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Values, Prices and Income Distribution in Actual Economies

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Modern Classical Economics and Reality

Part of the book series: Evolutionary Economics and Social Complexity Science ((EESCS,volume 2))

Abstract

This chapter estimates the proximities of labour values and production prices to market prices in real-world economies and explores at length the respective relationships amongst production prices, interindustry structure of production and changes in income distribution. The results finally suggest that value-based approximations of actual single-product economies could be considered as accurate enough, and the effective dimensions of those economies appear to be relatively low, that is to say, between two and three.

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Notes

  1. 1.

    This chapter draws on Mariolis et al. (2006, 2013), Tsoulfidis and Rieu (2006), Tsoulfidis and Mariolis (2007), Tsoulfidis (2008), Mariolis and Tsoulfidis (2009), Tsoulfidis and Paitaridis (2009) and Mariolis (2010a, 2015).

  2. 2.

    For the aggregation , we applied the method suggested by Miller and Blair (2009, pp. 160–164).

  3. 3.

    See footnote 4 in Chap. 2.

  4. 4.

    For an exploration of the relationships between alternative measures of deviation , see Chap. 4.

  5. 5.

    On the basis of an elegant proof, Shaikh (1984, pp. 55–59 and 80–82) argues that, in actual single-product economies, these deviations should be relatively low.

  6. 6.

    See footnote 12 in Chap. 2.

  7. 7.

    The wage curve in terms of commodity 3 switches from convex to concave at ρ ≅ 0.730. If wages are paid ex post, then this curve is strictly concave to the origin (negative price Wicksell effect ), while the other two curves are strictly convex.

  8. 8.

    At the time of this research, the SIOTs of the Greek economy were available for the years 1988 through 1998. For the available data as well as the construction of relevant variables, see the Appendix 1 at the end of this chapter.

  9. 9.

    It is observed that, for the case of the US economy, the results differ significantly from those of the aggregated model (reported in Sect. 3.2).

  10. 10.

    These estimates also measure interindustry ‘pure forward linkages’ (consider Eqs. 2.12 and 2.13).

  11. 11.

    The results for the other years were similar, and we decided not to report them for reasons of economy in space.

  12. 12.

    See, e.g. Hejl et al. (1967), Fink (1981), Ochoa (1984, Chap. 7), Shaikh (1984, 1998, 2012, 2016), Petrović (1987, 1988), Valtukh (1987, Chap. 4), Bienenfeld (1988), Cockshott et al. (1995), Chilcote (1997, Chaps. 6 and 7), Cockshott and Cottrell (1997), Tsoulfidis and Maniatis (2002), Val’tukh (2005), Zachariah (2006), Sánchez and Ferràndez (2010), Mariolis and Soklis (2011), Flaschel et al. (2012), Mariolis et al. (2012, pp. 58–62), Flaschel et al. (2013), Fröhlich (2013), Nakajima (2013), Iliadi et al. (2014), Li (2014a) and Sánchez and Montibeler (2015).

  13. 13.

    Also see, e.g. Harvie (2000), Izyumov and Alterman (2005), Yu and Feng (2007), Zachariah (2009), Karabarbounis and Neiman (2013) and Piketty (2013, Chap. 6).

  14. 14.

    See Soklis (2006, 2011, 2012, Chap. 6, 2015), Mariolis and Soklis (2007, 2010, 2014), Wirkierman (2012) and Garbellini and Wirkierman (2014). For the SUTs, see the Appendix 2 at the end of this chapter, which is based on Mariolis and Soklis (2010).

  15. 15.

    Soklis (2012) examines 79 SUTs of 11, quite diverse, economies and detects that the matrices \( {\left[\mathbf{B}-\mathbf{A}\right]}^{-1} \) contain negative elements. Consequently, all those systems are not ‘all-productive’.

  16. 16.

    The industries are indicated in Table 3.6.

  17. 17.

    The subscript C (F) refers to the case of circulating (fixed) capital.

