Employment Growth in Rural TVA Counties: Does Establishment Size Matter?

Transcription

1 Employment Growth in Rural TVA Counties: Does Establishment Size Matter? Mark S. Henry, David L. Barkley, Yu Bai, and JaeEspey September 2000 Contractor Paper About the Authors Mark Henry is a Professor in the Department of Agricultural and Applied Economics at Clemson University. David L Barkley is a Professor in the Department of Agricultural and Applied Economics at Clemson University. Yu Bai is a Research Assistant in the Department of Agricultural and Applied Economics at Clemson University. Jae Espey is a Research Associate 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. All staff and contractor papers are working papers and can be found on the TVA Rural Studies website

2 I. Introduction Recent research on earnings change in the TVA region suggests that the presence of a cluster of establishments producing similar products affects the pace of earnings growth in rural areas (Henry, Barkley, and Zhang, 1997). However, the impact of industry clusters on earnings growth varies by type of industry (twodigit SIC) and earnings changes are asymmetric industry clusters are associated with faster earnings growth in growing industries and faster earnings losses in declining industries. In this paper, we explore the possibility that these asymmetries are related to variation in the employment size distribution of establishments across the rural counties of the TVA region. The role of small, medium, and large businesses in rural growth is examined by assessing the effect the size distribution of establishments has on employment change in the rural counties of the TVA region. If bigger is better for rural employment growth, then a policy to recruit large plants may be preferred. Alternatively, if "small is beautiful," then efforts to support networks of small establishments may provide the greatest employment growth potential for rural areas. Related Literature Renewed interest by rural development analysts in the importance of the scale of production in manufacturing follows two lines. First is the longstanding Goldschmidt thesis that rural counties are better-off when the local farm size distribution is composed of moderate size family farms rather than a few large farms. This is a controversial assertion (see Hayes and Olmstead, 1984). Yet, the conventional wisdom of robust rural areas dependent on moderate sized family farms persists in many states and is supported by some academic research (e.g., Lobao, 1990). In recent work, Lyson and Tolbert (1996) and Tolbert (1998) extend the Goldschmidt (and related Ulmer and Mills thesis) to rural manufacturing. They hypothesize that like family farms an array of smaller manufacturing establishments in a rural county may be superior to a few large plants. Roughly, this is the thesis that small scale flexible and specialized production units are thought to be more efficient than large scale units (See Storper and Walker, 1989, for example). Birch (1999) also argues that small firms are the major source of new jobs in the United States. However, this is a controversial assertion and has been challenged by Davis, Haltiwanger, and Schuh (1996), among others, on the grounds that both the data (Dun and Bradstreet) and methods of growth accounting are seriously flawed. So the growth catalyst role of small versus large business is not well understood. Second, the success of industry clusters in selected regions has stimulated interest in regional industrialization policy that promotes formation of industrial districts based on agglomerations of targeted industrial activities. Examples of economic development policies grounded in industry districts are diverse and include the European Union policies aimed at creating innovative milieus and industrial districts in lagging regions of the European Union (Storper and Scott, 1992) and Rosenfeld s (1992) industrial hubs in his new rural policy paradigm for the United States. However, other analysts find that plant persistence over time is the key to sustained employment growth and that larger size establishments tend to be more persistent than small plants. For example, see Jensen et al. (1996) and Davis et al. (1996). So aggregate growth in a region may be closely linked to the size distribution of establishments in the industrial base. Research Issues The overall goal of this paper is to determine the implications for TVA rural (nonmetropolitan) employment growth from state and local policy that targets business recruitment efforts at networks of small establishments versus efforts to recruit largescale plants. Two principal areas of investigation are pursued to determine if policies promoting industrial districts of small establishments or policies to recruit large-scale plants affect rural employment most favorably. First, industries are identified that tend to have a few large establishments and the 1

