Growth: the Solow model

NOTE: The present notebook is coded with quarto, in the R programming language. The white margin to the right will be used to display code output.

This document relies heavily on the {tidyverse} ecosystem of packages. We load the tidyverse below as a prerequisite for the rest of the notebook - along with a few other important libraries.

Because we use WEBR, the notebook takes time to load because it fetches the data from the World Bank API - it thus requires an internet connexion.

To install R packages on your own device, use the install.packages() function as below (commented on purpose).

# install.packages("tidyverse")

Once they are installed, we can call/load/activate them in the current session.

library(tidyverse)    # Package for data wrangling
library(readxl)       # Package to import MS Excel files
library(latex2exp)    # Package for LaTeX expressions
library(quantmod)     # Package for stock data extraction
library(WDI)          # To access World Bank data
library(gtsummary)    # To format regression output
library(ggsci)        # Color scales
library(tidyfit)      # Enhanced regressions
library(ggrepel)      # Better labels on plots 
library(plm)          # Panel models

\(\rightarrow\) Don’t forget that code flows sequentially. A random chunk may not work if the previous ones have have not been executed.

The content of the notebook is heavily inspired from the book Advanced Macro-economics - An Easy Guide.

1 Growth: a central question in economics

For decades, economists have sought to understand and explain the drivers of growth. Let us understand why by looking at long-run patterns of economic output.

The Maddison Project Database compiles data for GDP over long chronological depths.

Below, we focus on a subset of countries for which a lot of points are available.

temp <- tempfile()
link <- "https://dataverse.nl/api/access/datafile/421302"
# 2020 version below in comments
#link <- "https://www.rug.nl/ggdc/historicaldevelopment/maddison/data/mpd2020.xlsx"
#lt_gdp <- read_excel(temp, sheet = 3)
#lt_gdp <- read_excel(temp, sheet = 3)
#country_list <- c("France", "United States", "Italy", "Mexico", "Poland", "South Africa")
download.file(link, temp)
lt_gdp <- read_excel(temp, sheet = 3, skip = 2) |>
  pivot_longer(-year, names_to = "country", values_to = "gdppc")

country_list <- c("FRA", "USA", "ITA", "MEX", "POL", "ZAF")

lt_gdp |>
  filter(year > 1700, country %in% country_list) |>
  ggplot(aes(x = year, y = gdppc, color = country)) + geom_line() +
  theme_classic() + ylab("GDP per capita") +
  theme(legend.position = c(0.3, 0.5),
        axis.title.x = element_blank())

What we see is an exponential pattern in growth. And the central question is: how come???
The purpose of the sessions to come will be to propose theoretical models that seek to reproduce this salient fact.

NOTE: for long term estimates of GDP, we refer to this tool and the corresponding paper.

2 The baseline model

In this section, we present the Solow model of growth.

2.1 Notations and first properties

We model aggregate output \(Y\) as a function of two inputs: capital \(K\) and labor \(L\):

\[Y=F(K,L),\] where the function \(F\) must satisfy a set of properties (which define neoclassical production functions and are sometimes called the Inada conditions):

  1. first, \(\frac{\partial F}{\partial K}>0\) and \(\frac{\partial F}{\partial L}>0\): with more capital or more labor, we are able to produce more (makes sense!).
  2. second, however: \(\frac{\partial^2 F}{\partial^2 K}<0\) and \(\frac{\partial^2 F}{\partial L^2}<0\): the returns to increasing inputs are diminishing. Increasing one input will only lead you so far…
  3. third, \(F\) exhibits constant returns to scale: \(F(aK, aL)= aF(K,L)\): doubling both inputs will exactly double production.
  4. fourth, \(\underset{K \rightarrow 0}{\lim}\frac{\partial F}{\partial K}=\underset{L \rightarrow 0}{\lim}\frac{\partial F}{\partial L}=\infty\) (marginal product of inputs is arbitrarily large when inputs are very small, i.e., the first inputs have the most importance) and reversely, \(\underset{K \rightarrow \infty}{\lim}\frac{\partial F}{\partial K}=\underset{L \rightarrow \infty}{\lim}\frac{\partial F}{\partial L}=0\) (marginal product vanishes when inputs are too large).

