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Ghulam Reza Paikar

An analysis of youth labor force participation in

Afghanistan: Evidence from Aybek city

Volume | 054 Bochum/Kabul | 2018 www.development-research.org | www.afghaneconomicsociety.org

An analysis of youth labor force participation in Afghanistan: Evidence from Aybek city

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An analysis of youth labor force participation in Afghanistan:

Evidence from Aybek city

Ghulam Reza Paikar

Keyword list

Youth labor force, labor market, labor supply, logit model, Aybek city

Abstract

Afghanistan has a large proportion of young people in its labor force and it also has a high rate of

youth unemployment. There are several demographic and socio-economic factors affecting

young people’s participation in the labor force. This case study investigated the factors

determining youth labor force participation in Afghanistan with empirical evidence from Aybek city.

An attempt was made to ascertain the determinants of young people’s economic activities. The

data for this case study consists of information from 329 young people and their households which

were collected through structured interviews. Binomial logistic regressions were used for the data

analysis. In this case study, we conclude that years of work experience and possessing English

language and computer skills increase the probability that a young person will participate in the

labor market. Young people’s participation in the labor market is also determined by gender.

Culturally, women are expected to work in the house and take care of children, so being female

may decrease the chance of labor force participation. Marital status, family size, and the education

level of the head of the household positively affected youth labor force participation. However,

assets and mediation (i.e., a middleman) in the process of employment have negative effects on

the probability that a young person will be economically active.

Description of the data

There is very limited research on the present topic. Afghanistan has a high level of

unemployment and a large proportion of young people in the labor force. Of the total population

of Afghanistan, 55.3 percent are youth between the ages of 15-24 years (ILO, 2013). In this case

study, an attempt was made to ascertain the factors influencing youth participation in the labor

market. Samangan province was selected as the study area. This province is divided into seven

districts that contain 674 villages. The population of this province is very young; 40.7 percent of

the population is 15 years old or older and of this group, 55.3 percent of males and 89.8 percent

of females are unemployed (UNFPA, 2015).

Based on the objectives and scope of the study, both primary and secondary data were

collected. The secondary data include published reports, national statistical data, working papers,


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and journal articles. The primary data analyzed in this paper are comprised of information about

the labor market participation of 329 young people residing in Aybek city of Samangan province.

This information was obtained through structured interviews with both male and female youth

between the ages of 15-35 years using a reasonably extensive questionnaire. The questionnaire

contains qualitative and quantitative information about a young person’s family background, his

or her partner’s characteristics, and personal information such as age, education, work

experience, and work skills. It also includes several micro and macro level parameters for the

measurement of employment and the reasons for unemployment.

The sample consists of 329 young people who are either economically active or

underemployed. The total sample population was categorized into three sub-samples. The first

category, which includes 110 economically active youth, consists of salary employed, wage

employed, and self-employed young people working in different types of occupations such as

school teacher, hospital nurse, baker, tailor, embroider, car mechanic, and waiter. The second

category of the sample population includes 109 graduated students who are either economically

active or underemployed. The third category, which includes both economically active youth and

young people actively seeking employment, was sampled from the study households. The first

two categories were selected randomly. The procedure of random sampling was carried out

according to the following steps. First, four schools (two public and two private) were selected

based on the lists of schools provided by the president of education of Samangan province, while

the city of Aybek has only one public hospital, so this hospital was included. Second, young

people who were salary employed in these institutions were randomly sampled from the lists of

school teachers and hospital nurses and cleaners. The same procedure was applied to wage

employed and self-employed youth. The lists for grocery stores, restaurants, and auto mechanics

were provided by the municipality of Aybek city. Wage employed and self-employed female youth

were interviewed from Tahmina women’s market using the same procedure.

The second category of the sample population contains 109 graduated students from

Samangan’s higher education institute. The students were randomly selected from the previous

two years’ graduation lists from the three faculties (economics, agriculture, and education) of this

institute. The sample includes both male and female students who were either employed or

looking for work.

The third category of the sample population was obtained using a lottery sampling method.

Because there were no lists available of youth who were underemployed, these respondents were

targeted using household surveys. This sample includes young people who were looking for a job

or economically active in different types of businesses. The procedure for sampling was based

on the following steps. First, from the seven districts of Aybek city, two were selected by lottery.

Second, the streets were selected by lottery from the lists created by the surveyors. Third, the

sample households were chosen by lottery from the selected streets.


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Research question and theoretical contextualization

There is a vast amount of empirical research on individual labor supply in developing countries.

