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Endogenous regressors can lead to biased estimates for causal effects using methods assuming regressor–error independence. To correct for endogeneity bias, the authors propose a new method that accounts for the regressor–error dependence using flexible semiparametric odds ratio conditional models; the approach requires neither parametric distributional assumptions nor tuning parameters for modeling endogenous regressors' distributions conditional on the error term and exogenous regressors. Inference is achieved via optimizing the profile likelihood concentrating on the parameters of interest. The proposed approach requires no use of instrumental variables (IVs), observed or latent, that must satisfy the stringent condition of exclusion restriction. Nonnormally distributed endogenous regressors are required for model identification with a normal error distribution. The approach's flexibility in capturing regressor–error dependence increases the capability of IV-free endogeneity correction and provides opportunities to improve the accuracy of causal effect estimation. Unlike existing IV-free methods, the proposed approach can handle discrete endogenous regressors with few levels, such as binary regressors or count regressors with small means, and is thus applicable to a plethora of applications involving such regressors. The authors demonstrate the versatility of the approach for binary, count, and continuous endogenous regressors using comprehensive simulation studies and empirical data. [ABSTRACT FROM AUTHOR]

BACKGROUND: Osteoporotic fracture is the most serious complication of osteoporosis. Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture, but the causal relationship between the two is unclear. OBJECTIVE: To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS: The genome-wide association study (GWAS) datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9, respectively. Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable, Mendelian randomization analyses with random-effects inverse variance weighted, MR-Egger regression, weighted median, simple model, and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture. Sensitivity analyses were performed to test the reliability and robustness of the results. Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION: The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture. Elevated abundance of Actinomycetales [odds ratio (OR)=1.562, 95% confidence interval (CI): 1.027-2.375, P=0.037), Actinomycetaceae (OR=1.561, 95% CI: 1.027-2.374, P=0.037), Actinomyces (OR =1.544, 95% CI: 1.130-2.110, P=0.006), Butyricicoccus (OR=1.781, 95% CI: 1.194-2.657, P=0.005), Coprococcus 2 (OR=1.550, 95% CI: 1.068-2.251, P=0.021), Family XIII UCG-001 (OR=1.473, 95% CI: 1.001-2.168, P=0.049), Methanobrevibacter (OR=1.274, 95% CI: 1.001-1.621, P=0.049), and Roseburia (OR=1.429, 95% CI: 1.015-2.013, P=0.041) would increase the risk of osteoporotic fractures in patients. Elevated abundance of Bacteroidia (OR=0.660, 95% CI: 0.455-0.959, P=0.029), Bacteroidales (OR=0.660, 95% CI: 0.455-0.959, P=0.029), Christensenellacea (OR=0.725, 95% CI: 0.529-0.995, P=0.047), Ruminococcaceae (OR=0.643, 95% CI: 0.443-0.933, P=0.020), Enterorhabdus (OR=0.558, 95% CI: 0.395-0.788, P=0.001), Eubacterium rectale group (OR=0.631, 95% CI: 0.435-0.916, P=0.016), Lachnospiraceae UCG008 (OR=0.738, 95% CI: 0.546-0.998, P=0.048), and Ruminiclostridium 9 (OR=0.492, 95% CI: 0.324-0.746, P=0.001) would reduce the risk of osteoporotic fractures in patients. We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method. That is, using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable, eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture. The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice, but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering. [ABSTRACT FROM AUTHOR]

Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause. First, we crisply articulate potential pitfalls for practitioners regarding the interpretation of compositional causes from the viewpoint of interventions and warn against attributing causal meaning to common summary statistics such as diversity indices in microbiome data analysis. We then advocate for and develop multivariate methods using statistical data transformations and regression techniques that take the special structure of the compositional sample space into account while still yielding scientifically interpretable results. In a comparative analysis on synthetic and real microbiome data we show the advantages and limitations of our proposal. We posit that our analysis provides a useful framework and guidance for valid and informative cause-effect estimation in the context of compositional data. [ABSTRACT FROM AUTHOR]

