Competitive vs Collaborative Essay

This is when members of the group are preoccupied in establishing that they are â€Å"right† [AWJ2]and that the others are â€Å"wrong.† The group member may not want to adapt any of their work to have it blend with the others. They may pressure others into thinking their way. Some groups may suffer â€Å"group think,† where a bad choice goes unquestioned because group members are unwilling to go against what appears to be a consensus. Solution: Separate the tasks so that there is little or no overlap of topics. Make sure that each person clearly perceives that there exists a clear reward for the effort expended in the group work. Group members should keep in mind to compromise when coming to agreements because not everyone will always agree. Group member leaves the class. A team member could potentially drop the class because either they just don’t want to take it anymore or because a family issue, or for other reasons. If this occurs after research section assignments, this leaves the group vulnerable without that section. If it was far enough along in the timeline, it would be very difficult to cope with problem because the group would have to start from scratch to complete the remaining sections and assignments. Solution: If the group has not heard from a team member all week, they should message that particular person to ask them about the assignments. If the team still does not hear from them, they should write an email to the professor inquiring about the other team member to find out if they dropped the class for any particular reason. If the team member has dropped the class or refuses participation, the group should divide the remaining sections and tasks to complete the project in a timely manner. Depending on the situation, the team should contact the professor for an extension or guidance for the particular case. Consequences: The obvious penalty for lack of communication and cooperation in group work would be a lower grade or even failure to complete the project. Overcoming obstacles like these in group projects is crucial to actively use and improve communication skills and to build teamwork experience used in the classroom as well as the workplace. Individual’s effort towards group work shows one’s ability to contribute meaningful information and skills to the worth of the total project. Although team members receive grades based on individual efforts, the project grade is collaborative at first. Each team member’s role and effort affects the initial group’s grade. In APA style, quotation marks are reserved for directly quoted materials. Never use quotes to set off a word or phrase. APA style does allow for the use of italics where terms might be new or confusing. None of the words in your check point are new or confusing or might be confusing to your reader so there’re is no reason to use quotes or italics.

Recession in India Essay

We have compiled the said report which helps in understanding what corrective steps were taken which helped the banks to emerge out of the turmoil. Financial Crisis The financial crisis of 2007 to the present is a crisis triggered by a liquidity shortfall in the United States banking system caused by the overvaluation of assets. It has resulted in the collapse of large financial institutions, the bailout of banks by national governments and downturns in stock markets around the world. In many areas, the housing market has also suffered, resulting in numerous evictions, foreclosures and prolonged vacancies. It is considered by many economists to be the worst financial crisis since the Great Depression of the 1930s. It contributed to the failure of key businesses, declines in consumer wealth estimated in the trillions of U. S. dollars, substantial financial commitments incurred by governments, and a significant decline in economic activity. Many causes have been suggested, with varying weight assigned by experts. Both market-based and regulatory solutions have been implemented or are under consideration, while significant risks remain for the world economy over the 2010–2011 periods. The collapse of a global housing bubble, which peaked in the U. S. in 2006, caused the values of securities tied to real estate pricing to plummet thereafter, damaging financial institutions globally. Questions regarding bank solvency, declines in credit availability, and damaged investor confidence had an impact on global stock markets, where securities suffered large losses during late 2008 and early 2009. Economies worldwide slowed during this period as credit tightened and international trade declined. Critics argued that credit rating agencies and investors failed to accurately price the risk involved with mortgage-related financial products, and that governments did not adjust their regulatory practices to address 21st century financial markets. Governments and central banks responded with unprecedented fiscal stimulus, monetary policy expansion, and institutional bailouts. | Background and causes The immediate cause or trigger of the crisis was the bursting of the United States housing bubble which peaked in approximately 2005–2006. Already-rising default rates on â€Å"subprime† and adjustable rate mortgages (ARM) began to increase quickly thereafter. An increase in loan packaging, marketing and incentives such as easy initial terms and a long-term trend of rising housing prices had encouraged borrowers to assume difficult mortgages in the belief they would be able to quickly refinance at more favorable terms. However, once interest rates began to rise and housing prices started to drop moderately in 2006–2007 in many parts of the U. S. , refinancing became more difficult. Defaults and foreclosure activity increased dramatically as easy initial terms expired, home prices failed to go up as anticipated, and ARM interest rates reset higher. Share in GDP of U. S. financial sector since 1860 Low interest rates and large inflows of foreign funds created easy credit conditions for a number of years prior to the crisis, fueling a housing construction boom and encouraging debt-financed consumption. The combination of easy credit and money inflow contributed to the United States housing bubble. Loans of various types (e. g. mortgage, credit card, and auto) were easy to obtain and consumers assumed an unprecedented debt load. As part of the housing and credit booms, the number of financial agreements called mortgage-backed securities (MBS) and collateralized debt obligations (CDO), which derived their value from mortgage payments and housing prices, greatly increased. Such financial innovation enabled institutions and investors around the world to invest in the U. S. housing market. As housing prices declined, major global financial institutions that had borrowed and invested heavily in subprime MBS reported significant losses. Falling prices also resulted in homes worth less than the mortgage loan, providing a financial incentive to enter foreclosure. The ongoing foreclosure epidemic that began in late 2006 in the U. S. continues to drain wealth from consumers and erodes the financial strength of banking institutions. Defaults and losses on other loan types also increased significantly as the crisis expanded from the housing market to other parts of the economy. Total losses are estimated in the trillions of U. S. dollars globally. While the housing and credit bubbles built, a series of factors caused the financial system to both expand and become increasingly fragile, a process called financialization. Policymakers did not recognize the increasingly important role played by financial institutions such as investment banks and hedge funds, also known as the shadow banking system. Some experts believe these institutions had become as important as commercial (depository) banks in providing credit to the U. S. economy, but they were not subject to the same regulations. These institutions as well as certain regulated banks had also assumed significant debt burdens while providing the loans described above and did not have a financial cushion sufficient to absorb large loan defaults or MBS losses. These losses impacted the ability of financial institutions to lend, slowing economic activity. Concerns regarding the stability of key financial institutions drove central banks to provide funds to encourage lending and restore faith in the commercial paper markets, which are integral to funding business operations. Governments also bailed out key financial institutions and implemented economic stimulus programs, assuming significant additional financial commitments. The crises culminated on Sept. 15th 2008 with Lehman Brothers filing for bankruptcy. It has been reported that JP Morgan helped drive Lehman into bankruptcy and kicked off the credit crises by forcing it to give up billions in cash reserves on the afternoon of Friday September 13, 2008. Growth of the housing bubble Main article: United States housing bubble A graph showing the median and average sales prices of new homes sold in the United States between 1963 and 2008 (not adjusted for inflation) Between 1997 and 2006, the price of the typical American house increased by 124%. During the two decades ending in 2001, the national median home price ranged from 2. 9 to 3. 1 times median household income. This ratio rose to 4. 0 in 2004, and 4. 6 in 2006. This housing bubble resulted in quite a few homeowners refinancing their homes at lower interest rates, or financing consumer spending by taking out second mortgages secured by the price appreciation. In a Peabody Award winning program, NPR correspondents argued that a â€Å"Giant Pool of Money† (represented by $70 trillion in worldwide fixed income investments) sought higher yields than those offered by U. S. Treasury bonds early in the decade. Further, this pool of money had roughly doubled in size from 2000 to 2007, yet the supply of relatively safe, income generating investments had not grown as fast. Investment banks on Wall Street answered this demand with the MBS and CDO, which were assigned safe ratings by the credit rating agencies. In effect, Wall Street connected this pool of money to the mortgage market in the U. S. , with enormous fees accruing to those throughout the mortgage supply chain, from the mortgage broker selling the loans, to small banks that funded the brokers, to the giant investment banks behind them. By approximately 2003, the supply of mortgages originated at traditional lending standards had been exhausted. However, continued strong demand for MBS and CDO began to drive down lending standards, as long as mortgages could still be sold along the supply chain. Eventually, this speculative bubble proved unsustainable. The CDO in particular enabled financial institutions to obtain investor funds to finance subprime and other lending, extending or increasing the housing bubble and generating large fees. A CDO essentially places cash payments from multiple mortgages or other debt obligations into a single pool, from which the cash is allocated to specific securities in a priority sequence. Those securities obtaining cash first received investment-grade ratings from rating agencies. Lower priority securities received cash thereafter, with lower credit ratings but theoretically a higher rate of return on the amount invested. By September 2008, average U. S. housing prices had declined by over 20% from their mid-2006 peak. As prices declined, borrowers with adjustable-rate mortgages could not refinance to avoid the higher payments associated with rising interest rates and began to default. During 2007, lenders began foreclosure proceedings on nearly 1. 3 million properties, a 79% increase over 2006. This increased to 2. 3 million in 2008, an 81% increase vs. 2007. By August 2008, 9. 2% of all U. S. mortgages outstanding were either delinquent or in foreclosure. By September 2009, this had risen to 14. 4%. Easy credit conditions Lower interest rates encourage borrowing. From 2000 to 2003, the Federal Reserve lowered the federal funds rate target from 6. 5% to 1. 