







































American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 6.71 

Volume. 10, Number 3; July-September, 2023; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadipub.com/Journals/index.php/aijbe| editorial@sadipub.com 

 

 

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SECTORAL EMPLOYMENT AND FDI INVESTMENTS IN GHANA: A 

COMPREHENSIVE ANALYSIS 
 

 

Riccardo .C, Ganau, Roberto, and Storper, M. 

Faculty of Business and Economics, Mendel University, Brno, Czech Republic 

https://doi.org/10.5281/zenodo.8234146 

Abstract: In the context of increasing foreign direct investment (FDI) inflows to the African continent, this 

study delves into the distribution and impact of FDI-registered projects across sectors in Ghana. While these 

investments have been anticipated to catalyze economic growth, the disparate contributions of sectors to the 

country's total Gross Domestic Product (GDP) raise questions about the effectiveness of FDI in driving sector-

specific development. This paper seeks to comprehensively investigate the influence of FDI and local 

investments on key sectors in the Ghanaian economy. The study concentrates on the agriculture, building & 

construction, manufacturing, and service sectors, meticulously analyzing the role of FDI in fostering job 

creation within these domains. Drawing on data spanning from 2001 to 2018, sourced from the Ghana 

Investment Promotion Centre, the research employs a battery of tests including multivariate, multicollinearity, 

unit-root, correlation, and auto-correlation analyses to unveil both short and long-term relationships among 

the variables. Ordinary least squares (OLS) regression provides a foundational basis for simple linear 

regression. Findings illuminate a positive and significant impact of FDI on the service sector, contrasted by 

its relatively muted influence on agriculture and manufacturing industries, where its significance wanes at the 

5% confidence level. Intriguingly, the study also reveals that employment generation, stemming from FDI-

registered projects, does not significantly affect the manufacturing sector. As an actionable recommendation, 

the paper suggests governmental provision of incentives to attract investment into underperforming sectors, 

thereby stimulating employment opportunities and fostering economic expansion. This study contributes 

substantively to the existing literature by shedding light on the nuances of FDI's contribution to the economy, 

offering insights into sector-specific responses to FDI, and deciphering the multifaceted factors that shape 

investment inflows. Ultimately, the findings underscore the importance of targeted policies for reinvigorating 

sectors and driving holistic economic advancement. 

Keywords: foreign direct investment (FDI), economic growth, sectoral distribution, Ghanaian economy, job 

creation 

 

Introduction: 

As foreign direct investment (FDI) flows to the African continent continue to rise, the distribution of FDI 

registered projects among sectors has fueled expectations of increased economic growth in Ghana. However, 

the contribution of each sector to total GDP varies. This paper aims to investigate the impact of registered 

projects through FDI and local investments on key sectors of the Ghanaian economy. The research focuses on 



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assessing the role of FDI in generating jobs in the agriculture, building & construction, manufacturing, and 

service sectors. The study uses data from the Ghana Investment Promotion Centre for the period 2001 to 2018. 

Through a series of tests, including multivariate, multicollinearity, unit-root, correlation, and auto-correlation 

analyses, the study determines the short and long-term relationships between variables. Ordinary least squares 

(OLS) regression is used to obtain a simple linear regression. Results indicate that FDI had a positive impact 

on the service sector, but lacked significant impact on agriculture and manufacturing industries at a 5% 

significance level. Additionally, employment creation through registered investment projects had no 

significant effect on the manufacturing sector. The paper recommends that the government provide incentives 

to attract more investors in the non-performing sectors to boost employment generation and facilitate 

economic growth. This study contributes to the existing literature on the significance of FDI's contribution to 

an economy, including the various sectors' response to FDI, and the factors that affect inflows. 

