Friday, December 12, 2014

Raghuram Rajan Busts Modi's 'Make in India'


Raghuram Rajan Busts Modi's 'Make in India' Reserve Bank ...

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7 mins ago - Reserve Bank of India governor Raghuram Rajan sounds caution on 'Make in India' plan TNN | Dec 13, 2014, 05.37AM IST Reserve Bank of India (RBI) governor ...

Reserve Bank of India governor Raghuram Rajan sounds caution on ‘Make in India’ plan


rajan
Reserve Bank of India (RBI) governor Raghuram Rajan.
NEW DELHI: With his former boss Manmohan Singh by his side, Reserve Bank of India (RBI) governor Raghuram Rajan had some words of caution on the government's "Make in India" initiative, saying the focus should be on domestic demand against the backdrop of slowing global growth.

The Narendra Modi government has unveiled the "Make in India" drive to make the country an industrial hub like China, boost theshare of manufacturing in the economy to 25% from the current 15-16% and create millions of jobs.

The plan is the centrepiece of the government's efforts to revive sluggish growth.

"Instead, I am counselling against an export-led strategy that involves subsidizing exporters with cheap inputs as well as an undervalued exchange rate, simply because it is unlikely to be as effective at this juncture," Rajan said in his Bharat Ram memorial lecture, titled "Make in India Largely for India," and made it clear that he was not advocating export pessimism.

"I am also cautioning against picking a particular sector such as manufacturing for encouragement, simply because it has worked well for China. India is different, and developing at a different time, and we should be agnostic about what will work," he said while applauding the broader objective of the drive launched by the government.

Singh, a former PM and RBI governor, also spoke about the country's growth potential, saying it could notch 8-9% growth, provided there was a national consensus on taking advantages of opportunities provided by a globalized world. It was a rare occasion when Rajan and the former PM shared the stage after Singh stepped down from his office.

Rajan said slow growing industrial countries would be much less likely to be able to absorb a substantial additional amount of imports in the foreseeable future.

"Other emerging markets certainly could absorb more, and a regional focus for exports will pay off. But the world as a whole is unlikely to be able to accommodate another export-led China," he said.

The former chief of the International Monetary Fund (IMF) said if external demand growth is muted then the country will have to produce for the internal market and called for creating the infrastructure to support this, including introduction of the Goods & Services Tax (GST).

"We are more dependent on the global economy than we think. That it is growing more slowly, and is more inward looking than in the past means that we have to look to regional and domestic demand for our growth — to make in India primarily for India," he said.

"A well designed GST Bill, by reducing state border taxes, will have the important consequence of creating a truly national market for goods and services, which will be critical for our growth in years to come," he said.

Rajan, who has been under pressure from the government to cut interest rates to help boost growth, said a central bank has to pay attention to financial stability in addition to inflation.



