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The Political Economy of Institutions and
Economic Development
Mark Gradstein
Lecture 1: Setting the Stage: Modern
Economic Growth – Facts and Theories
Part I: Main Issues
- Why are some countries rich and other
countries are poor?
- The role of state institutions in economic
development
2
- Focus on long run (say, decades-long)
growth
- In particular, no attention to relatively
short run fluctuations (business cycles?)
3
- Abstract from the current economic
crisis in the West
- Abstract from economies primarily
relying on minerals
4
General growth facts and implications
(Maddison, 2001)
Levels of per capita GDP (1990 int dollars)
1820 1870
1913
1950
1973
2001
West
1109 1882
3672
5649
13082
22509
Rest
578
606
860
1091
2072
3372
West:Rest 1.9
3.1
4.3
5.2
6.3
6.7
5
6
Growth rates of per capita GDP (annual avg)
1820- 1870-
1913-
1950-
1973-
70
1913
1950
1973
2001
West 1.06
1.57
1.17
3.72
1.95
Rest
0.82
0.65
2.83
1.75
0.06
7
8
Percentage of population living on less than $1/day,
1990
East Asia
29.6
Europe and Central Asia
0.5
Latin America and Caribbean
11.3
Middle East and North Africa
2.3
South Asia
41.3
Sub-Saharan Africa
44.6
World Bank, 2005
9
The Great Divergence:
The ratio of GDP per capita between the
richest region and the poorest region in
the world was only 1.1:1 in the year 1000,
a 2:1 in the year 1500 and 3:1 in the year
1820.
10
In the course of the ‘Great Divergence’ the
ratio of GDP per capita between the richest
region (Western offshoots) and the poorest
region (Africa) has widened considerably
from a modest 3:1 ratio in 1820, to a 5:1
ratio in 1870, a 9:1 ratio in 1913, a 15:1 in
1950, and a huge 18:1 ratio in 2001.
11
The Industrial Revolution is a watershed
in initiating the “Great Divergence”.
But it was preceded by the expansion of
the Atlantic trade, which changed the
economic map of Europe
12
Basic facts:
- Modern growth – starting in late18th-early
19th century
- Rising living standards, post-Malthusian
regime in the West
13
- Divergence, big time – Lant Pritchett,
Galor…
- Growth rates over the medium run are very
volatile.
14
Growth Correlates
Physical Investment
HCapital components: health, education
Institutional Quality
15
16
17
Association between years of schooling and
growth
18
Average life expectancy (years at birth)
1820
1900
1950
1999
West 36
46
66
78
Rest
26
44
64
24
19
Corruption, risk of expropriation are
negatively correlated with the level of
development;
The quality of government services is
positively correlated with it.
20
Generally,
Economic wellbeing translates, albeit
indirectly, into quality measures (health,
longevity, education)
21
Then why do certain societies fail to
improve their technologies, invest more
in physical capital, and accumulate more
human capital?
22
E.g.,
South
Korea
and
Singapore
managed to grow, while Egypt and
Senegal failed to take advantage of
growth opportunities.
23
Why some countries stagnate during
certain periods and grow fast during
other periods?
24
E.g., China before and after 1978;
Argentina in the first half of the 20th
century and in the second half of it.
25
Why, in recent history, rich countries
grow, whereas poor countries do not
catch up?
26
Part II: Theories and Approaches
Research program: causes of growth
- What determines economic prosperity?
- How does this prosperity translate into human
well being?
- What is the role of policies and institutions to
achieve these goals?
27
Approaches to explain growth
- Geography (Jared Diamond)
- Culture (Max Weber)
- Neoclassical emphasis on physical investment
(Solow)
- Endogenous growth theories – innovation, human
capital (Lucas, Romer)
- Institutions
28
Naïve Geography
Hypotheses:
Growth is inversely related to the proximity
to the equator;
Landlocked countries are at a disadvantage
29
But: Early civilizations (Egypt, Middle
East) emerged in hot places
More generally, reversals between North
and
South
in
terms
development over time
30
of
economic
Sophisticated geography:
Location interacted with technology (e.g.,
fire, heating allowed cold countries to grow
fast) – related to Jared Diamond’s “Guns,
Germs, Steel”
But then why does technology develop the
way it does?
31
Physical investment
Solow’s model (aggregate variant)
Y = AF(k) (population is constant;
physical capital is the only production
factor)
F(.) is a neoclassical production function
(diminishing marginal returns)
32
Implications:
- Convergence to a steady state
- Initial conditions are immaterial
- Growth: via the residual
- Capital flows to poor countries
33
But: empirically, no convergence
(slight divergence, if at all, across
countries in recent years; Great
Divergence in the longer run)
No particular flows to poor countries.
