DETERMINANTS OF LABOUR FORCE PARTICIPATION IN UGANDA

A CASE STUDY OF JINJA DISTRICT

ABSTRACT

The purpose of this study research was to find out the Determinants of Labor Force Participation in an Economy, a case study of Jinja District in Uganda. This further unveiled the effects, possible solutions,  the strategies of Labor force in trying to participate in an economy and examining what these strategies suggest about the respect of the right to work as a realistic objective relating age, education level and marital status.

The study adopted cross-section research design. Quantitative and qualitative approaches of data collection methods were used while carrying out the study where questionnaires were administered to various respondents.

The study found that age had a significant relationship with labour force participation presented by 53(91.4%) who agreed. The chi-square results showed that since the P-value (0.001) was less than 0.05 the confidence level. Also study findings revealed that education had an effect towards labour force presented by 54(93.1%) of the respondents. From the chi-square results, it was seen that since the P-value (0.002) was less than 0.05 the confidence level and marital status had a significant effect on labour force participation presented by 53(89.4%) and the P-value (0.003) was less than 0.05 the confidence level.

The study recommended that higher institutions like the Government, Non-Government Organizations, youth councils , ministries, universities, vocational institutions and the youth themselves among others were recommended to establish a youth development bank in Jinja locality to facilitate the provision of financial services to the youth, the Government of Uganda also to establish a full active and independent ministry of the labor that would help in identifying the problems that the laborers are facing and provide solutions to them.

CHAPTERONE:

INTRODUCTION

1.0 Introduction

This chapter presents the background of the study, the statement of the problem, the purpose of the study, the objectives of the study, the hypothesis, the scope of the study, as well as its significance and rationale.

1.1 Background to the Study.

Labor force participation is the proportion of the population aged between 14 and 64 years that is economically active that is all people who supply labor for the production of goods and services during a specified period. The real labor force consists of those who are employed and unemployed but actively looking for a job. Individuals, who are not working and not searching for a job, are not counted in the labor force number. Labor force participation is extensively used to assess the labor market and serves as a useful assessment of the labor market along with employment and unemployment rate. (Nyende, 2010)

Ugandan labour markets like many others in most of Sub‐Saharan African (SSA) are often

Characterized by dualism and there are large and growing regional inequalities in access to

formal (non‐agricultural) employment (Klasen, 2004). Increasingly therefore, nonstandard

Employment has emerged to represent a major form of labour market activity for a large number of working Ugandans.

In many SSA countries, as in many other developing countries, individuals who participate in labour markets are more likely to be  self-employed or, more generally, informal sector employment (Glick and Sahn, 1997). Yet, despite a growing awareness in the literature that low productive labour force participation in formal employment, is seen as a constraint to economic growth and poverty reduction (Klasen, 2004; Blackdenet al., 2006), such concerns have yet to be translated into an in‐depth analysis of the socio‐economic characteristics of labour force participation in the labour markets in SSA and Uganda in particular.

Although there are several explanations and evidence provided to account for labour market choices for Cling et al. (2007), a drawback of many of these studies, and one that we address here, is that they provide only a partial analysis of labour markets, either focusing on a specific region (as in Glick and Sahn, 1997) or sector of employment (usually wage employment) and none explores the determinants of labour force participation in the formal, informal and nonparticipation.

Surprisingly, no single extensive study has focused on Ugandan labour markets and more specifically on the socio‐economic characteristics of labour force participation. This is all  more surprising in the context of Uganda in particular where employment levels have changed from sector to another over time. However, perhaps more than most regions, labour markets in Uganda particularly have undoubtedly changed considerably since then, especially with respect to the labour force participation (occupational choice). But more importantly, the growing lack of functional health facilities, it has been affected and therefore impacting heavily on labour force participation. However, detailed studies focusing on labour force participation has not attracted much attention in Uganda and yet from a policy perspective, understanding its role is critical in the growth process and further reduction in poverty levels. Here the researcher examines labour market participation. In doing so, we investigate the determinants of labour force participation towards economic growth with a more focus on the socio‐economic characteristics.

1.2 Statement of the Problem

Determinants of labour force and economic growth has been the focus of intensive research effort in recent times. It has been verified that the labour force participation is very critical for the success of economic growth in the country (Kennedy and Hedley (2003).

