metadata: authors: 0: author: Go,Delfin Sia
1: author: Kearney,Marna
2: author: Korman,Vijdan
3: author: Robinson,Sherman
4: author: Thierfelder,Karen E.
count: Southern Africa
docna: 0: docna: Wage subsidy and labor market flexibility in
            south Africa
docty: Policy Research Working Paper
theme: Economic statistics, modeling and forecasting
majtheme: Economic management
subtopic: Mining & Extractive Industry (Non-Energy),Labor Markets,Skills Development and Labor Force Training,Food Security,Employment and Unemployment,Rural Labor Markets
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor,Finance and Financial Sector Development
abstracts: cdata!: In this paper, the authors use a highly
            disaggregate general equilibrium model to analyze the
            feasibility of a wage subsidy to unskilled workers in South
            Africa, isolating and estimating its potential employment
            effects and fiscal cost. They capture the structural
            characteristics of the labor market with several labor
            categories and substitution possibilities, linking the
            economy-wide results on relative prices, wages, and
            employment to a micro-simulation model with occupational
            choice probabilities in order to investigate the poverty and
            distributional consequences of the policy. The impact of a
            wage subsidy on employment, poverty, and inequality in South
            Africa depends greatly on the elasticities of substitution
            of factors of production, being very minimal if unskilled
            and skilled labor are complements in production. The desired
            results are attainable only if there is sufficient
            flexibility in the labor market. Although the impact in a
            low case scenario can be improved by supporting policies
            that relax the skill constraint and increase the production
            capacity of the economy especially towards labor-intensive
            sectors, the gains from a wage subsidy are still modest if
            the labor market remains very rigid.
repnme: repnme: Wage subsidy and labor market flexibility
            in south Africa
display_title: Wage subsidy and labor market
            flexibility in south Africa
listing_relative_url: /research/2009/03/10372263/wage-subsidy-labor-market-flexibility-south-africa
topicv3: Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Mining Sector m1327113 554,Skilled Workers m1504035 2732,Unemployment m1504051 2747
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor,Finance and Financial Sector Development
no_of_pages: 45
author_keys: 0: author_key: 93791
1: author_key: 667416
2: author_key: 717501
3: author_key: 452473
4: author_key: 435443
model_extractions: WPS4871 Policy ReseaRch WoRking PaPeR 4871 Wage Subsidy and Labor Market Flexibility in South Africa _Delfin S. Go Marna Kearney Vijdan Korman Sherman Robinson Karen Thierfelder_ The World Bank Africa Region Office of the Chief Economist March 2009
Policy ReseaRch WoRking PaPeR 4871

# **Abstract**

In this paper, the authors use a highly disaggregate general equilibrium model to analyze the feasibility of a wage subsidy to unskilled workers in South Africa, isolating and estimating its potential employment effects and fiscal cost. They capture the structural characteristics of the labor market with several labor categories and substitution possibilities, linking the economy-wide results on relative prices, wages, and employment to a micro-simulation model with occupational choice probabilities in order to investigate the poverty and distributional consequences of the policy. The impact of a wage subsidy on employment, poverty, and inequality in South Africa depends greatly on the elasticities of substitution of factors of production, being very minimal if unskilled and skilled labor are complements in production. The desired results are attainable only if there is sufficient flexibility in the labor market. Although the impact in a low case scenario can be improved by supporting policies that relax the skill constraint and increase the production capacity of the economy especially towards labor-intensive sectors, the gains from a wage subsidy are still modest if the labor market remains very rigid.

This paper—a product of the Office of the Chief Economist, Africa Region, in collaboration with the Povert Reduction & Economic Management Unit for Southern Africa—is part of a larger effort in the Africa Region to improve economic analysis of important policy issues. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The corresponing author may be contacted at dgo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Wage Subsidy and Labor Market Flexibility In South Africa**<sup></sup>

By Delfin S. Go, _The World Bank_ Marna Kearney, _Consultant_ Vijdan Korman, _The World Bank_ Sherman Robinson, _University of Sussex, UK_ Karen Thierfelder, _US Naval Academy_

>  The framework used in the paper is based on a World Bank technical assistance project to develop a CGE-micro simulation model for the South African National Treasury in a collaboration with IDS and the US Naval Academy. The purpose of the exercise is to illustrate the potential use of the framework for analysis of policy change.  The views expressed are those of the authors and do not necessarily reflect those of their respective institutions or affiliated organizations. The authors would like to thank Rita Almeida, Rob, Davis, Shantayanan Devarajan, Lawrence Edwards, David.Faulkner, Johannes Fedderke, M. Louise Fox, Jeffrey D. Lewis, Christopher Loewald, Konstantin Makrelov, Kuben Naido, Kalie Pauw, Ritva Reinikka, Matthew Simmonds, Rogier van den Brink, and Theo van Rensburg for helpful comments and suggestions.  We also thank B. Essama-Nssah and Konstantin Makrelov in helping to formulate the CGE-micro framework.
# **1. Introduction**

Despite a recent improvement in economic growth, unemployment in South Africa is still high. While the unemployment rate has declined from 29.4 percent in 2001 to 26.7 percent in 2005 (Statssa, 2006),<sup>1</sup> employment growth is, on average, only 2.1 percent per year (see, for example, Bhorat, 2005). That level of employment growth is slow relative to labor force growth and therefore insufficient to deal with the severity of the unemployment problem. Using a broader definition of unemployment to include discouraged workers, unemployment in South Africa is approximately 30 percent for men and 38 percent for women, and has almost doubled since the transition from Apartheid (Levinsohn, 2008). Reducing unemployment is therefore a major policy concern in South Africa and one policy option being debated is a wage subsidy scheme - see, for example, recommendations made by the Harvard Center for Interntional Ddevelopment (CID) South Africa Initiative and, in particular, the summary report of the International Panel on Growth in Hausmann (2008) as well as the policy options to alleviate unemployment in Levinsohn (2008).

The South African labor market presents an interesting economic issue - if there are wage and labor market rigidities in an economy, would a wage subsidy be able to reduce high structural unemployment? Using the particular institutional situation of South Africa, this paper investigates the circumstances by which a wage subsidy would generate significant employment effects. The methodology employed is a disaggregative economic framework – which combines a general equilibrium model commonly used in public finance to look at fiscal, welfare, and economy-wide effects of a policy change, and a micro-simulation model with occupational choice probabilities to examine the employment and distributional consequences at the micro level. In what follows, we briefly review the unemployment issues in South Africa and describe the approach adopted in the context of the paper’s objectives.

**_Why unemployment is high in South Africa._** There is an extensive literature about South Africa’s labor market issues, which are selectively summarized below. Three major and interrelated causes of unemployment are often cited – (i) insufficient economic growth, particularly in the tradable sectors; (ii) high real wages or labor cost; and (iii) labor market rigidities and other structural problems in the labor markets. In addition, other related factors cited include the participation pattern in the  labor force, the level of reservation wages, job search issues, and the impact of transfer payments. Bhorat and Leibbrant (1996), Bhorat and Oosthuizen (2005), and Banerjee, Galiani, Levinsohn, and Woolard (2007) provide a good overview.

A significant cause of unemployment in the past was the lack of economic growth during the 1970s, 1980s and 1990s (Fallon and Pereira da Silva (1994) and Lewis (2001)). Employment growth was therefore low (Standing, Sender, and Weeks (1996), Bhorat (2001)). Recently, however, unemployment has been high despite higher economic growth, suggesting that there are other underlying factors.

> 1 The rate is dependent on whether a “strict” or “expanded” definition is used.  The quoted numbers are for the former, which are more conservative or lower.  Even then, the unemployment rate is still high.

- 2 -
As South Africa liberalized and opened its economy to trade, production in agriculture and mining declined and production shifted towards capital-intensive manufacturing and highskilled services, exacerbating the weak demand for less-skilled labor. See Edwards (2001) and Fedderke, Shin, and Vase (1999) among others for a more detailed discussion. A key factor in the economic transformation and structural change of the South African economy is the relative decline of the tradable sectors, particularly the manufacturing sector but also agriculture and mining, where employment is traditionally generated. The absolute number of jobs in the three sectors declined between 1994 and 2004. Employment fell by 12 percent in the agricultural sector, by 29 percent in the mining sector, and by about 12 percent in the manufacturing sector. The non-tradable sectors such as finance and business services grew the most, but they primarily are primarily skilled labor-intensive. See, for example, Hausmann (2008) as well as Rodrik (2006).

Another significant factor is the rise in real wages, which directly dampens labor demand. This rise is not a recent phenomenon and has persisted for several decades. The average growth of real wages was about 1.3 percent per year in the 1980s and 1.5 percent per year in the 1990s. Lewis (2001) estimates that real wages for unskilled and semi-skilled workers in particular have risen by 150% from 1970 to 1999. At the same time, unemployment among unskilled and semiskilled workers rose significantly from less than 10 percent in 1970 to over 50 percent in 1999. The evolution of real wages is, however, subject to measurement and interpretation issues. Banerjee _et al._ (2007), for example, measure employed hours worked in “efficiency units” and find instead that “real wages per unit of human capital” have increased only slightly from 1995 to 2005. On the other hand, labor policy and minimum wage legislation since 1994, which were designed to correct the inequities and disparity of the Apartheid era, have significantly increased the indirect non-wage labor cost.

