The Influence of Aid for Trade on Human Development in South Asia

Abstract

Although South Asia has made some progress in human development over the last century, it still lags behind some other Asian countries. It is necessary to explore the factors that contribute to human development but have not yet been explored. Thus, the aim of this study is to explore the influence of aid for trade on human development in South Asia for the period 2005 to 2019. First, the study detected the unit root problem in the data using panel unit root tests and found that all data series (human development, aid for trade, population and remittances) are stationary at the level except foreign direct investment, which is stationary at the first difference. Second, the pooled mean group-autoregressive distributed lag (PMG-ARDL) model was employed to examine the short- and long-term influence of aid for trade on human development. The findings of the PMG-ARDL approach explored that aid for trade has a positive and significant influence on human development, both in the short and long run. Finally, this study prescribed some policy suggestions to improve human development in the region.

1. Introduction

Human development is one of the central considerations for the development of any country. It is not just about per capita income growth, but encompasses a multidimensional aspect of the community, such as the economic, social, political, health, education, law and security aspects. Sen recognized Haq as “the pioneer leader of the human development approach”. He connected the origin of this approach to a series of past and related thoughts including basic requirements, physical quality of life and discrepancies in living conditions. This human development approach addresses all these concerns [1]. Human development includes a variety of choices, as well as the freedom and resources to pursue these opportunities [2]. Ref. [3] argues that, rather than goods and income, the analysis of human development should be more linked to the choice of freedom of available opportunities. Ref. [4] mentioned that opportunities, achievements and freedom of choice are pioneers of human development. The Human Development Index was developed by the United Nations Development Program in the year 1990 and is widely used as a tool to assess human development. This human development index captures people’s basic quality of life by examining three aspects. These include a long and healthy life, knowledge and good living standards [2].

South Asia is in the Southern region of Asia. This region includes Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. In the year 2017, 216 million people subsisted on less than USD 1.90 a day, out of an estimated 736 million extremely poor people worldwide [5]. More than 850 million people over the age of 15 years cannot read or write, of which two-thirds of the population are women. Several people in South Asian countries do not have access to health services. According to the United Nations Children’s Fund, 5.4 million children, more than half of them newborns, do not live past their first five years of life [6]. These figures show the level of low human development in the lives of the country’s poor. The trend of the Human Development Index in South Asia is shown in

Table 1. Ranking South Asian countries based on Human development.

Country	Human Development Index	Rank
Sri Lanka	0.782	72
Bhutan	0.654	129
India	0.645	131
Bangladesh	0.632	133
Nepal	0.602	142
Pakistan	0.557	154

Source: [6].

, along with the ranking. Sri Lanka is ranked 72nd in the world, while Pakistan is ranked 154th in the world.

The World Trade Organization (WTO) held in Hong Kong in 2005 devised the Aid for Trade Initiative at the Ministerial Conference. This initiative aimed to support the 8th millennium development goal, i.e., to create a worldwide partnership for development, easing global trade and improving market access for developing countries, comprising quota-free and duty-free trade. The main aim of aid for trade is to expand competitiveness and active participation in local, country and world markets to expand trade-related infrastructure, improve supply-side capacity and improve market access; implement and adjust trade restructurings; and to help in the implementation of trade agreements. Aid for trade is mainly focused on economic infrastructure, competitiveness and export expansion to meet an extensive development program that includes poverty reduction and human development. The Aid for Trade Initiative links aid and trade policies in the quest to raise living standards and human development [7].

Food exports from low-income countries should be improved through government interventions (e.g., improving the sector’s labor market situation the production of high value-added food goods). These countries could create jobs at home, not abroad [8]. The true wealth of any country is its population, and the purpose of human development is to create an environment that allows people to live healthy, long, and creative lives. Human development is a development about expanding human potential and expanding human freedoms. It is about developing and improving people’s capabilities. This, in turn, empowers people to make choices and lead lives they are meant to value. The main difference in living standards between nations is the difference in human capital.

