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Faculty of Business and Law School of Accounting, Economics and Finance
ECONOMICS SERIES
SWP 2015/1
Spatial Aid Spillovers During Transition
Zohid Askarov and Hristos Doucouliagos
The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School or IBISWorld Pty Ltd.
Spatial Aid Spillovers During Transition
Zohid Askarov
and
Hristos Doucouliagos*
January 20th 2015
Abstract
We investigate whether development aid stimulates growth in transition economies, paying
particular attention to the possibility of spatial spillovers arising from aid. We find that aid
has a positive impact on growth of the recipient country. However, aid also appears to
generate adverse growth spillovers on other nations. In contrast, we find that growth in one
transition economy tends to spillover to bordering countries and there are significant
positive spatial spillovers from good policies. Spillovers are an important part of the growth
experience of transitional economies.
JEL codes: O4, O5, F35
Keywords: aid effectiveness, spatial spillovers, transition economies
Askarov: Centre for Economics and Financial Econometrics Research, Deakin University,
221 Burwood Highway, Burwood, Victoria, 3125, Australia: Email: [email protected].
*Doucouliagos (Corresponding author): Department of Economics, Deakin University, 221
Burwood Highway, Burwood, Victoria, 3125, Australia. Email: [email protected].
Telephone: 61 3 9244 6531.
1
1. Introduction
The aid-growth relationship has been explored extensively. However, studies have yet to
reach consensus on the impact of aid on economic growth, with numerous contradicting
results reported. While many studies conclude that aid contributes to growth, many others
find that aid has no robust effect on growth.1 One limitation with the extant studies is that
they consider growth and aid to be independent across countries, with little attention given to
spatial dimensions of aid effectiveness. Aid flowing into one country could plausibly impact,
either positively or adversely, upon the growth of other nations. Omitting spatial spillovers
can result in misspecification of the underlying data generating process, omitted variable bias,
and possibly incorrect inference regarding the net effect of aid. If there are positive (negative)
spillovers from aid, then conventional cross-country growth regressions can understate
(overstate) aid effectiveness.
The contribution of this paper is to analyze spatial spillovers arising from: (i)
development aid, (ii) economic growth, and (iii) other factors such as good policy. Instead of
examining a varied cross-section of heterogeneous countries, we focus on a specific group of
countries that share the common experience of having undergone (or undergoing) transition
towards a market economy.2 Transition economies present an interesting case study.
Transition involves the development of a market based economy, legal, political and
institutional reforms and trade and financial liberalization and integration (Svejnar 2002). The
1 Studies reporting positive growth effects include Hansen and Tarp (2001), Clemens et al. (2012), and Brückner
(2013). Studies reporting aid ineffectiveness include Easterly (2003), Easterly, Levine and Roodman (2004), and
Rajan and Subramanian (2008).
2 Cross-country samples typically contain excess heterogeneity that can blur underlying relationships. A
narrower sample is potentially less noisy and more informative. The cost of this is that our findings apply to a
unique group of countries whose experience need not generalize to other countries.
2
collapse of the Soviet Union in particular led to deep economic and political shocks in many
transition countries, especially those from Europe and Central Asia.3 Aid became an
important feature of the transition process for many countries, with aid as a share to GDP
been a major source of funds for several countries, e.g., Albania (28%), Armenia (24%),
Kyrgyzstan (33%), Tajikistan (30%), and Bosnia and Herzegovina (73%).
Between 1990 and 2012, donors provided more than 348 billion dollars (US 2012) in
aid to transition economies. Relatively little is known about the effects of these large aid
flows on growth. The two extant studies by Fischer, Sahay and Vegh (1996) and Askarov and
Doucouliagos (2015) both find that aid had a small positive effect on growth.4 However, both
studies ignore spatial effects. We show in this paper that aid given to transition economies
generates growth for the recipient. However, we also report evidence that aid generates
adverse spatial spillovers; the effects of aid on growth are not necessarily confined within the
recipient country. In contrast to aid, we find positive spatial spillovers arising from good
policies, most notably from restraining government expenditure, inflation, and recession.
Our innovation is to investigate spatial aid effectiveness. The spatial diffusion of
growth itself has been noted in numerous studies. International trade, foreign direct
investment, policy emulation, and technology are all potentially important channels for
growth spillovers.5 For example, Ades and Chua (1997) find that politically unstable
countries can create strong adverse spillovers on their neighbors’ growth by disrupting trade
flows and increased military outlays. Easterly and Levine (1998) identify several sources of
3 The dissolution of the Soviet Union is only part of the transition story; transition in several countries
commenced prior to this event.
4 Fischer et al. (1996) analyze 20 transition economies for the years 1992-1994, Askarov and Doucouliagos
(2015) examine a larger sample of 32 transition countries for the years 1990-2012.
5 Spillovers can also arise in financial markets, particularly through global share markets and credit exposure of
financial institutions.
3
spillovers, such as neighbors copying growth promoting policies, foreign investors finding it
easier to move into neighboring nations once success is achieved in one country, and positive
performance in one country spilling over to other countries through trade.6 Van Pottelsberghe
de la Potterie and Lichtenberg (2001) find that FDI is a channel for knowledge diffusion that
promotes growth spillovers. Emulation is also important. For example, policy makers might
find it difficult to accurately judge whether one policy is better than another. This uncertainty
may induce them to follow the policies of other jurisdictions (Simmons et al. 2006; Gilardi
2010). Trade and technological and knowledge spillovers are also major mechanisms of
growth spillovers (see Romer 1990; Coe and Helpman 1995; Paci and Pigliaru 2002). Growth
spillovers are more likely when a particular country is an important global supplier of a
commodity (e.g., energy in the case of Russia) or an important global manufacturer (e.g.,
China).
Spillovers among transition nations may also play a role in their growth experience.7
An important part of the transition process is integration with world markets. For example,
several Eastern European countries have become an integral part of Germany’s production
chain (Poirson and Weber 2011), and several are now members of the European Union.
Nevertheless, the links among transition economies remain strong, especially through trade
and migration. For example, Russia has yet to establish strong links with European nations,
other than via the energy sector. In contrast, trade between Russia and many transition
countries is particularly strong (IMF 2014). Moreover, remittances from Russia to several
transition countries (e.g., Armenia and Tajikistan) amount to more than 8% of GDP (IMF
2014). Many transition nations observe and often emulate developments in others. A case in
6 See also Holod and Reed III (2004), Berry and Baybeck (2005), and Bitzer and Kerekes (2008).
7 Spillovers may exist also between transitional economies and other nations. Our focus in this paper is on
spillovers among transitional economies.
4
point is the wave of privatizations. We also expect spillovers among transition countries
because of their common political and economic history.8 They have all, to a varying degree,
emerged from central planning and authoritarian regimes but their historical links continue.
For instance, many of the leaders of transitioning nations were members of the former Soviet
Union communist party politburo.
The structure of the paper is as follows. Section 2 discusses the possibility of spatial
aid effectiveness. Section 3 describes the spatial econometric methodology and data. Section
4 presents the empirical results which are then discussed in section 5. Section 6 concludes the
paper.
2. Spatial Aid Effectiveness?
There are several channels through which aid can potentially generate spatial spillovers on
growth.
2.1 Competition for aid
Competition for aid can generate positive spillovers. For example, if aid is received
conditionally on policy reforms, then a recipient country might be induced to improve its
policies by learning or emulating practice elsewhere. Donors are not motivated purely by
humanitarian needs. They may, for example, reward countries for their good behavior
(especially in terms of their democracy and human rights record), trade links, and political
ties (Younas 2008). Recipients can make themselves more attractive to donors through policy
and institutional reform. By pro-actively improving policy (or pressured by donors to do so),
recipients may create spatial spillovers that result in a “race-to-the-top”; spillovers are
8 Technology spillovers arising from inter-industry and intra-industry FDI are another channel for spillovers in
transition countries (Djankov and Hoekman 2000 and Damijan et al. 2013).
5
generated as countries compete with each other and learn and copy from each other. One
consequence of the resulting reforms might be to stimulate growth.
Competition for aid could also create negative spillovers. For example, policy makers
from other transition nations might divert their efforts away from policy reform and towards
lobbying for aid. Moreover, they can deliberately delay implementing necessary reforms and
vital infrastructure in order to strengthen their case as more deserving and in need of aid.
