16 Cost of Doing Business and Quality of Life factors: Previous research on the determinants of industry location and regional economic change suggest that the availability of an industry cluster is only one of numerous factors that may influence regional income gains or losses. Other potential influences on regional income change include the quality and cost of the labor force; availability and cost of public goods and services; and the local quality of life (Duffy, 1994; Wheat, 1986). Labor market conditions are represented by labor quality (percent of persons 25 and over with some college in 1980) and labor costs (average annual pay per employee in the rural counties of the CEA in 1980). All labor market variables are measured for the nonmetro counties in the CEAs for 1980 only as a general indicator of labor conditions across regions at the beginning of the period. This avoids simultaneity problems and reflects the paucity of annual wage data by two digit SIC industry in the nonmetro counties. Industry income growth will be positively related to labor quality if the firms activities require well-educated and trained workers. Industries with more routinized production processes may place less significance on labor quality in their location and expansion decisions. Labor costs and availability are hypothesized to be most important to the location and expansion decisions for industries with labor intensive production processes. These labor market characteristics will be less relevant for industries where labor costs are a small share of total value added. The availability and cost of public goods and services are represented by per capita local government expenditures. Per capita local government expenditures will be positively related with income change if the availability of public goods and services reduces the cost of conducting business at that location. In addition, labor costs may be lower in locations with high per capita public expenditures if the resulting public goods and services are associated with a higher local quality of life. Public expenditures per capita, however, are highly correlated with taxes per capita. Thus, public expenditures may be negatively related to income growth for industries that perceive few benefits from the public goods and services relative to the higher taxes associated with the provision of these goods. In sum, the cost of doing business and quality of life factors that represent the X vector in equation (5) include: NMWAGE: nonmetro average wage level, NMGOVPC: nonmetro average government expenditures per capita, NMLFQUAL: nonmetro labor quality (percent of the labor force with at least some college), NMPOVRT: nonmetro poverty index (percent of the population living below the poverty income level), IV. Empirical Results Localization Economies We find that localization effects are statistically significant in almost all of the fortyeight two digit SIC industries as shown in Table 4.1. Note that results for some industries do not include state or year dummies because of the small number of

17 17 observations or problems with collinearity. The industries that do not include these fixed effects controls for the error term are noted in the tables and listed after the full model results. In these industries, there may be fixed components in the error term that are related to states or years and thus the error term may not conform to the assumptions of the regression model. Given this caveat, we find that for growing regions, the localization effect is between 0 and 1 and significant at the.01 level for forty-four of the forty-eight industries. These forty-four industries have the weak form of localization economies. Having a higher initial income level yields a boost to absolute income growth but also reduces the rate of growth for regions that are expanding. In SICs 59 and 81 the localization parameters are significant and greater than one. Here both absolute change and growth rates are bolstered by larger initial income levels the strong form of localization economies. For SIC 36 electrical and electronic machinery, and SIC 45 air transportation there is not a significant localization effect. The top five industries in terms of the strength of the localization effect are: SIC 59, SIC 81, SIC 55, SIC 72, and SIC 51. In manufacturing, only SICs 24, 27 and 30 have localization parameters that are above.70 and thus approach the parameters for these leading trade and service industries. Interesting findings are obtained from the regressions on the declining regions. First, while most industries enjoy a localization effect in growing regions, they similarly suffered larger losses from larger initial income size as well. Ten industries have strong negative localization effects with the parameter estimates greater than Moreover, for more than half of the industries, the estimates of difference between growing and declining localization effects are statistically significant and the negative side of localization effect tends to be larger than the positive localization effects. In thirteen industries, there is no significant difference between the size of the localization parameters for growing and declining regions. Of the 35 industries with significant differences, only in three (SICs 25, 58 and 72 ), do the growth advantages outweigh the negative localization effects in declining regions. In the remaining 32 industries, the downside of localization economies exceeds the benefits to growth from localization economies. Indeed in ten industries mainly trade and services the negative localization effect is of the strong variety. Perhaps this is not unexpected given the well-known high rates of business failures as well as start-ups for smaller trade and service firms. O Uhuallachain and Satterthwaite (1992) argue that the growth and decline of an industry is an asymmetric process and our results support this assertion for most rural two digit industries. Table 4.1 Localization Effects SIC Localization Economy in Growing Part P-Value Localization Economy in Declining Part P-value Difference (Declining- Growing) P-Value

18 (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.d.) (n.y.) (n.y.) (n.y.) (n.d.) (n.y.) (n.y.) (n.y.) (n.y.) (n.y.) (n.y.) In Growing Column, BOLD estimates indicate the insignificant estimate at 15%, gray stands for significant estimates at 15%. N.d. Model without state dummy. n.y. model without year dummy.

19 19 Recalling Tables 3.2 and 3.3, we have nine kinds of localization effects as shown below. Growing years I (b<0) II (0<b<1) III ( b>1) Declining Years I (b<0) None None None II (0<b<1) None All other industries None III ( b>1) None SIC48,52,54,64, 66,73,86,87 SIC59, 81 All industries fall into three categories. Most are II and II combined with III; thus, both declining and growing industries tend to geographical convergence since regions with larger beginning period industry incomes grow (or decline) at a slower rate than regions with smaller beginning period clusters in these industries. The underlying trend is a self-balancing process: in growing years, along with the expansion of income, the growth rate is declining and thus regional industry income should converge over regions (which is a function of the initial size and other characteristics). In declining years, shrinking income is associated with slower rates of decline. If the declining and growing years happened alternatively and evenly, there would be only slight changes in industry incomes, because the effects are offset. However, this is usually not the case. Instead, for most industries, growth was more persistent than declines. (See the results displayed in Table 3.4). We conclude that TVA rural industry incomes, on balance, benefit from clusters in terms of added income growth potential. However, regions with larger beginning period incomes tended to grow at slower rates so that total industry income should converge across these rural regions in the TVA. Industrial Spillover effects For growing regions five industries, SIC25, SIC26, SIC29, SIC35, and SIC53, have significant and positive urban industrial spillover effects. For these industries, greater initial size of the Metro industry income is associated with greater growth in income in the same rural industry. In nine industries, the industry spillover effect is negative indicating an urban backwash effect. These nine industries are SIC 24, 32, 34, 56, 83, 59, 75, 81, and 87. In declining regions, eleven industries (SICs 20, 24, 28, 53, 64, 79, 44, 59, 66, 78, and 86) have positive coefficients on the Metro industry spillover effects. Recall that, since we use absolute changes on the dependent variable, larger initial metro industrial clusters are associated with greater rural industry declines. As urban industry income increases in these SICs, income in that industry located in the nearby rural areas will decline. However, five SICs(34, 63, 33, 60,75) have negative parameter estimates which suggests that larger Metro industry clusters reduce the decline in rural industry income an urban spillover effect. Differences in the urban spillover effect between growing and declining regions are significant in only nine industries. Compared to localization effects where thirty-five industries show significant differences, this suggests that the urban spillover effects are more symmetric in growing and declining regions. For SICs 24, 35, 64, 79, 25, 44, 59, 81 and 86 growth regions and declining regions responded differently to urban spillover