The economy-wide impact of harnessing human capital development and the case of Ethiopia: A dynamic computable general equilibrium model analysis

Yeshineh, Alekaw Kebede; Woldeyes, Firew Bekele Article The economy-wide impact of harnessing human capital development and the case of Ethiopia: A dynamic computable general equilibrium model analysis Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Yeshineh, Alekaw Kebede; Woldeyes, Firew Bekele (2025) : The economy-wide impact of harnessing human capital development and the case of Ethiopia: A dynamic computable general equilibrium model analysis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-23, https://doi.org/10.3390/economies13050137 This Version is available at: https://hdl.handle.net/10419/329417 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Popescu Received: 4 March 2025 Revised: 26 April 2025 Accepted: 28 April 2025 Published: 16 May 2025 Citation: Yeshineh, A. K., & Bekele Woldeyes, F. (2025). The EconomyWide Impact of Harnessing Human Capital Development and the Case of Ethiopia: A Dynamic Computable General Equilibrium Model Analysis. Economies,13(5), 137. https://doi.org/ 10.3390/economies13050137 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article The Economy-Wide Impact of Harnessing Human Capital Development and the Case of Ethiopia: A Dynamic Computable General Equilibrium Model Analysis Alekaw Kebede Yeshineh * and Firew Bekele Woldeyes Department of Macroeconomy and Finance Policy Research, Policy Studies Institute (PSI), Addis Ababa 2479, Ethiopia; firew[email protected] *Correspondence: [email protected] or alekaw[email protected] Abstract: This study uses a computable general equilibrium (CGE) model to analyze the impact of skilled and semi-skilled labor supply shocks on the Ethiopian economy and sectoral outputs. The study examines three policy scenarios: a 10% increase, a 15% increase, and a 20% increase in skilled and semi-skilled labor supply compared to a business-as-usual (BAU) scenario. The findings show that all three scenarios contribute to higher economic growth, investment, and exports. The impact on sectoral outputs is also significant, with the industry and services sectors performing better than the agriculture sector. In the 20% increase scenario, the real annual gross domestic product (GDP) growth rate is projected to be 0.79 percentage points higher than the business-as-usual scenario. Additionally, the annual growth rates of investments and exports are expected to be 2.69 and 2.31 percentage points higher, respectively, compared to their business-as-usual scenario counterparts. The agriculture sector experiences a slight increase of 0.16 percentage points in annual production compared to the business-as-usual scenario. Output in the industry sector also sees a rise of 1.61 percentage points higher than the business-as-usual scenario, while outputs in the services sector improve significantly. Overall, the study highlights the positive impact of increasing the supply of skilled and semi-skilled labor on the economy. This is mainly due to the higher productivity of skilled and semi-skilled workers, which contributes to increased economic growth. The findings suggest that governments should implement policies to enhance the supply of skilled and semi-skilled labor, such as investing in education and training programs. These measures would promote economic growth and improve living standards. Keywords: skill-biased labor supply shock; computable general equilibrium (CGE) model; social accounting matrix (SAM); learning-by-doing growth hypothesis; skill mismatch; business as usual (BAU) high growth (LS20); medium growth (LS15); low growth (LS10); investment in education and training programs 1. Introduction 1.1. Background of the Study Since the 1980s, the crucial role of human capital in driving economic growth and reducing poverty has been widely recognized. Over the years, various theories that explore the impact of human capital on economic growth and development have emerged. These theories underscore that human capital comprising the skills, talents, education, and abilities of the workforce plays a fundamental role in the development process. From a Economies 2025,13, 137 https://doi.org/10.3390/economies13050137 Economies 2025,13, 137 2 of 23 macroeconomic perspective, the accumulation of human capital enhances labor productivity, fosters technological innovation, increases returns on capital, and promotes sustainable growth. Human capital development is crucial for achieving sustainable development goals, as it enhances productivity, innovation, and economic growth (see Klarin,2018; Manioudis & Meramveliotakis,2022) . Consequently, it is a key element in the economywide production function and contributes significantly to poverty reduction. At the microeconomic level, education, which serves as a proxy for human capital, enhances individual employability in the labor market and increases earning potential. Thus, at an individual level, human capital directly influences labor productivity and earnings, making it an integral component of firm production. In this context, human capital refers to an individual’s ability and efficiency in transforming raw materials and capital into goods and services. There is a consensus that these skills can be developed through the educational system. In growth theory, Solow-Swan’s growth model is considered the main model of growth theory. Alternative economic growth models are often compared to it. The Solow-Swan growth model, also known as the neoclassical growth model, concludes that long-term economic growth cannot be achieved by simply accumulating capital without technological progress. This is because of the assumption of diminishing returns, which eventually limits growth (Savvides & Stengos,2009;Acemoglu et al.,2019;D. Romer,2019). Technological improvements are necessary to overcome the effects of diminishing returns, as they make factors, such as labor, more productive. In the human capital extended neoclassical growth model proposed by Mankiw et al. (1992), both physical and human capital are included in the production function. It was found that technological