Growth of Employment and Earnings in Tertiary Sector, 1983-2000
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Economic and Political WeeklyMarch 17, 2007974period, this sector accounts for more than half of formal tertiaryemployment in urban areas, and more than two-thirds in ruralareas. Third, females account for a small part of tertiary employ-ment in the formal sector, and surprisingly no more than 10 percent of informal tertiary employment, both in rural and urbanareas. It should, however, be remembered that we included onlyusual principal status (UPS) workers. Fourth, the share of theself-employed in the non-public part of tertiary employment ishigh but contrary to expectations, it is higher in the formal sectorof both the rural and urban economies.It will be interesting to know how the levels of employmentin the formal and informal segments of the tertiary sector comparewith those in manufacturing. Table 3 throws light on this question.It is seen that three-quarters of all employment outside agricultureand construction in the urban economy are in the tertiary sector.It is interesting to note that the tertiary sector has a larger presencerelative to manufacturing in the formal sector in rural areas (notpresented in Table 3).IIEmployment Elasticities by Broad SectorsHaving looked at the relative size of the tertiary sector for thelatest “thick” round of the NSS we now try to see the relativegrowth of this sector over time. It is also sometimes hypothesisedthat the tertiary sector is relatively labour-intensive. Thus, wecould expect employment elasticity to be high in this sector.The data presented in Table 4 combines output trends calculatedfrom the national accounts statistics with employment trendsobtained from the NSS to present a view of employment elasti-cities over a time period for sectors at one-digit nationalindustrialclassification (NIC 1987). The growth rates arecalculatedfor three periods separately between the 38th and 50throunds (1983-93); between the 43rd and 50th rounds (1987-93);and between the 50th and 55th rounds (1993 and 1999). Theemployment estimates are based on UPS workers.The major points to emphasise from Table 4 are that first,tertiary sector employment grew faster than manufacturing in allthree periods. The differential in the growth rates was muchhigher with respect to agriculture, particularly between the 50thand 55th rounds. However, we should remember that the em-ployment growth in the last period was disproportionatelyaffectedby the fall in employment growth rate in the agriculturalsector. Second, employment growth in the tertiary sector fell inthe second-half of the 1990s relative both to the 1987-93 periodand even in the longer 1983-93 decade. But this was entirelybecause of the decline in employment in community and socialservices. The table shows that compared to the 1983-93 decade,the decline in employment growth was marginal in financialservices. The rate of growth of employment increased in all othergroups and was particularly stronger in trade. Third, employmentelasticity mirrored the story of employment growth. The employ-ment elasticity fell slightly in all the other tertiary sectors butwas well above that in manufacturing in the last period.IIIProductivity Differentials between SectorsIs the employment growth in the tertiary sector being drivenbyhigh demand for labour or is labour entering this sector becauseoflack of jobs in other production sectors? In other words, is labourbeing pulled or pushed into this sector? A first cut at this questionTable 1: Change in the Sectoral Shares of EmploymentCountry1971-80 1980-91 1990-2000 AgricultureManufacturingTertiaryAgricultureManufacturingTertiaryAgricultureManufacturingTertiaryRepublic of Korea-14.48.36.0-17.35.012.9-7.6-6.714.5Taiwan, China1-15.611.13.7-6.61.78.9-5.0-4.19.2Thailand-1.40.31.7-10.53.27.3-15.34.310.2Malaysia-14.86.19.9-10.44.66.6-7.92.93.0Philippines2-1.4-0.72.1-6.2-0.66.7-7.80.37.6Indonesia -2.71.31.1-10.882.87.1India3-5.51.83.0-4.60.03.4-3.60.32.4Notes:1Figures for the first two periods are obtained from Mazumdar and Basu (1997), Table 3.2, p 38. For the last period figures are calculated from ILO yearbook data.2Calculated from ADB Key Indicators, 2001 for all periods.3Calculated from NSS adjusted by population from decadal census for all periods. The periods refer to 1973-83, 1983-93 and 1993-2000.Table 2: Distribution of Employment in Tertiary Sector:Formal and Informal 1999-2000(In percentage)CategoryFormalInformalMalesFemalesTotalMalesFemalesTotalRuralPublic64.182.866.8–––Private regularwage10.811.210.916.115.416.0Casual wage–––32.327.631.7Self-employed25.06.022.451.557.052.3Total100.0100.0100.0100.0100.0100.0Percentage ofrural tertiary19.13.122.267.610.177.8Percentage ofall tertiary7.71.39.027.44.131.5UrbanPublic52.764.354.5–––Private regularwage15.822.316.826.633.227.6Casual wage–––23.526.724.0Self-employed31.513.428.749.940.148.4Total100.0100.0100.0100.0100.0100.0Percentage ofurban tertiary30.65.636.154.29.763.9Percentage ofall tertiary18.23.321.532.35.838.0Source:Estimated from NSS unit level data of the 55th round.Table 3: Tertiary Employment as a Percentage of Total inManufacturing Plus Tertiary 1999-2000AreaFormalInformalMalesFemalesTotalMalesFemalesTotalRural13.62.316.046.86.953.7All-urban22.44.126.441.17.348.4Metro23.54.628.137.57.645.1Non-metro21.93.925.842.67.349.8Note:Total employment in manufacturing plus tertiary in each area=100.0.Source:Same as Table 2.
