Brett Romero

Data Inspired Insights

Tag: corruption

The Dark Side of Meritocracy

In recent years, discussion about economic concepts like inequality and income mobility has been everywhere. Thrust into the spotlight by the global financial crisis, they have rarely left the front pages thanks to Thomas Piketty’s Capital in the Twenty-First Century and a series of rolling financial crises in Europe. These days you can’t even enjoy your artisanal quail egg omelet and fair trade coffee without some bearded, tweed wearing, artisanal whiskey distilling, overgrown trust fund baby complaining about how unfair it all is, in between cashing rent checks from his parents.

When it comes to discussions of inequality though, one of the underlying assumptions that few are willing to challenge is that the drivers of inequality largely boil down to nepotism and inherited wealth, while the answer to most inequality based problems comes down to one idea: meritocracy.

What is a Meritocracy?

Meritocracy is a system in which the people who hold power (through democratically elected means or otherwise) are those that are most deserving based on individual merit. In common use, it is usually taken to be slightly broader than that – a world in which money and success are allocated, perfectly, to those who are deemed to deserve it the most.

In an increasingly polarized political system in the US, meritocracy – or ‘the right to rise’ – is often the only thing that politicians on both sides of the aisle seem to agree on. The ideal of meritocracy is so ingrained in the US that Americans are famous for their belief that hard work will be rewarded with untold wealth and success. But this belief is far from unique to the US. In Australia meritocracy has long been considered part of the national identity with politicians of all stripes often talking in jingoistic terms about ‘the fair go’.

For all the talk of meritocracy though, how feasible is it in the real world? What would be some of the major hurdles to implementing a more meritocratic system?

Defining Merit

The first question that should arise whenever meritocracy is discussed is how is merit defined? There are 4 basic criteria that most commonly are thought of as contributing to merit:

  1. Qualifications
  2. Work ethic
  3. Intelligence
  4. Experience

For almost all competitions where there are winners and losers – jobs, university positions or other – some combination of these traits will generally be used to decide a winner. To keep things simpler, let’s focus on the job market for now.

The first thing to consider when defining merit for a given job is that to have an accurate measure of merit, the criteria need to be modified for every position. Jobs requiring manual labor place a higher value on work ethic but little value on qualifications. Jobs in tech often place higher value on intelligence, but less on formal qualifications and, depending on the role, large amounts of experience can be seen as detrimental. Most jobs will require applicants to possess experience in one or more specific areas.

For the most part, this customization of criteria for each job is already being done – a job advertisement is essentially a statement of the criteria that merit will be assessed by. But the question is, are those criteria actually the correct ones to identify the best possible person for a given job? I believe the answer to that question is a resounding “no”. Let me explain why.

Let’s look at a common example that anyone who has tried changing sector, industry or country in his or her career will be able to relate to.

Imagine you have been working for around 10-15 years and have spent all of that time in one industry[1]. During that time you have picked up a lot of useful workplace skills, spreadsheets, experience with various applications, writing skills, general how-not-to-piss-everyone-off skills and so on. Now you want a new challenge that will require a lot of the skills you have, but in a different industry. You approach a recruiter, bright eyed and excited by the possibilities, but despite your best efforts to sell your skills as relevant, the recruiter basically discards your experience as worthless and tries to push you towards a low level role or something in your old industry.

This experience is a simple example that reveals an underlying truth – if we were being truly meritorious, there could be no fixed criteria for merit for any job because there is no way to preemptively identify what combination of skills and experience will ultimately prove to be the most valuable.

The possibilities for what combination of skills and experience lead to the best performance in a role are endless. Many successful business owners do not have MBAs. Many of the best investors on Wall Street do not come from finance backgrounds. Some of the best NFL punters are ex-Australian Rules Football players. What people who excel tend to have in common is a combination of skills and experiences that allows them to bring a different perspective to a problem.

Yet, despite history proving time[2] and time again[3] that different perspectives are often vital to important insights, it is a rare employer or recruiter that will take a bet on a candidate with ‘unusual experience’ rather than a candidate who ticks all the boxes. The reason for that is simple – it is safer. Choosing the candidate that ticks the boxes provides cover (“I gave you what you asked for”) and it gives a better guarantee of an acceptable level of performance. The unusual candidate could be fantastic – but they could also be a complete flop who turns out to be way out of their depth.

Unfortunately, this is only the first hurdle for a true meritocracy – if merit is a difficult thing to define, it is an even more difficult thing to measure.

Measuring Merit

Once we get past the step of deciding what criteria will determine the most deserving applicant, the next step is deciding who best meets those criteria. A quick look at the application process for college admission or a technical job will give you an instant appreciation for the lengths that people will go to try and get an accurate assessment of an applicant’s true merit.

