capital, knowledge and innovation is the major driver of economic growth (Romer, 1986; ..... Human capital is a people centric concept of development and.
CHAPTER ONE INTRODUCTION 1.1 Background to the Study Human capital stands as a paramount factor in the growth process of every economy. Gary Becker (1964) views that human capital is determined by education, training and health and it increases the growth rate of the economy. The endogenous growth model posits that human capital, knowledge and innovation is the major driver of economic growth (Romer, 1986; Lucas, 1988). According to Schultz (1993), human capital is characterised as the significant instrument to enhancing efficiency and maintaining the upper hand since it includes the improvement of aptitudes to increment financial benefit. A push to advance interest in human capital development is to certain to bring about speedy economic growth for the economy (Olaniyan and Okemakinde, 2008). Human capital investment is an integral factor to sustainable economic growth in Nigeria. Given the importance of human capital, it will be consistent to hypothesize that enhancing the human capital level, especially via education and training, is imperative to accomplish sustainable growth and development. (Oluwatobi and Ogunrinola, 2011). The Solow growth model explains how labour (L), capital (K) and knowledge (A) are the basic sources of economic growth in an economy (Solow, 1956). The model explains how knowledge is used to augment labour (AL) known as “effective labour.” In the endogenous growth model, human capital was portrayed as the abilities and learning that makes labourers beneficial and it has an expanding rate of return unlike physical capital and it can also be created by training and education (Romer and Lucas, 1986). Empirical studies have shown that human capital accumulation is the major determinant of long-run growth. It can in this way be declared that any channel that makes education, which
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is a procedure of creating human capital, more open to more individuals is a means that backs sustained growth and development (Oluwatobi, Olurinola and Taiwo, 2016). In recent times, Nigeria has made major efforts in accumulating physical capital and exploitation of natural resources for economic growth rather than human capital formation. Nigeria has recorded poor literacy rate over time; having 64 million adults that are illiterate and an average literacy rate of 59.19 percent (WDI, 2014). The major challenge of the Nigerian education system is caused by the consistent decline in the public expenditure allocated to education against the standard 26 percent recommended by the United Nations Educational, Scientific and Cultural Organization (UNESCO) for developing countries. Between 2003 and 2013, education expenditure changed from 8 percent to 21 percent of the aggregate spending plan in 2003 to about 6.3 percent, 7.8 percent, and 8.7 percent respectively from 2005 to 2007. It became 6.42 percent in 2009 and then 8.7 percent in 2013. In 2014, the legislature fundamentally increased education expenditure to 10.7 percent of the aggregate spending plan, however recent reports show that present spending levels have diminished well beneath 10 percent. Currently, education expenditure is 8 percent which is low compared to the UNESCO standard of 26 percent (World Bank, 2014). Nigeria has been growing over time but the level of unemployment and poverty are still on the increase and the inequality gap is widening up. Between 2006 and 2016, the GDP growth rate of the Nigerian economy was at an average rate of 5.17 percent per annum. In the second quarter of 2017, Nigeria’s GDP has grown by 0.55 percent and was recorded to be 1 percent on the second half of the year 2017 (World Bank, 2016). In the midst of this growth, there are still evidences of income inequality and lack of equal access to opportunities and the rate of poverty is adversely influenced. The concept of economic growth is abstract and vague because it just focuses in the increase of output and not growth aimed at reducing poverty,
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unemployment and inequality. There is therefore a need for growth that cuts across all the sectors of the economy which is termed ‘inclusive growth’ The idea of inclusive growth propels impartial open doors for economic agents in the midst of economic growth with benefits procured by each portion of society. It is growth that cuts across all sectors of the economy. Sen (1999) says that the legislature and the general population need to take a stab at an "inclusive growth" as well as "growth-meditated development" that lays emphasis on all parts of society, social and economic development. Inclusive growth enables individuals to add to and take advantage of economic growth (World Bank, 2010). According to the African Development Bank (2012), inclusive growth can be explained as economic growth that leads to increased access to opportunities while protecting the weak and vulnerable and observing justice and equality. It is economic growth aimed at ensuring productive employment for the citizens and it reduces poverty level (Ianchovichina and Lundstorm, 2009). This study is however focused on how the human capital investment will bring about inclusive growth in Nigeria. 1.2 Statement of the Research Problem Nigeria has been seen according to empirical studies as a developing country. There can be no huge development in any nation without sufficient human capital advancement and quality speculation. In the past, a significant part of planning in Nigeria was focused on the accumulation of physical capital for rapid economic growth and development without perceiving the impact of human capital investment on the growth and development of the economy (Ogujiugba and Adeniji, 2003). Research and development also have a role to play in developing human capital because it increases and widens the span of knowledge of individuals. In Nigeria, the percentage expenditure on research and development as at 2015 was 0.01% according to the Federal Institute of Industrial Research. There has been numerous research conducted on the subject matter of human capital investment and growth and majority
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of them have generated a positive result (Awe and Ajayi, 2010; Jaiyeoba, 2015). Mba, Mba, Ogbuabor and Ikpegbu (2013) carried out a study on human capital and growth and discovered that a positive relationship exists between the two concepts. Human capital is crucial to sustainable development according to their study and inclusive growth springs out from sustainable development. Their study also explains that more investment, especially public expenditure on human capital will lead to growth. In the Nigerian economy, growth has been occurring but the growth has not been inclusive in nature. There has been various studies on the concept of inclusive growth but a few researches have been carried out in relation to human capital. The Nigerian economy is growing at an average of 5.17 per annum but the inequality rate is still 0.43, unemployment rate is 7.5 percent and the poverty rate is 82.2 percent (World Bank, 2014). Public expenditure ranged over time from 0.73 and 10.48. The tertiary school enrolment rate has ranged from 2.31 and 13.40 from 1981-2013. Nigeria has experienced high economic growth but has not developed because the 5.17 percent growth rate has not translated to increase in the welfare and living standards of individuals. Therefore, the real question is what kind of growth will reduce poverty, inequality and unemployment rate all at the same time? Further, one may ask what exactly is needed to achieve this growth. Human capital investment has a paramount role to play in achieving inclusive growth in the Nigerian economy. However, scarcity of research highlights the need for further research on the impact of human capital investment on inclusive growth in Nigeria. 1.3 Research Questions The following research questions will help in driving the objectives of the study, thereby serving as a guideline. i.
What is the trend of inclusive growth in Nigeria?
ii.
What is the impact of human capital on inclusive growth in Nigeria?
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iii.
Does government investment on human capital promote inclusive growth in Nigeria?
1.4 Objectives of the Study The major aim of this study is to examine how human capital investment can impact inclusive growth in Nigeria. However, the specific objectives of this study are to: i.
Examine the trend of inclusive growth in Nigeria?
ii.
Assess the impact of human capital on inclusive growth in Nigeria.
iii.
Determine the impact of government investment on human capital on inclusive growth in Nigeria
1.5 Research Hypotheses Hypothesis 1: H0: Human capital investment does not have impact on inclusive growth H1: Human capital investment has impact on inclusive growth 1.6 Scope of the Study There are different speculations that can be made to contribute to human capital yet this study will concentrate on education as an impetus for human capital. This study will focus on analysing the impact of human capital investment on inclusive growth in Nigeria both descriptively and econometrically. There is no universal indicator for inclusive growth; however, McKinley (2010) constructed the inclusive growth composite index (IG index). This index has been used by Udah and Ebi (2015) to capture inclusive growth in Nigeria and will be adopted for this study because it is recognized and certified by the Asian Development Bank. This study will focus on secondary and tertiary education because these levels of education contributes largely to national development (Keller, 2004). The extent of this study will cover the year 1981 to 2015 because those are the years for which data are available and this will be confined to the Nigerian economy.
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1.7 Significance of Study According to Todaro and Smith (2003), investment in the development of human capital must be given direct consideration even in economies that are quickly developing. The nature of this study is prompted by the need to address the impact of human capital investment on inclusive growth. This study also empirically examines the possibility of attaining inclusive growth through investment in human capital and will examine the magnitude and the direction of the impact of human capital investment on inclusive growth. The policy recommendations that will spring up from this study will enable the government see the need for more investment in human capital and increase their allocation to the education sector and pave way for further studies on the subject matter. It will contribute to the general body of knowledge and pave way for further studies. This study will bring data and facts into play to assure the validity of the conclusion and analysis in other to aid economic planning and policy making. This study will aid the understanding of the human capital concept and the impact is has on inclusive growth and also shed more light on the subject matter of inclusive growth and the fact that inclusive growth deals with the pace and pattern of growth. 1.8 Method of Analysis and Data Sources The Autoregressive Distributed Lag estimation technique would be adopted to specify the nature of long run relationship between human capital investment and inclusive growth in the Nigerian economy using secondary data from 1981 to 2015. The Vector Error Correction Model will be adopted to check for an error correction mechanism between time series variables in the short run and to estimate the relationship that exists between the variables in the short run and reconcile the short run behaviour with the long run equilibrium; that is, it will restrict the long run behaviour of endogenous variables to incorporate short run equilibrium. The dataset for this study will be drawn from secondary sources. Information drawn from World Bank Development Index and Central Bank of Nigeria statistical bulletin.
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1.9 Outline of Chapters For a detailed and comprehensive analysis of the study, this study will be discussed in five chapters. Chapter 1 being the introduction to the study will entail the background to the study, the statement of problem, the questions this research seeks to answer, objective of the study, scope of the study, hypothesis of the study and the structure of the study. After which relevant literature related to the study; conceptual issues, relevant theories and empirical and methodological issues will be reviewed accordingly. Then the research methodology will be discussed as it examines the theoretical and analytical framework, model specification, techniques of estimation and data sources of the variables of study. In addition, the data estimation results will be discussed and the implication of the analysis will be reported. Finally, the entire study will be summarised and policy recommendations will be proffered.
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CHAPTER TWO LITERATURE REVIEW 2.1 Preamble The issue of low investment in the development of human capital is one of the of the reasons for the undeveloped state of the Nigerian economy characterized by high unemployment rate, high illiteracy rate, poor informal sector and other negative factors. A number of researchers have espoused this subject matter in past and recent times and most of them have failed to lay emphasis on the impact of human capital on inclusive growth. This study aims at examining this subject matter and hopes to provide useful details that will support further studies in this area of research. The concept of economic growth is now shallow because countries now grow without any positive changes in social welfare, reduction in poverty level, unemployment rate and inequality of access to opportunities. Improvement in the living standards of the people is now the primary objective of every country and paying attention to the concept of inclusive growth is now the major measurement of the performance of an economy and the aim of development policies (Asian Development Bank, 2014). This chapter contains conceptual definitions of key issues in the study. It also reviews relevant theories, existing literature and methodology that have contributed to the expansion of the subject matter for the purpose of a more comprehensive research and to enhance the readers’ understanding.
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2.2 Conceptual Review 2.2.1 Human capital The concept of human capital was introduced by Schultz (1961) in his paper but it was already explained by other economists such as J.S Mill, Adam Smith, and J.B Say. Later on, Gary Becker (1965) and Michael Grossman (1972) elaborated more on the topic. According to Gary Becker, the concept of human capital can be defined as the skill and knowledge base of people that increases their productivity of which the knowledge base is the most important factor which can be improved by education. He explained the relationship between education and the income of individuals. Becker explained that investment in the human capacity of an economy will provide a profitable health status for the labo-ur force and increase their standard of living which leads to sustained economic growth and development. Physical and human capital are the major factors that spur growth in an economy and the major reason for differences in the growth of nations can be traced to these factors. Grossman (1972) contributed to the subject matter of human capital by including the concept of health; the demand for health. He stated that a person’s health is regarded as a stock and it depreciates as the person ages with time and it can be improved when their health is invested upon. This model depends greatly on the human capital theory as proposed by Gary Becker. Human capital investment is the act of investing in the intellectual capacity of people so as to bring about economic returns. Jhingan (2005) stated that human capital investment is expenditure on education, health and social services and it also means expenditure on training and education. Sulaiman, Bala, Tijani, Waziri and Maji (2015) stated that the most beneficial means to developing human capital is through education from the least stage to the highest educational attainment level and the rate at which a country grows can be largely determined by the quality of the human capital in that economy.
