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Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement. Multivariable Mendelian randomization (MVMR) estimates the direct effects of multiple exposures on an outcome, but it lacks statistical power when exposures are highly correlated. We introduce an extension of MVMR that can identify clusters of correlated exposure signals and use it to assess the effects of metabolites on Alzheimer disease. [ABSTRACT FROM AUTHOR]

Organizational dehumanization, a concept that has garnered increasing scholarly attention, still faces two significant limitations within the current literature. First, there is a lack of rigorously validated scales in the field. Second, the effects of organizational dehumanization on the family domain have been largely overlooked. In light of these gaps, we embarked on a comprehensive research project comprising five studies (NTotal = 2635) to address these limitations. Our primary objectives were twofold: (1) to develop and validate a concise five‐item scale for measuring organizational dehumanization based on Caesens et al.'s (Eur. J. Work Org. Psychol., 26, 2017, 527‐540) 11‐item measure (Studies 1, 2 and 3) and (2) to investigate a novel spillover–crossover model of organizational dehumanization (Studies 4 and 5). Our results indicated that our proposed short scale has a good factorial structure and high reliability indices, correlates strongly with the 11‐item full scale, is invariant over time and demonstrates evidence for convergent, discriminant and incremental validity. In addition, using data from both employees and their family members, we showed that organizational dehumanization contributes to an increase in work‐to‐family conflict among employees, as perceived by their family members. This, in turn, heightens relationship tension within their family members, ultimately leading to a decline in their relationship satisfaction. Theoretical and practical implications and avenues for future research are also discussed. [ABSTRACT FROM AUTHOR]

Energy poverty remains a significant challenge in developing nations, as exemplified by the situation in Indonesia. The connection between energy poverty and insufficient financial resources to fulfill basic energy requirements is significant. Remittances hold the potential to address energy poverty. This study seeks to explore the contribution of remittances to alleviating multidimensional energy poverty among households in Indonesia. Data for this research are sourced from the Indonesian Family Life Survey (IFLS) from 2007 to 2014. This study uses traditional migrant-sending districts as an instrumental variable to address endogeneity concerns arising from the reverse causality between remittances and energy poverty. The findings indicate that remittances from migrant workers can help alleviate multidimensional energy poverty in Indonesia, as demonstrated by the two-stage least-squares (2SLS) methodology. Households receiving assistance use the funds to increase their energy consumption and alleviate energy deprivation by directing resources toward electricity, appliances, and communication services. Additionally, the study highlights that economic disparities play a role in shaping the relationship between remittances and energy poverty. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide new evidence on the relationship between trade openness and carbon dioxide (CO2) emissions in Africa. Based on recent data and an uncommon and more informative composite indicator of trade openness proposed by Squalli and Wilson, we use an augmented Stochastic Impact by Regression on Population, Affluence and Technology (STIRPAT) model. The empirical evidence using the Two-Stage Least Square estimation (2SLS) validates the "pollution haven" hypothesis and shows that trade openness increases CO2 emissions in Africa. However, the elasticity varies greatly depending on the different measures of trade openness used. Moreover, we find that trade openness is associated with an increase in CO2 emissions in North Africa, South Africa and West Africa, but rather has a negative effect on CO2 emissions in East Africa and in Central Africa. Furthermore, the quantile regression approach shows that the effect of trade openness is increasing on the 10th, 25th, 50th, and 75th quintiles, but decreasing at the 90th quintile, thus highlighting the "scale effect". The results obtained are robust even when using other indicators of environmental quality. The findings suggest that it is important to have a real measure of the level of trade openness of countries, which will allow African leaders in particular to take appropriate measures to strengthen barriers to the entry of technologies and polluting materials, and invest more in green technologies. [ABSTRACT FROM AUTHOR]

Folk religions are reviving in contemporary China. They are distinctive from institutionalized religions and share many qualities with the emerging religious phenomena in the West. Despite their significance, however, the lack of concise frameworks and causal analysis methods has hindered a deeper understanding of these religions. By integrating psychological and sociocultural perspectives, this study concentrates on subjective well-being and tailors a micro model applicable to analyzing Chinese folk religions, along with a novel instrumental variable for causal identification. Empirical evidence suggests that folk religions have negative effects on subjective well-being, resulting in a 17.3 percent decrease in subjective well-being compared to the sample mean. This is primarily due to believers seeing folk religions as mere means for achieving earthly targets, therefore suffering from strengthened negative senses and motivated radical behaviors. Nevertheless, investing in folk religions to a high degree might transform believers' attitudes from seeing folk religions as means to seeing them as ends, thereby alleviating the negativity. This alleviation is mainly derived from positive senses, self-regulation, and inclusive attitudes towards society. Hence, taking religions more seriously and not utilitarianly, or fulfilling unmet targets more effectively, are two paths to eliminate the negative effects. [ABSTRACT FROM AUTHOR]

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental designs: regression discontinuity design, difference-in-differences analysis, interrupted time series design, instrumental variable analysis, and propensity score analysis with examples in education research. [ABSTRACT FROM AUTHOR]

