A Study of Youth Unemployment in Tanzania and Statistical Analysis

Abstract

This paper\'s aim and purpose was to investigate the contributing elements, causes, effects, and remedies to Tanzania\'s problem of youth unemployment and to offer potential answers. The major goal was to determine how Tanzania\'s unemployment rate affected economic growth. The study used a survey approach, time series data from 1997 to 2017, and make used of the correlation analysis and regression analyses to analyze the issue and demonstrate the link between Tanzanian unemployment and economic growth. According to the study\'s findings, Tanzania\'s economic growth has been negatively impacted by unemployment but only marginally. According to the report, the government must act quickly to prevent an unanticipated rise in the unemployment rate because it is a significant barrier to social advancement.

Introduction

I. INTRODUCTION

One of the most obvious indicators of how well an economy is doing is the unemployment rate. While exceptionally low unemployment rates may be a symptom of an overheated economy, high unemployment rates are a sign of economic distress. According to academics and researchers, an expanding economy is characterized by greater investment opportunities, a higher employment rate (low unemployment), and a higher demand for goods and services. Whether Tanzania's economic progress has improved the lives of its residents, particularly in terms of employment prospects, is still a mystery given the country's high unemployment rate. The distribution of economic well being among the population is impacted by high unemployment rates. Lack of employment affects people's ability to purchase goods and services, lowering their standard of living in comparison to those who are employed.

In the majority of countries around the world, unemployment is one of the largest problems. It is a persistent issue that both industrialized and developing countries deal with. Unemployment is well defined as the state in which a person is not working or is not currently employed, or as the percentage of people who are qualified to work and are actively seeking employment but are unable to do so. Unemployment demonstrates a country's inability to fully use its work force (World Bank, 1994). Lack of labor force renewal increases the number of unemployed people, resulting in a high unemployment rate. A high rate of unemployment signals a lack of employment opportunities, growing poverty, and a low standard of living. More than 90 colleges and universities annually produce thousands of graduates, but firms still classify them as unqualified for the open positions, according to Danbirni M.I (2015). As a result, people become frustrated, which leads to social crimes including theft, drug trafficking, unauthorized immigration to rich countries, and prostitution in order to assure a higher level of living. There are several facets to the unemployment problem. In addition to working jobs that don't match their level of education and expertise, there are also situations where people are unemployed and make little money to cover their basic necessities. The worst case scenario is when people are looking for work but are having trouble finding, others 2 are ready to start up their own small enterprises but hindered by the existing poor macro economic situations.

Tanzania's unemployment issue is viewed as a problem for the country's growth on both an economic and social level. The rates of new job creation in Tanzania have not been keeping up with the growth in the labor force in the market for a number of years (World Bank, 2017). When Tanzania experienced a financial crisis in the 1970s, as evidenced by a decline in economic growth from 5% per year to an average of 2.6% per year in the 1980s and a further decline at a rate of 1% per year at the start of the 1990s, unemployment in Tanzania began to rise (World Bank, 2017). Lack of statistical data on population participation in economic activities in Tanzania, starting with the volume and types of employment. This results in unemployment since the governments failed to launches required actives policy and distributes the available resources in alleviating the problem of unemployment. Reviewing the data from many works of literature reveals the several causes that exacerbate Tanzania's unemployment issue. One of these factors is an increase in the number of people looking for work.

For instance, the number of people looking for work increased by 3.53 million over the past period, and this number is expected to rise in the near future due to the market's rising demand for college and university graduates. According to estimates, Tanzania's real Gross Domestic Product (GDP) growth rate increased from 7.1% in 2002 to 8.4% in 2007. However, some people have argued that this idea is contradictory. The unemployment rate increased from 2% in 2005 to 2.9 % in 2013, but declining to 2.3 % in 2016 as a result of government initiatives such the National Employment Policy. The causes for this include the nations' excellent economic growth at a pace of 7.2% and the jobless rate rising (World bank, 2017).This situation is shameful given that Tanzania has abundant natural resources, including land, minerals, and aquatic resources, which it is able to use to provide work possibilities for the country's overburdened youth.

