Technical efficiency, technological progress and productivity growth of large and medium manufacturing industries in Ethiopia: A data envelopment analysis

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 Technical efficiency, technological progress and productivity growth of large and medium manufacturing industries in Ethiopia: A data envelopment analysis Obsa Teferi Erena, Mesfin Mala Kalko & Sara Adugna Debele | To cite this article: Obsa Teferi Erena, Mesfin Mala Kalko & Sara Adugna Debele | (2021) Technical efficiency, technological progress and productivity growth of large and medium manufacturing industries in Ethiopia: A data envelopment analysis, Cogent Economics & Finance, 9:1, 1997160, DOI: 10.1080/23322039.2021.1997160 To link to this article: https://doi.org/10.1080/23322039.2021.1997160 © 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 05 Nov 2021. Submit your article to this journal Article views: 176 View related articles View Crossmark data GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Technical efficiency, technological progress and productivity growth of large and medium manufacturing industries in Ethiopia: A data envelopment analysis Obsa Teferi Erena 1 *, Mesfin Mala Kalko 2 and Sara Adugna Debele 1 Abstract: The purpose of this study is to assess empirically how the technical efficiency scores for 43 sub-sectors and their determinants over the period 2010 to 2017 show significant variation across the sub-sectors. The study applied a two-step approach for measuring technical efficiency and its determinants. A data envelopment analysis output-orientation (i.e. both CCR & BCC models) is used to estimate technical efficiency scores for 43 sub-sectors over the period 2010 to 2017. Malmquist productivity index (MPI) output orientation is also applied to compute technical efficiency change, technological progress, and productivity change. The estimated technical efficiency score shows significant variation across the subsectors. Thus, we used a Tobit regression model to scrutinize what defines the variation in technical efficiency scores using three years of panel data which covers 2015 to 2017. Moreover, the 43 sub-sectors were further grouped into 14 major subABOUT THE AUTHOR Obsa Teferi Erena is an Assistant Professor at the College of Business and Economics at Hawassa University, Ethiopia. His research interest include corporate governance, earning management, knowledge management, technology management, and financial accounting. He has published in Corporate Governance: The International Journal of Business in Society. Email: [email protected] Mesfin Mala Kalko is currently a Ph.D. Candidate at the Faculty of Management and Economics at Tomas Bata University in Zlin, Czech Republic. His research interest include corporate governance, corporate social responsibility, corporate finance, innovation, knowledge management, and financial accounting. He has published in Corporate Governance: The International Journal of Business in Society. Email: [email protected] Sara Adugna Debele is a Lecturer at the College of Business and Economics at Hawassa University, Ethiopia. Her research interest include financial accounting, corporate governance, and corporate social responsibility. She has published in Corporate Governance: The International Journal of Business in Society. Email: [email protected] PUBLIC INTEREST STATEMENT This paper uses data envelopment analysis to measure technical efficiency, technological change, and productivity growth in 43 manufacturing industries over the period 2010 to 2017. Malmquist productivity index (MPI) output orientation is also applied to compute technical efficiency change, technological change, and productivity change. The results show that the sector had experienced a 37 percent technical efficiency in overall average when the CCR model was used. The findings of the study would have implications for policymakers, government, and firm owners in that it provides an insight into the source of productivity growth in the sector. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Page 1 of 38 Received: 07 February 2021 Accepted: 20 October 2021 *Corresponding author: Obsa Teferi Erena, College of Business and Economics, Hawassa University, P.O. Box: 05, Hawassa, Ethiopia E-mail: [email protected] Reviewing editor: Christian Nsiah, School of Business, Baldwin Wallace University, Ohio, United States Additional information is available at the end of the article © 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. sectors and classified as public and private to examine whether there is a technical efficiency score discrepancy between the same sub-sectors operating under different ownership. For measuring overall technical efficiency, we used two output variables (i.e., value-added and operating surplus) and two input variables (i.e., total fixed assets and a total number of employees). When reducing the sub-sectors to fourteen major groups, the operating surplus was not included, thus we used valueadded and total sales as output variables and total fixed assets, the total number of employees, and cost of raw materials used in the production process as input variables. To shed light on the source of inefficiency, technical efficiency is decomposed into pure technical efficiency and scale efficiency. This study found that the sector had experienced a 37 percent technical efficiency in overall average when the CCR model was used. The study also claims that public owned subsectors are less likely to be efficient than private subsectors. The regression results show the capital expenditure ratio has a significant positive influence on technical efficiency. The Malmquist index result also shows, on average, the sector had registered a 10.5% technological progress and a 13% productivity growth over the period 2010–2017. The findings of the study would have implications for policymakers, government, and firm owners in that it offers an insight into the source of productivity growth in the sector. Subjects: Economics; Finance; Business, Management and Accounting Keywords: Technical efficiency; Technological progress; Productivity; DEA; Tobit Model; Ethiopian manufacturing sector 1. Introduction The manufacturing sector plays a crucial role in production, growth, and job creation (Naudé & Szirmai, 2012). It is the most important engine of long-term growth and development, both in developed and developing countries (McKinsey, 2012). In the former, for instance, in 2015 the manufacturing sector shares 12% and 19% of the gross domestic product (GDP) of the United States and Japan, respectively (UNCTAD, 2015) and it remains a vital source of innovation & competitiveness, making enormous contributions to research & development, exports, and productivity growth (McKinsey, 2012). While, in the latter, the manufacturing sector shares 27% and 16% of the GDP of China and India in 2015, respectively (UNCTAD, 2015) and it continues to provide a pathway from subsistence farming to rising incomes and living standards. For the least developed countries, such as Ethiopia, agriculture makes up the highest proportion of the economy. According to the Ethiopian CSA (2018), the net contribution of the manufacturing sector to the GDP growth rate increased from 0.4% in 2011 to 1.1% in 2017. The agriculture and services sectors accounted for 36.3% and 39.3% in 2017, respectively. This low contribution of the manufacturing sector to the GDP is a common feature of Sub-Saharan African countries. oFr instance, it takes 8.4% of Kenya`s GDP (Kenya Association of Manufacturers, 2018) and 2.4% of Djibouti`s GDP (United Nations, 2016). In this regard, Ethiopia has shifted its economic strategy from agricultural to industrial lead in 2011. The strategic plan was divided into two five-year plans, with Growth and Transformation Plan I, which covers from 2011 to 2015, and Growth and Transformation Plan II, which covers from 2016 to 2020. Nevertheless, the agriculture sector has continued to dominate the GDP of the country, which was followed by the service sector. Indeed, the manufacturing sector`s contribution to GDP demonstrates Ethiopia`s infant stage of manufacturing activities or industrialization. Empirical evidence (Ayelign & Singh, 2019; Oqubay, 2015; Hailu & Tanaka, 2015; UNDP, 2017) has also