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# Infrastructure aid for resource trade? The crossroads of strategy and sustainable development

Hendrik Kruse, Thais Nunez Rocha, Camelia Turcu

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## To cite this version:

Hendrik Kruse, Thais Nunez Rocha, Camelia Turcu. Infrastructure aid for resource trade? The crossroads of strategy and sustainable development. 2024. hal-04731539

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Document de Recherche du Laboratoire d'Économie d'Orléans

Working Paper Series, Economic Research Department of the University of Orléans, University Of Tours
and University Clermont-Auvergne, France

DR LEO 2024-14

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# Infrastructure aid for resource trade? The crossroads of strategy and sustainable development

Hendrik KRUSE

Thais NUNEZ-ROCHA

Camelia Turcu

MISE EN LIGNE / ONLINE | 10/10/2024

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Laboratoire d'Économie d'Orléans
Collegium DEG
Rue de Blois - BP 26739
45067 ORLÉANS Cedex 2

TÉL | (33) (0)2 38 41 70 37

MAIL | leo@univ-orleans.fr
www.leo-univ-orleans.fr
Infrastructure aid for resource trade? The crossroads of strategy
and sustainable development

October 10, 2024

Abstract

We investigate the claim that rich countries use development aid to ensure access to natural re-
sources. We provide a theoretical model that suggests that even an altruistic donor may be inclined
to allocate a higher share of their aid expenditure on infrastructure and other trade-promoting
measures if they rely on the recipient's resource exports for their own production. We use a panel
dataset from 2001 to 2011. Our results suggest that bilateral resource trade on average positively
affects the number of infrastructure projects and their average size. The effect seems to be driven
mostly by fuels and road infrastructure projects. While the effect of resources is weaker for land-
locked countries, we find that the transport capacity of the recipient's fleet of bulk carriers —used
in the maritime transport of many resources— reinforces the effect of resources on infrastructure
aid. Finally, we find a decreasing influence of resources over time.

**Keywords:** Foreign Aid, Resource exports, Political Economy, Trade costs, Infrastructure

JEL Codes: F18, F35, O13, Q01, Q56
# 1 Introduction

The fact that some resource-rich countries remain poor and the conjecture that they may fall victim to a “resource curse” is the subject of extensive economic research and debate (Frankel and Warner, 2001; Lederman and Maloney, 2007; Torres et al., 2012). It is of little surprise, then, that among the countries receiving development aid some resource-rich economies figure prominently. For instance, the Democratic Republic of Congo is one of the largest recipients of development aid in Africa and also a crucial exporter of minerals. Nigeria, the biggest oil exporter in Africa, also receives one of the largest sums of development aid on the continent in absolute terms (Kruse and Martínez-Zarzoso, 2021).

While the literature on aid allocation typically finds that commercial interest and political motives play a role in the allocation decision of donors (Alesina and Dollar, 2000; Dreher et al., 2008), resources per se have received less attention in the academic literature. With two notable exceptions. The first is Dreher and Fuchs (2015) who do not find evidence of an impact of resource endowment on the allocation of Chinese aid. The second Couharde et al. (2020), here the authors examine the role of oil in the aid distribution decisions of G7 donors and find that aid from these donors increases significantly with the oil endowment of recipient countries. They demonstrate, additionally, that their strategic interests in oil security influence their aid decisions and highlight the presence of competition among G7 donors for access to oil supplies.

However, the channel through which resource abundance or resource trade could affect development aid is complex. Donors could use aid as leverage to receive better bilateral contracts, but many resources are also traded on organized spot and futures markets (Ruta and Venables, 2012). Aid could help facilitate foreign direct investment that then triggers preferential trade, but this depends on the existence of an investor with capacity and expertise (Li et al., 2013).

In this study, we narrow down the focus and start from the observation of Venables (2016) that many resource-rich countries have insufficient infrastructure to benefit from their resources. This, in turn, also increases costs for donors who import (or consume indirectly) these resources. In this case, donors can increase the amount of bilateral aid dedicated to improving the existing infrastructure as a remedy. This entails benefits to the exporting state and to the donor due to lower input costs. The question that we want to answer is: Do donors give more infrastructure aid to countries from whom they receive more resources?

We contribute to the literature on aid allocation by adapting the framework by Dudley and Mont-
marquette (1976) and expanded by Younas (2008) to include resource imports and two different types of aid: income transfer and infrastructure aid. To our knowledge, we are the first to study the determinants of infrastructure aid empirically and the first to study the role of resources on aid, theoretically. We provide a theoretically founded estimation model, which main purpose is to guide the empirical analysis, to gauge the effect of resources on infrastructure aid allocation. Moreover, we contribute to the literature on the role of resource exports in economic development. Specifically, we show how resources can affect the development policy followed by donors.

Our theoretical framework suggests that the possibility of lowering the cost of resource trade increases the amount of infrastructure mostly for donors that have a low preference for making an impact. Thus, we consider the effect of resource exports relative to the donor's GDP per capita. We employ panel data from 2001 to 2011 using the aid project-level data from the DAC's Creditor Reporting System (CRS). Our findings suggest that resource trade in fact has a positive effect on average on the number and size of infrastructure projects. Moreover, we distinguish different types of resources and find that our results are driven mostly by fuels and to a lesser extent by forestry and metals. Additionally, we find that most of the effect is due to road infrastructure projects. Since most resource trade is sea-borne, we conjecture that donors have more of an interest to improve the recipient infrastructure if the deficiencies it creates explain a large fraction of total bilateral trade costs. We find that the effect of resources on infrastructure aid is statistically non-significant for landlocked countries. A good maritime infrastructure, on the other hand, strengthens the relationship between resource exports and infrastructure aid. Finally, we find the effect of resources on infrastructure aid to be decreasing over time. This is driven mostly by fuels, while the effect of the forestry trade shows a slight upward trend.

The topic is of potential policy importance for the following reasons. Recent studies find that on average infrastructure aid is effective in promoting exports (Calì and te Velde, 2011; Vijil and Wagner, 2012). However, as Alesina and Dollar (2000) argue the motivation of development aid may affect the impact it has on the local economy. If resource consideration drives the decision to initiate infrastructure projects this likely implies that infrastructure projects are tailored to facilitate resource exports and it is up to question if other sectors of the economy can benefit as well. Bonfatti and Poelhekke (2017) show that resource abundance is linked to an "interior-to-coast" orientation of road networks that favors overseas trade over trade among neighbors. Moreover, as the professed goals of development policy shift towards sustainable development and mitigation of climate change (Arndt and Tarp, 2017), persistent strategic goals linked to emission-intensive resources, such as fossil fuels,
may undercut the effectiveness of development assistance in this vein.

The remainder of the paper is organized as follows. In Section 2 we review the relevant literature. Section 3 expands the theoretical framework of Dudley and Montmarquette (1976) to include resources and infrastructure aid. Section 4 presents the empirical strategy. Our dataset is discussed in Section 5. Descriptive statistics are presented in Section 6. Results are summarized in Section 7. Section 8 concludes.

## 2 Literature Review

Our paper contributes to the literature on the allocation of aid. Most previous papers in the field have studied the determinants of aggregate aid. Moreover, the majority of the papers study aid from the supply side. We follow the same line and study the supply-side impact of aid. Nevertheless, our focus is not put on aggregate aid but on specific types of aid (i.e. infrastructure aid).

Dudley and Montmarquette (1976) provide one of the first theoretical models explaining the allocation of bilateral aid. In their model, the supply of aid is driven by the fact that donors wish to make an impact. Consumption of the subjectively measured impact of development aid is considered as a private good and weighed against consumption of a physical good. Thus, there is a demand in donor countries for making an impact that is driving the supply of aid. The way impact is captured is very flexible and can accommodate recipient needs, decreasing returns, as well as political determinants. Younas (2008) uses a similar framework but aggregates across donors. A slightly different perspective is offered by Djajić et al. (2004). While Dudley and Montmarquette (1976) measure the impact of aid instantaneously, Djajić et al. (2004) use a two-period model to study terms-of-trade effects. They show that in the presence of habit formation aid can lead to a second period terms-of-trade gain for the donor. Bueno de Mesquita and Smith (2007) provide a more pessimistic model in which donors give aid in exchange for policy concessions and the majority of inhabitants in recipient countries may suffer welfare reductions in contrast to the ruling elite. Djajić (2009) is the first to study the allocation decision between different types of aid. In his model, the donor may prefer more infrastructure aid instead of aid for immediate consumption because infrastructure is derived from the donor's export good, thus improving its terms of trade.

Many empirical studies confirm that the motivations for giving aid are not merely altruistic. Alesina and Dollar (2000) using a Panel from 1970 to 1994 find striking differences between donors. They report the importance of the colonial past, especially for France and the UK, and the role of the
US strategic interest in Israel and Egypt as political determinants. They also find that donors tend to reward democracies and open economies. Donors also take into account the needs of recipients to varying degrees and give more aid to countries with lower GDP per capita. Younas (2008) show that donors reward recipients that import goods in which donors have a comparative advantage. A commercial motive of donors to increase their own exports is in line with much of the gravity literature on development where aid is typically found to increase bilateral imports from the donor to the recipient. Martínez-Zarzoso (2015) provides a summary of the literature. Results in Kruse and Martínez-Zarzoso (2021), however, suggest that these effects do not compensate for the negative terms-of-trade effect the donor experiences. Nowak-Lehmann D. et al. (2013) find that recipient exports do not increase in response to development aid. Berthélemy (2006) uses the trade elasticities to classify donors as altruistic or egoistic. His findings show that Austria, Ireland, Nordic countries, and Switzerland pay comparatively little attention to commercial motives whereas Australia, France, and Italy are among the most egoistic donors. He uses a Panel data set and introduces recipient-specific fixed effects. Hoeffler and Outram (2011) distinguish between need, merit, and self-interest and find that most donors focus on need and self-interest and do not pay much attention to merit indicators such as growth and political reform. They use a panel data set with recipient and donor fixed effects and lag their explanatory variables by one period. Dreher and Fuchs (2015) study the allocation decisions of Chinese and find no evidence that it is more self-interested than the aid of traditional donors. They do not find an effect of the resource endowment of the recipient on aggregate Chinese aid. Couharde et al. (2020) show that the oil endowment of recipient countries increases the amount of aid received from seven major OECD donors (G7). Their empirical analysis covering 82 recipient countries over the 1980-2010 period also points out that strong competition for oil among the G7 donors does exist.

