**1. Introduction**

Industrial expansion is essential for socioeconomic development as it generates different opportunities—capital accumulation, structural changes, technological innovations, and productivity—that improve economic performance [1–3]. Industrialization or the shift from agriculture to the manufacturing sector is key to development, making development without industrialization an unthinkable process [1,4]. Industrial development is also the pathway for the structural transformation of an economy and society. High rates of economic growth and capital accumulation are essential but not adequate for structural transformation, unless complemented by industrialization [2]. Industrialization promotes economic diversification, inclusive growth, and the efficient utilization of resources, such as physical, human, and mineral resources, which help eradicate poverty [5].

The productivity advantage of manufacturing over other sectors is a major factor for pursuing sustained industrialization, along with the higher externalities that can arise from manufacturing growth [6]. Unlike agriculture and the service sectors, manufacturing accelerates convergence and, with its huge productivity advantages, will enable developing economies to catch up with their developed counterparts [4]. Different factors are attributed to industrial growth and productivity, including human or physical capital, labor, energy, innovations, and capacity utilization [3,7,8]. Among others, energy is critical for productivity and growth as it enables achieving both industrial development and structural transformation [9]. In fact, the use of energy is a precondition for the development of human society and more energy use is required for sustaining industrial

development [9]. Energy use is directly related to growth and economic development and is an essential input for all production and consumption activities [8,10].

The causal relationship between energy consumption and growth has been investigated in different countries but the results remain controversial with diverse outcomes in different countries based on the econometric approaches used and the time spans of the studies [10–14]. Some studies validate the positive effects of energy on growth and productivity [15–17], while others empirically confirm a negative impact of energy on growth and productivity [11,12]. Others find no causal relationship between the two empirically [10,18]. Here we use different econometric estimators for matters of sensitivity analysis of the result to evaluate how energy affects labor productivity in Ethiopian manufacturing.

In Ethiopia, the share of agriculture and services in gross domestic product (GDP) has been more than 60 percent and 20 percent, respectively, for decades, while manufacturing's contribution to GDP has been less than five percent, which, too, is attributable to other industries [19,20]. Currently, the service sector contributes 47 percent, agriculture 43 percent, and industry makes up the rest, leaving a very low share of GDP being contributed by manufacturing [21]. The existing literature confirms that Ethiopian people have been depending on agriculture for their livelihood for decades in terms of production and employment, with significantly small contributions from the manufacturing sector to the economy [22,23]. The dominance of, first, the agriculture sector and, later, the service sector shows premature deindustrialization in Ethiopia, while the low share of manufacturing implies output deindustrialization [21,24].

The low industry performance can be attributed to several factors such as inefficient use of labor, energy, human or physical capital, innovations and capacity utilization [3,8,24]. As established theoretically, energy is a significant factor in determining sustainable industrial production. However, the empirical relationship between energy and growth is mixed [10–14]. Besides, there are very few empirical studies on energy and productivity at the industry level. This motivated us to undertake this study on the empirical relationship between energy and labor productivity in the case of the Ethiopian manufacturing industry. Accordingly, this study addresses the following research question:

How does energy effect labor productivity in the Ethiopian manufacturing industry?

The analysis emphasizes the role of energy use in manufacturing labor productivity in Ethiopia. The study uses panel data for estimating the empirical model using a dynamic generalized method of moments (GMM) estimator. The estimation results confirm that energy use positively effects labor productivity in the manufacturing sector in Ethiopia. This implies that the efficient use of energy is a pillar of labor productivity in the Ethiopian manufacturing industry. Thus, this study adds to the existing literature by empirically confirming the relationship between energy use and labor productivity across different model specifications.

The rest of this research is organized as follows. Section 2 reviews the literature on energy and productivity. The empirical model and estimation approach are presented in Section 3, along with the definitions of the variables used in the model. Data are discussed in Section 4. A descriptive and regression-based analysis of the energy and labor productivity of the manufacturing sector in Ethiopia is discussed in Section 5. The final section gives the conclusion and the implications of the findings.

#### **2. Literature Review on Energy and Productivity Growth**

This section presents a general overview of the link between energy and productivity, followed by an empirical review of the relationship between energy and growth. It then discusses existing studies on the determinants of labor productivity. This helps establish the rationale for undertaking this study that links energy with labor productivity at the industry level in Ethiopia.