progress is still the main source of growth (Savvides & Stengos,2009). An alternative growth model to counter the impact of diminishing returns is to consider a production function that does not adhere to the principle of diminishing returns. This model, known as the AK model, assumes that the output is a linear function of capital. Consequently, economies characterized by this type of production function can continuously accumulate physical capital without experiencing diminishing returns. The AK model appears to be a modified version of the Harrod-Domar growth model, which posits that the rate of output growth is determined by the ratio of savings and capital-output ratios. The proposition of this growth model is that, given the low savings rate and high capital-output ratio resulting from capital scarcity in many developing countries, it is crucial to accelerate growth by increasing the savings rate. This will enable the absorption of the growing labor force and reduce poverty (Ararat,2009). However, in the endogenous growth model, the process of technical change or knowledge accumulation is not constant or predetermined; it depends on the specific features of the model. Knowledge accumulation can be understood as the adoption of new production techniques, the implementation of new management and organizational structures, and the accumulation of scientific knowledge. Such knowledge accumulation can enhance the level of human capital in the economy, which in turn has a positive effect on economy’s productive capacity (Savvides & Stengos,2009). Therefore, the accumulation of human capital is useful in overcoming the limitations of diminishing returns and can lead to economic growth, even in the absence of technological progress, at least in the short term. It is widely acknowledged that sustainable long-term growth can be achieved through continuous improvement of the production process through the adoption of new technologies and methods. In this context, P. M. Romer (1990) argues that the development of human capital leads to higher economic growth, as educated workers contribute to innovation through research and development activities (P. M. Romer,1990). Thus, human capital plays a Economies 2025,13, 137 3 of 23 crucial role in increasing productivity rates by facilitating the faster rate of innovation in domestic products and services, as well as by imitating technologies developed elsewhere. 1.2. Human Capital Development in Ethiopia Ethiopia, home to a population of over 120 million in 2021, is the second most populous country in Africa. The country has a youthful population, with 40% of Ethiopians under the age of 15. However, the agricultural sector, which is facing a decline in available land per farmer, may struggle to provide employment opportunities for this growing youth bulge. Schmidt and Woldeyes (2019) have shed light on this issue, indicating that without proactive management, these young individuals may face unemployment, leading to untapped potential and discontent. However, with the right policies in place, Ethiopia’s youthful population could become an asset in driving the country’s development agenda, which is aimed at poverty reduction. From an individual standpoint, research suggests that individuals with higher levels of education tend to have better employment prospects, higher earnings, and greater productivity compared to those with lower levels of education. These findings have provided a strong rationale for governments and households to invest a significant proportion of their resources in education. The hope is that these investments will yield long-term benefits for both the economy and households. Education is seen as an investment that equips individuals with the knowledge and skills necessary for improved employment opportunities and enhanced productive capacities. Recognizing that education plays a critical role in preparing the youth for the transition from an agrarian to a modern society, the Government of Ethiopia has allocated substantial funds to the education sector in recent years. As a result, the number of formal education centers has increased, leading to a significant rise in youth enrollment. Since 2015/16, school enrollment has exceeded 20 million and 2.4 million students in primary and secondary schools, respectively as shown in Table 1. The number of primary and secondary schools has reached 37,742 and 3687, respectively, in the 2019/20 academic year. The student population in primary and secondary schools has surpassed 20.4 million and 3.4 million, respectively, in 2019. Additionally, the number of technical and vocational education and training (TVET) centers has also seen growth. Table 1. Formal education developments. Particulars 2014/15 2015/16 2016/17 2017/18 2018/19 2019/20 2020/21 Primary schools 33,373 34,867 35,887 36,437 37,039 37,742 35,980 Secondary schools 2830 3156 3380 3589 3739 3687 3481 No. of universities 33 37 37 49 50 55 56 School enrolment Primary (million) 18.7 20 20.8 20.7 20.04 20.4 18.4 Secondary (million) 2.11 2.42 2.56 2.67 2.82 3.47 3.54 TVET (thousands) 265.75 304.14 302.08 292.38 317.73 386.81 283.97 Source: (National Bank of Ethiopia,2021). Despite the commendable increase in the number of students, there are concerns regarding the quality of education, as a large portion of primary school students do not progress to secondary education. Furthermore, there exist regional disparities in terms of access to education. The fact that students fail to complete secondary education decreases their likelihood of benefiting from employment opportunities. Economies 2025,13, 137 4 of 23 Ethiopia has also prioritized technical and vocational education and training (TVET) as part of its education strategy. The TVET centers aim to develop the skills of the youth and help them enter the labor market. As of 2015/16, there were over 300,000 students in TVET institutions. However, Krishnan and Shaorshadez (2013) argue that the TVET program in Ethiopia has been supply-driven, despite the strategy emphasizing flexibility in accommodating the demand for TVET students (Krishnan & Shaorshadez,2013). Investing in education is a crucial policy tool