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Sectoral Labour Flows and Agricultural Wages in India, 1983-2004: Has Growth Trickled Down?

This paper examines the evolution of poverty in India through the prism of agricultural wages and employment. It links the movement in wages (and hence poverty) to the fundamental process of sectoral labour flow that underlies economic development. It finds that despite the rapid growth of the non-farm sector, its success in drawing labour from land has been limited. Yet agricultural earnings have increased, demonstrating the pivotal role of agricultural productivity. The stock of the labour force already locked into agriculture is large and the best way to improve living standards would be to boost farm productivity.

SPECIAL ARTICLE

Sectoral Labour Flows and Agricultural Wages in India, 1983-2004: Has Growth Trickled Down?

Mukesh Eswaran, Ashok Kotwal, Bharat Ramaswami, Wilima Wadhwa

This paper examines the evolution of poverty in India through the prism of agricultural wages and employment. It links the movement in wages (and hence poverty) to the fundamental process of sectoral labour flow that underlies economic development. It finds that despite the rapid growth of the non-farm sector, its success in drawing labour from land has been limited. Yet agricultural earnings have increased, demonstrating the pivotal role of agricultural productivity. The stock of the labour force already locked into agriculture is large and the best way to improve living standards would be to boost farm productivity.

We would like to thank Paul Beaudry, Andrew Foster, Shubashis Gangopadhyay, Peter Lanjouw, Dilip Mookherjee, Suresh Tendulkar, Alessandro Tarrozi, the participants of the World Bank workshop on Equity and Development (7-8 December 2004) and the participants of the “Workshop on the Indian Economy: Policy and Performance 1980-2000” held at the Centre for India and South Asia Research, University of British Columbia in Vancouver (in June 2005) for their helpful comments. We are grateful to the Shastri Indo-Canadian Institute for financial support through the SHARP programme and to India Development Foundation, Gurgaon, for their hospitality and generous support.

Mukesh Eswaran (eswaran@econ.ubc.ca) and Ashok Kotwal (kotwal@econ.ubc.ca) are at the University of British Columbia, Bharat Ramaswami (bharat@isid.ac.in) is at the Indian Statistical Institute, and Wilima Wadhwa (wilima@vsnl.com) is with the Indian Statistical Institute and ASER Centre.

1 Introduction

T
his paper examines the evolution of poverty in India through the prism of agricultural wages and employment. While headcount ratios of poverty have been the focus of much of official and academic writings on the subject, looking at agricultural wages has its advantages both as a statistical m easure as well as a way of thinking about how growth trickles down to the poor.

Table 1 (p 47) displays a classification of rural households according to source of major earnings. The table is computed from National Sample Survey (NSS) consumption expenditure survey data for 2004-05. From the table, it is clear that h ouseholds that depend on earnings from unskilled labour (agricultural labour and other labour) account for more than 50% of the households that are poor according to the official poverty line. The corresponding figure for the non-poor p opulation is 32%. It would therefore seem that the earnings of manual labour households ought to be strongly correlated with poverty.

A large empirical literature in India has indeed confirmed the association of poverty with agricultural wages. A recent study that comprehensively documents this association is Kijima and Lanjouw (2005), which shows agricultural wage rates at the region level to be strongly (inversely) correlated with region level poverty rates in the three years between 1987 and 1999 for which such survey data were available. Sundaram (2001a) used the wage and employment data to construct synthetic measures of yearly earnings and showed that the movement in earnings was directionally consistent with the movement in poverty as measured by consumption expenditure surveys.

Deaton and Dreze (2002) argued that agricultural wages could be taken not just as a proxy for poverty but also as a poverty measure in its own right since it is the reservation wage of the very poor. It would also seem that it would be easier to theorise and model agricultural wages than it would be poverty measures which are complicated non-linear functions of underlying average income and income inequality. It is this last consideration that motivated this study to use agricultural wages as a measure of poverty.

To see this, consider a dual economy of the standard sort comprising a farm and a non-farm sector. The farm sector uses land and labour to produce a farm good. The poor in this economy are those who are assetless. In particular, the rural poor are the landless workers in agriculture. Because of labour mobility, the agricultural wage is also the floor wage in the non-farm economy. Thus, if there is full employment, poverty can decline only if a gricultural wages rise. The question is how will growth in this on the major time criterion. For an individual who is employed economy affect agricultural wages and the poor. on the usual status, their principal activity in terms of industry of

Growth comes about because of higher total factor productivity employment is also determined on the basis of major time crite(TFP) in the farm and non-farm sector. The connection between rion. The survey also records their “subsidiary” economic activity farm TFP and agricultural wage (and hence poverty) is quite in the remainder time. direct: at the same level of production inputs an increase in agri-Most work on employment and unemployment in India and in cultural TFP (e g, through better seeds or particular existing estimates of the secto-

Table 1: Classification of Rural Households according through irrigation that leads farmers to to Major Earnings Source, 2004-05ral allocation of labour force are based on

raise more crops or to switch to high-value crops) will raise the marginal product of labour and hence the wage. What is the relationship between non-farm TFP and agricultural wages? Here the link is through

Non-Poor Poor Households Households

Self-employed in non-agriculture 16.51 12.91

Agricultural labour 22.11 41.8

Other labour 10.29 12.13

Self-employed in agriculture 38.38 26.71

the usual status definitions (see, for instance, Chadha and Sahu 2002; Sundaram 2001a, b). However, the usual status definition does not take into account multiple economic activities that are charac

labour allocation: if an increase in non-farm Others 12.71 6.45 teristic of poor households. By the usual

TFP increases the value of the marginal Source: Computations from NSS data. status criterion, individuals with regular
product of labour in the non-farm sector, it Table 2: Size of NSS Employment Surveys wage employment constitute only 14% of
will draw labour away from agriculture and, 1983 1993-94 1999-2000 2004-05 the workforce. More than half of the work
given the diminishing returns due to land (a fixed factor), the agricultural wage will rise. The extent of the wage increase due to non- Number of individuals All 6,23,448 5,64,740 5,96,686 Rural 4,14,649 3,56,351 3,71,187 Urban 2,08,799 2,08,389 2,25,499 6,02,833 3,98,025 2,04,808 force is self-employed (53%); the great majority of them in agriculture and about one-third are casual wage workers
farm TFP growth would depend, of course, Number of households (Pappola 2007). Furthermore, over 80% of
on the amount of labour drawn away from All 1,20,897 1,15,409 1,20,578 1,24,680 female workers in unorganised manufac
agriculture. Rural 78,595 69,230 71,417 79,306 turing work out of their homes mostly in
This simple conceptual scheme justifies the use of agricultural wages as a poverty Urban 42,302 46,179 49,161 Number of primary sampling units All 12,210 11,602 10,106 45,374 12,502 subcontracting relationships where the intermediary supplies raw material and

measure.1 It is also suggestive of the Rural 7,924 6,951 5,999 7,944 buys back their output (Unni and Rani

m echanisms of trickle-down – that we must Urban 4,286 4,651 4,107 4,558 2005). For most of the labour force, there

look at farm TFP and the extent to which labour moves from the agricultural to the non-farm sectors. Hence the focus of this paper on the movement in agricultural wages and the sectoral labour flows from agriculture to the rest of the economy.