A. Statement Of The Problem

Theoretically, economic expansion is seen as key weapons for eradicating unemployment and poverty but also helps to raise the living standard of people. The primary objective of macroeconomic policies is to achieve strong and manageable output growth with low or tiny percentage of unemployment. According to Banda (2016), a growth in the GDP is predicted to result in higher employment rates and a decline in unemployment. This widely recognized economic theory, known as Okun's Law, is distinguished by the fictitious link between production and unemployment. According to Banda (2016), Okun's Law is one of the most well-known experimental links between production (Gross Domestic Product) and unemployment in macroeconomic theory and has thus been found to apply to many countries, primarily industrialized ones. While the situation in Tanzania appears to be different, recent reports from government authorities indicate that the nation is seeing economic development, despite the fact that the unemployment problem is continuously becoming worse. This raises the question of whether there is actually a link between unemployment and economic growth. Osinubi (2005) found that although economic growth has a vital role in reducing poverty and unemployment, it is insignificant since it cannot address all of the important causes of unemployment and poverty on its own. There is a justification for using alternative tactics that can make it possible to develop investment programs that will result in the creation of additional jobs, boost economic growth, and lower poverty and unemployment. As a result, it was decided that this study would investigate the relationship between Tanzanian economic growth and unemployment. The motivation for this study comes from the fact that Tanzania is currently dealing with severe unemployment problems and is urgently in need of solutions to the problem in order to continue progress.

B. Study's Objectives

The study's goals are broken down into general and targeted goals. This study's general goal was to examine how unemployment affects economic development in Tanzania, while its specific goals were to examine the relationship between employment and economic development there as well as the impact of the labor force, capital formation, and unemployment rate on that development.

C. Research Questions

1. What is the state of unemployment and economic growth in Tanzania
2. What is the effect of unemployment rate, labour force and capital formation on economic growth in Tanzan

II. LITERATURE REVIEW

The term unemployment refers to the total number of people who are willing and able to work but cannot find employment despite showing up for work at the going rate of pay. For these reasons, it suggests that being jobless is a sign of inactivity in a nation. Balami (2006) elaborates on inactivity as a circumstance in which the labour is forcibly laid off. It signifies that the labor force is ready and able to work but finds it challenging to find employment. Likewise, according to classical economics, unemployment is defined as the excess of labor over the labor demand that is accentuated by fluctuations in real pay. Real or traditional wages When true wages for work are not established above the market clearing level, unemployment results from an increase in the labor force in the market relative to the number of jobs available. In the same circumstances, unemployment was precisely defined by the International Labor Organization (2009) as a state of inactivity that occurs when persons who are jobless fervently desired employment within the previous four weeks. The number of unemployed people divided by the labor force yields the rate of unemployment, which is the frequency of unemployment expressed as a percentage.According to a Business Week analysis from 2011, there are more than 200 million unemployed individuals worldwide. This number is likely higher because the employment growth in more than two thirds of developed economies and half of least developed economies has slowed down.

According to Jhingan (2001), the number of people without a job in an economy can be thought of as a measure of unemployment, which is frequently expressed as a percentage of the workforce.

A. Unemployment In Tanzania

The number of people who are unemployed as a percentage of the workforce is a clear definition of the unemployment rate in Tanzania and other countries. People who are employed and those who are not, i.e., those who are not working but are actively seeking employment, both make up the workforce. The workforce excludes those who are retired, have children, or are not looking for work (Elder). Even in wealthy times, the unemployment rate can occasionally be set at a uniform rate of 4% to 5%. There are always some people who choose to move between different economic sectors and cities or towns. When the economy experiences recessions, unemployment also rises more significantly, sometimes reaching double digit levels. EES (2016) reports that the number of workers in Tanzania's mainland formal sectors increased from 2,334,969 in 2015 to 2,599,311 in 2016, representing an increase of 264,342 workers over the course of a year compared to the rise up to 193,618 workers data collected in 2014 and 2015. Additionally, the data shows that the private sector, which employed 1,748,695 people in 2016, more than double the amount of people employed by the public sector, which employed 850,616 people.Together, the private and public sectors show a gain in employment between 2015 and 2016, with the private sector seeing a larger increase (180,530 workers) than the public sector, which had an increase (83,813 workers) during that time.

Additionally, the results demonstrate that while unexpected employment decreased from 11.8 percent in 2015 to 7.1 percent in 2016, the percentage of consistent employment in the recognized industries increased from 88.2 percent to 92.9 percent in 2016. Unexpected employment is clearly unwarranted and frequently associated with unfavorable working conditions, as stated by Tanzanian laws and regulations. an assessment carried out over ten years, from 2006 to 2016. According to the integrated labor force survey from 2016, there were 2,194,392 unemployed people in total in 2006, which is approximately equal to 11.7% of the total workforce in a market. 16,627,133 people made up the estimated 18,821,525 workforce that was available at that time, of which 16,821,525 worked in unorganized sectors. 2,368,672 people, or 10.7% of the workforce, were anticipated to be unemployed in 2016. This is a significant portion of the population.22,152,320 people made up the total workforce in the market, of which 19,783,648 were identified as being employed. Of the employed population, 2,502,327 people were identified as working in unorganized sectors. It is believed that many Tanzanians are involved in livelihood related activities, which accounts for the country's lower unemployment rate compared to popular belief, which contrasts employment in the formal and unofficial sectors of the economy.