Economic growth is influenced by a number of factors in the labour force such as age, marital status, and education among others. Jinja has established mechanisms for labour force participation geared at improving economic growth. However, as mentioned earlier, there seems to be little improvement as far as economic growth is concerned in terms of poor quality of goods produced, poor standards of living, low per capita income and delayed service delivery among others which raise the concern. It is against this background that the researcher sought to establish the determinants of labour force participation in Jinja District.

1.3 Objectives of the Study

1.3.1 General Objectives

The general objective of this study was to determine the factors that affect labor force participation in Jinja district in Uganda.

1.3.2 Specific Objectives

(i) To analyze the effect of age on labour force participation.

1. ii) To analyze the effect of highest level of education on labour force participation.

iii) To analyze the effect of marital status on labour force participation.

1.4 Research Hypotheses

H1: There is no significant effect of age on labour force participation.

H2: There is no significant effect of the highest level of education on labour force participation.

H3: There is no significant effect of marital status to the labour force participation.

1.5 Scope of the Study

1.5.1 Subject Content Scope

This study intended to find out the determinants of labor force participation in Uganda basing on the socio-economic determinants which include gender, age, marital status, education and others.

1.5.2 Geographical Scope

To cover all the regions in the entire country was impossible because of the limited time frame and amount of funds for this research. For this reason, the research covered Jinja district. The choice of this area was due to the fact that, it has a large number of people, employed and unemployed and therefore it  provided good source of data for the study.

1.5.3 Time Scope

This study was carried out in April-June2017.

1.6 Significance of the Study

This study provided information on the economic characteristics of the population and its economic activity status that is, the employment, unemployment and underemployment.

By integrating household socio-economic factors in the overall study it provided an understanding of the mechanisms and effects of various government programs and policy measures on a comparative basis.

This study acted as the guide to further researchers in the same field of study, particularly it added on the existing literature.

CHAPTERTWO:

LITERATURE REVIEW

2.0 Introduction

This section looked at the related literature on the similar studies. It described in detail the information on the research objectives and some expected results of the research report.

Jinja district as my area of interest as a researcher, was surrounded by places like Kasowa, Mutai, Iganga in the eastern part of Uganda thus it was 00 degrees 12 east /0.500 degrees north 33.200 east (latitude 0.500, longitude 33.200). It was bordered by Kamuli in the north, Luuka district to the east, Mayuge in the south west, Buvuma district in the south, Buikwe district in the west and Kayunga district in the North West.

Relating to the Malthusian theory of population, Thomas Robert Malthus proposed a systematic theory of population that it grows at a geometric rate while food grows at an arithmetic rate. But in Jinja,as population grew it increased productivity and this contributed to economic growth as food production  was increased for both on commercial and subsistence basis hence avoiding the population trap as suggested by Malthus. Production of food on commercial basis avails disposable income amongst individuals in Jinja district since their output works as raw materials to most factories and industries as well and this has led to increased economic growth.

2.1 The Relationship between Age and Labour Force

The Labour force (economically active population) refers to those persons who supply labour for the production of goods and services, as well as the unemployed. In other words, it is the sum of the number of persons engaged in economic activities in the last 7 days and the number actively looking for work. In Uganda, the age range of 14-64 years is considered as the working age.

Persons are considered to be employed if they are of specified age (14 to 64 years) and they performed any work at all, for pay or profit (or pay in kind), or were temporarily absent from a job, for such reasons as illness, holidays or industrial dispute (UNDS 2009).

Age which could reflect the effects of human capital investment therefore has a positive effect on labour force participation with greater effect in the formal sector. The effect of age indicates that older workers are more experienced and thus more likely to obtain a job in the formal sector (Guntheret al, 2007).

Research from the United States and United Kingdom undertaken by Oreopoulos (2006, 2007) indicates that an additional year of schooling can have significant benefits for disadvantaged youth in terms of earnings, health and wellbeing, consistent with earlier United States research by Angrist and Krueger (1991).

Nationally, youths are defined as persons aged 18 to 30 years. However, the international definition of 15 to 24 years is somehow used.

Unemployment is high among the youth in the Greater Kampala areas. The youth unemployment rate was recorded at 18 per cent for the youth aged 15-24 years and 16 percent for youth aged 18-30 years. The finding shows notable difference of unemployment rates among sexes of youths. The unemployment rate for female youth aged 18-30 years is 25 percent compared to males 7 percent. The findings show that males are more likely to be unemployed in the economy compared to females (UNDS 2002/ 2003).

