Determinants of Capacity Utilization in Nigeria's Manufacturing Sector

Doctoral Thesis / Dissertation, 2022

211 Pages, Grade: 4.5




1.1 Background to the study

The manufacturing sector of an economy has always played, and will continue to play, a critical role in the rapid advancement of a rising economy. The relative importance of small and medium-sized enterprises, which make up the bulk of businesses in developing countries like Nigeria, has historically played a key role in accelerating growth and development in countries where their importance has been prioritized in economic planning strategies.

According to Aremu (2004), manufacturing enterprises play a critical role in the economy of any country, depending on its relative level of development. Furthermore, Gunu (2004) and Aremu (2010) claimed that manufacturing industries, particularly small and medium-sized ones, generate personal income, savings, job possibilities, and propel the real sector of a rising economy. These businesses are seen as the locomotives that propel entrepreneurial talents and local technical improvements that are required for capacity utilization.

Manufacturing sector growth and capacity utilization are two connected phenomena: the more the capacity utilized, the larger the outputs produced, and the faster the rise of manufacturing productivity or output. After gaining political independence in October 1960, the country focused extensively on the manufacturing sector in the late 1970s and early 1980s, with the goal of achieving economic and social independence.

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Figure 1.1 Trend Movement of Manufacturing Value Added (MVD) and Manufacturing Capacity Utilization Rate Source: Researcher's Computation, 2021

Manufacturing capacity utilization was 73.3 percent in 1981, with manufacturing value added to gross domestic product at 20.3 percent, but by 1995, manufacturing capacity utilization had dropped to 29.29 percent as electricity supply to manufacturing plants became erratic, roads were in poor condition, and the safety of manufacturers, i.e. bots, had become a concern. However, from 2003 and 2014, the average capacity utilization rate was quite low at 54 percent and grew to 60.5 percent with a 9.4 percent contribution to GDP, indicating that the manufacturing sector has had a very limited impact. Any economy's manufacturing sector can become a major driver of growth if it is adequately established. However, it is regrettable that the industry has underperformed expectations in Nigeria, resulting in a drop in industrial productivity and a contribution of less than 5% to the country's Gross Domestic Product (Udoh and Ogbuagu, 2012).

Establishing the conditions for strong economic growth at home is the first step in creating an economic climate that promotes manufacturing competitiveness. Fostering a climate that encourages significant corporate investment, particularly in the manufacturing sector, necessitates a risk-free economic environment. The manufacturing sector, for example, is growing the Nigerian economy's gross domestic product, although its contribution looks to be lower than planned. No industry can operate at full capacity without sufficient energy, as a reliable power supply is the primary driver of technological and social progress. There is almost no business or area of

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Figure 1.2. Trend Movement of Electricity Power Consumption Per Capita (ELPCP), Gross Fixed Capital Formation Percentage Contribution to GDP (GFCF_GDP) and Lending Interest Rate (LINTR)

Source: Researcher's Compilation, 2021

Nigeria is endowed with a diverse range of energy resources, including crude oil, natural gas, coal, hydropower, solar energy, and fissionable materials for nuclear energy, but it consistently faces an energy shortage, which is a major impediment to the country's industrial and technological development. Looking at the diagram (fig 1.2) above, it appears that the amount of power 3 consumed by each family has been steadily increasing over time. For example, in 1981, electricity consumption per capita was 51 kilowatts, rising to nearly 82 kilowatts in 1982. From 1983 to 2009, electricity consumption per capita fluctuated, reaching an all-time high of 157 kilowatts in 2012, which is still far below the standard requirement of ensuring a steady power supply in the country. The average power consumption in Nigeria is insufficient to maintain manufacturing enterprises' plants and machines operating at optimal levels, forcing them to rely on fossil fuel alternatives to power their plants and machines (Adegbamigbe, 2007 & Ajanaku, 2007).

Another determinant of capacity utilization in Nigeria's manufacturing sector is interest rate, which is a monetary policy tool and has long been a source of concern for monetary policymakers and investors. The working of interest rates is heavily influenced by the level of activity in an economy's manufacturing sector. The rate of interest involved in obtaining funds from banking institutions determines investment in the manufacturing sub-sector. Yet, as significant as interest rates are, the monetary authority is concerned by their tendency to exhibit unpredictable behavior, i.e., they fluctuate too much, as shown in the figure above. From 1981 to 1986, the loan rate fluctuated too much. The interest rate was approximately 9.5 to 9.8% in the early 1990s, but it climbed to 20.04 percent in 1991 and 32 percent in 1993. Higher interest rates tend to limit credit growth, making it more difficult for firms to obtain financing and produce at their maximum capacity, as well as for individuals to find and hold work.

