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Real Exchange Rate Volatility and

Employment Growth in South Africa: The

Case of Manufacturing

Trust R. Mpofu and Eftychia Nikolaidou

ERSA working paper 737

March 2018

Real Exchange Rate Volatility and Employment Growth in South

Africa: The Case of Manufacturing

Trust R. Mpofu1 and Eftychia Nikolaidou2

Abstract

This paper investigates the effect of exchange rate volatility on employment growth in South

Africa, a country that is characterised by high rates of unemployment and relatively high

exchange rate volatility. Employing the Autoregressive Distributed Lag (ARDL) cointegration

method over the period 1995Q3 to 2015Q2 and using a variety of specifications, results show

that real exchange rate volatility has a significant contractionary effect on manufacturing

employment growth. Furthermore, findings indicate that manufacturing output, wages and

exports, as well as the level of real effective exchange rate and long-term interest rates have a

significant impact on manufacturing employment growth.

1 Introduction

South Africa has one of the highest unemployment rates in the world (see figure 1). This

remains one of the key concerns to the policymakers in South Africa. Although there is a vast

literature that examines the causes of high unemployment rates from a microeconomic

perspective (see e.g., Bhorat, 2007; Banerjee, Galiani, Levinsohn, McLaren and Woolard,

2008)3, there is a limited work on the issue from a macroeconomic perspective. Exchange rate

movements are expected to impact employment (and, indirectly, unemployment) via the

profitability of the sectors in export-oriented activities. This is so because exchange rate

volatility changes the production costs of firms, and thus, causes uncertainty of future earnings.

Studies like Belke and Setzer (2003) and Belke and Kaas (2004) argue that hiring workings

respresents and investment in the senese that there are high costs to reversing this decision.

This is the reason why exchange rate volatility is potentially expected to affect employment

following the notion of “the option value of waiting” (Dixit, 1989).

1 Corresponding author: School of Economics, University of Cape Town, Rondebosch 7701, Cape Town, South Africa.

Email: trustmpofu@gmail.com 2 School of Economics, University of Cape Town, Rondebosch 7701, Cape Town, South Africa. Email:

efi.nikolaidou@uct.ac.za 3 These studies assert factors like the increase in labour supply after the end of apartheid, skill-biased technical change, the

role of trade unions and bargaining councils and labour regulation as possible causes

Existing empirical work on the e¤ects of exchange rate volatility on employment growthfocuses on developed countries (mainly the US and Germany) most likely because of dataavailability while there is a very limited number of empirical studies on the issue fordeveloping countries. Furthermore, the very limited work for developing countries tendsto investigate the e¤ects of exchange rate level on employment growth (see, inter alia,Frenkel, 2004; Kim, 2005; Frenkel and Ros, 2006; Galindo, Izquierdo and Montero, 2007)rather than the e¤ect of exchange rate volatility. The only studies for developing countriesthat look into the e¤ects of exchange rate volatility on employment are the ones by Belkeand Setzer (2003), Belke and Kaas (2004), Belke (2005) for Central and Eastern Europeancountries2 and by Demir (2010) for Turkey. To the best of our knowledge, there is onestudy by Ngandu (2008) for South Africa examining the impact of exchange rate level onemployment in all sectors. As such, this study contributes to the literature by focusingon the impact of real exchange rate volatility on employment growth given that the SouthAfrican currency (the rand) has a history of being more volatile compared to peers (see,inter alia, Hassan, 2015; Mpofu, 2016). Furthermore, this study uses a methodology thathandles the dynamic e¤ects of the variables. This way this paper overcomes the limitationof Ngandu�s (2008) paper that use a CGE model that is static and as such could nottackle dynamic issues related to the evolution of economic adjustments that arise from acurrency change. Finally, we use more recent data spanning from 1995 to 2015, a periodthat includes the post 2008-2009 global �nancial crisis era.

Several factors motivate this study. Firstly, some researchers, for example, Rodrik (2003)assert that a competitive and stable real exchange rate is crucial for economic development.Therefore, analysing the impact of exchange rate volatility on employment growth could beimportant and relevant in assisting the South African government�s policy of job creation.This follows the fact that South Africa has one of the highest unemployment rates in theworld.

Secondly, the study focuses on the manufacturing sector because the e¤ect of exchangerate volatility is more felt by the manufacturing and/or export sector. Besides, the man-ufacturing sector is vital to both growth and employment creation in most economies3.For instance, the past growth miracles of Japan and the East Asian Tigers (Singapore,Hong Kong, South Korea and Taiwan) provide evidence of how their export-oriented man-ufacturing sectors were in�uential in facilitating economic development and growth. Inaddition, Tkalec and Vizek (2010) argue that the signi�cance of the manufacturing sectorstems from the fact that it is the carrier of innovation, research, and development activitiesthat eventually spillover to other sectors and result in increased gross domestic product(GDP). The vitality of the manufacturing sector is supported by South Africa�s Ministerof Trade and Industry who states that, " The overriding goal of the Industry Policy ActionPlan (IPAP) in the policy context is to prevent industrial decline and support the growthand diversi�cation of South Africa�s manufacturing sector. The balance of internationalevidence is that manufacturing is the engine of growth and employment of all economiesthat have achieved high GDP and employment growth. Manufacturing can generate sig-ni�cant job creation directly as well as indirectly in a range of primary and service sectoractivities"4.

2These countries include Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Ro-mania, Slovak Republic, and Slovenia.

3See studies by Rodrik (2008) and Faulkner, Loewald and Makrelov (2013) for the debate about thismatter for South Africa.

4The Department of Trade and Industry 2013, Industrial Policy Action Plan, Economic Sectors and Em-

2

Thirdly, as �gure 2 shows, manufacturing value added as a percentage of GDP has had asubstantial steady decline between 1995 to about 2005, and a sharp decline thereafter. Thisconcept is referred in the literature as a sign of de-industrialization in Africa. Exploringwhether exchange rate volatility could have played a role in the observed decline in theshare of manufacturing value added as a percentage of GDP in South Africa also makes itan interesting study.

