Development aid: Does it work?
1 Introduction
There is a long-standing debate in development economics about whether development aid does
any good or simply gives the donors a clean conscience and allows them to forget their colonial
misdeeds. Angus Deaton, 2015 Nobel laureate, is a well-known critic; so is William Easterly,
former World Bank staff. Another former World Bank staff, Dambysia Moyo, wrote a book
claiming that the best thing that could happen to Africa would be if all African leaders received a
phone call announcing them that all sources of aid would stop in the next five years.
On the other hand, people like Jeffrey Sachs, founder of Harvard Center for Development Studies,
argues that there is too little aid to make a difference. In his view, aid would have an effect if it took
the form of a “big push” removing all the constraints to growth at once.
To get a feel for the debate, take a look at the following two clips:
http://www.youtube.com/watch?v=uUHf_kOUM74
http://www.youtube.com/watch?v=vzy8dafM89E
Does it matter a lot? Development aid is big money: A grand total $120 billion in 2009 from 23
member countries of the OECD DAC (Development Assistance Committee) through 156 bilateral
agencies (DDC in Switzerland, AFD in France, GTZ in Germany, DFID in England, etc.) and 263
multilateral agencies (World Bank, IMF, UNDP etc.).1 Basically 1% to 1.5% of recipient countries
GDP. Since 1970, total is about $2.98 trillion dollars 2008. This is all big money, so whether it
made any difference or not is a legitimate question. Who receives aid? The lion's share goes to
Africa:
Figure 1: Aid disbursements by recipient region (average 2000-2010)
1
Birdsall Kharas 2010.
1
Source : Ferro Wilson 2011
Who donates? The figure below shows the log of bilateral assistance in 2008 versus the log of GDP,
both expressed in current dollars. We can see that the size of the economy widely predicts the size
of aid flows. After adjusting depending on the size of the economy, the biggest donors (the greatest
vertical distance from the regression line) are not the former colonial powers (GB, France) but
rather the countries with strong social capital structure (Sweden, Norway, Denmark, Netherlands).
Figure 2: Total aid vs GDP of donor countries
10
USA
9
DEU
GBR
FRA
JPN
NLD
8
DNK
ESP
CAN ITA
SWE
NOR
AUS
BEL
CHE
AUT
7
IRL
FIN
PRT
GRC
KOR
6
LUX
NZL
10
12
14
16
lgdp
Source : WDI (GDP) ; Birdsall Kharas 2010 (ODA)
Evaluating the impact of aid is all the more important given that the budgetary situation of many
donors is not as good as it used to be after the Global Financial crisis, turning public opinion against
spending in general and against international aid in particular.
Figure 3: Public opinion and spending cuts
2
Source : Financial Times, 12 july 2010.
2. Prima facie evidence
Prima facie evidence is frankly not very encouraging, with growth plummeting in Sub-Saharan
Africa (SSA) precisely when aid flows grow (Figure 4). However a graph like this says nothing and
should be interpreted very cautiously. Easterly (2003) writes that it shows that aid is ineffective.
But countries get aid when they are sick. The picture is like saying, “people in hospital are sicker
than people in the street, therefore hospitals are useless”. There is a basic endogeneity problem that
makes the identification of aid’s impact very difficult. As usual, we will be struggling with this
identification problem through various econometric approaches.
Figure 4: Aid and SSA’s economic performance
3
Source : Easterly 2003
Before trying to assess aid’s impact, one should keep in mind that its objectives are multiple,
including
1. Reduce poverty
2 Improve health
3 Improve education
4 Accelerate growth.
It is difficult to evaluate its efficacy according to a single criterion, so some evaluations have
adopted a multi-criteria approach, like CGDev’s donor’s assessment by (Birdsall Kharas 2010)
(Table 1)
Table 1: Multicriteria evaluation of donor performance
4
This approach produces multidimensional performance ratings like in Figure 5.