This paper estimates the impact of participation in governance on member satisfaction with cooperatives using survey data from 960 land cooperative members in the Yangtze River Delta. The instrumental variable approach is used to address potential endogeneity. We find that participation in governance increases member satisfaction with cooperatives by approximately 26.2%. In particular, the impact of participation in governance is much higher for elite members with political connections than for ordinary members. The recognition of member rights and duties increases the likelihood of member participation in governance. To achieve the long‐term development of cooperative organizations, policy‐makers should encourage cooperative members to actively participate in governance, for example, by increasing cooperative members' understanding of their rights and responsibilities. [ABSTRACT FROM AUTHOR]

This paper estimates the causal effect of having young children aged 0–5 years on mothers’ labour force participation in rural India. To address the potential endogeneity in the fertility decision, I exploit Indian families’ preference for having sons. I leverage exogenous variation in the gender of older children aged 6 + years as an instrumental variable for having younger children aged 0–5 years in the family. IV estimates show that the mothers’ participation is significantly reduced by 9.9% due to the presence of young children aged 0–5 years in the household, with the negative effect mostly driven by mothers belonging to the highest income quartile; mothers with high education; and mothers residing in nuclear families. The findings highlight the need for investment in high-skilled jobs and formal childcare facilities to encourage mothers’ labour supply. Using the testable implications for the generalizability of LATE discussed in Angrist (The Economic Journal, 114: C52 C83, 2004), I show that the estimated causal effect is homogenous across compliers, always takers, and never takers and thus, generalizable to the whole population of interest. [ABSTRACT FROM AUTHOR]

This study investigates yardstick competition in local government spending in South Korea. By leveraging the exogenous variation introduced by by-elections, the study estimates the influence of neighbouring jurisdictions on local expenditures. The findings reveal no significant interdependence in spending among South Korean municipalities, which contrasts with previous empirical studies from other countries. The results suggest that conventional spatial econometric methods may overestimate the degree of interdependence in local spending. [ABSTRACT FROM AUTHOR]

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BACKGROUND: Many clinical research observations have indicated a close association between rheumatoid arthritis and osteoporosis as well as bone mineral density (BMD). However, it remains unclear whether there is a causal genetic relationship between rheumatoid arthritis and the development of osteoporosis and alterations of BMD. OBJECTIVE: To assess the potential causal relationship between rheumatoid arthritis and osteoporosis as well as BMD using a two-sample Mendelian randomization approach, provide meaningful insights from a genetic perspective into the underlying mechanisms and offer a reference for early prevention of osteoporosis and improvement in the progression of the disease. METHODS: We conducted a study using data from publicly available genome-wide association studies databases to identify single nucleotide polymorphisms associated with rheumatoid arthritis as instrumental variables (P < 5×10-8). The main outcomes of the study included osteoporosis and BMD at five different sites, including total body BMD, lumbar spine BMD, femoral neck BMD, heel BMD, and forearm BMD. The inverse variance-weighted method was used as the primary analysis method to evaluate causal effects. Weighted median, simple median, weighted mode and MR-Egger regression were used as supplementary analyses. Causal relationships between rheumatoid arthritis and the risk of osteoporosis and BMD were assessed using odds ratios (OR) and 95% confidence intervals (CI). Heterogeneity was assessed using Cochran’s Q test and horizontal pleiotropy was evaluated using MR-Egger intercept tests. RESULTS AND CONCLUSION: The inverse variance-weighted analysis demonstrated a positive association between genetically predicted rheumatoid arthritis and osteoporosis (OR=1.123, 95% CI: 1.077-1.171; P=4.02×10-8). Heterogeneity test (P=0.388) indicated no significant heterogeneity among the single nucleotide polymorphisms. MR-Egger intercept (P=0.571) tests did not detect horizontal pleiotropy, and sensitivity analysis showed no evidence of bias in the study results. There was no causal relationship between rheumatoid arthritis and BMD at the five different sites. The total body BMD (OR=1.000, 95% CI: 0.988-1.012; P=0.925), lumbar spine BMD (OR=0.999, 95% CI: 0.982-1.016; P=0.937), femoral neck BMD (OR=1.001, 95% CI: 0.986-1.016; P=0.866), heel BMD (OR=0.996, 95% CI: 0.989-1.004; P=0.419), and forearm BMD (OR=1.063, 95% CI: 0.970-1.031; P=0.996) indicated no significant association. MREgger intercept analysis did not detect potential horizontal pleiotropy (total body BMD: P=0.253; lumbar spine BMD: P=0.638; femoral neck BMD: P=0.553; heel BMD: P=0.444; forearm BMD: P=0.079). Rheumatoid arthritis may contribute to the development of osteoporosis through the interaction between chronic inflammation and bone formation, resorption, and absorption. Additionally, the use of glucocorticoids and the presence of autoantibodies (such as anti-citrullinated protein antibody) in patients with rheumatoid arthritis showed associations with osteoporosis. Future research should focus on monitoring systemic inflammatory markers, standardized use of glucocorticoids, and regular screening for osteoporosis risk in patients with rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Summary: Auxiliary variables with known population totals are extensively used in survey sampling to construct generalised regression (GR) estimators or optimal regression (OR) estimators of totals or means of study variables. This article explores the possibility of improving the efficiency of such estimators when continuous auxiliary variables are used in the regression estimation jointly with appropriate power functions of them, provided that the values of the auxiliary variables are known for all units in the population. The efficiency gain is determined analytically in the case of the OR estimator. A practical criterion for choosing the power functions that maximise the efficiency gain, involving the coefficient of determination in the regression fit of the study variable, is proposed for both the OR estimation and the more practicable, but generally less efficient, GR estimation. Furthermore, the effect of adding a power function of a continuous auxiliary variable in regression estimation is investigated when this variable is also used at the design stage. A simulation study shows that the joint use of a continuous auxiliary variable and a power function of it chosen according to the proposed criterion may improve considerably the efficiency of OR estimation, and much more the efficiency of GR estimation. [ABSTRACT FROM AUTHOR]