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Building human capital through education reduces the occurrence of health issues, death rate and life expectancy. It is a key factor for achieving economic growth and development and it contributes to political stability (Singh, 2012). Adelakun and Johnson (2011) explored the concept of human capital and stated that human capital is a relevant component for further production and for increasing the literacy rate in an economy. Manpower training increases productivity level in an economy which contributes to increased standard of living of the population and sustained economic growth and development. Mba, Mba, Ogbuabor and Ikpegbu (2013) explained that human capital is a tool to promoting competitive advantage because it involves training, education and empowerment programmes. Human capital is also a defined factor for reducing the poverty level in a country and from the macro level, human capital accumulation increases the productivity of the labour force, technological advancement, capital value and sustainable growth (Son, 2010). The concept of human capital is clear but it is difficult to measure, therefore, some proxies have been developed by some empirical literature such as literacy rate, school enrolment rate, years of schooling, government education expenditure and so on. Developing countries can catch up with developed countries if they can increase their stock of human capital and pay much more attention to education since it is a major determinant of economic growth (Solow, 1956). Hanushek (2013) stated that the human capital concept is hinged on the role research and development plays in its formation which leads to growth and development. He explained the role cognitive skills play in contribution to the human capital of an economy. Organization for Economic Co-operation and Development OECD, 2001) defined human capital as the combination of knowledge, skills and competencies embedded in an individual and developed with continuous investment in education, health care and training. Kanayo (2013) examined the concept of human capital and explained that human capital utilization is a booster of 10
economic growth and development in an economy by increasing per capita income through an increase in productivity. Human capital is a people centric concept of development and investment in the people drives the socio-economic development of an economy and improves the capacity of the human resource of a nation. The experience of the East Asian Tigers attests to the importance that human capital play in the process of ensuring significant economic growth in an economy (Jaiyeoba, 2015). Schultz (1961) identified five major categories of activities that boosts human capital (1) Health facilities and services; which includes all health expenditures that improves the health status and standard of living of people. (2) On the job training; which includes trainings coordinated by firms. (3) Formal education; which includes primary, secondary and tertiary education. (4) Study programs organized for adults. (5) Migration of individuals 2.2.2 Education This concept of education will be used as a measure for human capital in this study. Maheshwari (2012) defined education from different perspectives; to educate means to teach. On the under hand, from the Latin word, education means to lead forth. From Hindi, it means to learn. Maheshwari (2012) stated that education is expected to bring out the inherent abilities of a child. Recently, education has only been viewed as a concept to be used in school or only for those who have been to school or have certificates. This is a very shallow view of the concept of education. On a broader view, education is a lifelong all-round development process which bring about changes within us. Education should not be limited to the classroom or school rather it is a long term concept. Aristotle defined education as a sound mind in a sound body. The Nigerian Educational Research Council (1981) defined education as the process which individuals are prepared for real life situations and happenings. The relevance of quality education to the growth of an economy cannot be trampled upon. Education aids the increase
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in skill sets and the knowledge base of individuals which is a major booster of productivity, it promotes technological innovation via science and technology which is an ideal attribute of the 21st century country. Education also promotes the creation of new methods and techniques of doing things which engenders creativity. According to Callaway (1975), life is education and education is life. That is, there is need for a lifelong learning in the system. Therefore, the need for quality education should be emphasized in an economy. 2.2.3 Inclusive Growth The concept of inclusive growth was introduced by Kakwani and Pernia (2000). They discussed the concept of pro-poor growth which is the growth that involve the poor in the active administration of the economy. Ranieri and Ramos (2013) stated that inclusive growth is a major concept in most literature as pertaining to development and the making of policies in many countries. They explained that the concept of inclusive growth came up from the concept that increase in output and social equality should go together as people worked on how to place growth and equality side by side. They also discussed that growth would not solve social problems except the growth was redistributive in nature and it leads to the reduction of poverty, inequality and high unemployment rate. Inclusive growth is sustained economic growth; it is defined as the growth that cuts across all the sectors of the economy (Sen, 1999). The inclusive growth concept takes care of poverty, inequality and other developmental issues. Inclusive growth is borne out of investment in individuals and innovation. It is the major goal of policy making because it brings about poverty reduction, employment creation, equality if access and empowerment for the people (George, McGahan and Prabhu, 2012). Inclusive growth includes all the sectors of the economy in the growth process of the economy due to the fact that economic growth in itself cannot tackle such as poverty, unemployment and inequality from 1990s till present
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(Raheem, Isah and Adedeji, 2015). Recently, OECD espoused the concept of inclusive growth and the need for inclusive growth in the achievement of the Sustainable Development Goal (SDG) 8. The concept of inclusive growth has been recognized by many governments, organizations and institutions. There is also a notion that increase in the Gross Domestic Product (GDP) does not necessarily mean growth because some countries achieve high growth rates with increasing poverty level, unemployment and inequality; this is because such kind of growth is only accounted for by a part of the population and not the entire population. There is need for the entire population of a country to contribute to and benefit from the growth process of that country. Inclusive growth is the one of the solutions to the developmental problems of a country because it benefits all the sectors of the economy. Inclusive growth is an extended version of pro poor growth; it is the growth that is sustainable economically (Birdsall, 2007). According to OECD, human capital indicators are the major determinants of inclusive growth. The determinants of inclusive growth are inclusive institutions, human capital, employment opportunities, equality and social inclusion, social protection, progressive tax systems an d structural transformation. Inclusive growth captures the vulnerable so they can be contribute to and benefit from the growth of the economy. Ramos and Ranieri (2013) explained the concept of inclusive growth as a development strategy. The concept of inclusive growth came up as a result of finding ways to ensure equality in the process of economic growth. The African Development Bank also contributed to the definition of inclusive growth by explaining that inclusive growth promotes the equality of access to economic opportunities, protecting the disadvantaged, ensuring rule of law and supporting inclusive institutions. The Commission on Growth and Development (2008) defined inclusive growth as an umbrella term for equality, social protection and protected employment. The focus of inclusive growth
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is on productive employment instead of the distribution of income. It is concerned with expanding the economy size and the growth rate of the economy while ensuring there is productive employment and investment opportunities. Inclusive growth is sustained growth and it is aimed at reducing poverty level. For sustained growth to be recorded, growth should be cut across all sectors of the economy. There should be structural transformation to ensure this and there must also be unbiased institutions, free access to markets. The concept of inclusive growth is a long term concept and it focuses on working on the individual productivity and driving inclusive institutions. 2.2.4 Composite Inclusive Growth Index Comprehensive development as an idea has not had any broad pointer to capture it. Although, a number of indicators have been used to capture inclusive growth such as number of people below the minimum living standard and so on, the World Economic Forum, WEF (2014) used the Human Opportunities Index (HOI) to capture inclusive growth in their working paper on new growth model. McKinley (2010) constructed a composite inclusive growth index which has been certified by the Asian Development Bank (ADB). The index was constructed at the country level. It has various measures such as growth, productive employment and economic infrastructure, income poverty and equity with gender equality inclusive, human capabilities and social protection. These indicators suggests a diagnostic approach based on weights and scores which can help countries to assess their economic improvement in attaining inclusive growth (McKinley, 2010). It can be used to test how the inclusive growth goals of a country can be maximized. It uses non-income indicators of living standards and values individual capabilities. Vellala, Madala and Chhatopadhyay (2016) used this index to measure inclusive growth in India and stated that the composite index for inclusive growth is a unique approach. The higher
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the value, the greater the inclusive growth rate. This index has been tested in Bangladesh, Cambodia, India, Indonesia, Philippine, and Uzbekistan. 2.2.5 Measures of inclusive growth 1) Economic Growth: Economic growth relates to the growth of per capita income as this serves as a basis for the generation and improvement of economic opportunities and equality. This dimension is assigned the weight of 25 percent in the composite index. The growth of the per capita income should be supported by valued- added changes in industry, services and agriculture to find the growth trend. Per capita income does not align with the broad based increase in productive employment. To achieve inclusive growth, one must study and pay close attention to the pace and the pattern of growth that is paramount. (McKinsley, 2010). Inclusive growth is highly represented by the decent employment of economic growth as stipulated by World Bank (2009) and Ali and Son (2007). 2) Productive employment: The employment factor is a major concept of inclusive growth in an economy and it has generally been trampled upon. The Millennium Development Goals (MDGs) identified some indicators of employment such as the employment to population ratio and also the Sustainable Development Goals (SDGs) made mention of decent employment. There is inadequacy in the indicators accessible to measure productive employment as most of them are unable to provide information as pertaining to the employment quality. The lack of an adequate and reliable indicator of productive employment poses the need for the International Labour Organization (ILO) to collaborate with potential data sources in gathering employment data. However, due to the lack of reliable data and weakness in monitoring the progress of productive employment, this measure has been given a weight of 15 percent. 3) Economic infrastructure: Equality of access to infrastructure such as power, clean water, roads etc. is another measure of inclusiveness in an economy. Data in this aspect has not really been readily available although some indicators such as electric power consumption per capita has
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been identified. This measurement has also been ignored due to the extra attention paid to social infrastructure such as health, education and sanitation. This measure has been assigned the weight of 10 percent. 4) Income poverty and gender equality: The current motion towards the strategic framework of inclusive growth implies that the layman focus on tackling extreme poverty is too limiting (McKinsley, 2010). Income inequality has been on an increase in developing countries and it has an adverse effect on the non-poor population. Therefore, a total weight of 15 percent was assigned to income poverty. The issue of gender equality is also very important in promoting inclusive growth. Three dimensions are to be evaluated to achieve gender equality; education, health and employment. This measure was given the weight if 5 percent. 5) Human capabilities: The demand side of inclusive growth has been addressed but the supply side has not been given sufficient attention; in the sense that, the labour force has acquired the right skills and capacity to be employed productively and gain access to economic opportunities. The human capabilities are regarded as the outcome of human welfare and it boosts the pace of growth by generating additional income; talking about the concept of human capital which involves investment in health and education and equal access to them. (McKinsley, 2010). This measurement is regarded as the prioritized measurement of inclusive growth. Tandon and Zhuang (2007) stated that the lack of elementary capabilities is a sign of human poverty. Hence, this measurement was given the weight of 20 percent. 6) Social protection: This measurement focused basically on the poor and vulnerable who lack access to economic opportunities provided by inclusive growth (McKinsley, 2010). Tandon and Zhuang (2007) explained that inclusive growth focuses on increasing opportunities for all while providing social protection for the poor and vulnerable. The Asian Development Bank also stated that the major factors of inclusive growth are the opening up of equal opportunities and also eliminating poverty (Tandon and Zhuang, 2007). They explained various categories
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of social protection; labour market policies, social insurance programs, welfare schemes, child protection schemes and so on. This measurement has been assigned the weight of 10 percent. Table 2.1: Summary of dimensions, measures and weight assigned.
Dimensions Growth Productive employment Economic infrastructure Income poverty Gender equality Human capabilities Social protection Total
Measures/Indicators Per capita income Per capita GDP Electric power consumption Proportion of people living below the minimum living standard Ratio of boys to girls in primary and secondary education School enrolment ratio (education) Under-5 mortality rate (health) Total expenditures on transfer payments as a ratio of GDP
Weights (%) 25 15 10 15 5 10 10 10 100
Source: Diagnosis of Nigeria’s Inclusive Growth: A Composite Index Approach (Udah and Ebi, 2015)
2.2.6 Construction of Composite Index of Inclusive Growth The composite index is constructed on a weighted score of 0-10 for each year from 1985- 2013 based on the performance of each of the chosen indicators in terms of the ranking of its growth and the weight assigned to it (Udah and Ebi, 2015). The product of rank and the assigned weight gives the score for the indicator and the summation of all the indicator scores for that year gives the composite inclusive index for that year. Table 2.2: Example of inclusive growth index for 2013 Indicators Per capita income growth rate (Economic growth)
Rank Weight 5 25%
Score
Annual employment growth rate (Productive employment)
6
15%
0.9
Annual growth in electricity consumption per capita (Economic infrastructure) 2
10%
0.2
Annual growth in poverty reduction (Income poverty and gender equality)
4
15%
0.6
Annual growth of gender equality (Income poverty and gender equality)
4
5%
0.2
Annual rate in mortality under-5 per 1000 birth (human capability) Annual rate of school enrolment (human capability) Annual growth in transfer payments (social protection)
8 6 10
10% 10% 10%
0.8 0.6 1 5.55
Total
100%
1.25
Source: Diagnosis of Nigeria’s Inclusive Growth: A Composite Index Approach (Udah and Ebi, 2015) 17
2.2.7 Inclusive Growth in Nigeria Growth in Nigeria which has been fairly 6 percent which is decent enough is yet to translate to one that reduces poverty, inequality and unemployment. The African Development Bank (2013) defines inclusive growth as economic growth that occurs when economic opportunities are easily accessible by all and the vulnerable are protected in a fair environment. Nigeria’s poverty rate according to Ogbu, 2013 has risen from 52 percent in 2004 to 61 percent in 2010. The National Bureau of Statistics predicted that this poverty rising trend was going to keep occurring. In 2012, Nigeria ranked 153 out of 185 countries in the Human Development Index (HDI) with an index of 0.471 and a life expectancy of about 52.3 years. The income inequality in Nigeria is also rising; in 2004, it was 0.429 but in 2010 was found to be 0.447. The rising inequality rate led to a 41.4 percent loss in the Human Development Index (HDI), (United Nations Development Programme; UNDP, ----. The National Bureau of Statistics also discovered a steady rise in the rate of unemployment in Nigeria from 6 percent in the fourth quarter of 2014 to 14 percent in the fourth quarter of 2016. Kanayo (2011) discovered some factors restricting inclusive growth in Nigeria such as; macroeconomic policy inconsistency, instability and policy reversals, conflicts of macroeconomic goals, public sector dominance in production and consumption, pervasive rentseeking and corruption facilitated by government being the hub of economic activities, infrastructure inadequacy and decay, high volatility of major macroeconomic aggregates, weak institutional capacity for economic policy management and coordination, lack of sustainability of public finance at all levels of government, lack of effective coordination among the three tiers of government, large debt overhang and so on. Quite a number of these problems are institutional while others are caused by disharmony between goals and means.
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A number of policies in Nigeria have been targeted for inclusive growth but they lack proper implementation. The Millennium Development Goals (MDGs) of 2015 comprised of most of the inclusive growth dimensions but those goals never saw the light of the day in Nigeria. The goals were measurable, timely, attainable and realistic but they were not achieved in Nigeria. Also, taking a cue from the irrationality of the Vision 20:2020; this goal seems to have faded in Nigeria. 2.2.8 Human capital investment in Nigeria Since education is used in this study as a proxy for human capital, the trend of investment in the education of Nigeria will be examined in details. Nigeria is the most populous country with about 168 million people of which only 30 million are students. The educational system regulation is a shared responsibility of the Nigerian Federal, State and local governments. The literacy rate in Nigeria is an estimate of 61 percent. Nigeria is also characterized by a large number of children out of school and low adult literacy rates. There are 36 Federal universities, 37 state universities and 45 private universities in Nigeria which are all certified by the National Universities Commission (NUC). (United States Embassy in Nigeria, 2012). Omojomite (2010) explained that the Nigerian educational system has undergone two developmental phases; the phase of rapid growth which was from 1950-1980 which was as a result of the British educational system that was in vogue then and there was easy access to tertiary education and the second phase which was a phase of rapid fall was from 1981-2009 due to lack of adequate financial inputs, the dwindling oil revenues, the International Monetary Fund (IMF) Structural Adjustment Programme (SAP) and corruption. Omojomite (2010) regression showed that the only significant indicator of education expenditures was the government’s revenue.
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Nigeria has recorded poor literacy rate over time; having 64 million adults that are illiterates and an average literacy rate of 59.19 percent (WDI, 2014). The major challenge of the Nigerian education system is caused by the consistent decline in the public expenditure allocated to education against the standard 26 percent recommended by the United Nations Educational, Scientific and Cultural Organization (UNESCO) for developing countries. Between 2003 and 2013, education expenditure changed from 8 percent to 21 percent of the aggregate spending plan in 2003 to about 6.3 percent, 7.8 percent, and 8.7 percent respectively from 2005 to 2007. It became 6.42 percent in 2009 and then 8.7 percent in 2013. In 2014, the legislature fundamentally increased education expenditure to 10.7 percent of the aggregate spending plan, however recent reports show that present spending levels have diminished well beneath 10 percent. Currently, education expenditure is 8 percent which is low compared to the UNESCO standard of 26 percent (WDI, 2014). Tertiary education is a major contributor to the growth of an economy because it boosts individual productivity and encourages the development of ideas, technological innovation and knowledge base (Larocque, 2008). There is an increase in the gross tertiary enrolment rate for both male and female from 3 percent to over 10 percent in Nigeria from 1985 to 2011 (WDI, 2015). In real terms, tertiary education was found to be increasing in Nigeria but it is inconsistent and sluggish. Okuneye and Adelowokan (2014) stated that the tertiary enrolment is distorting as the government is putting not enough efforts in promoting school enrolment in Nigeria. The tertiary enrolment assessment in Nigeria shows that tertiary enrolment has been increasing over time attaining the highest growth rate of 219.63 percent in 1997 and lowest growth rate of 67.27 in 2004 (CBN- Annual report, 2010). The major distortions accounted for between these two times periods were traced to the political instability and industrial struggles.
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However, irrespective of the government policies enacted to promote schooling at all levels, school enrolment rate is still very low. It is shocking and heart breaking to see that the tertiary enrolment growth rate is still less than 15 percent; this is very inappropriate in relation to the Vision 20:2020. 2.3 Theoretical Review The theories of growth has been as far back as 1776. The first theory of growth was propounded by Adam Smith in his book ‘Wealth of Nations’ but didn’t get much attention. He discovered three sources of growth which all related to capital and labour growth, efficiency and foreign trade. There are no theories of inclusive growth as the concept of inclusive growth is addressed as a theory in itself. The fact that economic growth is a part of inclusive growth because for inclusive growth to occur, there has to be a sustained occurrence of economic growth;; this gives a basis for the theories of economic growth to be used in this study as theoretical foundation. In this specific field, there are various economic theories that have been used in advocating this study such as human capital theory, the Solow model and the endogenous growth model which help in explaining how human capital impacts the growth of the economy. 2.3.1 Human capital theory The concept of human capital dates back to Adam Smith (1776) and many classical economists. However, the use of this modern concept can be traced to the works of Theodore Schultz and Gary Becker of the Chicago school and also Jacob Mincer. Modern growth theories view human capital as a paramount factor for economic growth. Schultz (1961) explained human capital as those resources that are inherent in each human being which can be traded between the users and the owners to improve their respective living conditions.
