Challenges for youth employment in Pakistan: are they youth-specific?
Rate of return (Analysis)
|Publication:||Name: Pakistan Development Review Publisher: Pakistan Institute of Development Economics Audience: Academic Format: Magazine/Journal Subject: Business, international; Social sciences Copyright: COPYRIGHT 2010 Reproduced with permission of the Publications Division, Pakistan Institute of Development Economies, Islamabad, Pakistan. ISSN: 0030-9729|
|Issue:||Date: Autumn, 2010 Source Volume: 49 Source Issue: 3|
|Topic:||Event Code: 530 Labor force information Canadian Subject Form: Labour force Computer Subject: Return on investment|
|Product:||Product Code: E121930 Youth; E229000 Unemployment|
This paper analyses the patterns of and the challenges for youth
employment in Pakistan, and examines whether these challenges are
youth-specific. Using the 2005-2006 Labour Force Survey (LFS), the
analysis includes determinants of unemployment, determinants of working
in the formal sector, rate of return on education, and determinants of
working hours. The paper finds that many of the challenges to youth
employment in Pakistan are not youth-specific. Policies should thus
emphasise broader labour market reforms, even in the context of tackling
youth employment issues. Still, some challenges are youth-specific, such
as a higher youth unemployment rate and insufficient returns to
better-educated youth. To address these challenges, more youth-specific
interventions are needed.
JEL classification: J13, D01, O12
Keywords: Youth Employment, Labour Market, Poverty, Pakistan.
Youth employment is a challenging issue in many developing and transitional countries [Kolev and Saget (2005)]. The youth unemployment rate is usually two times to three times that of the adult unemployment rate. And this figure is probably an underestimate because it normally does not account for those who are "discouraged" in seeking work and remain "inactive" (neither in school nor in the labour market). For those employed, young people also suffer disproportionally from decent work deficit, measured in terms of working poverty and status in employment. The ILO (2010) shows that young people have a higher likelihood than adults of being among the working poor (with per capita expenditure below $1.25 a day), as the share of working poor youth in total youth employment was 28.1 percent in 2008.
To increase the awareness of and to stimulate more interventions around youth issues, the 2007 World Development Report [The World Bank Group (2007)] summarises the challenges to youth and stresses the necessity of investing in youth in developing countries, especially smoothing the transition from school to work and creating more opportunities for youth employment. However, before formal intervention plans are developed, a diagnostic analysis of the overall labour market, and specifically the youth labour market, should be carried out.
In the case of Pakistan, there is a growing recognition of the political urgency to respond to the challenges of youth employment. The challenges are multi-faceted. The transition from school to the labour market is not smooth; the youth unemployment rate is higher than the adult unemployment rate; many young people work as unpaid family workers, own-account, or casual wage workers; and due to cultural and other reasons, female youth are in worse shape than their male counterparts on various employment dimensions.
However, an analysis of youth employment cannot be separated from the overall labour market analysis. Pakistan has a unique labour market, and many characteristics of the youth labour market are present in the general labour market as well. Thus, to understand the challenges in the youth labour market, one must first know whether these challenges are youth-specific. Understanding these questions can better guide the policy-makers to allocate limited resources to youth-specific interventions or to interventions with a much broader target.
The objectives of this paper are to analyse the patterns and the challenges of youth employment in Pakistan and to determine whether these challenges are youth-specific. This paper uses the 2005-2006 Labour Force Survey (LFS), and the analysis covers various dimensions in the labour market, including determinants of unemployment, determinants of working in the formal sector, (1) rate of return on education, and determinants of working hours.
I find that many of the challenges to youth employment in Pakistan are not youthspecific. This suggests that policies should emphasise a broader labour market reform, even in the context of tackling youth employment issues. Still, some challenges are youthspecific, such as the high unemployment rate and insufficient returns to better-educated youth. For these challenges, more youth-specific interventions are needed. Having said that, the policy-makers also need to consider the long term returns of youth labour market policies, and the fact that problems at an early career-stage tend to replicate themselves later on. Thus, from these perspectives, even if many of the labour market challenges facing youth are not different from those facing adults, there are still reasons for dedicated youth employment policies.
The paper is organised as follows. Section 2 describes the data and the key variables used in the paper. Section 3 presents youth labour market trends and compares the key dimensions of youth employment and adult employment. Section 4 analyses the determinants of youth activities, and Section 5 analyses the difference between youth and adults on characteristics associated with being unemployed and working in the formal sector. Section 6 presents the empirical results on rate of return on education for both youth and adults, and Section 7 explores the determinants of total hours worked. Section 8 concludes.
The main analysis uses the Labour Force Survey (LFS) data from 2005-2006. Earlier rounds are also used for the trend analysis. The LFS is planned to be conducted every two years, but the actual frequency varies. The LFS collects a set of information on various dimensions of a country's civilian labour force, including socio-demographic characteristics, such as age, sex, marital status, level of education, current school enrollment, migration status, and employment information. Each round of the LFS consists of all urban and rural areas of all provinces defined in the Population Census. (2) In this paper, a "youth" is defined as someone between 15 and 24 years of age, and an "adult" as someone between 25 and 65 years of age. Definitions of the key variables used are in the Appendix 1 and the descriptive analysis is presented in Appendix 2.
3. YOUTH LABOUR MARKET TRENDS AND A STATIC COMPARISON OF YOUTH EMPLOYMENT AND ADULT EMPLOYMENT
3.1. The Trend of Youth Activities in Pakistan, from 1992 to 2006
In principle, youth can engage in any of the following activities: work and go to school; work only; go to school only; be unemployed; or be inactive (3) Figure 1 presents the trend of these five activities for male and female youth in both urban and rural areas. Overall, the activity trends for both male and female youth are similar; however, a higher percentage of youth work in the rural areas than in the urban area. In both urban and rural areas between 1992-1993 and 2005-2006, male youth were more likely to choose to work rather than to continue their education. One possible reason is that economic growth generated more job opportunities for youth, especially in fields that do not require higher education; thus there were no strong incentives for youth to pursue relatively higher education.
