The macroeconomic impact of ocean economy financing in South Africa

ABSTRACT

The global ocean plays a vital role in supplying seafood and employment, driving worldwide GDP growth. South Africa recognises the ocean economy's potential and invests in it to boost productivity, reduce unemployment, and spur economic expansion. However, challenges like outdated infrastructure, weak governance, and technological gaps hinder progress. Amidst rising demand for seafood, unregulated competition prevails. The study employed ARDL to test the long – and short-run relationships between ocean economy financing, economic growth, and unemployment from 1994 to 2019. The results show that there is a positive and statistically insignificant relationship between ocean economy financing economic growth, and unemployment. This study recommends that the government continue to increase a sustained investment in ocean economy infrastructure, addressing growth constraints, and adopting a comprehensive strategy for realising economic growth and job creating.

KEYWORDS:

• Ocean economy
• ARDL
• economic growth
• GDP
• unemployment

1. Introduction

Oceans cover more than 70% of the earth’s surface; it follows that oceans play a major role in supporting all life on the planet through generating oxygen, recycling nutrition, absorbing carbon dioxide, and regulating global climate and temperature (Cervigni and Scandizzo 2017). Almost three-quarters of the world’s cities and large towns are situated in close proximity to the sea, while approximately 40% of the world's population live close to 60 km from the sea. The major contributors to the world’s wealth are its seas and rivers in the form of trillions of goods and services (United Nations Economic Commission for Africa, (UCECA) 2016). Globally, oceans are the providers of both seafood and employment, are also responsible for continuing to feed the world’s population, and together represent the key driver of global gross domestic product (GDP) (Patil et al. 2016).

According to the Organisation for Economic Co-operation and Development (OECD 2016), the ocean economy is defined as the economic benefit and value realised from the earth’s oceans and coastlines. Globally, the ocean economy which consists of marine tourism, aquaculture, shipping, and other ocean activities approximately generate $2.5 trillion per year. However, the figure excludes the cultural and less tangible components that are significant to the health of the ocean. In addition, if the ocean economy is not sustained, its resources and benefits may not be guaranteed (OECD 2016). The World Wildlife Fund (WWF 2017) stresses that, with regard to oceans, acidity is increasing, biodiversity is declining, another waste is accumulating, and the temperature is rising. Further, the ecological crisis is affecting the ocean deeper and deeper which will affect both the future and the present of ocean health. Therefore, any investment guideline is needed and development policy in order to realise the full benefits of the ocean.

The ocean economy has been identified as another dimension for unlocking growth potential in the future of the global economy. In 2012 Rio de Janeiro held a United Nations Conference on sustainable development where the ocean economy concept was endorsed (OECD 2016). However, the ocean economy remains faced with a growing awareness of the heavy damage wrought on ocean ecosystems by human activities, which includes pollution, habitat destruction, overfishing, and the effect of climate change. Poor management of the ocean and economic activities linked to it has cost the global economy approximately $50 billion every year (Benkstein 2018). However, this figure does not take into consideration the specific ways in which overfishing, illegal fisheries and environmental degradation affect the livelihoods of coastal and riparian communities, more especially those countries that are still developing. It is in this context that ocean economy financing plays a major role in sustaining and ensuring growth in ocean economic activities.

The ocean economy is that part of the global economy that depends on the ocean as the input in the production process, and which, by virtue of geographic location, takes place under the sea, while ocean economy financing refers to investment flowing to ocean sectors (Operation Phakisa 2015). In this context, and according to the World Bank and the United Nations Department of Economic and Social Affairs (2017), many countries have turned to the oceans as a source of economic innovation, competitive advantage, and as a source of job creation. For this reason, ocean economy financing seeks to promote economic growth by taking into account social inclusion. Social inclusion would involve the preservation or improvement of livelihoods while at the same time protecting the environment (World Bank and United Nations Department of Economic and Social Affairs 2017).

South Africa is surrounded by a vast ocean space, which has a significant potential to provide growth but one that has not yet been tapped. The National Planning Commission (NPC) was established by former President Jacob Zuma, led by Traver Manuel, a former Minister of Finance, to draft a National Development Plan (NDP). The NDP was delivered in 2012 with the aim of increasing development in order to fight poverty, inequality, and unemployment by 2030 (NPC 2012). The challenges related to the implementation of this plan are not easily overcome due to their being substantial in scope and structure. In 2017 the unemployment rate in South Africa stood at 27.7%, while youth unemployed stood at an estimated rate of 67.4% (Statistics South Africa [StatsSA] 2019). South Africa has over the years achieved a sluggish growth rate which dipped further going into the recession in 2017 (Peyper 2017).

