The Nexus Of Corruption And Higher Income Part III

[I’ve been working on this for over a week now, hope you enjoy it. Since this is a very long post, I’ve split it into three parts. This is Part III]

Continued from Part II

Conclusion

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

Technical Notes:

1. Corruption data is from Transparency International’s Corruption Perception Index, from 1995 to 2011
2. GDP per capita data is taken from the IMF World Economic Outlook database (September 2011) – series code PPPPC.