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Transcript of Labour Mkt
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S P D I S C U S S I O N P A P E R
July 2006
NO. 0606
Youth in the Labor Marketand the Transition from
School to Work in Tanzania
Florence Kondylis andMarco Manacorda
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Youth in the Labor Market and the Transition from School to W
Tanzania
Florence Kondylis
&
Marco Manacorda
July 2006
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Youth in the Labor Market and the Transition from School to W
Tanzania1
Florence Kondylis* and Marco Manacorda**
1. Introduction
Tanzania, together with many other Sub-Saharan countries (see Guarce
2005), suffers from a severe youth unemployment and inactivity problem in
(Mjema 1997). Despite sustained growth in the second half of the last decad
1990s labor market outcomes have further deteriorated (Government of Tan
Although unemployment is by no means a problem unique to youths in Sub-Sa
the problem there is compounded by disappointing education outcomes th
prospects of youths appear rather dim and by the circumstance that work is o
asset for a large part of the population while no publicly provided insuranc
against the risk of unemployment is in place.
Despite unemployment being largely an urban phenomenon in Tanzania
outcomes of rural youths are not much rosier. Although rural children transition
ages into work (with no or little schooling or sometimes in combination with sc
example Beegle and Burke 2004; Beegle and others 2004.most end up in low
jobs on the household farm.This is a possibly a major reason behind increas
from the countryside to urban areas (U.S. Census Bureau 1995), even in the fac
deteriorating urban labor market prospects. Urbanization reflects wider demog
between 1957 and 2002 population grew fourfold,and this trend is not expect
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time soon,2 casting serious doubts on the possibility that the youth joblessness
disappear in the meantime.
Although we are not the first to document the level of youth joblessnes
(Mjema 1997; Government of Tanzania 2003; LO/FTF 2003), our paper aims
additional light on this phenomenon. First, we provide evidence on different d
youths' labor market performance. For this exercise we can rely on micro d
Tanzanian Integrated Labor Force Survey (ILFS) of 2000/01, a rather large hou
(approximately 11,000 households) that provides a rich array of information on
job search, schooling, training, and migration, together with basic information o
and their households' characteristics. Second we attempt to uncover the de
youths' labor market outcomes and to tease out significant predictors of labor m
and failure using simple regression tools.
The structure of the paper is as follows. Section 1 presents an overview
unemployment problem. Section 2 presents the data. Section 3 presents detaile
statistics on youths' labor market performance. Section 4 presents the regre
Section 5 concludes.
2. Youth in the labor marketBy now an extensive literature analyzes youths' labor market outcom
transition into adulthood in Organisation for Economic Co-operation and
(OECD) countries and especially the United States (for all, see OECD 1996
2000 and Ryan 2001). As discussed in Rees (1986), youths typically display
market attachment and lower employment rates than older workers. Some of
engaged in full time education or combine education with work; others devot
searching or move from one job to another as part of their investment in human
a process of mutual information gathering with employers. From this persp
joblessness reflects (a potentially efficient) mechanism of allocating workers to
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planner. Lower wages associated with lower experience levels or stronger pr
leisure also potentially imply lower disutility of being out of employment for yo
Not only do youths display higher rates of joblessness and unemployme
due to frictional reasons at any given point in time, but they also appear to be m
to the state of the economic cycle. The youth unemployment problem in mo
countries (and in particular in OECD countries following the oil shocks of
largely attributed to the weakness of the economy and overall lack of labor d
1986; Freeman and Wise 1982; Blanchflower and Freeman 2000a, 2000b; ILO
and Lemieux 2000). Disadvantaged youths in particular appear to bear a dis
share of the cost of economic downturns or weak labor demand in their area
(Freeman 1991; Freeman and Rodgers 1999). Reasons for the extreme vu
youths in the labor market to economic downturns have largely to do with their l
labor market skills (experience and sometimes education) and lower labor mark
(including lower job search), employment protection legislation, and hiring an
that often penalize recently hired workers. If aggregate demand matters, agg
does too. At given labor demand, a rise in the proportion of youths in the labor
to disproportionately affect the youths themselves, consistent with a world whe
adults are only imperfect substitutes in production. Excess supply relative to de
wages, employment, or both (Welch 1979; Card and Lemieux 2001; Koreman
2000). As young workers see their employment prospects deteriorate, not only
to work less, but they respond with adjustment at different margins, includ
probability of staying in school, residing with parents (Card and Lemieux 20
committing crime (Freeman 1996, 1999).Much less is known about the behavior of youths in developing countries
(1988) and O'Higgins (2003) both show that higher unemployment and job
among youths are widespread in many developing countries. Work by Guarcel
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A commonly held view about urban labor markets of developing cou
(youth) joblessness is a luxury accessible only to those from more advantaged
often proxied by their education. Unemployment is often regarded as an optio
youths queuing for a job in the public sector or waiting to fill a vacancy in the f
sector. In the presence of widespread poverty and in the absence of public
welfare nonemployment is just a nonviable option for the poor, who will h
option but making ends meet through informal and causal work. From this pe
youth unemployment problem should not per se be a source of major policy c
this is by enlarge a voluntary phenomenon,
In the rest of this paper we document youths' labor market outcomes in
we explicitly attempt to document what roleif anymarket forces play in
outcomes and how individuals respond to changing economic incentives.
particular that youth joblessness is by no means a voluntary phenomenon in Ta
last part of the paper we summarize these findings with an eye to the potenti
policymaker.
3. DataIn this section we present basic descriptive evidence on school attendan
market performance of teenagers and youths in Tanzania. For the purpose of thi
use micro data from the 2000/01 ILFS. The ILFS is a rather large sample su
individual observations in 11,158 households) collecting a rich array of in
several features of individuals' work activity, schooling and (off the) job-search
information on a number of individual and household characteristics.
In the rest we present evidence on individuals aged 1519 (teenagers
(youths) relative to individuals aged 3549 (prime-age individuals). We pre
results for men and women and for the main geographical areas of the cou
Salaam other urban areas and rural areas We refer sometimes to Dar Es Sala
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attended school. This proportion rises to 19% for prime-age men, suggesting an
in education outcomes across subsequent cohorts of men.
Table 1A: Labor Force Status and Schooling
MALES
(1) (2) (3) (4)
Age Group School Never
attended
Work Work &
School
No
Dar esSalaam
Teens 0.581 0.026 0.206 0.036 Youth 0.142 0.042 0.472 0.020 Prime-age 0.000 0.021 0.974 0.000
Urban
Teens 0.508 0.040 0.435 0.122 Youth 0.078 0.038 0.760 0.016
Prime-age 0.000 0.042 0.949 0.000
Rural
Teens 0.391 0.146 0.761 0.219 Youth 0.023 0.154 0.922 0.009 Prime-age 0.000 0.192 0.968 0.000
Patterns of work participation in column 3 are, to a large extent and in
mirror image of patterns of school attendance. Work here refers to any work aweek prior to the survey. The data include people with a job but temporarily a
While around 20% of male teenagers are working in urban areas, the c
proportion is 43% in other urban areas and 76% in rural areas. Similar pa
identified for youths, with an employment-to-population ratio that increases from
Es Salaam to 76% in other urban areas. By contrast, the majority of male yo
rural areas work, with an employment-to-population ratio of 92%. Teenag
participation rates are always below prime-age men's, which is on the order of
little variation across areas.
