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STANFORD CENTER FOR INTERNATIONAL DEVELOPMENT
Working Paper No. 324
Some Aspects of the Trends in Employment and Unemployment in Bihar and Kerala since the Seventies
by
T.N. Srinivasan*
Treb Allen**
May 2007
Stanford University 579 Serra Mall @ Galvez, Landau Economics Building, Room 153
Stanford, CA 94305-6015
* Samuel C. Park Jr Professor of Economics and Non-resident Senior Fellow, Stanford Center forInternational Development, Stanford University** Yale University
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Some Aspects of the Trends in Employment and Unemployment in Bihar and Kerala since the Seventies
T.N. Srinivasan and Treb Allen
JEL Codes: J21, J64 Keywords: employment trends, state-level, India 1. Introduction
In Srinivasan (2006), the trends in employment and unemployment since the early
seventies in India were analyzed. It pointed out that although there are many sources of data on
employment and unemployment, the definition of worker, employment status, etc. are not the
same in all sources and have even varied over time within the same source, as for example, in the
decennial population censuses. In addition, coverage in most sources is limited in terms of
geographical area, sectors, and in other ways. Some sources such as the Economic Census are of
recent origin while the population census goes back to 1881! The two main sources with all
India coverage are the population census (PC) and the Employment and Unemployment Surveys
(EUS) of the National Sample Survey Organisation (NSSO), although their methods of data
collection and their limitations differ. The EUS was carried out by the NSSO in its 9th round
(May-September 1955), also in the 17th-20th rounds for the urban sector, and again for rural and
urban sectors in the 27th round (1972-73). Only from the 32nd round (1977-78) has the EUS
become formally part of the national quinquennial household surveys of the NSSO using
essentially identical concepts of employment and unemployment. Apart from the large
quinquennial surveys, the NSSO also collects data annually from a smaller sample of households
distributed over the same number of first stage units as its normal socio-economic survey. The
report of the National Commission on Labour (NCL 2002) has a comprehensive discussion of
sources of data.
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The estimates of employment and unemployment from the rounds other than
quinquennial rounds in which EUS is conducted, particularly those meant for Enterprise Surveys
(ESs) (particularly at the state and regional levels), are suspected to be biased. However, no
concrete evidence has thus far been adduced in supported of suspected biases from these so-
called ‘thin’ rounds. Moreover, the sample sizes at the State and All India levels for these
rounds are sufficiently large to produce reliable estimates, albeit with higher sampling errors.
Many of the conceptual, measurement and data gathering problems relating to labour
statistics arise largely from the complexity of the Indian labour market. From the employee or
worker side, complexities arise from the fact that individuals (particularly females) frequently
move in and out of the work force within a year, and even those who participate in the work
force and are employed throughout the year could move from self-employment in their own
farms in one season to wage employment in another season within the same year. Self-
employment continues to be the single largest source of employment in the economy. Although
the proportion of population living in households whose major source of income is self
employment declined from 55.6% in 1987-88 to 50.9% in 1999-2000 in rural areas, it increased
slightly from 38.9% to 39.2% during the same period in urban areas (NSS, 2001, Table 4.2).
Also, an individual could be engaged in more than one economic activity at the same time or at
different times in a year.
From the employer side, the situation is just as complex. A farmer employs workers not
only from his/her own household but also hires agricultural laborers during peak agricultural
season. The same farmer would be employed as casual work (or looking for such work) outside
the farm during slack agricultural season. Outside of crop production activities, as the data from
the latest economic census show, 98.6% of the number of enterprises in existence in 2005 in the
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economy employed less than 10 workers.1 In the earlier census of 1998, this proportion was
similar at 98.1%, accounting for 76.5% of the number of usually working persons. A large
majority (61.3%) of the enterprises operated in rural areas. Also, 20% of rural and 15.5% of
urban enterprises operated with no premises (GOI 2006). It is very unlikely that enterprises
employing less than 10 workers would maintain written records of their activities. There is no
way one could gather data on their employment, other than by canvassing such enterprises
directly though a well designed survey or census. This is indeed what the Economic Census and
its follow up survey attempt to do. However, the census excludes a large share of the workforce
employed in crop production activities.
The focus of this paper is the EUS of the NSSO since it is the only comprehensive source
of data using the same concepts and methods of data collection over more than three decades.
Importantly, compared to PC, NSSO data are available for many more years. Our purpose is
twofold. First, we fit a simple trend regression to the data, from 32nd Round (1977-78) to 61st
Round (2005) for Bihar, Kerala and India on employment rate per 1000 persons (person-days),
unemployment rate per 1000 persons (person-days) in the labour force, employment status and
labour force participation rate per 1000 persons (person-days), taking into account that sample
sizes in terms of the number of households of various rounds were different.2 Observations from
each round are weighted by the square root of the sample size, thus placing more importance on
observations from the large quinquennial surveys (Section 2). The time trend analysis is meant
to extract the time patterns in the data efficiently. Also, the estimation allows for possible serial
1 GOI (2006). Strictly speaking, the data from the economic censuses refer to the number of positions and not to workers. Thus the same position could be held by different persons during a year. 2 In Srinivasan (2006) All-India data for the 27th Round (1972-73) were included. Since we did not have the data for Bihar and Kerala for the 27th round, in this paper we focus on comparable data for 1978-2005 for Bihar, Kerala and All India.
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correlation in the disturbance term in the regression equation, taking into account that the
observations are not evenly spaced over time.
It is important to stress that our time series analysis is basically descriptive. It is not a
structural economic analysis of the labour market based on a model of labour supply and demand
that brings in endogenous and exogenous determinants of both, including importantly variables
capturing labour market policies and regulations.3 Thus the trends are best viewed as trends in
labour market equilibria in a loose sense.
Second, besides fitting time trends in Section 2, we also analyze the time patterns of
employment, unemployment and being out of the work force within the seven day reference
period at the All India level. The observed time pattern enables an assessment of the belief that
there is considerable churning in the labour market because “the activity pattern of the
population, particularly in the unorganized sector, is such that during a week, and sometimes,
even during a day, a person, could pursue more than one activity.” (NSS 2005a, Report 506). If
this is the case, we should observe that the distribution of the number of days within a week of a
given activity status (employed, unemployed and not in work force) should be well dispersed.
We will see that this is not what we observe in general, although there is less persistence of
economic states for females than males.
In Section 3 we discuss the Kerala employment situation in some detail comparing our
findings from NSS data in Section 2 with three other studies (2005 Human Development Report
for Kerala, Kerala Economic Review (2006) and Zachariah and Irudaya Rajan (2005)). As is
well known, Kerala differs from most other states in India in its superior performance with
respect to social indicators relating to education and health. It also accounts for a large part of
emigration abroad of workers (and their return home). Its contribution to inter-state migration
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within India is also substantial. Although Kerala’s economic performance lagged behind the
national average for decades, recently Kerala seems to be catching up. Given the comparatively
high education levels of males and females in Kerala, the problem of unemployment of the
educated is a serious issue. It is therefore instructive to look at Kerala’s trends in some detail.
We offer some concluding observations in Section 4.
2. Trends in Employment, Unemployment, and Employment Status
2.1 Person and Person-Day Rates
Before describing the trends in employment and unemployment rates, I want to draw
attention to the fact that the important distinction between the person rate of usual (US) and
current weekly (CWS) statuses and the person-day rate of current daily status (CDS), seems to
have been ignored in the discussion of the employment issue in some of the official publications
(Planning Commission, 2005, 2002, 2001; MOF, 2004).
In the EUS, a person could be in one or combination of the following three broad activity
statuses during the relevant reference period (year, week or day): (i) working (i.e. being engaged
in economic activity), (ii) unemployed in the sense of not working, but either making tangible
efforts to seek work or being available for work if work is available and (iii) not working and not
available for work. Statuses (i) and (ii) correspond to being in work force and status (iii) to
being out of work force. It is possible for a person to be in all three statuses concurrently
depending on the reference period. Under such a circumstance, one of the three was uniquely
identified in the EUS as that person’s status by adopting either the major time or priority
criterion. The former was used in identifying the “usual activity status” and the latter for
“current activity status.” (NSS, 2005). More precisely, the principal usual activity status of a
person among the three was determined as follows: first it was determined whether the person
3 To the best of our knowledge, no such general equilibrium model is available in the empirical literature.
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spent a major part of the year in or out of the work force. Next, those who were in the work
force who spent a major part of their time during the 365 days preceding the date of survey in the
work force working (not working) were deemed as employed (unemployed) (i.e. major time
criterion). In addition to his or her principal activity in which a person spent a major part of his
or her time, he/she could have pursued some economic activity for a relatively shorter time
during the preceding year. This minor time activity was that person’s secondary activity.
The current weekly status of a person during a period of 7 days preceding the date of
survey is decided on the basis of a certain priority cum major time criterion. The status of
“working” gets priority over the status of “not working but seeking or available for work,” which
in turn gets priority over the status of “not working and not available for work.” A person is
classified as working (employed) while pursuing an economic activity, if he or she had worked
for at least one hour during the 7 day reference period. A person who either did not work or
worked for less than one hour is classified as unemployed if he or she actively sought work or
was available for work for any time during the reference week, even if not actively seeking work
in the belief that no work was available. Finally, a person is classified as not in the work force if
he or she neither worked nor was available for work any time during the reference period. The
current daily status of a person was determined on the basis of his/her activity status in each day
of the reference week using a priority-cum-major time criterion.4
Which of the three rates, namely “usual status (principal and secondary capacity work
combined)”, “weekly status” and “daily status” should be used estimating the levels and trends in
workforce or the number of unemployed? The first two of the three are “person rates”, that is,
they refer to persons, for example the number of persons employed or unemployed per 1000
persons in the population. The third is a person-day rate i.e. it refers to the number of person
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days employed or unemployed per 1000 person-days. Thus, if a person in the sample was
deemed to have worked (i.e. employed) for 3.5 days in the reference week, his employed person-
days is 3.5 and total person-days is seven so that his employed person-day rate is 0.5, i.e. 500
person days of employment in the week per 1000 person days. Averaging this daily rate over all
persons and multiplying it by the population figures will yield the total number of person-days of
employment per day.
The total number of person-days of employment is not the same as the total number of
employed persons. The reason is that a given total number of person-days of employment could
be distributed among the same number of persons in many ways so as to lead to different
numbers of persons employed. For example, consider a four person economy in which all four
participate in the work force and together they were employed for ten person-days in the week.
This yields a person-day rate of employment of 10 out of 28 or 36%. If the ten person-days are
distributed in a way that one person is employed for seven days, another for three days and the
remaining two are unemployed, then person-rate of employment is two out of four or 50%. On
the other hand, if it is distributed in a way that three persons work for three days each and one
person works for just a day, the person rate of employment is four out of four or 100%, given the
priority given to the status of employment! Unfortunately, official publications ignore the
distinction between persons and person-days, and possible heterogeneity among the population
in number of days worked.
For example, MOF (2004, Table 10.7, p209) purports to present the number of persons in
the work force, employed and unemployed, using daily status rates that refer to person-days.
