An input–output analysis of the impact of mining on the South African economy

Resources Policy 26 (2000) 17–30 www.elsevier.com/locate/resourpol

An input–output analysis of the impact of mining on the South African economy L.C. Stilwell

a,*

, R.C.A. Minnitt b, T.D. Monson c, G. Kuhn

d

a Allmine Projects, P.O. Box 14794, Farrarmere, 1518, South Africa Department of Mining Engineering, University of Witwatersrand, Private Bag 3, Wits, 2050, South Africa c School of Business and Economics, Michigan Technological University, Houghton, MI 49931, USA Manager-Economic Research and Development, Industrial Development Corporation of South Africa Limited, P.O. Box 784055, Sandton 2146, South Africa b

d

Abstract We use input–output techniques to analyse the impacts of gold, coal, and other mining activities upon the South African economy between 1971 and 1993. Our results suggest that the premise upon which the South African government’s proposed minerals policy is based, i.e. that “the mining industries have the capacity to generate wealth and employment on a large scale” (Republic of South Africa, Department of Minerals and Energy, 1998. A minerals and mining policy. White Paper, Department of Minerals and Energy, Republic of South Africa), may require further thought. Our estimated production and employment multipliers indicate that the impacts of marginal changes in mining production and employment were not significantly different from production and employment impacts of most other South African economic activities and that there were few linkages between mining and the rest of the economy. These results suggest that South African mining activities will increase income and employment only if exports increase or if policies are established to increase linkages between mining and the rest of the South African economy.  2000 Published by Elsevier Science Ltd. Keywords: Mining activities; Input–output analysis; Output multipliers; Income multipliers; Employment multipliers; Mineral policy; Mineral exports

Introduction The significance of South African minerals South African mineral resources are among the largest in the world. It has the largest reserves of gold, platinum, titanium, chromium, manganese and vanadium, the second largest reserves of zirconium, and significant reserves of phosphates, antimony, coal and nickel (see Table 1). South Africa currently produces about 21% of world gold output (down from its 38% average share during 1976 to 1996: Gold Institute, 1999) and is the fifth largest producer of diamonds. Diamonds are not included in Table 1 because no estimates of reserves are available. Davis (1994) (p. 24) states that despite this enormous natural endowment, South Africa remains a * Corresponding author. E-mail addresses: [email protected] (L.C. Stilwell), [email protected] (R.C.A. Minnitt), [email protected] (T.D. Monson), [email protected] (G. Kuhn). 0301-4207/00/$ - see front matter  2000 Published by Elsevier Science Ltd. PII: S 0 3 0 1 - 4 2 0 7 ( 0 0 ) 0 0 0 1 3 - 1

Third World country after more than 70 years of government planning, and describes its economic situation as in crisis. The role of minerals in economic development There is an extensive literature on the benefits of mineral wealth and its relationship to economic growth.1 At one extreme, Davis (1994) believes that economies richly endowed with mineral resources can export raw minerals to fuel economic growth. At the other extreme, Gelb (1988) views mineral endowments as a curse, and argues that economies with large mineral endowments have had lower rates of economic growth than countries 1 However, there has not been much analysis of the role of minerals in economic development in developed nations such as Australia, Canada and the United States. Input–output analyses of the role of minerals in these countries, as well as in major developing country mineral exporters such as Brazil and Chile, should be undertaken to provide comparisons to South Africa.

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Table 1 Major South African mineral reserves, 1996a

Mineral

Reserves

Rank in world reserves

Annual production

Rank in world production

Gold Platinum group Titanium Chromium Manganese Vanadium Zirconium Phosphate Rock Antimony Coal Nickel

40 154 t 62 816 t 146 Mt 3100 Mt 4000 Mt 12 500 kt 14 300 kt 2500 Mt 250 kt 55 333 Mt 11 800 kt

1 1 1 1 1 1 2 3 4 5 5

4967 t 189 t 930 kt 4971 kt 3240 kt 15 kt 260 kt 2790 kt 5137 t 206 Mt 34 kt

1 1 2 1 3 1 2 3 4 5 8

a

Years of life at current production 81 333 157 624 1235 845 55 896 49 268 348

Source: Republic of South Africa, Department of Minerals and Energy (1997).

that have relied on imported raw minerals. Gelb (1988) and Auty (1993) suggest that economic and political factors were the primary reasons why many countries have not benefited from their large mineral endowments. Sachs and Warner (1995) found that very few resourceabundant countries (two of the 18 studied) sustained a growth rate greater than 2% from 1970 to 1989. They argued that trade policies, investment rates, terms of trade volatility, income inequality, and bureaucratic inefficiencies were important reasons for the lack of growth in the countries studied. Edwards (1985) advocates another option for South Africa, namely the development of domestic secondary industries to beneficiate, or add value to, its minerals. Rather than exporting raw minerals, he suggests that South Africa could become a rich nation by using its mineral wealth as intermediate inputs to develop a strong manufacturing sector. There is a more basic reason for the failure of mineral rich developing countries to match the growth of mature but mineral deficient countries. Lane and Tornell (1995), quoted in Sachs and Warner (1995) (p. 4) contend that “resource-rich economies are subject to more extreme rent-seeking behaviour than resource-poor economies, as national politics (in the resource-rich countries) is oriented to grabbing the rents earned by the natural resource endowments”. New discoveries of mineral deposits lead to ‘feeding frenzies’ in a fight for the natural resource rents, which only serve to harm the public good. Hotelling (1931) (p. 144) noted this problem earlier: “Great wastes arise from the suddenness and unexpectedness of mineral discoveries, leading to wild rushes, immensely wasteful socially, to get hold of valuable property”. The major industrialised nations of the world are also involved in this debate, albeit in a vaguely defined form. Some may regard South Africa’s large and comprehensive mineral reserves to be of strategic importance to

their own economies. In a throwback to the days of colonial rule, and even mercantilism, some nations seek to secure sources of raw mineral inputs for their own manufacturing industries. They see the prime role of South Africa as an exporter of raw minerals, and its industrialization policies are regarded as ‘tools of apartheid’ and dismissed as ‘absurd’ (Davis, 1994: 3, 127). A brief description of the South African economy South Africa’s national income is roughly one-seventh that of the United Kingdom, but South Africa is by far the most industrialized nation in Africa. It has a welldeveloped, sophisticated financial system that operates in the First World, but has a typical Third Nation income distribution profile (du Toit and Falkena, 1995: 10). The disparity of income, due largely to the racial policies of the past, has resulted in a very narrow fiscal base. In 1997, personal taxes contributed about 38% of total tax revenue, corporate taxes contributed about 17% and indirect taxes (mainly levied on consumer goods and services) contributed the final 45%. However, 1.5% of the population accounted for 51.7% of personal taxes (South African Reserve Bank, 1999). The huge demands made by the poor for social expenditure, the high government wage bill, and large interest and debt repayment obligations of the government all seriously constrain fiscal policy (du Toit and Falkena, 1995: 28). Manufacturing and mining account for about 25% and 11% of GDP, respectively (see Table 2). Slightly less than 25% of GDP is exported. Gold exports represented 16.4% of total exports in 1998. Mining production remained roughly unchanged during the past several years while mining employment fell by about 30%, perhaps in part due to significant increases in wages and salaries throughout the economy. Mining employment now represents slightly more than 10.5% of total employment in non-agricultural formal sector activities.

