Econ Diversification and Poverty Rural India

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Published: Indian Journal of Labour Economics, Vol 48(2), 2005

Economic Diversification and Poverty in Rural India

Yoko Kijima and Peter Lanjouw*

(Foundation for the Advanced Studies in International Development, Tokyo,

and DECRG, The World Bank)

Abstract

We analyze National Sample Survey data for 1987/8, 1993/4 and 1999/0 to explorethe relationship between rural diversification and poverty. While there is little consensus

regarding the rate of poverty decline during the 1990s, we provide region-level estimates

that suggest that aggregate rural poverty fell slowly. Unlike earlier estimates, ourestimates correlate well with region-level NSS data on changes in agricultural wage rates.

We show that agricultural wage employment has grown over time, and that a growing

fraction of agricultural laborers are uneducated and have low caste status. On aggregatethe non-farm sector has not grown appreciably during the 1990s. Non-farm employment

is generally associated with education levels and social status that are rare among the

poor. Econometric estimates confirm that poverty reduction is more closely associated

with agricultural wages and employment levels than with non-farm employment growth.However, expansion of non-farm employment influences poverty indirectly, via an

impact on agricultural wages.

*We are grateful to Ananya Basu, Mukesh Eswaran, Andrew Foster, Himanshu, Stephen Howes, AshokKotwal, Jenny Lanjouw, Rinku Murgai, Martin Ravallion, Abhijit Sen, Alakh Sharma, Ravi Srivastava, and

Tara Vishwanath for helpful discussions. We also thank participants at the International Seminar on Wages

and Incomes in India held at IGIDR, Mumbai, December 12-14, 2004, and at the Public Forum Eye onIndia: Making Sense of the Fast Growing Economy of India held at the University of British Columbia,

Vancouver, Canada, June 23-25, 2005, for comments. The views in this paper are those of the authors and

should not be taken to reflect those of the World Bank or affiliated institutions. All errors are our own.


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Introduction

The performance of the Indian economy during the past decade-and-a-half has

been widely applauded both within India and in the international community. Growth

has accelerated substantially beyond the sluggish rates that were the norm in earlier

decades; and from which escape had previously seemed so elusive. A degree of

momentum appears to have been achieved that holds out a promise for rapid, and

sustained, growth in per-capita income levels for the countrys vast population.

What is less clear, however, is the extent to which overall economic progress has

translated into poverty reduction in India. There is a concern that while great strides were

achieved on aggregate, not all segments of the population may have benefited equally

from this growth. In particular, it is unclear as to how far the rural poor have seen

marked improvements in living standards during the past decade.

This paper presents evidence from household survey data on three inter-related

topics in an effort to ascertain whether rural living standards have improved significantly

over time. The paper first reviews evidence on rural poverty in India from three large-

scale surveys carried out in 1987/8, 1993/4 and 1999/0. The paper notes that there is

little consensus as to the rate of poverty decline during the 1990s, due to well-discussed

problems of comparability in the consumption module of the NSS surveys for 1993/4 and

1999/0. The paper draws on a recent literature that proposes a variety of adjustment

methodologies for restoring comparability, to produce a new set of region-level estimates

of poverty. These estimates indicate that there is considerable variation across regions in

the evolution of poverty, but suggest that at the all-India level poverty has fallen only

slightly during the 1990s. The paper shows that the estimates produced here correlate

well with data on changes in agricultural wages at the region level. Such a correlation

cannot be discerned with other estimates of poverty in the literature.The paper finds that at the all-India level there is little evidence of diversification

out of agriculture in rural areas. In particular, the data suggest that since 1987 there has

been no decline, indeed some growth, in the share of the adult population in rural areas

with primary occupation in agricultural wage labor. The paper confirms the historically

close association between poverty and agricultural wage employment. The strong

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association is seen not only in terms of the strikingly lower per capita consumption levels

amongst agricultural laborers, but also the generally low educational outcomes amongst

agricultural laborers and the high percentage of such laborers belonging to the lowest

castes. There is little evidence of broad, across the board, acceleration in agricultural

wage growth during the 1990s. In a majority of regions, real agricultural wage growth

between 1993/4 and 1999/0 is lower than what was observed during the 1980s. In some

regions real wages actually declined.

A sector that is increasingly looked to for impetus in rural poverty reduction is the

rural non-farm sector. This sector accounts for nearly half of rural household income in a

significant number of states in India. The sector is highly heterogeneous and can be

crudely broken up into three sub-sectors comprising: regular, salaried non-farm

employment; casual wage labor in the non-farm sector; and non-agricultural self-

employment activities. The former sub-sector is most clearly associated with relatively

high and stable incomes, while the latter two are more heterogeneous and can comprise

both productive as well as residual activities. NSS data indicate that during the reference

period there is little sign of significant growth, on aggregate, in the size of the non-farm

sector. Overall employment levels in the non-farm sector remained remarkably stable

between 1987 and 1999/0. The paper finds further, that the poor are hardly represented at

all in the regular non-farm sector and only slightly in non-farm self-employment. Only in

the case of casual wage-labor in the non-farm sector do the poor appear to have a

somewhat greater involvement in the non-farm sector than do the non-poor. Multinomial

logit models indicate that those with little or no education, or those who belong to the

scheduled castes or tribes, are much more likely to have agricultural wage labor as

principal occupation than any of the three non-farm occupations. Indeed, the probability

of employment in the regular non-farm sector is particularly strongly associated with high

education levels and high social status.

Despite little evidence of change, in terms of poverty reduction or diversification

out of agriculture, at the all-India level, NSS data reveal a great deal of heterogeneity at

the NSS region-level. This regional variation in can be exploited to study

econometrically the relationship between poverty, agricultural wage labor employment

and non-farm employment in rural India. The key finding from this analysis is that while

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the non-farm sector may not employ many of the poor, and therefore contributes

relatively little to poverty reduction in a direct way, expansion of this sector, particularly

the unskilled casual sub-sector, puts pressure on wage rates in agriculture and thus

indirectly exercises a significant influence on poverty rates.

The evidence assembled in this study from the NSS surveys thus offers some

empirical support to the view that the period between 1987/8 and 1999/0 was one of

limited poverty reduction in rural areas. But it is important, as well, to acknowledge the

limitations to the analysis here. The analysis stops in 1999/0 and we are unable to say

what has happened since that survey year. A new large-sample NSS round is expected in

2005 and these data will be keenly scrutinized, not least in order to resolve the

controversies regarding the evolution of measured poverty. The analysis is also

somewhat partial in its examination of sources of rural livelihoods. Notably, we do not

focused here on the relationship between poverty and agricultural production, nor on the

evolution of agricultural output over time. This omission is due to lack of data in the

NSS surveys on farm production and household incomes.

In the remaining sections of this paper we turn in greater detail to the empirical

underpinnings of the basic story outlined above. In Section II we consider the evolution

of poverty in India, and propose a new set of region-level estimates of poverty. Section

III looks at agricultural labor and agricultural wages. Section IV documents the size and

evolution of the non-farm sector between 1987/8 and 1999/0, and Section V exploits a

region-level panel dataset across the three surveys to bring together the arguments of the

preceding sections and Section VI offers some concluding comments.

II. Poverty 1987/8 1999/0

Much has been written about the difficulties surrounding comparisons of poverty

rates from the NSS surveys during the 1990s. A recent special issue of Economic and

Political Weekly (January 25, 2003) is devoted to the subject of poverty and its evolution

in India, and pays particular attention to the potentially important measurement problem

in the NSS surveys for the 1990s. The problem is well described in several of the papers

included in the special issue (Deaton, 2003a, Datt, Kozel and Ravallion, 2003, Sundaram

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and Tendulkar, 2003). Deaton and Kozel (forthcoming) provide a further update on the

state of the debate. To briefly summarize, consumption data in the 50th

round of the NSS

survey used a 30-day recall period for all goods. In the next thick round of the NSS

survey - the 55th

round (referring to 1999/0) - all households were asked to report

expenditures for both a 30-day and an alternative recall period (a 7-day recall for food

items and a 365-day recall period for non-food, low-frequency items). As Deaton

(2003a) argues, the results are unlikely to be comparable with those from a questionnaire

in which only the 30-day questions are used (as in the 50th

round). It seems likely that,

possibly inadvertently, households would try to reconcile their answers to questions that

refer to different recall periods, thereby compromising comparability with the earlier

consumption data.

