From Happiness Data to Economic Conclusions

There has been no progress in the modest interest in economic history in the top five economics journals since the 1970s. This is despite an apparent newfound enthusiasm for the field among economists. This chapter explains this contradiction by highlighting the differences between research in economic history and economics that exploits historical data. In so doing, the author defines what, in his view, constitutes good economic history research.

Almost all consumer and firm transactions are achieved using computers and as a result gives rise to increasingly large amounts of data available for analysts. The gold standard in Economic data manipulation techniques matured during a period of limited data access, and the new Large Data Analysis (LDA) paradigm we all face may quickly obfuscate most tools used by Economists. When coupled with an increased availability of numerous unstructured, multi-modal data sets, the impending 'data tsunami' could have serious detrimental effects for Economic forecasting, analysis, and research in general. Given this reality we propose a decision-aid framework for<i> <strong>Augmented-LDA (A-LDA) </strong></i>– a synergistic approach to LDA which combines traditional supervised, rule-based Machine Learning (ML) strategies to iteratively uncover hidden sources in large data, the artificial neural network (ANN) Unsupervised Learning (USL) at the minimum Helmholtz free energy for isothermal dynamic equilibrium strategies, and the Economic intuitions required to handle problems encountered when interpreting large amounts of Financial or Economic data. To make the ANN USL framework applicable to economics we define the temperature, entropy, and energy concepts in Economics from non-equilibrium molecular thermodynamics of Boltzmann viewpoint, as well as defining an information geometry, on which the ANN can operate using USL to reduce information saturation. An exemplar of such a system representation is given for firm industry equilibrium. We demonstrate the traditional ML methodology in the economics context and leverage firm financial data to explore a frontier concept known as behavioral heterogeneity. Behavioral heterogeneity on the firm level can be imagined as a firm's interactions with different types of Economic entities over time. These interactions could impose varying degrees of institutional constraints on a firm's business behavior. We specifically look at behavioral heterogeneity for firms that are operating with the label of 'Going-Concern' and firms labeled according to institutional influence they may be experiencing, such as constraints on firm hiring/spending while in a Bankruptcy or a Merger procedure. Uncovering invariant features, or <i><strong>behavioral data metrics </strong></i>from observable firm data in an economy can greatly benefit the FED, World Bank, etc. We find that the ML/LDA communities can benefit from Economic intuitions just as much as Economists can benefit from generic data exploration tools. The future of successful Economic data understanding, modeling, simulation, and visualization can be amplified by new A-LDA models and approaches for new and analogous models of Economic system dynamics. The potential benefits of improved economic data analysis and real time decision aid tools are numerous for researchers, analysts, and federal agencies who all deal with increasingly large amounts of complex data to support their decision making.

