This section of the paper reports and discusses the interpretation of the estimated coefficients obtained from the system-Generalized Method of Moments estimation technique in two parts. The first part focuses on the economic interpretation of the short and long results, and the second part presents the analysis of the causality test results.
Short-run and long-run analysis
Here, we present and interpret the short-run and long-run impact of port throughput on employment in Africa using the Generalized Method of Moments estimation technique. In addition, we briefly highlight the findings of other determinants of employment in Africa. These results (short- and long-run) are reported in Tables
3 and
4. It must be noted that, in each of the results tables, three estimation models are presented. While models 1 and 2 present the results for total and industry employment, model 3 reports the results for service employment, respectively. Before proceeding to the interpretation of the results, it is important to note that the validity of our estimated results depends on the model diagnostics. Specifically, our estimates rely on the absence of second-order autocorrelation and the validity of the instruments used. The last part of Table
3 indicates that none of our three models exhibit second-order autocorrelation, and the instruments used in our analysis are valid as confirmed by the Hansen J test's p-values.
Table 3
The short-run impact of port throughput on employment in Africa
lnEMP (− 1) | 0.943*** | 0.967*** | 0.966*** |
| (0.0205) | (0.0146) | (0.00619) |
lnPTP | 0.00751** | 0.00902** | 0.00500** |
| (0.00285) | (0.00380) | (0.00232) |
lnEDU | − 0.0187** | 0.0104 | 0.00265 |
| (0.00786) | (0.0103) | (0.00250) |
lnPDN | 0.00803** | − 0.00535*** | 0.00295 |
| (0.00341) | (0.00173) | (0.00207) |
lnINF | − 0.0152** | − 0.0213*** | − 0.00622* |
| (0.00692) | (0.00380) | (0.00316) |
lnINV | − 0.00323 | 0.00442 | 0.0111 |
| (0.00558) | (0.00935) | (0.00854) |
INC | − 0.000220 | − 0.00363 | 0.00192* |
| (0.000819) | (0.00226) | (0.00108) |
Constant | 0.227** | 0.0688 | 0.0494 |
| (0.105) | (0.0541) | (0.0289) |
Number of observations | 173 | 173 | 173 |
Number of groups | 26 | 26 | 26 |
Number of instruments | 22 | 22 | 23 |
AR2[p-value] | 0.289 | 0.243 | 0.279 |
Hansen[p-value] | 0.415 | 0.156 | 0.348 |
Table 4
The long-run impact of port throughput on employment in Africa
lnPTP | 0.1320** | 0.2773** | 0.1454*** |
| (0.0648) | (0.2167) | (0.0501) |
lnEDU | − 0.3286*** | 0.3196* | 0.0771 |
| (0.0446) | (0.1928) | (0.0765) |
lnPDN | 0.1412*** | − 0.1644* | 0.0858 |
| (0.0491) | (0.0841) | (0.0619) |
lnINF | − 0.2676** | − 0.6555* | − 0.1809** |
| (0.1169) | (0.3526) | (0.0834) |
lnINV | − 0.0568 | 0.1357 | 0.3241 |
| (0.1077) | (0.2601) | (0.2223) |
INC | − 0.0038 | − 0.1116 | 0.0558 |
| (0.0138) | (0.1000) | (0.0381) |
Constant | 3.9948 | 2.114216 | 1.4361 |
| 0.7528 | (1.4983) | (0.9367) |
Regarding the coefficients of the variable of interest, the results in Table
3 and
4 clearly show that the estimated coefficients measuring the impact of port throughput on total employment (model 1), industry employment (model 2), and service employment (model 3) are positive and statistically significant as expected, suggesting that improvements in port throughput hold significant potential for enhancing employment outcomes in Africa. The magnitude of the coefficients shows that for every 1% increase in port throughput, total employment increases by 0.007% (0.132%), industry employment increases by 0.009% (0.277%), and services employment increases by 0.005% (0.050%) in both the short-run (long-run). These coefficients are all statistically significant at the conventional levels of 1% and 5%. These outcomes obtained are in line with the positive impact of port throughput on employment revealed by Bottasso et al. (
2013), Seo and Park (
2018), and Hidalgo-Gallego and Núñez-Sánchez (
2023).