Ignoring these potential comparability problems produces estimates of poverty for

1999/0 that are dramatically lower than in 1993/4. Deaton and Drze (2002) present

estimates of the decline in poverty between the 50th and the 55th rounds based on

unadjusted figures and indicate, for example, that in rural areas poverty declined from an

all-India headcount rate of 33 percent to 26 percent.i

To what extent is this evidence of impressive poverty reduction driven by

the problems of non-comparability outlined above? A variety of approaches have been

proposed to correct poverty estimates for 1999/0 and render them comparable with

those for 1993/4 (see Deaton and Kozel, forthcoming, for a clear overview of the various

alternative approaches that have been proposed). In Table 1 we present adjusted

estimates of poverty for the 55th

round and suggest that at the all-India level rural poverty

has declined only from 33.1 to 30.9 percent between 1993/4 and 1999/0. The specific

adjustment methodology that is applied in Table 1 is motivated by recent contributions by

Sundaram and Tendulkar (2004) and Sen and Himanshu (2004a, 2004b). Sundaram and

Tendulkar (2004) argue that food consumption data were not contaminated as a result of

the changes in questionnaire design between the 50th and 55th rounds. Deaton and his

colleagues had earlier noticed that the consumption module concerning a certain sub-set

of intermediate goods had not changed during this period. The poverty estimates in

Column 3 are based on a model of consumption in the 50th

round in which total per capita

consumption in the 50th

round is regressed on a subcomponent of total consumption

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comprising food consumption and the consumption of the set of comparable intermediate

goods identified by Deaton and colleagues. We follow the methodology outlined in

Elbers, Lanjouw and Lanjouw (2003) and Kijima and Lanjouw (2003) to take the

parameter estimates from this regression model estimated in the 50th

round and apply

these to the same sub-component of consumption in the 55th

round. This allows us to

predict, at the level of each household in the 55th

round, a measure of consumption which

accords with the 50th

round definition of consumption.ii

Poverty estimates are then

calculated off of these predicted consumption levels. Sen and Himanshu (2004a, 2004b)

argue that this particular specification provides a more plausible basis for producing

comparable estimates of poverty than earlier approaches.

Table 1 reveals that changes in poverty at the national, and even state, level, mask

considerable heterogeneity in the evolution of poverty across regions. Nonetheless the

results indicate that progress in poverty reduction was relatively slow in most regions

and, indeed, in one case, Orissa, the estimates in column 3 suggest that poverty increased

significantly over the time period.

The estimates of poverty in Column 3 of Table 1 must be treated with caution.

They are based on assumptions that cannot be readily verified, and are certainly

pessimistic relative to most other estimates that have been proposed. The very basic

question of whether and how much poverty declined in rural India during the 1990s

remains moot, and it is unclear whether, absent new data, a consensus can be achieved.

In the meantime, we consider in the next section trends in employment in agricultural

labor, and in agricultural wages, to see to what extent these provide corroborating

evidence to the poverty estimates above.

III. Agricultural Employment and Agricultural Wages

There is a long-standing recognition that employment in casual agricultural wage

labor is strongly correlated with poverty in rural India (Lal, 1976, Singh, 1990, Lanjouw

and Stern, 1998, Sharma 2001, Sundaram, 2001, Himanshu, 2004). Both agricultural

wage employment shares and agricultural wage rates have consequently been scrutinized

as indicators of rural living standards and how these have been evolving over time.

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Agricultural Wage Labor Employment

Table 2 reveals the close association between per-capita consumption levels and

employment in agricultural wage labor. Respectively in each of the three NSS rounds

examined in this paper, it can be seen that the percentage of the working population (aged

15 and higher and reporting some economically gainful activity) with primary

employment in agricultural wage labor is highest in the two lowest consumption

quintiles. Indeed agricultural wage labor employment shares are highest by a large

margin in the lowest quintile. This suggests that not only is agricultural wage

employment a good correlate of the incidence of poverty in general, but that it is

particularly closely aligned with extreme poverty and may thus also be a good proxy for

measures of poverty that are sensitive to distance below the poverty line. In all three

years, the odds of being employed in agricultural wage labor fall monotonically as one

rises in the consumption distribution, and there is little evidence of this association

changing or weakening over time.

Table 3 demonstrates that two other important dimensions of well-being,

education levels and caste status, are also strongly and inversely correlated with

agricultural labor employment. Once again, there is little to suggest that over time, the

association between agricultural wage labor employment and low welfare in terms of

these other indicators has weakened. Anticipating somewhat the discussion in

subsequent sections, we can see that the relationship between non-agricultural

employment on the one hand, and educational and social status, on the other, is strong

and positive particularly in the case of regular employment in the non-farm sector.

Employment in agricultural labor shows some signs of expansion over the period

spanning the three most recent NSS surveys (Table 4). In 1987/8, about 20% of all males

aged 15 and above were employed in agricultural labor. This rose to 22.5% in 1993/4 and

grew slightly further to 23.2% by 1999/0. For women, although far fewer are judged to

be economically active, the importance of agricultural labor employment is even more

pronounced. And once again, the percentage of women employed in agricultural labor

has increased over time (from 11.2% in 1987/8 to 14.5% by 1999/0). Given that the

overall rural population was also growing during this time period, this percentage

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increase over time translates into quite sizeable increases in the number of agricultural

laborers in rural India during the late 1980s and the 1990s.

Table 5 indicates that agricultural wage labor employment varies markedly across

states (Appendix Table 1 provides a further disaggregation, to the NSS region level, for

the three survey years). In all three years, states with particularly high percentages of the

economically active population employed in agricultural labor include Andhra Pradesh,

Bihar, Karnataka, Maharashtra, Orissa and Tamil Nadu. Agricultural employment shares

grew monotonically in 6 out of the 16 major states between 1987/8, 1993/4 and 1999/0

(Andhra Pradesh, Himalchal Pradesh, Madhya Pradesh, Maharashtra, Orissa, and Tamil

Nadu). In another 7, the trend was not monotonic but employment shares in 1999/0 were

higher than they were in 1987/8 (Bihar, Gujarat, Karnataka, Punjab, Rajasthan, Uttar

Pradesh and West Bengal). Only in Assam, Haryana and Kerala is there some suggestion

of a decline in the importance of agricultural wage labor employment over time.

To summarize, scrutiny of agricultural wage labor employment shares indicates

that there is a strong correlation between employment in this sector of the rural economy

and lower living standards, whether expressed in terms of per-capita consumption levels

or in terms of broader dimensions of well-being such as education levels and social

status. This relationship does not appear to have weakened markedly over time. At the

same time, the importance of agricultural wage employment seems to have risen over the

1990s with most states ending the decade with higher agricultural employment shares

than at the end of the previous decade.

The evidence that wage labor accounts for an

increasing proportion of total employment in rural India has been widely noted in the

literature (see, for example, Visaria and Basant, 1993, for an earlier analysis). This is

sometimes interpreted as a trend towards proletarianisation of the labor force. This

term is often associated with a situation in which farmers, particularly smallholder

cultivators, are pushed out of agriculture into wage labor. However, proletarianisation

may also simply describe the shift away from self-employment (mainly in agriculture)

towards wage labor. In general, whether push or pull effects dominate across rural

India, varies with the experience of land legislation, population pressure on the land,

expansion of non-agricultural opportunities and related factors. The fact that

proletarianisation often takes place against a background of rising real agricultural wages

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(see below) suggests that there are pull factors at work.At the same time, the

observation that employment shares in agricultural wage labor have risen while at the

same time there has been a rise in the percentage of agricultural wage laborers who are

illiterate, suggests that agricultural labor has remained a last-resort employment option;

one where push factors are certainly not absent.

Agricultural Wages

The evidence presented above suggests that agricultural wage employment can

serve as a valuable window on living standards in rural areas. Agricultural wage rates

prevailing in rural areas provide a further perspective on the livelihoods of this segment

of the rural population. As has been pointed out by Deaton and Drze (2002),

agricultural wages can be viewed not only as useful proxies of poverty but can also be

seen as indicators of poverty in their own right insofar that they capture the reservation

wages of the rural labor force.

Agricultural wage data are available in India from a variety of sources. Himanshu

(2004) provides a detailed assessment of the different sources available, and documents

the differences in the methodologies and survey designs that are applied. He points to

serious problems of transparency as well as of comparability and interpretation across the

different wage series. He argues that calculation of agricultural wage rates directly from

the NSS surveys is not only feasible, but quite possibly yields more reliable figures than

are available from alternative sources.

Table 6 presents state-level estimates of real agricultural wages calculated from

our three NSS surveys. The resulting estimates for 1993/4 and 1999/0 accord quite

closely to those calculated separately by Himanshu (2004), but are not identical in so far

as the estimates here apply the Tornqvist intertemporal and spatial price indices proposed

by Deaton (2002), and also take 1993 as base year.iii

From Table 6 it can be seen that

alongside the growth in agricultural wage employment shares there has been growth in

agricultural wages between 1987/8 and 1999/0. Wages have risen monotonically in all

states except Assam and Punjab, where there appears to have been some decline in real

wages between 1993/4 and 1999/0. However, what is also apparent from Table 6 is that

the rate of growth of agricultural wages appears to have faltered somewhat between

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1993/4 and 1999/0. In 13 out of 16 states, wages grew less rapidly between 1993/4 and

1999/0 compared to the period 1987/8 - 1993/4. Agricultural wage growth accelerated in

only three states (Bihar, Kerala, and Gujarat).

