Misery index (economics)

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https://en.wikipedia.org/wiki/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 it 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.

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	3.45 – Dec 1952	13.63 – Jan 1948	13.63	3.45	-10.18
Dwight D. Eisenhower	1953–1960	9.26	2.97 – Jul 1953	10.98 – Apr 1958	3.28	9.96	+5.68
John F. Kennedy	1961–1962	7.14	6.40 – Jul 1962	8.38 – Jul 1961	8.31	6.82	-1.49
Lyndon B. Johnson	1963–1968	6.77	5.70 – Nov 1965	8.19 – Jul 1968	7.02	8.12	+1.10
Richard Nixon	1969–1974	10.57	7.80 – 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	7.70 – Dec 1986	19.33 – Jan 1981	19.33	9.72	-9.61
George H. W. Bush	1989–1992	10.68	9.64 – Sep 1989	14.47 – Nov 1990	10.07	10.30	+0.23
Bill Clinton	1993–2000	7.80	5.74 – Apr 1998	10.56 – Jan 1993	10.56	7.29	-3.27
George W. Bush	2001–2008	8.11	5.71 – Oct 2006	11.47 – Aug 2008	7.93	7.39	-0.54
Barack Obama	2009–2016	8.83	5.06 – Sep 2015	12.87 – Sep 2011	7.83	6.77	-1.06
Donald Trump	2017–Apr 2019 Incomplete data	6.29	5.32 – Feb 2019	7.44 – Feb 2017	7.30	5.60	-1.70

Variations

Harvard Economist Robert Barro created what he dubbed the "Barro Misery Index" (BMI), in 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.

Hanke has recently constructed a World Table of Misery Index Scores by exclusively relying on data reported by the Economist Intelligence Unit. 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.

Misery Index of 2016

Misery Index 2016
Ranked from worst to best
Rank	Country	Misery Index	Major Contributing Factor
1	Venezuela	573.4	Consumer Price
2	Argentina	83.8	Consumer Price
3	Brazil	75	Lending Rates
4	South Africa	44.9	Unemployment
5	Egypt	43.9	Consumer Price
6	Ukraine	36	Lending Rates
7	Azerbaijan	35.9	Consumer Price
8	Turkey	30.7	Lending Rates
9	Iran	29.3	Lending Rates
10	Colombia	28.7	Lending Rates
11	Greece	28.1	Unemployment
12	Russia	27.4	Lending Rates
13	Algeria	24.2	Unemployment
14	Kazakhstan	24.1	Lending Rates
15	Saudi Arabia	23	Unemployment
16	Peru	22.1	Lending Rates
17	Spain	19.1	Unemployment
18	Ecuador	18.4	Lending Rates
19	Indonesia	17.5	Lending Rates
20	India	17.1	Lending Rates
21	Portugal	15.5	Unemployment
22	Sri Lanka	14.8	Unemployment
23	Italy	14.7	Unemployment
24	Pakistan	14.4	Lending Rates
25	Chile	14.4	Unemployment
26	Mexico	12.5	Lending Rates
27	Belgium	11.8	Unemployment
28	Bulgaria	11.7	Unemployment
29	Poland	11.5	Unemployment
30	Canada	11.3	Unemployment
31	France	11.3	Unemployment
32	Australia	11.1	Unemployment
33	Finland	10.9	Unemployment
34	United Kingdom	10	Unemployment
35	Hong Kong	10	Lending Rates
36	Norway	9.8	Unemployment
37	Philippines	9.6	Unemployment
38	New Zealand	9.4	Lending Rates
39	United States	9.4	Unemployment
40	Vietnam	9.3	Lending Rates
41	Slovakia	9.2	Unemployment
42	Czech Republic	8.7	Unemployment
43	Ireland	8.6	Unemployment
44	Sweden	8.4	Unemployment
45	Austria	7.9	Unemployment
46	Denmark	7.8	Unemployment
47	Singapore	7.6	Lending Rates
48	Taiwan	7.1	Unemployment
49	Israel	6.8	Unemployment
50	Romania	6.4	Unemployment
51	Netherlands	6.3	Unemployment
52	Hungary	6.2	Unemployment
53	Malaysia	5.9	Lending Rates
54	Germany	5.5	Unemployment
55	Switzerland	5.5	Unemployment
56	South Korea	5.5	Unemployment
57	Thailand	4.9	Lending Rates
58	China	4.5	Lending Rates
59	Japan	3.8	Unemployment

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."

Misery and crime

Some economists 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. 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.