GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 184

Improvement of Socio-economic Conditions
and Distribution of Consumption
Expenditures: Case Study of India’s Poverty Decline over Two Decades

Takahiro Ito

March 2011; revised June 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Improvement of Socio-economic Conditions and Distribution of Consumption
Expenditures: Case Study of India’s Poverty Decline over Two Decades†(Previous title: Improvement in Socioeconomic Conditions
and Poverty Decline in India, 1983-2004)

Takahiro Ito‡
June 2012(First version: January 2010)

Abstract
This paper examines how the standard of living in India has improved over two decades,
focusing on the distribution of household-level consumption expenditures. The analysis isconducted using the DiNardo-Fortin-Lemieux (DFL) semi-parametric decomposition method,which offers two desirable features that enable us to avoid the traditional pitfalls of(semi-)macro-level poverty analysis. The estimation results indicate that regional
heterogeneity in poverty decline is very large, and different regional factors contribute to thedecline at different stages of development. From 1983 to 1993/94, regional education(measured by literacy rate) was the main engine of the decline, accounting for 85% of thetotal poverty decline in the period. However, in the decade that followed, labor market
conditions had a significant role in reducing poverty. In particular, wage and employment
growth in the non-agricultural sector was key in the improvement of living standards. In
addition, agricultural wage employment remains important in reducing poverty in rural areas.

†

I sincerely thank Sayako Kanda, Ayako Kondo, Kohei Kubota, Fumio Ohtake, Masaru
Sasaki, Yasuyuki Sawada, and the participants at various seminars for their helpful comments
and suggestions. I also acknowledge the financial support from the Resona Foundation for
Asia and Oceania, and a Grant-in-Aid for young scientists (start-up program) from the Japan
Society for the Promotion of Science (JSPS).
‡
Associate Professor, Graduate School for International Development and Cooperation,
Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Japan; Tel:
+81-82-424-6920; E-mail: ito@hiroshima-u.ac.jp
1

I. Introduction
Poverty is one of the most urgent political issues of our time. Poverty alleviation was set as
one of the Millennium Development Goals (MDGs) at the Millennium Summit of the United
Nation’s Millennium Project in September 2000. However, even today, India continues to be
one of the poorest countries in the world. Despite two decades of significant economic
growth, about 35% of the population (360 million people), accounting for approximately
one-third of the world’s poor, still lives on less than one purchasing power parity (PPP) dollar
a day (UNDP 2007).
Meanwhile, India has steadily succeeded in poverty reduction. Providing aprescription for poverty alleviation, a number of studies have investigated factors
contributing to poverty reduction in India.1 Poverty is strongly associated with agricultural
wage employment in rural areas. Moreover, such jobs are the last resort for low-skilled,
non-educated workers, thereby rendering them underpaid. In addition, agricultural laborers
have accounted for the greatest proportion of the working population through the 1980s,
1990s, which remain firmly unchanged in rural areas even today. Thus, it has been believedthat productivity growth in the agricultural sector is very important in reducing poverty(Deaton and Drèze 2002; and Sundaram 2001, 2007).
In addition, several quantitative studies have found a statistically significant

1

For the literature review on this topic, see Datt and Ravallion (2002).
2

correlation between poverty and agricultural wage. For example, Datt and Ravallion (1998b)
find that the agricultural productivity has a positive effect on reducing poverty using timeseries data. Moreover, in their series of works (Datt and Ravallion 1998a and Ravallion and
Datt 2002), they investigate the determinants of poverty decline at the state level, and revealthat the key factor contributing the most to poverty decline between the 1960s and 1980s is
the growth of productivity in the agricultural sector. Their results also indicate that
non-agricultural employments have a positive impact on reducing poverty and producing
better educational indicators such as high literacy rates, which contributes to poverty
alleviation.
Other studies using regional data produced similar results. For instance, Kijima andLanjouw (2005) reveal that poverty reduction during the 1990s is closely associated with
agricultural wages and employment levels. Lanjouw and Murgai (2010) also explain regional
heterogeneity in poverty alleviation. Their analysis shows that poverty ratios correlate wellwith region-level agricultural wage rates from the 1980s to the early 2000s. On the other hand,in the recent decade, the non-agricultural sector has also played the role of an engine forpoverty reduction, accounting for a considerable part of rural household income as well as therecent stagnation of the agricultural sector.
Taking these findings as the point of departure, this paper also examines how living
standards in India have improved over the last two decades, focusing on the distribution of3household-level consumption expenditures. The analysis is conducted using the
DiNardo-Fortin-Lemieux (DFL) semi-parametric decomposition method proposed by
DiNardo et al. (1996). This method offers two desirable features that enable us to avoid the
traditional pitfalls of (semi-)macro-level poverty analysis.
First, the DFL decomposition can depict distributional (heterogeneous) impacts offactors that seem to be associated with poverty reduction. This enables us to investigate theextent to which each factor improves living standards at each income (consumption) level,
and consequently, gain a better understanding of how poverty can be alleviated. The second
advantage concerns the study of poverty. Poverty analysis typically requires us to calculate
poverty measures, such as headcount poverty ratios and poverty gaps, at a certain(semi-)macro level. In the calculation of these aggregate poverty measures, for example,
information on individuals living above the poverty line is not used, even if their living
standards are very close to the line or they happen to be above the line at the time they weresurveyed. This also suggests that these poverty measures and the corresponding results
obtained are very sensitive to a shift of the poverty line. This is a critical issue especially fordeveloping countries such as India, where a large portion of the population lives around thepoverty line, making the determination of the poverty line and the measurement of poverty
problematic. However, the DFL decomposition, which focuses on distributional changes,
does not require such aggregation and has no information loss associated with the4