Since, as has been mentioned, on both a global and local scale, the ocean economy is becoming a new focal point in the discourse on growth and sustainable development, it is still faced with a series of challenges. These include the rising demand for seafood, inadequate economic incentives, ineffective governance institutions, lack of technological advances, and insufficient or inadequate management tools, all of which have led to unregulated competition among users (DEA 2017). This in turn has resulted in the unreported, unregulated, and over-exploitation of fishing, and, in some cases, the irreversible alteration of valuable aquatic resources and coastal areas (Operation Phakisa 2015). The oceans themselves remain saddled with problems related to environmental concerns that are in turn linked to climate change which constitutes a threat to the environment as well as to growth and development (Operation Phakisa 2015). Another major problem is ocean acidification, a growing environmental concern linked to climate change. Ocean acidification refers to the change in seawater chemistry caused by the absorption of excess atmospheric carbon dioxide by oceans. Moreover, ocean acidification is a threat to national growth and development, particularly in developing nations (Council for Scientific and Industrial Research [CSIR] 2016). The urgent importance of investment resides in addressing these challenges faced by oceans and their sustainability. South Africa is under pressure to address the triple development challenges and the strategies put forward so far are failing to yield significant results.

In this era of globalisation, no country can afford to depend solely on its land resources. Therefore, sourcing alternative resources is essential, especially for developing countries. This sourcing of resources from the sea area is available to South Africa, which has a very long coastline zone of over 3000 km, therefore representing a solid opportunity for development (McCarthy and Rubidge 2005). It is important to note that any successful and sustainable use made of the ocean economy depends on the nature and quality of the financing devised and employed by the government and private sector. Based on this assumption or argument, the current study seeks to evaluate the social returns on South Africa’s financial investment in its ocean economy through an assessment of the macroeconomic effects of ocean economy financing. The paper is organised into six sections: the first section is the introduction and background, the second section is an overview of economic growth, unemployment, and the ocean economy; the third section literature review, which includes theoretical and empirical review; the fourth section outlines the methodology, indicating the variables used in the analysis, and describing the econometric model, the fifth is the analysis and discussion of results and the last part is conclusion and recommendations.

2. Ocean economy overview

South Africa has a vast offshore ocean space and approximately 3924 kilometres of coastline, both of which factors highlight the relatively unexplored economic potential of its ocean economy (Operation Phakisa 2014). The ocean economy is defined by the Organisation for Economic Co-operation and Development (OECD) as the sum of the economic activities of ocean-based industries, together with the assets, goods, and services provided by marine ecosystems (OECD 2016). The concept ‘ocean economy’ refers to ‘that portion of the economy which relies on the ocean as an input to the production process or which, by virtue of geographic location, takes place on or under the ocean (Walker 2018).

In October 2014, the South African government announced that it would be implementing a number of ocean economy projects that would contribute more than R20 billion to the country's GDP by 2019 (Operation Phakisa 2014). The Operation Phakisa initiative forms part of the government’s NDP economic blueprint that aims to promote economic growth and job creation (SAinfo Reporter 2015).

South African former president Jacob Zuma adopted the Malaysian Big Fast Result Approach which was renamed Operation Phakisa (meaning hurry up in Sesotho) to address national key priorities, namely, a low economic growth, a high rate of unemployment, inequality, and poverty (Operation Phakisa 2014). Operation Phakisa is a results-oriented strategy that entails establishing clear goals and objectives, tracking progress, and making the outcomes public. Initially, Operation Phakisa focused on two areas: the ocean economy and health.

Operation Phakisa represents a new spirit of moving more quickly to meet the government's goals. At the time, the South African government's starting point was that South Africa was surrounded by vast ocean space and had not fully capitalised on the enormous potential of this untapped resource. Between 2014 and 2019, the government unlocked R29.4 billion in oceans economy investments, resulting in the creation of 7 093 direct jobs (Operation Phakisa 2019). In 2019, it was reported that R40.8 billion in investments had been made in the Oceans Economy, which had resulted in the creation of 7 385 direct jobs (Operation Phakisa 2019).

Operation Phakisa has six priority sectors as new growth areas; Maritime Transport and Manufacturing activities, such as coastal shipping, trans-shipment, boat building, repair, and refurbishment; offshore oil and gas exploitation; aquaculture; marine protection services and ocean governance; maritime transport and manufacturing, and two additional sectors which were later added: marine and coastal tourism and small harbour development (Operation Phakisa 2019).

The impact of South Africa’s Ocean economy bears similarities to other nations with significant coastal regions. Like many countries, South Africa’s Ocean economy contributes substantially to its GDP and employment, driven by sectors such as fisheries, maritime transport, tourism, and offshore energy extraction. Investment in maritime infrastructure, including ports and coastal facilities, is crucial for supporting trade, transportation, and tourism activities. However, South Africa's economy differs in structure, with a heavier reliance on primary sectors compared to many developed nations, impacting the relative importance of the ocean economy. Additionally, the South Africa faces unique developmental challenges such as poverty and inequality, necessitating targeted policies for inclusive growth, particularly in coastal communities. Environmental sustainability is a shared concern, with challenges like overfishing and marine pollution requiring concerted efforts for resource management (Guarnieri and De Leo 2022).

3. Literature review

This section presents a review of the literature on the relationship between ocean economy financing, economic growth, and unemployment. The section is divided into two subsections, the theoretical framework, and the empirical literature. The theoretical literature is drawn from theories that explain the variable of interest.

