Country Name Year Inflation 40 Nigeria 2000.0 6.933292 41 Nigeria 2001.0 18.873646 42 Nigeria 2002.0 12.876579 43 Nigeria 2003.0 14.031784 44 Nigeria 2004.0 14.998034
⬆:This script cleans Nigerian inflation data for easier analysis. It removes unnecessary metadata and reshapes the dataset from wide to long format using the melt function ensuring all values are numeric. It also filters the data from the year 2000 onwards and sorts it chronologically. The same cleaning approach can also be applied to unemployment and labor force datasets since they come from the same source(The World Bank).
⬆:This graph visualizes the relationship between Nigeria's Inflation Rate (red solid line) and Unemployment Rate (blue dashed line) from 2000 to 2025. It employs a dual-axis design to manage the different scales of the two metrics, with inflation measured on the left axis and unemployment on the right.
For the first fifteen years (2000–2015), both indicators fluctuated within a relatively stable range. Inflation generally stayed between 5% and 20% while unemployment remained low and steady, hovering near 4%. However, the period after 2015 marks a significant shift in economic stability. Unemployment saw a dramatic surge, peaking at nearly 5.8% around 2020 due to Covid before experiencing a sharp decline toward 3% by 2023.
In contrast, inflation has followed an aggressive upward trajectory in recent years. While it dipped slightly around 2020, it skyrocketed after 2021, breaking past 30% by 2024. This creates a striking divergence in the final years of the plot as unemployment figures appear to have stabilized at a historic low, inflation has surged to a historic high, suggesting that soaring costs of living have replaced job availability as the primary economic "push factor" for the country.
⬆:This stacked area chart provides a comprehensive look at Nigeria's growing labor force from 2000 to 2025, but its visual representation of employment (the green area) requires a critical economic lens to understand the reality on the ground. At a glance, the total labor force shows a massive, steady expansion, nearly doubling from 60 million to over 110 million people, which illustrates the country's significant demographic youth bulge. While the red area representing unemployment appears to shrink significantly after 2020, this trend is largely a reflection of a controversial shift in data methodology rather than a sudden abundance of high quality jobs.
The sharp drop in the red unemployed section corresponds with the 1-hour working rule adopted by the National Bureau of Statistics (NBS) in 2023 to align with International Labor Organization (ILO) standards. Under this definition, anyone who engages in at least one hour of economic activity for pay or profit in the preceding seven days is classified as "employed". This includes everything from small scale trade and subsistence farming to running minor errands. Consequently, the vast green area on the chart masks a deep seated crisis of underemployment and working poverty, where millions are technically employed but earn far too little to survive amidst Nigeria's triple digit inflation and a depreciated Naira.
This disconnect between statistical employment and economic stability is the primary driver of the "Japa" phenomenon, where skilled and unskilled Nigerians alike seek work elsewhere. Even though the chart shows a large employed population, the economy remains volatile due to soaring fuel prices, a high misery index, and a cost of living crisis that has outpaced the new minimum wage. For many represented in that green area, employment does not mean a stable career or financial security, instead, it represents a survival struggle that pushes them to look for better opportunities beyond Nigeria's borders.
Headline 2: UK Immigration Policy and Migration of Nigerian Healthcare Workers¶
Policy file exists: True Medical file exists: True Policy data shape: (9051, 18) Medical data shape: (6, 2) Policy columns: ['year', 'iso2', 'change_id', 'iso3', 'country_full_name', 'summary', 'change_mag', 'change_level', 'pol_area', 'pol_tool', 'target_group', 'target_origin', 'target_specific', 'change_restrict', 'u_complab_country_year_change_country', 'u_complab_country_year_change_country_code', 'u_complab_country_year_change_year', 'u_complab_country_year_change_change'] Medical columns: ['Year', 'Total Migrants']
Data Processing¶
The datasets are first cleaned and filtered to focus on UK-specific immigration policy changes and Nigerian healthcare migration data.
A cumulative policy index is then created, and both datasets are aligned to comparable time points. Standardization is used to compare the policy trend and migration trend on the same scale.
