




































AMERICAN FINANCE & BANKING REVIEW 7(1) (2022), 1-6 

1 

 

        Finance & Banking Review 
 

 AFBR VOL 7 NO 1 (2022) P-ISSN 2576-1226  E-ISSN 2576-1234 

 
        Available online at https://www.cribfb.com 

       Journal homepage: https://www.cribfb.com/journal/index.php/amfbr 

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THE NEXUS OF TRADE LIBERALIZATION AND UNEMPLOYMENT 

IN THE CONTEXT OF AFGHANISTAN              

                   
 Abdul Wahid Naderi (a)1    Ata Mohammad Nikzad (b)    Eralappa Thippeswamy (c)    Jayavantha Nayak (d)    

 

(a) Ph.D., Research Scholar, Department of PG Studies in Economics, University College, Mangalore-575001, India; E-mail: 

wahidnaderi5105@gmail.com 
(b) Ph.D., Research Scholar, Mangalore University, Mangalore, D.K, Karnataka, India; E-mail: ajmer.nikzad@gmail.com 
(c) Ph.D.; Associate Professor, Department of Economics, Field Marshal K M ,Cariappa College Madikeri, Karnataka, India; E-mail: 

ethippeswamy@yahoo.com 
(d) Ph.D.; Associate Professor & Coordinator, Department of PG Studies in Economics, University College, Mangalore – 575001, India; E-mail: 
jayavantha_nayak@yahoo.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 30th June 2022  

Accepted: 29th August 2022 

Online Publication: 4th September 2022 

 
Keywords: 

 

Unemployment, Trade Liberalization 

Afghanistan 

 
JEL Classification Codes: 

 

      E24, F1 

 

 

  

 
A B S T R A C T 
 
Despite this, there have been conducted outnumber of studies on the relationship between trade and 

unemployment around the world. The purpose of this study is to investigate the nexus between trade and 

unemployment, and whether trade creates or destroys jobs in the context of Afghanistan. To answer this 
question, the data was gathered from various sources including the World Bank, and the National 

Statistics and Information Authority of Afghanistan, from 1990 to 2018. Using ADF (Augmented Dicky 

Fuller) stationarity test, ARDL Bound test, and causality test. The empirical evidence showed only short-

run consequences in one variable which is Gross Domestic Products Per Capita. Further, the study 

employed diagnostic and stability tests to understand the fitness of the model. Hence, this study surely 

answers the questions and shows that there is no link between trade and unemployment. Finally, the 

study evinced only the influence of GDP Per Capita on unemployment. Besides, there is a unilateral 

causality running from GDP Per Capita toward Unemployment and also the study analyzed that GDP 
Per Capita has a negative and significant impact on Unemployment in the short run. Eventually, the 

study suggests that the government needs to reform policy in regard to tackling unemployment through 

domestic investment.  

 
 

© 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article distributed 
under the terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).  

            

 

INTRODUCTION 

Globalization is the act of interaction and integration, which has different economic, political, and social aspects. The 

increase in interactions and integrations causes growth in international trade and cultural exchange as well. Meanwhile, 

foreign trade is one of the main components of globalization. The most debatable issue over the impact of Trade on 

Unemployment has always been discussed, whether the trade is the creator or the destroyer of jobs “does opening up to 

international trade create or destroy jobs?” (Dividson et al., 1999; Felbermayr, 2011). In this logic, there are many 

controversial studies that show the contrast between different authors. For instance, Brecher (1974) and Helpman (2010) 

found a positive relationship between trade and unemployment. In another study, a negative relationship between both trade 

and unemployment has been shown (Felbermayr, 2011). Therefore, there is a huge public concern about the effect of trade 

and unemployment, some argue that free trade can increase the export market, which leads to a higher demand for the 

products, expanding domestic production, and finally creating more jobs.  

Afghanistan has suffered 4 decades of civil unrest which affected various government and private sectors, the 

unfortunate situation of the country raised the lack of jobs, stagnation of industries, and increased unemployment (Ashrafi 

& Kalaiah, 2021). A lot of people especially the young generation lost their jobs. According to the World Bank, the 

unemployment rate was increased by 11.20 percent in 2020 in Afghanistan. Unemployment is one of the most socio-

economic complications for economists and social welfare. The International Labour Organization (ILO) defines 

                                                      
1Corresponding author: ORCID ID: 0000-0001-8479-8715  
© 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/amfbr.v7i1.1793 

 
To cite this article: Naderi, A. W., Nikzad, A. M., Thippeswamy, E., & Nayak, J. (2022). THE NEXUS OF TRADE LIBERALIZATION AND 

UNEMPLOYMENT IN THE CONTEXT OF AFGHANISTAN. American Finance & Banking Review, 7(1), 1-6. 