Tests, interviews and essays are probably the most common tools used to assess merit but all can be (and are) gamed by people who understand the system. Material for tests can be rote learned with little to no understanding necessary. Interviews are notorious for being poor predictors of talent, which makes sense when you consider that the most confident people are often delusional. Essays, aside from providing evidence of basic writing skills, are assessed subjectively.

Even if these tools for assessing merit were designed in such a way as to prevent gaming the system, these are still three very narrow tests of ability. As Megan McArdle explains, the experience in China shows that selecting for people who do well on exams gives you… a selection of people who do well on exams.

Assumptions and Prejudice

One interesting side effect of the difficulty in determining merit is it leads to people basing their assessment on completely superficial qualities (at least partially). A good dress sense, physical attractiveness, and being an eloquent speaker are just some examples of relatively superficial qualities people use to assess intelligence and merit. As frustrating as this can be for the unshapely, poorly dressed, mumblers out there, these are all things that can be improved and worked on (at least to some degree). Others are subject to prejudices that cannot be addressed – the impact of race on the ability to get interviews, for example, is well established.

Another concerning trend is the increasing use of someone’s current level of success/wealth as an indicator of merit. That is, if someone is wealthy and/or successful, they must be someone who is highly intelligent and works harder than everyone else. This line of thinking is dangerous for two reasons:

  1. Too much value is placed on the opinions of wealthy and successful people – particular on topics outside their domain. Anyone who has listened to Clive Palmer or Donald Trump speak should know that is a mistake.
  2. The implicit assumption made when you believe wealthy and successful people are fully deserving of their place in the world is that anyone who is poor and unsuccessful is also fully deserving of their situation.

Evidence of this thinking is present everywhere to some degree, but seems particularly prevalent in the US[4], where TV shows like Shark Tank are extremely popular and prominent CEOs are regularly asked for their opinions on public policy issues.

This belief system can largely be explained as the flip side of the optimistic view American’s have of their economic prospects. As this paper from the Brookings Institution highlights, American’s are far more likely to believe hard work and intelligence will be rewarded and yet are second only to the UK in terms of how closely correlated a son’s earnings are to his fathers (i.e. hard work has the least chance of improving your situation). If you truly believe that hard work and intelligence is all you need to be successful, you must also believe that people who are currently experiencing success have those attributes.

Best Person for the Job

Going back to our problems with creating a meritocracy, everything discussed so far has overlooked a key factor in this endless quest to find the most deserving – people are not cogs that can be simply transferred in and out of a machine seamlessly. The person who ‘deserves’ the job on merit (provided we can define it and measure it accurately) is often NOT the best person for the job. The best person for the job is often determined by qualities such as:

  • how that person fits in with the team culture,
  • their personality type, and
  • how they respond to authority (or the lack thereof).

These traits are all key factors in how well someone will perform in a given role and yet none would typically be thought of as meritorious qualities.

This realization is not new. Employers and hiring firms have been pushing the idea of the ‘beer test’ (asking yourself which of the candidates would you most like to go for an after-work beer with) for some time now. But it does beg the question – what would happen if a company simply hired the ‘best’ candidates for each position without considering whether these people will work well together? Would that team be more productive than a team that hired less ‘deserving’ candidates but aimed to build a harmonious work place? The entire body of management knowledge (and every buddy cop movie ever made) would tell us otherwise.

Summary

One thing that becomes obvious when you start thinking about how a true meritocracy would actually work is how difficult it would be to implement:

  • Many of the criteria we associate with someone being deserving of a role or position are subjective or exclude applicants who would in fact be far superior.
  • Our methods of assessment are often deeply flawed, subject to gaming and our own prejudices.
  • Selecting the most objectively deserving candidates is not guaranteed to provide the best results anyway.

Yet, despite the reasons above hopefully being enough to give pause the next time someone begins expressing frustration with the current lack of meritocracy, all of these issues are only really logistical problems.

There is a good argument to be made that we can and should try to improve on all of these things – that we should aim to get better at identifying the right people and refine our methods of assessing skills. We should pay more attention to team fit and personalities when selecting the best candidate. It is hard to imagine the world being a worse place if employers were more open minded about what skills might be valuable to their company and sociopaths were less likely to impress during an interview.

However, the next question is how far should we take this. What does a truly meritocratic society look like and is it something we really want? That is the subject of discussion in Part II in this series on meritocracy.

 

[1] You can easily replace ‘industry’ in this story with ‘company’, ‘field’ or ‘country’.

[2] Einstein famously worked at a patent office where his work often exposed him to the transmission of electric signals and electrical-mechanical synchronization of time. Exposure to these topics helped him to arrive at his conclusions about the nature of light and the connection between space and time.

