# Global value chains: Do they impact the allocation of foreign aid?

Basak Bayramoglu, Jean-François Jacques, Julie Lochard

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Basak Bayramoglu, Jean-François Jacques, Julie Lochard. Global value chains: Do they impact the allocation of foreign aid?. Journal of Comparative Economics, 2025, 53 (4), pp.1001-1032. 10.1016/j.jce.2025.07.007. hal-05417354

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# Global value chains: Do they impact the allocation of foreign aid?

Basak Bayramoglu<sup>a, b, *</sup>, Jean-François Jacques<sup>b, c</sup>, Julie Lochard<sup>d, c</sup>

## ARTICLE INFO

Dataset link: https://data.mendeley.com/previ-
ew/ncf7dczycm=a=389a2ece-9e3f-43e8-a4d9-2
be3625527e8

Keywords:

Foreign aid

Global value chains

Trade

## ABSTRACT

The rise of global value chains (GVCs) in recent decades has induced significant changes in the geography of world production, with consequences for bilateral relations. What are the consequences of GVC-related bilateral trade for the allocation of bilateral foreign aid? Using data on bilateral aid from 22 donors to 127 recipient countries over 2000-2018, our findings, robust to endogeneity, show that a larger participation of the recipient country in GVCs increases the amount of aid allocated to that country. To rationalize these findings, we develop a theoretical model that provides a simple explanation for the existence of transfers among countries: foreign aid allows a donor country producing a final good to buy less expensive intermediate inputs from an upstream country. Overall, this suggests that donors allocate aid strategically to import inputs at a lower cost.

## 1. Introduction

World production is largely structured into global value chains (GVCs), which are related to the use of foreign value added (embodied in raw materials, intermediate inputs or “tasks”) in production for either domestic consumption or exports (Antràs, 2020).<fnref n="1" /> The rise of GVCs in recent decades<fnref n="2" /> has induced significant changes in the organization and geography of the world production of goods and services, with consequences for bilateral and multilateral trade relations (Amador and Cabral, 2016).

In this paper, we investigate whether GVC-related bilateral trade drives donors' allocation of bilateral foreign aid. The literature on foreign aid indicates that in addition to recipients' needs and merits, donor countries provide foreign aid to developing economies for strategic reasons to serve their own interests and that trade is a crucial element in such interests (e.g., Alesina and Dollar, 2000; Berthélemy, 2006; Hoeffler and Outram, 2011).<fnref n="3" /> This stream of literature has generally used simple measures of bilateral relationships, such as bilateral trade flows or trade openness indicators. Several studies focus more specifically on trade-related motives. Younas (2008) finds that donor countries tend to allocate more aid to those recipient countries where they export goods in which they have a comparative advantage (i.e., capital goods). In the same vein, Barthel et al. (2014) show that donors increase bilateral aid as a reaction to a rise in aid by other donors competing for export markets in the same receiving country. This result is in line with the evidence that donor countries use aid as a means to promote their own exports. In this study, unlike in the

https://doi.org/10.1016/j.jce.2025.07.007

Received 7 October 2024; Received in revised form 19 June 2025; Accepted 21 July 2025

Available online 12 September 2025

0147-5967/© 2025 The Authors. Published by Elsevier Inc. on behalf of Association for Comparative Economic Studies. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

<footnote><sup>a</sup> Université Paris-Saclay, INRAE, AgroParisTech, PSAE, Palaiseau, France</footnote>

<footnote><sup>b</sup> ERUDITE, Université Gustave Eiffel, France</footnote>

<footnote><sup>c</sup> INRAE, PSAE, Palaiseau, France</footnote>

<footnote><sup>d</sup> ERUDITE, Université Paris-Est Créteil, France</footnote>

<footnote>* Corresponding author at: ERUDITE, Université Gustave Eiffel, France.</footnote>

<footnote>E-mail address: basak.bayramoglu@inrae.fr (B. Bayramoglu).</footnote>

<footnote n="1">¹ A global value chain can be defined as "*a series of stages involved in producing a product or service that is sold to consumers, with each stage adding value, and with at least two stages being produced in different countries. A firm participates in a GVC if it produces at least one stage in a GVC*" (Antràs, 2020, p. 3).</footnote>

<footnote n="2">² GVC-trade steadily grew in the 1990s and stagnated after the 2008 global financial crisis (World Bank, 2020).</footnote>

<footnote n="3">³ Another aspect of donors' interest in the allocation of aid is to buy political support from recipient countries, for instance, in UN General Assembly votes (Alesina and Dollar, 2000; Dreher et al., 2008).</footnote>
abovementioned works, we are interested in how donor countries use development aid to guarantee the foreign *sourcing* of their exports. We argue that foreign aid may act as a subsidy to reduce the price of imported intermediate goods. The argument that aid can be used to guarantee the sourcing of essential imports and as a subsidy to guarantee the profitability of foreign direct investments by firms from the donor countries is already present in the early literature on aid (e.g. Maizels and Nissanke, 1984).

Two recent and more closely related papers show that aid is used to ensure access to natural resources. Couharde et al. (2020) find that the oil endowments of recipient countries increase the amount of aid that they receive from G7 donors and that highly oil-dependent donors provide more bilateral aid. Kruse et al. (2020) provide theoretical and empirical evidence showing that bilateral resource exports (i.e., raw materials, fuels, and mining and forestry products) from a recipient country positively affect the volume and value of aid in infrastructure allocated to that country. To investigate the relation between integration in GVCs and the allocation of foreign aid, we combine empirical and theoretical analyses. Our study gives more general conclusions, as it is not restricted to infrastructure aid, which represents only 8.5% of the value of total aid. Moreover, we extend the research question beyond that related to access to natural resources to all other manufacturing products and services embodied in donors' exports and consider, from a broad perspective, the entire production chain.<fnref n="4" /> Indeed, bilateral aid may be directed specifically at some developing countries because these recipients are the main suppliers of specific inputs or tasks in the production stage or providers of less expensive intermediate inputs.

Our study also contributes to the empirical literature on GVCs, which has benefited from the recent construction and availability of global input-output tables providing a detailed picture of international flows of commodities in different sectors by linking national input-output tables across countries.<fnref n="5" /> The measurement of GVCs and the participation of countries in this phenomenon have been extensively studied in the empirical literature (see, for instance, Hummels et al., 2001, Johnson and Noguera, 2012, Koopman et al., 2014, Johnson, 2014, Johnson, 2018, Timmer et al., 2014, Antràs, 2020, and Antràs and De Gortari, 2020). However, the consequences for development and spillovers from GVCs to developing countries remain less explored, even though developing countries are increasingly participating in GVC-trade (World Bank, 2020).

Using panel data on bilateral development aid from 22 donors to 127 recipient countries over the period 2000–2018, we evaluate how the dependence of donors through GVCs from specific recipients orientates the allocation of development aid. To conform with the general definition of GVCs, we measure GVC dependence with the use of foreign value added in production for exports (Antràs, 2020). The two-way relationship between aid and foreign value added may lead to endogeneity issues causing a threat to identification. We account for this endogeneity by using an instrumental variable approach, based on a shift-share instrument. Our empirical results first show that a rise in the amount of foreign value added from a recipient country in a donor country's exports increases the amount of aid allocated to that country. We also show that the impact of GVC-trade on the allocation of aid is particularly strong when recipient countries contribute to intermediate inputs or tasks in differentiated sectors (food and beverages, electrical and machinery, and transport equipment). Interestingly, we find no effect of GVC-trade in raw materials such as petroleum, chemical and nonmetallic mineral products, and in metal products.

To rationalize these findings, we develop a theoretical model, based on Antràs and De Gortari (2020), which provides a simple explanation for the allocation of bilateral aid along the global value chain: foreign aid allows a donor country producing a final good to buy less expensive intermediate inputs from an upstream country. This model assumes that governments in both donor and recipient countries are politically motivated and support business lobbies by providing foreign aid. There is evidence of lobbying for foreign aid by business lobbies from donor countries. Kimura and Todo (2010) identify Japan as a country where foreign aid is used to create economic opportunities for private investors. They also note that these investors are strategically included in government committees on foreign aid. In 2021, two French medias (Disclose and Mediapart) published an investigation report that pointed to the influence of international French firms in the allocation of French foreign aid.<fnref n="6" /> In our model, we show that governments have an interest in signing a bilateral cooperative agreement that stipulates the payment of monetary transfers from the downstream country to the upstream country, allowing the latter to grant subsidies to its exporting firm. These subsidies help reducing the production cost of the intermediate and final goods and, in turn, increase the production of the final good by the donor country. Our model also predicts a positive relationship between *foreign value added of the recipient country in the exports of the donor country and the amount of aid granted by that country*, that supports our empirical results.

Section 2 presents our data and methodology, and Section 3 presents our empirical results. In Section 4, we discuss endogeneity issues. In Section 5, we present our theoretical model offering a rationale for the existence of monetary transfers among countries along GVCs. Finally, in Section 6, we summarize our main findings.

<footnote n="4">⁴ By considering GVCs, we do not restrict ourselves to direct intermediate inputs but account for the sourcing of all inputs along the supply chain. If we take the example of vehicles that are produced for exports, then we account not only for the origin of the tires or engines embodied in these vehicles but also for the origin of the rubber, steel, etc., used to produce these inputs.</footnote>

<footnote n="5">⁵ Another strand of the literature relies on firm-level data that document firms’ input sourcing decisions and their import or export participation (Johnson, 2018).</footnote>

<footnote n="6">⁶ https://www.mediapart.fr/journal/dossier/international/les-nouvelles-derives-de-l-aide-francaise-au-developpement</footnote>
## 2. Data and methodology

### 2.1. Data

Our data for bilateral Official Development Assistance (ODA)<fnref n="7" /> come from the database on Credit Reporting System (CRS) reported by the OECD. We consider total aid commitments<fnref n="8" /> from 22 donor countries to 127 recipient countries in the time period 2000–2018. In robustness tests, we restrict the analysis to aid in allocable sectors (therefore excluding, for instance, humanitarian aid or debt relief) and take averages over three-year periods instead of annual observations to reduce the volatility of aid flows.

Our variable of interest intends to measure the dependence of donor countries on recipient countries through the foreign value added (FVA) embodied in the donor's exports. This standard GVC participation indicator is available at the country, industry and year levels in global input-output (I-O) tables.<fnref n="9" /> It has several advantages compared to the alternative proxy relying on trade flows and a product classification that identifies intermediate goods (such as broad economic categories, BEC). First, global (as well as national) I-O tables avoid arbitrariness in the classification of products, particularly for products that can be used both as an intermediate good and as a final good (e.g., flour) (Hummels et al., 2001). Second, global I-O tables allow us to account for imported inputs used 'indirectly' in the production of an export good, i.e., all inputs (including services) along the supply chain. Third, global I-O tables provide a better approximation of the foreign content of exports by measuring the value added by each country used to produce the export of goods or services.<fnref n="10" /> Moreover, considering the FVA embodied in countries' exports allows us to account for the dependence of donor countries in the production of not only finished goods but also semi-finished goods (Antràs and Chor, 2022). We use data on FVA from the UNCTAD Eora Global Value Chain database. The UNCTAD Eora database was initially constructed for the World Investment Report (UNCTAD, 2013) and has been extended to include all basic GVC indicators based on Eora Multi Regional Input Output (MRIO) data.<fnref n="11" /> This database provides the largest geographical coverage with 189 countries compared to other global input-output tables<fnref n="12" /> (see below and in the Appendix for a description of the data).

### 2.2. Stylized facts

We now present some descriptive statistics on GVC participation. Fig. 1 represents the evolution over time of FVA as a share of total exports for the top four donor countries (the US, Japan, Germany and France).

Small economies generally source more inputs from abroad than do large economies, such as the US or Japan. Moreover, we see a rising trend in all four countries before the financial crisis, consistent with the intensification of GVCs reported in the literature (e.g., World Bank, 2020). After 2008, the share of FVA is rather constant, except for that in Japan.

When we look at the origin region of the foreign value added (Fig. 2), we note the important role of the East Asia and Pacific region. This region represents the largest sourcing region for all top donors, except the US. These figures also show that countries are generally more dependent on regional than global GVCs. This finding is consistent with the evidence provided in World Bank (2020).<fnref n="13" />

Fig. 3 plots the logarithm of ODA commitments toward all recipient countries against the logarithm of FVA for the four top donor countries. The figure illustrates a strong and positive correlation: countries representing a larger share of FVA in donors' exports receive more aid. In the next section, we empirically investigate the existence of a causal relationship between FVA and the amount of aid.

### 2.3. Model specification and estimation method

Much of the empirical literature shows that the allocation of aid mainly depends on the variables reflecting recipient needs and 'merits' as well as donor interests (e.g., Alesina and Dollar, 2000; Berthélemy and Tichit, 2004; Berthélemy, 2006; Hoeffler and Outram, 2011). The estimated equations are generally close to a gravity-type setting, where bilateral aid flows depend on

<footnote n="7">⁷ Bilateral aid is the dominant channel of foreign aid: in the period 2008–2013, Development Assistance Committee (DAC) donors disbursed over 60% of ODA bilaterally and approximately 25% multilaterally. For some donors, such as the US, the share of bilateral aid has reached 72% (Gulrajani, 2016).</footnote>

<footnote n="8">⁸ Most studies in the aid literature use aid commitments rather than aid disbursements because commitments better reflect donors' intentions and are not affected by the recipient capacity.</footnote>

<footnote n="9">⁹ A description of global I-O tables is provided in Appendix B. Another standard GVC participation indicator captures the domestic value added used in another country's export production (forward GVC participation). In this study, instead, we focus on the backward GVC participation measure.</footnote>

<footnote n="10">¹⁰ In particular, the foreign value added is different from the 'import content of exports', or vertical specialization (VS) index of Hummels et al. (2001), because the VS index includes the value of imported inputs used to produce the domestic input embodied in other countries' exports (the 'double-counting residual'; see Johnson, 2018).</footnote>

<footnote n="11">¹¹ This database is publicly available: http://worldmrio.com/unctadgvc/ (see Casella et al., 2019).</footnote>

<footnote n="12">¹² Other global input-output databases such as the World Input-Output Database (WIOD) or the OECD Trade in Value Added (TIVA) provide data on only 40 to 60 countries, mostly developed countries. Such large geographical coverage is especially important in our case because most ODA recipients are developing countries.</footnote>

<footnote n="13">¹³ France and Germany appear to be more dependent on Europe and Central Asia as well as Middle East and North African countries, while the US appears to depend more on Latin American and Caribbean countries.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 1. Foreign value added (FVA) embedded in top donors’ exports overtime.</figcaption></figure>

Notes: FVA in % of total exports over time (2000–2018).

Data source: UNCTAD Eora.

<figure><img src="image_6.png" /><figcaption>Fig. 2. Foreign value added (FVA) embedded in top donors’ exports over origin regions.</figcaption></figure>

Notes: FVA in % of total exports (average over 2000–2018). We use the World Bank classification of regions (MENA: Middle East and North Africa; SSA: Sub-Saharan Africa; LAC: Latin America & the Caribbean; SA: South Asia; EAP: East Asia and Pacific; ECA: Europe and Central Asia).

Data source: UNCTAD Eora.

donor characteristics ($D_{it}$), recipient characteristics ($R_{jt}$) and bilateral characteristics capturing economic and cultural ties ($B_{ijt}$) (Eq. (1)).

$$ \ln ODA_{ijt} = \alpha_0 + \alpha_1 D_{it} + \alpha_2 R_{jt} + \alpha_3 B_{ijt} + v_i + v_j + \eta_t + \epsilon_{ijt}, \quad (1) $$

where $ODA_{ijt}$ denotes the total ODA commitments of donor country $i$ to recipient $j$ in year $t$ (in thousands US$); $v_i$ and $v_j$ are country fixed effects; and $\eta_t$ are year fixed effects, which control for the general evolution of ODA overtime. Donor characteristics ($D_{it}$) include, generally, the size of the total aid budget or, more globally, the GDP of the donor country. On the recipient side ($R_{jt}$), the allocation of aid mainly depends on the GDP per capita (GDPCap), which has been highlighted as a good proxy for recipient needs in the literature; population (Pop), accounting for the fixed costs of aid administration (Berthélemy, 2006); and institutional quality, used as a proxy for recipient ‘merits’ (index InstQ, which controls for the level of democracy). Bilateral variables
<figure><img src="image_7.png" /><figcaption>Fig. 3. ODA and foreign value added (FVA) for the top donors in 2010.
Notes: ODA commitments (on the vertical axis) and FVA (on the horizontal axis) in logs.
Data source: UNCTAD Eora and the OECD. See the text and Appendix for details.</figcaption></figure>

$(B_{ijt})$ capture economic and cultural ties, measured by bilateral distance (Distance), colonial relationships (Colony) and a common language (Comlang).<fnref n="14" /> All time-varying explanatory variables are lagged one year to reflect the fact that aid allocation decisions in year $t$ are based on past (generally, $t-1$) variables.