https://doi.org/10.46281/amfbr.v7i1.1793 

mailto:ajmer.nikzad@gmail.com
mailto:ethippeswamy@yahoo.com
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://orcid.org/0000-0001-8479-8715
https://doi.org/10.46281/amfbr.v7i1.1793
https://orcid.org/0000-0001-8479-8715
https://orcid.org/0000-0001-5948-0940
https://orcid.org/0000-0003-4540-0019
https://orcid.org/0000-0002-7430-2453


Naderi et al., American Finance & Banking Review 7(1) (2022), 1-6 

 

2 

“unemployment” as the number of people who are unemployed but available for doing work, including those who lost a job 

or voluntarily left work. Thus, we tried to present the relationship between trade and unemployment, and the current study 

was undertaken to examine Trade Liberalization and Unemployment in the context of Afghanistan. 

 

LITERATURE REVIEW 

In this section, the review of research articles has been undertaken for understanding the concepts, objectives, and 

methodology adopted and the results of those studies. Based on past research, it is possible to identify the research gap and 

hence the review of articles has been undertaken which is explained in this particular part of the study. 

There are numerous studies on the relationship between trade and unemployment, we try to go through both 

theoretical and empirical surveys. Historically, the relationship between trade liberalization and unemployment was studied 

using the Hecksher-Ohlin and Stolper-Samuelson theorems.  The H-O theorem predicts that countries will export goods 

produced with the rigorous that are abundantly available. Many of the trade models consider full employment of labor and 

all factors at all times and, accordingly, there is no recognition of any effect of trade and employment. The four employment 

theories are minimum wage theory (Brecher, 1974; Davis, 1998), implicit contract theory Matusz (1996), efficiency wage 

theory Matusz (1996), and job search theory (Davidson, Martin, & Matusz, 1999), (Moore & Ranjan 2005) incorporated 

with traditional models of (H-O and Ricardo-Viner). The latest theoretical developments presented two new forms of trade 

models-heterogeneity of firms (Helpman & Itskhoki, 2010; Helpman, Itskhoki, & Redding, 2010; Egger & Kreickemeier, 

2009) and offshoring or trade-in tasks (Batra & Beladi, 2010; Mithra & Ranjan, 2010; Ranjan, 2012 & 2013). The result of 

these studies showed the contradictory, complex and vague relationship between trade and aggregate employment. Hence, 

there is a need for empirical evaluation of how trade changes the level of equilibrium employment (Davidson and Matusz, 

2004). 

Most cases of empirical studies observed the impact of trade openness/trade liberalization. For example, 

Felbermayr et al. (2011) empirically analyzed 20 Organization for Economic Co-operation and Development (OECD) and 

the result is based on the consequence of panel and cross-sectional data. This study showed that over the long term; larger 

trade openness is related to a lower structural rate. As a result, the investigation into the relationship between trade openness 

and unemployment in developing economies has yielded conflicting results. In their intensive study of Malaysia, 

Nanthakumar et al. (2011) found out that the increase in trade balance had negative Granger non-causality effects on the 

severity of unemployment dynamics. Thus, trade liberalization is capable of increasing aggregate productivity in several 

sectors. Subsequently, the efficiency and performance of the economy raise the rate of labor utilization. Alawin (2013) 

assessment of trade balance and unemployment in Jordon, exploits quarterly data from 2000 to 2012. The findings of this 

study highlighted the lack of a long-run relationship between the balance of trade and the unemployment rate. According to 

these findings, the trade balance deficit causes unemployment in the short term and vice versa. Kim and Sun (2009) found 

that trade openness factors play a significant role in the labor market churning most industries like automobile, chemicals, 

and apparel sectors affected by the North American Free Trade Agreement (NAFTA). The results of these studies are that 

trade openness reduces aggregate unemployment in the inflexible labor market and conversely increases aggregate 

unemployment in the inflexible labor market. Hasan et al. (2012) studied the rate of unemployment and trade liberalization 

with the consideration of both urban and rural areas of the state. There was no sign of any decrease in the unemployment 

rate because of trade reform. On the other hand, trade openness declined the unemployment rate in urban areas. The findings 

of Dutt et al. (2009) study for 90 developing countries even removing control variables is a negative result for trade openness 

and unemployment rate. In addition, they pointed out weak support for the Hecksher-Ohlin theorem. 

 

MATERIALS AND METHODS 

The data required for this study was collected from various sources including World bank open data source, National 

statistics, and information Authority which encompass from 1990 till 2018, moreover dependent variable in this study is 

unemployment which is explained by independent variables including trade openness, term of trade, capital formation, and 

GDP per capita income.  

 

Variables used in this study 

 

Unemployment: Unemployment is the sum of the population involved in the Labour force of a country which the labor 

force generally encompasses a population aged from 15-64, further the unemployment population is a layer of a society that 

is actively looking for employment opportunities. Hence, this study analysis trade and its impact on unemployment. 

