Global Apparel Value Chain in the Post‐ MFA Era: Exploring Bangladesh's Competitive Edge

Highlights Machine Learning (ML) methods are offered as an alternative to conventional techniques for understanding global commodity trade. A formal comparison is made between ML results with those from the Poisson Pseudo Maximum Likelihood (PPML) Estimator, which is the most commonly used statistical technique for bilateral trade analysis. PPML takes into account fixed effects and the dynamic nature of panel data to provide a better fit and higher prediction accuracy than ML in the context of in-sample forecasts. ML methods have strengths in feature selection, better validation statistics, and stronger predictive power than PPML, especially with out-of-sample forecasts. Abstract. In international economics, there has been a steady stream of innovations to explain patterns of trade between and among countries with emerging techniques. The most recent - Poisson Pseudo Maximum Likelihood (PPML) estimator – corrects for a potential bias caused by the large proportion of zero observations in bilateral trade data. Alternatively, this study offers Machine Learning (ML) as an option, especially in the presence of finer data on bilateral trade patterns. Using monthly and HS-6-digit (product) level data, the study finds that the main advantage of PPML is its accuracy of forecasts in-sample, but feature selection is somewhat rigid due to the inclusion of a large number of pair-wise fixed effects. ML models have the advantage in selecting features when a long list of explanatory variables is to be considered. Model validation statistics such as MAE and RMSE favor ML methods, but PPML tends to yield higher goodness of fit. In the out-of-sample context, ML has better accuracy than PPML, and a one-step walk-forward ML approach further improves the accuracy of ML forecasts. While PPML has a rich research and application history, emerging ML techniques have sufficient room for improvement in their adaptation to economic analysis. Keywords: Beef, Bilateral Trade, Boosting, Machine learning, ML for Policy

Economists view international trade as an engine to the economic growth of nations. Specifically, international trade is believed to promote the efficient allocation of resources, allows countries to experience the benefits of scale, facilitates the diffusion of knowledge, fosters technological progress and encourages competition. As a result, policy makers continue to undertake socio-economic, legal, political and institutional reforms in order to boost international trade. The study examines the impact of bilateral trade in the Economic and Monetary Union of Central African States (CEMAC) on the economic growth of Cameroon for a forty six year period from 1972 to 2018 using data collected from the World Bank and the International Monetary Fund. Through the adoption of the Poisson Pseudo Maximum Likelihood Estimator (PPML), the study finds out that while Cameroon’s bilateral trade with other CEMAC member States had an insignificant impact on the economic growth of Cameroon. Also, total labour force, government investment in education, government investment in fixed capital and the creation of CEMAC as a trade bloc all have a positive impact on the economic growth of Cameroon. The study recommends that policy makers in Cameroon and the CEMAC region as a whole should put in place laws, policies, measures and structures that boost bilateral trade within the CEMAC region in a bid to foster the economic growth of Cameroon as well as the economic growth of the CEMAC region as a whole. Keywords: International trade, Economic growth, CEMAC, Poisson Pseudo Maximum Likelihood (PPML) Estimator. DOI: 10.7176/JESD/11-10-19 Publication date: May 31 st 2020

New evidence in the literature on trade effects of the euro often reports different estimates. In this paper, I investigate the impact of trade data, instead of methodology, on the estimation of the key coefficient. In particular, I apply both the log-linearized least squares (OLS) estimator and the Poisson pseudo-maximum likelihood (PPML) estimator to the structural gravity model and compare these estimates by using trade data from two of the most widely used sources (IMF DOTS and UN Comtrade) and by varying samples. One surprising result is that the OLS estimator yields coefficients of the euro with opposite signs for the two data sources, when a sample covering all countries is applied. It is as expected that the PPML estimator is much less sensitive to sample size than the OLS estimator, taking a data source as given. However, the variation in estimates caused by data sources and sampling is consistent for both estimators. It indicates that both estimators are not free from the measurement error issue. More findings include: (1) the discrepancy in OLS estimates derived for the two datasets persists across samples, but the magnitude varies; (2) the magnitude of the discrepancy in PPML estimates from the two datasets is more stable to sampling; (3) both OLS and PPML estimators are sensitive to sample compositions for a given sample size.

