This post isn’t a defense of corruption. It’s not an April Fool’s joke either. There’s no doubt that corruption weighs on an economy and on society through many different channels – through higher costs of doing business, to redistribution of income, through reducing the rewards of entrepreneurship, through social and economic inequality, through reducing the level of trust in society (indirectly contributing to all the problems listed above, and more).
There’s this meme I’ve been hearing and reading that if we can just handle corruption, Malaysia would easily become a high income nation.
The basis for this view is this seemingly convincing correlation between corruption and per capita income (this is the full dataset; the charts I’ve seen elsewhere are more simplistic):The data is taken from Transparency International’s Corruption Perception Index (CPI) from it’s inception in 1995 to the latest numbers published a few months back (with a scale ranging from zero being totally corrupt, to ten meaning completely corruption free).
The horizontal axis is GDP per capita in current international dollars adjusted for purchasing power parity, from the IMF World Economic Outlook database (September 2011 edition – 2011 data and some past years data for some countries are based on estimates).
The chart above tabulates values of the CPI against per capita income for every country which has a score under the CPI (except for Kosovo, which has no GDP numbers).
The scatter plot of the data suggests that countries with lower levels of corruption have higher levels of per capita income. Therefore, the reasoning goes, the route to becoming a higher income economy can be trodden by simply reducing the level of corruption. If corruption represents costs to economic growth and development, reducing it would ipso facto improve the income level; Quod Erat Demonstrandum (QED).
I wish it were so easy. You could just as easily say that as income levels increase, the incentive for indulging in corruption falls. Countries have low levels of corruption because they have high income. QED. And if you have a background in statistics or econometrics, you could also point out that the causal relationship might be two-way (corruption drives changes in income, AND income drives changes in corruption) or that income and corruption levels might be driven by a separate independent process, in which case the inverse causal relationship between corruption and income might be completely spurious i.e. there’s no real relationship at all, they just happen to move together.
In summary, corruption might cause income; OR income might cause corruption; OR both; OR neither. Correlation on its own does not really prove anything.
Now the ideal way to solve this conundrum is to figure out all the factors that contribute to income and corruption levels, throw them into a coherent model, and test the significance of the coefficient estimates.
That’s a little beyond the scope of a blog post. But there are ways to determine whether there is any causal relationship between corruption and income, without bringing in other variables.The analysis of the data will be very wonkish, so if you’re allergic to statistical analysis, I’d advise jumping straight to the end.
We should start off first by formalising the correlation into a regression. Using an unbalanced panel estimation with fixed effects on the sample data above (translation: we do a regression that covers all countries simultaneously over time), we get the following results (standard errors in parenthesis):Ln(GDP) = 8.49 (0.06) + 0.26*Ln(CPI) (0.04)
What this says is that a 1% increase in the CPI score is associated with a 0.26% increase in the level of income.
So if you go from a CPI score of 5, and manage to increase it to 6 (an increase of 20%), your associated income level should be on average 5% higher (the 95% confidence range would be between 7% and 3%). If you’re going from a score of 2 (e.g. Cambodia, Laos) to 7 (US, France), your income level would be between 45% to 85% higher (average: 65%).Would that kind of increase be sufficient to qualify as a high income nation? I’m not sure but I don’t think so, certainly not based on the examples I quoted. What about if we look at levels alone?
GDP = 8803 (894) + 965*CPI (205)
Since we’re dealing with current GDP numbers, we can evaluate this against the World Bank’s current threshold for high income, which happens to be USD12,195.
What this means is that all you need is a CPI score of about 3.5 (Thailand; El Salvador) to cross over into becoming a high income nation. Ahem.
That’s obviously not true, so we need to look into this a little deeper – the correlation, such as it is, isn’t really helpful at all, and can’t be relied upon to give a true picture of the relationship between corruption and income.
What about a non-linear relationship (log GDP against actual CPI score)? I tried it, and it’s not much different from the first attempt above (an increase of 1 point in the CPI score raises GDP per capita by 6%-8%; again not terribly convincing).
So back to first principles – what is the the CPI? It’s a continuous (not discrete) scale that ranges from zero to ten. Looking at the individual country scores and testing for unit roots suggest the CPI scores are mainly – though not all – I(0) variables i.e. the CPI scores are stationary variables. On the other hand, GDP per capita numbers are very obviously I(1) variables i.e. non-stationary variables.
