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Solow: Model and Measurement

Capital and labor share

f(kt,nt)=rtkt+wtnt=αf(kt,nt)ktkt+(1α)f(kt,nt)ntnt\begin{aligned} f(k_t,n_t) &= r_t k_t + w_t n_t \\ &= \alpha \frac{f(k_t,n_t)}{k_t}k_t + (1-\alpha)\frac{f(k_t,n_t)}{n_t}n_t \end{aligned}
α=rtktf(kt,nt)=1wtntf(kt,nt).\alpha = \frac{r_t k_t}{f(k_t,n_t)} = 1 - \frac{w_t n_t}{f(k_t,n_t)}.

Summary

Product approach:yt=ktαnt1αExpenditure approach:yt=ct+it+gt+(xtmt)Income approach:yt=rtkt+wtntLaw of motion:kt+1=(1δ)kt+it\begin{aligned} \text{Product approach:}\quad & y_t = k_t^\alpha n_t^{1-\alpha} \\ \text{Expenditure approach:}\quad & y_t = c_t + i_t + g_t + (x_t - m_t) \\ \text{Income approach:}\quad & y_t = r_t k_t + w_t n_t \\ \text{Law of motion:}\quad & k_{t+1} = (1-\delta)k_t + i_t \end{aligned}

Match the model to the data


Model and measurement

yt=rtkt+wtnty_t = r_t k_t + w_t n_t
α=rtktyt=1wtntyt\alpha = \frac{r_t k_t}{y_t} = 1 - \frac{w_t n_t}{y_t}

Model and measurement (continued)

δk=iδ=ik\delta k = i \qquad \Rightarrow \qquad \delta = \frac{i}{k}
δ=iy(ky)1\delta = \frac{i}{y}\left(\frac{k}{y}\right)^{-1}

Simplifying assumptions

  1. Closed economy

xt=mt=0x_t = m_t = 0
  1. Fixed savings rate

it=syti_t = s y_t

Reduced model

yt=f(kt,nt)yt=ct+ityt=rtkt+wtntkt+1=(1δ)kt+itit=syt\begin{aligned} y_t &= f(k_t,n_t) \\ y_t &= c_t + i_t \\ y_t &= r_t k_t + w_t n_t \\ k_{t+1} &= (1-\delta)k_t + i_t \\ i_t &= s y_t \end{aligned}

Per-capita formulation

yt=f(kt)yt=ct+ityt=rtkt+wtkt+1=(1δ)kt+itit=syt\begin{aligned} y_t &= f(k_t) \\ y_t &= c_t + i_t \\ y_t &= r_t k_t + w_t \\ k_{t+1} &= (1-\delta)k_t + i_t \\ i_t &= s y_t \end{aligned}

(All variables are per capita.)


The Solow model

δk=sy\delta k = s y
y=kαy = k^\alpha
k=(sδ)11αk^* = \left(\frac{s}{\delta}\right)^{\frac{1}{1-\alpha}}

Steady-state values

y=(sδ)α1αi=syc=(1s)yr=αδs\begin{aligned} y^* &= \left(\frac{s}{\delta}\right)^{\frac{\alpha}{1-\alpha}} \\ i^* &= s y^* \\ c^* &= (1-s)y^* \\ r^* &= \alpha \frac{\delta}{s} \end{aligned}

Studying the model numerically


Intertemporal choices


Question 1


Question 2


Question 3


Pseudo-code (Question 1)

α ← 0.35
δ ← 0.06
s ← 0.20
k* ← (s/δ)^(1/(1-α))

k1 ← 0.5 · k*
for t = 1,…,50:
    y_t ← k_t^α
    i_t ← s y_t
    k_{t+1} ← (1-δ)k_t + i_t

return k_51 / k*