This study employs an Autoregressive Distributed Lag (ARDL) cointegration methodwhich is able to estimate an error correction form of the model for the variables underinvestigation. The ARDL method has the advantage that enables consistent estimations insmall sample sizes given the short period covered in this study. Using various speci�cations,the results show that real exchange rate volatility has a signi�cant contractionary e¤ecton manufacturing employment growth and that the appreciation of the real exchange ratereduces employment growth. Furthermore, manufacturing output, manufacturing wages,exports and long-term interest rates have signi�cant e¤ects on manufacturing employmentgrowth. These �ndings suggest that macroeconomic policies that minimise exchange ratevolatility would increase employment growth in the manufacturing sector.

The paper is organised as follows: Section 2 presents a review of the literature. Section 3gives the overview of South Africa�s economy and the labour market. Section 4 de�nes thedata and methodology. Section 5 presents the results and section 6 concludes.

2 Literature Review

The relationship between exchange rate volatility and employment is stimulated from thetheory of uncertainty in exchange rate and investment. Exchange rate uncertainty has anegative impact on investment process when investment is characterised by irreversibili-ties. This is because uncertainty increases the value of the option to wait until the nextperiod before investing and hence a¤ecting employment decisions. Belke (2001) calls thistransmission mechanism of exchange rate volatility as "the investment channel" and statesthat its relevance is determined by the openness of the economy.

Exchange rate volatility a¤ects employment via investment because investment is an im-portant component of demand (Belke and Gros, 1998; Belke and Gros, 2002). This is alsobecause employment decisions are branded by some degree of irreversibility in the presenceof structural rigidities (Belke and Setzer, 2003). In other words, hiring workers representsan investment in the sense that there are costs incurred to reversing this decision becausesuch a decision is like a sunk cost that cannot be recovered or easily reversed should marketconditions change badly, which is also observed with investment expenditures (Caballeroand Pindyck, 1996).

Most of the empirical work on exchange rate movements and employment focuses on devel-oped countries using both exchange rate level (see, inter alia, Branson and Love, 1986; Bran-son and Love, 1988; Dekle, 1998; Gourinchas, 1999; Campa and Goldberg, 2001) based ona single country panel analysis (that is, they analyse di¤erent manufacturing sectors overtime) and exchange rate volatility based on time series analysis like OLS, VAR in �rst

ployment Cluster, IPAP 2013/14 � 2015/16. Cited at http://www.thedti.gov.za/news2013/ipap_2013-2016.pdf

3

di¤erence or ARDL cointegration (see e.g., Belke and Gros, 1998; Belke and Gros, 2002;Belke, 2001). Studies for developing countries that focus on exchange rate level and em-ployment/unemployment issues are based on either panel analysis using cross-countries(see e.g., Frenkel, 2004; Frenkel and Ros, 2006; Galindo, Izquierdo and Montero, 2007)or using a single country (Kim, 2005), or time series (see e.g., Ngandu, 2008; Alexan-dre, Bacao, Cerejeira and Portela, 2010). The limited number of studies for developingcountries that consider the impact of exchange rate volatility on employment issues arebased on either cross-country panel analysis (see e.g., Belke and Setzer, 2003; Belke andKaas, 2004; Belke, 2005; Belke, Kaas and Setzer, 2004) or a single country panel analysis,that is, they analyse the manufacturing �rms and sectors over time (see e.g., Demir, 2010).

Focusing on the studies that analyse the relationship between exchange rate volatility andemployment issues, the question that follows is, what proxy do these studies use to measureexchange rate volatility given that this variable is unobservable? One measure commonlyused is the standard deviation of the 12 month-to-month changes in the logarithm of thereal exchange rate (see e.g., Belke and Gros, 1998; Belke, 2001; Belke and Gros, 2002; Belkeand Setzer, 2003; Belke and Kaas, 2004; Belke, Kaas and Setzer, 2004; Belke, 2005; Demir,2010). Demir (2010) also uses two alternative measures namely: 12 month moving standarddeviation of the real exchange rate, a measure he argues is instrumental in capturing e¤ectsof volatility in the presence of sluggish adjustment in employment decisions and overlappingcontracts, and the GARCH(1,1) process.

Empirical �ndings from these studies are as follows: Belke (2001) employs an ARDLcointegration method and �nds that exchange rate volatility has a signi�cant negativee¤ect on total employment growth for Germany from 1973Q4-1996Q2. Belke and Gros(2002) use a VAR in �rst di¤erence and �nd that an increase in exchange rate volatilityreduces employment growth and increases unemployment rate for Germany over the period1973-1999. Belke and Setzer (2003) examine the impact of exchange rate volatility onunemployment rate in Czech republic, Hungary, Poland, and Slovak republic using paneldata analysis, and �nd that exchange rate volatility increases unemployment rate. Thesame procedure is done by Stirböck and Buscher (2000) who also �nd similar results. Belkeand Kaas (2004) examine the impact of real exchange rate volatility on total employmentgrowth in Central and Eastern European (CEE) countries. They employ a panel dataanalysis and �nd that real exchange rate volatility reduces employment growth. They alsoargue that one of the transmission channel of exchange rate volatility to employment isvia higher wages. This follows that uncertainty in labour demand may cause unions tonegotiate higher wages for their members and lead to higher unemployment rate. This issimilar to the �ndings by Andersen and Sørensen (1988) indicating that increased exchangerate volatility increases real wages and lowers employment. Belke and Göcke (2001) use anemployment index and also �nd the negative relationship between exchange rate volatilityand employment performance.

Demir (2010) employs a �rm level panel analysis in the manufacturing sectors over theperiod 1983-2005 to examine the impact of exchange rate volatility on employment growthin Turkey. He focuses on Turkey as it was among the forerunners of trade and �nancialliberalisation out of the developing economies in the early 1980s. He �nds that exchangerate volatility has a signi�cant contractionary employment e¤ect on manufacturing �rms.Demir (2010) asserts that the negative relationship between exchange rate volatility andemployment growth might be as a result of the e¤ect of increasing volatility reducingtotal supply of credit available from the banking system which then reduces investment as

4

cited by Bernanke and Gertler (1990). Sharpe (1994) shows that in markets with capitalmarket imperfections, �nancial constraints signi�cantly a¤ect �rm level �uctuations inemployment.

