Development & Health Economics · PhD Researcher · Lancaster University · Accra, Ghana
Producing rigorous evidence at the intersection of health systems, equity, and African development — translating research into policy that matters.
I am an economist — specialising in development and health economics — based in Accra, Ghana, with deep expertise in health systems strengthening, health financing, and implementation research across Sub-Saharan Africa.
My work sits at the intersection of rigorous quantitative analysis and real-world policy application — spanning non-communicable diseases, adolescent health, catastrophic health expenditure, environmental sustainability, and financial inclusion.
I bring together academia, international organizations, and frontline practitioners to translate evidence into change that endures.
I am an economist — specialising in development and health economics — based in Accra, Ghana. My work is rooted in a conviction that rigorous evidence, when connected to the right people and institutions, can meaningfully reshape how African societies address disease, inequality, and underdevelopment.
Currently pursuing a PhD in Health Economics and Policy at Lancaster University under a full NIHR scholarship, I focus on primary healthcare, non-communicable diseases, and service delivery across West Africa. My research draws on quantitative methods — econometrics, health technology assessment, and efficiency analysis — applied to questions that matter for African governments and communities.
I am a collaborator with the Global Burden of Disease (GBD) project at the Institute for Health Metrics and Evaluation (IHME), one of the world's leading global health research bodies. This collaboration grounds my work in the highest standards of global health measurement and accountability.
Beyond research, I serve as Head of Research and Policy at Rewrite to Build Africa — a sustainability-focused NGO driving evidence-based advocacy on climate change, environmental governance, and sustainable development across Africa.
I also have a commitment to academic teaching and mentorship. As a Teaching Assistant at GIMPA, I supported master’s students in econometrics and managerial economics — helping build the quantitative foundations of the next generation of African economists.
Ghana's illegal artisanal mining crisis — known locally as galamsey — represents one of the most urgent environmental and public health challenges facing the country. Illegal mining pollutes water bodies, degrades arable land, and devastates communities that depend on natural resources for their livelihoods and health.
Through Rewrite to Build Africa, I advocate for evidence-based environmental governance, accountability in extractive industries, and policies that protect Ghana's ecological heritage while creating sustainable economic pathways. This work connects my research on environmental sustainability to frontline advocacy and community engagement.
A track record spanning health economics research, quantitative analysis, international consulting, and policy development — across Ghana, West Africa, and global collaborations.
Alongside research and consulting, I have contributed to graduate and postgraduate economics education at GIMPA, teaching quantitative and applied economics courses across multiple master’s programmes.
My research agenda addresses some of the most pressing challenges in African health systems and development — producing evidence designed to inform policy, resource allocation, and service delivery reform across West Africa and beyond.
I work with international health organizations, governments, and NGOs to deliver evidence-based consulting across health economics, policy analysis, data management, and strategic research design.
I am building an early-career research network for Ghanaian and Nigerian researchers working at the frontiers of health financing, equity, and development economics — a community designed to accelerate impact and foster collaborative science.
Despite the growing body of African health economics research, early-career researchers in Ghana, Nigeria, and across Sub-Saharan Africa often face structural barriers to collaboration, mentorship, funding access, and publication. The networks and resources that accelerate careers in high-income settings are simply not equally available.
This planned research network is designed to change that — providing a structured community for rigorous, policy-relevant research in health financing, catastrophic health expenditure, equity, and the socioeconomic determinants of health. It is built on the conviction that African health challenges require African researchers, African data, and African solutions.
Are you an early-career health economist, development economist, or health researcher based in Ghana or Nigeria? We're building something meaningful — and your work belongs at the table.
Whether you're interested in research collaboration, consulting, the early-career network, or policy dialogue — I'd be glad to hear from you.
Thank you for reaching out. I'll be in touch shortly.
A space for honest reflection on research, policy, and the work of building a better Africa — written from the field, the conference room, and everywhere in between.
Ghana has just launched one of its most ambitious health commitments in recent memory — free primary healthcare for all citizens. And in a telling sign of intent, the rollout is beginning not in the cities, but in the most deprived rural communities.
Save before you spend. Avoid debt. Buy land before you buy a car. Sensible rules, all of them. But two people can face identical numbers and completely different realities, and the optimal choice can change with them.
Above average in what? Height, education, savings, debt? And should you even want to be? On benchmarks, the rooms we choose to stand in, and why the same person can be above average and below average on the same afternoon.
A friend gave me an idea during my undergraduate days that I have never quite put down since. Notes on the butterfly effect, the decisions that quietly become our lives, and the patterns families pass down without ever deciding to.
A simple theoretical model of why her options and his options move in opposite directions as they both age — and what a chart makes obvious that a conversation never quite can.
Heatwaves are breaking records across Europe. Floods keep returning to the same streets in Accra. They are not the same disaster, but they are no longer a coincidence either. Two continents, one June.
On AI agents, economics research, and the one skill that will separate serious scholars from the noise. The barrier to producing research is falling fast. What is not falling is the ability to know when the output is wrong.
Every civilisation-defining shift in history has created winners and losers — not based on who was smartest, but on who moved first. For the first time, Africa's youth have the tools, the timing, and the numbers to move first. The window is open. The question is whether we walk through it.
Ghana has just launched one of its most ambitious health commitments in recent memory — free primary healthcare for all citizens. And in a telling sign of intent, the rollout is beginning not in the cities, but in the most deprived rural communities. This is a policy worth watching closely, and worth understanding well.