Figure 5: Multidimensional donor performance rating
Alternatively, Ferro and Wilson (2011) test econometrically the adequacy of development aid to
needs as expressed by the companies in the World Bank’s Enterprise Surveys, where companies
divide the main obstacles to their activity among seven categories (labour, business climate, access
to credit, infrastructure, stability, rule of law, or trade). The targeting of Development aid is
measured through the the Creditor Reporting System, a database about assistance to development
from the OECD.
The estimating equation is
5
ln adrjt   d   r   j   Prj ,t 1  udrjt
(1)
where d is the donor country, r the recipient country, j is the type of obstacle, adrjt is the amount of
disbursed assistance targeted on constraint j in country r by donor country d, in millions dollars, in
year t, and Prj ,t 1 is the proportion of firms in country r giving constraint j as the main one for
year t-1.
If ̂ is significant, aid is correlated with firm perceptions of what are the biggest obstacles to them,
which is what we hope. This will give us an overall picture of adequacy of aid to needs.
A variant can be tested where interaction terms allow us to test different coefficients, one for each
type of constraint:
ln adrjt   d   r   j   Prj ,t 1   j 1  j  Prj ,t 1  I j   udrjt
(2)
1 for constraint j
Ij  
otherwise
0
(3)
7
where
In this case, the marginal effect of a perceived constraint, say trade-related (e.g. inefficient border
agencies, high import tariffs, etc), on aid is
adrjt
Prj
  j
(4)
Their sample is a panel of 67 developing countries over 2004-2008. Results are shown in Table 2.
Table 2: Firm perceptions of constraints to growth and aid allocation
6
Source : Ferro Wilson 2011, Table 3
The correlation is positive, and this is rather good news. The basic problem in the evaluation of
aid’s impact on the basis of donors criteria is that money is fungible. By that, we mean that when a
donor grants money for a program or project, it relaxes the Government’s overall budget constraint
and allows it to spend larger amounts where its wants to, not necessarily where the donor wants. Put
in plain terms: The donor gives for a hospital. This allows the government not to spend money for
the hospital, and instead spend that money on the armed forces.
The fungibility of aid money makes it logical to take as the performance criterion some single
objective on which both beneficiary and donors can agree. Here we will consider (unsurprisingly)
the impact aid has on growth, as, at least in principle, improvements in criteria 1-3 in our
classification above are correlated with growth at the country-time level.
2. The impact of aid on growth
Given that this whole business of adequacy of grants to needs, perceived or real, is marred by the
problem of money fungibility, a short-cut approach is to assess the overall, “reduced-form” effect of
aid on growth. This is justified by the fact that a whole lot of welfare indicators are closely
7
correlated to GDP per capita, so if aid helps GDP per capita to grow faster, all welfare indicators
can be expected to improve (or so the story goes…)
The basic equation to test the impact of aid on growth is
gi  0  1ai  xiβ  ui
(5)
where ai is the amount of aid per capita and xi is a vector of country characteristics influencing
growth (initial GDP, education levels, investment rates as in any Barro growth regression, and
whatever you want on institutions, social capital etc.)
Problem 1 : endogeneity of aid. If aid tends to go either to promising countries or, more likely, to
countries undergoing difficulties that translate into low growth, (5) will return biased estimates. In
order to take the endogeneity of aid to growth into account in the estimation, one should estimate
something like
gi  0  1ai  xiβ  ui
ai   0   1 gi  zi δ  vi
(6)
where aid is a function of growth for instance through crisis response. Equation system (6) is
basically what was estimated by IV techniques in the first generation of papers (e.g. Boone 94, 95);
where the coefficient of aid in the growth equation was typically not significant.
The problem in estimating this kind of equation through instrumental variable (e.g. 2SLS) is that it
is almost impossible to find at the same time strong instruments (correlated with aid) that also
satisfy the exclusion restriction (in plain English, recall that the exclusion restriction means that the
instrument for aid can influence growth, but only through its relationship with aid; not directly).