  18. 18.

    It may be noted that also \( \underset{j}{ \max}\left\{{m}_{jj}\right\} \) always occurs in the same industry, i.e. industry 10 (Manufacture of chemicals and chemical products, manufacture of rubber and plastic products), and it is in the range of 0.619 (year 1989) to 0.740 (year 1988).

  19. 19.

    This minimum is not identified from a visual inspection of Fig. 3.11. It is discerned from more detailed data as well as from the movement of κ 40 (see below).

  20. 20.

    The SIOTs and the corresponding levels of sectoral employment of the Finnish economy are provided via the Eurostat website (http://ec.europa.eu/eurostat). At the time of this research, they were available for the years 1995 through 2004 and describe 59 products, which are classified according to CPA (‘Classification of Product by Activity’; see the Appendix 1 at the end of this chapter). However, all the elements associated with the industry ‘Uranium and thorium ores’ equal zero and, therefore, we remove them from our analysis. Furthermore, since all labour and material inputs in the industry ‘Crude petroleum and natural gas; services incidental to oil and gas extraction excluding surveying’ equal zero, while the relevant product is imported to the system, we aggregate it with the industry ‘Metal ores’. Thus, we derive SIOTs of dimensions 57 × 57.

  21. 21.

    Industry classification: 1. Products of agriculture, hunting and related services; 3. Fish and other fishing products; services incidental of fishing; 6. Other mining and quarrying products; 7. Food products and beverages; 8. Tobacco products; 13. Pulp, paper and paper products; 15. Coke, refined petroleum products and nuclear fuels; 16. Chemicals, chemical products; 19. Basic metals; 21. Machinery and equipment n.e.c.; 23. Electrical machinery and apparatus n.e.c.; 24. Radio, television and communication equipment and apparatus; 26. Motor vehicles, trailers and semi-trailers; 27. Other transport equipment; 29. Recovered secondary raw materials; 45. Real estate services.

  22. 22.

    The vector of relative labour values (of Austrian production prices) can be considered as a constant-term (a linear) approximation of the vector of relative production prices, which is exact when profits equal zero (which is derived from the ‘rule of simple interest ’). At the actual values of the profit rate, the d − distance between the vector of actual production prices and the vector of Austrian production prices (of labour values) is 0.070 (is 0.188). Moreover, labour values and actual Austrian production prices are ‘equally’ accurate approximations of the market prices: the deviation of the latter from labour values is in the range of 0.337–0.353, while their deviation from the actual Austrian production prices is in the range of 0.322–0.368.

  23. 23.

    See, e.g. Kyn et al. (1967), Sekerka et al. (1970), Petrović (1987), Bienenfeld (1988), Da Silva and Rosinger (1992), del Valle Caballero (1993), Shaikh (1998, 2012, 2016) and Iliadi et al. (2014).

  24. 24.

    The latter ten SIOTs have been used by Iliadi et al. (2014), who empirically examine the monotonicity of the production price-profit rate curves: non-monotonic curves, expressed in terms of SSC, are observed in about 19 % (105/559) of the tested cases. The data are provided via the Eurostat website and describe 59 products, which are classified according to CPA. However, there are cases in which all the elements or only the labour inputs or, finally, only the material inputs associated with certain industries are equal to zero. In order to derive ‘Sraffa matrices’ (Krause 1981, pp. 177–178), i.e. matrices with strictly positive left P-F eigenvectors, we remove them from our analysis or we make the appropriate aggregations. For example, in each SIOT, all the material inputs associated with the industry ‘Private households with employed persons’ are equal to zero and, therefore, we remove this industry. Or, in the SIOTs of the German economy, (i) all the elements associated with the product ‘Uranium and thorium ores’ are equal to zero, and, therefore, we remove them and (ii) all labour and material inputs in the industry ‘Metal ores’ are equal to zero, while the relevant product is imported to the system, and, therefore, we aggregate it with the industry ‘Other mining and quarrying products’. Thus, n = 55 for Denmark, n = 56 for Finland, n = 56 for France, n = 56 for Germany, and n = 52 (year 1995) or n = 50 (year 2005) for Sweden.