aggregation. In this regard, the DFL decomposition method has an advantage overconventional approaches.
The remainder of this paper is structured as follows. Section II briefly discusses thetrend of poverty over the past two decades in India. The procedure for using the DFL methodwith nationwide Indian data is presented in Section III, and the estimation results are shownin Section IV. The results indicate that regional heterogeneity in poverty decline is very large,
and different regional factors contribute to poverty decline at different stages of development.
From 1983 to 1993/94, regional education (measured by literacy rate) was the main engine ofthe poverty decline, accounting for 85% of the total poverty decline in this period. In thedecade that followed, the labor market condition had a significant role in reducing poverty. In
particular, wage and employment growth in the non-agricultural sector was key in improving
living standards. In addition, agricultural wage employment remains important in reducing
poverty in rural areas. Section V concludes the paper.

II. Poverty decline over the past two decades
Figure 1 shows the changes in the distribution of monthly per-capita expenditures between
1983, 1993/94, and 2004/05. Data are from the National Sample Surveys (NSS) conducted
approximately every five years (“thick” rounds). As the “thick” NSS rounds, the 38th, 43rd,
50th, and 61st rounds are available, but the 55th round has a problem of comparability with5

other rounds due to the changes in questionnaire design. Therefore, this paper uses threerounds of the 38th (1983), 50th (1993/94), and 61st (2004/05). The sample used in theanalysis consists of data from all states other than the seven union territories and eight
northeastern states. The distribution of expenditures shown in Figure 1 is adjusted by thesector-state wide official poverty line to the 2004 urban price level. The vertical line is thenationwide-level official poverty line in 2004 (= Rs538.60).

[Figure 1]

As can be seen from the figure, the mode of expenditure distribution has beensteadily rising and the shape of the distribution has widened slightly. This implies that while
poverty has improved consistently between 1983 and 2004, inequality has not necessarily
been eradicated.
At the same time, this period also witnessed drastic changes in several social andeconomic factors (Table 1). The educational aspect showed great improvement: illiteracy
rates dropped as much as 50% and the population with formal education steadily increased inboth rural and urban areas.

[Table 1]
6Regarding employment in rural areas, the fraction of male adult population engaging
in the agricultural sector was stable during the 1980s and accounted for nearly 70% in
2004/05. Since the agricultural sector is the principal source of income for the poor, it has
been believed that poverty is closely associated with agricultural wage employment. As aresult, the productivity growth in the agricultural sector is expected to have a significant
impact on poverty reduction in rural areas. In fact, Table 1 shows that wage rates in the
agricultural sector in rural areas have increased steadily during this period, which implies thatpoverty decline has contributed to productivity growth in the agricultural sector to a certain
extent. Regarding employment in the non-agricultural sector, the fraction of urban maleworkers engaged in this sector is considerably high: 82% in 1983, 84% in 1993/94, and 87%
in 2004/05. Even in rural areas, the fraction of non-agricultural workers has risen moderately.
In addition, another representative data set indicates that the share of non-farm incomes intotal rural income increased from almost one-third in 1982 to 48% in 1999 (Foster andRosenzweig, 2004). We can therefore conclude that the expansion of this sector has
contributed to poverty reduction to a certain extent in both rural and urban areas.
Thus, in this period, education level and labor market conditions have changed
dramatically both in rural and urban areas. In the empirical analysis, we analyze whether andto what extent these factors contribute to poverty reduction in rural and urban areas (as can be7

seen in Figure 2) during the past two decades.