We evaluate the impacts of small high schools on youth risky behaviors and mental health in New York City, using a two-sample-instrumental-variable approach to address endogenous school enrollment. We find heterogeneous effects. School size, overall, has little effect. Among students most likely to attend small schools opened after an educational-achievement-oriented reform, however, diagnoses of violence-associated injuries and mental health disorders increased. Among students most likely to attend traditional small schools opened prior to the reform, pregnancy rates and diagnoses of mental health disorders declined. School focus is more important than school size as a determinant of student well-being outcomes. [ABSTRACT FROM AUTHOR]

This paper explores the relationship among education, knowledge, perception and disaster experience to investigate whether household disaster preparedness behaviour mitigates income losses. We employ instrumental variables approach and generate indigenous knowledge from a large‐scale dataset to examine responsiveness of disaster preparedness via unemployment and production. We identify disaster and climate knowledge perception as new determinant towards disaster risk reduction. Our findings suggest Disaster Preparedness Index (DPI) is almost 64% effective in mitigating household per capita net income loss in comparison with the mean via unemployment channel. We argue that informal education and community‐based training could bring more efficacies in this loss mitigation mechanism. [ABSTRACT FROM AUTHOR]

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non‐response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non‐respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non‐random missingness. Using worst case identification bounds that do not make assumptions about the non‐response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non‐response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility. [ABSTRACT FROM AUTHOR]

Complex contextual and sociopsychological factors influence the adoption of agricultural technologies like irrigation. This study used the sociopsychological Risk-Attitude-Norms-Abilities-Self-Regulation (RANAS) framework to examine the factors impacting irrigation adoption in Maharashtra (India). Logistic regression modelling was conducted based on cross-sectional surveys in 2019 and 2022, with interim interventions promoting risk-awareness and irrigation technology training. Effects of the interventions on the psychological variables in 2022 were corrected using instrumental variable regression. While micro-irrigation adoption rose from 36.9% to 62.8%, overall irrigation counterproductively decreased from 81.6% to 70.4%. Results indicated that wealth and risk-aversion remained relevant, while self-perceived ability and attitude towards irrigation became non-significant to irrigation adoption. This study highlights the unintended consequences of interventions and the necessity to also transform attitudes, and promote psychological ownership and trust to sustain irrigation technology adoption behaviour. These results could support stakeholders (e.g., policy makers, water authorities) in designing and implementing more sustainable interventions. [ABSTRACT FROM AUTHOR]

We address the semiparametric challenge of identifying and estimating generalized additive partial linear models with nonignorable missingness in the response. Identifiability is ensured under instrumental variable assumption that there is an instrumental covariate related to the prospensity but unrelated to the response variable, or the assumption that the conditional score function is linear in the response variable. We propose a new estimating equation for the prospensity by taking expectation of the unobservable part on a linear combination of all covariates rather than the covariates themselves. This estimating equation does not suffer from the typical curse of dimensionality. Then the unknown nonparametric function is approximated by polynomial spline basis functions and we construct estimating equations for mean of response based on the inverse probability weighting. Under some regular conditions, we establish asymptotic normality of the proposed estimators for parametric components and consistency of the estimators of nonparametric functions. Simulation studies demonstrate that the proposed inference procedure performs well in many settings. The proposed method is applied to analyze the household income dataset from the Chinese Household Income Project Survey 2013. [ABSTRACT FROM AUTHOR]

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.

