Academic literature on the topic 'Learning-by-exporting effect'

This review makes a significant contribution to the study of the “learning-by-exporting” effect. The article offers a detailed overview of the various views and studies on the subject. The work helps to review the evolution in the field of learning-by-exporting research through bibliometric analysis. Thirdly, this paper focuses on the most cited publications, as well as on the work of the last two or three years. Also, this article discusses the relationship between the learning-by-exporting and the self-selection hypothesis.

Abstract This paper examines the causal relationship between exporting and productivity in the manufacturing firms in Senegal using a unique firm-level panel data for the period 1998-2011. We control for endogeneity and sample selection by jointly estimating the productivity and the export-participation equations. Our results indicate strong evidence of both self-selection of the most efficient firms enter into the export market and effect of Learning in the export market. Findings show that firms with better financial health are likely to exports. Furthermore, the ownership of intangible assets like brevet and the quality of labour positively affect the probability to export of the manufacturing firms. We investigate the sectoral heterogeneity of the Learning-by exporting effect (LBE) and find evidence of a weak heterogeneity of the learning-by-exporting effect between the sectors. From a policy relevance, the evidence of learning-by-exporting suggests Senegal has much to gain from encouraging exports by helping domestic firms to overcome the barriers to enter into foreign markets by promoting access to intangible assets like brevet. Particularly, export promotion policies could be helpful, reducing the level of financial constraints faced by firms and indirectly enhancing their investment spending and productivity. As a driver of manufacturing exports, labour quality must be carefully considered in the perspectives of industrial development. Considerable efforts are required in the Senegalese educational system in order to match the training to the requirements of the labour market.

Following a growing literature, the authors test in this work the two hypotheses of self-selection and learning by exporting across different Indian manufacturing firms. Using matched sampling techniques, they estimate whether export-oriented firms are more efficient than non-exporters on the basis of the Indian Surveys of manufacturing firms for the period 2005-2013. The findings indicate that export entrants increase their productivity after entry but this increase is only temporary. In fact, the authors document a time-varying relationship between export participation and economic performance. This occurs for both total-factor productivity (TFP) and productivity growth. These results are consistent with those found in the previous literature for many countries. The only lasting significant effect that we find among the different measures of performances between exporters and non-exporters is that the former generates higher profits than their domestic counterparts.

Purpose The purpose of this study is to advance and test the idea that product exports and technology imports are complementary cross-border learning approaches for emerging market firms’ innovation performance. In addition, this paper also seeks to search for contextual variables that affect this complementarity. Design/methodology/approach This study takes systems approach to examine complementarity, combining a “productivity” and an “adoption” approach. In addition, interaction approach is also used as robustness check. Findings The authors show that the positive effect of export activity on firms’ growth rate is higher for firms that also engage in technology import, and vice versa. Furthermore, they show that, Ceteris paribus, firms’ adoption of one cross-border learning mechanism (e.g. entering export markets) positively influences the adoption of the other (e.g. technology import). Moreover, this complementarity is only significant for firms from province with low level of marketization. Research limitations/implications This inconsistency about learning-by-exporting and technology import on innovation can be resolved, at least partially, by the complementarities perspective. This paper also reveals two mechanisms of learning-by-exporting: the indirect effect of export on innovation through increasing the likelihood of adoption decision of importing technology and enhancing the positive effect of technology imports. Practical implications The potential of combining the two strategies should not be ignored by managers. To improve regional competitiveness, local governments should try best to improve the efficiency of customs to help firms realize the synergistic effect of learning-by- exporting and learning-by-technology-importing. Originality/value This study first explores the positive complementarity between the two cross-border learning mechanism in sharping EEEs 2019 innovation performance and identifies the condition to realize the synergistic effect of learning-by-exporting and learning-by-technology-importing.