[Figure 2]

III. Estimation procedure
DFL decomposition
This paper adopts the DiNardo-Fortin-Lemieux (DFL) semi-parametric decomposition
method (DiNardo et al., 1996) to investigate the determinants of poverty decline in India.
Thus, to estimate the contribution of each covariate to the poverty reduction, the extended
method is applied and briefly explained here.
First, the density of log consumption expenditures,, can be expressed as theintegral of the density of log expenditures, conditional on a set of attributes,
distribution of the attributes,
|

∈

∈

| ,

:

, |

d
d

|

| , over the

(1)
≡

;

,

where Ω is the domain of definition of attributes, andandindicate the period of

expenditures and attributes, respectively. Next, on the basis of the relationship expressed in
the last line, we can calculate the counter factual densities of consumption expenditures byintroducing combinations of different periods (e.g.,
8

,

).

We suppose that there are two attributes affecting consumption expenditures, that is,
,

,

and

. Under the assumption that the expenditure distribution does

not depend on the distribution of attributes, the density that would prevail at time
attributehas the same distribution as that at time

;

,

∬

| , ,

d

∬

| ,

|

(2)

|

d

|

is expressed as

,

| ,
where

if an

,
d

,

|

d

d

,

|

|

d
,

|

d

|

|

|

. Thus, the counter factual densities canbe calculated using actual densities with the help of “reweighting” functions.2 Once thereweighting function is obtained, counter factual densities are estimated using the weighted
kernel method:
;
where

,

,

∑∈

(3)

|

is the set of indices of the sample at time ,individual ,is the bandwidth, andis the sampling weight of∙ is the kernel function.

In addition to the effects of distributional changes in the attributes described above,
we can account for another counter factual situation wherein the effect of attributes on
consumption expenditures changes from that at time2

to that at time . Letting

For the calculation of the reweighting function, see Appendix A
9andbe the effects ofandat time , the density that would prevail at timesame impacts as at time
;

,
|

∑∈

|

,

,

where

has theis calculated as

,

∑∈

if

,

,

(4)

is the linear projection of

onto attributes(discussed in

the next sub-section). Similarly, counterfactual changes due to changes in the distribution andimpact of attributecan be calculated.

Thus, changes in the density of consumption expenditures from time

to timecan be decomposed as
;

,
;

,

;

,

,

, ,

(5)
;

,

,

, ,

;

,

,

, ,

;

,

,

,

,

;

,

,

,

,

;

,

,

,

,

;

,

,

,

,

;

,

,

,

,

;

,

,

,

,

;

,

,

,

,

where the first and third components on the right hand side represent the distributional effectof the attributes

andeffect of the attributes, respectively, and the second and fourth components represent theand, respectively, due to changes in their impacts; the last term
10denotes the effect of residual factors.

Estimation of consumption expenditures and empirical variables
As mentioned above, the DFL decomposition requires the estimation of the linear projectionof consumption expenditures to obtain parameter vectors. Suppose that the expenditure

function is expressed as follows:
ln

(6)

where

and

consumption level,

are the household- and region-level characteristics affecting household
andare the coefficient vectors to be estimated,

denote the region and time fixed effects, andandis the random error.

Regarding the empirical variables used in the analysis, household characteristics,, include the education level of adult members (aged 15 years and older), age and gendercompositions, social class, and religion. Education variables are the average schooling years
for members aged 15–29, 30–44, and 45–59 years. Age and gender compositions arecalculated as ratios to the total adult members in the households. For social class, a dummy
variable for households belonging to the scheduled castes/tribes (SCs/STs), which are thelowest classes in the social hierarchy, is employed. Because caste categories other thanSCs/STs are not available in the 38th (1983) round, the analysis in this paper also uses thisclassification: SCs/STs or non-SCs/STs.
11

The region-specific attributes,, include variables such as literacy rates,

composition of the male labor force, and other region-level characteristics. These variables
are calculated at the district level. The labor force is divided into five categories on the basisof employment status and sector: self-employed workers in the agricultural sector,
self-employed workers in the non-agricultural sector, wage workers in the agricultural sector,3
casual wage workers in the non-agricultural sector, and regular wage workers in the
non-agricultural sector. On the basis of this category, the fraction of male workers engaged ineach category to the entire male working population (aged between 20 and 60 years) is
calculated. Other district-level controls are the fraction of landless households, price level,and population. The summary statistics of these variables are shown in Table 2.

[Table 2]

IV. Estimation Results
Hypothetical changes in the expenditure distribution
Table 3 shows the OLS estimation results of Equation (6) for rural households (Panel A) andurban households (Panel B). A detailed discussion of the estimation results is then conducted
together with the results of the DFL decomposition.