2 Data: Measures of Earnings and Labour Force

Our data sources are the employment surveys of the National Sample Survey Organisation (NSSO). In this paper, we consider the surveys undertaken in 1983 (calendar year) and in 1993-94, 1999-2000 and 2004-05 (agricultural years, i e, July to June) – the so-called “thick rounds”. Table 2 provides information about the size of the sample in each of these years. The survey period is divided into four quarters and the sample design allots equal number of primary sampling units (villages in rural areas and blocks in urban areas) to each quarter. Thus, for instance, about 30,000 households were surveyed in each quarter of the 1999-2000 survey. The survey data do not report the day or week when the household is surveyed although the instructions for fieldwork state that within a quarter the fieldwork is spread uniformly over the different weeks. Note that the uniform allocation of household units across sub-rounds applies at the level of the state as well. Thus, in comparing outcomes at the state-level across NSS rounds, we can be sure that we do not have to adjust for seasonal factors.

For a given reference period (ranging from a year, week and half-day), individuals are classified as being in the workforce, unemployed or being out of the labour force. When the reference period is a year, the “usual” status of an individual is determined

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fore, work is seasonal, short-term and without tenure. Consequently, an individual’s activity status can vary even within as short a reference period as a week.

In this paper, therefore, we adopt measures of labour force based on the daily status of the individual derived from the data on the weekly disposition of time. As households are surveyed throughout the year (in equal numbers), the aggregates derived from weekly data are representative of annual aggregates. For the reference period of a week, the survey elicits an individual’s time disposition during each day of the week. For each day, i ndividuals are classified (their “daily” status) as being in the workforce, unemployed or being out of the labour force with a

Table 3: Assignment of Daily Status

Works Works More Works More Works Less Works Less than Works for More than than One Hour than One Hour than One Hour One Hour and Less than Four Hours and Less than and Less than and Is Seeking Is Seeking or One Hour and

Four Hours and Four Hours and or Available Available for Is Seeking Is Seeking or Is Seeking or for Work for Work for More or Available Available for Available for Four Hours than One Hour for Work Work for More Work for Less or More But Less than for Less than than One Hour than One Hour Four Hours One Hour

Employed 1.0 0.5 0.5 0 0 0
Unemployed 0 0.5 0 1 0.5 0
Out of labour force 0 0 0.5 0 0.5 1

weight of either 1.0 or 0.5. A weight of 1.0 corresponds to a full day and a weight of 0.5 corresponds to a half-day. Naturally, an individual can at most be assigned two activities with equal weight. The survey uses a priority and major time criterion to assign the activity status to each half-day. This is explained in Table 3. Summing the weights across days, we obtain for each individual in the survey, the weekly break-up of days in each of the three activity states. Therefore, for each individual one can calculate the total time spent working, being unemployed and out of the labour force.

For assigning the industrial classification code, a person who is considered to be employed for the day would be assigned at most two economic activities (with weights 0.5 apiece) decided on the major time criterion. A person who is employed for half-day only would be assigned one economic activity again on the major time criterion. Once again by summing the weights across days, we obtain for each individual in the survey, the weekly break-up of the days of employment into different economic activities.

For the reference period of a week and for each economic activity reported by an individual, the employment survey also reports the weekly earnings. A measure of daily earnings in the activity can be obtained by dividing the weekly earnings by the number of days worked in that particular activity. However, as wage data is not available for the self-employed, the survey does not report any earnings figures for them.

We adjust the raw earnings data to be theoretically consistent with the individual’s labour force status. An individual who was unemployed was assigned a zero earning (rather than a missing value) to reflect their status as being part of the labour force. Second, the earnings observations for individuals who were out of the labour force (unemployed or employed but unable to work and did not receive earnings) were set to “missing”.

To control for cost of living differences across time and across states, earnings have to be deflated. The Planning Commission uses the consumer price index for agricultural labourers and the consumer price index for urban manual workers to update its poverty line in nominal values. We use the deflator implicit in the Planning Commission poverty lines to deflate earnings across time and states.2 Real earnings are in terms of rural Maharashtra prices of 1999-2000.

3 Trends in Agricultural Earnings

Table 4 presents real weekly earnings and real daily earnings in agriculture for each of the NSS rounds.3 At the all India level, weekly earnings grew by 68% between 1983 and 2004-05.4 This translates into an annualised rate of growth of 2.5% per year. The average daily earnings grew

Table 4: Real Agricultural Earnings (Rs, in 1999

faster – 74% between 1983

Rural Maharashtra Prices)

Weekly Earnings Average Daily Earnings and 2004 or an annualised

2004-05 199.33 39.76 rate of 3.33% per year
1999-2000 188.62 38.55 (Tables 4 and 5). The rates
1993-94 163.42 31.10 of growth were higher in the
1983 118.50 22.81 first decade – 1983 to
% Increase 1983-2004 68.21 74.31 1993-04 – with annualised
1983-93 37.91 36.33 rates of 3.3% for weekly
1993-2004 21.97 27.86 earnings and 3.2% for daily
1999-2004 5.68 3.15 earnings. Both these rates

slowed down appreciably in the next decade – 1993 to 2004-05 – to 1.8 and 2.3% per year, respectively. And in the last five years – 1999 to 2004 – these rates have slowed down even further to 1.1 (weekly earnings) and 0.6% (daily earnings). The slowing down of the rate of increase in earnings correlates well with the findings of slower decline of poverty in the 1990s and of the slower increase in real consumption expenditures (Deaton and Dreze 2008; Sen and Himanshu 2005).

Table 5: Annualised Rates of Growth (%)

Table 5 presents GDP Non- Agriculture Agriculture Agriculture
the annual sectoral Farm GDP GDP Weekly ADE
growth rates of farm 1983-2004 5.77 7.09 2.62 Earnings 2.51 2.68
and non-farm GDP 1983-93 5.18 6.43 2.86 3.27 3.15
together with the 1993-2004 6.32 7.70 2.41 1.82 2.26

annualised growth 1999-2004 5.96 7.20 1.84 1.11 0.62

rates of daily wages and earnings in agriculture. The nonfarm sector has grown more rapidly in the decade 1993-94 to 2004-05 while farm GDP growth rate has gone the other way. The growth in earnings of agricultural labour seems to follow the trend in

  • (1) GDP: GDP at factor cost at 1993-94 prices;
  • (2) Agri GDP: GDP originating in agriculture, forestry and logging, and fishing;
  • (3) Non-Farm GDP: Residual = GDP – Agri GDP;
  • (4) Agri Weekly Earnings: Real weekly earnings in agriculture, Rural Maharashtra 1999-2000 prices;
  • (5) Agri ADE: Real average daily earnings in agriculture, Rural Maharashtra 1999-2000 prices;
  • Table 6: Employment Structure – Daily Status

    Agr Mfg CTT G&P Total

    All 2004-05 0.539 0.128 0.218 0.090 0.975

    1999-2000 0.580 0.121 0.189 0.089 0.979

    1993-94 0.611 0.114 0.148 0.108 0.981

    1983 0.634 0.118 0.133 0.099 0.984

    Males 2004-05 0.486 0.130 0.272 0.083 0.970

    farm sector GDP. This 1999-2000 0.529 0.125 0.233 0.088 0.976

    is particularly notice-1993-94 0.566 0.117 0.182 0.113 0.977

    able during the 1983 0.596 0.124 0.157 0.105 0.982 Females

    period 1999-2000 to

    2004-05 0.681 0.124 0.075 0.108 0.988

    2004-05 when growth

    1999-2000 0.723 0.108 0.066 0.093 0.990

    in both the farm

    1993-94 0.737 0.104 0.056 0.093 0.990 s ector GDP and agri-1983 0.744 0.102 0.063 0.084 0.992

    cultural earnings has Agr: Agriculture, Mfg: Manufacturing, CTT: Construction,

    Trade and Hotels, Transport, Storage and Communications, slowed substantially. G&P: Government Services, Education, Health, Community

    Services, Personal Services.