Therefore all researchers focused at age inform of number with how active it can be contributing to labour but the interest of youth specializing in some careers was not looked at, thus there was need to carryout research if the active labour specialize as doctors, engineers, accounts without focusing on un relevant areas. And analyses their effect on economic growth in Jinja and Uganda as well.

2.2 The Relationship between Marital Status and Labour Force Participation

The relationship between marital status and labor force participation differs by sex, with married men having a higher rate than never-married men, while the opposite is true for women. However, the differences are far smaller in the 1990s than in earlier years (Mugume, A., and Canagarajah, 2007). Further, it is interesting to note that the participation among married women with children under age 18 is now greater than among married women with no children less than 18 years old. In contrast, there is no effect of number or age of children on participation for men once age and marital status are controlled for. There are some intriguing differences between whites and blacks (UNDS 2002/ 2003). Most striking is the fact that among blacks, labor force participation is substantially higher for married than for single women, while the opposite is true among whites. On the other hand, the participation for single compared to married men are considerably lower among blacks than among whites. It shows how differences in labor force participation for women and men have changed by age since 1963. Men’s participation display the single peak with a sharp drop-off after age 50 that is the characteristic pattern for industrialized societies (Lisaniler  and  Bhatti ,2005)

Women’s participation in 1963, and even as late as 1980, displayed the double-peaked pattern that is still common in many societies, but by 1996 approached the male pattern. As for racial differences, among men the participation of blacks is lower than that of whites for all age groups, but among women blacks have substantially lower participation only up to about 30 years of age. Differences in labor force participation of various groups are clearly related to other demographic differences among these groups, such as marital status and levels of education. Also, age is related to education because younger people have been obtaining more schooling and is obviously also related to the age of the children in the family. An additional complication is the difficulty of separating the effect of age from the cohort effect. Labour force participation for men and women alike declines after the age of 50, but it is also the case that labor force participation for women who are currently 50 or older was lower at all ages than for younger women, while to a much lesser extent the opposite is true for men.(Goldstein, S. ,1972). Thus there was need to carryout research on single mothers who are no longer married but hardworking their contribution to labour force in Jinja area and Uganda as a whole since most researchers did not elaborate more about this issue.

2.3The Relationship between Education and Labour Force Participation

In the early days, girls received more education when families became more affluent and could afford to provide some schooling for all their children. It was mainly seen as a way to make them more interesting companions for their husbands and better mothers, who could, for instance, read the bible and help to teach their children. Nonetheless, it also helped to increase the supply of women who could be productive workers, so that employers were willing to hire them and to pay them enough to make paid work attractive (ILO, 2002). Consequently, in time, the prospect of better jobs came to be an important incentive for getting additional education, all the more so as life expectancy increased, and women could expect to spend more years in the labor market. It was also worth noting that there are a range of pathways for individuals to achieve higher educational attainment, including apprenticeships and vocational education and training. The proportion of young people not completing Year 12 but going on to complete an apprenticeship or other vocational qualification has risen over the past decade.

While levels of educational attainment are important, the benefits of additional schooling may not be fully realized unless the quality of the educational experience is high. Indeed, as Tunny (2006) and Hanushek and Woesman (2007) outline, differences in learning achievements matter more in explaining cross country differences in productivity and productivity growth than differences in the average years of schooling or enrolment rates. It is the knowledge acquired while engaged in schooling that counts; time in the classroom can be of little value if students are not gaining learning and skills from the experience. That said, human capital acquisition can occur through other channels, such as on‑the‑job training.

Kennedy and Hedley (2003) reported broad declines in participation by men across educational attainment levels between 1981 and 2001. The 2006 census showed more changes since 2001, principally the rise in participation by prime‑working‑age men with no post school qualification, which was discussed above. However, the data also show that the participation of older men with lower skill levels have risen sharply since 2001, almost offsetting a significant reduction over the period to 2001. This group experienced an increase in participation between 2001 and 2006 even greater than that observed for prime‑working‑age men with the same educational attainment.

Men with no post‑school qualifications aged 55 to 70 experienced an increase of 8.5 percentage points in their participation between 2001 and 2006, effectively reversing the decline in participation by this group in the period 1981 to 2001.

On a related note, Kennedy and Hedley (2003) described a decline in participation for men aged 55 to 59 in all educational groups between 1981 and 2001. This group has increased their participation between 2001 and 2006, but it remains below the 1981 level. This may be indicative of changing retirement patterns; where the 1981 data show steep declines in participation at ages 60 and 65 the 2006 data show a more gradual decline from age 55 onwards. As a result, participation are lower in 2006 relative to 1981 for men aged between 55 and 59, but higher for those aged over 60.

