Language and Labour n South Afrca A new approach for a new South Afrca Katy Cornwell Department of Econometrcs and Busness Statstcs Monash Unversty Clayton VIC 3800 Australa Phone +61 3 9905 2453 Fax +61 3 9905 5474 katy.cornwell@buseco.monash.edu Abstract Ths paper consders the role of language n labour earnngs n South Afrca over the perod 1996 to 1998. Our pooled cross-secton comprses of over 180,000 workng age adults, and the analyss consders the decson to partcpate n the labour force, employment prospects and labour earnngs. Models nclude varables for ndvdual mother tongue n addton to race. After condtonng on a number of soco-economc and demographc factors, we fnd the Englsh language to be one of the pvotal determnants of labour earnngs. These results are robust across two models of sample selecton. Such fndngs shed lght on the economc consequences of South Afrca s natonal polcy of lngustc heterogenety. The author would lke to thank Brett Inder and Ana Deumert for useful dscussons.
1. Introducton Unemployment n South Afrca has been metaphorcally descrbed as an untamed beast (Kngdon and Knght, 2004). Indeed, South Afrca s charactersed by unemployment rates amongst the hghest n the world, wth the most ferocous rates of up to 45% amongst black South Afrcans (see Table 1). Moreover, hardshp s not overcome once pad employment s found, partcularly for the majorty black South Afrcan populaton. As a resdue from the aparthed era, substantal dfferences n earnngs between racal groups reman. Table 1 demonstrates that on average whte South Afrcans are earnng almost four tmes as much as blacks. Hgh unemployment rates coupled wth dsparate labour earnngs have lead to numerous studes on the determnants of ncome, mostly focussng on the returns to educaton. The latest studes nclude the work of Keswell and Poswell (2002) and Serumaga-Zake and Naude (2003). The former work questons the emprcal relevance of the standard human captal theory of dmnshng margnal returns to educaton, and provdes a thorough overvew of the vast South Afrcan returns to educaton lterature. Serumaga-Zake and Naude (2003) utlse double hurdle and Heckman sample selecton models n examnng the prvate returns to educaton of black South Afrcan males and females. We extend ths work by consderng multple years of data from the South Afrcan October Household Surveys and ncorporatng addtonal varables. Aparthed dctated that race was the prmary determnant of educatonal and occupatonal opportunty. Wth well-documented evdence of the effect of educaton on earnngs, t was natural for the lterature to lnk race, educaton and earnngs n South Afrca. The breakdown of aparthed saw the formaton of the New South Afrca where black economc empowerment s recognsed as fundamental to redressng past mbalances and enablng the country to move on to acheve sustanable development and prosperty (southafrcanfo, 2004). Great efforts have been made to eradcate racal dscrmnaton and undo the njustces of the past, and the Ranbow Naton looks forward to the day when they can say wth confdence that race no longer determnes one s fate. Dsappontngly, studes contnue to fnd race dummes strongly sgnfcant n ncome and employment equatons. We would argue that a new South Afrca calls for a new approach to modellng the South Afrcan labour market: an approach whch looks much further than race n dentfyng the determnants of earnngs n the multlngual new South Afrca. 1
We begn to explore ths noton by examnng whether mother tongue language provdes a better nsght than race nto to what s of mportance to an ndvdual s relatve success n the labour market. Is t race per se that leads to hgher unemployment rates for black South Afrcans, or s t that Englsh s not ther natural mother tongue language, creatng a barrer of entry to employment and an mpedment to earnngs? It s ths aspect of the labour market whch we seek to address n ths paper. Our nterest s prmarly on ntroducng language as a potental determnant of labour earnngs. However, before the ndvdual s able to report earnngs, they must overcome two hurdles: the ndvdual must frst choose to partcpate n the labour force, and then from ths labour force pool the ndvdual must also be selected for employment. Recognsng a propensty for sample selecton bas, we model ncome usng two models of sample selecton: Cragg s (1971) double hurdle and Heckman s (1979) sample selecton model, wth mother tongue ncluded n addton to race and other soco-economc and demographc varables at each of the partcpaton, employment and ncome stages of the models. Gven that ths avenue s a new drecton for the labour earnngs lterature, our next secton s devoted to dscusson of the South Afrcan labour market n the context of language. Secton 3 follows wth a descrpton of the methodology, whle secton 4 ntroduces the data. Results a presented n secton 5, and dscusson follows n secton 6. 2. Language as the new drecton Embracng lngustc pluralsm n ts consttuton, the new South Afrca recognses and guarantees equal status to each of ts eleven offcal languages 1. However, hstorcal whte domnance n government and commerce s reflected n Englsh and Afrkaans beng the most commonly used languages n offcal and commercal publc lfe, despte the Afrcan languages of Xhosa and Zulu beng the more common languages spoken at home (see Table 2). In partcular, Englsh s eghth on the lst of mother tongues ranked accordng to frequency for the respondents n our sample. 1 The 11 Offcal languages of the Republc of South Afrca are Seped, Sesotho, Setswana, sswat, Tshvenda, Xtsonga, Afrkaans, Englsh, sndebele, sxhosa and szulu. 2