    This table suggests that the impressive growth in the non-farm GDP has not mattered much to agricultural earnings and poverty. If true, why is that? It is important to know the answer to this question because typically it is easier to increase the growth rate of the non-farm sector than the farm sector. Unlike the farm sector, the non-farm sector is not crucially dependent on a fixed factor like land. Furthermore, non-farm technology can be transferred more e asily to developing countries unlike farm technologies that may require substantial climatic adaptation.

    4 Sectoral Labour Flow and the Labour-to-Land Ratio

    As discussed in the introduction, the growth in non-farm sector productivity could affect agricultural wages by lowering the labour-to-land ratio in agriculture.5 Because of diminishing returns, agricultural wages (for a given level of productivity) are inversely related to the labour-to-land ratio that, in turn, depends on the capacity of non-agricultural sectors to draw labour from agriculture. Thus, when the expansion of non- agricultural sector results in a movement of labour away from agriculture, it not only confers benefits on the labour that moved (through perhaps higher wages in non-agriculture) but to all those still left in agriculture. This is the main conduit through which non-agricultural growth can have an impact on rural p overty in a country like India. The countries in east Asia that

    january 10, 2009

    Figure 1: Agricultural Earnings and Labour -Land Ratios: 1983-2004

    (Real Daily Wage)

    80

    60

    40

    20

    Kerala Punjab Haryana Assam Rajasthan Tamil Nadu UPKerala APGujarat WB Assam MP Karnataka Orissa MH Rajasthan Gujarat Haryana Punjab UP AP WB TamilNadu Bihar MH Karnataka Orissa MP Bihar

    100 200 300 400 500 600 Labour-Land Ratio

    wage 1983

    wage 2004

    saw rapidly rising living standards also experienced a swift reduction in the share of agriculture in the labour force.6 Even, in China, the percentage of labour force engaged in agriculture plummeted from 70% in 1979 to 47% in 1999. It is fruitful, therefore, to examine how the employment structure has changed in India from 1983 to 2004.

    Table 6 (p 48) shows the changes over the 21-year period (1983 to 2004) in the employment structure for males and females. The table is based on the one digit daily status classification of economic activities. However, instead of presenting the shares of all the eight sectors, we aggregate some of them to display the shares of four sectors: agriculture (including forestry and fishing); manufacturing; the aggregate of construction, trade and hotels, transport and communications (CTT); and lastly the aggregate of government services, health, education and various personal services (G&P). Employment shares of mining and of real estate and finance are not presented which is why the total of shares adds to a number slightly below one.

    In India, the reduction of labour force in agriculture has been nothing like what was witnessed in east Asia. In the 21-year period, agriculture’s share in the labour force declined by less than 10 percentage points from 63.4% to 53.9%. This change was largely driven by the change in employment structure for males. The share of agriculture in the labour force for males (measured in person days) declined from about 60% in 1983 to just under 50% in 2004-05.7 As the share of manufacturing has changed very little over these 21 years, the share of services has increased by about the same percentage. For females, the sectoral pattern of employment has changed much less. In 2004-05, 68% of female labour force continued to be employed in agriculture as compared to 74% in 1983. While men have moved primarily into construction, trade and transport, women have moved into manufacturing and government and personal services. It is noticeable that the sectoral pattern of employment of women has been v irtually stagnant between 1983 and 1999-2000. For both males and females, the diversification of employment away from agriculture has happened at a faster pace in the decade 1993-94 to 2004-05. For state-level experiences, the reader is referred to the tables in the Appendix A.2 to A.13 (pp 54-55).

    For agricultural wages, what matters is the labour-land ratio that may not always move in an opposite direction to agriculture’s share in the labour force. Because of labour force growth, labourland ratios can increase despite a fall in agriculture’s share in the labour force. For 15 major Indian states, Figure 1 plots the average real daily earnings (in 1999 rupees) in agriculture against the labour-land ratio (days of agricultural employment per h ectare of gross cropped area) for 1983 and 2004. It can be seen that for all but three states (Kerala, Haryana and Punjab), the labour use per hectare of land has increased over this period.8 With growing population and limited absorption of labour by the n on-farm sector, this is not surprising. Yet, for all states, real daily earnings have increased during this period. Quite clearly, if either farm TFP or agricultural inputs such as fertilisers had not increased during this period, agricultural wages would have declined. The contribution of agricultural productivity growth to the increase in wages and the decline in poverty is therefore e vident.

    It becomes interesting, therefore, to ask how much non-farm sector growth has contributed to the growth of agricultural wages. By constructing a counterfactual scenario of what would

    Table 7(a): Sectoral Employment (Males) by Cohort Groups: 1983

    Millions of Days Per Week Sectoral Shares in Total
    18-25 26-33 34-41 42-49 18-25 26-33 34-41 42-49
    Agriculture 153.86 114.96 109.61 70.88 0.59 0.52 0.55 0.55
    Mining 1.78 2.32 2.22 1.30 0.01 0.01 0.01 0.01
    Manufacturing 36.18 31.22 25.07 16.72 0.14 0.14 0.12 0.13
    Construction 10.46 8.68 7.11 4.08 0.04 0.04 0.04 0.03
    Trade and hotels 25.50 20.30 17.21 10.78 0.10 0.09 0.09 0.08
    Transport 10.04 10.95 10.03 5.88 0.04 0.05 0.05 0.05
    Finance and real estate 1.83 4.51 2.44 1.57 0.01 0.02 0.01 0.01
    Pub admn and servs 20.88 28.59 27.35 18.80 0.08 0.13 0.14 0.14
    Total 260.53 221.53 201.05 130.00 1.00 1.00 1.00 1.00

    Table 7(b): Sectoral Employment (Males) by Cohort Groups: 1993-94

    Millions of Days Per Week Sectoral Shares in Total
    28-35 36-43 44-51 52-59 28-35 36-43 44-51 52-59
    Agriculture 164.26 105.60 104.40 59.89 0.51 0.50 0.56 0.60
    Mining 3.53 2.91 2.12 0.75 0.01 0.01 0.01 0.01
    Manufacturing 40.48 26.32 20.51 9.74 0.13 0.13 0.11 0.10
    Construction 15.39 7.87 6.38 2.29 0.05 0.04 0.03 0.02
    Trade and hotels 33.94 22.45 16.73 8.74 0.11 0.11 0.09 0.09
    Transport 17.69 10.57 7.86 2.92 0.06 0.05 0.04 0.03
    Finance and real estate 5.10 4.69 2.54 1.20 0.02 0.02 0.01 0.01
    Pub admn and servs 39.55 30.12 27.32 14.23 0.12 0.14 0.15 0.14
    Total 319.95 210.52 187.85 99.76 1.00 1.00 1.00 1.00

    Table 7(c): Sectoral Employment (Males) by Cohort Groups: 2004-05

    Millions of Days Per Week Sectoral Shares in Total
    39-46 47-54 55-62 63-70 39-46 47-54 55-62 63-70
    Agriculture 143.09 90.50 82.98 41.68 0.47 0.50 0.63 0.74
    Mining 3.53 1.99 0.86 0.11 0.01 0.01 0.01 0.00
    Manufacturing 36.96 19.84 11.24 4.11 0.12 0.11 0.08 0.07
    Construction 19.55 8.21 4.89 1.26 0.06 0.05 0.04 0.02
    Trade and hotels 41.88 22.36 13.87 5.47 0.14 0.12 0.10 0.10
    Transport 19.30 9.44 4.92 0.80 0.06 0.05 0.04 0.01
    Finance and real estate 7.97 5.04 1.99 0.64 0.03 0.03 0.01 0.01
    Pub admn and servs 32.42 22.59 12.01 2.52 0.11 0.13 0.09 0.04
    Total 304.70 179.95 132.77 56.60 1.00 1.00 1.00 1.00