The data suggested that mature‑age men with no post‑school qualifications appear to be staying in the workforce much longer than they did 25 years ago, which may reflect improvements in health, or may instead be a temporary outcome resulting from exceptionally strong economic conditions. More generally, Australian men may be taking a more flexible approach to their retirement age, with expanded opportunities for part‑time work likely to have played a part in this development.(Mugume and Canagarajah, 2007)

However, there are a number of factors affecting labor’s participation that is government subsidization, political climate, and investors’ confidence as well, the relationship amongst age, marital status and education level determine most towards economic growth than other factors.

Therefore there was need to research about individuals who take long time acquiring high levels of education that is bachelor degrees, master’s degrees and PHD’s if they contribute towards quality of labour within the economy since it wasn’t indicated by the previous researchers.

CHAPTERTHREE:

RESEARCH METHODOLOGY

3.0 Introduction

This section looked at the methods and techniques that were used to conduct the study in order to achieve the stated objectives. It covered the research design, area of the study, study population, sampling and data collection as well as data analysis techniques in order to enhance the success of this research.

3.1 Research Design

A research design was a plan and structure of investigating in order to obtain answers to research questions (Kothari, 2009). In order to examine the factors that affect labor force participation, descriptive research design was used. The study was a survey in the form of cross sectional study in which data was collected once across a population through sampling. Quantitative and qualitative approaches were adopted. The former enhanced the understanding of the meaning of numbers, while the latter gave precise and testable expression to qualitative ideas.

3.2 Study Population

The study population included all people in the area. Particular emphasis was however put on youth between 14 1nd 64 years of age who are considered to be active labor force according to International Labor Organization and in this case, males and females towards economic growth as well.

3.3 Sample Selection

Given that the study area was relatively large, it was found necessary to reduce it by taking a representative sample. Cluster sampling technique was used.

3.4 Sample Size and Sample Selection

In the collection of data, there was need of defining the sample size of the population to be used as respondents while carrying out this research. With a sample size of 68 respondents at 90% level of confidence which the researcher wanted to achieve, the following formula for sample size determination was used and a sample was drawn as follows.

Where z is the level confidence (z=1.64 for 90% CI)

p=0.5q=1-p,           q=0.5

e=0.1

n =68

3.5 Data Collection Technique

3.5.1 Questionnaire

For in-depth understanding of the variable under study, self-administered questionnaire was used for the respondents. This basically involved the face to face verbal interaction with the respondents, asking questions pertaining to economy’s labor force participation in the area and recording down the answers from the respondents. This method was preferred because clarification was easily be sought especially on issues which cannot clearly be got using other methods such as observation.

3.6 Data Analysis Procedure

In order to ensure logical completeness and consistency of responses, data editing was carried out each day by the researcher. Identifying mistakes and data gaps was rectified as soon as possible. Once editing was done with, data was analyzed qualitatively and quantitatively. The qualitative data from interviews and secondary documents was analyzed using content analysis and logical analysis techniques.

Descriptive statistics were generated and used to describe each of the variables.

Bivariate analysis was done using spearman’s rank correlation coefficient and tested for significance at a 5% level of significance using SPSS.

The data tested for presence of heteroskedasticity, autocorrelation and multi-collinearity using the relevant tests using SPSS.

The t statistic was used to assess the significance of association between each of the socioeconomic characteristics and labour force participation. The model took the following form

Y =β i + β₂X₂ + β₃X₃ + β₄X₄+ β₅X₅ + β₆X₆ + β₇X₇+ ε.

Where             Y= Labour force participation,

X₂ = age measured in years of respondent

X₃=l, if education level is degree, D₃=0, otherwise.

X₄ = l, if education level is diploma, D₄=0, otherwise.

X₅ =1, if education level is primary, D₅=0, otherwise.

Benchmark category/base category is secondary education level.

X₆ = 1, if marital status is married, D₆ = 0, otherwise

X₇ = 1, if marital status is divorced, D₇ = 0, otherwise

Benchmark category/base category is single.

ε = error term

Dummy variables were created for the different levels of categorical variables (marital status and education level). The parameters were interpreted as the relative probability of participating in the one labour sector rather than no participation for each of the socio‐economic factors.

