Embed Size (px)
Citation preview
11
The relation between economic crises and
inequality: a long term perspective
A B Atkinson, Nuffield College, Oxford
Istat 2011
2
Based on joint research with Salvatore Morelli, University of Oxford, which forms part of the programme of the EMoD institute, directed by David Hendry and funded by INET.
3
1. Introduction: a two-way relationship?
2. Economic crises 1911-2010;
3. Which inequality of what?
4. Empirical evidence: case studies of Nordic and Asian crises;
5. Do crises lead to inequality?
6. Does inequality lead to crises?
7. What can we conclude so far?
44
1. Introduction: a two-way relationship?
Inequality
Economic crises:
financial crises andcollapses in output/consumption
5
DIFFERENT VIEWS:
How do crises affect inequality?
INEQUALITY FALLS: US 1929 Great Crash: “The upward drift [in inequality] accelerates from the turn of the century up to America’s entrance into World War I. Inequality fell between 1929 and the early years after World War II” (Williamson and Lindert, 1980, page 95).
“The share of years … that a country was exposed to a banking crisis has a substantive negative impact on top income shares” [5 year crisis reduces share of top 1 per cent by 1 percentage point]” (Roine, Vlachos and Waldenström, Journal of Public Economics, 2009).
6
How do crises affect inequality?
INEQUALITY RISES: Asian financial crisis of 1997: “After nearly a decade of either declining or stable trend since the mid 1980s, the family income inequality in Korea sharply increased in the course of the financial crisis, and remained high even after the economy recovered from the recession” (Lee, 2002).
“The current economic crisis has shown that it is the poor and vulnerable groups in society who are disproportionately affected by such shocks” (OECD, January 2011).
7
DIFFERENT VIEWS:
Does inequality increase the risk of crises?
NOT ON THE AGENDA: The indexes to three authoritative accounts of financial crises, by Kindleberger and Aliber (2005), Krugman (2009) and Reinhart and Rogoff (2009), contain neither “inequality” nor “income distribution”.
The US Financial Crisis Inquiry Commission, set up in 2009 to investigate “the most significant financial crisis since the Great Depression”, was charged with examining 22 specific areas. None of these refer to inequality.
8
Does inequality increase the risk of crises?
YES: According to Stiglitz, in the face of stagnating real incomes, households in the lower part of the distribution in the US borrowed to maintain a rising standard of living. This borrowing later proved unsustainable, leading to default and pressure on over-extended financial institutions.
According to Rajan, “growing income inequality in the United States stemming from unequal access to quality education led to political pressure for more housing credit. This pressure created a serious fault line that distorted lending in the financial sector.”
9
Does inequality increase the risk of crises?
YES: According to Fitoussi and Saraceno, “an increase in inequalities which depressed aggregate demand and prompted monetary policy to react by maintaining a low level of interest rate which itself allowed private debt to increase beyond sustainable levels. On the other hand the search for high-return investment by those who benefited from the increase in inequalities led to the emergence of bubbles. ... The crisis revealed itself when the bubbles exploded ... So although the crisis may have emerged in the financial sector, its roots are much deeper and lie in a structural change in income distribution that had been going on for twenty-five years” (2009, page 4).
1010
2. Economic crises 1911-2010Consider:
• systemic banking crises (not limited to a few banks);
• “collapses” in real consumption per capita.
The study of crises requires long run data: “a data set that covers only twenty-five years simply cannot give one an adequate perspective” (Reinhart and Rogoff, 2009).
It requires cross-country data: “to use history to gauge the probability and size distribution of macroeconomic disasters, it is hopeless to rely on the experience of a single country” (Barro, 2009, page 246).
11
The data challenge: Banking crisesWe have relied on three major sources to identify systemic banking crises:
• Bordo, Eichengreen, Klingebiel and Martinez-Peria, 2001;
• Reinhart and Rogoff, 2008, 2009 and Reinhart, 2010;
• Laeven and Valencia, 2009 and 2010.
They do not cover all the same countries or the same time periods, and they do not always agree.
We have applied a majoritarian criterion. Where there are only two entries (one data-base does not cover the country or period), and they disagree, we have in general included the crisis.
12
Figure 1 62 Banking crises in 25 countries over 100 years (exc war times)
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
US
Norway
Sweden
Finland
Iceland
India
Japan
Indonesia
Malaysia
Mauritius
Singapore
Argentina
Brazil
Australia
Canada
New Zealand
South Africa
France
Germany
Italy
Netherlands
Portugal
Spain
Switzerland
UK
13
The data challenge: Consumption crises
Barro defines consumption (or GDP) “disaster” as peak to trough decline of at least 10 per cent: e.g. consumption in Argentina fell from a peak in 1998 to a trough in 2002 by 22.5 per cent.