The extended model of bilateral aid allocation corresponds to Eq. (2):

$$ \ln ODA_{ijt} = \beta_0 + \beta_1 \ln GDP_{it-1} + \beta_2 \ln GDPCap_{jt-1} + \beta_3 \ln Pop_{jt-1} + \beta_4 InstQ_{jt-1} \\ + \beta_5 \ln Distance_{ij} + \beta_6 Colony_{ij} + \beta_7 Comlang_{ij} + \beta_8 \ln FVA_{ijt-1} \\ + v_i + v_j + \eta_t + \epsilon_{ijt}, \quad (2) $$

where $FVA_{ijt-1}$, our variable of interest, intends to measure the dependence of donor countries on recipient countries through the foreign (country $j$) value added embodied in the donor's (country $i$) exports. This model can be further refined to control for any time-varying country characteristics through the use of country-year fixed effects ($\mu_{it}$ and $\mu_{jt}$) (Eq. (3)). The use of country-year fixed effects has an additional advantage in our context as it simplifies the interpretation of our variable of interest (FVA) by controlling for the overall exports of country $i$.<fnref n="15" />

$$ \ln ODA_{ijt} = \lambda_0 + \lambda_1 \ln Distance_{ij} + \lambda_2 Colony_{ij} + \lambda_3 Comlang_{ij} + \lambda_4 \ln FVA_{ijt-1} \\ + \mu_{it} + \mu_{jt} + \epsilon_{ijt}, \quad (3) $$

In our baseline regressions, we estimate models corresponding to Eq. (2) and Eq. (3) using two main estimation methods.

To address the high prevalence of zero values in the aid variable, recent studies use count models, such as a Poisson pseudo maximum likelihood (PPML) or a negative binomial pseudo maximum likelihood (NBPML) estimator (e.g., Dreher et al., 2015; Cardwell and Ghazalian, 2018; Couharde et al., 2020; Kruse et al., 2020). PPML estimators can be applied in all models where the dependent variable is nonnegative. These estimators are commonly used in the trade literature with gravity models (see, for example, Yotov et al., 2016) and have several desirable properties in our context. First, they provide a natural way in which to account for zero values in the aid variable. Second, PPML estimators are consistent in the presence of heteroscedasticity

<footnote n="14">¹⁴ Colony is a dummy variable equal to one if the recipient and donor countries have ever had a colonial link and Comlang is a dummy variable equal to one if the donor and recipient countries share the same language. All variables and data sources are defined in Table 7 in Appendix B.</footnote>

<footnote n="15">¹⁵ If we do not include this latter variable in the model (or, alternatively, country-year fixed effects), then FVA depends on the level of country $i$'s exports; i.e., an increase in FVA for a given donor might reflect either an increase in the value of its own exports or a stronger dependence of the donor on recipient countries (Johnson, 2018).</footnote>
(Santos Silva and Tenreyro, 2006), make minimal assumptions about the data and outperform alternative models (Santos Silva and Tenreyro, 2006, Santos Silva and Tenreyro, 2011). Moreover, contrary to negative binomial regression models, PPML estimators have the advantage of being invariant to the scale of the dependent variable (Bosquet and Boulhol, 2014).<fnref n="16" />

Endogeneity is also a potential concern. The relation between aid and trade may go in either of the two directions and create a reverse causality bias (Suwa-Eisenmann and Verdier, 2007). Our theoretical framework also suggests a two-way relationship between bilateral aid and foreign value added. Moreover, potential omitted variables, as well as measurement error in the GVC indicator, might generate an endogeneity bias. Studies generally address this issue by using one-year lags, but using lags does not solve for endogeneity in the presence of autocorrelation. We propose two complementary strategies to address this issue. First, we add multiple fixed effects to reduce the risk of omitted variables.<fnref n="17" /> Second, we employ an instrumental variable method using a shift-share instrument (see Section 4).

# 3. Empirical results

## 3.1. Baseline results

To investigate the relationship between foreign value added and foreign aid, we estimate our baseline model using, first, a simple OLS estimator, where we take the logarithm of aid (and therefore delete zero values) and, second, a PPML estimator that accounts for these zero values in the dependent variable. The baseline results are reported in Table 1. We estimate a first model with control variables and separate country and year fixed effects (Eq. (2)) (Columns 1 and 3) and a second model (Eq. (3)), where we add country-year fixed effects to control for all time-varying country characteristics (including the size of countries and institutional characteristics) (Columns 2 and 4). Robust standard errors are clustered at the donor country level to allow for correlations among the unobserved determinants of bilateral allocations for each donor country.

We find that the control variables have the expected sign. In general, larger countries (in terms of GDP) tend to allocate more aid, while poorer countries tend to receive more aid. Population and the institutional quality index in recipient countries are only significant in the OLS specification (Column 1). Moreover, we find that economic and cultural ties between countries are important for the allocation of bilateral aid. Donors allocate less aid to more distant countries and give more aid to their former colonies or to countries that share the same language.

The GVC-related trade variable (foreign value added in exports, FVA) appears to play a significant role in the allocation of aid in all models (Columns 1 to 4). In our preferred specification (Column 4), the estimation coefficients indicate that a 10% increase in the recipient value added contained in donors' exports would increase ODA commitments to that country by 2.15%.

## 3.2. Heterogeneity across donor countries

We then investigate possible heterogeneity across donor countries. Fig. 4 displays the effect of FVA for each of the 22 donor countries.<fnref n="18" /> This investigation reveals some heterogeneity in the role of GVCs. Our results confirm that large donor countries (such as France, Germany, Japan, the UK or the US) are generally more prone to pursue their own self-interest, whereas for donors that have been traditionally categorized as more altruistic (Canada, Denmark, the Netherlands, Finland or Sweden), the impact of FVA is close to zero. We find a negative effect of FVA for two countries (Luxembourg and Ireland), which might be explained by the fact that their participation in GVCs is mainly concentrated in the financial and insurance sectors. These results also match, to a large extent, the grouping of donors into two categories ('altruistic' vs. 'egoistic') highlighted in several papers (Berthélemy, 2006; Hoeffler and Outram, 2011).<fnref n="19" />

## 3.3. Heterogeneity across sectors

We further decompose the GVC-trade effect across sectors. Global input-output tables report, for each country of exports, the value added contributed by all other countries in the world in 26 sectors (see the Appendix). The estimation results using a PPML estimator with donor-year and recipient-year fixed effects are reported in Table 2. We find a positive and strong effect of GVC-trade in agriculture and in several manufacturing sectors, namely, food and beverage (FVA_Food), electrical and machinery (FVA_ElecMac) and transport equipment (FVA_TranspEq). Interestingly, we find no effect of our GVC-trade variable for petroleum, chemical and

<footnote n="16">16 We use the Stata command *ppmlhdef* for estimation of pseudo Poisson regression models with multiple high-dimensional fixed effects (see Correia et al., 2020).</footnote>

<footnote n="17">17 Country-year fixed effects address endogeneity issues to a certain extent because they account for all time-varying observable and unobservable characteristics. We do not use three-way fixed effects (country-year and country-pair fixed effects) here because, in that case, the impact of the variable of interest (e.g., foreign value added) is only identified from time variation within a pair of countries. GVC participation changes slowly within a pair of countries (Fernandes et al., 2020). Therefore, identification also requires cross-country variations.</footnote>

<footnote n="18">18 Concretely, we estimate our base model (Eq. (3)) using a PPML estimator and replace the *FVA* variable with 22 interacted variables between the *FVA* variable and donor fixed effects.</footnote>

<footnote n="19">19 Berthélemy (2006) groups donors according to the estimated elasticity of aid to trade intensity obtained from fixed effect regression on bilateral aid commitments from 22 donors to 137 recipients between 1980 and 1999. The above author defines those donors for which the trade elasticity is significantly higher than for other donors as egoistic (Australia, France and Italy), while the altruistic cluster corresponds to donors with a corresponding elasticity that is significantly lower: Austria, Denmark, Ireland, Netherlands, New Zealand, Norway and Switzerland.</footnote>
<table><caption>**Table 1**
Baseline results.</caption><thead><tr><th rowspan="2">Method:<br>Dependent Variable:</th><th colspan="2">OLS-FE</th><th colspan="2">PPML-FE</th></tr><tr><th>ln ODA<br>(1)</th><th>ln ODA<br>(2)</th><th>ODA<br>(3)</th><th>ODA<br>(4)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td>0.367***<br>(0.0557)</td><td>0.412***<br>(0.0211)</td><td>0.125***<br>(0.0455)</td><td>0.215***<br>(0.0323)</td></tr><tr><td>ln(GDP_donor)</td><td>1.265**<br>(0.531)</td><td></td><td>0.544**<br>(0.222)</td><td></td></tr><tr><td>ln(GDPCapita_recipient)</td><td>-0.530***<br>(0.0919)</td><td></td><td>-0.497***<br>(0.185)</td><td></td></tr><tr><td>ln(Pop_recipient)</td><td>1.199**<br>(0.472)</td><td></td><td>-1.104<br>(0.760)</td><td></td></tr><tr><td>ln(InstQ_recipient)</td><td>0.250**<br>(0.0901)</td><td></td><td>-0.147<br>(0.109)</td><td></td></tr><tr><td>ln(Distance)</td><td>-1.113***<br>(0.285)</td><td>-1.078***<br>(0.0759)</td><td>-0.786***<br>(0.182)</td><td>-0.684***<br>(0.0680)</td></tr><tr><td>Colony</td><td>1.578***<br>(0.282)</td><td>1.576***<br>(0.0810)</td><td>1.079***<br>(0.160)</td><td>1.013***<br>(0.0819)</td></tr><tr><td>Comlang</td><td>1.158***<br>(0.163)</td><td>1.164***<br>(0.0531)</td><td>0.523***<br>(0.177)</td><td>0.505***<br>(0.0714)</td></tr><tr><td>Observations</td><td>29,619</td><td>32,218</td><td>38,446</td><td>41,381</td></tr><tr><td>R-squared</td><td>0.585</td><td>0.631</td><td>-</td><td>-</td></tr><tr><td>Donor FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Recipient FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Year FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Donor-year FE</td><td></td><td>x</td><td></td><td>x</td></tr><tr><td>Recipient-year FE</td><td></td><td>x</td><td></td><td>x</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor country (or donor-year) level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. The dependent variable is ODA commitments in log (Columns 1 & 2) or in level (Columns 3 & 4). All explanatory variables except Distance, Colony and Comlang are lagged one year. See the text for details.

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Foreign value added (FVA) estimates – Heterogeneity across donors.</figcaption></figure>
<table><caption>**Table 2**
Sectoral results.</caption><thead><tr><th colspan="2">Method: PPML-FE</th><th colspan="2">Dep. Var: ODA</th></tr></thead><tbody><tr><td>ln(FVA_Agr)</td><td>0.311***<br>(0.0595)</td><td>ln(FVA_ElecGas)</td><td>0.386*<br>(0.204)</td></tr><tr><td>ln(FVA_Fish)</td><td>-0.138***<br>(0.0441)</td><td>ln(FVA_Const)</td><td>0.231<br>(0.145)</td></tr><tr><td>ln(FVA_Mine)</td><td>0.0110<br>(0.0600)</td><td>ln(FVA_Repair)</td><td>0.180*<br>(0.0983)</td></tr><tr><td>ln(FVA_Food)</td><td>0.157***<br>(0.0417)</td><td>ln(FVA_Wtrade)</td><td>-0.0238<br>(0.153)</td></tr><tr><td>ln(FVA_Textile)</td><td>-0.0391<br>(0.0281)</td><td>ln(FVA_Rtrade)</td><td>-0.0232<br>(0.109)</td></tr><tr><td>ln(FVA_Wood)</td><td>-0.0905<br>(0.0704)</td><td>ln(FVA_Hotel)</td><td>-0.150**<br>(0.0753)</td></tr><tr><td>ln(FVA_Petrol)</td><td>-0.0247<br>(0.0866)</td><td>ln(FVA_Transp)</td><td>0.818***<br>(0.0991)</td></tr><tr><td>ln(FVA_Metal)</td><td>0.104<br>(0.0669)</td><td>ln(FVA_Pos)</td><td>-0.891***<br>(0.147)</td></tr><tr><td>ln(FVA_ElecMac)</td><td>0.244***<br>(0.0665)</td><td>ln(FVA_Fin)</td><td>-1.056***<br>(0.246)</td></tr><tr><td>ln(FVA_TranspEq)</td><td>0.254***<br>(0.0653)</td><td>ln(FVA_PubAdm)</td><td>-0.0612<br>(0.0923)</td></tr><tr><td>ln(FVA_OtherMan)</td><td>-0.106<br>(0.0647)</td><td>ln(FVA_Educ)</td><td>0.348***<br>(0.135)</td></tr><tr><td>ln(FVA_Recy)</td><td>-0.0351<br>(0.0435)</td><td></td><td></td></tr></tbody></table>

Notes: No. of observations included: 29,139. Robust standard errors are in parentheses and clustered at the donor-year level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. The dependent variable is ODA commitments in level, and the estimation method is PPML with multiple high-dimensional fixed effects (donor-year and recipient-year). Other explanatory variables (Distance, Colony, and Comlang) are included but not reported. The list of sectors is reported in Table 8 in the Appendix. See the text for details.

nonmetallic mineral products (FVA_Petrol) and for metal products (FVA_Metal). This finding contrasts with the results in Couharde et al. (2020) and Kruse et al. (2020) for oil, but these studies refer to resource trade or oil endowments, while we focus on value added trade flows.<fnref n="20" />

Overall, these sectoral estimates might indicate that the main channel through which foreign value added affects foreign aid is not related to the sourcing of raw materials but rather to the negotiation of lower prices of intermediate products. This interpretation is consistent with our theoretical model showing that the allocation of aid is motivated by the reduction in the price of imported inputs (see Section 5).<fnref n="21" /> The role of agriculture seems different from that of natural resources. Although GVCs in agriculture have expanded since the 1990s, agriculture still represents a small share of GVC-trade (World Bank, 2020). Moreover, the participation of developing countries in agriculture GVCs is mainly limited to the supply of a specific product, such as cocoa by Cote d'Ivoire or Ghana or coffee by Ethiopia or Uganda (World Bank, 2020). Our estimation results show that despite the small share of agriculture in GVC-trade, agriculture GVCs have a relatively large impact on the allocation of aid. It should be noted that global agricultural production is very dependent on certain producing countries for the import of chemical inputs, such as nitrogen fertilizers and phosphate-based products. Whether this specific dependence explains the allocation of foreign aid deserves further investigation using more disaggregated data.<fnref n="22" />

The estimation results for GVC-trade in services are more mixed. For some service sectors, we find a positive and large effect of GVC-trade (transport or education and health), while for others, we find either a negative or a nonsignificant effect. However, we do not want to put too much emphasis on these results because the data on services trade used to compute GVC indicators are generally more problematic than are those on goods trade (Johnson, 2018).

We further investigate specific characteristics of sectors, such as the level of competition in their supply chain or their strategic importance for donors. With regard to the level of competition, we expect donors to provide more aid in sectors of differentiated goods (where firms face less competition), such as manufactured goods or electronic products, in order to reduce the cost of these imported goods. In contrast, we expect less aid for imports of homogeneous products, such as raw materials and minerals, where there is more competition. In Fig. 6 in Appendix, we represent the correlation between the level of differentiation of products in our

<footnote n="20">²⁰ The difference in results might also come from a difference in the sample or in the model specification.</footnote>

<footnote n="21">²¹ Another possible explanation for the concentration of FVA positive estimates in differentiated sectors relates to contract enforcement. Several studies document the fact that contract enforcement issues are more problematic for differentiated goods than for goods with a reference price (e.g., Ranjan and Lee, 2007). This conclusion also conforms to the fact that GVCs often entail the exchange of customized inputs with contracts that are incomplete and difficult to enforce (Antrás and Chor, 2022).</footnote>

<footnote n="22">²² The Eora database that we use to compute GVC-trade has the largest country coverage but reports more aggregated sectors than do other global input-output tables (see Appendix B).</footnote>
<table><caption>**Table 3**

Robustness analysis – Alternative ODA measure.</caption><thead><tr><th>Method:</th><th>PPML-FE</th><th></th><th></th></tr><tr><th>Dependent Variable:</th><th>ODA Allocable Sect</th><th>AfT</th><th>ODA 3-y average</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td><strong>0.239</strong>***<br>(0.0405)</td><td><strong>0.242</strong>***<br>(0.0604)</td><td><strong>0.214</strong>***<br>(0.0419)</td></tr><tr><td>ln(Distance)</td><td>-0.707***<br>(0.0703)</td><td>-0.835***<br>(0.0858)</td><td>-0.718***<br>(0.104)</td></tr><tr><td>Colony</td><td>1.004***<br>(0.0791)</td><td>1.036***<br>(0.123)</td><td>1.024***<br>(0.128)</td></tr><tr><td>Comlang</td><td>0.513***<br>(0.0596)</td><td>-0.0178<br>(0.114)</td><td>0.523***<br>(0.107)</td></tr><tr><td>Observations</td><td>41,381</td><td>41,317</td><td>16,038</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td><td>x</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. FVA is lagged one year in Columns (1) and (2) and is computed as a three-year period average in Column (3). See the text for details.

Eora-26 sectors and the estimated sectoral coefficients on the FVA variable.<fnref n="23" /> This correlation is positive (correlation coeff=0.22). Electrical and machinery (elecmac) and transport equipment (transpeq) are both very differentiated sectors, and sectors for which FVA seems to matter in the allocation of aid. On the contrary, petroleum, chemical, and non-metallic mineral products (petrol), as well as mining and quarrying (mine), are mostly composed of subsectors with homogeneous goods for which we do not find any impact of FVA. Other sectors are in between, which suggests that other explanations might exist as well, such as the strategic importance of sectors for donor countries.

