ISSN No.: 2348-2753 LEADER

ISSN No.: 2348-2753

L E A D E R

A Quarterly Publication of Lead College of Management, Palakkad

Vol.6, No.4 January – March 2019

INTERNATIONAL JOURNAL OF BUSINESS MANAGEMENT

K. Thomas George Signs of Weakness in Global Economic Growth: Slowdown in Indian Economy 1

S. Radha Market Share Elasticity of Colour Television: An Empirical Study 17

K. Rajkumar Commercial Banks: Measurement of Financial and Economic Performance 31

A. Vijayalakshmi Testing the reliability of a Questionnaire: Cronbach’s Alpha Coefficient 53

K.A. Keerthi PrabhuImpact of M&A on Growth Acceleration and Structural Break in Indian Pharmaceutical Industry

64

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Signs of Weakness in Global Economic Growth: Slowdown in Indian Economy

Dr. K. Thomas George*Weak Europe prompts IMF to cut forecastFears sharp to slowdown on trade war

The International Monetary Fund (IMF) cut its world economic growth forecasts for 2019 and 2020 due to weakness in Europe and some emerging markets, and said failure to resolve trade tensions could further destabilise a slowing global economy.

In its second downgrade in three months, the global lender also cited a bigger-than-expected slowdown in China’s economy and possible “No Deal” Brexit as risks to its outlook, saying these could worsen market turbulence in financial markets. The IMF predicted the global economy to grow at 3.5% in 2019 and 3.6% in 2020, down 0.2 and 0.1 percentage point respectively from last October’s forecasts. The new forecasts, released on the eve of this week’s gathering of world leaders and business executives in the Swiss ski resort of Davos, show that policy-makers may need to come up with plans to deal with an end to years of solid global growth.

“After two years of solid expansion, the world economy is growing more slowly than expected and risks are rising,” IMF Managing Director Christine Lagarde told a briefing.

“Does that mean a global recession is arround the corner? No. But the risk of a sharper decline in global growth has certainly increased,” she said, urging policy-makers to be ready for a “serious slowdown” by boosting their ecnomies’ resilience to risks.

*Professor & Director, Lead College of Management, Dhoni, Palakkad

LEADER IJBM Volume - 6 Issue No. 4 January - March 2019

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Signs of weaknessThe downgrades reflected signs of weakness in Europe, with its export

powerhouse Germany feared by new fuel emission standards for cars and with Italy under market pressure due to Rome’s recent budget standoff with the Europe Union. Growth in the Europe zone is set to moderate from 1.8% in 2018 to 1.6% in 2019, 0.3% point lower than projected three months ago, the IMF said.

The IMF also cuts its 2019 growth forecast for developing countries to 4.5%, down 0.2% point from the previous projection and a slowdown from 4.7% in 2018.

Despite fiscal stimulus that offsets some of the impact of higher U.S tariffs, China’s economy will slow down due to the combined influence of needed financial regulatory tightening and trade tensions with the U.S., Growth in emerging and developing Asia will dip from 6.5% in 2018 to 6.3% in 2019 and 6.4% in 2020. China, which grew at 6.9% in 2017, compared with 6.7% by India, had growth rate of 6.6% in 2018. In the next two years 2019 and 2020 it is projected to grow at 6.2% each, the IMF said.

Beijing surpriseThe Chinese economy has grown faster than expected, but concerns over

stimulus remain

China’s economy is showing signs of a rebound. According to figures released by its National Bureau of Statistics on Wednesday, the Chinese economy grew at 6.4% in the first quarter of the current year compared to the same period last year. While this rate of growth is equal to the pace registered in the December quarter and faster than economists’ expectations of a 6.3% expansion, it is still slower than the growth rate of 6.8% recorded in the same period last year. Retail sales and factory output also showed strong growth momentum. The latest growth figure is seen as a sign that the Chinese government’s efforts over the last few quarters to stimulate what is the world’s second largest economy are beginning to have a positive effect. Total social financing grew by almost 40% to 8.2 trillion yuan in the first quarter of the year, pointing to a credit expansion that will boost growth in the coming quarters. With trade tensions with the United States subsiding significantly for now, export growth may accelerate, further boosting the Chinese economy. Chinese exports reached a five-month high in March, rising 14.2% when compared to the


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same month last year. The Chinese stock market has also been buoyed by the early signs of an economic turnaround and increased liquidity, with the CSI 300 index rising by over a third in value since the beginning of the year.

Gross domestic product growth that is generated largely by increased lending, however, poses the risk of losing momentum once the stimulus is withdrawn. Beijing, of late, has once again been prodding its banks to boost lending to public and private businesses, apart from implementing various fiscal measures to boost consumer spending. This could lead to a tricky situation where businesses that resort to heavy borrowing when credit is easily available become burdened with disproportionately high amounts of debt once the economic boom cycle reverses. Chinese authorities may eventually be forced to crack down on exuberant lending by banks when the economy is found to be overheating. It was such a crackdown that contributed to the fall in property prices in the last few years. For now, though, property prices have begun to rebound after restrictions on the real estate sector were eased lately, in an attempt to stimulate growth in the economy. The Chinese government is now walking a tightrope as it attempts to keep the momentum from slowing in the short term, even as market forces try to correct imbalances within the economy. Such macroeconomic policy, focussed too narrowly on the short term while ignoring the long-term consequences, however, does not bode well for either the Chinese economy or the wider global economy.

GDP growth slumps to 5.8%Slowdown in the economy was led by sluggish growth in the agriculture,

forestry and fishing sector (2.9% growth), the mining sector (1.3% growth) and manufacturing (6.9%).

India’s GDP grew at 5.8% in the January-March 2019 quarter, dragging down the full year growth to a five-year low of 6.8%. The unemployment rate in the country rose to a 45-year high of 6.1% in 2017-18, as per official data released on the first day of the second term of the Modi government.

Addressing a press conference on Friday, Economic Affairs Secretary Subhash Chandra Garg said the slowdown, caused by temporary factors such as liquidity crunch, is likely to continue in the April-June 2019 quarter, with the demand picking up from the second quarter onwards.


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Temporary factors“Slowdown in the fourth quarter GDP was due to temporary factors, like stress

in the NBFC sector affecting consumption finance. The first quarter of the current fiscal will also see relatively slower growth. From the second quarter onwards, we expect the growth and consumption to pick up,” Mr. Garg said.

Asked about India losing the fastest growing nation tag to China with a quarterly growth of 5.8%, Mr. Garg, who is also the Finance Secretary, said, “Quarterly numbers don’t matter…it is basically annual growth… At 6.8% annual growth, India is still the fastest growing nation… China is still lower.”

During the year, the slowdown in the economy was led by sluggish growth in the agriculture, forestry and fishing sector (2.9% growth), the mining sector (1.3% growth) and manufacturing (6.9%).

The sectors which saw growth rate of over 7% were public administration, defence and other services, construction, financial, real estate and professional services, and electricity, gas, water supply and other utility services.

“The consumption story is not dead,” the secretary said, adding that, “I am confident that with interest rates becoming much more favourable and credit coming back in the system…the consumption story will be very good.”

The unemployment data, which was released a day after Prime Narendra Modi took oath for the second term, confirms an earlier leaked version of this survey that claimed that joblessness was at a 45-year high.


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“It is a new design and a new matrix. It would be unfair to compare it with the past. This 45-year high is your interpretation. I don’t want to claim that it is 45-year low or high,” Statistics Secretary Pravin Srivastava told the media.

The data showed that 7.8% of the employable urban youth and 5.3% of employable rural youth was without jobs. Additionally, 6.2% male and 5.7% females across the country were jobless.

Further, as per the data, unemployment rate for males was higher in rural areas at 5.8% as against 3.8% for women, while in urban areas the rate for women was 10.8 against 7.1% for men

Deepening slowdownCan the RBI’s reduction in borrowing costs help check the demand slowdown?

India’s economy is inarguably slowing, and the latest estimates from the Central Statistics Office disconcertingly point to a deepening slowdown. GDP growth is projected to have eased to 6.6% in the October-December period. With the CSO now forecasting the full-year expansion at 7%, fiscal fourth-quarter growth is implicitly pegged at an even slower 6.5%. At that level, growth would have slowed to a seven-quarter low, giving the incumbent NDA government its slowest pace of annual growth. The data clearly reflect the pain points in the real economy that have been evident for some time now. For one, the farm sector continues to remain in trouble with GVA (gross value added) growth in agriculture, forestry and fishing having slowed sharply to 2.7% in the last quarter, from a 4.2% pace in July-September and 4.6% a year earlier. With rabi sowing showing a shortfall across most crops after a deficient north-east monsoon, and the abiding structural issues that have pushed a multitude of farmers into acute distress nowhere near resolution, it is hard to foresee an early revival in this crucial primary sector. This, in turn, continues to dog demand in the hinterland for manufactured products, from two-wheelers to tractors, and is evident in the consumption spending data. Growth in private final consumption expenditure eased appreciably to 8.4%, from the second quarter’s pace of 9.8%.

Manufacturing is another source of concern. The estimates for growth in GVA for the sector put the pace at 6.7%, weaker than the 6.9% posted in the second quarter and a rapid deceleration from the April-June period’s 12.4%. The latest Index of Industrial Production (IIP) figures also give little cause for optimism as manufacturing expansion in December slowed to 2.7%, from 8.7% 12 months earlier.


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RBI Governor Shaktikanta Das had in fact pointedly cited how “high-frequency and survey-based indicators for the manufacturing and services sectors” suggested a slowdown in the pace of activity, to help justify his vote last month for an interest rate cut to bolster growth. That most of the sectors comprising the broader services basket remain becalmed adds to the sense of disquiet. It remains to be seen if the RBI’s reduction in borrowing costs helps check the demand slowdown in the fourth quarter, an improvement in investment activity notwithstanding. Gross fixed capital formation, the key metric for investment demand, expanded by a healthy 10.6%, building on the second quarter’s 10.2% increase. Still, with military tensions with Pakistan on the boil, a long campaign for the general election ahead, uncertainties looming on the global trade and growth horizons, and little fiscal leeway to tease back momentum through increased spending, the economy appears headed for a period of uncertainty at least till the next government is in place.

Industrial growth declines to 20-month low, inflation up

Costlier food and fuel spur retail inflation to 5-month highIndustrial growth slowed in February to 0.1%, driven by an across-the-board

slowdown, especially in key sectors like manufacturing, mining, capital goods, and infrastructure, according to the latest official data.

Separate data showed that retail inflation quickened in March to 2.86% from 2.57% in February, driven in large part by the food and fuel sectors.


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Growth in the Index of Industrial Production (IIP) slowed in February from 1.44% in January.

Within the Index, the mining and quarrying sector saw growth slowing to 2% from 3.92% over the same period.

The manufacturing sector saw a contraction of 0.31% in February from 1.05% in January.

“The IIP data broadly indicates the slowing down of the economy, which was reflected in the quarterly GDP data,” said D.K. Srivastava, chief policy advisor, EY India.“The outlook should be thought of in terms of stimulating investment demand in the economy through monetary and fiscal measures,” he said.

“On the monetary side, steps have been taken through two successive rate cuts by the Reserve Bank of India,” Mr. Srivastava added. “On the fiscal side, however, the prospects were limited because both direct and indirect tax revenue collections have shown a shortfall compared to the revised estimates. So, in order to meet the 3.4% fiscal deficit target, it appears the government has gone in for curtailing expenditure in general, and capital expenditure in specific.”

Capital goods contractThe capital goods sector continued its contraction in February, contracting

8.84% compared with a contraction of 3.42% in the previous month.

Growth in the infrastructure sector slowed to 2.38% from 6.8%.

The electricity sector was the only sector that saw an acceleration in growth, coming in at 1.18% in February compared with a growth of 0.94% . The consumer non-durables sector also saw growth quickening, to 4.3% from 3.33% over the same period.

CPI inflation

Retail inflation, as measured by the consumer price index (CPI), quickened in March to a five-month high due to a speeding up of inflation in the food and fuel sectors. Inflation in the food and beverages segment of the CPI quickened to 0.66% in March compared with a contraction of 0.07% in February.


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“Inflation is still well below the average threshold of 4% and food prices have just turned positive, and vegetable prices are much less negative than the trend,” Mr. Srivastava added.

IIP growth slows to 1.7%, retail inflation rises to 2.57 %

Manufacturing growth moderates to 1.3% in January from 2.65% in December; CPI rises on firming food prices

Industrial activity slowed in January 2019 growing by just 1.7% due in large part to a deceleration in the manufacturing, electricity, and capital goods sectors, official data released on Tuesday showed. In a separate release, government data showed that retail inflation in February snapped a four-month declining trend by rising to 2.57%.

The Index of Industrial Production (IIP) saw growth slip below the 2% for the second time in three months in January, with the previous occurrence being the 0.32% growth seen in November 2018. Growth in the IIP was at 2.6 in December.

Within the IIP, the mining and quarrying sector was one of the only major sectors that saw growth accelerating, from a contraction of 0.39% in December to a growth of 3.9% in January.

“The slowdown in the IIP only confirms the national income data which also indicated a continuing slowdown,” D.K. Srivastava, chief policy advisor, EY


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India, said. “The sectors where the slowdown is happening are manufacturing and industry. Apart from services, all the sectors seem to be slowing.”

The manufacturing sector saw growth slowing to 1.3% in January from 2.65% in December. The electricity sector saw growth slowing to 0.8% from 4.45% over the same period. The capital goods sector contracted 3.2% in January, down from a growth of 5.9% in the previous month.

The construction sector witnessed the strongest growth of all the major sectors, of 7.9%, but this was still significantly slower than the 10% seen in December.

The consumer sector also saw growth slowing, with growth in the consumer durables sector slowing to 1.8% and in the consumer non-durables sector to 3.8% in January, from 2.93% and 5.35%, respectively, in the previous month.

“By March, government spending usually expands, but this time the signs of that are not very prominent because they are trying to cut down on capital expenditure to meet the revised fiscal deficit target,” Mr. Srivastava added, saying an expansion in government spending would have meant a recovery in IIP growth in coming months.

Declining inflationRetail inflation, as measured by the Consumer Price Index (CPI),quickened for

the first time in five months in February to 2.57% from 1.97% in January, mainly due to firming food prices, official data showed. Inflation in food and beverages sector stood at -0.07% in February compared with -1.29% in January. “The upward movement was driven primarily by a sequential rise seen in various food groups, except in vegetables,” B. Prasanna, head, global markets group, ICICI Bank said.

“Core inflation moved down slightly as expected, reflecting easing of input costs, pricing powers and growing slack in the economy. The earlier spikes seen in rural health and education seem to have stabilised.”

The housing sector saw inflation slowing marginally to 5.1% from 5.2% over the same period. The fuel and light sector saw inflation slowing to 1.24% in February from 2.12% in January.

“With inflation remaining below RBI’s target, inflationary expectations declining and growth profile weakening, RBI may front-load its monetary easing


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in the beginning of FY20,” said Devendra Kumar Pant, chief economist and senior director, India Ratings and Research. “However, with capacity utilisation still being low at 74.8% (2QFY18) and pending elections in April-May 2019, it is unlikely to spur investment demand in the economy.”

In slow mode: on manufacturing and inflation dataManufacturing, inflation data give monetary policy makers room for an interest rate cut

Manufacturing activity in the country continues to remain becalmed. The latest Index of Industrial Production data show that output across the broad sector expanded 1.3% in January, a clear loss of momentum from the 3% pace in December and a drastic slowdown from the 8.7% growth seen in January 2018. Overall, industrial output growth slumped to 1.7%, from 2.6% in December, and 7.5% a year earlier, as production in 12 of the 23 industry groups that comprise the manufacturing sector shrank from a year earlier. These are quick estimates that are likely to be revised. But the fact that key job-creating industries, including textiles, leather and related products, pharmaceuticals, rubber and plastic products, and motor vehicles, reported contractions hardly bodes well for the real economy. A look at the use-based classification of industries also gives little cause for cheer. Capital goods, a closely watched proxy for business spending plans, contracted 3.2%, a telling contrast with the 12.4% expansion posted 12 months earlier. A sustained revival on this vital front may still be some time away. A recent survey by IHS Markit of business activity expectations, conducted over two weeks in the latter half of February, shows that Indian businesses plan to curb outlays on hiring and capital spending, with sentiment on capex at a one-year low. And growth in consumer durables output was an anaemic 1.8% (7.6% in January 2018), another clear sign that spending on consumption of non-essentials remains in search of favourable winds.

If the IIP poses cause for concern, retail inflation data hardly provide much reassurance. While price gains measured by the Consumer Price Index accelerated to a four-month high of 2.57% in February, it is the persistent deflationary trend in the prices of some farm items that is deeply disquieting, reflecting as it does a collapse in pricing power in the agrarian heartland. Vegetables, fruits and pulses and products all posted negative rates of inflation from a year earlier, of –7.69%, – 4.62% and –3.82% respectively. While urban consumers may cheer the increased affordability of vegetables and fruits, rural demand for manufactured goods will remain depressed unless there is a meaningful turnaround in the farm sector’s economic fortunes.


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Looking ahead, with Saudi Arabia committed to deepening its production cuts in order to keep crude oil prices well-supported, it appears unlikely that India’s fuel and energy costs will stay soft for much longer. And with political parties sure to open the spending spigot in a bid to woo voters, inflationary impulses will quicken. For now, though, with growth slowing and inflation still comfortably within the Reserve Bank’s 2%-6% target range, monetary policy makers would feel justified in pressing ahead with one more interest rate cut at their meeting next month.

Exports rise 2.44%; trade deficit narrows

Merchandise exports grew to $26.67 billion in Feb., imports declined to $36.26 bn

A marginal 2.44% increase in exports as well as lower imports of gold and petroleum products in February, significantly narrowed the country’s trade deficit to $9.6 billion, according to data released by the Commerce Ministry on Friday.

India’s merchandise exports rose to $26.67 billion in February from $26.03 in the year-ago month mainly on account of higher shipments in sectors such as pharmaceutical, engineering and electronics.

Imports declined by 5.4% to $36.26 billion in the last month, narrowing the trade deficit to $9.6 billion. The gap between imports and exports was $12.3 billion in February 2018, and $14.73 billion in January 2019.