As a Ghanaian and a health economist, I have spent considerable time studying the quiet ways in which cost shapes health decisions across this country. The data is consistent, and it tells a sobering story: for many households, especially in rural and low-income communities, the decision to seek care is never purely medical. It is financial. The fear of a bill at the end of a consultation keeps people away from facilities until conditions become impossible to ignore — and by then, much harder to treat.
Ghana's newly launched free primary healthcare policy is a direct response to that reality. And it is barely weeks old. This is not the moment for sweeping verdicts — positive or negative. It is, however, exactly the right moment to understand what Ghana is attempting, why it matters, and what will need to go right for this vision to become a lived reality for every citizen. I want to hold two things together: genuine excitement about what has been launched, and honest reflection on the road ahead.
One of the most significant — and most encouraging — features of Ghana's rollout strategy is where it is beginning. Rather than launching first in urban centres where health infrastructure is comparatively stronger, the government has chosen to prioritise the most deprived rural communities. This is an equity-first approach, and it deserves recognition.
For too long, geography has been a silent determinant of health outcomes in Ghana. A child born in a remote northern district and a child born in Accra do not start life with the same access to care. By anchoring the pilot in the communities that have historically been left furthest behind, Ghana is signalling that universal healthcare must mean something in practice — not just in policy documents. The lessons learned from this rural-first approach will be critical in shaping how the policy scales nationally.
Beyond the rollout strategy, the policy itself addresses one of the most persistent barriers to healthcare in Ghana: cost. Financial barriers are among the most consistent predictors of delayed or foregone care — particularly among low-income households and women of reproductive age. Removing user fees at the primary care level is a direct intervention against that barrier.
Preventive care is at the heart of this vision. One of the most critical — and often underappreciated — pillars of free primary healthcare is its explicit focus on preventive activities. Ghana's primary care system has increasingly sought to energise preventive programming: health education, routine screenings, antenatal care, and community outreach. The logic is sound. Prevention is not only morally compelling; it is economically rational. Investing upstream in health behaviours and early detection saves far more than treating advanced disease.
When people do not fear a bill at the end of a consultation, they are more likely to present early — before a manageable condition becomes a crisis. This benefits individuals and a health system that is already stretched. Early detection and early treatment are cheaper than emergency responses and hospitalizations.
Perhaps nowhere is the case for robust free primary healthcare more urgent than in the context of non-communicable diseases (NCDs). Ghana, like much of Sub-Saharan Africa, faces what researchers call the “double burden” of disease: the unfinished agenda of infectious illness alongside the rapidly growing weight of NCDs — hypertension, diabetes, stroke, and cancers.
This is not an abstraction for me. Within my own extended family, I have lost close to three or four people to NCDs — hypertension, specifically. These were not sudden, unpredictable deaths. They were the end result of conditions that, caught early, are highly manageable. NCDs are, in the language of public health, ambulatory care-sensitive conditions — meaning that timely, quality primary care can prevent hospitalisation, complications, and death. The tragedy is not that these conditions are incurable; it is that they are being met too late.
“NCDs are ambulatory care-sensitive conditions. When caught early, they are manageable. When missed, they are devastating. Free primary healthcare, properly resourced, is our single strongest tool against this tide.”
Free primary healthcare, if genuinely implemented, positions Ghana to intercept this burden early. Routine blood pressure checks, blood glucose screening, and basic counselling on diet and lifestyle — these are not expensive interventions. But they require consistent access, and access requires removing the cost barrier at the facility door.
Every ambitious policy faces implementation realities, and asking hard questions early is an act of support — not scepticism. For free primary healthcare to fulfil its potential, Ghana will need to address some well-known structural challenges in its health system.
Many CHPS compounds and district hospitals already operate under significant strain — long queues, overstretched staff, and essential medicines that are intermittently out of stock. An influx of newly empowered patients, which is exactly what a successful free healthcare policy should produce, will place additional demand on these facilities. Managing that demand well requires investment in workforce, supply chains, and physical infrastructure running in parallel with the policy rollout.
Financing sustainability through the National Health Insurance Scheme (NHIS) — particularly timely reimbursements to facilities — will also be critical. Facilities that are not reimbursed promptly may begin to ration care informally, quietly undermining the very access the policy is designed to guarantee.
One innovation attracting growing attention alongside the policy is telemedicine — the use of digital platforms to extend healthcare consultations beyond the walls of physical facilities. In principle, telemedicine holds real promise for Ghana: a country with significant geographic disparities in access, where a patient in a remote district may travel hours to see a health professional.
But we must ask the harder question: how sustainable is this, really? Telemedicine requires reliable electricity, internet connectivity, digital literacy, and devices — preconditions that remain unevenly distributed, particularly in the rural communities where the rollout is beginning. Used wisely — as a complement to, not a replacement for, physical primary care infrastructure — telemedicine can extend Ghana's reach. But it must be piloted rigorously, evaluated honestly, and scaled only where the conditions for sustainability genuinely exist.
Ghana has done something significant. In a region where public health financing remains chronically underfunded, committing to free primary care — and choosing to begin with the most marginalised communities — is a statement of values and a standard to live up to.