  25. 25.

    For the Greek economy, Ω is in the range of 5.069 (year 1992) to 8.049 (year 1995; see Table 3.10) and, therefore, the difference \( {S}_{\max }-{S}_{\mathrm{sm}} \) is in the range of 0.016–0.031. For the other economies, Ω is in the range of 7.056 (Sweden, 1995) to 35.251 (Finland, 2004), and, therefore, \( {S}_{\max }-{S}_{\mathrm{sm}} \) is in the range of 0.026–0.115.

  26. 26.

    For the Greek economy, m = 12 (Basic metals and fabricated metal products) and M = 19 (Transports, water transport services, air transport services, post and telecommunications). For the French economy, m = 28 (Other transport equipment) and M = 51 (Education services).

  27. 27.

    See studies mentioned in footnotes 12 and 23 in this chapter, and Krelle (1977), Leontief (1985), Hamilton (1986), Özol (1984, 1991), Cekota (1988, 1990), Michl (1991), Petrović (1991), Fujimori (1992), Da Silva (1993), Marzi (1994), Angeloussis (2006), Han and Schefold (2006), Degasperi and Fredholm (2010), García and Garzón (2011) and Li (2014b).

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Authors and Affiliations

  1. Department of Public Administration, Panteion University, Athens, Greece

    Theodore Mariolis

  2. Department of Economics, University of Macedonia, Thessaloniki, Greece

    Lefteris Tsoulfidis

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Appendices

Appendix 1: Data Sources and Construction of Variables

3.1.1 A.1.1 Greece

The symmetric input-output tables of the Greek economy were available for the years 1988 through 1998 and at the 25 industries level of detail (Mylonas et al. 2000). However, the necessary data on employment and wage were not fully available for the year 1998, and so our analysis extends until the year 1997. From the 25 industries, only the first 19 are absolutely consistent with the requirements of our analysis: the concepts of labour values and production prices have no meaning in industries such as public administration and education, whereas the concept of output is problematic to industries such as finance and real estate. Thus, we decided to eliminate from our analysis the last 6 industries, making the necessary adjustments in the output vector.

These input-output tables are restricted to flow data so they do not include interindustry data on fixed capital stocks and also the noncompetitive imports . As a result, our investigation is restricted to a circulating capital model, where we cannot separate the foreign from the domestic sector of the economy. It is worth noting that the issue of noncompetitive imports leads to many complications (see Sect. 2.2.4).

The market price s of all products are taken to be equal to 1. Thus, the matrix of direct technical coefficients, A, is obtained by dividing element-by-element the inputs of each industry by its gross output . In accordance with most of the relevant empirical studies, we use wage differentials to homogenize the sectoral employment , i.e. the vector of inputs in direct homogeneous labour , l ≡ [l j ], is estimated as

$$ {l}_j=\left({\Lambda}_j{\overline{x}}_j^{-1}\right)\left[{w}_{\mathrm{m}j}{\left(\underset{j}{ \min}\left\{{w}_{\mathrm{m}j}\right\}\right)}^{-1}\right] $$

where Λ j , w mj , \( {\overline{x}}_j \) denote the total employment , money wage rate, in terms of market prices, and gross output of the industry j, respectively, and \( \underset{j}{ \min}\left\{{w}_{\mathrm{m}j}\right\} \) the minimum money wage rate in terms of market prices. Alternatively, the homogenization of employment could be achieved, for example, through the economy’s average wage rate; in fact, the empirical results are robust to alternative normalizations with respect to homogenization of labour inputs.

