Search results

The purpose of this paper is to shed light on the effect of high‐level human capital investment, using tertiary education as the proxy, on the urban‐rural income gap in China.

Using a panel dataset covering 28 provinces of China over the period from 1988 to 2007, this paper employs Hansen's method and two‐step GMM‐SYS estimator to estimate the threshold regression model and the dynamic fixed‐effect panel model, respectively.

The urban‐rural income gap is found to be related to high‐level human capital investment in an inverted U‐shaped pattern with respect to economic development level. The estimated threshold turning point is around 20,000 RMB GDP per capita. This estimate is sufficiently robust to model specifications and variants of the dependent variable.

The authors forecast that high‐level human capital investment could play a role in bridging the urban‐rural income gap at the national level by 2014, when China's GDP per capita assumes an annual growth rate of 7.5 percent.

This, it is believed, is the first research to find an inverted U‐shaped pattern for high‐level human capital investment and urban‐rural income gap nexus in China.

The literature that explores the relationship between human capital and economic growth has produced mixed results. It highlights the puzzle on the correlations between human capital and economic growth. This study contributes to this debate by offering an explanation of the puzzling effects.

Using the threshold model proposed by Kremer et al. (2013), the results document that there is a threshold effect in the human capital–growth nexus.

The findings illustrate that the relationship between human capital and economic growth is weakly positive up to a certain threshold level of governance; however, the relationship turns out to be positive once the threshold level has been achieved.

The mixed evidence on the human capital–growth relationship can be explained through institutional quality differences. The findings recommend that better governance is complementary to contribute to the productive use of human capital in achieving higher economic growth.

The purpose of this study is to analyze the mediating effect of human capital in foreign direct investment (FDI) and growth nexus and establish the threshold of human capital in 28 sub-Saharan African (SSA) countries over the period 1999–2017.

This study used a secondary source of data obtained from the World Development Indicator and used the system generalized method of moments and dynamic panel threshold regression (TR) to analyze the data.

This study found that FDI and human capital have no significant impact on the economic growth in SSA. However, when the interactive term of FDI and human capital was introduced in the model, the economic growth effect of FDI became positive and significant, while the coefficient of the interactive term is negative and significant. This presupposes that SSA does not have a sufficient high-quality workforce that can absorb and transform the spillover benefits of FDI into economic growth. As a result, this study applied the TR to determine the minimum level of human capital and established a threshold level at 63.91%.

It, therefore, becomes pertinent for policymakers in the SSA region to have a human capital policy to build up their absorptive capacities to fully take advantage of FDI.

The contribution of this study lies in establishing a threshold of human capital at 63.91% for countries in the SSA region.

The threshold regression framework is used to examine the effect of foreign direct investment on growth in Sub-Saharan Africa (SSA). The growth literature is awash with divergent evidence on the role of foreign direct investment (FDI) on economic growth. Although the FDI–growth nexus has been studied in diverse ways, very few studies have examined the relationship within the framework of threshold analysis. Furthermore, even where this framework has been adopted, none of the previous studies has comprehensively examined the FDI–growth nexus in the broader SSA. In this paper, within the standard panel and threshold regression framework, the problem of determining the growth impact of FDI is revisited.

Six variables are used as thresholds – inflation, initial income, population growth, trade openness, financial market development and human capital, and the analysis is based on a large panel data set that comprises 45 SSA countries for the years 1985–2013.

The results of this study show that the direct impact of FDI on growth is largely ambiguous and inconsistent. However, under the threshold analysis, it is evident that FDI accelerates economic growth when SSA countries have achieved certain threshold levels of inflation, population growth and financial markets development. This evidence is largely invariant qualitatively and is robust to different empirical specifications. FDI enhances growth in SSA when inflation and private sector credit are below their threshold levels while human capital and population growth are above their threshold levels.

The contribution of this paper is twofold. First, the paper streamlines the threshold analysis of FDI–growth nexus to focus on countries in SSA – previous studies on FDI-growth nexus in SSA are country-specific and time series–based (see Tshepo, 2014; Raheem and Oyınlola, 2013 and Bende-Nabende, 2002). This paper provides a panel analysis and considers a broader set of up to 45 SSA countries. Such a broad set of SSA countries had never been considered in the literature. Second, the paper expands on available threshold variables to include two new important macroeconomic variables, population growth and inflation which, though are important absorptive capacities but, until now, had not been used as thresholds in the FDI–growth literature. The rationale for including these variables as thresholds stems from the evidence of an empirical relationship between population growth and economic growth, see Darrat and Al-Yousif (1999), and between inflation and economic growth, see Kremer et al. (2013).