This volume contains contributed papers from the 7th international conference on ‘Applications of Physics in Financial Analysis (APFA)’ held at Tokyo on 1–5 March 2009. The conference was organized jointly by Tokyo Institute of Technology and Hitotsubashi University with support from the Research Institute of Economy, Trade, and Industry (RIETI), Physical Society of Japan, Japanese Economic Association, Information Processing Society of Japan, Japanese Society for Artificial Intelligence, and Japan Association for Evolutionary Economics. The first APFA conference (APFA1) was held in 1999 at Dublin, followed by APFA2 at Liege in 2000, APFA3 at London in 2001, APFA4 at Warsaw in 2003, APFA5 at Torino in 2006, and APFA6 at Lisbon in 2007. The 7th APFA conference, which is the first meeting held outside Europe, was attended by 223 researchers in physics and economics from 23 countries world-wide. In keeping with past APFA conferences, we paid special attention to issues in financial markets, which turned out to be very timely. The conference was held in March 2009, in the middle of the global financial crisis that originally started in the US and spread quickly to every corner of the world.The topic of the conference is ‘New Approaches to the Analysis of Large Scale Business and Economic data’. The rapid development of information and communication technology has enabled financial/non-financial firms to keep detailed records of their business activities in the form of, for example, tick-by-tick data in financial markets, point-of-sale (POS) data on individual household's purchasing activity, and interfirm network data describing relationships among firms in terms of suppliers/customers transactions and ownerships. This growth in the scope and amount of business data available to researchers has led to a far-reaching expansion in research possibilities. Researchers not only in social sciences but also in physics, mathematics, and information sciences have recently become interested in such datasets, conducting empirical investigations about various aspects of economic activities. Specifically, they have searched for regularities and ‘laws’ akin to the ones in natural science, successfully producing fascinating results, as shown in the papers contained in this volume.Each paper submitted for publication in this volume has gone through the refereeing process, and has been revised on the basis of comments and discussion at the conference as well as comments from the anonymous referees. Finally, 19 papers were accepted for publication. The editors are very grateful to the colleagues involved in the refereeing process for their rapid and careful reviewing of the papers.We thank Takayuki Mizuno, Koji Sakai, Hiwon Yoon and Hiroki Matsui for their support for the conference. We appreciate the administrative assistance provided by Yayoi Hatano of Hitotsubashi University, and Masahiko Ozaki, Masato Yamada and Tomoko Kase of RIETI. We are most grateful to the authors for their contributions, as well as to the participants, all of whom made this conference stimulating and enjoyable. Misako Takayasu Tokyo Institute of Technology, Japan Tsutomu Watanabe Hitotsubashi University, Japan RIETI, Japan Yuichi Ikeda Hitachi Research Laboratory, Hitachi Ltd, Japan Hideki Takayasu Sony Computer Science Laboratories, Inc, Japan

Older adults often require specialized health care expertise, but the effects of geriatrics-focused models of primary care have not been fully evaluated. To compare the effects of geriatrics-focused primary care vs traditional primary care for older patients in the Veterans Affairs (VA) health care system. In this cohort study, geriatrics-focused primary care and traditional primary care patient dyads matched on variables associated with geriatrics-focused primary care entry and outcomes were enrolled from VA medical centers with operational geriatrics-focused primary care clinics serving 500 or more patients annually in fiscal year 2016. Across 57 eligible sites, patients 65 years or older who transferred their care to geriatrics-focused primary care within the previous 12 months (exposure period) (n = 291) and their matched traditional primary care controls (n = 277) were enrolled. Study recruitment occurred from March 22, 2018, through December 11, 2019, with patient outcomes data collection completed June 24, 2021. Data were analyzed from June 2023 to January 2024. At least 2 geriatrics-focused primary care visits during the exposure period. Geriatrics-focused primary care involves interprofessional teams of clinicians with geriatrics expertise to provide comprehensive primary care for older veterans with complex needs. The primary outcome was home time (days not in a hospital unit, short-term nursing facility, inpatient rehabilitation, or an emergency visit) within 18 months of the exposure period. Secondary outcomes were completion of advance directives, assessments (falls, incontinence, and function), and medication quality (potentially inappropriate medications, Anticholinergic Drug Scale score). Patients were surveyed over 18 months about care experiences using Patient Perceptions of Integrated Care subscales, and Patient-Reported Outcomes Measurement System self-reported health and well-being measures. A total of 568 patients were enrolled (mean [SD] age, 80.5 [7.0] years; 558 [98.2%] male), including 291 in the geriatrics-focused primary care and 277 matched in the traditional primary care groups; most baseline variables were comparable between the 2 groups. Geriatrics-focused primary care was not associated with more home time (rate ratio, 0.97; 95% CI, 0.62-1.51) over 18 months. Geriatrics-focused primary care was associated with higher odds of assessment for falls (odds ratio [OR], 2.86; 95% CI, 1.62-5.04), incontinence (OR, 2.73; 95% CI, 1.69-4.42), and function (OR, 4.05; 95% CI, 2.39-6.85) at 18 months. Geriatrics-focused primary care was not associated with advance directive completion, medication quality, or self-reported health and well-being. In this cohort study, geriatrics-focused primary care did not increase time at home or other measures of self-reported health and well-being or integration of care within the first 18 months after care transition. However, increases in some process measures important for quality of care were observed, suggesting that further studies are needed to evaluate outcomes over a longer time frame.

