59 © 2022 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Economics and Empirical Research Vol. 9, No. 1, 59-66, 2022 ISSN(E) 2409-2622 / ISSN(P) 2518-010X DOI: 10.20448/ajeer.v9i1.3876 © 2022 by the authors; licensee Asian Online Journal Publishing Group The Significance of Foreign Direct Investment Registered Projects and Employment Creation in the Sectors of Ghana’s Economy Evans Yeboah1 Dacosta Boateng Agyei2 Ran Li3 Anita Sossoe4 Mavis Tetteh5 Aisha Amankwa6 ( Corresponding Author) 1,2,3,4,5,6Faculty of Business and Economics, Mendel University, Brno, Czech Republic. 1Email: xyeboah1@mendelu.cz Tel: +420608353123 2Email: xagyei@mendelu.cz Tel: +420608388700 3Email: xli2@mendelu.cz Tel: +420770608925 4Email: xsossoe@mendelu.cz Tel: +420605969060 5Email: xtetteh@mendelu.cz Tel: +420773154319 6Email: xamankw2@mendelu.cz Tel: +420605791191 Abstract The flow of foreign direct investment (FDI) into the Ghanaian economy has contributed significantly to the various sectors. This study seeks to assess the impact of registered investment (FDI and domestic) projects on employment generation in the agriculture, building & construction, manufacturing, and service sectors. The data for this study was obtained from the Ghana Investment Promotion Centre (GIPC) for the period 2001 to 2018. The Kwiatkowski-Phillips- Schmidt-Shin (KPSS) test showed a unit root existence in the time series. The regression results show no significant impact of registered investment projects on the agriculture and manufacturing sectors at a 5% significance level. Our findings also indicated that employment creation through registered investment projects has no effect on the manufacturing industry. The outcome also revealed that the service sector benefits more from FDI than the other sectors. It is recommended that the government boost these non-performing sectors with incentives to attract more investors. Keywords: FDI, Sectors, Registered projects, Employment, Impact, Economic growth. JEL Classification: E22, F21, O16, R53 Citation | Evans Yeboah; Dacosta Boateng Agyei; Ran Li; Anita Sossoe; Mavis Tetteh; Aisha Amankwa (2022). The Significance of Foreign Direct Investment Registered Projects and Employment Creation in the Sectors of Ghana’s Economy. Asian Journal of Economics and Empirical Research, 9(1): 59-66. History: Received: 10 February 2022 Revised: 23 March 2022 Accepted: 7 April 2022 Published: 26 April 2022 Licensed: This work is licensed under a Creative Commons Attribution 4.0 License Publisher: Asian Online Journal Publishing Group Funding: This study received no specific financial support. Authors’ Contributions: All authors contributed equally to the conception and design of the study. Competing Interests: The authors declare that they have no conflict of interest. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study followed all ethical practices during writing. Contents 1. Introduction ...................................................................................................................................................................................... 60 2. Literature Review ............................................................................................................................................................................ 60 3. Methodology and Data ................................................................................................................................................................... 61 4. Results and Discussion ................................................................................................................................................................... 62 5. Conclusion ......................................................................................................................................................................................... 65 References .............................................................................................................................................................................................. 66 mailto:xyeboah1@mendelu.cz mailto:xagyei@mendelu.cz mailto:xli2@mendelu.cz mailto:xsossoe@mendelu.cz mailto:xtetteh@mendelu.cz mailto:xamankw2@mendelu.cz https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://www.doi.org/10.20448/ajeer.v9i1.3876 https://orcid.org/0000-0002-0934-3996 https://orcid.org/0000-0002-8668-2086 https://orcid.org/0000-0001-5539-2783 https://orcid.org/0000-0001-7464-2872 https://orcid.org/0000-0002-1949-523X https://orcid.org/0000-0003-2724-3603 Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 60 © 2022 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study contributes to the existing literature on the assessment of the significance of FDI’s contribution to an economy, including the various sectors’ response to FDI and factors that affect the inflows. This paper presents the impact of FDI and domestic registered investment projects through regression analysis. 