The Impact of Aid for Trade on Income Inequality: The Case of Upper-Middle-Income Countries

doi:10.21203/rs.3.rs-4198879/v1

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The Impact of Aid for Trade on Income Inequality: The Case of Upper-Middle-Income Countries

https://doi.org/10.21203/rs.3.rs-4198879/v1

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AfT (Aid for Trade) in developing and less developed countries can help reduce income inequality while promoting exports. The present study analyses the effect of AfT on income inequality for 10 upper-middle-income countries using the Pesaran (2006) Common Correlated Effect estimator for 2003–2018. Total AfT had no significant impact on income inequality. Regarding specific AfT components, AfT inflows to develop infrastructure had no significant effect on income inequality. However, AfT funds for developing productive capacity significantly reduced income inequality. This suggests that directing AfT funds to increase production in sectors like agriculture, industry, fishing, and tourism can also make the income distribution more equitable in upper-middle-income developing countries. Finally, the relationship between per capita income and income inequality supported the Kuznets’ inverted U hypothesis.

JEL Codes: O10, F35, P45

Aid for Trade

Income Inequality

Foreign Aid

Developing countries seeking to achieve sustainable development and reduce poverty rely significantly on foreign aid (Garrett and Wanner, 2017). To rebuild their economies in the aftermath of World War II, Western European countries received foreign aid, termed Official Development Assistance (ODA), via the Marshall Plan. Since then, similar forms of support have been extended to developing and less developed countries facing economic crises or natural disasters. During the 1990s, however, the effectiveness of foreign aid was increasingly called into question, given the continued high poverty rates in many of these countries. Meanwhile, there was also increasing recognition that economic growth also depended on openness and exports. Hence, donor countries began requiring aid-receiving countries to reduce import restrictions and liberalize their trade policies. Meanwhile, to maximize the benefits of foreign trade for these countries, donors introduced aid for trade (AfT) initiatives, which combined foreign aid and trade strategies with the aim of raising living standards (Sardar et al., 2022; Van Der Sluis and Durowah, 2018; Lee and Oh, 2022). As Gnangnon (2020b) puts it, AfT refers to foreign aid that aims to expand developing countries’ participation in international trade.

AfT was launched in 2005 at the World Trade Organization’s (WTO) Hong Kong Ministerial Conference, although the WTO had already noted that trade is an important, but neglected, economic development tool. For example, the WTO argued during the 1986-93 Uruguay WTO negotiations that developing countries’ needs were being ignored. Likewise, the WTO claimed during the 2001 Doha Development Round that developing countries were not gaining much from international trade. The AfT initiative is thus a response to these debates, which aims to reshape foreign aid to focus on international trade. Currently, this approach is supported by many developed countries, such as the USA, Australia, Germany, and Japan (European Commission (EC), 2020).

Developing and less developed countries can particularly benefit from AfT given their inadequate production capacity and the difficulties and costs they face in integrating into international markets due to long procedures and insufficient infrastructure and institutions. These factors hinder their ability to compete internationally (Berrittella and Zhang, 2013; Alonso, 2016). AfT enables these countries to eliminate trade barriers, improve infrastructure, and negotiate bilateral commercial agreements. AfT also promotes private sector participation in international trade (EC, 2020; Ghimire et al., 2016), thereby supporting sustainable economic development (Garrett and Wanner, 2017). Although the United Nations’ 2030 sustainable development goals lack an explicit trade target, a number of other targets interrelate with trade (Lammersen and Hynes, 2016).

Total AfT has grown by about 6% annually, from 556.4 billion dollars in 2006 to 48.7 billion dollars in 2020, to become a key financial resource for low- and middle-income countries (OECD/WTO, 2022). At $30 billion annually, AfT currently represents about 30% of developing countries’ financial ODA, although the level is rising (De Melo and Wagner, 2015). The leading donors are European Union (EU) member states, which together provided 13.5 billion Euros in 2018, or about 30% of total AfT funding (EC, 2020). African, Asian, and South American countries are the largest AfT recipients (Berrittella and Zhang, 2013).