Interestingly, at the top of the table, the phrase “person-years” is used, suggesting that the
numbers in the table refer not to persons but to person-years. Apparently, MOF wants to have it
4 See section 2.3 below for details.
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both ways! Fortunately, in the latest economic survey (MOF, 2007, Table 10.4) the usual status
rates are used thus avoiding the mistaken use of daily status rates.
2.2 Employment, Unemployment and Employment Status: Time Trend Regressions
The following weighted regression was estimated from the data, taking into account that
our data are unequally spaced in time.
t t t t t tn E n t n n uα β= + + (1)
1
1
with ttt
t t
uun n
ρε−
−
= + (2)
Where tn : Number of households canvassed in the round of period t
tE : Employment Rate, Unemployment Rate or Employment Status
tu : Random disturbance terms with expectation zero and variance
( )2
21tnδρ−
tε : Independent and identically (over time) distributed random terms
with mean zero and variance 2δ
Since the various rounds covered different time spans (year, six months, etc.) and also
different year types (Calendar year, Agricultural years (July 1- June 30) etc), period t has been
defined so that the interval between any two consecutive t is a quarter of a year. Thus the slope
coefficient β represents the rate of change in the expected value of tE per quarter year. There
are only seven observations on person-day rates based on current daily status. This fact has to be
kept in mind in assessing the current daily status regressions.
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2.3 Results from the trend regression
Throughout the following discussion of employment trends, we focus only on the sign
and statistical significance of the trend rather than the magnitude of the trend coefficients, as the
units of the coefficient differ depending on the variable. For reasons explained in Section 2.1, let
us ignore the trends in person-day rates based on CDS and focus only on the person rates of US
(principal and secondary) and CWS.5 Table 5 summarizes the statistical significance and the
sign of each variable, which we discuss in detail below.
2.3.1 Employment Rates
Table 1 and Figure 1 to Figure 4 present the results for employment rates, namely,
number of persons employed per 1000 persons in the population of ages 5 and above. Since the
All India trends are discussed in detail in Srinivasan (2006), we will focus mostly on Bihar and
Kerala with only brief references to the All India story as appropriate.
Figure 1 and Figure 3 suggest that male rural and urban employment rates in Kerala by
and large exceed those in India as a whole, while those in Bihar tend to be below the national
average. A similar pattern is seen in urban female employment rates (Figure 4), though not for
rural females (Figure 3). The trends in Kerala and All India are similar except in two cases: US
for rural males and urban females. For rural males, the employment rate in Kerala is increasing,
while it is decreasing for all India, with both trends being statistically insignificant. For urban
females, Kerala employment rates show a decreasing but insignificant trend. All India data show
an increasing and also insignificant trend. On the other hand, the trends in Bihar are quite
different from (and in many cases the opposite of) the All India trends. The overall picture is
5 The CDS rates are available only for the quinquennial rounds so that we are restricted to only 8 observations over the period. This fact has to be kept in mind when assessing the trends in CDS in Table 1 to Table 4.
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that the employment situation in Kerala, with the exception of rural females according to US, has
not changed for the worse, and has improved significantly for rural males according to CWS.
On the hand, the situation in Bihar is disturbing: there is no case of a positive trend in
employment rates and a statistically significant downward trend for rural males (US and CWS),
rural females (US), urban males (CWS), and urban females (US).
2.3.2 Unemployment Rates
Table 2 and Figure 5 to Figure 8 present the trends in unemployment rates. As can be
seen from the figures, in general the level of unemployment is distinctly higher in Kerala as
compared to All India and Bihar. However, there is no evidence of a significant upward trend in
unemployment rate in Kerala – in fact, the rate according to US for urban males shows a
significant downward trend. This is consistent with the corresponding All India trend. Also, the
trends in employment in Table 1 and unemployment in Table 2 for Kerala are broadly consistent.
The trends in Bihar are once again disturbing: there is no evidence of a significant downward
trend in unemployment. Indeed, all measures for every demographic group show upward trends
in unemployment, and all coefficients with the exception of rural males according to CWS and
rural females by US and CWS are statistically significant.
2.3.3 Labor Force Participation Rates
Table 3 and Figure 9 to Figure 12 present the labor force participation rates. Several
interesting features of the data emerge from these. First, participation rates according to both US
and CWS in Kerala of males and urban females are higher than the All India average, while
participation rates in Bihar are below the All India Average. For rural females, both Kerala and
Bihar participation rates are below the All India average, with Bihar being the lowest.
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In terms of trends, at the All India level there is a significant upward trend in
participation rates according to CWS of males in rural and urban areas and also of females in
rural areas. Except in the case of urban males where it is positive and significant, all other US
rates show no trend. In Bihar, both US and CWS rates for males and females trend downward,
with the trend in US rates for all demographics except urban males being statistically significant.
In Kerala, trends in all CWS and US rates are upward except for urban males and rural females
according to US, but only the CWS trend for rural males is significant. Thus Kerala and All
India show stable or increasing labor force participation rates, while Bihar shows a disturbing
downward trend in some of the rates. However, a caveat is in order: the rates in Figure 9 to
Figure 12 are not age group specific; it is possible that participation rates reflect in part the
differing trends in age distribution among Bihar, Kerala, and all India. As is well known,
historically Kerala has low fertility rates and mortality rates. Table 15 to Table 18 depict the
distribution of employment by age group. An examination of the tables shows that the
population in Kerala is indeed slightly older; however, we have not conducted a rigorous
analysis of the data.
2.3.4 Employment Status
Employment status data are presented in Table 4 and Figure 13 to Figure 16. At the All-
India level, self-employment continues to be the dominant mode of employment for employed
persons, with more than 50% of males and females being self-employed in rural areas and
slightly less than 50% in urban areas. The share of regular wage / salaried employment is very
low in rural areas for males while it is a significant 40% or so for males and 35% for females in
urban areas. There is no discernible uniformity in the pattern of trends. In rural areas, self-
employment shows a significant downward trend for All India for both males and females no
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significant trend for either Bihar or Kerala. In urban areas, there is a significant upward trend for
males and no significant trend for females. In Kerala there is no significant trend for males or
females, while in Bihar, there is a significant upward trend for both.
Regular wage / salaried employment rates shows a significant downward trend for males
in Bihar and All India in rural and urban areas. For males in Kerala, there is a significant upward
trend in rural areas and no significant trend in urban areas. For females, there was a significant
upward trend in both rural and urban areas in Kerala and All India, but a downward trend in
Bihar (albeit not significant for rural females).
Casual labor employment rates at the All India level show a significant upward trend for
males in both rural and urban areas. Females also show downward but insignificant trend in
casual employment in both rural and urban areas. Interestingly, both in Bihar and Kerala there is
no trend for either males or females in either rural or urban areas! The upward trend in All India
for males (which some might see as a confirmation of the so-called prolitarization hypothesis)
and the absence of any trend in both states is intriguing and merits further analysis.
2.3.5 Changes within the reference week of employment status
We mentioned in Section 1 that there is a widely held belief that during a single week and
sometimes even during a single day people pursue more than one employment activity. This
leads to the expectation that the distribution of the number of (half) days within a week of a
given status such as employed (E), unemployed (UE), and in the labor force (LF) should be well
dispersed. Table 6 to Table 8 present the distribution within the reference week of E, UE, and
LF for those who are classified as E, UE, and LF according to CWS for All India, Bihar and
Kerala, respectively. We see a strong persistence for males in the employment states within the
week in Bihar and All India. Thus, of those classified as employed (unemployed, within labor
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force) in CWS, more than 80% (97%, 90%) are employed (unemployed, in the labor force) in all
seven days of the reference week. In Kerala, there is significantly less persistence with respect to
employment, as only 60% of males in rural areas and 66% in urban areas are classified as being
employed for all seven days. With respect to unemployment, there is as much persistence in
Kerala as in Bihar and All India and nearly as much persistence with respect to being in the labor
force.
The picture regarding females is somewhat different. As is commonly believed, the
persistence with respect to all three (E, UE, and LF) is comparatively less for females than males
in Bihar, Kerala, and All India. However, with respect to state of being unemployed, females
exhibit high persistence everywhere (though still less so than males). Once again, Kerala is
somewhat distinct, as the male-female differences in persistence rates are somewhat less than
either in Bihar or All India.6
3. Unemployment in Kerala
The Kerala Economic Review (2006) concludes that “unemployment is the single largest
problem of the Kerala economy today” (468). As we noted in Section 2.3.2, unemployment rates
in Kerala are higher than in Bihar or India as a whole, and as such, unemployment in Kerala is
indeed comparatively serious. Whether it is the ‘single most’ problem is difficult to judge since
the Review does not provide data on how less serious (whatever it means by that term) other
problems are. In any case, our discussion below suggests several reasons why a more nuanced
interpretation of the Kerala situation is warranted.
3.1 Unemployment Levels
6 In ongoing research, we fit a Markov transition model to the transition in status of employment (employed, unemployed, and not in workforce) from one day to the next within the seven day reference period. We have transition data for the quinquennial 38th, 43rd and 50th rounds and are currently waiting for data from the 61st round. Such data were not collected in the annual rounds.
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Unemployment rates in Kerala have continued their historic trend of being much higher
than the All India average. As discussed in Srinivasan (2006), however, the observed
unemployment reflects the outcomes of two distinct processes. The first is the labor supply
process, that is, the ex ante choice by individuals whether or not to participate in the labour force.
The second is the labor demand process that, conditional on the ex ante choice of individuals to
participate in the labor force, determines whether or not they are able to find employment.
Because the unemployment rate is calculated as a proportion of the labor force, it depicts the
outcome of the second process. An unemployment rate defined as a proportion of the general
population above age 5, rather than as a proportion of the labour force, is a better measure of the
joint outcome of the two processes.
Table 21 depicts the ratio of the Kerala unemployment rates to the national average using
both the proportion of the labor force and the proportion of the general population above the age
of 5. While the proportion of the general population unemployed in Kerala is still higher than
the national average, the ratio falls substantially for all demographics after accounting for the
higher than average labor force participation in Kerala. Hence, part of the explanation of the
distinctiveness of Kerala’s unemployment problem is simply a greater proportion of the
population willing to work.
3.2 Unemployment Trends
Zachariah and Rajan (2005), comparing two different household surveys, find that
unemployment has increased substantially (55% for males and 115% for females) in Kerala
between 1998-2003. They argue that the increase was caused by an influx of women into the
workforce, an ageing of the labor force, an increased proportion of persons with higher
educations, and emigration. Since these factors are all gradual demographic changes and are
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unlikely to show substantial “jumps” over short periods, we would expect that unemployment
should be rising gradually over a long period. As mentioned in Section 2.3.2, however, long
term unemployment trends in Kerala show no indication of rising; for urban males, there is
actually a statistically significant declining trend. Additionally, we find very little evidence of an
influx of women into the workforce during their period of study; according to NSS data, labor
force participation rates for women between 1998 and 2003 show only a slight rise for rural
females and a decline for urban females. Finally, as Figure 5 to Figure 8 demonstrate, there
exists substantial variation in the measurements of unemployment from year to year; hence, any
attempt to measure changes in unemployment from just two observations should be taken with a
grain of salt. Indeed, the NSS employment survey finds that, with the exception of urban males
as measured by CWS, unemployment rates in 2003 are actually substantially lower than the
unemployment rates in 1998 in Kerala for both CWS and US for all demographics! Hence,
although we do not know enough about the surveys Zachariah and Rajan use to calculate
unemployment rates, it cannot be ruled out that the increase in unemployment that Zachariah and
Rajan find could be statistically insignificant. If so, the factors they identify likely have little to
do with the “increase” in unemployment.
