Conrade Wages and wage elastctes for wne and table grapes n South Afrca B Conrade Abstract A survey of 90 wne and table grape farmers n the Western Cape puts the average wage for farm labour at R928 per month n 2003 and R23 per month n 2004. Output per worker has doubled snce 983. On farms wth grape harvesters, labour s 30 per cent more productve (48 ton/worker) than on farms where wne grapes are pcked by hand (37 ton/worker). At 9.75 tons per worker, table grapes are four tmes as labour-ntensve as wne grapes. Resdent men domnate the workforce on wne farms, whle the resdent female workforce s 20 per cent larger than the resdent male workforce on table grape farms. Seasonal workers contrbute a thrd of labour n table grapes, and brokers less than ten per cent n ether case. In a sngle-equaton short-run Hcksan demand functon, wage, output, captal levels and mechansaton ntenstes are hghly sgnfcant determnants of employment. Hgher wages decrease employment and larger output ncreases employment. More mechansaton, measured by the number of tractors used to produce a ton of frut, rases labour ntensty too. Grape harvesters could not be shown to reduce jobs. The ten per cent rse n the mnmum wage planned for March 2005 could reduce employment by 3.3 per cent n the wne ndustry and 5.9 per cent n the table grape ndustry, but t s more lkely that the wage ncrease wll be offset aganst fewer benefts. The average expected mpact s about the same as for all agrculture and manufacturng as a whole.. Introducton Agrculture s sheddng labour the world over. Lately, new legslaton governng farm labour may have ncreased the rate at whch jobs are lost n South Afrca (Newman et al, 997; South Afrcan Human Rghts Commsson, 2003:62). Gven the broad unemployment rate of close on 30 per cent (Klasen & Woolard, 999), t s mportant to know the effect of the mnmum wage on employment n every sector to whch t apples. In 970, agrculture employed 3 per cent of the economcally actve populaton n South Afrca. In 2002, only 6 per cent of the economcally actve reman n agrculture (Department of Agrculture, 2004). Employment levels School of Economcs, Unversty of Cape Town, Cape Town, 770, South Afrca. bconrad@commerce.uct.ac.za. 38
Conrade have fallen steadly at.7 per cent per year snce 970. There s some evdence that the trend may have accelerated durng the early 990s. The Department of Labour (200) reports that one n fve farm workers lost ther jobs between 990 and 996. In contrast, Western Cape farms shed almost no jobs between 985 and 2002 n spte of real wages rsng at 2.3 per cent per year over that perod. The mnmum wage was ntroduced at two levels n March 2003, the hgher of whch was bndng for wne farmers (Conrade, 2004). Mnmum wage laws per se do not create unemployment, but when bndng, the extent of dsemployment depends on the elastcty of labour demand, whch s ndustry specfc. Jobs are lost when the real wage grows faster than productvty or where relatve factor costs favour mechansaton. Some producton processes, such as frut pckng, are nherently less lkely to be mechansed. Ths could partly explan why the Western Cape has lost fewer jobs than the rest of the country. It also suggests that the demand for farm labour n the provnce s relatvely nelastc and that a bndng mnmum wage may cause fewer job losses here than elsewhere. Fallon and Lucas (998:) showed that wage elastctes for the non-farm sectors n South Afrca range from extremely nelastc n tobacco ( 0.06) and beverages ( 0.8) to qute elastc n textles ( 0.98) and wearng apparel ( 2.5). One of the few local estmates for agrculture places the wage elastcty for farm labour n KwaZulu-Natal on the elastc sde at about.4 (Latt & Neuwoudt, 985). In the US corn belt, the demand for farm labour s nelastc (O Donnell et al, 999) whle reported wage elastctes range from 0.32 n England and Wales to 0.79 n Scotland (Dckens et al, 995). Errngton et al (997) confrm the nelastc demand for farm labour England and Wales wth short-run elastctes of 0.2 and 0.20 and long-run elastctes of 0.43 and 0.53. In South Afrca, the overall wage elastcty for agrculture s estmated to be 0.59 (Balcombe et al, 2000), whch s not unlke the estmate of 0.55 found for manufacturng (Fedderke & Marott, 2002). But Fallon and Lucas s fndngs provde reasons to beleve that wthn agrculture, estmates could vary dramatcally from sector to sector. The results presented here, are specfc to the wne and table grape ndustres. Data come from a two-wave panel of 80 wne farmers surveyed n August 2003 and resurveyed n August 2004. In 2004, the survey was extended to nclude 40 table grape growers as well. Detals of data collecton and ntal fndngs are dscussed n Conrade (2004). The structure of the paper s smple: Secton II brefly touches on the data, Secton III presents the model 39