The protection of labor has the indirect consequence of increasing labor market rigidities through reduced labor mobility, increased frictions to exits from employment, as well as the additional cost of compliance to labor regulations and of the negotiation processes with labor unions – see, for example, Nattrass (2000) and Moolman (2003). In addition to the implied wage premia arising from unions and labor market institutions, it is plausible that the high concentration ratios in the output markets noted by Fedderke, Kularatne, and Mariotti (2006), Aghion, Bruan, and Federkke (2006), and Hausmann (2008) limited competition and investment, thus reinforcing the slow growth of employment in the formal sector. In a survey of 325 large South African manufacturing firms, Chandra, Moorty, Rajaratman, and Schaefer (2001) document the behavioral consequences of the various labor market legislations – firms tend to hire fewer workers, substitute capital for labor when expanding, employ temporary workers as opposed to hiring permanent workers, and rely more on sub-contracting services.

There are also structural issues underlying the South African economy and labor markets. A key manifestation of the structural problem is the complementarity or lack of substitution between skilled and unskilled workers, with the skills constraint dampening the employment growth of less-skilled workers – see Hausmann (2008) and Levinsohn (2008). Significant factors include the dualistic structure of the South African economy and the economic shifts towards more high-skilled and capital-intensive economic activities. Implicit in the shift towards capital-intensive sectors and their demand for skilled labor is the relative complementarity

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between capital and skilled labor, which adds to the rigidities in the factor markets. Furthermore, Aparteid left South Africa with a mismatch in the supply and demand of skills as a generation of workers did not receive the benefit of higher education. In this situation, equilibrium unemployment in the face of supply-side shocks and shifts would tend to be higher because the degree of coordination in wage-setting as well as real wage inflexibility would lead to less efficient supply-demand matching in South Africa (i.e. the Beveridge curve approach to unemployment). Moreover, factors such as hysteresis and persistence mechanisms, which were used to explain high unemployment in OECD countries, also point to the likelihood that a sustained period of high unemployment caused by weak aggregate demand can in turn cause a deterioration in the supply side of the economy, resulting in the long-term unemployed being detached from the labor force and a higher equilibrium unemployment rate.<sup>2</sup> The nonparticipation of the less-skilled who are jobless is a possible consequence of the structural problems in South Africa. A vestige of Apartheid is the geographical distance between where many of the unemployed reside and where firms are located. As a result, transportation cost is a deterrent to employment for less skilled labor; in effect, it creates a high threshold in their reservation wage. Various factors mitigate the necessity for immediate employment; these include income differences in the dual economic structure combined with within-household income transfers due to the availability of the old age pension or the employment of a family member in the formal sector also mitigate the necessity for immediate employment. See, for examples, Banerjee _et al._ (2007), Poswell (2002) and Dinkelman and Pirouz (2000), and Moll (1993).

**_Why a wage subsidy?_** Because of these structural issues in South Africa’s labor market, policy intervention such as a wage subsidy has become increasingly attractive.  Using a careful empirical analysis of individual-level changes and transitions in the labor market status observed from an extensive nationally representative panel of individual labor data, Banerjee _et al._ (2007) conclude that, because of the structural changes in the economy,  South Africa’s high level of unemployment is an “equilibrium” phenomenom; the decade-long high levels of unemployment appear to be a structural rather than a temporary aberration. Such structural unemployment cannot be solved by macroeconomic management or temporary swings in aggregage demand, but must be addressed by policy interventions affecting labor demand or supply such as wage subsidy, search subsidy, reduced regulations for first jobs and government employment. Banerjee _et al._ also note that there is much more churning in the South African  labor market than would be observed under the conventional view that the market is rigid.  However, much of the churning may reflect transitions or boundaries between searching  and non-searching that are more fluid between  being not economically active and informally employed than between any of those states entering into formal employment. Part of the reason noted by others is the small size of the informal sector, which does not provide a buffer between formal jobs and unemployment – Kingdon and Knight (2000) and Fallon and Lucas (1998). Another factor is the mismatch of skills noted above.

A basic justification for a wage subsidy is that it directly intervenes in the factor market to stimulate demand for less-skilled labor. Although a wage subsidy creates jobs in the short run,

> 2 See, for example, Nickell _et. al._ (2003). For a more general discussion, see Chapters 4 and 11 in Carlin and Soskice (2006).

- 4 -
in the long run, less skilled labor will be substituted for capital and skilled labor as less-skilled labor becomes relatively cheaper. Like any relative price change, there will be substitution and output/income effects from a wage subsidy, and the secondary or general equilibrium effects from the interaction of various goods and factor markets in the economy may be important. In this study, policy intervention occurs through factor demand for the less skilled formal labor, with a wage subsidy going directly to the producers. In this context, the substitution or complementarity of labor types affects the employment-generating capacity of the wage subsidy.

Alternatively, the wage subsidy can be given directly to employees if the structural problems are related to the supply of less-skilled labor. That is, the supply of less-skilled labor is hampered by a high reservation wage or “minimum” wage level that individuals are willing to accept in order to work. Labor supply and “unemployment” of the less-skilled are therefore in equilibrium and the measured high unemployment rates include the inactive (voluntary unemployment). In this context, a subsidy to individuals would affect their reservation wage and induce a higher proportion of less-skilled labor to participate in the labor market. This assumes, however, that factor demand, factor input complementarity, and real wage flexibility are not the major constraints in the labor market in South Africa.<sup>3</sup> In this study, the demand side will be the main area of investigation; we leave issues such as the estimation of a reservation wage and the labor market participation of the less-skilled worker for future research.

We also examine the sensitivity of the impact of a wage subsidy to two complementary policies aimed at alleviating labor market problems in South Africa – i) increasing the supply of skilled workers by removing restrictions on skilled immigrants or through training programs; and ii) facilitating the growth of economic activities (e.g. tradable sectors) where skill is less intensive. Levisohn (2008) recommended a wage subsidy and an immigration reform to encourage the immigration of skilled individuals as two key policy responses to alleviate unemployment in South Africa. We consider the worst case scenario, high complementarity between labor types, and examine whether the marginal or net impact of the wage subsidy would be greater in combination with either policy alternative.

Although this paper will not address design and implementation modalities of a wage subsidy in detail, there are several key elements that are important – i) targeting; ii) lowering of labor cost; iii) enhancement of the operation of the labor markets; and iv) ease of administration. Among alternative schemes that are publicly debated, a voucher scheme appears promising. The vouchers would go only to the unemployed or any subgroups being targeted such as new hires or entrants. A voucher scheme should reduce labor costs since producers eventually get the subsidy as the unemployed enter the market and seek jobs; it creates a missing market, enhancing interactions between producers and those still unemployed. Producers are still able to choose among voucher holders regarding who best fits their hiring needs. A voucher system should be easy to administer by making use of South Africa's existing transfer system. In particular, Levinsohn (2008) outlines what a well-targeted wage subsidy could constitute:

- a) Since unemployment is highest among the young, a targeted wage subsidy could facilitate the school-to-work transition, targeting recent school leavers. It should be

> 3 In addition, it will entail a very different labor market closure in that wages have to be flexible with labor supply responding to the changes in the market wage and its distance to the reservation wage of workers.

- 5 -
available to all South Africans after the age of 18 or as soon as they have completed schooling (to minimize the number of students that would leave school for a subsidized job). The subsidy would not expire to ensure that those who stay in school after the age of 18 are not penalized.

- b) Upon turning 18, each South African receives an account (“Subsidy Account”) into which the government places a sum of money (each person receiving the same amount). This money can only be used to subsidize the monthly wage that the individual receives while working for a registered firm. When the individual takes a job in the formal sector (in a registered firm), a fraction of the individual’s wage would be drawn from the individual’s Subsidy Account. The subsidy would be entirely portable and tied to the individual, not the firm.

- c) A critical component of the targeted wage subsidy is a probationary period during which subsidized workers may be dismissed at will. The period should allow the employer enough time to learn whether the employee is job-worthy but shorter than the total duration of the subsidy to ensure that workers can find an alternative job if the first one does not work.

Relative to various suggestions regarding a wage subsidy like Levinsohn (2008), this paper is therefore complementary in attempting to quantify the likely employment effects of a wage subsidy.

**_The approach of the paper._** To look at the employment effects of a wage subsidy, the distinguishing feature of the analysis is a disaggregative framework, which combines a multisector and multi-labor computable general equilibrium (CGE) model with a micro simulation model of South Africa along the line of work such as Bourguignon, Robilliard, and Robinson (2002), Savard (2006, 2003) and Essama-Nssah, Go, Kearney, Korman, Robinson, and Thierfelder (2007). Specifically, we use this framework to assess the likely impact of a wage subsidy on unemployment and its sensitivity to the relative complementarity or lack of substitution among factors of production and to the labor market conditions in South Africa. The paper examines several issues – i) Under what circumstances will a wage subsidy be effective or ineffective in reducing unemployment, particularly in the labor categories of unskilled or semiskilled workers where unemployment is concentrated? ii) How significant are the welfare and equity impacts on heterogenous households and on particular groups of labor and households? iii) What are the fiscal and economy-wide repercussions?  And in a worst case scenario, iv) can the employment effects of a wage subsidy be enhanced with other supporting measures such as an increase in the supply of skilled labor or an increase in output of low-skill labor-intensive sectors?