A way to examine the strategic importance of sectors for donors is to represent FVA embedded in the exports of top donors across sectors. This can help us to identify donor countries' dependence on imports of intermediate goods in certain sectors. For France, Germany, Japan, and the USA, the sectors that appear more frequently in the top five sectors are: 'Financial Intermediation and Business Activities', 'Mining and Quarrying', 'Petroleum, Chemical and Non-Metallic Mineral Products', and 'Electrical and Machinery' (see Fig. 7 in Appendix). In line with the predictions of our theoretical model, we expect that donors will provide relatively more aid for these sectors to reduce the cost of these imported goods. Our empirical results show that this effect is only confirmed for the sector of 'Electrical and Machinery'.

### 3.4. Robustness analysis

To test the robustness of our findings, we first investigate the effect of FVA for each recipient country. More precisely, we replace the variable FVA in our baseline model with 127 interaction variables measuring foreign value added in each recipient country. Reassuringly, we find that the FVA interacted coefficient is positive and significant at the 5% level for 51 of the 106 countries included in the estimation and negative and significant only for 16 countries. Moreover, this coefficient is positive and significant for those countries that have the largest GVC participation (China, India, Indonesia, Malaysia, and Algeria).<fnref n="24" />

Then, we test the robustness of our results with respect to the aid variable in Table 3. First, instead of total ODA commitments, we take ODA commitments in allocable sectors (CRS 450), which excludes de facto general budget support, food assistance, debt relief, humanitarian aid, and administrative costs of donors and flows to refugees in donor countries. Allocable assistance consists of flows that are allocated to specific sectors such as health, energy or agriculture, as well as multisector programs.<fnref n="25" /> This allocable assistance allows for a better estimate of the flows of aid commitments to developing countries and excludes unpredictable flows (such as humanitarian aid or debt relief). Therefore, we expect GVC-trade to have a larger effect on this category of aid. Even if the difference is probably not statistically significant, the FVA coefficient is larger in Column (1) of Table 3 compared to our baseline estimate (Column 4 of Table 1).

As an alternative to total ODA commitments, we also consider aid for trade (AfT) in Column (2).<fnref n="26" /> AfT has probably a more direct impact on the price of intermediate goods than does, for instance, aid for education or health. Here, again, the FVA coefficient is larger in Column (2) of Table 3 than in our baseline estimate (Column 4 of Table 1).

<footnote n="23">²³ We use Rauch (1999)'s product differentiation index and convert the SITC original classification into ISIC Rev.3 thanks to the algorithm developed by Liao et al. (2020) and Lenzen et al. (2013) for the correspondence between ISIC Rev.3 and Eora-26 sectors. Note that the Rauch classification is not available for services.</footnote>

<footnote n="24">²⁴ The overall estimation results are reported in Appendix (Table 9).</footnote>

<footnote n="25">²⁵ The total allocable sector category (CRS 450) includes the following four subcategories: social infrastructure and services (education, health, etc.; CRS 100), economic infrastructure and services (transport, communication, energy, etc.; CRS 200), production sectors (agriculture, industry, etc.; CRS 300), and multiple sectors (CRS 400).</footnote>

<footnote n="26">²⁶ The definition and identification of AfT is based on the recommendations of the WTO Aid for Trade Taskforce. According to this definition, AfT corresponds to five categories: Technical assistance for trade policy and regulations, Economic infrastructure, Productive capacity building, Trade-related adjustment, and Other trade-related needs. The OECD provides CRS codes that can be used as proxies for the first four categories.</footnote>
<table><caption>**Table 4**

Robustness analysis – Additional controls.</caption><thead><tr><th rowspan="2">Method:<br>Dependent Variable:</th><th colspan="4">PPML-FE<br>ODA</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td><strong>0.117</strong>***<br>(0.0351)</td><td><strong>0.215</strong>***<br>(0.0322)</td><td><strong>0.186</strong>***<br>(0.0341)</td><td><strong>0.096</strong>***<br>(0.0368)</td></tr><tr><td>ln(Distance)</td><td>-0.354***<br>(0.0782)</td><td>-0.686***<br>(0.0683)</td><td>-0.528***<br>(0.0585)</td><td>-0.228***<br>(0.0690)</td></tr><tr><td>Colony</td><td>0.737***<br>(0.0820)</td><td>1.011***<br>(0.0819)</td><td>1.012***<br>(0.0798)</td><td>0.750***<br>(0.0799)</td></tr><tr><td>Comlang</td><td>0.460***<br>(0.0669)</td><td>0.506***<br>(0.0714)</td><td>0.516***<br>(0.0705)</td><td>0.470***<br>(0.0657)</td></tr><tr><td>Exports</td><td>0.303***<br>(0.0298)</td><td></td><td></td><td>0.290***<br>(0.0304)</td></tr><tr><td>Comcur</td><td></td><td>-0.135<br>(0.121)</td><td></td><td>0.103<br>(0.138)</td></tr><tr><td>RTA</td><td></td><td></td><td>0.595***<br>(0.0988)</td><td>0.524***<br>(0.106)</td></tr><tr><td>Observations</td><td>41,381</td><td>41,381</td><td>41,381</td><td>41,381</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td><td>x</td><td>x</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$.

All explanatory variables, except Distance, Colony, Comlang and Comcur, are lagged one year. See the text for details.

Furthermore, instead of annual ODA commitments, we take the average over three-year periods for two reasons. First, commitments better reflect the intentions of donors but are recorded in the full amount of the transfer, regardless of the time required to complete disbursements. Second, annual aid flows are generally volatile, and taking a three-year average allows us to reduce this volatility (Barthel et al., 2014). The estimation results on three-year averages are reported in Column (3) of Table 3. Even if the number of observations is reduced, we obtain very similar results.

To further test the robustness of our results, we introduce additional bilateral variables that might be related to the amount of foreign value added and, at the same time, influence the allocation of aid. First, we account for the dummy variables for common currency (Comcur) and regional trade agreement (RTA) (Columns 2 and 3).<fnref n="27" /> The use of a common currency or the existence of a RTA might play a role in the allocation of aid. Moreover, RTAs are one way in which to reduce trade barriers among member countries. Consequently, one would expect that GVCs are particularly active among countries that have signed RTAs. Indeed, GVC activity is particularly intense among EU and ASEAN members, but it should be noted that other RTAs, such as Mercosur, appear to be much less successful in generating GVC participation among its members (World Bank, 2020, ch. 2).

Bilateral exports are a natural candidate for a bilateral variable. Several studies have shown that donor countries use aid to promote their own exports (e.g., Younas, 2008; Barthel et al., 2014). Therefore, we add in our baseline model the variable (Exports) measuring bilateral exports from donor to recipient countries (Column 1 of Table 4).<fnref n="28" />

In the last column (Column 4) of Table 4, we add all the variables together. The coefficient on the FVA variable is lower than that in the baseline results (Column 4 of Table 1), particularly when we add the export variable (Column 1);<fnref n="29" /> however, it remains statistically and economically significant. In Appendix, we also reproduce the comprehensive results from our baseline Tables (Tables 1 and 2) when we add bilateral exports. These estimation results confirm that there is an additional dependence through FVA even when we control for bilateral exports (see Tables 10 and 11). This is especially true for certain differentiated sectors, such as electrical and machinery, and transport equipment. In the next section, we further investigate the risk of endogeneity due to omitted variables, reverse causality and measurement errors.

For our baseline estimation, as Cardwell and Ghazalian (2018), we have opted for one-way clustering of the standard errors at the donor level. From a statistical perspective, this one-way clustering is also the most conservative option, as standard errors are clustered at the country level, which has the smallest sample size (22 donors instead of 127 recipients), leaving fewer degrees of freedom. In Appendix, we provide robustness tests with different structures of one-way clustering, at the bilateral (donor-recipient) level and at the recipient level (see Table 12). In all cases, our variable of interest (FVA) is statistically significant at the 1% level.<fnref n="30" />

<footnote n="27">²⁷ Two other potential omitted variables that might be related to the allocation of aid are foreign direct investment (FDI) and remittances. However, data are generally not available at the bilateral level for a large sample of countries and a long period of time.</footnote>

<footnote n="28">²⁸ We use the inverse hyperbolic sine (IHS) transformation for the export variable because it is defined for zero values (representing 4% of the sample), is similar to a log and can be interpreted as percentage impacts, except for small values of the export variable.</footnote>

<footnote n="29">²⁹ This is probably due to the fact that bilateral exports of donors to recipient countries are highly correlated with the imported inputs embedded in donors' overall exports.</footnote>

<footnote n="30">³⁰ While two-way clustering at the donor and recipient dimensions would allow for more robust variance of the standard error, we do not include it, as the estimates produced in Stata for two-way clustering may have certain limitations, depending on the nature of the data (in particular, if choos are multiplicative and not additive) (Davezies et al., 2018).</footnote>
Weidner and Zylkin (2021) show that three-way fixed effects PPML models may suffer from bias due to the incidental parameter problem. They propose a correction based on Taylor expansions to the point estimates and standard errors. There is a similar issue with the two-way model. In this case, the incidental parameter problem affects standard errors and not point estimates. This correction can be applied but necessitates a different cluster than the one currently used in the paper. Instead of clustering at the donor-year level, we need to cluster at the bilateral (donor–recipient) level. In Table 13 in Appendix, we report the results of our baseline model (corresponding to Table 1, column 4) with and without the bias correction (clustering at the bilateral level). As expected, standard errors increase, but our main coefficient of interest is still significant at conventional levels.

Finally, to test the robustness of our results, we isolate the effect of the FVA of a particular recipient on the aid directed toward that country, in order to exclude potential external influences that are shared across all recipient countries of a specific donor country. To do so, we use a control function approach in two steps. In a first step, we regress bilateral foreign value added (in log) on the average FVA of other recipients of a given donor (with donor-year and recipient-year fixed effects). In a second step, we estimate our baseline model with a PPML estimator and replace bilateral FVA with the residuals from the previous estimation. This allows us to focus solely on the recipient-specific effect and eliminate influences that are common to all recipient countries.<fnref n="31" /> The second step is estimated with bootstrapped standard errors (100 replications). Estimation results of this additional robustness test are reported in Table 14 in Appendix C. Residuals from the first step are statistically significant, positive and close to our baseline estimates.

## 4. Endogeneity

The relation between FVA (or, more generally, trade) and ODA might be plagued by endogeneity problems. The usual suspects are omitted variables, measurement errors and reverse causality. Our preferred specification absorbs all time-varying country characteristics through fixed effects, but time-varying bilateral unobservable factors might still introduce bias. Examples of omitted factors are FDI or remittances, which can affect both ODA and FVA. These flows are bilateral ones and thus cannot be controlled for using time-varying country fixed effects.<fnref n="32" /> The relation between aid and trade may go in either of the two directions and create a reverse causality bias (Suwa-Eisenmann and Verdier, 2007). Our theoretical framework, that is presented in the next section, also points to a two-way relationship between the share of foreign value added in exports and foreign aid. Furthermore, the construction of global input-output tables used to measure the GVC-related trade variable generally entails several assumptions and mathematical optimization algorithms, which might introduce some measurement errors (see Appendix B for details). In the case of reverse causality, our baseline estimator potentially overestimates the impact of FVA on aid, whereas with omitted factors and measurement errors, the OLS estimates may be upward or downward biased.<fnref n="33" />

Endogeneity issues are generally tackled with the use of an instrumental variable estimator. However, this estimator cannot be used here because the second-stage specification is nonlinear and includes high-dimensional fixed effects (donor-year and recipient-year).<fnref n="34" /> As an alternative strategy, we adopt a two-stage residual inclusion (2SRI) control function approach. This approach is essentially an instrumental variable method where the residuals from a first-stage estimation are included in the baseline model. These residuals should capture all the endogenous components of our variable of interest (FVA) and leave out only the exogenous component predicted by the instrument. Wooldridge (2015) provides a comprehensive overview of control-function regression techniques (see also Lin and Wooldridge, 2019).

We propose a shift-share approach to derive an instrumental variable for FVA. Shift-share instruments are computed as a weighted average of a set of shocks, with weights reflecting exposure shares (e.g., Borusyak et al., 2022). In Eq. (4) describing the construction of the shift-share instrument ($z_{ijt}$), $Sh_{ijst}$ is the share of sector $s$ in bilateral FVA in $t-5$ (i.e., five years before the current observation) and $W_{ijst}$ is (the log of) the sum of FVA in one sector $s$ from all recipient countries (except $j$) in all donors' exports (except $i$) (equivalent to 'world demand' for the sector $s$).<fnref n="35" />

$$z_{ijt} = \sum_{s=1}^{23} Sh_{ijst} W_{ijst}, \quad (4)$$

Similar 'Bartik-style' instruments have been used in a large number of studies (e.g., Autor et al., 2013).<fnref n="36" /> The results from the control function approach are presented in Table 5. In Column (1), we report the baseline estimate without controlling for

<footnote n="31">³¹ Such common factors may include cyclical fluctuations specific to the donor country or localized effects impacting certain groups of recipient countries (e.g., a heatwave affecting a particular region).</footnote>

<footnote n="32">³² Buelens and Tirpák (2017), for instance, provide evidence on a sample of 40 (mainly developed) countries of a positive association between bilateral FDI stock and countries' participation in GVC measured as the import content of bilateral exports.</footnote>

<footnote n="33">³³ In particular, if we think of bilateral FDI (or remittances) as a potential omitted factor, then the bias ultimately depends on the relationship between FDI (or remittances) and aid, which is both theoretically ambiguous and empirically debated (e.g., Harms and Lutz, 2006; Donabauer et al., 2016).</footnote>

<footnote n="34">³⁴ As argued by Angrist and Pischke (2009), “a forbidden regression crops up when researchers apply 2SLS reasoning directly to nonlinear models”. Most discussions have focused on the case of a nonlinear first stage, where the endogenous explanatory variables are, for instance, discrete (e.g. Wooldridge, 2014). But the problem also arises for models with a non-linear second stage.</footnote>

<footnote n="35">³⁵ For instance, for France-Vietnam ($ij$) in the agriculture sector, we compute the total FVA in agriculture coming from all countries (except Vietnam) embedded in the exports of all donors (except France). Therefore, we exclude not only the bilateral FVA in agriculture from Vietnam embedded in French exports but also the FVA in agriculture from Vietnam into other countries and that from all other countries embedded in French exports.</footnote>

<footnote n="36">³⁶ Jaeger et al. (2018) propose a “multiple instrumentation” procedure where they use multiple lags of the shock (“shift”) variable instrumented with lags of the standard shift-share instrument used in the literature in labor economics. They show that this simple solution allows them to estimate separately the short- and long-run effects of immigration (increased labor supply) on wages.</footnote>
endogeneity on a comparable sample. Our shift-share instrument is found to predict bilateral FVA in the first stage (bottom part of Col. 2 in Table 5). In the second stage, where we include the residuals from the first stage, we find a positive and significant coefficient for the FVA variable (Col. 2). The 2SRI point estimate appears larger than the corresponding (non-instrumented) coefficient obtained on the same sample (Col. 2 vs. 1), even if the difference is not statistically significant.<fnref n="37" />

Several recent studies discuss the identifying assumptions underlying the use of Bartik-like instruments (e.g., Goldsmith-Pinkham et al., 2020; Borusyak et al., 2022). A necessary condition for identification is that the (lagged) shares of each sector in bilateral FVA are random across countries or that the shocks are random across sectors. Because the first assumption is more debatable, we perform an additional test.<fnref n="38" /> We check that these shares are not highly correlated with country characteristics (see Goldsmith-Pinkham et al., 2020). To do so, we regress the share of each sector computed in 2000 on country characteristics (GDP of the donor, GDP per capita, population and institutional quality of the recipient, bilateral distance and colony and common language dummy variables) in 2005 for each of the 23 sectors. The results reproduced in Table 15 in Appendix show that R-squared and F-statistics are generally small and estimated coefficients are also small and insignificant or marginally significant.<fnref n="39" /> We undertake some complementary graphical analysis. We compute simple correlation graphs between sector-specific shares and the GDP of the donor country. These graphs reported in Appendix (Figs. 8 and 9) show no specific patterns in this relationship. This is also a reassuring signal in favor of the exogeneity of sector-specific shares.

We provide two complementary tests. First, we compute some placebo shocks in the spirit of He and Mei (2024). More precisely, we interact our sector-specific shares with placebo sector shocks drawn from a random normal distribution using the same mean and variance (0 and 5) than (He and Mei, 2024) and Adão et al. (2019). Then, we use this instrument instead of our shift-share in our control function regression. We iterate this procedure 100 times and keep coefficients on our FVA variables. We expect a non-significant coefficient for our variable of interest using these placebo shocks. Indeed, when using this instrument, coefficients on the FVA variable turn insignificant at the 5% level in 85% of the estimations.<fnref n="40" /> This test provides some reassurance that our estimates are not overly sensitive to the choice of sector-specific shares. Second, regarding the shift component, bias may result from artificial correlations between the sector-level shocks and the outcome variable. This may happen for instance during a global crisis. In an additional robustness check, we remove the two years from our sample (2007 and 2008) corresponding to the global financial crisis. Estimation results are displayed in columns (1) and (2) of Table 17 in Appendix C. In column (1) we report the corresponding (non instrumented) coefficient on the same sample. In column (2), when we account for endogeneity using a 2SRI control function, we still find a positive and significant (at the 5% level) effect for our variable of interest (FVA). In columns (3) and (4), we adopt a more conservative approach and exclude three years of our sample (2007, 2008 and 2009).

Even if these 2SRI results should be interpreted with caution because of their relative imprecision, they remain consistent with our baseline PPML estimates and suggest that the effect of FVA on the allocation of ODA is robust to potential endogeneity bias.

