WPS3990
Does Aid Help Improve Economic Institutions?1
Decio Coviello Roumeen Islam
European University Institute The World Bank
Abstract
Aid is expected to promote better living standards by raising investment and growth. But aid may also
affect institutions directly. In theory, these effects may or may not work in the same direction as those on
investment. This paper examines the effect of aid on economic institutions and finds that aid has neither a
positive nor a negative impact on existing measures of economic institutions. These results are found
using pooled data for non-overlapping five-year periods, confirmed by pooled annual regressions for a
large panel of countries and by pure cross-section regressions. We explicitly allow for time invariant
effects that are country specific and find our results to be robust to model specifications, estimation
methods and different data sets.
JEL Classification: O11; O19; O16
Keywords: Aid; Economic Institutions; Dynamic Panel
World Bank Policy Research Working Paper 3990, August 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
of ideas about development issues. An objective of the series is to get the findings out quickly, even if the
presentations are less than fully polished. The papers carry the names of the authors and should be cited
accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they
represent. Policy Research Working Papers are available online at http://econ.worldbank.org.
1This paper was written when Coviello worked in the World Bank Institute Poverty Reduction (WBIPR) of the
World Bank. We thank Anindya Banerjee, Bill Easterly, Fabio Schiantarelli, Arvind Subramanian, Luca A. Ricci,
Ariell Reshef, Jon Temple for helpful suggestions, comments and discussions. We also thank Fred Kilby for
providing us with a version of the Fraser data. Authors' contacts: rislam@worldbank.org, decio.coviello@iue.it.
1
Introduction
Institutions are shaped by history. Whatever other factors may affect
their form, institutions have inertia and "robustness".
R.D. Putnam, (1993), pg.8
How effective is aid in promoting better growth in poor countries? Over the last decade, new
data and techniques have been used to examine the relationship between aid and growth, the
urgency of the issue varying presumably with the tightness of aid budgets. Conflicting results
have been reported in these different studies. Some, notably, Clements et al (2004) and Dalgaard,
Hansen and Tarp (2004) among recent papers, find a positive or conditional relationship between
aid and growth. Papers attempting to nuance the relationship between aid and growth such as
Burnside and Dollar (2000) and World Bank (1998) find that aid promotes growth in good policy
environments. However, Hansen and Tarp (2001), Easterly (2003), Easterly, Levine and
Roodman (2004) and Roodman (2004) find that these results are not robust to different data sets
and specifications. Rajan and Subramanian (2005) reviewing the literature find no robust
relationship between aid and growth.
In these papers, the main hypothesis is that aid should raise growth by providing funds
for investment. As Rajan and Subramanian (2005) indicate, even assuming that all aid is invested
(and using plausible values for both the share of capital in income and the output capital ratio for
the average developing country), estimations show that a 1% increase in the ratio of aid to GDP
should at most raise long run growth by 0.16 percent. If half of aid is wasted or consumed this
coefficient of aid's impact on growth should be close to 0.1. This is not a large amount. They
also note that the coefficient on aid should be close to its impact on investment. Barro and Martin
(2004, Chapter 12) find a coefficient on the investment to GDP growth rate in the order of .03%;
thus aid should not be expected to have a large impact on growth through this channel. They
conclude that to justify higher coefficients found in the literature, one would have to assume that
aid has a substantial impact on total factor productivity growth- for example because it promotes
2
higher human capital, better institutions or better policies. This paper examines the effect of aid
on economic institutions and finds no robust evidence of either a positive or negative relationship
between aid and economic institutions.
Studies of the relationship between aid and growth find that much of aid does not
translate one for one into higher public investment and the effect varies tremendously between
countries (World Bank, 1998). The typical aid dollar finances 29 cents of public investment.
Even more noteworthy, studies have found that the share of GDP devoted to public capital
spending does not have a relationship to economic growth. This is because how spending
translates into public capital depends on the efficiency of government expenditures and the
relationship between budgeted and realized expenditures. For example, infrastructure services
have been shown to be important for growth (Serven and Calderon, 2004) but it is harder to show
that infrastructure expenditures are important (Hulten, 1996 and World Bank, 1998).
Even if aid does not affect investment and thus growth directly, aid could affect growth
(indirectly) through its impact on policies or institutions (or even skills/human capital). Some of
these changes could be productivity enhancing (or reducing) and could add value even if funding
public capital does not (see World Bank, 1998). For example, donors often attempt to strengthen
legal systems and property rights. There are countless projects funded that aim to reform the
judiciary and numerous laws adopted as a result of donor intervention, for example: bankruptcy,
accounting, disclosure, company, collateral, and land registration laws. It has been argued that
many countries underwent rapid economic liberalization (or deregulation, which is equivalent to
a particular form of institutional reform) because of pressures from the donor community. This
"Washington Consensus", though geared towards what was believed to be an improvement in
policies and institutions was often blamed for much of the vicissitudes of Russia and Latin
America in the 1990s. Explanations for the less than positive results have varied between an
underestimation of the time needed to implement institutional change or the too rapid pace of
3
liberalization (and accompanying change in institutions. In the case of the socialist countries this
might be expected to show up as less regulation).2
If aid worsened some institutions but supported public expenditures on investment, the
net effect on growth could be either positive or negative. Similarly, the impact would be
ambiguous if it improved some institutions but worsened others. If aid succeeded in raising
critical skills then the net effect of all three on growth could be nil, positive or negative. Or the
impact of aid may vary between the short and long runs.
Papers examining the impact of aid on certain aspects of institutional development in a
country have become popular. The potentially negative impact of aid on institutional
development is discussed in several papers, old and new. Aid can encourage rent seeking and
corruption, alleviate pressures to reform domestic institutions (such as taxation), keep
undemocratic or "bad" governments in power by providing them with resources for their cronies,
and generally weaken accountability. Alesina and Weder (2002) and Knack (2000) find that
foreign aid inflows lead to increases in corruption as agents vie for aid resources. Brautigam and
Knack (2004) contend that high levels of aid create moral hazard for donors and recipients,
exacerbate collective action problems and weaken local pressures for reform and for
accountability. In addition, the unpredictability and volatility of aid can have perverse effects on
fiscal policy (Celasun and Walliser, 2005) and donor fragmentation can reduce bureaucratic
quality as different donors put pressure on staff in government bureaucracies (Knack and
Rahman, 2004) to satisfy donors' own needs (for example needs for financial reporting).
Djankov et al (2005) find that foreign aid has a negative impact on democracy. They explain
these results by saying foreign aid could lead politicians in power to engage in rent-seeking
activities in order to appropriate these resources and to exclude other groups from the political
2 It is also believed that changes in formal laws and regulations (economic institutions) did not take into account
existing informal systems and other initial conditions (such as income and skill levels) and so tended to undermine
the transactions they were supposed to strengthen.
4
process. Thus political institutions are damaged because they become less representative and less
democratic. Some work on economic institutions Knack (2000) indicates that even measures of
economic institutions performed poorly as a result of aid. Svennsson (2000) finds that aid is
associated with more regulation and that more regulation is associated with more corruption.
But just as aid can have a negative effect on political institutions and corruption through
the rent seeking process, it may also have a positive effect on certain economic institutions.
These effects would be generated because (a) resources are needed to set up institutions and aid
provides these resources, (b) the skill building, knowledge dissemination (which provides
relevant information on economic outcomes to stakeholders, including voters and leaders within
existing political systems)3, policy advice and conditionality associated with aid aims to improve
institutions and (c) aid enhances incentives to undertake reforms (not necessarily investment).
Thus it is equally possible for aid to influence economic institutions positively. Presumably the
larger the flows of aid to a country, the higher in absolute terms is the flow of these other factors
to a country so that aid flows serve as proxy for the other dimensions of aid. In reality, there may
be countries where the two dimensions of aid are substitutes: countries receiving less money may
receive relatively more effective policy advice (so that countries with higher aid flows may show
less institutional development because the non-money factor is absent or unimportant or because
the negative effect of money overrides any positive effect of policy advice)4. We assume that
countries receiving higher aid flows also receive more policy advice and conditionality aimed at
strengthening institutions. Finally, as mentioned earlier, aid could have positive effects on some
types of institutions but have negative effects on others so that the overall effect may be zero.