Basically, there are three cases:
- \(F(aK, aL)= aF(K,L)\): constant returns to scale;
- \(F(aK, aL)> aF(K,L)\): increasing returns to scale;
- \(F(aK, aL)< aF(K,L)\): decreasing returns to scale.

The most common assumption is that returns to scale are constant.

One example below. We plot production as a function of labor force. As the labor force increases, production does too, but the marginal effect decays. In this example, with 25 people, I can produce a good worth 5M€, but 100 people will only produce 10M€ of goods. This is of course assuming constant capital. To further (more rapidly) increase the output, you would also need to put in more investment to the system.

tibble(labor = 1:100) |>
  ggplot(aes(x = labor)) + geom_function(fun = sqrt, color = "#2266DD") +
  theme_classic() + 
  geom_vline(xintercept = 25, linetype = 2) + 
  geom_vline(xintercept = 100, linetype = 3) + 
  geom_hline(yintercept = 5, linetype = 2) + 
  geom_hline(yintercept = 10, linetype = 3)

The most straightforward example of a function that satisfies these conditions is the Cobb-Douglas function with constant returns to scale:

\[Y=AK^\alpha L^{1-\alpha}, \quad \alpha\in(0,1),\] where \(A>0\) is some constant that can relate to technological capability or energy consumption. The value \(\alpha\) determines the relative importance of capital versus labor in output creation.
A generalization of Cobb-Douglas is \(Y=AK^\alpha L^{\beta}\) with \(\alpha+\beta \neq 1\)…

2.2 What some data says

Now, let’s have a closer look - with data! From the general Cobb-Douglas function \(Y = AK^aL^{b}\), we get: \[\log(Y)=\log(A)+a \log(K)+b\log(L), \tag{1}\]

which is a perfect form for a linear regression!
Below, we import the data and take a look at the univariate link between workforce size (proxy for labor) and GDP.

wb_growth <- WDI(                           # World Bank data
  indicator = c(
    "labor" = "SL.TLF.TOTL.IN",             # Labor force (# individuals)
    "savings_rate" = "NY.GDS.TOTL.ZS",      # Savings rate (% GDP)
    "pop" = "SP.POP.TOTL",                  # Population
    "pop_growth" = "SP.POP.GROW",           # Population growth
    "capital_formation" = "NE.GDI.TOTL.ZS", # Gross capital formation (% GDP)
    "gdp_percap" = "NY.GDP.PCAP.CD",        # GDP per capita
    "gdp" = "NY.GDP.MKTP.CD"                # Gross Domestic Product (GDP)
  ), 
  extra = TRUE,
  start = 1960,
  end = 2024) |>
  mutate(across(everything(), as.vector)) |>
  select(-status, -lending, -iso2c, -iso3c) |>
  filter(lastupdated == max(lastupdated)) |>
  arrange(country, year) |>
  mutate(capital_percap = capital_formation / labor)

wb_growth <- wb_growth |> filter(region != "Aggregates") # Remove continents & co. 

wb_growth |>
  ggplot(aes(x = labor, y = gdp, color = region)) + geom_point(size = 0.3, alpha = 0.5) + 
  scale_x_log10() + scale_y_log10(limits = c(10^7, 10^14)) + 
  geom_smooth(method = "lm", se = F) + theme_classic() +
  scale_color_d3() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(nrow = 4, byrow = TRUE)) +
  labs(title = "GDP & labor", caption = "Mind the log-log scale")

Gross capital formation is not necessarily exactly the best input, but it’s likely the one that is closest to a capital proxy in the World Bank data. So we use it anyway for the sake of providing some illustration.

Capital & investment

Usually, we are interested in the stock of capital, and not flows (net additions/subtractions). Often, we have the latter, which we write \(I_t\). Because we must assume a depreciation rate \(\delta\), the stock of capital is

\[K_t=(1-\lambda)K_{t-1}+I_t \tag{2}\]

(depending on timing, \(I_t\) can be replaced by \(I_{t-1}\)), and in the end,

\[K_t=(1-\delta)^tK_0 + \sum_{s=0}^tI_s (1-\delta)^{t-s}\] But this still requires \(K_0\)…
Note that reasonable values for \(\delta\) are ~5-6%.