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Gary Becker (1964) elaborated on the theory by describing human capital in terms of education as an investment which is set to produce returns in due time to the individual in terms of employment benefits and economic growth. This theory has increased the relevance of education to the growth of the economy because the more the investment in the individual’s capacities, the better and more productive they become and the more the economy grows. Therefore, education expenditures, training, health and so on are major investments in human capital. They are called human capital because people cannot be separated from their knowledge, skills, competencies, health or value system unlike their financial and physical assets that they can be separated from. Schultz (1960) explained that education lead to increase in the earning capacity of individuals; being an investment and the returns comes later via higher employment benefits. The major indicators of human capital are education and training. The speedy recovery of Germany and Japan can be attributed to their rugged investment in human capital (Schultz, 1962). The concept of human capital in contemporary times have been expanded in the sense that it does not only imply knowledge or skills but also competencies, attributes and attitudes (Becker, 2002). Human capital theory places education as an individual and as a public good and its reward are general and social. Becker (1964) stated that human capital is relevant for production activities. However, there are different sources of human capital differences among individuals; innate ability, schooling, school quality and non-schooling investments, training and pre-labour market influences. Acemoglu (2017) stated that there are two approaches to measuring human capital; objectives measures which is often imperfect and earning potential which is basically one’s actual earnings. David Gillies (2015) stated that human capital theory is one of the strongest foundation for educational policies in the world.
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2.3.2 Solow model The Solow Growth model was propounded by Robert Solow in the 1960s. It is a modification of the Harrod-Domar model; it makes provision for substitution between factors of production (labour and capital). It is the stand point from which most growth theories are built on. It is a model that supports that in a pure production economy, there should be capital accumulation. The Solow model provides a simplified model of the economy and explains the causes of economic growth and the reasons for the differences in the per capita income of countries (Acemoglu, 2008). The assumptions of the Solow Model are constant returns to scale; production function is homogeneous of degree one, it assumes that all individuals work, save and invest a fixed proportion of their income, it assumes a closed economy with no government, taxation nor subsidies, there are no financial markets, one composite good is produced with constant technology, flexible prices and wages, full employment of all factors of production, constant growth rate of the labour force and savings rate, diminishing returns to the use of labour and capital (Solow, 2002) The Solow model explains the occurrence of a steady state where depreciation and population growth play a role. The amount of capital in the economy isn’t affected by the population growth but population growth reduces the amount of capital per worker. The capital amount can be subject to depreciation which shows the rate at which the capital diminishes. Population growth and depreciation are the two factors found to be eating up the capital per worker in an economy. In order to stop this occurrence in an economy, there must be steady investment in capital so that new ones will be created to make up for the loss caused by the population growth and depreciation. The steady state is the state where the capital is maintained through investment in new capital. When new capital are invested, the economy will automatically work itself up to the steady state. The capital stock will only grow if the capital investment is
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larger than the loss but will shrink if the capital loss is higher than the capital investment and if the two are equal, there will be no change in the capital stock of the economy. The steady state depends on the saving rate in the economy. The higher the savings rate, the higher the capital per worker and the lower the savings rate, the lower the capital per worker in the economy. The Solow model also emphasized productive employment in the case of the addition of effective workers which is an indicator of inclusive growth. It states that the productivity of workers depends on human related factors such as the worker’s health and education. The output per worker is dependent on the efficiency of the workers; the more efficient the workers, the more productive they will be and the more output will emerge from their labour. The higher the savings rate, the higher the investment, the higher the capital stock per worker and the higher the output level per worker. It suggests that for growth to be sustained, there must be technical progress. Technology is augmented by labour and technological progress can result from investment in human capital. The Solow model suggests two major reasons for the differences in the growth rate of countries. First is if one country has a higher steady state income level than the other and the second is caused by difference in growth paths caused by difference in the points in the transition to the steady state. The implications of the Solow model are that economies with the same level of per capita income will conditionally converge and poorer countries are expected to grow faster than rich countries and catch up to the same per capita income when the countries have the same steady state level of income. The model predicts the convergence of countries over time but this has not been the case empirically. The growth rates of developing countries have not been more than that of developed countries as predicted by the model. Empirically, countries that have the same features have the tendency to converge for example, the OECD countries. The Solow model is 24
also criticized for assuming savings rate, growth rate of labour supply, workforce skills and the pace of technological change and it fails to provide the features of economies that cause them to grow over long periods of time. It failed to explain why technological progress is exogenous and it did not incorporate physical. Can we be okay with a model that does not explain differences in key parameters without explanation of these differences? Although, the limitations have proved to be barriers in this research, the Solow model is still appraised for its simple nature and for being a strong foundation for modern economic growth theory. This has led to the emergence of the endogenous growth model. 2.3.3 The Endogenous growth model The endogenous growth theory also known as the new growth theory was propounded by Paul Romer (1986) and Lucas (1988) to correct the shortcomings of the Solow model. The major objective of this theory is to position technological progress as an endogenous variable to be explained in the model. This model endogenizes some variables such as savings/ investment, technological progress and skill formation, population growth and long term per capita growth. The model takes into account multi sector models, more factors of production, externalities, economies of scale, monopolistic competition and open economy models. This theory has the following assumptions: technological advancement is seen as a public good, the human capital factor is emphasized, the government can affect the rate of growth and the growth rate is also influenced by the savings rate and it predicts GDP convergence between countries in the long run which is caused by technological advancement. This model postulates that economic growth is brought about due to an investment in human capital and investment in innovation and knowledge base. It explains that spill over effects and externalities of the knowledge based economy engenders sustained growth and it is largely dependent on the policies in place and inclusive institutions.
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The endogenous growth model is highly preferable because it constructs macroeconomic model from micro economic layers and focus was given to the concept of building human capital and the knowledge base of individuals and emphasizing the need for technological innovations. The endogenous growth model states that policies will promote innovation, global partnerships and healthy competition which will lead to sustained growth. This growth model explains that growth is sourced from internal sources and not external sources; sources of the growth are majorly human capital, knowledge and innovation. The endogenous growth theory posits that policies stimulate growth if the institutions are inclusive and innovation is supported and if the private sector invest in research and development, there will be technical progress. The institutions must ensure the protection of intellectual property, simplify the ease of doing business and involve individuals in research and development. The human capital factor is the major input of sustained growth according to this growth theory and technology is an endogenous factor. The endogenous growth model lays emphasizes on human capital because it explains why the returns on physical capital is not high in poor countries. It states that if the human capital factor is improved in poor countries, they have a high tendency to grow faster. The endogenous growth model explains the economic forces behind technical progress such as higher capital stock, human capital, research and development. The endogenous growth theory provides explanation to the conditional convergence by explaining the externalities and spill over effects of human capital and knowledge. The externality of human capital is caused by education which increases the human capital stock of the labour force. Empirically, investment in human capital is also as important as investment in physical capital. Lucas (1988) explained that growth does not lead to better standards of living without raising the returns on investment in human capital.
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2.4 Methodological and Empirical Review The growth theories from Solow model to the endogenous growth model have clarified that a relationship exists between investment in human capital and growth. However, empirical testing on the growth theory has yielded different results. Various empirical researchers have adopted various methods in conducting studies on the growth theory. Different data sets have been used so far and different definitions and measures of human capital which is factored in the production function have been used and this has resulted in an indefinite proof on the role human capital plays in the growth process. The Solow growth model and the endogenous growth model formed the foundation of empirical studies on how human capital development facilitates growth (Mandlebe, 2014). Matthew, Ogunnaike and Fasina (2006) studied the relationship between human capital investment and economic growth in Nigeria using secondary data from 1970-2004. The CobbDouglas production function and the OLS techniques were adopted to estimate the data. The results showed that labour force, government expenditure on education and gross capital formation has a positive and significant effect on GDP where government investment in education had the lowest effect. This is caused by the misallocation of resources by the government and corruption. The study showed that a positive and significant relationship exists between human capital investment and economic growth in Nigeria. However, human capital investment is important for the Nigerian economy to experience growth. They proposed that the revenue allocation to the educational sector should be increased and the funds should be accounted for. Dauda (2010) examined the relationship between human capital formation and economic growth. He adopted the endogenous growth model and also used primary, secondary and tertiary school enrolment as a measure for human capital and discovered that there was a long run positive relationship between human capital formation and economic growth. The study
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adopted the unit root tests, co-integration tests and the Error Correction Mechanism (ECM). This study discovered that there is a feedback mechanism between human capital formation and economic growth in Nigeria. The policy recommendation was that the government should put efforts to increasing the financial allocation to the development of human capital to achieve sustained growth. Awe and Ajayi (2010) studied the causal relationship between human capital investment and economic growth in Nigeria from 1975-2005 using the co-integration technique and the Error Correction Mechanism (ECM). The result showed a direct causal relationship between human capital investment and economic growth in Nigeria. They proposed that the budgetary allocations to the development of human capital should be increased. Adelakun (2011) carried out a study on the importance of the development of human capital to the growth of the Nigerian economy. He employed the Ordinary Least Square (OLS) framework to analyse the relationship of the two concepts using Gross Domestic Product as a measure for economic growth, total government spending on health and education, and primary, secondary and tertiary school enrolment as an indicator for human capital. The result showed a positive relationship between human capital development and economic growth. He recommended that stakeholders should come up with a proper means of investing in human capital and also inclusive institutions should be established to see to the human resource needs of the economy met and policies should be implemented to foresee the sustained growth of the economy. Matthew (2011) looked at the link between human capital investment and economic growth in Nigeria paying major attention to the role education plays in economic growth. The study adopted the unit root and the Augmented Dickey Fuller (ADF) test and found that government expenditure on education and economic growth were positively related and government expenditure on health and economic growth were negatively related. However, based on the
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results, the study recommended that the government should inject more financial resources to the education and the health sectors respectively, the study also proposed that the ten percent threshold proposed by the current national plan should be put in place. Amasomma and Nwosa (2011) evaluated the causal relationship between human capital development and economic growth in Nigeria from 1970-2009. The endogenous growth model was adopted in this study. They adopted the Vector Error Correction (VEC) and the pairwise granger causality and discovered that there was no causal relationship between human capital development and economic growth in Nigeria. The variables of study were found to be stationary at first differencing although, there was no co-integrating relationship. They recommended that the budgetary allocation to health and education should be increased and the proper establishment of vocational schools to promote economic growth. The causality was caused by the steady decline in the budgetary allocations to education and health. There is need for an increase in the budgetary allocations to these sectors and to establish vocational institutions which will promote economic growth. Oluwatobi and Ogunrinola (2011) analyzed the relationship between human capital development and economic growth in Nigeria. They adopted the secondary data analysis method and the Augmented Solow growth model. They discovered that there is a positive relationship between government recurrent expenditure on human capital and economic growth while capital expenditure was negatively related to economic growth. Isola and Alani (2012) studied the individual role of human capital indicators in economic growth in Nigeria from 1980-2005.This study adopted the growth accounting model in which Gross Domestic Product is stated as a function of labour and capital. OLS technique was adopted to analyse the impact adult literacy rate, life expectancy, labour growth, capital growth rate and a dummy variable has on the growth rate of the GDP. The result showed a significance of human capital to growth in Nigeria and also, a positive and significant relationship exists
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between health and economic growth. They recommend that more investment should be made to the education and health components of economic growth. Adelowokan (2012) evaluated the impact of education and health spending had on economic growth in Nigeria from 1970-2010 using a static regression model. It was found that human capital has a long run impact on economic growth using the Engle-Granger two step cointegration technique. This study discovered that government investment on education and health had positive relationship on economic growth. Mehrara and Musai (2013) studied the causal nexus between education and GDP in developing countries using the panel unit root tests and the panel Cointegration analysis from 1970-2010. The results showed a strong causal relationship between education and GDP in these countries. It was found that capital accumulation and GDP boosts education in developing countries; therefore, the rapid growth of the economy translates to educational improvement. They recommended that practice-oriented training should be promoted in schools and the educational system should match the needs of the labour market in order to promote employment opportunities and long term economic growth. Kanayo (2013) empirically studied the nexus between human capital and economic growth from 1970-2010. Secondary data was used in this study and the time series of the data set was selected to prevent the problems of spurious correlation. The Error Correction Mechanism was adopted to examine the relationship between human capital and economic growth. Results found that investment in human capital development has a significant impact on economic growth while the relationship between capital expenditure on education was found to be insignificant to the growth of the economy due to the low utilization of expenditure in Nigeria. It also showed that post-primary education enrolment and tertiary education enrolment are positively related to growth. Recommendation was that the Nigerian educational system should
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be re-structured to ensure quality education at all levels. This would require proper planning, increase in investment in the educational sector and rebasing the growth fundamentals. Mba, Mba, Ogbuabor and Ikpegbu (2013) studied the importance of human capital on economic growth; examining their relationship from 1977-2011. They adopted the OLS technique and Gross Domestic Product was adopted as an indicator for economic growth. Their results showed that human capital development and economic growth are positively related. They recommended that the manpower needs of the economy needed to be revisited; work place policies should put in place to promote economic growth and the spending on health and education should be utilized efficiently so as to ensure quality education and health care systems in the economy. Vincent, Nwosu and Okonma (2013) analysed the relationship between real GDP and investment in human capital from 1980-2012. The Vector Autoregressive (VAR) approach, the Johansen co-integration test and the granger causality tests were adopted as the research method. The results showed that capital spending and recurrent expenditure on human capital and real GDP are co-integrated. It also showed a causal nexus between capital and recurrent expenditure on human capital to real GDP in the long run. Capital expenditure was discovered to be more important in explaining growth that the recurrent expenditure on human capital investment. They recommended that the capital spending on human capital should be increased. Aliyu (2013) evaluated the effect of human capital formation on economic growth in Nigeria with times series data spanning from 1981-2007 by adopting the Johansen Cointegration technique and the vector error correction technique. The result of the study show that human capital has a strong impact on economic growth. The policy recommendation was for the government to provide public subsidies and company tax concessions for the job on training in
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the private sector and also, they should direct the focus on linkages with employers of labour to generate demand for skills. Akpolat (2014) studied the long run relationship between physical and human capital on GDP using panel data of 13 developed and 11 developing countries from 1970-2010. Gross fixed capital formation was used as a measure of physical capital while education expenditures and life expectancy at birth were used as human capital indicators. Panel Dynamic Ordinary Least Squares (DOLS) and Fully Modified OLS (FMOLS) panel co integrated regression models were adopted to decipher the magnitude and sign of the Cointegration relationship and to compare the impact of physical and human capital variables based on the countries. However, the impact of physical and human capital on GDP in developed countries were found to be greater that of developing countries while the impact of life expectancy at birth on GDP was found to be higher in the developing countries. Ada and Acaroglu (2014) analysed the nexus between economic growth and human capital investment from 1990 to 2011 in 15 Middle East and North Africa countries using panel data estimation was adopted alongside the co-integration method and augmented Solow model. The results showed that if the quality of education is improved, the gross domestic product per capita would increase, thereby making growth more effective. It was also discovered that public spending for human capital has no significance on Gross Domestic Product per capita in the Middle East and North African countries and they claim that there is a political aspect to the study. It was also discovered that the results of the study is not in line with the augmented Solow model. Anaduka (2014) evaluated the effect of human capital development on economic growth with quarterly time series data from 1999-2012 using national output as a measure for economic growth. The study adopted the augmented Solow model and the study showed that there is a significant relationship between human capital development and output. This explains that
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human capital development is paramount to the growth of the Nigerian economy in order for it to achieve economic growth that is sustainable. The study also revealed the existence of an inelastic relationship between human capital development and national output. Hence, the government should give human capital development a high priority and efforts should be made in developing human capabilities and skills set via increase educational funds. Jaiyeoba (2015) examined the relationship between human capital investment in education and health in Nigeria from 1982-2011. The Johansen co-integration and the OLS technique were adopted. The results showed that there is a long run nexus between government expenditure on education and health and economic growth. They also discovered that the investment in education in Nigeria is insignificant and it is below the United Nations benchmark. The study recommended that to boost economic growth and eliminate poverty in Nigeria, the government should formulate policies to promote spending in the education and health sectors. Atanda, Adebosin and Adewusi (2015) analysed the effect of human capital investment on economic growth in Nigeria with time series data from 1970-2009. They adopted the endogenous growth model and examined the variables using the Augmented Dickey Fuller unit root test and the Engle granger Cointegration test and discovered that there is a long run relationship between human capital investment and economic growth in Nigeria. They regresses real output on private and public capital investment, accumulation of human capital and the openness of the economy. The results showed that private and public sector capital investment and human capital accumulation are paramount factors that boosts economic growth in Nigeria. Anyanwu, Adam, Obi and Yelwa (2015) examined the relationship between human capital and economic growth in Nigeria using time series data from 1981-2010. The endogenous growth model was adopted within the Autoregressive distributed lag (ARDL) framework. The results showed a co-integrating relationship between growth and development factors; also, the
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findings show that human capital had a largely insignificant positive effect on economic growth in Nigeria. The study proposed that the government should invest more in the development process of human capital and should increase the budgetary allocations to education and health and they should pay close attention to school enrolment. Omitogun, Osoba and Tella (2016) examined the interactive effects of the nexus between human capital and economic growth form 1986-2014. They adopted the augmented Solow growth model. This study adopted secondary data on education and health spending, real GDP and gross capital formation from Central Bank statistical bulletin and analyzed the data using FMOLS technique. The results show a positive and significant relationship between the interactive effects of human capital indicators and economic growth in Nigeria. They recommended that the government should ensure policy mix rather than using a policy in terms of human capital development. 2.5 Summary of Gaps Identified in the Literature This chapter reviewed the major concepts of the study, the theories and the empirical works on the subject matter. Most of the empirical literature confirmed a positive relationship existence between human capital investment and economic growth. Studies testing the nexus between human capital and inclusive growth are rare especially for Nigeria. Hence, this study seeks to cover this identified literature gap. In the empirical literature reviewed, there were scarce literature that emphasized the welfare implications of the link between human capital and economic growth; this study therefore seeks to fill the gap introducing the inclusive growth concept since it covers the welfare implications of growth. The literature reviewed made less mention of the sustainability of growth but majorly concentrated on the link between human capital investment and economic growth. They paid less attention to how the problem of growth with little trace of development in Nigeria problem can be achieved. This study seeks to also close that gap.