Competition for aid would in such cases have an adverse spillover effect on growth.
2.2 Technology and knowledge spillovers
Aid can generate growth spillovers in similar ways that FDI creates spillovers. For
example, aid can stimulate a recipient’s growth by financing imports of investment goods.
The resulting technology transfer can increase capital productivity and stimulate endogenous
technical change. Aid spent on imports of investment goods might thus generate technology
spillovers to other countries. Aid given in the form of technical assistance can also contribute
to knowledge spillovers. Moreover, the successful entry of new technology financed by aid
flowing into one country might make it easier for the entry of new technology into other
nations. Aid can thus serve as a direct mechanism of knowledge transfer and knowledge
spillovers. These knowledge spillovers might then stimulate growth in other nations.
2.3 Migration and brain drain
Migration and labor mobility are important to development. Aid’s effect on migration
is complex and it remains a relatively underexplored area of research. Aid agencies often
argue that aid helps retain skilled labor and prevent brain drain. This assertion has been
challenged. For example, Faini and Venturini (1993), Berthélemy et al. (2009) and Ontiveros
6
and Verardi (2012) point to the existence of thresholds: some aid might increase incomes
making it easier for people to finance their emigration.
Aid might stimulate growth in one country, drawing skilled labor and entrepreneurs
from neighboring countries, thereby adversely affecting their growth. Even temporary
migration can impact on growth.9 Legal migration between many transition economies is
fairly widespread (there is also likely to be significant illegal migration). Indeed, ‘brain drain’
has affected several transition economies (United Nations 2002). Migration can also arise
from individual aid projects. For example, skilled labor could migrate from one World Bank
funded project in one jurisdiction to another World Bank funded project in a different
jurisdiction. On the other hand, it is possible that by generating opportunities for emigration,
aid can also create an incentive to acquire human capital, thereby having a positive impact on
skill formation in the labor exporting country (Beine, Docquier and Rapoport 2001).
Chandra, Head and Tappata (2014) argue that cross-border migration can be induced
by exchange rate movements.10 Aid can affect exchange rates via “Dutch disease”, if aid
flows are sufficient large and they are spent on domestic goods and services. This can then
impact upon the recipient’s real exchange if it alters the relative price of non-tradeables to
tradeables; see Rajan and Subramanium (2011) who present evidence on Dutch disease
caused by aid.
9 If aid contributes to the development of a welfare state, then it might also entice unskilled immigrants into the
recipient country. Migration could also disrupt civil society. The effects of these influences on growth also need
to be considered.
10 Chandra et al. (2014) also note that cross-border migration is linked with cross-border shopping. If aid does
impact upon the recipient’s exchange rate it can stimulate neighboring nations by encouraging cross-border
shopping.
7
2.4 Institution displacement
Aid can contribute to growth by improving institutions in the recipient country. However, this
might come at the expense of forcing some undesirable activities to move to neighboring
countries. For example, if aid reduces corruption and crime in the recipient country, it might
force these activities to move elsewhere and neighbors might be a relatively easier target.11
Similarly, rent seeking activities might shift over the border (or to other transition nations),
resulting in an adverse effect on growth and development in neighboring countries. It might
be relatively easier for this process to occur between transition economies as they retain some
of the ties forged during their years of central planning. However, the opposite is also
possible. If aid succeeds in reducing corruption and crime in one country, the successful
programs could be copied elsewhere. Aid could also assist with collaborative efforts between
nations to fight corruption and crime.
On the other hand, aid might erode the quality of the recipient country’s institutions if
it elicits rent-seeking and corruption intended to access aid funds (Beck and Laeven 2006;
Djankov, Montalvo and Reynal-Querol 2008). Ruling elites can use aid to buy-off their
political opponents and to finance political repression. Consequently, aid might serve to
preserve existing regimes rather than transform them. This would then reduce pressure on
other nations to instigate regime reform.
Competition for aid, technology and knowledge transfers, migration and institution
displacement can create a range of positive and negative spillovers. The net effect is an
empirical matter.
11 The literature is divided on the effects of aid on corruption in the recipient country. For example, Tavares
(2003) finds that aid reduces corruption while Svensson (2000) argues that aid can stimulate corruption. Little is
known about aid’s spatial impact on corruption in other countries.
8
3 Model specification and data
The two key challenges to the empirical exploration of spatial aid effectiveness are the
specification of the growth model that captures the underlying economic processes and
estimation from which we can infer causality.
3.1 Specification
We commence with a standard (aspatial) generic growth model:
ititit ug += βx' , (1)
where git is the growth rate of per capita GDP, itx is a vector of explanatory variables
(including aid), β is the vector of parameters, and uit is the error term. We extend this model
by including two spatial variables: spatial aid and spatial growth. If only aid is spatially
lagged the growth model becomes:
ititaitit ug ++= Awβx '' ρ , (2)
where Aw it' is the weighted measure of aid received by other countries, w is the weight
matrix (discussed below) and αρ is the regression coefficient on spatial aid. If only growth is
spatially lagged, the growth model becomes:
ititjtgit ug ++= βxgw ''ρ , (3)
where gw jt' is the weighted measure or ‘spatial lag’ of the dependent variable (growth in
other countries) and gρ is the regression coefficient on spatial growth. This model is formally
known as the Spatial Autoregressive model (SAR). Allowing for both spatial aid and spatial
growth the growth model becomes:
ititaitjtgit ug +++= Awβxgw ''' ρρ . (4)
9
Eqn. (4) can be extended to the Spatial Durbin model (SDM), where the itx vector is also
spatially lagged (Anselin 1988; LeSage and Pace 2009):
itititjtgit ug +++= Xθwβxgw '''ρ , (5)
where θ is a vector of parameters relating to the spatially lagged X vector, of which is aid is
one element. A somewhat simpler model that serves as a useful baseline is the so-called
Spatial Lag in X model (or SLX):
itititit ug ++= Xθwβx '' . (6)
Eqn. (6) differs from Eqn. (5) by excluding the spatial growth term ( gw jt' ). We use
equations (1) and (6), the standard aspatial and the SLX models, as baseline models.12 Our
preferred model is the SDM (Eqn. 5), as this is the more general model that includes spatially
lagged growth and spatially lagged independent variables. One criticism of SAR-type models
is that a statistically significant spatial growth lag ( gρ ) might simply be a result of model
misspecification; spatial growth effects might simply be the result of other variables that have
a spatial dimension. The SDM model helps to identify the underlying causal channels by
allowing all variables to have a spatial component.13 The SDM offers a much richer
framework for empirical analysis, exploring spatial growth lags conditional on spatial lags in
other variables.
The estimated models also include time fixed effects as these are particularly
important in distinguishing between spatial correlation and spatial causation. Time fixed
effects help to account for cross-sectional dependence in the data (unobservable common
factors or shocks). The transition experience affected the majority of these countries in a
12 One advantage of the SLX model is that it avoids the need to find instruments for spatial growth.
13 Another attractive feature of the SDM model (Eqn. 5) is that it produces unbiased coefficient estimates even if
the true data-generating process is a spatial error model.
10
similar way and the time dummies can control for these effects. We also estimate versions
that include country random effects.
Our main interest lies on the regression coefficients on aid, the weighted aid term )( aρ
and the spatial growth term )( gρ . A positive (negative) coefficient on )( aρ means that aid in
one country creates positive (negative) growth spillovers on others. A positive (negative)
coefficient on )( gρ means that growth in one country creates positive (negative) growth
spillovers on others.14 However, we are also interested in identifying the channels (if any)
through which growth spillovers arise and, hence, we are also interested in the θ vector of
parameters from the SDM model (Eqn. 5).
3.2 Data
We employ a panel data set consisting of an unbalanced panel of all 32 transition
countries, for the period 1990 to 2012.15,16 We follow Brückner (2013) and use annual data.
However, the results are broadly similar when we use five-year averages. The main source of
data is the World Development Indicators (WDI). The growth rate of per capita GDP is
employed as the dependent variable.17 Aid is measured as per capita aid in constant prices.
The estimated growth model includes several variables that are standard in the
empirical growth literature. Specifically, we control for convergence, ethnic fractionalization,
assassinations (political violence), policy variables (budget balance, inflation, and openness),
14 Negative spillovers can arise when growth in one country draws resources at the expense of others.
15 Most of the estimation uses an unbalanced panel (1990 to 2012). Estimations using spatial GMM and
maximum likelihood use a balanced dataset (1995 to 2011). In both cases countries are retained in the sample
even when they cease to receive aid.