In addition, empirical studies suggest an unambiguous negative link from exchange ratevolatility to investment because increasing exchange rate volatility causes higher interestrates. The interest rates represents the monetary policy channel and the rise in themmight represent a restrictive monetary policy which attracts capital �ows in the presenceof current account de�cits and �ghts against in�ation. As a result, increasing interest ratesnegatively a¤ect employment because this causes the borrowing costs to rise and henceinvestments of all kinds may be reduced including the hiring of new employees (Nickell andNicolitsas, 1999). Exchange rate volatility can directly a¤ect �rm�s employment decisionsthrough its e¤ects on sales, pro�ts, and investment risk and planning5.

Regarding exchange rate levels, empirical studies �nd that exchange rate appreciation re-sults in a reduction in employment in the US manufacturing sector (Branson and Love,1988). Gourinchas (1999) also found similar results for France, while Campa and Gold-berg (2001) found that an exchange rate depreciation resulted in an increase in the USmanufacturing industries. Frenkel (2004) and Frenkel and Ros (2006) �nd a signi�cantnegative e¤ect of real exchange rate appreciation on employment in four Latin Americancountries. Likewise, Kim (2005) �nds a signi�cant negative e¤ect of real appreciation onemployment in Korea. Furthermore, Galindo, Izquierdo and Montero (2007) �nd that realexchange rate depreciation increases employment growth in countries with high degrees oftrade openness when analysing nine Latin American countries. However, their results alsoshow that real exchange rate depreciation has negative e¤ects on employment in industrieswith high debt denominated in foreign currency (i.e. the US dollar). These examplesjustify the need to include real exchange rate level in the model.

Reviewing the theoretical literature, it seems that exchange rate volatility could have eithera negative or positive e¤ect on employment growth. At the same time, most empiricalstudies support a negative e¤ect. The hypothesis tested in this study is that exchangerate volatility adversely a¤ects manufacturing employment growth in South Africa. Thisstudy focuses on the manufacturing sector because it is a major source of employmentexpansion in South Africa given that it has a large number of unskilled workers. Assuch, the poor performance of the manufacturing sector contributes signi�cantly to SouthAfrica�s unemployment problem. Moreover, the manufacturing sector is more likely to bea¤ected by the e¤ects of exchange rate volatility given its exposure to exports. The nextsection provides an overview of South Africa�s economy and the labour markets.

3 Overview of South Africa�s economy and the labourmarket

Since its independence in 1995, many developments have taken place in South Africa�slabour market and monetary policy stance. As such, this section shows stylised factsof some of the key labour market indicators and the developments in the manufacturing

5See also Sharpe 1994 about the e¤ects of sales on employment.

5

sector which further motivate why we study the impact of real exchange rate volatility onemployment growth.

During the study period, unemployment rate increased from 16.90% in 1995 using thenarrow de�nition to 28.85% in 2003 before declining to reach 25.35% in 2015. We usethe narrow de�nition of unemployment rate because it is the international comparatorto the de�nition of unemployment rate formally adopted by the International LabourOrganisation in 19826. Although the unemployment rate has been on a downward trendsince 2003, it is still one of the highest in the world. Figure 1 shows the trend of theunemployment rate in selected countries. In 1995, only Spain had unemployment ratehigher than South Africa�s (20.75% vs 16.90%) compared to other countries like Argentinaat 16%, Brazil at 4.64%, China at 2.90%, France at 10.03%, Germany at 10.40%, Malaysiaat 3.10%, Mexico at 4.70%, Portugal at 7.15%, Russia at 8.30% and Turkey at 6.60%.However, by 2015, South Africa�s unemployment rate was at 25.35%, seconded by Spainat 22.10%, then Portugal at 12.43%, France at 10.40% and Turkey at 10.30% while therest of the economies have unemployment rates below 10%.

[Insert Figure 1 Here]

According to Belke and Setzer (2003) the negative relationship between exchange ratevolatility and employment should be strong if the labour market is characterised by rigidi-ties which improve the bargaining position of the workers. The labour markets in SouthAfrica are broadly considered to be rigid. For instance, Bhorat and Cheadle (2009) showthat in the late 1990s hiring costs (that measure all social security and health costs) and�ring costs (�nancial and legislative provisions for retrenching workers) were fairly rigidwhile hiring (employment contracts) and �ring (dismissal clauses) regulations were �exible.But, by 2006 the South African economy was characterised by high levels of hiring and�ring rigidities although with �exible hiring and �ring costs. This is due to high values ofhiring and �ring rigidities (44.00 and 40.00 respectively for South Africa vs 29.91 and 33.43respectively for upper-middle income countries), and low values of hiring and �ring costsfor South Africa relative to other upper-middle income countries and the global averagesusing the World Banks�Cost of Doing Business (see table 1).

[Insert Table 1 Here]

Emphasis is placed on hiring and �ring costs and hiring and �ring rigidities because em-pirical studies linking the theory of labour market rigidities and unemployment rate �ndthat these rigidities have the strongest and most signi�cant e¤ect on unemployment rate(Bernal-Verdugo, Furceri and Guillaume, 2012; Nickell, 1997). Besides, Nickell (1997)states that some rigidities do not cause high unemployment rate. For instance, he �ndsthat rigidities such as union density, union coverage index and employment protection in-dex (strength of the legal framework governing hiring and �ring) have a strong impact onunemployment rate, meaning that these rigidities increase unemployment rate. However,

6Narrow unemployment rate is de�ned as unemployed individual who did not work in the last sevendays but actively looked for work whilst broad unemployment is narrow unemployment rate plus thosewho were not working but would accept a suitable job if o¤ered even though they are not looking for worknow (and in some cases includes seasonal workers and contract workers as well).

