Are We Actually Getting Better? I Let 35 Years of Data Answer That
Tools: Google Colab, Python, wbdata, pandas, NumPy, matplotlib, seaborn, World Bank Open Data API
Most of my articles are about Nigeria. I thought to write something different this time.
I listen to Drinks and Mics by Nairametrics, where the hosts — Sir Ugo Dre, Sir Arnold, Sir Tunji — and sometimes The Oracle 😂😂 also join them, alongside guests from different sectors, to talk about different topics ranging from the Nigerian economy to our capital market and geopolitics. As someone without a finance background, I have noted a lot of things down from their conversations, which I then had to read about just to understand.
During one of the episodes, someone said that we are actually getting better as a country compared to where we are coming from and how bad things used to be. But most Nigerians do not agree with that statement. I cannot even say that to some of my colleagues at work — they will tear me to pieces.
So what I wanted was not only to see things for myself, but to also compare the Nigerian economy to other African economies, just to see how well or badly we are performing. That is why I decided to do this research.
The Setup
Before I get into the findings, here is exactly how this was built — because reproducibility matters.
Everything was pulled directly from the World Bank Open Data API using Python’s wbdata library. No manual spreadsheets. No copy-pasting from Wikipedia. One API call per country, five indicators, 1990 to 2024.
INDICATORS = {
'NY.GDP.MKTP.CD': 'GDP_USD', # GDP in current USD
'NY.GDP.MKTP.KD.ZG': 'GDP_Growth', # Real GDP growth rate
'FP.CPI.TOTL.ZG': 'Inflation', # CPI inflation
'SL.UEM.TOTL.ZS': 'Unemployment',# Unemployment rate
'BX.KLT.DINV.CD.WD': 'FDI', # Foreign direct investment
}The result was a clean DataFrame: 245 rows, 7 columns, zero missing values after interpolation — and one outlier that needed serious investigation before a single chart was drawn (more on that shortly).
Finding 1: Nigeria’s GDP Did Not Collapse. The Naira Did.
This is the finding that reframes everything.
Nigeria’s nominal GDP peaked at $668 billion in 2014. By 2024, it had fallen to $252 billion — a 62% drop in dollar terms. On the surface that looks catastrophic.
But here is what the growth rate chart shows at the same time: Nigeria grew positively in almost every year except 2016 and 2020. The economy, measured in naira, kept moving. What collapsed was the exchange rate.
When the naira devalues against the dollar, the same amount of local economic activity appears smaller when converted to USD. A country can simultaneously show positive real GDP growth and shrinking nominal GDP in dollar terms. These two things are not contradictions, they are measuring different things.
This is the currency translation effect. And it is one of the most misunderstood concepts in Nigerian economic discourse. When people say “Nigeria’s economy has shrunk,” they are often looking at the dollar number without asking why the dollar number changed.
That saidover the full 35-year period, Nigeria averaged 4.1% annual growth. Third in the group. Behind Ethiopia (6.8%) and Egypt (4.4%). South Africa the most industrialised economy on the continent averaged just 2.0%.
So are we getting better? In growth terms, relative to peers — yes. In currency stability terms that is a separate, harder conversation.
Finding 2: The Inflation Story Is the Most Damning Chart in This Project
I want you to look at this heatmap and find Morocco.
Morocco’s inflation has been below 7% in virtually every single year since 1990. Their average over 35 years: 2.6%.
Now find Nigeria.
Nigeria’s average over 35 years: 18.7%. The highest in the group. We hit 57% inflation in 1993 — that dark red cell during the Abacha era economic crisis. We have never sustainably stayed below 10% for any extended period since 2015.
This matters for forex traders and investors more than almost any other single number. The reason is Purchasing Power Parity — the principle that says, over the long run, countries with higher inflation see their currencies depreciate against countries with lower inflation. Morocco’s Dirham is one of Africa’s most stable currencies. The Naira is not. The inflation chart explains a significant portion of why.
For anyone on a trading desk or in a treasury function, chronic inflation above 10% is not just an economic problem — it is a directional signal on currency.
Finding 3: Before You Trust a Number, Investigate It
This one is not about Nigeria. It is about Egypt and about data literacy.
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Egypt’s 2024 FDI came in at $46.6 billion. Every other year in their 35-year history sits between $0.2 billion and $11.6 billion. A Z-score of 5.15 flagged it immediately as an extreme outlier.
I could have left it. The number is technically real. But real does not always mean representative.
