| Ryan Pitylak |
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Does Aid Starve the Developing World? By: Ryan Pitylak 05/03/2006 University of Texas-Austin
1. Introduction The effect of aid on GDP is
important because many developing countries receive foreign aid. It is important to understand whether foreign
aid negatively or positively impacts the lives of the people living in the
developing country, and GDP is one measure of this impact. My approach is designed to look at the
difference between the countries that are extremely underdeveloped and those
that are just less developed. My
hypothesis is that the countries that are the most underdeveloped need aid so
much that the aid will actually boost their GDP levels. My hypothesis is extended to include the
notion that the less developed countries that receive aid actually receive
lower GDP levels because the aid actually diverts incentives, and people do not
increase their marginal productivity to labor, and therefore GDP drops for that
reason. For the most underdeveloped
countries, I assume that the support infrastructure to gain higher marginal
productivity levels do not exist, and therefore aid doesn’t change incentives
that much. This intuition comes from
looking at some of the least developed countries, like those in Sub-Saharan
Africa. To caveat this point, the people
in these least developed countries could raise their marginal productivity
levels, as argued by William Easterly in The
Elusive Quest for Growth, but changes in these countries need to occur
before major change to these workers lives can happen. 2. Analytical Approach and Data My analysis is about the effect of
aid on GDP, holding several other determinants of GDP constant. The information came from the World Bank
Indicators database that was available through the The dependent
variable is GDP Per Capita, which is GDP per capita according to current
international dollars of the observation, because the change in real GDP per
capita is good measure of the growth, or decline, of a country. The independent variables in this regression
are FDI, which is net foreign direct investment of the observation, year, which
is the year of the observation, Inflation, which is the inflation of the
observation, health, which is a measure of both private and public health
expenditure, and different measures of foreign aid. FDI is held constant because it is presumed
that FDI is a measure of the desirability to invest in the country. Countries that are more desirable to invest
in long-term capital investments, such as FDI, typically have sounder policies
and institutions. Year is held constant
because it is presumed that the situation in each country changes from year to
year according to effects that are specific to that year, and not specific to
our main thesis about what is the effect of foreign aid on GDP. Health is held constant because it is
presumed that countries that spend more money on health have a better economy,
and therefore GDP will be inflated too high for good countries unless I hold
health constant. Multiple aid variables
are regressed in the different tests. My
initial hypothesis is that the effect of aid on GDP lags by four years. That seemed like an appropriate amount of
time for the aid money to be distributed and the effects of the aid to be felt on
GDP levels. Now to explain the
different the different measures of aid: Aid is a measure of the effect of aid
on the same year’s GDP. Aid Lag 1 is the
measure of the effect of foreign aid received in the previous year on GDP. Aid Lag 2 is the measure of the effect of
foreign aid received two years ago on GDP.
Aid Lag 3, Aid Lag 4, and Aid Lag 5 all follow the same format. Multiple ways of looking at Aid, ranging from
the effect of the current year’s foreign aid on GDP to the effect of foreign
aid from five years ago on GDP, were important to isolate the actual effect of
foreign aid on GDP. Regressions were run
separately for the least developed countries and the less developed
countries. The least developed countries
included Table 0a. Lowest Countries Variables
Table 0b.
I found it odd that
the aid variable increases with each lagged year for the low
countries, but this was verified through manual inspection of the data. Table 1. Regression of
the Determinants of GDP in the lowest developed countries between
1997-2001
The Log ( Aid ) is statistically significant in regression 1b, but
there might be first order correlation.
This might also seem statistically significant because of the fixed
effects of each country. Table 2. Regression of
the Determinants of GDP in the low developed countries between
1997-2001
Aid was not
significant in OLS regression, but the log ( aid ) was
significant. The problem with this is
that the significance may come from variations by year or variations by
country. To resolve these conflicts, I
ran an Xtreg in Stata that
adjusted for the fixed effects of each country, and I also added dummies for each
year. The base year was 1997. Regression 3b below shows that Log ( Aid ) is still statistically significant.
Interestingly, the
log of aid was still significant in regression 3b, but the significance lowered
to being only significant at the 10% level.
To try to find out if the lagged variables have any effect, regression 3c
shows these results. What’s odd here is
that the lagged aid values are all negative, and significant from lagged years
one through four, but the aid in the current year, which is statistically
significant, has a positive effect on GDP.
To test whether the lagged variables should have been added, I ran an
F-test to see if the aidlag variables were jointly
significant. The F-Test result was F(5,36)=2.35. This
is significant at the 10% level. Overall, the added
sum of the lagged aid variables from regression 3c results in a positive
number, which means that over the course of the 6 years, aid has a positive
effect on GDP. This is consistent with
the results found in regression 3b that show that the log of aid has a positive
effect on GDP. However, there may be a
serial correlation problem. Table 5 will
answer this question. The following is the information for
the low developed countries. None of the
aid variables were statistically significant.
This was surprising because some of the aid variables were significant
in Table 2. There appears to be a
difference between the two sets of countries, as expected. Table 4.
Regression adjusting for the fixed effects of country of the Determinants
of GDP in the low developed countries between 1997-2001
I also tried adding
the aid variables together to see it was significant when I added aid plus aid
lagged one year through five years. This
variable was not significant in the regression adjusting for the fixed effects
of each country. For the lowest
countries, testing for first order correlation found that the correlation
between the residuals and the residuals from one year lagged resulted in a rho of .52. To find
if first order correlation was creating the significance of aid in the
regression that adjusted for fixed effects, I rho
differenced the Y and X variables. The
result is: Table 5. Regression of
the Determinants of GDP, adjusting for first order correlation, in the lowest
developed countries between 1997-2001
For the lowest
countries, it appeared as if the log of aid was statistically significant when
I rho’d all the dependent non-dummy variables, but
when adjusting for the fixed effects of each country, and holding each year
constant, the log of aid became non-statistically significant. This was determined by using only the Log
(Aid Rho’d) variable, and no Aid or Aid Lagged
variables and running an Ordinary Least Squares regression (5a), and also by
running a regression adjusting for the fixed effects of country (not printed,
but it was not statistically significant). Regression 5a was statistically significant,
but the regression adjusting for the fixed effects of country found that the
statistical significance of Log (Aid Rho’d) came from
the fixed effects of the country. 3. Conclusion The effect of aid
on GDP is still partially unclear, but it appears that aid does not
statistically effect GDP. Aid appears to negatively effect
GDP when we aggregate all of the countries together and do not account for
difference among the countries, but when we look at differences among the
countries and adjust for these fixed effects, the effect of aid on GDP becomes
non-statistically significant.
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Copyright (c) 2006
Ryan Pitylak All rights reserved.
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