[3] Steve Jobs often talked about the importance of a calligraphy class he took in shaping what fonts were best used in operating systems.

[4] Australia’s proclivity to tall poppy syndrome does have some positive side effects

Does Wealth Inequality Impact Growth?

I recently read a paper entitled Does wealth inequality matter for growth? The effect of billionaire wealth, income distribution, and poverty[1] that has been getting some coverage in economic circles. One of the reasons for the coverage is that income and wealth inequality has become a major discussion point in economics, since the release of Thomas Piketty’s Capital in the Twenty-First Century.

The other reason for the attention is that the paper, although implicitly agreeing with Thomas Piketty’s conclusion that inequality is detrimental to economic growth, puts a twist on the conclusion. This paper, through a series of statistical models, provides evidence to suggest wealth inequality in itself does not impact economic growth, but that wealth inequality that arises due to government corruption impacts on economic growth.

Reading between the lines, this conclusion essentially reverses the prescription Piketty has been arguing for (greater intervention from government to redistribute wealth) and instead implies the opposite, that government should be reduced and basically get out of the way.

At a high level, there are two reasons I wanted to review this paper. These reasons are:

  1. To highlight the importance of skepticism when reading headlines based on scientific literature, and
  2. To provide an example of how a lack of domain knowledge[2] can cause problems in the world of statistics.

The Setup

The basic experiment setup is as follows. The authors (Sutirtha Bagchi and Jan Svejnar) took the Forbes List of World Billionaires for four years, 1987, 1992, 1996[3] and 2002. They then split the billionaires in these lists into two groups: those that have seemingly gained their wealth through political connections, and those that apparently gained their wealth independent of political connections.

Once grouped, the researchers aggregated the wealth of the billionaires by country to calculate politically connected billionaire wealth, politically unconnected wealth, and (adding these two pools together) total billionaire wealth – for each country in the dataset.

To normalize this measure of billionaire wealth across countries, they then divided the billionaire wealth for each country in each year by the total GDP for that country in that year. This provided a measure of billionaire wealth (politically connected, politically unconnected and total) as a percentage of GDP[4], which was taken to be a measure of inequality.

In addition to the three variables for each country – politically connected wealth inequality, politically unconnected wealth inequality, and total wealth inequality the authors also added a number of other variables, including measures of poverty, income inequality, income level (as measured by real GDP per capita), levels of schooling and the price level of investment[5].

Using linear regression, these variables were then used to predict GDP growth per capita for the following five years (after the year the variables corresponds to). For example, the variables for the year 1987 were used to predict the GDP growth per capita for the years 1988 to 1992.

Without getting too deep into how linear regression works, this approach was informative because it allowed for an assessment of the impact of each variable on growth, assuming all the other variables were held constant. With a variety of models constructed, the authors were able to assess what impact politically connected inequality had on growth, assuming politically unconnected inequality, income, poverty levels, schooling levels and the price level of investment were held constant.

The other big benefit of using linear regression is that it provides information about which of the variables used in a model are actually useful (“found to be significant”) in making a prediction. Essentially, variables that are found to not be significant can be excluded from the model with little or no decrease in the accuracy of the model.

Before moving on to the results, please be aware, for the sake of brevity, I am greatly simplifying the experimental setup, and completely ignoring a range of robustness and other testing the authors did. For those details, you will need to read the full paper.

The Results

At a high level, the results of the models constructed suggested the following in relation to the impact of inequality on growth:

  1. Politically connected wealth inequality (regardless of how it is normalized) was found to be a statistically significant predictor of growth. In all cases the coefficient was negative, indicating the higher the level of wealth, the lower the predicted growth.
  2. Politically unconnected wealth inequality (regardless of how it is normalized) was not found to be a significant predictor of growth.
  3. Wealth inequality (when political connectedness is ignored) can be a significant predictor of growth depending on how it is normalized[6]. When found to be significant, higher levels of billionaire wealth led to lower levels of predicted growth.
  4. Income inequality was found to be a significant predictor of growth in only one of the 12 models constructed. In the case where it was found to be significant, greater income inequality led to predictions of higher growth.

In addition, the model also provided some other interesting conclusions:

  1. The level of income in a country was found to be a significant predictor of growth in all cases. The models suggested that the higher the level of income in a country, the lower the predicted growth[7].
  2. The level of poverty was not found to be a significant predictor of growth in any of the models constructed.
  3. The level of schooling (for males or females) was not found to be a significant predictor of growth in any of the models constructed.

Caveats and Problems

Already from some of the findings above, you probably have some questions about the results. Poverty and schooling and income inequality have no impact on economic growth? The conclusions can change based on how billionaire wealth is normalized? You are right to be skeptical, but lets break down why.