As per the data, the drop in imports was mainly on account of sharp decline in inward shipments of gold and petroleum products.


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Gold import fallsWhile the import of gold fell by about 11% to $2.58 billion in February, as

against $2.89 billion in the corresponding month last fiscal, inward shipments of petroleum products were down by nearly 8% to $9.37 billion. During the April-February period of the current fiscal year, exports grew 8.85% to $298.47 billion, while imports rose by 9.75% to $464 billion. The trade deficit has widened to $165.52 billion during the 11 months of the current fiscal from $148.55 billion compared to the year-ago period, the data said.

Non-petroleum and non-gems and jewellery exports in February 2019 stood at $19.87 billion, as compared to $18.90 billion in the year-ago month. Non-petroleum and non-gems and jewellery exports in April-February 2018-19 were $217.43 billion, as against $201.95 billion in the comparative period last fiscal.

President of exporters’ body FIEO Ganesh Kumar Gupta said 18 out of 30 major product groups were in positive territory, with most of them with marginal growth during the month. “However, with this trend, we will be able to achieve merchandise exports of about $330 billion, the highest ever exports for a fiscal,” he added.

Meanwhile, RBI said services exports in January 2019 were $17.75 billion, registering a negative growth of 1.02% over December 2018.

Wholesale price inflation quickens to 2.93% in FebruaryFood and fuel prices turn costlier

Wholesale price inflation in February snapped a three-month declining trend by rising to 2.93% on the back of firming food and fuel prices, official data released on Thursday showed.

Growth in the wholesale price index (WPI) accelerated in February from 2.76% in January, the lowest level it had touched since March 2018.


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Within the index, the primary articles category saw inflation quickening to 4.84% in February from 3.54% in January. The food articles category witnessed inflation quickening to 4.28% from 2.34% over the same period.

“At a more disaggregated level, inflation in some of the select cereal items such as wheat, jowar, bajra, maize, barley is now in double digits,” Sunil Kumar Sinha, principal economist, India Ratings and Research, wrote in a note. “Pulses, which as a category was witnessing deflation till November 2018, is now witnessing double digit inflation.”

“It is difficult to say whether this is a lagged impact of the government announcement of raising MSPs to 1.5 times of production costs or the lower sowing area under rabi crops,” Mr. Sinha added.

The crude petroleum and natural gas segment also saw inflation quickening in February, to 5.87% from 3.87% in January. Overall, the fuel and power segment saw inflation moving up to 2.23% from 1.85% over the same period.

The manufactured products sector was one of the few to see inflation easing in February to 2.25% from 2.61% in the previous month

Capital highTo retain the confidence of foreign investors, macroeconomic management is the key

Foreign investors appear to have rediscovered India. The inflow of foreign capital into India’s stock market in the month of March hit a high of $4.89 billion, the biggest foreign inflow into Indian stocks since February 2012. As a result, the stock market rose a solid 8% in March. Foreign investment in Indian equities stood at $2.42 billion in February, as against a net outflow of $4.4 billion during the same month a year earlier, and is expected to be strong in April as well. Both cyclical and structural factors are behind this sudden uptick in foreign investment that has helped the rupee make an impressive comeback. The rupee has appreciated by about 7% since early October, when it was reeling at around 74 against the dollar. Last year, India received more foreign direct investment than China for the first time in two decades. While the Chinese economy has been slowing down considerably in the last one year, India has emerged as the fastest-growing major economy. Doubts over the robustness of the GDP calculation method notwithstanding, it is clear that investors expect India to be a major source of global growth in the coming years.


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Other short-term reasons may also be behind some of the recent inflow of capital into the country. For one, there is a sense among a section of investors that their fears of political instability are misplaced. More important, there are clear signs that western central banks have turned dovish. Both the Federal Reserve and the European Central Bank, for instance, have promised to keep interest rates low for longer. This has caused investors to turn towards relatively high-yielding emerging market debt. Indian mid-cap stocks, which suffered a deep rout last year, are now too attractive to ignore for many foreign investors.

The return of foreign capital is obviously a good sign for the Indian economy. But policymakers need to be careful not to take foreign investors for granted. Other emerging Asian economies will be competing hard to attract foreign capital, which is extremely nimble. Any mistake by policymakers will affect India’s image as an investment destination. To retain investor confidence, whichever government comes to power after the general election this summer will need to increase the pace of structural reforms and also ensure proper macroeconomic management with the help of the Reserve Bank of India. Long-pending reforms to the labour and land markets are the most pressing structural changes that will affect India’s long-term growth trajectory. The high fiscal deficit of both the Centre and the State governments and the disruptive outflow of foreign capital are the other macroeconomic challenges. These are some issues that need to be solved sooner rather than later.

Don’t suppress data, economists tell govt.


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‘Any statistics that doubt Centre’s achievements seem to get revised or stifled’A group of 108 economists and social scientists working around the world,

have issued an appeal to convince the Indian government to stop suppressing uncomfortable data, restore access to public statistics and re-establish the independence and integrity of institutions.

“For decades, India’s statistical machinery enjoyed a high level of reputation for the integrity of the data it produced on a range of economic and social parameters,” the economists from institutions ranging from the Massachusetts Institute of Technology, University of California, Berkeley and Harvard University to Jawaharlal Nehru University, Delhi University and the Indian Statistical Institute, wrote in their appeal.

“It was often criticised for the quality of its estimates, but never were allegations made of political interference influencing decisions and the estimates themselves,” they added.

“Lately, the Indian statistics and the institutions associated with it have however come under a cloud for being influenced and indeed, even controlled by political considerations.”

The economists termed economic statistics as a “public good” that was vital for policy-making and informed public discourse in democracies where citizens seek accountability from their governments. The economists cited the government’s GDP data as one key troublesome area. First, they said that the figures released in 2015 with the new base year of 2011-12 were problematic as they “did not square with related macro-aggregates.”

Thereafter, the economists said, each successive GDP data release had come with associated problems, especially the latest data for the demonetisation year of 2016-17, which revised growth upwards by 1.1 percentage points to 8.2%.

Back series dataFurther, they said that the treatment of the back series data, and the involvement

of the NITI Aayog in the process did “great damage to the institutional integrity of the autonomous statistical bodies”. Finally, they cited the controversy surrounding the employment data that erupted recently. “In fact, any statistics that cast an iota of doubt on the achievement of the government seem to get revised or suppressed on the basis of some questionable methodology,” they said.


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“This is the time for all professional economists, statisticians, independent researchers in policy — regardless of their political and ideological leanings — to come together to raise their voice against the tendency to suppress uncomfortable data, and impress upon the government authorities, current and future, and at all levels, to restore access and integrity to public statistics, and re-establish institutional independence and integrity to the statistical organisations,” they added.

The national and global reputation of India’s statistical bodies is at stake, the economists concluded.

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Market Share Elasticity of Colour Television: An Empirical Study

S. RADHA *

1.1 INTRODUCTIONMarket share is often used to describe the position and success of a company in an

industry. Many companies view it as an important organizational goal. Accordingly it can be said that market share influences the organizational thinking and strategic planning of various companies. The existing literature considers market share to be one of the most important indicators of organizational success and the implication is that bigger the market share, more successful the company would be. Gale and Buzzell (1993) suggested that market share could be an important determinant of profitability in the medium to long term. They stated that larger market share is both a reward for providing better value to the consumer and a means of realising lower costs.

The importance of market share is also acknowledged in the Boston Consulting Group Matrix as a key indicator of industry growth (Lynch 2000). This is not surprising as companies with market leader status tend to derive profitability from their economies of scale capability as well as their established branding (Buzzell et al...1975).

Porter (1985) in his strategic planning model suggests that a firm with a low market share can succeed by developing a well focused strategy and a firm with high market share can succeed through cost leadership or a differentiated strategy.

In Product Evaluation Matrix developed by Yoram and Henry Claycamp (1976), the current and projected positions of a company’s products are examined on three measures viz. sales, market share and profitability.

*Faculty, Einstein Academy of Management Studes, Vadavalli, Coimbatore - 641046


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Generally it is accepted that increased market share can be equated with success, whereas decreased market share is a manifestation of unfavourable actions/inactions by a firm and is usually equated with failure.

This paper envisages to study the market share of major CTV brands in India with reference to the following research questions:

1. Who are the major players in the CTV market in India?2. What are their relative market shares?3. What are the inter and intra brand variations in market share during the

reference period?4. What are the price elasticities of market share of the selected brands?

With the above research questions in the background, the present study focused on market share analysis of the selected CTV brands in India with reference to the following objectives:

1.2 OBJECTIVES OF THE STUDYThe broad objectives of the study are

1. To review the literature and theoretical framework relating to various aspects of market share analyses,

2. To study the growth of the sales of the reference brands and to analyse their market share,

3. To study inter and intra brand market share variations,4. To estimate price elasticity of market share and5. To draw inferences for policy formulation.1.3 METHODOLOGY1.3.1 SCOPE

The study covers the period 2015-2019. The sample includes the CTV brands: viz: Samsung, LG, Sony, Micromax, Haier, Panasonic, Philips which are popular brands in Indian T.V. market.

Buzzell et al. (1975) is of the firm view that market share is important for infrequently purchased products than frequently purchased because infrequently purchased products tend to be durable, higher in unit cost, and are often complex and


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difficult for buyers to evaluate, as there is a bigger risk in wrong choice. Further market share is more important to businesses when buyers are fragmented rather than concentrated.

We fully agree with Buzzell et al.’s opinion and therefore we have focused our study on a consumer durable product viz: T.V. mentioned above. The brands which are in the market for a reasonable period are considered for the purpose of analysis.

1.3.2 DATA SOURCEThe study is based on secondary data collected from Centre for Monitoring Indian

Economy (CMIE) database and Capitaline Plus published by Capital Market Public Limited, Mumbai, which covers more than 6500 listed companies falling in nearly 20 industrial classifications.

1.3.3 DATA ANALYSISTo analyse the data, all conventional statistical, mathematical and econometric

tools have been used. The important techniques identified for application in the study are as follows:

1.3.3.1 GROWTH RATETo compute the annual growth rates, the formula used by the World Bank

(2000) is adopted. The least squares growth rate “r” is estimated by fitting a linear regression trend line to the logarithmic annual values of variable in the relevant period. The regression equation takes the form

Ln Xt = a + bt

which is equivalent to the logarithmic transformation of the compound growth equation

Xt = Xo (1 + r)t

In this equation, X is the variable,

‘t’ is time

a= log x0 and

b = Ln (l+r), 'a' and 'b' are the parameters to be estimated.


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If b* is the least square estimate of ‘b’, the average annual growth rate `r’ is obtained as [exp (bt – 1)] and is multiplied by 100 to express it as a percentage.

1.3.3.2. ELASTICITY OF MARKET SHARETo calculate the elasticity of market share, the following equation is used.

ΔMSi Pi eij = -------------- X ---------------- Δ Pj MSj

where MS = Market Share; P = Price

1.3.4. Variables included in the Model1.3.4.1. Market Share

Market share of a brand in a particular product market can be defined as a proportion of a brand’s sales to its industry sales. Indeed, market share is often used to describe the position of a brand within its industry. This is calculated as the ratio of a brand’s actual sales to the total industry sales of a particular product.

1.3.4.2 Price: Current market price of the brand

1.3.4.3 Authentic Marketing:Authentic marketing is not the art of selling what you make but knowing what

to make. It is the art of identifying and understanding customer needs.

To William Davidow, “While great devices are invented in the laboratory, great products are invented in the marketing department”. Peter Drucker observes that, “If you can’t bring something special to the market you don’t belong in it”. The winners are those who carefully analyse needs, identify opportunities and create value-laden- offers for target customer groups that competitors can’t match.

1.3.4.4 Market Share Change:Generally, it is accepted that increased market share can be equated with success,

whereas decreased market share is a manifestation of unfavourable actions/inactions by a firm and is usually equated with failure. But are perceived market share movements meaningful in regard to strategy formulation? In any event, the market position of any firm can be influenced by intentional or involuntary actions.


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i) Increase in Market Share:Market share can be increased in a number of ways: by enhancing the perceived

value of the products or by reducing the market price, or a combination of both measures. Increase in market share can also be achieved if a number of competitors opt out of the market for whatever reasons. However, it can be argued that price reductions are only a short term measure to increase market share — they are likely to be matched by competitor price reductions. Accordingly, the enhancing of product value appears to be the only realistic method of increasing market share.

Finlay (2000), suggests that increased market share occurs when:

¢ Current markets are not saturated for the types of offer the firm is making; ¢ Present customers can be induced to buy more; ¢ Increased economies of scale provide significant competitive advantages;

and ¢ The firm has spare production or distribution capacity.

ii) Decrease in Market ShareDecrease in market share may be as a result of increased competition or as

a deliberate decision not to invest in new or improved products. Finlay (2000) suggests that decreased market share occurs when:

¢ There is no money available for the enhancements needed to retain market share;

¢ The firm’s markets are being hit by cheap imports; and ¢ The firm’s reputation has suffered and cannot be reclaimed.

iii) Static market shareA static market share may be as a result of a decision not to increase the current

market share but to consolidate instead. On the other hand, despite efforts to increase the market share, the firm has only managed to retain its current share.

Finlay (2000) suggests that a static market share occurs when: ¢ An owner-manager wishes their firm to remain the same size as it is now, for

example, because they are near retirement, or because they do not wish to relinquish control to others;

¢ Market share is not an important driver of profit; and ¢ No funding is available to support market penetration or extensions.


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1.4 Earlier studies related to Market Share Erosion:The Austrian economist Schumpeter (1934) developed the concept of

perennial gale of creative destruction” to explain the dynamic market process by which market leaders and challengers engage in “an incessant race to get or to keep ahead of one another” (Kirzner, 1973: 20). The outcome of this market process is the inevitable and eventual market share erosion and dethronement experienced by market share leaders over time through the process of competition (Schumpeter, 1934, 1950). In this study, the author modeled this dynamic process in terms of four dimensions of competitive activity and used these characteristics to predict market share erosion and dethronement.

Schumpeter argued that once a leading market position is won by alert competitive action, a leading firm inevitably finds itself dogged by imitators. That is, without further aggessive action of their own, all industry leaders will eventually succumb to the moves of more aggressive rivals.

Ferrier et al. (1999) explored the extent to which dethronement and market share erosion were function of the competitive behaviors or actions of industries’ market share leaders and their respective number two challengers.

Specifically they had developed and tested a set of hypotheses concerning the characteristics of competitive actions carried out by market share leaders and challengers and the impact of these competitive behaviors on the erosion of market share gap between the two and the likelihood of leader dethronement based on seven years of data collected in 41 industries. They suggested that leaders were more likely to experience market share erosion and/or dethronement to industry challengers with less competitively aggressive and simpler repertoires of actions more slowly.

1.5 Earlier studies related to Market Share and Price:Bucklin et al. (1998) analysed the relationship between market share elasticities

and brand switching probabilities. They derived a theoretical relationship between the aggregate market share elasticity matrix and the aggregate brand switching matrix on the basis of a logit model of heterogeneous consumes choosing among competing brands in a product class. Aggregate cross-elasticities were shown to be proportional (through a single scaling constant) to their corresponding aggregate row-conditional brand switching probabilities.


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Aggregate own-elasticities were shown, to be proportional (through the negative of the same scaling constant) to one minus their corresponding aggregate row-conditional repeat purchase probabilities. An empirical analysis conducted on household scanner panel data in the liquid laundry detergent category showed that the theoretical correspondence held as a very good approximation. An illustrative use of the relationship in estimating aggregate (store-level) models of market share indicated that the relationship helps improve to predictive validity in a holdout period.

Shuba et al. (2000) made an attempt to study the market share response and competitive interaction: the impact of temporary, evolving and structural changes in prices. Managing pricing is a challenging task due to the significant impact on shares and the likelihood of strong consumer and competitor reaction. The major contributions of this paper are to assess comprehensive share response to temporary, evolving and structural changes in prices and to determine the level of market share as a function of levels of prices.

For the empirical analysis, they examined two consumer product categories and found that it was valuable to distinguish among temporary, evolving and structural changes in prices, as their impact on market shares tended to differ. Further, they found that subsequent competitive reaction would influence predictions of price response. Accordingly, it was important for managers to use conjectures regarding competitive price reactions in assessing the impact of policy changes.

Sen (2005) attempted to find out whether increasing the market share of smaller firms would result in lower prices through empirical evidence from the Canadian retail gasoline industry. Employing monthly data on average retail prices and market shares across eleven Canadian cities between 1991 and 1997, he found that more aggregate market share in the hands of independent retailers was correlated with higher retail prices, but indirectly associated with lower prices through the corresponding fall in market concentration among vertically integrated firms. The sum of these impacts was negative as indirect effects were larger in magnitude than corresponding direct effects.

It is clear from the above review that only very few studies have been conducted on market share erosion and price elasticity. Hence, this study has been undertaken to examine market share gain or erosion and price elasticity of market share.


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1.6 Results and Discussion:1.6.1. Colour Television - Brandwise Sales Growth:

Table:1 Colour Television Sets Sold: 2015-2019 (units in millions)Brand 2015 2016 2017 2018 2019 GR r2

Samsung 50.94 51.84 68.11 65.44 67.37 20.00 0.86Sony 23.38 33.6 28.51 33.89 32.76 17.04 0.67LG 25.09 30.06 31.38 38.24 35.1 23.37 0.81Micromax 20.04 21.8 22.77 25.19 28.08 10.74 0.69Haier 17.83 10.8 16.04 20.74 23.27 12.58 0.63Panasonic 16.7 17.5 17.33 17.18 23.4 6.78 0.74Philips 10.02 14.4 13.86 19.32 24.02 22.66 0.93Total 164 180 198 220 234 23.35 0.99

Figure:1 Colour Television Sets Sold in Millions

Table.1 presents the details of brand-wise sales of Colour Television sets during 2015-2019. The aggregate industry sales in 2015 was 164 million units and it had increased to 234 million units in 2019, thus recording a rate of growth of 23.35 percent.

