Growth without jobs is meaningless

By Dr. Stephen LARTEY

Ghana has restored macroeconomic stability. Now comes the harder question: stability for whom? Answering that demands a model that counts GDP, jobs, and wages at the same time — and Ghana has the data to run one.

When the National Development Planning Commission’s Chairman, Dr Nii Moi Thompson, called for a “3D Growth” framework at the IYA Business Roundtable last month, he was not just proposing a new dashboard. He was making a methodological argument: that the way Ghana measures, forecasts, and evaluates economic policy needs to change.

Under the proposed approach, economic performance is assessed not just by output growth, but by three coequal dimensions — GDP, jobs, and wages. “Growth without jobs is meaningless,” Dr Thompson told the Roundtable. “Growth without rising incomes is unsustainable.”

This is the right framing for Ghana in 2026. The country is emerging from an IMF programme with macroeconomic stability restored — inflation falling, the cedi appreciating, fiscal reforms biting — but with persistent labour-market weakness. The Ghana Statistical Service’s most recent Quarterly Labour Force Survey puts national unemployment at 13.0 percent in Q3 2025, with youth unemployment for the 15–24 age group at 32.5 percent. About two in three employed Ghanaians work in vulnerable, informal arrangements. GDP growth is recovering, but the connection between output growth and household welfare has thinned.

The forthcoming National Development Plan, regional consultations under the NDPC, and the launch of the Ghana Infrastructure Plan in October 2025 are all converging on a question that demands an answer: what mix of productivity reforms, sectoral investment, and macro stability will generate broad-based, employment-intensive growth?

Figure 1. The 3D Growth Framework: what a properly specified CGE model can quantify in one assessment.

To answer that, you need a model that can do three things at once. You need a model that traces a shock through the entire economy — capturing not just the direct effect of, say, a new port or power station, but the indirect effects on supplier industries, wages across labour types, and household incomes by region and quintile. You need a model that distinguishes between productivity-led growth and capital-deepening growth, because these have very different employment signatures. And you need a model that handles unemployment honestly, rather than assuming all labour markets clear at full employment.

That model is the Computable General Equilibrium (CGE) model, and Ghana has the data to run one.

What a CGE Model Does That Simpler Tools Cannot

Most economic forecasting in Ghana relies on macroeconometric models that project GDP, inflation, and fiscal aggregates. These are valuable for short-horizon stability questions. They are not designed to answer 3D Growth questions, because they typically lack two features that matter: sectoral disaggregation and household disaggregation.

A CGE model is built on a Social Accounting Matrix — a structured table that records every flow of goods, services, factor payments, taxes, and transfers between sectors, households, government, and the rest of the world in a base year. Ghana’s most recent SAM, developed by the International Food Policy Research Institute (IFPRI) using 2022 data, distinguishes nine production sectors (cash crops, food crops, livestock, agro-processing, mining, energy, manufacturing, services, public services), ten household groups (five rural quintiles, five urban quintiles), and three labour types (unskilled, primary, secondary-plus).

When a policy shock enters this framework — say, a 1.2 percent of GDP envelope deployed as new infrastructure investment in energy — the model traces the impact through every channel: new capital raises energy-sector output, which raises demand for inputs from other sectors, which raises labour demand across the economy, which raises wages, which raises household income, which raises consumption, which feeds back into demand. The model finds the new equilibrium where all markets simultaneously clear.

Figure 2. How a CGE model traces a single policy shock through the economy. Each box is a model equation; each arrow a market-clearing condition.

This is general equilibrium thinking, made operational. Every shock produces a vector of outcomes: a GDP number, but also a sectoral output decomposition, an employment effect by labour type, a wage change by skill level, and a household income response by rural-urban quintile. That is precisely the 3D dashboard Dr Thompson called for.

The International Food Policy Research Institute’s CGE work has been applied across more than thirty countries to answer exactly these questions. In Ghana specifically, recent published research has applied dynamic CGE methods to the 24-hour Economy proposal (Abdul-Salam, 2024), with results suggesting real GDP could be substantially higher than a business-as-usual path under sustained policy support.

The Labour-Market Choice That Matters Most

For 3D Growth analysis, the single most important modelling choice is how the labour market closes.

The standard textbook treatment assumes full employment: any new labour demand is met by perfectly elastic supply at a market-clearing wage. Under this closure, a productivity shock produces no employment effect — only wage effects. Every additional unit of demand for labour shows up as a wage increase, not a new job.

This is the wrong closure for Ghana. With 13 percent unemployment, 32 percent youth unemployment, and substantial labour underutilisation across regions, the labour market is plainly not at full employment. Imposing the textbook closure would systematically zero out the jobs dimension of any policy assessment — exactly the dimension that 3D Growth elevates.