C l 4 h i f bi i k d h
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circumstance that rural teenagers are able to provide their work services on t
farm, without the need for a lengthy job search or formal contractual arra
addition, lower household income in these areas makes these individuals pot
likely to work even while still in school, while the lack of substantial alte
opportunities other than on the household farm makes the return to search quite
Column 5 analyses the proportion of individuals who are neither at work
(sometimes defined as jobless; see Ryan 2001).3 This column provides a first
the problems that young individuals face in Tanzanias labor market. Aro
teenagers and 40% of youths are neither at school nor at work in Dar Es
corresponding proportions in other urban areas are 18% for both teenagers a
rural areas, joblessness is lower and on the order of 7% for both age groups.
Table 1B: Labor Force Status and SchoolingFEMALES
(1) (2) (3) (4)
Age Group School Never attended Work Work &
School
Dar es Salaam
Teens 0.442 0.036 0.265 0.027
Youth 0.054 0.043 0.374 0.009 Prime-age 0.000 0.120 0.693 0.000
Urban
Teens 0.368 0.061 0.445 0.089 Youth 0.025 0.050 0.672 0.000 Prime-age 0.000 0.157 0.892 0.000
Rural
Teens 0.343 0.186 0.762 0.169
Youth 0.012 0.213 0.924 0.007 Prime-age 0.000 0.474 0.951 0.000
Note: The table reports the proportion of teenagers (aged 1519), youths (aged 2024)individuals (aged 3549) who report being enrolled in school (column 1), never having (column 2), in work (column 3), combining work and school (column 4) and neither in wo(column 5) in Dar Es Salaam, other urban areas, and rural areas. All data are weightweights
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Looking at women's labor force status (table 1B), notable differen
genders emerge. Women are less likely to be in school relative to men of
This is particularly evident in urban areas: in Dar Es Salaam the proportio
teenagers and youths in school are 44% and 5% respectively (that is, 14 perc
and 9 percentage points respectively less than boys of the same age). In othe
the proportions of female teenagers and youths in school are 37% and 2%
(12 percentage points and 5 percentage points respectively less than boys
age). In rural areas, where boys' school attendance is lower, differences betw
boys are less evident, with a proportion of female teenagers in school o
proportion of female youths in school of 1% (that is, 5 percentage
percentage point respectively less than boys of similar age).
Column 2 investigates whether these differences are due to girls being
enroll in school in the first places. The proportion of teenage and young gir
attended school is around 4% in Dar Es Salaam and between 5% and 6% i
areas, hence exhibiting little difference with respect to boys. This suggests
on average less likely to remain in school that boys are. The proportion o
young girls who never attended school is much higher in rural areas: 1
respectively, or between 4 percentage points and 5 percentage points mo
Although girls appear to do worse than boys in terms of school attendance,
with older individuals shows that recent cohorts of women have e
remarkable progress relative to men in both rural and urban areas. The
prime-age women who never attended school is 12% in urban areas (10 perc
more than men), 16% in other urban areas (12 percentage points more th47% in rural areas (27 percentage points more than men)
As with men, female employment ratios increase with age in all ar
are at their lowest in Dar Es Salaam and at their highest in rural areas. As
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percentage point in other urban areas. Differences are statistically sig
differences emerge in rural areas. The pattern is reversed among youths, a
are less likely to be in work than young boys. Here differences range from
points lower in Dar Es Salaam to 9 percentage points lower in other urban
no differences emerge between girls and boys in rural areas. One potentia
for this pattern is that girls in urban areas drop out of school earlier than b
the labor market earlier. However, as they age, some of them tend to withd
labor market, as they get gradually absorbed by childrearing and ot
activities, while potentially a smaller proportion of school leavers enter the
This is confirmed by an analysis of employment to population ratios amo
women that shows a negative female-male gap. The differences in the em
population ratios between prime-age women and men are 28 percentage po
Dar Es Salaam and 5 percentage points lower in other urban areas. Differ
areas are on the order of only 1 percentage point.
Column 4 shows that girls are also less likely than boys to comb
education. This is a largely a reflection of the fact that fewer women are
time. If one standardizes the proportion of those combining work and scho
4) to the proportion in school (in column 1), results are very similar for men
so that, conditional on being in school, the probability of work is similar
girls. Finally, column 5 reports the proportion of women neither at school no
one could have expected from the data presented in the previous columns in
girls are more likely to be jobless than boys. This likely partly reflects lower
of women together with potentially lower demand for their work services. Aappears that young girls aged 2024 (youths) are at greater risk of being nei
nor in work. For example, the proportion of jobless women rises from 32%
in Dar Es Salaam to 58% for youths and falls to 30% for prime-age
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discernible differences in the prevalence of joblessness between boys ands
areas.
In sum, there is evidence that a non-negligible proportion of the pop
out of school and starts to work at a rather early age, especially in rural area
girls drop out earlier and enter the labor market sooner than boys. Ho
increasing proportion of individuals drops out of school, the chances of findi
to fall in urban areas. Whereas, most men eventually appear to get absorbed
market, a large proportion of women remains out of the labor market; esp
Es Salaam, possibly devoting their time to home production. In rural a
suggest a smoother transition, with a large proportion of individuals tran
work at early ages. This is true for both men and women. It is important
emphasize that this smoother transition in rural areas might be the result o
being required to work at early ages in order to guarantee their household's
survival, together with lower returns to education and job search. Rural job
provide only subsistence for many individuals. In this sense, such quicker t
possibly associated with worse lifetime outcomes for individuals in rural a
those in urban areas.
Employment
In this section we concentrate on the characteristics of those in
Tables 2A and 2B report the distribution of work by occupation, togethe
information on hours of work and underemployment.
Columns 1 to 5 report the proportion in work split into five catego
paid employment (employees), self-employed (split between those with
employees), those performing unpaid work in the family nonagricult
(typically shops), and those working on their own farm. Work for pay inclu
both in cash and in kind. The data refer to the individual's main occupation
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prime-age men these proportions are 55%, 37%, and 9%. Similarly, th
working in the family business (columns 4 and 5 together) in the three areas
and 94% for teenagers and 5%, 29%, and 86% for prime age men. Self
(columns 2 and 3 together) interests respectively 27%, 17%, and 2% of t
40%, 34%, and 5% of prime-age men. One possible interpretation of these
paid employment might require a lengthy job search, and access to self
might require either capital or access to credit, with both these conditions b
harder to fulfill for younger individuals.5
Column 6 presents data on total hours of work in all jobs. In gener
urban areas tend to work more hours. Prime-age men work on average 58 ho
Dar Es Salaam and 62 hours in other urban areas. In rural areas average hour
lower, on the order of 54 hours. Both teens and youths tend to work less th
men, but patterns across areas largely reflect those of prime-age men's. Av
of hours of work among teenagers is on the order of 53, 44, and 43 respectiv
Salaam, other urban areas, and rural areas respectively. For youths, these nu
58, and 52 respectively.6
Data on hours of work include all jobs held by individuals. A n
proportion of individuals in Tanzania hold at least two jobs. Column 7proportion. Multiple job holding is particularly widespread in other urban
rural areas and is more common among prime-age men than among teenage
For example only 2% of youth in employment have a second job in D
compared with around 8% of prime-age men. In rural areas these figures
5In addition a compositional effect is likely to be at work. This is because, as the labor force agproportion of it is composed by individuals with higher education. These trends potentcircumstance that more educated individuals are more likely to enter into paid employment or tbusiness (especially with employees) than less educated individuals. Regression results (not rep
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24%. Overall it appears that young individuals work fewer hours than prime
are less likely to hold more than one job.