3.1. Theoretical literature review

The relationship between ocean economy financing and macroeconomic indicators has been explored through an analysis of established economic theories. Growth theories, including Solow (1956) and Romer (1986), emphasise the importance of input factors and productivity growth in driving long-term economic growth. Solow suggests that investment enhances output, while Romer highlights the combination of physical capital and investment as catalysts for innovation in production processes. Barro (1990) introduces the role of government expenditure in economic growth, implying that investing in the ocean economy sector can directly contribute to production and economic performance, creating jobs and value. Increased investment, driven by government action, can promote productivity and growth. Similarly, Barro's model recognises government spending as a fiscal policy tool with a lasting impact on growth. Efficient public spending can lead to optimal economic outcomes, while excessive or inefficient spending can hinder growth.

However, endogenous growth models often disregard real-world complexities and are less applicable to developing economies. These models overlook variables like poor infrastructure, institutional inefficiencies, and market imperfections that impede growth. The models also neglect the political barriers that can hinder innovation. Government decisions’ long-run effectiveness in promoting growth is questioned, and excessive government spending can lead to inefficiencies, high debt, and reduced private investment.

The relationship between government expenditure and growth is not linear. Government spending initially contributes positively to growth until a point, after which it can hinder growth. The endogenous growth model suggests that productive government spending is crucial for favourable growth outcomes. Deficit financing can lead to growth reduction due to interest burdens unless compensated by positive impacts. In this context, productive public expenditure can enhance growth in the long run if unproductive spending is curbed.

In conclusion, the relationship between ocean economy financing and macroeconomic indicators can be understood through the lens of various economic theories. Productive government spending and investment in the ocean economy sector have the potential to contribute positively to economic growth, creating jobs and value. However, the real-world complexities of developing economies and the nuances of government expenditure's impact on growth need to be carefully considered in policymaking.

3.2. Empirical literature review

Ocean economy has recently gained widespread attention globally as a new economic frontier with the potential to boost economic growth and employment (Cervigni and Scandizzo 2017; OECD 2016; Scandizzo, Cervigni, and Ferrarese 2018). A sizeable body of literature has in recent decades focused on estimations of the contribution of the global ocean economy, and local economies, to economic growth and job creation. Various studies have examined the impact of financing the ocean economy on economic growth, employment, and value-added in different regions. These studies provide insights into the potential benefits and challenges associated with ocean economy investments.

Shields, O’Connor, and O’Leary (2005) estimated the role of Ireland’s ocean economy on economic growth with an investment of €60 million. According to Shields, O’Connor, and O’Leary (2005), close to 22,000 jobs would be directly or indirectly created by the ocean sector which was contributing €1 billion to Ireland’s overall gross national product (GNP). However, the report by Shields, O’Connor, and O’Leary (2005), was unclear as to the specific ways in which the investment overall would affect the economy of Ireland. The OECD (2016) reported that the global ocean economy contributed around $1.5 trillion to the world's gross value added (GVA) in 2010, creating 31 million direct jobs. The future projection estimated a sustainable ocean economy value of $3 trillion by 2030, while an unsustainable scenario was projected at $2.8 trillion. The European Commission (2017) noted that the European ocean sector, supported by the European Maritime and Fisheries Fund, employed over 5 million people and generated nearly $500 billion in annual revenue. The global ocean economy's output was estimated at $1.3 trillion in 2017, with the potential to double by 2030.

Kaidou-Jeffrey et al. (2018) demonstrated that ocean-based activities in the Caribbean region contributed $1.5 trillion in 2010, potentially rising to $3 trillion by 2030, generating over 40 million jobs. South Africa’s Ocean economy contributed R56 billion to GDP and created 316,000 jobs in renewable energy and fisheries (Walker 2018). Colgan (2013) conducted a Cost–Benefit Analysis of the U.S. Great Lakes, showing positive GDP impact from ocean-related activities, despite some negative effects on employment during specific periods. The Baltic Sea Region study (Hoegh-Guldberg et al. 2015) projected €32 billion in additional revenue and 550,000 jobs by 2030, although environmental stress affected growth. Zhao (2013) used input-output analysis to find that China's ocean economy contributed less than 10% of GDP, mainly from traditional industries. Scandizzo, Cervigni, and Ferrarese (2018) used a CGE model for Mauritius, predicting the ocean economy to more than double in a decade. Wignaraja, Collins, and Kannangara (2018) expected India's ocean economy to account for over 20% of GDP by 2025. Vega and Hynes (2017) reported that Ireland's ocean economy positively impacted GDP and employment. European Commission (2018) noted an increase in direct employment and investment in the blue economy from 2009 to 2016.

