Tag: corruption prevention

Two Types of Corruption

There are really two types of corruption.

Type 1:  Speed-Money Corruption

In India, bureaucrats sometimes deliberately delay the processing of applications in the hope of getting a bribe.  A citizen who needs, say a water connection, has to pay a bribe to a clerk to get their application looked at.  If you are the victim of a crime, and go to the local police station, the policeman will expect a small bribe to register your complaint.

This seems like a fairly harmless form of corruption.  Some people defend it as necessary in a free market, as a mechanism for the differentiation of services.  It actually seems very like a tip.  But in this case, you have to pay before you get the service.

But if you take a closer look at this form of bribe and think about the economics of it, you see that what is being demonstrated is a form of rent-seeking behaviour.  And you will see that it has the harmful side-effect of encouraging inefficiency.

As time progresses, the processes get slower and slower and newer hurdles and pain points are introduced to make people fork out more and more money, and everyone ends up losing – paying a heavy price for encouraging such practices – because of the resulting inefficiencies.

If there’s one take-away from the above discussion, it would be this:  in this form of corruption, the inefficient people get rewarded, and you end up encouraging inefficiency.

Type 2:  Man-In-The-Middle Corruption

The second kind of corruption is the man-in-the-middle kind of corruption. This is the corruption you encounter when people’s money passes through the hands of a middle-man tasked with procuring services for them.

In the realm of public services, like the construction of roads and schools, that middle-man is government.

In the presence of corruption, the middle-man ends up selecting the service-provider who pays the highest bribes, not the service-provider who does the best job.

This leads to a market where the lowest-quality service provider wins.

Economic theory also suggests that this drives the highest-quality vendors out of the market.

That conclusion follows from the work of George A. Akerlof. He described it in a paper titled ‘The Market for “Lemons”: Quality Uncertainty and the Market Mechanism’.

In the case of India, it is possible that the migration of computer scientists and engineers out of India can be explained in part by the pressures of such a process.

We can also compare the two forms of corruption in terms of how damaging they are.

Impact Analysis

1)  Speed-Money Corruption

Let’s say that a citizen needs to get a job worth X dollars/rupees done.  He needs to pay a bribe that’s usually of the order of 3% to 10% of X.

With the cost of the bribe factored in, the value/expenditure ratio is above 90%.  So, the loss is small.

If we count the money taken as a bribe as a benefit to society (assuming the bribe-taker uses it well – maybe he donates it to charity) then the value/expenditure ratio is 100%.

So, speed money corruption doesn’t hurt too badly.  It leads to a small loss if any per transaction.

The other type of corruption is far more damaging.  Here’s why:

2)  Man-in-the-Middle Corruption

Things are much worse (much more damage is done) when a middleman decides to spend the money that you pooled in (tax money) injudiciously.

So let’s say X dollars/rupees of our tax money is in the hands of a middleman.

Let’s say the middleman takes a bribe from a vendor to buy something worthless (I’ll give you some examples of this shortly).

The middleman who made the purchase would get 3% to 10% of the value of the sale from the crooked vendor (this is called a kickback).

Now assuming the middleman spends the money he gets very well (let’s say he donates it to charity), that is the only value the tax-payer gets for the transaction.

So, for X dollars spent, the taxpayer gets a maximum benefit of X/10.

That means the value/expenditure ratio is less than 10%.

Now, here are some examples of possible cases of man-in-the-middle corruption in India.

There is an article in the BBC titled “India scraps $753 Finmeccanica helicopter deal“.  It is about the purchase by the Indian government of 12 helicopters for transporting VIPs at a cost of $753 million. Each helicopter cost around $60 million.  The kickbacks paid are estimated to be a whopping $67.6 million.

In comparison, the cost of India’s space mission to Mars is $73 million.

So, Indian officials were willing to pay as much for a helicopter as it would cost to fund a space mission to Mars.

The helicopters were also quite useless because there are thousands of military helicopters already in use in India to ferry VIPs.

The argument that the Generals who bought the helicopter put forth was that the helicopter would allow the bodyguard of the Prime Minister to stand upright while accompanying him to landings at high altitudes.

That would have been an expenditure of 753 million dollars for a value of 67.6 million (provided the officials used the bribe money well) which is a less than 10% value/expenditure ratio.