What actually happened: the UAE’s sovereign wealth fund ADQ signed a $35 billion deal to develop the Ras El Hekma coastal city on Egypt’s Mediterranean coast — the largest single foreign investment in Egypt’s history. One deal. One year. It pushed Egypt from 32nd to 9th globally in FDI rankings overnight.
# ── Pull Egypt FDI specifically to inspect ───────────────────────────────────
egypt_fdi = df[df['Country'] == 'Egypt'][['Year', 'FDI']].set_index('Year')
print("=" * 45)
print(" EGYPT FDI INFLOWS — FULL HISTORY (USD Billion)")
print("=" * 45)
print(egypt_fdi.to_string())
print(f"\n Mean FDI (all years) : ${egypt_fdi['FDI'].mean():.2f}B")
print(f" Max FDI : ${egypt_fdi['FDI'].max():.2f}B "
f"(Year: {egypt_fdi['FDI'].idxmax()})")
print(f" Median FDI : ${egypt_fdi['FDI'].median():.2f}B")
# ── Check if it is a statistical outlier using Z-score ───────────────────────
from scipy import stats
egypt_fdi['Z_Score'] = np.abs(
stats.zscore(egypt_fdi['FDI'].fillna(0))
)
print(f"\n Z-Score Analysis (values above 2.0 are outliers):")
print(egypt_fdi[egypt_fdi['Z_Score'] > 2.0].to_string())For a forecasting model, including that number as-is would cause the model to predict $40+ billion FDI for Egypt every year going forward. That is not a forecast again.
The value was capped at $12 billion and documented. This is called normalisation. It is standard practice in credit analysis and econometric modelling. The lesson: a number with a Z-score of 5 is not an answer. It is a question.
Finding 4: Every Economy Has a Different Engine
This is the most technically interesting chart in the project and the one that challenged my assumptions the most.
A correlation matrix for each country reveals what actually drives each economy. The findings differ significantly across countries:
Ethiopia (FDI ↔ GDP = 0.91): The strongest correlation in the entire dataset. Ethiopia’s economic size is almost entirely explained by foreign investment flows. Take FDI away and the engine stalls.
Algeria (GDP ↔ Unemployment = -0.90): Near perfect. When oil revenues grow, unemployment drops. When they contract, it rises. There is almost no buffer from other sectors. Pure resource economy behaviour.
Morocco (FDI ↔ Unemployment = -0.74): Foreign investment in Morocco creates jobs more directly than anywhere else in the group because the FDI goes into manufacturing and services, not just extraction.
Nigeria (GDP Growth ↔ Unemployment = -0.57): Growth does reduce unemployment here but the correlation is weaker than you would expect for an economy of Nigeria’s size and population. Growth has historically been concentrated in oil — a capital-intensive sector that does not hire millions of people. This is the structural jobs problem hiding inside the growth numbers.
So — Are We Getting Better?
The composite scorecard ranks all seven countries across all five indicators. Lower average rank = stronger overall performance in 2024.
Morocco ranks best overall consistent, stable, diversified. Ethiopia is the growth champion but fragile. South Africa leads in GDP size and FDI but carries a 32.3% unemployment rate , a structural crisis that no amount of GDP growth seems to resolve.
Where does Nigeria land?
Mid-table. Held back almost entirely by one thing: inflation. We rank 7th. last on that single metric. Remove inflation from the scorecard and Nigeria’s position improves considerably. Fix inflation and the naira, and much of the GDP story corrects itself.
So back to the statement from the podcast. Are we getting better compared to where we were coming from?
The honest answer the data gives is: it depends on what you measure.
Growth rate — yes, relatively. FDI trend — no, declining. Inflation — still the hardest problem we have. Currency — the 2024 CBN reforms are recent, and the data is still coming in.
It is also worth noting that we have been seeing more inflow from foreign investors recently. Since this is historical data up to 2024, it does not fully capture that shift — but we can at least see the early signs of it in the massive growth we have been experiencing in our capital market, plus the fact that we have recently been upgraded to frontier market status, which will take effect in September.
The person on that podcast was not wrong. But neither are my colleagues who push back. Both sides are looking at real numbers. They are just looking at different ones.
That is exactly why this kind of analysis matters.
What Comes Next
This is Phase 1. The data is clean, saved, and documented.
Phase 2 will use these 35 years of data to train forecasting models — ARIMA for individual country projections, and a VAR (Vector Autoregression) model that forecasts all five indicators simultaneously across all seven countries, capturing cross-economy spillover effects that single-country models miss.
The goal is to generate my own 2025 and 2026 predictions — built from first principles, not borrowed from an IMF PDF.
