(p.856) Appendix 7.1 Data Sources and Methods for Chapter 7
(p.856) Appendix 7.1 Data Sources and Methods for Chapter 7
II. Original world and US manufacturing profit rates 1960–1989 (Christodoulopoulos 1995)
Figure 7.13 World Manufacturing Average and Incremental Rates of Profit, 1970–1989
Figure 7.14 US Manufacturing Average and Incremental Rates of Profit, 1960–1989
The 1994 International Sectoral Database (ISDB) (OECD 1994) contained annual data, now discontinued, from which it was possible to derive measures of gross operating surplus, that is, GDP minus Indirect Business Taxes (net of subsidies) minus Employee Compensation, gross capital stock, and gross investment for various OECD countries. This was used to derive measures of average and incremental rates of profit by world industry.1 In order to achieve comparability and consistency across countries and industries, the analysis was limited to the period 1970–1990 and focused on the profitability of eight manufacturing industries (Food, Textiles, Paper, Chemicals, Minerals, Metals and Metal Products, Machinery and Equipment, Other Manufacturing products) across eight countries (United States, Japan, Canada, Germany, France, Italy, Belgium, and Norway). World totals for gross operating surplus, gross capital stock, and gross investment were calculated for each industry, using PPP exchange rates to make the translation into US dollars. This data was then used to calculate average and incremental profit rates for each industry at the (developed) world level.
III. Average and incremental profit rates for US industries 1987–2005 (Shaikh 2008)
Figure 7.15 Average Rates of Profit in US Industries, 1987–2005
Figure 7.16 Deviations of US Industry Profit Rates from Average
Figure 7.17 Incremental Rates of Profit in US Industries, 1987–2005
Figure 7.18 Deviations of US Industry Incremental Profit Rates from Average
(p.857) Since the US BEA now only calculates net capital stock, the rate of profit on total capital is defined here as the ratio of nominal net profits (gross profits minus depreciation) to current-cost net capital stock. On the other hand, since gross investment figures are widely available and are independent of the debatable assumptions needed to estimate capital stocks, the incremental rate of profit is defined as the ratio of the change in nominal gross profits to lagged nominal gross investment. Further details of the derivation and use of these and other relevant variables are listed in (1) to (6).
1. The basic flow variables were taken from the US Bureau of Economic Analysis (BEA) Gross-Domestic-Product-(GDP)-by-Industry tables 1947–97 GDPbyInd_VA_NAICS and 1998–2005 GDPbyInd_VA_NAICS, available at http://www.bea.gov/industry/gdpbyind_data.htm. From these were calculated current Gross Value Added (GVA), Employee Compensation (EC), Gross Operating Surplus (GOS),2 the price index for GVA (VAPI) which was used to create real GVA (GVAR), and employment data on Full- and Part-Time Employees (FTPE), Self-Employed Persons (SEP), and Full-Time Equivalent Employees (FEE). All of these were available for 1987–2005 except SEP and FEE, which were only available for 1998–2005.
2. For each sector a wage equivalent (WEQ) was calculated by applying the average full-time wage per worker (w ≡ EC/FEE) to SEP, and the resulting value was subtracted from GOS to create Gross Profits (PG). This was done because the NIPA calculation of GOS implicitly treats all of the income of proprietors and partners (i.e., of self-employed persons) as profit-type income. Since SEP and FEE were only available for 1998–2005, the 1987 ratios of FEE/FTPE and PEP/FTPE were used along with 1987–1997 values of FTPE to fill in these earlier years.
3. Current Cost Capital Stock (K), Gross Investment (IG), and Current Cost Depreciation (DEP) for each sector, and the quantity index for Net Capital Stock (KQI) were taken from the following BEA Wealth tables: Table 3.1ES. Current-Cost Net Stock of Private Fixed Assets by Industry; Table 3.4ES. Current-Cost Depreciation of Private Fixed Assets by Industry; and Table 3.7ES. Historical-Cost Investment in Private Fixed Assets by Industry; and Table 3.8ES. Chain-Type Quantity Indexes for Investment in Private Fixed Assets by Industry, all downloaded on November 8, 2007, last revised on August 8, 2007. The industries in the Wealth tables were matched to those in the NIPA accounts, which required aggregating sectors 50–51 and 69–70 in the former tables. Real capital stocks (KR) were created by scaling up the quantity index using the base-year (2000) values of current cost stocks.