Capital formation is also a factor that influences the manufacturing sector's capacity utilization. Capital formation affects economic growth through determining the industrial sector's capacity to produce. The most important limitation to long-term economic growth has been identified as a lack of capital. In the meantime, understanding the impact of capital formation is a necessary precondition for planning a policy intervention aimed at achieving economic growth. According to Jhingan (2006), the process of capital formation involves three interrelated conditions: (a) the existence of real savings and their growth; (b) the existence of credit and financial institutions to mobilize savings and direct them to desired channels; and (c) the use of these savings for capital goods investment. In 1986, the Nigerian government recognized the need for increased capital formation and embarked on an economic reform that turned the focus to the private sector. The public sector reforms were anticipated to ensure that interest rates were positive in real terms and that savings were encouraged, ensuring that investment funds for the manufacturing sector were freely available. Aside from that, the reforms were supposed to boost labor productivity and efficiency, as well as the efficient use of economic resources, raise aggregate supply, reduce unemployment, and provide a low inflation rate. For example, gross fixed capital formation in Nigeria averaged 89.4 and 86 percent of GDP in 1981 and 1982, respectively, before dropping to the lowest average of 14.90 percent of GDP in 2013. The above chart in fig 2 above illustrates that the government has been neglectful in the field of capital accumulation, since the expenditure profile has shifted more to recurrent rather than capital expenditures in recent years. She didn't spend much of her capital on capital goods like machinery, instruments, or factories, or on increasing the stock of raw materials, completed goods, or bettering general investments. That isn't good enough for a country that is trying to develop.

Finally, the importance of deposit money bank credits in the efficient and effective performance of the manufacturing sector cannot be overstated, which is why one of the broad policy objectives of the Federal Government's Appropriation Bill in recent years has been to achieve a high economic growth rate, i.e. GDP of at least 5%, by better mobilizing and prudently using economic resources. Banks must be efficient mediators in mobilizing and channeling deposits to the productive sector of the economy, particularly manufacturing. Despite ongoing regulatory efforts to attract credit to the manufacturing sector, Nigerian manufacturing firms remain undesirable for deposit money bank loans with low interest rates (Ogar, Nkamare, & Effiong,. 2014). According to the central bank of Nigeria's 2009 report, commercial banks' loans and advances to the manufacturing sector have consistently departed from mandated minimums practically throughout the regulatory era.

1.2. Statement of the problem

Despite the fact that Nigeria's population has been growing, which should translate to a high potential for personnel in the industrial sector, this has not been the case because enterprises lack the ability or financial resources to cater to or employ the country's growing labour force. And, given that the majority of enterprises in the country are labour-intensive, as opposed to their counterparts in more developed countries, which are more mechanized and less labour-intensive, the Nigerian economy should have a low level of idle capacity. Medium and small businesses are widely regarded as a powerful tool for alleviating poverty and promoting economic growth and development around the world.

Despite the fact that manufacturing firms' contribution is widely acknowledged, industrialists nonetheless confront several obstacles that limit their progress and survival. Because manufacturing activities can only thrive in a good investment environment, such as stable financial market systems for accessing credits at low rates of interest, and affordable corporate tax, little work has been done in the Nigerian manufacturing industry until recently, especially in terms of the components and determinants of full or optimal capacity utilization.

The unpredictable performance of Nigeria's industrial sector has harmed the economy's growth and, as a result, exacerbated the unemployment and crime rates. It has also increased demand for imported commodities, making the domestic economy highly vulnerable to changes in overseas prices. Basically, the manufacturing sector's poor performance has been attributed to the banking sector's unwillingness to appropriately support the manufacturing sector (Levine, 1997; Hassan, Sanchez, & Yu, 2011). Furthermore, the central bank's monetary policy and the government's trade policies have not been supportive of the industrial sector. The financial sector is, after all, expected to be a primary driving force boosting output and engineering the expansion of manufacturing companies, which may be done by making financing available to manufacturers at a cheap interest rate in order to cut operating costs and enhance productivity. However, emerging countries, particularly Nigeria, have failed to do this, and the manufacturing sector has virtually disappeared, contributing very little to the economy in terms of output and jobs (Shahbaz, 2009).

The challenges confronting Nigeria's manufacturing sector are simply due to the country's inability to develop suitable machines and technology on its own, its overreliance on foreign technology, and a lack of capital to acquire it, and as a result, the country's technology base is so weak due to insufficient investment in research, innovation, and development. Furthermore, the sector's mistreatment and malfunctioning as a result of poor financing, epileptic power supply, dilapidated and obsolete infrastructure, insufficient capital accumulation, high-interest rates, and persistent inflation, perennial security challenges, smuggling, and massive importation of capital goods can all be considered major determinants of capacity utilization.

In light of the foregoing, the study investigated the determinants of capacity utilization in the Nigerian manufacturing sector in order to determine which of the determinants of capacity utilization contributes the most to boosting manufacturing firms' ability to utilize available productive resources and, as a result, make appropriate policy recommendations on the way forward.

1.3 Objectives of the Study.

The broad objective of this study is to investigate the determinants of capacity utilization in the Nigerian manufacturing sector. The specific objectives of the study are to:

1. determine the effect of electricity power consumption on average capacity utilization rate in the manufacturing sector of Nigeria.
2. analyze the effect of gross fixed capital formation on average capacity utilization rate in the manufacturing sector in Nigeria.
3. evaluate the impact of lending interest rate on average capacity utilization rate in the manufacturing sector of Nigeria.
4. investigate the effect of importation on average capacity utilization rate in the manufacturing sector of Nigeria.
5. determine the influence of credit to manufacturing sector on average capacity utilization rate in the manufacturing sector of Nigeria.
6. evaluate the impact of labour force on average capacity utilization rate in manufacturing sector of Nigeria.