This is why man-in-the-middle corruption is particularly dangerous and damaging.  It can lead to a country’s resources being siphoned off without conferring any benefits upon the country.

Another example I can give you is that of the excessive expenditure over the last few years on the import of arms.

In the years 2011, 2012 and 2013, India spent $12 billion to buy aircraft from Europe, and a further $12 billion to buy 120 Sukhoi planes from Russia, and $10 billion for just ten Boeing transport planes (each plane costs $1 billion – no tenders were issued).  [This World Bank report suggests that corrupt governments overspend on defence procurement because of the lack of transparency in such deals.]

And the Indian government is spending so much money on weapons imports while running a deficit (India is borrowing money to finance I believe 50% of its budget).

What is scarier is that the Indian central government’s annual education budget is only $12 billion, at a time when there are 400 million Indians who don’t get a basic education, and when India graduates less than 100 computer science PhD students each year (thousands of Indian students go overseas for graduate studies).

But it’s actually much worse.  More than 250,000 farmers have killed themselves since the mid-1990s — a figure that may be a fraction of the truth as local authorities willfully misreport “accidental” deaths.  Every second child born in India is malnourished. Nearly two million children under the age of five die every year from preventable illness as common as diarrhoea.  Of those who survive, half are stunted owing to a lack of nutrients. The national school dropout rate is 40 per cent.

The above numbers highlight the dangers of man-in-the-middle corruption and make clear why it is imperative to put a stop to it.  We can see from the above that both types of corruption lead to inefficiency, but the second type of corruption can lead to the large-scale misallocation of tax money (leading to poverty, hunger and death on an unimaginable scale).

We have proposed some methods to prevent this type of corruption in the following articles:


Using text analytics to prevent O(log n) failure of democratic institutions

In this article, we discuss an Achilles’ heel present in many democratic institutions.  We claim that many democratic institutions can be made to fail in ‘log n’ time (exponentially fast) if patronage (nepotistic) networks are allowed to grow unfettered.

We then support the claim using real-world examples of the failure of democratic institutions (political and otherwise) and discuss why such failure has not been observed in polity in India.

We also look at how text analytics can be used to detect (and consequently enable steps to be taken to prevent) such failures.

The Weakness

In democratic institutions, voting mechanisms are used to confer upon one individual (from a field of eligible candidates), powers and responsibilities as specified in the charter of the institution.

In some cases, the powers that accrue to the elected individual are so great that they enable him to use them to ensure his or her re-election.  There are two methods available to such an individual.

The first is to pay off the electoral college and secure re-election.  This method is an O(n) algorithm.  The quantum of resources required to secure re-election is proportional to the number of people who need to be suborned.  So, this method only works in cases where electoral colleges are small (for example, in the case of committees deciding job appointments).

A faster method of suborning large numbers of people exists.  The establishment of a hierarchy of patronage can leverage the dynamics of social networks to speedily (in log n time) corrupt very large numbers of people.

It works like this:  The person who is elected to head the country appoints as immediate subordinates only people who are on account of tribal or ethnic affiliations expected to be loyal to him/her. This appointment is often accompanied by a monetary exchange in the form of a bribe paid by the appointee to secure the post.  Such a monetary exchange helps cement the loyalty since the person making the payment becomes dependent on their superior’s continuation in power to recoup the money spent. In other words, the immediate subordinates are forced to invest in their superiors’ careers.

The subordinates so appointed in turn appoint people loyal to themselves to positions below them, in order to recover their investment in the person above them and so on.  Very soon, the entire government machinery becomes beholden directly or indirectly to the person at the top for their jobs and has a vested interest in keeping the person at the top in power.

In some countries, this effectively transforms the democratically elected ‘president’ into a dictator for life. 

Example 1

The first example of such failure is possibly (and I am just illustrating a point – no disrespect intended) the government of Cameroon.  The President of Cameroon has been in power since the 1980s.  The President is impossible to replace in spite of rampant corruption and economic mismanagement because all the tribal chiefs and officials in Cameroon are beholden to the President.

All these officials and chiefs try and recoup their investment by rent-seeking behavior (you will need their good offices if you wish to do any business in Cameroon).

The resulting economic climate doesn’t necessarily encourage the growth of entrepreneurship or investment in Cameroon.

Example 2

Iraq for many years was ruled by a dictator who had managed to suborn an entire political system.  Iraq once had a participatory democratic system.  But the use of patronage by Saddam led to Iraq coming under totalitarian rule.

