Transcript Mankiw 5/e Chapter 6: Unemployment

The lessons of growth theory
…can make a positive difference in the
lives of hundreds of millions of people.
These lessons help us
 understand why poor
countries are poor
 design policies that
can help them grow
 learn how our own
growth rate is affected
by shocks and our
government’s policies
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Huge effects from tiny differences
In rich countries like the U.S.,
if government policies or “shocks”
have even a small impact on the
long-run growth rate,
they will have a huge impact
on our standard of living
in the long run…
slide 1
Huge effects from tiny differences
percentage increase in
standard of living after…
annual
growth rate
of income per
capita
…25 years
…50 years
…100 years
2.0%
64.0%
169.2%
624.5%
2.5%
85.4%
243.7%
1,081.4%
slide 2
Huge effects from tiny differences
If the annual growth rate of
U.S. real GDP per capita
had been just
one-tenth of one percent higher
during the 1990s,
the U.S. would have generated
an additional $449 billion of income
during that decade
slide 3
slide 4
International Evidence on Investment
Rates and Income per Person
Income p er
person in 1992
(logarithmic scale)
10 0, 00 0
Canada
Denmark Germany
U.S.
10 ,0 00
Mexico
Egypt
Fi nl and
Brazil
Pakist an
Ivory
Coast
U.K.
Israel
FranceItaly
Singapore
Peru
Indonesia
1, 00 0
Zimbabwe
India
Chad
10 0
Japan
0
Uganda
5
Kenya
Cameroon
10
15
20
25
30
35
40
Investment as p ercentage of output
(average 1960 –1992)
slide 5
Income p er
person in 1992
(logarithmic scale)
International Evidence on Population
Growth and Income per Person
100,000
Germany
U.S.
Denmark
Canada
Israel
10,000
U.K.
Italy
Japan
Fi nl and France
Mexico
Singapore
Egypt
Brazil
Pakist an
Peru
Indonesia
1,000
Cameroon
Ivory
Coast
Kenya
India
Zimbabwe
Chad
100
0
1
2
Uganda
3
4
Population growth (percent per year)
(average 1960 –1992)
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Examples of technological progress
 1970: 50,000 computers in the world
2000: 51% of U.S. households have 1 or more computers
 The real price of computer power has fallen an average of
30% per year over the past three decades.
 The average car built in 1996 contained more computer
processing power than the first lunar landing craft in 1969.
 Modems are 22 times faster today than two decades ago.
 Since 1980, semiconductor usage per unit of GDP has
increased by a factor of 3500.
 1981: 213 computers connected to the Internet
2000: 60 million computers connected to the Internet
slide 7
Policies to promote growth
Four policy questions:
1. Are we saving enough? Too much?
2. What policies might change the saving
rate?
3. How should we allocate our investment
between privately owned physical capital,
public infrastructure, and “human capital”?
4. What policies might encourage faster
technological progress?
slide 8
1. Evaluating the Rate of Saving
 Use the Golden Rule to determine whether
our saving rate and capital stock are too high,
too low, or about right.
 To do this, we need to compare
(MPK   ) to (n + g ).
 If (MPK   ) > (n + g ), then we are below the
Golden Rule steady state and should increase s.
 If (MPK   ) < (n + g ), then we are above the
Golden Rule steady state and should reduce s.
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1. Evaluating the Rate of Saving
To estimate (MPK   ), we use
three facts about the U.S. economy:
1. k = 2.5 y
The capital stock is about 2.5 times one
year’s GDP.
2.  k = 0.1 y
About 10% of GDP is used to replace
depreciating capital.
3. MPK  k = 0.3 y
Capital income is about 30% of GDP
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1. Evaluating the Rate of Saving
1. k = 2.5 y
2.  k = 0.1 y
3. MPK  k = 0.3 y
To determine  , divided 2 by 1:
k
0.1 y

k
2.5 y

0.1
 
 0.04
2.5
slide 11
1. Evaluating the Rate of Saving
1. k = 2.5 y
2.  k = 0.1 y
3. MPK  k = 0.3 y
To determine MPK, divided 3 by 1:
MPK  k
k
0.3 y