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Table 2 South African economic indicators, 1998a Data category

Data

Gross Domestic Product Exports Manufacturing Production Index (1995=100) Value of mineral sales, 1998 Value of gold sales, 1998 Mining Production Index (1995=100) Mining employment 1998 Mining Employment Index (1995=100) Employment in non-agricultural formal sector activities Total Employment Index (1990=100) Wages and Earnings Index (1990=100) Consumer Price Index (1995=100) Producer Price Index (June 1995=100) Share of mining in GDP Share of manufacturing in GDP Population (estimate at 30 June 1998)

R 659 billion R 146 billion 103.6 R 71 billion R 24 billion 99.2 540 000 70.3 5 160 000 84.6 280.9 130.3 121.0 10.8% 25.5% 42 million

a Sources: Statistics South Africa (1999a) and International Monetary Fund (1999).

The role of mining in the South African economy Compared with the 1970s and 1980s, mining, especially gold mining, declined in importance to the South African economy (see Table 3). Coal and other mining activities experienced large increases in the values of their sales after 1980 that continued into the 1990s. The value of gold sales rose slightly while the volume of gold production fell. Mining’s shares of GDP and mining employment have steadily decreased since 1990. In total, mining activities contributed R71 billion (or 10.7%) to GDP in 1998—down from 12% in 1996. Gold export sales fell to 16.4% of total exports. In 1995, exported mineral sales accounted for 42.63 billion. Mineral exports then account for 77.3% of total mineral

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sales. All gold produced in South Africa was sold in the export market and 67% of other minerals were produced for the export market. In 1993, the last year for which input–output (I–O) tables were available, the shares of gold mining, other mining and coal mining in GDP placed them in fourth, 12th and 22nd position in an aggregated 35-sector distribution of GDP. Lower ore tonnages and ore grades were the main reasons for the continued decline in gold mining’s contribution to the GDP. In contrast, strong world demand caused increased output from non-gold mining sectors during the 1990s. However, these increases were not large enough to stop the decline in the importance of mining in the South African economy.2 Proposed minerals policy The South African government claims that: “South Africa’s mining industry is supported by an extensive and diversified resource base, and has since its inception been a cornerstone of the South African economy. The changes which have come about in our country make it necessary to prepare the industry for the challenges which are facing all South Africans as we approach the twenty first century” (Republic of South Africa, Department of Minerals and Energy, 1998: 1). The government’s proposed minerals policy is based upon the premise that “the mining industries have the capacity to generate wealth and employment on a large scale” (Republic of South Africa, Department of Minerals and Energy, 1998: 4). A concise, comprehensive description of mining’s relationships with the rest of the South African economy 2 Sources for information in this paragraph are Republic of South Africa, Department of Minerals and Energy, 1999; Statistics South Africa, 1999b.

Table 3 The role of mining in the South African economya 1970 Value of mineral sales (billions of rands) Value of gold sales (billions of rands) Value of coal sales (billions of rands) Value of other mineral sales (billions of rands) GDP (billions of rands) Mining’s share of GDP Mining Production Index (1990=100) Gold Production Index (1990=100) Other Mining Production Index (1990=100) Mining Employment Index (1990=100) Total exports (billions of rands) Mineral exports/total exports Gold exports/total exports

1.56 0.83 0.11 0.62 12.5 13% 117.6 167 51.1 85 2.4 NA 35%

1980 15.50 10.40 1.50 3.61 60.3 26% 99.9 112 87.3 80 19.9 NA 52%

1990 41.55 18.99 8.18 14.38 276.1 15% 100.0 100.0 100.0 98 60.9 54% 31%

1995 55.13 23.47 12.82 18.83 484.6 11% 100.1 87 111.5 73 101.5 42% 23%

1996 63.10 26.48 14.81 21.81 542.7 13% 98.8 82 112.7 70 126.1 40% 21%

a Sources: Statistics South Africa (1996), Republic of South Africa, Department of Minerals and Energy (1997) and International Monetary Fund (1999).

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is necessary to guide policymakers if mining is to be the major basis for future economic development. However, the nature of these relationships is not generally well understood.3 No such description exists despite the mass of other statistical information on mining. In this paper, we use I–O techniques to address this shortcoming. The next section discusses the application of I–O techniques to South African mining activity. Our results follow and a concluding section summarises implications of these results for future economic development in South Africa. The appendices provide estimates of all multipliers over the period studied.

The application of I–O analysis to South Africa I–O analysis I–O techniques numerically model the relationships among the productive sectors of an economic system.4 By showing details of the flow of goods and services among industries, they describe the process of production, the use of goods and services, and the income generated in production. They are subject to the following assumptions: 1. the models are final demand driven; 2. different production activities can be grouped into homogeneous sectors, each producing one product; 3. there is no substitution of intermediate inputs; 4. each sector’s demand or intermediate inputs changes in direct proportion to output from that sector; 5. no technological change occurs (Kuhn and Jansen, 1997). By 1961, I–O techniques had developed to represent “a great simplification of theoretical models but a considerable elaboration and refinement of statistical data, to the point where theory and empirical implementation meet” (Barna, 1961: 3). However, I–O analysis is still not universally accepted. Its major shortcoming is the fixed coefficients assumption that does not account for either input substitution or technological change (Rose and Miernyk, 1989). Two other limitations of I–O techniques and GDP estimation should temper conclusions about South Africa. First, GDP estimates, whether derived from I–O techniques or from expenditure analysis, do not account for natural resource depletion. Consequently, GDP tends to exaggerate the wealth of economies, such as South Africa, in which resource extraction plays a large part 3 This deficiency may be due to the size and complexity of the industry, which in 1997 produced over 56 different minerals and employed close to half a million people. 4 The history of I–O techniques dates to Leontief (1936).

