The New Kaldor Facts: Ideas, Institutions, Population, and Human
Lecture 1: Basic Models of Growth - University of WarwickSome Kaldor™s Fact 1 Per Capita output...
Transcript of Lecture 1: Basic Models of Growth - University of WarwickSome Kaldor™s Fact 1 Per Capita output...
Lecture 1: Basic Models of Growth
Eugenio Proto
February 18, 2009
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 1 / 12
Some Kaldor�s Fact
1 Per Capita output grows over time, and its growth rate does not tendto diminish
2 Physical Capital per worker grows over time3 The growth rate of output per worker di¤ers substantially acrosscountries
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 2 / 12
Some Kaldor�s Fact
1 Per Capita output grows over time, and its growth rate does not tendto diminish
2 Physical Capital per worker grows over time
3 The growth rate of output per worker di¤ers substantially acrosscountries
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 2 / 12
Some Kaldor�s Fact
1 Per Capita output grows over time, and its growth rate does not tendto diminish
2 Physical Capital per worker grows over time3 The growth rate of output per worker di¤ers substantially acrosscountries
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 2 / 12
Di¤erent paths of Growth
0
10000
20000
30000
40000
50000
60000
70000
80000
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
UnitedStatesSpain
Argentina
Kaldor facts do not apply to stagnating countriesMacroeconomic growth consider growing countriesDevelopment and growth proceed ed separatelyGenerating a unique model for growth and development is still faraway
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 3 / 12
Di¤erent paths of Growth
0
10000
20000
30000
40000
50000
60000
70000
80000
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
UnitedStatesSpain
Argentina
Kaldor facts do not apply to stagnating countries
Macroeconomic growth consider growing countriesDevelopment and growth proceed ed separatelyGenerating a unique model for growth and development is still faraway
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 3 / 12
Di¤erent paths of Growth
0
10000
20000
30000
40000
50000
60000
70000
80000
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
UnitedStatesSpain
Argentina
Kaldor facts do not apply to stagnating countriesMacroeconomic growth consider growing countries
Development and growth proceed ed separatelyGenerating a unique model for growth and development is still faraway
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 3 / 12
Di¤erent paths of Growth
0
10000
20000
30000
40000
50000
60000
70000
80000
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
UnitedStatesSpain
Argentina
Kaldor facts do not apply to stagnating countriesMacroeconomic growth consider growing countriesDevelopment and growth proceed ed separately
Generating a unique model for growth and development is still faraway
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 3 / 12
Di¤erent paths of Growth
0
10000
20000
30000
40000
50000
60000
70000
80000
1950
1954
1958
1962
1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
UnitedStatesSpain
Argentina
Kaldor facts do not apply to stagnating countriesMacroeconomic growth consider growing countriesDevelopment and growth proceed ed separatelyGenerating a unique model for growth and development is still faraway
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 3 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdt
per cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � na
no ponzi game: limt!∞fa(t) exp[�R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)
increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,
Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Ramsey-Samuelson model
Household behavior
size: L(t) = ent
Utility U =R ∞0 u[c(t)]L(t)e
�ρtdtper cap. wealth acc.: a = w + ra� c � nano ponzi game: limt!∞fa(t) exp[�
R t0 [r(v)� n]dvg � 0
Firms�Behavior
Y = F (K , L) with L = L(t)T (t)increasing and concave in K and L,Constant Return to scale:
F (λK ,λL) = λF (K , L)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 4 / 12
Static Equilibria
Household Optimal choice:
maxcU(c)
w + ra� c � na � 0
limfa(t) exp[�Z t
0[r(v)� n]dvg � 0
Euler Condition
r = ρ+ [�u00(c)cu0(c)
] � ˙c/c
with u(c) = c1�θ�11�θ (CIES):
˙c/c = (1/θ)(r � ρ)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 5 / 12
Static Equilibria
Household Optimal choice:
maxcU(c)
w + ra� c � na � 0
limfa(t) exp[�Z t
0[r(v)� n]dvg � 0
Euler Condition
r = ρ+ [�u00(c)cu0(c)
] � ˙c/c
with u(c) = c1�θ�11�θ (CIES):
˙c/c = (1/θ)(r � ρ)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 5 / 12
Static Equilibria
Household Optimal choice:
maxcU(c)
w + ra� c � na � 0
limfa(t) exp[�Z t
0[r(v)� n]dvg � 0
Euler Condition
r = ρ+ [�u00(c)cu0(c)
] � ˙c/c
with u(c) = c1�θ�11�θ (CIES):
˙c/c = (1/θ)(r � ρ)
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 5 / 12
Static Equilibrium (cont�d)
Firms�optimal Choice
maxF (K , L)� (r + δ)K � wL =
max f (k)� (r + δ)k � wT (t)
FOCsf 0(k) = r + δ
[f (k)� k f 0(k)]ext = w
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 6 / 12
Static Equilibrium (cont�d)
Firms�optimal Choice
maxF (K , L)� (r + δ)K � wL =
max f (k)� (r + δ)k � wT (t)
FOCsf 0(k) = r + δ
[f (k)� k f 0(k)]ext = w
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 6 / 12
Steady state
Dynamics (k = a )
˙k = f (k)� c � (x + n+ δ)k˙c/c = (1/θ)(f 0(k)� δ� ρ� θx)
limfa(t) exp[�Z t
0[r(v)� n]dvg = 0
Equilibrium
˙c = 0! f 0(k�) = δ+ ρ+ θx˙k = 0! c� = f (k�)� (x + n+ δ)k�
with˙yy= α
˙kk= 0
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 7 / 12
Steady state
Dynamics (k = a )
˙k = f (k)� c � (x + n+ δ)k˙c/c = (1/θ)(f 0(k)� δ� ρ� θx)
limfa(t) exp[�Z t
0[r(v)� n]dvg = 0
Equilibrium
˙c = 0! f 0(k�) = δ+ ρ+ θx˙k = 0! c� = f (k�)� (x + n+ δ)k�
with˙yy= α
˙kk= 0
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 7 / 12
Steady state analysis
Growth in steady state
˙cc
=∂�C (t)e (n+x )t
�∂t
/C (t)e(n+x )t
= c � x = 0
˙kk
=∂�K (t)e (n+x )t
�∂t
/K (t)e(n+x )t
= k � x = 0
˙yy
= y � x = α˙kk= 0
y (per capita income) in steady state grows with T (t) = ext
GROWTH IS EXOGENOUS
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 8 / 12
Steady state analysis
Growth in steady state
˙cc
=∂�C (t)e (n+x )t
�∂t
/C (t)e(n+x )t
= c � x = 0
˙kk
=∂�K (t)e (n+x )t
�∂t
/K (t)e(n+x )t
= k � x = 0
˙yy
= y � x = α˙kk= 0
y (per capita income) in steady state grows with T (t) = ext
GROWTH IS EXOGENOUS
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 8 / 12
AK model
Consumer behavior exactly as in Ramsey
Firms behavior and static equilibrium
Y = AK
y = f (k) = Ak
capital = human capital, knowledge, public good...no raw labour , w = 0r = A� δ
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 9 / 12
AK model
Consumer behavior exactly as in Ramsey
Firms behavior and static equilibrium
Y = AK
y = f (k) = Ak
capital = human capital, knowledge, public good...no raw labour , w = 0r = A� δ
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 9 / 12
AK model
Consumer behavior exactly as in Ramsey
Firms behavior and static equilibrium
Y = AK
y = f (k) = Ak
capital = human capital, knowledge, public good...
