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on Discrete Choice Models |
| By: | Martin Eckhoff Andresen; Manudeep Bhuller; Alfred L{\o}vgren |
| Abstract: | This paper studies how workers' beliefs about pay shape the tradeoffs between pay and workplace amenities. We design a multi-stage incentivized survey experiment that combines hypothetical choice experiments with elicited beliefs about starting salaries in real jobs and randomly varies the provision of explicit pay information. Although stated preferences imply sizable willingness to pay for amenities consistent with prior literature, baseline beliefs about salaries in real jobs are systematically biased along two margins: respondents under-predict starting salaries by 18% and expect higher-amenity jobs to pay more, substantially over-predicting the amenity-pay gradient. Exposure to pay information raises mean pay beliefs for similar jobs by 4% and reduces belief dispersion by 15%, but does not alter the strong positive association between perceived pay and advertised amenities, leaving the amenity-pay tradeoffs in stated choices essentially unchanged. While workers have strong preferences for workplace amenities, the tradeoffs they perceive deviate sharply from those present under full information. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.02503 |
| By: | David Levinson; Isaac Mann (TransportLab, School of Civil Engineering, University of Sydney) |
| Abstract: | We introduce Access Surplus as a welfare measure that frames accessibility in a market-like form: the inverse cumulative cost to reach the next opportunity is the ‘supply, ’ and the willingness to pay for one more choice is the ‘demand.’ The area where demand exceeds supply, up to a natural stop point, is Access Surplus . The metric avoids arbitrary cut-offs, is additive over residents, links clearly to project effects, and stays transparent when only origin–destination times and counts are available. |
| Keywords: | transportation, accessibility |
| JEL: | R40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:nex:wpaper:paper-2025-07 |
| By: | Isaac Mann; David M. Levinson (TransportLab, School of Civil Engineering, University of Sydney) |
| Abstract: | The value of travel time and reliability are significant economic parameters in canonical transport Cost–Benefit Analysis. Our study employs connected vehicle data paired with Sydney, Australia’s, extensive toll road network to introduce a novel approach to valuing these metrics. Toll uptake makes the time–money trade-off explicit: travellers pay to avoid congestion. While toll choices have long been used to infer time valuation, a networkwide approach incorporating passive revealed preferences has not yet been explored. We design choice sets using methods termed route ‘observation’ and ‘generation’, and estimate time and reliability valuations using mixed-path size logit. Our findings align closely with official estimates used in project appraisal, and set the stage for panel revealed preference studies as connected vehicles occupy more of the vehicle fleet. |
| Keywords: | transportation, road transport, transport networks, automated vehicles |
| JEL: | R40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:nex:wpaper:paper-2025-08 |
| By: | Wenyang Hao; Yixin Fang; David Levinson (TransportLab, School of Civil Engineering, University of Sydney) |
| Abstract: | User-organized Pre-pooled Ride-hailing (UPR) is a user-coordinated form of shared mobility that layers social media coordination on top of commercial ride-hailing within bounded trust-based communities (e.g., campuses, workplaces, or residential compounds). We designed a comprehensive survey with 24 choice scenarios and integrated sociodemographic, revealed-behavior, and attitudinal measures including environmental perceptions, then estimated mixed logit models for all respondents and for organizers versus followers. UPR is most competitive for longer, daytime trips and for first-/last-mile access to metro hubs, offering a lower per-person cost than solo ridehailing and faster door-to-door travel than public transport. UPR choice is negatively associated with ride-hailing and platform ride-pooling use, implying substitution. Climate-mitigation beliefs increase UPR choice probabilities, while stronger trust requirements and privacy concerns constrain uptake. The findings highlight how environmental perceptions and community trust jointly shape decentralized pooling and its potential role as a low-impact complement to transit in fringe areas. |
| Keywords: | transportation |
| JEL: | R40 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:nex:wpaper:paper-2026-14 |
| By: | Yingshuo Wang; Xian Sun; Yanhang Li; Zhichao Fan; Zexin Zhuang |
