




































American Finance & Banking Review; Vol. 3, No. 1; 2018 

ISSN 2576-1226    E-ISSN 2576-1234 

Impact Factor: 4.1 

Published by Centre for Research on Islamic Banking & Finance and Business   
 

 

5 

The Determinants of the Foreign Direct Investment on the 

Macroeconomic Variables: The Case of the Algerian Economy 
 

 

Ahmed Smahi
1 

 

1
Department of Economics, University Abou –Bakr Belkaied, Tlemcen, Algeria 

 

Correspondence: Ahmed Smahi, Department of Economics, University Abou –Bakr Belkaied, Tlemcen, 

Algeria. Tel: 00(213)551846777. E-mail:delevery10@gmail.com 

 

To cite this article: Smahi, A. (2018). The Determinants of the Foreign Direct Investment on the 

Macroeconomic Variables: The Case of the Algerian Economy. American Finance & Banking Review, 3(1), 5-

11. Retrieved from http://www.cribfb.com/journal/index.php/amfbr/article/view/136 

 

 
Received: September 10, 2018         Accepted: September 15, 2018        Online Published: September 24, 2018 
 

 
Abstract 

Foreign direct investment in Algeria as a percentage of GDP represented 0.9% during the last decade. The goal 

of this study is to assess the effect of Foreign Direct Investment on Algerian economy through an empirical 

analysis by applying the bounds testing ARDL and ECM-ARDL using annual data for the period 1970-2014. As 

far as the role of FDI is concerned, we shall try to highlight its effect that may show causal relationships to non-

hydrocarbon GDP, non-hydrocarbon export, industry and employment in long run. Our estimation of an ARDL 

model indicates that the political and macroeconomic stability are not enough to attract FDI to help non-

hydrocarbon sectors drive economic growth. 
 
Keywords: Algerian Economy, FDI, ARDL Model 
 
 
1. Introduction 

Foreign direct investment (FDI) is a crucial factor to stimulate economic growth for many countries especially in 

less developed ones that cannot rely solely upon their own resources to promote their economies. It is known that 

from the early seventies the need for FDI was not so strong for Socialist Algeria which relied on its own 

resources as well as international credits for its own development that focuses on petrochemicals, steel and 

plastics as key  industries for economic growth. Considering that FDI was viewed as the extension of colonialism 

Boumedienne's planning and his socialist management concentrated on   public dominance over all sectors of the 

Algerian economy instead of promoting investment by attracting foreign direct investment. Between 1980 and 

1990 the FDI flow increased at an average rate of about 7 percent a year compared with average rates of 0.08 

percent as a percentage of GDP. 

The persistence of a low level in foreign direct investment flows since the 1990s (black decade) has been 

associated with an average rate of 3 % of annual FDI inflows. However, in 1999, FDI remained remarkably high 

as a percentage of GDP as it rose to 0.6 percent. 

FDI inflows varied between 1 and 2 billion dollars during last decade. From  2001 to 2014, Even though  

Algerian economy has been characterized by some political and macroeconomic stability,  it remains that its 

attractive potential to FDI was not up to its expectations as foreign investors are still reluctant to take the 

decision to transfer their assets to Algerian market.  

The goal of this study is to assess however, the effect of Foreign Direct Investment on Algerian economy 

through an empirical analysis by applying the bounds testing ARDL and ECM-ARDL using annual data for the 

period 1970-2014. 

The rest of the paper is organized as follows. In section 2 we present a literature review on the relationship. 

Section 3 presents the model and the methodology, followed by the results and discussion in Section 4, and 

finally, section 5 presents the main conclusion. 



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2. Literature review 

Many studies have highlighted the different impacts of FDI on macroeconomic variables such as GDP growth, 

exports, unemployment rates, inflation, industrial sector, the stock market, etc…. 

Firstly, Solow 1956 as among the oldest pioneer in the theorization of FDI emphasized the crucial role of 

technological progress as a specific investment to explain economic growth followed by the Harrod-Domar 

model of economic growth (See Sato1964). Kaldor 1963, Findlay 1978, Lucas 1988, Romer 1989, Barro 1990, 

Robelo 1991, Frankel and Romer 1999 advanced second generation theories that developed endogenous input to 

FDI. 