(Winter-Nelson, 1995). Second, GDP measures may understate the value of production in economies with large informal sectors. This issue is important in South Africa where there are five million non-agricultural formal sector employees in a population of slightly more than 40 million (Table 2). In comparison, there are about eight million non-agricultural formal sector employees in Australia, the population of which is one half the size of that of South Africa.5 Despite these problems, virtually all developed countries, as well as many developing countries, use I–O techniques for national income accounting. Proponents argue that the high level of disaggregation in I–O analysis reduces the likelihood that technological change will affect the product mix. Rose (1995) also explains that fixed coefficients pertain only to intermediate sectors. Tables expressed in value terms require only fixed value shares, which in I–O analysis can change over time. Further, in recent years, computable general equilibrium (CGE) and social accounting matrices (SAM) represent newer I–O based models that offer solutions to some of the inherent problems found in I–O analysis (Rose, 1995). South African I–O tables Statistics South Africa (SSA) compiles and publishes official statistics, including I–O tables. It first published South African I–O tables in 1967 and then in 1971, 1975, 1978, 1981, 1984, 1988, 1989 and 1993 (Statistics South Africa, 1971–1993).6 The limitations of I–O analysis notwithstanding, the South African tables are a continuous commentary on the South African economy over a period of 22 years. They are the best available reference to trace the contribution of the various sectors to GDP during that time. Tables from 1971 to 1993, except for 1988, comprise the data for this analysis. We aggregated all the SSA transaction tables into 35 sectors to simplify calculation and interpretation of results.7 The manufacturing industries contained in the SSA tables (SIC code numbers 3111 to 3869) were grouped into 12 sectors containing

5 The analysis in this paper was based on data used by the South African government in its economic planning. Since the informal sector is important, the government should place high priority on examining and collecting data on its importance. 6 The 1967 table was not used in our analysis since it did not show imports separately. The 1988 table was unavailable. The tables for 1967 to 1989 are final tables. The 1993 table is preliminary and is compiled using the RAS method from national accounts’ production and expenditure data. See O’Connor and Henry (1975) or Stilwell (1999) for more on the RAS technique. 7 In 1993, the latest year of issue, the SSA classified the South African economy in 90 sectors. Table A1 gives the SIC codes for the aggregated 35 sectors. The I–O data are available upon request from L.C. Stilwell.

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

industries of a generally similar nature and having the same first two digits of the SIC code. Mining activities are grouped into three sectors—coal mining, gold mining, and other mining. All other sectors were left unchanged. Table 4 gives an idea of the lack of linkages between mining activities and the rest of the South African economy. The table contains two sets of technical coefficients for the three mining sectors from the 1993 I–O table.8 Columns 1–3 give the technical coefficients for intermediate purchases by the three mining activities in 1993.

21

Columns 4–6 give technical coefficients for intermediate sales of the three mining sectors in 1993. Insignificant technical coefficients (aij⬍0.0010 or less than 0.0010 rands per rand of output) are given in bold print. There were 15, 19, and 15 sectors of the South African economy that were insignificant suppliers of intermediate inputs to coal, gold, and other mining, respectively. The sum of the technical coefficients (total intermediate inputs) for each mining sector was less than the average of 0.4479 for all 35 sectors in the I–O table. Gold mining intermediate purchases were 56% lower than the average for the 35 sectors.

Table 4 Technical coefficients for mining activities, 1993 (bold type indicates coefficients ⬍0.0010) Mining sector purchases from the sectors below (1–3) or mining sector sales to the sectors below (4–6)

Intermediate purchases of

Intermediate sales of

(1)

(2)

(3)

(4)

(5)

(6)

Coal mining

Gold mining

Other mining

Coal mining

Gold mining

Other mining

Agriculture, forestry and fishing Coal mining Gold mining Other mining Food, liquor, beverages, tobacco Textiles, clothing, cordage, leather Wood and furniture Paper, printing, publishing Chemicals, plastics, petroleum, rubber Pottery, glass, non-metallic minerals Iron, steel, non-ferrous basic industries Fabricated metal products Engines, machinery, equipment Electrical machinery, appliances Motor vehicles, parts, accessories Railway, other transport equipment Jewelry, related articles Other manufacturing industries Electricity, gas, steam Water supply Building construction Civil engineering, other construction Wholesale, retail, motor trade Catering and accommodation services Transport and storage Communication Finance and insurance Real estate Business services Machinery, equipment renting, leasing Medical, other health, veterinary services Other services—profit seeking Other services—non profit seeking Others, scrap, government services

0.0017 0.0001 0.0000 0.0008 0.0009 0.0043 0.0030 0.0009 0.0820 0.0025 0.0016 0.0251 0.0435 0.0268 0.0176 0.0046 0.0000 0.0009 0.0507 0.0016 0.0002 0.0003 0.0481 0.0000 0.0375 0.0010 0.0000 0.0018 0.0000 0.0075

0.0005 0.0003 0.0000 0.0003 0.0002 0.0032 0.0140 0.0003 0.0224 0.0010 0.0017 0.0113 0.0119 0.0041 0.0007 0.0105 0.0000 0.0004 0.0605 0.0022 0.0081 0.0008 0.0114 0.0000 0.0034 0.0008 0.0000 0.0014 0.0000 0.0009

0.0002 0.0038 0.0000 0.0002 0.0001 0.0034 0.0030 0.0001 0.0707 0.0021 0.0032 0.0127 0.0326 0.0077 0.0096 0.0051 0.0000 0.0005 0.0616 0.0079 0.0050 0.0007 0.0328 0.0000 0.0219 0.0009 0.0000 0.0016 0.0000 0.0077

0.0000 0.0001 0.0003 0.0038 0.0011 0.0007 0.0004 0.0030 0.0170 0.0135 0.0258 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0622 0.0049 0.0001 0.0000 0.0000 0.0003 0.0003 0.0000 0.0000 0.0017 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0336 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0012 0.0008 0.0003 0.0002 0.0002 0.0000 0.0002 0.0004 0.0781 0.0255 0.0346 0.0018 0.0019 0.0004 0.0011 0.0000 0.2240 0.0009 0.0000 0.0004 0.0066 0.0135 0.0000 0.0002 0.0010 0.0000 0.0000 0.0001 0.0000 0.0007

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000 0.0000 0.0759

0.0000 0.0000 0.0257

0.0000 0.0000 0.0877

0.0019 0.0000 0.0063

0.0000 0.0000 0.0000

0.0000 0.0002 0.0000

Total intermediate purchases/sales

0.4410

0.1980

0.3827

0.1437

0.0336

0.3943

8

Technical coefficients (aij) are the inter-industry purchases of producing sector i from supply sector j divided by the value of sector i production.

The technical coefficients in columns (4) to (6) give sales of the three mining sectors to the 35 sectors of the South African economy. Here, the sectors highlighted in bold represent those sectors in which the three mining sectors had insignificant sales. There were 22, 33, and

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22 sectors of the South African economy that were insignificant purchasers of intermediate inputs from coal, gold, and other mining, respectively. The sum of the technical coefficients (total intermediate sales) for coal and gold mining were far below the average (0.4479) of all sectors’ intermediate sales. Gold, in particular, only sold 0.0336 rands per rand of output to other South African industries. Inter-industry sales of coal mining output were less than half of the average and were mainly confined to the electricity and iron and steel sectors. Sales to chemicals and related, iron and steel, jewellery and related, and construction activities caused the other mining sector to have intermediate sales above the average across all sectors. I–O techniques to estimate the impact of increased mining activity GDP and employment multipliers, estimated from I– O tables, measure the effects of changes in a particular activity’s output or employment upon all other activities throughout the economy. The South African I–O tables produced total GDP and type II total GDP multipliers and total employment and type II total employment multipliers for the gold, coal, and other mining sectors as well as for the other 32 South African economic activities. Total GDP multipliers estimate the direct, indirect, and induced effects of marginal increases in a sector’s value added on GDP. The direct effect is the increased value added of the sector. The indirect effect is value added by other sectors needed to satisfy the sector’s increased demand for intermediate inputs. The induced effect is increased demand due to household incomes generated in producing the intermediate and final outputs. Type II total GDP multipliers are the ratios of the direct, indirect, and induced effects to the direct effects of the increased marginal output. Total employment multipliers estimate the number of jobs created per one million monetary units of marginal output from a sector. Type II total employment multipliers give the number of jobs created throughout the economy per job created in the sector in which the marginal increase in output occurred.

