Improve Allocation Efficiency Research Articles

The effects of digital economy on manufacturing resource allocation efficiency across Chinese cities: a spatial economic perspective

ABSTRACT The development of digital economy provides significant impacts on spatial distribution of industries. Using microdata from Chinese manufacturing enterprises and adopting a spatial economic perspective, this study reveals that the digital economy exerts non-linear impacts on resource allocation. As the level of urban digital economy improves, the efficiency of manufacturing resource allocation shows an inverted U-shaped trend, and high hierarchy cities exhibit higher critical values. The study finds a positive spatial correlation in manufacturing resource allocation efficiency across cities; however, the growth of digital economy in neighbouring cities may lead to backwash effects and impede local improvements in allocation efficiency. The mechanism analysis indicates that the digital economy primarily affects spatial resource allocation through urban agglomeration economies and diseconomies. Consequently, economic agents and policy makers in cities should consider the dual effects of ‘location optimization’ and ‘transforming the economy from substantial to fictitious’ during digital transformation processes to select optimal development paths and improve allocation efficiency.

Sustainable urbanization and green total factor productivity: evidence from China’s new-type urbanization plan

Sustainable urbanization is significant in developing countries. This paper studies whether Chinese-type sustainable urbanization, that is new-type urbanization, promotes green total factor productivity (GTFP). We find that the new-type urbanization implementation in China, on average, significantly promotes GTFP by 3.2%. The positive effect of new-type urbanization on GTFP is correlated with promoting innovation, especially green innovation, and improving allocation efficiency. We do not find clear evidence for industrial upgrading, including industrial advancement and rationalization. This is because industrial upgrading is a gradual process and cannot be achieved shortly. Instead, we find that pilot cities might screen the entry of new firms and keep polluting firms out comparatively. The heterogeneous results indicate that the promotion effects on GTFP are more salient in regions with strong environmental regulation and adequate factor endowments. We have some practical implications for sustainable development in developing countries.

Patent strategies in the digital revolution: How businesses use digital technology patents to optimize resource allocation efficiency

This study assesses how digital technology patents affect resource allocation efficiency in China's nonfinancial firms (2009–2021) using a benchmark regression model. It finds that both the quantity and quality of these patents significantly improve allocation efficiency, especially in highly digitalized sectors. The analysis suggests that patents enhance efficiency via better innovation but their impact varies across enterprise types. Recommendations are made based on these findings.

Search Frictions in Many‐to‐one Markets*

This article studies how search frictions affect competition and matching efficiency in many‐to‐one loan markets where a borrower requires support from multiple investors and coordination is desired but absent. We develop a dynamic search model and show that borrowers employ mixed strategies in quoting interest rates. More importantly, we find that in many‐to‐one markets, the rate dispersion caused by search frictions facilitates coordination and hence improves allocation efficiency relative to a no friction environment. Further, we empirically present stylized facts consistent with the theoretical predictions and structurally estimate the impact of search frictions on matching efficiency.

Impact of consumer foresight on efficient overselling

AbstractOverselling is commonly adopted in the travel and hospitality sectors where a good or service is sold in excess of actual supply. We examine the impact of consumer foresight on efficient overselling when there are two dimensions of uncertainty, namely, early consumers are uncertain about their service valuations and the seller is uncertain about late demand arrival. We show that when consumers are naïve and have no foresight to anticipate future events, the seller resorts to the use of partial refunds and involuntary cancellation when the mandatory compensation for seller‐initiated cancellation is low, resulting in efficiency loss. When consumers are sophisticated and have perfect foresight on future events, efficiency is achieved when the seller sells the entire capacity in advance and relies solely on voluntary cancellation to re‐sell units when late demand warrants it. Refund complements overselling both by improving allocation efficiency in involuntary cancellation and by mitigating the cost of overselling when consumers have limited foresight. Unlike the social planner, the seller may suffer from consumer foresight. Our findings pinpoint the mandatory compensation in involuntary cancellation as a strategic tool for the social planner to tilt the seller's preference in the seller‐initiated cancellation policy to achieve efficient overselling.

Optimal overconfidence in the presence of information manipulation

This paper studies overconfidence in a decision-making model with an information manipulation stage. We demonstrate overconfidence discourages information manipulation and thereby improves allocation efficiency. The ideal level of overconfidence compromises between this discouragement effect and the information loss brought on by non-Bayesian weight. Overconfident leadership benefits the organisation and this conclusion is robust to a more general setting of information manipulation. Results of comparative static analysis are reported.