B. Marxist Theory Of Unemployment

Karl Marx established this thesis in 1863. According to the argument, the economy's persistent unemployment is a result of capitalism's depraved workings. By intentionally depleting the labor force, capitalists have a negative impact on the labor markets by raising unemployment, which in turn lowers labor demand and lowers wages. Accordingly, the theory suggested that the best approach to solve unemployment is to abandon capitalism economic systems in favor of socialist ones in order to avoid the wage competition they foster,While Okun's theory of unemployment idea concentrated on defining the relationships between unemployment and economic financial growth. According to the hypothesis, unemployment has a detrimental impact on economic growth in any given economy. It is obvious that a percentage drop in unemployment results in a 3% boost in economic growth (Okun, 1962). Only 0.3 percent decreases in unemployment are possible if the economy's growth rates have exceeded its rate of growth by 1%. Kwani (2005) revealed that Okun's theory of unemployment accurately described the sign of fact of negative ties between unemployment and financial growth in determining the veracity of this theory. The accuracy of the theory was assessed using real GDP data from the United States, and the findings accurately supported the theory's hypothesized relationships between unemployment and economic growth. Additionally, Freeman (2007) suggests that if real GDP output increases by 3% and unemployment decreases by 0.3%, it would show that the rise in real GDP performance for each percentage fall in unemployment rates deviates from a global average of 2% growth in real GDP.

III. RESEARCH METHODOLOGY

This essay aims to investigate what constitutes youth unemployment in Tanzania. The multinomial logistic regression model (MNL), which expands on logistic regression by supporting more than two discrete outcomes, is used in this study. The multinomial logistic model predicts the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables.

This model is used when there are more than two categories and the dependent variable is nominal /categorical, that is the dependent variable falls into any one of a set of categories which cannot ben ordered in any meaningful way (Greene, 2003). The choice of the model based on its relevance in handling categorical data and its frequence usage in studie related to labor market problems.The following is a description of the multinomial logistic analysis' general model:

Where: K is the number of potential outcomes, is a regression coefficient, Yt is the dependent variable over which the probability distribution is specified, Xt is the set of explanatory variables in the model. The young employment situation in Tanzania serves as the model's dependent variable. The study uses both the national definition of youth as someone between the ages of 15 and 34 and the internationally accepted definition of youth as someone between the ages of 15 and 24. The employment situation of young people in Tanzania is divided into three groups, including employed, unemployed, and inactive.This is an example of a potential result from the study model, which looks at the likelihood of teenagers being employed, jobless, or inactive given various demographic factors. Employed status in Tanzania comprised paid employees, independent contractors working outside of traditional agriculture, unpaid family caregivers, and traditional agriculture workers, according to ILFS (2006). While the final two go under the category of casual youth employment, the first two fall under the category of official youth employment. Both official and informal employment categories are used to represent employed youth for the purposes of this study, however we only include traditional agricultural laborers in the informal group. Although traditional agriculture and unpaid family caregivers account for more than 80% of Tanzania's economic activities, the inclusion of this sector in labor market analyses has been contested (Wamuthenya, 2010). This is because of the nature, scope, and productivity of informal sector employment, particularly in developing nations like Tanzania where peasant farming is predominant in small-scale operations. Traditional agriculture is included in this study's definition of informal work, however unpaid family helpers are not included because they do not accurately represent employment.This study uses the International Labor Organization's (ILO) definition of unemployment, which refers to those who are available for and actively

looking for work but have not worked more than one hour during the short reference period (O'Higgins, 1997). Additionally, we define inactive adolescents as individuals who, during the reference period, are neither employed nor unemployed for reasons such as performing only domestic work in their own homes, attending school full-time, being ill, retired, or choosing not to work (ILFS, 2006).