Kilby (2004) finds that donor funds favor more heavily regulated economies and promote
deregulation. Remner (2004) finds aid to be associated with increased public spending but
reduced tax effort. Bannerjee and Rondinelli (2003) find that aid does not have a systematic
3If aid is used to disseminate policy impacts, aid may change leaders' incentives within political institutions.
4In practice we are not able to disentangle these separate effects in our regressions.
5
influence on the decision to begin privatization, that the impact of aid on privatization is limited
to technical assistance and that in countries with "better" governance, aid may be more important
or effective in the privatization process. Tavares (2003) finds that aid reduces corruption.
Other potential impacts of aid on factors that affect growth are also possible. The aid
literature has examined the negative effects of real exchange rate appreciation following high aid
inflows on trade and growth. A recent example of these types of papers is that by Rajan and
Subramanian (2005) who find that aid inflows have systematic adverse effects on a country's
competitiveness, measured as a decline in the share of labor intensive and tradable goods
industries in the manufacturing sector. In order to understand how aid affects development it is
important to examine aid's impact on a broader set of factors.
In this paper, we revisit the issue of aid and institutions, focusing particularly on the
"economic" institutions measures. Historical development and the cross country growth
literature have both demonstrated that different types of political institutions and conditions have
supported growth. More specifically, having more or less democracy does not predict growth
outcomes. But economic institutions matter. (Acemoglu et al. (2002, 2004, 2005)) and they are
not linked in a one to one predetermined relationship with political institutions. Figures 1-4 show
the correlation between a measure of democracy from polity4 and alternative measures of
economic institutions from ICRG and the Fraser Institute. Correlations between different
measures of economic and political institutions are not large over decades (though they are
usually significant) as shown in Tables 1and 2 below and tables A1 and A2 in the appendix.
6
Table 1: Correlations between democracy and measures of economic institutions
1984 1997
bq corr polity4 bq corr polity4
bq 1 1
corr 0.8276 1 0.6971 1
polity4 0.6069 0.5634 1 0.4555 0.5783 1
Table 2: Correlations between democracy and measures of economic institutions
Average 1984-1997
avg_bq avg_corr avg_polity4
avg_bq 1
avg_corr 0.829 1
avg polity4 0.5942 0.6002 1
*Notes for Tables 1 and 2:
bq= bureaucratic quality, corr= corruption, from
ICRG; polity4 (from polity IV index, normalized
0-1)= democracy measure, avg= average.
7
Figure 1: Change in World Institutions: corruption and democracy, 1984-1997
Figure 2: Change in World Institutions: bureaucratic quality and democracy, 1984-1997
8
Figure 3: Change in World Institutions: property rights and democracy, 1970-2000
Figure 4: Change in World Institutions, regulation and democracy, 1970-2000
9
The main conclusion of this paper is that aid does not have an impact on different measures of
economic institutions once we explicitly control for country effects that are time invariant and
are country specific. We conclude that estimations showing that aid either negatively or
positively affects institutions omit historical determinants of institutions that are important in
shaping today's institutions. Once we account for these factors, we find no robust effect of aid on
institutions. We do both cross section and dynamic panel data (GMM) analysis to ask the
question whether aid has any effect on a variety of measures of economic institutions. Our results
are robust to different definitions of aid (for example we distinguish between technical assistance
and other forms of lending), to a number of specifications and to different datasets.
Furthermore, we find that the results in the literature that find a negative relationship
between aid and democracy (a measure of political institutions) and a positive relation between
aid and corruption are not robust to specification or estimation methods and vanish once we
explicitly consider country fixed-effects. These results are not shown here.5
Data on Aid and Economic Institutions
For the panel regressions in this paper we use indicators of economic institutions from
two main datasets for the panel regressions-the ICRG and Fraser Institute datasets. As a
robustness check we also use an indicator to measure institutional development more narrowly.
This measure is linked to institutional development in the financial sector. We focus on a subset
of indicators that represent our notion of economic institutions those that support economic
transactions but not through the political process. Indicators of democracy, checks and balances
within government and political stability (or civil wars) fall in the latter category. For the ICRG
dataset, available for 1984-2002 we use measures of bureaucratic quality and corruption as
indicators of "economic" institutions (in contrast to government stability, internal conflicts,
5But are available upon request.
10
ethnic tensions, democracy and accountability which do not fall in this group). The ICRG data
are available on an annual basis for 113 countries.6
In the annual data we selected five indicators from the ICRG data set, (see details in the
data appendix table). The variables we considered are bureaucratic quality, corruption, law and
order, expropriation risk, or repudiation risk.7
The other data we use are five yearly data from the Fraser Institute available for the
period 1970-2000. Two indicators are of interest: freedom from government regulation and legal
structure/security of property rights. The first variable ranges from 0 (low freedom=high
regulation) to 10 (high freedom=low regulation). This indicator is an aggregate of 15 sub-indices
and is called "Regulation of Credit, Labor, and Business. This index reflects a mix between what
is commonly accepted as desirable institutional outcomes and simply, less regulation. For
example, under most conditions it is desirable that administrative procedures are not an
important obstacle to setting up a business, that the percentage of deposits held in private banks
is high (as opposed to deposits in public banks), that businesses are generally free to set their
own prices and that interest rate controls and regulations do not lead to negative rates. But the
indicator also includes a measure of whether hiring and firing practices are determined by private
contract (as opposed to some government regulations). It is not clear how much regulation would
be considered "good" in all contexts. For those who would contend that the indicator picks up
mostly "less regulation", it would help test the hypothesis that donors successfully promoted the
Washington Consensus model of "liberalization, privatization and competition". Alternatively, it
would be a test of whether donors supported better institutions.
The second indicator measures aspects of: a) judicial independence, b) impartial courts,
c) protection of intellectual property rights, d) military interference in rule of law and the
political process, and e) the integrity of the legal system.
6Note that other authors, e.g. Knack (2000and 2004) also use the ICRG data.
7In the empirical section we do not present all the regressions due to space constraints but these are all available
upon request.
11
Finally, we use another measure of institutional quality that is sector specific. It may be
argued that most of the existing measures of institutional quality are too broad (and subjective) to
be good indicators of the effects of aid on institutional quality. One way to narrow the analysis is
to consider institutional quality in a particular sector or a set of institutions geared towards
promoting a particular transaction- we choose the financial sector. Following Clague et al.
(1999), we think that measures of financial assets kept in the banking system, namely M2 minus
currency in circulation, are a good proxy for institutional development in the financial sector.
The reasoning is that individuals would be very unlikely to hold money in banks if the legal and
regulatory framework did not sufficiently lower the risk of bank failure or expropriation so as to
make bank deposits desirable. Donors have put a lot of aid funds and policy advice into financial
system development, specifically bank privatisation and banking sector regulation and
supervision.
Three types of aid flows are used. The first is Effective Development Assistance over real
GDP (AID), as in Chang et al., 1998. The second is Net Overseas Development Assistance over
real GDP (ODA) and includes technical assistance, as in Alesina and Weder (2002) and technical
assistance completed as the difference between ODA-AID. This variable is available for 1975-
1995 from Chang et al (1998). Missing values are extrapolated based on a regression of EDA on
Net ODA. The standard measure of aid (Official Development Assistance, ODA) lumps
concessional loans together with grants if the loan's grant element exceeds 25%, while Effective
Development Assistance converts these loans to their grant equivalent and thus provides a better
measure of long term resource flows.