available to policymakers. The Incheon Declaration for implementing Sustainable Development Goal 4 recommends allocating 4% to 6% of the GDP or 15% to 20% of total government expenditure to education (UNESCO, 2015). In Ethiopia, as illustrated in Figure 1, the share of education expenditure as a proportion of total government expenditure has increased, reaching 24% in 2018 compared to 14% in 1994. In fact, Ethiopia has surpassed the internationally set target of 20% total expenditure on education. Figure 1. Education expenditure (% total government expenditure). Source: Ministry of Finance (2021–2022). As illustrated in Figure 2, Ethiopia has consistently allocated more than 4% of its GDP to education since 2005. This indicates a substantial commitment to advancing education within the country. Consequently, there has been notable progress in expanding access to education. Nevertheless, there is anecdotal evidence suggesting that the anticipated impact on development has not been fully realized. According to Pritchett (2001), this lackluster performance in various developing nations can be attributed to factors such as unfavorable governance conditions, diminishing marginal returns, and inadequate education quality (Pritchett,2001). As of 2018, the total education expenditure amounted to USD 3.4 billion (or ETB 92.6 billion), showing significant growth from just USD 1 billion a decade ago. Over the past few decades, education expenditure has increased both in terms of share and absolute levels, as well as per capita. Despite the substantial public investment in education, the share of national income/gross national income (34%) in 2016 still went to laborers without formal education, although there was a slight decrease compared to 2011. Progress in this area has been slower than anticipated, considering the amount invested in the education sector. Economies 2025,13, 137 5 of 23 Figure 2. Education expenditure (% GDP). Source: Ministry of Finance (2021–2022). The educational strategy has been carefully designed, with special attention given to technical and vocational education and training (TVET) as well as science and technology education. Furthermore, it is expected that the human capital development strategy will prioritize the linkage between universities (and TVET institutions) and industries. It is widely acknowledged that in-service training plays a crucial role in enhancing human capital development, as workers are more likely to specialize and acquire skills with moderate levels of training. Tables 2and 3illustrate an interesting trend: a shift in the utilization of human capital within the economy. The proportion of unskilled labor (Lab— No Education) has declined from 36.47% in 2011 to 34.04% in 2015/16, while the share of skilled labor (Lab—Tertiary Education) has increased from 5.70% in 2011 to 8.46% in 2015/16. This is accompanied by a rise in the contribution of non-agricultural capital, which has grown from 26.45% in 2011 to 36.76% in 2015/16. These figures indicate a structural transformation, suggesting that the economy is shifting its focus towards sectors with higher productivity, such as industry and services, and away from agriculture. Given the government’s substantial investment in the education sector, which facilitates access to formal education and in-service training for a large proportion of the youth population, it is crucial to evaluate the contribution of skilled and semi-skilled workers to Ethiopia’s economic growth. Table 2. The share of factors in value added in 2011. Factors of Production Share of Income Lab—No Education 36.47 Lab—Primary Education 7.09 Lab—Secondary Education 7.37 Lab—Tertiary Education 5.70 Capital Land Rural 11.88 Capital Livestock Rural 4.07 Non-Agricultural Capital 26.45 Source: IFPRI SAM for 2011 (IFPRI,2011). Economies 2025,13, 137 6 of 23 Table 3. The share of factors in value added in 2016. Factors of Production Share of Income Lab—No Education 34.05 Lab—Primary Education 7.37 Lab—Secondary Education 3.29 Lab—Tertiary Education 8.46 Capital Land Rural 7.32 Capital Livestock Rural 2.75 Non-Agricultural Capital 36.76 Source: PSI-EU SAM (PSI-EU,2016). 1.3. Objective The objective of this study is to analyze the economy-wide impact of skilled biased technological progress on economic growth and income distribution. The specific objectives are the following: (i) Analyze the effect of human capital development on economic growth and on sectoral output growth. (ii) Analyze the effects of skilled and semi-skilled labor supply shock on economic growth and other macroeconomic performances. (iii) Analyze the contribution of skilled and semi-skilled labor supply shock on sectoral growth and structural transformation. (iv) Investigate the effect of skilled biased labor supply shocks on factor income, government revenue and income changes by types of households. This study is organized as follows. Section 2provides a brief review of the literature on the relationship between human capital and economic growth. Section 3describes the database and methodology used in the study. Section 4presents the empirical results and discusses the main findings. Finally, Section 5concludes the study and offers recommendations based on the findings. 2. Literature Review The theory of economic growth has undergone significant development throughout various historical periods, resulting in the emergence of different perspectives. Modern growth theory, which traces its origins back to the influential work of Frank Ramsey in 1928, posits that prioritizing savings over current consumption yields higher levels of future consumption and output. However, in the aftermath of World War II, growth economists began formulating models based on the economies of developed nations, placing emphasis on factors like capital accumulation, technological advancements, and human capital as catalysts for economic growth. The Harrod-Domar model, which is widely regarded as the first growth model in modern growth theories, emerged in the aftermath of the Great Depression. It provided a historical perspective and analyzed economic growth. The model synthesized the findings of studies conducted by Roy Harrod