0%. [31] This was done to soften the effects of the collapse of the dot-com bubble and of the September 2001 terrorist attacks, and to combat the perceived risk of deflation. [32] U. S. current account or trade deficit Additional downward pressure on interest rates was created by the USA’s high and rising current account (trade) deficit, which peaked along with the housing bubble in 2006. Ben Bernanke explained how trade deficits required the U. S. to borrow money from abroad, which bid up bond prices and lowered interest rates. Bernanke explained that between 1996 and 2004, the USA current account deficit increased by $650 billion, from 1. 5% to 5. 8% of GDP. Financing these deficits required the USA to borrow large sums from abroad, much of it from countries running trade surpluses, mainly the emerging economies in Asia and oil-exporting nations. The balance of payments identity requires that a country (such as the USA) running a current account deficit also have a capital account (investment) surplus of the same amount. Hence large and growing amounts of foreign funds (capital) flowed into the USA to finance its imports. This created demand for various types of financial assets, raising the prices of those assets while lowering interest rates. Foreign investors had these funds to lend, either because they had very high personal savings rates (as high as 40% in China), or because of high oil prices. Bernanke referred to this as a â€Å"saving glut. † A â€Å"flood† of funds (capital or liquidity) reached the USA financial markets. Foreign governments supplied funds by purchasing USA Treasury bonds and thus avoided much of the direct impact of the crisis. USA households, on the other hand, used funds borrowed from foreigners to finance consumption or to bid up the prices of housing and financial assets. Financial institutions invested foreign funds in mortgage-backed securities. The Fed then raised the Fed funds rate significantly between July 2004 and July 2006. This contributed to an increase in 1-year and 5-year adjustable-rate mortgage (ARM) rates, making ARM interest rate resets more expensive for homeowners. This may have also contributed to the deflating of the housing bubble, as asset prices generally move inversely to interest rates and it became riskier to speculate in housing. USA housing and financial assets dramatically declined in value after the housing bubble burst. Sub-prime lending U. S. subprime lending expanded dramatically 2004-2006 The term subprime refers to the credit quality of particular borrowers, who have weakened credit histories and a greater risk of loan default than prime borrowers. The value of U. S. subprime mortgages was estimated at $1. 3 trillion as of March 2007, with over 7. 5 million first-lien subprime mortgages outstanding. In addition to easy credit conditions, there is evidence that both government and competitive pressures contributed to an increase in the amount of subprime lending during the years preceding the crisis. Major U. S. investment banks and government sponsored enterprises like Fannie Mae played an important role in the expansion of higher-risk lending. Subprime mortgages remained below 10% of all mortgage originations until 2004, when they spiked to nearly 20% and remained there through the 2005-2006 peak of the United States housing bubble. A proximate event to this increase was the April 2004 decision by the U. S. Securities and Exchange Commission (SEC) to relax the net capital rule, which permitted the largest five investment banks to dramatically increase their financial leverage and aggressively expand their issuance of mortgage-backed securities. This applied additional competitive pressure to Fannie Mae and Freddie Mac, which further expanded their riskier lending. Subprime mortgage payment delinquency rates remained in the 10-15% range from 1998 to 2006, then began to increase rapidly, rising to 25% by early 2008. Fannie Mae, the nation’s biggest underwriter of home mortgages, has been under increasing pressure from the Clinton Administration to expand mortgage loans among low and moderate income people†¦ In moving, even tentatively, into this new area of lending, Fannie Mae is taking on significantly more risk, which may not pose any difficulties during flush economic times. But the government-subsidized corporation may run into trouble in an economic downturn, prompting a government rescue similar to that of the savings and loan industry in the 1980s. A 2000 United States Department of the Treasury study of lending trends for 305 cities from 1993 to 1998 showed that $467 billion of mortgage credit poured out of Community Reinvestment Act (CRA)-covered lenders into low and mid level income borrowers and neighborhoods. Nevertheless, only 25% of all sub-prime lending occurred at CRA-covered institutions, and a full 50% of sub-prime loans originated at institutions exempt from CRA. While the number of CRA sub-prime loans originated were less than non-CRA sub-prime loans originated, it is important to note that the CRA sub-prime loans were the more â€Å"vulnerable during the downturn, to the detriment of both borrowers and lenders. For example, lending done under Community Reinvestment Act criteria, according to a quarterly report in October of 2008, constituted only 7 percent of the total mortgage lending by the Bank of America, but constituted 29 percent of its losses on mortgages. Economist Paul Krugman argued in January 2010 that the simultaneous growth of the residential and commercial real estate pricing bubbles undermines the case made by those who argue that Fannie Mae, Freddie Mac, CRA or predatory lending were primary causes of the crisis. In other words, bubbles in both markets developed even though only the residential market was affected by these potential causes. Predatory lending Predatory lending refers to the practice of unscrupulous lenders, to enter into â€Å"unsafe† or â€Å"unsound† secured loans for inappropriate purposes. A classic bait-and-switch method was used by Countrywide, advertising low interest rates for home refinancing. Such loans were written into extensively detailed contracts, and swapped for more expensive loan products on the day of closing. Whereas the advertisement might state that 1% or 1. 5% interest would be charged, the consumer would be put into an adjustable rate mortgage (ARM) in which the interest charged would be greater than the amount of interest paid. This created negative amortization, which the credit consumer might not notice until long after the loan transaction had been consummated. Countrywide, sued by California Attorney General Jerry Brown for â€Å"Unfair Business Practices† and â€Å"False Advertising† was making high cost mortgages â€Å"to homeowners with weak credit, adjustable rate mortgages (ARMs) that allowed homeowners to make interest-only payments. â€Å". When housing prices decreased, homeowners in ARMs then had little incentive to pay their monthly payments, since their home equity had disappeared. This caused Countrywide’s financial condition to deteriorate, ultimately resulting in a decision by the Office of Thrift Supervision to seize the lender. Former employees from Ameriquest, which was United States’s leading wholesale lender,[60] described a system in which they were pushed to falsify mortgage documents and then sell the mortgages to Wall Street banks eager to make fast profits. [60] There is growing evidence that such mortgage frauds may be a cause of the crisis. [60] Deregulation Further information: Government policies and the subprime mortgage crisis Critics have argued that the regulatory framework did not keep pace with financial innovation, such as the increasing importance of the shadow banking system, derivatives and off-balance sheet financing. In other cases, laws were changed or enforcement weakened in parts of the financial system. Key examples include: * Jimmy Carter’s Depository Institutions Deregulation and Monetary Control Act of 1980 (DIDMCA) phased out a number of restrictions on banks’ financial practices, broadened their lending powers, and raised the deposit insurance limit from $40,000 to $100,000 (raising the problem of moral hazard). Banks rushed into real estate lending, speculative lending, and other ventures just as the economy soured. * In October 1982, U. S. President Ronald Reagan signed into Law the Garn–St. Germain Depository Institutions Act, which provided for adjustable-rate mortgage loans, began the process of banking deregulation, and contributed to the savings and loan crisis of the late 1980s/early 1990s. * In November 1999, U. S. President Bill Clinton signed into Law the Gramm-Leach-Bliley Act, which repealed part of the Glass-Steagall Act of 1933. This repeal has been criticized for reducing the separation between commercial banks (which traditionally had a conservative culture) and investment banks (which had a more risk-taking culture). In 2004, the U. S. Securities and Exchange Commission relaxed the net capital rule, which enabled investment banks to substantially increase the level of debt they were taking on, fueling the growth in mortgage-backed securities supporting subprime mortgages. The SEC has conceded that self-regulation of investment banks contributed to the crisis. * Financial institutions in the shadow banking system are not subject to the same regulation as d epository banks, allowing them to assume additional debt obligations relative to their financial cushion or capital base. This was the case despite the Long-Term Capital Management debacle in 1998, where a highly-leveraged shadow institution failed with systemic implications. * Regulators and accounting standard-setters allowed depository banks such as Citigroup to move significant amounts of assets and liabilities off-balance sheet into complex legal entities called structured investment vehicles, masking the weakness of the capital base of the firm or degree of leverage or risk taken. One news agency estimated that the top four U. S. banks will have to return between $500 billion and $1 trillion to their balance sheets during 2009. This increased uncertainty during the crisis regarding the financial position of the major banks. Off-balance sheet entities were also used by Enron as part of the scandal that brought down that company in 2001. * As early as 1997, Federal Reserve Chairman Alan Greenspan fought to keep the derivatives market unregulated. With the advice of the President’s Working Group on Financial Markets, the U. S. Congress and President allowed the self-regulation of the over-the-counter derivatives market when they enacted the Commodity Futures Modernization Act of 2000. Derivatives such as credit default swaps (CDS) can be used to hedge or speculate against particular credit risks. The volume of CDS outstanding increased 100-fold from 1998 to 2008, with estimates of the debt covered by CDS contracts, as of November 2008, ranging from US$33 to $47 trillion. Total over-the-counter (OTC) derivative notional value rose to $683 trillion by June 2008. Warren Buffett famously referred to derivatives as â€Å"financial weapons of mass destruction† in early 2003. Increased debt burden or over-leveraging Leverage ratios of investment banks increased significantly 2003-2007 U. S. households and financial institutions became increasingly indebted or overleveraged during the years preceding the crisis. This increased their vulnerability to the collapse of the housing bubble and worsened the ensuing economic downturn. Key statistics include: * Free cash used by consumers from home equity extraction doubled from $627 billion in 2001 to $1,428 billion in 2005 as the housing bubble built, a total of nearly $5 trillion dollars over the period, contributing to economic growth worldwide. U. S. home mortgage debt relative to GDP increased from an average of 46% during the 1990s to 73% during 2008, reaching $10. 5 trillion. * USA household debt as a percentage of annual disposable personal income was 127% at the end of 2007, versus 77% in 1990. * In 1981, U. S. rivate debt was 123% of GDP; by the third quarter of 2008, it was 290%. * From 2004-07, the top five U. S. investment banks each significantly increased their financial leverage (see diagram), which increased their vulnerability to a financial shock. These five institutions reported over $4. 