In Section II above we described the controversy concerning the comparability of

consumption poverty estimates between the 1993/4 and 1999/0 surveys. Do our adjusted

poverty estimates correlate closely with real agricultural wages? Table 7a presents

Pearson and Spearman rank correlation coefficients of region-level poverty estimates and

the accompanying regional-level agricultural wage data, in turn for the different survey

years and for a number of different adjusted poverty estimates.iv

All of the correlation

coefficients are very significant and negative indicating that, indeed, poverty rates,

regardless of the year of the survey or of the adjustment methodology used to establish

comparability between 1993/4 and 1999/0, are lower in those regions where agricultural

wages are higher. There is little obvious basis for preferring one adjustment

methodology over another in terms of whether this results in a closer correlation of

poverty with agricultural wages.

Table 7b looks at changes in poverty andchanges in agricultural wages. Between

1993/4 and 1999/0 the correlation is consistently negative between poverty growth and

agricultural wage growth. However, only in the case of the adjusted poverty estimates

presented in Table 1 is the relationship strong and significant. This finding provides

some empirical support in favour of the poverty estimates produced in this study.

IV. Non-Farm Employment

As described in Section I, the rural non-farm sector is widely looked to as a

source of momentum for rural growth and poverty reduction. Employment patterns in

the non-farm sector have been widely scrutinized for evidence of economic dynamism in

rural areas. Visaria and Basant (1993) carefully examine National Sample Survey and

Census data and document a clear increase in the share of non-agricultural employment

in the rural workforce during the 1980s, with the trend more clearly evident among males

than among female workers. In addition, the evidence appears to point to a more rapid

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expansion of tertiary sector employment rather than of secondary sector employment, and

that the bulk of employment growth is of a casual nature, rather than permanent.

Fisher, Mahajan and Singha, (1997) conclude that between 18-25% of rural

employment occurred in the non-farm sector at the beginning of the 1990s. An important

observation made in this study was that approximately one-fifth of total rural non-farm

employment was estimated to be generated by public sector services, primarily public

administration and education (see also Sen, 1996). Other important sectors in terms of

employment shares were found to include retail trade, personal services, construction,

wood products and furniture, over-land transport, and textiles. While manufacturing

activities are often the first that come to mind when discussing the non-farm sector, the

study showed that services are easily as important.

A study by Acharya and Mitra (2000) draws on multiple rounds of National

Sample Survey data (spanning the period 1984-1997), and also two rounds of the

Economic Census (corresponding to 1990 and 1998) to ask whether the positive nonfarm

employment trends of the 1980s have continued through the 1990s. They find little

evidence of further expansion. At the all-rural India level they find that employment in

the secondary and tertiary sectors grew from about 22% of the workforce in 1983 to

about 25% by 1987-8. They found no evidence of further growth during the 1990s; the

last NSS survey they examined (thin round for 1997) indicated an employment rate of

about 24%. The authors note considerable variation across states in the degree of

occupational diversification (with states such as Kerala, Punjab, Haryana, Gujarat and

Tamil Nadu clearly more diversified than others), but observe no clear evidence of

growth in nonfarm employment rates during the 1990s occurring in any state other than

Kerala (Acharya and Mitra, 2000).v

An important recent paper by Foster and Rosenzweig (2003a) provides a theoretical

exposition of how the non-farm economy interacts with the farm economy - building on

the great heterogeneity of non-farm activities in rural areas, and highlighting the

importance of general-equilibrium relationships. The authors argue that a key distinction

has to be made between traded and non-traded goods and services, and they emphasize

the significance of wage and salary employment in non-farm activities as opposed to the

self-employment activities that have traditionally been the focus of attention. Foster and

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Rosenzweig (2003a and 2003b) analyze NCAER data from roughly 250 villages covering

the period 1971, 1982 and 1999 to study the evolution of the non-farm economy in rural

India. These data permit the authors to calculate non-farm incomes, and they show that

non-farm income shares have increased significantly during this time period.vi

Foster

and Rosenzweig suggest that a growing rural based export-oriented manufacturing sector

can be expected to have an important pro-poor impact in rural India, possibly more

significant than that which can be expected from agriculture-led growth. This follows

from their observation that rural diversification tends to be more rapid and extensive in

places where agricultural wages are lower and where agricultural productivity growth has

been less marked. Although the Foster and Rosenzweig study employs different data

definitions and conventions than NSS-based studies (including the present study) their

evidence is suggestive of a very significant rise in non-farm employment shares during

their study period. They suggest that by 1999 about 44% of males aged 25-44 had

primary employment in the non-agricultural sector by 1999. These figures appear higher

than what NSS data suggest (although the figures cannot be directly compared as they

refer to different age groups and different employment definitions). An important

question is whether expansion of the non-farm sector observed by Foster and Rosenzweig

occurred steadily during their reference period, or whether it took place in fits and starts.

As others have already suggested, and we shall see further below, NSS data indicate that

between 1987/8 and 1999/0 there was little expansion of non-farm employment in rural

areas.

Non-Farm Employment Shares

Table 5 indicated employment shares between agricultural labor, cultivation, and

non-farm activities by year and state. Non-farm employment is broken down into three

categories: regular employment (generally salaried), casual employment (daily wage) and

self-employment/own enterprise activities. This distinction is intended to reflect to some

extent the very different characteristics of non-farm activities in rural areas

characteristics that are important in terms of defining the desirability of such jobs. A

general typology that appears to resonate with findings from many village studies is that

regular non-farm employment is typically highly sought-after in rural areas as it is

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associated not only with high incomes, but crucially also with a degree of stability. Non-

farm self employment activities can be both residual, last resort options as well as high

return, productive, activities, but whether they are of the former or latter variant generally

depends on the amount of capital resources that can be brought to the activity. Casual

non-farm wage employment is generally thought to be less demeaning to a worker than

agricultural wage labor, but returns may be only marginally higher and the nature of the

work may be both physically demanding as well as hazardous (construction, rickshaw

pulling, industrial workshops, etc.).

Between 1987/8, 1993/4 and 1999/0 overall employment shares in non-farm

activities have hovered around 25-30%, with no clear evidence of growth over time

(Table 5). In each respective survey year, wage and salary employment has tended to

account for about 14-15% of overall employment, with the balance made up by self-

employment/own-enterprise activities. Again, there is little evidence of change over

time. There are large differences across states in terms of the importance of the non-farm

sector. In Kerala, non-farm employment shares were as high as 71% in 1999/0, and the

importance of regular employment in this state grew significantly over time (from under

30% to nearly 40%). In States such as Madhya Pradesh, Bihar, and Maharashtra, the

sector has still to make its presence felt.

Non-Farm Employment and Consumption Quintiles

The distinction between regular, casual and self- employment in the non-farm

sector is well reflected in Tables 8a-8c, documenting the relationship between non-farm

employment and consumption quintiles in each of the respective survey years.vii

In all

three survey years, regular non-farm employment occurred disproportionately in the top

quintile of the per capita consumption distribution (Table 8a). While overall

employment shares in regular non-farm employment hovered around 6% throughout this

period, the relative frequency of such employment in the top quintile was more than two

times higher, while in the bottom quintile it was barely 2%.

Overall employment shares in casual non-farm activities are only slightly higher

than for regular non-farm employment shares (Table 8b). But the distribution across

consumption quintiles is quite different. Casual wage employment in the non-farm sector

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generally occurs most frequently in the lowest quintiles of the consumption distribution.

The odds of employment in casual non-farm wage labor are less than one in the top

quintiles and greater than one in the bottom three quintiles. Again, there is little evidence

of change in these patterns over time.

Non-farm self-employment activities tend to be more evenly distributed over the

consumption distribution, indicating that both poor households as well as rich households

are involved in such activities (Table 8c). On balance, however, the odds of self-

employment in the non-farm sector are slightly higher in the top three quintiles of the

consumption distribution suggesting that such activities are more frequent amongst the

relatively better off.

As was seen in the case of agricultural wage labor, the patterns of employment

observed across the consumption distribution also tend to be repeated in terms of other

dimensions of well-being such as education and caste status (Table 3). Education levels

and social status are generally highest amongst those with regular non-farm employment

while casual non-farm employment is more common amongst the illiterate and scheduled

caste and scheduled tribe households. Over time there is some suggestion that casual

non-farm employment has become slightly more strongly correlated with higher

education levels. This is consistent with gradually rising education levels in rural India

over time, and a tendency for those with some education to crowd out the uneducated in

casual non-farm employment (see further below).viii

Non-Farm Employment Probabilities

Of course, education and social status are not just of interest as intrinsic indicators

of welfare, but are also likely to play an instrumental role in determining income or

consumption levels influencing for example, individuals access to non-farm

opportunities. The relationship between occupational choice and household

characteristics is explored more systematically in Appendix Tables 2-4 on the basis of

multinomial logit models of occupational choice for each survey year (Appendix Table 1

provides descriptive statistics of the explanatory variables employed in these models).