1.4 Research Questions.

The research seeks to provide answers to the following questions:

1. What is the effect of electricity power consumption on average capacity utilization in the manufacturing sector of Nigeria?
2. What is the effect of gross fixed capital formation on average capacity utilization rate in the manufacturing sector of Nigeria?
3. What is the impact of lending interest rate on average capacity utilization
rate in the manufacturing sector of Nigeria?
4. What is the effect of importation on average capacity utilization rate in the manufacturing sector of Nigeria?
5. What is the influence of credit to manufacturing sector on average capacity utilization rate in the manufacturing sector of Nigeria?
6. What is the impact of labour force on average capacity utilization rate in the manufacturing sector of Nigeria?

1.5. Research Hypotheses

The statement of the hypotheses will be stated in null form as follows:

H01: Electricity power consumption has no significant effect on average capacity utilization rate in the Manufacturing sector of Nigeria at 5% level of significance.

H02: Gross fixed capital formation has no significant effect on average capacity utilization rate in the manufacturing sector of Nigeria at 5% level of significance.

H03: Lending interest rate has no significant impact on average capacity utilization rate in the manufacturing sector of Nigeria at 5% level of significance.

H04: Importation has no significant effect on average capacity utilization rate in the manufacturing sector of Nigeria at 5% level of significance.

H05: Credit to manufacturing sector has no significant influence on average capacity utilization in the manufacturing sector of Nigeria at 5% level of significance.

H06: Labour force has no significant impact on average capacity utilization in the manufacturing sector of Nigeria at 5% level of significance.

1.6 Significance of the Study.

The importance of manufacturing firms in an economy determines, to a large extent, the flexibility of the economic system to meet future requirements for the goal of being productive, efficient, and achieving the set macro-economic goals, which expressly translate to higher living standards for the populace. The following groups will benefit greatly from this research:

1. Manufacturers and Entrepreneurs.This is because understanding the factors that influence capacity utilization will provide producers with insight into total resource usage and how to boost output without increasing production costs. Empirical study into the rate of Capacity Utilization, particularly among industrial enterprises in developing countries such as Nigeria, has been limited; one reason for this appears to be a lack of reliable and sufficient data. The manufacturing sector offers immense potential for job development, wealth creation, and poverty alleviation, according to Nigeria's commendable medium-term policy document (National Economic and Empowerment Strategy — NEEDS) (Borodo & Alhaji, 2009). Manufacturing has become the catalyst for developing countries like Nigeria to profit from globalization, bridging the gap with the high-income industrialized world, because to rapid technical advancements, extensive liberalization, and the proliferation of internalized production (Mike, 2010). Furthermore, optimal capacity utilization among enterprises is critical in the expansion process and continues to receive significant attention in modern economic literature, not just in emerging but also established countries.
2. Government:Capacity utilization not only improves the fortunes of industry owners, but it also aids in the creation of jobs in all economies. The study will help the government and monetary authorities understand the necessity of investing in the industrial sector, which is one of the country's most important strategic sectors. The capacity utilization rate is an essential statistic for businesses since it can be used to evaluate operating efficiency and cost structure. It can be used to calculate the rate at which unit costs rise or fall. When production increases, the average cost of manufacturing decreases. This indicates that the higher the capacity utilization, the lower the cost per unit, giving a company a competitive advantage. This is why many huge corporations strive to produce at or near full capacity (100%) as much as possible.
3. The academic world:The study's findings will add to the existing literature on the current state of the manufacturing sector in Nigeria and its contribution to GDP. The study's conclusions, based on our empirical findings and analysis, will be extremely useful to researchers who will rely on their additions to current knowledge for future research.
4. Monetary Authorities:The findings of this study will aid monetary authorities in appraising the manufacturing sector's performance in Nigeria, particularly in terms of its contribution to economic growth.
5. Policymakers and Economic Planners:The findings of this study will be extremely useful to policymakers in devising and executing appropriate policy measures to accelerate economic growth in the manufacturing sector.

1.7 Scope of the study

The study is primarily focused on Nigeria's economy. The study focused mostly on the determinants of capacity utilization in Nigeria's manufacturing sector in order to discover which of these determinants contributes the most to the manufacturing sector's output. The study focused on the years 1981 to 2019, as available data allowed, and used a few key variables such as Average Manufacturing Capacity Utilization Rate (AMCUR) as the dependent variable, and Electricity Power Consumption Per Capita (ELPCP), Gross Fixed Capital Formation (GFCF), Lending Interest Rate (LINTR), Goods Importation (GSIMP), and Inflation Rate (INFR) as the independent variables. The reason for the choice of the chosen period was because of from 1981-2019 is significant because it was the phasing in of structural adjustment policies (SAP) in Nigeria from the 1980s and the fiscal restructuring reforms in the mid-1990s which had its thrust in changing the structure of the economy from reliance on only oil to a transitional diversified tradable agricultural base and subsequently to a robust industrial sector. The study reviewed the concept of capacity utilization and conceptualize the determinants of capacity utilization in Nigeria while reviewing various theories that lays support to the importance of capacity utilization. The specified model was estimated using the Autoregressive Distributive lag Model (ARDL) to determine the level of impact that one variable has on another. The study made use of E-views 11 statistical software for estimation and computation of results, while the available time series data of interest was obtained from World Development Indicators (WDI) from World Bank and CBN annual statistical bulletin 2019.



