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 }}

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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 Trumanalign="Center" | 1948–1952align="Center" | 7.88align="Center" | 03.45 – Dec 1952align="Center" | 13.63 – Jan 1948align="Center" |13.63align="Center" | 3.45align="Center" | -10.18
Dwight D. Eisenhoweralign="Center" | 1953–1960align="center" | 9.26align="Center" | 02.97 – Jul 1953align="Center" | 10.98 – Apr 1958align="Center" | 3.28align="Center" | 9.96align="Center" |+5.68
John F. Kennedyalign="Center" | 1961–1963align="center" | 7.14align="Center" | 06.40 – Jul 1962align="Center" | 08.38 – Jul 1961align="Center" | 8.31align="Center" | 6.82align="Center" | -1.49
Lyndon B. Johnsonalign="Center" | 1963–1968align="center" | 6.77align="Center" | 05.70 – Nov 1965align="Center" | 08.19 – Jul 1968align="Center" | 7.02align="Center" | 8.12align="Center" | +1.10
Richard Nixonalign="Center" | 1969–1974align="center" | 10.57align="Center" | 07.80 – Jan 1969align="Center" | 17.01 – Jul 1974align="Center" | 7.80align="Center" | 17.01align="Center" | +9.21
Gerald Fordalign="Center" | 1974–1976align="center" | 16.00align="Center" | 12.66 – Dec 1976align="Center" | 19.90 – Jan 1975align="Center" | 16.36align="Center" | 12.66align="Center" | -3.70
Jimmy Carteralign="Center" | 1977–1980align="center" | 16.26align="Center" | 12.60 – Apr 1978align="Center" | 21.98 – Jun 1980align="Center" | 12.72align="Center" | 19.72align="Center" | +7.00
Ronald Reaganalign="Center" | 1981–1988align="center" | 12.19align="Center" | 07.70 – Dec 1986align="Center" | 19.33 – Jan 1981align="Center" | 19.33align="Center" |9.72align="Center" | -9.61
George H. W. Bushalign="Center" | 1989–1992align="center" | 10.68align="Center" | 09.64 – Sep 1989align="Center" | 14.47 – Nov 1990align="Center" | 10.07align="Center" | 10.30align="Center" | +0.23
Bill Clintonalign="Center" | 1993–2000align="center" | 7.80align="Center" | 05.74 – Apr 1998align="Center" | 10.56 – Jan 1993align="Center" | 10.56align="Center" | 7.29align="Center" | -3.27
George W. Bushalign="Center" | 2001–2008align="center" | 8.11align="Center" | 05.71 – Oct 2006align="Center" | 11.47 – Aug 2008align="Center" | 7.93align="Center" | 7.39align="Center" | -0.54
Barack Obamaalign="Center" | 2009–2016align="center" | 8.83align="Center" | 05.06 – Sep 2015
align="Center" | 12.87 – Sep 2011align="Center" | 7.83align="Center" |6.77align="Center" | -1.06
Donald Trumpalign="Center" | 2017–2020align="center" | 6.91align="Center" |05.21 – Sep 2019
align="Center" | 15.03 – Apr 2020align="Center" | 7.30align="Center" | 8.06align="Center" | +0.76
Joe Bidenalign="Center" | 2021–2023align="center" | 10.16align="Center" |06.79 – Feb 2024
align="Center" | 11.29 – Jun 2021align="Center" | 7.70align="Center" | 6.79align="Center" | -0.91
{{cite web|url=http://www.miseryindex.us/indexbyPresident.aspx |title=US Misery Index by President }}

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 | 2020{{Cite web |last=Hanke |first=Steve H. |date=14 April 2021 |title=Hanke's 2020 Misery Index: Who's Miserable and Who's Happy? |url=https://www.nationalreview.com/2021/04/hankes-2020-misery-index-whos-miserable-and-whos-happy |access-date=23 March 2022 |website=National Review}}

! 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}}3827.6330.8
style=text-align:left|{{flag|Zimbabwe}}547.0414.7
style=text-align:left|{{flag|Syria}}N/A225.4
style=text-align:left|{{flag|Yemen}}N/A116.2
style=text-align:left|{{flag|Ghana}}N/A86.8
style=text-align:left|{{flag|Barbados}}N/A31.5
style=text-align:left|{{flag|Sudan}}193.9176.1
style=text-align:left|{{flag|Lebanon}}177.1190.337
style=text-align:left|{{flag|Suriname}}145.380.5