Objective To explore the bidirectional causal relationships between periodontitis and asthma using the two-sample Mendelian randomization (MR) method to provide a basis for exploring the etiology and formulating preventive and therapeutic measures of periodontitis and asthma. Methods We performed two-sample bidirectional Mendelian randomization analysis using publicly released European genome-wide association studies (GWAS) statistics for periodontitis (n = 34 615) and asthma (n = 408 422). The inverse variance weighted (IVW) method was employed as the main approach to estimate the bidirectional causal relationships between periodontitis and asthma. In addition, weighted median (WM), MR-Egger regression, maximum likelihood, and Mendelian randomization robust adjusted profile score (MR-RAPS) were used as supplementary analyses. Sensitivity analyses were conducted using Cochran's Q test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out analysis. Results A total of 12 and 43 single-nucleotide polymorphisms (SNPs) were included as instrumental variables for periodontitis and asthma, respectively. The results of IVW, WM, MR-Egger regression, maximum likelihood, and MR-RAPS showed that periodontitis was not causally related to the risk of asthma (IVW: OR: 1.003, 95% CI: 0.973-1.035, P = 0.828, WM: OR: 0.990, 95% CI: 0.951-1.031, P = 0.641; MR-Egger regression: OR: 0.988, 95% CI: 0.960-1.028, P = 0.573; maximum likelihood: OR: 1.003, 95% CI: 0.972-1.035, P = 0.834; MR-RAPS: OR: 1.002, 95% CI: 0.970-1.036, P = 0.890) among the European population, and no causal effect of asthma on periodontitis was found (IVW: OR: 1.021, 95% CI: 0.938-1.111, P = 0.633, WM: OR: 1.011, 95% CI: 0.894-1.142, P = 0.866; MR-Egger regression: OR: 1.042, 95% CI: 0.824-1.319, P = 0.731; maximum likelihood: OR: 1.021, 95% CI: 0.938-1.112, P = 0.631; MR-RAPS: OR: 1.017, 95% CI: 0.931-1.110, P = 0.713) among the European population. Cochran's Q test showed no heterogeneity among the included instrumental variables, MR-PRESSO test found no horizontal pleiotropy, and the leave-one-out method did not identify outlier SNPs. Conclusion The results of this study, based on European genetic data, do not support a bidirectional causal association between periodontitis and asthma in the European population.

BACKGROUND: Osteonecrosis is a common refractory disease in clinical practice, and observational studies have suggested that micronutrients may have a prognostic role in osteonecrosis. However, the specific causal association between micronutrients and osteonecrosis is not known. OBJECTIVE: To explore the causal association between micronutrients and osteonecrosis by Mendelian randomization using summary data from a large population-based genome-wide association study (GWAS) for clinical diagnosis and treatment. METHODS: The required exposure and outcome data (calcium, magnesium, iron, vitamin E, carotenoids, retinol & osteonecrosis) were extracted from the IEU OpenGWAS database, GWAS catalog database, and FinnGen database. Data were analyzed by bidirectional Mendelian randomization with inversevariance weighted as the primary study method, and weighted median method, simple mode method, weighted mode method, and MR-Egger regression to complement the results. The reliability of the data was then verified through sensitivity analyses. RESULTS AND CONCLUSION: (1) The results found a positive correlation between serum iron concentration and osteonecrosis, while no correlation was found for other micronutrients. There was no reverse causality in all the data. (2) The results of sensitivity analysis showed a robust causality. (3) By Mendelian randomization method, this study provided evidence of causality between serum iron concentration and osteonecrosis, and understanding the causality of micronutrient elements on osteonecrosis can help in the clinical diagnosis and treatment of osteonecrosis, which is of great clinical significance. [ABSTRACT FROM AUTHOR]

We examine the impact of firm social media engagement on sales performance, answering “whether,” “what,” and “how” questions. The study uses a quasi-experimental design in a social e-commerce setting, for which propensity score matching and difference-in-differences methods quantify a mean 20.67% sales increase after firm social media adoption. We also find that firms that sell low-involvement products benefit more from social media adoption, compared to those that sell high-involvement products. Further, in terms of how to manage social media engagement, we find that informative content, in general, is effective for sales of high-involvement products, whereas promotional content, a new type of content discovered in this study, is more beneficial for sales of low-involvement products. Meanwhile, more social media followers generate better firm sales performance. We used instrumental variables and the control function method to address endogeneity issues and conducted robustness checks to support our conclusions. This study sheds light on the value of firm social media, particularly regarding industry differences and firm know-how. [ABSTRACT FROM AUTHOR]

Household consumption pattern has been used as an indicator for measuring the level of household welfare in Indonesia. The availability of labor in formal or informal sector is needed by the society to fulfill household needs. This is especially the case with informal sector that still dominates higher in Indonesia because this sector is a widely open alternative for the society in Indonesia. However, it often overlooked that the welfare of these jobs affects the transmission mechanism of intra-household job allocation, including women workers. This study analyzes women’s labor participation in the formal and informal sectors on household welfare on Sumatra Island. The method of this research is based on analysis of the data of Indonesia family Survey (IFL 5) in 2014. This data provided by the Indonesian Central Bureau of Statistics or Badan Pusat Statistik (BPS) offers a longitudinal on household in Indonesia. This research use sample of 2.610 household The data is analysed using Two Stage Least Square (2SLS) through variable instruments (IV). The results show that women household heads who work in the informal sector have a higher welfare than the formal sector and women household heads who are widowed tend to have low welfare. The regional government is expected to be able to maximize human development policies to reduce the gaps of household welfare based on gender, especially for women.