Background: International trade is crucial for economic growth, particularly for landlocked developing countries like Nepal. Despite regional trade agreements like SAFTA, Nepal faces a persistent trade deficit within the SAARC region, obstructed by structural constraints and political tensions. Purpose: Using a gravity model, this study will analyse the determinants of Nepal's bilateral trade and exports with SAARC countries. It sought to assess the effectiveness of regional integration efforts for Nepal and identify key economic and policy factors shaping its trade performance. Methods: A panel dataset for seven SAARC countries (2009-2022) was analysed using the Poisson Pseudo-Maximum Likelihood (PPML) estimator. Models for total trade and exports incorporated traditional gravity variables (GDP, distance), socio-economic factors (exchange rates, tariffs, economic freedom), policy-related dummies (common border, BIMSTEC, India blockade, COVID-19), and an interaction term between tariffs and economic freedom. Findings: This research reveals that economic size and geographic distance significantly affect trade flows. Similarly, Partner country GDP and common borders positively influenced trade. Further, Nepal's exchange rate depreciation has negatively affected trade. The effect of several dummy variables on total trade flow and exports is diverse, such as the economic blockade by India, which has shown significant negative effects on exports. While COVID-19 negatively impacted total trade, it positively influenced exports. Higher economic freedom was found to reduce the adverse impact of tariffs on both total trade and exports. Conclusion: While Nepal's landlocked geography poses trade barriers, strong institutions help mitigate these constraints. Shifts in GDP-trade patterns reflect evolving export potential, though currency depreciation remains challenging. Strengthening governance, trade infrastructure, and export diversification is essential for deeper regional integration. Keywords: International Trade, Export Performance, Gravity Model, PPML estimators, SAARC JEL Codes: F14, F15, F18

Theory-consistent (‘structural’) gravity models are commonly used to model bilateral trade. Extensive simulations suggest that the Poisson Pseudo-Maximum Likelihood (PPML) estimator performs well in that setting. However, an influential review by (‘HM’) includes a simulation where PPML exhibits significant bias, which leads them to recommend a toolbox approach, including PPML and other estimators. I show that PPML’s bias is related to the noisiness of the data. A more realistic error variance assumption reveals that even for the same distribution type as HM, PPML’s bias is 4% rather than the 27% they report.

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This paper contributes to the literature on the discussed effects of digitization on bilateral trade in African countries over the period 1995–2021. Our methodology consists of two main steps. First, we calculate original digital divide indicators using both an absolute and bilateral approach. In the second step, we link the calculated indicators to measures of bilateral trade. Given the characteristics of the data, we apply the Poisson Pseudo Maximum Likelihood (PPML) estimator. Subsequently, we deepen the empirics by disaggregating the effect of the digital divide on bilateral exports and imports, estimating the effect of the bilateral digital divide (including the sign of the gap) on bilateral trade, further controlling for omission bias by augmenting the basic specification of the gravity model, using several estimators competing with PPML, further controlling for fixed effects and multilateral resistances with PPML with High Dimensional Fixed Effects, controlling for endogeneity with IV-Poisson regression, then by addressing the issue of uncertainty with Bayesian techniques. All these techniques led us to the key finding that the digital divide is a limiting factor for bilateral trade in Africa. In other words, the distance of countries from complete digital coverage and digital inequalities between countries hurt bilateral trade in Africa.

This article explores global competitiveness in the apparel industry and its implications on the competitive performance of China and Bangladesh through the lens of transaction cost theory (TCT). The study has been conducted based on qualitative method of social science research. The primary evidence for this study has been collected through semi-structured in-depth personal interviews with key resource persons in international trade. The study finds that rising competition in global apparel trade largely influences Bangladesh’s competitiveness in the apparel industry compared to China. However, China’s apparel industry faces challenges from rising production costs. It also finds that unrestricted trade flows among Asian competitors through bilateral and comprehensive trade agreements have severe implications on Bangladesh’s competitiveness in apparel sector, while trade protectionism remains the key barrier towards China’s share of exports in global apparel market. The findings of the study will be crucial for both Chinese and Bangladeshi policy makers and stakeholders to address existing impediments, including future challenges to gain a competitive advantage in their apparel exports. The study is among the first to compare the international competitiveness of apparel industries between the two major apparel exporting countries by using TCT.

The main purpose of this study is to understand whether the trade cost influences the export of Readymade Garment (RMG) export of Bangladesh. Theoretically, with an increase in the volume of exported goods, the costs will also increase. Therefore, this study is to examine the impact of trade costs on the readymade garment export performance of Bangladesh for the period of 2007 -2015. This study applied two types of panel data models: pooled OLS model and the Poisson-Pseudo Maximum Likelihood (PPML) estimation procedure have been employed in the estimation. The main contribution of the paper compared to other studies on Bangladesh lies in its approach in addressing the impact of trade costs on exports and using PPML gravity equation estimation. The results of the study indicate that trade costs have a significant negative impact on exports of Bangladesh. The major findings show that trade cost has a negative and significant impact on Bangladesh's bilateral RMG export performance. This implies that the Bangladeshi government should work to reduce domestic trade costs in order to boost efficiency and sustainably promote RMG export.