Similar failures can be seen in post WW2 Libya and Syria, and in Stalin’s Russia.


One common feature of many of the countries where such failure has occurred is that they have hierarchical social structures in which respect and obedience can be commanded by people higher in the hierarchy from people lower in the hierarchy.

Cameroon has a culture of veneration of elders and tribal leaders.  So do the countries of the Arab world.

India also has somewhat similar cultural traits.  So, it is very interesting to see that a similar process of deterioration of democracy has not been observed in India.

One explanation is that India was saved by its own heterogeneity.  India is made up of distinct linguistic regions and ethnic groups.  It would be impossible for tribal and ethnic hierarchies to span India’s many linguistic and ethnic boundaries.

So, even if one province has failed, that would not trigger failure in neighboring provinces, and the regional despot would not be strong enough to change the Indian constitution.

Examples of such failure can arguably be observed to have happened already in Karnataka and in Tamil Nadu.  In Karnataka, a couple of mining barons managed to become ministers in the government and managed to exercise a lot of control over it for a number of years. Had Karnataka been an independent political entity, they might at some point have attempted to change the constitution to give themselves more power and possibly unlimited tenure.

In the state of Tamil Nadu, the ruling politicians are suspected of building patronage networks around themselves in order to facilitate rent-seeking behaviour.  If Tamil Nadu had been an independent political entity, I suspect that it might have lost its democratic character, since the number of people below the Chief Minister with a vested interest in keeping him/her in power would have become too big to permit free and fair elections to take place.

But what is interesting is that in India, though democracy could have failed at the level of the states (controlled by politicians who had suborned a large part of the mechanism of government) the possible failure did not take place or proved reversible.  That is probably because no kinship networks extend to all parts of India.

The fragmentation must have protected the constitution and the electoral system.  That in turn allowed constitutional and legal frameworks and electoral politics to correct the failures.

In Karnataka, the corrupt mining barons and the Chief Minister who supported them ended up behind bars.  In Tamil Nadu, some family members of the corrupt Chief Minister ended up behind bars.  In both states, the parties that had engaged in corruption were voted out of power.

So, failure through networks of patronage might be difficult to engineer in India because of the extremely heterogeneous nature of its society (a result of size and diversity).


Why would someone intending to build a patronage network only elevate kith and kin to positions of power?

As in, why did the dictators in Iraq and Syria choose to pack the governing organizations with people from their own community or tribe?

One possible answer emanates from the work of George Price (http://www.bbc.co.uk/news/magazine-24457645), who showed that altruism exhibited towards close relatives can have concrete benefits for a selfish individual.

I quote from the article:

Price’s equation explained how altruism could thrive, even amongst groups of selfish people.

It built on the work of a number of other scientists, arguably beginning with JBS Haldane, a British biologist who developed a theory in the early 1950s. When asked if he would sacrifice his own life to save that of another, he said that he would, but only under certain conditions. “I would lay down my life for two brothers, or eight cousins.”

Here is the Wikipedia page describing the Price equation (http://en.wikipedia.org/wiki/Price_equation).

So, it is possible that the elevation of kith and kin minimizes the possibility that the people so elevated might one day turn against their patron (they might be more likely to exhibit non-selfish behavior towards their patron).

Detection using Text Analytics

One is tempted to ask whether it is possible to detect at an early stage the process of failure through patronage of a democratic system.

The answer is yes.  It appears to be possible to build a protective mechanism that can uncover and highlight the formation and growth of nepotistic patronage networks.

The BBC article on nepotism in Italy examines research on the detection of nepotism in Italy, and I quote:

Prof Perotti, along with others, has published revealing studies of university teachers, showing the extraordinary concentration of surnames in many departments.

“There is a scarcity of last-names that’s inexplicable,” says fellow academic, Stefano Allesina. “The odds of getting such population densities across so many departments is a million to one.”

Take the University of Bari, where five families have for years dominated the dozens of senior positions in Business and Economics there. Or consider the University of Palermo, where more than half the entire academic population has at least one relative working within the institution.

I happened to find one of Allesina’s papers titled “Measuring Nepotism through Shared Last Names: The Case of Italian Academia” on the internet.