3.3 Female Unemployment
Much has been made about the substantial levels of unemployment among women.
According to the 2005 Human Development Report for Kerala, “If a single fact were to convey
the intensity of the problem of unemployment in Kerala, it is that unemployment among women
is two to three times higher than among men” (109). The Kerala Economic Review (2006) finds
that in 1999-2000, the unemployment rate for women was almost 50% in rural areas and more
than 50% in urban areas. Our results largely corroborate the seriousness of this problem. As
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mentioned in Section 2.3.2, while male unemployment rates show long-term declines in Kerala
(significant for urban males according to US, insignificant for urban males according to CWS
and Rural Males for both measures), female unemployment rate trends are positive (albeit
insignificant). The fact that not only are female unemployment rates substantially higher than
those for males, but the difference between the two is increasing, is indeed distressing. The
employment trends are similar: while rural males show a significant increase in employment
rates over time (as measured by CWS), rural females show a significant declining trend (as
measured by US). There does, however, seem to be a bright side to this story: both rural and
urban females show a statistically significant positive trend in regular wage / salaried
employment and negative insignificant trends in self-employed and casual labor employment.
This trend is consistent with the Kerala Economic Review’s (2006) conclusion that “women
avoid low paid and low status manual work, wherever possible” (105).
3.4 Unemployment and Education
As mentioned in the introduction, Kerala is commonly considered one of the superior
achievers in regards to education. Partly because of this, the problem of unemployment has
largely been interpreted as a problem of the educated. The 2005 Human Development Report for
Kerala argues that “the problem of unemployment in Kerala is basically one of educated
unemployment” (111). Zachariah and Irudaya Rajan (2005) write, “Education is an important
factor in determining the level of unemployment in Kerala, as most of the unemployed are
educated” (24).
To analyze the importance of education on unemployment, we disaggregate employment
status by level of education for Kerala, Bihar, and All India. Table 9 to Table 12 present our
results. As is immediately evident, those with secondary and higher levels of education have
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higher than average unemployment rates in Kerala for all demographics, with the exception of
very well educated urban males. While the 2005 Human Development Report for Kerala claims
that unemployment rates decline for education levels past secondary, our tables indicate an at
best mixed picture for urban and rural males, and an increase in unemployment rates as
education level increases for urban and rural females.
Table 13 and Table 14 depict the changes in unemployment rates (as a proportion of the
general population above 5 and the labor force, respectively) between 1994 and 2000 for Kerala
by level of education. Although the same caveat as above for interpreting trends from two
observations applies, the tables suggest that unemployment among higher educated males is
declining. However, there is no clear trend for higher educated females. This is largely
consistent with the story presented in the 2005 Human Development Report for Kerala of women
“continu[ing] in the educational stream in Kerala in the absence of ‘desired’ employment
opportunities” (110).
There is a puzzling element in Table 9 to Table 12 relating to the proportion of
population 15 years and above in each of the six education levels. As is to be expected, the
proportion not literate in Bihar exceeds that of Kerala and All India in all four tables.
Surprisingly, the proportions with higher secondary and graduate and above levels of education
of urban males in Bihar consistently exceed those in Kerala, while being roughly the same as in
India as a whole (except that in 2000, Bihar has a higher proportion with graduate education and
above). Comparing Kerala to India, Kerala’s relative dominance is in all levels of education up
to and not above secondary levels for males (rural and urban). For females on the other hand, the
dominance is in all levels of education in rural areas, while in urban areas, fewer females have a
graduate or above level of education. This comparison raises two questions. First, does it reflect
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a relative under emphasis of higher education, albeit with no gender bias or even a bias towards
females? Second, since the data do not include emigrants, do they point to selective emigration,
that is, do relative more of the highly educated emigrate out of Kerala?
3.5 Unemployment and Age
There has been some discussion about how unemployment differs depending on the age
of the worker. As mentioned earlier, Zachariah and Rajan (2005) argue that one reason
unemployment “increased” in Kerala during their period of study was because of the ageing of
the population. The Kerala Economic Review (2006) notes that unemployment rates are
particularly high among young workers aged 15-29 years. The 2005 Kerala Human
Development Report also concludes that the youth have a “high unemployment rate” (111). To
examine this claim, we disaggregate employment status (employed, unemployed, out of
workforce) by age groups. Table 15 to Table 18 depict our results. An examination of the
unemployment rates for Kerala youth (ages 15-29) confirms that their rates are substantially
higher than any other age category for all demographics. However, in India as a whole, young
persons in the workforce also have high unemployment rates. Therefore, it is not clear that
Kerala has any more of a problem with unemployed younger workers than the rest of India.
Table 22 depicts the ratio of Kerala unemployment rates to the national average by age group.
As is evident, in 2000, the highest two ratios were for workers between ages 30-39 – not the age
group traditionally considered the youth! This indicates that Kerala unemployment among
young workers is not as disproportionately high as the unemployment among middle age
workers.
Table 19 and Table 20 depict the change in unemployment rates (as a proportion of the
general population above 5 and the labor force, respectively) in Kerala between 1994 and 2000.
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19
While trends inferred from just two observations should not be taken as conclusive evidence, the
tables indicate that unemployment rates are largely declining for the younger age groups. In both
tables, all demographics except for rural males show declines in unemployment rates for ages
20-24. In contrast, there are substantial increases in unemployment for middle aged (30-44
years) workers in all demographics (except for 30-34 urban females and 40-44 rural females).
This suggests that greater emphasis in future research should be places on unemployment in
middle aged workers.
3.6 Persistence of Unemployment
There is some disagreement in the literature about how persistent unemployment is in
Kerala. Zachariah and Rajan (2005) find that a large majority of the unemployed in 1998 were
not unemployed in 2003, suggesting the persistence of unemployment was relatively low. In
contrast, the 2005 Human Development Report for Kerala finds that the average waiting time for
young educated workers is 5.2 years for males and 7 years for females and that chronic
unemployment – 183 or more days spent without work – is over 4 times the national average.
The analysis of the length of spells of employment and unemployment would require panel data
over extended periods of time. We do not have such data. However, although strictly speaking
the persistence of employment and unemployment during the reference week is not an exact
proxy for the length of corresponding spells, it is nonetheless a proxy. As mentioned in Section
2.3.5, persistence in Kerala is substantially lower than the national average for all demographics,
suggesting that movement in and out of employed status is more common in Kerala. However,
the persistence of unemployment during the reference week in Kerala is slightly higher than the
national average for all demographics, indicating that unemployed persons in Kerala are slightly
less likely to move out of that status. This suggests a mixed picture: in Kerala on the one hand,
15March07
20
persons who were employed at least one half day during the reference week are more mobile
across employed states, while persons who were actively searching for work but were not
employed for at least one half day were more likely to continue to actively search for employed
all seven days during the reference week.
3.7 Unemployment and Migration
2005 Kerala Human Development Report, Kerala Economic Review (2006), and
Zachariah and Irudaya Rajan (2005) all emphasize the importance of emigration, particularly to
the Gulf States, to the employment story in Kerala. While we are unable to say much about
emigration, as the NSS survey only counts persons currently living in the household (or more
precisely, persons currently eating out of the same kitchen), Table 23 shows the number of
migrants per 1000 persons by the years since they have arrived for Bihar, Kerala, and All India.
As is evident, Kerala has a larger proportion of migrants than either Bihar or All India. The
difference is especially pronounced for migrants who have been living in Kerala for 0-5 years.
This fact suggests that in-migrants in Kerala are likely to be more recent, which could reflect the
phenomenon referred to by Zachariah and Irudaya Rajan (2005) of in-migration from
neighboring states.
4. Conclusion7
We begin with some general remarks. Our analysis of long term trends in employment,
unemployment and labour force participation is based on using available data from thick and thin
rounds of NSS. The reason is that using the data from the thick rounds only can lead to
misleading conclusions. For example, if we use only the data from quinquennial rounds 38th
(1983-84), 43rd (1987-88) and 50th (1993-94) and focus on males who constitute the
overwhelming majority (in excess of 75%) of those employed, we find that although the signs of
15March07
21
the changes of the three (US, CWS and CDS) employment rates are the same except in one
instance, the magnitudes of the change are very different (see Table 24). If instead of using the
inappropriate CDS rates, one used CWS rates, aggregate employment growth between 1983 and
1999-00 would have been faster in rural areas, slower in urban areas and faster overall. But
between 1983 and 1987-88 on the other hand, the use of CWS would lower the growth of
employment both in rural and urban areas. The point is not only that it matters which of the
three employment rates is used for projecting aggregate employment, but also whether the data
from all available rounds are used, since these data do not in general support a slow down in
employment rates in India as a whole.
Based on data from just three quinquennial rounds, not only have official publications
and academic writers wrongly concluded that employment growth has slowed since the reforms
of 1991, but in attempting to explain the slow-down, they have also identified a fall in
“employment elasticity” as the culprit. For example, MOF (2004, p.207) suggests, “In view of
the declining employment elasticity of growth, observed during 1994-2000, the Special Group
[constituted by the Planning Commission on targeting ten million employment opportunities per
year over the Tenth Plan period] has recommended that over and above employment generated in
process of present structure of growth, there is a need to promote certain identified labour
intensive activities” (Planning Commission 2002). The Planning Commission (2005, Table 8.1)
generates its estimates of employment generated during the Tenth Plan using observed
employment elasticities and actual GDP growth. Srivastava (2006, Table 18) also computes
trends in employment elasticities and comments on its decline.
Unfortunately, such projections and policy pronouncements based on them have no
analytical foundation. Elementary economics would suggest that the observed employment in
7 This section was written by T.N. Srinivasan. Treb Allen is not to be held responsible for it.
15March07
22
any period represents equilibrium between labour supply and labour demand. In principle, both
supply and demand functions could shift over time. For example, GDP growth, ceteris paribus,
would shift the labour demand function outward. Similarly, growth of the number of individuals
in the prime working ages due to population growth, ceteris paribus, shift the supply curve
outward. Depending on the relative strengths of these shifts almost any trend (up, down or no
change) in equilibrium employment is possible. In other words, the so-called “employment
elasticity” is not a deep behavioral parameter and can take on any value.