UN voting patterns have been used as a proxy for political alliances in most of the aforementioned studies and are typically found to exert a positive impact on aid. Dreher et al. (2008) use a disaggregated data set to show that causality can also run the other way. Their evidence suggests that US aid, in particular budget support and grants, are used to influence recipients voting behavior in the General Assembly with similar results for other donors.

The importance of studying the determinants of aid allocation is emphasized by (Alesina and Dollar, 2000) who stress that self-interested motivations may affect the effectiveness of aid. The determinants of infrastructure aid have to our knowledge not been studied in detail empirically. Several papers have shown that infrastructure aid helps increase the recipients' exports. Calì and te Velde (2011) and Vijil
and Wagner (2012) study the effect of infrastructure aid on overall exports and find significant and positive effects. The importance of infrastructure for exports has been stressed by Limao and Venables (2001) for road infrastructure and Blyde and Molina (2015) for a composite index of port, airport, and ICT infrastructure among others.

Concerning resources, Venables (2016), among others, cites weak infrastructure as one of the key reasons why developing countries have difficulty exploiting resource richness. In principle, resource exporters can choose between air and ocean cargo. Hummels and Schaur (2013) construct a measure of time-sensitivity of sectors based on the difference in freight rates between shipping by plane or ship since shipping by ship is more time-consuming but cheaper. Blyde and Molina (2015) compare this measure across sectors. Resource sectors differ in their degree of time sensitivity. Nonmetallic minerals range in the more time-sensitive half of the distribution, but most resources are found at the bottom of the list, for instance, Coal and Lignite. This implies that most resources rely on the existence of appropriate ports and infrastructure linking ports to resource exploitation sites. The importance of ports on aggregate trade has been studied by Clark et al. (2004).

## 3 Theoretical Framework

As in Dudley and Montmarquette (1976) we study development aid from a supply-side perspective. Donors treat the subjectively measured impact of the aid they give as a private good. They face a trade-off between the consumption of a final good and the impact of their development assistance. We extend the Dudley and Montmarquette (1976) model, however, in two ways. First, in our model donors rely on resource imports from recipients to produce a final consumption good. Secondly, donors do not only decide whom to give aid to but also whether aid should be in the form of an income transfer for immediate consumption or dedicated to fund infrastructure projects as in Djajić (2009).

The donor $d$ maximizes the following utility function:

$$U_d = f(X_d, H_d) \quad \text{with} \quad H_d = H_d^{TF} + H_d^{\tau} \quad (1)$$

where $H_d$ consumption of the subjectively measured impact of foreign aid and $X_d$ represents the total consumption of the other good of the donor. $H_d^{TF}$ measures the impact of an aid transfer on all recipients which is defined as the sum of the impact on all recipients individually as in Dudley and Montmarquette (1976): $H_d^{TF} = \sum_{r \in R} H_{dr}^{TF}$. $H_d^{\tau}$ measures the impact of aid for infrastructure and is
defined in an analogous manner: $H_d^\tau = \sum_{r \in R} H_{dr}^\tau$.

The subjectively measured impact of an aid transfer is defined following Dudley and Montmarquette (1976) as:

$$H_{dr}^{TF} = n_r^{\alpha} \left( \frac{a_{dr}}{y_r} \right)^{\gamma} \qquad (2)$$

where $n_r$ is the population of the recipient country, $a_r$ is per capita aid transfer, and $y_r$ is per capita GDP. $\alpha$ measures the extent of a small country bias. For $\alpha = 1$, every citizen in every recipient country has an equal weight in the donor's objective function. For $0 < \alpha < 1$, citizens in larger countries get a lower weight. The elasticity $\gamma$, indicates the extent of decreasing returns in the creation of impact. Since we expect the marginal effect of development aid to be decreasing, we expect $0 < \gamma < 1$.

Within each country suppose there is a continuum of possible infrastructure aid projects $\omega_r \in \Omega_r$. The impact of aid for each infrastructure project depends on a different set of variables than the income transfer. Let:

$$H_{dr}^{\tau} = \int H_{dr}^{\tau}(\omega_r) d\omega_r, \quad \text{where} \quad H_{dr}^{\tau}(\omega_r) = \left(\frac{n_r}{A_r}\right)^{\alpha_{1\tau}} v_r^{\alpha_{2\tau}} \left(\frac{a_{dr}^{\tau}(\omega_r)}{I_r}\right)^{\gamma\tau} \qquad (3)$$

$A_r$ denotes the area of the recipient country. The rationale behind including area is that the larger the country the more difficult to devise an infrastructure project that will benefit the entire population. $v_r$ is the urbanization rate in the recipient country following Canning (1998). $a_{dr}^\tau(\omega_r)$ is bilateral infrastructure aid committed to any project $\omega_r$. We regard infrastructure as a public good and therefore do not measure it in per capita terms, unlike income transfers. $I_r$ is a measure of the quality of existing infrastructure. The better the existing infrastructure, the less improvement is needed. Hence, we would expect $\alpha_{1\tau}, \alpha_{2\tau}, \gamma_\tau > 0$ as before.

The budget constraint of the donor takes a different form than in Dudley and Montmarquette (1976). Suppose the donor produces $\bar{x}_d$ units of a final consumption good using a Leontief technology requiring $\lambda_{dr}$ units of a resource good produced uniquely in $r$ per unit of output, so $\lambda_{dr}$ represents natural resources imports of $d$ from $r$. The import of said resource is subject to iceberg trade costs $\tau_{dr} > 1$. The donor's budget constraint thus reads:

$$X_d = \bar{x}_d \left( 1 - \sum_{r \in R} \lambda_{dr} \tau_{dr} \right) - \sum_{r \in R} \left( n_r a_{dr} + \int a_{dr}^{\tau}(\omega_r) d\omega_r \right) \qquad (4)$$

We assume that infrastructure aid affects trade costs in a way that is proportional to the subjectively
measured impact. To facilitate mathematical tractability, let $\frac{\partial \tau_{dr}}{\tau_{dr} \partial a_{dr}^{\tau}} = -\frac{\partial H_{dr}^{\tau}}{\partial a_{dr}^{\tau}}$. Income transfers $a_{dr}$ do not have an effect on trade costs.

The first-order conditions for income transfers imply:

$$ \frac{f_{H_{dr}^{TF}}}{f_{X_d}} = \frac{y_r^{\gamma} n_r^{1-\alpha}}{\gamma a_{dr}^{\gamma-1}} \qquad (5) $$

As in Dudley and Montmarquette (1976) the right-hand side can be interpreted as the shadow price of an impact via transfer in country r for country d. Since impact via transfer and via infrastructure aid are perfect substitutes and impacts in different countries are perfect substitutes, this has to be equal across recipients and types of aid in equilibrium such that:

$$ k_d = \frac{f_{H_{dr}^{TF}}}{f_{X_d}} = \frac{f_{H_{dr}^{\tau}}}{f_{X_d}} \quad \forall r \in R \qquad (6) $$

where $k_d$ is the marginal rate of substitution between aid impact and the other good, hence is measuring the importance of making an impact versus own interests represented by own consumption. As a consequence, the ensuing expression for aid as an income transfer is the same as in Dudley and Montmarquette (1976):

$$ a_{dr} = \left[ \frac{\gamma k_d}{y_r^{\gamma} n_r^{1-\alpha}} \right]^{\frac{1}{1-\gamma}} \qquad (7) $$

(7) shows that the dependence of the donor on the resource import does not affect the magnitude of the income transfer.

The first order conditions for aid for any infrastructure project $\omega_r$ imply:

$$ k_d = \frac{1 + \lambda_{dr} \bar{x}_d \frac{\partial \tau_{dr}}{\partial a_{dr}^{\tau}(\omega_r)}}{\gamma_{\tau} n_r^{\alpha_{1\tau}} A_r^{-\alpha_{1\tau}} \nu_r^{\alpha_{2\tau}} a_{dr}^{\tau}(\omega_r)^{\gamma_{\tau}-1} I_r^{-\gamma_{\tau}}} \qquad (8) $$

using $\frac{\partial \tau_{dr}}{\tau_{dr} \partial a_{dr}^{\tau}} = -\frac{\partial H_{dr}^{\tau}}{\partial a_{dr}^{\tau}}$ and rearranging we get:

$$ a_{dr}^{\tau}(\omega_r) = \left(1 + \frac{1}{k_d} \tau_{dr} \lambda_{dr} \bar{x}_d\right)^{\frac{1}{1-\gamma_{\tau}}} \left[ \frac{\gamma_{\tau} k_d n_r^{\alpha_{1\tau}} \nu_r^{\alpha_{2\tau}}}{I_r^{\gamma_{\tau}} A_r^{\alpha_{1\tau}}} \right]^{\frac{1}{1-\gamma_{\tau}}} \qquad (9) $$

While resources do not affect income transfers, they do increase the amount of infrastructure aid according to (9). Equations (9) and (7) have similar terms in brackets. The crucial difference is that (9) has an additional factor by which resource trade inflates the total amount of infrastructure aid. Due to the 1+ within this factor, $\lambda_{dr} = 0$ is just a special case of the model. According to equation
(9), the elasticity of infrastructure aid with respect to resource trade is not necessarily constant, but depends on $k_d$. Intuitively, donors that are willing to pay a higher shadow price to make an impact put less weight on resource considerations.