confirmed that the sector had faced several problems, such as limited access to and interrupted electricity power, a low level of export performance and competition, a shortage Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 and irregular supply of domestic raw materials, limited access to and poor quality of internet services, and weak logistic support. Technical efficiency is the ability of a firm to produce as much output as possible with a specified level of inputs, given the existing technology. It can also be a situation wherein it is impossible, with current technical knowledge, to increase output from given inputs or produce a given output using less than one input without using more of another input (Farrell, 1957). Efficiency is a major problem in Ethiopia. Because Ethiopia`smanufacturing industries were not operating at full capacity (Hailu & Tanaka, 2015), there was a need to improve the sector`s efficiency (Bekele & Belay, 2007). Ethiopia has very limited capital but an abundant workforce, and hence its industries are predominantly labor-intensive instead of capital-intensive. Productivity growth might come from the enhancement of productivity based on catching up capability and innovation by effective use of human capital in the labor market and adoption of new technology. Conversely, switching from labor-intensive to capital-intensive would increase productivity if an optimal benefit is achieved from technology change. The Ethiopian manufacturing sector tends to be labor-intensive. This is common in the least developed countries because of the presence of a massive pool of unemployed labor force (Wu, 1993). Being labor-intensive or capital-intensive might not result in efficiency or inefficiency, but being able to produce additional units of production (output) while keeping input constant or reducing input while keeping output constant could lead to the efficient frontier. There have been contradicting results in the literature that suggest capital-intensive firms are more efficient than labor-intensive one. For example, Arrow et al. (1961) suggested that differences in the efficiency of firms arise due to variations in the efficiency of the labor force. Wu (1993), using econometric and group-wise analyses, finds that labor-intensive firms are relatively more efficient than capital-intensive firms. In contrast, Alvarez and Crespi (2003) and Sun et al. (1999) suggest firms that are capital-oriented tend to be more efficient. Similarly, Li and Zhao (2017) indicated that capital-intensive firms were found to perform better and have higher stock value than labor-intensive firms. There have been few studies on measuring the productivity of manufacturing firms in Ethiopia. For example, Goshu et al. (2017) proposed a framework for measuring productivity in manufacturing companies. Tsegay et al. (2018) applied conventional OLS and panel data to test determinants of performance in manufacturing with regard to the textile and garment industry. Rao and Tesfahunegn (2015) used the Cobb-Douglas production function to examine the performance of manufacturing industries. Abegaz (2013), Hailu and Tanaka (2015), and Ayelign and Singh (2019) estimate the technical efficiency and total productivity changes of medium and large manufacturing firms using a comprehensive panel data set annually collected by the central statistical agency. Bekele and Belay (2007) analyzed technical efficiency and its determinants in the grain mill products manufacturing industry, using the stochastic frontier model. Moreover, the recent study by Oqubay (2018) analyzed the structure and performance of manufacturing industries. Most of the studies stated here have employed a linear function, stochastic frontier approach to computing technical efficiency score which is subject to model diagnostic tests such as the normal distribution of residual terms (which is a part of technical inefficiency score), model identification, and specification. In addition, no attempt has been made in these prior studies to examine what defines the variation in technical efficiency scores of the firms under study. Thus, this study attempts to fill this gap by using two-fold analyses: (1) measuring technical efficiency and total productivity growth using a non-linear programming approach, data envelopment analysis approach, and (2) a Tobit regression model has been employed to analyze determinants of technical efficiency. Furthermore, a comparative analysis has been performed to understand whether the technical efficiency score differs between public-owned industrial groups and privately owned industrial groups. We assume that this study contributes to the body of knowledge in two ways. First, it has used a more comprehensive analysis to answer the question that addresses why some firms are more efficient than other firms? This question has been partially answered by identifying the potential Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 firm-specific factors that largely define a firm technical efficiency score. Second, the study asserts that public-owned firms are less likely to be efficient than privately-owned firms. The important question to be raised here is: do resource providers worry about their firm’s technical efficiency and productivity growth? This question may not be relevant and sound where there are strong shareholders/resource providers’ laws and regulatory provisions that impose duties and responsibilities on the management of the company. However, in developing countries such as Ethiopia, shareholders` law and other provisions are scarce and limited in their application, so public-owned firms are assumed to be less efficient than those firms that operate under private investors. The best resolution to this problem would be privatizing public-owned firms. Ethiopia is currently working on a privatization strategy. The remainder of the paper is organized as follows: Section 2 presents a review of literature relevant to this study. Section 3 presents the methodology employed in the study. Section 4 reports results and discussion and Section 5 presents the conclusion. Sections 6 & 7 present practical implications and limitations and suggestions for further studies, respectively. 2. Literature review 2.1. The link between the relative technical efficiency and technological change to productivity growth Productivity growth permits a company to increase profit and market share at the micro-level, and it assists a country to create jobs, counteract inflation, and force the necessary industrial restructuring at the macro-level (J.D. Lee & Heshmati, 2009, p.1). There is widespread agreement among academic researchers in the field of growth theory, policymakers, and businessmen that productivity raise is essential for continued economic growth (J.D. Lee & Heshmati, 2009, p.1). In one of the original contributions to economic growth theory, the investigations of economic growth by Abramovitz (1956), Denison (1962), and Kendrick (1956), productivity/efficiency was considered transcendent for clarifying a noteworthy portion of growth, as Griliches (1998) indicated. In these studies, the authors wanted to review the behavior of growth rates of physical and labor capital as well as the growth rates of per capita production within the USA. From their conclusions, they asserted that much of the growth was because of productivity or, agreeing to Abramovitz (1956), the measure of our ignorance. Having confirmed the significance of productivity for economic growth, Denison (1962) contended that one of the explanations for its acceleration rested in economies of scale, but this might not be directly influenced. Among the contributions of Solow (1956) and Swan (1956), who introduced productivity into an economic growth model, where it had been called technical progress. The growth model was supported by the analysis of a neoclassical production function, which assumed constant returns to scale and decreasing returns on inputs. Solow (1956) stated technical progress was an increasing factor of scale by which production was multiplied. Meanwhile, Swan (1956) said technical progress was initially neutral but increased its responsibility for rises in output that were not caused by rises in capital or labor and indirectly increased production by increasing the contribution of capital. In contrast to those models, endogenous models appeared within which technical progress would be internal to the model of economic growth. Among these studies are Lucas (1988), Romer (1986, 1990), who were also known for their attention to increasing incomes at scale and the consideration of models in flawed equilibrium, assuming equilibrium in monopolistic competition and the inclusion of human capital stock in the production function. Though, considering TFP, technical progress, or technological change, there is also the model of Mankiw et al. (1992) which wanted to defend Solow’s contributions to economic growth by finding solutions to some of the critiques indicated in the original model. Thus, it was treated as an augmented Solow model with human capital, and to the authors, that alternation better fit the explanation of the growth of nations. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 In a similar vein, the mainstream approaches to economies of innovation by Acemoglu and Zilibotti (2001) pointed out that many technologies used by the least developed countries (LDCs) are developed in the advanced economies and are designed to make optimum use of the skills of these richer countries’ workforces. Differences in the supply of skills create a mismatch between the requirements of these technologies and the skills of LDC workers and lead to low productivity in the LDCs. Even when all countries have equal access to new technologies, this skill-technology disparity can lead to sizable differences in TFP and production per worker. For example, the evolutionary approach of Nelson and Winter (1973) to the economics of innovation indicated diffusion processes for new technologies and the existence of significant differences among firms in terms of profitability, the technology used, lead to differences in productivity and growth. Similarly, Geroski et al. (1993) in their studies on the profitability of 721 innovating manufacturing firms in the UK, found the number of innovations achieved by manufacturing firms had a positive impact on operating profit. They also indicated innovative firms were more profitable than noninnovative firms in general, although the effect of specific innovation types on firm profit margin was only modest in size. The term economic efficiency refers to the use of resources to maximize the production of goods and services. In absolute terms, the situation can be called economically efficient if: (1) no one can be made better-off without making someone else worse-off, (2) no additional output can be obtained without increasing the amount of input, and (3) production proceeds at the lowest possible per-unit cost (Sullivan & Sheffrin, 2007 p. 15). Efficiency can be categorized into technical, allocative, or the combination of the two (i.e. total economic efficiency) based on the scope of efficiency targeted (Bhat et al., 2001). Technical efficiency means producing maximum output with given inputs, or equivalently, using minimum inputs to produce a given output (Farrell, 1957). Farrell (1957) considered a production function for a fully efficient firm and analyzed technical efficiency for a production firm as the ratio of the output of any given firm to that of a fully efficient firm. Allocative efficiency deals with the minimizing of the cost of production with a proper combination of inputs for a given level of output and a set of input prices, assuming that the entity examined is working at full technical efficiency. These technical and allocative efficiencies can be combined as a measure of economic efficiency. TFP can effectively contribute to output growth by improvements in technology and efficiency, as these are two determinants of TFP, under constant returns to scale. If returns to scale are variable, TFP growth can be generated by technical change, efficiency improvement, and scale effects. This also reinforces the potential role played by technical efficiency in determining productivity and therefore the need for the relation of assumptions to accommodate inefficiency and efficiency variations. Technical efficiency reflects firm-specific technical knowledge and effort (Page, 1980), the will, skills, and determination of employees and management (Aigner et al., 1977; L.-F Lee & Tyler, 1978), and the effects of work stoppages, managerial skills, material bottlenecks, worker efforts and other disruptions to production (L.-F Lee & Tyler, 1978). To explore sources of productivity growth in the presence of inefficiencies, it is essential to appropriately model production technology and inefficiencies among economic agents. Data envelopment analysis (DEA) has been extensively used to analyze productivity growth and inefficiencies. Data envelopment analysis represents a method of analysis that can serve as an aid in identifying best practice performance in the utilization of resources amongst firms of a similar category. Such identification can highlight where the most significant benefits can be made from efficiency improvements and assist organizations to realize their maximum potential. Measurement tools such as DEA are useful in situations where government bodies operate in markets, which are distorted by prices closely controlled by the government, subsidies, and a lack of contestability. In these cases, the same old market indicators of performance such as profitability and rates of return cannot be used to measure an organization’s economic performance accurately. Despite this, governments and the public at large are still worried that these organizations operate efficiently. In these situations, DEA provides comparative monitoring that identifies Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 variations and hence provides encouragement and direction for the improvement of the performance (Abbott & Doucouliagos, 2003). Most of the prior literature on productivity focused on input productivity like labor or capital as a measure of input efficiency. A rise in the level of productivity reflects a rise in the efficiency of inputs. Hence, the same level of inputs can produce higher output levels, which suggests a reduction in the cost of production. In other words, it reflects betterment in the input qualities. A study conducted by Bhatia (1990) on misleading growth rates in the manufacturing sector argued that unstable socio-demographic changes and lower levels of technology are causing low productivity in India as compared to the United Kingdom and the United States. In his study of the manufacturing sector in India using data for 21 years (from 1965 to 1985), it was pointed out that factor efficiency was influenced by the factor of production, socio-demographic, sociopolitics, development and management of the human resource, workplace, and working condition where a higher capital-labor intensity ratio is associated with a higher level of technology. 2.2. Manufacturing sector of Ethiopia Ethiopia began its first series of economic reform programs in 1992. The reform programs are aimed at reorienting the economy from a command to a market economy, rationalizing the role of the state, and creating legal, institutional, and policy environments to enhance private-sector investment. Different sectoral policies, strategies, and plans were developed and implemented in an effort to make the manufacturing industry play a great role in the economy. As a result of the economic reforms and priorities given to the sector, its contribution to the economy has increased from 11.4% in 2003/2004 to 13.4% in 2010/11 and within the industry, the construction and manufacturing sub-sectors have registered a high growth rate of 12.8% and 12.1% respectively (MoFED, 2011). The fact that the contribution of the manufacturing sector to GDP is minimal exhibits the infant stage of manufacturing activities or industrialization in Ethiopia. This low contribution of the manufacturing sector to the GDP is the common feature of most developing countries that are especially found in Sub-Saharan African countries. The share of the manufacturing value added (MVA) is one of the indicators which pave the way to assess the sector’s performance against other economies. The Ethiopian manufacturing sector is dominated by food products and beverages and nonmetallic mineral manufacturing sub-sectors. In 2017, the