Fiscal, Labour Market and Distributive Effects of Educational Reforms: A Hybrid Simulation Study of Free Senior High School Policy in Ghana

Fiscal, Labour Market and Distributive Effects of Educational Reforms: A Hybrid Simulation Study of Free Senior High School Policy in Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fiscal, Labour Market and Distributive Effects of Educational Reforms: A Hybrid Simulation Study of Free Senior High School Policy in Ghana Robert Naatey Angmor, Ricahard Kwabena Nkrumah, Francis Kwaw Andoh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7934227/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study combines the GHAMOD microsimulation model, which is from the pool of SOUTHMOD models by UNU-WIDER, and IFPRI’s RIAPA dynamic CGE model to investigate the fiscal and economy-wide impacts of educational reforms in Ghana’s Free Senior High School (FSHS) policy. Introduced in 2017/2018, the FSHS policy aims to improve access to secondary education, particularly for the poor. However, like several other subsidy interventions in developing countries, the policy has introduced significant fiscal and implementation challenges as well including overstretched facilities, impaired subsidy payment by the government for feeding, teaching and learning materials. Thus, putting a strain on quality of education. The paper simulates the current universal FSHS policy and six alternative reforms that explore efficiency, fiscal sustainability, and social equity. Specifically, we evaluate policy effectiveness and distributive effects of the status quo ‘universal FSHS grant’ with targeted scenarios with respect to (1) a beneficiary’s boarding/residency status (2) poverty status of beneficiary students (means-tested), and (3) capped benefit for all beneficiaries. These scenarios are evaluated on long term impacts on government savings, poverty, inequality, sectoral growth, and labour market outcomes up to 2040. Our findings indicate that targeted reforms do not only lead to increased fiscal savings, which when reinvested offsets non-market incomes of households from the limited grant transfer by stimulating sectorial growth, employment and positive distributive impact on households. We are able to demonstrate that simpler and less-costly non-means-tested schemes based on residency or capped benefits could be deployed to mitigate the fiscal constraints posed by the current form of the policy while yielding significant positive fiscal and distributive impacts. The findings from the study contribute to deepening the FSHS policy discourse in Ghana. Specifically, the simulated reforms could guide the pursuit toward a targeted FSHS policy which is fair and fiscally sustainable. Microeconomics CGE Free Senior High School (FSHS) Simulation Fiscal GHAMOD Distributive Effects Social Protection Educational Reform Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The Free Senior High School (FSHS) policy aligns closely with Ghana’s commitments under the United Nations Sustainable Development Goals (SDGs), particularly SDG 4, which aims to ensure inclusive and equitable quality education for all. By promoting universal access to secondary education, the policy holds promise for addressing several of Ghana’s key development challenges (Chanimbe & Prah, 2020 ). It is anticipated that it will foster economic growth by creating a more educated workforce and support social cohesion by reducing poverty and inequality. However, like several other subsidy interventions in developing countries, the policy has introduced significant fiscal and implementation challenges as well including overstretched facilities, impaired subsidy payment by the government for feeding, teaching and learning materials (Shamo, 2023 ). Thus, putting a strain on quality of education. Over the period of its implementation FSHS in Ghana, the government’s fiscal pressures were further impaired by the double whammy of COVID-19 and the war in Europe between 2020–2022 leading to an IMF bailout in 2023 (Atuahen, Agyei & Frimpong, 2024 ). These have, therefore, invited clarion calls for urgent reforms. Suggestions from a section of scholars and stakeholders have been in favour of a more targeted approach that allows wealthier families to contribute to education costs while supporting free education for the poor might be a viable solution to ensure the program’s sustainability and effectiveness. Hence, by simulating six alternative reform scenarios, this study contributes directly to the discourse on subsidy scheme designs in education in general and the FSHS policy in particular. The proposition to allow wealthier families to pay for secondary education while offering it for free to the poor has also spurred debate about the potential impact on the quality and inclusiveness of the public education system. Critics argue that if wealthier families are allowed or even encouraged to contribute financially, they may seek private or alternative educational options instead of participating in the public system. This could lead to further stratification, where public schools primarily serve lower-income students, and wealthier students migrate to private schools. Such an outcome might undermine the inclusivity goals of the Free SHS policy and reduce the social cohesion fostered by a shared educational experience. From a welfare economics perspective, provision of education generates positive externality in the form of human capital that is often not captured in the market price, leading to overpricing. In this regard, the externality unaccounted for in the market price of education provides a justification for subsidies in education. In addition, financial barriers could restrict access to education leading to full or partial excludability against the poor in the attainment of capability potential. Hence, in most instances, formal education, especially basic education, is subsidized to eliminate or reduce financial hurdles to improve access to quality education in the short term and promote economic development in the long term through human capital development. The human capital theory by Becker ( 1964 ), Mincer ( 1974 ) and Schultz ( 1961 ) supports this notion. These authors view education as a productive investment that enhances individuals’ skills and future earnings, Hence, by eliminating financial barriers, access to education is enhanced, especially for low-income and rural households, thereby increasing human capital accumulation. The improved access is crucial for poverty alleviation through improved labour market outcomes and income growth (Becker, 1993 ; Dang & Dabalen, 2019 ). Likewise, by eliminating educational fees, the Free SHS program expands educational opportunities for marginalised groups, particularly low-income and rural households, thereby enhancing their agency and life prospects (Sen, 1993 ). Education is seen not only as a tool for income generation but as a means of achieving broader well-being and social participation. As Robeyns ( 2005 ) notes, achieving real equality requires more than formal access; it demands substantive opportunities. Furthermore, the approach highlights that inequality reflects deeper disparities in individuals’ capacity to convert resources into valuable outcomes (Nussbaum, 2011 ). Thus, educational subsidies, as in the case of the FSHS, functions as a redistributive policy that promotes inclusive human development and social justice. Empirical evidence shows that by expanding access to education leads to improved income levels and long-term poverty alleviation, particularly in low-income settings (Filmer & Fox, 2014 ; UNESCO, 2020 ). In Latin America, specifically Brazil and Mexico, educational reforms involving conditional cash transfer programs such as “Bolsa Família” and “Oportunidades” incentivized school attendance among marginalized populations, which has led to noticeable increases in enrolment and retention rates (UNESCO, 2020 ). In Asia, educational reforms in countries like India and Indonesia have prioritized teacher training and curriculum modernization to meet labour market demands, contributing to higher graduation rates and better job prospects for graduates (Dhar, 2021 ). Also, Honorati and Santos ( 2024 ) found that a policy-driven increase in school construction in Indonesia led to improved adult wages and reduced poverty among low-income groups. While these regions have unique cultural and socioeconomic landscapes, their shared focus on reforming second-cycle education highlights the critical role of equitable access in fostering economic mobility and reducing social inequalities. In general, improving access to secondary education is associated with significant returns in the labour market, surpassing even primary education in its poverty-reducing effects (Montenegro & Patrinos, 2022 ). In Ghana, evidence points to a marked increase in high school enrolment, with gross enrolment rising from 17.2% in 2016 to 30.7% in 2018 (MoE, 2017). Beyond enrolment, a growing body of research examines the distributional effects of education policies, especially in terms of how benefits are shared across different income groups. A study by Duflo, Dupas, Spelke and Walsh ( 2024 ) showed that access to secondary education in Ghana significantly increases lifetime earnings and improves intergenerational mobility, particularly for children from rural and poor households. Similarly, Psaki et al. ( 2022 ) revealed that removing financial barriers to education improves enrolment and completion rates, especially among girls and the poor. Furthermore, Adu-Ababio and Osei ( 2018 ) used microsimulation models to estimate the potential redistributive effects of Free SHS reforms, finding that targeted policies yield greater reductions in income inequality than untargeted ones. Their findings suggest that while universal access promotes equity in opportunity, carefully designed reforms that address both geographical and income-based disparities have a stronger impact on long-term inequality reduction. Simulations of Free SHS reform scenarios have demonstrated that targeting benefits to the poorest quintiles or capping transfers for wealthier households can significantly improve the policy’s redistributive impact (Adu-Ababio & Osei, 2018 ). Evidence from cross-country studies further corroborates that increased investment in secondary education correlates with lower Gini coefficients, especially in countries with strong public education systems (Mazurek, Fernández-García & Pérez-Rico, 2021 ; UNESCO, 2018 ). Recent empirical studies increasingly highlight the importance of aligning education financing reforms with equity objectives using advanced quantitative tools. Microsimulation models and benefit incidence analysis have emerged as key methodologies for evaluating the distributional effects of education policies (Sologon, Doorley & O’Donoghue, 2023 ). However, other strand of scholars, for example, Getachew ( 2024 ) found that universal education subsidies tend to be regressive in nature unless they are specifically targeted to the poor. On Ghana’s FSHS policy, particularly Essuman ( 2019 ) found that higher-income households could disproportionately benefit from the policy due to existing inequalities in educational preparedness and geographic access. Considering these, some scholars advocate for hybrid models that combine universal access with targeted support to ensure that the poor receive a fairer share of public education spending (Grosh, Leite, Wai-Poi & Tesliuc, 2022 ). Despite the extensive empirical literature on the impacts of educational reforms on access, poverty reduction, income equality, and labour market outcomes, several notable gaps remain. First, many studies (Adu-Ababio & Osei, 2018 ; Essuman, 2019 ; Chanimbe & Prah, 2020 ) focus predominantly on enrolment and completion outcomes, with less emphasis on the long-term macroeconomic and fiscal implications of universal education subsidies, such as Ghana’s FSHS. By ignoring the fiscal implications of the educational reforms, these studies overestimate or underestimate the impacts of educational subsidies given the intersectoral linkages and government fiscal levers. To the best of the authors’ knowledge, no study has attempted to assess the impact of alternative FSHS reforms in a manner as explored in this study by linking a microsimulation with a dynamic computable general equilibrium to assess how targeting mechanisms affect fiscal, labour market outcomes and income distribution. Moreover, existing analyses often lack comprehensive assessments of how education reforms interact with labour markets across skill levels, and how these interactions translate into broader social welfare outcomes over time. The lack of comprehensive empirical assessment of educational reforms from an economy-wide perspective obscures fiscal trade-offs and multi-sectoral impacts but isolate changes to household consumption welfare and demand for education from the rest of the economy. This study aims to fill these gaps by employing a dynamic, economy-wide model linked bottom-up with a detailed tax-benefit microsimulation model to simulate and compare various FSHS policy scenarios, assessing their implications for government expenditure, income distribution, sectoral growth, and labour market dynamics. This approach provides a more comprehensive understanding of the policy’s long-term socioeconomic effects. The remaining sections of the paper present empirical methods including modelling approach, data sources and model calibration, macro closure rules for the general equilibrium model. Subsequently, the third section presents the results from the model estimations while, finally, in-depth discussion of the results and conclusions and policy implications are presented in the fourth and last sections. 