Probability Of Inspection Research Articles

An experimental study on the sale of counterfeit products under monitoring policies

This study examines the decision behaviour of a seller who may sell either a genuine product or a deceptive counterfeit product under a monitoring policy characterised by an inspection frequency and a penalty level. A unique feature of selling deceptive counterfeit products is that the seller incurs significant losses when the deceptive counterfeit product is inspected, but enjoys significant gains if it is not. We conduct a laboratory experiment to investigate the seller’s behaviour regarding his/her selling strategy. In contrast to the standard theoretical predictions, we observe that the seller’s strategy exhibits a “deviate-to-equal-probability” effect, a diminishing marginal impact of the penalty level, and dynamic behavioural biases such as the “gambler’s fallacy” and speculation. A behavioural model is developed using bounded rationality and prospect theory to describe the observed behaviours, including the perception of the inspection probability in an inverse “S-shaped” pattern. Our results imply that, when designing effective monitoring policies, policymakers need to discreetly choose inspection frequencies depending on the penalty levels and to take into account sellers’ dynamic behavioural biases.

Perverse Consequences of Well Intentioned Regulation: Evidence from India’s Child Labor Ban

AbstractAlthough bans against child labor are a ubiquitous policy tool, there is very little empirical evidence on their effectiveness. In this paper, we examine the consequences of India’s landmark legislation against child labor, the Child Labor (Prohibition and Regulation) Act of 1986. Using data from employment surveys conducted before and after the ban, and using age restrictions that determined whom the ban applied to, we show that the relative probability of child employment increases and child wages (relative to adult wages) decrease after the ban. Our main specification relies on comparing changes in work probabilities over time for children of the same age but with siblings who are rendered either eligible or ineligible for legal work when the ban is implemented. The increases in the probability of economic activity are largest for children (i) in areas where the industries targeted by the ban play a larger role in local labor markets, (ii) in areas where the probability of employer inspections is higher, and (iii) in families that are poorer. These results are consistent with a theoretical model building on the seminal work of Basu and Van (1998) and Basu (2005), where families use child labor to reach subsistence constraints and where child wages decrease in response to bans, leading poor families to utilize more child labor. We also find decreases in child participation in schooling (for younger children only) and no economically meaningful change in household outcomes like assets or calorie intake.

Imperfect Inspection of a System With Unrevealed Failure and an Unrevealed Defective State

This paper proposes a model of inspection of a protection system in which the inspection outcome provides imperfect information of the state of the system. The system itself is required to operate on demand typically in emergency situations. The purpose of inspection is to determine the functional state of the system and consequently whether the system requires replacement. The system state is modeled using the delay time concept in which the failed state is preceded by a defective state. Imperfect inspection is quantified by a set of probabilities that relate the system state to the outcome of the inspection. The paper studies the effect of these probabilities on the efficacy of inspection. The analysis indicates that preventive replacement mitigates low-quality inspection and that inspection is cost-effective provided the imperfect inspection probabilities are not too large. Some derivative policies in which replacement is “postponed” following a positive inspection are also studied. An isolation valve in a utility network motivates the modeling.

Environmental enforcement and compliance in developing countries: Evidence from India

Abstract Effective implementation of environmental regulations is an important concern for emerging economies that face serious environmental degradation. In this paper we analyze compliance and enforcement of environmental regulations in India. In particular, we model: (i) plant-level compliance with water and air pollution control laws in the state of Punjab, and (ii) the decisions of the regulatory agency, namely, the Punjab Pollution Control Board to enforce these laws through inspections and other administrative actions. The two decisions are interrelated. For a sample of 117 large water polluting plants and 109 large air polluting plants the probability of inspection influences plant-level compliance and vice versa. We also find enforcement activity is targeted towards frequent violators. Plants that belong to dirty industries are more stringently monitored but those belonging to more profitable firms less so. Plants with high abatement costs and those that are new comply less frequently.

Announced or Surprise Inspections and Oligopoly Competition

Abstract To enforce compliance, regulators often choose between announced or unannounced (surprise) inspections. We analyze the impact of these inspection regimes on firms’ compliance choices in a multiple stage oligopoly game of quantity competition with endogenous compliance, monitoring and avoidance. In equilibrium, whether unannounced inspections achieve a higher level of compliance than announced inspections depends on the number of firms, demand and the cost of compliance. Furthermore, the impact on compliance of increasing the fine, the supervisor’s wage or the probability of inspections also depends on market size and structure and may be non-monotonic. Finally we provide conditions under which a welfare maximizing regulator will prefer an unannounced to an announced regime. Thus, our results suggest that when choosing the appropriate inspection regime, regulators should account for market characteristics, especially if compliance maximization is the objective.

