An analysis of staffing efficiency in U.S. manufacturing: 1983 and 1989

A DEA framework is used to examine changes in administrative employment in U.S. manufacturing industries between 1983 and 1989, using data collected by the U.S. Department of Labor. Among other findings, the analysis suggests that production technology is an important factor in explaining inter-industry differences in administrative staffing. In addition, "best practice" staffing efficiencies for Batch industries are shown to hold a distinct (and statistically significant) advantage over that for Line industries. On related issues, this research uncovers no evidence of the "dramatic decreases" in overhead staffing that were suggested in the popular business press during this time period. Clear structural differences in administrative staffing intensities, however, are noted with respect to manufacturing production technology. In their usage of overhead staff, Batch industries tend to be more "professional-worker" intensive, while Line industries are relatively more "non-professional-worker" intensive. These patterns hold up over time and are statistically con-firmed in an analysis of DEA "cones".

[1]  W. Rushing,et al.  The Effects of Industry Size and Division of Labor on Administration , 1967 .

[2]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[3]  John H. Freeman Environment, Technology, and the Administrative Intensity of Manufacturing Organizations , 1973 .

[4]  D. Pugh,et al.  The Context of Organization Structures , 1969 .

[5]  W. B. Johnston Workforce 2000: Work and Workers for the 21st Century. , 1987 .

[6]  R. Hayes Restoring our competitive edge , 1984 .

[7]  Joan C. Woodward Industrial Organization: Theory and Practice , 1966 .

[8]  L. Pondy Effects of Size, Complexity, and Ownership on Administrative Intensity , 1969 .

[9]  John H. Bishop Is a Skills Shortage Coming , 1992 .

[10]  Budget,et al.  STANDARD INDUSTRIAL CLASSIFICATION MANUAL, 1987. , 1987 .

[11]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[12]  Lawrence M. Seiford,et al.  Recent developments in dea : the mathematical programming approach to frontier analysis , 1990 .

[13]  Peter T. Ward,et al.  Overhead surgery or media hyperbole? An examination of manufacturing employment structure in high and low tech industries, 1983 and 1989 , 1993 .

[14]  C. Northcote Parkinson,et al.  Parkinson's Law or the Pursuit of Progress , 1958 .

[15]  Seymour Melman,et al.  The rise of administrative overhead in the manufacturing industries of The United States, 1899-1947 , 1951 .

[16]  Jack E. Adams,et al.  The Future Impact of Automation on Workers , 1987 .

[17]  C. Perrow A FRAMEWORK FOR THE COMPARATIVE ANALYSIS OF ORGANIZATIONS , 1967 .

[18]  David J. Miller,et al.  Configurations of strategy and structure: Towards a synthesis , 1986 .

[19]  Bernard P. Indik The Relationship Between Organization Size and Supervision Ratio , 1964 .

[20]  Henry Mintzberg,et al.  The Structuring of Organizations , 1979 .

[21]  A. Charnes,et al.  Auditing and accounting for program efficiency and management efficiency in not-for-profit entities , 1980 .

[22]  R. Banker Maximum likelihood, consistency and data envelopment analysis: a statistical foundation , 1993 .

[23]  James D. Thompson Organizations in Action , 1967 .

[24]  R. Färe,et al.  Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach , 1994 .

[25]  J. Child Parkinson's Progress: Accounting for the Number of Specialists in Organizations , 1973 .

[26]  R. Färe,et al.  Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries , 1994 .

[27]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.