In this unit, we will consider a lot of explanations for variation in GDP per capita
Focus on things that are external or a country cannot control
Today and next class (and discussion) will be about Foreign Aid
But first, today, we will have to spend some time talking about the empirics of development literature
All comes down to regression models
Development papers are very empirical
Take data (of varying quality) from countries around the world and try to see:
What factors explain variation in GDP per capita around the world?
Dependent variable \(\color{#e64173}{Y}\): GDP per capita (or growth rate)
Independent variables \(\color{#e64173}{X_1, X_2, \cdots X_k}\): things that can plausibly affect GDP per capita
$$Y=\beta_0+\beta_1X+u$$
\(\beta_0\): intercept \((Y\) when \(X=0)\)
\(\beta_1\): slope, the marginal effect of \(X\) on \(Y\): \(\frac{\Delta Y}{\Delta X}\)
\(u\): error term, contains everything other than \(X\) that affects \(Y\)
This idea of a “line through data points” is simplified, just to give you intuition
If you want to know more, take my econometrics class
Some data on economic freedom and gdp per capita
Suppose we want to estimate a regression model
$$\widehat{\text{gdp per capita}} = \beta_0 + \beta_1 \, \text{ef}$$
$$ \widehat{\text{GDP per capita}} = -86,400 + 14704 \, \text{ef} $$
Intercept \((\beta_0)\): -86,400
Slope \((\beta_1)\): 14,704
Regression results are presented in a table like this
Often includes multiple models (columns) and multiple independent variables (rows)
GDP per capita | |
---|---|
Intercept | -86400.07 *** |
(13361.77) | |
Economic Freedom | 14704.30 *** |
(1935.13) | |
N | 112 |
R-Squared | 0.34 |
SER | 15881.92 |
*** p < 0.001; ** p < 0.01; * p < 0.05. |
Again, marginal effect of Economic Freedom on GDP per capita is 14704.
\(R^2\) indicates 34% of variation in GDP per capita is explained by this (very simple) model
GDP per capita | |
---|---|
Intercept | -86400.07 *** |
(13361.77) | |
Economic Freedom | 14704.30 *** |
(1935.13) | |
N | 112 |
R-Squared | 0.34 |
SER | 15881.92 |
*** p < 0.001; ** p < 0.01; * p < 0.05. |
Any good statistics class will repeat: correlation does not imply causation
But it can, with the right tools
Classic (simplified) procedure of a randomized control trial (RCT) from medicine
Treatment Group
Control Group
Random assignment to groups ensures that the only differences between members of the treatment(s) and control groups is receiving treatment or not
Selection bias: (pre-existing) differences between members of treatment and control groups other than treatment, that affect the outcome
Treatment Group
Control Group
(Selection Bias)
If treatment is randomly assigned for a large sample, it eliminates selection bias!
Treatment and control groups differ on average by nothing except treatment status
Creates ceterus paribus conditions in economics: groups are identical on average (holding constant age, sex, height, etc.)
Treatment Group
Control Group
Professors Esther Duflo and Abhijit Banerjee, co-directors of MIT's @JPAL, receive congratulations on the big news this morning. They share in the #NobelPrize in economic sciences “for their experimental approach to alleviating global poverty.”
— Massachusetts Institute of Technology (MIT) (@MIT) October 14, 2019
Photo: Bryce Vickmark pic.twitter.com/NWeTrjR2Bq
Angus Deaton
Economics Nobel 2015
The RCT is a useful tool, but I think that is a mistake to put method ahead of substance. I have written papers using RCTs...[but] no RCT can ever legitimately claim to have established causality. My theme is that RCTs have no special status, they have no exemption from the problems of inference that econometricians have always wrestled with, and there is nothing that they, and only they, can accomplish.
Deaton, Angus, 2019, “Randomization in the Tropics Revisited: A Theme and Eleven Variations”, Working Paper
Lant Pritchett
“People keep saying that the recent Nobelists "studied global poverty." This is exactly wrong. They made a commitment to a method, not a subject, and their commitment to method prevented them from studying global poverty.”
“At a conference at Brookings in 2008 Paul Romer (last years Nobelist) said: "You guys are like going to a doctor who says you have an allergy and you have cancer. With the skin rash we can divide you skin into areas and test variety of substances and identify with precision and some certainty the cause. Cancer we have some ideas how to treat it but there are a variety of approaches and since we cannot be sure and precise about which is best for you, we will ignore the cancer and not treat it.”
