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Abstract

Trade has a crucial role in a country’s economic development. This study scrutinizes the impact of terms of trade (TOT), labor force, and capital on the United States’ economic growth. This study aims to investigate the short and long-run impacts of Capital, Labor, and Terms of Trade on the economic growth of the United States. To accomplish the study’s goals, this study analyzed a time series of annual data for the United States from 1980 to 2021. The Philipps-Perron test, the model of Augmented Dickey-Fuller (ADF) model, and the Autoregressive Distributed lag (ARDL) of the bound tests were applied to test whether the data is stationary or not. We employed an Autoregressive Distributed lag (ARDL) unit root model to examine the study variables’ short- and long-term associations. The outcomes of the unit root of the (ADF) test explain that both the Trace and Maximum Eigenvalues values showed that the study’s variables were stationary at both level and first difference, but stationarity was not there at the first difference using the PP model. At I(0) and I(1), the log(GDP), labor, capital, and log(TOT) stayed stationary. However, the estimated results of the (ARDL) model revealed that the economic growth of the United States was negatively and significantly affected by the (TOT), but economic growth, capital, and the labor force have favorable long and short-run relationships. The results of ECM(-1) imply that 7% of adjustment from disequilibrium to equilibrium from short-run to long-run. This study’s findings suggest that policymakers and governments ease trade between countries to achieve the highest economic growth.

Keywords

terms of trade gross domestic product economic growth import export

Introduction

World trade is a significant element that significantly subsidizes a nation’s economic growth (Abbass et al., 2022). The terms of trade reported a high impact on productivity (Kehoe & Ruhl, 2008). The terms of trade are a basic component of trade that gives information about the behavior of trade flow in a country (Jebran et al., 2018a, 2018b). The terms of trade can examine as the total ratio of export and import of a country during a specific period that can be written as $TOT = P_{EX} / P_{IM} \times 100$ . Whereas $P_{EX}$ represents the total price of export of a country and $P_{IM}$ represents the total price of imports of a country. Simply, the terms of trade epitomize the ratio of a nation’s exports and imports at a specific time. Any change in export or import might impact the term of trade positively or negatively. Furthermore, GDP, which is the total added worth of a nation’s services and goods over a specific period, has been used as a proxy for economic growth (Al-Bakr & Al Salman, 2016). As informed by Nancy (2021) and Jebran et al. (2018), economic growth was positive and significantly impacted by the terms of trade.

Over the decades, the United States had the largest economy in the world, having a total of 22.9 trillion dollars’ worth of Gross Domestic Product in 2022. The total export of the United States is 224.4 billion, and the total import is testified at 314.1 billion dollars for the above-mentioned year. These findings revealed that the United States is a big world importer. It is the main reason to test the term of trade impact on the United States’ economic growth. The previous studies reported mixed findings on how economic growth is affected by the term trade. In contrast, (Kalumbu & Sheefeni, 2014) study’s findings showed a negative relationship between the terms of trade and economic growth. At the same time, other studies revealed a positive correlation between economic growth and terms of trade (Grimes, 2006; Wong, 2010). The rise in export prices more than imports can be the cause of a positive effect of the TOT on the financial development of a country. However, (Sun & Heshmati, 2010) conducted a study in China using panel data analysis promoting rapid economic growth. The trade structure and global trade volume toward export results positively affect China’s regional productivity. Because of trade opening, the industrial revolution, and marvelous monetary development, China has appeared as the second-biggest economy in the world (Jiang et al., 2023).

Trade has a long history but has become increasingly vital for a nation’s economic growth and progress in recent years. The long and short-run relationship between capital and labor is also important to examine the impact on a country’s economic growth. Capital investment in a country leads to productivity which further creates new jobs. Labor attraction is also important as more production needs more labor as well. In a study by Jebran et al. (2018) conducted in Pakistan, the findings showed that labor and capital have a long and short-run relationship and a significant impact on productivity. Based on the current body of knowledge, this study has the innovation by taking the recent United States data from 1980 to 2021. It will provide recent results that can be compared with further studies. As a result, this study investigates how terms of trade, labor, and capital impact the United States’ economic growth.

It should be mentioned that non of the existing literature addressed the impact of terms of trade on the United States’ economic growth by using the ARDL long and short-run models. This study contributes to the existing body of knowledge by setting specific objectives and adding new insights reporting the latest findings. As no studies were found, those have used the specific variables and methods used to analyze the United States’ 42 years of annual data from 1980 to 2021, that impact of terms of trade on economic growth. By using the ARDL long and short-run model is also the essence of this study from 1980 to 2021. Furthermore, we investigate the shocks of the terms of trade on the response of GDP, Gross capital, and Labor by applying the impulse and response test (Holtz-Eakin et al., 1988). Therefore, this study has set two specific objectives. First, to examine the impact of terms of trade, labor, and capital on the United States’ economic growth. Second, to investigate how terms of trade, labor, and capital affect United States’ economic growth in the long run as well as in the short run.