former made up about 26% of the establishments in the manufacturing sector (CSA, 2018). The relatively high number of food products and beverage manufacturing industries is mainly explained by the high local input content and the availability of large local markets for food products and beverages (Befekadu & Berhanu, 2000). In 2017, grain mill products manufacturing firms (GMPMF) contributed about 35% of the manufacturing of food products and beverages industrial group (CSA, 2018). Industries such as metal processing, electrical and electronics, chemical, and other engineering industries, which help build technical capabilities and dynamism, have not yet been developed. Most manufacturing exports are focused on agriculture, including drinks, clothes, shoes, and semi-processed hides. On the other hand, most capital goods and manufactured consumer goods are imported into Ethiopia, which is also heavily reliant on the importation of fuel. On the policy facet, the government is committed to creating a favorable environment for attracting direct foreign investment and promoting domestic investment. A variety of foreign companies from China, India, Turkey, and Japan are presently competing in the country to leverage this opportunity. The preferential dutyfree trade access provided by Ethiopia to the United States of America and European Union markets also provides strategic opportunities (Hailu & Tanaka, 2015). Ethiopia has abundant resources that can provide valuable inputs for light manufacturing, namely, cattle, which can be used as an input for making leather and leather products; forests, which can be used as an input for the furniture industry; cotton, which can be used as an input for the garment industry; and agricultural land and lakes are used to provide inputs for agroprocessing industries (Dinh et al., 2012). Moreover, Ethiopia has plentiful low-cost labor, which Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 gives it a comparative advantage in less-skilled, labor-intensive sectors (Dinh et al., 2012; Sonobe et al., 2009). In such light manufacturing areas as leather products and apparel, textile, wood products industries, it has a good opportunity for low-cost manufacturing exports. 2.3. Determinants of technical efficiency of manufacturing firms in Ethiopia The aim of this section is to identify the factors that affect each firm’s efficiency levels. These determinants of technical efficiency can be summarized as follows: 2.3.1. Capital expenditure Prior studies on capital investment in fixed assets indicated a significant and positive relationship between capital expenditures in fixed assets and productivity growth (Abdi, 2008; Delong & Summers, 1991; Gort et al., 1999; Gumbau-Albert & Maudos, 2002; Sala-i-Martin, 1997). For instance, Delong and Summers (1991) found a rising 1% investment share in machinery and equipment could lead to a 0.2 to 0.3% rise in long-run productivity growth. In support of their findings, Sala-i-Martin (1997) pointed out that a 1% increase in equipment investment could cause a 0.2% rise in output growth, while a 1% rise in non-equipment investment could lead to a 0.06% rise in productivity growth of output. Similarly, Gumbau-Albert and Maudos (2002) indicated that differences in efficiency are typically due to a higher ratio of investment to physical capital if it is believed that new production technologies are integrated into new capital purchases, and that technological improvement accelerates the growth of efficiency/productivity in the sector. Thus, we propose new capital investment in fixed assets is positively associated with firm efficiency in manufacturing firms in Ethiopia. 2.3.2. Capital intensity The relationship between capital intensity and technical efficiency has been studied by many scholars with inconsistent results (Latruffe et al., 2004; Mathijs & Vranken, 2000; Sun et al., 1999; Wu, 1993). An important finding of previous studies indicates that capital intensity, measured as capital divided by labor, has a significant and positive impact on technical efficiency in the food, machinery, and electronics sectors of Chinese manufacturing industries (Sun et al., 1999). They pointed out that a rise in the utilization of capital inputs such as machinery and equipment in relation to labor, or capital deepening, is expected to improve productivity and lead to a growth in technical efficiency in these industries. Similarly, Mathijs and Vranken (2000) indicated more capital-intensive farms are efficient in Bulgarian crop farms. In contrast, Latruffe et al. (2004) pointed out more capital-intensive farms are less efficient in crop and livestock farms in Poland. Hence, we propose capital intensity is positively associated with manufacturing firm efficiency in Ethiopia. In prior literature, capital intensity is measured as the ratio of total assets (book value) to the total number of employees (Blomström & Persson, 1983). Abenoja and Lapid (1991) measured capital intensity as the ratio of the gross book value of fixed assets to the total number of production workers. Following Abenoja and Lapid (1991), for this study, we used the ratio of the book value of machinery and equipment of the establishment to the total number of production workers. 2.3.3. Account-book ratio To our knowledge, extant research has not addressed the impact of internal control on manufacturing firm efficiency. When sound internal controls are maintained and effectively monitored, they are an important aid to enhancing productivity and effectiveness. Firms that maintain proper record-keeping are assumed to be efficient. Firms that keep a complete book of account are in a better position to prudently plan and track the day-to-day operations of their production unit (Bekele & Belay, 2007). This will aid them to improve their technical efficiency level by preventing waste of resources. In this study, the account-book ratio, as a proxy for internal control, is measured as the ratio of firms that maintain books of account to the total number of firms in the industry. Thus, we hypothesize that the account-book ratio is positively related to the technical efficiency of manufacturing firms in Ethiopia. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 2.3.4. Skill intensity Prior literature on the relationship between skill intensity and technical efficiency indicates that skill-intensive firms are more capital-intensive, larger in size, tend to be exporters, and more productive (Bernard & Jensen, 1999). Firms with better managerial skills tend to have higher earnings, production, and technical efficiency (Kirkley et al., 1998). Similarly, Ray (1997) indicates an increase in the proportion of non-production white-collar and managerial staff might impose certain rigidities in the production process, causing slow adjustments to variations in demand. In this study, skill intensity is measured as the ratio of production workers to total employees. Thus, we expect a positive correlation between skill intensity and technical efficiency. This expectation is also consistent with conventional trade theory, which claims that firms with higher skill intensity specialize in higher quality products and tend to be more profitable and efficient (Whang, 2016). 2.3.5. Industry size Theoretical research on the relationship between firm size and efficiency indicates that larger firms benefit from economies of scale and operate at lower average costs of production, implying that firm size has a positive impact on efficiency. Similarly, a theory developed as a model of firm growth by Jovanovic (1982) indicates larger firms are more efficient than smaller ones. This result is an outcome of a selection process, in which efficient firms grow/prosper and survive, while inefficient firms stagnate or leave the industry. Furthermore, empirical studies on the firm size and efficiency relationship indicate various results. For instance, Lundvall and Battese (2000) indicated technical efficiency increases with firm size. Sun et al. (1999) also pointed out a rise in the size of firms is likely to promote a firm’s market share and competitiveness, which in turn is expected to improve a firm’s access to new technology, scale efficiency, innovative capability, and productivity. These improvements tend to improve the firm’s technical efficiency. Similarly, Sur et al. (2018) indicated a positive association between size and technical efficiency. They found that as the capacity of a firm increases, its efficiency also increases. In contrast, Betancourt and Clague (1975) assumed a negative association between efficiency and firm size. They argue small firms adopt more appropriate technology and foster competitive factors and product markets with their flexibility to respond to changes in technology, product markets, and markets. Hence, we hypothesized that firm size is positively correlated to technical efficiency. 