Secondly, there are many empirically studies that focus on the positive impact of FDI on macroeconomic 

variables, Choe (2003) used Granger causality test to detect some impacts of FDI to economic growth in 80 

developed and developing countries for the period 1971 – 1995. Using similar technique, Al-Iriani (2007) found 

bidirectional causality between FDI and economic growth in GCC countries during the period from 1970 to 

2004. 

Chowdhury and Mavrotas (2006) pointed out in their study the existence of Bidirectional causality in Malaysia 

and Thailand using Lag-augmented vector autoregression for the period 1969-2000. Shaikh (2010) found a 

significant relationship between economic growth and foreign direct investment inflows (FDI) in Malaysia 

during the period 1970 to 2005.  

On the contrary, De Mello (1999) found week impact for FDI effects on economic growth in 32 developed and 

developing countries in the period 1970- 1990. Manuchehr and 

Ericsson (2001) confirmed a null impact between Finland and Denmark as far as the impact of FDI in both 

economies is concerned since the 1970s. Zenasni and Benhabib (2015), using a Granger causality test for the 

period 1980-2013, found that FDI had a positive but a negligible effect on Algerian economic growth whilst 

concomitantly domestic investment exhibited significant effects. 

Moreover, Belloumi (2014) examined the relationship between foreign direct investment (FDI), trade openness 

and economic growth by applying the bounds testing (ARDL) Model for the period from 1970 to 2008. His 

results suggested that there is no significant Granger bidirectional causality between FDI and economic growth 

particularly in the short run. Dritsaki and stiakakis (2014) applied for Croatia a ECM-ARDL Model using annual 

data for the period 1994-2012 and arrived to the conclusion that there is a negative sign of FDI to lead to 

substantial economic growth in Croatia. Additively, Sarkar (2007) presented a negative relationship between FDI 

and economic growth in 51 less developed countries from 1970 to 2002. 

3. Model and methodology 
3.1. Data sources  

The sample comprises 45 annual observations for the period 1970 - 2014.The sources of our variables are 

collected from different issues of International financial Statistics, world development indicators and the Bank of 

Algeria. 

 3.2. The Econometric approach  

The ARDL model is used to analyze cointegration series for short and long-run dynamics, even when the  time-

series  are stationary I(0)  or  integrated  of  order I(1). The variables may include a mixture of stationary and 

non-stationary time-series for ARDL Bounds testing approach proposed by Pesaran (1997), Pesaran, Smith and 

Shin (2001) and Pesaran et al. (2001). In addition, the bounds testing procedure (Pesaran et al., 2001) proposed 

in this study is robust for small sample (AbdPattichis, 1999; Mah, 2000; and Tang and Nair, 2002, Halim et al 

2008). 

Our variables are FDI, FDIt-1, NHGDP, NHEXP, EMPL and INDVA that represent respectively non-

hydrocarbon GDP, non-hydrocarbon export, industry and employment. 

The mathematical representation of an ARDL regression model is: 

 

invt = β0 + β1invt-1 + .....+ βkinvt-p + αNHGDP0t + α1NHEXPt-1 + α2indvat-2 + α3emplt-3+ + ε………… (1) 

Where: 

 εt is a random "disturbance" term.  

 β0= Intercept of the function  

 β1, α0, α1, α2, α3 are parameter estimates. 

Before presenting empirical results of the ARDL model, we apply the following econometric steps needed for 

stationary Test of the data.  Firstly, we use the Augmented Dickey-Fuller & Philips-Perron test then we proceed 

to determine the F-test for ARDL Model. 



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4. Result and discussion 

4.1.Stationary test results  

Before estimating the ARDL bounds approach, we use the Augmented Dickey-Fuller (1979, 1981) and Phillips 

and Perron, (1988) tests for stationary and non-stationary time-series. The results are represented in table (1) 

showing that all variables are integrated of order one (I (1)) except the non hydrocarbon GDP and industry 

variables, though they are stationary at levels (I (0)) 

 

Table 1: Stationary test results 

  Variables            ADF     PP 

    

 Level                 First difference Level First difference  Level First difference 
    

    Inv -1.89                        -10.21***               -1.89 -9.92***   
    

   NHGDP -2.95**                    - 4.71**   - 2.95**   -4.94*** 

   NHEXP -1.91                     -5.76*** -1.92    -5.76*** 
    

   Indva -3.58**                 -8.67*** 

                 -

3.61***           -14.93*** 
    

 unmpl  -0.90                    - 5.42***                -1.22                 -5.44*** 
    

    

 

*show values are significant at 10 % level with MacKinnon (1996). 