Angus Deaton
Economics Nobel 2015
“Lant Pritchett is so fun to listen to, sometimes you could forget that he is completely full of shit.”
Even if a study is internally valid (used statistics correctly, etc.) we must still worry about external validity:
Is the finding generalizable to the whole population?
If we find something in India, does that extend to Bolivia? France?
Subjects of studies & surveys are often WEIRD: Western, Educated, and from Industrialized Rich Democracies
IN MICEhttps://t.co/mLuKBRhsAb
— justsaysinmice (@justsaysinmice) September 15, 2020
RCTs are considered the “gold standard” for causal claims
But society is not our laboratory (probably a good thing!)
We can rarely conduct experiments to get data
Instead, we often rely on observational data
This data is not random!
Must take extra care in forming an identification strategy
To make good claims about causation in society, we must get clever!
Economists often resort to searching for natural experiments
Some events beyond our control occur that separate otherwise similar entities into a "treatment" group and a "control" group that we can compare
e.g. natural disasters, U.S. State laws, military draft
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
Previous GDP per Capita
Investment share of GDP
Macroeconomic variables
Foreign Aid
Geography
Culture
Political institutions
Recall we are limited by the data we can find and measure
A lot of variables used are proxy variables that are correlated with something we care about, but can't measure
Burnside and Dollar (2000: 852)
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
$$ \begin{align*} \text{GDP per capita growth} &= \beta_0 + \beta_1 \text{Initial GDP} + \beta_2 + \text{Ethnic fractionalization}+ \\ & \beta_3 \text{Assassinations} + \beta_4 \text{Distance from equator} + \cdots \\ \end{align*} $$
Each column is a particular model
Number next to each variable (row) is the marginal effect of that variable on outcome variable
Number in parentheses below it is the standard error of the estimate
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
We restrict our exploration to foreign aid for the purpose of causing economic growth/development
NOT aid for humanitarian crises or natural disaster recovery
NOT aid for military/peacekeeping efforts
NOT aid for specific causes (i.e. reduce malaria)
Historical ideas behind foreign aid as a necessary, desirable, or admirable goal of rich, Western countries
A moral imperative to “bring civilization” to lesser developed countries
Rudyard Kipling, “The White Man's Burden” (1899)
1940s-1950s: World War II ends
1945-1951: Marshall Plan to rebuild war-torn Europe
1946: Harrod-Domar model -> "financing gap"
1950s-1960s:
1960 Rostow's stages of growth model
Cold War starts, “Red scare” in U.S.
Foreign aid to “Rhird World” takes off, in part to protect it from Soviets
John F. Kennedy addressing USAID
“There is no escaping our obligations: our moral obligations as a wise leader and good neighbor in the interdependent community of free nations – our economic obligations as the wealthiest people in a world of largely poor people, as a nation no longer dependent upon the loans from abroad that once helped us develop our own economy – and our political obligations as the single largest counter to the adversaries of freedom.” – John F. Kennedy
Source: USAID
1982: Mexico announces it can no longer finance its debts
1980s Latin American debt crisis
Also in Africa and Middle East
IMF and World Bank begin to make general loans (instead of for specific projects) to developing countries, conditional on "structural adjustment"
Governments would be required to make growth-enhancing policy changes in exchange for loans
What were these? Eventually became known as...
Rodrik, Dani, 2006, "Goodbye Washington Consensus, Hello Washington Confusion?" Journal of Economic Literature 44(4): 973-987
27% say it's about right
Foreign aid is less than 1% of our federal spending
L: Roy Harrod (1900-1978)
R: Evsey Domar (1914-1997)
“Knife's Edge” equilibrium: a single savings rate and ICOR that permits stable growth
Highly simplistic, yet extremely influential
L: Roy Harrod (1900-1978)
R: Evsey Domar (1914-1997)
“Financing gap” between “required” investment rate (predicted from model) and a country's actual saving rate
Low income countries can't increase savings \(\implies\)foreign aid from countries with higher savings will lead directly to rapid growth1
1 Remember this argument!
William Easterly
1957-
“To sum up, Domar's model was not intended as a growth model, made no sense as a growth model, and was repudiated as a growth model. So it was ironic that Domar's growth model became, and continues to be today, the most widely applied growth model in economic history,” (p.28).
Poverty trap argument:
In order to escape poverty, people must invest in capital goods to improve productivity
In order to invest, people must first save some of their income
Low-income people need to spend all of their income on subsistence
Thus, they are trapped in a poverty trap
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In this unit, we will consider a lot of explanations for variation in GDP per capita
Focus on things that are external or a country cannot control
Today and next class (and discussion) will be about Foreign Aid
But first, today, we will have to spend some time talking about the empirics of development literature
All comes down to regression models
Development papers are very empirical
Take data (of varying quality) from countries around the world and try to see:
What factors explain variation in GDP per capita around the world?