Determining Wealth is Difficult

The first problem, and it is one explicitly acknowledged by the authors, is that measuring wealth (and therefore wealth inequality) is very difficult. Most of the difficulty arises from determining the wealth of the rich. In some cases, it is relatively straightforward to determine wealth – for example if the billionaire’s wealth is tied up in one company (e.g. Bill Gates and Mark Zuckerberg). But in other cases, particularly with inherited wealth, the assets are diversified, held in a large number of holdings, trusts and companies across the world. In some further cases, it is extremely difficult to value the assets of a billionaire due to the unique nature of the assets (this is why Donald Trump’s worth is always the subject of debate).

To get around this, problem, the authors have relied on the Forbes list of billionaires. In terms of billionaire wealth, this is probably the best researched list of billionaires available, but by Forbes own admission “It’s less about the [net worth] number, per se… this is a scorecard of who the most important people are.”

Is Billionaire Wealth a Good Predictor of Wealth Inequality?

The authors built their measure of wealth inequality using the wealth of billionaires. But does this make sense even if we assume the Forbes list is accurate? There are two main problems I see with this approach.

The first is that ‘billionaire’ is an arbitrary cutoff point. Extremely wealthy people with wealth over the billion-dollar cut off one year regularly fall out of the three comma club the following year. For smaller countries with very few billionaires, this can have an outsized impact on their measure of wealth inequality from year to year.

The second issue is that looking at billionaire wealth tells you nothing about the distribution of wealth below the $1 billion mark. An example is provided in Chart 1 below.

Chart 1 – Wealth Distribution Across Two Hypothetical Countries

What Chart 1 shows is two hypothetical countries with 10 people each and the same amount of total wealth. Country 1 has two people who are extremely wealthy (but not billionaires), while the rest are far less wealthy. In Country 2, we have one billionaire but a much more even distribution of wealth amongst the rest of the population. Looking at the chart we would conclude that Country 1 has a higher level of inequality, but if we calculate inequality based on methodology used in the paper, Country 2 will be determined to be more unequal than Country 1. In fact, Country 1 would be assigned an inequality value of 0.

Obviously this is an exaggerated example, but it illustrates the point that there is a lot that could be happening below the $1 billion mark that is completely ignored by the measure used. I would also argue that the distribution of wealth amongst the population who are not billionaires is going to be much more important for growth than the ratio of billionaires to everyone else.

What is Politically Connected?

This is the part of the experiment setup that will probably end up being the most contentious, and relates back to the lack of domain knowledge. The problem is the authors could not possibly know of every billionaire on the list, the circumstances of how they accrued their wealth, and make a judgment call on whether political connections were a necessary precondition. As a result, they had to rely on various news sources to draw their conclusions and this led to some interesting outcomes.

For those that read the Wonkblog piece I linked to earlier, you may have noticed a chart in which Australia was adjudged to have 65% of billionaire wealth over the four years looked at being politically connected, putting it the same range as India and Indonesia. To most Australians this would be a hugely surprising result given Australia’s strong democratic tradition, strong separation of powers and prominence of tall poppy syndrome[8].

Generously, the authors of the paper provided me with the classifications that led to this number and it boils down to the fact that they have classified Kerry Packer as a politically connected billionaire. For those that know of Packer (pretty much every Australian) it would seem ridiculous to class him in the same bracket as Russian oligarchs or Indonesian billionaires who benefitted from the corrupt Suharto regime. But for someone who is not from Australia, they had to make this judgment based on newspaper clippings talking about Packer’s lobbying efforts.

In the case of Australia, having a high percentage of politically connected billionaire wealth has little impact. Once politically connected billionaire wealth (i.e. Kerry Packer’s wealth) is taken as a ratio of GDP, the number becomes very small because Packer’s wealth is dwarfed by the relatively large Australian economy. But what about other countries? How have various judgment calls impacted their inequality measures and therefore the model?

As I mentioned at the start of this section, it is unreasonable to expect the authors to be able to know how every billionaire worldwide accrued their wealth and the role of the government in that process. Additionally, the fact that there may be issues with some classifications does not mean we should throw away the results. However, it does mean any conclusions we draw from the results should be caveated with this problem in mind.

Unknown Unknowns

The final problem comes down to the high level question of what drives economic growth.

When you consider all the different things that can impact on the economic growth of a country over the course of five years, you quickly realize there are an almost unlimited number of factors. Commodity prices, what is happening in the economies of major trading partners, weather patterns, population growth, war, immigration, fiscal policy, monetary policy, the level of corruption and the regulatory environment are just some of the factors that can have a major impact on growth.