What the policy needs now is not cynicism, but constructive accompaniment. Sustained investment in health workers, medicines, and facilities. A financing architecture that does not buckle under pressure. Preventive programming treated as the engine of the system, not an afterthought. And the voices of rural communities kept firmly at the centre — because the people in those queues will know, before any report does, whether this is working.
Ghana has taken its first step. With the right support and the right accountability, this journey can go the distance.
Michel is an economist specialising in development and health economics, based in Accra, Ghana, with expertise in health systems strengthening, health financing, and implementation research across Sub-Saharan Africa. The views expressed here are his own.
Every civilisation-defining shift in history has created winners and losers, not based on who was smartest, but on who moved first. For the first time, Africa's youth have the tools, the timing, and the numbers to move first. The window is open. The question is whether we walk through it.
Every generation, if it is paying attention, recognises the moment it is standing in. Not in real time, rarely. Usually only in hindsight, when historians have named the turning point, drawn the timeline, and made it all look inevitable. But some shifts are visible as they happen. The emergence of artificial intelligence as a general-purpose tool is one of them. And for Africa, a continent of 1.4 billion people, the youngest population on earth, and a long history of arriving late to someone else's revolution, this one may be different. This time, we have the chance to be early.
To understand what AI represents, it helps to zoom out, far out. Human civilisation has not progressed in a straight line. It has moved in punctuated leaps, each one triggered by a technology or idea so fundamental that it did not just change how people worked. It changed who people were, how they organised, and what was possible.
The Agricultural Revolution, roughly 12,000 years ago, was the first great reordering. Hunter-gatherer bands gave way to settled communities. Surplus food created specialisation. Specialisation enabled trade, writing, governance, and eventually cities. Everything we call civilisation rests on that first leap.
The Industrial Revolution, beginning in Britain in the 1760s, was the second. Steam power and mechanised production broke the ceiling on human physical output. It took a century to spread globally, but when it did, it restructured economies entirely, shifting populations from farms to factories, creating the modern city, and generating both extraordinary wealth and extraordinary inequality depending on where in the world you happened to be standing.
The Digital Revolution of the 1990s was the third. The internet collapsed distance. Information, once locked in libraries, universities, and the offices of powerful institutions, became portable and eventually nearly free. It created new industries, dissolved old ones, and gave individuals access to tools and audiences that once required corporate budgets.
Each of these revolutions shared something: they rewarded early movers and penalised latecomers. Africa, for various reasons including colonial extraction, structural underfunding, and geographic and political fragmentation, arrived late to both the Industrial and Digital revolutions. The consequences are visible in our infrastructure, our institutions, and the persistent gaps in global wealth and knowledge production.
Which is why the fourth revolution deserves our full attention.
What separates the AI revolution from its predecessors is not just its power. It is its speed. When ChatGPT launched in November 2022, it reached 100 million users in two months. Not two years. Two months. To appreciate how extraordinary that is, consider how long previous technologies took to reach the same milestone.
Sources: ITU, Statista, Reuters, UBS analysis (Jan 2023). Figures are approximate global user milestones.
The telephone took 75 years. Television took 13. The internet took 7. Facebook took 4.5 years. The iPhone, which transformed the world in its own right, took 3.5 years. ChatGPT took two months.
This is not just a faster version of what came before. This is a qualitatively different kind of diffusion, made possible by smartphones, cloud infrastructure, and the internet backbone that now reaches billions of people worldwide. The tools of this revolution ride on infrastructure that already exists. They do not require a new factory, a new power grid, or a new network to be built. They are available, today, to anyone with a phone and a data connection.
And that changes everything for Africa.
“Africa missed the Industrial Revolution because it was colonised. It arrived late to the Digital Revolution because of infrastructure gaps. The AI Revolution has no such barriers to entry. The tools are here, they are free, and the clock is running.”
Africa has 1.4 billion people. More than 60 percent of the continent's population is under the age of 25. By 2050, one in four people on earth will be African. This is not a demographic footnote. It is a structural advantage of historic proportions, but only if that young population is equipped with the skills and tools of the current economy, not the last one.
Africa has also demonstrated, more than once, that it can leapfrog. When mobile banking arrived in the mid-2000s, much of Africa had no meaningful banking infrastructure to speak of. Rather than building the bricks-and-mortar system that took the West a century, African societies leapt directly to mobile money. M-Pesa in Kenya is now studied in economics departments around the world as a textbook case of leapfrogging, skipping an entire developmental stage by adopting the superior technology at the right moment.
AI offers the same opportunity, at a larger scale. A young person in Accra, Kampala, Nairobi, or Lagos today has access to tools that, just five years ago, were available only to well-funded research teams and large technology firms. And crucially, many of these tools are free, at least for now.
This is perhaps the most important point, and I want to say it directly: the tools are free right now because the companies building them are in a land-grab phase. They are competing for users, for data, for market dominance. The generous free tiers across Claude, ChatGPT, Gemini, Perplexity, Canva AI, Notion AI, and GitHub Copilot are a strategic offer, not a permanent one.
The window will not stay open indefinitely. As these platforms mature and monetise more aggressively, what is free today will increasingly sit behind paywalls. The young person in Accra who learns these tools today will be building on a foundation. The one who waits five years may find the entry cost has risen significantly.