Containment measures implemented to curb the spread of coronavirus (COVID‐19) disrupted labour markets across the world. While several studies focus on the impact of containment measures on job losses, this paper documents the effect of containment measures (specifically variation in the strictness of these measures) on employment patterns or transitions. COVID disruption can be viewed as multiple shocks to the labour market (as against a single shock). This is important because labour income is a key driver of aggregate inequality in developing countries, and such disruption has a knock‐on effect on the achievement of the Sustainable Development Goals. Therefore, we estimate the causal relationship between coronavirus containment measures and employment dynamics in South Africa. Exploiting the timing of interviews and within individual variation, we find that containment measures impact employment transitions. Specifically, stricter containment measures have a positive effect on the transition into unemployment, and the converse is true for the transition into employment. The implication of this causal relationship for development, ongoing management of COVID‐19 and future pandemics is discussed. [ABSTRACT FROM AUTHOR]

Although the low-carbon transition has enormous potential for improving economic and environmental benefits, it may also lead to social equity issues, trapping certain regions in a low-carbon dilemma. Based on panel data from the Yangtze River Economic Belt for the period 2007–2019, this study empirically analyzes the impact of carbon decoupling on energy equity, employing the carbon emission trading policy and natural wetlands as joint instrumental variables. The findings reveal that: (1) from 2007 to 2019, the average level of carbon decoupling in the Yangtze River Economic Belt was recorded at 0.415, maintaining a weak decoupling state overall and nearly forming an "inverted V" shape; (2) in the instrumental variable regression results, for every 1% increase in the level of carbon decoupling, the energy equity in the Yangtze River Economic Belt decreased by an average of 0.095% (p <.01); (3) environmental burden played a partial mediating role between carbon decoupling and energy equity, with the mediation effect value being −0.007 (p <.05); (4) green energy consumption and marketization can variably mitigate the adverse effects of carbon decoupling. Examining the sustainability of the economic-environmental system from the perspective of energy equity contributes to shaping a low-carbon economy that maintains social justice and sustainability, thereby avoiding falling into a low-carbon trap. [ABSTRACT FROM AUTHOR]

Developing countries have taken substantial measures to liberalize protected domestic markets to facilitate international trade. However, market liberalization often depends on policies of export expansion and innovations, raising the question: Is export expansion sufficient for productivity gains of developing countries? How does export expansion mediate the relationship between innovations and productivity gains? Are further policy interventions necessary? Hence, this study examines the impacts of developing countries' exports to China on productivity gains from 1992-2022. Further, it investigates how innovations mediate the relationship between export expansion and productivity gains. The 2SLS method is applied for estimation, which addresses the endogeneity by using export expansion to China from other developing countries. The findings reveal that exports positively impact productivity driven by China's rise. Innovations and learning by exporting significantly mediate the association between key variables. It highlights how developing countries benefit from China's growth, contributing insights to trade and economic growth literature. [ABSTRACT FROM AUTHOR]

BACKGROUND: Multiple observational studies have suggested a potential association between telomere length and musculoskeletal diseases. However, the underlying mechanisms remain unclear. OBJECTIVE: To investigate the genetic causal relationship between telomere length and musculoskeletal diseases using two-sample Mendelian randomization analysis. METHODS: Genome-wide association study summary data of telomere length were obtained from the UK Biobank. Genome-wide association study summary data of 10 common musculoskeletal diseases (osteonecrosis, osteomyelitis, osteoporosis, rheumatoid arthritis, low back pain, spinal stenosis, gout, scapulohumeral periarthritis, ankylosing spondylitis and deep venous thrombosis of lower limbs) were obtained from the FinnGen consortium. Inverse variance weighting, Mendelian randomization-Egger and weighted median methods were used to evaluate the causal relationship between telomere length and 10 musculoskeletal diseases. Inverse variance weighting was the primary Mendelian randomization analysis method, and sensitivity analysis was performed to explore the robustness of the results. RESULTS AND CONCLUSION: (1) Inverse variance-weighted results indicated a negative causal relationship between genetically predicted telomere length and rheumatoid arthritis (odds ratio=0.78, 95% confidence interval: 0.64-0.95, P=0.015) and osteonecrosis (odds ratio=0.56, 95% confidence interval: 0.36-0.90, P=0.016). No causal relationship was found between telomere length and the other eight musculoskeletal diseases (all P > 0.05). (2) Sensitivity analysis affirmed the robustness of these causal relationships, and Mendelian randomization-Egger intercept analysis found no evidence of potential horizontal pleiotropy (all P > 0.05). (3) This Mendelian randomized study supports that telomere length has protective effects against rheumatoid arthritis and osteonecrosis. However, more basic and clinical research will be needed to support our findings in the future. [ABSTRACT FROM AUTHOR]