2. Methods This study employs a bottom-up approach linking a tax-benefit microsimulation to a computable general equilibrium (CGE) in a macroeconomic framework. The tax-benefit model is based on UNU-WIDER’s SOUTHMOD model for Ghana (GHAMOD). GHAMOD is a tax-benefit microsimulation model for Ghana that is both highly adaptable and user-friendly. It allows the user to compare the effects of different benefit policy scenarios on poverty, inequality, and tax revenues (UNU-WIDER, 2018). The model can simulate several other policies including the Livelihood Empowerment Against Poverty (LEAP) program, the School Capitation Grant, the Free Senior High School Program, social contributions (employer and employee), and taxes (presumptive, VAT, excises). The model estimates the distributional effects of tax and benefit schemes on individuals and households through payments and receipts between government and and households. Although the model is non-behavioural (Adu-Ababio, Osei, Pirttila, & Rattenhuber, 2023), it allows for the inclusion of the variance in attributes of individuals and households to examine the distributional effects of tax and benefit reforms. We pass the changes in government expenditure to the CGE that is based on IFPRI’s Rural Agriculture Investment and Policy Analysis (RIAPA) model. Source: Authors’ construct (2025) The CGE model further estimates the economywide impacts of government reforms in educational reverberating through multiple sectors within the Ghanaian economy. This is possible through forward and backward linkages between the education sector and the rest of the economic sectors. The result could be assessed in the form of full impacts of educational reforms on household poverty, inequality, jobs creation, overall and sectorial GDP growth. 2.1. Microsimulation in GHAMOD First, the FSHS policy is modelled as a universal government subsidy in senior high education. Hence, the subsidy reduces the expenditure on education by eligible households. Thus, given a household expenditure function of the form: $$\:y={p}_{1}{q}_{1}+{p}_{2}{q}_{2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where \(\:y\) is total expenditure, \(\:{p}_{1}\) and \(\:{q}_{1}\) are, respectively, the price and quantity of senior high school (SHS) education. Also, given that \(\:{p}_{2}\) and \(\:{q}_{2}\) , respectively, represent the composite price and quantities of all other goods and services consumed by the household, then for any change in welfare due to an increase in price now depends on the change in quantities consumed when prices are normalised at equilibrium. Thus, the new expenditure function post reform given as $$\:{y}^{{\prime\:}}={{p}^{{\prime\:}}}_{1}{{q}^{{\prime\:}}}_{1}+{p}_{2}{{q}^{{\prime\:}}}_{2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ solved simultaneously with the initial expenditure function yields a change in quantity demanded $$\:{\Delta\:}{q}_{1}=-{q}_{1}d{p}_{1}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ which is shown to be dependent on change in quantity of good 1 (education) and the relative price movements: \(\:{\Delta\:}{p}_{1}/{p}_{1}\) . Finally, changes in quantities consumed, \(\:{\Delta\:}{q}_{1}\) is affected by consumer elasticities, which is typically between \(\:-1<ϵ<0\) , as well as changes in government expenditure, \(\:{\Delta\:}\text{e}\text{x}\text{p}\) , in funding the subsidy: $$\:{\Delta\:}exp=-\:{\sum\:}_{h=1}^{H}{y}_{1,h}d{p}_{1}\left(1-{ϵ}_{1}\left({s}_{1}-d{p}_{1}\right)\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ Where \(\:{s}_{1}\) is initial subsidy before the reform. The final change in government expenditure, \(\:{\Delta\:}exp\) , is passed to the CGE model as a shock to government’s transfer to eligible households. 2.2. Modelling FSHS Reforms in the CGE Model One simple way to implement the FSHS subsidy reforms in the CGE macroeconomic framework is to modify government’s expenditure by the changes in the rates of subsidy on education. This would significantly reduce consumption of education since government is a major financier of education. The consequence would be a significant change in the market price and total supply. The approach, however, affects household consumption only through distortions in the supply as if the reforms are targeting production subsidies. On the contrary, the FSHS subsidies are consumption based. Thus, reforms affect the cost-sharing apportionment between the government and households. In that regard, this study, first, treats the implementation of the policy as a form of lump-sum transfer (inverse of income tax) to household, which is modified through simulated cost-sharing reforms. The effect on households’ expenditure on education is direct through changes in income. In this regard, we show the relevant equations in the IFPRI CGE model affected by our analysis as described below. All other equations and variables in the core CGE model remain unchanged. 2.2.1. Production module Profit maximization drives producer’s behaviours in the market. In this module, the capitalist producer combines factors such as capital (K), labour (L) and intermediate materials (M) in a nested constant elasticity of substitution (CES) function to maximize profits through by influencing quantity supplied. The nested production function is in two layers. In the bottom layer, capital and labour are combined into an aggregate input KL with imperfect substitutability. The aggregate KL input is, therefore, combined at the top layer with intermediate materials in fixed proportions (Leontief function). In both the inputs and the products markets, each producer is considered as small, independent and a price-taker. This means, prices are exogenously determined through the interaction of demand and supply in the market. Profits are redistributed as returns on capital to providers of capital (i.e., enterprises and households) which are partly reinvested through savings and the remaining going into taxes and consumption. 2.2.2. Household module Household income comprises labour wages, capital payments, remittance income, and transfer payments from enterprises and the government. The latter is the conduit through which FSHS reforms affect household incomes, consumption and welfare. That is, households’ disposable income, after income taxes, is spent on goods and services including education. The remainder is then saved for future consumption and investment purposes. In the CGE model, household consumption is modelled as simple linear expenditure system (LES) which is tractable and with least data requirements. 2.2.3. Government module Government derives incomes through taxes and tariff revenues, and foreign income. On the other size, government expenditures include government consumption, transfer payments to households and other countries, and government saving. The consumption expenditure of the government also includes the expenditure on FSHS. 2.2.4. Rest of the world module We apply, in this module, the Armington (1969) proposition of an imperfect substitution between domestic and foreign goods which drives price differences between goods produced domestically and abroad. The price differentiation consequently drives a country’s imports and exports. A CES aggregation function combines imported and domestic goods into an aggregate Armington commodity, which is the total domestic supply. The aggregate Armington commodity goes into consumption, investment, and intermediate inputs. Similarly, a constant elasticity of transformation (CET) aggregation function splits domestic production into goods for domestic consumption and exported (Wang et al., 2017c). For simplicity, we go by the small-country assumption for Ghana, in which case international trade prices are determined by international markets. 2.2.5. Poverty, Inequality and social welfare module The recursive dynamic CGE model in this study is liked to a microsimulation module for poverty analysis. The poverty module is based on the latest living standard survey for Ghana (GLSS 7) that captures demography, income and consumption of 14,009 households. Changes in consumption due to real and policy shocks in incomes and employment are passed from representative households in the macro CGE model to individual household units in the living standard survey to ascertain the number of individuals lifted out or pushed into poverty. Additionally, our macro model captures household welfare changes using the Hicksian equivalent variation (EV) method to measure the impact of educational subsidy reforms on the consuming public. The Hicks equivalent variation is calculated as the change in consumer utility, which considers prices and quantities of education (number of eligible household members currently attending SHS) and other consumer goods, between baseline and the reform scenarios. A positive value of EV denotes an improvement in social welfare while a negative EV implies a decline in social welfare due to the reforms. 2.3. Macro closure rules Macro closure refers to rules for achieving equilibrium in three macro balances: the balances for the government, the savings-investment account, and the current account on the trade with rest of the world. First, government savings are set to be flexible by adjusting endogenously to restore balance to the government account while keeping direct tax income rates fixed. In relation to this study, this rule ensures that the government does not reconfigure revenue (direct tax) handles in its response to real shocks (changes in transfers to households) but accommodate the changes through adjustments in its savings. On the current account front, exchange rates are allowed to adjust freely in response to trade flows with the rest of the world. In that regard, foreign savings are taken to be exogenously determined in the model. Lastly, for the savings-investment balance, we assume a historically consistent balanced closure with a scaled marginal propensity to consume (MPS) for domestic institutions. By this closure, not only does investment change to affect economic growth, other components of absorption (consumption (C), government spending (G)) equally adjust to produce the resultant GDP growth. Other macro closure rules implemented in this study include assumptions on factor employment and mobility, and a national benchmark price for relative price determination. Labour market is considered to surplus supply who unemployed and mobile while capital (including land) is fully employed and activity specific. All markets are cleared at the equilibrium price levels determined endogenously in the model. 2.4. Data sources and model calibration The most recent version of GHAMOD (version 2.7) uses data from the Ghana Living Standard Survey Rounds 6 and 7 (GLSS 6 & 7). The data of interest in this study was derived from the GLSS 7, which is the most recent wave of the GLSS at the time of conducting this study. Hence, the model is calibrated to simulate the costs and benefits associated with the FSHS policy, incorporating detailed household-level data (GLSS 7) to assess its distributional effects across various income groups. The main database for the CGE model calibration, on the other hand, is the 90-sector social accounting matrix (SAM) 2019 constructed based on the Supply-and-Use Table (SUT) 2013 for Ghana. The SUT provides a complete and consistent accounting framework of intra and inter-sectorial and market linkages suitable for CGE modelling. Additional data sources for the SAM include the national accounts, the IMF Financial Statistics, agriculture and enterprise surveys, the living standard surveys, among others. Baseline population, labour supply and GDP growth rates were set to official statistics from the Ghana Statistical Service and national budget documents. Trade (Armington) and production elasticities were calibrated using estimates from the GTAP 11 database. 2.5. The Baseline Context Ghana’s FSHS policy is a universal subsidy to all Ghanaian students attending a public senior high school placed by the Computerised School Selection and Placement System (CSSPS). The government spends a fixed amount of Gh¢1002.47 (US $ 187.06) 1 per annum on each resident beneficiary and Gh¢648.47 per annum on each non-resident beneficiary. These amounts cover the costs of tuition, meals, and other related fees (Chanimbe & Dankwah, 2021 ). The difference between the residential benefit amount and the non-residential benefits are mainly due to meals, boarding facility charges. Each student is expected to enjoy the subsidy for 3 consecutive years, which is the entire duration of senior high school education in Ghana. Baseline estimation using the 2017 GLSS data shows that the most FSHS benefit would accrue to poor rural households (see Fig. 2 ), whereas the urban poor tend to receive the least benefit due to the number of children enrolled in senior high school, distributed across resident and non-residential status at the beginning of the policy. Source: Authors’ computation (2025) using GLSS 7 dataset In the 2019 SAM, education expenditure of GH¢10.1 billion (US $ 1.88 billion) constitutes the second largest government expenditure after health expenditure. A simulation of the GHAMOD model puts government expenditure on the FSHS to be GH¢1.5 billion (US $ 279.97 million), about a tenth of the total government expenditure on education. In the dynamic CGE model, Ghana’s economy is expected to grow at 6 percent per annum in the business-as-usual scenario with a 2.3 percent annual population growth. Finally, the FSHS policy is expected to increase the supply of labour with secondary education or higher for those who continue to tertiary education. Hence, we assume a decline in supply growth rate of labour with no education or primary education in both rural and urban areas into the long term, between 1.8 to 1.2 percent per annum while increasing same for secondary and tertiary education levels, especially in urban areas (see Fig. 3 ). Source: Authors’ own estimates 2.6. Scenario Design The analysis explores proposed reforms in the FSHS policy and examines the impacts on fiscal balance, labour market outcomes and distributive effects on households. Table 1 describes each policy reform implemented in this study. The impact assessment of each reform scenario is implemented from 2020 to 2040. Table 1 Policy Descriptions Reform Scenario Policy Description Scenario 1 - Non-Means-Tested Targeting for Non-resident Only [NMTT-NR Only] A targeted FSHS benefit for non-resident beneficiaries only, regardless of poverty status. This non-means-tested approach modifies what is known to be the status-quo by targeting only non-resident students, irrespective of poverty status. This means, non-resident beneficiaries receive Gh¢648.47 (US $ 121.01) pa. This significantly reduces the financial burden on the government as benefits accrue to only a section of students. Scenario 2 - Non-Means-Tested Targeting for Resident Only [NMTT-R Only] A targeted Free SHS benefit provided only to resident