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Sid Harth
Real GDP Growth (Percent change) 2012 2013 2014 Turkmenistan 11.1 10.2 10.1 Chad 8.9 3.9 9.6 Mongolia 12.4 11.7 9.1 Democratic Republic of the Congo 7.2 8.5 8.6 Côte d'Ivoire 10.7 8.7 8.5 Myanmar 7.3 8.3 8.5 Mozambique 7.2 7.1 8.3 Ethiopia 8.8 9.7 8.2 Sierra Leone 15.2 20.1 8.0 China 7.7 7.7 7.4 The Gambia 5.3 6.3 7.4 Lao P.D.R. 7.9 8.0 7.4 Tanzania 6.9 7.0 7.2 Cambodia 7.3 7.4 7.2 Sri Lanka 6.3 7.3 7.0 Uzbekistan 8.2 8.0 7.0 Nigeria 4.3 5.4 7.0 Mauritania 7.0 6.7 6.8 Burkina Faso 9.0 6.6 6.7 Panama 10.8 8.4 6.6 Qatar 6.1 6.5 6.5 Zambia 6.8 6.7 6.5 Bhutan 6.5 5.0 6.4 Niger 11.1 4.1 6.3 Philippines 6.8 7.2 6.2 Bangladesh 6.3 6.1 6.2 Tajikistan 7.5 7.4 6.0 Republic of Congo 3.8 3.3 6.0 Rwanda 8.8 4.7 6.0 Mali 0.0 1.7 5.9 Uganda 2.8 5.8 5.9 Malaysia 5.6 4.7 5.9 Papua New Guinea 8.1 5.5 5.8 Malawi 1.9 5.2 5.7 Togo 5.9 5.1 5.6 India 4.7 5.0 5.6 Vietnam 5.2 5.4 5.5 Djibouti 4.8 5.0 5.5 Benin 5.4 5.6 5.5 Nepal 4.8 3.9 5.5 Kenya 4.6 4.6 5.3 Brunei Darussalam 0.9 -1.8 5.3 Dominican Republic 2.7 4.6 5.3 Bolivia 5.2 6.8 5.2 Indonesia 6.3 5.8 5.2 Gabon 5.5 5.6 5.1 Cameroon 4.6 5.5 5.1 Georgia 6.2 3.2 5.0 São Tomé and Príncipe 4.0 4.0 5.0 Colombia 4.0 4.7 4.8 Burundi 4.0 4.5 4.7 Kazakhstan 5.0 6.0 4.6 Saudi Arabia 5.8 4.0 4.6 Senegal 3.4 3.5 4.5 Ghana 8.8 7.1 4.5 Maldives 0.9 3.7 4.5 Azerbaijan 2.2 5.8 4.5 Botswana 4.3 5.9 4.4 Namibia 5.0 4.3 4.3 Lesotho 6.0 5.7 4.3 United Arab Emirates 4.7 5.2 4.3 Pakistan 3.8 3.7 4.1 Kyrgyz Republic -0.9 10.5 4.1 Paraguay -1.2 13.6 4.0 Nicaragua 5.0 4.6 4.0 Ecuador 5.1 4.5 4.0 Angola 5.2 6.8 3.9 Comoros 3.0 3.5 3.9 Bahrain 3.4 5.3 3.9 Fiji 1.8 4.6 3.8 Algeria 3.3 2.8 3.8 Haiti 2.9 4.3 3.8 Korea 2.3 3.0 3.7 Seychelles 2.8 3.5 3.7 Ireland -0.3 0.2 3.6 Peru 6.0 5.8 3.6 Costa Rica 5.1 3.5 3.6 New Zealand 2.5 2.8 3.6 St. Kitts and Nevis -0.9 3.8 3.5 Morocco 2.7 4.4 3.5 Jordan 2.7 2.9 3.5 Vanuatu 1.8 2.2 3.5 Taiwan Province of China 1.5 2.1 3.5 FYR Macedonia -0.4 2.9 3.4 Guatemala 3.0 3.7 3.4 Oman 5.8 4.8 3.4 Mauritius 3.2 3.2 3.3 Guyana 4.8 5.2 3.3 Suriname 4.8 4.1 3.3 Poland 2.0 1.6 3.2 Afghanistan 14.0 3.6 3.2 United Kingdom 0.3 1.7 3.2 Armenia 7.1 3.5 3.2 Zimbabwe 10.6 3.3 3.1 Madagascar 2.5 2.4 3.0 Sudan -2.7 3.3 3.0 Turkey 2.1 4.1 3.0 Honduras 3.9 2.6 3.0 Hong Kong SAR 1.6 2.9 3.0 Lithuania 3.7 3.3 3.0 Kiribati 2.8 2.9 3.0 Singapore 2.5 3.9 3.0 Iceland 1.5 3.3 2.9 Australia 3.6 2.3 2.8 Uruguay 3.7 4.4 2.8 Tunisia 3.7 2.3 2.8 Hungary -1.7 1.1 2.8 Kosovo 2.8 3.4 2.7 Luxembourg -0.2 2.1 2.7 Latvia 5.2 4.1 2.7 Guinea-Bissau -2.2 0.3 2.6 Liberia 8.3 8.7 2.5 Czech Republic -1.0 -0.9 2.5 Israel 3.0 3.2 2.5 Guinea 3.8 2.3 2.5 Romania 0.6 3.5 2.4 Mexico 4.0 1.1 2.4 Tonga 0.5 0.8 2.4 Slovak Republic 1.8 0.9 2.4 Trinidad and Tobago 1.2 1.6 2.3 Canada 1.7 2.0 2.3 Montenegro -2.5 3.5 2.3 Tuvalu 0.2 1.3 2.2 Egypt 2.2 2.1 2.2 Malta 1.1 2.9 2.2 United States 2.3 2.2 2.2 Swaziland 1.9 2.8 2.1 Sweden 0.9 1.6 2.1 Albania 1.1 0.4 2.1 Eritrea 7.0 1.3 2.0 Belize 4.0 0.7 2.0 Chile 5.5 4.2 2.0 Samoa 1.5 -1.1 2.0 Yemen 2.4 4.8 1.9 Antigua and Barb
Sid Harth
GDP based on PPP valuation (Current international dollar (Billions)) 2012 2013 2014 China 14,774.4 16,149.1 17,632.0 United States 16,163.2 16,768.1 17,416.3 India 6,357.5 6,776.0 7,277.3 Japan 4,530.3 4,667.6 4,788.0 Germany 3,442.8 3,512.8 3,621.4 Russia 3,396.2 3,491.6 3,558.6 Brazil 2,896.5 3,012.9 3,072.6 France 2,490.2 2,534.5 2,586.5 Indonesia 2,225.2 2,389.0 2,554.3 United Kingdom 2,247.2 2,320.4 2,434.9 Mexico 2,007.2 2,058.9 2,143.5 Italy 2,043.4 2,035.4 2,065.9 Korea 1,623.8 1,697.0 1,789.8 Saudi Arabia 1,472.1 1,553.1 1,651.7 Canada 1,466.5 1,518.4 1,578.9 Spain 1,485.0 1,488.8 1,533.6 Turkey 1,367.0 1,443.5 1,512.1 Islamic Republic of Iran 1,250.0 1,244.3 1,283.6 Australia 1,013.6 1,052.6 1,100.4 Nigeria 909.3 972.6 1,057.8 Taiwan Province of China 937.1 970.9 1,021.6 Thailand 923.6 964.5 990.1 Egypt 878.0 909.8 945.4 Poland 870.1 896.8 941.4 Argentina 888.2 927.9 927.4 Pakistan 793.5 835.1 884.2 Netherlands 774.4 780.3 798.1 Malaysia 652.5 693.6 746.8 Philippines 591.2 643.1 694.6 South Africa 640.8 662.6 683.1 Colombia 566.7 602.0 641.5 United Arab Emirates 534.4 570.6 605.0 Algeria 500.8 522.6 551.7 Venezuela 538.0 553.3 545.7 Bangladesh 460.8 496.0 535.6 Vietnam 443.9 475.0 509.5 Iraq 472.4 499.6 494.5 Belgium 447.4 455.0 467.1 Singapore 403.5 425.3 445.2 Switzerland 417.5 432.0 444.7 Sweden 405.4 418.2 434.2 Kazakhstan 367.6 395.5 420.6 Chile 374.2 395.6 410.3 Hong Kong SAR 366.1 382.5 400.6 Austria 370.2 376.7 386.9 Romania 353.3 371.2 386.5 Peru 333.1 357.6 376.7 Ukraine 386.9 392.5 373.1 Norway 321.1 328.0 339.5 Qatar 276.1 298.4 323.2 Czech Republic 286.0 287.6 299.7 Greece 284.9 278.0 284.3 Kuwait 272.5 275.4 283.9 Portugal 268.6 268.8 276.0 Israel 245.7 257.5 268.3 Morocco 228.1 241.7 254.4 Denmark 236.4 240.9 248.7 Myanmar 201.6 221.5 244.3 Hungary 223.7 229.6 239.9 Ireland 209.8 213.3 224.7 Finland 217.7 218.3 221.5 Sri Lanka 183.2 199.5 217.1 Ecuador 162.3 172.1 182.0 Angola 153.2 166.1 175.5 Belarus 162.9 166.8 171.2 Uzbekistan 142.8 156.5 170.3 Azerbaijan 147.6 158.5 168.4 Oman 146.3 155.6 163.6 Sudan 145.2 152.3 159.5 New Zealand 144.4 150.7 158.7 Slovak Republic 140.6 144.0 149.9 Ethiopia 113.9 126.7 139.4 Dominican Republic 119.4 126.8 135.7 Kenya 118.5 125.8 134.7 Tunisia 115.4 119.7 125.1 Bulgaria 116.8 119.6 123.3 Guatemala 107.2 112.9 118.7 Ghana 94.7 103.0 109.4 Yemen 96.2 102.3 106.0 Libya 144.3 126.6 103.3 Tanzania 78.2 84.9 92.5 Serbia 86.3 89.7 90.7 Croatia 86.1 86.6 87.3 Turkmenistan 65.6 73.4 82.2 Jordan 72.9 76.2 80.2 Lebanon 75.1 77.4 80.1 Lithuania 72.0 75.4 79.0 Panama 64.5 71.0 77.0 Côte d'Ivoire 59.1 65.2 72.0 Costa Rica 64.4 67.6 71.2 Bolivia 60.4 65.4 70.0 Uruguay 63.0 66.8 69.8 Cameroon 58.8 62.9 67.2 Nepal 59.2 62.4 66.9 Uganda 57.7 61.9 66.7 Zambia 52.7 57.1 61.8 Afghanistan 55.9 58.8 61.7 Bahrain 54.5 58.3 61.6 Slovenia 58.4 58.7 60.5 Paraguay 47.5 54.7 57.9 Democratic Republic of the Congo 45.8 50.5 55.7 El Salvador 47.7 49.2 50.9 Luxembourg 46.8 48.5 50.7 Cambodia 42.3 46.1 50.3 Latvia 4
Sid Harth
GDP, current prices (U.S. dollars (Billions)) 2012 2013 2014 United States 16,163.2 16,768.1 17,416.3 China 8,386.7 9,469.1 10,355.4 Japan 5,937.9 4,898.5 4,769.8 Germany 3,427.9 3,636.0 3,820.5 France 2,688.2 2,807.3 2,902.3 United Kingdom 2,470.6 2,523.2 2,847.6 Brazil 2,247.7 2,246.0 2,244.1 Italy 2,014.4 2,072.0 2,129.3 Russia 2,017.5 2,096.8 2,057.3 India 1,858.7 1,876.8 2,047.8 Canada 1,821.4 1,826.8 1,793.8 Australia 1,555.6 1,505.9 1,482.5 Korea 1,222.8 1,304.5 1,449.5 Spain 1,323.2 1,358.7 1,400.5 Mexico 1,185.7 1,260.9 1,295.9 Netherlands 823.6 853.8 880.4 Indonesia 877.8 870.3 856.1 Turkey 788.6 820.0 813.3 Saudi Arabia 734.0 748.5 777.9 Switzerland 631.2 650.4 679.0 Nigeria 467.1 521.8 594.3 Sweden 523.9 558.9 559.1 Poland 490.7 517.7 552.2 Argentina 603.0 610.3 536.2 Belgium 483.2 508.3 527.8 Norway 500.0 512.6 511.6 Taiwan Province of China 475.3 489.1 505.5 Austria 394.7 416.1 436.1 United Arab Emirates 372.3 402.3 416.4 Islamic Republic of Iran 398.0 367.1 402.7 Colombia 369.8 378.4 400.1 Thailand 366.0 387.3 380.5 Denmark 315.2 330.6 347.2 South Africa 382.3 350.8 341.2 Malaysia 305.0 313.2 336.9 Singapore 286.9 297.9 307.1 Israel 257.2 290.6 305.0 Hong Kong SAR 262.6 274.0 292.7 Philippines 250.2 272.1 289.7 Egypt 262.3 271.4 284.9 Finland 255.9 267.4 276.3 Chile 266.3 277.0 264.1 Greece 248.6 241.8 246.4 Ireland 222.1 232.2 245.8 Iraq 216.0 229.3 232.2 Portugal 212.3 220.1 228.2 Algeria 207.8 212.5 227.8 Kazakhstan 203.5 231.9 225.6 Qatar 189.9 202.5 212.0 Venezuela 298.4 227.2 209.2 Peru 192.7 202.4 208.2 Romania 169.2 188.9 202.5 New Zealand 170.4 181.6 201.0 Czech Republic 196.4 198.5 200.0 Vietnam 155.6 170.6 187.8 Bangladesh 141.7 161.8 186.6 Kuwait 174.1 175.8 179.3 Ukraine 176.5 178.3 134.9 Angola 115.3 124.2 131.4 Hungary 124.6 132.3 129.7 Morocco 95.9 103.8 112.6 Ecuador 87.5 93.7 100.5 Slovak Republic 91.4 95.8 100.1 Oman 75.4 77.1 80.5 Azerbaijan 68.7 73.5 77.9 Belarus 63.6 71.7 77.2 Sri Lanka 59.4 66.7 71.6 Sudan 63.2 66.7 70.0 Myanmar 55.8 56.8 65.3 Luxembourg 55.2 60.4 63.9 Uzbekistan 51.2 56.8 63.1 Kenya 50.4 55.0 62.7 Dominican Republic 60.4 61.3 62.5 Croatia 56.0 57.4 58.3 Guatemala 50.4 53.8 58.3 Uruguay 50.0 55.7 55.6 Bulgaria 51.3 53.0 55.1 Costa Rica 45.4 49.6 50.5 Slovenia 46.3 48.0 49.9 Ethiopia 42.6 46.0 49.9 Libya 81.9 65.5 49.3 Tunisia 45.2 47.0 49.1 Lithuania 42.3 46.5 48.7 Turkmenistan 35.2 40.8 47.5 Lebanon 43.0 45.0 47.5 Yemen 35.4 40.4 45.5 Panama 35.9 40.5 44.7 Serbia 38.1 42.5 42.6 Tanzania 28.5 33.3 36.6 Jordan 31.0 33.9 36.6 Ghana 41.7 47.8 35.5 Bolivia 27.3 30.8 34.1 Bahrain 30.7 32.8 34.0 Côte d'Ivoire 27.7 32.1 34.0 Latvia 28.4 31.0 32.8 Democratic Republic of the Congo 27.5 29.9 32.7 Cameroon 26.5 29.3 32.2 Paraguay 24.9 29.1 31.3 Trinidad and Tobago 26.4 27.7 29.6 Estonia 22.7 24.9 26.4 Uganda 21.2 22.9 26.1 Zambia 24.9 26.8 25.6 El Salvador 23.8 24.3 25.1 Afghanistan 20.3 20.7 21.7 Cyprus 22.8 21.9 21.3 Gabon 17.9 19.3 20.7 Nepal 18.9 19.2 19.6 Honduras 18.5 18.5 19.4 Bosnia and Herzego