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CHAPTER THREE THEORETICAL FRAMEWORK AND METHODOLOGY 3.1 Preamble This chapter aims at examining the theory and economic variables that this research model will be based upon. The Endogenous growth model shows the nexus between human capital and growth which will be highlighted in the first section. The second section provides the analytical framework of the study. The third section includes the research method, model specification and the techniques of estimation. The fourth section explains some issues relating to the methodology of the model. 3.2 Theoretical framework The framework of this study will be based on the endogenous growth model. The endogenous growth model was propounded by a group of growth theorists (Kenneth Arrow, Hirofumi Uzawa, Miguel Sidrauski, Paul Romer, Robert Lucas, Serguio Rebelo, Ortigueira and Santos) in the mid-1980s to address the limitations of the Solow model by introducing human capital and knowledge as the major determinants of growth and technical progress. It is also known as the new growth theory. However, the most striking endogenous growth models sprung out from Romer (1990) and Lucas (1988). Note that there are specific differences in the two models. Romer (1990) models growth based on technology and innovation in which human capital plays a role in the occurrence of the former. There are three sectors in Romer’s model: the final products sector, the intermediate sector and the research sector. Lucas (1988) states that human capital is a factor of production. Both theories however, lead to growth through technical progress. In the Lucas model, there are just two sectors- the goods sector and the education sector.
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The distinctive characteristic of the Lucas model is that human capital is explained as a factor of production and growth would occur endogenously, it has to be sourced from a constant or an increasing marginal returns to human capital accumulation. There are two sectors as assumed in the Lucas model- the goods sector where physical capital and human capital is used for production activities and the second sector where only human capital is used in the production process. If there is a constant or increasing returns in the second sector, then growth occurs. Endogenous growth theory further explains that human capital is an endogenous mechanism that leads to technological growth. This is because human capital accumulation boosts labour and physical capital productivity and prevents the depreciation of capital accumulated. Lucas (1988) postulated that the productivity level of an economy depends on the average level of human capital and not the aggregate level of human capital. Therefore, it is not the total knowledge base of a firm but the average skills and knowledge base of the economy that spurs the growth process. Romer (1994) stated that growth is as a result of endogenous forces such as human capital, technology, innovations, research and development and knowledge rather than exogenous forces. The endogenous growth model emphasizes the role of knowledge as the key driver of growth as well as the role institutions and policies play in technological progress and innovation. The production function includes human capital development through training, education and physical capital. The long run aggregate production function is Y=AKα (uhL) 1-α Where Y= Aggregate output A= Technology
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(3.1)
K= Capital stock L= Labour stock u is the total time invested in working by labourers and h is the human capital stock. The term uh is the human capital and uhL is the total effective labour force. α is the percentage increase in the output from a one percentage change in capital. The technology level is constant. 0 < α < 1 where the value of α is positive and it lies between 0 and 1. The production function can be expressed in per capita terms as: y= Aƙ α (uh)1-a
(3.2)
Where k is the per capita physical capital stock and there is a constant returns to scale in physical capital and human capital. Lucas (1988) explained that human capital investment has effects on production internally and externally. That is, increased skill set will boost economic productivity due to individual efficiency and also produce spill over effects. Human capital growth rate is dependent on the time spent on skill acquisition as it eventually increases the growth rate of per capita income. Endogenous growth theory basically explains that development of human capital in form of education and other means such as health, training and so on spurs the growth process of an economy. This growth is a self-sustaining growth and can therefore lead to growth that cuts across all sectors of the economy by creating productive employment opportunities, reducing poverty and inequality and eradicating gender disparity. Stern (1991) stated that the endogenous growth theory has contributed to understanding the determinants of growth in developing countries; as the theory contributed to the growth process of the East Asian Tigers. The Nigerian economy where there is lower than expected public investment in human capital, the legislature can work with this theory by building up inclusive establishments and investing in the general population to empower them and boost economic productivity.
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3.3 Methodological Approach African Development Bank (AfDB, 2011) stated that inclusive growth cannot occur without adequate human capital investment. However, from the theoretical and empirical review, it is expected to observe a positive link between human capital and growth. Human capital if effectively utilized should spur growth in the economy which should in turn decrease poverty level, unemployment and inequality. The quality of human capital also matters because two countries may have the same level of human capital investment but their growth and efficiency rates may differ. However, the endogenous growth model explains the relationship between human capital and growth. Sustained growth is a necessary condition for inclusive growth in an economy and human capital has a vital role to play in it. Essentially, human capital promotes innovation, entrepreneurship, economic productivity, employment and economic equality in the economy. Entrepreneurship promotes innovation and invention. Productive employment also occurs when an increase in economic productivity is merged with employment opportunities. Productive employment is an important aspect of inclusive growth because one of the major goals of inclusive growth is to increase the equal distribution of income amongst the poor and vulnerable in the economy. Human capital development through education and empowerment programs can also reduce poverty and inequality levels in the economy. Empowerment programs will enable them to contribute and benefit from the growth process of the economy. Human capital development also reduces gender inequality because individuals will be aware of their individual human rights. There will be lesser gender discrimination because the educated individuals will be aware of the equality of human rights through human right orientation programs. This will reduce the sexual orientation imbalance because educated
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individuals will be aware of the equality of human rights. Gender equity is also a catalyst for inclusive growth. Economic growth coupled with productive employment, poverty and reduction in inequality and gender inequality will lead to inclusive growth. 3.3.1 Model Specification The endogenous growth model is a modification of the Solow growth model; it includes human capital and incorporates positive externalities resulting for the accumulation of human capital. It also emphasizes the relevance of education, training and technology in the growth process of an economy. Lucas (1988) and Romer (1986) stated that growth in an economy was created internally. Lucas also stated that human capital investments have spill-over effects that boosts technical progress through the human capital externalities (Oketch, 2006). The specification allows for identification of the channels through which human capital and other variables influence growth over time. A standard production function which applies to the endogenous growth model is specified as follows: Y=f (A, K, L)
(3.3)
Where; Y= output A= technological change K= Physical capital input L= Labour capital input This model provides a theoretical foundation for this study because it relates human capital to economic production activities. However, Yt = At K t α Lt β
(3.4)
Where α and β represents the percentage increase in the output resulting from a one percentage change in capital and labour respectively. The model assumes an increasing returns to scale on production where the summation of alpha and beta is greater than unity, that is α + β > 1.
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Given this theoretical relationship, the model is specified as follows: INGRt = f (SEDUt, TEDUt, PEDt, GFCFt)
(3.5)
Where f is a functional relationship INGRt: Inclusive Growth Rate SEDUt: Secondary School Enrolment Rate TEDUt: Tertiary School Enrolment Rate PEDt: Public Expenditure on Education GFCFt: Gross Fixed Capital Formation Explicitly, the model is expressed as: INGR = SEDU + TEDU + PED + GFCF The explicit model could be expressed in econometric estimation form as INGRt = β0 + β1 SEDUt + β2 TEDUt + β3 PEDt + β4 GFCFt + 𝜇t
(3.6)
Where β0 is the intercept term, β1 -β4 represents the elasticity of the coefficients and 𝜇 is the stochastic error term. The Cobb Douglas form of the model is specified as: INGRt = β0 . LSEDUt β1 . LTEDUt β2 . LPEDt β3 . LGFCFt β4 . 𝜇t
(3.7)
In order to obtain a more explicit and estimable linear function of Equation (3.5), the variables on both sides are transformed into their natural logs (L) to obtain the following: LINGRt = β0 + β1 LSEDUt + β2 LTEDUt + β3 LPEDt + β4 LGFCFt + 𝜇t
(3.8)
The logarithm is used because of magnitude of the parameters and in light of the measure of elasticity. Natural logarithm of all the variables was used to make the variables more comparable; also because the variables are expected to grow exponentially and the model is specified using a Cobb-Douglas production function. β0 is the constant parameter of which inclusive growth equals holding other variables constant. β1 LSEDUt is the elasticity of inclusive growth with respect to secondary school enrolment; β2
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LTEDUt is the elasticity of inclusive growth with respect to tertiary school enrolment; β3 LPEDt is the elasticity of inclusive growth with respect to public expenditure on education; β 4 LGFCFt is the elasticity of inclusive growth with respect to gross fixed capital formation and Uti is the error term which includes the impact of other measures of inclusive growth that are not included in the model. 3.3.2 A priori Expectations The expected signs of the co-efficient of the explanatory variables are: β0 > 0, β1 > 0, β2 > 0, β3 >0, β4 >0 This implies that the sign of the intercept term β0 is expected to be positive. That is, holding other explanatory variables constant, the intercept value equals inclusive growth. The co-efficient of LSEDUt (β2) is expected to show a positive sign. This implies that holding other explanatory variables constant, a percentage increase in Secondary Education will lead to a percentage increase in inclusive growth. The co-efficient of LTEDUt (β2) is expected to show a positive sign. This implies that holding other explanatory variables constant, a percentage increase in Tertiary Education will lead to a percentage increase in inclusive growth. The co-efficient of LPEDt (β2) is expected to show a positive sign. This implies that holding other explanatory variables constant, a percentage increase in Public Expenditure on Education will lead to a percentage increase in inclusive growth. The co-efficient of LGFCFt (β3) is expected to show a positive sign. This implies that holding other explanatory variables constant, a percentage increase in Gross Fixed Capital Formation will lead to a percentage increase in inclusive growth. 3.3.3 Estimation Technique The estimation techniques adopted in this model is the Autoregressive Distributed Lag estimation technique and the Vector Error Correction Model (VECM).
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Co-integration is used to test for the stationarity of time series data and the order of integration of the series. A stationary series is one with a mean and variance which does not vary at any point in time. Non stationary series on the other hand is one with mean and variance that vary over time. Time series data are mostly found to be unreliable and may not always be stationary. Therefore, suitable techniques should be applied to non-stationary data in order to prevent the occurrence of a spurious regression result. Therefore, in this study, the Augmented Dickey Fuller (ADF) Test for stationarity will be conducted on the time series data and then, the co-integration test will be conducted in order to analyse the long run equilibrium relationship between the variables in the study. However, in this study, the Autoregressive Distributed Lag (ARDL-Bounds) co-integration technique proposed by Pesaran, Shin and Smith (2001) will be adopted. This approach has some statistical advantages over other co-integration techniques; it provides valid results for variables whether they are I (0) or I (1) or mutually co-integrated and it also provides very efficient and consistent test results in small and large sample sizes. Also, the ARDL approach allows the regression variables to tolerate different optimal lags. In this study, the ARDL co-integration technique is relevant because of the different orders of integration. Then, a Vector Error
Correction Mechanism (VECM) test will be conducted in order to test for the speed of adjustment of variables to long run equilibrium. The data analysis will be done using the Eviews 9 econometric software and Microsoft Excel for inputting the data set. 3.3.3.1 Test of stationarity The behaviour and properties of a data series are influenced by the stationarity of the data series as the presence of unit root could cause the time series analysis result to be unpredicted. There are several tests for stationarity such as the Dickey Fuller, Augmented Dickey Fuller (ADF), Phillips-Perron test, the Kwiatkowski-Phillips-Schmidt-Shin (KPSS), the run sequence plot,
42
the Zivot-Andrew test and so on. This study will focus on the Augmented Dickey Fuller Test (ADF). The Augmented Dickey Fuller Test takes care of possible serial correlation in the error term by adding the lagged difference terms of the dependent variable. It will be used to test for the presence of unit root. The Augmented Dickey Fuller Test tests for unit root when the error term are correlated. Time series can be differentiated to the first or second order to make it stationary. A time series that is stationary at levels is known to be integrated of order 0 denoted by I (0) but if it is differentiated to make it stationary, it is denoted by I (1). First difference stationary time series appear quite often in most economic and business statistics (Granger, 1986). 3.3.3.2 Auto Regressive Distributed Lag However, in this study, the Autoregressive Distributed Lag (ARDL-Bounds) co-integration technique proposed by Pesaran, Shin and Smith (2001) will be adopted. This approach has some statistical benefits over other co-integration techniques; it provides valid results for variables whether they are I (0) or I (1) or mutually co-integrated and it also provides very valid and consistent test results in small and large sample sizes. Also, the ARDL approach allows the regression variables to tolerate different optimal lags. In this study, the ARDL co-integration technique is relevant because of the different order of integration. The F-statistics tests the significance of the lagged levels of the variables in the error correction form of the ARDL model and also to ascertain the presence of a long run relationship among the underlying variables. The F-statistics distribution is asymptotic and non-standard irrespective of the order of integration of the variables. However, Peresan et al. (1996) provided critical values for different numbers of regressors which can be employed to overcome the problem of the nonstandard nature of the asymptotic distribution of the F-statistics. In the bound testing, if the calculated F-statistic exceeds the Upper Critical Bound (UCB), then the series are co-integrated; and if it below the Lower Critical Bound (LCB), there is no co-integration.