[FIGURE 1 OMITTED]
In both rural and urban areas, the percentage of unemployed male youth reached its peak in 2001-2002. (4) This is consistent with the economy dynamic: in 2001-2002 a severe drought and a devastating earthquake hit the economy badly, but by 2003 the economy had recovered and grew quite rapidly until 2006. The trend for female youth activities in the rural areas is more dynamic than that in the urban areas. In the rural areas, female youth employment rate decreased from 1992-1993 to 1998-1999, but then steadily increased. The school enrollment rate also increased, accompanied by a sharp decline in the rate of "inactivity". These findings are consistent with earlier analysis on youth vulnerability in Pakistan [Sparreboom and Shahnaz (2007)]. Among youth workers, the trends in status in employment and industry share are quite stable. (5)
3.2. Youth Employment and Adult Employment Compared
In this section, I use the latest LFS data 2005-2006 and compare youth employment and adult employment by labour force participation (LFP) rate, unemployment rate, employment status, and industry allocation (Table1).
The LFP rate for male youth is significantly less than that for male adults. However, the male youth unemployment is almost triple the male adult unemployment rate. This is not particular to Pakistan, but very common in many developing countries and developed world [ILO (2010)]. The overall LFP rate for women is very low, reflecting the fact that women are discouraged from participating in the labour force. The unemployment rate is high for both female youth and female adults, roughly 10 percent. The reasons are complex. Culturally, women's mobility is restricted; historically, many jobs have been fulfilled by men, even those which are more likely to be performed by women (such as housekeeping) in other countries. Women are usually less educated thus skill mis-match also plays some role for the low labour force participation rate.
In terms of status in employment, more adults than youth are salaried workers, employers, or own-account workers. There are two possible reasons. First, adults are more competitive in the formal job market, and second, many youth begin their careers in the informal sector (as unpaid family workers, for example) and then gradually become ownaccount or salaried workers as they get older. Little difference exists in their industry allocations between youth and adult workers, except that male youth are more likely to be in the manufacturing sector while male adults are more likely to be in the service sector.
4. YOUTH EDUCATION AND EMPLOYMENT DECISIONS
Youth face a decision whether to continue their education, join the labour force, or stay inactive. A rational decision depends on many factors, including the marginal utility to the household of youth continuing in school, working, or staying inactive, the income constraints, time constraints, and outside opportunities for youth to work. The theoretical model of education and employment decisions has been substantively discussed in the child labour literature(6) [Bhalotra and Heady (2003); Edmonds (2007)]. In this paper I adapt the reduced form for the youth education and employment decisions.
Y(EW,E,W,H)=F(age,sex,X), ... ... ... ... ... ... ... (1)
where Y is the decision, EW is both in the labour force and in school, E is in school, W is in the labour force, and H is inactive; HH is household demographic information, and X is the vector of variables including individual and household characteristics. (7)
The four choices can be sequential or simultaneous. For example, youth can choose whether to go to school, then choose whether to join the labour force or stay inactive. Alternatively the choices can be made simultaneously. Because the order of the decisions is subjective in a sequential model, this paper uses a simultaneous model--i.e., a multinomial logit model--to understand which factors are significant in contributing to youth activity decisions [Ersado (2005)]. Four choices (to be both in labour force and in school; in school; in labour force; and inactive) are estimated, with school as the base outcome [Green (2003)].
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]
where j=0 is in school, j=l is in school and in labour force,j=2 is in labour force, and j=3 is staying inactive. The relative risk ratios (RRR) are presented.
Table 2 presents the results for male youth and female youth activity decisions. Two of the findings are common for male and female youth. First, household head's education and employment status play a significant role in determining youth activity decisions. Youth in a family with a better-educated head and/or employed head (in either the agricultural sector or the non-agricultural sector) are more likely to be in school and less likely to be in the labour force or be inactive. Second, the ratio of working individuals to non-working individuals in a household has a significant and positive effect on youth's activity decisions, both in statistics and in magnitude. This ratio reflects two specific effects: the income effect and the network effect. The network effect in this context refers to youth who are exposed to greater job opportunities if there are more employed individuals in the family. (8) Since the income effect is the opposite of the network effect (a wealthier family is more likely to keep youth in school), this finding suggests that the network effect outweighs the income effect and the family network can be instrumental in helping youth to find a job and start to work. This finding is consistent with findings from the Pakistan Investment Climate Assessment (PICA) survey that about 50 percent of firms hire employees through a network of family or friends [Hou (2008)].
The male youth decision model also shows that older and married male youth are more likely to work. Male youth in families with more young children (0-5 or 6-14 years old) or female youth (15-24 years old) are more likely to work. On the contrary, male youth in a family with more adults or more male youth (15-24 years old) are more likely to be in school. This finding suggests that households tend to put male youth to work when there are younger children or more girls in the same age range.
The female decision model shows that the majority of female youth remain inactive, neither in the labour force nor in school. Older and married female youth are less likely to be in school than their younger and unmarried counterparts. Household demographic structure plays an important rote in determining the activities of female youth. Female youth in families that have more young children (0-5 or 6-14 years old) are more likely to be in the labour force or to stay inactive; female youth in families that have more men in the same age range (15-24 years old) are less likely to be in the labour force and more likely to be inactive; female youth in families that have more older men and women are more likely to be in school. This finding implies that female youth are less likely to be in school when domestic needs increase (such as caring for young children) and there are fewer substitutes (such as older women).
In summary, the analysis shows that in addition to age and sex, some household characteristics are highly correlated with the youth activity decision. Among them, education of household head, household demographic structure, and the ratio of working to non-working individuals are very important determinants.
5. WHO IS MORE LIKELY TO BE UNEMPLOYED AND TO BE A FORMAL WORKER?
This paper uses a two-part model to understand the likelihood of being unemployed and being a formal worker, if employed. This multiple-part model has been widely used in the health economics literature [Duan and Chau (1987); Manning and Newhouse, et al. (1987); Ruiz and Amaya, et al. (2007)]. in this paper, the two-part model separates the process into two stages, with the first being unemployed and the second being a formal worker, if employed.
Part I uses a LOGIT model to estimate the likelihood of being unemployed:
Prob[unemployed=1 | labour market participation=1]=[[alpha].sub.1] +[[beta].sub.1][CHI]+[[epsilon].sub.1], ... (2)
where [beta], is a vector of coefficients and [CHI] represents a set of independent variables.
Part II also uses a LOGIT model:
Prob[formal worker=1 | unemployed=0] = [[alpha].sub.1]+[[beta].sub.1] [CHI]+[[epsilon].sub.1], ... ... ... (3)
where [[beta].sub.2] is a vector of coefficients and X represents a set of independent variables. The respective error terms are symbolised by [[epsilon].sub.1] and [[epsilon].sub.2]. It is assumed that [EPSILON]([[epsilon].sub.1])=[EPSILON]([[epsilon].sup.2]) = 0.