Across the brands, maximum growth of 23.37 per cent was witnessed in LG followed by 22.66 per cent in Philips and 20.00 percent in Samsung. The growth rate of other premium brands were, Sony (17.04 percent), Haier (12.58 percent) and Micromax (10.74 percent). Minimum growth rate of 6.78 percent was noticed in Panasonic.


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It is surprising to note that other popular brands such as Onida, Videocon were not at all in the visibility during the period under reference. The entire analysis clearly shows the dominance of foreign brands in the Indian Colour Television market.

1.6.2. Market Share of Colour Television:Table.2 Brandwise Market Share

Brand Y-2015 Y-2016 Y-2017 Y-2018 Y-2019 x σn-1 σn cvSamsung 31.06 28.80 34.40 29.75 28.79 30.56 2.34 2.09 7.65 Sony 14.26 18.67 14.40 15.40 14.00 12.75 6.67 5.97 52.31 LG 15.30 16.70 15.85 17.38 15.00 16.05 0.98 0.88 6.11 Micromax 12.22 12.11 11.50 11.45 12.00 11.86 0.36 0.32 3.04 Haier 10.87 6.00 8.10 9.43 9.95 8.87 1.89 1.69 21.31 Panasonic 10.18 9.72 8.75 7.81 10.00 9.29 0.99 0.89 10.66 Philips 6.11 8.00 7.00 8.78 10.26 8.03 1.60 1.43 19.92

Figure:2 Market Share of CTV


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Table.2 presents brand-wise market share of C.T.V. for the period 2015-2019.

In the initial year of our analysis, Samsung indicated the highest market share of 31.06 percent, thus emerging market leader in the C.T.V. segment and continued to be the market leader during the entire period under review.

LG was the challenger to Samsung in all the years except 2016. In that year, Sony had shown some improvement over LG but lost it in the very next year (2017).

As the period progressed, all reference brands have experienced wider fluctuations in their relative market share. In the terminal years, the market share of all brands except Philips had declined and the reduction in their respective market share was marginal which indicates that all brands have sustained their market share without much erosion.

Philips has shown encouraging improvement in their market share in 2019 vis-à-vis 2016. Statistical results show that Samsung had an unassailable supremacy in the inter brand variation 14 percent up against the challenger. Sample S.D and C.V values clearly indicate that there was not much variations in the intra brand market share.

1.6.3. Changes in Market Share: Gain / ErosionTable.3 Changes in Market Share

Brand 2016 2017 2018 2019 Net Gain (or) Net Erosion

Gain Loss Gain Loss Gain Loss Gain Loss -2.27 (E)Samsung -2.26 5.6 -4.65 -0.96 -0.86 (E)

Sony 4.41 -4.27 1 -1.4 -0.30 (E)LG 1.4 -0.85 1.53 -2.38 -0.22 (E)Micromax -0.11 -0.61 -0.05 0.55 -0.92 (E)Haier -4.87 2.1 1.33 0.52 -0.18 (E)Panasonic -0.46 -0.97 -0.94 2.19 -0.18 (E)Philips 1.89 -1 1.78 1.48 +4.15 (G)

Table.3 presents the details of changes in market share during 2015-2019. Net Gain / Erosion clearly indicates that all brands (except Philips) have suffered erosion in their relative market share during the period under review. However,


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the quantum of erosion was marginal in all brands (except Samsung where the net erosion was 2.27 percent) which shows that all brands have sustained their market share despite acute competition.

Surprisingly Philips was the only brand which indicated significant gain in its relative market share indicating healthy marketing practices. Since the net erosion was minimal in all brands it is inferred that different brands employ effective marketing strategies to cement their market positioning.

1.6.4. Proportionate Changes in Market Share:Table.4 Proportionate Changes in Market Share ∆ MS / MSBrand 2016 2017 2018 2019Samsung -7.27 19.44 -13.52 -3.23Sony 30.92 -22.87 0.69 -0.90LG 9.15 -5.08 9.65 -13.70Micromax -0.90 -5.03 -0.43 4.80Haier -44.80 35.00 16.42 5.51Panasonic -4.52 -9.98 -10.74 28.04Philips 30.92 -12.50 25.43 16.86

Table.4 presents the details of proportionate changes in the market share during the period under review.

It is evident that all brands have experienced wider fluctuations in their ratios, the highest being found in Sony, Haier and Philips. The abnormal fluctuations were specifically due to push and pull factors in the marketing strategies and technological innovations. The data clearly indicates that market for C.T.V. remains highly complex and dynamic.

1.6.5. Average Price:Table.5 indicates the Average Price of the reference brands during 2015-2019.

The average price of Sony remained maximum than other brands. On the other side, the average price of Micromax was the minimum. Despite charging minimum average price, why Micromax could not capture sizable market share is an issue for further research.


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Table.5 Average Price of the C.T.VBrand Average Price

2015 2016 2017 2018 2019Samsung 17765 17574 15663 14999 15134Sony 23799 21077 22990 18853 20373LG 19990 18990 18527 16499 14999Micromax 11940 10490 9659 9894 8499Haier 17048 14660 13890 12199 10990Panasonic 19900 18181 15598 13428 12490Philips 25999 16499 15690 12999 10999

1.6.6. Changes in Average Price:

Table.6 Changes in Average PriceBrand Change in Price (∆ Price)

Net Reduction 2016 2017 2018 2019Samsung -191 -1911 -664 135 2631Sony -2722 1913 -4137 1520 3426LG -1000 -463 -2028 -2000 5491Micromax -1000 -831 235 -1395 2991Haier -2388 -770 -1691 -1209 6058Panasonic -1719 -2583 -2170 -938 7410Philips -9500 -809 -2691 -2000 15000

Table.6 details the changes in average price during 2016-2019. It is evident that almost all brands have invariably reduced their product prices conforming that all brands have strongly adopted price manipulation as an effective strategy to retain their market share. The maximum net price reduction during the period was by Philips (Rs.15,000). Other brands such as Panasonic (Rs.7410), Haier (Rs.6058), LG (Rs.5491), Sony (Rs.3426), Samsung (Rs.2631) also have considerably reduced their product prices during the period under review.

Despite price reduction, Samsung alone sustained its market share whereas Panasonic, LG, Sony and Haier couldn’t achieve much, which goes to show that besides price there are other significant factors which influence market share.


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1.6.7. Proportionate Change in Price:Table.7 Proportionate Change in Price (Percentage)

Brand ∆ P / PNett Reduction

2016 2017 2018 2019Samsung -1.08 -10.87 -4.24 0.9 15.29Sony -11.43 9.07 -18 8.06 12.3LG -5 -2.44 -10.95 -12.12 30Micromax -8.37 -7.9 2.43 -14.1 27.94Haier -14 -5.25 -13.12 -9.91 42.28Panasonic -8.64 -14.2 -13.92 -6.98 43.74Philips -36.54 -4.9 -17.15 -15.39 73.98

Table.7 exhibits the details of proportionate change in C.T.V prices during the period under review.

It is evident that Philips has the maximum reduction of 73.98 followed by Panasonic (43.74) and Haier (42.28). The ratio was comparatively high in LG (30) and Micromax (27.94) also.

In other words, all brands taken for review have considerably reduced their product prices to sustain their market share.

It is inferred that since all brands have adopted price manipulation as a strategy they have succeeded in retaining their market position but failed in grabbing additional share.

1.6.8. Market Share Elasticity:If elasticity = 1, the implication is that proportionate change in market share is

equal to proportionate change in price. If elasticity = 0, the market share is inelastic, if Elasticity > 1, a small change in price leads to more than proportionate change in market share and vice versa.

From Table.8 it is evident that the average elasticity of all brands was >1 except Micromax, confirming to the theoritical postulate that any change in price leads to more than proportionate change in the market share eitherway. Therefore, the brands have to be very cautious while manipulating price.


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Table.8 Market Share Elasticity

Brand∆ MS/∆P x P/MS Average

Elasticity2016 2017 2018 2019Samsung 6.73 1.79 3.19 3.59 3.825Sony 2.70 2.52 0.04 11.17 4.11LG 1.83 2.08 0.88 1.13 1.48Micromax 0.12 0.64 0.18 0.34 0.32Haier 3.20 6.67 1.25 0.56 2.92Panasonic 0.52 1.43 0.77 4.02 1.68Philips 0.85 2.55 1.48 1.10 1.5

Theoretically there are five types of elasticity in marketing literature. They are

Elasticity = 1, Elasticity > 1, Elasticity < 1, Elasticity = 0 and Elasticity equal to infinity.

Conclusion:The following conclusions emerge from the empirical study.

1. Samsung has recorded the maximum sales during the period under review.2. Samsung continues to be the market leader.3. Foreign brands dominate Indian CTV market.4. LG was the challenger.5. As the period progressed all brands have experienced wider fluctuation in their

market share.6. All brands sustained their relative market share despite acute competition.7. The analysis clearly indicates that price is not the only variable which influences

market share and8. Any change in price leads to more than proportionate change in market share.

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3

Commercial Banks: Measurement of Financial and Economic Performance

K. RAJKUMAR*

3.1. Financial performance (soundness) analysis using CAMELTo gauge the financial soundness and there by to evaluate the efficiency of the

banks, regulators all over the world have resorted to CAMELS. CAMEL’S ratings are the result of the Uniform Financial Institutions Rating System, the internal rating system used by regulators for assessing financial institutions on a uniform basis and identifying those institutions requiring special supervisory attention. Regulators assign CAMELS ratings both on a component and composite basis, resulting in a single CAMELS overall rating. When introduced in 1979, the system had five components. A sixth component—sensitivity to market risk—was added in 1996. The CAMEL supervisory criterion in banking sector is a significant and considerable improvement over the earlier criteria such as frequency, check, spread over and concentration. The six components of the new CAMEL model are: • C—Capital adequacy • A—Asset quality • M—Management • E—Earnings • L—Liquidity • S—Sensitivity to market risk.

CAMEL’S framework can be used to rate the banks as well as rank them based on their performance in the ratios. Regulators normally assign rating and those banks which fall below with composite CAMELS ratings of 4 or 5, are deemed to be “problem” banks and may be subject to regulatory enforcement actions. The alternative method which is used by many researchers is ranking of CAMEL ratios.

In the early 90’s, the banking sector has experienced a paradigm change under the waves of LPG (liberalization, privatization and globalization). Accordingly, Reserve Bank of India provided a framework for the performance appraisal of

*Asst. Professor, Sri Krishna Institute of Management, Coimbatore


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the current strength of the system, operations and the performance of the banks. Padmanabhan Working Group (1995).

Present study has employed CAMEL ranking model to analyse the financial performance (soundness) of the selected sample banks. The major reason for choosing CAMEL instead of CAMELS is that, the former fits more into the methodology and objectives of the present study, whereas the latter is more suitable for on-site inspection for collecting feedback through questionnaire. Due to aforementioned reasons, DEA- both oriented CCR and BCC models were adopted to measure technical, pure, scale efficiencies as well as returns to scale of the selected banks under study.

H0 : There is no significant difference in financial performance in terms of CAMEL ratios across different bank groups.

H0 : There is no significant difference in OTE across different bank group.H0 : There is no significant difference in PTE across different bank group.H0 : There is no significant difference in SE across different bank group.H0 : There is no significant difference in RTS across different bank group.H0: There is no significance difference in Efficiency in terms of Koopman’s ratio

across different bank groups.

3.2. Concepts used in CAMEL analysis As discussed earlier in the current study financial efficiency is tested using

CAMEL framework. After analyzing financial statements of the various banks under study, four sub-parameters were adopted in measuring the bank performance in terms of Capital adequacy, Asset quality, Management efficiency, Earning quality and Liquidity. The sub-parameters chosen under each of the CAMEL acronym are:

3.2.1 Capital adequacy: Capital adequacy is a reflection of the inner strength of a bank which determines the volume of operations and would enable a bank to sustain its stability during the times of crisis. Hence capital adequacy has a bearing on the overall performance of a bank. Capital adequacy indicates whether the bank has enough capital to absorb unanticipated losses and reduction in asset values that could otherwise cause a bank to fail, and provide protection to depositors and creditors in the event of liquidation. The balance sheet of the bank cannot be expanded beyond the level determined by the capital adequacy ratio.


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3.2.2 Asset Quality: Asset quality is an important parameter to test the financial credibility of the banks and their risk exposure. The purpose to measure asset quality is to ascertain the composition of non-performing assets (NPAs) as a percentage of total assets. The sub-parameters directly assess the performance of bank’s assets and also provide an insight into the bank’s attitude towards risk and risk management.

3.2.3 Management efficiency: Management efficiency means adherence to set of norms, ability to plan and respond to changing environment, leadership and administrative capability of the bank. Management efficiency is measured not only in terms of increasing revenue but also decreasing cost. Management efficiency is another quintessential component of the CAMEL model which ensure the growth and stability of a bank.

3.2.4 Earning quality: Earning quality ratios are used to measure the ability of the bank to earn profit compared to expenses. It shows the bank’s overall efficiency and performance as it examines the bank’s investment decisions as compared to their debt situations. It primarily determines the profitability of a bank and indicates sustainability and growth of future earnings. It also attracts the attention of the equity holders who are interested in the ultimate returns which depend on the earning quality.

3.2.5 Liquidity: Liquidity for a bank is the quantum of assets which are easily convertible into cash in order to meet their short term obligations. Liquidity is the ability of the bank to meet financial obligations including customer’s demand for cash across the counter and lack of liquidity will have an undesirable impact on the credibility of the bank. The liquidity ratios indicate the bank’s short term solvency and its ability to pay-off the liabilities.

3.3. Efficiency: One of the important economic dimensions for ensuring the success of an organization is the efficiency with which it uses its resources. In the performance evaluation of firms, estimating their efficiency is of paramount importance. Lovell (1993) defines productivity as the ratio of its output to its input. Sengupta (1995), Cooper, Seiford and Tone (2007) defines both productivity and efficiency as the ratio between output and input. According to Berger and Mester (1997) two most important economic efficiency concepts are cost and profit efficiency. In economics, a firm needs to be technically efficient along with good financial performance, to achieve 100% efficiency. Drawing inspirations from Koopmans (1951) and Debreu (1951), Farrell (1997) was first to measure efficiency empirically.


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3.3.1 Technical Efficiency: According to Farrell (1957), the concept of efficiency measurement can be divided into two components, namely technical efficiency (TE) and allocative efficiency (AE). According to him, technical efficiency is the firm’s ability to obtain maximal output from a given set of inputs while allocative efficiency means the firm’s ability to use inputs in optimal proportions, given their respective prices and production technology. Pareto-Koopmans concept of efficiency (Pareto (1909), Koopmans (1951) says “A DMU (decision-making unit) is fully efficient if and only if it is not possible to improve any input or output without worsening some other input or output.

Technical efficiency is necessary to achieve allocative efficiency. The figure 3.1 shows the classic framework by Farrell which decomposes overall efficiency into technical and allocative (price) efficiency. Consider the case of a simple output (Y) that is produced by using two inputs (X1, X2). Under the assumption that the production function Y=f(X1, X2) is linearly homogeneous, the efficient unit isoquant, Y=1, shows all technically efficient combinations. In Figure 3.1, P represents a firm, country, individual, etc., that also produces at Y=1, but uses higher levels of inputs, and is therefore less efficient in a technical sense. The magnitude of the efficiency can be expressed as the ratio between optimal and actual resource use (OR/OP). By taking into account the iso-cost line (representing relative factor prices), we can identify allocative efficiency. Any point on the line Y=1 has technical efficiency, but only Q receives technical efficiency at minimum cost. Allocative (price) efficiency can be expressed as the ratio between minimum price and actual cost (OS/OR), and overall efficiency is the product of technical and allocative efficiency.

Figure 3.1: Technical, allocative, and overall efficiency

Source: Farrel M.J. The measurement of productive efficiency. Journal of the Royal Statistical Society Series A 1957;120(3):253-90.


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3.3.2 Scale Efficiency: Another important constituent of efficiency is scale efficiency. A firm is said to be scale efficient when its size of operations is optimal so that any modifications on its size will render the unit less efficient. Scale efficiency is obtained by dividing the overall technical efficiency by the pure technical efficiency. If a firm is functioning at optimum scale, it will achieve scale efficiency which is the potential productivity gain due to its optimum production scale.

3.3.3 Returns to Scale: Another important constituent of efficiency in performance evaluation is Returns to scale. It refers to how much additional output can be obtained when we change all inputs proportionately. Returns to scale are significant in efficiency analysis as there are links between returns to scale and productivity and efficiency.

The present study focuses both an evaluation of Financial Performance as well as efficiency analysis. Performance evaluation has been carried out through financial ratio analysis or parametric or non-parametric approaches. The inherent limitations of the financial ratio analysis coupled with advances in management sciences have led to the development of alternate methods such as non-parametric Data Envelopment Analysis and parametric Stochastic Frontier Approaches to measure efficiencies. Berger & Humphrey (1997) opined that the idea of performance evaluation is to separate banks that are performing well from those which are functioning not so well as their peers in the market. Researchers are fond of DEA or Stochastic frontier methods in analyzing bank efficiency. However, bank regulators screen banks by evaluating banks’ liquidity, solvency and overall performance to enable them to intervene when there is a need and to evaluate the potential problems (Casuet al, 2006). Thus the present study has adopted CAMEL framework, which is the regulatory framework adopted by the supervisors to assess the financial performance (soundness) of banks and non-parametric Data envelopment Analysis (DEA) to measure technical efficiency.

3.4. Measurement of efficiency: There are two general methodologies that are commonly used to measure efficiency. They are: parametric approach using econometric techniques; and, nonparametric approach using linear programming method. Both differ mainly in how they handle the random error and their assumptions regarding the shape of the efficient frontier.

Data envelopment analysis (DEA), is a mathematical programming method for evaluating the relative efficiency of decision making units (DMUs) with multiple


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inputs and multiple outputs. DEA model was first proposed by Charnes et al. (1978) based on earlier work initiated by Farell (1957). It was later extended by Banker et al. (1984).In a standard non-parametric approach, for determining efficiency of a firm, firms under the study are called decision-making units (DMUs). A typical firm may use many inputs and produce several outputs. A single virtual input and single virtual output which is called in DEA literature as a production possibility set, is constructed empirically by enveloping the inputs and outputs data set, where a parametric transformation function is not assumed. This is considered as the advantage of DEA compared with parametric models. (This ratio of virtual output to virtual input is defined as a relative measure of efficiency.) The efficient frontier of a production possibility set enables the relative efficiency evaluation. The efficiency score distinguishes between efficient and inefficient DMUs by establishing whether a DMU is located on the efficient frontier or inside the production possibility set. Also, the efficiency score indicates how distanced the DMU is from the efficient frontier. DEA empirically identifies the efficient frontier of a set of DMUs based on the input and output variables.

