The economy-wide impact of increasing wages in the South African mining sector, a CGE approach

by Gloria Kgalalelo Setou

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Abstract

This paper examines the economy-wide impact of increasing wages by an average of 117 per cent for the majority of mineworkers in the South African mining sector. This is in line with the demands by the mineworkers, particularly in the platinum and gold sectors, which saw them embark on major strike action in 2012, 2014 and also eminent in 2015 to demand a minimum wage of R12500. 2012 and 2014 were crises years for the South African mining industry. The incidents at Marikana and the subsequent strike action which ensued over wage disputes, will forever be engraved in the memories of many South Africans, not only for the tragic loss of 44 lives on that fateful day of 16 August 2012 , but also because they marked a turning point for South Africa's mining industry and raised critical questions of whether the mining charter was being complied with, what a minimum wage is or should be and also whether mining companies are in a position to afford to meet the demands of the mineworkers. Computable general equilibrium modelling (CGE) is the methodology used and mining sector wages are shocked by 117 per cent; which is the average percentage increase in wages demanded by the majority of mine workers earning between R6001 and R8000 to evaluate the overall economic impact both in the short run and in the long run. An Upgem14 model of the University of Pretoria is used which comprises of 14 sectors and 14 industries of the South African economy.

JEL Codes: J31 - Wage Level and Structure; Wage Differentials

Keywords: Wages, wage demands, minimum wage, strike action

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Table of contents

CONTENT

CHAPTER 1 PAGE

1 Introduction 5 1.1 Overview 5 1.2 Economic developments in the mining sector 5

CHAPTER 2

2 Literature review 7 2.1 History and evolution of mining in South Africa 7 2.2 Minerals currently mined in South Africa 10 2.2.1 Mining Production 10 2.2.2 Mining Sales 15 2.2.3 Mining exports and exchange rate 17 2.2.4 Mining profitability 18 2.2.5 Market Capitalization 18 2.3 Mining working conditions and Labour law 19 2.4 Mining contribution to GDP and Employee compensation 23 2.5 Mining sector employment per skills level and wages 25 2.6 Mining wages in other economies (evidence from abroad) 27 2.7 Problem Statement 29

CHAPTER 3

3 Methodology (CGE model) 32 3.1.1 Introduction 32 3.1.2 Description of model and database 32 3.1.3 Description of model closure 33 3.1.4 A back of the envelope (BOTE) representation of UPGEM14 34

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CHAPTER 4 PAGE

4 Data Analysis 37 4.1 Interpretation and explanation of results 37 4.1.1 Standard DPSV short run closure 37 4.1.2 Standard DPSV Long run closure 37

CHAPTER 5

5 Conclusion and Recommendation 40 5.2 Discussion 40 5.3 Conclusion 40

References 41

List of tables and figures

List of tables Page

2.4.1 Gross domestic product by industry 23 2.4.2 Compensation of employees 24 2.5.1 Mining sector employment per skills level and wages 23 3.1.4.1 UPGEM10 BOTE model 36

List of figures

2.1.1 Gold ore 8 2.1.2 SA’s platinum corridor 9 2.2.1.1 Physical volume of mining production 10 2.2.1.2 South Africa’s mined gold production, 1940-2011 11 2.2.1.3 Platinum ore 11 2.2.2.1 Mineral sales 15 2.2.3.1 Mineral exports and real effective exchange rate 17 2.3.1 Mining in Africa 19 2.6.1 Mario Go’mez, one of 33 Chillean miners trapped underground in 2010 27 2.7.1 Marikana strike 2012 30 2.7.2 Marikana strike 2014 30 3.1.3.1 Short-run closure in UPGEM14 model 33 3.1.3.2 Long-run closure in UPGEM14 model 34

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1. INTRODUCTION

1.1 Overview

The year 2012 was a crisis year for the South African mining industry. The incidents at Marikana and the subsequent strike action over wage disputes, will forever be engraved in the memories of many South Africans, not only for the tragic loss of 44 lives on that fateful day of 16 August 2012 , but also because they marked a turning point for the industry. Thirty four (34) platinum sector mineworkers in Marikana lost their lives in a violent confrontation with members of the South African police force, one of the saddest and most regrettable episodes in the history of the mining sector which necessitates that the right options are taken to ensure that it is an occurrence that is never repeated (South African chamber of mines, 2012/13: 9).

Marikana unleashed some centripetal forces within the mining industry, and exposed socio- economic realities that spurred organised business, organised labour and government, as the three main industry role players, to recognise the need for closer co-operation to avoid similar tragedies in the future. This also served to acknowledge the need to protect a sector that remains critical to the growth and development of the national economy.

1.2 ECONOMIC DEVELOPMENTS IN THE MINING SECTOR

According to the South African Chamber of mines, since the advent of the global financial crisis, the South African mining sector has been marred by volatility ascribed mainly to the slowdown in economic growth in China; which is one of the main importers of South African minerals; the below potential growth performance of the American economy, and the on-going economic recession in Europe. These negative international developments caused sharp reductions in commodity prices as a result of reduced demand

Over a six-month period into the financial crisis, the price of gold dropped by more than 20per cent and a declining demand for catalytic convertors in the vehicle-manufacturing industry was the cause of a similar drop in the price of platinum. For the South African mining industry this translated into a situation where more than 50per cent of gold and platinum operations found themselves in loss- making situation; three of the world’s top rating agencies: Standard & Poor’s, Moody’s and Fitch downgraded South African’s sovereign credit rating as a result.

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Compounding these damaging interventions were the results of a Fraser Institute survey of mining countries, in which South Africa was downgraded 10 places to position 64 out of 93 countries.

Given mining linkages and induced impacts on many other parts of the economy, it is not surprising that the mining industry is a significant investor in the South African economy. On a direct basis mining accounts for 12per cent of total investment in the economy (public and private), and accounts for 19per cent of total private sector investment. If indirect multipliers and induced effects are considered, the total contribution of mining to fixed investment is estimated at about 25per cent of the total. So one-quarter of all investment in the economy is somehow related to mining according to the South African chamber of mines.

Mining production registered an increase of 12 per cent year-on-year in December 2013, with the highest positive growth having been recorded for building materials at 39,9 per cent. The main contributors to the 12 per cent increase in production were iron ore (contributing 5.8 percentage points), PGMs (contributing 3.6 percentage points) and gold (contributing 1.8 percentage points). Mineral sales also increased by 7.9 per cent year-on-year in November 2013 with the main contributors being iron ore and PGMs, contributing 6.8 and 4.8 percentage points respectively. Gold was however a major negative contributor to mineral sales, contributing -8.0 percentage points (Statistics South Africa, 2013: 4)

The South African mining industry, now the fifth largest in the world, accounts for over 8per cent of South Africa’s GDP on a direct basis. A recent study by Quantec and the Industrial Development Corporation (IDC) found that in 2012 the mining sector helped to create 1 365 892 jobs in the South African economy. This accounts for approximately 14 per cent of the total formal non-agricultural employment in the country. Mining created 524 632 jobs directly and another 841 260 jobs were created in the industries that either supply goods and services to the mining sector, or use mining products for downstream value addition, or which are related to the spending multipliers from mining and mining employees in the economy. The social multiplier of mining is very significant for South Africa. Given a dependency ratio of about 10 to 1, this means that about 13 600 000 people were directly dependant for the daily food on their table on the 1 365 892 jobs created by the mining sector.

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2 Literature review

2.1 History and evolution of mining in South Africa

Mining in South Africa dates back to very long before our current age, It was a diamond found in Orange River that started mining in South Africa in 1867. Not long after, gold was discovered in Pilgrim’s Rest and in Barberton. This led to an even bigger gold discovery at the rocky hills of the Witwatersrand in Gauteng where an estimated 40per cent of the total gold ever found in the world came from.

According to miningsa other precious metals and minerals that can be found in the country are platinum, chrome, vanadium, manganese, vermiculite, zirconium, limonite, rutile, and palladium. South Africa is also the 3rd largest coal exporter in the world.