The following is the structure of our paper. We start by reviewing the relevant literature on how TOT, labor, and capital, affect economic growth. Next, the study explained the methodological background and models employed. Then, we present our results of the different models applied. Next, in the discussion section, we critically discuss the study’s findings and compare them with the existing literature. Finally, the paper offers a conclusion, study limitations, and suggestions for additional research and asks readers to think about the consequences of the policy recommendations based on study’s findings.

Literature Review

Although trade has been around for a century, it has recently become more important for a country’s economic development and growth (Chan & Al-Hawamdeh, 2002). As per (OEC, 2020), one of the largest exports from the United States to other nations is petroleum gas, valued at 34.7 billion dollars. Other notable exports include integrated circuits, which were worth 44.2 billion dollars; cars, valued at 47.6 billion dollars; crude oil, valued at 52.3 billion dollars; and refined oil, valued at 58.4 billion dollars. Furthermore, Germany, Canada, Mexico, China, and China are the United States’ top five export partners, with a combined export value of 218 billion dollars, 196 billion dollars from Mexico, 122 billion dollars from China, and 59.2 billion dollars from Germany. On the other hand, imports are also a part of American business. However, on the other hand, the United States is also involved in imports. (Infante-Amate et al., 2022) examine Latin America has a highly significant influence on material flow in the world. He also showed a negative impact of trade on economic development from 1900 to 2016. Singh (2023) explain the long-run positive impact of TOT on Indian economic growth between 1990 and 2018 (Jawaid & Waheed, 2011). Term of trade (TOT) positively impacts the economic growth of 94 developed and developing countries. They took three variables, Labor, Gross Capital, and Term of trade, to test their impact of them on the economic growth of 94 countries. Results investigate the positive and significant impact of TOT, Gross Capital, and Labor on 94 developed and developing countries from 2004 to 2008. Tomic et al. (2020) examine the term of trade has consistent long-run and short-run relationship on international trade of developed countries (Yang, 2021). The term of trade has had a positive impact on savings from 2000 to 2019 (Freitas et al., 2020). The term of trade has a positive impact on economic and election outcomes (Askari et al., 2009). The imports and exports have a positive impact on the US economic growth.

The statistics showed that the main import partners of the United States are China, ranked first with 438 billion dollars. Mexico ranked second with 326 billion dollars, Canada ranked third with 264 billion dollars, Germany ranked fourth with total imports of 116 billion dollars, and Japan ranked fifth with 112 billion dollars. The main imports include cars, computers, packaged medicaments, broadcasting equipment, motor vehicles, parts, and accessories. The recent trend of July 2022 showed that United States exports were recorded at 176 billion dollars against the imports of 271 billion dollars resulting in an adverse trade balance of 95.5 billion dollars. These statistics are of immense importance as the variation in the country’s exports or imports also fluctuates the ratio of the terms of trade. As a result, trade terms may affect a country’s economic growth.

Jebran et al. (2018) examined how the term of trade affected China’s economic expansion. They evaluated the time series annual data from 1980 to 2013 using the ARDL model, and the results showed that terms of trade had a significant negative impact on China’s economic growth both in the short- and long term. The study’s results also demonstrated that the unit root test was performed to report a considerable negative impact on the terms of trade on the growth of the Chinese economy at the first difference level. Additionally, Jawaid and Raza (2013) examined how terms of trade affected India’s economic growth using annual time series data for the years 1980 through 2010. Their research findings showed a significant association between terms of trade and economic growth in both the long- and short-term.

Fatima (2010) observed how terms of trade affected Pakistan’s economic growth from 1990 to 2008, and the results revealed how much trade differed between developed and developing nations. Additionally, developed countries received higher trade export prices than developing nations. The findings, however, indicated that the terms of trade had a negative and significant impact on Pakistan’s economic growth. According to Basnet et al. (2021), the terms of trade have a long-term, significantly favorable influence on the economic growth of South Asian nations, including Pakistan, Malaysia, the Philippines, Bangladesh, Thailand, India, Sri Lanka, and Indonesia. Ahamad (2018) also examined 10 years of data from 2008 to 2017 to examine the effect of global and international trade on Bangladesh’s economic growth. He conducted investigations using the Ordinary Least Square technique to determine how international commerce and trade affect economic growth and how it affects economic growth in Bangladesh. The findings indicated a strong positive relationship between international commerce and trade and Bangladesh’s economic growth.