The literature review has shown the diverse impacts of ocean economy financing on regions, including potential gains in economic output, employment, and value-added. The perspectives on the impact of ocean economy financing, reflecting both positive and potentially conflicting views. The overall economic impact of investment, or the lack thereof, on these ocean sectors remains unclear, as do the distinctions in this regard between developed and developing countries and between various sectors in these economies. One of the reasons for this appears to be the magnitude of the impact of ocean economy financing on economic growth, even though this financing may affect various countries and various sectors of the economy unequally. In developed countries, such as Ireland (Shields, O’Connor, and O’Leary 2005; Moriss and O'donghue 2012), the United States (Kildow and Colgan 2005), and those within the European Union (OECD 2016 and European Commission 2017), the ocean economy plays a significant role in driving economic growth, creating employment opportunities, and contributing to overall national and regional GDP. Several empirical studies provide insights into the impact of the ocean economy on developed nations with projections indicating substantial future potential and highlighted as major contributors to GDP and employment. In contrast, developing countries like South Africa, Mozambique, and China face different challenges and opportunities in harnessing their ocean economies. Studies conducted in these regions, such as those by Turpie and Wilsn (2011), Zhao (2013), and Zhao, Hynes, and He (2014), highlight the importance of traditional industries like fishing and tourism, alongside emerging sectors such as marine biomedicine and seawater utilisation. However, challenges such as limited data availability, infrastructure gaps, and financing constraints need to be addressed to fully realise the potential of the ocean economy in these regions. In addition, conflicting views also arise when considering challenges such as environmental stress (Hoegh-Guldberg et al. 2015), sector-specific fluctuations (Colgan 2013), and the need for sustainable practices.

The review of this literature has revealed a gap in the literature on the specific impact of investment in the ocean economy on economic growth, and most literature was based on static models such as CBA, cobb–Douglas, I-O, and ECG. None of these studies have attempted the use of ARDL. The ARDL model stands out compared to static methods due to its dynamic analysis capabilities, incorporation of lagged variables, flexibility in handling both stationary and non-stationary data, and ability to test for causality (Nkoro and Uko 2016). Unlike static models, ARDL avoids imposing structural biases and offers robustness to small sample sizes (Nkoro and Uko 2016), making it well-suited for empirical analysis of the impact of ocean economy financing on economic growth. By capturing both short-term dynamics and long-term relationships between variables, the ARDL model provides valuable insights into the complex interactions within the ocean economy and its effects on broader economic growth dynamics.

The lack of a comprehensive understanding of the impact of ocean economy funding on factors such as economic growth and employment originates from methodological differences, the complexity of interactions, data limitations, environmental considerations, regional specificity, and the long-term effects of investments. Methodological disparities in existing studies contribute to conflicting views, while the interconnected nature of the ocean economy makes it difficult to isolate specific effects. Moreover, varying data quality and regional characteristics hinder comprehensive analysis, alongside the challenge of balancing economic development with environmental sustainability.

4. Data and methodology

The selection of unemployment and economic growth as key variables in the study on the impact of ocean economy financing in South Africa originates from their critical importance to the South African developmental agenda. Unemployment is a pressing socioeconomic issue in South Africa, with high levels contributing to poverty and inequality. Economic growth, on the other hand, is a fundamental goal for any nation, driving improvements in living standards, infrastructure development, and overall growth. By examining how investment in the ocean economy affects these key indicators, policymakers can better understand the sector's role in promoting job creation, poverty alleviation, and sustainable economic development. The study on the impact of ocean economy financing in South Africa, aligned with initiatives like Operation Phakisa, holds significant promise in addressing the country's developmental challenges, including unemployment and stagnant economic growth.

This section presents the data and outlines the methodological approach of the study. The Autoregressive Distributive Lag (ARDL) technique was used to estimate the short and long-run relationship between ocean economy financing and economic growth and unemployment. The ARDL technique was selected because of its compatibility with the small sample size and allows variables to be integrated in different order, I (0) or I(1). Table 1 shows the description of the data and the sources of the data, including the discussion and the justification of variables.

Table 1. Data description.

Acronym	Variable	Description	Source
OEF	Ocean Economic Financing	OEF refers to government expenditure on the ocean sectors. These include the following: marine transport and manufacturing, offshore oil and gas exploration, aquaculture, marine protection services, ocean governance, coastal and marine tourism, and small harbours (Kildow & Colgan, 2005; Operation Phakisa 2014).	National Treasury
GDP	Gross Domestic Product	GDP is a monetary measure of the market value of all final goods and services produced in a given period. The GDP growth rate is measured as the percentage increase or decrease of GDP from the previous measurement cycle (Mohr and Fourie 2008).	World Bank
UNEM	Unemployment	The unemployment rate in South Africa is a measure of the underutilisation of the available labour supply. This also describes the inability of the economy to create employment for those who want to work but are not working (ILO 2020). Unemployment is measured as a percentage of the unemployment rate in South Africa.	World Bank
BF	Business Freedom	An overall indicator of the efficiency of government regulation of business. The ease of doing business can promote trade flows (Depken and Sonora 2005).	Heritage foundation
GI	Government Integrity	A trustworthy government is free from corruption and able to fulfil its commitment to protecting the interests of investors. Government corruption undermines public expenditure (Qiongzhi and Qi 2011).	Heritage Foundation
LR	Lending rate	The rate of interest that is paid on a loan. A high rate of interest affects the trade balance. Lower interest rates increase exports (Batra and Beladi 2013).	World Bank