It is believed that the mining industry caused the foreign settlers to choose South Africa over other African countries. The British came because of the diamonds and the first Boer War of 1880 erupted because of the annexing of certain diamond fields. The Second Boer War was heavily influenced on the other hand by gold mining. From all this fighting emerged “Randlords” who were an elite group of foreign entrepreneurs that monopolized diamond and gold mines up to the time of World war one (WWI). Some of the Randlords include Cecile Rhodes and the De Beers consolidated Mines. The history of these Randlords is about ordinary Europeans from humble beginnings who made it rich and became the new elite; many of them even got so far as to receive titles. The South African currency, the Rand, originates from the fact that gold was discovered and mined on the Witwatersrand. (mininginsa.co.za).

Diamond and gold discoveries played an important part in the growth of the early South African Republic. A site northeast of Cape Town was discovered to have rich deposits of diamonds, and thousands rushed to the area of Kimberley in an attempt to profit from the discovery. The British later annexed the region of Griqualand West, an area which included the diamond fields. In 1868, the republic attempted to annex areas near newly discovered diamond fields, drawing protests from the nearby British colonial government. These annexations later led to the First Boer War of 1880- 1881.

Gold was later discovered in the area known as Witwatersrand, triggering what would become the Witwatersrand Gold Rush of 1886. Like the diamond discoveries before, the gold rush caused thousands of foreign expatriates to flock to the region. This heightened political tensions in the area, ultimately contributing to the Second Boer War in 1899.

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The gold mining industry continued to grow throughout much of the early 20th century, significantly contributing to the tripling of the economic value of what was then known as the Union of South Africa. In particular, revenue from gold exports provided sufficient capital to purchase much-needed machinery and petroleum products to support an expanding manufacturing base.

Platinum deposits were later discovered in 1923 by Mr Adolf Erasmus near Naboomspruit, in central Transvaal.In 1924 Mr Andries Lombaard obtained platinum grains from a stream bed on the farm Maandagshoek in the Lydenburg district and brought this discovery to the attention of Dr Hans Merensky, who devised a prospecting programme to establish the source of these platinum grains. Merensky later discovered a layered gently dipping platiniferous pyroxenite on the same farm, Maandagshoek. Although he did not realize it at the time, he had found the world's largest known repository of the platinum-group elements(PGE) later to be known as platinum-group metals (PGMs).

Production of platinum has continued ever since, and today three large-scale mining concerns are engaged in winning platinum from the Bushveld Complex. These are J.C.L's Rustenburg Platinum Mines Ltd, Gencor's Impala Platinum Ltd, and Lonrho's Western Platinum Ltd. The Merensky Reef had been traced on the eastern limb of the Bushveld Complex for a distance of 150 km, and in the western limb for about 200 km (Fig.1), thus making it by far the largest platinum deposit in the world. (Hochreiter, kennedy, muir, and woods)

Figure 2.1.2 South Africa’s platinum corridor

As of 2007, the South African mining industry employed 493,000 workers. The industry represented 18per cent of South Africa's $588 billion USD Gross Domestic Product.

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2.2 Minerals currently mined in South Africa

2.2.1 Mining sales

Figure 2.2.2.1 Mineral Sales

Figure 8 Figure 9

Figure 10 Figure 11

Figure 12 Figure 13

Figure 14 Figure 15

Source: StatsSa mining production and sales, December 2014

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From figure 8 above mining sales have been increasing exponentially over the past decade. Total mineral sales including gold increased from around R10 billion in the beginning of 2003 to R30.7 billion rand at the end of 2014, an increase of 211 per cent just over a decade. Excluding gold, mining sales increased from 6.7 billion in January 2003 to 27.4 billion at the end of 2014, an increase of 306 per cent in 11 years.

From figure 9 above manganese ore sales increased by 710 per cent over a decade from R174.3 million in January 2003 to R1,4 billion at the end of 2014. Sales for Platinum Group Metals (PGMs) also registered an increase of 141 per cent over the same period from R2.9 billion in the beginning of 2003 to R7.0 billion at the end of 2014.

Figure 10 depicts that gold sales only increased by 7.7 per cent over the last decade from R3.1 billion beginning of 2003 to R3.4 billion at the end of 2014, this coincides with a slowdown in gold production as discussed on page 9 and affirms that South Africa’s gold deposits are being depleted.

Coal sales rose by 310 per cent over the decade under review as depicted by figure 11, from R2.0 billion at the beginning of 2003 to R8.4 billion at the end of 2014. Iron ore sales on the other hand increased exponentially by 1387 per cent; a greater percentage than all the other minerals; from R308 million to R4.6 billion.

From figure 12 above Chromium sales increased by a measurable 967 per cent over a decade from R102 million in January 2003 to R1,1 billion at the end of 2014. Sales for copper also recorded an increase of 208 per cent over the same period from R154 million at the beginning of 2003 to R474 million at the end of 2014.

Figure 13 shows that sales for building materials (granite or norite) declined by 40 per cent over the last decade from R71 million in January 2003 to R43 million at the end of 2014. Sales for building materials (lime and limestone) on the other hand registered an increase of 143 per cent over the same period from R99.6 million at the beginning of 2003 to R242 million at the end of 2014.

Nickel sales rose by 141 per cent over the decade under review as depicted by figure 14, from R218 million at the beginning of 2003 to R524 million at the end of 2014. Sales for other metallic minerals also increased markedly by 361 per cent from R215 million to R994 million.

Figure 15 shows that building material sales rose by 234 per cent over the last decade from R266 million in January 2003 to R888 million at the end of 2014. Sales for other building materials also increased by 536 per cent from R95 million to R603 million.

2.2.3 Mining Exports and Exchange rate

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Figure 2.2.3.1 Mineral exports and real effective exchange rate

Mineral exports and real effective exchange rate mineral product exports

Rand million Index Exports of articles of stone, plaster, cement, asbestos, 60000 140 mica, ceramic, glassware

50000 120 Exports of precious or semi- precious stones, metals & 100 40000 articles thereof 80 Exports of base metals & articles 30000 60 thereof 20000 40 Total mineral exports 10000 20

0 0 … … … … … … … … … … ------Average real effective exchange 10 11 12 13 14 - - - - - rate Sep Sep Sep Sep Sep May May May May May Jan Jan Jan Jan Jan

The real effective exchange rate1 depicted by the orange line on the graph above, shows that the exchange rate of the South African (SA) Rand against a basket of currencies of its major trading partners has been depreciating between January 2010 and December 2014 from 108 index points to 81 index points; following the global financial crisis of 2008/2009 which led to the reduced demand of some Rand denominated commodities ranging from manufactured goods to financial market products like equities (find source). Total mineral exports have however increased measurably from R24 billion in January 2010 to R49 billion in December 2014 as shown by the blue line from the graph above as it became relatively cheaper to exports minerals because of the weaker real effective exchange rate of the Rand. (find source). Exports of mineral products, increased the most and tripled to R23 billion in value by the end of 2014, and they were followed by exports of precious or semi- precious stones, metals and articles thereof which increased by about R6 billion in value over the period reviewed in the graph.

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2.4 Mining contribution to GDP and compensation of employees