Abstract Objectives The goal of the research was to assess the quantitative relationship between median progression-free survival (PFS) and median overall survival (OS) specifically among patients with relapsed/refractory multiple myeloma (RRMM) based on published randomized controlled trials (RCTs). Methods Two bibliographic databases (PubMed and Embase, 1970–2017) were systematically searched for RCTs in RRMM that reported OS and PFS, followed by an updated search of studies published between 2010 and 2022 in 3 databases (Embase, MEDLINE, and EBM Reviews, 2010–2022). The association between median PFS and median OS was assessed using the nonparametric Spearman rank and parametric Pearson correlation coefficients. Subsequently, the quantitative relationship between PFS and OS was assessed using weighted least-squares regression adjusted for covariates including age, sex, and publication year. Study arms were weighted by the number of patients in each arm. Results A total of 31 RCTs (56 treatment arms, 10,450 patients with RRMM) were included in the analysis. The average median PFS and median OS were 7.1 months (SD 5.5) and 28.1 months (SD 11.8), respectively. The Spearman and Pearson correlation coefficients between median PFS and median OS were 0.80 (P

In this work we propose a functional concurrent regression model to estimate labor supply elasticities over the years 1988 through 2014 using Current Population Survey data. Assuming, as is common, that individuals' wages are endogenous, we introduce instrumental variables in a two-stage least squares approach to estimate the desired labor supply elasticities. Furthermore, we tailor our estimation method to sparse functional data. Though recent work has incorporated instrumental variables into other functional regression models, to our knowledge this has not yet been done in the functional concurrent regression model, and most existing literature is not suited for sparse functional data. We show through simulations that this two-stage least squares approach greatly eliminates the bias introduced by a naive model (i.e. one that does not acknowledge endogeneity) and produces accurate coefficient estimates for moderate sample sizes. [ABSTRACT FROM AUTHOR]

Background: Serum lipids were found to be correlated with chronic kidney disease and cardiovascular disease. Here, we aimed to research the potential causal associations between five serum lipid parameters and the risk of diabetic nephropathy using several Mendelian Randomization methods. Methods: Genetic data was obtained from the UK Biobank datasets. Causal effects were estimated using multiple MR methods. Heterogeneity and pleiotropy tests were performed. Results: MR analysis revealed that HDL-C and TG exhibited causal associations with diabetic nephropathy (P<0.05). Similar trends were not observed for other lipid parameters. Conclusions: Our research has suggested links between HDL-C, TG and diabetic nephropathy. The findings could contribute to further elucidation of the disease etiology. Strengths and limitations of this study: This article only uses Mendel randomization method to analyze the relationship between blood lipids and diabetes nephropathy, which is more convincing when combined with population data. [ABSTRACT FROM AUTHOR]

Theory predicts that societies with higher social capital have lower levels of government corruption. This hypothesis has been difficult to evaluate empirically because of widespread reliance on aggregated, country-level measures of trust and corruption, and reverse causality. We utilize micro-level international data from 34 African nations and two alternative measures of both trust and corruption to re-evaluate the evidence of this theorized relationship. To account for reverse causality, we use historical transatlantic slave exportation data as an instrumental variable. Slave trade has historically lowered interpersonal trust in Africa, which has had a long lasting effect on past and present levels of corruption. Our findings indicate that a higher level of personalized trust leads to more corruption, whereas a higher level of generalized trust leads to less corruption. This result is robust to alternative specifications of regression models, including different measures of trust and experienced versus perceived types of corruption. [ABSTRACT FROM AUTHOR]

Instrumental-variables estimation is an approach commonly used to evaluate the effect of a program in case of noncompliance. However, when the binary treatment status is misreported, standard techniques are not sufficient to point identify and consistently estimate the effect of interest. We present a new command, ivbounds, that implements three partial identification strategies developed by Tommasi and Zhang (2024, Journal of Econometrics 238: 105556) to bound the heterogeneous treatment effect when both noncompliance and misreporting of treatment status are present. We illustrate the use of the command by reassessing the benefits of participating in the 401(k) pension plan on savings in the United States. [ABSTRACT FROM AUTHOR]

ADHD is associated with an increased risk of injury. Causal evidence for effects of pharmacological treatment on injuries is scarce. We estimated effects of ADHD medication on injuries using variation in provider preference as an instrumental variable (IV). Using Norwegian registry data, we followed 8051 patients who were diagnosed with ADHD aged 5 to 18 between 2009 and 2011 and recorded their ADHD medication and injuries treated in emergency rooms and emergency wards up to 4 years after diagnosis. Persons with ADHD had an increased risk of injuries compared to the general population (RR 1.35; 95% CI: 1.30–1.39), with higher risk in females (RR 1.47; 95% CI: 1.38–1.56) than males (RR 1.23; 95% CI: 1.18–1.28). The between-clinics variation in provider preference for ADHD medication was large and had a considerable impact on patients' treatment status. There was no causal evidence for protective effects of pharmacological treatment on injuries overall for young individuals with ADHD characterized by milder or atypical symptoms. However, there was an apparent effect of pharmacological treatment over time on the risk of injuries treated at emergency wards in this patient group. [ABSTRACT FROM AUTHOR]

In this article, we study the problem of parameter estimation for measurement error models by combining the Bayes method with the instrumental variable approach, deriving the posterior distribution of parameters under different priors with known and unknown variance parameters, respectively, and calculating the Bayes estimator (BE) of the parameters under quadratic loss. However, it is difficult to obtain an explicit expression for BE because of the complex multiple integrals involved. Therefore, we adopt the linear Bayes method, which does not specify the form of the prior and avoids these complicated integral calculations, to obtain an expression for the linear Bayes estimator (LBE) for different priors. We prove that this LBE is superior to the two‐stage least squares estimator under the mean squared error matrix criterion. Numerical simulations show that our LBE is very close to the real parameter whether the variance parameters are known or unknown, and it gradually approaches BE as the sample size increases. Our results indicate that this instrumental variable approach is valid for measurement error models. [ABSTRACT FROM AUTHOR]