What about quantities per capita? Let’s look at our capital proxy.

wb_growth |>
  ggplot(aes(x = capital_percap, y = gdp_percap, color = region)) + geom_point(size = 0.3, alpha = 0.5) + 
  scale_x_log10() + scale_y_log10(limits = c(100, 150000)) + 
  geom_smooth(method = "lm", se = F) + theme_classic() +
  scale_color_d3() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(nrow = 4, byrow = TRUE)) +
  labs(title = "GDP & capital per capita", caption = "Mind the log-log scale")

Clearly, the slopes are strange; the variable is likely not really suitable for this exercise.

We can then try to estimate the \(a\) and \(b\) constant in the model defined in Equation 1.

lm(log(gdp) ~ log(labor) + log(capital_formation), 
   data = wb_growth |> 
     filter(gdp > 0, labor > 0, capital_formation > 0) |>
     select(gdp, labor, capital_formation) |> na.omit()) |>
  tbl_regression(intercept = T)

Characteristic

Beta

95% CI

1

p-value
(Intercept) 8.2 7.8, 8.7 <0.001
log(labor) 0.93 0.91, 0.95 <0.001
log(capital_formation) 0.64 0.54, 0.74 <0.001
1

CI = Confidence Interval

In this case, as we did not impose constant returns to scale, we see that the latter are in fact increasing… with the data & model (& estimation method) we use of course.

In fact, there are potentially large differences across countries and years; hence a panel model would be more suitable. Below, we enforce two-way fixed effects (\(c_t\) and \(d_i\)):

\[\log(Y_{t,i})= a\log(K_{t,i})+b\log(L_{t,i})+c_t+d_i+e_{t,i}. \]

plm(
  formula = "log(gdp) ~ log(labor) + log(capital_formation)", 
  data = wb_growth |> 
    filter(gdp > 0, labor > 0, capital_formation > 0) |>
    mutate(country = as.factor(country)) |>
    select(gdp, labor, capital_formation, country, year) |> na.omit(),
  effect = "twoways",
  model = "within",
  index = c("country", "year")
) |>
  tbl_regression(intercept = T)

Characteristic

Beta

95% CI

1

p-value
log(labor) 0.53 0.47, 0.59 <0.001
log(capital_formation) 0.14 0.11, 0.17 <0.001
1

CI = Confidence Interval

Here the results are substantially different - and seem to point towards decreasing returns to scale.

2.3 A few simplifications

To further ease notations, we can use constant returns in the following way and focus on per capita output (e.g., assuming uniform competence across time and across the population). The idea is simply to equate labor and population and to write:

\[Y=F(K,L)= LF(K/L,1).\] We can then think in terms of per capita quantities, which we write in lowercase letters: \(y=Y/L\), \(k=K/L\). The model becomes simply: \[y = f(k),\] and, for instance with Cobb-Douglas, we get \[y=Ak^\alpha.\]

This is simpler, as their is only one input and the important parameter, \(\alpha\), features here only once!

2.4 Laws of motion

Until now, we have not mentioned an important dimension in models, time. Production occurs every year, but also every month, every week, and every day! First, let us fix things for labor (say, (active) population). The model is not intended to explain or focus on this particular variable, hence it makes a simple assumption: \[L_t=L(t)=e^{nt}\]

We then introduce an important notation: the differential operator with respect to time, represented with a dot: \[\dot{L}=\frac{\partial L}{\partial t}=ne^{nt} \quad \Longrightarrow \quad \frac{\dot{L}}{L}=n.\]

Clearly \(n\) is the growth rate of labor. Usually, it is assumed nonnegative, but who knows what surprises the future will reveal? Some countries already experience degrowth in population…

url <- "https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/ddf--datapoints--total_population_with_projections--by--geo--time.csv"
pop <- read_csv(url)
colnames(pop) <- c("country", "year", "pop")

pop |>
  filter(country %in% c("fra", "usa", "chn"), year > 1800) |>
  ggplot(aes(x = year, y = pop, color = country)) +
  geom_line() + theme_classic() +
  theme(axis.title = element_blank(),
        title = element_text(face = "bold"),
        legend.title = element_text(face = "bold")) +
  ggtitle("Population (log-scale)") +
  scale_y_log10()

The projections for 2100 reveal stark changes.

pop |>
  filter(year == 2100) |>
  arrange(desc(pop)) |>
  head(15) |>
  ggplot(aes(x = pop/10^6, y = reorder(country, pop))) +
  geom_col() + theme_classic() +
  theme(axis.title = element_blank(),
        title = element_text(face = "bold"),
        legend.title = element_text(face = "bold")) +
  ggtitle("Population in 2100 (in millions)")

In addition, \(n\) is fixed exogenously, but there are models that endogenize it so that it is determined/constrained by other factors.