## 3.1 Minimum project size

So far, we assumed that infrastructure projects can be arbitrarily small, that is, $a_{dr}^\tau$ can be arbitrarily close to zero. In order to increase plausibility we posit a minimum size per project, in effect, more effective projects have a higher portion of fixed costs <fnref n="1" />. Moreover, let $\theta_r(\omega_r)$ denote the effectiveness of the specific infrastructure project, unobserved by the researcher. $\theta_r(\omega_r)$ is assumed to follow a uniform distribution over the recipient-specific support $\theta_r(\omega_r) \in [0, \bar{\theta}_r]$. $\theta_r(\omega_r)$ measures the rate at which aid translates into infrastructure, as a fixed cost parameter.

We assume that the distribution of the minimum size is due to the same parameter $\theta_r(\omega_r)$ that also measures aid effectiveness:

$$a(\omega_r)_{dr}^\tau > \theta_r(\omega_r)F_r \quad (10)$$

For an infrastructure project to get funding, it is required that $H(\omega_r)_{dr^\tau} > 0$, which implies that there is a maximum value $\theta_r^*$, such that any project with $\theta_r(\omega) > \theta_r^*$ will not get funded. Using (9) this maximum value is given by:

$$\theta_r^* = \left(1 + \frac{1}{k_d} \tau_{dr} \lambda_{dr} \bar{x}_d\right)^{\frac{1}{1-\gamma_r}} \left[ \frac{\gamma_r k_d n_r^{\alpha_{1\tau}} \psi_r^{\alpha_{2\tau}}}{I_r^{\gamma_r} A_r^{\alpha_{1\tau}}} \right]^{\frac{1}{1-\gamma_r}} \frac{1}{F_r} \quad (11)$$

Given the distributional assumptions and multiplying the share of projects that will get funding, $\Pr(\theta_r(\omega_r) > \theta_r^*)$, by $\Omega_r$, we obtain the number of infrastructure projects:

$$N_{dr}^\tau = \left(1 + \frac{1}{k_d} \tau_{dr} \lambda_{dr} \bar{x}_d\right)^{\frac{1}{1-\gamma_r}} \left[ \frac{\gamma_r k_d n_r^{\alpha_{1\tau}} \psi_r^{\alpha_{2\tau}}}{I_r^{\gamma_r} A_r^{\alpha_{1\tau}}} \right]^{\frac{1}{1-\gamma_r}} \frac{1}{F_r \bar{\theta}_r} \Omega_r \quad (12)$$

which is a function of the same determinants as the amount per project in (9) and in addition fixed costs determinants.

In order to determine the average size of an infrastructure project $\bar{a}_{dr}^\tau$ in country r let $G(\theta_r)$ denote the cumulative distribution function. We can write:

<footnote n="1">¹For instance, in order to build a canal for ocean-going vessels that has a high impact on the host country, a lot of construction work is needed. Filling potholes on the other hand can start with a single pothole, but the returns may be less high</footnote>
$$ \bar{a}_{dr}^{\tau} = \theta_{r}^{* -1} \int_{0}^{\theta_{r}^{*}} a_{dr}^{\tau}(\theta_{r}) dG(\theta_{r}) \qquad (13) $$

Using our distributional assumption and plugging in (9)

$$ \bar{a}_{dr}^{\tau} = (1 - \gamma_{\tau}) F_{r}^{-\frac{\gamma_{\tau}}{1-\gamma_{\tau}}} \left( 1 + \frac{1}{k_{d}} \tau_{dr} \lambda_{dr} \bar{x}_{d} \right)^{\frac{1}{(1-\gamma_{\tau})^{2}}} \left[ \frac{\gamma_{\tau} k_{d} n_{r}^{\alpha_{1\tau}} v^{\alpha_{2\tau}}}{I_{r} \gamma_{\tau} A_{r}^{\alpha_{1\tau}}} \right]^{\frac{1}{(1-\gamma_{\tau})^{2}}} \qquad (14) $$

According to (14) the average amount of infrastructure aid per project depends on the same characteristics as the number of observations in (12) with the exception of $\bar{\theta}_r$ and $\Omega_r$. The coefficients differ, however.

In consequence, the total amount of infrastructure aid flowing a donor $d$ to any recipient $r$ can be seamlessly decomposed as follows:

$$ \int_{\theta_r^*}^{\infty} a_{dr}^{\tau}(\omega_r) d\omega_r = N_{dr}^{\tau} \times \bar{a}_{dr}^{\tau} \qquad (15) $$

# 4 Empirical Implementation

We estimate (12) and (14) separately, following the decomposition in (15). Since the model exhibits constant elasticities, we use a Poisson Pseudo-Maximum Likelihood (PPML) estimator to correct for heteroskedasticity bias (Santos Silva and Tenreyro, 2006). We prioritize the use of a PPML model over an Ordinary Least Squares (OLS) for two reasons. First, heteroscedasticity will not result in biased estimates. Second, zero-trade observations can be included; the PPML estimator remains consistent with and without the inclusion of the zero-trade observations. Adding high-dimensional fixed effects, we are able to control for unobservable variables in our model.

For comparability, we use the same set of explanatory variables for both dependent variables. Let $\eta_{drt}^s$ denote the linear predictor related to each model, where $s$ represents the specification. We have:

$$ \eta_{drt}^{s} = \beta_{0}^{s} + \beta_{1}^{s} \ln IA_{r} + \beta_{2}^{s} \ln I_{r}(t-1) + \beta_{3}^{s} \ln \left( 1 + \frac{1}{k_{dt}} R_{drt}(t-1) \right) + \beta_{Z}^{s} Z_{drt}(t-1) + \Psi_{dt} + \zeta_{r} \qquad (16) $$

where $IA_r$ is infrastructure aid from third countries (Davies and Klasen, 2019), $I_{r(t-1)}$ is infrastructure index. $R_{drt}$ is bilateral resource exports from the recipient $r$ to the donor $d$ at time $t$ ($\tau_{dr} \lambda_{dr} \bar{x}_d$). One key issue is the treatment of $k_{dt}$ within the resource factor, which is determined by characteristics
of the donors' objective function. Our baseline assumption is that richer donors are likely to pay a higher shadow price for making an impact than poorer donors. In order to reflect this assumption we assume that $k_{dt}$ equals the donor's GDP per capita. We will perform robustness checks by trying alternative assumptions. First, we will use $k_{dt} = 1$ which implies the assumption that own consumption and making an impact are perfect substitutes. Second, assuming a quasi-linear utility function one arrives at $k_{dt} = X_{dt}$, which we derive by subtracting total aid given per capita from GDP per capita. This is similar to our initial assumption but introduces potential endogeneity concerns since aid for infrastructure affects $k_{dt}$ in this case.

$Z_{dr(t-1)}$ represents other control variables from the literature. We control for bilateral imports of the recipient from the donor country to control for the possibility that the donor gives aid to gain access to the recipient market (Younas, 2008). Moreover, we control for overall exports from the recipient to the donor to make sure that resource exports specifically are driving our results.<fnref n="2" /> Finally, we add to our infrastructure aid regression standard determinants of bilateral development aid per capita (Hoeffler and Outram, 2011) $\Psi_{dt}$ such as colony.<fnref n="3" /> In addition, we control for UN voting behavior in the General Assembly as a political determinant of aid. Dreher et al. (2008) report a strong link between UN voting behavior and aid for the US. Results are also reported in Table A.2. $k_{dt}$ are donor-specific and time-varying shadow prices of making an impact. $\zeta_r$ represents an idiosyncratic error term. The estimated models are therefore the following. For the number of infrastructure projects, we have:

$$N_{rdt}^{\tau} = \exp(\eta_{rdt}^{N}) + \varepsilon_{rdt}^{N} \quad (17)$$

and equivalently for the value of bilateral infrastructure aid flows:

$$a_{rdt}^{\tau} = \exp(\eta_{rdt}^{V}) + \varepsilon_{rdt}^{a} \quad (18)$$

where $\varepsilon_{rdt}^N$ and $\varepsilon_{rdt}^a$ are error terms.

Estimating (17) and (18) using PPML has several advantages over a standard log-linearized estimation with OLS. Santos Silva and Tenreyro (2006) have shown that log-linearizations of constant elasticity models are prone to heteroskedasticity bias due to Jensen's inequality. In contrast, for PPML to be unbiased and consistent, only the conditional mean has to be specified correctly. The type of

<footnote n="2">²We add 1 to maintain consistency with our measure of bilateral resource exports.</footnote>

<footnote n="3">³Further control variables like population, GDP per capita, GDP growth, corruption, democracy, distance, RTAs, WTO membership, urbanization, and common language are also used and reported in Table A.2</footnote>
heteroskedasticity and the actual distribution of the dependent variable are irrelevant for unbiasedness and consistency. Furthermore, PPML can account for zero aid flows and performs well when zeroes are frequent (Santos Silva and Tenreyro, 2011).