Table 2.4.1 Gross Domestic Product by Industry

Quarterly gross domestic product by industry at constant 2010 prices (R million) Year Quar-ter Agricultur Mining Manu- Electricity, Constructi Wholesale Transport, Finance, General Personal Total value Taxes GDP at Total value e, forestry and facturing gas and on , retail and storage real estate governm e services added at less market added at and quarrying w ater motor and com - and nt basic subsidies prices basic fishing trade; m unicatio business services prices on prices catering n services products excluding and agricultur accom mo e dation 1993 47 128 240 906 229 535 47 482 38 685 196 038 89 829 227 132 301 521 84 458 1 443 975 155 407 1 601 287 1 396 847 1994 50 851 242 110 235 732 50 236 39 806 200 939 93 961 235 431 304 527 88 572 1 487 294 164 413 1 652 528 1 436 443 1995 40 732 234 605 251 055 51 240 41 219 212 795 103 921 243 688 307 091 93 459 1 531 913 172 092 1 703 757 1 491 181 1996 50 507 232 728 254 570 56 774 42 050 220 668 110 260 260 223 313 021 95 943 1 596 253 180 502 1 777 018 1 545 746 1997 50 962 236 684 261 443 58 988 43 498 221 551 118 640 272 469 315 491 95 912 1 637 756 185 775 1 823 221 1 586 794 1998 48 261 236 448 260 920 55 272 40 934 224 431 125 165 278 742 313 130 101 869 1 649 220 183 659 1 832 337 1 600 959 1999 51 253 233 137 262 486 54 996 40 378 241 488 131 674 292 987 310 458 105 810 1 693 749 182 666 1 876 313 1 642 496 2000 53 662 230 573 283 747 56 701 42 659 261 048 142 603 302 304 307 582 110 908 1 768 274 185 549 1 955 118 1 714 612 2001 51 891 230 342 292 827 54 603 44 760 266 008 151 017 326 995 304 708 113 402 1 819 554 188 216 2 007 906 1 767 663 2002 55 264 232 646 301 026 56 514 47 361 272 126 164 608 347 519 306 891 116 196 1 888 705 193 319 2 082 206 1 833 441 2003 55 640 240 547 296 498 58 181 51 001 279 380 175 057 364 197 315 384 122 654 1 945 016 198 505 2 143 612 1 889 375 2004 56 118 244 179 311 011 62 129 55 647 294 461 183 613 389 889 321 418 124 796 2 032 496 208 478 2 241 244 1 976 378 2005 57 695 246 685 330 306 65 450 62 281 315 210 193 334 412 149 335 265 129 535 2 140 471 218 873 2 359 516 2 082 776 2006 54 544 245 224 351 572 67 688 68 780 333 999 203 224 451 896 345 531 136 298 2 258 757 232 539 2 491 296 2 204 213 2007 56 168 243 662 370 389 70 000 79 455 352 698 218 488 484 675 361 636 143 807 2 380 979 243 862 2 624 841 2 324 811 2008 67 072 230 663 378 964 67 522 87 300 358 880 226 136 511 716 381 768 149 216 2 459 238 249 363 2 708 601 2 392 166 2009 65 802 218 830 338 692 66 337 94 759 354 870 225 712 517 114 393 921 148 015 2 424 053 242 887 2 666 940 2 358 251 2010 65 605 230 350 358 699 67 940 95 453 370 581 229 499 523 526 404 647 148 561 2 494 860 253 148 2 748 008 2 429 256 2011 66 464 228 645 369 261 68 879 95 809 384 768 236 439 544 997 422 695 152 163 2 570 121 266 166 2 836 287 2 503 657 2012 66 861 221 972 376 126 68 801 97 804 398 585 242 233 561 079 437 734 155 295 2 626 489 272 758 2 899 247 2 559 629 2013 67 880 230 908 378 933 68 375 100 468 405 983 247 062 578 127 451 214 158 167 2 687 117 276 272 2 963 389 2 619 237 Source: StatsSA

From table 1 above mining sector contribution to Gross Domestic Product (GDP) at market prices was worth R240 906 million, which translates to 15 per cent of GDP in 1993, and while GDP increased by 34 per cent between 1993 and 2003, the mining sector did not grow and actually contracted by 0.1 per cent during that decade thus resulting in the mining sector contribution of R240 547 million accounting for only 11 per cent of GDP in 2003. GDP further grew by 38 per cent to R2 963 389 million between 2003 and 2013 while the mining and quarrying sector further receded by 4 per cent to R230 908 million and accounted for only 8 per cent of GDP in 2013. The mining sector has therefore been contracting over the past two decades spanning from 1993 to 2013 and its contribution to GDP has almost halved over that period and this is also evident from the total physical volume of mining production as discussed in section… which remained almost unchanged over the period reviewed.

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Table 2.4.2 Compensation of employees

Quarterly compensation of employees (R million) Year Quar-ter Agricultur Mining Manu- Electricity, Constructi Wholesale Transport, Finance, General Personal Total com- e, forestry and facturing gas and on , retail and storage real estate governm e services pensation and quarrying water motor and and nt of fishing trade; comm unic business services employee catering ation services s and accom mo dation 1993 5 089 15 927 46 467 3 903 9 643 29 502 18 026 21 196 55 255 14 610 219 618 1994 5 703 16 628 51 151 4 310 10 023 31 727 19 592 24 132 63 435 17 093 243 793 1995 6 432 18 566 56 905 4 932 10 983 35 742 22 532 28 386 72 021 20 013 276 512 1996 6 936 19 969 60 413 6 092 11 491 38 850 25 580 31 998 86 292 22 419 310 039 1997 7 435 22 061 64 001 6 672 12 365 42 608 27 850 37 111 96 416 25 753 342 270 1998 8 347 23 650 72 678 6 734 12 488 46 575 30 054 43 776 103 527 28 208 376 036 1999 8 847 26 185 76 254 7 772 12 593 55 069 31 416 49 087 109 398 31 643 408 264 2000 9 446 28 435 83 595 8 178 12 879 62 196 33 963 55 086 116 447 36 447 446 672 2001 10 018 31 484 89 316 8 219 13 138 62 706 36 600 63 471 123 704 40 006 478 662 2002 11 659 34 843 100 404 9 880 13 824 67 707 38 048 74 261 136 004 45 700 532 330 2003 13 430 36 008 105 133 10 846 15 588 75 041 42 313 84 450 151 039 50 781 584 630 2004 13 036 38 856 112 755 11 293 17 460 84 180 46 788 98 008 166 287 55 241 643 905 2005 12 222 42 042 121 244 12 451 20 023 92 397 51 135 114 144 181 765 60 863 708 286 2006 12 891 49 535 134 467 13 844 23 600 104 592 56 134 130 445 196 420 66 255 788 183 2007 14 709 58 221 154 326 15 460 32 229 113 750 61 219 152 782 220 257 74 786 897 740 2008 16 660 69 675 177 186 17 623 37 604 126 629 65 628 171 443 257 245 81 043 1 020 736 2009 18 391 75 601 185 106 20 006 41 938 134 301 68 776 175 084 301 744 85 246 1 106 193 2010 19 494 86 399 205 680 22 091 44 287 148 043 74 663 196 298 347 326 90 425 1 234 707 2011 19 990 95 980 218 564 26 054 47 920 162 313 82 690 218 248 388 536 97 816 1 358 111 2012 21 621 109 062 238 082 29 541 52 376 177 323 88 294 236 653 416 538 104 362 1 473 852 2013 23 120 118 281 260 722 32 430 55 990 189 364 94 677 264 570 459 691 111 801 1 610 647 Source: StatsSA

From table 2 above total compensation of employees in the mining sector in 1993 was R159 27 million, and as a percentage of total compensation of employees by the South African economy was 7 per cent. Total compensation of employees by the South African economy amounted to R219 618 million in 1993 and increased by 166 per cent between 1993 and 2003 while the mining sector wage bill grew by 126 per cent during that decade. Compensation of employees by the mining sector amounted to R36008 million in 2003 accounting for only 6 per cent of total compensation of employees by the South African economy. Total compensation of employees by the South African economy further grew by 175 per cent to R1 610 647 million between 2003 and 2013 while the compensation of employees by the mining and quarrying sector surpassed it and grew by 228 per cent to R118 281 million and accounted for 7 per cent of the total South African wage bill in 2013. The mining sector wage bill has therefore been increasing measurably over the past two decades spanning from 1993 to 2013 in line with general price increases, however, as a percentage of the total wage bill, total compensation of employees by the mining sector has remained relatively unchanged around the 7 per cent level.