Quantile treatment effects (QTEs) characterize different causal effects of treatment on outcome distribution. Propensity score (PS) methods are commonly employed for estimating QTEs in non-randomized studies. Previous studies have shown that insufficient and unnecessary adjustment for covariates in PS models leads to bias and efficiency loss of treatment effects. Confounders may vary across different quantiles of outcome distribution. To allow for the selection of covariates related to outcome at interesting quantiles corresponding to QTE, we proposed quantile adaptive lasso (QAL) to select covariates that can provide unbiased and efficient QTE estimation. QAL utilized linear quantile regression models to construct penalty weights. Theoretical analyses showed that QAL enjoys several asymptotic properties. Simulations showed the superiority of QAL over existing methods in variable selection and QTE estimation. We applied QAL to datasets from the China Health and Retirement Longitudinal Study to explore the impact of smoking on the severity of depression. [ABSTRACT FROM AUTHOR]

Enabled by recent technological advances and the substantial growth of the sharing economy, electric bike-sharing (EBS) has experienced rapid growth in medium-sized Chinese cities, yet its impact on for-hire vehicle (FHV) services remains insufficiently studied. Using a six-month longitudinal dataset from Yancheng, a representative medium-sized city in China, we employ an instrumental variable method to address potential endogeneity and provide quantitative empirical analysis. The analysis identifies a significant substitution effect, where a 1% increase in EBS trips corresponds to a 0.810% decline in FHV ridership. Through heterogeneity analyses, this study reveals that the substitutive effect of EBS is stronger in central downtown, which has denser infrastructure, while its impact diminishes in peripheral districts. Furthermore, unfavorable weather conditions mitigate the substitutive effect, as users increasingly rely on FHVs for their reliability and comfort during unfavorable conditions. The findings of this study highlight the necessity of integrating EBS into the electrified shared mobility ecosystem in a balanced manner to prevent disruptions to the existing transportation network and provide valuable guidance for sustainable and stable transportation planning in medium-sized cities and similar urban contexts. [ABSTRACT FROM AUTHOR]

When choosing estimands and estimators in randomized clinical trials, caution is warranted, as intercurrent events, such as, due to patients who switch treatment after disease progression, are often strongly associated with patients' time-varying prognostic factors. Consequently, for patients who did experience intercurrent events, there are typically no comparable patients who did not. Statistical analyses may then easily lure one into making large implicit extrapolations, which often go unnoticed. We will illustrate this problem of implicit extrapolations using a real oncology case study, with a right-censored time-to-event endpoint, in which patients can cross over from the control to the experimental treatment after disease progression, for ethical reasons. We address this by developing an estimator for the survival risk ratio contrasting the survival probabilities at each time t if all patients would take experimental treatment with the survival probabilities at those times t if all patients would take control treatment up to time t, using randomization as an instrumental variable to avoid reliance on no unmeasured confounders assumptions. This doubly robust estimator can handle time-varying treatment switches and right-censored survival times. Insight into the rationale behind the estimator is provided and the approach is demonstrated by reanalyzing the oncology trial. [ABSTRACT FROM AUTHOR]