The empirical models are presented in an equation format as follows: (1) $\mathrm{GD}{P}_t={\beta }_0+{\beta }_1\mathrm{OE}{F}_t+{\beta }_{2}B{F}_t+{\beta }_{3}G{I}_t+{\beta }_{4}L{R}_t+{\beta }_{5}F{C}_t+{\beta }_{6}O{P}_t+{\mu }_t$(1) (2) $\mathrm{UNE}{M}_t={\beta }_0+{\beta }_1\mathrm{OE}{F}_t+{\beta }_{2}B{F}_t+{\beta }_{3}G{I}_t+{\beta }_{4}L{R}_t+{\beta }_{5}F{C}_t+{\beta }_{6}O{P}_t+{\mu }_t$(2) where:

• $\mathrm{GD}{P}_t$ The gross domestic product growth rate in South Africa.

• $\mathrm{UNE}{M}_t$ The total unemployment rate in South Africa

• $\mathrm{OE}{F}_t$ total government capital expenditure of ocean sectors which includes aquaculture, coastal and marine tourism, marine protection and ocean governance, marine transport and manufacturing, and small harbour development.

• $\mathrm{BF}$ business freedom in South Africa.

• $G{I}_t$ government integrity

• $L{R}_t$ lending rate,

• FC dummy for the financial crisis (1 is for the years 2008 and 2009 when there was a financial crisis and 0 for the other years)

• OP dummy for Operation Phakisa (1 for the period after Operation Phakisa was incepted since 2014 and other years is 0)

• ${\mu }_t$ error term.

Where β₀, and β₁ are coefficients of the explanatory variables, t represents the time series and μ is the error term. The error term represents the influence of the omitted variables.

5. Empirical results

Table 2 shows the statistical synopsis of variables used in this research, namely GDP, unemployment, entrepreneurship, trade, ocean economy financing, business freedom, government integrity, and lending rate.

Table 2. Descriptive statistics.

	GDP	UNEM	OEF	BF	GI	LR
Mean	2.66	24.31	21.46	74.52	3.85	13.09
Std. Dev.	1.73	2.55	0.87	7.18	0.09	3.89
Skewnes	−0.31	−0.81	−0.72	0.38	0.06	0.70
Kurtosis	2.83	4.24	3.07	2.046	2.67	2.33
J-B	0.46	4.54	2.27	1.61	0.12	2.60
Proba	0.79	0.10	0.32	0.44	0.93	0.27
	26	26	26	26	26	26

Sources: Authors’ own computation using World Bank, National Treasury, Heritage Foundation (2021).

Table 2 shows the descriptive statistics. GDP has a mean value of 2.66%, unemployment 24.31%, ocean economy financing 21.46. Each of these mean values represents the average of the annual values between 1994 and 2019. GDP has a standard deviation value of 1.73%, which is almost the size of the mean. This means that there is a wide deviation from the mean. Unemployment is 2.55%, ocean economy financing 0.87. These values are almost the size of the means, and they all show a narrow deviation from the mean. GDP has a skewness value of – 0.31%, unemployment is – 0.81%. This means that the skewness has a long-left tail. Thus, all are normally distributed. GDP has a kurtosis value of 2.83%, and ocean economy financing is 2.27. All of these are normally distributed, while the kurtosis value of unemployment is 4.24%. GDP has a Jargue-bera value of 0.46, and ocean economy financing is 2.27. The probability value is greater than 5% which means that the series is normally distributed, while unemployment is 4.54%, the probability is not greater than 5%, and the series is not normally distributed. Having discussed the descriptive statistics Table 3 below discusses the unit root tests which as conducted using three tests, namely Augmented Dicky Fuller (ADF), Philips Perron (PP), Dicky-fuller GLS (ERS).

Table 3. Stationary tests.

Order of Integration	Variable	ADF	PP	Dicky fuller
		Trends and Intercept	Trends and Intercept	Trends and Intercept
Levels	BF	−1.994	−1.994	−2.064
1st Difference	BF	−4.837***	−4.838***	−5.062***
Levels	UNEM	−2.010	−2.010	−2.076
1st Difference	UNEM	−5.088	−5.049**	−4.474***
Levels	GDPG	−3.105	−3.072	−3.250
1st Difference	GDPG	−5.637***	−13.183***	−5.898***
Levels	GI	−3.271	−3.158	−3.434
1st Difference	GI	−4.442***	−4.395***	−4.660***
Levels	LR	−3.305	−2.374	−3.392
1st Difference	LR	−4.501***	−4.511***	−4.226***
Levels	OEF	−2.273	−2.021	−2.396
1st Difference	OEF	−4.721***	−15.575***	−6.506***

Note: (*; **; ***) indicate significance at 10%; 5% and 1% level of significance respectively.

Sources: Author’s own computation using World Bank, Heritage Foundation, and National Treasury (2020).

