Farmer suicides and agrarian crisis in developing countries

Indian agriculture has been grappling with loss-making propositions like fragmented land holdings, depleting water table levels, deteriorating soil quality, skyrocketing input costs and low yields. Haunted by debt, small and marginal farmers are committing suicide: without credible collateral for loans from public sector lending institutions, they are forced to borrow money at exorbitant rates from moneylenders. Tragically, as India rises to the top slot among global economies, the number of farmers killing themselves every year is rising. In 2015, over 13,000 farmers took their lives. Maharashtra topped the list with 433 deaths, followed by Karnataka (1,569), Madhya Pradesh (13,000), Tamil Nadu (606) and Rajasthan (373).

The Indian farmer, once a rallying election symbol for many political parties, has lost his clout and voice with the political establishment. He is the orphan of growth in an elitist mindset. His voice is drowned in the coveting cacophony of pro-market lobbyists who have captured all levers of the establishment. Ministers, top civil servants and opinion makers rub shoulders at seminars organised by CII, FICCI, ASSOCHAM and foreign-funded economic think tanks, where they wax eloquent about corporate concessions while recommending withdrawal of subsidies for the poor.



published by Indian Express column by Prabhu Chawla

http://www.newindianexpress.com/prabhu-chawla/column/2017/jun/11/to-redeem-rural-india-in-lament-destroy-the-elitist-mindset-of-pro-corporate-politics-1615234.html

prabhuchawla@ newindianexpress.com

Disruption of the traditional offshore business

Coming of RPA and the newer technologies

A recent KPMG report argues that the rising cost of labor is causing BPO to become an non-viable option and that technologies, such as RPA, AI, and other cognitive and automation platforms, are advancing to create more sustainable options. According to this report, more and more companies are turning to “machines with rapidly advancing capabilities for understanding, learning, communicating, and problem-solving. Robotic process automation (RPA)—this convergence of low-cost, easy-to-implement process automation, coupled with machine learning, data analytics, and cognitive innovations—is creating a new class of digital labor.” The same report suggests this trend will cause companies to turn to more advanced machines rather than relying on BPOs for the work they need to complete, thus eventually eliminating the need for human workers and BPOs.


Recently, great numbers of business process outsourcers have turned to robotic software to automate back office processes, such as FAO, HR management, and procurement – processes that normally require a human employee to accomplish. It has been estimated that one half to 70% of the work done by shared service, captive, or outsourced operations can be automated using robotic software. This technology not only drastically reduces the cost of labor for business process outsourcers, but it produces better quality work and allows employees to focus on tasks that are much more meaningful. Here are some hard facts that suggest a decrease in the use of BPO is possible. Although it is still a hotspot for outsourcing, India is experiencing a decline in outsourcing. Between 2011 and 2014, the number of deals worldwide declined by 61% and value of these deals shrunk from $206.8 billion to $120.4 billion.






Ever since Trump’s inaugural speech emphasizing an America-first message future for outsourcing is uncertain in America, where global labor markets are being disrupted by various flavors of travel bans to the United States, the specter of a wall being built at the US-Mexico border that costs more than the entire Space-X program, a reform of H1B visas that could likely dismantle the traditional outsourcing model, and a curious thing called Brexit that could change the global trade landscape forever, one might be forgiven for feeling slightly disoriented. 


But here are three scenarios that I am seeing for 2017: [Phil Fersht, 2017]


RPA will remain undefined. Over the next 12 months, the perception of RPA will remain blurred. RPA capabilities will fold into broad propositions such as Digital Workforces or Cognitive Automation. This is adding to the continuing confusion around RDA. Extending on that, in 18 months we won’t talk about RPA anymore. Most of the leading technology providers will have been acquired and RPA is a reality in the back-office.

M&A through ISVs. Buyers will have to do scenario planning for acquisitions. While this might bring broader capabilities, licensing costs are likely to increase as well. Pega’s acquisition of OpenSpan is the template for such developments. Beyond the tool providers, the automation pure plays such as Symphony and GenFour are likely to be equally absorbed by larger consultancies.

The emergence of an Automation Ecosystem: We already have seen the impact of Watson, as it is starting to evolve into an ecosystem. Suffice it to say, IBM could be the driving force to extend those capabilities, as we have argued some time ago. But it could equally be one of the tool providers significantly expanding its reach. As stated, we are seeing the providers in the Winner’s Circle moving toward the notion of orchestrating much broader automation capabilities. At the same time, we are seeing providers like Blue Prism and UiPath being deployed in IT-centric scenarios such as IT Help Desk and Application Management, pointing to a convergence of scenarios and tool sets. 

Cloud based Business Process


Outsourcing providers have long employed automation. But a recent report from management consulting firm A.T. Kearney highlights the growth in business process as a service (BPaaS), which lets companies hand off routine chores via the cloud to software systems that perform those chores without human intervention. Any activity that is repeatable and rules-based is a good candidate for such automation, said Cliff Justice, a partner for innovation and enterprise solutions at KPMG. “Payroll, AP, order entry—all of those activities follow a set of rules and parameters and workflows.” Johan Gott, a principal in the private equity practice at A.T. Kearney, said that while technology enables BPaaS, it is at heart a new business model. “Even though it’s not core technology, it’s the more disruptive trend that we’re seeing,” he said. Spending on BPaaS, in fact, is expected to reach $13.7 billion in 2016, up from $12.95 billion in 2015 (Gartner, Forecast Analysis: Public Cloud Services, Worldwide, 1Q16 Update, May 2016).




While automating processes would involve considerable effort and IT resources for an individual company, achieving automation by using BPaaS via the cloud is a much simpler and less expensive option.”BPaaS can unlock an enormous potential for growth in BPO services by dramatically expanding the customer pool to smaller and midsized customers,” according to the A.T. Kearney report. “The standardized offerings of BPaaS are particularly well suited to smaller companies, which have neither the volume to enter into large contracts, nor the need to outsource more than a few relatively simple processes.” 



Disruption of BPO

The election of Mr Trump to the Oval pretty much just hammered in the final nail in the coffin for the traditional IT outsourcing market as we know it. The Republicans control the House, the Senate and Trump has a huge mandate to impose his will, not dissimilar from Obama and his healthcare reforms.  Change is going to happen and it will likely have a very significant impact on global IT and BPO service delivery. The bad news for the offshore industry  is that Trump’s protectionist policies are going to accelerate reality and actions will be direct in the form of raising the H1B minimum salary to $100,000 per year, encouraging cloud-based standardized service providers,and intelligent automation of the existing business process. [Phil Fersht, 2017] The attacks are reverberating at companies with production and IT operations in countries like India, China and the Philippines, outsourcing executives say. Some companies are looking for U.S.-based alternatives, while vendors that provide outsourced services are pushing automation as a cost-effective way to re-shore work—but not necessarily jobs.