Table 1: Summary Statistics of youth (International definition)

The model's explanatory variables (independent variables) include marital status, location, gender, education level, and skill set. Since the 2011 survey was not undertaken, the 2006 Tanzania Integrated Labor Force Survey (ILFS) stands as the most recent available source of labor force information for this study. According to ILFS, (2006) 66.8% of young people are employed, with 12.7% of them being unemployed and 20.6% being inactive, in both formal and informal sectors of the economy. According to statistics, 48.1% of young people who are active are men and 51.9% are women. Of these young people, 33.1% reside in urban areas and 66.9% live in rural areas. Only 0.3% of young people have abilities acquired through professional training, according to statistics on young people's skills, whereas 99.7% do not. Statistics on education levels reveal that 14.9% of youth completed elementary education and went on to higher education, whereas 52.5% of young only completed primary education.

IV. RESULTS AND DISCUSSION

In order to facilitate comparison and benchmarking with research on youth employment in other nations, the analysis of the determinant elements of youth unemployment in Tanzania was undertaken using the international definition of young people. Using a multinomial logistic model (MLM), we estimate the likelihood that young people with specific characteristics will be either unemployed or inactive as opposed to being employed, using the employed category as the reference point/preference group. First, we looked at the significance of the link between the independent and dependent variables in our model. This was essential to figuring out how well the model could forecast the dependent variable. Five independent factors (sex, location, married status, education, and abilities) were added to the original model, which did not include any independent variables. A Chi Square test was used to determine whether the addition of the independent variables improved the model. With a Chi square of 1831095.032, 16 degrees of freedom, and a 5% level of significance, the test findings demonstrate the existence of a statistically significant association between the dependent variable and a collection of independent variables (Table 2). According to the test findings, the independent factors that were included in the model have a connection to the dependent variable; as a consequence, they help to lower the model's error rate and can successfully predict the model's dependent variable, which is the youth employment status. In addition to testing the overall impact of the collection of independent variables on the model, we also looked at how each independent variable affected the overall link between the dependent variable and individual independent variable

Table 2: model testing

The impact of each of the study's five independent variables on the MLM error reduction as determined by 2-log likelihood statistics was examined. The results of the likelihood ratio tests show that, at the 5% level of significance, all five independent variables (sex, location, skills, marital status, and education) were significant (Table 3). This suggests that all five independent variables were important factors in the study model's explanation of the variation in young people's job status and that their inclusion in the model helps to reduce error.

Table 3: Variable testing

The findings of the tests conducted on the factors influencing youth unemployment in Tanzania point to two MLM equations, the first of which separates a variable with a statistically significant link from those who are employed. The second equation separates factors with a statistically significant association to discriminate between employed and inactive youth. According to table 4 below, the factors that can be used to separate young people who are unemployed from those who are in employment are: gender (male), location (urban), skills (young people without skills), marital status (single, married, widowed), and educational attainment (primary not completed, primary completed). The same characteristics that distinguished between unemployed and employed youth were statistically significant, with the exception of marital status, where only the single and married variables were significant.

Table 4: Parameter Estimates

Analyzing the role of each independent variable in differentiating between unemployed youth from employed youth, we find that all five independent variables of the model play a significant role. The results show that being a male made a youth person in Tanzania about 4% less likely to be unemployed over being employed. This indicates that male youth have high chance being employed over being unemployed as compared to female youth persons. These results were consistent with the findings presented in previous studies such as Isengard (2003) in Germany and Mlatsheni &Rospabe (2002) which also reported that gender was among the key factor for youth unemployment. These studies also support the findings that women youth were discriminated hence male youth had a high chance of being employed than female youth.

The results on the impact of youth location on their employment status show that living in urban areas made the youth person about five times more likely to be unemployed over being employed. This indicates that it is easy for youth people to be employed in rural areas than in urban areas of Tanzania especially in agriculture sector due to the informal nature of employment in rural areas.

In Urban areas the youth are more constrained formal employment requirements such as education, skills and experience which most of them do not have. The results were consistent with by Mpanju (2012) in the country which also indicated that the unemployment rate was higher in rural areas than in urban areas of Tanzania. The results also show that being a youth without skills made a youth person in Tanzania about 23% less likely to be unemployed over being employed. This implies that youth people without skills are more likely to be employed that skilled youth. Given the country education system skills are acquired either in vocational training colleges or at higher education such as universities and colleges. The youth person who has only completed primary education or secondary education does not possess any skills required in the job market, hence they engage in informal employment. For the skilled youth, market competition for the job, experiences and their preferences for formal employment make them more likely to be unemployed over being employed. The findings of the study were consistent with previous findings such as Isengard (2003) in Germany, Awogbenle & Iwuamandi (2010) in Nigeria, Mlatshani & Rospabe (2002) in South Africa, Bruno and Cazes (1998) in France which all indicated that skills was an important determinant factor in both formal and informal employment.   