We employ the multinomial logit model to explore the individual and household

characteristics that are associated with the probability of nonfarm employment in rural

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India (see Greene, 1993 for a useful exposition of this model). We consider seven broad

occupations in rural areas: agricultural casual wage employment; regular farm

employment; cultivation; nonfarm regular employment; nonfarm casual wage (daily

wage) employment; nonfarm own-enterprise activities; and other (plus non-working).ix

Our explanatory variables comprise a selection of individual and household

characteristics. At the individual level we consider the age, educational status, and

caste/religious status of each person.

At the household level, we have information on the size of the household to which

each person belongs and the households per-capita landholding. The latter might proxy

wealth and contacts, and thereby provide some indication of the extent to which

individuals are better placed to take advantage ofopportunities in the nonfarm sector.x

The multinomial model requires that a particular occupational category be

designated as the numeraire against which all results should be compared. We have

chosen agricultural wage labour as the comparison group. This implies that parameter

estimates for the categories which are included should be interpreted not as correlates of

employment in a given occupational category, but as indicators of the strength of

association of a particular explanatory variable with the respective occupational category

relative to the same explanatory variable with agricultural labour. To ease interpretation

we consider direct parameter estimates and then produce some derived Tables that

summarise the impact of specific explanatory variables.

The multinomial logit models confirm that relative to agricultural wage labor, the

probability of employment in any of the three non-farm sub-sectors is consistently lower

for those who belong to the scheduled castes and for those with no education. This

pattern remains unchanged across the three survey years. These findings are summarized

in Tables 9 and 10 presenting the predicted probabilities of employment in the various

occupations at mean values of the explanatory variables. For example, the first cell in

Table 9 indicates that the predicted probability of employment in agricultural labor

would be about 26.6% if all individuals were scheduled castes or scheduled tribes (with

education levels and other characteristics corresponding to the overall average in the

population). This probability would fall to 16.9% if their caste status were switched to

non-SC/ST. It is important to recognize that these are stylized probabilities in reality

15


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SC/STs would have education levels and landholdings well below the national average

as well. Table 9 indicates that the effect of caste status on regular non-farm employment

probabilities appears to operate indirectly through the differential education and

landholdings of SC/STs instead of directly. Holding these other characteristics constant

(at their national average), predicted employment in regular non-farm employment is not

markedly lower for SC/STs. With casual and self-employment in the non-farm sector,

evidence of caste differences are more readily discernable. In general, there is little

evidence of marked changes in the role of social status in determining occupational status

over time.

Table 10 documents confirms the clear association of education with employment

in non-farm activities. Predicted probabilities of regular non-farm employment in all

three survey years increase markedly with education levels (at mean values of other

characteristics), while they fall sharply in the case of agricultural labor, and more

moderately in the case of casual non-farm employment. There is little evidence of a

strong role for education in self-employment activities. Once again, this is possibly the

consequence of the heterogeneity in the kind of self-employment activities that take

place.

Finally, the multinomial models in Appendix Tables 2-4 also suggest that the

probability of employment in regular non-farm activities and nonfarm self-employment

(relative to agricultural labor) is significantly higher for those with higher per-capita

landholdings. Lanjouw and Stern (1998) argue, on the basis of a detailed village study in

Uttar Pradesh, that information networks and ability to pay bribes are important

determinants of access to the better-paying and more attractive non-farm jobs. It is

possible that per-capita landholdings are proxying such assets here.

V. Poverty Reduction, Agricultural Wages and Non-Farm Employment

We conclude the analysis in this paper by drawing on the considerable variation

across NSS regions and over time to bring together the three strands of the analysis:

poverty, agricultural labor and non-farm employment. Table 11 presents estimates of two

models estimated on the basis of a NSS-region-level panel dataset where, for each NSS

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region, changes in poverty, agricultural employment and wages, and non-farm

employment are available for two spells: 1987/8-1993/4 and 1993/4-1999/0.

In the first model, we attempt to understand the factors that explain changes in

poverty over time. There has been extensive research along these lines undertaken in

recent years by Datt and Ravallion (1997, 2002) and Ravallion and Datt (1996, 1999) on

the basis of a state-level panel dataset spanning about 40 years and starting in the late

1950s. A consistent message from this literature is that poverty reduction in India falls

with higher farm yields, development spending, and non-agricultural output, and that

poverty rises with higher inflation. A further observation is that initial conditions also

matter: states with higher initial levels of education and infrastructure were observed to

achieve more rapid poverty reduction. Although Ravallion and Datt pay close attention to

the important role played by non-agricultural output growth on poverty (and note that the

elasticity of poverty with respect to non-agricultural output varies considerably by state)

the data they analyze do not allow them to focus specifically on the rural non-farm

sector. The region-level dataset we have constructed from three rounds of NSS data is

not as rich that which has been constructed at the state-level, but it does offer an

opportunity to enquire specifically into the relationship between rural poverty and rural

non-farm employment (as well as to study the relationship between poverty and

agricultural employment and wage rates).

In the first model reported in Table 11 the percentage change in poverty is regressed

on percentage changes in: agricultural wages; agricultural wage employment share;

regular non-farm employment share; casual non-farm employment share; non-farm self-

employment share; the proportion of land under irrigation; and the proportion of landless

households. xi

In addition to these indicators of change over time, we also include as

control variables the base year values of the same variables for their respective spells, as

well as the base-year headcount rate, a dummy representing the 1987/8-1993/4 spell, and

dummies for each of the major Indian states.xii It is clear that our data do not allow us to

control very well for important determinants of poverty reduction such agricultural

productivity growth and development spending (it is unlikely that our proxy variable,

proportion of land irrigated, can fully capture these effects). As a result, the results in

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this model have to be viewed as highly tentative. Nevertheless, the results are

suggestive.xiii

From the first model in Table 11 we can see that, all else equal, poverty falls

significantly with increases in agricultural wages, and rises with growth in agricultural

employment shares. Thus the simple correlations identified in Section III are robust to

the inclusion of additional control variables. An increase in agricultural wages of 10% is

associated with a 4% fall in poverty, while a 10% increase in the percentage of the

population employed as agricultural laborers is associated with a 1.6% increase in the

headcount rate. Controlling for changes in agricultural wages and employment rates,

poverty does not appear to vary with changes in non-farm employment, irrespective of

sub-sector. As agriculture intensifies (proxied by an expansion in land under irrigation)

poverty is also observed to fall. Growth in landlessness does not appear to independently

correlate with changes in poverty. Controlling for changes over time, higher initial levels

of agricultural wages are also associated with larger reductions in poverty, and the larger

the initial share of non-farm self-employment and of land under irrigation, the larger the

reduction in poverty. The estimates suggest, further, that poverty fell more sharply in

those regions with higher initial levels of poverty. This provides some support to the

notion that conditional on other changes and base-year characteristics there was some

convergence amongst regions in poverty levels (although measurement error is very

likely to also be playing a role). Relative to Bihar, and controlling for other explanatory

variables, states such as Andhra Pradesh, Gujarat, Karnataka, Kerala, Madhya Pradesh,

Maharashtra, Rajasthan and Tamil Nadu enjoyed more rapid declines in poverty during

our reference period.

In our model of poverty reduction there is only very limited evidence of a role for the

non-farm sector. This is consistent with our earlier analysis indicating that few of the

poor appear to gain access to non-farm jobs, and that even on the margin there is little

evidence that the poor would participate in an expansion of the non-farm sector.xiv Does

this mean that non-farm employment has no role to play in reducing poverty? One

possible route through which non-farm employment influences poverty is via an impact

of the non-farm sector on agricultural wages. In our second model we regress the

percentage change in agricultural wages on changes and base year employment levels in

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sub-sectors of non-farm employment, proportion of land irrigated and percentage of

landlessness. Again we include as further controls a dummy for the 1987/8-1993/4 spell

and state dummies. This specification is much in the spirit of an earlier analysis by

Sheila Bhalla (1993), based on state-level time series data covering the period 1971/2 to

1983/4, in which it was found that non-farm employment exerted a more discernable

impact on agricultural wages than did agricultural productivity.xv

In this model we can see that expansion of casual non-farm employment is

strongly correlated with growth in agricultural wages. This is consistent with a process

of labor market tightening: while the poor may find it difficult to gain access to even

casual non-farm employment, the siphoning off of the non-poor out of agricultural labor

and into casual non-farm employment, puts pressure on agricultural wages. This rise in

agricultural wages, then helps to reduce overall poverty levels. We find further that

agricultural wages tend to rise less rapidly in those regions with high initial wage levels,

and with high initial shares of agricultural employment. There is also some evidence

that, all else equal, a higher initial share of regular non-farm employment is associated

with a more rapid rise in agricultural wages. Conditional on all other variables, the

model suggests that wage growth was slower during the 1987/8-1993/4 period. This is in

contrast with our unconditioned impression of slower wage growth between 1993/4-

1999/0 in Section III. Wage growth appears to have been particularly strong in the states

of Haryana and Punjab.