5.2. Characteristics Associated with Unemployment
The most striking finding is that, for male youth, the higher the level of education, the greater the likelihood of being unemployed (Table 3). Compared with male youth without any formal education, the likelihood of being unemployed is 1.74 times greater for male youth with a matric degree (equivalent to a high school diploma), 2.2 times greater for male youth with an inter degree (higher than matric but less than a bachelor's degree), and 3.37 times greater for male youth with a bachelor's degree or above. This could be skill-mismatch between demand and supply. For example, better-educated male youth are more likely to seek job opportunities that require a higher level of skills, but such opportunities are rare for youth in Pakistan, Thus, over time, the probability of being unemployed is higher for better-educated youth. It could also because youth has unrealistic expectations of jobs. This pattern is very different from that in developed countries, where the youth unemployment rate falls as the level of education rises [Nickell (1996); Nickell (1996); O'Higgins (1997)].
Yet this pattern is unique to male youth. Education is not an important factor in determining unemployment for male adults because none of the educational variables are significant in the regression, There are at least two possible explanations for this youth- specific pattern. First, economic pressures might make it necessary for adults with higher levels of education to accept jobs with fewer educational requirements. Second, because there is less demand for individuals with higher education, adults are more likely than youth with a similar education level to get the job, largely because adults are more experienced. However, the pattern for female adults is different. Female adults with inter or higher degrees are less likely than their male counterparts to be unemployed.
There are some regional variations in youth unemployment and adult unemployment. The patterns are not identical but are very similar for youth and adults within each gender. In contrast, the gender difference is much bigger than the youth-adult difference. This gender difference has deep social historical roots. The prevailing traditional cultural restrictions on women in Pakistan [Amin (1995); Hakim and Aziz (1998)] have affected many aspects of women's life including mobility, marriage, education and employment. However, with economic growth and efforts to empower women in recent years, women's roles have improved both within and outside of households. More women are getting education and are more involved in their employment decisions, but efforts are still needed for significant changes in labour market outcome indicators.
The analysis does not control for the remittance due to data limitations. However, it has to be acknowledged that remittance plays important roles in Pakistan's economy and labour market [Adam (1998)]. The current contribution of foreign remittances is more than 4 percent of GDP [Ahmed and Sugiyarto, et al. (2010)] and evidence shows increased investment in productive assets among households receiving remittances [Arif (2009)]. Literature also show that the migrants to Gulf countries are more of young people with higher education because of the expected higher returns in working in foreign countries [Arif (2009]. However, when examining the effect of migration within the country, I find that on average both adult male and youth male in these households have higher unemployment probability. On the contrary, the unemployment likelihood for female adults is decreased, probably due to more employment opportunities in the urban areas if assuming direction of domestic migration is more from rural to urban areas.
Other household characteristics are also significant in determining unemployment likelihood and the effects are quite similar for youth and adults. Individuals in a household with more employed members arc less likely to be unemployed. Though there could be many confounding factors, the larger network with more employed family members and relatives could help unemployed youth and adults find jobs.
5.3. Characteristics Associated with Working in the Formal Sector
This section compares the characteristics associated with working in the formal sector for youth and adults of each gender, respectively. Pakistan's informal sector is large. As specified in the previous section, the informal sector consists of workers who are self- identified as unpaid family workers, own-account workers, or casual wage workers. Formal sector workers consist of workers who are self-identified as salaried employees or employers. Eighteen percent of youth and 27 percent of adults are employed in the formal sector.
The variables that are significantly associated with working in the formal sector are similar for youth and adults (Table 4). Education plays an important role for all groups. However, the likelihood of being employed in the formal sector is much greater for male adults than for male youth with the same level of education. Similar patterns are also found between female youth and female adults. This finding suggests that it might take youth with higher education quite some time to find a job in the formal sector; before that, these youth have to work in the informal sector. Better-educated females are more likely to be employed in the formal sector than their male counterparts. This is true for both youth and adults.
Another important set of variables in determining employment in the formal sector is the status in employment of other household members, and these effects are very similar across the four groups. An individual seeking work in the formal sector who has family members employed in the formal sector has a higher probability of working in the formal sector. This is again because of the network effect of family, However, these variables might be correlated with other unobserved characteristics; thus the estimation is subject to the omitted-variable bias.
In summary, the variables that determine youth employment in the formal sector are not very specific to youth, except for education. There arc two main points. First, it is very likely that better-educated youth will begin working in the informal sector and then move to the formal sector, given the different findings for youth and adults. Second, although in general the labour market does reward workers with better education by placing them in the formal sector, the reward is not very linear. There is little difference in the probability of working in the formal sector between individuals with middle or primary school education and individuals with no formal education. This is especially true for youth.
6. RETURN ON EDUCATION
This study follows the standard human capital earnings function developed by Mincer :
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)
where In y is the log of weekly earnings, s is schooling, X is a vector of other characteristics that might influence the earnings, and u is a residual with zero mean. Various methods have been developed to address the selection bias and the endogeneity issues in the estimation of the rate of return on education [Griliches (1977, 1979)], and many have been applied to studies in developed countries. Since the objective of this section is to compare the rate of return for adults and youth, this paper uses the simplest method, the ordinary least squares (OLS). Thus, the interpretation should not be focused on the absolute return on education for youth and adults, but rather on the comparison of the two. It should also be noted that the sample is restricted to wage employees (salaried workers and casual wage workers) because LFS only reports earnings for wage employees. Since only levels of education are reported, the rate of return on education is level-based rather than year-based.
In general, the rate of return on education significantly and progressively increases with higher levels of education (Table 5). This is consistent with the finding from Aslam (2009), which shows the return to an additional year of schooling ranges from 7 to 11 percent for men and from 13 to 18 percent for women. (9) The finding is also similar to that in Kingdon and Soderbom (2008), which shows that conditional on occupation, education consistently and substantially raises earnings. (10) However, the analysis also reveals three additional points.
First, there is no significant difference in earnings between youth with primary education and youth without any formal education. This is also true for female adults, but not for male adults. This implies that the labour market might not sufficiently reward individuals with limited education in these disadvantaged groups. This could have the devastating consequence that some families might be reluctant to send their children to school if they perceive that completion of primary education would not increase their future earnings prospects.
Second, it seems that the return on education is compounded with experience (or age). This conclusion comes from the comparison of rate of return on education between youth and adults, for males and females respectively. Coefficients at all education levels are significantly higher for adults than for youth, and such differences increase with the level of education.