6

he �nds that rigidities such as the tax burden on labour, the unemployment bene�t system,and working time have either no impact or little impact on unemployment rate.

Moreover, the labour laws in South Africa improve the bargaining position of the workers.The main labour laws in South Africa include the Labour Relations Act (LRA) 66 passedin 1995, the Basic Conditions of Employment Act (BCEA) 75 passed in 1997, and theEmployment Equity Act (EEA) 55 passed in 1998. The LRA is the centrepiece of labourlaw and all other labour laws are subordinate to this law. The LRA states that everyworker has the right to form and join a trade union, to participate in the activities andprogrammes of a trade union and to strike. The BCEA addresses issues such as hours ofwork, overtime, meal intervals, annual leave, sick leave and remuneration to mention but afew. The EEA promotes equal opportunity and fair treatment as well as a¢ rmative actionto redress racial imbalances that negatively a¤ected Black people (Africans, Coloured, andIndians), women and people with disabilities7.

As for monetary policy developments, in March 1995, the South African governmentadopted a �oating exchange rate regime following the removal of the dual exchange rateregime which had been in place since the mid-1980s. This means that the South Africancurrency is determined by market forces. The South African Reserve Bank (SARB), how-ever, can participate in the foreign exchange market and such activities can in�uence theexchange rate. It is because of this reason that the government was under pressure to in-tervene to in�uence the currency with the hope of stimulating exports which then increasesoutput and thus positively a¤ecting employment. The SARB asserts that its participationin the foreign exchange rate market is to build up the foreign exchange reserves and shouldbe seen as the management of international liquidity and not the exchange rate policytarget. Between 1998 and early 2000, the SARB followed an informal in�ation targetingsystem but since February 2000, the SARB adopted a formal in�ation targeting system.These developments are important in trying to explain the overall performance of randvolatility and its level.

On the other hand, the manufacturing sector performed poorly during the period of thisstudy. Figure 2 shows that the manufacturing value added as percentage of GDP declinedfrom 21.35% in 1995 to 13.22% in 2015. At the same time, manufacturing sector hasbeen characterised by falling employment while real manufacturing exports increased dur-ing the period under review (with slight decreases in 2002/2003 and 2008/2009 period).This contrasting transformation between export performance and employment makes it aninteresting case study to explore the e¤ects of real exchange rate volatility and the levelof the exchange rate on manufacturing employment growth. This suggests that the ex-change rate might not have been competitive enough (even though it depreciated between1995-2001) which could have attracted more demand of South Africa�s manufactured ex-ports. This notion that the exchange rate might not have been competitive is supportedby studies which investigate the South African real equilibrium exchange rates (see e.g.Saayman, 2007; MacDonald and Ricci, 2004). Saayman (2007) uses the rand per US dollarexchange rate and �nds that the currency was overvalued from 1995-2002 with a small

7These legislations have amendments: as amended by Labour Relations Amendment Act 42 of 1996,Labour Relations Amendment Act 12 of 2002; as amended by Basic Conditions of Employment AmendmentAct 11 of 2002. Other Acts mentioned in line with these amendments include: Intelligence Services Act65 of 2002, Electronic Communications Security (Pty) Ltd Act 68 of 2002, General Intelligence LawsAmendment Act 52 of 2003, Prevention and Combating of Corrupt Activities Act 12 of 2004, PublicService Amendment Act 30 of 2007, and Skills Development Amendment Act 37 of 2008.

7

undervaluation between mid-1998 and early 1999. MacDonald and Ricci (2004) use thereal e¤ective exchange rate for South Africa and �nd that the currency was overvaluedfrom 1995Q1-2002Q1 except between 1998Q1 and 1999Q1.

[Insert Figure 2 Here]

Moreover, Edwards and Alves (2006) state that the lack of re-structuring exports towardsthe dynamic high technology products is one of the reasons why South African manufactur-ing exports performed poorly during the 1990s as well as lagging the exports performanceof East Asian economies. They also argue that the real depreciation of the rand during the1990s contributed extensively towards growth in the manufacturing exports but the volatil-ity of the exchange rate may have contributed to the poor export performance relative toother developing economies.

Following the international evidence which shows manufacturing as the engine of growthand employment leads us to compare South Africa and other least developed countrieswhich have built their successes on a dynamic manufacturing sector. One such countryis Malaysia. In the mid-1980s South African and Malaysian economies were similar byhaving identical output per head, total factor productivity, dependence on mining andhuman capital levels which were very close. Table 2 shows these �gures. However, theevolution of these two countries diverged as time progressed. Rodrik (2008) shows thatSouth Africa�s economy had roughly 12 percent of its total labour force employed in themanufacturing sector whilst Malaysia�s was less than 8 percent in the mid-1980s. But bythe year 2000, Malaysia�s workforce reached 16 percent whilst South Africa�s dropped toabout 7 percent. Moreover, Malaysian industrialization experience came after a periodof what looked like a continuous downward trend in manufacturing in the early 1980s.Rodrik (2008) argues that it is possible to reverse a deterioration trend in manufacturingperformance by utilising adequate policy framework.

[Insert Table 2 Here]

4 Data, Model Speci�cation, and EstimationMethod

This paper uses quarterly time series data for South Africa from 1995Q3 to 2015Q2 ob-tained from the South Africa Reserve Bank and Datastream. This period is chosen becausethe South African government adopted the �oating exchange rate regime in March 1995which exposes the currency to large swings. All the variables are seasonally adjusted us-ing TRAMO/SEATS8 ARIMA tools. This is done to remove cyclical seasonal movementsthat are common in time series data observed at monthly and quarterly frequency. Thevariables are de�ned as follows:

The dependent variable is employment (Lemp) which is measured as the logarithm ofthe number of employees in the formal manufacturing sector. Independent variables areas follows: Loutput is the logarithm of manufacturing gross value added at 2010 constant

8TRAMO stands for Time Series Regression with ARIMA noise, missing values and outliers. SEATSstands for Signal Extration in ARIMA Time Series.

