Skill-proximate occupations for non-post-secondary-educated workers in Ghana

In upcoming research, my colleague and I posit that the skill content of a worker’s current occupation is a high dimension piece of information that can function as a job market signal, particularly for low wage, non-post-secondary educated workers. Using the 2013 Skills Towards Employability and Productivity (STEP) Survey data for Ghana, we construct a skill vector for every occupation consisting of a skill measure incorporating routine versus nonroutine; and manual versus cognitive intensity. We then create an occupation relatedness measure across all occupations at the 3-digit ISCO level. The fact that many jobs in different industries share common skills but differ substantially in wages suggests that there may be incomplete information in the labor market and potential pathways for low educated workers to become skilled through alternative routes (STARs). The figure below illustrates the resulting relatedness plot of occupations with node size denoting employment shares and node color representing wages (dark blue being the highest).

Our results show that in 2016/17, there were approximately 1.2 million individuals whose skill profile based on current work is proximate to the skill profile of a higher paying occupation. We call these STARs after Blair et al (2020). Of these, 46 percent were workers with less than post-secondary education in low wage occupations who have skills to transition to a higher wage role in their wage category. Another 344,840 (28.3 percent) were workers with less than post-secondary education in middle wage occupations who have skills to transition to a higher wage role in their wage category. An estimated 292,151 (23.9 percent) were non-post-secondary-educated workers who have skillsets to transition to higher wage work. Finally, there were just 18,379 (1.5 percent) workers with less than post-secondary education who are in high-wage roles.

Comments

Mapping the Blast Radius: What Happens When the "World's Factory" Stops?

In global trade, "efficiency" often masks "fragility." We know that China is central to the global electronics supply chain, but how central? And if that node were to go dark, who would feel the shockwaves first? To answer this, I moved beyond standard trade statistics and built a network simulation using the OECD Inter-Country Input-Output (ICIO) Tables (2023 Edition) . This dataset maps the DNA of the global economy, tracking every dollar of input across 66 countries and 45 industries. 1. The Blast Radius: Tracing the Contagion I treated the global economy as a directed graph and simulated a total supply shock to Chinese Electronics (CHN_C26) . By tracing the flow of inputs across three tiers of buyers, I visualized the "Blast Radius" of this disruption. Fig 1: The Supply Chain Cascade. The shock originates in China (Red) and immediately hits "Tier 1" assembly hubs (Dark Blue) before cascading to global consumers...

Turkey's Informality Tax

A general-equilibrium model of Turkey's dual labour market says the cost of taxing formal work doesn't show up where we usually look for it. The bottomline: Making Turkey's transfer to the unemployed a third more generous raises unemployment from 8.6% to 9.2% . That is the honest cost, and it is not large. How you pay for it matters more than whether you pay for it. Funded by payroll taxes, the poor end up 0.6% worse off than before the transfer was raised — the policy defeats itself. Funded by VAT, they are 0.5% better off . The reason is not unemployment, which is nearly identical under both. It is informality . A higher payroll tax pushes formal jobs into the unregistered sector, where the wage is 43% lower and nothing is taxed. Turkey is not on the wrong side of the payroll-tax Laffer curve — revenue peaks around 53%, well above today's ~37.5% wedge. But the marginal cost...

The Labor Market Impacts of AI in Türkiye: Beyond Occupational Exposure Analysis

Beyond Exposure AI and Labor Markets · Türkiye Occupational exposure to AI identifies where technology may affect work, but not whether employment will contract, reorganize, or expand. Applying the Richmond (2026) AI Jobs Transition Framework to Türkiye's 2024 Household Labour Force Survey, the dominant high-exposure archetype is not automation. It is reorganization. Mpumelelo Nxumalo and Dhushyanth Raju · Summary of the working paper Beyond Exposure: AI and Labor Market Reorganization in Türkiye Most discussion of AI and jobs runs on a single number: how much of an occupation's task content a model can perform. That number identifies where technology reaches. It does not say what happens next. An exposed occupation may shed workers, or it may keep them while the work itself is rebuilt around the technology. Those are different outcomes, they call for different policy, and exposure alone cannot tell them apart. Applying the Richmond (202...