1 trillion in debt for fiscal year 2007, about 30% of USA nominal GDP for 2007. Lehman Brothers was liquidated, Bear Stearns and Merrill Lynch were sold at fir e-sale prices, and Goldman Sachs and Morgan Stanley became commercial banks, subjecting themselves to more stringent regulation. With the exception of Lehman, these companies required or received government support. * Fannie Mae and Freddie Mac, two U. S. Government sponsored enterprises, owned or guaranteed nearly $5 trillion in mortgage obligations at the time they were placed into conservatorship by the U. S. government in September 2008. These seven entities were highly leveraged and had $9 trillion in debt or guarantee obligations, an enormous concentration of risk; yet they were not subject to the same regulation as depository banks. Boom and collapse of the shadow banking system In a June 2008 speech, President and CEO of the New York Federal Reserve Bank Timothy Geithner  Ã¢â‚¬â€ who in 2009 became Secretary of the United States Treasury  Ã¢â‚¬â€ placed significant blame for the freezing of credit markets on a â€Å"run† on the entities in the â€Å"parallel† banking system, also called the shadow banking system. These entities became critical to the credit markets underpinning the financial system, but were not subject to the same regulatory controls. Further, these entities were vulnerable because of maturity mismatch, meaning that they borrowed short-term in liquid markets to purchase long-term, illiquid and risky assets. This meant that disruptions in credit markets would make them subject to rapid deleveraging, selling their long-term assets at depressed prices. He described the significance of these entities: In early 2007, asset-backed commercial paper conduits, in structured investment vehicles, in auction-rate preferred securities, tender option bonds and variable rate demand notes, had a combined asset size of roughly $2. trillion. Assets financed overnight in triparty repo grew to $2. 5 trillion. Assets held in hedge funds grew to roughly $1. 8 trillion. The combined balance sheets of the then five major investment banks totaled $4 trillion. In comparison, the total assets of the top five bank holding companies in the United States at that point were just over $6 trillion, and total assets of the entire banking system we re about $10 trillion. The combined effect of these factors was a financial system vulnerable to self-reinforcing asset price and credit cycles. Paul Krugman, laureate of the Nobel Prize in Economics, described the run on the shadow banking system as the â€Å"core of what happened† to cause the crisis. He referred to this lack of controls as â€Å"malign neglect† and argued that regulation should have been imposed on all banking-like activity. Financial markets impacts Impacts on financial institutions 2007 bank run on Northern Rock, a UK bank The International Monetary Fund estimated that large U. S. and European banks lost more than $1 trillion on toxic assets and from bad loans from January 2007 to September 2009. These losses are expected to top $2. 8 trillion from 2007-10. U. S. banks losses were forecast to hit $1 trillion and European bank losses will reach $1. 6 trillion. The IMF estimated that U. S. banks were about 60 percent through their losses, but British and eurozone banks only 40 percent. One of the first victims was Northern Rock, a medium-sized British bank. The highly leveraged nature of its business led the bank to request security from the Bank of England. This in turn led to investor panic and a bank run in mid-September 2007. Calls by Liberal Democrat Shadow Chancellor Vince Cable to nationalise the institution were initially ignored; in February 2008, however, the British government (having failed to find a private sector buyer) relented, and the bank was taken into public hands. Northern Rock’s problems proved to be an early indication of the troubles that would soon befall other banks and financial institutions. Initially the companies affected were those directly involved in home construction and mortgage lending such as Northern Rock and Countrywide Financial, as they could no longer obtain financing through the credit markets. Over 100 mortgage lenders went bankrupt during 2007 and 2008. Concerns that investment bank Bear Stearns would collapse in March 2008 resulted in its fire-sale to JP Morgan Chase. The crisis hit its peak in September and October 2008. Several major institutions either failed, were acquired under duress, or were subject to government takeover. These included Lehman Brothers, Merrill Lynch, Fannie Mae, Freddie Mac, Washington Mutual, Wachovia, and AIG. Credit markets and the shadow banking system TED spread and components during 2008 During September 2008, the crisis hit its most critical stage. There was the equivalent of a bank run on the money market mutual funds, which frequently invest in commercial paper issued by corporations to fund their operations and payrolls. Withdrawals from money markets were $144. 5 billion during one week, versus $7. 1 billion the week prior. This interrupted the ability of corporations to rollover (replace) their short-term debt. The U. S. government responded by extending insurance for money market accounts analogous to bank deposit insurance via a temporary guarantee and with Federal Reserve programs to purchase commercial paper. The TED spread, an indicator of perceived credit risk in the general economy, spiked up in July 2007, remained volatile for a year, then spiked even higher in September 2008, reaching a record 4. 65% on October 10, 2008. In a dramatic meeting on September 18, 2008, Treasury Secretary Henry Paulson and Fed Chairman Ben Bernanke met with key legislators to propose a $700 billion emergency bailout. Bernanke reportedly told them: â€Å"If we don’t do this, we may not have an economy on Monday. † The Emergency Economic Stabilization Act, which implemented the Troubled Asset Relief Program (TARP), was signed into law on October 3, 2008. Economist Paul Krugman and U. S. Treasury Secretary Timothy Geithner explain the credit crisis via the implosion of the shadow banking system, which had grown to nearly equal the importance of the traditional commercial banking sector as described above. Without the ability to obtain investor funds in exchange for most types of mortgage-backed securities or asset-backed commercial paper, investment banks and other entities in the shadow banking system could not provide funds to mortgage firms and other corporations. This meant that nearly one-third of the U. S. lending mechanism was frozen and continued to be frozen into June 2009. According to the Brookings Institution, the traditional banking system does not have the capital to close this gap as of June 2009: â€Å"It would take a number of years of strong profits to generate sufficient capital to support that additional lending volume. † The authors also indicate that some forms of securitization are â€Å"likely to vanish forever, having been an artifact of excessively loose credit conditions. While traditional banks have raised their lending standards, it was the collapse of the shadow banking system that is the primary cause of the reduction in funds available for borrowing. Global effects A number of commentators have suggested that if the liquidity crisis continues, there could be an extended recession or worse. The continuing development of the crisis has prompted in some quarters fears of a global economic collapse although the re are now many cautiously optimistic forecasters in addition to some prominent sources who remain negative. The financial crisis is likely to yield the biggest banking shakeout since the savings-and-loan meltdown. Investment bank UBS stated on October 6 that 2008 would see a clear global recession, with recovery unlikely for at least two years. Three days later UBS economists announced that the â€Å"beginning of the end† of the crisis had begun, with the world starting to make the necessary actions to fix the crisis: capital injection by governments; injection made systemically; interest rate cuts to help borrowers. The United Kingdom had started systemic injection, and the world’s central banks were now cutting interest rates. UBS emphasized the United States needed to implement systemic injection. UBS further emphasized that this fixes only the financial crisis, but that in economic terms â€Å"the worst is still to come†. UBS quantified their expected recession durations on October 16: the Eurozone’s would last two quarters, the United States’ would last three quarters, and the United Kingdom’s would last four quarters. The economic crisis in Iceland involved all three of the country’s major banks. Relative to the size of its economy, Iceland’s banking collapse is the largest suffered by any country in economic history. At the end of October UBS revised its outlook downwards: the forthcoming recession would be the worst since the Reagan recession of 1981 and 1982 with negative 2009 growth for the U. S. , Eurozone, UK; very limited recovery in 2010; but not as bad as the Great Depression. The Brookings Institution reported in June 2009 that U. S. consumption accounted for more than a third of the growth in global consumption between 2000 and 2007. â€Å"The US economy has been spending too much and borrowing too much for years and the rest of the world depended on the U. S. consumer as a source of global demand. With a recession in the U. S. and the increased savings rate of U. S. consumers, declines in growth elsewhere have been dramatic. For the first quarter of 2009, the annualized rate of decline in GDP was 14. 4% in Germany, 15. 2% in Japan, 7. 4% in the UK, 18% in Latvia, 9. 8% in the Euro area and 21. 5% for Mexico. Some developing countries that had seen strong economic growth saw significan t slowdowns. For example, growth forecasts in Cambodia show a fall from more than 10% in 2007 to close to zero in 2009, and Kenya may achieve only 3-4% growth in 2009, down from 7% in 2007. According to the research by the Overseas Development Institute, reductions in growth can be attributed to falls in trade, commodity prices, investment and remittances sent from migrant workers (which reached a record $251 billion in 2007, but have fallen in many countries since). The has stark implications and has led to a dramatic rise in the number of households living below the poverty line, be it 300,000 in Bangladesh or 230,000 in Ghana. By March 2009, the Arab world had lost $3 trillion due to the crisis. In April 2009, unemployment in the Arab world is said to be a ‘time bomb’. In May 2009, the United Nations reported a drop in foreign investment in Middle-Eastern economies due to a slower rise in demand for oil. In June 2009, the World Bank predicted a tough year for Arab states. In September 2009, Arab banks reported losses of nearly $4 billion since the onset of the global financial crisis. U. S. economic effects Real gross domestic product — the output of goods and services produced by labor and property located in the United States — decreased at an annual rate of approximately 6 percent in the fourth quarter of 2008 and first quarter of 2009, versus activity in the year-ago periods. The U. S. unemployment rate increased to 10. 1% by October 2009, the highest rate since 1983 and roughly twice the pre-crisis rate. The average hours per work week declined to 33, the lowest level since the government began collecting the data in 1964. Effects of Recession on India There is, at least in some quarters, dismay that India has been hit by the crisis. This dismay stems from two arguments. The Indian banking system has had no direct exposure to the sub-prime mortgage assets or to the failed institutions. It has very limited off-balance sheet activities or securitized assets. In fact, our banks continue to remain safe and healthy. So, the enigma is how can India be caught up in a crisis when it has nothing much to do with any of the maladies that are at the core of the crisis. The second reason for dismay is that India’s recent growth has been driven predominantly by domestic consumption and domestic investment. External demand, as measured by merchandize exports, accounts for less than 15 per cent of our GDP. The question then is, even if there is a global downturn, why should India be affected when its dependence on external demand is so limited? The answer to the above frequently-asked questions lies in globalization. First, India’s integration into the world economy over the last decade has been remarkably rapid. Integration into the world implies more than just exports. Going by the common measure of globalization, India’s two-way trade (merchandize exports plus imports), as a proportion of GDP, grew from 21. 