However, Kalumbu and Sheefeni (2014) analyzed how international trade affected the seven regions’ economic growth in 2009, and their study’s results showed that this impact is both positive and antagonistic significantly. Furthermore, the international trade environment, cultural factors, infrastructure, technology, and economic factors play a crucial role in increasing international trade. The literature has reported mixed findings impacting economic growth. Onyike et al. (2020), using the ARDL technique, the relationship between investment, remittances, human capital, and economic growth is explored over the short- and long terms. According to the findings, both long- and short-term economic growth was positively and significantly impacted by human capital. The study by Jebran et al. (2018) revealed that capital and labor had a considerably favorable impact on China’s economic growth over the long and short run. In addition, the analysis identified a robust unidirectional causality between terms of trade and labor force, whereas a significant bidirectional causal link was found between capital, labor, and economic growth. Wong (2010) also examined the data gathered from Korea and Japan, and the results showed that an increase in the ratio of the term of trade ratio decreases per capita real GDP. From the literature and discussion cited above, it is concluded that the relationship between economic growth and terms of trade is not linear.

Nabine (2009) mentioned that it is essential that theoretical growth has received attention between trade policies and economic development (Javaid et al., 2022). Afghan refugees have a positive and significant long-run relationship with the Pakistan labor market by applying the ARDL bound test from 1979 to 2020 (Durkin, 2001). There has a positive relationship between (Trade & Human capital) and GDP. (Oliveira, 2011) trade capital positively impacts South America’s economic growth (Adegboyega et al., 2017). Capital and Labor positively impact Nigeria’s economic growth (Bun & Winter, 2022). Labor and capital positively impacted Netherlands’ productivity from 2001 to 2017 (Basovskaya & Basovskiy, 2022). There is positive human capital & labor and Russian incomes. Ahmed and Kialashaki (2023) showed a weak relationship between FDI spillover and economic growth of most important Asian-pacific countries. Additionally, skilled human capital positively affects productivity. Musa Ahmed (2012) examines human capital significantly impacts the economic growth of top eight Asian countries.

Based on the literature, researchers investigated the impact of Human capital, Labor, FDI, and TOT on the economic growth of different regions and countries. Specific studies on the GDP and its components in China, Pakistan, India and other developed countries were reported in the literature. Hence, the literature is silent, particularly on the US study variables we considered examining. Therefore, this study aimed to fill the gap in the literature to analyze how terms of trade, labor force, and gross capital affect the economic growth of the United States from 1980 to 2021. Furthermore, to fill the methodological gap, we investigated the shocks of the terms of trade on the response of GDP, Gross capital, and Labor by applying the impulse and response test by Holtz-Eakin et al. (1988).

Method

Study Design

Using a quantitative study strategy, this study was conducted to investigate how the labor force, gross capital, and terms of trade influence American economic growth. For this purpose, time series annual data has been used to investigate trend analysis. Times series analysis deals with data collected at consistent intervals or over time. The data was obtained from the World Bank from 1980 to 2021 (TheWorldBank, 2022). This study has taken the Import, Export, Capital, Labor, and GDP of the United States in terms of USD. In the literature, mainly two proxies were used for terms of trade, that is, income terms of trade and net barter trade of trade (Jebran et al., 2018). For the analysis, this study has used the net barter term of trade as a proxy. Terms of trade are calculated as a ratio/percentage of total exports and imports of the United States. The capital as a variable was taken as the total gross capital the United States can produce in a year, and labor was taken as the total labor force of the United States over the specific period of 1980 to 2021. In addition, this study has used GDP as a representation of the United States’ economic growth. For regression modeling, GDP is taken as the dependent variable, although import, export, terms of trade, labor, and capital were taken as independent variables.

An Econometric Model for the Study

The study analyzes the impact of terms of trade (TOT), labor, and capital, on the United States’ economic growth. This study has followed similar models as those used by Jebran et al. (2018); Nancy (2021) in their studies. To use GPD as a representation of economic growth and the term of trade (TOT) as the fraction of exports and imports. GDP as a function of TOT is written as follows Equation (1).

$GD P_{t} = f (Labor, Capital, TOT)$ (1)

Where; GDP_t represents the Gross Domestic Product over a time period t, and term of the trade (TOT) represents the exports and imports ratio over the time period t. Capital represents the total gross capital, and labor denotes the total labor force of the United States.

The above Equation (1) can be expressed in Equation (2) for a log-linear model or short-run and long-run models to scrutinize the association between GDP and TOT, labor, and Capital.

$\begin{matrix} LogGD P_{t} = β_{0} + β_{1} LogTO T_{t} + β_{2} LogCapita l_{t} \\ + β_{3} LogLabo r_{t} + μ \end{matrix}$ (2)

Where; $LogGD P_{t}$ is the log of GDP over time t; $β_{0}$ = constant; $β_{1}$ , $β_{2}$ and $β_{3}$ = coefficient of the ith independent variables and $TO T_{t}$ , $Capita l_{t}$ and $Labo r_{t}$ = independent variable; and $μ$ = error term.

For estimation of long-term dynamics, the linear model can be further expanded into error correction model Equation (3) as follows.

