Journal of International Commerce, Economics and Policy

The withdrawal of India from negotiations on the world’s largest trade pact, the Regional Comprehensive Economic Partnership (RCEP), in November 2019 was a major setback for proponents of regional economic unity. This paper tries to evaluate India’s concerns and also the way forward for India. A review of trade between RCEP countries post-implementation is done to analyze India’s stand against joining the block. India decided to opt out of the agreement because of adverse trade balance, impact on the dairy sector, economic slowdown, experience with Free trade agreements (FTAs), China factor, data localization, rules of origin, and the experience of ASEAN countries with Sino-FTA have been some of the factors responsible for this decision. Bilateral trade agreements with Japan, Malaysia, Singapore, Thailand, Korea, Rep.and ASEAN were already in place. So, trade between India and 12 RCEP member countries would not have changed after India’s inclusion in RCEP. There was fear of a massive surge in imported manufactures from China and dairy imports from Australia and New Zealand. This paper also evaluates the long-term repercussions of this decision and whether India missed out on becoming part of the global value chain n having greater market access through this block. India’s experience following protectionist policies did not yield the desired results in the past. So, an in-depth analysis has been done to validate if India has made a mistake by not joining RCEP or if India’s concerns and fears were justified and its regional future.

In this study, we investigate the potential effects of the Regional Comprehensive Economic Partnership (RCEP) agreement on Vietnam’s rice exports from 2000 to 2016. For this analysis, we employ the panel data augmented gravity model with some modifications. The estimated results suggest a positive relationship between Vietnam’s per capita export and the capita real gross domestic product (GDP) of the importer, Vietnam’s rice production, export price, and ASEAN dummy. We confirm that there is a negative relationship between Vietnam’s per capita rice export value and import country agricultural land and import tariffs. Additionally, this paper forecasts Vietnam’s rice export value per capita at each market in RCEP partners by using the autoregressive model. Our forecast results show that among RCEP members, Vietnam has potential markets if import tariff reductions are lowered in Singapore, Brunei, Korea, Japan, and Malaysia. To reap the benefits of rice exports’ potential, Vietnam must move from low-grade and cheap rice to high-quality rice production instead of expanding rice production and exports with the following implications: changing its present rice trading partners to potential markets and adopting market-oriented policies to meet the international demand and to increase the export elasticity of rice.

The paper analyzes the interaction of economic growth and inflation rate on accounting for both sources of capital investment and global shocks. The analysis method is a time-varying coefficient Bayesian vector autoregression (TVC-BSVAR) model applied in a quarterly data sample of the Vietnam economy over Q2/2001–Q4/2022. The global factors, including the world economic growth and the inflation rate, are the most important drivers of domestic growth and inflation rate. A greater world growth rate raises the domestic growth rate while a higher world inflation rate increases the domestic inflation rate. If the world economy falls into stagflation with declining growth and a rising inflation rate, Vietnam’s economic growth reduces while the inflation surges. The monetary and fiscal policy can be employed to recover domestic economic growth but with an associated surge in the domestic inflation rate.

The firm-level determinants of outward foreign direct investment (OFDI) intensity of Indian manufacturing firms are analyzed using a random-effects Tobit model for a panel dataset covering over 17,550 firms from 1992–1993 to 2018–2019. The explanatory variables are based on a theoretical synthesis of Dunning’s (1980) [Towards an eclectic theory of international production: Some empirical tests. Journal of International Business Studies, 11(1), 9–31] ownership–location–internationalization (OLI) model and Mathew’s (2002) [Competitive advantage of the latecomer firm: A resource-based account of industrial catch-up strategies. Asia Pacific Journal of Management, 19(4), 467–488] linkage–leverage–learning (LLL) model. The empirical evidence reveals that policy liberalization, size (up to a threshold level), age, business group ownership structure, and the extent of foreign earnings strongly impact the OFDI intensity of Indian manufacturing enterprises. In contrast, outsourcing intensity, technological efforts, product differentiation, export intensity, and use of imported intermediate inputs were found to have a moderate positive impact. A moderate negative impact was found for factors like the global financial crisis, debt-equity ratio, and technology imports, and a strong negative effect for foreign ownership and public-sector ownership structure variables.

We critically analyze and compare the analytical structures of the international monetary and financial trilemmas. The first structure says that countries cannot have all three of fixed exchange rates, free capital mobility, and monetary autonomy. The second argues that countries cannot have all three of financial globalization, absence of financial crises, and financial autonomy. While the monetary trilemma is often described as being based on the Mundell–Fleming model, this is true only under the assumption of perfect capital mobility. Imperfect mobility can be caused by factors other than capital controls and in this case the constraints of the monetary trilemma need not be binding in the short run. In order to have a mechanism to provide balance of payments equilibrium, however, the theory of economic policy demonstrates that the monetary trilemma must be binding in the long run. While pointing to an important issue the financial trilemma formulation does not provide the most useful way to analyze the tradeoffs generated by financial globalization. More useful for this purpose is the literature on international externalities and public goods. We also argue that for many countries, giving up monetary autonomy will be more costly than giving up financial policy autonomy.