While the pronouncements on the slow down in employment growth since 1993-94 are
based on inappropriate measurement and invalid employment elasticity analysis, and the long
term trends in US and CWS employment rates do not support such pessimistic pronouncements,
there is no denying the fact that during the six decades since independence, with the state playing
a dominant role in the economy, and a conscious attempt at industrialization, the industrial
structure of employment in the economy has changed extremely slowly. Primary activity
(mostly agriculture) is still the dominant source of employment. The industrialization strategy
that emphasized investment in capital intensive, heavy industry on the one hand and promoted
small scale industry (SSI) through reservation of many products for production by SSI only on
the other, has failed to substantially increase employment. This failure is seen from the
stagnation since 1977-78 in the share of the secondary sector as a source of employment for rural
males and an alarming fall in the share of manufacturing in both rural and urban areas. The only
redeeming feature is a slow rising trend in the small share for both males and females in rural
areas. As is well known, historically the transformation of less developed economies into
developed ones has consisted in shifting workforce from employment in lower productivity
primary activities to higher productivity secondary and tertiary sectors. Viewed from this
15March07
23
perspective, Indian development strategy has thus far been disappointing. Despite the fact that
recent rapid growth has been led by rapid growth of the service sector rather than manufacturing,
any expectation that India can leap-frog the stage of manufacturing growth and shift less
educated and unskilled workers employed in agriculture and other primary activities with lower
productivity to employment in high productive service activities is extremely unrealistic.
One of the contributors to the dismal performance is the set of labour laws enacted after
independence. These made it costly for large enterprises to hire workers for long term
employment. Once hired, workers could not, in effect, be dismissed for economic reasons
because of the costly and time consuming procedure for dismissal. The 2005-2006 Economic
survey (MOF 2006, p.209) notes, “these laws apply only to the organized sector. Consequently,
these laws have restricted labour mobility, have led to capital-intensive methods in the organized
sector and adversely affected the sector’s long run demand for labour.” Interestingly, the survey
notes that “perhaps there are lessons to be learnt from China in the area of labour reforms.
China, with a history of extreme employment security, has drastically reformed its labour
relations and created a new labour market, in which workers are highly mobile. Although there
have been many layoffs and open unemployment, high rates of industrial growth especially in
the coastal regions helped their redeployment.” However, the survey fails to point out that in the
Special Economic Zones (SEZs) in the coastal areas of China,
employers were free to hire and fire workers and 100 percent foreign ownership was allowed,
whereas in India’s recently legislated SEZs, the power to exempt them from labour laws is in the
hands of the governments of the states in which they happen to be located.
Given the slow change in employment structure in the context of faster output growth,
and its implications for the poor as noted earlier, it is understandable that an expanded
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24
Employment Guarantee program is being implemented. N.S.S. Narayana, Kirit Parikh and I
(1988) long ago analyzed the growth-enhancing and poverty reducing potential of a well-
designed (i.e. creating productive assets) and well-executed (i.e. involving no leakage to the non-
poor) rural work program. I very much hope that the current program would indeed be well-
designed and well-executed. But, it is important to note that even if it is, it can only be palliative
and not one that will eradicate poverty once and for all within a recognizable time horizon
(Srinivasan, 2005). The latter goal has been the vision of our founding fathers and mothers.
Realizing that vision requires, in my mind, not only a deepening, widening and acceleration of
economic reforms, but also a rethinking of our agricultural policies ranging from price supports,
input subsidies and credit to foreign trade.
Developing a foundation for policy that is based upon sound analysis of variations across
states and over time is obviously essential for effective policy formulation; crude aggregate
projections void of any economic foundation are no substitutes. Projections based on
“employment elasticities” are crude. I am not dismissing valuable and informative studies by
scholars cited by Srivastava (2006). However, they do have some limitations. For the reason
that a large majority of Indian workers are employed in agriculture and allied activities, a large
number of studies are addressed to analyzing the determinants of employment in agriculture.
Srivastava (2006) also presents a model of such determinants and estimates it econometrically
carefully allowing for the endogeneity of some of the determinants. Yet it must be said that few,
if any, of the studies look at the observed employment levels and returns to labour as being
determined in an equilibrium between supply and demand, with both supply and demand being
shifted by exogenous variables including policy and technology. The analysis of the informal
and formal employment outside of agriculture is less extensive. I should say that the scholars in
15March07
25
the past were limited by the data available to them that was largely of an aggregate nature. Now
that the NSSO has made available the rich household level data from the quinquennial and
annual rounds of EUS, it should be possible to analyze the determinants of household labour
supply, including occupational choice decisions and of labour demand decisions of producers
such as farmers and owners of household enterprises. I very much hope many such studies will
be undertaken.
Turning to the states of Kerala and Bihar, our findings show that the trends in Bihar are
extremely disturbing. On the other hand, the trends in Kerala are much more in line with the all-
India picture. In many ways, Bihar typifies many of the disadvantages of a land-locked country
in not benefiting significantly from India’s globalization. Unless the road, rail and air
connectivity of Bihar to the rest of India improves substantially, Bihar will not be able to attract
domestic and foreign investment needed to accelerate its growth enough to catch up with the rest
of India. We did not dwell on other aspects of the poor development in Bihar. It is worth
emphasizing that apart from its poor infrastructure, Bihar also suffers from poor levels of
education and health of its population. Kerala has achieved its demographic transition from high
mortality/high fertility state to a low mortality/low fertility state quite some time ago. According
to the latest (2005-06) National Family Health Survey data its fertility rate at 1.93 in 2005-06 is
below replacement level and its neighboring state of Tamil Nadu has an even lower fertility of
1.80. By contrast, Bihar’s total fertility rate is 4.00 which is much higher than the all-India
average 2.68. Serious governance issues also plague Bihar, though they are of course not absent
elsewhere in India.
Kerala is in many ways a paradox. Its being a costal state with centuries of maritime
trade and relations with the rest of the world and its achievements in health and education should
15March07
26
have enabled it to become the Indian counterpart of special coastal economic zones of China that
boomed with China’s globalization. Yet Kerala is not one of the faster growing states of India
since India began to globalize. Could it be that Kerala shot itself in the foot by its having one of
the more hostile investment climates in India with even less flexibility in its labour laws than
elsewhere in India? Could it be that the so-called Kerala model put the cart before the horse by
emphasizing welfare measures ahead of growth? After all, that is how India’s labour laws were
presciently described long ago by Professor P.C. Mahalanobis (1969, p.442 and 1961, p.157):
… certain welfare measures tend to be implemented in India ahead of economic growth, for example, in labour laws which are probably the most highly protective of labour interest in the narrowest sense, in the whole world. There is practically no link between output and remuneration; hiring and firing are highly restricted. It is extremely difficult to maintain an economic level of productivity or improve productivity … the present form of protection of organized labour, which constitutes, including their families, about five or six percent of the whole population would operate as an obstacle to growth and would also increase inequalities.
15March07
27
Works Cited 2005 Human Development Report for Kerala (2006), “Chapter 7: Reckoning Caution: Educated
Unemployment and Gender Unfreedom,” Center for Development Studies: Thiruvananthapuram.
GOI (2006), “Provisional Results of Economic Census 2005: All India Report,” Government of
India, Ministry of Statistics and Programme Implementation, Central Statistical Organization, New Delhi. http://www.mospi.gov.in
Kerala Economic Review 2006 (2007), “Chapter 19: Labour and Unemployment,” Kerala State
Planning Board, http://www.keralaplanningboard.org/html/Economic%20Review%202006/Chap/Chapter19.pdf.
MOF (2006) Economic Survey, 2005-06, New Delhi, Ministry of Finance.
MOF (2004) Economic Survey 2003-04, New Delhi, Ministry of Finance.
Narayana, N.S.S., Kirit S. Parikh and T.N. Srinivasan (1988), “Rural Works Programs in India: Costs and Benefits,” Journal of Development Economics 29 (2): 131-56.
NCL (2002) Report of the National Commission on Labour, New Delhi, Ministry of Labour. NSS (2001) Employment and Unemployment in India, Parts I and II, Report No. 458 (55/10/2),
New Delhi, National Sample Survey Organisation. NSS (2005) Employment and Unemployment Situation in India, January-June 2004, Report No.
506 (60/10/1), New Delhi, National Sample Survey Organization. Planning Commission (2005) Mid-term Appraisal of the 10th Five Year Plan (2002-2007), New
Delhi, Planning Commission. Planning Commission (2002) Report of the Special Group on Targeting Ten Million
Employment Opportunities Per Year, New Delhi, Planning Commission. Srinivasan, T.N. (2005), “Guaranteeing Employment: a Palliative?” Chennai, The Hindu
Srivastava, R.S. (2006), “Trends in Rural Employment in India with Special Reference to Agricultural Employment,” forthcoming in the World Bank’s India Employment Report.
Zachariah, K. C. and S. Irudaya Rajan (2005), “Unemployment in Kerala at the Turn of the
Century: Insights from CDS Gulf Migration Studies,” Working Paper 374, August.