One might wonder whether these differences in hours worked across
groups reflect differences in the supply or the demand for labor. In what c
admittedly imperfect measure of the imbalance between the demand and
hours of work across age groups, we report an indicator of underemployme
8. This measures the proportion of individuals who work fewer than 40 hou
declare a desire to work more hours.7 It is interesting to observe that this
always the highest among young individuals. For instance, in Dar Es S
teenagers and 4% of youths declare being underemployed. For prime-
proportion is only 1%. In rural areas the corresponding proportions are re
5%, 6%, and 3%.
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13
Table 2A: Job Characteristic
MALES
(1) (2) (3) (4)
Occupation
Age Group Employee Self-employedwith employees
Self-employed,
no employees
Unpaid
family
worker
Dar es SalaamTeens 40.76 0.00 27.20 22.75 Youth 47.38 1.72 38.36 6.65 Prime-age 54.46 10.32 30.00 0.00
Urban
Teens 15.05 0.21 16.74 5.24 Youth 21.77 3.06 22.49 3.82
Prime-age 36.94 5.54 28.48 0.38
Rural
Teens 3.63 0.04 2.32 3.47 Youth 5.62 0.34 4.63 0.83 Prime-age 8.95 0.99 4.44 0.25
Note: The table reports the characteristics of those in work. Columns 15 report the occemployed with no employees, unpaid family worker, and working on own farm). Colpositive hours. Column 7 reports the proportion holding more than one job. Column 8 r
wishing to work more hours. See also notes to table 1A.
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14
Table 2B: Job Characteristic
FEMALES
(1) (2) (3) (4)
Occupation
Age Group Employee Self-employed
with
employees
Self-employed,
no employees
Unpaid
family
worker
Dar es SalaamTeens 52.06 0.42 26.12 15.24 Youth 36.38 2.77 53.82 4.37 Prime-age 27.22 3.27 55.79 0.39
Urban
Teens 16.77 1.27 11.72 20.86 Youth 19.08 1.08 35.18 6.51
Prime-age 14.12 5.85 28.40 2.88
Rural
Teens 1.86 0.00 1.91 5.16 Youth 1.32 0.28 1.94 1.38 Prime-age 2.29 0.19 3.03 0.78
Note: See notes to table 2A.
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Table 2B reports the employment characteristics of women. Teenage g
be more likely to work as employees than teenage boys in urban areas (52% an
Es Salaam and other urban areas respectively, versus 41% and 15% for boys) a
to work in the family enterprise. Changes in the distribution of women's emp
the life cycle appear rather different from men's. As with men, the p
nonagricultural self-employment rises with age in each area (from 27% to 59
Salaam, from 13% to 36% in other urban areas, and from 2% to 3% in rural a
proportion engaged in unpaid nonagricultural family work falls (from 15% to le
Dar Es Salaam, from 21% to 3% in other urban areas, and from 5% to 1% in
However, in contrast to men, the proportion in salaried employment falls (from
in Dar Es Salaam and from 17% to 14% in other urban areas) or stays const
rural areas), while engagement in the household farm rises. In urban area
working women are less likely to be in paid employment or to be self-em
employees than men are and more likely to be self-employed with no employe
in the family enterprise than prime-age men are. In rural areas most working w
engage in work on the household farm. These women account for 94% of work
rural areas (compared with 85% of working men). These patterns might ref
opportunities in the access to salaried employment for women compared with mdue to their lower labor market characteristics (for example, education) or a
gender discrimination. The need to take care of their children and families mig
salaried dependent employment a less attractive opportunity for women in Tanz
Information on hours shows that on average women tend to work few
men. Differences in average hours of work between prime-age women vary betw
in urban areas to minus 5 in rural areas. The same does not apply for teen
average differences in hours of work among teenage girls and boys range fro
value of 3 in Dar Es Salaam to a negative value of 1 elsewhere. This is consis
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Underemployment is larger for females than for males. For example, 14% of fem
Dar Es Salaam declare being underemployed compared with only 4% of men.
In sum, the career profiles of men and women appear rather differ
become older, urban men tend increasingly to move away from work in t
enterprise or farm toward salaried employment and self-employment. At the sa
individuals tend to work more hours. This is a combination of true job changes,
the fact that those who leave school later tend to be more likely to engage in
self-employment and to work more hours (plus possibly differences across coh
majority of working men in rural areas are engaged in work in the household f
movements toward salaried employment and self-employment that are qualitativ
those in urban areas are observed here.
As they grow older, women in urban areas increasingly work either as s
with no employees or in the family farm. In part, this might reflect a moveme
salaried employment due to the need for more flexible working arrangement
attend to domestic duties. In rural areas, women's participation is higher at any a
is little indication that rural women withdraw from the labor market. Almost all
work on the household farm. Women tend to work fewer hours than men, but
more likely to declare being underemployed. In this sense, lower labor market athe part of women does not seem to be completely ascribable to their lower
There is evidence that women in Tanzania find it particularly hard to access the
in urban areas, probably because of a combination of discrimination and lower m
Inactivity and unemployment
So far we have concentrated on the characteristics of those in work. T
3B report instead of information on the characteristics of those out of work an
have already documented the levels of joblessness (that is, those out of bo
school) in tables 1A and 1B. The first column of table 3A reports the propor
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Salaam and highest in rural areas among teenagers and youths, paralleling
employment. Activity rates among teenagers increase from 39% in Dar Es Sala
rural areas. Participation rates for youths vary between 79% in Dar Es Salaam
rural areas. For prime-age men, participation is almost universal, with the
inactive individuals varying between 1% in Dar Es Salaam to 3% in rural an
areas. Unemployment ratesthe measure generally used to ascertain jo
developed countriesare reported in column 2. These are obtained as the ratio
number of (strictly) unemployed individuals and the number of active individu
to the ILO definition. Unemployment rates are remarkably high among teenag
in urban areas. There is virtually no unemployment in rural areas. For teenage
range from 47% in Dar Es Salaam to 13% in other urban areas. Similar pa
among youths: unemployment rates are on the order of 40% in Dar Es Salaam
other urban areas. Interestingly, unemployment rates are virtually zero amo
men. Male unemployment in Tanzania is hence primarily an urban
disproportionately affecting young workers.
