Human Capital Development, Infrastructural Development and Industrial Sector Productivity in Nigeria

This study examines relationship between human capital development, infrastructural development and industrial sector in Nigeria for the period 1991 through 2014. The objective of the study is to identify some critical economic and social factors that influence industrial sector productivity in Nigeria. To ascertain the relationship between our variables of studies, secondary source of data was employed and extracted from World Development Indicators. Using an Ordinary Least square (OLS) estimation technique, the study established that human capital development has positive and significant effect on industrial sector productivity while infrastructural development has positive but insignificant effect on industrial sector productivity in Nigeria. Thus, the study recommends effective negotiation of debt relief from Paris club and other foreign debt to enable the government have excess funds to invest on pro-poor intervention project, transparency in governance and implementation of fiscal budget with post evaluation.


INTRODUCTION
A developmental focused economy must upgrade its infrastructure and concurrently improve the quality of human capital if it is to achieve sustainable economic growth through improved productivity of industrial sector; this is contained in the report of Asian development Bank (ADB) on the country's growth prospects. Economist Theodore Schultz invented the term (human capital) in the 1960s to reflect the value of our human capacities. He believed human capital was like any other type of capital; it could be invested in through education, training and enhanced benefits that will lead to an improvement in the quality and level of production.
According to Todaro and Smith (2011), human capital is productive investments embodied in human persons, including skills, abilities, ideals, health, and locations, often resulting from expenditures on education, on-the-job training programs and medical care. Improvements in productive efficiency from investment in education raise the return on a lifesaving investment in health. In 2010, the OECD (the Organization of Economic Co-operation and Development) encouraged the governments of advanced economies to embrace policies to increase innovation and knowledge in products and services as an economical path to continued prosperity. International policies also often address human capital flight, which is the loss of talented or trained persons from a country that invested in them, to another country which benefits from their arrival without investing in them.
Infrastructure is frequently defined as a set of basic physical and organizational structures and facilities required for operation of an economy. Infrastructure can be divided into economic infrastructure (transport, telecommunication, energy provision and sewage) and social infrastructure (law enforcement, security provision, education and health system. The economic infrastructure Development will go a long way in contributing to both economic growth and economic development (Srinivasu & Rao, 2013).
How is this human capital and infrastructural development of importance to developing countries, particularly, Nigeria? Economic development of Nigeria can be facilitated and accelerated by the presence of infrastructure. If these facilities and services are not in place, development will be very difficult and in fact can be likened to a very scarce commodity that can only be secured at a very high price and cost (Srinivasu & Rao, 2013).
Large number of empirical studies did not analysis human capital development, infrastructural development and industrial sector productivity in a single study, however, some of the empirical studies have shown the relationship between two of the variables (taking either human capital development or infrastructural development as an explanatory variable to economic growth or industrial sector performance).
Empirical studies on Infrastructural development and Industrial sector productivity have shown that public capital has important explanatory power for why some countries have managed to industrialize, while others have not (Anders 2009).Focusing exclusively but critically on the power supply situation in Nigeria, Emeka (2008) argued that despite huge funds government had committed into the power sector between (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007), Nigeria with population of over 140 million was only able to generate less than 3,000 MW as against over 10,000 MW needed to transform the economy of the country. He further identified several causes of this inadequate power supply and argued that this precarious situation has serious negative implications for the operations of industrial sector in the country, as most organizations spent fortunes generating their own power. This situation represents a major setback on the country's quest for industrial development.
On human capital development and industrial sector productivity; empirical studies have it that firm performance in relation to human capitals should not be regarded as a phenomenon that only adds 'more zeros' in a firm's profits; it is rather transforming the entire workforce as the most 'valuable assets' in order for the organization to pave ways for greater achievements via innovativeness and creativity. However, government expenditure on education maintained a positive long run relationship with index of industrial production while government expenditure on health and Gross Capital Formation exhibited long run negative relationship with the dependent variable (Simon, 2012), strong positive relationship in banking industry (Oyinlola et al., 2014). both government expenditure on administration and government expenditure on economic services have negative relationships with industrial productivity (Tawose 2012).Very many of the empirical studies supported the fact that human capital development has a positive and significant influence on productivity of industrial sector, the only point of clarification was that -government expenditure on education maintained a positive long run relationship with index of industrial production while government expenditure on health and Gross Capital Formation exhibited long run negative relationship with index of Industrial production (Simon 2012).
The summary of empirical literature reviewed on diseases, longevity and labour productivity is shown in Table 1: Majority of the workers in the transport industry were unskilled with only a few workers possessing higher degrees, despite the positive relationship observed between human resource effect and the productivity One sector of the economy may not give a general view for the whole sectors.