Lterature on the economcs of language n the labour market s lmted. The majorty nvolve consderaton of the role of language on labour market nteractons and earnngs for mmgrants and Hspancs n the Unted States. Dscusson tends to fnd consensus n favour of lngustc homogenety. The theoretcal bass for the mmgrant/hspanc lterature s generally pnned to the noton of language as the facltator of communcaton. In ths sense, language can be seen as the medum for communcaton exchange, whereby lngustc heterogenety ncreases the transacton costs of ths exchange and consequently n the absence of blngualsm, less exchange wll take place between those speakng dfferent languages. Consder the mplcatons of ths for the ndvdual job seeker n the labour market. Informaton about jobs flows through open channels of communcaton. The ndvdual s prvy to these channels of nformaton dependng on her ablty to communcate wth the people n these channels. If Englsh s the domnant language used n the work envronment, the Englsh-speakng ndvdual can tap nto nformaton drectly from the pool of the employed and also drectly to the employer. Consequently, the Englsh-speaker holds an advantage on the employment front over the non-englsh speaker. Ths suggests that job search may not only be facltated by language channel, but also lmted by t, and hence lngustc dsadvantage would present tself n both the partcpaton decson and the employment outcome. The lterature advocates that earnngs may also be ted to language knowledge. Where access to occupaton s determned by language channel, a worker may fnd themselves n low pad occupatons relatve to skll. Kossoudj (1988) suggests that there could also be some element of ndvdual choce to be among peers of the same language background, thereby maxmsng ndvdual utlty rather than ncome. Indeed, our sample data ndcates that Englsh mother tongue adds an average earnngs premum n the order of 200% over the most wdely spoken Afrcan language, Zulu (Table 2). From the employer s vewpont, communcatve ablty s a form of human captal n that t enhances productvty, and productvty s lnked to earnngs. The communcator s better able to convey ther comparatve skll advantage, from whch the employer s able to realse any productvty gans from specalsaton. McManus (1985) compares a group of employees sharng a lngua franca wth a group who are unable to communcate. He suggests that n the latter case, the dvson of tasks would be accordng to average characterstcs of the group wth no allowance for personal varaton, whereas the former would be more productve n the sense that productvty gans from specalsaton could be realsed. Hence, those wth hgh 3
levels of communcaton would fnd themselves deemed more productve and awarded accordngly wth hgher ncome. Furthermore, by acceleratng the absorpton of nformaton, communcaton mproves the return to educaton (McManus et al 1983), such that employers may choose to tran workers wth hgh language sklls n new technology more readly than those wth lmted language sklls, enablng the employee wth hgh language sklls to clmb further up the promotonal and therefore ncome ladder. Emprcal applcaton corroborates ths theoretcal dscusson, revealng that language attrbutes play an mportant role n earnngs for mmgrants and Hspancs. Grener (1984) s able to use language to explan up to one thrd of the relatve wage dfference between Anglo and Hspanc men. Kossoudj (1988) concurs n her selecton bas corrected specfcaton of a random utlty model for occupaton and earnngs. Other studes have been able to ncorporate Englsh profcency. For nstance, Rvera-Batz (1990) uses test-based Englsh profcency measures to examne the mpact on earnngs, fndng t to be a major factor. Mora (2003) models a standard Mnceran earnngs functon wth Englsh fluency, geographcal regon and ethncty as condtonng varables, fndng there to be a great deal of nteracton between educaton, experence and schoolng. Interestngly, for males wth no educaton and no experence, Mora (2003) suggests that those who speak Englsh earn sgnfcantly less than those who do not speak Englsh, yet ths result s reversed at hgher levels of schoolng: for a male wth 12 years of educaton, Englsh language profcency adds an earnngs premum of 30%. Small pockets of studes have also looked at the effect of mnorty languages on educatonal outcomes n developng economes, however, whle nterestng, these are manly descrptve and ther emphass on the mplcatons for blngual educaton dstracts from the focus of our study concernng labour market outcomes. Moreover, lke the work on mmgrants, these studes are concerned wth the mplcatons of a mnorty populaton group beng unable to converse n one offcal and domnant language. The case of South Afrca, however, s unque n that through a hstory of poltcal dscrmnaton, the languages of the mnorty populaton group domnate commerce and offcal lfe, yet t s the majorty and also the poorest populaton group who speak the languages whch could well be the mnor n the labour market context. We turn now to our own analyss n the hope of sheddng lght on ths stuaton. 4