Boosting the Hodrick-Prescott Filter

The use of longitudinal survey data in economic research is considered with special reference to the estimation of gross flows. Alternative models of measurement error for discrete variables are reviewed. A common assumption is that errors on different occasions are independent. Two reasons for departures from this assumption are unit heterogeneity, where different units have different misclassification rates, and state heterogeneity, where different rates apply to individuals in different underlying states which partition the observed states. The effect of assuming independent errors in the presence of heterogeneity is considered. Approaches to adjusting for measurement error in gross flow estimation are reviewed.

BackgroundAsthma exacerbations remain a major challenge in asthma management, often due to delayed recognition and limitations of conventional monitoring tools such as peak flow meters and symptom questionnaires. These tools are typically effort dependent or retrospective, making them less suited for continuous, real-time monitoring. A novel, smartphone-based respiratory-responsive vocal biomarker (RRVB) may offer an accessible and noninvasive approach for dynamic assessment of respiratory health. This RRVB has previously demonstrated generalizable performance in cross-sectional cohorts across multiple respiratory conditions, including asthma, chronic obstructive pulmonary disease, and COVID-19, in populations spanning India and the United States. This study extended this work by evaluating the real-world, longitudinal performance of the same RRVB tool for daily asthma exacerbation monitoring via smartphones in home settings.ObjectiveThis study aimed to evaluate the efficacy of the RRVB as a convenient real-time tool for monitoring asthma exacerbations and respiratory states in a real-world, longitudinal setting.MethodsIn this prospective cohort study, 84 adult patients with asthma were enrolled from an academic medical center and followed for 90 days. Participants submitted daily 6-second voice samples and conducted peak expiratory flow measurements and surveys, including symptom reports and asthma control assessments. RRVB scores were generated in real time on the app. Asthma states (normal function, mild event, and exacerbation) were defined based on both peak expiratory flow and self-reported well-being. Risk ratios were calculated to assess the predictive validity of RRVB scores for identifying exacerbation events. Engagement was measured via frequency of completed sessions, and participant experience was evaluated through exit surveys.ResultsRRVB scores significantly stratified asthma states. The risk of experiencing an exacerbation was 2.15 times higher (95% CI 1.62-2.85; P<.001) with elevated RRVB scores and 3.57 times higher (95% CI 2.70-4.73; P<.001) using normalized scores adjusted for individual characteristics. RRVB scores did not significantly correlate with the Asthma Control Test (risk ratio=1.17, 95% CI 0.96-1.44; P=.12), highlighting its role as a momentary signal rather than a proxy for longitudinal control. Engagement was moderate or higher (≥26 total app sessions) in 58% (49/84) of participants. Among survey respondents, 93% (43/46) found the app easy to use, 89% (41/46) reported a positive overall experience, and 87% (40/46) indicated that they would use a similar tool in the future. Fewer participants (32/46, 70%) reported understanding the RRVB scores, suggesting a need for improved score interpretability and user guidance in future implementations.ConclusionsThe RRVB tool demonstrated effective real-time detection of asthma exacerbations and dynamic respiratory states, supporting its potential as a noninvasive, user-friendly, and physiologically grounded digital biomarker for asthma monitoring. These findings provide foundational evidence for broader deployment and integration of voice-based tools to support proactive, real-world asthma management.Trial RegistrationClinicalTrials.gov NCT05850390; https://clinicaltrials.gov/study/NCT05850390