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18
Table 3A: Unemployment and Ina
MALES
(1) (2) (3) (4)
Active
(ILO)
Unemployment
rate (ILO)
Unemployment-
to-population
ratio
Availabl
& no
search
Dar es
Salaam
Teens 0.386 0.465 0.179 0.027 Youth 0.790 0.403 0.318 0.020 Prime-age 0.989 0.015 0.015 0.004
Urban
Teens 0.498 0.126 0.063 0.035 Youth 0.850 0.106 0.090 0.016
Prime-age 0.965 0.017 0.016 0.005
Rural
Teens 0.773 0.016 0.012 0.025 Youth 0.936 0.015 0.014 0.019 Prime-age 0.974 0.007 0.007 0.007
Note: The table reports the characteristics of those out of work. Columns 1 repoColumn 2 reports the proportion strictly unemployed conditional on being in the l
percentage of the population in that age group. Column 4 reports the proportion of for work (as a proportion of the population). Column 5 reports the proportion unapopulation). Columns 6 and 7 report the proportion in long-term unemployment (onor more). Column 8 provides an estimate of in unemployment duration. See text for d
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19
Table 3B: Unemployment and Ina
FEMALES
(1) (2) (3) (4)
Active
(ILO)
Unemployment
rate (ILO)
Unemployment
-to-population
ratio
Available
& no
search
av
Dar es Salaam
Teens 0.466 0.431 0.201 0.061 Youth 0.679 0.449 0.305 0.091 Prime-age 0.778 0.109 0.085 0.056
Urban
Teens 0.472 0.057 0.027 0.118 Youth 0.750 0.104 0.078 0.083 Prime-age 0.906 0.015 0.014 0.041
Rural
Teens 0.767 0.007 0.005 0.031 Youth 0.935 0.012 0.011 0.021 Prime-age 0.954 0.004 0.003 0.007 Note: See notes to table 3A.
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One might wonder whether these data provide a good indication of
joblessness among different age groups. To the extent that fewer youths are acti
active individuals do not include those in school), one might suspect that this
inflates the prevalence of unemployment among this group. In particular, if
school are those with a lower probability of finding a job, these figures ove
extent of unemployment for a random individual in that age group. By contrast
have no job opportunities stay in school or declare themselves inactive, this
opposite bias. An alternative measure of joblessness relates the number of unem
entire population, abstracting from whether these individuals are in school or
active or not). The unemployment-to-population ratio is reported in column 3.
even if one standardized unemployment to a much larger population at risk (tha
population in that age group), unemployment prevalence is still higher amon
teenagers. Figures for prime-age individuals are virtually identical to the ones
given that, as shown in column 1, essentially all prime-age men participate
market.
Although we have so far concentrated on unemployment, it is important
not all of those out of work (or school) are strictly unemployed. Columns 4 anproportion of individuals who are available to take a job if offered one and ha
for work in the week preceding the survey and those who self-declare
respectively. These are groups with increasingly lower labor market attachm
group includes truly idle individuals. The proportion of available individuals wh
having searched over the previous week is rather small at all ages and in all are
market status is most prevalent among teenagers and on the order of 3% of the
all areas. Figures on inactivity show, rather worryingly, that 5% of teenager
youths in Dar Es Salaam are idle. The proportions in other urban areas are
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The figures above show that urban teenagers and youths are at very high
unemployed. However these data are unable to tell us whether these individuals
out of unemployment or whether they get stuck in unemployment for long pe
Columns 5 and 6 report the proportion of long-term unemployed among thos
take a job (whether searching or not). Column 5 reports the proportion of those
one year of job search. Column 6 shows the proportion of those with at least two
search. These are sometimes labeled very long-term unemployed. In urban
unemployment is more likely to occur, long-term unemployed accounts for a
more of the overall unemployment pool. What is remarkable is that long-term u
is particularly widespread among youths. This is a major difference with respec
countries in the 1990s, where long-term unemployment is thought to have be
largely for older individuals (Machin and Manning 1999). More than 70% of
youths in urban areas are long-term unemployed. This contrasts with a propo
term unemployed on the order of 3750% for prime-age men. In rural
unemployment is almost nonexistent, the data show that the few who d
unemployed transition quite rapidly through this state. The proportion
unemployment varies between 16% and 24% depending on age among rural me
Column 7 reports the average duration of unemployment as estimated inflows and unemployment prevalence.8 Average unemployment duration is rem
in urban areas, especially for youths, consistent with the observation that thes
have a disproportionate risk of being long-term unemployed. Average durati
Salaam varies between 3.6 years for teenagers and 9.1 years for youths. In othe
these figures are respectively 5 and 5.7. Notice that unemployment duration is
Dar Es Salaam for prime-age men (3.4 years compared with 2 years in other urb
still much lower than for teenagers and youths.
8 Notice that cross-sectional dataincluding thesetypically report information on unemploym
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The question naturally arises as to why so many young individuals are in
looking for a job. In table 4A we report the subjective reasons provided by thes
for not looking or for not being available tout court. As columns 1 to 6 s
proportion of individuals answers that they are not looking for a job due
expectation of finding one. This proportion is particularly high for teenagers
urban areas. For example, in Dar Es Salaam 66% of individuals report this as th
for not looking. This compares with 21% among youths (and zero among prime
relevant share of those not looking reports to be waiting for a reply to a job a
waiting for a job to start. No systematic patterns can be detected through areas o
Only in urban areas is there a large proportion of inactive youths and teenagers
declaring not to be looking due to their involvement in home duties. Columns 7
reasons provided by those not available. Between 20% and 30% of these inact
report being involved in household chores. The relative importance of this exp
as individuals age. Rather interestingly, between 11% and 46% of teenagers,
the area, report being inactive due to sickness or disability. This accounts for an
proportion of inactive youths. These worrisome figures are most likely the
HIV/AIDS epidemic. Although there is no way to ascertain this with the
evidence from other sources suggests that youths are at the highest risk oHIV/AIDS in Tanzania, one of the countries with the highest prevalence in
Africa.9 The residual category other accounts for a large share of the inacti
appears that inactivity hides some productive employment in the household,
overestimate of the true extent of joblessness among young individuals. It is un
whether the category other includes individuals who have stopped being ava
poor labor market prospects.
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23
Table 4A: Reasons for not Looking or
MALES
(1) (2) (3) (4) (5)
Reason
not look
Thought would
not find
Waiting for
job or reply
Off-
season
Household
duties
Temporar
Dar es
Salaam
Teens 64.48 13.58 0.00 3.16 3.60Youth 25.55 32.48 0.00 2.68 0.00Prime-age 0.00 100.00 0.00 0.00 0.00
Urban
Teens 28.28 48.83 0.00 7.53 1.48Youth 51.83 21.84 0.00 18.02 0.00Prime-age 16.20 7.01 38.32 0.00 0.00
RuralTeens 17.43 22.11 8.25 30.70 1.32Youth 19.92 27.88 8.68 37.59 0.00Prime-age 33.80 39.35 17.72 9.13 0.00Note: The table reports the characteristics of those out of work who report not looking for a job one (columns 79). In each column the table reports the distribution of the main self-reported retable 1A.
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For women, several interesting patterns emerge. As column 1 of Tab
activity rates are higher among teenage women than teenage men in Dar Es Sal
activity rate for women is 47% (8 percentage points higher than for men). In rur
is no substantial difference between teenage boys and girls, with a teenage fe
rate on the order of 77% (the same as men's). In other urban areas girls are sligh
to be active than men (47% compared with an activity rate for teenager m
Although, womens activity rates increase with age, the rise is less pronounced
and hence men overtake women by the time they reach prime age. Participa
prime-age women are on the order of 95% in rural areas (2 percentage points l
and 78% in Dar Es Salaam (20 percentage points less than men). Although act
lower for women than for men, a higher or equal proportion of active
unemployed at least in Dar Es Salaam, as shown in column 2. In Da
unemployment rates are 43% for female teenagers (3 percentage points less tha
for female youths (5 percentage points more than men), and 11% among prim
(10 percentage points more than men). In other urban areas women are less
unemployed than men when in their teens (6% compared with 13%), but wome
likely to be unemployed when they are in between the ages of 20 and 24 (10
with 11%). As with men, there is no unemployment in rural areas. Urban uhence is a similar problem for young males and females.