2.1 Conceptual Framework

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Figure 2.1. Conceptual framework model on determinants of capacity utilization in Nigeria's manufacturing sector

Source: Researcher's Compilation, 2021

2.1.2 Capacity Utilization.

The concept of capacity utilization has no universally accepted meaning. This is due to the fact that different disciplines, such as political economy and organizational development, have diverse perspectives on capacity concerns. Capacity Utilization (CU) and capacity are difficult to define, let alone interpret and quantify consistently and consistently. Understanding capacity use and measuring it is essential for correctly designing a capacity management program, particularly when capacity utilization is controlled by specific constraints (Kirkley, James & Dale 2002). Capacity utilization perception is fundamentally linked to output. CU is frequently mentioned in discussions of applied and theoretical issues at both the macro and microeconomic levels, as its significance for business decision-makers grows. Excess capacity among enterprises, for example, indicates that there are components of monopolistic tendencies within certain industries (Ezu, Gideon, Sarah, Anyeneh, Ogbonnaya, 2019).

Cassel (1937) is credited with the most important work on the economic notion of capacity utilization; he distinguished between excess capacity of fixed elements (short run cost curves) and excess capacity of all factors (long run cost curves). Cassel went on to say that because the absolute technical upper limit of output obtained from fixed factors is unlikely to be within the domain of actual economic operations, capacity output should be defined as the output at which average total costs are at their lowest.

One of the forerunners of capacity utilization, Klein (1960), observed that while the term capacity is frequently used in economic research, comparatively little attention is paid to a precise theoretical explanation of the idea. The term is used as a self-definitive term, and it is assumed that there is agreement on its meaning. However, if we were to embark on the work of evaluating capacity, whether for a corporation, an industry, or the entire economy, we would undoubtedly face numerous theoretical challenges, necessitating the establishment of a clear conceptual foundation as a starting point. Excess capacity, according to Chamberlin (1935), one of the first writers to pay attention to the concept of capacity utilization, is the result of imperfect competition, which leads to inefficiency in an economic organization.

Capacity underutilization is defined as a decrease in stock size between two peak years where capacity utilization is the extent to which an enterprise or a nation uses its installed productive capacity. It is the relationship between output that is produced with the installed equipment and the potential output which could be produced with it, if capacity was fully used (Ezu 2019).

Optimal capacity utilization, according to Klein (1960) and Hickman (1964), should be distinguished and viewed as the level of output associated with the entire competitive equilibrium. However, Padma (1991), claims that empirical implementation of the economic concept of capacity utilization encounters difficulties, partly due to the challenge of cost function estimate and, more importantly, due to critical uncertainties about whether the long-term average cost curve genuinely curves up. Klein (1960) utilized the concept of an economic production function to try to encapsulate the concept of maximum output. He claimed that capacity utilization is a composite index of all fully utilized components, including capital stock and other variables.

Furthermore, because capacity utilization is not a complete replacement for capital stock because it is dependent on other elements of production, it has aided in the economic extension of the idea of capacity. Another pioneer in the subject of capacity, Johansen (1968), confirms Klein's theory that plant capacity utilization is equivalent to the firm's maximal output.

Similarly, more recent research by Fare (1984), Fare and Grosskopf, and Kokkelenberg (1989) have obtained utilization rate and firm capacity measurements based on Klein and Johansen's earlier definitions. Padma (2010) goes on to say that capacity utilization can also refer to the most efficient level of output, implying economic capacity because it explicitly considers economic factors such as cost considerations, which would otherwise be overlooked by an engineering or technical conceptual definition. Capacity utilization is calculated using industry indexes for the Federal Reserve survey in the United States. This capacity utilization index attempts to capture the highest level of output that a corporation can sustain within the structure of a logical work program, given enough availability of inputs to operate the equipment and machinery prepared and taking into account routine downtime. The indirect use of capacity utilization metrics, according to Longs et al. (1973), is crucial in the design of econometric models and also serves as a validation test for the series under consideration. Models of capital and price formation are two examples of its applications. However, it should be recognized that the causes for capacity are far from exhaustive. The capacity output is influenced by the opinions and judgments of business owners and entrepreneurs inside a single firm, as well as the quality of inputs, the impact of managerial competences, and so on. “It can be claimed, for example, that managers of a firm are the greatest judges of what capacity utilization is...”, as Panic (1978) put it.

As a result, the availability of variable inputs and their associated costs, managerial goals and competencies, and fixed capital stocks all have a significant impact on capacity output. As a result, it's safe to presume that management has the ability to adjust capacity utilization rates based on a variety of criteria (NBS, 2019).