8

prices at time t-1 (we use lagged values to control for endogeneity problems). This variableis used to control for manufacturing demand shocks and productivity changes. Lwages isthe logarithm of real wages in the manufacturing sector at time t-1. We use lagged valuesto control for the possibility of contemporaneous e¤ects of exchange rate volatility onemployment growth through higher wages and the reverse causality from labour demand(Demir, 2010). Intr is the real interest rate calculated as nominal interest rate minusin�ation rate. This paper uses the yield on government bonds-10 years and more, whichrepresents the long-term interest rates. Lexport is the logarithm of real exports at timet-1. We use one-lagged value of exports due to the potential endogeneity between exportperformance and exchange rate volatility. Expvol is an interaction term between exchangerate volatility at time t and exports at time t-1. We use this variable to control for hedginge¤ects by the manufacturing �rms involved in foreign trade given that these �rms are likelyto have better knowledge and access to foreign �nancial markets.

Real exchange rate volatility (vol) is measured as the 12 month moving standard deviationof the �rst di¤erence of the logarithm real e¤ective exchange rate. Real e¤ective exchangerate is the trade weighted real exchange rate for the 20 trading partners of South Africabased on manufacturing goods. There is no consensus in the literature on how to measurethis variable because it is an unobservable variable. In this study we use the moving stan-dard deviation of the real e¤ective exchange rate because this measure would be signi�cantin capturing the e¤ects of volatility in the presence of sluggish adjustment in employmentdecisions and overlapping contracts as argued by Demir (2010). Lreer is the logarithm ofreal e¤ective exchange rate, where an increase is the real appreciation. We use this variableto control for the level e¤ects.

Trend takes the value of 1 in 1995Q3, 2 in 1995Q4, 3 in 1996Q1 e.t.c. We use this variableto capture the impact of technical progress on labour demand. Find0809 is the dummyvariable that takes the value of one for the period 2008-2009 and zero otherwise to controlfor the 2008/2009 global �nancial crisis. Ltax is the logarithm of tax payable by personsand individuals as percentage of total revenue. We use this variable to control for labourmarket rigidities. Lunemp is the log of one-lagged unemployment rate. We also use thisvariable to proxy labour market rigidities.

Using time series data to estimate empirical models requires the variables used to bestationary. Although the ARDL does not require variables to be integrated of the sameorder (i.e. they can be a mixture of I(0) and I(1)), we cannot use this approach in thepresence of I(2) variables. This implies that unit root tests should be conducted beforecarrying out any analysis. Accordingly, we apply the augmented Dickey-Fuller (ADF) andPhillips-Perron (PP) tests to �nd the order of integration of the variables. Table 3 showsthe results for the unit root tests, with panel A showing the ADF test and panel B showingthe PP test. Panel A shows that two variables are integrated of order zero {I(0)} and �vevariables are integrated of order one {I(1)}, while panel B shows that one variable is I(0)but at the 10% level of signi�cance and six variables are I(1). The key stylised facts ofthe stationary variables are shown in table 4, which shows the summary statistics of thesevariables.

[Insert Tables 3 and 4 Here]

In analysing the impact of real exchange rate volatility and other variables on employment

9

growth in the manufacturing sector, we follow Belke (2001) and Demir (2010) with somemodi�cations. Our regression equation is as follows:

Lempt = �0 + �1trendt + �2Loutputt�1 + �3Lwagest�1 + �4Intrt (1)

+�5V olt + �6L exp ortt�1 + �7 exp volt + �8find0809t + "t

where the variables are as de�ned above. Other speci�cations of equation 1 include Lreertand Ltaxt to control for the level e¤ect of the exchange rate and the labour market rigiditiesrespectively.

Equation 1 is estimated by the ARDL approach to cointegration. This approach has anadvantage over other cointegration methods (both single equation cointegration methods,for example, fully modi�ed OLS and dynamic OLS, and non single equation, for instance,Johansen (1988)) in that it performs better in small samples (Pesaran and Shin, 1999).The other advantage is that it works even when the underlying variables are I(0) only, I(1)only or a mixture of I(0) and I(1) unlike the cointegration methods of Engle and Granger(1987), Johansen (1988), and Stock and Watson (1988) that concentrate on cases in whichthe underlying variables are I(1) only (Pesaran, Shin and Smith, 2001). Therefore, thebounds testing procedure by Pesaran et al. (2001) allow to test for the existence of along-run relationship when the orders of integration of the underlying regressors are notknown with certainty. This follows the low power of unit root tests that leads to always bea certain degree of uncertainty with respect to the order of integration of the underlyingvariables (Belke and Polleit, 2006).

Before estimation, it is necessary to allocate appropriate lags of the regressors in ARDLcointegration method. This is advantageous because it simultaneously corrects for residualserial correlation and the problem of endogenous regressors. This appropriate augmen-tation of the order of the ARDL model leads to two important facts. First, the OLSestimators of the short-run parameters are

pT -consistent with the asymptotically singular

covariance matrix. Second, the ARDL based estimators of the long-run coe¢ cients aresuper-consistent. Therefore, valid inferences on the long-run parameters can be made us-ing standard normal asymptotic theory (Pesaran and Shin, 1999). This approach has anadditional advantage of yielding consistent estimates of the long-run coe¢ cients that areasymptotically normal irrespective of whether the underlying regressors are I(0) or I(1) ormutually cointegrated (Pesaran and Shin, 1999).

5 Results

The �ndings from the regression analysis are in tables 5 and 6, which show the long-run es-timates and the error correction model (ECM) respectively using alternative speci�cations(columns 1-6). We estimate equation 1 taking two factors into account. First, the mostappropriate lag speci�cation is needed. This paper uses the Akaike Information Criterion(AIC) to establish the appropriate lag speci�cation. We set the maximum lag order atfour to estimate di¤erent ARDL models because it is consistent with most estimationsusing quarterly data. Second, one should ensure that there is no serial correlation in the

10

residuals. We use the LM test to check for serial correlation and �nd that there is none asshown in table 5. In column 5, however, we use �ve lags of the dependent variable whileother variables are still set at four lags to correct for serial correlation found when themaximum lags was set at four for the dependent variable.