Sid Harth
GDP based on PPP valuation Current international dollar (Billions) 2 United States 17,416 4 Japan 4,788 5 Germany 3,621 8 France 2,587 12 Italy 2,066 7 Brazil 3,073 10 United Kingdom 2,435 11 Mexico 2,143 3 India 7,277 1 China 17,632 16 Spain 1,534 15 Canada 1,579 14 Saudi Arabia 1,652 9 Indonesia 2,554 25 Argentina 927 27 Netherlands 798 24 Poland 941 18 Islamic Republic of Iran 1,284 19 Australia 1,100 17 Turkey 1,512 30 South Africa 683 Venezuela 17,632 Romania 17,632 Belgium 17,632 Switzerland 17,632 29 Philippines 695 13 Korea 1,790 23 Egypt 945 Algeria 17,632 Sweden 17,632 22 Thailand 990 21 Taiwan Province of China 1,022 26 Pakistan 884 6 Russia 3,559 28 Malaysia 747 20 Nigeria 1,058 1980 1990 2000 2010 2011 2012 2013 2014 Source: IMF World Economic Outlook, October 2014
Sid Harth
Democratic Republic of the Congo 17,416 13 Korea 1,449 26 Norway 512 21 Nigeria 594 27 Taiwan Province of China 505 Finland 17,416 Greece 17,416 Egypt 17,416 9 Russia 2,057 Hong Kong SAR 17,416 23 Poland 552 29 United Arab Emirates 416 Thailand 17,416 1980 1990 2000 2005 2010 2011 2012 2013 2014 Source: IMF World Economic Outlook, October 2014
Sid Harth
If only somebody stops Modi from making vulgar slogans, like 'make in India,' ask an expert, Arun Jaitley is not that person, get all facts, figures, charts, diagrams, astrologic charts not scientific, nor reliable, he may come to a single conclusion. NOT IN MILLION YEARS, Mr Modiji. World GDP Ranking 2014 | Data and Charts list of countries by GDP Historical Data 1970-2013 GDP at current US$ GDP at current PPP int.$ Real GDP Growth GDP by country GDP per capita GDP per capita Ranking Database G20 Economic Forecast: GDP growth, Inflation, Unemployment, Government Debt, Current Account Balance, External Debt "A purchasing power parity (PPP) between two countries, A and B, is the ratio of the number of units of country A’s currency needed to purchase in country A the same quantity of a specific good or service as one unit of country B’s currency will purchase in country B. PPPs can be expressed in the currency of either of the countries. In practice, they are usually computed among large numbers of countries and expressed in terms of a single currency, with the U.S. dollar (US$) most commonly used as the base or “numeraire” currency" - Global Purchasing Power Parities and Real Expenditures. 2005 International Comparison Program. The World Bank. GDP, current prices U.S. dollars (Billions) 1 United States 17,416 3 Japan 4,770 4 Germany 3,820 5 France 2,902 6 United Kingdom 2,848 8 Italy 2,129 2 China 10,355 11 Canada 1,794 24 Argentina 536 15 Mexico 1,296 14 Spain 1,400 16 Netherlands 880 10 India 2,048 19 Saudi Arabia 778 12 Australia 1,483 7 Brazil 2,244 22 Sweden 559 25 Belgium 528 20 Switzerland 679 18 Turkey 813 30 Islamic Republic of Iran 403 17 Indonesia 856 South Africa 17,416 28 Austria 436 Venezuela 17,416 Denmark 17,416
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics Rajan, U, A. Seru and V. Vig, 2008, The Failure of Models that Predict Failure: Distance, Incentives and Defaults . Chicago GSB Research Paper No. 08-19 . Reinhart, C. and K. Rogoff, 2008, This Time is Different: A Panoramic View of Eight Centuries of Financial Crises . Manuscript, Harvard University and NBER. Scheinkman, J. and M. Woodford, 1994, Self-Organized Criticality and Economic Fluctuations, American Economic Review 84 (Papers and Proceedings), 417-421
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The Financial Crisis and the Systemic Failure of Academic Economics Friedman, J., 1987, Exploratory projection pursuit, Journal of the American Statistical Association , 82, 249-266 Hellwig, M. F., 2008, Systemic Risk in the Financial Sector: An Analysis of the Subprime-Mortgage Financial Crisis , MPI Collective Goods Preprint, No. 2008/43. Hendry, D., 2009, The Methodology of Empi rical Econometric Modelling: Applied Econometrics Through the Looki ng-Glass, forthcoming in The Handbook of Empirical Econometrics , Palgrave. Hendry, D.F., 1995. Dynamic Econometrics . Oxford University Press: Oxford. Hendry, D.F. and H-M. Krolzig, 2005, The Properties of Automatic Gets Modelling, Economic Journal, 115 , C32--C61. Hoover, K., S. Johansen, and K. Juselius, 2008, Allowing the data to speak freely: The macroeconometrics of the cointe grated vector autoregression. American Economic Review 98, 251-55. Juselius, K., 2006, The cointegrated VAR model: Econ ometric Methodology and Empirical Applications . Oxford Universi ty Press: Oxford. Juselius, K. and M. Franchi, 2007, Taking a DSGE Model to the Data Meaningfully, Economics–The Open-Access, Open-Assessment E-Journal, 4. Kindleberger, C.P., 1989, Manias, Panics, and Crashes: A History of Financial Crises . MacMillan: London. Krahnen, J.-P. and C. Wilde, 2006, Risk Transfer with CDOs and Systemic Risk in Banking. Center for Financial Stud ies, WP 2006-04. Frankfurt. Krahnen, J.-P., 2005, Der Handel von Kreditr isiken: Eine neue Dimension des Kapitalmarktes Perspektiven der Wirtschaftspolitik 6, 499 – 519. Leijonhufvud, A., 2000, Macroeconomic Instability and C oordination: Selected Essays , Edward Elgar: Cheltenham. Lo, A., D. V. Repin and B. N. Steenbarger, Fear and Greed in Financial Markets: A Clinical Study of Day-Traders, American Economic Review 95, 352-359. Lux, T. and F. Westerhoff, 2009, Economics crisis, Nature Physics 5, 2 – 3. Lux, T., 2009, Stochastic Behavioral Asset Pricin g Models and the Stylized Facts, chapter 3 in T. Hens and K. Schenk-Hoppé, eds., Handbook of Financial Markets: Dynamics and Evolution. North-Holland: Amsterdam,161 – 215. Minsky, H.P., 1986, Stabilizing an Unstable Economy . Yale University Press: New Haven.
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics References Allen, F. and A. Babus, 2008, Networks in Finance . Wharton Financial Institutions Center Working Paper No. 08-07. Available at SSRN: Anderson, P.W., 1972, More is different, Science 177, 393-396. Aoki, M. and H. Yoshikawa, 2007, Reconstructing Macroeconom ics: A Perspective from Statistical Physics and Combinatorial Stochastic Processes . Cambridge University Press: Cambridge and New York. Bagehot, W., 1873, Lombard Street: A Descript ion of the Money Market. Henry S. King and Co.: London. Banerjee, A., 1992, A simple model of herd behaviour, Quarterly Journal of Economics , 108 , 797–817. Boesch, F. T., F. Harary, and J. A. Kabe ll, 2006, Graphs as models of communication network vulnerability: Connec tivity and persistence, Networks , 11, 57 - 63. Brigandt, I. and A. Love, ‘Reductionism in Biology’ in the Stanford Encyclopedia of Philosophy . Campos, J., N.R. Ericsson and D.F. Hendry, 2005, Editors' Introduction to General to Specific Modelling , 1 - 81, Edward Elgar: London. Chamley, C. P., 2002, Rational Herds: Economic Models of Social Learning. Cambridge University Press: Cambridge. Coates J.M. and J. Herbert, 2008, Endogenous steroids and financia l risk taking on a London trading floor, Proceedings of the National Academy of Sciences , 6167 – 6172. Cogley, T. and T. Sargent, 2008, The market pric e of risk and the equity premium: A legacy of the Great Depression?, Journal of Monetary Economics , 55, 454-476 . Criado, R., J. Flores, B. Hernández-Bermej o, J. Pello, and M. Romance, 2005, Effective measurement of network vulnerability under random and intentional attacks , Journal of Mathematical Modelling and Algorithms , 4, 307-316. Eichengreen, B., 2008, Origins and Responses to the Crisis , unpublished manuscript, University of California, Berkeley. Föllmer, H., 2008, Financial uncertainty, risk m easures and robust preferences, in: Yor, M, ed., Aspects of Mathematical Finance , Springer: Berlin