43
If the calculated F- statistic is between the UCB and the LCB, then decision about co-integration is inconclusive. In simpler terms, if the computed F- statistics lies in the UCB, the 𝐻0 should be rejected and conclude there is a long run relationship but if the F-statistics lies in the LCB, the 𝐻0 should not be rejected and
therefore conclude that there is no long run relationship The ARDL bound testing approach to co-integration uses (p+1) formula to estimate the number of regressions. The p value indicates the maximum number of lags utilized and k shows the total number of variables. The lag length is picked using the minimum values of both the Akaike Information Criteria (AIC) and the Schwarz Bayesian Criterion (SBC). The hypothesis is the null of non-existence of a long run relationship against the existence of a long run relationship which is illustrated as:
𝐻0 : 𝜆1 = 𝜆2 = 𝜆3 = 𝜆4 = 0 (No long-run relationship) Against 𝐻1 : 𝜆1 ≠ 𝜆2 ≠ 𝜆3 ≠ 𝜆4 ≠ 0 (Long-run relationship exists) The presence of a long run relationship indicates that we can proceed with our analysis. The model below is specified to determine the long run co-integrating relationship between the variables. 𝑝 ΔINGRt = β0 + λ1 SEDUt-1 + λ2 TEDUt-1 + λ3 PEDt-1 + λ4 GFCFt-1 + β1 ∑𝑖=1 ∆SEDUt−1 + β2
∑𝑝𝑖=1 ∆TEDUt−1 + β3 ∑𝑝𝑖=1 ∆PEDt−1 + β4 ∑𝑝𝑖=1 ∆GFCFt−1 + μti
(3.9)
Where 𝑝 = lag length of the model Δ = first difference of the operator μ = error term When the long run relationship between the variables have been established, the stable long run model is estimated as:
44
𝑝
𝑝
𝑝
𝑝
ΔINGRt = β0 + β1 ∑𝑖=1 ∆ SEDUt−1 + β2 ∑𝑖=1 ∆ TEDUt−1 + β3 ∑𝑖=1 ∆ PEDt−1 + β4 ∑𝑖=1 ∆ GFCFt−1 + μti
(3.10) 3.3.3.3 Vector Error Correction Model The Vector Error Correction Mechanism is also known as the speed of adjustment. It is used to test for an Error Correction Mechanism of variables in the short run. It is used to explain the dynamic interrelationships among variables that are stationary. The co-integration is limited in the sense that it does not account short run disequilibrium. The error correction constructed by Engle and Granger shows if there is a disequilibrium in the short run. It reconciles the short run and the long run behaviour of an economic variables. The Error Correction Mechanism is majorly used to estimate the speed in which the variables in the short run reconciles the short run behaviour with the long run equilibrium. It is also known as the equilibrium correction mechanism. The result is economically significant when the co-efficient of the error term lies between 0 and 1 and t-statistics is greater than 2 to ensure that it is statistically significant. For this study, the VECM model is specified as; ΔINGRt = β0 + β1 Σ𝑝𝑖=1 ΔPEDUt−1 + β2 Σ𝑝𝑖=0 ΔSEDUt−1 + β3 Σ𝑝𝑖=0 ΔPEDt−1 + β4 Σ𝑝𝑖=0 ΔGFCFt−1 + γECMt-1 + μt
(3.11)
Where 𝑝 = lag length of the model ECMt-1 stands for the error correction term lagged for one period γ stands for the coefficient for determining the speed of adjustment 3.4 Definition of Variables and Data Sources The data used in this study are annual data from 1981-2015 sourced from the World Development Indicators (WDIs) and the Central Bank of Nigeria (CBN) Statistical Bulletin.
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Level of Secondary Education: This study adopts the secondary school enrolment as a measure of human capital because this level of education is a pre-requisite for the adoption of technology. It is a paramount stage in every child’s development and it is also a link between the primary and tertiary education. This is a stage where young adults develop physically, mentally and emotionally. United Nations Educational, Scientific and Cultural Organization UNESCO (2012) asserts that to prepare young adults for life in a fast changing world, secondary school education should be taken seriously. Secondary education is important in the growth process because workers without a secondary school education could be considered as unqualified in the labour force. Secondary education prepares young adults toward useful and productive living within the society and higher education. Taiwo (2015) observed that secondary education is paramount because it is a source of mid-level manpower production. Secondary school education also diversifies its curriculum to cater for talent differences and also equip the young adults with relevant skill set for standard living in the modern science and technology age. Level of Tertiary Education: This study also adopts the tertiary school enrolment as a measure of human capital because it the major educational level that contributes to national development through high level of man power training. Tertiary education helps in value development and promotes national and global partnership. The national policy on education explains that tertiary education equips individuals with the relevant skill set to be self-reliant and to further develop the spirit of creativity imbibed in them from the basic education. The tertiary education is the educational level where there is rich knowledge and information sharing that will equip individuals with relevant skills to impact change in the economy. Public Expenditure on Education: Public expenditure on education is the amount of government funds directed towards the educational sector of the economy. It can be divided into capital and recurrent expenditure. An increase in the government’s expenditure on
46
education will promote inclusive growth in the sense that, the individuals will have sufficient skill set and knowledge base to participate in the domestic and the global economy. Therefore there should be a positive relationship between public expenditure on education and inclusive growth. Gross Fixed Capital Formation: The Gross Fixed Capital Formation has be adopted in this study as a proxy variable for capturing physical capital. Investment in physical capital should be encouraged because it has the potentials of speeding up the production process as human capital alone cannot work all out. Therefore, there should be a positive relationship between the level of gross capital formation and inclusive growth. Table 3.1: Overview of the variables of the model
A Priori Expected Source Expectations Outcome
Measurement Composite Inclusive Growth Index
β1 > 0 β2 > 0
+ +
WB (World Bank) WB (World Bank)
Ratio of public expenditure on education to total government education [%] β2 > 0
+
CBN
β3 > 0
+
WB (World Bank)
Gross secondary school enrolment rate Gross tertiary school enrolment rate
Annual percentage growth of gross fixed capital formation
Source: Researcher’s computation
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CHAPTER FOUR DATA ANALYSIS AND DISCUSSION OF RESULTS 4.1 Preamble This chapter presents and discusses the results of the variables in the study; presents the descriptive statistics; mean, median, standard deviation and kurtosis. This chapter also examines the graphical and trend analyses of educational attainment, public expenditure on education and inclusive growth. Section 4.3 presents the regression results based on the model specified in the previous after which the empirical results of the regression and the major findings will be discussed. 4.2 Trend and Descriptive Analysis of Data 4.2.1 Statistical Analysis of Data Descriptive statistics consists of the quantitative analysis of the variables of study. That is, the mean, median, minimum, maximum, standard deviation, skewness and the kurtosis of the data included in the study. The median is the middle number of the series and it is often seen as a social measure as its value is better reporting value than the mean. The mean value is the typical value average of the series and it summarizes the whole time series data over a single period with a single variable. The mean and median serves as measures of central tendency while standard deviation is the sum of squared deviations from the mean. A smaller mean in contrast with standard deviation implies that there is a likelihood of the presence of a low coefficient of variance and vice versa. The standard deviation is used as a statistical quantity to make statistical inferences and predictions about a general population. The skewness and kurtosis are designed to provide information about the scores distribution. Skewness shows the measure the asymmetry of the distribution of the series around the mean.
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Gross Fixed Capital Formation has the highest mean and standard deviation.
Public
expenditure on education has the lowest mean while Secondary enrolment rate has the lowest standard deviation. All the variables have their mean higher than their standard deviation. This means that all the variables are likely to have a low coefficient of variation. Table 4.1: Descriptive Statistics of Variables.
LINGR
LSEDU LTEDU
LPED
LGFCF
Mean
1.196753 3.41884 1.789507
1.73405 22.761
Median Maximum
1.321756 3.45205 1.789582 2.054124 3.78047 2.432698
1.82938 22.1449 2.35897 25.1747
Minimum
-0.10536
2.83372 0.837248
-0.3147 21.4249
Std. Dev. Skewness Kurtosis Jarque-Bera Probability Sum
0.455295 0.19685 0.477719 -0.94707 -0.7821 -0.25028 3.935885 3.72908 1.858786 6.509479 4.34324 2.264695 0.038591 0.11399 0.322276 41.88634 119.659 62.63275
0.52105 1.227 -1.9816 0.84117 8.23886 2.29616 62.9308 4.84996 0 0.08848 60.6916 796.635
Source: Researcher’s Computation using E-views 9. The asymmetry of a distribution of series around the mean is measured by the skewness and the skewness of a symmetric distribution is zero. The variables LINGR, LSEDU, LTEDU, LPED are negatively skewed while LGFCF is positively skewed. The Jarque-Bera test is used to test if a sample is normally distributed or not. The greater the values are than zero, the lower the value of the Jarque-Bera statistic and the more normally distributed the sample is. The result above shows that the probability values of the Jarque-Bera test are greater than zero; this implies that the samples are normally distributed. The peak or the flatness of the distribution of a set of series is measured by using Kurtosis. If the value of the kurtosis is greater than 3, then the distribution is leptokurtic, it is peaked relative to the normal. If the value is less than 3, the distribution is platykurtic, it is flat relative to the
49
normal. The LINGR, LSEDU and LPED are leptokurtic while LTEDU and LGFCF are platykurtic. 4.2.2 Trend Analysis of Data Trend analysis involves the collection and examination of the pace and pattern of movement of the variables of study over the time period under review. This section show trend analysis of the variables of study in Nigeria from 1981-2015. The graphical analysis of inclusive growth, secondary education, tertiary education, public expenditure on education and gross fixed capital formation are presented and discussed below. Figure 4.1a Trend analysis of Inclusive Growth in Nigeria LINGR 2.4 2.0 1.6 1.2 0.8 0.4 0.0 -0.4 1985
1990
1995
2000
2005
2010
2015
Source: Researcher’s Computation using E-views 9. The inclusive growth rate of the Nigerian economy was positive as at 1981. The lowest point was recorded in 1994 and the peak was in 2004. The trend of inclusive growth in Nigeria has been plagued by steady fluctuations. Figure 4.1b: Trend analysis of secondary education in Nigeria LSEDU 3.8
3.6
3.4
3.2
3.0
2.8 1985
1990
1995
2000
2005
2010
2015
Source: Researcher’s Computation using E-views 9.
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From 1981 to 1984, secondary education increased steadily but declined in 1986 due to the Structural Adjustment Programme (SAP). The peak rate was recorded in 2011 and the lowest rate in 1999. Figure 4.1c: Trend analysis of tertiary education in Nigeria LTEDU 2.8
2.4
2.0
1.6
1.2
0.8 1985
1990
1995
2000
2005
2010
2015
Source: Researcher’s Computation using E-views 9. Tertiary education in Nigeria has been on a steady increase from 1981 till 2006 where there was a sharp decrease in the tertiary enrolment rate after which it continue to grow steadily. The peak tertiary enrolment rate was recorded in 2015 and the lowest rate was recorded in 1981. Figure 4.1d: Trend analysis of Public expenditure on education in Nigeria LPED 2.4 2.0 1.6 1.2 0.8 0.4 0.0 -0.4 1985
1990
1995
2000
2005
2010
2015
Source: Researcher’s Computation using E-views 9. Public expenditure on education in Nigeria has been plagued with fluctuations characterized majorly by a decrease overtime. The peak was recorded in 2013 and the lowest expenditure on education was recorded in 1991.
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Figure 4.1e: Trend analysis of Gross fixed capital formation in Nigeria LGFCF 26
25
24
23
22
21 1985
1990
1995
2000
2005
2010
2015
Source: Researcher’s Computation using E-views 9. The general trend of gross fixed capital formation in Nigeria is filled with declines and fluctuations. The lowest point was recorded in 1995 and its peak was recorded in 2014. However, it has unsteadily increased since 1995. 4.2.3 Educational attainment, Public expenditure on education and inclusive growth This section conducts a trend and comparative analysis of the variables of interest in the study. Section 4.2.1 represents the average inclusive growth index in Nigeria for the period under review, it also includes a comparison in trend between real GDP per capita and inclusive growth index. A graphical representation of the analysis is also represented. Section 4.2.2 represents a comparative and graphical analysis of educational statistics in Nigeria. Section 4.2.3 represents a graphical analysis of the ratio of public education expenditure to total public expenditure. 4.2.4 Inclusive Growth in Nigeria Inclusive growth is basically sustained growth; growth that cuts across all the sectors of the economy. Human capital research has spanned across various subject matters but few research has been done as to human capital relating to inclusive growth. However, an examination of the inclusive growth of the Nigerian economy shows a stagnant result.