Valuing photovoltaic power plants by compound real options

Meeting electricity demand with renewable generation requires spending trillions of dollars in investments exposed to financial risk. Renewable projects have multiple strategic real options to manage investment risk, but they are usually priced separately in isolation. This paper proposes a framework for valuing multistage decisions in a grid-scale photovoltaic (PV) power plant under uncertainty. The project is modelled as a compound portfolio of American sequential and mutually exclusive options to defer, expand and relocate. The compound options are jointly priced by a hierarchically nested least-squares Monte Carlo algorithm. The framework incorporates uncertainties related to decreasing costs in solar technology and the emergence of more profitable renewable tariffs elsewhere. The results show that the value of the compound option portfolio significantly exceeds the value of the single deferral option commonly considered in practice. Ignoring expansion and relocation flexibilities may lead to underinvestment by mispricing the value of PV projects. Incorporating these options encourages early commitment of the PV investment. Moreover, summation of individual option prices may not be a good approximation of the true portfolio value, as interactions among options exist. These findings may improve allocation efficiency of renewable investments, accelerate energy transition and benefit investors, funding agencies and regulators.

Characterization of Transplant Center Decisions to Allocate Kidneys to Candidates With Lower Waiting List Priority

Allocation of deceased donor kidneys is meant to follow a ranked match-run list of eligible candidates, but transplant centers with a 1-to-1 relationship with their local organ procurement organization have full discretion to decline offers for higher-priority candidates and accept them for lower-ranked candidates at their center. To describe the practice and frequency of transplant centers placing deceased donor kidneys with candidates who are not the highest rank at their center according to the allocation algorithm. This retrospective cohort study used 2015 to 2019 organ offer data from US transplant centers with a 1-to-1 relationship with their local organ procurement organization, following candidates for transplant events from January 2015 to December 2019. Participants were deceased kidney donors with a single match-run and at least 1 kidney transplanted locally and adult, first-time, kidney-only transplant candidates receiving at least 1 offer for a locally transplanted deceased donor kidney. Data were analyzed from March 1, 2022 to March 28, 2023. Demographic and clinical characteristics of donors and recipients. The outcome of interest was kidney transplantation into the highest-priority candidate (defined as transplanted after zero declines for local candidates in the match-run) vs a lower-ranked candidate. This study assessed 26 579 organ offers from 3136 donors (median [IQR] age, 38 [25-51] years; 2903 [62%] men) to 4668 recipients. Transplant centers skipped their highest-ranked candidate to place kidneys further down the match-run for 3169 kidneys (68%). These kidneys went to a median (IQR) of the fourth- (third- to eighth-) ranked candidate. Higher kidney donor profile index (KDPI; higher score indicates lower quality) kidneys were less likely to go to the highest-ranked candidate, with 24% of kidneys with KDPI of at least 85% going to the top-ranked candidate vs 44% of KDPI 0% to 20% kidneys. When comparing estimated posttransplant survival (EPTS) scores between the skipped candidates and the ultimate recipients, kidneys were placed with recipients with both better and worse EPTS than the skipped candidates, across all KDPI risk groups. In this cohort study of local kidney allocation at isolated transplant centers, we found that centers frequently skipped their highest-priority candidates to place kidneys further down the allocation prioritization list, often citing organ quality concerns but placing kidneys with recipients with both better and worse EPTS with nearly equal frequency. This occurred with limited transparency and highlights the opportunity to improve the matching and offer algorithm to improve allocation efficiency.

Risk factors of intraoperative blood transfusion in pediatric craniotomy for intracranial tumor resection: a 10-year analysis.

A pediatric craniotomy for intracranial tumor removal brings both high bleeding and blood transfusion risks. The aim of this study was to identify the risk factors for intraoperative blood transfusion in this procedure. The secondary outcome was to investigate postoperative complications and clinical outcomes related to blood transfusion. A retrospective analysis was performed among children who underwent a craniotomy for brain tumor resection at a tertiary hospital over a 10-year period. The pre- and intraoperative variables were examined and compared between the transfusion and nontransfusion groups. One hundred seventy-two patients (58%) received intraoperative blood transfusions among a total of 295 craniotomies in 284 children. Factors associated with blood transfusion were body weight ≤ 20 kg (adjusted odds ratio [AOR] 5.286, 95% confidence interval [CI] 2.892-9.661; p < 0.001), American Society of Anesthesiologists (ASA) physical status III-IV (AOR 6.860, 95% CI 1.434-32.811; p = 0.016), preoperative hemoglobin ≤ 11 g/dl (AOR 3.610, 95% CI 1.406-9.265; p = 0.008), tumor size ≥ 45 mm (AOR 2.117, 95% CI 1.214-3.693; p = 0.008), and duration of operation ≥ 6 hours (AOR 3.816, 95% CI 1.736-8.385; p = 0.001). Postoperative infection of other systems, other complications, duration of mechanical ventilation, and intensive care unit and hospital length of stay were found to be significantly higher in the transfusion group. Lower body weight, higher ASA physical status, preoperative anemia, large tumor size, and prolonged duration of surgery were found to be significant factors for predicting intraoperative blood transfusion in pediatric craniotomy. The identification and modification of risks from intraoperative blood transfusion can be beneficial in reducing the probability of transfusion and improving allocation efficiency of limited blood component resources.