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    Table 8(a): Sectoral Employment (Females) by Cohort Groups: 1983

    Millions of Days Per Week

    Sectoral Shares in Total

    18-25 26-33 34-41 42-49

    18-25 26-33 34-41 42-49

    Agriculture 61.79 54.79 54.40 35.49 0.74 0.73 0.74 0.76

    Mining 0.55 0.39 0.52 0.17 0.01 0.01 0.01 0.00

    Manufacturing 10.59 7.31 6.62 3.51 0.13 0.10 0.09 0.07

    Construction 2.03 1.77 1.63 0.73 0.02 0.02 0.02 0.02

    Trade and hotels 2.15 2.70 3.37 2.17 0.03 0.04 0.05 0.05

    Transport 0.37 0.25 0.30 0.13 0.00 0.00 0.00 0.00

    Finance and real estate 0.27 0.25 0.13 0.08 0.00 0.00 0.00 0.00

    Pub admn and servs 5.80 7.59 6.86 4.68 0.07 0.10 0.09 0.10

    Total 83.54 75.05 73.83 46.96 1.00 1.00 1.00 1.00

    Table 8(b): Sectoral Employment (Females) by Cohort Groups: 1993-2004

    Millions of Days Per Week Sectoral Shares in Total
    28-35 36-43 44-51 52-59 28-35 36-43 44-51 52-59
    Agriculture 84.64 59.01 53.40 25.97 0.73 0.72 0.76 0.76
    Mining 0.77 0.36 0.33 0.08 0.01 0.00 0.00 0.00
    Manufacturing 11.35 7.35 4.68 2.75 0.10 0.09 0.07 0.08
    Construction 1.98 1.70 0.96 0.30 0.02 0.02 0.01 0.01
    Trade and hotels 4.83 3.23 3.45 1.47 0.04 0.04 0.05 0.04
    Transport 0.41 0.33 0.21 0.04 0.00 0.00 0.00 0.00
    Finance and real estate 0.66 0.37 0.19 0.04 0.01 0.00 0.00 0.00
    Pub admn and servs 11.54 9.26 6.65 3.39 0.10 0.11 0.10 0.10
    Total 116.18 81.60 69.87 34.05 1.00 1.00 1.00 1.00

    Table 8(c): Sectoral Employment (Females) by Cohort Groups: 2004-05

    Millions of Days Per Week Sectoral Shares in Total
    39-46 47-54 55-62 63-70 39-46 47-54 55-62 63-70
    Agriculture 80.95 47.58 38.55 11.58 0.70 0.72 0.77 0.73
    Mining 0.40 0.20 0.10 0.02 0.00 0.00 0.00 0.00
    Manufacturing 11.69 4.73 3.64 1.53 0.10 0.07 0.07 0.10
    Construction 2.32 0.89 0.46 0.08 0.02 0.01 0.01 0.01
    Trade and hotels 5.88 3.78 2.58 1.34 0.05 0.06 0.05 0.08
    Transport 0.54 0.35 0.11 0.05 0.00 0.01 0.00 0.00
    Finance and real estate 0.77 0.46 0.20 0.01 0.01 0.01 0.00 0.00
    Pub admn and servs 13.72 8.11 4.49 1.24 0.12 0.12 0.09 0.08
    Total 116.27 66.10 50.14 15.84 1.00 1.00 1.00 1.00

    have happened if non-farm TFP was held constant at 1983 levels, Eswaran et al (2008) estimate the contribution of the non-farm sector (in the period 1983 to 1999) to be at the most 22%, confirming the primary role of agricultural productivity in increasing agricultural wages.

    5 Employment Shifts: Who Moves Out of Agriculture

    In this section, we examine the sectoral patterns of employment (at the one-digit level) disaggregating the population into cohorts of eight-year age intervals, in order to see which age groups are the most mobile. In 1983, we start off with the following age cohorts: 18-25, 26-33, 34-41 and 42-49. In 1993-94, these cohorts become the age groups 28-35, 36-43, 44-51 and 52-59, respectively and in 2004-05, these cohorts are in the age-groups 39-46, 47-54, 55-62 and 63-70, respectively.

    Tables 7(a)-7(c) (p 49) concern males for the years 1983, 1993-94 and 2004-05. Tables 8(a)-8(c) are similar tabulations for females. The first four columns of each of the tables are the employment numbers (in millions of person days per week) for each of the cohorts at the one-digit industrial classification. The last four columns of these tables are the employment proportions.

    50

    From the proportions data, it is clear that it is only the youngest cohort in 1983 of age 18-26 that shows a change in employment structure over time. Fifty-nine per cent of males in this cohort were employed in agriculture in 1983. By 2004-05, this figure had come down to 47%. From the information on the labour force days in different sectors, it can be seen that the labour force in agriculture for this male cohort actually increased between 1983 and 1993-94. However, the proportion declined because employment in the other sectors expanded even more. This must be because the males in the cohort who were out of the labour force (presumably studying) in 1983 went more into the non-farm sectors than into the farm sector in 1993-94.

    The other male cohorts do not show much change in their employment structure over time. Because of life cycle effects, labour supply of the older cohorts (in 1983) declines with time and this seems to happen proportionately among all the sectors. As these cohorts are older, they do not experience the addition of more educated members into the labour force as seen in the 18-26 group. The oldest cohort in 1983 sees an increase in the share of agriculture principally because exit from other sectors (because of retirement) is faster than from agriculture.

    The story for females is similar to that of males. The only change that occurs is in the cohort that is in the age group 18-26 in 1983. Compared to males, the decline in percentage share of agriculture is muted. The employment structure for older females in 1983 continues to be frozen in later years much like that of the older male cohorts.

    6 Education and the Role of the Non-farm Sector

    The previous section suggested that the shift out of agriculture is associated with education, since it is the young males (and to a lesser extent, young females) who are out of the labour force in

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    Table 9: Average Earnings of Males with No Education for Cohort 34-42them. The sectoral probabilities of employment are approxi

    2004-05 1983

    mated by the sectoral proportions of employment of the relevant

    Sectors w p p w p w/W w p p w p w/W

    1i1i1i1i1i1i 10i0i0i0i0i0i 0

    sub-population.

    Agriculture 227.02 0.59 133.65 0.49 135.80 0.68 92.28 0.56

    Tables 9 and 10 show the results for wage workers who do not

    Mining 426.92 0.02 8.45 0.03 281.08 0.03 8.39 0.05

    have literacy skills. For illiterate males, agricultural activity

    Manufacturing 357.91 0.10 37.25 0.14 217.86 0.09 18.74 0.11 Construction 303.13 0.18 54.69 0.20 249.00 0.08 20.95 0.13 accounts for 59% of working days in 2004 as opposed to 68% in

    Trade and hotels 319.43 0.03 9.07 0.03 150.63 0.02 3.21 0.02 1983. Notice that the entire shift is into construction with the rest Communications and

    of the sectoral distribution remaining virtually unchanged

    Transport 417.10 0.04 17.67 0.06 215.07 0.04 8.08 0.05

    between the two years. It is interesting, however, that this shift

    Finance and real estate 179.51 0.00 0.13 0.00 212.09 0.00 0.27 0.00

    has happened largely between 1999 and 2004 – the sectoral dis-

    Pub admn and servs 351.14 0.04 12.47 0.05 218.10 0.06 13.15 0.08 W

    = 273.38, W = 165.08. tribution was virtually unchanged between 1983 and 1999. In

    10

    1999, the proportion in agriculture of this cohort of illiterate

    Table 10: Average Earnings of Females with No Education for Cohort 34-42

    males was 66%.