The Offshore industry has been living with single-digit revenue growth for some time now and is unable to kick-start growth in a big way amid the global macroeconomic and political uncertainties. These companies in India especially are already feeling the pinch financially, and are making blatant attempt to appease the Trump administration by announcing local hiring in USA in a big way. Infosys said following the release of its March quarter results that it would hire about 10,000 people in the U.S. over the next two years. The company plans to open four innovation center hubs in the U.S. Even as this announcement from Infosys created a furor over its potential implication for jobs in India, Cognizant said recently it will rationalize its cost structure by bringing the employee base in line with demand. The company said on the earnings call it intends to ramp up hiring in the U.S., while at the same time reduce dependence on the H-1B visas. For the top offshore companies, there was a steep drop in H-1B visas filing for the year 2017.



Indian IT outsourcing firms are also exploring other options, including setting up near-shore centers or facilities closer to the U.S, with Mexico being the most-sought-after center. Apart from the option helping to face the challenges of a protectionists environment in the U.S., the cost of doing business also comes down by roughly 50 percent. Nasscom says that India’s IT industry contributes to over $2 billion in annual taxes in the US, with a cumulative of $20 billion over 10 years, while generating 4,11,000 jobs in the US as in 2015. The industry says that US companies benefit from outsourcing as they can allocate resources to critical work locally.”Indian IT sector must now brace for further troubled times ahead. The sector was already battling both cyclical challenges (due to changes and shifts in financial services, healthcare verticals) as well as secular challenges (i.e., cloud shift, automation, pricing pressure, insourcing) impacting revenue growth. A sub 10% growth for FY17 is certain,” Arup Roy, Research Director, Gartner said in a statement on November 9. 

However the companies that  don’t diversify their portfolios away from pure body-shopping and process competencies to a technology-driven advantages and that have a big bunch of complacent employees are going to face music in near term from the disruptive wave of automation.While many Asian countries are gearing up to this challenge, the IT sector in India is facing existential crisis largely of its own making because it became complacent and overconfident even as technologies and markets changed.

References

  1. On the Eve of Disruption, https://www.atkearney.com/documents/10192/7094247/On+the+Eve+of+Disruption.pdf/49fa89fa-7677-4ab8-8854-5003af40fc8e, A.T. Kearney, 2016
  2. RPA and BPO minor changes or real disruption, https://www.uipath.com/blog/rpa-and-bpo-minor-changes-or-real-disruption, UIPATH, 2016
  3. RPA 2017 report, http://www.horsesforsources.com/RPA-provider-blueprint-snapshot_022217, Phil Fersht, 2017

IT Fraud and Consumerism

Consumerism and Frauds in the Offshore IT field

Introduction

During the initial years of Westernization of Indians, many intellectuals saw the world as an undivided humanity that knows no barrier or religion, race, class, and nationality (Datta, 2003). Enduring many invasions through ages, the Indian had a broad and inclusive concept of world that emphasized amongst so many religions what we had was one among many religion. Rabindranath Tagore, the Nobel Laureate in Literature from Bengal, captured this essential oneness of mankind and visualized a universal man in Indian philosophy in his famous Nobel-winning Gitanjali:

“When one knows thee, then alien there in none,

Then no door is shut. Oh, grant me my prayer that

I may never lose the bliss of the touch of the one

In the play of the many.”

Neo-liberal Mafia

Off late due to increased westernization from 80’s, many religious gurus are professing faith in neo-liberalism that includes market fundamentalism, consumerism, welfare retrenchment, and liberal governance, away from Gandhi’s idea of Hindu economics. These revivalist gurus are professing a mix of economic efficiency, ambitious individualism beyond the traditional Hindu society, selfish narcissism, acquisitiveness and excessive materialism for their followers that are taking over the traditional Hindu ethos of toleration and equilibrium in public life. This new culture is feeding the consumer culture and exploiting the traditional Hindu ethos for the sake of new technocratic global-consumer middle class concentrated in few cities.

The Indian people had firsthand experience of this new naked commercialization where huge amounts of money was lost in the bubble busts after bull runs aided by mass hysteria without taking the operating P/E of the sectors into consideration. The new consumer class that is getting huge flows of capital from West, when examined closely, appear both self-centered and riven by paradoxes, seeking validation for their lives from Hindu evangelist gurus even as they acquire the latest consumer gadgets. At the same time this group hasn’t taken the mantle of leadership in religion-socio-economic development, and when compared to similar groups in China or Japan or Korea, they have a  reputation for creating chaos and confusion. (Deb)

This dichotomy in daily ethos among these new adherents of the urban revivalist agenda has created vast number of problems for an average Indian. The neo-liberal professors of this movement such as Subramanya Swamy have paid a nominal lip service to the vast population groups in the country while vocally professing their god given rights for the unbridled consumerism that has sees no responsibility. Some of these new jingoist adherents have identified a caste-superiority based logic in placid Hindu society that legitimizes their dominant position in High-Technology directorships, in Corporate world, in Faculty positions and in Government positions . Researchers have found that the vast masses at the base of the Indian economic pyramid are also affected by the spread of consumer culture. “Increasing desires to consume branded goods that are advertised through television is …a consistent and recurring theme.” Moreover, “intertwined cultural processes of conspicuous consumption, normative change [imposing a link between consumer goods and morality], and [interpersonal] competition” mark narratives of low caste Indian consumers. They reflect an increasingly consumerist content of Indian media that depicts the mythic lifestyles of the rich and famous. (Belk, 2008)

Satyam Scam

For example, during the High technology growth of Hyderabad in 2000’s, this new revivalist mafia tried to hijack the technology growth for their own selfish purpose while locking the vast sections of the population in their flawed pyramid of the new-liberal agenda. The case of Satyam computers highlight the nefarious potential of loose-canons that would burst the high-technology growth (only among Indians). The fraud committed by the founders of Satyam is a testament to the fact that “the science of conduct” is swayed in large by neo-liberal agenda, ambition/greed, and hunger for power, money, fame and glory. Satyam fraud spurred the government of India to tighten CG norms to prevent recurrence of similar frauds in the near future. The government took prompt actions to protect the interest of the investors and safeguard the credibility of India and the nation’s image across the world. If the government didn’t take action in time the scandal had the potential to spiral into mass hysteria that would have jeopardized the entire IT sector that employed 2.5 million people around that 2009.