4. Imputed values for owner-occupied-housing (OOH) were removed from the real estate industry values of GVA (space rent line 134 minus intermediate input line 135), GOS (GVA minus taxes net of subsidies (line 135 minus line 136), and DEP (line 140), there being no imputation made for EC, using NIPA Table 7.12. Imputations in the National Income and Product Accounts, Bureau of Economic Analysis, downloaded on November 4, 2007 at 12:55:31 p.m., last revised on August 1, 2007. But whereas the BEA NIPA accounts now allocate all imputed values for OOH to the real estate sector, it still splits the Wealth stock components of OOH imputations between Farms and Real Estate, which had to be removed using Table 5.1. Current-Cost Net Stock of Residential Fixed Assets by Type of Owner, Legal Form of Organization, Industry, and Tenure Group, lines 15–16, respectively. A similar (p.858) adjustment was made for IG, using Table 5.7. Historical-Cost Investment in Residential Fixed Assets by Type of Owner, Legal Form of Organization, Industry, and Tenure Group, lines 15–16.
5. Inventories were added to the capital stocks of manufacturing and wholesale/retail trade industries, using NIPA Table 1BU. Real Manufacturing and Trade Inventories, http://www.bea.gov/national/nipaweb/nipa_underlying/SelectTable.asp and Table 2AUI. Implicit Price Deflators for Manufacturing and Trade Sales, both downloaded on November 8, 2007, last revised on February 3, 2004. The 1987–2005 average ratio of real inventories to real capital stock ratio in each sector was taken to be its normal ratio, and this was used in conjunction with annual real capital stocks to create annual normal inventories for each sector. These were then converted to current cost inventories using the implicit price deflators for manufacturing and trade sales.
For the construction industry, data on inventories of materials and supplies was available from the 1992, 1997, 2002 Economic Census of Construction, Table 3. The value of construction work was available for establishments reporting inventories, reporting no inventories, and non-reporting. The ratio of the construction sales of the first two sets was used to split the last set into subcomponents with and without inventories, the inventory sales ratio of the first set was applied to the first subcomponent of the last set to estimate its inventory levels, and this was added to reported inventories to get an overall total. The average inventory/GVA ratio for 1992, 1997, and 2002 (which was stable around 4%) was then used to define a normal ratio, and this was used to estimate annual normal inventory stocks in the construction sector. The same ratio was also applied to the sector’s fixed investment in equipment and structures in order to estimate normal inventory investment. Total capital and investment were defined as the sums of their fixed and inventory components.
In the Insurance and Related Activities industry, total reserves were calculated as the sum of checkable deposits and currency, money market funds and security RPs in US Flow of Funds Tables L.116, Property-Casualty Insurance Companies (lines 2–3) and L.117, Life Insurance Companies (lines 2–3), downloaded January 8, 2008, 10:30 p.m. Since the ratio of reserves to net current-cost capital declined over time and fluctuated from one year to the next, its normal level was defined by its exponential trend. This trend value was then applied to annual capital stocks to get the normal reserve stocks, and to annual investment flows to get the normal investment in reserves, the resulting figures being added to fixed capital stocks and investment to get total capital stock and investment. A similar procedure was followed for the Banking and Finance industry, which encompasses commercial banks, savings banks, and credit unions, with reserves defined as the sum of vault cash and currency, reserves at the Federal Reserve, banks’ own checkable and time deposits and currency (but not that of their customers), and Fed Funds and RP’s, as taken from US Flow of Funds table L.109 (lines 2–4), L.114 (lines 2–5), and L.115 (lines 2–4).
