Data Files Research Articles

Credit Market Frictions and the Linkage Between Dispersion and Macro Uncertainty

Cross-sectional dispersion and macro uncertainty (volatility of aggregate economic variables) are conceptually distinct. However, empirically, they both comove and are countercyclical. This paper builds a general equilibrium model and demonstrates that credit market frictions allow cross-sectional dispersion to drive time-varying macro uncertainty endogenously. In the model, as firm-level productivity becomes more dispersed, more firms are pushed to the left tail of the productivity distribution, resulting in more defaults and a depletion in aggregate net worth. This mechanism generates countercyclical leverage and aggregate volatility. The model implies that the government can inject equity in economic downturns to stabilize aggregate volatility. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00166 .

Designing File Storage and Sharing System

Background: Files stored on a network are anticipated to be exchanged securely and efficiently. An internet-based file management platform that enables users to securely create, save, organize, collaborate, and share files. Sharing is a recently developed service idea that is frequently sought after by researchers and students. The suggested work offers many forms of data files, including documents and multimedia files. File storage has been a widely used storage method for many years. Materials and Methods: The proposed method used to build the website is Visual Studio 2015 with SQL server Database to provide file storage with different data type storage and it provides a service as an IT repository file storage system to help student and teachers to communicate with each other in academic way for publishing files in easy and in a flexible manner. Results: The proposed system results showed that the website can provide file upload with supported files are ".doc", ".docx", ".pptx", ".pdf", ".zip", ".rar", ".mp4", ".flv", ".avi", ".PDF", ".DOCX" and ".DOC". Results showed that the system uploaded file size 5M, 10 and 25 M smoothly. Conclusion: The work provides a flexible way to store and sharing files for different fields and we use it for education purposes. The results showed that the proposed system is very flexible in the process of uploading various files at a very high speed, due to the ease of uploading files to the server and accessing them by various students in the college.

A Computation of the Ninth Dedekind Number Using FPGA Supercomputing

This manuscript makes the claim of having computed the \(9\) th Dedekind number, D(9). This was done by accelerating the core operation of the process with an efficient FPGA design that outperforms an optimized 64-core CPU reference by 95 \(\times\) . The FPGA execution was parallelized on the Noctua 2 supercomputer at Paderborn University. The resulting value for D(9) is 286386577668298411128469151667598498812366. This value can be verified in two steps. We have made the data file containing the 490 M results available, each of which can be verified separately on CPU, and the whole file sums to our proposed value. The paper explains the mathematical approach in the first part, before putting the focus on a deep dive into the FPGA accelerator implementation followed by a performance analysis. The FPGA implementation was done in Register-Transfer Level using a dual-clock architecture and shows how we achieved an impressive FMax of 450 MHz on the targeted Stratix 10 GX 2,800 FPGAs. The total compute time used was 47,000 FPGA hours.

The Impact of Code Bundling on Medicare Volume and Reimbursements Within Endoscopic Sinus Surgery.

In 2018, Medicare introduced new codes to the Endoscopic Sinus Surgery (FESS) and balloon sinus dilation (BSD) families of Current Procedural Terminology (CPT) codes. Using the Medicare Part B National Summary Data File from 2010 to 2022, an interrupted time-series analysis examined trends in volume and reimbursements before and after 2018. Prior to 2018, volume and reimbursements for FESS grew at a mean rate of 2.5% ± 2.2% per year and 6.9% ± 6.6% per year, respectively, before reimbursements decreased significantly in 2018 by -13.9% (P = .014), leading to a stabilization of volume (growth of 0.72%, P = .602). Volume and reimbursements for BSD saw rapid growth from 2011 to 2015 which plateaued prior to the introduction of bundled codes and did not appear to change significantly in 2018 (-0.6%, P = .306 and 11.9%, P = .392, respectively). In addition to concurrent devaluation of FESS and BSD codes, bundling appears to have further contributed to falling reimbursements in rhinology.

Increased Identification of Vaccines for Vaccine Safety Surveillance Through Linkage With the Minnesota Immunization Information Connection as of December 31, 2023.

