Search results

Chinese multinational enterprises’ (MNEs) unprecedented, aggressive cross-border mergers and acquisitions (CBMAs) have led to several studies examining Chinese CBMAs, which importantly has also led to some degree of “theorising”. This study aims to undertake a “non-theoretical” fact-finding exercise before any theorising and empirical “causal” examination for a better understanding of the phenomenon (the rise of Chinese CBMAs).

This study uses a “stylised facts” approach which documents “empirical regularities” concerning Chinese CBMAs and thus guides new research questions.

Several facts are documented. Firstly, both the value and frequency of Chinese CBMAs are catching up to greenfield investments, with CBMA deals being larger in scale but lower in frequency. Secondly, Chinese CBMAs show a global reach away from the regional orientation of their early years. Thirdly, Chinese MNEs are possibly transforming their value chain with industrial upgrading as an aim. Fourthly, Chinese “full” acquisitions of targets have surged, especially in OECD countries, suggestive of Chinese MNEs’ “radical” acquisition approaches.

The gathered facts lend support to the view of the need for such fact-finding exercises to explicate and shed “new” light on the phenomenon (beyond our “current” views/beliefs). An understanding of the underlying trends beyond bare facts can also identify new knowledge, which can in turn provide new directions for research.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

Tax evasion and money laundering have become important sources of illicit financial flows in developing countries. Foreign capital flows used by shell corporates are generally with no real economic activities but motivated by harmful tax practices, thereby inducing loss of revenue for developing countries. Despite the coercive actions, such as backlisting of noncooperative jurisdictions to anti-money laundering and countering terrorism financing standards, illicit financial activities are still eroding the tax base in developing countries. The purpose of the paper is to assess the blacklisting effectiveness as a coercive policy against illicit financial activities.

This paper applies a propensity score matching strategy to a sample of 118 developing jurisdictions from 2009 to 2017 to evaluate changes in illicit financial activities following the blacklisting.

The results show that rather than altering illicit inflows in blacklisted countries, financial restrictions have produced the inverse, causing a boomerang effect on financial crime activities. The illicit share of capital inflows increases on average by 6 percentage points and 0.7% of GDP following the blacklisting. These results are robust to alternative matching methods and to the hidden bias problem.

Most of the previous research analyzed the link between blacklisting and fiscal revenues. However, here, the study analyzes whether blacklisting makes countries more cooperative in terms of fighting illicit financial flows.

This paper argues that through information and communication technology (ICT) adoption, the youth will be engaged in all nodes of the agricultural value chains, thereby improving the level of employment and reducing post-harvest losses. The study examines the determinants of ICT adoption among the youth. In addition, it estimates the impact of ICT adoption on youth employment in agriculture towards the actualisation of the Sustainable Development Goals (SDGs), particularly SDG-8, to promote inclusive and sustainable economic growth, productive employment and decent work for all.

The study engages data from Wave 4 (2018/2019) of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The logit regression, the propensity score matching and the inverse probability weighted regression adjustment are used as the estimation techniques.

The study underscores that educational level, access to electricity, location, age and income are significant determinants of ICT adoption among the youth. The findings also show that the youth's average weekly engagement in agricultural activities is about 24 h. In addition, the result reveals that ICT adoption can increase youth agricultural employment by approximately 21%. The mean difference indicates that those with access to ICT participate in agricultural activities more than their counterparts without ICT access by 29.46%.

One of the limitations of the study is that some of the variables such as insecurity, social protection/safety nets, that may have a significant influence on youth agricultural participation where not included in the model due to data constraint. As a recommendation for further studies, given data availability, such variables should be considered when examining youth-agricultural employment nexus.

Since ICT adoption has a significant impact on agricultural employment, this study proposes improved infrastructure facilities such as reliable power supply, lowering the cost of mobile and data subscriptions and better education facilities should be prioritised at all localities. This will enable the youth to embrace agriculture and help improve their socioeconomic welfare and livelihood.