3

Agricultural wage works can be further divided into casual and regular employment. However, thefraction of regular workers in this sector is very low, and it is combined with casual workers.
12

[Table 3]

The DFL decomposition results are shown in Figures 3 (from 1983 to 1993/94) and
4 (from 1993/94 to 2004/05), in which the observed and hypothetical changes in the
expenditure distribution are depicted.
As shown in Figures 3 and 4, the household-level variables do not serve to explain
the increase in consumption expenditures significantly (Graphs 2 to 5). The only exception is
the household composition, which has a negative effect on living standards from 1983 to
1993/94 and a positive effect from 1993/94 to 2004/05. Regarding the education level inhouseholds, the OLS estimation results in Table 3 indicate that household-level education
appears to explain adequately the cross-sectional variation of expenditure, although its
influence seems to decrease over the years. Consequently, the increase in the number ofeducated members does not contribute to the secular improvement in living standards. As forcaste membership, Graph 4 of Figures 3 and 4 indicate that caste membership has no effect.
This might be explained from the results in Table 3: while the disadvantage of SCs/STs
membership seems slightly improved in rural areas (positive coefficients in the second andthird columns in Panel A), the gaps between SCs/STs and other castes increased in urban
areas (negative coefficients in the second and third columns in Panel B).
13

[Figure 3]

The results for district/region level variables are depicted in Graphs 6 to 11 of
Figures 3 and 4. From 1983 to 1993/94 (Figure 3), the literacy rate has a significant positive
impact on poverty decline during this period, while the district-level employment situation
does not. In addition, Graph 11 indicates that other district/state variables such as price leveland population also have significant impacts on the improvement of living standards.
Regarding the results for the poverty reduction from 1994/95 to 2004/05 (Figure 4), unlike
the results for the previous decade, the literacy rate does not have any impact, while theemployment situation does. In particular, agricultural wage employment (Graph 8),
non-agricultural casual employment (Graph 9), and non-agricultural regular employment(Graph 10) contributed to a great extent to the poverty reduction during this period.

[Figure 4]

Further decompositions of regional heterogeneity
To investigate the poverty alleviation impact of the regional characteristics in-detail,further decompositions are implemented. Figures 5 to 6 show the decomposition results for14the impact of the region-level variables, wherein decompositions are made on the basis ofrural or urban, and distributional effect or price effect. From 1983 to 1993, the impact ofliteracy rate (district level) accounted for 85% of the total poverty decline during the period(Graph 1 of Figure 5A). The figure shows that while the rising rates of literacy dominate theimpact in rural areas (Graph 2-R), an increase in the positive externality of literacy dominatedthat in urban areas (Graph 3-U). On the other hand, in this period, the employment situation
does not contribute to poverty decline significantly (Figures 5B to 5C). In rural areas,
however, the productivity/wage growth in the non-agricultural sector (Graph 3-R of Figure
5B and Graph 3-R of Figure 5E) and that of the non-agricultural casual employment (Graph
2-R of Figure 5D) have an influence on the poverty reduction to a certain extent.

[Figure 5]

For 1994/95 to 2004/05, the results contrast starkly with those for the previous
decade: while literacy rate does not contribute to the poverty decline, the employment
situation does. The positive effect from the rise in literacy rate is offset by the decrease insocial returns to literacy (Figure 5A). In contrast, the labor market situation makes a huge
contribution to poverty decline during this period. In rural areas, the increases in the wages ofcasual workers both in the agricultural (Graph 3-R of Figure 6C) and non-agricultural sectors
15

(Graph 3-R of Figure 6D), and employment growth in the non-agricultural sector (Graph 2-Rof Figure 6D and Graph 2-R of Figure 6E) serve as engines for poverty reduction. In urban
areas, the productivity/wage growth in the non-agricultural sector dominates the impacts ofpoverty reduction (Graph 3-U of Figure 6B and Graph 3-U of Figure 6E). The
non-agricultural employment, in total, accounts for 61.5% of poverty decline during thisperiod.

[Figure 6]

VI. Conclusion
This paper addressed the long-standing issue of poverty in India using nationwide survey data
covering the period from 1983 to 2004. The data suggest that during the reference period,
while the Indian economy displayed consistent poverty reduction, it also witnessed modest
growth in the size of non-agricultural employment and productivity growth in both
agricultural and non-agricultural sectors. In addition, educational standards have also steadily
improved.
The analysis in this paper examined factors contributing to poverty alleviation inIndia, focusing on the distributional changes in consumption expenditures. The main findings
from the analysis are as follows. First, regional heterogeneity in poverty decline is very large,16