State-of-the art non-destructive techniques for defects detection in nanocomposites foam-core sandwich panels containing carbon nanotubes: IR thermography and microwave imaging

In this article, the state-of-the art of infrared (IR) thermography and microwave non-destructive (NDT) testing for inspection of defects in carbon nanotubes-based nanocomposite sandwich panels has been presented. Different types of defects such as holes, notches and inclusion (Teflon) have been simulated in the polymeric foam-core of sandwich panels. The infrared (IR) thermography and microwave methods have been conducted to assess the simulated defects in the sandwich panel. The thermography results revealed that the thermal energy absorbance was higher in sandwich panel containing multi-walled carbon nanotubes (MWCNTs), however, various types of defects were well detected in both specimens with and without MWCNTs. From the result of microwave imaging, the same probability of NDT inspection was observed for detecting subsurface defects in sandwich panel and its nanocomposites. The use of low content of MWCNT (0.5 wt%) did not significantly affect on the microwave absorption properties of sandwich panel among NDT procedure, however some changes of reflection coefficient amplitude in selected frequency bands were observable.

ND BIM-integrated knowledge-based building management: Inspecting post-construction energy efficiency

Abstract Inspection of sustainable performance during post-construction has become increasingly essential. However, conventional operation and maintenance processes are limited with a high probability of inaccurate manual building inspections and the lack of real-time input of dynamic factors. In this regard, engagement of an Integrated Knowledge-based Building Management System using nD BIM applications (nD BIM-IKBMS) is anticipated to promote promising resolutions. The proposed system is expected to provide simulation-based supervisory control while automatically detecting and diagnosing operational faults. Following the literature-based conceptual model developed in our past research, this study aims to generate and verify the proposed framework theoretically, as the second step of nD BIM-IKBMS research and development series, concentrating on functional modeling. Notwithstanding which, it is difficult to describe a cross-disciplinary system following a single framework. As the technology evolves, the framework can not be generalized into new scenarios. Towards this end, the study is followed through the complexity theory lens. A highly flexible framework, considering that each part wherein has an opportunity to evolve, was developed. Following the principles of axiomatic design, the proposed framework was established. Ultimately, this research generates a range of detailed 3-hierarchical Icam DEFinitions for Function Modeling 0 (IDEF0) diagrams outlining the proposed nD BIM-IKBMS structure. This study identifies the interoperability of the proposed framework by standardizing communication protocols, data formats, naming convention, evaluation systems, and modulization. While this research is limited to implementing our nD BIM-IKBMS framework, real-world projects will be utilized in our upcoming steps of R&D series for validation purposes.

Machine learning for environmental monitoring

Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. We demonstrate how machine-learning methods can inform the efficient use of these limited resources while accounting for real-world concerns, such as gaming the system and institutional constraints. Here, we predict the likelihood of a facility failing a water-pollution inspection and propose alternative inspection allocations that would target high-risk facilities. Implementing such a data-driven inspection allocation could detect over seven times the expected number of violations than current practices. When we impose constraints, such as maintaining a minimum probability of inspection for all facilities and accounting for state-level differences in inspection budgets, our reallocation regimes double the number of violations detected through inspections. Leveraging increasing amounts of electronic data can help public agencies to enhance their regulatory effectiveness and remedy environmental harms. Although employing algorithm-based resource allocation rules requires care to avoid manipulation and unintentional error propagation, the principled use of predictive analytics can extend the beneficial reach of limited resources. Machine learning using big data can enhance environmental law monitoring. Applied to the US Clean Water Act, such methods can help public agencies to increase the likelihood of inspecting non-compliant facilities up to sevenfold.

Will China's building sector participate in emission trading system? Insights from modelling an owner's optimal carbon reduction strategies

Building sector is a significant contributor to the global warming and thus the control of carbon emissions from buildings has received unprecedented attention. While China is pioneering in including building sector in its Emission Trading System (ETS) pilots, there is few practical trading. This study investigates the reasons of lack trading via exploring a building owner's optimal strategy that is based on a multi-objective optimization model to achieve required carbon emissions reduction with minimal incremental costs. The investigated emissions reduction strategies include adopting low-carbon technologies, purchasing emission permits from ETS market, and non-compliance. A typical four-star hotel in Shenzhen, China is selected as an empirical case to validate the proposed model. The result shows that non-compliance is the preferable strategy by the owners, and there is no permits trading from the carbon market. Key influencing factors that affect the owners’ strategic choice are further investigated with various scenarios and it is found that the probability of government environmental inspection, the penalty for non-compliance, and an owner's reputation loss will to a large extent change an owner's strategy. These findings provide a quantitative rationale for policymakers to reformulate existing initiatives and mechanisms to invigorate the ETS market in the building sector.