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Figure 1: Rural Male Employment Rates
Rural Male Employment Rates: 1978-2005
400
420
440
460
480
500
520
540
560
580
600
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 2: Rural Female Employment Rates
Rural Female Employment Rates: 1978-2005
0
50
100
150
200
250
300
350
400
450
500
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
15March07
29
Figure 3: Urban Male Employment Rates
Urban Male Employment Rates: 1978-2005
400
420
440
460
480
500
520
540
560
580
600
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 4: Urban Female Employment Rates
Urban Female Employment Rates: 1978-2005
0
50
100
150
200
250
300
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
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30
Figure 5: Rural Male Unemployment Rates
Rural Male Unemployment Rates: 1978-2005
0
20
40
60
80
100
120
140
160
180
200
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
in th
e La
bor F
orce
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 6: Rural Female Unemployment Rates
Rural Female Unemployment Rates: 1978-2005
0
50
100
150
200
250
300
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
in th
e La
bor F
orce
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
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31
Figure 7: Urban Male Unemployment Rates
Urban Male Unemployment Rates: 1978-2005
0
20
40
60
80
100
120
140
160
180
200
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
in th
e La
bor F
orce
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 8: Rural Female Unemployment Rates
Urban Female Unemployment Rates: 1978-2005
0
50
100
150
200
250
300
350
400
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
in th
e La
bor F
orce
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
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32
Figure 9: Rural Male Labor Force Participation Rates
Rural Male Labor Force Participation Rates: 1978-2005
450
470
490
510
530
550
570
590
610
630
650
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 10: Rural Female Labor Force Participation Rates
Rural Female Labor Force Participation Rates: 1978-2005
0
50
100
150
200
250
300
350
400
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
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33
Figure 11: Urban Male Labor Force Participation Rates
Urban Male Labor Force Participation Rates: 1978-2005
450
470
490
510
530
550
570
590
610
630
650
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
Figure 12: Urban Female Labor Force Participation Rates
Urban Female Labor Force Participation Rates: 1978-2005
0
50
100
150
200
250
300
350
400
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Bihar - Usual
Bihar - Usual (Trend)
Bihar - CWS
Bihar - CWS (Trend)
Kerala - Usual
Kerala - Usual (Trend)
Kerala - CWS
Kerala - CWS (Trend)
All India Usual
All India - Usual (Trend)
All India - CWS
All India - CWS (Trend)
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Figure 13: Rural Male Employment Status
Rural Male Employment Status: 1978-2005
0
100
200
300
400
500
600
700
1978 1983 1988 1993 1998 2003
Date
Per
1000
Per
sons
Em
ploy
edBihar - Self Employed
Bihar - Self Employed(Trend)Bihar - Regular Wage /SalariedBihar - Regular Wage /Salaried (Trend)Bihar - Casual Labor
Bihar - Casual Labor(Trend)Kerala - Self Employed
Kerala - Self Employed(Trend)Kerala - Regular Wage /SalariedKerala - Regular Wage /Salaried (Trend)Kerala - Casual Labor
Kerala - Casual Labor(Trend)All India - Self Employed
All India - Self Employed(Trend)All India - Regular Wage /SalariedAll India - Regular Wage /Salaried (Trend)All India - Casual Labor
All India - Casual Labor(Trend)
Figure 14: Rural Female Employment Status
Rural Female Employment Status: 1978-2005
-100
0
100
200
300
400
500
600
700
800
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Empl
oyed
Bihar - Self Employed
Bihar - Self Employed(Trend)Bihar - Regular Wage /SalariedBihar - Regular Wage /Salaried (Trend)Bihar - Casual Labor
Bihar - Casual Labor(Trend)Kerala - Self Employed
Kerala - Self Employed(Trend)Kerala - Regular Wage /SalariedKerala - Regular Wage /Salaried (Trend)Kerala - Casual Labor
Kerala - Casual Labor(Trend)All India - Self Employed
All India - Self Employed(Trend)All India - Regular Wage /SalariedAll India - Regular Wage /Salaried (Trend)All India - Casual Labor
All India - Casual Labor(Trend)
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Figure 15: Urban Male Employment Status
Urban Male Employment Status: 1978-2005
0
100
200
300
400
500
600
700
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Empl
oyed
Bihar - Self Employed
Bihar - Self Employed(Trend)Bihar - Regular Wage /SalariedBihar - Regular Wage /Salaried (Trend)Bihar - Casual Labor
Bihar - Casual Labor(Trend)Kerala - Self Employed
Kerala - Self Employed(Trend)Kerala - Regular Wage /SalariedKerala - Regular Wage /Salaried (Trend)Kerala - Casual Labor
Kerala - Casual Labor(Trend)All India - Self Employed
All India - Self Employed(Trend)All India - Regular Wage /SalariedAll India - Regular Wage /Salaried (Trend)All India - Casual Labor
All India - Casual Labor(Trend)
Figure 16: Rural Female Employment Status
Urban Female Employment Status: 1978-2005
0
100
200
300
400
500
600
700
1978 1983 1988 1993 1998 2003
Date
Per 1
000
Pers
ons
Empl
oyed
Bihar - Self Employed
Bihar - Self Employed(Trend)Bihar - Regular Wage /SalariedBihar - Regular Wage /Salaried (Trend)Bihar - Casual Labor
Bihar - Casual Labor(Trend)Kerala - Self Employed
Kerala - Self Employed(Trend)Kerala - Regular Wage /SalariedKerala - Regular Wage /Salaried (Trend)Kerala - Casual Labor
Kerala - Casual Labor(Trend)All India - Self Employed
All India - Self Employed(Trend)All India - Regular Wage /SalariedAll India - Regular Wage /Salaried (Trend)All India - Casual Labor
All India - Casual Labor(Trend)
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Table 1: Employment Rates
Type of Labor
Reference Period8
Bihar (1978-2005)
Kerala (1978-2005)
All India (1978-2005)
US (PS + SS) -1.073668** (-2.63)
.2620723 (0.92)
-.0310515 (-0.41)
CWS -.7974901* (-1.97)
.5165638* (2.13)
.1803534 (1.62) Rural Male
CDS -.6434321** (-4.30)
-.1820937 (-1.02)
-.354677** (-3.50)
US (PS + SS) -1.429334*** (-3.40)
-.9917814** (-2.53)
-.2378543 (-1.28)
CWS -.4173406 (-1.37)
.0518032 (0.20)
.3866298** (2.45) Rural Female
CDS -.2337504 (-0.97)
-.0479303 (-0.39)
-.0912835 (-0.77)
US (PS + SS) -.8598631 (-1.68)
.1225451 (0.67)
.4276876*** (3.52)
CWS -.9630869* (-1.98)
.2008281 (1.43)
.5038361*** (6.93) Urban Male
CDS -.0054495 (-0.03)
.0578602 (0.17)
.4823205** (5.64)
US (PS + SS) -.5168156** (-2.83)
-.4643276 (-1.46)
.0945996 (0.70)
CWS -.2981782 (-1.71)
.1160696 (0.60)
.169256 (1.39)
Urban Female
CDS .0294205 (0.30)
.3179382 (1.85)
.2092145* (2.81)
Robust t-values reported in parentheses. *** significant at .01, ** significant at .05, * significant at .1 8 US (PS + SS): Usual Status (Principal and Secondary) per 1000 persons CWS: Current Weekly Status per 1000 persons CDS: Current Daily Status per 1000 person-days
15March07
37
Table 2: Unemployment Rates Type of Labor
Reference Period9
Bihar (1978-2005)
Kerala (1978-2005)
All India (1978-2005)
US (PS + SS) .0681397*** (3.08)
-.2738219 (-1.32)
.012938 (0.71)
CWS .073804 (0.81)
-.1100033 (-0.30)
-.0682063 (-0.82) Rural Male
CDS .5673392** (4.01)
.8356645* (2.38)
.5506425*** (5.76)
US (PS + SS) .0273698 (0.83)
.9675017 (1.71)
-.0010813 (-0.01)
CWS .057974 (0.67)
1.221984 (1.37)
-.1375166 (-1.15) Rural Female
CDS .5757978** (3.99)
1.269532 (1.37)
.3063981 (1.52)
US (PS + SS) .3911752*** (3.83)
-.4648153** (-2.46)
-.1644696*** (-4.99)
CWS .4981617** (2.95)
-.2593744 (-0.84)
-.2506032*** (-4.11) Urban Male
CDS .1466589 (1.13)
-.6310474 (-1.04)
-.1554512 (-0.94)
US (PS + SS) .564872** (2.64)
1.356236 (1.53)
-.0150578 (-0.11)
CWS .6171387* (1.90)
1.565698 (1.25)
-.2732304 (-1.72)
Urban Female
CDS .4498174 (0.88)
-.0944449 (-0.07)
-.1820823 (-0.80)
Robust t-values reported in parentheses. *** significant at .01, ** significant at .05, * significant at .1 9 US (PS + SS): Usual Status (Principal and Secondary) per 1000 persons in the labour force CWS: Current Weekly Status per 1000 persons in the labour force CDS: Current Daily Status per 1000 person-days in the labour force
15March07
38
Table 3: Labor Force Participation Rates Type of Labor
Reference Period10
Bihar (1978-2005)
Kerala (1978-2005)
All India (1978-2005)
US (PS + SS) -1.0472** (-2.51)
.128129 (0.68)
-.0326748 (-0.46)
CWS -.5692172 (-1.41)
.4632069***(4.91)
.1560479* (1.99) Rural Male
CDS -.4269362 (-1.83)
.3243148* (2.98)
-.0572606 (-0.71)
US (PS + SS) -1.421028*** (-3.46)
-.7269848 (-1.56)
-.1719245 (-0.91)
CWS -.4222321 (-1.40)
.5301264 (1.29)
.4020053** (2.21) Rural Female
CDS -.1631295 (-0.61)
.3438458 (0.84)
-.0147332 (-0.15)
US (PS + SS) -.6126826 (-1.26)
-.050119 (-0.46)
.3439545** (2.59)
CWS -.6297179 (-1.25)
.0949791 (0.51)
.4235174*** (4.41) Urban Male
CDS -.0242581 (-0.15)
-.3684077* (-3.04)
.4823274** (4.65)
US (PS + SS) -.483419** (-2.58)
.1269123 (0.26)
.1427134 (0.95)
CWS -.2482998 (-1.43)
.5875656 (1.40)
.1614862 (1.13)
Urban Female
CDS .0273658 (0.21)
.3539256 (1.57)
.2373662* (2.63)