Aims to provide an analytical framework investigating the accumulation of human capital in an OLG framework characterized by a continuous interplay between human capital distribution and individual choice of accumulation. This leads to a wide variety of dynamics. Generally, more equal economies tend to accumulate a higher human capital but other cases are possible. The accumulation is characterized by bimodality or multimodality in the human capital distribution and by an endogenous poverty trap.

Many countries have experienced, or are experiencing, urbanization. One such example is China. Even though the large‐scale rural‐urban migration seems chaotic on the surface, there are certain underlying forces driving individual decisions. The purpose of this paper is to provide some understanding of the relationship between human capital, migration, and occupational choices.

The paper starts with an overlapping generations model. Human capital plays various roles across different occupations – it does not affect the income of farmers, it affects income of workers linearly, and it has increasing returns in rural non‐farm business. The paper then derives income profiles for individuals with heterogeneous human capital, and finds the human capital thresholds of occupations. The paper calibrates the model to China, and simulates the model to answer two questions: how does an improving human capital distribution affect rural wages, quantities of migrants and return migrants? How does a fast‐growing urban wage rate affect rural wages, quantities of migrants and return migrants?

First, depending on the initial human capital level, policies aiming to enhance human capital may have different impacts on migration. If the initial human capital level is low, these policies will yield more permanent migrants; on the contrary, if the initial human capital is at a relatively high level, then a shrinking permanent migrant class with a growing entrepreneur class can be expected. This results in an inverted U‐shaped relation between the initial human capital level and the size of the permanent migrant class. Second, even though the non‐farm business of return migrants helps raise rural wages, the income inequality between rural and urban areas is not eliminated and migration is persistent. Third, borrowing constraints limit the size of rural non‐farm businesses and slow down the development of rural industry. The fourth and final point is that, migration costs discourage labor mobility and reduce the quantities of both permanent migrants and entrepreneurs.

This is an original paper on this subject.

This study investigates the impact of international remittances on the economic growth of remittance-receiving countries, using data from 113 developing countries between 1990 and 2015.

The authors used a novel approach to address the potential endogeneity of remittances. The authors estimated bilateral remittances and use them to create weighted indicators of remittance-sending countries, which the authors then use as instruments for remittance inflows to remittance-receiving countries.

The results indicate that while remittances have a positive impact on economic growth in developing countries with high human capital, they do not contribute to growth in developing countries with low human capital. The authors also examined the channels through which remittances affect growth. The findings suggested that remittances do not impact labor supply in developing countries with high human capital, but they reduce labor supply in countries with low human capital. Additionally, remittances increase investment in physical capital in developing countries with high human capital, but they do not have an effect on investment in developing countries with low human capital.

The authors investigated the impact of remittances on economic growth using a novel approach to address the endogeneity of remittances. Additionally, the authors examined the different indirect channels through which remittances can impact economic growth, such as their effect on labor supply and investment.

This paper aims to empirically analyze the role of FDI technology spillover effects in the development patterns of ASEAN.

The paper is based on the analytical framework of Borensztein by utilizing time‐series data between 1990 and 2008 in ASEAN countries. Models (1)‐(3) estimate parameters by adopting OLS, and Model (4) uses dummy saturation methods in PC Give, Oxmetrics 6 to determine significant dummy variables automatically. By judging the overall significance of each variable and the model, Model (4) has a substantial improvement, and is finally selected to judge the education threshold of each country.

The empirical results render support to the existence of technology spillovers in ASEAN. The regression results also suggest that it is necessary to combine FDI technology spillovers with domestic human capital, that is, in the process of promoting economic development. China's FDI in ASEAN requires lower education threshold, and also has positive effect on economy growth in six countries of ASEAN.

As this paper investigates data from many countries, there are some unavoidable differences in the statistical sources and the calibre of data. It is inevitable to have some inaccuracies. This paper tries to process the data into similar calibres and units.

The paper examined whether total FDI, intra‐ASEAN FDI and FDI from China have played exactly the same role in ASEAN countries. Accordingly, the paper put forward suggestions after combining the characteristics of FDI from China with different local situations of ASEAN host countries.

The paper, respectively, takes data of total FDI, intra‐ASEAN FDI, FDI from China into the FDI variable in the model, and get three sets of results for each country. The year dummy variable is newly added to the original framework of Borensztein in order to test whether the dependent variable can be affected to a large extent by potential great changes in macro‐economies.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.