In the next section, we develop a simple theoretical model that provides a rationale for our empirical findings.

## 5. Theoretical framework

Our simple model is based on Antràs and De Gortari (2020) and provides a rationale for the existence of monetary transfers among countries in the context of GVCs. A global value chain indicates that the final product is produced on intermediate goods that are produced on other intermediate goods, constituting a chain of production for which each element creates a value. Their question is to determine the best location for each stage in the value chain. They show in the partial equilibrium version of the model that the monopolistic firm in the downstream country at the end of the chain can choose the optimal path.

We borrow the same analytical framework, but for the sake of simplicity, we focus on the last links in the chain: the country of the supplier firm (upstream country) and the country of the final good firm (downstream country). We show that these two countries have an interest in signing a cooperative agreement that stipulates the payment of monetary transfers from the downstream country to the upstream country, allowing the latter to grant subsidies to its firm. We show that owing to these subsidies, it is possible to obtain a final good price that is lower than the price that would be obtained in the absence of such an agreement. Assuming imperfect competition, higher positive profits appear for each monopoly supported by a national export lobby that defends the interests of the firm with its respective government. A monetary transfer that reduces the supplier's production costs leads to higher monopoly revenues and positive profits, which also benefit the lobbies and ruling political parties in both countries.

The framework of Antràs and De Gortari (2020) is relevant here because the existence of a value chain implies that firms in the chain that are downstream outsource their production of intermediate goods along the chain, and the value added at each stage can be easily derived. As in Antràs and Helpman (2004), we assume that there are negotiations between upstream and downstream producers. Differently from Antràs and Helpman (2004), we assume that negotiations between firms move to the level of the states

<footnote n="37">³⁷ The two confidence intervals overlap. The estimate on the FVA variable in our preferred specification on the same sample is 0.172 (column 1 of Table 5), and the corresponding 95% confidence interval is: [0.117; 0.227]. In our 2SRI specification, when we bootstrap standard errors, we obtain a point estimate of 0.326 and a corresponding 95% confidence interval [0.042; 0.610] (column 2 of Table 5).</footnote>

<footnote n="38">³⁸ We also compute our shift-share instrument using a longer lag (seven instead of five years) for the share of sector *s* in bilateral FVA (*Sh*<sub>jst</sub>). The estimation results (available upon request) using this alternative shift-share instrument are quite similar compared to the baseline result.</footnote>

<footnote n="39">³⁹ More specifically, these results indicate that country characteristics explain generally less than 10% of the variance of sector-specific shares (sometimes more for service sectors) (see Table 15 and Table 16 in Appendix C).</footnote>

<footnote n="40">⁴⁰ It is close to zero in average (mean 2.55) with a large standard deviation (39.66) and the median is about 0.71.</footnote>
<table><caption>**Table 5**
2SRI control function estimates.</caption><thead><tr><th>Method:</th><th colspan="2">PPML-FE</th></tr><tr><th>Dependent Variable:</th><th>ODA</th><th></th></tr><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td>0.172***<br>(0.0279)</td><td>0.326**<br>(0.145)</td></tr><tr><td>ln(Distance)</td><td>-0.708***<br>(0.0658)</td><td>-0.703***<br>(0.0705)</td></tr><tr><td>Colony</td><td>0.963***<br>(0.0924)</td><td>0.973***<br>(0.0929)</td></tr><tr><td>Comlang</td><td>0.546***<br>(0.0761)</td><td>0.543***<br>(0.0827)</td></tr><tr><td>Residuals ShiftShare</td><td></td><td>-0.162<br>(0.139)</td></tr><tr><td>Observations</td><td>29,850</td><td>29,850</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td></tr><tr><td></td><td colspan="2">First stage<br>Dep. var: ln(FVA)</td></tr><tr><td></td><td>(1)</td><td>(2)</td></tr><tr><td>ShiftShare</td><td></td><td>1.131***<br>(0.098)</td></tr><tr><td>KP Wald F-stat</td><td></td><td>132.56</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. All explanatory variables, except Distance, Colony and Comlang, are lagged one year. In Column (1), FVA is not instrumented. Column (2) reports the first-stage and second-stage control function estimates. Bootstrapping methods are used in the second stage (500 replications). See the text for details.

and that lobbies play the role of intermediaries for firms to obtain transfers as subsidies. These transfers are negotiated to support the exporting firms of the donor country by reducing the cost of their imported inputs.

We further assume that these transfers allow the recipient country to impose a lower customs tariff on imported inputs than on imports of consumer goods (foreign aid as a disguised subsidy granted to the upstream firm). This assumption is supported in the data by a positive correlation between per capita aid receipts and the average tariff differential (tariff on final goods minus tariff on inputs) set by recipient countries, as shown in Fig. 5.<fnref n="41" /> As shown in this figure, the countries that set the highest tariff differential on their imports tend to receive more per capita ODA. We also examine the same relationship between per capita ODA receipts and the mean tariff differential for the top four donors in Fig. 10 in Appendix C. Apart from Japan, we consistently find a positive correlation between the two variables: top donors provide more aid to those countries that set the largest tariff differential (tariff on final goods minus tariff on inputs) on their imports on average. In a similar vein, Lahiri and Raimondos-Møller (1997) demonstrated, in a three-country model, that foreign aid tied to a trade reform in the recipient country can eliminate the transfer paradox. In such a scenario, aid tied to tariff cuts from the recipient country can result in a Pareto improvement. With regard to the welfare of the recipient country, if the pre-reform tariff is higher than the optimal one, the efficiency gains permitted by the tariff cut compensate for the welfare loss resulting from negative terms of trade (higher import price for the recipient country).

## 5.1. Model presentation

We assume that *n* countries indexed by *i* or *j* produce an intermediate or a final good using a local composite factor incorporating the factors of production (labor and capital) and a bundle of local and imported intermediate inputs. The cost of the composite factor is $c_i$. A final good of consumption is produced after *n* stages of production, with each stage being denoted by *i* or *j*. Therefore, unfinished goods are progressively transformed and circulate among countries depending on two factors: the local cost of production of each country and the transportation cost between the two countries. Transportation cost $\tau_{i,j} \ge 1$ from country *i* to country *j* denotes the units of good produced in country *i* for country *j* to receive one unit of that good (iceberg trade coefficient). The whole production process for each final good follows a path among countries. All along the path, the total costs (production and transportation costs) must be minimized. The optimal path among countries is denoted by a series, *l*, of countries $l = l(1), l(2), ..., l(n)$. The numbers *i*, *j* = 1, ..., *n* here indicate the stage of production. The cost of an intermediate good at stage *n* transported and delivered from country *l*(*n* − 1) to country *l*(*n*) is as follows:

$$p_{l(n-1)}^{n-1} \tau_{l(n-1)} l(n) \quad (5)$$

<footnote n="41">⁴¹ To construct this figure, we combine our data on foreign aid coming from the OECD with tariff data from Shapiro (2021). The tariff database contains data on approximately 40 countries, most of which are developed countries. Only seven countries available in this database are receiving aid.</footnote>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Mean tariff differential and total per capita ODA receipts (in log).</figcaption></figure>

Notes: Total per capita ODA receipts are on the vertical axis and the mean tariff differentials (tariff on final goods - tariff on inputs) are on the horizontal axis. Both variables are in logs and for 2007.

Data source: OECD and Shapiro (2021). See the text for details.

where $p_{(n-1)}^{n-1}$ is the unit cost of production in country $l(n-1)$ of intermediate good $n-1$ in optimal path $l$.

In the model of Antràs and De Gortari (2020), in partial equilibrium, each firm in the value chain produces an intermediate (or final) good with a Cobb-Douglas constant-returns-to-scale technology, which combines a local input, denoted by $I_L^n$, and an imported input (intermediate good), denoted by $I_I^n$:

$$y^n = (I_L^n)^{a_n} (I_I^n)^{1-a_n} \qquad (6)$$

where $y^n$ is the quantity of the intermediate good produced at the final stage $n$ of production, with elasticity $\alpha_n < 1$.

The local input production costs are given by the following:

$$a_{l(n)}^n c_{l(n)} \qquad (7)$$

where $a_{l(n)}^n$ is the unit factor requirement at stage $n$ in country $l(n)$.

The production cost of the imported input is given by Equation (5). This price takes into account the cost of transport $\tau_{l(n-1)l(n)}$. Consequently, the price of the final good is as follows:

$$p^n = (a_{l(n)}^n c_{l(n)}^n)^{a_n} (p_{l(n-1)}^{n-1} \tau_{l(n-1)l(n)})^{1-a_n} \qquad (8)$$

It is therefore reasonable to argue that the downstream firm will seek to reduce the price of its intermediate good ($n-1$) by reducing the price of its imported input.

In order to demonstrate the plausibility of a transfer from the downstream country (firm) to the upstream country (firm), which would result in a reduction in price, it is necessary to examine the final stage of production (for the final good), denoted by $n$. We assume that each firm is supported by a national export lobby that defends the interests of the firm with its respective government. Governments will jointly seek to increase the profit of their firm as well as consumer surplus coming from the consumption of the final good. The government of the upstream firm will subsidize its firm by reducing the price of its imported input. The upstream firm can therefore lower its price, so does the downstream firm. The channels of aid allocation are complex. Based on empirical observations (Shapiro (2021)), we assume that aid results in a decrease in the tariff applied by the recipient country to the imported intermediate good compared to the tariff of the consumption good, creating a gap between the two tariffs.

Moreover, in our model, governments in both countries are politically motivated, i.e., politicians are serving not only the public interest but also their own private interest. Based on the lobbying model of Grossman and Helpman (1994), we implicitly assume that the export business lobby in each country offers electoral campaign contributions in exchange for foreign aid policy. The objective of the lobby group is to promote the exports of domestic firms in world markets.

This form of lobbying resembles collective lobbying that can be undertaken by a trade association. We do not explicitly model the details of lobbying activity (demand and supply of lobbying), but the economic mechanisms we propose would apply to both individual and collective lobbying. The existing empirical evidence (Bombardini and Trebbi (2012), Bombardini and Trebbi (2020), Kim and Osgood (2019), Huneeus and Kim (2018), and Saha (2020)) indicates that the majority of firms (regardless of size) engage in collective lobbying, and large firms can adopt a dual strategy of both individual and collective lobbying.
The role of lobbies is well documented in the literature on trade agreements. Protrade business interests are known to have played a significant role in the expansion of trade agreements into areas beyond trade, such as the protection of intellectual property rights pushed by pharma companies or trade in services pushed by financial firms (Rodrik, 2018a and Rodrik, 2018b). Empirical work based on data on US firms (Kim, 2017 and Blanga-Gubbay et al., 2025) reveals that exporting firms are more likely to lobby to reduce tariffs and that multinational firms are more likely to lobby on trade agreements. Moreover, Shapiro (2021) shows that trade policy differs along the supply chain and argues that this pattern is consistent with lobbying influence: firms may lobby for higher tariffs on their final goods and for lower tariffs on those goods they use as inputs. There is also anecdotal evidence of lobbying for foreign aid by business lobbies from donor countries (Anwar, 2006; Michaelowa, 1996; Sogge, 2015). Ravi Kanbur's experience from Ghana (Kanbur, 2000), which motivates the work of Villanger (2006), demonstrates the pressure from companies that can influence aid disbursements. An investigation conducted by two French medias (Disclose and Mediapart) suggests that French foreign aid, channeled through the French Development Agency (AFD) between 2015 and 2019, primarily benefited French companies. Using a firm-level analysis, Chauvet and Ehrhart (2018) show a positive effect of foreign aid on firm sales' growth in recipient countries.

## 5.2. Model resolution

Our theoretical model is a two-stage dynamic model. In the first stage, countries negotiate the amount of monetary transfers to obtain a lower price for the imported input and increase the profits of both firms. In the second stage, knowing the levels of transfer, the firms determine optimal quantities and prices. We solve the model by backward induction. We start by analyzing the second stage of the game.

### Monopoly of the final good (downstream firm)

Suppose that the demand curve of the monopoly that produces the final good is written as follows:

$$y^n = A_n(p^n)^{-\epsilon_n} \qquad (9)$$

where $A_n$ is a constant parameter and $\epsilon_n > 1$ represents the price elasticity of demand.

The profit of the downstream monopoly is given by the following:

$$\pi^n = p^n y^n - (a_{l(n)}^n c_{l(n)}) I_L^n - (p_{l(n-1)}^{n-1} \tau_{l(n-1)l(n)}) I_I^n \qquad (10)$$

Profit maximization then leads to the monopoly price, which is written as follows:

$$p^n = B_n(a_{l(n)}^n c_{l(n)})^{\alpha_n} (p_{l(n-1)}^{n-1} \tau_{l(n-1)l(n)})^{1-\alpha_n} \qquad (11)$$

with the constant parameter $B_n$:

$$B_n = \frac{1}{\frac{(\epsilon_{n-1})}{\epsilon_n} (\alpha_n)^{\alpha_n} (1 - \alpha_n)^{1-\alpha_n}} \qquad (12)$$

By expressing the factor demands $(a_{l(n)}^n c_{l(n)}) I_L^n$ and $(p_{l(n-1)}^{n-1} \tau_{l(n-1)l(n)}) I_I^n$ as a function of $p^n y^n$, the monopoly profit is then rewritten at the optimum as follows:

$$\pi^n = p^n y^n \left( \frac{1}{\epsilon_n} \right) \qquad (13)$$

where,

$$p^n y^n = A_n(p^n)^{1-\epsilon_n} \qquad (14)$$

Note that the demand for the imported input derived from the maximization of the monopoly profit is given by:

$$I_I^n = y^{n-1} = A_{n-1}(p^{n-1})^{-\epsilon_{(n-1)}} \qquad (15)$$

with,

$$A_{n-1} = A_n(\tau_{l(n-1)l(n)})^{-\epsilon_{n-1}} \frac{\epsilon_n - 1}{\epsilon_n} (1 - \alpha_n) B_n^{1-\epsilon_n} (a_{l(n)}^n c_{l(n)})^{\alpha_n} (1-\epsilon_n) \qquad (16)$$

$$\epsilon_{(n-1)} = 1 - (1 - \alpha_n)(1 - \epsilon_n) \qquad (17)$$

### Monopoly of the intermediate good (upstream firm)

It is assumed that the government of stage n pushed by an export lobby (which protects its monopoly) transfers funds to the upstream country of stage n-1 also pushed by its lobby (which also protects its monopoly). We assume that the transfer makes it possible to finance a tariff reduction s on the importation of intermediate goods of the country n-1 compared to the tariff t previously applied to all imported goods, as suggested by the positive correlation between transfer and tariff differential highlighted in Fig. 5. Foreign aid allows for tariff relief required by the upstream country (as a disguised subsidy granted to the upstream firm). All imported goods of the country n-1 are subject to taxation by t, although the intermediate goods benefit from a tariff cut by s.

Consequently, we assume that the price of the imported inputs that firm n-1 must use is:

$$p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t - s. \qquad (18)$$
where *t* represents an ad valorem tariff for all imported goods (consumption and intermediate goods), and *s* represents an ad valorem subsidy which reduces the tariff on the imported intermediate good. For simplicity, as in Lahiri and Raimondos-Møller (1997), we assume that country *n* (the donor country) does not apply any tariffs.

The profit of the upstream monopoly is equal to the following:

$$ \pi^{n-1} = p^{n-1} y^{n-1} - (a_{l(n-1)}^{n-1} c_{l(n-1)}) I_{L}^{n-1} - (p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + (t-s)) I_{I}^{n-1} \quad (19) $$

Profit maximization then leads to the following monopoly price:

$$ p^{n-1} = B_{n-1} (a_{l(n-1)}^{n-1} c_{l(n-1)})^{\alpha_{n-1}} (p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + (t-s))^{1-\alpha_{n-1}} \quad (20) $$

with the constant parameter $B_{n-1}$:

$$ B_{n-1} = \frac{1}{\left(\frac{\epsilon_{(n-1)-1}}{\epsilon_{(n-1)}}(\alpha_{n-1})^{\alpha_{n-1}}(1 - \alpha_{n-1})^{1-\alpha_{n-1}}\right)} \quad (21) $$

By expressing the factor demands as $(a_{l(n-1)}^{n-1} c_{l(n-1)}) I_{L}^{n-1}$ and $(p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + (t-s)) I_{I}^{n-1}$ as a function of $p^{n-1} y^{n-1}$, the upstream monopoly profit is then rewritten at the optimum as follows:

$$ \pi^{n-1} = p^{n-1} y^{n-1} \left( \frac{1}{\epsilon_{(n-1)}} \right) = \frac{A_{n-1}}{\epsilon_{(n-1)}} (p^{n-1})^{1-\epsilon_{(n-1)}} \quad (22) $$

with,

$$ p^{n-1} y^{n-1} = A_{n-1} (p^{n-1})^{1-\epsilon_{n-1}} \quad (23) $$

and $p^{n-1}$ from Eq. (20).

We define the value added of the producer at stage $n-1$ as the revenue minus the consumption of intermediate good:

$$ V A^{n-1} = p^{n-1} y^{n-1} - (p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t-s) I_{I}^{n-1} \quad (24) $$

$$ \Leftrightarrow V A^{n-1} = A_{n-1} (p^{n-1})^{1-\epsilon_{n-1}} \left(1 - \alpha_{n-1} \left(1 - \frac{1}{\epsilon_{n-1}}\right)\right) \quad (25) $$

**Proposition 1.** At the (economic) optimum of the producers *n* et *n* − 1, the value added of the recipient country (of stage *n* − 1) depends positively on transfers *s*.