 

Trade Openness: measured as the sum of total imports and exports as a ratio of the GDP. Further Trade Openness indicates 

the country’s involvement in global trade, (literature). 

 

Terms of Trade: measured as the percentage ratio of the export unit value indexes to the import unit value indexes, 

calculated relative to the base year 2000. In another word, the term of trade represents how much of a countries’ export unit 

can purchase units of import or export prices divided by import prices and multiplied by 100 (TOT). 

 

Domestic Investment: This variable is proxied by real gross capital formation measured as a percent of the GDP, moreover 

capital formation is the accumulation or aggregate of net capital of a country during a year. (DOMINVS).  

 



Naderi et al., American Finance & Banking Review 7(1) (2022), 1-6 

 

3 

The study employed the ARDL approach which is a proper model to estimate fewer data. Further, the study is 

calculating the short-run and long-run relationship among variables through the ARDL model. However, the bound ARDL 

test is applicable on stationarity order I(0) level and stationarity order I(1) or first different and also can be employed on a 

mixture of both stationarity orders (level and first different). Moreover, the ARDL test can crash and gives misleading result 

when the stationarity order I(2) arises. Therefore, to avoid the spurious result it’s necessary to check the unit root test. In 

addition, to find out the unit root test, the study used ADF (Augmented Dicky Fuller) stationarity test to point out the 

stationarity of the variables (Ashrafi & Kaliah, 2020). 

The result from ADF (Augmented Dickey-Fuller) test revealed that all the variables signalized at first difference 

stationarity order. Therefore, it is fit to run the ARDL test due to the small data size and stationarity at 1st difference. In 

addition, variables in the table are described as follows lun: Unemployment, Lgdpc: Gross Domestic Products Per Capita, 

Lop: Openness, Ltot: Term of Trade, Lcf: Capital Formation.  

 

RESULTS AND DISCUSSION 

ARDL Bound Test  

The ARDL test developed by Pesaran et al. (1999) is a comprehensive test for different stationarity levels (level, first 

difference) as well as a small sample size. 

 

Table 1. ARDL Bound Test 

 
C 0.0106 0.0050 2.1141 0.0479 

LUN 1.5509 0.1751 5.6451 0.0000 

LCF -0.0017 0.0042 -0.3518 0.7288 

LGDPC -0.0001 0.00117 -2.3116 0.0322 

LOP -0.0156 0.0099 -0.3518 0.7644 

LTOT -0.0111 0.04568 -0.1852 0.8550 

R-squared 0.7560 

Adjusted R-
squared 

0.6790 

F-statistic 9.8164 

F-probability 0.0000 

Durbin-
Watson stat 

1.9017 

 
Test Statistic Value Significant  Lower Bound Upper Bound 

F-Statistic 2.7604 10% 2.45 3.5 

K 4 5% 2.88 4.01 

  2.5% 3.25 4.49 

  1% 3.74 5.06 

                                 Source: Authors computation using EViews11 

 

The above table shows that the dependent variable: Unemployment is positive and probable. Hence, it indicates 

that the lagged period of Unemployment, itself has a positive influence on the current period. Besides, GDP per capita is 

negative, and probable it illustrated that GDP per capita is influencing unemployment negatively. Further to understand the 

long-run association we apply the bound test. Moreover, the result from the bound test and F-statistic is 2.77 and this value 

should be compared to the Pesaran critical value of 5%. Nevertheless, to find out about the exitance of the long-run 

relationship of variables we compare the F-statistic to the bound table: Lower bound at 5% is 2.88 and the upper bound at 

5% is 4.1. Moreover, the guideline is, that if the F-statistic is greater than the upper bound then there is a long-run association 

among variables but if the F-statistic is lower than the lower bound then there is no long-run relationship among variables. 

Thus, the result evinces that there is no long-run relationship among variables as per the guideline of the bound table. 

Similarly, if the bound test ruled out the presence of a long-run relationship, then we continue with the ARDL to find out 

the short-run causality relationship.  

 

Table 3. ARDL Test 

 
Variables  Coefficient Std. Error t-Statistic Prob. 

