Institutional determinants of inward FDI: Evidence from Pakistan

Uddin, Moshfique; Chowdhury, Anup; Zafar, Sheeba; Shafique, Sujana; Liu, Jia

doi:10.1016/j.ibusrev.2018.10.006

Cited by 64 publications

(65 citation statements)

References 95 publications

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“…In line with prior studies [2,4,5,46,55,72,73,112,115,116,118,119], the correlation coefficients of the selected measures are reported in Table 6. We ascertain high level of correlation amid institutional dimensions as in previous studies [2,4,13,23,28].…”

Section: Descriptive Statistics and Correlationssupporting

confidence: 64%

Exploring Foreign Direct Investment–Economic Growth Nexus—Empirical Evidence from Central and Eastern European Countries

2019

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This study aims to examine the link between foreign direct investment (FDI) inflows and economic growth, also considering several institutional quality variables, as well as sustainable development goals (SDGs) set in the 2030 Agenda for Sustainable Development. By estimating panel data regression models for a sample of 11 Central and Eastern European countries, from 2003 to 2016, the empirical outcomes provide support for a non-linear relationship between FDI and gross domestic product per capita. Regarding institutional quality, it is found that control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability positively influence growth, while political stability and absence of violence/terrorism is not statistically significant. Moreover, SDGs such as poverty, income distribution, education, innovation, transport infrastructure, and information technology are noteworthy drivers of growth. The outcomes of panel fully modified and dynamic ordinary least squares partly confirm the findings. The panel vector error-correction model Granger causalities provide support for a short-run one-way causal association running from FDI to growth and a long-run two-way causal connection among FDI and growth. Furthermore, in the long run, unidirectional causal relationships running from each institutional quality indicator to economic growth and FDI are set out.

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Section: Descriptive Statistics and Correlationssupporting

confidence: 64%

Exploring Foreign Direct Investment–Economic Growth Nexus—Empirical Evidence from Central and Eastern European Countries

2019

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“…Furthermore, in the south-south FDI flow might be harmful to the development of local institutions, particularly in natural resource-rich countries. Uddin's [25,26] research by using multivariate OLS (Ordinary Least Square) regression in Pakistan found that institutional factors contribute to attracting FDI significantly. Meanwhile in Arab countries institutional quality and country risk are important factors in determining inward FDI [27].…”

Section: Institutional Factorsmentioning

confidence: 99%

“…Analyzed by using Extreme Bound Analysis Chaneriga et al [28] in their research found that geopolitical determinants have a relationship with inward FDI. Meanwhile, evidence from Pakistan shows that a strong political environment in the host country has a negative effect on attracting FDI [25].…”

Section: Political Relationsmentioning

confidence: 99%

China’s Outward FDI in Indonesia: Spatial Patterns and Determinants

Supriyadi

Wang

2018

Sustainability

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China has gradually become an important world investor with the implementations of its “Going Global” and “Belt and Road” strategy initiatives. Indonesia is the world’s largest archipelagic country, and has the largest economy of the ASEAN (Association of Southeast Asian Nations). Therefore, Indonesia is an important node for China’s implementation of its “Belt and Road” initiative. However, the existing research results regarding China’s OFDI (Outward Foreign Direct Investment) in Indonesia have indicated that little focus has been placed on the distribution patterns and determinants at the provincial levels. In this study, spatial analysis and mathematical statistics methods were used to analyze the temporal and spatial pattern evolution characteristics of China’s OFDI in Indonesia for the period ranging from 2006 to 2016. Also, the mechanism of the pattern evolution was quantitatively identified. The results obtained in this research study indicated the following: (1) China’s OFDI in Indonesia was observed to be characterized by fluctuations in investment amounts and rising numbers of investment projects, also, the investment amounts and number of projects were spatially dispersed; (2) the overall spatial evolution of China’s OFDI in Indonesia was found to be characterized by certain patterns, such as “west higher than the east, and south higher than the north” patterns. Moreover, the direction of the investment gravity center was determined to be obviously distinct during different periods; (3) high-level investments were found to be focused in only a few provinces, meanwhile, the majority of the provinces continued to be in a low-level stable state of investment from China; (4) this study’s OLS (Ordinary Least Square) regression and step-wise regression models revealed that institutional factors, political relations, and human resources were the most important factors which had affected China’s OFDI in Indonesia during the study period.