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2.5 Mining sector employment per skills level and wages

Table 2.5.1 Mining sector employment per skills level and wages

Labour Market Dynamics in South Africa 2013 Row percentage

Salary category R1 - R200 R201 - R500 R501 - R1 000 R1 001 - R1 500 R1 501 - R2 500 R2 501 - R3 500 R3 501 - R4 500 R4 501 - R6 000 R6 001 - R8 000

Main industry grouped Main occupation grouped Legislators; senior Mining and quarrying officials and managers 0 0 0 0 0 0 1.1 0.1 3 Professionals 0 0 0 0 0 0.2 0.3 1.2 4.9 Technical and associate professionals 0 0 0 0 0.8 1.2 0.8 4.9 2.9 Clerks 0 0.2 0.2 0.4 1 2.2 5.6 4.2 12 Service workers and shop and market sales workers 0 0 0 0 1.9 6.8 2.4 14.5 0 Skilled agricultural and fishery workers ------Craft and related trades workers 0 0.1 0.3 0.4 1.1 1.8 4.2 8.4 8 Plant and machine operators and assemblers 0 0.1 0.2 1.4 1.6 2.6 6.7 21.9 25.1 Elementary Occupation 0 0 0.3 0.7 3.3 3.8 8.1 22 25 Domestic workers ------

Salary category R8 001 - R11 000 R11 001 - R16 000 R16 001 - R30 000 R30 001 - R37 500 R37 501 - R54 167 R54 168 - R62 500 R62 501- R70 800 R70 801 - R83 300 R83 301 OR MORE Total N=

Main industry grouped Main occupation grouped Legislators; senior Mining and quarrying officials and managers 4.6 17.8 11.7 1.9 31.5 0 9.8 0 18.6 100 179756530.8 Professionals 6.1 11.8 26.1 10 3.1 0 21.9 0 14.3 100 315811954.5 Technical and associate professionals 6.3 26.5 39.5 2.1 0 0 7.7 0 7.2 100 183814796.2 Clerks 18.8 15.9 35.5 2 0 2.1 0 0 0 100 229342860 Service workers and shop and market sales workers 26.1 2.1 11.6 0 34.5 0 0 0 0 100 49570273.3 Skilled agricultural and fishery workers ------100 0 Craft and related trades workers 9.3 18.3 31.2 3.6 10.2 0 0 0 3.2 100 1043592800 Plant and machine operators and assemblers 21.6 15 4 0 0 0 0 0 0 100 716666571.3 Elementary Occupation 18.7 9.8 8.2 0 0 0 0 0 0 100 456947548 Domestic workers ------100 0

Source: StatsSA

From table 3 above the employment category of legislators; senior officials and managers make up 5.6 per cent of those employed in the mining sector; they generally earn between R8000 and R83301 or more. The majority (31.5 per cent) of those employed in this category earns between R37501 and R54167, followed by 18.6 per cent earning between 83301 and more, while 17.8 per cent of this category earn between R11001 and R16000 and 11.7 per cent earn between R16001 and R30000.

The majority (33 per cent) of those employed in the mining sector; fall in the craft and related trades workers category of employment. The majority of this category (31.2 per cent) earn between

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R16001 and R30000, followed by 18.3 per cent who earn between R11001 and R16000. 10 per cent earn between R37501 and R54167 and 9 per cent between R8001 and R11000.

The second majority (22.6 per cent) of those employed in the mining sector fall in the plant, machine operators and assemblers’ category and the majority (25 per cent) within this category earn between R6001 and R8000. 22 per cent of those employed in this category earns between R4501 and R6000, the other 22 per cent earns between R8001 and R11000, and a further 15 per cent between R11001 and R16000. This is the category in which rock drillers within the mining sector fall.

14 per cent of those employed in the mining sector fall are in the elementary occupation category, where the majority (25 per cent) earn between R6001 and R8000, followed by 22 per cent earning between R4501 and R6000. 18.7 per cent earn between 8001 and 11000 and a further 10 per cent earn between R11001 and R16000. The miners generally fall within both the elementary occupation category and the plant, machine operators and assemblers category. In both these categories the majority (25 per cent) earn between R6001 and R8000 and the second majority earn between R4501 and R6000. The two categories jointly make up 36.6 per cent of the mining sector workforce, which makes up the majority, yet they earn far less than the minority category of legislators; senior officials and managers who make up 5.6 per cent of those employed in the mining sector, the majority of which earn between R37501 and R54167.

There are other factors of course that would affect salary levels which include education levels, skills, experience and competence which generally determine the level at which appointments are made but if the majority are sharing a small piece of the remuneration pie, it does not augur favourably for bridging the income inequality gap which according to the National Development Plan is at 0.6 nationally as measured by the gini coefficient 1

2.6 Mining wages in other economies (evidence from abroad)

The shortage of miners is particularly acute in Australia, the world's biggest source of iron ore and the world's second-biggest gold producer. The Minerals Council of Australia estimates the country needs an additional 86,000 workers by 2020, to complement a current work force estimated at 216,000. "It's a tight labor market and difficult cost environment," said Ian Ashby, president of BHP Billiton Ltd.'s iron-ore division. To attract workers, BHP and other companies are building recreation centers, sports fields and art galleries in hardscrabble mining company towns. BHP said rising manpower and capital costs reduced earnings by $1.2 billion during the first half of 2011, The

15 average salary in the Australian mining industry was about 108,000 Australian dollars, or about US$110,000, in 2010, which includes some part-time and lower-skilled workers and is well above the A$66,594 average for all Australians, according to the Australian government's Bureau of Statistics (ABS).

The ABS employee earnings and hours report showed that the mining industry continues to pay the highest wages in Australia, with average take home earnings sitting at $2,388.20 as of May 2012. The average hourly rate in the sector was reported to be $52.30. “In the Mining industry, 63 per cent of full-time adult non-managerial employees earned weekly total cash earnings of more than $2,000 per week,” ABS director of labour employer surveys, Mike Scott said.

The mining sector in Australia pays so much that David Nichols, author of "The Bogan2 Delusion", a sociological book about the riches of blue-collar Australians, was quoted saying: "I have civil-servant friends who talks about giving it all up and going to the work in the mines. Bogan referring to Australian slang for an uneducated blue-collar worker.

Chilean professionals working in the mining industry earn the highest salaries in Latin America and the sixth highest in the world, reveals a recent survey by global recruitment firm Hays. The study, which looked at 37 countries worldwide and had over 10,000 respondents, said Chilean mine employees earn an average annual salary of $97,537, not so far from the global average of $98,787.According to the survey results, published by Latin Pacific Business News, the second best paid miners are Brazilians, with an average annual salary of $76,800, followed by Peru ($73,100), Colombia ($61,100), Mexico ($56,100), Bolivia ($52,300), and Argentina ($41,600). The figure is quite significant, considering that Chile’s average household disposable income is a mere $11,000 a year, as a study by the Organization for Economic Cooperation and Development (OECD), published by Business Insider shows.

In contrast, foreign miners working in Bolivia earn the highest salaries in all South America with annual average income of $156,900. They are closely followed by non-nationals working in Peru ($ 137,200) and Argentina ($133,300). Worldwide, Bolivia and Peru are in fourth and ninth place respectively in terms of places that best paid foreign miners, while Chile was ranked 23

The Hays survey also looked at day rates for contractors as these "tend to be more in tune with short-term changes in industry demand." Australia has the highest rates at US$710/day, followed by Europe (US$450), North America (US$420) and then South America (US$350). The lowest rates were found in Africa (US$260) and Asia (US$240).

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The highest paid mining employees in the world are in Norway where average annual salaries are US$158,700, followed by Australia (US$137,100) and Canada (US$101,800), according to the survey. The Hays survey looked at pay levels for 12 different functions in the mining industry, including operations, procurement, the supply chain and general management. South African miners in comparison, the majority earn between R6001 and R8000 per month which translates to annual packages of between R120 000 and R160 000 or US$ 10000 and US$ 13333 respectively. This is far less, approximately a tenth of what countries like Norway, Australia and Canada pay the miners.

2.7 Problem Statement

In 2007 the South African National Union of Mineworkers, which represents the nation's mineworkers, engaged in a series of talks with the Chamber of Mines, an industry group. The meetings also saw the participation of the Commission for Conciliation, Mediation and Arbitration, a body with mediation authority over the dispute. On 27 November 2007, the National Union of Mineworkers announced that South African mineworkers would go on strike to protest at unsafe working conditions.[24] The strike took place on 4 December, and impacted over 240,000 workers at 60 sites across the country, including mines devoted to the production of gold, platinum, and coal.[25][26]

2012 Lonmin strike[edit]

The Lonmin strike, dubbed a wildcat3 strike was a strike in August 2012 in the Marikana area, close to Rustenburg, South Africa at a mine owned by Lonmin one of the world's largest primary producers of Platinum Group Metals (PGMs). Miners were protesting for a minimum wage of R12500, an increase of R8000 for a mineworker earning R4500, the lowest of the income spectrum of the majority, and an increase of R4500 for a mineworker earning R8000, the highest of the income spectrum for the majority. This translates to a 178 per cent and 56 per cent increase in wages for the lowest earner and the highest earner of the majority respectively. http://www.sahistory.org.za/archive/glitter-gold

A series of violent confrontations occurred between platinum mine workers on strike and the South African Police Service on Thursday, 16 August 2012, and resulted in the deaths of 34 miners, as well as the injury of an additional 78 miners. Between 9 August 2012 and 16 August, a total of 44 people died some of whom were police officers. Occurring in the post-apartheid era, it was the deadliest incident of violence between police and the civilian population in South Africa since the 1960 Sharpeville Massacre and prompted the South African President, Jacob Zuma to declare a 6 day long week of mourning.