beneficiaries, regardless of poverty status. This non-means-tested approach also alters the status-quo by targeting only resident students. Hence, beneficiary resident students receive Gh¢1002.47 (US $ 187.06) pa. This reduces the financial burden on the government compared to the status-quo. Scenario 3 – Means-Tested Targeting for Non-Resident Only [MTT-NR Only] A targeted Free SHS benefit provided only to poor non-resident beneficiaries. This targeted approach expands access by encouraging patronage of poor households of community day-schools or non-residential access to elite public schools. This reform poses less financial and infrastructure strain on government’s purse due to absence of boarding and feeding costs. Scenario 4 – Means-Tested Targeting for Resident Only [MTT-R Only] A targeted Free SHS benefit provided only to poor resident beneficiaries. This reform seeks to allocate benefit of Gh¢1002.47 (US $ 187.06) pa to only residential students from poor households while requiring the poor to cater for tuition and other non-resident fees of Gh¢648.47 (US $ 121.01) pa. The non-poor households, on the other hand, would cater for both tuition and residential fees. This reform ensures that the poor who require to be admitted to boarding facilities due to longer distances from home to choice schools are accommodated. This, therefore, ensures that lack of proximity to choice schools does not become a barrier to education Scenario 5 – Means-Tested Targeting for both Resident and Non-Resident [MTT-All] A targeted FSHS benefit provided only to poor households, covering both resident and non-resident beneficiaries. Implementing a means-tested approach, where only students from poor households benefit from both tuition and residential fees. This substantially aligns with the original policy intent to increase access for students who face financial barriers. By allowing only wealthier families to pay, the policy could channel resources toward those most in need, ensuring that students from low-income backgrounds receive the needed support to attain secondary education. At the same time, affluent families can contribute financially to create an additional revenue stream, which could be reinvested in improving the quality of education in public schools, including infrastructure, learning materials, and teacher training. Scenario 6 – Non-Targeted Capped Benefit [NTCB-All] A non-targeted FSHS benefit provided to all beneficiaries but capped at the benefit amount payable to non-resident students In this reform, government subsidy is capped at the benefit amount payable to non-resident students, i.e., Gh¢648.47 (US $ 121.01) pa for all type of students. This guarantees a minimum benefit amount sufficient for non-residential fees. Any person that wishes to be admitted to residential facilities would be required to top up the difference for resident students. Source: Authors’ construct (2025) 3. Results The results as presented in this section highlight the fiscal, labour market, and social welfare implications of various reforms of Ghana's Free Senior High School (FSHS) policy. Each result shows the effect of each alternative FSHS reform in comparison with the business-as-usual ‘universal subsidy’ scenario. We first discuss the relative impact of the reforms on government savings from the GHAMOD microsimulation, which is fed to the dynamic CGE model to produce the fiscal, labour market and distributive effects on the general economy and households. 3.1. Fiscal effects Figure 4 provides a clear comparative analysis of the fiscal savings associated with different FSHS policy targeting strategies in Ghana. To begin with, government savings from the education subsidy reforms is highest (US $ 173.38 million) under means-tested targeting of non-residential students only (MTT-NR) followed by non- means-tested targeting of non-residential students only (NMTT-NR) with savings of $ 173.09 million. This is accounted for by the sheer number of non-residential beneficiaries over those with residential status although the benefit amount for non-resident student is less than the those of residential status. The results, however, means that whether targeting the poor or not, limiting subsidy to non-residential students would yield similar savings for the government. Hence, targeting the poor makes little difference if education subsidy is limited to non-resident students since poor households are most likely to switch to non-resident status if subsidies are limited. In this regard, the distributive impacts, as will be shown in subsequent paragraphs, differ significantly. Strictly speaking, subsidy reform which includes payments to residential students yield lesser government savings compared with non-resident students which further dwindles when all two strands of students are considered. Source: Authors’ estimation using GHAMOD microsimulation (2025) The non-means-tested capped benefit for all students (NTCB-All) scenario, which represents a capped but expansive beneficiary base, results in moderate savings of $ 61.11 million. Notably also, the means-tested targeting for all students (MTT-All) scenario produces the lowest savings of $ 7.29 million. These two reflect the trade-off between simultaneous expansion in beneficiaries and declining government savings over the status quo “universal FSHS subsidy”. In line with the foregoing analysis, all reform scenarios demonstrate higher growth in government savings, with the gap between the baseline and the reforms widening significantly after 2030 (see Figure A1 in the appendix). 3.2. Sectorial growth effect The education sector GDP growth rates, as shown in Fig. 5 , are directly affected by changes in household consumption, private investment and government expenditure over time. Specifically, by reducing subsidies on education, increases the market price of education which causes a fall in household demand for education. However, the inelastic nature of demand for education minimizes the fall in demand but escalate consumption expenditure, squeezing out essential needs like food and health. The effect on final demand is further offset by scaled investments and government expenditure in education arising from its savings through the alternative reforms. Since government is the largest consumer of education, its scaled expansion in expenditure ensures that sectorial growth outperforms the BAU scenario until after 2030. Generally, education sector GDP growth decreases after 2030 where population growth would cause a decline in household demand to overweigh government savings in the sectorial absorption. The decline in growth rate is steeper, except for NMTT-NR and NMTT-R, that manage to stay above the BAU into 2040. Source: Authors’ construct (2025) For visualisation purpose, the annual growth rate axis starts at 3 per cent instead of the origin. 3.3. Sectorial growth Figure 6 depicts the sectorial growth rates for different scenarios in the Ghanaian economy, including Agriculture, Industry, Services, Education, and the overall National growth rate. The baseline scenario depicting the current state of the FSHS policy shows relatively lower growth rates across all sectors compared to the other scenarios, while NMTT-NR, which targets the Free Senior High School (FSHS) policy benefits solely towards non-resident beneficiaries regardless of poverty status, exhibits the highest growth rates, particularly in Services and Education. In contrast, MTT-All, which fully targets the FSHS benefits to poor households (both resident and non-resident), shows the lowest growth rates, especially in the Agriculture and National growth rates. The other intermediate scenarios, such as NMTT-R, MTT-R and NTCB-All, fall between these extremes, indicating the significant impact that the targeting approach for the FSHS policy can have on the sectorial growth rates in Ghana. Source: Authors’ construct (2025) 3.4. Labour market effect In all of the reforms, there is a notable increase in labour labour employment relative to the baseline for all workers primary irrespective of educational background (Fig. 7 ). Growth in employment more than doubles in targeting of non-residential students (NMTT_NR) for all workers. The highest gainers are the workforce with primary or tertiary education while uneducated labour gains the least growth in employment, which are still improvements over the BAU. On the other hand, growth in labour returns (wages) declines in comparison with the BAU. This is occasioned by the increasing number of workers employed, thus lowering wages per worker. Labour with primary and, in some instances, tertiary education have a decline in growth rate in wages below the BAU due to excess supply of labour. Interestingly, labour with no formal education consistently benefits the most across all reforms, with rising employment growth notably, especially in NMTT-R through NTCB-All, suggesting that targeted subsidies or redirected fiscal savings would open employment opportunities for low-skilled workers. Source: Authors’ construct (2025) Data labels on the uneducated and labour with secondary education are omitted for readability 3.5 Poverty effect Figure 8 presents the estimated impact of various FSHS reform simulations on poverty in Ghana, using the headcount ratio (P0) and the poverty gap (P1). All reforms show a reduction in poverty incidence and intensity compared to the baseline. The highest reduction in poverty is seen under NMTT-NR. Interestingly, all means-tested schemes rather yield lower impact on poverty unlike the opposite, non-means-tested schemes due to lower government savings and scaled-investments spillover effects. Rural households see the most substantial poverty reductions, particularly in NMTT-NR and NMTT-R, and across all other reforms (see Figure A2 and A3 in the appendix). Source: Authors’ construct (2025) 3.6. Inequality effect Figure 9 shows a notable reduction in inequality by 2040, with point changes declining to around 0.27 or slightly below, whereas the baseline scenario remains virtually unchanged or even slightly elevated. Again by 2040, urban inequality consistently declines across all simulations (see Figure A4 in the appendix). This pattern suggests that while the universal FSHS policy maintains existing levels of inequality, targeted reforms lead to a more equitable distribution of resources and opportunities over time. Specifically, reforms like NMTT-R and MTT-R, focusing on resident students only and pro-poor targeting of resident students only respectively, achieve the most significant reductions in inequality, reinforcing the idea that redistributive targeting mechanisms enhance equity. These results are consistent with the capability approach and human capital theories, which stipulate that broadening access to education among disadvantaged groups contributes to closing structural inequality gaps. Source: Authors’ construct (2025) 3.7. Social welfare effect In the baseline universal FSHS grant, households in urban areas are the greatest net gainers in welfare (1.30 percentage point change) compared with rural areas (0.92 percentage point change) (Fig. 10 ). Per the result shown Fig. 10 , with the exception of in the NMTT-NR scenario which targets non-resident beneficiaries, targeting by either residency, poverty status capped benefits would significantly bridge the gap between urban and rural dwellers. Poor households, both rural and urban, see greater improvements, indicating that targeted reforms are more effective at boosting welfare among the poor (see Figure A5 in the appendix). Source: Authors’ construct (2025) 3.8. Comparative macro performance Figure 11 presents the elasticities of growth, poverty, and employment across six simulation scenarios for the FSHS policy reforms. Each scenario is evaluated on three dimensions: growth, poverty and employment. The results show that NMTT-R which targets resident beneficiaries, yields the highest growth elasticity and achieves a substantial employment elasticity, but it is associated with the largest reduction in poverty. NTCB-All which is the non-targeted capped benefit, also demonstrates strong performance with high growth and employment elasticities, though its poverty reduction impact is more modest. The other scenarios exhibit moderate to low elasticities, with NMTT-NR and MTT-All producing notable growth and employment effects but less pronounced poverty reduction. Source: Authors’ construct (2025) 4. Discussion The comparative analysis of Ghana’s Free Senior High School (FSHS) policy reforms demonstrates that alternate targeting strategies significantly outperform the baseline universal model in expanding access to education and promoting equity. Although universal coverage under the baseline provides widespread access, its efficiency in fostering long-term educational sector growth is limited. The Capability Approach (Sen, 1999 ) highlights that expanding real educational opportunities, especially for disadvantaged groups, enhances both individual agency and collective well-being. Accordingly, targeted reforms serve to bolster the capability sets of low-income and rural youth, aligning with the policy's foundational goals. From a Human Capital perspective (Becker, 1964 ), channelling resources into populations with the highest marginal returns such as non-residents who typically face greater educational access constraints, is not only equitable but economically prudent. In evaluating inequality outcomes, the analysis has shown a significant decline in inequality, with means-tested residency targeting. This finding aligns with the insights of Nussbaum ( 2011 ) and Eden, Chisom and Adeniyi ( 2024 ), who emphasise that equitable access to social goods, such as education, is crucial for addressing structural disparities. In contrast, the baseline scenario maintains or slightly increases inequality, highlighting the limitations of universal approaches in transforming opportunity landscapes. The reforms highlight how reallocating benefits based on socioeconomic status or residential status can mitigate inequality more effectively. This supports empirical evidence from developing countries that targeted interventions in secondary education yield more equitable long-term outcomes (Psacharopoulos & Patrinos, 2018 ). Furthermore, the analysis reinforces the importance of designing educational interventions that are both inclusive and redistributive. The