Moreover, recent advances in PPML estimation using Iteratively Reweighted Least Squares (IRLS) by Larch et al. (2019) and Correia et al. (2020) facilitate the inclusion of high-dimensional fixed effects.<fnref n="4" /> We include donor-year fixed effects to control for $k_{dt}$. Fally (2015) showed that including fixed effects in a Poisson model is equivalent to conditioning on the total of the dependent variable within each group. Thus, the use of donor-year fixed effects implies that we are capturing determinants of the allocation decision of the donor only. In addition, we add recipient fixed effects in some regressions to control for $\Omega_r$ and $F_r$ in (12) and (14).

For endogeneity issues in all models, we use combinations of the three sets of fixed effects, that is, donor-year fixed effect, recipient fixed effect, recipient-year fixed effect (Borchert et al. (2022) and Dhingra et al. (2023)) and we discuss this largely in what follows. In both regressions, endogeneity is a potential concern. In fact, in the model infrastructure aid is given in order to reduce trade barriers and increase bilateral trade. This could give rise to reverse causality and simultaneity bias. Since suitable instruments are lacking, we attempt to close the reverse transmission channel using a combination of measures. First, we follow Hoeffler and Outram (2011) in using one-year lags. It is well known that in the presence of auto-correlation using lags will not fully remove possible endogeneity. Two control variables play the role of changing this transmission channel. First, if past values of infrastructure aid affect lagged resource trade, they do so by first affecting the quality or quantity of infrastructure and, second, they would be expected to raise overall exports. We close these transmission channels by including both an infrastructure index and aggregate exports as control variables. Moreover, our use of donor-time fixed effects implies that the association between bilateral aid flows and bilateral resource trade flows is driven by aid relative to all other recipients of the donor in question. This reduces the plausibility of reverse causality concern. Finally, infrastructure is a public good and the donor cannot exclude other trading partners of the recipient from benefiting. To the extent that infrastructure aid increases trade, this should affect all trade flows not only the bilateral ones between donor and recipient. For this reason, we perform robustness checks including recipient-time fixed effects to further address endogeneity concern. That being said, as Dreher and Fuchs (2015) we remain cautious in interpreting our results and do not maintain that we identify a strictly causal relationship.

<footnote n="4">⁴We use the user-written command *ppmlhdfe* provided by Correia et al. (2020) to implement this approach</footnote>
In all result tables, we present two types of standard errors. First, we report standard errors clustered at the country-pair level in order to account for auto-correlation in $a_{rdt}^T$ arising from projects extending several years.<fnref n="5" /> Second, we report more conservative standard errors clustered at the donor level. On the one hand, this takes into account that the decisions may be correlated for each donor. On the other hand, according to Abadie et al. (2023) these standard errors will be too high unless the population of donors is notably larger than the sample of donors. Since most donors are included in our dataset and we are not interested in making statements about “other possible donors”, donor clustered standard errors will understate significance levels. We report them merely as a conservative robustness check.

# 5 Data

We use a Panel data set from 2002 to 2010. See Table A.1 for a list of the countries in our sample.

Our data on development aid is based on the OECD's Development Assistance Committee's (DAC) Creditor Reporting System (CRS). The CRS data is based on individual projects. We obtain our variable of the total value of bilateral infrastructure aid by summing up the value of all infrastructure projects by country-pair and year. We identify infrastructure projects as belonging to the sector "Transport and Storage". We ignore projects that have multiple recipients. The number of aid projects is the number of observations in the category of infrastructure projects by country-pair and year. One key advantage of the CRS data is that in addition to the sector of a development aid project, also their purpose is reported. This allows us to distinguish between different types of infrastructure projects —those targeting air, rail, road and water transport.

Data on our main explanatory variables of interest, bilateral trade, is taken from BACI (Gaulier and Zignago, 2010). We define resources using the broad definition of the WTO (2010, pp. 204–205) including raw material, fuels and mining products, and forestry products.

Our Index of Infrastructure is taken from Donaubauer et al. (2016). We use their sub-index for transport infrastructure. This index comprises indicators for road, rail, air, and water infrastructure and is derived using an unobserved component model. The availability of this index limits our sample period until 2011. In addition, we use the dead weight tonnage (DWT) of the specialized fleet of the recipient to proxy for resource-specific maritime infrastructure. In accordance with the Donaubauer et al. (2016) index, DWTs are normalized by the recipient country's area. While Donaubauer et al.

<footnote n="5">⁵On average, an infrastructure project lasts between 4 and 6 years in our dataset.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 1: Different infrastructure projects</figcaption></figure>

(2016) include the DWT of the total merchant fleet of the recipient, we additionally obtain data on DWTs for oil tankers and bulk carriers separately from UNCTAD. Landlocked countries do not typically have a merchant fleet or ports. To tackle this problem, the log of the merchant fleet's DWTs and the number of ports will be set to zero when missing.<fnref n="6" /> Furthermore, we use a landlocked dummy obtained from the CIA's coastline data.

As for our remaining variables, GDP per capita, GDP growth, population, and area are from the World Bank's World Development Indicators (WDI). As further controls for political connections, we include RTA and WTO membership. RTA membership is from de Sousa (2012) and WTO membership from Kruse and Martínez-Zarzoso (2021). UN voting patterns are from Voeten (2013). Our measure is defined as the percentage of times the recipient country voted against the donor.

# 6 Descriptive Statistics

In our dataset, aid for transport and storage accounts for 8.5 percent of the value of total aid. 8.2 percent of infrastructure projects are financed by concessional loans. Figure 1 shows the composition of infrastructure aid focusing on the main transport-related purposes. Road projects account for the majority of projects and the largest part of the overall value of aid. Projects related to railroads are fewer (around 7 percent of the total) but account for the second largest share of the value of infrastructure aid. Projects related to water and air infrastructure play a smaller role. Other projects

<footnote n="6">⁶In fact, the only landlocked country included in the World Port Index is Paraguay with a single port, the Port of Asunción.</footnote>
<table><caption>Table 1: Share of maritime trade for resources</caption><thead><tr><th>Resource Sector</th><th>Mean</th><th>SD</th><th>Min</th><th>Median</th><th>Max.</th></tr></thead><tbody><tr><td>Forestry</td><td>0.94</td><td>0.03</td><td>0.87</td><td>0.94</td><td>0.97</td></tr><tr><td>Fuels</td><td>0.95</td><td>0.02</td><td>0.92</td><td>0.95</td><td>0.98</td></tr><tr><td>Metals</td><td>0.84</td><td>0.04</td><td>0.76</td><td>0.84</td><td>0.89</td></tr><tr><td>Other</td><td>0.62</td><td>0.15</td><td>0.33</td><td>0.69</td><td>0.79</td></tr><tr><td>Total</td><td>0.79</td><td>0.14</td><td>0.33</td><td>0.83</td><td>0.98</td></tr></tbody></table>

Source: Own calculation based on Hummels and Schaur (2013) data.

include funding for transport policy management, storage, and education relating to transport and storage.

Most resource trade is sea-borne, and thus, we expect that it mostly benefits from infrastructure linking exploitation sites to ports. Table 1 reports the share of maritime trade by resource category based on data for the US from Hummels and Schaur (2013). Forestry products and fuels are almost exclusively transported by ship. The average share of maritime trade (over the years 1991 to 2005, respectively) for both categories is 94 and 95 percent. For metals, the average is 84 percent. Only when looking at other resources, do we find that air-borne trade accounts for a larger part of the trade value in some years, but on average maritime trade still accounts for 62 percent of the traded value.

# 7 Results

Table 2 reports the results using our baseline assumption for the shadow price of making an impact $k_{dt} = GDPpc_{d}$. Columns (1) and (2) report the results for equation (17) and columns (3) and (4) for equation (18). We report results using recipient-fixed effects in columns (1) and (3), and more conservative results using recipient-year fixed effects in columns (2) and (4). For brevity, we report only the most important variables in the model. The coefficients for other control variables are reported in Table A.2 in the Appendix.

In all four columns, we find a positive and statistically significant effect of resource exports on the number and average size of infrastructure aid when using standard errors clustered by country pairs. Using standard errors clustered by donor reduces the level of significance in all cases. The coefficients remain weakly statistically significant at the 5 percent level in columns (1), and (4), turn insignificant in (2), and are significant at the 10 percent level in column (3). This implies that increased natural resources exports of the recipient to the donor in the precedent year trigger an average increase of
<table><caption>Table 2: Baseline models - PPML</caption><thead><tr><th rowspan="2"></th><th colspan="2">Number of projects</th><th colspan="2">Average value</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Infrastructure aid from ROW (in logs)</td><td>-0.062<br>(-4.034)***<br>[-4.608]***</td><td>-0.438<br>(-15.724)***<br>[-9.422]***</td><td>-0.085<br>(-2.927)**<br>[-3.166]**</td><td>-1.394<br>(-18.980)***<br>[-26.731]***</td></tr><tr><td>Infrastructure index<sub>t-1</sub></td><td>0.061<br>(0.518)<br>[0.581]</td><td></td><td>-0.903<br>(-2.826)**<br>[-2.710]**</td><td></td></tr><tr><td>Total Exports<sub>t-1</sub> (+1 in logs)</td><td>-0.025<br>(-0.485)<br>[-0.382]</td><td>-0.001<br>(-0.018)<br>[-0.012]</td><td>-0.137<br>(-1.785)<sup>+</sup><br>[-2.880]**</td><td>-0.139<br>(-2.402)<sup>*</sup><br>[-1.807]<sup>+</sup></td></tr><tr><td>Total Imports<sub>t-1</sub> (+1 in logs)</td><td>0.461<br>(5.312)***<br>[6.462]***</td><td>0.345<br>(6.357)***<br>[5.729]***</td><td>0.088<br>(0.750)<br>[0.796]</td><td>-0.062<br>(-0.732)<br>[-0.808]</td></tr><tr><td>Natural resource exports<sub>t-1</sub><br>with k<sub>dt</sub> = GDPpc<sub>dt</sub> (+1 in logs)</td><td>0.229<br>(2.749)**<br>[2.174]*</td><td>0.145<br>(2.587)**<br>[1.582]</td><td>0.264<br>(2.469)<sup>*</sup><br>[1.835]<sup>+</sup></td><td>0.302<br>(2.851)**<br>[2.165]*</td></tr><tr><td>Colony</td><td>0.765<br>(3.408)***<br>[4.425]***</td><td>0.646<br>(3.213)**<br>[4.211]***</td><td>0.539<br>(1.681)<sup>+</sup><br>[1.830]<sup>+</sup></td><td>0.414<br>(1.376)<br>[1.753]<sup>+</sup></td></tr><tr><td>Observations</td><td>12691</td><td>9751</td><td>2063</td><td>2061</td></tr><tr><td>Pseudo R2</td><td>0.50</td><td>0.57</td><td>0.56</td><td>0.93</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient-year FE</td><td></td><td>YES</td><td></td><td>YES</td></tr></tbody></table>