RESEARCH METHODOLOGY Theoretical Framework
In this study, we start with a simple Cobb Douglas production function of output (X) with physical capital stock (K) and labour force (L) as the two basic inputs. The Labour force can be further replaced with stock of human capital (H) since we assume the labour input to be conditioned for the average level of education.
Where the sum of α and β is one, and α and β are the efficiencies of capital and labour respectively. A is the average level of technology common to capital and labour. Taking after the work of Tomasz (2012), the general production function is given by where µ is a positive constant, AYS gives average years of schooling and AYE represents average years of working experience in a given country/region. Parameters θ and ρ represent average individual private returns to schooling and experience respectively. Substituting (5) into and (4) and dividing both sides by L we obtain the formula for real output per human capital x: Equation 6 is the industry production function expressed for labour productivity.

Model Specification
In specifying a model that will explain the interrelationship between human capital development, infrastructural development and industrial sector productivity, there is the possibility of encountering endogeneity problem. That is, any of the three variables may correlate with the error term when specified as explanatory variable. Thus, industrial sector productivity will be treated as endogenous variable in the model while other variables are treated exogeneity

Relationship between Human Capital, Infrastructural Development and Industrial sector productivity in Nigeria
To establish relationship between the three contending variables above, a human and physical production function in which industrial sector productivity is treated as regressand is formulated. The model is formulated to explore physical capital (K) and human capital (H) complementarity impact on long-run economic growth, however, the model is modified to fit the purpose of our study. More so, some factors have been found to be relevant in explaining the growth of industrial sector productivity but within the range of identifying main determinants (human capital and infrastructural development) and they include: 1. Education and Health -Tadaro and Smith (2011) stated that education plays a key role in the ability of a developing country to absorb modern technology and to develop the capacity for self-sustaining growth and development. Moreover, health is prerequisite for increases in productivity, and successful education relies on adequate health as well. 2. Energy provision will be considered as a factor under economic infrastructure while education and health is synonymous to social infrastructure (also factor considered under human capital) The model assumed constant returns to each factor. It is implicitly specified as follow; where α and µt are the intercept and error term respectively, b1 + b2 = b for HTtwhich comprises (SANFt + WATRst). Table 2 shows the apriori expectation. Productivity GER (+ ) HT ( + ) SANF (+) WATRs (+) ELEC ( + ) NB: All the signs in parentheses represent the apriori expectation relative to industrial productivity.

Estimation Techniques
Estimating equation 4 using ordinary least square (OLS) techniques, there is every tendency that we face some challenges on endogeneity issue, correlation between the disturbances (autocorrelation), unequal variances of error terms (heteroskedasticity) and correlation between the lagged dependent variables and the error term, hence making OLS estimator biased and inconsistent. These identified problem can be reduced to a reasonable level if not eliminated by using Generalized Least Squares (GLS), as it takes such information into account explicitly and therefore capable of producing estimators that are BLUE.

Sources of Data and Measurement of Variables
The data is sourced from World Development Indicators (WDI) online database published by World Bank organization. Time series data spanning from 1991 and 2014 were used. The choice of these explanatory variables and the periods covered were informed by the extent of data availability.
In this study, the variables of interest are Level of Industrial Sector Productivity (INDVA) which is the industry value added, education level (GER) considering enrolment at primary level, The health factor(HT) looking at the improved sanitisation facilities (SANF), access to clean water (WATRs), Provision of energy (G) which is the electricity production from hydroelectric sources(ELEC).