2 per cent in 1997-98, the year of the Asian crisis, to 34. 7 per cent in 2007-08. Second, India’s financial integration with the world has been as deep as India’s trade globalization, if not deeper. If we take an expanded measure of globalization, that is the ratio of total external transactions (gross current account flows plus gross capital flows) to GDP, this ratio has more than doubled from 46. 8 per cent in 1997-98 to 117. 4 per cent in 2007-08. Importantly, the Indian corporate sector’s access to external funding has markedly increased in the last five years. Some numbers will help illustrate the point. In the five-year period 2003-08, the share of investment in India’s GDP rose by 11 percentage points. Corporate savings financed roughly half of this, but a significant portion of the balance financing came from external sources. While funds were available domestically, they were expensive relative to foreign funding. On the other hand, in a global market awash with liquidity and on the promise of India’s growth potential, foreign investors were willing to take risks and provide funds at a lower cost. Last year (2007/08), for example, India received capital inflows amounting to over 9 per cent of GDP as against a current account deficit in the balance of payments of just 1. 5 per cent of GDP. These capital flows, in excess of the current account deficit, evidence the importance of external financing and the depth of India’s financial integration. So, the reason India has been hit by the crisis, despite mitigating factors, is clearly India’s rapid and growing integration into the global economy. The contagion of the crisis has spread to India through all the channels – the financial channel, the real channel, and importantly, as happens in all financial crises, the confidence channel. India’s financial markets – equity markets, money markets, forex markets and credit markets – had all come under pressure from a number of directions. First, as a consequence of the global liquidity squeeze, Indian banks and corporates found their overseas financing drying up, forcing corporates to shift their credit demand to the domestic banking sector. Also, in their frantic search for substitute financing, corporates withdrew their investments from domestic money market mutual funds putting redemption pressure on the mutual funds and down the line on non-banking financial companies (NBFCs) where the MFs had invested a significant portion of their funds. This substitution of overseas financing by domestic financing brought both money markets and credit markets under pressure. Second, the forex market came under pressure because of reversal of capital flows as part of the global deleveraging process. Simultaneously, corporates were converting the funds raised locally into foreign currency to meet their external obligations. Both these factors put downward pressure on the rupee. Third, the Reserve Bank’s intervention in the forex market to manage the volatility in the rupee further added to liquidity tightening. The transmission of the global cues to the domestic economy has been quite straight forward – through the slump in demand for exports. The United States, European Union and the Middle East, which account for three quarters of India’s goods and services trade are in a synchronized down turn. Service export growth is also likely to slow in the near term as the recession deepens and financial services firms – traditionally large users of outsourcing services – are restructured. Remittances from migrant workers too are likely to slow as the Middle East adjusts to lower crude prices and advanced economies go into a recession. Beyond the financial and real channels of transmission as above, the crisis also spread through the confidence channel. In sharp contrast to global financial markets, which went into a seizure on account of a crisis of confidence, Indian financial markets continued to function in an orderly manner. Nevertheless, the tightened global liquidity situation in the period immediately following the Lehman failure in mid-September 2008, coming as it did on top of a turn in the credit cycle, increased the risk aversion of the financial system and made banks cautious about lending. The purport of the above explanation is to show how, despite not being part of the financial sector problem, India has been affected by the crisis through the pernicious feedback loops between external shocks and domestic vulnerabilities by way of the financial, real and confidence channels. Effect on Banks The actual effect of recession was only realised in February 2008 in Banking Industry. Before this there were lot of questions and queries regarding whether the U. S. recession will have any impact on India or Indian banking sector. In Feb 2008, the markets suddenly crashed the actual picture came in front. The effects which came across the banking sector are as follow * Credit Card and loan settlements. As soon as the impact of recession was realized by the banking sector, the Indian banking system came into the mode of consolidation. Each and every bank started reviewing their NPA’s and the amount of lending they have done which is yet to be recovered. Bank concentrated more on retail loans and Credit Card payments. The first priority for bank was to recover such amount which was unpaid from their customers. The banks hired external agencies for calling up clients and requesting them to settle their respective dues. This in turn created a panic in the customers mind. The banks in order to recover their dues and make the process fast provided attractive offers to its customers. For e. g. By settling the entire amount by cash there were discounts which were given amounting to about 5% of the entire due amount. * Call money market. In the initial stages of recession there was lot of demand for short term cash amongst the bank as the bank needed to fulfil the requirement of CRR and SLR. The money which was lended by the bank were taking time to recover and therefore there was a sudden requirement of short term money. The interest rate which were use to be at 5-6% grow up to 14-15% for a time period of 11-15 days. These requirements by few banks were enchased fully by other banks which were low on lending. The banks like ING Vysya bank, Yes Bank, IDBI Bank were amongst the few who were lending through call money market to other banks. * Fixed Deposit Rates Before recession hit the market FD rates were at a sky high level. Lot of private sector banks as well as public sector banks were offering interest rates in long term period upto 11-12%. When the recession kicked in the money demand for long term had almost finished. This was because of the reason that banks were in the mode of consolidation and did not want to lend further till the time most of the money was recovered. The bank deposit rates came down to a level of 6-7% as there was ample liquidity in the banking sector because of funds being not given ahead as loans. * Private banks became unpopular. During recession looking at the bankruptcy of foreign banks there was panic in the mind of investors even in india. There were lot of question that were raised whether the private sector banks who take exposure in foreign securities are safe in investing or not. During this period only there was a news which came for ICICI Bank. ICICI Bank had taken direct exposures in securities which issued by Lehman Brothers and Merill Lynch. In fact even few of public sector banks had taken similar exposures but since public sector banks were backed up by the government, there was a comfort factor amongst the investors. If we look at what happened with ICICI Bank, the liquidity was ample and it was just a few percentage of exposure that has gone as bad debt but other private marked players like HDFC Bank and Kotak Mahindra Bank encashed on these opportunities and placed their canopies next to each and every branch and ATM of ICICI Bank. There was a lot of panic which was created within the investors and they wanted to park their funds in a safer bank. Many of them shifted to nationalized banks and others were diverted to other private banks. This not only hampered the image of ICICI Bank but also created a bad image of Indian Private Banks. They were much difficulties which were faced by these banks to get additional deposits from investors and even retain theri clients who were shifting toward nationalised banks. * Diversifying and churning of funds. While the recession was impacting the country and the banking system there were informations that were given to the investors that the government insures on Rs. 1 lakh for any particular individual. This was misinterpreted by lot of investors in what they believed was with respect to one particular bank. With these being public diversification started. Each investor to safeguard his/her money started opening many accounts in different banks and keeping the funds equal in all. There was a lot of churning which happened from private sector banks to public sector banks as there were lot of uncertainity about funds being saved in a private sector bank. Investor created portfolios in different nationalised banks because of which private sector banks faced decline in their interest earnings as well as corpus and faced losses. After a while this myth was broken by RBI governor that the government only ensures Rs. lakh in totality no matter how many banks an investor has. * Lending Choked. The banks private sector as well as public sector were uncertain with what more negative impact were forthcoming. This resulted in, banks not at all lending to retail and corporate which were related to infrastructure or real estate. The cycle of churning of funds had suddenly stopped. Many projects which were about to start or were half way completed we re forced to put their projects on hold as no additional funds were being provided. This created commotion in real estate market which resulted in decline of prices. Even on retail side many of the housing loans were rejected which propelled the negativity more. Even for Large Cap companies the banks were demanding additional securities in cash apart from normal tangible assets. Even for processing loans for investors who had excellent credit history, the banks put ahead lot of extra conditions and terms. This further created panic and investors postponed their financial goals and loans were not applied for. After a while many loan divisions of banks were shut down and the employees were shifted to other departments were asked to leave. This even further increased the liquidity with banks. * Banks Investment Primary earning for any bank is through lending. Loans were not being processed and since the banks were uncertain of what more negative impact will come the banks were desperately looking out for other avenues to make money. The most safest option available with banks was to invest in G-Secs (Government of India Securities). Many banks started heavily into govt. Securities and bonds. These securities were traded quite highly at that period. Other sources including were through reverse repo and short term lending to different banks. During this time period much more focus was given to income from wealth management as markets has been corrected and banks insisted on educating the investors to park their funds in the equity market. Though the banks were heavily investing in G-Secs and other bonds it was not enough for their survival. Sooner or later the banks had to lend where they make the maximum profit. * Unemployment During the time of recession many jobs were lost in all the sectors. The similar effect was seen in banking but it was not in totality but few departments specific. The maximum hits were taken by two divisions which suffered most during the recession time. The first being the Wealth management division of banks. Though the feeling was correct that the markets have come down and valuations are excellent, it was very difficult convincing the investors. This resulted in many job losses in wealth management department of all the banks as revenue was expected which was not possible to generate. The next division which suffered was the loan division. The lay off’s happened more as the departments closed down and were not functional at all. Most of the bank had outsourced the servicing part as it was cheaper compared to keeping the existing team operational. Close to 1100 jobs were lost in the matter of 3 months in the entire banking sector. There were lot of apprehensions in the mind of new jonnies and soon working for a retail bank became unpopular. * Nationalised Banks popularity During all these events the only player in banking who were waiting to claw back the market shares were the nationalised bank. There was enough panic in retail investor’s regarding their funds being safe and sound, which the nationalised banks encashed fully. Maximum number of promotional activities and advertisement were given by them in the news paper and new channels. Even the investors responded to them equally and more than willingly because the backing up of the government was more than enough to provide a relief factor. Even in terms of employment, soon the nationalised banks became very popular and the people who were asked to leave from private banks where looking out for safe options to enter again. They were not willing to take any more risk. With this the bank got best of the aggressive talent in cheap prices. What corrective measures were taken? Decrease in CRR and repo rates. RBI again cuts repo rates ; CRR to inject additional liquidity of Rs 20,000 crore January 2, 2009: On a review of current global and domestic macroeconomic situation, the Reserve Bank has decided to take the following further measures: Repo Rate To reduce the repo rate under the liquidity adjustment facility (LAF) by 100 basis points from 6. 