Vulnerable groups have low income and face borrowing constraints. What is the effect of pension security on lifelong consumption? Since 2011, China has been piloting old-age security among rural residents, which provides opportunities to study this issue. We numerically simulate the impact of old-age security on rural residents' consumption based on the overlapping generations framework and empirically test the findings. The empirical results show that pension security increases per capita expenditure during the working and retirement periods by 2.04% and 0.91%, respectively. The current excessively high average payment rate (exceeding the numerical simulation threshold of 0.1) has a negative moderating effect on the two consumption periods. The replacement rate, which primarily exhibiting a wealth effect, positively moderates consumption in both periods. Our research has theoretical and practical implications for optimizing social security and promoting welfare among low-income groups. [ABSTRACT FROM AUTHOR]

In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented; however, estimating biased causal effects by including some invalid IVs poses some risks. Invalid IVs are those IV candidates that are associated with unobserved variables. To solve this problem, we developed a novel strategy using negative control outcomes (NCOs) as auxiliary variables. Using NCOs, we are able to select only valid IVs and exclude invalid IVs without knowing which of the instruments are invalid. We also developed a new two‐step estimation procedure and proved the semiparametric efficiency of our estimator. The performance of our proposed method was superior to some previous methods through simulations. Subsequently, we applied the proposed method to the UK Biobank dataset. Our results demonstrate that the use of an auxiliary variable, such as an NCO, enables the selection of valid IVs with assumptions different from those used in previous methods. [ABSTRACT FROM AUTHOR]

This study examines the effects of urbanization on household health expenditure. Using a unique bi-annually household-level dataset from 2012–2016 from Vietnam, we obtain key findings as follows. To mitigate possible endogeneity concerns, we utilize a two-stage least squares regression (2SLS) approach, employing the development of information and communication (ICT) infrastructure at the province level as an instrumental variable (IV). The key findings can be summarized as follows. First, urbanization significantly reduces Vietnamese households' inpatient and outpatient health expenses. Second, the self-treatment expenses of households increase as the process of urbanization advances. In addition, we perform various robustness checks, encompassing different measures of urbanization, the use of lag of urbanization as an additional IV, and the plausible exogenous IV. The outcomes derived from these rigorous sensitivity analyses substantiate the reliability and consistency of our key results. Finally, we propose different ways to explain these results, including health insurance expenses and household income. [ABSTRACT FROM AUTHOR]

In survival contexts, substantial literature exists on estimating optimal treatment regimes, where treatments are assigned based on personal characteristics to maximize the survival probability. These methods assume that a set of covariates is sufficient to deconfound the treatment-outcome relationship. However, this assumption can be limited in observational studies or randomized trials in which non-adherence occurs. Therefore, we propose a novel approach to estimating optimal treatment regimes when certain confounders are unobservable and a binary instrumental variable is available. Specifically, via a binary instrumental variable, we propose a semiparametric estimator for optimal treatment regimes by maximizing a Kaplan–Meier-like estimator of the survival function. Furthermore, to increase resistance to model misspecification, we construct novel doubly robust estimators. Since the estimators of the survival function are jagged, we incorporate kernel smoothing methods to improve performance. Under appropriate regularity conditions, the asymptotic properties are rigorously established. Moreover, the finite sample performance is evaluated through simulation studies. Finally, we illustrate our method using data from the National Cancer Institute's prostate, lung, colorectal, and ovarian cancer screening trial. [ABSTRACT FROM AUTHOR]

Exploiting quasi-random assignment to NYC arraignment judges with varying propensities to detain, we use a judge fixed effect instrumental variable approach to estimate the impact of pretrial detention on several case outcomes: guilty plea, conviction, and carceral sentence. We find that any period of pretrial detention increases the likelihood of a guilty plea by 23 percentage points, a conviction by 24 percentage points, and a carceral sentence by 35 percentage points. Stratified analyses show differences in the size of the effect by charge severity and race: felony defendants experienced a larger effect on all case outcomes; non-Black defendants experienced a larger effect on guilty pleas and convictions; and Black defendants experienced a larger effect on carceral sentences. [ABSTRACT FROM AUTHOR]

Energy poverty and climate change are global challenges. While the Belt and Road Initiative (BRI) has fostered energy cooperation and alleviated energy poverty in BRI countries, its impact on climate change requires careful examination. This study investigates the impact of energy poverty alleviation on carbon emissions in BRI countries and analyzes the underlying mechanisms. In particular, this study constructs an integrated energy poverty assessment framework that includes three dimensions of energy accessibility, cleanliness, and affordability and utilizes the entropy-TOPSIS method to assess the status of energy poverty in 70 BRI countries. Subsequently, an empirical analysis using the Gini index as an instrumental variable was conducted to explore the impact of energy poverty alleviation on carbon emissions and the specific mechanisms. It is found that alleviating energy poverty in BRI countries will lead to an increase in per capita carbon emissions. However, improving energy cleanliness is effective in reducing per capita carbon emissions, while improving energy affordability has no significant impact on carbon emissions. This study shows that energy poverty alleviation and carbon emission reduction are two non-conflicting sustainable development goals. BRI countries can synergize energy poverty alleviation and carbon emissions reduction by focusing on clean energy development and providing energy subsidies. [ABSTRACT FROM AUTHOR]