Satyam fraud details

From being India’s IT “crown jewel” and the country’s “fourth largest” company with high-profile customers, the outsourcing firm Satyam Computers has become embroiled in the nation’s biggest corporate scam in living memory (Bhasin, 2009)

Satyam ownership model was flawed from the perspective of good corporate governance. There may be three factors responsible for this. The factors are not the causes of global and colossal fraud, but they provide an enabling environment for abuse and delusion.

  1. First, being a publicly owned company, Satyam could raise capital inexpensively if its existing shareholders assigned it a high value. Hence, in order to attract capital from public, it was under pressure to overstate profits to keep the company’s bonds and equities in high esteem. The promoters formed informal partnerships with this neo-liberal mafia all over the world targeting the Hindu temples, Christian and Muslim groups to develop a profitable relationship in the High-Technology sector based on false promises.
  2. Second, the promoter of the company, Mr. B. Ramalinga Raju, owned a very small fraction of the ownership stock. He diluted his holding from 25.6 % in 2001 to 3.6 % in 2009. He could overstate profits with the objective of influencing other shareholders. This ensured that the whole operation was risk free for the Owners in case of volatility in the IT sector.
  3. Third important factor for flawed ownership model may be, Satyam could preserve its fictitious profits without having to pay big taxes because its profits were protected significantly from the normal tax laws. They do not pay taxes on fictitious revenues and 22 profits. There are no penalties. The belief that exempting firms such as Satyam from service tax and corporate income tax will make them competitive is a little ridiculous. Satyam would not have overstated its revenues and profits if it had to back both with real cash. A big part of the blame for the colossal fraud thus belongs to India’s trade and fiscal policy makers who gave an uneven advantage to the neo-liberal technology mafia while ignoring the basic fundamentals of the High technology and its impact on the vast reaches of the population.

The owners maintained a consumer relation with the neo-liberal mafia over the period of 2 decades and won numerous corporate awards all recommended by this mafia. In 2007, Ernst & Young awarded Mr. Raju with the ‘Entrepreneur of the Year’ award. On April 14, 2008, Satyam won awards from MZ Consult’s for being a ‘leader in India in CG and accountability’. In September 2008, the World Council for Corporate Governance awarded Satyam with the ‘Global Peacock Award’ for global excellence in corporate accountability”. The company provided vast sums of money to this neo-liberal mafia by funding many higher education institutes such as IIIT, CCMB etc… thereby ensuring and addicting to consumerism the placid Hindu masses.

The promoters successfully cashed out of the company in an immoral relationship with the neo-liberal mafia over the period of 10 years. The cashed money was used in funding the real-estate companies and the socio-educational entities that would support this neo-liberal agenda and in future lay the foundations of the political takeover of the State governments. The owners were successful in creating a huge network of bogus companies that catered to this neo-liberals while systematically subjugating the vast populations to the consumerism. The owners of Satyam in an unethical relationship with this neo-liberal mafia wrongfully tried to influence fiscal and monetary policy of the Southern States by systematically taking over the social, agricultural, financial, educational, governmental, and meteorological aspects of the morbid agrarian population using an aggressive socio-economic agenda that created a new ecosystem of these fraud companies. The idea was to take over the top positions in the corporate, financial, judicial, religious and political eco-sphere of this new ecosystem.

polyp_cartoon_climate_extinction1

The government acted swiftly by arresting numerous managers for Income Tax evasion and the directors on numerous criminal charges. However the promoters of Satyam were able to show accounting fraud and go to prison while the neo-liberal mafia behind the company is free.

Requirement for newer controls

However this episode highlights the lack of controls at the government level on managing the IT growth and the neo-liberal mafia. The neo-liberal mafia was successful in promoting Mr. Raju as the poster boy of IT revolution and got an international audience with likes of Bill Gates, Bill Clinton, Hillary Clinton etc.. and subsequently benefited in the western countries such as United States and Canada by monopolizing many jobs in number of sectors.

The limits and responsibilities of operating a IT company catering to rich western clients were not defined properly in the existing company law. This is high-time the bureaucrats open their eye to this new pyramid scheme wrecking havoc on the age-old society in India. There should be harsher criminal punishments for people caught manipulating socio-political-economic environment for selfish greed.

Works Cited

Belk, V. a. (2008). Weaving a web: subaltern consumers, rising consumer culture, and television. Sage.

Bhasin, M. L. (2009). Creative Accounting Practices at Satyam Computers. Creative Commons Attribution 4.0 .

Datta, S. (2003). W (h) ither Indian Mind . IJT.

Deb, S. https://www.thenation.com/article/spoils-indian-democracy/.https://www.thenation.com/article/spoils-indian-democracy/.

DNA Analysis of my past

OverviewAutosomal DNA testing is the method by which you can trace your ancestry by having your autosomes analyzed.  Autosomal DNA is inherited from both parents, but Y-chromosomal DNA (Y-DNA) is inherited only from father to son, and mitochondrial DNA (mtDNA) is inherited only from our mother.

Results:

Following are the results. Based on autosomal analysis there are elements of Haihaya race, Cholas/Telugu Cholas, Elam, Pallava/Kambojas in the results. 

Pallavas and Cholas ruled over wide areas in South East Asia and in South India. Kambhoja/Pallava had Altaic, Caucasian and Siberian ancestry. Haihaya were part of Yadu tribe and match to wide set of kings in India especially Gujarat, Karnataka, MP/Maha, UP, Rajastan, Chattisgarh etc…

Nandi coins of Kalachuri Haihayas and Pallavas.



y HAPLOGROUP H1a1
Y Haplogroup H and it’s sub clades appear to be around 30,000 to 40,000 years old. 

H and sub clades appear to primarily be found in South Asia and the Middle East. The only known H* population of Europe are the Roma / Romany Gypsy people who are confirmed to be in Y Haplogroup H1a – M82. By some estimates the Romany version of Y Haplogroup H1a1 – M82 maybe only 2000 years old.



mtDNA HAPLOGROUP U2


U2 is a rare and interesting haplogroup. There was a U2 sample found in ancient remains dating to about 35,000 years ago at Kostenki, Russia. Around that time U2 appears to have spread both to Europe and to south Asia. Several suclades (U2a, U2b and U2c) are found most often in south Asia. U2d is found mostly in the Near East and southwest Asia, and U2e is found mostly in Europe. It is estimated to be about 19,000 years old and it might have been among early Europeans, or it might have migrated to Europe with Neolithic farmers or herders.