When economists build models to predict growth, they make choices about what factors they believe are the major drivers of growth. In this case, the authors have used factors like income levels, schooling and poverty levels. But what about some of the other factors mentioned above? Could these factors have better explained growth than politically connected wealth inequality?

This choice of variables is further complicated by the interrelatedness of the factors impacting growth. Is population growth driving economic growth, or is it because population growth indicates higher levels of immigration? Is government corruption holding back growth, or is it that corruption is siphoning off money from schooling and other public services?

When it comes to the models in the paper, the key question is if politically connected billionaire wealth is really impacting growth, or if it is simply acting as a proxy for some other measure (or measures). For example, are high levels of politically connected billionaire wealth dragging on growth, or is this measure acting as a proxy for the level of corruption in an economy and/or the prevalence of inefficient government created monopolies – which are the real drags on growth? Unfortunately, there is no definitive way to know the answer to these questions.

Conclusions

As mentioned at the outset, inequality and its impact on growth and the economy in general has been a popular topic of discussion in economic circles for the last 1-2 years. In many ways, it is the defining economic discussion of our time and has the potential to shape economic policy for a generation.

In an effort to provide more information in that debate, the authors of this particular paper deserve plenty of credit for taking an innovative approach to a difficult problem. However, at least in my mind, the results raise more questions then they answer.

That, it should be noted, is not a criticism, but is often the outcome of research and experiments. Results can often be confusing or misleading, and can only later be explained properly through further research. This is all part of the scientific method. Hypotheses are created, challenged, and either proved incorrect or strengthened. They are always subject to be proven wrong.

Unfortunately this nuanced process is not one that lends itself to catchy headlines and this is where we find one of the key problems with reporting of scientific results. Most authors, including the authors of this paper, are fully aware of the limitations of their findings. That is why you will find the conclusions section filled with words like ‘suggests’, ‘possibly’ and ‘could’. But those words do not make for good stories and so the qualifiers tend to get left out.

It is for this reason, if you are interested in the results of a particular paper or study, it always worth looking at the detail. With that, I’ll leave the final word to Sutirtha and Jan (emphasis mine):

“These and other examples, together with our econometric results, suggest that the policy debate about sources of economic growth ought to focus on the distribution of wealth rather than on the distribution of income. Moreover, particular attention ought to be paid to politically connected concentration of wealth as a possible cause of slower economic growth. Further research in this area is obviously needed, especially with respect to the effects of wealth inequality at different parts of the wealth distribution, the possibly declining effect of unequal distribution of income on growth, and the role of poverty.”

 

[1] S. Bagchi, J. Svejnar, Does wealth inequality matter for growth? The effect of billionaire wealth, income distribution, and poverty, Journal of Comparative Economics(2015), http://dx.doi.org/10.1016/j.jce.2015.04.002

[2] Domain knowledge is knowledge of the field that the data relates to.

[3] A change in the methodology used by Forbes to compile the list between 1997 and 2000 led them to instead choose 1996.

[4] The authors also try normalizing by other factors, such as population and physical capital stock, but this doesn’t substantially change the results of the model.

[5] A measure of how expensive it is to invest in capital within a country.

[6] When normalized by population, billionaire wealth is found to be a better predictor of growth than politically connected wealth.

[7] This may seem strange, but actually nicely captures a phenomenon in economics where lower income countries experience higher growth as they ‘catch-up’ to higher income countries.

[8] A perceived tendency to discredit or disparage those who have achieved notable wealth or prominence in public life.

Corruption in Kosovo: A Comparative Analysis

Cross posted from OpenDataKosovo.org:

Previously in Part I of this series, we looked at corruption in Kosovo from the perspective of Kosovo civil servants, as documented in a United Nations Development Programme (UNDP) report entitled Gender Equality Related Corruption Risks and Vulnerabilities in Civil Service in Kosovo[1].

In Part II we are now going to look at global corruption perception statistics compiled by Transparency International to consider how Kosovo compares internationally.

An International Comparison of Corruption

Transparency International is an organization that works to reduce corruption[2] through increasing the transparency of Governments around the world. Arguably Transparency International’s most well known contribution is the Corruption Perceptions Index (CPI), an index measuring “the perceived levels of public sector corruption worldwide”. In 2014[3] the CPI was calculated by aggregating 12 indices and data sources collected from 11 different independent institutions specializing in governance and business climate analysis over the past 24 months. The 2014 CPI covered 175 countries, including Kosovo.

In addition to the CPI, Transparency International does its own survey and data collection in the form of the Global Corruption Barometer (GCB survey). The GCB survey focuses on the public’s opinion of corruption within their own country, and in 2013 (the latest edition of the GCB available at the time of writing) collected the opinions of over 114,000 people across 107 countries – including Kosovo.

So what did these two reports show?