Research into methods for collecting product economic inventory data for inclusion into a comparative product life cycle assessment (LCA) are limited, while the rules governing such LCA based comparative product studies are strict in their requirements. As comparative assertions are to be made, it would seem appropriate that such studies be approached in a robust manner. Reference is made to the use of product stakeholders as the source of product economic inventory data for input into LCA and yet research reveals numerous examples of studies in which these stakeholders were evidently absent. Research shows that it is highly likely that in studies of comparative products, the stakeholders of some or all of the products may be absent, or unable or unwilling to cooperate in a comparative product study. Alternatives to stakeholder cooperation in product data collection are limited, leading to the question as to how a comparative product LCA, in support of a comparative product environmental claim statement, could be addressed from a product economic inventory data collection perspective. The criminal justice system has been shown to use a variety of analytical instruments to convict individuals based on “beyond reasonable doubt” evidence. To what extent could this forensic approach be used to derive product economic inventory data for input to a comparative LCA study and how would this data differ from the alternative collection methods? To what extent can such a forensic type method be used to establish intended product like-functionality for comparison purposes and to identify the individual product materials composition, associated conversion processes and country-of-origin, all of this information being of value as product economic inventory data for LCA input? Research shows a significant difference in comparative product ranking when a forensic approach is applied to an existing study that was reliant on assumptions in the absence of stakeholders. Many of these existing studies also show that their authors were potentially lacking in product technologically relevant knowledge, this being a reported critique of LCA. Interpretation of the results of forensic studies would require the study author to have such knowledge in order to interpret the results. The use of technologically relevant product knowledge and a forensic approach to product economic inventory data collection could help in rendering comparative product environmental claims more robust.

Starting for the good results reached in physics researchers in economics and finance have been interested in testing nonlinear dependence and chaos in economic models and data. A wide variety of reasons for this interest have been suggested, including an attempt to improve the forecasting accuracy of linear time series models and to better explain the dynamics of the underlying variables of interest using a richer class of models than that permitted by limiting the set to the linear case. But chaos theory has not had the same impact in the economics as it has in the hard sciences like physics. The literature did not provide a solid support for chaos as a consequence of the high noise level that exists in most economic time series, the relatively small sample sizes of data, and the weak robustness of chaos tests for these data.

Through a number of strategic investments by various funding organisations, the UK academic community has access to a unique and expansive range of digital data resources. The Economic and Social Data Service (ESDS), supported by the Economic and Social Research Council (ESRC) and the Joint Information Systems Committee (JISC) is a national data service. ESDS provides access and support for an extensive range of key economic and social data, both quantitative and qualitative, spanning many disciplines and themes. It comprises a number of specialist data services that promote and encourage data usage in teaching and research. While individual datasets are used extensively in academic research, they are signifi cantly under used in learning and teaching programmes within Higher Education (HE), at both undergraduate and postgraduate levels, and are rarely used in Further Education (FE). As a service provider of the ESDS, the UK Data Archive (UKDA) is in a strong position to offer its data resources to the learning and teaching communities for developing materials that might be more appealing to teachers than raw data. This activity requires advice and input from instructors in the classroom on how to develop the pedagogic aspects of learning resources: which content to extract; how to contextualise and apply raw data; where to position such resources in the learning process; and on the usability and functionality of the digital resources created. This paper describes the UKDA Survey Data in Teaching project (SDiT), funded under the JISC Exchange for Learning (X4L) Programme. The project’s goals were to increase the use of real data sources in the classroom, and in a more ambitious sense, to help improve the data literacy of those studying social sciences, from school students age 16-19 to postgraduates. The project created a set of free teaching and learning data and statistics-oriented resources based on the study of crime in society, and were based on learning strategies that encourage the teaching of research methods within a substantive context. This paper addresses both the positive experiences and challenges that arose from running the project.