**Proof.** see Appendix A.

The interpretation is simple: the transfer (subsidy) *s* reduces the price of the monopoly *n* − 1 which increases its revenue, because the demand elasticity $\epsilon_{n-1}$ is greater than 1. This in turn increases its value added. In addition, the reduction in the price of the monopoly *n* − 1 decreases the marginal cost of the monopoly *n* which in turn reduces the price of the monopoly of final good *n*. We can also show that the profits of both monopolies *n* and *n* − 1 depend positively on $VA^{n-1}$, and thus on transfers *s* (see Appendix A).

We now turn to the first stage of the game. In the first-stage of the model, as their incentives align, the two politically motivated governments will seek to negotiate a bilateral cooperative agreement allowing for the allocation of foreign aid for export purposes. Given that the model is in partial equilibrium, governments in both countries maximize the objective function *V* which is the weighted sum of consumer payoff (which is the difference between consumer surplus and the welfare loss of the transfer), and the profits of downstream and upstream firms. The consumer surplus is equal to $\frac{\epsilon_n}{\epsilon_{n-1}}\pi^n$, calculated from the demand function $y_n = A_n p_n^{-\epsilon_n}$ of the donor country of the final good. The welfare loss is $WL(s)$ which is associated with the payment of monetary transfers by the donor country due to the funding and political cost of aid giving. This can raise the question of funding but in this partial equilibrium model, the budget does not need to be balanced. So the objective function of the governments is:

$$ V = \frac{\epsilon_n}{\epsilon_n - 1} \pi^n - W L(s) + \lambda_n \pi^n + \lambda_{n-1} \pi^{n-1} \quad (26) $$

where parameters $\lambda_n > 0$ and $\lambda_{n-1} > 0$ represent the relative weights attached to campaign contributions.<fnref n="42" />

The welfare loss $WL(s)$ is set arbitrarily. We assume it an increasing and strictly convex function. We consider the following welfare loss function:

$$ WL(s) = a + \frac{b}{2}s^2 \quad (27) $$

with $a > 0$ and $b > 0$.

To obtain a Pareto-improving agreement in this case, it suffices to consider the maximum of the consumer payoff function $\frac{\epsilon_n}{\epsilon_{n-1}}\pi^n - WL(s)$ reached for a value $s^*$. This function is concave in *s* increasing and then decreasing. Any value higher than $s^*$ would degrade the payoff of the consumers of the donor country and would thus not be politically stable to a domestic political pressure.<fnref n="43" />

<footnote n="42">⁴² We follow Goldberg and Maggi (1999) solution to the Grossman-Helpman lobbying model (Grossman and Helpman, 1994) and assume that the government and lobby reach an efficient bargain.</footnote>

<footnote n="43">⁴³ If we had considered the two groups of agents, the donor and recipient country as in Appendix A, the Pareto improving subsidy would be greater than $s^*$.</footnote>
**Proposition 2.** (a) The maximum value of the function $V$ that is Pareto optimal for both donor and recipient countries is that which maximizes the payoff function of the consumers of the donor country; (b) At the (political) optimum of the governments, the value added of the recipient country depends positively on transfers $s$, if and only if $2s < p_{t(n-2)}^{n-2} \tau_{t(n-2)t(n-1)} + t$ (condition S).

**Proof.** Regarding point (a), it suffices to show that the profits of the monopolies of the donor and recipient country are an increasing and convex function of $s$. See Appendix A for (a) and (b).

Regarding Proposition 2a, although Pareto optimal for both countries, the long-term stability of such an agreement is questionable. Indeed, we can rationalize the cooperative choice between players as a Nash equilibrium of a dynamic game, which is a basic game repeated infinitely (possibly with an exogenous probability of stopping at the end of each basic game). The basic game considered in Appendix A has been streamlined to involve only two players: the donor and recipient countries. It is understood that the two governments will negotiate at each stage an optimal subsidy (which is greater than $s^*$ with three players). Once the agreement has been signed, one or both governments may choose to deviate from their initial commitment. Each deviation is individually profitable, as the monopoly (recipient of the subsidy) is able to maintain a high price for its intermediate good due to its monopoly position. Conversely, the donor country may not provide the transfer if the supplier's price has already been reduced. In the event of a deviation by either of the two players, the agreement will not be signed at the subsequent stage (this is the “trigger strategy”). In this analytical framework, if the discount rate between two periods is sufficiently low, meaning that future gains retain significant value, the players have an interest in following the agreement. This is a Nash equilibrium but with payoffs on the Pareto frontier (“Folk Theorem” with Friedman’s (1971) version, for example). In reality, this means that if an agreement is not respected by at least one of the two parties, the donor and/or recipient country will look for other trading partners.

**Proposition 2b** indicates that for a low subsidy level, countries have an interest in increasing the subsidy of the recipient country to reduce the price. Otherwise, if the subsidy is already high, a sufficiently high value added (sufficiently low price) does not need to be increased further by an increase in the subsidy because of the increasing marginal welfare loss.

**Proposition 3.** The share of the value added of the recipient country in the exports (in value) of the donor country depends positively on transfers $s$ if:

(i) Condition S is verified and the elasticity of exports in relation to the value of the final good production is strictly less than unity, or

(ii) Condition S is not verified and the elasticity of exports in relation to the value of the final good production is strictly greater than unity.

**Proof.** See Appendix A.

**Proposition 3** indicates that the share of the value added in exports, FVA in the data, is positively linked to the subsidy if the elasticity is less than 1 and, if Condition S is verified. This share is also positively related to $s$ if Condition S is not verified and, if the elasticity is greater than 1. The explanation follows. The share is a ratio; if the elasticity of exports is less than 1, then an increase of the value added increases this ratio (the denominator increases less than the numerator). Because of Condition S, the value added increases as $s$ increases. Consequently, the share is positively linked to $s$ in that case. In contrast, if the elasticity of exports is greater than 1, the share decreases as the value added increases. If Condition S is not verified, then the value added increases as $s$ decreases. Consequently the share is positively linked to $s$ in that case too.

These results show that a positive relationship can exist between the share of value added originating from the recipient country in the donor's exports and the transfers. This positive relationship is observed when the transfers are low and the export elasticity is less than unity, or when the transfers are high and the export elasticity is greater than unity.

The condition of (economic) optimality of the monopolies and the condition of (political) optimality of the two governments together determine the equilibrium. Thus, our theoretical model also points to a two-way relationship between the share of foreign value added in exports and foreign aid. In the empirical section, endogeneity issues have been tackled using an instrumental variable approach.

## 6. Conclusions

Despite the proliferation of trade involving global value chains in recent decades, its economic consequences on developing countries have been overlooked in the literature. In this study, we conduct both empirical and theoretical analyses to investigate whether GVC-related bilateral trade is a driver of donor countries' allocations of bilateral foreign aid.

The empirical assessment provides a robust and causal estimate of the effects of GVC-related trade on the allocation of foreign aid, accounting for the bidirectional relationships between aid and trade. For a given donor, the amount of foreign aid depends positively on the share of foreign value added originating from the aid recipient country in the exports of the donor country. Our baseline estimate suggests an elasticity of aid with respect to imported foreign value added of 0.215. Our simple theoretical model offers a rationale for this positive effect. As imported goods are intermediate goods produced abroad, the value added associated with these imported goods is higher when their price is high. The donor country producing the final good thus has an interest in transferring funds to countries to reduce the price. Export business lobbies interested in maintaining trade flows in the two countries push for these foreign aid flows. Other stories are also compatible with our main empirical findings. Countries might give more aid
to recipients that are important suppliers of intermediate goods to promote political stability and contract enforcement or improve infrastructure in these countries. However, these explanations are generally complementary to that provided in this study.

We also investigate possible heterogeneity across donor countries and across sectors. Regarding donors, our empirical results are in line with previous research showing that large donor countries (such as France, Germany, Japan, the UK or the US) use foreign aid strategically. Moreover, we find that for donors that have been traditionally categorized as being more altruistic, the impact of GVC-trade on foreign aid allocation is negligible. Our sectoral results indicate that the GVC-trade effect mainly holds for differentiated sectors (food and beverages, electrical and machinery, and transport equipment). These findings are consistent with our theoretical model and suggest that the main channel through which foreign value added affects foreign aid is not related to the sourcing of raw materials but rather to the negotiation of lower prices of intermediate products with the objective of increasing the profits of firms in both countries.

Overall, our study offers some important insights for policy-makers and development agencies. In particular, we show that donor countries' interests, particularly trade interests involving GVCs, are quantitatively important in the allocation of bilateral foreign aid, opening up the question of whether foreign aid needs to be granted in a multilateral or bilateral way to avoid strategic allocation and the resulting fragmentation of aid in some developing countries.

Despite the adoption of several international initiatives to reduce aid fragmentation, such as the Paris Declaration in 2005, the Accra Agenda for Action in 2008 and the Busan Declaration in 2011, the trend toward aid fragmentation continues. A study undertaken by the World Bank (2022) shows that the increase in foreign aid commitments in real terms in the period 2000-19 was accompanied by an increase in the number of donor countries and agencies. Our empirical results have important policy implications. Firstly, they suggest that the development of GVCs may exacerbate aid fragmentation by concentrating aid allocation in recipient countries incorporated into GVCs. These countries can offer lower-cost intermediate products that are used in the exports of donor countries. Secondly, there are potential unintended consequences of channeling aid primarily to GVC-linked countries. These include perpetuating economic dependency or undermining the development of small or least-developed countries with limited production network involvement. It is acknowledged that GVC integration fosters economic growth, with a 1% increase in GVC participation estimated to increase per capita income by more than 1% (World Bank, 2020). However, if foreign aid is strategically granted to recipient countries that are already integrated into GVCs, there is a risk of creating a double-barrelled situation, where “left-behind” recipient countries miss out on the benefits of both GVC-driven economic growth and GVC-driven foreign aid.

It is important to note a number of limitations of our study. Firstly, the theoretical and empirical analyses do not account for the competition among donors to gain export market share in a recipient country, which could alter the dynamics of foreign aid allocation and exacerbate aid fragmentation. An extension of the study to include spatial lag models (see, for instance, Barthel et al. (2014), Steinwand (2015), Davies and Klasen (2019), Couharde et al. (2020), and Baccini et al. (2022)) could be an interesting avenue for future research. Secondly, the current analysis is at best disaggregated at sectoral GVCs. For instance, certain intermediate products are vital for production, such as critical raw materials for the green energy transition and chips for the electronics industry. Research into product-level GVCs and their impact on the allocation of foreign aid would be a worthwhile area to explore in future research.

## CRediT authorship contribution statement

**Basak Bayramoglu:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Jean-François Jacques:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Julie Lochard:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Acknowledgments

The authors are grateful for comments and suggestions from the co-editor and the two reviewers, which have helped to improve the paper considerably. We would like to thank Brian Copeland, Yannick Guyonvarch, Christophe Hurlin, Hugo Molina, Clément Nedoncelle and Thais Nunez-Rocha for detailed comments and suggestions, and seminar and conference participants at Paris 1, INRAE, Paris-Est Créteil, ICDE, and ETSG for useful comments.

## Appendix A. Theory

**Proof of Proposition 1.** The profit of firm $n-1$ is written:

$$ \pi^{n-1} = p^{n-1} y^{n-1} - a_{l(n-1)}^{n-1} c_{l(n-1)} I_L^{n-1} - (p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t - s) I_I^{n-1} \quad (28) $$

We define the value added of the producer at stage $n-1$ as the revenue minus the consumption of intermediate good:

$$ VA^{n-1} = p^{n-1} y^{n-1} - (p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t - s) I_I^{n-1} \quad (29) $$

By replacing the optimal demand of the imported factor, the value added is written:

$$ VA^{n-1} = A_{n-1} (p^{n-1})^{1-\varepsilon_{n-1}} (1 - \alpha_{n-1} (1 - \frac{1}{\varepsilon_{n-1}})) \quad (30) $$
with

$$p^{n-1} = \frac{\epsilon_{n-1}}{\epsilon_{n-1}-1} \left[ \frac{p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t-s}{1-\alpha_{n-1}} \right]^{1-\alpha_{n-1}} \left[ \frac{a_{l(n-1)}^{n-1} c_{l(n-1)}}{\alpha_{n-1}} \right]^{\alpha_{n-1}} \quad (31)$$

as $\epsilon_{n-1} = 1 - (1 - \alpha_n)(1 - \epsilon_n) > 1$, we note that an increase in $s$ leads to a decrease in the monopoly price $n-1$, and to an increase in the value added $VA^{n-1}$.

Link between monopoly profits and value added.

$$\pi^{n-1} = \frac{A_{n-1}}{\epsilon_{n-1}} (p^{n-1})^{1-\epsilon_{n-1}} = \frac{VA^{n-1}}{\epsilon_{n-1}(1 - \alpha_{n-1}(1 - \frac{1}{\epsilon_{n-1}}))} \quad (32)$$

$$\pi^n = \frac{A_n}{\epsilon_n} (p^n)^{1-\epsilon_n} = \frac{A_n}{\epsilon_n} \left[ \frac{\epsilon_n}{\epsilon_n-1} \left[ \frac{p_{l(n-1)}^{n-1} \tau_{l(n-1)l(n)}}{1-\alpha_n} \right]^{1-\alpha_n} \left[ \frac{a_{l(n)}^{n} c_{l(n)}}{\alpha_n} \right]^{\alpha_n} \right]^{1-\epsilon_n} \quad (33)$$

$$\Leftrightarrow \pi^n = \frac{A_n}{\epsilon_n} \left[ \frac{\epsilon_n}{\epsilon_n-1} \left[ \frac{(\tau_{l(n-1)l(n)})}{1-\alpha_n} \right]^{1-\alpha_n} \left[ \frac{a_{l(n)}^{n} c_{l(n)}}{\alpha_n} \right]^{\alpha_n} \right]^{1-\epsilon_n} (p^{n-1})^{1-\epsilon_{n-1}} \quad (34)$$

$$\Leftrightarrow \pi^n = \frac{A_n}{\epsilon_n} \left[ \frac{\epsilon_n}{\epsilon_n-1} \left[ \frac{(\tau_{l(n-1)l(n)})}{1-\alpha_n} \right]^{1-\alpha_n} \left[ \frac{a_{l(n-1)}^t c_{l(n)}}{\alpha_n} \right]^{\alpha_n} \right]^{1-\epsilon_n} \frac{VA^{n-1}}{A_{n-1}(1-\alpha_{n-1}(1-\frac{1}{\epsilon_{n-1}}))} \quad (35)$$

**Proof of Proposition 2.** We define two positive constants:

$$D_n = \frac{A_n}{\epsilon_n} \left[ B_n (a_{l(n)}^n c_{l(n)})^{a_n} (\tau_{l(n-1),l(n)})^{1-a_n} \right]^{1-\epsilon_n} \quad (36)$$

and

$$E_n = \frac{A_{n-1}}{\epsilon_{n-1}} \quad (37)$$

so that

$$\lambda_n \pi_n + \lambda_{n-1} \pi_{n-1} = (\lambda_n D_n + \lambda_{n-1} E_n) (p_{l(n-1)}^{n-1})^{1-\epsilon_{n-1}}$$

The payoff function of the consumers is:

$$\frac{\epsilon_n}{\epsilon_n - 1} \pi_n - WL(s) = \frac{\epsilon_n}{\epsilon_n - 1} \pi_n - \left(a + \frac{b}{2}s^2\right)$$

$$\frac{d(\lambda_n \pi_n + \lambda_{n-1} \pi_{n-1})}{ds} = [-\alpha_{n-1}^2 (1 - \epsilon_{n-1}) p_{n-1}^{1-\epsilon_{n-1}}] [\lambda_n D_n + \lambda_{n-1} E_n] \frac{1}{p_{l(n-2)}^{n-2} \tau_{l(n-2)(n-1)} + t-s} > 0$$

$$\frac{d(\frac{\epsilon_n}{\epsilon_{n-1}} \pi_n - WL(s))}{ds} = [-\alpha_{n-1}^2 (1 - \epsilon_{n-1}) p_{n-1}^{1-\epsilon_{n-1}}] \left[ \frac{\epsilon_n}{\epsilon_n - 1} D_n \right] \frac{1}{p_{l(n-2)}^{n-2} \tau_{l(n-2)(n-1)} + t-s} - bs = 0$$

because

$$\frac{d p^{n-1}}{ds} = - \frac{\alpha_{n-1}^2 p^{n-1}}{p^{n-2} \tau_{l(n-2)l(n-1)} + t-s} \quad (38)$$

Moreover, as

$$VA^{n-1} = A_{n-1} (p^{n-1})^{1-\epsilon_{n-1}} (1 - \alpha_{n-1}(1 - \frac{1}{\epsilon_{n-1}})) \quad (39)$$

we have

$$\left[ \frac{VA^{n-1}(-\alpha_{n-1}^2(1-\epsilon_{n-1}))}{A_{n-1}(1-\alpha_{n-1}(1-\frac{1}{\epsilon_{n-1}}))} \right] \left[ \left( \frac{\epsilon_n}{\epsilon_n-1} \right) D_n \right] = bs(p_{l(n-2)}^{n-2} \tau_{l(n-2)l(n-1)} + t-s) \quad (40)$$

The term on the left hand side is increasing in $VA^{n-1}$. By deriving the term in the right hand side with respect to $s$ we have the result.