3. Methodology Our analyss s prmarly concerned wth the factors whch contrbute to monthly earnngs for all South Afrcans. Pror to reportng ncome, the ndvdual must frst choose to partcpate n the labour force. Of course, n South Afrca partcpaton n the labour force does not guarantee employment, and so a further decson on behalf of the employer must be made to draw the ndvdual from the labour force pool. Only once the ndvdual s employed do they report ncome. Accordngly, only a subsample of all South Afrcans are employed and able to report earnngs. It s lkely that the soco-economc characterstcs of the employed are dfferent to those who are not, and lkewse, the characterstcs of labour force partcpants are dfferent from non-partcpants. In partcular, unobservable characterstcs affectng the decson to work would be correlated wth the unobservable characterstcs affectng ncome. Selectvty bas would arse, therefore, f we were to make statements about the determnants of earnngs for all South Afrcans based on the observed earnngs of the subset whom are employed. The approprate model must be one whch copes wth sample selecton at each stage of partcpaton and employment. We specfy a sample selecton model wth the prmary dependent varable of nterest of the form I I I I y = x β + u, where x I s the vector of soco-economc and demographc explanatory varables, I vector of unknown coeffcents and u the error term. β I the There are two latent decson functons: (1) The partcpaton decson: P I * P P P = x β + u, wth ndcator varable 5
I P R S P f I > = T 1 * 0. 0 otherwse Such that the ndvdual partcpates n the labour force f I P* > 0. And (2) The employment decson: E I * E E E = x β + u, such that the ndvdual s selected for employment f I E* > 0 and hence I E R S E f I > = T 1 * 0. 0 otherwse These choces are partally observed: we do not observe the employment outcome for the non-partcpant nor the ncome for unemployed and non-partcpants. Correspondngly, the partcpaton equaton s defned over the whole South Afrcan workng age populaton, the employment equaton over labour force partcpants and the ncome equaton over those who are employed. We utlse 2 models of ncome determnaton whch ncorporate sample selecton correcton factors: the double hurdle model and Heckman s sample selecton model wth two sample selecton mechansms. Sequental double hurdle model We assume that the employment decson s subsequent to the partcpaton decson. In ths I case y s observed only f I P = 1 and I E = 1, and we have a sequental model whereby employment s ndependent of partcpaton. Ths ndependence assumpton s somewhat dubous, however, snce t s not lkely to make consderable dfference to the results, we leave t for future work. For the model specfcaton relaxng ths assumpton, the reader s referred to Maddala (1993). 6
Assumng normalty of the error terms, the sequental double hurdle model nvolves estmatng two separate probt models for partcpaton and employment. From these estmated models we obtan two correcton factors P λ = e j P P φ x $ β P P x $ Φe β j and E λ = e j E E φ x $ β, E E x $ Φe β j where φ. bg and Φ bg. are, respectvely, the probablty densty and cumulatve dstrbuton functons of the standard normal dstrbuton. Restrctng the sample to those employed, ncome s regressed on a number of socoeconomc and demographc varables (outlned n secton 4) as well as both the obtaned correcton factors. Heckman s sample selecton model In estmaton, Heckman s model dffers from the sequental double hurdle model n ts ncluson of the two correcton factors. The Heckman partcpaton probt and ts correspondng correcton term are dentcal to those of the double hurdle. In modellng the employment probt, however, the correcton factor from the partcpaton equaton s ncluded as an addtonal varable. The second correcton factor s then obtaned as E λ = e j E E φ x $ β, E E x $ Φe β j where x E now ncludes λ P as an addtonal varable. The second correcton factor alone s then ncluded n the ncome equaton as an addtonal regressor. 7
4. Data Data for ths study s extracted from the South Afrcan October Household Surveys of 1996 through to 1998. Ths provdes a mult-stage cluster sample of some 188,985 workng age adults. Our double hurdle and Heckman models characterse ncome as the prmary contnuous dependent varable of nterest. Partcpaton n the labour market and employment are two hurdles whch must be overcome before an ndvdual s observed as recordng ncome. Adoptng a Mnceran form to our dependent varable, ncome s taken as the natural logarthm of monthly ncome and deduced from waged and/or self-employed sources. Partcpaton and employment are the secondary bnary dependent varables. In the partcpaton equaton, the dependent varable takes a value of 1 where the ndvdual partcpates n the labour force and 0 otherwse. Smlarly, the dependent varable n the employment equaton takes a value of 1 where the person s employed. For ths second bnary varable, the