labour mkt

8/6/2019 Labour Mkt

1/60

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

8/6/2019 Labour Mkt

2/60

Youth in the Labor Market and the Transition from School to W

Tanzania

Florence Kondylis

&

Marco Manacorda

July 2006

8/6/2019 Labour Mkt

3/60

8/6/2019 Labour Mkt

4/60

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

8/6/2019 Labour Mkt

5/60

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

8/6/2019 Labour Mkt

6/60

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

8/6/2019 Labour Mkt

7/60

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

8/6/2019 Labour Mkt

8/60

8/6/2019 Labour Mkt

9/60

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


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

8/6/2019 Labour Mkt

10/60

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


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

8/6/2019 Labour Mkt

11/60

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

8/6/2019 Labour Mkt

12/60

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

8/6/2019 Labour Mkt

13/60

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

8/6/2019 Labour Mkt

14/60

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

8/6/2019 Labour Mkt

15/60

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%.

8/6/2019 Labour Mkt

16/60

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


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.

8/6/2019 Labour Mkt

17/60

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


Note: See notes to table 2A.

8/6/2019 Labour Mkt

18/60

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

8/6/2019 Labour Mkt

19/60

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

8/6/2019 Labour Mkt

20/60

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.

8/6/2019 Labour Mkt

21/60

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


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


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

8/6/2019 Labour Mkt

22/60

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


Urban


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.

8/6/2019 Labour Mkt

23/60

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

8/6/2019 Labour Mkt

24/60

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

8/6/2019 Labour Mkt

25/60

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.

8/6/2019 Labour Mkt

26/60

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.

8/6/2019 Labour Mkt

27/60

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

8/6/2019 Labour Mkt

28/60

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.

8/6/2019 Labour Mkt

29/60

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


36.49 20.49 10.26 29.08 0.00

Rural


47.23 25.34 14.32 0.00 8.13

Note: See notes to table 4A.

8/6/2019 Labour Mkt

30/60

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

8/6/2019 Labour Mkt

31/60

Table 5B: Job Search Methods

FEMALES

(1) (2) (3)

Enquiry with

employer

Family and

friends

Attempt to st

own busine

Dar es Salaam


Urban


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

8/6/2019 Labour Mkt

32/60

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

8/6/2019 Labour Mkt

33/60

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

8/6/2019 Labour Mkt

34/60

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

8/6/2019 Labour Mkt

35/60

8/6/2019 Labour Mkt

36/60

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.

8/6/2019 Labour Mkt

37/60

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)


-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.

8/6/2019 Labour Mkt

38/60

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


-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

8/6/2019 Labour Mkt

39/60

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

8/6/2019 Labour Mkt

40/60

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

8/6/2019 Labour Mkt

41/60

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

8/6/2019 Labour Mkt

42/60

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

8/6/2019 Labour Mkt

43/60

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

8/6/2019 Labour Mkt

44/60

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

8/6/2019 Labour Mkt

45/60

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

8/6/2019 Labour Mkt

46/60

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

8/6/2019 Labour Mkt

47/60

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

8/6/2019 Labour Mkt

48/60

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.

8/6/2019 Labour Mkt

49/60

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

8/6/2019 Labour Mkt

50/60

References

Beegle, Kathleen and K. Burke (2004), 'Why Children Arent Attending SchooRural Tanzania', Journal of African Economies, 13(2), 333-355, 2004.

Beegle, Kathleen, Rajeev Dehejia, and Roberta Gatti (2005), 'Child Labor andShocks', (forthcoming) Journal of Development Economics.

Blanchflower, David G. and Richard B. Freeman (2000a), 'Introduction', in BlaG. and R. B. Freeman (ed.s) Youth Employment and Joblessness Countries, University of Chicago Press and NBER, 2000.

Blanchflower, David G. and Richard B. Freeman (2000b), 'The Declining Ecoof Young Workers in OECD Countries', in Blanchflower D. G. and R(ed.s) Youth Employment and Joblessness in Advanced Countries, Chicago Press and NBER, 2000.

Borjas, George (1999), 'The Economic Analysis of Immigration,' HandbooEconomics, 3.

Card, David (2001), 'Immigrant Inflows, Native Outflows, and the Local La

Impacts of Higher Immigration', Journal of Labour Economics, Vol. 19(

Card, David. and Thomas Lemieux (2000), Adapting to Circumstances: TheWork, School, and Living Arrangements Among North American YoFreeman (ed.s) Youth Employment and Joblessness in AdvanceUniversity of Chicago Press and NBER, 2000.