As we do not know much about bilateral aid allocation criteria, instruments are likely to be weak,
even if we knew those criteria, they would be likely to be related to the recipient country’s growth
performance, violating the exclusion restriction.
Frankly, there is no perfect fix for that. We will see later on a paper by Nunn and Qiang with a
super-smart identification strategy for U.S. food aid, a particular type of aid.
Problem 2 : Heterogeneity of effects
This issue has given rise to even more debate than reverse causality. The issue is whether a given
dollar of aid has more impact in some policy environments than in others, and whether that should
be taken into account in both the estimation of impact and in the allocation of aid.
Suppose that a scatter point of growth vs. aid at the recipient-country level looked like Figure 6.
Figure 6: Fictitious data on growth and aid with heterogeneous effects
8
4
2
0
-2
-4
0
2
4
6
aid
growth
Fitted values
In such a setting, a “naïve” regression of growth on aid of the form
gi  0  1ai  ui
(7)
would return no impact :
Because it would pick up only average effects shown by the regression line in Figure 6. The correct
specification in such a case would be a regression of growth on aid with an interaction term
between aid and the “hidden variable” in Figure 6, which could be for instance the quality of
domestic policies in the recipient country:
gi  0  1ai  2 pi  3 pi ai  ui
If we did that on the fictitious data of Figure 6, we would get the following results :
9
(8)
with the marginal effect on aid being
gi
ai
 1   3 pi  1.65  3.15  0.54   0.0551
pi
1
2
(9)
This is what Burnside and Dollar (2000) did in a paper that had a huge impact.
How to measure the "quality" of domestic policy? One approach would be to take the World Bank’s
CPIA index, which rates the quality of government policies in each country. Instead, they try to
assess it as part of their estimation procedure. Their approach is to regress growth on a whole bunch
of policy variables and aid:
gi  0  1ai  pi α  xiβ  ui
 0  1ai   2 si  3 i   4 SWi
(10)
In (10), s is the government budget surplus,  is the rate of inflation, and SW is the Sachs-Warner
openness variable.2 Therefore, the policy quality vector is essentially about macro policy. They then
take the component of growth that is explained by these variables as their policy-quality index
through an auxiliary regression whose results are shown in Table 3.
Table 3: Burnside-Dollar’s results—determination of weights
2
In case you forgot, a closed economy is one where one of the following conditions is verified : average tariff over
40%, coverage ratio of non-tariff barriers is higher than 40%, foreign-exchange premium is bigger than 20% for 10
years, a socialist economy, or the presence of an export monopoly.
10
As aid looks non-significant (in accordance with the results of the papers of first generation, and, in
their argument, because the regression looks at average effects like in Figure 6), they take it
out. They then recover the regression coefficients and build a quality index of quality over politics.
pˆ i  const.  ˆ1si  ˆ2 i  ˆ SWi
 1.28  6.85si  1.4 i  2.16SWi
(11)
which is equal to the predicted growth based on the values of the three variables for a country with
the average of all the other features that are taken into consideration in the growth regression.
In the second step, they put the index in two main regressions (growth and aid), with a square term
for aid, in case the effect would be non-linear:
gi  0  1ai  2 pˆ i  3 pˆ i ai  4 pˆ i ai 2  xiβ  ui
ai   0   1 gi   2 pˆ i  zi δ  vi
(12)
Then they consider the first one by 2SLS, i.e. using the predicted value of ai in the second equation
as an instrument for ai. (or by OLS, ignoring the problem of aid’s endogeneity).
In this approach, the interaction term in the first equation allows us to treat the problem of
heterogeneity of effects, and the inclusion of quality of policies index in the second equation allows
us to see if aid is effectively targeted to those countries whose policies are good (two tests for the
price of one – the paper proposes a combined answer to the two initial questions + an implicit
evaluation of the targeting’s performance).
