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The relationship between farm size and land productivity is a hotly debated issue in the study of agricultural economics and development economics. The purpose of this paper is to explore the causes leading to the inverse productivity relationship by examining the relationship between farm size and factor inputs.

With a large panel data set of farm households in China during 2010–2011, this study uses the factor demand models to examine the relationship between farm size and per-mu labor and non-labor inputs while employing a stochastic frontier production function in determining the difference of labor efforts in farming operation across farm sizes. Moreover, the models for value-added margins and profits are used to further determine producer behavior of small-size farms.

Results of this study show that, as compared to larger farms, smaller farms not only utilize more labor and non-labor inputs per mu, but also benefit from a higher labor effort. Moreover, smaller farms concentrate more on grain output and cash costs while focusing less on the family labor input costs in an effort to maximize value-added margins rather than profits. The higher yields on smaller farms are thus a result of the utilization of a relatively higher level of labor and non-labor inputs along with skilled-oriented precision farming technology. The inverse productivity relationship is explained by the behavior of small-size producers with employment constraints, leading to smaller farms generating a higher yield than larger farms.

While Sen (1966), Feder (1985), Eswaran and Kotwal (1986) and others have theoretically derived the causal relationship between the incomplete factor markets, especially incomplete labor markets, and the inverse productivity, empirical studies to test the causal relationships are limited. In particular, a solid foundation based on an empirical analysis is lacking when it comes to explaining the inverse productivity in China. Results of this study are expected to have significant policy implications in terms of the understanding of small-size producer behavior and the associated mechanism underlying the inverse relationship between farm size and land productivity.

The purpose of this paper is to examine the long-term relationship between farm size and productivity in China at the national level.

In contrast to the micro-data examination conducted by earlier literature, in this study, the authors use household aggregate panel data on 29 provinces in China for 1988–2012. Using the panel data PMG model, the authors control the factor of difference in land quality due to the fixed effect in each province, and the authors consider the difference in the long-run coefficients of farm size and land productivity rather than the difference in their short-run relationship. Thus, the authors examine the long-term relationship between farm size and productivity. Furthermore, the authors examine the robustness of this relationship in the long-term using samples of rice, wheat and corn production by region.

In contrast with the findings presented previously, the authors find that the relationship between farm size and agricultural productivity is statistically positive in the long term.

The relationship between farm size and agricultural productivity is a key research issue in agricultural and development economics. In China, many studies have provided evidence of the inverse relationship between farm size and agricultural productivity at the family farm level. However, this inverse relationship seems to reflect specific regions and specific periods in the relationship between farm size and land productivity. At the nationwide level, in the long-term, this is not an inverse but a positive relationship. It is desirable to expand farm size for the long-term development of agriculture.

The purpose of this paper is to build a neural network (NN) inverse model for the multi-band unequal-power Wilkinson power divider (WPD). Because closed-form expressions of the inverse input–output relationship do not exist, the NN becomes an appropriate choice, because it can be trained to learn from the data in inverse modeling. The design parameters of WPD are the characteristic impedances, lengths of the transmission line sections and the isolation resistors. The design equations used to train the NN inverse model are based on the even–odd mode analysis.

An inverse model of a multi-band unequal WPD using NNs is presented. In inverse modeling of a microwave component, the inputs to the model are the required electrical parameters such as reflection coefficients, and the outputs of the model are the geometrical or the physical parameters.

For verification purposes, a quad-band WPD and a penta-band WPD are designed. The results of the full-wave simulations verify the validity of the design procedure. The resulting NN model outperforms traditional time-consuming optimization procedures in terms of computation time with acceptable accuracy. The designed WPDs using NN are implemented by microstrip lines and verified by using full-wave analysis based on high-frequency structure simulator (HFSS). The results of the microstrip WPDs have good agreements with the corresponding results obtained by using ideal transmission line sections.

The associated time-consuming procedure and computational burden in realizing WPD through optimization are major disadvantages; needless to mention the substantial increase in optimization time because of the multi-band design. NNs are one of the best candidates in addressing the abovementioned challenges, owing to their ability to process the interrelation between electrical and geometrical/physical characteristics of the WPD in a superfast manner.

The purpose of communicating with your esteemed magazine is your continuous support with Arab researchers and your sponsorship of research on the Arab world.

The research design was based on the APA style, and the methodology used in this research study is statistical and applied analysis to reach the desired results.