2.3.6. Advertising expense According to the Resource-Based Theory of the firm, firms that invest in R&D and advertising are more likely to create firm-specific assets that cannot be imitated by their rivals/competitors and serve as the foundation for their long-term competitive advantage. Firms’ advertisement and R&D choices, according to Geroski (1995), may help to better capture relative efficiency and its evolution over time. Firms that obtain innovations and conduct advertising improve their efficiency, making them more likely to succeed. Extant literature on the relationship between advertising expenses and technical efficiency is scarce. OCED (2014) indicated that advertising is an example of firms` responses to competition and is associated with improved productivity. Firms that spend money on advertising tend to be more productive than their competitors. Similarly, advertising expenditures may also be thought of as endogenous sunk costs, as Sutton (1991) suggests, strengthening the firms’ perceived brand reputation and increasing customers’ willingness to pay for their goods. As a result, advertising is supposed to boost survival chances. Comanor and Wilson (1967) suggest that advertising has an anticompetitive impact because it increases entry barriers, softening the resilience of competition. Following Özçelik and Taymaz (2004), in this study, the advertising ratio is measured as the ratio of advertising expense to total sales. Thus, we hypothesize that advertising expense is positively associated with technical efficiency. 3. Methodology The manufacturing sector is one of the rapidly growing sectors in Ethiopia. According to the CSA (2018) report, about 3,529 large and medium manufacturing companies are operating in Ethiopia. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 sectors have efficiency scores more than the average, while twenty-seven (27) sub-sectors are found with an efficiency score less than the average. The overall average efficiency scores of the sector have increased from 0.344 in 2011 to 0.507 in 2012 as some sub-sectors have shown improvement in resource utilization to produce products. Therefore, by adopting best practices, the sector could have on average produced more products by 49.3% than actually produced from the current level of inputs quantity. The number of efficient sub-sectors slightly declined from 2012 to 2015. Three sub-sectors were efficient in 2013, whereas two sub-sectors were in 2014 and 2015. The average efficiency scores also declined from 2012 to 2016. Moreover, the average technical efficiency per sub-sector over the observation years (2010 to 2017) ranges from 0.122 for spinning, weaving & finishing of the textile to 0.916 for tobacco products. This implies there is a high-efficiency variance among the sampled sub-sectors over the observation years. This result is consistent with some prior studies such as Hailu and Tanaka (2015) who find there is technical efficiency score variation across Ethiopian manufacturing firms, suggesting shortage of raw materials supply was the main cause of the relative technical inefficiency of the sector. On overall average, the sector understudy had a 37% technical efficiency value from 2010 through 2017. Our result is very consistent with the recent study by Ayelign and Singh (2019), who found that in the overall average Ethiopian medium and large-scale manufacturing industries registered 36.7% technical efficiency over the period 1996–2015. Low technical efficiency and productivity seem to be a common problem in manufacturing industries in Sub-Saharan African countries. For instance, the Kenyan manufacturing sector had low overall productivity and large productivity differences across industries (World Bank, 2014). Diaz and Sanchez (2007) indicated consistent findings that most industries in the manufacturing sector were inefficient and the inefficiency was greater among large firms than small firms. Abegaz (2013) addresses that Ethiopian manufacturing industries have the capability to use imported technology, but improvement and adoption of the technology are weak. This means that the sector has not utilized its maximum capacity. UNCTAD (2015) also provides evidence that the lack of access to and sharing of R&D facilities continues to hinder the ability of local firms to take advantage of opportunities both within Ethiopia and in other emerging markets. The World Economic Forum’s Global Competitiveness Index (GCI) ranks Ethiopia 109 th out of 140 countries with a score of 3.7 out of 7.0 in the 2015–16 report. It reflects that Ethiopian firms are not as competitive in the international market because innovative activity in the industry is very low. In conjunction with this data, the research and development (R&D) share of GDP was 0.5% in 2015 (UNCTAD, 2015). The aforementioned factors would collectively affect the efficiency of the sector. 4.4. BCC model results The BCC model evaluates whether increasing, decreasing, or constant returns to scale would be taken to improve the efficiency value found. CRS arises when a percentage increase in (all) inputs produces the same percentage increase in outputs. However, VRS occurs when a proportionate increase in inputs produces a smaller (larger) proportionate increase in outputs (W. W Cooper et al., 2006, p.125). This assumption of the BCC model decomposed into decreasing returns to scale and increasing return to scale. In a decreasing return to scale, an increase in input produces a smaller increase in output. An increasing return to scale arises when an increase in inputs yields a larger increase in output. A VRS model allows the best practice level of outputs to inputs to vary with the size of industries and it also measures the efficiency of the management in utilizing inputs that are free of scale efficiency. The CRS technical efficiency of the sample industries is divided into pure technical efficiency (PTE) and scale efficiency (SE) in the BCC model. PTE measures the extent to which an industry can increase its output (in fixed proportion) while remaining within the VRS frontier. Thus, technical efficiency measures the industry’s overall success in maximizing its output. SE reflects the extent to which an industry projected to the VRS efficiency frontier can further increase its output (again Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table 2. Technical efficiency of sub-sectors computed by CCR model under CRS assumption Sub-sectors 2010 2011 2012 2013 2014 2015 2016 2017 Average Processing & preserving of meat, fruit & vegetables 0.27 0.094 0.163 0.114 0.276 0.171 0.054 0.151 0.16 Vegetable & animal oils and fats 0.168 0.16 0.272 0.311 0.348 0.399 0.692 0.803 0.39 Dairy 0.259 0.31 0.285 0.499 0.39 0.355 0.326 0.486 0.36 Grain mill 0.118 0.122 0.194 0.185 0.172 0.341 0.141 0.232 0.188 Animal feeds 0.237 0.137 0.36 1 0.12 0.29 0.358 0.788 0.41 Bakery 0.113 0.276 0.839 0.154 0.145 0.159 0.145 0.135 0.24 Sugar and sugar confectionery 1 0.68 0.35 0.415 0.444 1 0.538 0.517 0.618 Macaroni & spaghetti 0.259 0.237 1 0.161 0.19 0.071 0.095 0.198 0.27 Food products n. e.c. 0.179 0.163 0.262 0.377 0.464 0.279 0.319 0.162 0.27 Distilling, rectifying & spirits 0.794 0.682 0.682 0.255 0.288 0.293 0.147 0.31 0.431 Wines 0.745 0.884 1 0.854 0.797 0.45 0.562 0.771 0.757 Malt liquors & malt 0.391 1 0.88 0.843 0.464 0.197 0.781 0.677 0.65 Soft drinks & mineral water 0.455 0.6 0.226 0.198 0.315 0.59 0.135 0.119 0.32 Tobacco products 1 0.685 1 1 1 0.648 1 1 0.916 Spinning, weaving & textiles 0.133 0.079 0.287 0.182 0.03 0.09 0.071 0.11 0.122 (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table2. (Continued) Sub-sectors 2010 2011 2012 2013 2014 2015 2016 2017 Average Cordage, rope, twine & netting 0.155 0.358 