Those DMUs which are located on the efficient frontier, compared to others, use minimum productive resources given the outputs (input-conserving orientation), or maximize the output given the inputs size (output-augmenting orientation), and are called as reference units or peer units within the sample of banks. These Pareto-efficient banks have a benchmark efficiency score of unity that no individual bank’s score can surpass. In addition, it is not possible for the Pareto-efficient DMU to improve any input or output without worsening some other input or output. It is significant to note that the efficient frontier provides a yardstick against which to measure the relative efficiency of all other banks that do not lie on the frontier. Those DMUs which do not lie on the efficient frontier are deemed to be relatively inefficient (i.e., Pareto non-optimal DMUs) and receives a TE score between 0 and 1. The efficiency score of each bank can be interpreted as the radial distance to the efficient frontier. In short, the DEA forms a non-parametric surface frontier (more formally a piecewise-linear convex isoquant) over the data points to determine the efficiency of each bank relative to this frontier. Using actual data for the banks under consideration, DEA employs linear programming technique to construct efficient or best-practice frontier. DEA can estimate efficiency under the assumption of constant return to scale and variable returns to scale. The below diagram shows the different orientation of DEA with respect to CRS and VRS models.


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Figure 3.2.Basic DEA model classifications

Source: Author’s constructionCharnes et al. (1978) developed a DEA model, which attained huge popularity

and known as CCR DEA model. The CCR model is based on the assumptions of constant returns-to-scale (CRS), strong disposability of inputs and outputs, and convexity of the production possibility set. The application of CCR model not only provides technical efficiency scores for individual banks but also provides vital information on input and output slacks, and reference set for inefficient banks.

A DMU is CCR efficient if:

1. θ *= 1 and there exists at least on optimal (v*,u*), with v* > 0 and u*>02. Inputs slacks are equal to 0 (i.e. )

Thus a DMU under CCR is 100% efficient if and only ifθ *= 1 and all slacks are equal to 0 (i.e. ), Otherwise, DMU0 is CCR-inefficient. The CRS assumption is only appropriate when all DMUs are operating at optimal scale. However, factors like imperfect competition and constraints in finance may cause a DMU not to operate at optimal scale.

Charnes has also proposed other variant models under CCR which are input-oriented CCR model, Output oriented CCR model and Base oriented models.

Input-oriented CCR models are models where DMUs are deemed to produce a given amount of outputs with the smallest possible amount of inputs. (Inputs are controllable)Output-oriented models are models where DMUs are deemed to produce with given amounts of inputs the highest possible of outputs. (Outputs are controllable). Base-oriented models are models where DMUs are deemed to produce


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the optimal mix of inputs and outputs. (Both inputs and outputs are controllable). Current study has adopted CCR input oriented model to measure overall technical efficiency.

Figure 3.3 projection of an inefficient unit on the frontier with the three possible model under CCR

Source: Data envelopment Analysis, A comprehensive Text with model, Applications, references by William W.Cooper, Lawrence M.Seiford and Kaoru Tone, 2007

The above figure describes the simple case of a single-input single output production system. Point I constitutes the benchmark for inefficient DMU D in the input-oriented model. The relative efficiency of D is given by the ratio of distances

. Point O is the projection of D in the output-oriented model. The relative efficiency of D is then . Finally, point B is the base-projection of D in the base oriented model.

3.4.1 BCC model: Introduced by Banker, Chames and Cooper (1984), this model measures technical efficiency as the convexity constraint ensures that the composite unit is of similar scale size as the unit being measured. The resulting efficiency is always at least equal to the one given by the CCR model, and those DMUs with the lowest input or highest output levels are rated efficient. Unlike the CCR model, the BCC model allows for variable returns to scale.The BCC model is anextension of the CCR model to allow for returns-to-scale to be variable. Thus, BCC model computes efficiency scores corresponding to the assumption ofvariable returns-to-scale (VRS). It is a more flexible than the CCR model since itallows for constant, increasing, and decreasing returns-to-scale. Banker et al.(1984) showed that solutions to CCR and


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BCC models allow a decomposition oftechnical efficiency (TE) into pure technical efficiency (PTE) and scale efficiency(SE) components. Like the CCR model, BCC model also has two variants: input-orientedBCC model and output-oriented BCC model. Current study is using input – oriented BCC model.

A DMU is considered to be efficient according to both CCR and BCC model only when it meets the conditions explained above as its efficiency scores are equal to 1 and both input and output slacks are zero.

Figure 3.4 Most efficient point on the production

Source: Data envelopment Analysis, A comprehensive Text with model, Applications, references by William W.Cooper, Lawrence M.Seiford and Kaoru Tone (2007)

Above diagram shows the example of a DMU which is efficient by way of both CCR and BCC input oriented model. It is the most technically efficient point as it is producing on the point where CRS is tangential to VRS using the most efficient output-input combination.

3.5. Technical efficiency - scales of measurementData Envelopment Analysis (DEA) uses mathematical programming rather

than regression in the efficiency measurement. Here, one circumvents the problem of specifying an explicit form of the production function and makes only a minimum number of assumptions about the underlying technology. Farrell (1957) formulated a linear programming model to measure the technical efficiency of a firm with reference to a benchmark technology characterized by constant returns to scale.


40

In DEA, we construct a benchmark technology from the observed input-output bundles of the firms in the sample by keeping the following general assumptions about the production technology without specifying any functional form. In DEA models following assumptions are made.

¢ Assumption 1 : All actually observed input-output combinations are feasible. An input-output bundle (x, y) is feasible when the output bundle y can be produced from the input bundle X. Suppose that there is a sample of N firms from an industry producing m outputs from n inputs. Let xj =(xij, x2j,…,xnj) be the input bundle of firm j(j = 1,2,…,N) and yj = (y1j, y2j,…, ymj) be its observed output bundle. Then, by (A1) each (xj, yj)(j =1,2,…, N) is a feasible input-output bundle.

¢ Assumption 2: The production possibility set is convex. Consider two feasible input-output bundles (xA, yA)and(xB, yB) . Then the (weighted) average input-output bundle ),( yx ,where BA xxx )1( λλ −+= and BA yyy )1( λλ −+= for some λ satisfying 00 ≤≤ λ is also feasible.

¢ Assumption 3: Inputs are freely disposable. If (x0, y0) is feasible, then for any x ≥ x0, (x, y0) is also feasible.

¢ Assumption 4: Outputs are freely disposable. If (x0, y0) is feasible, then for any y ≤ y0, (x0, y) is also feasible. If additionally we assume that constant returns to scale holds,

¢ Assumption 5: f (x, y) is feasible, then for any k≥ 0, (kx, ky) is also feasible. ¢ It is possible to empirically construct a production possibility set satisfying

assumptions (A1-A5) from the observed data without any explicit specification of a production function. Consider the input-output pair )ˆ,ˆ( yx where ∑=

Nj

j xx1

ˆ µ , ∑=N

jj yy

1

ˆ µ ,∑ =N

j1

,1µ and ∑ =≥N

j Nj1

).,...,2,1(0µ By (A1-A2), )ˆ,ˆ( yx is feasible.

Now, by (A3), if )ˆ,(,ˆ yxxx ≥ is also feasible. Next, by (A4), if ),(,ˆ yxyy ≤ is feasible. Thus, using (A1-A4), the input-output combination )ˆ,ˆ( yx is feasible. If, additionally, constant returns to scale is assumed, )ˆ,ˆ( ykxk is also a feasible bundle for any k≥ 0.. Define xkx ˆ~ = and

yky ˆ~ = for some .0≥k Then, by construction,∑ ∑≤N N

jj yky

1 1

~ µ and ∑≥N

jj xkx

1

~ µ .

Next define .jj kµλ = Then 0≥jλ and ∑ =N

j k1

.λ But k is only restricted to be non-negative. Hence, beyond non-negativity, there are no additional restrictions on the jλ s.


41

Therefore, based on the observed input-output quantities and under the assumptions (A1-A5), we can define the production possibility set or the technology set as follows:

. } ) N ,..., 2 , 1 = j ( ; 0 ; y y ; x x : )y ,x jj

j

N

1j=

jj

N

1j=≥≤≥ ∑∑ µµµ( { = T C

Here the superscript C indicates that the technology is characterized by constant returns to scale.

The input-oriented measure of technical efficiency of any firm t under Variable Returns to Scale, requires the solution of the following LP problem due to Banker, Charnes, and Cooper (BCC):

Let ) ,..., , ;( *n

*2

*1

* λλλθ be the optimal solution. Define . x = x = x t*j*j

N

1=j

t* θλ∑ Then

) y ,(x tt* is the efficient input-oriented projection of )y ,x( tt onto the frontier and

. = ) y ,x ( TE *ttVI θ

The CCR model is only appropriate when all decision making units (DMUs) are running at an optimal scale. In practice, some factors may prevent a DMU from operating at optimal scale, such as financial and legal constraints, imperfect information etc. Coelli (1996) highlighted that the use of the CRS specification when some of the DMUs are not running at optimal scale will result in measures of technical efficiency which are mixed up with scale efficiency. To overcome this problem, Banker et al (1984) suggested their model known as the BCC model. It improved the CCR model by introducing a variable that represents the returns to scale. The BCC model allows a calculation of technical efficiency that is free from the scale efficiency effects. So, the research repeated the trial using the BCC model which assumes variable returns to scale (VRS). The effect of the scale assumption on


42

the measure of capacity utilization is demonstrated in below figure. Four data points (A, B, C, and D) are used to estimate the efficient frontier and the level of capacity utilization under both scale assumptions. The frontier defines the full capacity output given the level of fixed inputs. With constant returns to scale, the frontier is defined by point C for all points along the frontier, with all other points falling below the frontier (hence indicating capacity underutilization). With variable returns to scale, the frontier is defined by points A, C and D, and only point B lies below the frontier i.e. exhibits capacity underutilization. The capacity output corresponding to variable returns to scale is lower than the capacity output corresponding to constant returns to scale.

Figure 3.5 - CRS and VRS frontiers (CCR and BCC efficient points)

Source: William w Cooper, Lawrence M. Seiford and Kaoru Tone, “Data envelopment Analysis” second edition (2007)

The total efficiency measures derived under the assumptions of constant returns to scale (CRS) represents overall technical efficiency (OTE). It provides an outlook about the inefficiencies in the production due to input/output configuration as well as the size of operations. The efficiency measures corresponding to VRS assumption represents pure technical efficiency (PTE) which measures inefficiencies due to only managerial under performance.

3.5.1 Inputs and outputs slacks – slacks and projection based analysis: As explained earlier, to be both CCR and BCC efficient, a DMU should have input and output slacks equal to zero. This is a constraint, and those DMU even after having efficiency score under BCC equal to 1, will not be considered fully (100%) efficient,


43

unless they have slacks, both inputs and outputs values equal to 0. Slacks can be explained better with the help of the below diagram.

Figure 3.6 Efficiency measurement and Input slacks

Source: Coelli, “an introduction to efficiency and productivity analysis”: page no: 165

As shown in the diagram above, where the firms using input combinations C and D are the two efficient firms that define the frontier, and firms A and B are inefficient firms. However, it is questionable as to whether the point A’ is an efficient point since one could reduce the amount of input X2 used by the amount CA’ and still produce the same output. This is known as input slack. With multiple input and outputs, output slacks also occurs. According to Farrell measure of technical efficiency and any non-zero input or output slacks should be reported to provide an accurate indication of technical efficiency of a firm in DEA analysis. Koopmans (1951) provides a stricter definition of technical efficiency which is equivalent to stating that a firm is only technically efficient if it operates on the frontier and furthermore that all associated slacks are zero.

3.5.2 Scale EfficiencyThe measure of SE provides the ability of the management to choose the optimum

size of resources, i.e., to decide on the bank’s size or in other words, to choose the scale of production that will attain the expected production level. It can be used to indicate the increase in productivity caused by moving to the technically optimal production scale. In other words, SE relates to the most efficient scale of operation in the sense of maximising average productivity. A scale efficient bank has the same


44

level of OTE and PTE. However, inappropriate size of a bank (too large or too small) may sometimes be a cause of technical inefficiency. In sum, technical inefficiency can be thought of being attributable to pure technical and scale inefficiencies. Pure technical inefficiency is mainly due to managerial (controllable) and environmental (uncontrollable) factors, while scale inefficiency is caused by operating at non-optimal scale i.e., either with increasing returns-to-scale (at suboptimal scale) or with decreasing returns-to-scale (at supra-optimal scale). A bank is scale efficient if it operates at constant returns-to-scale.

To quantify a measure of TE, we need to find out the divergence between actual production and production on the boundary of the feasible production set. This set summarizes all technological possibilities of transforming inputs into outputs that are available to the bank. A bank is technically inefficient if production occurs within the interior of this production set. A measure of SE can be obtained by comparing TE measures derived under the assumptions of constant returns-to-scale (CRS) and variable returns-to-scale (VRS).

Based on CCR and BCC scores, scale efficiency is defined as :

Let the CCR and BCC scores a DMU be θ*CCR and θ* BCC, respectively. The scale efficiency is defined by

SE is not greater than one. For a BCC-efficient DMU with Constant returns to scale (CRS) characteristics, i.e., in the most productive scale size, its scale efficiency is one. The CCR score is called the overall (Global) technical efficiency (TE), since it takes no account of scale effect as distinguished from PTE. On the other hand, BCC expresses the PTE under variable returns to scale circumstances as explained in chapter 3. These concepts demonstrate a decomposition of efficiency as relationship as:

θ*CCR = θ*BCC X SE or

[Technical Eff. (TE)] = [Pure Technical eff. (PTE) X [Scale eff. (SE)]

This decomposition depicts the sources of inefficiency, i.e., whether it is caused by inefficient operation (PTE) or by disadvantageous conditions displayed by the scale efficiency (SE) or by both.


45

Below figure depicts scale efficiency for a single input-output case.

Figure 3.7 Scale efficiency in DEA

Source: Author’s construction

For the BCC-efficient A with IRS, its scale efficiency is given by:

SE (A) = θ*CCR (A) = LM / LA < 1; which denotes that A is operating locally efficient (PTE = 1) and its overall inefficiency (TE) is caused by its failure to achieve scale inefficiency (SE) represented by LM/LA as shown in the diagram. For DMUs B and C, their scale efficiency is one, i.e., they are operating at the most productive scale size. Their technical efficiency is also one so they are both scale and technically efficient for both the CCR and BCC models. For the BCC-inefficient DMU E, we have SE (E)

Which is equal to the scale efficiency of the input- oriented BCC projection R. The decomposition of E is

TE (E) = PTE (E) X SE (E) or

Thus E’s overall inefficiency is caused by the technically inefficient operation of E and at the same time by the disadvantageous scale condition of E measured by PQ/PR.


46

The above outlined scale efficiency is for input-oriented, as the current study is using input-oriented method.

3.5.3 Returns to Scale explained:In DEA the envelopment surface, which is the efficiency frontier created by the

efficient firms, will differ depending on the scale assumptions that underpin the model. Two scale assumptions are generally employed in DEA: constant returns to scale (CRS), and variable returns to scale (VRS). The latter encompasses both increasing and decreasing returns to scale. CRS reflects the fact that output will change by the same proportion as inputs are changed (e.g. a doubling of all inputs will double output); VRS reflects the fact that production technology may exhibit increasing, constant and decreasing returns to scale. Returns to scale under DEA is explained below:

In a single-output, single-input technology characterized by the production possibility set

});(:),{( axxfyyxT ≥≤=

where

y = f(x) is the production function showing the maximum quantity of output y producible from input x and a is the minimum input scale below which the production function is not defined. When there is no minimum scale, a equals 0. At some specific point (x, y) on this production function, the average productivity is

.)(xxfAP =

Locally increasing returns to scale holds at this point if a small increase in x results in an increase in AP. Similarly, diminishing returns to scale exists when AP declines with an increase in x. Under constant returns, an increase in x leaves AP unchanged. Thus,

dxdAP is positive under increasing returns, negative under

diminishing returns, and 0 under constant returns.

For an efficient input-output combination (x0, y0) satisfying y0 = f(x0).

Let x1 =β x0, and f(x1) = y1. Further, assume that y1 =α y0 . Thus, α y0 = f( β x0). Clearly, α will depend on β . Thus, α(β) = max α : ( β x0, α y0) ∈T.

For any efficient pair (x, y),


47

α(β)y = f(βx).

Differentiating with respect to β,

(')(' xfy =βα ).()(') xfx βαβ =

Further, at β=1,

.)()(')1(' εα ==

xfxxf

Thus, at (x, y),

1)1(' >α implies increasing returns to scale,

1)1(' =α implies constant returns to scale, and

1)1(' <α implies diminishing returns to scale.

The below diagram shows the returns to scale measurement as explained above.

Figure 3.8 Returns to scale

Source: Author’s construction3.6 Methodology

Berger and Humphrey (1997) in their research on the efficiency analysis of banks, found that out of 130 studies reviewed, more than half of the studies used nonparametric techniques and 60 used parametric which suggest no approach dominate the other. The parametric approach has the advantage of allowing noise in the measurement of inefficiency. However, the approach needs to specify the functional form for the production, cost or profit function. Non-parametric is simple


48

and easy to calculate, since it does not require specification of functional form (Coelli, 2004). However, it suffers from the drawback that all deviations from the best-practice frontier are attributed to inefficiency since it does not allow for noise to be taken into account.

