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1. Organizational Healthcare Benefits as Signals of Values

   https://doi.org/10.1007/s10869-025-10050-7  

   Moran, Lauren H; French, Kimberly A; Fletcher, Keaton A; Garcia, Spencer C; Todd, Trevor (July 2025, Journal of Business and Psychology)

   Abstract Organizations spend trillions of dollars per year on their employee benefits packages. One reason for this may be that benefits packages are key tools for organizations to signal their values. We draw on signaling theory to understand how employees interpret and react to healthcare benefits as a function of (1) benefit universality, (2) benefit political contentiousness, and (3) individual political orientation. We collect two cross-sectional studies that capture reactions to four healthcare benefits: cancer treatment, reproductive care, abortion-facilitation, and gender-affirming care benefits. We find healthcare benefits signal several underlying organizational qualities, including support for employee health and well-being. Signaling support for employee health and well-being was less closely fitted to abortion-facilitative benefits and gender-affirming care benefits compared to more universal and less contentious benefits (cancer treatment and reproductive care benefits), especially among political conservatives. Similarly, abortion-facilitative benefits and gender-affirming care benefits were evaluated less positively and seen as less important and of lower utility than cancer treatment benefits and non-abortive reproductive care benefits, especially among those who identify as politically conservative. The findings extend knowledge of how and why employee reactions to benefits may differ, test under-developed aspects of signaling theory (signal fit, features and individual differences that modify fit), and inform organizational practice regarding benefit offerings. 

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2. The role of states in U.S. immigration: A study of population dynamics and subnational immigration laws

   https://doi.org/10.1016/j.ssresearch.2023.102909  

   Anadón, Isabel J (August 2023, Social Science Research)

   Legislative action on issues of immigration emerged prominently across and within US states throughout the 2000s. The emerging literature on this topic demonstrates the political motivations driving anti-immigrant laws that negatively impact the mobility of Hispanic/Latino and Foreign-born populations across US states. Considerable research identifies the political mechanisms driving restrictive state-level immigration policies. Despite the growth of this scholarly work, the impact of these laws within states requires further study. This paper broadens the approach to the study of restrictive state-level omnibus immigration laws (OILs) using a rich dataset to uncover the effects of these laws on compositional change for undocumented, foreign-born, and Hispanic/Latino populations from 2005 to 2017. Using a quasi-experimental design, I show that by passing omnibus immigration laws, states shape demographic patterns of Foreign-born populations. Specifically, I find that states that pass omnibus immigration laws experience a decrease in undocumented and Foreign-born populations relative to states that did not pass similar laws. Effects are estimated each year after the passage of OILs, providing additional insight into the temporal impact of omnibus immigration laws on the settlement patterns of these groups. I conclude by discussing the theoretical implications of the multiple interior immigration law and policies, specifically at the state level, and their salience in shaping population dynamics across the United States. 

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3. State Bans on Affirmative Action and Talent Loss among Blacks and Latinos in the United States

   https://doi.org/10.1525/esr.2020.43.2.58  

   Lutz, Amy; Bennett, Pamela R.; Wang, Rebecca (January 2020, Ethnic studies review)

   null (Ed.)

   Althoughaffirmativeactionincollegeadmissionsisconstitutionallypermissible,several states prohibit it. We investigate whether bans push black and Latino students fro m in-state public selective colleges to other types of postsecondary institutions, thus contributing to talent loss among these groups. Unlike most other studies, we analyze national data (the High School Longitudinal Study of 2009) so that we can follow students across state lines. We find no evidence that students from ban states shift from one type of selective college to another; that is, from in-state public flagships to in-state private ones or selective colleges in other states. However, the odds of attending a nonselective college, instead of an in-state public selective college, are al most three times higher among blacks and Latinos in ban states compared with their counterparts in states without bans. We argue that bans on affirmative action may contribute to talent loss among black and Latino students. 

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4. Transferring Legal Natural Language Inference Model from a US State to Another: What Makes It So Hard?

   https://doi.org/10.18653/v1/2023.nllp-1.21  

   Kwak, Alice; Forte, Gaetano; Bambauer, Derek; Surdeanu, Mihai (January 2023, Proceedings of the Natural Legal Language Processing Workshop 2023)

   This study investigates whether a legal natural language inference (NLI) model trained on the data from one US state can be transferred to another state. We fine-tuned a pre-trained model on the task of evaluating the validity of legal will statements, once with the dataset containing the Tennessee wills and once with the dataset containing the Idaho wills. Each model’s performance on the in-domain setting and the out-of-domain setting are compared to see if the models can across the states. We found that the model trained on one US state can be mostly transferred to another state. However, it is clear that the model’s performance drops in the out-of-domain setting. The F1 scores of the Tennessee model and the Idaho model are 96.41 and 92.03 when predicting the data from the same state, but they drop to 66.32 and 81.60 when predicting the data from another state. Subsequent error analysis revealed that there are two major sources of errors. First, the model fails to recognize equivalent laws across states when there are stylistic differences between laws. Second, difference in statutory section numbering system between the states makes it difficult for the model to locate laws relevant to the cases being predicted on. This analysis provides insights on how the future NLI system can be improved. Also, our findings offer empirical support to legal experts advocating the standardization of legal documents. 

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5. Understanding mental health trends during COVID-19 pandemic in the United States using network analysis

   https://doi.org/10.1371/journal.pone.0286857  

   Kobayashi, Hiroko; Saenz-Escarcega, Raul; Fulk, Alexander; Agusto, Folashade B. (June 2023, PLOS ONE)

   Meira, Karina Cardoso (Ed.)

   The emergence of COVID-19 in the United States resulted in a series of federal and state-level lock-downs and COVID-19 related health mandates to manage the spread of the virus. These policies may negatively impact the mental health state of the population. This study focused on the trends in mental health indicators following the COVID-19 pandemic amongst four United States geographical regions, and political party preferences. Indicators of interest included feeling anxious, feeling depressed, and worried about finances. Survey data from the Delphi Group at Carnegie Mellon University were analyzed using clustering algorithms and dynamic connectome obtained from sliding window analysis. Connectome refers to the description of connectivity on a network. United States maps were generated to observe spatial trends and identify communities with similar mental health and COVID-19 trends. Between March 3rd, 2021, and January 10th, 2022, states in the southern geographic region showed similar trends for reported values of feeling anxious and worried about finances. There were no identifiable communities resembling geographical regions or political party preference for the feeling depressed indicator. We observed a high degree of correlation among southern states as well as within Republican states, where the highest correlation values from the dynamic connectome for feeling anxious and feeling depressed variables seemingly overlapped with an increase in COVID-19 related cases, deaths, hospitalizations, and rapid spread of the COVID-19 Delta variant. 

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