17

The strike continued again in the first five months of 2014 which saw 70 000 mineworkers from major platinum producers such as Impala Platinum, Anglo American Platinum and Lonmin Platinum Mines based in the Rustenburg in the North West Province down tools. These mine workers belong to a newly formed trade union, Association of Mines and Construction Union (AMCU) under the leadership of Joseph Mathunjwa. The mines affected lost about 40 percent of platinum production as a result of the strike and subsequent shutdown that started in January 2014. The strike took around 440 000 ounces of platinum out of production. The three companies, Impala Platinum, Amplats and Lonmin suffered a total revenue loss of about R24.1billion during the strike and a further loss of R10.6 billion in wages. The five month long strike affected both the workers and the mining companies.

The South African mining industry shed 20,000 jobs in the 12 months leading to June 2013, and that trend was set to continue due to low margins, cost pressures and volatile commodity prices. Additionally, labour costs in the mining sector account for 45% to 50% of total cost, while the global average was 30 to 40% of total cost, with employee efficiency 10 times higher. The direct impact of the mining strike on first quarter 2014 growth was clearly evident. The Gross Domestic Product (GDP) reduction of 1.3% was reported. This resulted in an economic growth of only 0.6% in quarter 1, 2014.

During the five month strike, mineworkers’ debts increased. Without salaries, mineworkers’ dependence on credit increased and they were forced to borrow for basic necessities such as food, clothes and school fees for their children. It was reported that the average miner’s accumulated debt had increased and they were paying back R5 000 per month. Other mineworkers who had taken out loans were unable to pay their debt during the strike. Miners lost 45% of their annual income, and it would take them roughly 2.5 years to recoup it through the recently negotiated wage increase.

In May 2014, the newly appointed Minister of Mineral Resources, Ngoako Ramatlhodi appointed a task team to restart delayed negotiations and find an amicable solution. On 7 June 2014, Ramatlhodi announced that he would pull out of negotiations if a deal was not reached by 9 June 2014. In June 2014, AMCU argued for fixed wage increase over four years to meet the R12 500 goal by 2017.

After five months of striking, both the platinum companies and AMCU settled for a pay increase spread over three years. On 23 June 2014, a deal was reached between the platinum companies and AMCU. The three year agreement stated that workers who were currently earning less than R12 500

18 will receive a R1 000 increase in 2014 and in 2015. In 2016, the same workers will receive a R950 increase. After the wage increases, the minimum salary would be R8, 000 a month. The mining companies agreed to avoid future job loss as they sought to operate more efficiently.

On 24 June 2014, the deal was officially signed and workers started to return to work on 25 June 2014. It was expected that a return to full production would take three months. AMCU announced that it would continue to work to increase the minimum wage to R12, 500 by the year 2017. By the time a deal was reached, the strike had become the longest and most costly in South African history.

After the strike, Anglo American Platinum, Amplats, announced that it planned to sell four mines and two joint ventures because of the five-month long strike action. Those assets included Amplats’s Consolidated Union-Rustenburg mine in the North West Province, and the Pando JV in Limpopo Province which is jointly owned with platinum mining company Lonmin’s subsidiary company, Eastern Platinum. The Bapo Ba Mogale community owned Bapo Ba Mogale mining company, as well as the Johannesburg Stock Exchange (JSE) -listed exploration company, Mvelaphanda Resources. Amplats also considered discarding its Bokoni JV project which the company owned with triple-listed platinum company Atlatsa Resources based in Burgersfort in Limpopo Province.

The mining sector strike also re-opened the debate on the living wage and the minimum wage. It therefore becomes crucial to investigate what the overall economic impact would have been, had the salaries of miners being adjusted to R12500 by an average of 117 per cent for the majority of miners earning between R4500 and R8000.

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3. METHODOLOGY (CGE MODEL)

3.1 Introduction

The main objective of this paper is to simulate and assess the impact of a real wage increase policy of 117 percent on the general macro-economic variables and on output of various industries and households of the South African economy using Computable General Equilibrium (CGE) modelling techniques, in particular the GEMPACK software package software and the UPGEM 14 model solved using GEMPACK. The focus is on both the short run and long run impact.

3.2 Description of model and database

The UPGEM 14 model is derived from the theoretical framework based on the ORANI-G generic single-country CGE model of Australia and is designed for comparative-static simulations (Horridge, 2000) using the 2011 Supply-Use (SU) tables of South Africa published by Statistics South Africa (Bohlmann and Van Heerden, 2014) which make up its database. It is a 14 sector / industry CGE model, which cover the following sectors: Agriculture, Mining, Manufacturing, Electricity and water, Construction, Trade, Transport and Communications, Business, General government and other services. The model covers 14 commodities: Agriculture, Mining, Manufacturing, Electricity and water, Construction, Trade, Transport and Communication, Business, General government and other services. Those commodities are either imported or domestic i.e. 2 sources. 11 occupation types are covered in the model and they are: Managers, Professional, Technicians, Clerks, Service, Managers, Professional, Technicians, Clerks, Service, and Agric. There are 2 commodity margins in the model, Trade margins and Transport Margins.

UPGEM14 model takes into account all interlinkages in the economy and recognizes all main user categories namely: industries, households, investors, government and the rest of the world. It further identifies three primary factors, namely: capital, land and labour; it has one representative household and one central government. UPGEM14 is based on neoclassical theory assumptions; therefore, consumers maximize utility while producers minimize costs (Bohlmann and Van Heerden, 2014). Industries minimize costs based on a constant returns to scale (CRS) production function and given input prices. Imperfect substitutability between imported and domestic goods is modelled using Armington’s CES assumption. The model assumes that all sectors are competitive and markets clear (Giesecke and Schilling, 2010; Horridge, 2000). The numeraire assumption used in UPGEM14 for the nominal exchange rate is phi and is therefore kept exogenous. (Bohlmann and Van Heerden, 2014).Technological change and all tax rates are exogenous in the model both in the long run and short run.

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3.3 Description of model closure

The model closure describes how the modelling environment is set by stipulating which variables are exogenous, with the ones not explicitly stipulated as exogenous being stated as endogenous (as the case in the 2- page specification of the short run closure command file attached in the Appendix A). CGE models contain more variables that equations; the model closure should therefore be designed in a way that reflects the desired economic environment under which the simulation is running (Bohlmann and Van Heerden, 2014).

In this paper a both a short run and long run policy closure are used to evaluate and quantify the effect of a proposed policy exogenous shock i.e. (increasing wages in the mining sector by 117 per cent) on the overall economy. In a general short run closure, i.e. where the model is designed to simulate a particular policy effect in the short run using comparative static model, the following variables are fixed or exogenous: real wage (f1lab_io), technological change ( a1cap a1lab_o alind a1prim altot a2tot) capital stocks (x1cap), private consumption (x3tot), Investment (x2tot_i) and government consumption (x5tot). Reference can be made to figure….Appendix A: exhibit 202, Causation in a short-run closure and figure…Appendix A: Specification of short-run closure command file, which depict the variables that are exogenous in a short-run simulation. In this paper however, it is to be noted that the exogenous variable p1lab_p, which is wages by industry and occupation, is shocked in the UPGEM14 short-run command file (cmf) to enable us to assess its effect on other macroeconomic variables. This implies that the policy effect being investigated will normally be stated as a shock in model (please refer to page 2 of specification of the short –run closure command file attached in the Appendix A).

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Figure 3.1.3.1 : Short-run closure in UPGEM14 model

In the long-run closure, variables that are endogenous in the short-run closure become exogenous, and those that were exogenous in the short run become endogenous except for technological change which remains exogenous in both the short run and the long run closures as shown in figure… Appendix A, exhibit 205, causation in Long run closure.