1. Introduction Every country aims to achieve economic growth through sound macroeconomic policies. Over the past decades, many nations have experimented with different avenues through which this goal can be attained (Evans, Samuel, & Prince, 2021). Investment is an important factor in the composition of aggregate expenditure in every economy and has a significant influence on growth through productivity levels and job creation (Okwu, Oseni, & Obiakor, 2020). However, foreign direct investment (FDI) can also contribute to the development of a country through the accumulation of physical and human capital or by enhancing total productivity (Rodan, 1997). Over the past years, host countries have exploited these impacts of FDI (Alhassan, Gakpetor, Kyei, & Akomeah, 2018). In terms of economic growth and improving people’s welfare, Ghana has recognized the role of FDI in supporting its economic activities. FDI is also beneficial to the home country as it helps in the expansion of the market, lowering cost factors and other tariff measures (World Bank, 2010). The flow of FDI to the African continent is projected to shrink between 25% and 40% in 2020, based on the gross domestic product (GDP) growth forecast and some investment-specific factors (UNCTAD, 2020). Since Ghana accepted the structural adjustment program formulated by the World Bank in response to the economic crisis in early 1980, the country has attracted many foreign investors. Since 1983, the government of Ghana has executed these policies more consistently than the government of any other African country (Sayre, 1994). FDI inflows have increased the competition among the business sectors in Ghana. The country has witnessed significant investment in transport, health, education, and electricity (Ebenezer, Agoba, & Abebreseh, 2017). These investments are split between wholly foreign-owned and joint ventures (between Ghanaians and foreign investors). Investment inflows into the various sectors of the Ghanaian economy are distributed as registered projects. The distribution to the sectors is captured by the Ghana Investment Promotion Centre (GIPC) quarterly reports during the investment’s fiscal period. The GIPC is an institution formed under the GIPC Act 1994 (Act 478) to advocate, interrelate and ease investment in Ghana (Evans & Rose, 2021). According to the GIPC, steady economic growth has been the result of a massive expansion in FDI inflow into Ghana; from 2006 to 2010, FDI inflow into Ghana increased at a compound yearly growth rate of 41%, from $636 million to $2.527 billion (Evans et al., 2021). Between 1994 and 2013, there were a total of 4,714 registered investment projects, of which 22.14% were in the manufacturing sector, 29.29% were in the service sector, 16.35% in general trading, 8.67% in building and construction, 8.54% in the tourism sector, 5.17% in liaison, and 5.13% and 4.66% in the agriculture and export trade sectors, respectively (Kusi, 2012). Meanwhile, between January and June 2021, Ghana recorded 122 projects with an estimated total investment of US$874.01 million (GIPC, 2021). Conversely, quantitative findings have shown that FDI’s impact on global employment is more modest and is more important in host developing countries than in host developed nations, especially in production areas (Unctad, 1999). FDI facilitates employment in the host country, as most jobs are created through registered projects in the economy’s various sectors. The number of jobs expected to result from FDI is split among Ghanaians and expatriates. Employment creation generates short-term opportunities that have a direct impact or create additional lasting livelihoods (Evans et al., 2021). This paper focuses on the effect of registered projects on the various sectors of the Ghanaian economy. 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, Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 61 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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 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 sub- sectors, 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 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 ( 𝛽 ̂𝑗) indicates the relative variance of the j-th coefficient of regression. It holds that VIF ( 𝛽 ̂𝑗)  1. If VIF ( 𝛽 ̂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. Var ( 𝛽 ̂𝑗)= 𝜎 ̂𝑒 2 ( 1−𝑅𝑗 2).∑ (𝑥𝑗− �̅�)2𝑛 𝑖=1 = Var ( 𝛽 ̂𝑗)= 𝜎 ̂𝑒 2 ∑ (𝑥𝑗− �̅�)2𝑛 𝑖=1 (1) Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 62 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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 = ∑ (𝑒𝑡−𝑒𝑡−1)2𝑇 𝑡=2 ∑ 𝑒𝑡 2𝑇 𝑡 (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. 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 + 𝜀𝑡 (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, 𝛽2, 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 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. Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 63 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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 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 R- squared 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 Statistics 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. Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 64 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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. 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. Figure 3. Time series plot (FDI and domestic employment) in the sectors. Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 65 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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 non- significant 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 Statistics 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 non- significant coefficient does not provide any economic meaning to those variables. Model 5 is burdened with first- order 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. Variables Coefficient 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 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 Asian Journal of Economics and Empirical Research, 2022, 9(1): 59-66 66 © 2022 by the authors; licensee Asian Online Journal Publishing Group 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. 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