The core objective of this study is to assess the influence of aid for trade on human development in South Asia. The study area included South Asian countries including Bangladesh, Bhutan, India, Nepal, Sri Lanka and Pakistan. Afghanistan and Maldives are not included in the study due to the unavailability of important data. South Asian countries face severe poverty, sluggish GDP growth, high death rates and low rates of education. This region therefore requires significant attention to propose some effective and efficient policies related to aid for trade to improve human development in South Asia. Unfortunately, much less literature was found on the role of aid for trade in human development in South Asia. The study’s findings will give decision-makers in South Asia a clear and useful road map for developing suitable economic policies regarding human development in terms of aid for trade.

It is evident from the literature that many studies have been carried out related to aid for trade, but those studies that explore the impacts of aid for trade employ quantitative analyses and limit their focus to evaluating the effect of aid for trade on trade [9], economic growth [10] and export diversification [11] and do not consider its role in human development. So far, we have not found any studies for South Asia in this regard. Thus, the findings of the present study exploring the influence of aid for trade on human development for South Asia fill this gap. This research is also a pioneering study that will explore the effect of aid for trade on human development in South Asia. In addition, this study will also examine the effects of various determinants such as population, inflation, remittances and foreign direct investment on human development in South Asia. Based on the gap identified in the literature, this paper asks the question: does aid for trade affect human development in South Asia?

The remainder of the manuscript is formatted as follows: the second part of the article is related to the literature review. The third and fourth parts consist of the research methodology and the discussion of the findings, respectively. The last section of the manuscript is related to the conclusions and policy recommendations.

2. Literature Review

The literature review of this study is twofold: the first part provides a theoretical background, and the second part explains an empirical view of past work directly or indirectly linked to this study.

Official Development Assistance (ODA), or more commonly known as foreign aid, began after World War II [12,13,14]. Developing countries have received assistance when they were suffering from economic crises, natural disasters and emergencies. While some assistance is given by governments, some is provided by non-governmental organizations (NGOs) [15,16,17]. Lack of supply-side capacity limitations and a lack of adequate trade-related infrastructure in the developing region result in the receiving country being unable to take advantage of international trade and pursue overall continued economic growth in general and human development. The World Trade Organization (WTO) established Aid for Trade for developing countries in 2005 to remove these barriers to trade. The World Trade Organization created a task force in February 2006 to operationalize aid for trade based on the needs of developing nations. The task force does not provide suggestions on the financial means needed, where the money should come from or how it should be prioritized. The OECD provides data corresponding to all categories defined by the World Bank Task Force and the number of studies was based on the OECD data definitions. The OECD defines aid for trade categories as economic infrastructure, where aid goes to the transport, energy, storage and communications sectors to combine domestic and international markets, and building productive capacity in the real sectors such as financial and banking services, business, forestry, fisheries, agriculture, industry, mining and tourism to promote production and export diversification and maximize comparative advantage, regulation and trade policy. Trade officials are being trained to formulate trade policies and lead the effective implementation of trade negotiations. Other aid for trade initiatives help with other trade-related challenges. For example, [7,18] explored the influence of international aid on quality of life, employing the HDI (Human Development Index) as a proxy for quality of life. For the years 1974 to 1985, the ordinary least squares were used to assess the purpose of the study. For democratic and autocratic countries, Kosack performed different cross-country analyses. The results suggest that aid is affective for quality of life, but aid is more affective on improving quality of life in democratic countries compared to autocratic countries.