Fare Evasion in Transit Networks

Public transit systems in major urban areas usually operate under deficits and therefore require significant subsidies. An important cause of this deficit, particularly in the developing world, is the high fare evasion rate mainly due to an ineffective control policy or the lack of it. In this paper we study new models for optimizing fare inspection strategies in transit networks based on bilevel programming. In the first level, the leader (the network operator) determines probabilities for inspecting passengers at different locations, while in the second level, the followers (the fare-evading passengers) respond by optimizing their routes given the inspection probabilities and travel times. To model the followers’ behavior we study both a nonadaptive variant, in which passengers select a path a priori and continue along it throughout their journey, and an adaptive variant, in which they gain information along the way and use it to update their route. For these problems, which are interesting in their own right, we design exact and approximation algorithms, and we prove a tight bound of 3/4 on the ratio of the optimal cost between adaptive and nonadaptive strategies. For the leader’s optimization problem, we study a fixed-fare and a flexible-fare variant, where ticket prices may or may not be set at the operator’s will. For the latter variant, we design an LP-based approximation algorithm. Finally, employing a local search procedure that shifts inspection probabilities within an initially determined support set, we perform an extensive computational study for all variants of the problem on instances of the Dutch railway and the Amsterdam subway network. This study reveals that our solutions are within 5% of theoretical upper bounds drawn from the LP relaxation. We also derive exact nonlinear programming formulations for all variants of the leader’s problem and use them to obtain exact solutions for small instance sizes. The e-companion is available at https://doi.org/10.1287/opre.2016.1560 .

Calculation of Misjudgment Probability for Product Inspection Based on Measurement Uncertainty

It is important to research into the misjudgment probability of product inspection based on measurement uncertainty, which is of great significance to improve the reliability of inspection results. This paper mainly focused on total inspection and sampling inspection methods and regarded the misjudgment probability as the index to provide quantitative misjudgment risk results for both producer and consumer sides. Through the absolute probability and the conditional probability model, the estimation formula of the total inspection misjudgment rate is deduced, respectively, and the calculation methods of qualification determination and misjudgment rate of the full inspection results are studied. According to the total inspection misjudgment rate, the methods of misjudgment rate of sampling inspection and qualification determination of measurement results are researched. The misjudgment rate of measurement results is calculated based on the exhaustive method and the Monte-Carlo simulation. The estimation results show that the misjudgment probabilities calculated by absolute probability models can be used as the basis for the selection of the measurement plan for product inspection. The misjudgment probability calculated by conditional probability models is more directly to reflect the risks for both producer and consumer sides, and it prompts inspectors to make decisions more carefully.

Regulatory performance of audit tournaments and compliance observability

This paper examines the effectiveness of traditional regulatory schemes and newly emerging social information schemes for achieving compliance. Our experiment focuses on two stochastic audit schemes for enforcing regulatory compliance. In the Random Audit mechanism firms are randomly chosen for inspection. In the Tournament Audit mechanism the probability of inspection increases with the degree of estimated underreporting. To study the effects of social information, the experiment varies the observability of identity, output, and compliance decisions. Optimal output is theoretically independent of the auditing scheme, but equilibrium reporting is higher under the Tournament mechanism than Random auditing. Experimental findings are broadly consistent with the theoretical predictions for reporting, but deviate modestly for output. In particular, we find that average output is lower and reporting is higher in the Tournament treatment compared to the Random Audit treatment. At the individual level, a majority of participants misreported in most periods. Social observability does not affect output or reporting significantly in either of the audit treatments.

Regulatory Inspection Regimes and Oligopoly Competition

To enforce compliance with product safety standards, regulatory agencies employ supervisors to conduct inspections and impose fines on firms that violate these standards. In most industries, the designated regulators choose between various inspection regimes, such as announced or unannounced surprise inspections. We conduct a positive analysis of the impact of these regimes on firms' safety choices in a multiple stage oligopoly game with endogenous quality and quantity competition. In equilibrium, whether surprise inspections achieve a higher level of safety depends on the number of firms, the cost of safety provision and demand. Furthermore, the impact on safety of increasing the fine, the supervisor's wage or the probability of inspections may be non-monotonic and also depends on market size and structure. Finally, we show that in some cases, neither unannounced, nor announced inspections can attain the socially optimal safety level. However in such cases, unannounced inspections minimize the distortion from socially optimal safety.

Improve Compliance with Limited Resources by a Three-Group Inspection Regime

Frequent monitoring and relatively high fines are usually necessary to bring about improvements in environmental quality, but more challenging for many countries with limited human, material, and financial resources is to put them into practice. This paper developed a three-group model of a state-dependent enforcement in a repeated game to improve the policy implementation under limited inspection capacities. A certain number of firms are grouped (group 1, group 2, group 3) for different supervision intensity (e.g., the order of inspection probability corresponding to each group is p1 < p2 < p3) based on their environmental performance. The optimal policy parameters, such as inspection probability of each group and the probability that a firm found in compliance is moved to a better reputation group, were obtained as the basis for regulator’s policy making. Numerical simulations indicated that the three-group inspection regime can significantly increase compliance rate as compared with static enforcement with the same monitoring probability. Among the number of firms in each group under steady state conditions, group 2 had the most, group 1 was the second, and group 3 had the smallest. Analysis and prediction of a three-group reputation example provided a good experiment for the model. The results give a practical reference for the policy makers with inspection capacity constraints to achieve higher compliance rate.