Note: t-statistics based on standard errors clustered by pair (donor) in parentheses (brackets). + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$. All regressions include Population, Area, GDP per capita, GDP per capita growth, UNGA votes against, Corruption and Democracy indicators, distance, RTAs, WTO membership, urbanization and common language as further control variables. Results for control variables are reported in Table A.2 in the Appendix.

development aid in infrastructure from the donor of 16% to 26%.

Regarding control variables, in all four columns, we find a statistically significant negative effect of infrastructure aid from other countries. This is in accordance with expectations, because infrastructure is a public good, and, hence, a higher level of infrastructure aid from other donors will make it harder to find a suitable project opportunity. The quality of the existing infrastructure has a negative and statistically significant coefficient only in columns (3) and (4) for the average size of an infrastructure project. In line with results from other studies (Alesina and Dollar, 2000), we find that a past colonial relationship increases both the number of infrastructure projects and their average size. Recipients that import more from their donors' host on average more infrastructure projects. This effect is statistically significant at the 0.1 percent level in columns (1) and (2). For the average size of an infrastructure project, we do not find a statistically significant impact.

As argued above, looking at the effect of total exports can be an indicator to gauge what role reverse
<table><caption>Table 3: Robustness - PPML: Different $k_{dt}$</caption><thead><tr><th rowspan="2"></th><th colspan="2">Number of projects</th><th colspan="2">Average value</th></tr><tr><th>(1)<br>$k_{dt} = 1$</th><th>(2)<br>$k_{dt} = X_d$</th><th>(3)<br>$k_{dt} = 1$</th><th>(4)<br>$k_{dt} = X_d$</th></tr></thead><tbody><tr><td>Infrastructure aid from ROW (in logs)</td><td>-0.063<br>(-3.742)***<br>[-4.021]***</td><td>-0.062<br>(-4.034)***<br>[-4.608]***</td><td>-0.086<br>(-2.893)**<br>[-3.099]**</td><td>-0.085<br>(-2.927)**<br>[-3.166]**</td></tr><tr><td>Infrastructure index</td><td>0.071<br>(0.622)<br>[0.685]</td><td>0.061<br>(0.518)<br>[0.581]</td><td>-0.845<br>(-2.699)**<br>[-2.481]*</td><td>-0.903<br>(-2.827)**<br>[-2.711]**</td></tr><tr><td>Total Exports (+1 in logs)</td><td>-0.012<br>(-0.169)<br>[-0.156]</td><td>-0.025<br>(-0.485)<br>[-0.382]</td><td>-0.155<br>(-2.094)*<br>[-2.393]*</td><td>-0.137<br>(-1.786)<sup>+</sup><br>[-2.881]**</td></tr><tr><td>Total Imports (+1 in logs)</td><td>0.480<br>(4.906)***<br>[6.115]***</td><td>0.461<br>(5.312)***<br>[6.462]***</td><td>0.078<br>(0.666)<br>[0.702]</td><td>0.088<br>(0.750)<br>[0.796]</td></tr><tr><td>Natural Resource Exports<sub>t-1</sub><br>with $k_{dt} = 1$ (+1 in logs)</td><td>0.059<br>(1.324)<br>[1.352]</td><td></td><td>0.121<br>(3.101)**<br>[2.458]*</td><td></td></tr><tr><td>Natural Resource Exports<sub>t-1</sub><br>with $k_{dt} = X_d$ (+1 in logs)</td><td></td><td>0.229<br>(2.749)**<br>[2.173]*</td><td></td><td>0.264<br>(2.471)*<br>[1.836]<sup>+</sup></td></tr><tr><td>Colony</td><td>0.788<br>(3.492)***<br>[4.500]***</td><td>0.765<br>(3.408)***<br>[4.425]***</td><td>0.571<br>(1.861)<sup>+</sup><br>[2.063]*</td><td>0.539<br>(1.681)<sup>+</sup><br>[1.830]<sup>+</sup></td></tr><tr><td>Observations</td><td>12691</td><td>12691</td><td>2063</td><td>2063</td></tr><tr><td>Pseudo R2</td><td>0.50</td><td>0.50</td><td>0.56</td><td>0.56</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr></tbody></table>

Note: t-statistics based on standard errors clustered by pair (donor) in parentheses (brackets). + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$. All regressions include Population, Area, GDP per capita, GDP per capita growth, UNGA votes against, Corruption and Democracy indicators, distance, RTAs, WTO membership, urbanization and common language as further control variables.

causality may play. The coefficient of total exports is statistically non-significant in the regressions explaining the number of infrastructure projects. For the average size of infrastructure projects, the effect is negative and statistically significant in columns (3) and (4). Infrastructure aid is associated only with higher resource exports, but not with more exports in general. This increases our confidence that our results are not driven by reverse causality.

## 7.1 Robustness checks

Table 3 reports results for alternative assumptions regarding $k_{dt}$. In columns (1) and (3), we assume $k_{dt} = 1$. In terms of the theoretical framework outlined in Section 3, this would be tantamount to
assuming that making an impact and own consumption are perfect substitutes. In columns (2) and (4), we assume $k_{dt} = X_d$, derived by subtracting from the donor's GDP total aid commitments. This is tantamount to assuming a quasi-linear utility function. The disadvantage is, that by construction infrastructure aid is included in $k_{dt}$ leading to endogeneity concerns that could downward bias these results.

The results from Table 3 show that assumptions regarding $k_{dt}$ are not innocuous. Although the effect of natural resource exports remains positive in all four columns when assuming $k_{dt} = 1$ the effect on the number of infrastructure projects becomes statistically non-significant in column (1). However, it does retain statistical significance with respect to the average size of the infrastructure project in column (3). Here we observe the same magnitude, only the minimum average increase of development aid in infrastructure from donor goes to 13%.

Since the assumption that $k_{dt} = 1$ implies that making an impact and own consumption are perfect substitutes for the donor, we deem this the least plausible configuration for $k_{dt}$. When assuming $k_{dt} = X_{dt}$, our results in columns (2) and (4) are virtually identical than when using $k_{dt} = GDPpc_{dt}$ in Table 2.

One possible concern could be that infrastructure aid affects trade costs and therefore the lag of resource trade will be spuriously correlated with past values of infrastructure. This may cause a bias if the error terms are auto-correlated. In order to see if this is a concern, we replace the actually observed resource trade flow with a hypothetical frictionless trade value. This hypothetical value is constructed based on the gravity equation (Yotov et al., 2016), where bilateral trade corresponds to the product of sectoral expenditure of the importer (donor) and sectoral production of the recipient (recipient) divided by world production. We proxy these values using the sum of exports and imports. Then, we replace $R_{rd(t-1)}$ in (16) by the once-lagged hypothetical trade value.

Results are reported in Table A.5 in the Appendix. For the number of infrastructure projects, the effect remains significant although the point estimates vary less depending on the dimension of the fixed effects. In columns (1) and (2), the effect remains statistically significant at the 5 and 10 percent levels, respectively. It is only for the average size of infrastructure projects when estimated with recipient-year fixed effects that the coefficient turns non-significant.

Finally, Tables A.3 and A.4 in the Appendix report results by donor using year and recipient fixed effects. Note that we report results only for donors for which we have more than 100 observations. Regarding the average size of infrastructure projects in Table A.4 for most donors we do not have a
sufficient amount of non-zero observations. Only for South Korea do we find a positive and significant effect of resource exports on infrastructure aid to the recipient in question. For the number of infrastructure projects, the results in Table A.3 we obtain results for a broader set of countries. We find a positive and significant effect of resource exports on infrastructure aid for Australia, Austria, Belgium-Luxembourg, France, Ireland, Italy and Norway. For some donors, notably Denmark and Sweden, we find a negative and significant effect.

## 7.2 Different types of resources

In this section, we investigate whether the effect changes by the type of resources we are looking at. Resources differ not only by the mode of transportation —oil and gas are usually transported in specifically designed tankers, whereas many minerals are often transported in bulk carriers or even by air cargo—but also by the time sensitivity and by the degree that they can exploit economies of scale. For instance, different types of resources may depend on different harbor qualities. While bulk freight can often be loaded using power shovels, other resources such as oil and gas require special equipment.