The influence of foreign aid on human development indicators was used to analyze aid efficacy. The impacts of two types of foreign aid were examined in the study. Bilateral aid that goes directly to recipient countries from donor countries and other projected aid that is delivered to poor nations through non-governmental organizations. Two-stage least squares (2SLS) model and the generalized method of the moments (GMM) system were used. The study found that foreign aid is useful in human development based on its findings. The study also found that assistance from non-governmental organizations (NGOs) is more beneficial than official bilateral assistance in decreasing newborn mortality rates. The study found that aid has a small but considerable role in education [19]. Ref. [20] explored that aid contributes to public spending and therefore contributes to well-being. Well-being measured by the human development index is a 0 to 1 level that measures various aspects of quality of life, such as health, education and income. For a panel dataset of 38 countries over the 1980–1998 period, quintile regressions on welfare states were used. The study suggested that aid promotes human development by supporting government spending that improves measures of well-being. Aid-associated infant mortality and well-being have increased human development and decreased infant mortality. Aid is most effective in nations with lower levels of human development, such as those at the bottom center of the welfare distribution. The research found evidence that aid is more involved in countries with lesser levels of human development indicators, as increased investment in health reduces child and infant mortality rates and increases government spending on primary and secondary education. Aid has a beneficial influence on the extensively used metrics of educational achievement. Government spending on social services, i.e., sanitation, education and health are most likely to benefit the poor. Research has suggested that aid has an impact on well-being by affecting government spending [20]. The research study was conducted for Rwanda, one of the poorest countries in the world, with a high proportion of people employed in agriculture. The researcher used the aid for trade subcategories of trade-related policy and trade facilitation to examine the effect of aid for trade on the costs and welfare of trade. The study found that market access and the costs of trade are two of the most important factors in human well-being. According to their findings, a 1% reduction in transport costs increases producer costs by 21%, which decreases human well-being by 7%. They concluded that trade policy assistance is the most cost-saving in terms of trade, while trade-facilitation trade assistance generates the greatest welfare gains. Based on empirical findings, they concluded that lowering transport costs benefits the poor [21]. The effectiveness of foreign direct investment and aid for trade on human development was examined for 91 underdeveloped countries in different income groups between agricultural and non-agricultural countries. The study used random effects and fixed effects models. Empirical results showed that aid for trade flows had a positive influence on poverty reduction, but the influence varies between different income groups in less developed countries. The results also found that aid for trade works best at reducing poverty when it is allocated to investment and trade rules and regulations, and when it is allocated to economies with low dependence on agriculture. Aid for trade focused on investment and trade laws and regulations also increases net FDI flows, but aid for trade focused on productive capacity decreases them [22].

Keeping in view the above discussion, a gap was found in the previous literature. Most previous studies have focused on foreign aid. Foreign aid includes a variety of goals, some of which do not directly address human development, with little research exploring the influence of aid for trade on human development. Moreover, no research has yet investigated the influence of aid for trade on human development in South Asia. Therefore, this study fills this gap in the literature and also provides literature for future studies.

3. Methodology

The main objective of this study is to assess the influence of aid for trade on human development in South Asia, using balanced panel data for the period 2005 to 2019. Two South Asian countries, namely, Afghanistan and Maldives, were not included due to the unavailability of data on some variables. Human development was taken as a dependent variable and measured by the human development index (HDI). It is a statistical tool employed to compute the human development of a country, developed by the United Nations. This index contains three main components: people’s health, education and standard of living. Aid for trade was chosen as the main independent variable in this study because it was identified as the most relevant variable to address the problem of human development in this region for a few reasons. The reason for applying aid for trade instead of ODA (official development assistance) has a variety of objectives, some of which do not always promote human development, but aid for trade focuses on improving people’s livelihoods as the second most essential objective [23]. Thus, the use of aid for trade (AFT) is more suitable compared to official development assistance (ODA) for the purpose of the study. The study used other important variables, including remittances, population, inflation and foreign direct investment, that contribute to human development that have been identified by previous studies [24,25,26,27].

Remittances received directly into households have a significant and positive influence on human wellbeing. This increases the disposable income of recipient families and the effect on health, education, consumer spending, saving behavior, and asset accumulation [28]. Ref. [25] stated that population has an adverse effect on human development, that is, due to lack of resources, the more people there are, the more the performance of human development decreases. The effect of inflation on human development is based on the research outcomes from several studies. Ref. [26] explored that inflation adversely affects human development. Inflation disrupts the availability of basic needs such as food, clothing, education, health and shelter. Foreign direct investment helps human development through both direct and indirect channels when foreign investors invest directly in providing some form of social assistance to the poor and the indirect channel that provides opportunity for private sector employment, capital accumulation and FDI can bring human development [27].

Table 2. Description of the variables.

Abbreviation	Variables	Unit	Data Sources
HDI	Human development index	Index	[6]
AFT	Total aid for trade	Million (constant 2020 US dollar)	[29]
POP	Population growth	Percentage	[5]
CPI	Inflation	Consumer price index	[5]
REM	Remittances	Million (Current US dollar)	[5]
FDI	Foreign direct investment	Million (Current US dollar)	[5]

elaborates description of the variables used in the study [5,6,29].

