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Abstract
Drucker J. An evaluation of competitive industrial structure and regional manufacturing employment change, Regional Studies. This paper examines the relationship between regional industrial structure and employment change in the manufacturing sector and nineteen subsectors in the United States from 1987 to 1997. The relative associations of economic diversity, industrial specialization and competitive structure with economic performance are assessed using a non-causal regression framework. Multiple facets of industrial structure at the regional scale, including competitive structure, are considered together by exploiting confidential microdata to construct and evaluate detailed metrics across broad geographic and industrial ranges. The findings suggest the importance of industrial competitive structure for understanding regional employment change, economic performance and industrial development.
Drucker J. 竞争产业结构与区域製造业就业变迁的评估，区域研究。本文检视美国自 1987 年至 1997 年製造业部门与十九个次部门中，区域产业结构与就业变迁的关联性。本研究将运用非因果回归框架，评估经济多样性、产业专殊化与竞争结构和经济表现之间的相对关联。本研究将透过运用机密微资料，一併考量区域尺度中产业结构的多重面向，包含竞争结构，以此建构并评估横跨广阔地理及产业范畴的详细数据。研究发现显示出产业竞争结构之于理解区域就业变迁、经济表现与产业发展的重要性。
Drucker J. Une évaluation de la structure industrielle compétitive et la variation de l'emploi régional dans le secteur de la fabrication, Regional Studies. Cet article examine le rapport entre la structure industrielle régionale et la variation de l'emploi dans le secteur de la fabrication et dans dix-neuf sous-secteurs aux États-Unis entre 1987 et 1997. À partir d'un cadre de régression non causal, on évalue le rapport relatif de la diversité économique, de la spécialisation industrielle et de la structure compétitive à la performance économique. On considère ensemble les multiples facettes de la structure industrielle à l’échelle régionale, y compris la structure compétitive, en exploitant des micro-données confidentielles pour construire et évaluer des mesures détaillées à travers de vastes gammes géographiques et industrielles. Les résultats suggèrent l'importance de la structure compétitive industrielle pour comprendre la variation de l'emploi régional, la performance économique et le développement industriel.
Drucker J. Bewertung der industriellen Wettbewerbsstruktur und der Veränderungen bei der regionalen Beschäftigungsquote im produzierenden Sektor, Regional Studies. In diesem Beitrag wird die Beziehung zwischen der regionalen Branchenstruktur und den Veränderungen bei der Beschäftigungsquote im produzierenden Sektor sowie in 19 Subsektoren in den USA im Zeitraum von 1987 bis 1997 untersucht. Mit Hilfe eines nichtkausalen Regressionsrahmens werden die jeweiligen Beziehungen zwischen wirtschaftlicher Vielfalt, Branchenspezialisierung und Wettbewerbsstruktur einerseits und der Wirtschaftsleistung andererseits analysiert. Die vielfältigen Facetten der Branchenstruktur auf regionaler Ebene, einschließlich der Wettbewerbsstruktur, werden gemeinsam berücksichtigt, indem vertrauliche Mikrodaten zur Entwicklung und Bewertung von detaillierten Maßstäben für ein breites Spektrum von geografischen und industriellen Bereichen genutzt werden. Die Ergebnisse verdeutlichen die Wichtigkeit einer industriellen Wettbewerbsstruktur für das Verständnis von Veränderungen bei der regionalen Beschäftigungsquote, Wirtschaftsleistung und industriellen Entwicklung.
Drucker J. Evaluación de la estructura industrial competitiva y del cambio de empleo manufacturero regional, Regional Studies. En este artículo se analiza la relación entre la estructura industrial regional y el cambio de empleo en el sector manufacturero y los diecinueve subsectores en los Estados Unidos de 1987 a 1997. Mediante una estructura de regresión no causal, se evalúan las asociaciones relativas entre la diversidad económica, la especialización industrial y la estructura competitiva con el rendimiento económico. Se consideran en conjunto las multifacetas de la estructura industrial a escala regional y la estructura competitiva al aprovechar microdatos confidenciales para construir y evaluar valores métricos detallados en amplios márgenes geográficos e industriales. Los resultados indican la importancia de la estructura competitiva industrial para entender el cambio de empleo regional, el rendimiento económico y el desarrollo industrial.
Acknowledgements
Part of the research was conducted while the author was a Special Sworn Status researcher of the US Census Bureau at the Triangle and Chicago Census Research Data Centers. The contents of this study have been screened to ensure that no confidential data are revealed. All contents and conclusions expressed are solely the responsibility of the author and do not necessarily reflect the views of any of the supporting organizations or the US Census Bureau.
Funding –
This work was supported by awards from the National Science Foundation [grant number NSF-BCS 0423900], the Ewing Marion Kauffman Foundation [Dissertation Fellowship], and the North American Regional Science Council [Benjamin Stevens Fellowship]. Additional support for the Triangle Census Research Data Center came from the National Science Foundation [grant numbers NSF-SES 0004322 and NSF-ITR 0427889].