3 employment performance of these industries, vis a vis industries with smaller size plants, are examined for both the rural and urban counties of regional economies. These industries tend to be vertically integrated and may yield small local linkage effects. However, they also may tend to persist for longer time periods generating more sustained sources of employment growth. This information will enable policy makers to recognize the potential benefits of large-scale plants in rural areas. Second, neither the existence of groups of small plants nor the availability of potentially attractive locations for future clusters necessarily imply that the promotion of industrial districts (through targeted recruitment, development, and retention programs) is an advisable local rural development strategy. Industrial districts are heterogeneous, differing in core industrial sectors, organizational structure of member firms (e.g., large, vertically-integrated branch plants versus small, specialized, independent operations), interdependencies and linkages between firms, and organization of production within clusters members (e.g., small-batch, specialized, flexible processes versus larger-scale, standardized, routine processes). This research examines the growth potential of rural industries by type of manufacturing and by the size distribution of plants in the industry. This information will aid local decision makers as they select among industrial sectors for targeting, and as they determine the appropriate infrastructure investment and financial assistance for industry location incentives. II. Research Approach Rural areas with a significant manufacturing base can be expected to experience positive manufacturing employment growth for several reasons. First, there is a tendency for manufacturing plants to co-locate near other plants in same industry. This is often attributable to localization economics and Marshall-Arrow-Romer (MAR) external economies with labor pooling economies an integral component. Second, a size of region effect may attract new or expanded manufacturing employment in an industry. If the county has a large overall manufacturing base, it should, via input-output linkages, be able to offer a wide array of inputs and markets to new or expanding plants. Third, rural areas are expected to add employees as the national economy grows. If a particular industry is growing faster than the national average, and if a rural area has a larger initial share of that industry, then rural area growth will be faster overall. Finally, the thesis of this paper is that the initial size distribution of plants in a county will affect employment growth. If larger plants are more persistent through time (see Davis et al., 1996), then a few larger plants may be better for employment growth than an array of smaller plants. Alternatively, the Birch (1999) hypothesis that smaller firms create most new jobs is supported if rural areas with a larger number of small plants grow faster than areas with a few large plants. The empirical models developed by Dumais et al. (1997) provide the basis for the analysis of the effect of establishment size on subsequent employment change after controlling for other growth factors. County employment data for each fourdigit manufacturing SIC (NAICS) sector are obtained from County Business Patterns data enhanced by a private vendor to provide estimates of non-disclosed data (1981, 1992, and 1996, Claritas, Inc.). These data are aggregated to the two- and three-digit level and used to estimate equations of employment change for nonmetropolitan TVA counties and for multi-county Component Economic Areas, CEAs, with a urban core and rural hinterland (see Johnson, 1995, for procedures used to group counties). The county-level and CEA growth equations are presented below. County Regressions There is one regression on total employment change in rural TVA counties for each two-digit manufacturing SIC for the period And there are regressions for the three-digit SICs that represent the twenty fastest and slowest growing manufacturing industries in the United States. States include all the states in the TVA region. (1) log(1+ detij) = a0 + a1 N50t-1ij + a2 N100t- 1ij + a3 N250t-1ij + a4 Sijt-1 + a5 Sit-1 + e, where, the dependent variable, detij, is the change in employment in year t, county i, and SIC group j. N50t-1ij = the number of small manufacturing establishments in the beginning 2

4 period. These plants have fewer than 50 employees. Does a larger group of small plants in the industry yield larger increases in future employment holding the number of medium and large plants constant? N100t-1ij = the number of medium manufacturing establishments in the beginning period; these plants have more than 50, but fewer than 249 employees; i.e., does a larger group of medium plants in the industry yield larger increases in future employment holding the number of small and large plants constant? N250t-1ij = the number of large manufacturing establishments in the beginning period; these plants have fewer than 250 or more employees; i.e., does increasing the number of large plants in the industry yield larger increases in future employment holding the number of small and medium plants constant? Sijt-1 = the initial share of employment in industry j in county i: tendency to co-locate near other plants in same industry effect. Sit = the initial share of all TVA manufacturing employment in county i: size of region effect if the county has a large overall manufacturing base it should (via I/O linkages) be attractive to manufacturing plants in sector j. Log transformations allow elasticity estimates in equation (2) and reduce the influence of outliers in both equations. Note that in equations (1) one is added to employment change (except for changes of -1) to avoid losing observations with zeros for this variable, ln (1)=0; Moreover, for negative values on the lefthand side variable, we transform the observation to -Ln 1+ (detij). See Dumais et al. (1997) for a similar approach. CEA Regressions. Using a reduced form model of small region change similar to that developed in Henry, Barkley and Zhang (1998) and earlier by O huallachain and Satterthwaite (1992), regression based tests are made for the direction and magnitude of establishment size effects on rural employment growth. The TVA rural counties are grouped by functional economic areas to provide controls for the effects of urban growth in nearby urban centers. These Component Economic Areas (CEAs) are defined in Johnson (1995) and illustrated for the TVA states in Henry et al. (1998). The nonmetro counties in each CEA comprise a set of rural areas each linked to an urban core the Metropolitan Statistical Area (MSAs) within the CEA. This model examines the influence on employment growth from: the size distribution of establishments, the presence of rural localization economies, urban to rural spillover effects, and economic area input cost characteristics. (2) E tn -E t-1n =a exp δ1 N50 t-1n exp exp δ3 N250 t-1n E β1 t-1m exp where for industry i, δ2 N100 t-1n (Xb + e) N50 t-1n = Number of establishments with fewer than 50 employees in the nonmetro area of the CEA in the beginning period. N100 t-1n = Number of establishments with fewer than 250 employees and more than 50 employees in the nonmetro area of the CEA in the beginning period. N250 t-1n = Number of establishments with 250 or more employees in the nonmetro area of the CEA in the beginning period. E tn = Employment in year t for industry i in the NonMetro counties of a CEA. E t-1n = Employment in year t-1 for industry i in the NonMetro counties of a CEA. E t-1m = Employment in year t-1 for industry i in the Metro counties of a CEA. X = Vector of NM beginning period characteristics: input costs (wage rates, educational attainment levels of the nearby labor force, and geographic size of the CEA.) b = Vector of parameters to be estimated; e = error term. Log transformation yields a linear in the logs equation (3) that is amenable to estimation by ordinary least squares regression techniques. Estimates of parameters can be used to test for 3

