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,

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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

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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

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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.

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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)

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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)

15March07

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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34

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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36

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

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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

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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

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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

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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%

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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

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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