Macroeconomic Effects of 'Free' Secondary Schooling in the Developing World

Layer 1: Overview

This paper asks whether publicly funded (“free”) secondary schooling in developing countries raises GDP per capita. The question is policy-relevant because many low-income countries — including Ghana, Kenya, Tanzania, Uganda, and others listed in the paper’s appendix — have recently adopted or are considering such policies, motivated by the combination of low secondary enrollment (roughly one-third of secondary-school-age children enrolled in the poorest countries, versus near-universal enrollment in rich countries) and evidence that credit constraints keep talented students out of school.

The analysis is built around an overlapping-generations (OLG) model with heterogeneous households and credit constraints, estimated to match experimental evidence from a randomized controlled trial (RCT) in Ghana (Duflo, Dupas, and Kremer, 2021). The RCT randomly offered full four-year scholarships covering 100 percent of tuition and fees to approximately two thousand poor but high-ability students who had passed the Basic Education Certificate Examination (BECE) but had not enrolled in Senior High School (SHS). Scholarship winners were 27 percentage points more likely to complete secondary school than the control group, scored 0.16 standard deviations (equivalent to 7.6 percent wage gains in the model) higher on math and literacy tests, and experienced a 10.6 percent decline in fertility after 12 years.

The model departs from standard human capital OLG models in three ways. First, it incorporates an explicit opportunity cost of schooling: teenagers who attend SHS forgo labor income during ages 15–19, which is economically significant given that secondary-school-age individuals are near their prime working years in developing countries. Second, the model includes a merit-based entrance exam (the BECE), so that removing the exam requirement as part of free schooling causes negative selection — the new marginal students induced to attend have lower average ability than those already attending. Third, the model features education-dependent fertility: more-educated households have fewer children (estimated fertility of 2.07 per less-educated family vs 1.19 per more-educated family, in line with Ghanaian Demographic and Health Survey data). The model also incorporates imperfect substitutability between skilled and unskilled labor (elasticity of substitution set to 4, following long-run cross-country estimates), savings wedges that match low liquid asset holdings, and Ghana’s actual progressive income tax schedule.

The model is estimated using the Simulated Method of Moments (SMM) targeting ten moments — five non-experimental (aggregate population growth rate of 2.2 percent per year, aggregate SHS completion rate, SHS completion in the top and bottom test-score quartiles of the control group, and variance of the permanent component of log wages) and five experimental or quasi-experimental (RCT treatment effects on human capital, fertility, overall SHS completion, the Q4 vs Q1 difference in SHS completion, and the intergenerational schooling correlation from administrative data).

The central quantitative finding is that nationwide free secondary schooling — eliminating both fees and the entrance-exam requirement — raises secondary school completion by about 12 percentage points (from 30 percent to 42 percent of the population) but reduces GDP per capita by approximately 1 percent in the long run. The 95 percent confidence interval for the GDP effect excludes any positive value (lower bound -4.2 percent, upper bound -0.7 percent), so the model can statistically reject any positive GDP impact. The direct fiscal cost of the policy is 1.4 percent of GDP, implying a total cost (direct cost plus lost GDP) of approximately 2.4 percent of GDP. Taxes per capita increase by 1.4 percent. Adult earnings rise by about 1.2 percent, but this is more than offset by a 7.5 percent decline in child earnings (the opportunity cost of schooling for newly enrolled students). The skilled-to-unskilled wage ratio falls by about 10 percent, reflecting general-equilibrium wage compression from the expanded supply of secondary graduates.

Three counterfactual experiments decompose the negative GDP result. (i) Eliminating the opportunity cost of schooling reverses the GDP effect from -1.0 percent to +2.9 percent, a swing of nearly 4 percentage points — the dominant channel. (ii) Holding the ability distribution of new secondary attendees to match the experimental sample (removing negative selection) moves GDP from -1.0 percent to essentially 0, accounting for about 1 percentage point of the gap. (iii) Holding fertility constant for new secondary attendees moves GDP from -1.0 percent to +1.2 percent, contributing about 2.2 percentage points. When all three channels are shut down simultaneously, GDP rises by 6.9 percent — close to the naive back-of-the-envelope projection of 6 percent based on the RCT’s test-score estimates.

