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The paper aims to develop a theoretical model to explain the exact process through which the scale effect works to create a possible wedge between a perception-based ranking like the “Corruption Perception Index (CPI) ” and the axiomatic “absolute costs of corruption”-based ranking of economies with low enforcement against corruption.

The paper takes into account corruption both at the “high” and “low” levels of bureaucracies, where the bribes are paid sequentially at the two levels. The bribes are endogenously determined at the equilibrium using a sequential game approach.

The paper finds that in the absence of coalition between the two levels of bureaucrats, both the absolute level of corruption and the welfare level of the economies are expected to vary inversely with the perceived corruption frequency. The paper also explores the possibility of a stable coalition between the “high” and “low” level bureaucrats and shows that with the perception of a stable coalition being formed, the negative monotonic relation between the corruption frequency and the absolute size of corruption breaks down.

First, the paper argues that the ranking of the economies with low enforcement against corruption on the basis of perceived corruption frequency may not reflect the ranking of the economies according to their absolute size of corruption; it points out that the perceived higher corruption frequency in an economy as reflected in CPI can be an indicator of both the lower size of “high” level corruption and absolute size of corruption in the economy. Particularly, this happens in economies where coalition between the “high” and “low” level officials does not form. Second, it identifies the exact way in which the scale effect works to create a difference in the CPI ranking and the axiomatic “absolute costs of corruption”-based ranking and explains why similar difference would exist if “absolute costs of corruption”-based ranking is derived from all the sources of hard data on corruption. Third, it explains why a stable coalition between the “high” and “low” level bureaucrats in economies with low enforcement does not usually form.

This paper aims to theoretically find out whether investments could close the formal-informal wage gap in India.

The paper builds a general equilibrium model of a developing economy with a large informal sector and a capital-intensive formal sector with sector-specific capital and incorporates endogenous demand.

With homothetic preferences, a small initial wage premium and elastic relative demand, investment in the formal sector is likely to close the wage gap, but the gap persists with non-homothetic preferences. However, investment in the informal sector is unlikely to close the wage gap with either type of preferences.

Though labour market distortions in developing economies leading to a formal-informal wage gap are well-documented in the development literature, little attention has been given to the question of whether such a gap would close over time.

Just as materials undergo acceptance sampling to improve quality, so should the human resources undergo acceptance sampling to improve the quality they contribute to a product. The importance of high quality human resources in computer‐integrated manufacturing (CIM) production environments is particularly important because of the technology that is at risk to operator control. Suggests that the development of a quality assurance programme for screening human resources that work in CIM environments improves product quality and reduces technology risk. Outlines a quality assurance programme for human resources, along with an illustrative application of how acceptance sample methods can be used in the programme.

One of the most contentious issues concerning benefit–cost analyses of environmental and other regulatory programs has been the valuation of reductions in mortality risks. The conceptual basis for most valuation exercises has been the value of a statistical life (VSL). However, despite decades of both theoretical and empirical research on the meaning and measurement of the VSL concept, there is no consensus concerning the validity of the results it produces in actual applications. In this paper, we review the development and application of the VSL approach and then propose what we believe to be a better way to value changes in mortality hazard.

“Economics is a Serious Subject.” Edwin Cannan.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.