style=text-align:left|{{flag|Libya}}105.760.3
style=text-align:left|{{flag|Argentina}}95.0156.192
style=text-align:left|{{flag|Iran}}92.173.3
style=text-align:left|{{flag|Angola}}60.693.518
style=text-align:left|{{flag|Madagascar}}60.463.6
style=text-align:left|{{flag|Brazil}}53.461.785
style=text-align:left|{{flag|South Africa}}49.383.492
style=text-align:left|{{flag|Haiti}}48.995.4
style=text-align:left|{{flag|Kyrgyzstan}}47.140.977
style=text-align:left|{{flag|Nigeria}}45.647.2
style=text-align:left|{{flag|Eswatini}}42.763.1
style=text-align:left|{{flag|Lesotho}}42.451.6
style=text-align:left|{{flag|Peru}}42.234.835
style=text-align:left|{{flag|Zambia}}41.632
style=text-align:left|{{flag|South Sudan}}41.2176.1
style=text-align:left|{{flag|Turkey}}41.2101.601
style=text-align:left|{{flag|Namibia}}40.755.7
style=text-align:left|{{flag|Gabon}}40.562.4
style=text-align:left|{{flag|Congo}}40.361.5
style=text-align:left|{{flag|Botswana}}39.764.023
style=text-align:left|{{flag|Iraq}}39.542.3
style=text-align:left|{{flag|São Tomé and Príncipe}}39.362.3
style=text-align:left|{{flag|Liberia}}39.126.32
style=text-align:left|{{flag|Jamaica}}38.641
style=text-align:left|{{flag|Malawi}}37.963.5
style=text-align:left|{{flag|Jordan}}37.956.3
style=text-align:left|{{flag|Guinea}}36.838.9
style=text-align:left|{{flag|Uruguay}}36.730.296
style=text-align:left|{{flag|Armenia}}36.733.7
style=text-align:left|{{flag|Montenegro}}36.252.653
style=text-align:left|{{flag|Tunisia}}36.146.905
style=text-align:left|{{flag|Ethiopia}}36.161
style=text-align:left|{{flag|Honduras}}35.842.2
style=text-align:left|{{flag|India}}35.822.58
style=text-align:left|{{flag|Panama}}35.719.21
style=text-align:left|{{flag|Colombia}}35.444.531
style=text-align:left|{{flag|Mongolia}}35.442.98
style=text-align:left|{{flag|Georgia}}34.852.5
style=text-align:left|{{flag|Uzbekistan}}34.144.4
style=text-align:left|{{flag|Dominican Republic}}34.027.2
style=text-align:left|{{flag|Ukraine}}33.5110.003
style=text-align:left|{{flag|Saudi Arabia}}33.124.603
style=text-align:left|{{flag|Algeria}}32.750.2
style=text-align:left|{{flag|Pakistan}}32.552.6
style=text-align:left|{{flag|Costa Rica}}32.437.077
style=text-align:left|{{flag|Paraguay}}32.043.7
style=text-align:left|{{flag|Trinidad and Tobago}}31.521.98
style=text-align:left|{{flag|Greece}}31.331.128
style=text-align:left|{{flag|Mauritius}}30.429.884
style=text-align:left|{{flag|Gambia}}30.241.2
style=text-align:left|{{flag|Cape Verde}}29.926.3
style=text-align:left|{{flag|Bolivia}}29.918.9
style=text-align:left|{{flag|Kazakhstan}}29.543.854
style=text-align:left|{{flag|Guatemala}}29.326.3
style=text-align:left|{{flag|Burundi}}28.741
style=text-align:left|{{flag|Philippines}}28.319.552
style=text-align:left|{{flag|Azerbaijan}}28.238.131
style=text-align:left|{{flag|Spain}}28.228.16
style=text-align:left|{{flag|North Macedonia}}28.150.4
style=text-align:left|{{flag|Belize}}27.8
style=text-align:left|{{flag|Democratic Republic of the Congo}}27.438.64
style=text-align:left|{{flag|Equatorial Guinea}}27.131.8
style=text-align:left|{{flag|Comoros}}26.237.1
style=text-align:left|{{flag|Myanmar}}26.250.4
style=text-align:left|{{flag|El Salvador}}26.028.4
style=text-align:left|{{flag|Mozambique}}25.836.9
style=text-align:left|{{flag|Nicaragua}}25.718.725
style=text-align:left|{{flag|Mexico}}25.620.3
style=text-align:left|{{flag|Sri Lanka}}24.399.634
style=text-align:left|{{flag|Chile}}23.936.846
style=text-align:left|{{flag|Albania}}23.825.6
style=text-align:left|{{flag|Bosnia and Herzegovina}}23.875.9
style=text-align:left|{{flag|Iceland}}23.521.525
style=text-align:left|{{flag|Ecuador}}23.317.5
style=text-align:left|{{flag|Fiji}}23.217.5