If you want to know the difference, here’s a sample of the data for Australia:
In the first graph, the CPI numbers mainly fluctuate between 8.6 to 8.8, with the exception of a couple of years. That’s what a stationary variable looks like – it fluctuates around a central point through time. The GDP data however is continuously rising across time i.e. it’s non-stationary.
There are exceptions; for a subset of countries, the CPI is generally rising with GDP per capita, and for another subset, we have the opposite – the CPI score is falling but GDP per capita is rising. But on the whole, the general case is of a fairly stable CPI score with a continuously rising GDP per capita.
And this gives a partial solution to the problem – the CPI score, as constructed, cannot have a long term causal relationship with GDP per capita. You need an absolute, not relative, equivalent measure to properly define the relationship between corruption and income. Changes in stationary I(0) variables cannot “explain” long term changes in I(1) variables, you need to have variables of the same order of integration.
But all hope is not lost – if you can’t make the CPI data non-stationary, it’s fairly trivial to transform GDP per capita data into stationary data by taking the difference in values between each period. In other words, it’s theoretically valid to examine the relationship between the CPI score and real GDP growth.
So, starting all over again, here’s the same dataset but tabulating the CPI score on the vertical axis, and real GDP per capita growth on the horizontal axis:
And one look is all you need – there is no strong relationship between corruption and economic growth. Changes in the level of corruption don’t appear to be associated with changes in the rate of growth. There might be a relationship between corruption and the variance of growth (wider scatter at low CPI scores), but not the level of growth itself.
More formally (standard errors in parenthesis):
GDP growth = 0.045 (0.01) + 0.001*CPI (0.002)
The intercept (0.45) is statistically significant, but the coefficient for the CPI (0.001) is not statistically significant from zero – rather strongly so (p-value=0.6275).
[BTW, we’ve just discovered the trend estimate for world real GDP per capita growth over the last 15 years (0.045 = 4.5%).]
Does GDP growth affect corruption? Not hardly:
CPI = 4.33 (0.15) + 0.11*GDP growth (0.23)
Same story as above – the intercept is statistically significant, but the coefficient for GDP growth is not (p-value again at 0.6275).
The obvious conclusion is that the correlation between the CPI score and real GDP per capita is spurious – they’re both being driven by (an)other unidentified process(es). I’ll admit that finding surprised me – I expected to find a relationship, even if a very weak one. What could be the possible reasons behind this?
The idea that corruption has a dampening effect on income levels and/or growth is intuitively appealing, yet the data doesn’t appear to support any causal relationship of any kind. In fact, the conclusion appears to be that the relationship is technically spurious – corruption affects neither the level or growth of income, nor does income affect the level or rate of corruption (or should I say, the perception of corruption).
Here’s some of the reasons why I think the results came out the way they do:
- Accuracy of the dataset – The CPI scores are composites of surveys of business people on their experience with corruption in their respective countries. Taking the CPI scores as given means accepting that the CPI number accurately reflects actual corruption. That may not be true for a number of reasons, such as differences between opinion and actuality, or instances of corruption that might not impinge on the business community (NFC is a good example, since it allegedly involves CBT, rather than bribery). I also suspect the CPI score says more about the level of trust in public institutions as much as actual experience of corruption.
- Lags in the data – Because the CPI score is a reflection of business community perception of corruption rather than its incidence, there might be a lag structure to the data. For example, if a corruption case is exposed today, it might raise the perception of corruption now (a lower CPI score) even though the actual corruption might have occurred years before. That suggests increasing transparency might have a short term perverse effect on the CPI score, before returning perception returns to its “true” level.
- Variance in the dataset – While TI puts in considerable effort at arriving at a definitive CPI score (and kudos to them for trying), the variance of the scores in the individual surveys can be pretty wide – as much as 1 point or more. That means the data can be a bit “fuzzy”, especially for those countries with lower scores – the variance is noticeably smaller for countries with high CPI scores. In which case OLS regression analysis (which works towards minimising errors) might not be capturing the true relationship, simply because the distribution of the actual level of corruption might be too wide.