UK policy rows: 262
Policy direction counts:
change_restrict
1.0 120
-1.0 89
Name: count, dtype: int64
Policy index preview:
year PolicyChange PolicyIndex
0 2000 4.0 -5.0
1 2001 2.0 -3.0
2 2002 -4.0 -7.0
3 2003 2.0 -5.0
4 2004 -1.0 -6.0
5 2005 -1.0 -7.0
6 2006 -6.0 -13.0
7 2007 -3.0 -16.0
8 2008 -3.0 -19.0
9 2009 -5.0 -24.0
year PolicyChange PolicyIndex
17 2017 2.0 -40.0
18 2018 4.0 -36.0
19 2019 5.0 -31.0
20 2020 0.0 -31.0
21 2021 0.0 -31.0
22 2022 0.0 -31.0
23 2023 0.0 -31.0
24 2024 0.0 -31.0
25 2025 0.0 -31.0
26 2026 0.0 -31.0
Healthcare migration columns detected:
['Year', 'Total Migrants']
Healthcare migration preview:
year HealthcareMigration
0 2000 83822
1 2005 125003
2 2010 189847
3 2015 236603
4 2020 286251
year HealthcareMigration
1 2005 125003
2 2010 189847
3 2015 236603
4 2020 286251
5 2024 324007
Merged dataset:
year PolicyChange PolicyIndex HealthcareMigration
0 2000 4.0 -5.0 83822
1 2005 -1.0 -7.0 125003
2 2010 0.0 -24.0 189847
3 2015 -5.0 -43.0 236603
4 2020 0.0 -31.0 286251
5 2024 0.0 -31.0 324007
Merged shape: (6, 4)
Standardized comparison:
year PolicyIndex HealthcareMigration Policy_norm HealthcareMigration_norm
0 2000 -5.0 83822 1.243039 -1.334051
1 2005 -7.0 125003 1.108657 -0.890172
2 2010 -24.0 189847 -0.033596 -0.191235
3 2015 -43.0 236603 -1.310231 0.312736
4 2020 -31.0 286251 -0.503935 0.847879
5 2024 -31.0 324007 -0.503935 1.254842
UK Immigration Policy Trends¶
The cumulative policy index illustrates the evolution of UK immigration policy between 2000 and 2026. The overall downward trend indicates a gradual shift toward a more restrictive policy environment over time.
This movement toward restrictiveness becomes more pronounced from the late 2000s onwards. This period coincides with increasing political and economic pressure to control migration following the global financial crisis, which led to greater emphasis on reducing net migration levels.
The sharp decline in the policy index around 2015 reflects a concentration of restrictive policy measures introduced during this period. This aligns with broader policy developments under the UK government, including the implementation of stricter immigration controls associated with the Immigration Acts of 2014 and 2016. These policies aimed to tighten access to employment, housing, and public services, and formed part of a wider strategy to discourage migration and reduce overall inflows.
However, it is important to note that the sharp drop in the index reflects the cumulative effect of multiple policy changes rather than a single dramatic shift in policy severity. The index captures the frequency of restrictive measures, meaning that several smaller policy interventions occurring close together can produce a pronounced decline.
From around 2019 onwards, the index stabilizes, suggesting a period with fewer significant policy changes. This may reflect a transition toward policy consolidation, where existing frameworks remained in place rather than being substantially expanded. Overall, the pattern indicates that UK immigration policy has evolved through phases of tightening followed by relative stability, rather than continuous large-scale transformation.
UK Immigration Policy vs Nigerian Healthcare Worker Migration¶
The comparison between UK immigration policy and Nigerian healthcare worker migration reveals a divergence in trends over time.
Migration trends continue to increase over time, even during periods where the policy index indicates greater restrictiveness.
While the policy index indicates a shift toward greater restrictiveness—particularly in the period leading up to 2015—migration from Nigeria continues to increase steadily. This suggests that immigration policy alone does not determine migration flows.
Instead, the persistence of rising migration despite tightening policy conditions points to the importance of strong push factors in Nigeria. These may include economic instability, limited employment opportunities, and structural challenges within the healthcare system, which encourage outward migration regardless of destination-country policy constraints.
At the same time, demand-side factors in the UK play a significant role. Ongoing shortages in the healthcare sector create sustained demand for foreign-trained professionals, which can offset the effects of restrictive immigration policies.
Overall, the evidence suggests that UK immigration policy acts as a regulatory framework that shapes the conditions under which migration occurs, but does not fully control the volume of migration. Migration patterns are therefore better understood as the outcome of an interaction between push factors in the origin country, pull factors in the destination country, and the policy environment that mediates movement.
Correlation matrix:
Policy_norm HealthcareMigration_norm
Policy_norm 1.000000 -0.821628
HealthcareMigration_norm -0.821628 1.000000
Summary statistics:
PolicyIndex HealthcareMigration Policy_norm HealthcareMigration_norm
count 6.000000 6.000000 6.000000 6.000000e+00
mean -23.500000 207588.833333 0.000000 -1.480297e-16
std 14.882876 92775.179238 1.000000 1.000000e+00
min -43.000000 83822.000000 -1.310231 -1.334051e+00
25% -31.000000 141214.000000 -0.503935 -7.154374e-01
50% -27.500000 213225.000000 -0.268765 6.075080e-02
75% -11.250000 273839.000000 0.823094 7.140937e-01
max -5.000000 324007.000000 1.243039 1.254842e+00
Correlation Analysis¶
The correlation analysis shows a strong negative relationship between the standardized Policy Index and standardized Healthcare Migration.