$\begin{matrix} LogGD P_{t} = Φ_{1 i} \sum_{i = 0}^{q} Δ LogGD P_{t - i} + Φ_{2 i} \sum_{i = 0}^{q} Δ LogTO T_{t - 1} \\ + Φ_{3 i} \sum_{i = 0}^{q} Δ LogCapita l_{t - 1} + Φ_{4 i} \sum_{i = 0}^{q} Δ LogLabo r_{t - 1} + μ \end{matrix}$ (3)

The short-run relationship model can be shown in Equation (4) as follows.

$\begin{matrix} LogGD P_{t} = ψ_{1 i} \sum_{i = 0}^{q} Δ LogGD P_{t - i} + ψ_{2 i} \sum_{i = 0}^{q} Δ LogTO T_{t - 1} \\ + ψ_{3 i} \sum_{i = 0}^{q} Δ LogCapita l_{t - 1} + ψ_{4 i} \sum_{i = 0}^{q} Δ LogLabo r_{t - 1} + μ \end{matrix}$ (4)

In both the above Equations (3) and 4, the other variables are the same as in Equation (2), except $LogGD P_{t - i}$ which represents the Gross Domestic Product of one lagged period error correction term. Furthermore, $LogTO T_{t - 1}$ shows the one lagged value of error correction terms of trade (TOT), $LogCapita l_{t - 1}$ demonstrations of the one-lagged error correction value of Capital and $LogLabo r_{t - 1}$ shows the one lagged error correction value of Labor, while $Δ$ is first difference value and $Φ_{1 i}$ , $Φ_{2 i}$ , $Φ_{3 i}$ , and $Φ_{4 i}$ explain the long-run coefficient in Equation (3). However, $ψ_{1 i}$ , $ψ_{2 i}$ , $ψ_{3 i}$ , and $ψ_{4 i}$ explain the short-run coefficient in Equation (4). The same modeling was applied in the previous -efficient is reported literature, as reported by Jebran et al. (2018). The sign of the terms of trade co-efficient is reported as both positive and negative in the literature (Infante-Amate et al., 2022; Tomic et al., 2020). Therefore, this study expects both the positive and negative signs of the terms of trade co-efficient. Which is confirmed by the results both in the long and short run.

We utilized the Augmented Dickey-Fuller (ADF) test, which is common in the literature, to find whether the time series data is stationary or not (Jebran et al., 2018). Dickey and Fuller (1979) give a new concept to test autoregressive related to time series data called the ADF unit root test. In 1984, they developed the Dickey-Fuller test to make it easier to test the autoregressive unit root test constructed on the Dickey-Fuller test on a time series sample. The ADF model comprises null and alternative hypotheses; thus, we need to see the p-value presented as a guide to whether the data we use in a study is stationary or non-stationary to check the hypothesis results. However, Peter and Perron (1988) also established Philipps-Perron Test (PP-Test) for unit root to test the serial correlation between variables. Hence, Unit root test Equation (5) is mentioned below:

$P_{t} = β_{0} P_{t - 1} + β_{1} X_{e} + Φ$ (5)

We are going to test a time series value $P_{t}$ during the period of t, where $β_{0}$ and $β_{1}$ are explanatory variables, $X_{e}$ is represents an exogenous variable, and $Φ$ represents a model’s error term.

The Augmented Dickey-Fuller (ADF) model is a unit root test Equation (6), which explains the null hypothesis $α_{0}$ = 1 that takes the coefficient of lag(1) of $Y_{t}$ .

Null Hypothesis: $α_{0}$ =1

$Y_{t} = c + β_{0} + α_{0} y_{t - 1} + ψ Δ Y_{t - 1} + μ$ (6)

Where; $Y_{t}$ is a time series value during the period of t; $α_{0}$ and $ψ$ are explanatory variables; $y_{t - 1}$ is representing time series first lag; $Δ Y_{t - 1}$ shows representing time series at the time (t-1) first difference lag.

For the high-order regression model, the above Equation (6), the ADF unit root test, can be modified into Equation (7).

$\begin{matrix} P_{t} = c + β 0 + α 0 y_{t - 1} + þ_{1} Δ P_{t - 1} + þ_{2} Δ P_{t - 2} \\ + þ_{3} Δ P_{t - 3} . . \dots . + þ_{p} Δ P_{t - n} + ϵ \end{matrix}$ (7)

Here, we have added just more different terms, which are $Δ Y_{t - 1}$ , $Δ P_{t - 2}$ , $Δ P_{t - 3}$ and $Δ P_{t - n}$ respectively.

To further scrutinized our data, (Engle & Granger, 1987) gave a concept of testing for co-integration, which combines the problem of test of unit root and tests with parameters unknown under $H_{0}$ . In addition, Johansen and Juselius (2009) developed the ARDL model to test the co-integration analysis between two or more variables. Wong (2010) and Pesaran et al. (2001) have used the ARDL bound test models in their studies to test the short and long-run impact. The models applied in our study are the Augmented Dickey-Fuller (ADF) and ARDL models to test the short and long-run relationship between the United States’ economic growth and term of trade TOT, Labor, and Capital. However, the Johansen test is an improved form of Clive Granger and the Phillips-Ouliaris tests Johansen test has two subtests: Trace test and the Maximum test.