    2004-05 1983

    The increase in expected earnings for this group is therefore

    Sectors w p p w p w/W w p p w p w/W

    1i1i1i1i1i1i 10i0i0i0i0i0i 0

    entirely due to higher earnings in agriculture and construction

    Agriculture 150.36 0.72 108.79 0.65 92.72 0.76 70.11 0.73

    and not due to any major sectoral shifts of employment. How-

    Mining 238.44 0.01 3.46 0.02 161.83 0.02 2.60 0.03 Manufacturing 173.90 0.06 9.97 0.06 97.05 0.07 7.23 0.08 ever, while agricultural earnings for illiterate males increased by

    Construction 233.08 0.07 17.42 0.10 106.60 0.06 6.07 0.06 67% during this period, earnings in construction increased by Trade and hotels 163.11 0.01 1.89 0.01 109.05 0.00 0.37 0.00 only 22%. Among the non-farm sectors construction commands Communications and

    the least industry premium (over agricultural earnings), followed

    Transport 177.20 0.00 0.41 0.00 117.31 0.00 0.36 0.00

    by trade and hotels. Mining commands a very high premium but

    Finance and real estate 114.33 0.00 0.04 0.00 103.02 0.00 0.04 0.00

    employs very few people. The change in the contribution of agri-

    Pub admn and servs 222.56 0.12 25.76 0.15 104.10 0.09 9.31 0.10 W1 = 167.72, W0 = 96.09. culture to the total earnings of this group mirrors the changes in the employment structure – it falls from 56% to 49% while that of 1983 and who are presumably acquiring education that are more construction increases from 13% to 20%. likely to be employed in the non-farm sector. To make this con-For illiterate females, the contribution of agriculture to nection explicit, this section considers the role of the non-farm their total income is much higher – 73% in 1983 and 65% in 2004. sector in the earnings of workers differentiated by their educa-Between 1983 and 2004, their dependence on agriculture for tion levels. employment falls only slightly from 76% to 72%, with the shift A well-known feature of earnings data is that even after con-being entirely into the government and private services sector. trolling for education and age, earnings differ between indus-Expected earnings of illiterate females grew by 75% during this tries. In India, earnings in agriculture are typically the lowest. period, but most of it is accounted by the increase in agricultural Other sectors earn a premium over agricultural earnings. Sup-earnings given the high dependence of women on agriculture. pose W0 is the expected earnings of an illiterate person in 1983.

    Table 11: Average Earnings of Males with Middle School Education for Cohort 34-42

    Then 2004-05 1983

    n Sectors w p p1i w p1i w/W w p p0i w p0i w/WW0 = Σ p0i w0i ...(1)

    1i1i1i1i10i0i0i0i0

    Agriculture 245.60 0.19 47.13 0.09 170.78 0.07 12.05 0.03

    i=1

    Mining 911.12 0.02 15.98 0.03 346.25 0.01 5.00 0.01

    where w01 is the average earnings in sector i, p01 is the probability

    Manufacturing 518.76 0.21 110.77 0.21 343.83 0.21 72.44 0.21

    of obtaining employment in sector i and n is the number of sec-

    Construction 368.80 0.11 40.52 0.08 298.63 0.02 6.16 0.02

    tors. Similarly, if W1 denotes the expected earnings of an illiter-

    Trade and hotels 506.28 0.12 62.54 0.12 283.15 0.05 13.03 0.04 ate in 2004, then Communications and

    n transport 683.74 0.14 99.01 0.18 359.00 0.12 42.85 0.12 W1 = Σ p1i w1i ...(2) i=1

    Finance and real estate 583.51 0.03 19.81 0.04 350.93 0.03 11.35 0.03

    Pub admn and servs 856.87 0.16 141.30 0.26 373.90 0.49 181.71 0.53

    Notice that expected earnings in 2004 could be different

    W1 = 537.05, W = 344.58.

    from that in 1983 either because of an increase in sectoral 0

    Table 12: Average Earnings of Females with Middle School Education for Cohort 34-42

    e arnings or because the sectoral probabilities of employment

    2004-05 1983

    change or both.

    Sectors w p p w p w/W w p p w p w/W

    1i1i1i1i1i1i 10i0i0i0i0i0i 0

    If the agricultural sector is indexed by 1, then the contribution

    Agriculture 161.22 0.23 37.24 0.11 120.30 0.03 3.99 0.01 of this sector to the total income of the illiterates in each year is Mining 246.87 0.01 1.26 0.00 0.00 0.00 0.00 0.00

    given by Manufacturing 222.85 0.15 33.84 0.10 185.45 0.06 12.03 0.04

    ρ0 = p01 w01/W0 and ρ1 = p11 w11/W1 ...(3) Construction 276.67 0.03 8.40 0.03 320.69 0.00 1.39 0.00

    To obtain the estimates of (1), (2) and (3), we compare the Trade and hotels 281.53 0.04 10.96 0.03 288.26 0.01 4.24 0.02

    Communications and

    cohorts in the prime working age group of 34-42 in 1983 and in

    transport 636.58 0.02 13.40 0.04 277.56 0.05 15.20 0.05

    2004-05.9 This is done separately for males and females and for

    Finance and real estate 477.80 0.00 2.22 0.01 431.44 0.02 8.80 0.03

    different education levels. The self-employed are not included

    Pub admn and servs 433.90 0.52 224.36 0.68 292.15 0.81 236.00 0.84 in this exercise since there is no earnings data available for W1 = 331.68, W0 = 281.64

    Economic & Political Weekly

    EPW
    january 10, 2009 51

    We, therefore, see that the non-farm sector has played a limited role in accounting for the higher earnings of male illiterates and none at all for female illiterates. How does the impact vary with education level? To answer this, we repeat the exercise in the earlier section for individuals who have completed middle or secondary school. The results are displayed in Tables 11 and 12 (p 51). Note that here too the earnings figures (as well as the s ectoral proportions of employment) exclude the self-employed.

    Notice that the contribution of agriculture drops dramatically for individuals who have completed middle school. Note that it is lower than agriculture’s share in employment because of the much higher earnings in other sectors. There is something else noteworthy here: 93% of males in this group were employed in sectors other than agriculture in 1983 whereas only 81% of them were so employed in 2004. This is surprising since the non-farm sectors are expected to have created employment for this group during the 1980s and 1990s. Indeed, construction, trade, transportation have all increased their share of employment over the time period. It is the government and personal services segment that has dropped its employment share from 49% in 1983 to 16% in 2004. This is what is primarily responsible for the reduction of the contribution of non-farm sectors in the total earnings of this group.

    Within the non-farm economy, four sectors account for most of the expected earnings. These are manufacturing, communications and transport, real estate and finance and the sector consisting of government, social and personal services.

    7 Educational Premia

    In the last section we saw that the non-farm sector demands a wage premium over what a worker with certain age and education characteristics can get in agriculture. It pays to get non-farm sector jobs and the probability of getting these jobs rises with education. In trying to assess the contribution of the growth in non-farm sectors toward poverty removal, we can ask the following important question: would the contribution have been greater if a much larger proportion of the population was educated? In other words, where is the bottleneck – in the rate at which the educated workforce is being generated or in the rate at which employment opportunities are being created? We can get some idea by looking at what is happening to the educational premia over time.