Finally, by assuming that workers do not save and that their consumption has the same composition as the vector of the final consumption expenditures of the household sector, f T, directly available in the input-output tables , the commodity vector defining the real wage rate is estimated as

$$ {\mathbf{b}}^{\mathrm{T}}=\left[\underset{j}{ \min}\left\{{w}_{\mathrm{m}j}\right\}{\left(\mathbf{e}{\mathbf{f}}^{\mathrm{T}}\right)}^{-1}\right]{\mathbf{f}}^{\mathrm{T}} $$

where e represents the vector of market prices (also see Okishio and Nakatani 1985, pp. 66–67; Ochoa 1989, p. 428). It goes without saying that the empirical results (on the deviations of production prices from labour values) are robust to the assumption that a certain relatively small fraction of wages, s w , is saved; in that case, the vector of the real wage would be equal to \( \left[\left(1-{s}_w\right)\underset{j}{ \min}\left\{{w}_{\mathrm{m}j}\right\}{\left(\mathbf{e}{\mathbf{f}}^{\mathrm{T}}\right)}^{-1}\right]{\mathbf{f}}^{\mathrm{T}} \).

3.1.2 A.1.2 Japan

The input-output tables of Japan are available from the OECD STAN database at the 35 industries level of detail. Industry 34 (Government and producer services) is eliminated from the analysis for it plays no role in the formation of the general profit rate and production prices. For the same reason, industry 35 (Other services) is also eliminated. In fact, the input-output tables for both industries report zero operating surplus .

The vector of direct labour coefficients is estimated using the wage bill of each industry (the product of annual wage times the number of employees) from the input-output table for each year of our analysis. The problem with this estimation is that the self-employed population is not accounted for. For this purpose, we created an index of self-employment calculated as the ratio of the total employed population (the number of employees plus the self-employed) to the number of employees. The estimation of the self-employed population is absolutely necessary for our analysis since in an economy such as the Japanese, self-employment is widespread. For example, in agriculture in the year 1970, the number of self-employed was almost 12 times higher than that of the employees and dropped to approximately 6 times higher in the year 1990. The information on employment is available in the OECD STAN database. For a few industries, we could not collate data on the number of self-employed for the years 1970 and 1975, and so we used their percentage of the employed population in the year 1980. As the number of self-employed in these particular industries happened to be relatively small, it follows that our results are robust to this treatment. The industry names are given in Table 3.6.

3.1.3 A.1.3 China

The input-output tables of China for the year 1997 are available from the OECD STAN database at the 40 industries level of detail. Two industries 33 (Renting of machinery and equipment) and 34 (Computer and related activities) were eliminated from the analysis for they contain no data. The available input-output tables are restricted to flow data, and so our analysis will be carried out in terms of a circulating capital model. The flow vectors and matrices have been derived in a similar way with the above. The industries of the economy are the following:

  1. 1.

    Agriculture

  2. 2.

    Mining

  3. 3.

    Food

  4. 4.

    Textiles

  5. 5.

    Wood

  6. 6.

    Paper

  7. 7.

    Petroleum

  8. 8.

    Chemical

  9. 9.

    Pharmaceuticals

  10. 10.

    Rubber

  11. 11.

    Other nonmetallic mineral products

  12. 12.

    Iron and steel

  13. 13.

    Nonferrous metals

  14. 14.

    Fabricated metal products

  15. 15.

    Machinery and Equipment n.e.c.

  16. 16.

    Office accounting and computing machinery

  17. 17.

    Electrical machinery and apparatus n.e.c.

  18. 18.

    Radio and TV

  19. 19.

    Medical, precision and optical instruments

  20. 20.

    Motor vehicles

  21. 21.

    Building and repairing of ships and boats

  22. 22.

    Aircrafts and spacecraft

  23. 23.

    Railroad and transport equipment, n.e.c.

  24. 24.

    Manufacturing n.e.c.

  25. 25.

    Electricity, gas and water

  26. 26.

    Construction

  27. 27.

    Wholesale and retail trade

  28. 28.

    Hotels and restaurants

  29. 29.

    Transportation and storage

  30. 30.

    Communications

  31. 31.

    Finance insurance

  32. 32.