and Evsey Domar and explained an economy’s growth rate in terms of savings and capital (Harrod,1939;Domar,1946). Essentially, the model is a Keynesian model of economic growth that links an economy’s growth rate to the level of savings and capital. According to the Harrod-Domar growth model, the maximum rate of capital stock growth is determined by the ratio of the savings rate to the capital output ratio, assuming a historically determined and constant savings rate and capital output ratio (Ararat,2009). If we assume that the rate of capital stock growth is the same as the rate of Economies 2025,13, 137 7 of 23 economic growth, and that the capital-output ratio is constant, then the economic growth rate becomes an increasing function of the savings rate. The implication of the Harrod-Domar model is that, due to a low savings ratio but a high capital output ratio in developing countries, there will be a low growth a priori that cannot absorb the rapidly growing population. To address this, countries must accelerate economic growth beyond the traditional limits of the savings rate and capital output ratio by increasing the savings rate to a level that generates sufficient rates of economic growth to absorb the new labor force. The model assumes a direct relationship between output and capital stock, emphasizing the importance of physical capital accumulation (Savvides & Stengos,2009). However, the Harrod-Domar model and other earlier models neglected the role played by technological advance and human capital in economic growth in their analyses. On the other hand, the Solow-Swan neoclassical growth model is widely regarded as a foundational growth theory against which alternative models are refined and referenced. The model demonstrates that at the steady state, there is zero growth. An increase in savings leads to temporary growth, but without sustained increases, growth cannot be sustained indefinitely. The Solow-Swan model also provides insights into how economies can achieve an alternative steady state with positive economic growth (Savvides & Stengos,2009; Ararat,2009). The model is based on a production function that incorporates diminishing returns in the factors of production (capital and labor) and constant returns to scale. It also assumes the Inada conditions 1 and the essentiality 2 of capital, as well as a constant savings rate where households save a fixed proportion of their income (Savvides & Stengos,2009; Acemoglu,2009;Robert & Xavier,2004;Philippe & Peter,2009).The Solow-Swan model demonstrates how economic policy can boost the rate of economic growth by increasing the savings rate. However, this increase in growth is not sustainable in the long run, as it eventually leads to zero economic growth and stagnation. Consequently, countries with higher savings rates will have higher per capita income in the steady state than poorer countries, but they will experience zero economic growth in the long term. In order for long-term growth to occur, there needs to be a way to overcome diminishing returns and allow for the accumulation of productive inputs over time, such as through the introduction of technological progress. The key takeaway from the basic Solow-Swan model is that capital accumulation alone, without technological progress, cannot drive long-term economic growth. As diminishing returns set in, the economy will reach a point of steady state and cease to grow. This steady state property remains unchanged even with the extension of the Solow-Swan model by Mankiw et al. (1992), which incorporates an aggregate output function involving physical capital, human capital, and labor measured in efficiency units. Another extension of the Solow-Swan model, known as the Cass-Koopmans-Ramsey model (CKR model), relaxes the assumption of constant savings and incorporates consumer maximizing behavior, but it does not lead to qualitatively different equilibrium outcomes from the basic SolowSwan model. According to endogenous growth theory, the accumulation of human capital plays a crucial role in economic growth. Various cross-country studies suggest that educational attainment can significantly contribute to overall output production in an economy. The process of technological change and knowledge accumulation takes various forms, ranging from basic research to practical skills used at the firm level. Implementation of new production techniques, management practices, and organizational structures are all part of this knowledge accumulation process. Each form of knowledge accumulation has a different impact on the productive capacity of the economy. Economies 2025,13, 137 8 of 23 Therefore, the endogenous growth model introduces the concept of human capital, which refers to the skills and knowledge that enhance workers’ productivity. Recent studies, including Aghion et al. (2021), demonstrate that skilled workers facilitate technological diffusion, particularly within high-tech industries. This finding aligns with the conclusions of Bahar et al. (2020) from the World Bank, which indicate that the migration of skilled labor stimulates innovation in destination countries and contributes to economic growth. The implication is that unlike physical capital, which exhibits constant returns, human capital yields increasing rates of return. As a result, economies never reach a steady state, and growth does not slow down as capital accumulates. It is crucial to acknowledge that modern growth theory has faced criticism from various economists. They contend that the theory oversimplifies the complexities involved in influencing economic growth. Additionally, some argue that the theory is more suited to developed countries rather than developing ones. Nevertheless, notwithstanding these limitations, modern growth theory retains its significance as a framework for comprehending the determinants of economic growth. Notably, it has been employed to shape policies in numerous countries across the globe. 