Here it is:  http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021160

I quote from the paper:

Both types of analysis (Monte Carlo and logistic regression) showed the same results: the paucity of names and the abundance of name-sharing connections in Italian academia are highly unlikely to be observed at random. Many disciplines, accounting for the majority of Italian academics, are very likely to be affected by nepotism. There is a strong latitudinal effect, with nepotistic practices increasing in the south. Although detecting some nepotism in Italian academia is hardly surprising, the level of diffusion evidenced by this analysis is well beyond what is expected.

Concentrating resources in the “healthy” part of the system is especially important at a time when funding is very limited and new positions are scarce: two conditions that are currently met by the Italian academic system. Moreover, promoting merit against nepotistic practices could help stem the severe brain-drain observed in Italy.

In December 2010, the Italian Parliament approved a new law for the University. Among other things, the new law forbids the hiring of relatives within the same department and introduces a probation period before tenure. The analysis conducted here should be repeated in the future, as the results could provide an assessment of the efficacy of the new law.

This analysis can be applied to different countries and types of organizations. Policy-makers can use similar methods to target resources and cuts in order to promote fair practices.

The analysis that Allesina performed (a diversity analysis of last names) is a fairly easy text analytics task and provides a clue to the solution to the problem.

Such an analysis can unearth nepotism and allow steps to be taken to prevent it.

Extensions of the method can also be used to determine if, as in the case of Iraq and Syria, a certain community or ethnicity or family has taken over or is beginning to take over the governing mechanisms of a country.

And if the problem is identified early enough, it might give the constitutional, legal and electoral institutions of a democracy a fighting chance at protecting themselves and lead to a correction of the failure.

Using text analytics to detect fraud related to government tenders

In my last article, I talked about using statistics to detect fraud.  I’d promised to write about methods for detecting and preventing fraud in the issuing of tenders.

The floating of tenders is the primary mechanism by which governments – often the biggest economic force in a geographical area – procure services from private organizations.

If the bidding process is compromised, contracts might end up not going to the best or most efficient vendor as a stakeholder (the tax-payer) would desire.

That in turn results in bad roads, poor infrastructure, delayed projects, under-spending on education, over-spending on military purchases and other problems associated with bad governance. (See: https://aiaioo.wordpress.com/2012/11/18/tools-for-the-mind-and-how-you-can-change-the-world/).

It also stands to reason that if you could detect tendering fraud, you could solve quite a few of the problems that affect places where corruption is rife.

So how do you tell if a tender has been fairly issued or if it has been gamed to the benefit of a certain participant?

An unfair procurement process can use one of the following methods to ensure that a contract is awarded to a favored party:

Method 1:  Choice of pre-selection criteria

One method used to favour a certain party is the introduction of unnecessary qualifying conditions in the tender that have nothing whatsoever to do with the the end product or service to be procured.

These conditions are added to the tender in order to ensure that only a chosen small set of bidders meet all the conditions for participation in the tender process.

Method 2:  Cancellation and reissuing of the tender

I have been given to believe (by various sources) that in India/China, 3% of the size of the deal is the norm for kickbacks.

If a very efficient bid is placed, and it brings the cost of the service down so that the 3% kickbacks do not translate into a lot of money or if the winner of the bid refuses to pay a bribe, procurement officials might be able to subvert the process by coming up with reasons to cancel or terminate the tender.

They can then reissue the tender with tighter criteria intended to disqualify the uncooperative bidder.

My Experiences

Now, I must tell you that since the beginning of my career as an entrepreneur in India, I have come across numerous stories of terminated tenders, or of the disqualification of firms from a bidding process because they bid too low to be able to pay much by way of bribes.

I have personally walked into a tendering meeting where the government officials began with the words: “Ladies and gentlemen, we are proud to welcome you to our campus today.  We are extremely sorry that we cannot entertain you the way you entertain us when we come to your campuses.”

The tender was being issued for a software project that I felt should have taken a team of 3 engineers no more than 6 months to deliver.  But the tender stated that only firms with a minimum of “100 crores in revenue each year for the past 5 years,” (approximately 20 million USD each year) could bid for the project.  There were only 6 other firms in the room.

When the officials realised that Aiaioo Labs was a small firm, they suggested we leave.

They said, “There should be some other things we can work on with you.  Let’s meet some other time.”

Over the years, I began to wonder if there was any way that I as a tax payer might protect myself from bad deals (corrupt or price-inefficient deals) entered into by government middlemen with my tax money.