11
Results are shown in Table 4.
Table 4: Burnside and Dollar’s regression results (main regression)
The interaction term is positive but significant only in OLS: Instrumentation through the second
equation in (12) kills the effect or makes it barely significant (only at the 10 percent level),
suggesting that much of the significance of the effect observed under OLS is driven by simultaneity
bias.
Marginal effect of aid using OLS (4) column:
gi
ai
 ˆ1  ˆ3 pi  2ˆ 4ai pi
pi

0
not signif.
 0.20  1.2  2  0.019ai  1.2  0.24  0.0228ai
ˆ3
pi
(13)
pi
 0.24  0.036  0.203
So if the aid as a percentage of GDP rose from 1.6% (0.016) to 2% (0.02), an increase of 0.004, the
average growth would increase from 1.1% per year (0.011) to 1.18% per year (0.011 +
0.203 × 0.04)-less than 1% of acceleration in growth. Passing aid to 5% of GDP, growth would rise
to 1.7% per year.
Worse: Three years later, Easterly, Levine and Roodman paper showed that even these weak results
were not robust enough to proceed to an extension of the sample.
12
Source : Easterly, Levine et Roodman (2004)
Other papers questioned the approach of Burnside and Dollar on a more substantial basis. For
example, Guillaumont and Chauvet suggested a better identification of the aid’s determinants (the
2e equation, the weak link in all this story) from shocks (terms of trade, climate shocks, etc.). By
doing this, the impact of aid depends on shocks, not policies.
3 Can aid have perverse effects?
Potential perverse effects of aid include
o Corruption
o Distortions in prices (disguised dumping)
o The “Dutch disease”
"Dutch disease" refers to a macroeconomic syndrome related to the exploitation of natural resources
(origin: gas in the Netherlands in the 1970s):
o Capital inflow + booming exports causing an appreciation of the currency
13
o Boom of a sector (mining) causes a local inflation (wages, prices of goods, services, real estate
etc.)
The combination of the two (appreciation + inflation) causes a stronger real appreciation that
undermines the country's competitiveness in all other sectors.
Pushing it to the extreme, the mining sector cannibalizes the rest and the country is deindustrializing (examples: Netherlands s 70, Nigeria, Great Britain s 80).
The curse of aid: in monetary terms, aid has the same effect as an influx of capital: it allows the
maintenance of an unrealistic exchange rate which penalizes all tradable sectors of the economy.
In real form, aid has the same effect as a massive dumping: discourages local production and
creates annuities.
Collier and Hoeffler (2004) show that, against a background of civil conflict, aid tends to strengthen
the central Government (due to fungibility which allows the government to increase security
spending) and therefore discourages rebellions, which reduces the incidence of conflict. Other
arguments go in the reverse direction, suggesting that enlarges the “pie” over which belligerants
want to fight.
Is this true? A recent study (Nunn Qiang 2011) suggests that aid may well contribute to trigger
conflicts. This is a quite important issue: over the last 40 years, 98% of the deaths deriving from
conflicts (civil or international wars) occurred in developing countries and three quarters of these
conflicts are civil wars (Nunn Qiang 2011).
Nunn and Qiang (2011) applied a very original identification strategy to the problem by analyzing
the potential contribution of US food aid to conflicts in recipient countries paying very careful
attention to avoiding any endogeneity trap. The original motivation of the U.S. Food Aid program is
nicely summarized in the following quote from John F. Kennedy:
Nunn and Qiang argue, instead, that US food aid may well consist of dumping US agricultureal
surpluses in bumper-crop years. This is apparent in Figure 7. This suggests a nice identification
strategy: Use as an instrumental variable for U.S. food aid the level of precipitations in Kansas the
year before (given that much of the wheat that is dumped as food aid is produced in Kansas).