What can African youth build with these tools today? A health worker in a rural district can use AI to get a differential diagnosis checklist in under thirty seconds, cross-referenced with local disease prevalence data. A small business owner can generate professional marketing copy, financial projections, and a business plan without hiring a consultant. A university student can use AI as a research partner, one that reads the literature, summarises it, flags contradictions, and suggests gaps, compressing years of methodological learning into months. A developer can build and ship software products alone that previously required a team. A farmer can get agronomic advice, climate projections, and market price data in a single conversation.
None of these require a PhD. None require expensive hardware. They require curiosity, a smartphone, and the willingness to learn by doing.
“The young person in Accra who masters these tools today is not behind. For perhaps the first time in a generation, they are exactly where the frontier is.”
I believe in this opportunity deeply. But I would not be doing my job as an economist if I did not note the risks alongside the possibilities.
AI also displaces. Call centres, routine data processing, certain categories of writing and translation, and entry-level legal and financial analysis are roles that employed many young Africans, and that AI will increasingly handle at lower cost. The same revolution that creates opportunity for those who harness it will eliminate certain rungs on the economic ladder that less-equipped workers depended on.
There is also the question of whose AI this is. The large language models were trained predominantly on English-language content, on Western cultural assumptions, on data generated in high-income contexts. The subtle biases this introduces in medical advice, legal reasoning, and financial guidance are real, and the communities least served by AI's training data are often those with the least recourse when it gets things wrong.
These are not reasons to disengage. They are reasons to engage critically: to build African datasets, to train African engineers and AI researchers, to advocate within international forums for AI governance frameworks that do not simply export Silicon Valley's assumptions to the rest of the world.
History does not distribute revolutions fairly. But it does offer windows. The Agricultural Revolution rewarded those who adopted it and marginalised those who did not. The Industrial Revolution built the modern West on the labour, often coerced, of people who saw little of its benefits. The Digital Revolution created a new global elite while leaving billions in what researchers call the digital divide.
The AI Revolution is, right now, being written. The rules of who benefits are not yet fixed. The infrastructure of who gets to build, who gets to profit, and whose problems get solved is still being laid. Africa, with its youth, its mobile connectivity, its tradition of adaptive ingenuity, and its immense unmet needs that AI could help address, has a genuine claim to this moment.
The question is not whether AI will reshape the world. It already is. The question is whether the young economist in Tamale, the developer in Lagos, the nurse in Kigali, and the entrepreneur in Nairobi will be among those doing the reshaping, or among those being reshaped.
The tools are in your hands. The moment is now. Use it.
Michel is an economist specialising in development and health economics, based in Accra, Ghana. He writes on evidence, equity, technology, and African development. The views expressed here are his own.
I will be honest with you. When I first heard about AI agents that could do work on your behalf, I was sceptical. I am a development economist. My work involves messy panel datasets, fragile Stata do-files, half-finished manuscripts, and the kind of nuanced policy arguments that take weeks to sharpen. I did not see how a chatbot, however clever, was going to change that. Then I started using AI tools in my daily workflow. And something shifted.
The bottleneck in academic and policy research is rarely the thinking. It is the execution: the hours spent reformatting references, cleaning datasets, reorganising outputs, drafting that third version of a results section, or turning a 40-page technical report into a two-page brief for a policymaker who will read it in a taxi.
These tasks are not intellectually trivial. But they are time-consuming in ways that quietly erode the space you have for the work that actually requires your expertise. This is exactly where AI agents enter the picture.
Unlike a standard AI chat interface, an AI agent operates as a genuine assistant. You describe an outcome, point it to your files, and it works through a multi-step plan: reading, writing, executing code, and reporting back. The figure below maps where AI capability is strongest, where human expertise remains essential, and where the two must work together.
Figure 1
The Research Task Spectrum: AI, Human, and Shared Territory
Bar lengths are illustrative, reflecting the relative dominance of AI vs. human judgement across common research tasks. Shared tasks require informed human oversight of AI outputs.
What the figure makes clear is that the most powerful capabilities of AI agents sit at the execution layer, not the reasoning layer. AI can clean, synthesise, draft, and execute. It cannot determine whether your identification strategy is credible, whether your model violates a core assumption, or whether your finding is contextually meaningful for the populations you are studying.
As these tools become more capable, the ethics of their use in research becomes more, not less, important. Four principles should anchor any serious researcher's engagement with AI.
Disclose AI assistance in your methodology. Readers and reviewers deserve to know which parts of the research process involved AI-generated outputs.
Never feed sensitive, confidential, or identifiable data into AI systems without proper data-sharing protocols and institutional clearance.
The researcher remains fully responsible for every output. AI is a tool, not a co-author. Errors in AI-generated content are your errors.
Every AI output must be interrogated, not accepted. Treat it as a capable but fallible draft that requires your expertise to validate.
These are not abstract principles. They are the minimum conditions under which AI-assisted research can be trusted: by peer reviewers, by policymakers, and by the communities whose lives our findings are meant to improve.
We are entering an age where running a regression, building a model, or generating a policy brief will soon require very little technical friction. A reasonably smart person with access to the right tools will be able to produce outputs that, on the surface, look credible. The barrier to producing research is falling fast.
What is not falling is the ability to know when the output is wrong. When the specification is flawed. When the coefficient does not make theoretical sense. When the model has quietly violated an assumption the AI did not flag. That scrutiny requires a pre-AI foundation: years spent understanding econometrics from first principles, reading literature before you could search it with a chatbot, debugging code line by line before an agent could do it for you.