EMPIRICAL FINDINGS Descriptive Statistics
The empirical analysis of this study starts from descriptive statistics of variables used in the study with the aim of verifying their characteristics; hence, graphical presentation below reveals the relationship between industry value in Nigeria and various independent variables as adopted in the study between 1991 and 2014.
The trends of Industrial Sector Productivity in Nigeria measured by the industry value added from the year 1991 to 2014 is illustrated in Figure 1. The trend in the values of the variable is that of a rising pattern, increasing sharply at one time and slightly at other times. Between the years 1991 and 1998, a slightly stable trend was observed until after this period when a sharp decline, though temporary, was observed. Following this period, the Industry Value Added increased sharply in the year 2002 and ever since then, the rising trend has continued. Though on a fluctuating pattern, the rising trend continued till the last years of the period under study.

Source: Author's Computation using EVIEWS
The various factors/variables accounting for the observed trend in the Industrial Sector Productivity in Nigeria include access to power supply, improved sanitation, water supply and the literacy levels. These are discussed explicitly below. . Jarque-Bera statistic shows that all the variables have insignificant p-values. Both Kurtosis and Jarque-Bera statistic confirm that the time series data were normally distributed. Hence, the data are suitable for analysis on parametric considerations, particularly in estimating the OLS regression model.

Unit Root Test
Unit root test was conducted on the panel data to avoid spurious regression which tends to accept a false relationship or reject a true relationship as a result of the use of non-stationary data for the analysis. The Augmented Dickey Fuller (ADF) procedure was adopted in testing for existence of unit root in the panel data and the order of integration of all the variables.
Since a spurious regression is not desirable, testing for stationarity is a prerequisite when working with panel data. This transforms the non-stationary data into stationary data by means of differencing. The results of the Unit Root Test are summarized in the table 4 below; Results as presented in the table 4 show that the ELEC, INDVA and GER have unit roots (i.e. not stationary) at levels. However, the SANF and WATRS are stationary at levels, meaning that we rejected the null hypothesis at level which states that they have unit root. By first differencing, ELEC, INDVA and GER, however, were all stationary. This means that for these variables, we accepted the null hypothesis at level which states that they have unit root, but rejected the null hypothesis at first difference. All were stationary at second difference, indicating the absence of unit root in the variable data. Having tested for stationarity therefore, the time series data are suitable for analysis.

Co-Integration Test
The result of the Johansen's co-integration test is given below in Table 5. There are two methods displayed in the table above, the trace statistics and Max Eigen-statistic. Trace statistic agreed that there are two (2) co-integrating equations. This is confirmed with the rejection of the null hypothesis at 5% level while also the z-test assumes there is two (2) cointegrating equation owing to the p-values (0.0001 and 0.0226) being less than 0.05 level.  The F-statistic shows overall significance of model. The F-statistic is significant at 5% level since the probability of its value (0.000000) is less than the 0.05 critical level.
We therefore conclude that Human Capital (particularly, the social infrastructural factorsaccess to clean waters and access to improved sanitization facilities) cum economic infrastructure have significant effects on Industrial Sector Productivity in Nigeria. The more the people having access to improved water supply, improved sanitation facilities, access to education, and probably access to electricity the more the industrial productivity in Nigeria.
The Durbin-Watson statistic which equals 1.74 shows the absence of serial autocorrelation. This means that there is independence of observation among the regressors independent of the error terms. It indicates independence of observations or no autocorrelation.

CONCLUSION
This study examined relationship that exists in human capital development, infrastructure and industrial sector productivity in Nigeria. The periods covered for the study was between 1991 and 2014. We further analysed that human capital development variables include access to education (gross primary school enrolment), access to clean water and improved sanitization facilities while infrastructure is measured by electricity production from hydroelectric source. Our regression estimate revealed that human capital development has positive and significant effect on the industrial sector productivity (using Industry value added as proxy) while infrastructure, although has positive but insignificant effect on the productivity sector in Nigeria.
The result from this study reflects some improvement efforts of Nigeria government in attaining some of the Millennium Development Goals, ranging from increasing net enrolment rate in basic education in primary schools, reduction of maternal and child mortality that may result in increased capacity for human capital, access to safe drinking water and slightly improvement in sanitization facilities in the country since 2005.
Nevertheless, the study held that higher productivity in industrial sectors of Nigerian economy is explained significantly by improved level of sanitization facilities, access to clean water and increasing enrolment of pupils in primary schools while infrastructures are also positively related but insignificant.