Economic and Political WeeklyMarch 17, 2007975is to see if there are major productivity differentials orif theproductivity differential is increasing vis-à-vis the productionsector as revealed by sectoral GDP figures. These data are givenin Table 5.The average productivity in the tertiary sector as a whole ispulled up by the high value of the financial sub-sector but seemsto be above the level of manufacturing (in 2000) in most sectorsexcept trade (where it is 20 per cent lower). There is a suggestionthat the trade-manufacturing differential might have slipped overtime. Between 1983 and 2000 productivity in trade relative toits base (agriculture) remained practically constant (in real terms)but went up by more than 40 per cent in manufacturing. Thisallowed manufacturing productivity to go significantly abovetrade but it is interesting to see that this differential was estab-lished only recently – between the 50th and 55th rounds.Not all sub-sectors of the tertiary sector, however, suffered thefate of NIC sub-group 6, ie, trade. Both finance (group 8) andcommunity and social services (group 9) improved their relativeproductivity vis-à-vis manufacturing. In business services(group7), the relative improvement of productivity seems to havebeen under way since the 43rd round. But in the community andsocial services (group 9) the relative improvement was prominentonly between the 50th and 55th rounds. The surge in salariesin the public sector is reflected in the large increase in productivitybetween these two rounds in this group.The above pattern suggests that there is indeed some evidencetosupport the general perception that some sub-groups like trade(group 6) have had a relatively large influx of labour pushingdown its relative productivity to some extent, while others likebusiness services in group 7 have improved their position dueto demand factors.However, a study of trends in average relative productivity cancarry us only so far in our understanding about the trends inrelative earnings at which labour is being absorbed in the tertiarysector. For a more complete understanding we need to look atthe way in which the entire distribution of earnings (or incomes)has been changing in the tertiary sector in response to the highrate of growth of employment in this sector.Before getting into a further analysis of the tertiary sector onthe basis of unit level data, it will be worthwhile to discuss thelimitation of the database that we have used.IVLimitations of NSS DataWe need to be aware of the limitations of the main source ofour data, the NSS, before proceeding further. First, a large shareof employment in India is in the “self-employed” category. Thereis an inherent difficulty in allocating income accruing from self-employment when more than one earner from the same householdis in an income-earning activity. Households from different self-employed activities by different members of the household wouldbe typically pooled together. There is no way of distinguishingthe individual contributions of individual earners. Hence, theincome we can deal with is household income, and we cannormalise for the size of the household. Further, it is generallyaccepted that figures on expenditure given by the respondentinthe household are more reliable than those on income. Thus,we use the measure of household welfare as given by meanexpenditure per capita.When we are comparing levels of household welfare acrosssectors we need to identify the principal occupation of theTable 4: Employment Elasticities for Different Sectors and PeriodsNIC 1987 ClassificationGrowth Rates of GrossGrowth Rates of Employment Elasticity of EmploymentDomestic Product (GDP)38th-50th43rd-50th50th-55th38th-50th43rd-50th50th-55th38th-50th43rd-50th50th-55thAgriculture (0)2.724.772.881.372.190.720.5030.4590.250Mining (1)5.806.085.224.213.82-4.210.7260.628-0.806Manufacturing (2-3)5.245.217.342.001.361.920.3820.2620.262Electricity, gas, etc (4)7.937.906.943.873.94-2.300.4880.499-0.331Construction (5)4.645.266.355.490.136.401.1830.0241.007Trade, hotels, etc (6)5.315.559.173.933.376.340.7410.6080.691Transport, etc (7)5.795.609.393.523.525.350.6080.6290.570Finance, insurance, etc (8)9.6210.488.365.344.365.570.5550.4160.666Community, social and otherservices (9)5.414.908.573.594.41-1.180.6630.900-0.137Tertiary sector (6 to 9)6.406.548.823.773.903.050.5900.5970.346Note:We have also calculated employment elasticities based on UPSS (it includes both principal and subsidiary status workers) employment estimates. Butthe broad trends are similar and are not presented here.Table 5: Labour Productivity by Broad Sectors 1983-2000NIC 1987 ClassificationLabour Productivity (UPS)Labour Productivity Index (UPS)55th50th43rd38th55th50th43rd38thAgriculture (0)13,34911,75210,11610,223100100100100Mining (1)129,57973,75464,80262,920971628641615Manufacturing (2-3)46,99934,44427,54724,801352293272243Electricity, gas, etc (4)239,870139,433111,41093,2471,7971,1861,101912Construction (5)34,40634,49225,55137,543258294253367Trade, hotels, etc (6)42,83836,59332,29831,866321311319312Transport, etc (7)60,53748,31042,87138,468453411424376Finance, insurance, etc (8)303,895259,820184,626171,0292,2762,2111,8251,673Community, social and other services (9)47,72927,13726,38722,588358231261221Tertiary sector (6 to 9)61,21644,14437,98533,950459376375332Source:Same as Table 4.
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