Misery index (economics)
{{Short description|Economic indicator measuring economic and social cost}}
[[File:Misery Index.webp|thumb|{{center|Misery Index}}
{{legend|#2A85BE|Misery Index|outline=#004D80}}
{{legend-line|#FFD932 solid 3px|Unemployment rate }}
{{legend-line|#EE220C solid 3px|Inflation rate CPI }}
]]
The misery index is an economic indicator, created by the American 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.{{cite web|url=http://inflationdata.com/articles/misery-index/|title=The US Misery Index|work=Inflationdata.com}}
Misery index by US presidential administration
class="wikitable sortable"
|+ Index = Unemployment rate + Inflation rate (lower number is better) | |||||||
align="Center" |President || Time Period || Average || align="Center"| Low || align="Center"| High || align="Center" | Start || align="Center" | End || align="Center" | Change | |||||||
---|---|---|---|---|---|---|---|
Harry Truman | align="Center" | 1948–1952 | align="Center" | 7.88 | align="Center" | | 3.45 – Dec 1952align="Center" | 13.63 – Jan 1948 | align="Center" |13.63 | align="Center" | 3.45 | align="Center" | -10.18 |
Dwight D. Eisenhower | align="Center" | 1953–1960 | align="center" | 9.26 | align="Center" | | 2.97 – Jul 1953align="Center" | 10.98 – Apr 1958 | align="Center" | 3.28 | align="Center" | 9.96 | align="Center" |+5.68 |
John F. Kennedy | align="Center" | 1961–1963 | align="center" | 7.14 | align="Center" | | 6.40 – Jul 1962align="Center" | | 8.38 – Jul 1961align="Center" | 8.31 | align="Center" | 6.82 | align="Center" | -1.49 |
Lyndon B. Johnson | align="Center" | 1963–1968 | align="center" | 6.77 | align="Center" | | 5.70 – Nov 1965align="Center" | | 8.19 – Jul 1968align="Center" | 7.02 | align="Center" | 8.12 | align="Center" | +1.10 |
Richard Nixon | align="Center" | 1969–1974 | align="center" | 10.57 | align="Center" | | 7.80 – Jan 1969align="Center" | 17.01 – Jul 1974 | align="Center" | 7.80 | align="Center" | 17.01 | align="Center" | +9.21 |
Gerald Ford | align="Center" | 1974–1976 | align="center" | 16.00 | align="Center" | 12.66 – Dec 1976 | align="Center" | 19.90 – Jan 1975 | align="Center" | 16.36 | align="Center" | 12.66 | align="Center" | -3.70 |
Jimmy Carter | align="Center" | 1977–1980 | align="center" | 16.26 | align="Center" | 12.60 – Apr 1978 | align="Center" | 21.98 – Jun 1980 | align="Center" | 12.72 | align="Center" | 19.72 | align="Center" | +7.00 |
Ronald Reagan | align="Center" | 1981–1988 | align="center" | 12.19 | align="Center" | | 7.70 – Dec 1986align="Center" | 19.33 – Jan 1981 | align="Center" | 19.33 | align="Center" |9.72 | align="Center" | -9.61 |
George H. W. Bush | align="Center" | 1989–1992 | align="center" | 10.68 | align="Center" | | 9.64 – Sep 1989align="Center" | 14.47 – Nov 1990 | align="Center" | 10.07 | align="Center" | 10.30 | align="Center" | +0.23 |
Bill Clinton | align="Center" | 1993–2000 | align="center" | 7.80 | align="Center" | | 5.74 – Apr 1998align="Center" | 10.56 – Jan 1993 | align="Center" | 10.56 | align="Center" | 7.29 | align="Center" | -3.27 |
George W. Bush | align="Center" | 2001–2008 | align="center" | 8.11 | align="Center" | | 5.71 – Oct 2006align="Center" | 11.47 – Aug 2008 | align="Center" | 7.93 | align="Center" | 7.39 | align="Center" | -0.54 |