According to Christiano (1981), entrepreneurs may interpret capacity utilization to indicate practical or preferred capability at the individual level of the organization. The former is thought to be the highest output a company can achieve given its fixed factors of production and other operating conditions. Preferred capacity, on the other hand, is determined by the degree of market demand prevailing at the time and what the enterprises wish to manufacture under such conditions. Capacity utilization is a notion in economics that relates to how much a company or a country really uses its installed production capacity. As a result, it refers to the link between actual production produced with installed equipment and prospective output that may be created if capacity was completely utilized on a different day (Crotty, 2002). According to Strange (1999), if market demand increases, capacity utilization increases, and if demand decreases, capacity utilization decreases, implying that there is a link between capacity utilization and market demand. Capacity utilization metrics are frequently monitored by economists and bankers for signs of inflationary pressures. When capacity utilization rates are between 82 and 85 percent, price inflation is expected to rise. As a result, capacity utilization and domestic pricing have a link. Many statistical evidences demonstrate that many industries in industrialized capitalist economies suffer from chronic surplus capacity, according to (Crotty, 2002). Excess Capacity suggests that there isn't enough demand to justify increasing output. As a result, critics of market capitalism contend that the system is inefficient since at least 20% more output might be produced and sold if purchasing power was more evenly distributed. However, regardless of economic situations, a level of utilization that is somewhat below the maximum prevails.

Capacity Utilization refers to the extent that an enterprise or a country puts its installed production capacity to use. It is the relationship between the actual output produced and the maximum potential output (Satik, 2020)

The concept of capacity utilization is frequently mentioned in discussions of theoretical and applied problems at both the microeconomic and macroeconomic levels, according to Shaikh and Moudud (2004). Excess capacity, for example, is frequently mentioned as evidence of monopolistically competitive firm activity and is widely utilized in business cycle research to assess the state of appropriateness of economic policy. It's also useful for estimating the relative importance of determinants of investments, imports, and other things in econometric models. As a result, capacity utilization is linked to output. The capacity utilization rate is calculated by multiplying the manufacturing output index by the manufacturing capacity index. The term "capacity" refers to the manufacturing sector's highest level of output that can be sustained. Short-term changes in capacity utilization rates typically reflect changes in industrial output because capacity estimations grow slowly over time. The concept of capacity utilization is linked to output, according to both economists and industrialists. However, the rate of increase of manufacturing capacity fluctuates over time as a result of technical advancement and changing levels of corporate investment. When a result, the capacity utilization rate is a measure of economic slack, and as the economy's slack shrinks, enterprises often confront greater production costs in order to increase output further. However, this is another way that capacity usage affects output. Firms may be forced to hire untrained workers or reintroduce older, less efficient plant and equipment. Because of increases in industrial productivity, analysts in the United States of America predict capacity will not be a constraint on economic expansion in the foreseeable future (Satik, 2020). Measurement of Capacity Utilization

Capacity utilization is often measured at the plant level in the economy for goods-producing businesses. The data is presented as an average percentage rate by industry and across the economy, with 100% denoting full capacity. The operational rate is another name for this rate. When the operating rate is high, this is referred to as "overcapacity," but when the operating rate is low, it is referred to as "excess capacity" or "surplus capacity." Observed rates are frequently converted into indices (Brendt and Morrison, 1981).

The validity of statistical measures of capacity utilization has been a source of discussion among economists, as much depends on the survey questions asked and the valuation methods employed to assess production. Furthermore, due to changing technology, the efficiency of production may alter over time. The ratio of actual output to prospective output is one of the most common definitions of capacity utilization rate. However, there are at least eight alternative ways to define potential output:

- The Engineering Measurement
- The Economic Utilization Rate Measurement
- The Output Gap Measurement
- Rapid appraisal techniques
- Surveys and expert opinion
- Peak-to-peak analysis
- Stochastic Production Frontiers (SPF)
- Data Envelopment Analysis (DEA)

Engineering Measurement:The highest quantity of output that can be produced in the near run with the existing stock of capital is referred to as potential output. As a result, a common definition of capacity utilization is the weighted average of the ratio of a firm's actual output to the maximum that might be generated per unit of time using current plant and equipment (Johanson, 1968). Output can be quantified in either physical units or market values, but it is usually measured in market values. However, as output rises before the ultimate physical limit of production is reached, most enterprises' average cost of production is likely to rise as well. Higher average expenses may result, for example, from the requirement to work additional shifts, do greater plant maintenance, and so on.

Economic Utilization Rate Measurement:It's a metric for determining the ratio of actual output to the amount of output at which the average cost of production starts to grow. According to Brendt (1981), enterprises are asked how much they can increase production from existing plant and equipment without increasing unit costs. Normally, this measure would produce a rate roughly 10% higher than the ‘engineering measurement,' but time series data analysis revealed the same trend over time.

Output Gap Measurement: In this case, a derivative indicator is utilized to calculate the output gap percentage (% OG) as actual output (AO) less potential output (PO) divided by potential output multiplied by 100%.

(AOPO)-PO. * 100 percent OG Equals (AO.PO)-PO.

To this aim, the economic concept of capacity means that the firm is employing the plant size that permits the output to be produced at the lowest average cost for a particular output level and state of technology. Alternatively, the firm is producing the output level for which the present plant was designed for a certain plant size (Statista 2019).