sons of rankings9 of mining sector multipliers to those of other multipliers. Comparison of multipliers in Table 5 indicates that total GDP multipliers for mining activities did not differ significantly from the average multiplier of all 35 sectors. The last three rows show that multipliers for coal, gold, and other mining fell and rose with no pattern in ranking over the period. Gold mining had the highest ranked mining multiplier during the past 10 years (ranked as eighth in 1993, i.e. with seven sectors having higher multipliers, and ninth in 1989). The coal mining multiplier never ranked higher than seventh (in 1971) and the multiplier for other mining never ranked higher than 17th (in 1989). The conclusion, then, is that mining’s importance to South African GDP did not change much, if at all, over the period.10 Not once between 1971 and 1993 did the total GDP multiplier of any mining sector deviate positively or negatively by more than one standard deviation from the mean. In 1993, the distances of the mining sector multipliers from the mean were coal mining +0.36 standard deviations, gold mining +0.61 standard deviations, and other mining ⫺0.36 standard deviations. Over the period, the coal mining deviation ranged between +0.77 and ⫺0.36 standard deviations from the mean, gold mining between +0.61 and ⫺0.28 standard deviations, and other mining between ⫺0.19 and ⫺0.56 standard deviations. In contrast, the largest multipliers deviated positively from the mean by between 2.47 and 3.29 standard deviations and the smallest multipliers deviated by between ⫺1.70 and ⫺2.66 standard deviations from the mean. Activities with multipliers higher than mining multipliers all produced services with the exception of building construction. Sectors that consistently had multipliers more than two standard deviations above the mean were financial institutions and insurance services and other non-profit seeking services. The slight increase in the gold mining multiplier suggests that it developed somewhat stronger linkages with the rest of the economy. In 1971, the gold mining total GDP multiplier was 1.3% greater than the mean. Beginning in 1984, the gold mining total multiplier began to diverge positively from the mean and, by 1993, was 8.2% greater than the mean. At the same time, gold mining’s declining relative importance as a producer of export income11 and its increasing share of GDP between

Results Total GDP multipliers Appendix B gives estimates of all 35 total GDP multipliers in each of the 7 years of I–O data analysed. Table 5 contains the arithmetic means and standard deviations of each year’s total GDP multipliers as well as compari-

9 The rankings are: 1, multiplier with the highest value; 35, multiplier with the lowest value. 10 Examination of each year’s I–O table indicates that the shares of value added in mining and other activities did not change significantly over the period studied. 11 In 1971, South African gold production reached record levels and gold accounted for 59% of the value of all mineral exports; in 1993, gold accounted for 49% of the value of mineral exports (Republic of South Africa, 1910–1993).

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Table 5 Analysis—total GDP multipliers Measure

1971

1975

1978

1981

1984

1989

1993

Arithmetic mean Standard deviation (SD) SDs of largest multiplier from meana SD of coal mining from mean SD of gold mining from mean SD of other mining from mean SD of smallest multiplier from mean Coal mining multiplier ⬎/⬍ mean? Gold mining multiplier ⬎/⬍ mean? Other mining multiplier ⬎/⬍ mean? No. multipliers ⬎ coal mining No. multipliers ⬎ gold mining No. multipliers ⬎ other mining

1.51 0.22 3.29 0.31 0.08 ⫺0.41 ⫺1.82 ⬎ ⬎ ⬍ 6 13 24

1.41 0.19 2.47 0.71 ⫺0.18 ⫺0.23 ⫺2.20 ⬎ ⬍ ⬍ 7 20 21

1.41 0.21 2.78 ⫺0.13 ⫺0.13 ⫺0.56 ⫺2.11 ⬍ ⬍ ⬍ 17 18 27

1.36 0.20 2.58 ⫺0.14 ⫺0.28 ⫺0.19 ⫺2.66 ⬎ ⬍ ⬍ 17 21 19

1.46 0.17 2.77 ⫺0.27 ⫺0.21 ⫺0.21 ⫺1.70 ⬍ ⬍ ⬍ 19 18 17

1.38 0.17 2.83 ⫺0.16 0.38 ⫺0.22 ⫺1.71 ⬍ ⬍ ⬍ 15 8 16

1.46 0.20 2.79 ⫺0.36 0.61 ⫺0.36 ⫺2.64 ⬍ ⬎ ⬍ 23 7 22

a

(sector multiplier⫺mean multiplier)/standard deviation=number of standard deviations of the multiplier from the mean.

1989 and 1993 indicates that South Africa is now more reliant on a shrinking gold output. The same conclusion cannot be reached for coal mining and other mining activities. At best, multipliers on coal mining and other mining did not change. Type II total GDP multipliers These multipliers differ from total GDP multipliers as they measure the impact of a marginal increase in the income within a sector upon GDP while total GDP multipliers measure the impact of a marginal increase in a sector’s value added on GDP. Appendix C provides estimates of these total GDP multipliers for the 7 years studied. Table 6 gives arithmetic means and standard deviations of all 35 multipliers and the ranking of each mining sector’s type II total GDP multipliers in each year. Table 6 indicates that income generated in nearly all other activities in the South African economy had more

important impacts upon GDP than incomes generated in mining activities. Mining activities’ type II total GDP multipliers did not deviate by more than one standard deviation from the mean and these deviations were always negative. The gold mining multiplier ranged between ⫺1.21 and ⫺0.63 standard deviations below the mean, coal mining between ⫺0.86 and ⫺0.30 standard deviations below the mean and other mining between ⫺0.86 and ⫺0.59 below the mean. The coal mining type II total GDP multiplier never ranked higher than 19 (in 1993), the gold mining multiplier never ranked above 28 (in 1971 and 1989) and the other mining multiplier never ranked above 24 (in 1984). Further, there were no significant increases in the impact of increased mining income on GDP. In contrast, the large and smallest multipliers in each year deviated from the mean by between 2.5 and 4.48 and 1.01 and 1.52 standard deviations, respectively. The household sectors (all years) and the ‘others, scrap and government services’ (all years except 1981) consistently had sig-

Table 6 Analysis—type II total GDP multipliers Measure

1971

1975

1978

1981

1984

1989

1993

Arithmetic mean Standard deviation (SD) SDs of largest multiplier from meana SD of coal mining from mean SD of gold mining from mean SD of other mining from mean SD of smallest multiplier from mean Coal mining multiplier ⬎/⬍ mean? Gold mining multiplier ⬎/⬍ mean? Other mining multiplier ⬎/⬍ mean? No. multipliers ⬎ coal mining No. multipliers ⬎ gold mining No. multipliers ⬎ other mining