Third, there seems to be a scarcity premium for educated females in the labour market. This finding is based on a comparison of coefficients between male adults and female adults with similar education levels. Compared with their less-educated counterparts, women with more education receive much higher returns than men. However, the average earnings of women are still much lower than those of men, even in the categories of higher-educated ones. These findings are similar to the findings of Kingdon and Soderbom [Kingdom and Soderbom (2008)], who use a different survey and sample.
7. LABOUR SUPPLY--TOTAL HOURS WORKED
The labour supply models usually consist of two stages, the first being the decision to work and the second being hours worked. Since the earnings data are not reported for informal sector workers, I use the following models to estimate hours worked.
First, I restrict the sample to paid employees and estimate the hours worked:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (5)
where [[beta].sub.1] is the coefficient for earnings, X is the vector of other variables that may influence the hours worked, and u is the unobserved variables with zero mean.
Second, I include all workers in the labour market in both formal and informal sectors. Since earnings are not reported for unpaid family workers and own-account workers, employment status (ES) rather than earnings data are included.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (6)
In the last model, I use the earnings equation (Equation 4) to predict the earned wages for unpaid family workers and own-account workers, assuming that the return on education is the same for paid employees and informal sector workers. This is a strong assumption because of the selection bias between workers in the formal sector and workers in the informal sector. Thus, the results have to be interpreted with caution. In the case, the model is as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (7)
where earnings' is the predicted earnings using Equation 4.
Table 6 presents the regression results of characteristics associated with total hours worked for male workers. The sample in column 1 and column 4 includes only paid employees. The sample in the other columns includes workers in both formal and informal sectors. Column 3 and column 6 use earnings predicted from the earnings equation (Equation 4) for male adults, male youth, female adults, and female youth respectively. Table 7 presents the female case using the same structure.
Total hours worked in the past week increase when earning increases, and the results do not vary between the sample of wage workers (column 1 and column 4) and the sample with all workers (column 3 and column 6). Youth respond more than adults to the same incremental earnings increase. For example, male youth work 0.99-1.04 more hours and male adults work only 0.18-0.2 more hours for an increase in wages of 1,000 Rs a week. Female are more responsive than male to higher earnings.
Working hours are different among workers with different employment status. Unpaid family workers work less than salaried workers in all four groups. Among unpaid family workers, on average, young men work 5.6 hours less; adult males work 1.5 hours less; young women work 12 hours less; and adult women work 10 hours less than salaried workers in their respective groups. In general, casual wage workers also work less than salaried workers, but this is not true for male adults.
Other findings include that better-educated workers work less; migrated workers work longer hours; and workers tend to work much longer hours in the transportation, wholesale, and retail sectors than in other sectors.
Youth employment in Pakistan faces many challenges, but some of them are also common in the overall labour market. Thus, interventions at this stage should focus more on improving the overall labour market performance rather than on narrowly targeting youth employment. Generating more employment opportunities, for both adults and youth, and creating a job portal that would allow employees and workers to share employment information should be priorities in Pakistan. At the same time, focusing on long-term investment in human capital through formal and informal education and strategically strengthening the links between education and the labour market would greatly benefit youth in the long run [Fasih (2008)].
Still, there are some challenges that are youth-specific. The most striking one is that the unemployment rate is much higher for better-educated youth, and the initial earnings of better-educated youth are not very different from those of less-educated youth (as compared to wages for adults with similar education levels). Thus, youth-specific interventions should be implemented to generate more (well-paying) job opportunities for better-educated youth, to smooth the transition from school to the labour force, and to help youth realise their investment in education. These interventions would have long-term benefits to economic growth by leading to higher household incomes and influencing households to invest in youth education.
DESCRIPTION OF DATA AND KEY VARIABLES
The sampling takes two stages in the Pakistan Labour Force Survey. The first stage is the selection of the primary sampling units (PSU), (11) defined as enumeration blocks (12) in the urban areas and mouzas/dehs/villages in the rural areas. The second stage is the selection of secondary sampling unit (SSUs). A specified number of households--i.e. 12 from each urban sample PSU and 16 from each rural sample PSU --are selected using systemic sampling techniques with a random starting point.
Several researchers who have studied the labour market in Pakistan have use the Pakistan Integrated Household Survey (PIHS) or Pakistan Social and Living Standard Measurement (PSLM) Survey. Here I use the LFS because: (1) the LFS data are a representative sample of the labour force; (2) the LFS asks specifically whether the individual (10 years old or above) was actively looking for a job if he/she did not work in the past week, an essential question for defining unemployment; (3) the survey instruments are the same in each round, facilitating trend analysis over a period of years; (13) and (4) the LFS reports the number of hours worked but the PIHS does not Definition of key variables is listed below.
Employed and Unemployed
In the LFS, "employed" is defined as "do any work for pay, profit, or family gain during the past week, for at least one hour on any day" or "help to work for family gain in a family business or family farm during the past week" or "have a job or enterprise such as a shop, business, farm, or service establishment, even if did not work last week for some reason." "Unemployed" is defined as not engaging in any of the activities listed above but available for work during the past week.
Employment Status and Formal Worker
This paper categorizes employment status into five groups: employer, own-account workers, unpaid family workers, salaried workers, and casual wage workers. (14) Of these, workers in the informal sector consist of own-account workers, unpaid family workers, and casual wage workers.
Earnings and Hours Worked
Earnings are reported as the total amount earned (both in cash and in kind) from the main work source over the past week. Only paid employees (salaried workers and casual wage workers) report weekly earnings. Total hours worked is the sum of the hours worked in the past seven days for main occupations and for any subsidiary occupations.
Migration is defined as a dummy and equal to 1 if an individual lived in a district for less than 10 years.
Author's Note: I am thankful to anonymous referees of this journal for their comments on earlier versions of this paper. The usual disclaimer applies.
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(1) The formal sector defined here is based on status in employment, i.e. salaried workers and employers. Similarly, the informal sector consists workers who are unpaid family worker and own-account workers.
(2) Some rounds exclude Azad Jammu, Kashmir, North Areas, Federally Administered Tribal Area (FATA), military restricted areas, and protected areas of Khyber Pakhtunkbwa. The population of the excluded areas constitutes about 3 percent of the population.
(3) Inactiveness means that youth do not participate in the labour force or enrolled in school. However, inactive youth might engage in many domestic activities. This is especially the case for female youth.