Dependent variable \(\color{#e64173}{Y}\): GDP per capita (or growth rate)
Independent variables \(\color{#e64173}{X_1, X_2, \cdots X_k}\): things that can plausibly affect GDP per capita
$$Y=\beta_0+\beta_1X+u$$
\(\beta_0\): intercept \((Y\) when \(X=0)\)
\(\beta_1\): slope, the marginal effect of \(X\) on \(Y\): \(\frac{\Delta Y}{\Delta X}\)
\(u\): error term, contains everything other than \(X\) that affects \(Y\)
This idea of a “line through data points” is simplified, just to give you intuition
If you want to know more, take my econometrics class
Some data on economic freedom and gdp per capita
Suppose we want to estimate a regression model
$$\widehat{\text{gdp per capita}} = \beta_0 + \beta_1 \, \text{ef}$$
$$ \widehat{\text{GDP per capita}} = -86,400 + 14704 \, \text{ef} $$
Intercept \((\beta_0)\): -86,400
Slope \((\beta_1)\): 14,704
Regression results are presented in a table like this
Often includes multiple models (columns) and multiple independent variables (rows)
GDP per capita | |
---|---|
Intercept | -86400.07 *** |
(13361.77) | |
Economic Freedom | 14704.30 *** |
(1935.13) | |
N | 112 |
R-Squared | 0.34 |
SER | 15881.92 |
*** p < 0.001; ** p < 0.01; * p < 0.05. |
Again, marginal effect of Economic Freedom on GDP per capita is 14704.
\(R^2\) indicates 34% of variation in GDP per capita is explained by this (very simple) model
GDP per capita | |
---|---|
Intercept | -86400.07 *** |
(13361.77) | |
Economic Freedom | 14704.30 *** |
(1935.13) | |
N | 112 |
R-Squared | 0.34 |
SER | 15881.92 |
*** p < 0.001; ** p < 0.01; * p < 0.05. |
Any good statistics class will repeat: correlation does not imply causation
But it can, with the right tools
Classic (simplified) procedure of a randomized control trial (RCT) from medicine
Treatment Group
Control Group
Random assignment to groups ensures that the only differences between members of the treatment(s) and control groups is receiving treatment or not
Selection bias: (pre-existing) differences between members of treatment and control groups other than treatment, that affect the outcome
Treatment Group
Control Group
(Selection Bias)
If treatment is randomly assigned for a large sample, it eliminates selection bias!
Treatment and control groups differ on average by nothing except treatment status
Creates ceterus paribus conditions in economics: groups are identical on average (holding constant age, sex, height, etc.)
Treatment Group
Control Group
Professors Esther Duflo and Abhijit Banerjee, co-directors of MIT's @JPAL, receive congratulations on the big news this morning. They share in the #NobelPrize in economic sciences “for their experimental approach to alleviating global poverty.”
— Massachusetts Institute of Technology (MIT) (@MIT) October 14, 2019
Photo: Bryce Vickmark pic.twitter.com/NWeTrjR2Bq
Angus Deaton
Economics Nobel 2015
The RCT is a useful tool, but I think that is a mistake to put method ahead of substance. I have written papers using RCTs...[but] no RCT can ever legitimately claim to have established causality. My theme is that RCTs have no special status, they have no exemption from the problems of inference that econometricians have always wrestled with, and there is nothing that they, and only they, can accomplish.
Deaton, Angus, 2019, “Randomization in the Tropics Revisited: A Theme and Eleven Variations”, Working Paper
Lant Pritchett
“People keep saying that the recent Nobelists "studied global poverty." This is exactly wrong. They made a commitment to a method, not a subject, and their commitment to method prevented them from studying global poverty.”
“At a conference at Brookings in 2008 Paul Romer (last years Nobelist) said: "You guys are like going to a doctor who says you have an allergy and you have cancer. With the skin rash we can divide you skin into areas and test variety of substances and identify with precision and some certainty the cause. Cancer we have some ideas how to treat it but there are a variety of approaches and since we cannot be sure and precise about which is best for you, we will ignore the cancer and not treat it.”
Angus Deaton
Economics Nobel 2015
“Lant Pritchett is so fun to listen to, sometimes you could forget that he is completely full of shit.”