An example for the stability of the cooperative agreement. Here we show that the agreement can be obtained by a repeated game between the two countries. The basic game considered here includes two players, the donor country and the recipient country, each having two alternative strategies after signing an agreement. The donor country, to provide the transfer or not; the recipient country, to fix a low price which takes into account the subsidy or to fix a high price which does not take into account the subsidy. Because the upstream firm is a monopoly, the downstream firm has no choice than to accept. The maximum payoffs for both players are obtained when the donor country actually provides the transfer $\frac{\epsilon_n}{\epsilon_n-1} \pi^n(s^*) - WL(s^*) + \pi^n(s^*)$, and the recipient country fixes its price which takes into account the subsidy $\pi^{n-1}(s^*)$. This is the case where they both respect the agreement. The worst payoffs are obtained when the donor country does not transfer $\frac{\epsilon_n}{\epsilon_n-1} \pi^n(0) + \pi^n(0)$, and the recipient country fixes its high price which does not
include the transfer $\pi^{n-1}(0)$. It means that they both do not respect the agreement. These last payoffs are Nash payoffs. To obtain a Nash equilibrium, the following condition (C) must hold for a sufficiently low level of transfer $s$:

$$ \alpha^2 < 1 - \alpha $$

If the donor deviates from the worst case (he gives the transfer which has been negotiated but the recipient country does not fix the low price), then the donor loses. On the other hand, if the recipient deviates from the worst case (it decreases its price without receiving the transfer), then the recipient loses.

To obtain such result, we can simply show that to not respect the agreement is a dominant strategy for the recipient country. If the donor provides the transfer, the recipient is better off not to decrease its price because it receives the total subsidy $s^* I_I^{n-1}$ but it does not decrease its price, so its profit is $\pi^{n-1}(0)$ (for $s = 0$). We recall that $I_I^{n-1}$ is the amount of intermediate good imported by the upstream monopoly. If Condition (C) holds, then we have:

$$ \pi^{n-1}(0) + s^* I_I^{n-1} > \pi^{n-1}(s^*) $$

On the other hand, if the donor does not grant the transfer, the recipient is also better of not to put a low price. If Condition (C) holds, we have:

$$ \pi^{n-1}(0) > \pi^{n-1}(s^*) - s^* I_I^{n-1} $$

We note these two inequalities are identical. The inequality holds when Condition (C) is verified for a sufficiently small $s$.

**lemma.** If $\alpha^2 < 1 - \alpha$, then $\pi^{n-1}(0) > \pi^{n-1}(s) - s I_I^{n-1}$ for a sufficiently small $s$.

**Proof.** First remark that both terms of the inequality are equal for $s = 0$.

To obtain this inequality in the neighborhood of $s = 0$, we show that the derivative of the right-hand side of this inequality with respect to $s$ is strictly negative at $s = 0$, if Condition (C) holds. To show that, simply remark that the first-order condition with respect to the imported intermediate good for the upstream monopoly is:

$$ (p_{n-2}\tau_{n-2} + t - s)I_I^{n-1} = (1 - \alpha)\frac{\epsilon_{n-1} - 1}{\epsilon_{n-1}}p^{n-1}y^{n-1} $$

Because

$$ \pi^{n-1}(s) = \frac{p^{n-1}y^{n-1}}{\epsilon_{n-1}} $$

we have:

$$ sI_I^{n-1} = \frac{s(1-\alpha)(\epsilon_{n-1} - 1)}{p_{n-2}\tau_{n-2} + t - s}\pi^{n-1}(s) $$

So the derivative of $\pi^{n-1}(s) - sI_I^{n-1}$ with respect to $s$ for $s = 0$ is equal to:

$$ \frac{(\epsilon_{n-1} - 1)}{(p_{n-2}\tau_{n-2} + t - s)^2}\pi^{n-1}(s)[\alpha^2 - (1 - \alpha)] $$

So this derivative is strictly negative at $s = 0$, if Condition C is verified.

To conclude, in a repeated game if the discount rate is small enough, by the Folk theorem (Friedman 1971), the optimal payoffs can be obtained as a Nash equilibrium. These payoffs are those of the cooperative agreement with a transfer.

**Proof of Proposition 3.** The revenue of producer $n$ is expressed as a linear function of the value added of producer $n-1$:

$$ p_n y_n = \pi_n \epsilon_n = D_n \epsilon_n \frac{VA_{n-1}}{(A_{n-1}(1 - \alpha_{n-1}(1 - \frac{1}{\epsilon_{n-1}})))} \quad (41) $$

Suppose that the exports are a function of revenue $X_n = f(p_n y_n)$. They will thus be function of $VA_{n-1}$. Let us then derive the share of value added in exports $\frac{VA_{n-1}}{f(p_n y_n)}$ with respect to the value added. We obtain:

$$ \frac{f(p_n y_n) - f'(p_n y_n) p_n y_n}{f^2} \quad (42) $$

This derivative is positive if the elasticity is less than 1, which means that the share of value added in exports increases with value added. The latter is itself positively linked to the subsidy if Condition S is verified (low subsidy).

## Appendix B. Data

We consider 22 donor countries and 127 recipient countries over the period 2000–2018 (see Table 6 below). These 22 donor countries correspond to the ‘historical’ donors for which the OECD reports some ODA data at the beginning of our time period, i.e., in 2000. The GVC participation data are available for 189 countries, but due to missing data for the other variables, our sample is restricted to 142 countries. Furthermore, we exclude 15 countries due to insufficient data quality for the GVC/FVA variable (see https://worldmrio.com/unctadgvc/). Ultimately, our sample includes 127 recipient countries listed in the table below (see Table 6).
<caption>**Table 6**</caption>

List of countries.

**Donor countries**

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the UK, the US

**Recipient countries**

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Bangladesh, Belize, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo Dem. Rep., Cook Islands, Costa Rica, Cote d'Ivoire, Croatia, Cuba, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt Arab Rep., El Salvador, Equatorial Guinea, Eswatini, Fiji, Gabon, Gambia, Georgia, Ghana, Grenada, Guatemala, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Iran Islamic Rep., Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Korea Dem. People's Rep., Kosovo, Kyrgyz Republic, Lao PDR, Lebanon, Lesotho, Liberia, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Mauritania, Mauritius, Mexico, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Nicaragua, Niger, Nigeria, Niue, North Macedonia, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Rwanda, Samoa, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Solomon Islands, Somalia, South Africa, Sri Lanka, St. Lucia, St. Vincent and the Grenadines, Suriname, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, Uruguay, Uzbekistan, Vanuatu, Venezuela RB, Vietnam, West Bank and Gaza, Zambia

<caption>**Table 7**</caption>

Data description and sources.

<table><tr><td>ODA</td><td>Bilateral ODA commitments in thousands US$. Source: Credit Reporting System (CRS), OECD.</td></tr><tr><td>FVA</td><td>Foreign value added in exports in thousands US$. For each donor/exporting country, it corresponds to the value added contributed by the recipient country. Source: UNCTAD Eora Global Value Chain database (see Casella et al., 2019).</td></tr><tr><td>GDP, GDPCapita, Population</td><td>GDP and GDP per capita are in thousands US$. Source: World Development Indicators (WDI), World Bank</td></tr><tr><td>Distance; Colony; Comlang</td><td>Bilateral distance, colony and common language dummies come from the CEPI GeoDist database (Mayer and Zignago, 2011). Distance is the bilateral distance (in km) between the donor and recipient country. Colony is a dummy variable equal to one if the donor and recipient countries have ever had a colonial link. Comlang is a dummy variable equal to one if the donor and recipient countries share a common language.</td></tr><tr><td>RTA</td><td>The regional trade agreement dummy is computed using information from the WTO and comes from the CEPI gravity database.</td></tr><tr><td>Comcur</td><td>Comcur is a dummy variable equal to one when the donor and recipient country share a common currency. Data come from José de Sousa's webpage (see de Sousa, 2012).</td></tr><tr><td>Exports</td><td>Bilateral exports from the donor to recipient country. Data come from the International Monetary Fund Direction of Trade Statistics (IMF DOTS).</td></tr><tr><td>InstQ</td><td>Voice and accountability index from the World Bank (World Governance Indicators). The index “captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media”. Index values range from -2.5 to +2.5. The higher the index is, the higher the institutional quality.</td></tr></table>

## Data on GVC-related trade

There are three widely used global input-output tables: the World Input-Output Database (WIOD), the Trade in Value Added Database (TiVA) from the OECD and the Eora MRIO (Multi Regional Input Output) database. We choose the Eora MRIO database because of its largest country coverage and longest time period. On the downside, the sectoral coverage is more aggregate and thus less precise than are those of the WIOD or TiVA databases (World Bank, 2020). The Eora and similar statistics obtained from other GVC databases are generally consistent with one another (see Aslam et al., 2017; Casella et al., 2019).

These global input-output tables use disaggregated trade data to link national input-output tables across countries. The limitations of this macrolevel approach to measure GVCs are well known (e.g., Johnson, 2018). I-O tables require extensive data cleaning, reconciliation and extrapolation. For instance, for many countries, I-O tables are only available in benchmark years.
<table><caption>Table 8
Eora26 sectors.</caption><thead><tr><td>Variable name</td><td>Corresponding sector</td></tr></thead><tbody><tr><td>FVA_Agr</td><td>Agriculture</td></tr><tr><td>FVA_Fish</td><td>Fishing</td></tr><tr><td>FVA_Mine</td><td>Mining and Quarrying</td></tr><tr><td>FVA_Food</td><td>Food and Beverages</td></tr><tr><td>FVA_Textile</td><td>Textiles and Wearing Apparel</td></tr><tr><td>FVA_Wood</td><td>Wood and Paper</td></tr><tr><td>FVA_Petrol</td><td>Petroleum, Chemical and Non-Metallic Mineral Products</td></tr><tr><td>FVA_Metal</td><td>Metal Products</td></tr><tr><td>FVA_ElecMac</td><td>Electrical and Machinery</td></tr><tr><td>FVA_TranspEq</td><td>Transport Equipment</td></tr><tr><td>FVA_OtherMan</td><td>Other Manufacturing</td></tr><tr><td>FVA_Recy</td><td>Recycling</td></tr><tr><td>FVA_ElecGas</td><td>Electricity, Gas and Water</td></tr><tr><td>FVA_Const</td><td>Construction</td></tr><tr><td>FVA_Repair</td><td>Maintenance and Repair</td></tr><tr><td>FVA_Wtrade</td><td>Wholesale Trade</td></tr><tr><td>FVA_Rtrade</td><td>Retail Trade</td></tr><tr><td>FVA_Hotel</td><td>Hotels and Restaurants</td></tr><tr><td>FVA_Transp</td><td>Transport</td></tr><tr><td>FVA_Post</td><td>Post and Telecommunications</td></tr><tr><td>FVA_Fin</td><td>Financial Intermediation and Business Activities</td></tr><tr><td>FVA_PubAdm</td><td>Public Administration</td></tr><tr><td>FVA_Educ</td><td>Education, Health and Other Services</td></tr><tr><td>FVAPrivate</td><td>Private Households</td></tr><tr><td>FVA_Others</td><td>Others</td></tr><tr><td>FVA_Reexp</td><td>Re-export and Re-import</td></tr></tbody></table>

Furthermore, the technical features of I-O tables, such as sector classification, may differ across countries. However, global input-output tables also cause more specific problems related to the identification of inputs in international trade data and to the tracking of these inputs behind borders. To address the first issue, one solution is to use some classification scheme to differentiate between final goods and inputs. Another solution consists of using some proportionality assumption or mathematical optimization algorithms. Assumptions are also used to solve the second problem and determine the proportion of imported and domestic inputs in an industry. Moreover, one generally also needs to make the assumption that imported inputs are used with equal intensity in production for domestic and export markets.

We use the UNCTAD Eora database based on the Eora MRIO data (see Casella et al., 2019, and Lenzen et al., 2012, Lenzen et al., 2013). Our main GVC-dependence variable is the foreign value added (FVA) embodied in exports. For total foreign value added, we use “country-to-country” data reporting, and for each exporting country, we use the value added contributed by all other countries.

For sectoral value added (SFVA), we use “sector-to-country” data reporting for each country of exports, the value contributed by all other countries in the world in each sector. These data are only available until 2017. For most countries, the disaggregation consists of 26 sectors listed in the table below (Table 8). For some countries in our sample (28 recipient countries), Eora reports more disaggregated sectors. In that case, we reaggregate sectors to the Eora26 using the concordance table provided by Eora. Note that in a few cases, some subsectors correspond to several Eora26 sectors. In that case, we assign the subsector to the Eora26 sector that represents the largest share (more than 50%). In our estimations, we disregard the last three sectors (Private Households, Others and Re-export and Re-import).

## Appendix C. Additional Figures and Tables

See Figs. 6–10 and Tables 9–17.

## Data availability

https://data.mendeley.com/preview/ncf7dczycm?a=389a2ece-9e3f-43e8-a4d9-2be3625527e8.
<figure><img src="image_10.png" /><figcaption>Fig. 6. Proportion of differentiated subsectors vs FVA estimated coefficients.</figcaption></figure>

Source: Authors’ calculations. In the vertical axis, we represent the proportion of subsectors with differentiated products according to Rauch (1999). In the horizontal axis, we represent coefficients on the FVA variable for different sectors (corresponding to estimation results reported in Table 2). We use the algorithm developed by Liao et al. (2020) to convert Rauch’s SITC original classification into ISIC Rev3 and Lenzen et al. (2013) for the correspondence between ISIC Rev3 and Eora26 sectors.

<figure><img src="image_11.png" /><figcaption>Fig. 7. Foreign Value Added per sector for the four top donors (average 2000–2018). Source: Authors’ calculations.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Fig. 8. Correlation between the shares of each manufacturing sector in FVA in 2005 and the GDP of the donor country (in log). Source: Authors’ calculations.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 9. Correlation between the shares of each service sector in FVA in 2005 and the GDP of the donor country (in log). Source: Authors’ calculations.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 10. Mean tariff differential and per capita ODA from top donors (in log).</figcaption></figure>

Notes: Per capita ODA receipts from each donor country on the vertical axis and mean tariff differential (tariff on final goods - tariff on inputs) on the horizontal axis. Both variables are in log and computed for 2007.

Data source: OECD and Shapiro (2021). See text for details.
<table><caption>**Table 9**