    To capture this educational premium we estimated the following regression:

    ln Wij = β0+ B1 ' + B2 'Cij + B3 'Nij + δj + εij

    Eijwhere i indexes the individual and j indexes the state, W is earnings, Eis a vector of dummy variables indicating the individual’s education level, C is a vector of dummy variables for the individual’s cohort, Nis a vector of interaction variables between the education and cohort dummies and δis a fixed effect specific to the state. Since there are six educational classes in the 61st round and only five in the 38th round we have collapsed the educational classes into four groups that would be compatible across the two rounds: (1) illiterates, (2) primary, (3) middle school, and

    (4) graduates (high school graduates and also university gradu-ratio across different time periods have provoked great interest ates). The coefficients on educational dummies allow us to deter-because of what it might say about the effectiveness of different mine the educational premium for each cohort. government policies. This paper pursues a complementary and

    An illiterate worker belonging to the cohort 3 (i e, age group different approach. 34-42) had an all India average weekly earnings of Rs 126 in 1983 The paper looks at agricultural wages as an index of incomes while for a worker with primary education the figure was Rs 153. of the poor. By doing so, the paper is able to link the movement in Thus, the wage premium for a worker with primary school edu-wages (and hence poverty) to the fundamental process of sectocation over an illiterate worker was Rs 27. Similarly, the wage ral labour flow that underlies economic development. This way premia for middle school and graduates over illiterate workers we can begin to look at the mechanisms by which economic were Rs 96 and Rs 224, respectively. The results for the 61st growth can reduce poverty. round show that these premia have increased to Rs 86, Rs 197 Despite the rapid growth of the non-farm sector, its success in and Rs 696, respectively. For the next older cohort, the increase drawing labour from land has been limited. Yet agricultural in premia is even greater. earnings have increased demonstrating the pivotal role of agri-

    What this indicates to us is that if more middle school and cultural productivity. It could be argued, however, that the hishigh school graduates were available in 2004 they would have torical experience is not useful for assessing future priorities and found employment in industry and services.10 The main reason policies. With an even higher growth rate of the non-farm sector why the non-farm sector has not been able to contribute more to and a corresponding massive shift of labour, farm productivity poverty removal is that most of the employment it creates is for might not be that relevant to poverty dynamics. Note though that educated workers rather than for the illiterates and primary as access to non-farm sector jobs is closely tied to education, we school graduates. find that it is only the young male cohorts that show labour mobil

    ity. Older males and females of all ages are directly affected by 8 Concluding Remarks slowdown in agricultural growth. The stock of labour force The poverty debate in India has revolved around the movement already locked into agriculture is large and the best way to in the headcount ratios of poverty. As this is also the poverty improve their living standards would be the most direct one – of measure that is tracked by the government, the changes in this boosting farm productivity.

    Notes Desai, S and M B Das (2004): “Is Employment Driving Economics in India (New Delhi: Oxford University India’s Growth Surge?”, Economic & Political Press).

    1 See Eswaran and Kotwal (1993) for the precise Weekly, 3 July, 39 (27): 3045-51. Sen, Abhijit and Himanshu (2005): “Poverty and Inemodel on which our framework is based.

    Deaton, A (2005): “Prices and Poverty in India, 1987-quality in India” in A Deaton and V Kozel (ed.),

    2 The Planning Commission price deflators have 2000” in A Deaton and V Kozel (ed.), The Great Indian The Great Indian Poverty Debate (New Delhi, been criticised for using outdated weights.

    Poverty Debate (New Delhi, India: MacMillan). India: MacMillan).

    D eaton and Tarozzi (2005) and Deaton (2005) Deaton, A and J Dreze (2002): “Poverty and Inequal-Sundaram, K (2001a): “Employment-Unemploymenthave constructed alternative price deflators ity in India, a Re-examination”, Economic & Politi-Situation in Nineties: Some Results from NSS 55th that use more appropriate weights for the cal Weekly, 7 September, 37 (33): 3729-48. Round Survey”, Economic & Political Weekly, 17

    c omponents in the consumption basket. Their work does not, however, provide a price deflator

    Deaton and Dreze (2008): “Nutrition in India: Facts March, 36(11): 931-40. for 1983. and Interpretations”, Princeton University, http:// – (2001b): “Employment and Poverty in 1990s: Furweblamp.princeton.edu/chw/papers/deaton_dreze_ ther Results from NSS 55th Round Employment

    3 Although we use the terms wages and earnings interchangeably, the information in NSS data india_nutrition.pdf Unemployment Survey, 1999-2000”, Economic & captures earnings rather than wages. The two

    Deaton, A and A Tarozzi (2005): “Prices and Poverty Political Weekly, 11 August, 36 (32): 3039-49. can differ, for instance, because of piece rate con-Unni, Jeemol and Uma Rani (2005): “Home Based

    in India” in A Deaton and V Kozel (ed.), The tracts. Work in India: A Disappearing Continuum of

    Great Indian Poverty Debate (New Delhi, India: MacMillan).

    4 The experience of states is diverse. State-wise Dependence?” Working Paper No 160, Gujarat earnings are given in Table A.1 in the Appendix. Eswaran, M and A Kotwal (1993): “A Theory of Real Institute of Development Research. Wage Growth in

    5 This is not the only channel. Other channels could

    LDCs”, Journal of Table A.1: State-wise Real Agricultural Wages (Rs in 1999 Rural Maharashtra Prices)

    be through reducing price of agricultural inputs

    Development Econo- Weekly Earnings

    Average Daily Wages

    or reducing the price of the consumption basket mics, 42, 243-70. 2004-05 1999-00 1993-94 1983

    2004-05 1999-00 1993-94 1983

    of agricultural workers. Eswaran, M, A Kotwal,

    6 Of course, in several other countries like Taiwan AP 202.91 210.45 169.65 123.50 40.77 39.37 32.04 22.89

    B Ramaswami and and Indonesia the increases in agricultural pro-

    Assam 267.11 204.48 198.38 220.13 47.46 38.26 36.48 36.25

    W Wadhwa (2008): ductivity preceded the industrial expansion and

    “How Does Poverty Bihar 203.01 171.11 141.14 109.36 38.51 34.39 24.96 20.33

    also played an important role in increasing rural Decline: Suggestive

    wages. Gujarat 195.65 179.06 163.77 134.59 39.18 37.00 33.77 26.05

    Evidence from India, 7 Note that the employment shares are for the

    Haryana 325.96 286.81 218.82 239.60 55.49 65.70 40.31 39.81

    1983-1999”, Bread entire economy – there is no division between the

    Policy Paper No 14, Karnataka 191.60 192.39 152.40 95.69 38.07 35.77 29.19 18.20

    rural and urban sectors.

    http://ipl.econ.duke.

    8 The increase has been marginal in Madhya Kerala 344.63 309.49 251.23 186.19 78.71 76.69 54.26 44.28

    edu/bread/papers/ Pradesh and Rajasthan. policy/p014.pdf MP 170.54 162.65 155.62 103.07 32.42 28.38 26.87 17.14 9 We could do this exercise for different cohorts –

    Kijima, Y and P Lanjouw MH 161.28 166.62 139.94 98.97 31.48 36.38 25.60 18.55

    the results are not very different. Hence we chose (2005): “Economic to illustrate with only one cohort and we picked Diversification and

    Orissa 192.04 133.32 135.32 86.16 35.50 26.98 24.94 15.88

    the cohort in the prime working age. Poverty in Rural Punjab 301.51 346.92 359.98 219.21 54.59 59.89 57.44 41.08 10 For a contrary view, see Desai and Das (2004). India”, Indian Journal

    Rajasthan 278.60 259.22 231.27 155.61 50.62 44.74 39.04 29.03

    of Labour Economics,

    Tamil Nadu 195.50 194.46 148.46 85.17 44.27 46.54 32.31 19.69

    48, 349-74.