3.78 2.26 4.41 ⫺0.51 ⫺0.66 ⫺0.65 ⫺0.88 ⬍ ⬍ ⬍ 23 29 28

3.51 1.98 4.48 ⫺0.61 ⫺0.76 ⫺0.60 ⫺0.87 ⬍ ⬍ ⬍ 28 33 27

3.49 1.77 4.13 ⫺0.65 ⫺0.79 ⫺0.71 ⫺0.95 ⬍ ⬍ ⬍ 27 32 29

3.29 1.39 2.66 ⫺0.61 ⫺0.78 ⫺0.55 ⫺0.90 ⬍ ⬍ ⬍ 28 32 25

3.54 1.40 2.50 ⫺0.61 ⫺0.80 ⫺0.56 ⫺0.97 ⬍ ⬍ ⬍ 27 32 24

3.40 1.87 3.78 ⫺0.52 ⫺0.61 ⫺0.67 ⫺0.98 ⬍ ⬍ ⬍ 25 29 30

3.57 1.70 4.17 ⫺0.34 ⫺0.77 ⫺0.55 ⫺1.13 ⬍ ⬍ ⬍ 19 32 26

a

(sector multiplier⫺mean multiplier)/standard deviation=number of standard deviations of the multiplier from the mean.

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Table 7 Analysis—total employment multipliers Measure

1971

1975

1978

1981

1984

1989

1993

Arithmetic mean Standard deviation (SD) SDs of largest multiplier from meana SD of coal mining from mean SD of gold mining from mean SD of other mining from mean SD of smallest multiplier from mean Coal mining multiplier ⬎/⬍ mean? Gold mining multiplier ⬎/⬍ mean? Other mining multiplier ⬎/⬍ mean? No. multipliers ⬎ coal mining No. multipliers ⬎ gold mining No. multipliers ⬎ other mining

41.83 12.21 2.26 2.26 0.98 ⫺0.49 ⫺1.20 ⬎ ⬎ ⬍ 0 5 24

34.68 9.49 2.25 2.25 ⫺0.09 ⫺0.20 ⫺2.74 ⬎ ⬍ ⬍ 0 18 20

36.17 11.20 2.16 0.39 ⫺0.10 ⫺0.97 ⫺2.42 ⬎ ⬍ ⬍ 12 19 30

32.04 9.46 2.31 ⫺0.15 ⫺0.52 ⫺0.53 ⫺2.55 ⬍ ⬍ ⬍ 19 26 27

31.54 8.89 1.73 ⫺0.32 ⫺0.11 ⫺0.61 ⫺2.60 ⬍ ⬍ ⬍ 19 18 17

29.60 7.98 1.73 ⫺0.12 0.77 ⫺0.82 ⫺2.68 ⬍ ⬎ ⬍ 20 7 28

26.88 8.46 2.16 ⫺0.24 0.80 ⫺0.48 ⫺2.59 ⬍ ⬎ ⬍ 21 8 25

a

(sector multiplier⫺mean multiplier)/standard deviation=number of standard deviations of the multiplier from the mean.

nificantly more positive impacts on the economy than all other sectors, i.e. with multipliers more than two standard deviations from the mean. These comparisons also imply that income from mining activities, which the South African government hopes will carry the economy ahead into the future, had among the least important impacts on GDP of all South African economic activities. Total employment multipliers These multipliers relate the number of jobs created in the economy for every one million rand increase in a sector’s output. Appendix D gives estimated total employment multipliers for all sectors in all years studied. Table 7 gives the same statistical comparisons found in Tables 5 and 6, namely arithmetic means and standard deviations of each year’s employment multipliers and the rankings of mining sector multipliers. Mining sector employment multipliers were generally higher ranked than their GDP multipliers, which suggests that mining activities generated more jobs but lower incomes than many other activities. This comparison may indicate that wages in the mining sectors were generally lower than in other sectors. In 1993, the average employment multiplier (weighted by output) for all sectors excluding mining was 26.5. The average employment multiplier for the combined mining industries, also weighted by output, was 27.9. Only ‘catering and accommodation services’ (in 1975, 1978, 1981) and ‘agriculture, forestry and fishing’ (in 1993) had employment multipliers more than two standard deviations from the mean. With the exception of coal mining in 1971 and 1975, mining activities had about the same employment impacts as other sectors. The coal mining employment multiplier never ranked higher than 13th after 1975 and was 21 in 1993. The highest ranking of the gold mining employment multi-

plier was 9 (in 1993) and the other mining’s employment multiplier never ranked higher than 20 (in 1975). In 1993, the gold mining multiplier was 0.80 standard deviation above the mean, coal mining 0.24 standard deviation below the mean, and other mining 0.49 standard deviation below the mean. The conclusion is that the mining industries did not, apart from coal mining in 1971 and 1975, have significantly different impacts upon employment per unit of mining income than other South African economic activities, although gold mining has increased in importance since 1981. There has also been a general trend of decreasing employment linked to increased output, and the actual number of new jobs created by increased mining sector output diminished over the period. These decreases were due to inflation12 and increased mechanization, particularly in the coal and other mining sectors. In 1971, an increase of R1m in output from the coal mining industry generated 69.5 new jobs. This figure decreased every year to 24.8 in 1993, placing it in 22nd position. Gold mining ranked sixth in 1971, with 53.8 new jobs per R1m increase, and ninth in 1993, but with 33.7 new jobs per R1m. Other mining ranked 25th in 1971 and 26th in 1993. The employment multiplier for ‘machinery and equipment renting and leasing’ decreased the most (⫺69%) between 1971 and 1993 (see Appendix D). The second largest decrease (64%) was coal mining. Gold mining (37%) and other mining (36%) showed the ninth and tenth largest decreases, respectively. The importance of coal, gold, and other mining as generators of employment decreased more rapidly than 33 other sectors (coal), 26 other sectors (gold) and 25 other sectors (other mining), respectively. These observations suggest that, while increased min-

12 We were unable to obtain appropriate price indices to deflate the I–O data to real values.

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

25

Table 8 Analysis—type II total employment multipliers Measure

1971

1975

1978

1981

1984

1989

1993

Arithmetic mean Standard deviation (SD) SDs of largest multiplier from meana SD of coal mining from mean SD of gold mining from mean SD of other mining from mean SD of smallest multiplier from mean Coal mining multiplier ⬎/⬍ mean? Gold mining multiplier ⬎/⬍ mean? Other mining multiplier ⬎/⬍ mean? No. multipliers ⬎ coal mining No. multipliers ⬎ gold mining No. multipliers ⬎ other mining