The findings on the impact of skills in the likeliness of youth to be employed or not was also supported by the findings on the impact of education level on the youth employment status. The results of the study show that the youth who have completed primary education are 35% less likely to be unemployed compared to being employed while a youth person who has not completed primary education is 55% less likely to be unemployed as compared to being employed. This indicates that their high change for the youth without primary education to employed that those who have completed primary education as a results of most of population engaging in informal employment which is less favored by youth with education. The findings on the impact of education level supports the finding by UNICEF (2002) which indicate that most of the youth do not complete secondary education or vocational education hence are constrained in acquiring formal employments. The results on role of marital status in differentiating youth person employment status show that being a single, married or widowed increased the likelihood that the youth person would be unemployed over being employed by about 67%, 60% and 25% respectively. This is explained by the fact that the standard definition of the youth lies in the age between 15 to 24 years and most of the youth with such age in the country are still engaged in education hence single and unemployed. For the married and widowed youth especially for female ones are likely not to be employed as they stay at home and take care for the family.

The findings on the second equation in MLM indicate the role of each independent variable in the model in differentiating between inactive youth from employed youth. The results show that male youth are 32.6% less likely to be inactive over being employed. This indicates that the chances are high for female youth to be inactive over being employed Tanzania while male youth have high change of being employed rather than being inactive.

The results on geographical location show that a youth person who lives in urban area is about 2.6 more likely to be unemployed over being employed. Like in the first MLM equation, the youth people are more likely to be employed in rural areas where informal employment prevail more that formal employment. Likewise the results on skills level of the youth people show that youth without skills are about 54% less likely to be unemployed over being employed. The results also show that youth person who is single is about 3 times more likely to be inactive over being employed while married youth are about 46% less likely to be inactive over being employed. This is due to the fact that married youth have more responsibilities of taking care of the family which require them to work while most of single youth still depend on the parents hence less motivated to be employed.

Conclusion

The study\'s objective was to investigate the elements that contribute to youth unemployment in Tanzania and offer solutions for the issue. The factors of unemployment in Tanzania are examined using the Multinational Logistic Regression Model (MLM) in this study. Youth unemployment status was the study\'s dependent variable, and it was divided into three groups: employed, jobless, and inactive youth. The National Bureau of Statistics\' 2006 integrated labor force survey, which is the most recent survey to date, provided secondary data for the study. According to the study\'s findings, gender, geography, education, skills, and marital status all have a significant role in determining the differences in young people\'s job situation in Tanzania.The study\'s findings indicate that gender is a crucial factor in determining unemployment, and that male youth have a higher likelihood of finding job than female youth do. The geographic location of the young individuals was shown to be a key determinant, and it was discovered that young people residing in metropolitan regions had a five-fold greater likelihood of being unemployed than employed. The findings regarding the educational status of youth demonstrate that due to their participation in informal employment activities, youth who have not completed their primary education as well as those who have but did not pursue further education are less likely to be unemployed than those who have employment.The influence of skills on youth employment status results, which showed that unskilled young were roughly 2.3 less likely to be unemployed than employed, confirmed the results on schooling. The study\'s findings also show that marital status was a crucial determining factor, with young adults who were single or married having a higher risk of being unemployed than those who were employed than young adults who were widowed, divorced. Many recommendations are made based on the study\'s findings. The first is that the government and policymakers reassess the laws and regulations governing the job market to encourage a smooth transition for young people from education to the labor market.The study\'s findings demonstrate that young people with higher education levels and those with skills are more likely to be unemployed than employed. To make sure that all young people with education or skills realize their investments in education and contribute to the development of the country, it is crucial for the government to develop specific interventions, particularly in the creation of more formal jobs and strengthening job market regulation relating to young people. The study\'s findings also demonstrate the issue of gender imbalance in the labor market, with the data indicating that young men are more likely to be employed than unemployed. To ensure that all young people with the same level of skills or education have an equal opportunity, the government and policy makers should enhance the laws and regulations relating to gender balance in the job market. The report suggests that the government encourage the formalization of the informal work sector in order to encourage more young people to participate in a variety of activities that are currently regarded as informal. This will contribute to a reduction in the issue of young unemployment, particularly for educated and skilled adolescents in urban and rural areas. The study suggests that the national board of statistics (NBS) enhance its data collecting and categorization on young people in Tanzania. The integrated labor force survey has so far provided some extremely generic data, such as education level, which omits information on young people with secondary, college, or university degrees.

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Copyright

Copyright © 2023 Vaileth Elias Chacha, Dr Hu Junjuan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.