There are two empirically fundamental questions related to disparities in the level of economic development across nations. Economists inquire why some economies are richer than others, and what accounts for the huge increases in real incomes over time [25,26]. The extensive dispersion of output growth rates across countries is documented economically [27]. A comparison between countries shows that countries that at one time had similar levels of per capita income consequently followed very different patterns, with some seemingly caught in long-term stagnation while others were able to sustain high growth rates [28].

Among others, productivity is a determining factor of growth at the national and industrial levels, with increasing globalization and the expansion of competitive industrial product markets [16,29]. High industrial labor productivity results in lower per unit costs and increases firms' ability to compete in global markets [16]. There are several determinants of labor productivity, including human or physical capital, energy, and technology [29–31]. Energy is an essential input that constrains or induces productivity growth in different firms. It is an essential factor of production that is required in all economic processes [29,31]. This basic production input in economic activities provides a conducive platform for industrial growth and productivity. The efficient use of energy leads to the higher productivity of resources and a more dynamically competitive economy that can respond to the required economic transition from agriculture to industry dominated structure [32].

Energy has countless ways of empowering human beings through increasing productivity, powering industrial and agricultural processes, alleviating poverty, and facilitating sound social and economic development [33]. Limited access to energy cripples economic growth and development, which makes universal access to energy a major emphasis of the sustainable development goals [9]. The increased availability and use of energy increases productivity and enhances economic development [34]. Energy is primarily associated with the provision of power for agricultural or industrial production [35,36]. In fact, sustainable development and modern industry require reliable, affordable, and energy services available for all on a sustainable basis [9,33]. However, access to energy is limited and is accompanied by low quality and poor reliability, affordability, and availability [9]. Energy can be measured in terms of cost or value and can be disaggregated into electricity or other forms of energy based on types. It is possible to measure energy consumption in equivalent kilowatt hours (KWh) [37].

Energy use is a major stimulating factor in industrial productivity [16,32]. Public services and industrial production require access to energy use [12]. Recently, the demand for energy has been increasing, with the world having a population of over 7.2 billion, which is increasing [38]. Access to energy in Africa is low—for every ten people in sub-Saharan Africa (SSA), only four have access to electricity compared to the global access of nine out of ten people having access to energy; 57 percent of the global deficiency in access to electricity energy comes from SSA [9].

There is an increasing interest in identifying energy's role in productivity, as empirical findings on their causal relationship are mixed [13,39]. For instance, Schurr et al. [40] presents the association between energy consumption and growth in the national product (GNP) in the United States over the period 1880-1955. These authors identified two trends in the pattern of the energy share in relation to GNP. The share of energy to GNP was rising, until it declined persistently after the war. This change in the trend is attributed to a compositional change in the national output to light industries, which use less energy compared to heavy manufacturing industries and services and is also due to major improvements in the efficiency of energy conservation in light industries. In a follow-up to the study by Schurr et al. [40], Schurr [41] explored the link between energy use, productive efficiency, and energy efficiency from the 1920s to 1981. His study indicated that energy intensity, defined as energy's share in GNP, declined when multifactor productivity increased during the study period. Unlike the share of energy in output, which is attributed to technological advances that increased overall productive efficiency, energy intensities in terms of factor inputs increased over the study period. This ultimately led to an increase in the final output, which was more than the consumption of energy.

The role of electrification and non-electricity energy in productivity growth for the USA's economy is examined by Jorgenson [42]. His study confirms that electricity energy is related to productivity growth. However, there is also a strong association between non-electricity energy and productivity growth in the US economy. In another related study, Boudreaux [43] examined the impact of electricity energy on manufacturing productivity in the US from 1950 to 1984. This study showed that growth in electricity energy accounted for 79 percent of the value added to the manufacturing sector. Empirically, the study showed that the decline in energy growth accounted for the slowdown in productivity and output growth.

The role of energy in productivity growth in the European Union countries is assessed by Murillo-Zamorano [44] who empirically confirmed that energy is a fundamental input in productivity change. In another related study, the relationship between energy and labor productivity was examined by studying the effect of renewable and non-renewable energy in European countries over the period from 1995 to 2015 using the production frontier approach [45]. This study showed that renewable and non-renewable energy had an effect on the growth of the countries in the European Union. Based on his study, the author concluded that non-renewable energy had a positive impact, leading to divergence, while renewable energy had a negative impact, leading to convergence.