sample s restrcted to labour force partcpants. The October Household Surveys from 1996 onwards ntroduced a new queston regardng the mother tongue of the respondent. Gven that South Afrca embraces eleven offcal languages, t s of partcular nterest whether mother tongue language nfluences the dfferent aspects of labour force partcpaton, employment and earnngs. We also condton on a number of soco-economc and demographc varables ncludng populaton group, gender, household head, educaton level, rural resdency, martal status, age and tme dummes. Lnear regresson splnes for educaton level are used to allow dfferng slopes across lower and upper prmary, lower and upper secondary, tertary and other levels of educatonal attanment. For the ncome equaton, we also nclude hours worked and a dummy varable for employment n the nformal sector. The marred dummy s omtted from the employment equaton for dentfcaton. A more comprehensve descrpton of the varables s provded n Table 3. In each of the double hurdle and Heckman models, we consder unemployment under both the offcal and expanded defntons, wth lttle varaton n results. 8
5. Results 5.1 Labour force partcpaton Table 4 presents results for the partcpaton decson under both the offcal and broad defntons of unemployment. The move from the offcal to broad defnton means that some ndvduals move from beng non-partcpants to unemployed partcpants. These people could perhaps be called dsheartened workers as opposed to dscouraged workers: they would work f gven the chance, but are not actvely seekng work. Whle the move from offcal to broad does not alter the qualtatve drectons of the results, some varables become sgnfcant upon use of the broad defnton. Note that the estmated sequental double hurdle and Heckman partcpaton models are dentcal n ths frst stage. All of the race dummes are sgnfcant n the partcpaton models even after controllng for language. We fnd that coloured South Afrcans are more lkely to partcpate, whle Asan and whte South Afrcans are sgnfcantly less lkely to partcpate n the labour market than are blacks. Ths could be reflectve of the old money syndrome of whtes: whte South Afrcans have hstorcal wealth as a safety net n harder tmes, enablng them to drop out of the labour force much more easly than the nvarably poorer black South Afrcan. As one would expect, males, household heads and marred persons are much more lkely to partcpate than ther respectve base counterparts under the broad defnton. Ths result hghlghts the mportance of the household as an economc and cultural unt n South Afrca: the decson to partcpate n the labour force s made n conjuncton wth the cultural oblgatons of the extended famly. Under the narrower defnton of unemployed, marrage does not appear to be statstcally sgnfcant. Educatonal attanment s mportant for labour force partcpaton. Those wth Standard 1 educaton are more lkely to enter the labour force than those wth no educaton. The slope eases off for those wth Standard 2 through to Standard 4, yet remans margnally steeper than the base of no educaton. The drecton s reversed for those n lower secondary school (Standards 5 to 7). Ths could reflect two opposng effects. These people may comprse the dscouraged workers who do not enter the labour force because they lack a suffcent educatonal standard to fnd work. Wth the model defned over the entre workng age populaton, these ndvduals could also be those currently n school and hence not partcpatng n the labour force at ths stage. Standard 8 and above educaton sgnfcantly 9
mproves the lkelhood that the ndvdual wll jon the labour force. Ths reflects the fact that these people perceve themselves as more employable; perhaps schoolng has mproved avalablty of nformaton about employment networks and prospects. The model provdes some qute nterestng nsght nto language as a determnant of labour force partcpaton. Results under the broad defnton suggest that when the language spoken at home s any language other than Englsh, the ndvdual s less lkely to partcpate n the labour force. Indeed, t seems that language s one of the key factors n labour market partcpaton under ths defnton. When one cannot communcate n ther mother tongue, doors close and networks cannot be formed, forcng even those wllng to work to become dscouraged. Of the eght language categores, t s two of the common Afrcan languages, Xhosa and Seped, whch record extremely large negatve margnal effects. Mother tongues of Zulu, Sesotho and Setswana become nsgnfcant upon narrowng the unemployed to the offcal defnton. One could deduce from ths result that those who speak these partcular languages at home are the dscouraged rather than the dsheartened workers: they are forced out of the labour market altogether and are not just smply not lookng for work. Fnally, whle the labour force has expanded over the three years, the partcpaton rate of rural dwellers s lower than the urban. 5.2 Employment outcomes Results for employment outcomes are presented n Tables 5 and 6. There s lttle dfference between the double hurdle and Heckman models under ths specfcaton. We note that the Heckman model s sample selecton correcton term s nsgnfcant under the offcal defnton, but hghly sgnfcant under the broad defnton. The unemployment rate appears to have rsen over the three year perod. Race dummes are agan sgnfcant, wth Asan, coloured and whte South Afrcans all more lkely to be employed than black South Afrcans. Males and household heads are more lkely to be employed than females and those other than the household head. Interestngly, under the offcal unemployment defnton, rural dwellers are more lkely to be employed than urban dwellers, yet ths result s reversed under the broad defnton. 10