The results for the long-run estimates of the ARDL models in table 5 are in levels. As such,the long-run relationship between real exchange rate volatility and employment growth isdi¢ cult to quantify or attach a meaning even though it has the expected negative signand signi�cant in �ve out of six speci�cations. With this in mind, the more importantresults are found in the ECM speci�cation for it shows the variables as growth rates. Thealternative speci�cations in table 6 show a statistically signi�cant (at 1% level for columns4-6, at 5% level for columns 1-2, and at 10% level for column 3) and robust negative e¤ectof real exchange rate volatility on employment growth. The economic signi�cance of the�ndings, holding other control variables at their sample means suggests that for a onestandard deviation increase in real exchange rate volatility reduces employment growthin the range of 0.15-0.33 percentage points (that we call the impact factor in table 6).The negative e¤ect exerted by real exchange rate volatility on employment growth foundin this paper is similar to other studies that used other methodologies e.g. Demir (2010)using �rm level manufacturing panel, others using cross-country panel (see e.g., Belke andSetzer, 2003; Belke and Kaas, 2004; Belke, Kaas and Setzer, 2004) and others using VARin �rst di¤erence (see e.g., Gros, 1996; Belke and Gros, 1998; Belke and Gros, 2002).

[Insert Tables 5 and 6 Here]

For the level value of the real e¤ective exchange rate, we �nd that in the long-run, thecoe¢ cients are insigni�cant (see columns 4-6). However, in the short-run we �nd that anincrease (appreciation) of the real exchange rate leads to a signi�cant negative e¤ect onemployment growth as shown in columns 4 and 6 in table 6. As for long-term interest rates,table 5 shows that an increase in the interest rates leads to a signi�cant and negative e¤ecton employment level. However, the short-run impact follow a more complicated distributedlag, that is, having signi�cant coe¢ cients with unexpected signs.

The results in table 5 also show that increasing exports leads to a signi�cant and robustnegative e¤ect on the level of employment. For example, a one percent increase in exportswill result in the level of employment declining in the range of 0.32-0.47 percent. This isnot according to our expectations but this �nding might suggest that South Africa�s exportgoods (mostly primary products) are not in high demand in the global market as muchas goods with a high income elasticity of demand (such as high technological products).Demir (2010) �nds similar results for Turkey. Our �ndings are in line with Edwards andAlves (2006) claim that the lack of re-structuring exports towards the dynamic high tech-nology products is one of the reasons why South Africa�s manufacturing exports performedpoorly during the 1990s and lagging the exports performance of East Asian countries whoseeconomies grew due to export-oriented growth path. These results suggest that the SouthAfrican government would bene�t from exporting goods of high quality or goods whosedemand increases more as income increases. The short-run results (column 5 of table 6)further support the negative e¤ect of exports on employment growth. Other speci�cationshave negative signs but insigni�cant. In addition, the lagged values have positive andsigni�cant values.

11

On the other hand, controlling for real exchange rate volatility, that is, an increase in theinteraction term between exports and real exchange rate volatility results in an increasein both the level of employment and employment growth. Table 5, further shows that aone percent increase in manufacturing output signi�cantly lead to an increase in the levelof employment in the range of 0.73-1.9 percent. Columns 2 and 5 in table 6 show that anincrease in output growth leads to an increase in employment growth. However, the laggedvalues of output growth have a negative and signi�cant impact. As expected, table 6 showthat real wage growth reduces employment growth.

The control variables for labour market rigidities, tax and unemployment rate, give mixedresults. Overall, unemployment rate is found with a negative and insigni�cant impact inthe long-run. But in the short-run it has a positive and signi�cant e¤ect. This means thattotal unemployment rate does not discourage the hiring of workers in the manufacturingsector probably due to the fact that most workers hired in the manufacturing sector in theshort-run are unskilled yet total unemployment rate includes both skilled and unskilledworkers. On the other hand, tax is found with positive and signi�cant impact in thelong-run (see column 5) but in the short-run, it has a signi�cant and negative impact onemployment growth. The dummy variable for the 2008/2009 global �nancial crisis is foundwith a negative and highly signi�cant impact on employment growth in all speci�cation.This is expected given that about a million jobs were lost in the manufacturing sector asa result of this crisis. The trend variable which we use to proxy technological progress isfound with a negative and signi�cant e¤ect on the level of employment. This result couldbe attributed to labour substitution.

Finally, table 6 shows that in all six speci�cations, the estimated error correction coe¢ cients{ECM(-1)} have the expected negative signs which are signi�cant at 1% level in the rangeof -0.3653 to -0.5591. The correct sign for the ECM(-1) result con�rms the existence of along-run relationship and suggests that the speed of adjustment from short-run dynamicsto the long-run equilibrium is moderate.

6 Conclusion

South Africa�s unemployment rate has persistently remained high and this has left concernsto the policymakers. The rand has been on average more volatile compared to its peers.As such, this paper empirically examines the impact of real exchange rate volatility onemployment growth in the manufacturing sector, making a contribution to the relevantliterature. We apply the ARDL cointegration approach to analyse this impact given itsproperty of providing consistent estimates in small sample sizes given the limited period ofour study from 1995Q3 to 2015Q2. The results suggest that real exchange rate volatilityhas a contractionary and signi�cant e¤ect on manufacturing employment growth. So, the�ndings suggest that the negative developments in South Africa�s labour market can bepartly attributed to the exchange rate �uctuations. One of the policy conclusions basedon the results found in this paper is that suppressing exchange rate volatility should bringsubstantial bene�ts for the manufacturing sector in South Africa.

The contractionary e¤ect of real exchange rate volatility on employment growth is con-sistent with other studies (see e.g., Belke, 2001; Belke and Gros, 2002; Belke and Set-zer, 2003; Belke and Kaas, 2004; Demir, 2010). The results also show that the appreciation

12

of the real e¤ective exchange rate leads to a decrease in employment growth. This suggeststhe problem of Dutch disease. This is not surprising given the exchange rate literature inSouth Africa which shows that the rand is one of the commodity currencies in the world.That is, as the demand for commodities increase, the rand appreciates and negatively a¤ectother sectors like manufacturing.