The correlation coefficient is -0.8216. This means that as the UK Policy Index decreases, indicating more restrictive immigration policy, Nigerian healthcare worker migration increases.
This result suggests that healthcare migration is not explained by immigration policy alone. Instead, migration appears to be shaped by a combination of UK labor demand, push factors in Nigeria, and wider structural conditions.
Regression Analysis¶
To further test the relationship between UK immigration policy and Nigerian healthcare worker migration, a simple Ordinary Least Squares (OLS) regression model is estimated.
The dependent variable is HealthcareMigration, which represents the number of Nigerian healthcare workers migrating to the UK.
The independent variable is PolicyIndex, which represents the cumulative openness or restrictiveness of UK immigration policy. Lower values indicate more restrictive policy conditions.
The regression model is specified as:
[ HealthcareMigration_i = \beta_0 + \beta_1PolicyIndex_i + \epsilon_i ]
This model allows us to estimate whether changes in UK immigration policy are statistically associated with changes in Nigerian healthcare worker migration.
OLS Regression Results
===============================================================================
Dep. Variable: HealthcareMigration R-squared: 0.675
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 8.310
Date: Mon, 27 Apr 2026 Prob (F-statistic): 0.0449
Time: 01:00:46 Log-Likelihood: -73.222
No. Observations: 6 AIC: 150.4
Df Residuals: 4 BIC: 150.0
Df Model: 1
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
const 8.723e+04 4.82e+04 1.809 0.145 -4.67e+04 2.21e+05
PolicyIndex -5121.7702 1776.676 -2.883 0.045 -1.01e+04 -188.926
==============================================================================
Omnibus: nan Durbin-Watson: 1.271
Prob(Omnibus): nan Jarque-Bera (JB): 0.292
Skew: 0.224 Prob(JB): 0.864
Kurtosis: 2.017 Cond. No. 54.3
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
C:\Users\judit\AppData\Local\Programs\Python\Python312\Lib\site-packages\statsmodels\stats\stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.
warn("omni_normtest is not valid with less than 8 observations; %i "
Note: The warning regarding the normality test is due to the small sample size (n = 6). This does not affect the regression results but indicates that normality tests are not reliable in this case.
Regression Results Interpretation¶
The OLS regression results show a statistically significant negative relationship between UK immigration policy openness and Nigerian healthcare worker migration.
The coefficient for PolicyIndex is -5121.77 with a p-value of 0.045. This means that a one-unit decrease in the Policy Index, representing a shift toward more restrictive immigration policy, is associated with an increase of approximately 5,122 Nigerian healthcare workers migrating to the UK.
The model has an R-squared value of 0.675, meaning that approximately 67.5% of the variation in Nigerian healthcare worker migration is explained by the Policy Index. This indicates a relatively strong model fit for a simple regression model.
The F-statistic is 8.310 with a probability value of 0.0449, suggesting that the overall regression model is statistically significant at the 5% level.
The 95% confidence interval for the PolicyIndex coefficient is approximately [-10,100, -189], which does not include zero. This supports the conclusion that the relationship between immigration policy and healthcare migration is negative and statistically significant in this dataset.
| Metric | Value | |
|---|---|---|
| 0 | Coefficient for PolicyIndex | -5121.7700 |
| 1 | P-value for PolicyIndex | 0.0449 |
| 2 | R-squared | 0.6750 |
| 3 | Adjusted R-squared | 0.5940 |
| 4 | F-statistic | 8.3100 |
| 5 | Model p-value | 0.0449 |
| 6 | Number of observations | 6.0000 |
Summary of Regression Evidence¶
The regression summary confirms that the Policy Index is a meaningful predictor of Nigerian healthcare worker migration in this dataset.
However, the results should be interpreted carefully. The analysis is based on only six observations, which limits statistical reliability. Therefore, the findings should be treated as exploratory rather than causal.
The regression provides evidence of a strong association, but it does not prove that restrictive immigration policy causes migration to increase. Other factors, such as UK healthcare labour shortages, Nigerian economic conditions, wage differences, and professional opportunities, may also influence migration flows.
Analysis¶
The results suggest that UK immigration policy functions more as an enabling or constraining framework than as the primary driver of migration.
The steady increase in Nigerian healthcare worker migration, even during periods of greater policy restriction, indicates that push factors in Nigeria are highly significant. These likely include limited professional opportunities, economic instability, and pressures within the domestic healthcare system.
At the same time, the UK continues to attract healthcare workers because of labor shortages and stronger employment opportunities. Migration patterns are therefore best understood as the result of an interaction between push factors in Nigeria, pull factors in the UK, and the policy environment that regulates movement.
Despite statistical significance, the small sample size means the results should be interpreted cautiously and viewed as indicative rather than causal.