A number of studies have evaluated whether AfT inflows achieve their aim of increasing developing and less developed countries’ foreign trade volume (e.g., Mowlaei, 2017; Gnangnon, 2019; Nathoo et al., 2021) and reducing poverty (e.g., Gnangnon, 2020a; Menon and Melendez, 2020). However, to the best of our knowledge, only one study (Gnangnon, 2020b) has directly assessed whether AfT inflows reduce income inequality. For 65 countries, Gnangnon (2020b) found that AfT inflows were associated with lower wage inequality. Furthermore, this reduction was associated with higher exports of labour-intensive manufacturing products. Given that, for many people, wages represent either their total income or most of it, one can assume that wage inequality can closely proxy for income inequality (Siddique, 2021; Urata and Narjoko, 2017). Indeed, empirical studies generally measure income inequality using either the Gini coefficient or wage inequality.

The present study applies the Gini coefficient to examine the effect of AfT on income inequality. If AfT can reduce income inequality, this will indicate an additional benefit beyond its main function of raising export volumes in recipient countries. This in turn could suggest that donor countries aiming to support developing countries should provide foreign aid mostly as AfT.

One of the world’s major problems is the current increase in income inequality in both developing and developed countries (Huang et al., 2022). High income inequality has many negative effects, including societal disruption and conflict, political and economic instability, poor governance, limited access to healthcare, rising criminality, and lower economic growth and labour productivity (Fang and Qamruzzaman, 2021; Siddique, 2021; Levin et al., 2022). Hence, a critical goal of sustainable development is addressing income inequality (Urata and Narjoko, 2017), which in turn requires identification of its causes, as reflected in the large amount of empirical research in this area.

Income inequality is a multifactorial problem, including demographic, political and institutional, economic and financial, globalization and technological elements. Some argue that the quadrupling of international trade since 2000 is a key cause of growing income inequality (Huang et al., 2022), which in turn raises the question of the effect of AfT on income inequality. According to Berrittella and Zhang (2013), AfT can help reduce both poverty and inequality through its main function of increasing developing and less developed countries’ human, institutional, and physical capacity so that they can participate in foreign trade. That is, despite not directly aiming to reduce income inequality, AfT can help do so by helping these countries to expand exports specifically and grow the economy more generally. Our study tests this claim by analysing upper-middle-income countries using the Westerlung and Edgerton (2007) Lagrange Multiplier (LM) cointegration test and the Pesaran (2006) Common Correlated Effect (CCE) estimation method. The analysed period covers 2003 to 2018.

The rest of this paper is organized as follows. Section 2 discusses the relationship between AfT, foreign trade, and income inequality while Section 3 reviews the relevant literature. Section 4 presents the method and data set while Section 5 reports the analysis results. Section 6 concludes the paper.

As already outlined, by addressing poverty, AfT can potentially reduce income inequality via several direct or indirect channels. For example, OECD (2010) showed that AfT can bring poor people into the market system by minimizing supply-side restrictions and trade costs while increasing exports (De Melo and Wagner, 2015; Sardar et al., 2022; Gnangnon, 2020a). That is, growth in foreign trade and the economy can decrease poverty and inequality. However, while there is broad agreement that developing and less developed countries with inadequate domestic markets can grow their economies more quickly by liberalizing foreign trade, there is disagreement regarding international trade’s effects on income inequality in these countries. More specifically, AfT will not do this unless trade-led growth occurs in those sectors that have the most low-income employees. When it does, it can indeed reduce income inequality (Durowah, 2017; De Melo and Wagner, 2015).

The relationship between foreign trade and income inequality is usually explained in terms of the Heckscher-Ohlin and Stolper-Samuelson theories. According to the Heckscher-Ohlin theory, each country has a comparative advantage in certain products, so it should specialize in exporting these. According to the Stolper-Samuelson theory, developing and less developed countries should also consider both product and factor prices. In particular, because these countries have more labour, their export efforts should be focused on labour-intensive products. The resulting international trade will then raise labour employment and wages, thereby reducing income inequality within the country.

As already noted, the integration of developing countries into the global market through trade reforms has helped increase their economic growth. Yet, it has also worsened income inequality in many cases (United Nations (UN), 2019), particularly since 1990. This simultaneous increase in income inequality and trade needs explanation because it contradicts the predictions of the Stolper-Samuelson model (Lin and Fu, 2016). Gnangnon (2020b), for example, suggests that the wage gap between skilled and unskilled workers is widening because the growing trade in technologically advanced products has increased demand for skilled workers. Similarly, Borrs and Knauth (2021) conclude that the demand for skilled labour, and hence wage inequality, has grown since China and Eastern Europe countries joined the international market system. They suggest that trade can explain about 15% of this rise in wage inequality.