Model Specification and Estimation Strategy
Cross-Section Estimation
In this section, as in Knack (2001), Knack (2004), Brautigam and Knack (2004) and
Kilby (2005) we consider the effect of aid on economic institutions in a pure cross-section of
12
countries. Since the main source of variation is given by cross-country differences we are forced
to omit country effects (fixed effects). The equations we estimate are of the following form:
yi = + y0 + Xi + Ai +i (1)
i
Where yi represents the change in the quality of economic institutions, and y0 is a i
variable representing the initial level of institutional quality. This specification is used since
initial institutional effectiveness is believed to determine subsequent development (institutions
are persistent) and to catch a regression toward the mean effect. It is also the specification used
in a number of recent papers (such as those mentioned above). The variable Ai represents three
different types of aid flows: overall development assistance (oda), development assistance net of
technical assistance (aid) and technical assistance (ta). For the cross country regressions, our
main specification assesses the effect of aid flows in the specifications shown in (1), though our
main results remain unchanged if the model is specified in levels or in pure changes, that is,
without the initial level of institutions.8 For the panel data, GMM analysis, we focus on a
dynamic equation in levels, though we also consider the model in changes. In the levels
equations, we capture the effect of past aid flows on institutions through lagged institutions on
the right hand side.
In all but the most basic specifications we control for the initial level of GDP or
concurrent level of GDP. Real GDP computed as LGDP above, is in 1985 dollars. Other
conditioning variables used in the robustness checks differ across specifications and are added
drawing from the theory of what determines institutional quality as well as the empirical
literature on institutions. These variables are legal origin, to account for past historical/political
influences on institutions, latitude (to account for geographical factors), settler mortality rates a
la Acemoglu et al. (2002), ethnic fractionalisation, and trade openness to account for the effect of
trade and competition on institutions. Following Mulligan and Shleifer (2003) who find that
8We do not think the regression in levels (with aid flows) or in pseudo-changes (with initial institutions) to be the
most appropriate specification but have estimated these equations in order to reproduce those in recent papers.
13
more heavily populated states in the US are more heavily regulated, we believe that population
has a direct effect on institutions. Institutions being costly to set up, more populated countries
would tend to be more heavily regulated or have more complex institutions.9
We run both ordinary least squares (OLS) and instrumental variables (IV) regressions to
instrument for aid. We used two sets of instruments. The first instrument set considers only lags
of the log of gnp per capita and the log of population. For instance, to insure exogeneity we
consider lags 7 and 11 (depending on the data used), the first, to instrument for the regressions
using the ICRG data and the second for those using Fraser data and the CIM indicator. Although
not reported, we checked for the first stage F-statistics of the excluded instruments (in order to
see if the instruments are weakly correlated with the endogenous regressors, (see Stock Wrigth
and Yogo [2004]). All the first stage regressions report an F-test greater than 10.
The second instrument set is larger and it is composed of the first instrument set and a
series of dummies for ex-colonies, and for Sub-Saharan Africa, Europe, Central America, and
Egypt, as in Rajan and Subramanian (2005). In all the IV regressions, since we have more
instruments than endogenous regressors, we can test for the validity of the instruments using the
Hausman tests. In all the specifications we do not reject the null hypothesis of instrument
validity. Although ten lags seem long enough, it is clearly possible that countries' economic/
institutional situation of ten years ago could affect the level of economic institutions today. It is
hard to find truly exogenous variations in aid particularly since donors do not give aid
randomly.10
Tables 3 and 4, report summary statistics.
9Regressions not presented here are available from the authors upon request.
10 Given that the specification we consider includes the initial level of institutions, we believe that considering only
aid as endogenous can lead to severe inconsistency in the estimation of the effect of aid on institutions in the cross
section estimates. The initial level of institutions is itself correlated with the unobservable country effect and the
initial level of institutions is itself endogenous.
14
Table 3: Descriptive Statistics Panel Table 4: Descriptive Statistics Cross-Section
Variable Available N Obs Mean Std. Dev. Min Max Variable Available N Obs Mean Std. Dev. Min Max
reg 5 years 70-94 680 5.44 1.15 2.5 8.8 reg8500 104 0.64 0.95 -0.8 3.6
linireg85 104 5.31 1.15 2.5 7.3
prop " " 648 5.46 1.91 1.1 9.6 prop8500 108 0.79 1.28 -2 4.4
liniprop85 108 5.11 1.8 1.7 8.3
icrge " " 602 4.58 1.96 0 10 bur_qu9095 113 0.27 0.71 -2 2
bur_qu90 113 3.26 1.6 1 6
cim " 110 844 0.77 0.15 0 1 corr9095 113 0.24 1 -3 4
corr90 113 3.38 1.52 0 6
rule_law Annual 90-100 1497 3.33 1.69 0 6 exprisk9095 113 1.98 1.8 -3.6 6
exprisk90 113 6.75 2.18 2 10
bur_qual " " 1497 3.28 1.64 0 6 repud9095 113 1.54 1.64 -2 6.4
repud90 113 6.07 2.34 1.6 10
ethn_tens " " 1497 3.79 1.61 0 6 eth9095 113 0.96 1.24 -1.5 4.8
eth90 113 3.5 1.64 0 6
repud " " 1457 6.29 2.28 1 10 cim8500 117 0.04 0.08 -0.26 0.51
linicim85 118 0.79 0.13 0.34 0.98
exp_risk " " 1457 6.89 2.23 1.5 10 lgnp9095 100 0.1 0.37 -1.48 0.9
laid 88 0.91 2.09 -6.36 4.51
corr " " 1500 3.41 1.55 0 6 lpop84 97 16.25 1.49 12.29 20.76
lgnp84 85 7.38 1.32 4.79 9.65
oda " " 763 1.67 2.54 -0.02 16.29 lpop8500 120 0.26 0.18 -0.05 0.81
lgdp8500 110 0.1 0.32 -0.61 1.08
loda " " 572 -0.19 1.81 -9.67 2.79 lpop75 120 15.6 1.71 11.7 20.52
lgdp75 109 7.76 0.97 5.83 10.58
aid " " 763 0.91 1.77 -3.8 12.67 latitude 176 18.51 24.09 -36.89 63.89
legal_origin 176 2.63 1.81 1 9
laid " " 462 -0.69 1.98 -14.09 2.54 eng_legor 176 0.31 0.46 0 1
french_legor 176 0.38 0.49 0 1
gdpg " " 770 1.81 3.51 -13 32.29 germ_legor 176 0.03 0.18 0 1
scand_legor 176 0.03 0.17 0 1
lgdp " " 773 7.94 1.04 5.83 10.75 social 176 0.17 0.38 0 1
islam 176 0.05 0.22 0 1
popg " " 838 1.84 1.47 -5.86 16.62 centam 121 0.06 0.23 0 1
easia 121 0.07 0.26 0 1
lpop " " 840 15.79 1.7 11.59 20.91 egypt 121 0.01 0.09 0 1
ssa 121 0.22 0.42 0 1
syr " " 663 1.27 1.12 0.01 5.09 logmort0 64 4.65 1.25 2.15 7.99
15
Estimation Results:
Table 5 reports the cross section results for 3 variables from the ICRG data set. The first
column presents the results of the OLS estimation for the base specification for aid (using the
first instrument set)11 with GDP and population. Subsequent columns show the IV estimation
instrumenting for aid and the third column shows the model with a fuller specification that
includes most of the above-mentioned variables. The model in pure changes is not shown but the
results are similar. The regressions in Table 4 show that even the OLS regressions do not
indicate any significant relationship between aid and institutional measures.
Table 6 shows similar estimations for the two indicators from the Fraser data set. In this
table, column 4 shows the estimation in pure changes (differencing all variables). Table 6 shows the
only dependent variables among the several tested that have a consistent relationship with aid-the
variable from the Fraser data set measuring the extent of regulation. These results indicate that aid is
quite robustly associated with more regulation. High values of this indicator suggest that the economy
is over regulated. Aid seems to have consistently raised regulation; an alternative way to interpret this
result is that aid has not been a force for market liberalisation.12 Table 7 shows the results for our
financial sector specific institutional variable or contract intensive money. Table 6 indicates that
some specifications using this variable show a negative relationship between aid and institutions.
The OLS cross section results in Table 5, 6, and 7 indicate that it is possible to get a
significant relationship between aid flows and institutions. But in all cases except one, these
results are not robust across alternative specifications. In other words, any positive or negative
relationship between aid and institutions is not robust to the addition of control variables or to
using IV techniques, with the exception of the indicator "regulation".
11Similar results are obtained using oda and ta.