as certain previous studies have pointed out (see, for example, Datt and Ravallion 1998a).
However, although household characteristics explain the cross-sectional variation of living
standards across households, they do not explain the time-series improvement in living
standards. In contrast, regional characteristics explain a large fraction of poverty decline.
Second, different factors contribute to poverty decline at different stages ofdevelopment. Our results indicate that from 1983 to 1993/94 regional education (measured byliteracy rate) is very important, and in the decade that followed, labor market conditions had asignificant role in reducing poverty. In particular, wage and employment growth in the
non-agricultural sector was key in the improvement of living standards. It should also bementioned that agricultural wage employment is still important in reducing poverty in rural
areas. In fact, our results imply that the total poverty alleviation impact of agricultural
employment is larger than that of non-agricultural regular employment in rural areas. Thismight reflect the fact that a large fraction of the rural poor is engaged in agricultural-related
jobs. Moreover, it is also striking that our results indicate no tradeoff between agricultural
growth and nonfarm growth in reducing rural poverty. Each sector affects poverty reduction
through a difference channel: while the poverty alleviation effect of the nonfarm sector ismainly attributed to employment growth, that of the agricultural sector mostly results mostly
from the productivity growth. The role of the nonfarm sector development in reducing
poverty is somewhat ambiguous empirically (Ravallion and Datt 1996, 2002), the results in17this paper imply that the growth of the nonfarm sector complements the agricultural sector. In
addition, the results also suggest that the dissemination of basic education is an essential stepprior to labor market development. It is very interesting to know what factors contribute to
reducing poverty at what stage of development, and further studies are needed to clarify thisissue.

Appendix A. Estimation of the reweighting function for the DFL decomposition
In the case that there are two attributes x = (g, h), two reweighting functions are calculated
because we have two counterfactual situations, described as follows:
;

,

,

| ,

|

;

,

,

| ,

d

|

d
|

Applying Bayes’ rule, ψg|h(x) and ψx(x) can be calculated as follows:
,
|

,

,
,

|

,

|

,

|

,

,

|

,

,

|

,

|

|

|

,

|

|

|
|

|

,

|

,

|
|

(A1)

|

,

|

|

,

|

|

,

,

|

|

,

,

|

|

,

|

|

|

|

,

|

|

|

|

(A2)
.

Conditional and unconditional probabilities in Equations (A1) and (A2) can be estimated by
the probit/logit model. In our analysis, the trinomial logit model is used because we use
three-year data.
18

References
Datt, Gaurav and Martin Ravallion. 1998a. “Why Have Some Indian States Done Better than
Others at Reducing Poverty.” Economica 65 (257): 17–38.
——1998b. “Farm productivity and rural poverty in India.” Journal of Development Studies
34(4): 62-85.
——2002. “Is India’s Economic Growth Leaving the Poor Behind?” Journal of Economic
Perspectives 16 (3): 89–108.
Deaton, Angus and Jean Drèze. 2002. “Poverty and Inequality in India: A Re-Examination.”
Economic and Political Weekly 37(36): 3729–3748.
DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. 1996. “Labor Market Institutions
and the Distribution of Wages, 1973–1992: A Semiparametric Approach.” Econometrica
64 (5): 1001–1044.
Foster, Andrew D. and Mark R. Rosenzweig. 2004. “Agricultural Productivity Growth, Rural
Economic Diversity, and Economic Reforms: India, 1970–2000.” Economic Development
and Cultural Change 52 (3): 509-542.
Kijima, Yoko and Peter Lanjouw. 2005. “Economic Diversification and Poverty in Rural
India.” Indian Journal of Labour Economics 48 (2): 349–374.
Lanjouw, Peter and Rinku Murgai. 2010. “Poverty Decline, Agricultural Wages, and
Non-Farm Employment in Rural India: 1983–2004.” Agricultural Economics 40(2): 243–
263.
Ravallion, Martin, and Gaurav Datt. 1996. “How Important to India’s Poor Is the Sectoral
Composition of Economic Growth?” World Bank Economic Review 10 (1):1–25.
——2002. “Why has Economic Growth been More Pro-Poor in Some States of India than
Others.” Journal of Development Economics 68(2): 381–400.
Sundaram, Krishnamurthy. 2001. “Employment and Poverty in 1990s: Further Results fromNSS 55th Round Employment-Unemployment Survey, 1999–2000,” Economic andPolitical Weekly 36 (32): 3039–3049.
——2007. “Employment and Poverty in India, 2000–2005.” Economic and Political Weekly
42(30): 3121–3131.
United Nations Development Programme (UNDP). 2007. Human Development Report
2007/2008: Fighting Climate Change: Human Solidarity in a Divided World. New York,
Palgrave Macmillan.
19Figure 1: Changes in the expeenditure disttribution

Sourrce: Author'ss calculationn and drawiing from Naational Sam
mple Surveys
ys (NSS) 198
83,
19933/94, and 20004/05.
Notee: Expendituures are adju
usted by thee state-wise official pov
verty line too the 2004 priceplevell in urban arreas. The veertical line iis the all-Ind
dia level poverty line inn urban areaas in20044.