Rapid appraisal techniques

Rapid assessment (RA) is a participatory research approach that was devised to collect data when traditional data collection methods were not feasible. The strategy is frequently utilized in underdeveloped nations where records or information are lacking and the most expedient method of obtaining data is to rely on the recall of fishery participants. The technique focuses a special emphasis on gathering local knowledge and blending it with outside knowledge.

RA is primarily an informal mode of data collecting that combines elements of both formal surveys and information extraction through the application of expert knowledge. The method is both exploratory and interactive. It usually entails rapid and progressive learning, with data being reviewed and altered in the field to allow for more clarification or re-estimation (Christino, 1982).

Informal interviews with important players in the fishery are a big aspect of the technique. Fishers, fisher representatives, and anyone with input into the production process (e.g., chief fisher or villager in charge of "managing" the fishery) are key participants. As a result, the technique is time-consuming because to the large number of people who must be questioned in the field.

Questions about current and past catch levels, as well as activity levels and anticipated activity levels, might be asked for capacity measuring purposes. When quantitative estimates are impossible to get, relative estimates can be created using drawings and diagrams. For instance, ten dots could indicate the current catch and twelve dots could represent the best-ever catch. The average catch composition can be seen with the use of a pie chart, with each piece indicating the participants' perception of species composition. Questions concerning how much fishing activity may increase, why it is at its current level, and potential obstacles that would limit fishing activity can also be posed (Padma, 2010).

The data is gathered in the field and quantified to the greatest extent possible. Other quantitative data is available to supplement the information (e.g. quantity of sales on a central market can be used as a benchmark). Participants are re-interviewed, and the assembled data is submitted for validation and cross-checking. It's possible that you'll have to repeat this step multiple times. As a result of this repetition, estimations can be fine-tuned to offer values that are believable to the fishery's participants.

At the absolute least, the approach is expected to enable qualitative estimations of capacity and capacity usage. Depending on the level of knowledge in the fisheries, more precise capacity and catch estimations may be attainable.

Surveys and expert opinion

Surveys can be used to gather subjective but quantitative capacity estimates. In other industries, such surveys are frequently undertaken to measure capacity output. In the United States, the Federal Reserve and the Census Bureau, for example, use surveys to estimate capacity and capacity utilization in a variety of industries to supplement more directly quantifiable figures (Raimi and Adeleke 2009).

This, like RA, is especially effective when data is scarce or non-existent. Participants can be polled to determine their present catch and activity (for example, days fished) as well as provide subjective estimations of their potential activity and capture. A survey may involve less labor than a RA, but it also limits the opportunity for industry response and clarification of the analysis.

To evaluate capacity and potential overcapacity, several separate surveys may be required, each aimed at various segments of the business. Individual participants (for example, anglers) may be asked to estimate their catch, effort, and potential effort. From this, an estimate of each individual's prospective catch (i.e. output capacity) can be calculated (assuming a linear relationship between potential effort and potential catch). It may be possible to calculate catch estimates by species in some instances. The more disaggregated the data request, the higher the risk of inaccuracies compounding, especially if the interviewee is providing most of the information from memory. If precise information on a species level is required, some type of logbook program should be implemented, with fishermen recording their captures as they occur (Raimi and Adeleke, 2009).

The degree to which current and recent activity records are easily available will affect the dependability of survey estimates. If the information is purely relied on memory (i.e., the fishermen do not keep records), there is a significant risk

of overestimating (or underestimating) the average catch and possible effort. As a result, the capacity output estimate is likely to be erroneous or inaccurate. The information gathered by such a survey should be considered indicative rather than exact. When anglers keep solid records, potential bias is reduced, allowing for more extensive data collection and development (Padma, 2010).

The size and representativeness of the sample will also affect the accuracy of survey results. It will help to increase the accuracy of the estimates if a diverse group of anglers is surveyed. Increased sample size also improves reliability, albeit there is a trade-off between reliability and survey cost. Doubling the sample size does not increase the results' dependability, but it does level out potential inaccuracies (WDI, 2019).

Additional data from anglers can be gathered at the same time as the minimum data needed for capacity estimation. More details about the fishing activity, the boat (size, engine power if mechanized), and the gear utilized may be included. Complete fishing activity reports, including costs and earnings, are also possible. The amount of data acquired through the survey will be determined by the final use of the data as well as the expense of gathering it (Padma, 2010). An estimate of the total number of participants in the fishery is necessary to estimate capacity from survey data (unless the survey includes all participants - a census of producers). A second survey of regional industry representatives (e.g., head fishermen) or purchasers may be required. A survey like this might be used to assess participation rates (the number of boats or people) in the fisheries.

Expert surveys (e.g., biologists, industry representatives) can also be used to assess capacity output and usage. This could be a faster way than taking a sample from individual fisheries participants and estimating capacity from the ground up. If expert opinions differ, however, some subjective weighting of 23

each viewpoint is required to generate a composite estimate, or more formal procedures are required (Hsu, 1979).