Figure 7: U.S. food aid and wheat production
14
Source : Nunn Qian 2011
Then, they show that the incidence of conflicts seems to correlate with food aid/production in
Kansas :
Source : Nunn Qian 2011
Based on this preliminary evidence, they regress the incidence of conflicts in country i (region R
(i) such as Africa, Asia etc.) over the amount of food assistance received by this country from the
United States in the year t, considering all the control variables that you can imagine. Sample: 113
countries over 29 years (1976-2004).
The identification problem is the usual one: Aid is very probably endogenous to the conflict; i.e.
there is food aid because there is a conflict. But the instrumental-variable approach avoids this trap:
15
The fact that there is a conflict in Uganda does not make it rain in Kansas. The only problem is that
the instrumental variable varies by year, not by recipient country.
Their strategy is to interact a recipient country receives on average over the period of sampling
(which varies across recipients but not over time) with rainfall in Kansas (which varies across years
but not across recipients), thus generating an instrument that varies across time and recipients.
ait  0  1Tt 1  ai  2 Pt 1  ai  3ci ,t 1  zit   Ri t  i  uit
(14)
The second stage equation regresses the impact of (a dummy variable for a civil or international
war) conflict over aid, instrumented by the first one:
cit  0  1ait  xit γ   Ri t  i  uit
(15)
The estimate answers to the following question: do the recipient countries have a stronger incidence
of conflicts than non-recipient countries after a year of grain oversupply in the United States?
Source : Nunn Qian 2011
Mechanism of conflict’s activation: the aid amounts issues; If there is no democratic institutions to
resolve conflicts, the dispute over annuities degenerates. Nunn and Qiang found a 6 times greater
effect for those countries that never had a civilian Government.
Example: Somalia (see Maren, M (1997), The Road to hell: The ravaging effects of foreign aid and
international charity, NY, NY: The Free Press).
"The government had launched a cynical campaign: First you starve them, then attract them to
central areas with food, then cart them off to where you want them." "That had been the
government's plan, carried out with the assistance, unwitting sometimes, of local foreign charities
using donated by schoolchildren and old ladies and working-class families in church."
16
4. Impact evaluation at the micro level
The assessment of aid projects is generally quite primitive. Over more than 80 projects of assistance
to trade from the World Bank, only 4 with a more or less serious assessment. It is even worse for
bilateral donors (European Commission, etc.). there is currently a controversy over how evaluation
should take place, with scholars at MIT’s JPAL research center advocating for the systematic use of
randomized control trials (RCTs) whereas other people like Angus Deaton have contested the
superiority of RCTs over traditional econometric methods. In general, there is a wealth of methods
available, illustrated in Figure 8.
Figure 8: Evaluation methods available
Main IE methods
Experimental
RCT
Encouragement
design
REQUIRE DELIBERATE PROJECT DESIGN
Quasi-experimental
Pipeline
RDD
DID
Matching-DID
REQUIRE CERTAIN
PROJECT
CHARACTERISTICS
None of them is perfect and there is a number of inevitable trade-offs, illustrated in Figure 9.
Highly relevant outcome variables such as aggregate (country-level) export performance, growth or
employment are likely to be related only in very complicated way to policy interventions and
projects on the ground; that’s what I call “long causal chains”. By contrast, very operational
performance measures like container “dwell times” (how long a container remains stranded in
customs or in a port) may be quite correlated with the effectiveness of interventions, but typically
politicians don’t give a damn.
Then the other inevitable trade-off is between
o cross-country econometrics, where general empirical regularities are identified (good “external
validity”), but only loosely because there are so many confounding influences/endogeneity
biases (see our discussion earlier in this chapter and throughout the course) (poor “internal
validity”), and
o impact evaluations, where program effects are identified sharply (good internal validity) but
results may fail to hold in other settings (poor external validity).
There is no real way out of these key trade-offs.