Another explanation for growing income inequality is that the large companies that currently dominate international trade have successfully kept profits to themselves (UN, 2019). A further factor may be that skills and technology development have become increasingly important, thereby reducing subcontracted, unskilled workers’ bargaining power and wages (Dorn et al., 2021).

A more recent approach to theorizing these relationships is the Kuznets (1955) inverted U curve hypothesis. This predicts that participating in international trade will initially increase income inequality in less developed countries due to the focus on high-income sectors. Later, however, income inequality will fall as these societies become more equal, democratic, and economically advanced (Huang et al., 2022). In short, foreign trade may have unexpected effects on income inequality that may occur via various channels (UN, 2019). In general, even if foreign trade does not decrease poverty directly, it may do so indirectly by reducing poverty through the higher employment and wages resulting from economic growth (Lammersen and Hynes. 2016; Fang and Qamruzzaman, 2021; Page, 2007). Another indirect influence may occur through beneficial structural changes, such as the shift in many East Asian countries from predominantly agricultural to manufacturing economies. This has led to a burgeoning middle-income class (UN, 2019). AfT can also reduce income inequality indirectly by funding the development of productive capacities, particularly in agriculture, such as for training, irrigation, energy sources, and fertilizers (De Melo and Wagner, 2015). Such initiatives can have significant effects on family incomes given the importance of agriculture to developing countries’ GDP (Lammersen and Hynes. 2016).

Another poverty-alleviating effect of AfT in developing countries occurs through its support for small and medium-sized enterprises to make them more competitive internationally. These enterprises can provide new income streams for otherwise underemployed or unemployed workers (Gnangnon, 2020b). Given that women tend to own smaller businesses than men, this focus of AfT can also reduce gender-based income inequality specifically by empowering women to increase their incomes by participating in international markets (OECD/WTO, 2022; Lammersen and Hynes. 2016). In short, AfT can indirectly alleviate poverty by encouraging foreign trade strategies supportive of women and young people (Gnangnon, 2020a).

Gnangnon (2020a) demonstrated that AfT can also reduce poverty by increasing the diversity of a country’s exports. AfT funding can enable these countries to develop the necessary economic infrastructure to easily access up-to-date technology and global communication networks. This is important because countries with more diversified exports tend to have more sustainable growth and create more employment opportunities (UNCTAD, 2018). Furthermore, products tend to be more complex in those countries with more advanced institutions and a skilled, well-unionized workforce that consequently has strong collective bargaining power. However, the diversity of export products may have varying effects on income inequality depending on whether it differentially affects skilled and unskilled workers’ wages (Gnangnon, 2020b). Le et al. (2020), for instance, reported an inverted-U relationship between export product diversity and income inequality for 90 countries between 2002 and − 2014.

Finally, AfT can attract more foreign direct investment (FDI) to less developed countries (Nguyen et al., 2023), particularly for infrastructure projects. By stimulating economic growth and technology and knowledge transfers, FDI can help these countries’ local companies become more productive and internationally competitive. This in turn, can help reduce income inequality (Hayashikawa, 2008).

There has been extensive research into the effect of foreign aid on poverty and income inequality (Wang et al.: Maqbool and Ali: Younsi et al.). In contrast, research regarding the specific effect of AfT on poverty is rare. Van Der Sluis and Durowah (2018), for example, reported that AfT reduced poverty in 91 countries, although the effects varied. Specifically, they were largest in less developed countries; they were larger if AfT was used for infrastructure or new trade policies; and were larger if the recipient country’s economy relied less on agriculture. For Rwanda, Diop et al. (2005) found that reducing transportation costs was associated with rising export product prices and lower poverty. From their literature review, De Melo and Wagner (2015) concluded that AfT can increase productivity in agriculture, which is a major employer of poorer people. More specifically, such people can increase their incomes if AfT is used for road construction and other infrastructure to connect rural regions to major markets. From their analysis of countries in South and East Asia, Africa, and the Pacific, Ashenafi and Dong (2022) concluded that reducing tariffs stimulates agricultural production, which in turn raises unskilled workers’ incomes. Finally, focusing on six South Asian countries (Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka), Sardar et al. (2022) found that AfT has improved human development.