Unit root tests, augmented Dickey-Fuller and Phillips-Perron tests, indicate that the bilateral real exchange rates of six small Asian countries (India, Indonesia, Korea, the Philippines, Singapore, and Thailand) and three larger countries (Germany, the U.K., and the U.S.) are not stationary, and each has a unit root over the period January 1973 to December 1989 (i.e., a period of flexible exchange rates). This result does not support purchasing power parity (PPP);The theory of Generalized Purchasing Power Parity (Generalized-PPP) is developed due to the failure of PPP. Generalized-PPP states that the bilateral real exchange rates are, in general, non-stationary since the real fundamental variables are generally non-stationary. If the real fundamental variables of some countries share common trends, these countries' real exchange rates will share the same common trends. By sharing the common trends, these countries' real exchange rates are cointegrated, and there exists at least one stationary linear combination of the real exchange rates. Indeed, PPP is just a special case of Generalized-PPP;Tests for the performance of Generalized-PPP by Johansen's multivariate cointegration methodology are presented. There is evidence in support of Generalized-PPP for the Asian countries as long as the Singapore real exchange rate is included. It is likely that the Singapore real exchange rate is the main linkage for the existence of Generalized-PPP in Asia. On the other hand, Generalized-PPP does hold for each of the small Asian countries (except India) with the three large countries (Germany, the U.K., and the U.S.);The existence of Generalized-PPP implies that there is an error correction model. Using this model, it is possible to use the resulting impulse response functions to trace out the time paths of the various small Asian countries' real exchange rates for shocks in the real exchange rates of larger countries. Both the results of variance decomposition and the impulse response functions indicate that the real exchange rate movements in the small Asian countries are influenced by the shocks in large countries' real exchange rates, especially the largest country, the U.S.

Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).
 Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.
 Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.
 Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.

This study examines the impact of the European Union's Generalised Scheme of Preferences Plus (GSP+) on Pakistan's fruit and vegetable exports, utilising the Poisson Pseudo Maximum Likelihood (PPML) estimation technique with data spanning the period from 2004 to 2023. PPML is particularly suited for gravity models, as it effectively addresses common trade data issues such as zero trade flows and heteroskedasticity. To ensure robustness, Negative Binomial Regression (NBR) is also employed, which accounts for over-dispersion in the data. The analysis begins with a standard gravity model and is then extended to an augmented model by incorporating additional variables. The findings reveal that the GSP+ scheme has a significant impact on Pakistan's fruit and vegetable exports, with consistently positive and statistically significant coefficients across both estimation techniques. Economic size and population also positively influence trade flows, while distance has a negative impact, aligning with predictions from the gravity model. The study underscores the transformative potential of GSP+ in enhancing export performance, as demonstrated by the exceptional growth rates observed in partner countries such as Portugal, Ireland, Malta, Romania, and Bulgaria following their GSP+ status. Policy recommendations include maximizing GSP+ benefits, diversifying export markets, and ensuring adherence to international quality standards.

Climate change and the pressure to decarbonize, as well as energy security concerns, have drawn the attention of policymakers and the industry to hydrogen energy. To advance the hydrogen economy at a global scale, research and innovation progress is of significant importance, among others. However, previous studies have provided only limited quantitative evidence of the effects of research and innovation on the formation of a global hydrogen market. Instead, they postulate rather than empirically support this relationship. Therefore, this study analyzes the effects of research and innovation measured by scientific publications, patents, and standards on bilateral hydrogen trade flows for 32 countries between 1995 and 2019 in a gravity model of trade, using regression analyses and Poisson Pseudo Maximum Likelihood (PPML) estimation. The main results of the PPML estimation show that research and innovation progress is indeed associated with increased trade, especially with patenting and (international) standardization enhancing hydrogen export volumes. As policy implications, we derive that increased public R&D funding can help increase the competitiveness of hydrogen energy and boost market growth, along with infrastructure support and harmonized standards and regulations.

In the last decade, China’s trade with Africa increased faster than its overall foreign trade. This article focusses on the role of real exchange rates in this growth. A ‘bilateral real exchange rate’ augmented trade gravity model applied to China’s trade with 49 African countries over the period 2000–2011 shows that the real appreciation of most African currencies relative to the renminbi favoured China’s exports to these countries, but had no impact on China’s imports from Africa. This real appreciation of African currencies is explained by three main factors: the decision to peg them to other currencies (in particular to the euro), the amount of export of raw materials from African countries and the amount of financial assistance from international donors including China. Thus, a kind of detrimental sequence exists in Africa’s relationship with China: China’s imports of raw materials and its economic co-operation are among the factors explaining the appreciation of African real exchange rates, which itself stimulates China’s exports of manufactured goods, and so restricts Africa’s own industrial development.

U.S. real exchange rate fluctuations and relative price fluctuations