0.218 0.08 0.123 0.242 0.171 0.086 0.179 Knitting mills 0.02 0.018 0.032 0.317 0.27 0.03 0.161 0.333 0.147 Wearing apparel except fur apparel 0.084 0.214 0.424 0.122 0.195 0.162 0.517 1 0.339 Tanning & dressing of leather, luggage & handbags 0.11 0.258 0.32 0.484 0.299 0.142 0.195 0.898 0.33 Footwear 0.12 0.194 0.809 0.148 0.265 0.316 0.127 0.161 0.26 Wood & wood products 0.035 0.083 1 0.785 0.041 0.153 0.192 0.181 0.308 Paper 0.251 0.281 0.19 0.078 0.143 0.243 0.33 0.442 0.244 Publishing & printing services 0.261 0.226 0.245 0.383 0.227 0.342 0.482 0.225 0.298 Basic chemicals 0.264 0.182 0.376 0.419 0.308 0.289 0.491 0.111 0.3 Paints, varnishes & mastics 0.889 1 1 0.771 0.897 0.907 0.309 0.607 0.79 Pharmaceuticals & medicinal chemicals 0.305 0.682 0.435 0.536 0.595 0.172 0.425 0.175 0.415 Soap and detergents cleaning 0.332 0.228 0.771 0.469 0.263 0.371 0.239 0.218 0.361 Chemical products n.e.c. 1 0.077 0.374 0.38 1 0.258 0.307 0.642 0.504 Rubber 0.14 0.182 0.577 0.169 0.353 0.366 1 0.894 0.46 Plastic 0.282 0.224 0.258 0.321 0.281 0.185 0.261 0.216 0.25 (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table2. (Continued) Sub-sectors 2010 2011 2012 2013 2014 2015 2016 2017 Average Glass 0.21 0.23 0.547 0.364 0.374 0.623 0.226 0.309 0.36 Structural clay 0.104 0.213 0.183 0.101 0.191 0.132 0.084 0.182 0.14 Cement, lime & plaster 1 0.862 1 0.324 0.946 0.496 0.23 0.242 0.63 Articles of concrete & plaster 0.096 0.129 0.251 0.185 0.315 0.479 0.213 0.277 0.24 Non-metallic mineral 0.089 0.132 0.34 0.314 0.098 0.151 0.083 0.143 0.168 Basic iron & steel 0.278 0.294 0.509 0.377 0.344 0.417 0.291 0.321 0.35 Structural metal 0.327 0.142 0.409 0.208 0.141 0.414 0.174 0.284 0.262 Cutlery, hand tools & general hardware 0.358 0.288 0.69 0.057 0.381 0.35 0.153 0.218 0.31 Other fabricated metal 0.395 1 1 0.554 0.394 0.622 0.225 0.028 0.52 Other generalpurpose machinery 0.281 0.255 0.158 0.165 0.405 0.518 0.269 0.289 0.29 Parts & accessories for motor vehicles 0.658 0.361 1 0.692 0.457 1 0.273 0.344 0.59 Passenger cars, commercial vehicles & busses 0.26 0.375 0.491 1 0.558 0.042 1 0.437 0.52 Furniture 0.308 0.176 0.386 0.224 0.238 0.156 0.197 0.302 0.24 Mean 0.343 0.344 0.507 0.385 0.361 0.347 0.325 0.373 0.37 Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 in fixed proportions) while remaining within the CRS frontier. Thus, SE measures the extent to which a firm can increase output by moving to a part of the frontier with more beneficial returns to scale characteristics. The decomposition is needed to identify the sources of inefficiency by comparing the PTE and SE. When PTE exceeds SE, the source of inefficiency is due to scale inefficiency (inappropriate selection of scale size). In other words, if there is a difference between the technical efficiency score (CRS technical efficiency and VRS PTE), then it demonstrates scale inefficiency. Conversely, if SE is higher than PTE, then the source of inefficiency is due to poor utilization of inputs, i.e., pure technical inefficiency. Table 3 reports results obtained from BCC model VRS. The results entail CRS technical efficiency, VRS technical efficiency, and scale efficiency. We present the results of the most recent three years observations in the data set, 2015–2017 for clarity. In 2015, the number of technically efficient sub-sectors was two (2), about 5% of the sample when VRS TE was assumed, and five (5), 11.6% when CRS TE was assumed. Three sub-sectors found with scale efficiency, suggesting an appropriate selection of inputs and operating on the most productive scale size. Out of the inefficient sub-sectors, 38 sub-sectors experienced poor utilization of inputs as the source of inefficiency is pure technical inefficiency. However, two subsectors had higher PTE than SE which implies that the source of inefficiency is scale inefficiency. This indicates the industries are operating at an inappropriate scale. When looking at the type of scale, twenty-seven (27) sub-sectors (e.g., dairy products; bakery products; footwears; rubber products) appeared to have an increasing return to scale, showing that a proportionate increase in inputs yields a larger proportionate in the outputs. These industries would improve their efficiency by expanding the scale of operation. In contrast, eleven (11) industries (e.g., furniture; soap & detergent cleaning; plastic products; cement, lime & plaster) experienced a decreasing return to scale i.e., a proportionate increase in inputs produces a lower proportionate increase in outputs. This implies the industries have supra-optimal scale size (i.e. operates at the rising portion of long-run average cost curve) and thus, downscaling is needed for achieving efficiency frontier. Five sub-sectors, bakery products, sugar and sugar confectionery, macaroni and spaghetti, tanning and dressing of leather, and parties and accessories for a motor vehicle operate at a flatter portion of the long-run average cost curve that means a constant return to scale. About 11.6% of the sub-sectors have experienced PTE as computed by VRS in 2016. Three subsectors (tobacco products, rubber products, and passenger cars, commercial vehicles & busses) appeared efficient both by CRS TE and VRS TE. All of the scale-inefficient sub-sectors experienced a decreasing return to scale, i.e., an increase in proportionate usage of input produces the less proportionate increase in outputs. On average, the sample sub-sectors recorded a 0.531 of PTE and a 0.629 of SE suggesting the source of inefficient are pure technical inefficient. Generally, the sector was at poor resource management and converting inputs to the output. When looking in 2017, four sub-sectors, namely soft drink and mineral water, tobacco products, wearing apparel, and passenger cars, commercial vehicles and busses observed as pure technical efficiency as measured by VRS whereas, three sub-sectors (footwears, tobacco products, and soft drink and mineral water) were scale efficient. Out of the inefficient sub-sectors, 20 sub-sectors experienced a decreasing return to scale and 16 sub-sectors experienced an increasing return to scale. On average, pure technical efficiency scores declined in 2017 indicating some sub-sectors had used excess inputs to produce output as compared to in 2016. However, a scale efficient score on average shows improvement in the year. The mean of scale efficiency was higher than the mean of pure technical efficiency. It reveals the source of technical inefficiencies of the sector is pure Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 technical inefficient. The industries need to properly manage their input utilization in order to be efficient. On average, the industries had faced a 49% of pure technical inefficiency over the observation periods. It shows a firm’s inability to exploit inputs due to the poor skills of both operatives and management. The overall scale efficiency of 76% shows an appropriate section of production scale by the industries. It implies the organizational source of inefficiency. The Ethiopian manufacturing sector is characterized by a cheap labor force. This would benefit the sector in minimizing production cost as wage/salary is low. It could also be detrimental to the sector because of the inappropriate utilization of the resources by unskilled, low-cost labor forces. In essence, cheap labor implies less-skilled labor or labor incentive. It can be assumed that being labor or capitalintensive would not yield a guarantee for efficiency, but the quality of labor or capital utilized could determine the efficiency of a firm. In other words, a firm can be efficient, if it`s able to gain the optimum benefit from the resources utilized such as labor, capital, raw materials, or electric power. When looking at the annual average efficiency score in each observation period, the lowest average TE score was reported in 2016. The highest average TE and PTE were observed in 2012. The SE with the highest efficiency score was observed in 2010. The sample sub-sectors had shown higher SE in all observation periods and in the overall average. We further classify the sub-sectors considered in the previous analysis into fourteen major subsectors under both government and private ownership in order to measure technical efficiency and compare whether public sub-sectors are more efficient or not