2 .5 y

0.3
MPK 
 0.12
2.5
Hence, MPK   = 0.12  0.04 = 0.08
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1. Evaluating the Rate of Saving
 From the last slide: MPK   = 0.08
 U.S. real GDP grows an average of 3%/year,
so n + g = 0.03
 Thus, in the U.S.,
MPK   = 0.08 > 0.03 = n + g
 Conclusion:
The U.S. is below the Golden Rule steady state:
if we increase our saving rate, we will have faster
growth until we get to a new steady state with
higher consumption per capita.
slide 13
2. Policies to increase the saving rate
 Reduce the government budget deficit
(or increase the budget surplus)
 Increase incentives for private saving:
 reduce capital gains tax, corporate income
tax, estate tax as they discourage saving
 replace federal income tax with a
consumption tax
 expand tax incentives for IRAs (individual
retirement accounts) and other retirement
savings accounts
slide 14
3. Allocating the economy’s investment
 In the Solow model, there’s one type of
capital.
 In the real world, there are many types,
which we can divide into three categories:
– private capital stock
– public infrastructure
– human capital: the knowledge and skills
that workers acquire through education
 How should we allocate investment among
these types?
slide 15
4. Encouraging technological progress
 Patent laws:
encourage innovation by granting temporary
monopolies to inventors of new products
 Tax incentives for R&D
 Grants to fund basic research at universities
 Industrial policy:
encourage specific industries that are key for
rapid tech. progress
(subject to the concerns on the preceding slide)
slide 16
CASE STUDY:
The Productivity Slowdown
Growth in output per person
(percent per year)
1948-72
1972-95
Canada
2.9
1.8
France
4.3
1.6
Germany
5.7
2.0
Italy
4.9
2.3
Japan
8.2
2.6
U.K.
2.4
1.8
U.S.
2.2
1.5
slide 17
Explanations?
 Measurement problems
Increases in productivity not fully measured.
– But: Why would measurement problems
be worse after 1972 than before?
 Oil prices
Oil shocks occurred about when productivity
slowdown began.
– But: Then why didn’t productivity speed up
when oil prices fell in the mid-1980s?
slide 18
Explanations?
 Worker quality
1970s - large influx of new entrants into
labor force (baby boomers, women).
New workers are less productive than
experienced workers.
 The depletion of ideas
Perhaps the slow growth of 1972-1995 is
normal and the true anomaly was the rapid
growth from 1948-1972.
slide 19
The bottom line:
We don’t know which of these
is the true explanation,
it’s probably a combination
of several of them.
slide 20
CASE STUDY:
I.T. and the “new economy”
Growth in output per person
(percent per year)
1948-72
1972-95
1995-2000
Canada
2.9
1.8
2.7
France
4.3
1.6
2.2
Germany
5.7
2.0
1.7
Italy
4.9
2.3
4.7
Japan
8.2
2.6
1.1
U.K.
2.4
1.8
2.5
U.S.
2.2
1.5
2.9
slide 21
CASE STUDY:
I.T. and the “new economy”
Apparently, the computer revolution didn’t affect
aggregate productivity until the mid-1990s.
Two reasons:
1. Computer industry’s share of GDP much
bigger in late 1990s than earlier.
2. Takes time for firms to determine how to
utilize new technology most effectively
The big questions:
 Will the growth spurt of the late 1990s continue?
 Will I.T. remain an engine of growth?
slide 22
Money supply measures, April 2002
_Symbol
C
Assets included
Amount (billions)_
Currency
$598.7
M1
C + demand deposits,
travelers’ checks,
other checkable deposits
1174.0
M2
M1 + small time deposits,
savings deposits,
money market mutual funds,
money market deposit accounts
5480.1
M3
M2 + large time deposits,
repurchase agreements,
institutional money market
mutual fund balances
8054.4
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The social costs of inflation
…fall into two categories:
1. costs when inflation is expected
2. additional costs when inflation is
different than people had expected.
slide 28
The costs of expected inflation:
1. shoeleather cost
 def: the costs and inconveniences of reducing
money balances to avoid the inflation tax.
   i
  real money balances
 Remember: In long run, inflation doesn’t
affect real income or real spending.
 So, same monthly spending but lower average
money holdings means more frequent trips to the
bank to withdraw smaller amounts of cash.
slide 29
The costs of expected inflation:
2. menu costs
 def: The costs of changing prices.
 Examples:
– print new menus
– print & mail new catalogs
 The higher is inflation, the more frequently
firms must change their prices and incur
these costs.
slide 30
The costs of expected inflation:
3. relative price distortions
 Firms facing menu costs change prices infrequently.
 Example:
Suppose a firm issues new catalog each January. As
the general price level rises throughout the year, the
firm’s relative price will fall.
 Different firms change their prices at different times,
leading to relative price distortions…
 …which cause microeconomic inefficiencies
in the allocation of resources.
slide 31
The costs of expected inflation:
4. unfair tax treatment
Some taxes are not adjusted to account for inflation,
such as the capital gains tax.
Example:
 1/1/2001: you bought $10,000 worth of Starbucks
stock
 12/31/2001: you sold the stock for $11,000,