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics problems within one institute on other parts of the system (even across national borders). Certainly, before deciding a bout the bail-out of a large bank, this implies an understanding of the network. One should know whether its ba nkruptcy would lead to widespread domino effects or whether contagion would be limited. It seems to us that what regulators provide currently is far from a reliable assessment of such after effects. Such analysis has to be supported by more tr aditional approaches: Leverage of financial institutions rose to unprecedented levels pr ior to the crisis, par tly by evading Basle II regulations through special inve stment vehicles (SIVs). The hedge fund market is still entirely unregulated. The interplay between leverage, connectivity and system risk needs to be investigated at the aggreg ate level. It is highly likely, that extreme leverage levels of interconnected institutions will be found to im pose unacceptable social risk on the public. Prudent capital requirements would be necessa ry and would require a solid scientific investigation of the above aspect s rather than a pre-analytic laissez-faire attitude. We also have to re-investigat e the informational role of fi nancial prices and financial contracts. While trading in stock markets is usually interpreted as at least in part transmitting information, this information transmission seems to have broken down in the case of structured financial products. It seems th at securitization has rath er led to a loss of information by anonymous intermediation (often multiple) between borrowers and lenders. In this way, the informational component has been outsourced to rating agencies and typically, the buyer of CDO tr anches would not have spent any effort himself on information acquisition concerning his far away counterparts. However, this centralized information processing instead of the dispersed one in traditional credit relationships might lead to a severe loss of information. As it tu rned out, standard loan default models failed dramatically in recent years (Rajan et al, 2008). It should also be noted that the price system itself can exacerbate the difficulties in the financial market (see Hellwig, 2008). One of the reasons for the sharp fall in the as set valuations of major banks was not only the loss on the assets on which their derivatives were based, but also the general reaction of the markets to these assets. As markets became awar e of the risk involved, all such assets were written down and it was in this way that a sma ll sector of the market “contaminated” the rest. Large parts of the asset holdings of majo r banks abruptly lost much of their value. Thus the price system itself can be de stabilizing as expectations change. On the macroeconomic level, it would be desi rable to develop early warning schemes that indicate the formation of bubbles. Combinations of indicators with tim e series te
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics 5. A Research Agenda to Cope with Financial Fragility The notion of financial fragility implies th at a given system might be more or less susceptible to produce crises. It seems clear that financial innovations have made the system more fragile. Apparently, the exis ting linkages within the worldwide, highly connected financial markets have generated th e spillovers from the U.S. subprime problem to other layers of the fina ncial system. Many financial i nnovations had the effect of creating links between formerly unconnected player s. All in all, the degree of connectivity of the system has probably increased enormous ly over the last decades. As is well known from network theory in natural sciences, a more highly connected system might be more efficient in coping with certain tasks (maybe distributing risk components), but will often also be more vulnerable to shocks and – sy stemic failure! The systematic analysis of network vulnerability has been undertaken in th e computer science and operations research literature (see e.g. Criado et al ., 2005). Such aspects have, however, been largely absent from discussions in financial economics. Th e introduction of new derivatives was rather seen through the lens of general equilibrium models: more contingent claims help to achieve higher efficiency. Unfortunately, the claimed efficiency gains through derivatives are merely a theoretical implication of a highly stylized model and, therefore, have to count as a hypothesis. Since there is hardly any supporting em pirical evidence (or even analysis of this question), the claimed real-world effici ency gains from deriva tives are not justified by true science. While the economic argument in favor of ever new derivatives is more one of persuasion rather than evidence, importa nt negative effects have been neglected. The idea that the system was made less risky with the development of more derivatives led to financial actors taking positions with extreme degrees of leverage and the danger of this has not been emphasized enough. As we have mentioned, one totally neglected area is the degree of connectivity and its interplay with the stability of the system (see Bo esch et al. (2006). We believe that it will be necessary for supervisory author ities to develop a perspective on the network aspects of the financial system, collect appr opriate data, define measures of connectivity and perform macro stress testing at the system level. In this way, new measures of financial fragility would be obtained. This would also require a new area of accompanying academic research that looks at agent-based models of the fina ncial system, performs scenario analyses and develops aggregate risk measures. Network theory and the theory of self-organized criticality of highly connected systems would be appropriate starting points. The danger of systemic risk means that regula tion has to be extended from indivi
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics question are the movements between different exchange rate regimes and the deregulation of financial markets over the 70s and 80s. In summary, it seems to us that much of contemporary empirical work in macroeconomics and finance is driven by the pre-analytic belief in the validity of a certain model. Rather than (mis)using statistics as a means to illustrate these beliefs, the goal should be to put theoretical models to scientific test (as the naïve believer in positive science would expect). The current approach of using pre-selected models is problematic and we recommend a more data-driven methodology. Instead of star ting out with an ad- hoc specification and questionable ceteris paribus assumptions, the key features of the data should be explored via data-analytical tools and specification tests. David Hendry provid es a well-established empirical methodology for such e xploratory data analysis (H endry, 1995, 2009) as well as a general theory for model selection (Hendr y and Krolzig, 2005); clustering techniques such as projection pursuit (e.g. Friedman, 1987) might provide alternatives for the identification of key relati onships and the reduction of complexity on the way from empirical measurement to theoretical models. Cointegrated VAR models could provide an avenue towards identification of robust structures within a set of data (Juselius, 2006), for example, the forces that move equilibria ( pushing forces , which give rise to stochastic trends) and forces that co rrect deviations fr om equilibrium ( pulling forces , which give ri se to long-run relations). Interpreted in this way, the ‘general-to-specific’ empirical approach has a good chance of nesting a multivar iate, path-dependent data-generating process and relevant dynamic macroeconomic theories. Unlike approa ches in which data ar e silenced by prior restrictions, the Cointegrated VA R model gives the data a rich co ntext in which to speak freely (Hoover et al ., 2008). A chain of specification tests and estimated statistical models for simultaneous systems would provide a benchmark for the subsequent development of tests of models based on economic behavior: significant and robust rela tions within a simultaneous system would provide empirical regularities that one would attempt to explain, while the quality of fit of the statistical benchmark would offer a confidence band for more ambitious models. Models that do not reproduce (even) approximately the qualit y of the fit of statistical models would have to be rejected (the ma jority of currently popular macroeconomic and macro finance models would not pass this test). Again, we see here an aspect of ethical responsibility of researchers: Economic policy mo dels should be theoretically and empirically sound. Economists should avoid gi ving policy recommendations on the base of models with a weak empirical grounding and shou ld, to the extent possible, make clea