Virtual crossmatch for deceased donor kidney transplantation in the United States: A survey of histocompatibility lab directors and transplant surgeons

Virtual crossmatch (VXM) is used as an alternative to or in conjunction with a cell-based physical crossmatch (PXM) for assessing HLA (human leukocyte antigen) compatibility prior to deceased donor kidney transplantation (DDKT). Data on practice patterns and perceptions regarding VXM use in the US are limited. We performed a survey of US HLA directors and transplant surgeons regarding HLA testing and crossmatch strategies. 53 (56 %) HLA directors and 68 surgeons (representing ∼ 23 % of US transplant centers) completed the survey. Both groups agreed that VXM could reduce cold ischemia time (CIT), costs and improve allocation efficiency. VXM use increased following the 2021 kidney allocation change. Reducing CIT was the primary reason for favoring VXM over PXM. Preference for VXM reduced as candidates’ panel reactive antibodies increased. Regulations, program policies and limitations of HLA technology were cited as important reasons for preferring PXM over VXM. Surgeons reported similar perceptions, but findings are limited by the low response rate. Finally, half the labs reported lacking specific protocols for VXM use. In conclusion, improved HLA technology and protocols along with changes to institutional procedures and policy regulations are needed for safer expansion of VXM in DDKT.

The role of bypass filters in deceased donor kidney allocation in the United States

Kidney transplant centers set organ offer filters enabling all candidates at their center to be bypassed during allocation of deceased donor kidneys from the UNOS Organ Center. These filters aim to increase allocation efficiency by preemptively screening out offers unlikely to be accepted. National data were used to compare filter settings of 175 centers in 2007 and in 2019. We examined characteristics of centers whose settings became increasingly restrictive over time, and associations between filter settings and organ offer acceptance. Overall, centers became more open to receiving offers over time, from a median 62% of filters open to receiving national offers in 2007 to 73% in 2019. Intravenous drug use filter settings changed most, from 63 to 153 willing centers. Centers with more open filter settings had higher transplant volume and offer acceptance ratios across all risk categories despite preemptively screening out fewer offers compared to centers with less open settings, but similar transplant rates. There was significant geographic heterogeneity in the distribution of centers with more open filter settings. Current center bypass filters may impact patients' access to transplantation without achieving their full potential for improving allocation efficiency.

Adapting Serosurveys for the SARS-CoV-2 Vaccine Era.

Population-level immune surveillance, which includes monitoring exposure and assessing vaccine-induced immunity, is a crucial component of public health decision-making during a pandemic. Serosurveys estimating the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in the population played a key role in characterizing SARS-CoV-2 epidemiology during the early phases of the pandemic. Existing serosurveys provide infrastructure to continue immune surveillance but must be adapted to remain relevant in the SARS-CoV-2 vaccine era. Here, we delineate how SARS-CoV-2 serosurveys should be designed to distinguish infection- and vaccine-induced humoral immune responses to efficiently monitor the evolution of the pandemic. We discuss how serosurvey results can inform vaccine distribution to improve allocation efficiency in countries with scarce vaccine supplies and help assess the need for booster doses in countries with substantial vaccine coverage.

Efficiency of Palm Oil Companies in Indonesia: A DEA Approach

The purpose of this study was to investigate technical, allocative, cost and scale efficiency of palm oil companies in Indonesia. The data used cross sectional data on Indonesian Manufacturing Industries that were collected in the annual surveys by Indonesia’s Central of Statistics/BPS for 2015 which is registered under KBLI code 10431. Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (DMUs). The input of the palm oil production system are capital, labor, materials, land and energy. While output include industrial product in terms of CPO/PKO and biogas. The finding shows that Riau Island, North Sulawesi and Papua have high score of efficiency in terms of TE, AE, CE and SE, while West Java and Banten province at low level in terms of cost and allocative efficiency. Nevertheless, on nationwide the score of allocative and cost efficiency still low. Hence, all the input efficiencies still need to be improved, and also reduce cost of production. Beside that, the companies should make an improvement by enhancing management capabilities and improving allocation efficiency.