The HealthPartners' Vaccine Safety Datalink (VSD) team maintains standardized files of vaccines from medical and pharmacy claims and electronic health records (established data sources) for safety surveillance. Since 2021, for selected vaccines, data from the Minnesota Immunization Information Connection (MIIC), Minnesota's immunization information system, have been added to the HealthPartners' VSD files. We examined how MIIC data have enhanced the identification of novel and routine vaccines. We describe the approach to incorporating MIIC data. We determined and compared the number and proportion of vaccines identified from established data sources with the additional capture of vaccine data identified from MIIC, in which age group and period of observation varied by vaccine. As of December 31, 2023, of 1 099 411 people in the HealthPartners' VSD cohort, 1 001 400 people (91%) were linked with an MIIC record. Across all data sources, for the full cohort, >2.7 million COVID-19 vaccine doses were recorded since 2020, >4000 mpox vaccine doses since 2022, >7.3 million influenza vaccine doses since 2004, >600 000 human papillomavirus (HPV) vaccine doses since 2006, and >1.1 million diphtheria and tetanus toxoids and acellular pertussis (DTaP) vaccine doses since 2004. For COVID-19 vaccines, about 30% of vaccine doses were exclusively captured from MIIC, with the remaining 70% from established data sources. For the mpox vaccine, about 42% were exclusively from MIIC. For influenza, HPV, and DTaP vaccines, about 20%, 14%, and 17%, respectively, were exclusively identified from MIIC. Incorporation of data from state immunization information systems into existing vaccine data files can enhance monitoring on the safety of novel vaccines administered outside traditional health care settings and can enhance data quality for routine childhood and adult vaccines.

The Digital Lives of the Poor: Entertainment Traps and Information Isolation

Smartphones have enabled the delivery of life-improving information services to base-of-the-pyramid (BOP) consumers. However, little is known about how the poor interact with the digital world. Through a novel app we developed to investigate real-time smartphone usage, we identify an unnoticed barrier to digital information access by the poor—data shortages. By analyzing over 9.4 million minutes of smartphone usage data from 929 residents of a Mumbai settlement, we find that entertainment consumes 61% of their phone time. Our data reveal that under universally adopted monthly data plans, low-income individuals binge on YouTube and social media, resulting in data shortages and information isolation in the late-plan period. We offer a practical operational solution to this problem—shorter data-replenishment cycles—which serve as a commitment device to curb binge usage. We randomly assign participants to a “capped plan”—with daily data usage caps—or a standard (monthly) plan. Assignment to the capped plan increases late-plan access of invites to health camps sent via WhatsApp, increases attendance at these in-person camps by 27%, and reduces social media binge usage. Most participants (particularly those with low self-control and high fear of missing out) prefer the capped plan, even when costlier—clearly signaling demand. Because capped plans are inherently cheaper to provide, offering them could enable providers to increase BOP customer value and expand access. Our results suggest an opportunity to amplify the impact of life-improving services targeted at the poor by leveraging users’ interactions with smartphone technology. This paper was accepted by Victor Martínez de Albéniz, operations management. Funding: Financial support from The Wheeler Institute for Business & Development is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4989 .

A robust audio steganography technique based on image encryption using different chaotic maps

The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

A Supervised Approach to Zero-Shot Learning for Field Classification of Texts: Leveraging File Data for Improved Text Categorisation

A Supervised Approach to Zero-Shot Learning for Field Classification of Texts: Leveraging File Data for Improved Text Categorisation