substantial reduction in inequality observed in NMTT-R and MTT-R exemplifies how targeted support enhances substantive freedoms, particularly for those structurally disadvantaged by geography, gender, or income. While universal benefits can formalise access, they do not address the conversion factors that impede equal educational attainment. These reforms, therefore, shift the policy narrative from aggregate enrolment statistics to real opportunity equality, making a compelling case for pro-poor targeting as a more transformative approach. In macroeconomic terms, the study has shown that targeted reforms also deliver greater fiscal efficiency. NMTT-NR and MTT-NR generate the highest government savings compared to the baseline, demonstrating the value of targeting in reducing unnecessary expenditure while preserving access. These savings, when reinvested in complementary sectors or educational quality improvements, stimulate broader economic development. Government savings under the reform scenarios exhibit accelerated growth from 2030 onward, particularly under NMTT-NR and NMTT-R, highlighting their long-run sustainability (see Figure A1 in the appendix). This trajectory supports the Human Capital Theory’s assertion that efficient allocation of educational resources enhances both fiscal stability and future productivity. These reforms align with the broader policy goal of achieving universal access in a fiscally constrained environment without sacrificing the quality or equity of outcomes. The employment and sectoral growth results further highlight the macro-level advantages of the reform models. While composite labour outcomes decline slightly under reform scenarios, gains in employment for low-skilled (no formal education) workers suggest that targeted subsidies may stimulate demand in labour-intensive sectors or reduce informal sector unemployment. Additionally, higher national and services sector growth under NMTT-NR and NMTT-R reflect the economy-wide multipliers of smart educational investments (see Fig. 6 ). These outcomes were echoed by Moayed, Guggenheim and von Chamier ( 2021 ), who argued that subsidies that minimise regressive leakage while maximising redistributive impact are more likely to foster inclusive economic growth. The balance between fiscal savings and job creation under targeted reforms reinforces their advantage over the baseline, which achieves broad access but fails to leverage education for optimal macroeconomic transformation. At the microeconomic level, the reform scenarios yield stronger poverty reduction outcomes than the baseline. All simulations reduce both the poverty headcount (P0) and poverty gap (P1), with NMTT-NR and NMTT-R achieving the most substantial improvements (see Fig. 8 ). This confirms Antwi ( 2021 ), who found that FSHS significantly increased completion rates, particularly among girls and low-income students. Notably, NMTT-NR and NMTT-R achieve the largest reductions in the poverty gap. From a capability approach standpoint, these reforms enhance the substantive freedoms of households by removing one of the most critical financial barriers to upward mobility. Reducing educational costs allows poor households to redirect resources toward other necessities, thereby improving their overall well-being and resilience. Similarly, social welfare analysis further substantiates distributive impacts of the FSHS reforms on households. In NMTT-NR and NMTT-R, welfare gains are disproportionately higher for urban and rural poor households respectively, illustrating how well-designed targeting mechanisms can address spatial and class disparities (see Figure A5 in the appendix). The capability approach theorem offers a useful lens here. It highlights the centrality of equalising the means to achieving well-being than simply distributing nominal benefits. Hence, reforms that prioritise targeting by need rather than uniformity of access create more meaningful improvements in the lives of the most deprived. Finally, the study has demonstrated that reforms that target the resident or non-resident poor students tend to outperform broader universal strategies. This is especially relevant for rural areas, where poverty impacts are more pronounced and educational access more constrained. The consistent gains in both poverty metrics and social welfare under targeted reforms suggest that these policies are better aligned with national poverty alleviation goals and international development goals like the SDGs 4 and 10. 5. Conclusion and Policy Recommendations This study set out to demonstrate evidence that reforming Ghana’s Free Senior High School (FSHS) policy from a universal benefit structure to more targeted approaches can yield significant fiscal, social, and macroeconomic benefits. Specifically, this has been shown that by alternative reforms, targeting non-resident students, especially those from poor households, generates the highest government savings and substantial reductions in the poverty headcount, particularly in rural areas. Thus, reforms targeting students based on residency also shows strong fiscal and distributive impacts, suggesting that spatial targeting is an effective pathway for optimising impact. Importantly, while all reforms outperformed the business-as-usual case in terms of efficiency and equity, the degree of improvement varied depending on whether targeting is based on residency, poverty status, or a combination of both. These outcomes align with the theoretical underpinnings of this study: the Human Capital Theory and the Capability Approach, which both emphasize the value of expanding educational opportunities for disadvantaged groups to reduce structural inequalities and promote long-term economic development. The macroeconomic implications of the reform scenarios are equally notable. The simulations demonstrated that targeted policies not only lead to increased government savings but also stimulate growth and employment, particularly in the services and education sectors. Even reform options such as the capped universal benefit (NTCB-All) that yielded moderate fiscal and employment benefits, still outperformed the current universal policy. These findings support a transition toward more fiscally sustainable and economically productive education financing models. Based on these findings, the study recommends a phased transition over the long term toward a more targeted FSHS policy, without leaving the poor out of the safety net. This approach would allow for optimal fiscal allocation while preserving equity in access to secondary education, freeing resources for the expansion in investment in essential goods and services that also benefit the poor and the entire economy. We have also demonstrated that a much simpler and less-costly schemes such as non-means-tested targeting based on residency or capped benefits yield significant positive impacts that could be readily adopted to mitigate the fiscal constraints posed by the current form of the policy. Declarations Conflict of interest The authors declare that there are no known financial or personal relationships that could have appeared to influence the work reported in this paper. Funding This research received no external funding. Data availability The data used in this study were obtained from the Ghana Living Standards Survey Round 7 (GLSS 7), conducted by the Ghana Statistical Service (GSS). The dataset is publicly available upon request from the Ghana Statistical Service through their official data access portal at https://microdata.statsghana.gov.gh/index.php/catalog/97 References Adu-Ababio, K., & Osei, R. D. (2018). Effects of an education reform on household poverty and inequality: A microsimulation analysis on the free Senior High School policy in Ghana (No. 2018/147). WIDER Working Paper. Anlimachie, M. A., & Avoada, C. (2020). Socio-economic impact of closing the rural-urban gap in pre-tertiary education in Ghana: context and strategies. 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14:33:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55270,"visible":true,"origin":"","legend":"\u003cp\u003eShare of FSHS benefits by household groups\u003c/p\u003e\n\u003cp\u003eSource: Authors’ computation (2025) using GLSS 7 dataset\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/a078eba5b2509d9163943ea1.png"},{"id":94445476,"identity":"de98f1c0-fb8d-4ead-ab82-14c8352fe803","added_by":"auto","created_at":"2025-10-27 14:33:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56932,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration of baseline of growth rates in labour supply\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own estimates\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/f22af5c2cee35186cce4871c.png"},{"id":94445533,"identity":"f28cab83-2baa-4c60-abfb-a2605065d567","added_by":"auto","created_at":"2025-10-27 14:33:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38702,"visible":true,"origin":"","legend":"\u003cp\u003eGovernment savings from FSHS\u003c/p\u003e\n\u003cp\u003eSource: Authors’ estimation using GHAMOD microsimulation (2025)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/d6b035e3a57f79c1cf1d16b7.png"},{"id":94445537,"identity":"4d5f225c-63e5-4d42-9d5f-4e7b3c357a9e","added_by":"auto","created_at":"2025-10-27 14:33:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74852,"visible":true,"origin":"","legend":"\u003cp\u003eEducation sector GDP growth\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFor visualisation purpose, the annual growth rate axis starts at 3 per cent instead of the origin.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/cdf81e8b56fefc903732a8c5.png"},{"id":94445283,"identity":"979da7b7-2460-454e-82c8-cf5c6d2fa5e3","added_by":"auto","created_at":"2025-10-27 14:32:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51813,"visible":true,"origin":"","legend":"\u003cp\u003eSectorial growth\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/51846fbc164422c5b02fbaf7.png"},{"id":94445470,"identity":"aea2ce92-9f1f-4474-a9d4-6f9e5483212d","added_by":"auto","created_at":"2025-10-27 14:33:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72342,"visible":true,"origin":"","legend":"\u003cp\u003eLabour employment and wages\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData labels on the uneducated and labour with secondary education are omitted for readability\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/c14f84f887067062c1f642fe.png"},{"id":94445213,"identity":"2600b041-2c25-4ca7-b917-9095f8ca4a15","added_by":"auto","created_at":"2025-10-27 14:32:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39654,"visible":true,"origin":"","legend":"\u003cp\u003ePoverty headcount and gap\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/3d8a8d76e8c29679415e8eb5.png"},{"id":94445059,"identity":"d53a36b6-e95e-4ca2-9cc8-3712fee0ef89","added_by":"auto","created_at":"2025-10-27 14:32:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":35326,"visible":true,"origin":"","legend":"\u003cp\u003eInequality\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/d57ad778a0fad23022e3390d.png"},{"id":94445357,"identity":"472ba22d-4cbe-440c-83dd-1af3d64603b6","added_by":"auto","created_at":"2025-10-27 14:33:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":35025,"visible":true,"origin":"","legend":"\u003cp\u003eSocial welfare\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/775e70351ea021b0caf6a9bb.png"},{"id":94445064,"identity":"91eafcb9-73cd-497e-be58-a9cc8822bd83","added_by":"auto","created_at":"2025-10-27 14:32:38","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":37963,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth, poverty and employment\u003c/p\u003e\n\u003cp\u003eSource: Authors’ construct (2025)\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/eb765a5e76d83a6530b2c9eb.png"},{"id":94465543,"identity":"ea0d38c5-06fa-48de-8d98-d5c10f80e6af","added_by":"auto","created_at":"2025-10-27 15:16:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/65e0b5f7-a5ae-489b-a00e-d2f9fe4aa312.pdf"},{"id":94445746,"identity":"64caf430-c7d8-4b84-b903-f758bd0eef5d","added_by":"auto","created_at":"2025-10-27 14:33:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38772,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7934227/v1/2ed185cf36f2ed4f4403b7b9.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFiscal, Labour Market and Distributive Effects of Educational Reforms: A Hybrid Simulation Study of Free Senior High School Policy in Ghana\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Free Senior High School (FSHS) policy aligns closely with Ghana\u0026rsquo;s commitments under the United Nations Sustainable Development Goals (SDGs), particularly SDG 4, which aims to ensure inclusive and equitable quality education for all. By promoting universal access to secondary education, the policy holds promise for addressing several of Ghana\u0026rsquo;s key development challenges (Chanimbe \u0026amp; Prah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is anticipated that it will foster economic growth by creating a more educated workforce and support social cohesion by reducing poverty and inequality. However, like several other subsidy interventions in developing countries, the policy has introduced significant fiscal and implementation challenges as well including overstretched facilities, impaired subsidy payment by the government for feeding, teaching and learning materials (Shamo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, putting a strain on quality of education. Over the period of its implementation FSHS in Ghana, the government\u0026rsquo;s fiscal pressures were further impaired by the double whammy of COVID-19 and the war in Europe between 2020\u0026ndash;2022 leading to an IMF bailout in 2023 (Atuahen, Agyei \u0026amp; Frimpong, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These have, therefore, invited clarion calls for urgent reforms. Suggestions from a section of scholars and stakeholders have been in favour of a more targeted approach that allows wealthier families to contribute to education costs while supporting free education for the poor might be a viable solution to ensure the program\u0026rsquo;s sustainability and effectiveness. Hence, by simulating six alternative reform scenarios, this study contributes directly to the discourse on subsidy scheme designs in education in general and the FSHS policy in particular.