VI. Concluding Comments

This study has examined the three most recent quinquennial rounds of National

Sample Survey data to enquire into the extent and speed of poverty decline in rural India

during the 1990s, and to explore the inter-relationship between rural poverty, agricultural

wage labor employment and the rural non-farm economy. Our main findings can be

summarized as follows.

We suggest that while there is clear evidence of a significant decline in poverty between

1987/8 and 1993/4, there is little consensus on the rate of decline of measured poverty

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beyond 1993/4-1999/0. This is due to difficulties in comparing household consumption

across the last two surveys because of changes in questionnaire design. We have

presented estimates that attempt to correct for these problems of non-comparability and

these estimates point to a slowing in the rate of poverty decline. We have shown that

unlike the more optimistic scenarios that have been proposed, these more modest

estimates of poverty decline correlate well with NSS data on changes in agricultural wage

rates.

Consistent with earlier studies we find evidence that employment over time in

agricultural labor has increased in absolute numbers and in terms of percentage of the

labor force. We find that employment in agricultural labor is strongly correlated with

low consumption levels.

We have shown that the composition of the agricultural labor force is changing over

time. We observe that agricultural laborers are increasingly made up of those with no

education and low social status. This finding is consistent with the notion that agricultural

laborer remains a last resort option for the rural population as has often been

suggested in the literature.

Despite the growth in agricultural labor force, real wages have been rising over time

(continuing a trend that started in the early 1970s). Like several other commentators, we

find that wage growth has not accelerated during in the 1990s. In fact, our calculations

suggest that between 1993/4 and 1999/0 wage growth was lower than between 1987/8

and 1993/4.

Consistent with other researchers we document a sizeable non-agricultural sector in

rural India. While this sector appears to have grown in step with overall population

growth, we find no evidence that the non-farm sector has increased in share of total

employment during the 1990s. This latter finding appears to be robust within NSS data.

However, studies based on other data sources have suggested that growth in the non-farm

sector has been more pronounced.

We suggest that non-farm employment comprises three sub-sectors: regular

employment, casual employment and self-employment. We document that regular non-

farm employment is associated with high consumption levels, but show that those with

low education levels, low social status, and low wealth are not well-represented in this

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sub-sector. Education, social status and wealth seem less relevant for employment in

casual non-farm employment, although there is some evidence that lower levels of

education are helpful in gaining access to casual non-farm employment. Self-

employment in the non-farm sector seems to be particularly heterogeneous, comprising

both last resort as well as productive activities. On balance, involvement in this sub-

sector also appears to require some education, wealth and social status.

While the overall picture for India as a whole suggests that there has been no

acceleration in the rate of poverty decline or in the rate of diversification out of

agriculture, we are able to draw on the marked variation across NSS regions and over

time, to pursue the impact of diversification on rural poverty. Tentative econometric

estimates from a region-level panel dataset covering the three survey years indicate that

poverty reduction is more clearly associated with changes in agricultural wages and

agricultural wage-labour employment levels, than with expansion of non-farm

employment opportunities. This does not mean that non-farm employment is not relevant

to poverty reduction however; expansion of non-farm employment, particularly the

casual non-farm employment that most directly employs the poor and assetless, is

strongly associated with rising agricultural wages.

Thus, policy makers aiming to alleviate poverty should continue to explore options

for promoting the non-farm sector. This study suggests that efforts should focus on the

promotion of non-farm opportunities that do not impose barriers to entry. These efforts

can be expected not only to directly raise the income levels of the poor who gain access

to such jobs. They are also likely to contribute to poverty reduction by raising the wages

received by those who remain employed as agricultural labourers.

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Table 1: Poverty in Rural India 1987/88 1999/00(1) (2) (3)

State NSS region 43rd 50th 55th

AdjustedEstimates (s.e.)

Andhra Pradesh 35.01 29.17 24.25

Coastal 30.68 31.26 14.46 (1.4)Northern 35.99 26.05 29.03 (1.6)

Western 33.55 38.57 39.49 (1.6)

Southern 54.46 21.89 37.14 (3.5)

Assam 36.13 35.43 43.68

Eastern 30.67 29.18 39.80 (3.0)

Western 40.13 39.55 46.19 (2.6)

Hills 25.31 30.98 44.70 (8.3)

Bihar 54.55 48.57 47.86

Southern 52.98 52.62 51.67 (2.3)

Northern 55.66 49.26 43.62 (1.7)

Central 53.87 44.37 51.75 (2.2)

Gujarat 38.82 32.45 23.69Eastern 48.48 34.23 37.49 (4.9)

Northern 35.22 32.13 23.63 (3.6)

Southern 30.06 41.11 27.34 (5.4)

Dry Areas 57.71 38.70 22.61 (5.0)

Saurashtra 28.63 21.62 9.33 (2.1)

Haryana 13.70 17.01 5.26

Eastern 19.71 19.15 4.53 (1.5)

Western 6.52 13.88 6.64 (1.8)HimachalPradesh 11.51 17.10 7.79 (1.1)

Karnataka 39.77 37.90 29.01

Coastal 20.94 12.09 6.47 (2.7)Eastern 30.88 22.32 11.60 (2.7)

Southern 42.31 39.60 19.41 (2.9)

Northern 44.60 45.24 41.56 (2.7)

Kerala 23.57 19.48 10.96

Northern 30.54 21.77 17.78 (1.8)

Southern 18.86 17.96 6.20 (0.9)

Madhya Pradesh 43.67 36.60 39.50

Chattisgar 48.77 38.83 46.86 (2.6)

Vindhya 38.69 32.35 37.24 (3.9)

Central 46.70 45.65 40.17 (3.8)

Malwa 38.22 23.82 27.66 (3.6)

South 52.43 42.47 51.55 (4.2)Western 52.19 64.89 47.89 (4.5)

Northern 22.96 15.20 18.48 (3.2)

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(1) (2) (3)

State region 43rd 50th 55th

AdjustedEstimates (s.e.)

Maharashtra 42.98 42.89 32.42

Coastal 31.77 19.09 23.46 (4.2)Western 32.82 29.72 17.75 (2.2)

Northern 48.33 53.30 46.02 (4.2)

Central 52.16 53.41 30.54 (2.8)

InlandEastern 51.54

55.59 44.49 (3.4)

Eastern 44.81 55.18 55.72 (4.1)

Orissa 50.19 43.47 53.22

Coastal 39.22 38.97 38.10 (2.3)

Southern 76.02 63.23 88.65 (2.2)

Northern 51.99 39.26 55.51 (2.9)

Punjab 6.61 6.15 5.22

Northern 5.67 3.58 5.04 (0.9)

Southern 7.98 9.54 5.43 (1.2)

Rajasthan 35.29 23.00 20.18

Western 30.37 21.54 17.43 (2.0)

Northern 30.56 15.02 17.00 (1.9)

Southern 63.81 42.42 34.03 (4.5)

Eastern 32.16 30.54 21.71 (4.5)

Tamil Nadu 48.94 38.46 30.81

Northern 62.87 49.54 46.24 (3.2)

Coastal 42.71 24.77 22.39 (2.7)

Southern 54.47 42.10 26.81 (2.8)

Inland 32.36 29.84 22.15 (2.8)

Uttar Pradesh 35.01 28.63 27.62

Himalayan 7.85 13.15 14.60 (3.1)

Western 25.93 16.95 17.93 (1.2)

Central 35.17 37.10 37.75 (2.2)

Eastern 42.85 33.81 32.57 (1.3)

Southern 53.89 50.97 25.72 (4.5)

West Bengal 36.04 25.07 28.56

Himalayan 14.45 37.59 31.17 (4.1)

Eastern 48.32 29.97 36.67 (3.0)

Central 33.33 20.15 19.20 (1.7)

Western 33.17 21.15 29.16 (3.0)

All-Rural 39.25 33.07 30.91

Note: Headcount Estimates for 1987/88 and 1993/4 are calculated directly from the respective data rounds.

The Adjusted Estimates of headcounts from the 55 th round are based on a model of 50th round total consumption on 30-day

comparable non-food consumption plus 30-day food consumption, using the methodology described in Kijima and Lanjouw(2003).