1.1. Objective   

The motivation for this paper is the result of the recent increase in FDI flow to the African continent, from 

which Ghana is not an exception. The distribution of FDI registered projects among the various sectors has 

inspired a high level of expectation of economic growth in Ghana. The contribution of each sectoral level is 

measured as a proportion of total GDP. As numerous studies have shown FDI to play a significant role in many 

economies, there is a need for us to assess the significance of FDI and local investments in the sectors of the 

Ghanaian economy. This study has two main goals. The first is to investigate the impact of registered projects 

through FDI and domestic investments on the agriculture, building & construction, manufacturing, and service 

sectors. The second is to examine the employment created through investment registered projects and how 

this is distributed among the selected sectors. 

2. Literature Review  

The effect of FDI influx into the industrial, construction, and service sectors on economic growth was 

investigated in a panel of 16 Central, Eastern, and Southern European CESE nations, using data from different 

periods between 1998 and 2012. The analysis of the decomposition of FDI showed that FDI in the industrial 

and service sectors has a positive and significant impact on economic growth (Miteski & Stefanova, 2017). 

Another study considered the impact of FDI in the agriculture, manufacturing, and service sectors on economic 

growth. This empirical analysis used panel data from 2000 to 2015 from five countries: China, Pakistan, India, 

Bangladesh, and Sri Lanka. The results revealed that FDI in manufacturing has the greatest potential to 

increase economic advancement compared to investment in other sectors (Haider & Muhammad, 2016).   

Other studies have evaluated the relationship between FDI and growth at the sector level. In one study, the 

effect was examined using a panel cointegration test followed by a random-effects model. The results showed 

that at the sector level, growth affects FDI, but FDI does not affect growth (Areej & Shahid, 2017). Another 

study applied the autoregressive distributed lag (ARDL) method to investigate the relationship between FDI 

and growth in the mining sector using data from1988 to 2018. The results indicated that in this sector, FDI has 

a significant positive relationship with a country’s GDP in the long run. FDI in mining was revealed to have 

relatively greater effects compared to FDI in non-mining sectors and domestic investment (Plaxedes & 

Seetanah, 2020).  

Investigating the nature and behavior of total and sectoral FDI inflow in South Asian countries in recent years, 

another study adopted a holistic approach to studying and analyzing the FDI-growth dynamics. The results 

showed that the impact of FDI in South Asia is influenced by the sectoral composition of the FDI (Saswata, 

Nitya, & Bhawna, 2020). Furthermore, the relationship between FDI and income inequality has been analyzed. 

One study estimated the impact of FDI from a sector perspective and identified 3 major sectors: the primary 



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sector, manufacturing industry, and services. Using panel data for 13 economies from 1980 to 2009, the study 

found a positive effect of FDI on income inequality in the service and manufacturing sectors (Macarena, 2016).   

Using a multiple linear regression model and ordinary least squares (OLS) estimation, the influence of FDI 

on economic growth has been examined. One study distinguished ten different sectors in the United States. 

According to its findings, not all forms of FDI appear to be advantageous to host economies. However, certain 

industries have a favorable impact on economic growth, while others have a negative effect (Donny, 2018). 

Another study used a sample of 10 CEE for the period 1995–2019 and looked at the system determinants and 

transmission mechanisms of the sectoral structure of FDI inflows. This study followed on from earlier 

research, and the empirical component included the construction of a panel model. The results showed that 

the most effective strategy to attract developmentally-efficient FDI is to change the local economy's structure 

through explicit industrial and investment policies (Mario, Kusanović, & Jakovac, 2021). Using the Vector 

Autoregressive (VARs) model, FDI has been shown to have a considerable beneficial impact on economic 

growth in both the short and long run (Saidatulakmal & Abdillahi, 2021). A study revealed that, in the long 

run, both the rate of FDI inflows and the rate of foreign tourism have had a favorable impact on the rate of 

economic growth in Estonia (Amin & Glenn, 2021). Using sectoral data as the primary source of information 

to determine the direct effect of FDI on GDP, another analysis forecasted that FDI in the industry, tourism, 

and agriculture sectors has an overall highly favorable and significant impact on GDP over a ten-year period 

(Ram & Seema, 2018).  