16 We adopt the IMF classification of countries as transition economies.
17 Using growth data from the Penn World Tables (version 8) produces essentially the same results.
11
FDI, capital,18 population growth, two regional dummies (Europe and Asia, with Central Asia
as the base), oil resources, religion, and time dummies.19 Additionally, we include a country
specific recession variable,20 years of socialism and a variable (State) that classifies transition
nations into new nation states and countries that were sovereign states prior to 1989. The
main motivation for adopting this specification comes from prior studies on transition
economies (e.g., De Melo, Denizer, Gelb, and Tenev 2001; Askarov and Doucouliagos
2015).
Table 1 reports descriptions of the variables, the data source and summary statistics.
Appendix A lists the 32 countries included in the sample. In our sample, average per capita
aid is $49.23 (US constant prices) and the average rate of growth was 2.5 per cent per annum.
There is however much variation in the growth performance of transition countries.
TABLE 1 ABOUT HERE
3.3 Spatial weights
18 Some authors exclude capital as aid’s effect on growth might operate through capital (e.g., Burnside and
Dollar, 2000). Excluding capital from the growth model does not alter the results presented in the text. A simple
(unconditional) growth regression that uses only lagged aid yields a coefficient on aid of 0.037 and a t-statistic
of 4.75 (n = 682); regressing contemporaneous aid yields a coefficient of 0.033 and a t-statistic of 3.51 (n =
705).
19 In unreported regressions we also considered institutional quality measured by the World Bank’s Worldwide
Governance Indicators. This variable was never statistically significant and it was subsequently removed from
the modeling as doing so increases the available sample size.
20 This was derived following the procedure of Brückner and Ciccone (2011). We regress the log of per capita
income against country fixed effects, time fixed effects and a country specific time trend. A binary variable is
then constructed taking a value of 1 when the actual level of output is less than the predicted value.
12
We employ two alternate sets of weights for the weight matrix, w used in Eqns. (2) to
(6).21 First, we construct Neighbor weights for each country by assigning a value of 1 if they
share a contiguous border and 0 otherwise. This means that countries that do not share a
common border are assumed to experience no spatial spillovers. Second, we use the inverse
distance (1/Distance) between capital cities as weights. This is preferable as it means that all
transition countries can potentially influence each other but geographically closer countries
exert more influence. Different weights are considered in order to explore the robustness of
results, but more so because they convey different information regarding interactions between
transition economies.22
3.4 Endogeneity
The aid and the spatially lagged aid variables could be endogenous to growth. For example,
aid might also be allocated on the basis of a country’s growth experience. 23,24 Consequently,
OLS estimates will be biased. The spatially lagged growth variable will also cause
endogeneity. Spatial spillovers mean that the growth rate in country i effects country j and at
the same time country j effects country i. We adopt three approaches to address endogeneity.
21 The same weights are used for all spatially lagged variables. The spatial weights matrix is row-standardized.
22 Other weighing schemes are possible. For example, trade or political linkages can be used. However, such
economic and political weights are unlikely to be exogenous. In contrast, geographical distance serves as a
useful exogenous set of weights that is also a good proxy for trade.
23 Faster growing economies generate more profitable aid projects thereby making them more attractive to
potential donors.
24 One can argue that it was inevitable that growth would have accelerated after the initial collapse in output that
occurred with the end of central planning. Consequently, it only appears as if aid caused growth. However,
growth experience has been very variable, with income in several countries yet to return to its 1990 level (see
Askarov and Doucouliagos 2015).
13
First, following Clemens et al. (2012) we use lagged aid and extend this logic to using
lagged spatial aid and lagged spatial growth. This allows aid and spatial aid to effect growth
over time. The logic behind this approach is that lagged aid is exogenous to future growth. A
counter argument is that if aid is given in anticipation of growth, then past aid allocations are
determined by expected future outcomes. Rajan and Subramanian (2008) argue that lagged
aid is not exogenous if there is serial correlation in growth. Conversely, Askarov and
Doucouliagos (2015) argue that lagged aid is endogenous to expected growth and not to
realized growth as revealed by the data.
A second approach to tackling endogeneity is to instrument aid, spatial aid, and
spatial growth. Instrumentation of aid follows Brückner’s (2013) method by constructing an
adjusted aid series where the response of aid to growth has been removed.25 We first estimate
the effect that real per capita GDP growth has on aid, using two-stage least squares that
involves instrumenting growth:
ititiit eycaaid +Δ+=Δ )ln()ln( , (7)
where )ln( itaidΔ is the logarithmic change of aid per capita, )ln( ityΔ is the logarithmic
change of real per capita GDP, ia are country fixed effects that capture unobservable
differences across countries, and ite is error term. Then using estimates from Eqn. (7), we
construct an adjusted aid series where the response of aid to per capita GDP growth is
removed:
)ln()ln()ln( ititit ycaidaid Δ−Δ=Δ ∗ , (8)
where ∗Δ )ln( itaid is the measure of aid that is free from endogeneity bias and is used as an
instrument for )ln( itaidΔ . This series was then suitably transformed into a spatial
25 A wide range of alternate instruments have been proposed in the literature (e.g., Burnstein and Dollar 2000;
Rajan and Subramanian 2008). We found that these approaches produced weak instruments for our sample.
14
instrumented aid variable using the various weighing schemes. Appendix B details the
derivation of the adjusted aid series (Eqns. 7 and 8).
The first two approaches (lags and construction of an aid series free of the effects of
growth) involve estimators commonly used in empirical analysis (OLS and IV). Our third
approach is to use estimators that have been developed specifically for spatial econometrics
to accommodate endogeneity in the spatial lag; the spatial GMM, Generalized Spatial Two
Stage Squares, and maximum likelihood estimators (see Kelejian and Prucha 1998, 1999;
Kelejian et al. 2004).26
4. Results
Column 1 of Table 2 reports estimates of a conventional growth model that excludes spatial
dimensions in the growth process. These results indicate convergence and the importance of
macroeconomic policy variables (budget balance and inflation). Ethnic fractionalization,
assassinations and population growth all have a negative relationship with growth, while
domestic capital and oil resources have a positive association with growth. Recession has the
expected negative sign. The initial state variables (years of socialism and nature of the state)
appear to have no effect on subsequent growth during transition. Interestingly, neither
openness nor FDI appear to have a statistically significant effect on growth. The Catholic and
Protestant variables have positive and statistically significant coefficients, while Muslim has
a negative coefficient. Aid has a positive and statistically significant coefficient. This result is
consistent with prior studies of transition economies (Fischer et al. 1996; Askarov and
Doucouliagos 2015).
TABLE 2 ABOUT HERE
26 See, however, Gibbons and Overman’s (2012) critique of these estimators.
15
The spatial analysis results are reported in columns 2 to 9 of Table 2, where the
growth model is extended with the addition of the spatial aid term (the weighted aid received
by all other transition countries) and then the addition of the spatial lag of growth (the
weighted growth of all other transition countries); Eqns.(2) and (4), respectively. These
estimates will be biased in the presence of endogeneity. Even if aid is not endogenous to
growth, spatial growth is clearly endogenous to growth. This issue can be accommodated in
various ways. A simple way is to apply OLS using lagged aid and lagged spatial aid. This
approach also has the benefit of allowing the possibility that the effect of aid on growth
arrives with a time lag (Clemens et al. 2012). Similarly, lagging the spatial growth variable
can address the simultaneity between this variable and the dependent variable. This approach
is particularly valid if growth spillovers occur with a lag rather than contemporaneously.
These results are reported in columns 6 to 9. In columns 2, 3, 6 and 7 the spatial lags are
constructed using neighborhood weights. That is, the weights are based on whether countries
have a contiguous border. In columns 4, 5, 8 and 9 the spatial lags are constructed with
weights that use the inverse distance between capital cities, with more distant countries given
less weight.
Aid has a positive and statistically significant coefficient regardless of the
specification and weighting scheme.27 Spatially lagged aid (Aid spatial) always has a
negative coefficient, though this is not always statistically significant when inverse distance
weights are used. The spatial growth variable (Growth spatial) is statistically significant
when contiguity weights are used, indicating positive growth spillovers among neighbors.