Results

In the CPI, Kosovo performs poorly, placing 110th out of 175 countries with a score of 33 out of 100 (unchanged from 2013). To give some perspective, Kosovo finished equal 110th with 4 other countries – Albania, Ecuador, Ethiopia, and Malawi. This placed it behind Argentina (107th), Mexico (103rd), China (100th), India (85th) and Greece (69th), countries that are often associated with high levels of corruption. Finally, this was the lowest ranking for any country in the Balkans region (tied with Albania).

Chart 1 – GCB Survey Q6 – Perceptions of Corruption by Institution for 6 Countries

WAC_2_1

The GCB survey, however, shows that the people in Kosovo have a different perception of corruption in several areas to that reported in the CPI. Based on the responses to question 6[4] (see Chart 1) and question 7[5] (see Chart 2) of the GCB survey, people in Kosovo are somewhat more optimistic about the levels of corruption in their country than the low rating on the CPI might indicate. Kosovo scores well in several areas:

  • Only 16% of people reported having paid a bribe in the last 12 months. This placed Kosovo 35th out of the 95 countries that provided a response to question 7.
  • 46% of Kosovars generally believe their public institutions to be corrupt or extremely corrupt. This sounds high but actually puts Kosovo ahead of the US (47%) and only slightly behind Germany (40%). The results for certain institutions were even better:
    • The Military is believed to be corrupt or extremely corrupt by only 8% of those interviewed – only four countries had a lower percentage than Kosovo on this part of question 6.
    • NGOs and Religious bodies were also seen as uncorrupt by large majorities.
    • 44% of people believed public officials and civil servants were corrupt, placing Kosovo ahead of Germany, France and the US, among others.

Chart 2 – GCB Survey Q7 – Reports of Bribes Paid by Institution for 6 Countries

WAC_2_2

But not all the results were positive. Questions 1[6], 4[7] and 5[8] in the GCB survey in particular highlight a more pessimistic outlook:

  • In response to question 1, 66% of Kosovars stated that they believed corruption had increased over the past 2 years, while only 8% believed it had decreased.
  • In response to question 4, 74% of Kosovars stated they believed their Government is run by large entities largely or entirely for their own benefit.
  • In response to question 5, only 11% of Kosovars surveyed believed the actions of their Government in the fight against corruption are effective.

What does all this mean? Why does Kosovo perform so poorly on the CPI, and on some GCB survey questions, but on other questions the perceived level of corruption of people in Kosovo is comparable to some developed nations?

Perceptions vs. Reality

One of the issues when looking at the results of the GCB survey is that the responses to most of these questions are subjective. What constitutes corruption or extreme corruption varies by country and culture based on what people are used to living with. What someone in South Asia or sub-Saharan Africa considers standard practice and harmless may be considered unbelievably corrupt by people in other parts of the world.

These different standards are really highlighted when we compare the percentage of people believing an institution is corrupt with the number of people reporting to have paid a bribe to that institution, using questions 6 and 7 of the GCB survey. There are four institutions that appear as options for both questions, allowing us to make a direct comparison:

  1. Education
  2. Judiciary
  3. Medical and Health, and
  4. Police

In the comparison (see Chart 3), we find numerous examples where the percentage of people that reported paying bribes was higher than the percentage of people who believed the institution was corrupt. The implication of this finding is that significant numbers of people in these countries believe that paying a bribe is not a sign of corruption.

Chart 3 – Comparison of Perceived Corruption with Bribes Paid

WAC_2_3

Kosovo and most developed nations were examples of the opposite case – they generally reported relatively high numbers of people who believed the four comparable institutions were corrupt, and relatively low percentages of people reporting bribes being paid. Bribery, of course, is not the only form of corruption, and this result could simply be an indicator that different forms of corruption are more prevalent in these countries. But it could also be an indicator that people in some countries are particularly cynical about the fidelity of their institutions.

To get a better sense of how concerned people really are about corruption, lets now take a look at some of the responses to other questions in the survey.

Is a Person’s Willingness to Take Action a Better Indicator?

One of the questions asked on the survey that could potentially reveal some further information was question 10 – “Are you willing to get involved in the fight against corruption?” Respondents were then provided with a range of activities, both active and passive, and were requested to indicate whether they would be willing to participate.

At a high level, the responses to this question appear to show an inverse correlation between the value of the CPI for a country and how willing people in that country were to do something active to fight corruption. In other words, the higher the percentage of people willing to do something active to fight corruption, the lower the CPI index for that country (i.e. a higher level of corruption).

Using a statistical model (such as regression), we can check whether this relationship is real and how strong it is. However to do this, we need to consider countries with regimes that punish dissent and crack down on protests and/or organizations that might try to combat corruption. In these countries, you would expect to have a low percentage of people willing to take action against corruption despite corruption being high.