Conrade specfcaton and secton IV dscusses the econometrc results. The paper closes wth a summary of estmates wage elastctes and ther polcy mplcatons. 2. Descrptve statstcs Selected descrptve statstcs are gven n Table. It s clear that wne grapes are dfferent from table grapes n most respects. Wne farms are sgnfcantly larger n terms of output and frut produced, although table grape operatons employ more workers. Female farm workers and seasonal workers, of whom the majorty are female, are more readly employed on table grape farms than on wne farms. On wne farms, regular men domnate (59% of the workforce), whle table grape farms employ fewer regular men than regular women. Counter to popular belef (South Afrcan Human Rghts Commsson, 2003:43), women are employed n ther own rght on table grape farms, although not on wne farms. At 32 per cent, or 43.3 full-tme labour equvalents, seasonal workers are an mportant part of the workforce on table grape farms. Seasonal workers are not mportant on wne farms. Labour provded by labour brokers comprses less than ten per cent of the workforce n both ndustres. The level of mechansaton, as measured by the number of tractors, s the same n both ndustres, but varable mechansaton cost, measured as the amount spent on fuel, s sgnfcantly hgher on table grape farms than on wne farms. Table : Selected sample descrptve statstcs 2003 wne 2004 wne Table grape Entre sample Varable Unts Mean Mean Mean Mean Std dev n Output ton 393 533 036 386 20 88 wne grapes % of crop 77 63 0.6 60 37 88 table grapes % of crop 2 0.5 98 22 4 88 Unt cost R/ton 5 676 6255 255 2309 6 Employment FTEs 33.7 34.8 8.6 52. 80.8 90 resdent men 9.2 9.5 29.6 2.5 7.0 90 wves of resdent men 2.8.8 35.5 7.7 7.0 87 contract labour 2.9 2.8.8 4.6 2.0 88 seasonal workers.5 2.6 43.3 0.7 36.2 88 Average wage R/month 928 035 227 039 346 8 Labour productvty ton/fte 4.8 43.9 9.75 36. 20.5 88 Tractors number 5.9 6.0 6.8 6.2 4.3 88 Tractor productvty ton/tractor 228 243 57 29 05 88 Grape harvesters number 0.40 0.37 NA 0.4 0.47 5 Fuel R000 72 67 59 92 6 73 Fuel productvty ton/r000 fuel 2.5 2.9 9.6 9. 0.4 7 From August 2003 to August 2004, the wne ndustry saw some permanent women replaced seasonal workers, but the changes were not statstcally 40
Conrade sgnfcant at the fve per cent level. In some cases, the change s due to a reclassfcaton of the same ndvduals. Wne farms use more permanent labour than other agrculture n the Western Cape. The 2002 Agrcultural Census reports an average workforce of 29 workers per farm for the Western Cape, and sets the contrbuton of part-tme and seasonal workers at 55 per cent (StatsSA, 2004). The average wne farm n ths sample produces 40 tons of wne grapes and 340 tons of other frut, consstng mostly of frut for cannng. It s large and expensve compared to the ndustry average. Accordng to SAWIS (2004) the average wne farm produces only 278 tons of grapes, and more than 80 per cent of wne farmers produce less than 500 tons per year. The dfference s mostly due to what one regards as a farm. SAWIS counts producer numbers, or wne brands, whle ths survey counts operatonal unts. In addton, wne makng or frut packng are ncluded n the scope, and thus cost, of a farm, whle VINPRO calculates unt cost of producton from a lst of grape growng actvtes. VINPRO estmates the average cost of producton n ths area to be about R760 per ton (Van Wyk, 2004). The average table grape farm produces 008 tons of table grapes and 28 tons of other frut, ncludng wne grapes and ctrus. Unt cost of producton s R255 per ton for the sample as a whole, but s sgnfcantly hgher n the table grape ndustry (R6255/ton) than n the wne ndustry (± R550/ton). Ths s not surprsng gven that table grapes are packaged on the farm whle most wne grapes are processed centrally. Table grape cost thus ncludes the cost of packng, coolng and shppng whle cellar costs are only ncluded for a small number of wne farms. Even f one ncludes the estmated processng costs of roughly R900 per ton (SAWIS, 2004), table grapes are stll sgnfcantly more expensve to produce than wne grapes. The average wage on wne farms ncreased sgnfcantly from R925 per month n 2003 to R035 per month n 2004. Table grape workers earn hgher wages, on average R227 per month n 2004. Average wage was calculated by dvdng the total wage bll for the prevous fnancal year by the number of workers employed n August of each year. Accordng to the Census, the Western Cape s average wage was R662 per month for all farm labour and R49 per month for permanent labour n 2002 (StatsSA, 2004). Recalculated for the observed rato of permanent to casual staff, the 2002 provncal average s R053 per month, whch s 9 per cent hgher than the wage recorded here. Labour productvty, measured n tons of frut produced per full-tme labour equvalent, s four tmes hgher on wne farms (± 43 ton/fte) than on table grape farms (9.75 ton/fte). Labour productvty on wne farms ncreased by 4