The trace test, the first stage of the Johansen test, may calculate the co-integration of the data from various time series. One of two ideas can account for the trace test results:

$H_{0}$ = Variable K must be less or equal to m-1

$H_{1}$ = Variable K must be greater or equal to m-1

The following two assumptions are mentioned in the Maximum Eigenvalue test, the second step of the Johansen test, along with details on the co-integration of several time series:

$H_{0}$ = K has co-integration

$H_{1}$ = K+1 has co-integration

Results and Explanation

The descriptive statistics were used to summarize the study’s average variables (Muhammad et al., 2022), that is, terms of trade, GDP, capital, and labor. The average of study variables was used due to the large dataset shown in Table 1. The total terms of trade results showed that values less than 100 explain that the United States is an importing country. Average values of GDP, capital, and labor showed that there has a positive relationship between them (TheWorldBank, 2022).

Table 1.

Descriptive Statistics of the Study Variables (Average).

Period	TOT	GDP	Capital	Labor
1980–1984	88.79136	12.5306	11.8993	-
1985–1989	76.13466	12.6910	12.0596	-
1990–1994	91.48476	12.8156	12.1309	8.1173
1995–1999	87.01828	12.9329	12.2819	8.1474
2000–2004	69.64562	13.0440	12.3939	8.1706
2005–2009	69.69689	13.1493	12.4826	8.1903
2010–2014	80.26163	13.2105	12.5058	8.1983
2015–2021	79.87051	13.3065	12.6202	8.2139

Source. TheWorldBank (2022) and Authors’ own source.

According to Wadad et al. (2011), the unit root test is performed since most macroeconomic variables are trending and are generally non-stationary. Table 2 shows the findings of the Augmented Dickey-Fuller and Phillips and Person models, which report different outcomes at both levels and the first difference. The findings reported that almost all values at the level and first difference are significantly negative. It reflected on testing the stationarity of the variables at both levels and the first difference. Still, the Phillips and Person (PP) test reported that all values in the level are significant, showing that the variables are stationary at levels. Still, in the first difference, these are not stationary at the level; the variables are integrated at I(1). The same method was reported in the literature and showed the same results (Nabine, 2009; Nancy, 2021).

Table 2.

Unit Root Test: PP and ADF in Level and First Difference.

Variables	Model Specification	ADF		PP
Variables	Model Specification	Levels	First Diff	Levels	First Diff	Inference
GDP	Trend	−0.0770	−1.2330***	1.0017***	0.8322***	L(1)
GDP	Intercept	−0.0259***	−0.6547***	0.9003***	−0.1406	L(1)
TOT	Trend	−0.1857***	−0.4879***	0.9926***	0.5422***	L(1)
TOT	Intercept	−0.1728***	−0.4817***	0.8802***	0.5256***	L(1)
Labor	Trend	−0.0795	−0.6272**	1.000***	0.8284***	L(1)
Labor	Intercept	−0.0478***	−0.2639	0.9637***	0.1814	L(1)
Capital	Trend	−0.0784	−0.8612***	1.0017***	0.4111***	L(1)
Capital	Intercept	−0.0616**	−0.6548***	0.7883***	0.0971	L(1)

Source. Authors’ own source.

***

, **, and * are representing the significant level of interval at 1%, 5%, and 10% respectively.

Co-integration is used to assess if two or more time series can be combined linearly. Even if each series separately has a stochastic trend or is non-stationary. They are connected to create an equilibrium connection over the long term because they are expected to move near together, and their difference will be stable and stationary (Schall et al., 1990). Johansen co-integration test results are shown in Table 3. The findings reported that Trace statistic values of hypotheses 0 and 1 are lower than the critical values at a 5% confidence interval. It showed no co-integration, and the values of the Trace statistic of hypotheses 0 and 1 were 61.70 and 47.21, respectively. The critical values of hypotheses 0 and 1 were 23.31 and 29.68, respectively. In contrast, the Maximum Eigenvalues test results showed that the Maximum Eigenvalues statistic of hypotheses 0 and 1 were reduced than the critical values of hypotheses 0 and 1. It postulates that a long-run association between the terms of trade and the United States’ economic growth and the values of Maximum Eigenvalues are 38.41 and 27.07. The critical values of hypotheses 0 and 1 are 9.08 and 20.98, respectively.

Table 3.

Johansen Co-Integration Test.

Test	Trace test		Maximum Eigenvalue test
CE(s) of No.	Statistics	Critical value	Eigenvalue test	Critical value
Hypothesis	Trace	0.05	Maximum	0.05
None	61.7264	47.21	38.4089	27.07
At most 1	23.3175	29.68	9.0868	20.97

Source. Authors’ own source.