Imperfect Targeting and Advertising Strategies

The ability to successfully target consumers has been substantially affected by recent developments in digital markets, such as improvements in tracking technologies or General Data Protection Regulation. In this paper, we set up a game theoretic model to examine the implications of such changes in targeting success on firms’ targeting strategies and profits. We explicitly consider that firms can target different consumer groups—that is, high- or low-valuation consumers—and that targeting is imperfect—that is, a firm may not reach the intended consumer. We find that a higher targeting success rate has a nonmonotone effect on firms’ profits—that is, lowering profits if targeting is rather imprecise but raising profits if targeting is relatively accurate. If the probability of successful targeting is low, firms target high-valuation consumers (competition for cherries). More fine-tuned targeting then amplifies competition and decreases profits. Instead, if targeting is sufficiently precise, more fine-tuned targeting increases profits because firms segment the market by targeting different consumer groups. Improvements in the targeting ability also have profound effects on the number of products a firm offers and on the profitability of offering products that appeal not only to a single consumer group. First, although increased targeting success raises the attractiveness of introducing an additional product, it also heats up competition. A firm may therefore optimally reduce the number of products it sells if targeting success increases. Second, if products have a broader appeal, market segmentation is more likely to occur. However, aggregate profits are lower compared with when products are narrow. This paper was accepted by Joshua Gans, business strategy. Funding: Financial support from the German Research Foundation [DFG Project 462020252] is gratefully acknowledged. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.03632 .

Does Supply Concentration Encourage Cooperation? Evidence from Airlines

One of a firm’s key strategic decisions is whether to concentrate its input purchases in a small number of suppliers versus spreading them among many suppliers. We propose that supplier concentration solves an interfirm free-riding problem: By internalizing externalities between suppliers, it incentivizes buyer-supplier cooperation. Guided by a simple theoretical model, we investigate this hypothesis on slot exchanges between major airlines and their outsourced regional airline partners during inclement weather, a setting where externalities between suppliers are ubiquitous. We find robust evidence that a regional airline engages in more frequent slot exchanges with its major airline partner when it operates a larger share of the major’s outsourced flights. We also find that this positive effect of concentration on mutual cooperation increases in the size of the externalities between regionals. Our results suggest that, in contrast with Porter’s classic five forces framework, supplier concentration can serve as a governance instrument for buyer-supplier collaborations. More broadly, our paper provides novel evidence on task concentration as a tool to solve free-riding problems in multiagent settings. This paper was accepted by Joshua Gans, business strategy. Funding: This paper received financial support from the Spanish Research Agency (MICIU/AEI) [Grants PID2022-136983NB-I00, MCIN/AEI /10.13039/501100011033]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03455 .

DoeDat: Enhanced Roundtripping of Crowdsourced Specimen Annotations

The DoeDat platform was launched by Meise Botanic Garden in 2018 to capture label data from imaged herbarium specimens by inviting volunteer contributors (Groom et al. 2018). It has since facilitated data capture from specimens of other natural history collections (Helminger et al. 2020, Mitrache et al. 2023), as well as digitised content from various other disciplines, such as historical photographs, posters and postcards. Volunteers may simply transcribe handwritten and/or typed text, but often also interpret the sparse and scattered information on the image, including trying to georeference its original location. As of April 2024, almost 650.000 tasks have been completed, of which more than 470.000 were herbarium specimens from Meise. DoeDat supports domain standards, including Darwin Core, and follows most of the currently drafted MIDS (Minimum Information about a Digital Specimen) guidelines as to what data is captured for natural history specimens. However, images have to be pre-loaded into the server storage for each project and captured data gets exported as one or more CSV files per project. These data files then still need to be processed before they can be ingested into the local management system (Engledow et al. 2023). Often the data are also subjected to additional quality control before they get openly published. This can result in the pipeline from image to openly published annotations being quite time and labour-consuming. As the biodiversity infrastructure landscape moves more towards FAIR (Findable, Accessible, Interoperable, Reusable) open data, DoeDat will adapt accordingly. This includes digital objects that are easy to annotate. Furthermore, image servers following IIIF (International Image Interoperability Framework) greatly standardise the access and portability of media content, drastically changing the way images are being dealt with. We envision upgrading the DoeDat platform to load images and any required metadata as IIIF manifests, greatly streamlining the process of adding new content and tracking provenance. The transcriptions should be accessible for external systems, loading the updated image manifests and publishing them as annotations such as nanopublications.

Role of inpatient rehabilitation facility functional measures to predict community discharge after stroke.