. (2001), 'Can Falling Supply Explain the Rising Return to College for YA Cohort-Based Analysis', Quarterly Journal of Economics, 116 (2), 705

Freeman, Richard B. (1991), 'Crime and the Employment of Disadvantaged YWorking Papers, w3875, October 199

Freeman, Richard B. (1993), 'Labor Markets and Institutions in Economic DAmerican Economic Review, vol. 83(2), 1993. 403-08.

Freeman, Richard B. (1996), 'Why Do So Many Young American Men CommWhat Might We Do About It?', Journal of Economic Perspectives, V

1996, 25-42.Freeman, Richard B. (1999), 'The Economics of Crime', in Handbook of Labo

3, C. North-Holland, 1999.

Freeman, Richard B. and David I. Lindauer (1999), 'Why not Africa?', NBpapers w6942 February 1999

Y h L b M k P bl I N C d C

8/6/2019 Labour Mkt

51/60

Youth Labor Market Problem: Its Nature, Causes, and ConsequeConference Volume, University of Chicago Press and NBER, 1982.

Government of Tanzania (2003), Unemployment issues: Perspectives from thLabour, Youth Development and Sports, Poverty Policy Week 2000Presentations and Papers, available at: www.povertymondownloads/povpolweek /unemployment .doc

Guarcello, Lorenzo, Marco Manacorda, Furio Rosati, Jean Fares, Scott LyonValdivia (2005), 'School-to-Work Transitions in Sub-Saharan Africa: AUCW Working Paper, n.15, November 2005.

ILO (2000), Employing Youth: Promoting Employment-Intensive Growth, ILO

Koreman, Sanders and David, Neumark (2000), 'Cohort Crowding and Markets: A Cross-National Analysis', in Blanchflower D. G. and R. B. FYouth Employment and Joblessness in Advanced Countries, UniversitPress and NBER, 2000.

LO/FTF (2003), Profile of the Labour Market and Trade Unions in TanzaCouncil - Danish Trade Union Council for International Development

April 2003.Machin, Steve, and Alan Manning, (1999) 'The Causes and Consequences of lon

unemployment in Europe', in Handbook of Labor Economics, North-HolPublishers, 1999.

Mjema, Godwin D., (1997) 'Youth Unemployment in Tanzania: New SCombating an Old Problem', Journal of Population Studies and DevelopNo. 1, 1997.

OHiggins, Niall (2003), 'Trends in the Youth Labour Market in Developing aCountries', World bank, Social Protection Discussion Paper Series, n. 2003.

OECD, (1996), 1996 Employment Outlook, Growing into work: youth and the lover the 1980s and 1990s (Chapter 4), Paris, 1996.

OECD, (1998), 1998 Employment Outlook, Getting started, settling in: the treducation to the labour market (chapter 3), Paris, 1998.

OECD, (1999), Preparing Youth for the 21st Century: The Transition from EdLabour Market, Paris, 1999.

OECD, (2000), From Initial Education to Working Life - Making Transitions2001.

R ig M k R (1988) 'L b M k t i L I m C t i ' i
http://www.povertymonitoring.go.tz/http://www.povertymonitoring.go.tz/

8/6/2019 Labour Mkt

52/60

Rosenzweig, Mark R., (1988) 'Labor Markets in Low-Income Countries', in Development Economics, 1, North-Holland, 1988.

Ryan, Paul (2001), 'The School-to-Work Transition: A Cross-National Perspecof Economic Literature, Volume 39, Issue 1, 2001, 34-92.

Shitundu, Joseph M. (2005), Youth Employment Inventory of Existing Programmes in Tanzania, Background Paper, World Bank Social ProJuly 2005.

Sderbom, Mns, Francis Teal, Anthony Wambugu and Godius Kahyarara Dynamics of Returns to Education in Kenyan and Tanzanian Manufactfor the Study of African Economies Working Papers, Oxford University17, January 2004.

UNDP (2005), Human Development Report 2005, 2005.

US Census Bureau (1995), Population Trends Tanzania, U.S. Department oEconomics and Statistics Administration, Bureau of the CensusSeptember 1995.

Welch, Finis (1979) 'Effects of Cohort Size on Earnings: The Baby Boom Bab

Bust', Journal of Political Economy, n. 87, vol. 5, 1979, S65-S97.