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Table 2: Agricultural Wage Employment and Consumption Quintiles 1987-1999

% of working population with primary employment in agriculturalwage labor (average odds)

Per CapitaConsumption

Quintiles 1987 1993 19991 0.428 (1.623) 0.502 (1.647) 0.486 (1.532)

2 0.327 (1.241) 0.358 (1.174) 0.388 (1.225)

3 0.238 (0.902) 0.276 (0.906) 0.297 (0.938)

4 0.188 (0.713) 0.207 (0.678) 0.235 (0.740)

5 0.112 (0.425) 0.133 (0.436) 0.133 (0.420)

Total 0.264 (1.00) 0.305 (1.00) 0.317 (1.00)

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Table 3: Distribution of Occupations by Education and Social Groups

(Adult Male Individuals aged 15 and above)

Agriculturallabor Cultivator

Nonfarmregular

Nonfarmcasual

Nonfarmself-

employed

Farm

regularNot

working

1987

Not literate 31.3 36.9 2.4 9.0 11.4 3.1 6.0Primary completed 14.1 38.9 5.9 7.6 17.0 1.1 15.4

Secondary completed 4.0 28.8 18.0 2.3 12.8 0.3 33.8

University completed 2.2 23.2 34.3 0.7 13.5 0.1 26.1

Non SC/ST 15.4 39.8 7.1 6.0 14.7 1.1 15.8

SC/ST 32.1 27.3 4.7 10.2 10.2 3.4 12.1

Non Muslim 19.9 37.1 6.6 6.9 12.8 1.8 14.9

Muslim 20.3 30.2 5.6 9.2 19.8 0.8 14.2

1993

Not literate 36.5 39.4 2.2 6.9 9.4 1.2 4.4

Primary completed 18.2 41.7 5.2 8.5 14.3 0.3 11.7Secondary completed 6.1 32.4 14.1 3.0 11.2 0.2 33.2


Non SC/ST 16.0 42.5 7.1 5.4 13.2 0.5 15.3

SC/ST 37.9 27.5 5.0 8.5 8.4 1.0 11.6

Non Muslim 22.8 38.9 6.6 6.1 10.8 0.7 14.3

Muslim 20.0 30.4 5.7 8.2 21.7 0.4 13.8

1999

Not literate 39.1 34.1 1.8 8.4 10.1 1.4 5.3

Primary completed 20.1 36.7 4.7 10.0 13.6 1.1 13.9

Secondary completed 7.4 33.0 12.5 4.4 13.0 0.6 29.2University completed 3.5 29.1 31.3 1.5 14.1 0.5 19.9

Non SC/ST 17.5 38.5 6.9 6.5 13.8 0.9 16.0

SC/ST 36.5 24.9 5.1 10.4 8.6 1.2 13.3

Non Muslim 23.4 35.6 6.4 7.4 11.0 1.0 15.1

Muslim 21.7 23.3 5.5 10.3 23.0 0.6 15.7

Note: The figures are summed up to 100 in each educational or social category.

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Table 4: Distribution of Occupation in Rural India (Individuals Aged 15 and above)

Male Female

1987 1993 1999 1987 1993 1999

Agricultural labor 19.9 22.5 23.2 11.2 14.0 14.5

Cultivator 36.4 38.1 34.4 15.0 14.1 13.7

Nonfarm regular 6.5 6.5 6.3 1.0 1.0 1.0

Nonfarm casual 7.2 6.3 7.7 2.9 1.5 1.6

Nonfarm selfemployed 13.5 11.8 12.2 5.5 2.7 3.2

Farm regular 1.7 0.7 1.0 0.4 0.1 0.3

Not working 14.8 14.2 15.2 64.1 66.6 65.9

Note: Not working includes attending school, being unemployed, engaged in domestic duties, recipients

of rent, pension, and remittance, and beggars and prostitutes.

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Table 5: Employment Share among Economically Active Adult Population: 1987/88 - 1999/00

StateAgricultural

Labor Cultivator

Casual

NonfarmRegularNonfarm

NonfarmSelf-

EmployeFarm

Regular

TotalAgricultural

Sector

TotalNonfarmSector

1987

All India 29.8 39.5 6.5 8.1 14.2 1.9 71.2 28.8

Andhra Pradesh 39.4 28.6 4.9 8.0 17.7 1.3 69.3 30.6Assam 13.8 44.7 16.9 12.8 10.4 1.4 59.9 40.1

Bihar 35.1 40.0 4.4 4.7 11.2 4.6 79.7 20.3

Gujarat 30.7 30.9 7.5 17.8 10.0 2.9 64.5 35.3

Haryana 17.9 47.3 8.8 5.8 18.2 2.0 67.2 32.8

HP 4.7 69.7 6.5 9.1 10.0 0.0 74.4 25.6

Karnataka 40.1 33.1 5.5 8.6 11.3 1.4 74.6 25.4

Kerala 24.8 6.5 14.5 29.4 24.8 0.0 31.3 68.7

Madhya Pradesh 27.2 54.5 3.7 3.4 8.4 2.7 84.4 15.5

Maharashtra 36.3 37.7 7.3 6.7 10.0 2.1 76.1 24.0

Orissa 34.6 32.8 5.6 6.7 18.8 1.5 68.9 31.1

Punjab 20.8 34.6 12.6 5.8 20.1 6.1 61.5 38.5

Rajasthan 12.5 47.0 3.9 15.1 20.7 0.8 60.3 39.7Tamil Nadu 40.3 22.3 10.9 9.1 16.7 0.7 63.3 36.7

Uttar Pradesh 19.7 57.6 4.4 4.1 13.1 1.2 78.5 21.6

West Bengal 33.3 30.4 9.2 7.6 18.6 0.8 64.5 35.4

1993

All India 34.3 39.3 6.6 7.1 12.1 0.6 74.2 25.8

Andhra Pradesh 46.1 28.7 4.5 5.2 14.6 0.8 75.6 24.3

Assam 15.6 41.3 15.7 15.3 11.5 0.7 57.6 42.5

Bihar 42.3 39.2 4.1 2.8 11.2 0.4 81.9 18.1

Gujarat 41.3 33.4 7.7 8.9 8.7 0.1 74.8 25.3

Haryana 18.9 41.0 13.0 10.9 15.4 0.7 60.6 39.3

HP 7.8 65.2 9.5 9.0 8.3 0.2 73.2 26.8Karnataka 39.6 37.3 5.6 5.4 11.9 0.1 77.0 22.9

Kerala 27.3 5.1 13.8 35.1 18.6 0.0 32.4 67.5

Madhya Pradesh 33.2 53.7 3.7 3.4 5.0 1.0 87.9 12.1

Maharashtra 43.3 35.5 7.2 4.7 8.0 1.2 80.0 19.9

Orissa 39.5 36.8 4.7 4.4 14.1 0.4 76.7 23.2

Punjab 29.1 34.9 11.9 7.4 14.7 1.9 65.9 34.0

Rajasthan 16.1 57.4 5.0 11.9 9.0 0.6 74.1 25.9

Tamil Nadu 45.4 21.4 10.0 9.4 13.6 0.2 67.0 33.0

Uttar Pradesh 21.0 55.6 5.1 5.0 13.0 0.3 76.9 23.1

West Bengal 30.8 27.9 9.9 9.4 20.8 1.1 59.8 40.1

Cont.

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Table 5, cont.

StateAgricultural

Labor Cultivator

Casual

NonfarmRegularNonfarm

NonfarmSelf-

EmployeFarm

Regular

TotalAgricultural

Sector

TotalNonfarmSector

1999

All India 35.2 36.7 6.6 8.3 12.1 1.1 73.0 27.0

Andhra Pradesh 47.4 27.5 5.5 5.5 13.3 0.8 75.7 24.3Assam 9.0 37.0 9.0 18.9 14.2 11.9 57.9 42.1

Bihar 41.2 36.5 3.6 5.3 13.1 0.3 78.0 22.0

Gujarat 40.0 34.4 7.2 9.1 9.0 0.3 74.7 25.3

Haryana 17.7 40.3 15.4 10.5 15.0 1.1 59.1 40.9

HP 10.0 52.9 13.5 13.2 9.8 0.6 63.5 36.5

Karnataka 40.8 35.2 5.0 8.5 9.9 0.6 76.6 23.4

Kerala 21.7 3.8 13.8 38.4 19.4 2.8 28.3 71.6

Madhya Pradesh 37.4 47.1 3.6 4.8 6.5 0.5 85.0 14.9

Maharashtra 44.4 33.8 7.5 5.9 7.4 1.0 79.2 20.8

Orissa 45.3 29.9 4.9 6.0 13.5 0.5 75.7 24.4

Punjab 22.7 33.1 12.8 11.2 15.1 5.1 60.9 39.1

Rajasthan 15.7 56.0 5.7 11.1 11.2 0.4 72.1 28.0Tamil Nadu 45.8 17.5 11.8 10.1 14.0 0.9 64.2 35.9

Uttar Pradesh 20.7 50.7 6.2 6.6 15.3 0.6 72.0 28.1

West Bengal 34.7 24.8 5.9 9.2 23.6 1.8 61.3 38.7

Note: Economically active adult population is defined as those who are between 15 and 60 years of age and

engaged in work such as all the market activities for pay or profits (except prostituted, begging, smuggling

etc.) and non-market activities relating to the agricultural sector for own consumption and construction of

private or community facilities free of charge. Non-farm employment is defined as workers in sectors otherthan agriculture by using industry code. Employment status is defined in NSS as following. Regular

salaried employee is a person who gets in return salary or wages on a regular basis but not on daily basis,

casual wage labor is a person who earn wage according to the terms of the daily or periodic work contract,and self-employed are persons who operate their own farm or non-farm enterprises.