Barack Obama | align="Center" | 2009–2016 | align="center" | 8.83 | align="Center" | | 5.06 – Sep 2015 align="Center" | 12.87 – Sep 2011 | align="Center" | 7.83 | align="Center" |6.77 | align="Center" | -1.06 |
Donald Trump | align="Center" | 2017–2020 | align="center" | 6.91 | align="Center" | | 5.21 – Sep 2019 align="Center" | 15.03 – Apr 2020 | align="Center" | 7.30 | align="Center" | 8.06 | align="Center" | +0.76 |
Joe Biden | align="Center" | 2021–2023 | align="center" | 10.16 | align="Center" | | 6.79 – Feb 2024 align="Center" | 11.29 – Jun 2021 | align="Center" | 7.70 | align="Center" | 6.79 | align="Center" | -0.91 |
Variations
Harvard Economist Robert Barro created what he dubbed the "Barro Misery Index" (BMI), in 1999.{{cite news|url=http://www.businessweek.com/stories/1999-02-21/reagan-vs-dot-clinton-whos-the-economic-champ|archive-url=https://web.archive.org/web/20121022195246/http://www.businessweek.com/stories/1999-02-21/reagan-vs-dot-clinton-whos-the-economic-champ|archive-date=October 22, 2012|title=Reagan Vs. Clinton: Who's The Economic Champ?|author=Robert J. Barro |work=Bloomberg|date=22 February 1999 }} 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.{{cite web|url=http://www.cato.org/publications/commentary/misery-mena|title=Misery in MENA|author=Steve H. Hanke|date=March 2011|publisher=Cato Institute: appeared in Globe Asia}}
In 2013 Hanke constructed a World Table of Misery Index Scores by exclusively relying on data reported by the Economist Intelligence Unit.{{cite web|url=http://www.cato.org/publications/commentary/measuring-misery-around-world|title=Measuring Misery around the World|author=Steve H. Hanke|date=May 2014|publisher=Cato Institute: appeared in Globe Asia}} This table includes a list of 89 countries, ranked from worst to best, with data as of December 31, 2013 (see table below).
File:World Table of Misery Index Scores.png
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.{{cite book|url=http://bnarchives.yorku.ca/259/|title=Capital as Power: A Study of Order and Creorder |author=Nitzan, Jonathan |author2=Bichler, Shimshon |year=2009|pages=384–386|publisher=Routledge|series=RIPE Series in Global Political Economy}}
Hanke's Misery Index
{{Static row numbers}}
class="wikitable sortable static-row-numbers" style="text-align:right;"
|+ Ranked from worst to best | ||
rowspan=2 | Country/Territory
! rowspan=2 | 2022{{cite web |title=Hanke’s 2022 Misery Index |date=May 18, 2023 |first=STEVE H. |last=HANKE |url=https://www.nationalreview.com/2023/05/hankes-2022-misery-index/}} | ||
---|---|---|
style=text-align:left|{{flag|Venezuela}} | 3827.6 | 330.8 |
style=text-align:left|{{flag|Zimbabwe}} | 547.0 | 414.7 |
style=text-align:left|{{flag|Syria}} | N/A | 225.4 |
style=text-align:left|{{flag|Yemen}} | N/A | 116.2 |
style=text-align:left|{{flag|Ghana}} | N/A | 86.8 |
style=text-align:left|{{flag|Barbados}} | N/A | 31.5 |
style=text-align:left|{{flag|Sudan}} | 193.9 | 176.1 |
style=text-align:left|{{flag|Lebanon}} | 177.1 | 190.337 |
style=text-align:left|{{flag|Suriname}} | 145.3 | 80.5 |
style=text-align:left|{{flag|Libya}} | 105.7 | 60.3 |
style=text-align:left|{{flag|Argentina}} | 95.0 | 156.192 |
style=text-align:left|{{flag|Iran}} | 92.1 | 73.3 |
style=text-align:left|{{flag|Angola}} | 60.6 | 93.518 |
style=text-align:left|{{flag|Madagascar}} | 60.4 | 63.6 |
style=text-align:left|{{flag|Brazil}} | 53.4 | 61.785 |