Conrade fve per cent n 2004. In 983, the average product of labour n the study area (statstcal regon 8) was 20 tons per worker (StatsSA, 987). Wne grapes are dfferent from most other frut nsofar as they can be pcked by machne, makng captal and labour drect substtutes n ths case. There s a clam that grape pckng machnes could reduce a wne farm s harvest labour requrement from a hundred workers to two workers (Smb & Alber, 2000). In ths survey, 35 per cent of respondents already own a grape pckng machne and another 20 per cent rent a quarter of a machne on average. The capactes of these machnes vary wdely and part-tme use s dffcult to estmate accurately. However, the average labour productvty on farms wth a grape harvester s 29 per cent hgher (48 ton/fte) than on farms where a grape harvester s not used (37 ton/fte). As more farms adopt grape harvesters, labour productvty wll rse, employment wll most lkely fall and real wages mght ncrease. Tractors are the measure of mechansaton for whch relable data are easest to collect. Fuel data, whle more accurate (tractors can be dle), s more dffcult to record accurately at the farm-level. The average farm has 6.2 tractors and spent R92 000 on fuel n the prevous fnancal year. There s no statstcal dfference between the number of tractors on wne farms and table grape farms, but the average expendture on fuel s sgnfcantly hgher on table grape farms. Lke labour productvty, tractor productvty s hgher on wne farms (± 235 ton/tractor) than on table grape operatons (57 ton/tractor). Fuel productvty s twce as hgh on wne farms as on table grape farms. 3. Model specfcaton Economc theory usually assumes that prces (wages) and quantty (employment levels) are endogenous n compettve markets, but n the presence of mnmum wages ths may not be the case. The choce of model crtcally depends on the presence of smultanety. If there s smultanety, 2SLS estmaton s consstent and effcent whle OLS s nconsstent. If on the other hand, there s no smultanety, OLS s consstent and effcent whle 2SLS s consstent but neffcent (Pndycke & Rubnfeld, 998:353). Hausman s specfcaton test 2 s used as a prelmnary dagnostc before the models are dscussed n more detal. 2 See Appendx below. 42
Conrade 3. Determnants of labour demand In the smple neoclasscal model of proft maxmsaton, a frm produces output by choosng optmal combnatons of captal and labour to maxmse profts. The short-run cost functon s a constraned verson of proft maxmsaton n whch captal and output levels are held constant. C = C( w, K, y) By Shephard s lemma, the condtonal labour demand functon s the partal dervatve of the short-run cost functon wth respect to wage. The basc estmatng equaton s obtaned by takng the natural logarthms: ln(empl) = 0 ln(wage) 2 ln(output) 3 ln(captal level) u where empl wage output u employment n full tme labour equvalents average nomnal wage calculated from prevous year s wage bll and current full-tme equvalent employment sze of the harvest n tons stochastc error term A hgher wage s expected to reduce employment and hgher output to ncrease employment. Gven the double log specfcaton, the coeffcent on wage can be read off drectly as a short-run Hcksan wage elastcty (Errngton et al, 997). Captal level s proxed by expendture on fuel and the presence of certan labour-savng equpment such as grape harvesters. fuel gh fuel expendture n R000 number of grape pckng machnes used (owned and rented) If one beleves that captal and labour are substtutes n producton, the a pror expectaton on fuel s negatve, but several authors have found that captal and labour are complements (Latt & Neuwoudt, 985; O Donnell et al, 999; Fedderke & Marott, 2002). Ths suggests that producton on some farms (and frms more generally) s more ntensve than on others; those farms that use more labour also use more machnes. Nevertheless, the a pror expectaton about the coeffcent on gh s stll negatve snce these machnes drectly save labour. 43