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Table 6: Real Agricultural Wage (Daily, Rs. 1993 All India Price Level)

Percent Change

1987 1993 1999 1987-93 1993-99

Andhra Pradesh 10.84 15.32 19.04 0.413 0.243

Assam 22.94 23.79 23.12 0.037 -0.028

Bihar 14.61 16.31 22.50 0.116 0.380

Gujarat 15.37 17.17 20.39 0.117 0.188

Haryana 22.07 29.04 37.92 0.316 0.306

Himachal Pradesh 25.07 28.71 32.63 0.145 0.137

Karnataka 8.55 14.49 18.27 0.695 0.261

Kerala 24.28 31.06 42.03 0.279 0.353

Madhya Pradesh 12.62 15.92 17.00 0.261 0.068

Maharashtra 8.18 14.19 18.42 0.735 0.298

Orissa 10.55 16.16 16.34 0.532 0.011

Punjab 27.04 40.82 37.68 0.510 -0.077

Rajasthan 16.34 23.70 28.51 0.450 0.203

Tamil Nadu 9.66 18.69 23.35 0.935 0.249

Uttar Pradesh 13.00 21.79 23.58 0.676 0.082

West Bengal 18.12 23.29 26.00 0.285 0.116

Note: Agricultural wage is calculated by taking means of all wages of workers involving agricultural casual

operations such as ploughing, sowing, transplanting, weeding, harvesting, and other cultivation activities.

Tornqvst intertemporal and spatial price indexes calculated by Deaton (2002) are used for adjusting cost ofliving differences. Percent change in the last 2 rows is calculated by (wage t - wage t-1) / wage t-1.

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Table 7a: Region-level Correlations of Agricultural Wages and Poverty (Levels)

Region-level poverty estimates Year-specific Region-Level Agricultural Wages

Pearson Correlation

(prob val)

Spearman Rank

Correlation (prov val)

1987/8 - 0.60 (0.0001) - 0.59 (0.0001)1993/4 - 0.77 (0.0001) - 0.80 (0.0001)

1999/0

(This paper, Table 1)

- 0.66 (0.0001) - 0.66 (0.0001)

1999/0

(Deaton 2003c)

- 0.65 (0.0001) - 0.68 (0.0001)

1999/0

(Kijima and Lanjouw, 2003,

Table 4, column 2)

- 0.66 (0.0001) - 0.70 (0.0001)

1999/0

(Kijima and Lanjouw, 2003,

Table 4 column 3)

- 0.57 (0.0001) - 0.60 (0.0001)

Table 7b: Region-level Correlations of Changes in Agricultural Wages between

1993-1999 and Changes in Poverty

Changes in Region-level poverty

estimates (1993-1999)

Changes in Region-Level Agricultural Wages (1993-1999)

Pearson Correlation

(prob val)

Spearman Rank

Correlation (prov val)

This paper - 0.29 (0.03) - 0.29 (0.03)

Deaton (2003c) - 0.21 (0.12) - 0.13 (0.35)

Kijima and Lanjouw (2003)Table 4, column 2

- 0.25 (0.06) - 0.15 (0.25)

Kijima and Lanjouw (2003)

Table 4, column 3

- 0.06 (0.67) - 0.03 (0.80)

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Table 8a: Regular Non-Farm Employment and Consumption Quintiles 1987-1999

% of working population with primary employment in regular non-farm employment (average odds)


Quintiles 1987 1993 19991 0.027 (0.435) 0.025 (0.387) 0.021 (0.339)

2 0.039 (0.625) 0.038 (0.594) 0.037 (0.601)

3 0.051 (0.819) 0.055 (0.867) 0.047 (0.768)

4 0.072 (1.166) 0.079 (1.239) 0.073 (1.183)

5 0.131 (2.106) 0.140 (2.209) 0.148 (2.400)

Total 0.062 (1.00) 0.063 (1.00) 0.062 (1.00)

Table 8b: Casual Non-Farm Wage Employment by Quintile 1987-1999

% of working population with primary employment in casual non-farm employment (average odds)


Quintiles 1987 1993 1999

1 0.093 (1.246) 0.070 (1.073) 0.086 (1.109)

2 0.079 (1.058) 0.076 (1.156) 0.088 (1.124)

3 0.080 (1.081) 0.069 (1.045) 0.080 (1.016)

4 0.067 (0.899) 0.061 (0.924) 0.074 (0.943)

5 0.050 (0.667) 0.048 (0.737) 0.059 (0.756)

Total 0.074 (1.00) 0.066 (1.00) 0.078 (1.00)

Table 8c: Non-Farm Self-Employment by Quintile 1987-1999

% of working population self-employed in the non-farm sector

(average odds)

Per Capita

ConsumptionQuintiles 1987 1993 1999

1 0.102 (0.724) 0.087 (0.711) 0.104 (0.807)

2 0.128 (0.910) 0.114 (0.934) 0.120 (0.829)

3 0.147 (1.043) 0.124 (1.015) 0.131 (1.014)

4 0.162 (1.150) 0.142 (1.165) 0.140 (1.081)

5 0.171 (1.211) 0.152 (1.246) 0.158 (1.224)

Total 0.141 (1.00) 0.122 (1.00) 0.129 (1.00)

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Table 9 Predicted Probabilities of Access to Occupations (evaluated at mean characteristics)


Nonfarmregular

Nonfarmcasual labor

Nonfarmself-

employed

Farm

regular Not working

1987

SC/ST 26.6 31.0 5.8 8.9 10.5 2.8 14.3

non SC/ST 16.9 38.8 6.8 6.4 14.7 1.2 15.1Muslim 21.1 31.3 5.9 9.1 17.2 1.0 14.3

non Muslim 19.8 37.0 6.5 6.9 13.0 1.8 14.9

1993

SC/ST 33.4 32.3 5.8 7.5 8.2 1.1 11.8

non SC/ST 21.6 42.6 5.6 6.0 12.3 0.7 11.2

Muslim 23.1 33.7 5.9 7.7 16.6 0.5 12.6

non Muslim 26.0 40.0 5.6 6.3 10.2 0.9 11.1

1999

SC/ST 33.7 29.1 5.5 9.0 8.5 1.1 13.1

non SC/ST 23.7 37.7 5.3 7.0 12.7 1.2 12.4Muslim 25.1 26.6 5.8 9.6 18.0 0.6 14.3

non Muslim 27.4 36.0 5.2 7.4 10.4 1.2 12.3

Note: Employment probabilities are predicted after estimating multinomial logit model of 6 broad occupation

categories on individuals characteristics such as age, educational status, and caste, and households

characteristics such as per capita land holdings and the number of household members. The regression results

are provided in Appendix. The probabilities for SC/ST, for example, are predicted by assuming that thepopulation belong to entirely SC/ST.

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Table 10 Predicted Probabilities of Access to Occupations (evaluated at mean characteristics)


Nonfarmregular

Nonfarmcasual labor

Nonfarmself-

employedFarm

regular Not working

1987

Not literate 24.0 38.0 3.7 7.6 12.0 2.3 12.5



1993

Not literate 29.5 41.7 2.8 6.4 9.9 1.1 8.7

Primary completed 16.9 39.3 7.3 8.1 15.0 0.3 13.1

Secondary completed 7.5 29.3 18.5 3.5 12.5 0.2 28.5


1999

Not literate 31.7 36.7 2.5 7.9 10.1 1.2 9.9



Note: Employment probabilities are predicted after estimating multinomial logit model of 6 broad occupation

categories on individuals characteristics such as age, educational status, and caste, and households

characteristics such as per capita land holdings and the number of household members. The regression results

are provided in Appendix. The probabilities for education level with secondary, for example, are predicted byassuming that the population belong to entirely secondary education.