    References

    Pappola, T S (2007): UP 207.28 185.81 165.78 126.50 42.89 39.25 31.26 23.31 Chadha, G K and P Sahu (2002): “Post-Reform “Em ployment Trends”

    WB 185.45 192.14 164.50 122.93 38.73 40.55 34.09 26.38

    Setbacks in Rural Employment”, Economic & in K Basu (ed.), The

    All India 199.33 188.62 163.42 118.50 39.76 38.55 31.10 22.81

    Political Weekly, 25-31 May, 37 (21), 1998-2026. Oxford Companion to

    Economic & Political Weekly

    EPW
    january 10, 2009 53

    Table A.2: State-wise Employment Structure (Daily Status All Persons – 2004-05) Table A.5: State-wise Employment Structure (Daily Status All Persons – 1983)
    Agriculture Manufacturing Construction, Government Services, Total Agriculture Manufacturing Construction, Government Services, Total
    Trade & Hotels, Education, Health, Trade & Hotels, Education, Health,
    Transport, Storage Community Services, Transport, Storage Community Services,
    and Communications Personal Services and Communications Personal Services
    AP 0.540 0.120 0.214 0.095 0.970 AP 0.641 0.115 0.136 0.094 0.986
    Assam 0.648 0.040 0.190 0.114 0.991 Assam 0.708 0.044 0.128 0.112 0.992
    Bihar 0.660 0.077 0.192 0.054 0.982 Bihar 0.728 0.084 0.101 0.070 0.982
    Gujarat 0.533 0.177 0.194 0.076 0.979 Gujarat 0.609 0.146 0.125 0.110 0.990
    Haryana 0.480 0.146 0.255 0.097 0.978 Haryana 0.618 0.093 0.140 0.135 0.985
    Karnataka 0.601 0.106 0.188 0.079 0.975 Karnataka 0.637 0.125 0.138 0.080 0.979
    Kerala 0.307 0.142 0.354 0.145 0.948 Kerala 0.442 0.179 0.197 0.152 0.970
    MP 0.663 0.084 0.160 0.073 0.979 MP 0.747 0.077 0.085 0.066 0.975
    MH 0.511 0.128 0.221 0.102 0.963 MH 0.596 0.131 0.160 0.098 0.985
    Orissa 0.584 0.119 0.202 0.076 0.981 Orissa 0.670 0.101 0.110 0.102 0.982
    Punjab 0.445 0.151 0.277 0.107 0.980 Punjab 0.589 0.128 0.153 0.118 0.987
    Rajasthan 0.581 0.100 0.221 0.073 0.975 Rajasthan 0.740 0.078 0.112 0.062 0.991
    Tamil Nadu 0.412 0.217 0.238 0.098 0.964 Tamil Nadu 0.495 0.193 0.169 0.124 0.981
    UP 0.577 0.129 0.210 0.071 0.987 UP 0.688 0.102 0.114 0.090 0.994
    WB 0.427 0.175 0.256 0.114 0.971 WB 0.495 0.176 0.171 0.137 0.979
    All India 0.539 0.128 0.218 0.090 0.975 All India 0.634 0.118 0.133 0.099 0.984
    Table A.3: State-wise Employment Structure (Daily Status All Persons – 1999-2000) Table A.6: State-wise Employment Structure (Daily Status Males – 2004-05)
    Agriculture Manufacturing Construction, Government Services, Total Agriculture Manufacturing Construction, Government Services, Total
    Trade & Hotels, Education, Health, Trade & Hotels, Education, Health,
    Transport, Storage Community Services, Transport, Storage Community Services,
    and Communications Personal Services and Communications Personal Services
    AP 0.603 0.099 0.178 0.099 0.979 AP 0.483 0.112 0.274 0.089 0.959
    Assam 0.587 0.043 0.172 0.183 0.986 Assam 0.623 0.040 0.219 0.108 0.990
    Bihar 0.703 0.078 0.132 0.066 0.978 Bihar 0.637 0.070 0.220 0.054 0.980
    Gujarat 0.564 0.141 0.193 0.082 0.980 Gujarat 0.447 0.210 0.247 0.069 0.972
    Haryana 0.497 0.157 0.229 0.095 0.978 Haryana 0.390 0.173 0.319 0.091 0.974
    Karnataka 0.633 0.115 0.162 0.069 0.979 Karnataka 0.544 0.105 0.247 0.073 0.968
    Kerala 0.332 0.172 0.330 0.125 0.959 Kerala 0.283 0.119 0.448 0.091 0.942
    MP 0.725 0.073 0.120 0.066 0.984 MP 0.612 0.082 0.203 0.078 0.975
    MH 0.535 0.131 0.211 0.098 0.975 MH 0.416 0.154 0.291 0.093 0.953
    Orissa 0.658 0.099 0.150 0.073 0.979 Orissa 0.563 0.100 0.242 0.074 0.979
    Punjab 0.497 0.141 0.246 0.099 0.982 Punjab 0.378 0.171 0.345 0.082 0.976
    Rajasthan 0.635 0.083 0.190 0.065 0.972 Rajasthan 0.477 0.111 0.297 0.080 0.965
    Tamil Nadu 0.445 0.207 0.230 0.094 0.976 Tamil Nadu 0.344 0.212 0.307 0.088 0.952
    UP 0.613 0.119 0.177 0.079 0.988 UP 0.525 0.134 0.256 0.070 0.985
    WB 0.460 0.183 0.240 0.096 0.980 WB 0.437 0.146 0.297 0.088 0.968
    All India 0.580 0.121 0.189 0.090 0.980 All India 0.486 0.130 0.272 0.083 0.970
    Table A.4: State-wise Employment Structure (Daily Status All Persons – 1993-94) Table A.7: State-wise Employment Structure (Daily Status Males – 1999-2000)
    Agriculture Manufacturing Construction, Trade & Hotels, Government Services, Education, Health, Total Agriculture Manufacturing Construction, Trade & Hotels, Government Services, Education, Health, Total
    Transport, Storage and Communications Community Services, Personal Services Transport, Storage and Communications Community Services, Personal Services
    AP 0.642 0.098 0.144 0.098 0.982 AP 0.542 0.102 0.228 0.101 0.972
    Assam 0.692 0.034 0.132 0.127 0.984 Assam 0.569 0.036 0.196 0.182 0.984
    Bihar 0.740 0.056 0.114 0.076 0.986 Bihar 0.685 0.072 0.151 0.067 0.975
    Gujarat 0.567 0.162 0.145 0.110 0.984 Gujarat 0.480 0.175 0.239 0.081 0.975
    Haryana 0.518 0.108 0.202 0.159 0.987 Haryana 0.450 0.173 0.260 0.091 0.975
    Karnataka 0.636 0.112 0.125 0.102 0.975 Karnataka 0.578 0.116 0.212 0.070 0.976
    Kerala 0.439 0.148 0.243 0.139 0.969 Kerala 0.325 0.133 0.409 0.089 0.956
    MP 0.744 0.064 0.092 0.078 0.978 MP 0.675 0.077 0.155 0.073 0.980
    MH 0.568 0.123 0.162 0.123 0.975 MH 0.426 0.164 0.279 0.099 0.969
    Orissa 0.693 0.085 0.115 0.090 0.984 Orissa 0.638 0.082 0.178 0.078 0.976
    Punjab 0.535 0.124 0.191 0.138 0.989 Punjab 0.445 0.158 0.294 0.083 0.979
    Rajasthan 0.668 0.070 0.154 0.080 0.972 Rajasthan 0.534 0.095 0.258 0.076 0.963
    Tamil Nadu 0.477 0.201 0.179 0.121 0.978 Tamil Nadu 0.389 0.205 0.290 0.084 0.969
    UP 0.665 0.099 0.131 0.095 0.990 UP 0.574 0.124 0.211 0.077 0.986