Several other studies have investigated how AfT influences international trade volumes. Vijil and Wagner (2012), for example, reported that AfT that is focused on infrastructure, trade policies, and regulations was associated with higher total exports. From their analysis of 143 countries, Lee and Oh (2022) found that AfT to reform foreign trade policies and regulations had a positive effect on exports in Asian countries. The researchers suggested that this was due to government support for liberalizing trade in this region. Finally, Ghimire et al. (2016) who analysed 121 developing countries, also found a positive association between AfT and export volumes.

Despite the large number of studies into the relationship between foreign trade and income inequality, there is still no consensus. Some studies conclude that international trade reduces income inequality (e.g., Dorn et al., 2021; Nguyen, 2020; Goldberg and Pavcnik, 2007; Jaumotte et al., 2013; Berrittella and Zhang, 2013; Calderon and Chong, 2001), whereas others conclude the opposite (e.g., Khan et al., 2021; Wagle, 2007; Ghazali, 2009; Roser and Cuaresma, 2016), and a third group find no significant relationship (e.g., Agusalim and Pohan, 2018; Beaton et al., 2017; Dabla-Norris et al., 2015). The findings can also vary between countries. For instance, from their analysis of 100 countries, Zhu et al. (2023) reported that trade in digital services was associated with income inequality in middle- and upper-income countries but not in lower-income countries. Another factor is the theoretical model used. Thus, Urata and Narjoko (2017) found that applying certain models indicated that trade liberalization reduced wage inequality whereas other models indicated the opposite. The authors argued that these results critically depended on each country’s specific characteristics regarding the labour market, capital inflows, and trade policies. Another factor is system of government, with Lin and Fu (2016) reporting that foreign trade reduces income inequality in autocracies but increases it in democracies. They explained this in terms of export focus, with autocracies focusing on primary goods whereas democracies focus on manufacturing. Finally, Mumuni and Abille (2022) found that trade liberalization in 30 African countries ultimately reduced income inequality, although it initially made it worse.

The present study investigated the effect of AfT on income distribution in countries classified by the World Bank (2024) as upper-middle-income countries, i.e., countries with a per capita Gross National Income (GNI) of 4,466 − 13,845 dollars in 2022. Based on the available data, the following 10 countries were analysed for 2003–2018: Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Indonesia, Mexico, Paraguay, Thailand, and Turkey. Income inequality was represented by Gini coefficients obtained from Solt (2020) while the World Bank database and the OECD database provided data for the GDP and AfT variables, respectively. All variables were transformed into their logarithmic forms before being used in the models.

Model (1) was used to evaluate the impact of AfT on income inequality:

Model 1: ${GINI}_{it}={\beta }_{0}+{\beta }_{1}{AfT}_{it}+{{\beta }_{2}GDP}_{it}+{\beta }_{3}{GDP}_{it}^{2}+{ϵ}_{it}$ (1)

Here, ${GINI}_{it}$ and ${AfT}_{it}$ represent, respectively, the Gini coefficient and total AfT disbursements. The model uses the ratio of AfT disbursements to GDP. Economic growth, represented by real GDP per capita (${GDP}_{it}$), was used as the control variable while the square of real GDP per capita (${GDP}_{it}^{2}$) was added as an explanatory variable to test the validity of the Kuznets (1955) inverted-U hypothesis. This hypothesis predicts that income inequality and income level are initially positively associated during economic development, whereas they become negatively associated beyond a certain economic development threshold. Hence, the model predicts an inverted U-shaped relationship between the two variables. In terms of the model above, it predicts that coefficients ${\beta }_{2}$ and ${\beta }_{3}$ are positive and negative, respectively.

The present study also tested whether two groups of AfT components can explain income inequality, namely infrastructure-focused AfT and production capacity-focused AfT. Following Nguyen et al. (2023), in Model (2) below, AfT infrastructural funding for improving communication, energy production and supply, storage facilities, and transportation networks is represented by ${AfT}_{inf}$. In Model (3) below, AfT production capacity funding for improving agriculture, forestry, and fishing, industry, mining, tourism, banking, and financial services are represented by ${AfT}_{prod}$.