Energy and income causality for ten emerging markets, excluding China because of limited data availability and the G-7 countries, is examined in Soytas and Sari [17]. Their results show the bidirectional causality in Argentina, causality running from energy to GDP in France, Germany, Japan, and Turkey and causality running from GDP to energy consumption in Italy and Korea. The nexus between energy and growth for 20 net importer and exporter countries from 1971 to 2002 using the panel vector correction model is investigated by Mahadevan and Asafu-Adjaye [46]. Their findings show that for energy exporter developed countries this causal relationship is bidirectional, while for developing countries energy stimulates growth in the short term.

The effect of energy consumption and human capital on economic growth for 130 oil-exporting and developed countries from 1981 to 2009 is investigated by Alaali et al. [15]. Using GMM, they estimate an augmented neoclassical growth model, including education and health as human capital along with energy consumption. Their results show that energy had a positive and significant effect on growth. The empirical relationship between energy consumption and gross domestic product for six Gulf Cooperation Council (GCC) countries using cointegration and causality methods is investigated by Al-Irani [13]. His results show a unidirectional causal relationship running from GDP to energy consumption, but not the other way around. Moghaddasi [11] investigated the role of energy consumption in total-factor productivity in Iranian agriculture using the Solow residual model and their results show a negative impact, which they attribute to cheap and inefficient use of energy in this sector.

Kebede et al. [12] investigated energy demand in east, west, central, and south sub-Saharan countries using time series cross-sectional data for 20 countries for a 25-year time span. Their results show that energy demand was positively related to GDP, the population growth rate, and agricultural expansion, while it was negatively correlated with industrial development and the price of petroleum. The causal relationship between energy consumption and economic growth for 11 sub-Saharan African countries is investigated by Skinlo [18] using the ARDL bound test and Granger causality. His results show that there was cointegration between energy use and economic growth in seven countries included in the study: Ghana, Cameron, Senegal, Cote d'Ivoire, Zimbabwe, Gambia, and Sudan. In Sudan and Zimbabwe, the Granger causality ran from economic growth to energy use while in Cameroon and Cote d'Ivoire he found no Granger causality between energy consumption and economic growth.

Wolde-Rufael [39] investigated the causal relationship between energy consumption and economic growth for 17 African countries using the variance decomposition factor and impulse response analysis. The variance decomposition analysis confirmed that labor and capital were important, while energy was not as important as these factors. A meta-analysis using a multinomial logit model for 174 samples was conducted by Chen et al. [10] to explore the relationship between energy and GDP, with controversial results that show that the time span, econometric model, and selection characteristics affected the debatable outcomes of the casual relationship significantly.

The second part of this section explores labor productivity and its determinants, as studied by different researchers. Su and Heshmati [30] studied the development and source of labor productivity in 31 provinces of China during 2000-09. They used a fixed effects model adjusted for heteroscedasticity to estimate the coefficients' fixed assets, average labor wage, total volume of business, post and telecommunications, and profits, which had a positive effect on labor productivity. Accounting for heterogeneity, Velucchi and Viviani [47] examined the determinants of labor productivity in Italian firms using panel data and a quantile regression. Their results show that human capital and assets had a strong positive impact on fostering the productivity of low productive firms compared to high productive ones. Islam and Syed-Shazali [48] studied the impact of the degree of skills, research and development (R&D), and a favorable work environment on the productivity of labor-intensive manufacturing industries in Bangladesh. Their results confirmed a positive correlation between productivity and the degree of skills and the work environment, though it was a weak correlation; R&D had a strong positive correlation with productivity in Bangladesh.

Recently, Heshmati and Rashidghalam [49] studied the determinants of labor productivity in manufacturing and service sectors in Kenya using the World Bank Enterprise Survey database for 2013. Their findings confirm a positive effect of capital intensity and wages on labor productivity while female participation reduced productivity in these sectors. In a comparative study, Nagler and Naudé [50] examined the factors determining the labor productivity of non-farm enterprises in rural sub-Saharan Africa in Ethiopia, Nigeria, Uganda, and Malawi using the World Bank's Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) database. They found that rural enterprises were less productive than urban enterprises. By estimating Heckman selection and using panel data models, their study confirmed that education and credit availability induced enterprises' labor productivity.

Samuel and Aram [51] studied the main factors that helped or hindered the realization of industrial productivity in Africa. The study concluded that financial development, economic development, the labor market's flexibility, and the real effective exchange rate were clear determinants of industrialization in the entire region. In a time-series analysis Otalu and Anderu [7], the determinants of industrial sector growth in Nigeria were examined using the cointegration and error correction model (ECM). Their results show that both labor and capital had significant effects on economic growth. The exchange rate showed a positive and significant impact, signifying that currency appreciation might be detrimental to the growth of the industrial sector. In addition, the authors also found that these factors had a more permanent and not a transitory effect on industrial output.

