With the current sources of US supply chains being more diversified than before, China’s share in US goods imports is declining while Mexico becomes the largest exporter to the US market in 2023. However, can Mexico use this trade diversion to successfully outweigh China in US supply chains? This paper thus investigates whether the Mexico manufacturing sector is competitive enough to completely replace its Chinese counterparts and rise to a strategically vital supplier for the US economy. Based on multiple empirical evidence, we find that although US supply chain sources are shifting from China to Mexico, the major part of the value added of Mexican exports to the US market is generated in China. Moreover, our evidence shows that Mexico’s exports to the US concentrate on low-skill sectors, while China’s mainly consists of high-skill goods. Further discussion shows that the current US trade shift is highly likely due to China’s FDI inflows to Mexico’s traditionally strong export sector, motor vehicles. However, this shift is not significant enough for Mexico to become a capable substitute for China in the US supply chains. We conclude that the "trade diversion" strategy alone cannot support Mexico’s role in reducing the US supply chain dependence on China. Therefore, the US should better consider how to establish a sustainable trade framework that fosters stable cooperation with China.

Risks in Public Private Procurement mass housing project (PPP-MHP) initiatives are emerging and this requires early risk identification and allocation to achieve the goal and sustenance of the scheme. The study, being a follow-up of a Delphi survey, elicits the opinion of respondents on the probability of occurrence and severity of identified risks in PPP-MHPs in Nigeria. The study adopts a quantitative research design approach by administering structure questionnaire survey on identified PPP-MHPs partners in Abuja, Nigeria. Data analysis was performed using descriptive and inferential statistical tools such as Mean item score (MIS), standard deviation, and Kruskal Wallis analytical techniques with the aid of SPSS software packages. The findings show that all the listed risk factors were found to be extremely high, very high, high, or moderate in terms of occurrence while all the listed risk factors recorded a very high level of severity on the delivery of PPP-MHPs. The top ten (10) risk factors frequently associated with PPP-MHPs are non-availability of finance, high finance cost, non-involvement of the host community, poor execution of housing policies, corruption and lack of respect for law, wrong perception of housing need by low-income earners, Illegal title to land, land acquisition and site availability, level of demand for the mass housing projects and unstable value of local currency. The respondents differs significantly on 29 risk factors in terms of occurrence and 40 risk factors in term of severity. The study, therefore, recommends that risk management culture should be highly encouraged among the PPP Partners in the sector. The study intends to enumerate the rate of occurrence of some itemized risk factors and their severities on the delivery of PPP – procured mass housing projects in Nigeria and the need to bookmark these risk factors in ensuring the sustainability of the PPP mass housing scheme.

The study accessed the critical risk factors in public-private partnership (PPP)-procured mass housing project (MHP) delivery in Nigeria. The research design adopts a quantitative approach, using well-structured questionnaires distributed to stakeholders involved in PPP-MHPs i.e. consultants, in-house professionals, contractors, and the organized private sector (OPS) registered with PPP departments in the Federal Capital Territory Development Authority (FCDA) Abuja, Nigeria. The instrument relates to the background information of respondents and the risk peculiar to PPP-MHP. Sixty-three (63) risk factors were submitted for the respondents to rank using Mean Item score (MIS) for risk occurrence and its severity, while risk significance index (RI) was used to determine the risk impact. Fuzzy Synthetic Evaluation (FSE) method was subsequently applied to determine the risk criticality groups and the overall risk level in the sector. The fuzzy set theory deals with ambiguous, subjective and imprecise judgments peculiar to decision making in construction project risk assessment. It aims to provide a synthetic evaluation of an object relative to a fuzzy decision environment with multiple criteria that requires qualitative linguistic terms. The findings show that thirty-one (31) risk factors were critical in the sector while financial and micro-economic risk group is contributing most significantly to the overall risk level in PPP-MHPs in Nigeria. The top 10 risk factors in the sector include availability of finance, high finance cost, the unstable value of the local currency, lack of creditworthiness, influential economic events (boom/recession), high bidding cost, poor financial market, financial attraction to project investors, interest rate volatility, inflation rate volatility, corruption and lack of respect for the law, non-involvement of the host community and poor execution of housing policies. The implication for practice is that having known the risk group contributing most significantly to the overall risk level in PPP-MHPs, adequate financial and budgetary allocation should be made available before embarking on such venture so as to sustain the scheme in the country. The study is one of the recent researches conducted on housing, since the procurement option is novel in the sector. The study is of immense value to PPP actors in providing necessary information required to formulate risk response methods in minimize the identified risk impact sector.