Table 3 shows the ADF, PP, and Dicky Fuller test were conducted, revealing that the variables are non-stationary at levels but stationary at first differences. This indicates first-order integration (I (1)) for all series in all the tests.

The results for the bounds test shown in Table 4 reveal that there is a long-run and short-run relationship among the variables. Firstly, the GDP model has an F-statistic value of 18.06, which is greater than all the Bounds critical values, which is 4.01%, and the null hypothesis is rejected. Unemployment has an F-statistic value of 8.48, which is greater than all of the Bounds critical values, which is 4.01%, a null hypothesis is rejected. The following section presents a long-run and short-run analysis of the results for the GDP and UNEM model.

Table 4. ARDL Bound test.

Dependent variable	F-stats	1%	5%	10%
GDP	18.06	Cointegration	Cointegration	Cointegration
UNEM	8.48	Cointegration	Cointegration	Cointegration
		Level of significance	Lower I (0)	Upper I (1)
Critical values		1%	3.74	5.06
		5%	2.86	4.01
		10%	2.45	3.52

Sources: Author’s own computation with World Bank data, Heritage Foundation and National Treasury (2020).

Table 5 shows the long and short-run model, the results show that ocean economy financing has a positive relationship with economic growth in the long run while there is a negative effect in the short run, and both are statistically insignificant. This means that the null hypothesis of the relationship between ocean economy financing and economic growth cannot be rejected. Similarly, Shields, O’Connor, and O’Leary (2005), in their study, examined the impact of ocean economy financing on Ireland’s economic growth and show that there was no evidence of any positive impact on the country’s economic growth. Furthermore, Wignaraja, Collins, and Kannangara (2018) state the growth of the ocean economy is still hindered by a lack of infrastructure. Notwithstanding, a study by Colgan (2013) highlights a positive effect of ocean economy financing on USA economic growth. From this one can be deduced that ocean economy financing has a probability of producing different results in different countries. More especial developed countries show a positive effect of ocean economy to economic growth. Vega and Hynes (2017) ocean economy in Ireland has a positive effect on economic growth. In addition, OECD (2016) reported a positive effect between ocean economy and economic growth while Hoegh-Guldberg et al. (2015) ocean economy was underperforming. According to Sumaila et al. (2021), the contribution of the South African ocean economy to GDP has declined since 2010 from 4.4% to 4.2% in 2019. According to Walker (2018), several maritime initiatives under Phakisa might help South Africa meet its 5% growth target by 2019 was not achievable as the growth rate of South Africa’s economy in 2019 was 4.2%.

Table 5. Long and short run model.

Variable	GDP	UNEM
Long run
Log(OEF)	0.275 [0.488]	−2.020 [−1.229]
BF	0.056*** [2.598]	−0.371*** [−2.475]
GI	4.502*** [2.415]	−10.292 [−0.715]
LR	−0.485*** [−7.139]	1.401 [2.267]
FC	−3.813*** [−9.465]	9.288 [2.397]
OP	−0.751*** [−3.066]	−0.096 [−0.045]
Short run
DLog (OEF)	−0.572 [−1.210]	−0.695 [−1.395]
D(BF)	0.141*** [3.318]	−0.212*** [−5.233]
D(GI)	8.535*** [2.313]	−8.385*** [−3.175]
D(LR)	−0.191*** [−1.356]	0.482*** [4.533]
D(FC)	−7.229*** [−7.901]	3.197*** [5.172]
D(OP)	−1.424*** [−2.899]	−0.033 [−0.045]
CointEq(−1)*	−1.895*** [−11.613]	−0.344*** [−3.899]

Note: (*; **; ***) indicate significance at 10%; 5% and 1% level of significance respectively.

Source: Author’s own computation using World Bank, National Treasury, and Heritage Foundation (2020).

Business freedom and government integrity have a positive and significant effect on economic growth in both the long run and short run. The dummy variables representing the period after the global financial crisis and Operation Phakisa have a negative sign. This is the evidence that the period after the global financial crisis was characterised by low economic growth, while the negative coefficient of the Operation Phakisa dummy variable suggests that, since the inception of Operation Phakisa in 2014, economic growth has been on a downward trend. The error correction (ECM) coefficient of the GDP model is negative and significant at – 1.89. This suggests that speed adjustment confirms the long-run relationship. This implies that any deviation from the long-run equilibrium of only 1.8% is corrected.