This study investigates the impact of smoking on the labor market performance of college graduates, utilizing representative data from South Korea. To mitigate endogeneity concerns, we employ the average cigarette price during participants' college years as an instrumental variable for smoking behaviors. The findings reveal a detrimental effect of smoking on the labor market outcomes of college students. Smokers are significantly less likely to secure employment, and if employed, they tend to earn lower salaries. Notably, these adverse effects are more pronounced among female students. Through a behavioral and human capital lens, we discern the mechanism by which smoking impairs labor market performance. While smoking may initially contribute to positive aspects such as enhanced social activities, subjective mental health, and psychological stability among college students, it is concurrently associated with lower academic achievement, impeding human capital accumulation. These results suggest that although smoking may yield short-term benefits, it undermines long-term human capital development. The implications advocate for governmental intervention to mitigate the negative consequences of youth smoking. [ABSTRACT FROM AUTHOR]

We examine the causal effects of PM2.5 exposure on the burden of long-term care (LTC) by matching a satellite-based PM2.5 (particulate matter smaller than 2.5 micrometers (µm) in diameter) dataset with a nationally representative longitudinal study in China from 2011 to 2018. We find significant adverse effects of PM2.5 exposure--instrumented by thermal inversions--on the LTC burden. A 10 µg/m³ increase in annual PM2.5 exposure increases average monthly hours of LTC and the associated financial costs by 28 h and CNY 452, respectively. The effects are greater for those who had never smoked nor experienced severe PM2.5 pollution (annual average PM2.5 > 35 µg/m³) in the previous 5 years. We also find that as PM2.5 increases, chronic diseases, particularly cardiovascular diseases, could lead to a higher likelihood of LTC dependency but reduce the total hours and costs of LTC provision. Finally, we find that PM2.5 reduces the total years of LTC need, suggesting that PM2.5 increases LTC costs by increasing the severity of LTC dependency, rather than the duration of LTC need. Our findings can assist policymakers in planning for LTC provisions and clean air policies. [ABSTRACT FROM AUTHOR]

This article investigates the effect of sanitation on education at the municipal level in Brazilian cities. We measure the effect of the share of people living in households with access to piped water and a bathroom on educational indicators of the population aged 6–14. To take care of the well‐recognized endogeneity between sanitation and education, we estimate a panel‐data instrumental variable model. We use the municipalities that had their sanitation services privatized by other subnational instances as an instrumental variable. The results indicate that an increase of 1 percentage point in access to sanitation increases grade promotion rate by 0.334 pp., decreases age‐grade distortion rate by 0.464 pp., and decreases school dropout by 0.33 pp. We show evidence of complementarity between educational and sanitation policies, and the educational impacts of the latter can be used as leverage to convince politicians of the necessity to provide better water and sanitation conditions, especially to the poor population. [ABSTRACT FROM AUTHOR]

This paper considers the problem of inferring the causal effect of a variable Z on a dependently censored survival time T. We allow for unobserved confounding variables, such that the error term of the regression model for T is dependent on the confounded variable Z. Moreover, T is subject to dependent censoring. This means that T is right censored by a censoring time C, which is dependent on T (even after conditioning out the effects of the measured covariates). A control function approach, relying on an instrumental variable, is leveraged to tackle the confounding issue. Further, it is assumed that T and C follow a joint regression model with bivariate Gaussian error terms and an unspecified covariance matrix, such that the dependent censoring can be handled in a flexible manner. Conditions under which the model is identifiable are given, a two-step estimation procedure is proposed, and it is shown that the resulting estimator is consistent and asymptotically normal. Simulations are used to confirm the validity and finite-sample performance of the estimation procedure. Finally, the proposed method is used to estimate the causal effect of job training programs on unemployment duration. [ABSTRACT FROM AUTHOR]

The current study explores the possibility of a causal connection between a worker's religious attendance and wage. Numerous studies in the literature have already demonstrated a positive correlation between these two variables. For effective policy applications, however, it is necessary to find out whether there exists a causal connection between them. The study uses the US data and tests the hypothesis that religious attendance of middle-aged workers has a positive causal effect on their wages. Following an instrumental variable approach, the current study tests this hypothesis and demonstrates that religious attendance is very much likely to have a causal effect on middle-aged workers' earnings. This has important policy implications and thus deserves further attention from researchers and policymakers. [ABSTRACT FROM AUTHOR]

Estimation of causal treatment effects has attracted a great deal of interest in many areas including social, biological and health science, and for this, instrumental variable (IV) has become a commonly used tool in the presence of unmeasured confounding. In particular, many IV methods have been developed for right-censored time-to-event outcomes. In this paper, we consider a much more complicated situation where one faces interval-censored time-to-event outcomes, which are ubiquitously present in studies with, for example, intermittent follow-up but are challenging to handle in terms of both theory and computation. A sieve maximum likelihood estimation procedure is proposed for estimating complier causal treatment effects under the additive hazards model, and the resulting estimators are shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate the finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study. [ABSTRACT FROM AUTHOR]