17
Figure 9: Key trade-offs in evaluation
Choice of outcomes
o Interesting/important outcome -> long causal chain -> weak identification
o Strong identification -> short causal chain -> unexciting outcome
Relevance
Visibility
Identification of
causal chain
Export,
Growth,
Unemployment
TRADE-OFF 1
Container
dwell times
Length of
causal chain
Relevance of outcomes
Better policy relevance comes at the
price of weaker identification
Internal validity (ability to
identify a causal relation)
Investigation approach
o Impact evaluation -> strong internal
validity
o Cross-country econometrics: strong
external validity
IE
TRADE-OFF 2
Cross-country econometrics
External validity (ability to
derive generalizable results)
4.1 Identification with a control group
Here the argument is that a proper program evaluation needs to have a control group, as simple
before-after comparisons are liable to all sorts of confounding influences. This is visible in Figure
10, where Senegal’s horticulture exports seem to soar after the implementation of an EU technical
assistance program, the Pesticides Initiative Program (PIP).
Figure 10: The illusion of a before-after comparison
18
Début du
programme
Source : Jaud Cadot 2010
However, it is not really clear that it is the effect of the program we are observing. There may be
overall influences on all observed individuals confounding the treatment effect. Let us define
treatment-group status
1 if 𝑖 is treated
𝐺𝑖 = {
0 otherwise
(16)
and treatement period
1 after the programm′ s begnning
𝑇𝑃𝑡 = {
0 before
To avoid the problem of "confounding influences", we must define a control group which is not
subjected to the treatment, and we compare the performance of individuals after vs. before
treatment (difference 1) for individuals in treatment vs. for those in the control group (difference 2).
This method is called “difference-in-differences” (DID) regression. It makes it possible to control
for situations like that shown in Figure 11. Figure 11 illustrates another situation where DID
estimation is needed, because there is a cisis affecting all individuals after 2006.
Figure 11: A situation where DID estimation is needed
19
30.00
0.00
10.00
20.00
Program year
2000
2002
2004
2006
2008
2010
year
treated
control
Formally, a DID regression equation looks like this. Let y be e.g. exports level or growth
and xi a vector of individual characteristics:
yit  0  1TGi  2TPt  3TGi  TPt  xiβ  uit
Treatment effect
In the case of the PIP’s evaluation, DID regression results are as follows
Clearly, they are a lot less spectacular than a before-after comparison would suggest.
4.2 Experimental methods
20
(17)
Problem: Correlation between the likelihood of being treated and individual characteristics
Examples
o Education
o Voluntary technical assistance programs
The most direct method to filter the selection effects is randomization.
Sources of difficulties
o 'Acceptability': ethics, political, more easy with lotteries among the beneficiaries, or with the
“incentives design” which consists in randomizing the promotion of the program between
different regions, which generates various participation rates at random rates
o Size of sample/cost; a randomized impact assessment can easily cost you 200-300'000 $
o External validity: an impact assessment cannot claim to generate general lessons.
4.3 Quasi-experimental methods
The key problem in quasi-experimental method is one of selection. In principle, treatment status
should not correlate with pre-treatment individual characteristics that can impact future
performance. But this is rarely the case. If a program goes to the worst performers (e.g.
development aid) and we do not correct for selection, we underestimate the treatment effect (true
treatment is larger than measured). If the program goes instead to the best performers (e.g.
voluntary assistance to technical assistance) and we do not correct for selection, we overestimate the effect of treatment (true treatment is smaller than measured).
Suppose that growth during the treatment correlates somehow with growth before the treatment
(because growth is persistent). If it happened that treated firms in a given program (say, an exportpromotion program) enrolled into the program because they had a period of fast growth just before
the treatment that made them interested in getting export promotion, we might attribute their faster
growth during the program to a “treatment effect” while, in reality, it is just a “selection effect”:
Firms enrolled into the program because of the fast growth.