U2 overall correlates best to the ANE (WHG, IMO, is related but later) component present in Europe. ANE significantly overlaps India and Europe, the two places where U2 is also present. ANE shows no affinity to the SW Asian components (part EEF, and something termed basal Eurasian). ANE did enter northern parts of West Asia, especially the Caucasus, but that is in the post-Neolithic timeframe – as Stuttgart has no ANE. 


The provenance of the ancient U2 (from Jean’s compilation) are in concordance with the ANE scenario:
Russia Kostenki 14 U2 11467, 12308, 12372, 1811, 16051
Germany Blätterhöhle, Hagen 11000ybp U2eSweden Motala 2 8000ybp U2e1
Russia 7500 ybp U2e 2 samplesSweden Motala 12 8000ybp U2e

So that seems to confirm multiple waves of migration of U2e into Europe. We know that U2e was in Europe by 11 kya, but U2e almost certainly did not arrive in Europe until after the LGM (unless the age estimate for U2e is significantly wrong). U2e originated in the Black Sea/Caspian Sea refuge 20 kya, and then expanded into India and western Europe after the LGM. The Mesolithic European U2e might have been mostly replaced during the Neolithic, and another wave of U2e associated with Indo-Europeans expanded into Europe in the Bronze age. 
U4 is another group that seems to have expanded from the East European plain after the LGM, and U5 seems to have expanded from western Europe to the east after the LGM. As the ice retreated and bands of hunter-gatherers expanded northward in both western and eastern Europe, it seems possible that there could have been east-west diffusion of U5, U4 and U2e. But the LGM origin of U2e would have been in a southern refuge, perhaps around the Black Sea and Caspian Sea, and this could also explain its early arrival in India and Pakistan. 


Kostenki has 4 extra mutations (at markers 542, 711, 13269, & 15262) so it forms its own subclade parallel to U2a, U2b, U2e etc. So this suggests that U2 was widespread, perhaps from Russia to India, with at least 6 different lineages surviving ca 35 kya. The ancestor of U2e was one of those lineages, but the MRCA of U2e probably lived around 20 kya almost certainly somewhere in southwest Asia. 

Autosomal analysis

MDLP K23b Oracle Rev 2014 Sep 16



Admix Results (sorted):


# Population Percent

1 South_Indian 57.73

2 South_Central_Asian 32.25

3 Caucasian 3.84

4 Tungus-Altaic 2.09

5 Melano_Polynesian 1.83

6 East_Siberian 1.13



Finished reading population data. 620 populations found.

23 components mode.


——————————–


Least-squares method.


Using 1 population approximation:

1 GujaratiD_GIH_ @ 3.736310

2 Velamas_ @ 4.651088

3 Velama_ @ 5.156967

4 Punjabi_Lahore_PJL_ @ 5.980797

5 Lambadi_ @ 6.006914

6 GujaratiC_GIH_ @ 6.677832

7 Telugu_Kannada_ @ 7.009757

8 Kanjar_ @ 7.088082

9 Bengali_ @ 7.657912

10 Dharkar_ @ 7.682353

11 Meghawal_ @ 7.834942

12 TN_Brahmin_ @ 8.407625

13 Srivastava_ @ 8.646338

14 Piramalai_Kallar_ @ 9.188696

15 Naidu_ @ 10.071475

16 Marwadi_Middle_caste_ @ 10.241793

17 Muslim_India_ @ 10.359948

18 Brahmin_Tamil_ @ 10.717767

19 Brahmins_UP_ @ 11.587666

20 Lodhi_ @ 11.936786


Using 2 populations approximation:

1 50% GujaratiD_GIH_ +50% Velama_ @ 3.722667



Using 3 populations approximation:

1 50% Piramalai_Kallar_ +25% Punjabi_Gujjar_ +25% Vysya_ @ 3.181633



Using 4 populations approximation:

+++++++++++++++++++++++++

1 Pakistani_ + Velama_ + Velamas_ + Vysya_ @ 3.036024

2 Pakistani_ + Velamas_ + Velamas_ + Vysya_ @ 3.043674

3 Piramalai_Kallar_ + Punjabi_Gujjar_ + Velama_ + Vysya_ @ 3.078188

4 Brahmin_Tamil_ + Velama_ + Velamas_ + Velamas_ @ 3.109920

5 Pakistani_ + Velama_ + Velama_ + Vysya_ @ 3.111543

6 Punjabi_Gujjar_ + Velamas_ + Vysya_ + Vysya_ @ 3.128267

7 Piramalai_Kallar_ + Punjabi_Gujjar_ + Velamas_ + Vysya_ @ 3.128717

8 Naidu_ + Punjabi_Gujjar_ + Velama_ + Vysya_ @ 3.143112

9 Brahmin_Tamil_ + Velamas_ + Velamas_ + Velamas_ @ 3.143669

10 Pakistani_ + Piramalai_Kallar_ + Piramalai_Kallar_ + Velama_ @ 3.149175

11 Naidu_ + Punjabi_Gujjar_ + Velamas_ + Vysya_ @ 3.154916

12 Brahmin_Tamil_ + GujaratiD_GIH_ + Velama_ + Velama_ @ 3.161455

13 GujaratiD_GIH_ + Punjabi_Gujjar_ + Vysya_ + Vysya_ @ 3.162226

14 Sindhi_ + Velama_ + Vysya_ + Vysya_ @ 3.167185

15 Pakistani_ + Piramalai_Kallar_ + Piramalai_Kallar_ + Velamas_ @ 3.171693

16 Naidu_ + Pakistani_ + Piramalai_Kallar_ + Velamas_ @ 3.178530

17 Piramalai_Kallar_ + Piramalai_Kallar_ + Punjabi_Gujjar_ + Vysya_ @ 3.181633

18 Brahmin_Tamil_ + Velama_ + Velama_ + Velamas_ @ 3.181908

19 Naidu_ + Pakistani_ + Piramalai_Kallar_ + Velama_ @ 3.182143

20 Brahmin_Tamil_ + GujaratiD_GIH_ + Velama_ + Velamas_ @ 3.187350


=========================================================

ALTERNATE ANALYSIS WITH FEWER POPULATION
MDLP K16 2xOracle and OracleX4


Kit TXXXXX


Admix Results (sorted):


# Population Percent

1 Indian 68.41

2 Caucasian 13.67

3 SouthEastAsian 9.01

4 Siberian 2.96

5 Australian 2.49

6 Oceanic 1.94

7 Steppe 0.99

8 Amerindian 0.52


Single Population Sharing:


# Population (source) Distance

1 Marwadi (Rajasthan) 2.98

2 Scheduled_Caste (Tamil_Nadu) 4.28

3 Velama (Andhra_Pradesh) 4.3

4 Piramalai_Kallars (Tamil_Nadu) 5.38

5 Naidu (Tamil_Nadu) 5.6

6 Gupta (Rajput) 5.82

7 Kanjars (Punjab) 5.87

8 Muslim (India) 6.12

9 Dharkar (Uttar_Pradesh) 6.18

10 Vysya (India) 6.72

11 Lodi (Pakistan) 6.76

12 Tharu (Nepal) 6.76

13 Thakur (Maharashtra) 6.81

14 Scheduled_Caste (Uttar_Pradesh) 6.92

15 Dusadh (Uttar_Pradesh) 6.95

16 Kurmi (Nepal) 6.97

17 Balija (Andhra_Pradesh) 7.01

18 Hallaki (Â Uttara_Kannada) 7.1

19 GujaratiB (Gujarat) 7.32

20 Lambadi (Karnataka) 7.55


Mixed Mode Population Sharing:


#   Primary Population (source) Secondary Population (source) Distance

1  85.5% Marwadi (Rajasthan) + 14.5% GujaratiD (Gujarat) @ 2.49

2  83.7% Marwadi (Rajasthan) + 16.3% GujaratiC (Gujarat) @ 2.57

3  88.2% Marwadi (Rajasthan) + 11.8% Punjabi (Lahore) @ 2.66

4  71.6% Marwadi (Rajasthan) + 28.4% Scheduled_Caste (Tamil_Nadu@ 2.67

5  79.9% Marwadi (Rajasthan) + 20.1%Piramalai_Kallars (Tamil_Nadu)@ 2.75

6  84% Marwadi (Rajasthan) + 16% Naidu (Tamil_Nadu) @ 2.84

7  88.2% Marwadi (Rajasthan) + 11.8% Dusadh (Uttar_Pradesh)@ 2.86

8  99.2% Marwadi (Rajasthan) + 0.8% Melanesian (Bougainville) @ 2.86

9  91.6% Marwadi (Rajasthan) + 8.4% Sakilli (Tamil_Nadu) @ 2.87

10   93% Marwadi (Rajasthan) + 7% Madiga (Telangana) @ 2.88

11   92.6% Marwadi (Rajasthan) + 7.4% North_Kannadi (Kerala) @ 2.88

12  93.1% Marwadi (Rajasthan) + 6.9% Chamar (Uttar_Pradesh) @ 2.89

13  94.8% Marwadi (Rajasthan) + 5.2% Bengali (Bangladesh_BEB) @ 2.89

14  93.7% Marwadi (Rajasthan) + 6.3% Bhil (Maharashtra) @ 2.91

15  83.3% Marwadi (Rajasthan) + 16.7%Velama (Andhra_Pradesh) @ 2.91

16   94.5% Marwadi (Rajasthan) + 5.5% Hakkipikki (Karnataka) @ 2.92

17   92.9% Marwadi (Rajasthan) + 7.1% Kamsali (Andra_Pradesh)@ 2.93

18   93.3% Marwadi (Rajasthan) + 6.7% Kol (Uttar_Pradesh) @ 2.93

19   90.6% Marwadi (Rajasthan) + 9.4% Gupta (Rajput) @ 2.94

20   92.8% Marwadi (Rajasthan) + 7.2% Kurmi (Nepal) @ 2.94


-==============================================

Alternate international analysisDodecad K7b Oracle results:

Kit TXXXXX


Admix Results (sorted):


# Population Percent

1 South_Asian 62.66

2 West_Asian 34.01

3 Siberian 1.35

4 East_Asian 1.33

5 Southern 0.65


Single Population Sharing:


# Population (source) Distance

1 GIH30 (Dodecad) 2.74

2 INS30 (SGVP) 2.77

3 Velamas (Metspalu) 3.23

4 Tharus (Metspalu) 5.29

5 Dharkars (Metspalu) 6.12

6 Tamil_Nadu_Scheduled_Caste (Metspalu) 6.99

7 Iyer (Dodecad) 7.01

8 Muslim (Metspalu) 7.07

9 Kanjars (Metspalu) 7.1

10 Iyengar (Dodecad) 7.19

11 Indian (Dodecad) 7.31

12 Kurumba (Metspalu) 7.73

13 Brahmins_from_Tamil_Nadu (Metspalu) 7.93

14 Uttar_Pradesh_Scheduled_Caste (Metspalu) 9.42

15 Dusadh (Metspalu) 9.78

16 Piramalai_Kallars (Metspalu) 9.93

17 Kshatriya (Metspalu) 11.65

18 Kol (Metspalu) 11.74

19 Cochin_Jews (Behar) 12.35

20 Chenchus (Metspalu) 14.42


Mixed Mode Population Sharing:


#   Primary Population (source) Secondary Population (source) Distance

1   94.7% Velamas (Metspalu) + 5.3% Turkmens (Yunusbayev) @ 1.11

2   85.2% Kurumba (Metspalu) + 14.8% Brahui (HGDP) @ 1.15

3   85.2% Kurumba (Metspalu) + 14.8% Balochi (HGDP) @ 1.26

4   91.4% Velamas (Metspalu) + 8.6% Burusho (HGDP) @ 1.31

5   95.6% Velamas (Metspalu) + 4.4% Nogais (Yunusbayev) @ 1.34

6   94.6% Velamas (Metspalu) + 5.4% Tajiks (Yunusbayev) @ 1.36

7   95.5% Velamas (Metspalu) + 4.5% Uzbeks (Behar) @ 1.39

8   95.7% Velamas (Metspalu) + 4.3% North_Ossetians (Yunusbayev) @ 1.4

9   95.7% Velamas (Metspalu) + 4.3% Balkars (Yunusbayev) @ 1.41

10   95.4% Velamas (Metspalu) + 4.6% Hazara (HGDP) @ 1.45

11   95.7% Velamas (Metspalu) + 4.3% Kumyks (Yunusbayev) @ 1.45

12   95.4% Velamas (Metspalu) + 4.6% Iranian (Dodecad) @ 1.47

13   95.6% Velamas (Metspalu) + 4.4% Kurd (Dodecad) @ 1.51

14   95.9% Velamas (Metspalu) + 4.1% Abhkasians (Yunusbayev) @ 1.51

15   95.8% Velamas (Metspalu) + 4.2% Adygei (HGDP) @ 1.52

16   95.4% Velamas (Metspalu) + 4.6% Iranians (Behar) @ 1.52

17   95.9% Velamas (Metspalu) + 4.1% Turks (Behar) @ 1.54

18   96% Velamas (Metspalu) + 4% Georgians (Behar) @ 1.54

19   95.7% Velamas (Metspalu) + 4.3% Kurds (Yunusbayev) @ 1.55

20   95.8% Velamas (Metspalu) + 4.2% Chechens (Yunusbayev) @ 1.56


==========================================

Ancient Eurasia matches

Ancient Eurasia K6 Oracle results:
gedrosia K6 Oracle

Kit Txx


Admix Results (sorted):