Furthermore, the results show that manufacturing output growth has a signi�cant andpositive impact on employment growth while manufacturing wage growth has a negativeimpact. Long-term interest rates negatively a¤ects employment growth. Manufacturingexports are also found to have a signi�cant negative e¤ect on manufacturing employmentgrowth. This suggests that the South African government would bene�t in re-structuringits exports to have positive e¤ects on manufacturing employment growth in the long-run.

However, one limitation of this study is the lack of reliable panel data for the manufac-turing sectors in South Africa. Such data would provide extra features like the borrowingconstrains, di¤erent levels of skills for the employees and di¤erent levels of technologicalknowledge which will be important to be investigated further. Given the picture of ourresults, there is a need for more case studies for developing countries that analyse theimpact of exchange rate volatility on employment growth given that these countries aremostly susceptible to large exchange rate swings.

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16

Figure 1: Unemployment trends for selected countries (1995-2015)

010

2030

Unem

ploym

ent r

ate 

(%)

1995 2000 2005 2010 2015Year

Argentina BrazilChina FranceGermany MalaysiaMexico PortugalRussia South AfricaSpain Turkey

7 Appendix

Table 1: Mean Measures of Regulation, by Income levelArea of Regulation UMI South Africa Global averageRigidity of Hiring 29.91 44.00 34.33Rigidity of Hours 40.57 40.00 42.40Rigidity of Firing 33.43 40.00 33.26Aggregate Employment Index 34.64 41.33 36.66Hiring Costs 17.31 2.40 15.62Firing Costs 44.63 24.00 51.34Source: Bhorat & Cheadle (2009)

Note: UMI refers to Upper Middle Income Countries. The numbers range from 0 to 100.

The higher the number then the more rigid is the category in question.

Table 2: South Africa vs MalaysiaCountry Y/L (K/Y) H/L A Mining share of GDPSouth Africa 0.25 0.959 0.568 0.46 0.111Malaysia 0.267 1.004 0.592 0.45 0.103Source: Rodrik (2008)

17

Figure 2: Manufacturing value added (% of GDP)

1214

1618

2022

Man

ufac

turin

g va

lue a

dded

 (% o

f GDP

)

1995 2000 2005 2010 2015Year

18

Table 3: Unit Root TestsPanel A (i): ADF-levelVariable t-Stat 1%CV 5%CV 10%CVLemp -2.2153 -4.0784 -3.4677 -3.1606Loutput -2.4364 -4.0800 -3.4685 -3.1611Lwages -2.4970 -4.0784 -3.4677 -3.1606Intr -4.1580*** -4.0800 -3.4685 -3.1611Vol -4.3483*** -4.0800 -3.4685 -3.1611Lexport -2.5703 -4.0784 -3.4677 -3.1606Ltax -0.7050 -4.0800 -3.4685 -3.1611Panel A (ii)-ADF-�rst di¤erenceLemp -8.4900*** -2.5949 -1.9450 -1.6141Loutput -5.7537*** -2.5949 -1.9450 -1.6141Lwages -8.5916*** -2.5949 -1.9450 -1.6141Lexport -8.6065*** -2.5949 -1.9450 -1.6141Ltax -13.3997*** -2.5949 -1.9450 -1.6141Panel B (i): PP-levelLemp -2.4103 -4.0784 -3.4677 -3.1606Loutput -1.8628 -4.0784 -3.4677 -3.1606Lwages -2.4970 -4.0784 -3.4677 -3.1606Intr -2.7257 -4.0784 -3.4677 -3.1606Vol -3.2395* -4.0784 -3.4677 -3.1606Lexport -2.6989 -4.0784 -3.4677 -3.1606Ltax -1.1320 -4.0784 -3.4677 -3.1606Panel B (ii): PP-�rst di¤erenceLemp -8.5051*** -2.5949 -1.9450 -1.6141Loutput -5.6997*** -2.5949 -1.9450 -1.6141Lwages -8.5980*** -2.5949 -1.9450 -1.6141Intr -4.9943*** -2.5949 -1.9450 -1.6141Vol -5.5730*** -2.5949 -1.9450 -1.6141Lexport -8.6198*** -2.5949 -1.9450 -1.6141Ltax -13.4216*** -2.5949 -1.9450 -1.6141Notes: CV stands for critical values. ADF and PP stands for the Augmented

Dickey-Fuller and Phillips-Perron unit root tests. The null hypothesis: series

has a unit root (*MacKinnon 1996 one-sided p-values). ***,**,*

denotes rejection of null hypothesis at the 1%, 5%, and 10% respectively.

Variables are de�ned in section 4.

19

Table4:DescriptiveStatistics

Lemp

Loutput

Lwages

Intr

Vol

Lexport

Lreer

Ltax

Mean

-0.0028

0.0053

0.0037

-0.0604

0.0394

0.0156

-0.0032-0.0014

Median

-0.0027

0.0049

0.0034

-0.0733

0.0358

0.0233

-0.00130.0050

Maximum

0.1094

0.0384

0.1213

4.2166

0.0782

0.1456

0.0790

0.1054

Minimum

-0.0845

-0.0693

-0.0557

-3.3834

0.0191

-0.1773

-0.0954-0.1608

Std.Dev

0.0183

0.0177

0.0271

1.3753

0.0159

0.0617

0.0324

0.0429

Skewness

1.8944

-1.2192

0.9332

0.3940

0.7560

-0.5181

-0.0387-0.8988

Kurtosis

24.03266.4548

6.8379

3.9205

2.6868

3.5372

3.4715

5.2370

Jarque-Bera

1503.3958.8594

59.95094.8336

7.8483

4.4842

0.7515

27.1094

Probability

0.0000

0.0000

0.0000

0.0892

0.0198

0.1062

0.6868

0.0000

Sum

-0.2185

0.4153

0.2934

-4.7700

3.1115

1.2308

-0.2491-0.1067

SumSq.Dev

0.0261

0.0245

0.0571

147.5272

0.0197

0.2965

0.0819

0.1437

Observations

7979

7979

7979

7979

Notes:Seesection4forthede�nitionofthevariables.Allthevariablesarestationaryhere.