5 per cent to 5. 5 per cent with immediate effect. Reverse Repo Rate To reduce the reverse repo rate under the LAF by 100 basis points from 5. 0 per cent to 4. 0 per cent with immediate effect. Cash Reserve Ratio To reduce the cash reserve ratio (CRR) of scheduled banks by 50 basis points from 5. 5 per cent to 5. 0 per cent from the fortnight beginning January 17, 2009. The reduction in the CRR will inject additional liquidity of around Rs. 20,000 crore to the financial system. It is expected that the reduction in the policy interest rates and the CRR will further enable banks to provide credit for productive purposes at appropriate interest rates. The Reserve Bank on its part would continue to maintain a comfortable liquidity position in the system. Background to announcement of present monetary stimulus by RBI: The global financial situation continues to be uncertain. Since the official recognition of recession in the US, the UK, the Euro area and Japan, the downside risks to the global economy have increased. Concomitantly, the policy initiatives in the advanced economies are geared towards managing the recession and defusing potentially deflationary trends. The US has reduced the Federal Funds Rate to 0 – 0. 25 per cent. Several other advanced and emerging economies such as Japan, Canada, Republic of Korea, Hong Kong and China too have reduced their policy rates. India’s financial sector has remained resilient even in the face of global financial turmoil that is so deep and pervasive. Our financial markets continue to function in an orderly manner. India’s growth trajectory has, however, been impacted both by the financial crisis and the follow-on global economic downturn. This impact has turned out to be deeper and wider than earlier anticipated. Concurrently, because of global developments coupled with supply and demand management measures at home, inflation is on the decline. Reflecting these developments, the Reserve Bank has adjusted its policy stance from demand management to arresting the moderation in growth. In particular, the aim of these measures was to augment domestic and forex liquidity and to ensure that credit continues to flow to productive sectors of the economy. Notably, since mid-September 2008, the Reserve Bank has reduced the repo rate under the liquidity adjustment facility (LAF) from 9. 0 per cent to 6. 5 per cent, reduced the reverse repo rate under the LAF from 6. 0 per cent to 5. 0 per cent and the cash reserve ratio from 9. 0 per cent to 5. per cent How it helped? With these measures of RBI there was ample liquidity which was created in the market which forced the bank to lend out to companies as the funds in the banks were lying ideal and making no money for the bank. This actually started the lending process of the banks. * Role of fiscal stimulus package by government. There is a relationship between budget deficits and the hea lth of the economy, but is certainly not a perfect one. There can be massive budget deficits when the economy is doing quite well – the past few years of the United States being a prime example. That being said, government budgets tend to go from surplus to deficit (or existing deficits become larger) as the economy goes sour. This typically happens as follows: 1. The economy goes into recession, costing many workers their jobs, and at the same time causing corporate profits to decline. This causes less income tax revenue to flow to the government, along with less corporate income tax revenue. Occasionally the flow of income to the government will still grow, but at a slower rate than inflation, meaning that flow of tax revenue has fallen in real terms. 2. Because many workers have lost their jobs, there is increased use of government programs, such as unemployment insurance. Government spending rises as more individuals are calling on government services to help them out through tough times. 3. To help push the economy out of recession and to help those who have lost their jobs, governments often create new social programs during times of recession and depression. FDR’s â€Å"New Deal† of the 1930s is a prime example of this. Government spending then rises, not just because of increased use of existing programs, but through the creation of new programs. Because of factors one, the government receives less money from taxpayers, while factors two and three, the government spends more money. Money starts flowing out of the government faster than it comes in, causing the government’s budget to go into deficit. * How it helped? With the government spending more the government securities started declining in performance. As more and more securities were being issued the interest rate on securities started rising which has a direct impact on the gsec return. This again closed one more avenue of investment for banks as they were investing heavily into them instead of lending it out to corporate. This in all diverted the funds of the bank to the needful and thus started the lending process again. Future outlook In India there is evidence of economic activity slowing down. Real GDP growth has moderated in the first half of 2008 / 09. The services sector too, which has been our prime growth engine for the last five years, is slowing, mainly in construction, transport and communication, trade, hotels and restaurants sub-sectors. For the first time in seven years, exports have declined in absolute terms for three months in a row during October-December 2008. Recent data indicate that the demand for bank credit is slackening despite comfortable liquidity in the system. Higher input costs and dampened demand have dented corporate margins while the uncertainty surrounding the crisis has affected business confidence. The index of industrial production has shown negative growth for two recent months and investment demand is decelerating. All these factors suggest that growth moderation may be steeper and more extended than earlier projected. There are also several structural factors that have come to India’s aid. First, notwithstanding the everity and multiplicity of the adverse shocks, India’s financial markets have shown admirable resilience. This is in large part because India’s banking system remains sound, healthy, well capitalized and prudently regulated. Second, our comfortable reserve position provides confidence to overseas investors. Third, since a large majority of Indians do not participate in equity and asset markets, the negative impact of the wealth loss effect that is plaguing the advanced economies should be quite muted. Consequently, consumption demand should hold up well. Fourth, because of India’s mandated priority sector lending, institutional credit for agriculture will be unaffected by the credit squeeze. The farm loan waiver package implemented by the government should further insulate the agriculture sector from the crisis. Finally, over the years, India has built an extensive network of social safety-net programmes, including the flagship rural employment guarantee programme, which should protect the poor and the returning migrant workers from the extreme impact of the global crisis. RBI’s policy stance Going forward, the Reserve Bank’s policy stance will continue to be to maintain comfortable rupee and forex liquidity positions. There are indications that pressures on mutual funds have eased and that NBFCs too are making the necessary adjustments to balance their assets and liabilities. Despite the contraction in export demand, we will be able to manage our balance of payments. It is the Reserve Bank’s expectation that commercial banks will take the signal from the policy rates reduction to adjust their deposit and lending rates in order to keep credit flowing to productive sectors. In particular, the special refinance windows opened by the Reserve Bank for the MSME (micro, small and medium enterprises) sector, housing sector and export sector should see credit flowing to these sectors. Also the SPV set up for extending assistance to NBFCs should enable NBFC lending to pick up steam once again. The government’s fiscal stimulus should be able to supplement these efforts from both supply and demand sides. What Industry experts think? Mentioned below is what the senior experts in banking think of how the banking sector survived the crisis. 1). Mr. Anil Kumar Gupta (Vice President) Wealth management division- North and east region ING VYSYA BANK LTD. â€Å"The banking sector is very strong in India. Especially with the help of a governing body like RBI monitoring all the banks in Indian. † â€Å" I would say that step’s that were taken by the RBI in terms of rate cuts made so much liquidity in banking system that they were compelled to lend out to corporate. The recession gets more dangerous if the spending cycle by the people of the country or the lending cycles by the banks are put on a hold. † 2). Mr. Manavjeet Awasty (Senior Vice President) CITI BANK LTD- North â€Å"The ratio’s that the banks need to maintain because of RBI like CRR and SLR are the life savers for any banking firm. During financial crisis the condition of bankruptcy comes only when liquidity is crunched. The ratio’s which are maintained makes sure that enough liquidity is available in the system. † When the turnaround comes Over the last five years, India clocked an unprecedented 9% growth, driven largely by domestic consumption and investment even as the share of net exports has been rising. This was no accident or happenstance. True, the benign global environment, easy liquidity and low interest rates helped, but at the heart of India’s growth were a growing entrepreneurial spirit, rise in productivity and increasing savings. These fundamental strengths continue to be in place. Nevertheless, the global crisis will dent India’s growth trajectory as investments and exports slow. Clearly, there is a period of painful adjustment ahead of us. However, once the global economy begins to recover, India’s turn around will be sharper and swifter, backed by our strong fundamentals and the untapped growth potential. Meanwhile, the challenge for the government and the RBI is to manage the adjustment with as little pain as possible. Conclusion To conclude, we would say that the Indian banking sector is very strong in terms of its maintaining the said regulations and to follow the rule implied by its governing body which is RBI. The necessary steps were taken during the financial crisis which helped the banking sector to emerge out of the crisis without any major disturbance.