Model 2: ${GINI}_{it}={\beta }_{0}+{\beta }_{1}{AfT}_{inf,it}+{{\beta }_{2}GDP}_{it}+{\beta }_{3}{GDP}_{it}^{2}+{ϵ}_{it}$ (2)

Model 3: ${GINI}_{it}={\beta }_{0}+{\beta }_{1}{AfT}_{prod,it}+{{\beta }_{2}GDP}_{it}+{\beta }_{3}{GDP}_{it}^{2}+{ϵ}_{it}$ (3)

All variables were transformed into their logarithmic forms before being used in the models.

The first step in investigating whether AfT reduces income inequality, was to perform a cross-sectional dependency analysis to determine whether a shock in one country affects other countries. This is necessary to determine the appropriate unit root and cointegration tests for analysing each variable. For this purpose, we used the Breusch and Pagan (1980) LM test and the Pesaran (2004) Cross-section Dependence Lagrange Multiplier (CDLM) and Cross-section Dependence (CD) tests. While the CDLM test provides consistent estimates so long as the cross-sectional dimension remains smaller than the time dimension, it produces extreme size distortions in the opposite case, whereas the CD test does not face this problem.

Equations 4, 5, and 6 provide, respectively, the test statistics for the LM, CDlM, and CD tests (Pesaran, 2004):

$$LM=\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}{T}_{ij{\widehat{p}}_{ij}^{2}}$$

$CDLM=\sqrt{\frac{1}{N(N-1)}}\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}{\widehat{(Tp}}_{ij}^{2}$ -1) (5)

$$CD=\sqrt{\frac{2T}{N(N-1)}}\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}\widehat{{\rho }_{ij}}$$

Here, ${\widehat{p}}_{ij}$; is the estimate of the pairwise correlation of the residuals. The tests are based on the hypothesis of no cross-sectional dependency. Table 1, which summarizes the test results, indicates that the series is characterized by cross-sectional dependency.

Table 1

**Results of Cross-Sectional Dependency Tests**
Variables	LM	CDLM	CD
GINI	95.380 (0.000)	5.311 (0.000)	-2.075 (0.019)
GDP	84.746 (0.000)	4.190 (0.000)	-2.349 (0.009)
GDP²	85.833 (0.000)	4.304 (0.000)	-2.369 (0.009)
AfT_inf	119.280 (0.000)	7.830 (0.000)	-2.037 (0.021)
AfT_prod	94.355 (0.000)	5.203 (0.000)	-1.603 (0.054)
AfT	123.948 (0.000)	8.322 (0.000)	-2.105 (0.018)
Note: Values in parentheses are P-values.

The unit root properties of the series were tested using Smith et al.’s (2004) test of cross-sectional dependency, which offers 5 different test statistics:$\stackrel{-}{t}$, $\stackrel{-}{Max}$, $\stackrel{-}{LM}$, $\stackrel{-}{WS}$, and $\stackrel{-}{Min}$. $\stackrel{-}{LM}$ is a modification of of Solo’s (1984) LM while $\stackrel{-}{t}$ is a modification of Im et al.’s (2003) IPS. Smith et al. (2004) developed $\stackrel{-}{Max}$ based on Leybourne (1995) and $\stackrel{-}{WS}$ test statistics based on ADF statistics. $\stackrel{-}{Min}$ is a stronger form of LM. As can be seen from Table 2, which presents the unit root test results, all variables were I(1) at 5% significance, thereby indicating that the variables have a cointegration relationship.