Even if a study is internally valid (used statistics correctly, etc.) we must still worry about external validity:
Is the finding generalizable to the whole population?
If we find something in India, does that extend to Bolivia? France?
Subjects of studies & surveys are often WEIRD: Western, Educated, and from Industrialized Rich Democracies
IN MICEhttps://t.co/mLuKBRhsAb
— justsaysinmice (@justsaysinmice) September 15, 2020
RCTs are considered the “gold standard” for causal claims
But society is not our laboratory (probably a good thing!)
We can rarely conduct experiments to get data
Instead, we often rely on observational data
This data is not random!
Must take extra care in forming an identification strategy
To make good claims about causation in society, we must get clever!
Economists often resort to searching for natural experiments
Some events beyond our control occur that separate otherwise similar entities into a "treatment" group and a "control" group that we can compare
e.g. natural disasters, U.S. State laws, military draft
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \cdots + \beta_kX_{ki} +u_i$$
Previous GDP per Capita
Investment share of GDP
Macroeconomic variables
Foreign Aid
Geography
Culture
Political institutions
Recall we are limited by the data we can find and measure
A lot of variables used are proxy variables that are correlated with something we care about, but can't measure
Burnside and Dollar (2000: 852)
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
$$ \begin{align*} \text{GDP per capita growth} &= \beta_0 + \beta_1 \text{Initial GDP} + \beta_2 + \text{Ethnic fractionalization}+ \\ & \beta_3 \text{Assassinations} + \beta_4 \text{Distance from equator} + \cdots \\ \end{align*} $$
Each column is a particular model
Number next to each variable (row) is the marginal effect of that variable on outcome variable
Number in parentheses below it is the standard error of the estimate
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
Burnside, Craig and David Dollar, 2000, "Aid, Policies, and Growth," American Economic Review 90(4): 847-868
We restrict our exploration to foreign aid for the purpose of causing economic growth/development
NOT aid for humanitarian crises or natural disaster recovery
NOT aid for military/peacekeeping efforts
NOT aid for specific causes (i.e. reduce malaria)
Historical ideas behind foreign aid as a necessary, desirable, or admirable goal of rich, Western countries
A moral imperative to “bring civilization” to lesser developed countries
Rudyard Kipling, “The White Man's Burden” (1899)
1940s-1950s: World War II ends
1945-1951: Marshall Plan to rebuild war-torn Europe
1946: Harrod-Domar model -> "financing gap"
1950s-1960s:
1960 Rostow's stages of growth model
Cold War starts, “Red scare” in U.S.
Foreign aid to “Rhird World” takes off, in part to protect it from Soviets
John F. Kennedy addressing USAID
“There is no escaping our obligations: our moral obligations as a wise leader and good neighbor in the interdependent community of free nations – our economic obligations as the wealthiest people in a world of largely poor people, as a nation no longer dependent upon the loans from abroad that once helped us develop our own economy – and our political obligations as the single largest counter to the adversaries of freedom.” – John F. Kennedy
Source: USAID
1982: Mexico announces it can no longer finance its debts
1980s Latin American debt crisis
Also in Africa and Middle East
IMF and World Bank begin to make general loans (instead of for specific projects) to developing countries, conditional on "structural adjustment"
Governments would be required to make growth-enhancing policy changes in exchange for loans
What were these? Eventually became known as...
Rodrik, Dani, 2006, "Goodbye Washington Consensus, Hello Washington Confusion?" Journal of Economic Literature 44(4): 973-987
27% say it's about right
Foreign aid is less than 1% of our federal spending
L: Roy Harrod (1900-1978)
R: Evsey Domar (1914-1997)
“Knife's Edge” equilibrium: a single savings rate and ICOR that permits stable growth
Highly simplistic, yet extremely influential
L: Roy Harrod (1900-1978)
R: Evsey Domar (1914-1997)
“Financing gap” between “required” investment rate (predicted from model) and a country's actual saving rate
Low income countries can't increase savings \(\implies\)foreign aid from countries with higher savings will lead directly to rapid growth1
1 Remember this argument!
William Easterly
1957-
“To sum up, Domar's model was not intended as a growth model, made no sense as a growth model, and was repudiated as a growth model. So it was ironic that Domar's growth model became, and continues to be today, the most widely applied growth model in economic history,” (p.28).
Poverty trap argument:
In order to escape poverty, people must invest in capital goods to improve productivity
In order to invest, people must first save some of their income
Low-income people need to spend all of their income on subsistence
Thus, they are trapped in a poverty trap