style=text-align:left|{{flag|South Africa}} | 49.3 | 83.492 |
style=text-align:left|{{flag|Haiti}} | 48.9 | 95.4 |
style=text-align:left|{{flag|Kyrgyzstan}} | 47.1 | 40.977 |
style=text-align:left|{{flag|Nigeria}} | 45.6 | 47.2 |
style=text-align:left|{{flag|Eswatini}} | 42.7 | 63.1 |
style=text-align:left|{{flag|Lesotho}} | 42.4 | 51.6 |
style=text-align:left|{{flag|Peru}} | 42.2 | 34.835 |
style=text-align:left|{{flag|Zambia}} | 41.6 | 32 |
style=text-align:left|{{flag|South Sudan}} | 41.2 | 176.1 |
style=text-align:left|{{flag|Turkey}} | 41.2 | 101.601 |
style=text-align:left|{{flag|Namibia}} | 40.7 | 55.7 |
style=text-align:left|{{flag|Gabon}} | 40.5 | 62.4 |
style=text-align:left|{{flag|Congo}} | 40.3 | 61.5 |
style=text-align:left|{{flag|Botswana}} | 39.7 | 64.023 |
style=text-align:left|{{flag|Iraq}} | 39.5 | 42.3 |
style=text-align:left|{{flag|São Tomé and Príncipe}} | 39.3 | 62.3 |
style=text-align:left|{{flag|Liberia}} | 39.1 | 26.32 |
style=text-align:left|{{flag|Jamaica}} | 38.6 | 41 |
style=text-align:left|{{flag|Malawi}} | 37.9 | 63.5 |
style=text-align:left|{{flag|Jordan}} | 37.9 | 56.3 |
style=text-align:left|{{flag|Guinea}} | 36.8 | 38.9 |
style=text-align:left|{{flag|Uruguay}} | 36.7 | 30.296 |
style=text-align:left|{{flag|Armenia}} | 36.7 | 33.7 |
style=text-align:left|{{flag|Montenegro}} | 36.2 | 52.653 |
style=text-align:left|{{flag|Tunisia}} | 36.1 | 46.905 |
style=text-align:left|{{flag|Ethiopia}} | 36.1 | 61 |
style=text-align:left|{{flag|Honduras}} | 35.8 | 42.2 |
style=text-align:left|{{flag|India}} | 35.8 | 22.58 |
style=text-align:left|{{flag|Panama}} | 35.7 | 19.21 |
style=text-align:left|{{flag|Colombia}} | 35.4 | 44.531 |
style=text-align:left|{{flag|Mongolia}} | 35.4 | 42.98 |
style=text-align:left|{{flag|Georgia}} | 34.8 | 52.5 |
style=text-align:left|{{flag|Uzbekistan}} | 34.1 | 44.4 |
style=text-align:left|{{flag|Dominican Republic}} | 34.0 | 27.2 |
style=text-align:left|{{flag|Ukraine}} | 33.5 | 110.003 |
style=text-align:left|{{flag|Saudi Arabia}} | 33.1 | 24.603 |
style=text-align:left|{{flag|Algeria}} | 32.7 | 50.2 |
style=text-align:left|{{flag|Pakistan}} | 32.5 | 52.6 |
style=text-align:left|{{flag|Costa Rica}} | 32.4 | 37.077 |
style=text-align:left|{{flag|Paraguay}} | 32.0 | 43.7 |
style=text-align:left|{{flag|Trinidad and Tobago}} | 31.5 | 21.98 |
style=text-align:left|{{flag|Greece}} | 31.3 | 31.128 |
style=text-align:left|{{flag|Mauritius}} | 30.4 | 29.884 |
style=text-align:left|{{flag|Gambia}} | 30.2 | 41.2 |
style=text-align:left|{{flag|Cape Verde}} | 29.9 | 26.3 |
style=text-align:left|{{flag|Bolivia}} | 29.9 | 18.9 |
style=text-align:left|{{flag|Kazakhstan}} | 29.5 | 43.854 |
style=text-align:left|{{flag|Guatemala}} | 29.3 | 26.3 |
style=text-align:left|{{flag|Burundi}} | 28.7 | 41 |
style=text-align:left|{{flag|Philippines}} | 28.3 | 19.552 |
style=text-align:left|{{flag|Azerbaijan}} | 28.2 | 38.131 |
style=text-align:left|{{flag|Spain}} | 28.2 | 28.16 |
style=text-align:left|{{flag|North Macedonia}} | 28.1 | 50.4 |
style=text-align:left|{{flag|Belize}} | 27.8 | |
style=text-align:left|{{flag|Democratic Republic of the Congo}} | 27.4 | 38.64 |
style=text-align:left|{{flag|Equatorial Guinea}} | 27.1 | 31.8 |
style=text-align:left|{{flag|Comoros}} | 26.2 | 37.1 |
style=text-align:left|{{flag|Myanmar}} | 26.2 | 50.4 |
