Monopoly Capital and Management: Too Many Bosses and Too Much Pay?

Abstract:

The mainstream or neoclassical economics view that labor is rewarded according to its productivity has been extended to managers and management teams as justification for the levels of compensation that they receive. Additionally, the management concept of “span of management” or “span of control” has been used to explain the total number of and per employee number of managers in any organization along with the assumption that the appropriate span of management is where the marginal productivity of the last manager employed should equal his/her marginal cost, or wage. Or, at least, this is supposed to be the case in competitive industries in the short run, or in all industries in the long run. On the other hand, heterodox economists hold different views of the roles and purposes of managers within organizations and attempt to explain these through either the view of managers exploiting workers on behalf of owners or the view of managers exploiting both workers and owners in order to advance their own agendas. This article examines managerial compensation and intensity from both traditional/mainstream and alternative views (mostly using David Gordon’s theory of a “bureaucratic burden” existing in most U.S. industries).

Keywords:

• big business
• bureaucracy
• economic systems
• managers
• productivity

JEL Classification Codes::

• B51
• D24

Notes

1 This is usually defined in Gordon’s writings as the ratio of administrative and managerial personnel to clerical, service and production workers. Most of the data used in his analyses either preceded or were from the 1980s.

2 There are many sources to cite here and beyond the scope of this article. See Felipe and McCombie (2014) on a summary of the debate on capital and its productivity and see Christopher Brown (2005) on labor and measuring its productivity. Meanwhile, Herbert Simon (1957) notes that many management terms, such as “span of management,” come closer to being vague “proverbs” rather than being solid guidelines on how to actually organize or manage a firm.

3 Baran (1957, 37) writes that neither maximizing efficiency or minimizing costs is necessary nor desired in large corporations. Large corporations have “skyrocketing expense accounts, exorbitant salaries paid to executives making no contributions to the firms’ output but drawing revenues on the strength of their financial connections, personal influence, or character traits making them particularly adapted to corporate politics.”

4 Harry Braverman (1974, Chapter 2) notes that the central function of any management system is control over the workers. This is more important than any other management task according to him.

5 William Baumol (1962) argued that one primary goal of managers was to increase firm size in terms of employees, assets, etc. This was deemed by them to be more important than maximizing profits.

6 David Landes (1986) took issue with many of Marglin’s historical interpretations of how and why hierarchies developed and thought Marglin’s analysis flawed.

7 There is also data available for the years 2015 to 2017 at the BLS website. To create an index of business size, U.S. Census Bureau Concentration Ratios for 2002, 2007, and 2012 are used for the years 2002 to 2014. Unfortunately, the 2017 ratios are not yet available, and so the time span used for the analysis is 2002-2014. Also, before 2002, industry data was formatted in the Census Bureau’s Standard Industry Classification (SIC) code, and was only available at the two and three digit SIC level, and from 1988 to 1995, only a handful off industries were surveyed, and only results for these surveyed industries were disclosed annually. Therefore, NAICS data (year 2002 and beyond) provided the least amount of “gaps” in data continuity. Also, data from the Census at the 4-digit industry level were not available for mining and construction. Public administration, although important to a capitalist economy, was not considered because productivity levels are problematic to calculate and evaluate. For example, would taxes collected per government employee or government spending per government employee be considered as outputs or inputs?

8 The BLS website is not clear in its definition of chief executives much less chief executive officers, or CEOs. In the data files, under the broad occupational category of “Management Occupations,” “Chief Executives” are given as one specific type of management category along with general and operations managers, financial managers, and others. However, there were sometimes more chief executives listed for an industry than there were number of firms or enterprises listed as being part of an industry suggesting that there could be more than one chief executive per firm. When the author wrote the BLS for a clear definition of the term “Chief Executive,” the reply given in the Appendix was received. Since it is not entirely clear whether Chief Executive refers to a CEO, this article uses the term “chief officers” on the chance that some companies may have more than one CEO, or more likely, a CEO and his/her management team or lieutenants in a firm have been labeled as chief executives by the BLS because they have not been defined as Chief Information Officers, or Chief Operations Officers, (BLS Correspondence February 27, 2017).

9 Upon request, the author will provide additional tables showing models based upon grouping the data by industry and/or tables showing models based on fixed effects regression.

10 Since Chief Officers make the most important strategic decisions within organizations, it was felt important to assess the impact of their pay.

11 For ROA and Return on Equity (ROE) after taxes, the Almanac gave two numbers, ROA and ROE after taxes for all firms of all sizes regardless of whether they earned net income and another for firms reporting only net income. The analysis for this article used ROA and ROE after taxes when available for all firms regardless of their earning net income or not. When this was not provided, ROA and ROE for those reporting only net income was used. Unfortunately, data for all 4-digit industries were not available, which limited sample size in some models. For ROA and ROE, if data were not available at the 4-digit level, ROA and ROE at the 3-digit level were used when available from the Census Bureau’s Quarterly Financial Report Data (U.S. Census Bureau 2002–2014). Unfortunately, no data for average price/earnings ratio per industry was found.