Before using the PMG-ARDL approach, it is essential to ensure data consistency and data stability. We examine the data for the unit root problem. For this, this study applied a panel unit root test to identify the non-stationarity issue of each variable. The mean, variance and covariance of the data are inconstant over time, indicating the problem of unit root. Data containing the presence of a unit root can lead to unreliable and biased estimates [30]. The following Equation (1) expresses a simple AR(1) model that can be used to explain the unit root problem:

$${\mathrm{Y}}_{\mathrm{it}}=\varnothing {\mathrm{Y}}_{\mathrm{it}-1}+{\mathrm{e}}_{\mathrm{it}}$$

(1)

In Equation (1), Y represents any data series, Ø shows the parameter, e_it depicts the residual term and subscript t − 1 shows number of cross-sections_. There are three possible descriptions of unit root detection in the autoregressive model: when Ø = 1, the series has a unit root; when Ø > 1, the series is explosive; and when Ø < 1, the data are free from the unit root issue. The Levin and Lin (LL) unit root test is employed in this study, developed by [31], to detect the non-stationary issue. There are three kinds of test equations, i.e., (i) neither intercept nor trend, (ii) intercept but no trend variable and (iii) intercept and trend in the model. The second type of equation, which has a single intercept but no trend variable in the model, was used in this study. Equation (2) shows a mathematical form of both tests with an intercept but no trend variable:

$$\mathrm{\Delta }{\mathrm{Y}}_{\mathrm{it}}={\mathrm{a}}_0+{Ø\mathrm{Y}}_{\mathrm{it}-1}+{\displaystyle \sum }_{\mathrm{s}=1}^{\mathrm{p}}{\mathsf{\beta }}_{\mathrm{i}}\mathrm{\Delta }{\mathrm{Y}}_{\mathrm{it}-\mathrm{i}}+{\mathrm{e}}_{\mathrm{it}}$$

(2)

where Y is a variable, ${\mathrm{a}}_0$ represents the intercept term, Ø shows the slope coefficient of the variable Y and ${\mathrm{e}}_{\mathrm{it}}$ shows the residual term. Two subscripts, such as t and I, represent time period and number of cross-sections, respectively. The sum of “s” gives the number of lags employed in both tests starting from 1 to p.

In order to investigate the long- and short-term influence of aid for trade on human development, this study used the Panel Autoregressive Distributed Lag (ARDL) model based on the pooled mean group (PMG) and mean group (MG) estimators developed by [32,33], respectively. The PMG imposes a constraint on the homogeneity of the long-term coefficients between the cross-sections, while the heterogeneity of the short-term coefficient varies. As there are no restrictions on the mean group (MG) coefficient, it is free to vary and be heterogeneous in both the short and long run. However, according to [32], the PMG estimator offers more efficient estimates than the MG estimator in the long term. To confirm this assumption of long-term homogeneity, [34] was used in this investigation to verify if the PMG and MG estimators differ significantly from one another. Equation (3) shows the dynamic autoregressive distributed lag (ARDL) technique:

$$\begin{array}{cc}\hfill {\mathrm{Ln}}_{ }{\mathrm{HDI}}_{\mathrm{it}}={\mathsf{\beta }}_{\mathrm{o}}& +{\displaystyle {\displaystyle \sum }_{\mathrm{d}=1}^{\mathrm{a}-1}}{ \mathsf{\beta }}_{1\mathrm{d}}\mathrm{\Delta }{\mathrm{LnHDI}}_{\mathrm{it}-\mathrm{d}}+{\displaystyle {\displaystyle \sum }_{\mathrm{e}=0}^{\mathrm{b}-1}}{ \mathsf{\beta }}_{2\mathrm{e}}\mathrm{\Delta }{\mathrm{Ln}}_{ }{\mathrm{AFT}}_{\mathrm{it}-\mathrm{e}}\hfill \\ & +{\displaystyle {\displaystyle \sum }_{\mathrm{h}=0}^{\mathrm{e}-1}}{\mathsf{\beta }}_5\mathrm{h}\mathrm{\Delta }{\mathrm{LnPOP}}_{\mathrm{it}-\mathrm{h}}+{\displaystyle {\displaystyle \sum }_{\mathrm{I}=0}^{\mathrm{f}-1}}{ \mathsf{\beta }}_{6\mathrm{I}}\mathrm{\Delta }{\mathrm{LnCPI}}_{\mathrm{it}-\mathrm{L}}\\ & +{\displaystyle {\displaystyle \sum }_{\mathrm{j}=0}^{\mathrm{g}-1}}{ \mathsf{\beta }}_{7\mathrm{j}}\mathrm{\Delta }{\mathrm{LnREM}}_{\mathrm{it}-\mathrm{j}}+{\displaystyle {\displaystyle \sum }_{\mathrm{k}=0}^{\mathrm{h}-1}}{ \mathsf{\beta }}_{5\mathrm{k}}\mathrm{\Delta }{\mathrm{LnFDI}}_{\mathrm{it}-\mathrm{k}}+0{\mathsf{\delta }}_1{\mathrm{LnHDI}}_{\mathrm{it}-1}\\ & +{\mathsf{\delta }}_2{\mathrm{LnAFT}}_{\mathrm{it}-1}+{\mathsf{\delta }}_3{\mathrm{LnPOP}}_{\mathrm{it}-1}+{\mathsf{\delta }}_4{\mathrm{LnCPI}}_{\mathrm{it}-1}+{\mathsf{\delta }}_5{\mathrm{LnREM}}_{\mathrm{it}-1}\\ & +{\mathsf{\delta }}_6{\mathrm{LnFDI}}_{\mathrm{it}-1}-{\mathsf{\delta }}_8{\mathrm{ECT}}_{\mathrm{it}-1}+{\mathrm{e}}_{\mathrm{it}} \end{array}$$

(3)

Here, ∆ indicates the integration process after making the data stationary, the slopes of the short- and long-term parameters are represented by β and δ, and ${\mathrm{e}}_{\mathrm{it}}$ express the residuals of cross-section i at time t in the model. The error correction term (${\mathrm{ECT}}_{\mathrm{i},\mathrm{t}-1})$ determines the model’s dynamic stability. If the value of the ECT term is significantly negative, then the model is regarded as dynamically stable, and the short-run instability in the model itself stabilizes in the long run. This study tests four hypotheses. These include: (1) Aid for trade positively and significantly affects human development. (2) Population negatively and significantly affects human development. (3) The consumer price index (CPI) negatively and significantly impacts human development. (4) Foreign remittances contribute positively and significantly to human development. Finally, (5) foreign direct investment (FDI) contributes positively and significantly to human development.

4. Results and Discussion

Table 3. The outcomes of Levin and Lin tests.

Variables		Intercept			First Difference
	t-Stat.	Prob.	t-Stat	Prob.	Bandwidth	Conclusion
LnHDI	4.43 *	<0.01	--	--	2	I(0)
LnAFT	−3.50 **	<0.05	--	--	2	I(0)
LnPOP	−15.83 *	<0.01	--	--	2	I(0)
LnCPI	−4.62 *	<0.01	--	--	2	I(0)
LnREM	−5.33 *	<0.01	--	--	2	I(0)
LnFDI	0.61	0.73	8.028	<0.01	2	I(1)

*, **, show significance at 1% and 5%, respectively.

and

Table 4. The outcomes of the Im Pesaran and Shin (IPS) unit root tests.

Variables		Intercept		First Difference
	t-Stat	Prob.	t-Stat	Prob.	Conclusion
LnHDI	−2.13 **	<0.05	--	--	I(0)
LnAFT	−4.35 *	<0.01	--	--	I(0)
LnPOP	−4.26 *	<0.01	--	--	I(0)
LnCPI	−2.06 **	<0.05	--	--	I(0)
LnREM	−2.83 **	<0.05	--	--	I(0)
LnFDI	−1.07	0.14	−8.29 *	<0.01	I(1)

*, **, indicate significance at 1% and 5%, respectively.

present findings of the Levin and Lin [31] and Im Pesaran and Shin [35] panel unit root tests. Here, the null hypothesis (H0) is that the data series is non-stationary, against the alternative hypothesis (H1) that the data series is stationary. The decision rule states that the null hypothesis (H0), which indicates that the data series is non-stationary, is accepted if the value of p is larger than the 5% critical value (0.05). Furthermore, we adopt the alternative hypothesis (H1), which states that the data series is stationary if the value of p is less than the 5% critical value (>0.05). The outcomes of panel unit root tests confirm that the variables LnHDI, LnAFT, LnPOP, LnCPI and LnREM are integrated at level I(0), while LnFDI is integrated at first difference, i.e., I(1).