5.53 5.70 4.98 ⫺0.68 ⫺0.66 ⫺0.53 ⫺0.68 ⬍ ⬍ ⬍ 33 32 28

4.34 2.31 3.07 ⫺1.17 ⫺1.11 ⫺0.85 ⫺1.26 ⬍ ⬍ ⬍ 33 32 29

4.24 2.66 4.70 ⫺0.85 ⫺0.88 ⫺0.42 ⫺1.03 ⬍ ⬍ ⬍ 31 33 21

3.82 1.74 3.11 ⫺0.86 ⫺1.10 ⫺0.44 ⫺1.34 ⬍ ⬍ ⬍ 28 33 21

4.00 1.64 2.88 ⫺0.82 ⫺1.25 ⫺0.18 ⫺1.47 ⬍ ⬍ ⬍ 26 33 17

3.59 1.37 2.58 ⫺0.75 ⫺1.20 ⫺0.36 ⫺1.51 ⬍ ⬍ ⬍ 26 33 18

3.93 1.52 2.35 ⫺0.40 ⫺1.37 ⫺0.42 ⫺1.55 ⬍ ⬍ ⬍ 18 33 19

a

(sector multiplier⫺mean multiplier)/standard deviation=number of standard deviations of the multiplier from the mean.

ing production will no doubt lead to increased employment, the trend has been to a smaller, not larger, number of jobs per unit of output. As evidence of this trend, consider that employment in the mining industry dropped about 39% from an average of 660,000 in 1992 to 405,000 at the end of 1998 while the volume of total mining output was essentially unchanged (Republic of South Africa, Department of Minerals and Energy, 1999). Type II total employment multipliers These multipliers estimate the number of jobs created in the economy for every job created in a particular sector. Appendix E gives estimates of these multipliers for all sectors in each year studied. Table 8 gives results similar to those found in Tables 5–7. Type II total employment multipliers are a truer indication of value of a sector’s employment creation to the rest of the economy than total employment multipliers. An example illustrates this conclusion. Gold mining’s total employment multiplier of 33.7 in 1993 placed it in ninth position as a generator of employment (Appendix D). However, its type II total GDP multiplier (Appendix C) placed it third from the last in terms of income flowing back to the economy. This lower flow of income offsets the impact of increased employment in gold mining, and the type II total employment multiplier shows this result. In all years except 1972 and 1975, increased employment in gold mining had a lower impact on employment than any other sector except agriculture (see Appendix D). Likewise, higher type II total GDP multipliers for coal mining and other mining suggest that employment in these sectors had larger impacts on the economy than gold mining. Their higher type II total employment multipliers confirm this result.13 13 However, the size of the industry concerned must be taken into account when considering type II employment multipliers. For

Sectors with significantly different employment positive impacts (multipliers more than two standard deviations from the mean) were jewellery and related (1971, 1975, 1978, 1981), food, liquor, beverages and tobacco (1971, 1975, 1981, 1984, 1989, 1993), machinery and equipment renting and leasing (1975), and households (1989, 1993). The coal, gold, and other mining type II employment multipliers never ranked higher than 20th (1993), 33rd (1971, 1975), and 18th (1984), respectively. Notwithstanding these rankings, coal and other mining type II employment multipliers show a general increasing trend. The gold mining multiplier changed little. However, type II employment multipliers for all three mining sectors remain well below the mean across all sectors

Conclusions The opening paragraph of the government’s White Paper (Republic of South Africa, Department of Minerals and Energy, 1998) states that “South Africa’s mining industry has since its inception been a corner stone of South Africa’s economy” (p. 1). While true, this statement should be qualified to the extent that mining’s contribution to the economy is largely dependent upon direct exports of unbeneficiated minerals. This statement especially applies to coal and gold mining. Their intermediate purchases from other South African economic activities are relatively lower than the average across all sectors. Likewise, their intermediate sales to other South instance, gold mining’s type II employment multiplier (1.838) was third lowest in 1993, but gold mining employed 435,000 people. Thus, almost 800,000 jobs were linked to the gold mining industry, of which 365,000 were created through its linkage to other sectors. By way of contrast, the water supply sector had a type II employment multiplier of 7.0408 (the third largest) but, because it only employed 7000 people, it created slightly more than 42,000 additional jobs.

26

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

African economic activities are relatively lower than the average across all sectors. In the case of gold, it is virtually nil. Furthermore, multipliers estimated for mining activities are generally comparable to, if not smaller than, the average across all sectors. A basic premise of the White Paper is that the South African mining industry “has the capacity to generate wealth and employment opportunities on a large scale” (p. 4). Our estimated total and type II total GDP multipliers indicated that mining activity in South Africa had about the same impact on GDP as other economic activities. These estimates mean that investment in the minerals industry will generate value added at the same degree as investment in other sectors. Total employment and type II total employment multipliers demonstrate that both output from and employment in the mining industries do not create employment in the rest of the economy to any significantly greater degree than output and employment in other economic activities. We therefore conclude that the basic premise upon which the White Paper is based may be misplaced.

Future investment in the mining industry should not be assumed ipso facto to be a cornucopia of benefits to the economy. South African gold production has become a smaller share of worldwide gold production. A large share of other South African mineral production is sold in export markets and the South African mining industry will increasingly need to compete in a volatile international base minerals market. The manner by which South Africa can benefit from mineral reserves other than gold is an area for urgent comprehensive research. Likewise, consideration should be given to policies to increase linkages between mining and other South African economic activities.

Acknowledgements The authors thank several anonymous referees for their comments on a previous draft of this article.

Appendix A. The 35 aggregated sectors used in this study Sector No.

Description

1100 2100 2400 2800 3111–3140 3211–3240 3310–3320 3411–3420 3511–3560 3610–3699 3710–3720 3811–3819 3821–3829 3831–3839 38400–38403 3852–3859 3901 386,3902/3/9 4100 4200 5100 5200 61,62 6300 7100 7200 81,82 8310 8320 8330 9330 9700 9800 9900 REM

Agriculture, forestry and fishing Coal mining Gold mining Other mining Food, liquor, beverages and tobacco Textiles, clothing, cordage and leather Wood and furniture Paper, printing and publishing Chemicals, plastics, petroleum and rubber Pottery, glass, refractory and other non-metallic minerals Iron, steel and non-ferrous basic industries Fabricated metal products Engines, machinery and equipment Electrical machinery and appliances Motor vehicles, parts and accessories Railway and other transport equipment Jewellery and related articles Other manufacturing industries Electricity, gas and steam Water supply Building construction Civil engineering and other construction Wholesale and retail trade and motor trade Catering and accommodation services Transport and storage Communication Financial institutions and insurance services Real estate Business services Machinery and equipment renting and leasing Medical, dental and other health and veterinary services Other services—profit seeking Other services—non profit seeking Others, scrap and government services Remuneration of employees

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

27

Appendix B. GDP multipliers SIC-CODE

Description

1971

1975

1978

1981

1984

1989

1993

1100 2100 2400 2800 3111–3140 3211–3240 3310–3320 3411–3420 3511–3560 3610–3699 3710–3720 3811–3819 3821–3829 3831–3839 38400–38403 3852–3859 3901 386,3902/3/9 4100 4200 5100 5200 61,62 6300 7100 7200 81,82 8310 8320 8330 9330 9700 9800 9900