The extant literature helps us to understand the various aspects of young people’s labor force

participation and the different employment statuses, as observed throughout the world. Most of

the existing research is related to the traditional theory of the household. A theoretical model of

time allocation was first developed and introduced by Mincer and Becker in which they used time

as a commodity for utility maximization and considered the household behavior of time allocation

(Becker G. , 1965), (Mincer, 1962). The model explains that the labor supply depends on the

wages offered in the labor market and in turn, the offered wage rates depend on differences in

human capital such as education, work experience, skills, and training. The likelihood that a labor

supplier is employed in the labor market and the number of hours a labor supplier works are

functions of the market wage and the individual’s demand for leisure; that is, if the wage offer

exceeds the value of leisure time, the labor supplier will participate in the labor market.

It also is important to note that family members can influence young people’s participation

in the labor market. Youth with a well-educated parent or sibling may have greater advantages in

finding a job in the labor market compared to youth with the same amount of schooling because

the former would receive good advice and assistance in finding a position (Mazzota, 2010).

Other researchers have analyzed the impact of early employment on educational

achievement. In developing nations, most young people start working during childhood, which

leads to fewer opportunities and reduced earning later in life (Rosati & Rossi, 2003) (Heady,

2000). Similarly, in a comparison study, researchers found that boys who entered the labor market

before the age of 12 earned 20 percent less and were 8 percent more likely to be in the lowest

income bracket than boys who started working after the age of 12 (Ilahi et al., 2005). Although

many young people enter the labor market early on in life, many fail to find employment. Empirical

studies have shown that young people in general spend 1.4 years in intermittent work and

joblessness (Fares & Dhushyanth, 2006). In addition, the long-term nature of youth

unemployment may affect their labor market earnings. This means that youth unemployment

adversely affects their earnings for as long as ten years (Mroz & Timothy, 2006).

The determinants of unemployment for youth in the labor market have been studied and

investigated by many economists. For example, an empirical study analyzed the dynamics of

youth unemployment and found that the shortage of attractive jobs, instability, and frequent

turnover were the major sources of teenage unemployment (Clark & Summers, 1982). In India,

the main reasons for unemployment among Indian youth were the lack of education and work

experience (Visaria, 1998).


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Afghanistan is a challenging country for young people. More than three decades of conflict

and war have devasted the country’s institutions. This conflict has resulted in a battered economy

and a country full of scars caused by the intense fighting. Youth unemployment in Afghanistan is

a significant problem and thus is an important topic of study in order to ascertain the causes of

this phenomenon. Conflict and war can be understood as the main causes of the problem of youth

unemployment. This problem results in millions of young people migrating to Iran, Pakistan, and

countries in Europe and North America to seek better futures. The situation is highly complicated

for Afghan youth. For instance, there are places among the streets of Kabul and other provinces

where workers wait in the hope that someone will arrive and offer them a job for minimum wages

for construction work as plumbers, carpenters, and so forth.

There are also young university students who are unable to find work after years of trying.

They have the necessary qualifications and desire the chance to put the skills they have learned

in the service of the country, but there are few job opportunities in the labor market.

Given the importance of the youth labor market dividend and their contribution to economic

development, this study analyzes the position of youth in the labor market. It seeks to determine

the demographic and socio-economic factors influencing youth labor market participation. More

specifically, the study aims to obtain a clear answer to the question: which factors determine youth

labor market participation?

Field research design and methods of data collection

The dependent variable (whether a man works or not) is dichotomous or binary and takes the

value “1” if a man is employed and “0” if a man is unemployed. To analyze the binary variable,

logistic regression was used (McCullagh & Nelder, 1989). The logit model assumes the following

cumulative probability density function:

𝑃 =1

1+𝑒−(𝛽0+𝛽𝑋𝑖) ………….1

Where “P” is the probability that a man works or is employed, “e” is the exponential value, 𝛽 is

the coefficient, and “𝑋𝑖” is the explanatory variable.