Relative to a recent CGE application of the wage subsidy issue in South Africa in Pauw and Edwards (2006), the present analysis contributes the following additional features – i) cross substitution among labor categories is differentiated using a translog (instead of a traditional nested CES) formulation, which will allow for different degrees of complementarity between higher skilled and lower skilled labor in various sectors, closer but different substitution among lower skilled labor in different sectors, and greater but different degrees of complementarity between high skilled labor and capital in various sectors; ii) the addition of the micro simulation model also allows for the welfare and equity analysis of a policy reform with the full

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heterogeneous information contained in the household and labor force surveys; and iii) combination of the wage subsidy and complementary policies. To deal with parameter uncertainty due to the lack of reliable empirical estimates of the elasticity of substitution among factors of production, we evaluate the impact of wage subsidy over alternative sets of low, medium, and high elasticities. The CGE cum micro-simulation framework has the wage earnings equations and multi-nomial logit functions of occupational choices from the micro data linked to the CGE model like Bourguignon, Robilliard, and Robinson (2002). The link and reconciliation between the two models is essentially a recursive top-down iteration similar to Savard (2006, 2003) and Essama-Nssah _et al._ (2007).<sup>4</sup> The model is used as a “measuring instrument” rather than a forecasting or planning model. By abstracting from other policy issues or the temporal aspects of South Africa’s recent growth (e.g., terms of trade shocks, investment growth etc.), it holds everything else constant and focuses on measurement of the marginal employment effects of a wage subsidy and the sensitivity to alternative degrees of labor market flexibility and to some supporting measures suggested to alleviate the labor market problems. The model does not address the design and implementation elements of a wage subsidy.

**_Organization._** The paper is structured as follows:  section 2 provides an overview of the economic framework, a CGE cum micro-simulation model, with emphasis on its distinctive aspects and structural features imposed to portray the South African economy and its labor market situation; section 3 discusses the simulations and the results as well as suggestions for further research; and section 4 draws general conclusions.

# **2. The Economic Framework and its Applications to South Africa**

The economic framework is an extension of the CGE cum micro-simulation model in EssamaNssah _et al._ , (2007). The top layer is a CGE model for South Africa with data for 2003, using the modeling approach described in Lofgren, Harris, and Robinson (2001). See Kearney (2004) for a detailed description of the model features. The bottom layer is a micro-simulation described in Korman (2006), which pulls together the micro observations of the Labor Force Survey (LFS: 2000) and Income and Expenditure surveys (IES: 2000).<sup>5</sup> In what follows, we focus on the features relevant for analysis of the economy, labor market, degrees of complementarity among labor types and capital, closure rules, and the micro behavior of labor and households.

> 4 A bottom-up iteration is possible but not employed in the present study. A two-way iteration is best used if there are dynamic feedbacks from factor accumulation as well as changes in the demand structure, which are planned for future applications.

> 5 These surveys are nationally representative and conducted by Statistics South Africa. Both surveys are mostly based on the same sample of households; therefore, we combined data from these two surveys using individual’s unique identification code.

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**_Economic structure of South Africa._** The CGE model has 43 production activities.<sup>6</sup> For reporting purposes, the output results by activity are aggregated into three categories: agriculture, industry, and services (see table 1 for the composition of the aggregate categories).<sup>7</sup>

**Table 1:  CGE Model Sectors**

|**AGRICULTURE**|**INDUSTRY**|**SERVICES**|
|---|---|---|
|Agriculture|Coal Mining|Electricity & Gas & Steam|
||Gold & Uranium Ore Mining|Water Supply|
||Other Mining|Construction & Civil Engineering|
||Food|Catering & Accommodation|
||Beverages & Tobacco|Wholesale & Retail Trade|
||Textiles|Transportation & Storage|
||Wearing Apparel|Communication|
||Leather & Leather Products|Financial Services|
||Footwear|Business Services|
||Wood & Wood Products|Health & Community & Social & Personal Services|
||Paper & Paper Products|Other Producers|
||Printing & Publishing & Recorded Media|Government Services|
||Coke & Refined Petroleum Products||
||Basic Chemicals||
||Other Chemicals & Man-Made Fibers||
||Rubber Products||
||Plastic Products||
||Glass & Glass Products||
||Non-metallic Minerals||
||Basic Iron & Steel||
||Basic Non-ferrous Metals||
||Metal Products Excluding Machinery||
||Electrical Machinery||
||TV & Radio & Communication Equip||
||Professional & Scientific Equip||
||Motor Vehicles Parts & Accessories||
||Other Transport Equipment||
||Furniture||
||Other Industries||

Source: CGE model social accounting matrix (SAM) database.

Agriculture accounts for 4 percent of value added, industry accounts for 27 percent, and service accounts for 69 percent (Figure 1).

> 6 Full detail of the South Africa CGE model can be found in Essama _et. al._ (2007) and Kearney (2004); for a version of the model used to analyze Value Added Taxes (VAT) see Go et al. (2005). In this description we comment on new features of the model important for an analysis of a wage subsidy.

7 Note, we disaggregate crude oil from other mining, as described in Essama et al (2007).

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<!-- Start of picture text --> Figure 1: Aggregate Activity Share of Value Added Figure 2: Formal Employment by Skill Level (2000)<br>60.0%<br>55.0%<br>4% 50.0%<br>45.0%<br>27% 40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>69%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br> Legislators, senior Professionals Technical and associate Semi-Skilled Elementary Occupation<br>Agriculture Industry Service officials and managers professionals Skill Levels<br>Source: Authors’ calculations from a Social Accounting Source: Authors’ calculations from LFS 2000.<br>Matrix (SAM) for South Africa, 2003.<br>% o f fo rm a l e m p lo y e d<br><!-- End of picture text -->

**_Labor in South Africa._** There are three types of labor (formal, self-employed, and informal) and three skill levels (high-skilled, semi-skilled, and low-skilled) within each type of labor. Value added is allocated to primary factors and summarized in Table 2.

Table 3 shows the distribution of employment by sector and occupation. About 6 people out of ten are employed in the services sector. About the same ratio are engaged in formal sector work. With respect to the distribution of skills, the data show that about 12 percent of those employed are highly skilled; over 45 percent of labor in South Africa is employed in the lowskilled and medium-skilled formal sector and another 19 percent in the informal sector.

**Table 2: Value Added Shares**

||Agriculture|Industry|Services|
|---|---|---|---|
|Capital|0.76|0.54|0.45|
|High-skilled formal labor|0.03|0.12|0.25|
|Semi-skilled formal labor|0.02|0.12|0.18|
|Low-skilled formal labor|0.11|0.15|0.04|
|Self-employed labor*|0.04|0.03|0.04|
|Informal labor*|0.04|0.04|0.04|

*Self-employed and informal labor are further distributed by skill (not shown).

Source: Authors’ calculations from a SAM for South Africa, 2003.

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**<u>Table 3: Employment by Sector and Occupation</u>**

|Occupational Types|Agriculture|Industry|Services|Total|
|---|---|---|---|---|
|1. Formal Low –Skilled Workers|6.0|2.9|5.7|14.6|
|2. Formal Semi –Skilled Workers|6.2|8.7|16.5|31.3|
|3. Formal High- Skilled Workers|0.7|1.3|9.6|11.6|
|4. Informal Sector Workers*|2.7|2.5|13.9|19.2|
|5. Self-employed*|9.1|2.8|11.5|23.4|
|Total|24.6|18.2|57.2|100.0|

Notes: *Self-employed and informal labor are further distributed by skill (not shown). Source: Authors’ calculations from LFS 2000.

Self-employed and informal sector workers make up about 43 percent of the total employed labor force. A large proportion of informal labor (including domestic workers) and self-employed are working in the services sector, which is the biggest employer of the work force and also employs the largest share of the high-skilled workers.

**Table 4:   Formal Wage Employment by Economic Sector, 2000 (%)**

|Economic Sector|Working,VeryPoor*|Working,Poor**|Non-<br>Poor***|
|---|---|---|---|
|Agriculture|36.5|16.0|1.1|
|Mining|2.1|8.0|7.3|
|Manufacturing|11.0|18.0|18.8|
|Electricity, gas and water supply|0.5|1.0|1.6|
|Construction|5.6|6.0|2.8|
|Wholesale and retail trade|23.3|23.0|12.1|
|Transport|2.3|5.0|6.8|
|Financial Services|5.2|9.0|13.9|
|Community, Social and Personal||||
|Services|9.8|14.0|35.6|
|Private households|3.6|2.0|0.2|
|Overall(allsectors)|100|100|100|
|Notes:||||

*Working, very poor: annual wage for working very poor is calculated using R1000 per month (2004).Using the CPI for 2004 and 2000, the annual wage of working very poor comes to about R9,695. [1000/(123.8/100)]*12.

**Working, Poor: annual wage for working poor is calculated using R2500 per month (2004) benchmark   In 2000 prices, the annual wage for this group is about R24,233.[2500/(123.8/100)]*12.

***Non poor: Formal workers <u>are those with an annual income higher than R24,233.</u>

<u>Source: Authors’ calculations from LFS 2000.</u>

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Since the wage subsidy is given to employers of formal wage workers, we describe briefly the characteristics of the formal labor market from the LFS (2000) and IES (2000) surveys. Figure 2 shows formal employment by skill level. High-skilled formal workers<sup>8</sup> constitute 24 percent of the total formal work force. Semi-skilled workers<sup>9</sup> constitute 55 percent of the total formal employment and low-skilled workers that are defined as elementary occupations<sup>10</sup> constitute about 21 percent of the formal wage labor market. Overall about 72 percent of formal employment is characterized by either low- or semi-skilled workers.