\u003c/p\u003e\u003cp\u003eThe proposition to allow wealthier families to pay for secondary education while offering it for free to the poor has also spurred debate about the potential impact on the quality and inclusiveness of the public education system. Critics argue that if wealthier families are allowed or even encouraged to contribute financially, they may seek private or alternative educational options instead of participating in the public system. This could lead to further stratification, where public schools primarily serve lower-income students, and wealthier students migrate to private schools. Such an outcome might undermine the inclusivity goals of the Free SHS policy and reduce the social cohesion fostered by a shared educational experience.\u003c/p\u003e\u003cp\u003eFrom a welfare economics perspective, provision of education generates positive externality in the form of human capital that is often not captured in the market price, leading to overpricing. In this regard, the externality unaccounted for in the market price of education provides a justification for subsidies in education. In addition, financial barriers could restrict access to education leading to full or partial excludability against the poor in the attainment of capability potential. Hence, in most instances, formal education, especially basic education, is subsidized to eliminate or reduce financial hurdles to improve access to quality education in the short term and promote economic development in the long term through human capital development. The human capital theory by Becker (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), Mincer (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) and Schultz (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1961\u003c/span\u003e) supports this notion. These authors view education as a productive investment that enhances individuals\u0026rsquo; skills and future earnings, Hence, by eliminating financial barriers, access to education is enhanced, especially for low-income and rural households, thereby increasing human capital accumulation. The improved access is crucial for poverty alleviation through improved labour market outcomes and income growth (Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Dang \u0026amp; Dabalen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Likewise, by eliminating educational fees, the Free SHS program expands educational opportunities for marginalised groups, particularly low-income and rural households, thereby enhancing their agency and life prospects (Sen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEducation is seen not only as a tool for income generation but as a means of achieving broader well-being and social participation. As Robeyns (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) notes, achieving real equality requires more than formal access; it demands substantive opportunities. Furthermore, the approach highlights that inequality reflects deeper disparities in individuals\u0026rsquo; capacity to convert resources into valuable outcomes (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, educational subsidies, as in the case of the FSHS, functions as a redistributive policy that promotes inclusive human development and social justice.\u003c/p\u003e\u003cp\u003eEmpirical evidence shows that by expanding access to education leads to improved income levels and long-term poverty alleviation, particularly in low-income settings (Filmer \u0026amp; Fox, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Latin America, specifically Brazil and Mexico, educational reforms involving conditional cash transfer programs such as \u0026ldquo;Bolsa Fam\u0026iacute;lia\u0026rdquo; and \u0026ldquo;Oportunidades\u0026rdquo; incentivized school attendance among marginalized populations, which has led to noticeable increases in enrolment and retention rates (UNESCO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Asia, educational reforms in countries like India and Indonesia have prioritized teacher training and curriculum modernization to meet labour market demands, contributing to higher graduation rates and better job prospects for graduates (Dhar, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, Honorati and Santos (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that a policy-driven increase in school construction in Indonesia led to improved adult wages and reduced poverty among low-income groups. While these regions have unique cultural and socioeconomic landscapes, their shared focus on reforming second-cycle education highlights the critical role of equitable access in fostering economic mobility and reducing social inequalities. In general, improving access to secondary education is associated with significant returns in the labour market, surpassing even primary education in its poverty-reducing effects (Montenegro \u0026amp; Patrinos, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Ghana, evidence points to a marked increase in high school enrolment, with gross enrolment rising from 17.2% in 2016 to 30.7% in 2018 (MoE, 2017). Beyond enrolment, a growing body of research examines the distributional effects of education policies, especially in terms of how benefits are shared across different income groups. A study by Duflo, Dupas, Spelke and Walsh (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that access to secondary education in Ghana significantly increases lifetime earnings and improves intergenerational mobility, particularly for children from rural and poor households. Similarly, Psaki et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed that removing financial barriers to education improves enrolment and completion rates, especially among girls and the poor. Furthermore, Adu-Ababio and Osei (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used microsimulation models to estimate the potential redistributive effects of Free SHS reforms, finding that targeted policies yield greater reductions in income inequality than untargeted ones. Their findings suggest that while universal access promotes equity in opportunity, carefully designed reforms that address both geographical and income-based disparities have a stronger impact on long-term inequality reduction. Simulations of Free SHS reform scenarios have demonstrated that targeting benefits to the poorest quintiles or capping transfers for wealthier households can significantly improve the policy\u0026rsquo;s redistributive impact (Adu-Ababio \u0026amp; Osei, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEvidence from cross-country studies further corroborates that increased investment in secondary education correlates with lower Gini coefficients, especially in countries with strong public education systems (Mazurek, Fern\u0026aacute;ndez-Garc\u0026iacute;a \u0026amp; P\u0026eacute;rez-Rico, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent empirical studies increasingly highlight the importance of aligning education financing reforms with equity objectives using advanced quantitative tools. Microsimulation models and benefit incidence analysis have emerged as key methodologies for evaluating the distributional effects of education policies (Sologon, Doorley \u0026amp; O\u0026rsquo;Donoghue, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, other strand of scholars, for example, Getachew (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that universal education subsidies tend to be regressive in nature unless they are specifically targeted to the poor. On Ghana\u0026rsquo;s FSHS policy, particularly Essuman (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that higher-income households could disproportionately benefit from the policy due to existing inequalities in educational preparedness and geographic access. Considering these, some scholars advocate for hybrid models that combine universal access with targeted support to ensure that the poor receive a fairer share of public education spending (Grosh, Leite, Wai-Poi \u0026amp; Tesliuc, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the extensive empirical literature on the impacts of educational reforms on access, poverty reduction, income equality, and labour market outcomes, several notable gaps remain. First, many studies (Adu-Ababio \u0026amp; Osei, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Essuman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chanimbe \u0026amp; Prah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) focus predominantly on enrolment and completion outcomes, with less emphasis on the long-term macroeconomic and fiscal implications of universal education subsidies, such as Ghana\u0026rsquo;s FSHS. By ignoring the fiscal implications of the educational reforms, these studies overestimate or underestimate the impacts of educational subsidies given the intersectoral linkages and government fiscal levers.\u003c/p\u003e\u003cp\u003eTo the best of the authors\u0026rsquo; knowledge, no study has attempted to assess the impact of alternative FSHS reforms in a manner as explored in this study by linking a microsimulation with a dynamic computable general equilibrium to assess how targeting mechanisms affect fiscal, labour market outcomes and income distribution. Moreover, existing analyses often lack comprehensive assessments of how education reforms interact with labour markets across skill levels, and how these interactions translate into broader social welfare outcomes over time. The lack of comprehensive empirical assessment of educational reforms from an economy-wide perspective obscures fiscal trade-offs and multi-sectoral impacts but isolate changes to household consumption welfare and demand for education from the rest of the economy. This study aims to fill these gaps by employing a dynamic, economy-wide model linked bottom-up with a detailed tax-benefit microsimulation model to simulate and compare various FSHS policy scenarios, assessing their implications for government expenditure, income distribution, sectoral growth, and labour market dynamics. This approach provides a more comprehensive understanding of the policy\u0026rsquo;s long-term socioeconomic effects.\u003c/p\u003e\u003cp\u003eThe remaining sections of the paper present empirical methods including modelling approach, data sources and model calibration, macro closure rules for the general equilibrium model. Subsequently, the third section presents the results from the model estimations while, finally, in-depth discussion of the results and conclusions and policy implications are presented in the fourth and last sections.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study employs a bottom-up approach linking a tax-benefit microsimulation to a computable general equilibrium (CGE) in a macroeconomic framework. The tax-benefit model is based on UNU-WIDER\u0026rsquo;s SOUTHMOD model for Ghana (GHAMOD). GHAMOD is a tax-benefit microsimulation model for Ghana that is both highly adaptable and user-friendly. It allows the user to compare the effects of different benefit policy scenarios on poverty, inequality, and tax revenues (UNU-WIDER, 2018). The model can simulate several other policies including the Livelihood Empowerment Against Poverty (LEAP) program, the School Capitation Grant, the Free Senior High School Program, social contributions (employer and employee), and taxes (presumptive, VAT, excises). The model estimates the distributional effects of tax and benefit schemes on individuals and households through payments and receipts between government and and households. Although the model is non-behavioural (Adu-Ababio, Osei, Pirttila, \u0026amp; Rattenhuber, 2023), it allows for the inclusion of the variance in attributes of individuals and households to examine the distributional effects of tax and benefit reforms. We pass the changes in government expenditure to the CGE that is based on IFPRI\u0026rsquo;s Rural Agriculture Investment and Policy Analysis (RIAPA) model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003cp\u003eThe CGE model further estimates the economywide impacts of government reforms in educational reverberating through multiple sectors within the Ghanaian economy. This is possible through forward and backward linkages between the education sector and the rest of the economic sectors. The result could be assessed in the form of full impacts of educational reforms on household poverty, inequality, jobs creation, overall and sectorial GDP growth.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Microsimulation in GHAMOD\u003c/h2\u003e\u003cp\u003eFirst, the FSHS policy is modelled as a universal government subsidy in senior high education. Hence, the subsidy reduces the expenditure on education by eligible households. Thus, given a household expenditure function of the form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:y={p}_{1}{q}_{1}+{p}_{2}{q}_{2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e is total expenditure, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{1}\\)\u003c/span\u003e\u003c/span\u003e are, respectively, the price and quantity of senior high school (SHS) education. Also, given that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{2}\\)\u003c/span\u003e\u003c/span\u003e, respectively, represent the composite price and quantities of all other goods and services consumed by the household, then for any change in welfare due to an increase in price now depends on the change in quantities consumed when prices are normalised at equilibrium. Thus, the new expenditure function post reform given as\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}^{{\\prime\\:}}={{p}^{{\\prime\\:}}}_{1}{{q}^{{\\prime\\:}}}_{1}+{p}_{2}{{q}^{{\\prime\\:}}}_{2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003esolved simultaneously with the initial expenditure function yields a change in quantity demanded\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}{q}_{1}=-{q}_{1}d{p}_{1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhich is shown to be dependent on change in quantity of good 1 (education) and the relative price movements: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{p}_{1}/{p}_{1}\\)\u003c/span\u003e\u003c/span\u003e. Finally, changes in quantities consumed, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{q}_{1}\\)\u003c/span\u003e\u003c/span\u003e is affected by consumer elasticities, which is typically between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-1\u0026lt;ϵ\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e, as well as changes in government expenditure, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\text{e}\\text{x}\\text{p}\\)\u003c/span\u003e\u003c/span\u003e, in funding the subsidy:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}exp=-\\:{\\sum\\:}_{h=1}^{H}{y}_{1,h}d{p}_{1}\\left(1-{ϵ}_{1}\\left({s}_{1}-d{p}_{1}\\right)\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{1}\\)\u003c/span\u003e\u003c/span\u003e is initial subsidy before the reform. The final change in government expenditure, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}exp\\)\u003c/span\u003e\u003c/span\u003e, is passed to the CGE model as a shock to government\u0026rsquo;s transfer to eligible households.