If fast growth is a permanent characteristic of treated firms, no problem: this can be controlled by
fixed effects. But what if they had fast growth after a change of management a few years before the
program? Propensity-score matching can help reduce the bias due to selection effect by “handpicking” a control group with characteristics similar to those of the treatment group. In our case, we
would use lagged growth as a firm characteristic and “match” treated firms with high-growth
control firms, as in Figure 12.
Figure 12: A case where treated firms were fast growers before treatment
21
3
2
Pre-program growth
matching
0
1
Low weight or discarded
0
.5
1
log firm size
Control group
1.5
2
Treatment group
The propensity-score matching (PSM) procedure treats this problem in two steps. In the first,
treatment status is regressed on individual characteristics:
Pr i  TG   f  xi 
(18)
In (18), characteristics are taken as time-invariant and evaluated at the beginning of the sample
period. But we could also re-estimate that in every period for programs in which firms or
invididuals enrol when they want, and that’s what we would do if we wanted to use lagged growth
as one of the determinants of enrolment.
From step 1, we retrieve predicted treatment probabilities (“propensity scores”), pˆ i , and use them to
assign weights to control-group observations when matching them with treatment-group
observations. For each treated individual i, we form a “customized” control group with weights wij
assigned to control-group individuals j with lower weights on those having propensity scores that
differ a lot from i’s. The DID estimator is then
ˆ PSM DID  iTG  yi   jCG wij y j 
(19)
yi  yi ,post-traitement  yi ,pre-traitement
(20)


where
Example : Export promotion in Tunisia, where DID produces severely biased results without
matching.
Table 5: FAMEX treatment effects estimated by DID without matching
22
Forwarding degree
Estimator
Outcome
Total exports
R-squared
Nb. destinations
R-squared
Nb. products
R-squared
TY (k = 0)
OLS
(1)
TY+1 (k = 1)
OLS
(2)
TY+2 (k = 2)
OLS
(3)
TY+3 (k = 3)
OLS
(4)
TY+4 (k = 4)
OLS
(5)
TY+5 (k = 5)
OLS
(6)
0.774***
[0.187]
0.550
0.890***
[0.204]
0.552
0.445**
[0.220]
0.551
0.600***
[0.227]
0.551
0.519*
[0.282]
0.544
0.852***
[0.290]
0.551
0.338***
[0.033]
0.413
0.355***
[0.033]
0.417
0.323***
[0.037]
0.416
0.288***
[0.036]
0.417
0.305***
[0.043]
0.412
0.353***
[0.045]
0.418
0.290***
[0.039]
0.414
0.285***
[0.041]
0.415
0.240***
[0.042]
0.415
0.230***
[0.044]
0.414
0.265***
[0.054]
0.411
0.337***
[0.053]
0.416
21,077
18,638
13,802
9,197
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
18,805
21,089
Fixed effects included in 3 regressions above
Firm
Yes
Yes
Sector-year
Yes
Yes
Table 6: Same thing after matching
Forwarding degree
Estimator
Outcome
Total exports
R-squared
Nb. destinations
R-squared
Nb. products
R-squared
TY (k = 0)
PS weighted
(1)
TY+1 (k = 1)
PS weighted
(2)
TY+2 (k = 2)
PS weighted
(3)
TY+3 (k = 3)
PS weighted
(4)
TY+4 (k = 4)
PS weighted
(5)
TY+5 (k = 5)
PS weighted
(6)
0.411**
[0.171]
0.793
0.486**
[0.200]
0.770
0.208
[0.216]
0.765
0.080
[0.280]
0.762
0.009
[0.349]
0.746
0.144
[0.327]
0.740
0.104***
[0.022]
0.840
0.111***
[0.027]
0.825
0.076**
[0.032]
0.813
0.022
[0.033]
0.807
-0.014
[0.046]
0.787
0.039
[0.045]
0.783
0.086***
[0.031]
0.799
0.081**
[0.037]
0.783
0.062
[0.042]
0.773
-0.025
[0.047]
0.761
-0.009
[0.053]
0.755
0.072
[0.055]
0.755
21,077
18,638
13,802
9,197
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
18,805
21,089
Fixed effects included in 3 regressions above
Firm
Yes
Yes
Sector-year
Yes
Yes
Effect of matching
23
Predicted
Observed
FAMEX
No FAMEX
Average Export value per firm (KTD)
Average Export value per firm (KTD)
No FAMEX
6000
5000
4000
3000
2000
1000
0
2004
2005
2006
2007
2008
2009
2010
FAMEX
6000
5000
4000
3000
2000
1000
0
2004
2005
2006
2007
2008
2009
2010
4.4 Externalities
Philosophical problem of the impact assessment if it is used to evaluate enterprise assistance
programs.