Table 2

Results of Unit Root Tests
Variables	$\stackrel{-}{\varvec{t}}$stat.	$\stackrel{-}{\varvec{M}\varvec{a}\varvec{x}}$Stat.	$\stackrel{-}{\varvec{L}\varvec{M}}$Stat.	$\stackrel{-}{\varvec{M}\varvec{i}\varvec{n}}$Stat.	$\stackrel{-}{\varvec{W}\varvec{S}}$Stat.
GINI	-2.069 (0.435)	-1.517 (0.390)	4.818 (0.505)	3.479 (0.450)	-2.356 (0.312)
GDP	-1.922 (0.631)	-1.367 (0.724)	4.068 (0.680)	2.482 (0.833)	-1.866 (0.850)
GDP²	-1.774 (0.767)	-1.475 (0.672)	3.524 (0.814)	2.713 (0.750)	-1.926 (0.789)
AfT_inf	-2.024 (0.538)	-1.867 (0.243)	5.435 (0.505)	4.996 (0.212)	-2.287 (0.309)
AfT_prod	-2.401 (0.197)	-2.155 (0.076)	5.724 (0.192)	5.112 (0.066)	-2.788 (0.025)
AfT	-2.262 (0.311)	-2.058 (0.133)	5.417 (0.300)	4.870 (0.114)	-2.650 (0.103)
First Difference
GINI	-2.896 (0.020)	-2.287 (0.037)	6.900 (0.010)	5.409 (0.027)	-3.098 (0.003)
GDP	-5.115 (0.002)	-3.720 (0.001)	9.035 (0.002)	8.068 (0.001)	-3.597 (0.001)
GDP²	-5.054 (0.002)	-3.717 (0.001)	9.015 (0.002)	8.060 (0.001)	-3.602 (0.001)
AfT_inf	-3.889 (0.001)	-3.655 (0.000)	9.735 (0.000)	9.093 (0.000)	-4.315 (0.000)
AfT_prod	-4.494 (0.000)	-3.997 (0.000)	9.266 (0.000)	8.969 (0.000)	-4.499 (0.000)
AfT	-5.201 (0.000)	-4.801 (0.000)	10.325 (0.000)	10.146 (0.000)	-5.378 (0.000)
Note: Values in parentheses are P-values.

The cointegration equation slope coefficients were tested for heterogeneity using Pesaran and Yamagata’s (2008) Delta test, which provides reliable results for large samples, whereas the Delta adj test provides reliable results for small samples (Pesaran and Yamagata, 2008, p.57). As can be seen from Table 3, the test results reject the null hypothesis that the slope coefficients are homogeneous across countries. That is, the model is heterogenous.

Table 3

**Results of Homogeneity Tests**
Test	Model 1		Model 2		Model 3
	Stat.	P-value	Stat.	P-value	Stat.	P-value
Delta	11.637	0.000	9.041	0.000	8.592	0.000
Deta adj.	14.035	0.000	11.436	0.000	10.868	0.000

Westerlung and Edgerton’s (2007) LM test was used to evaluate the long-term cointegration relationship between the independent variables and the dependent variable. This test is applicable regardless of cross-sectional dependency and can provide consistent, unbiased results for small samples. Table 4 presents the LM test bootstrap results, which is applicable in cases of cross-sectional dependency. For all three models, the results confirm the test hypothesis that the variables are cointegrated.

Table 4

**Results of Cointegration Test**
Test	Model 1		Model 2		Model 3
	LM stat.	P-value	LM stat.	P-value	LM stat.	P-value
LM test	10.779	0.952	9.098	0.997	12.430	0.860

Given the cointegration of the variables, the Pesaran (2006) CCE method, which controls for heterogeneity, was applied to estimate the long-term coefficients. This multi-factor residual model has a number of advantages: it distinguishes between observed and unobservable common effects with individual-specific regressors in the model; it allows common factors to differentially affect individual units; it permits correlations between common factors themselves and with individual-specific regressors; and it accepts heteroskedastic and correlated error terms in the model. Another advantage is that the CCE estimator requires the common factors and explanatory variables to be exogenous and stationary. The Kapetanios et al. (2006) CCE method also provides consistent results in cases in which the variables contained unit roots. That is, the CCE estimator can yield unbiased results in small samples whether T < N or N < T cases. Furthermore, the results are not affected by the series’ cointegration and unit root properties (Pesaran, 2006; Kapetanios et al., 2006).

Table 5

**Results of the Long-Run Coefficient Estimation**
	Model 1		Model 2		Model 3
	Coef.	P-value	Coef.	P-value	Coef.	P-value
GDP	9.895646	0.062	4.295494	0.049	5.848511	0.027
GDP²	-1.285536	0.057	− .5785821	0.046	− .7743133	0.027
AfT_inf			.0001324	0.880
AfT_prod					− .0019812	0.004
AfT	− .0001235	0.856
c	-11.37355	0.375	-4.071291	0.178	-6.580219	0.052

Table 5 presents the results of the long-run coefficient estimations. Overall, these show that total AfT disbursements have no statistically significant effects on income inequality, indicating that AfT disbursements cannot explain income inequality trends in the countries examined in this study. These findings also contradict previous studies reporting that AfT reduces poverty (Van Der Sluis and Durowah, 2018; Ashenafi and Dong, 2022). According to Model 1, both GDP and the square of GDP affect income inequality at the 10% significance level, with positive and negative coefficients, respectively. This suggests that Kuznets’ inverted-U hypothesis is valid for upper-middle income countries. That is, AfT initially increases income inequality when these countries grow economically but later decreases it after a certain level of economic growth.