A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

Increasing the number of cores is one of the most effective methods to enhance performance. However, an extensive experimental study on mobile edge computing (e.g., Android devices) indicates that the memory management system has gradually become a key performance bottleneck. Studies on improving memory management mainly focus on exploring the trade-off between avoiding fragmentation and improving allocation efficiency. From our previous research, we know that the fragmentation is no longer a crucial bottleneck; instead, inter- and intra-thread behavior should be focused on, and thus, we introduce memory management based on thread behaviors (MMBTB). Unfortunately, it lacks a unified optimization program interface and good architecture. Consequently, in this paper, we propose a memory resource management at operating system (OS) layer of mobile edge computing, called the thread-oriented memory management layer (TOMML) to address this problem, which follows the microkernel architecture pattern and can meet the user’s requirements for selecting plug-ins to achieve different optimization goals. This paper is divided into several sections as follows. First, we demonstrate the efficiency of TOMML through theoretical simulation and experimentation. The experimental result is that TOMML can improve memory allocation efficiency by 12%–20%. Furthermore, we introduce a plug-in to save power, which can further promote 6%–25% bank free compared with previous excellent research.

Optimization of County-Level Land Resource Allocation through the Improvement of Allocation Efficiency from the Perspective of Sustainable Development.

Land resources provide stable support for economic development in China. However, due to the scarcity of land, the contradiction between agricultural land protection and construction land expansion is prominent. Under such circumstances, optimal allocation of land resources between agricultural and nonagricultural uses is vitally important. In view of the fact that land resources are indispensable inputs for production activities in agricultural and nonagricultural sectors, reducing the efficiency loss of land resource allocation between agricultural and nonagricultural uses is the only way to optimize the process. Counties are the basic administrative units in China, and their improvement of allocation efficiency will help optimize nationwide land resource allocation. This paper constructs models for estimating county-level land resource allocation efficiency from the perspective of sustainable development and searches for countermeasures to improve allocation efficiency. W County is used as an example to demonstrate how to choose these targeted countermeasures. It is concluded that the best way to optimize county-level land resource allocation between agricultural and nonagricultural uses can be found by estimating allocation efficiency from the perspective of sustainable development.

Strategy-Proof Mechanism for Provisioning and Allocation Virtual Machines in Heterogeneous Clouds

In this paper, we address the problem of heterogeneous physical machines resource management (HPMRM); that is, providing and allocating multiple virtual machine (VM) instances from heterogeneous physical machines to maximize social welfare. Although existing allocation mechanisms allocate VMs to users through the single-mapping mechanism, such allocations cannot guarantee maximum social welfare or efficient utilization of multiple types of resources for cloud providers. Thus, we consider the multi-mapping mechanism, which permits mapping VMs allocated to one user to physical machines for VM provisioning and allocation. This can result in improved social welfare and lead to less resource fragmentation. We formulate the HPMRM problem in an auction-based setting, and design optimal and approximate mechanisms to solve it. In addition, we show that our proposed mechanism is strategy-proof; that is, our proposed mechanism drives the system into an equilibrium where no users have incentives to maximize their own profit by untruthfully reporting their requests. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. We also perform experiments to investigate the performance of our proposed approximation mechanism compared to the optimal mechanism. Experimental results demonstrate that our proposed approximation mechanism can obtain near optimal solutions and significantly improve allocation efficiency, while generating greater social welfare.

Kidney Donor Profile Index Does Not Accurately Predict the Graft Survival of Pediatric Deceased Donor Kidneys.

The new deceased donor kidney allocation algorithm uses a Kidney Donor Profile Index (KDPI) based on donor characteristics to predict graft survival and divides kidneys into 4 quality groups (ie, KDPI-A, -B, -C, and -D). Pediatric kidneys constitute 10% to 12% of deceased donor kidneys. We hypothesized that KDPI would not accurately predict pediatric donor graft survival and superior predictive models could be created. Scientific Registry of Transplant Recipients data for years 2000 to 2010 for transplants from child (<10 years) and adolescent (10-17 years inclusive) donors into first-time adult recipients were analyzed with graft failure as the principle outcome. Two novel indices, Child Donor Index (CDI) and Adolescent Donor Index (ADI), were developed using stepwise variable deletion to identify significant model covariates in a Cox Regression. Pediatric donor kidneys were then classified into the 4 quality groups based on both KDPI and CDI/ADI scores. The performance of the KDPI, CDI, and ADI models were compared with respect to the 4 quality groups defined by the new allocation system. The KDPI did not effectively discriminate between quality groups (P > 0.05 for all but 1 comparison) in Kaplan-Meier survival analyses. The CDI and ADI included novel variables (eg, body mass index percentiles) and successfully discriminated between quality groups (P < 0.05 by log rank test). The Net Reclassification Index showed improvement when switching from KDPI to CDI and ADI, with values of 0.09 (P < 0.001) and 0.073 (P < 0.001), respectively. The KDPI does not accurately predict pediatric kidney graft survival. Alternative indices can improve allocation efficiency.