# Population Percent

1 Ancestral_North_Eurasian 37.18

2 Ancestral_South_Eurasian 28.92

3 Natufian 20.55

4 East_Asian 13.32

5 Sub_Saharan 0.03


Single Population Sharing:


# Population (source) Distance

1 Punjabi_PJL 2.77

2 GujaratiD 3.8

3 GujaratiC 6.62

4 Bengali 7.48

5 GujaratiB 10.65

6 GujaratiA 13.46

7 Punjabi 14.51

8 Burusho 14.59

9 Sindhi 16.9

10 Pathan 19.6

11 Palliyar 19.92

12 Kalash 20.61

13 Pashtun_Afghan 22.66

14 Kurd_SE 25.13

15 Paniyas 25.56

16 Balochi 26.16

17 Brahui 26.91

18 Tajik 27.25

19 Makrani 28.67

20 Baloch_Iranian 30.75


Mixed Mode Population Sharing:


#   Primary Population (source) Secondary Population (source) Distance

1   95.9% Punjabi_PJL + 4.1% Pima @ 0.82

2   95.5% Punjabi_PJL + 4.5% Clovis @ 0.85

3   96.7% Punjabi_PJL + 3.3% Eskimo @ 0.88

4   66.9% GujaratiD + 33.1% Bengali @ 0.98

5   97.1% Punjabi_PJL + 2.9% Nganasan @ 0.98

6   95.7% Punjabi_PJL + 4.3% Kusunda @ 0.99

7   97.1% Punjabi_PJL + 2.9% Tibetan @ 1

8   96.3% Punjabi_PJL + 3.7% Sherpa @ 1

9   89.9% GujaratiD + 10.1% Kharia @ 1.03

10   97.4% Punjabi_PJL + 2.6% Ulchi @ 1.03

11   97.3% Punjabi_PJL + 2.7% Ami @ 1.03

12   97.4% Punjabi_PJL + 2.6% Han @ 1.04

13   97.4% Punjabi_PJL + 2.6% Dai @ 1.04

14   96.7% Punjabi_PJL + 3.3% Cambodian @ 1.06

15   97.3% Punjabi_PJL + 2.7% Mongola @ 1.09

16   74.6% Punjabi_PJL + 25.4% Bengali @ 1.15

17   96.8% Punjabi_PJL + 3.2% Kalmyk @ 1.16

18   96.5% Punjabi_PJL + 3.5% Altaian @ 1.23

19   96.3% Punjabi_PJL + 3.7% Kyrgyz @ 1.35

20   94.4% GujaratiD + 5.6% Kusunda @ 1.6

Ancient Eurasia K6 4-Ancestors Oracle


This program is based on 4-Ancestors Oracle Version 0.96 by Alexandr Burnashev.

Questions about results should be sent to him at: Alexandr.Burnashev@gmail.com

Original concept proposed by Sergey Kozlov.

Many thanks to Alexandr for helping us get this web version developed.


gedrosia K6 Oracle


Admix Results (sorted):


# Population Percent

1 Ancestral_North_Eurasian 37.18

2 Ancestral_South_Eurasian 28.92

3 Natufian 20.55

4 East_Asian 13.32



Finished reading population data. 136 populations found.

6 components mode.


——————————–


Least-squares method.


Using 1 population approximation:

1 Punjabi_PJL @ 2.847092

2 GujaratiD @ 3.869269

3 GujaratiC @ 6.711767

4 Bengali @ 7.637662

5 GujaratiB @ 10.779576

6 GujaratiA @ 13.664439

7 Punjabi @ 14.699165

8 Burusho @ 14.971711

9 Sindhi @ 17.026245

10 Pathan @ 19.882652

11 Palliyar @ 20.130989

12 Kalash @ 20.914474

13 Pashtun_Afghan @ 23.068459

14 Kurd_SE @ 25.343739

15 Paniyas @ 25.889154

16 Balochi @ 26.328218

17 Brahui @ 27.076447

18 Tajik @ 27.917334

19 Makrani @ 28.867464

20 Baloch_Iranian @ 30.972765


Using 2 populations approximation:

1 50% Bengali +50% GujaratiC @ 1.831478



Using 3 populations approximation:

1 50% Bengali +25% GujaratiC +25% GujaratiC @ 1.831478



Using 4 populations approximation:

+++++++++++++++++++++++++++++++++++++++++++

1 Bengali + GujaratiD + Punjabi_PJL + Punjabi_PJL @ 1.013111

2 Bengali + GujaratiD + GujaratiD + Punjabi_PJL @ 1.084096

3 Bengali + Punjabi_PJL + Punjabi_PJL + Punjabi_PJL @ 1.140579

4 Bengali + GujaratiD + GujaratiD + GujaratiD @ 1.305963

5 Bengali + GujaratiB + GujaratiB + Palliyar @ 1.548610

6 Bengali + GujaratiC + Punjabi_PJL + Punjabi_PJL @ 1.568302

7 Bengali + GujaratiC + GujaratiD + Punjabi_PJL @ 1.750346

8 Burusho + GujaratiB + GujaratiD + Paniyas @ 1.767009

9 Bengali + GujaratiB + Palliyar + Punjabi @ 1.781465

10 Burusho + GujaratiD + GujaratiD + Palliyar @ 1.798610

11 Bengali + Bengali + GujaratiC + GujaratiC @ 1.831478

12 Bengali + Bengali + GujaratiC + GujaratiD @ 1.848223

13 Burusho + GujaratiB + Paniyas + Punjabi_PJL @ 1.853450

14 Burusho + GujaratiC + GujaratiD + Palliyar @ 1.930839

15 Burusho + GujaratiC + Palliyar + Punjabi_PJL @ 1.992579

16 Burusho + GujaratiD + Palliyar + Punjabi_PJL @ 1.995188

17 Bengali + GujaratiC + GujaratiD + GujaratiD @ 2.021405

18 Bengali + Bengali + GujaratiC + Punjabi_PJL @ 2.038647

19 Bengali + GujaratiA + GujaratiB + Palliyar @ 2.043208

20 Bengali + GujaratiB + Palliyar + Sindhi @ 2.081966


=============================================================

mtDNA U2 information and frequency in India

H1a genetic distance between different groups

Distribution of H-M82 (H1a1)

