Task 1:
The Ghost of the Financing Gap by William Easterly

What are the main assumptions of the “Financing Gap model”?

The Financing Gap model implies the following testable propositions: aid will go into investment one for one, and there will be a fixed linear relationship between growth and investment in the short run.

What is the ICOR?

Incremental Capital Output Ratio (ICOR) is the additional capital required to increase one unit of output. This ratio is used to measure the efficiency of an industrial unit or country as an economic unit. The lesser the ICOR, more efficient the organization.

What are the testable implications of the Financing Gap model?

The predictions are about the short-run evolution of aid, investment, and growth.

Donor conditionality - In light with the Growth theory. A lump sum transfer like aid would have no effect on the rate of investment. There is a moral hazard problem with giving aid on the basis of a “financing gap.” Recipient countries will have an incentive to maintain or increase the “financing gap” by low saving (i.e. high consumption) so as to get more aid. Even if the donor puts savings conditions on the aid, the conditions may not be credible if the recipient perceives the donor as soft-hearted. These theoretical perspectives are inconsistent with the one to-one aid to investment link postulated in the Financing Gap Model

The constant ICOR reflects the endogenous steady state response of both growth and investment to the model parameters and to policies like the tax rate. It does not represent a linear causal relationship between physical capital and output, because any increase in physical capital with human capital held constant will run into diminishing returns. The ICOR also would not remain constant during the transition to a new steady state after such an increase in physical investment.

How does Easterly test them?

Aid to investment - the presumption was that aid will go one for one into investment. Through this test he is asking whether investment and aid jointly evolved the way that the users of this model expected. 88 aid recipient countries on which we have data spanning the period 1965-95 The dependent variable here is Investment/GDP and the independent variable is Overseas Development Assistance (ODA)/GDP. Investment to growth- the linear growth-investment relationship What we really want to know is if the relationship has some predictive power, i.e. if we can predict growth with a constant ICOR 138 countries with at least 10 observations on growth and lagged investment (the median number of observations is 36). include a constant (remember the ICOR model does not have one) and then do a test of its significance.

What does he find?

Aid to investment – to show how many of these countries show a significant and positive effect of foreign aid on investment, with a coefficient greater than or equal to one? Just 6 of the 88 countries pass the test of a positive and significant coefficient greater than or equal to one. found a zero coefficient on aid in a cross-section investment regression The Financing Gap Model aid advocates anticipated that aid would go into investment, not into tiding countries over droughts. According to the result, investment and aid did not evolve the way they expected. Investment to growth - the linear growth-investment relationship There are four countries that pass all the tests of a positive and significant relationship between growth and investment, a “zero” constant, and an ICOR between 2 and 5.21. Four economies that pass the tests are an unusual assortment: Israel, Liberia, Reunion (a French colony), and Tunisia.

Remembering the few countries where the aid-to-investment link worked as expected, we can say that the Financing Gap model fits one country: Tunisia. Unfortunately, 1 success out of 138 countries is likely to have occurred by chance even if the model had no empirical validity – which so far the evidence says it does not.

Task 2:
Steady state of an economy

\[ Y_t=AK^{\alpha}_{t-1} \] \[ K_t=K_{t-1}(1-\delta)+sY_t\] \[ A=5, \alpha=0.4, \delta=0.07, s=0.3\]

A <- 5
s <- 0.3
alpha <- 0.4
delta <- 0.07

K <- rep(NA, 200)
Y <- rep(NA, 200)

K[1] <- 1

for(t in 2:200){
  Y[t] <- A*K[t-1]^alpha
  K[t] <- K[t-1]*(1-delta)+(s*Y[t])
}

plot.ts(K)

plot.ts(Y)

Now, assign delta a random value from distribution of 0.07 and standard deviation 0.05:

Y<- rep(NA, 200) K<- rep(NA, 200) delta<- rep(NA, 200) K[1]<- 1 A<- 5 alpha<- 0.4 s<- 0.3

for (t in 2.200){ delta[t]<- rnorm(1, 0.07, 0.05) Y[t]<- AK[t-1]^alpha K[t]<- K[t-1](1-delta[t])+(s*Y[t]) } plot.ts(K)

Y<- rep(NA, 200)
K<- rep(NA, 200)
delta<- rep(NA, 200)
K[1]<- 1
A<- 5
alpha<- 0.4
s<- 0.3

for (t in 2.200){
delta[t]<- rnorm(1, 0.07, 0.05)
Y[t]<- A*K[t-1]^alpha
K[t]<- K[t-1]*(1-delta[t])+(s*Y[t])
}