Robust t-values reported in parentheses. *** significant at .01, ** significant at .05, * significant at .1 10 US (PS + SS): Usual Status (Principal and Secondary) per 1000 persons CWS: Current Weekly Status per 1000 persons CDS: Current Daily Status per 1000 person-days
15March07
39
Table 4: Employment Status Type of Labor
Reference Period
Bihar (1978-2005)
Kerala (1978-2005)
All India (1978-2005)
Self-employed
-.3112701 (0.24)
.3591831 (0.45)
-.5201889*** (-5.30)
Regular wage / salaried
-.4882537** (-2.80)
.6200682** (3.08)
-.1748983*** (-3.11) Rural Male
Casual labor .1454584 (0.18)
-1.016041 (-1.14)
.7546508*** (6.70)
Self-employed
.1388536 (0.09)
-.2634871 (-0.39)
-.5272238*** (-3.17)
Regular wage / salaried
-.0754834 (-0.44)
1.825321***(5.64)
.0611338** (2.48) Rural Female
Casual labor -1.824096 (-0.60)
-1.823258 (-1.41)
-.3430518 (-1.25)
Self-employed
2.932231*** (4.70)
.456647 (0.92)
.2849969*** (3.32)
Regular wage / salaried
-3.119039*** (-8.93)
-.0663956 (-0.20)
-.5164836*** (-14.38) Urban Male
Casual labor -.1600996 (-0.47)
-.094833 (-0.15)
.2109622* (1.88)
Self-employed
3.173837*** (3.38)
.2617548 (0.25)
-.218251 (-0.71)
Regular wage / salaried
-2.350479*** (-4.43)
2.029064***(3.57)
.8327468*** (8.12)
Urban Female
Casual labor -.9562397 (-1.08)
-1.948652 (-1.60)
-.5647687 (-1.68)
Robust t-values reported in parentheses. *** significant at .01, ** significant at .05, * significant at .1
15March07
40
Table 5: Summary of Trend Coefficients Topic Group Negative
(significant) Negative (insignificant)
Positive (insignificant)
Positive (significant)
Rural Male 2 Rural Female 1 1 Urban Male 1 1
Employment
Urban Female 1 1 Rural Male 1 1 Rural Female 2 Urban Male 2 Unemployment
Urban Female 2 Rural Male 1 1 Rural Female 1 1 Urban Male 2
Bih
ar
Labor Force Participation Rate
Urban Female 1 1 Rural Male 1 1 Rural Female 1 1 Urban Male 2
Employment
Urban Female 1 1 Rural Male 2 Rural Female 2 Urban Male 1 1 Unemployment
Urban Female 2 Rural Male 1 1 Rural Female 1 1 Urban Male 1 1
Ker
ala
Labor Force Participation Rate
Urban Female 2 Rural Male 1 1 Rural Female 1 1 Urban Male 2
Employment
Urban Female 2 Rural Male 1 1 Rural Female 2 Urban Male 2
Unemployment
Urban Female 2 Rural Male 1 1 Rural Female 1 1 Urban Male 2
All
Indi
a
Labor Force Participation Rate
Urban Female 2
15March07
41
Table 6: Within Reference Week Distribution of Labor Force (Percent) for 1999-2000 for All India Rural Males6 Rural Females6 Urban Males6 Urban Females6
Number of Days / Week
E UE LF E UE LF E UE LF E UE LF
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.5 0.02 0.00 0.01 0.08 0.00 0.06 0.01 0.06 0.01 0.15 0.09 0.131.0 0.47 0.13 0.27 1.09 0.67 0.94 0.32 0.00 0.21 1.01 0.60 0.941.5 0.14 0.04 0.10 0.86 0.54 0.81 0.04 0.04 0.03 0.56 0.03 0.512.0 1.18 0.19 0.52 3.33 0.96 2.72 0.49 0.08 0.20 2.09 1.00 1.612.5 0.13 0.05 0.07 0.98 0.56 0.93 0.05 0.01 0.03 0.82 0.02 0.743.0 1.88 0.11 0.72 4.03 0.81 2.97 0.81 0.09 0.29 2.56 1.04 1.883.5 0.64 0.15 0.48 12.79 4.83 12.48 0.30 0.17 0.23 8.66 3.45 8.204.0 3.72 0.19 1.41 6.83 0.61 4.84 1.73 0.19 0.69 3.28 0.69 2.354.5 0.23 0.07 0.13 0.67 0.26 0.64 0.09 0.05 0.05 0.35 0.40 0.375.0 4.25 0.15 2.06 6.12 0.55 4.46 2.70 0.14 1.31 3.13 0.26 2.175.5 0.26 0.09 0.18 0.64 0.21 0.60 0.10 0.00 0.07 0.30 0.00 0.306.0 4.06 0.10 2.74 4.06 0.30 3.15 5.69 0.25 4.16 4.65 0.22 3.526.5 0.13 0.00 0.10 0.11 0.00 0.11 0.14 0.00 0.10 0.12 0.00 0.107.0 82.90 98.73 91.21 58.40 89.70 65.30 87.53 98.91 92.62 72.31 92.20 77.18
Table 7: Within Reference Week Distribution of Labor Force (Percent) for 1999-2000 for Bihar Rural Males6 Rural Females6 Urban Males6 Urban Females6
Number of Days / Week
E UE LF E UE LF E UE LF E UE LF
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.5 0.01 0.00 0.01 0.08 0.00 0.03 0.03 0.00 0.03 0.00 0.00 0.001.0 0.25 0.47 0.16 1.40 0.00 1.23 0.15 0.00 0.09 0.70 0.00 0.621.5 0.31 0.21 0.23 1.58 4.38 1.27 0.03 0.00 0.00 0.40 0.00 0.352.0 1.04 0.00 0.39 4.71 4.12 3.94 0.17 0.00 0.03 1.86 1.67 1.842.5 0.36 0.43 0.18 1.90 5.51 2.03 0.12 0.04 0.10 0.19 0.00 0.173.0 2.14 0.09 0.65 6.67 6.05 5.29 0.62 0.12 0.19 3.63 0.00 1.973.5 1.15 0.32 0.80 10.26 16.91 10.60 0.30 2.01 0.40 8.79 12.55 9.184.0 3.68 0.40 1.21 8.67 0.00 6.37 1.25 0.25 0.49 3.91 0.00 2.174.5 0.60 0.21 0.33 1.06 0.00 1.13 0.22 0.00 0.15 0.08 0.93 0.175.0 4.24 0.00 1.79 6.48 0.79 4.50 1.89 0.00 0.91 3.17 0.00 2.245.5 0.42 0.00 0.21 0.82 1.81 0.86 0.09 0.00 0.05 0.00 0.00 0.006.0 2.19 0.53 1.17 2.21 0.11 1.73 1.35 0.00 0.96 0.64 0.00 0.906.5 0.27 0.00 0.23 0.16 0.00 0.11 0.09 0.00 0.08 0.00 0.00 0.007.0 83.33 97.35 92.64 54.02 60.32 60.90 93.69 97.59 96.52 76.63 84.86 80.37
15March07
42
Table 8: Within Reference Week Distribution of Labor Force (Percent) for 1999-2000 for Kerala Rural Males6 Rural Females6 Urban Males6 Urban Females11
Number of Days / Week
E UE LF E UE LF E UE LF E UE LF
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.5 0.06 0.00 0.00 0.23 0.00 0.19 0.16 0.00 0.14 0.19 0.00 0.141.0 1.62 0.00 0.49 2.16 0.67 1.17 0.85 0.00 0.35 1.37 0.38 1.151.5 0.17 0.00 0.09 1.29 0.00 0.99 0.22 0.00 0.20 1.12 0.00 0.722.0 3.54 0.00 0.78 4.22 0.74 2.45 2.02 0.00 0.66 3.32 0.00 2.022.5 0.17 0.00 0.10 0.99 0.00 0.81 0.07 0.00 0.00 0.63 0.10 0.283.0 4.97 0.00 1.04 5.44 0.00 2.72 3.28 0.00 1.00 3.68 0.00 1.843.5 1.11 0.00 0.72 16.01 1.33 12.88 0.85 0.00 0.63 8.64 2.50 7.094.0 9.47 0.00 2.36 5.56 1.54 2.63 6.03 0.00 2.16 4.94 1.66 3.374.5 0.31 0.00 0.10 0.34 0.00 0.23 0.07 0.00 0.02 0.55 0.77 0.605.0 6.96 0.14 2.94 6.32 0.00 3.66 6.20 0.58 2.84 4.68 0.35 0.005.5 0.10 0.00 0.04 0.15 0.00 0.07 0.04 0.00 0.03 0.00 0.00 2.626.0 12.21 0.00 7.56 9.74 0.00 5.60 13.87 0.00 9.77 8.68 0.00 5.016.5 0.10 0.00 0.09 0.19 0.00 0.21 0.14 0.00 0.15 0.26 0.00 0.207.0 59.19 99.86 83.68 47.36 95.73 66.39 66.18 99.42 82.05 61.93 94.25 74.95 11 E: Distribution of persons classified as employed (according to current weekly status) by number of half-days employed during the reference week. Note that persons classified as employed according to current weekly status by definition have worked at least one-half day during the reference week, so the zero days / week cell is necessarily 0%. UE: Distribution of persons classified as unemployed (according to current weekly status) by number of half-days unemployed during the reference week. Note that persons classified as unemployed according to current weekly status by definition have not been employed for any half day and have been actively seeking or available for work for at least one half day during the reference week, so the zero days / week cell is necessarily 0%. LF: Distribution of persons in the labor force (unemployed or employed according to current weekly status) by number of half-days in the labor force (unemployed or employed) during the reference week. Note that persons reporting 0 days in the labor force are not in the labor force, so the zero days / week cell is necessarily 0%.
15March07
43
Table 9: Percent Distribution of Current Weekly Activity of Rural Males ages 15 years and above by
Education Not
literate Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
Bihar 91.1 88.6 88.1 83.9 70.2 69.8 68.5 76.8 54.3 70.0 67.1 83.7 83.0 83.0
Kerala 68.7 60.5 84.3 75.6 76.2 71.9 63.5 63.0 43.0 53.4 70.7 78.2 74.7 69.8
Empl
oyed
All India 88.5 85.3 88.0 84.8 73.5 74.1 69.5 71.2 65.4 68.6 79.7 81.5 83.1 80.7
Bihar 1.8 1.9 2.1 2.9 2.0 3.2 4.3 4.3 5.2 7.5 16.6 9.2 2.8 3.0
Kerala 1.0 1.8 2.1 3.8 5.9 9.7 11.0 10.0 9.6 14.9 17.1 11.1 5.7 7.7
Une
mpl
oyed
All India 1.6 2.4 1.7 2.5 2.9 3.2 4.8 4.7 6.5 5.5 11.1 8.6 2.6 3.2
Bihar 7.1 9.5 9.9 13.3 27.8 27.0 27.3 19.0 40.5 22.5 16.4 7.1 14.3 14.0
Kerala 30.3 37.7 13.6 20.6 17.9 18.5 25.6 27.0 47.4 31.7 12.2 10.6 19.6 22.5
Out
of t
he
Wor
kfor
ce
All India 9.9 12.3 10.3 12.7 23.6 22.7 25.7 24.1 28.1 26.0 9.2 9.9 14.3 16.1
Bihar 49.6 49.8 19.1 18.8 13.8 13.2 9.7 10.5 4.5 3.8 3.4 3.7 100 100
Kerala 6.5 7.2 35.1 31.4 33.5 33.0 16.7 18.8 4.4 5.3 3.8 4.2 100 100
Prop
ortio
n of
Po
pula
tion
All India 41.2 37.3 27.7 26.0 15.4 17.8 8.9 10.7 4.2 5.0 2.7 3.3 100 100
Note: Distributions may not sum to exactly 100% due to rounding.
15March07
44
Table 10: Percent Distribution of Current Weekly Activity of Rural Females ages 15 years and above
by Education Not
literate Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
Bihar 23.9 27.8 10.1 14.7 8.9 9.4 8.1 10.9 6.6 5.5 22.3 8.1 21.6 24.5
Kerala 31.2 25.9 31.1 29.1 19.9 21.6 23.2 18.4 16.5 16.0 32.1 27.7 26.1 24.1
Empl
oyed
All India 43.8 43.1 34.3 33.8 23.2 23.9 21.2 22.4 20.3 17.6 33.0 28.8 39.6 38.0
Bihar 0.4 0.6 0.2 0.9 0.0 0.9 2.1 0.2 4.3 0.1 3.9 3.4 0.5 0.7
Kerala 1.3 1.1 0.9 1.6 3.1 4.0 10.1 11.6 12.0 9.8 29.8 30.2 4.0 5.3
Une
mpl
oyed
All India 1.0 1.2 0.9 0.9 1.3 1.4 3.6 3.1 6.5 3.8 15.2 13.7 1.2 1.4
Bihar 75.7 71.6 89.6 84.4 91.1 89.7 89.8 88.9 89.1 94.4 73.8 88.5 77.9 74.8
Kerala 67.6 73.0 68.0 69.4 77.0 74.4 66.7 70.0 71.5 74.2 38.2 42.1 69.9 70.7
Out
of t
he
Wor
kfor
ce
All India 55.2 55.7 64.8 65.3 75.5 74.7 75.2 74.5 73.3 78.6 51.8 57.5 59.2 60.6
Bihar 84.0 78.8 8.6 11.4 4.2 5.2 2.3 3.3 0.7 0.8 0.3 0.4 100 100
Kerala 14.0 16.2 36.3 31.2 29.1 27.7 14.4 15.4 4.1 5.5 2.2 3.9 100 100
Prop
ortio
n of
Po
pula
tion
All India 70.8 65.2 16.7 17.4 7.3 9.6 3.6 4.9 1.2 1.9 0.5 0.9 100 100
Note: Distributions may not sum to exactly 100% due to rounding.