Conrade Pckng up on the dea of ntensty of producton, the followng captal productvty varable s defned: tracton number of tractors output Tracton measures the ntensty of mechansaton ndependent of farm sze. The expected sgn s postve snce frms that have hgh machne productvty wll also have hgh labour productvty and vce versa. Theoretcally one expects unque labour demand functons for each of the products produced. A crop dummy allows one to separate table grapes from wne grapes. A postve coeffcent would ndcate that table grapes are more labour ntensve than wne grapes. cropd crop dummy, = table grapes and 0 = wne grapes Cash wages are not the only cost of employment. Farm workers n the study area receve extensve free benefts whch are not accurately quantfed. Benefts are expected to reduce employment snce they ncrease the cost of employment. The provson of free electrcty s used to proxy all benefts. electrcty = free electrcty provded, 0.5 = electrcty subsdzed, 0 = workers pay for electrcty Crop type and mechancal grape pckng, as captured by grape harvesters, potentally affect wage elastcty. There s no a pror expectaton regardng the sgn of the nteracton between cropd and lnwage. Accordng to the Hcks- Marshall rules of elastcty (Hcks, 932:242), demand should be more elastc where the wage bll contrbutes a larger porton of total costs (table grapes) and more elastc where the technology permts nput substtuton (wne grapes). The nteracton between gh and lnwage s expected to have a negatve coeffcent, snce the opportunty to substtute a machne for workers s greater on farms where grape harvesters are used. Fnally, t s possble that the demand for certan knds of labour s more elastc than for other knds. To ths end, four addtonal employment varables were defned: men women seas broker regular men wth headcount = FTE the wves of regular men wth headcount adjusted for actual hours mostly pckng labour recruted by the farmer n FTE labour provded by a thrd party n FTE 44
Conrade Insofar as resdent men are the domnant source of labour, one expects the demand for ther labour to be less elastc than for that of women who often have only casual status n the eyes of many farmers (Conrade, 2004). There s no obvous expectaton wth regards to the elastcty of seasonal and contract labour. If the farmer vews casual labour as extra, the demand for ts labour wll be relatvely elastc, but where farmers have replaced permanent staff wth casual labour, demand should be no dfferent. 4. Results and dscusson Labour demand on grape farms s generally nelastc. Results deterorate n proporton to the share of a partcular labour type n overall employment. Between 85 and 89 per cent of the varaton n total employment (Table 3) s explaned, whle only about 80 per cent of the varaton n the employment of permanent men (Table 4) could be explaned. For the wves of regular men (Table 5), the overall ft drops to between 63 and 7 per cent, but n ths model wage s no longer statstcally sgnfcant at any reasonable level of confdence. In the models of the demand for seasonal workers (Table 6) about 64 per cent of the varaton s explaned, whle the coeffcent on wage s only sgnfcant at the 5 per cent level of confdence. Accordng to Table 3, wage, farm sze, fuel expendture and mechansaton ntensty are hghly sgnfcant determnants of overall employment. Coeffcents have the expected sgns. A hgher wage reduces employment and larger farms employ more people. Ths study confrms the result that frms usng more machnes also use more labour, snce both lnfuel and tracton carry postve sgns. Surprsngly, grape harvesters could not be shown to reduce overall employment, but table grape farmers employ sgnfcantly larger workforces than wne farmers. The coeffcent on the nteracton term of lnwage and cropd n Model 3 s not statstcally sgnfcant at fve per cent, mplyng that at the fve per cent level of confdence there s no statstcal dfference n the wage elastctes for the two ndustres. However, f one s wllng to accept the p-value of 0.079 for the nteracton term, the demand for labour n table grape producton (-0.59) s almost twce as elastc as the demand for labour on wne farms (-0.33). In Models 3 and 4 the Ramsey RESET tests (Gujarat, 995:465) fal to reject the null hypothess that the models have no omtted varables, whle Breusch-Pagan tests (Gujarat, 995:379) fal to reject a constant varance hypothess and Whte s tests (Gujarat, 995:379) fal to reject a null hypothess of homoskedastcty. 45