Water is critical for agriculture in developing countries and climate change has created more extreme weather events. In this study, we examine the role of villages' year-round irrigation in ensuring households' irrigation to cope with weather shocks and investigate the effects of irrigation on income and poverty of rural households. Our results show that the availability of villages' year-round irrigation significantly increases the share of irrigated land area at the household level, which leads to higher crop income and household income, and lower poverty. Therefore, developing year-round irrigation is recommended. [ABSTRACT FROM AUTHOR]

In this study, we investigate a penalty-based two-stage least square estimator in regression models when the exploratory variables are correlated with the error term. We propose a two-stage Bridge estimator to overcome this endogeneity problem in high-dimensional data. Our proposed estimator enjoys remarkable statistical properties such as consistency and asymptotic normality. As special cases, this method deals some ill-condition situations such as the multicollinearity as well as the sparsity. Performance of the proposed estimators is demonstrated by simulation studies and it is compared to the existing estimators. An application in real data set is presented for illustration. [ABSTRACT FROM AUTHOR]

Objective To explore the causal relationship between lipoprotein (a) [Lp (a)] and cerebral small vascular disease (CSVD) by Mendelian randomization (MR) analysis. Methods Using genome-wide association study (GW AS) public database, the single nucleotide polymorphisms independently associated with Lp (a) (P < 5 X 10"8) were selected as instrumental variables (I Vs). The main outcomes of this study were white matter hyperintensity (WMH), cerebral micro hemorrhage, and lacunar infarction. Inverse variance weighted (IVW) mothod was used to evaluate the causal relationship between Lp (a) and CSVD, Sensitivity analysis was carried out by MR-Egger and MR-PRESSO to evaluate the pleiotropy of IVs. Furthermore, weighted median method, simple median method, and maximum likelihood ratio method were used to test the reliability of the results. Results IVW results showed that there was a positive risk relationship between Lp (a) and WMH (Oi=L083, 95%CI: 1.035-1.134, P < 0,001), but Lp(a) was not correlated with cerebral microhemorrhage (P=0.568) and lacunar infarction (P=0.651). MR-Egger analysis showed that the MR analysis of Lp (a) and WMH was not affected by horizontal pleiotropy (P=0.721). Conclusion MR analysis shows that Lp (a) is related to the increased risk of WMH. [ABSTRACT FROM AUTHOR]

Self-employment is a field of special importance to developing countries in view of its positive role on socio-economic development. This study exploits a unique nationally representative database using the 1949–1985 China Demographic Data, the 2016 China Labour Force Dynamic Survey, and the 1959–1999 China Drought Disaster Data. Through a difference-in-differences estimation, we reveal that the Great Chinese Famine bear significantly upon individuals' self-employment decisions. Specifically, a 1% increase in famine intensity results in a 3.89% and 2.31% decrease in the probability of self-employment and self-employed entrepreneurs entering the agricultural sector, respectively. Using rainfall as an instrument, we further show that the documented relationship is causal. Overall, we provide strong empirical evidence that famine exposure significantly affects one's self-employment decisions, and can help design policies aiming to promote self-employment and the development of small businesses. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) requires strong unverifiable assumptions to estimate causal effects. However, for categorical exposures, the MR assumptions can be falsified using a method known as the instrumental inequalities. To apply the instrumental inequalities to a continuous exposure, investigators must coarsen the exposure, a process which can itself violate the MR conditions. Violations of the instrumental inequalities for an MR model with a coarsened exposure might therefore reflect the effect of coarsening rather than other sources of bias. We aim to evaluate how exposure coarsening affects the ability of the instrumental inequalities to detect bias in MR models with multiple proposed instruments under various causal structures. To do so, we simulated data mirroring existing studies of the effect of alcohol consumption on cardiovascular disease under a variety of exposure-outcome effects in which the MR assumptions were met for a continuous exposure. We categorized the exposure based on subject matter knowledge or the observed data distribution and applied the instrumental inequalities to MR models for the effects of the coarsened exposure. In simulations of multiple binary instruments, the instrumental inequalities did not detect bias under any magnitude of exposure outcome effect when the exposure was coarsened into more than 2 categories. However, in simulations of both single and multiple proposed instruments, the instrumental inequalities were violated in some scenarios when the exposure was dichotomized. The results of these simulations suggest that the instrumental inequalities are largely insensitive to bias due to exposure coarsening with greater than 2 categories, and could be used with coarsened exposures to evaluate the required assumptions in applied MR studies, even when the underlying exposure is truly continuous. [ABSTRACT FROM AUTHOR]

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method—the doubly-ranked method—has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects—that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed. [ABSTRACT FROM AUTHOR]

In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly proposed method provides a highly efficient and robust alternative to the existing quasi-maximum likelihood estimation or GMM estimation, and can be implemented using the existing R software package conveniently. Under mild conditions, the consistency and asymptotic normality of the resulting estimators are established. The finite sample properties of the proposed method are investigated through Monte Carlo simulation studies. Finally, the Boston house price data and crime data of Tokyo are analyzed to illustrate the usefulness of the proposed estimation method. [ABSTRACT FROM AUTHOR]