2.1. FDI and Employment Generation  

The impact of FDI inflows on low- and high-skilled workers' employment and wages in Mexico's 

manufacturing and service sectors has been investigated. The study used a quarterly panel dataset spanning 

Mexico's 32 states from 2005 to 2018. According to the findings, increased FDI influx into the manufacturing 

sector had a favorable influence on low- and high-skilled employment. In the service sector, however, the 

results are inconclusive throughout the model for both types of employment (Eduardo, Ozuna, & Zamora, 

2020).   

Another study indicated a general positive correlation between external investment and local employment at 

the national level, although it identified significant variances between regions and sectors (Riccardo, Ganau, 

& Storper, 2022). Using Johansen's cointegration approach and Toda and Yamamoto's Granger causality test, 

other researchers investigated the long-run link between outbound FDI and employment in China. According 

to the data, outward FDI from China resulted in favorable job development, particularly in the tertiary sector 

(Huiqun & Lu, 2011).  

Another study examined the impact of FDI and economic growth in Turkey on overall employment and female 

employment. The findings demonstrated that FDI harms overall employment and female employment, 

whereas economic growth has a beneficial impact on overall employment and female employment (Umit & 

Alkan, 2016). Using suitable descriptive analysis, a further study analyzed the impact of FDI on job creation 

in India. The results demonstrated that the impact on job creation in India is obvious, but FDI inflows may not 

play a key role in the country's growth rate. (Ronismita & Swapnamoyee, 2020). A single equation error 

correction model was used to examine the impact of FDI on employment in Macedonian industrial sectors. 

The findings showed that FDI and human costs are statistically significant determinants that positively affect 

employment in the manufacturing subsectors, implying that, as a result of their interaction, companies with 

FDI may have higher productivity (Dimitar, 2017). In another study, using panel data from 1994 to 2017, the 

authors examined the impact of FDI on youth unemployment in the Southern African Development 

Community (SADC) area. The findings suggested that FDI has a slight impact on lowering youth 



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unemployment in the SADC region (Dadirai et al., 2021). Finally, providing a general overview of the flow 

of FDI to Ghana by considering the overall number of registered projects and using employment creation to 

assess their significance, Yeboah and Anning (2020) showed that Ghanaians enjoyed about 85% of the total 

jobs created between 2013 and 2018.  

3. Methodology and Data  

This study seeks to investigate the comparative influence of FDI and domestic registered projects and 

investment on employment generated in the various sectors of the Ghanaian economy. However, to avoid 

having too wide a focus, we have focused on the agriculture, building & construction, manufacturing, and 

service sectors. To assess the impact of FDI on an economy, a series of tests must be carried out to ascertain 

the short and long-run relationships between the variables. These tests include multivariate, multicollinearity, 

unit-root, correlation, and auto-correlation (among the error terms) analyses. These tests are carried out to 

obtain a simple linear regression using ordinary least squares (OLS). The study used secondary data from 

GIPC for the period 2001 to 2018.   

First, a summary statistic was carried out of all the variables to obtain the means and standard deviations; these 

are shown in Table 1. Moreover, Figure 1 shows the time trends of FDI projects in the various sectors. We 

tested for unit root presence in the variables using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. Under 

the null hypothesis (𝐻0), 𝜇𝑡 is constant, and the variance of 𝜀𝑡 is zero. On the other hand, under the alternative 

hypothesis (𝐻1), 𝜇𝑡 is a random walk, and the variance of 𝜀𝑡 is positive. The KPSS test thus shows a unit root 

presence in each of the variables (agriculture sector, building & construction sector, manufacturing sector, and 

service sector). It is known that time series involve a different approach to the analysis of economic data 

(Granger, 1981).   

 Secondly, a multicollinearity test was carried out using variance inflation factors (VIF). The symptoms of 

multicollinearity in a regression model include an increase in the variance of regression coefficients. The VIF 

approach (  ̂𝑗)  indicates the relative variance of the j-th coefficient of regression. It holds that VIF (  ̂𝑗)  1. If 

VIF (  ̂𝑗) exceeds the limit of 10, it is an indication of severe multicollinearity in the model. The variance of 

the j-th regression coefficient can be written as in Equation 1.  