Commercial Fixture Essay

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Principles of Fire Behavior - Research Proposal Example Passive fire protection refers to measures that are generally built into buildings during construction in accordance with fire codes. The following will outline various modes and techniques of both suppressant agents and of fire suppressing strategies. In the suppression of wildfires, it should first be noted that wildfires that do not threaten human habitation generally are allowed to burn. Wildfires are part of a natural process, and as such they are not considered to be a bad thing. Where wildfires become a problem is when they threaten human habitation. Water is the most common fire suppressant agent, but the mode of delivery depends upon the location and terrain of the wildfire. For instance, water can be delivered by plane or by helicopter. These same planes or helicopters can be used to deliver various chemical fire suppressing agents as well. Firefighters also repel or parachute, which is referred to as smoke jumping, into areas in order to set up pre-emptive fire suppressing measures. On the ground, firefighters use small water or chemical agent pumps in order to douse and control small fires from spreading. Additionally, they will carry chainsaws in order to construct firebreaks, which is simply removing combustible mate rials from an area in order to prevent the spread of fire. Furthermore, tanker trucks carrying thousands of gallons of water are utilized to deliver water or other suppressant agents to where they are needed (Berry 2007). Human life is considered the most important factor in fire suppression. After this, various aspects such as the protection of property, health, safety, and ecological factors are taken into consideration. In the worst cases of enormous fires, the preservation of human life is the only possible action that can be taken. Cost and safety or firefighters is taken into consideration as well. Another technique in wildfire suppression is the creation of

Miss.Independent Essay Example for Free

Miss.Independent Essay Abstract We survey the phenomenon of the growth of ? rms drawing on literature from economics, management, and sociology. We begin with a review of empirical ‘stylised facts’ before discussing theoretical contributions. Firm growth is characterized by a predominant stochastic element, making it di? cult to predict. Indeed, previous empirical research into the determinants of ? rm growth has had a limited success. We also observe that theoretical propositions concerning the growth of ? rms are often amiss. We conclude that progress in this area requires solid empirical work, perhaps making use of novel statistical techniques. JEL codes: L25, L11 Keywords: Firm Growth, Size Distribution, Growth Rates Distribution, Gibrat’s Law, Theory of the Firm, Diversi? cation, ‘Stages of Growth’ models. ? I thank Giulio Bottazzi, Giovanni Dosi, Ha? da El-Younsi, Jacques Mairesse, Bernard Paulr? , Rekha Rao, e Angelo Secchi and Ulrich Witt for helpful comments. Nevertheless, I am solely responsible for any errors or confusion that may remain. This version: May 2007 †  Corresponding Author : Alex Coad, Max Planck Institute of Economics, Evolutionary Economics Group, Kahlaische Strasse 10, D-07745 Jena, Germany. Phone: +49 3641 686822. Fax : +49 3641 686868. E-mail : [emailprotected] mpg. de 1 #0703 Contents 1 Introduction 3 2 Empirical evidence on ? rm growth 2. 1 Size and growth rates distributions . . . . 2. 1. 1 Size distributions . . . . . . . . . . 2. 1. 2 Growth rates distributions . . . . . 2. 2 Gibrat’s Law . . . . . . . . . . . . . . . . 2. 2. 1 Gibrat’s model . . . . . . . . . . . 2. 2. 2 Firm size and average growth . . . 2. 2. 3 Firm size and growth rate variance 2. 2. 4 Autocorrelation of growth rates . . 2. 3 Other determinants of ? rm growth . . . . 2. 3. 1 Age . . . . . . . . . . . . . . . . . 2. 3. 2 Innovation . . . . . . . . . . . . . . 2. 3. 3 Financial performance . . . . . . . 2. 3. 4 Relative productivity . . . . . . . . 2. 3. 5 Other ? rm-speci? c factors . . . . . 2. 3. 6 Industry-speci? c factors . . . . . . 2. 3. 7 Macroeconomic factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4 4 5 9 9 11 14 15 18 18 19 23 25 26 28 29 3 Theoretical contributions 3. 1 Neoclassical foundations – growth towards an ‘optimal size’ . . . . 3. 2 Penrose’s ‘Theory of the Growth of the Firm’ . . . . . . . . . . . 3. 3 Marris and ‘managerialism’ . . . . . . . . . . . . . . . . . . . . . 3. 4 Evolutionary Economics and the principle of ‘growth of the ? tter’ 3. 5 Population ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 31 32 34 35 38 . . . . . . . 39 39 40 43 44 45 46 49 5 Growth of small and large ? rms 5. 1 Di? erences in growth patterns for small and large ? rms . . . . . . . . . . . . . 5. 2 Modelling the ‘stages of growth’ . . . . . . . . . . . . . . . . . . . . . . . . . . 51 51 53 6 Conclusion 56 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Growth strategies 4. 1 Attitudes to growth . . . . . . . . . . . . . . . . . . . 4. 1. 1 The desirability of growth . . . . . . . . . . . 4. 1. 2 Is growth intentional or does it ‘just happen’ ? 4. 2 Growth strategies – replication or diversi? cation . . . 4. 2. 1 Growth by replication . . . . . . . . . . . . . 4. 2. 2 Growth by diversi? cation . . . . . . . . . . . . 4. 3 Internal growth vs growth by acquisition . . . . . . . 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . #0703 1 Introduction The aim of this survey is to provide an overview of research into the growth of ? rms, while also highlighting areas in need of further research. It is a multidisciplinary survey, drawing on contributions made in economics, management and also sociology. There are many di? erent measures of ? rm size, some of the more usual indicators being employment, total sales, value-added, total assets, or total pro? ts; and some of the less conventional ones such as ‘acres of land’ or ‘head of cattle’ (Weiss, 1998). In this survey we consider growth in terms of a range of indicators, although we devote little attention to the growth of pro? ts (this latter being more of a ? nancial than an economic variable). There are also di? erent ways of measuring growth rates. Some authors (such as Delmar et al. , 2003) make the distinction between relative growth (i. e. the growth rate in percentage terms) and absolute growth (usually measured in the absolute increase in numbers of employees). In this vein, we can mention the ‘Birch index’ which is a weighted average of both relative and absolute growth rates (this latter being taken into account to emphasize that large ? rms, due to their large size, have the potential to create many jobs). This survey focuses on relative growth rates only. Furthermore, in our discussion of the processes of expansion we emphasize positive growth and not so much negative growth. 1 In true Simonian style,2 we begin with some empirical insights in Section 2, considering ? rst the distributions of size and growth rates, and moving on to look for determinants of growth rates. We then present some theories of ? rm growth and evaluate their performance in explaining the stylised facts that emerge from empirical work (Section 3). In Section 4 we consider the demand and supply sides of growth by discussing the attitudes of ? rms towards growth opportunities as well as investigating the processes by which ? rms actually grow (growth by ‘more of the same’, growth by diversi? cation, growth by acquisition). In Section 5 we examine the di? erences between the growth of small and large ? rms in greater depth. We also review the ‘stages of growth’ models. Section 6 concludes. 2 Empirical evidence on ? rm growth To begin with, we take a non-parametric look at the distributions of ? rm size and growth rates, before moving on to results from regressions that investigate the determinants of growth rates. 1 2 For an introduction to organizational decline, see Whetten (1987). See in particular Simon (1968). 3 #0703 2. 1 Size and growth rates distributions A suitable starting point for studies into industrial structure and dynamics is the ?rm size distribution. In fact, it was by contemplating the empirical size distribution that Robert Gibrat (1931) proposed the well-known ‘Law of Proportionate E? ect’ (also known as ‘Gibrat’s law’). We also discuss the results of research into the growth rates distribution. The regularity that ? rm growth rates are approximately exponentially distributed was discovered only recently, but o? ers unique insights into the growth patterns of ? rms. 2. 1. 1 Size distributions The observation that the ? rm-size distribution is positively skewed proved to be a useful point of entry for research into the structure of industries. (See Figures 1 and 2 for some examples of aggregate ? rm size distributions. ) Robert Gibrat (1931) considered the size of French ? rms in terms of employees and concluded that the lognormal distribution was a valid heuristic. Hart and Prais (1956) presented further evidence on the size distribution, using data on quoted UK ? rms, and also concluded in favour of a lognormal model. The lognormal distribution, however, can be viewed as just one of several candidate skew distributions. Although Simon and Bonini (1958) maintained that the â€Å"lognormal generally ? ts quite well† (1958: p611), they preferred to consider the lognormal distribution as a special case in the wider family of ‘Yule’ distributions. The advantage of the Yule family of distributions was that the phenomenon of arrival of new ? rms could be incorporated into the model. Steindl (1965) applied Austrian data to his analysis of the ? rm size distribution, and preferred the Pareto distribution to the lognormal on account of its superior performance in describing the upper tail of the distribution. Similarly, Ijiri and Simon (1964, 1971, 1974) apply the Pareto distribution to analyse the size distribution oflarge US ? rms. E? orts have been made to discriminate between the various candidate skew distributions. One problem with the Pareto distribution is that the empirical density has many more middlesized ? rms and fewer very large ? rms than would be theoretically predicted (Vining, 1976). Other research on the lognormal distribution has shown that the upper tail of the empirical size distribution of ? rms is too thin relative to the lognormal (Stanley et al. , 1995). Quandt (1966) compares the performance of the lognormal and three versions of the Pareto distribution, using data disaggregated according to industry. He reports the superiority of the lognormal over the three types of Pareto distribution, although each of the distributions produces a best-? t for at least one sample. Furthermore, it may be that some industries (e. g. the footwear industry) are not ? tted well by any distribution. More generally, Quandt’s results on disaggregated data lead us to suspect that the regu4 #0703 larities of the ? rm-size distribution observed at the aggregate level do not hold with sectoral disaggregation. Silberman (1967) also ? nds signi? cant departures from lognormality in his analysis of 90 four-digit SIC sectors. It has been suggested that, while the ? rm size distribution has a smooth regular shape at the aggregate level, this