Finance:Misery index (economics)

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Short description: Economic indicator measuring economic and social cost
Misery Index
  Misery Index
  Unemployment rate
  Inflation rate CPI

The misery index is an economic indicator, created by economist Arthur Okun. The index helps determine how the average citizen is doing economically and is calculated by adding the seasonally adjusted unemployment rate to the annual inflation rate. It is assumed that both a higher rate of unemployment and a worsening of inflation create economic and social costs for a country.[1]

Misery index by US presidential administration

Index = Unemployment rate + Inflation rate (lower number is better)
President Time Period Average Low High Start End Change
Harry Truman 1948–1952 7.88 03.45 – Dec 1952 13.63 – Jan 1948 13.63 3.45 -10.18
Dwight D. Eisenhower 1953–1960 9.26 02.97 – Jul 1953 10.98 – Apr 1958 3.28 9.96 +5.68
John F. Kennedy 1961–1963 7.14 06.40 – Jul 1962 08.38 – Jul 1961 8.31 6.82 -1.49
Lyndon B. Johnson 1963–1968 6.77 05.70 – Nov 1965 08.19 – Jul 1968 7.02 8.12 +1.10
Richard Nixon 1969–1974 10.57 07.80 – Jan 1969 17.01 – Jul 1974 7.80 17.01 +9.21
Gerald Ford 1974–1976 16.00 12.66 – Dec 1976 19.90 – Jan 1975 16.36 12.66 -3.70
Jimmy Carter 1977–1980 16.26 12.60 – Apr 1978 21.98 – Jun 1980 12.72 19.72 +7.00
Ronald Reagan 1981–1988 12.19 07.70 – Dec 1986 19.33 – Jan 1981 19.33 9.72 -9.61
George H. W. Bush 1989–1992 10.68 09.64 – Sep 1989 14.47 – Nov 1990 10.07 10.30 +0.23
Bill Clinton 1993–2000 7.80 05.74 – Apr 1998 10.56 – Jan 1993 10.56 7.29 -3.27
George W. Bush 2001–2008 8.11 05.71 – Oct 2006 11.47 – Aug 2008 7.93 7.39 -0.54
Barack Obama 2009–2016 8.83 05.06 – Sep 2015
12.87 – Sep 2011 7.83 6.77 -1.06
Donald Trump 2017–2020 6.91 05.21 – Sep 2019
15.03 – Apr 2020 7.30 8.06 +0.76
Joe Biden 2021–2023 10.16 07.47 – Aug 2023
11.29 – Jun 2021 7.70 7.47 -0.23

[2]

Variations

Harvard Economist Robert Barro created what he dubbed the "Barro Misery Index" (BMI), in 1999.[3] The BMI takes the sum of the inflation and unemployment rates, and adds to that the interest rate, plus (minus) the shortfall (surplus) between the actual and trend rate of GDP growth.

In the late 2000s, Johns Hopkins economist Steve Hanke built upon Barro's misery index and began applying it to countries beyond the United States. His modified misery index is the sum of the interest, inflation, and unemployment rates, minus the year-over-year percent change in per-capita GDP growth.[4]

Hanke has recently constructed a World Table of Misery Index Scores by exclusively relying on data reported by the Economist Intelligence Unit.[5] This table includes a list of 89 countries, ranked from worst to best, with data as of December 31, 2013 (see table below).

World Table of Misery Index Scores as of December 31, 2013.

Political economists Jonathan Nitzan and Shimshon Bichler found a negative correlation between a similar "stagflation index" and corporate amalgamation (i.e. mergers and acquisitions) in the United States since the 1930s. In their theory, stagflation is a form of political economic sabotage employed by corporations to achieve differential accumulation, in this case as an alternative to amalgamation when merger and acquisition opportunities have run out.[6]