This paper examines the effects of institutions and financial development on workers’ remittances in sub-Saharan Africa. To achieve the objectives, a two-step system generalized method of moment (GMM) was utilized on a panel dataset of 38 countries in the region from 2005 to 2022. Empirical findings from the study reveal that some institutional indicators, namely, control of corruption, government effectiveness, political stability and rule of law have a significant impact in attracting remittances to SSA. Further evidence reveals that financial development indicators notably domestic credit to the private sector and domestic credit to the private sector by banks have a significant impact on remittances inflow in the region. Financial development indicators are found to complement some of the institutional indicators, such as control of corruption, political stability, government effectiveness, and rule of law on the inflow of remittances to the region. Government efforts should be geared toward improving the institutional framework and ensuring financial market development to increase the amount of remittances inflow to the region.

This study explores the application of machine learning models to predict dividend payouts for publicly listed companies in the Vietnamese stock market. The authors compared various machine learning algorithms to identify the most suitable one for this task. Based on our evaluation, the test shows that the Random Forest algorithm outperformed other methods in terms of accuracy, F1 score, and time efficiency. Furthermore, we analyzed the feature’s importance to understand the key factors driving dividend payouts. The results can help investors, managers, and government agencies make informed decisions and improve financial market transparency. The study also highlights the potential of using machine learning algorithms to automate and enhance the credit rating process, leading to more accurate and real-time assessments of business creditworthiness in Vietnam.

This study provides unique insights into the link between economic policy uncertainty (EPU) and stock market index in Sub-Saharan African (SSA) countries. Basically, the dearth of evidence in this perspective, especially in the context of SSA motivates this new assessment. Thus, on account of the monthly longitudinal series for 11 SSA countries over 2014M1–2023M6, the novel multiple structural break test in panel (MSBP) and cross-sectional ARDL (CS-ARDL) provide the following upshots: first, the high level of EPU worsens the region’s stock index in the short run; second, there is a long-term negative but insignificant effect of EPU on the stock index; third, EPU significantly reduces the stock index in the three identified structural break regimes, but outside it, it is insignificant; fourth, the devastating influence of EPU on stock prices is more pronounced during the COVID-19 era demonstrating that the response of the region’s stock market to EPU is highly sensitive to global shocks. The policy implication from the major findings is for the government authorities to put up plans that can strengthen the stock markets’ resilience to economic policy-induced uncertainty.

Using plant-level data for 2008-2017, we examine the impact of the adoption of ISO 14000 by Indian manufacturing plants on their energy intensity. The propensity-score-matching difference-in-difference (PSM-DID) estimator is used to assess the effect. Our results show that the adoption of ISO 14000 tends to reduce energy intensity in Indian manufacturing plants, especially among the low and low-medium technology industries. ISO 14000 lowers energy intensity in industrial plants in energy-intensive industries, and the effect is stronger for the group of such industries covered under the Perform–Achieve–Trade (PAT) scheme of the Indian government, a market-based compliance mechanism for energy efficiency improvement.

This study aims to use a measure of earnings management to predict companies whose financial statements have problems. This is an identification measure other than common measures to predict financial statement fraud such as measuring by the M-Score or the Z-score model that many previous studies have applied. In the income management measure, the author uses a measure of abnormal cash flow and abnormal expense flow to consider whether the corporate financial statements have problems or not. To do this, the author uses data from listed non-financial enterprises in the period from 2018 to 2022, with machine learning and deep learning algorithms, of which we focus on three main algorithms: ANN, SVM and RF. The results show that identifying problematic financial statements based on abnormal cash flows is quite effective with an accuracy of over 78% for the SVM method, while if using the RF method, the accuracy reaches over 82% but it is required to accept an increased processing time.

The primary objective of this study is to investigate the determinants of foreign direct investment, with a specific focus on per capita GDP (GDP), alongside various risk factors including economic risk (ER), financial risk (FR), political risk (PR), and geopolitical risk (GPR), in the context of Turkey. This investigation employs an innovative wavelet methodology to analyze the relationships among these variables. The empirical analysis entails the application of wavelet coherence, as well as multiple wavelet coherence (MWC) and partial wavelet coherence (PWC) techniques, to evaluate the strength and significance of relationships between different time series while considering the influence of other related time series. Utilizing quarterly data spanning from 1988Q1 to 2020Q4, this study seeks to fill a gap in the existing literature by examining the nexus between these factors in both time and frequency domains, a novel approach in this field. The findings from the wavelet coherence analysis reveal that GDP, PR, FR, and ER positively impact FDI inflows, whereas GPR exerts a negative influence across various time and frequency intervals. Moreover, the outcomes of MWC and PWC analyses underscore the significant effects of the control variables on the relationships under investigation. Based on these findings, the study puts forth noteworthy policy implications aimed at fostering a more conducive environment for FDI in Turkey.