Table 4 shows the F-statistic, critical bound values, the Breusch-Godfrey LM autocorrelation, and the matrix list lags values. The findings reported that the value of the F-statistic (5.557) is greater as compared to the critical bound I(0) and critical bound l(1), which represented a long-run relationship between TOT, labor, capital, and GDP. Breusch-Godfrey LM autocorrelation explained that there is no serial correlation, while matrix list lags reported that the ARDL model we applied had used lags (1, l, 1, 1).

Table 4.

ARDL Bound Test.

F-statistic value	L(0)	L(1)	p-value	Lag length
5.557	2.72	3.77	.05	1

Source. Authors’ own source.

The long-run bond by using ARDL model results are presented in Table 5. The examination aimed to see the relationship between the United State GDP, terms of trade, capital, and labor in the long run. Estimated outcomes indicated that the terms of trade coefficient have a significantly positive (0.00042) effect on the United States’ economic growth at a 10% confidence level which is not so strong. Still, when we took results at first lags then, there is a significant and negative (−0.00055) impact of the term of trade on the USA economic growth at a 1% confidence interval. ΔGDP is also significant at 1%, and the coefficient value is (0.9270). The control variables, such as capital and total labor force, both have positive and significant results at a 1% confidence interval of 1.2496 and 0.2768, respectively. The results are compared with those (Fatima, 2010; Nabine, 2009; Nancy, 2021)

Table 5.

ARDL Model Long-Run Relationship.

Dependent V = GDP	Co-efficient	ARDL (1 1 1 1)	p-values
Variables	Co-efficient	Standard error	p-values
ΔGDP	0.9270***	0.0438	.00
TOT	0.00042*	0.00022	.06
L1. TOT	−0.00055***	0.00018	.00
Labor	1.2498***	0.2893	.00
Capital	0.2768***	0.0268	.00
Constant	−0.8272	0.9839	.41

Source. Authors’ own source.

***

, **, and * are representing the significant level of interval at 1%, 5%, and 10% respectively.

The value of lagged ECM is negative and statistically insignificant. The lagged value of ECM examines the speed of adjustment from disequilibrium to equilibrium from short-run to long-run. The results of ECM(−1) imply that 7% of adjustment from disequilibrium to equilibrium from short-run to long-run. Empirically observing the short-term link between the research variables, as shown in Table 6, is one of the study’s goals. To examine the short-run relationship, we apply the ARDL short-term link between the GDP, terms of trade, labor, and capital in the United States. The TOT was found insignificant. However, after taking the first lags, the results demonstrated a positive coefficient of terms of trade (0.00055), capital (1.0763), and labor (2.4153) had a considerable influence on the expansion of the US economic growth. ΔGDP is insignificant, and the co-efficient value is (−0.07295), similar to (Jebran, Iqbal, Bhat, & Ali, 2018) and (Basovskaya & Basovskiy, 2022).

Table 6.

ARDL Model Short-Run Relationship.

Dependent variable = GDP	Co-efficient	ARDL (1 1 1 1)	p-values
Variables	Co-efficient	Standard error	p-values
ΔGDP	−0.07295	0.0439	.11
TOT	−0.0017	0.0022	.44
L1. TOT	0.00055***	0.00018	.00
Labor	2.4154*	1.3686	.09
L1. Labor	1.0736***	0.3206	.00
Capital	0.3976	0.2430	.11
L1. Capital	0.2478***	0.0233	.00
Constant	−0.8272	0.9839	.41
R-squared	0.9485
Adj R-squared	0.9322
Breusch-Godfrey LM test	Prob >chi2	0.4848
White’s test	Prob >chi2	0.4140

Source. Authors’ own source.

***

, **, and * are representing the significant level of interval at 1%, 5%, and 10% respectively.

There have three different techniques to test the causality between variables. Still, in our study, we used the Granger causality test to check the causal effect between the terms of trade, gross capital, labor force and GDP. Table 7 shows the findings of the Granger causality test. The findings reported a causality between terms of trade with (GDP, capital, and Labor) similar to those (Jawaid & Raza, 2013). GDP doesn’t have to cause an effect on TOT, Capital, and Labor, similar to (Jebran et al., 2018).

Table 7.

Granger Causality Test.

Null Hypothesis	Observations	F-statistic	p-value
GDP does not Granger cause with TOT	41	0.411	.980
GDP does not Granger cause with Labor	41	2.813	.245
GDP does not Granger cause with Capital	41	0.013	.993
GDP does not Granger cause with All	41	5.191	.520
TOT does Granger cause with GDP	41	7.290	.026
TOT does Granger cause with Labor	41	7.220	.027
TOT does Granger cause with Capital	41	8.901	.012
TOT does Granger cause with All	41	12.816	.046
Labor does not Granger cause with GDP	41	0.501	.778
Labor does not Granger cause with TOT	41	0.146	.929
Labor does not Granger cause with Labor	41	3.921	.141
Labor does Granger cause with Capital	41	11.976	.063
Capital does not Granger cause with GDP	41	0.529	.767
Capital does not Granger cause with TOT	41	1.120	.571
Capital does not Granger cause with Labor	41	3.459	.177
Capital does not Granger cause with Capital	41	9.060	.170

Source. Authors’ own source.