On the basis that perception of a “crisis” depends on expectations regarding the growth of consumption, we apply the Barro criterion before 1950, but a cut-off of 5 per cent after 1950.
14
Figure 2 55 consumption "collapses" in 25 countries over 100 years (exc war)
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
US
Norway
Sweden
Finland
Iceland
India
Japan
Indonesia
Malaysia
Mauritius
Singapore
Argentina
Brazil
Australia
Canada
New Zealand
South Africa
France
Germany
Italy
Netherlands
Portugal
Spain
Switzerland
UK
15
Systemic banking crisis
No systemic banking crisis
TOTAL
Consumption “collapse”
18 37 55
No consumption “collapse”
44
TOTAL 62
1616
3. Which inequality of what?
• Inequality of what? Earnings versus income versus consumption versus wealth;
• Snapshots versus lifetime outcomes; inequality of opportunity;
• Which part of the income parade should we be watching?
• Horizontal dimensions of inequality.
First need to clarify
17
Income
The income “parade”
Poverty line
“Middle class”
1. Overall inequality: Gini coefficient
3. Poverty rate
2. Top income share
Chartbook of economic inequality: 5 indicators
+ (4) top earnings decile and (5) top wealth share
18
Inequality: the data challenge• Crises are rare events, so that we need a long run of years;
• To explore the impact of a crisis, we need to be able to monitor change year by year: we need annual series;
• For the present crisis, we lack up-to-date distributional data for many countries;
• For past years, we cannot simply download annual series on inequality covering a range of countries;
• Data have to be pieced together from a variety of national sources; data for earlier parts of the century are hard (or impossible) to find;
• Priority given to time series consistency over cross-country comparability.
19
Figure 3 Banking crises for which distributional data
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
US
Norway
Sweden
Finland
Iceland
India
Japan
Indonesia
Malaysia
Mauritius
Singapore
Argentina
Brazil
Australia
Canada
New Zealand
South Africa
France
Germany
Italy
Netherlands
Portugal
Spain
Switzerland
UK
62 banking crises: distributional data for 35.
Nordic crises
Asian crises
20
Figure 4 consumption "collapses" for which distributional data
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
US
Norway
Sweden
Finland
Iceland
India
Japan
Indonesia
Malaysia
Mauritius
Singapore
Argentina
Brazil
Australia
Canada
New Zealand
South Africa
France
Germany
Italy
Netherlands
Portugal
Spain
Switzerland
UK
55 consumption collapses: distributional data for 33
21
Figure NO1 Economic crises and inequality in Norway 1911-2010
0
5
10
15
20
25
30
35
40
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
Vertical line indicates start of banking crises; rectangle shows consumption collapse (peak to trough)
Pe
r c
ent
100
110
120
130
140
150
160
170
Pe
r c
ent
Gini coefficient, equivalised (EU-scale) household income,weighted by personsShare of top 1 per cent in gross income
Per cent living in households with equivalised (EU-scale)disposable income below 60 per cent medianShare of top 1 per cent in total wealth
Earnings at top decile as % median, series 1 (RH scale)
Earnings at top decile as % median, series 2 (RH scale)
4. Empirical evidence: case studies of Nordic and Asian crises
22
Figure FIN1 Economic crises and inequality in Finland 1911-2010
0
5
10
15
20
25
30
35
40
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001Vertical line indicates start of banking crisis; rectangle shows consumption collapse (peak to trough)
Per
cen
t
100
110
120
130
140
150
160
170
180
190
200
Per
cen
t
Income Distribution Survey, equiv after tax income using EU scalehousehold income, weighted by persons
Share of top 1 per cent in gross income, series 1
Share of top 1 per cent in gross income, series 2
Per cent below 60 per cent of median
Top decile of earnings (RH scale)
23
Figure JA1 Economic crises and inequality in Japan
0
10
20
30
40
50
60
70
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
Vertical line indicates start of banking crisis
Per
cen
t
100
110
120
130
140
150
160
170
180
190
200
Per
cen
t
Gini coefficient, Income RedistributionSurvey
Share of top 1 per cent in gross income
Share of top 0.1 per cent in gross income
Per cent below 60% median
Wealth Gini coefficient
Earnings top decile as per cent of median(RH scale)
24
Figure SI1 Economic crises and inequality in Singapore 1911-2010
0
10
20
30
40
50
60
1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
Vertical line indicates start of banking crisis; rectangle shows consumption collapse (peak to trough)
Per
cen
t
100
125
150
175
200
225
250
Per
cen
t
Gini coefficient among employed population,series 1
Gini coefficient among households, ranked byincome from work, series 2
Gini coefficient among employed households,income from work after government benefits andtaxes, series 3Share of top 1 per cent in gross income
Share of top 10 per cent in gross income
Earnings at upper quintile as % median (RH scale)
25
Figure US1929 Window diagram
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5
Gini coefficient
Income share top 1 per cent, disposableincome (exc capital gains)
Income share top 1 per cent, disposableincome (inc capital gains)
Share of top 1 per cent in total wealth
Figure US2007 Window diagram
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5
-16.0
-12.0
-8.0
-4.0
0.0
4.0
8.0
Gini coefficient
Income share top 1 per cent, disposableincome (exc capital gains)Income share top 1 per cent, disposableincome (inc capital gains)Percent in poverty
Top decile as % median (Right hand scale)
Figure US1984-88 Window diagram
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5
-15.00
-12.00
-9.00
-6.00
-3.00
0.00
3.00
6.00
9.00
12.00
Gini coefficient
Income share top 1 per cent, disposable income (exc capitalgains)Income share top 1 per cent, disposable income (inc capitalgains)Percent in poverty
Share of top 1 per cent in total wealth
Top decile as % median (Right hand scale)
?