World Bank (2005), Tanzania: Sustaining and Sharing Economic GrowtEconomic Memorandum and Poverty Assessment, Washington

8/6/2019 Labour Mkt

53/60

Social Protection Discussion Paper Series Titles

8/6/2019 Labour Mkt

54/60

Social Protection Discussion Paper Series Titles

No. Title

0607 Youth Labor Market in Burkina Faso: Recent Trendsby Daniel Parent, July 2006

0606 Youth in the Labor Market and the Transition from School to Work Tanzaniaby Florence Kondylis and Marco Manacorda, July 2006

0605 Redistributing Income to the Poor and the Rich: Public Transfers in America and the Caribbeanby Kathy Lindert, Emmanuel Skoufias and Joseph Shapiro, August (online only)

0604 Uninsured Risk and Asset Protection: Can Conditional Cash TransfePrograms Serve as Safety Nets?by Alain de Janvry, Elisabeth Sadoulet, Pantelis Solomon and RenoJune 2006

0603 Examining Conditional Cash Transfer Programs: A Role for IncreasInclusion?by Bndicte de la Brire and Laura B. Rawlings, June 2006 (online

0602 Civil-service Pension Schemes Around the World

by Robert Palacios and Edward Whitehouse, May 2006 (online only0601 Social Pensions Part I: Their Role in the Overall Pension System

by Robert Palacios and Oleksiy Sluchynsky, May 2006 (online only

0543 Complementing Natural Disasters Management: The Role of SocialProtectionby Renos Vakis, February 2006

0542 Brazils Bolsa Escola Program: The Role of Local Governance inDecentralized Implementationby Alain de Janvry, Frederico Finan, Elisabeth Sadoulet, Donald NeKathy Lindert, Bndicte de la Brire and Peter Lanjouw, December

0540 Demographic Alternatives for Aging Industrial Countries: Increased

8/6/2019 Labour Mkt

55/60

0540 Demographic Alternatives for Aging Industrial Countries: IncreasedFertility Rate, Labor Force Participation, or Immigrationby Robert Holzmann, December 2005 (online only)

0539 Disability, Poverty and Schooling in Developing Countries: ResultsHousehold Surveysby Deon Filmer, November 2005 (online only)

0538 Food Aid and Food Security in the Short- and Long Run: Country Efrom Asia and sub-Saharan Africaby Carlo del Ninno, Paul A. Dorosh and Kalanidhi Subbarao, Novem

2005 (online only)

0537 The New Pensions in Kazakhstan: Challenges in Making the Transitby Richard P. Hinz, Asta Zviniene and Anna-Marie VilamovskaSeptember 2005 (online only)

0536 Households Vulnerability to Shocks in Zambiaby Carlo del Ninno and Alessandra Marini, September 2005 (online

0535 Linking Community Empowerment, Decentralized Governance, andService Provision through a Local Development Frameworkby Louis Helling, Rodrigo Serrano, David Warren, September 2005 only)

0534 Youth Employment in the MENA Region: A Situational Assessmenby Nader Kabbani and Ekta Kothari, September 2005

0533 Measuring Risk Perceptions: Why and Howby Joachim De Weerdt, July 2005

0532 Implementing Means-Tested Welfare Systems in the United Statesby Kathy Lindert, June 2005 (online only)

0531 La Focalizacin En El Programa De Desarrollo Humano OportunidaMexicoby Mnica Orozco y Cecilia Hubert, June 2005 (online only)

0530 Sistema de Identificacin de la Poblacin Objetivo: SIPO en Costa

0528 Focalizacin De Programas En Chile: El Sistema CAS

8/6/2019 Labour Mkt

56/60

gby Osvaldo Larraaga, June 2005 (online only)

0527 Reforming Brazil's Cadastro nico to Improve the Targeting of the Famlia Programby Bndicte de la Brire and Kathy Lindert , June 2005 (online onl

0526 Designing and Implementing Household Targeting Systems: LessonLatin American and The United Statesby Tarsicio Castaeda and Kathy Lindert, with Bndicte de la BrirFernandez, Celia Hubert, Osvaldo Larraaga, Mnica Orozco and R

Viquez, June 2005 (online only)Also available in Portuguese.