12Or that the "Washington Consensus" view is substantiated.
16
As in Acemoglu et al. (2005) we consider the role of history to be crucial in shaping
institutions today; this means that we consider the cross-sectional results estimation done in
previous works to be driven by the omission of country effects that are correlated with the initial
level of institutions. We observe a large and significant effect of the past level of institutions on
the change of institutions and on the level itself in all the specifications, estimation techniques,
and considering different indicators of institutions.
17
Table 5. Regressions with Rule of Law, Bureaucracy, Corruption
Rule of Law Bureaucracy Corruption
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
(OLS) (IV Aid) (IV Aid) (OLS) (IV (IV Aid) (OLS) (IV Aid) (IV Aid)
Aid)
aid -0.08 -0.13 -0.06 -0.08 -0.05 -0.03 -0.06 -0.03 0.02
(log) -0.64 -0.07 -0.06 -0.05 -0.07 -0.05 -0.06 -0.06 -0.05
initial institution -.67*** -.72*** -.67*** -.40*** -.36*** -.40*** -.52*** -.51*** -.54***
(in year 1990) -0.086 -0.08 -0.07 -0.07 -0.06 -0.07 -0.08 -0.08 -0.08
lpopulation -1.12 -3.31 0.61 0.37 -2.7 .15* -0.86 -3.93* -0.44
(`90~'95) -2.61 -2.68 -2.29 -1.9 -2 -1.85 -1.82 -2.13 -1.72
lgnp .63* .69** 1.11*** .45* 0.38 .54** 0.29 0.3 .63**
(`90~'95) -0.32 -0.31 -0.26 -0.25 -0.31 -0.27 -0.26 -0.29 -0.28
English Legal Origin -0.24 -.43* -.65**
-0.28 -0.26 -0.31
French Legal Origin -.7** -.79*** -.83**
-0.32 -0.26 -0.32
Latitude .01** 0.003 0.008
-0.006 -0.003 -0.004
Constant 3.16*** 3.47*** 3.21*** 1.36*** 1.57*** 1.92*** 1.96*** 2.17***
-0.38 -0.35 -7.43 -0.26 -0.29 -0.35 -0.36 -0.35
# obs 79 62 62 79 62 62 79 62 62
R2 0.4 0.81 0.85 0.33 0.48 0.6 0.43 0.52 0.62
Notes:
1) dependent variable is indicator of institutional quality.
2) instrument 1 for aid (lgnp and lpop in 1984) is used. Instrument 2 not shown but basic story similar.
3) robust standard error is reported in the brackets below the coefficient
4) ***designates significance at .01 level, ** at .05 level, * at .10 level.
18
Table 6. Regressions with Regulation and Property Rights
Regulation Property Rights
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
(OLS) (IV Aid) (IV Aid) Changes (OLS) (IV Aid) (IV Aid) Changes
(IV) (IV)
Aid -.14*** -.22*** -.22*** -.33*** .03* -.09** -.06* 0
(log) -0.04 -0.06 -0.06 -0.09 -0.08 -0.11 -0.11 -0.13
Initial Institution -.47*** -.47*** -.49*** -.60*** -.63*** -.61***
(in year 1985) -0.08 -0.08 -0.09 -0.1 -0.1 -0.1
lpopulation -1.07 -0.76 -0.8 -0.95 -0.99 0.998 -0.85 -0.9
(`85~'00) -0.72 -0.76 -0.77 -1.11 -1.15 -1.3 -1.3 -1.65
lgnp -.25** -0.39 -.45* -1.11*** .93** 0.7 0.61 0.22
(`85~'00) -0.25 -0.26 -0.26 -0.38 -0.41 -0.45 -0.47 -0.54
English Legal Origin 0.3 0.53
-0.3 -0.43
French Legal Origin .02* 0.11
-0.28 -0.44
Latitude 0.001 0.01
-0.004 -0.01
Constant 3.85*** 4.09*** 4.06*** 2.19*** 3.49*** 4.12*** 3.52** 1.04***
-0.49 -0.59 -0.58 -0.49 -0.66 -0.72 -0.82 -0.56
# obs 75 74 74 74 78 76 76
R2 0.61 0.59 0.61 0.27 0.5 0.49 0.25
(uncentered)
Notes:
1) Dependent variable is change in the institutional indicator, 1985~2000. For column 4, the variables
are all specified in changes.
2) instrument 1: log population in 1975, log gnp in 1975
3) robust standard error is reported in the brackets below the coefficient
4) ***designates significance at .01 level, ** at .05 level, * at .10 level.
19
Table 7. Regressions with CIM and aid
Model 1 Model 2 Model 3 Model 4 Model 5
(pure change,OLS) (pure changes,IV1 Aid) (pure changes,IV2 Aid) (IV1 Aid) (IV2 Aid)
Aid 0.0009 -0.003 -0.002 -.012* -.01*
(log) -0.0037 -0.005 -0.005 -0.006 -0.006
Initial CIM -.29** -.29**
(in year 1985) -0.14 (-1.97)
Population -0.059 -0.06 -0.06 -.07* -.07*
(`85~'00) -0.043 -0.049 (-1.33) -0.04 -0.04
GDP .039*** .038** .039** .04** .04**
(`85~'00) -0.013 -2.37 -2.43 -0.017 -0.017
Constant .063*** 0.08 .08*** .35** .34**
-0.019 -0.028 -0.027 -0.14 -0.14
# obs 94 87 87 87 87
R2 0.27 0.26 0.27 0.36 0.36
(uncentered)
Notes:
1) Dependent variable CIM is Contract-Intensive Money, 1985~2000.
2) instrument 1: log population in 1975, log gnp in 1975
instrument 2: log population in 1975, log gnp in 1975, dummy for Central America, East Asia, Europe and Sub-
Saharan Africa
3) robust standard error is reported in the brackets below the coefficient
4) ***designates significance at .01 level, ** at .05 level, * at .10 level.
20
Dynamic Panel GMM Estimation
Cross-section methods are simple and easy to interpret, but have weaknesses.
Relationships may be artificially created by unobserved heterogeneity that is caused by a number
of factors such as history, mortality rates, geography, or endowments of resources (country
effects that are not time varying-in the case of economic institutions). The use of panel data can
overcome this problem. We can look at relationships over time (so we can add within country
variation to the across country variation), to see whether increases in aid are followed by
changes in economic institutions, and to distinguish between short-run and long-run effects. The
main estimated equation is the following one:
yi = ( -1)yi (2)
,t ,t-1+ 'Xi +t +i +i
,t ,t
Or alternatively,
yi = yi (3)
,t ,t-1+ 'Xi +t +i +i
,t ,t
Where y_it represents the level of the different institutional indicators for country (i) at time (t),
y_it-1 is its own lag. t, is a time trend attempting to catch a common co-movement between the
regressors and the dependent variable, in the estimates we consider both a linear trend and a set
of time dummies but we report results only with time dummies. i are the country specific
effects and i are the i.i.d stochastic disturbances. X_it are the regressors. Among them we
,t
consider the lagged level of the natural log of aid flows.
The following section provides details on the variables considered and a discussion of the
results. In this section we describe the methodological aspects of estimating (dynamic) models
with panel data. There are several econometrics methods which can be used to estimate
equations like (2), or equivalently (3) where the number of countries (N) is larger than the
number of time periods for which data are available (T). Because of the assumptions on the
21
presence of country specific effectsi , and on the error terms, i.i.d.i ; the Ordinary Least
,t
Squares (OLS) and the Instrumental Variables estimators13 of , and in the level equation (2)
and (3) are biased and inconsistent. This is so because the explanatory variable yi ,t-1is by
definition positively correlated with the error term i + i due to the country fixed effect. This
,t
correlation does not vanish as the number of countries in the sample gets larger, nor as the
number of time periods increases. As discussed in Bond et al. (2001) and in Bond (2002), the
OLS levels estimator of the autoregressive coefficient (which suffers from omitted variable bias)
is considered that upper bound for the true estimates. The Within Groups estimator eliminates
these sources of inconsistency by transforming the equations so as to eliminatei . 14
yi =yi (4)
,t ,t-1+ 'Xi +t + i
,t ,t
The original observations are expressed in first differences, as in equation (4), and OLS
estimates performed. Even if the country effect i is removed by the first difference
transformation, for panels where the number of time periods is small relative to the number of
countries the first difference transformation induces a "non-negligible" correlation between the
transformed lagged dependent variable and the transformed error term. The above correlation
can be shown to be negative and does not vanish as the number of countries gets larger15.