Formal wage workers in agricultural and retail trade sectors are relatively poor. Based on income thresholds from a recent study on South Africa (Altman, 2007), formal workers can be classified into three groups: i) very poor, ii) working poor, and iii) not poor. The working poor refers to anyone who is employed by the definition of the South African Labor force survey (also in line with the International Labor Organization (ILO) definition), working and earning less than R2,500 per month. This threshold is close to that chosen by National Treasury as the minimum level below which workers are exempt from income tax.<sup>11</sup> Table 4 reflects only formal employment by economic sectors. Agricultural and trade sectors hold the largest share of very poor or poor workers. Manufacturing sectors also have relatively large shares of working poor formal workers (18 percent). On the other hand, non-poor workers are mainly employed in services although manufacturing and financial sectors also have significant shares of non-poor workers, with 19 and 14 percent respectively.

<!-- Start of picture text --> Figure 3: Poverty Profile of Formal Wage Figure 4: Earnings by Gender<br>Workers by Skill Types (2000 prices)<br>Formal Wage Workers by Skill Types Average Earnings by Gender Groups<br>90% 50,000<br>80% 45,000<br>70% 40,000<br>35,000<br>60%<br>30,000<br>50%40% Working Ver PoorWorking PoorWorking Non-Poor 25,000 FemaleMale<br>20,000<br>30%<br>15,000<br>20%<br>10,000<br>10% 5,000<br>0% Low Skill Med Skill High Skill - Formal Wage Informal Wage Self-employment<br>Skill Level Labor Types<br>Source: Authors’ calculations from LFS 2000. Source: Authors’ calculations from LFS 2000.<br>Proportion of formal workers Average Annual Earnings(2000)<br><!-- End of picture text -->

- 8 High skilled formal workers include legislators, senior officials, professionals, technical and associate professionals.

> 9 Semi skilled workers are: clerks, service workers and shop and market sales workers, skilled agricultural and fishery workers, craft and related trades workers, plant and machine operators and assemblers. 10 Low-skilled workers include elementary jobs.

11 The minimum level of annual income subject to income tax was R32,000 in 2004. We converted this value in 2000 prices using CPI to make it comparable to our study. In 2000 prices, the minimum annual income would be about R25, 848.

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Evidence confirms that low-skilled, low-wage, individuals are trapped in poverty. About 85 percent of low-skilled workers in the formal economy are either very poor or working poor (Figure 3). On the other hand, almost 85 percent of people with high-skill levels are non-poor. Skill level is an important determinant of poverty within the working population.

**<u>Table 5: Average Earnings of Labor Market by Education Level</u>**

|Education Level|Annual|AverageEarnings(ran|d-2000 prices)|
|---|---|---|---|
||Formal Labor|Informal Labor|Self-employed|
|No Schooling|15,028|5,942|8,367|
|Grade 0-6|16,710|6,773|9,400|
|Grade 7-9|22,983|10,614|12,776|
|Grade 10-12<br>|44,327|16,139|35,696|
|National Technical Certificate<sup>12</sup>|60,628|43,417|72,725|
|Degree and Post Graduate|119,939|124,957|147,296|
|Overall Average|41,582|11,728|25,829|
|Source: Authors’ calculations from LFS2000.||||

To explore further the link between skill level and poverty, Table 5 reports three types of earnings by different level of education. For all three types of workers, earnings increase sharply with education, an attribute closely linked with skill level. For example, a worker with a college degree in the formal sector earns, on average, about 8 times as much as a worker with no schooling. The disparity between a degree-holder and a worker with no schooling is even larger in the informal sector or for the self-employed sector.

As the education level rises, average annual earnings also rise. On average, individuals with higher education earn more than twice that of individuals who have graduated from grades 10-12. When compared with different types of labor, self-employed with higher education earn the most compared to the formal and informal labor with the same level of education. On the other hand, at lower levels of education, formal workers earn more until grade 12. There are other dimensions of earning disparity among formal sector workers. Two most commonly discussed aspects are: rural/urban disparity and disparity based on gender. For instance, average earnings are significantly higher for those who are working in urban areas. The differential is largest for the self-employed.

**<u>Table 6: Average Earnings of Labor Market by Regions (2000 prices)</u>**

|Region|An|nual AverageEarnin|gs(rand)|
|---|---|---|---|
||Formal Labor|Informal Labor|Self-employed|
|Rural|21,872|9,360|13,299|
|Urban|48,028|13,310|41,692|
|Overall Average|41,582|11,728|25,829|
|Source: Authors’ calculations from LFS 2000.||||

12 National Technical Certificate includes three levels, NTCi- NTCiii, which are equivalent to high school grades 1012.

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The data show significant gender differences in average earnings (Figure 4). Differentials are three times more prominent in the self-employed group than in other labor types. Male workers earn, on average, about 60 percent more than female workers in informal labor. Wage differentials are smallest for formal wage workers, where male workers earn, on average, about 20 percent more than female workers. Although income differences due to gender or education are not distinguished explicitly in the CGE model, they are captured in the earnings or wage functions of the micro-simulation.

**_Relative complementarity or low substitution among factors of production –_** is a key assumption in the model. Each economic activity can use nine labor categories plus capital in production. For reporting purposes, all skill levels of the self-employed are aggregated into a single input, self-employed labor; likewise for informal labor. In the production technology, it is assumed that substitution possibilities among inputs differ and the following structure is used: (1) it is difficult to substitute low-skilled labor for high-skilled labor in any of the three labor categories; (2) it is easy to substitute across labor categories for the same skill (i.e. a high-skilled formal worker is a good substitute for a high-skilled informal worker or a high-skilled selfemployed worker);  and (3) as the skill level of labor increases, it is more difficult to substitute capital for labor. In the CGE model, this behavior is represented using a translog production function.<sup>13</sup> The degree of substitution among labor inputs in production is important when measuring what impact a wage subsidy for low- and medium-skilled formal workers will have on unemployment.

**Table 7: Translog Elasticity Multipliers**

||Capital|High-<br>skilled<br>formal|Med-<br>skilled<br>formal|Low-<br>skilled<br>formal|High-<br>skilled<br>self|Med-<br>skilled<br>self|Low-<br>skilled<br>self|High-<br>skilled<br>informal|Med-<br>skilled<br>informal|Low-<br>skilled<br>informal|
|---|---|---|---|---|---|---|---|---|---|---|
|Capital|0|0.5|1|1.5|0.5|1|1.5|0.5|1|1.5|
|High-skilled formal||0|1|0.25|1.5|1|0.25|1.5|1|0.25|
|Med-skilled formal|||0|1|1|1.5|1|1|1.5|1|
|Low-skilled formal||||0|0.25|1|1.5|0.25|1|1.5|
|High-skilled self|||||0|1|0.25|1.5|1|0.25|
|Med-skilled self||||||0|1|1|1.5|1|
|Low-skilled self<br>High-skilled<br>informal<br>Med-skilled<br>informal<br>Low-skilled<br>informal|||||||0|0.25<br>0|1<br>1<br>0|1.5<br>0.25<br>1<br>0|

Source: CGE model database.

> 13 All activities except coal, gold, other mining, and refined petroleum use a translog production function; coal, gold, other mining, and refined petroleum use a constant elasticity of substitution (CES) production function with the assumption that it is difficult to substitute among inputs so the elasticity of substitution is low (0.2).

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**Table 8: Reference elasticity of substitution in production, by activity**

|Activity|Elasticity|Activity|Elasticity|
|---|---|---|---|
|Agriculture, Forestry, and Fisheries|0.60|Metal Products Excluding Machinery|0.60|
|Coal Mining|0.20|Machinery and Equipment|0.25|
|Gold and Uranium Ore Mining|0.20|Electrical Machinery|0.60|
|Other Mining|0.20|TV, Radio, and Communication Equip|0.60|
|Food|0.60|Professional and Scientific Equip|0.60|
|Beverages and Tobacco|0.40|Motor Vehicles Parts and Accessories|0.25|
|Textiles|0.30|Other Transport Equipment|0.25|
|Wearing Apparel|0.60|Furniture|0.25|
|Leather and Leather Products|0.60|Other Industries|0.60|
|Footwear|0.29|Electricity, Gas, and Steam|0.60|
|Wood and Wood Products|0.25|Water Supply|0.60|
|Paper and Paper Products|0.60|Construction and Civil Engineering|0.60|
|Printing, Publishing, and Recorded Media|0.34|Wholesale and Retail Trade|0.60|
|Coke and Refined Petroleum Products|0.44|Catering and Accommodation|0.60|
|Basic Chemicals|0.60|Transportation and Storage|0.60|
|Other Chemicals and Man-Made Fibers|0.60|Communication|0.60|
|Rubber Products|0.44|Financial Services|0.60|
|Plastic Products|0.44|Business Services|0.60|
|Glass and Glass Products|0.35|Health, Community, Social, and Personal Services|0.60|
|Non-metallic Minerals|0.61|Other Producers|0.60|
|Basic Iron and Steel|0.25|Government Services|0.60|
|BasicNon-ferrousMetals|0.25|||

Source: CGE model database.

Input substitution possibilities vary by production activity. A set of multipliers (Table 7) are applied to all sectors, providing similar “structure”or “nesting”of elasticities; however, sectors have different reference elasticities (Table 8). Given the lack of empirical estimates regarding the exact magnitudes of factor substitution, we provide sensitivity tests and consider three cases - low substitution elasticities, base substitution elasticities, and high substitution elasticities. In the base case, the reference elasticities of substitution in Table 8 are multiplied directly by the factors in Table 7. The resulting base case numbers correspond generally to conservative numbers found in various CGE works, including Essama-Nssah _et al._ , (2007) and Kearney (2004). Low substitution elasticity values are one half those reported in Table 7, high substitution elasticity values are two times those reported in Table 7. When the production process is assumed to be constant elasticity of substitution (CES), the values in Table 8 are used.