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics question are the movements between different exchange rate regimes and the deregulation of financial markets over the 70s and 80s. In summary, it seems to us that much of contemporary empirical work in macroeconomics and finance is driven by the pre-analytic belief in the validity of a certain model. Rather than (mis)using statistics as a means to illustrate these beliefs, the goal should be to put theoretical models to scientific test (as the naïve believer in positive science would expect). The current approach of using pre-selected models is problematic and we recommend a more data-driven methodology. Instead of star ting out with an ad- hoc specification and questionable ceteris paribus assumptions, the key features of the data should be explored via data-analytical tools and specification tests. David Hendry provid es a well-established empirical methodology for such e xploratory data analysis (H endry, 1995, 2009) as well as a general theory for model selection (Hendr y and Krolzig, 2005); clustering techniques such as projection pursuit (e.g. Friedman, 1987) might provide alternatives for the identification of key relati onships and the reduction of complexity on the way from empirical measurement to theoretical models. Cointegrated VAR models could provide an avenue towards identification of robust structures within a set of data (Juselius, 2006), for example, the forces that move equilibria ( pushing forces , which give rise to stochastic trends) and forces that co rrect deviations fr om equilibrium ( pulling forces , which give ri se to long-run relations). Interpreted in this way, the ‘general-to-specific’ empirical approach has a good chance of nesting a multivar iate, path-dependent data-generating process and relevant dynamic macroeconomic theories. Unlike approa ches in which data ar e silenced by prior restrictions, the Cointegrated VA R model gives the data a rich co ntext in which to speak freely (Hoover et al ., 2008). A chain of specification tests and estimated statistical models for simultaneous systems would provide a benchmark for the subsequent development of tests of models based on economic behavior: significant and robust rela tions within a simultaneous system would provide empirical regularities that one would attempt to explain, while the quality of fit of the statistical benchmark would offer a confidence band for more ambitious models. Models that do not reproduce (even) approximately the qualit y of the fit of statistical models would have to be rejected (the ma jority of currently popular macroeconomic and macro finance models would not pass this test). Again, we see here an aspect of ethical responsibility of researchers: Economic policy mo dels should be theoretically and empirically sound. Economists should avoid gi ving policy recommendations on the base of models with a weak empirical grounding and shou ld, to the extent possible, make clea
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics ‘moods’ among the population. This would capture the psychological component of the business cycle which – though prominent in ma ny policy-oriented discussions – is never taken into consideration in cont emporary macroeconomic models. It is worth noting that understanding the form ation of such low-level equilibria might be much more valuable in coping with major ‘efficiency losses’ by mass unemployment than the pursuit of small ‘inefficiencies’ due to societal decisions on norms such as shop opening times. Models with interacting heteroge neous agents would also open the door to the incorporation of results from other fields : network theory has been mentioned as an obvious example (for models of networks in finance see Allen and Babus, 2008). ‘Self-organized criticality’ theo ry is another area that seem s to have some appeal for explaining boom-and-bust cycles (cf. Scheinkman and Woodford, 1992). Incorporating heterogeneous agents with imperfect knowledge would also provide a better framework for the analysis of the use and dissemination of information through market operations and more direct links of communi cation. If one accepts that the dispersed economic activity of many economic agents could be described by stat istical laws, one might even take stock of methods from statistical physics to mode l dynamic economic systems (cf. Aoki and Yoshikawa, 2007; Lux, 2009, for examples). 4. Robustness and Data-Driven Empirical Research Currently popular models (in particular: dyna mic general equilibrium models) do not only have weak micro foundations, th eir empirical performance is fa r from satisfactory (Juselius and Franchi, 2007). Indeed, the relevant stra nd of empirical economics has more and more avoided testing their models and has instead turned to calibrati on without explicit consideration of goodness-of-fit. 7 This calibration is done using “deep economic parameters” such as parameters of utility functions derived from microeconomic studies. However, at the risk of bei ng repetitive, it should be empha sized that micro parameters cannot be used directly in the paramete rization of a macroeconomic model. The aggregation literature is full of examples that point out the possible “fallacies of composition”. The “deep parameters” only seem sensible if one considers the economy as a universal organism without interactions. If intera ctions are important (as it seems to us they are), the restriction of th e parameter space imposed by using micro parameters is inappropriate. Another concern is nonstationa rity and structural shifts in the underlying data. Macro models, unlike many financial models, are ofte n calibrated over long time horizons which include major changes in the regulatory framew ork of the countries investigated. Cases in 7 It is pretty obvious how the currently popular class of dynami c general equilibrum models would have to ‘cope’ with the current financ
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The Financial Crisis and the Systemic Failure of Academic Economics simply incomprehensible from the viewpoint of this approach. In order to develop models that allow us to deduce macro events from microeconomic regularities, economists have to rethi nk the concept of micro foundations of macroeconomic models. Since economic activity is of an essentiall y interactive nature, economists’ micro foundations should allow for the interactions of economic agents. Since interaction depends on differences in inform ation, motives, knowledge and capabilities, this implies heterogeneity of agents. For inst ance, only a sufficiently rich structure of connections between firms, households and a di spersed banking sector will allow us to get a grasp on “systemic risk”, domino effects in the financial sector, and their repercussions on consumption and investment. The dominan ce of the extreme form of conceptual reductionism of the representative agent has pr evented economists from even attempting to model such all important phenomena. It is the flawed methodology that is the ultimate reason for the lack of applicability of the st andard macro framework to current events. Since most of what is relevant and inte resting in economic lif e has to do with the interaction and coordination of ensemble s of heterogeneous economic actors, the methodological preference for single actor models has extremely handicapped macroeconomic analysis and prevented it from approaching vital topics. For example, the recent surge of research in network theory has received relatively scarce attention in economics. Given the established curriculum of economic programs, an economist would find it much more tractable to study adulte ry as a dynamic optimization problem of a representative husband, and derive the optimal time path of marital infidelity (and publish his exercise) rather than investigating fina ncial flows in the banking sector within a network theory framework. This is more than unf ortunate in view of the network aspects of interbank linkages that have become apparent during the current crisis. In our view, a change of focus is necessary that takes seriously the regularities in expectation formation revealed by behavioral research and, in fact, gives back an independent role to expectations in economic mo dels. It would also be fallacious to only replace the current paradigm by a representa tive ‘non-rational’ actor (as it is sometimes done in recent literature). Rather, an appropriate micro foundation is needed that considers interaction at a certain level of complexity a nd extracts macro regularities (where they exist) from microeconomic models with dispersed activity. Once one acknowledges the importa nce of empirically based be havioral micro foundations and the heterogeneity of actors, a rich spect rum of new models becomes available. The dynamic co-evolution of expectations and economic activity would allow one to study out-of-equilibrium dynamics and adaptive ad