style=text-align:left|{{flag|El Salvador}} | 26.0 | 28.4 |
style=text-align:left|{{flag|Mozambique}} | 25.8 | 36.9 |
style=text-align:left|{{flag|Nicaragua}} | 25.7 | 18.725 |
style=text-align:left|{{flag|Mexico}} | 25.6 | 20.3 |
style=text-align:left|{{flag|Sri Lanka}} | 24.3 | 99.634 |
style=text-align:left|{{flag|Chile}} | 23.9 | 36.846 |
style=text-align:left|{{flag|Albania}} | 23.8 | 25.6 |
style=text-align:left|{{flag|Bosnia and Herzegovina}} | 23.8 | 75.9 |
style=text-align:left|{{flag|Iceland}} | 23.5 | 21.525 |
style=text-align:left|{{flag|Ecuador}} | 23.3 | 17.5 |
style=text-align:left|{{flag|Fiji}} | 23.2 | 17.5 |
style=text-align:left|{{flag|Mauritania}} | 23.2 | 45.4 |
style=text-align:left|{{flag|Morocco}} | 22.8 | 36.565 |
style=text-align:left|{{flag|New Zealand}} | 22.2 | 22.441 |
style=text-align:left|{{flag|Belarus}} | 22.0 | 39.2 |
style=text-align:left|{{flag|Italy}} | 22.0 | 26.451 |
style=text-align:left|{{flag|Oman}} | 21.6 | 11.3 |
style=text-align:left|{{flag|United Kingdom}} | 22.5 | 17.659 |
style=text-align:left|{{flag|Egypt}} | 20.9 | 41.832 |
style=text-align:left|{{flag|Indonesia}} | 20.9 | 21.727 |
style=text-align:left|{{flag|Kenya}} | 20.8 | 29.264 |
style=text-align:left|{{flag|Vanuatu}} | 20.4 | 18.3 |
style=text-align:left|{{flag|Kuwait}} | 20.3 | 8.6 |
style=text-align:left|{{flag|Papua New Guinea}} | 20.1 | 18 |
style=text-align:left|{{flag|Russia}} | 19.9 | 33.202 |
style=text-align:left|{{flag|Nepal}} | 19.9 | 37.18 |
style=text-align:left|{{flag|Romania}} | 18.5 | 32.271 |
style=text-align:left|{{flag|Serbia}} | 18.4 | 41.138 |
style=text-align:left|{{flag|France}} | 18.4 | 19.935 |
style=text-align:left|{{flag|Croatia}} | 18.3 | 25.5 |
style=text-align:left|{{flag|Hong Kong}} | 18.2 | 18.191 |
style=text-align:left|{{flag|Canada}} | 18.1 | 20.676 |
style=text-align:left|{{flag|Malta}} | 18.0 | 11.062 |
style=text-align:left|{{flag|Portugal}} | 18.0 | 18.615 |
style=text-align:left|{{flag|Uganda}} | 17.6 | 35.235 |
style=text-align:left|{{flag|Mali}} | 17.5 | 32.7 |
style=text-align:left|{{flag|Estonia}} | 17.1 | 34.692 |
style=text-align:left|{{flag|Latvia}} | 17.1 | 35.49 |
style=text-align:left|{{flag|Slovenia}} | 17.0 | 19.919 |
style=text-align:left|{{flag|United States}} | 16.7 | 16.882 |
style=text-align:left|{{flag|Moldova}} | 16.4 | 52.9 |
style=text-align:left|{{flag|Cyprus}} | 16.3 | 20.6 |
style=text-align:left|{{flag|Slovakia}} | 16.2 | 32.051 |
style=text-align:left|{{flag|Bulgaria}} | 16.0 | 24.6 |
style=text-align:left|{{flag|Laos}} | 16.0 | 52.16 |
style=text-align:left|{{flag|Australia}} | 15.9 | 20.059 |
style=text-align:left|{{flag|Burkina Faso}} | 15.9 | 26.3 |
style=text-align:left|{{flag|Cuba}} | 15.8 | 102 |
style=text-align:left|{{flag|Czech Republic}} | 15.7 | 22.2 |
style=text-align:left|{{flag|Cameroon}} | 15.5 | 19 |
style=text-align:left|{{flag|Belgium}} | 15.4 | 20.608 |
style=text-align:left|{{flag|Hungary}} | 14.8 | 40.242 |
style=text-align:left|{{flag|Singapore}} | 14.6 | 15.986 |
style=text-align:left|{{flag|Austria}} | 14.5 | 17.063 |
style=text-align:left|{{flag|Lithuania}} | 14.5 | 32.87 |
style=text-align:left|{{flag|Malaysia}} | 14.5 | 9.075 |
style=text-align:left|{{flag|Guinea-Bissau}} | 14.4 | 17.2 |
style=text-align:left|{{flag|Israel}} | 14.4 | 12.384 |
style=text-align:left|{{flag|Luxembourg}} | 14.3 | 18.316 |