This study investigated the association between stroke severity, functional status measured by the Inpatient Rehabilitation Facility-Patient Assessment Instrument (IRF-PAI), and community discharge from IRF. Aim one examined the association between National Institutes of Health Stroke Scale (NIHSS) scores measured during the acute care stay and IRF admission functional status, measured by the admission IRF-PAI self-care and mobility functional measures, to deduce if functional measures can serve as a proxy for stroke severity. Aim two investigated the ability of the NIHSS and IRF-PAI admission functional measures to predict community discharge from IRF after stroke. Retrospective cohort study using electronic health records and Uniform Data System. Medical Record file data from January 1, 2018, to December 30, 2019. Academic hospital-based IRF. Five hundred forty-four patients transferred from acute care hospital to IRF after an ischemic or hemorrhagic stroke. Exclusion criteria included a transient ischemic attack, discharge against medical advice, death during IRF stay, or readmission to acute care within 48 hoursof IRF admission. Not applicable. Admission IRF-PAI self-care and mobility scores and discharge status from IRF. Of the 544 patients, 76.7% had community discharge. NIHSS scores were significantly associated with IRF-PAI admission self-care scores across each NIHSS stroke category. There was no statistically significant association between NIHSS and IRF-PAI admission mobility score. IRF admission self-care and mobility scores were each statistically significant predictors of community discharge (odds ratio [OR] = 1.10, 95% confidence interval [CI]: 1.03-1.17; OR = 1.10, CI: 1.03-1.18, respectively). NIHSS scores were not a statistically significant predictor of community discharge (OR = 0.70, CI: 0.47-1.04) from IRF. IRF-PAI self-care functional measure is associated with the NIHSS and can serve as a proxy for stroke severity. IRF-PAI self-care and mobility measures each predict community discharge.

Local Agglomeration and Household Mortgage Debt

Using detailed household data, we find that households working in locally agglomerated economies have high mortgage loans and are more likely to have mortgage loans. Channel testing shows that local agglomeration increases housing and mortgage demand as well as mortgage supply. The increase in mortgage loans is driven by purchases of larger houses and smaller down payments. Mechanism analyses document that local agglomeration affects mortgage loans by increasing career upward potential and providing downside protection. Specifically, we find that the impact of local agglomeration is stronger for skilled employees with enhanced prospects. In addition, local agglomeration weakens the negative relation between unemployment and mortgage loans, which supports the downside-protection role of local agglomeration. These results hold under instrumental variables analysis, difference-in-difference analysis, and a set of robustness checks. Overall, our findings highlight the importance of local labor market composition in household mortgage debt. This paper was accepted by Kay Giesecke, finance. Funding: F. F. Chen acknowledges the financial support provided by the Fundamental Research Funds for the Central Universities; the MOE Project of Key Research Institute of Humanities and Social Science in University [Grant 22JJD790094]; and the 111 Project [Grant B18033]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03486 .

How Does a Health Insurance Program Covering 500 Million Poor Impact Credit Market Outcomes?

I study the impact of the world’s largest publicly funded health insurance plan for the poor on credit markets. India launched a health insurance plan that covered 500 million beneficiaries. The fact that opposition-ruled states did not implement the program for political reasons allows me to compare border districts of implemented and nonimplemented states within a difference-in-differences framework. I find that loan delinquency reduced significantly in implemented districts. Increased liquidity for loan repayment seems to be the mechanism at work. I rule out the federal-level ruling party’s influence as an explanation. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01039 .

Unleashing the Crowd: The Effect of Social Networks in Crowdfunding Markets

We identify the crucial role social networks play in crowdfunding markets. Investors are 50% more likely to fund projects that their peers support and are 11.2% more likely to fund projects from regions where they share strong social ties, given a one-standard-deviation change in the variables. More influential peers exert a greater influence, especially in the case of riskier projects, and the peer effects are amplified in crowdfunding platforms that prioritize transparency and accountability. Social ties transmit information about economic conditions in project locations, and they complement the influence of peer effects. Furthermore, the social network effects affect project funding outcomes and can be particularly valuable in mitigating the adverse effects of natural disasters. Our findings suggest that social networks play a significant role in crowdfunding markets by increasing investor awareness, disseminating information, and ultimately influencing capital allocations. This paper was accepted by Kay Giesecke, finance. Funding: L. Peng acknowledges the Keynes Fund, the Krell research grant, and the Wassermann research grant for financial support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01364 .