Previous studies have shown that educational expectations of individuals with high socioeconomic status (SES) are relatively unaffected by low academic performance, a phenomenon called "sticky expectations." However, this result might be biased by endogeneity and reverse causality between academic achievement and educational expectations. Using data from the Trends in International Mathematics and Science Study from 11 countries with a strict school-entry rule and building on the well-established finding that children born in the months before the school-entry cutoff underperform at school, we use birth month as an instrument to identify the causal effect of early academic achievement on parents' expectations of university completion by parental education. Our findings based on the instrumental variable (IV) regression show that the moderation of social origin in the relationship between children's performance and parental expectations is moderately overestimated in cross-sectional data. Nonetheless, the stickiness of high-SES parental expectations is confirmed in the IV model, proving that parental expectations are less affected by children's early achievement when the parents are highly educated. [ABSTRACT FROM AUTHOR]

Empirical evidence on the links between biomass fuel and human capital development is limited. This paper estimates the impact of biomass cooking fuel on child schooling using a panel of household survey data (2011-2016) from Ethiopia. Applying an instrumental variable approach and the extended probit model, we find that using biomass cooking fuel decreases the probability of school enrollment and increases the probability of school absenteeism. This result is predominantly driven by work-related reasons and is more prominent among boys. Consequently, improved access to cleaner cooking fuels could have positive impacts on important development indicators such as better educational outcomes. [ABSTRACT FROM AUTHOR]

The global obesity rate has risen at an alarming rate in recent decades, and “fatness” has become an increasingly serious public health problem. At the same time, the loss of working hours and increased medical costs caused by air pollution have a wide range of direct and indirect effects on the health of the population and the macroeconomy. Against this background, using data from the China Family Panel Survey (CFPS) and thermal inversion as instrumental variables, this study analyzes the effect of air pollution on the risk of obesity among residents. We employ a two-stage least squares method to identify the effects of air pollution on the risk of obesity. The findings indicate that for a 1 μg/m³ increase in the annual average PM2.5 concentration at the county level, the obesity level increases significantly by 0.0286. This result is credible after a series of robustness checks; male groups, less-educated groups, and rural residents are more sensitive to the negative effects of air pollution. Finally, policy suggestions are provided. [ABSTRACT FROM AUTHOR]

Background: Emerging research indicates that gut microbiota (GM) are pivotal in the regulation of immune-mediated bone diseases. Nonunion, a bone metabolic disorder, has an unclear causal relationship with GM and immune cells. This study aims to elucidate the causal relationship between GM and nonunion using Mendelian Randomization (MR) and to explore the mediating role of immune cells. Methods: Using a two-step, two-sample Mendelian randomization approach, this study explores the causal link between GM and nonunion, as well as the mediating role of immune cells in this relationship. Data were sourced from multiple cohorts and consortiums, including the MiBioGen consortium. GM data were derived from a recently published dataset of 473 gut microbiota, and nonunion data were obtained from genome-wide association studies (GWAS). Results: MR analysis identified 12 bacterial genera with protective effects against nonunion and seven bacterial genera associated with a higher risk of nonunion, including Agathobacter sp000434275, Aureimonas, Clostridium M, Lachnospirales, Megamonas funiformis, and Peptoccia. Reverse MR analysis indicated that nonunion does not influence GM. Additionally, MR analysis identified 12 immune cell types positively associated with nonunion and 14 immune cell types negatively associated with nonunion. Building on these findings, we conducted mediation MR analysis to identify 24 crucial GM and immune cell-mediated relationships affecting nonunion. Notably, Campylobacter D, Megamonas funiformis, Agathobacter sp000434275, Lachnospirales, Clostridium E sporosphaeroides, and Clostridium M significantly regulated nonunion through multiple immune cell characteristics. Conclusions: To our knowledge, our research results are the first to emphasize a causal relationship between the gut microbiome and nonunion, potentially mediated by immune cells. The correlations and mediation effects identified in our study provide valuable insights into potential therapeutic strategies targeting the gut microbiome, informing global action plans. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Material and Methods Results Conclusion To estimate the causal effects of smoking and cessation on tooth loss using instrumental variable (IV) analysis.Data from the Behavioural Risk Factor Surveillance System (BRFSS), conducted from 1995 to 2006, 2008, 2010, and 2012 in 50 U.S. states and Washington DC, were merged with cigarette pack prices and smoking ban data from the CDC ‘Tax Burden on Tobacco’ and ‘State Tobacco Activities Tracking and Evaluation’ (STATE) System. Prices and bans served as IVs for ever smoking and cessation in a control function IV model for self‐reported edentulism and loss of 6+ teeth. IV models were adjusted for age, sex, race/ethnicity, educational attainment, type 2 diabetes, state unemployment rates, state health expenditures, and state fixed effects.Ever smoking cigarettes increased the risk of edentulism by 2.6% (95% CI: 2.3%–2.8%) and the risk of loss of 6+ teeth by 10.7% (95% CI: 10.0%–11.3%). Smoking cessation reduced risks of edentulism and loss of 6+ teeth by 7.6% (95% CI: −7.8% to −742%) and 13.5% (95% CI: −13.7% to 13.3%), respectively.This study provides robust evidence that smoking increases tooth loss risk, while cessation significantly reduces it, emphasizing the oral health benefits of tobacco control policies. [ABSTRACT FROM AUTHOR]