The dot notation \(\dot{L}\) stands for the derivative of the \(L\) function with respect to time. It is the infinitesimal increase of \(L\) when \(t\) (time) increasing by just a tiny bit. Importantly, this assumes that time is continuous. This is somewhat far-fetched when we think in terms of data, as many indicators are only available on a yearly basis, with a few at the monthly frequency (e.g., inflation).

Nevertheless, with the advent of big data and nowcasting, higher frequencies are becoming more common. For instance, the OECD used to propose a real-time GDP tracker that was updated at a weekly rate, but it seems this project was terminated.

The most interesting part of the model pertains to capital (it’s the only variable driving output). Indeed, we have: \[\dot{y}=\frac{\partial }{\partial t}Ak(t)^\alpha = \alpha A \dot{k} k^{\alpha-1}=\alpha A \dot{k}\frac{k^\alpha}{k}=\alpha \frac{\dot{k}}{k}y \quad \Leftrightarrow \quad \frac{\dot{y}}{y}=\alpha \frac{\dot{k}}{k} .\]

The (relative) growth rate of output is proportional to that of capital (per capita).

It is assumed that capital dynamics are driven by two opposite forces: investment (in production apparatus) and capital depreciation:

\[\dot{K}=\frac{\partial K}{\partial t}= sY-\delta K \ \Leftrightarrow \ \frac{\dot{K}}{L}=sy-\delta k , \tag{3}\]

where \(s \in [0,1]\) is called the savings rate and measures how much of the current output value is reinvested (in the economy as a whole here). The constant \(\delta>0\) is the rate at which capital value decays.

Now, it’s easier to use the simple model, the one that considers per capita quantities. What’s the law of motion of \(K/L\)? Because \(\dot{L}=n L\), it’s \[\dot{k}=\frac{\partial (K/L)}{\partial t}=\frac{\dot{K}L-K\dot{L}}{L^2}=\frac{\dot{K}-Kn}{L}=\dot{K}/L-kn.\] If we plug this into (Equation 3), we get \[\dot{k}=sy - k (\delta + n), \tag{4}\]

where we see that the dynamics of per capita capital is: saved capital minus a depreciation rate \(\delta + n\).

2.4.1 Illustration

Below, we enforce a savings rate of 10%, n=0.02 and \(\delta=0.05\). We have \(k^*=sy/(n+\delta)=sA(k^*)^\alpha/(n+\delta)\), which implies \(k^*=(sA/(n+\delta))^{1/(1-\alpha)}\).
We work with \(\alpha = 1/2\) and \(A=1\) for simplicity.

s <- 0.1
n_delta <- 0.07
k_star <- (s/(n_delta))^2
tibble(x = 0:10) |>
  ggplot(aes(x = x)) +
  geom_function(fun = function(x) 0.1*(x^0.5), color = "#EE4422") + 
  geom_function(fun = function(x) 0.07*x, color = "#EEBB11") + 
  theme_classic() + xlab("k (capital)") + 
  theme(axis.title.y = element_blank(),
        axis.text = element_blank()) +
  geom_vline(xintercept = k_star, linetype = 2, color = "grey") + 
  geom_hline(yintercept = sqrt(k_star)*s, linetype = 2, color = "grey") +
  geom_label(aes(x = 9, y = 0.3), label = "saved output", color = "#EE4422", size = 3, fill = "white") +
  geom_label(aes(x = 9, y = 0.6), label = "depreciation", color = "#EEBB11", size = 3, fill = "white")

3 Steady states

3.1 Balanced growth paths, focused on capital

Balanced growth paths (BGP) are parameter combinations that lead to constant (relative) growth rates. Because the key quantities (growth rates) do not change anymore, this can be perceived as a form of equilibrium. We are interested in these situations as it characterizes a stable version of the model - they are also called steady states. For instance, in the specification outlined above, this would mean that \(\dot{k}/k\) is constant. Given Equation 4, it is equal to \[\gamma_k=\frac{\dot{k}}{k}=\frac{sy}{k}-(n+\delta), \tag{5}\] hence, we see that the relative rate still depends on \(k\). For it to be constant, we would need \(k\) to be constant too and hence, this requires \(\dot{k}=0\)! In this case, the BGP implies that all quantities be constant, which seems rather unrealistic.