This effect is weaker among more distant economies.
27 The regression coefficients do not fully reflect aid’s effect on growth, as spillovers and feedback effects also
need to be considered. These effects are discussed below.
16
A second approach to tackling endogeneity is to instrument aid. As noted in Section 3
(and detailed in Appendix B), our instrumentation for aid follows Brückner’s (2013) method
of constructing an adjusted aid series where the response of aid to growth has been removed.
Table 3 presents these results, instrumenting aid and spatial aid (columns 1 and 2) and then
also instrumenting spatial growth (column 3). Estimation of columns 1 and 2 is via OLS.
Column 3 uses two-step GMM in order to provide consistent estimates in the presence of
endogeneity. Spatially lagged growth is instrumented using spatially lagged policy and
spatially lagged oil-to-GDP, all lagged one year (see Appendix B for details).28 Column 4
uses the Generalized Spatial Two Stage Squares (GS2SLS) (see Kelejian and Prucha, 1998
and 1999). This estimator uses spatially lagged values of all exogenous variables as
instruments for the spatial lag (spatial growth in our case). The aid series is again adjusted for
the effects of growth on aid. Columns 5 to 7 repeat using inverse distance weights. The
results for aid itself confirm the findings of Table 2; (instrumented) aid exerts a positive and
statistically significant effect on growth. However, spatially lagged aid is now no longer
statistically significant. Spatial growth retains its positive sign and statistical significance
when contiguity weights are used.
TABLE 3 ABOUT HERE
Tables 2 and 3 consider only spatial spillovers arising from growth and aid. However,
growth spillovers could arise from other variables. Consequently, the SAR model represented
by Eqn. (3) might be incorrectly specified. Hence, it is important to extend the analysis by
considering other sources of spatial spillovers beyond just aid and growth itself. For example,
both ethnic fractionalization and assassinations can potentially impact on other nation’s
28 Note however that the Cragg-Donald and Kleibergen-Paap tests indicate that our instrumentation of spatial
growth is not as successful when inverse distance weights are used.
17
growth by transmitting political instability across borders. Budget and inflation are policy
variables whose impact can also spill over into other nations. A case in point is that policy
tends to be copied and transmitted across countries (Gilardi 2010). Moreover, inflation
expectations may not be independent across countries and integration may lead to the
international transmission of inflation (Ciccarelli and Mojon 2010). Oil and other natural
resources could have either positive or negative spillover effects on other countries. Religion
is included in the growth model to explore the effects of religiosity on growth (Barro and
McCleary 2003). We allow for the possibility that the effects of religiosity could also spill
over into other nations. The political initial state variables (Socialism and State) can also
create spillovers. For example, compared to sovereign states, the newly independent nation
states face a more difficult task of establishing institutions and these difficulties might disrupt
their neighbors. Spatial lags in initial GDP enable the testing of cross-regressive effects (Rey
and Montouri 1999). Finally, spatial lags in the recession variable enable testing of contagion.
The results are reported in Table 4, with and without spatial growth (i.e., Eqns. 5 and
6, or the SDM and SLX models, respectively).29 The SDM model includes spatial lags in all
RHS variables, except for the time dummies and the two regional dummies. The estimates
reported in Table 4 use lags in aid, spatial aid and spatial growth. These results again indicate
aid effectiveness in the recipient nation and adverse aid spillovers to other nations.
TABLES 4 AND 5 ABOUT HERE
Our final (and preferred) estimates make use of the panel nature of the data to
estimate a random effects SDM. We prefer a random effects specification because we are
also interested in exploring spatial spillovers from several variables that are time invariant
(e.g., ethnic fractionalization, religion, and State), which would drop out of a fixed effects
specification. Estimation of the random effects SDM is through maximum likelihood that 29 Other than spatial aid and spatial growth, we treat all the other spatial lags as exogenous.
18
explicitly accommodates the endogeneity of the spatial growth lag (see LeSage and Pace
2009). These results use inverse distance weights and are presented in Table 5.30 Column 1
reports results using contemporaneous aid, column 2 uses lagged aid and in column 3 we use
the instrumented aid series.
While the regression coefficients are informative, additional information can be
drawn by considering the spatial lags and calculating direct, indirect and total effects of
individual variables (LeSage and Pace 2009). These are calculated for the estimates of Table
5 (see columns 4 to 9). The direct effects are measured by the own-partial derivative.
Spillovers are captured by the indirect effects measured by the cross-partial derivatives.
Below we predominantly draw upon these results for statistical inference.
5. Discussion
All the results confirm direct aid effectiveness for transition economies. Whether we
use contemporaneous, lagged or instrumented aid, aspatial or spatial models, aid’s own-
partial derivative is positive and statistically significant. Thus, we can conclude that aid has
been effective in generating growth, on average, in the individual transition economies that
receive it. There is, however, some evidence of negative cross-partial derivatives, i.e.,
adverse spatial spillovers arising from aid. The spatial aid term has in most cases a negative
coefficient and it is statistically significant in models that condition on other spatial variables.
However, this term becomes less statistically significant when we instrument aid. We can
conclude from the preponderance of the results that aid has a positive effect on growth in the
countries that directly receive it, but there also appears to be an adverse effect on the growth
of other nations. Indeed, table 5 shows that taking into account both the positive effect on the
30 Recall that we prefer inverse distance weights as they allow all transition countries to be connected, not just
those with a common border.
19
recipient’s growth and the adverse effect on the growth of others, the net (or total) effect is
negative when lagged aid is used (column 6) and it is zero when aid is instrumented (column
9).
Several other spillover effects are evident among transition economies. We find
evidence of spatial growth spillovers for contiguous transition countries but not for nations
further away. This evidence of spatial growth spillovers holds even when we dig deeper into
the drivers of spatial spillovers. Good policy is important to both an individual country and to
others. Government budget balance has a direct positive effect plus a positive spillover effect;
running deficits is detrimental to growth. Inflation is also a drain on an individual country as
well as others; inflation is transmitted across borders. Similarly, the Recession variable
suggests the transmission of recession across borders. It appears that good policy has a
positive effect on growth in a given transition country and it also has a positive spillover
effect on other transition countries. Taken together, the adverse spillovers from inflation and
recession suggest the importance of coordinating policy responses across countries.
Ethnic fractionalization appears to have no effect on other nations. These is some
evidence of adverse spillovers from assassinations, though this is not robust (compare tables
4 and 5). Similarly, while there is some evidence of positive spillovers from FDI, this is not
robust.31 Domestic capital is important to growth but it does not appear to generate spillovers.
The initial political state variable (State) indicates that spillovers are larger when they arise
from transition economies that were formerly independent (sovereign) nations.
In addition to convergence, we find some evidence of cross-regressive effects; growth
of transition economies is affected by their initial level of income and also by the initial
31 Some of the results suggest that openness is directly detrimental to growth (tables 2, 3, and B2). Table 5
suggests that FDI might have an adverse direct effect on growth. However, the level of statistical significance is
low in both cases.
20
income level of other transitional economies. This is consistent with the notion of transitional
economies as a convergence club.
The spatial lags in all three religion variables (Catholic, Protestant, and Muslim) are
statistically significant, with both Catholic and Protestant having robust positive direct and
indirect effects on growth. Evidently, religiosity has been an important factor for growth in
transition economies during the period studied. It appears that compared to other religions
(e.g., Eastern Orthodox, Buddhist, etc.) countries with these three religions are more likely to
generate spillovers. One explanation for this might be that countries with these religious
groups might be more able to translate this cultural similarity into trade and economic
linkages that result in growth spillovers.
6. Conclusions
Numerous studies have investigated the effects of aid on economic growth. These
studies assume that the effect of aid on growth is limited within the geographical boundaries
of the aid receiving nation. Moreover, the extant studies abstract from the possibility of
spillovers from other variables, especially growth and policy spillovers; spatial dependence
has been ignored in the aid effectiveness literature. This paper explores the role of aid in
promoting growth in transition countries, with a focus on spatial effects from geographically
close nations and nations experiencing similar transformation.
Four main findings emerge from the analysis. First, aid appears to have had a positive
effect on growth. To the extent that rising incomes are important for popular and political
support for continued transition, then aid has also contributed to the transition process itself.