Purpose The purpose of this paper is to contribute to the literature on learning by exporting by investigating whether an increase in the complexity of exported products contributes to higher productivity at the firm level. Design/methodology/approach The study implements an empirical analysis for Estonian manufacturing firms involved in exporting for the period 2008–2014, adding product complexity as an explanatory variable in the production function estimation. An increase in product complexity is interpreted as an indirect proxy for an increase in firm capabilities, capturing both tangible and intangible elements of competitiveness and reflecting the learning effects. Findings A relatively weak correlation between product complexity and productivity was found using a simple OLS estimation – exporters with higher product complexity have generally higher productivity levels. Somewhat surprisingly, no evidence for the learning by exporting was found among exporters, meaning that the increased complexity does not seem to be a channel for productivity upgrading. This result seems to be robust, irrespective of estimation methods and sampling preferences. Research limitations/implications The sample is representative of exporting firms. Practical implications The results show that the pursuit to more complex product does not necessarily contribute to productivity for exporting firms. The findings suggest that the firm-level upgrading due to increased export orientation is likely to take place through the other channels like moving up in global value chains and differentiating by product quality. Originality/value This is one of the first papers to investigate the effect of product complexity on productivity at a firm level. The results provide new insights into the learning-by-exporting hypothesis, with focus on potential learning among the existing exporters.

This paper “opens a black box” in examining how and under what conditions do firms achieve productivity gains by exporting, conventionally known as the learning-by-exporting (LBE) effect. We extend the current theoretical paradigm by proposing that exporters utilize strategic decisions pertinent to innovativeness, production capability, and human capital so as to leverage knowledge and resources obtained from exporting in order to achieve productivity gains. We test and validate our hypotheses with panelized data of roughly 250,000 Chinese firms over a 7-year period (2001-2007). We also show that the salience of these mediation mechanisms is contingent upon ownership structure and industry characteristics: Non-state-owned enterprises and firms in industries with medium export intensity or medium and high new product development intensity effectuate more learning through these conduits than their counterparts. The multimediation mechanism LBE model offers useful implications for academia, practitioners, and policy makers.

The hypothesis of learning-by-exporting hinges largely based on the argument that the exporters are exposed to knowledge and technology to foreign markets and they learn and become more productive and innovative. However, firms from developing countries not only export to industrialized economies but also to less developed countries. The natural questions arises that what if a firm from developing countries directs its exports to a country at a similar or lower level of technological developed. Would there still be productivity gains to be made? We attempt to test the effects of destination of exports on firms’ productivity and innovation for a sample of the Indian manufacturing firms. Our findings indicate that a positive learning effect is flowing from developed countries to productivity and innovation of the Indian firms. However, in the case of exporting to developing countries including China, we find weak or negative effects. Furthermore, our results also suggest that in-house R&D and foreign technology enhances the absorption capacity of firms, which in turn help firms in learning and gaining through exporting to technologically advanced countries.

This thesis uses Chinese firm-level data to investigate the relationships between the export premium, firm productivity and wage inequality. Using Chinese annual survey data for all state-owned firms and other non-state-owned firms with sales on mainland China over 5 million RMB, the author finds that there is a series of premiums for exporters compared with non-exporters. On average, exporters pay higher wages, produce more, sell more, add more value, employ more labour, have higher capital intensity, and have higher productivity (based on 1999-2003 data). Firms with relatively high export values will also be relatively more productive. Quantile results show that the premium decreases with the increase of the quantile. In addition, the export premium declines over time and across the industries, provinces and ownership types, and the higher the export intensity, the lower the export premium. The thesis further investigates the question: what determines the export premium – the selection effect or learning-by-exporting effect? First, the author uses the Olley and Pakes (1996) method to control both selection and simultaneity bias to estimate the reliable firm productivity. Then the author tests the self-selection and learning-by-exporting effects both parametrically and non-parametrically. The author finds both strong self-selection and learning-by-exporting effects at the aggregate level. The higher the productivity the firm has today, the easier for the firm to export tomorrow. The learning-by-exporting effect is the most significant in the second yearafter exporting. However, at the more disaggregated level, no significant learning effect is found within sectors and within middle and western provinces. A significant learning effect is found in eastern provinces. The learning-by-exporting effect across different ownership types is not robust to different testing methods. In addition, the author uses Chinese privately-owned firm-level survey data to investigate the heterogeneous export premium associated with different levels of trade. Firms engaged in international trade have higher premiums than firms which trade only across province borders. Firms which trade across province borders have higher premiums than firms that only trade within their province. Furthermore, export premium deviation between international trade and interprovincial trade is much smaller compared with the export premium deviation between interprovincial trade and inter-county trade. This finding implies that compared with the inter-county premium, the premium at interprovincial level is similar to the premium at the international level (though the former is actually less than the latter). The export premium caused by the self-selection effect can reflect the trade cost and it tells that trading goods across provincial borders within China is as onerous as crossing national borders. The next question to consider is whether engaging in international trade causes the wage inequality between firms to increase? To find out the answer, the author adopts a two-stage estimation strategy to study the effect of international trade on wage inequality. The first stage uses the Chinese annual survey firm-level data to calculate the wage inequality indexes—Gini and Theil of each province; as well as two dimensions of trade openness—intensive margin and extensive margin of each province. The second stage uses the panel data to study the impacts of trade margins on wage inequality between provinces. The results show that the variation of trade openness itself can explain nearly 70 percent of variation of wage inequality across China’s provinces and the extensive margin has a larger impact on increasing wage inequality than the intensive margin. Instrumental variable (IV) regression results imply that with one unit of increase in trade openness, the intensive margin increases wage inequality by nearly one unit and the extensive margin increases wage inequality by 1.2 to 1.3 units.
Thesis (Ph.D.) -- University of Adelaide, School of Economics, 2012