(4) Note that the percentage of unemployed youth is different from the unemployment rate for youth. The former uses all youth as the denominator while the latter uses all youth in the labour force as the denominator.
(5) Tables are available from the author upon request.
(6) The term of "child labour" is commonly used in the context of economic activity by children (aged less than 15), while "youth" refers to those age 15-24.
(7) The author would like to control for the consumption or income variable, but LFS does not contain the consumption module and only reports the earnings for paid employees. Thus, the income variable is not usable since it only captures part of the household income. I controlled for household head education, which is positively correlated with income and expenditures both in magnitude and significance.
(8) The Investment Climate Assessment Survey also shows that firms hire through "family or friends" more frequently.
(9) The author used the Pakistan Integrated Household Survey (PIHS) data, and the sample was restricted to wage and salaried employee.
(10) The authors also use the PIHS data and broaden the analysis to wage, self-employed, and agricultural workers.
(11) Sample PSUs is drawn with probability proportional to size method.
(12) Enumeration blocks are defined as 200-250 households on average with well-defined boundaries and maps.
(13) PSLM2004-2005 uses a slightly different employment module than PIHS2001-2002 and PIHS2005-2006. This makes the trend analysis difficult to interpret.
(14) Casual wage workers include casual paid employees, workers paid a piece rate or according to the work performed, and paid non-family apprentices. Own-account workers include self-employed workers, owner cultivators, share croppers, and contract cultivators.
APPENDIX 2 Descriptive Analysis of Male Youth, Male Adults, Female Youth and Female Adults Male Male Female Female Youth Adults Youth Adults Age 18.9 40.7 19.0 39.4 Married 13.1% 87.3% 32.2% 88.7% No any Formal Education 24.1% 40.7% 45.8% 73.5% Below Primary Education 5.5% 4.4% 3.1% 2.1% Middle Level 20.5% 15.0% 15.0% 8.4% Matric Level 23.3% 11.7% 13.6% 4.3% Inter Level 17.8% 14.5% 13.8% 6.2% Bachelor's 6.5% 5.9% 5.8% 2.5% Post-graduation Level 2.3% 7.7% 3.0% 2.9% Migration within Past 10 3.7% 5.0% 6.4% 5.0% Years Household Characteristics No. of Members, 0-5, Female 0.66 0.88 0.76 0.90 No. of Members, 0-5, Male 0.69 0.91 0.81 0.93 No. of Members, 6-14, Female 1.07 1.07 1.03 1.16 No. of Members, 6-14, Male 1.17 1.18 1.15 1.26 No. of Members, 15-24, Female 1.20 0.95 2.04 0.87 No. of Members, 15-24, Male 2.15 0.92 1.25 0.91 No. of Members, 25-65, Female 1.40 1.54 1.38 1.77 No. of Members, 25-65, Male 1.40 1.83 1.50 1.53 No. of Members, 66+ 0.20 0.21 0.22 0.25 Household Size 9.93 9.48 10.13 9.59 No. Work/ No. Not Work 0.73 0.60 0.61 0.54 Urban 37.1% 36.0% 36.1% 33.8% Punjab 53.3% 54.0% 55.4% 54.9% Sindh 26.9% 26.4% 23.4% 23.9% Khyber Pakhtunkhwa 15.6% 14.6% 18.0% 16.7% Balochistan 4.3% 5.0% 3.2% 4.6% Number of Observations 22797 37743 21514 37500
Table 1 A Static Comparison between Youth Labour and Adult Labour, 2005-2006 Male Male Female Female Youth Adults Routh Adults LFP Rate and Unemployment Rate LFP Rate 73% 95% 18% 22% Unemployment Rate 8% 3% 10% 9% Employment Status in the Urban Area Employer 0% 3% 0% 0% Own-account Workers 20% 39% 18% 21% Unpaid-family Workers 22% 7% 26% 17% Salaried Workers 33% 37% 32% 40% Casual Workers 26% 15% 24% 22% Employment Status in the Rural Areas Employer 0% 0% 0% 0% Own-account Workers 20% 54% 9% 16% Unpaid-family Workers 47% 12% 71% 68% Salaried Workers 12% 18% 6% 5% Casual Workers 21% 15% 15% 11% Industry Allocation Agriculture 37% 35% 61% 73% Manufacturing 18% 12% 23% 11% Construction 9% 7% 1% 0% Wholesale and Retail 18% 18% 2% 2% Transportation 7% 8% 0% 0% Services 11% 17% 13% 13% Other 1% 3% 0% 0% Hours Worked and Earnings in the Previous Work Hours Worked (Hours) 49.95 51.97 34.55 35.06 Earning per Week Rs) 856.00 1501.17 571.88 949.99 Note. 1. weighted data arc used. Table 2 Determinants of Youth Activity Decisions Male Youth In School In LF Inactive and LF Age 0.94 2.19 *** 0.98 [0.23] [0.27] [0.22] Marred 1.5 3.61 *** 5.54 *** [0.39] [0.52] [1.05] Household Head Educ. (Middle and below) 0.93 0.56 *** 0.65 *** [0.10] [0.03] [0.07] Educ. (Matric and above) 1.14 0.17 *** 0.28 *** [0.12] [0.01] [0.03] Married 1.57 ** 1.15 * 0.97 [0.28] [0.10] [0.15] Female 0.89 0.43 *** 0.73 [0.28] [0.06] [0.21] Female and Married 0.71 0.52 *** 0.72 [0.26] [0.09] [0.25] Employed (in Agricultural 0.66 *** 0.44 *** 0.57 *** Sector) [0.10] [0.03] [0.08] Employed (in Non- 0.56 *** 0.52 *** 0.68 *** agricultural