Since “P” denotes the probability of a young person’s employment but is not directly

observable, a binary (0,1) variable was constructed. The derivative of the regression equation

from the logistic probability density function looks like:

𝑃 =1

1+𝑒−𝑌=

𝑒𝑌𝑖

1+𝑒𝑦𝑖 ………….. 2

Where,

𝑌𝑖 = 𝛽0 + 𝛽𝑖𝑋𝑖


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The above equation shows the cumulative logistic distribution function. If P (the probability of an

employed man) is given the equation (2), then the probability that a man is unemployed is given

the following equation:

1 − 𝑃 =1

1+𝑒𝑌𝑖 ………….3

We may write this as,

𝑃

1−𝑝=

1+𝑒𝑌𝑖

1+𝑒−𝑌𝑖 ………….4a

(𝑃

1−𝑃) = 𝑒𝑦𝑖 …………4b

Where 𝑃

1−𝑃 is the odd ratio in favor of a man’s employment status. Taking the natural log of the

equation (4b),

𝑙𝑛 (𝑃

1 − 𝑃) = 𝑌𝑖 = 𝛽0 + 𝛽𝑖𝑋𝑖

The above equation is called a logit model. The occurrence of 𝑋𝑖 may increase between the (0,1)

interval, which shows the effects of the different explanatory variables on the probability that a

man is employed or unemployed.

We estimated partial derivatives to explain the impact of the independent variables on the

probability of employment. The probability derivatives are given by the following equation:

𝜕𝑃

𝜕𝑋𝑖= 𝑃𝑗(1 − 𝑃𝑗)𝛽𝑖

Where “P” is the probability of employment or youth labor force participation.

Our model assumes that these categories of employment are independent of each other.

The parameters for each category of decision-making in each model were obtained from the

estimation of a single maximum likelihood logit. Table 1 contains all variables with their definitions,

which were included in the bivariate logistic regression.

Table 1: Definition of the variables

Acronym Definition of the variables

Age Youth’s age in years

Sex 1 if a youth is male, 0 otherwise

MS 1 if a youth is married, 0 otherwise

Un_educ 1 if a youth has no years of schooling, 0 otherwise

PEL 1 if a youth has primary education level, 0 otherwise


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SEL 1 if a youth has secondary education level, 0 otherwise

HEL 1 if a youth has high school education level, 0 otherwise

BEL 1 if a youth has bachelor education level, 0 otherwise

Y_educ A youth’s education in years

EX A youth’s job experience in years

S 1 if a youth has some English language skills, computer skills, other skills, 0

otherwise

HHS Total household size

H_educ Head of the household’s education in years

Remittance The amount of remittance a youth’s family receive in AFNS

Assets The amount of assets like houses, cars, and others in AFNS divided by 1,000,000

P_educ Partner’s years of schooling of married man and woman

P_income Per month income of married youth’s partner

Ins 1 if insecurity caused youth’s unemployment, 0 otherwise

Mm 1 if youth’s unemployment is caused by intervention (middleman), 0 otherwise

H_incm Per month total income of the household divided by 1,000 AFNS

This paper uses both quantitative and qualitative approaches to data analysis. Table 2

reports the mean and standard deviation of the sample variables. The sample data show that, on

average, nearly half of the population has a bachelor’s degree education level. However, the data

also indicate that 18 percent of young people have no years of schooling. This means that a

substantial proportion of young people are illiterate and thus illiteracy is a challenging problem

among the youth population. The data show that, on average, a young person has more than two

years of work experience and 45 percent of the sample population has some computer, English

language, and other skills which assist them in finding work, according to the usual job

requirements of the labor market. In terms of family background, there are, on average, eight

people living together, and the head of the household has seven years of schooling. On average,

16 percent of respondents reported that mediation (i.e., a middleman) is the main reason that

they were able to find a job in the labor market.

Table 2: Descriptive statistics of the survey data

Characteristics Mean Std. deviation

Age 26.84 7.55

Sex 0.48 0.50

MS 0.49 0.50


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Un_educ 0.18 0.387

PEL 0.04 0.195

SEL 0.12 0.478

HEL 0.22 0.576

BEL 0.49 0.658

Y_educ 10.63 6.19

EX 2.58 4.37

S 0.45 0.858

HHS 8.07 3.612

H_educ 6.98 6.095

Remittance 4.27 9.769

assets 14.14 17.77

P_educ 2.22 6.40

P_income 3.35 9.44

ins 0.07 0.25

mm 0.16 0.36

H_incm 38.892 49.40

N 329

Source: Estimates by author using survey data from Aybek city (2018)

Figure 1: Reasons for youth unemployment

24.89

10.04

3.49

7.42

22.7121.40

13.97

9.17

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Diagrammtitel


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In this survey, respondents identified some of the obstructions affecting their participation

in the labor market. Several micro and macro factors were proposed to respondents and a high

percentage reacted to “decrease in investment” as the main cause of unemployment in

Afghanistan. In addition, the existence of mediation and bribery in the process of obtaining

employment, the lack of job experience, the lack of important skills like English and knowledge of

computers, the reduction of international aid, and insecurity are reported as the main causes of

unemployment among young people, thus affecting their labor force participation.