6. The NAICS data set has sixty-one individual private industries, plus an overall aggregate (All Private Industries) and several sub-aggregates such as Total, Durable, and Nondurable Manufacturing. Detailed descriptions of each industry are available online (StatCanada 1997). Particular care was taken focus on industries that were dominated by profit-driven enterprises and were also competitive on a world scale. This led to the exclusion of thirty-one of the original sixty-one private industries, with a concomitant redefinition of the overall rate of profit and incremental rate of profit. The first set of industries was excluded if they were dominated by nonprofit activities enterprises (e.g., arts, museums, educational services, and social services) or if the available data on the wages of employees significantly understated (p.859) the wage-equivalent of the proprietors and partners (say as in the case of law firms or medical offices).3 Such considerations applied to Administrative and Support Services, Ambulatory Health Care Services, Educational Services, Funds and Other Financial Vehicles, Hospitals and Nursing and Residential Care Facilities, Other Service Except Government (which include Religion, Grant Making, Civic, Professional and Similar Organizations), Performing Arts, Spectator Sports, Museums, and Related Activities, Legal Services, Computer Systems Design and Related Services, and Miscellaneous Professional, Scientific, and Technical Services, Publishing Industries; and Social Assistance. These sectors typically had either extremely low or negative “profit rates” (e.g., Educational Services), or very high ones (e.g., Administrative and Support Services, and the various sub-sectors of Professional, Scientific, and Technical Services). Finally, another eighteen industries were excluded because either their average or incremental rates of profit had period averages below 5% (several even had negative or near zero averages).4 These were deemed internationally uncompetitive on a world scale. The full list of excluded industries is available in Shaikh (2008, appendix B).
IV. Average and incremental profit rates for Greek manufacturing 1962–1991 (Tsoulfidis and Tsaliki 2011)
Figure 7.19 Deviations of Greek Manufacturing Profit Rates from Average Profit Rate, 1962–1991
Figure 7.20 Deviations of Greek Manufacturing Incremental Profit Rates from Average Incremental Rate, 1962–1991
Source: Tsoulfidis and Tsaliki 2011: 19, fig. 4, and 30, fig. 5.
V. Incremental rates of profit for OECD industries 1988–2003
Figure 7.21 OECD Industries, Deviations of Incremental Rates of Profit from their Average (Using PPP Exchange Rates)
1. Data source: OECD STructral ANalysis (STAN) Database (OECD 2003) provides investment and profit data which were used to calculate IROP. However, since it does not provide capital stock data, it was not possible to calculate the average rate of profit.
2. The variables used for IROP analysis from STAN were Gross Fixed Capital Formation (GFCF), which is gross investment; and Gross Operating Surplus and mixed income (GOPS), which is gross profit. The latter was either directly available or could be constructed as the sum of Net Operating Surplus and mixed income (NOPS) and Consumption (p.860) of Fixed Capital (CFC). Lack of data prevented the removal of the remuneration of the wage equivalent (WEQ) of the self-employed (see chapter 6, section VIII, and appendix 6.7.II).
3. The STAN database (OECD 2003) was used because it covered roughly thirty OECD countries. The subsequent version of this database covered only eighteen countries and excluded even those such as Canada and the United Kingdom.
4. Since the two main series were not always available, we restricted our data to points that included both variables. Our final therefore begins in 1987 and includes only those industry averages comprising three or more countries.
5. Sectors that were considered to be dominated by nonprofit activities enterprises (e.g., arts, museums, educational services, and social services) were excluded, as were ones in which such as law firms or medical offices in which the wage equivalent of GOPS is likely to be large (see the preceding section II.6). The final list of included sectors is indicated in figure 7.21.
6. The variables were in local currency units (and in euros for EMU countries in post-euro years), so they were converted to Purchasing Power adjusted US Dollars (International Dollars) using the Penn World Table (PWT) 6.2 Purchasing Power Parity (PPP) data.
7. The PPP-converted variables were aggregated across countries for each industry and the IROP calculated as the change in GOPS divided by GFCF of the preceding year.
(2) Gross Operating Surplus ≡ Gross Value Added − Employee Compensation − Taxes on Production and Imports.
(3) I thank George Smith and Denise McBride of the Bureau of Economic Analysis for helping us identify potential sectors.
(4) Duménil and Lévy (2004, 84–85) argue that in two of these industries, Pipeline Transportation and Railroad Transportation, the extremely low measured rates of profit were primarily due to the fact that the BEA methods yield excessively high values for their capital stocks because of the very long service lives the BEA assigns to pipelines and railroad tracks.