style=text-align:left|{{flag|Bangladesh}} | 14.0 | 20.107 |
style=text-align:left|{{flag|Poland}} | 13.9 | 33.761 |
style=text-align:left|{{flag|Vietnam}} | 13.4 | 14.839 |
style=text-align:left|{{flag|Bahrain}} | 13.2 | 22.2 |
style=text-align:left|{{flag|Central African Republic}} | 13.2 | 35.4 |
style=text-align:left|{{flag|Netherlands}} | 13.0 | 14.973 |
style=text-align:left|{{flag|Ireland}} | 12.9 | 8.602 |
style=text-align:left|{{flag|Finland}} | 12.8 | 21.629 |
style=text-align:left|{{flag|Norway}} | 12.8 | 13.542 |
style=text-align:left|{{flag|Sweden}} | 12.7 | 29.198 |
style=text-align:left|{{flag|Thailand}} | 12.6 | 10.219 |
style=text-align:left|{{flag|Denmark}} | 11.8 | 15.785 |
style=text-align:left|{{flag|United Arab Emirates}} | 11.8 | 13 |
style=text-align:left|{{flag|Tanzania}} | 11.6 | 25.132 |
style=text-align:left|{{flag|Chad}} | 11.6 | 23.34 |
style=text-align:left|{{flag|Tonga}} | 11.4 | 88.1 |
style=text-align:left|{{flag|Germany}} | 10.9 | 16.381 |
style=text-align:left|{{flag|Côte d'Ivoire}} | 10.8 | 11.622 |
style=text-align:left|{{flag|Rwanda}} | 10.6 | 69.192 |
style=text-align:left|{{flag|Niger}} | 10.5 | 9.77 |
style=text-align:left|{{flag|Togo}} | 9.5 | 10.95 |
style=text-align:left|{{flag|Switzerland}} | 8.6 | 8.518 |
style=text-align:left|{{flag|South Korea}} | 8.3 | 12.515 |
style=text-align:left|{{flag|China}} | 8.3 | 13.1 |
style=text-align:left|{{flag|Japan}} | 8.1 | 9.071 |
style=text-align:left|{{flag|Qatar}} | 5.3 | 13.591 |
style=text-align:left|{{flag|Taiwan}} | 3.8 | 9.399 |
style=text-align:left|{{flag|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."{{cite journal|author1=Di Tella, Rafael |author2=MacCulloch, Robert J. |author3=Oswald, Andrew |year=2001|url=http://www.people.hbs.edu/rditella/papers/AERHappyInflation.pdf|title=Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness|journal=American Economic Review|volume=91|issue=1|pages=335–341, 340|doi=10.1257/aer.91.1.335|s2cid=14823969 }}
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.{{cite journal|url=https://ideas.repec.org/a/eee/ecolet/v102y2009i2p112-115.html|title=New evidence from the misery index in the crime function|author1=Tang, Chor Foon |author2=Lean, Hooi Hooi |journal=Economics Letters|year=2009|author-link2=Hooi Hooi Lean|volume=102|issue=2|pages=112–115|doi=10.1016/j.econlet.2008.11.026}} 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|date=May 2015}}
Data sources
The data for the misery index is obtained from unemployment data published by the U.S. Department of Labor ([https://research.stlouisfed.org/fred2/series/UNRATE U3]) and the Inflation Rate ([https://research.stlouisfed.org/fred2/series/CPIAUCNS/ 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
{{reflist}}
External links
- [http://www.miseryindex.us The current and historical Misery Index]
- [https://web.archive.org/web/20150214232054/http://miseryindex.us/indexbyPresident.aspx The Misery Index by President]
- [http://www.miseryindex.us/indexbyyear.aspx The Misery Index By Year]
- [http://www.cato.org/publications/commentary/misery-mena Cato Publications Commentary]
- [http://ptien.faculty.wesleyan.edu/files/2012/05/LovellTien2000.pdf Correlations of Misery Index and Consumer Sentiment]
{{Deprivation Indicators}}
{{Poverty}}
Category:Political science theories