20

Figuree 2: Observed Chhanges in the Ex
xpenditure Distriibution
A: F
From 1983 to 199
93/94

B: Froom 1993/94 to 2004/05

Note: See the note in Figure 1.
21

Figgure 3: Decomposition of Changges in the Expen
nditure Distributiion (1983-1993//94)

22

Figu
ure 4: Decompossition of Changees in the Expenditure Distribution (1993/94-2004
4/05)

23

Figure 5: Deccomposition by ssectors and channels of impact (1983-1993/94)
A: Impact of Literacy Rate (11983-1993/94)

24

B: Immpact of Non-Aggric. Self-Employ
yment (1983-199
93/94)

25

C: Impact of Agric.. Wage Employm
ment (1983-1993
3/94)

26

D: Imppact of Non-Agriic. Casual Emplo
oyment (1983-1993/94)

27

E: Impaact of Non-Agricc. Regular Emplloyment (1983-1
1993/94)

Note: Figures in graphs are thhe percentage off the total poverty
y decline during the period.

28

Figure 6: Decom
mposition by secctors and channeels of impact (19993/94-2004/05))
A: Impact of L
Literacy Rate (19993/94-2004/05)

29

B: Impact of Non-Agriic. Self-Employm
ment (1993/94-2
2004/05)

30

C: Immpact of Agric. W
Wage Employmeent (1993/94-200
04/05)

31

D: Impacct of Non-Agric. Casual Employ
yment (1993/94--2004/05)

32

E: Impacct of Non-Agric. Regular Employ
yment (1993/94-2004/05)

Note: See the note in Figure 55.

33

Table 1: Changes in economic conditions in India
Rural

Urban

1983

1993/94

2004/051983

1993/94

2004/05

549.0

604.9

680.7

581.3

658.2

757.6

Primary completed

11.1

10.7

12.8

15.7

12.6

12.4

Middle completed

8.3

11.1

15.0

16.6

16.6

18.2

Secondary completed

4.7

8.8

12.3

17.7

24.2

25.3

University completed

0.8

1.6

3.3

6.7

10.3

16.2

Agricultural sector

40.6

52.3

67.2

34.9

48.2

54.5

Non-agricultural sector

90.8

74.5

150.1

102.6

117.3

165.2

Agricultural sector

78.2

75.5

69.4

11.9

10.4

7.5

Self employment

46.1

42.7

41.6

6.7

5.5

4.7

Regular employment

2.1

0.9

0.7

0.5

0.4

0.3

Casual employment

30.0

31.9

27.1

4.6

4.5

2.5

Non-agricultural sector

19.9

22.9

28.3

82.0

84.3

87.3

Self employment

10.0

11.2

13.8

30.4

32.6

37.4

Regular employment

5.3

5.9

6.7

38.9

38.7

37.6

Casual employment

4.5

5.8

7.8

12.7

13.0

12.4

Monthly per capita expenditure(in 2004 Rs.)
Education level (%)

Wage rates (2004 Rs.)

Empolyment (%)

Note: All figures are adjusted using sampling weights.

34

Table 2: Summary statistics of empirical variables
Panel A: Rural Areas

Log of monthly per capita expenditure
Average schooling years
Members aged 15 to 29
Members aged 30 to 44
Members aged 45 to 59
Household sizeAge composition
15 to 29
30 to 44
45 to 59
60 over
Female ratio
Scheduled castes / tribes
Religion
MuslimChristianSikh
Buddhist
Jaina
Other religions
Landless dummy
Region-level characteristics
Literacy rate
Ratio of the non-agric. self-employed
Ratio of the agric. wage workers
Ratio of the non-agric. casual workers
Ratio of the non-agric. regular workers
Price index*
Ratio of landlessLog of population
No. of observations