Figure 3.1.3.2 : Long-run closure in UPGEM14 model

Source: Adapted from Horridge (2000)

22 3.3 A back-of-the-envelope (BOTE) representation of UPGEM14

BOTE models are used to describe the economic mechanism responsible to explain and trace the impacts of the shock applied to the economy. In this paper, we used an adapted version of Adams (2005) BOTE model to explain the UPGEM14 model mechanism responsible for our main macroeconomic findings. Adams (2005) stylised macroeconomic model outlines a general approach of how to interpret macroeconomic results from CGE models such as GTAP.

The adapted BOTE model used in this paper is presented in Table 1. Equation (1), explains GDP at market prices simply as the sum of consumption, investment, government expenditure and the trade balance (exports minus imports). Equation (2) represents South Africa’s production function, relating GDP at factor costs to the primary factors (capital and labour) and technological change. Equation (3) represents the relationship between real GDP at market prices and real GDP at factor costs accounting for the value of ad-valorem taxes.

Equation (4) represents the economy’s consumption function and Equation (5) explains government consumption; these two equations move together, therefore, consumption and government consumption are assumed to be equal. Equation (6) relates the volume of imports to real GDP, the real exchange rate and the ad-valorem tax. Increases of the exchange rate (real depreciation) will be reflected as a decline in competitiveness in South Africa. Equation (7) represents the export equation in the model; where, exports are inversely related to the exchange rate and to the level of economic activity in the country. Equation (8), describes the level of investment in the economy as the ratio of investment to capital and an exogenous variable that represents the ratio of investment to capital in a specific year. Equation (9) defines the real exchange rate as the ratio of the price of GDP at market prices to the price GDP in the rest of the world converted to the local currency via the nominal exchange rate.

Equation (10), relates the price of GDP at market prices to the price of GDP at factor cost, to the ad- valorem tax rate and the price at which this tax rate is applied, in this paper, the general sales tax in the petrochemical commodities enters the model through this equation.

Equation (11) shows the inverse relationship of the terms of trade (price of exports relative to the price of imports) to the value of exports and the average price of imports. Equation (12) shows the relationship of the terms of trade with the price of consumption, while equation (13) shows the

23 relationship of the terms of trade with the price of government consumption. Equation (14) relates the capital to labour ratio to the real price of labour (nominal wage) and the real price of capital (nominal rental of capital). Equation (15), explains the relationship between real factor prices. Equation (16) depicts the real price of labour as a function of the real wage rate, the inverse of the terms of trade and the ad-valorem tax. Finally, equation (17) derives the real price of capital as a function of the rate of return on capital and the inverse of the terms of trade and the ad-valorem tax.

The main idea of using this BOTE model is to determine the first point of impact of the exogenous shock, in our case to trace the effect of a 5 percent increase in a general sales tax on the petrochemical commodities with simultaneous recycling of tax revenue to all industries and households; and then to be able to track and explain all the effects of this shock in the overall economy (Adams, 2005).

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Table 3.4.1: UPGEM14 BOTE Model1

Y


r
= C
r
+ I
r
+ G
r
+
X
r
− M
r

(1)

Y
r
∗ A
r
= F

L
r
, K
r

(2)













P



r
∗ Y
r
= P


∗ Y
r
+ P
r
+ Y
r
+ T
r
(3)




P
r
∗ C
r
= Ω
r
∗ P



r
∗ Y
r
(4)




P
r
∗ G
r
= ᴦ
r
∗ P



r
∗ Y
r
(5)
M
r
= F
Y


r
, RER
r
,
(6)



(
)

X
r
= F

− RER
r

∗ Y

r
(7)



= Φ
r
(8)

RER
r
=


(9)



∗



















P


r = F




P


r , T r , P r , Taxrec
(10)
TOT
r
= (11)









∗(

(
)

= (12)









(





)

= (13)









(





)

(
) = F



(14)






(
)

RP
r





=
(15)





RP
r
= RW
r
∗ F

(16)









,(


(
)
RP
r
= ROR
r
∗ F

(17)









,(


(
) Source: Adapted from Adams (2005)

1For a full description of the variables in the BOTE model and the exogenous/endogenous status of each variable refer to Appendix A at the end of this paper

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4 Data Analysis and interpretation of results

In this section, both the short-run and long-run closure and BOTE model specified in the previous section to help us interpret the results of a policy shock simulating a 100 percent increase in mining nominal wages for elementary mine workers and operators using the UPGEM14 model. Since the simulation results of the shocks applied to the economy largely depend on the model closure, it is essential to keep the type of closure used in mind. In policy simulations, such as the ones evaluated in this paper, the differences in the outcomes between the baseline and the policy simulation are due to the policy shocks, therefore, these differences can be interpreted as the effects of the policy (Dixon et al., 2013). The variable being shocked is kept exogenous in both the short-run and the long-run.

The policy shock was applied to the UPGEM14 model in the following way2:

1) f1lab_o the occupation specific wage shifter was increased for both coal mining and other mining, for both elementary mineworkers and operators by 37 percent because even though elementary workers and operators were demanding a 100 per cent increase in their nominal wages, they make up 37 per cent of the mining sector workforce.

# percentage increase in wages demanded by operators and elementary mineworkers in the mining sector# ! shock f1lab("coalmining","operators") = 100; ! shock f1lab("coalmining","elementary") = 100;

! shock f1lab("othmining","operators") = 100; ! shock f1lab("othmining","elementary") = 100;

# Shock in occupation specific wage shifter for operators and elementary mineworkers in the mining sector which make up 37% of the workforce#

shock f1lab_o("coalmining") = 37; shock f1lab_o("othmining") = 37;

2The short-run and long-run command files and the excerpt of the TABLO code affected by this simulation can be found in Appendix B and Appendix C at the end of this paper

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The empirical results of the policy simulation are presented next. The analysis will start with the presentation of the main macroeconomic results using the BOTE model specified in the methodology section to trace the overall impact of the policy shock in the economy, followed by the main contributors to the changes in GDP and lastly, a brief industry analysis.

4.1 Macroeconomic results

Table 4.1.1 Macroeconomic results

Description Macros shortrun longrun Percenta Percenta ge ge change change Aggregate revenue from all indirect taxes (change) delV0tax_csi -1995.61 -1544.52 Aggregate employment: wage bill weights employ_i -0.21 0 Overall wage shifter f1lab_io -2.41 -2.59 Ratio, investment/consumption f2tot 0 -1.46 Ratio, consumption/ GDP f3tot 0.24 0.49 GDP price index, expenditure side p0gdpexp -1.18 -1.3 Real devaluation p0realdev 1.19 1.31 Terms of trade p0toft 0.43 0.95 Average capital rental p1cap_i -1.37 -0.91 Average nominal wage p1lab_io -1.01 -1.58 Average land rental p1lnd_i -5.04 -8.74 Consumer price index p3tot -1.01 -1.38 Government price index p5tot -2.48 -2.75 Inventories price index p6tot 18.6 6.41 PCap / p1prim_i pCap_p1prim -0.15 0.43 Effective price of labour, incl labour-saving tech change pLabEff -1.01 -1.58 PLabEff / p1prim_i pLabEff_p1prim 0.22 -0.25 Average real wage realwage 0 -0.21 Utility per household utility 0 1.47 Check = w0gdpexp - w0gdpinc ... should be tiny w0gdpdif 0 0 Nominal GDP from expenditure side w0gdpexp -1.24 -1.34 Nominal GDP from income side w0gdpinc -1.24 -1.34 Nominal GNE w0gne -1.32 -1.34 Aggregate revenue from all indirect taxes w0tax_csi -0.59 -0.46 Aggregate payments to capital w1cap_i -1.37 -1.17 Aggregate payments to labour w1lab_io -1.21 -1.58 Aggregate payments to land w1lnd_i -5.04 -8.74 Aggregate primary factor payments w1prim_i -1.33 -1.46 Aggregate nominal investment w2tot_i -0.92 -1.84 Total nominal supernumerary household expenditure w3lux -1.01 0.08 Nominal total household consumption w3tot -1.01 -0.86 Import volume index, C.I.F. weights x0cif_c -0.53 -0.75 Check = x0gdpexp - x0gdpinca ... should be tiny x0gdpdif 0 0 Real GDP from expenditure side x0gdpexp -0.06 -0.05 Real GDP at factor cost (change) x0gdpfac -0.11 -0.13 Real GDP from the income side (change) x0gdpinc -0.06 -0.05 x0gdpinc as pct change x0gdpinca -0.06 -0.05 Real GNE x0gne 0 0.25 Import volume index, duty-paid weights x0imp_c -0.53 -0.75 Aggregate capital stock, rental weights x1cap_i 0 -0.27 Aggregate land stock, rental weights x1lnd_i 0 0 Aggregate primary factor use (excludes tech change) x1prim_i -0.11 -0.13 Aggregate real investment expenditure x2tot_i 0 -0.94 Real household consumption x3tot 0 0.53 Export volume index x4tot -0.73 -1.7 Aggregate real government demands x5tot 0 0.53

27

The increase of 37 per cent in the nominal wages in the mining sector for elementary mine workers and operators will result in aggregate employment (employ_iop) decreasing by 0.21 per cent in the short run because the nominal cost of labour in the mining sector would have increased, prompting employers/producers to demand and use less labour. In the long run, aggregate employment will not register any change because it is fixed. As the mining sector spends more on labour, there will be less money to invest in capital for their production processes, thus leading to a decrease in the average capital rental (p1cap_i) of 1.37 per cent in the short-run. A decrease of a lesser magnitude, 0.91 per cent is registered in the long run. In the long run rate of return on capital is fixed.