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Modelling FSHS Reforms in the CGE Model\u003c/h2\u003e\u003cp\u003eOne simple way to implement the FSHS subsidy reforms in the CGE macroeconomic framework is to modify government\u0026rsquo;s expenditure by the changes in the rates of subsidy on education. This would significantly reduce consumption of education since government is a major financier of education. The consequence would be a significant change in the market price and total supply. The approach, however, affects household consumption only through distortions in the supply as if the reforms are targeting production subsidies. On the contrary, the FSHS subsidies are consumption based. Thus, reforms affect the cost-sharing apportionment between the government and households. In that regard, this study, first, treats the implementation of the policy as a form of lump-sum transfer (inverse of income tax) to household, which is modified through simulated cost-sharing reforms. The effect on households\u0026rsquo; expenditure on education is direct through changes in income. In this regard, we show the relevant equations in the IFPRI CGE model affected by our analysis as described below. All other equations and variables in the core CGE model remain unchanged.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Production module\u003c/h2\u003e\u003cp\u003eProfit maximization drives producer\u0026rsquo;s behaviours in the market. In this module, the capitalist producer combines factors such as capital (K), labour (L) and intermediate materials (M) in a nested constant elasticity of substitution (CES) function to maximize profits through by influencing quantity supplied. The nested production function is in two layers. In the bottom layer, capital and labour are combined into an aggregate input KL with imperfect substitutability. The aggregate KL input is, therefore, combined at the top layer with intermediate materials in fixed proportions (Leontief function). In both the inputs and the products markets, each producer is considered as small, independent and a price-taker. This means, prices are exogenously determined through the interaction of demand and supply in the market. Profits are redistributed as returns on capital to providers of capital (i.e., enterprises and households) which are partly reinvested through savings and the remaining going into taxes and consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Household module\u003c/h2\u003e\u003cp\u003eHousehold income comprises labour wages, capital payments, remittance income, and transfer payments from enterprises and the government. The latter is the conduit through which FSHS reforms affect household incomes, consumption and welfare. That is, households\u0026rsquo; disposable income, after income taxes, is spent on goods and services including education. The remainder is then saved for future consumption and investment purposes. In the CGE model, household consumption is modelled as simple linear expenditure system (LES) which is tractable and with least data requirements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Government module\u003c/h2\u003e\u003cp\u003eGovernment derives incomes through taxes and tariff revenues, and foreign income. On the other size, government expenditures include government consumption, transfer payments to households and other countries, and government saving. The consumption expenditure of the government also includes the expenditure on FSHS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Rest of the world module\u003c/h2\u003e\u003cp\u003eWe apply, in this module, the Armington (1969) proposition of an imperfect substitution between domestic and foreign goods which drives price differences between goods produced domestically and abroad. The price differentiation consequently drives a country\u0026rsquo;s imports and exports. A CES aggregation function combines imported and domestic goods into an aggregate Armington commodity, which is the total domestic supply. The aggregate Armington commodity goes into consumption, investment, and intermediate inputs. Similarly, a constant elasticity of transformation (CET) aggregation function splits domestic production into goods for domestic consumption and exported (Wang et al., 2017c). For simplicity, we go by the small-country assumption for Ghana, in which case international trade prices are determined by international markets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5. Poverty, Inequality and social welfare module\u003c/h2\u003e\u003cp\u003eThe recursive dynamic CGE model in this study is liked to a microsimulation module for poverty analysis. The poverty module is based on the latest living standard survey for Ghana (GLSS 7) that captures demography, income and consumption of 14,009 households. Changes in consumption due to real and policy shocks in incomes and employment are passed from representative households in the macro CGE model to individual household units in the living standard survey to ascertain the number of individuals lifted out or pushed into poverty.\u003c/p\u003e\u003cp\u003eAdditionally, our macro model captures household welfare changes using the Hicksian equivalent variation (EV) method to measure the impact of educational subsidy reforms on the consuming public. The Hicks equivalent variation is calculated as the change in consumer utility, which considers prices and quantities of education (number of eligible household members currently attending SHS) and other consumer goods, between baseline and the reform scenarios. A positive value of EV denotes an improvement in social welfare while a negative EV implies a decline in social welfare due to the reforms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Macro closure rules\u003c/h2\u003e\u003cp\u003eMacro closure refers to rules for achieving equilibrium in three macro balances: the balances for the government, the savings-investment account, and the current account on the trade with rest of the world. First, government savings are set to be flexible by adjusting endogenously to restore balance to the government account while keeping direct tax income rates fixed. In relation to this study, this rule ensures that the government does not reconfigure revenue (direct tax) handles in its response to real shocks (changes in transfers to households) but accommodate the changes through adjustments in its savings. On the current account front, exchange rates are allowed to adjust freely in response to trade flows with the rest of the world. In that regard, foreign savings are taken to be exogenously determined in the model. Lastly, for the savings-investment balance, we assume a historically consistent balanced closure with a scaled marginal propensity to consume (MPS) for domestic institutions. By this closure, not only does investment change to affect economic growth, other components of absorption (consumption (C), government spending (G)) equally adjust to produce the resultant GDP growth. Other macro closure rules implemented in this study include assumptions on factor employment and mobility, and a national benchmark price for relative price determination. Labour market is considered to surplus supply who unemployed and mobile while capital (including land) is fully employed and activity specific. All markets are cleared at the equilibrium price levels determined endogenously in the model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data sources and model calibration\u003c/h2\u003e\u003cp\u003eThe most recent version of GHAMOD (version 2.7) uses data from the Ghana Living Standard Survey Rounds 6 and 7 (GLSS 6 \u0026amp; 7). The data of interest in this study was derived from the GLSS 7, which is the most recent wave of the GLSS at the time of conducting this study. Hence, the model is calibrated to simulate the costs and benefits associated with the FSHS policy, incorporating detailed household-level data (GLSS 7) to assess its distributional effects across various income groups. The main database for the CGE model calibration, on the other hand, is the 90-sector social accounting matrix (SAM) 2019 constructed based on the Supply-and-Use Table (SUT) 2013 for Ghana. The SUT provides a complete and consistent accounting framework of intra and inter-sectorial and market linkages suitable for CGE modelling. Additional data sources for the SAM include the national accounts, the IMF Financial Statistics, agriculture and enterprise surveys, the living standard surveys, among others. Baseline population, labour supply and GDP growth rates were set to official statistics from the Ghana Statistical Service and national budget documents. Trade (Armington) and production elasticities were calibrated using estimates from the GTAP 11 database.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5. The Baseline Context\u003c/h2\u003e\u003cp\u003eGhana\u0026rsquo;s FSHS policy is a universal subsidy to all Ghanaian students attending a public senior high school placed by the Computerised School Selection and Placement System (CSSPS). The government spends a fixed amount of Gh\u0026cent;1002.47 (US\u003cspan\u003e$\u003c/span\u003e187.06)\u003csup\u003e1\u003c/sup\u003e per annum on each resident beneficiary and Gh\u0026cent;648.47 per annum on each non-resident beneficiary. These amounts cover the costs of tuition, meals, and other related fees (Chanimbe \u0026amp; Dankwah, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The difference between the residential benefit amount and the non-residential benefits are mainly due to meals, boarding facility charges. Each student is expected to enjoy the subsidy for 3 consecutive years, which is the entire duration of senior high school education in Ghana. Baseline estimation using the 2017 GLSS data shows that the most FSHS benefit would accrue to poor rural households (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas the urban poor tend to receive the least benefit due to the number of children enrolled in senior high school, distributed across resident and non-residential status at the beginning of the policy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; computation (2025) using GLSS 7 dataset\u003c/p\u003e\u003cp\u003eIn the 2019 SAM, education expenditure of GH\u0026cent;10.1\u0026nbsp;billion (US\u003cspan\u003e$\u003c/span\u003e1.88\u0026nbsp;billion) constitutes the second largest government expenditure after health expenditure. A simulation of the GHAMOD model puts government expenditure on the FSHS to be GH\u0026cent;1.5\u0026nbsp;billion (US\u003cspan\u003e$\u003c/span\u003e279.97\u0026nbsp;million), about a tenth of the total government expenditure on education. In the dynamic CGE model, Ghana\u0026rsquo;s economy is expected to grow at 6 percent per annum in the business-as-usual scenario with a 2.3 percent annual population growth. Finally, the FSHS policy is expected to increase the supply of labour with secondary education or higher for those who continue to tertiary education. Hence, we assume a decline in supply growth rate of labour with no education or primary education in both rural and urban areas into the long term, between 1.8 to 1.2 percent per annum while increasing same for secondary and tertiary education levels, especially in urban areas (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; own estimates\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Scenario Design\u003c/h2\u003e\u003cp\u003eThe analysis explores proposed reforms in the FSHS policy and examines the impacts on fiscal balance, labour market outcomes and distributive effects on households. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes each policy reform implemented in this study. The impact assessment of each reform scenario is implemented from 2020 to 2040.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePolicy Descriptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReform Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolicy Description\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 1\u003c/b\u003e - \u003cb\u003eNon-Means-Tested Targeting for Non-resident Only [NMTT-NR Only]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA targeted FSHS benefit for non-resident beneficiaries only, regardless of poverty status.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis non-means-tested approach modifies what is known to be the status-quo by targeting only non-resident students, irrespective of poverty status. This means, non-resident beneficiaries receive Gh\u0026cent;648.47 (US\u003cspan\u003e$\u003c/span\u003e121.01) pa. This significantly reduces the financial burden on the government as benefits accrue to only a section of students.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 2\u003c/b\u003e - \u003cb\u003eNon-Means-Tested Targeting for Resident Only [NMTT-R Only]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA targeted Free SHS benefit provided only to resident beneficiaries, regardless of poverty status.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis non-means-tested approach also alters the status-quo by targeting only resident students. Hence, beneficiary resident students receive Gh\u0026cent;1002.47 (US\u003cspan\u003e$\u003c/span\u003e187.06) pa. This reduces the financial burden on the government compared to the status-quo.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 3\u003c/b\u003e \u0026ndash; \u003cb\u003eMeans-Tested Targeting for Non-Resident Only [MTT-NR Only]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA targeted Free SHS benefit provided only to poor non-resident beneficiaries.