5 the presence of urban cluster spillover effects (β) and rural plant size (δ) effects. (3) Ln[1+ (E tn -E t-1n )]= Ln a + δ1 N50 t-1n + δ2 N100 t-1n + d3 N250 t-1n + β1 Ln(1+E t-1m ) + γ2 Ln(Wage) + γ3 Ln (Area) + γ4 Ln (Educ) + e Note that in equation (3) one is again added to the employment change and beginning period employment values to avoid losing observations with zeros for these variables, ln (1)=0. Moreover, for negative values on the left hand side variable, we transform the observation to -Ln 1+ (E tn -E t-1n ). The model in equation (3) examines rural growth determinants across rural regions and over three time periods, and the subperiods: and The subperiods are of potential interest Equation (3) is estimated for all two-digit SICs in Manufacturing (SIC 20-39) and selected threedigit SICs as noted above. Data sources are County and City Databook, enhanced County Business Patterns (Claritas, Inc.), and other Census Data for 1980 and III. Results Manufacturing Employment Trends in Rural TVA Counties, Two-Digit SIC Industries As an indication of the relative importance of manufacturing sectors to the rural TVA areas, the average number of industry employees and establishments, per county, are computed for TVA rural counties. The number of small establishments (fewer than 50 employees), medium establishments (50 to 250 employees) and big establishments (more than 250 employees) in the average rural county are also compiled for each manufacturing sector. The twenty industries in manufacturing, SIC 20 to 39, are identified in Table 1. Mean county employment (1981 versus 1996) across the two-digit manufacturing industries in the rural counties of the TVA region is displayed in Figure 1. As expected, employment in Textile Mill Products (SIC 22) and Apparel and Other Textile Products (SIC 23) were mainstays of the TVA rural economy in 1981 each contributing almost 400 jobs per rural county. The wellknown decline in employment in these industries is illustrated in Figure 1 but, as of 1996, they continue to be the two leading sources of manufacturing employment in the rural TVA region. However, SIC 24 (Lumber and Wood Products) and SIC 20 (Food Processing) each have grown over the period to a level near that of SIC 23 each supplying about 250 jobs per county. Other sectors with prominent increases in employment in rural TVA counties were SIC 30 (Rubber and Misc. Plastics), SIC 35 (Industrial Machinery and Equipment) and SIC 37 (Transportation Equipment). The remaining sectors had smaller changes in employment from 1981 to In rural TVA counties, all two-digit industries, except for SIC 20 and SIC 31, had an increase in establishment numbers from 1981 to 1996 as illustrated in Figure 2. The average county had about 10 Lumber and Wood products (SIC 24) establishments in 1981 increasing to about 12 in While most of these establishments are small sawmills and logging operations, the number per county is dramatically higher than any other industry. SIC 35 (Industrial Machinery and Equipment) averaged about 4 establishments per county in 1996, and SIC 27 (Printing and Publishing) had approximately 3 plants per rural county in All other two-digit manufacturing industries have fewer than two establishments, on average, in the rural counties of the TVA region. The size distribution of establishments in each industry is displayed in Figures 3a and 3b. Looking first at Food Processing (SIC 20), note that the share of larger plants has increased as the total number of plants has declined from 1981 to In Textiles (SIC 22) and Apparel, (SIC 23) the share of small plants increased along with the number of plants even as average employment in these industries declines. While small establishments dominate in terms of total establishment numbers in all industries, larger plants are prominent in SIC 22 and 23 and in SIC 36 (Electronic and Other Electric Equipment). Summary tabulations for mean employment, mean number of establishments, and the size distribution of establishments are provided in Table 2. From 1981 to 1996, the average number of plants grew in each two-digit SIC manufacturing industry in the TVA region with the exceptions of SIC 20 and SIC 31. This result is somewhat surprising for the Textile and Apparel industries since both industries experienced substantial declines in employment 4