Peak-to-peak analysis

The peak-to-peak technique presupposes that the level of inputs and outputs are directly proportional. The data is used to create a catch-per-unit input index (for example, catch per day or catch per boat). The assumption is that maximum catch-per-unit input equates to full capacity utilization. Given harvesting technology and capital stock, the peaks are thought to indicate years when the fishery was producing its maximum output in the short term (Klein and Summers 1966). As a result, lower catch rates are thought to imply capacity underutilization.

The method also accounts for technological advancements over time, therefore the difference in catch rates between two peak years is thought to be due to technological advancements. The predicted change in technology between the peaks, which is considered to be a linear trend, is used to estimate “capacity” catch rates in the years between the peaks. (Researchers have, however, developed extremely sophisticated analytical approaches in recent years to better discern trends in technology improvement.) The ratio of the observed catch rate to the derived "capacity" catch rate is then used to calculate capacity utilization. The product of the level of inputs and the "capacity" catch rate is used to calculate capacity output (WDI, 2019).

This strategy has the advantage of just requiring knowledge about one input and one output. As a result, in all mathematical approaches for estimating capacity and capacity utilization, it represents the most widely applicable and least demanding data (Kirkley and Squires 1999). The technique, on the other hand, has the drawback of not accounting for changes in stock between years or any other structural changes that alter input-output connections. Changes in capture rates are thought to be solely due to technological advancements.

Ballard and Roberts (1977), Ballard and Blomo (1978), and Hsu (1979) have all used peak-to-peak analysis in fisheries. Kirkley and Squires (1999), provided additional details on the technique, including the mathematical formulation of the approach.

Stochastic production frontiers (SPF)

The greatest expected output for a given set of inputs is indicated by stochastic production boundaries. They are founded on the concept that output is a function of the level of inputs and the efficiency with which the producer uses those inputs, and they are derived from production theory. The output associated with the best practice usage of the inputs is statistically evaluated, while simultaneously acknowledging the stochastic nature of the data originating from mis- or un-measured determinants of production (Nasim, Sinha & Singh, 2018).

Inefficiency and random error are commonly blamed for the discrepancy between actual and potential output (i.e. the stochastic element in production). Methods for separating the random component from the efficiency component have been developed, allowing for a more accurate estimation of prospective production. That is, significant output levels that may have occurred by coincidence rather than as a result of usual practice have little impact on the estimations. As a result, derived capacity output metrics are compatible with the preceding definition of production under normal operating conditions (World Bank, 2019). In a wide number of industries, SPF approaches have been used to assess technological efficiency (including fishing). While these techniques are based on efficiency theory, they can easily be tweaked to provide estimates of capacity usage. When vessel level data is available, this is accomplished by including only fixed inputs into the production function, such as boat numbers (in aggregated analyses) or engine power, boat size, or any measure of capital inputs (Nasim et al 2018). The frontier output for a certain size (for example) of boat is effectively determined by the boats of that size that produced the greatest output, taking into account swings in output levels that can be attributed to "luck." Lower output levels would suggest inefficient input use as well as capacity underutilization (Nasim et al 2018).

One advantage of the SPF technique over peak-to-peak analysis is that it allows for the inclusion of several inputs from the manufacturing process. While the technique can be used with just one input and output, it also allows for the recognition of other accessible data at the level of fishing inputs or other production factors. As a result, all accessible input data can be combined in a single study to give a single capacity utilization measure. This can include biomass stock information (where available), allowing the effects of stock changes to be incorporated directly into the research. As a result, low output levels in some years due to low resource stock levels would not be wrongly attributed to capacity underutilization (World Bank, 2019).

The method can also be used to assess changes in efficiency through time, as opposed to those caused by technological progress. While peak-to-peak analysis assumed that any variation in the catch rate was due to technological changes, the SPF technique may identify such changes as well as those related to utilization separately. In order to identify the effects of changes in technology and stock abundance on catch rates, independent identification of the impacts from resource stock fluctuations necessitates including information on stock levels into the study. Alternatively, the SPF can discern between embodied and disembodied technical change when data is available (Padma 2010).

The method can be used with either aggregated (fleet level) data or data from individual fishing vessels. The latter is the most preferable, albeit at this aggregation level, special attention must be made to isolate noise, efficiency, and utilization changes individually for estimation. Individual vessel capacity and capacity utilization estimates can then be aggregated to the fleet level, albeit aggregation concerns must be recognized.

However, there are several limits to the technique. The typical technique, like peak-to-peak, can only be used to evaluate capacity utilization for a single output. Some type of aggregation, or more complex estimate methodologies and approximations, may be required for multispecies fisheries. When fisheries management is done on a species-by-species basis, the resulting measurements of capacity usage and capacity output may be difficult to comprehend (e.g. using quotas) (Hsu, 2003).

In order to estimate the frontier, one must additionally give a functional form for the production function. Many of the underlying technology's functional specifications impose undesired or perhaps impossible constraints on the underlying manufacturing technology (e.g. the Cobb-Douglas, which is a multiplicative function, imposes unitary elasticity of substitution between inputs). Flexible functional forms, on the other hand, are widely available and reduce the number of limits imposed on the underlying technology. Because the flexible forms allow parametric examination of various features of the underlying technology, the fact that SPF requires technology specification should not be seen as a significant constraint of the technique (Padma 2018).