Do Consumers Respond to Social Movements? Evidence from Gender-Stereotypical Purchases After #MeToo

This paper explores whether and how consumers respond to global social movements challenging systemic discrimination and stereotypes. We examine the impact of the #MeToo movement on the market for products with stereotypical markers of femininity. Our analysis of high-frequency stockout and price data from a leading global fashion retailer spans from January 2017 to December 2018 and covers 32 countries, or 89% of the Organization for Economic Cooperation and Development (OECD) population. Using a triple-difference approach across time, countries, and products, we identify changes in product-level stockouts, corresponding to a drop in the purchase of stereotypically “feminine” items. Our findings suggest a sudden shift in demand, with no immediate adjustments in product assortments or pricing. We discuss potential driving mechanisms, as well as implications for product management and marketing in marketplaces where segmentation is gender based. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02352 .

Good Prophets Know When the End Is Near

We consider a class of online decision-making problems with exchangeable actions, where in each period a controller is presented an input type drawn from some stochastic arrival process. The controller must choose an action, and the final objective depends only on the aggregate type-action counts. Such a framework encapsulates many online stochastic variants of common optimization problems with knapsack, bin packing, and generalized assignment as canonical examples. In such settings, we study a natural model-predictive control algorithm. We introduce general conditions under which this algorithm obtains uniform additive loss (independent of the horizon) compared with an optimal solution with full knowledge of arrivals. Our condition builds on the recently introduced compensated coupling technique, providing a unified view of how uniform additive loss arises as a consequence of the geometry of the underlying decision-making problem. Our characterization allows us to derive uniform loss algorithms for several new settings, including the first such algorithm for online stochastic bin packing. It also lets us study the effect of other modeling assumptions, including choice of horizon, batched decisions, and limited computation. In particular, we show that our condition is fulfilled by the above-mentioned problems when the end of the time horizon is known sufficiently long before the end. In contrast, if at a late stage, there is still uncertainty about the end of the time horizon we show that such uniform loss guarantees are impossible to achieve. We use real and synthetic data to illustrate our findings. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was supported by the Division of Mathematical Sciences [Grant 1839346]; the Division of Electrical, Communications and Cyber Systems [Grant 1847393]; and the Army Research Laboratory [Grant W911NF-17-1-0094]. The authors also gratefully acknowledge support from the NSF [Grants ECCS-1847393, CNS-195599, and DMS-1839346], AFOSR [Grant FA9550-23-1-0068], an ARL award [Grant W911NF-17-1-0094], and ARO MURI [Grant W911NF-19-1-0217]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04307 .

Stock Return Autocorrelations and Expected Option Returns

We show that the return autocorrelation of underlying stock is an important determinant of expected equity option returns. Using an extended Black-Scholes model incorporating the presence of stock return autocorrelation, we demonstrate that expected returns of both call and put options are increasing in the return autocorrelation coefficient of the underlying stock. Consistent with this insight, we find strong empirical support in the cross-section of average returns of equity options. Our paper highlights the necessity to control for stock return autocorrelation when studying option return predictability. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03071 .

Nonsubstitutable Consumption Growth Risk

Standard applications of the consumption-based asset pricing model assume that goods and services within the nondurable consumption bundle are substitutes. We estimate substitution elasticities between different consumption bundles and show that households cannot substitute energy consumption by consumption of other nondurables. As a consequence, energy consumption affects the pricing function as a separate factor. Variation in energy consumption betas explains a large part of the premia related to value, investment, and operating profitability. For example, value stocks are typically more energy intensive than growth stocks and thus riskier, because they suffer more from the oil supply shocks that also affect households. This paper was accepted by Lukas Schmid, finance. Funding: C. Schlag gratefully acknowledges research and financial support from the Leibniz Institute for Financial Research SAFE. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01269 .

Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem. We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00492 .