Introduction: The early prediction of sepsis based on machine learning or deep learning has achieved good results.Most of the methods use structured data stored in electronic medical records, but the pathological characteristics of sepsis involve complex interactions between multiple physiological systems and signaling pathways, resulting in mixed structured data. Some researchers will introduce unstructured data when also introduce confounders. These confounders mask the direct causality of sepsis, leading the model to learn misleading correlations. Finally, it affects the generalization ability, robustness, and interpretability of the model. Methods: To address this challenge, we propose an early sepsis prediction approach based on causal inference which can remove confounding effects and capture causal relationships. First, we analyze the relationship between each type of observation, confounder, and label to create a causal structure diagram. To eliminate the effects of different confounders separately, the methods of back-door adjustment and instrumental variable are used. Specifically, we learn the confounder and an instrumental variable based on mutual information from various observed data and eliminate the influence of the confounder by optimizing mutual information. We use back-door adjustment to eliminate the influence of confounders in clinical notes and static indicators on the true causal effect. Results: Our method, named CISepsis, was validated on the MIMIC-IV dataset. Compared to existing state-of-the-art early sepsis prediction models such as XGBoost, LSTM, and MGP-AttTCN, our method demonstrated a significant improvement in AUC. Specifically, our model achieved AUC values of 0.921, 0.920, 0.919, 0.923, 0.924, 0.926, and 0.926 at the 6, 5, 4, 3, 2, 1, and 0 time points, respectively. Furthermore, the effectiveness of our method was confirmed through ablation experiments. Discussion: Our method, based on causal inference, effectively removes the influence of confounding factors, significantly improving the predictive accuracy of the model. Compared to traditional methods, this adjustment allows for a more accurate capture of the true causal effects of sepsis, thereby enhancing the model's generalizability, robustness, and interpretability. Future research will explore the impact of specific indicators or treatment interventions on sepsis using counterfactual adjustments in causal inference, as well as investigate the potential clinical application of our method. [ABSTRACT FROM AUTHOR]

Despite relaxed of COVID-19 mask mandates at the time of this research, individuals in some countries, including Japan, continued to wear masks. This research suggests that childhood experiences of wearing school uniforms could influence anti-COVID-19 mask-wearing preferences in adulthood. Utilizing macro data on exogenous variations from the expansion of the apparel industry and quasi-experimental survey data, we establish causal links of childhood educational experiences. Findings indicate younger cohorts with school uniform experiences tend to persist in wearing masks when near others, even without mandates. This reflects the impact of childhood experiences on later-life behaviors through the development of other-regarding preferences. [ABSTRACT FROM AUTHOR]