In addition, prime-age women are also at high risk of unemployment, a
Es Salaam. An analysis of columns 4 and 5 shows that a much higher proporti
than men are available but not looking or unavailable. For example, in Dar E
proportion of female youths available but not searching is 9% (compared with
and the proportion of idle youths is 18% (compared with 7% for men). Idlen
from teenagers to youth and then stay constant (at around 17%) in urban areas
this rate increases and then decreases in other urban areas. Most likely, women
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true for all age groups in all areas except teenagers in Dar Es Salaam. Ave
among female youths in Dar Es Salaam is 5.9 years (compared with 9.1 years fo
other urban areas this is 5.4 years (compared with 5.2 years for men). Obviou
non-negligible proportion of women enter into inactivity, an option largely u
men, this might explain why observed durations are shorter for women than for
Table 4B illustrates that more than half of female teenagers and yout
Salaam not looking for a job declare being discouraged, that is, do not to expec
A smaller proportion of women than men declare waiting for a response fro
employer or for a job to start. As expected, women are more likely than men
looking due to family reasons. More women than men report not looking du
season in rural areas. This perhaps reflects the more cyclical nature of jobs for
for men in the agricultural sector. Not surprisingly a higher proportion of idle w
being involved in household duties as the main reason for not being avproportion tends to increase with age in urban areas, consistently with the notion
women effectively are engaged in productive work at home.
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26
Table 4B: Reasons for not Looking or
FEMALES
(1) (2) (3) (4) (5)
Reason
not look
Thought
would
not find
Waiting
for job or
reply
Off-
season
Household
duties
Temporarily
ill
Dar es
Salaam
Teens 59.98 8.50 0.00 12.47 1.00 Youth 67.49 2.72 0.00 24.56 0.00 Prime-age
42.85 19.90 16.65 17.02 0.00
Urban
Teens 16.67 28.89 1.65 48.00 0.00 Youth 15.37 17.94 3.48 47.06 1.78 Prime-age
36.49 20.49 10.26 29.08 0.00
Rural
Teens 17.15 18.31 20.80 28.63 0.00 Youth 22.39 5.15 18.50 46.57 0.00 Prime-age
47.23 25.34 14.32 0.00 8.13
Note: See notes to table 4A.
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Tables 5A and 5B analyze the job search activities among the (strictly) unempl
inquires with potential employers are the most widespread search method.
individuals age, they are less likely to use informal search channels (askin
relatives) than to ask directly employers or to attempt to start their own
example, the proportion of unemployed teenagers asking family and friends is 1
Salaam, 29% in other urban areas, and 36% in rural areas. Conversely, the p
those attempting to start their own business are 6%, 3%, and 28% respecti
prime-age men only 9% use family and friends as their favorite job search chan
Salaam, 0% in other urban areas, and 6% in rural areas. The proportion of pr
attempting to start their own business is 37% in Dar Es Salaam, 11% in other
and 30% in rural areas. Overall these data show the difficulties that youths face
a job. They hardly attempt to start their own business, which, as already mentio
access to capital, and hence is a less viable opportunity. Young individuals are muse informal channels, perhaps due to their lower chances of finding jobs throu
applications with employers. No substantial differences emerge in the pattern
between men and women.
Table 5A: Job Search Methods
MALES
(1) (2) (3)
Enquiry with
employer
Family and
friends
Attempt to st
own busine
Dar es Salaam
Teens 75.31 18.96 5.73Youth 58.37 30.76 10.87
Prime-age 64.26 8.91 26.83
Urban
Teens 67.79 28.56 2.19Youth 79.25 12.93 7.82Prime-age 89.46 0.00 10.54
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Table 5B: Job Search Methods
FEMALES
(1) (2) (3)
Enquiry with
employer
Family and
friends
Attempt to st
own busine
Dar es Salaam
Teens 55.16 38.40 5.23Youth 53.55 29.21 17.24Prime-age 36.46 19.44 41.21
Urban
Teens 63.13 21.85 15.03Youth 59.14 17.44 20.27Prime-age 56.90 11.95 31.15
Rural
Teens 44.33 0.00 55.67Youth 67.73 0.00 27.58
Prime-age 69.07 0.00 30.93Note: See notes to tables 5A.
In sum, we have illustrated that urban unemployment is prima
phenomenon in Tanzania (and to a lower extent a problem for prime-age wom
Salaam). Unemployment figures are likely to underestimate the extent of jobles
(small) proportion of individuals declare being available but not looking
expectation of not finding a job. ILO inactivity rates are also remarkably high. S
individuals are engaged in household chores, so they are somewhat involved
activities. Some may be discouraged and have abandoned their search activity.
that a non-negligible proportion of individuals declare being inactive due to h
This accounts for as much as 4% of men and 2% of women aged 2025 in DaAlthough there is no way to check this in the data, it is plausible that this rem
inactivity rates are to be ascribed to the widespread prevalence of HIV/AIDS.
Women seem to fare even worse than men. Although dropping out of th
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One further piece of evidence that emerges from the analysis is th
unemployment is particularly widespread, especially among youths. Rather th
and out of the labor market in an attempt to gain employment for life, you
Tanzania remain out of the labor force for very long periods of time. Girls te
lower than average unemployment durations than boys but this is most likely a
the fact that some of them transit to inactivity, an option rarely pursued by men.
Prime-age women also appear to suffer from remarkably high rates of u
and underemployment, at least in Dar Es Salaam. Participation is also lower fo
for men in urban areas. Although this might signal that their productivity at ho
than the wage they are offered in the market (or that they have stronger preferen
production relative to market activities), an additional (and not mutual
explanation is that low participation is the result of low labor market prospects.
In rural areas unemployment and underemployment are not major prages and across both sexes. Both participation and employment are rema
Obviously this says very little about the quality of jobs these individuals hold a
standards of living.
5. Determinants of Youths' Labor Force StatusIn this section we investigate further teenagers' and youths' labor for
schooling choices using simple regression tools. In particular, we concentrate o
aggregate indicators of the state of the local labor market, and conditional on th
to tease out what individual characteristics predict employment, unemploymen
attendance among Tanzanian youths.
For different labor market outcomes Y, we run the following regr
(1) YiR = 0 + XR'1 + Xi'2 + uiR
where i denotes a generic individual living in region R. The XR's
characteristics the Xi's are individual characteristics and u is an error te
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local labor demand we use the prime-age employment-to-population ratio in th
region of residence. We use employment of prime age individuals because this
the group with highest labor market attachment and whose employment is mos
exogenous to that of youths. After some experimentation with the data we decid
the employment-to-population ratio of individuals aged 3544.