 ̂ 𝜎 ̂  

 ̂ ̅ ̂ )=  𝑛𝑖 ̅                                      

(1)  

The last test is to verify that there is no autocorrelation between predicted variables and the error terms from 

the regression outputs. Using the Durbin-Watson (DW) autocorrelation test, the hypotheses are H0: There is 

no first-order autocorrelation, and H1: there is first-order autocorrelation. The calculation for this test is shown 

in Equation 2.  

∑ 

𝑑 =                                                                                                       (2)  

The DW test is not capable of testing for a higher order of autocorrelation of the error terms. The rule of DW 

states that 1.5< d <2.5 is the no autocorrelation range.   

Model Equation 3 contains non-significant regressors (Agriculture and Manufacturing sectors). The p-value 

of the explained sum of squares reduction F-test suggests that non-significant coefficients are zeros and can 

be removed from the model. The backward elimination method can be applied to remove the non-significant 

explanatory variables and enhance the performance of the resulting model. It begins with the removal of the 

non-significant coefficients as indicated by the high p-value.  After applying the backward elimination method, 

we arrived at model Equation 4.  



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In model Equation 4, the constant is non-significant, and it is affected by pure heteroskedasticity. Pure 

heteroskedasticity is due to a correct model specification and does not cause a systematic error (bias).   

Because the error term does not have a constant variance, it is necessary to find out which regressor is causing 

the heteroskedasticity. Heteroskedasticity violates classical assumption number five, which makes model 4 

less than ideal. After applying the principles and steps for handling pure heteroskedasticity, we obtained model 

Equation 5 by removing the manufacturing sector from the equation.   

𝑇𝑜𝑡𝑎𝑙 𝐹𝐷𝐼 𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑠𝑡=𝛽0 + 𝛽1𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒𝑡 + 𝛽2Building and Construction𝑡 + 𝛽3Manufacturing𝑡 + 

𝛽4Service𝑡 + 𝜀𝑡                                                                                                     (3)  

𝑇𝑜𝑡𝑎𝑙 𝐹𝐷𝐼 𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑠𝑡= 𝛽0 +𝛽1Building and Construction𝑡+𝛽2Manufacturing𝑡 + 𝛽3Service𝑡 +   (4) 

𝑇𝑜𝑡𝑎𝑙 𝐹𝐷𝐼 𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑠𝑡 = 𝛽0 +𝛽1Building and Construction𝑡 + 𝛽2Service𝑡 + 𝜀𝑡                (5)  

To assess FDI registered projects’ impact on the total number of jobs, we considered the number of jobs created 

in the selected sectors. The total number of jobs for Ghanaians and expatriates in each of the sectors is modeled 

on the overall employment from FDI. Model Equations 6 and 7 are generated by the logarithm transformation 

of each of the variables. The estimate of the expected number of jobs to be created from the registered 

investment projects is thus:  

𝑙𝑛Total FDI employment 𝑡=𝛽0 + 𝛽1 𝑙𝑛Agriculture𝑡 + 𝛽2 𝑙𝑛Building and Construction𝑡 + 𝛽3𝑙𝑛Manufacturing𝑡 + 

𝛽4𝑙𝑛Service𝑡 +𝜀𝑡                                                                                                     (6)  

𝑙𝑛Total FDI employment 𝑡= 𝛽0 +𝛽1 𝑙𝑛Agriculture𝑡 + 𝛽2 𝑙𝑛Building and Construction𝑡 + 𝛽3𝑙𝑛Service𝑡 + 𝜀𝑡 (7)  

Under the model estimation of the impact of FDI registered projects, the total of FDI projects is the dependent 

variable, whereas the agriculture, building & construction, manufacturing, and service sectors are the 

explanatory variables. The total number of FDI registered projects is measured in hundreds, whereas the total 

FDI employment is measured in thousands. 𝛽1,2, 𝛽3,𝑎𝑛𝑑  𝛽4 are the regression coefficients, while 𝜀𝑡 indicates 

the error term, and 𝛽0 represents the constant term of the obtained model. All the analyses were carried out 

using Gretl software.   