Agriculture Coal mining Gold mining Other mining Food, liquor, beverages, tobacco Textiles, clothing Wood and furniture Paper, printing, publishing Chemicals, plastics, petroleum Pottery, glass, non-metallic minerals Iron and steel Fabricated metal products Engines and machinery Electrical machinery Motor vehicles Railway and other transport eqpt. Jewellery and related Other manufacturing Electricity and gas Water Building construction Other construction Wholesale/retail trade Catering and accommodation Transport and storage Communication Financial institutions/insurance Real estate Business services Machinery/equipment renting Medical services Other profit seeking services Other non profit services Others, scrap, and govt. services

1.28 1.58 1.53 1.42 1.35 1.27 1.53 1.47 1.25 1.48 1.48 1.55 1.49 1.41 1.11 1.51 1.54 1.49 1.42 1.35 1.57 1.55 1.66 1.50 1.79 1.84 2.24 1.16 1.57 1.46 1.37 1.60 2.04 1.53

1.21 1.55 1.38 1.37 1.28 1.25 1.43 1.32 0.99 1.39 1.38 1.43 1.39 1.31 1.09 1.42 1.36 1.40 1.45 1.31 1.56 1.50 1.54 1.45 1.60 1.67 1.89 1.12 1.62 1.24 1.30 1.60 1.88 1.39

1.23 1.38 1.38 1.29 1.29 1.27 1.46 1.34 0.97 1.39 1.38 1.50 1.44 1.40 1.02 1.44 1.34 1.37 1.30 1.26 1.58 1.51 1.59 1.46 1.52 1.69 1.98 1.13 1.65 1.20 1.30 1.53 1.88 1.35

1.13 1.33 1.30 1.32 1.23 1.23 1.36 1.26 0.82 1.35 1.37 1.42 1.43 1.29 1.03 1.46 1.33 1.31 1.29 1.23 1.48 1.44 1.49 1.38 1.49 1.61 1.88 1.12 1.62 1.17 1.27 1.56 1.81 1.35

1.25 1.41 1.42 1.42 1.29 1.37 1.48 1.40 1.19 1.42 1.39 1.50 1.49 1.39 1.21 1.61 1.47 1.40 1.36 1.59 1.60 1.54 1.63 1.46 1.54 1.70 1.94 1.16 1.66 1.23 1.33 1.34 1.84 1.51

1.20 1.35 1.44 1.34 1.26 1.28 1.37 1.34 1.09 1.31 1.25 1.37 1.34 1.30 1.21 1.54 1.33 1.32 1.32 1.28 1.40 1.39 1.52 1.38 1.45 1.51 1.85 1.13 1.60 1.17 1.34 1.59 1.84 1.38

1.31 1.39 1.58 1.39 1.38 1.40 1.50 1.45 1.18 1.44 1.38 1.48 1.47 1.45 1.33 1.60 0.94 1.41 1.38 1.35 1.59 1.56 1.66 1.54 1.58 1.57 2.01 1.11 1.61 1.17 1.42 1.64 1.83 1.54

Appendix C. Type II GDP multipliers SIC-CODE

Description

1100 2100 2400 2800 3111–3140 3211–3240 3310–3320 3411–3420 3511–3560 3610–3699

Agriculture Coal mining Gold mining Other mining Food, liquor, beverages, tobacco Textiles, clothing Wood and furniture Paper, printing, publishing Chemicals, plastics, petroleum Pottery, glass, non-metallic minerals Iron and steel Fabricated metal products Engines and machinery Electrical machinery Motor vehicles Railway and other transport eqpt. Jewellery and related

3710–3720 3811–3819 3821–3829 3831–3839 38400–38403 3852–3859 3901

1971

1975

1978

1981

1984

1.93 2.64 2.17 2.19 6.92 3.78 4.46 3.64 3.41 3.39

1.90 2.12 1.67 2.16 6.55 3.95 4.29 3.43 3.74 3.27

1.96 2.15 1.78 1.97 6.27 3.88 4.44 3.48 3.55 3.27

1.88 2.09 1.64 2.25 6.06 3.56 3.76 3.38 3.28 3.02

2.39 2.34 1.85 2.49 7.03 4.06 4.34 3.90 3.62 3.67

3.99 4.19 3.81 4.24 4.48 4.56 6.80

3.33 4.04 3.46 4.08 4.32 3.82 6.01

3.80 4.72 3.86 3.84 4.68 4.02 4.94

3.88 4.04 3.60 3.52 4.24 4.16 6.78

4.35 4.73 4.06 3.94 5.52 3.19 5.42

1989 2.04 2.43 2.23 2.10 5.68 3.44 3.70 3.35 3.24 2.76

1993 2.54 3.06 2.06 2.57 6.31 3.53 3.92 3.65 3.77 2.83

3.06 2.87 3.62 3.75 3.36 3.37 3.28 3.20 4.02 4.33 2.93 3.57 9.27 6.92 (continued on next page)

28

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

Appendix C. Type II GDP multipliers (continued) SIC-CODE

Description

1971

386,3902/3/9 4100 4200 5100 5200 61,62 6300 7100 7200 81,82 8310 8320 8330 9330 9700 9800 9900

Other manufacturing Electricity and gas Water Building construction Other construction Wholesale/retail trade Catering and accommodation Transport and storage Communication Financial institutions/insurance Real estate Business services Machinery/equipment renting Medical services Other profit seeking services Other non profit services Others, scrap, and govt. services

3.65 2.27 2.26 5.72 5.47 2.68 3.93 2.25 2.15 3.81 1.51 1.87 317.56 1.92 2.38 2.70 13.73

1975 3.29 2.63 2.32 4.82 4.27 2.41 4.69 2.45 1.96 3.02 1.37 2.01 2.58 1.90 2.35 2.81 12.40

1978 3.49 2.23 2.37 5.02 4.61 2.60 4.63 2.28 2.16 3.50 1.36 2.04 1.66 1.90 2.43 2.86 10.78

1981 3.75 2.32 2.43 4.78 4.04 2.50 4.25 2.36 2.13 3.11 1.34 2.03 1.61 1.84 2.49 2.74 6.98

1984 4.13 2.43 2.84 4.85 4.59 2.51 4.62 2.60 2.11 3.40 1.41 2.20 1.80 1.94 2.10 3.10 6.70

1989 2.68 2.36 3.20 4.93 4.34 2.55 3.91 2.46 1.90 3.14 1.37 2.30 1.91 2.28 2.62 2.74 10.49

1993 3.03 2.49 3.52 5.23 4.18 2.89 4.06 2.77 2.30 3.94 1.23 2.37 1.70 2.43 2.89 3.39 10.67