After analyzing the pros and cons of both parametric and non-parametric analysis, current study decided to adopt the non-parametric model DEA for testing the technical efficiency. The reasons for adopting DEA are as follows:

y In order to compare the performance of banks under distinct ownership, the study needs a reference unit with a single efficiency score, DEA provides a single efficiency score for each DMU;

y The study has the objective of performance evaluation where by it needs to not only differentiate the banks based on efficiency/inefficiency but needs to suggest ways, by which inefficient units can be projected on the efficient envelop. DEA highlights areas of improvements for each single DMU by providing reference sets;

y DEA focus on a best-practice frontier, instead of on population central-tendencies.

y Under DEA every unit is compared to an efficient unit or a combination of efficient units. The comparison, therefore, leads to sources of inefficiency of units that do not belong to the frontier;

y Current study uses multiple inputs and outputs. DEA is adaptable for multiple inputs and outputs

y In comparing financial and technical efficiencies, the focus will be on the management performance. DEA is considered to be more suitable for management performance rather than the stochastic frontier analysis which focus primarily on micro economic issues

y DEA doesn’t require any preconceived assumptions as it is based on linear programming techniques

y DEA enable us to identify the specific DMU that can be used as the benchmark against the inefficiency DMUs. One of the primary objective of the current study is find out the most efficient banks among the various categories of banks functioning in India.


49

Due to aforementioned reasons, DEA-both input oriented CCR and BCC models were adapted to measure technical, pure, scale efficiencies as well as return to scale (RTS) of the related banks under study.

3.6.1 Input and output selection for measuring technical, pure technical and scale efficiency

There are five approaches which are widely used to define the bank inputs and outputs under DEA studies.

They are: - (i) production approach and (ii) intermediation approaches, (iii) asset approach, (iv) user cost approach and (v) value added approach.

In the production approach, banks are viewed as producers of loans and deposits account services using available inputs. Under this approach the outputs are measured by the number of accounts serviced as opposed to the rupee value and interest expenses are excluded from the total cost.

Under the intermediation approach banks are considered as intermediaries between liability holders and those who receive bank funds and output is measured by the rupee value of earning assets of the bank with inputs being labour, capital and deposits.

The asset approach is was first suggested by Sealey and Lindley (1977) it treats banking activity, focusing exclusively on the role of banks as financial intermediaries between depositors and final uses of bank assets. The main criticism leveled at the intermediation and asset approaches is that they do not take into consideration the substantial amount of resources that the banks devote intoacquiring deposit funds, particularly demand and savings deposits (Berger and Humphrey, 1992).

Hancock (1985) was the first to apply the user cost approach to banking, which determines whether a financial product is an input or an output on the basis of its net contribution to bank revenue. If the financial returns on an asset exceed the opportunity cost of the funds or alternately, if the financial costs of a liability are less than the opportunity cost, they are considered as outputs; otherwise, they are considered as inputs. This approach identifies the inputs of the production process in the banking industry as “the net cost a bank must sustain in a given period of time in order to hold one unit of the financial instrument associated with the service”. In operational terms, user cost is calculated as the difference between all the revenues and all the costs (including the opportunity cost) generated by a financial


50

instrument in the bank’s portfolio. For example, the cost of using a bank loan can be approximated by the difference between the interest rate on a riskless security of equal amount (opportunity cost) and the expected yield of the loan. Here, deposits are included among output.

The value-added approach, as developed by Berger et al. (1987), differs from the asset and user cost approaches in that it considers all liability and asset categories to have some output characteristics rather than distinguishing inputs from outputs in a mutually exclusive way. This approach identifies any balance sheet item (assets or liabilities) as output if it contribute to the banks’ value added (i.e., business associated with the consumption of real resources), otherwise it is considered as an input or non-relevantoutput. Under this approach, the major categories of produced deposits (e.g., demand, term and saving deposits) and loans (e.g., mortgages and commercial loans) are viewed as important outputs because they form a significant proportion of value added.

In choosing the appropriate approach, Berger and Humphrey (1997) suggested that the intermediation approach is the most appropriate for evaluating the entire bank because it is inclusive of interest expense (income paid to depositors), which often accounts for one-half to two third of total costs. Meanwhile, he recommended that the production approach is more appropriate for evaluating the efficiency of the bank’s branches because branches process customer documents for the banks as a whole. In majority of the empirical literature, intermediation approach is used as opposed to the production approach. Hence the current study has adopted intermediation approach in selecting input and output variables for computing the various efficiency scores of the selected banks as it fit more into the performance objectives of the study.

The current study has selected 4 inputs which are: (i) The number of employees (labour);(ii) Interest expenses;(iii) Non-interest expenses; and(iv) Total deposits

And the chosen outputs are:(i) Advances (total loans);(ii) Interest income; and(iii) Non-interest income


51

These inputs are chosen as they directly results in the production of chosen outputs through the intermediary function of the bank. The efficiency scores calculated will capture the ability of the banks to create loans, interest income, non- interest income using the inputs like labour, interest and non- interest expended. The inclusion of non – interest expense and income created by the bank enable us to know whether the banks are succeeded in taking advantage of the recent changes in the Indian banking industry where the new generation private banks and vastly experienced foreign banks have drifted from the traditional banking services and ventured into non-traditional banking activities. All the input and output variables except labour are measured in millions of rupees. The number of employees and deposits created in the bank together create lo ans (advances). In creating the loans, the cost involved is interest and also there would be non-interest expenses. Whether they have used the resources optimally can be verified by the output that are created which is the interest income and non-interest income as well as loans. Efficiency of the firms is gauged by minimising the inputs to maximise the outputs. A low efficiency scores attained by the bank clearly postulate the inability of such banks to walk with the market changes along with their peers hence a wastage of resources too. Some notable studies in banking efficiency analysis, like Hassan (2002), Sufian and Majid (2007) have also used inputs and outputs that is been adopted in in this study. The below diagram shows intermediation approach of banks whereby they transfer the inputs to outputs.

Figure 3.9 banks as Intermediaries turn inputs into outputs

Source: Author’s construction based on the review of literature conducted for this study


52

Hamim, Sayed, Naziruddin (2006) anlaysed 47 studies conducted on the efficiency evaluation using DEA and found that out of the 49 studies conducted during the period 1985-2005, 34 of them have used Intermediation approach. Below table shows the number studies that have used the inputs and outputs employed in the current study.

Table 3.1. Frequency of the use inputs-outputs used in the current study compared with other studies

Inputs OutputsLabour (39)Deposits (15)Interest expense (10)Non-interest expense (8)

Loans or advances (13)Interest income (8)Non-interest income (13)

The above table shows the frequency of the inputs and outputs selected for this study used in similar studies.

3.7 Conclusion:The present study has been envisaged to evaluate the financial performance and

Efficiency of commercial banks in India under four ownership categories viz: SBI, Public sector, Private sector and Foreign Banks. To assess the financial performance we have used CAMEL parameters. In case of efficiencies our focus is on technical, pure technical and scale efficiencies for which we have used DEA. The theoretical and conceptual framework relations to the CAMEL and Efficiencies have been discussed in this chapter. It should be emphasised that this study strictly adopts the models of Ferrel (1957). Coelli (2004), Cooper, Seiford and Tone (2007) for the estimation of performance and efficiency parameters.

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Testing The Reliability of A Questionnaire: Cronbach’s Alpha Coefficient

A. VIJAYALAKSHMI*

IntroductionIn Survey research, constructing a questionnaire is a paramount task of a

researcher. There are various instruments to collect primary data, but the most prominent one is a structured questionnaire. Since a questionnaire consists of a anumber of items in a defined order, it is essential to test the Reliability (internal consistency) of the items incorporated in the questionnaire.

Even a well-defined problem and systematically gathered data will be of no use if the items in the questionnaire are inconsistent (or) unreliable. Hence, testing the reliability (consistency) of a questionnaire becomes one of the most important tasks in a survey researrch. The objective of this paper is to discuss the methodological aspects of testing the reliability (or) internal consistency of a questionnaire.

Scaling techniqueResearchers quite often face measurement problem, especially when the

concepts such as attitudes and opinions measured are complex and abstract differ and they do not possess the standardised measurement tools. Among the scales, the most prominent and widely applied scale to measure the attitude/response of the respondents is Likert-type scale (also known as summated scale).

Often data collected in Business Management related to attitudes, emotions, opinions and personalities involves the use of Likert-Type scales. As individuals attempt to quantify constructs which are not directly measurable they often use multiple item scales and summated ratings to quantify the construct of interest.

4

*Faculty, Einstein Academy of Management studies, Vadavalli, Coimbatore – 641046.


54

Summated Scales (Likert-type Scales):Summated scales consist of a number of statements which express either a

favourable or unfavourable attitude towards the given object to which therespondent is asked to react. The respondent indicates his/her agreement or disagreement with each statement in the questionnaire. Each response is given a numerical score, indicating its favourableness or unfavourableness, and the scores are totalled to measure the respondent’s attitude. In other words, the overall score represents the respondent’s position on the continuum of favourableor unfavourableness towards an issue. For example, when asked to express opinion whether the respondent considers their job quite pleasant, the respondent may respond in any one of the following ways:

i. Strongly agreeii. Agreeiii. Undecidediv. Disagree, andv. Strongly disagree

These five points constitute the scale. The above example is a 5 point scale, which at one extreme of the scale there is strong agreement with the given statement and at the other is strong disagreement, and between them lie intermediate points.

ReliabilityThe two most important and fundamental characteristics of any measurement

procedure are reliability and validity. Reliability is defined as the extent to which a questionnaire, test, observation or any measurement procedure produces the same results on repeated trials. In short, it is the stability or consistency of scores over time or across raters. Keep in mind that reliability pertains to scores not people. Thus, in research we would never say that someone was reliable. The extent to which they agree on the scores for each contestant is an indication of reliability. Similarly, the degree to which an individual’s responses (i.e. their scores) on a survey would stay the same over time is also a sign of reliability. An important point to understand is that a measure can be perfectly reliable and yet not be valid.

Assessing the Three Aspects of ReliabilityThere are three aspects of reliability, namely: equivalence, stability and internal

consistency (homogeneity). The first aspect, equivalence, refers to the amount of agreement between two or more instruments that are administered at nearly the same


55

point in time. Equivalence is measured through a parallel forms procedure in which one administers alternative forms of the same measure to either the same group or different group of respondents. The higher the degree of correlation between the two forms, the more equivalent they are. Another situation in which equivalence will be important is when the measurement process entails subjective judgements or ratings being made by more than one person. The same can be said for a situation in which multiple individuals are observing ‘behaviour’. The observers should agree as to what constitutes the presence or absence of a behaviour as well as the level to which the behaviour is exhibited. In these scenarios equivalence is demonstrated by assessing interrater reliability which refers to the consistency with which observers or raters make judgments. The procedure for determining interrater reliability is:

# of agreements / # of opportunities for agreement x 100.

Thus, a situation in which raters agrees a total of 75 times in 90 opportunities (i.e., unique observations or ratings) produces 83% agreement. (75/90 = .83 x 100 = 83%)

The second aspect of reliability, stability, is said to occur when the same or similar scores are obtained with repeated testing with the same group of respondents. In other words, the scores are consistent from one time to the next. Stability is assessed through a test-retest procedure that involves administering the same measurement instrument to the same individuals under the same conditions after some period of time. Test-test reliability is estimated with correlations between the scores at Time 1 and those at Time 2 (to Time x).

The third and last aspect of reliability is internal consistency (or homogeneity). Internal consistency concerns the extent to which items on the test or instrument are measuring the same thing. If, for example, you are developing a test to measure organizational commitment you should determine the reliability of each item.

If the individual items are highly correlated with each other you can be highly confident in the reliability of the entire scale. The appeal of an internal consistency index of reliability is that it is estimated after only on test administration and therefore avoids the problems associated with testing over multiple time periods. Internal consistency is estimated via the split-half reliability index, coefficient alpha (Cronbach, 1951) index or the Kuder-Richardson formula 20 (KR-20) (Kuder & Richarson, 1937) index. The split-half estimate entails dividing up the test into


56

two parts (e.g. odd / even items or first half of the items / second half of the items), administering the two forms to the same group of individuals and correlating the responses. Coefficient alpha and KR-20 both represent the average of all possible split-half estimates. The difference between the two is when they would be use to assess reliability. Specifically, coefficient alpha is typically used during scale development with items that have several response options (i.e. 1 = strongly disagree to 5 = strongly agree) whereas KR-20 is used to estimate reliability for dichotomous (i.e. Yes/No ; True/False) response scales. The formula to compute KR-20 is:

KR-20 = N / (N-1) [ 1- Sum (piqi) / Var (X) ]

Where Sum (piqi) = sum of the product of the probability of alternative responses; and to calculate coefficient alpha:

α = N/(N-1) [1 – Sum (piqi) / Var (X)]

Where N = # items

SumVar (Yi) = sum of item variances

Var(X) = composite variance (Allen & Yen, 1979)

The higher the reliability value the more reliable the measure. The general convention in research has been prescribed by Nunnally and Bernstein (1994) who state that one should strive for reliability values of 0.70 or higher. Reliability values increase as test length increases (see Gulliksen, 1950). That is, the more items you have in your scale to measure the construct of interest, the more reliable your scale will become. However, the problem with simply increasing the number of scale items when performing applied research is that respondents are less likely to participate and answer completely when confronted with the prospect of replying to a lengthy questionnaire. A well-developed yet brief scale may lead to higher levels of respondent participation.

Alpha testCronbach’s alpha test allows researchers to select adequate set of questions

for developing a scale needed to measure a particular construct of interest. It is a measure of reliability (or) internal consistency. This measure was originally developed in a context where set of questions (also known as items) are asked to a


57

group of individuals with the objective of measuring a specific construct. The extent to which all questions contribute positively towards measuring the same concept is known as internal consistency. This is a key element for evaluating the quality of the overall score. Cronbach’s alpha is one of the most widely-used measures of internal consistency.

Case study:Testing the reliability of a questionnaire

For the purpose of testing the reliability (or) internal consistency of the questionnaire, the study has utilized- a portion of an actual questionnaire used in a management study entitled “Study of work culture; its dimensions, measurement and impact on work performance”

The details of the questionnaire and concepts are presented in the successive sections.

I. The Employee Commitment/lnvolvement QuestionnaireThe employee commitment / involvement questionnaire was developed to

measure the extent of commitment / involvement shown by the employees on the awareness of the company’s annual targets, machine capacity, work plan,processing style, competence of their colleagues, standards of quality re( ‘aired, rejections and delivery schedule.

The second dimension pertains to the degree of competence / shortcom ‘igs, which includes competency of the superiors to reduce rejection r lte, maximize capacity utililzation and error rectification.

The third dimension relates to the degree to closeness of the work behaviour which includes identification of wastages; bring them to the notice of the superiors, cost reduction schemes, overtime duty, absenteeh m, innovative practices forproductivity improvement and consultatic ns among the peer group to improve job performance.

The fourth dimension relates to the degree of reaction of the employees when the Company/ Department achieve targets, sense of belonging and feeling pros d of the country.

To find the reliability of the questionnaire a pilot study has been conducted and the results are presented in the successive sections.


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Administration of Employee Commitment / Involvement questionnaireThe Employee Commitment / Involvement questionnaire was administered

to the respondents individually and the following instructions were given. ‘Please give your views on the factors listed below. There is no right or wrong answer. Your frank answers are the best. Your response will be kept confidential and will not be made available to your superiors. If any of the factors is not applicable to you please write : N.A. Please do not omit any time.

Respond on the degree of competence on a scale 1 to 5.

1. Below average / Near

2. Average / Rarely

3. Above average / Sometimes

4. Good / Frequently

5. Very good / Always

S.No. Particulars Response

1. The competency available with our superiors for achieving reduced rejection / required quality standards is found to be

1 2 3 4 5

2. The competency available with out superiors for maximising capacity utilisation is found to be

1 2 3 4 5

3. The awareness of employees on the short comings / likely deviations on job plan given by their respective superiors is found to be

1 2 3 4 5

4. The employees are found to discuss with their respective superiors for rectification of shor t comings / likely deviations of the job plans, prior to implementation

1 2 3 4 5


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Table 1: SELECTED RESPONSE TO THE GIVEN QUESTIONNAIRE

EmployeeQUESTIONS / ITEMS

Q1 Q2 Q3 Q41 4 3 3 32 3 4 3 33 3 4 4 44 3 4 4 45 3 4 3 36 3 4 3 47 4 4 4 48 3 3 3 39 2 2 2 310 4 4 4 4

Total 32 36 33 35

Tools of analysisReliability test: Cronbach’s Alpha coefficient

In order to have a keen understanding in the methodological aspects of Cronbach’s Alpha Test, an attempt has been made in this section to explain the methodology through both manual calculation and as well as using SPSS1 software.

Cronbach’s Alpha coefficient test: A manual computation:

It is one of the most widely used measures of internal consistency of items in a questionnaire. The mathematical representation of Cronbach’s Alpha coefficient test is as follows;

Whereα = Cronbach’s Alpha valueK = Number of items on questions

= The variance associated with item i= The variance associated with the total (or sum) of all k item scores.


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Results:For the given set of questions, the responses were recorded in table 1, and

accordingly the following statistics were computed and presented in Table 2.

TABLE 2: COMPUTED STATISTICAL VALUE FOR THE DATA PRESENTED IN TABLE 1

S.No. Particulars Value1. Mean Employees total score 13.802. S.D of Employees total score 3.2683. Variance of Employees total score 10.684. Sum of items variances 4.925. Number of items 4

Cronbach’s Alpha Coefficient = ( (4 / (4-1) ) * (1 - 4.92 / (10.68 ^ 2)) ) = 4 / 3 * (1 – 4.92 / 10.68) = 4 / 3 * (1 - 0.461) = (1.33) * (0.539)Cronbach’s Alpha Coefficient = 0.717

SEM (Standard Error of Measurement) =

= 3.268 ( = 3.268 * 0.532 SEM = 1.74

Discussion:Since the calculated Cronbach’s alpha coefficient 0.717 which is greater than

0.70 benchmark value prescribed by Nunnally & Bernstein (1994), it is concluded that the items incorporated in the questionnaire have internal consistency. Hence the items included in the questionnaire are statistically reliable and can be used for data collection.

However, manual calculation of Cronbach’s alpha does not provide any information regarding addition or deletion of some items in the questionnaire and its impact on the numerical value of Cronbach’s alpha. This is accomplished through statistical package, SPSS.