**_Macroeconomic closures._** At the macro level, we assume that government’s real spending, real investment, and aggregate foreign savings are constant. Private savings adjust in order to maintain a fixed total investment in the economy and all changes affect household consumption.  This is a standard approach in public finance analysis of revenue and welfare issues as it provides the results of the wage subsidy in isolation of other macroeconomic adjustment shocks, e.g., from any changes in investment or government expenditure.<sup>14</sup> Domestic

14The crowding out of private investment is therefore not the focus. The other option of adjusting government expenditure in the budget, while feasible, is constrained by the indirect links between public services and household

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savings (savings by institutions or households) are assumed to adjust and the economic and welfare effects are driven primarily by changes in net household income and consumption as the cost of higher wage subsidies filter through the economy.

Unlike traditional tax models, however, there will be a resource effect as the subsidy will lower wage cost and raise employment given the labor market behavior of the model. The structural features of the labor markets in South Africa are treated in a similar fashion as in Essama _et al._ (2007), Go _et al._ (2005) and Lewis (2001). Structural unemployment is specified for low-skilled and semi-skilled formal workers, with sticky real wages, while the other labor markets clear in equilibrium. All other factors are mobile across all production activities and are fully employed, with the exception of capital in agriculture, coal, gold, and other mining which are treated as activity specific.<sup>15</sup> The wage subsidy is introduced much like a “negative wage tax” that lowers the labor cost to employers; it affects only the low-skilled and semi-skilled workers where significant unemployment exists, but covers employers in all activities except coal, gold, other minerals, petroleum, and government services. Although there are already great details in terms of sectors and labor categories, the CGE model cannot target further the wage subsidy to the young or new job entrants, as for example formulated by Levinsohn (2008), without the additional complexity of adding a demographic component to labor market behavior. Likewise, at the micro-simulation level explained below, an increase in employment is drawn from the pool of unemployed among the low- and semi-skilled based primarily on their economic and individual characteristics (such as education, experience, gender, etc.) that affect their probabilities of being hired. The incorporation of demographic dynamics is clearly an area for future research.

In the government budget, government savings are ‘flexible’, but with investment and government spending fixed, this is just a modeling device to shift the adjustment to the households. It is in fact equivalent to the imposition of a lump-sum tax on household income. The wage subsidy is therefore not free and the fiscal cost will depend on the interaction among the resource effects of increased employment and gross domestic product (GDP), the dampening effects on household income from the implied lump-sum tax, and their economy-wide effects on the revenue of existing taxes. One advantage of a general equilibrium approach is that all the economy-wide or direct and indirect effects are observed. Since tax revenue from other sources will likely adjust upward, the net cost of the program is not the full expenditure on wage subsidies. What is not financed from the revenue effect of existing taxes is the net fiscal cost; it is also the size of the implied lump-sum tax on households. Because the first best option of lumpsum taxation is normally not feasible, we also look at a real or existing tax instrument like the social security tax and examine the implications for changes in household income taxes following a wage subsidy as well as possible distributional impacts.

**_Micro-behavior of labor and households._** A micro-simulation model is used to explain the income generation processes and the expenditure patterns at the household level based on parameterization of the information contained in the household survey data.  The LFS provides

> income/consumption/welfare.  To examine the impact on household income and welfare, those links will need to be spelled out.

> 15 With this specification, we present a long run view of the adjustment process, achieving equilibrium sectoral employment except those sectors in which capital is assumed to be sector-specific.

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detailed information on labor supply, employment, unemployment, formal wages, informal wages, and self-employed income, and a number of socio-economic characteristics of individuals and households. The IES survey contains detailed data on household expenditure patterns, labor and non-labor incomes of household, and a number of socio-economic characteristics of households. When the two databases are combined and observations with missing sampling weights are dropped, the number of individuals in our database is 103,732 from 26,214 households. We rely on household weights from the IES data to generate economy-wide results.

The specification of our model of the income-generation process at the individual or household level is described in more details in Essama _et. al._ (2007) and Korman (2006). The model has three components: (a) a multinomial logit model of the allocation of individuals across occupational states, based on individual and labor characteristics; (b) a model of the determinants of earnings (such as education, gender, union membership, urban-rural location, head of household, marital status, etc.); and (c) an aggregation rule for computing household income from the contribution of its employed members. We assume that other types of non-labor income, such as interest and rent incomes or transfers, are exogenous. The sum of formal and informal wages and self-employment income by all wage earners and self-employed people in a household and other non-labor income make up total household income. The econometric modeling of the income generation processes includes the estimation of wage functions and occupational probability functions for formal labor, informal labor, and self-employed workers by skill-type and by economic sectors (see the Annex for details).

**_Macro-micro links._** The communication between the CGE model and the microsimulation model is a top-down approach. The CGE model translates the impact of the shocks and policies through changes in relative prices of commodities and factors, and through levels of employment.  The micro-simulation model takes these changes as exogenous and translates them into changes in household behavior which underpins changes in earnings, occupational status, and gains and losses of per capita income as indicative measures of welfare. A series of steps are taken to ensure outcomes from the micro-simulation model are consistent with the aggregate results from the CGE model both before and after the shock. In particular, the consistency constraints require that the occupational choices predicted by the micro-simulation model match the employment shares in the CGE model.  Similarly, simulated earnings at the micro level must match macro predictions.<sup>16</sup> Because the base years for the social accounting matrix (SAM,2003) and the survey data (2000) in our study of South Africa are different, we employ percent changes to communicate changes in employment, wages, and prices from the CGE to the micro simulation.  This allows us to retain the more recent numbers in the macro accounts as well as the familiar poverty and inequality measurements of the micro data.<sup>17</sup>

In the case of employment changes, the CGE model provides estimates of the percent change in employment by category for each simulation. The micro simulation model generates exactly the same percent changes in the individual labor force data set by moving individuals into (or out of) that specific labor category. For example, when a labor category expands, the

> 16 Bourguignon, Robilliard and Robinson (2002) explain that benchmark consistency could be achieved by ensuring that the calibration of the CGE is compatible with the consistency constraints.

> 17 See Essama _et. al._ (2007) for details.  Savard (2006) and  Robillard and Robinson (2006) also discussed approaches for achieving consistency  between household survey data  and the national accounts.

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micro simulation model uses unemployed individual’s estimated maximum utilities (i.e., summation of predicted probabilities plus the error or unobservable term) of being in each employment category (including the unemployed group). When moving individuals from the unemployed pool to the employed group, we used the following information about unemployed people: (i) their skill type, and (ii) the economic sector in which they were previously employed before they became unemployed. This information is utilized in the process of moving individuals into labor market.

# **3. Simulations**

The employment impact of a wage subsidy to low and medium-skilled formal labor largely depends on two sets of factors: i) the relative complementarity of the factors of production; and ii) labor market constraints due to either a limited amount of skilled labor and capital or the size of the unskilled and medium-skilled intensive sectors in the economy. We devise two sets of simulations to test the sensitivity of a wage subsidy to key factors. We also look at the microeconomic impact of a wage subsidy on households assuming the middle range of substitution elasticities in production.

## **_Set 1 of Simulations:_** _Sensitivity of the Impact of Wage Subsidy to the Relative Complementarity of the Factors of Production_

The magnitude of the employment gains from a wage subsidy depends upon the assumptions about factor substitution in production. Three scenarios assuming low, medium, and high elasticities of substitution between factors of production are performed to illustrate the employment creating potential of a wage subsidy. In the presence of technological constraints and labor market rigidities, the elasticities of substitution would be rather low—as may be the case in South Africa. As technology improves and/or labor market rigidities are removed, the elasticities of substitution should increase and the employment creating potential of a wage subsidy would be larger.

We consider a range of values for a wage subsidy to all production activities except coal, gold, other mining, refined petroleum, and government services. As seen in Table 9, for a 10 percent wage subsidy, the employment gains range from 1.9 percent when the economy is assumed to be inflexible in production to 7.2 percent when the economy is assumed to be flexible in production. The wage subsidy expands employment of low and medium-skilled formal labor in all three sectors. The agricultural sector shows a large percentage increase in employment, but given the sector’s relatively small share in total employment, the contribution to the change in total employment is relatively low. The agricultural sector’s employment creation potential rises rapidly as the elasticities of substitution rise (for example, employment of low-skilled formal labor increases from 5.1 percent to 21.2 percent). Further research is needed on the agricultural sector to assess its true employment potential, given the seasonality of the sector’s employment as well as the existing institutional rigidities such as land reform and minimum wages. The factors in fixed total supply (high-skilled formal labor, informal labor, and self-employed labor) are released from the services sector as the economy adjusts to the wage subsidy.

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**Table 9: Employment change (%) of 10% wage subsidy** **<u>to low-skilled and medium-skilled formal labor</u>**

||Base|Low|Medium|High|
|---|---|---|---|---|
|Low-skilled formal labor|3451.5|3.3|6.7|12.2|
|Agriculture|761.6|4.9|10.8|21.2|
|Industry|1069.7|2.1|4.1|7.1|
|Services|1620.2|3.3|6.5|11.2|
|Medium-skilled formal labor|3207.0|3.0|6.2|12.2|
|Agriculture|34.7|3.8|8.6|18.4|
|Industry|432.2|2.5|5.0|9.8|
|Services|2740.1|3.1|6.4|12.5|
|High-skilled formal labor|1300.7|0.0|0.0|0.0|
|Agriculture|16.4|0.3|1.0|2.5|
|Industry|133.4|0.2|0.3|0.4|
|Services|1150.9|0.0|0.0|-0.1|
|Informal labor|2913.4|0.0|0.0|0.0|
|Agriculture|301.8|0.0|1.8|5.0|
|Industry|357.3|0.1|0.2|0.3|
|Services|2254.3|0.0|-0.3|-0.7|
|Self-employed labor|346.3|0.0|0.0|0.0|
|Agriculture|18.2|0.3|1.4|3.8|
|Industry|43.1|0.1|0.1|0.0|
|Services|285.1|0.0|-0.1|-0.3|
|Total labor force|11218.9|1.9|3.8|7.2|

Source: CGE model simulations.