The Government should use taxpayer money to help companies only if there is a market deficiency
(otherwise the companies should pay for the services provided):
Informational externalities: If a company explores a new market and succeeds, it demonstrates the
profitability of this market, others imitate it, therefore, annuities are dispelled (the information is a
public good), and anticipating that the company provides less resources it would be socially
optimal.
It is only in a case of this kind that the program can be justified. However
o If there are treatment’s effects, it means the benefits of the expansion into new markets are
internalized by the recipient firms, therefore, we do not need the program
o If there is no treatment effect, it means that either the program has no effect or the benefits are
spread among the control group, which would precisely be good justification for the program!
24
Table 7: Cost-benefit analysis
TY
TY+1
(a) Total exports of all FAMEX firms (in millions of TD)
(b) Total counterfactual exports of all FAMEX firms without treatment (in millions of TD)
(c) Additional exports generated by FAMEX program (in millions of TD)
1352.26 1433.42
896.53 881.67
455.73 551.75
(d) Discounted (using 8% rate) additional FAMEX exports (in millions of TD)
455.73 507.61
(e) Total profits (using 5% rate) generated by additional FAMEX exports (in millions of TD)
22.79
25.38
6.84
7.61
Tax revenue collected on total profits generated by additional FAMEX exports (30%
(f)
corporate tax rate) (in millions of TD)
Total cost of grants provided to firms + administrative and operational costs of FAMEX
(g)
program (in millions of TD)
(h) Public benefit/cost ratio = (f)/(g)
TY+2
TY+3
TY+4
TY+5
Total
Ratio
Difference across FAMEX firms and
counterfactual is not significant
-
-
-
-
-
-
-
-
14.20
48.17
14.45
14.20
1.02
(i) Net after-tax total profits generated by additional exports (in millions of TD) = (e)-(f)
(j) Amount paid for by FAMEX firms as matching grants (in millions of TD)
(k) Private benefit/cost ratio = (i)/(j)
15.95
9.44
(l) Total public and private cost of FAMEX program = (g)+(j)
(m) Overall benefit/cost ratio = (e)/(l)
23.64
17.77
-
-
-
-
33.72
9.44
3.57
2.04
References
Birdsall, Nancy, and H. Kharas (2010), Quality of Official Development Assistance Assessment;
Washington, DC: Center for Global Development.
Burnside, Craig, and D. Dollar (2000), “Aid, Policies, and Growth”; American Economic Review
90, 847-868.
Collier, Paul, and A. Hoeffler (2004), "Greed and grievance in civil war”; Oxford Economic Papers
56, 563-595.
25
Easterly, William (2003), “Can Foreign Aid Buy Growth?”; mimeo, New York University.
—, R. Levine and D. Roodman (2004), “Aid, Policies, and Growth: Comment”; American
Economic Review 94, 774-780.
Ferro, Esteban, and J. Wilson (2011), “Foreign Aid and Business Bottlenecks: A Study of Aid
Effectiveness”; World Bank Policy Research Working Paper 5546; Washington, DC: The World
Bank.
Nunn, Nathan, and N. Qian (2011), “Aiding Conflict: The Unintended Consequences of U.S. Food
Aid on Civil War”; mimeo, Harvard University.
26