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis targeted approach expands access by encouraging patronage of poor households of community day-schools or non-residential access to elite public schools. This reform poses less financial and infrastructure strain on government\u0026rsquo;s purse due to absence of boarding and feeding costs.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 4\u003c/b\u003e \u0026ndash; \u003cb\u003eMeans-Tested Targeting for Resident Only [MTT-R Only]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA targeted Free SHS benefit provided only to poor resident beneficiaries.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis reform seeks to allocate benefit of Gh\u0026cent;1002.47 (US\u003cspan\u003e$\u003c/span\u003e187.06) pa to only residential students from poor households while requiring the poor to cater for tuition and other non-resident fees of Gh\u0026cent;648.47 (US\u003cspan\u003e$\u003c/span\u003e121.01) pa. The non-poor households, on the other hand, would cater for both tuition and residential fees. This reform ensures that the poor who require to be admitted to boarding facilities due to longer distances from home to choice schools are accommodated. This, therefore, ensures that lack of proximity to choice schools does not become a barrier to education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 5\u003c/b\u003e \u0026ndash; \u003cb\u003eMeans-Tested Targeting for both Resident and Non-Resident [MTT-All]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA targeted FSHS benefit provided only to poor households, covering both resident and non-resident beneficiaries.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImplementing a means-tested approach, where only students from poor households benefit from both tuition and residential fees. This substantially aligns with the original policy intent to increase access for students who face financial barriers. By allowing only wealthier families to pay, the policy could channel resources toward those most in need, ensuring that students from low-income backgrounds receive the needed support to attain secondary education. At the same time, affluent families can contribute financially to create an additional revenue stream, which could be reinvested in improving the quality of education in public schools, including infrastructure, learning materials, and teacher training.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScenario 6\u003c/b\u003e \u0026ndash; \u003cb\u003eNon-Targeted Capped Benefit [NTCB-All]\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eA non-targeted FSHS benefit provided to all beneficiaries but capped at the benefit amount payable to non-resident students\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIn this reform, government subsidy is capped at the benefit amount payable to non-resident students, i.e., Gh\u0026cent;648.47 (US\u003cspan\u003e$\u003c/span\u003e121.01) pa for all type of students. This guarantees a minimum benefit amount sufficient for non-residential fees. Any person that wishes to be admitted to residential facilities would be required to top up the difference for resident students.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results as presented in this section highlight the fiscal, labour market, and social welfare implications of various reforms of Ghana's Free Senior High School (FSHS) policy. Each result shows the effect of each alternative FSHS reform in comparison with the business-as-usual \u0026lsquo;universal subsidy\u0026rsquo; scenario. We first discuss the relative impact of the reforms on government savings from the GHAMOD microsimulation, which is fed to the dynamic CGE model to produce the fiscal, labour market and distributive effects on the general economy and households.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Fiscal effects\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a clear comparative analysis of the fiscal savings associated with different FSHS policy targeting strategies in Ghana. To begin with, government savings from the education subsidy reforms is highest (US\u003cspan\u003e$\u003c/span\u003e 173.38\u0026nbsp;million) under \u003cem\u003emeans-tested targeting of non-residential students only (MTT-NR)\u003c/em\u003e followed by non-\u003cem\u003emeans-tested targeting of non-residential students only (NMTT-NR)\u003c/em\u003e with savings of \u003cspan\u003e$\u003c/span\u003e173.09\u0026nbsp;million. This is accounted for by the sheer number of non-residential beneficiaries over those with residential status although the benefit amount for non-resident student is less than the those of residential status. The results, however, means that whether targeting the poor or not, limiting subsidy to non-residential students would yield similar savings for the government. Hence, targeting the poor makes little difference if education subsidy is limited to non-resident students since poor households are most likely to switch to non-resident status if subsidies are limited. In this regard, the distributive impacts, as will be shown in subsequent paragraphs, differ significantly. Strictly speaking, subsidy reform which includes payments to residential students yield lesser government savings compared with non-resident students which further dwindles when all two strands of students are considered.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; estimation using GHAMOD microsimulation (2025)\u003c/p\u003e\u003cp\u003eThe \u003cem\u003enon-means-tested capped benefit\u003c/em\u003e for all students (NTCB-All) scenario, which represents a capped but expansive beneficiary base, results in moderate savings of \u003cspan\u003e$\u003c/span\u003e61.11\u0026nbsp;million. Notably also, the \u003cem\u003emeans-tested targeting\u003c/em\u003e for all students (MTT-All) scenario produces the lowest savings of \u003cspan\u003e$\u003c/span\u003e7.29\u0026nbsp;million. These two reflect the trade-off between simultaneous expansion in beneficiaries and declining government savings over the status quo \u0026ldquo;universal FSHS subsidy\u0026rdquo;. In line with the foregoing analysis, all reform scenarios demonstrate higher growth in government savings, with the gap between the baseline and the reforms widening significantly after 2030 (see Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eA1\u003c/span\u003e in the appendix).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Sectorial growth effect\u003c/h2\u003e\u003cp\u003eThe education sector GDP growth rates, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, are directly affected by changes in household consumption, private investment and government expenditure over time. Specifically, by reducing subsidies on education, increases the market price of education which causes a fall in household demand for education. However, the inelastic nature of demand for education minimizes the fall in demand but escalate consumption expenditure, squeezing out essential needs like food and health. The effect on final demand is further offset by scaled investments and government expenditure in education arising from its savings through the alternative reforms. Since government is the largest consumer of education, its scaled expansion in expenditure ensures that sectorial growth outperforms the BAU scenario until after 2030. Generally, education sector GDP growth decreases after 2030 where population growth would cause a decline in household demand to overweigh government savings in the sectorial absorption. The decline in growth rate is steeper, except for NMTT-NR and NMTT-R, that manage to stay above the BAU into 2040.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003cp\u003e\u003cem\u003eFor visualisation purpose, the annual growth rate axis starts at 3 per cent instead of the origin.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Sectorial growth\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the sectorial growth rates for different scenarios in the Ghanaian economy, including Agriculture, Industry, Services, Education, and the overall National growth rate. The baseline scenario depicting the current state of the FSHS policy shows relatively lower growth rates across all sectors compared to the other scenarios, while NMTT-NR, which targets the Free Senior High School (FSHS) policy benefits solely towards non-resident beneficiaries regardless of poverty status, exhibits the highest growth rates, particularly in Services and Education. In contrast, MTT-All, which fully targets the FSHS benefits to poor households (both resident and non-resident), shows the lowest growth rates, especially in the Agriculture and National growth rates. The other intermediate scenarios, such as NMTT-R, MTT-R and NTCB-All, fall between these extremes, indicating the significant impact that the targeting approach for the FSHS policy can have on the sectorial growth rates in Ghana.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Labour market effect\u003c/h2\u003e\u003cp\u003eIn all of the reforms, there is a notable increase in labour labour employment relative to the baseline for all workers primary irrespective of educational background (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Growth in employment more than doubles in targeting of non-residential students (NMTT_NR) for all workers. The highest gainers are the workforce with primary or tertiary education while uneducated labour gains the least growth in employment, which are still improvements over the BAU. On the other hand, growth in labour returns (wages) declines in comparison with the BAU. This is occasioned by the increasing number of workers employed, thus lowering wages per worker. Labour with primary and, in some instances, tertiary education have a decline in growth rate in wages below the BAU due to excess supply of labour. Interestingly, labour with no formal education consistently benefits the most across all reforms, with rising employment growth notably, especially in NMTT-R through NTCB-All, suggesting that targeted subsidies or redirected fiscal savings would open employment opportunities for low-skilled workers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003cp\u003e\u003cem\u003eData labels on the uneducated and labour with secondary education are omitted for readability\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.5 Poverty effect\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the estimated impact of various FSHS reform simulations on poverty in Ghana, using the headcount ratio (P0) and the poverty gap (P1). All reforms show a reduction in poverty incidence and intensity compared to the baseline. The highest reduction in poverty is seen under NMTT-NR. Interestingly, all means-tested schemes rather yield lower impact on poverty unlike the opposite, non-means-tested schemes due to lower government savings and scaled-investments spillover effects. Rural households see the most substantial poverty reductions, particularly in NMTT-NR and NMTT-R, and across all other reforms (see Figure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eA2\u003c/span\u003e and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eA3\u003c/span\u003e in the appendix).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Inequality effect\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows a notable reduction in inequality by 2040, with point changes declining to around 0.27 or slightly below, whereas the baseline scenario remains virtually unchanged or even slightly elevated. Again by 2040, urban inequality consistently declines across all simulations (see Figure \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003eA4\u003c/span\u003e in the appendix). This pattern suggests that while the universal FSHS policy maintains existing levels of inequality, targeted reforms lead to a more equitable distribution of resources and opportunities over time. Specifically, reforms like NMTT-R and MTT-R, focusing on resident students only and pro-poor targeting of resident students only respectively, achieve the most significant reductions in inequality, reinforcing the idea that redistributive targeting mechanisms enhance equity. These results are consistent with the capability approach and human capital theories, which stipulate that broadening access to education among disadvantaged groups contributes to closing structural inequality gaps.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Social welfare effect\u003c/h2\u003e\u003cp\u003eIn the baseline universal FSHS grant, households in urban areas are the greatest net gainers in welfare (1.30 percentage point change) compared with rural areas (0.92 percentage point change) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Per the result shown Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003e, with the exception of in the NMTT-NR scenario which targets non-resident beneficiaries, targeting by either residency, poverty status capped benefits would significantly bridge the gap between urban and rural dwellers. Poor households, both rural and urban, see greater improvements, indicating that targeted reforms are more effective at boosting welfare among the poor (see Figure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003eA5\u003c/span\u003e in the appendix).