| Abstract: | Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price sometimes increases predicted demand, and implied willingness-to-pay estimates are frequently negative or implausible. We propose a two-stage adapter that embeds foundation model predictions within a utility-maximization framework. In the first stage, we estimate a standard choice model whose parameters are constrained to obey economic theory. In the second stage, we freeze those parameters and train a correction term that incorporates the foundation model's predictions as additional information. The result is a model that inherits the foundation model's accuracy gains while guaranteeing monotonic price-demand relationships under policy perturbation and producing analytically computable trade-off measures. On two transportation datasets, the adapter recovers up to 13 percentage points of accuracy over a standard logit model while maintaining perfect economic consistency, something neither the raw foundation models nor conventional distillation achieve. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.26559 |
| By: | Shakeeb Khan; Tatiana Komarova; Denis Nekipelov |
| Abstract: | Semiparametric discrete choice models are widely applied in economics, yet a fundamental tension arises when covariates are discrete as regression coefficients that are point identified under continuous regressors may become only partially identified. We show that this is not merely an identification problem but creates serious estimation pathologies. Classical estimators, including the maximum score estimator of Manski (1975), not only have population maximizers that are outer regions of the identified set (Komarova (2013)) but also converge to a random set drawn from a finite collection of deterministic regions that partition that outer region. To resolve this failure, we introduce the Random Set Quantile (RSQ) estimator which extracts the $\tau$-quantile of the classical estimator for $\tau \in (1/2, 1)$. We prove this result for a class of widely used models, which includes binary/multinomial choice and discrete outcome panel data models. This construction is consistent and locally robust across the full parameter space, including precisely those configurations where classical estimators break down. A feasible implementation based on the $m$-out-of-$n$ bootstrap inherits both properties. We apply the methodology to the 2019 UK General Election, where the discrete support of Brexit-related covariates generates the partial identification our theory analyzes. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.02200 |
| By: | Ehlert, Martin (WZB Berlin Social Science Center); Schimke, Benjamin (University of Wuppertal) |
| Abstract: | Micro-credentials such as online certificates or digital badges have been promoted as flexible tools to enhance employability, especially given the growing demand for continuing education and training. Yet, little is known about how employers value such credentials in hiring decisions and whether they function as complements or substitutes for other qualification signals. Drawing on signaling and human capital theory as well as the digital divide literature, this study examines the hiring value of digital and in-person micro-credentials relative to formal qualifications and work experience. The empirical analysis is based on a pre-registered discrete choice experiment conducted with 1, 380 human resource professionals in Germany, who evaluated 12, 048 applicant profiles across 24 occupations requiring higher education. Results based on conditional logit models show that digital micro-credentials do not increase hiring probabilities and are valued significantly less than equivalent certificates obtained through in-person courses. This difference is independent of recruiters’ prior experience with digital micro-credentials and largely driven by trust in the signal quality of the two credential formats. We also find that in-person micro-credentials issued by universities improve hiring chances compared to other providers. Furthermore, among applicants with weaker field-of-study matches, in-person micro-credentials can enhance employability, indicating a partially substitutional signaling function, while this is not the case for digital micro-credentials. These findings suggest that in strongly institutionalized labor markets such as Germany, employers continue to prioritize established (micro-)qualifications over emerging digital forms. At the same time, this is evidence against a digital divide in terms of outcomes. |
| Date: | 2026–06–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:8a7h6_v1 |
| By: | Dusel, Sara; von Keyserlingk, Marina A. G.; Wieck, Christine |