The right alternative is a wage-curve closure, drawing on Blanchflower and Oswald’s empirical regularity (Blanchflower & Oswald, 1994) that real wages are negatively related to local unemployment with an elasticity of around -0.1. Under this closure, additional labour demand is split between jobs (more workers employed) and wages (higher pay for existing workers), with the split governed by the elasticity parameter. A new infrastructure project does both: it pulls workers out of unemployment, and it bids up wages for those already employed. Both effects matter, and both are quantified.

Figure 3. Why closure choice matters. The textbook full-employment closure zeroes out the jobs dimension; the wage-curve closure correctly splits effects between employment and wages — the honest 3D answer for an economy with 13 percent unemployment.

This is the closure that lets you populate the 3D dashboard with credible numbers, not just one of the three numbers.

What a Properly Specified CGE Model Could Tell Us About the New Economy

The Ghana Infrastructure Plan, the forthcoming Consolidated National Development Plan, the 24-Hour Economy initiative, and the broader Big Push agenda all share a structural feature: they propose to deploy substantial public resources — measured in percentage points of GDP — into targeted sectors over a multi-year horizon. The political question is whether they will deliver. The analytical question is how to know in advance.

A properly specified CGE assessment of such a programme can answer at least seven questions that pure GDP forecasting cannot:

1. Aggregate employment. How many net new jobs over the horizon? How do they distribute across skill levels?

2. Wage trajectories. Do wages rise faster than inflation? Which labour types benefit most?

3. Distributional impact. Do rural or urban households gain more? Which quintiles see the largest income gains?

4. Sectoral targeting. Does investment in energy actually expand energy-sector output, or does it crowd out other sectors? Where does the value-added growth land?

5. Capital versus productivity channels. Is the policy delivering through new physical capacity or through reform-driven productivity? These have very different speeds and depreciation profiles.

6. Cost-effectiveness. What is the implicit cost per job-year created? How does it compare to international benchmarks for similar programmes?

7. Interaction effects. When multiple pillars run simultaneously, do they reinforce each other, or do they compete for the same labour and capital pool? The answer determines whether you can simply add up policy effects or whether resource crowding requires a discount.

None of these questions can be answered by GDP forecasting alone. All of them are answerable in a CGE framework.

What the Model Cannot Do — and What to Do About It

Honest methodology requires being explicit about limits.

A standard CGE model cannot natively distinguish between formal and informal sector dynamics unless the SAM splits production accordingly. Ghana’s IFPRI 2022 SAM does not separate formal and informal production. This matters because Dr Thompson rightly emphasised that 92 percent of Ghanaian businesses operate informally, contributing only 27 percent of GDP. If a New Economy policy is partly about formalising economic activity — not just expanding aggregate output — then standard CGE will miss that mechanism. The fix is a formal-informal SAM extension, which is technically feasible but requires additional survey work to estimate the splits credibly.

CGE results are also conditional on closure choices, elasticity parameters, and base-year structures. Honest reporting requires sensitivity analysis across plausible parameter ranges. A single point estimate is misleading; a robustness band is informative.

Finally, CGE is an ex ante simulation tool, not an empirical evaluation. It tells you what the model predicts under stated assumptions. The actual outcome depends on implementation quality, external shocks, and behavioural responses the model cannot fully capture. CGE should inform policy design and target-setting; it cannot substitute for monitoring and evaluation of actual programme rollout.

Where This Leaves the Policy Conversation

Ghana is having an economic conversation about three things at once: how to measure progress, how to design the New Economy package, and how to ensure implementation translates plans into outcomes. The 3D Growth framing connects all three.

For measurement, NDPC’s proposal moves the country toward a more honest dashboard. For policy design, the framework demands tools that can quantify all three dimensions — and standard macroeconometric forecasting cannot. For implementation, the same tools provide a reference path: if the model says the energy pillar should generate X jobs over Y years under stated investment, then deviations from that path become a diagnostic signal for monitoring teams.

What this argues for, concretely, is a CGE-based ex-ante assessment capacity housed within Ghana’s national development planning architecture. The IFPRI 2022 SAM and recent published Ghana CGE work provide a foundation. The wage-curve closure, calibrated to current GSS labour force data, provides the right labour-market framework. The remaining requirement is the institutional decision to invest in this analytical capacity and to use its results to inform decision-making across NDPC, the Ministry of Finance, and sector ministries.

The technical tools exist. The data exists. The political moment — with the IMF programme ended, the Consolidated National Development Plan in preparation, and the Ghana Infrastructure Plan launching — is exactly when this analytical capacity matters most.

Dr  Lartey is an economist with a PhD in Economics from the University of Sussex, UK, specialising in institutions, fiscal policy, monetary and macroeconomic policy, and causal inference.

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