15March07
45
Table 11: Percent Distribution of Current Weekly Activity of Urban Males ages 15 years and above by Education
Not literate
Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
Bihar 85.9 85.3 75.6 82.5 92.8 64.9 57.3 59.3 43.3 41.4 65.3 72.8 67.1 69.2
Kerala 65.1 41.1 78.0 72.7 73.9 74.9 63.0 64.1 51.9 48.0 80.2 82.6 71.8 69.0
Empl
oyed
All India 84.8 80.7 83.4 80.9 70.8 71.7 66.4 66.2 59.2 60.0 80.6 79.9 75.3 73.7
Bihar 2.1 2.7 3.6 3.0 4.9 2.9 6.6 5.5 5.6 5.2 13.1 14.1 5.7 5.9
Kerala 4.6 2.5 4.6 4.7 7.7 8.5 10.2 10.1 13.0 6.4 7.2 4.7 7.4 7.1
Une
mpl
oyed
All India 1.9 2.5 3.0 3.5 4.8 4.9 4.7 4.3 5.9 5.4 5.4 5.8 4.1 4.3
Bihar 12.0 12.1 20.8 14.5 32.4 32.2 36.2 35.3 51.1 53.4 21.6 13.2 27.2 25.0
Kerala 30.3 56.4 17.5 22.6 18.4 16.6 26.8 25.8 35.1 45.7 12.6 12.7 20.8 23.9
Out
of t
he
Wor
kfor
ce
All India 13.3 16.9 13.6 15.6 24.4 23.4 28.8 29.5 34..9 34.6 14.0 14.4 20.6 22.0
Bihar 21.0 19.9 17.7 14.7 17.6 14.9 16.7 18.0 11.5 12.4 15.5 20.1 100 100
Kerala 3.6 4.1 30.4 24.8 31.3 33.3 19.0 21.3 7.0 8.2 8.7 8.2 100 100
Prop
ortio
n of
Po
pula
tion
All India 16.2 14.5 23.3 19.8 18.4 19.3 17.5 19.1 11.0 11.5 13.5 15.6 100 100
Note: Distributions may not sum to exactly 100% due to rounding.
15March07
46
Table 12: Percent Distribution of Current Weekly Activity of Urban Females ages 15 years and above by Education
Not literate
Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
1994
2000
Bihar 11.6 15.9 7.9 4.9 4.9 5.5 7.2 4.3 4.8 2.9 16.9 14.6 9.7 10.6
Kerala 24.3 14.4 25.0 25.8 18.6 18.0 17.1 22.4 22.9 19.8 40.2 35.3 22.5 21.8
Empl
oyed
All India 26.5 24.2 17.9 16.0 11.4 11.4 12.2 11.4 13.4 12.1 29.1 26.1 19.9 17.9
Bihar 0.6 0.8 1.8 0.2 0.2 1.0 0.2 0.8 1.7 2.5 8.0 6.5 1.2 1.3
Kerala 1.5 0.2 1.4 0.6 5.9 4.7 11.9 11.9 8.6 13.1 22.0 19.5 6.3 6.4
Une
mpl
oyed
All India 0.6 0.5 0.9 0.6 1.9 1.2 2.6 1.7 3.7 2.6 7.3 4.9 1.9 1.4
Bihar 87.9 83.2 90.3 94.9 94.9 93.6 92.6 95.0 93.5 94.6 75.1 79.0 89.1 88.1
Kerala 74.2 85.4 73.6 73.6 75.5 77.3 71.0 65.7 68.5 67.1 37.8 45.1 71.2 71.8
Out
of t
he
Wor
kfor
ce
All India 72.9 75.4 81.2 83.5 86.7 87.4 85.2 86.8 82.9 85.3 63.6 69.0 78.2 80.7
Bihar 50.2 46.4 14.8 14.3 11.4 11.5 10.9 12.7 6.5 7.4 6.3 7.6 100 100
Kerala 11.3 10.4 30.1 25.0 27.7 29.3 18.1 20.3 6.6 8.2 6.2 6.8 100 100
Prop
ortio
n of
Po
pula
tion
All India 36.3 32.0 21.6 19.6 14.3 15.8 12.8 14.0 7.2 8.7 7.8 9.9 100 100
Note: Distributions may not sum to exactly 100% due to rounding.
15March07
47
Table 13: Percentage Change in Distribution of Unemployment per 1000 persons in Kerala from 1994 to 2000 by Education
Not literate
Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
Rural Male 80.00% 80.95% 64.41% -9.09% 55.21% -35.09% 35.09%
Rural Female -15.38% 77.78% 29.03% 14.85% -18.33% 1.34% 32.50%
Urban Male -45.65% 2.17% 10.39% -0.98% -50.77% -34.72% -4.05%
Urban Female -86.67% -57.14% -20.34% 0.00% 52.33% -11.36% 1.59%
Table 14: Percentage Change in Distribution of Unemployment per 1000 persons in the labor force in
Kerala from 1994 to 2000 by Education Not
literate Literate to primary Middle Secondary Higher
Secondary Graduate & above Total
Rural Male 101.38% 96.91% 65.41% -7.22% 19.53% -36.18% 40.14%
Rural Female 1.85% 85.31% 15.93% 27.49% -9.79% 8.34% 35.65%
Urban Male -13.12% 9.04% 8.01% -2.31% -41.27% -34.65% -0.15%
Urban Female -76.44% -57.14% -14.02% -15.45% 45.84% 0.61% 3.75%
15March07
48
Table 15: Percent Distribution of Current Weekly Activity of Rural Males ages 15 years and above by age
1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000Bihar 46.41 46.72 75.74 80.52 90.48 80.32 95.34 95.54 96.75 96.78 98.3 96.1 98.06 98.17 95.59 94.37 83.46 76.92Kerala 24.91 26.39 68.31 66.24 85.33 82.37 94.33 88.13 94 90.24 94.77 94.48 94.22 91.92 91.53 84.51 67.3 54.75
All India 53.42 47.27 6.41 80.39 92.28 90.94 95.71 94.76 96.27 95.8 96.54 95.73 96.09 95.27 93.82 92.55 73.94 67.87
Bihar 2.84 5.42 5.39 6.31 5.39 5.11 3.13 2.3 1.66 1.25 1.13 1.25 0.34 0.74 0.99 0.97 0.47 1
Kerala 9.62 12.4 13.74 17.47 9.41 13.75 3.48 6.76 3.08 6.05 1 1.91 1.51 1.25 0 3.59 0.48 1.56
All India 3.15 4.63 11.64 6.72 4.35 5.14 1.88 2.86 1.44 1.73 1.21 1.59 0.89 1.53 0.93 1.42 0.8 1.07
Bihar 50.75 47.85 5.39 13.16 4.13 4.57 1.54 2.16 1.59 1.98 0.56 2.65 1.6 1.09 3.41 4.66 16.07 22.08
Kerala 65.47 61.21 13.74 16.28 5.26 3.88 2.19 5.11 2.93 3.71 4.22 3.61 4.26 6.83 8.47 11.91 32.21 43.7
All India 43.43 48.1 11.64 12.89 3.37 3.92 2.41 2.38 2.29 2.47 2.25 2.68 3.02 3.2 5.24 6.03 25.27 31.06
Bihar 9.76 8.92 9.76 8.92 7.27 6.76 6.41 6.32 6.13 6.19 4.52 4.95 4.28 4.33 4.02 3.49 9.21 9.14
Kerala 10.19 10.66 10.19 10.66 9.06 9.77 7.28 6.7 7.1 6.2 5.78 5.29 5.78 6.34 3.79 4.39 13.18 14.12
All India 10.53 10.05 10.53 10.05 8.38 8.04 6.75 6.65 6.27 6.54 4.96 5.26 4.58 4.74 3.86 3.7 9.96 10.26
Une
mpl
oyed
Out
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ce
Prop
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Po
pula
tion
50-5425-29 55+Year
Empl
oyed
30-34 35-39 40-44 45-49Age 15-19 20-24
Table 16: Percent Distribution of Current Weekly Activity of Rural Females ages 15 years and above by age
1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000Bihar 11.49 11.05 16 20.39 22.43 24.32 26.57 29.28 29.62 34.36 27.24 33.61 27.26 33.47 28.46 26.65 15.97 18.02Kerala 8.36 9.37 18.32 19.51 24.2 23.81 36.01 30.43 40.71 40.62 44.09 39.09 38.76 32.03 28.68 33.24 19.33 13.43
All India 29.6 25.04 2.2 32.53 41.69 40.51 47.96 46.83 50.65 50.5 50.58 51.21 49.74 48.77 44.94 43.81 25.13 23.99
Bihar 0.44 1.22 0.74 0.83 0.35 0.74 0.29 0.55 0.69 0.29 0.58 0.32 0.61 0.54 0.36 1.07 0.31 0.32
Kerala 4.76 7.62 12.3 16.62 7.48 9.03 3.19 4.03 0.85 3.3 2.28 1.13 0.65 1.73 0.78 1.49 0.15 0.45
All India 1.61 2.27 2.2 2.79 1.46 1.65 1.18 1.3 1.02 0.86 1.06 0.73 0.99 0.77 0.69 0.89 0.47 0.44
Bihar 88.07 87.74 83.26 78.78 77.22 74.93 73.14 70.16 69.69 65.35 72.18 66.08 72.13 65.98 71.19 72.28 83.72 81.66
Kerala 86.88 83.01 69.38 63.87 68.32 67.16 60.8 65.53 58.45 56.09 53.64 59.77 60.59 66.23 70.54 65.27 80.53 86.12
All India 68.79 72.69 62.52 64.68 56.84 57.84 50.86 51.87 48.34 48.64 48.35 48.05 49.27 50.46 54.37 55.3 74.41 75.57
Bihar 8.21 7.59 8.21 7.59 8.44 8.4 7.81 7.52 5.88 6.18 5.3 5.36 5.16 4.14 3.85 3.59 8.81 8.78
Kerala 9.89 10.45 9.89 10.45 10.26 8.94 7.36 7.3 7.64 7.77 4.9 6.06 4.9 6.39 4.24 4.3 14.05 14.95
All India 9.53 9.25 9.53 9.25 9.35 8.82 7.19 7.46 6.22 6.72 5.22 5.18 4.82 4.6 3.78 3.62 10.04 10.46
Une
mpl
oyed
Out
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ce
Prop
ortio
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Po
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tion
50-5425-29 55+Year
Empl
oyed
30-34 35-39 40-44 45-49Age 15-19 20-24
15March07
49
Table 17: Percent Distribution of Current Weekly Activity of Urban Males ages 15 years and above by age
1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000Bihar 23.84 23.09 39.87 45.16 75.08 70.54 90.46 93.14 97.47 95.95 98.5 97.09 96.58 96.57 93.29 95.21 57.17 60.5Kerala 24.73 20.78 62.18 60.29 85.91 86.92 93.98 91.17 92.92 88.34 96.27 94.27 91.56 91.68 87.6 89.58 57.42 46.94