The main takeaway

This leads to the main conclusion: the Solow models does not generate endogenous growth in equilibrium!

In the above example, the equilibrium point is where the red and yellow curves meet, i.e., where the increase obtained from the saved output is offset by the natural depreciation of the capital.

If we take the Cobb-Douglas example, for which \(y=Ak^a\), the condition is \(sy - k (\delta + n)\), i.e, \[sAk^a=k(\delta +n) \Longleftrightarrow k^{a-1}=\frac{\delta + n}{sA} \Longleftrightarrow k^*=\left(\frac{sA}{\delta + n} \right)^{1/(1-a)} \tag{6}\]

Below, we plot \(sy/k\) and \(n+\delta=0.01+0.05=0.06\) (population growth and capital depreciation), with \(y=Ak^{1/2}\), \(A=1\) and \(s=0.1\). The point where the curves cross is \(s/k^{1/2}=n+d\), i.e., \(k=(s/(n+d))^2\) in our example. It marks the zone where capital will increase versus the one where it will shrink.

n_d <- 0.06
a <- 0.5
s <- 0.1
solution <- (s/n_d)^2
tibble(x = 1:10) |>
  ggplot(aes(x = x)) + geom_function(fun = function(x) (s/(x^a)), color = "#2266DD") +
  theme_classic() + xlab("k (capital)") + 
  theme(axis.title.y = element_blank(),
        axis.text = element_blank()) +
  geom_vline(xintercept = solution, color = "grey") + 
  annotate("text", label = "capital \nshrinks \nto the \nright", vjust = 1,
           x = solution + 0.1, y = s, color = "grey", hjust = 0, size = 3) +
  annotate("text", label = "capital \ngrows \nto the \nleft", vjust = 1,
           x = solution - 0.1, y = s, color = "grey", hjust = 1, size = 3) +
  geom_hline(yintercept = n_d, linetype = 2, color = "#EEBB11") +
  annotate("text", label = "k*", x = solution + 0.1, y = n_d, hjust = 0, vjust = -0.5) + 
  geom_label(aes(x = 9, y = 0.035), label = "sy/k", color = "#2266DD", size = 3, fill = "white") +
  geom_label(aes(x = 9, y = n_d), label = "n+d", color = "#EEBB11", size = 3, fill = "white")

We recall that, in the model, the capital, \(k\), is the key driver of growth. Its growth rate, \(\gamma_k\) in Equation 5.

To reach the steady state (BGP), it is required that \(\dot{k}>0\) when \(k<k^*\) or \(\dot{k}<0\) when \(k>k^*\). In both cases, this means

\[\left. \frac{\partial \dot{k}}{\partial k} \right|_{k=k^*}<0.\] In the Cobb-Douglas model, from (Equation 4), we have

\[\frac{\partial \dot{k}}{\partial k}=sy'(k)- (n+\delta) = s (A\alpha k^{\alpha -1}) - (n+\delta),\] and substituting (Equation 6),

\[sA\alpha \left( \left(\frac{sA}{\delta + n} \right)^{1/(1-a)} \right) ^{\alpha-1}-(n+\delta)=sA\alpha\left(\frac{sA}{n+\delta} \right)^{-1}-(n+\delta)=(\alpha-1)(n+\delta).\] To be negative, this expression requires \(\alpha <1\), i.e., diminishing returns.