12 Concentration ratios are only published by the Census Bureau every five years. Therefore, and because there is not much variation in their numbers every five years (except for slight increases on average), the 2002 concentration ratios were used for that year as well as for 2003 and 2004 data; and the 2007 ratios were used for that year as well as for 2005, 2006, 2008, and 2009; and the 2012 ratios were used for that year as well as 2010, 2011, 2013, and 2014. Admittedly, concentration ratios developed by the Census Bureau have limitations. The ratios are based only on U.S. firms (foreign competitors are not considered) and local market concentration (at a city or metro area level) is not considered. Only national level data is considered. The Herfindahl-Hirschmann Index (HHI) is an alternative measure, but the Census only publishes this for manufacturing firms (U.S. Census Bureau, Economic Census, 2002, 2007, and 2012). Despite its limitations, the ratio is used here as one part of a variable to indicate some type of market power that an industry has.

13 This is a dummy variable where industries are labeled as to whether they are part of an overall industry (agriculture, mining, manufacturing, and others) that is one of five of the heaviest unionized industries in the United States (US BLS 2017b). A value of 1 indicated that a 4-digit industry was part of an overall industry grouping that had heavy unionization whereas a value of 0 indicated that an industry was not part of such a larger industry category. Unfortunately, no data source was found that showed the extent of unionization for the 4-digit industries for the years covered in this article. According to the BLS, the top five major non-governmental industries with the heaviest union concentration for these years were utilities, transportation and warehousing, telecommunications, construction, and educational services. However, since the data used for this article did not include any for construction, manufacturing industries were coded as a 1 since this overall industry included the next highest level of unionization in the United States, especially in many regions of the United States (US BLS 2017a).

14 For example, the assets turnover ratio (Sales/Average Total Assets) was highly correlated with the Big Business Index because its reciprocal, Assets/Sales, is part of the index.

15 The almanacs of financial ratios sometimes dropped some industry categories and added new ones over the years as did the Census Bureau. The almanacs usually had data on anywhere from 190 to 200 industries or industry groupings, although some values were not given every year for every industry. Roughly and on average for each year: 2% of the industries examined in this study were utilities, around 44% from manufacturing (which typically had the greatest amount of data available), around 11% for retailing, around 5% for transportation and warehousing, around 5% for information services, around 19% for F/I/RE, around 11% for professional services, around 1% for arts, entertainment, and recreational services, and around 2% for other services. When it came to overall employment: less than 1% of the employment numbers were for utilities, around 15% for manufacturing, around 10% for F/I/RE, around 15% for wholesaling and and retailing, around 3% for transportation and warehousing, around 27% for professional services, around 5% for information services, and around 24% for all other services and arts, entertainment, and recreation. These numbers are somewhat close to what the U.S. Bureau of Labor Statistics reports for industry employment levels (U.S. Bureau of Labor Statistics 2017a).

16 The notation Pr/E is used and not P/E so as to avoid confusion with the short hand notation for Price/Earnings ratio, or P/E.

17 Different models were also developed that used BBI and the variables representing managerial intensity and managerial pay as independent variable to predict the different productivity, firm performance, and exploitation measurements. The results are similar to those presented in Table 4 in that the different managerial variables are similarly correlated with the different dependent variables as they are with BBI with some exceptions. These results can be furnished by the author upon request.

18 Admittedly, more of what front-line workers used to do in many organizations is now being done by people who have the title of manager or supervisor. The latter group may have some managerial duties but could still be doing a lot of work similar to what his/her subordinates do. There is also a desire of many firms to avoid overtime costs for many front or first-line workers, and so some of them have been “promoted” to the management ranks, given some supervisory duties and been given salaries although they do much of the same work that their subordinates perform. This phenomenon could also be contributing to bureaucratic bloat and at the same time, helping to reduce costs outside of management and supervisory salaries.

19 Also, there is the claim, along the lines of the quote from Baran (1957), that many new people hired in by firms tend to be friends, relatives, and former classmates of current employees who are performing well. This applies to those who work within the managerial ranks as well. Therefore, employers are making safe bets when they hire such new people since these people are more likely to support the current corporate culture because they are already the friends and/or relatives of people who work at the company. As some of my friends who have done well in getting jobs and promotions in the corporate world have often said, “It is not what you know, but who you know.”

20 Regarding CEO compensation, some recent studies have shown that the best paid CEOs have often had the worst performance by their companies. Despite this, average CEO pay and bonuses continue to go up (Adams 2014).

Additional information

Notes on contributors

Thomas E. Lambert

Thomas Lambert is an assistant professor and applied economist in the Equine Industry Program and Economics Departments at the College of Business, University of Louisville in Louisville, Kentucky.