52
In 1981, inclusive growth was 3.35 according to the composite index. The highest level during the period of review was 7.8 in 2004 while the least level was 0.9 in 1994. When this trend was compared with that of other countries that are at the same level as the Nigerian economy, considering the GDP per capita. Also, McKinley (2010) computed the Composite inclusive growth index for the countries. Table 4.2a: Level Gaps in Real GDP per capita (Current US $) and Inclusive Growth Index in Nigeria Countries
GDP Per Capita in 1981 ($) GDP Per Capita in 2015($) Annual GDP Growth Rate (%) Inclusive Growth Index
Bangladesh Egypt India Indonesia Nigeria Philippines
242.2 526.4 275.9 612.5 806.5 731.7
1210.16 3547.71 1613.19 3336.11 2655.16 2878.34
5.61 3.81 1.66 5.36 5.82 1.19
5.15 5.47 5.7 4.4 4.85 3.8
Source: Researcher’s Computation Nigeria’s GDP per capita was greater than that of other five countries as at 1981 but by 2015, Philippines, Egypt and Indonesia had surpassed Nigeria by $223.16, $892.55 and $680.95 respectively. Even with the highest annual growth rate, Nigeria is still ranked fourth amongst these countries based on their inclusive growth. India has the highest inclusive growth index even has it has a low annual GDP growth rate. Therefore, this explains that high GDP growth rate does not necessarily translate into inclusive growth. However, the factors slowing down the inclusive growth rate of the Nigerian economy has been identified by Ogujiuba (2011); inconsistency of macroeconomic policies, unstable macroeconomic variables and goals, weak institutions and high rate of corruption. The trend analysis of inclusive growth in Nigeria has been examined in this study and it shows that in the first decade, Nigeria recorded an average inclusive growth index of 2.22. The second decade recorded an average inclusive growth index of 1.915. This explains the sharp fall in the average inclusive growth index between two decades. This fall is attributed to the military
53
regime active from 1987-1999. The third decade shows an inclusive growth average of 3.985 which is the peak. This is almost double of that of the second decade. This increase has been traced to the transition of the Nigerian economy from military to civil rule. The last time period recorded an average inclusive growth index of 2.73 which is a fall in the Nigerian inclusive growth index. In total, the average Nigerian inclusive growth rate from 1981-2015 was 2.71 which is far less than the average score scale of 0-10. Table 4.2b: Average inclusive growth index in Nigeria over time Period
Average Inclusive Growth Index
1981-1990 1991-2000 2001-2010 2011-2015
2.22 1.915 3.985 2.73
Source: Researcher’s Computation Economic growth does not necessarily translate into inclusive growth although, it is crucial in order for economy to achieve sustained growth. The inclusive growth trend in Nigeria in relation to the economic growth is fair enough as some years of high and low inclusive growth is related with some high or low economic growth. The inclusive growth trend in Nigeria compared with the economic growth is fairly stable in relation to the excess fluctuations in the economic growth trend. However, even as economic growth is a necessary condition for inclusive growth, inclusive growth is sustained growth. Below is represented the economic growth level captured by GDP growth rate (%) plotted against the inclusive growth index
54
Figure 4.2a: Trends in GDP Growth Rate and Composite Inclusive Growth Index (19812015) 40 20 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 -20 INGR
GDP
Source: Researcher’s Computation 4.2.5 Education in Nigeria Education coupled the acquisition of relevant skill set is the basic catalyst for sustained growth and economic development in any economy. Education investment is crucial in order for an economy to experience growth matched with low unemployment, inequality and poverty rate. Formal education can be divided into three stages; the primary education, secondary education and the tertiary education. The benefits of education are both economic and non-economic. A major economic benefit of education is sustained economic growth and non-economic benefits are; health, low income inequality, low fertility rate and so on. The major aim of primary education is to empower individuals with basic skills that are relevant for human living such as arithmetic, reading and writing. Secondary education is brought about in order aid comprehension and familiarize the individuals with existing technology. Tertiary education equips individuals with the necessary skills to contribute to technical progress, growth and development through innovation, research and development. The United Nation’s Development Programme (UNDP) Human Development Reports computed the education index by using the mean years of schooling and expected years of schooling to capture education in 187 countries. Nigeria ranked 152 as at 2016 in the education index with a value of 0.425. Nigeria has maintained this value since 2010; this value is about
55
half of the value of other countries; Australia (0.927), New Zealand (0.917), Norway (0.910), United States of America (0.890) and Canada (0.850). The Nigeria’s Vision 20:2020 depends majorly on industrialization for it to be achieved. This calls for the need for investments in the skill set of the labour force; to acquire technical skills, vocational skills and entrepreneurial skills. Figure 4.2b: Gross primary, secondary and tertiary school enrolment ratio (%) in Nigeria 120
100 80 60
40 20 0
SEDU
TEDU
PEDU
Source: Researcher’s Computation Figure 4.2b shows the level of primary, secondary and tertiary education in Nigeria. The trend shows that a gap exists between primary, secondary and tertiary education between 1981 and 2015. There was a sharp increase in the secondary enrolment rate 1998 and in 2010 and the tertiary enrolment rate has been on a steady increase from 1981 to 2015. There is also a gap between secondary and tertiary education within this time period. Secondary education was 38.599 in 2015 while tertiary education was 11.385. Tertiary and secondary education is essential for technical progress and skill acquisition. The government is the major contributor to the education sector. However, the next section examines government efforts in contributing to education in the Nigerian economy. 4.2.6 Public Expenditure on Education in Nigeria Public expenditure is government expenditure on goods and services in an economy. It can be recurrent or capital. In relation to education, recurrent expenditure consists of payment of 56
teachers and staff salaries, purchase of teaching materials and so on while capital expenditure consists of the construction or renovation of school, libraries and so on. The enrolment rate of students at all levels depends on accessibility and affordability of schools, all thing being equal. Figure 4.2c illustrates the public spending on education as a percentage of the total expenditure in Nigeria. The government expenditure in Nigeria ranged between 0.73 and 10.58 percent during the period of study. The lowest government expenditure on education was recorded in 1992 and the highest education expenditure was recorded in 2013. The average amount within the period of study is 6.25 percent. Figure 4.2c: Public expenditure on Education as a Ratio of Total Government Expenditure on Education in Nigeria (%), 1981-2015
PED 12 10 8 6 4 2 2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
0
Source: Researcher’s Computation The Nigerian government has spent an average of 6.25 percent of its annual budget on education. United Nations Educational, Scientific and Cultural Organization (UNESCO) recommended that for any economy to grow and develop their educational sector, they must invest a minimum of 26 percent of their total expenditure on education annual budget and 7 percent of their GDP on education. 4.3 Econometric Results This section analyses the link between the independent variables and the dependent variables using time series data from 1981-2015. This analysis will be carried out using the Augmented 57
Dickey Fuller test, Autoregressive Distributed Lag and the Vector Error Correction Model. These tests will be conducted using Eviews 9. 4.3.1 Unit Root Test The Augmented Dickey Fuller (ADF) test was adopted in this study to test for unit root. The rule of thumb for this test is that the null hypothesis should be rejected if the absolute value of the ADF test is greater than the critical value 1 percent, 5 percent and 10 percent and if it is lesser, the null hypothesis should not be rejected. The variables LINGR, LSEDU and LPED are integrated of I (0) while LTEDU and LGFCF are integrated of I (1). Hence we say that they are stationary at levels and first difference respectively. The results of the unit root test provides information to justify the choice of the ARDL procedure for co-integration analysis as the appropriate estimation technique. However, the results of the unit root test reveals that the variables consists of a mix of I(0) and I(1) series therefore, making the ARDL technique a most preferred choice of estimation technique for this study. Table 4.3a: Unit Root Test Variables ADF Test Statistics 5% Critical Value Order of Integration LINGR LSEDU LTEDU LPED LGFCF
-3.405534 -5.72485 -5.988639 -5.443327 -4.027852
-2.951125 -2.954021 -2.954021 -2.963972 -2.954021
I(0) I(0) I(1) I(0) I(1)
Source: Researcher’s Computation using EViews 9 4.3.2 Autoregressive Distributed lag (ARDL) The bounds testing procedure also known at the Autoregressive Distributed lag (ARDL) estimation technique is employed to determine the long run relationship between human capital investment and inclusive growth in Nigeria. The major of this estimation technique is that it can handle relationships irrespective of whether the independent variables are I(0) or I(1). The 58
ARDL approach does not involve the pre-testing issues related with the traditional cointegration analysis which requires the classification of variables into I (0) and I (1). The Akaike Information Criterion was used and a maximum lag order of 3 was employed for the autoregressive distributive lag model. The F-statistics from the bound test is carried out to investigate whether the regressors are jointly significant. The bounds test is conducted by placing certain restrictions on the estimated long run co-efficient of inclusive growth, secondary enrolment rate, tertiary enrolment rate, public expenditure on education and gross fixed capital formation. The computed F-statistics is given as 6.50799749 and it is compared to the upper and lower bounds at 5% significance Table 4.3b: Estimation of ARDL Selected Model: ARDL(1, 3, 2, 2, 1) Variable Coefficient LINGR(-1) 0.393103 LSEDU 1.505563 LTEDU(-2) 1.967498 LPED 0.323782 LGFCF 0.647377 C 1.16834 R-squared 0.761312 Adjusted R-squared 0.588927 S.E. of regression 0.301523 Sum squared resid 1.636491 Log likelihood 2.164876 F-statistic 4.416334 Prob(F-statistic) 0.002152
Std. Error t-Statistic 0.14898 2.638636 0.686244 2.193918 0.879987 2.235826 0.138438 2.338832 0.242123 2.673748 2.341886 0.498888 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
Prob.* 0.0167 0.0416 0.0383 0.0311 0.0155 0.6239 1.21516 0.47029 0.7397 1.38096 0.95226 2.2692
Source: Researcher’s Computation using EViews 9 From the table, all the variables have their hypothesized signs. In addition, all the variables are statistically significant at their respective lags. The coefficient of LSEDU shows a positive relationship between LSEDU and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in secondary enrolment rate would lead to a more than proportionate increase in inclusive growth by 1.5
59
percent. This supports the apriori expectation as secondary education and inclusive growth is supposed to have a positive relationship. The coefficient of LTEDU shows a positive relationship between LTEDU and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in tertiary enrolment rate would lead to a more than proportionate increase in inclusive growth by 1.96 percent. This supports the apriori expectation as tertiary education and inclusive growth is supposed to have a positive relationship. The coefficient of LPED shows a positive relationship between LPED and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in public expenditure on education as a percentage of total expenditure would lead to a less than proportionate increase in inclusive growth by 0.32 percent. This supports the apriori expectation as in public expenditure on education as a percentage of total expenditure and inclusive growth is supposed to have a positive relationship. Therefore, an increase in public expenditure on education will contribute to inclusive growth in Nigeria. The coefficient of LGFCF shows a positive relationship between LGFCF and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in gross fixed capital formation would lead to a more than proportionate increase in inclusive growth by 0.64 percent. This supports the apriori expectation as gross fixed capital formation and inclusive growth is supposed to have a positive relationship. Therefore, an increase in physical capital in Nigeria will contribute to inclusive growth in Nigeria. The goodness of fit of the model (R-squared) and adjusted R-squared are 0.761312 and 0.588927 respectively. Reporting the adjusted R-squared value which incorporates the degree of freedom, about 76 percent of variations in the dependent variable, LINGR is being explained by the independent variables, LSEDU, LTEDU, LPED, LGFCF respectively. This shows a good fit of the short run ARDL model. The Durbin-Watson statistic value is 2.2692, indicating
60
the absence of autocorrelation in the model. The F- statistics shows the joint significance of the variable included in the model. 4.3.3 Bounds Test Here, the Akaike Information Criterion was used and the F-statistics shows the joint significance of the explanatory variables included in the model. The bounds test is conducted by placing certain restrictions on the estimated long run coefficients of variables under review. Table 4.3c: ARDL Bounds Test
F-statistic 6.507949 Critical Value Bounds Significance Lower Bound 10% 2.45 5% 2.86 2.50% 3.25
Upper Bound 3.52 4.01 4.49
Source: Researcher’s Computation using EViews 9 Number of regressors (K): 4 Optimal lag length of the model: 3 The null hypothesis (𝐻0 : 𝜆1 = 𝜆2 = 𝜆3 = 𝜆4 = 0 ) there is no long-run relationship against the alternative hypothesis (𝐻1 : 𝜆1 ≠ 𝜆2 ≠ 𝜆3 ≠ 𝜆4 ≠ 0 ) there is a long-run relationship. If the F-statistics is greater than the upper bound, then we can say that there is a co-integrating relationship among the I (0) and the I (1) variable, therefore, we assume that there is a short run to long run relationship among the I (0) and the I (1) variables. If the F-statistics is between the upper bound and the lower bound, then we can conclude that the relationship is inconclusive and the result is spurious. If the F-statistics is lower than the lower bound, then we can conclude that there is no co-integrating relationship among I (0) and I (1) variables. From the tables above, the F-statistics is greater than the upper bound at all levels of significance therefore, we can conclude that there is a co-integrating relationship among the I (0) and I (1) variables. 61
4.3.4 Long Run Model Estimation This result shows the existence of a long run relationship among the variables of study. The long run coefficient of the variables are estimated in this section. Table 4.3d: Estimation of the ARDL Long Run Co-efficient
Long Run Coefficients AIC- ARDL ((1, 3, 2, 2, 1) Dependent variable: LINGR Variable
Coefficient
LSEDU LTEDU LPED LGFCF C
1.015048 0.261761 0.665136 0.082307 1.925104
Std. Error 1.57866 0.5013 0.45087 0.18079 3.78034
t-Statistic Prob. 0.64298 0.52216 1.47523 0.45527 0.50924
0.5283 0.6079 0.1574 0.6544 0.6168
Source: Researcher’s Computation using EViews 9 The estimates of the long run co-efficient based on the ARDL model are summarized in Table 4.3d. All variables are in logarithm hence each estimated co-efficient may be interpreted as measures of long run constant elasticity. The result reveals that the point estimates are remarkably in terms of magnitude and the signs being of no consequence. All the explanatory variables included in the baseline are not statistically significant in the long run although significant in the short run. The estimated long run coefficient of LSEDU, LTEDU, LPED, LGFCF all show expected sign although the variables are not statistically significant. The coefficient of LSEDU shows a positive relationship between LSEDU and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in secondary enrolment rate would lead to a more than proportionate increase in inclusive growth by 1.015 percent. This supports the apriori expectation as secondary education and inclusive growth is supposed to have a positive relationship.
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The coefficient of LTEDU shows a positive relationship between LTEDU and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in tertiary enrolment rate would lead to a more than proportionate increase in inclusive growth by 0.26 percent. This supports the apriori expectation as tertiary education and inclusive growth is supposed to have a positive relationship. The coefficient of LPED shows a positive relationship between LPED and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in public expenditure on education as a percentage of total expenditure would lead to a less than proportionate increase in inclusive growth by 0.66 percent. This supports the apriori expectation as in public expenditure on education as a percentage of total expenditure and inclusive growth is supposed to have a positive relationship. Therefore, an increase in public expenditure on education will contribute to inclusive growth in Nigeria. The coefficient of LGFCF shows a positive relationship between LGFCF and inclusive growth in the long run. Holding all explanatory variables constant, a one percent increase in gross fixed capital formation would lead to a more than proportionate increase in inclusive growth by 0.082 percent. This supports the apriori expectation as gross fixed capital formation and inclusive growth is supposed to have a positive relationship. Therefore, an increase in physical capital in Nigeria will contribute to inclusive growth in Nigeria. 4.3.5: Choice of Maximum Lag In this study, the maximum lag length for the ARDL estimation is automatically chosen by the estimation software, Eviews 9. It is however essential that finding the appropriate lag length for each of the underlying variables in the ARDL model to have Gaussian error terms that is, standard normal error terms that do not suffer from non-normality, autocorrelation and heteroskedasticity. In order to select the appropriate model, it is necessary to determine the optimal lag length by using proper model selection criteria such as Akaike Information
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Criterion (AIC), Schwarz Information Criterion (SIC) or Hannan-Quinn Criterion (HQC). In this study, the AIC is selected for the model. Using the AIC, the appropriate lag length for the estimation was found to be ARDL (1, 3, 2, 2, 1) for the dependent variable first and the subsequent independent variables in the model. This implies that the maximum lag length for the dependent variable, LINGR is 2 and for the independent variables, LSEDU, LTEDU, LPED and LGFCF are 3, 2, 2, and 1 respectively. As model selection criteria, the model with the smallest AIC estimates or small standard errors and high R-squared performs relatively better. The estimates from the best performed becomes the long run coefficients. This lag length selection is appropriate for the estimation of the ARDL bounds test for co-integration which immediately follows. Figure 4.3a: Choice of Maximum Lag
Source: Researcher’s Computation using EViews 9 4.3.6 Vector Error Correction Model The Vector Error Correction is used to examine the speed of adjustment or feedback effect and is derived from the error term of the co-integration method. It checks for short run disequilibrium that is the extent to which any previous period disequilibrium is being corrected in the long run. The VECM model is used for estimation due to the presence of co-integrating the variables in the short run and it reconciles the short run behaviour with the long run equilibrium. That is, it restricts the long run behaviour of endogenous variables to incorporate 64
short run disequilibria. The short run deviations are corrected through the series of adjustments. The stability condition of the VECM can only be satisfied when the coefficient has a negative sign and lies between 0 and 1 with a t-statistics that is greater than two to ensure statistical significance. A positive coefficient indicates a divergence while a negative coefficient indicates convergence. The coefficient of the error term has a negative sign and it is statistically significant for this model. This shows that there is a long run convergence between inclusive growth and the independent variables. That is, the speed of adjustment each period or year is 60 percent. Table 4.3e: Estimation of VECM Results Cointegrating Form Variable Coefficient Std. Error t-Statistic Prob. D(LSEDU) 1.505563 0.68624 2.193918 0.0416 D(LTEDU(-1)) 1.967498 0.87999 2.235826 0.0383 D(LPED) 0.323782 0.13844 2.338832 0.0311 D(LGFCF) 0.647377 0.24212 2.673748 0.0155 CointEq(-1) -0.606897 0.14898 -4.073688 0.0007 Cointeq = LINGR - (1.0150*LSEDU + 0.2618*LTEDU + 0.6651*LPED + 0.0823*LGFCF + 1.9251 )
Source: Researcher’s Computation using EViews 9 The results of the estimation gives the short run relationships among the variables. The result reveals that though the coefficient of all the independent variables lie between 0 and 1, all the variables are positive and statistically significant. This implies that the error correction is -meaningful taking place in the isolated independent variables; LSEDU, LTEDU, LPED and LGFCF. This result confirms the positive relationship between; LSEDU, LTEDU, LPED and LGFCF. The result shows that a positive relationship exists in relation to LSEDU and LTEDU in relation to inclusive growth in the short run. The coefficient shows that 60 percent of errors in the current period will be corrected in the subsequent period respectively which implies a more than average speed of adjustment. The error correction mechanism is statistically significant.