Table 5. The results of Panel ARDL.

Long-Run Elasticities
		PMG Estimator			MG Estimator
Variables	Parameters	Values	St. Error	Prob.	Values	St. Rror	Prob.
LnAFT	δ1	0.005 **	0.002	<0.05	0.006 **	0.001	<0.05
LnPOP	δ4	−0.16 **	0.004	<0.05	−0.50 **	0.10	<0.05
LnCPI	δ5	−0.18 *	0.03	<0.01	−0.004 **	0.01	<0.05
LnREM	δ6	0.04 **	0.01	<0.05	0.09 **	0.01	<0.05
LnFDI	δ7	0.01 *	0.002	<0.01	0.036 *	0.006	<0.01
Short-run Elasticities
∆LnAFT	${\mathsf{\beta }}_1$	0.004 **	0.001	<0.05	0.001 **	0.001	<0.05
∆LnPOP	${\mathsf{\beta }}_4$	0.03	0.11	0.7	−1.02	2.63	0.7
∆LnCPI	${\mathsf{\beta }}_5$	−0.04 **	0.02	0.05	−0.05	0.03	0.19
∆LnREM	${\mathsf{\beta }}_6$	0.01 **	0.01	0.05	0.001 **	0.01	0.05
∆LnFDI	${\mathsf{\beta }}_7$	0.003 **	0.009	<0.05	0.006 **	0.002	<0.05
Constant	${\mathsf{\beta }}_2$	0.10	0.19	0.58	0.10	0.19	0.58
ECT		−0.26 **	0.07	<0.05	−0.22 **	0.07	<0.05

Note: *, ** show significance levels of 1% and 5%, respectively.

represents the outcomes of the PMG-ARDL and MG-ARDL models. The PMG estimator coefficients are constrained to be homogeneous across the country in the long run but vary from country to country in the short run. The MG estimator coefficients estimate specific parameters for each country. Furthermore, comparing the findings of the PMG-ARDL and MG-ARDL estimates depicts how the empirical coefficients of the efficient model are affected by different estimation approaches. This study used the Hausman test [34] to ensure consistency and a more efficient estimator between the PMG (pooled mean group) and the MG (mean group) estimators. The Hausman test follows the chi-square distribution. The statistical value of the Hausman test is 5.84, being statistically insignificant at 5%. Therefore, we accepted the null hypothesis that the PMG estimator is more reliable and effective than the MG. The Hausman test results demonstrated that the PMG estimator is efficient as compared to the MG estimator.

The findings show that aid for trade has a positive and significant effect on human development both in the long and short terms. The current study explored the effect of total aid for trade on human development. The findings demonstrate that aid for trade has a valuable and statistically significant long- and short-term influence on human development. Thus, we accept the hypothesis that aid for trade positively and significantly affects human development. Moreover, in the long term, the value of the AFT parameter is 0.005 and is significant at 5%. In South Asia, a 1% increase in aid for trade results in a 0.005 percent improvement in human development. In the short term, the consequences of AFT are the same. In the short term, the coefficient of aid for trade is 0.004, which is significant at 5% as the probability value is smaller than 0.05. This shows that a 1% rise in assistance for trade will result in a 0.004 percent improvement in human development in south Asia. In the short run, aid for trade provides opportunities for trade and jobs that stimulate the income of the peoples that facilitate human development [36]. Aid for trade in the long run affects human development through many ways. The increase in commercial activity and the supply of job opportunities result from the improvement in trade-related infrastructure. In the surrounding areas, local people have started their own business hotels, restaurants, transport and communication centers. Private banks have also started to open their branches. Trade-induced services are growing, which has favorable impacts on the lifestyle of the local people and, as a result, improves human development [37]. Aid for trade utilizes productive capacities that influence human development by increasing government revenue from domestic taxes, reducing costs, creating services and better governance [38]. Trade-related capacity assistance has played a positive role in the private sectors, reducing costs, developing new products and enhancing their exports. Moreover, the findings are linked to the goals of the development community, for example, increasing worker skills, improving workers’ health, better working conditions, reducing poverty, creating jobs and enhancing human development. Consumers benefited from lower prices [39]. Ref. [40] showed that improved human development and a supportive environment for employees will lead to successful corporate performance even in developing countries.