These findings imply that the effect of port throughput on employment outcomes can be seen from the fact that an increase in port throughput generates demand for a range of services, including cargo handling, transportation, warehousing, port administrators, and customs clearance, which create employment opportunities for both skilled and unskilled workers (Bottasso et al.
2013; Seo and Park
2018; Wang and Zhang
2020). In addition, the Port serves as a gateway for the facilitation of imports and exports, leading to increased trade and economic activity. This can result in the creation of more jobs in various sectors, including transportation, logistics, and manufacturing. Furthermore, port throughput reflects the revenue/income generated by port authorities to the state (government) which would be used to stimulate economic activities and bolster the government's ability to employ the citizenry in various sectors of the economy (Bottasso et al.
2013; Shan et al.
2014; Seo and Park
2018; Hidalgo-Gallego and Núñez-Sánchez (
2023). The AfDB (
2018a,
b) report indicates that ports have the potential to serve as catalysts for industrialization in Africa, while the highlighted the need for policies to support the formalization of port-related services in Africa, to promote decent work and employment growth, given the positive employment benefit to be derived from port operations.
Interestingly, we find the impact of port throughput to be greater in terms of the size of the coefficient in the industry employment model compared to the total and service employment models. A similar finding was revealed by Bottasso et al. (
2013). The greater effect of port throughput on industry employment could be due to an increase in demand for raw materials and intermediate goods, stimulating the growth of manufacturing industries. This can result in the creation of jobs in the manufacturing sector, which is typically associated with industry employment.
Regarding the results of the other potential determinants of employment, interesting outcomes were obtained. The impact of inflation on total employment, industry, and service employmentwas found to be negative and statistically significant in both the short and long run. The potential explanation is that high inflation rates can create uncertainty and instability in the economy, which can discourage investment and reduce business confidence. This can lead to reduced economic activity and job creation. Additionally, high inflation rates can affect the cost of borrowing and access to credit, further impacting business investment and job creation. This finding is in line with the study by Kassouri (
2024) who reported a negative impact of inflation on employment.
The study also finds a positive significant effect of population density on total employment while the impact of population density on employment in the industrial sector was negative and significant. The positive impact of population density on total employment may be due to the increase in demand for goods and services and the potential for economies of scale in Africa. Thus, while several potential factors may explain this relationship, one possible explanation is that a larger population can create increased demand for goods and services, which in turn can generate more job opportunities. Furthermore, as the population grows, businesses may have greater opportunities to achieve economies of scale, leading to increased employment. Investments in infrastructure and public services can also contribute to job creation. The result obtained are consistent with the studies by Sobieralski (
2021), Wang and Zhang (
2020), Fageda and Gonzalez-Aregall (
2017), Seo and Park (
2018), and Johnson et al. (
2017). On the contrary, the negative impact of population density on industrial employment could stem from the fact that in densely populated areas, land becomes scarce and expensive. This means industrial companies may struggle to find affordable space for factories, warehouses, or other facilities. The high cost of land can deter new industrial ventures or force existing ones to relocate, leading to a decrease in industrial employment opportunities. This outcome confirms the study by Kassouri (
2024), and Hidalgo-Gallego and Núñez-Sánchez (
2023), which found a negative effect of population density on employment.
The results obtained for education effect on total employment were positive and significant while it enters negatively and significantly for industrial employment. The implication of the positive impact of education on employment is that education fosters critical thinking, problem-solving, and creativity—essential skills in today's rapidly changing job market. Well-educated individuals are better equipped to adapt to new technologies, industries, and job requirements, making them more resilient to economic fluctuations and technological advancements. Consequently, they have a higher likelihood of gaining employment easily. The positive significant effect of education on total employment obtained in this study is in line with those obtained by Sobieralski (
2021), Wang and Zhang (
2020), Fageda ad Gonzalez-Aregall (
2017), and Seo and Park (
2018). The possible explanation for the negative impact of education on industrial employment could be that higher levels of education may lead to increased awareness of labor rights, workplace safety regulations, and environmental concerns among industrial workers. While these are generally positive developments, they can also impose additional costs and administrative burdens on industrial employers. Compliance with regulations may require investments in training, safety equipment, and environmental controls, which can increase operational expenses and potentially reduce employment levels in the short term. Studies that have reported a negative impact of education on employment include Sobieralski (
2021).