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4.3.7 Diagnostic Test In order to ascertain that the results obtained from the ARDL bounds testing procedure and other tests are reliable and free from erroneous assumptions, we carry out some post-estimation diagnosis tests. These include the normality test, heteroskedasticity test, serial correlation test and the Ramsey Reset tests. Figure 4.3f: Diagnosis Test Test Breusch-Pagan-Godfrey Serial Correlation LM Test Breusch-Pagan-Godfrey Heteroskedasticity Test Jarue-Bera Normality Test Ramsey RESET Test (log likelihood ratio)
Test statistics Prob 0.97645 0.398 1.575979 0.2018 0.38131 0.82642 1.296857 0.2706
Source: Researcher’s Computation using EViews 9 The results of the estimated ARDL model is confirmed from the table above as passes all the diagnostic tests against serial correlation, heteroskedasticity and normality of errors. The Ramsey RESET test also suggests that the model is well specified. These are confirmed from their respective F-statistic probability values as they are large and greater than 0.5 by rule of thumb. Therefore, the null hypothesis of no serial correlation in the estimated ARDL model is accepted and this result is corroborated by the Durbin-Watson statistic of 2.2692 Further, the null hypothesis of no heteroskedasticity is accepted, given the observed value to be greater than 0.5. The P-value of the Jarque-Bera normality test greater than 0.5 indicating the acceptance of a normally distributed series. Finally, the Ramsey RESET test confirms that the ARDL model is correctly specified as its p-value is greater than 0.05, indicating that the value of the fitted speed parameter is not different from zero. 4.3.8 Structural Stability Test The structural stability test via the recursive residual is necessary because ARDL is sensitive to structural breaks. The Cumulative Sum (CUSUM) examines whether the regression co efficient is changing systematically while the Cumulative Sum of Square (CUSUMSQ) checks 66
whether the coefficient changes suddenly. Structural breaks become viable when we notice an unexpected shift in the time series which can result in large forecasting errors and unreliability of the model. Figure 4.3b: Cumulative Sum
Figure 4.3c: Cumulative Sum of Square
15
1.6
10
1.2
5
0.8 0
0.4 -5
0.0
-10
-0.4
-15 1998
2000
2002
2004 CUSUM
2006
2008
2010
2012
1998
2014
2000
2002
2004
2006
2008
CUSUM of Squares
5% Significance
2010
2012
2014
5% Significance
Source: Researcher’s Computation using EViews 9 A CUSUM chart is a plot of the cumulative variance between succeeding values and a target value. It can also be described as cumulative sum of the deviation of each sample value from the target value. Shifts in the mean are easy to predict on the CUSUM chart as they show the changes that occur in the slope of the plotted points. Brown, Durbin and Evans (1975) stated that the null hypothesis of a stable parameter will be rejected if the respective plot crosses any of the straight lines either the upper critical bound or the lower critical bound. If the plot lies between the upper critical bound and the lower critical bound, then the null hypothesis is not rejected. Structural shifts occurs when there is a shift in the points. If one or more of the points crosses the point plotted at the upper critical limit or lower critical limit, then there is an issue of recursive residual. From the CUSUM and CUSUMSQ charts depicted above, there are no issues of recursive residuals in terms of the mean and variance respectively. This shows that no variable is sensitive to structural breaks. However, we can conclude that both the long run and short run estimates are stable and there is no form of structural breaks. Therefore, the model estimation result is stable, efficient and reliable.
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4.4 Test of Hypotheses This study aims to shows the impact of human capital investment on inclusive growth in Nigeria. This chapter show the empirical analysis of the variables under review to show the relationship between the variables of study and to know if they meet the a priori expectations stated. The estimation results show that there is a convergent long run relationship between human capital investment and inclusive growth in Nigeria. To test the hypothesis stated in the study, examine the t-ratio of the estimated model. If the coefficient of all the variables are statistically significant, we can reject the null hypothesis and accept the alternative hypothesis that human capital investment has an impact on inclusive growth. In the short run, human capital investment has a significant impact on inclusive growth but in the long run, the impact of human capital investment on inclusive growth is not significant. Therefore, from the analysis, a conclusion can be drawn on the hypothesis of the study: H0: Human capital investment does not have impact on inclusive growth H1: Human capital investment has impact on inclusive growth The short run estimation result of the ARDL model show that the t-statistics of the coefficient of variables have a positive and significant relationship with inclusive growth. Although, the ARDL bounds test result show that there is a long run relationship, this relationship is not significant. Hence, reject the null hypothesis and conclude that human capital investment has an impact on inclusive growth. 4.5 Discussion of Results The trend, descriptive and econometric analysis of the variables were portrayed and explained accordingly. The analysis was carried out mainly to determine the relationship between human capital and inclusive growth in Nigeria. All variables were revealed to be stationary at levels and first difference. The ARDL and VECM model results reveal that there is a positive and
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significant relationship between human capital and inclusive growth which is in line with the apriori expectations. The result show that human capital investment has a positive impact on inclusive growth in Nigeria. Secondary enrolment rate, tertiary enrolment rate, public expenditure on education and gross fixed capital formation showed a positive relationship in the VECM. The VECM results shows that 60 percent of errors in the current period will be corrected in subsequent time periods. Working papers on inclusive growth claim that a positive relationship exists between human capital and inclusive growth, this study accepts that inference and favours public expenditure on education as well as the human capital itself. Hence, all the null hypotheses of this study should be rejected. The structural stability test was also conducted and this showed that there was no form of structural breaks occurrence among the variables of interest. 4.6 Implications of Findings This chapter presented and discussed the results as pertains to the variables of interest in this study. A trend analysis was carried out as relating to the variables; education, inclusive growth and public expenditure on education. The cross country analysis carried out on inclusive growth and GDP per capita and GDP growth rate confirmed that though economic growth is a necessary but not a sufficient condition. The pace and pattern of growth of the primary, secondary and tertiary enrolment rate was also shown. Econometric analysis was carried out and it showed that the variables are integrated of order one and zero. Logarithmic transformations were used to examine the variables in order to achieve linearity. It showed that a positive relationship exists between inclusive growth and the independent variables. The apriori expectations were met for all variables. The error correction coefficient showed that 60 percent of errors in the current period will be corrected in the subsequent period respectively which implies a more than average speed of adjustment.
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Also, the diagnosis test was carried out and it showed that the model is properly specified, no autocorrelation, no heteroskedasticity and it is normally distributed. The structural breaks test also showed that there are no issues of recursive residuals in terms of the mean and variance respectively and no variable is sensitive to structural breaks in the long and short run. In summary, the econometric results were favourable and the apriori expectations were met.
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CHAPTER FIVE SUMMARY, RECOMMENDATIONS AND CONCLUSION 5.1 Preamble The previous chapter dealt with trend analysis, regression analysis and data interpretation obtained from various sources from 1981 to 2015. This chapter provides a summary on the study as well as the empirical findings. Recommendations will also be proffered as a guide to enlighten both individuals and government on the importance of human capital investment in achieving inclusive growth and policy actions that can be adopted to achieve the goal of inclusive growth. 5.2 Summary The Nigerian economy has been recorded as one of the fastest growing economies in the world and the largest economy in Africa with a nominal GDP of $568 billion. The buoyancy of the economy notwithstanding, Nigeria is still plagued with a GDP per capita that is barely $2000 and a high rate of inequality, poverty and unemployment. In spite of the constant investment of the government in education, the education enrolment rates are still low compared to the enrolment rate of other countries. This is traced to the failure of the government in aiding the attainment of growth that translates to development and the growth of the economy that carries the citizens along. The Lucas endogenous growth theory was not found to be applicable in the Nigerian economy. However, this view stands to contradict the findings of some empirical studies in Nigeria. An attempt to resolve this issue led to a comprehensive reviews of related literature. A vivid examination of related literature revealed a number of methodological shortcomings and gaps in previous studies. The analytical framework of the study was traced to the Lucas endogenous theory. It shows the transmission from human capital investment to sustained growth in
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Nigeria. A structural model was specified due to the dynamic nature of the relationship between human capital investment and inclusive growth. The Autoregressive Distributed Lag (ARDL) was adopted to test for long run relationship between the dependent and the independent variables of study. The Vector Error Correction Mechanism (VECM) was adopted to check for an error correction mechanism between the variable sin the short run. The results of the estimated equation reveals that investment in human capital has a positive impact on inclusive growth in Nigeria which is the expected result. This goes in line with the Lucas endogenous growth theory that states that the accumulation of human capital is the engine of growth. All the variables had a positive and statistically significant effect on inclusive growth in the short run but were statistically insignificant in the long run. This means that all the variables contributed to a large extent to inclusive growth in the short run but their contribution was not significant in the long run. 5.3 Recommendations 1. Human capital investment and inclusive growth The positive effect of human capital investment on inclusive growth is in line with the Lucas endogenous growth theory. This study lays emphasis on the quantity of human capital that is, investment in human capital. The Nigerian educational system is characterised by low enrolment, low quality and wide differences in student performance based on socioeconomic background (WEF, 2015). There is therefore a need for investment in human capital in Nigeria. The current state of the educational system in Nigeria, especially tertiary education is demeaning. Nigeria has been said to produce a set of incompetent and lazy graduates that lack the basic skills necessary for survival in the 21st century. This has posed as a barrier to inclusive growth, therefore, there is need for the Nigerian educational system to be restructured. These issues can be addressed through the restructuring of the academic curriculum at each level of education by including more applied, vocational and practical courses, increasing the quality
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and quantity of the teachers and faculties in these institutions, introducing the concept of lifelong learning, promotion of expanded internship, work-study programmes, industrial trainings and hands on experiences and the promotion of research, innovation and development through the establishment of research and development centres. These policies, if properly applied to the economy will spur an increase in the level of human capital in the economy, which will translate to inclusive growth in Nigeria. The result shows a positive and significant relationship between public expenditure on education and inclusive growth. Though the goal of primary education for all is yet to be achieved, the policy has contributed to the increase in enrolment at all levels. Hence, if this policy is introduced at the secondary and tertiary level, it will be sure to cause a remarkable change. This policy, though a good one, cannot be achieved due to the financial status of a majority of the population. There is therefore need for government spending to promote human capital in Nigeria. Investment in human capital has remarkable effects in that it will make the people more skilled and create employment for the people, thereby ensuring a constant source of income for them which will raise them from poverty and narrow the inequality gap in the economy. A valid model for financing higher education should address two broad areas of public interests; the need for inclusive education and candid concentration on talent management. Government can invest in human capital to promote inclusive growth by allocating 26 percent of the annual budget and 7 percent of the GDP in the education sector, reducing the corruption level, bureaucracy and red tapism of officials in the educational sector, awarding scholarships to the poor and vulnerable, economic empowerment of families, increase in the allocations of fund to research and development, establishment of functional libraries research and development centres at strategic areas.
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2. Inclusive Education The Education for All movement was commissioned by UNESCO at the World Economic Forum in 2000. The main aim of this movement was the provision of quality basic education for all children, youth and adults before the year 2015. 164 countries pledged to undertake this movement and accomplish it on or before 2015, Nigeria inclusive. This goal was most certainly not achieved by Nigeria, as Nigeria still has the primary school enrolment rate of barely 50 percent. This movement had six main goals as stated by UNESCO (2016), they include, expanding and improving basic education, especially for the vulnerable, ensuring that by 2015 all children have access to quality primary education, equal education for all to meet learning needs, improve adult literacy rate by 50 percent by 2015, and equal access to basic education for all, eliminating gender inequality in schools by 2005, and achieving gender equality in schools by 2015, with the major aim of ensuring girls enjoy full and equal access to basic education of good quality, and improving all areas of education and ensuring the success of all as relating to recognised and measurable learning outcomes, especially in literacy, numeracy and basic life skills. These goals are very clear, realistic and unambiguous. Hence, Nigeria needs to retake up this pledge as a National policy instead and implement all these goals into the educational sector. The tertiary school enrolment rate is a function of both the primary and secondary school enrolment rates. Therefore, if Nigeria seeks to achieve the goal of inclusive growth, this policing system if effectively ran, reviewed and managed will take the Nigerian economy from where it is now, to where it wants to be in the nearest future. 3. National Education Policy on Education The national education policy on education already contains the educational goals stated in terms of their relevance to the needs of individuals and the society at large. The national policy
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on education was introduced as a result of the 1969 National Curriculum Conference which was organized to address the pressing educational issues in Nigeria. The National Policy on Education was first published in 1977 and other subsequent editions in subsequent periods. An educational planning and monitoring committee was set up in the process. This policy instrument is a tool to achieving the national goals of which inclusive growth is a major goal. This policy combination emphasizes the concept of lifelong learning, expansion of educational training facilities, mind training, skill acquisition, and quality education. This policy is enough in itself to develop the Nigerian educational system if properly implemented. The policy covers all the sectors of the Nigerian educational system starting from basic education to the highest educational attainment in Nigeria even the vocational and technical education. The major issue with the Nigerian educational system is the lack of policy implementation because there are enough policies in place. Adesina (1982) identified some challengers of policy implementation in Nigeria; overestimation of available resources, under estimation of costs and implementation, over reliance of external support and inaccurate statistical data. Also, the communication process, the capability problem and the dispositional conflicts issue. In addition, Okoroma (2006) states that the Nigerian system is plagued with lack of political will and corruption and this has posed as a challenge to the progress of the Nigerian educational system. This study suggests that these educational regulations should be flexible enough to suit every political regime. 5.4 Conclusion Economists are regularly interested in acquiring dependable estimates of the causal effects and long run relationship of one variable on another. These sound and authentic estimates can be used in the prediction of the consequence of policy changes, assuming all other factors are held constant.
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Standard regression analysis can sometimes fail to yield dependable estimates for the following reasons; omitted variable bias, reverse causality or simultaneous equation bias and measurement error. The inability to recognise and report these issues could be due to adoption of an irrelevant economic theory, which would result in misspecification of the theoretical foundations of the estimations. These issues, when considered in the analysis of the relationship between human capital investment and inclusive growth in Nigeria brought new insight. Conscious attempts should be made by future studies to address the limitations of this study stated above. However, this study believes that the Nigerian educational system does not need any more policies as the National Policy on Education contains it all. Nevertheless, attempts at enhancing the efficacy and efficiency of human capital investment in Nigeria, should appraise the recommendations summarised above. 5.5 Limitations of the Study and Suggestions for Further Studies This study undoubtedly has vital implications for growth model estimates. Though this study has faced a number of constraints, they are issues that should be addressed by future studies. These issues, if properly addressed will improve the results of this study. A constraint faced by this study is the lack of data sets available for the Nigerian economy to measure fully the quality of the Nigerian educational system. It is very vital to note that a hundred percent enrolment rate may not guarantee high inclusive growth rate. Similarly, a few highly skilled individuals in the economy will make little or no difference in inclusive growth as inclusive growth is about carrying most, if not all of the people in the economy along in growth. The quantity and quality of education are both very vital for high inclusive growth. Hence, future studies should integrate these two sides in the endogenous growth model. This study concentrated on the amount allocated and not the efficient utilisation, which is as important as
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the allocated amount. Future studies should address this issue as it relates to the high level of corruption and mismanagement of these allocated funds in the education sector. Another major constraint of this study is the lack of variety of measurement for inclusive growth in Nigeria. There was also no direct theoretical foundation for linking inclusive growth to any other economic or social issue. This is why the Lucas theory of endogenous growth was used as the theoretical foundation of this study. The absence of a theory or model of inclusive growth also proved to be a great limitation in this study. In view of these limitations, the results obtained from this study should be meticulously examined. The lack and unavailability of data on the quality of education, other measures of education, and inclusive growth could be addressed by further studies. Also, Lucas’ (1988) opinion that everything cannot be theorised on at the same time could also be address by further studies. Earlier studies on this subject matter have been based on two principal assumptions. First, investment in human capital will always translate into inclusive growth. Hence, inclusive growth should be regressed on human capital using majorly education as its proxy variable. Second, public expenditure on education has the same effect as the human capital itself on inclusive growth. Lastly, this study has shown that human capital and inclusive growth are statistically significant in the short run but insignificant in the long run. However, there long run relationship between human capital investment and inclusive growth but the relationship is insignificant. This study revealed that human capital investment has a positive relationship with inclusive growth in Nigeria. Hence, if the government seeks to influence the speedy attainment of inclusive growth, it could be done through investment in human capital. This study also looked at inclusive growth from a more economic perspective. The concept was properly explained
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from the point of view of an economist and the economic implications were focused on than the political. The welfare implications of this relationship is embodied in positive relationship between human capital investment and inclusive growth, such that an increase in the human capital investment in the economy will affect inclusive growth positively. Human capital promotes economic growth, productive employment, poverty and inequality reduction and gender equality, which all together will translate into inclusive growth. This therefore means that whether in isolation of growth or not, human capital investment has implications on the welfare of the individuals in the economy, in that it increases the welfare level of individuals in the economy. Inclusive growth on its own has great impact on the welfare of individuals in the economy, as the growth is mainly people centric and is spurred by their activities. The results obtained from this study can be used in drawing the conclusion that human capital investment is one of the main ways Nigeria can get out of the challenge of growth without development. This may be attributed to the fact that most developed economies, if not all have high enrolment rates at all levels of education, which manifests itself in high development of the economy. Countries like Canada, United States of America, Australia and Norway have tertiary enrolment rates of 84, 89.5, 88.5 and 80 percent respectively, while Nigeria is still battling with an enrolment rate of barely 14 percent. Due to the high level of investment in human capital by these countries, their economies are buoyant and have achieved high level of economic growth that has translated to economic development. This is a major reason why these countries are regarded as advanced economies. Hence, if Nigeria seeks to solve the problem of growth without development, human capital investment is the suggested way out of it. The total government education expenditure as a ratio of total government expenditure has a positive relationship in inclusive growth in both the long and short run. In a nutshell, this study
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has contributed to contemporary knowledge and the understanding of the relationship that exists between human capital investment and inclusive growth in Nigeria.