3. Methods and Database 3.1. Introduction Ethiopia possesses a significant human capital with a projected population of over 120 million by 2021. The presence of a large young population in a country enhances the potential for bolstering human capital and supporting the economy. Acknowledging the importance of human development, the Government of Ethiopia has allocated a substantial budget to the education sector. In a country like Ethiopia, where considerable resources are invested in human capital development, labor assumes a crucial role as a means of livelihood for many individuals. Consequently, labor utilization and labor-related policies exert far-reaching implications on the economy and livelihoods. Therefore, in order to assess the impact of alternative labor utilization and labor-related policies, it is essential to employ a comprehensive framework that considers the economy as a whole. This entails analyzing the contribution of human capital development to economic growth and other associated effects. By utilizing a Computable General Equilibrium (CGE) model, it becomes possible to delineate the entire economy and examine the influence of policy changes on both the overall economy and its sectoral breakdown. Furthermore, this framework allows us to evaluate the distributional impacts of policy changes on households and government income, a crucial aspect for financing development projects and other public services. This study employs a Computable General Equilibrium (CGE) model analysis to gain deeper insights into the contribution of education or human capital development to economic growth and various sectoral effects in Ethiopia. 3.2. The CGE Model Description and Scenarios In this study, we utilize the recursive dynamic version of the IFPRI CGE model (Thurlow,2008;Dorosh et al.,2011). 3 The model effectively simulates the operation of an economy and tracks comprehensive backward and forward linkages between various economic actors. The model is designed as a system of simultaneous linear and non-linear equations, in line with the behavior of economic agents. Additionally, it incorporates the economic environment in which these agents function. This environment is commonly characterized by market equilibrium conditions, macroeconomic balances, and dynamic updating equations (Thurlow,2008). Economies 2025,13, 137 15 of 23 4.1.2. Impact on Sectoral Output The major effects of different skilled and semi-skilled labor supply shocks on the resulting sectoral compositions are outlined in Table 7. Table 7. Simulation results of sectoral output growth. Sector BAU LS10 LS15 LS20 % Change from BAU LS10 LS15 LS20 Agriculture 5.42 5.51 5.54 5.58 0.09 0.13 0.16 Industry 4.94 6.47 6.51 6.55 1.53 1.57 1.61 Services 4.54 5.47 5.52 5.58 0.93 0.99 1.04 Total VAD FC 5.02 5.76 5.81 5.86 0.74 0.79 0.84 Source: Model simulation results based on PSI-EU-SAM. First, the agriculture sector experiences the lowest benefits across all scenarios. In the high growth scenario (LS20), the annual production of the agriculture sector only increases marginally by 0.16 percentage points compared to the BAU scenario. Similarly, in the medium growth scenario (LS15), the growth in the agriculture sector output is modest, with only a 0.13 percentage point increase compared to the BAU scenario. This increase is still lower than the impact on GDP. In other words, the impact on agriculture, in proportion, is lower than the impact on GDP. It is found in the literature that the impact of tertiary education on agricultural productivity can be insignificant and even negative, particularly (Reimers & Klasen,2013). Our analysis shows that agriculture benefits less from skilled and semi-skilled labour compared to industry and service sectors, which are more knowledgeintensive. In addition to the above mentioned findings in the literature, this could be due to structural challenges that limit access to modern technology which requires higher level of education, such as very small land size which average land holding less than 1 hector per holder. In the high growth scenario, the impact on GDP is on average 0.84 percentage points higher per annum compared to the BAU scenario. Similarly, in the medium growth scenario, the impact on GDP is 0.74 percentage points higher compared to the BAU scenario. In the low growth scenario (LS10), the impact on agricultural outputs is still minimal but positive, and much smaller than the impact on GDP. The reason for the low impact on agriculture in the simulation results is that the agriculture sector relies more on unskilled labor, whose supply is reduced as more workers migrate to the industry and services sectors following improved human capital development. Second, although the contribution to GDP of the industry sector is still small, its role has been improving over time. Under the high growth scenario (PS20), the industry’s contribution to GDP is 1.61 percentage points higher than the BAU scenario. Similarly, under the medium growth scenario (LS15), the industry’s contribution increases by 1.57 percentage points compared to the BAU scenario. The impact on the services sector is also relatively better compared to agriculture in all scenarios. In the high growth scenario (LS20), the services sector grows by approximately 1.04 percentage points higher than the growth in the BAU scenario. In the medium growth scenario (LS15), the services sector grows by nearly 1.0 percentage point higher than the growth in the base case (BAU) scenario. Likewise, in the low growth scenario (LS10), the services sector grows by a margin of 0.93% compared to the growth under the BAU scenario, which is still higher than the impact on GDP. The higher impact on the industry sector is partly due to the moderate skill-intensive nature of manufacturing activities compared to agricultural activities. The same is true for the services sector. Economies 2025,13, 137 16 of 23 4.1.3. Factor Income Effect Table 8presents the impact of different scenarios on employment income in the country. The scenarios demonstrate a decrease in income share for unskilled and semi-skilled labor by 2026, attributed to a decline in the supply of unskilled labor and an increase in the supply of skilled and semi-skilled labor. The annual compounded growth rates also indicate a decrease in wage income for these labor categories. However, the total income for skilled labor