6 in the rural TVA counties from 1981 to This suggests that the Textile and Apparel industries are closing larger, and perhaps older, plants first and continuing production with fewer employees in plants that have become more capital intensive. It also may reflect the increased share of nonwoven fabrics in domestic textile plants and the decline of traditional labor-intensive apparel plants that are losing market share to foreign plants. Another notable trend is the large increase in Food Processing (SIC 20) employment while the number of plants has declined. This is consistent with the expansion and births of larger meat products plants in the rural TVA region over this period. Employment growth across the two-digit manufacturing industries is mixed ranging from a low of -63% for Leather products, SIC 31, to the high of 91.4% for SIC 37. Big losers include SIC 22 (-14.7%), SIC 23 (-30.7%), while other fast growth industries include SIC 30 (Rubber and Plastics), SIC 38 (Instruments), SIC 35 (Industrial Machinery) and SIC 27 (Printing and Publishing). Slower growth occurred in SIC 24 (Wood Products), SIC 25 (Forestry), and SIC 34 (Fabricated Metal Products). In the following sections, the role that size of the plants played in the observed trends in rural employment is examined using the regression models described in equations (1) to (3). Two sets of regression results are presented based on county-level or CEA-level observations. County Regressions The results from estimation of equation (1) using observations on rural TVA counties are displayed in Table 3 for the time period. The dependent variable is the natural log of the change in employment in the two-digit manufacturing industry from 1981 to Looking first at the F test, each of the regressions, with the exception of SIC 21, which has very few observations, is highly significant. Jointly, the five explanatory variables explain a significant share of the variation in TVA rural county employment change over this period. The County variable is the share of total TVA region manufacturing employment in a county a proxy for the size of region effect. If the county has a large overall manufacturing base it should (via I/O linkages) be attractive to manufacturing plants in general. The results in Table 3 generally support the positive impact of a larger starting period manufacturing employment base on industry specific employment growth. Seventeen of the parameters are positive and thirteen of those are significant at the.10 level or smaller. The three negative parameters are not statistically different from zero. Employment growth in SIC 30, 35, 34 each have highly elastic responses to an increase in the size of the county employment base. The county size effect has little or no impact on employment change in SIC 20, 21, 22, 23, 25, 29, and 36 mostly nondurable manufactured products. The remaining SICs have moderately strong employment impacts associated with the size of the region. The Industry variable is the share of beginning period industry employment in the county the tendency to co-locate near other plants in same industry effect. The expectation is that labor-pooling effects are important at the industry level to firms seeking a reliable source of industry specific labor skills. That is, firms might be attracted to counties that have more employment in similar industries. Alternatively, firms may seek locations that have fewer competitors for the local labor supply. Results in Table 3 show that only five industries have a positive same industry effect on subsequent employment change and none are statistically significant. In contrast, fifteen of the parameters are negative and seven are statistically different form zero at the.10 level or smaller. Firms in SIC 24, 35, 36, 26, 27, 22, and 28 are most likely to close plants, avoid building new plants, or to contract employment in existing plants in rural TVA counties with larger concentrations of establishments in the same industry. The three variables that capture the role of the size of plant on subsequent employment change are the number of Small, Medium, and Large plants in the county in the initial period (1981). Results suggest that the plant size distribution does affect industry employment change but in a mix of ways across these SIC groups. In each of the 20 industry groups, at least one parameter on the number of Small, Medium, and Large plants in the county is significant at least at the.10 level suggesting that size matters to employment change. In SIC 20, Food Processing ( p value of.14), SIC 26, Paper and Allied Products, and SIC 35, Industrial Machinery and Equipment, large plants provide a boost to industry employment growth. However, counties with 5