Second, at given local labor demand (that is, adult employment), a rise
of young workers is likely to have an effect on their employment prospect
include in the model the share of teenagers and youths over total working a
(1560) in each region. Because rural-to-urban migration is high in Tanzania (a
about 28% of the population of teenagers and youths in urban areas), in order to
share of youths and teenagers in the population in urban areas we are restricted
who declare being born in that area. This allows us to control for the potential bi
stem in our regressions due to endogenous migration. If migration is stronger where labor demand is also stronger (see Card 2001), including the share of yo
(rather than natives) would overestimate the (presumably negative) correla
youths' labor supply and their employment rate.10
We include average travel time (in hours) to the closest secondary
additional measure of local opportunities. These data come from the Hous
Survey of 2000/01, which provides information on the distance to a large numb
infrastructure, including secondary school, for each household in the samp
aggregated the data by region and rural/urban status and included this variable
side of the regressions.
We also include a number of individual controls. First, we include a dum
denoting whether the individual received any type of training in his life. This
on-the-job training, such as apprenticeship, and off-the-job training, suc
vocational education. Second, to measure returns to migration we include a dum
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individuals' family background in determining their labor market outcomes, we
the average years of education of all other household members. Because ea
household members education is correlated with his or her age and sex, we al
condition for the age and sex structure of the individual's household by includin
of males and females in each five-year age cell in the household (coefficients no
Additionally, all the regressions control for the following covariates. Fir
four education dummy variables: never attended school (0 years of education
basic (16 years of completed education), completed basic (7 years of complete
and at least one year of secondary education (8 years of education or more). T
the circumstance that participation rises and school attendance falls as individu
and that this in turn might depend on the level of education we include unrestr
variables for potential experience (age-education-7). To control for differe
structure we include dummy variables for the individual's relationship to the ho(head, spouse, child, other relative, domestic employee, or unrelated family m
also include quarter-of-year dummy variables to allow for potential seasona
employment. Finally, to control for potentially unobserved differences betwee
city and other urban areas we include a dummy variable for residence in Dar
Standard errors (in brackets) are clustered by region of residence.
Before presenting the regression results, some care must be exerted in in
regression coefficients. Although one would ideally want to include onl
variables on the right hand side for the ordinary least squares estimates to c
interpretation, one might be skeptical that this is the case in our regressions. Fo
individuals with otherwise better labor market prospects are more likely to acqu
and receive training, this would lead to the erroneous conclusions that tr
employment. Similar concerns arise with the variables reflecting the age a
structure of the household or the child's relationship to the household head
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33
Table 6A: Determinants of Teenagers' and Youths' Labor Fo
MALES
(1) (2) (3)Work School School & work
URBAN
Adult employmentrate
1.075** 0.092 0.581** 0.
(0.378) (0.186) (0.228) (0Share youth -0.480** 0.004 -0.322*** -0
(0.213) (0.118) (0.110) (0Time to school 0.055 0.111 0.050 0.(0.112) (0.081) (0.067) (0
Training 0.137*** -0.185*** -0.004 0.(0.024) (0.023) (0.013) (0
Migrant -0.034 -0.052* -0.009 -0(0.038) (0.026) (0.015) (0
Head of householdeducation
0.001 0.020*** -0.004 0.
(0.007) (0.004) (0.005) (0
Observations 2004 2004 2004 20R-squared 0.331 0.517 0.241 0.
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34
Table 6A: (contd.) Determinants of Teenagers' and Youths' Labo
MALES
(1) (2) (3)
Work School School & work
RURAL
Adult employment rate 0.762 0.141 0.544 (0.465) (0.182) (0.390)
Share youth 0.275 0.066 0.199 (0.334) (0.140) (0.337)
Time to school -0.044 -0.004 -0.025** (0.026) (0.007) (0.012)
Training 0.032 -0.094*** -0.040** (0.023) (0.024) (0.015)
Migrant -0.007 -0.009 0.016 (0.037) (0.013) (0.020)
Head of householdeducation
-0.016*** 0.010** 0.002
(0.002) (0.004) (0.004)
Observations 3971 3971 3971 R-squared 0.150 0.464 0.247 Note: The table reports the coefficient of a regression of each of the dependent variables 3544) local employment population ratio by sex, share of individuals aged 1524 in the pwere born in that area), average travel time to secondary school (in hours), a dummy varvariable for migrant status, and average years of education of other household members. Avariables for level of education (0 years of education, 16 years of education, 7 years ovariables for potential experience (age-education-7), dummy variables for relationship
domestic employee, and unrelated family member), a dummy variable for residence in Darother household members in each five-year age cell, separately by sex. All regressionsgeneralized least squares with weights given by sampling weights. Numbers in parentheses *** Significant at the 1% level.** Significant at the 5% level.* Significant at the 10% level.
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35
Table 6B: Determinants of Teenagers' and Youths' Labor Fo
FEMALES
(1) (2) (3)Work School School & work
URBAN
Adult employment rate 0.410* 0.068 0.067 0(0.197) (0.097) (0.065) (0
Share youth -0.652* 0.165 -0.164 -0(0.326) (0.179) (0.136) (0
Time to school -0.002 0.075 0.023 -0(0.189) (0.098) (0.082) (0
Training 0.148** -0.177*** -0.027** 0(0.059) (0.024) (0.010) (0
Migrant 0.031 -0.036 -0.007 0(0.038) (0.021) (0.018) (0
Head of householdeducation
-0.015 0.013 -0.010 -0
(0.012) (0.009) (0.007) (0
Observations 2383 2383 2383 2R-squared 0.272 0.446 0.161 0
RURAL
Adult employment rate 0.079 0.368*** 0.168 -0(0.540) (0.123) (0.323) (0
Share youth -0.017 -0.025 -0.041 0(0.285) (0.093) (0.165) (0
Time to school -0.017 -0.028*** -0.023** 0(0.019) (0.004) (0.010) (0
Training 0.046* -0.085** -0.030 0(0.024) (0.030) (0.022) (0
Migrant -0.010 -0.009 -0.006 -0(0.028) (0.012) (0.011) (0
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Among urban boys, local labor demand seems to have a pronounced
these different margins of adjustment except for school attendance. The estimat
in column 1 (1.07) implies that a 10 percentage point rise in adult employme
approximately equal rise in youths' employment. Column 2 shows that this do
school attendance. However columns 3 and 4 show that a rise in local labor dem
a significant increase in both the probability of combining work and school (
probability of full time work (0.49). The rise in employment following a rise
demand hence comes in approximately equal proportions from a rise in pa
among students and a fall in inactivity (0.59, in column 6).
If local labor demand seems to affect urban boys' labor force status, loca
also appears to matter. Consistent with theory, local labor supply shows syste
opposite sign of local labor demand. The point estimates in the table sho
percentage point rise in the share of youths in the working age population leademployment of 4.8 percentage points and a rise in full-time school of 3.3 perc
Again an improvement in the state of the local labor market appears to have
overall school attendance (column 2) but it tends to increase the share of
combining work and school.
An analysis of the other rows shows that distance from school infrastruc
appear a biding constraint for urban boys. Training instead is associated
employment and lower schooling. Although one might infer from this that tr
employment, as already discussed, at an alternative interpretation is tha
administered with higher probability to those in work or at least to those
probability of work. Migrant boys are less likely to be in school, but there is
correlation with employment or inactivity. The negative effect of migratio
attendance most likely reflects the circumstance that most individuals migrate a
left school. Higher household education increases boys' probability of school at
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labor market outcomes in Tanzania: urban individuals from more educated h
more likely to be in school and less likely to be inactive.