Based on the Cobb Douglas production function, Real GDP will respond to a decrease of 0.21 per cent in employment if capital is fixed, by decreasing by almost half of 0.21 per cent. This matches the decrease in real GDP from the expenditure side (x0gdpexp) of 0.06 per cent in the short run, which decreases by almost the same magnitude in the long run, 0.05 per cent. In both the short-run and the long-run, real GDP from the expenditure side (x0gdpexp) is endogenous or responsive.

The terms of trade (P0toft) will respond by increasing by 0,43 per cent in the short-run and by 0.95 per cent in the long-run because price of imports will be growing at a slower rate than export prices due to increased cost of production. Import volumes (x0imp_c) will however decrease by 0,53 per cent in the short-run and by 0.75 per cent in the long-run mainly due to reduced market demand as more money is directed towards paying higher wages.

Aggregate capital stock (x1cap_i) will not respond to an increase in nominal wages in the mining sector for elementary mine workers and operators, because it is fixed. In the long-run it will decline by 0.27 per cent because more money being spent on nominal wages than capital accumulation. This will lead to a decline of 0.94 per cent in aggregate real investment expenditure (x2tot_i) in the long- run. In the short-run aggregate real investment expenditure will not change because it is fixed.

Real household consumption (x3tot) and aggregate government demand (x5tot) will change by the same magnitude and increase by 0.53 per cent in the long run as wages feed through the economy and transmit into higher household consumption and increased tax income by government because of higher wages, thus leading to increased aggregate government demand. In the short run both Real household consumption and aggregate government demand will not change because they are fixed.

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4.2 Industry Results This section analyses and discusses the effects that the policy shock had on industries. This analysis focuses on evaluating who were the main winners and losers in the economy.

Table 4.2.2 Industry results

Industry Industry value added(x1tot) longrun shortrun 1 Agriculture 1.13 0.41 2 CoalMining -4.23 -2.9 3 OthMining -7.84 -4.34 4 FoodBevTob 1.11 0.43 5 PetroChem 0.56 0.43 6 OthManuf 0.91 0.8 7 Electricity -0.16 0.03 8 Water -0.13 0.03 9 Construction -1.03 -0.03 10 TradeAccom 0.87 0.6 11 TransComm 0.68 0.35 12 Business 0.78 0.39 13 GenGov 0.56 0.04 14 OthServices 1.17 0.44

As depicted in table 4.1.2 above and Figure 4.1.1, various industries like mining and construction show that when nominal wages are increased by 37 per cent for both elementary mineworkers and operators by in the mining sector, these sectors will slow down by 2.9 per cent for coal mining, 4.34 for other mining, thus registering a slower growth than the real GDP negative growth of 0,06 per cent in the short-run. Construction will slow down by 0.03 per cent in the short run. In the long-run, more sectors will be affected negatively by the policy shock, with mining growth worsening and receding by 4.23 and 7.84 per cent for coal mining and for other mining respectively. Electricity, water and construction will register a negative growth of 0.16, 0.13 and 1.03 per cent respectively in the long run. This is because these sectors are labour intensive and will generally employ less labour when nominal wages increase.

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Figure 4.1.1 Industry results

All industries that register positive growth in the short run as a result of the policy shock, still grow positively in the long run and register an improvement. These industries include agriculture, food and beverages, petro chemicals, manufacturing, trade and accommodation, transport and communication, Business, general government and other services sectors. The sector that gains the most in the short-run is manufacturing and in the long-run it is other services. The sector that loses the most both in the short run and long run is the mining sector followed by construction.

30 Table 4.1.3 Industry Sale

SALE Industry 1 Intermediate 2 Investment 3 Household 4 Export 5 Government 6 Stocks 7 Margins Total

1 Agriculture 0.492 0 0.383 0.125 0 0 0 1 2 CoalMining 0.528 0 0.008 0.469 0 -0.004 0 1 3 OthMining 0.413 0 0.001 0.567 0 0.018 0 1 4 FoodBevTob 0.217 0 0.705 0.073 0 0.005 0 1 5 PetroChem 0.58 0.001 0.253 0.167 0 -0.002 0 1 6 OthManuf 0.465 0.173 0.166 0.218 0 -0.022 0 1 7 Electricity 0.69 0 0.286 0.015 0 0.009 0 1 8 Water 0.517 0 0.457 0 0 0.026 0 1 9 Construction 0.196 0.741 0.021 0.002 0 0.04 0 1 10 TradeAccom 0.138 0 0.059 0.057 0 -0.009 0.756 1 11 TransComm 0.596 0 0.226 0.083 0 -0.007 0.101 1 12 Business 0.655 0.022 0.288 0.029 0 0.006 0 1 13 GenGov 0.14 0 0.045 0 0.807 0.008 0 1 14 OthServices 0.389 0 0.582 0.027 0 0.002 0 1 Total 0.422 0.075 0.214 0.12 0.092 0 0.077 1

Several industries like mining and construction will respond to the policy by displaying subdued growth compared to other sectors, because a large portion (74 per cent) of the construction industry sales or production goes towards investment as evident from table 4.1.3 above but investment is fixed in both the short-run and long-run thus leading to this slow growth in construction. Almost all general government production goes (80, 7 per cent) goes towards government consumption which is fixed in the short run, thus explaining the benign 0,04 per cent growth in the sector in the short- run following a nominal wage increase. In the long run general government consumption is responsive and increases by 0.56 per cent.

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Table 4.1.4 Industry Employment

Industry Short run Long run Employment Employment by industry by industry (x1lab) (x1lab) 1 Agriculture 1.37 1.92 2 CoalMining -9.5 -10.03 3 OthMining -10.8 -12.9 4 FoodBevTob 0.83 1.56 5 PetroChem 0.84 1.01 6 OthManuf 1.16 1.2 7 Electricity 0.07 0.44 8 Water 0.12 0.6 9 Construction -0.06 -0.52 10 TradeAccom 1.27 1.36 11 TransComm 1.06 1.31 12 Business 0.93 1.33 13 GenGov 0.04 0.67 14 OthServices 1.08 1.73

The effect of the policy shock will cause industries like mining and construction which are labour intensive to shed jobs in both the short-run and the long-run. employees also tend to move within these industries. Employment will be shed more in the long run than in the short run because it takes some time for the economy to adjust to labour price increases. Using equation (16), it can be said that since the real price of labour, terms of trade and ad-valorem tax are all increasing, real wages are also increasing. This can be explained by the fact that in the model’s long-run policy closure, employment is assumed to be exogenous (full-employment).. The services industries i.e. business, government and other services which account for around 45% of the wage bill in the economy will gain employment both in the short run and in the long run because of minimal increases in the cost of labour or nominal wages.