1983
Mean Std. Dev.
6.340
0.589

1993/94
Mean Std. Dev.
6.439
0.521

2004/05
Mean Std. Dev.
6.560
0.481

2.100
1.473
0.545
6.607

3.544
3.223
1.982
3.165

2.647
1.976
0.821
6.094

4.079
3.763
2.577
2.824

3.304
2.724
1.204
6.049

4.440
4.335
3.126
2.875

0.248
0.165
0.110
0.068
0.493
0.286

0.205
0.158
0.152
0.131
0.168
0.452

0.260
0.183
0.115
0.069
0.487
0.305

0.214
0.169
0.166
0.141
0.167
0.461

0.254
0.198
0.118
0.074
0.492
0.314

0.212
0.176
0.171
0.152
0.166
0.464

0.087
0.020
0.021
0.006
0.001
0.002
0.083

0.282
0.139
0.142
0.074
0.033
0.048
0.275

0.094
0.016
0.020
0.006
0.001
0.003
0.129

0.292
0.127
0.139
0.078
0.029
0.054
0.335

0.105
0.016
0.021
0.006
0.001
0.003
0.043

0.306
0.125
0.143
0.076
0.027
0.057
0.204

0.320
0.151
0.113
0.054
0.294
0.132
0.050
0.046
0.072
0.053
1.016
0.109
0.085
0.082
14.0510.474
65,646

35

0.412
0.158
0.127
0.063
0.294
0.135
0.069
0.074
0.077
0.057
1.016
0.091
0.129
0.111
14.0060.349
57,381

0.509
0.162
0.160
0.071
0.245
0.147
0.103
0.084
0.084
0.066
1.003
0.092
0.043
0.051
14.3230.579
63,720Panel B: Urban Areas

Log of monthly per capita expenditure
Average schooling years
Members aged 15 to 29
Members aged 30 to 44
Members aged 45 to 59
Household sizeAge composition
15 to 29
30 to 44
45 to 59
60 over
Female ratio
Scheduled castes / tribes
Religion
MuslimChristianSikh
Budhist
Jaina
Other religions
Landless dummy
Region-level characteristics
Literacy rate
Ratio of the non-agric. self-employed
Ratio of the agric. wage workers
Ratio of the non-agric. casual workers
Ratio of the non-agric. regular workers
Price index
Ratio of landlessLog of population
No. of observations

1983
Mean Std. Dev.
6.423
0.600

1993/94
Mean Std. Dev.
6.540
0.588

2004/05
Mean Std. Dev.
6.705
0.610

4.074
3.806
1.729
6.387

4.808
4.962
3.703
3.013

4.182
4.654
2.267
5.706

5.135
5.423
4.380
2.567

4.803
5.290
3.028
5.632

5.442
5.667
5.013
2.805

0.291
0.176
0.103
0.058
0.478
0.152

0.230
0.168
0.148
0.120
0.182
0.359

0.290
0.205
0.112
0.060
0.477
0.154

0.237
0.182
0.163
0.129
0.183
0.361

0.290
0.221
0.128
0.070
0.482
0.175

0.238
0.192
0.177
0.145
0.179
0.380

0.164
0.030
0.012
0.007
0.010
0.007
0.481

0.370
0.170
0.111
0.086
0.101
0.081
0.500

0.160
0.025
0.014
0.010
0.007
0.003
0.527

0.367
0.155
0.117
0.099
0.086
0.055
0.499

0.165
0.023
0.014
0.009
0.011
0.002
0.259

0.371
0.149
0.119
0.096
0.103
0.048
0.438

0.640
0.144
0.311
0.115
0.037
0.057
0.121
0.074
0.418
0.138
1.028
0.070
0.483
0.213
13.2850.902
35,473

0.712
0.116
0.335
0.089
0.036
0.045
0.126
0.066
0.409
0.122
1.031
0.095
0.528
0.203
13.9130.822
38,508

0.774
0.126
0.386
0.118
0.019
0.037
0.133
0.085
0.381
0.135
1.013
0.128
0.260
0.160
13.636
1.141
36,509Note: All statistics are adjusted using sampling weights. Except that the price index isthe state-level variable, all the region-level characteristics are aggregated at the districtlevel.

36

Table 3: Estimation results for monthly per-capita expenditures (OLS)
Panel A: Rural Areas
1983
Household-level Variables
Ave. sch. years: aged 15 to 29
Ave. sch. years: aged 30 to 44
Ave. sch. years: aged 45 to 59
Household size
Aged 15 to 29
Aged 30 to 44
Aged 45 to 59
Aged over 60
Female ratio
Scheduled castes / tribes
MuslimChristianSikh
Budhist
Jaina
Other religions
Landless dummyDistrict-level Variables
Literacy rate
Self-employed in non-agric.
Wage workers in agric.
Casual workers in non-agric.
Regular workers in non-agric.
Price index
Landless
Log of population
Intercept
No. of observations
Adjusted R-squared