    WB 0.462 0.197 0.190 0.131 0.979 WB 0.472 0.160 0.267 0.078 0.977
    All India 0.611 0.114 0.148 0.108 0.981 All India 0.529 0.125 0.233 0.088 0.976
    54 january 10, 2009 Economic & Political Weekly
    EPW
    Table A.8: State-wise Employment Structure (Daily Status Males – 1993-94) Table A.11: State-wise Employment Structure (Daily Status Females – 1999-2000)
    Agriculture Manufacturing Construction, Government Services, Total Agriculture Manufacturing Construction, Government Services, Total
    Trade & Hotels, Education, Health, Trade & Hotels, Education, Health,
    Transport, Storage Community Services, Transport, Storage Community Services,
    and Communications Personal Services and Communications Personal Services
    AP 0.585 0.096 0.184 0.110 0.975 AP 0.713 0.094 0.089 0.095 0.991
    Assam 0.683 0.031 0.149 0.120 0.983 Assam 0.686 0.081 0.034 0.191 0.993
    Bihar 0.719 0.054 0.129 0.083 0.985 Bihar 0.783 0.102 0.047 0.061 0.993
    Gujarat 0.493 0.194 0.180 0.114 0.980 Gujarat 0.769 0.059 0.081 0.083 0.991
    Haryana 0.449 0.120 0.242 0.174 0.985 Haryana 0.750 0.070 0.063 0.111 0.994
    Karnataka 0.593 0.105 0.159 0.113 0.969 Karnataka 0.750 0.114 0.057 0.065 0.986
    Kerala 0.431 0.121 0.295 0.116 0.964 Kerala 0.351 0.278 0.111 0.226 0.966
    MP 0.698 0.068 0.118 0.090 0.974 MP 0.837 0.063 0.042 0.050 0.992
    MH 0.472 0.153 0.210 0.135 0.969 MH 0.772 0.058 0.064 0.094 0.989
    Orissa 0.676 0.078 0.133 0.096 0.983 Orissa 0.713 0.144 0.071 0.059 0.987
    Punjab 0.500 0.138 0.220 0.130 0.988 Punjab 0.722 0.067 0.039 0.168 0.996
    Rajasthan 0.567 0.092 0.208 0.097 0.964 Rajasthan 0.853 0.056 0.042 0.041 0.992
    Tamil Nadu 0.408 0.196 0.239 0.127 0.971 Tamil Nadu 0.557 0.211 0.109 0.113 0.990
    UP 0.633 0.103 0.153 0.100 0.988 UP 0.774 0.100 0.037 0.086 0.996
    WB 0.476 0.174 0.213 0.114 0.977 WB 0.388 0.319 0.081 0.206 0.993
    All India 0.566 0.117 0.182 0.113 0.977 All India 0.723 0.108 0.066 0.093 0.990
    Table A.9: State-wise Employment Structure (Daily Status Males – 1983) Table A.12: State-wise Employment Structure (Daily Status Females – 1993-94)
    Agriculture Manufacturing Construction, Government Services, Total Agriculture Manufacturing Construction, Government Services, Total
    Trade & Hotels, Education, Health, Trade & Hotels, Education, Health,
    Transport, Storage Community Services, Transport, Storage Community Services,
    and Communications Personal Services and Communications Personal Services
    AP 0.602 0.115 0.164 0.102 0.983 AP 0.740 0.101 0.074 0.078 0.993
    Assam 0.705 0.041 0.141 0.104 0.992 Assam 0.743 0.050 0.027 0.167 0.988
    Bihar 0.712 0.081 0.113 0.075 0.980 Bihar 0.837 0.061 0.049 0.045 0.992
    Gujarat 0.534 0.179 0.152 0.121 0.987 Gujarat 0.766 0.075 0.051 0.101 0.993
    Haryana 0.584 0.102 0.161 0.135 0.983 Haryana 0.795 0.059 0.041 0.101 0.996
    Karnataka 0.601 0.117 0.167 0.090 0.974 Karnataka 0.727 0.128 0.054 0.078 0.988
    Kerala 0.446 0.153 0.244 0.124 0.967 Kerala 0.462 0.228 0.089 0.206 0.985
    MP 0.694 0.087 0.107 0.081 0.969 MP 0.849 0.055 0.034 0.048 0.987
    MH 0.512 0.162 0.195 0.112 0.981 MH 0.757 0.064 0.067 0.099 0.987
    Orissa 0.663 0.094 0.113 0.110 0.979 Orissa 0.744 0.106 0.061 0.074 0.984
    Punjab 0.590 0.128 0.165 0.104 0.987 Punjab 0.736 0.046 0.031 0.183 0.996
    Rajasthan 0.659 0.097 0.150 0.081 0.988 Rajasthan 0.863 0.027 0.050 0.047 0.987
    Tamil Nadu 0.444 0.194 0.212 0.125 0.975 Tamil Nadu 0.603 0.208 0.070 0.110 0.992
    UP 0.660 0.107 0.131 0.095 0.992 UP 0.803 0.085 0.036 0.072 0.997
    WB 0.505 0.170 0.188 0.115 0.978 WB 0.383 0.323 0.064 0.220 0.990
    All India 0.596 0.124 0.157 0.105 0.982 All India 0.737 0.104 0.056 0.093 0.990
    Table A.10: State-wise Employment Structure (Daily Status Females – 2004-05) Table A.13: State-wise Employment Structure (Daily Status Females – 1983)
    Agriculture Manufacturing Construction, Government Services, Total Agriculture Manufacturing Construction, Government Services, Total
    Trade & Hotels, Education, Health, Trade & Hotels, Education, Health,
    Transport, Storage Community Services, Transport, Storage Community Services,
    and Communications Personal Services and Communications Personal Services
    AP 0.637 0.134 0.113 0.104 0.988 AP 0.716 0.114 0.082 0.080 0.991
    Assam 0.774 0.039 0.043 0.140 0.997 Assam 0.731 0.065 0.028 0.172 0.995
    Bihar 0.767 0.110 0.058 0.058 0.993 Bihar 0.787 0.094 0.055 0.052 0.988
    Gujarat 0.749 0.093 0.059 0.093 0.994 Gujarat 0.809 0.056 0.050 0.083 0.998
    Haryana 0.781 0.057 0.039 0.115 0.992 Haryana 0.797 0.041 0.025 0.134 0.998
    Karnataka 0.720 0.109 0.066 0.093 0.988 Karnataka 0.720 0.144 0.070 0.055 0.990
    Kerala 0.368 0.201 0.115 0.282 0.966 Kerala 0.432 0.251 0.066 0.229 0.977
    MP 0.779 0.087 0.061 0.062 0.989 MP 0.857 0.058 0.038 0.035 0.988
    MH 0.707 0.077 0.078 0.120 0.982 MH 0.773 0.064 0.087 0.069 0.994
    Orissa 0.644 0.174 0.090 0.079 0.987 Orissa 0.691 0.123 0.101 0.076 0.990
    Punjab 0.689 0.079 0.029 0.196 0.993 Punjab 0.572 0.135 0.049 0.232 0.988
    Rajasthan 0.793 0.078 0.066 0.058 0.995 Rajasthan 0.886 0.043 0.042 0.027 0.998
    Tamil Nadu 0.529 0.223 0.118 0.115 0.985 Tamil Nadu 0.603 0.190 0.079 0.122 0.994
    UP 0.764 0.111 0.044 0.075 0.994 UP 0.805 0.081 0.044 0.069 0.999
    WB 0.376 0.315 0.058 0.238 0.987 WB 0.437 0.207 0.071 0.276 0.991
    All India 0.681 0.124 0.075 0.108 0.988 All India 0.744 0.102 0.063 0.084 0.992
    Economic & Political Weekly january 10, 2009 55
    EPW

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