Second, there is some evidence (though it is not always robust) that aid also generates
negative spatial spillovers. This suggests that the impact of aid may extend beyond the
recipient’s national borders. The net effect of aid is thus smaller than what a conventional
21
cross-country growth regression would suggest. We speculate that part of the explanation
behind the adverse spatial aid effect relates to the effects of migration and adverse spillovers
generated by institutional displacement.
Our third finding is that there appear to be positive growth spillovers among
neighboring transition economies. Fourth, there are spatial spillovers arising from budget
balance, inflation and recession, reflecting the benefits of good policy. Evidently, good policy
in one country has a positive effect on others. One mechanism behind this might be
emulation; successful policies in one country are copied by other nations. It might also reflect
the benefits of inter-jurisdictional competition, e.g., through yardstick competition (Besley
and Rosen 1999). The international transmission of inflation and recession highlights the
importance of policies to curtail these phenomena.
While our results relate to the transition group of countries, spillovers from aid and
good policy are likely to occur elsewhere. An understanding of spillovers is critical to
improving aid effectiveness and generating growth for developing and emerging economies.
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Table 1 Summary statistics, variable descriptions and data sources
Variable N Mean Std. Dev. Description Source Aid 720 49.229 62.044 Net official development assistance and official aid
received (constant 2011 US$), per capita World Bank (WDI)
Asia 736 0.156 0.363 Regional dummy variable Authors Assassin 664 0.104 0.414 Politically motivated murder or attempted murder
of a high government official or politician The Cross-National Time-Series (CNTS) Data Archive
Budget 671 -3.416 5.425 Government balance (% of GDP) EBRD and ADB Capital 712 25.114 8.600 Gross capital formation (% of GDP) World Bank (WDI) Catholic 736 17.087 28.151 Catholics as % of population in 1980 La Porta et al. (1999) Ethnicity 736 0.375 0.160 Index of ethnolinguistic fractionalization Alesina et al. (2003) Europe 736 0.688 0.464 Regional dummy variable Authors FDI 664 4.990 5.996 Foreign direct investment, net inflows (% of
GDP) World Bank (WDI)
GDP per capita Growth
717 2.535 9.095 The dependent variable, GDP per capita growth (annual %)
World Bank (WDI)
Inflation 712 132.250 713.213 Change in Consumer Price Index (annual %) World Bank (WDI) Initial GDP 696 7.704 1.128 Natural log of initial GDP per capita World Bank (WDI) Muslim 736 19.038 30.686 Muslims as % of population in 1980 La Porta et al. (1999) Openness 706 96.717 33.331 Openness at 2005 constant prices (%) World Penn Tables
(version 8) Oil 734 3.758 8.797 Oil rents (% of GDP) World Bank (WDI) Policy 655 4.363 6.757 Policy index based on Burnside and Dollar (2000).
See footnote 33 for details. Own calculation
Popgrowth 736 0.270 1.190 Population growth rate (%) World Bank (WDI) Protestant 736 4.062 12.051 Protestants as % of population in 1980 La Porta et al. (1999) Recession 736 0.477 0.500 Country specific recession index based on
Brückner and Ciccone (2011). See footnote 21 for details.
Own calculation
Socialism 736 52.969 16.442 Years under central planning de Melo et al. (2001) State 736 0.844 0.871 Independence and development of state
institutions de Melo et al. (2001)
27
Table 2 Baseline aspatial and spatial growth models, OLS
Contemporaneous Lagged Aspatial
Eqn. (1) (1)
Neighbor Eqn. (2)
(2)
Neighbor Eqn. (4)
(3)
1/distance Eqn. (2)
(4)
1/distance Eqn. (4)
(5)
Neighbor Eqn. (2)
(6)
Neighbor Eqn. (4)
(7)
1/distance Eqn. (2)
(8)
1/distance Eqn. (4)
(9) Initial GDP -3.831*** -3.509*** -3.283*** -3.591** -3.424** -3.500*** -3.446*** -3.461*** -3.397*** (-3.56) (-3.68) (-3.68) (-2.42) (-2.33) (-3.75) (-3.81) (-3.75) (-3.76) Ethnicity -5.491* -6.430** -7.311*** -6.758*** -7.058*** -6.493** -7.172*** -7.486*** -7.804*** (-1.78) (-2.23) (-2.71) (-3.02) (-3.16) (-2.34) (-2.66) (-2.82) (-3.03) Assassin -1.768** -1.804** -1.777** -1.848** -1.736* -1.538 -1.560 -1.621 -1.564 (-2.12) (-2.16) (-2.10) (-2.10) (-1.97) (-1.58) (-1.56) (-1.62) (-1.54) Budget 0.272*** 0.285*** 0.241*** 0.281*** 0.253** 0.291*** 0.270*** 0.290*** 0.269*** (3.85) (3.90) (2.94) (3.29) (2.63) (3.80) (3.28) (3.86) (3.37) Inflation -0.003** -0.003** -0.002* -0.003** -0.003* -0.003** -0.002 -0.003** -0.003* (-2.21) (-2.12) (-1.67) (-2.59) (-1.97) (-2.03) (-1.64) (-2.10) (-1.79) Openness -0.012 -0.017* -0.011 -0.015 -0.013 -0.017* -0.013 -0.016* -0.014 (-1.22) (-1.84) (-1.19) (-1.27) (-1.12) (-1.79) (-1.39) (-1.71) (-1.55) FDI -0.074 -0.062 -0.082 -0.060 -0.064 -0.057 -0.073 -0.046 -0.052 (-1.03) (-0.93) (-1.20) (-0.79) (-0.83) (-0.88) (-1.08) (-0.76) (-0.82) Capital 0.231** 0.224** 0.213** 0.232* 0.227* 0.220** 0.219*** 0.230*** 0.231*** (2.49) (2.52) (2.57) (1.93) (1.88) (2.56) (2.59) (2.60) (2.62) Popgrowth -2.003*** -2.195*** -2.265*** -2.060** -2.137*** -2.484*** -2.680*** -2.323*** -2.437*** (-3.05) (-3.06) (-3.21) (-2.72) (-2.87) (-3.20) (-3.36) (-3.21) (-3.30) Europe -0.972 -0.141 -0.403 -0.945 -1.154 0.146 -0.102 -0.770 -0.975 (-0.66) (-0.09) (-0.25) (-0.91) (-1.08) (0.10) (-0.07) (-0.54) (-0.70) Asia -3.660* -3.045 -4.062* -4.084 -4.155 -2.389 -3.163 -3.959* -4.133* (-1.78) (-1.51) (-1.94) (-1.20) (-1.26) (-1.19) (-1.54) (-1.83) (-1.89) Aid 0.034*** 0.037*** 0.037*** 0.037*** 0.037*** 0.038*** 0.038*** 0.039*** 0.040*** (3.27) (3.26) (3.35) (3.04) (2.99) (2.94) (3.03) (2.92) (2.95) Aid spatial - -0.023** -0.024** -0.035 -0.038 -0.031** -0.032** -0.067** -0.070** (-1.97) (-2.04) (-1.01) (-1.13) (-2.38) (-2.46) (-2.19) (-2.25) Growth spatial - - 0.351*** - 0.325* - 0.220** - 0.228 (3.21) (1.79) (2.31) (1.33) Oil 0.163** 0.210*** 0.201*** 0.222*** 0.217** 0.203*** 0.200*** 0.212*** 0.211*** (2.54) (3.85) (3.74) (2.83) (2.72) (3.69) (3.64) (3.77) (3.74) Recession -0.854** -1.288** -1.153** -1.309** -1.273** -1.393** -1.238** -1.441*** -1.367** (-2.15) (-2.41) (-2.26) (-2.27) (-2.19) (-2.56) (-2.46) (-2.60) (-2.56) Socialism -0.028 -0.055** -0.065*** -0.044 -0.048 -0.062** -0.069*** -0.054** -0.057** (-1.41) (-2.36) (-2.70) (-1.36) (-1.54) (-2.53) (-2.70) (-2.43) (-2.51) State -0.234 0.588 0.758 0.495 0.475 0.539 0.693 0.426 0.445 (-0.44) (0.82) (1.05) (0.63) (0.61) (0.81) (1.01) (0.67) (0.70) Catholic 0.030* 0.064*** 0.054** 0.064** 0.060* 0.062** 0.058** 0.055** 0.054** (1.80) (2.60) (2.42) (2.11) (2.01) (2.53) (2.48) (2.44) (2.43) Protestant 0.052** 0.075*** 0.071*** 0.077** 0.074** 0.069*** 0.068*** 0.063*** 0.062*** (2.45) (3.20) (3.28) (2.30) (2.24) (3.05) (3.01) (2.80) (2.76) Muslim -0.043* -0.016 -0.011 -0.034 -0.030 -0.007 -0.003 -0.028 -0.025 (-1.89) (-0.60) (-0.42) (-1.09) (-0.98) (-0.25) (-0.10) (-1.34) (-1.20) Wald [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Adjusted R2 0.472 0.480 0.502 0.475 0.482 0.479 0.488 0.479 0.482
Notes: The dependent variable is per capita GDP growth. Robust t-statistics reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Wald is a joint test for the statistical significance of all variables. Constant and time fixed effects included in all estimates but not reported. Number of observations varies between 598 and 600 depending on the use of lags. All estimations use OLS. Column 1 reports the standard (aspatial) model. Columns 2 to 5 report results with aid, spatial aid and spatial growth treated as contemporaneous, using neighbor weights or inverse distance weights. Columns 6 to 9 report results with aid, spatial aid and spatial growth are lagged one period, using neighbor weights or inverse distance weights.