Human development is influenced by population growth. It has an impact on the well-being of developing nations, particularly in terms of economic growth, education, health, food security and housing deprivation [25]. The findings of population growth support the above statement in this study. In the long term, the coefficient value of population growth is negative and significant at 5%. The long-term outcome of population growth shows that a 1% rise in population growth will bring about a 0.16% decline in human progress, while the result of short-term population growth is also negative but statistically insignificant. We also accept the hypothesis that population negatively and significantly affects human development.

Inflation has long been one of the most significant elements in human development, as it has a negative influence on human growth and wellbeing. Inflation has a considerable and detrimental influence on human development [26]. The coefficient of inflation indicates that a 1% increase in inflation will result in a 0.18% decrease in human development in the long run. The coefficient of inflation in the short run is −0.041, which is significant at 5%, decreasing human development in south Asia by 0.04%. Therefore, we accept the hypothesis that the consumer price index (CPI) negatively and significantly impacts human development.

Remittances are the major source of foreign exchange. It also plays a vital role in human development [41]. Remittances have a positive coefficient and are important at 5% level of significance. Furthermore, we accepted the hypothesis that foreign remittances contribute positively and significantly to human development. Based on the findings, a 1% increase in remittances results in 0.04% increase in human development for south Asian countries over the long run. But the coefficient value of remittances is also positive, but insignificant in the short run. Many families in underdeveloped countries depend on remittances as their main source of income. Remittances are known for their ability to improve living conditions by providing better food, basic healthcare and education [42].

It was observed from results that the FDI showed a positive influence on Human development. Thus, the hypothesis that foreign direct investment (FDI) contributes positively and significantly to human development is accepted. We found that a 1% rise in FDI caused an increase in human development among south Asian countries by about 0.01% in the long term. The coefficient of FDI is positive, but statistically significant in the short run. Recently, human capital has gained more attention, FDI contributes to human capital a higher level through skill development, provision of advanced technology and income generation with direct effect on human development and helps to improve misery level of the poorest country [43].

The error correction term (ECT), which adjusts the short-term imbalance in the long-term, reflects the dynamic stability of the model. For dynamic stability to exist, the value of the error correction term (ECT) coefficient must be significant and negative. The coefficient value of the error correction term is −0.26, which is negative and significant at the 5% level. Thus, it is shown that the estimated panel ARDL model is back to equilibrium with an adjustment speed of 26% per anum in South Asia.

Table 6. The findings of diagnostic tests.

Diagnostic Tests	Test Statistics
Hausman test	5.89 (0.54)
Normality test	0.90 (0.62)

reflects the findings of the Jarque-Bera test [44] to determine the normality of the residual term. The Jarque-Bera test is commonly conducted for large data sets. The Jarque–Bera test follows the F distribution. The conclusion was made based on the probability of the F-statistic value; the rule of thumb suggests if the p-value of the F-statistic is less than 5%. We reject the alternative hypothesis (H1) that the residual term is not normally distributed. Moreover, the p-value obtained from the study is 0.62, which is greater than 0.05. It states that the null hypothesis (H0) is accepted, which implies that the data is normally distributed.

Table 7. Summary of the descriptive statistics of the data series.