The results of this study provide an interesting perspective on the factors that influence changes in variables in Iraq: The prevailing patterns of temperature in Iraq are characterized by a clear pattern, and it is noticeable that they are linked to patterns of distance connection, but it is affected by some local influences, including the different terrain in Iraq. The highest values recorded by the correlation relationship were for the oscillation NAO and EA-WR with −0.812, −0.805, respectively. It is clear from the results that the correlation values are different between the northern and southern stations regions, and that the most influential appeared during the month of January, meaning that the effects of the remote correlation patterns are clearer during the winter season. Most of the correlation values of temperature were at the significance level of 0.5 and 0.1, in addition to that, the correlation was inverse, meaning that when the values of the distance correlation patterns rise, the temperature rates drop above the study stations and vice versa. The effect of SOI appeared during the months of December, April and May, and it recorded a strong correlation with the temperature variable. Most of the oscillations showed different effects with temperature, and it cannot be inferred that the effect is subjected and affected by the oscillation without reference to the extensions of pressure systems and their effects on the local conditions over Iraq.

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The topic of the research is one of the modern topics that researchers have not taken up in a large way, and the apparent thesis in the research is one of the effects of the newly observed climate changes with the aim of addressing and limiting the effects of these phenomena on the Arab world.

The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

The purpose of this paper is to posit and empirically demonstrate that positive and negative leader behaviors have a linear relationship with subordinate outcomes. The authors challenge this notion, and test a model where leader positive and negative behaviors have a curvilinear relationship (inverse-U shaped) with subordinate job satisfaction.

Cross-sectional design, based on a sample of 131 employees working across organizations and industries in the USA. Subordinates provided information on all study measures.

The authors show that higher levels of positive and negative behaviors from the leader will not generate a corresponding linear increase in employees’ satisfaction. Instead, the relationship is non-linear, with diminishing returns in subordinate job satisfaction for positive leader behaviors and higher ones for negative leader behaviors. In addition, subordinates with high levels of hardiness are more satisfied with positive leader behaviors, and report less dissatisfaction with negative leader behaviors.

Limitations are cross-sectional design, self-reported data, measurement of a limited number of leader behaviors as representative of leader positive and negative behaviors, and focus on only one dependent construct (subordinate job satisfaction).

Above a certain point, leaders’ positive behaviors have limited effect on increasing subordinates’ job satisfaction. Likewise, leaders’ negative behaviors decrease subordinates’ job satisfaction only above specific levels of leader behaviors.

The authors challenge this notion of linearity by theorizing and demonstrating that subordinates’ job satisfaction is influenced by leader positive and negative behaviors in non-linear relationship characterized by an inverse-U-shaped and a specific increase and decrease pattern.

The purpose of this study is to explain the adjustment rate made to target capital structures by listed non-financial firms in Pakistan during the courses of their life cycles and to determine what factors influence their adjustment rates.

The study used multivariate analysis to classify 39 years (1972-2010) of unbalanced panel data from listed non-financial Pakistani firms in terms of their growth, maturity and decline stages. Further, it used a fixed-effects panel data model to determine the factors that influence capital structure and adjustment rates during the life-cycle stages of firms.

The study observed a low–high–low leverage pattern during the growth, maturity and decline stages of businesses in line with tradeoff theory. Furthermore, the study observed an adjustment rate for growing firms of between 49.3-37.9 per cent, for mature firms of between 35.5-17.5 per cent and for declining firms of between 22.2-15.1 per cent toward their respective leverage targets. Furthermore, it was found that growing firms have higher leverage adjustment rates because, by having more investment opportunities, these firms can alter their capital structures easily by changing the composition of their new issues.

Erratic economic conditions in Pakistan have created an uncertain business environment. Therefore, even mature Pakistani firms remain skeptical about the sustainability of positive trends among current economic indicators. Furthermore, to avoid uncertainty, Pakistani firms grab short-term opportunities by using quickly available short-term debt as a main financing source. Government should introduce long-term policies that will stabilize the business environment and strengthen the financial, as well as the judicial, institutions of the country so that these firms may benefit from long-term investment opportunities and access more options for raising external financing. The results of this study will also help policymakers for other Asian economies where the capital markets are underdeveloped and where firms have higher leverage ratios, such as Thailand, Indonesia and Malaysia.

This is the first study in Pakistan that has used a multivariate approach to classify firms into their different life-cycle stages and to discover the leverage adjustment rates of firms during those life-cycle stages.

The purpose of this paper is to analyze the performance and the relationship between competitiveness, real gross domestic product (GDP) growth and human development in 20 countries of the Latin America and Caribbean region during the 2006-2015 period. The main argument to uphold here is that – from the perspective of virtuous circle – countries with better conditions of competitiveness are those with better economic performance and with better conditions for human development.

Time series data were organized at three levels: individual countries, groups of nations and Latin America and Caribbean as a whole. Indicators used were: index of competitiveness, rates of change in real GDP and Human Development Index. Cluster analysis tests were performed: data ranges were determined and quintiles were established. Countries were ranked in five categories and comparative position matrices were determined for each variable. Linear correlations between indexes were calculated. Linear correlation coefficients were determined in terms of groups of countries and considering Latin America and Caribbean as a whole.