0525 Unequal Prospects: Disparities in the Quantity and Quality of Labouin sub-Saharan Africaby John Sender, Christopher Cramer and Carlos Oya, June 2005

0524 Pension Supervision: Understanding International Practice and Coun

Contextby Richard P. Hinz and Anca Mataoanu, May 2005

0523 Social Security Coverage in Latin Americaby Rafael Rofman, May 2005

0522 A Technology White Paper on Improving the Efficiency of Social SProgram Delivery in Low Income Countries: An Introduction to Av

and Emerging Mobile Technologiesby Mike Gallaher, May 2005 (online only)

0521 Aging and Poverty in Africa and the Role of Social Pensionsby Nanak Kakwani and Kalanidhi Subbarao, May 2005 (online only

0520 Delivery Mechanisms of Cash Transfer Programs to the Poor in Banby Shaikh S. Ahmed, May 2005 (online only)

0519 Portability Regimes of Pension and Health Care Benefits for InternaMigrants: An Analysis of Issues and Good Practicesby Robert Holzmann, Johannes Koettl and Taras Chernetsky, May 2(online only)

0517 Household Vulnerability and Childrens Activities: Information Nee

8/6/2019 Labour Mkt

57/60

yHousehold Surveys to Measure their Relationshipby Diane Steele, May 2005 (online only)

0516 The Effect of Child Labor on Mathematics and Language AchievemLatin Americaby Mario A. Sanchez, Peter F. Orazem, and Victoria Gunnarsso, Ma

0515 The Inter-Generational Persistence of Child Laborby Patrick M. Emerson and Andr Portela Souza, May 2005

0514 How Does Working as a Child affect Wage, Income and Poverty as Adult?by Nadeem Ilahi, Peter F. Orazem, and Guilherme Sedlacek, May 20

0513 Dynamics of Child Labor: Labor Force Entry and Exit in Urban Braby Suzanne Duryea, Jasper Hoek, David Lam and Deborah Levison2005

0512 The Responses of Child Labor, School Enrollment, and Grade Repethe Loss of Parental Earnings in Brazil, 1982-1999by Marcelo Crtes Neri, Emily Gustafsson-Wright, Guilherme SedlaPeter F. Orazem, May 2005

0511 Child Labor, Schooling, and Poverty in Latin Americaby Guilherme Sedlacek, Suzanne Duryea, Nadeem Ilahi, and MasaruMay 2005

0510 Changing Patterns of Child Labor around the World since 1950: TheIncome Growth, Parental Literacy and Agricultureby Victoria Gunnarsson, Peter F. Orazem, and Guilherme Sedlacek, 2005

0509 Disability and Social Safety Nets in Developing Countries

by Sophie Mitra, May 2005

0508 Social Funds: A Review of Public Sector Management and InstitutioIssuesby Mukhmeet Bhatia, May 2005

0505 A Guide to Multisector Labor Market Models

8/6/2019 Labour Mkt

58/60

by Gary S. Fields, April 2005

0504 Multinational Enterprises and Training Revisited: Do International SMatter?by Niels-Hugo Blunch and Paula Castro, March 2005

0503 Community-Based Health Insurance & Social Protection Policyby Steven Tabor, March 2005 (online only)

0502 Towards a Better Understanding of the Nature, Causes and Consequ

Youth Labor Market Disadvantage: Evidence for South-East Europeby Alexandre Kolev and Catherine Saget, March 2005 (online only)

0501 A Lecture on the Political Economy of Targeted Safety Netsby Lant Pritchett, January 2005

0420 Can Conditional Cash Transfer Programs Improve Social Risk ManLessons for Education and Child Labor Outcomes

by Elisabeth Sadoulet, Frederico Finan, Alain de Janvry and Renos VDecember 2004

0419 Measuring Welfare for Small but Vulnerable Groups. Poverty and Din Ugandaby Johannes G. Hoogeveen, September 2004

0418 Institutional Analysis Toolkit for Safety Net Interventionsby Inke Mathauer, August 2004

0417 Trade Union Participation in the PRSP Processby Lawrence Egulu, August 2004

0416 A New Approach to Social Assistance: Latin Americas ExperienceConditional Cash Transfer Programs

by Laura B. Rawlings, August 20040415 Shocks and Coffee: Lessons from Nicaragua

by Renos Vakis, Diana Kruger and Andrew D. Mason, July 2004

8/6/2019 Labour Mkt

59/60

8/6/2019 Labour Mkt

60/60

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

labour mkt

Documents