Table 5 also shows that ocean economy financing reduces unemployment both in the long and the short run but is statistically insignificant. This means that the null hypothesis of the relationship between ocean economy financing and unemployment cannot be rejected. Unemployment has been on the rise in South African for almost a decade and shows no sign of decline. Most recently unemployment has risen to reach 34.4% (StatsSA 2021) due to COVID 19 pandemic. However, Operation Phakisa estimated that the South African ocean economy would create 77 100 direct jobs by 2019. According to Freight News (2019), while over R40 billion was invested in targeted ocean sectors between 2014 and 2019, less than 10,000 of the 77,100 direct jobs promised to be created by 2019 were created. This means whilst the ocean economy has the probability to reduce unemployment this has not been met with much success in South Africa. A similar finding has also been reported by Colgan (2013) in the U.S who found a negative impact of the ocean economy on employment between 2007 and 2009. Colgan (2013) alludes to the depletion of natural resources, particularly in the fisheries industry. The Green Connection (2021) also confirms the ocean economy's failure to generate the number of jobs projected by the programme. It is, therefore, no wonder that this study fails to reject the null hypothesis of no relationship between ocean economy financing and unemployment during the period under review. Notwithstanding this finding, this paper does not in any way suggest that the ocean economy financing does not have the potential and the capability to create jobs in South Africa; a comprehensive strategy that includes the ocean economy should be adopted but certainly not on its own alone.

The ocean economy faces challenges from climate change, evidenced by extreme weather events, rising sea levels, and ocean acidification, which disrupt various sectors differently. Fisheries encounter challenges from shifting fish stocks and habitat loss, impacting catches and livelihoods. The degradation of marine ecosystems due to overexploitation, harmful fishing techniques, carbon emissions, and plastic pollution results in a decline in fish biomass and biodiversity (Guarnieri and De Leo 2022). Climate change alters fish distribution and abundance, affecting fishing opportunities (Nichols 2019; Palacios-Abrantes et al. 2022; Rogers et al. 2019). Coastal tourism is negatively affected by beach erosion and coral bleaching, influencing revenue and employment due to the effects of climate change and industrial activities (Dimopoulos, Queiros, and van Zyl 2022; Hernández et al. 2023). The shipping industry encounters difficulties from changes in sea routes and vulnerabilities in ports, which impede trade (Liu et al. 2022: Viljoen and Joubert 2016). Uncertainties surround renewable energy sources such as offshore wind and wave power due to fluctuating ocean conditions, impacting the feasibility of projects (Köpke, Mielniczek, and Stolz 2023; Lyden et al. 2022). These sectoral impacts emphasise the complexity of sustainable ocean resource management amidst climate change.

Climate change and environmental stressors have significant impacts on the ocean economy. These impacts include rising sea levels, ocean acidification, increasing sea surface temperature, extreme weather events, and changes in primary productivity and distribution of marine species. These changes pose major threats to the sustainable development of marine industries and ecosystems, affecting both market-based goods and nonmarket services provided by the ocean (Gaines et al. 2023; Kong et al. 2018; Tisdell 2017). The negative consequences of climate change outweigh the positive effects, making it crucial for governments to take measures to mitigate these adverse effects (Sumaila and Hotte 2012). The economic impacts of climate change on marine ecosystems are already being observed, with potential losses in coral reefs and changes in the yield of exploited marine species. As climate change continues to worsen, the economics and management of marine fisheries are expected to be affected, impacting the world's market benefits and non-market benefits provided by marine fish resources.

Business freedom and government integrity have a negative relationship with unemployment both in the long and short run meaning that both these variables lead to a reduction in unemployment albeit that government integrity is statistically insignificant in the long run and not in the short run. The lending rate variable shows a positive and statistically significant relationship with unemployment both in the long run and short run. The dummy financial crisis shows a positive and statistically significant relationship with unemployment meaning that during the time of the financial crisis, unemployment increases. The final control variable is OP representing the period of the implementation of operation Phakisa program. The results of this research show a negative and statistically insignificant relationship with unemployment meaning no relationship between Operation Phakisa program and job creation during the period under review cannot be rejected. This implies that the Operation Phakisa program with all its good intentions cannot go alone in fighting unemployment in South Africa. It is, therefore, no wonder that the number of jobs created under the Operation Phakisa program was less than those that were targeted (Freight News 2019).

The error correction (ECM) coefficient of unemployment mode is negative and significant at – 0.34. This implies that any deviation from the long-run equilibrium of only 34% is corrected. A further step to be taken to estimate the model would be checking the model’s adequacy before making a forecast. Therefore, the following tables present the diagnostic tests.

Table 6 shows the diagnostic tests of the GDP and UNEM models. The residuals are normally distributed in the model as evidenced by non-rejection of the null hypothesis using the Jarque-Bera test and no serial correlation in the residuals. Heteroscedasticity, where the null hypothesis (H_o) claims that residuals are homoscedasticity, and the null hypothesis is not rejected at 5% level, of significance. The model is correctly specified, evidenced by the probability value of 0.69% which is greater than the 5% level of significance.

Table 6. Diagnostic tests.

Details	GDP	UNEM
Jarque-Bera	0.795[0.671]	0.51 [0.77]
Autocorrelation	3.506 [0.088]	0.327 [0.727]
Heteroskedasticity	0.598 [0.806]	0.565 [0.803]
RESET TEST	0.16 [0.69]	0.379 [0.710]

Sources: Author’s own computation using World Bank, National Treasury, and Heritage Foundation data (2020)

6. Conclusions and recommendations

Ocean economy financing has a positive and statistically insignificant relationship with GDP meaning that it has the potential to grow the economy in the long run provided structural constraints in South Africa are addressed. This means that there is no relationship between ocean economy financing and GDP. This implies that the economic growth strategy in South Africa should go beyond just the ocean economy. A comprehensive economic growth strategy for South Africa is needed. Concerning unemployment, it can be concluded that ocean economy financing has a negative and statistically insignificant relationship with unemployment meaning that the is no relationship between ocean economy and unemployment. Once again as in the case of GDP reported above ocean economy financing cannot be regarded as a panacea for resolving the unemployment problem in South Africa. A comprehensive strategy to fight unemployment in South Africa is needed.