Results in rural areas in the bottom part of the table are qualitatively sim
in general less precise. In general, local labor demand displays no statistical
effect on rural boys' labor force status. As with the results on local labor deman
significant effect of the youth share of labor force status. Possibly this lack
effects reflects the circumstance that boys in rural areas are largely emp
household farm and hence that their employment prospects are largely indep
state of the local labor market. An alternative explanation for the lack of signif
labor supply variable is that emigration from rural areas occurs when
opportunities worsen, so that as labor supply increases youth employment falls
coefficient that is biased toward zero in the regressions.
Unlike in urban areas, in rural areas distance to school appears to be determinant of youths' labor force status, affecting the probability of combin
school with a negative coefficient (0.025). This is consistent with the notio
school distance acts as a fixed cost of school attendance, hence reducing the i
individuals to combine work and school. The coefficients on the other variab
line with the ones in urban areas. Similar to urban areas, higher househo
increases school attendance, but in rural areas it is associated with a fall in empl
slight rise in inactivity. Boys from more privileged backgrounds give up work
for either school or leisure in rural areas while in urban areas they stay in scho
remaining unemployed or inactive.
The results in table 6B also illustrate significant effects of aggregate dem
force status and schooling decisions of girls in urban areas. Results are qualita
to the ones found for boys. A noticeable difference though is that changes i
demand largely appear to affect the margin between inactivity and full time w
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changes in the state of the local labor market. Aggregate labor supply also app
important determinant of labor force status among girls' in urban areas. Results
the ones found for boys. No appreciable difference can be detected between b
insofar as the other coefficients go.
For girls in rural areas results are similar to the ones found for boys
market conditions, whether on the side of demand or supply, appear to
Surprisingly, stronger labor demand is associated with higher school attendance
Prima facie, this result is hard to rationalize. Distance to school emerges
important determinant of labor force status, with increasing school distance lea
in part time school and hence an overall fall in school attendance.
We have performed a number of robustness checks on the data (not re
first concern is that because some prime-age individuals are household me
youths whose behavior we want to study, this might lead to biased estimates olocal labor demand. This bias stems from the correlation between differe
members' labor supply due to reasons other than local labor demand and supply
patterns of substitution or complementarity in individuals' labor force statu
household or added worker effects). To check how relevant this problem is fo
individual, we have computed prime-age employment to population rate
employment of prime-age individuals in his household. Results are essentially u
Second we have computed adult employment using different age brack
(those aged 2549 or those aged 3549). In general results are qualitatively sim
less precise.
Third, we have estimated the effect of local labor demand and supply i
of the other (potentially endogenous) covariates included in the models in table
This is to address the concern that the inclusion on endogenous variables mi
consistency of the estimates of the (arguably exogenous) indicator for the stat
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areas. While an improvement in local labor demand translates into a rise in em
both boys and girls, the effect is larger for boys since a non-negligible propo
tends to combine work with school as demand becomes stronger, while the s
happen for girls. For both boys and girls stronger local labor demand ten
joblessness, that is, to increase employment rates among those who would h
been out of school. We find no evidence of local labor demand affecting school
potentially suggesting that widespread school dropout in Tanzania at earlier age
the need for these children to engage in work but rather the potentially high coreturns to schooling (for evidence on low returns to education at least at low and
levels, see Sberdom and others (2004). This matches well with the observatio
in combination with work is not uncommon and with the fact that labor market
poor in urban areas. At a given level of local demand, a rise in the aggregate sup
to the labor market also appears to depress individuals' labor market prospects.Local labor demand and supply indicators appear to explain little of th
employment across rural areas. This might be due to the fact that ru
disproportionately employed on the household farm and hence isolated from t
market. Endogenous out-migration might also partly explain this lack of results.
Distance to school infrastructure appears to be a constraint in rural areas
school distance tends to reduce the incentive to combine work with school, he
school attendance with no significant effect on work at the extensive margin.
Fourth, individuals' socioeconomic background, as proxied by the aver
of other household members, is a strong predictor of young individuals' labor
being associated with a fall in work (in rural areas) and joblessness (in urban are
in schooling.
6. Discussion and conclusionsOur analysis of the Tanzanian youth labor markets illustrate that the aggregat
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areas are 45 times more likely to be jobless, compared with a youth-to-adult u
ratio of about 2 in most developed countries (ILO 2000). In addition, altho
(developed) countries for which data are available higher unemployment preva
youths is typically associated with shorter durations (Machin and Mannin
opposite is true in urban Tanzania. This makes the youth unemployment proble
in Tanzania than in most other developed countries.
Our analysis also illustrates that the youth unemployment is unlikely to
individuals queuing for rationed well paying jobs. Contrary to the widespreaddeveloping countries joblessness is a luxury for the better-off, we find that
individuals from more advantaged family backgrounds tend to attend school
less likely to be jobless in urban areas, suggesting that joblessness is a more se
for the poor.
Women and in particular young women appear rather penalized in this in urban areas start working at earlier ages than boys, sometimes in menial an
jobs characterized by long working hours, but their transition to the labor mark
life cycle is slower, as a substantial proportion of women gets absorbed in hom
(child bearing and child rearing in particular). Although one might ar
confinement of a large (although arguably decreasing) proportion of women
production activities might simply be the result of preferences and cultur
presented evidence that a large number of women self-classify as involuntary u
underemployed and that improvements in labor market conditions lead to a dro
inactivity rates. Both these two pieces of evidence suggests that a substantial
women (and in particular young women) remains inactive due to poor
prospects.
A separate though related question regards rural areas. Here unemp
joblessness do not appear to be major problems, although as emphasized in
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rural to urban areas, despite (and potentially causing) increasing jobless in
signals that the employment prospects of rural youths are not rosier than
counterparts.
An analysis of the reasons why the labor market in Tanzania is unable
available workers goes beyond the scope of this paper. One can speculate, tho
economic reforms of the mid-1990s that put the country on a path of fiscal d
macroeconomic stabilization and led to extensive privatization (World Ba
disproportionately those who were attempting to enter the labor market for tThese trends seem not to have been so far compensated by the sustained
registered since the mid-1990s. Additionally, massive demographic changes
rising proportion of youths in the labor market together with increasing urba
certainly further deteriorated the labor market prospects of recent cohorts of ur
By contrast, one can state with a certain degree of confidence that some osometimes invoked in more developed countries for the (youth) unemployment
as minimum wages, union power, employment protection legislation, or
incentives associated with welfare are all unlikely to apply to Tanzania (or in ge
developing countries), where the labor market is by enlarge unregulated
essentially nonexistent (if not for public sector workers), and unions extend
formal (public sector) (Freeman 1993; LO/FTF 2003).
This evidence immediately raises the question of how young individuals
jobs and how policy could intervene where such mechanisms of adjus
inadequate. One possible explanation is that Tanzanian youths engage in some f
and informal work that is not recorded in the ILFS, so that our measure of u
and joblessness largely overestimates the nature of the problem. This point ce
further research.