As discussed in Bond et al. (2001) and in Bond (2002), we consider the Within Groups
estimator of the autoregressive coefficient as a lower bound for our estimates. Because of the
13We refer to estimation that considers aid as the only endogenous regressor and only instruments for aid (as in
cross section specifications).
14When T=2 the Within Group estimator and the First Difference estimators delivers the same data point estimates.
If T>2 the two estimators are different. In the estimation we consider Fixed Effect Estimates, see Wooldridge
(2003).
15 However, the correlation induced by the transformation vanishes, and the Within Groups estimator is a consistent
estimator when T is large.
22
opposite nature of the bias of the two above estimators, we would expect better estimators-that is
the true parameters- to lie between the OLS and the Within Groups estimates, or at least to not be
significantly higher than the former or significantly lower than the latter.
Several consistent estimators potentially bounded between the OLS and the Within
Groups estimators are proposed by the econometrics literature but have not been commonly used
to disentangle the effect of aid on institutions. We focus here only on two of them namely the
"Differenced Estimator" proposed and developed by Holtz-Eakin, Newy and Rosen (1988) and
Arellano and Bond (1991) and the "System Estimator" developed by Arellano and Bover (1995)
and further developed in Blundell and Bond (1997). The main advantage of considering these
Generalized Method of Moments estimators is that both GMM estimators allow us to control for
potential endogeneity of all explanatory variables, country fixed effects and temporary
measurement error. Aware of the severe bias problems and instrument validity issues in the case
of a persistent (time-variant) measurement error, we leave the discussion on the performance of
both the Difference and the System estimators to Hauk et al. (2004). By following Bond et al.
(2001), in the robustness check of the results we assume that the measurement error is transient
and there is a permanent additive measurement error that is considered time-invariant and thus
absorbed in the country effect. To allow for the mentioned problem we repeated the estimates
using as instruments the lagged levels starting from (t-3) instead of (t-2) and the lagged
difference starting from (t-2) instead of (t-1).16
GMM estimators control for endogeneity by using "internal instruments", that is,
instruments based on lagged values of the explanatory variables. These methods do not allow us
to control for full endogeneity but for a weak version of it. More precisely, we assume that the
explanatory variables are only "weakly exogenous", which means that they can be affected by
16Results are the same and not reported but available on request.
23
current and past realizations of institutions but must be uncorrelated with future unanticipated
shocks to institutions (the error term). Thus, the above correlation assumption implies that future
unpredictable innovations in institutions do not affect current foreign aid. This assumption is
commonly shared and accepted in the growth literature (the first application is Levine et al.
(2000), for details see Bond et al. (2005)) and for the first time discussed in the aid and
institutions literature.
To consistently estimate equations (2) and (3), where potentially all the regressors, Xi ,
,t
can be endogenous, and to account for errors in the differenced equation being correlated with
the lagged dependent variable we need some additional assumptions. First we assume that the
error term i is i.i.d, and second that the explanatory variables Xi are weakly exogenous such
,t ,t
that in the GMM estimation, as suggested by Arellano and Bond (1991), we can exploit the
following orthogonality conditions:
E[yi ,t-s(i -i )] = 0s 2 ¸ 2; t = 3; :::::; T (5)
,t ,t-1
E[Xi ,t-s(i - i )] = 0s 2 ¸ 2; t = 3; :::::; T (6)
,t ,t-1
The GMM estimator based on the above orthogonality conditions is commonly defined as
the "Differenced Estimator". The two orthogonality conditions only satisfy one of the two
requirements needed by the GMM estimators. As showed by Alonso-Borrego and Arellano
(1996) and by Blundell and Bond (1997), when the explanatory variables are persistent over time
and this is the case when we deal with variables like institutions which have an autoregressive
coefficient that is always greater than 0.7 - lagged levels of these variables are weak instruments
for the regression equation in first differences. In these cases severe problems of identification
24
can lead to bias and could result in a poorly performing differenced estimator, see Arellano et al.
(2002), Wright (2005), Bond et al. (2005). 17
To reduce the weak instruments bias18 of the Difference-Estimator, Arellano and Bover
(1995) and Blundell and Bond (1997) propose a different GMM estimator: the System Estimator.
The System Estimator, using some mild stationarity assumptions, relies on a system of equations,
namely the regression in differences, (eq. (4)), added to the regression in levels, (eq. (3)). The
system estimator exploits the orthogonality condition of equations (5) and (6) for the equation in
differences. For the equation in levels, lagged differences of the corresponding variables are used
as instruments. Under mean stationary assumptions, these are shown to be appropriate
instruments; that is the additional conditions required are that there be no correlation between the
differences of the variables and the country specific effects:
E[(yi ,t+ p- yi ,t+q )i] = 0 ; E[(Xi ,t+ p- Xi ,t+q)i] = 0 p,q (7)
The additional moment conditions for the second part of the system, the regressions in levels are:
E[(yi ,t-s - yi,t-s-1 )(i + i )] = 0 s = 1, (8)
,t
and
E[(Xi ,t-s- Xi ,t-s-1)(i + i )] = 0 s = 1 (9)
,t
The extra orthogonality conditions help in gaining identification in the case where the
dependent variable (institutions) comes from a very persistent series. For instance, in the extreme
case of a unit root the difference, (yi ,t-s - yi,t-s-1) would be a pure innovation and so the best
instrument for an AR (1) process. For all the estimates we present a test for the extra assumptions
17 Monte Carlo experiments show that the weakness of the instruments can increase the small-sample bias of the
GMM estimates and make inference unreliable, Staiger and Stock (1997), Stock et al. (2002).
18It is worth noting that the bias properties under weak instruments, in this case the presence of a highly persistent
series, are well known only for the autoregressive coefficient.
25
required by the System-Estimator. As in Huang and Temple (2005), we compare the model that
considers the assumptions of eq. (5) and eq. (6), and the model that adds the assumptions of eq.
(7) and eq. (8). The test consists in estimating both the unrestricted and the restricted models
using the two different sets of moments conditions, and comparing their (two-step)
Sargan/Hansen statistics using an incremental Sargan test of the form:
~ ^
DRU = n(J( ) - J( )) ~ r 2 (10)
~ ^
Where J( ) is the minimized GMM criterion for the restricted model, and J( ) for the
unrestricted model, n is the number of observations and r is the number of restrictions. Bond et
al. (2001), and Bond and Windmeijer (2005) discuss the statistical properties of these tests.
To test the validity of our preferred specifications we will consider two sets of commonly
adopted tests. The first is a Sargan/Hansen test of over-identifying restrictions and the second is
the Arellano and Bond autocorrelation test. The latter examines the hypothesis that the error term
i is not serially correlated in the equation in levels. We choose the model's lag structure and
,t
the instrumentation strategy by looking at the best combination of results from these tests. To
avoid model "over fitting" that is, having more instruments than countries- throughout the paper
our reduced instrument set uses no lags longer than (t-4). As discussed in Bowsher (2002), using
fewer moment conditions improves the power of the Sargan/Hausman test.
Estimation Results
This section presents the results of our panel data analysis in which we estimate
regressions in the form of equation (3). We consider a dynamic specification for institutions and
two different information sets. The first one, the basic information set, considers GDP and the
lag of institutions as the only control variables to explain the level of institutions. The second
one, the full information set, considers different control variables depending on data availability.
26
For instance variables representing human capital are not available on annual basis and thus are
not included in the full information set in the annual regressions.