The significance level of p-values is set at 5%. The p-values can be used as an index of the “strength of the 

evidence” against the null hypothesis (H0) (Fisher, 1925).    

Having chosen the statistic from the data for this study and the probability associated with this statistic, if the 

probability is smaller than 5%, we reject H0.  According to the literature, the proposed level of p=0.05 means 

that a “1 in 20 chance is being exceeded by chance”, and this is a suitable limit for statistical significance 

(Fisher, 1935). Fisher explained that it is usual and convenient for experimenters to take 5% as a standard 

level of significance and to ignore all outcomes which fail to reach this standard (Fisher, 1925). This leads to 

their elimination from further discussion. 

Table 1. Summary statistics.  

Variable  Mean  Median  S.D.  Min  Max  

Total FDI Projects  252.7  202.0  109  138.0  514.0  

Service  76.8  63.5  42.7  37.0  195.0  

Manufacturing  53.2  51.0  13.0  39.0  86.0  

Building and Construction  22.8  19.0  14.8  8.00  61.0  

Agriculture  10.0  10.5  4.63  1.00  16.0  

4. Results and Discussion  

The summary statistics of the variables in Table 1 show that the service sector had the highest median with 

63.5%, followed by the manufacturing sector with 51%, and building & construction with 19%, whereas the 



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agriculture sector had the lowest median with 10.5%. Similarly, the time series plots in Figure 1 show an 

upward trend of FDI-distributed projects in the service, manufacturing, and building & construction sectors, 

while the agriculture sector had a downward trend. In addition, Table 2 below shows the results of the 

multicollinearity test of the regression outputs. The table shows no multicollinearity among the variables.   

Model 1 in Table 3 shows a non-significant impact of FDI registered projects on the agriculture and 

manufacturing sectors. The constant of model 1 is also non-significant. However, the impact on the service 

and building & construction sectors is significant. The regression output for model 2 is indicated in Table 4; 

the constant is zero because it is not statistically significant. However, the coefficient of the manufacturing 

sector became statistically significant after applying backward elimination to the agriculture sector.  

  

  

  
Figure 1. Time series trends per sector.  

Table 2. Multicollinearity test.  

Variables  Variance inflation factor  

Service  2.981  

Manufacturing  1.325  

Building and Construction  3.259  

Agriculture  1.112  

 

  



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Table 3. Model1 estimation.  

 Variables  Coefficient  Std. Error  t-ratio  p-value  

Constant  7.056  27.32  0.2584  0.8002  

Service  1.414  0.245  5.780  6.38e-05***  

Manufacturing  1.113  0.535  2.083  0.0576*  

Building and Construction  2.791  0.736  3.795  0.0022***  

Agriculture  1.320  1.378  0.958  0.3553  

 Model 1 variants.   

Regression Statistics  Figure  Regression Statistics  Figure  

Mean dependent var  251.67  S.D. dependent var  108.62  

Sum squared residuals  8073.49  S.E. of regression  24.920  

R-squared  0.959  Adjusted Rsquared  0.947  

F (4, 13)  77.55  P-value(F)  6.15e-09  

Log-likelihood  −80.49  Akaike criterion  170.98  

Schwarz criterion  175.44  Hannan-Quinn  171.60  

rho  −0.480  Durbin-Watson  2.919  

Note: Significance codes:  ‘***’ 0.001, ‘*’ 0.05.  

Table 4. Model 2 estimation.  

 Variables  Coefficient  Std. Error  t-ratio  p-value  

Constant  14.50  26.12  0.55  0.5875  

Service  1.44  0.243  5.92  3.73e-05***  

Manufacturing  1.17  0.529  2.21  0.0439**  

Building and Construction  2.82  0.732  3.86  0.0017***  

 Model 2 variants.   