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Table 4.1.5 Industry basic prices of domestic goods

Industry Basic price of domestic goods (p0dom) short-run long-run 1 Agriculture -0.57 -1.18 2 CoalMining 3.09 4.44 3 OthMining 3.14 5.76 4 FoodBevTob -1.22 -1.65 5 PetroChem -0.45 -0.35 6 OthManuf -0.55 -0.58 7 Electricity -1.38 -0.43 8 Water -2.01 -1.05 9 Construction -1.52 -1.31 10 TradeAccom -1.11 -1.92 11 TransComm -1.03 -1.6 12 Business -1.44 -1.92 13 GenGov -2.5 -2.77 14 OthServices -1.2 -1.91

It is evident from table 4.1.5 above that basic prices of domestic goods in the mining sector will also increase both in the short run and in the long run as a result of the policy shock of increasing occupation specific wage shifter for both coal mining and other mining, for both elementary mineworkers and operators by 37 percent. In the short run, basic prices of domestic goods in the mining sector will increase by 3.09 per cent in the coal mining sector and by 3.14 per cent in other mining. In the long run, basic prices of domestic goods in respective sub-sectors of the mining sector will increase by 4.44 per cent and 5.76 per cent. Basic prices of domestic goods in other sectors will decline, with the greatest decline registered in the general government services sector by 2.5 per cent in the short-run and by 2.77 per cent in the long run. Services sectors are not as labour intensive as the primary and the secondary sectors of the economy.

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Table 4.1.5 Consumer Welfare

Industry Household purchaser price Household demand Household Sales short run long run short run long run short run long run 1 Agriculture -0.58 -1.17 -0.15 0.45 -0.04 0.22 2 CoalMining 2.73 3.91 -1.3 -1.32 -0.01 -0.01 3 OthMining 0.7 1.28 -0.61 -0.42 0 0 4 FoodBevTob -1.11 -1.57 0.04 0.6 0.12 0.55 5 PetroChem -0.48 -0.59 -0.19 0.24 0.01 0.12 6 OthManuf -0.44 -0.58 -0.2 0.24 0.02 0.1 7 Electricity -1.35 -0.42 0.13 0.18 0.05 0.06 8 Water -2 -1.04 0.36 0.4 0.17 0.19 9 Construction -1.51 -1.3 0.18 0.5 0 0.01 10 TradeAccom -0.8 -1.39 -0.07 0.53 0.02 0.06 11 TransComm -0.93 -1.44 -0.03 0.55 0.03 0.17 12 Business -1.41 -1.88 0.14 0.71 0.06 0.22 13 GenGov -2.48 -2.75 0.54 1.04 0.03 0.05 14 OthServices -1.16 -1.84 0.06 0.7 0.07 0.47

Source: UPGEM14 simulation results

The effect of the policy shock will result in household purchaser prices generally declining across industries in both the short run and the long run due to higher wages which will improve the cost of living of a number of households. Household purchaser prices for the mining sector will however increase as products from the sector increase in prices due to higher mining wages which are production costs.

Household demand will generally increase for products of different industries, both in the short run and in the long run. Declines in household demand are registered for a few sectors like mining and petrochemicals, products of which households do not utilize much, and to a minimal extent agriculture, manufacturing, trade and transport in the short run.

Household sales will generally benefit from the policy shock. Increases are recorded across most industries in both the long run and the short run and especially for the food and beverages industry and other services in the long run. Household sales reduced marginally for the agriculture and the coal mining sectors by 0.04 and 0.01 per cent respectively in the short run.

Using equation (2), it can be explained that due to labour being fixed in the long run, and capital stock decreasing, GDP at factor cost is decreasing. Finally, using equation (1), the decrease of Real GDP by 0.06 per cent in the short run and 0.05 per cent in the long run can be explained. Overall,

34 households are better off, prices of all commodities except for mining are declining and households are demanding more of all the other commodities as depicted in table 4.1.5 above.

4.3 Main Contributors to the changes in GDP

To put the main macroeconomic results into perspective, it is useful to look at the main contributors to the changes in GDP.

Table 4.1.6 Contribution to GDP

GDP component Percentage change short run long run 1 Consumption 0 0.31 2 Investment 0 -0.18 3 Government 0 0.11 4 Stocks 0 0 5 Exports -0.22 -0.52 6 Imports 0.16 0.23

From the discussion in section 4.1 above that the overall impact of the policy shock will cause real GDP at calculated from the expenditure side to decrease by 0.06 per cent in the short run and 0.05 per cent in the long run. It can be seen in table 4.1.6 above, that the main contributors to this negative change in GDP are exports in the short run, and investment in the long run. Exports will reduce by 0.22 per cent in the short run and by 0.52 per cent in the long run, because prices of local commodity exports are likely to increase as labour costs which are also production costs increase thus reducing the competitiveness and consequently demand for our exports. Increase in labour costs as per the policy shock will also crowd out investment in the long run.

5. Conclusion A 14-sector Computable General Equilibrium (CGE) model for the South African economy was used to analyse the impact of a policy shock simulating a 37 percent increase in the occupation specific wage shifter for both coal mining and other mining, for both elementary mineworkers and operators.

It was found, that the policy leads to a contraction in GDP with real GDP decreasing by 0.06 per cent in the short run and by 0.05 per cent in the long run. In the short-run private investment,

35 consumption expenditure by households and government consumption are fixed and in the long run consumption expenditure by households and government consumption increase by 0.31 and 0.11 per cent respectively. However, the volume of exports and investment declined. Overall, macroeconomic results were mixed. Labour intensive industries and investment were losers with households being relative winners as they benefit from higher mining sector wages. This policy shock will only affect GDP negatively by a minimal 0.06 per cent in the short run and 0.05 per cent in the long run but overall it is beneficial to household welfare and particularly to miners, who work hard under the not so favourable conditions, and their families.

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References

South African chamber of mines. 2012/13. Annual report: Putting South Africa first. [online]

Price Waterhouse Coppers. 2011. SA mine review of trends in the South African mining industry. [online]

Statistics South Africa. 2013 December. Statistical release P2041: Mining production and sales. [online]

Singh, S. (2014). Effects of the mining strikes on the South African economy. Available: http://www.miningreview.com/effects-of-the-mining-strikes-on-the-south-african-economy/ [accessed on 22 October 2014].

Adams, P.D. (2005). Interpretation Results from CGE Models Such as GTAP. Journal of Policy Model, 27(2005): 941-959.

Bohlmann, H.R. & Van Heerden, J.H. (2014). Structure of the UPGEM Database. EKN819 Lecture Notes on Course Disc. Department of Economics, University of Pretoria.

Bohlmann, H.R. & Van Heerden, J.H. (2014). Introduction to CGE Modelling. EKN819 Lecture Notes on Course Disc. Department of Economics, University of Pretoria.

Dixon, P.B., Koopman, R.B. & Rimmer, M.T. (2013) The MONASH Style of Computable General Equilibrium Modeling: A Framework for Practical Policy Analysis. Chapter 2 in the Handbook of Computable General Equilibrium Modeling. Dixon, P.B. & Jorgenson, D.W. (Editors). North-Holland, Amsterdam.

Fullerton, D. & Metcalf, G.E. (1997). Environmental Taxes and the Double Dividend Hypothesis: Did You Really Expect Something for Nothing?. National Bureau of Economic Research (NBER), Working Paper No W6199

Giesecke, J.A. & Schilling, C. (2010). The Economic Impact of the New Zealand Fiscal Stimulus Package. New Zealand Economic Papers, 44(3):231-257.

Harrison, W.J. & Pearson, K.R. (1996) Computing Solutions for Large General Equilibrium Models Using GEMPACK. Computational Economics, 9:83-127.

Horridge, J.M (2000). ORANI-G: A General Equilibrium Model of the Australian Economy. CoPS/IMPACT Working Paper OP-93. Centre of Policy Studies, Monash University.

Horridge, J.M., Meeraus, A., Pearson, K.R & Rutherford, T.F. (2013) Solution Software for Computable General Equilibrium Modeling. Chapter 20 in the Handbook of Computable General Equilibrium Modeling. Dixon, P.B. & Jorgenson, D.W. (Editors). North-Holland, Amsterdam.

Van Heerden, J.H., Blignaut, J. Mabugu, M., Gerlagh, R., Hess, S., Tol, R., Horridge, M., Wit, M. & Letsoalo, T. (2006). Redistributing Environmental Tax Revenue to Reduce Poverty in South Africa:

37

The Cases of Energy and Water. South African Journal of Economic and Management Science, 9(4): 537-552.

World Bank (2012). World Development Indicators. The World Bank, Washington DC.

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