    Real estate

  33. 35.

    Research and development

  34. 36.

    Other business activities

  35. 37.

    Public administration

  36. 38.

    Education

  37. 39.

    Health and social work

  38. 40.

    Social services

3.1.4 A.1.4 Korea

Data limitations mainly on sectoral employment , wages, depreciation and capital stock restricted the analysis to 27 industries level of detail. In this classification, ‘nonproductive’ industries, such as, for example, the real estate and the public administration (whose output is really the wages of workers employed), are also included. The methodology applied for the construction of A, l and b T was similar to that applied to other countries. Since there is no matrix of capital stock coefficients, A C, published for the Korean economy, we had to create one from the available data. To this end, we used the published fixed capital flow matrices companions of input-output tables of the years 1995 and 2000. This matrix allocates the gross fixed capital formation of each industry to itself and others. We use this matrix to form weights, assuming—in the absence of an actual capital stock matrix—that capital stock is allocated among producing industries in a way similar to that of gross investment . A gross capital stock vector corresponding to the 27 input-output industry detail is fortunately published by Shin (2005). This vector was allocated to each industry according to the weights that we formed with the fixed capital formation (for details, see Tsoulfidis and Rieu (2006)). Depreciation coefficients are directly provided by the Bank of Korea, and they are derived through the fixed capital flow matrices companions of the input-output data for the years 1995 and 2000. The depreciation coefficients matrix, A D, is derived in a way similar to that of A. The industries of the economy that we used in our analysis are the following:

  1. 1.

    Agriculture, forestry, and fisheries

  2. 2.

    Mining and quarrying

  3. 3.

    Food, beverages and tobacco

  4. 4.

    Textile products and leather products

  5. 5.

    Wood and paper products

  6. 6.

    Printing, publishing and reproduction of recorded media

  7. 7.

    Petroleum and coal products

  8. 8.

    Chemicals and allied products

  9. 9.

    Nonmetallic mineral products

  10. 10.

    Primary metal products

  11. 11.

    Fabricated metal products

  12. 12.

    General machinery and equipment

  13. 13.

    Electronic and other electric equipment

  14. 14.

    Precision instruments

  15. 15.

    Transportation equipment

  16. 16.

    Furniture and other manufacturing products

  17. 17.

    Electric, gas, and water services

  18. 18.

    Construction

  19. 19.

    Wholesale and retail trade

  20. 20.

    Eating and drinking places, and hotels and other lodging places

  21. 21.

    Transportation and warehousing

  22. 22.

    Communications and broadcasting

  23. 23.

    Finance and insurance

  24. 24.

    Real estate and business service

  25. 25.

    Public administration and defence

  26. 26.

    Educational and health service

  27. 27.

    Social and other services

3.1.5 A.1.5 UK and USA

The input-output data for both countries are provided in the STAN basis of OECD. In the case of the UK, we managed to put together employment data (employed and self-employed). Provided that the wage data are available in the input-output tables , all we needed to carry out was an estimate of the wage equivalent of the self-employed population to obtain the total wage bill actually given and the imputed one for the self-employed population. The capital flow matrix for the case of the UK is provided in the OECD STAN database along with data on capital stock, which we corresponded to each of the 33 industries. The US input-output data were collated from the same source, and the industry names (the same with the UK) are given in Table 3.9.

3.1.6 A.1.6 Canada

The input-output tables of Canada are available from the OECD STAN database at the 34 industries level of detail. A, l and b T are created following the standard procedure. The sectoral wages are given as a row of the input-output tables, whereas the sectoral average wage is derived by estimating the number of employees of each industry. The so-derived sectoral wage is multiplied by the index of self-employment calculated by the ratio of the total employed population (the number of employees plus the self-employed) to the number of employees. The information on employment in thousands is available in the OECD STAN database. The industries of the economy that we used in our analysis are the following:

  1. 1.

    Agriculture

  2. 2.

    Mining

  3. 3.

    Food

  4. 4.