As a policy comparison, an economy-wide improvement in schooling quality that raises test scores by 0.1 standard deviations (a conservative estimate consistent with randomized teacher-incentive interventions in India and Kenya) raises GDP per capita by 2.7 percent and increases SHS completion by 13.8 percentage points — more than free schooling and at lower fiscal cost (the policy pays for itself in equilibrium). Improving schooling quality avoids the negative selection and opportunity-cost channels because it raises human capital for both new and inframarginal students.

On welfare and distribution, the policy is predominantly redistributive. The bottom 25 percent of parents gain welfare equivalent to a 7.3 percent increase in lifetime consumption, while the top 25 percent lose 4.2 percent. For children, the bottom 25 percent gain 23 percent in consumption-equivalent welfare, while the top 75 percent lose about 5.3 percent. These distributional predictions are validated against a new nationally representative survey of 3,500 Ghanaian households (conducted by the authors in August–September 2022): households with at most a JHS education were 3.1 percentage points more likely to support the policy than average, while those with SHS education or more were 5.2 percentage points less likely — remarkably close to the model’s predicted values of 2.6 and 5.9 percentage points, respectively. The authors conclude that free secondary schooling in developing countries is primarily a redistributive policy and not an efficient path to economic growth at current levels of schooling quality.

Layer 2: Deep Dive

What is the identification strategy and what are the main threats to it?

The paper uses a two-step strategy. First, it estimates the OLG model using SMM, with the experimental moments from Duflo, Dupas, and Kremer’s (2021) RCT serving as the key identifying variation. The RCT randomly assigned scholarships to poor but high-ability students in Ghana who had passed the BECE but had not enrolled in SHS, making the treatment effect on schooling completion, test scores, and fertility credibly causal in partial equilibrium. Second, the estimated model is used to compute general-equilibrium counterfactuals for a nationwide policy. The main threats to validity are: (a) external validity of the RCT sample to the general population — the sample is explicitly ‘smart kids from poor families,’ which the authors account for through the negative-selection counterfactual; (b) the model misses on the intergenerational schooling correlation (model: 0.32 vs data: 0.45) and on the treatment effect on SHS completion (model: 21.3 pp vs data: 27 pp), though the authors show in Appendix C that forcing the model to match these moments does not reverse the negative GDP conclusion (a 40 percent higher schooling cost parameter yields a -0.8 percent GDP result vs -1.0 percent baseline; a 15 percent higher ability-persistence parameter yields -2.0 percent); (c) abstracting from human capital externalities (Lucas 1988 type spillovers) and crime reduction effects of education — the authors note these omissions but argue the low estimated effects of the policy make them unlikely to matter quantitatively; and (d) partial equilibrium of the RCT itself — the authors assume no general-equilibrium effects of the experiment since it covered only 2,064 students.

What are the three main mechanisms and how are they distinguished empirically?

The three channels are (i) opportunity cost — attendees ages 15–19 forgo labor income; (ii) negative selection — removing the BECE requirement means new marginal students have lower average ability than current attendees; (iii) differential fertility — newly educated households reduce fertility, shifting the long-run population distribution toward less-educated (higher-fertility) households, diluting the share of educated workers over time. The paper isolates each channel through sequential counterfactual experiments: (i) is isolated by eliminating the option for ages-15–19 children to work (forcing the choice between schooling and idleness), which raises the GDP effect from -1.0 to +2.9 percent; (ii) is isolated by artificially boosting the ability of new secondary attendees to match the experimental sample’s ability distribution, which moves GDP from -1.0 to approximately 0; (iii) is isolated by setting new attendees’ fertility to the uneducated-household level, which moves GDP from -1.0 to +1.2 percent. The magnitudes reveal that the opportunity cost channel is the largest (approximately 4 pp swing), followed by the fertility channel (approximately 2.2 pp), and then the selection channel (approximately 1 pp).

What heterogeneity is documented?