All India 34.34 30.65 11.24 64.23 88.36 86.75 95.43 94.95 97.34 95.95 96.72 96.19 95.95 95.71 92.77 92.36 56.05 50.15
Bihar 6.39 6.03 45.56 15.72 13.74 18.07 5.2 5.47 0.82 0.6 0.38 0.77 0 0.43 0.45 0 0.53 0.15
Kerala 11.8 12.9 18.17 16 10.75 8.28 4.22 7.02 3.38 8.56 2.53 2.95 3.57 3.71 0 2.21 1.71 1.46
All India 5.35 5.94 23.53 10.93 6.96 7.84 2.62 2.87 1.21 1.89 0.96 1.3 0.88 1.15 1.16 0.78 0.58 0.69
Bihar 69.76 70.88 45.56 39.12 11.17 11.39 4.34 1.39 1.71 3.45 1.12 2.14 3.42 3 6.26 4.79 42.31 39.34
Kerala 63.47 66.32 18.17 23.71 3.34 4.8 1.8 1.81 3.7 3.1 1.19 2.79 4.87 4.61 12.4 8.22 40.87 51.6
All India 60.31 63.41 23.53 24.84 4.68 5.41 1.94 2.18 1.45 2.16 2.32 2.51 3.17 3.13 6.06 6.86 43.37 49.16
Bihar 12.55 11.7 12.55 11.7 7.34 8.67 6.18 6.17 6.84 6.62 5.94 5.8 5.14 5.56 3.96 3.82 7.24 8.32
Kerala 10.65 9.63 10.65 9.63 10.35 8.56 7.66 8.19 7.23 7.48 5.78 6.42 5.78 5.97 4.41 4.72 12.38 14.73
All India 11.04 10.85 11.04 10.85 9.61 9.66 7.61 7.38 7.09 7.36 6.09 6.41 5.18 5.49 3.87 4.03 8.69 9.47
Une
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oyed
Out
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Prop
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Po
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tion
50-5425-29 55+Year
Empl
oyed
30-34 35-39 40-44 45-49Age 15-19 20-24
Table 18: Percent Distribution of Current Weekly Activity of Urban Females ages 15 years and above by age
1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000Bihar 3.66 5.86 5.54 3.99 10.4 7.78 10.62 9.69 13.24 20.62 15.12 13.98 13.46 14.28 15.24 13.9 9.06 11.5Kerala 10.61 8.16 19.32 13.91 19.83 26.3 22.45 26.25 32.88 33.92 36.38 38.19 35.06 32.16 35.85 30.31 15.12 10.48
All India 11.33 9.31 5.37 14.13 19.56 17.61 24.28 21.71 27.18 25.38 29.15 26.05 28.5 24.39 25.97 24.58 13.23 11.4
Bihar 1.88 1.55 4 3.16 2 1.04 0.8 0.85 0.17 3.03 0 0.62 0 0 0 0.06 0 0
Kerala 7.34 5.79 15.12 18.77 12.59 13.26 10.66 8.86 1.22 3.89 0.93 2.56 0.44 0.65 2.23 0 0.21 0
All India 2.12 1.7 5.37 4.09 2.87 2.19 1.54 1.06 0.73 0.74 0.48 0.38 0.64 0.46 0.29 0.19 0.14 0.15
Bihar 94.46 92.59 90.46 92.85 87.59 91.18 88.58 89.46 86.59 76.35 84.88 85.4 86.54 85.72 84.76 86.04 90.94 88.5
Kerala 82.05 86.05 65.56 67.32 67.58 60.44 66.89 64.89 65.9 62.19 62.69 59.25 64.51 67.19 61.92 69.69 84.67 89.52
All India 86.55 88.99 79.13 81.78 77.57 80.2 74.17 77.22 72.09 73.89 70.38 73.57 70.87 75.15 73.75 75.23 86.63 88.45
Bihar 10.61 10.44 10.61 10.44 7.6 7.55 7.29 7.39 7.23 7.13 5.64 6.1 5.23 4.88 3.27 3.66 7.15 8.69
Kerala 10.08 8.83 10.08 8.83 10.76 10.2 8.12 8.59 7.86 7.53 5.95 6.2 5.95 6.47 3.94 4.2 14.06 15.23
All India 10.39 10.34 10.39 10.34 9.71 9.46 7.84 8.04 7.38 7.79 5.63 5.9 4.89 5.11 3.73 3.71 9.65 10.45
Une
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Prop
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Po
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50-5425-29 55+Year
Empl
oyed
30-34 35-39 40-44 45-49Age 15-19 20-24
15March07
50
Table 19: Percentage Change in Distribution of Unemployment per 1000 persons according to Current Weekly Activity in Kerala from 1994 to 2000 by Age
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Rural Male 28.90% 27.15% 46.12% 94.25% 96.43% 91.00% -17.22% - 225.00%
Rural Female 60.08% -76.04% 20.72% 26.33% 288.24% -50.44% 166.15% 91.03% 200.00%
Urban Male 9.32% -11.94% -22.98% 66.35% 153.25% 16.60% 3.92% - -14.62%
Urban Female -21.12% -71.37% 5.32% -16.89% 218.85% 175.27% 47.73% -100.00% -100.00%
Table 20: Percentage Change in Distribution of Unemployment per 1000 persons in the labor force according to Current Weekly Activity in Kerala
from 1994 to 2000 by Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Rural Male 14.74% 24.63% 44.02% 100.23% 98.04% 89.77% -14.94% - 291.20%
Rural Female 23.62% -41.85% 16.46% 43.71% 267.37% -42.86% 210.70% 62.04% 321.04%
Urban Male 18.57% -7.26% -21.80% 66.37% 151.69% 18.50% 3.64% - 4.31%
Urban Female 1.50% -25.64% -13.69% -21.62% 187.57% 152.03% 59.84% -100.00% -100.00%
15March07
51
Table 21: Ratio of Kerala Unemployment Rate to All India Unemployment Rate
US CWS US CWS US CWS US CWS US CWS US CWS US CWS US CWS2005 5.7 4.7 33.5 21.6 2.9 3.1 27.1 31.9 3.4 2.7 10.8 6.1 1.7 1.7 8.2 8.32004 4.5 4.1 35.8 20.5 0.9 2.0 15.3 16.3 2.6 2.3 9.9 4.9 0.5 1.1 3.4 3.32003 5.1 4.7 43.1 27.9 2.8 3.3 17.2 20.6 3.1 2.8 10.7 5.2 1.6 1.8 3.8 3.22002 4.5 6.1 56.3 45.1 3.2 4.8 37.7 37.7 2.6 3.4 14.7 7.6 1.8 2.6 9.0 6.62002 7.0 5.8 22.6 26.4 1.8 2.9 41.0 54.3 4.0 3.2 5.4 4.2 1.1 1.7 7.8 6.82001 7.5 7.6 54.2 27.4 2.2 2.6 18.2 20.5 4.4 4.2 13.0 4.3 1.3 1.5 3.8 3.22000 6.2 4.9 43.0 18.5 2.3 3.2 23.8 23.3 3.6 2.7 11.8 4.1 1.4 1.8 6.0 5.11998 6.0 5.9 32.3 34.0 3.2 3.1 24.1 26.5 3.5 3.4 8.0 5.7 1.8 1.7 5.7 5.01997 9.3 6.7 57.1 81.9 2.6 3.0 32.0 35.6 5.4 3.7 11.7 12.3 1.5 1.6 6.1 5.41996 9.5 9.2 43.3 50.3 4.1 4.4 15.7 41.3 5.8 5.5 8.2 8.5 2.4 2.5 2.6 6.31995 9.3 7.0 53.6 31.8 4.6 4.4 24.3 29.0 5.3 3.9 11.0 4.1 2.7 2.5 5.0 4.91994 7.0 4.3 36.7 15.6 3.0 3.3 18.3 17.3 4.0 2.4 9.7 3.5 1.8 1.9 4.6 3.81990 8.3 8.3 42.7 24.2 4.2 5.3 40.0 31.9 4.8 4.8 13.0 5.5 2.5 3.0 9.9 5.61988 9.4 6.5 18.8 23.1 3.7 4.2 24.0 27.4 5.3 3.4 6.3 5.0 2.2 2.4 6.3 5.51983 7.7 3.8 16.7 4.2 2.6 2.3 6.6 4.5 4.5 2.0 6.1 0.8 1.5 1.2 1.7 0.71978 26.2 7.2 19.8 14.9 5.9 4.5 14.1 11.3 12.0 4.1 12.2 4.2 3.8 2.7 5.7 2.6
Average 8.3 6.1 38.1 29.2 3.1 3.5 23.7 26.8 4.6 3.4 10.1 5.4 1.8 2.0 5.6 4.8
Urban FemaleUnemployment per 1000 PersonsUnemployment per 1000 Persons in the Labor Force
Year Rural Male Rural Female Urban Male Urban FemaleRural Male Rural Female Urban Male
Table 22: Ratio of Kerala Unemployment Rate to All India Unemployment by Age
1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 2000 1994 20003.1 2.7 1.2 2.6 2.2 2.7 1.9 2.4 2.1 3.5 0.8 1.2 1.7 0.8 0.0 2.5 0.6 1.53.0 3.4 5.6 6.0 5.1 5.5 2.7 3.1 0.8 3.8 2.2 1.5 0.7 2.2 1.1 1.7 0.3 1.02.2 2.2 0.8 1.5 1.5 1.1 1.6 2.4 2.8 4.5 2.6 2.3 4.1 3.2 0.0 2.8 2.9 2.13.5 3.4 0.8 4.6 4.4 6.1 6.9 8.4 1.7 5.3 1.9 6.7 0.7 1.4 7.7 0.0 1.5 0.02.9 2.9 2.1 3.7 3.3 3.8 3.3 4.1 1.9 4.3 1.9 2.9 1.8 1.9 2.2 1.8 1.3 1.1
20-24 25-29 50-54 55+Year
Rural Male
30-34 35-39 40-44 45-49Age 15-19
Rural FemaleUrban Male
Urban FemaleAverage
15March07
52
Table 23: Number of Migrants per 1000 Persons by Years since Migration (1999-2000) Years since Migration 0 – 5 6 – 9 10 – 14 15+ Total
Bihar 5 1 2 3 11 Kerala 77 28 30 73 208 Rural
Males All India 29 9 9 21 70 Bihar 54 37 39 152 282 Kerala 123 48 65 236 446 Rural
Females All India 95 50 59 222 424 Bihar 59 21 16 31 126 Kerala 134 28 39 69 269 Urban
Males All India 107 34 36 80 257 Bihar 81 40 37 164 323 Kerala 159 53 57 167 435 Urban
Females All India 130 54 60 172 416
Table 24: Change in Employment Rate (%) Rural Areas Urban Areas
US(PS+SS) CWS CDS US(PS+SS) CWS CDS
1983 to 1987-88 - 0.46 - 0.37 3.9 -1.17 0.00 0.35
1983 to 1993-94 1.10 3.91 4.6 1.76 3.80 4.86
1983 to 1999-00 -3.10 -0.19 -0.82 1.18 3.45 3.59
1987-88 to 1993-94 2.60 5.35 0.60 2.16 3.36 3.78
1987-88 to 1999-00 - 1.48 1.19 - 4.59 2.37 3.45 2.72