Age as a proxy for potental experence has a dmnshng effect on the probablty of employment. Educatonal attanment has a mxed effect on employment outcomes. Under the offcal defnton, those wth Standard 1 educaton are actually less lkely to be employed than those wth no educaton. Under the broad defnton, however, educaton holds no advantage n employment prospects untl secondary school level. In fact, the Heckman model fnds lower secondary schoolng (Standards 5-7) to reduce the chances of employment, ndcatng that vocatonal skll may be more mportant than these levels of educaton. Returns from upper secondary and tertary educaton have a large postve effect on the probablty of employment. These results reconcle wth Keswell and Poswell (2002) who fnd that returns to educaton accelerate rather than dmnsh. These results are not surprsng, consderng that the government has njected substantal funds nto mprovng educaton. In fact, South Afrca can boast school enrolment ratos hgher than other countres n the developng world. In both selecton models and under both unemployment defntons, Englsh mother tongue affords the ndvdual much greater success n employment outcomes. The model suggests that those who speak one of the fve lsted Afrcan languages at home are somewhere n the order of 20% less lkely to be employed than an Englsh mother tongued ndvdual wth the same soco-economc characterstcs. And ths s the estmated outcome even after controllng for race and the level of educaton of the ndvdual. 5.3 Earnngs The two sample selecton models provde smlar estmates for earnngs, as shown n Tables 7 and 8. All the approprate sample selecton correcton terms are sgnfcant. We agan observe postve coeffcents on male, household head and marred dummes. The models suggest that any educaton mproves earnngs. Whle Standards 1 through to 7 provde smlar returns to educaton, completon of secondary educaton, and even more so, tertary educaton, greatly mprove the earnngs potental of the ndvdual South Afrcan. The Mnceran proxy for experence, age, combned wth age 2, has a postve but dmnshng effect on earnngs. Whle sgnfcant, hours worked has lttle partal effect on ncome, owng manly to the small varaton n hours worked for ndvduals n the sample. Informal sector and rural workers have lower ncomes than those n formal jobs and the urban area respectvely. 11
Most relevant to ths paper are the results on race and language. Whte South Afrcans appear to earn a premum over the other racal groups, yet the models consstently suggest that ncomes of Asans and coloured South Afrcans are nsgnfcantly dfferent to blacks wth the same soco-economc and demographc characterstcs. Ths mples that whle earnngs dfferentals do stll exst, South Afrca may be well on ts way to breakng down dscrmnaton on the bass of race alone. Englsh mother tongue provdes an ncome premum above all the other languages. Sesotho, the domnant language n the Free State and Lmpopo, records the lowest partal elastcty, suggestng that ncomes are 40% less than those whose mother tongue s Englsh. The Afrkaans dummy has a surprsngly large negatve estmated sem-elastcty. Notably, Afrkaans speakers are drawn from a wde range of racal backgrounds, the majorty coloured and whte. Indeed, the effect on ncome for a coloured South Afrcan to speak Afrkaans speaker could well be dfferent to that of a whte South Afrcan. Prelmnary study nto the nteracton of colour and Afrkaans dummy varables has been undertaken wth some nterestng results. For the addtve model n ths paper, we fnd the coloured dummy varable to be nsgnfcant, yet when ths effect s nteracted wth Afrkaans, we fnd sgnfcant race and language effects. Brefly, our results ndcate that beng a coloured South Afrcan wth an Afrkaans mother tongue has a greater detrmental effect on ncome than havng an Afrkaans mother tongue and beng a whte South Afrcan. One explanaton could be that Afrkaans speakng coloureds may be concentrated n partcular low-wage occupatons. 6. Dscusson Ths paper has examned the mportance of language for labour force partcpaton, employment and earnngs n South Afrca. The estmated models suggest that Englsh mother tongue language s mportant for success n the labour market, even after condtonng on race and level of educatonal attanment. However, t s recognsed that ths study has a number of shortcomngs. Frstly, nformaton on language profcency s a mssng yet mportant pece of the puzzle. The ndvdual possessng hgher profcency and thus potentally better communcatve sklls would fnd themselves n a better barganng poston for jobs than those whom are less well off n ther ablty to communcate va language. Unfortunately, the October Household Surveys only provde nformaton on the language spoken at home, whch we term the mother tongue. No 12