3.7 Ethical Considerations

In order to get data from the Jinja, I obtained an introductory letter from the Department of Economics and Statistics, Kyambogo University this was presented to factory and industry officers in records department.

3.8 Anticipated Limitations to the Study and Solutions

The time frame available was not enough to do a detailed study. However as a researcher, I committed myself to the study and make proper personal programming and preparation.

Still as the researcher, I faced the challenge of insufficient funds which however was managed by getting some funds from my parents, relatives and friends.

CHAPTER FOUR

DATA PRESENTATION, INTERPRETATION AND ANALYSIS

4.0. Introduction

The chapter elaborates the biographic descriptions of the respondents in terms age, level of education and marital status perceptions about the effective participation of labour force in an economy in Uganda.

The chapter further presents responses concerning the study objectives which include; finding out the determinants of labour force in Jinja district, finding out the factors that contribute to economic growth in Jinja district and finding out the relationship between labour force and economic growth in an organization or institution.

4.1 Background Information on the Respondents

With regard to background characteristics of the respondents, a number of variables were investigated. The study involved respondents of varying characteristics which enabled me as the researcher to get sufficient information on the study variables as follows.

4.1.1. Marital Status of the Respondents

As a researcher, I looked at the marital status of the respondents so that to link it with labour force participation and economic growth motivation and employees’ performance in organization as the table below shows.

Table 4.1 Marital Status of Respondents

Source: Primary Data 2017

According to the table 4.1 above, considering to the column of valid percent, it shows that in Jinja district as my area of study, 75.9% of the respondents were single and 24.1% of the respondents were married thus single individuals within Jinja district and Uganda as well are more likely to supply labour to firms and industries than married partners/couples.

This indicated that single individuals do concentrate more on work since they don’t have family responsibilities hence contributing to economic growth than married respondents.

4.1.2 Age Group of the Respondents

As a researcher, I looked at the age group of the respondents so that to link it with labour force participation and economic growth motivation and employees’ performance in organization as the table below show

Table 4.2 Age Group

According to the study, thus in table 4.2 above, it was revealed that 36 members of the total respondents were between 18- 30 age group taking up the highest percentage of 44.8, 18 members of the total respondents were found in the age group of 31-40 occupying 31.0% of total respondent, 8 members were observed in the age group of 41-50 taking up a percentage of 13.8 of the total participant and finally the research observed only 6 members who were aged above 51. As a researcher this was because young individuals of age group 18-30 are energetic thus are more likely to supply labour even for more hours than those of old age group.

4.1.3 Level of Education

As a researcher, I looked at the level of education of the respondents so that to link it with labour force participation and economic growth, motivation and employees’ performance in organization as the table below shows

Table 4.3 Level of Education

From the table 4.3 above, it indicates that the study was conducted mainly in the people in Jinja district who had a tertiary education as it constituted 41.4% of the total respondents while those who had studied with a secondary education were 37.9%. These were followed by those who had ended in primary level who constituted 19.0% in Jinja district. According to the analysis above, the interpretation was the educated or the literate are more likely to be employed and supply labour since they acquired more skills than others as indicated according to their levels of education above.

Figure 4.1: Time spent working

From the above figure 4.1, it is clearly indicated that most of the respondents had spent between 2-5 years in service while working for in Jinja district and they constitute 78.98%.  This was followed by workers who have worked above 5 years with a percentage of 12.28% while those who had spent less than 5 years in service were 8.77%.

4.2 Effect of Age on Labour Force Participation

Table 4.4 Effect of Age on Labour Force Participation

H₁: There is no significant effect of age on labour force participation.

According to the study objective one in table 4.4, 33 individuals that is 56.9% of the respondents agreed that age has an effect on labour force followed by 20 individuals thus 34.5% who strongly agreed, this was discovered due to a number of reasons given by individuals who agreed and the researcher as well since people with young age are willingly and can supply labour within Jinja. 3 respondents with 5.2% did not agree, 1 respondent with 1.7% strongly disagreed thus discovered by the researcher that individuals with older age supply less labour since some of them are coming to retirement age. This was the reason as to why this was represented by a smaller percentage respectively.

The chi-square table above shows that since the P-value (0.001) is less than 0.05 the confidence level, we reject the null hypothesis and conclude that age has a significant effect on labour force participation. The observation here is that a bigger percentage supported that age has a significant effect on labour force participation.

4.3 Effect of the Level of Education on Labour Force Participation

Table 4.5 Effect of the Level of Education on Labour Force Participation

H₂: There is no significant effect of the level of education on labour force participation.