20

Table5:Long-runestimatesoftheARDLmodels

Variable

(1)

(2)

(3)

(4)

(5)

(6)

Vol

-14.2222*

-15.1850*

-16.7440

-30.5018*

-17.8416**

-35.9420*

(8.4701)

(7.8455)

(10.5843)

(16.1938)

(6.7204)

(19.9546)

Intr

-0.0082*

-0.0064)

0.0044

-0.0161**

-0.0056

-0.0162**

(0.0042)

(0.0041)

(0.0039)

(0.0069)

(0.0037)

(0.0077)

Lexport

-0.3315***

-0.3245***

-0.3285**

-0.4299**

-0.4738***

-0.4679*

(0.1021)

(0.0924)

(0.1544)

(0.1838)

(0.1387)

(0.2336)

Expvol

2.0870*

2.2116*

2.5059

4.2278*

2.5317**

4.9789*

(1.2218)

(1.1301)

(1.5246)

(2.2564)

(0.9620)

(2.7771)

Loutput

0.7316***

1.0407***

0.9054**

0.5678

1.9329***

0.5705

(0.2458)

(0.3163)

(0.4134)

(0.4455)

(0.5072)

(0.5494)

Lwages

0.2887*

0.1078

0.0483

-0.1754

-0.2004

-0.3186

(0.1485)

(0.1974)

(0.2095)

(0.3860)

(0.1882)

(0.4900)

Ltax

0.1275

0.3939***

(0.0865)

(0.1290)

Lunemp

-0.0686

-0.0078

(0.1190)

(0.1247)

Lreer

-0.0507

-0.0472

-0.0563

(0.1078)

(0.0617)

(0.1199)

Trend

-0.0047**

-0.0056***

-0.0037

-0.0022

-0.0070***

-0.0015

(0.0020)

(0.0019)

(0.0023)

(0.0033)

(0.0015)

(0.0040)

Find0809

-0.0351

-0.0499*

-0.0399

-0.0725

-0.0677**

-0.0890

(0.0284)

(0.0295)

(0.0391)

(0.0487)

(0.0291)

(0.0612)

SerialCorrelation

3.8602[0.4153]

7.9030[0.0952]

6.2080[0.1841]

6.9334[0.1394]

7.0032[0.1357]

6.2729[0.1797]

AdjustedR^2

0.9466

0.9478

0.9469

0.9481

0.9458

0.9484

Notes:***,**,*indicate1%,5%,and10%levelofsigni�cance.Valuesin()representthestandarderrors.Valuesin[]representtheprobability.

21

Table6:ErrorCorrectionrepresentationoftheARDLmodels

Variable

(1)

(2)

(3)

(4)

(5)

(6)

DLemp1

0.2694**

0.2513*

0.2573**

0.1531

0.0953

0.1696

DLemp2

0.1255

0.1365

0.1318

0.0297

0.2352**

0.0254

DLemp3

0.4362***

0.4433***

0.5049***

0.4853***

0.3563***

0.4796***

DVol

-10.2021**

-11.1309**

-7.5032*

-16.5999***-13.6304***-16.5112***

DIntr

0.0012

0.0008

0.0030*

-0.0000

0.0013

0.0003

DIntr1

0.0036*

0.0038*

0.0054**

0.0042**

0.0046**

DIntr2

0.00024

0.0019

0.0032*

0.0018

0.0030*

DIntr3

0.0047***

0.0052***

0.0060***

0.0057***

0.0055***

DLexport

-0.0395

-0.0438

-0.0366

-0.0365

-0.1138***

-0.0294

DLexport1

0.1183***

0.1259***

0.0570

0.1727***

0.1199***

0.1628***

DLexport2

-0.0003

-0.0105

-0.0479

0.0404

0.0401

DLexport3

0.0801**

0.0709**

0.1030***

0.0982***

Dexpvol

1.5073**

1.6375***

1.1190*

2.3812***

1.9644***

2.3635***

Dexpvol1

0.0608*

0.0582

DLoutput

0.1835

0.2686**

0.1610

0.1254

0.4582***

0.1185

DLoutput1

-0.5435***

-0.6402***

-0.5241***

-0.6079***

-0.7369***

-0.6422***

DLoutput2

-0.2838*

-0.3505**

-0.1952

-0.3331**

-0.6592***

-0.2920*

DLwages

-0.1212

-0.1323

-0.2355***

-0.2093**

-0.1233

-0.2507***

DLwages1

-0.0077

0.0488

0.0669

0.1190

0.1637*

DLwages2

-0.2488***

-0.2002**

-0.2044**

-0.1918**

-0.1766**

DLtax

0.0691

0.0756

DLtax1

-0.2138***

DLtax2

-0.1753***

DLtax3

-0.1444**

DLunemp

0.0778**

0.0587*

DLunemp1

0.0566

DLunemp2

0.0578

DLreer

-0.2674***

-0.0582

-0.2835***

Find0809

-0.0307**

-0.0354***

-0.0204*

-0.0400***

-0.0417***

-0.0425***

Constant

1.8001***

0.8262***

1.6742***

5.1328***

-3.0410***

5.3834***

ECM(-1)

-0.4963***

-0.5344***

-0.3653***

-0.4095***

-0.5591***

-0.3681***

Impactfactor

-0.2040

-0.2226

-0.1501

-0.3320

-0.2726

-0.3302

Notes:Theterm

Ddenotestaking�rstdi¤erence,i.e.DVOL=Vol-Vol(-1)andDLemp1=Lemp(-1)-Lemp(-2).***,**,*

indicate1%,5%,and10%levelofsigni�cance.

22