Sector) [0.08] [0.03] [0.08] Migration within Past 10 0.83 1.13 1.08 Years [0.19] [0.12] [0.24] No. of Members, 0-5, Female 1.34 *** 1.39 *** 1.19 *** [0.07] [0.04] [0.06] No. of Members, 0-5, Male 1.53 *** 1.41 *** 1.22 *** [0.08] [0.04] [0.06] No. of Members, 6-14, 1.47 *** 1.35 *** 0.99 Female [0.06] [0.03] [0.04] No. of Members, 6-14, Male 1.27 *** 1.33 *** 1.19 *** Female [0.05] [0.03] [0.05] No. of Members, l5-24, 1.23 *** 1.14 *** 0.85 *** Female [0.05] [0.02] [0.04] No. of Members, 15-24, 0.71 *** 0.80 *** 1.11 *** Male [0.03] [0.02] [0.04] No. of Members, 25-65, 1 0.87 *** 0.86 ** Female 10.071 [0.03] [0.06] No. of Members, 25-65, 0.56 *** 0.58 *** 0.96 Male [0.04] [0.02] [0.05] No. of Members, 661 0.84 0.71 *** 0.97 [0.09] [0.04] [0.09] No. Work /No.Non-work 44.22 *** 34.94 *** 3.82 *** [3.93] [2.86] [0.57] Urban 0.78 *** 0.70 *** 0.53 *** Number of Observations 22392 22392 22392 Female Youth In School Inactive and LP In LF Age 1.78 2.11 *** 1.77 *** [0.96] [0.38] [0.24] Marred 1.51 11.26 *** 26.37 *** [0.86] [2.19] [4.86] Household Head Educ. (Middle and below) 1.29 0.43 *** 0.39 *** [0.32] [0.03] [0.02] Educ. (Matric and above) 0.73 0.18 *** 0.13 *** [0.19] [0.02] [0.01] Married 1.21 1.31 ** 0.87 [0.43] [0.17] [0.08] Female 0.6 0.46 *** 0.44 *** [0.31] [0.10] [0.07] Female and Married 0.43 0.78 0.82 [0.31] [0.19] [0.15] Employed (in Agricultural 0.23 *** 0.65 *** 1.07 Sector) [0.09] [0.07] [0.09] Employed (in Non- 0.47 *** 0.43 *** 0.87 ** agricultural Sector) [0.13] [0.04] [0.06] Migration within Past 10 0.82 0.82 1.1 Years [0.43] [0.12] [0.13] No. of Members, 0-5, Female 1.37 * 1.80 *** 1.32 *** [0.23] [0.08] [0.05] No. of Members, 0-5, Male 1.54 *** 1.78 *** 1.33 *** [0.24] [0.08] [0.05] No. of Members, 6-14, 1.50 *** 1.46 *** 1.07 *** Female [0.14] [0.04] [0.02] No. of Members, 6-14, Male 1.15 1.44 *** 1.19 *** Female [0.12] [0.04] [0.03] No. of Members, l5-24, 1.17 1.14 *** 0.92 *** Female [0.11] [0.04] [0.02] No. of Members, 15-24, 0.67 *** 0.68 *** 1.05 ** Male [0.07] [0.02] [0.02] No. of Members, 25-65, 0.91 0.76 *** 0.82 *** Female [0.15] [0.04] [0.03] No. of Members, 25-65, 0.39 *** 0.54 *** 0.92 ** Male [0.07] [0.03] [0.03] No. of Members, 661 0.89 0.79 *** 0.85 *** [0.20] [0.06] [0.05] No. Work /No.Non-work 20.99 *** 22.04 *** 2.05 *** [2.46] [1.97] [0.17] Urban 0.55 *** 0.44 *** 0.38 *** Number of Observations 21262 21262 21262 Note: 1. Variables included but not reported: age squared, province dummies; 2. Standard errors are in brackets; 3. *** p<0.01, ** p<0.05, * p<0.1 Table 3 Characteristics Associated with Unemployment Male Youth Adults Age 1.19 0.77 [0.97] [.12.08] *** Age Squared 0.99 1 [1.56] [14.32] *** Married 0.37 0.2 [8.09] *** [19.88] *** Had Training 1.17 0.84 [0.75] [0.79] Education (No Formal Educationas Comparison) Below Primary Education 1.24 0.89 [1.54] [0.72] Primary Education 1.27 1.01 [2.56] ** [0.06] Middle 1.07 1.05 [0.66] [0.46] Matric 1.74 1.06 [5.74] *** [0.65] Inter 2.2 1.23 [5.56] *** [1.70] * Bachelor or Above 3.37 1.06 [7.12] *** [0.50] Migration within Past 1.28 2.14 10 Years [1.72] * [7.64] *** Household Size 0.89 0.97 [11.12] *** [3.87] *** No. Employed / 0.02 0.06 No. not Employed [28.06] *** [20.58] *** Urban 1.44 1.24 [5.76] *** [3.28] *** Provinces (Punjab asComparison Group) Sindh 0.54 0.69 [7.60] *** [4.52] *** Khyber Pakhtunkhwa 0.81 1.52 [2.52] ** [5.56] *** Balochistan 0.54 0.4 [.5.17] *** [6.28] *** Number of Observations 15584 34716 Female Youth Adults Age 1.39 0.75 [0.87] [8.35] *** Age Squared 0.99 1 [1.10] [10.56] *** Married 1.48 0.67 [2.14] ** [3.46] *** Had Training 1.42 1 [1.02] [0.00] Education (No Formal Educationas Comparison) Below Primary Education 1.19 0.8 [0.52] [0.62] Primary Education 1.33 0.79 [1.38] [1.15] Middle 1.19 1.52 [0.65] [1.61] Matric 1.42 1.04 [1.67] * [0.22] Inter 1.15 0.58 [0.55] [2.05] ** Bachelor or Above 1.03 0.66 [0.091 [2.23] ** Migration within Past 1.45 0.64 10 Years [1.38] [1.71] * Household Size 0.93 0.97 [3.94] *** [2.47] ** No. Employed / 0.03 0.07 No. not Employed [15.22] *** [17.55] *** Urban 1.28 1.96 [1.69] * [6.21] *** Provinces (Punjab asComparison Group) Sindh 1.15 1.28 [0.69] [1.74] * Khyber Pakhtunkhwa 3.09 3.37 [6.95] *** [10.69] *** Balochistan 0.85 1.39 [0.51] [1.55] Number of Observations 3213 7073 Note: 1. Absolute value of z statistics in brackets. 2. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. Table 4 Characteristics Associated with Being a Worker in the Formal Sector Male Youth Adults Age 1.11 1.18 [0.72] [13.34] *** Age Squared 1 1 [0.30] [13.54] *** Married 1.01 1.06 [0.09] [1.22] Had Training 1.71 1.55 [2.92] *** [4.86] *** Education (No Formal Education as Comparison) Below Primary Education 1.02 1.28 [0.14] [3.17] *** Primary Education 1.02 1.26 [0.22] [4.87] *** Middle 0.93 1.83 [0.88] [12.45] *** Matric 1.24 2.64 [2.76] *** [22.31] *** Inter 1.84 3.75 [5.28] *** [23.73] *** Bachelor's or Higher 2.38 6.04 [5.91] *** [35.62] *** In Agricultural Sector 0.13 0.1 [20.85] *** [39.19] *** Number of other Family 0.63 0.55 Members are Employer [2.50] ** 14.56] *** Own-account Workers 0.74 0.87 [7.45] *** [5.21] *** Unpaid Family Workers 0.83 0.63 [5.48] *** [17.63] *** Salaried Workers 2.67 2 [28.00] *** [29.38] *** Casual Wage Workers 0.67 0.84 [10.89] *** [6.84] *** Migration within 1.5 1.4 Past 10Years [3.62] *** [5.86] *** Household Size 0.96 0.97 [4.15] *** [5.06] *** Urban 1.15 0.76 [2.51] ** [8.63] *** Provinces (Punjab as Comparison Group) Sindh 0.89 1.29 [1.97] ** [7.21] *** Khayber Pakhtunkhwa 0.68 1.22 [4.861 *** 14.501 *** Balochistan 0.6 1.65 [5.02] *** [10.54] *** Number of Observations 14579 34403 Female Youth Adults Age 0.94 1.16 [0.14] [3.18] *** Age Squared 1 1 [0.18] [3.01] *** Married 0.93 0.9 [0.33] [0.85] Had Training 1.18 1.29 [0.46] [0.74] Education (No Formal Education as Comparison) Below Primary Education 0.36 1.16 [1.63] [0.42] Primary Education 0.6 0.71 [1.70] * [1.55] Middle 1.08 1.01 [0.26] [0.04] Matric 4.16 6.44 [6.36] *** [10.81] *** Inter 7.37 9.1 [7.59] *** [10.16] *** Bachelor's or Higher 12.61 17.3 [8.69] *** [15.60] *** In Agricultural Sector 0.02 0.01 [7.30] *** [13.73] *** Number of other Family 1.15 0.29 Members are Employer [0.31] [3.74] *** Own-account Workers 0.63 0.61 [4.20] *** [5.26] *** Unpaid Family Workers 0.69 0.69 [3.77] *** [4.50] *** Salaried Workers 1.53 1.59 [4.63] *** [6.42] *** Casual Wage Workers 0.58 0.68 [5.42] *** [4.33] *** Migration within 1.73 1.56 Past 10Years [1.80] * [2.16] ** Household Size 1.05 1 [1.50] [0.23] Urban 1.04 1.06 [0.24] [0.48] Provinces (Punjab as Comparison Group) Sindh 1.91 1.21 [2.91] *** [1.21] Khayber Pakhtunkhwa 2.25 1.64 [3.49] *** [2.93] *** Balochistan 2.25 2.68 [1.91] * [3.17] *** Number of Observations 3087 6805 Note: 1. Absolute value of z statistics in brackets. 2. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. Table 5 Rate of Return on Education Male Female Youth Adults Youth Age 0.28 0.06 -0.12 [6.44] *** [13.04] *** [0.82] Age Squared -0.01 0 0 [5.28] *** [10.98] *** [1.01] Married 0.01 0.12 -0.03 [0.57] [7.11] *** [0.39] Education (No Formal Education as Comparison) Below Primary Education 0.02 0.1 0.25 [0.60] [3.68] *** [1.92] * Primary Education 0.02 0.15 0.05 [0.80] [8.92] *** 10.63] Middle 0.05 0.25 0.24 [2.31] ** [13.48] *** [2.13] ** Matric 0.14 0.43 0.01 [5.81] *** [26.69] *** [0.10] Inter 0.31 0.6 0.19 [8.13] *** [28.95] *** [2.04] ** Bachelors or Higher 0.64 1.05 0.63 [13.21] *** [63.09] *** [6.84] *** Urban 0.02 0.17 0.21 [1.20] [15.49] *** [3.81] *** Provinces (Punjab as Comparison Group) Sindh -0.05 -0.03 0.29 [2.97] *** [2.21] ** 14.19] *** Khyber Pakhtunkhwa -0.13 0 0.29 [5.74] *** [0.05] [3.33] *** Baluchistan 0.2 0.19 0.75 [6.65] *** [11.01] *** [4.72] *** Number of observations 6167 14456 962 Female Adults Age 0.06 [3.95] *** Age Squared 0 [3.07] *** Married 0.06 [1.49] Education (No Formal Education as Comparison) Below Primary Education 0.22 [1.54] Primary Education 0.13 [1.50] Middle 0.38 [3.55] *** Matric 1.03 [16.58] *** Inter 1.12 [15.86] *** Bachelors or Higher 1.67 [32.75] *** Urban 0.16 [3.99] *** Provinces (Punjab as Comparison Group) Sindh 0.23 [4.75] *** Khyber Pakhtunkhwa 0.07 [1.24] Baluchistan 0.51 [4.67] *** Number of observations 1905 Note: 1. Absolute value of t statistics in brackets. 2. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. Table 6 Characteristics Associated with Total Hours Worked in the Previous Week for Male Workers Male Youth 1 2 3 Weekly Earnings 1.04 [6.30] *** Actual or Predicted 0.99 Weekly Earnings [5.32] *** Own Account Workers 0.15 0.13 [0.43] [0.37] Unpaid Family 5.63 -5.62 Workers [16.05] *** [15.91] *** Casual Wage Workers 0.92 0.78 [2.65] *** [2.23] ** Age 2.28 4.2 4.05 [2.78] *** [7.18] *** [6.90] *** Age-squared 0.05 0.09 0.09 [2.61] *** [6.16] *** [5.93] *** Married 0.23 0.46 0.39 [0.51] [1.37] [1.16] Had Training 2.13 1.94 1.82 [1.87] * [1.99] ** [1.85] * Below Primary 1.34 0.7 0.5 Education [2.22] ** [1.57] [1.13] Primary Education 0.79 0.33 0.28 [2.01] ** [1.13] [0.96] Middle 0.14 1.23 1.27 [0.33] [3.95] *** [4.04] *** Matric -1.17 1.38 1.5 [2.53] ** [4.14] *** 14.46] *** Inter 4.1 3.71 0.98 [5.50] *** [6.61] *** [6.96] *** Bachelors or Higher 8.76 7.06 8.06 [9.08] *** [9.04] *** [10.00] *** Migration within 10 2.28 1.82 1.82 Years [3.71] *** [3.42] *** [3.42] *** Manufacture 