In the energy literature, the contribution of energy use to productivity in practice is controversial, with some studies claiming that energy use is a fundamental pillar of productivity growth, while others argue that energy has little effect on productivity growth [10,44]. In studies on labor productivity, energy seems to be missing as a major determinant factor in explaining labor productivity [47–51]. Furthermore, there is little focus on investigating the explicit role of energy in labor productivity from the manufacturing industry's perspective [47–49]. Most growth theories fail to include energy use as a pillar of productivity or as one argument for the growth differences between nations [45].

Thus, this study adds to the existing literature by addressing the controversial nature of previous studies' results by empirically investigating the association between energy and productivity in the Ethiopian manufacturing industry. It also considers energy as a major variable of interest for explaining labor productivity in addition to capital and technical changes. This link is investigated from the manufacturing industry's perspective. Moreover, this study uses different model specifications to confirm the consistency of this relationship by using both static and dynamic panel data estimators for the manufacturing industry groups.

## **3. Model Specification and Estimation**

## *3.1. Model Specification*

Productivity is a fundamental indicator for assessing economic performance [52]. In general terms, productivity can be defined as the ratio of total output produced to the inputs used. There are different measures of productivity, which can be classified as multifactor productivity measures and single factor measures of productivity [53]. The former relate output to a bundle of inputs, while the latter measure the ratio of output to a single input [52]. For instance, labor productivity is defined as the ratio of the quantity index of gross output to the quantity index of labor input [53]. Among other factors, energy is a key driver of economic growth and industrialization as it enhances the productivity of labor, capital, and other factors of production. In fact, energy use has received considerable attention as a pillar of productivity in the literature on energy economics, but with mixed empirical results for different countries on the causal relationship between the two [13,15,46].

This study empirically investigates the relationship between energy use and labor productivity in Ethiopian manufacturing industries. Like labor and capital production factors, energy is seen as an essential factor for economic development [15]. The production function is a useful tool for analyzing the technological relationship between labor, capital, other inputs, and the output produced [54]. The production function which relates output to the vector of inputs is mostly used for analyzing productivity [55,56]. Accordingly, in this study, the production function developed by Cobb and Douglas [57] is used for estimating the productivity of labor in the manufacturing sector in Ethiopia. The Cobb–Douglas production function, with two inputs in its basic form [58,59], is represented as:

$$
\Upsilon = \mathrm{AL}^{\alpha} \mathrm{K}^{\beta} \tag{1}
$$

where Y denotes the quantity of production or output or its value, L represents labor or its value, and K stands for the value of capital. α and β are parameters of inputs labor and capital respectively and A is technology. This standard production function can be generalized to include more inputs such as energy and other material inputs:

$$\mathbf{Y} = \mathbf{A} \mathbf{L}^{\alpha} \mathbf{K}^{\beta} \mathbf{E}^{\gamma} \tag{2}$$

where the other variables are defined in the same manner as in Equation (1). E stands for energy inputs in the production process and γ denotes a parameter to be estimated as a coefficient for energy input. We can linearize the production function by log transformation as:

$$\text{LogY} = \log\text{A} + \alpha \text{logL} + \beta \log\text{K} + \gamma \log\text{E} + \text{U} \tag{3}$$

$$\begin{array}{c} \text{if } \alpha + \beta + \gamma > 1, \text{IRS} \\ \text{if } \alpha + \beta + \gamma < 1, \text{DRS} \\ \text{if } \alpha + \beta + \gamma = 1, \text{CRS} \end{array} \tag{4}$$

where α, β, and γ stand for elasticities of production with respect to labor, capital, and energy respectively. Equation (3) is the first model to be estimated to decide production's returns to scale in the manufacturing industry in Ethiopia. The sum of the parameters will give us a measure of the returns to scale from a proportional increase in inputs. If the sum of the parameters is greater than one we have increasing returns to scale (IRS); if the sum is less than one, we get decreasing returns to scale (DRS); if the sum is one then the returns to scale are constant (CRT).

