econ.studio
Solow–Swan Growth Model
Section 7 of 9
Section 7

Comparative Statics, Evidence, and Extensions

How parameters shift the steady state

The steady-state formulas k=(s/(n+g+δ))1/(1α)k^* = \bigl(s/(n+g+\delta)\bigr)^{1/(1-\alpha)} and y=(s/(n+g+δ))α/(1α)y^* = \bigl(s/(n+g+\delta)\bigr)^{\alpha/(1-\alpha)} let us read off how each parameter moves the long-run equilibrium.

**Savings rate ss**: a higher ss shifts the investment curve sf(k)sf(k) upward. At the old kk^* actual investment now exceeds break-even investment, so kk rises until a new, higher kk^* is reached. Output per worker is permanently higher, but the long-run growth rate of yy returns to gg. Saving more lifts the level, not the growth rate.

**Population growth nn**: a higher nn steepens the break-even line (n+g+δ)k(n+g+\delta)k. Each unit of capital must now be spread across more workers, so less remains per worker in steady state. High population growth \Rightarrow lower kk^* and yy^*.

**Technology growth gg**: a higher gg also steepens the break-even line — each unit of technology needs more capital to stay current — so kk^* in effective-worker terms falls. But gg directly raises the growth rate of Y/LY/L, so living standards rise faster. Higher gg lowers the level of kk^* but raises the long-run growth rate of per-worker output.

**Depreciation δ\delta**: faster depreciation raises the effective cost of maintaining capital, steepening the break-even line and lowering kk^* and yy^*. Countries with faster-depreciating capital stocks are poorer in steady state.

Comparative statics summary

Parameter changeEffect on k*Effect on y*Effect on long-run growth rate of Y/L
ss \uparrow++0 (positive only during transition)
nn \uparrow--0
gg \uparrow--++
δ\delta \uparrow--0
α\alpha \uparrow++0

The central question: can differences in saving explain cross-country income gaps?

The richest countries are roughly 30 to 60 times richer than the poorest. Can the Solow model explain this through differences in saving rates? The answer is a clear no — and understanding why is the single most important lesson the model teaches.

The elasticity argument

From y=(s/(n+g+δ))α/(1α)y^* = \bigl(s/(n+g+\delta)\bigr)^{\alpha/(1-\alpha)}, the elasticity of yy^* with respect to ss is:

εy,s=α1α12(with α=13)\varepsilon_{y^*,s} = \frac{\alpha}{1-\alpha} \approx \frac{1}{2} \quad (\text{with } \alpha = \tfrac{1}{3})

This means a **1% rise in ss raises yy^* by only about 12\tfrac{1}{2}%.** The square-root relationship is a direct consequence of diminishing returns to capital.

Now run the numbers. Suppose a rich country saves 40% and a poor country saves 10% — a 4-to-1 ratio in saving rates. The predicted income ratio is 41/2=24^{1/2} = 2. That is a 2× income gap, not 30–60×.

To generate a 30× income gap through saving alone, you would need srich/spoor=302=900s_{\text{rich}}/s_{\text{poor}} = 30^2 = 900. Saving rates are bounded between 0 and 1. The math is impossible.

Steady-state income y* as a function of the saving rate s

With α=1/3\alpha = 1/3 the elasticity of yy^* with respect to ss is α/(1α)=12\alpha/(1-\alpha) = \tfrac{1}{2}. A 4× difference in saving rates (10% vs 40%) produces only a **2× difference in yy^***. To generate the observed 30–60× income gaps through saving alone would require saving-rate differences of 302=90030^2 = 900 to 602=360060^2 = 3600 — impossible. The gap must lie elsewhere: in technology AA.

The Lucas Paradox: the flip side of the same coin

There is a second way to see the same problem. If income gaps were really due to capital differences, poor countries would have very little capital and therefore — by diminishing returns — very high marginal products of capital (MPK).

With α=1/3\alpha = 1/3, MPK =αkα1=αy/k= \alpha k^{\alpha - 1} = \alpha y / k. If the US–India capital-per-worker ratio is 20:1, the predicted MPK ratio is 202/37.420^{2/3} \approx 7.4. Capital should flood into poor countries, equalising returns.

In reality, capital does not flow to poor countries at anything like this rate. Robert Lucas (1990) called this the Lucas Paradox. Possible resolutions: institutional weakness, sovereign risk, missing human capital, and information asymmetries all reduce the effective return to capital in poor countries — consistent with the view that AA differs, not just kk.