style=text-align:left|{{flag|Mauritania}}23.245.4
style=text-align:left|{{flag|Morocco}}22.836.565
style=text-align:left|{{flag|New Zealand}}22.222.441
style=text-align:left|{{flag|Belarus}}22.039.2
style=text-align:left|{{flag|Italy}}22.026.451
style=text-align:left|{{flag|Oman}}21.611.3
style=text-align:left|{{flag|United Kingdom}}22.517.659
style=text-align:left|{{flag|Egypt}}20.941.832
style=text-align:left|{{flag|Indonesia}}20.921.727
style=text-align:left|{{flag|Kenya}}20.829.264
style=text-align:left|{{flag|Vanuatu}}20.418.3
style=text-align:left|{{flag|Kuwait}}20.38.6
style=text-align:left|{{flag|Papua New Guinea}}20.118
style=text-align:left|{{flag|Russia}}19.933.202
style=text-align:left|{{flag|Nepal}}19.937.18
style=text-align:left|{{flag|Romania}}18.532.271
style=text-align:left|{{flag|Serbia}}18.441.138
style=text-align:left|{{flag|France}}18.419.935
style=text-align:left|{{flag|Croatia}}18.325.5
style=text-align:left|{{flag|Hong Kong}}18.218.191
style=text-align:left|{{flag|Canada}}18.120.676
style=text-align:left|{{flag|Malta}}18.011.062
style=text-align:left|{{flag|Portugal}}18.018.615
style=text-align:left|{{flag|Uganda}}17.635.235
style=text-align:left|{{flag|Mali}}17.532.7
style=text-align:left|{{flag|Estonia}}17.134.692
style=text-align:left|{{flag|Latvia}}17.135.49
style=text-align:left|{{flag|Slovenia}}17.019.919
style=text-align:left|{{flag|United States}}16.716.882
style=text-align:left|{{flag|Moldova}}16.452.9
style=text-align:left|{{flag|Cyprus}}16.320.6
style=text-align:left|{{flag|Slovakia}}16.232.051
style=text-align:left|{{flag|Bulgaria}}16.024.6
style=text-align:left|{{flag|Laos}}16.052.16
style=text-align:left|{{flag|Australia}}15.920.059
style=text-align:left|{{flag|Burkina Faso}}15.926.3
style=text-align:left|{{flag|Cuba}}15.8102
style=text-align:left|{{flag|Czech Republic}}15.722.2
style=text-align:left|{{flag|Cameroon}}15.519
style=text-align:left|{{flag|Belgium}}15.420.608
style=text-align:left|{{flag|Hungary}}14.840.242
style=text-align:left|{{flag|Singapore}}14.615.986
style=text-align:left|{{flag|Austria}}14.517.063
style=text-align:left|{{flag|Lithuania}}14.532.87
style=text-align:left|{{flag|Malaysia}}14.59.075
style=text-align:left|{{flag|Guinea-Bissau}}14.417.2
style=text-align:left|{{flag|Israel}}14.412.384
style=text-align:left|{{flag|Luxembourg}}14.318.316
style=text-align:left|{{flag|Bangladesh}}14.020.107
style=text-align:left|{{flag|Poland}}13.933.761
style=text-align:left|{{flag|Vietnam}}13.414.839
style=text-align:left|{{flag|Bahrain}}13.222.2
style=text-align:left|{{flag|Central African Republic}}13.235.4
style=text-align:left|{{flag|Netherlands}}13.014.973
style=text-align:left|{{flag|Ireland}}12.98.602
style=text-align:left|{{flag|Finland}}12.821.629
style=text-align:left|{{flag|Norway}}12.813.542
style=text-align:left|{{flag|Sweden}}12.729.198
style=text-align:left|{{flag|Thailand}}12.610.219
style=text-align:left|{{flag|Denmark}}11.815.785
style=text-align:left|{{flag|United Arab Emirates}}11.813
style=text-align:left|{{flag|Tanzania}}11.625.132
style=text-align:left|{{flag|Chad}}11.623.34
style=text-align:left|{{flag|Tonga}}11.488.1
style=text-align:left|{{flag|Germany}}10.916.381
style=text-align:left|{{flag|Côte d'Ivoire}}10.811.622
style=text-align:left|{{flag|Rwanda}}10.669.192
style=text-align:left|{{flag|Niger}}10.59.77
style=text-align:left|{{flag|Togo}}9.510.95
style=text-align:left|{{flag|Switzerland}}8.68.518
style=text-align:left|{{flag|South Korea}}8.312.515
style=text-align:left|{{flag|China}}8.313.1
style=text-align:left|{{flag|Japan}}8.19.071
style=text-align:left|{{flag|Qatar}}5.313.591
style=text-align:left|{{flag|Taiwan}}3.89.399
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}}