Efficiency and capacity utilization estimation is a difficult statistical problem. Fortunately, special software has been developed that simplifies the econometric estimation of the measurements (Sena, 1999). However, a number of assumptions must be made in terms of model specification and distributional assumptions about the measure of capacity utilization, and because this is a statistical process, the findings may differ significantly from one model to the next. Choosing the most suited model from a variety of options necessitates extensive testing. The analysis should ideally be carried out by someone with econometrics experience who is aware of the potential statistical issues that may arise.

Only a few attempts have been made to predict stochastic production frontiers for fisheries (Kirkley, Squires and Strand, 1995 and 1998; Coglan, Pascoe and Harris, 1999; Sharma and Leung, 1999; Squires and Kirkley, 1999). These have generally focused on efficiency estimation rather than capacity assessment. These techniques have been applied to capacity estimation by Kirkley and Squires (1999) and Kirkley, Paul, and Squires (2001).

Data Envelopment Analysis (DEA)

The mathematical programming technique Data Envelopment Analysis (DEA) is used to estimate technological efficiency and capacity utilization. It works in a similar way to SFP in that it estimates a production frontier and counts inefficiency and capacity utilization as deviations from the frontier. Unlike SPF, however, it does not require the data to be transformed into any certain functional form of the production frontier, and it can analyze both single and many outputs. Because species-specific metrics may be generated, capacity measures for a given species can be aggregated over multiple fleet segments and fisheries (Harris, 1999). As a result, capacity estimates and goal capacity measures can be directly compared. Capacity utilization measures at the fleet level can give managers more information about where capacity management is most needed. The technique has the disadvantage of not accounting for random fluctuations in the data. As a result, the frontier would be defined by an above-normal catch (due to “luck”), and all capacity measurements would be made in relation to this amount of output, which does not correspond to typical operating conditions (Padma 2018). As a result, capacity utilization measures may be lower than they would be if favorable random factors were removed. An “unlucky” vessel, on the other hand, would be considered to be working well below capacity. This is less of a problem because vessel output is predicted to be higher under normal conditions. These issues can be mitigated to some extent by averaging data over several years, which reduces the impact of random changes. However, information on variations in capacity utilization during the time period being studied is lost in the process (Kirkley et. al 2001).

2.1.3 Electricity Generation Rate in Nigeria

According to the Nigerian Economist, electricity generating in Nigeria began in Lagos in 1886 with the usage of generators to supply 60 kilowatts (1987). Tin miners built a 2 MW plant on the Kwali River in 1923, and six years later, the Nigerian Electricity Supply Company, a private company based near Jos, was founded to manage a hydro-electric plant at Kura to supply electricity to the mining industry. After that, United Africa Company formed a private firm in Sapele to support the activities of African Timber and Plywood Company Simpson (1969). Between 1886 and 1945, electricity generation was relatively limited, with most of the power going to Lagos and other commercial areas like Jos and Enugu's mining industries, according to Simpson (1969). The colonial government established an electricity department inside the Public Works Department, which erected generating sets in a number of cities to serve government reservation areas and commercial centers Adedeji, Adesina, and Akanji (2016). The Nigerian Legislative Council began efforts to integrate the energy business in 1950, when it passed a bill establishing the Nigerian Electricity Corporation, which was charged with creating and supplying electricity. Economist from Nigeria (1987). PWD's energy sector activities as well as the generating sets of Native Authorities were taken over by ECN. The company was in charge of 46 megawatts of electricity in 1951. Between 1952 and 1960, the company-built coal-fired turbines in Lagos' Oji and Ijora neighborhoods and began planning a transmission network to connect the power plants to other commercial districts. ECN constructed a 132 KV transmission line connecting Lagos to Ibadan through Shagamu in 1961, and the Western System was formed in 1965 when this line was extended to Oshogbo, Benin, and Ughelli. The Niger Dams Authority (NDA), a statutory body established in 1962 to develop and operate dams between the Niger and Kaduna rivers, went on to commission a 320MW hydro power plant at Kainji in 1969, with the electricity generated sold to PHCN. The National Electric Power Authority was formed in 1972 when the NDA and ECN amalgamated to become the National Electric Power Authority (NEPA). NEPA was Nigeria's largest electrical company until power sector reforms led to the formation of the Nigerian Power Holding Company and, later, the privatization of electricity generation and distribution.

Nigeria, with a population of 160 million people, produces and consumes 4,000 megawatts of power, which is only 2.5 percent of what South Africa, which has a smaller population, produces and consumes. At the signing of a memorandum of understanding with a German Technical Partner (GIZ), Dr Mu'azu Babangida Aliyu, the former Governor of Niger State, stated that the country required 35,000 megawatts of electricity to achieve steady power supply. According to Aliyu, the country's electricity supply in 2014 was somewhere around 5,000 megawatts, which was woefully inadequate.


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Determinants of Capacity Utilization in Nigeria's Manufacturing Sector
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determinants, capacity, utilization, nigeria, manufacturing, sector
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Justin Alugbuo (Author), 2022, Determinants of Capacity Utilization in Nigeria's Manufacturing Sector, Munich, GRIN Verlag,


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