61

Cronbach’s Alpha coefficient test using SPSS softwareThis package helps a researcher not only to check the internal consistency

through α value, but also facilitates to decide whether a particular item should be deleted for want of internal consistency.

The results obtained from SPSS package for the data presented in table are as follows;

TABLE 3: CRONBACH’S ALPHA COEFFICIENT TEST USING SPSSReliability Statistics

Cronbach’s Alpha Cronbach’s Alpha Based on Standardized Items

N of Items

0.717 0.720 4

Item StatisticsQuestions Mean Std.

DeviationN

Q1 3.2 0.6 10Q2 3.6 0.67 10Q3 3.3 0.64 10Q4 3.5 0.50 10Item-Total Statistics

Questions

Scale Mean if Item

Deleted

Scale Variance if Item

Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach’s Alpha if

Item Deleted

Q1 17.1000 12.100 0.671 0.750 0.682Q2 16.6000 8.627 0.714 0.874 0.628Q3 17.1000 12.767 0.623 0.659 0.777Q4 16.6000 9.378 0.773 0.800 0.613Scale Statistics

Mean Variance Std. Deviation N of Items

13.80 10.68 3.268 4


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Additional information available from SPSS results are:The values presented in column “Correlated item-total correlation” are

the correlation between each item and the total score from the questionnaire. In a reliable scale all items should correlate with the total score and more importantly the values must be greater than 0.30. In this case, all the questions have correlation value more than 0.30 indicating reasonable degree of correlation with the total scores.

A very low correlation value implies that, the particular item does not correlate very well with the scale overall, and hence this variable has to be excluded from the questionnaire in order to increase the internal consistency of the items.

The values in the column “Cronbach’s Alpha if item deleted” are the overall alpha coefficient if a particular item is deleted from the questionnaire.

Tips improve reliability of a questionnaire: y Clear conceptualisation.

y Standardisation: By giving programmes to the investigators will help them to follow a standard criterion while collecting data, which will increase the reliability of the response.

y Increase the number of items in the questionnaire.

y Using more precise measurement technique (scaling techniques).

y Using multiple indicators to measure a particular concept: For example, if the study is about anxiety, the researcher can use a variety of indicators like heart beat rate, Glucose level, BP, etc. to supplement the concept; and

y Finally, pilot testing and replication are the most import methods to increase the reliability of a questionnaire.

Conclusion:The Cronbach’s Alpha reliability coefficient normally ranges between 0 and

1. However, there is actually no lower limit to this coefficient. The closer Cronbach’s alpha coefficient is to 1.0 the greater the internal consistency of the items in the scale. Based upon the formula

α = rK / (1 + CK — 1)r), where K is the number of items considered and ‘r’ is the mean of the inter-item correlations, the size of alpha is determined by both the number


63

of items in the scale and the mean inter-item correlations. George and Mallory (2003) provide the following rules of thumb: >0.9 — Excellent; >0.8 — Good; >0.7 — Acceptable; >0.6 — Questionable; >0.5 — Poor and <0.5 — Unacceptable.

Factor analysis is a method to determine the dimensionality of a scale. When using Likert-Type scales it is imperative to calculate and report Cronbach’s alpha coefficient for internal consistency reliability for any scales or subscales one may be using. The analysis of the data then must use these summated scales or subscales and not individual items. If one does otherwise, the reliability of the items is at best probably low and at worst unknown. Cronbach’s alpha does not provide reliability estimates for single items.

64

Impact of M&A on Growth Acceleration and Structural Break In Indian Pharmaceutical

Industry

K.A. KEERTHI PRABHU*

3.1 IntroductionMergers and acquisitions have always been taken as the one of the most

convenient and profitable strategy to survive in the competitive environment. The intent is to gain competitive advantage, capitalizing larger market share, creating profits through synergistic effect and economies of scale thereby bringing competencies and capabilities for a sustainable corporate performance. The rapid changing technologies, fast moving economies, the positive impact of globalization, accelerated revenues has triggered this activity manifold in recent times (Neha Duggal, 2015).

Studies by Ravi Sanker & Rao K.V ,1998; Jayakumar.S, 1999; Canagavally.R, 2000; Pawaskar, 2001; Ms.Surjit Kaur, 2002; Gopinath, 2007; Nayyar, 2007; Vanitha & Selvam, 2007 and Mantravedi & Reddy, 2007 have confirmed the conclusion of Puri, 1981; Healy et al, 1992; Cantwell & Santangelo, 2002 and Fred et al, 2005 that market power, cost cutting, access to market or technology, to attain a mixture of synergies and economies of scale are the reasons why firms agree for and adopt M&A as a growth strategy, and there exists significant difference in the performance of acquirer firms after merger or takeover. Bradley et al, 1987; Berkovitch & Narayanan, 1993; Beena, 2004 & 2010; Gondhalekar & Bhagwat, 2005 have reported positive financial synergy effects in acquirer firms.

Another studies by Viji, 2014; Allirani, 2013; Sivamurugan, 2010; Kannan and Raveendran, 2009; Saravanakumar, 2009; Nagaraj, 2008; Balakrishnan and

5

*Research Scholar, Dept. of Econometrics, Bharathiar University, Coimbatore - 46.


65

Parameswaran, 2007; Rodrik and Subramanaian, 2005; Nagaraj, 2003; Rani and Unni, 2004; Balakrishnan and Suresh Babu, 2003; Golder, 2000 and Unni et al, 2001 have categorically established that policy shift/ policy change trigger new growth dynamics in Indian manufacturing industry with reference to Economics Reform Policies of 1991. Prominent firms in Indian pharmaceutical industry opted for M&A strategy in the year 2004-05 to augment their growth prospects.

From the existing literature allirani & viji studied the impact of M&A on machinery and chemical industry respectively. Since there are no studies on the impact of M&A strategy on growth in Indian pharmaceutical industry, this study has been envisaged to answer the following research question.

1. What is the impact on M&A strategy on growth in terms of selected economic indicators?

2. What is the timing of structural break in selected growth indicators?To answer the above research questions we have selected 21 firms of Indian

pharmaceutical industry during pre (1995-2004) and post (2005-14) merger period.

3.2 Growth Indicators This chapter presents a discussion on the growth of Indian Pharmaceutical

Industry during pre (1995 to 2004) and post (2005-2014) merger periods with reference to the following growth indicators viz; Gross Fixed Capital Formation, Employment and Output.

In January 2005, India amended its patent laws governing pharmaceuticals, bringing them into conformance with the WTO TRIPs agreement. Under the new patent law, Indian drug makers can no longer manufacture and market reverse-engineered drugs patented by foreign pharmaceutical firms. Multinational pharmaceutical firms have entered India after 2005 using the same resource base. Firms started performing more mergers and acquisitions deals, and form other alliances with domestic and foreign pharmaceutical firms to sustain increasing pressure on profit margins (Kale, 2007).


66

3.3 Average (Annual Trend) growth and Growth Acceleration / Deceleration3.3.1 Model

For estimating the acceleration in the growth rates, we have used the following semi-logarithmic specification of a non-linear (quadratic)1 equation

221ln ttt ββα ++=Χ (1)

where Xt = Gross Fixed Capital Formation measured in terms of Gross Fixed Assets (GFA)

t = time period

t2 = cumulative time period

α , β 1, β 2= parameters to be estimated

The coefficient 1β is interpreted as average annual trend growth and 2β gives the rate of acceleration or deceleration in the average growth (Kaur, 2007). The estimated results obtained from the regression (1) are presented in Table 3.2.

3.3.2. Gross Fixed Capital Formation (GFCF) Since β1 the industry was statistically significant at 1 percent level. Hence the

annual growth of gross fixed capital formation was 9.8 percent during the period under review. Regarding acceleration even though β2 was positive, we could not draw any inference because β2 was not statistically significant. Across the firms, the growth rate was significant in 7 firms only the maximum being 25.3 percent in Piramal Enterprises Ltd and lowest growth of 3.9 percent was recorded in two firms viz: Granules India Ltd and Indoco Remedies Ltd. In the remaining firms also, growth rates were positive but not significant.

Regarding growth acceleration in GCFC, β2 was positive and statistically significant in 7 firms indicating significant acceleration in the trend growth. In majority of the remaining firms also the trend growth accelerated (positive β2) but its significance could not be ascertained. As such the growth scenario of GFCF in this industry was not that much encouraging during the period under review.

3.3.3 Employment In case of industry employment, β1is positive and β2is negative and both

are statistically highly significant at 1 percent level, implying 5.3 percent growth in average employment during the period. The deceleration in the growth rate of employment was about 0.7 percent. Since the parameter which signifies the


67

deceleration (β2) in the trend growth is statistically highly significant it is inferred that the employment growth in this industry significantly decelerated.

Firm wise analysis indicates that 5 firms experienced de-growth in average employment. In four other firms, the annual average employment growth was not statistically significant. As regards trend growth, almost all the firms recorded deceleration in trend growth confirming jobless growth in this industry.

3.3.4 Output Regarding output, the industry’s annual average growth rate was 6.9 percent

with meager growth acceleration of 0.05 percent over time. It may be observed that output growth was lesser than the rate of growth of GFCF.

Growth in output was visible in most of the firms in which maximum of 18.5 percent was found in Cipla Ltd followed by 17 percent growth in Sun Pharmaceutical Indus Ltd. As regards growth trends, five firms experienced deceleration and remaining 16 firm’s acceleration and the growth scenario in output of this industry appears to be in tune with the national scenario during the period under review.

Table: 3.1. Average Annual Growth Trends in Indian Pharmaceutical Industry during 1995-2014.

Acquirer firmsGross Fixed Capital Formation Employment Output

α β1 β2 DW α β1 β2 DW α β1 β2 DW

Alembic Ltd.7.23* 0.106 0.002

1.1712.32* -0.004 -0.0006

1.7410.10* 0.065 0.0006

1.41(-19.84) (-0.94) (-0.74) (-78.31) (-0.16) (-0.74) (-40.67) (-1.63) (-0.04)

Aurobindo Pharma Ltd.

7.18* 0.209 0.0031.39

9.17* -0.002 -0.00052.12

11.70* 0.033** 0.00031.53

(-23.85) (-1.43) (-1.32) (-193.22) (-0.20) (-1.19) (-97.17) (-1.84) (-0.44)

Cipla Ltd.7.52* 0.198* 0.006*

2.2410.35* 0.030* -0.005*

1.2811.40* 0.185 0.002

1.91(-28.83) (-4.28) (-3.36) (-161.92) (-3.03) (-2.65) (-25.18) (-1.29) (-0.65)

Glaxosmithkline Pharmaceuticals Ltd.

7.53* 0.226** 0.0021.05

11.99* 0.033** -0.004*1.35

10.14* 0.115 0.003**1.94

(-29.74) (-2.14) (-0.99) (-98.64) (-2.44) (-2.54) (-50.89) (-0.46) (-2.38)

Granules India Ltd.

7.05* 0.039 -0.0021.15

8.74* 0.05* -0.006*1.72

10.58* 0.044*** -0.002**1.05

(-25.95) (-0.72) (-0.90) (-102.37) (-2.84) (-5.35) (-68.98) (-1.8) (-2.15)Hindustan Antibiotics Ltd.

8.09* 0.232* 0.003**1.12

11.81* 0.061** -0.0021.08

10.87* 0.068* 0.00041.69

(-11.65) (-2.91) (-1.74) (-63.06) (-2.01) (-0.84) (-162.54) (-6.3) (-1.12)Indoco Remedies Ltd.

8.05* 0.039 -0.0021.65

11.74* 0.05* -0.005*1.29

10.58* 0.044*** -0.002**1.05

(-25.95) (-0.72) (-0.90) (-102.37) (-2.84) (-5.35) (-68.98) (-1.8) (-2.15)Intas Pharmaceuticals Ltd.

7.40* 0.082 -0.00071.24

10.51* 0.018** -0.00061.92

6.09* 0.087* -0.0041.42

(-25.15) (-1.56) (-0.35) (-211.73) (-2.06) (-1.68) (-63.57) (-3.42) (-0.43)

Jubilant Life Sciences Ltd.

7.23* 0.126 0.0021.96

10.32* -0.004 -0.00061.86

9.10* 0.065 0.00061.37

(-19.84) (-0.94) (-0.74) (-78.31) (-0.16) (-0.74) (-40.67) (-1.63) (-0.04)


68

Kopran Ltd.8.18* 0.189 0.003

1.3611.17* -0.002 -0.0005

1.069.70* 0.133** 0.0003

1.56(-22.85) (-1.43) (-1.32) (-193.22) (-0.20) (-1.19) (-97.17) (-1.84) (-0.44)

Merck Ltd.8.29* 0.212* 0.003*

1.838.82* 0.002* 0.002*

2.2110.13* 0.168 0.006*

1.78(-28.57) (-3.64) (-1.77) (-107.58) (-2.90) (-3.74) (-42.5) (-1.38) (-3.36)

Mylan Laboratories Ltd.

7.45* 0.209* 0.005*2.12

11.07* 0.02 -0.006*1.32

9.58* 0.157* -0.0011.73

(-27.31) (-3.5) (-2.67) (-149.39) (-1.56) (-4.29) (-103.1) (-3.46) (-1.56)

Natural Capsules Ltd.

5.66* 0.108 -0.00042.53

10.42* -0.037 0.004*1.08

7.96* 0.007 0.003*1.65

(-14.05) (-1.52) (-0.15) (-74.86) (-1.64) (-3.43) (-46.35) (-0.22) (-2.96)

Pfizer Ltd.5.53* 0. 236** 0.002

1.9410.99* 0.044** -0.006*

1.659.14* 0.115 0.003**

1.95(-19.74) (-2.14) (-0.99) (-98.64) (-2.44) (-2.54) (-50.89) (-0.46) (-2.38)

Piramal Enterprises Ltd.

6.29* 0.253* 0.003***1.08

10.82* 0.002* 0.004*2.2

11.13* 0.168 0.006*1.78

(-28.57) (-6.64) (-1.77) (-161.58) (-2.90) (-3.74) (-3 3.71) (-1.38) (-3.36)Sun Pharmaceutical Inds. Ltd.

8.72* 0.221** 0.0021.32

12.61* 0.061* -0.005*1.14

12.67* 0.170* 0.0012.1

(-38.68) (-2.44) (-1.42) (-200.44) (-4.94) (-5.02) (-108) (-3.98) (-0.90)

Suven Life Sciences Ltd.

9.09* 0.067 -0.00021.41

10.08* 0.00001 -0.0041.49

11.62* 0.059 0.00021.45

(-25.38) (-1.52) (-0.09) (-47.54) (-0.003) (-1.12) (-41.24) (-1.69) (-0.13)

Themis Medicare Ltd.

7.45* 0.169* -0.005*1.87

8.07* 0.02 -0.006*1.96

10.58* 0.057* -0.0011.7

(-27.31) (-3.5) (-2.67) (-149.39) (-1.56) (-4.29) (-103.1) (-3.46) (-1.56)

Wanbury Ltd.7.52* 0.198* 0.006*

1.2610.35* 0.012* -0.006*

1.229.40* 0.085 -0.002

1.93(-28.83) (-4.28) (-3.36) (-161.92) (-3.03) (-2.65) (-25.18) (-1.29) (-0.65)

Wockhardt Ltd.6.27* 0.185* -0.002

1.3511.96* 0.011* -0.005**

2.357.25* 0.143* 0.0003

1.72(-15.97) (-2.66) (-0.61) (-99.47) (-6.98) (-2.25) (-42.46) (-5.17) (-0.29)

Wyeth Ltd.7.20* 0.045 -0.0001

1.669.12* 0.051 -0.003

1.3810.01* 0.036 0.0003

1.79(-38.98) (-0.83) (-0.25) (-113.96) (-0.64) (-1.65) (-54.12) (-1.09) (-0.28)

Indian Pharmaceutical Industry

8.72* 0.098* 0.0061.69

25.11 0.053* -0.0071.9

11.76* 0.069* 0.00051.71

(-99.25) (8.73) (-5.34) (-168.92) (-6.38) (-6.28) (-244.25) (-5.35) (-0.87)

Note: Figures in parentheses are t valuesβ1:- Average Annual Trend Growth; β2:- Rate of Acceleration or Deceleration in the Average Growth; DW:- Durbin Watson *= Statistically Significant at 1 % level. ** = Statistically Significant at 5 % level and *** = Statistically Significant at 10 % level.

3.3.5 Timing of Structural Break: Econometric ModelIn order to capture the impact of inorganic growth strategies during 2005 (i.e. the

bench mark taken for analysis) in Indian pharmaceutical industry, the present study separately covers two sub-periods naming them as pre-merger period and post-merger


69

period. The pre-merger period (henceforth Period I) from 1995 to 2004 and post-merger period (henceforth Period II) from 2005-2014 has been taken for analysis. It must be noted here that the impact of any policy change can only be found after a year of its implementation. In this study an equal lag length has been maintained as suggested by Balakrishnan (2005) in order to compare the average trend growth rate between the two periods. To put it in another way, the under lying exercise compares whether trend growth rates in the two periods are significantly different.

To carry out the analysis, the trend growth rates for the two periods (using a semi-logarithmic linear trend equation) are estimated separately.

tt 1ln βα +=Χ (2)for Period I (i.e. 1995-2004), t = 1…….10.

tt 1''ln βα +=Χ (3)

for Period II (i.e. 2005-2014) , t = 11……20.

Now, in order to investigate the issue of a structural difference in the trend equations of the two (pre and post-merger) periods, we tested the null hypothesis that the set of co-efficient in Period I [equation (2)] is equal to the set of coefficient in Period II [equation (3)]. Assuming the two equations to be structurally the same we test the null hypothesis (jointly):

'αα = '

11 ββ =

To find the differences in the coefficients (intercept as well as slope) in the pre and post- merger periods a “dummy variable approach”2 has been employed, we introduced both additive3 (D) and multiplicative4 (D multiplied by t) dummies to equation (2) and estimated the following expanded regression equation by polling all 20 observations.

iiit utDtD ++++=Χ )(ln 211 ββαα (4)1. The “dummy variable” is preferred to “Chow test” approach as it explicitly tells us whether the two regression equations are different on account of differences in the coefficient of the slope or of the intercept or coefficients of both the parameters. For details, The appropriate Chow test is ( )

( ) ( ) ( )[ ]knnkUR

URR FknnRSS

KRSSRSSF 2,

2121

~2/

/−+−+

−=

], see Gujarati (2013).2. It checks for the change in the intercept of trend equation. 3. It finds whether the trend rate of growth in the post-merger period has improved or not.