3.2 Sensitivity analysis

What if the output increases, e.g., if the productivity factor \(A\) becomes larger by 30%?

tibble(x = 1:10) |>
  ggplot(aes(x = x)) + theme_classic() + xlab("k (capital)") +
  geom_function(fun = function(x) (s/(x^a)), color = "#427FEE") +
  geom_function(fun = function(x) (1.3*s/(x^a)), color = "#0B42A3") +          
  theme(axis.title.y = element_blank(),
        axis.text = element_blank()) +
  geom_vline(xintercept = solution, color = "grey") +
  geom_vline(xintercept = (1.3*s/n_d)^2, color = "#444444") +
  geom_hline(yintercept = n_d, linetype = 2, color = "#EEBB11") +
  annotate("text", label = TeX("$k_1^*$"), x = solution + 0.1, y = n_d, hjust = 0, vjust = -0.2) + 
  annotate("text", label =  TeX("$k_2^*$"), x =  (1.3*s/n_d)^2 + 0.1, y = n_d, hjust = 0, vjust = -0.2) + 
  geom_label(aes(x = 9, y = 0.035), label = "sy/k", color = "#427FEE", size = 3, fill = "white") +
  geom_label(aes(x = 9, y = 0.045), label = "1.3sy/k", color = "#0B42A3", size = 3, fill = "white") +
  geom_label(aes(x = 9, y = n_d), label = "n+d", color = "#EEBB11", size = 3, fill = "white")

Optimal capital stock increases temporarily. Over a long horizon, the relative growth rate \(\gamma_k\) will shrink back to zero, but the levels of capital and output will have risen (permanently) by some strictly positive amount. A similar pattern would occur if the savings rate \(s\) increased suddenly. Such shocks can for instance come from government policies.

3.3 What about consumption?

In the model, consumption is what is not saved, i.e., \(c=(1-s)y\).

Upon equilibrium, the growth rate in Equation 5 is equal to zero, hence \[sy^* =k^*(\delta+n) \tag{7}\] and long-term consumption is \[c^*=y^*-(\delta + n)k^*,\] but in this expression, \(y^*\) and \(k^*\) depend on the savings rate \(s\); see for instance Equation 6: \(k^*=\left(\frac{sA}{\delta + n} \right)^{1/(1-a)}\). The long-term (steady) consumption can thus be influenced by \(s\). Since \(y=Ak^a\), this gives \[c^*= A\left(\frac{sA}{\delta + n} \right)^{a/(1-a)}-(\delta+n)\left(\frac{sA}{\delta + n} \right)^{1/(1-a)}.\]

We illustrate this shape in the plot to the right. We see that there is in fact an optimal level for \(s\)… perhaps this should be endogenized directly within the model with agents that maximize their consumption stream (more on that later!).

c_star <- function(s, a, A, d_n){
  A*(s*A/n_d)^(a/(1-a)) - n_d*(s*A/n_d)^(1/(1-a))
}

tibble(s = seq(0.1,0.9, by = 0.1)) |>
  ggplot(aes(x = s)) + theme_classic() + xlab("s (savings rate)") + ylab("consumption") + 
  geom_function(fun = c_star, args = list(a = 0.5, A = 1, d_n = 0.06), aes(color = "a = 0.5")) +
  geom_function(fun = c_star, args = list(a = 0.6, A = 1, d_n = 0.06), aes(color = "a = 0.6")) +          
  theme(legend.position = c(0.5, 0.5),
        axis.text = element_blank())

4 The cross-section of income

Ok, so the model is not great at generating stable long-term growth. But maybe it can explain differences in GDP per capita across countries…

We recall the optimal from Equation 7 and Equation 6: \[y^*=A^{1/(1-\alpha)}\left( \frac{s}{n+\delta} \right)^{\alpha/(1-\alpha)}.\]

If we take the log,

\[\log(y^*) = \frac{1}{1-\alpha}\log(A) + \frac{\alpha}{1-\alpha}\log\left(\frac{s}{n+\delta} \right)\]

Capital depreciation is hard to measure, so let’s fix it at \(\delta=0.1\) (a slightly high value).

wb_growth <- wb_growth |> 
  group_by(country) |>
  mutate(pop_growth = mean(pop_growth, na.rm = T)) |>
  ungroup() |>
  mutate(x = log(savings_rate/(0.1 + pop_growth)))
lm(log(gdp_percap) ~ x, 
   data = wb_growth |> 
     filter(gdp_percap > 0, x > 0) |>
     select(gdp_percap, x) |> na.omit()) |>
  tbl_regression(intercept = T)

Characteristic

Beta

95% CI

1

p-value
(Intercept) 5.5 5.4, 5.6 <0.001
x 0.97 0.94, 1.0 <0.001
1

CI = Confidence Interval

So we have that

\[\frac{\log(A)}{1-\alpha}=5.5, \quad \frac{\alpha}{1-\alpha} = 0.97\]

This gives \(\alpha=0.49\) and \(A=e^{2.7}=14.8\).