increases, despite a decrease in wage rates, due to a higher proportion of skilled labor being employed in the economy compared to the BAU scenario. The greatest increase in income for skilled labor is observed in the low growth (LS10) scenario, followed by the medium growth (LS15) scenario, possibly due to a moderate decline in wage rates in these scenarios. Table 8. Simulation results of factor income. Annual Compounded Average % Changes from INITIAL Labour Type BASE LS10 LS15 LS20 lab-n 4.53 4.26 4.31 4.36 lab-p 5.47 4.29 4.30 4.30 lab-s 5.47 4.98 4.97 4.97 lab-t 1.35 3.91 3.83 3.75 Capital Land Rural 6.17 6.8 6.86 6.92 Capital Livst Rural 5.61 5.72 5.78 5.83 Non-Agg-capital 4.86 2.88 2.94 3.00 Source: Model simulation results based on PSI-EU-SAM. Income from rural land and livestock shows a slight increase in all scenarios, with the highest increase in rental income observed in the high growth (LS20) scenario. This suggests that the productivity of land and livestock improves with the increase in skilled and semi-skilled labor supply. On the other hand, income from non-agricultural capital is lower in all scenarios compared to the BAU scenario. This may be attributed to the low marginal productivity of capital resulting from the sudden increase in supply of skilled and semi-skilled labor, without a corresponding increase in capital. 4.1.4. Impact on the Income of Government and Poor Households Figure 5illustrates that the change in government revenue, compared to the business as usual (BAU) scenario, exhibits an upward trend. Specifically, under the low growth (LS10) scenario, government revenue surpasses its BAU equivalent by 0.29%. While the agricultural sector may not be a primary source of direct tax revenue for the government, a surge in skilled and semi-skilled labor presents a significant opportunity. This is because these workers are typically employed in industries and services, which are generally easier and more efficient to tax than agriculture. Consequently, a shift towards a labor supply biased towards skilled individuals can result in a substantial increase in government tax revenue. In fact, in the 2015/16 period, tax revenue from agricultural income and rural land use fees accounted for only 0.04% of GDP (Mengistu et al.,2018). Consequently, it is noteworthy that government revenue experiences a marginal increase by augmenting the outputs of non-agricultural sectors. Economies 2025,13, 137 17 of 23 Figure 5. Government income (% change). Source: Model simulation results based on PSI-EU-SAM. A thorough analysis of the macro simulation results reveals a significant surge in imports and a decline in household consumption. Given that imports contribute significantly to tax revenue, the increase in imports has generated more tax revenue than the potential loss of revenue due to the decrease in domestic consumption. In simpler terms, the fiscal impact of the shock is reflected in the reduction of domestic tax revenues resulting from lower household consumption and the increase in international trade taxes from higher imports. This holds true across all scenarios, with the high growth (LS20) scenario generating more revenue compared to the others. This is because income in the BAU scenario is derived from self-employment, which is not subject to taxation. However, in the high growth (LS20) and medium growth (LS15) scenarios, increased income arises from formal sector employment, which is taxable due to productivity improvements. Although it is interesting to understand the full extent of income distribution resulting from changes in labor utilization, it is particularly important to examine the impact on individuals living in poverty and the middle class (excluding those in the top 20%), as any change in their income can have significant consequences. Table 9presents the income distribution among different household groups in all scenarios. In comparison to the business as usual (BAU) scenario, income decreases for all types of households. However, in relation to the initial income level, rural households experience a slight increase in income compared to urban households. Table 9. Simulation results of income distribution by poverty household groups. Annual Compound % Changes from INITIAL Household Type INITIAL BASE LS10 LS15 LS20 Rural poor 164.83 4.81 4.47 4.52 4.57 Rural middle class 529.38 4.67 4.25 4.30 4.34 Rural rich 162.03 4.44 4.04 4.07 4.10 Urban poor 19.96 4.00 3.36 3.40 3.43 Urban middle income 179.50 4.28 3.50 3.54 3.57 Urban rich 303.14 4.00 3.64 3.65 3.66 Source: Model simulation results based on PSI-EU-SAM. To understand these patterns, it is important to note that the expansion of skilled and semi-skilled labor has a minimal impact on the income of those living in poverty, as they already lack skills. When considering the overall impact, the decline in income is significant in the low growth (LS10) scenario. In summary, it is worth noting that in all scenarios, the increase in skilled and semi-skilled labor supply shock has a negative impact compared to the business as usual (BAU) scenario. Economies 2025,13, 137 18 of 23 5. Conclusions and Recommendations We conducted a comprehensive analysis of the effect of human capital on the economy using the Computable General Equilibrium (CGE) model approach based on the Social Accounting Matrix (SAM) of PSI to investigate the impact of skill-biased labor supply shocks on the Ethiopian economy. We developed three plausible scenarios for CGE analysis: high growth (LS20) scenario, medium growth (LS15) scenario, and low growth (LS10) scenario. These scenarios were compared to a “Business As Usual” (BAU) scenario to assess the potential effects. In the high growth (LS20) scenario, which models a 20% increase in skilled and semiskilled labor supply, we observed substantial improvements in GDP, non-agricultural sector output, exports, fixed investments, imports, and net indirect tax. Similarly, the medium growth (LS15) scenario, assuming a 15% increase in skilled and semi-skilled labor supply, demonstrated positive impacts on growth indicators. Finally, the low growth (LS10) scenario, with a 10% increase in skilled and semi-skilled labor supply, allowed