70

t = 1………20

where Di = 0 for all the observations for Period I (i e, 1995 to 2004) and 1 for the observations for Period II (i e, 2005-2014).From this it would follow:

tDXE

i

t10 βα +=

= for Period I (5)

and ( ) ( ) ttDXE

i

t212111 γγββαα +=+++=

= for Period II. (6)

It is important to note here that equations (2) and (3) are same as equations (5) and (6) respectively, with ( )11' ααγα +== and ( )212

'1 ββγβ +== . The coefficient

1α is the differential intercept (it tells by how much the value of the intercept in Period II differs from that of Period I ) and 2β is the differential slope coefficient (indicating the difference in the slopes of regression lines) for the period for which D1 =1 , i e, post-merger period. The signs of these coefficients indicate the direction of change in the intercept and slope during Period II of the study.

3.4 Gross Fixed Capital Formation (GFCF) As for as growth of capital is concerned there is almost unanimity among the

economists such as Uchikawa (2001) Balakrishan and Suresh Babu (2003) Mazumdar and Sakar (2004) Rani and Unni (2004) Nagaraj (2008) Kannan and Ravendran (2009)that since the early 1980’s there has been acceleration in capital intensification in the manufacturing sector. Thus the emerging hypothesis is that the Indian pharmaceutical industry showed more inclination towards capital intensification after M&A and this section seeks to test that hypothesis. This section discusses the average annual growth in gross fixed capital formation and its acceleration /deceleration in Indian pharmaceutical industry during pre (1995 to 2004) and post (2005-2014) merger periods.

3.4.1 Pre-Merger Period (1995-2004)

The average annual trend growth of gross fixed capital formation (Table – 3.2) during Period I was 6.8 percent. Across the firms, β1was statistically significant in 11 firms, and the growth rates ranged between the maximum of 10.3 percent in Merck Ltd. and minimum of 1.7% in Indoco Remedies Ltd. The rate of capital accumulation was very impressive in 8 acquirer firms, and their growth rates were higher than industry’s growth rate during the period under study. However the inter-firm growth rates varied widely indicating uneven capital intensification.


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3.4.2 Post-Merger Period (2005-2014) During Period II, the average growth rate accelerated to 7.1 percent and across

the firmsβ1was statistically significant only in 5 out of 21 firms; and ranged between 10.2 percent in Sun Pharmaceutical Indus Ltd and a minimum de-growth of 3.2 percent in Natural Capsules Ltd. The rate of capital intensification as evidenced by β1appears to be not uniform and unbalanced among acquirer firms, since the inter-firm variations in growth rates were much higher during Period II. To test whether the trend growth equation for Period II is structurally different from that of Period I, we have estimated the following equation with additive (D) and multiplicative (Dt) dummies:

Ln iiit utDtDGFCF ++++= )(ln 211 ββααwhere, GFCF is Gross Fixed Assets in constant pricest = time periodDi= additive dummyDiti= multiplicative dummyα , 1α , 1β , 2β = parameters to be estimatedui= stochastic elementAnd the results are as follows:

LnGFCFt = 8.72 + 0.59Di + 0.002t – 0.03Dit + ui 2R = 0.91D.W = 1.84

(118.72) (3.89) (7.98) (–0.69)

t = 1…….20.In the above regression, the ‘t’ values of both differential intercept ( )1α and

differential slope co-efficient ( )2β are statistically significant at 1 percent and 10 percent level respectively, we infer that the regressions for the two periods are structurally different. Hence the estimated trend growth of 7.0 % for Period II is significantly different from the annual growth of 6.8% estimated for Period I. As the value of ‘F’5 statistic was 9.36 which is statistically significant at 1 percent level we conclude that the growth rates of the two period are statistically different.

The Chow test also confirms that the parameters of pre and post-merger period growth estimates are signi6ficantly different at the industry level as well as in 18 acquirer firms. The emerging conclusion is that M&A has not led to capital intensification and as such the hypothesis that M&A leads to capital intensification does not hold.


72

Table: 3.2. Average Annual Trend Growth of Gross Fixed Capital Formatio in Indian Pharmaceutical Industry during pre and post-merger periods.

Acquirer firms

Pre – Merger Post- Merger Test of Stability:

Chow Test Calculated

Value of F*

Statistical Difference

α β1 DW α β1 DW

Alembic Ltd. 7.52* 0.043

2.125.02* 0.056

1.69 2.51 Significant(-41.89) (-1.92) (-10.93) (-1.65)


9.95* 0.0642.31

10.14* 0.0671.64 3.23 Significant

(-54.94) (-1.54) (-16.56) (-0.52)

Cipla Ltd. 6.55* 0.083***

1.358.60* 0.086*

1.56 1.68 Significant(-13.59) (-1.88) (-9.85) (-4.9)


8.97* 0.077*1.77

8.79* 0.0571.88 1.92 Significant

(-31.83) (-4.28) (-5.19) (-2.86)

Granules India Ltd. 7.77* 0.043

1.7310.13* 0.052**

1.59 1.71 Not Significant(-45.63) (-1.16) (-9.94) (-2.02)

Hindustan Antibiotics Ltd.

8.18* 0.064*1.23

9.62* 0.0811.87 2.14 Significant

(-29.93) (-2.12) (-12.68) (-1.07)

Indoco Remedies Ltd.

8.39* 0.0171.31

9.01* 0.0371.22 2.23 Significant

(-50.15) (-0.3) (-14.88) (-0.36)Intas Pharmaceuticals Ltd.

76.60* 0.0340.75

8.20* 0.0391.36 1.13 Not

Significant(-31.89) (-1.06) (-10.25) (-0.67)


6.18* 0.0241.72

9.62* 0.047***1.99 2.25 Significant

(-29.93) (-0.12) (-12.68) (-2.00)

Kopran Ltd. 8.71* 0.078*

1.368.60* 0.087

1.42 1.57 Significant(-48.88) (-4.91) (-11.74) (-1.2)

Merck Ltd. 8.97* 0.103*

1.86.79* 0.099

1.09 2.05 Significant(-31.83) (-4.28) (-5.19) (-2.86)


8.60* 0.0341.09

8.20* 0.0591.36 1.41 Significant

(-31.89) (-1.06) (-10.25) (-0.67)


8.39* 0.0272.31

9.01* 0.0321.22 1.23 N o t

Significant(-50.15) (-0.3) (-14.88) (-0.36)

Pfizer Ltd. 5.22* 0.082*

1.518.87* 0.085

1.84 4.15 Significant(-44.78) (-4.08) (-14.46) (-0.46)


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8.24* 0.061*1.31

8.00* 0.096**1.96 2.33 Significant

(-23.48) (-4.28) (-11.78) (-2.66)

Sun Pharmaceutical Inds. Ltd.

4.59* 0.095**1.8

7.46* 0.102**2 2.26 Significant

(-13.42) (-2.21) (-5.72) (-2.44)


9.95* 0.0622.31

10.14* 0.0671.8 3.23 Significant

(-54.94) (-1.54) (-16.56) (-0.52)


8.94* 0.071*1.93

9.63* 0.0621.15 4.42 Significant

(-46.38) (-3.06) (-14.33) (-0.56)

Wanbury Ltd. 8.40* 0.032

1.0910.28* 0.041

2.65 2.42 Significant(-28.63) (-0.56) (-10.95) (-1.63)

Wockhardt Ltd. 6.22* 0.064*

1.519.87* 0.067

1.84 4.15 Significant(-44.78) (-4.08) (-14.46) (-0.46)

Wyeth Ltd. 8.40* 0.069*

1.868.21* 0.072

2.77 2.51 Significant(-20.61) (-3.31) (-10.68) (-1.1)

IndianP h a r m a c e u t i c a l Industry

49.63 0.068* 2.68 15.72 0.0702.78 5.2 Significant

(8.93) (0.065) (12.15) (-0.51)

Note: Figures in parentheses are t valuesβ1:- Average Annual Trend Growth; β2:- Rate of Acceleration or Deceleration in the Average Growth; DW:- Durbin Watson; *= Statistically Significant at 1 % level. ** = Statistically Significant at 5 % level and *** = Statistically Significant at 10 % level.

3.4.3 Structural Break in Gross Fixed Capital Formation To examine the timing of the turnaround in the growth rates, we have considered

both additive and multiplicative dummies beginning from the mid-1990. This is to test whether the turnaround in growth occurred in response to inorganic growth strategies after M&A. After fitting a semi – logarithmic trend equation over the entire period from 1995 to 2014, the equation with both additive and multiplicative dummies beginning from 1996 onwards we re-estimated. The estimated regression equation using gross fixed capital formation data for the entire period i.e. 1995 to 2014 is;

lnGFCFt = 7.02 + 0.07 +ut t = 1………….18(28.34) (27.33)

From table 3.3, it is found that the sign of the coefficient of the multiplicative dummy (D*t) is positive and significant at 10 percent indicating that acceleration in growth occurred from 1998 onwards. The period 2002 showed deceleration in


74

growth (although the coefficient is negative statistically significant) up to 2004. The acceleration reemerged from 2006 to 2012. Despite the fact structural break surfaced in 1998, it was deteriorated during Period II up to 2004. From the result it is inferred that the turnaround break in trend rate became visible in 1998 to 2001 and 2006 to 2012.

Thus it is evident that structural break had occurred during pre M&A as well as post M&A and therefore M&A cannot be considered as a causative factor for structural break in capital intensification process.

Table: 3.3. Search for Structural Break in Gross Fixed Capital Formation of Indian Pharmaceutical Industry during pre and post-merger periods.

Dummy Variable

Intercept()

Additive Dummy ()

Time()

Multiplicative Dummy ()

beginning 1996

7.431*(27.433)

-0.005(-0.016)

-0.056(-0.292)

0.152(0.807) 0.969 1.78

beginning 1997

7.231*(49.039)

0.149(0.934)

0.015(0.149)

0.081(1.071) 0.980 1.33

beginning 1998

7.034*(54.556)

0.428**(2.451)

0.185*(3.250)

0.091***(1.998) 0.972 1.62

beginning 1999

7.013*(62.013)

0.426**(2.694)

0.201*(5.056)

0.104**(2.601) 0.973 1.64

beginning 2000

7.076*(68.897)

0.363**(2.282)

0.159*(5.439)

0.073**(2.308) 0.971 1.68

beginning 2001

7.124*(75.645)

0.313***(1.964)

0.148*(6.086)

0.057***(2.174) 0.970 1.67

beginning 2002

7.131*(82.079)

0.236(1.509)

0.153*(7.979)

-0.054***(-2.609) 0.973 1.56

beginning 2003

7.178*(85.486)

0.180(1.016)

0.137*(8.055)

-0.038**(-1.955) 0.969 1.65

beginning 2004

7.219*(86.341)

0.147(0.702)

0.122*(8.393)

-0.027***(-1.792) 0.967 1.46

beginning 2005

7.259*(89.342)

0.167(0.673)

0.114*(8.571)

0.020(1.864) 0.965 1.38

beginning 2006

7.269*(93.410)

0.153***(1.794)

0.117*(9.493)

0.017***(1.903) 0.964 1.34


75

beginning 2007

7.278*(97.261)

0.158(1.331)

0.112*(10.414)

0.015**(1.401) 0.964 1.35

beginning 2008

7.272*(102.234)

0.037(1.002)

0.112*(11.772)

0.010***(-0.961) 0.964 1.27

beginning 2009

7.315*(104.432)

0.194(0.804)

0.107*(12.260)

0.014**(-0.566) 0.963 1.28

beginning 2010

7.321*(108.372)

0.356(0.648)

0.105* 0.021**0.963 1.28

(13.275) (0.714)beginning 2011

7.329*(113.546)

0.719(1.020)

0.104*(14.536)

0.036**(1.030) 0.964 1.36

beginning 2012

7.337*(130.217)

2.050**(2.357)

0.102*(17.171)

0.096***(2.303) 0.971 1.36

beginning 2013

7.332*(135.286)

3.087**(2.484)

0.104*(19.220)

0.144(2.495) 0.971 1.51

Note: Figures in parentheses are t valuesDW: - Durbin Watson; *= Statistically Significant at 1 % level. ** = Statistically Significant at 5 % level and *** = Statistically Significant at 10 % level.

3.5 Employment There has been considerable debate in India about the impact of growth on

employment especially in the organized manufacturing sector for different periods since early 1980’s and there is unanimity amongst scholars that the organized manufacturing sector registered “jobless growth” during 1980-81 to 1990-91,Goldar (2000). The employment stagnation was also confirmed by World Bank (1989), Fallon and Lucas (1993), Papola (1994), Ghose (1994), Nagaraj (1994), Kannan (1994) Bhalotra (1998) and Dutta Roy (1998). Kannan and Raveendran(2009) and Sivamurugan (2010) confirm jobless growth during post reform period.

Indeed, most studies that have dealt with the likely effects of policy changes on the employment situation in India have been pessimistic about the prospects of employment growth in the post-1991 also (Mundle 1992, 1993; Deshpande 1992; Bhattacharya and Mitra 1993; Mitra 1993; Agarwal and Goldar 1995; Kundu 1997). In this section we are examining the impact of M&A on the growth of employment in Indian pharmaceutical industry. Further, employment growth in the Indian pharmaceutical industry at the aggregate and acquirer firm level are reported in table 3.5.


76

3.5.1 Pre-Merger Period (1995-2004)The average growth of employment (Table 3.4) during Period I was 3.2 percent.

Among the acquirer firms, 12 firms have recorded positive growth and remaining 9 firms de-growth during Period I. The growth rates ranged between the maximum of 8.7 percent in Kopran Ltd and a minimum of 1.7 percent in Suven Life Sciences Ltd.

3.5.2 Post-Merger Period (2005-2014) The post M&A scenario of the industry in terms of employment was dismal with

de-growth of 3.1 percent and 12 out of 21 firms recorded de-growth in employment after M&A. Thus it is evident that M&A has actually aggravated de-growth in employment as evidenced by 70 percent of firms considered in the analysis.

The present study confirm the findings of Goldar (2000), Balakrishnan and Sureshbabu (2003) Kannan and Raveendran (2009) and Sivamurugan (2010) regarding the declining and negative growth in employment of Indian pharmaceutical industry and is often described as “jobless growth”. They further, argued that the negative growth of employment is due to the labour saving technological advancement in the Indian manufacturing industry after technological tie up with other firms.

To test whether the trend growth of Period II is structurally different from that of Period I we have estimated the following equation with additive (D) and multiplicative (Dt) dummies:

where, ln EMPT = number of employees Di = additive dummy Dt = multiplicative dummy

α , 1α , 1β , 2β = parameters to be estimatedui= stochastic element

and the results are as follows:


77

t = 1…….20.

In the above regression, the‘t’ values of both differential intercept ( )1α and differential slope co-efficient ( )2β are statistically significant at 1 percent, we infer that the regressions for the two periods are structurally different. Hence the estimated trend growth of (-) 4.1% for Period II is significantly different from the trend growth of 3.2% estimated for Period I. As the value of ‘F’ statistic was 7.21 which is statistically significant at 1 percent level we conclude that the growth rates of the two periods are statistically different also. The Chow test also confirms that the parameters of growth estimates during Period I and II are significantly different at the industry level as well as across acquirer firms. All the statistical tests categorically confirm that M&A has actually aggravated de-growth in employment

Table: 3.4. Average Annual Trend Growth of Employment in Indian Pharmaceutical Industry during pre and post-merger periods.

Acquirerfirms

Pre merger period Post merger period

Test of Stability:

Chow Test Calculated Value of F*

Statistical Difference

α β1 DW α β1 DWAlembic Ltd. 9.18*

(-143.56)0.057(-0.145)

1.37 11.37*(-27.93)

-0.045***(-3.35) 1.64 2.56 Significant


11.15*(-184.25)

-0.073(-0.49)

1.75 12.85*(-34.42)

-0.057**(-3.37) 1.84 2.67 Significant

Cipla Ltd. 12.37*(-102.84)

0.039*(-10.67)

2.57 12.57*(-56.38)

-0.046*(-5.37) 2.87 9.37 Significant

Glaxosmithkline Pharamaceutical Ltd.

16.39*(-242.86)

0.35(-0.37)

2.45 11.76*(-86.37)

-0.038**(-3.86) 2.75 6.37 Significant

Granules India Ltd.

12.84*(-265.42)

-0.064***(-1.46)

1.43 13.37*(-37.38)

-0.076*(-7.08)

1.54 9.56 Significant


18.13*(-183.25)

0.006(-0.68)

2.36 11.97*(-35.45)

-0.087**(-3.37)



12.63*(-98.68)

0.075*(-2.96)

1.65 13.68*(-74.27)

-0.068*(-3.87)


Intas Phara-masticals Ltd.

18.94*(-157.9)

-0.038**(-0.05)

1.52 12.36*(-73.37)

-0.059*(-6.48)



10.53*(-164.35)

-0.086***(-1.57)

2.12 9.84*(-37.91)

0.051*(-3.84)



78

Kopran Ltd. 11.03*(-137.06)

0.087**(-3.35)

1.86 12.69*(-25.69)

-0.041**(-3.63)


Merck Ltd. 18.93*(-226.37)

-0.046**(-2.36)

1.76 15.48*(-39.7)

-0.163*(-5.85)



15.44*(-224.47)

0.059(-2.46)

1.37 11.36*(-58.47)

-0.036(-1.36)



19.01*(-87.28)

0.046*(-6.53)

1.73 10.37*(-37.57)

0.095*(-5.26)

1.76 8.59Significant

Pfizer Ltd. 2.57*(-296.86)

0.086(-0.41)

1.29 8.47*(-89.58)

-0.035**(-3.48)



21.03*(-186.4)

0.078*(-4.89)

2.28 15.48*(-26.58)

-0.035**(-3.37)


Sun Pharamaceutical Inds. Ltd.