For the case of income and investment, though a positive and negative relationship was revealed based on how employment was measured, the effect is not statistically significant for investment on all the measures of employment used while the impact is only positive and statistically significant in the short run for income and service employment. The result obtained by Kassouri (
2024), Hidalgo-Gallego and Núñez-Sánchez (
2023), Seo and Park (
2018), and Bottasso et al. (
2013) is in line with the positive significant effect of income on service employment revealed in this paper. The positive impact of income on service employment is multifaceted and can be explained through several means. For instance, Higher income levels generally lead to greater disposable income for individuals. As people have more money to spend on non-essential goods and services, there is an increased demand for services such as dining out, travel, entertainment, personal care, and luxury goods. This heightened demand creates opportunities for service providers to expand their operations and hire more employees to meet the needs of consumers. Though insignificant, the positive relationship between investment and employment (industrial and service employment) revealed in this study is not surprising as overall, investment stimulates economic growth, fosters innovation, and creates job opportunities across both service and industrial sectors. By promoting business expansion, enhancing productivity, supporting technological innovation, developing infrastructure, and empowering SMEs, investment initiatives contribute to sustainable employment generation and economic development. This outcome is in tandem with the study by Kassouri (
2024) regarding the positive impact of investment on employment. The negative effect of investment on total employment revealed in this study suggests that, over the years, investment in African countries has not contributed positively to employment creation within the continent.
Results of causality analysis
As earlier indicated in the introduction and estimation technique sections, another important contribution of the paper is to establish whether causality runs from employment to port throughput. To do so, we employ the Dumitrescu and Hurlin (
2012) Granger causality method in assessing the direction of causality between employment and port throughput. It must be noted that the null hypothesis of Dumitrescu and Hurlin's (
2012) Granger causality test states that there is no causality between port throughput and employment, while the alternative states that there is a causality between port throughput and employment. The rule of thumb is that if the P-value of Dumitrescu and Hurlin's (
2012) test is insignificant at the 5% level, then the null hypothesis is accepted, and the conclusion is that there is no causality between port throughput and employment. However, if the p-value is significant at the 5% level, then we fail to reject the alternative hypothesis and conclude that there is a causality between port throughput and employment. The analysis of this is presented in Table
5.
Table 5
Dumitrescu and Hurlin (
2012) Granger causality test results
Total employment | | | | 0.000 |
Industry employment | | | | 0.000 |
Service employment | | | | 0.000 |
port throughput | 0.000 | 0.000 | 0.000 | |
According to the results of Dumitrescu and Hurlin's (
2012) Granger causality test presented in Table
5, based on the p-value, we established a bi-directional causal relationship between measures of employment that is total, service, and industry employment and port throughput in Africa.
The possible explanation for the bi-directional causal relationship between port throughput and total employment is that increased port throughput can lead to increased economic activity, which can create job opportunities and stimulate employment growth. At the same time, increased employment can lead to increased demand for goods and services, which can drive up port throughput as more goods are imported and exported. This relationship is supported by Gossling and Scott (
2015), who found that port throughput is positively associated with economic growth and job creation in developing countries, including those in Africa. Another report by the AfDB (
2017a,
b), and World Bank (
2018) showed that investments in port infrastructure can stimulate economic growth and job creation by facilitating international trade and attracting foreign investment. On the other hand, increased employment can also lead to increased demand for goods and services, which can lead to increased port throughput.
Concerning the implication for the bi-directional relationship between port throughput and service employment and port throughput in Africa, the reason could be that increased port throughput can lead to increased economic activity, which can create job opportunities in the service sector, such as logistics, transportation, and warehousing. At the same time, increased service employment can lead to increased demand for port services, such as cargo handling and customs clearance as postulated by Sánchez-Soriano et al. (
2018), and Song et al. (
2018).
As far as the bi-directional causal relationship between industry employment and port throughput is concerned, it could be that increased port throughput can lead to increased economic activity, which can create job opportunities in industries such as manufacturing, construction, and mining. At the same time, increased industry employment can lead to increased demand for port services, such as shipping and logistics. This relationship is supported by Kramberger et al. (
2019).
In sum, the bi-directional causal relationship between measures and port throughput revealed in this study highlights the importance of investing in port infrastructure and promoting job creation in the service and industrial sectors to drive economic growth and development in Africa.