may merely be due to a statistical aggregation e? ect rather than a phenomenon bearing any deeper economic meaning (Dosi et al, 1995; Dosi, 2007). Empirical results lend support to these conjectures by showing that the regular unimodal ? rm size distributions observed at the aggregate level can be decomposed into much ‘messier’ distributions at the industry level, some of which are visibly multimodal (Bottazzi and Secchi, 2003; Bottazzi et al. , 2005). For example, Bottazzi and Secchi (2005) present evidence of signi? cant bimodality in the ? rm size distribution of the worldwide pharmaceutical industry, and relate this to a cleavage between the industry leaders and fringe competitors. Other work on the ? rm-size distribution has focused on the evolution of the shape of the distribution over time. It would appear that the initial size distribution for new ? rms is particularly right-skewed, although the log-size distribution tends to become more symmetric as time goes by. This is consistent with observations that small young ? rms grow faster than their larger counterparts. As a result, it has been suggested that the log-normal can be seen as a kind of ‘limit distribution’ to which a given cohort of ? rms will eventually converge. Lotti and Santarelli (2001) present support for this hypothesis by tracking cohorts of new ? rms in several sectors of Italian manufacturing. Cabral and Mata (2003) ? nd similar results in their analysis of cohorts of new Portuguese ? rms. However, Cabral and Mata interpret their results by referring to ? nancial constraints that restrict the scale of operations for new ? rms, but become less binding over time, thus allowing these small ?rms to grow relatively rapidly and reach their preferred size. They also argue that selection does not have a strong e? ect on the evolution of market structure. Although the skewed nature of the ? rm size distribution is a robust ? nding, there may be some other features of this distribution that are speci? c to countries. Table 1, taken from Bartelsman et al. (2005), highlights some di? erences in the structure of industries across countries. Among other things, one observes that large ? rms account for a considerable share of French industry, whereas in Italy ? rms tend to be much smaller on average. (These international di? erences cannot simply be attributed to di? erences in sectoral specialization across countries. ) 2. 1. 2 Growth rates distributions It has long been known that the distribution of ? rm growth rates is fat-tailed. In an early contribution, Ashton (1926) considers the growth patterns of British textile ? rms and observes 5 US 86. 7 69. 9 87. 9 16. 6 5. 8 Western Germany 87. 9 77. 9 90. 2 23. 6 11. 3 78. 6 73. 6 78. 8 13. 9 17. 0 France Italy 93. 1 87. 5 96. 5 34. 4 30. 3 74. 9 8. 3 UK Canada Denmark 90. 0 74. 0 90. 8 30. 2 16. 1 92. 6 84. 8 94. 5 25. 8 13. 0 Finland Netherlands 95. 8 86. 7 96. 8 31. 2 16. 9 86. 3 70. 5 92. 8 27. 7 15. 7 Portugal Source: Bartelsman et al. (2005: Tables 2 and 3). Notes: the columns labelled ‘share of employment’ refer to the employment share 6 26. 4 17. 0 33. 5 10. 5 12. 7 13. 3 13. 0 6. 5 16. 8 Total economy 80. 3 39. 1 32. 1 15. 3 40. 7 40. 5 30. 4 27. 8 18. 3 31. 0 Manufacturing 21. 4 11. 5 35. 7 6. 8 12. 0 12. 7 9. 9 5. 3 11. 4 Business services Ave. No. Employees per ? rm of ? rms with fewer than 20 employees. 20. 6 33. 8 12. 1 46. 3 33. 4 33. 0 41. 9 39. 8 Business services Total economy Manufacturing Share of employment (%) Business services Total economy. Manufacturing Absolute number (%) Table 1: The importance of small ? rms (i. e. ?rms with fewer than 20 employees) across broad sectors and countries, 1989-94 #0703 #0703 1 Pr 1998 2000 2002 0. 1 0. 01 0. 001 1e-04 -4 -2 0 s 2 4 6 Figure 1: Kernel estimates of the density of ?rm size (total sales) in 1998, 2000 and 2002, for French manufacturing ? rms with more than 20 employees. Source: Bottazzi et al. , 2005. Figure 2: Probability density function of the sizes of US manufacturing ? rms in 1997. Source: Axtell, 2001. that â€Å"In their growth they obey no one law. A few apparently undergo a steady expansion.. . With others, increase in size takes place by a sudden leap† (Ashton 1926: 572-573). Little (1962) investigates the distribution of growth rates, and also ? nds that the distribution is fat-tailed. Similarly, Geroski and Gugler (2004) compare the distribution of growth rates to the normal case and comment on the fat-tailed nature of the empirical density. Recent empirical research, from an ‘econophysics’ background, has discovered that the distribution of ? rm growth rates closely follows the parametric form of the Laplace density. Using the Compustat database of US manufacturing ? rms, Stanley et al. (1996) observe a ‘tent-shaped’ distribution on log-log plots that corresponds to the symmetric exponential, or Laplace distribution (see also Amaral et al. (1997) and Lee et al. (1998)). The quality of the ? t of the empirical distribution to the Laplace density is quite remarkable. The Laplace distribution is also found to be a rather useful representation when considering growth rates of ? rms in the worldwide pharmaceutical industry (Bottazzi et al. , 2001). Giulio Bottazzi and coauthors extend these ? ndings by considering the Laplace density in the wider context of the family of Subbotin distributions (beginning with Bottazzi et al., 2002). They ? nd that, for the Compustat database, the Laplace is indeed a suitable distribution for modelling ? rm growth rates, at both aggregate and disaggregated levels of analysis (Bottazzi and Secchi 2003a). The exponential nature of the distribution of growth rates also holds for other databases, such as Italian manufacturing (Bottazzi et al. (2007)). In addition, the exponential distribution appears to hold across a variety of ? rm growth indicators, such as Sales growth, employment growth or Value Added growth (Bottazzi et al. , 2007). The growth rates of French manufacturing ? rms have also been studied, and roughly speaking a similar shape was observed, although it must be said that the empirical density was noticeably fatter-tailed than the Laplace (see Bottazzi et al. , 2005). 3 3 The observed subbotin b parameter (the ‘shape’ parameter) is signi? cantly lower than the Laplace value of 1. This highlights the importance of following Bottazzi et al. (2002) and considering the Laplace as a special 7 #0703 1998 2000 2002 1998 2000 2002 1 prob. prob. 1 0. 1 0. 01 0. 1 0. 01 0. 001 0. 001 -3 -2 -1 0 1 2 -2 -1. 5 -1 conditional growth rate -0. 5 0 0. 5 1 1. 5 2 conditional growth rate. Figure 3: Distribution of sales growth rates of French manufacturing ? rms. Source: Bottazzi et al. , 2005. Figure 4: Distribution of employment growth rates of French manufacturing ? rms. Source: Coad, 2006b. Research into Danish manufacturing ? rms presents further evidence that the growth rate distribution is heavy-tailed, although it is suggested that the distribution for individual sectors may not be symmetric but right-skewed (Reichstein and Jensen (2005)). Generally speaking, however, it would appear that the shape of the growth rate distribution is more robust to disaggregation than the shape of the ?rm size distribution. In other words, whilst the smooth shape of the aggregate ? rm size distribution may be little more than a statistical aggregation e? ect, the ‘tent-shapes’ observed for the aggregate growth rate distribution are usually still visible even at disaggregated levels (Bottazzi and Secchi, 2003a; Bottazzi et al. , 2005). This means that extreme growth events can be expected to occur relatively frequently, and make a disproportionately large contribution to the evolution of industries. Figures 3 and 4 show plots of the distribution of sales and employment growth rates for French manufacturing ?rms with over 20 employees. Although research suggests that both the size distribution and the growth rate distribution are relatively stable over time, it should be noted that there is great persistence in ? rm size but much less persistence in growth rates on average (more on growth rate persistence is presented in Section 2. 2. 4). As a result, it is of interest to investigate how the moments of the growth rates distribution change over the business cycle. Indeed, several studies have focused on these issues and some preliminary results can be mentioned here. It has been suggested that the variance of growth rates changes over time for the employment growth of large US ? rms (Hall, 1987) and that this variance is procyclical in the case of growth of assets (Geroski et al. , 2003). This is consistent with the hypothesis that ? rms have a lot of discretion in their growth rates of assets during booms but face stricter discipline during recessions. Higson et al. (2002, 2004) consider the evolution of the ? rst four moments of distributions of the growth of sales, for large US and UK ?rms over periods of 30 years or more. They observe that higher moments of the distribution of sales growth rates have signi? cant cyclical patterns. In case in the Subbotin family of distributions. 8 #0703 particular, evidence from both US and UK ? rms suggests that the variance and skewness are countercyclical, whereas the kurtosis is pro-cyclical. Higson et al. (2002: 1551) explain the counter-cyclical movements in skewness in these words: â€Å"The central mass of the growth rate distribution responds more strongly to the aggregate shock than the tails. So a negative shock moves the central mass closer to the left of the distribution leaving the right tail behind and generates positive skewness. A positive shock shifts the central mass to the right, closer to the group of rapidly growing ? rms and away from the group of declining ? rms. So negative skewness results. † The procyclical nature of kurtosis (despite their puzzling ? nding of countercyclical variance) emphasizes that economic downturns change the shape of the growth rate distribution by reducing a key parameter of the ‘spread’ or ‘variation’ between ? rms. 2. 2 Gibrat’s Law. Gibrat’s law continues to receive a huge amount of attention in the empirical industrial organization literature, more than 75 years after Gibrat’s (1931) seminal publication. We begin by presenting the ‘Law’, and then review some of the related empirical literature. We do not attempt to provide an exhaustive survey of the literature on Gibrat’s law, because the number of relevant studies is indeed very large. (For other reviews of empirical tests of Gibrat’s Law, the reader is referred to the survey by Lotti et al (2003); for a survey of how Gibrat’s law holds for the services sector see Audretsch et al. (2004). ) Instead, we try to provide an overview of the essential results. We investigate how expected growth rates and growth rate variance are in? uenced by ? rm size, and also investigate the possible existence of patterns of serial correlation in ? rm growth. 2. 2. 