so your nominal capital gain was $1000 (10%).
 Suppose  = 10% in 2001.
Your real capital gain is $0.
 But the govt requires you to pay taxes on your
$1000 nominal gain!!
slide 32
The costs of expected inflation:
4. General inconvenience
 Inflation makes it harder to compare nominal
values from different time periods.
 This complicates long-range financial planning.
slide 33
Additional cost of unexpected inflation:
arbitrary redistributions of purchasing power
 Many long-term contracts not indexed,
but based on e.
 If  turns out different from e,
then some gain at others’ expense.
Example: borrowers & lenders
• If  > e, then (r  ) < (r  e)
and purchasing power is transferred from
lenders to borrowers.
• If  < e, then purchasing power is transferred
from borrowers to lenders.
slide 34
Additional cost of high inflation:
increased uncertainty
 When inflation is high, it’s more variable and
unpredictable:
 turns out different from e more often, and
the differences tend to be larger (though not
systematically positive or negative)
 Arbitrary redistributions of wealth
become more likely.
 This creates higher uncertainty, which makes
risk averse people worse off.
slide 35
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Recent episodes of hyperinflation
10000
percent growth
1000
100
10
1
Israel
1983-85
Poland
1989-90
Brazil Argentina
Peru
Nicaragua Bolivia
1987-94 1988-90 1988-90 1987-91 1984-85
inflation
growth of money supply
slide 37
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Business Cycles
Business Cycles
– Business cycles are 2-year to 5-year
fluctuations around trends in real GDP and
other related variables
– A recession is a large fall in the growth of real
GDP and related variables
• A depression is an especially large
recession
slide 39
Business Cycles
slide 40
Real GDP Growth in the United States
10
Percent change
from 4 quarters
8
earlier
Average growth
rate = 3.5%
6
4
2
0
-2
-4
1960
1965
1970
1975
1980
1985
1990
1995
2000
slide 41
Recessions in the U.S. since World War II
Year and quarter
of peak in RGDP
Number of quarters
until trough in RGDP
Change in RGDP,
peak to trough (%)
1948:4
2
-1.7
1953:2
3
-2.7
1957:3
2
-3.7
1960:1
3
-1.6
1970:3
1
-1.1
1973:4
5
-3.4
1980:1
2
-2.2
1981:3
4
-2.9
1990:2
3
-1.5
No simple regular or cyclical pattern: output changes very
considerably in size and spacing
slide 42
Behavior of the Components of
Output in Recessions
Component of
GDP
Consumption
Durables
Nondurables
Services
Average
Share in GDP
(%)
Average Share in fall in GDP in
recessions relative to normal
growth (%)
8.4
25.8
29.5
15.6
11.2
9.1
4.7
10.7
0.7
20.9
11.7
40.6
Net Export
-0.4
-12.3
Gov’t Purchases
20.6
3.3
Investment
Residential
Business Fixed
Inventories
Fluctuations are distributed very unevenly over the
components of output
slide 43
Cyclical Behavior of
Key Macroeconomic Variables
Procyclical variable
– An economic variable that moves in the “same”
direction as aggregate economic activity
industrial production, consumption, investment,
employment, real wage, inflation, stock prices
Countercyclical variable
– An economic variable that moves in the “opposite”
direction as aggregate economic activity
unemployment
slide 44
Supply shocks
A supply shock alters production costs,
affects the prices that firms charge.
(also called price shocks)
Examples of adverse supply shocks:
 Bad weather reduces crop yields, pushing up
food prices.
 Workers unionize, negotiate wage increases.
 New environmental regulations require firms to
reduce emissions. Firms charge higher prices to
help cover the costs of compliance.
(Favorable supply shocks lower costs and prices.)
slide 45
CASE STUDY:
The 1970s oil shocks
 Early 1970s: OPEC coordinates a reduction in
the supply of oil.
 Oil prices rose
11% in 1973
68% in 1974
16% in 1975
 Such sharp oil price increases are supply shocks
because they significantly impact production
costs and prices.
slide 46
CASE STUDY:
The 1970s oil shocks
The oil price shock
shifts SRAS up,
causing output and
employment to fall.
In absence of
further price
shocks, prices will
fall over time and
economy moves
back toward full
employment.
P
P2
LRAS
B
SRAS2
A
P1
SRAS1
AD
Y2
Y
Y
slide 47
CASE STUDY:
The 1970s oil shocks
70%
12%
Predicted effects of
the oil price shock:
• inflation 
• output 
• unemployment 
60%
…and then a gradual
recovery.
10%
50%
10%
40%
8%
30%
20%
6%
0%
1973
4%
1974
1975
1976
1977
Change in oil prices (left scale)
Inflation rate-CPI (right scale)
Unemployment rate (right scale)
slide 48
CASE STUDY:
The 1970s oil shocks
60%
Late 1970s:
As economy was
recovering,
oil prices shot up
again, causing
another huge
supply shock!!!
14%
50%
12%
40%
10%
30%
8%
20%
6%
10%
0%
1977
1978
1979
1980
4%
1981
Change in oil prices (left scale)
Inflation rate-CPI (right scale)
Unemployment rate (right scale)
slide 49
CASE STUDY:
The 1980s oil shocks
40%
1980s:
A favorable
supply shock-a significant fall
in oil prices.
As the model
would predict,
inflation and
unemployment
fell:
10%
30%
8%
20%
10%
6%
0%
-10%
4%
-20%
-30%
2%
-40%
-50%
1982
1983
1984
1985
1986
0%
1987
Change in oil prices (left scale)
Inflation rate-CPI (right scale)
Unemployment rate (right scale)
slide 50