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Comparative macro performance\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e11\u003c/span\u003e presents the elasticities of growth, poverty, and employment across six simulation scenarios for the FSHS policy reforms. Each scenario is evaluated on three dimensions: growth, poverty and employment. The results show that NMTT-R which targets resident beneficiaries, yields the highest growth elasticity and achieves a substantial employment elasticity, but it is associated with the largest reduction in poverty. NTCB-All which is the non-targeted capped benefit, also demonstrates strong performance with high growth and employment elasticities, though its poverty reduction impact is more modest. The other scenarios exhibit moderate to low elasticities, with NMTT-NR and MTT-All producing notable growth and employment effects but less pronounced poverty reduction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; construct (2025)\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe comparative analysis of Ghana\u0026rsquo;s Free Senior High School (FSHS) policy reforms demonstrates that alternate targeting strategies significantly outperform the baseline universal model in expanding access to education and promoting equity. Although universal coverage under the baseline provides widespread access, its efficiency in fostering long-term educational sector growth is limited. The Capability Approach (Sen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) highlights that expanding real educational opportunities, especially for disadvantaged groups, enhances both individual agency and collective well-being. Accordingly, targeted reforms serve to bolster the capability sets of low-income and rural youth, aligning with the policy's foundational goals. From a Human Capital perspective (Becker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), channelling resources into populations with the highest marginal returns such as non-residents who typically face greater educational access constraints, is not only equitable but economically prudent.\u003c/p\u003e\u003cp\u003eIn evaluating inequality outcomes, the analysis has shown a significant decline in inequality, with means-tested residency targeting. This finding aligns with the insights of Nussbaum (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Eden, Chisom and Adeniyi (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who emphasise that equitable access to social goods, such as education, is crucial for addressing structural disparities. In contrast, the baseline scenario maintains or slightly increases inequality, highlighting the limitations of universal approaches in transforming opportunity landscapes. The reforms highlight how reallocating benefits based on socioeconomic status or residential status can mitigate inequality more effectively. This supports empirical evidence from developing countries that targeted interventions in secondary education yield more equitable long-term outcomes (Psacharopoulos \u0026amp; Patrinos, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the analysis reinforces the importance of designing educational interventions that are both inclusive and redistributive. The substantial reduction in inequality observed in NMTT-R and MTT-R exemplifies how targeted support enhances substantive freedoms, particularly for those structurally disadvantaged by geography, gender, or income. While universal benefits can formalise access, they do not address the conversion factors that impede equal educational attainment. These reforms, therefore, shift the policy narrative from aggregate enrolment statistics to real opportunity equality, making a compelling case for pro-poor targeting as a more transformative approach.\u003c/p\u003e\u003cp\u003eIn macroeconomic terms, the study has shown that targeted reforms also deliver greater fiscal efficiency. NMTT-NR and MTT-NR generate the highest government savings compared to the baseline, demonstrating the value of targeting in reducing unnecessary expenditure while preserving access. These savings, when reinvested in complementary sectors or educational quality improvements, stimulate broader economic development. Government savings under the reform scenarios exhibit accelerated growth from 2030 onward, particularly under NMTT-NR and NMTT-R, highlighting their long-run sustainability (see Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eA1\u003c/span\u003e in the appendix). This trajectory supports the Human Capital Theory\u0026rsquo;s assertion that efficient allocation of educational resources enhances both fiscal stability and future productivity. These reforms align with the broader policy goal of achieving universal access in a fiscally constrained environment without sacrificing the quality or equity of outcomes.\u003c/p\u003e\u003cp\u003eThe employment and sectoral growth results further highlight the macro-level advantages of the reform models. While composite labour outcomes decline slightly under reform scenarios, gains in employment for low-skilled (no formal education) workers suggest that targeted subsidies may stimulate demand in labour-intensive sectors or reduce informal sector unemployment. Additionally, higher national and services sector growth under NMTT-NR and NMTT-R reflect the economy-wide multipliers of smart educational investments (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These outcomes were echoed by Moayed, Guggenheim and von Chamier (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who argued that subsidies that minimise regressive leakage while maximising redistributive impact are more likely to foster inclusive economic growth. The balance between fiscal savings and job creation under targeted reforms reinforces their advantage over the baseline, which achieves broad access but fails to leverage education for optimal macroeconomic transformation.\u003c/p\u003e\u003cp\u003eAt the microeconomic level, the reform scenarios yield stronger poverty reduction outcomes than the baseline. All simulations reduce both the poverty headcount (P0) and poverty gap (P1), with NMTT-NR and NMTT-R achieving the most substantial improvements (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This confirms Antwi (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that FSHS significantly increased completion rates, particularly among girls and low-income students. Notably, NMTT-NR and NMTT-R achieve the largest reductions in the poverty gap. From a capability approach standpoint, these reforms enhance the substantive freedoms of households by removing one of the most critical financial barriers to upward mobility. Reducing educational costs allows poor households to redirect resources toward other necessities, thereby improving their overall well-being and resilience. Similarly, social welfare analysis further substantiates distributive impacts of the FSHS reforms on households. In NMTT-NR and NMTT-R, welfare gains are disproportionately higher for urban and rural poor households respectively, illustrating how well-designed targeting mechanisms can address spatial and class disparities (see Figure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003eA5\u003c/span\u003e in the appendix). The capability approach theorem offers a useful lens here. It highlights the centrality of equalising the means to achieving well-being than simply distributing nominal benefits. Hence, reforms that prioritise targeting by need rather than uniformity of access create more meaningful improvements in the lives of the most deprived.\u003c/p\u003e\u003cp\u003eFinally, the study has demonstrated that reforms that target the resident or non-resident poor students tend to outperform broader universal strategies. This is especially relevant for rural areas, where poverty impacts are more pronounced and educational access more constrained. The consistent gains in both poverty metrics and social welfare under targeted reforms suggest that these policies are better aligned with national poverty alleviation goals and international development goals like the SDGs 4 and 10.\u003c/p\u003e"},{"header":"5. Conclusion and Policy Recommendations","content":"\u003cp\u003eThis study set out to demonstrate evidence that reforming Ghana\u0026rsquo;s Free Senior High School (FSHS) policy from a universal benefit structure to more targeted approaches can yield significant fiscal, social, and macroeconomic benefits. Specifically, this has been shown that by alternative reforms, targeting non-resident students, especially those from poor households, generates the highest government savings and substantial reductions in the poverty headcount, particularly in rural areas. Thus, reforms targeting students based on residency also shows strong fiscal and distributive impacts, suggesting that spatial targeting is an effective pathway for optimising impact. Importantly, while all reforms outperformed the business-as-usual case in terms of efficiency and equity, the degree of improvement varied depending on whether targeting is based on residency, poverty status, or a combination of both. These outcomes align with the theoretical underpinnings of this study: the Human Capital Theory and the Capability Approach, which both emphasize the value of expanding educational opportunities for disadvantaged groups to reduce structural inequalities and promote long-term economic development.\u003c/p\u003e\u003cp\u003eThe macroeconomic implications of the reform scenarios are equally notable. The simulations demonstrated that targeted policies not only lead to increased government savings but also stimulate growth and employment, particularly in the services and education sectors. Even reform options such as the capped universal benefit (NTCB-All) that yielded moderate fiscal and employment benefits, still outperformed the current universal policy. These findings support a transition toward more fiscally sustainable and economically productive education financing models.\u003c/p\u003e\u003cp\u003eBased on these findings, the study recommends a phased transition over the long term toward a more targeted FSHS policy, without leaving the poor out of the safety net. This approach would allow for optimal fiscal allocation while preserving equity in access to secondary education, freeing resources for the expansion in investment in essential goods and services that also benefit the poor and the entire economy. We have also demonstrated that a much simpler and less-costly schemes such as non-means-tested targeting based on residency or capped benefits yield significant positive impacts that could be readily adopted to mitigate the fiscal constraints posed by the current form of the policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no known financial or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data used in this study were obtained from the Ghana Living Standards Survey Round 7 (GLSS 7), conducted by the Ghana Statistical Service (GSS). The dataset is publicly available upon request from the Ghana Statistical Service through their official data access portal at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://microdata.statsghana.gov.gh/index.php/catalog/97\u003c/span\u003e\u003cspan address=\"https://microdata.statsghana.gov.gh/index.php/catalog/97\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdu-Ababio, K., \u0026amp; Osei, R. D. (2018). \u003cem\u003eEffects of an education reform on household poverty and inequality: A microsimulation analysis on the free Senior High School policy in Ghana\u003c/em\u003e (No. 2018/147). WIDER Working Paper.\u003c/li\u003e\n\u003cli\u003eAnlimachie, M. A., \u0026amp; Avoada, C. (2020). 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Roadmap for Equality in Education: Problems, Solutions and Implementation Strategies. \u003cem\u003eDinamika Ilmu\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 11-28.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The exchange rate used is 1 USD\u0026thinsp;=\u0026thinsp;5.3590 Ghanaian Cedi, based on the average exchange rate in 2019.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Cape Coast","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CGE, Free Senior High School (FSHS), Simulation, Fiscal, GHAMOD, Distributive Effects, Social Protection, Educational Reform","lastPublishedDoi":"10.21203/rs.3.rs-7934227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7934227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study combines the GHAMOD microsimulation model, which is from the pool of SOUTHMOD models by UNU-WIDER, and IFPRI\u0026rsquo;s RIAPA dynamic CGE model to investigate the fiscal and economy-wide impacts of educational reforms in Ghana\u0026rsquo;s Free Senior High School (FSHS) policy. Introduced in 2017/2018, the FSHS policy aims to improve access to secondary education, particularly for the poor. However, like several other subsidy interventions in developing countries, the policy has introduced significant fiscal and implementation challenges as well including overstretched facilities, impaired subsidy payment by the government for feeding, teaching and learning materials. Thus, putting a strain on quality of education. The paper simulates the current universal FSHS policy and six alternative reforms that explore efficiency, fiscal sustainability, and social equity. Specifically, we evaluate policy effectiveness and distributive effects of the status quo \u0026lsquo;universal FSHS grant\u0026rsquo; with targeted scenarios with respect to (1) a beneficiary\u0026rsquo;s boarding/residency status (2) poverty status of beneficiary students (means-tested), and (3) capped benefit for all beneficiaries. These scenarios are evaluated on long term impacts on government savings, poverty, inequality, sectoral growth, and labour market outcomes up to 2040. Our findings indicate that targeted reforms do not only lead to increased fiscal savings, which when reinvested offsets non-market incomes of households from the limited grant transfer by stimulating sectorial growth, employment and positive distributive impact on households. We are able to demonstrate that simpler and less-costly non-means-tested schemes based on residency or capped benefits could be deployed to mitigate the fiscal constraints posed by the current form of the policy while yielding significant positive fiscal and distributive impacts. The findings from the study contribute to deepening the FSHS policy discourse in Ghana. Specifically, the simulated reforms could guide the pursuit toward a targeted FSHS policy which is fair and fiscally sustainable.\u003c/p\u003e","manuscriptTitle":"Fiscal, Labour Market and Distributive Effects of Educational Reforms: A Hybrid Simulation Study of Free Senior High School Policy in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:12:16","doi":"10.21203/rs.3.rs-7934227/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e36da290-16fc-498d-bfb4-407560ba9cd9","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56791502,"name":"Microeconomics"}],"tags":[],"updatedAt":"2025-10-27T11:12:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:12:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7934227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7934227","identity":"rs-7934227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}