Limited public resources allocation model based on social fairness using an extended VIKOR method

Purpose Different from manufacturing resources allocation problems, the prices and amounts of limited public service resources could not be changed with the consumers’ requirements and social fairness is the most important objective for improving allocation efficiency. To measure social fairness reasonably, the purpose of this paper is fourfold: first, divide social fairness into longitudinal comparative fairness and crosswise comparative fairness, therefore providing their calculation formula and describing the comprehensive fair degree by using the interval numbers. Second, the comparison regulations of interval numbers are given and the corresponding features are also described. Third, an extension of VIKOR method is put forward for evaluating social fairness of different allocation alternatives with interval numbers. Finally, a numerical example illustrates the proposed method and clarifies the main results developed in the paper. Design/methodology/approach In this paper, the author depicts the social fair degree as an interval number, and thus proposes the comparison method between any two interval numbers. Based on the basis procedure of the VIKOR method, the paper proposes an extension of the fuzzy VIKOR method with the interval numbers to rank and select the compromise allocation alternatives. Finally, a numerical example illustrates the practicability of the proposed method. Findings The comparison of interval numbers is very important when the author evaluates the decision alternatives. Through analyzing the present comparison methods, the paper proposes the simple method of comparing the interval numbers, which can obtain the same results with the above two methods. The fuzzy VIKOR method, a popular multi-criteria decision-making method, focusses on ranking and selecting from a set of alternatives in a fuzzy environment. For the fuzzy value, the paper also proposes the extension of the VIKOR method to perform an evaluation and get the compromise alternatives. Originality/value According to the huge customers’ requirement, how to improve their social fair degree has become the focus in public service resources allocation, where the social fair degree may be a comprehensive concept which includes the fair degree compared with all the other allocation alternatives and the fair degree compared with the other small group under the same allocation alternative. In the paper, the author defines the above two types of fair degree and then depicts the comprehensive fair degree as their integration, which will be interval numbers.

Globalization, Financial Crisis and Contagion: Time-Dynamic Evidence from Financial Markets of Developing Countries

Financial integration among economies has the benefit of improving allocation efficiency and diversifying risk. However the recent global financial crisis, considered as the worst since the Great Depression has re-ignited the fierce debate about the merits of financial globalization and its implications for growth especially in developing countries. This paper examines whether equity markets in emerging countries were vulnerable to contagion during the recent financial meltdown. Findings show: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru, Venezuela and Columbia, Latin American countries were least affected; (3) Africa and Middle East emerging markets were averagely contaminated with the exceptions of Kenya, Namibia, Nigeria, Morocco, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon. Results have two important policy implications. Firstly, we confirm that Latin America was most prepared to brace the financial crisis, implying their fiscal and monetary policies are desirous of examination and imitation. Secondly, we have confirmed that strategic opening of the current and capital accounts based on empirical evidence for a given region/country as practiced by India is a caution against global economic and financial shocks.

Speculations in option markets enhance allocation efficiency with heterogeneous beliefs and learning

Many studies investigate the impact of heterogeneous beliefs in the first moment, while very few in the second moment. This is partially due to continuous-time setup which makes it difficult to incorporate heterogeneous beliefs in the second moment. In a two-period exponential–normal model with Bayesian learning, I demonstrate that heterogeneous prior variances give rise to the economic value of option markets. Investors speculate in option market and public information improves allocation efficiency of markets only when there is heterogeneity in prior variances. Heterogeneity in mean is neither a necessary nor a sufficient condition for generating speculations in option markets. With heterogeneous beliefs, options are non-redundant assets which can facilitate side-betting and enable investors to take advantage of the disagreements and the differences in confidence. This fact leads to a higher growth rate in the investors’ certainty equivalents and, thus, a higher equilibrium interest rate. Furthermore, option exhibits a unique feature of enabling signal precision to affect the ex ante risk premium of underlying asset, which quadratic derivative and stock do not have.