Analysis on Trilateral Game of Government Agency, Emission-Dependent Firm and Carbon Verification Institutions under Cap-and-Trade Scheme

To reduce carbon emission, cap-and-trade (C&amp;T) is regarded as one of the effective environmental regulations around the world, which brings huge challenges to generating firms. In this paper, a tripartite game model on carbon abatement is built to analyze their optimal strategies under the C&amp;T scheme. Based on the equilibrium of the game, some suggestions are presented to give the government agency to promote the efficiency of C&amp;T scheme, that is, introducing carbon verification institution and doing heavier fine to firm will decrease the probability of firm deception and relieve the burden of the government agency; setting reward to carbon verification institution and an appropriate increase in fine of carbon verification institution will decrease the probability of supervisor inspection, further saving supervision costs.

REGULATION OF POLLUTION IN THE LABORATORY: RANDOM INSPECTIONS, AMBIENT INSPECTIONS, AND COMMITMENT PROBLEMS

ABSTRACTWe compare experimentally a traditional random inspection policy and a variant where the agency may carry out a preliminary inspection of the level of ambient pollution before implementing any individual inspection. Since the agency may have an incentive to announce high inspection probabilities and then secretly renege on its announcement to avoid implementing costly inspections, we are also interested in the agency's commitment power. We find that overall, ambient inspections increase efficiency but the effect is weaker than expected when the agency has no commitment power; and polluters' reactions to the lack of commitment power of the agency vary depending on whether the agency uses ambient inspections or not.

Increasing the Probability of Fault Detection in Non Perfect Inspection Model of Delay Time Analysis with Compromise on Inspection Time

This study separates the real inspection content (soft portion) within the total maintenance inspection activity and attempts to repeat the same some additional number of times during actual inspection. Effect of repetition of soft portion on inspection related time, fault detection probability and the consequence variable of down time per unit time is analyzed. Statistical test proves that both the inspection time and probability of fault detection has nearly same rate of influence on the consequence variable though in opposite direction. A factor ω is introduced to account for the proportion of soft portion over the maintenance inspection time. As the number of repetitions of soft portion is increased for a given value of ω, it is found from analysis that the new set of inspection time and probability of fault detection improves downtime per unit time until an optimum number of repetitions is reached. Improvement is better as the value of ω is on the lower side. The practitioner is to take this possibility of soft repeatable portion of maintenance inspection time into account while estimating these two input parameters when employing delay time methodology as a preventive maintenance strategy.

Cocaine: The Complementarity Between Legal and Illegal Trade

AbstractI show that the decreased price of cocaine in its major destination markets is partly explained by a lower smuggling cost, which is itself the consequence of an increased international trade in legal goods. First, because more legal imports are associated with a bigger number of transporters and therefore with a bigger supply of potential drug smugglers. Second, because, as the number of legal shipments grows, the individual inspection probability decreases, lowering the risk born by the smugglers and thus their compensation. The cocaine market provides evidence of a complementarity between legal and illegal trade.

Pollution standards, technology investment and fines for non-compliance

In this paper, we analyze whether it is socially desirable that fines for exceeding pollution standards depend not only on the degree of non-compliance but also on technology investment efforts by the polluting firms. For that purpose, we consider a partial equilibrium framework where a representative firm chooses the investment effort and the pollution level in response to an environmental policy composed of a pollution standard, an inspection probability and a fine for non-compliance. We find that the fine should strictly decrease with the investment effort when (i) there are administrative costs of sanctioning; (ii) the optimal policy induces non-compliance; and (iii) either the fine is sufficiently convex in the degree of non-compliance or the investment effort decreases marginal abatement costs significantly.

Performance evaluation and analysis of a JIT-Kanban production system with sampling inspection

A just-in-time (JIT)–Kanban production system with sampling inspection is considered in this paper, where the processing do not always meet the requirement of the quality. Under this production environment, characteristics and components of the corresponding inventory management system are identified, relations and interactions between these components are explained and consequently, inventory growth and replenishment pattern of finished products to customers are investigated. Furthermore, the considered JIT–Kanban is formulated as a generalised Poisson queuing model where both the arrival rate and the processing rate are assumed to be Poisson process and state dependent, accordingly the state transition diagram is developed. Steady-state probabilities as a function of arrival rate, processing rate and sampling inspection policy (defects percentage and inspection probability) are derived. Consequently, the effects of different sampling inspection policies on the system’s measures of performance are evalua...