    Textiles

  5. 5.

    Wood

  6. 6.

    Paper

  7. 7.

    Petroleum

  8. 8.

    Chemical

  9. 9.

    Rubber

  10. 10.

    Other nonmetallic mineral products

  11. 11.

    Machinery and equipment

  12. 12.

    Fabricated products

  13. 13.

    Machinery and equipment

  14. 14.

    Office machinery

  15. 15.

    Electrical machinery

  16. 16.

    Radio and TV

  17. 17.

    Motor vehicle

  18. 18.

    Aircrafts

  19. 19.

    Railroad equipment

  20. 20.

    Railroad equipment

  21. 21.

    Manufacturing n.e.c.

  22. 22.

    Utilities

  23. 23.

    Construction

  24. 24.

    Wholesale and retail trade

  25. 25.

    Hotels and restaurants

  26. 26.

    Transportation

  27. 27.

    Communications

  28. 28.

    Finance insurance and real estate

  29. 29.

    Computer equipment

  30. 30.

    Other business

  31. 31.

    Public administration

  32. 32.

    Education

  33. 33.

    Health

  34. 34.

    Social services

3.1.7 A.1.7 Denmark, Finland, France, Germany and Sweden

The described products (and their correspondence to CPA) are the following:

  1. 1.

    (CPA: 01). Products of agriculture, hunting and related services

  2. 2.

    (02). Products of forestry, logging and related services

  3. 3.

    (05). Fish and other fishing products; services incidental of fishing

  4. 4.

    (10). Coal and lignite; peat

  5. 5.

    (11). Crude petroleum and natural gas; services incidental to oil and gas extraction excluding surveying

  6. 6.

    (12). Uranium and thorium ores

  7. 7.

    (13). Metal ores

  8. 8.

    (14). Other mining and quarrying products

  9. 9.

    (15). Food products and beverages

  10. 10.

    (16). Tobacco products

  11. 11.

    (17). Textiles

  12. 12.

    (18). Wearing apparel; furs

  13. 13.

    (19). Leather and leather products

  14. 14.

    (20). Wood and products of wood and cork (except furniture); articles of straw and plaiting materials

  15. 15.

    (21). Pulp, paper and paper products

  16. 16.

    (22). Printed matter and recorded media

  17. 17.

    (23). Coke, refined petroleum products and nuclear fuels

  18. 18.

    (24). Chemicals, chemical products and man-made fibres

  19. 19.

    (25). Rubber and plastic products

  20. 20.

    (26). Other nonmetallic mineral products

  21. 21.

    (27). Basic metals

  22. 22.

    (28). Fabricated metal products, except machinery and equipment

  23. 23.

    (29). Machinery and equipment n.e.c.

  24. 24.

    (30). Office machinery and computers

  25. 25.

    (31). Electrical machinery and apparatus n.e.c.

  26. 26.

    (32). Radio, television and communication equipment and apparatus

  27. 27.

    (33). Medical, precision and optical instruments, watches and clocks

  28. 28.

    (34). Motor vehicles, trailers and semi-trailers

  29. 29.

    (35). Other transport equipment

  30. 30.

    (36). Furniture; other manufactured goods n.e.c.

  31. 31.

    (37). Secondary raw materials

  32. 32.

    (40). Electrical energy, gas, steam and hot water

  33. 33.

    (41). Collected and purified water, distribution services of water

  34. 34.

    (45). Construction work

  35. 35.

    (50). Trade, maintenance and repair services of motor vehicles and motorcycles; retail sale of automotive fuel

  36. 36.

    (51). Wholesale trade and commission trade services, except of motor vehicles and motorcycles

  37. 37.

    (52). Retail trade services, except of motor vehicles and motorcycles; repair services of personal and household goods

  38. 38.

    (55). Hotel and restaurant services

  39. 39.

    (60). Land transport; transport via pipeline services

  40. 40.

    (61). Water transport services

  41. 41.

    (62). Air transport services

  42. 42.