The main findings can be summarized as follows. Increases in the flow of aid are not
followed by improvements or declines in the quality of economic institutions. For the whole
sample, and sub samples used in the robustness check, such as considering Sub-Saharan
countries only, or East Asian countries, this result is robust across different estimation methods,
and to variation in the choice of moment conditions. Not surprisingly the annual data results are
less precise due to the instrumentation strategy but in all the cases we do not reject the restriction
of b1+b2=0 representing a 2 year effect of the flow of aid on institutions (see Table 8). 19
For all the specifications we consider OLS, and Within Groups (WG) estimates, and two
versions of the "Differenced" GMM estimator. All models include a full set of time dummies.
The first version of the Dif-GMM (1) is the one-step version of the GMM estimator with White-
Huber heteroskedasticity-robust standard errors, while the Dif-GMM (2) is the two step version
of the GMM estimator. The Dif-GMM (2) estimator always uses the small sample correction for
standard errors of the two step GMM estimates, (see Windmeijer (2005)). We also consider a
two-step version of the System estimator, with standard errors robust to heteroskedasticity and
autocorrelation and Windmeijer (2005) corrected. For all the different GMM estimators, for the
sake of consistency of the results, we consider the same set of instruments following the
selection rules discussed in the previous section.
Table 9 reports the first set of results on the legal structure/property rights variable from
the Fraser Institute. As the table shows, there are reasons to believe that this model and the
selection of the instrument is properly specified and that the first lag of the dependent variable is
19Though the coefficients are individually significant and of opposite sign during the first and second years, for the
annual data we believe that this reflects noise in the annual data rather than a systematic effect of aid on institutions
that changes sign over the years. In any case, the 2 year effect is zero.
27
sufficient to account for the persistency of institutions. All the specification tests in both the
System and the Difference estimators pass the desired thresholds. Moreover, the incremental
Sargan test does not reject the null hypothesis; the extra moment conditions are valid. It is worth
noting that the autoregressive parameters estimated by both Dif-GMM and the Sys-GMM are
both positive and statistically significant and do lie in the interval defined by the OLS and the
WG estimates. This confirms the validity of our instruments and it also hints that, for this
specification, the instruments are not weak. Looking at all the tables together, we note that,
across all the estimation methods, there is strong evidence that an increase in (the flow) of aid
does not affect the level of institutions.
28
Table 8: Aid and Institutions: Basic Specification, Annual Data, GMM
Dep. Var: I i ,t Corruption Bur. Qual. Repudiation Rule of Law Exp Risk
I 0.893** 0.876** 0.908** 0.906*** 0.92**
i , t - 1 (0.041) (0.043) (0.038) (0.037) (0.034)
ln( AID 0.024 0.016 0.097* 0.084** 0.125**
i,t -1 )
(0.032) (0.037) (0.050) (0.036) (0.051)
ln(AIDi ,t-2 ) -0.04 0.047** -0.041 -0.059** -0.074*
(0.030) (0.018) (0.045) (0.026) (0.043)
ln( GNPPC 0.03 0.097** 0.224** 0.117** 0.055
i,t -1)
(0.041) (0.038) (0.071) (0.042) (0.045)
Unit Root
= 1 [0.010] [0.004] [0.018] [0.012] [0.021]
Long Run Effects (1) -0.153 0.524 0.61 0.265 0.636
1 + 2
1 - = 0 [0.616] [0.153] [0.154] [0.398] [0.207]
Long Run Effects (2)
1 + 2 = 0 [0.585] [0.143] [0.171] [0.400] [0.146]
Sargan/Hansen Test [0.213] [0.646] [0.765] [0.316] [0.258]
m(1) [0.001] [0.000] [0.000] [0.000] [0.000]
m(2) [0.034] [0.241] [0.153] [0.015] [0.163]
No. Observations 877 877 877 877 877
Notes:
Reported here are the results using two step system GMM estimation. Robust standard error are
reported in "()"
The dependent variables are the annual level of Expatriation Risk,Bureaucratic Quality, Repudiation, Epropriation
Risk, Corruption (ICRG). Robust standard error are reported in () parenthesis.
The value reported for the GMM estimations are computed considering a reduced instrument set. We use
lags t-2 up to t-3 of the dependent variable and lag t-2 only of Aid, and the log(gnppc) in both the difference
and the system estimations, while the first lag differences of Institutions, Aid and the log of gnppc are
added (under the mean stationarity assumption) in the system estimation.
Long Run Effects (1): is the test (b1+b2)/(1-a)=0 and Long Run Effects(2): is the test (b1+b2)=0.
Long Run Effects (1) is a non linear test of restrictions where the standard errors are computed using the
delta method, while Long Run Effects (2) is just a test for the numerator and is a linear test of restrictions.
Estimated coefficient and robust standard errors for the time dummies are omitted but available on request.
The values reported for the Sargan/Hansen test are the p-values for the null hypothesis of instrument validity
The values reported for m(1) m(2) are the p-values for the Arellan-Bond test for AR(1) and AR(2) disturbances
in the first differences equations. Under H0: it is tested if the residuals are not first (second) order
serially correlated."[ ]" report the p-values of the tests.
Data are for 12 years, between 1984-1995, as used in Alesina et al. (2002).
29
Table 9: Aid and Institutions: Legal Structure, Basic Specification, five-year Data.
Dep. Var: I W.G OLS levels Dif-GMM(2) SYS-GMM
i , t
I 0.173** 0.598** 0.258** 0.318**
i , t - 1 (0.059) (0.041) (0.075) (0.074)
ln( AID 0.182 0.093** 0.163 0.016
i,t -1)
(0.095) (0.041) (0.245) (0.111)
ln( GDPPPC i,t -1) 0.505 0.454** -0.206 0.532**
(0.359) (0.083) (0.850) (0.246)
Unit Root
= 1 [0.0000] [0.0000] [0.805] [0.009]
Sargan/Hansen Test - - [0.157] [0.104]
Incremental Sargan - - - [0.190]
m(1) - - [0.003] [0.001]
m(2) - - [0.236] [0.241]
No. Observations 331 331 237 331
Notes:
The dependent variable is the five year level of Legal Structure (FRG). "W.G" is
the Within Groups estimation. "Dif-GMM(2)" is the two step step difference
GMM estimation.
"SYS-GMM" is the two step system GMM estimation. Robust standard error
while not robust for the Within Groups estimator. More precisely, in columns
(v) and (vi) are computed Windmeijer's corrected standard errors,
while Huber-White sandwich corrected estimates in column (iv).
Column (iii) reports Praise-Winsten AR(1) regressions corrected,
and for heteroskedasticity a la' White.
The value reported for the GMM estimations are computed considering a
reduced instrument set. We use lags t-2 up to t-4 f or all the estimators
and for all the instruments. Estimated coefficient and robust standard errors for
the time dummies are omitted but available on request.
The values reported for the Sargan/Hansen test are the p-values fot the null
hypothesis of instrument validity.
The value reported for the Incrental Sargan test is the p-value for the joint
tests the extra moments used for the system estimator. Under Ho: we test the
validity of the extra moments.
The values reported for m(1) m(2) are the p-values for the Arellan-Bond test
for AR(1) and AR(2) disturbances in the first differences equations.
Under H0: it is tested if the residuals are not first (second) order serially correlated.
"[ ]" report the p-values of the tests. Data are for 5 years, between 1970-2000.
** Significant at 5 %, * Significant at 10%
30
Table 10: Aid and Institutions (GMM system): Basic Specification, five-year Data
Dep. Var: I Property Regulation C.I.M
i , t
I 0.318** 0.684** 0.768**
i , t - 1 (0.074) (0.079) (0.122)
ln( AID i ,t - 1) 0.016 -0.014 0.0008
(0.111) (0.048) (0.004)
ln(GDPPPCi ,t-1) 0.532** 0.07 0.026**
(0.246) (0.098) (0.013)
Sargan/Hansen Test [0.104] [0.434] [0.860]
Incremental Sargan [0.190] [0.784] [0.875]
m(1) [0.001] [0.001] [0.003]
m(2) [0.241] [0.694] [0.642]
No. Observations 331 332 438
Notes:
The dependent variable is the five year level of Property, Regulation and CIM,
respectively. "SYS-GMM" is the two step system GMM estimation. Robust standard,
error are reported in "()" which is computed Windmeijer's corrected standard errors.