7 more large plants in SIC 30, 31, 33, 38, and 39 tend to lose more jobs. On an industry basis, larger numbers of small plants in 1981 stimulated employment growth in SIC 22, 24, 25, and 27 from 1981 to Small plants tended to reduce employment gains in SIC 20, 28, 29, 30, 31, 33, 38, and 39. The medium size plants tended to reduce employment in eleven of the industries. Taking a positive spin when looking at the three size influences across a given industry, smaller is better in SIC 22 (Textiles), 24 (Lumber and Wood), 25 (Furniture), and 27 (Printing and Publishing). In contrast, "big is beautiful" in SIC 20 (Food Processing), 26 (Paper and Allied Products), and 35 (Industrial Machinery). Only in SIC 30 (Rubber and Plastics) do medium size plants, perhaps, provide a basis for more rapid employment gains. The reader should note that the county regressions may not capture regional influences on county employment change. Employment change across the two-digit industries is examined in the next section using observations on multi-county functional economic regions in the TVA states. CEA Regressions Two-digit SIC As noted above, TVA rural counties are grouped by functional economic areas (Component Economic Areas) as defined in Johnson (1995) to provide controls for the effects of urban growth in nearby urban centers. The nonmetro counties in each CEA comprise a set of rural areas each linked to an urban core a Metropolitan Statistical Areas (MSAs). Using the model described in equation (2), rural TVA employment growth impacts are estimated from the size distribution of establishments, the presence of rural localization economies, urban to rural spillover effects, urbanization economies, and economic area input cost characteristics. Estimates of parameters can be used to test for the presence of urban cluster spillover (β) effects and rural plant size (δ) effects. The model in equation (3) examines rural growth determinants across three time periods, and the subperiods: and Equation (3) is estimated for all twodigit SICs in Manufacturing (SIC 20-39). Results are displayed in Tables 4a, 4b, and 4c. Looking first at the period, note that the regressions are all significant with the exceptions of SIC 21 and SIC 31 two industries with very few observations. There are four variables included that capture: (1) the influence of urban spillovers (METEMP = Log of Metro area employment in the CEA in 1981); (2) the beginning period cost of labor (LWAGE= the log of the mean manufacturing wage in the rural part of the CEA; (3) the size of the region (LAREA = log of the geographic size of the CEA in square miles); and, (4) the beginning period human capital level (LEDUC= log of the mean number of years of schooling of the adult population in the rural counties of the CEA). As a control for available land area, LAREA is generally positively associated with employment change over each of the three time periods although only 5 or 6 industries seem to be significantly affected by land area in the CEA region. Educational levels are significantly associated with employment growth only in a handful of industries and mostly as a negative influence on employment growth. Considering only statistically significant parameter estimates ( p values.10 or less), LEDUC enters negatively in SIC 22, 24, and 25 in the period; negatively in SIC 23, 24, 26, 35 and 36 in the period; and negatively in SIC 24 in the period. Apparently, firms in these sectors continue to seek rural areas with relatively low levels of educational attainment perhaps in a quest for low cost labor. On the positive side, LEDUC was positively associated with employment growth in SIC 33 over the period, and with employment growth in SIC 27 and 39 over the period. Surprisingly, given the more recent emphasis on education and economic growth, no sectors associated higher educational attainment with expanding employment in the period. The other side of the local educational coin is the area wage rate. One might expect that sectors that were avoiding areas with higher educational attainment would also avoid areas with higher wage rates. Results in Tables 4a c suggest that only a few sectors were attracted to low wage areas: higher wages reduced employment growth from 1981 to 1996 only in SIC 34 (Fabricated Metal Products). For , higher wages meant slower employment growth in SIC 23, 35, and 38. Alternatively, employment grew faster with 6