**show values are significant at 1% level with MacKinnon (1996). 

***show values are significant at 5 % and 1 level with MacKinnon (1996). 

4.2. Cointegration test 

Secondly in order to detect the best optimal lags length, we use several tests such as :  the Akaike information 

criterion (AIC) test (1974, 1976), the Hannan-Quinn criterion (HQC), (1979) and the Schwarz Criterion (SC) 

(1978). The ARDL model used in long and short run are expressed as follows according to the choice of the 

equations that present more advantages with less value in former tests. 
 
4.3. Long-Run 

……. (2) 

… (3) 

…. (4) 

…. (5) 

……. (6) 

In order to determine the long-run effect of FDI on Algerian macroeconomic variables, we compute the F-

statistic compared with the critical value tabulated by Pesaran et al. (2001) at the 5 percent level. On the basis of 

Wald Test results in different equation :(2), (3), (4), (5), (6) , we accept the null hypothesis (H0) and reject (H1) 

as the alternative hypothesis, (no existence of cointegration) in long run among the variables.  

 

 

 
and 

 

 
and 

 

 
and 

 



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8 
 

 
 
and 

 

 
On the basis of the results in Table (2), we may conclude that there is no effect of foreign direct investment on 

the Algerian macroeconomics variables in the long-run. 

 

Table 2. Long run results 

 Dependent  

variable: 

GDPHH 

(Equation 3) 

Dependent 

 variable: 

NHEXP 

(Equation 4) 

Dependent 

 variable: 

INDVA 

(Equation 5) 

    Dependent 

   variable: 

   Unmp 

    (Equation 6) 

 
 variables    coefficients     variables   coefficients    variables   coefficients       variables   coefficients   variables coefficients 

 
   FDI t-1           0,098             FDI t-1              0,442                           FDI t-1              -0,106                  FDI t-1              -0,396  

    GDPHH t-1       -0,009              NHEXP t-1       0,032           INDVA t-      10,005                    Unmp t-1           -0,324  

   NHEXP t-1         0,060              GDPHH t-1       -1,165             GDPHH t-1          0,004                   GDPHH t-1        0,019  

   INDVA t-1         -0,434              INDVA t-1         2,003           NHEXP t-1          0,040         NHEXP t-1     -0,413  

   Unmp t-1              0,071              Unmp t-1             0,047           Unmp t-1               0,064                    INDVA t-1        0,350  

    R2                             0,710               R2                            0,750            R2                            0,800                    R2                            0,680  

    F-Statistic        2,180              F-Statistic        3,350           F-Statistic        4,050                     F-Statistic      2,190  
 variables coefficients 

    D-W                 2,000               D-W                 2,360            D-W                2,350                     D-W               2,270  

 
   serial  

   correlation       NO 

              serial  

   correlation       NO 

          serial  

          correlation        NO       
                    serial  

                    correlation       NO       

          *show values are significant at 5 % 
 

4.4. Short-Run 

The mathematical representation of the cointegration analysis in the short run is: 

  (7) 

              
(8) 

) 

   
(9) 

3 

 (10) 

) 



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 (11) 

In the short run, all dependent macroeconomic variables exhibit a four cointegration relationship with foreign 

direct investment. We note furthermore, in table 3, through econometric diagnostic tests, the absence of serial 

correlation while Durbin Watson seems to be good with high R
2
 - more than 60 percent in all models- except for 

the last one. It is clear to show in the first model that a change in the non hydrocarbon GDP by one percent leads 

to an increase of non hydrocarbon exports by 0.17%, while a change in FDI shows a negative sign which implies 

that there is statistically an insignificant effect and a decrease in the non hydrocarbon GDP by 0.09%. 