Table 4 reports results for different types of resources. For brevity, we do not report results for our control variables that are included in all regressions. For any resource type $R^*$ we use the following decomposition to separate its effect from remaining resources:

$$ \ln \left( 1 + \frac{1}{k_{dt}} R_{rd(t-1)} \right) = \ln \left( 1 + \frac{R_{rd(t-1)} - R_{rd(t-1)}^*}{k_{dt}} \right) + \ln \left( 1 + \frac{R_{rd(t-1)}^*}{k_{dt} + R_{rd(t-1)} - R_{rd(t-1)}^*} \right) \quad (19) $$

For the number of infrastructure projects, we find positive and statistically significant results for all three resource types we isolate. For forestry products (column 1) and metals (column 3) the effect of the remainder of resource exports retains a positive and statistically significant impact. When isolating the effect of oil and gas, the impact of the rest of the resource exports turns insignificant. When looking at the average size of infrastructure projects in columns (4) to (6), oil and gas have the same effect, while forestry and metal are statistically non-significant.

Thus, while oil seems to be the most important determinant of infrastructure aid, we conclude that both forestry and metals do play a role for the number of infrastructure projects. The fact that the rest of infrastructure projects turns non-significant in column (2) can be explained by the fact that the rest includes resources other than forestry and metal that may have no, or different effects on
<table><caption>Table 4: Different types of resources</caption><thead><tr><th rowspan="2"></th><th colspan="3">Number of projects</th><th colspan="3">Average value</th></tr><tr><th>(1)<br>Timber</th><th>(2)<br>Oil</th><th>(3)<br>Metals</th><th>(4)<br>Timber</th><th>(5)<br>Oil</th><th>(6)<br>Metals</th></tr></thead><tbody><tr><td>Exports of Timber<sub>t-1</sub></td><td>1.373<br>(3.145)**<br>[3.131]**</td><td></td><td></td><td>0.337<br>(0.301)<br>[0.301]</td><td></td><td></td></tr><tr><td>Exports of Oil<sub>t-1</sub></td><td></td><td>0.333<br>(3.303)***<br>[3.050]**</td><td></td><td></td><td>0.387<br>(3.286)**<br>[3.286]**</td><td></td></tr><tr><td>Exports of Metal<sub>t-1</sub></td><td></td><td></td><td>0.282<br>(2.090)*<br>[1.730]<sup>+</sup></td><td></td><td></td><td>0.171<br>(0.604)<br>[0.604]</td></tr><tr><td>Rest of Resource Exports<sub>t-1</sub> (+1 in logs)</td><td>0.240<br>(2.893)**<br>[2.266]*</td><td>0.119<br>(1.412)<br>[0.969]</td><td>0.226<br>(2.746)**<br>[2.201]*</td><td>0.266<br>(1.780)<sup>+</sup><br>[1.780]<sup>+</sup></td><td>0.163<br>(0.837)<br>[0.837]</td><td>0.263<br>(1.836)<sup>+</sup><br>[1.836]<sup>+</sup></td></tr><tr><td>Observations</td><td>9751</td><td>9751</td><td>9751</td><td>2061</td><td>2061</td><td>2061</td></tr><tr><td>Pseudo R2</td><td>0.57</td><td>0.57</td><td>0.57</td><td>0.93</td><td>0.93</td><td>0.93</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr></tbody></table>

Note: t-statistics based on clustered (two-way clustered) standard errors in parentheses (brackets). + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$.
infrastructure aid.

## 7.3 The role of maritime trade and types of projects

So far, we have treated infrastructure and trade costs as separate determinants of infrastructure aid. In this section, we investigate whether some determinants of trade costs change the way resource trade affects the probability of infrastructure aid. Moreover, given the importance of maritime trade, we investigate the effect of resources on different types of infrastructure projects.

The rationale for doing this is that most infrastructure aid is linked to improvements of internal trade costs. The effect this has on the price of resource inputs for donor countries depends on how important internal trade costs in the recipient country are in total bilateral trade costs. This leads to the following hypotheses based on the assumption that most resource trade is sea-borne. Resources from landlocked countries have to be transported through a transit country before reaching a port. Since this implies that trade costs accrue due to transport in the transit country, the role of internal trade costs in the landlocked country is lower. Consequently, we expect that allocation decisions for landlocked countries will be driven less by the resource considerations of the donor. More specifically, to the extent that resource trade is linked to more road and railway infrastructure aid to facilitate maritime exports of goods from the interior, this effect should be weaker for landlocked countries.

Moreover, maritime infrastructure is of particular importance. We focus on the dead-weight tons of the fleet of oil tankers and bulk carriers. Similar to Donaubauer et al. (2016), we use these as proxies for the resource-specific quality and capacity of the maritime trade infrastructure. A high level of capacity implies that, on the one hand, infrastructure projects addressing the capacity of water infrastructure will be affected less by resources. (An exception could occur if there are navigable internal rivers.) On the other hand, given a sufficient capacity for maritime trade, internal trade costs may play a relatively important role for international trade costs. Thus, donors may give more aid for internal infrastructure projects, notably road and railway, if resource specific maritime infrastructure is already relatively high.

Table 5 reports results. Again, we report only results for our variables of interest in this exercise to save space. The effect of natural resources on infrastructure projects appears to be by and large driven by road infrastructure. We find a highly statistically significant and positive coefficient in column (3). The effect on the total number of projects in column (1) is less strong albeit still statistically significant. The effect on railway (column 2) or water (column 4) projects is statistically non-significant on average.
<table><caption>Table 5: Maritime transport and resources</caption><thead><tr><th rowspan="2"></th><th colspan="4">Number of projects</th></tr><tr><th>(1)<br>All</th><th>(2)<br>Rail</th><th>(3)<br>Road</th><th>(4)<br>Water</th></tr></thead><tbody><tr><td>Natural resource exports<br>k<sub>dt</sub> = GDPpc<sub>dt</sub> (+1 in logs)</td><td>0.229<br>(2.738)<sup>**</sup><br>[2.185]<sup>*</sup></td><td>0.182<br>(1.429)<br>[1.520]</td><td>0.385<br>(3.431)<sup>***</sup><br>[3.236]<sup>**</sup></td><td>-0.066<br>(-0.909)<br>[-0.558]</td></tr><tr><td>×1(Landlocked)</td><td>-0.151<br>(-0.974)<br>[-1.076]</td><td>0.316<br>(1.439)<br>[1.312]</td><td>-0.477<br>(-1.932)<sup>+</sup><br>[-1.771]<sup>+</sup></td><td>0.285<br>(0.672)<br>[0.655]</td></tr><tr><td>× DWT of oil tanker fleet<br>by area (in logs)</td><td>-0.007<br>(-3.637)<sup>***</sup><br>[-2.129]<sup>*</sup></td><td>-0.004<br>(-0.999)<br>[-0.612]</td><td>0.003<br>(2.546)<sup>*</sup><br>[2.930]<sup>**</sup></td><td>-0.006<br>(-1.704)<sup>+</sup><br>[-1.222]</td></tr><tr><td>× DWT of bulk carrier fleet<br>by area (in logs)</td><td>0.005<br>(2.535)<sup>*</sup><br>[3.595]<sup>***</sup></td><td>-0.002<br>(-0.437)<br>[-0.625]</td><td>0.004<br>(1.532)<br>[2.725]<sup>**</sup></td><td>0.007<br>(2.323)<sup>*</sup><br>[3.424]<sup>***</sup></td></tr><tr><td>Observations</td><td>12691</td><td>7079</td><td>12190</td><td>8521</td></tr><tr><td>Pseudo R2</td><td>0.50</td><td>0.57</td><td>0.53</td><td>0.46</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr></tbody></table>

Note: t-statistics based on clustered (two-way clustered) standard errors in parentheses (brackets).

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.

As expected, the interaction of resource exports with a landlocked dummy bears a negative sign in for the sum of all projects in column (1) although the effect is statistically non-significant. For road infrastructure projects, we find a negative effect that is statistically significant at the ten percent level. The overall effect of resource trade on road infrastructure projects is, in fact, statistically non-significant in landlocked countries. For water related infrastructure and railway projects, however, we find positive albeit non-significant coefficients.

Our results also confirm our hypothesis, that the capacity for maritime trade affects the allocation decision of the donor and the role of resource trade. Surprisingly, however, the fleet of oil tankers appears to play a slightly different role than bulk carriers. The capacity of oil tankers reduces the role of resources in the allocation decision of the donor in the overall sample (column 1) and for water infrastructure projects (column 4). In both cases, the capacity of bulk carriers has a positive and significant effect. For rail and road infrastructure projects (columns 2 and 3) are similar. The capacity of neither type of vessel affects the role of resources in explaining the allocation of railway projects. However, both types of vessel capacity increase the impact of resource trade on road projects. This is in line with the hypothesis that maritime capacity increases the returns for the donor from improving internal infrastructure in the recipient country.
<figure><img src="image_6.png" /><figcaption>Figure 2: Effect on the number of infrastructure projects over time</figcaption></figure>

## 7.4 The effect over time

The strategic goal of facilitating access to resource may not only affect the efficacy of infrastructure aid, but also undermine the increasingly important goal of mitigating climate change. This is especially true for trade in fossil fuels which are responsible for the lion's share of CO2 emissions. If the donors' strategic and altruistic motives align increasingly over time to combat climate change, we should expect a reductions in the effect of resources on aid over time, especially for fuels.