The following gives a summary of most of the studies which specifically tested for M82, showing its distribution in different part of the world


Region/Ethnicity Country/Population Size H1a freq. (%) Reference

East/Southeast Asia Tibet 156 0 Gayden et al. 2007

East/Southeast Asia Cambodia 6 16.67 Sengupta et al. 2006

East/Southeast Asia Cambodia/Laos 18 5.56 Underhill et al. 2000

East/Southeast Asia Japan 23 0 Sengupta et al. 2006

North Asia Siberia 18 0 Sengupta et al. 2006

Middle East and North Africa Qatar 72 1.39 Cadenas et al. 2008

Middle East and North Africa United Arab Emirates 164 1.84 Cadenas et al. 2008

Middle East and North Africa Yemen 62 0 Cadenas et al. 2008

Middle East and North Africa Saudi Arabia 157 0.64 Abu-Amero et al. 2009

Middle East and North Africa Oman 121 0 Abu-Amero et al. 2009

Middle East and North Africa Egypt 147 0 Abu-Amero et al. 2009

Middle East and North Africa Somalia 201 0 Abu-Amero et al. 2009

Middle East and North Africa Lebanese 916 0 Abu-Amero et al. 2009

Middle East and North Africa Jordan 146 0 Abu-Amero et al. 2009

Middle East and North Africa Iraq 203 0 Abu-Amero et al. 2009

Middle East and North Africa Turkish 523 0.19 Cinnioglu et al. 2004

Middle East and North Africa Iran 150 2 Abu-Amero et al. 2009

Middle East and North Africa Iran 938 1.2 Grugni et al. 2012

Roma-Europe Slovakian 62 30.65 Pamjev et al. 2011

Roma-Europe Portuguese 126 16.67 Gusmao et al. 2008

Roma-Europe Kosovo, Belgrade, Vojvodina 88 43.18 Regueiro et al. 2011

Roma-Europe Bulgarian 248 39.52 Gresham et al. 2001

Roma-Europe Spanish 27 18.52 Gresham et al. 2001

Roma-Europe Croatians 377 20.16 Battaglia et al. 2009

Roma-Europe Macedonians 257 13.23 Perièiæ et al. 2005

Roma-Europe Hungarian 424 16.98 Pamjav et al. 2011

Roma-Europe Lithuvenian Roma 20 50 Gresham et al. 2001

Balkans Greeks 92 0 Battaglia et al. 2009

Balkans Albanians 55 0 Battaglia et al. 2009

Balkans Bosniacs 324 0 Battaglia et al. 2009

Balkans Slovenians 75 0 Battaglia et al. 2009

Balkans North-East-Italians 67 0 Battaglia et al. 2009

Balkans Hungarians 53 0 Battaglia et al. 2009

Balkans Czechs 75 0 Battaglia et al. 2009

Balkans Poles 99 0 Battaglia et al. 2009

Balkans Ukrainians 92 1.1 Battaglia et al. 2009

Balkans Herzegovinians 141 0 Perièiæ et al. 2005

Balkans Serbians 113 0.9 Perièiæ et al. 2005

Caucasus Caucasians 1789 0 Yunusbayev et al. 2011

Caucasus Georgians 66 0 Battaglia et al. 2009

Caucasus Balkarians 38 2.6 Battaglia et al. 2009

South Asia Nepal 188 4.25 Gayden et al. 2007

South Asia Afghanistan 204 3.43 Haber et al. 2012

South Asia Malaysian Indians 301 18.94 Pamjav et al. 2011

South Asia Terai-Nepal 197 10.66 Fornarino et al. 2009

South Asia Hindu New Delhi 49 10.2 Fornarino et al. 2009

South Asia Andhra Pradesh Tribals 29 27.6 Fornarino et al. 2009

South Asia Northwest India 842 14.49 Rai et al.2012

South Asia South India 1845 20.05 Rai et al.2012

South Asia Central India 863 14.83 Rai et al.2012

South Asia North India 622 13.99 Rai et al.2012

South Asia East India 1706 8.44 Rai et al.2012

South Asia West India 501 17.17 Rai et al.2012

South Asia Northeast India 1090 0.18 Rai et al.2012

South Asia Andaman Island 20 0 Thangaraj et al. 2003

Will Robots spell trouble for the BPO industry

Introduction

Robotic Process Automation (RPA) is the new technology driven business process automation set to take over the Business Process Outsourcing (BPO). The saying on the streets is, if a BPO provider is embracing the benefits of RPA or other transformative technology in the next year, they’re going to have plenty of business.  Providers who refuse to innovate may find themselves in the dust. RPA can help BPO providers get up to speed and offer great new services to their existing clients.  Traditional outsourcing won’t disappear overnight, but RPA can take those relationships to a new level today.

RPA

The current sophistication of the technology is still at beginner level for integration in the process pipeline. The impact on process automation is estimated at 20-40% only of the overall customer requirements, however with the increased Artificial Intelligence research and IoT technology improvements there is a huge scope in future for this number to increase. Today’s emerging RPA tools, such as Automation Anywhere, Blue Prism and UiPath, would cost around one ninth of a Full Time Equivalent (FTE) person working in, say, the UK or US, or a third of the cost of an FTE working offshore (say India) and could replace up to 20 FTE after process re-engineering. What RPA does is completely skew the business case dynamics of outsourcing: large, global organisations, such as Infosys, Wipro, TCS, Capgemini, Capita, etc, who have built their business model around employing more and more people, will now have to completely change their whole mindset to cope with the opportunities and threats that RPA brings. [Andrew Burgess, 2016] A recent CIO Journal article noted that the market for RPA is expected to jump from $183 million in 2013 to $4.98 billion by 2020. Further, The Age of Smart Process Automation (SPA) that uses AI with machine-learning capabilities just is around the corner. The outsourcing global organizations are investing heavily in this technology, For example, Cognizant acquired Trizetto; Wipro has created an AI platform called Holmes; TCS is working on an AI platform called Ignio; and Infosys has announced a major investment in automated capabilities. While RPA is likely to cannibalise existing revenue streams of the BPO players to an extent, BPO players can offset this by adopting an annuity-based business model where the players generate revenues by selling robotic software and also by managing every robot that they operate for their clients.