Several dimensions of heterogeneity are documented. In the experimental sample, the treatment effect on SHS completion is not particularly skewed toward high-ability students: the difference in treatment effects between the top and bottom test-score quartiles is only 4 percentage points in the data (and 3 in the model), implying broadly similar gains across the ability distribution within the selected sample. In the estimated model’s misallocation analysis, the attendance probability plot (Figure 3) shows that the highest-ability children are fairly likely to attend SHS even when born to low-ability parents — suggesting relatively low misallocation in the estimated model compared to the stylized high-misallocation case. On welfare, the paper documents large heterogeneity by income quartile: the bottom 25 percent of parents gain 7.3 percent in consumption-equivalent welfare while the top 25 percent lose 4.2 percent; for children the bottom 25 percent gain 23 percent while the top 75 percent lose about 5.3 percent. Welfare also differs across generations: gains for grandchildren who always exist are smaller (9 percent) than for children (12 percent), reflecting the compounding fertility effect. The survey confirms these patterns across urban/rural, male/female, and across the Volta (42.3 percent average support for free SHS) and Ashanti (78.2 percent average support) regions of Ghana.

What robustness checks are run?

The authors report three robustness checks in Appendix C. First, they increase the schooling cost parameter ΨS by 40 percent to force the model to match the (currently undershot) treatment effect on SHS completion; the free schooling policy then produces a -0.8 percent GDP result (vs -1.0 percent baseline) and a 14 percent increase in attendance (vs 12 percent baseline) — the conclusion is unchanged. Second, they increase the ability-persistence parameter ρ by 15 percent to match the intergenerational schooling correlation; the result is a -2.0 percent GDP decline and a 4 percent attendance increase — the GDP decline is larger, so if anything the baseline is too generous to free schooling. Third, they experiment with lower values of the elasticity of substitution between skilled and unskilled labor (down to 1.4 from the baseline value of 4) and report no substantive change in conclusions. The authors also use bootstrapped 95 percent confidence intervals for all aggregate predictions, which is unusual in general-equilibrium counterfactual exercises in macroeconomics.

How does the paper relate to and differ from closely related prior work?

The paper is most closely related to Abbott, Gallipoli, Meghir, and Violante (2019) and Daruich (2020), both of which study public education expansions in the United States and find largely positive effects on GDP and welfare. The authors argue the contrast with their pessimistic findings reflects lower school quality in developing countries — in a rich-country setting, opportunity costs are lower relative to the returns to schooling. Hendricks and Schoellman (2014) find similar negative selection of college students in the US as enrollment expands, lending support to the selection channel. Khanna (2023) documents substantial declines in the relative wages of skilled workers after an education expansion in India, consistent with the model’s 10 percent skilled-to-unskilled wage compression, though Khanna’s short-run effects are larger due to lower short-run elasticity of substitution. In terms of methodology, the paper follows Daruich (2020) in using RCT evidence to discipline an OLG model, and is the first paper to do so for the macroeconomic effects of education policy in the developing world. The paper also builds on the macro-development literature emphasizing school quality (Hanushek and Woessmann, 2007; Schoellman, 2012) over average years of schooling as the proximate cause of low human capital in poor countries.

What are the policy implications and their scope conditions?

The central policy implication is that free secondary schooling in developing countries, at current low levels of schooling quality, is primarily redistributive rather than growth-enhancing. Countries considering free schooling should expect secondary enrollment to rise substantially (by around 12 percentage points in the baseline) but GDP per capita to fall or stay flat. The alternative of improving schooling quality — modeled as a 0.1 standard deviation increase in test scores, using teacher incentives or additional teachers at a cost of approximately US$5.78 per student per year (based on Mbiti et al. 2019 in Tanzania) — raises GDP by 2.7 percent and schooling enrollment by even more (13.8 percentage points), while paying for itself in equilibrium. A key scope condition: the negative GDP finding is driven by the combination of high opportunity costs of schooling (secondary-school-age workers have economically significant labor income in developing countries), negative selection from removing merit requirements, and low schooling quality that limits the human capital return per year of schooling. In rich countries where these conditions do not hold, the same policy has been found to be beneficial. The paper also shows (Table 6) that maintaining the entrance-exam requirement alongside free schooling substantially mitigates the GDP decline (-0.3 percent vs -1.0 percent), and that keeping both the test and a positive fee results in approximately zero GDP change — suggesting that the test-requirement component of the policy design is important.