Increased employment from the wage subsidy leads to increased GDP. For a 10 percent wage subsidy, GDP increases from 0.6 percent (low substitution elasticities) to 2.4 percent (high substitution elasticities), see Table 10. **Table 10: Percent Change in Real GDP given 10% wage subsidy to low-skilled and medium-skilled formal labor**

||Base|Low<br>M|edium|High|
|---|---|---|---|---|
||(Billion R)|10%|10%|10%|
|Absorption|1231.0|0.6|1.3|2.4|
|Household Consumption|786.3|1.0|2.0|3.8|
|Fixed investment|200.3|0.0|0.0|0.0|
|Inventory|5.3|0.0|0.0|0.0|
|Government consumption|239.1|0.0|0.0|0.0|
|Exports|339.8|0.6|1.1|2.1|
|Imports|-319.4|0.6|1.2|2.3|
|GDPatmarket prices|1251.5|0.6|1.3|2.4|

Source: CGE model simulations.

The modeling results estimate that a 10 percent wage subsidy with low elasticities of substitution will cost R19.7 billion (in 2003 rand, see Table 11). However, real GDP increases as employment increases, and tax revenues will also increase, offsetting the cost of the wage subsidy. As a result, the effective cost of the wage subsidy is 75 percent of the total wage subsidy bill, in the low elasticity case. If one assumes the economy is more flexible, the effective net cost of the wage subsidy falls to 55 percent of the wage subsidy bill. The wage subsidy per job

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created is relatively high, at R90,758 per job created for the low elasticity case, because the wage subsidy is provided to all low-skilled formal and medium-skilled formal labor hired, not just to the additional workers. The cost per job created declines dramatically as the economy becomes more flexible.

**Table 11 Government revenue (billion rand) and fiscal cost of wage subsidy** (10% wage subsidy to low-skilled and medium-skilled formal labor)

||Base|Low|Medium|High|
|---|---|---|---|---|
|||10%|10%|10%|
|Direct tax|169.0|172.7|173.5|174.9|
|Indirect tax|||||
|Tariffs|8.3|8.3|8.4|8.5|
|Domestic|62.5|63.1|63.5|64.3|
|NetVAT|69.6|70.2|70.7|71.7|
|Totaltax revenue|309.4|314.3|316.1|319.3|
|Additional tax revenue<br>(revenue effectfromexisting taxes due toincreased employment and GDP)||4.9|6.8|10.0|
|Wage subsidy cost|0.0|-19.7|-20.5|-21.9|
|Net wage subsidy cost (implied lump sum tax)||-14.7|-13.7|-11.9|
|Effectivewage subsidyrate (percent ofthe costnot covered by therevenue effect)||74.9|67.0|54.5|
|Wage subsidy cost perjob created (Rperjob)||90758.4|47406.5|27039.8|

Source: CGE model simulations.

The employment and GDP effects increase as the subsidy rate increases. Here we report the changes for the base case. As seen in Figure 5, total employment growth ranges from 1.8 percent for a 5 percent wage subsidy to 11 percent for a 25 percent wage subsidy. GDP growth ranges from 0.6 to 3.3 percent.

**Figure 5: Employment and GDP changes in response to wage subsidies, medium elasticity case**

<!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>0.05 0.1 0.15 0.2 0.25<br>Wage subsidy<br>Low -skilled Formal Labor Medium-skilled Formal Labor<br>Total Labor Force GDP at market prices<br>Percent change<br><!-- End of picture text --> Source: CGE model simulations.

The current model specification assumes that the wage subsidy only affects the number employed without affecting the market wages. Alternatively, the presence of labor unions means that some of the wage subsidy is collected by union workers in the form of higher wages. To

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show the sensitivity of our results to union behavior, we consider the case in which the union claims half of the wage subsidy in the form of higher wages. Using the medium elasticity values and a 10 percent wage subsidy, we find that the employment gains are 1.7 percent, compared to 3.8 percent in the absence of unions—the employment gains are more than twice as large in the absence of increased wages.

Replacing the implied lump-sum tax with a real tax, we impose a social security tax to finance about two-thirds of the wage subsidy cost. It this scenario, the social security tax (sst) pushes the cost of the program to a subset of household income groups—it is imposed on income groups earning R24,000 and R100,000 to partially finance the wage subsidy. These households are primarily from the income deciles 7 to 9. Lower household deciles do not fall in the tax base and are therefore excluded, while the uppermost household deciles are not considered because the incomes are mainly non-wage. The social security tax is implemented as a direct tax with no incentive effects on the employer; it effectively replaces the implied lumpsum tax necessary to finance the wage subsidy. The direct income tax rate, including the social security tax, goes from 0.087 to 0.117 for the seventh income decile group, 0.108 to 0.146 for the eight income decile group, and 0.136 to 0.182 for the ninth income decile group; other direct taxes are raised further to finance the rest of the wage subsidy cost, but do not have to increase as much as would be the case without the social security tax. The impact of the financing scheme is evident when looking at household welfare—there is a dramatic decline in the net gains income for the households paying the social security tax. Next, we look at the more detailed impact on households for the medium case of the elasticities of substitution.

**_Impact on households – medium elasticity case._** Looking at the medium case in Table 9, a wage subsidy to the formal sector employers leads to an increase in employment in the formal sector, particularly in the agricultural sector.  The importance of wage subsidy policy is to create new jobs for low-skill and medium-skill formal unemployed individuals in the labor force while encouraging employers to hire new employees with these skill groups and reducing their wage bill.  As a result, employment increases by 3 percent and the new workers are making non-zero wages.  While there are differences in earnings of newly employed workers depending on their age, racial composition, and regional disparities, they are beneficiaries of the subsidy scheme since they start making non-zero earnings, are out of the unemployed pool, and, as a result their welfare increases.

Although a wage subsidy is primarily focused on increasing jobs, the average wage may be affected by the wages of new entrants, depending on their level of experience, education etc. For low- and medium-skill workers in the industrial sector, new entrants are drawn primarily from unemployed young black Africans, who tend to have less work experience and less education than a college degree. As a result, there is a decline in the average wage because new entrants earn much lower than average wages (Figure 6). Regardless of skill level, workers in agriculture gain from implementation of a 10 percent _ad-volerem_ wage subsidy policy. Average wage gains vary from 2.3 percent for low skill workers to 3.3 percent increase for high skill workers in the agricultural sector. On the other hand, for low- and medium-skill workers in the

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service sector, average wage increases are negligible, while high-skill workers gain above 2 percent in the same sector (Figure 6).<sup>18</sup>

**Figure 6:  Changes in Relative Wages from  a Wage Subsidy in Formal Sector**

<!-- Start of picture text --> 4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Agri-Low skill Agri-Med skill Agri-High skill Indust-Low Indus-Med Indust-High Service-Low Service-Med Service-High<br>skill skill skill skill skill skill<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br>-5.0%<br>-6.0%<br>Occupational Types<br>Source: Author’s calculations<br>Figure 7: Income Gains from a Wage Subsidy for Workers in Formal Sector<br>12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>Agri-Low skill Agri-Med skill Agri-High skill Indust-Low Indus-Med Indust-High Service-Low Service-Med Service-High<br>skill skill skill skill skill skill<br>Occupational types<br>Percentage Change in Average Wages<br> Percentage gains from wage subsidy<br><!-- End of picture text --> Source: Author’s calculations Source: Author’s calculations 18 In the CGE model, the average real wage for low- and medium- skilled formal labor type is fixed economy wide, but wage differences exist by activities by activity.

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Overall aggregate income gains for formal sector workers are still substantial, ranging from 0.6% to 11%, particularly for those in the agricultural and service sectors (Figure 7). The total income gains reflect both changes in employment and changes in average wages.

We also examine the impact of a social security tax imposed on households with income between R24 000 and R100 000 to partially finance the wage subsidy with the proceeds. The households subject to the new tax are in high income deciles (mainly the top 3 deciles - except the richest decile where the primary income sources are non-wage). As the social security tax is not likely to affect employers’ behavior, there is little impact on the employment level and the structure of the economy from the macro results. As expected, however, there are differences at the household level.

Households are generally better off and their welfare increases with a wage subsidy with or without a social security tax (Figure 8). Relative to the baseline, the imposition of a social security tax affects mainly those households subjected to the new tax - the upper income deciles (7, 8, 9 deciles). Income gains by household income group range from 2% (for the richest group) to 20% (for the poorest). But with a social security tax imposed on the top 3 deciles (excluding the richest), the gains will likely disappear for households in these higher 3 income deciles. For example, households in the ninth decile become net losers from the imposition of social security tax.