1 Gibrat’s model Robert Gibrat’s (1931) theory of a ‘law of proportionate e? ect’ was hatched when he observed that the distribution of French manufacturing establishments followed a skew distribution that resembled the lognormal. Gibrat considered the emergence of the ?rm-size distribution as an outcome or explanandum and wanted to see which underlying growth process could be responsible for generating it. In its simplest form, Gibrat’s law maintains that the expected growth rate of a given ? rm is independent of its size at the beginning of the period examined. Alternatively, as Mans? eld (1962: 1030) puts it, â€Å"the probability of a given proportionate change in size during a speci? ed 9 #0703 period is the same for all ? rms in a given industry – regardless of their size at the beginning of the period. † More formally, we can explain the growth of ? rms in the following framework. Let xt be the size of a ? rm at time t, and let ? t be random variable representing an idiosyncratic, multiplicative growth shock over the period t ? 1 to t. We have xt ? xt? 1 = ? t xt? 1 (1) xt = (1 + ? t )xt? 1 = x0 (1 + ? 1 )(1 + ? 2 ) . . . (1 + ? t ) (2) which can be developed to obtain It is then possible to take logarithms in order to approximate log(1 + ? t ) by ? t to obtain4 t log(xt ) ? log(x0 ) + ? 1 + ? 2 + . . . + ? t = log(x0 ) + ?s (3) s=1 In the limit, as t becomes large, the log(x0 ) term will become insigni? cant, and we obtain t log(xt ) ? ?s (4) s=1 In this way, a ? rm’s size at time t can be explained purely in terms of its idiosyncratic history of multiplicative shocks. If we further assume that all ? rms in an industry are independent realizations of i. i. d. normally distributed growth shocks, then this stochastic process leads to the emergence of a lognormal ? rm size distribution. There are of course several serious limitations to such a simple vision of industrial dynamics. We have already seen that the distribution of growth rates is not normally distributed, but instead resembles the Laplace or ‘symmetric exponential’. Furthermore, contrary to results implied by Gibrat’s model, it is not reasonable to suppose that the variance of ? rm size tends to in? nity (Kalecki, 1945). In addition, we do not observe the secular and unlimited increase in industrial concentration that would be predicted by Gibrat’s law (Caves, 1998). Whilst a ‘weak’ version of Gibrat’s law merely supposes that expected growth rate is independent of ?rm size, stronger versions of Gibrat’s law imply a range of other issues. For example, Chesher (1979) rejects Gibrat’s law due to the existence of an autocorrelation structure in the growth shocks. Bottazzi and Secchi (2006a) reject Gibrat’s law on the basis of a negative relationship between growth rate variance and ? rm size. Reichstein and Jensen (2005) reject Gibrat’s law 4 This logarithmic approximation is only justi? ed if ? t is ‘small’ enough (i. e. close to zero), which can be reasonably assumed by taking a short time period (Sutton, 1997). 10 #0703after observing that the annual growth rate distribution is not normally distributed. 2. 2. 2 Firm size and average growth Although Gibrat’s (1931) seminal book did not provoke much of an immediate reaction, in recent decades it has spawned a ? ood of empirical work. Nowadays, Gibrat’s ‘Law of Proportionate E? ect’ constitutes a benchmark model for a broad range of investigations into industrial dynamics. Another possible reason for the popularity of research into Gibrat’s law, one could suggest quite cynically, is that it is a relatively easy paper to write. First of all, it has been argued that there is a minimalistic theoretical background behind the process (because growth is assumed to be purely random). Then, all that needs to be done is to take the IO economist’s ‘favourite’ variable (i. e. ?rm size, a variable which is easily observable and readily available) and regress the di? erence on the lagged level. In addition, few control variables are required beyond industry dummies and year dummies, because growth rates are characteristically random. Empirical investigations of Gibrat’s law rely on estimation of equations of the type: log(xt ) = ?+ ? log(xt? 1 ) + (5) where a ? rm’s ‘size’ is represented by xt , ? is a constant term (industry-wide growth trend) and is a residual error. Research into Gibrat’s law focuses on the coe? cient ?. If ? rm growth is independent of size, then ? takes the value of unity. If ? is smaller than one, then smaller ? rms grow faster than their larger counterparts, and we can speak of ‘regression to the mean’. Conversely, if ? is larger than one, then larger ? rms grow relatively rapidly and there is a tendency to concentration and monopoly. A signi?cant early contribution was made by Edwin Mans? eld’s (1962) study of the US steel, petroleum, and rubber tire industries. In particular interest here is what Mans? eld identi? ed as three di? erent renditions of Gibrat’s law. According to the ? rst, Gibrat-type regressions consist of both surviving and exiting ? rms and attribute a growth rate of -100% to exiting ? rms. However, one caveat of this approach is that smaller ? rms have a higher exit hazard which may obfuscate the relationship between size and growth. The second version, on the other hand, considers only those ?rms that survive. Research along these lines has typically shown that smaller ? rms have higher expected growth rates than larger ? rms. The third version considers only those large surviving ? rms that are already larger than the industry Minimum E? cient Scale of production (with exiting ? rms often being excluded from the analysis). Generally speaking, empirical analysis corresponding to this third approach suggests that growth rates are more or less independent from ? rm size, which lends support to Gibrat’s law. 11 #0703 The early studies focused on large ? rms only, presumably partly due to reasons of data availability. A series of papers analyzing UK manufacturing ? rms found a value of ? greater than unity, which would indicate a tendency for larger ? rms to have higher percentage growth rates (Hart (1962), Samuels (1965), Prais (1974), Singh and Whittington (1975)). However, the majority of subsequent studies using more recent datasets have found values of ? slightly lower than unity, which implies that, on average, small ? rms seem to grow faster than larger ? rms. This result is frequently labelled ‘reversion to the mean size’ or ‘mean-reversion’. 5 Among a large and growing body of research that reports a negative relationship between size and growth, we can mention here the work by Kumar (1985) and Dunne and Hughes (1994) for quoted UK manufacturing ? rms, Hall (1987), Amirkhalkhali and Mukhopadhyay (1993) and Bottazzi and Secchi (2003) for quoted US manufacturing ? rms (see also Evans (1987a, 1987b) for US manufacturing ? rms of a somewhat smaller size), Gabe and Kraybill (2002) for establishments in Ohio, and Goddard et al. (2002) for quoted Japanese manufacturing ? rms. Studies focusing on small businesses have also found a negative relationship between ? rm size and expected growth – see for example Yasuda (2005) for Japanese manufacturing ? rms, Calvo (2006) for Spanish manufacturing, McPherson (1996) for Southern African micro businesses, and Wagner (1992) and Almus and Nerlinger (2000) for German manufacturing. Dunne et al. (1989) analyse plant-level data (as opposed to ? rm-level data) and also observe that growth rates decline along size classes. Research into Gibrat’s law using data for speci? c sectors also ? nds that small ? rms grow relatively faster (see e. g. Barron et al. (1994) for New York credit unions, Weiss (1998) for Austrian farms, Liu et al. (1999) for Taiwanese electronics plants, and Bottazzi and Secchi (2005) for an analysis of the worldwide pharmaceutical sector). Indeed, there is a lot of evidence that a slight negative dependence of growth rate on size is present at various levels of industrial aggregation. Although most empirical investigations into Gibrat’s law consider only the manufacturing sector, some have focused on the services sector. The results, however, are often qualitatively similar – there appears to be a negative relationship between size and expected growth rate for services too (see Variyam and Kraybill (1992), Johnson et al. (1999)) Nevertheless, it should be mentioned that in some cases a weak version of Gibrat’s law cannot be convincingly rejected, since there appears to be no signi? cant relationship between expected growth rate and size (see the analyses provided by Bottazzi et al. (2005) for French manufacturing ? rms, Droucopoulos (1983) for the world’s largest ? rms, Hardwick and Adams (2002) for UK Life Insurance companies, and Audretsch et al. (2004) for small-scale Dutch services). Notwithstanding these latter studies, however, we acknowledge that in most cases a negative relationship between ? rm size and growth is observed. Indeed, 5 We should be aware, however, that ‘mean-reversion’ does not imply that ? rms are converging to anything resembling a common steady-state size, even within narrowly-de? ned industries (see in particular the empirical work by Geroski et al. (2003) and Ce? s et al. (2006)). 12 #0703 it is quite common for theoretically-minded authors to consider this to be a ‘stylised fact’ for the purposes of constructing and validating economic models (see for example Cooley and Quadrini (2001), Gomes (2001) and Clementi and Hopenhayn (2006)). Furthermore, John Sutton refers to this negative dependence of growth on size as a ‘statistical regularity’ in his revered survey of Gibrat’s law (Sutton, 1997: 46). A number of researchers maintain that Gibrat’s law does hold for ? rms above a certain size threshold. This corresponds to acceptance of Gibrat’s law according to Mans? eld’s third rendition, although ‘mean reversion’ leads us to reject Gibrat’s Law as described in Mans? eld’s second rendition. Mowery (1983), for example, analyzes two samples of ? rms, one of which contains small ? rms while the other contains large ? rms. Gibrat’s law is seen to hold in the latter sample, whereas mean reversion is observed in the former. Hart and Oulton (1996) consider a large sample of UK ? rms and ? nd that, whilst mean reversion is observed in the pooled data, a decomposition of the sample according to size classes reveals essentially no relation between size and growth for the larger ? rms. Lotti et al. (2003) follow a cohort of new Italian startups and ? nd that, although smaller ? rms initially grow faster, it becomes more di? cult to reject the independence of size and growth as time passes. Similarly, results reported by Becchetti and Trovato (2002) for Italian manufacturing ? rms, Geroski and Gugler (2004) for large European ? rms and Ce? s et al. (2006) for the worldwide pharmaceutical industry also ? nd that the growth of large ? rms is independent of their size, although including smaller ? rms in the analysis introduces a dependence of growth on size. It is of interest to remark that Caves (1998) concludes his survey of industrial dynamics with the ‘substantive conclusion’ that Gibrat’s law holds for ? rms above a certain size threshold, whilst for smaller ? rms growth rates decrease with size. Concern about econometric issues has often been raised. Sample selection bias, or ‘sample attrition’, is one of the main problems, because smaller ? rms have a higher probability of exit. Failure to account for the fact that exit hazards decrease with size may lead to underestimation of the regression coe? cient (i. e. ?). Hall (1987) was among the ? rst to tackle the problem of sample selection, using a Tobit model.