Conrade Table 3: Demand for all workers Varable Model Model 2 Model 3 Model 4 Dependent varable ln(empl) ln(empl) ln(empl) ln(empl) ln(wage) -0.32** -0.34** -0.334** -0.390** (-3.64) (-5.20) (-4.94) (-6.5) ln(sze) 0.556** 0.563** 0.570** 0.6** (6.84) (7.03) (9.63) (9.86) ln(fuel) 0.423** 0.49** 0.339** 0.328** (7.2) (7.23) (7.28) (7.05) ln(tracton) 0.274** 0.276** 0.28** 0.289** (3.52) (3.55) (4.23) (4.34) cropd dropped dropped 2.820**.036** (2.79) (6.37) cropd ln(wage) 0.5** 0.6** -0.255 (3.8) (3.20) (-.77) gh 0.397-0.0 (0.4) (-.73) gh ln(wage) -0.073 (-0.52) electrcty -0.23** -0.22** -0.87** -0.93** (-4.5) (-4.5) (-3.88) (-4.00) constant.502*.676**.679** 2.067** (2.49) (3.35) (3.33) (4.53) n 35 35 7 7 adjusted R 2 0.85 0.85 0.89 0.89 Ramsey RESET 2.58 2.65.58.67 prob. 0.06 0.05 0.9 0.8 Breusch-Pagan 4.32 4.27.5 0.34 prob. 0.04 0.04 0.28 0.56 Whte 5.39 47.52 35.46 34.48 prob. 0.04 0.0 0.27 0.2 T-statstcs n parentheses; * sgnfcant at 5%, ** sgnfcant at % Accordng to Table 4, the demand for regular men s smlar to the demand for total labour. Hgher wages sgnfcantly reduce the number of permanent men employed and larger farms employ more regular men than smaller farms. Hgher fuel spendng and more ntensve use of machnes are assocated wth more employment. Agan table grape farms employ more workers than wne grape operatons, but ths tme the crop dummy and the cropd-lnwage nteracton term are not sgnfcant at the fve per cent level. To argue that the demand for regular men s more elastc on table grape farms (-0.44) than on wne grape farms (-0.20), one needs to accept a p-value of 0.46 on the nteracton term. Ths s usually not done, but gven the small number of table grape growers n the current sample, the data defntely suggest that demand for regular men n the two ndustres could be very dfferent from each other. Benefts do not seem to affect the number of regular men employed and grape harvesters could not be shown to reduce employment. In Models 8 and 9 the Ramsey RESET test fals to reject the null hypothess that the models have no 46
Conrade omtted varables, whle Breusch-Pagan tests fal to reject a constant varance hypothess and Whte s test fal to reject a null hypothess of homoskedastcty. Table 4: Demand for all permanent men Varable Model 5 Model 6 Model 7 Model 8 Model 9 Dependent varable ln(men) ln(men) ln(men) ln(men) ln(men) ln(wage) -0.50-0.47-0.209** -0.205** -0.258** (-.65) (-.62) (-3.02) (-2.69) (-3.82) ln(sze) 0.60** 0.59** 0.609** 0.62** 0.624** (7.08) (7.06) (7.40) (8.82) (9.02) ln(fuel) 0.296** 0.305** 0.296** 0.224** 0.23** (4.77) (4.98) (4.87) (4.27) (4.09) ln(tracton) 0.232** 0.29** 0.223** 0.248** 0.255** (2.8) (2.69) (2.74) (3.3) (3.4) cropd dropped dropped dropped 2.05 0.445** cropd ln(wage) 0.073 0.075 0.075-0.237 (.9) (.95) (.95) (-.46) gh.4.086 (.08) (.05) gh ln(wage) -0.8-0.76-0.09 (-.2) (-.8) (-.95) electrcty -0.05 (-0.93) constant -0.354-0.378-0.02 0.308 0.667 (-0.56) (-0.59) (-0.02) (0.54) (.30) n 35 35 35 7 7 adjusted R 2 0.82 0.82 0.82 0.79 0.79 Ramsey RESET 7.06 7.03 7.29 2.48 2.46 prob. 0.0002 0.0002 0.0002 0.06 0.06 Breusch-Pagan 8.55 8.58 8.67 0.6 0.4 prob. 0.004 0.0034 0.0032 0.43 0.52 Whte 67.75 6.47 43.48 33.99 23.62 prob. 0.0007 0.0002 0.0027 0.07 0.2 T-statstcs n parentheses; * sgnfcant at 5%, ** sgnfcant at % Models of the demand for the labour suppled by the wves of regular men are presented n Table 5. Whle these models provde a reasonable ft, wth sgnfcant coeffcents on lnsze, lnfuel and lntracton, the coeffcent on lnwage s not statstcally dfferent from zero at any reasonable level of sgnfcance for Models 0 to 3. Grape harvesters and the free benefts could not be shown to reduce the number of women employed, but the average table grape operaton employs about four and a half more permanent women than the average wne farm. The Ramsey RESET tests on Models 2 and 3 fal to reject the null hypothess of no omtted varables and the results of the Breusch-Pagan and Whte tests ndcate the absence of heteroskedastcty. Gven that all varables n Model 3 are sgnfcant at the fve per cent level, there s no reason to drop any of them, but snce the majorty of permanent women n the sample are employed on table grape farms, the lnwage-cropd nteracton term prevents the 47