Regression Statistics   Figure  Regression Statistic s  Figure  

Mean dependent variance   251.67  S.D. dependent var   108.7  

Sum squared residuals   8644.09  S.E. of regression   24.84  

R-squared   0.956  Adjusted R-squared   0.947  

F (3, 14)   103.69  P-value(F)   8.47e-10  

Log-likelihood   −81.11  Akaike criterion   170.21  

Schwarz criterion   173.78  Hannan-Quinn   170.70  

rho   −0.402  Durbin-Watson   2.78  

Note: Significance codes:  ‘***’ 0.001, ‘**’ 0.01. 

The coefficients of model 1 show a positive response from the various sectors in response to FDI and local 

registered investment projects. The DW value shows a higher negative serial correlation. The percentage of 

variation explained in the dependent variable was about 96%. Model 2 in Table 4 shows autocorrelation due 

to the DW test value.   



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Model 3 in Table 5 indicates that the total of FDI registered projects has a positive impact on both the service 

and building & construction sectors. However, the significance level of the service sector is higher than that 

of the building & construction sector. Also, the constant has become statistically significant (nonzero).  

Model 3 shows no serial correlation based on the figure for DW in the output. However, the information 

criterion has increased compared to models 1 and 2. Figure 2 indicates a normal distribution of the error term 

from the regression output.  

Table 5. Model 3 estimation.  

 Variables  Coefficient  Std. Error  t-ratio  p-value  

Constant  65.46  13.86  4.722  0.0003***  

Service  1.440  0.272  5.286  9.14e-05***  

Building and Construction  3.319  0.783  4.236  0.0007***  

 Model 3 variants.  

Regression Statistics   Figure  Regression Statistics  Figure  

Mean dependent variance   251.67  S.D. dependent var  108.66  

Sum squared residual   11671.78  S.E. of regression  27.89  

R-squared   0.94  Adjusted R-squared  0.934  

F (2, 15)   121.48  P-value(F)  5.42e-10  

Log-likelihood   −83.81  Akaike criterion  173.62  

Schwarz criterion   176.29  Hannan-Quinn  173.99  

rho   −0.159  Durbin-Watson  2.298  

Note: Significance codes:  ‘***’ 0.001.  

  
Figure 2. Normality test result from model 3 estimation output. 

To assess the impact of FDI and local registered investment projects on employment creation in the sectors, 

we needed to use the total estimated number of jobs created. The values for the time series were transformed 

into logs for a correct model specification. Figure 3 shows the log transformation of the time series plots for 

the agriculture, building & construction, manufacturing, and service sectors. 



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Figure 3. Time series plot (FDI and domestic employment) in the sectors.  

The regression output from model 4 in Table 6 on the employment impact of FDI on the sectors shows that 

the coefficients of the manufacturing and building & construction sectors are non-significant. This means that 

the nonsignificant p-values of the regressors need to be removed from the model to obtain the final regression 

model (model 5).  

Table 6. Model 4 estimation.  

Variables  Coefficient  Std. Error  t-ratio  p-value  

Constant  0.787  1.556  0.504  0.6221  

l_Agriculture  0.278  0.053  5.230  0.0002***  

l_Service  0.319  0.098  3.263  0.0062***  

l_BuildingConst  0.181  0.086  2.096  0.0562*  

l_Manufacturing  0.373  0.186  2.006  0.0661*  

Model 4 variants.   

Regression Statistics  Figure  Regression Statist ics  Figure  

Mean dependent variance  9.901  S.D. dependent var   0.992  

Sum squared residual  1.570  S.E. of regression   0.347  

R-squared  0.906  Adjusted R-squared   0.877  

F (4, 13)  31.39  P-value(F)   1.44e-06  

Log-likelihood  −3.589  Akaike criterion   17.179  

Schwarz criterion  21.63  Hannan-Quinn   17.793  

rho  −0.258  Durbin-Watson   2.483  

Note: Significance codes:  ‘***’ 0.001 ‘*’ 0.05. 

The results of model 5 in Table 7 indicate a significant impact of FDI on employment in the agriculture, 

building & construction, and service sectors. The constant is statistically significant and nonzero. However, 

the agriculture and service sectors respond more significantly to FDI than the building & construction sector. 