28
Table 3 Spatial growth model, instrumented aid and growth
Neighbor weights 1/Distance weights Aid
Instrumented (1)
Aid Instrumented
(2)
Growth instrumented
(3)
GS2SLS
(4)
Aid Instrumented
(5)
Aid Instrumented
(6)
Growth instrumented
(7)
Initial GDP -4.591*** -4.450*** -3.787*** -5.919*** -4.562** -4.464** -3.893** (-3.12) (-3.13) (-2.62) (-6.76) (-2.13) (-2.09) (-2.07) Ethnicity 2.828 1.953 -0.797 3.000 2.362 2.306 3.333 (0.59) (0.43) (-0.14) (0.91) (0.37) (0.35) (0.54) Assassin -1.507 -1.512 -1.927* -.247 -1.544 -1.422 -1.265 (-1.51) (-1.50) (-1.83) (-0.42) (-1.57) (-1.47) (-1.41) Budget 0.223*** 0.178** 0.166* 0.248*** 0.217** 0.190** 0.159* (3.32) (2.38) (1.91) (3.26) (2.71) (2.14) (1.77) Inflation -0.003* -0.002 -0.002* -0.012*** -0.003* -0.002 -0.002** (-1.82) (-1.39) (-1.75) (-4.42) (-2.00) (-1.48) (-2.07) Openness -0.025** -0.020** -0.013 0.007 -0.025* -0.023* -0.014 (-2.55) (-1.99) (-1.07) (0.53) (-1.89) (-1.86) (-1.10) FDI -0.095 -0.120 -0.125 -0.100** -0.093 -0.099 -0.110 (-0.87) (-1.06) (-0.92) (-2.18) (-0.63) (-0.68) (-0.80) Capital 0.254** 0.247** 0.209 0.280*** 0.247 0.244 0.244 (2.13) (2.16) (1.56) (6.78) (1.49) (1.45) (1.58) Popgrowth -1.224* -1.294* -1.729*** -1.386*** -1.196** -1.290** -1.479*** (-1.75) (-1.84) (-3.74) (-3.46) (-2.34) (-2.66) (-3.48) Europe 3.018 2.748 0.983 2.769 2.949 2.777 1.680 (1.46) (1.35) (0.41) (1.42) (1.08) (0.96) (0.62) Asia -2.786 -3.828* -5.449 -5.514* -2.780 -2.724 -2.550 (-1.30) (-1.74) (-1.61) (-1.78) (-0.73) (-0.76) (-0.79) Aid 0.037*** 0.036*** 0.030** 0.031*** 0.037** 0.037** 0.033** (2.72) (2.65) (2.34) (5.91) (2.27) (2.24) (2.23) Aid spatial 0.007 0.001 -0.002 0.009 -0.019 -0.017 -0.006 (0.34) (0.03) (-0.12) (0.98) (-0.46) (-0.51) (-0.22) Growth spatial - 0.326** 0.732*** 0.434*** - 0.309 0.841 (2.54) (2.79) (3.23) (1.44) (1.31) Oil 0.225*** 0.220*** 0.199*** 0.241*** 0.223*** 0.221*** 0.229*** (3.63) (3.63) (2.86) (4.39) (2.79) (2.75) (3.05) Recession -0.735 -0.688 -0.504 -1.194** -0.810 -0.793 -0.758 (-1.28) (-1.24) (-1.02) (-2.47) (-1.45) (-1.40) (-1.43) Socialism 0.005 -0.003 -0.014 0.011 0.006 0.002 -0.005 (0.32) (-0.19) (-0.46) (0.27) (0.19) (0.08) (-0.17) State 2.207** 2.373** 2.352* 2.500*** 2.127 2.112 2.174 (2.07) (2.21) (1.90) (3.03) (1.48) (1.45) (1.62) Catholic 0.096** 0.088** 0.064* 0.121*** 0.093* 0.092* 0.088** (2.44) (2.33) (1.87) (4.53) (1.97) (1.94) (2.06) Protestant 0.132*** 0.128*** 0.113*** 0.116*** 0.131*** 0.130*** 0.118*** (4.50) (4.47) (4.61) (2.72) (3.72) (3.72) (4.04) Muslim -0.030 -0.026 -0.023 -0.042 -0.030 -0.025 -0.015 (-1.23) (-1.06) (-0.65) (-1.41) (-0.71) (-0.63) (-0.38) Cragg-Donald F stat. 27.06 13.67 Kleibergen-Paap F stat. 16.57 5.94 Stock-Yogo crit.val.(10 %) 19.93 19.93 Hansen J-stat 0.332 0.169 Wald [0.000] [0.000] [0.000] [0.000] Adjusted R2 0.432 0.451 0.433 0.439
Notes: The dependent variable is per capita GDP growth. Aid is measured as the change in the logarithm of per capita aid instrumented using Brückner’s (2013) approach (see Appendix B). Robust t-statistics reported in parentheses. Wald is a joint test for the statistical significance of all variables. *** p<0.01, ** p<0.05, * p<0.1. Constant and time fixed effects are included in all estimates but not reported. Number of observations is 514. Columns 1, 2, 5 and 6 estimated using OLS. Columns 3 and 7 use two-step GMM. Column 4 uses GS2SLS.