Based on the findings, the study proposes the following recommendations for increasing a return on investment in the ocean economy sectors in South Africa. Although the ocean economy has been predicted to be the cornerstone for promoting present and future economic growth and job opportunities in South Africa (Operation Phakisa 2014), levels of investment in a sustainable ocean economy remain drastically low, an indication that much needs to be done to promote a sustainable ocean economy in the country. According to the National Treasury (2019a; 2019b), investment has been steadily declining as a percentage of GDP, reaching a low of 17.7% in the third quarter of 2018.

Therefore, a combination of low growth in employment, as well as in investment and productivity, continues to restrain economic growth in South Africa. Thus, the findings of the study indicate that the South African government should be encouraged to increase investment in all of the country’s ocean economy sectors to promote the productivity of its ocean economy and the sustainability of its ocean and its resources. This financing includes economic policy instruments as well as other finance mechanisms available for South Africa to create incentives and to generate revenue for the sustainable use of the ocean and sustainable future economic growth.

Whilst the government is encouraged to implement economic instruments which can create incentives for actors in the ocean economy continuously, the government should not turn a blind eye to various negative externalities that may result from such investments in the near and long-term future. These negative externalities include pollution, climate change, depletion of marine life and species, as well as bunkering, to mention a few possible threats which should be anticipated and addressed, for example by levying taxes, fees or charges, licence requirements etc.

Concerning the effects of ocean economy financing on unemployment, which, according to the results of the current study. The persistent unemployment problem in South Africa is generally and pervasive, one that affects all sectors of the economy, and is not specifically or solely linked to the ocean economy. Unemployment in South Africa has been on the rise since the early 2000s without any sign of decline due to various factors, and a detailed discussion of which falls beyond the scope of this study. However, the matter of structural unemployment in South Africa needs to be addressed by both government and private sector as it pertains to the skills shortage. Since, as has been mentioned, the ocean economy, and some sectors, in particular, require certain high-level skills, the usefulness and effectiveness of ocean economy financing on unemployment may be somewhat compromised. For this reason, this study recommends that government should continue to encourage institutions of higher learning to offer more programmes relating to maritime studies. In this context, colleges and other higher education institutions should be encouraged and supported in providing appropriate curricula that provide young people with the knowledge and skills for specialist technical positions in the ocean economy. Government should also provide scholarships, internships, and graduate programmes for individuals who want to pursue careers in the ocean and maritime sectors.

To harness ocean economy financing and stimulate growth while reducing unemployment, a comprehensive approach is vital. This approach encompasses the promotion of Public-Private Partnerships (PPPs), offering incentives to encourage private sector investment, establishing dedicated Blue Economy Funds, investing in capacity building and skills development, nurturing technology and innovation hubs, integrating sustainable investment criteria, and enacting policy and regulatory reforms to create an enabling environment. By leveraging these strategies, nations can unlock the full potential of the ocean economy, driving sustainable economic development, and generating job opportunities to alleviate unemployment.

The study recommend that future research should focus investigating the effectiveness of government and private sector investment on ocean economy. In addition, also designing an ocean economy GDP decomposition satellite account. Furthermore, a full cost–benefit analysis (CBA) should be applied in future studies in this area.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Research Foundation (NRF) under the grant Black Academic Advancement Programme (BAAP) [grant number 120625].

Notes on contributors

Weliswa Matekenya

Dr. Weliswa Matekenya holds a PhD in the Department of Economics from Nelson Mandela University (NMU). Her research interests include Development Economics, Maritime Economics, Environmental Economics, Public Sector, and Macroeconomics. Weliswa's contributions to the academic sphere have not gone unnoticed. In 2022, she was honoured with the Emerging Researcher of the Year. Furthermore, she has been awarded two research grants from the National Research Foundation (NRF), further solidifying her standing as a distinguished researcher.

Ronney Ncwadi

Professor Ronney Ncwadi holds a PhD in Economics from Nelson Mandela University (NMU); His research interest is in Macroeconomics, Public Finance, International Finance, and Applied Econometrics. He has published both in local and international journals and has read papers at academic conferences both in South Africa and abroad. He was a Director in the Macroeconomic analysis Unit of the Eastern Cape Provincial Treasury between 2008 and 2010. He was also an Associate Professor and a Deputy Dean of the Faculty of Management and Commerce at Fort Hare University. Prof Ncwadi is currently a Full Professor of Economics and Director of the School of Economics, Development and Tourism at Nelson Mandela University in Port Elizabeth.