What does the paper find about misallocation in the estimated model?

The estimated model exhibits relatively low misallocation. The misallocation concept refers to situations where high-ability children of poor parents are kept out of secondary school by borrowing constraints even though the net-present-value of additional schooling exceeds the cost. The paper shows (Figure 2) that economies can have similar aggregate secondary enrollment rates of around 30 percent but very different degrees of misallocation — one where enrollment is low because returns are low (low-misallocation case), and one where enrollment is low because high-ability children are credit-constrained (high-misallocation case). The estimated model falls closer to the low-misallocation case (Figure 3), with the highest-ability children fairly likely to attend SHS even if born to low-ability parents. This finding is consistent with the modest increase in SHS completion induced by free schooling (12 percentage points) relative to the experimental treatment effect on the selected sample (27 percentage points): most high-ability children are already attending, so there is limited room for a free schooling policy to reduce misallocation.

What does the welfare analysis reveal about the puzzle of large welfare gains alongside a GDP decline?

The paper documents an apparent puzzle: the free schooling policy reduces long-run GDP per capita by 1 percent but produces large positive welfare gains for parents (average 3.9 percent in consumption-equivalent welfare) and even larger gains for children (average 12.4 percent). The resolution is that (a) welfare gains for parents come entirely from redistribution — the very poor gain 7.3 percent while the rich lose 4.2 percent, and the progressive tax schedule is the mechanism; (b) the welfare gains for the children’s generation partially reflect large gains to the small number of previously misallocated children who now attend secondary school (the bottom 25 percent of children gain 23 percent, primarily through income gains for those who previously could not afford school); and (c) these gains erode across generations — grandchildren who always exist gain less (9 percent vs 12 percent for children), because the grandchildren who would only have existed without the free schooling policy (i.e., the ‘unborn’ due to reduced fertility among educated households) would have experienced disproportionately large gains (almost 17 percent). The composition of the population thus shifts toward those experiencing smaller gains, compounding over generations and producing the long-run GDP decline.

What is the role of the entrance exam design in free schooling policy outcomes?

The paper shows that how access is structured matters as much as whether schooling is free. In the main analysis, free schooling eliminates both fees and the BECE entrance requirement, consistent with Ghana’s 2017 policy. In alternative simulations (Table 6), free schooling that maintains the existing entrance requirement (a ‘relaxed test’ policy) produces a GDP decline of only -0.3 percent instead of -1.0 percent. Free schooling that keeps the test at full stringency (so fewer new students gain access) produces essentially no change in GDP (-0.0 percent), but also a much smaller increase in secondary attendance (3.0 pp vs 11.8 pp). Eliminating only the test requirement while keeping a positive fee produces a -0.4 percent GDP decline. These results confirm that the negative selection channel is a quantitatively important driver of the adverse GDP effect and is specifically activated by the removal of the merit requirement.

How is the model estimated and what moments does each parameter primarily identify?

The model is estimated by SMM minimizing the sum of squared differences between model moments and their data counterparts, using a vector of 10 parameters (fertility parameters νJ and νS; schooling efficiency ηS; goods cost of schooling ΨS; intergenerational altruism b; exam score noise σε; Gumbel taste-shock scale θ; savings wedge χ; ability persistence ρ; ability shock standard deviation συ). Six parameters are chosen directly from the literature or normalization (A, α, β, r*, λ, σζ). Ten moments are targeted: population growth rate (primarily identifies νJ, νS), aggregate SHS completion rate and quartile completion rates (identify ηS, b, ΨS, χ), variance of the permanent component of wages (identifies συ, ρ), and five experimental moments from the Duflo et al. RCT (treatment effects on human capital, fertility, SHS completion, the Q4–Q1 completion difference, and the intergenerational schooling correlation). Confidence intervals are bootstrapped by re-sampling the five experimental moments 100 times, treating the non-experimental moments as fixed. The Jacobian matrix (Appendix Table C.1) and sensitivity matrix (Appendix Table C.2) are computed following Kaboski and Townsend (2011) and Andrews, Gentzkow, and Shapiro (2017) to document identification.