ndcaton s gven of ablty to speak other languages, nor of ther profcency. Indeed, for a black South Afrcan to speak Englsh at home could mply that the household has had an hstorcally more fortunate exstence than other black South Afrcans under aparthed. In ths case, Englsh mother tongue could be an ndcaton of class. Data on multple language profcency would allow some dstncton between a class effect and the degree to whch language ablty matters n the labour market. Moreover, n attackng the 2001 census, Donnelly (2003) labels responses to a queston seekng to dentfy a sngle mother tongue from a generalsed lst as unrevealng. The sgnfcance of the mother tongue dummes should be taken wth cauton. We have not condtoned on magsteral dstrct n our model. It could well be that language s proxyng for area, whereby the regons where partcular languages are spoken are n fact the poorer areas of South Afrca wth hgher unemployment rates. Indeed, the segregaton of whte and black South Afrcans durng aparthed meant that whole geographcal areas were desgnated accordng to ther perceved poltcal status. Smlarly, we must be careful to dstngush the extent to whch language determnes earnngs drectly, as opposed to language determnng occupaton type, whch n turn determnes earnngs. Further analyss could nclude nteracton effects between combnatons of race, tme, educaton and language. Despte the fact that the South Afrcan government spends a large proporton of ts budget on schools, t may be that educatonal attanment matters consderably more for those wth Afrcan mother tongues. The ncorporaton of more data and a race/tme nteracton effect may reveal some nterestng results concernng race as a determnant of employment and earnngs snce aparthed. Fnally, we recognse that the decson to jon the queue for a job s not ndependent of the probablty of the ndvdual fndng herself employed. Accordngly, our model specfcaton should be altered to accommodate the resultng correlaton n the errors. Despte the lmtatons, these tentatve results are qute marked: a black South Afrcan wth no educaton who speaks Englsh at home (albet paradoxcal) s modelled as more lkely to partcpate n the labour force, more lkely to be employed, and predcted to earn a hgher ncome than, for example, a Xhosa speakng Afrcan wth otherwse dentcal characterstcs. Such a result has mportant mplcatons for polcy concernng South Afrca s multlngual poltcal and socetal stance. The polcy drectve flowng from the mmgraton lterature would pont n the drecton of abolton of multlngualsm n favour of one offcal language: Englsh. However, South 13
Afrca s stuaton s unque, and not smply from a moral or ethcal vewpont. In the mmgraton lterature, mmgrants represent a small mnorty group speakng a mnor language. In South Afrca, we have the domnant (most populous) group speakng a number of seemngly mnor languages, whle the less populous group speaks the major language as a result of mbalanced hstorcal factors. The message of ths paper s that, from the pont of vew of the ndvdual black South Afrcan lookng to enhance ther employment prospects, ther prorty ought to be to learn the major language, Englsh. However, ths does not necessarly extend to the government by suggestng adopton of Englsh as the unversal offcal language of South Afrca. The new South Afrca s all about freedom and equty: the South Afrcan consttuton embraces freedom of the people through allowng and facltatng each populaton group to communcate n ther own language. It would therefore go aganst the sprt of the consttuton to revert to a sngle offcal language, partcularly f t were the language of the mnorty (least populous) group. The mmgrant/mnorty language lterature suggests that technologcal progress s thwarted by lack of communcaton va language between workers, and moreover, snce comparatve skll advantages cannot be communcated, producton results at an overall average rate of skll level rather than beneftng from specalsaton accordng to skll. However, multlngualsm s not always costly (Coulmas, 1992). Consder the case of multlngual communcaton va a translator, for example. Effcency gans can be realsed by mnorty language groups learnng the more populous Afrcan languages, rather than many Afrcans learnng one language to communcate wth the mnorty populaton. Hence, rather than Englsh as the domnant language n commerce beng pushed upon the non-englsh speakng populaton, commerce tself could be adapted to embrace the Afrcan language and subculture. Consequently, commerce would then concde wth South Afrca s poltcal agenda. What seems to be needed s a progressve race narratve that s able to challenge the neolberal war on the poor wthout abandonng the need for blacks to be the authors of ther own destny. It s for the hstorcally domnant bodes to learn to lsten, empathse and follow, wthout crowdng out the voces of the margnalsed. To do otherwse s to turn soldarty nto mperalsm. Mngxtama, 22/6/04. 14
