Misery index (economics)

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
  Misery Index
  Unemployment rate
  Inflation rate CPI

Misery index by US presidential administration
Index = Unemployment rate + Inflation rate (lower number is better)
PresidentTime PeriodAverageLowHighStartEndChange
Harry Truman1948–19527.8803.45 – Dec 195213.63 – Jan 194813.633.45-10.18
Dwight D. Eisenhower1953–19609.2602.97 – Jul 195310.98 – Apr 19583.289.96+5.68
John F. Kennedy1961–19637.1406.40 – Jul 196208.38 – Jul 19618.316.82-1.49
Lyndon B. Johnson1963–19686.7705.70 – Nov 196508.19 – Jul 19687.028.12+1.10
Richard Nixon1969–197410.5707.80 – Jan 196917.01 – Jul 19747.8017.01+9.21
Gerald Ford1974–197616.0012.66 – Dec 197619.90 – Jan 197516.3612.66-3.70
Jimmy Carter1977–198016.2612.60 – Apr 197821.98 – Jun 198012.7219.72+7.00
Ronald Reagan1981–198812.1907.70 – Dec 198619.33 – Jan 198119.339.72-9.61
George H. W. Bush1989–199210.6809.64 – Sep 198914.47 – Nov 199010.0710.30+0.23
Bill Clinton1993–20007.8005.74 – Apr 199810.56 – Jan 199310.567.29-3.27
George W. Bush2001–20088.1105.71 – Oct 200611.47 – Aug 20087.937.39-0.54
Barack Obama2009–20168.8305.06 – Sep 2015
12.87 – Sep 20117.836.77-1.06
Donald Trump2017–20206.9105.21 – Sep 2019
15.03 – Apr 20207.308.06+0.76
Joe Biden2021–20219.7707.70 – Jan 2021
11.29 – Jun 20217.7010.19+2.49

[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]
Rank Country Misery
Index
1 Venezuela3827.6
2 Zimbabwe547.0
3 Sudan193.9
4 Lebanon177.1
5 Suriname145.3
6 Libya105.7
7 Argentina95.0
8 Iran92.1
9 Angola60.6
10 Madagascar60.4
11 Brazil53.4
12 South Africa49.3
13 Haiti48.9
14 Kyrgyzstan47.1
15 Nigeria45.6
16 Eswatini42.7
17 Lesotho42.4
18 Peru42.2
19 Zambia41.6
20 South Sudan41.2
21 Turkey41.2
22 Namibia40.7
23 Gabon40.5
24 Congo40.3
25 Botswana39.7
26 Iraq39.5
27 São Tomé and Príncipe39.3
28 Liberia39.1
29 Jamaica38.6
30 Malawi37.9
31 Jordan37.9
32 Guinea36.8
33 Uruguay36.7
34 Armenia36.7
35 Montenegro36.2
36 Tunisia36.1
37 Ethiopia36.1
38 Honduras35.8
39 India35.8
40 Panama35.7
41 Colombia35.4
42 Mongolia35.4
43 Georgia34.8
44 Uzbekistan34.1
45 Dominican Republic34.0
46 Ukraine33.5
47 Saudi Arabia33.1
48 Algeria32.7
49 Pakistan32.5
50 Costa Rica32.4
51 Paraguay32.0
52 Trinidad and Tobago31.5
53 Greece31.3
54 Mauritius30.4
55 Gambia30.2
56 Cape Verde29.9
57 Bolivia29.9
58 Kazakhstan29.5
59 Guatemala29.3
60 Burundi28.7
61 Philippines28.3
62 Azerbaijan28.2
63 Spain28.2
64 North Macedonia28.1
65 Belize27.8
66 Democratic Republic of the Congo27.4
67 Equatorial Guinea27.1
68 Comoros26.2
69 Myanmar26.2
70 El Salvador26.0
71 Mozambique25.8
72 Nicaragua25.7
73 Mexico25.6
74 Sri Lanka24.3
75 Chile23.9
76 Albania23.8
77 Bosnia and Herzegovina23.8
78 Iceland23.5
79 Ecuador23.3
80 Fiji23.2
81 Mauritania23.2
82 Morocco22.8
83 New Zealand22.2
84 Belarus22.0
85 Italy22.0
86 Oman21.6
87 United Kingdom22.5
88 Egypt20.9
89 Indonesia20.9
90 Kenya20.8
91 Vanuatu20.4
92. Kuwait20.3
93 Papua New Guinea20.1
94 Russia19.9
95   Nepal19.9
96 Romania18.5
97 Serbia18.4
98 France18.4
99 Croatia18.3
100 Hong Kong18.2
101 Canada18.1
102 Malta18.0
103 Portugal18.0
104 Uganda17.6
105 Mali17.5
106 Estonia17.1
107 Latvia17.1
108 Slovenia17.0
109 United States16.7
110 Moldova16.4
111 Cyprus16.3
112 Slovakia16.2
113 Bulgaria16.0
114 Laos16.0
115 Australia15.9
116 Burkina Faso15.9
117 Cuba15.8
118 Czech Republic15.7
119 Cameroon15.5
120 Belgium15.4
121 Hungary14.8
122 Singapore14.6
123 Austria14.5
124 Lithuania14.5
125 Malaysia14.5
126 Guinea-Bissau14.4
127 Israel14.4
128 Luxembourg14.3
129 Bangladesh14.0
130 Poland13.9
131 Vietnam13.4
132 Bahrain13.2
133 Central African Republic13.2
134 Netherlands13.0
135 Ireland12.9
136 Finland12.8
137 Norway12.8
138 Sweden12.7
139 Thailand12.6
140 Denmark11.8
141 United Arab Emirates11.8
142 Tanzania11.6
143 Chad11.6
144 Tonga11.4
145 Germany10.9
146 Côte d'Ivoire10.8
147 Rwanda10.6
148 Niger10.5
149 Togo9.5
150 Switzerland8.6
151 South Korea8.3
152 China8.3
153 Japan8.1
154 Qatar5.3
155 Taiwan3.8
156 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.

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.
  2. "US Misery Index by President".
  3. Robert J. Barro (22 February 1999). "Reagan Vs. Clinton: Who's The Economic Champ?". Bloomberg. Archived from the original on October 22, 2012.
  4. Steve H. Hanke (March 2011). "Misery in MENA". Cato Institute: appeared in Globe Asia.
  5. Steve H. Hanke (May 2014). "Measuring Misery around the World". Cato Institute: appeared in Globe Asia.
  6. Nitzan and Bichler (2009). Capital as Power: A Study of Order and Creorder. RIPE Series in Global Political Economy. Routledge. pp. 384–386.{{cite book}}: CS1 maint: uses authors parameter (link)
  7. Hanke, Steve H. (14 April 2021). "Hanke's 2020 Misery Index: Who's Miserable and Who's Happy?". National Review. Retrieved 23 March 2022.
  8. Di Tella, Rafael; MacCulloch, Robert J.; Oswald, Andrew (2001). "Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness" (PDF). American Economic Review. 91 (1): 335–341, 340. doi:10.1257/aer.91.1.335. S2CID 14823969.
  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.
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