What are the survey design details and how well does it validate the model?

The authors conducted a new nationally representative household survey in Ghana in August–September 2022, covering 3,500 households selected via two-stage cluster sampling from seven regions accounting for about 61 percent of the Ghanaian population. Respondents were asked whether eight categories of government expenditure should be abolished, cut substantially, cut somewhat, maintained, or expanded. For free SHS, respondents with at most a JHS education were 3.1 percentage points more likely to support the policy than average; those with SHS education or more were 5.2 percentage points less likely. These empirical patterns align closely with the model’s predicted values of 2.6 and 5.9 percentage points respectively. The pattern is robust across urban/rural subsamples, male/female subsamples, and across the Volta and Ashanti regions (which differ substantially in overall support levels — 42.3 percent vs 78.2 percent — but maintain the same qualitative pattern of lower-educated households being more supportive). The one discrepancy is that the model over-predicts the support of JHS-educated households who have children enrolled in SHS.

Key Concepts

Opportunity cost of schooling: In this paper’s model, the foregone labor income of teenagers aged 15–19 who attend secondary school rather than work. This cost persists even when the school fee is eliminated by government policy and is identified as the single largest channel explaining why free secondary schooling reduces rather than raises GDP per capita in developing countries, contributing approximately 4 percentage points to the adverse GDP effect.

Negative selection of new students: The reduction in average ability of the marginal students who enter secondary school once both fees and the merit-based entrance exam are eliminated. The existing pool of secondary attendees was positively selected by the entrance exam, so broadening access induces a lower-ability pool of new entrants, reducing the average human capital gain per new graduate. The paper estimates this channel accounts for approximately 1 percentage point of the adverse GDP gap relative to the back-of-the-envelope projection.

Differential fertility by education: The model feature by which secondary-educated households have significantly fewer children (parameter νS = 0.19 implying 2.4 children per family) than non-secondary-educated households (νJ = 1.07 implying 4.1 children per family). When free schooling induces more households to obtain secondary education, aggregate fertility falls, and crucially the share of high-ability households in the long-run population declines because those households now have fewer children, reducing the long-run supply of educated workers and contributing approximately 2.2 percentage points to the adverse GDP gap.

Misallocation of talent: In this paper’s sense: the situation in which high-ability children of poor parents are prevented by borrowing constraints from attending secondary school even though the net-present-value of additional schooling exceeds the combined goods and opportunity costs. The paper finds that the estimated model of Ghana corresponds more closely to a low-misallocation economy (Figure 3), meaning the highest-ability children attend SHS at fairly high rates regardless of parental income, so the scope for free schooling to reduce misallocation is limited.

Balanced growth path: In this paper: a recursive competitive equilibrium in which aggregate population grows at a constant rate while the relative distribution of households across individual states (ability, education, assets) is stationary, and household policy functions are independent of the aggregate population level. All policy counterfactuals are conducted by introducing a policy into the balanced growth path and computing transition dynamics to the new balanced growth path.

Schooling quality (ηS): The efficiency parameter governing how much human capital a student of given ability acquires from a year of secondary schooling, defined in the production function h(z,S) = z · ηS. In the estimated model, ηS = 5.66, implying an annual return to education of 7.9 percent for the experimental sample. The paper shows that a policy raising ηS (schooling quality) by enough to increase average test scores by 0.1 standard deviations raises GDP by 2.7 percent and expands SHS enrollment by 13.8 percentage points, outperforming free schooling on both counts.

Savings wedge (χ): A wedge between the international market rate of return on capital (r*) and the return available to households in the model (r = r* - χ), calibrated to match the low savings rates observed in low-income economies. In the estimated model χ = 0.09, implying households earn approximately 2 percent per year on savings. Together with the borrowing constraint (no borrowing against children’s future income), this ensures that poor parents cannot save their way out of the constraint preventing them from sending high-ability children to school.