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88
APPENDICES APPENDIX A: TIME SERIES DATA FOR ALL VARIABLES Year
INGR
SEDU
TEDU
PED
GFCF
1981
3.35
17.0086
2.31
8.82
2.1512E+10
1982
2.45
20.91
2.66
7.61
1.6423E+10
1984
1.45
28.6849
2.99
6.38
4053948000
1985
4.75
29.1736
3.39
5.55
3454841000
1986
4.55
27.083
3.5471301 5.22
3140100000
1987
2.85
27.0726
3.4812801 2.04
3278489000
1988
4.55
31.6635
3.8541501 6.44
2762657000
1989
5.35
24.132
4.1224098 8.28
2845302000
1990
4
24.5958
4.2996872 4.53
4382926000
1991
3.95
29.4374
4.5105948 2.31
3761777000
1992
4.2
30.1466
4.7215025 0.73
3735332000
1993
3.25
30.8559
4.9324102 3.61
2139415000
1994
0.9
31.5651
5.1433178 5.46
2019424000
1995
1.15
32.2744
5.3542255 4.42
2017059000
1996
2.45
32.9836
5.5651331 4.49
2550595000
1997
2.35
33.6929
5.7760408 4.06
2993589000
1998
2.35
34.4021
5.9869485 5.23
2752912000
1999
1.9
23.4156
6.0830898 3.91
2508842000
2000
4.15
24.4599
6.3846024 7.07
3255314000
2001
3.45
26.8612
6.593698
5.87
3345603000
2002
4.15
29.421
6.8027936 9.21
4144047000
89
2003
4.1
31.4532
9.6407804 6.48
6700671000
2004
7.8
34.752
9.8531599 6.05
6494736000
2005
5.25
34.6991
10.40532
6.23
6127632000
2006
3.35
34.1874
8.8780853 7.83
1.2021E+10
2007
4.05
31.6138
9.1571391 8.43
1.5396E+10
2008
3.75
35.098
9.4361929 6.92
1.7318E+10
2009
4.45
38.9045
9.7152467 5.49
2.0487E+10
2010
4.85
43.8367
9.9943005 5.18
6.2707E+10
2011
5.25
36.8313
10.273354 10.13
6.5793E+10
2012
2.75
37.2733
10.552408 10.48
6.7717E+10
2013
5.55
37.7153
10.831462 10.58
7.5511E+10
2014
3
38.1573
11.110516 8.38
8.575E+10
2015
2.35
38.5992
11.38957
7.1329E+10
7.13
APPENDIX B: AUGMENTED DICKEY-FULLER TEST RESULTS Unit Root Test at Level Null Hypothesis: LINGR has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-3.405534
0.0177
Test critical values: 1% level
-3.639407
5% level
-2.951125
90
10% level
-2.614300
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: LSEDU has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-3.242139
0.0260
Test critical values: 1% level
-3.639407
5% level
-2.951125
10% level
-2.614300
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: LTEDU has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-1.824315
0.3628
Test critical values: 1% level
-3.639407
5% level
-2.951125
10% level
-2.614300
91
*MacKinnon (1996) one-sided p-values. Null Hypothesis: LPED has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-3.264041
0.0248
Test critical values: 1% level
-3.639407
5% level
-2.951125
10% level
-2.614300
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: LGFCF has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
0.021747
0.9542
Test critical values: 1% level
-3.639407
5% level
-2.951125
10% level
-2.614300
*MacKinnon (1996) one-sided p-values.
92
Unit Root at First Difference Null Hypothesis: D(LTEDU) has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-5.988639
0.0000
Test critical values: 1% level
-3.646342
5% level
-2.954021
10% level
-2.615817
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(LGFCF) has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=8) t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.027852
0.0038
Test critical values:
1% level
-3.646342
5% level
-2.954021
10%level
-2.615817
93
APPENDIX C: ARDL ESTIMATION
A snmccx dscmzx Dependent Variable: LINGR Method: ARDL Date: 03/23/18 Time: 12:40 Sample (adjusted): 1984 2015 Included observations: 32 after adjustments Maximum dependent lags: 1 (Automatic selection) Model selection method: Akaike info criterion (AIC) Dynamic regressors (3 lags, automatic): LSEDU LTEDU LPED LGFCF Fixed regressors: C Number of models evalulated: 256 Selected Model: ARDL(1, 3, 2, 2, 1) Variable
Coefficient Std. Error
t-Statistic
Prob.*
LINGR(-1)
0.393103 0.148980
2.638636
0.0167
LSEDU
1.505563 0.686244
2.193918
0.0416
LSEDU(-1)
-1.296725 0.719177
1.803069
0.0881
LSEDU(-2)
1.447886 0.712811
-2.031234 0.0573
LSEDU(-3)
-1.040694 0.542363
1.918815
0.0710
LTEDU
1.633449 0.853581
1.913644
0.0717
LTEDU(-1)
-0.175187 1.125112
0.155706
0.8780
LTEDU(-2)
1.967498 0.879987
2.235826
0.0383
LPED
0.323782 0.138438
2.338832
0.0311
94
LPED(-1)
-0.610996 0.157173
-3.887425 0.0011
LPED(-2)
0.690884 0.151227
4.568521
0.0002
LGFCF
0.647377 0.242123
2.673748
0.0155
LGFCF(-1)
-0.597425 0.202628
-2.948383 0.0086
C
1.168340 2.341886
0.498888
R-squared
0.761312
0.6239
Mean dependent var 1.215163
Adjusted R-squared 0.588927
S.D. dependent var
0.470285
S.E. of regression
0.301523
Akaike info criterion 0.739695
Sum squared resid
1.636491
Schwarz criterion
Log likelihood
2.164876
Hannan-Quinn criter. 0.952255
F-statistic
4.416334
Durbin-Watson stat
Prob(F-statistic)
0.002152
1.380955
2.269202
*Note: p-values and any subsequent tests do not account for model selection.
APPENDIX D : BOUNDS TEST
ARDL Bounds Test Date: 03/23/18 Time: 12:41 Sample: 1984 2015 Included observations: 32 Null Hypothesis: No long-run relationships exist Test Statistic
Value
k
95
F-statistic
6.507949
4
Critical Value Bounds Significance
I0 Bound
I1 Bound
10%
2.45
3.52
5%
2.86
4.01
2.5%
3.25
4.49
1%
3.74
5.06
Test Equation: Dependent Variable: D(LINGR) Method: Least Squares Date: 03/23/18 Time: 12:41 Sample: 1984 2015 Included observations: 32 Variable
Coefficient Std. Error
t-Statistic Prob.
D(LSEDU)
1.505563
0.686244
2.193918 0.0416
D(LSEDU(-1))
0.407192
0.643101
0.633170 0.5346
D(LSEDU(-2))
-1.040694 0.542363
-1.918815 0.0710
D(LTEDU)
1.633449
0.853581
1.913644 0.0717
D(LTEDU(-1))
1.967498
0.879987
2.235826 0.0383
D(LPED)
0.323782
0.138438
2.338832 0.0311
96
D(LPED(-1))
-0.690884 0.151227
-4.568521 0.0002
D(LGFCF)
0.647377
0.242123
2.673748 0.0155
C
1.168340
2.341886
0.498888 0.6239
R-squared
0.782265
Mean dependent var -0.001302
Adjusted R-squared0.625011
S.D. dependent var
0.492392
S.E. of regression 0.301523
Akaike info criterion 0.739695
Sum squared resid 1.636491
Schwarz criterion
Log likelihood
2.164876
Hannan-Quinn criter. 0.952255
F-statistic
4.974548
Durbin-Watson stat
Prob(F-statistic)
0.001071
1.380955
2.269202
APPENDIX D: LONG RUN COEFFICIENT
ARDL Cointegrating And Long Run Form Dependent Variable: LINGR Selected Model: ARDL(1, 3, 2, 2, 1) Date: 03/23/18 Time: 12:42 Sample: 1981 2015 Included observations: 32 Cointegrating Form Variable
Coefficient Std. Error
t-Statistic
97
Prob.
D(LSEDU)
1.505563 0.686244
2.193918
0.0416
D(LSEDU(-1))
1.447886 0.712811
2.031234
0.0573
D(LSEDU(-2))
-1.040694 0.542363
-1.918815 0.0710
D(LTEDU)
1.633449 0.853581
1.913644
0.0717
D(LTEDU(-1))
1.967498 0.879987
2.235826
0.0383
D(LPED)
0.323782 0.138438
2.338832
0.0311
D(LPED(-1))
-0.690884 0.151227
-4.568521 0.0002
D(LGFCF)
0.647377 0.242123
2.673748
CointEq(-1)
-0.606897 0.148980
-4.073688 0.0007
Cointeq = LINGR - (1.0150*LSEDU
0.0155
+ 0.2618*LTEDU +
0.6651*LPED + 0.0823*LGFCF + 1.9251 )
Long Run Coefficients Variable
Coefficient Std. Error
t-Statistic
Prob.
LSEDU
1.015048 1.578657
0.642982
0.5283
LTEDU
0.261761 0.501302
0.522163
0.6079
LPED
0.665136 0.450871
1.475225
0.1574
LGFCF
0.082307 0.180789
0.455268
0.6544
C
1.925104 3.780340
0.509241
0.6168
98
APPENDIX E: CHOICE OF OPTIMAL LAG Akaike Information Criteria (top 20 models) .92
.88
.84
.80
.76
ARDL(1, 2, 2, 2, 3)
ARDL(1, 1, 2, 2, 2)
ARDL(1, 0, 3, 2, 1)
ARDL(1, 3, 2, 3, 3)
ARDL(1, 0, 2, 3, 1)
ARDL(1, 2, 2, 2, 2)
ARDL(1, 3, 3, 2, 3)
ARDL(1, 1, 2, 2, 1)
ARDL(1, 3, 3, 3, 1)
ARDL(1, 2, 2, 2, 1)
ARDL(1, 0, 2, 3, 2)
ARDL(1, 3, 3, 2, 2)
ARDL(1, 0, 2, 2, 2)
ARDL(1, 3, 2, 3, 2)
ARDL(1, 3, 2, 2, 3)
ARDL(1, 0, 2, 2, 1)
ARDL(1, 3, 2, 2, 2)
ARDL(1, 3, 2, 3, 1)
ARDL(1, 3, 2, 2, 1)
ARDL(1, 3, 3, 2, 1)
.72
APPENDIX E: DIAGNOSTICS TEST 12
Series: Residuals Sample 1984 2015 Observations 32
10
8
6
4
2
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-3.94e-15 -0.051577 0.451560 -0.484975 0.229761 0.084883 2.492888
Jarque-Bera Probability
0.381310 0.826417
0 -0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Table 4.4b: Serial Correlation LM Test: Breusch-Pagan-Godfrey Breusch-Godfrey Serial Correlation LM Test: F-statistic
0.976450
Prob. F(2,16) 99
0.3980
Obs*R-squared
3.480932
Prob. Chi-Square(2) 0.1754
Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic
1.575979
Prob. F(5,26)
0.2018
Obs*R-squared
7.442662
Prob. Chi-Square(5) 0.1897
Scaled explained SS 1.757804
Prob. Chi-Square(5) 0.8815
Ramsey RESET Test Equation: UNTITLED Specification: LINGR LINGR(-1) LSEDU LSEDU(-1) LSEDU(2) LSEDU( -3) LTEDU LTEDU(-1) LTEDU(-2) LPED LPED(-1) LPED(2) LGFCF LGFCF(-1) C Omitted Variables: Squares of fitted values Value
df
Probability
t-statistic
1.138796
17
0.2706
F-statistic
1.296857
(1, 17)
0.2706
F-test summary:
100
Mean Sum of Sq. df
Squares
Test SSR
0.115992
1
0.115992
Restricted SSR
1.636491
18
0.090916
Unrestricted SSR
1.520498
17
0.089441
Unrestricted Test Equation: Dependent Variable: LINGR Method: ARDL Date: 03/27/18 Time: 03:04 Sample: 1984 2015 Included observations: 32 Maximum dependent lags: 1 (Automatic selection) Model selection method: Akaike info criterion (AIC) Dynamic regressors (3 lags, automatic): Fixed regressors: C Variable
Coefficient Std. Error t-Statistic Prob.*
LINGR(-1)
0.674664
0.288036
2.342293
0.0316
LSEDU
2.299061
0.974064
2.360276
0.0305
LSEDU(-1)
2.103477
1.005331
2.092324
0.0517
LSEDU(-2)
-2.625899 1.252963
-2.095751 0.0514
LSEDU(-3)
1.820066
0.870497
2.090836
0.0519
LTEDU
3.059046
1.511257
2.024174
0.0590
LTEDU(-1)
0.639629
1.188137
0.538346
0.5973
101
LTEDU(-2)
-3.994222 1.982214
-2.015031 0.0600
LPED
0.594072
2.166537
LPED(-1)
-1.051597 0.417126
-2.521053 0.0220
LPED(-2)
1.086051
0.378035
2.872883
0.0106
LGFCF
1.205949
0.546129
2.208177
0.0413
LGFCF(-1)
-1.104199 0.488288
-2.261370 0.0371
C
1.287161
0.553581
FITTED^2
-0.369633 0.324582
R-squared
0.778230
Mean dependent var 1.215163
Adjusted R-squared 0.595596
S.D. dependent var 0.470285
S.E. of regression
Akaike info criterion 0.728679
0.299067
0.274203
2.325152
0.0448
0.5871
-1.138796 0.2706
Sum squared resid 1.520498
Schwarz criterion
1.415743
Log likelihood
3.341131
Hannan-Quinn criter. 0.956421
F-statistic
4.261146
Durbin-Watson stat 2.232458
Prob(F-statistic)
0.002880
*Note: p-values and any subsequent tests do not account for model selection.
102
103