8 higher wage levels in SIC 30 from 1981 to 1996 and SIC 22, 26, and 39 from 1992 to Looking at the final control variable, METEMP, there is a general pattern of urban backwash effects. Larger employment levels in the metropolitan counties of the CEA resulted in lower rural employment growth in SIC 24, 27, and 30 from 1981 to 1996; in SIC 27, 30, 35, and 36 from 1992 to 1996; and in SIC 27 and 37 from 1981 to In no sector did larger beginning period employment spread to proximate rural counties a surprising result given findings for South Carolina in Henry et al. (1997). Turning to the effects of the size distribution of plants on subsequent employment change, results vary importantly by the time period of analysis. Over the entire period, 1981 to 1996, large plants were a stimulus to employment growth in SIC 26, 30, 36, 38, and perhaps in SIC 20 (see Figure 4c and 4d). Alternatively, large plants resulted in larger job losses in SIC 31 and 32. However, the subperiod analysis suggests that there are important changes in the role of large plants. CEAs with more large plants in 1981 saw substantial employment growth until 1992 only in Food Processing, SIC 20 (See Figures 4a and 4b). In all other sectors, more large plants in 1981 seem to be associated with slower employment growth through 1992 (11 of the 19 industry parameters on the N250 variable are negative and significant at the.10 level or less). In stark contrast to the period, more large plants in 1992 had a positive impact on employment through 1996 in 17 of the 20 industries, with 9 of the parameters significantly different from zero at least at the.10 level (See Figures 4e and 4f). This large plant reversal may reflect the closing of older, large plants during the ups and downs of the economy from 1981 to The steady expansion of the national economy since 1992 probably resulted in an expansion of employment in large plants that survived (modernized) during the 1980s and in the construction of new large plants during the early 1990s. What was the role of the small and medium size plants during this period? The parameter estimates on the N50 and N100 variables suggest the following for the most recent period: fewer small textile plants (SIC 22) and more medium size plants aided employment growth. Small plants were good job generators in SIC 25, 27, 29, 32, and 37. Medium size plants aided employment growth in SIC 33, 34, 35, 38, and 39. During the period, counties with more small plants gained jobs in SIC 32, 33, 35, 37, and 39 while losing more jobs in SIC 36. Medium size plants stimulated growth in SIC 26 and 38, but were associated with job losses in SIC 20, 29, 35, and 37. Again it is helpful to look at these two subperiods since the national economy performed quite differently in each period. Three-Digit SIC Industries Because of the large number of three-digit SIC industries (using the 1977 definitions), the ten fastest and ten slowest growing sectors are identified from national CBP data files. These are identified in Table 5. As shown in Table 6, the industries that grew most rapidly, adding at least 100,000 jobs during the period, were SIC 308 (Misc. Plastics Products), SIC 275 (Commercial Printing), SIC 201 (Meat Products), SIC 384 (Medical Instruments and Supplies), and SIC 381 (Engineering and Scientific Instruments). Industries adding between 40,000 and 100,000 jobs were SIC 367 (Electronic Components and Accessories), SIC 359 (Industrial Machinery, NEC), SIC 371 (Motor Vehicles and Equipment), SIC 243 (Millwork, Plywood and Structural Members), and SIC 239 (Miscellaneous Fabricated Textile Products). In contrast, industries losing at least 100,000 jobs include: SIC 366 (Household Audio and Visual Equipment), SIC 331 (Blast Furnace and Steel), SIC 353 (Construction and Related Machinery), SIC 372 (Aircraft and parts), SIC 357 (Office Equipment includes typewriters and calculating machines that have been largely displaced by electronic computers over this period), SIC 233 (Women s and Misses Outerwear), and SIC 232 (Men s and Boys Furnishings). Losing between 75,000 and 100,000 employees were SIC 314 (Footwear, except rubber), SIC 373 (Ship and Boat Building and Repair), and SIC 332 (Iron and Steel Foundries). We use these ten fast and ten slow growth industries as our sample of three-digit industries for analysis of rural TVA employment change. The fast growth sectors seem to offer the most opportunity, while the slow growth industries are the biggest threats to employment growth. 7