Appendix D. Employment multipliers SIC-CODE

Description

1971

1100 2100 2400 2800 3111–3140 3211–3240 3310–3320 3411–3420 3511–3560 3610–3699

Agriculture Coal mining Gold mining Other mining Food, liquor, beverages, tobacco Textiles, clothing Wood and furniture Paper, printing, publishing Chemicals, plastics, petroleum Pottery, glass, non-metallic minerals Iron and steel Fabricated metal products Engines and machinery Electrical machinery Motor vehicles Railway and other transport eqpt. Jewellery and related Other manufacturing Electricity and gas Water Building construction Other construction Wholesale/retail trade Catering and accommodations Transport and storage Communication Financial institutions/insurance Real estate Business services Machinery/equipment renting Medical services Other profit seeking services Other non profit services Others, scrap, and govt. services

58.25 69.49 53.75 35.79 46.75 41.23 52.40 36.57 28.24 40.60

51.61 56.03 33.85 32.82 40.28 37.97 44.28 29.28 21.23 34.88

53.59 40.54 35.08 25.35 42.62 42.67 52.71 32.46 21.02 36.09

44.60 30.58 27.11 27.04 36.39 38.29 41.48 28.10 15.94 30.73

46.97 28.71 30.53 26.13 35.77 40.15 42.62 28.61 20.30 30.24

43.41 28.65 35.72 23.05 34.43 37.92 40.33 26.24 18.69 29.18

45.15 24.84 33.69 22.85 33.81 33.83 39.98 26.31 18.58 28.13

36.32 40.75 37.02 35.12 28.73 38.19 38.13 40.72 27.72 28.12 43.43 50.59 41.57 56.03 40.74 61.79 58.72 10.23 27.20 33.19 29.98 28.16 40.08 44.93

30.64 33.86 30.07 28.93 25.61 33.66 30.01 35.72 28.87 28.52 39.40 37.39 34.75 53.24 36.06 42.92 42.94 8.72 32.34 18.91 31.28 27.90 44.57 40.63

31.63 38.15 34.44 34.15 26.34 38.53 25.81 39.80 21.44 26.52 46.72 41.82 40.59 60.40 35.82 45.82 53.51 9.03 34.63 14.67 32.57 28.25 43.68 43.46

29.00 31.65 30.51 27.89 22.88 35.40 27.86 45.50 20.31 25.64 39.53 35.73 33.47 53.93 32.88 42.25 43.60 7.96 34.93 15.00 28.30 26.44 37.98 40.49

27.15 32.57 30.30 28.73 25.94 39.24 30.21 39.06 19.78 25.17 39.10 36.28 34.10 46.57 30.46 37.50 42.24 8.45 35.65 15.05 24.27 17.95 36.27 40.41

22.54 29.53 27.84 26.59 24.03 34.86 26.20 29.52 18.39 22.30 36.75 35.48 31.84 42.25 28.83 29.33 40.15 8.20 35.54 15.57 25.30 25.64 33.42 38.60

20.38 27.58 24.36 29.26 22.48 25.91 18.97 28.21 16.49 18.11 36.90 33.79 29.01 30.37 26.16 24.60 40.41 5.01 25.09 10.44 23.61 21.49 29.31 38.96

3710–3720 3811–3819 3821–3829 3831–3839 38400–38403 3852–3859 3901 386,3902/3/9 4100 4200 5100 5200 61,62 6300 7100 7200 81,82 8310 8320 8330 9330 9700 9800 9900

1975

1978

1981

1984

1989

1993

L.C. Stilwell et al. / Resources Policy 26 (2000) 17–30

29

Appendix E. Type II employment multipliers SIC-CODE

Description

1971

1975

1978

1981

1984

1989

1993

1100 2100 2400 2800 3111–3140 3211–3240 3310–3320 3411–3420 3511–3560 3610–3699 3710–3720 3811–3819 3821–3829 3831–3839 38400–38403 3852–3859 3901 386,3902/3/9 4100 4200 5100 5200 61,62 6300 7100 7200 81,82 8310 8320 8330 9330 9700 9800 9900

Agriculture 1.44 Coal mining 1.66 Gold mining 1.78 Other mining 2.51 Food, liquor, beverages, tobacco 9.52 Textiles, clothing 2.99 Wood and furniture 3.12 Paper, printing, publishing 5.06 Chemicals, plastics, petroleum 5.69 Pottery, glass, non-metallic minerals 3.28 Iron and steel 5.71 Fabricated metal products 4.30 Engines and machinery 4.77 Electrical machinery 5.26 Motor vehicles 4.89 Railway and other transport eqpt. 5.91 Jewellery and related 14.17 Other manufacturing 3.62 Electricity and gas 5.70 Water 3.10 Building construction 5.69 Other construction 3.14 Wholesale/retail trade 2.83 Catering and accommodations 2.50 Transport and storage 3.30 Communication 1.88 Financial institutions/insurance 4.34 Real estate 7.83 Business services 3.67 Machinery/equipment renting 33.89 Medical services 2.44 Other profit seeking services 5.26 Other non profit services 5.98 Others, scrap, and govt. services 5.27

1.44 1.64 1.78 2.37 9.01 3.10 3.22 5.04 6.20 3.24 4.83 4.21 4.96 5.45 4.82 4.41 11.43 3.30 5.53 2.83 4.85 4.02 2.71 2.42 3.03 2.03 3.93 6.50 2.86 10.27 1.91 5.16 5.05 4.07

1.49 1.99 1.91 3.11 8.07 2.87 3.00 4.65 6.07 3.16 5.33 4.49 5.02 4.60 4.83 3.81 16.73 3.01 5.59 2.88 3.94 3.59 2.69 2.15 2.87 2.28 3.62 6.33 3.07 5.38 1.92 4.91 4.94 3.77

1.49 2.32 1.90 3.05 8.11 2.71 2.92 4.47 5.75 3.16 5.21 4.53 5.16 4.44 5.08 4.16 9.24 2.09 4.73 2.70 3.74 3.37 2.67 2.00 2.81 2.09 3.61 5.75 2.47 3.17 1.90 5.40 4.72 3.07

1.59 2.65 1.95 3.70 8.74 2.72 2.95 5.06 7.29 3.61 5.59 4.27 4.92 4.45 5.55 2.71 5.45 2.52 5.12 5.68 3.85 3.58 2.58 2.36 3.10 2.22 3.61 6.62 2.34 4.33 2.24 4.54 4.98 3.15

1.52 2.56 1.95 3.10 7.12 2.45 2.66 4.80 5.61 2.59 4.54 3.24 3.56 3.58 5.05 2.96 6.45 2.63 5.02 4.35 3.29 2.81 2.52 2.35 2.82 2.15 3.17 5.27 2.21 4.07 2.55 5.18 4.92 2.96

1.58 3.33 1.84 3.29 7.50 2.57 2.53 4.38 5.68 2.58 4.86 3.28 4.41 2.71 5.61 6.07 3.78 2.75 5.54 7.04 3.36 2.84 2.92 4.26 3.35 2.88 3.22 4.33 2.87 4.81 2.54 6.43 5.83 2.69

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