Using the South African Revenue Service and National Treasury firm-level panel data for 2009–17, this paper investigates how global value chain-related trade affects the export performance of manufacturing firms in South Africa. In particular, the paper uses extant classifications of internationally traded products to identify different categories of global value chain-related products and compares the productivity premium of international traders for these different categories. Also, the paper investigates possible differences in learning-by-exporting effects across the identified categories of global value chain-related products by estimating the effect of exporting before and after entry into foreign markets. The results confirm that global value chain-related trade is associated with a higher productivity premium compared with traditional trade. However, within the categories of exporters, only the firms that trade in global value chain-related products and simultaneously engage in research and development in the post-entry periods appear to learn from exporting.

Abstract Decision making in new fields with little data available relies heavily on physics-based simulation models. However, due to a lack of full understanding of the physical processes governing flow in the unconventional resources, data-driven modeling has emerged as an alternative and complimentary tool to create recovery forecasts that honor the available data. Transfer Learning provides an opportunity to start early-stage analysis of the asset before adequate data becomes available. New challenges in the energy industry as well as shifting dynamics in both domestic and global supply and demand has encouraged some of the petroleum exporting countries in the Middle East to strategize the development of unconventional resources. In this research we have developed a data-driven Transfer Learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed Transfer Learning method is developed on real-world data from several thousand horizontal multistage wells in the Eagle Ford super-basin in South Texas. In this method we have integrated reservoir engineering domain expertise in the data pre-processing and feature generation steps. We have also considered the temporal and spatial balancing of the training data to assure that the predictive models honor the real practice of unconventional field development. Our full cycle Transfer Learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. This workflow enables reservoir engineers to experiment with multiple hypothetical scenarios and observe the impact of additional data in the learning process. We use the developed workflow to examine the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East. Existence of all liquid types of oil, condensate and dry gas in the Eagle Ford has resulted in training a model flexible enough to be tested on various types of assets in a new location. We first present the successful deployment of our model within the Eagle Ford. Next, we use the information from major formations such as Tuwaiq Mountain and Hanifa and show the value of a pre-existing model from a fully-developed shale play on achieving acceptable accuracies with minimal information available in a new field. Our model is developed by data types with relatively low resolution that minimizes overfitting effects and allows generalization to different geologies with basin-wide accuracy. This approach allows conducting accelerated assessment of various sections of a large asset to enhance field development planning processes. This is a first example of such an effort on a basin scale that examines the effectiveness of Transfer Learning on some of the major unconventional plays in the Middle East region. This workflow allows investigating the relationship among geologic and petrophysical variables, drilling and completion parameters, and productivity of a large group of wells in a new asset.