34
Why does not capital (always) flow to
poor countries, where its productivity
should be large?
Why divergence in income levels across
countries, with persistent – perhaps
growing – inequality?
35
One
possibility:
human
capital
externalities.
Examples:
information
innovations; research ideas
36
exchanges;
In the presence of externalities, and with
complementarity
between
physical
and
human capital, the former will flow to
countries
with
high
reinforcing growth there.
37
human
capital,
This
is
also
differentials
consistent
for
same
with
people
wage
across
countries; and even across regions within a
country (take a software engineer in Silicon
Valley vs same in, say, Iowa)
38
Disaggregate variant of Solow
Successive generations of families (a parent
and a child); population size constant over
time;
Initial income level is given, y0
39
Family
BC
(allocation
between
consumption and capital – physical or
human)
yt = ct + kt+1
40
Production function:
yt = AktαKtβ, 0<α,β<1; β is the extent of the
spillover
(cap letters designate aggregate values)
41
Parents are altruistic:
U(ct,yt+1) = ln(ct) + ln(yt+1)
They allocate family budget to max own
utility
42
Solution through utility max:
ct = yt/(1+α),
kt+1 = Kt+1 = αyt/(1+α),
yt+1 = A[αyt/(1+α)]α+β
43
If α+β<1 (such as when β=0), Solovian
dynamics;
If α+β=1, constant growth rate;
If α+β>1, the growth rate increases along
the path
44
But: empirical evidence suggest that the
magnitudes
of
HC
externalities
are
relatively small – 1-4 percent.
In contrast, to account for the vast disparity
in living standards across countries, they
should
have
been
enormous.
[The
technique used is called growth accounting.]
45
An Aside on Growth Accounting
The objective of growth accounting is to
decompose the economic growth rate of a
country into contributions from different
factors.
46
Aggregate production function:
Yt = AtKαt Nt1−αt
the capital stock Kt and the size of the labor
force Nt, as well as
total factor productivity (TFP) At.
Only
capital
and
observable in the data.
47
labor
are
actually
Productivity (“residual”) serves as a catchall for anything else that is left unexplained
by the other two factors.
If GDP goes up by more than can be
explained through capital and labor alone,
we interpret the increase as caused by
productivity.
48
Under perfect competition among firms the
marginal product of each factor equals its
price (r – interest rate, w – wages);
and the capital and labor shares for the
economy are:
Capital Share = rtKt/Yt = α
Labor Share = wtNt/Yt = 1 − α
49
The actual capital share for a country can be
found in national income and product
statistics; in most industrialized countries,
the capital share is between 0.3 and 0.4,
with the labor share varying between 0.6
and 0.7.
50
We can now decompose economic growth
into separate contributions from capital,
labor, and productivity. Growth rates can
be computed as (natural) log-differences;
the growth rate of output in a given year, for
example, can be computed as
logYt+1 − log Yt.
51
Taking logs of the production function
gives:
log Yt = log At + α log Kt + (1 − α) log Nt
The growth rate of output can therefore be
expressed as:
logYt+1−log Yt = log At+1−log At + α (log
Kt+1−log Kt) + (1 − α) (log Nt+1 − log Nt)
52
The impact of any given factor of
production on output growth is proportional
to its share in output. Thus, if the capital
share is one-third and the labor share is twothirds, and if the capital stock increases by
three percent in a given year, this growth
would translate into just one percent growth
in output.
53
Can be extended to include human capital.
A measure of human capital is needed.
Standard: years of schooling (Barro-Lee).
The
resulting
growth
accounting
is
incompatible with changes in the number of
years of schooling.
54
Poor
countries
expanded
schooling
relatively more than richer countries – yet
have grown less.
Further, growth rates are volatile; years of
schooling are not.
55
Could be due to the measurement of human
capital: quantity vs quality.
Hanushek and Woessman suggest that the
latter may account for what the former fails
to do.
56
Their measure of the quality of human
capital is based on PISA (Program for
International Student Assessment) – across
country comparable results in math and
reading tests delivered to 15 y old students.
57
Test scores and long-run economic growth
Note: Added-variable plots of a regression of the average annual rate of growth (in percent) of
real GDP per capita in 1960–2000 on the initial level of real GDP per capita in 1960, average test
scores on international student achievement tests, and average years of schooling in 1960.
Source: Hanushek and Wößmann (2007)
58
But: reverse causality and omitted variables
are
both
serious
challenges
interpretation of the association.
59
to
the
Lant Pritchett; Bils and Klenow…:
Ec development brings about human capital
investments (not the other way around);
Many instances of futile investments in
education – in Africa and elsewhere.
60