| Abstract: | Despite a plethora of experiments on animals’ preferences for different resources, elicited through animals’ choices and willingness to work, the insights gained from this body of work remain largely unexploited in economics. Non-anthropocentric cost-benefit analysis (CBA) may fill this gap given that it attempts to evaluate impacts on animals using data describing the animals’ own perspective. However, economic frameworks that have used animal preference data to make inferences on animals’ monetary willingness to pay (WTP) remain limited and leave important conceptual issues unaddressed. The aim of this conceptual study is to explore novel pathways on how experimental findings describing animals’ preferences can be used for making inferences on animals’ monetary WTP in CBA. In an innovative approach, we use empirical insights, captured through an extensive literature review of animal experiments, as the main input for a process of conceptual reflection in economics. Framed within our assumptions, we present three main results: two novel concepts of farm animals’ WTP, the first based on published animal experiments and the second calling for refined experimental designs. Third, we suggest some refinements to the existing concepts of animals’ WTP. This work provides novel approaches to integrating animals’ WTP into CBA that, when adopted, would allow for greater economic representation of animal welfare into policy evaluations. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2026–06–10 |
| URL: | https://d.repec.org/n?u=RePEc:ags:uhgewp:402760 |
| By: | Gregory J. Martin; Shoshana Vasserman; Cameron Pfiffer |
| Abstract: | Using a novel dataset covering the complete history of individual-level web traffic and digital subscriptions from a major metropolitan newspaper in the United States between 2020 and 2024, we investigate consumers' willingness to pay for different categories of news content, with particular focus on the kinds of coverage believed to generate civic externalities. Our identification strategy relies on the quasi-random arrival of paywall events which force consumers to subscribe if they wish to continue reading. Using this variation, we estimate a model of consumer demand and construct the optimal staff allocation for the paper under different counterfactual revenue models: a fully subscription-based model and a fully ad-supported model. Our results suggest that readers are willing to pay for local reporting, and that measures of demand based only on time-use substantially underestimate the value of “hard” news coverage on topics like local politics and public health. However, digital subscription revenues alone are insufficient to cover staff costs even at the highest revenue-generating sections of the paper. We use our model to estimate the subsidy required to expand the newspaper's production of investigative coverage. |
| JEL: | L23 L82 P0 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35289 |
| By: | Kinyua, Martin |
| Abstract: | The study presented by this Working Paper provides a foundational economic valuation of habitat protection within the Ruthumbi Forest block of the critical Mt. Kenya Afri-montane ecosystem. Using the Contingent Valuation Method (CVM), the 2002 study estimates rural households’ willingness to pay (WTP) for the protection and preservation of the forest, establishing a "Pre-treatment" baseline immediately prior to the implementation of the Forests Act of 2005. The survey targeted 215 households from two selected geographical locations forming part of the forest community, defined by the study as people living within a five-kilometer radius of the forest edge. To address inherent challenges of non-market valuation including the prevalence of invalid responses—particularly protest zeros—that could introduce sample selection bias, a Tobit model with sample selection was employed, estimated via the Heckman two-step procedure. The first stage used a Probit model to predict the probability of providing a valid WTP response, while the second stage incorporated the Inverse Mills Ratio into the WTP function to correct for selection bias. Empirical results confirmed significant sample selection (ρ significant at 1%), validating the need for this correction. The adjusted model revealed that household income and respondent age significantly influence WTP, with mean monthly WTP estimated at KSh 125.00 per household. However, to provide a statistically robust and conservative estimate for policy purposes, a population-wide mean of KSh. 86.35 was utilized for aggregation. This figure, derived from the Tobit expected value of the dependent variable, accounts for non-participation across the broader community. Aggregated across the target forest-adjacent population, this results in a 2002 baseline ecosystem service value of KSh. 145, 627.15 per month, or KSh. 1, 747, 526.00 annually. Further, the study findings supported the policy conclusion that forest communities are aware of forest benefits and are willing to pay for communal management—laying groundwork for community-based forest governance. By documenting these parameters before the institutionalization of Participatory Forest Management (PFM), this research serves as a primary longitudinal anchor. It offers essential benchmarks for evaluating return on investment (ROI) of Kenyan forest policy reforms and climate-resilience strategies from 2002 to 2026 and beyond. |