Findings revealed that decreasing conditions in competitiveness and economic growth indicators are the representative situation since 2009. The most competitive country in the region is Chile, and the weakest is Venezuela. Nevertheless, all Latin American and Caribbean countries analyzed seem to have made progress in terms of human, economic and social development. Regarding correlations, Dominican Republic showed an inverse relationship between competitiveness and economic growth, while Jamaica and Venezuela showed inverse relationships between competitiveness and human development. At the individual country level, no statistically significant relationship between economic growth and human development was detected.

Findings highlight the necessity of future research that result in a deeper understanding of the transmission mechanisms between economic and social performance in Latin American and Caribbean countries. Particular reasons at the micro level that explain improvements or deteriorations in competitiveness and human development must also be analyzed. Based on the degrees of freedom, time series could have included more years, but a lack of information was found for some countries. It would also be necessary to observe each particular case considering the type of economy, production characteristics and export/import composition.

Results complement the existing literature by exploring competitiveness and its relationship with economic and social variables in developing countries. The authors also believe that this paper is relevant for macroeconomic and social policy debates involving competitiveness and human well-being in this region of the world.

This paper supports an important argument: human well-being and national development must be the ultimate goal of competitiveness. Traditional literature focuses on levels and determinants of competitiveness in developed countries, but it usually does not take into account social and human aspects of the process in developing countries. Little attention has been paid to analyze the relationship between competitiveness and socioeconomic variables in developing countries. Methods and findings of this paper complement the existing literature by studying the relationships among competitiveness, real GDP growth and human development in Latin American and Caribbean countries, using correlation analysis.

This paper investigates the link between equity and credit markets for the government‐sponsored mortgage institutions, Fannie Mae and Freddie Mac, during the period from January 2007 until December 2008. Before the financial crisis, investors perceived these real estate finance institutions as quasi state guaranteed.

By examining Fannie Mae and Freddie Mac during 2007 and 2008, this study extends existing research on the link between equity and credit markets. The authors employ univariate time series regression and vector autoregressive models to analyze the comovements over time and the lead‐lag relationship for equity returns, CDS spread changes, and bond spread changes.

The results provide evidence for equity returns and credit spreads of CDS and bonds being inversely related and adjusting simultaneously. The relationship between equity and credit markets intensifies during periods of heightened risks. The link between equity returns and bond spread changes is more robust in an environment of slightly elevated risk, while the relationship between equity and CDS markets intensifies during times of extreme stress. It was also found that the link between equity and credit markets completely breaks down as government intervention in the form of regulatory changes and ultimately, conservatorship, materializes.

Investors active in equity and credit markets need to be aware of the relevance of the prevailing capital market regime and the role of external effects such as government support and bailout.

There is a growing body of empirical research employing event studies and regression analyses on the firm level to examine the link between equity and credit default swaps. Yet, to the authors' knowledge this relationship has not been explored specifically for quasi guaranteed institutions. However, given the growing number of at least partly state owned real estate finance institutions, this specific focus is important to understand future expected risk compensation of equity and credit investors. The paper ask what lessons are to be learnt from the current financial crisis about investor protection in quasi guaranteed financial institutions.

This study aims to investigate the effect of corporate social responsibility (CSR) disclosure on firm performance, considering both firm profitability and firm value in an emerging market, India.

The study examines the effect of CSR disclosure on firm performance using panel regressions for the final sample that consists of 386 companies listed in the BSE 500 index, India. It covers all major players in the capital market for ten years from 2007–2016.

The result shows a trend toward the negative effect of CSR disclosure on firm profitability and firm value in India; this negative effect is mainly influenced by environmental disclosure score and social disclosure score. An adverse effect of firm profitability and firm value on CSR disclosure is also observed to underline the inverse relationship.

The study provides implications to consumers, investors, managers and policymakers. Firstly, consumers have to be more aware of CSR initiatives of companies, and they should support those companies to do more. Secondly, investors can use the ESG disclosure score as a signal for the level of CSR activities, which negatively affects firm performance. Thirdly, managers have to consider CSR more seriously and spend CSR amount wisely after proper research and not just to meet the mandatory limit. In addition, managers have to take necessary actions to make the public aware of the CSR activities of the company to gain an advantage in the future. Finally, policymakers have to give more emphasis on the promotion of CSR activities to reach the ultimate consumers who lie in the remote areas of the country, and more awareness has to be given to them regarding CSR activities.

The findings contribute to the literature by providing insights on CSR disclosure and firm performance relationship in India, an emerging market with increasing international attention where such studies are scant and less clear, especially after the amendments in the Companies Act, 2013. Furthermore, the measurement of CSR disclosure using environmental, social and governance (ESG) score is novel in the Indian context.