1993/94
2004/05
Difference from the coefs. in 1983

0.019
0.028
0.025
-0.020
0.440
0.544
0.572
0.430
-0.090-0.185-0.031-0.0140.220-0.0920.073
0.073-0.035(0.001)**(0.001)**(0.001)**(0.001)**(0.015)**(0.021)**(0.018)**(0.017)**(0.015)**(0.006)**(0.010)**(0.016)(0.019)**(0.025)**(0.050)(0.047)(0.008)**-0.007-0.006-0.002-0.0090.001
0.014-0.096-0.030
0.018
0.030
0.025
-0.052-0.115-0.0970.146-0.1570.000(0.001)**(0.001)**(0.002)(0.002)**(0.020)(0.027)(0.023)**(0.023)(0.019)(0.007)**(0.013)*(0.023)*(0.021)**(0.035)**(0.077)(0.063)*(0.011)-0.011-0.008-0.005-0.014
0.033
0.104
0.022
0.106
0.043
0.027
0.023
0.093
-0.1700.112
0.105-0.0280.084(0.001)**(0.001)**(0.002)**(0.002)**(0.020)(0.027)**(0.022)(0.022)**(0.019)*(0.007)**(0.012)(0.023)**(0.020)**(0.039)**(0.075)(0.054)(0.015)**0.273-0.063-0.0010.221
0.021-0.671
0.214-0.0427.349(0.030)**(0.050)(0.029)(0.067)**(0.069)(0.033)**(0.035)**(0.009)**(0.136)**0.002 (0.029)
0.173 (0.062)**-0.040 (0.034)-0.101 (0.072)
0.330 (0.084)**
0.136 (0.053)**-0.063 (0.041)
0.067 (0.011)**-1.003 (0.191)**
186,684
0.396-0.104
0.164
0.072
0.007
0.254
0.492
-0.0950.080-1.505(0.031)**(0.060)**(0.034)*(0.071)(0.082)**(0.053)**(0.062)(0.011)**(0.187)**37

Panel B: Urban Areas
1983
Household-level Variables
Ave. sch. years: aged 15 to 29
Ave. sch. years: aged 30 to 44
Ave. sch. years: aged 45 to 59
Household size
Aged 15 to 29
Aged 30 to 44
Aged 45 to 59
Aged over 60
Female ratio
Scheduled castes / tribes
MuslimChristianSikh
Budhist
Jaina
Other religions
Landless dummyDistrict-level Variables
Literacy rate
Self-employed in non-agric.
Wage workers in agric.
Casual workers in non-agric.
Regular workers in non-agric.
Price index
Landless
Log of population
Intercept
No. of observations
Adjusted R-squared

1993/94
2004/05
Difference from the coefs. in 1983

0.011
0.029
0.025
-0.048
0.494
0.524
0.437
0.324
-0.161-0.124-0.0630.064
0.129-0.031
0.194
0.055-0.008(0.001)**(0.001)**(0.001)**(0.002)**(0.022)**(0.030)**(0.029)**(0.028)**(0.019)**(0.010)**(0.011)**(0.031)*(0.029)**(0.046)(0.046)**(0.046)(0.008)-0.0030.001-0.002-0.013-0.079-0.019
0.009
0.054
0.025
-0.0120.020-0.031-0.087-0.1040.001-0.101-0.014(0.001)**(0.001)(0.001)(0.003)**(0.032)*(0.043)(0.041)(0.038)(0.027)(0.014)(0.014)(0.040)(0.035)*(0.054)(0.060)(0.103)(0.011)-0.0100.003
0.002-0.0140.006-0.048
0.129
0.301
0.082
-0.0630.004-0.002-0.033
0.057
0.098
0.074
0.022

(0.001)**(0.001)*(0.002)(0.005)**(0.037)(0.049)(0.048)**(0.044)**(0.030)**(0.016)**(0.017)(0.039)(0.040)(0.056)(0.061)(0.076)(0.013)

0.027
0.059
0.010
0.197
0.279
-0.716
0.075
0.023
6.570

(0.039)(0.068)(0.115)(0.082)*(0.081)**(0.105)**(0.031)*(0.005)**(0.137)**0.267 (0.050)**-0.039 (0.112)-0.613 (0.182)**-0.410 (0.124)**-0.116 (0.122)-0.193 (0.078)*
0.031 (0.038)-0.028 (0.006)**
0.632 (0.156)**
110,490
0.427

0.230
0.250
0.339
-0.0510.281-0.204-0.0430.030-0.411(0.053)**(0.102)*(0.167)*(0.114)(0.113)*(0.083)*(0.045)(0.006)**(0.143)**

Note: All estimates are adjusted using sampling weights. Huber-White
heteroskedasticity-robust standard errors are in parentheses. The coefficient estimates inthe second and third columns are those on the interaction terms with the year dummy.
Region fixed effects are controlled, but not reported here. Single asterisk (∗) and double
asterisks (∗∗) denote that the coefficient is statistically significant at the 10%, 5%, and
1% levels, respectively.

38