Comparing the information criteria in model 4 to model 5, it is clear that model 4 has the lowest information 

criteria, but a nonsignificant coefficient does not provide any economic meaning to those variables. Model 5 

is burdened with firstorder autocorrelation. Regarding model 5, the constant, agriculture, and service sectors 

were below a 1% significance level, while the building & construction sector was around 2%.   

Table 7. Model 5 estimation.  



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Variables  Coeffici ent  Std. Error  t-ratio   p-value  

Constant  3.610   0.731  4.938   0.0002***  

l_Agriculture  0.251   0.057  4.425   0.0006***  

l_Service  0.335   0.107  3.110   0.0077***  

l_BuildingConst  0.223   0.092  2.418   0.0298**  

 Model 5 variants.   

  Regression Statistics  Figure  Regression Statistics   Figure  

Mean dependent variance  9.90  S.D. dependent var   0.992  

Sum squared residual  2.06  S.E. of regression   0.383  

R-squared  0.87  Adjusted R-squared   0.851  

F (3, 14)  33.3  P-value(F)   1.25e-06  

Log-likelihood  −6.1  Akaike criterion   20.14  

Schwarz criterion  23.6  Hannan-Quinn   20.53  

rho  0.28  Durbin-Watson   1.367  

Note: Significance codes:  ‘***’ 0.001 ‘**’ 0.01. 

The results indicated that from 2001 to 2018, the distribution of FDI registered projects among the various 

sectors was not significant in the agriculture and manufacturing sectors. This implies that greater effort is 

needed to enhance the performance of both the agriculture and manufacturing sectors in terms of attracting 

FDI and domestic investment. Regarding employment creation from FDI through the registered projects, only 

the manufacturing sector seemed not to have a significant response in terms of the number of jobs generated 

through investment during the selected period. A critical point of the analysis is that more FDI projects are 

allocated to the service sector than to other sectors in the Ghanaian economy. The recent efforts in the 

manufacturing sector on the part of the current administration seek to address the low performance in that 

sector. The excellent performance of the building & construction sector in terms of FDI employment is due to 

the huge investment in housing and construction activities in the country in recent years. The results of all the 

models show that the manufacturing sector’s responses to FDI and local investment were at a 5% significance 

level, which indicates a less significant impact. However, based on the results, we cannot rule out that FDI 

and domestic investment have no effect on the manufacturing sector. We excluded the significance level of 

investment in the manufacturing sector as a result of our restriction to a 5% significance level. The R-squared 

from all the models indicates an excellent fit.  

  

5. Conclusion  

This study has confirmed the significance of FDI and domestic investment registered projects distributed 

among the agriculture, building & construction, manufacturing, and service sectors. The KPSS test indicated 

a unit root presence in the selected time series variables. OLS regression showed that registered FDI projects 

have no significant effect on the agriculture and manufacturing sectors. However, the building & construction 

and service sectors enjoy a significant impact from the registered investment projects. On the other hand, when 

testing for the influence of FDI on the employment created in the selected sectors, no significant effect was 

found on job creation in the manufacturing sector. Conversely, FDI did have a positive impact on employment 

generated in the agriculture, building & construction, and service sectors. This study has significant 

implications for policymakers and the government of Ghana since the outcome showed that some sectors are 



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not responding optimally to FDI and domestic registered investment projects. Manufacturing is an essential 

tool for transforming an economy, and there is a need for the government to improve the investment situation 

in the manufacturing sector. However, there are fewer registered projects in the agriculture sector, although it 

serves as a source of employment for most people in the country. It would be helpful for the government to 

boost these non-performing sectors with incentives to attract more investors. Also, there is a need to modernize 

the agriculture sector to enhance its efficiency. Based on the results, the service sector performs better than 

the other sectors. However, this outcome may not be sufficient to explain the factors behind the non-

performance of the manufacturing sector in terms of employment creation from investment. As the results 

confirm that the agriculture and manufacturing sectors are not responding optimally to FDI and domestic 

investments, it would be good to allocate resources and incentives to boost their performance. The findings 

apply to the situation in Ghana and would differ for other countries. 

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