| Keywords: | Contingent Valuation; Tobit-Heckman Model; Habitat Protection; Payment for Ecosystem Services; Afro-montane Ecosystems; Communal Management; Rural Households, Kenya; 2002 Socio-Economic Baseline; Longitudinal Baseline; Kenya Forest Policy |
| JEL: | C01 C3 C31 C34 Q23 Q28 Q5 Q51 Q57 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128908 |
| By: | Ivan Korolev |
| Abstract: | This paper provides a practical guide to estimating semiparametric and nonparametric fixed-effects panel data models using the mgcv package in R. The focus is implementation: handling fixed effects with unit indicators, first differencing, or penalized unit effects; specifying smooth terms; and conducting cluster-robust inference. Monte Carlo experiments compare \code{mgcv::bam} estimators with linear and fixed-series spline estimators. Simulations suggest that penalized splines adapt to unknown smoothness and estimate functions accurately in the designs studied here. A penalty-adjusted cluster-robust covariance estimator yields tests with near-nominal size for finite-dimensional parameters, and confidence bands provide accurate coverage for centered unknown functions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.12739 |
| By: | Aditya Saxena; Deepjyoti Das; Alireza Ermagun; David Levinson (TransportLab, School of Civil Engineering, University of Sydney) |
| Abstract: | Keywords: Urban freight Crowd shipping Willingness to participate User participation Personality trait ICLV model This study investigates the determinants of willingness to participate in crowd shipping (WTP-CS) for the working population within the Mumbai Metropolitan Region. It explores socioeconomic factors, personality traits, and travel correlates to WTP-CS by testing an integrated choice and latent variable (ICLV) modeling framework. Four conclusions are drawn. First, longer travel times and higher costs diminish WTP-CS, while economic incentives (e.g., free Wi-Fi services) are positively associated with WTP-CS. Second, lower-income individuals are positively inclined toward crowd shipping, while females and older individuals display less inclination toward crowd shipping. Third, increasing the number of services offered by crowd shippers nega tively affects WTP-CS. Fourth, individuals with higher levels of the openness personality trait exhibit a positive inclination toward WTP-CS, whereas those with higher conscientiousness tend to exhibit a more reserved attitude toward WTP-CS. The findings emphasize the role of individual traits in shaping participation behaviors in crowd shipping initiatives, and in contrast to most existing crowd shipping studies that focus on users or developedcountry contexts, provide new evidence on supply-side participation among working commuters in emerging markets in a less-studied region. |
| Keywords: | transportation, road transport, freight transport |
| JEL: | R40 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:nex:wpaper:paper-2026-11 |
| By: | Chensong Huang; Changyu Chen; Chenwei Lin; Hanjia Lyu; Xian Xu; Jiebo Luo |
| Abstract: | LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate this distinction using the St. Petersburg game as a controlled testbed, a classical paradox in which the expected payoff is infinite, yet humans typically report low, finite willingness to pay. We evaluate 28 LLMs with a structured prompt suite that includes the original game; controlled decision variants that perturb truncation, repeated play, numeric endowment, and occupational identity; a human-perspective prompt that asks models to reason as human decision makers; and paired comparisons between base models and their instruction-tuned counterparts. In the original game, most models generate finite bids, creating the appearance of human-like risk behavior. However, this outcome-level resemblance masks substantial mechanism-level differences. The controlled variants reveal that rather than maintaining human-like behavior seen in the original game, models often shift to conditionally and computationally rational behavior. Human-cue prompting and instruction tuning often lower bids and reduce some visible pathologies, but most mechanism-level response patterns remain largely unchanged. These findings show that behavioral alignment in risk decision-making can be surface-level: LLMs may produce human-like risk decisions without exhibiting human-consistent mechanisms. High-stakes evaluations of LLM decision-making should therefore move beyond outcome similarity and examine whether the alignment is supported by mechanism-level consistency. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.04978 |