According to the study objective two in table 4.5, 40(69.0%) of the respondents agreed with education has an effect towards labour force followed by 14(24.1%) who strongly agreed. According to the researcher with responses obtained, it was noted that educated people contribute greatly towards economic growth because they are so innovative 5.2% with three respondents did not agree thus innovativeness and creativity of labour in Jinja is a biggest contributor

Similarly from the chi-square table above it was seen that since the P-value (0.002) is less than 0.05 the confidence level, we reject the null hypothesis and conclude that education level has a significant effect on labour force participation.

4.4 Effect of Marital Status on Labour Force Participation

Table 4.6 Effect of Marital Status on Labour Force Participation

H₃: There is no significant effect of marital status on labour force participation

According to the study objective three in table 4.6, 34(56.6.0%) of the respondents agreed that marital status has an effect on labour force participation followed by 19(32.8%) who strongly agreed, 3(5.2%) did not agree.

Similarly from the chi-square table above it was seen that since the P-value (0.003) is less than 0.05 the confidence level, we reject the null hypothesis and conclude that there is a significant effect of marital status on labour force participation.

Test for heteroskedasticity

Using the Park test

Hypotheses

Ho: There is no heteroskedasticity in the residuals.

Ha: There is heteroskedasticity in the residuals.

Intercept = 5.903341577 p value= 0.892556 n= 68

We reject the null hypothesis and conclude that there is heteroskedasticity in the series (p<0.05).

Test for autocorrelation

Using the Breusch pagan test for serial correlation

Hypothesis

Ho: There is no serial correlation in the residuals.

Ha: There is serial correlation in the residuals.

Intercept= 17.56196021 p value= 0.0053131 n=68

We reject the null hypothesis and conclude that there is serial correlation in the residuals (P<0.05).

Test for multicollinearity

Hypothesis

H1: There is no significant effect of age on labour force participation.

H2: There is no significant effect of the highest level of education on labour force participation.

H3: There is no significant effect of marital status to the labour force participation.

F= 0.131451 p value = 0.03185933 n=68

We reject the null hypothesis and conclude that the explanatory variables are not correlated (P<0.05).

4.7 Presentation of model

Table 4.7.2: Binary Logistic model

A Dependent Variable: labor force participation

Y =β i + β₂X₂ + β₃X₃ + β₄X₄+ β₅X₅ + β₆X₆ + β₇X₇+ ε.

Y = 5461.614 – 3647.734 D₂ – 3294.948 D₃ + 2622.38 D₄ + 3154.674 D₅ – 2254.91 D₆ – 1552.63 D₇

An increase in age by I year would on average increase that rate of labor force participation by 3647.734 keeping other factors constant. This statistically significant since the p-value (0.006<0.05) therefore there is a significant relationship between age and labor force participation

The coefficient for degree (3294.948), means  that those with  degree holders level  of  education spend  less  incomes (5461.615-3294.948=2167.135) than  those  with  secondary level of  education. However this  more  so  showed  that  those  with primary level of  education on  average  spent  more  income  on  daily  basis  than those  with  no education level (2167.135>1813.881). The p value (0.459 > 0.05) thus, statistically insignificant at 5% level of significance.

The coefficient for tertiary (2622.38) implies that those with tertiary education level on average spend more incomes (5461.615+2622.38=8083.3995) than those with secondary level of education. The P value (0.292 > 0.05) meaning it’s statistically insignificant. From results tertiary level of spend more income on daily basis than those with non formal and primary.

The coefficient for male (3154.674) implies that male household heads on average spend more incomes (5461.615 + 3154.674 = 8616.289) than female household heads. The p value (0.163 >0.05) implying it’s statistically insignificant at 5% level of significance.

The coefficient for unemployed (-1552.63) implies that unemployed household heads on average spend less incomes 5461.615 – 2254.91 = 3206.705) than employed household heads. The P value (0.041 < 0.05), implies that it’s statistically significant at 5% level of significance.

The table above shows that the coefficient for self-employed (-22054.91) implies than self employed on average spend less incomes (5461.615 -1552.63 = 3908.985) than employed household heads. This is statistically significant since the p value (0.000 < 0.05) at 5% level of significance. However, self employed household heads spend more incomes than unemployed households.