- Non-linear relationship between corruption and income – There’s the possibility that corruption only affects national income and growth at certain ranges of corruption. That may be true especially at the bottom of the income scale, as the relative costs of corruption on society might be larger. Past a certain income level, the costs of corruption might rapid diminish. Applying the same analysis to subsets of the data might reveal a causal relationship. I’d tend to discount this explanation though, as looking at the individual country scatterplots tends to show a relatively stable value of the CPI against higher and higher income levels.
- The fallacy of composition – Corruption is often seen to be a dead loss to the economy, but that’s only true at the level of the individual economic agent. It largely isn’t true for the economy as a whole. Money spent on bribery for instance transfers wealth and income from the briber to the bribed – one loses and one gains. But from the perspective of GDP, the difference in terms of growth and spending will only be seen in terms of the differences in marginal consumption and saving between the two parties. If the bribed spends as much as the briber, then total expenditure within the economy doesn’t change. Only if the briber has a smaller propensity to consume will income levels and growth be negatively affected (incidentally, that suggests that venal corruption should be more tolerated than large scale corruption), or if the expenditure takes place elsewhere, e.g. buying condos in Australia instead of in Malaysia. What this means is that the impact of corruption should primarily be seen through rising income and wealth inequality (the distribution of income), and/or through capital flight, but not in GDP or GDP growth.
Any or all of the factors above could be in play, or just as likely, that the analysis I’ve done is correct and there’s some other factor driving both variables – income inequality for instance, or the integrity of social and political institutions, for example.
But the bottom line here is that the available evidence that I’ve been able to come up with just doesn’t support a causal link between income and corruption, or vice versa.
I’ll conclude with the actual data for Malaysia; it’s illustrative of problem (CPI score against GDP per capita; red line is the estimated regression):
Malaysia shows a negative relationship between the CPI score and GDP per capita – so if you believe that there is a causal relationship, the way to increase our income level is to increase the perceived level of corruption. That obviously can’t be right. A more plausible explanation? The CPI score is actually pretty stable from 1995 to 2008 – I wonder what happened then (*cough*).
Moving on (CPI score against GDP per capita growth; red line is estimated regression):
Here the estimated relationship is slightly positive (higher CPI leads to higher growth), but the sample coefficient is statistically indistinguishable from zero; in other words, there’s no detectable relationship.
The most appealing explanation I can come up with for the data is that increased transparency post-2008 and the proliferation of online news channels and social media activism, has seen evidence of past corruption increasingly surfacing and that’s been reflected in a higher perceived level of corruption (lower CPI score).
It isn’t that corruption is increasing, it’s that we’re more aware of it and increasingly intolerant of it. Which is a good thing, and signals in a way our increasing development as a society. But I don’t expect that reducing corruption will, on its own, help Malaysia become a high income nation.
- Corruption data is from Transparency International’s Corruption Perception Index, from 1995 to 2011
- GDP per capita data is taken from the IMF World Economic Outlook database (September 2011) – series code PPPPC.
Here are some comments:
I do work with statistics but in the manufacturing sector using minitab..seems there is no correlation of CPI with GDP growth primarily because CPI is not measurable but a perception and we know we have citizens who put down the perception of our country…getting a positive perception on corruption in Malaysia would be impossible.
First of all, thanks for the awesome study. It has really given us a lot of insight on corruption and economic growth.
I think you may have said this, but not exactly in these words. Also, it wasn’t exactly clear from your 2nd part. May I know how you had defined GDP growth or GDP per capita growth. If my assumption is correct, and that you had used year-on-year growth, then I think it would not be too surprising to encounter almost no correlation. My take on this is that, from year to year, I would think that any number of factors would affect GDP growth (global economic conditions, commodity prices, bad weather, etc). Corruption would be a pretty insignificant factor for short-run growth. Perhaps you may have tried this, but I think there should be a much stronger link between corruption and long-term growth. It’s hard to say how long term is long term though. Plus, I think you may have been handicapped by the short CPI data.
Also, another small issue that I am not entirely sure is that, perhaps, developed countries (which I assume tend to be less corrupt), will tend to have smaller growth rates, courtesy of the fact that their GDP is already at a high base. So, if your data does not go far back enough to reflect the transformation from a low income economy to a high income economy, it will create sort of a distortion in the sense “low corruption may be correlated with low growth”.
I am not trying to poke holes into your study here. I really think that it has definitely given us a lot of things to think about. Really appreciate it.