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

Using a desktop review of literature, the effect of illegal capital flows on the economic performance of Sub-Saharan Africa is examined. The review focus on articles with attention to illegal capital flows and their effects on the economic performance of Sub-Saharan Africa as a whole. By way of sampling method, purposive sampling was used, and so the desktop review focused purposively on articles published on issues of illicit financial flows and their effects on the economic performance of Ghana and Sub-Saharan Africa as a whole. The review found a high propensity of trade mis-invoicing and thus high illicit financial flows, transactions across boarders from developing countries and for that matter Sub-Saharan Africa to the developed economies. Therefore, the research recommends that customs divisions in sub-Saharan Africa should have up-to-date commodity-level world pricing information to make relatively better comparisons to detect mis-pricing and avoid such falsification and manipulation in trade. Given the high propensity of trade mis-invoicing resulting in high illicit financial flows, we recommend that cross-border transactions from developing sub-Saharan African countries be subjected to heightened scrutiny to curtail any potential traces of falsification in trade for tax evasion.

In today’s world of a globalized economy and ubiquitous digital transactions, businesses are hungry for ways to increase transaction efficiency and security. In the real economy, solutions that scale to fit transaction volume or velocity are equally valuable. This is true for clearing and settlement and for the day-to-day needs of buyers and sellers alike. Clever observers of both cash and digital transactions can spot cases where technology that supports transaction security or safety can strengthen consumer-borrower ties, mitigate default risks, and reduce recidivism. In general, a cloud solution for payment processing and merchant services solves two major barriers to optimum business technology: lack of scalability and lack of security [1]. The extension of current practice has its advantages, but new solutions unlock significant opportunities for both consumers and financial institutions [2]. The focus of this work is on the provisioning of cloud-based payment processing and merchant services to financial institutions and established global organizations, although the options available with these services mean they are potentially applicable to a wide range of group entities, including non-trading organizations, pension administrators, and group treasurers. With the increased attention to cybersecurity, a mass of data is available to assist the IT departments of the major payment processors, merchants, and acquirers to get cybersecurity on the radar of C-level executives [3]. The case is put forward for the increased targeting of and reporting to the Board’s Audit, Risk, and Liability Committees of publicly held payment processors and merchants to reduce fraud losses and mitigate the reputation and class action lawsuit risk due to data breaches. The progress of technology in the payment sector requires all stakeholders to have a collective approach in order to mitigate fraud and cybersecurity-related risks in new products and services to enhance consumer confidence and the proportion of retail cashless transactions [4].

The paper analyses the extent to which crude oil price shocks impact GDP growth, exchange rate, interest rate and inflation of an emerging oil exporting economy, Ghana. The Structural Vector Autoregressive model is used to analyse the quarterly data from 2009q1 – 2020q4. The results showed that exchange rate and GDP growth respond positively but temporal to the impulse of crude oil price. In contrast, inflation and interest rate respond negatively to crude oil price shock. Specifically, the exchange rate appreciates in the initial quarter and begins to depreciate, whereas GDP growth experiences an increase in the first two quarters and also reduces afterwards. Crude oil price shocks to the Ghanaian economy follow the conventional behaviour of the impact of crude oil on macroeconomic indicators. The positive impact of the price shock on GDP growth and exchange rate is not much reflecting the fact that Ghana is an emerging oil-producing country with low production and export level. Ghana’s prospects in the oil and gas sector should not just be a mere hoax. Policies should be directed toward petroleum exploration and production efforts since the energy transition endanger benefits for future exploitation. Policies should be implemented to attract competitive players locally and internationally in the oil industry. The shock of crude oil prices is beginning to show evidence based on this study. Therefore government must consider recognising the importance of other economic sectors in order not be become heavily dependent on oil.

The number of poor people continues to rise in Africa, despite a slow decline in the poverty rate. Africa with a population of 422 million poor people, representing about 70 per cent of the world's poorest people shows that the global burden of poverty has shifted from the rest of the world to Africa. This paper discussed the causes of poverty on the continent and various responses by stakeholders toward accelerating its poverty reduction. It was found that with the available statistics and projections, Africa will still fall short of eradicating poverty by 2030, but it can bring it to a low level. The study, therefore, recommends that African policymakers should aim for growth that is inclusive and sustainable. International support from the world bank, ODA, G7, and others will play a vital role, especially through technology and resource transfers, also African continent need to improve its resource mobilization.

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31^st December, 2021 and the data for the period starting from 9^th November, 2017 to 31^st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criterion tests. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.