**Figure 8: Income gains and Loses  at the Household Level from simulations**

<!-- Start of picture text --> 25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>-5.0%<br>Population Deciles<br>Sim 1 Sim2<br> Percentage Changes in Household Level Income<br><!-- End of picture text --> Source: Author’s calculations. Notes: Simulation 1:10 percent wage subsidy given to employers for low and semi skill in formal sector. Simulation 2: Adding social security tax to simulation 1

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**_Overall poverty and inequality decline._** Tables 12 and 13 report the Foster-GreerThorbecke (FGT) poverty headcount ratio and general entropy indices with particular focus on the Gini coefficient. About 1.6 percent of households move out of poverty with the implementation of the wage subsidy, with the head count ratio declining from 49.1 to 47.6 percent.<sup>19</sup> The employment effect also offsets the addition of a social security tax with a 1.5 percent reduction in the poverty rate.

**<u>Table 12: Poverty Indicators, by Region, Population Deciles, and Education  Level</u>**

||<br>**Pover**|<br>**ty Indicato**|<br>**rs (%)**|<br>**Variation f**|<br>**rom Base**|<br>**Difference**|
|---|---|---|---|---|---|---|
||**Base**|**Sim1**|**Sim2**|**Sim1**|**Sim2**|**Sim2-Sim1**|
|**_1. National Headcount Rati_**|**_o_**||||||
|Proportion of Poor|0.491|0.476|0.477|-1.6|-1.46|0.09|
|**_2. Regional Decomposition_**<br>|**_(Headco_**<br>|**_unt ratio)_**<br>|||||
|Urban|0.335|0.319|0.320|-1.6|-1.51|0.08|
|Rural|0.721|0.706|0.707|-1.5|-1.39|0.09|
|**_3. Population Deciles_**<br>|||||||
|Poorest|0.952|0.929|0.929|-2.3|-2.24|0.03|
|2|0.934|0.909|0.909|-2.5|-2.53|0.01|
|3|0.884|0.859|0.860|-2.5|-2.39|0.07|
|4|0.796|0.768|0.769|-2.8|-2.69|0.11|
|5|0.561|0.538|0.539|-2.2|-2.15|0.07|
|6|0.351|0.343|0.343|-0.8|-0.80|0.00|
|7|0.186|0.179|0.183|-0.7|-0.29|0.42|
|8|0.114|0.107|0.108|-0.7|-0.54|0.12|
|9|0.078|0.074|0.074|-0.5|-0.45|0.02|
|Richest|0.085|0.050|0.050|-3.6|-3.55|0.03|
|**_4. Level of Education of He_**<br>|**_ad of the_**<br>|**_Househol_**<br>|**_d (Headc_**<br>|**_ount Ratio)_**<br>|||
|No Schooling|0.783|0.774|0.774|-0.9|-0.86|0.01|
|Grade 0-6|0.620|0.605|0.606|-1.5|-1.36|0.17|
|Grade 7-9|0.479|0.447|0.448|-3.2|-3.09|0.13|
|Grade 10-12|0.257|0.241|0.241|-1.6|-1.57|0.05|
|NTC Level|0.146|0.126|0.126|-2.0|-1.99|0.00|
|Degree andPost Graduate|0.078|0.066|0.067|-1.2|-1.05|0.10|

Notes: Poverty line is taken as 1 dollar per day. Exchange rate is rand 6.95 /1 dollar in 2000 prices. Per capita income is used as a welfare measure of household.

Simulation 1: 10 percent wage subsidy given to employers for low- and semi-skill in formal sector. Simulation 2: A social security tax is introduced relative to simulation 1.

> 19 These results are based on using $1 as a poverty line per day per person.

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**-** **<u>Table13: Generalized Entropy Indices Inequality Indicators</u>**

||<br>**Inequalit**|<br>**y Indicat**|<br>**ors**|<br>**Variation **|<br>**from Base**|**Difference**|
|---|---|---|---|---|---|---|
||**Base**|**Sim1**|**Sim2**|**Sim1**|**Sim2**|**Sim2-Sim1**|
|**1. National Level**|||||||
|General Entropy (0)|1.22|1.19|1.19|-0.02|-0.02|0.00|
|General Entropy (1)|1.20|1.18|1.19|-0.02|-0.02|0.01|
|Gini Coefficient|0.72|0.71|0.72|-0.4|-0.4|0.1|
|**2. Regional Decomposition**<br>|||||||
|**Urban**|||||||
|General Entropy (0)|1.02|1.00|1.00|-0.02|-0.02|0.00|
|General Entropy (1)|1.01|0.99|1.00|-0.02|-0.02|0.01|
|Gini Coefficient|0.67|0.67|0.67|-0.5|-0.5|0.3|
|**Rural**|||||||
|General Entropy (0)|1.13|1.12|1.11|-0.01|-0.01|-0.01|
|General Entropy (1)|1.46|1.43|1.43|-0.02|-0.02|0.00|
|Gini Coefficient|0.71|0.71|0.70|-0.5|-0.5|-0.2|
|**3. Population Deciles (Gini Co**|**efficient)**||||||
|Poorest|0.55|0.57|0.57|2.0|2.0|0.0|
|2|0.52|0.55|0.55|3.2|3.2|0.0|
|3|0.52|0.53|0.53|1.5|1.5|0.0|
|4|0.43|0.45|0.45|1.8|1.8|0.0|
|5|0.37|0.39|0.39|2.0|2.0|0.0|
|6|0.39|0.39|0.39|0.4|0.4|0.0|
|7|0.40|0.40|0.40|0.3|0.3|0.0|
|8|0.34|0.35|0.35|0.8|0.8|0.0|
|9|0.32|0.32|0.32|-0.1|-0.1|0.0|
|Richest|0.44|0.44|0.44|-0.2|-0.2|0.0|
|**4. Level of Education of head**<br>|**of the ho**|**usehold (**|**Gini Coeff**|**icient)**|||
|No Schooling|0.62|0.62|0.61|0.00|-0.4|-0.4|
|Grade 0-6|0.61|0.61|0.60|-0.01|-0.6|-0.4|
|Grade 7-9|0.62|0.61|0.60|-0.01|-0.8|-0.2|
|Grade 10-12|0.60|0.59|0.60|-0.01|-0.7|0.3|
|NTC Level|0.50|0.46|0.47|-0.03|-3.3|0.5|
|Degree andPost Graduate|0.53|0.53|0.53|0.00|-0.4|0.3|

Notes: Simulation 1: 10 percent wage subsidy given to employers for low- and semi-skill in formal sector. Simulation 2: Adding social security tax to simulation 1.

In addition to overall poverty, rural poverty also decreases about 1.5 percent from 72.1 percent to 70.6 percent; urban poverty declines 1.6 percent. Poorer households gain more than richer households. The decomposition of headcount poverty ratio by population deciles shows that poorer households on average gain more than richer households (see Table 12). For instance, the poverty rate falls on average by more than 2 percentage points for lower deciles of

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the population. Other potential winners in terms of poverty levels include – i) households with heads completing an education level of grade 7-9; individuals with technical and vocational school degrees; iii) higher skilled workers. Overall, the differences in poverty rates between the two simulations are very minimal.

All inequality indicators improve (Table 13). The Gini coefficient declines about half a percentage point from 72 to 71.5 percent. A similar decline is also observed at the regional level. The magnitudes of these changes are similar in both simulations. While we observe variations from the base case in both simulations, the differences are not significant.

**_Set 2 of Simulations:_** _Sensitivity of the Employment Effects of a Wage Subsidy to Measures That Ease the Skill Constraint or Promote Labor Intensive Activities, Assuming Low Elasticity of Substitution among Factors of Production_

The positive impact of the wage subsidy on employment, poverty, and inequality hinges on a critical assumption – that the elasticities of substitution among factors of production are those of the medium case. In the low case, the impact will likely be minimal (see employment effects in Table 9 for example). Lowering the cost of less-skilled labor to employers with a wage subsidy will not generate an employment kick when factors of production are relatively complementary and the constraint is the supply of skilled workers (or capital). Given some uncertainty regarding the degree of labor market rigidity, we consider further sensitivity tests for the low elasticity scenario. In particular, we consider the effect of a wage subsidy given: (i) a 5% increase in the supply of skilled labor; (ii) a 5% increase in the supply of skilled labor and capital; (iii) a 5% increase in the supply of skilled labor and capital and a 10% production subsidy to activities with high value added shares in low-skilled and medium-skilled labor; and (iv) for each of the three interventions from (i) to (iii), a marginal increase in the low substitution elasticities among factor inputs.

Introducing the measures above also addresses, in a partial or simplified way, some of the second-best effects of a wage subsidy—a wage subsidy essentially introduces a distortion to offset other distortions that have resulted in high unemployment of low-skilled and mediumskilled labor in South Africa. A wage subsidy can be viewed as a short term solution, while the increase in the availability of skilled-labor and capital and the increased substitution possibility among factor inputs addresses the longer term adjustments. By design, however, the accumulation of skills and capital as well as changes in the substitution elasticities in the simulations are limited to what may be easily attained in the short term in order to test the sensitivity of the wage subsidy to these factors and examine any interesting interactions.

_Policy intervention I_ – under the original low elasticity case (Set A in Table 14), the constraint that there are too few skilled workers is relaxed and the supply is increased by 5 percent. The amount of change or actual measures for bringing this about are not the focus, but the measures could range from the removal of restrictions on skilled immigrants in the short-term or through training programs in the longer run. For simplicity, we assume that existing public expenditures or training programs can be realigned to bring this about without additional fiscal cost. The employment impacts of a 10 percent wage subsidy, summarized in Table 14, are indeed positive. However, looking at the marginal employment effects of the wage subsidy

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given the policy intervention (column iv) and comparing them with the reference case of a wage subsidy alone (without the policy intervention), the employment gains (column v) are negligible – very slightly positive for medium-skilled labor and slightly negative for low–skilled labor. This is likely due to the fact that skilled workers are also highly complementary to capital, which is kept constant.