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics empirical research. Much of th is literature shows that human subjects act in a way that bears no resemblance to the rational expect ations paradigm and also have problems discovering ‘rational expectations equilibria’ in repeated experimental settings. Rather, agents display various forms of ‘bounded rati onality’ using heuristic decision rules and displaying inertia in their r eaction to new information. They have also been shown in financial markets to be strongly influenced by emotional and hormonal reactions (see Lo et al. , 2005, and Coates and Herbert, 2008) Econom ic modeling has to take such findings seriously. What we are arguing is that as a modeling requirement, internal consistency must be complemented with external consistency : Economic modeling has to be compatible with insights from other branches of science on hum an behavior. It is highly problematic to insist on a specific view of humans in econom ic settings that is irreconcilable with evidence. The ‘representative agent’ aspect of many cu rrent models in macroeconomics (including macro finance) means that modelers subscribe to the most extreme form of conceptual reductionism (Lux and Westerhoff, 2009): by assumpti on, all concepts applicable to the macro sphere (i.e., the economy or its financial system) are fully reduced to concepts and knowledge for the lower-level domain of the indi vidual agent. It is worth emphasizing that this is quite different from the standard reductionist concept that has become widely accepted in natural sciences. The more stan dard notion of reductionism amounts to an approach to understanding the nature of complex phenomena by reducing them to the interactions of their parts, allowing for new, emergent phenomena at the higher hierarchical level (the concept of ‘more is different’, cf. Anderson, 1972). Quite to the contrary, the representative agen t approach in economics has simply set the macro sphere equal to the micro sphere in a ll respects. One could, indeed, say that this concept negates the existence of a macro s phere and the necessity of investigating macroeconomic phenomena in that it views th e entire economy as an organism governed by a universal will. 6 Any notion of “systemic risk” or “coordination failure” is necessarily absent from, and alien to, such a methodology. For natural scientists, the distinction between micro-level phenomena and those originating on a macro, system-wide scale from the interaction of microscopic units is well-known. In a dispersed system, the current crisis would be seen as an involuntary emergent phenomenon of the microeconomic activity. The conceptual reductionist paradigm, however, blocks from the outset a ny understanding of the interplay between the micro and macro levels. The differences betw een the overall system and its parts remain 6 The conceptual reductionist approach of the representative ag ent is also remarkably different
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The Financial Crisis and the Systemic Failure of Academic Economics probabilistic projections that cover a whole range of speci fic models (cf., Föllmer, 2008). The theory of robust control provides a toolbox of techniques that could be applied for this purpose, and it is an approach that should be considered. 3. Unrealistic Model Assumption s and Unrealistic Outcomes Many economic models are built upon the twin assumptions of ‘rational expectations’ and a representative agent. ‘Rational expectati ons’ forces individuals’ expectations into harmony with the structure of the economist’s own model. This concept can be thought of as merely a way to close a model. A behavi oral interpretation of rational expectations would imply that individuals and the econom ist have a complete understanding of the economic mechanisms governing the world. In this sense, rational expe ctations models do not formalize expectations as such: they ar e not written down as a component of the model according to some empirical observation of th e expectation formation of human actors. Thus, even when applied economics research or psychology provide insights about how individuals actually form exp ectations, these insights cannot be used within RE models. Leaving no place for imperfect knowledge and ad aptive adjustments, rational expectations models are typically found to have dynamics that are not smooth enough to fit economic data well. Technically, rational expectations models are often framed as dynamic programming problems in macroeconomics. But, dynamic programming models have serious limitations. Specifically, to make them analytically tractab le, researchers assume representative agents and rational expectations, which assume aw ay any heterogeneity among economic actors. Such models presume that there is a single m odel of the economy, which is odd given that even economists are divided in their views about the correct model of the economy. While other currents of research do exist, economic policy advice, particularly in financial economics, has far too often been based (c onsciously or not) on a set of axioms and hypotheses derived ultimately from a highly limited dynamic control model, using the Robinson approach with ‘rational’ expectations. The major problem is that despite its many refine ments, this is not at all an approach based on, and confirmed by, empirical research. 5 In fact, it stands in star k contrast to a broad set of regularities in human behavior discove red both in psychology and what is called behavioral and experimental economics. The co rner stones of many models in finance and macroeconomics are rather maintained despite all the contradictory evidence discovered in 5 The historical emergence of the representative agent paradigm is a mystery. Ironically, it appeared over the 70s after a period of intense discussions on the problem of aggregation in economics (that basically yielded negative results such as the impossibility to demonstrated ‘
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The Financial Crisis and the Systemic Failure of Academic Economics of identical micro strategies leads to synchronous behavior and mechanic contagion. This simultaneous application might generate an unexpected macro outcome that actually jeopardizes the success of the underlying micro st rategies. A perfect illustration is the U.S. stock market crash of October 1987. Triggere d by a small decrease of prices, automated hedging strategies produced an aval anche of sell orders that out of the blue led to a fall in U.S. stock indices of about 20 percent within one day. With the massive sales to rebalance their portfolios (along the lines of Black and Scholes), the rele vant actors could not realize their attempted incremental adjustments, but ra ther suffered major losses from the ensuing large macro effect. A somewhat different aspect is the danger of a control illusion : The mathematical rigor and numerical precision of risk management and as set pricing tools has a tendency to conceal the weaknesses of models and assumptions to those who have not developed them and do not know the potential weakness of the assumptions and it is indeed this that Eichengreen emphasizes. Naturally, models are only approxi mations to the real world dynamics and partially built upon quite heroic assumptions (most notoriously: Normality of asset price changes which can be rejected at a confidence level of 99. 