The value reported for the GMM estimations are computed considering a reduced
instrument set. We use lags t-2 up to t-4 for all the estimators and for al the instru-
ments.
Estimated coefficient and robust standard errors for the time dummies are omitted
but available on request. The values reported for the Sargan/Hansen test are the
p-values fot the null hypothesis of instrument validity. The value reported for the
Incremental Sargan Test are the p-values to test the moments used for the system
estimator. Under Ho: we test the validity of the extra moments.
The values reported for m(1) m(2) are the p-values for the Arellan-Bond test for
AR(1) and AR(2) disturbances in the first differences equations. Under H0: it is
tested if the residuals are not first (second) oreder serially correlated."[ ]" report
the p-values of the tests. Data are for 5 years, between 1970-2000.
** Significant at 5 %, * Significant at 10%
31
Only in the pooled OLS estimation is there an effect of aid and it is positive. Previous
work based on pure cross-section data, the Kilby (2004) panel data and the Djankov et al. (2005)
estimations also find the OLS estimators to be significant. The effects of economic development
on economic institutions are positive and statistically significant at 5%. This result confirms that
countries' legal structure is affected by the level of economic development. In all the
specifications we observe that the autoregressive coefficient of institutions is positive and
statistically significant. Given the discussion of the previous section we interpret only the
estimates of the Differenced and the System estimator. Institutions are persistent and the
probability of having higher level of institutions today is monotonically increasing in the level of
past year's value of institutions.
Table 10 reports the summary of the System Estimation for our legal structure/property
rights variable, regulation and contract intensive money. As expected the estimated coefficients
for the lagged dependent variable are always positive and statistically significant at 5%. The
coefficient on the level of (the natural log) GDP per capita is positive and significant for legal
structure and contract intensive money at 5% while it is not for Regulation. Although, we have
considered a very parsimonious specification given the few theoretical models and possible
explanations for the evolution of economic institutions, all the tests for specification and
instruments validity suggest that the selected model is well specified. For instance, for all three
indicators the Sargan/Hansen tests do not reject the null of instrument validity and the Arellano-
Bond test for autocorrelation rejects the null of first order serial correlation for the AR(1) type,
but not for the AR(2) type. All the incremental Sargan tests suggest that the extra assumptions
used in the "System-GMM" estimation are valid. Finally, the unit root tests suggest that although
institutions are very persistent they do not have a unit root.
32
All the estimation details for regulation and contract intensive money are not reported but
available on request. For both indicators we followed the same rules to select the best
specification and the number of instruments as discussed for legal structure. Table 8 reports the
estimation results for the five ICRG indicators. We report the "System" estimates only though
others are available on request20. The table reports tests for the presence of a unit root and for
long run effects (or rather the effect over two years). For all the specification and for all the
indicators we find no evidence of a joint effect of aid. In contrast to Alesina and Weder (2002),
once we consider the "System" estimator we sometimes find that both the first and the second
lag of aid are statistically significant but with opposite sign (except for bureaucratic quality
which is positively associated with more aid). We also considered the specification with
contemporaneous aid and the first lag of aid and observe the same change in signs). In contrast to
previous results, we also test the joint (2 year) effect of aid on economic institutions we confirm
the zero effect as in the five year data.
Conclusion
This paper examines the effect of aid on institutions, specifically institutions that affect
economic transactions between private agents or private agents and the state. It excludes
institutions that regulate political relationships between the state and citizens. Thus our focus is
not on institutions of democracy or those that determine political stability or voice and
accountability (though these may affect economic outcomes). In the very long run, one may
argue that the nature of economic and political institutions is linked. In the shorter run, as the
paper discusses, we do not see convergence. A priori, there is no reason why there should be a 1-
1 relationship between the precise design of political institutions and the effectiveness of
20For the annual data we follow the same procedures to select the model and to assess that the estimates are indeed
bounded between the within group estimator (fixed effect) and the pooled OLS.
33
economic institutions (which also vary in design). Moreover, a continuous process of what might
be termed "shocks" for want of a better word, such as changes in external trade, natural events,
technology, or health, may provide windows of opportunity for changing certain institutions (for
example economic institutions) before they have an impact on other types of institutions.
So we ask to what extent aid can affect economic institutions. This is an important
question since donors attempt to raise growth in recipient countries not just by providing capital
for investment but by advising on policies and institutions designed to raise the productivity of
investment. We find no empirical evidence to suggest that aid has a robust and significant impact
on institutional quality using several measures of institutions. The only exception to this was
aid's impact on regulation as defined by one indicator from the Fraser Institute (aid is seen to
favor more regulation).
These results suggest that in general aid cannot be said to have affected institutional
quality. One caveat is that the data could be refined much further. For example, the influence of
aid targeted to a particular sector could be assessed with measures of institutional quality in that
sector. For this, more work on clearing up the aid data is needed and better estimates of
institutional quality would help. We tried to assess the impact of aid in a particular sector, the
financial sector, but found no effect.
34
Bibliography:
Acemonglu, D., S. Johnson, and J. Robinson (2002), "Reversal of Fortune: Geography and
Institutions in the Making of the Modern World Income Distribution", Quarterly Journal
of Economics, November, Vol. 117, 1231-1294.
Acemoglue, D., and S. Johnson (2005), "Unbundling Institutions", Journal of Political Economy,
October, Vol. 113, 949-995.
Acemoglu, D., S. Johnson, and J. Robinson (2005), "Institutions as the Fundamental Cause of
Long-Run Growth", Handbook of Economic Growth (Philippe Aghion and Stephen
Durlauf, eds.) North Holland, December.
Alesina A., and D. Dollar (2000),"Who Gives Foreign Aid to Whom and Why?" Journal of
Economic Growth, Vol 5. No.1, 33-63.
Alesina, A. and B. Weder (2002), "Do Corrupt Governments Receive Less Foreign Aid?" The
American Economic Review, Vol. 92, 4, 1126-1137.
Alonso-Borego, C., and M. Arrellano (1999), "Symetrically Normalizad Instrumental Variable
Estimation Using Panel Data", Journal of Business and Economic Statistics, Vol. 17.
Arellano, M. and S. Bond (1991), "Some Tests of Specification for Panel Data: Montecarlo
Evidence and an Application to Employment Equation ", Review of Economic Studies,
Vol. 58, 277-297.
Arellano, M. and O. Bover (1995), "Another Look at the Instrumental Variable Estimation of
Error- Components Models", Journal of Econometrics, Vol.68, 1.
Banerjee ,S.G.and D.A Rondinelli. (2003), "Does Foreign Aid Promote Privatization?
Empirical Evidence from Developing Countries", World Development Report.
Barro, R. and , X. S. Martin. (2004), Economic Growth, MIT Press.
Blundell, R. and S. Bond (1997), "Initial Conditions and Moments Restrictions in Dynamic
Panel Data Models", Journal of Econometrics, Vol.118, 115-143.
Bond, S., Hoeffeer A., and J. Temple (2001), "GMM Estimation of Empirical Growth Models",
CEPR Discussion Paper 3048.
Burnside, C. and D. Dollar (2000), "Aid, Policies, and Growth", American Economic Review,
Vol. 90, 4, September, 847-868.
35
Bond, S. (2002), "Dynamic Panel Data Models: A Guide To Micro Data Methods and Practice",
CEMMAP Working Paper CWP09/02.
Bond S., and M. Soderbom (2005), "Adjustment Costs and the Identification of Cobb Douglas
Production Functions", The Institute for Fiscal Studies, Working Paper, 05/04. Oxford.
Brautigam, D. and S. Knack (2004), "Foreign Aid, Institutions, and Governance in Sub-Saharan
Africa", Economic Development and Cultural Change, Vol. 52, 1, 25585.
Calderon, C. and L. Serven (2004), "The Effects of Infrastructure Development on Growth and
Income Distribution", Central Bank of Chile working paper, #270.
Celasun, O and J. Walliser, (2005), "Predictability of Budget Aid: Experiences in Eight African
Countries", Preliminary draft.