Fortunately, it seems possible to use text analytics to detect and alert an ombudsman to possible fraud in the issuing of tenders.  Below is a description of how such a method might work.

Using text analytics to detect irrelevant selection constraints

If tenders for procuring very different products have very similar pre-selection criteria, they could be flagged as suspicious.

The reason this method might work is that relationships between corrupt officials and client firms can take a considerable amount of time to form (because of the risks involved and the consequent need for caution).  It is easy for corrupt officials to change the favored vendor very frequently.

That would mean that they would have to keep the criteria of selection of firms more or less unchanged across widely varying tenders and over long periods of time.  So, you might find that tenders small and large, for hardware or for software, (in other words, tenders for different services), but issued by the same organization might – if the tender process has become unfair – employ more or less the same set of selection criteria irrespective of what is being purchased.

Tools can be developed to detect these similarities and flag them up for review.  Such tools would have to be able to detect the portions of the tender document that are related to the bidder, and the portions of the document that are related to the product or service requested.  It would then have to measure the similarity between the bidder-related sections of the tender documents.  It might also be possible to extract only the qualifying criteria and look for similarities there.  It might also be possible to analyse the bidder selection criteria to see if any criteria might be irrelevant to a project, or incompatible with the requirements of the project.

Using text analytics to detect reissued tenders

If a new tender document’s product or service description sections resemble those in an older tender – and if the issuing organization remains the same, it might be possible that the tender has been reissued.  If it is further found that a very low bid had won the bidding in the previous round of tendering, and that the earlier tender had been cancelled, this could be used as a flag to alert an ombudsman.

Using text analytics to detect vendor-oriented constraints

If many of the conditions for participation in the tender are company-specific (properties of companies such as size or earnings) as opposed to capability-specific (experience in a certain technology space), it might raise a red-flag.

Systems to manage tenders

It might be possible to analyse tenders for fraud if tenders are stored in and managed using a tender management system with fraud detection analytics that both serves as a repository for tender documents, as well as manages the submission of bids and monitors the selection procedure and the life-cycle of projects. This would allow governments to maintain not just a history of tender issuers, but also a history of vendors.  By so doing, governments would be able to determine which vendors are reliable and which are not.

Moreover, it would give people issuing and evaluating tenders more confidence in a low bidder (there is always a danger in projects that someone could bid too low and win the project, but then not be able to execute) and hence help reduce costs. So, a tender fraud detection tool could possibly help governments make better decisions regarding vendors of services and reduce corruption in the process of issuing tenders for the procurement of services and products for government.

Graph Algorithms for Fraud Detection

Text analytics algorithms are difficult and expensive to develop.  Fortunately there are other ways to detect tendering fraud. Patterns of favoritism in tender outcomes can be detected from a bipartite graph of issuing organizations and beneficiaries.  If tenders from a particular issuing organization are found to repeatedly favour a specific vendor from a large field of vendors (more than random probabilities would allow), the organization and vendor could be flagged. Price comparisons across tenders can also be made to determine if any prices have exceeded the price range for similar purchases (this will again require text analytics).

Some Theory

There has been a lot of work on corruption by economists in the last 10 years.  One interesting equation that models corruption is the Klitgaard Corruption Equation. The equation is C = R + D – A where C stands for Corruption, R for Rent (quantum of possible illegal earnings from being in a position of responsibility where corruption is possible) and A stands for Accountability. These concepts are explained very well in the following article http://seekyt.com/define-corruption/ (from where I got the following image as well). But Klitgaard does not model one variable that can impact corruption – and that variable is choice. If you increase the choices available to a purchaser, the opportunities for avoiding corruption increase and the likelihood of corrupt transactions occurring decreases.

For example, if everyone in a certain location must only obtain a service from the government office serving their locality, then a person who does not want to pay a bribe does not have the option of travelling to a different office to obtain the service without paying a bribe.  So, if the officer at the local office is corrupt and demands a bribe for rendering a service, then the person has no choice but to cough up the bribe.  This happens in a lot of government offices where registrations have to be performed.  Increasing the choice of service provider lessens the likelihood of people being trapped into giving bribes.

The same forces are at work in the case of tenders.  The strategy of a corrupt tendering official is to artificially reduce the choices of the selectors to only firms that will pay a bribe. Computer systems that fight tendering corruption work by preventing the artificial restriction of choices.