To account for this, we need to have some sort of indicator of how worried people are about speaking out in their country. The best piece of information that we have from the GCB survey that can serve this purpose was the question asking if the respondent would be willing to report corruption.

Using these two pieces of information, we can try to test the following hypotheses:

  1. A high percentage of people willing to take action against corruption in a given country is indicative of a high level of corruption.
  2. A low percentage of people willing to take action against corruption in a given country, but a high level willing to report corruption is indicative of a low level of corruption.
  3. A low percentage of people willing to take action against corruption in a given country, and a low level willing to report corruption is indicative of a high level of corruption in a repressive regime.

Based on these hypotheses, we also expect that there would be no (or very few) cases where there is high percentage of people willing to take action against corruption and a low level of people willing to report corruption.

Building a Model

Using our two pieces of information described above, and with the assumption that the CPI is the most accurate indicator of the true level of corruption within a country[9], we can build a model to predict CPI for each country and test our hypotheses. The formula for this model will be as follows:

Where:

Yi = the actual value of CPI for country i

β0 = a constant

Xi1 = the percentage of people willing to do something active to fight corruption[10] in country i

β1 = a constant applied to Xi1

Xi2 = the percentage of people willing report an incidence of corruption in country i

β2 = a constant applied to Xi2

εi = the residual or error

Using ordinary least squares (OLS) and the data for the 101 countries for which the CPI and the two variables (X1 and X2) described above are provided, the results of the model is as follows:

β0 β1 β2
Coefficient 27.9735 -0.8417 0.8798
Standard Error 4.8427 0.0705 0.0738
R2 66.7%

The first thing to note is that the coefficients support the three hypotheses we mentioned above:

  1. A strongly negative coefficient β1 indicates that the larger the percentage of people willing to do something active to fight corruption, the lower the predicted CPI.
  2. A strongly positive coefficient β2 indicates that the larger the percentage of people willing to report corruption, the higher the predicted CPI.

General Insights

Aside from providing support for our hypotheses, the other thing this model reveals is the countries that are not very well explained by this model. Chart 4 shows the CPI predicted by the model as compared to the actual CPI value for 2014.

Chart 4 – Predicted CPI vs. Actual CPI by Country

WAC_2_4

At a high level, we can split the chart into two parts:

  1. Points below and to the right of the line reflect countries where the actual level of the CPI was lower than the predicted level
  2. Points above and to the left of the line reflect countries where the actual level of the CPI was higher than the predicted level.

Starting with the first group – countries that were more corrupt than the model predicted – these cases appear to fall into two categories:

  • Conflict Affected Countries – In these cases, of which Sudan is the most extreme example, there was typically a low percentage of people willing to do something active to fight corruption, and therefore the CPI was predicted to be significantly higher than it is in reality. This is likely to be due the citizenry of these countries facing more immediate problems. This pattern was seen across Sudan, Afghanistan, Iraq, Libya and South Sudan.
  • Other – In these cases, of which Russia was the best example, there was generally a high percentage of people willing to report corruption (86% for Russia) and a relatively low percentage of people willing to do something active to fight corruption (47% in Russia). As a result the model predicted a relatively high CPI. The explanation for this is not as clear as above, but the evidence would seem to suggest that the people in these countries are either not aware of the high level of corruption present in their country, or that they have a significantly different opinion as to what constitutes corruption.

Contrasting with the above cases, we can also see there are countries above and to the left of the line in Chart 4. This represents countries that were less corrupt than the model predicted. In these cases the responses to the two questions were indicative of a country with a higher level of corruption than actually existed. The following were two interesting cases:

  • Finland – the model was thrown off by a surprisingly low percentage of people willing to report corruption. Of the respondents from Finland, only 65% of people surveyed reported they would be willing to report corruption – a surprisingly low percentage for a country with a CPI value of 89. In fact, Finland and Japan were the only countries with a CPI above 60 that reported a percentage below 80% for this question.
  • The United States – neither of the data points used for the US in the model were hugely abnormal for countries in the same CPI range. 80% of people said they would be willing to report corruption (a little lower than you would expect) and 50% said they be willing to do something active to fight corruption (a little higher than you would expect). Both of these potentially show a slightly higher level of mistrust in government than other developed nations, something that does tie in with the politics of large parts of the US.

Unlike the above examples, Kosovo appeared fairly typical for the model. Let’s now take a deeper look into the results of the model for Kosovo.

Insights for Kosovo

For Kosovo, the model was able to fairly accurately predict the CPI using the two variables described. Kosovo has both a high percentage of people willing to do something active to fight corruption (80%) and a high percentage of people willing to report corruption (84%). As a result, the model predicted a high level of corruption in Kosovo, a CPI of 35, which was just below the actual CPI value of 33.