As labor productivity shows how effectively labor inputs are converted into outputs [60], we take production or output per employee to measure labor productivity. There are two ways of doing this. First, if one is interested in the scale effects of energy and capital use on labor productivity, then the right-hand side of the equation to include all inputs in the original form per labor, while the left side is

measured as productivity—that is, output is divided by labor. In this case, labor, on the right-hand side, represents the scale of production as:

$$\frac{\Upsilon}{\mathcal{L}} = \frac{\mathcal{A}\mathcal{L}^{\alpha}\mathcal{K}^{\beta}\mathcal{E}^{\mathcal{Y}}}{\mathcal{L}}\tag{5a}$$

$$\mathbf{Y}/\mathbf{L} = \mathbf{A}\mathbf{L}^{\alpha-1}\mathbf{K}^{\beta}\mathbf{E}^{\gamma} \tag{5b}$$

$$\mathrm{LogY/L}\_{\mathrm{it}} = \log \mathrm{A}\_{\mathrm{it}} + (\infty - 1) \log \mathrm{L}\_{\mathrm{it}} + \beta \log \mathrm{K}\_{\mathrm{it}} + \gamma \log \mathrm{E}\_{\mathrm{it}} + \mathrm{U}\_{\mathrm{it}} \tag{5c}$$

$$
\mathfrak{p} = \mathfrak{a} - 1; \text{then}, \mathfrak{a} = \mathfrak{p} + 1 \tag{5d}
$$

$$\text{LogY}/\text{L}\_{\text{it}} = \lambda + \rho \log \text{L}\_{\text{it}} + \beta \log \text{K}\_{\text{it}} + \gamma \log \text{E}\_{\text{it}} + \text{t}\_{\text{it}} + \text{U}\_{\text{it}} \tag{5e}$$

where the dependent variable is labor productivity, which measures the scale effect of the factors on labor productivity. Value of energy is used for the manufacturing industry as a major variable of interest. Labor is a control variable that represents the scale of production and is defined as the number of employees in the industry group. The second key control variable is capital, which is defined as the value of the industry groups' fixed assets. All variables are in logarithm form, so that the coefficients are defined elasticities. T represents the trend, which is included for capturing the technical change effect. U represents the error term of the panel model and subscripts i and t represent the industry sector and time period respectively. U contains unobservable sector- and time-specific effects. βs are unknown coefficients of the explanatory variables, where λ is the constant term.

Equations (6a) and (6b) represent the third model, which measures the intensity effect of factors on labor productivity. The other way of specifying the model is by dividing the right-hand side variables (L, K, E) with labor to express energy and capital in the form of capital intensity and energy intensity, respectively, while the L ratio will end up in the intercept. Thus, the third model to be estimated is written as:

$$\frac{\chi}{\text{L}} = \left(\frac{\text{A}}{\text{L}}\right) \left(\frac{\text{L}}{\text{L}}\right)^{\alpha} \left(\frac{\text{K}}{\text{L}}\right)^{\beta} \left(\frac{\text{E}}{\text{L}}\right)^{\gamma} \tag{6a}$$

$$\frac{\Upsilon}{\Pi} = \left(\frac{\rm A}{\rm L}\right) \left(\frac{\rm K}{\rm L}\right)^{\beta} \left(\frac{\rm E}{\rm L}\right)^{\gamma}; \left(\frac{\rm L}{\rm L}\right)^{\alpha} = 1^{\alpha} = 1\tag{6b}$$

For all the three models to be estimated, an error term is included and the models are linearized and transformed into logarithm forms before estimation. The third model to be estimated (7a) measures energy and capital intensity and their effect on labor productivity in manufacturing industrial groups in Ethiopia:

$$\log(\text{Y}/\text{L})\_{\text{it}} = \mu + \beta \log \mathbf{k}\_{\text{it}} + \gamma \log \mathbf{e}\_{\text{it}} + \mathbf{t}\_{\text{it}} + \mathbf{u}\_{\text{it}} \tag{7a}$$

$$\text{LogMLP}\_{\text{it}} = \alpha + \beta \text{logCapital Intensity}\_{\text{it}} + \gamma \text{logEnergy Intensity}\_{\text{it}} + \lambda \text{trend}\_{\text{it}} + \text{U}\_{\text{it}} \tag{7b}$$

where manufacturing labor productivity is the dependent variable defined as the manufacturing output of an industry group per employee. μ is the intercept, β is a slope coefficient for capital intensity, γ is a slope coefficient for energy intensity, while t stands for time trend to represent a shift in the production function over time and thus λ is the rate of technological change. U is the error term in the model with i and t representing industry group and time respectively. It follows an error component structure consisting of industry effects and random error components.