| By: | Wenyang Hao; David Levinson (TransportLab, School of Civil Engineering, University of Sydney) |
| Abstract: | A heterogeneous ensemble method combines multiple models to improve predictive accuracy, robustness, and generalizability compared to any individual model. In this paper, we introduce a novel Baggingenhanced Stacking Heterogeneous Ensemble Method (BESHEM) designed to capture the complexity and nonlinearity inherent in travel mode choice modeling. BESHEM integrates linear, tree-based, probabilistic, instance-based, and neural network-based models through nested bagging and stacking strategies, significantly outperforming conventional ensemble methods. We apply BESHEM to analyze User-organized Pre-pooled Ride-hailing (UPR), an emerging mobility mode among suburban university campus communities in China, which combines the flexibility of ride-hailing with the collaborative mechanisms and cost-effectiveness of traditional carpooling. We evaluate and compare BESHEM against twenty representative base models and four established ensemble strategies using a comprehensive dataset from UPR users and non-users, encompassing socioeconomic attributes, travel scenarios, and attitudinal perceptions. After comparing BESHEM with all benchmark ensembles and base models, we find that when the meta-learner is set to Extra Trees, BESHEM achieves the highest prediction accuracy among all competing methods. Feature importance analyses reveal that UPR adoption is positively influenced by previous ride-sharing experience, medium- to long-distance metro-integrated travel scenarios, and perceived safety among female users, while negatively affected by short-distance competitive travel alternatives and privacy concerns. |
| Keywords: | transportation, transport networks |
| JEL: | R40 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:nex:wpaper:paper-2026-03 |
| By: | Sta. Romana, Leonardo L. |
| Abstract: | The voluntary carbon market, used by companies to buy credits to "offset" part of their remaining emissions, has recently faced integrity issues due to inaccurate, sometimes even fraudulent, claims of emissions avoidance from projects preventing deforestation. Several recent "best practices" in the Amazon are noteworthy in terms of rebuilding trust in the market for offsets based on the carbon-absorbing abilities of nature itself. On the supply side, a developer and two Brazilian start-ups focused on forest restoration projects are presented. On the demand side, the big US tech firms and global corporates buying the carbon credits are discussed. Their willingness to pay a premium price for the high-quality carbon offsets are noted. An innovative World Bank Amazon-inspired financial instrument used to mobilize private institutional capital is also explained. |
| Keywords: | Nature-based Solutions, , Forest Protection, Forest Restoration, Deforestation, Forest Degradation, Carbon Market, Carbon Removal, Carbon Offsets, Amazon Rainforest, Biodiversity |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341033 |
| By: | Till Köveker; Philipp Cremer |
| Abstract: | Empirical evidence on price premiums for green intermediate products is scarce, especially for energy-intensive basic materials. Evidence on such green premiums is relevant, as they may affect companies incentives to invest in green production technologies. Moreover, green premiums are important for the design of green support programmes, as support levels could be adjusted for companies’ green revenues. This paper proposes a new approach for estimating green premiums for basic materials. Basic material buyers’ additional willingness to pay for green inputs is estimated based on their reported internal carbon prices. This green willingness to pay is used to construct a demand curve for green basic materials. Short-to medium-term green supply is derived from low-carbon basic material production facilities that have been announced or are under construction. The proposed methodology is then applied to estimate and predict green premiums in the steel sector. The results indicate that green steel premiums will be too low and too transient to generate significant incentives to invest in green primary steel production facilities. Other policies such as effective carbon prices and carbon contracts for difference are and will be central in driving the green steel transition. Green steel premiums may only play a complementary role in the first years of the transition |
| Keywords: | Green Premium, Internal Carbon Price, Willingness to Pay, Green Steel, Steel Industry, Decarbonization, Climate Policy |
| JEL: | Q02 L61 Q59 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2167 |