The low elasticity case, which is half the multipliers of Table 7 times the reference elasticities of Table 8, is close to a Leontief fixed-coefficient technology in some activities. The implicit substitution elasticities, for example, between high-skilled and low-skilled workers and between capital and high-skilled workers in mining, metal products, machinery, vehicles and transport equipment, etc., are close to 0.12 – a high degree of complementarity among these factors or a fairly rigid factor market.

_Policy intervention II_ involves policy intervention I plus 5 percent growth in capital. This mimics the increase in productive capacity in the economy through capital accumulation or productivity change. Relaxing the capital constraint in addition to skills will bring about a higher marginal employment of a wage subsidy and the employment gains relative to the reference case are clearly positive.

_Policy intervention III_ involves policy intervention II plus a 10 percent production subsidy to production activities with high value added shares to low- and medium-skilled formal labor. The output subsidy, in effect, redirects the increase in capital towards more labor intensive sectors. The targeted sectors are the ones with 40 percent of value added to either lowskilled formal labor or medium-skilled formal labor, except gold which does not get a wage subsidy. They are: textiles, apparel, wood and wood products, printing, publishing, and recorded media, rubber products, plastic products, machinery and equipment, other transport equipment, furniture, and other producers.

The results are in line with the expected outcomes in most cases. The marginal employment gains relative to the reference case improve with additional policy interventions (and note the policy interventions are cumulative). The only two exceptions are in the low elasticity case. The marginal effect of a wage subsidy on employment, given a 5 percent increase in skilled labor (intervention I) is not better than the effect of a wage subsidy alone— employment for low-skilled labor does increase by 3.3 percent when there is a wage subsidy in addition to the expansion of the supply of skilled labor. However, this is slightly below the employment gains of a wage subsidy alone. In the model, skilled labor and low-skilled labor are poor substitutes in production (see Table 7). When it is difficult to substitute skilled labor for low-skilled labor, the employment response to a wage subsidy is not as great when there is an additional supply of skilled labor. When the labor market is less rigid (Set B), the wage subsidy has a bigger marginal effect on the employment of low-skilled labor given an increased supply of skilled labor.

A similar situation arises for medium-skilled labor in policy intervention III for low production substitutability (Set A). In this case, the question is, “why is the marginal effect of a wage subsidy smaller in the case of policy intervention III compared to policy intervention II?” Since policy intervention III is policy intervention II plus a production subsidy to low and

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medium-skilled formal labor, one would expect that the marginal employment gains would be highest with policy intervention III. As seen in Table 7, it is difficult to substitute mediumskilled formal labor and capital; as a result, the employment response to a production subsidy to medium-skilled intensive sectors is dampened. When it is easier to substitute medium-skilled formal labor and capital, as is the case in Set B, the results show that the marginal employment effects of a wage subsidy for medium-skilled labor are higher as policy interventions expand. This is not the case for low-skilled formal labor; it is easier to substitute capital for low-skilled labor and the marginal effects of a wage subsidy are higher in policy intervention III than policy intervention II.

_Improving Labor Market Flexibility at the Margins (Set B)._ For each supporting measure, we also examine the sensitivity of the impact of the wage subsidy under a slight improvement in the labor market flexibility by reducing somewhat the degree of complementarity among factors of production. We do not consider how this may be brought about but suggestions by many include reduction of regulations for new job entrants and for government employment.

More specifically, Set B in Table 14 provides a slightly higher low elasticity case with elasticities computed as 0.75 times the reference values instead of 0.50 times the reference values. The results now show that the marginal employment effects of a wage subsidy are all higher. The employment gains relative to the reference case are also now clearly higher for the increase in skills or other measures. The employment gains of a production subsidy over intervention II are also now established.

Labor market flexibility matters a great deal in determining the employment impact of a wage subsidy, especially in the lower elasticity range. Moreover, for the two sets of lower values of the elasticity of substitution between high-skilled and low-skilled labor and between high-skilled labor and capital, the improvement ranges of 0.1-0.2 to 0.2-0.3 at the lowest end and from 0.2-0.3 to 0.3-0.4 in the next level - all still below 0.50 or the Cobb-Douglas case of 1.0. The increase in labor market flexibility being considered in Set B is very modest.

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**Table 14: Sensitivity Of Wage Subsidy Effect On Employment  To** **<u>Supporting Measure that Ease Skill Constraint or Promote Labor Intensive Activities</u>**

|<br>% change in employment<br>10% wage<br>subsidy<br>(reference<br>case)*<br>(i)|<br> <br>Policy<br>Intervention<br>(ii)|<br>10% wage<br>subsidy plus<br>policy<br>intervention<br>(iii)|<br>Marginal effect<br>of wage<br>subsidy,<br>given policy<br>intervention<br>(iv)=(iii)-(ii)|<br>Employment gains<br>of wage subsidy<br>plus policy over<br>wage subsidy alone<br>(v)=(iv)-(i)|
|---|---|---|---|---|
|I. Intervention: 5% Increase in the Supply of Skill<br>Set A. Low el<br> <br>|ed Labor<br>asticity case (0.5*<br>|Reference Elastic<br>|<br>ities)<br>||
|medium-skilled formal<br>3.086<br> <br>|1.146<br>|4.297<br>|3.151<br>|0.068<br>|
|low-skilled formal<br>3.464|3.173|6.508|3.335|-0.130|
|Set B. Slightly higher<br> <br>|low elasticity case<br>|(0.75*Reference<br>|Elasticities)<br>||
|medium-skilled formal<br>4.814|0.828|5.766|4.938|0.124|
|low-skilled formal<br>5.360|2.830|8.348|5.518|0.158|
|II. Intervention: Intervention I plus 5% Increase|in Capital||||
|Set A. Low el<br> <br>|asticity case (0.5*<br>|Reference Elastic<br>|ities)<br>||
|medium-skilled formal<br>3.086|4.929|8.277|3.348|0.265|
|low-skilled formal<br>3.464|5.530|9.099|3.569|0.104|
|Set B. Slightly higher<br>|low elasticity case|(0.75*Reference|Elasticities)||
|medium-skilled formal<br>4.814|4.767|9.920|5.183|0.339|
|low-skilled formal<br>5.360|6.154|11.897|5.743|0.383|
|III. Intervention: Intervention II plus 10% produ<br>Medium-Skilled Labor<br>Set A. Low el|ction subsidy to A<br>asticity case (0.5*|ctivities with High<br>Reference Elastic|Value Added Sha<br>ities)|res in Low-Skilled and|
|medium-skilled formal<br>3.086|6.644|9.915|3.271|0.188|
|low-skilled formal<br>3.464|7.927|11.498|3.571|0.106|
|<br> <br>Set B. Slightly higher|<br>low elasticity case|<br>(0.75*Reference|<br>Elasticities)||
|medium-skilled formal<br>4.814|6.998|12.175|5.177|0.363|
|low-skilled formal<br>5.360|9.023|14.870|5.847|0.487|

_Source:_ CGE model simulations.

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# **4. Conclusions**

The impact of a wage subsidy on unemployment very much depends on the elasticities of substitution of factors of production and on the structural characteristics of the labor market. In the medium elasticity case, a wage subsidy will have the intended impact on employment of low and semi-skilled formal labor and will generate improvements in terms of poverty and inequality. Depending on the elasticities, we find that overall employment gains range from 1.9 to 7.2 percent. Although a wage subsidy to employers is expensive per job created, because the subsidy goes to all low- and medium-skilled formal labor, the expansion of the labor supply will increase GDP and generate some offsetting tax revenue from existing tax instruments, particularly in the medium and high elasticity cases. The net cost may be financed by a lumpsum such associal security tax in order to maintain its employment effects, which would have no incentive effects on the employer.

However, there is much uncertainty regarding the degree of labor market flexibility in South Africa and the impact on employment, poverty, and inequality will likely to be minimal if factors of production turn out to be highly complementary to one another, as would be expected with labor market rigidities and structural problems. Under the low range of elasticity values, the employment gains of a wage subsidy may be improved somewhat by supporting policies to relax the skill constraint, to increase the productive capacity of the economy, or to redirect production increases through economic incentives towards labor-intensive sectors.

Nonetheless, the employment gains from the introduction of complementary measures to the wage subsidy in the low elasticity case still appear modest relative to the medium elasticity case. Hence, our view is that labor market flexibility is a critical factor. In fact, the combination of a wage subsidy and some marginal easing of the skills and capital constraints and of policies to improve labor market flexibility appears promising as a short-term package of measures towards the long-term solution of the unemployment problem. It is, however, still an interim step and any enduring effort will require tackling the underlying factors to the unemployment in South Africa.

In addition to labor market flexibility, several fundamental factors are suggested by the literature review in the introduction. The presence of high mark-ups and concentration ratios in industries point to the significance of imperfect competition, scale economies, and trade policies in affecting labor market outcomes. The decline of the tradable sector and its employment impact also suggest the importance of Dutch disease or the real exchange rate in a resource rich country. Temporal or dynamic effects of schooling and training on human capital are another set of issues. To this end, the inclusion of imperfect competition and scale economies in a general equilibrium framework is feasible but it will require significant changes in the analysis as elaborated, for examples, by Harris (1984), Devarajan and Rodrik (1989), and Willenbockel (1994). The same is true with introducing dynamics, particularly at the micro-simulation level. Since no model, no matter how elaborate, will be able to address all these factors, these are areas suggested for future research.

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