9999.... Anyone who has attended a course in first-year statistics can do this within minutes). Of course, considerable progress has been made by m oving to more refined models with, e.g., ‘fat-tailed’ Levy processes as their driving factors. However, while such models better capture the intrinsic volatility of markets, their improved perf ormance, taken at face value, might again contribute to enhancing th e control illusion of the naïve user. The increased sophistication of extant mode ls does, however, not overcome the robustness problem and should not absolve the modelers fr om explaining their limitations to the users in the financial industry. As in nuclear physic s, the tools provided by financial engineering can be put to very different uses so that what is designed as an instru ment to hedge risk can become a weapon of ‘financial mass destructio n’ (in the words of Warren Buffet) if used for increased leverage. In fact, it appears that derivative posit ions have been built up often in speculative ways to profit from high retu rns as long as the downside risk does not materialize. Researchers who develop such m odels can claim they are neutral academics – developing tools that people ar e free to use or not. We do not find that view credible. Researchers have an ethical res ponsibility to point out to the public when the tool that they developed is misused. It is the responsibility of the researcher to make clear from the outset the limitations and underlying assumptions of his models and warn of the dangers of their mechanic application. What follows from
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The Financial Crisis and the Systemic Failure of Academic Economics calculators. Simultaneously with Black-Scholes option pricing, the same principles led to the widespread introduction of new strategies under the heading of por tfolio insurance and dynamic hedging that just tried to implement a theoretically risk-free portfolio composed of both assets and options and k eep it risk-free by frequent re balancing after changes of its input data (e.g., asset prices). For structured pr oducts for credit risk, the basic paradigm of derivative pricing – perfect replication – is not applicable so that one has to rely on a kind of rough-and-ready evaluation of these cont racts on the base of historical data. Unfortunately, historical data were hardly avai lable in most cases which meant that one had to rely on simulations with relatively arbi trary assumptions on corre lations between risks and default probabilities. This makes the th eoretical foundations of all these products highly questionable – the equivalent to build ing a building of cement of which you weren’t sure of the components. The dramatic recent rise of the markets fo r structured products (most prominently collateralized debt obliga tions and credit default swaps - CDOs and CDSs) was made possible by development of such simulation-based pr icing tools and the adoption of an industry-standard for thes e under the lead of rating agencies. Barry Eichengreen (2008) rightly points out that the “development of mathematical methods designed to quantify and hedge risk encourag ed commercial banks, investment banks and hedge funds to use more leverage” as if the very use of the mathematical methods diminished the underlying risk. He also notes that the models were estimated on data from periods of low volatility and thus could not deal with the arriva l of major changes. Worse, it is our contention that such major change s are endemic to the economy and cannot be simply ignored. What are the flaws of the new unregulated fi nancial markets which have emerged? As we have already pointed out in the introduction, th e possibility of systemic risk has not been entirely ignored but it has been defined as lying outside the res ponsibility of market participants. In this way, moral hazard concerning systemic risk has been a necessary and built-in attribute of the system . The neglect of the systemic part in the ‘normal mode of operation’, of course, implies that external effects are not taken pr operly into account and that in tendency, market participants will igno re the influence of their own behavior on the stability of the system. The interesting aspect is more that this was a known and accepted element of operations. Note that the blame should not only fall on market participants, but also on the deliberate ignoring of the systemic ri sk factors or the failu re to at least point them out to the public amounts to a sort of academic ‘moral hazard’ . There are some additional aspects as well: as set-p
Sid Harth
The Financial Crisis and the Systemic Failure of Academic Economics explanation--the researchers did not know the models were fragile. We find this explanation highly unlikely; fi nancial engineers are extremel y bright, and it is almost inconceivable that such bright individuals did not understand the limitations of the models. A second, more likely explanation, is that they did not consid er it their job to warn the public. If that is the cause of their failure, we believe that it involves a misunderstanding of the role of the economist, and involves an ethical breakdown. In our view, economists, as with all scientists, have an ethical responsibility to comm unicate the limitations of their models and the potential mi suses of their research. Currently, there is no ethical code for professional economic scientists. There should be one. In the following pages, we identify some majo r areas of concern in theory and applied methodology and point out their connection to crisis phenomena. We also highlight some promising avenues of study that may provide guidance for future researchers. 2. Models (or the Use of Models) as a Source of Risk The economic textbook models applied fo r allocation of scarce resources are predominantly of the Robinson Crusoe (repr esentative agent) type. Financial market models are obtained by letting Robinson manage his financial affairs as a sideline to his well-considered utility maximiza tion over his (finite or infini te) expected lifespan taking into account with correct probabilities all pote ntial future happenings. This approach is mingled with insights from Walrasian general equilibrium theory, in particular the finding of the Arrrow-Debreu two-period model that all uncertainty can be eliminated if only there are enough contingent claims (i.e., appropriate derivative instruments). This theoretical result (a theorem in an extremely styli zed model) underlies the belief shared by many economists that the introduction of new cl asses of derivatives can only be welfare increasing (a view obviously originally shared by former Fed Chairman Greenspan). It is worth emphasizing that this view is not an empirically grounded belief but an opinion derived from a benchmark model that is much too abstract to be confronted with data. On the practical side, mathematical portfolio and risk management models have been the academic backbone of the tremendous increase of trading volume and diversification of instruments in financial markets. Typical ly, new derivative products achieve market penetration only if a certain i ndustry standard has been esta blished for pricing and risk management of these products. Mostly, pric ing principles are derived from a set of assumptions on an ‘appropriate’ process for the underlying asset, (i.e., the primary assets on which options or forwards are written) toge ther with an equilibrium criterion such as arbitrage-free prices. With that mostly co mes advice for hedging the inherent risk of a der