Using Wave 4 of the LSMS-ISA, logit regression, propensity score matching and the inverse probability weighted regression adjustment, makes this study one of the very few to examine the impact of ICT adoption on agricultural employment among the youth in Nigeria. It implies that this study has provided empirical evidence and expanded the frontiers of knowledge on the extent to which ICT adoption influences youth agricultural employment in Nigeria.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

The study's objective is to examine the relevance of globalization in affecting the size of the shadow economy in selected African nations.

To do this, the authors employ the KOF globalization index and implement both static and dynamic common correlated mean group estimators on a panel of 24 African nations from 1995–2017. This technique accommodates the issue of cross-sectional dependence, sample bias and endogenous regressors. Panel threshold analysis is also conducted to establish the nonlinearity between globalization and the shadow economy. To examine the causality between the variables, the study employs Dumitrescu and Hurlin's panel causality test.

The results show that globalization reduces the size of the shadow economy. The results of the nonlinear analysis suggest a U-shaped relationship. Overall globalization has a threshold impact of 48.837%, economic globalization has 45.615% and political globalization has 66.661% while social globalization has a threshold value of 35.744%. The results of the panel causality show that there is a bidirectional causality between the two variables.

The results suggest that the government and other relevant authorities need to introduce capital controls and other policy measures to moderate the degree of social, political and cultural diffusion. Appropriate policies should be formulated to monitor the extent of African economic openness to other continents to maximize the gains from globalization.

Apart from being the first study in the African region that evaluates the relevance of globalization in controlling the shadow economy, it also analyzes the dynamics and threshold analysis between the two variables using advanced panel econometrics which makes the study unique. The study suggests that globalization tools are useful for affecting the size of the shadow economy in Africa. This study provides fresh empirical evidence on the impact of globalization on the shadow economy in the case of Africa.

There is extensive literature on the determinants of job tenure insecurity. However, very little is known about the individual drivers of labour market insecurity. Additionally, while a piece of literature shows that volunteering improves workers' income, no study considers volunteering as an activity which could help workers to feel more confident about their perception of labour market insecurity if they lost or resigned their jobs. Therefore, purpose of this paper is to study whether workers who volunteer are less likely to perceive labour market insecurity.

The paper employs data from the sixth European working conditions survey which provides a great deal of information on working conditions. For the empirical investigation, probit model as well as robustness analysis have been implemented.

Results show that employees who do voluntary activities have a greater likelihood of declaring perceived labour market insecurity, which is nearly 3 percentage points lower, than employees who do not volunteer. Findings suggest that governments need to improve the relationship between for-profit and non-profit sectors to encourage volunteering.

This is the first study which considers volunteering as an activity which could help workers to feel more confident about their perception of “labour market insecurity”. Most of the studies on “labour market insecurity” do not focus on the workers individual characteristics but mainly on the labour markets institutional characteristics and welfare regimes differences.

This paper aims to examine the impact of intra-industry trade on business cycle synchronization within the Economic Community of West African States (ECOWAS). ECOWAS region is characterized by limited intracommunity trade and a low level of foreign direct investment.

First, this research uses the two-digit level harmonized system classification to measure intra-industry trade, which is straightforward to interpret and compute, making it suitable for countries with low trade intensity. Second, it uses the system generalized method of moments (system-GMM) to examine the dynamic relationship between variables and address endogeneity concerns.

The results obtained from the system-GMM estimation reveal a positive and significant correlation between intra-industry trade intensity and business cycle synchronization, as well as an inhibiting effect of economic freedom on the relationship between intra-industry trade and business cycle synchronization. These results highlight the need to implement policies aimed at reducing tariff barriers, improving financial integration and intensifying production.

This research analyze the link between intra-industry trade and business cycle synchronization within the ECOWAS. It also analyze the role of economic freedom on the link between intra-industry trade and business cycle synchronization.

Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.

Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.

Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.

Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.

The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).