2.51 1.17 1.09 [4.15] *** [3.03] *** 12.80] *** Construction 7.63 3.4 3.58 [12.46] *** [7.11] *** 17.43] *** Wholesale and Retail 2.51 5.68 5.68 [3.72] *** [16.49] *** [16.45] *** Transportation 3.62 6.33 6.19 [5.13] *** [13.32] *** [12.91] *** Service -2.11 1.54 1.53 [3.26] *** [3.70] *** [3.65] *** Other Industry -2.12 0.63 0.39 Male Adults 4 5 6 Weekly Earnings 0.2 [3.13] *** Actual or Predicted 0.18 Weekly Earnings [2.68] *** Own Account Workers 1.17 1.37 [5.73] *** [6.50] *** Unpaid Family 1.56 -1.42 Workers [4.77] *** [4.29] *** Casual Wage Workers 0.4 0.64 [1.51] [2.37] ** Age 0.27 0.29 0.27 [2.95] *** [5.15] *** [4.80] *** Age-squared 0 0 0 [2.66] *** [6.94] *** [6.67] *** Married 0.68 0.71 0.61 [1.99] ** [2.94] *** [2.53] ** Had Training 1.51 0.86 0.96 [2.54] ** [1.72] * [1.91] * Below Primary 0.69 0.79 0.74 Education [1.31] [2.28] ** [2.13] ** Primary Education 0.03 0.78 0.77 [0.08] [3.74] *** [3.67] *** Middle 0.05 0.59 0.54 [0.13] [2.48] ** [2.24] ** Matric 2.3 0.07 0.16 [7.20] *** [0.34] [0.72] Inter 4.93 2 2.11 [12.01] *** [6.52] *** [6.65] *** Bachelors or Higher 7.44 4.48 5.07 [19.79] *** [15.95] *** [15.50] *** Migration within 10 1.61 0.65 0.72 Years [4.32] *** [2.18] ** [2.38] ** Manufacture 0.72 2.54 2.39 [1.57] [9.22] *** [8.50] *** Construction 6.8 2.58 2.73 [15.13] *** [7.53] *** [7.88] *** Wholesale and Retail 3.86 6.92 6.79 [7.00] *** [31.57] *** [30.73] *** Transportation 3.62 7.12 7.16 [7.52] *** [24.55] *** [24.49] *** Service -4.49 -0.33 -0.25 [10.20] *** [1.30] [0.98] Other Industry -3.39 0.71 0.57 Note: 1. Absolute t statistics in bracket. 2. *significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. 3. Other variables in the model but not reported include: household size, household demographic structure, household head information, and province dummies. Table 7 Characteristics Associated with htal hours Worked in the Previous Week for Female Workers Female Youth 1 2 3 Weekly Earnings 1.86 [4.54] *** Actual or Predicted 1.66 Weekly Earnings [4.02] *** Own-account Workers 936 9.2 [9.12] *** [8.98] *** Unpaid Family 12.74 -12.59 Workers [12.17] *** [12.01] *** Casual Wage Workers 5.96 5.85 [5.56] *** [5.43] *** Age 2.43 1.42 1.5 [1.09] [1.18] [1.25] Age-.squared 0.06 0.03 0.03 [1.02] [0.98] [1.07] Married 2.47 1.68 1.62 [2.01] ** [2.66] *** [2.55] ** Had training 1.15 0.56 1.15 [0.51] [0.32] [0.65] Below Primary -5.31 0.65 -0.54 Education [2.66] *** [0.57] [0.47] Primary Education 3.9 1.4 1.45 [3.12] *** [2.14] ** [2.24] ** Middle 5.39 0.83 1.66 [3.05] *** [0.91] [1.80] * Matric 7.02 3.66 3.8 [4.97] *** [4.28] *** [4.45] *** Inter 9.22 6.23 6.72 [5.29] *** [5.12] *** [5.49] *** Bachelors or Higher 12.2 8.6 -10.01 [6.85] *** [6.58] *** [7.42] *** Migration within 10 5.66 2.86 2.8 Years [3.63] *** [2.91] *** [2.86] *** Manufacturing 5.51 1.33 1.31 [4.50] *** [1.94] * [1.91] * Construction 1.83 5.39 5.77 [0.59] [1.94] * [2.04] ** Wholesale and Retail 5.82 10.77] 10.69 [1.79] * [6.04] *** [6.01] *** Transportation 9.18 14.02 13.35 [2.82] *** [5.23] *** [4.99] *** Service 0.46 -0.21 -0.1 [0.31] [0.19] [0.09] Other Industry 2.99 6.32 5.06 Female Adults 4 5 6 Weekly Earnings 1.08 [5.39] *** Actual or Predicted 1.05 Weekly Earnings [5.30] *** Own-account Workers -7.22 -7.02 [9.31] *** [8.99] *** Unpaid Family 10.12 9.93 Workers [12.61] *** [12.27] *** Casual Wage Workers 3.22 3.03 [4.071 *** [3.79] *** Age 0.1 0.43 0.38 [0.36] [3.45] *** [3.08] *** Age-.squared 0 -0.01 -0.01 [0.60] [3.90] *** [3.73] *** Married 2.04 1.18 1.35 [2.58] ** [2.45] ** [2.79] *** Had training 1.75 2.88 2.68 [0.96] [1.93] * [1.79] * Below Primary -1.49 -3.19 -3.58 Education [0.70] [3.15] *** [3.53] *** Primary Education 2.95 0.21 0.12 [2.27] ** [0.35] [0.20] Middle -1.07 -.1.39 1.8 [0.65] [1.35] [1.73] * Matric 4.57 3.36 4.48 [4.18] *** [4.16] *** [5.41] *** Inter 4.96 2.76 -3.77 [4.00] *** [2.70] *** [3.60] *** Bachelors or Higher 7.75 -3.85 -6.36 [7.41] *** [4.89] *** [6.99] *** Migration within 10 2.63 0.55 0.47 Years [2.44] ** [0.79] [0.67] Manufacturing 0.37 -4.04 3.93 [x.76] *** [7.33] *** [7.12] *** Construction 1.7 5.38 4.6 [0.66] [2.28] ** [1.91] * Wholesale and Retail 0.82 9.07 9.07 [0.24] [0.06] *** [10.04] *** Transportation [0.89 13.15 12.99 [3.93] *** [5.41] *** [5.35] *** Service 1.63 -0.85 -0.67 [1.76] * [1.12] [0.88] Other Industry 3.98 7.4 6.38 Note: 1. Absolute t statistics in bracket. 2. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. 3. Other variables in the model but not reported include: household size, household demographic structure, household head information, and province dummies.
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