us to assess the economy-wide and sectoral output effects. Our findings revealed four noteworthy results. Firstly, both GDP and non-agricultural sector output experienced significant growth in the high growth (LS20) and medium growth (LS15) scenarios, as well as in the low growth (LS10) scenario although the impact is minimal. Export performance, fixed investments, imports, and net indirect tax also showed promising improvements under these scenarios. Secondly, growth in these scenario surpassed that of the BAU scenario, indicating that skill-biased labor expansion has the potential to support further growth. This implies that there is room for growth before reaching a limit. Exports, gross fixed investments, imports, government revenue, and tax revenues substantially increased in these scenarios. Thirdly, the impact on poor households in the LS20 scenario was comparable to that of the BAU scenario, implying that this scenario yields minimal negative outcomes for these vulnerable groups. Lastly, we observed that the response to skill-biased labor supply shocks is not immediate. Instead, notable effects materialize several years after the implementation of the shock, which can be attributed to the time required to internalize the skills and knowledge of human capital. This finding aligns with the learning-by-doing growth hypothesis. In conclusion, our study underscores the importance of human capital in driving economic growth. We highlight the potential benefits of skill-biased labor supply shocks and stress the time lag in experiencing their effects. These findings contribute to a deeper understanding of the role of human capital in economic development. The study concludes that implementing appropriate policy measures is crucial to stimulate the economy and optimize the allocation of available resources for sustainable economic growth. Failure to take immediate action will result in suboptimal outcomes with lasting impacts. Policies aimed at sustaining growth and efficiently utilizing abundant resources, such as labor, in developing countries should be evaluated not only for their immediate growth impact but also for future development. It is of utmost importance to align skills training and labor market demands to ensure that an excess supply of labor yields positive effects on the economy. As such, employment and education policies should be synchronized with the economy’s needs, and their effectiveness should be continuously monitored. Based on the study’s findings, we recommend that governments: • Invest in education and training to enhance the supply of skilled and semi-skilled labor, thus facilitating economic diversification beyond agriculture. • Design educational programs that equip graduates with skills that are currently in demand in the labor market. This will improve employability and reduce skill mismatches. • Regularly monitor education and employment policies to evaluate their effectiveness in aligning with the economy’s requirements, making adjustments as necessary. Economies 2025,13, 137 19 of 23 • Expand financial inclusion to entrepreneurs and small businesses, enabling their growth and job creation, particularly in non-agricultural sectors where governments can collect increased tax revenues. Author Contributions: This research was initiated by F.B.W., with the concept note further developed in collaboration with researchers from PSI. The theoretical frameworks and secondary data analysis were conducted by A.K.Y. In relation to the CGE model simulation, both F.B.W. and A.K.Y. performed the simulation analyses and interpreted the results. Both authors contributed to and discussed the literature review. All authors have read and agreed to the published version of the manuscript. Funding: This study was conducted with funding from African Economic Research Consortium (AERC), under the AERC collaborative project on “Human Capital Development in Africa”, Grant No. RC21639. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The main dataset used (PSI_EU SAM) is publicly available at PSI websites and JRC Publications Repository (https://publications.jrc.ec.europa.eu/repository/handle/ JRC118413, accessed on 6 February 2024) and annual reports of Ministry of Finance (MoF) and National Bank of Ethiopia (NBE). It is also furnished upon request. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A The CGE Model Specification The following tables provide a complete listing of the model’s variables, parameters and equations. Although these tables describe the South African model, it is largely based on the equation listing found in the Standard Computable general Equilibrium (CGE) Model of Lofgren et al. (2002) International Food Policy and Research Institute (Lofgren et al.,2002). However, the equation numbers do not correspond to those found in Lofgren et al. (2002). Rather, the ordering of equations follows the description of the model found in Section 2of this paper. Table A1. Model sets, parameters, and variables. Symbol Sets Explanation Symbol Explanation a∈AActivities c∈CMR(⊂C)Regionally imported commodities a∈ALEO(⊂A)Activities with a Leontief function at the top of the technology nest c∈CMNR(⊂C)Non-regionally imported commodities c∈CCommodities c∈CT(⊂C)Transaction service commodities c∈CD(⊂C)Commodities with domestic sales of domestic output c∈CX(⊂C)Commodities with domestic production c∈CDN(⊂C)Commodities not in CD f ∈FFactors c∈CE(⊂C)Exported commodities i∈INS Institutions (domestic and rest of world) c∈CEN(⊂C)Commodities not in CE i ∈INSD(⊂INS)Domestic institutions c∈CM(⊂C)Aggregate imported commodities i∈INSDNG(⊂INSD)Domestic non-government institutions c∈CMN(⊂C)Commodities not in CM h ∈H(⊂INSDNG)Households Economies 2025,13, 137 20 of 23 Table A1. Cont. Symbol Sets Explanation Symbol Explanation Parameters cwtscWeight of commodity cin the CPI pwmcImport price (foreign currency) dwtscWeight of commodity cin the producer price index pwmrcr Import price by region (foreign currency) icaca Quantity of cas intermediate input per unit of activity aqdstcQuantity of stock change icdcc′ Quantity of commodity cas trade input per unit of c’ produced and sold domestically pgc Base-year quantity of