Figure 3
The Informed Supervision Model
How rigorous researchers should engage with AI-generated research outputs
Step 3 is irreducible. No prompt, no tool, and no agent can substitute for the researcher's disciplinary knowledge at the point of validation. This is where your training becomes your competitive advantage.
The researchers who will thrive are not those who use AI the most. They are those who use it with the most informed scepticism, treating every output as a draft to be interrogated, not a result to be published.
Your background is not baggage from a pre-AI world. In a landscape where anyone can generate a regression table, it is your most important qualification.
Michel is an economist specialising in development and health economics, based in Accra, Ghana. Doctoral Researcher at Lancaster University, GBD Collaborator at IHME. The views expressed here are his own.
I woke up yesterday to messages about Accra underwater again. Kaneshie Adabraka Spintex Kwame Nkrumah Interchange, the same names that show up every rainy season, like a roll call nobody wants to take. The University of Ghana suspended lectures. Somewhere in Madina or Mallam, a family is dragging a soaked mattress into a courtyard, trying to figure out what is salvageable.
Eleven years ago, almost to the day, June 3 took over 150 lives at the Goil station near Circle. This June 3rd, the rains came back and Accra flooded again. Same communities. Same images. Same calls for action that echo last year's almost word for word.
At the same time, on the other side of the world, France just recorded its hottest day since records began in 1947. Two children were found unconscious in a car in Carpentras. Satellite images show ground surface temperatures over 50°C in parts of France and Spain.
None of this caught anyone by surprise who was paying attention. Scientists have said for years that a warmer atmosphere holds more moisture, which means heavier rain when it finally lets go. A rapid attribution study by World Weather Attribution confirmed the kind of heat France experienced this June would have been virtually impossible without our emissions. We did not fail to predict the impossible. We made it normal, on schedule, roughly as forecast.
In Ghana, it is not a mystery either. The President of the Ghana Institution of Engineers said it plainly: we have done things the wrong way for thirty or forty years. Wetlands that once absorbed rainfall have been built on. Drains were never sized for the rain we get now. So when the rain comes, it has nowhere to go except into people's living rooms.
The honest answer is no, and not by accident. Climate change did not sneak up on anyone with a research budget. It crept up on the people who could not afford to plan for it, while the people who could have funded that planning kept deferring the cost. In both Accra and Carpentras, the systems built for a more stable climate were not upgraded fast enough. The difference is that Europe had more margin for that failure.
I think about this professionally too, spending my days inside datasets trying to turn patterns of human suffering into policy. Numbers do not move anyone until you put a face to them. A two-year-old in a car in France. A trader in Makola whose stock floated away overnight.
What strikes me, sitting here in Accra with my own city still drying out, is how unevenly this lands even though the sky does not discriminate. Europe has cooling shelters and early warning systems. Ghana is rolling out parametric flood insurance for Greater Accra, a real step forward, but infrastructure takes years and floods take hours. That gap is where ordinary people keep losing everything they have built.
Adaptation has to stop being a side conversation to mitigation. The heat in Europe and the floods in Accra are not waiting for us to finish debating emissions targets. We need drainage built for the rainfall we actually get now, not infrastructure inherited from colonial-era master plans, and financial protection that moves at the speed of disaster rather than bureaucracy.
What the numbers rarely capture is the compounding. A flood that wipes out a trader's stock in Kaneshie is also a flood that might pull a child out of school for the term, delay a routine clinic visit, push a family into debt that takes two years to clear. The climate crisis does not arrive as a single headline.
But naming the problem is not the same as solving it. What is missing is not more conviction. It is the economic evidence that would tell us what works, where, at what cost, and for whom.
As I write this, the rain over Accra has only slowed, not stopped. The Meteorological Agency has forecast more through the peak of this season. Whether it will flood again is not really in question. The harder question is whether we will finally be able to say what it would have cost to prevent it.
Here is what I keep returning to as an economist. We talk endlessly about climate action, about net zero targets and emissions pledges. But almost nobody has seriously costed the other side of the equation: what does it actually cost to prepare the countries that are most exposed? And what does it cost when we do not?
The Notre Dame Global Adaptation Initiative (ND-GAIN) tracks two things simultaneously for every country: how vulnerable it is to climate change, and how ready its institutions, infrastructure and economy are to respond. When you plot African countries on both dimensions at once, the picture is stark. On the ND-GAIN scale, a positive vulnerability score means a country is more exposed to climate impacts than the global average; a positive readiness score means stronger institutional capacity to respond. Most African countries have both high vulnerability and low readiness, a combination that concentrates climate risk precisely where adaptive capacity is thinnest.
Each dot is a country. Orange = African countries · Blue = European countries · Gold = Rwanda (outlier). African countries cluster top-left (high exposure, low readiness); European countries cluster bottom-right (low exposure, high readiness). Rwanda is the African exception — strong readiness despite low income. Source: Notre Dame Global Adaptation Initiative (ND-GAIN), 2023.
Chad and Niger score 0.15 on the vulnerability index, placing them among the five most exposed countries on earth. Guinea-Bissau (0.13), Sudan (0.13), Somalia (0.12) and Sierra Leone (0.12) follow closely. What they share is not just exposure. Their readiness scores are all deeply negative, meaning that the countries carrying the greatest climate burden have the least institutional and financial capacity to absorb it.