The retrospective voting theory suggests that citizens vote for governing parties in response to distributive benefits. Knowing this, governments may reward voters by providing particularistic benefits—i.e., pork—prior to elections. Previous studies, however, do not account for the endogeneity. We address this problem by focusing on disaster relief and exploiting exogeneity of disaster. In particular, by using maximum hourly rainfall as an instrumental variable for disaster relief, we analyze the causal effect of disaster relief on incumbent's electoral outcomes. Our analyses of Japanese data in the past few decades indicate that disaster relief increased governing parties' vote share. Specifically, when the disaster relief per capita increases from zero to its mean, the predicted value of the governing parties' vote share increases by 2.8 and 5.4% points in the lower and upper chambers, respectively. The finding is consistent with retrospective voting behavior. Moreover, our results imply that the incumbent's electoral gain is brought about by persuading voters from oppositions to governing parties rather than by mobilizing supporters of governing parties. [ABSTRACT FROM AUTHOR]

Traffic-related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult because high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007 to 2016 (n  = 3 570 272 births) and computed hourly wind estimates for residential addresses within 500 m of high-traffic roads (ie, annual average daily traffic >25 000 vehicles) (10.9% of births). We matched pregnant individuals predominantly upwind with pregnant neighbors downwind of the same road segment (n  = 37 631 pairs). Living downwind was associated with a decrease of 11.6 g (95% CI, -18.01 to -5.21) in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50 m was associated with a decrease of 36.3 g (95% CI, -67.74 to -4.93) in term birth weight and living 51-100 m downwind was associated with an odds ratio of 3.68 (95% CI, 1.71-7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads. This article is part of a Special Collection on Environmental Epidemiology. [ABSTRACT FROM AUTHOR]

We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal regimes. Furthermore, identification of the expected outcome under superoptimal regimes in nonexperimental studies requires the same assumptions as identification of value functions under conventional optimal regimes when the treatment is binary. To illustrate the utility of superoptimal regimes, we derive identification and estimation results in a common instrumental variable setting. We use these derivations to analyse examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival. [ABSTRACT FROM AUTHOR]

By applying Resilience Index Measurement Analysis to data from Tajikistan, this paper measures food insecurity resilience capacity. Another objective of this paper is to construct and integrate coping strategies into resilience discussions. The final objective is to analyze the role of resilience capacity and coping strategy in food security with an Instrumental Variable approach. Our results generally confirm that resilience and coping strategies increase food security, determined by food expenditure, household adequacy of fruit and vegetable consumption, and household food expenditure share. Moreover, resilience capacity has a moderating role in mitigating negative impacts of shocks on food security. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of physical activity on well-being. We use data from the Aspects of Daily Life survey provided by the Italian National Institute of Statistics, for the period 2013–2019. To capture the effect of sport participation on life satisfaction we face the problem of endogeneity. We solve this issue from a methodological point of view by using an instrumental variable (IV) ordered probit model, where the instrument has been identified in living nearby to an equipped green area. Our investigation suggests that physical activity is positively and strongly associated with life satisfaction, showing that the probability of being very happy is very high (70%) among individuals practicing sport regularly. For this reason, policies targeted at promoting and supporting sport practice play a key role in determining the subjective well-being of citizens. Our results also indicate a significant association between life satisfaction and gender, and between life satisfaction and inability to work, revealing the need to foster inclusive policies to reduce differences in well-being among groups of population. [ABSTRACT FROM AUTHOR]

Our objective is to test for evidence of information asymmetry in the mortgage servicing market. Does the sale of mortgage servicing rights (MSR) by the initial lender to a second servicing institution unveil any residual asymmetric information? We are the first to analyze the originator's selling choice of MSR. We use a large sample of U.S. mortgages that were securitized through the private-label channel during the period of January 2000 to December 2013 (more than 5 million observations). We propose a new nonparametric instrumental variable testing procedure to account for potential endogeneity. For robustness, we present parametric analyses to corroborate our results using instrumental variables. Our empirical results provide strong support for the presence of second-stage asymmetric information in the mortgage servicing market during the period of analysis and before the risk retention reform of 2014. [ABSTRACT FROM AUTHOR]