The  empirical  results of  FDI on non hydrocarbon exports  identified in equation 9 in Table 3  show  through 

some coefficients that  one  percent  change  in  non   hydrocarbon GDP and industry sector leads  to  0.72%  rise   

and  1.76 drop respectively  on non hydrocarbon exports . The foreign direct investment appears to have had a 

negligible effect on the Algerian non hydrocarbon export. Finally, we find another negligible effect of NHGDP, 

NHEXP and FDI on industry value added whose coefficient does not exceed 0.05. 

 

Table 3: short run results 

 Dependent  

variable: 

GDPHH 

(Equation 8) 

Dependent 

 variable: 

NHEXP 

(Equation 9) 

Dependent 

 variable: 

INDVA 

(Equation 10) 

    Dependent 

   variable: 

   Unmp 

    (Equation 11) 

 
variables     coefficients     variables     coefficients       variables   coefficients      variables   coefficients   variables coefficients 

 
  d (inv(-1))        0,009           d(inv(-1))*        -0,020         d(inv(-1))*      -0,069                d(inv(-1))         -0,002  

 d(nhexp(-1))*    0,178            d(gdphh(-1))*     0,722          d(gdphh(-1))     0,046                  d(indva(-1))        0,155  

 d(indva(-1))         0,277            d(indva(-1))*      -1,699             d(inv(-1))*         -0,011                 d(NHEXP(-1)     -0,051 
 

 d(unmp(-1))        -0,137            d(unmp(-1))          -0,247           d(unmp(-1)         0,030          d (gdphh(-1)    -0,116  

 ECT (-1)                 0,124             ECT (-1)*             -0,238           ECT (-1)              -0,031                  ECT (-1)*            0,991  

  R2                                  0,710              R2                                   0,620            R2                               0,600                  R2                                 0,680  

 F-Statistic*           2,180              F-Statistic*          2,560           F-Statistic          1,720                   F-Statistic*        4,350  
 variables coefficients 

 D-W                      2,000              D-W                     1,960            D-W                  1,790                   D-W                   1,300  

 
 serial  

 correlation            NO 

             serial  

    correlation            NO 

          serial  

          correlation         NO       
                  serial  

                  correlation         YES       

 

4.5. ECM t-1 Results  

We use the Error correction coefficient (ECM) as signal to explain that the deviation in the long-run relationship 

will be fed into its short-run dynamics, See Granger J. (1987). Thus, it may be better that ECM t-1 should be 

negative and significant. 

Table 3 reports the results for ECM t-1. Speed of adjustment for models 2 and 3 that allow correcting long run 

equilibrium at 26 and 3% respectively, with negative and significant coefficient. Thus, Model 1 shows a positive 

and statistically insignificant error correction coefficient. This cannot be interpreted as a good sign for the 

converging relationship in the long run between non-hydrocarbon GDP and foreign direct investment in Algeria.  

Moreover, the ECM t-1 of unemployment as dependant variable presents the problem of autocorrelation. Also, 

this result confirms the absence of any structural change of FDI to converge towards equilibrium in the long run. 
4.6. CUSUM and CUSUMSQ Test  

Having found a significant and negative of ECM t-1 coefficient in equation 9 and 10: (Figures 1 and 2), the 

CUSUM (cumulative sum) and CUSUMSQ (CUSUM squared) tests are then introduced to  check for the 

stability of the relationship in the short run dynamics within a long run equilibrium, Brown et al. (1975).  

 



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-15

-10

-5

0

5

10

15

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance    

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance   

Figure 01: CUSUM and CUSUMSQ Test of FDI impact on non-hydrocarbon exports 

   

-15

-10

-5

0

5

10

15

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance   

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance    

Figure 02: CUSUM and CUSUMSQ Test of FDI impact on industry 

5. Conclusion  
In this paper, we investigated if Foreign Direct Investment has an effect on Algerian macroeconomic variables 

(non-hydrocarbon GDP, non-hydrocarbon export, industry and employment). The estimation through the bounds 

testing ARDL and ECM-ARDL allows detecting that FDI is ineffective and presents a negligible impact on non-

hydrocarbon export as well as industry in the short run. In the long run, our estimation using  cointegration 

analysis does not highlight a dynamic relationship between first, FDI and non hydrocarbon economic growth, 

second, FDI and unemployment and third, FDI and non hydrocarbon Exports. 

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