References Coulmas, F., 1992, Language and Economy, Blackwell Publshers, Oxford. Cragg, J., 1971, Some Statstcal Models for Lmted Dependent Varables wth Applcaton to the Demand for Durable Goods, Econometrca, 39, 5, 829-844. Donnelly, S., 2003, Language and the Census, Mal & Guardan, 14/8/03. Grener, G., 1984, The Effect of Language Characterstcs on the Wages of Hspanc Amercan Males, Journal of Human Resources, 19, 25-52. Heckman, J., 1979, Sample Selecton Bas as a Specfcaton Error, Econometrca, 47, 1, 153-162. Kngdon, G. and J. Knght, 2004, Unemployment n South Afrca: The Nature of the Beast, World Development, 32, 3, 391-408. Keswell, M. and L. Poswell, 2002, How Important s Educaton for Gettng Ahead n South Afrca?, CSSR Workng Paper No. 22, Cape Town. Kossoudj, S., 1988, Englsh Language Ablty and the Labor Market Opportuntes of Hspanc and East Asan Immgrant Men, Journal of Labor Economcs, 6, 2, 205-228. Maddala, 1983, Lmted Dependent and Qualtatve Varables n Econometrcs, Cambrdge Unversty Press. McManus, W., 1985, Labour Market Costs of Language Dsparty. An Interpretaton of Hspanc Earnngs Dfferences, Amercan Economc Revew, 75, 4, 818-827. McManus, W., W. Gould and F. Welch, 1983, Earnngs of Hspanc Men: The Role of Englsh Language Profcency, Journal of Labour Economcs, 1, 2, 101-130. Mncer, J., 1974, Schoolng, Experence, and Earnngs. New York: Columba Unversty Press. 15
Mngxtama, A., 2004, Let Black Voces Speak for the Voceless, Mal & Guardan, 22/6/04. Mora, M., 2003, An Overvew of the Economcs of Language n the U.S. Labor Market: Presentaton Notes, Amercan Economc Summer Mnorty Program Presentaton Notes, Unversty of Colorado, Denver. Rvera-Batz, F., 1990, Englsh Language Profcency and the Economc Progress of Immgrnts, Economcs Letters, 34, 295-300. Serumaga-Zake, P. and W. Naude, 2003, Prvate Rates of Return to Educaton of Afrcans n South Afrca for 1995: a Double Hurdle Model, Development Southern Afrca, 20, 4, 515-528. Southafrcanfo, 2004, http://www.southafrca.nfo/ess_nfo/sa_glance/demographcs/language.htm. 16
Table 1 Average sample unemployment rates and average monthly earnngs by offcal race classfcaton Unemployment rate Average monthly earnngs Offcal Expanded Afrcan 28.22 45.40 1582 Asan 11.70 15.51 3218 Coloured 14.12 21.86 1736 Whte 3.80 5.67 5674 Overall 22.56 37.40 2213 Source: 1996-1998 October Household Surveys. Table 2 Number of respondents and average monthly earnngs by mother tongue Number of Respondents Average monthly earnngs Afrcan Asan Coloured Whte Total Afrkaans 1283 132 19020 10277 30712 2752 Englsh 448 5158 2569 6139 14314 4662 Seped 16110 0 24 3 16137 1835 Sesotho 17207 3 48 16 17274 1414 Setswana 18694 0 119 5 18818 1530 Xhosa 32336 4 94 46 32480 1502 Zulu 38583 16 45 10 38654 1594 Other than Englsh 19842 258 170 326 20596 1739 Total/Overall 144503 5571 22089 16822 188985 2213 Source: 1996-1998 October Household Surveys. 17
Table 3 Varable defntons Varable ASIAN COLOUR WHITE MALE HEAD ED1 ED2-4 ED5-7 ED8-10 EDTERT EDOTHER RURAL MARRIED AGE AGE 2 HOURS Y1996 Y1997 Y1998 INFORMAL XHOSA ZULU SEPEDI SESOTHO SETSWANA AFRIKAANS OTHERL Descrpton Dummy varables for populaton group, takng a value of 1 where the respondent s offcally classfed as Asan, Coloured and Whte respectvely. Base: Afrcan. Gender dummy takng the value of 1 where the respondent s male. Dummy varable takng the value of 1 where the respondent s regarded as the head of the household. Hghest level of educatonal attanment. Lnear regresson splnes were used to allow dfferng slopes across Standards 1-10, tertary and other levels of educatonal attanment. Dummy varable takng the value of 1 where the respondent resdes n a rural area. Dummy varable takng the value of 1 where the respondent s marred. Used n partcpaton and ncome equatons only. Age and Age 2 to allow for a nonlnear effect. Ths would also capture Mnceran potental experence. Hours worked n the last 7 days. Tme dummes to allow for dfferent ntercepts n each year. A dummy takng the value of 1 for employment n the nformal sector. Derved from the man category of occupaton and/or, for self-employed persons, an absence of regstraton of the busness for VAT or wth the regster of companes, the Commssoner of unemployment nsurance or the Commssoner of workmen s compensaton. Dummy varables takng the value of 1 where the language spoken at home s Isxhosa/Xhosa, Iszulu/Szulu/Zulu, Seped/Northern Sotho, Setswana/Tswana, Afrkaans and a language other than these 7, respectvely. Englsh s the language of partcular nterest, rankng 8 th out of all possble responses. Hence, 8 categores were chosen. Base: Englsh 18
Table 4 Partcpaton Double Hurdle & Heckman Offcal Estmates t statstc Margnal effects Broad Estmates t statstc Margnal effects C -4.2822-103.253 0.0000-4.4936-108.480 0.0000 ASIAN -0.1956-5.726-0.0540-0.3240-9.320-0.1048 COLOUR 0.1363 4.856 0.0428 0.0653 2.280 0.0235 WHITE -0.1356-4.666-0.0384-0.2342-7.900-0.0780 MALE 0.4512 63.778 0.1552 0.4180 59.050 0.1596 HEAD 0.4912 57.527 0.1705 0.3989 45.300 0.1520 ED1 0.0444 7.485 0.0135 0.0619 10.620 0.0223 ED24-0.0307-2.679-0.0091-0.0566-5.010-0.0198 ED57-0.0573-5.788-0.0168-0.0786-8.070-0.0274 ED810 0.2078 26.292 0.0668 0.2410 30.840 0.0899 EDTERT 0.1524 10.664 0.0481 0.0712 4.840 0.0257 EDOTHER -0.7662-10.250-0.1603-0.5559-7.090-0.1653 RURAL -0.2973-38.654-0.0785-0.2295-29.960-0.0765 MARRIED 0.0774 9.519 0.0238 0.0065 0.790 0.0023 XHOSA -0.3939-12.766-0.0994-0.2266-7.200-0.0756 ZULU -0.1885-6.149-0.0522-0.0298-0.950-0.0105 SEPEDI -0.3169-9.855-0.0829-0.2179-6.660-0.0729 SESOTHO -0.1576-4.987-0.0442-0.0555-1.720-0.0194 SETSWANA -0.1835-5.831-0.0509-0.0237-0.740-0.0084 AFRIKAANS -0.0547-3.009-0.0160-0.0585-3.160-0.0205 OTHERL -0.1638-5.319-0.0459-0.1036-3.290-0.0358 AGE 2.1732 156.485 0.4864 2.5077 183.550 0.7900 AGE2-0.2708-161.519-0.0606-0.3158-189.610-0.0995 Y1997 0.0025 0.302 0.0007 0.0200 2.400 0.0071 Y1998 0.1358 14.391 0.0426 0.1059 11.260 0.0385 19