29
Table 4 Spatial Durbin and Spatial Lag in X growth models
Lagged aid and growth SDM
Neighbor Eqn. (6)
SLX Neighbor Eqn. (5)
SDM 1/Distance
Eqn. (6)
SLX 1/Distance
Eqn. (5) (1) (2) (3) (4) Initial GDP -5.174*** -5.247*** -3.774*** -3.784*** (-3.54) (-3.60) (-3.49) (-3.47) Ethnicity -3.471 -4.202 -6.145** -6.129** (-1.12) (-1.36) (-2.16) (-2.16) Assassin -1.468 -1.512 -1.158 -1.161 (-1.54) (-1.57) (-1.20) (-1.20) Budget 0.233*** 0.222*** 0.231*** 0.229*** (2.84) (2.59) (2.99) (2.98) Inflation -0.003*** -0.003*** -0.002** -0.002** (-3.27) (-3.22) (-2.17) (-2.14) Openness -0.021 -0.018 -0.018 -0.017 (-1.49) (-1.27) (-1.51) (-1.47) FDI -0.031 -0.034 -0.052 -0.052 (-0.62) (-0.68) (-0.87) (-0.88) Capital 0.196*** 0.186*** 0.215*** 0.215*** (2.66) (2.63) (2.65) (2.64) Popgrowth -2.573*** -2.626*** -2.674*** -2.665*** (-2.86) (-2.92) (-3.44) (-3.47) Europe 2.700 1.847 -0.249 -0.290 (0.77) (0.51) (-0.07) (-0.09) Asia 1.622 1.305 -2.616 -2.604 (0.46) (0.36) (-0.70) (-0.70) Aid 0.029*** 0.030*** 0.035*** 0.035*** (2.73) (2.80) (2.68) (2.66) Aid spatial -0.026*** -0.026*** -0.082** -0.081** (-2.61) (-2.65) (-2.08) (-2.09) Growth spatial - 0.223** - 0.051 (2.07) (0.31) Oil 0.277*** 0.286*** 0.239*** 0.239*** (3.75) (3.79) (3.90) (3.88) Recession -1.562** -1.555** -1.293*** -1.289*** (-2.38) (-2.42) (-2.62) (-2.63) Socialism -0.069* -0.074* -0.117*** -0.117*** (-1.66) (-1.81) (-2.64) (-2.64) State 1.059 1.067 1.131 1.137 (1.55) (1.57) (1.26) (1.26) Catholic 0.073*** 0.067*** 0.049** 0.049** (2.94) (2.94) (2.14) (2.14) Protestant 0.114*** 0.113*** 0.054* 0.054* (2.92) (2.93) (1.77) (1.76) Muslim -0.016 -0.022 -0.012 -0.012 (-0.67) (-0.97) (-0.47) (-0.45)
Spatial Lags in X vector
Initial GDP -0.344 0.132 -4.549*** -4.622 (-0.32) (0.13) (-2.85) (-1.55) Ethnicity 6.366 7.345 1.651 3.421 (1.29) (1.52) (0.23) (0.33) Assassin -2.598** -2.559** -2.683* -3.426 (-2.42) (-2.37) (-1.72) (-1.58) Budget 0.476*** 0.400** 0.142 0.302
30
(2.63) (2.39) (0.58) (0.87) Inflation 0.003*** 0.003*** 0.003*** 0.003*** (2.77) (3.10) (4.42) (4.05) Openness -0.063** -0.051* -0.149*** -0.192*** (-2.06) (-1.68) (-2.75) (-3.20) FDI 0.196** 0.194** 0.330* 0.280 (2.52) (2.53) (1.84) (1.55) Capital -0.037 -0.073 -0.125 -0.234 (-0.48) (-0.91) (-1.02) (-1.32) Popgrowth -0.750 -0.958 -1.771* 0.066 (-0.43) (-0.54) (-1.88) (0.04) Oil -0.048 -0.086 0.192 0.120 (-0.42) (-0.75) (0.97) (0.51) Recession 0.989 1.381* -2.087 -2.908* (1.26) (1.72) (-1.10) (-1.78) Socialism 0.006 0.019 0.252 0.420* (0.06) (0.20) (1.18) (1.84) State 0.408 0.414 3.641 4.485 (0.26) (0.26) (0.91) (1.01) Catholic 0.135** 0.128** 0.212*** 0.366*** (2.37) (2.36) (2.88) (3.09) Protestant 0.193* 0.183* 0.597** 0.236 (1.93) (1.87) (2.60) (0.71) Muslim 0.073 0.081 0.050 -0.060 (1.31) (1.41) (0.56) (-0.38) Wald [0.000] [0.000] [0.000] [0.000] Adjusted R2 0.526 0.532 0.580 0.579
Notes: The dependent variable is the growth rate of per capita GDP. Robust t-statistics reported in parentheses. Wald is a joint test for the statistical significance of all variables. *** p<0.01, ** p<0.05, * p<0.1. Constant and time fixed effects are included in all estimates but not reported. Number of observations is 597. All estimations use OLS. Aid, spatial aid and spatial growth are all lagged one period, using neighbor weights (columns 1 and 2) and inverse distance weights (columns 3 and 4).
31
Table 5 Spatial Durbin growth model with random effects
Contemporaneous aid
Lagged aid
Instrumented aid
Lagged aid Instrumented aid
(1) (2) (3) Direct effect
(4)
Indirect effect
(5)
Total Effect
(6)
Direct effect
(7)
Indirect effect
(8)
Total Effect
(9) Initial GDP -5.650*** -5.681*** -7.899*** -5.738*** -5.004** -10.742*** -7.943*** -1.442 -9.384*** (-3.76) (-3.77) (-4.11) (-4.54) (-2.53) (-6.70) (-4.90) (-0.62) (-5.06) Ethnicity -0.903 -1.628 2.467 -1.159 29.886 28.727 2.928 22.279 25.208 (-0.24) (-0.42) (0.47) (-0.28) (1.42) (1.22) (0.55) (0.76) (0.78) Assassin -0.653 -0.395 -0.534 -0.353 1.329 0.976 -0.500 0.226 -0.273 (-1.11) (-0.68) (-0.93) (-0.56) (0.41) (0.29) (-0.81) (0.07) (-0.08) Budget 0.320*** 0.319*** 0.274*** 0.322*** 0.584** 0.906*** 0.278*** 0.611** 0.889*** (4.31) (4.34) (3.73) (4.57) (2.02) (3.10) (3.95) (2.13) (3.08) Inflation -0.012*** -0.012*** -0.011*** -0.012*** -0.026*** -0.037*** -0.010*** -0.017* -0.027*** (-4.51) (-4.48) (-4.08) (-4.53) (-2.73) (-3.91) (-4.07) (-1.85) (-2.93) Openness 0.014 0.015 0.013 0.017 0.086* 0.104** 0.016 0.051 0.067 (1.27) (1.35) (1.10) (1.54) (1.88) (2.12) (1.28) (1.15) (1.42) FDI -0.081* -0.076* -0.089** -0.078 -0.125 -0.203 -0.090* -0.088 -0.178 (-1.80) (-1.70) (-1.99) (-1.57) (-0.61) (-0.92) (-1.82) (-0.41) (-0.78) Capital 0.283*** 0.278*** 0.301*** 0.277*** 0.206 0.484** 0.301*** 0.266 0.566*** (6.85) (6.76) (7.47) (6.45) (1.17) (2.47) (7.06) (1.50) (2.89) Popgrowth -2.242*** -2.547*** -1.598*** -2.586*** -0.335 -2.921** -1.638*** -0.670 -2.309 (-5.75) (-6.47) (-4.10) (-7.18) (-0.23) (-2.04) (-4.56) (-0.43) (-1.47) Europe 9.487** 9.189** 13.625*** 9.506** 0.983 10.490** 13.923*** 1.755 15.678*** (2.32) (2.25) (2.76) (2.32) (0.69) (2.27) (2.98) (0.82) (2.78) Asia 4.228 4.243 6.413 4.691 0.549 5.240 6.859 0.956 7.815 (0.92) (0.90) (1.02) (1.02) (0.53) (1.02) (1.19) (0.63) (1.16) Aid 0.023*** 0.025*** 0.027*** 0.024*** -0.065*** -0.041** 0.027*** -0.025 0.002 (4.01) (4.56) (5.49) (4.37) (-3.83) (-2.31) (5.60) (-1.60) (0.10) Aid spatial -0.041*** -0.061*** -0.025* (-2.68) (-3.94) (-1.66) Growth spatial 0.054 0.071 0.085 (0.51) (0.67) (0.81) Oil 0.308*** 0.314*** 0.378*** 0.317*** 0.278 0.596** 0.381*** 0.175 0.557** (4.10) (4.15) (5.21) (4.51) (1.20) (2.43) (5.25) (0.71) (2.18) Recession -1.299*** -1.339*** -1.222** -1.536*** -4.809** -6.345*** -1.401*** -2.578 -3.979* (-2.61) (-2.71) (-2.52) (-3.28) (-1.99) (-2.60) (-3.05) (-1.23) (-1.88) Socialism -0.033 -0.039 0.013 -0.033 0.255 0.223 0.020 0.151 0.170 (-0.75) (-0.89) (0.21) (-0.82) (1.16) (1.05) (0.35) (0.51) (0.60) State 1.891** 1.829** 2.864*** 1.856** 9.430** 11.286*** 2.912*** 13.894** 16.805*** (2.37) (2.28) (2.95) (2.42) (2.39) (2.65) (3.11) (2.56) (2.91) Catholic 0.081*** 0.079*** 0.112*** 0.082*** 0.453*** 0.535*** 0.117*** 0.449*** 0.566*** (3.24) (3.17) (3.75) (3.33) (4.00) (4.70) (3.79) (3.16) (4.02) Protestant 0.106* 0.100* 0.196*** 0.104** 0.635** 0.739** 0.202*** 1.066*** 1.268*** (1.90) (1.81) (2.61) (2.03) (2.15) (2.24) (2.93) (2.60) (2.79) Muslim 0.001 0.005 -0.015 0.009 0.366* 0.376* -0.008 0.688*** 0.680*** (0.02) (0.14) (-0.39) (0.35) (1.87) (1.87) (-0.23) (3.09) (2.91)
Spatial X
Initial GDP -4.089* -4.123** -0.527 (-1.90) (-1.96) (-0.20) Ethnicity 25.801 26.413 18.192 (1.38) (1.41) (0.69) Assassinations 0.482 1.163 0.203 (0.17) (0.41) (0.07) Budget 0.511** 0.523** 0.537** (1.98) (2.04) (2.11) Inflation -0.019** -0.022** -0.014* (-2.16) (-2.54) (-1.71) Openness 0.065* 0.077** 0.046 (1.74) (2.06) (1.21) FDI -0.095 -0.103 -0.063 (-0.55) (-0.60) (-0.37) Capital 0.216 0.168 0.209 (1.25) (0.99) (1.23) Popgrowth -0.199 -0.245 -0.663 (-0.14) (-0.17) (-0.47) Oil 0.180 0.204 0.098 (0.76) (0.87) (0.38) Recession -3.033* -4.279** -2.254
32
(-1.69) (-2.29) (-1.33) Socialism 0.239 0.266 0.172 (1.12) (1.23) (0.61) State 8.227** 8.594** 12.332** (2.05) (2.13) (2.33) Catholic 0.396*** 0.404*** 0.389*** (4.24) (4.35) (3.35) Protestant 0.604** 0.566** 0.926*** (2.36) (2.21) (2.85) Muslim 0.339* 0.326* 0.608*** (1.96) (1.90) (3.33) Wald [0.000] [0.000] [0.000] R2 0.457 0.466 0.455
Notes: The dependent variable is the growth rate of per capita GDP. Wald is a joint test for the statistical significance of all variables. *** p<0.01, ** p<0.05, * p<0.1. Constant and time fixed effects are included in all estimates but not reported. Number of observations is 544. All estimations use maximum likelihood. Column 1 uses contemporaneous aid. In column 2 aid is lagged. In column 3 aid is instrumented. Columns 4 to 9 report the direct, indirect and total effects of the explanatory variables on growth.