However, aside from proving the accuracy of the model in this case, these high values reveal important information about the people of Kosovo. It reveals Kosovars do believe corruption is an issue, and that they are willing to do something about it.

Summary

Overall, there are positives and negatives for Kosovo that can be taken from the Transparency International data. On the negative side, the CPI highlights that corruption is a significant issue in Kosovo. Even in a region with consistently low CPI scores (the best performer is Slovenia with a score of 58) Kosovo is a significant underperformer. The most disappointing aspect of this underperformance is that Kosovo has had the significant advantage of 15 years of assistance from various international agencies in setting up infrastructure for good governance.

That said, there is a big positive that comes from the GCB survey data, and it is also potentially an important clue as to the best way forward for Kosovo and the international organizations involved in the region. That positive is that the people of Kosovo appear to be aware of the issues of corruption in their country, and more importantly, they are very willing to take an active role to fight it. Compared to Albania, a country with the same CPI as Kosovo, almost twice the percentage of survey respondents stated they were willing to do something active to fight corruption in Kosovo (80% vs. 44%), and significantly more people said they were willing to report corruption (84% vs. 51%).

What this suggests is that, if harnessed effectively, anti-corruption efforts in Kosovo could be very popular, and therefore powerful. But the right strategies have to be implemented and publicized to garner public support.

Somewhat unsurprisingly, we believe a key strategy has to be raising awareness of how data can be used to reduce corruption and bring about change. This can apply equally to data that is currently collected by government agencies but isn’t publically released, or new datasets that the public can assist in collecting. With the right data and right analysis, these datasets can help to improve governance in numerous ways including:

  • exposing systematic corruption
  • identifying gaps in anti‑corruption controls, and
  • better targeting of anti-corruption efforts.

Using this open data approach also helps reduce reliance on the bravery of individual whistleblowers. Although whistleblowers are often vital in helping to identify incidents and even patterns of corruption, the fact is that, even in developed nations, they will always risk retaliation and other subtler forms of retribution (reduced career prospects, being ostracized by their peers and generally being perceived as untrustworthy).

Overall, what the results of the Transparency International data shows us is that, with better coordination and targeting of anti-corruption efforts, there is the potential to actively involve large numbers of Kosovars. If that can be achieved and funneled into meaningful strategies, the future of Kosovo could be very bright indeed.

Have any suggestions for ways data could be used to fight corruption? Disagree completely? Feel free to leave your thoughts in the comments!

 

[1] Gender Equality Related Corruption Risks and Vulnerabilities in Civil Service Kosovo, United Nations Development Programme. November 2014. Gender Corruption final Eng.pdf

[2] Defined by Transparency International ‘… as “the abuse of entrusted power for private gain”. Corruption can be classified as grand, petty and political, depending on the amounts of money lost and the sector where it occurs.’

[3] The methodology for compiling the CPI is reviewed on a yearly basis with data sources added and removed as needed.

[4] “To what extent do you see the following categories in this country affected by corruption?” – responses of “corrupt” or “extremely corrupt” recorded as a positive response.

[5] “In your contact or contacts with the institutions have you or anyone living in your household paid a bribe in any form in the past 12 months?“

[6] “Over the past 2 years, how has the level of corruption in this country changed?”

[7] “To what extent is this country’s government run by a few big entities acting in their own best interests?”

[8] “How effective do you think your government’s actions are in the fight against corruption?”

[9] By their own admission, Transparency International’s CPI is not a perfect measure of corruption. Corruption by its nature is hidden and so there is no objective measure of the true level of corruption. However, the CPI is currently the most respected measure of corruption available and so we make the assumption that it is also the most accurate for the purposes of constructing this model.

[10] Taken as the average of the percentage of people who said they would take part in a peaceful protest and the percentage of people who said they would join an organization that works to reduce corruption as an active member

Women and Corruption Issues in Kosovo

For those that don’t know, over the past couple of months I have been spending time working with a tech startup/NGO here in Pristina called Open Data Kosovo. The main aim of the organization is to encourage and facilitate the release of data and other information by the government of Kosovo (and related bodies) in order to increase transparency and reduce corruption. So far they have been fantastically successful, getting both national and international media attention, which is all the more impressive when you consider they are only now coming to the end of their first year of existence.

One of the main things I have been working on since joining is putting together some analysis of the various datasets they have been publishing online to see what conclusions can be provided to the public that might help create a more informed discussion of the issues. The first piece has now been published on the Open Data Kosovo website and we are excited to see what kind of feedback we get. If you want to take a look, please click the link below:

More women in leadership would probably reduce corruption, but is there a more effective way? 

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