Figure 1 shows the fluctuation of the United States GDP, Exports, and Imports from 1980 to 2021. On the Y-axis, we mentioned GDP, Exports, and Imports values in million, and on X-axis, we have years from 1980 to 2031. Results examine the upward trend of GDP, Exports, and Imports from 1980 to 2021, but imports values are higher than exports. Additionally, the trend of GDP, Exports, and the future trend from 2021 to 2031 is also mentioned in Figure 1.

Figure 1.

US GDP, Export and Import.

In Figure 2, we mention the term of trade (TOT). Outcomes explain the downward trend of TOT from 1980 to 2021. On the Y-axis, we have the terms of trade from 0 to 120, in X-axis, we have 50 years from 1980 to 2031. The outcomes examine that all values are lower than 100, which indicates that the US is an importing country. Furthermore, the trend of TOT examines that the terms of trade line fallowing downwards.

Figure 2.

TOT Trend.

Figure 3 shows the CUSUM square test. On the X-axis, we have years from 1995 to 2021 in Y-axis, we have CUSUM square from 0 to 1. Results show that the band line lies between a 5% significant level and showed a model is a good fit for the study. Furthermore, outcomes also indicate that the residuals of the United States economic growth equation do not have structural instability.

Figure 3.

CUSUM Graph.

Holtz-Eakin et al. (1988) examine that shocks of independent variables impact the fluctuation of dependent variables. Impulse graph results showed an upward trend showing the short and long-run association between GDP and terms of trade and between capital and terms of trade. There is also a stable and upward trend association between labor and terms of trade in the short and long run. In contrast, a stable short and long-run association was also found between the terms of the trade itself. Additionally, the impulse and response of variables over the short and long-run are shown in Figure 4. TOT response on GDP, Capital, and Labor that examines TOT with Labor positively influences long- and short-run relationships. But TOT with GDP and Gross Capital have a negative relationship in the short-run but a positive influence in the long-run.

Figure 4.

Impulse and Response function.

Discussion

Trade activities are considered the backbone of the economies (Muhammad & Ximei, 2022). More trade helps in attracting capital, labor, and technology. We adopted models in our study similar to those Ahamad (2018); Jebran et al. (2018); Nancy (2021) to scientifically examine how the terms of trade affect the expansion of the US economic growth. The Augmented Dickey-Fuller (ADF) results show that all variables are stationary at a level and the first difference. However, the Phillips and Person (PP) models show that all variables are stationary at a level but not at the first difference. However, Jebran et al. (2018) used the ADF and PP test for the stationarity of the research variables and obtained different results using data empirically tested about Pakistan. Our results are in contrast to their findings in this regard. However, the Phillips and Person (PP) tests describe the TOT’s positive and statistically significant impact on the USA’s economic growth. Our findings of the Augmented Dickey-Fuller model are also similar to those Kalumbu and Sheefeni (2014; Baum and Lewbel (2019); MacKinnon (2013).

To test the Co-integration and the Bound test of ARDL, the study examines whether a long-run association exists between the terms of trade and the United States’ economic growth. The estimated outcomes of the co-integration showed a long-run relationship between (the term of trade, gross capital, and labor force), and economic growth. In addition, the results of the ARDL long-run model of our studies reported that the terms of trade have a weak positive impact at a 10% confidence interval, but L.TOT a negative and strongly significant at a 1% confidence interval. Capital and labor have a significantly positive impact on economic growth, similar to those Fatima (2010); Nabine (2009); Nancy (2021) and Basovskaya and Basovskiy (2022). While those of Jebran et al. (2018) and Infante-Amate et al. (2022) documented negative results. But all these researchers do not empirically scrutinize the influence of the terms of trade, labor, and capital on the United States’ economic growth. To compare the study’s findings to Jawaid et al. (2020). An extensive, positive, and significant association between economic growth and TOT was discovered using a co-integration test. The findings examine the terms of trade of Bangladesh, Kuwait, Australia, Hong Kong, Canada, Japan, and the United Kingdom, significantly strengthening those countries’ economies. Furthermore, trade agreements with China and the United Arab Emirates significantly negatively affected economic growth. However, Sikandar et al. (2021) also explored the association between exports and GDP, and the findings investigate a favorable long-run association between exports and economic growth (Bahmani-Oskooee et al., 2016). Mexico has a long-run bilateral trade relationship with 13 trade partners by applying the ARDL model (Kassouri & Altıntaş, 2020). ARDL model examines the term of trade shocks’ impact on the real exchange rate of 23 products of African exporting countries.