Clague, C., P. Keefer, S. Knack S., M.Olson (1999), "Contract-Intensive Money: Contract
Enforcement, Property Rights, and Economic Performance", Journal of Economic
Growth, Vol. 4, 185-211.
Clemens, M., S. Radelet and R. Bhavnani, (2004), "Counting chickens when they hatch: the
short-term effect of aid in growth", Center for Global Development WP No.44.
Collier, P., and D. Dollar (1999), "Aid Allocation and Poverty Reduction", WBPRWP
Collier, P., and D. Dollar (2004), "Development effectiveness: What have we learned?",
Economic Journal, Vol. 114, 244271.
Dalgaard, C., H. Hansen, F. Tarp (2004), "On the Empirics of Foreign Aid and Growth", The
Economic Journal, Vol. 114, 191216.
Djankov, S., J.G. Montalvo, and M. Reynal-Querol, "The curse of aid" Mimeo, World Bank.
Dollar, D., and V. Levin (2004), "The Increasing Selectivity of Foreign Aid, 1984-2002", World
Bank Policy Research Working Paper.
Easterly, W. (2003), "Can Foreign Aid Buy Growth?", Journal of Economic
Perspectives,Vol.17,3, 23-48.
Easterly, W., R Levine, and D. Roodman (2004), "New Data, New Doubts: A Comment on
Burnside and Dollar's- Aid, Policies, and Growth", The American Economic Review,
forthcoming.
Fielding, D. and G. Mavrotas (2005), "The Volatility of Aid", Mimeo, United Nation University,
WIDER.
36
Hansen, H. and F. Tarp (2001), "Aid and growth regressions", Journal of Development
Economics, Vol. 64, 2, 547-570.
Hauk, W.R. and R. Wacziarg (2004), "A Monte Carlo Study of Growth Regressions", NBER
Working Paper Series.
Heckelman, J. and S. Knack (2005), "Political Institutions and Market-Liberalizing Policy
Reform", Mimeo, The World Bank.
Holtz-Eakin, D. and W. Newey, H. Rosen (1988), "Estimating Vector Auto regressions with
Panel Data", Econometrical, Vol.56, 1371-1395.
Hulten, C. (1996), "Infrastructure Capital and Economic Growth: How Well You Use it May Be
More Important than How Much You Have", NBER Working Paper Series, #W5847.
Huang, Y., and J. Temple (2005), "Does External Trade Promote Financial Development?"
Discussion Paper #05/575, University of Bristol.
Kilby, C. (2005), "Aid and regulation", The Quarterly Review of Economics and Finance, Vol.
45, 2, 325-345.
Kilby, C. (2005), "World bank Lending and regulation", Economics Systems, Vol. 29, 384-407.
Knack, S. (2004), "Does Foreign Aid Promote Democracy?" International Studies Quarterly,
Vol. 48, 1, 251-266.
Knack, S. (1996), "Aid Dependence and the Quality of Governance: Cross-Country Empirical
Tests", World Bank Working Paper, wps2396.
Knack S., and A. Rahman (2004), "Donor Fragmentation and Bureaucratic Quality in Aid
Recipients", World Bank Policy Research Working Paper, 3186.
Kraay, A. (2005), "Aid, Growth and Poverty Foreign Aid and Macroeconomic Management",
Mimeo, IMF.
Levine, R., N. Loayza, and T. Beck, (2000), "Financial Intermediation and Growth: Causality
and Causes". Journal of Monetary Economics.
Mulligan, C.B., and A. Shleifer (2003), "Population and Regulation", NBER Working Paper,
190, December.
Putnam, R.D. (1994),"Making democracy at work: civic traditions in modern Italy", Princeton
University Press.
Rajan, R., and A. Subramanian (2005), "Aid and Growth: What Does the Cross-Country
Evidence Really Show?", IMF WP 05/127.
37
Rajan, R., and A. Subramanian, "What Undermines Aid's Impact on Growth?", NBER Working
Paper No. 11657
Roodman, D. (2003), "The Anarchy of Numbers: Aid, Development, and Cross-country
Empirics", Center for Global Development Working Paper.
Staiger, D., and J.H. Stock (1997), "Instrumental Variable Regression With Weak Instruments",
Econometrica, May,Vol. 65, 5, 557-586.
Stock, J.H, J.H. Wright, J.H, M. Yogo (2002), "A Survey of Weak Instruments and Weak
Identification in Generalized Method of Moments", Journal of Business and Economic
Statistics, October, Vol.4, 20, 518-529.
Svensson, J. (1999), "Aid, Growth and Democracy", Economics and Politics, Vol. 11, 3, 275-
297.
Tavares, J. (2003), "Does foreign aid corrupt", Economics Letters, Vol.79, 1, 99- 106.
Windmeijer, F. (2005),"A Finite Sample Correction for the Variance of Linear Efficient Two-
Step GMM Estimators", Journal of Econometrics, 126, 25-51.
38
Data Appendix:
Variable Code Data source Note
Initial GDP per capita LGDP Summers and Heston, 1991, Natural logarithm of GDP/capita for first year of
updated using GDPG period; constant 1985 dollars
Institutional quality 1 Prop, Reg Fraser Institute: Disaggregate indicators, available on the web.
http://www.freetheworld.com/2005
/2005Dataset.xls
Institutional quality 2 ICRG PRS Group's IRIS III data set (see Revised version of variable. Computed as the av-
Knack and Keefer, 1995) erage of the three compo-nents still reported
Institutional quality 3 CIM Contract Intesive Money, M2=M1+quasimoney=34+35
IFC data, 2005 Currency comes from line 14a of International
Financial Statistics, "currency outside deposit
money banks." M 2 is defined by IFS as the sum
of money and quasi-money, or the sum of lines
14a (currency outside banks), 24 (demand
deposits), 15 (time deposits
Sub-Saharan Af-rica SSA World Bank, 2003 Codes nations in the south-ern Sahara as sub-
Saharan
East Asia EASIA Dummy for China, Indonesia,
South Korea, Malaysia,
Philippines, and Thailand,
following Burnside and Dollar
Effective Devel-opment Assis- AID Chang et al., 1998; DAC, 2002; Available values for 197595 from Chang et al.
tance/ real GDP IMF, 2003; World Bank, 2003; Miss-ing values extrapolated based on
Summer and Heston, 1991 regression of EDA on Net ODA. Con-verted to
1985 dollars with World Import Unit Value
index from IMF, series 75. GDP computed like
LGDP above
Net Overseas De-velopment ODA DAC, 2002; IMF, 2003; World Like AID exception using ODA from DAC
Assistance/real GDP Bank, 2003; Summer and Heston, Real GDP expressed in PPP
Population LPOP World Bank, 2003 Natural logarithm
Mean years of secondary SYR Barro and Lee, 2000
schooling among those over 25
39
Tables and Figures:
Table A1: Correlations between economic and political indices, 1970 and 2000
1970
prop reg polity4 przdemaug fhpolrigau
prop 1
reg 0.2424 1
0.1606
polity4 0.5156 0.569 1
0.0002 0.0001
przdemaug 0.4296 0.4506 0.868 1
0.0023 0.0027 0
fhpolrigaug 0.6278 0.6219 0.9017 0.8446 1
0 0 0 0
2000
prop reg polity4 przdemaug fhpolrigau
prop 1
reg 0.6644 1
0
polity4 0.377 0.4439 1
0 0
przdemaug . . .
1 1 1
fhpolrigaug 0.5431 0.5662 0.9004 . 1
0 0 0 1
40
Table A2: Correlations between economic and political indicators, Average 1970-2000
Average 1970-2000
prop reg polity4 przdem fhpolrig
avg_prop 1
avg_reg 0.4469 1
0
polity4 0.604 0.4346 1
0 0
przdem 0.5272 0.3697 0.8938 1
0 0 0
fhpolrig 0.6712 0.5212 0.9565 0.8812 1
0 0 0 0
Notes: Table A1 and A2
Sources: prop rights index (from Fraser, see appendix), regulation (Fraser).
Polity4 (see above)
Fhpolrig: gmented Freedom House political rights index (w/Bollen data)
(normalized 0-1)
41