In Finance and Accounting, many deals are mature and rooted in legacy models, the work is highly transactional, and buyers have been stuck with the same FTE loads for years (or decades). But the real reason why F&A is starting to deliver real potential for R-BPO is the simple lack of widely accepted enterprise F&A SaaS which can fix the dysfunction of a process, with a broad-brush implementation and hefty license fee. We are seeing it in pockets with SaaS solutions such as Workday FM, Netsuite and even FinancialForce, but it’s the ultimate failure of F&A to over-rely on legacy technology, maintain strict controls that defy collaboration, and keep bloated numbers of people to deliver legacy processes that is creating a huge potential new market for robotic-led processing and human augmentation. [Phil Fersht, 2016] Forrester estimates that RPA and machine learning will cause the number of U.S. “cubicle workers” to decrease by 16%, or 12 million workers, by 2025. KPMG suggests the worldwide total could be as much as 100 million jobs. “In the next 15 years, it’s likely that 45 percent, and maybe up to 75 percent, of existing offshore jobs in the financial services sector will be performed by robots, or more precisely, robotic process automation (RPA),” stated Cliff Justice, KPMG LLP (KPMG) Advisory principal.

“That should translate into enormous costs savings of up to 75 percent for firms that get on board.” [KPMG]

To imagine the scale of potential impact on the current industry, the BPO sector globally is currently worth over $300bn. In India alone, more than 3 million people are employed doing BPO work; in the Philippines there are another million. Across Europe and the US millions more workers earn their living through BPO. RPA will have the potential to impact every single one of those jobs. The Indian BPO industry had revenues of Rs 1.86 lakh crore in financial year 2016, according to Nasscom. It employs 1.1 million people exclusively for outsourcing business. India’s share of the global BPO sourcing market is around 38 per cent. However, apart from Governance issues , pursuing arbitrage in established outsourcing destinations such as India, Philippines & China is becoming less desirable due to rising commodity and living costs. Moreover, outsourcing of labor intensive and rules-based processes leads to human-errors and makes the business vulnerable to security breaches and fraud. Replacing with RPA from the perspective of enterprises or end clients, can lead to significant benefits such as improved efficiency, reduction in the number of FTEs required to handle a process, cost savings, and improved ability to reach meet the SLA targets and KPIs. In some of the traditional markets there will be no significant change likely in the approach of large US companies until the new administration settles in and drafts new laws to deal with these newer technologies and the offshore industry.

Offshore Regulations

But there is no specific law in India that regulates outsourcing transactions except in relation to telecommunication services. Most Indian BPO companies follow the global standards of certification. However, data privacy and integrity concerns related to outsourcing have emerged as the biggest concerns for the clients of Indian BPOs. Nasscom is in talks with the government to set up a nodal agency to monitor, collate and disseminate information on international IT frauds involving Indian entities.

Frauds

There were many instances of fraud in the offshore industry particularly the IRS telephone impersonation scam.  There were instances where victims in the US were threatened with tax investigation by call centre executives of these firms pretending to be officials from the IRS. Software industry experts and officials from enforcement agencies in the country feel that absence of regulations to monitor BPOs, high unemployment rate and slow conviction in criminal cases have together made India a hub for such activity.

The Treasury Inspector General for Tax Administration (TIGTA) has only seen a rise in the IRS impersonation scam in the US with an average loss of more than $5,700 per taxpayer. “The Treasury Inspector General for Tax Administration, or TIGTA, has received reports of more than one million contacts since October 2013. TIGTA is also aware of more than 6,700 victims who have collectively reported over $38 million (Rs 253 crore) in financial losses as a result of tax scams,” a July 2016 release of TIGTA stated. As a part of its consumer awareness and protection program, TIGTA released several alerts and YouTube videos explaining the modus operandi of IRS impersonators. “TIGTA is concerned that the recent arrests in India will not bring a total halt to the IRS telephone impersonation scams,” said J Russell George, Treasury Inspector General for Tax Administration, in an email response to The Sunday Express. “Members of the public cannot and must not let their guard down,” he added.

Regarding incident where BPO employees allegedly duped over 6,000 US citizens of at least Rs 500 crore, “This particular incident is not much of a BPO issue. We should not call them (Thane call centres) BPOs. These are companies run by criminals to commit fraud. Having said that, we have recognised the issue and are closely working with law enforcement agencies,” R Chandrasekhar, president of Nasscom, said. “We are ready to provide whatever help possible to the police to get to the bottom of such cases. We are committed to make India safer,” he said.

Conclusion

Notwithstanding some frauds, Since the reality is that India brings a great advantage to the IT and BPM (Business Process Management) industry through low-cost and simplicity, there is a great chance that the indian players will have a slice of the RPA in the long run.

References

  1. How robotics is changing the face of Business Process Outsourcing, Robohub, Andrew Burgess, 2016.
  2. Why it’s time for Robotic-BPO to break the mold of legacy F&A engagements, HorsesforSources, Phil Fersht, 2016.
  3. Rise of the robots, KPMG Report.

Corporate fraud in Software industry

Unprecedented corporate fraud in Software History

An estimated $1.5 billion (Rs 7600 crore) may have disappeared in the fraud confessed to in 2009 by the now-jailed chairman of Satyam Computer Services Ltd. Satyam’s failures were many and systemic—from a weak auditing process to ineffective board oversight to a leader intent on committing fraud. For corporate leaders, regulators, and politicians in India, as well as for foreign investors, this “Enron moment” demanded a reassessment of the country’s progress in corporate governance. The resignations of an unprecedented 620 independent directors over the following year added to the mounting concerns.

As a consequence, India’s ranking in the CLSA4 Corporate Governance Watch 2010 slid from third to seventh in Asia. The CLSA report stated that India “has failed to adequately address key local governance challenges such as the accountability of promoters (controlling shareholders), the reputation of relatedparty transactions, and the governance of the audit profession.” The ensuing debate over reform approaches has raised such questions as, “How well are India’s companies being governed?” “Why the failures?” “Where were the regulators?” “What must be done to ensure that directors abide by best practice?”

http://www.business-standard.com/article/opinion/pratip-kar-lessons-in-corporate-governance-110051000024_1.html

http://www.ifc.org/wps/wcm/connect/1fe292804a4785e6824d9faa52ef3b86/PSO_23_Pratip.pdf?MOD=AJPERES