government demand icecc′ Quantity of commodity cas trade input per exported unit of c’ qinvc Base-year quantity of private investment demand icercc′r Quantity of commodity cas trade input per exported unit of c’ from region rshi fif Share for domestic institution iin income of factor f icmcc′ Quantity of commodity cas trade input per imported unit of c′shiiii′ Share of net income of i′to i (i′∈INSDNG′;i∈INSDNG) icmrcc′r Quantity of commodity cas trade input per imported unit of c’ from region rtaaTax rate for activity a intaaQuantity of aggregate intermediate input per activity unit tinsiExogenous direct tax rate for domestic institution i ivaaQuantity of aggregate intermediate input per activity unit tins01i 0–1 parameter with 1 for institutions with potentially flexed direct tax rates mpsiBase savings rate for domestic institution i tmcImport tariff rate mps01i0–1 parameter with 1 for institutions with potentially flexed direct tax rates tmrcr Regional import tariff pwecExport price (foreign currency) tqcRate of sales tax pwercr Export price by region (foreign currency) trns f rif Transfer from factor f to institution i Greek Symbols αa a Efficiency parameter in the CES activity function δt cCET function share parameter αva a Efficiency parameter in the CES value-added function δva f a CES value-added function share parameter for factor fin activity a αac c Shift parameter for domestic commodity aggregation function γm ch Subsistence consumption of marketed commodity cfor household h αq cArmington function shift parameter θac Yield of output cper unit of activity a αt cCET function shift parameter ρa aCES production function exponent αm c Shift parameter in the CES regional import function ρva a CES value-added function exponent αe c Shift parameter in the CES regional export function ρac c Domestic commodity aggregation function exponent βaCapital sectoral mobility factor ρq cArmington function exponent βm ch Marginal share of consumption spending on marketed commodity cfor household hρt cCET function exponent δa aCES activity function share parameter ρm c Regional imports aggregation function exponent δac ac Share parameter for domestic commodity aggregation function ρe c Regional exports aggregation function exponent Economies 2025,13, 137 21 of 23 Table A1. Cont. Symbol Sets Explanation Symbol Explanation δq cArmington function share parameter ηa f at Sector share of new capital υfCapital depreciation rate Exogenous Variables CPI Consumer price index MPSADJ Savings rate scaling factor (=0 for base) DTINS Change in domestic institution tax share (=0 for base; exogenous variable) QFSfQuantity supplied of factor FSAV Foreign savings (FCU) TINSADJ Direct tax scaling factor (=0 for base; exogenous variable) GADJ Government consumption adjustment factor WFDISTf a Wage distortion factor for factor fin activity a IADJ Investment adjustment factor Endogenous Variables AWFa f t Average capital rental rate in time t QFf a Quantity demanded of factor ffrom activity a DMPS Change in domestic institution savings rates (=0 for base; exogenous variable) QGcGovernment consumption demand for commodity DPI Producer price index for domestically marketed output QHch Quantity consumed of commodity c by household h EG Government expenditures QHAach Quantity of household home consumption of commodity cfrom activity afor household h EHhConsumption spending for household QINTAaQuantity of aggregate intermediate input EXR Exchange rate (LCU per unit of FCU) QINTca Quantity of commodity cas intermediate input to activity a GOVSHR Government consumption share in nominal absorption QINVcQuantity of investment demand for commodity GSAV Government savings QMc Quantity of imports of commodity c INVSHR Investment share in nominal absorption QMRcr Quantity of imports of commodity c by region r Endogenous Variables Continued PMSi Marginal propensity to save for domestic non-government institution (exogenous variable) QERcr Quantity of exports of commodity c to region r PAaActivity price (unit gross revenue) QQc Quantity of goods supplied to domestic market (composite supply) PDDcDemand price for commodity produced and sold domestically QTcQuantity of commodity demanded as trade input PDSc Supply price for commodity produced and sold domestically QXAaQuantity of (aggregate) value-added PEcExport price (domestic currency) QXcAggregated quantity of domestic output of commodity PERcr Export price by region (domestic currency) QXACac Quantity of output of commodity c from activity a PINTAaAggregate intermediate input price for activity aRWFfReal average factor price Economies 2025,13, 137 22 of 23 Table A1. Cont. Symbol Sets Explanation Symbol Explanation PKf t Unit price of capital in time t TABS Total nominal absorption PMcImport price (domestic currency) TINSiDirect tax rate for institution i (i∈INSDNG) PMRcr Import price by region (domestic currency) TRIIii′ Transfers from institution i′to i (both in the set INSDNG) PQcComposite commodity price WFfAverage price of factor PVAaValue-added price (factor income per unit of activity) YFfIncome of factor f PXcAggregate producer price for commodity YG Government revenue PXACac Producer price of commodity cfor activity aYIiIncome of domestic non-government institution QAaQuantity (level) of activity YIFi f Income to domestic institution i from factor f QDcQuantity sold domestically of domestic output ∆Ka f at Quantity of new capital by activity afor time t QEcQuantity of exports Source: South African Model and Lofgren et al. (2002). Notes 1 Marginal product of capital (or labour) approaches infinity as capital (or labour) goes to 0 and approaches 0 as capital (or labour) goes to infinity. 2An input is essential if a strictly positive amount is needed to produce a positive amount of output. 3Detailed description of the models and their workings are presented in the Appendix A. 4 That is, in this scenario, skilled and semi-skilled labour supply is increased by 2.97% (2.7 + 2.7 × 0.1) and in the second scenario, skilled and semi-skilled labour increases by 3.105% and in the last scenario ther rate of increase of skilled and semi-skilled labour is 3.24%. 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