Hanke's 2020 Misery Index

Ranked from worst to best[7]
Country/Territory 2020 2022
 Venezuela 3827.6 330.8
 Zimbabwe 547.0 414.7
 Syria N/A 225.4
 Yemen N/A 116.2
 Ghana N/A 86.8
 Barbados N/A 31.5
 Sudan 193.9 176.1
 Lebanon 177.1 190.337
 Suriname 145.3 80.5
 Libya 105.7 60.3
 Argentina 95.0 156.192
 Iran 92.1 73.3
 Angola 60.6 93.518
 Madagascar 60.4 63.6
 Brazil 53.4 61.785
 South Africa 49.3 83.492
 Haiti 48.9 95.4
 Kyrgyzstan 47.1 40.977
 Nigeria 45.6 47.2
 Eswatini 42.7 63.1
 Lesotho 42.4 51.6
 Peru 42.2 34.835
 Zambia 41.6 32
 South Sudan 41.2 176.1
 Turkey 41.2 101.601
 Namibia 40.7 55.7
 Gabon 40.5 62.4
 Congo 40.3 61.5
 Botswana 39.7 64.023
 Iraq 39.5 42.3
 São Tomé and Príncipe 39.3 62.3
 Liberia 39.1 26.32
 Jamaica 38.6 41
 Malawi 37.9 63.5
 Jordan 37.9 56.3
 Guinea 36.8 38.9
 Uruguay 36.7 30.296
 Armenia 36.7 33.7
 Montenegro 36.2 52.653
 Tunisia 36.1 46.905
 Ethiopia 36.1 61
 Honduras 35.8 42.2
 India 35.8 22.58
 Panama 35.7 19.21
 Colombia 35.4 44.531
 Mongolia 35.4 42.98
 Georgia 34.8 52.5
 Uzbekistan 34.1 44.4
 Dominican Republic 34.0 27.2
 Ukraine 33.5 110.003
 Saudi Arabia 33.1 24.603
 Algeria 32.7 50.2
 Pakistan 32.5 52.6
 Costa Rica 32.4 37.077
 Paraguay 32.0 43.7
 Trinidad and Tobago 31.5 21.98
 Greece 31.3 31.128
 Mauritius 30.4 29.884
 Gambia 30.2 41.2
 Cape Verde 29.9 26.3
 Bolivia 29.9 18.9
 Kazakhstan 29.5 43.854
 Guatemala 29.3 26.3
 Burundi 28.7 41
 Philippines 28.3 19.552
 Azerbaijan 28.2 38.131
 Spain 28.2 28.16
 North Macedonia 28.1 50.4
 Belize 27.8
 Democratic Republic of the Congo 27.4 38.64
 Equatorial Guinea 27.1 31.8
 Comoros 26.2 37.1
 Myanmar 26.2 50.4
 El Salvador 26.0 28.4
 Mozambique 25.8 36.9
 Nicaragua 25.7 18.725
 Mexico 25.6 20.3
 Sri Lanka 24.3 99.634
 Chile 23.9 36.846
 Albania 23.8 25.6
 Bosnia and Herzegovina 23.8 75.9
 Iceland 23.5 21.525
 Ecuador 23.3 17.5
 Fiji 23.2 17.5
 Mauritania 23.2 45.4
 Morocco 22.8 36.565
 New Zealand 22.2 22.441
 Belarus 22.0 39.2
 Italy 22.0 26.451
 Oman 21.6 11.3
 United Kingdom 22.5 17.659
 Egypt 20.9 41.832
 Indonesia 20.9 21.727
 Kenya 20.8 29.264
 Vanuatu 20.4 18.3
 Kuwait 20.3 8.6
 Papua New Guinea 20.1 18
 Russia 19.9 33.202
   Nepal 19.9 37.18
 Romania 18.5 32.271
 Serbia 18.4 41.138
 France 18.4 19.935
 Croatia 18.3 25.5
 Hong Kong 18.2 18.191
 Canada 18.1 20.676
 Malta 18.0 11.062
 Portugal 18.0 18.615
 Uganda 17.6 35.235
 Mali 17.5 32.7
 Estonia 17.1 34.692
 Latvia 17.1 35.49
 Slovenia 17.0 19.919
 United States 16.7 16.882
 Moldova 16.4 52.9
 Cyprus 16.3 20.6
 Slovakia 16.2 32.051
 Bulgaria 16.0 24.6
 Laos 16.0 52.16
 Australia 15.9 20.059
 Burkina Faso 15.9 26.3
 Cuba 15.8 102
 Czech Republic 15.7 22.2
 Cameroon 15.5 19
 Belgium 15.4 20.608
 Hungary 14.8 40.242
 Singapore 14.6 15.986
 Austria 14.5 17.063
 Lithuania 14.5 32.87
 Malaysia 14.5 9.075
 Guinea-Bissau 14.4 17.2
 Israel 14.4 12.384
 Luxembourg 14.3 18.316
 Bangladesh 14.0 20.107
 Poland 13.9 33.761
 Vietnam 13.4 14.839
 Bahrain 13.2 22.2
 Central African Republic 13.2 35.4
 Netherlands 13.0 14.973
 Ireland 12.9 8.602
 Finland 12.8 21.629
 Norway 12.8 13.542
 Sweden 12.7 29.198
 Thailand 12.6 10.219
 Denmark 11.8 15.785
 United Arab Emirates 11.8 13
 Tanzania 11.6 25.132
 Chad 11.6 23.34
 Tonga 11.4 88.1
 Germany 10.9 16.381
 Côte d'Ivoire 10.8 11.622
 Rwanda 10.6 69.192
 Niger 10.5 9.77
 Togo 9.5 10.95
  Switzerland 8.6 8.518
 South Korea 8.3 12.515
 China 8.3 13.1
 Japan 8.1 9.071
 Qatar 5.3 13.591
 Taiwan 3.8 9.399
 Guyana −3.3