The F-probability (0.0081)<0.05, implied that there was significant of relationship between education level of  household head  and  daily  expenditure of  house hold head, in this  case therefore the null hypothesis was rejected in favor of the alternative and continued to indicate that the higher the education level of households the higher the average daily household expenditure.

The F-probability (0.0081) <0.05 signifies the null hypothesis is rejected in favors of the  alternative and therefore meant that there exists a relationship between gender, employment status and  slum poverty.

This further indicated that household expenditure was higher for the male-headed households as compared to the female-headed households, this therefore is an indication that women emancipation can be a key pillar in the real improvement of households income that   can gradually help in the fight against poverty.

CHAPTER FIVE

DISCUSSION OF FINDINGS CONCLUSIONS AND RECOMMENDATIONS

5.0 Introduction

This chapter presents a discussion of the findings of the study, the conclusions and recommendations made from the study. The study was purposely done to investigate the determinants of labour force participation in Jinja district. The study involved a sample size of 68 respondents from the information about the labour force issue in the area was collected for analysis and presentation.

5.1. Discussion of the Findings

5.1.1 Effect of age on labour force participation

Study findings in chapter four revealed that showed that age has a significant relationship with labour force participation presented by 53(91.4%) who agreed. The chi-square results showed that since the P-value (0.001) was less than 0.05 the confidence level, the null hypothesis was rejected and concluded that age has a significant effect on labour force participation. And this was also related to Mugume, A., and Canagarajah, S. (2007). Employment, Labour Markets and poverty in Uganda World Bank, Uganda Country Office.

5.1.2 Effects of level of education on Labour Force Participation

Study findings in table 4.5, revealed that education has an effect towards labour force presented by 54(93.1%) of the respondents. From the chi-square results, it was seen that since the P-value (0.002) was less than 0.05 the confidence level, null hypothesis was rejected and concluded that education level had a significant effect on labour force participation. This is in agreement with what Kennedy and Hedley (2003) says, towards labour force participation and influence of education attainment and scholars that is Hanushek and woesman (2007):‛The Role of Education Quality to Increased Productivity of Workers’

5.1.3 Effects of marital status on Labour Force Participation

Findings revealed that marital status has a significant effect on labour force participation presented by 53(89.4%). It was seen that since the P-value (0.003) was less than 0.05 the confidence level. The study concluded that there is a significant effect of marital status on labour force participation. This finding concurs with (Mugume, A., and Canagarajah, 2007) who established that the relationship between marital status and labor force participation differs by sex, with married men having a higher rate than never-married men, while the opposite is true for women. However, the differences are far smaller in the 1990s than in earlier years. Further, it is interesting to note that the participation among married women with children under age 18 is now greater than among married women with no children less than 18 years old. In contrast, there is no effect of number or age of children on participation for men once age and marital status are controlled for.

5.2 Conclusions

The study concludes that age, level of education and marital status are all major determinants of labour force participation.

The study concluded that there is a significant relationship between age and labour force participation since the P-value (0.001) was less than 0.05 the confidence level.

Education also has an effect towards labour force since the P-value (0.002) was less than 0.05.

And finally marital status had a significant effect on labour force participation since the P-value (0.003) was less than 0.05 the confidence level.

5.3 Recommendations

Basing on the study findings, the following recommendations were highlighted as follows;

It is important for further studies to be carried out in order to do justice to all the labour tools to workers that influence the economic growth performance. With the limitations identified above, the ability to generalize the result of this study is restricted.

It is very pertinent at this juncture to suggest that more research should be conducted on the relationship and influence of economic growth and development performance using many private and public companies

The study also recommended that there should a greater balance between employees’ needs and economic needs. It is the duty of all stake holders to ensure that this is achieved as this will reduce employees’ selfishness at their places of work.

Refresher courses should be given to managers to improve upon their management skill so as to effectively and appropriately tackle employees’ diverse needs. This can contribute towards reducing on the rate of labour force turn over in private firms in Uganda..

The intended reward should be awarded in a kind of ceremony to an employee and announced appropriately to motivate others. The lack of communication was seen as main barrier in employee’s motivation. During interview sessions an employee complained about the lack of announcement of rewards on time, thus the motivation effectiveness suffers. It is suggested to communicate reward to employees in proper ceremony on time, so that they can be better motivated.

5.4 Area for Further Studies

The study recommends that further studies should be carried out on;

1. The effectiveness of labour force on performance of an organization in Jinja and Uganda as well.
2. The relationship between contribution of labour force and labour turn over to economic growth
3. How labour force past work experience and their attitudes affect economic growth.
4. How participation of labour can influence a company’s performance and how it can be improved.
5. How labour culture can be an influence on the economy.