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The Financial Crisis and the Systemic Failure of Academic Economics Kindleberger (1989), and Hyman Minsky (1986), to name a few prominent examples. This tradition, however, has been neglected and even suppressed. The most recent literature pr ovides us with examples of blindness against the upcoming storm that seem odd in retrospect. For example, in their analysis of the risk management implications of CDOs, Krahnen (2005) and Krahnen and Wilde (2006) mention the possibility of an increase of ‘systemic risk.’ But, they conclude that this aspect should not be the concern of the banks engaged in th e CDO market, because it is the governments’ responsibility to provide cos tless insurance against a syst em-wide crash. On the more theoretical side, a recent and prominent strand of literature essentially argues that consumers and investors are too risk averse because of their memory of the (improbable) event of the Great Depression (e.g., Cogley a nd Sargent, 2008). Much of the motivation for economics as an academic discipline stems fr om the desire to explain phenomena like unemployment, boom and bust cycles, and financial crises, but the dominant theoretical model excludes many of the aspects of the economy that will likely lead to a crisis. Confining theoretical models to ‘normal’ time s without consideration of such defects might seem contradictory to the focus that the aver age taxpayer would expect of the scientists on his payroll. This failure has deep methodological roots. The often heard definition of economics—that it is concerned with the ‘allocation of scar ce resources’—is short- sighted and misleading. It reduces economics to the study of optimal decisions in well-specified choice problems. Such research generally loses track of the inherent dynamics of economic systems and the instability that accompanies its complex dynamics. Without an adequate understanding of these processes, one is likely to miss the ma jor factors that influence the economic sphere of our societies. 3 The inadequate definition of econo mics often leads researchers to disregard questions about the coordination of actors and the possibility of coordination failures. Indeed, analysis of these issues woul d require a different type of mathematics than that which is generally used now by many prominent economic models. Many of the financial economists who devel oped the theoretical models upon which the modern financial structure is built were we ll aware of the strong and highly unrealistic restrictions imposed on their models to assure stability. Yet, financial economists gave little warning to the public about th e fragility of their models; 4 even as they saw individuals and businesses build a financial system based on their work. There are a number of possible explanations for this failure to warn the public. One is a “lack of understanding” 3 For example, the German members of this group of author s share a vivid memory of a prominent economic adviser in their c
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The Financial Crisis and the Systemic Failure of Academic Economics 1. Introduction The global financial crisis has revealed the need to rethink fundamentally how financial systems are regulated. It has also made clear a systemic failure of th e economics profession . Over the past three decades, economists have largely developed and come to rely on models that disregard key fact ors—including heterogeneity of decision rules, revisions of forecasting strategies, and changes in the soci al context—that drive outcomes in asset and other markets. It is obvious, even to the casual observer that these models fail to account for the actual evolution of the real-world ec onomy. Moreover, the current academic agenda has largely crowded out research on the inherent causes of fi nancial crises. There has also been little exploration of early in dicators of system crisis and potential ways to prevent this malady from developing. In fact, if one browses through the academic macroeconomics and finance literature, “systemic crisis” appears like an otherworldly event that is absent from economic models. Most models, by de sign, offer no immediate handle on how to think about or deal with this recurring phenomenon. 2 In our hour of gr eatest need, societies around the world are left to gr ope in the dark without a theory. That, to us, is a systemic failure of the economics profession . The implicit view behind standard models is that markets and ec onomies are inherently stable and that they only te mporarily get off track. The majority of economists thus failed to warn policy makers about the threatening system crisis and ignored the work of those who did. Ironically, as the cr isis has unfolded, economists have had no choice but to abandon their standard models and to produce hand-waving common-sense remedies. Common-sense advice, although us eful, is a poor substitute for an underlying model that can provide much-needed guidance for developi ng policy and regulation. It is not enough to put the existing model to one side, observi ng that one needs, “exceptional measures for exceptional times”. What we need are models capable of envisaging such “exceptional times”. The confinement of macroeconomics to models of stable states that are perturbed by limited external shocks and that neglect the intrinsic recurrent boom-and-bust dynamics of our economic system is remarkable. After all, worldwide financial a nd economic crises are hardly new and they have had a treme ndous impact beyond the immediate economic consequences of mass unemployment and hyper in flation. This is even more surprising, given the long academic legacy of earlier economists’ study of crisis phenomena, which can be found in the work of Walter Bage hot (1873), Axel Leijonhuvfud (2000), Charles 2 Reinhart and Rogoff (2008) argue that the current financial cr isis differs little from a long chain of similar crises in developed and developing countries. We certainly share their view. The problem is
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The Financial Crisis and the Systemic Failure of Academic Economics* David Colander, Department of Economics Middlebury College Middlebury, VE, USA Hans Föllmer Department of Mathematics Humboldt University Berlin Berlin, Germany Armin Haas Potsdam Institute for Climate Impact Research Potsdam, Germany Michael Goldberg Whittemore School of Business & Economics University of New Hampshire Durham, NH, USA Katarina Juselius Department of Economics University of Copenhagen Copenhagen, Denmark Alan Kirman GREQAM, Université d’Aix-Marseille lll, EHESS et IUF Marseille, France Thomas Lux 1 Department of Economics University of Kiel & Kiel Institute for the World Economy Kiel, Germany Brigitte Sloth Department of Business and Economics University of Southern Denmark Odense, Denmark Abstract : The economics profession appears to have b een unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold. In our view, this lack of understanding is due to a misallocation of research efforts in economics. We trace the deeper roots of this failure to the profession’s insistence on constructing models that, by design, disregard th e key elements driving outcomes in real-world markets. The economics profession has failed in communicating the limitations, weaknesses, and even dangers of its preferred models to the p ublic. This state of affa irs makes clear the need for a major reorientation of focus in the research economists undertake, as well as for the establishment of an ethical code that would ask economists to understand and communicate the limitations and potential misuses of their models. Keywords: financial crisis, academic moral ha zard, ethic responsibility of researchers * This opinion paper is the outcome of one week of intense discussions within the working group on ‘Modeling of Financial Markets’ at the 98 th Dahlem Workshop, 2008. We are grateful to Carlo Jaeger and Rupert Klein for organizing this stimulating meeting and to Deirdre McCloskey and other participants for helpful comments.
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Kiel Institute for the World Economy, Düsternbrooker Weg 120, 24105 Kiel, Germany Kiel Working Paper 1489 | February 2009 The Financial Crisis and the System ic Failure of Academic Economics David Colander, Hans Föllmer, Armin Haas, Michael Goldberg, Katarina Juselius, Alan Kirman, Thomas Lux, and Brigitte Sloth Abstract: The economics profession appears to have been unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold. In our view, this lack of understanding is due to a misallocation of research efforts in economics. We trace the deeper roots of this failure to the profession’ s insistence on constructing models that, by design, disregard the key elements driving outcomes in real-world markets. The economics profession has failed in communicating the limitations, weaknesses, a nd even dangers of its preferred models to the public. This state of affairs makes clear the need fo r a major reorientation of focus in the research economists undertake, as well as for the establishment of an ethical code that would ask economists to understand and communicate the limitations a nd potential misuses of their models.

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