DUN (-1) 0.9889 0.1751 5.6451 0.0000 

DUN (-2) -0.3053 0.1700 -1.7958 0.0884 

DOP  -0.0030 0.0099 5.6451 0.7644 

DGDPC -0.0270 0.0117 -2.3116 0.032 

DCF -0.0015 0.0042 -0.3518 0.7288 

DTOT -0.0008 0.0045 -0.1852 0.8550 

C 0.0106 0.0050 2.1141 0.0479 

R-squared 0.7560 

F-stat   9.8164 0.0000 

Durbin-Watson stat 1.9017 

Source: Authors computation using EViews11 

 



Naderi et al., American Finance & Banking Review 7(1) (2022), 1-6 

 

4 

The table above shows the dynamic short run, in the short run only Gross domestic product per capita has a negative 

and significant effect on Unemployment. Further, it revealed that there is a short-run causality running from Gross Domestic 

Products Per Capita toward unemployment, and the result of table 3 indicated that if the GDP Per Capita changes by 0.01 

units it changes Unemployment by -0.02 units. Moreover, the model passed the diagnostic and stability tests which have 

shown in tables4 and 5 and Figures 1 and 2 respectively. 

The model is run against the serial correlation of (Breusch-Godfrey serial correlation LM test) to point out the 

exitance of serial correlation on the model. 

 

Table 4. Breusch-Godfrey serial correlation LM test 

 

 

 

Source: Authors computation using EViews11 

The result from the above table shows that there is no serial correlation in the model and as the probability value 

is more than 0.05 significant value then we accept the Null Hypothesis. Moreover, the model run against Heteroskedasticity 

test of Breusch-Pagan-Godfrey to obtain the presence of Homoskedasticity of variables 

Table 5. Heteroskedasticity Test Breusch-Pagan-Godfrey 

Source: Authors computation using EViews11 

 

The Result from the table-5 pointed out the existence of Homoskedasticity, as the P-value is greater than 0.05 

significant value. Therefore, we accept the Null Hypothesis. 

Similarly, the model tested against Jarque-Bera’s normality test and the result indicated that the p-value is greater 

than 0.05 and we accept Null Hypothesis that the data is normally distributed. The study plotted CUSUM and CUSUMQ to 

see the stability of the model. 

 

   
                                                    Figure 1. CUSUM Test 

  Source: Authors computation using EViews11 

 

 
                                                          Figure 2. CUSUMQ Test 
                                            Source: Authors computation using EViews11 

 

CUSUM and CUSUMQ figures from the stability test indicated that the model is fit as in the figure it shows that 

CUSUM of squares and CUSUM is significant. Similarly, the null hypothesis tells us that the model is not stable while the 

Breusch-Godfrey serial correlation LM test 

Null Hypothesis: No serial Correlation at up to 2 lags  

F-statistic 1.9529 Prob.  0.1724 

Obs R-squared 4.8577 Prob. Chi-Square 0.0881 

Heteroskedasticity Test Breusch-Pagan-Godfrey 

Null Hypothesis: Homoskedasticity 

F-statistic 1.1366 Prob. F 0.3793 

Obs. R-squared 6.8672 Prob. Chi-Squared 0.3333 

Scaled Explained SS 2.5070 Prob. Chi-Squared 0.8677 



Naderi et al., American Finance & Banking Review 7(1) (2022), 1-6 

 

5 

residual test shows that the CUSUM and CUSUM of Square are significant at 5%. Thus, the model used in this study is 

stable. 

 

CONCLUSIONS  

The article has referenced many studies that were carried out to understand the relationship between trade liberalization and 

unemployment around the world. Hence, the purpose of this study focused on understanding the connection between trade 

liberalization and unemployment in Afghanistan. Data were gathered from various sources including World Bank, National 

Statistic, and Information Authority of Afghanistan encompassed from 1990 to 2018. Consequently, ADF (Augmented 

Dicky Fuller) stationarity test evinced the first-order stationarity of the data. Similarly, ARDL Bound test depicted the 

absence of a long-run relationship and the presence of a short-run. Further, the empirical evidence showed short-run 

consequences in one variable which is Gross Domestic Products Per Capita. Furthermore, the study pointed out that there 

is a unidirectional causality running from GDP Per Capita toward Unemployment, and GDP Per Capita has indicated a 

negative and significant impact on Unemployment in the short run. Moreover, this study calculated the result and found that 

there is no relationship between trade and unemployment in Afghanistan which neither rejects nor accepts the question 

raised “does opening up to international trade create or destroy jobs”. Finally, the study suggested that the government needs 

to reform policy regarding tackle down unemployment through domestic investment. 

 

 

 
Author Contributions: Conceptualization, A.W.N. and A.M.N.; Methodology, A.W.N. and A.M.N.; Software, A.M.N.; Validation, A.W.N. and A.M.N.; 

Formal Analysis, A.W.N. and A.M.N.; Investigation, A.W.N. and A.M.N.; Resources, A.W.N.; Data Curation, A.W.N. and A.M.N.; Writing – Original 
Draft Preparation, A.W.N.; Writing – Review & Editing, A.M.N.; Visualization, A.M.N.; Supervision, E.T., and J.N.; Project Administration, A.M.N.; 

Funding Acquisition, .W.N. and A.M.N. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 
Acknowledgments:  N/A 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.    

 

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