Notes
1 The meta-analysis conducted by Beaudry and Schiffauerova (Citation2009) found that only twenty-five of the sixty-seven studies reviewed examined competition together with diversity and specialization externalities. In the majority of these twenty-five studies, one or more of the three characteristics failed to generate decisive results. Similarly, thirteen of the thirty-one studies reviewed by De Groot et al. (Citation2009) considered the three traits together, of which eight yielded inconclusive findings for at least one of the externality types.
2 There are methodological concerns prevalent in industrial structure and agglomeration research such as unobserved heterogeneity and possible endogeneity. There may be additional characteristics of firms, industries or regions not included in the analyses presented that influence manufacturing employment change, and endogeneity may arise if the firms most likely to grow also are the best at selecting locations that support employment increases. In a non-causal framework, these concerns are not paramount as the goal is to compare rather than to isolate and specify precisely the magnitude of each independent relationship.
3 For example, employment may decline as productivity and capital intensity increase. Also, regional employment change may feed back into enduring industrial structure characteristics, though such a causal pathway is not a likely explanation for the major findings of this study (see note 13 below).
4 The CM data were accessed via the Longitudinal Research Database (LRD), a dataset that contains all of the information from the various years of the CM as well as the Annual Survey of Manufacturers. Some of the listed works refer to the LRD rather than to the CM.
5 The concept of industrial structure concentration intrinsically involves comparisons between a set of plants or firms and the individuals comprising that set, leading to possible ambiguities in indicator interpretation. Here, the application of several ratios as indicators that differ chiefly in their numerators assists in attributing and evaluating the findings.
6 Robustness checks were conducted by setting the threshold as low as six and as high as fifty firms in the regional industry, producing results with qualitative interpretations that are the same as those presented.
7 Combes et al. (Citation2004) and Frenken et al. (Citation2007) have proposed refinements to measuring economic diversity, but the appropriate level of industrial disaggregation at which to apply multiple measures of diversity and their intersection with other co-location concepts remains obscure. For example, at what degree of similarity among firms do agglomeration effects switch from being cross-industry (diversity) externalities to within-industry (specialization or competition) externalities?
8 In using the 1999 definitions, this study errs on the side of inclusivity; relatively small counties and rural counties that had little interaction with central cities and the immediately surrounding urbanized areas in the earlier portion of the decade will not alter analytical outcomes extensively. Checks using primary metropolitan statistical areas (PMSAs) instead of CMSAs yield similar results to those presented.
9 Disclosure protections preclude divulging the particular regions included for each subsector.
10 Although the initial level of manufacturing employment is also the denominator of the concentration ratio, its inclusion helps to distinguish the effects of regional scale from competitive structure. The correlation between the two variables is not large (approximately 0.4). The Herfindahl–Hirschman and Rosenbluth indices do not introduce this overlap and thus provide a robustness check for the specification of the competitive structural variable.
11 Several alternate specifications were tested, including using the percentage change in employment as the dependent variable, using the local (that is, competitive) term from a classical shift–share decomposition as the dependent variable, substituting firm counts or total value added for employment in the dependent variable, adding lagged concentration and diversity measures, specifying macro-regional controls by Census Divisions rather than Census Regions, and breaking the decade into two five-year intervals. Each of these specifications produced results in support of the substantive findings and interpretations described in the text. White's general heteroskedasticity test does not reject homoskedasticity at conventional significance levels, and the heteroskedasticity-adjusted probability values are little different than the standard estimates.
12 Standard tests (global Moran's I and LaGrange multiplier statistics) suggested the existence of positive spatial dependence with a lag interpretation dominant. Yet a spatial lag model using an inverse distance weights matrix based on MSA and CMSA centroids yielded coefficient estimates presenting no substantive differences from the ordinary least squares estimates displayed other than for the Census Region dummy variables. The same held true for a spatial error model. The possibility remains of spatial processes that operate at sub-regional scales or with respect to patterns other than those reflected by MSA boundaries and centroids.
13 The regression analysis does not on its own provide direct evidence of the direction of the causal link between concentration and employment change. Regional employment change leading to greater concentration is a logical possibility, yet detailed examination of the CM microdata reveals that declines in regional industrial structure concentration during the time period are associated with greater than proportional employment losses from large establishments, which would imply a positive sign for the concentration variable coefficient. Therefore, if employment change does in some cases lead to increases in concentration, the estimate in actually may understate the effect of concentration on manufacturing employment change.Disclosure limitations disallow the provision of lists or counts of regions that might elucidate further the contrast further among regions with differing levels of concentration; however, similar comparisons conducted by the author involving alternate distributional intervals, such as smaller fractions of standard deviations, yielded comparable substantive conclusions.
14 A more precise figure would require descriptive statistics for the economic diversity measure that are not approved for release.
15 Regional descriptive statistics and correlations are not approved for disclosure.
16 The relationship between regional scale and diversity may have a causal as well as a statistical explanation (Duranton and Puga, Citation2001).
17 Variable correlations are available from the author upon request.
18 Diagnostic tests revealed little or no spatial dependence for most industries, and employing either a spatial lag or a spatial error model did not alter the results substantively for any of the sectors (for more description and caveats, see note 12 above).
19 Fig. A1 includes the control variables as well, with interpretations analogous to those discussed above for the manufacturing-wide analysis. Tables following the format of that present estimated coefficient signs and significance ranges along with observation counts and coefficients of determination for each subsector are available from the author upon request.