Conrade wage elastcty from beng estmated accurately. In Model 4 the nteracton term s dropped, producng a statstcally sgnfcant coeffcent on lnwage of -0.95, whch s smlar to the wage elastcty estmated for ther husbands. The result of Ramsey s RESET test fals to reject the null hypothess of no omtted varables and the results of the Breusch-Pagan and Whte tests agan ndcate the absence of heteroskedastcty for Model 4. Table 5: Demand for labour suppled by the wves of regular men Varable Model 0 Model Model 2 Model 3 Model 4 Dependent varable ln(women) ln(women) ln(women) ln(women) ln(women) ln(wage) -0.035-0.035-0.093-0.078-0.95* (-0.24) (-0.24) (-0.85) (-0.7) (-2.00) ln(sze) 0.46** 0.46** 0.5** 0.49** 0.57** (3.49) (3.49) (5.2) (5.02) (5.26) ln(fuel) 0.44** 0.44** 0.285** 0.294** 0.272** (4.3) (4.3) (3.85) (3.99) (3.68) ln(tracton) 0.25 0.25* 0.290** 0.284** 0.298** (.96) (.96) (2.74) (2.67) (2.78) cropd dropped dropped 4.75** 4.438** 0.944** (2.63) (2.79) (9.62) cropd ln(wage) 0.29* 0.29* -0.468* -0.500* (2.22) (2.22) (-2.06) (-2.20) gh.4 (0.72) gh ln(wage) -0.89-0.024 (-0.82) (-.6) electrcty -0.23* -0.67* -0.24 (-2.02) (-2.02) (-.62) constant -0.793-0.393-0.0-0.22 0.573 (-0.80) (-0.48) (-0.3) (-0.26) (0.78) n 30 30 66 66 66 adjusted R 2 0.63 0.63 0.7 0.7 0.7 Ramsey RESET 2.60 2.5 0.38 0.3 0.23 prob. 0.06 0.06 0.77 0.82 0.87 Breusch-Pagan 0.53 0.57 0.03 0.6 0.03 prob. 0.47 0.45 0.86 0.69 0.86 Whte 37.86 24.83 9.92 5.38 3.98 prob. 0.34 0.64 0.94 0.88 0.78 T-statstcs n parentheses; * sgnfcant at 5%, ** sgnfcant at % The demand for seasonal labour n Table 6 s determned by the same varables as the demand for permanent labour, but a smaller sample sze generally produces fewer sgnfcant coeffcents. As expected, table grape farms employ sgnfcantly more seasonal workers than wne grapes, but n Model 9 lnsze, lntracton and lnfuel are not sgnfcant at the fve per cent level. In Model 20, both lnsze and lntracton become sgnfcant at one per cent or better f lnfuel s dropped. The Ramsey RESET test does not reject the null hypothess of no omtted varables and for Model 20. Nether Model 9 nor Model 20 has a heteroskedastcty problem. The provson of benefts and the 48
Conrade use of grape harvesters do not affect the demand for seasonal workers. The wage elastctes n Models 9 and 20 are only sgnfcant at 2 and per cent respectvely, but they are almost double the wage elastcty for regular men. Table 6: Demand for seasonal workers Varable Model 5 Model 6 Model 7 Model 8 Model 9 Model 20 Depend varable ln(seas) ln(seas) ln(seas) ln(seas) ln(seas) ln(seas) ln(wage) -0.300-0.247-0.305-0.502-0.49-0.485 (-0.49) (-0.50) (-0.73) (-.6) (-.58) (-.6) ln(sze).95*.85* 0.674* 0.7* 0.685* 0.942** (2.7) (2.20) (2.33) (2.5) (2.42) (5.67) ln(fuel) -0.206-0.99 0.257 0.236 0.245 (-0.45) (-0.44) (.4) (.07) (.0) ln(tracton).529*.528* 0.593 0.64 0.628 0.832** (2.50) (2.53) (.74) (.8) (.85) (2.87) cropd dropped dropped 5.352 2.233** 2.234** 2.467** (.22) (7.47) (7.72) (9.89) cropd ln(wage) 0.20 0.9-0.450 (0.7) (0.7) (-0.7) gh -.24-0.038 (-0.6) (-0.08) gh ln(wage) 0.59 (0.5) electrcty -0.394-0.39-0.256-0.276 (-.03) (-.04) (-0.99) (-.08) constant 3.878 3.545 0.562.845.86 2.073 (0.88) (0.94) (0.9) (0.78) (0.78) (0.9) n 5 5 86 86 86 88 adjusted R 2 0.4 0.6 0.64 0.64 0.64 0.65 Ramsey RESET 0.87 0.93.3.20.49.39 prob. 0.46 0.43 0.28 0.3 0.22 0.25 Breusch-Pagan 0.38 0.38 3.89 3.59 3.83 3.64 prob. 0.54 0.54 0.05 0.06 0.05 0.06 Whte 42.27 3.33 34.9 34.95 2.37 2.29 prob. 0.9 0.30 0.29 0.9 0.32 0.50 T-statstcs n parentheses; * sgnfcant at 5%, ** sgnfcant at % 5. Wage elastctes and ther polcy mplcatons The estmated wage elastctes are summarsed n Table 7. The demand for contract labour could not be estmated wth a sngle equaton model for the current sample. There are varous reasons why ths s the case. Frstly, labour brokers contrbute a small share of total labour so that there s smply not enough data avalable at ths pont. In addton, labour brokers probably operate n a market where employment and wage are determned smultaneously. Furthermore, estmatng the full-tme equvalent labour provded by labour brokers requre strong assumptons snce farmers do not normally record the hours or days worked by contract labour. Whle the wves of regular men contrbute a larger share of overall labour, especally on table 49
Conrade grape farms, collectng data on ther employment was smlarly challengng. Even regular women rarely work full-tme, agan requrng a whole range of assumptons to convert a body count nto full-tme equvalent employment. Fnally, t s possble that not all employers vew women n the same way. Some may actually employ women on charty prncples rather than straghtforward productvty grounds explanng why the relatonshp between employment and wage s noser than for the other types of labour. The small number of observatons for seasonal labour smlarly hampered the estmaton of ndustry specfc wage elastctes. Table 7: Wage elastctes by farm and labour type Wne grapes Table grapes All employment -0.33-0.59 Regular men -0.2-0.44 Wves of regular men -0.20-0.20 Seasonal workers -0.49-0.49 Contract labour not estmated not estmated The demand for labour on grape farms s nelastc. For overall labour demand, the wage elastcty n table grapes s smlar to Balcombe et al s (2000) estmate for agrculture as a