Regarding the specific components of AfT, the results for Model 2 show that infrastructure-focused AfT funding has no statistically significant effect on income inequality in receiving countries. However, economic growth and income inequality follow a statistically significant inverted U-shaped relationship at the 5% significance level. In contrast, the results for Model 3 show that productive capacity-focused AfT inflows significantly reduce income inequality in upper-middle-income countries, albeit to a very limited extent. As with the first two models, there is an inverted-U-shaped relationship between economic growth and income inequality.

Our findings provide mixed support for previous studies. First, regarding the relationship between AfT disbursements and production capacity, we support De Melo and Wagner (2015), who reported that poverty can be reduced by focusing AfT funding on agriculture. Conversely, regarding infrastructure-focused AfT funding, our findings contradict De Melo and Wagner’s (2015) conclusion that such funding can reduce poverty. Second, regarding the inverted U-shaped relationship between economic growth and income inequality, our findings agree with Paweenawat and McNown (2014), who investigated Thailand. On the other hand, they disagree with Zungu et al. (2021), who investigated 13 Southern African Development Community (SADC) countries.

While foreign aid primarily aims to promote economic development in developing and less developed countries, ultimately, it also aims to reduce poverty. Given research showing that such aid in the form of ODA has not been so effective in achieving this latter aim, it has been suggested that the promotion of foreign trade in these countries could be more effective. Proponents argue that by combining foreign aid and trade policies, AfT can better stimulate recipient countries’ economic growth through exports. In particular, increasing exports can create new job opportunities and raise wages. Hence, AfT may be an important financial tool for reducing poverty and income inequality.

Using Westerlung and Edgerton’s (2007) cointegration test, the present study analysed the impact of AfT on income inequality for six upper-middle-income countries between 2003 and 2018. Given that the variables were cointegrated, the long-run coefficients were estimated using the Pesaran (2006) CCE estimator. The analysis indicated that total AfT disbursements had no statistically significant effect on income inequality in the six countries. However, two specific AfT components (infrastructural-focused and productive capacity-focused) had differential effects on income inequality. More specifically, the former component had no significant effect whereas the latter component significantly reduced income inequality. The results also supported the Kuznets hypothesis, implying that economic growth and income inequality form an inverted-U-shaped relationship in the investigated countries.

In conclusion, the present findings show that AfT inflows can reduce income inequality in upper-middle income countries, but only if they aim to increase production capacity. As Hayashikawa (2008) argues, AfT funding can help poorer people in recipient countries so long as it promotes foreign trade and economic growth specifically in those sectors that provide the most employment for low-income workers. That is, AfT funding in developing countries to boost productivity in sectors like agriculture and fisheries, industry, and tourism may help reduce income inequality. This is because the higher production capacity due to AfT inflows creates new job opportunities that particularly favour poor people, including women, who tend to work in these sectors. Thus, production-focused AfT funding in such countries can reduce income inequality as well as increase foreign trade. In contrast, it can be predicted that infrastructure-focused AfT funding (e.g., for energy production and transportation) will have no noticeable effect on income inequality. This may be because infrastructural development has already passed a certain threshold level in upper-middle-income countries, such that poorer people will not benefit significantly from further infrastructure-focused AfT inflows. However, it should also be noted that the present study only analysed a short period while these countries have only just begun developing sectors like renewable energy. Consequently, macroeconomic indicators may not yet be reflecting such infrastructure investments. Overall, our analysis indicates that policymakers should aim to increase in upper-middle-income countries in order to reduce income inequality. Meanwhile, donor countries should increase AfT funding for projects related to recipient countries’ production sectors.

Author Contribution

AA prepared empirical results and tables. AA and JTL wrote the main text jointly. Both authors reviewed the manuscript.

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The Impact of Aid for Trade on Income Inequality: The Case of Upper-Middle-Income Countries

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Abstract

1. Introduction

2. Theoretical Framework AfT, Foreign Trade, and Income Inequality

3. Literature Review

4. Data and Models

5. Empirical Results

6. Conclusion

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