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“…The development and prosperity of this market can have a great impact on economic growth. Moreover, high economic growth can then lead to the advancement of, and influence on, international interactions (Uddin et al, 2019). It is impossible to achieve desirable economic development and growth without the existence of efficient financial institutions and appropriate financing (Coulibaly et al, 2018;Alam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performances of over-the-counter companies in developing countries using a stochastic dominance criterion and a PSO-ANN hybrid optimization model

Nasseri¹,

Jamshidi²,

Yazdifar³

et al. 2020

JAAR

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PurposeWith suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and investigates these methods for a developing country, thus providing a novel approach to the application of banking and finance. Our specific objectives are to employ a stochastic dominance criterion to evaluate the performances of over-the-counter (OTC) companies in a developing country and to analyze them with a hybrid model involving particle swarm optimization and artificial neural networks.Design/methodology/approachIn order to achieve these aims, the authors conduct a case study of OTC companies in Iran. Weekly and daily returns of 36 companies listed in this market are calculated for one year during 2014–2015. The hybrid model is particularly interesting, and the results of the study identify first-, second- and third-order stochastic dominances among these companies. The study’s chosen model uses the best performing combination of activation functions in our analysis, corresponding to TPT, where T represents hyperbolic tangent transfers and P represents linear transfers.FindingsOur portfolios are based on the shares of companies ranked with respect to the stochastic dominance criterion. Considering the minimum and maximum numbers of shares to be 2 and 10 for each portfolio, an eight-share portfolio is determined to be optimal. Compared with the index of Iran OTC during the research period of this study, our selected portfolio achieves a significantly better performance. Moreover, the methods used in this analysis are shown to be as efficient as they were in the capital markets of developed countries.Research limitations/implicationsThe problem of optimizing investment portfolios has to allow for correlations among returns from the financial maintenance period under consideration if an asymmetric distribution of returns exists (Babaei et al., 2015). Therefore, it is desirable to select an appropriate criterion in order to prepare an optimal portfolio and prioritize investment options. Although a back propagation technique is very popular in artificial neural (ANN) training, it is time-consuming to train a network in this way, and other methods such as particle swarm optimization (PSO) should be considered instead. In the hybrid combination of PSO and ANN, it is not the structure of a neural network that changes. Rather, the weighting method and the training technique chosen for the network are the important aspects, and these relate to PSO, so the only role ANN plays in this process is to reduce the errors.Practical implicationsThe hybrid model combining ANN and PSO is seen to be considerably successful for generating optimal results and appropriate activation functions. These results are consistent with the theoretical findings of Das et al. (2013) and an application of the simple PSO in a study conducted by Pederson and Chipperfield (2010). Our research results also confirm the efficiency of stochastic dominance criteria as noted in the studies conducted by Roman et al. (2013), ANN as in a study carried out by Kristijanpoller et al. (2014) and PSO as in studies conducted by Liu et al. (2015) and Deng et al. (2012). These studies were carried out in the capital markets of developed countries, whereas the authors’ analysis relates to a developing country.Originality/valueThe authors deduce that the tools and methods whose efficiency was proven in the capital markets of developed countries also apply to, and demonstrate efficiency in, two novel applications of portfolio optimization within developing countries. The first of these is gaining familiarity with the theory and practice of these research tools and the methods that enrich financial knowledge of investors in developing countries. The second of these is the application of tools and methods identified by investors in the capital markets of developing countries, which enables optimal allocation of financial resources and growth of the markets. The authors expect that these findings will contribute to improving the economies of developing countries and thus help with economic development and facilitation of improving trends.

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Institutional determinants of inward FDI: Evidence from Pakistan

Cited by 64 publications

References 95 publications

Exploring Foreign Direct Investment–Economic Growth Nexus—Empirical Evidence from Central and Eastern European Countries

Exploring Foreign Direct Investment–Economic Growth Nexus—Empirical Evidence from Central and Eastern European Countries

China’s Outward FDI in Indonesia: Spatial Patterns and Determinants

Evaluating the performances of over-the-counter companies in developing countries using a stochastic dominance criterion and a PSO-ANN hybrid optimization model

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