33
Appendix A. Average aid and per capita growth, transition economies, 1990-2012
Country Average per capita aid (US$)
Average per capita growth (%)
1. Albania 120.9 3.4 2. Armenia 97.6 5.4 3. Azerbaijan 27.5 3.9 4. Belarus 12.9 3.7 5. Bosnia and Herzegovina 205.1 11.8 6. Bulgaria 30.2 1.9 7. Cambodia 47.6 5.6 8. China 2.0 9.1 9. Croatia 31.2 1.0
10. Czech Republic 21.6 1.8 11. Estonia 46.2 2.7 12. Georgia 88.2 -0.2 13. Hungary 22.2 1.1 14. Kazakhstan 13.3 2.7 15. Kyrgyzstan 64.6 -0.4 16. Lao 72.7 4.6 17. Latvia 33.0 2.2 18. Lithuania 34.4 2.3 19. Macedonia 102.3 0.7 20. Moldova 56.8 -1.4 21. Mongolia 111.9 2.8 22. Poland 37.2 3.8 23. Romania 16.7 1.6 24. Russia 8.6 0.9 25. Serbia 126.7 -0.5 26. Slovak Republic 23.6 2.3 27. Slovenia 24.2 1.9 28. Tajikistan 31.7 -1.4 29. Turkmenistan 9.6 3.6 30. Ukraine 12.3 -0.8 31. Uzbekistan 7.1 2.0 32. Vietnam 25.7 5.4
34
Appendix B. Derivation of Aid Adjusted for Growth
We followed Brückner (2013) in instrumenting aid. We commence by instrumenting growth
using a measure of policy and oil resources.32 Policy is an important factor for growth in
transitional economies and many of these economies are also oil producers. Hence, these two
variables can be expected to be important drivers of growth. The first stage regression results
reported in column 1 of Table B1 below confirm that both policy and oil are important
drivers of growth. Both variables are also statistically significant in explaining aid (column
2), but neither variable is important in explaining aid conditional on growth (columns 5 and
6) confirming that the exclusion restriction is not violated. Hence, it appears that policy and
oil are useful instruments for growth and will enable the construction of an aid series that is
free from the effects of growth. Column 3 reports the results of estimating an OLS version of
Eqn. (7). These results are potentially biased if growth is driven by aid. Column 4 reports the
IV version of Eqn. (7). That is, in column 4, growth is instrumented by policy and oil. The
coefficients from these estimates are then used to construct the adjusted aid series, Eqn. (8).
Column 4 confirms that less aid is received as transitional economies grow. Table B2 reports
baseline (aspatial) growth regressions using the instrumented aid series. Column 1 uses all
available annual data, while column 2 uses 5-year averages. Aid contributes directly to
growth in recipient transition economies.
32 We followed Burnside and Dollar (2000) and constructed a policy measure using budget surplus, inflation and
openness. Each component is weighted by its coefficient in an initial growth regression, giving: Policy = 6.86 +
0.335xBudget surplus - 0.008xInflation - 0.003xOpenness.
35
Table B1: Derivation of Aid Adjusted for Growth
(1) (2) (3) (4) (5) (6) Δln(GDP
per capita) Δln(Aid/Pop) Δln(Aid/Pop) Δln(Aid/Pop) Δln(Aid/Pop) Δln(Aid/Pop)
OLS OLS OLS IV IV IV Δln(GDP per capita) - - -3.147*** -3.181*** -2.679*** -3.454*** (-5.19) (-5.09) (-3.42) (-3.85) Policy 0.004** -0.013** - - -0.003 - (2.72) (-2.58) (-0.65) Oil/GDP 0.005*** -0.014** - - - 0.004 (4.19) (-2.09) (0.66) Hansen J, p-value 0.299 Exact. Exact. Cragg-Donald F stat. 22.16 8.60 22.47 Kleibergen-Paap F stat. 35.03 4.78 4.55 Stock-Yogo crit.val.(10 %) 19.93 16.38 16.38 Country FE Yes Yes Yes Yes Yes Yes Observations 649 543 572 543 543 543 Adjusted R2 0.185 0.032 0.178 0.074 0.078 0.066
Notes: The dependent variable in column 1 is the growth rate of per capita GDP and in columns 2 to 6 it is the growth rate of per capita aid. Standard errors clustered by country. *** p<0.01, ** p<0.05, * p<0.1.
Table B2: Aspatial growth models with instrumented aid
Annual data (1)
5-year averages (2)
Initial GDP -2.428** -3.509*** (-2.19) (-3.68) Ethnicity 1.270 -6.430** (0.28) (-2.23) Assassin -1.663* -1.804** (-1.71) (-2.16) Budget 0.192*** 0.285*** (2.99) (3.90) Inflation -0.003* -0.003** (-1.74) (-2.12) Openness -0.025** -0.017* (-2.56) (-1.84) FDI -0.061 -0.062 (-0.56) (-0.93) Capital 0.205* 0.224** (1.80) (2.52) Popgrowth -1.084 -2.195*** (-1.55) (-3.06) Europe 2.852 -0.141 (1.34) (-0.09) Asia 0.438 -3.045 (0.21) (-1.51) Aid 0.038*** 0.037*** (2.59) (3.26) Oil 0.151*** 0.210*** (2.71) (3.85) Recession -0.439 -1.288** (-0.75) (-2.41) Socialism -0.013 -0.055** (-0.76) (-2.36) State 1.148 0.588 (1.28) (0.82)
36
Catholic 0.052* 0.064*** (1.66) (2.60) Protestant 0.083*** 0.075*** (3.46) (3.20) Muslim -0.000 -0.016 (-0.02) (-0.60) N 514 128 Wald [0.000] [0.000] Adjusted R2 0.396 0.546
Notes: The dependent variable is the growth rate of per capita GDP. Aid is measured as the change in the logarithm of per capita aid instrumented using Brückner’s (2013) approach (see Table B1). Wald is a joint test for the statistical significance of all variables.*** p<0.01, ** p<0.05, * p<0.1. Constant and time fixed effects are included in all estimates but not reported. Column 1 uses annual data. Column 2 uses 5-year averages.