9 In the following sections, the role that size of the plants played in these observed trends is again examined using the regression models described in equations (1) and (3). TVA Rural County Regressions The three-digit SIC industries are examined using model (1) to evaluate the role that the size distribution of plants in 1981 had on subsequent employment growth in the threedigit SIC sectors that had the greatest employment change over the 15 year period from 1981 to Results from model (1) are presented in Table 7. Each of the regressions is highly significant as indicated by the small p values for the F test on the joint significance of the parameter estimates. As expected, counties with larger shares of all TVA manufacturing at the beginning of the period generally have faster employment growth in the industries that are expanding and in SIC 357, which is declining nationally. However, looking at the Industry percentage parameters, only in SIC 381 and 366 did county localization effects improve employment growth in that industry. Expanding industries SIC 243 and 308 tended to seek rural TVA counties that had a smaller presence in that industry in Declining industries SIC 331 and 353 tended to lose more employees if the county share of employment in those sectors was larger than the average county. The impact on employment growth from having large plants in the county is quite mixed. In the growth industries SIC 201 (Meat Processing), SIC 275 (Commercial Printing), SIC 308 (Miscellaneous Plastic Products), more large plants was a substantial force in capturing more employment over the period. However, employment growth was slower in SIC 367 (Electronic Components), and especially in SIC 381 (Search and Navigation Equipment), if the county had more large plants in Counties with more large plants in 1981 exacerbated employment losses in SIC 233 (Women s and Misses Outerwear), 314 (Footwear Except Rubber), 332 (Iron and Steel Foundries), 366 (Communications Equipment), and 372 (Aircraft and Parts). In general, counties with more small plants in the growing industries were likely to see larger employment losses with the exception of SIC 359 (Industrial Machinery, NEC). A similar conclusion holds for medium sized plants except for SIC 308 (Miscellaneous Plastic Products), where more starting period medium sized plants generated greater employment gains. Among the ten slowest growing industries, more small plants also were associated with employment losses. However, in general, counties with large numbers of medium and large plants had larger employment losses (based on larger estimated coefficient). CEA Regressions Three-digit SIC For the multicounty CEA observations, the national nonmetro employment trends are emphasized for two subperiods, and The top ten growth and declining industries in rural counties across the United States are identified in Tables 8 and 9 for the two subperiods. Actual employment changes for each period are displayed in Tables 10 and 11. Regression results using the model in equation (3) for the CEA observations are reported in Table 12 for the period and in Table 13 for the period. Because there are fewer observed three-digit establishments than in the two-digit industries and since there are only 107 CEAs, regressions are more variable in significance than with the county observations. Using a p value of.10 or less as a rule, 15 of the 20 three-digit regressions are significant for the period. Looking first at the period, improved education attainment is not important to employment growth except in SIC 314, and in SIC 243 it hinders job growth. Lower wage rates attracted more employment in SIC 242 but higher wages were associated with employment growth in SIC 243 and 275. Metropolitan spread effects are important in SIC 201, 209, 314, and 349. However, larger metro area employment has a backwash effect on SIC 242. Plant size is important in several industries. Meat Packing (SIC 201) employment growth is favored by large plants. Small plants boost employment in SIC 209, 243, 275, 349, 359, and 371. Medium size plants favor employment growth in SIC 384. Large plants are associated with subsequent employment loss in SIC 209, 221, 222, 234, 242, 314, 322, 359, and 362. In the period, land area, wage rates, and educational attainment levels again have little influence on employment growth. And only in SIC 243 and 308 is rural employment change affected by urban backwash 8

10 effects. Like the earlier period, large Meat Processing Plants are associated with faster employment growth. Large plants also play a dominant role in employment growth in SIC 228, 243, 245, 262, 308, 362, and 371. In none of these industries is a greater frequency of large plants in 1992 associated with subsequent employment losses. Over this time period and for these industries, these findings support the hypothesis by Davis et al. (1996) that large plants tend to be more persistent through time than small plants and thus provide a more stable source of job growth than small plants. However, having more medium size plants in 1992 also added to employment growth in SIC 201, 233, 243, 245, 249, 344, 359, and 371, but added to employment losses in SIC 236. More small plants added to employment growth in SIC 234, 236, 245, 275, and 359 and increased job losses in SIC 233. These results support the Birch thesis that smaller is better for employment growth. IV. SUMMARY AND CONCLUSIONS The role that establishment size plays in manufacturing employment change in rural TVA rural counties varies substantially across industries and time periods. From the analysis of the employment data in the previous section we conclude the following: Over the entire period, with county observations and within broad two-digit industry groups, large plants yielded the greatest potential for rural employment change both growth and decline. However, looking within these broad groups, several industry patterns emerge. Examples include: Food Processing (SIC 20) in general, and especially Meat Products (SIC 201), are more likely to expand in large plants. Printing and Publishing establishments (SIC 27) have both small and large plant positive effects on growth but in SIC 275, Commercial Printing, it is the large plants that aid growth. In SIC 30, Rubber and Miscellaneous Plastics Products, large plants are especially likely to be associated with employment decline, but in SIC 308, Miscellaneous Plastics, large and medium plants stimulate employment gains. In SIC 31, Leather And Leather Products, the larger the plant size the greater the reduction in employment, and this holds in SIC 314, Footwear, except rubber. Similar stories for the other manufacturing industries in rural TVA counties can be constructed by inspection of the results in Tables 3 and 6. In general, larger plants do no better or worse than small plants across the board in stimulating persistent manufacturing growth over this entire period. Large plants do, typically, have a bigger absolute impact on subsequent county employment change as revealed by inspection of the parameter estimates in Tables 3 and 6. The functional economic region (CEA) analyses provide another perspective. For the period, large plants in the broad twodigit industries (with the important exception of Food Products) tended to be associated with employment loss, while small plants were more of a positive influence on job expansions. However, in the more recent period, large plants generally were associated with stronger job gains in the rural regions. At the three-digit level, large Meat Products, SIC 201, plants again emerge as a leader in providing added employment to rural TVA regions and in SIC 356, large plants went against the tide of falling national employment in general industrial machinery. In the period, bigger was better in SIC 243 (Millwork, Plywood, Structural), and in SIC 245 (Wood Buildings/Mobile Homes), although all three size plant groups fared well. SIC 308, Miscellaneous Plastics, also benefited from large plants supporting the county level analysis. SIC 362, Electrical Industrial Apparatus, had the greatest boost from large plants across the growth industries and SIC 371, Motor Vehicles and Equipment, also gained more in large as well as medium sized plants. Smaller was best in SIC 234, Women s and Children s Undergarments, and SIC 236, Girls and Children s Outerwear both declining textile sectors. This suggests that it is the smaller establishments that are best able to innovate and compete with this type of imported textile products. However, it is the large plants in SIC 9