PHP153 - KEY STAKEHOLDERS PERCEPTIONS OF HEOR USAGE IN LEBANON

Purpose This paper aims to study employee mental health in relation to workplace design and indoor environmental quality (IEQ) when working from home, which has received little attention. The trend toward hybrid working urges for more knowledge. Design/methodology/approach Through a mixed data collection method, this study analyzed potential relationships (between mental health, workplace design and IEQ) from information obtained through a cross-sectional survey, repeated point-in-time surveys and desk-based IEQ sensors at home. Data were collected in April 2020 during a national COVID-19 lockdown in The Netherlands amongst 36 subjects. They all worked full time from home in this period and together completed 321 point-in-time surveys. The three data sets were combined and analyzed using bivariate and path analysis. Findings Outcomes indicate that subjective and objective IEQ conditions, workplace suitability and distraction affect employee mental health in the home workplace in a similar way as in the office. Being satisfied with the noise level increases concentration, self-reported well-being and engagement. High sound pressure levels (&gt;58 dB) increased tension or nervous feelings. Originality/value To the best of the authors’ knowledge, this study is one of the first to explore employee mental health in relation to simultaneously assessed (perceived and measured) multiple IEQ parameters in the home workplace.

Lighting metrics and standards are tested against spaces when designing the built environment, but humans move between different indoor and outdoor spaces throughout their day. This 'room versus human' relationship differs between individuals and likely changes how light affects wellbeing between persons even in the same indoor environments during working hours. Understanding this relationship is crucial for optimizing living and working environments. In this paper, we explore the relationship between light exposure and wellbeing of 20 university students using sensors which are wearable and thus detect human light exposure directly. A comprehensive data collection methodology is employed, including data-logging worn spectral irradiance sensors and smartwatch-administered surveys. These tools gather timeseries spectral irradiance data at 1-minute intervals and qualitative data through periodic surveys administered four times daily. The surveys capture participants' sleep quality, mood, and alertness, alongside daily journals where participants document their activity schedules and locations. This dual collection method aims to provide insights into how varied light exposures—including in shared environments—influence sleep and alertness. Our analysis uses Repeated Measure Correlation (rmcorr) and Pearson correlation to assess the impact of melanopic irradiance on alertness, sleep pattern and quality, considering both individual behaviours and environmental contexts. By analyzing correlations between light exposure, activity patterns and self-reported wellbeing, we aim to elucidate how architectural designs and daily routines influence light exposure and subsequent health outcomes. Findings will contribute to a reevaluation of light exposure standards and student lighting patterns, enhancing the understanding of behavioural choice and lighting's role in daily life. This study lays the groundwork for future large-scale applications, leading to personalized lighting strategies that promote circadian health within university spaces and homes.

WW10: MULTINATIONAL ECONOMIC STUDIES: LESSONS LEARNED FROM TRIAL-BASED AND MODELLING STUDIES

The Economics and Business Data Center (EBDC), founded as a cooperation of the University of Munich (LMU) and the Ifo Institute for Economic Research in 2008, has the aim of opening new fields of economic research by providing innovative datasets of German companies. These datasets contain part of the survey data of the Ifo Institute as well as external balance-sheet and financial statement data. More precisely, in a first step, micro data from the Ifo Business Survey and from the Ifo Investment Survey has been linked with balance-sheet data from the enterprise databases Amadeus and Hoppenstedt. As a result, we can offer two extensive datasets of German companies that allow for the simultaneous research of rather qualitative aspects contained in the Ifo micro data together with quantitative accounting or structural company information: the EBDC Business Expectations Panel (focus: cyclical economic factors/balance-sheet data) and the EBDC Business Investment Panel (focus: investment/balance-sheet data). Both company panels contain historical as well as current data and are updated regularly. In general, because of the high levels of confidentiality and data security the Ifo Institute ensures its panel members, the EBDC company panels can only be used for research purposes on the premises of the Ifo Institute. Furthermore, the data is only provided in an anonymized way and with a one-year time lag. Its use is subject to strict security precautions. The aim of this paper is to give an overview on the data sources, and to describe the scope of and access to the EBDC data. Furthermore, it describes some research projects taking advantage of these datasets up till now.