Ghana sits in a more ambiguous position. Our vulnerability score of -0.02 is actually relatively low by African standards, reflecting geography and some institutional depth. But our readiness score of -0.01 means we are essentially treading water, with adaptive capacity that does not match even our current exposure, let alone what is coming.
Now here is the economic question that almost nobody has answered rigorously. UNEP estimates that developing countries need between $215 billion and $387 billion per year for climate adaptation. They currently receive roughly $21 billion. That is a gap of more than $150 billion annually, sustained over decades, in the countries that did the least to create the problem.
But even that framing understates the research gap. We know the aggregate financing shortfall. What we do not have, in any rigorous form for most African countries, is a cost-effectiveness frontier for adaptation: which interventions, per dollar spent, save the most lives, protect the most assets, and preserve the most productive capacity in the face of specific climate hazards? That calculation has been done extensively for vaccines and health interventions. It has barely been attempted for flood barriers, early warning systems, heat-resilient housing, or parametric insurance in African urban settings.
As a development economist, that gap is what I find most troubling. Not because the money is not available, though it largely is not. But because without that evidence, the money that does flow cannot be allocated rationally. We are asking governments and donors to invest in adaptation without the benefit-cost evidence that would justify and guide those investments. That is not climate policy. That is guesswork with consequences measured in lives.
The research agenda is clear. We need rigorous economic evaluation of climate adaptation interventions in African cities and rural settings: cost per life protected, cost per household asset preserved, marginal returns to drainage investment versus insurance versus early warning systems. We need it disaggregated by country, by hazard type, and by income group. And we need it produced by African researchers who understand the institutional and political economy constraints that determine whether evidence actually reaches policy.
The floods in Accra will come again. The question of what it would have cost to prevent them, and what we are willing to pay next time, is one our field has an obligation to answer.
Michel is an economist specialising in development and health economics, based in Accra, Ghana. He writes on evidence, equity, climate, and African development. The views expressed here are his own.
I've been thinking about this dating pool issue for men and women for a while now, mostly from conversations with friends, and I decided to put my thoughts into a simple model.
Note: this is theoretical. I don’t have real data. It’s a predictive model built to test an idea, not a study.
A woman in her early twenties has options. Men her age want her. Men older than her want her. The pool is wide. Then, somewhere around thirty, it starts closing. This is what everyone means by the “sweet spot.” I wanted to see if it actually shows up in a model.
For HerEarly on, her options span a wide age range: men her age, men older, sometimes much older. That window stays open through her mid-twenties. Nothing in the numbers says it should close anytime soon.
But it does. Past thirty, something shifts. The men who were options at twenty-five have mostly settled down. What’s left gets thinner. More already married, more still not ready to commit. Her pool doesn’t just plateau. It shrinks. And this tends to be exactly when the family starts asking questions at every gathering.
For HimFollow a man through the same years and the story runs the other way. His mid-twenties are fine, but they are not his peak. By his early thirties, he is seen as a serious option for a wider range of people than before. He has income now, he has settled, supposedly. Nobody at the family house is pressing him. He still has “time.”
As her pool contracts, his expands. Same decade. Opposite direction. Here’s what that looks like when you put numbers on it.
Green is his pool. Red is hers. When a line rises, that pool is growing, meaning more realistic options at that age. When a line falls, that pool is shrinking. Where the two lines cross is the turn.
Drag back to twenty-one. Both lines sit close together. Push past thirty and watch them pull apart. Her line bends down, his keeps climbing. That crossing point is the turn everyone talks about at family gatherings, now drawn as a chart.
Why Her Line BendsThink of her pool as everyone available, minus the already-married. That second group grows fastest through her late twenties and early thirties. A lot of the men who were options at twenty-five are simply gone by thirty-three. Not moved on from her specifically, just gone from the pool entirely.
Her pool didn’t shrink because she changed. It got depleted while she wasn’t looking.
Why His Line ClimbsHis pool widens because the age range society considers fair game for him keeps stretching. At twenty-five he’s mostly picking from a few years either side. By thirty-five, that band is much wider. Not a better pool. Just a bigger net, and one he didn’t have to earn.
Her pool doesn’t shrink forever. Divorce and widowhood reopen the market. Her line recovers a little past thirty-eight. It’s not as permanent as the popular story suggests.
A bigger pool isn’t a better one. This chart measures how many options exist, not how good they are. Width is not the same as quality.
The model rests on one assumption. That men prefer same-age-or-younger, women prefer same-age-or-older. Common, but not universal. Change it, and the two lines stay much closer together.
His advantage is social permission: it loosens the older he gets. Her disadvantage is a shrinking pool: it tightens. Though not as permanently as the popular story suggests. Her line does recover a little past thirty-eight; the “everyone’s already taken” idea is overstated.
The sweet spot isn’t superstition. It’s what a depleting pool looks like, sitting right next to one that keeps growing, for the person on the other side.
I have a friend. His name is Bruno Felalaga, and we have been friends for a very long time now, since 2013. One of the things I have always enjoyed about our friendship is that we talk a lot. We can talk about almost anything, life, people, ideas, decisions, why things happen the way they do, and somehow there is usually something to take away from those conversations.
He is a very insightful guy. Sometimes he will say something quite casually, and you may not even realise how profound it is until much later.