than private sub-sectors. To do the analysis, 2015 and 2017 observations were taken, assuming the sub-sectors are operating in the same environment and market. The results presented in Table 4 and 5 indicates, on average, private-owned sub-sectors had registered better total technical efficiency (0.821), PTE (0.939) and SE (0.876) than public-owned sub-sectors and they are relatively efficient. When comparing subsector to sub-sector, public-owned food products and beverages and machinery and equipment are efficient both under CRS and VRS models, whereas the same sub-sectors under private ownership are inefficient. On average, the technical inefficiency of public-owned sub-sectors highly driven by pure technical inefficiency suggesting they had poorly managed usage of resources. In contrast, scale inefficiency contributes more to the total inefficiency of private sub-sectors. It implies private-owned sub-sectors showed an inappropriate combination of resource use in 2015. In 2017, the efficiency score has declined for both public and private sub-sectors in terms of total technical efficiency (CRS), PTE & SE (VRS). It is noted that the mean efficiency scores of private sub-sectors are much higher than public-owned sub-sectors, indicating private sub-sectors better manage resources and appropriately mix inputs to obtain the maximum benefit. Indeed, the number of private firms in each sub-sector is quite larger than the number of public firms in the same sub-sector. One of the reasons for the imbalance number of firms is privatization. The government sells state-owned firms to private investors that cause a decline in public firms and increase private firms at the same time. Given this practical issue, we assume the variation between the efficiency of the private and public firms might partially be defined by size (in terms of total asset proxy or number of employees proxy). The result is consistent with Alvarez and Crespi (2003) and Gumbau-Albert and Maudos (2002) findings that public firms on average tend to be less efficient as compared to private firms. 4.5. Determinants of technical efficiency: Tobit regression results The important finding observed in this study is the capital expenditure ratio has a positive effect on technical efficiency, suggesting a significant investment in new capital would lead the firm to an efficiency level. A firm with advanced machinery and equipment more likely to engage in innovative products which in turn increases production and sales performance when minimizing the length of production time, inventory and accounts receivable turn over and other irrelevant costs. On the other hand, a higher depreciation and maintenance cost would offset the advantage of Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table 3. Technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE), and type of scale (TFS) Subsectors 2015 2016 2017 TE PTE SE TFS TE PTE SE TFS TE PTE SE TFS Processing & preserving of meat, fruit & vegetables 0.171 0.174 0.983 irs 0.054 0.059 0.913 drs 0.151 0.198 0.762 drs Vegetable & animal oils and fats 0.399 0.419 0.95 irs 0.692 0.757 0.915 drs 0.803 0.808 0.993 irs Dairy 0.355 0.363 0.977 irs 0.326 0.357 0.914 drs 0.486 0.491 0.989 irs Grain mill 0.341 0.465 0.733 drs 0.141 0.477 0.295 drs 0.232 0.375 0.617 drs Animal feeds 0.29 0.335 0.866 irs 0.358 0.468 0.764 drs 0.788 0.869 0.907 drs Bakery 0.159 0.159 1 - 0.145 0.326 0.446 drs 0.135 0.184 0.734 drs Sugar and sugar confec tionery 1 1 1 - 0.538 1 0.538 drs 0.517 0.518 0.999 - Macaroni & spaghetti 0.071 0.071 0.994 - 0.095 0.22 0.431 drs 0.198 0.277 0.713 drs Food products n. e.c. 0.279 0.281 0.994 irs 0.319 0.386 0.827 drs 0.162 0.303 0.535 drs Distilling, rectifying & spirits 0.293 0.294 0.994 irs 0.147 0.352 0.417 drs 0.31 0.346 0.896 drs Wines 0.45 0.495 0.909 irs 0.562 0.617 0.91 drs 0.771 0.789 0.977 irs Malt liquors & malt 0.197 1 0.197 drs 0.781 1 0.781 drs 0.677 1 0.677 drs (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table3. (Continued) Subsectors 2015 2016 2017 TE PTE SE TFS TE PTE SE TFS TE PTE SE TFS Soft drinks & mineral water 0.59 0.741 0.796 drs 0.135 0.673 0.2 drs 0.119 0.307 0.387 drs Tobacco products 0.648 0.658 0.986 irs 1 1 1 - 1 1 1 - Spinning, weaving & textiles 0.09 0.226 0.398 drs 0.071 0.274 0.259 drs 0.11 0.217 0.507 drs Cordage, rope, twine & netting 0.242 0.247 0.981 irs 0.171 0.213 0.8 drs 0.086 0.086 0.995 - Knitting mills 0.03 1 0.03 irs 0.161 0.185 0.87 drs 0.333 0.641 0.519 irs Wearing apparel except fur apparel 0.162 0.164 0.992 irs 0.517 0.981 0.527 drs 1 1 1 - Tanning & dressing of leather, luggage & handbags 0.142 0.142 0.999 - 0.195 0.311 0.627 drs 0.898 0.9 0.997 irs Footwear 0.316 0.317 0.998 irs 0.127 0.239 0.529 drs 0.161 0.161 1 - Wood & wood products 0.153 0.155 0.99 irs 0.192 0.587 0.327 drs 0.181 0.182 0.995 irs Paper 0.243 0.245 0.991 irs 0.33 0.372 0.886 drs 0.442 0.443 0.999 irs Publishing & printing services 0.342 0.343 0.998 irs 0.482 0.888 0.543 drs 0.225 0.652 0.346 drs (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table3. (Continued) Subsectors 2015 2016 2017 TE PTE SE TFS TE PTE SE TFS TE PTE SE TFS Basic chemicals 0.289 0.3 0.961 irs 0.491 0.564 0.872 drs 0.111 0.117 0.942 irs Paints, varnishes & mastics 0.907 0.93 0.973 irs 0.309 0.344 0.898 drs 0.61 0.608 0.998 irs Pharmac euticals & medicinal chemicals 0.172 0.17 0.993 irs 0.425 0.627 0.678 drs 0.18 0.176 0.998 - Soap and detergents cleaning 0.371 0.37 0.998 drs 0.239 0.564 0.424 drs 0.22 0.34 0.639 drs Chemical products n. e.c. 0.258 0.27 0.97 irs 0.307 0.456 0.674 drs 0.64 0.666 0.964 irs Rubber 0.366 0.42 0.883 irs 1 1 1 - 0.89 0.92 0.972 irs Plastic 0.185 0.3 0.622 drs 0.261 0.971 0.269 drs 0.22 0.621 0.347 drs Glass 0.623 0.73 0.854 irs 0.226 0.296 0.764 drs 0.31 0.313 0.987 drs Structural clay 0.132 0.14 0.931 irs 0.084 0.118 0.708 drs 0.18 0.183 0.991 irs Cement, lime & plaster 0.496 0.87 0.567 drs 0.23 0.594 0.388 drs 0.24 0.755 0.321 drs Articles of concrete & plaster 0.479 0.67 0.716 drs 0.213 0.662 0.321 drs 0.28 0.466 0.594 drs Nonmetallic mineral 0.151 0.15 0.992 irs 0.083 0.163 0.511 drs 0.14 0.143 0.999 - (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table3. (Continued) Subsectors 2015 2016 2017 TE PTE SE TFS TE PTE SE TFS TE PTE SE TFS Basic iron & steel 0.417 0.61 0.679 drs 0.291 0.99 0.294 drs 0.32 0.714 0.45 drs Structural metal 0.414 0.65 0.635 drs 0.174 0.705 0.247 drs 0.28 0.407 0.697 drs Cutlery, hand tools & general hardware 0.35 1 0.35 irs 0.153 0.158 0.97 drs 0.22 0.226 0.961 irs Other fabricated metal 0.622 0.74 0.843 irs 0.225 0.239 0.943 drs 0.03 0.03 0.963 irs Other generalpurpose machinery 0.518 0.6 0.864 irs 0.269 0.499 0.54 drs 0.29 0.309 0.938 irs Parts & accessories for motor vehicles 1 1 1 - 0.273 0.67 0.408 drs 0.34 0.533 0.645 drs Passenger cars, comm ercial vehicles & busses 0.042 0.05 0.87 irs 1 1 1 - 0.44 1 0.437 Irs Furniture 0.156 0.29 0.532 drs 0.197 0.471 0.419 drs 0.3 0.874 0.345 drs Mean 0.347 0.46 0.837 0.325 0.531 0.629 0.37 0.492 0.784 Note: TEtotal technical efficiency computed by CRS model; PTEpure technical efficiency computed by VRS model; SEscale efficiency; TFStype of scale; drs-decreasing return to scale; irs-increasing return to scale;—Dashconstant return to scale. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table7. (Continued) Sun-sectors EFFCH TECHCH PTECH SECH TFPCH Paper 1.084 1.107 1.084 1 1.2 Publishing & printing services 0.979 1.102 1.11 0.882 1.079 Basic chemicals 0.883 1.052 0.89 0.992 0.93 Paints, varnishes & mastics 0.947 1.055 0.943 1.004 0.999 Pharmaceuticals & medicinal chemicals 0.924 1.116 0.923 1.001 1.031 Soap and detergents cleaning 0.942 1.107 0.999 0.942 1.042 Chemical products n.e.c. 0.939 1.028 0.944 0.995 0.965 Rubber 1.304 1.153 1.304 1 1.503 Plastic 0.962 1.109 1.06 0.908 1.068 Glass 1.057 1.143 1.052 1.005 1.208 Structural clay 1.083 0.975 1.074 1.008 1.056 Cement, lime & plaster 0.817 1.37 0.961 0.85 1.119 Articles of concrete & plaster 1.163 1.119 1.181 0.984 1.302 Non-metallic mineral 1.07 1.062 1.04 1.029 1.137 Basic iron & steel 1.021 1.156 1.143 0.893 1.18 Structural metal 0.98 1.241 1.031 0.95 1.216 Cutlery, hand tools & general hardware 0.931 1.098 0.93 1.001 1.023 Other fabricated metal 0.687 1.172 0.69 0.996 0.805 Other general-purpose machinery 1.004 1.345 1.01 0.994 1.35 Parts & accessories for motor vehicles 0.911 1.126 0.968 0.942 1.027 Passenger cars, commercial vehicles & busses 1.077 1.097 1 1.077 1.181 (Continued) Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 Table7. (Continued) Sun-sectors EFFCH TECHCH PTECH SECH TFPCH Furniture 0.997 1.001 1.095 0.911 0.999 Mean 1.022 1.105 1.032 0.991 1.13 Where: EFFCH-efficiency change; TECHCHtechnology change; PTECHpure technical efficiency change; SECH—scale efficiency change; TFPCH-total factor productivity change. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 technological retrogression was shown which in turn caused negative productivity growth. The technical efficiency index shows worsening from 2013 to 2016. Generally, the industries are more focused on technological progress or innovation than technical efficiency during the study period. 5. Conclusions This study estimates technical efficiency and total productivity growth of medium and large-scale manufacturing sub-sectors using census data annually collected by the Ethiopian Central Statistical Agency. A technical efficiency score is computed by a data envelopment analysis, whereas the Malmquist productivity index has been employed to estimate total productivity change. Furthermore, a censored Tobit regression model was used to identify potential factors which can define the variation in total technical efficiency scores. The result shows that on average, mediumand large-scale manufacturing sub-sectors registered a 0.37 (37%) efficiency score over the study periods 2010 to 2017. It suggests that on average, the sector could minimize its input quantity by 63% without altering the level of production or could produce about 63% of production from the resources assumed in the observation periods. In practice, the sector has been suffering from a lack of adequate materials, electric power interruption, and skilled labor. It also could not appropriately utilize the available resources. Thus, it can be concluded that the manufacturing sector should look into its resource utilization methods to obtain the optimal benefit. The Malmquist productivity index shows on average, the sub-sectors made technological progress by 10.5%. The technological change index positive value indicates a decline in the quantity of output produced by a similar quantity of input. It indicates progress in innovation, which has greatly contributed to positive productivity growth in 31 sub-sectors. It suggests that most sub-sectors have paid more focus on technological change than technical efficiency change. Moreover, productivity grew by 13% over the study periods, 2010 to 2017, which is less than 2% per annum. As productivity is the linear combination of catch-up and frontier shift, firms need to balance these factors in order to improve productivity. Furthermore, we observed that the total technical efficiency scores computed by a constant return to scale model show considerable variations among the sub-sectors under consideration. To understand the determinant factors that can cause a firm to be more efficient or less efficient, a censored Tobit regression was run and the results showed that capital expenditure ratio and account book ratio has a significant positive effect on technical efficiency. The capital expenditure ratio indicates long-term investments that can increase a firm`s future cash flow, which in turn improves technical efficiency. On the other hand, the account book ratio reflects firm internal control practice which involves financial management practices and how business transactions are recorded, maintained, and processed into information helpful to decision-makers in planning, directing, and controlling activities. It can be inferred that a firm that designs an effective Table 8. The Malmquist index summary of annual means Year TE TeChE PTE SE TFP 2011 1.062 1.201 1.12 0.948 1.276 2012 1.544 0.869 1.767 0.873 1.341 2013 0.744 1.225 0.68 1.094 0.911 2014 0.957 1.354 1.036 0.925 1.296 2015 0.948 0.873 0.827 1.147 0.828 2016 0.931 1.46 1.236 0.754 1.36 2017 1.132 0.912 0.876 1.292 1.033 Mean 1.022 1.105 1.032 0.991 1.13 Note: All Malmquist indexes represent geometric means (Coelli, 1996). TE-total technical efficiency; TeChE-technological change; PTE-pure technical efficiency; SEscale efficiency; TFP-total factor productivity. Erena et al., Cogent Economics & Finance (2021), 9: 1997160 https://doi.org/10.1080/23322039.2021.1997160 accounting system and financial management practices more likely to be efficient. The coefficient of capital intensity is positive but not statically strong. This study also finds public-owned subsectors are less efficient than privately owned sub-sectors. Even though the sector shows total productivity progress, it is still insignificant when compared to other industries. For example, the sector's net contribution to GDP in 2016/17 was 1.1%, while agriculture and services accounted for 36.3% and 39.3%, respectively (CSA, 2018). Growth in the manufacturing sector is expected to be a positive function of the GDP. To meet these expectations, the sector needs to be efficient. Efficiency in the sector can be improved by strengthening the corporate governance structure (Bris et al., 2008), enhancing R&D, innovations, and utilization of information technology. Finally, human resource development is another aspect that needs to be addressed. The skills of personnel working in manufacturing should match the changing requirements of these industries, which are forced upon us by globalization. Without a competent workforce, it is difficult to compete, particularly in this type of knowledge-based industry. 6. Practical implications of the study The findings of the study would have implications for policymakers and firm owners in that it offers an insight into the source of productivity growth, competitiveness, and areas for further improving the manufacturing sector. Policymakers would also use the findings in designing strategic plans towards increasing the productivity of the sector. The main source of productivity is internal factors which involve optimal usage of existing resources or producing the optimal production from the existing input resources. Thus, considerable due attention should be given to the productivitydriven growth strategy than the foreign direct investment-driven growth strategy of the sector. 7. Limitations and future research directions Our study has two potential limitations. First, our analysis focuses mainly on medium and largescale firms, excluding small manufacturing firms that make up a large percentage of the sector. Future research may focus on small and medium enterprises (SME) in developing countries like Ethiopia. Second, the limitations in the data also forced us to use sectoral data instead of firm data, which would have allowed a deeper and more interesting or novel analysis. There are several factors recommended in the empirical literature (Alvarez & Crespi, 2003) like export performance, firm owner education level, import performance, research & development, but we could not include these variables in the model due to data unavailability. Acknowledgements The authors are thankful to the Office of the Vice President for Research and Technology Transfer of Hawassa University and Internal Grant Agency of the Faculty of Management and Economics, Tomas Bata University in Zlin (Grant Number: IGA/FaME/2020/003) for financial support towards carrying out this research. The authors also would like to thank Prof. Christian Nsiah and two anonymous reviewers for their time and effort devoted to critical review, helpful and constructive comments throughout the revision process. Funding This work was supported by the Hawassa University and Tomas Bata University in Zlin (IGA/FaME/2020/003). Author details Obsa Teferi Erena 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0003-4304-5359 Mesfin Mala Kalko 2 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-5153-4764 Sara Adugna Debele 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-9832-7049 1 College of Business and Economics, Hawassa University, Hawassa, Ethiopia. 2 Faculty of Management and Economics, Tomas Bata University in Zlin, Zlin, Czech Republic. Disclosure statement No potential conflict of interest was reported by the author(s). 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