In order to see if this is the case, we re-estimated (12) allowing coefficients to vary by year. Furthermore, we employed the composition from Section 7.2 to focus on fuels. Figure 2 illustrates the results and reports 95 percent confidence intervals. The coefficients of aggregate resource trade on infrastructure aid is decreasing as shown in Figure 2a. In fact, the effect becomes statistically non-significant after 2010. A similar pattern, albeit less pronounced, materializes for fossil fuels in Figure
2c. No clear pattern emerges for metals trade (Figure 2d), while for forestry we find an increasing effect after 2007 (Figure 2b). On the one hand, the overall reduction of the impact of resources and fuels in particular can be interpreted as evidence that strategic goals shift away from resources and align with fight against CO2 emissions. It is unclear, on the other hand, how the increase in the role of forestry trade relates to attempts to reduce deforestation (Arndt and Tarp, 2017). If the recent shifts in the strategic goals relating to resources mark a permanent change in donors' reconciliation of professed and strategic goals of development assistance, remains to be seen.

# 8 Conclusion

Giving infrastructure aid benefits not only the recipient country but can also benefit the donor if it relies on resource imports from the recipient country. In this paper, we incorporate this notion into an aid allocation model that allows for different types of aid. In the model, bilateral resource exports affect bilateral infrastructure aid because donors can benefit from lower input prices. We apply the model empirically to data from the OECD's Creditor Reporting System.

Our results suggest that for the period of 2001 to 2011 resource exports affect the allocation decision of donors, but that these effects differ in nuanced ways. First, our model suggests that richer donors put less weight on resource considerations. Second, our empirical results indicate that oil has the strongest effect on infrastructure aid. We find statistically significant effects for forestry and metals, as well. Third, most of the effect is driven by road infrastructure projects. Our results suggest that resources increase the number of road infrastructure projects more the bigger the capacity of the maritime infrastructure of the recipient.

We leave for further research the question of what effect this has on the effectiveness of infrastructure aid. Earlier studies on aid allocation stress the importance of the motives of giving aid on the effectiveness of aid (Alesina and Dollar, 2000). While on one hand infrastructure aid may improve the recipients' capacity to benefit from its resource richness, infrastructure projects tailored to fit the needs of the resource sector may do little to improve conditions of other sectors. If institutions are weak it may even improve the government's ability to extract rents at the expense of its subjects. Our results do not imply that negative effects will necessarily prevail, but point to a new direction of inquiry in the effectiveness of sectoral aid flows.
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# Appendix A: Tables

<table><caption>Table A.1: List of Countries</caption><caption>Panel A: Recipients</caption><tbody><tr><td>Albania</td><td>Ecuador</td><td>Lebanon</td><td>Saudi Arabia</td></tr><tr><td>Algeria</td><td>Egypt</td><td>Liberia</td><td>Senegal</td></tr><tr><td>Angola</td><td>El Salvador</td><td>Madagascar</td><td>South Africa</td></tr><tr><td>Argentina</td><td>Ethiopia</td><td>Malawi</td><td>Sri Lanka</td></tr><tr><td>Armenia</td><td>Gabon</td><td>Malaysia</td><td>Sudan</td></tr><tr><td>Azerbaijan</td><td>Gambia</td><td>Mali</td><td>Suriname</td></tr><tr><td>Bangladesh</td><td>Ghana</td><td>Mexico</td><td>Syria</td></tr><tr><td>Bolivia</td><td>Guatemala</td><td>Moldova</td><td>Tanzania</td></tr><tr><td>Brazil</td><td>Guinea</td><td>Mongolia</td><td>Thailand</td></tr><tr><td>Burkina Faso</td><td>Guinea-Bissau</td><td>Morocco</td><td>Togo</td></tr><tr><td>Cameroon</td><td>Guyana</td><td>Mozambique</td><td>Tunisia</td></tr><tr><td>Chile</td><td>Haiti</td><td>Myanmar</td><td>Turkey</td></tr><tr><td>China</td><td>Honduras</td><td>Nicaragua</td><td>Uganda</td></tr><tr><td>Colombia</td><td>India</td><td>Niger</td><td>Ukraine</td></tr><tr><td>Congo, DR</td><td>Indonesia</td><td>Nigeria</td><td>Venezuela</td></tr><tr><td>Congo, Rep.</td><td>Iran</td><td>Pakistan</td><td>Vietnam</td></tr><tr><td>Costa Rica</td><td>Iraq</td><td>Panama</td><td>Yemen</td></tr><tr><td>Côte d'Ivoire</td><td>Jamaica</td><td>Papua New Guinea</td><td>Zambia</td></tr><tr><td>Croatia</td><td>Jordan</td><td>Paraguay</td><td>Zimbabwe</td></tr><tr><td>Cuba</td><td>Kazakhstan</td><td>Peru</td><td></td></tr><tr><td>Dominican Rep.</td><td>Kenya</td><td>Philippines</td><td></td></tr></tbody></table><table><caption>Panel B: Donors</caption><tbody><tr><td>Australia</td><td>France</td><td>Korea, Rep.</td><td>Spain</td></tr><tr><td>Austria</td><td>Germany</td><td>Kuwait</td><td>Sweden</td></tr><tr><td>Belgium-Luxembourg</td><td>Greece</td><td>Netherlands</td><td>USA</td></tr><tr><td>Canada</td><td>Ireland</td><td>New Zealand</td><td>United Arab Emirates</td></tr><tr><td>Denmark</td><td>Italy</td><td>Norway</td><td>United Kingdom</td></tr><tr><td>Finland</td><td>Japan</td><td>Portugal</td><td></td></tr></tbody></table>
<table><caption>Table A.2: Baseline models - PPML: Control variables</caption><thead><tr><th rowspan="2"></th><th colspan="2">Number of projects</th><th colspan="2">Average value</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Population<sub>t-1</sub> (in logs)</td><td>-2.124<br>(-1.820)<sup>+</sup><br>[-1.329]</td><td></td><td>5.123<br>(1.790)<sup>+</sup><br>[1.446]</td><td></td></tr><tr><td>GDP pc<sub>t-1</sub> (in logs)</td><td>-0.439<br>(-1.299)<br>[-1.964]<sup>*</sup></td><td></td><td>0.004<br>(0.010)<br>[0.018]</td><td></td></tr><tr><td>Growth GDP pc<sub>t-1</sub></td><td>0.006<br>(1.141)<br>[1.268]</td><td></td><td>0.026<br>(0.927)<br>[2.659]<sup>**</sup></td><td></td></tr><tr><td>UNGA votes against<sub>t-1</sub> (share)</td><td>0.823<br>(1.062)<br>[1.089]</td><td>0.168<br>(0.169)<br>[0.227]</td><td>1.672<br>(0.747)<br>[0.793]</td><td>0.284<br>(0.108)<br>[0.118]</td></tr><tr><td>Corruption<sub>t-1</sub></td><td>-0.101<br>(-1.282)<br>[-1.335]</td><td></td><td>-0.388<br>(-2.457)<sup>*</sup><br>[-2.419]<sup>*</sup></td><td></td></tr><tr><td>Democracy<sub>t-1</sub></td><td>0.069<br>(1.134)<br>[1.413]</td><td></td><td>-0.174<br>(-1.081)<br>[-1.817]<sup>+</sup></td><td></td></tr><tr><td>Distance (in logs)</td><td>0.302<br>(1.686)<sup>+</sup><br>[2.076]<sup>*</sup></td><td>0.195<br>(1.606)<br>[1.606]</td><td>-0.251<br>(-0.985)<br>[-0.762]</td><td>-0.054<br>(-0.301)<br>[-0.226]</td></tr><tr><td>RTA<sub>t-1</sub></td><td>0.097<br>(0.707)<br>[1.253]</td><td>-0.115<br>(-0.750)<br>[-0.916]</td><td>0.704<br>(2.329)<sup>*</sup><br>[1.962]<sup>*</sup></td><td>0.431<br>(1.183)<br>[1.132]</td></tr><tr><td>Both WTO<sub>t-1</sub></td><td>-0.087<br>(-0.322)<br>[-0.592]</td><td></td><td>0.181<br>(0.484)<br>[0.402]</td><td></td></tr><tr><td>Urbanization<sub>t-1</sub> (share)</td><td>-0.009<br>(-0.218)<br>[-0.431]</td><td></td><td>0.005<br>(0.088)<br>[0.147]</td><td></td></tr><tr><td>Common Language</td><td>0.282<br>(1.445)<br>[1.195]</td><td>0.338<br>(2.087)<sup>*</sup><br>[2.000]<sup>*</sup></td><td>-0.617<br>(-2.005)<sup>*</sup><br>[-4.012]<sup>***</sup></td><td>-0.386<br>(-1.619)<br>[-1.874]<sup>+</sup></td></tr><tr><td>Observations</td><td>12691</td><td>9751</td><td>2063</td><td>2061</td></tr><tr><td>Pseudo R2</td><td>0.50</td><td>0.57</td><td>0.56</td><td>0.93</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient FE</td><td>YES</td><td></td><td>YES</td><td></td></tr><tr><td>Recipient-year FE</td><td></td><td>YES</td><td></td><td>YES</td></tr></tbody></table>