Robustness - Recipient specific FVA effects.</caption><thead><tr><th>Recip_code</th><th>Recip_name</th><th>Coeff_In(FVA)</th><th>p-value</th><th>[95% conf. interval]</th></tr></thead><tbody><tr><td>AFG</td><td>Afghanistan</td><td>-0.085</td><td>0.126</td><td>-0.19 0.02</td></tr><tr><td>AGO</td><td>Angola</td><td>-0.104</td><td>0.342</td><td>-0.32 0.11</td></tr><tr><td>ALB</td><td>Albania</td><td>0.366</td><td>0</td><td>0.24 0.49</td></tr><tr><td>ARG</td><td>Argentina</td><td>0.526</td><td>0</td><td>0.33 0.72</td></tr><tr><td>ARM</td><td>Armenia</td><td>0.316</td><td>0</td><td>0.17 0.46</td></tr><tr><td>ATG</td><td>Antigua and Barbuda</td><td>-0.335</td><td>0.008</td><td>-0.58 -0.09</td></tr><tr><td>AZE</td><td>Azerbaijan</td><td>0.184</td><td>0.069</td><td>-0.01 0.38</td></tr><tr><td>BDI</td><td>Burundi</td><td>0.205</td><td>0.05</td><td>0.00 0.41</td></tr><tr><td>BGD</td><td>Bangladesh</td><td>-0.027</td><td>0.688</td><td>-0.16 0.11</td></tr><tr><td>BIH</td><td>Bosnia and Herzegovina</td><td>0.053</td><td>0.359</td><td>-0.06 0.17</td></tr><tr><td>BLZ</td><td>Belize</td><td>0.567</td><td>0.006</td><td>0.16 0.97</td></tr><tr><td>BOL</td><td>Bolivia</td><td>-0.014</td><td>0.847</td><td>-0.16 0.13</td></tr><tr><td>BRA</td><td>Brazil</td><td>0.207</td><td>0.228</td><td>-0.13 0.54</td></tr><tr><td>BTN</td><td>Bhutan</td><td>-0.699</td><td>0</td><td>-0.95 -0.45</td></tr><tr><td>BWA</td><td>Botswana</td><td>0.126</td><td>0.602</td><td>-0.35 0.60</td></tr><tr><td>CAF</td><td>Central African Republic</td><td>-0.069</td><td>0.303</td><td>-0.20 0.06</td></tr><tr><td>CHL</td><td>Chile</td><td>1.422</td><td>0.002</td><td>0.54 2.30</td></tr><tr><td>CHN</td><td>China</td><td>0.951</td><td>0</td><td>0.47 1.43</td></tr><tr><td>CIV</td><td>Cote d'Ivoire</td><td>0.523</td><td>0</td><td>0.38 0.67</td></tr><tr><td>CMR</td><td>Cameroon</td><td>0.689</td><td>0</td><td>0.46 0.91</td></tr><tr><td>COD</td><td>Congo. Dem. Rep.</td><td>0.000</td><td></td><td></td></tr><tr><td>COK</td><td>Cook islands</td><td>0.000</td><td></td><td></td></tr><tr><td>COL</td><td>Colombia</td><td>0.326</td><td>0</td><td>0.19 0.46</td></tr><tr><td>COM</td><td>Comoros</td><td>0.000</td><td></td><td></td></tr><tr><td>CPY</td><td>Cabo Verde</td><td>-0.446</td><td>0.03</td><td>-0.85 -0.04</td></tr><tr><td>CRI</td><td>Costa Rica</td><td>-0.206</td><td>0.066</td><td>-0.43 0.01</td></tr><tr><td>CUB</td><td>Cuba</td><td>0.856</td><td>0.018</td><td>0.15 1.56</td></tr><tr><td>DJI</td><td>Djibouti</td><td>0.069</td><td>0.523</td><td>-0.14 0.28</td></tr><tr><td>DMA</td><td>Dominica</td><td>0.000</td><td></td><td></td></tr><tr><td>DOM</td><td>Dominican Republic</td><td>-0.095</td><td>0.141</td><td>-0.22 0.03</td></tr><tr><td>DZA</td><td>Algeria</td><td>0.766</td><td>0</td><td>0.56 0.98</td></tr><tr><td>ECU</td><td>Ecuador</td><td>-0.061</td><td>0.453</td><td>-0.22 0.10</td></tr><tr><td>EGY</td><td>Egypt. Arab Rep.</td><td>0.232</td><td>0.002</td><td>0.08 0.38</td></tr><tr><td>FJI</td><td>Fiji</td><td>1.844</td><td>0</td><td>1.35 2.34</td></tr><tr><td>GAB</td><td>Gabon</td><td>0.516</td><td>0</td><td>0.29 0.74</td></tr><tr><td>GEO</td><td>Georgia</td><td>0.250</td><td>0.001</td><td>0.10 0.40</td></tr><tr><td>GHA</td><td>Ghana</td><td>0.312</td><td>0.001</td><td>0.12 0.50</td></tr><tr><td>GMB</td><td>Gambia. The</td><td>0.516</td><td>0.001</td><td>0.20 0.83</td></tr><tr><td>GNB</td><td>Guinea-Bissau</td><td>0.000</td><td></td><td></td></tr><tr><td>GNQ</td><td>Equatorial Guinea</td><td>0.000</td><td></td><td></td></tr><tr><td>GRD</td><td>Grenada</td><td>0.000</td><td></td><td></td></tr><tr><td>GTM</td><td>Guatemala</td><td>-0.038</td><td>0.588</td><td>-0.18 0.10</td></tr><tr><td>HND</td><td>Honduras</td><td>-0.100</td><td>0.163</td><td>-0.24 0.04</td></tr><tr><td>HRV</td><td>Croatia</td><td>0.293</td><td>0.018</td><td>0.05 0.54</td></tr><tr><td>HTI</td><td>Haiti</td><td>0.174</td><td>0.007</td><td>0.05 0.30</td></tr><tr><td>IDN</td><td>Indonesia</td><td>0.217</td><td>0.003</td><td>0.07 0.36</td></tr><tr><td>IND</td><td>India</td><td>0.470</td><td>0</td><td>0.23 0.71</td></tr><tr><td>IRN</td><td>Iran. Islamic Rep.</td><td>0.542</td><td>0</td><td>0.38 0.71</td></tr><tr><td>IRQ</td><td>Iraq</td><td>0.465</td><td>0</td><td>0.29 0.64</td></tr><tr><td>JAM</td><td>Jamaica</td><td>0.111</td><td>0.157</td><td>-0.04 0.27</td></tr><tr><td>JOR</td><td>Jordan</td><td>0.051</td><td>0.371</td><td>-0.06 0.16</td></tr><tr><td>KAZ</td><td>Kazakhstan</td><td>0.414</td><td>0</td><td>0.26 0.57</td></tr><tr><td>KEN</td><td>Kenya</td><td>-0.029</td><td>0.578</td><td>-0.13 0.07</td></tr><tr><td>KGZ</td><td>Kyrgyz Republic</td><td>0.192</td><td>0.003</td><td>0.06 0.32</td></tr><tr><td>KHM</td><td>Cambodia</td><td>-0.182</td><td>0.002</td><td>-0.30 -0.07</td></tr><tr><td>KIR</td><td>Kiribati</td><td>0.000</td><td></td><td></td></tr><tr><td>LAO</td><td>Lao PDR</td><td>-0.419</td><td>0</td><td>-0.64 -0.20</td></tr><tr><td>LBN</td><td>Lebanon</td><td>-0.021</td><td>0.811</td><td>-0.19 0.15</td></tr><tr><td>LBR</td><td>Liberia</td><td>0.299</td><td>0.001</td><td>0.13 0.47</td></tr><tr><td>LCA</td><td>St. Lucia</td><td>0.000</td><td></td><td></td></tr><tr><td>LKA</td><td>Sri Lanka</td><td>-0.027</td><td>0.674</td><td>-0.15 0.10</td></tr><tr><td>LSO</td><td>Lesotho</td><td>0.161</td><td>0.321</td><td>-0.16 0.48</td></tr><tr><td>MAR</td><td>Morocco</td><td>0.584</td><td>0</td><td>0.39 0.78</td></tr><tr><td>MDG</td><td>Madagascar</td><td>0.257</td><td>0.003</td><td>0.09 0.43</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 9 (continued).</caption><thead><tr><th>Recip_code</th><th>Recip_name</th><th>Coeff_In(FVA)</th><th>p-value</th><th colspan="2">[95% conf. interval]</th></tr></thead><tbody><tr><td>MDV</td><td>Maldives</td><td>-0.291</td><td>0.008</td><td>-0.50</td><td>-0.08</td></tr><tr><td>MEX</td><td>Mexico</td><td>0.122</td><td>0.075</td><td>-0.01</td><td>0.26</td></tr><tr><td>MHL</td><td>Marshall Islands</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>MKD</td><td>North Macedonia</td><td>0.078</td><td>0.221</td><td>-0.05</td><td>0.20</td></tr><tr><td>MLI</td><td>Mali</td><td>-0.133</td><td>0.021</td><td>-0.25</td><td>-0.02</td></tr><tr><td>MMR</td><td>Myanmar</td><td>0.233</td><td>0.123</td><td>-0.06</td><td>0.53</td></tr><tr><td>MNE</td><td>Montenegro</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>MNG</td><td>Mongolia</td><td>0.222</td><td>0.015</td><td>0.04</td><td>0.40</td></tr><tr><td>MOZ</td><td>Mozambique</td><td>-0.199</td><td>0.001</td><td>-0.31</td><td>-0.08</td></tr><tr><td>MRT</td><td>Mauritania</td><td>0.547</td><td>0</td><td>0.44</td><td>0.65</td></tr><tr><td>MUS</td><td>Mauritius</td><td>1.311</td><td>0</td><td>0.83</td><td>1.79</td></tr><tr><td>MWI</td><td>Malawi</td><td>-0.177</td><td>0.044</td><td>-0.35</td><td>0.00</td></tr><tr><td>MYS</td><td>Malaysia</td><td>0.848</td><td>0.004</td><td>0.27</td><td>1.43</td></tr><tr><td>NAM</td><td>Namibia</td><td>0.157</td><td>0.063</td><td>-0.01</td><td>0.32</td></tr><tr><td>NER</td><td>Niger</td><td>-0.086</td><td>0.159</td><td>-0.20</td><td>0.03</td></tr><tr><td>NGA</td><td>Nigeria</td><td>0.164</td><td>0.111</td><td>-0.04</td><td>0.36</td></tr><tr><td>NIC</td><td>Nicaragua</td><td>-0.205</td><td>0.104</td><td>-0.45</td><td>0.04</td></tr><tr><td>NIU</td><td>Niue</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>NPL</td><td>Nepal</td><td>-0.260</td><td>0</td><td>-0.38</td><td>-0.14</td></tr><tr><td>NRU</td><td>Nauru</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>PAK</td><td>Pakistan</td><td>0.173</td><td>0.017</td><td>0.03</td><td>0.31</td></tr><tr><td>PAN</td><td>Panama</td><td>-0.390</td><td>0.04</td><td>-0.76</td><td>-0.02</td></tr><tr><td>PER</td><td>Peru</td><td>0.288</td><td>0.002</td><td>0.11</td><td>0.47</td></tr><tr><td>PHL</td><td>Philippines</td><td>0.219</td><td>0.086</td><td>-0.03</td><td>0.47</td></tr><tr><td>PLW</td><td>Palau</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>PNG</td><td>Papua New Guinea</td><td>1.339</td><td>0</td><td>0.91</td><td>1.77</td></tr><tr><td>PRK</td><td>Korea. Dem. People's Rep.</td><td>-0.791</td><td>0</td><td>-0.99</td><td>-0.60</td></tr><tr><td>PRY</td><td>Paraguay</td><td>0.090</td><td>0.403</td><td>-0.12</td><td>0.30</td></tr><tr><td>PSE</td><td>West Bank and Gaza</td><td></td><td>0.000</td><td></td><td></td></tr><tr><td>RWA</td><td>Rwanda</td><td>0.106</td><td>0.303</td><td>-0.10</td><td>0.31</td></tr><tr><td>SEN</td><td>Senegal</td><td>0.209</td><td>0</td><td>0.09</td><td>0.32</td></tr><tr><td>SLB</td><td>Solomon Islands</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>SLE</td><td>Sierra Leone</td><td>0.255</td><td>0.003</td><td>0.08</td><td>0.43</td></tr><tr><td>SLV</td><td>El Salvador</td><td>0.017</td><td>0.817</td><td>-0.13</td><td>0.16</td></tr><tr><td>SOM</td><td>Somalia</td><td>-0.519</td><td>0</td><td>-0.72</td><td>-0.32</td></tr><tr><td>STP</td><td>Sao Tome and Principe</td><td>-0.050</td><td>0.809</td><td>-0.46</td><td>0.36</td></tr><tr><td>SUR</td><td>Suriname</td><td>1.829</td><td>0</td><td>1.54</td><td>2.12</td></tr><tr><td>SWZ</td><td>Eswatini</td><td>-0.081</td><td>0.349</td><td>-0.25</td><td>0.09</td></tr><tr><td>SYC</td><td>Seychelles</td><td>0.344</td><td>0.01</td><td>0.08</td><td>0.61</td></tr><tr><td>SYR</td><td>Syrian Arab Republic</td><td>0.071</td><td>0.254</td><td>-0.05</td><td>0.19</td></tr><tr><td>TCD</td><td>Chad</td><td>0.195</td><td>0.001</td><td>0.08</td><td>0.31</td></tr><tr><td>TGO</td><td>Togo</td><td>0.775</td><td>0</td><td>0.45</td><td>1.09</td></tr><tr><td>THA</td><td>Thailand</td><td>0.834</td><td>0</td><td>0.54</td><td>1.13</td></tr><tr><td>TJK</td><td>Tajikistan</td><td>0.103</td><td>0.391</td><td>-0.13</td><td>0.34</td></tr><tr><td>TKM</td><td>Turkmenistan</td><td>0.242</td><td>0</td><td>0.11</td><td>0.37</td></tr><tr><td>TLS</td><td>Timor-Leste</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>TON</td><td>Tonga</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>TUN</td><td>Tunisia</td><td>0.265</td><td>0.001</td><td>0.10</td><td>0.42</td></tr><tr><td>TUR</td><td>Turkey</td><td>0.280</td><td>0</td><td>0.13</td><td>0.43</td></tr><tr><td>TUV</td><td>Tuvalu</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>TZA</td><td>Tanzania</td><td>-0.204</td><td>0.005</td><td>-0.35</td><td>-0.06</td></tr><tr><td>UGA</td><td>Uganda</td><td>-0.074</td><td>0.321</td><td>-0.22</td><td>0.07</td></tr><tr><td>UKR</td><td>Ukraine</td><td>0.135</td><td>0.083</td><td>-0.02</td><td>0.29</td></tr><tr><td>URY</td><td>Uruguay</td><td>1.056</td><td>0</td><td>0.66</td><td>1.45</td></tr><tr><td>UZB</td><td>Uzbekistan</td><td>0.407</td><td>0</td><td>0.21</td><td>0.60</td></tr><tr><td>VCT</td><td>St. Vincent and the Grenadines</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>VEN</td><td>Venezuela. RB</td><td>-0.107</td><td>0.035</td><td>-0.21</td><td>-0.01</td></tr><tr><td>VNM</td><td>Vietnam</td><td>0.523</td><td>0</td><td>0.39</td><td>0.66</td></tr><tr><td>VUT</td><td>Vanuatu</td><td>-0.505</td><td>0</td><td>-0.78</td><td>-0.23</td></tr><tr><td>WSM</td><td>Samoa</td><td>1.013</td><td>0</td><td>0.74</td><td>1.29</td></tr><tr><td>XKX</td><td>Kosovo</td><td>0.000</td><td></td><td></td><td></td></tr><tr><td>ZAF</td><td>South Africa</td><td>0.197</td><td>0.028</td><td>0.02</td><td>0.37</td></tr><tr><td>ZMB</td><td>Zambia</td><td>-0.103</td><td>0.2</td><td>-0.26</td><td>0.05</td></tr></tbody></table>

Notes: This Table reports the estimation results (coefficient, p-value and 95% confidence interval) for the interaction terms between the FVA variable and dummy variables for the recipient countries. Control variables are not reported. Robust standard errors are clustered at the donor-year. See the text for details.
<table><caption>**Table 10**
Baseline results adding bilateral exports.</caption><thead><tr><th rowspan="2">Method:<br>Dependent Variable:</th><th colspan="2">OLS-FE</th><th colspan="2">PPML-FE</th></tr><tr><th>ln ODA<br>(1)</th><th>ln ODA<br>(2)</th><th>ODA<br>(3)</th><th>ODA<br>(4)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td>0.307***<br>(0.0494)</td><td>0.348***<br>(0.0200)</td><td>0.0329<br>(0.0513)</td><td>0.117***<br>(0.0351)</td></tr><tr><td>Exports</td><td>0.237***<br>(0.0444)</td><td>0.223***<br>(0.0145)</td><td>0.354***<br>(0.0561)</td><td>0.303***<br>(0.0298)</td></tr><tr><td>ln(GDP_donor)</td><td>1.287**<br>(0.562)</td><td></td><td>0.473**<br>(0.218)</td><td></td></tr><tr><td>ln(GDPCapa_recipient)</td><td>-0.666***<br>(0.105)</td><td></td><td>-0.686***<br>(0.197)</td><td></td></tr><tr><td>ln(Pop_recipient)</td><td>1.155**<br>(0.476)</td><td></td><td>-0.944<br>(0.743)</td><td></td></tr><tr><td>InstQ_recipient</td><td>0.205**<br>(0.0879)</td><td></td><td>-0.153*<br>(0.0786)</td><td></td></tr><tr><td>ln(Distance)</td><td>-0.773***<br>(0.271)</td><td>-0.774***<br>(0.0746)</td><td>-0.377*<br>(0.218)</td><td>-0.354***<br>(0.0782)</td></tr><tr><td>Colony</td><td>1.354***<br>(0.234)</td><td>1.373***<br>(0.0715)</td><td>0.736***<br>(0.134)</td><td>0.737***<br>(0.0820)</td></tr><tr><td>Comlang</td><td>1.058***<br>(0.133)</td><td>1.066***<br>(0.0490)</td><td>0.477***<br>(0.122)</td><td>0.460***<br>(0.0669)</td></tr><tr><td>Observations</td><td>29,619</td><td>32,218</td><td>38,446</td><td>41,381</td></tr><tr><td>R-squared</td><td>0.585</td><td>0.631</td><td>-</td><td>-</td></tr><tr><td>Donor FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Recipient FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Year FE</td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Donor-year FE</td><td></td><td>x</td><td></td><td>x</td></tr><tr><td>Recipient-year FE</td><td></td><td>x</td><td></td><td>x</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor country (or donor-year) level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. The dependent variable is ODA commitments in log (Columns 1 & 2) or in level (Columns 3 & 4). All explanatory variables except Distance, Colony and Comlang are lagged one year. We use the inverse hyperbolic sine (IHS) transformation for the export variable. See the text for details.