REFERENCES

Goldstein, S., 1972: The Influence of Labour Force Participation and Education on Fertility in

Thailand, Population Studies

AngristJ. D, 1991: ‛Does Compulsory School Attendance Affect Schooling and Earnings?’

Blackden ,Aramoran and Moloney: Civilian Labour Force Participation of Age, Sex,  And

Ethnicity

Glick and Sahn (1997): Female Work Participation and Gender Differences in Western Pengal

Cling, Langreean and Roubaud (2007) :  Population And Labour Force Senarios  of European

Union

Glick and Sahn (1997): Female Work Participation and Gender Differences in Western Pengal

InternationalLabourOrganisation (2002) a Publication of Labour Force Participation in Sub-Saharan africa

Gunther, Isabel and AndreyLaunov (2007). ‘Competitive and Segmented Informal

Underdevelopment, and Job‐search Activities in LDCs. Journal of Development Labour Markets’ Mimeo. January, IZA, Bonn

Hanushekandwoessman(2007):‛The Role of Education Quality to Increased Productivity of

Workers’

Kennedy and Hedley (2003): Labour force participation and influence of education attainment

Klasen, S.2005. “Population Growth, Economic Growth, and Poverty Reduction in Uganda:

Theory and Evidence.”Discussion Paper Series, issue no. 125. Department of Economics, University of Goettingen, Germany.

Lisaniler F.G. and F. Bhatti (2005): ‘Determinants of Female Labor Force Participation: AStudy

of North Cyprus’; Review of Social, Economic and Business Studies;

Kothari, 2009: The Urban Informal Sector in Developing Countries: Employment, Poverty, and

Environment. Geneva, Switzerland: International Labour Organization.

Ministry of Gender, Labour and Social Development (2006).Labour Market Information Status

Report for Uganda

Mugume, A., and Canagarajah, S. (2007). Employment, Labour Markets and poverty in Uganda.

World Bank, Uganda Country Office.

Nyende- M. (2010): ‘Socio-economic Investigation into Determinants of Labour Force

Participation in Labour markets: Evidence from Uganda. Mimeo.Economic Policy Research Centre, Makerere University, Uganda.

Oreopoulos P. (2006) “National Bureau of Economic Research ” in Cambridge

Tunny (2007): Gender, Emotions and Labour markets – Asian and Western Perspectives

Uganda National Delivery Survey. Uganda Bureau of Statistics, 2002/2003 and 2009: Government of Republic of Uganda

Encyclopedia of the nation’s>>Africa>>Uganda

QUESTIONNAIRE

I am MWESIGWAJUNIOR, a third year student from Kyambogo University, carrying out a study on DETERMINANTS OF LABOUR FORCE PARTICIPATION IN AN ECONOMY. You are one of the respondents randomly selected to participate in this study. The information given shall be treated with at most confidentially.

DEMOGRAPHIC CHARACTERISTICS

1. What is your date of birth?
2. What is your sex?
3. i) Male ii) Female
4. What is your marital status? i) Single                           ii) Married

iii) Divorced                     iv) others……………….

4. What is your Educational level?
5. i) Degree    ii) Diploma

iii) Secondary                    iv) Primary

1. vi) Others (specify)………………………….

VARIABLE SPECIFICATIONS

1. Do you participate in the labor market?
2. a) Yes
3. b) No

2. If no, why don’t you participate?
3. a) Lack of necessary skill
4. b) Retired
5. c) Disability
6. D) Others (please specify)……………………
7. If yes, for how long have you been in labour market?

……………………………………………………………………………………………..

4. Are you contented with the work you are currently doing?
5. a) Yes
6. b) No
7. How would you like the labour market to operate in the economy according to your opinion?

………………………………………………………………………….

6. Does age have any effect towards labour force?
7. a) Yes
8. b) No

7) If yes, describe how according to your opinion. And if no, skip to question 8.

……………………………………………………………………………..

8) Is there any effect of the level of education towards labour force?

1. a) Yes
2. b) No

9) If yes, explain how, otherwise skip to question 10.

…………………………………………………………

10) Does marital status have any effect towards labour force participation?

1. a) Yes
2. b) No

11) If yes, elaborate according to your view.

……………………………………………………………………………

Thank you for your time and cooperation

.