Task 1: The Ghost of the Financing Gap

Question 1 - What are the main assumptions of the “Financing Gap model”

The main assumptions of Financing Gap Model are:

Question 2 - What is the ICOR

ICOR is the Investment Capital Output Ratio and it is a measure of a country’s efficiency at converting or using capital in the prudction of output. A higher ICOR means that more capital is required per unit of production therefore a lower ICOR is desired as it it represents a higher efficiency. It is possible for the ICOR to be misleading as it does not take into account the substitution of capital with labour, nor does it factor in a tax imposed on goods that has reduced demand. A high ICOR may also be the rsult of high investment and low population growth as evidenced by places such as Japan and South Korea. A constant ICOR is only possible in a steady-state economy whilst a fluctiating economy is a sign of growth or trasitioning.

Question 3 - What are the testable implications of the Financing Gap model

The first Testable implication of the financing gap model is that aid will go directly to investment on the ratio of 1:1 allowing for savings rates to rise, which is evidenced by the growth of the nation’s GDP and a greater than 1:1 increase in investment. The Second testable implication of the model is that the relationship between growth and investment is fixed and linear.

Question 4 - How does Easterly test them

Easterly Tests his theories and the model’s robustness by running regressions. He tests whether the Investment/GDP dependent variable is affected by the explanitory variable AID/GDP in the short run. He finds that only 6 of the 88 countries tested have a coefficient greater than or equal to 1. This result indicates a positive relationship. This would imply that many countries are just spending aid. Easterly also tests the investment-to-growth relationship in the short run by regressing (Lagged Investment/GDP)/Growth to see if the constant ICOR is able to accurately predict growth. Easterly tests the statistical significance of the relationship and finds that only 4 out of 138 countries have statistically significant results with a with a zero constant and an ICOR between 2 and 5. There is only one country that completely fits the financing gap model predictions and that is Tunisia. Easterly postulates that this is far less likely to be due to the robustness of the model and far more likely due to random chance.

The idea that “investment is necessary but not sufficient” is also tested by Easterly by looking at IOR rates and investment rates that have been lagged by one time period. He finds that at the normal ICOR growth rate of 3.5, nine tenths of the sample violate the “necessary” condition when looking at the data from 1950-1952. Over 4 years he found that 49% of countries analysed required foreign investment when they had an OCR of 2, and none when an ICOR of 5 was used. This is further evidence that in the short run, investment is not necessary for growth.

Question 5 - What does he find Easterly concludes that the financing gap model fails all the empirical, statistical and theoretical tests performed on the model and therefore one can conclude that filling a financial gap to meet the investment requirement will not lead to a rise in investement or growth in the short or long-run. The flawed model however will still be used due to current incentives and common practices in the International Financing Institutions such as the World Bank and the IMF even though it is used in academia due to it’s fallacies. Easterly suggests that the institutions and researchers should devote more time and effort in critiquing older models still used as they may not sustain under rigourous testing.

Simulating a Steady State

Equation for K

\(K_{t} = s * Y_{t} + (1-delta) * K_{t-1}\)

Equation for Y

\(Y_{t} = A * K^a_{t-1}\)

K <- rep(NA, 200)
Y <- rep(NA, 200)
alpha <- 0.4
K[1] <- 1
s <- 0.3
delta <- 0.07
A <- 5
Y[1] <- A*K[1]^alpha
for (t in 2:200) {
  Y[t] <- A* K[t-1]^alpha
  K[t] <- s*Y[t] + (1-delta)*K[t-1]}

plot.ts (K)

plot.ts (Y)

Simulating for Random Depreciation

Y <- rep(NA, 200)
K <- rep(NA, 200)
K[1] <- 1
delta <- rnorm(200,0.07,0.05)
alpha <- 0.4
s <- 0.3
A <- 5
Y[1] <- A* K[1]^alpha
for (t in 2:200) {
  Y[t] <- A* K[t-1]^alpha
  K[t] <- s*Y[t] + (1-delta[t])*K[t-1]}

plot.ts(Y)

plot.ts(K)