26.27*(-186.79)

0.079(-0.15)

2.93 11.47*(-26.95)

-0.045***(-2.36)



22.68*(-147.9)

-0.017**(-2.56)

1.27 12.37*(-37.95)

-0.059*(-6.48)



18.78*(-295.86)

0.046(-0.06)

2.37 12.47*(-84.48)

-0.047**(-3.05)


Wanbury Ltd. 12.64*(-275.45)

-0.057*(-2.46)

1.95 12.95*(-112.83)

-0.039*(-4.69)


Wyeth Ltd. 9.74*(-196.45)

-0.058*(-4.96)

1.96 11.98*(-102.81)

-0.086*(-4.78)


Indian Pharamasceutical Industry

15.64*(-312.96)

0.032*(-5.44)

1.26 15.36*(-47.45)

-0.041*(-4.45)


Note: Figures in Parentheses are t valuesβ1: Average Annual Trend Growth: β2: Rate of Acceleration or Deceleration in

the Average Growth;

DW: Durbin Watson; *= Statistically Significant at 1% level, ** -= Statistically Significant at 5% level and

*** = Statistically Significant at 10% level

3.5.3 Structural Break in EmploymentTo examine the timing of the turnaround in the growth rates, we have considered

both additive and multiplicative dummies beginning from the mid-1990’s. This is to test whether the turnaround in growth occurred in response to inorganic growth strategies such as M&A. After fitting a semi – logarithmic trend equation over the


79

entire period from 1995 to 2014, the equation with both additive and multiplicative dummies beginning from 1996 onwards were re-estimated. The estimated regression equation for the entire period i.e. 1991 to 2012 is as follows;

lnEmptt = 18.61 + 0.026 +ut t = 1………….18(232.33) (2.02)

Table 3.5 present the results of structural break in employment. It is found that the sign of the coefficient of multiplicative dummy (D*t) was positive, right from the beginning indicating acceleration occurred from 1996, onwards and became highly significant from 2001 onwards and continued up to 2006 and the period 2009 showed deceleration in growth up to 2011. Moreover, acceleration reemerged form 2012.

Thus it is evident the M&A has aggravated deceleration in employment growth which is prominent after 2001.

Table: 3.5. Search for Structural Break in Employment of Indian pharmaceutical Industry during pre and post-merger periods.

Dummy Variable

Intercept(α)

AdditiveDummy

(Di)

Time(t)

MultiplicativeDummy (Dt) R2 DW

Di=1 beginning 1996

18.367* -0.894 -0.051 0.1980.789 1.567

(20.790) (-0.567) (-0.097) (0.459)Di=1

beginning 199718.659* -0.742 0.060 0.132

0.796 1.929(31.478) (-1.134) (0.221) (0.359)

Di=1beginning 1998

18.711* -0.794 0.039 0.1370.869 2.024

(36.698) (-1.562) (0.238) (0.797)Di=1

beginning 199918.612* -0.924*** 0.058 0.117

0.851 2.123(45.845) (-1.860) (0.592) (0.949)

Di=1 beginning 2000

18.568* -1.027** 0.084 0.1120.887 2.181

(51.321) (-2.335) (0.882) (1.152)Di=1

beginning 200118.645* -1.131** 0.061 0.131***

0.879 2.179(56.727) (-2.497) (0.856) (1.753)

Di=1 beginning 2002

18.619* -1.135** 0.068 0.137***0.881 2.176

(56.728) (-2.629) (0.813) (1.758)


80

Di=1 beginning 2003

18.658* -1.161** 0.051 0.146**0.886 2.171

(61.629) (-2.548) (0.816) (2.269)Di=1

beginning 200418.719* -1.039** 0.041 0.146**

0.889 2.203(67.051) (-2.179) (0.754) (2.671)

Di=1 beginning 2005

18.658* -0.849*** 0.051 0.138**0.911 2.302

(72.274) (-1.673) (1.034) (2.648)Di=1

beginning 200618.636* -0.576 0.062 0.115**

0.917 1.989(78.971) (-1.019) (1.551) (2.269)

Di=1 beginning 2007

18.387* -1.163 0.122* 0.0910.868 1.873

(71.779) (-1.364) (3.249) (1.529)Di=1

beginning 200818.556* -0.647 0.084* 0.092

0.873 2.061(77.472) (-0.782) (3.261) (1.474)

Di=1 beginning 2009

18.476* 0.675 0.126* -0.0480.882 2.257

(85.075) (0.573) (4.694) (-0.059)Di=1

beginning 201018.323* 0.841 0.124* -0.069

0.868 1.921(83.045) (0.558) (5.552) (-0.251)

Di=1 beginning 2011

18.252* 0.347 0.141* -0.0730.871 1.772

(81.343) (0.178) (6.347) (-0.956)Di=1

beginning 201218.177* -0.107 0.162* 0.023***

0.839 1.671(84.851) (-0.056) (8.843) (1.887)

Di=1 beginning 2013

18.254* -0.874 0.156* 0.081**0.872 1.693

(88.076) (-0.143) (8.667) (2.067)Note: Figures in parentheses are t valuesDW: - Durbin Watson; *= Statistically Significant at 1 % level. ** = Statistically Significant at 5 % level and*** = Statistically Significant at 10 % level.

3.6 Output Uchikawa (2001) Ranni and Unni (2004) Nagaraj (2009) Kuar (2007) Kannan

and Raveendran (2009) have established that policy shifts have augmented output growth in terms of GVA with reference to 1991 policy reforms.

On the other hand, Golder (2000) Balakrishnan and Suresh Babu (2003) Balakrishnan (2005) and Delong (2004) argues that acceleration began in the early


81

or mid-1980sand mid 1990s could not be due to policy initiatives. Rodrik (2004) joins Delong in stating that the reforms undertaken in the 1990’s cannot be accepted as a turning point in the Indian manufacturing.

As the debate continues for and against the positive impact of policy changes on the growth acceleration of Indian manufacturing industries, we made an attempt to re-examine the issue with reference to corporate restructuring/inorganic growth strategies through M&A in Indian pharmaceutical industry. The estimated results for Period I and II are presented in table 3.6.

3.6.1 Pre-Merger Period (1995-2004)The average trend growth of output during Period I was 7.6 percent. Across

the acquirer firms, the growth coefficient was positive in all firms and statistically significant in almost 16 firms indicating a well spread output growth performance during Period I of the study. The growth rate ranged between the maximum of 22.3 % in Merck Ltd. and a minimum of 2.8 % was recorded in Indoco Remedies Ltd. Out of 21 acquirer firms, almost 16 firms outweighed the growth rate of the entire industry. Thus it is evident that even before M&A, the respondent firms recorded impressive growth in output.

3.6.2 Post-Merger Period (2005-2014)The industry’s growth rate substantially increased to 13.2 % during Period II.

The intra-firm growth rates were positive and significant in 16 acquirer firms and the growth impetus was vibrant during post-merger period. The rate of growth ranged between the maximum of 26.7 % in Glaxosmithkline Pharmaceuticals Ltd., and a minimum of 3.1 % was recorded in Siemens Ltd. 3 out of 21 firms have recorded higher than industry’s growth rate during Period II of the study. Thus evidencing that M&A has had positive impact on the output growth.

To test whether the trend growth equation of Period II is structurally different from that of Period I, we have estimated the following equation with additive (D) and multiplicative (Dt) dummies:

where,Output = Gross Value Added in constant terms


82

Di = additive dummyDt = multiplicative dummyα , 1α , 1β , 2β = parameters to be estimatedui= stochastic element

and the results are as follows;

lnOutputt = 14.19 – 0.203Di +0.029t 0.006+Dit + ui2R = 0.95

D.W = 1.91

(264.60) (-0.46) (9.16) (6.33)t = 1…….20.

Table: 3.6. Average Annual Trend Growth of Output in Indian Pharmaceutical Industry during pre and post-merger periods.

Acquirer firmsPre – Merger Post- Merger

Test of Stability:

Chow Test Calculated Value of

F*

StatisticalDifference

α β1 DW α β1 DW

Alembic Ltd 15.48* 0.056*

1.3817.47* 0.041**

1.34 0.81 Not Significant(-162.57) (-7.46) (-31.56) (-2.34)


12.56* 0.059***0.91

8.96* 0.084*2.48 0.79 Not

Significant(-38.21) (-2.14) (-23.28) (-5.28)

Cipla Ltd 14.56* 0.031

0.8114.08* -0.056**

2.59 2.67 Significant(-69.59) (-0.79) (-34.51) (-2.34)


12.25* 0.089*1.83

9.34* 0.267*1.24 1.02 Not

Significant(-158.3) (-9.21) (16.63) (-4.71)

Granules India Ltd.

8.98* 0.078*1.45

9.76* 0.0741.67 0.83 Not

Significant(-79.14) (-5.34) (-8.48) (-0.94)


14.34* 0.068*1.31

12.56* 0.045**1.43 0.88 Not

Significant(-167.29) (-7.46) (-41.52) (-2.31)


10.43* 0.028*0.67

7.68* 0.047**1.62 1.40 Not

Significant(-134.23) (-5.56) (-45.23) (-3.56)

Intas Pharma-ceuticals Ltd.

12.34* 0.059*0.67

12.68* 0.034**1.61 0.54 Not

Significant(-134.51) (-5.64) (-54.32) (-3.53)


83


8.67* 0.068*1.52

9.57* 0.056*1.38 1.71 Not

Significant(-152.58) (-9.64) (-42.78) (-7.98)

Kopran Ltd. 14.67* 0.076*

1.5015.46* 0.156*

2.23 2.46 Significant(-265.76) (-15.57) (-61.57) (-11.57)

Merck Ltd. 10.54* 0.223

0.8921.67* -0.051**

1.56 1.23 Not Significant(-69.57) (-0.58) (-34.56) (-4.32)


25.12* 0.167**1.23

7.56* 0.178*2.34 3.45 Significant

(-67.45) (-3.38) (-15.47) (-6.37)


12.45* 0.0490.51

14.67* -0.056**1.41 2.61 Significant

(-69.67) (-10.26) (-31.45) (-2.34)

Pfizer Ltd. 23.34* 0.056*

1.9811.65* 0.129*

1.67 1.09 Not Significant(-42.45) (-2.73) (-16.69) (-7.83)


26.76* 0.057*1.54

10.64* 0.087*1.08 2.61 Not

Significant(-164.45) (-21.75) (-63.85) (-12.76)Sun Pharma-ceutical Inds. Ltd.

13.97* 0.0451.87

15.54* 0.076*1.67 1.11

Not Significant

(-45.58) (-4.34) (-21.56) (-3.87)

Suven Life Sci-ences Ltd.

9.23* 0.158*1.56

8.23* 0.256*1.43 2.23 Significant

(-56.67) (-16.68) (-24.65) (-12.45)


12.45* 0.054*2.41

15.66* 0.31**1.45 1.11 Not Significant

(-94.6) (-6.87) (-14.76) (-3.67)

Wanbury Ltd. 10.45* 0.049*

1.5521.76* 0.34***

1.51 1.21 Not Significant(-76.89) (-6.56) (-32.98) (-1.48)

Wockhardt Ltd. 24.11* 0.043

3.4321.42* 0.143*

1.67 1.31 Not Significant(-46.76) (-2.56) (-13.65) (-3.65)

Wyeth Ltd. 32.45* 0.087*

1.3210.76* 0.057*

1.42 1.06 Not Significant(-123.29) (-9.34) (-29.91) (-7.76)

Indian Pharma-ceutical Industry

27.53* 0.076*1.87

13.52* 0.132*2.82 3.97 Significant

(-142.28) (-7.46) (-98.42) (-12.76)

Note: Figures in parentheses are t valuesβ1:- Average Annual Trend Growth; β2:- Rate of Acceleration or Deceleration in the Average Growth;DW: - Durbin Watson; *= Statistically Significant at 1 % level. ** = Statistically Significant at 5 % level and*** = Statistically Significant at 10 % level.

In the above regression, the‘t’ values of both differential intercept ( )1α and differential slope coefficient ( )2β and statistically significant at 1 percent, we


84

infer that the regressions for the two periods are structurally different. Hence the estimated trend growth 13.2 % for Period II is significantly different from the trend growth of 7.6 % recorded for during Period I.

As the value of ‘F’ statistic was 6.27 which are statistically significant at 1 percent level we conclude that the growth rates of the two periods are statistically different also.

The Chow test also confirms that the parameters of pre and post-merger period growth estimates are significantly different at the industry level as well as in 16 acquirer firms.

3.6.3 Structural Break in Output To examine the timing of the turnaround in the growth rates, we have considered

both additive and multiplicative dummies beginning from the mid-1990’s. This is to test whether the turnaround in growth occurred in response policy changes / inorganic growth strategies.

After fitting a semi – logarithmic trend equation over the entire period from 1995 to 2014, the equation with both additive and multiplicative dummies beginning from 1996 onwards were re-estimated. The estimated regression equation using output data for the entire period i.e. 1995 to 2014 is as follows;

lnOutputt = 12.22 + 0.086 +ut t = 1………….18(48.15) (11.49)

From table 3.7, it is found that the sign of the coefficient of the multiplicative dummy (D*t) is negative signifying deceleration in output growth occurred from 1996 onwards and it became significant at 5 percent level in 1998 and persisted up to 2001. There after it become positive and statistically significant till the end of our analysis. From the results it is inferred that the turnaround or significant break in trend rate became visible right from 2005 onwards and continued till 2013. Implying that M&A has augmented output growth in Indian Pharmaceutical industry.


85

Table: 3.7. Search for Structural Break in Output of Indian Pharmaceutical Industry during pre and post-merger periods.

Dummy Variable

Intercept(α)

AdditiveDummy (Di)

Time(t)

MultiplicativeDummy (Dt) R2 DW

Di=1beginning 1996

12.325* 0.341 0.086 -0.0410.957 1.123

(68.784) (1.258) (1.134) (-0.512)Di=1

beginning 199712.296* 0.387** 0.167* -0.051

0.962 1.278(76.764) (2.632) (3.412) (-0.965)

Di=1beginning 1998

12.323* 0.523* 0.145* -0.061***0.975 1.293

(112.918) (4.419) (5.357) (-1.827)Di=1

beginning 199912.317* 0.543* 0.213* -0.071**

0.979 1.347(156.807) (4.789) (6.234) (-3.521)

Di=1beginning 2000

12.821* 0.671* 0.261* -0.048**0.984 1.421

(167.43) (4.112) (9.325) (-2.754)

Di=1beginning 2001

12.411* 0.734* 0.171* -0.062*0.976 1.621

(179.156) (6.141) (12.698) (-5.049)Di=1

beginning 200212.312* 0.732* 0.169* 0.078*

0.989 1.732(182.201) (4.673) (16.387) (4.213)

Di=1beginning 2003

12.315* 0.746* 0.202* 0.073*0.992 1.867

(221.265) (5.321) (19.513) (4.953)

Di=1beginning 2004

12.304* 0.690* 0.173* 0.074*0.979 1.960

(212.133) (4.732) (20.180) (3.543)Di=1

beginning 200512.401* 0.624* 0.213* -0.078*

0.987 1.798(202.689) (5.204) (22.467) (-4.247)

Di=1beginning 2006

12.421* 0.789* 0.214* -0.0580.996 1.756

(221.467) (4.612) (23.724) (-0.735)Di=1

beginning 200712.453* 0.875* 0.231* -0.071

0.993 1.723(234.211) (4.567) (23.644) (-1.078)

Di=1beginning 2008

12.429* 1.034* 0.167* -0.0560.988 1.621

(245.577) (4.487) (23.573) (-1.622)Di=1

beginning 200912.434* 1.067** 0.145* -0.058**

0.990 1.525(240.309) (3.856) (27.678) (-3.490)

Di=1beginning 2010

12.378* 1.027** 0.215* -0.059**0.991 1.512

(221.238) (3.111) (29.589) (-3.670)


86

Di=1beginning 2011

12.378* 1.037 0.178* -0.071***0.990 1.477

(278.213) (1.526) (29.459) (-1.834)Di=1

beginning 201212.567* 2.030*** 0.238* -0.201**

0.989 1.268(230.456) (1.911) (29.519) (-2.050)

Di=1beginning 2013

12.533* 4.290** 0.159* -0.188**0.985 1.011

(237.754) (2.487) (31.270) (-2.379)

Note: Figures in parentheses are t valuesDW: - Durbin Watson; *= Statistically Significant at 1 % level.** = Statistically Significant at 5 % level and*** = Statistically Significant at 10 % level.

3.7 ConclusionThis paper has been envisaged to answer the following questions:

a. Whether M&A strategy triggered new growth dynamics in Gross Fixed Capital, Employment and Output? If so,

b. What is the timing of structural break? (Trigger)Growth rate of Gross Fixed Capital Formation was higher during Pre-M&A

period and M&A has not led to capital intensification because structural break in the growth of capital formation occurred in the year 1998 itself. Hence M&A cannot be considered as a causative factor for structural break in capital intensification process.

In employment 12 out of 21 firms recorded de-growth after M&A and situation worsened after 2005. It is evident that M&A has actually aggravated de-growth in employment as evidenced by 70 percent of firms considered in the analysis.

The present study confirms the findings of Goldar, 2000; Balakrishnan & Sureshbabu, 2003; Kannan & Raveendran, 2009; Saravanakumar, 2009; Sivamurugan, 2010; Allirani, 2013 and Viji, 2015 regarding the declining and negative growth in employment of Indian pharmaceutical industry and is often described as “jobless growth”.

They further, argued that the negative growth of employment is due to labour saving technological advancement in the Indian manufacturing industry after technological tie up with other firms.


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Output growth was higher during Period II and the turnaround or structural break became visible right from 2005 onwards and continued till 2013. Hence based on the analysis we could derive mixed results which are as follows;

i. The hypothesis that M&A would lead to capital intensification does not hold in this industry.

ii. M&A has aggravated de-growth in employment in 70 percent of the firms.iii. However output growth was higher after M&A, and the significant structural

break occurred in 2005.M&A strategy has no effect on GFCF, negative impact on Employment and

positive on output. Timing of structural break GFCF (1998), Employment (2001) and Output (2005).

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