<table><caption>**Table 11**
Sectoral results adding bilateral exports.</caption><thead><tr><th colspan="2">Method: PPML-FE<br>Dep. Var: ODA</th><th></th><th></th></tr></thead><tbody><tr><td>ln(FVA_Agr)</td><td>0.282***<br>(0.0536)</td><td>ln(FVA_ElecGas)</td><td>0.366*<br>(0.199)</td></tr><tr><td>ln(FVA_Fish)</td><td>-0.106**<br>(0.0418)</td><td>ln(FVA_Const)</td><td>0.157<br>(0.135)</td></tr><tr><td>ln(FVA_Mine)</td><td>-0.0088<br>(0.0606)</td><td>ln(FVA_Repair)</td><td>0.117<br>(0.0962)</td></tr><tr><td>ln(FVA_Food)</td><td>0.0909**<br>(0.0424)</td><td>ln(FVA_Wtrade)</td><td>-0.0310<br>(0.145)</td></tr><tr><td>ln(FVA_Textile)</td><td>-0.0421<br>(0.0276)</td><td>ln(FVA_Rtrade)</td><td>-0.0245<br>(0.101)</td></tr><tr><td>ln(FVA_Wood)</td><td>-0.0705<br>(0.0639)</td><td>ln(FVA_Hotel)</td><td>-0.119<br>(0.0771)</td></tr><tr><td>ln(FVA_Petrol)</td><td>-0.0178<br>(0.0894)</td><td>ln(FVA_Transp)</td><td>0.680***<br>(0.0928)</td></tr><tr><td>ln(FVA_Metal)</td><td>0.0616<br>(0.0654)</td><td>ln(FVA_Post)</td><td>-0.922***<br>(0.126)</td></tr><tr><td>ln(FVA_ElecMac)</td><td>0.154***<br>(0.0580)</td><td>ln(FVA_Fin)</td><td>-0.760***<br>(0.238)</td></tr><tr><td>ln(FVA_TranspEq)</td><td>0.232***<br>(0.0615)</td><td>ln(FVA_PubAdm)</td><td>0.004<br>(0.0867)</td></tr><tr><td>ln(FVA_OtherMan)</td><td>-0.103*<br>(0.0594)</td><td>ln(FVA_Educ)</td><td>0.349**<br>(0.142)</td></tr><tr><td>ln(FVA_Recy)</td><td>-0.0251<br>(0.0428)</td><td>Export</td><td>0.304***<br>(0.0286)</td></tr></tbody></table>

Notes: No. of observations included: 28,632. Robust standard errors are in parentheses and clustered at the donor-year level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. The dependent variable is ODA commitments in level, and the estimation method is PPML with multiple high-dimensional fixed effects (donor-year and recipient-year). We use the inverse hyperbolic sine (IHS) transformation for the export variable. Other explanatory variables (Distance, Colony, and Comlang) are included but not reported. The list of sectors is reported in Table 8 in the Appendix. See the text for details.
<table><caption>**Table 12**
Different clustering.</caption><thead><tr><th>Method:</th><th colspan="3">PPML-FE</th></tr><tr><th>Dependent Variable:</th><th colspan="3">ODA</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td><strong>0.215</strong>***<br>(0.0323)</td><td><strong>0.215</strong>***<br>(0.0762)</td><td><strong>0.215</strong>***<br>(0.0402)</td></tr><tr><td>ln(Distance)</td><td>-0.684***<br>(0.0680)</td><td>-0.684***<br>(0.133)</td><td>-0.684***<br>(0.0673)</td></tr><tr><td>Colony</td><td>1.013***<br>(0.0819)</td><td>1.013***<br>(0.192)</td><td>1.013***<br>(0.0857)</td></tr><tr><td>Comlang</td><td>0.505***<br>(0.0714)</td><td>0.505***<br>(0.194)</td><td>0.505**<br>(0.0888)</td></tr><tr><td>Observations</td><td>41,381</td><td>41,381</td><td>41,381</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Cluster</td><td>donor-year</td><td>donor-recipient</td><td>recipient-year</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year level (col. 1), bilateral level (col. 2) and recipient-year level (col. 3). *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. All explanatory variables, except Distance, Colony and Comlang, are lagged one year. See the text for details.

<table><caption>**Table 13**
Correcting for the incidental parameter problem.</caption><thead><tr><th>Method:</th><th colspan="2">PPML-FE</th></tr><tr><th>Dependent Variable:</th><th colspan="2">ODA</th></tr><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td><strong>0.215</strong>***<br>(0.0762)</td><td><strong>0.215</strong>**<br>(0.0981)</td></tr><tr><td>ln(Distance)</td><td>-0.684***<br>(0.133)</td><td>-0.684***<br>(0.168)</td></tr><tr><td>Colony</td><td>1.013***<br>(0.192)</td><td>1.013***<br>(0.237)</td></tr><tr><td>Comlang</td><td>0.505***<br>(0.194)</td><td>0.505**<br>(0.240)</td></tr><tr><td>Observations</td><td>41,381</td><td>41,381</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the bilateral (donor-recipient) level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. All explanatory variables, except Distance, Colony and Comlang, are lagged one year. Column (1) reports estimation results without correction (with a cluster at the bilateral level) and column (2) with correction for the incidental parameter problem. See the text for details.

<table><caption>**Table 14**
Excluding common factors – 2nd stage Control Function.</caption><thead><tr><th>Method:</th><th colspan="2">PPML-FE</th></tr><tr><th>Dependent Variable:</th><th colspan="2">ODA</th></tr><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>ln(Distance)</td><td>-0.784***<br>(0.0620)</td><td></td></tr><tr><td>Colony</td><td>1.045***<br>(0.0833)</td><td></td></tr><tr><td>Comlang</td><td>0.524***<br>(0.0800)</td><td></td></tr><tr><td>Residuals</td><td>0.171***<br>(0.0339)</td><td></td></tr><tr><td>Observations</td><td>43,376</td><td></td></tr><tr><td>Donor-year FE</td><td>x</td><td></td></tr><tr><td>Recipient-year FE</td><td>x</td><td></td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. This table reports second-stage control function estimates, using bootstrapping methods (100 replications). This estimation includes residuals from a first stage estimation where we regress bilateral foreign value added (in log) on the average of partners of each partner FVA (in log) (with donor-year and recipient-year fixed effects). See the text for details.
<caption>**Table 15**</caption>

Relationship between sector shares (2000) and country characteristics (2005) - Agriculture and Manufacturing.

<table><thead><tr><th rowspan="2">Dependent Variable:</th><th colspan="12">Share of each sector in bilateral FVA (2000)</th></tr><tr><th>Agr (1)</th><th>Fish (2)</th><th>Mine (3)</th><th>Food (4)</th><th>Textile (5)</th><th>Wood (6)</th><th>Petrol (7)</th><th>Metal (8)</th><th>ElecMac (9)</th><th>TranspEq (10)</th><th>OtherMan (11)</th><th>Recy (12)</th></tr></thead><tbody><tr><td>ln(GDP_donor)</td><td>-0.003<br>(0.003)</td><td>0.0006<br>(0.0009)</td><td>0.006**<br>(0.003)</td><td>-0.002*<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.0003<br>(0.0004)</td><td>0.003**<br>(0.001)</td><td>0.004**<br>(0.002)</td><td>0.003*<br>(0.001)</td><td>0.0001<br>(0.0003)</td><td>-0.0002<br>(0.0002)</td><td>0.00006<br>(0.00006)</td></tr><tr><td>ln(GDPCapita_recipient)</td><td>-0.02***<br>(0.002)</td><td>-0.0001<br>(0.0002)</td><td>0.03***<br>(0.001)</td><td>0.003***<br>(0.0008)</td><td>-0.002***<br>(0.0004)</td><td>-0.003***<br>(0.0007)</td><td>0.01***<br>(0.001)</td><td>0.005***<br>(0.001)</td><td>0.004***<br>(0.0010)</td><td>0.002***<br>(0.0005)</td><td>-0.0009***<br>(0.0002)</td><td>0.0004***<br>(0.00003)</td></tr><tr><td>ln(Pop_recipient)</td><td>0.003***<br>(0.0007)</td><td>-0.0005***<br>(0.0001)</td><td>0.02***<br>(0.001)</td><td>-0.002***<br>(0.0004)</td><td>0.004***<br>(0.0005)</td><td>0.0002<br>(0.0004)</td><td>0.009***<br>(0.0005)</td><td>0.002***<br>(0.0003)</td><td>0.003***<br>(0.0005)</td><td>-0.0006***<br>(0.0002)</td><td>-0.0005***<br>(0.00008)</td><td>0.0003***<br>(0.00003)</td></tr><tr><td>InstQ_recipient</td><td>0.03***<br>(0.003)</td><td>0.001**<br>(0.0005)</td><td>-0.02***<br>(0.002)</td><td>0.002<br>(0.001)</td><td>0.003***<br>(0.0007)</td><td>-0.001<br>(0.0009)</td><td>-0.002<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.001)</td><td>0.002***<br>(0.0004)</td><td>0.0007**<br>(0.0003)</td><td>-0.00005<br>(0.00004)</td></tr><tr><td>ln(Distance)</td><td>0.01**<br>(0.006)</td><td>0.003***<br>(0.0005)</td><td>0.006**<br>(0.003)</td><td>0.009***<br>(0.002)</td><td>-0.005**<br>(0.002)</td><td>-0.002<br>(0.0010)</td><td>-0.01***<br>(0.003)</td><td>-0.02***<br>(0.003)</td><td>-0.01***<br>(0.002)</td><td>-0.0003<br>(0.0007)</td><td>0.001***<br>(0.0003)</td><td>0.0003***<br>(0.00006)</td></tr><tr><td>Colony</td><td>0.02*<br>(0.01)</td><td>-0.003<br>(0.002)</td><td>0.0009<br>(0.02)</td><td>0.01***<br>(0.003)</td><td>0.003<br>(0.005)</td><td>-0.003<br>(0.004)</td><td>-0.007<br>(0.005)</td><td>-0.004<br>(0.008)</td><td>0.01<br>(0.007)</td><td>0.003<br>(0.003)</td><td>-0.003*<br>(0.001)</td><td>-0.0005***<br>(0.0001)</td></tr><tr><td>Comlang</td><td>-0.003<br>(0.007)</td><td>0.002<br>(0.002)</td><td>-0.01***<br>(0.003)</td><td>0.001<br>(0.003)</td><td>-0.004*<br>(0.002)</td><td>0.004<br>(0.003)</td><td>0.007<br>(0.006)</td><td>-0.002<br>(0.003)</td><td>-0.003<br>(0.004)</td><td>-0.002***<br>(0.0006)</td><td>0.002**<br>(0.0008)</td><td>0.00003<br>(0.0001)</td></tr><tr><td>Observations</td><td>2,266</td><td>2,178</td><td>2,266</td><td>2,266</td><td>2,266</td><td>2,244</td><td>2,266</td><td>2,242</td><td>2,263</td><td>2,145</td><td>2,244</td><td>1,826</td></tr><tr><td>R-squared</td><td>0.076</td><td>0.046</td><td>0.153</td><td>0.063</td><td>0.076</td><td>0.013</td><td>0.124</td><td>0.080</td><td>0.052</td><td>0.033</td><td>0.036</td><td>0.087</td></tr><tr><td>F-stat</td><td>48.35</td><td>44.26</td><td>232.20</td><td>27.72</td><td>13.32</td><td>15.89</td><td>101.85</td><td>60.31</td><td>10.30</td><td>13.33</td><td>11.92</td><td>89.20</td></tr></tbody></table>

Notes: Cross-sectional estimates. Robust standard errors are in parentheses and clustered at the donor level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$.

<caption>**Table 16**</caption>

Relationship between sector shares (2000) and country characteristics (2005) - Services.

<table><thead><tr><th rowspan="2">Dependent Variable:</th><th colspan="12">Share of each sector in bilateral FVA (2000)</th></tr><tr><th>ElecGas (13)</th><th>Const (14)</th><th>Repair (15)</th><th>Wtrade (16)</th><th>Rtrade (17)</th><th>Hotel (18)</th><th>Transp (19)</th><th>Post (20)</th><th>Fin (21)</th><th>PubAdm (22)</th><th>Educ (23)</th></tr></thead><tbody><tr><td>ln(GDP_donor)</td><td>0.0001<br>(0.0004)</td><td>-0.0006**<br>(0.0003)</td><td>-0.0010***<br>(0.0003)</td><td>-0.002<br>(0.002)</td><td>-0.002**<br>(0.0009)</td><td>-0.001**<br>(0.0006)</td><td>-0.0007<br>(0.002)</td><td>-0.001***<br>(0.0005)</td><td>-0.003**<br>(0.001)</td><td>-0.001***<br>(0.0003)</td><td>-0.001*<br>(0.0006)</td></tr><tr><td>ln(GDPCapita_recipient)</td><td>0.0009***<br>(0.0002)</td><td>-0.003***<br>(0.0002)</td><td>-0.0005***<br>(0.0001)</td><td>-0.003***<br>(0.0006)</td><td>0.010***<br>(0.0007)</td><td>-0.002***<br>(0.0002)</td><td>0.001*<br>(0.0006)</td><td>-0.004***<br>(0.0002)</td><td>-0.01***<br>(0.001)</td><td>-0.002***<br>(0.0003)</td><td>-0.001***<br>(0.0002)</td></tr><tr><td>ln(Pop_recipient)</td><td>-0.0003<br>(0.0002)</td><td>-0.004***<br>(0.0002)</td><td>-0.0010***<br>(0.0001)</td><td>-0.0008<br>(0.0008)</td><td>0.006***<br>(0.0006)</td><td>-0.003***<br>(0.0001)</td><td>-0.0005**<br>(0.0002)</td><td>-0.006***<br>(0.0001)</td><td>-0.01***<br>(0.0001)</td><td>-0.003***<br>(0.0002)</td><td>-0.004***<br>(0.0002)</td></tr><tr><td>InstQ_recipient</td><td>-0.005***<br>(0.0004)</td><td>-0.006***<br>(0.0002)</td><td>0.00008<br>(0.0002)</td><td>0.0004<br>(0.0007)</td><td>0.003**<br>(0.001)</td><td>-0.0002<br>(0.0003)</td><td>0.001<br>(0.001)</td><td>-0.003***<br>(0.0002)</td><td>-0.01***<br>(0.001)</td><td>-0.002***<br>(0.0002)</td><td>-0.003***<br>(0.0003)</td></tr><tr><td>ln(Distance)</td><td>-0.005***<br>(0.001)</td><td>0.0004<br>(0.0008)</td><td>0.002**<br>(0.0009)</td><td>0.003<br>(0.002)</td><td>0.02***<br>(0.002)</td><td>0.002***<br>(0.0005)</td><td>-0.001<br>(0.002)</td><td>0.004***<br>(0.0008)</td><td>0.0009<br>(0.002)</td><td>0.003***<br>(0.0008)</td><td>0.005***<br>(0.001)</td></tr><tr><td>Colony</td><td>-0.007***<br>(0.001)</td><td>-0.003***<br>(0.0010)</td><td>-0.002**<br>(0.0009)</td><td>-0.003<br>(0.006)</td><td>-0.003<br>(0.005)</td><td>0.0001<br>(0.004)</td><td>-0.003<br>(0.004)</td><td>-0.001<br>(0.004)</td><td>-0.003<br>(0.006)</td><td>-0.002**<br>(0.001)</td><td>0.0008<br>(0.005)</td></tr><tr><td>Comlang</td><td>-0.003***<br>(0.0009)</td><td>0.0008<br>(0.0006)</td><td>-0.00003<br>(0.0007)</td><td>0.004<br>(0.004)</td><td>-0.004<br>(0.002)</td><td>-0.001<br>(0.001)</td><td>-0.010**<br>(0.004)</td><td>0.001<br>(0.001)</td><td>0.02***<br>(0.003)</td><td>-0.0009<br>(0.0008)</td><td>0.002<br>(0.003)</td></tr><tr><td>Observations</td><td>2,266</td><td>2,266</td><td>1,694</td><td>1,848</td><td>2,266</td><td>2,222</td><td>2,266</td><td>2,244</td><td>2,266</td><td>2,244</td><td>2,222</td></tr><tr><td>R-squared</td><td>0.044</td><td>0.370</td><td>0.175</td><td>0.023</td><td>0.077</td><td>0.205</td><td>0.016</td><td>0.423</td><td>0.196</td><td>0.268</td><td>0.253</td></tr><tr><td>F-stat</td><td>205.89</td><td>445.79</td><td>66.42</td><td>31.78</td><td>60.17</td><td>100.01</td><td>5.51</td><td>512.38</td><td>77.76</td><td>154.80</td><td>187.09</td></tr></tbody></table>

Notes: Cross-sectional estimates. Robust standard errors are in parentheses and clustered at the donor level. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$.
<table><caption>**Table 17**
2SRI control function estimates (excluding the global financial crisis).</caption><thead><tr><th>Sample:</th><th colspan="2">Without 2007-2008</th><th colspan="2">Without 2007-2008-2009</th></tr><tr><th>Method:</th><td colspan="2">PPML-FE</td><td colspan="2"></td></tr><tr><th>Dependent Variable:</th><td colspan="2">ODA</td><td colspan="2"></td></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>ln(FVA)</td><td><strong>0.165</strong>***<br>(0.031)</td><td><strong>0.348</strong>**<br>(0.144)</td><td><strong>0.144</strong>***<br>(0.031)</td><td><strong>0.280</strong>*<br>(0.159)</td></tr><tr><td>ln(Distance)</td><td>-0.713***<br>(0.075)</td><td>-0.706***<br>(0.079)</td><td>-0.722***<br>(0.079)</td><td>-0.717***<br>(0.087)</td></tr><tr><td>Colony</td><td>0.897***<br>(0.105)</td><td>0.909***<br>(0.108)</td><td>0.872***<br>(0.113)</td><td>0.881***<br>(0.113)</td></tr><tr><td>Comlang</td><td>0.560***<br>(0.085)</td><td>0.555***<br>(0.094)</td><td>0.543***<br>(0.092)</td><td>0.539***<br>(0.101)</td></tr><tr><td>Residuals ShiftShare</td><td></td><td>-0.191<br>(0.158)</td><td></td><td>-0.142<br>(0.151)</td></tr><tr><td>Observations</td><td>25,230</td><td>25,230</td><td>22,946</td><td>22,946</td></tr><tr><td>Donor-year FE</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Recipient-year FE</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td></td><td colspan="2">First stage<br>Dep. var: ln(FVA)</td><td colspan="2"></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>ShiftShare</td><td></td><td>1.16***<br>(0.11)</td><td></td><td>1.25***<br>(0.09)</td></tr><tr><td>KP Wald F-stat</td><td></td><td>109.18</td><td></td><td>175.04</td></tr></tbody></table>

Notes: Robust standard errors are in parentheses and clustered at the donor-year. *** $p < 0.01$, ** $p < 0.05$, and * $p < 0.1$. All explanatory variables, except Distance, Colony and Comlang, are lagged one year. In Columns (1) and (3), FVA is not instrumented. Columns (2) and (4) report the first-stage and second-stage control function estimates. Bootstrapping methods are used in the second stage (500 replications). See the text for details.

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