whole, whle the estmate for wne grapes s much lower. The ndustry dfferental s to be expected, snce labour s a bgger cost tem on table grape than on wne grape farms. For men, the ndustry dfferental s smlar, but due to nosy data no ndustry dfferental could be establshed for women and seasonal workers. The most surprsng result s that the wage elastcty for women on wne farms s of a smlar magntude as the wage elastcty for men. Many farmers consder the wves of regular men as casual labour only to be employed when there s work, usually durng the harvest season. But almost n the same breath these farmers also talk of the need for both husband and wfe to work n order to get by, and of wantng to keep money on the farm rather than pay t out to contract workers. Ted housng thus provdes job securty n an envronment of rsng real farm wages. As expected, the demand for seasonal workers s more elastc than the demand for regular workers. The proposed ten per cent wage ncrease scheduled for March 2005 wll cause very few addtonal job losses, despte the current low nflaton envronment whch causes most of the nomnal ncrease to regster as a real ncrease. Frst, the estmates n Table 7 are low. Even n the table grape ndustry, a ten per cent real wage ncrease would reduce employment only by sx per cent at most. Second, farm workers stll receve a sgnfcant porton of ther wages n knd, and as long as employers are able to offset some of the cash wage aganst 50
Conrade fewer benefts, the effectve wage ncrease wll be lower that the statutory ncrease. Seasonal workers wll be more at rsk than permanent staff, snce they receve fewer benefts that can be offset aganst hgher wages. From labour s pont of vew, low wage elastctes are good news snce t means that labour stands to beneft from hgher mnmum wages wthout facng proportonal dsemployment, at least n the short-run. In the wne and table grape ndustres, more people could be lfted out of poverty at a gven mnmum wage than would have been the case had the demand for labour been more elastc. To date, experence wth the mnmum wage has been n a postve product prce envronment for the wne ndustry. When product prces rse, employers fnd t relatvely easy to meet statutory ncreases. If wne prces fall by 5 per cent, as t could ths season, the ncrease n the mnmum wage planned for March 2005, mght meet wth more resstance than have been observed so far. The strong Rand puts smlar pressure on table grape farmers. Fnally, demand for labour s derved from the demand for the product and s a functon of producton technology. These wage elastcty estmates for wne and table grapes are probably smlar to those that apply n other tree frut ndustres, but could be dramatcally dfferent from the wage elastctes that apply n feld crops or lvestock producton. Gven the mportance of accurate estmates of wage elastcty n the process of settng mnmum wage polcy, t s essental to extend ths knd of analyss to the other mportant agrcultural ndustres before any further nterventons are made. Acknowledgements Ths survey was generously funded by the Natonal Research Foundaton and the paper benefted from detaled comments by Suzanne McCoskey of the US Naval Academy as well as nput from Corne van Walbeek and Murray Lebbrandt of UCT and two anonymous revewers, but as always, any remanng mstakes are entrely my own. References Balcombe K, Baley A, Morrson J, Rapsomanks G & Thrtle C (2000). Stochastc bases n techncal change n South Afrcan agrculture. Agrekon 39(4):495-503. 5
Conrade Conrade BI (2004). The effect of the level of the agrcultural mnmum wage n the Western Cape wne ndustry after one year. Presented at AEASA, Somerset West, 22-23 September 2004. Department of Agrculture (2004). Abstract of Agrcultural Statstcs. http://www.nda.agrc.za/. Department of Labour (200). Determnaton of Employment Condtons n South Afrcan Agrculture. http://www.polty.org.za/. Dckens R, Machn S, Mannng A, Metcalf D, Wadsworth J & Woodland S (995). The effect of mnmum wages on UK agrculture. Journal of Agrcultural Economcs 46():-9. Errngton AJ, Harrson Mayfeld L, Khatr Y & Townsend R (997). Estmatng the prce elastcty for famly and hred farm labour n England and Wales. Appled Economcs 29:56-574. Fallon P & Lucas R (998). South Afrcan labor markets: Adjustments and nequaltes. Dscusson paper 2, The World Bank Southern Afrca Department. Fedderke JW & Marott M (2002). Changng labour market condtons n South Afrca: A sectoral analyss of the perod 970 997. The South Afrcan Journal of Economcs 70(5):830 864. Gujarat DN (995). Basc Econometrcs. Thrd edton. McGraw-Hll Internatonal Edtons, New York, VSA. Hcks JR (932). The Theory of Wages. Macmllan and Co, London, UK. Klasen S & Woolard I (999). Levels, trends and consstency of employment and unemployment fgures n South Afrca. Development Southern Afrca 6()3-35. Latt EA & Neuwoudt WL (985). A supply and demand analyss of regular Black labour n Natal. Agrekon 24(2):-7. Newman RA, Ortmann GF & Lyne MC (997). Farm labour remuneraton, labour legslaton and commercal farmers perceptons n KwaZulu-Natal. Agrekon 36():73-84. O donnell CJ, Shumway CR & Ball VE (999). Input demands and neffcency n US agrculture. Amercan Journal of Agrcultural Economcs 8(4):865-880. 52