    (63). Supporting and auxiliary transport services; travel agency services

  43. 43.

    (64). Post and telecommunication services

  44. 44.

    (65). Financial intermediation services, except insurance and pension funding services

  45. 45.

    (66). Insurance and pension funding services, except compulsory social security services

  46. 46.

    (67). Services auxiliary to financial intermediation

  47. 47.

    (70). Real estate services

  48. 48.

    (71). Renting services of machinery and equipment without operator and of personal and household goods

  49. 49.

    (72). Computer and related services

  50. 50.

    (73). Research and development services

  51. 51.

    (74). Other business services

  52. 52.

    (75). Public administration and defence services; compulsory social security services

  53. 53.

    (80). Education services

  54. 54.

    (85). Health and social work services

  55. 55.

    (90). Sewage and refuse disposal services, sanitation and similar services

  56. 56.

    (91). Membership organisation services n.e.c.

  57. 57.

    (92). Recreational, cultural and sporting services

  58. 58.

    (93). Other services

  59. 59.

    (95). Private households with employed persons

Appendix 2: A Note on the Supply and Use Tables

The symmetric input-output tables can be derived from the ‘System of National Accounts’ framework of Supply and Use Tables (SUTs; see, e.g. United Nations 1999, Chaps. 2, 3, and 4; Eurostat 2008, Chap. 11), introduced in 1968 (see United Nations 1968, Chap. 3). Given that in the SUTs there are industries that produce more than one commodity, and commodities that are produced by more than one industry, it follows that the SUTs could be considered as the counterpart of joint production systems (see, e.g. Flaschel 1980, pp. 120–121; Bidard and Erreygers 1998, pp. 434–436). By contrast, in the SIOTs, there is no industry that produces more than one commodity nor commodity that is produced by more than one industry, and, therefore, the SIOTs could be considered as the counterpart of single production systems. Since joint production is the empirically relevant case (Steedman 1984; Faber et al. 1998; Kurz 2006), SUTs constitute a more realistic ‘picture’ (in the sense of multiproduct output resulting from a single plant or process) of the actual economic system than SIOTs. It has to be noted, however, that some of the ‘joint’ products that appear in the SUTs may result from statistical classification and, therefore, they do not correspond to the genuine notion of joint production (see, e.g. Semmler 1984, pp. 168–169; United Nations 1999, p. 77).

The SUTs are not necessarily ‘square ’, i.e. the number of produced commodities does not necessarily equal the number of operated industries (see, e.g. United Nations 1999, p. 86; Eurostat (2008), p. 295). Square matrices are obtained by applying aggregation methods (then some important information may be lost). Moreover, in the Supply Tables, goods and services are measured at current ‘basic price s’, while in the Use Tables, all intermediate costs are measured in current ‘purchasers’ price s’. The derivation of the SUT at basic prices may be based on the method proposed by United Nations (1999, Chap. 3 and pp. 228–229).

For a review of the methods used to convert the SUT into SIOT, see, e.g. ten Raa and Rueda-Cantuche (2003, pp. 441–447). Amongst the various available methods, the so-called ‘Commodity Technology Assumption’ is the only one that fulfils a set of important properties of the input-output analysis (see Jansen and ten Raa 1990). However, the ‘commodity technology assumption ’ is possible to generate economically insignificant results, i.e. negative elements in the input-output matrix. Ten Raa and Rueda-Cantuche (2013) offer a critical review of the various procedures proposed to overcome this inconsistency, while Lager (2007), Mariolis (2008) and Soklis (2009b) argue that the v. Neumann-Sraffa treatment of joint-product systems constitutes a preferable approach, not based on any of the restrictive (and debatable) assumptions of the conversion methods.

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Mariolis, T., Tsoulfidis, L. (2016). Values, Prices and Income Distribution in Actual Economies. In: Modern Classical Economics and Reality. Evolutionary Economics and Social Complexity Science, vol 2. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55004-4_3

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