Table 5 Employment Double Hurdle Model Offcal Estmates t statstc Margnal effects Estmates Broad t statstc Margnal effects C -0.9545-12.561 0.1699-1.4479-22.268 0.0738 ASIAN 0.2160 3.714 0.0556 0.2601 5.049 0.0941 COLOUR 0.2963 6.543 0.0734 0.3235 8.300 0.1153 WHITE 0.6780 13.647 0.1378 0.6942 16.085 0.2212 MALE 0.1541 13.163 0.0408 0.2578 26.651 0.0933 HEAD 0.5856 43.792 0.1251 0.6152 55.822 0.2013 ED1-0.0339-3.358-0.0097-0.0097-1.241-0.0037 ED24 0.0394 2.080 0.0110 0.0215 1.461 0.0082 ED57-0.0262-1.624-0.0075-0.0178-1.380-0.0068 ED810 0.0836 6.524 0.0228 0.0904 8.458 0.0339 EDTERT 0.3614 15.365 0.0867 0.4428 21.023 0.1528 EDOTHER -0.8625-6.746-0.3099-1.2169-11.311-0.4390 RURAL 0.0436 3.460 0.0121-0.1214-11.736-0.0470 XHOSA -0.6238-12.051-0.2153-0.7493-16.598-0.2914 ZULU -0.4845-9.445-0.1618-0.5784-12.915-0.2275 SEPEDI -0.6094-11.348-0.2096-0.6662-14.246-0.2609 SESOTHO -0.4056-7.700-0.1325-0.4776-10.405-0.1883 SETSWANA -0.3750-7.132-0.1214-0.5017-10.981-0.1978 AFRIKAANS -0.0800-2.269-0.0234-0.0897-2.871-0.0346 OTHERL -0.4392-8.459-0.1449-0.4547-10.026-0.1793 AGE 0.7488 26.601 0.7317 0.7394 31.074 0.4586 AGE2-0.0650-18.317-0.0635-0.0630-21.022-0.0391 Y1997-0.0309-2.181-0.0089-0.0312-2.680-0.0119 Y1998-0.1504-9.954-0.0452-0.0479-3.776-0.0184 20
Table 6 Employment Heckman Model Offcal Estmates t statstc Margnal effects Estmates Broad t statstc Margnal effects C -0.7763-2.506 0.2188-3.4204-14.616 0.0003 ASIAN 0.2206 3.762 0.0541 0.1738 3.307 0.0692 COLOUR 0.2924 6.393 0.0692 0.3439 8.798 0.1363 WHITE 0.6813 13.636 0.1309 0.6343 14.495 0.2440 MALE 0.1414 5.822 0.0360 0.3747 22.662 0.1482 HEAD 0.5729 22.751 0.1169 0.7135 45.155 0.2711 ED1-0.0351-3.450-0.0097 0.0069 0.868 0.0027 ED24 0.0402 2.143 0.0107 0.0066 0.451 0.0026 ED57-0.0245-1.498-0.0067-0.0413-3.131-0.0163 ED810 0.0776 4.733 0.0204 0.1603 11.960 0.0638 EDTERT 0.3591 15.097 0.0822 0.4441 20.978 0.1747 EDOTHER -0.8459-6.467-0.2975-1.3329-12.247-0.3802 RURAL 0.0520 2.740 0.0138-0.1851-14.608-0.0722 XHOSA -0.6130-11.177-0.2058-0.8128-17.735-0.2774 ZULU -0.4793-9.217-0.1553-0.5881-13.100-0.2132 SEPEDI -0.6010-10.820-0.2012-0.7278-15.357-0.2545 SESOTHO -0.4015-7.560-0.1271-0.4943-10.739-0.1830 SETSWANA -0.3700-6.954-0.1159-0.5101-11.140-0.1882 AFRIKAANS -0.0787-2.227-0.0221-0.1077-3.431-0.0423 OTHERL -0.4350-8.308-0.1391-0.4845-10.633-0.1798 AGE 0.6766 5.407 0.5476 1.6064 15.827 0.7293 AGE2-0.0559-3.565-0.0452-0.1732-13.436-0.0786 Y1997-0.0308-2.175-0.0085-0.0256-2.197-0.0101 Y1998-0.1538-9.514-0.0446-0.0181-1.382-0.0072 λ P -0.0470-0.594-0.0130 0.5407 8.733 0.2106 21
Table 7 Income Double Hurdle Model Offcal Broad Estmates t statstc Estmates t statstc C 4.3683 26.500 4.3549 28.010 ASIAN 0.0143 0.440 0.0089 0.270 COLOUR 0.0497 1.860 0.0457 1.710 WHITE 0.4157 14.710 0.4200 15.070 MALE 0.3919 28.540 0.3775 31.730 HEAD 0.1065 6.740 0.0994 6.830 ED1 0.0709 10.350 0.0694 10.240 ED24-0.0138-1.070-0.0136-1.060 ED57 0.0243 2.150 0.0220 1.940 ED810 0.0873 8.550 0.0883 8.720 EDTERT 0.1720 14.670 0.1687 14.350 EDOTHER -1.1025-18.510-1.0809-18.230 RURAL -0.4181-35.440-0.3944-39.820 MARRIED 0.0939 11.460 0.0865 10.680 XHOSA -0.2411-7.750-0.2244-7.220 ZULU -0.1407-4.790-0.1283-4.340 SEPEDI -0.1291-4.000-0.1247-3.880 SESOTHO -0.4065-13.640-0.3993-13.360 SETSWANA -0.2337-7.870-0.2167-7.230 AFRIKAANS -0.1997-12.760-0.2022-12.910 OTHERL -0.1308-4.470-0.1321-4.540 AGE 0.8597 13.110 0.8939 13.620 AGE2-0.0922-11.200-0.0965-11.680 HOURS 0.0013 5.470 0.0013 5.490 INFML -0.4351-49.740-0.4339-49.550 Y1997 0.1118 12.430 0.1129 12.530 Y1998 0.2082 18.080 0.1938 18.380 λ P 0.1762 3.750 0.1739 4.180 λ E -0.2402-4.500-0.1585-4.170 22
Table 8 Income Heckman Model Offcal Broad Estmates t statstc Estmates t statstc C 4.9139 65.690 4.9670 66.030 ASIAN 0.0355 1.110 0.0317 0.990 COLOUR 0.0445 1.670 0.0396 1.480 WHITE 0.4376 15.810 0.4359 15.890 MALE 0.3521 40.660 0.3430 37.340 HEAD 0.0794 5.710 0.0707 5.170 ED1 0.0653 9.760 0.0642 9.640 ED24-0.0093-0.720-0.0088-0.680 ED57 0.0291 2.590 0.0288 2.570 ED810 0.0703 7.700 0.0682 7.460 EDTERT 0.1720 14.660 0.1697 14.480 EDOTHER -1.0669-18.150-1.0519-17.820 RURAL -0.3883-44.510-0.3756-41.420 MARRIED 0.0887 10.970 0.0881 10.900 XHOSA -0.2223-7.260-0.2079-6.700 ZULU -0.1373-4.680-0.1272-4.310 SEPEDI -0.1172-3.650-0.1089-3.400 SESOTHO -0.4033-13.540-0.3963-13.260 SETSWANA -0.2272-7.660-0.2160-7.210 AFRIKAANS -0.1972-12.610-0.1983-12.680 OTHERL -0.1278-4.370-0.1250-4.300 AGE 0.6345 25.030 0.6277 25.880 AGE2-0.0633-22.770-0.0628-23.420 HOURS 0.0013 5.420 0.0013 5.470 INFML -0.4345-49.670-0.4333-49.470 Y1997 0.1110 12.340 0.1114 12.390 Y1998 0.1918 17.970 0.1858 17.950 λ E -0.1668-3.340-0.1591-4.260 23