A second possibility is that young individuals engage into illegal or pos
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on victimization rates show alarming property crime and robbery rates in D
(23.1% and 8.2, compared with, say, rates of 10% and 0.6% for the United State
prostitution might be another way for young people, especially women, to mak
Obviously if crime, illegal, or hazardous activities are the response to the poor
prospects of youths in Tanzania, this might provide an additional rationa
intervention aimed at the youth unemployment problem.
Finally, without social protection, households might be the ultimate
welfare to youths. This might come through cohabitation and shared living arreven through resource pooling by members of the same household living apart.
some household members from rural to urban areas might indeed be a w
households to spread the risk of economic downturns affecting differently c
countryside (Rosenzweig 1988). More rural-biased development (after deca
bias), and in particular land redistribution might certainly relieve some of the pr
urban labor market (Mjema 1997) although it is not at all obvious whether this
to guarantee sustained growth.
In principle there are alternative ways for youths to cope with labor m
Policy intervention can be potentially important (OECD 1999). One strategy
school. If the alternative to school is inactivity or unemployment, one might w
many youths in Tanzania appear to drop out early in the absence of sub
opportunities. We found no evidence that school enrollment is affected by th
local labor market, although we found that in urban areas boys are more likel
work with school in good times. In rural areas distance to school infrastru
remains an impediment to school attendance. Policies aimed at improving
possibly via an improvement in school quality and school construction shou
alleviate some of the problems in the youth labor market.
Another mechanism through which youths typically react to the poo
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differences in employment across urban areas suggests that migration of
capital) do not respond sufficiently to local economic incentives. Potential gain
from increasing mobility across urban areas in turn fostered from a m
information system about the existence of vacancies.
In the face of scarce salaried employment opportunities a third strategy f
no employment is to set up one's own business. We have shown that young in
substantially less likely to be self-employed than prime-age individuals are
difficulties in access to credit, a lack of entrepreneurial culture and skillsframework that has long discouraged small (informal) enterprises (Mjema 1997)
to explain why this avenue is rarely pursued by Tanzanian youths. Again
suggest room for policy intervention.
However the strongest inefficiencies in the labor market (and else
economy) arise from poor enforcement of property rights, lack of legal pr
widespread corruption coupled with an overly bureaucratic government (
Lindauer 1999; LO/FTF 2003).
It must be emphasized that the government of Tanzania is not indi
problem of youth unemployment and that a number of policies have been im
proposed (by the government) and activities launched (by nongovernmental
and international organizations) since the mid-1980s. Since the early 2000s
intervention in youth unemployment has experienced a new impulse.
To the best of our knowledge, no formal evaluation of such policies exi
hard to discuss their impact and to derive policy prescriptions. However, an an
polices proposed and (partly) implemented during the 1990s that aim to promote
and growth and that involved as (possibly indirect) beneficiaries the youths
conclusion that these efforts were in general ineffective (Shitundu 2005). Un
and the very limited number of beneficiaries, both due largely to the small bud
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Since the late 1990s and early 2000s, and in recognition of the dramatic
problem, the youth employment problem has acquired a much more prominen
government agenda. This is reflected in a number of legislative acts or policy p
identify (youth) employment growth as one of the aims of government interven
number of policy actions that have since then been launched or are being in t
taking place (Shitundu 2005; World Bank 2005)
These polices include both supply-side and demand-side interventio
supply side, one major policy is the Primary Education Development Programwhich abolished school fees and is apparently responsible for an unpreced
primary school enrollment after more than a decade of stagnation. Access to a
secondary education has also improved by halving school fees, constructing
introducing scholarships (under the Secondary Education Development
COBET, a program in force since the 1990s, provides remedial education to sch
apparently with appreciable results. Efforts to reform the vocational school sy
and Folk colleges) are on the agenda but still to be implemented. The IL
Promoting Gender Equality and Decent Work throughout Life also aims
apprenticeship and skill training.
Major interventions on the demand side have been directed to improve
climate, to provide credit to micro-entrepreneurs (for example, the SELF
financed by the Inter-African Development Bank and the government of Tanz
provide public jobs with potential training content (through public-private partn
as the DSM Solid Waste Management Programme, the ILO Integrated Urban
Promotion, which is part of the Jobs for Africa Programme, and Com
Programmers under the TASAF scheme).
Finally, some efforts are being made to increase the match between th
the demand of labor (see the newly labor created Labor Exchange Centre in D
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for example) and to strengthen technical capability in government offices
employment issues.
Although it is still too early to evaluate the overall impact of these
general impression one gets from a review of these (actual or planned) interve
youth unemployment has recently acquired a much greater emphasis in the pol
Tanzania, financial resources have increased substantially to match the scale of
and a much larger amount of coordination has been achieved. All these element
the government of Tanzania is potentially on the right path toward fightiunemployment problem.
7. Summary of main findingsLabor force status and schooling choices
Three-fourths of teenagers and youths in Tanzania live in rural areas. School attendance is far from universal, even among teenagers, and is hi
areas.
Although the majority of urban youths attend school sometime in their lifeproportion of rural youths do not.
Work participation is higher in rural areas, where school in combinationalso widespread.
Idleness is a phenomenon affecting a large proportion of urban youths and Girls are more likely to have never attended school and to drop out from
boys are.
Urban girls are more likely to work than boys are, although by adulthood reversed.
Characteristics of those working
Hours of work increase with age. Like employment patterns, young females in Dar Es Salaam work mo
young males do, although by adulthood this pattern is reversed.
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In contrast to men, the proportion of females in salaried employment fwhile engagement in the household farm rises.
Characteristics of those not working
Urban youths (and prime-age women to less extent) are at disproportibeing unemployed compared with prime-age men.
Inactivity rates are also high among urban youths and teenagers. Lack of job opportunities is often invoked by youths as a reason for not
job.
Inability and sickness explain a significant share of the prevalence of inact Unemployed youths and teenagers are less likely to report waiting to fi
more likely to use informal search channels, and less likely to attempt town business than prime-age individuals are.
Youths are more likely to be long-term (or very long-term) unemployed thindividuals are.
Girls (and women) in general display shorter unemployment durations thado, but this might be due to their unemployment spells ending in inactivity
Determinants of youths labor force status and schooling choices
Local labor demand and supply are important determinants of teenagersemployment in urban Tanzania.
Excess labor demand increases employment and reduces inactivity in urba Urban boys (but not urban girls) are more likely to combine work with
local labor demand is stronger.
School enrollment is unresponsive to the state of the local labor market. Labor market status of rural teenagers and youths is largely unresponsive
the local labor market.
Distance to school is an important factor explaining low school attendanceyouths and teenagers. Family background is a strong predictor of school enrollment and labor fo In urban areas teenagers and youths from better-off families are more li
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Summary Findings
Although we are not the first to document the level of youth joblessness
in Tanzania (Mjema 1997; Government of Tanzania 2003; LO/FTF
2003), our paper aims to shed some additional light on this phenomenon.
First, we provide evidence on different dimensions of youths' labor
market performance. For this exercise we can rely on micro data from
the Tanzanian Integrated Labor Force Survey (ILFS) of 2000/01, a rather
large household survey (approximately 11,000 households) that provides
a rich array of information on employment, job search, schooling,
training, and migration, together with basic information on individuals'
and their households' characteristics. Second we attempt to uncover
the determinants of youths' labor market outcomes and to tease outsignificant predictors of labor market success and failure using simple
regression tools.
HUMAN DEVELOPMENT