5. Do crises lead to inequality?
Window diagrams
26
After
\ = /
TOTAL
Before
/4 6 4 14
= 3 3 6 12
\ 1 2 2 5TOTAL
8 11 12 31
Did inequality rise before and fall after? Classification of 31 banking crises (4?).
ClassicUS 1929
Evidence from all 25 countries 1911-2010
27
After
\ = /
TOTAL
Before
/6 3 4 13
= 3 1 6 10
\ 4 2 2 8TOTAL
13 6 12 31
Did inequality rise before and fall after? Classification of 33 consumption collapses.
Evidence from all 25 countries 1911-2010
ClassicUS 1929
28
GINI coefficientIdentified crisis
No identified crisis
TOTAL
Higher inequality
2 5 7No higher inequality
4 10 14
TOTAL
6 15 21
Level of inequality in 2007 compared with ten years earlier and identification of a banking crisis in 2007-8
6. Does inequality lead to crises? Evidence from all countries 1911-2010
29
A matter of judgment:• classification of banking crises (B);
• classification of consumption collapses (B/A);
• identification of direction of change in inequality (C/B).
The data do not lend themselves to straightforward statistical tests.
30
1. Banking model, with competitive consumption:Increased demand for consumer borrowing to finance desired consumption to keep up with those whose earnings are rising faster; banks respond by raising rates but take on more risk. Change in inequality (top, overall and bottom) is causal.
2. Banking model, with introduction of securitization:Change in banking practices with introduction of securitization, taking on greater risk to an extent that is greater the higher the degree of inequality. Level of inequality (overall and bottom) is jointly causal.
3. Banking model, with shift in remuneration practices:Remuneration becomes tied more closely to sales, so that banks behave more like sales maximisers than maximisers of shareholder value, increasing the exposure to risk. Observe increased top inequality and increased risk of crisis. Co-incident, not causal.
Interpretation: Co-incident or causal?
31
4. Financial sector model, with bubbles:Asset bubble draws skilled workers into financial sector, causing wage dispersion to rise. Change in inequality (top) is co-incident, not causal.
5. Political economy model of monetary policy:In response to rise in inequality, uses deregulation of banking for distributional reasons. Change in inequality (overall and bottom) is causal.
6. Political economy model of deregulation:Increased inequality at the top leads to lobbying for deregulation. Change in inequality (top) is co-incident.
7. Political economy model of pensions:Government decides to reduce size of welfare state. Loss of income to current beneficiaries causes inequality to rise. Households respond by saving more in private pensions, driving up equity prices, and by “buy-to-let” purchases of housing, driving up house prices. Change in inequality (bottom) is co-incident, not causal.
3232
7. What can we conclude?
• Economic inequality has many dimensions; here focused on income and its components, but some of the most important dimensions of inequality may be those not measured, such as inequality of opportunity;
• Heterogeneity is important; different parts of the distribution may change differently: it depends which part of the parade we are watching; different parts are relevant to different explanatory models;
• The role of inequality in the origins of crises and the distributional impact of banking crises may differ over time: “this time it may be different”; in the US there was a rise in overall inequality leading up to the 1929 and S+L crises, but this was not the case with the present crisis, where the increase was at the top; on the other hand, in terms of levels of inequality, 2007 may be more like 1929 than the 1980s;
3333
7. What can we conclude (continued)• Outside the US, the history of crises in different countries round
the world does not suggest that either rising or high levels of inequality have been adduced as significant causal factors; there is a range of possible mechanisms, but it is not evident that there is a smoking gun;
• Cannot write off high inequality as a temporary feature of bubbles; in the US the only sustained period of inequality-reduction was in the early 1940s; quite a number of European (and Asian) countries have seen inequality and poverty rise after a banking crisis;
• On the other hand, it is not the case that there is a general upward trend in income inequality.