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Table 11: Correlates of Poverty Reduction and Agricultural Wage Growth Multivariate OLS% Change in Regional Headcounts

(1987-1993 and 1993 -1999)

% Change in Regional Agricultural

Wages(1987-1993 and 1993-1999)

% change in (prob val) (prob val)

Agricultural wages -0.391*** (


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parameter estimates in the above models unchanged, but would have pointed to a highly

non-robust positive relationship between expansion of regular non-farm employment andgrowth in poverty.

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Appendix Table 2: Occupational Choice by Multinomial Logit Model in 1987Not

working CultivatorNonfarmregular

Nonfarmcasual

Nonfarmselfemployed

farmregular

Age -0.606 0.026 0.171 0.038 0.059 -0.023

(-91.39) (5.92) (20.64) (5.38) (10.31) (-1.94)

Age squared 0.008 -0.000 -0.002 -0.001 -0.001 0.000

(87.87) (-0.21) (-15.11) (-6.49) (-6.45) (0.60)

Literate but below primary 0.534 0.477 1.166 0.466 0.796 -0.327

(11.59) (17.66) (22.39) (11.68) (23.56) (-4.18)

Primary completed 1.482 0.717 1.769 0.653 1.196 -0.354

(38.46) (25.68) (35.66) (16.16) (35.47) (-4.28)

Middle completed 2.682 1.120 2.664 0.737 1.580 -1.049

(65.52) (32.48) (50.83) (14.39) (38.95) (-7.15)

Secondary completed 3.821 1.433 4.137 0.697 2.119 -0.464

(68.13) (27.77) (67.61) (8.59) (37.36) (-2.54)

University completed 4.754 1.515 5.234 0.112 2.661 -1.644

(32.45) (10.43) (36.22) (0.39) (17.75) (-1.76)

SC/ST -0.337 -0.700 -0.501 -0.095 -0.801 0.302(-11.51) (-34.28) (-13.77) (-3.15) (-29.06) (6.03)

Muslim 0.073 -0.218 -0.014 0.320 0.330 -0.668

(1.71) (-6.83) (-0.27) (7.20) (9.30) (-5.82)

Number of household members 0.123 0.160 0.053 0.006 0.094 0.061

(31.39) (50.63) (10.91) (1.22) (24.41) (7.47)

Per capita land owned (ha.) 2.774 3.799 1.362 0.189 -0.194 1.947

(44.97) (68.30) (16.69) (1.93) (-2.36) (16.13)

Constant 6.346 -2.248 -6.671 -1.977 -2.973 -2.494

(61.94) (-27.84) (-43.56) (-15.94) (-28.62) (61.94)

Number of observations 108580log likelihood -143487

Pseudo R2 0.198Note: Numbers in the parentheses are z-values.

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Nonfarmcasual

Nonfarmselfemployed

farmregular

Age -0.689 0.003 0.144 0.026 0.061 0.001

(-86.99) (0.54) (16.19) (3.46) (9.85) (0.04)

Age squared 0.009 0.000 -0.001 -0.001 -0.001 -0.001

(82.59) (4.24) (-10.87) (-4.99) (-7.19) (-0.65)

Literate but below primary 0.456 0.278 1.060 0.496 0.723 -0.260

(8.27) (9.50) (17.72) (11.56) (19.82) (-2.13)

Primary completed 1.262 0.586 1.657 0.836 1.074 -0.746

(26.16) (18.76) (28.23) (19.25) (28.22) (-4.54)

Middle completed 2.506 0.878 2.458 0.881 1.470 -0.719

(55.63) (26.30) (43.84) (18.42) (37.16) (-4.00)

Secondary completed 3.818 1.194 3.761 0.858 1.855 -0.507

(72.47) (27.67) (64.24) (13.18) (37.81) (-2.26)

University completed 5.156 1.561 5.359 -0.024 2.727 0.498

(37.94) (11.92) (40.24) (-0.08) (20.30) (1.10)

SC/ST -0.502 -0.904 -0.569 -0.225 -0.926 -0.112(-15.55) (-40.99) (-15.03) (-6.97) (-31.72) (-1.29)

Muslim 0.312 -0.098 0.227 0.349 0.631 -0.461

(6.35) (-2.74) (3.88) (6.99) (16.43) (-2.45)


(29.10) (48.15) (8.77) (5.18) (25.81) (6.89)

Per capita land owned (ha.) 4.379 5.601 2.710 -0.236 0.887 4.252

(52.44) (73.57) (25.83) (-1.68) (8.53) (22.68)

Constant 7.215 -2.024 -6.565 -1.955 -3.174 -4.218

(60.00) (-22.88) (-39.00) (-14.66) (-28.03) (-11.74)



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Nonfarmcasual

Nonfarmselfemployed

farmregular

Age -0.706 0.008 0.086 0.044 0.070 0.040

(-95.46) (1.61) (9.86) (6.14) (11.41) (2.35)

Age squared 0.009 0.000 -0.000 -0.001 -0.001 -0.000

(91.05) (3.67) (-3.95) (-7.91) (-8.06) (-2.03)

Literate but below primary 0.444 0.322 1.096 0.493 0.646 0.181

(8.32) (10.66) (16.14) (12.10) (17.36) (1.81)

Primary completed 1.164 0.615 1.815 0.794 0.992 0.385

(25.19) (19.66) (28.60) (19.40) (26.10) (3.81)

Middle completed 2.094 0.911 2.649 0.932 1.353 0.114

(50.23) (29.98) (46.07) (23.02) (37.01) (1.02)

Secondary completed 3.429 1.331 3.846 0.950 1.916 0.710

(72.67) (35.78) (65.82) (18.23) (45.24) (6.07)

University completed 4.665 1.556 5.346 0.630 2.629 1.130

(44.23) (15.74) (51.04) (3.84) (25.78) (4.33)

SC/ST -0.372 -0.770 -0.459 -0.107 -0.825 -0.482(-12.38) (-35.46) (-12.47) (-3.63) (-29.21) (-6.70)

Muslim 0.292 -0.308 0.236 0.374 0.655 -0.636

(6.58) (-8.75) (4.24) (8.54) (18.37) (-4.52)


(23.37) (45.64) (5.56) (2.63) (22.46) (3.91)

Per capita land owned (ha.) 4.360 5.930 2.873 -1.781 0.691 1.496

(47.56) (71.08) (24.99) (10.86) (6.00) (5.30)

Constant 7.974 -2.154 -5.907 -1.962 -3.324 -4.202

(70.16) (-24.08) (-35.72) (-15.66) (-29.44) (-13.56)



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End notes

i These figures are based on the revisions to the official poverty lines propsed by Deaton and Dreze and

spatial and temporal price indices proposed by Deaton and Tarozzi (2000) and Deaton (2003b).ii Kijima and Lanjouw (2003) provide a detailed description of the method and also outline how the

precision of the predicted poverty estimates can be assessed.iii

Himanshu (2004) cautions against calculation of agricultural wage rates from the 1987/8 round of theNSS, arguing that the unit record data do not produce wage rates that are readily comparable to wage

estimates for that year published by the NSSO itself. We have chosen to calculate and report the 1987/8wage rates, but acknowledge that they may be less reliable than estimates for the other two years.iv Kijima and Lanjouw (2005) present region-level estimates of agricultural wages as well as employmentshares in both agricultural and non-agricultural activities.v In his exhaustive examination of NSS data between 1977/8 to 1999/0 Vaidyanathan (2001) observes

many of the same trends reported here.vi Foster and Rosenzweigh (2003b) suggest that non-farm income shares grew from just under a third in

1982 to nearly 50% in 1999. A study by Lanjouw and Shariff (2002) based on a different NCAER datasetfor 1993 calculated a rural non-farm income share of 37%. This is suggestive of steady growth of the non-

farm sector throughout the 1980s and 1990s a trend which NSS employment data do not appear to

corroborate (see below).vii Lanjouw and Shariff (2003) observe very similar patterns across income quintiles in NCAER data for1993/4.viii As already noted earlier, the correlation between agricultural wage employment and illiteracy has

strengthened during this period.ix We concentrate in this analysis on reportedprincipal occupation of males, and are unable to consider, as

a result, the set of issues associated with combining farm with nonfarm activities during the course of, say,

an agricultural year (with its associated peak and slack seasons).x It is often noted that the market for the purchase and sale of land is rather thin in rural India, as opposedto the market for landuse tenancy (see Jayaraman and Lanjouw, 1999). Landholdings may therefore be

reasonably exogenous in the kind of models estimated here.xi The comparable region-level poverty rates for 1999/0 employed in this exercise are those reported in

column 3 of Table 1.xii Both specifications reported in Table 11 also includes two dummy variables representing, respectively,

the Inland South region of Andhra Pradesh, and the Western region of Haryana. These two dummies were

interacted, respectively, with the percentage change in regular non-farm employment and in agriculturalwages. In terms of these two characteristics the dummies are extreme outliers and failure to control for the

first explicitly would have left most parameter estimates unchanged,

Econ Diversification and Poverty Rural India

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