no raw labour , w = 0r = A� δ
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 9 / 12
AK model
Consumer behavior exactly as in Ramsey
Firms behavior and static equilibrium
Y = AK
y = f (k) = Ak
capital = human capital, knowledge, public good...no raw labour , w = 0
r = A� δ
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 9 / 12
AK model
Consumer behavior exactly as in Ramsey
Firms behavior and static equilibrium
Y = AK
y = f (k) = Ak
capital = human capital, knowledge, public good...no raw labour , w = 0r = A� δ
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 9 / 12
AK modelSteady state
Dynamics (k = a )
k = (A� δ� n)� c/kc/c = (1/θ)(A� δ� ρ)
limfk(t)e�(A�δ�ρ)tg = 0
Equilibrium
c/c = cons
k/k = c/cy/y = k/k = cons
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 10 / 12
AK modelSteady state
Dynamics (k = a )
k = (A� δ� n)� c/kc/c = (1/θ)(A� δ� ρ)
limfk(t)e�(A�δ�ρ)tg = 0
Equilibrium
c/c = cons
k/k = c/cy/y = k/k = cons
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 10 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RKDepreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RKDepreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RK
Depreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RKDepreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RKDepreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .
De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with Human capital
Firms produceY = F (H,K ),
let Y = Kf (H/K )
Market determines RH ,RKDepreciation rates δH , δK
In equilibrium
f (H/K )� f 0(H/K )(1+H/K ) = δK � δH
unique value for H/K .De�ne A = f (H/K ) and we obtain a AK model
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 11 / 12
Model with learning by doing and spillover
Yi = F (Ki ,KLi ) = LiF (ki ,K )
K is the aggregate (physical or human) capital, since ki = k thenK = kL
F (k,K )/k = f (K/k) = f (L) and
F1(k,K ) = f (K/k)� f 0(K/k)Kk2iki = f (L)� f 0(L)L
private marginal product of capital is non decreasing in k..
c/c = (1/θ)[f (L)� Lf 0(L)� δ� ρ] constant
Generates long run growth
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 12 / 12
Model with learning by doing and spillover
Yi = F (Ki ,KLi ) = LiF (ki ,K )
K is the aggregate (physical or human) capital, since ki = k thenK = kL
F (k,K )/k = f (K/k) = f (L) and
F1(k,K ) = f (K/k)� f 0(K/k)Kk2iki = f (L)� f 0(L)L
private marginal product of capital is non decreasing in k..
c/c = (1/θ)[f (L)� Lf 0(L)� δ� ρ] constant
Generates long run growth
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 12 / 12
Model with learning by doing and spillover
Yi = F (Ki ,KLi ) = LiF (ki ,K )
K is the aggregate (physical or human) capital, since ki = k thenK = kL
F (k,K )/k = f (K/k) = f (L) and
F1(k,K ) = f (K/k)� f 0(K/k)Kk2iki = f (L)� f 0(L)L
private marginal product of capital is non decreasing in k..
c/c = (1/θ)[f (L)� Lf 0(L)� δ� ρ] constant
Generates long run growth
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 12 / 12
Model with learning by doing and spillover
Yi = F (Ki ,KLi ) = LiF (ki ,K )
K is the aggregate (physical or human) capital, since ki = k thenK = kL
F (k,K )/k = f (K/k) = f (L) and
F1(k,K ) = f (K/k)� f 0(K/k)Kk2iki = f (L)� f 0(L)L
private marginal product of capital is non decreasing in k..
c/c = (1/θ)[f (L)� Lf 0(L)� δ� ρ] constant
Generates long run growth
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 12 / 12
Model with learning by doing and spillover
Yi = F (Ki ,KLi ) = LiF (ki ,K )
K is the aggregate (physical or human) capital, since ki = k thenK = kL
F (k,K )/k = f (K/k) = f (L) and
F1(k,K ) = f (K/k)� f 0(K/k)Kk2iki = f (L)� f 0(L)L
private marginal product of capital is non decreasing in k..
c/c = (1/θ)[f (L)� Lf 0(L)� δ� ρ] constant
Generates long run growth
Eugenio Proto () Lecture 1: Basic Models of Growth February 18, 2009 12 / 12