Note: t-statistics based on clustered (two-way clustered) standard errors in parentheses (brackets). + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$. Additional control variables included in the regressions in Table 2.
<table><caption>Table A.3: Results by Donor</caption><caption>Dependent variable: Number of infrastructure projects</caption><thead><tr><th>Country</th><th>Infrastr. aid from ROW</th><th>Tot. Exp.</th><th>Tot. Imp.</th><th>N.Res. Exp.</th><th>Infrastr. Idx.</th><th>Obs.</th><th>Ps.- R2</th></tr></thead><tbody><tr><td>Australia</td><td>0.03<br>(0.64)</td><td>-0.80<br>(-1.30)*</td><td>-1.28<br>(-1.67)*</td><td>0.71<br>(1.66)*</td><td>2.28<br>(3.53)***</td><td>153</td><td>0.68</td></tr><tr><td>Austria</td><td>-0.00<br>(-0.01)</td><td>-0.40<br>(-0.62)</td><td>-0.61<br>(-0.72)*</td><td>1.81<br>(1.55)*</td><td>0.72<br>(0.47)</td><td>118</td><td>0.20</td></tr><tr><td>Belgium-Luxembourg</td><td>-0.01<br>(-0.22)</td><td>-0.51<br>(-2.33)*</td><td>0.74<br>(2.03)*</td><td>0.49<br>(2.21)*</td><td>-0.04<br>(-0.07)</td><td>313</td><td>0.37</td></tr><tr><td>Germany</td><td>0.06<br>(1.12)*</td><td>0.08<br>(0.23)</td><td>-0.82<br>(-1.95)*</td><td>0.13<br>(0.50)</td><td>-0.06<br>(-0.09)</td><td>366</td><td>0.36</td></tr><tr><td>Denmark</td><td>-0.12<br>(-0.88)*</td><td>-0.08<br>(-0.40)</td><td>0.11<br>(0.24)</td><td>-3.00<br>(-1.13)*</td><td>0.15<br>(0.26)</td><td>143</td><td>0.28</td></tr><tr><td>Spain</td><td>0.07<br>(1.47)*</td><td>-0.23<br>(-0.87)*</td><td>0.52<br>(1.47)*</td><td>0.21<br>(0.60)</td><td>0.35<br>(0.66)</td><td>459</td><td>0.31</td></tr><tr><td>France</td><td>0.04<br>(1.39)*</td><td>-0.48<br>(-1.26)*</td><td>-0.17<br>(-0.52)</td><td>0.84<br>(2.43)*</td><td>-0.44<br>(-0.95)*</td><td>505</td><td>0.25</td></tr><tr><td>United Kingdom</td><td>0.04<br>(0.58)</td><td>0.24<br>(0.88)*</td><td>1.53<br>(3.80)***</td><td>0.20<br>(0.51)</td><td>0.88<br>(1.26)*</td><td>234</td><td>0.23</td></tr><tr><td>Ireland</td><td>-0.06<br>(-0.32)</td><td>0.07<br>(0.84)*</td><td>-0.27<br>(-1.80)*</td><td>0.94<br>(0.88)*</td><td>-2.12<br>(-1.71)*</td><td>123</td><td>0.79</td></tr><tr><td>Italy</td><td>0.13<br>(1.58)*</td><td>-0.46<br>(-0.97)*</td><td>-0.75<br>(-0.89)*</td><td>0.95<br>(1.06)*</td><td>-0.82<br>(-0.84)*</td><td>228</td><td>0.40</td></tr><tr><td>Japan</td><td>-0.00<br>(-0.04)</td><td>0.02<br>(0.27)</td><td>0.08<br>(0.79)*</td><td>-0.03<br>(-0.35)</td><td>-0.17<br>(-1.19)*</td><td>654</td><td>0.49</td></tr><tr><td>Korea, Rep.</td><td>0.01<br>(0.34)</td><td>0.37<br>(2.58)</td><td>-0.31<br>(-1.27)*</td><td>-0.57<br>(-2.58)</td><td>-1.10<br>(-3.06)***</td><td>293</td><td>0.43</td></tr><tr><td>Norway</td><td>0.02<br>(0.32)</td><td>-0.11<br>(-0.86)*</td><td>-0.09<br>(-1.09)*</td><td>1.93<br>(3.36)***</td><td>-0.43<br>(-0.95)*</td><td>243</td><td>0.38</td></tr><tr><td>Sweden</td><td>0.02<br>(0.19)</td><td>-0.03<br>(-0.16)</td><td>-0.73<br>(-2.45)</td><td>-3.65<br>(-2.16)*</td><td>-0.70<br>(-0.79)*</td><td>258</td><td>0.28</td></tr><tr><td>USA</td><td>0.01<br>(0.27)</td><td>-0.34<br>(-1.98)*</td><td>0.79<br>(1.47)*</td><td>-0.79<br>(-2.69)</td><td>0.16<br>(0.37)</td><td>626</td><td>0.60</td></tr></tbody></table>

<footnote>Note: t-statistics based on standard errors clustered by recipient in parentheses. + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$. Results only reported for countries with more than 100 observations. All regressions include control variables and year and recipient fixed effects.</footnote>
<table><caption>Table A.4: Results by Donor</caption><caption>Dependent variable: Average size (value) of infrastructure projects</caption><thead><tr><th>Country</th><th>Infrastr.<br>aid from<br>ROW</th><th>Tot.<br>Exp.</th><th>Tot.<br>Imp.</th><th>N.Res.<br>Exp.</th><th>Infrastr.<br>Idx.</th><th>Obs.</th><th>Ps.-<br>R2</th></tr></thead><tbody><tr><td>Spain</td><td>0.30<br>(1.81)*</td><td>3.56<br>(2.01)*</td><td>-0.97<br>(-0.36)</td><td>-2.10<br>(-2.99)***</td><td>4.63<br>(2.71)</td><td>148</td><td>0.96</td></tr><tr><td>France</td><td>-0.17<br>(-1.68)*</td><td>-0.38<br>(-0.50)</td><td>1.70<br>(1.28)*</td><td>-0.12<br>(-0.24)</td><td>-0.46<br>(-0.33)</td><td>161</td><td>0.75</td></tr><tr><td>Japan</td><td>-0.02<br>(-0.45)</td><td>0.17<br>(0.71)*</td><td>0.21<br>(0.50)</td><td>0.07<br>(0.57)</td><td>-1.89<br>(-2.66)</td><td>470</td><td>0.70</td></tr><tr><td>Korea, Rep.</td><td>0.26<br>(1.00)*</td><td>-6.44<br>(-4.94)***</td><td>-1.98<br>(-0.78)*</td><td>0.69<br>(0.90)*</td><td>10.34<br>(5.39)***</td><td>118</td><td>0.88</td></tr><tr><td>USA</td><td>0.34<br>(3.20)***</td><td>-0.83<br>(-1.86)*</td><td>0.36<br>(0.52)</td><td>-0.24<br>(-1.39)*</td><td>-0.19<br>(-0.17)</td><td>295</td><td>0.85</td></tr></tbody></table>

<footnote>Note: t-statistics based on standard errors clustered by recipient in parentheses. + $p < 0.1$, * $p < 0.05$, ** $p < 0.01$, *** $p < 0.001$. Results only reported for countries with more than 100 observations. All regressions include control variables and year and recipient fixed effects.</footnote>
<table><caption>Table A.5: Frictionless trade - PPML</caption><thead><tr><th rowspan="2"></th><th colspan="2">Number of projects</th><th colspan="2">Average value</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Infrastructure aid from ROW (in logs)</td><td>-0.062<br>(-3.814)***<br>[-4.052]***</td><td>-0.438<br>(-15.272)***<br>[-8.248]***</td><td>-0.088<br>(-3.005)**<br>[-3.079]**</td><td>-1.408<br>(-17.914)***<br>[-33.500]***</td></tr><tr><td>Infrastructure index<sub>t-1</sub></td><td>0.081<br>(0.652)<br>[0.678]</td><td></td><td>-0.876<br>(-2.853)**<br>[-2.438]*</td><td></td></tr><tr><td>Total Exports<sub>t-1</sub> (+1 in logs)</td><td>0.032<br>(0.757)<br>[0.644]</td><td>0.029<br>(0.953)<br>[0.653]</td><td>-0.094<br>(-1.292)<br>[-1.586]</td><td>-0.015<br>(-0.272)<br>[-0.252]</td></tr><tr><td>Total Imports<sub>t-1</sub> (+1 in logs)</td><td>0.434<br>(4.944)***<br>[5.223]***</td><td>0.316<br>(5.859)***<br>[6.026]***</td><td>-0.008<br>(-0.064)<br>[-0.055]</td><td>-0.090<br>(-1.003)<br>[-0.806]</td></tr><tr><td>Hypothetical frictionless Resource Exports<sub>t-1</sub><br>k<sub>dt</sub> = GDPpc<sub>dt</sub> (+1 in logs)</td><td>0.179<br>(2.104)*<br>[2.015]*</td><td>0.174<br>(1.954)<sup>+</sup><br>[1.752]<sup>+</sup></td><td>0.629<br>(3.647)***<br>[3.740]***</td><td>0.081<br>(0.521)<br>[0.368]</td></tr><tr><td>Colony</td><td>0.745<br>(3.242)**<br>[3.858]***</td><td>0.612<br>(3.015)**<br>[3.633]***</td><td>0.383<br>(1.211)<br>[1.325]</td><td>0.451<br>(1.378)<br>[1.782]<sup>+</sup></td></tr><tr><td>Observations</td><td>12691</td><td>9751</td><td>2063</td><td>2061</td></tr><tr><td>Pseudo R2</td><td>0.50</td><td>0.57</td><td>0.57</td><td>0.93</td></tr><tr><td>Donor-year FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Recipient FE</td><td>YES</td><td></td><td>YES</td><td></td></tr><tr><td>Recipient-year FE</td><td></td><td>YES</td><td></td><td>YES</td></tr></tbody></table>

<footnote>Note: t-statistics based on standard errors clustered by pair (donor) in parentheses (brackets). + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. All regressions include Population, Area, GDP per capita, GDP per capita growth, UNGA votes against, Corruption and Democracy indicators, distance, RTAs, WTO membership, urbanization and common language as further control variables.</footnote>