11 226, Textile Finishing, Except Wool, that seem to do the best in maintaining jobs in this component of the textile sector. Finally, the medium size establishments fared well in a wide range of growing industries (SIC 201, 243, 245, 249, 344, 359, and 371). Within the declining textile industry, medium sized plants did well in SIC 233, but in SIC 236, medium sized plants tended to lose jobs. In conclusion, nonmetro industries whose employment growth benefited from groups of large, small, and medium scale operations are identified. Rural communities may use this information to assess the employment growth potential for small, medium, and large scale establishments and to identify manufacturing industries in the SIC groups evaluated in this report to target in their economic development strategy. Future research at targets within the four-digit category would provide added guidance on industry targeting for rural TVA counties. 10

12 V. REFERENCES Birch, D., et al Who s Creating Jobs? Cognetics, Inc. Cambridge, MA. Davis, S., Haltiwanger, J., and S. Schuh Job Creation and Destruction. MIT Press. Cambridge, MA. Dumais, G., Ellison, G., and E. L. Glaeser Geographic Concentration as a Dynamic Process. NBER Working Paper Ellison, G., and E. L. Glaeser Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach. NBER Working Paper Ellison, G., and E. L. Glaeser Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach. Journal of Political Economy 105:5: Goldschmidt, W Small Business and the Community: A Study in the Central Valley of California on the Effects of Scale of Farm Operation. Report of the Special Committee to Study Problems of American Small Business. Print 13: U.S. GPO, Washington, DC. Hayes, M. N., and A. Olmstead Farm Size and Community Quality. American Journal of Agricultural Economics 66: Henry, M., D. Barkley, and Y. Zhang Industry Clusters in the TVA Region: Do They Affect Development of Rural Areas? Report to TVA Rural Studies, University of Kentucky, Lexington, Contractor Paper Jensen, J. B., and R. H.McGuckin Firm Performance and Evolution: Empirical Regularities in the U.S. Microdata. Center for Economic Studies Discussion paper, CES 96-10, U.S Bureau of the Census, U.S. Department of Commerce. Johnson, K. P Redefinition of the BEA Economic Areas. Survey of Current Business February: Lobao, L Locality and Inequality. SUNY Press: Albany, NY. Lyson, T., and C. Tolbert Small Manufacturing and Nonmetropolitan Socioeconomic Well-Being. Environmental and Planning A 28: Mills C., and M. Ulmer Small Business and Social Welfare. Report of the Smaller War Plants Corporation to the Special Committee to Study Problems of American Small Business. U.S. Senate, 79 th Congress, 2 nd Session, Document No. 135 U.S. GPO, Washington, DC. O huallachain, B., and M. Satterthwaite Sectoral Growth Patterns at the Metropolitan Level: An Evaluation of Economic Development Incentives. Journal of Urban Economics 31(1): Rosenfeld, S. A Competitive Manufacturing: New Strategies for Regional Development. Center for Urban Policy Research. New Brunswick, NJ. Storper, M., and R. Walker The Capitalist Imperative. Basil Blackwell: NY. Storper, M., and A. J. Scott (eds.) Pathways to Industrialization and Regional Development. Routledge: London. Tolbert, C. M Local Capitalism, Civic Engagement, and Socioeconomic Well-Being in the Nonmetropolitan United States. Paper presented at the Annual Meeting of the Rural Sociological Society. Portland, OR. 11

13 VI. Appendix Other variables that were employed in regressions not reported here include: E t-1nb = Employment in year t-1 in the CEA for the five industries with the largest backward linkages to industry i (US IO model-implan). E t-1nf =Employment in year t-1 in the CEA for the five industries with the largest forward linkages to industry i (US IO model-implan). U = Labor force in year t-1 in the MSA region of the CEA. These variables were either highly correlated with others in the model (1) or (3) or were mostly zero yielding little or no variation across the CEAs. Accordingly, they were deleted to improve model performance. 12

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