Conrade Pndyck PS & Rubnfeld DL (998). Econometrc Models and Economc Forecasts. Fourth edton. McGraw-Hll,Boston. (SAWIS) South Afrcan Wne Industry Informaton & System (2004). Annual Booklet. http://www.saws.co.za/. Smb T & Alber M (2000). Agrcultural employment crss n South Afrca. Trade and Industry Polcy Secretarat (TIPS) workng paper 3_2000. South Afrcan Human Rghts Commsson (2003). Fnal report on the nqury nto human rghts volatons n farmng communtes. http://www.sahrc.org.za/. STATSSA (987). Census of Agrculture 98. Reports no 06-0-7 and 06-0-8. Government Prnters, Pretora. STATSSA (2004). Census of commercal agrculture 2002 (Summary). Statstcal release P0 http://www.statssa.gov.za/. Van Wyk G (2004). De ekonomese volhoubaarhed van wyndrufverboung n Worcester- en Rawsonvlle-streke. VINPRO vr Kaapse Wynprodusente. www.vnpro.co.za. 53
Conrade Appendx Hausman s test of endogenety The lterature descrbes slghtly dfferent versons of Hausman s specfcaton test. Consder the followng structural demand and supply equatons for a labour market: Demand: e = 0 w 2 y 3 c u [] Supply: e = 4 5 w 6 s u 2 [2] The demand-sde model s farly standard: Employment (e) s a functon of the wage (w), output (y) and captal (c) and a stochastc error term (u). Labour suppled (e) s modelled as a functon of wage (w), the level of benefts provded (s) and a stochastc error term (u2), rather than the usual alternatve wage snce the alternatve wage would not vary across farms n the same dstrct. The reduced form system of the structural equatons above s: Wage: w = π 0 π y π 2 c π 3 s v [3] Employment e = π 4 π 5 y π 6 c π 7 s v 2 [4] In order to pck the rght model to estmate labour demand, the queston s f w s correlated wth u n equaton [] or not. Hausman s null hypothess s no smultanety, n other words that w s uncorrelated wth u. If ths hypothess holds, OLS s consstent and effcent. Both Gujarat (995:670) and Pndyck and Rubnfeld (998:353) frst regress wage on the exogenous varables and a range of nstruments, that s, estmatng [3]: wˆ ˆ π 0 ˆ π y ˆ π 2c ˆ π 3 = s [5] such that = ˆ ˆ [6] w w v and then substtutes [6] nto []: ( wˆ v ) 2 y 3c u e = 0 ˆ [7] 54
Conrade 55 Gujarat (995:670) estmates [7] drectly, regressng employment on exogenous varables, the ftted wage and the ftted resdual from the wage regresson. Accordng to Gujarat, Hausman s null hypothess requres a statstcally nsgnfcant coeffcent on the ftted resdual varable n [7]. Pndycke and Rubnfeld (998: 353) agree wth the substtuton n [7], but substtute [6] n agan to be able to regress employment on wage (not wage-hat). ( ) ( ) u c y v w u c y v v w u c y v w e 3 2 0 3 2 0 3 2 0 ˆ ˆ ˆ ˆ ˆ = = = [8] It s clear from [8] that the coeffcent on the ftted resdual should be zero f wage s exogenous to employment. Table 2 reports the results of Hausman s specfcaton test for the Pndyck and Rubnfeld verson of the test. The t-tests on the ftted resduals fal to reject the assumpton of ndependence between wage and employment n four out of fve cases. Further analyss thus proceeds wth OLS, except for brokers for whch sngle equaton estmaton s not approprate and 2SLS estmaton does not produce sgnfcant results.
56 Table 2: Results of Hausman s specfcaton test Dep varable lnwage lnsze lnfuel lntracton cropd electrcty womeneq funben ftted res c adj. R 2 ln(wage) -0.037 0.090-0.048 0.27** -0.037-0.254-0.066 6.74** 0.08 (-0.46) (.42) (-0.56) (2.7) (-0.60) (-.63) (-.7) (22.9) ln(empl) -.0** 0.572** 0.36** 0.280**.2** 0.677 6.27** 0.89 (-2.8) (9.00) (7.9) (3.94) (2.) (.86) (2.66) ln(men) -0.304 0.633** 0.207** 0.304** 0.432** 0.077.22 0.78 (-0.78) (9.6) (3.79) (3.94) (3.97) (0.20) (0.48) ln(women) 0.05 0.523** 0.262** 0.309** 0.898** -0.26-0.798 0.70 (0.03) (5.24) (3.33) (2.77) (5.72) (-0.38) (-0.22) ln(seas) -.05 0.65* 0.286 0.589 2.373** 0.567 5.35 0.63 (-0.5) (2.2) (.2) (.66) (4.72) (0.28) (0.40) ln(broker) 3.83* 0.445-0.309 0.494-0.885-4.27* -28.* 0.03 (2.3) (.43) (-.24) (.44) (-.87) (-2.56) (-2.58) T-statstcs n parentheses; * sgnfcant at 5%, ** sgnfcant at %