Results

The results of this case study, using binomial logistic regression for the analysis of data collected

from 329 young people comprising the sample population, appears in Table 3. The bivariate

logistic regression expresses the probability of a young person participating in the labor market

and being employed in economic activity or not, as a function of a set of predictors as displayed

in Table 1. The estimation of the regression was used to infer the degree of confidence that the

predicted value can be the actual value with a given input of the independent variables (𝑋𝑖).

Table 3: Binomial logistic analysis for the sample data

VARIALBLES Coefficient Std. error

Sex -1.084** 0.444

MS 0.658* 0.389

S 0.991*** 0.332

EX 0.790*** 0.118

FS 0.105* 0.058

H_educ 0.212*** 0.034

Asset -0.057*** 0.016

Mm -6.436*** 1.397

Constant -2.288*** 0.735

Nagelkerke R square .710

Mean & std.dev of

dependent

Variable

.54 .499

N 329

Source: Estimates by author using SPSS statistical software *** significant at 1% level ** significant at 5% level * significant at 10% level

As Table 3 shows, youth employment is determined by gender. There is a negative and

significant correlation between a young person’s gender and the probability that s/he will


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participate in the labor market. In the labor market, women contribute less than men for several

reasons. Culturally, women are expected to perform chores around the house and take care of

their children and they are not allowed to participate in the labor market. In addition, there are

barriers to women’s education as many are not allowed to continue their education to higher levels

or they quit school at the primary or secondary level, while some families do not send them to

school during their childhood. Consequently, women’s potential earnings will decrease in the

labor market and there may be little chance of them securing employment. Marital status

significantly affected the probability of young people’s labor force participation. That is, marriage

makes young people more responsible for their family which they need to support financially.

The results of this case study demonstrate a highly positive and significant correlation

between young people’s skills (e.g., English language and computer skills) and their chance of

finding a job in the labor market. Years of work experience positively affects the probability of

young people’s labor force participation. A year’s increase in work experience of a young person

may increase his or her participation in the labor market by a probability of 79 percent.

Family background may also affect the probability of a young person participating in the

labor force. Family size and the head of the household’s education level impacts youth

employment. There is a highly positive and significant correlation between the head of the

household’s level of education and the young person’s employment. This finding is in line with the

empirical study of Mazzotta (2008) in which she concluded that graduates from deprived family

backgrounds find it more difficult to obtain employment than do graduates from affluent family

backgrounds. Assets of the family in the form of apartments, houses, cars, and stores negatively

affected young people’s labor force participation.

Another factor that determines youth labor force participation relates to mediation in the

process of employment. An intervention by a third party in the process of employment includes a

middleman who uses his or her power or bribe money to obtain a job for the targeted youth. This

practice may produce some negative effects in the labor market. First, other eligible labor

suppliers are not considered, so productivity may decrease. Second, this practice may discourage

other young people from participating in the labor force.

The value of Nagelkerke R-square defines the extent and validity of the operational

function and also explains how robustly the model works to predict the effects of the explanatory

variables on the dependent variable.


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Conclusion

In this paper, we studied the determinants of youth labor force participation using a case study of

Aybek city in Samangan province. An attempt was made to ascertain a clear answer to the

question: which factors influence young people’s decisions to participate in the labor market? To

address this question, we examined empirical survey data collected from Aybek city. The following

conclusions are derived based on this case study.

Young people’s skills such as having knowledge of the English language and computers

and the number of years of work experience are positively associated with their labor force

participation. Each additional year of experience increases the probability of a young person being

economically active. Marital status makes young people more responsible for their families and

thus increases the likelihood that they will participate in the labor market.

Culturally, women are expected to do chores around the house and take care of the

children, so being female may decrease their chance of being economically active in the labor

market. Family size and each additional year of education of the head of the household increase

the probability of a young person participating in the labor market.

In the case of Afghanistan, interventions in the process of employment by a third party

(middleman) is a significant problem in the labor market as it negatively affects youth labor force

participation. Although the middleman uses his or her power or bribe money (taken from the

employee) to employ the targeted person, this may intensify the disparities between the rich and

poor in the society as well as decrease the productivity of the labor supply because the eligible

labor supplier is not being employed. Preventing this illegal action by the government may

increase youth labor force participation.

Another finding of this case study is that assets have a negative effect on young people’s

participation in the labor market. It is recommended that future studies investigate the effects of

different types of assets (e.g., having agricultural land and farms) on youth employment in rural

areas.


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