Variables	Bangladesh	Bhutan	India	Nepal	Pakistan	Sri Lanka
HDI
Mean	0.81	0.59	0.59	0.54	0.69	0.75
Max	0.85	0.65	0.64	0.60	0.74	0.78
Min	0.79	0.52	0.53	0.48	0.64	0.72
St. dev.	0.02	0.04	0.03	0.04	0.03	0.01
AFT
Mean	488.67	28.62	1684.32	93.94	233.10	214.34
Max	1316.12	47.40	3253.25	120.17	608.96	339.04
Min	124.13	18.67	586.77	68.70	45.85	87.22
St. dev.	415.27	8.77	793.35	15.45	158.70	71.74
POP
Mean	1.1	1.1	1.2	0.8	2.1	0.7
Max	1.1	1.4	1.5	1.8	2.2	1.1
Min	0.9	1	1.0	0.0	2	0.1
St. dev.	1.1	0.1	0.19	0.5	0.09	0.2
CPI
Mean	120.33	120.2	119.5	123.2	117.7	111.5
Max	179.67	167.1	180.4	188.7	182.7	155.5
Min	69.15	4.84	68.04	68.50	85.7	58.2
St. dev.	36.15	32.69	38.45	41.42	40.8	30.3
REM
Mean	11,864.19	20.61	58,317.2	4723.75	13,427.6	5125.91
Max	18,363.86	58.15	83,332	8286.63	22,252	7261.85
Min	4314.50	2.24	22,125	2111.82	4280	1975.54
St. dev.	4054.51	19.56	17,987.2	2397.44	6310.15	2058.60
FDI
Mean	1645.21	22.20	33,553.14	69.36	2532.2	801.27
Max	2831.15	75.27	50,610.65	196.27	5590	1614.04
Min	456.52	2.65	7269.41	0.995	859	272.4
St. dev.	772.80	22.87	11,483.12	61.15	1434.36	355.68

articulates the summary of the descriptive statistics of the variables used in this study. Summary of descriptive statistics comprise average, minimum value, maximum value followed by standard deviation. The highest average HDI value recorded for Bangladesh (0.81), and the lowest for Nepal (0.54). The highest aid for trade $1684.32 million received by India, which is the highest aid among all other countries, with the smallest $28.62 billion received by Bhutan. The highest population growth was recorded in India (1.2%) and the lowest (0.7%) in Sri Lanka. Highest average value of consumer price index 123.2 for Nepal, with lowest CPI value 111.55 for Sri Lanka. India has highest average value of remittances at $58,317.2 million, lowest average value for Bhutan at $20.61 million. India has highest average FDI $33,553.14 million, and Bhutan has the lowest FDI $22.20 million.

5. Conclusions

The core objective of this study was to explore the influence of the aid for trade on human development using balanced panel data for the period 2005–2019. The study also explored the influence of population growth, inflation, remittances and foreign direct investment on human development. First, the study detected the unit root issue in the data using panel unit root techniques including the Levin and Lin (LL) and Im Pesaran and Shin (LPS) tests. Second, the study used the panel ARDL model to evaluate the short- and long runs influence of aid for trade and determinants on human development. The outcomes of panel unit root tests show that the date series LnHDI, LnAFT, LnPOP, LnCPI and LnREM are stationary at level while LnFDI is stationary at first difference. The findings of the panel ARDL model suggested that Aid for trade has a significant and positive influence on human development in South Asia. Whereas population, inflation and remittances are also positively and significantly affecting human development in the long run in South Asia.

6. Policy and Managerial Implications

Based on study’s findings, important and stimulating policy suggestions emerge. First, aid for trade has been found to be an effective tool for human development in South Asia. It should be encouraged through the development of effective and efficient trade policies. In this way, human development in South Asia would be improved. Second, the study founded a high population growth rate not favorable to human development. It adversely affects human development. Therefore, it is necessary to reduce the high population growth rate in south Asia. Third, the high rate of inflation is not an ideal situation for any economy. Likewise in the study’s findings, the high inflation rate is leading to reduced human development in South Asia. In addition, it can create uncertainty regarding the money market. The governments of the South Asian economies will have to maintain a controlled inflation rate to improve human development in the region. Fourth, the study explored that remittances plays a positive role for human development in South Asia. Governments should institute such policies that encourage South Asian citizens to work in foreign countries. Fifth, the study results indicate that foreign direct investment enhances human development in South Asia. Thus, measures must be taken to establish a suitable environment for businesses to attract foreign direct investment in the country.

7. Limitations and Future Direction of the Study

This study analyzed data from 2005 to 2019 due to data availability. Afghanistan and Maldives were excluded from this study due to unavailability of data on some variables. An expanded sample that includes the entire South Asian region will provide researchers with a better understanding of the problem. Future study can be extended to analyzes how policy changes can impact changes in the allocation of aid for trade funds for initiatives focused on human development in the recipient country, in addition the study can be extended to analyze the difference in effect of multilateral aid for trade to bilateral aid for trade in human development. Multilateral aid is financed by rich countries and distributed by international financial institutions such as the United Nations Development Programs, World Bank and Regional Banks, while bilateral aid is managed by donor country agencies.