We examine the causal effect of early retirement on medication use using Danish registry data. A reform in early retirement schemes in 2006 gradually increased eligibility ages from 60 to 64 differentially across birth cohorts. This enables an instrumental variable design that was applied using novel g-estimation methods that alleviate bias in binary outcome IV models. Our data allow studying patterns in the short run (ages 59½–60½) and in the long run (ages 57–63). For those who were eligible already at age 60, retirement did not change overall medication use. However, when investigating medication and population subgroups, we see that painkiller use decreases and hypertension medication as well as mental health medication use increase after retirement in almost all population subgroups. Moreover, males as well as the blue-collar occupation subgroups do show decreases in overall medication use after early retirement. In conclusion, our analyses reveal that retirement can have important heterogeneous health effects across population groups and are potentially informative about the welfare benefits of social insurance more broadly. [ABSTRACT FROM AUTHOR]

This paper explores the potential causal relationship between political orientation and education investment by using panel data from 21 OECD countries from 1970 to 2020 and utilizing estimators that address endogeneity (i.e. 2SLS, System GMM, and Lewbel 2SLS). In particular, using communist influence as a physical instrument for political orientation, we find a positive impact of the right political orientation on education investment, and the impact of the left orientation is negative. The positive impact from the right orientation is also stronger than the negative impact from the left. Moreover, these core results are robust to alternative measures of political orientation and education investment, alternative estimators that address endogeneity, and the moderation effect of innovation. [ABSTRACT FROM AUTHOR]

Abstract Background Exercise habits significantly influence the mental health of university students. However, previous research has often neglected the endogeneity issues in this context, leading to biased estimates and limiting the ability to establish a clear causal relationship. This study aimed to address endogeneity issues and investigate the causal effect of exercise habits on the mental health of university students. Methods This cross-sectional study was conducted involving 1,120 university students from China. Data on demographic characteristics, exercise habits, and mental health were collected using both online and offline questionnaires. To evaluate the mental health benefits of exercise habits, we employed both the instrumental variable (IV) approach and ordinary least squares (OLS) regression. Results The OLS estimates revealed a positive association between exercise habits and positive affect (β = 0.179, p

Abstract This article investigates the short- and long-term impacts of retirement on loneliness using panel data from the Survey of Health, Aging, and Retirement in Europe. To identify causal effects, we exploit differences in retirement rules across and within countries and use retirement thresholds in an instrumental variable setting. On average, we find that entering retirement leads to a reduction in loneliness in the long run and no clear effect in the short run. The reduction is driven by individuals being less likely to feel socially isolated and lacking companionship. Our results suggest that individuals adapt to retirement by increasing their activity levels and reap the benefits in terms of reduced loneliness and social isolation. The heterogeneity analysis shows that this is particularly true among the higher educated. The heterogeneity analysis also reveals that retirement increases feelings of loneliness for women in the short term and that the effect seems to be driven by women lacking companionship when their partner is not yet retired.

Abstract Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers’ perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers’ weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies.

Unmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables. A negative control outcome is associated with the confounder but not causally affected by the exposure in view, and a negative control exposure is correlated with the primary exposure or the confounder but does not causally affect the outcome of interest. We introduce an outcome confounding bridge function that depicts the relationship between the confounding effects on the primary outcome and the negative control outcome, and we incorporate a negative control exposure to identify the bridge function and the average causal effect. We also consider the extension to the positive control setting by allowing for the nonzero causal effect of the primary exposure on the control outcome. We illustrate our approach with simulations and apply it to a study about the short-term effect of air pollution on mortality. Although a standard analysis shows a significant acute effect of PM2.5 on mortality, our analysis indicates that this effect may be confounded, and after double negative control adjustment, the effect is attenuated toward zero.