During my undergraduate days, Bruno introduced me to one idea that has stayed with me ever since: the butterfly effect.
The way I came to understand it was simple. A butterfly flaps its wings somewhere. It is a tiny movement, almost insignificant. But that small disturbance changes something, which changes something else, which affects something else, and much further down the chain, the eventual outcome can be completely different.
The example that made it stick for me was this. A butterfly flaps its wings somewhere in the United States. Nothing about that movement looks like it matters to anyone. But the air it displaces nudges the air beside it, and that nudges the next, and weeks later, thousands of miles away, a hurricane is forming over Hawaii. Not because the butterfly was powerful, but because the conditions were already sitting there, waiting for something to tip them.
The deeper point is not really about butterflies or hurricanes. It is that small changes at the beginning can have consequences far greater than we could ever have anticipated.
For some reason, that idea never left me. And the older I get, the more I realise that Bruno had introduced me to something that goes far beyond weather or chaos theory. It is also a useful way of thinking about life.
The butterfly effect belongs to chaos theory, and is closely associated with the work of meteorologist Edward Lorenz, who showed that very small differences in starting conditions could lead to very different outcomes over time. He put the question in the title of a 1972 talk: does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?
Think about some of the biggest things that have happened in your life. How many of them actually looked big when they started?
At the time, nothing dramatic happened. It was just another day. But years later, you look backwards and realise that one seemingly ordinary decision quietly changed the direction of your life. And sometimes, you never even realise it.
That is what fascinates me.
And I think nowhere is this more powerful than within the family.
We tend to think families are shaped by the big moments: marriage, the birth of a child, financial difficulties, divorce, illness, death, success. Of course, those things matter. But increasingly, I think families are also shaped by the tiny decisions nobody outside the home will ever know happened.
None of these decisions looks particularly historic when it happens. But then comes the ripple.
Because children are watching. And children inherit much more from us than our names, our faces, or whatever property we may leave behind. They inherit patterns.
They learn what love looks like by watching us love. They learn what conflict looks like by watching us disagree. They learn whether men apologise. Whether women are heard. Whether anger means shouting or conversation. Whether vulnerability is weakness. Whether affection should be expressed or merely understood. Whether problems are confronted or buried until everyone learns to live around them.
Then those children grow up. They enter friendships carrying those lessons. They enter relationships carrying them. They enter marriages carrying them. Eventually, some of them become parents themselves. And suddenly, a decision made quietly inside one household twenty or thirty years earlier is living inside another household entirely.
Perhaps a father rarely expressed affection because his own father never did. His son grows up believing that this is simply how men behave. He becomes a father. He loves his children deeply, but struggles to tell them. Nobody deliberately decided to create an emotionally distant family. Yet three generations later, the pattern remains.
But what gives me hope about this idea is that the butterfly effect works in both directions. If small harmful decisions can compound, so can good ones.
Those decisions may seem small. But perhaps somewhere down the line, a child will love differently because of them. And that child may raise another child differently. And suddenly, what looked like one ordinary decision has travelled further than the person who made it will ever know.
Essay · Data
Above average in what? And, more to the point, should you even want to be?
Are you above the average Ghanaian? What about the average Nigerian? The average African?
Before you answer, there is a small problem.
Above average in what?
Height? Education? Income? Savings? Fitness? Career?
Or, since we are being honest, your number of sexual partners?
And perhaps the more interesting question is this: is being above average always a good thing?
I have always been fascinated by averages. Not because of the arithmetic, but because an average gives you something surprisingly powerful: a place to locate yourself.
Take height. The average adult Ghanaian man is roughly 169 cm, about 5’6½". If you are 5’10", congratulations, you are above average. But you didn’t exactly work hard for those extra inches.
Now take education. Only about 13% of Ghanaian adults who had ever attended school reported tertiary education as their highest level of attainment in the 2021 Census. If you have a university degree, suddenly being “above average” feels more meaningful.
And then there is the statistic nobody puts on LinkedIn: lifetime sexual partners. The 2022 Ghana Demographic and Health Survey reports very different averages for men and women.
If you discover that you are comfortably above that average too...
Congratulations?
Maybe. Maybe not. 😂
And that is precisely the point.
My rule is simple.
You probably want to be above average in savings, useful skills, physical activity and perhaps education. You probably want to be below average in debt, unnecessary spending and preventable health risks.
But it gets more interesting when you stop comparing yourself with a country and start comparing yourself with the people around you.
Think about your closest friends.
Income. Career. Savings.
Fitness. Reading. Discipline. Ambition.
Where do you sit?
Suppose you realise that you are below average. Your friends are learning more, exercising more, taking bigger opportunities and generally pushing themselves harder than you are.
That sounds terrible.
I think it might be excellent news. You may be in exactly the right room.
Sometimes being below average means everybody around you has something to teach you. Their normal becomes the standard pulling you upward.
The opposite should make us think too. If you are always the most ambitious, most disciplined, most informed or most accomplished person in the room, being above average may feel good. But perhaps the better question is: am I still being stretched?
This isn’t about ranking your friends by salaries or qualifications. Human beings are obviously worth more than that. It is about recognising something quieter.
Here is my favourite thing about averages. You can change your average without changing anything about yourself.
Imagine you have a master’s degree.
Put yourself in a random room representing Ghana’s adult population and, educationally, you are relatively unusual.