Furthermore, the Granger Causality test reported a causal effect between terms of trade with (GDP, capital, and Labor) similar to Jawaid and Raza (2013). GDP doesn’t have to cause an effect on TOT, Capital, and Labor, similar to Jebran et al. (2018) and Holtz-Eakin et al. (1988). The shocks of independent variables impact the fluctuation of dependent variables. In our study, Impulse graph results showed an upward trend showing the short and long-run association between GDP and terms of trade and between capital and terms of trade. The impulse graph showed that TOT with Labor positively influences long- and short-run relationships. But TOT with GDP and Gross Capital have a negative relationship in the short-run but a positive influence in the long-run. Results are compared to Shi et al. (2015) and Boyaci et al. (2022). However, Majeed (2016) used panel data from 65 developing nations from 1965 to 2010 to logically analyze and experimentally check the relationship between trade and economic growth. His analysis demonstrated that trade had a favorable short- and long-term influence on economic growth. The results of our study also indicated that TOT had a favorable effect on economic growth. Tahir and Hayat (2020), to apply the autoregressive distributed lagged model (ARDL) approach, an empirical analysis was performed on the data from 1989 to 2018. They found that trade openness and economic growth had a positive and statistically significant relationship. Nabine (2009) used the augmented aggregate production function growth model to test his study hypothesis and found no causal relationship among exports, imports, economic growth, and foreign direct investment. The OLS model and Grange causality test were used to get the results. Capital and labor have a favorable effect on Nigaria’s economic growth (Adegboyega et al., 2017). Between 2001 and 2017, labor and capital had a positive effect on the productivity of the Netherlands (Bun & Winter, 2022). Russian incomes and human capital are in positive territory (Basovskaya & Basovskiy, 2022).

Conclusion

Trade has a long history, but for the past few decades, international trade and commerce have significantly influenced the growth of the economy of the developed and developing world. It brings happiness in the form of increasing living standards. Economic growth is much concerned with terms of trade. The estimated results of the terms of trade (TOT) show that the United States is an importing country. There could be two reasons. First, purchasing power is higher than selling power, negatively impacting economic growth. Second, the price of imported products is comparatively higher than that of exported products, negatively affecting the United States’ economic growth.

Additionally, our study looked at this effect and empirically showed that the terms of trade, capital, and labor force impact US economic growth in the long and short run from 1980 to 2021. The Bound test outcomes revealed a long-run relationship between the terms of trade and the United States’ economic growth. The terms of trade have a positive and weak impact on economic growth, but at first lags, TOT has a strong negative and statistically significant impact on US economic growth. Furthermore, in the short-run, at the first lags, TOT has a positive influence on economic growth. However, both the control variables, labor and capital, significantly positively impact economic growth in the long and short run. The findings Granger causality reported a causal effect between terms of trade with (GDP, capital, and Labor). GDP doesn’t have to cause an effect on TOT, Capital, and Labor. The United States is an importing country; other nations, especially the developing world, may take advantage of exporting as more as possible to balance their balance of payments. It will help them attract more investment in new industries, reducing the curse of unemployment and poverty.

Policy Implications

The results of the study explain that the United States is an importing country; therefore, the terms of trade have an adversative effect on economic growth. Based on our study findings and analysis, the authors suggest that the United States needs to make strike policies for investors to invest in a country. Also, the government can change friendlier investment policies for investors to increase export productivity. Government should provide funds to local investors to increase export productivity. The findings also suggest revising the export and import policies to decrease the import volume. To increase their foreign exchange reserves, developing and underdeveloped nations can benefit from exporting more to the United States to balance their payments. Additionally, we recommend developing successful import and export strategies, which may include limiting the cost and volume of exports compared to imports.

Limitations of the Study

The study carries some limitations and shortcomings. First, the data sampled focused on the United States from 1980 to 2021. Second, by using terms of trade, gross capital, and labor force as independent variables, the study’s findings were constrained, but this does not imply that only these factors affect economic growth. We applied the Ordinary List Square OLS model, the Phillips and Person (PP), the Augmented Dickey-Fuller ADF model, and Autoregressive distributed lag (ARDL). The findings may differ due to changes in the dataset by changing countries as a sample. However, findings may differ by applying other research models, that is, the Production function and Gravity model.

Future Research

The study also provides some future direction for further empirical analysis. An extensive dataset may be used by taking monthly observations to deepen the analysis. A comparative study may be conducted among developed countries to know their differences. In addition, more independent variables such as capital stock, investment, trade Openness, exchange rate, financial development, employment, etc., to check to examine the more accurate impact on economic growth.

Footnotes

The First author is thankful to the People Republic of China for providing a Ph.D. scholarship. We are also thankful to the participants of the study.

Author Contributions

Each author has a substantial contribution to the paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Umer Shahzad

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How Terms of Trade Impact Economic Growth: The Case of the United States

Abstract

Keywords

Introduction

Literature Review

Method

Study Design

An Econometric Model for the Study

Results and Explanation

Discussion

Conclusion

Policy Implications

Limitations of the Study

Future Research

Footnotes

Author Contributions

Declaration of Conflicting Interests

Funding

ORCID iD

References