Criticism

A 2001 paper looking at large-scale surveys in Europe and the United States concluded that unemployment more heavily influences unhappiness than inflation. This implies that the basic misery index underweights the unhappiness attributable to the unemployment rate: "the estimates suggest that people would trade off a 1-percentage-point increase in the employment rate for a 1.7-percentage-point increase in the inflation rate."[8]

Misery and crime

Some economists, such as Hooi Hooi Lean, posit that the components of the Misery Index drive the crime rate to a degree. Using data from 1960 to 2005, they have found that the Misery Index and the crime rate correlate strongly and that the Misery Index seems to lead the crime rate by a year or so.[9] In fact, the correlation is so strong that the two can be said to be cointegrated, and stronger than correlation with either the unemployment rate or inflation rate alone.[citation needed]

Data sources

The data for the misery index is obtained from unemployment data published by the U.S. Department of Labor (U3) and the Inflation Rate (CPI-U) from the Bureau of Labor Statistics. The exact methods used for measuring unemployment and inflation have changed over time, although past data is usually normalized so that past and future metrics are comparable.

See also

References

  1. ↑ "The US Misery Index". Inflationdata.com. http://inflationdata.com/articles/misery-index/. 
  2. ↑ "US Misery Index by President". http://www.miseryindex.us/indexbyPresident.aspx. 
  3. ↑ Robert J. Barro (22 February 1999). "Reagan Vs. Clinton: Who's The Economic Champ?". Bloomberg. http://www.businessweek.com/stories/1999-02-21/reagan-vs-dot-clinton-whos-the-economic-champ. 
  4. ↑ Steve H. Hanke (March 2011). "Misery in MENA". Cato Institute: appeared in Globe Asia. http://www.cato.org/publications/commentary/misery-mena. 
  5. ↑ Steve H. Hanke (May 2014). "Measuring Misery around the World". Cato Institute: appeared in Globe Asia. http://www.cato.org/publications/commentary/measuring-misery-around-world. 
  6. ↑ Nitzan, Jonathan; Bichler, Shimshon (2009). Capital as Power: A Study of Order and Creorder. RIPE Series in Global Political Economy. Routledge. pp. 384–386. http://bnarchives.yorku.ca/259/. 
  7. ↑ Hanke, Steve H. (14 April 2021). "Hanke's 2020 Misery Index: Who's Miserable and Who's Happy?". https://www.nationalreview.com/2021/04/hankes-2020-misery-index-whos-miserable-and-whos-happy. 
  8. ↑ Di Tella, Rafael; MacCulloch, Robert J.; Oswald, Andrew (2001). "Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness". American Economic Review 91 (1): 335–341, 340. doi:10.1257/aer.91.1.335. http://www.people.hbs.edu/rditella/papers/AERHappyInflation.pdf. 
  9. ↑ Tang, Chor Foon; Lean, Hooi Hooi (2009). "New evidence from the misery index in the crime function". Economics Letters 102 (2): 112–115. doi:10.1016/j.econlet.2008.11.026. https://ideas.repec.org/a/eee/ecolet/v102y2009i2p112-115.html. 

External links



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