If we use time-series data to estimate country-specific \(\alpha\) and \(A\) (total productivity factor).

wb_growth |>
  filter(gdp_percap > 3000, is.finite(capital_percap)) |>
  group_by(country) |>
  mutate(n = n()) |>
  filter(n > 32) |>
  group_by(country) |>
  regress(log(gdp_percap) ~ log(capital_percap), m("lm")) |>
  coef() |>
  select(country, term, estimate) |>
  pivot_wider(names_from = "term", values_from = "estimate") |>
  mutate(alpha = exp(`log(capital_percap)`),
         A = exp(`(Intercept)`)) |>
  filter(alpha > 0.01, alpha < 0.99) |>
  ggplot(aes(x = alpha, y = A)) + geom_point() + geom_text_repel(aes(label = country)) +
  scale_x_log10() + scale_y_log10() + theme_classic()

We only show the countries for which \(\alpha \in (0,1)\) but even this leads to \(A\) taking very different values.
Somewhat surprisingly, the link between the two values is positive.

Lastly, let’s try a hybrid approach via a panel model in which the alpha will be common to all countries but the fixed effects coding the \(A_i\) differ…

fit <- plm(
  formula = "log(gdp_percap) ~ x", 
  data = wb_growth |> 
    filter(gdp_percap > 0, labor > 0, savings_rate > 0) |>
    mutate(country = as.factor(country)) |>
    mutate(x = log(savings_rate/(0.05 + pop_growth))) |>
    select(gdp_percap, labor, country, year, x) |> na.omit(),
  effect = "twoways",
  model = "within",
  index = c("country")
) 
fit |>  tbl_regression(intercept = T)

Characteristic

Beta

95% CI

1

p-value
x 0.08 0.06, 0.10 <0.001
1

CI = Confidence Interval

The result is very different again. \(\alpha\) is surprisingly low. The productivity factors reflect variations in GDP per capita.

alpha <- 0.08/(1+0.08)
exp(fixef(fit)*(1-alpha)) |> as.list() |> data.frame() |> 
  pivot_longer(everything(), names_to = "country", values_to = "A") |>
  head(12)
country A
Albania 927.8194
Algeria 699.2470
Angola 728.5867
Argentina 1767.5416
Armenia 911.9709
Australia 6748.4841
Austria 6435.0851
Azerbaijan 459.7826
Bahamas..The 4637.4428
Bahrain 3551.2624
Bangladesh 186.1108
Barbados 2623.0058

5 Simulation: a try

Let’s see what happens if we try to simulate the motion of capital:

\[\dot{k}_t=s Ak_t^\alpha - k_t (\delta + n),\]

A simulation is not necessarily obvious; here we follow the Euler discretization scheme (with unit step) and use \(A=1\).
We wrap the code in a function that depends on the important parameters and we allow for some randomness (just out of curiosity).

s <- 0.1       # Savings rate
delta <- 0.05  # Capital depreciation
n <- 0.02      # Population growth
alpha <- 0.8   # Cobb-Douglas parameter
n_k <- 100     # Number of points

gen_k <- function(alpha, s, delta, n, sigma){
  k_dot <- 0
  k <- 100  
  for(t in 2:100){
    k_dot[t] <- s*k[t-1]^alpha - k[t-1]*(delta+n) + sigma*rnorm(1)
    k[t] <- k[t-1] + k_dot[t]
  }
  k
}

And plot the result for two values of the savings rate (without randomness).

If the savings rate is too low, the capital stock decreases…
We show the equilibrium value \(k^*\) with the horizontal line.

What happens with a bit of noise?

6 Takeaways

\(\rightarrow\) The Solow model does not generate (steady) growth endogenously. It is not able to produce one of the most salient stylized fact in macroeconomics… The next sessions will try to propose extensions that intend to address this limitation.