Growth accounting and the Solow residual

Solow (1957) turned the model into a measurement tool. Starting from Y=Kα(AL)1αY = K^\alpha (AL)^{1-\alpha}, take logs and differentiate with respect to time:

Y^=αK^+(1α)L^+(1α)g\hat{Y} = \alpha \hat{K} + (1-\alpha)\hat{L} + (1-\alpha)g

Rearranging gives the Solow residual — the portion of output growth not explained by measured capital and labour:

TFP growth=Y^αK^(1α)L^\text{TFP growth} = \hat{Y} - \alpha \hat{K} - (1-\alpha)\hat{L}

For the US from 1909–1949, Solow found that roughly 87% of output growth per worker came from the residual (technology), and only 13% from capital deepening. This result both confirmed the model's prediction that technology is the engine of long-run growth and reinforced the central question: if capital explains only 13% of US growth, it cannot explain 30–60× cross-country income gaps.

What the residual measures
Everything that raises output beyond what is predicted by capital and labour inputs: technological progress, organisational improvements, learning-by-doing, better resource allocation, and — importantly — our measurement errors.
Limitations of the residual
It measures ignorance as much as technology. Human capital mismeasurement, variable factor utilisation, economies of scale, and sectoral reallocation all inflate the residual. Mankiw, Romer, and Weil (1992) showed that adding human capital dramatically reduces it.

No long-run growth without technology

Capital accumulation alone cannot sustain long-run per-worker growth. As kk rises, diminishing returns drive f(k)f'(k) down. Eventually the return to additional investment falls below the break-even rate and k˙\dot k drops to zero.

Sustained growth in living standards requires g>0g > 0. But gg is exogenous in this model — it arrives from outside, like manna. The model tells us technology matters enormously; it does not explain where technology comes from. This silence is the primary motivation for endogenous growth theory.

Conditional convergence

Countries with the same fundamentals (ss, nn, gg, δ\delta, α\alpha) but different starting points converge to the same steady state. Poorer ones grow faster because they are further below kk^*, where diminishing returns have not yet fully bitten.

This is conditional convergence: a country grows faster the further it is below its own kk^*. A poor country with a low savings rate is racing toward its own lower steady state, not toward the US. Cross-country regressions confirm conditional (but not unconditional) convergence.

Critiques and limitations

Exogenous technology
The model takes gg as given. It tells us long-run growth requires technological progress but gives no theory of where progress comes from. This gap motivated endogenous growth theory (Romer 1986, 1990; Lucas 1988).
Exogenous savings rate
Real households optimise intertemporally. The Ramsey–Cass–Koopmans model replaces the fixed ss with a utility-maximising household, making the savings rate respond to fundamentals.
No institutions, geography, or culture
Cross-country income differences span a factor of 60. The model attributes these entirely to differences in ss, nn, and A0A_0. Institutions, geography, culture, and history are absent — a major limitation that the residual-based literature has had to confront.
No natural resources or environment
Capital and labour are the only inputs. Natural resources, land, and environmental constraints do not appear.

Extensions of the Solow framework

Human capital: Mankiw–Romer–Weil (1992)

The most influential extension adds human capital HH as a third factor:

Y=KαHβ(AL)1αβ,α+β<1Y = K^\alpha H^\beta (AL)^{1-\alpha-\beta}, \quad \alpha + \beta < 1

With αβ1/3\alpha \approx \beta \approx 1/3, the augmented model explains about 78% of cross-country income variation (versus 59% for the basic model). The implied convergence speed is 2.7%\approx 2.7\% per year — much closer to the empirical estimate of 2%\approx 2\% per year than the basic model's 5.3%\approx 5.3\%.

Crucially, MRW also shows that accounting for human capital reduces the implied capital share to reasonable levels and substantially shrinks the Solow residual, reconciling some — but not all — of the technology gap.

Endogenous growth: the AK model

What if diminishing returns to capital are absent? Suppose the aggregate production function is linear in capital:

Y=AKk^=sAnδY = AK \quad \Rightarrow \quad \hat{k} = sA - n - \delta

The long-run growth rate of kk is sAnδsA - n - \delta, which is now a function of ss. The savings rate affects not just the level of output but the long-run growth rate — the opposite of the Solow result. This is the foundation Romer (1986) built on, by micro-founding why MPK need not fall.

Open economy

With perfect capital mobility the domestic capital stock is pinned by the world interest rate rwr_w:

f(k)=rw+δf'(k^*) = r_w + \delta

kk^* is now determined externally, not by the domestic savings rate. A rise in ss leads to a current-account surplus as saving flows abroad, not to higher domestic capital. The saving-income link that drives comparative statics in the closed economy disappears entirely.

Check yourself

  1. Why does the Solow–Swan model converge to a unique steady state? Which two features of f(k)f(k) guarantee it?
  2. What happens to kk^* and yy^* when the savings rate ss permanently increases? Trace through the diagram and the formula.
  3. The elasticity of yy^* with respect to ss is α/(1α)1/2\alpha/(1-\alpha) \approx 1/2. Use this to explain why saving-rate differences cannot account for 30–60× income gaps.