Now walk into a room full of master’s graduates. Same person. Same degree. Suddenly, you are ordinary.
Now enter a room filled with PhD students and professors. Same person. Same degree. Same afternoon. But now you might be below average.
What happened? You changed rooms.
I think there is something powerful about deliberately entering rooms where your achievements stop being impressive.
You can be one of the strongest people in your workplace and then attend a conference where almost everybody has achieved something you haven’t. You leave thinking: wow, I’m behind.
Maybe. Or perhaps you just discovered a new average worth chasing.
There is, of course, a trap. Choose the wrong comparison group and averages become poisonous.
Social media does this brilliantly. You can be doing extremely well relative to people of your age, background and circumstances, yet feel hopelessly behind because the fifty people appearing on your screen are CEOs, professors, millionaires, founders and people who somehow announce a major achievement every Tuesday.
So perhaps the lesson isn’t that we should constantly try to be above average. It is that we should choose our averages intelligently.
Compare yourself nationally when you need perspective. Compare yourself professionally when you want to know where your career stands. Look at the people around you when you want to understand what your environment is making normal.
And every now and then, forget everybody else and compare yourself with the person you were five years ago.
Because averages are useful mirrors. But they are terrible measures of human worth. Being below average doesn’t mean you are failing. Being above average doesn’t mean you are succeeding.
So perhaps the question isn’t simply: am I above average?
Compared with whom?
In what?
And is this even an average worth beating?
Sources: Ghana Statistical Service, 2021 Population and Housing Census (Volume 3D, Literacy and Education) · Ghana Demographic and Health Survey 2022 · Ghana STEPS Survey 2023. Height figures are rounded national averages and vary by age cohort. The curve above is an illustrative distribution, not fitted data.
Personal Finance · Economics
Knowing the rule is financial literacy. Knowing when it applies to you is something else.
These are generally sensible rules. But lately, I have been wondering whether they are always right for everyone.
Take the popular advice about emergency funds. The conventional wisdom is that before investing seriously, you should have enough saved to cover several months of expenses. That way, when something unexpected happens, you do not have to borrow.
Makes perfect sense.
But consider someone who understands this rule perfectly and deliberately chooses another approach. Instead of keeping several months of expenses in cash, they invest most of the money and accept that, if an emergency comes, they may have to borrow temporarily or liquidate an investment.
Another person would never do that. They would rather sacrifice the potential return for the security of knowing the cash is there.
Which of them is financially literate?
Perhaps both. The difference may simply be their circumstances and their willingness, and capacity, to take risk.
This is another interesting one. As a young person, one of the golden rules I heard was: don’t rush to buy a car. Buy land. Build. Then think about a car.
Again, there is wisdom in this. If someone spends a huge proportion of their income on an expensive car purely for status, while having no savings or investments, questioning that decision is understandable. The car depreciates and demands maintenance, insurance and fuel, while land may appreciate.
But suppose the car is not primarily a luxury.
What if reliable transport saves you two or three hours every day? What if it lets you work more efficiently, pursue opportunities you previously could not, or significantly improves your quality of life?
Meanwhile, the plot of land you are being encouraged to buy may sit untouched for the next ten years.
Is buying the car first automatically a bad financial decision? I am not sure it is that simple.
We tell people to live within their means, and rightly so. But what happens when your income stays relatively constant while rent, food, transport and electricity keep climbing?
And in many African settings, an individual’s income is rarely just about the individual. There may be parents to support, siblings who need help, school fees, medical bills, funerals, weddings and family responsibilities that arrive without warning. These do not fit neatly into the personal finance models we read about.
Then come the other choices.
Save the money, or pay for another degree that might raise your future income?
Buy a house, or keep renting and invest the difference?
Hold cash while inflation eats its purchasing power?
Start a business, or keep the security of a salary?
Invest every extra cedi, or is enjoying some of it today also legitimate?
The more I think about these questions, the less convinced I become that financial literacy should simply be about teaching people rules. Perhaps we need to talk more about financial judgment.
Financial literacy matters. We should understand interest, inflation, debt, diversification, compounding, liquidity and risk. But knowing these concepts does not automatically tell you what decision to make.
Economics gives us a useful way of thinking about this. We talk about making the optimal choice. But optimal according to what?
A decision is optimal given certain constraints, preferences, risks and information. Change those conditions and the optimal choice changes with them.
The numbers are identical. The financial realities are not.
That does not mean every decision can be defended with “well, this works for me.” Some decisions are simply reckless. Buying a luxury car you clearly cannot afford in order to impress people is difficult to describe as judgment. Borrowing constantly at very high interest to maintain a lifestyle beyond your income does not become a sophisticated strategy just because you can name the trade-off.
I have written all this almost in the third person, observing how people make financial decisions and questioning the rules we hand them. But I am probably in the same dilemma.
I ask myself these questions too.
Should I keep this money or invest it?
Should I buy this now or wait?
Am I being prudent, or simply too cautious?
Am I taking a calculated risk, or finding an intelligent explanation for something I already wanted to do?
That last question may be the most important one.
So this is not an argument against emergency funds. It is not an argument for borrowing to invest. And it certainly isn’t an argument that every young person should buy a car before buying land.
It is a question about how we think about money. Perhaps financial rules should be treated as starting points rather than universal instructions.
Maybe the better question is not “what is the right way to manage money?” but this: