Causal effects of corporate taxes on private firms' earnings management: a regression discontinuity analysis

PurposeThe authors emphasize the information role of earnings management and how it may be used to “mislead some stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting numbers.” Specifically, the authors examine the causal effect of tax incentives on private firms' earnings management based on a corporate tax reform in China.Design/methodology/approachIn December 2001, China implemented a tax collection reform which moved the collection of corporate income taxes from the local tax bureau to the state tax bureau. This reform results in exogenous variations in the effective tax rate among similar firms established before and after 2002. The authors apply a regression discontinuity design and use the generated variation in the effective tax rate to investigate the impact of taxes on firm earnings management.FindingsThe authors find that tax reduction substantially increases private firms' incentives to manage earnings information, and such effect is particularly pronounced when tax collection intensity and government interventions are low. Further evidence shows that lower tax rates stimulate firms' investment, inventory turnover and recruitment of skilled human capital. A plausible mechanism is that private firms signal a promising outlook by managing earnings to attain greater financing and improve investment/operation levels when financial constraints are removed.Originality/valueFirst, the authors present the causal effects of tax incentives on private firm's earnings management, which deepens the authors’ understanding on the determinants of firm's earnings information production. Second, this study also contributes to the literature on tax-induced earnings management. Third, the authors believe that this topic offers clear policy implications and would be of particular interest to regulators.

Identifying the potential role of insomnia on multimorbidity: A Mendelian randomization phenome-wide association study in UK Biobank

Objectives: To identify the breadth of potential causal effects of insomnia on health outcomes and hence its possible role in multimorbidity. Design: Mendelian randomisation (MR) Phenome-wide association study (MR-PheWAS) with two-sample Mendelian randomisation follow-up. Setting: Individual data from UK Biobank and summary data from a number of genome-wide association studies. Participants: 336,975 unrelated white-British UK Biobank participants. Exposures: Standardised genetic risk of insomnia for the MR-PheWAS and genetically predicted insomnia for the two-sample MR follow-up, with insomnia instrumented by a genetic risk score (GRS) created from 129 single-nucleotide polymorphisms (SNPs). Main outcomes measures: 11,409 outcomes from UK Biobank extracted and processed by an automated pipeline (PHESANT). Potential causal effects (i.e., those passing a Bonferroni-corrected significance threshold) were followed up with two-sample MR in MR-Base, where possible. Results: 437 potential causal effects of insomnia were observed for a number of traits, including anxiety, stress, depression, mania, addiction, pain, body composition, immune, respiratory, endocrine, dental, musculoskeletal, cardiovascular and reproductive traits, as well as socioeconomic and behavioural traits. We were able to undertake two-sample MR for 71 of these 437 and found evidence of causal effects (with directionally concordant effect estimates across all analyses) for 25 of these. These included, for example, risk of anxiety disorders (OR=1.55 [95% confidence interval (CI): 1.30, 1.86] per category increase in insomnia), diseases of the oesophagus/stomach/duodenum (OR=1.32 [95% CI: 1.14, 1.53]) and spondylosis (OR=1.57 [95% CI: 1.22, 2.01]). Conclusion: Insomnia potentially causes a wide range of adverse health outcomes and behaviours. This has implications for developing interventions to prevent and treat a number of diseases in order to reduce multimorbidity and associated polypharmacy.

Polynomial Mendelian Radomization reveals widespread non-linear causal effects in the UK Biobank

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

Instead of laboratory: Analyzing data from natural experiments (Nobel Memorial Prize in Economic Sciences 2021)

The 2021 Nobel Prize in Economic Sciences was awarded to David Card, Joshua Angrist, and Guido Imbens for advancing methodology to establish casual relationships in economics. Their approach brought the notion of the natural experiment, situations in which heterogeneous reactions of different groups of people to chance shocks or policy changes allows to elicit causal effects, to the forefront of empirical analysis, and spearheaded a revolution in development of statistical methods needed to analyze the data. After the initial contributions in labor economics and economics of education, the new approach has become a new standard in economic sciences.

Genetic liability to rheumatoid arthritis on autism and autistic traits: polygenic risk score and Mendelian randomization analyses

Higher prevalence of autism in offspring born to mothers with rheumatoid arthritis has been reported in observational studies. We investigated (a) the associations between maternal and offspring’s own genetic liability for rheumatoid arthritis and autism-related outcomes in the offspring using polygenic risk scores (PRS) and (b) whether the effects were causal using Mendelian randomization (MR). Using the latest genome-wide association (GWAS) summary data on rheumatoid arthritis and individual-level data from the Avon Longitudinal Study of Parents and Children, United Kingdom, we constructed PRSs for maternal and offspring genetic liability for rheumatoid arthritis (single-nucleotide polymorphism [SNP] p-value threshold 0.05). We investigated associations with autism, and autistic traits: social and communication difficulties, coherence, repetitive behaviours and sociability. We used modified Poisson regression with robust standard errors. In two-sample MR analyses, we used 40 genome-wide significant SNPs for rheumatoid arthritis and investigated the causal effects on risk for autism, in 18,381 cases and 27,969 controls of the Psychiatric Genetics Consortium and iPSYCH. Sample size ranged from 4992 to 7849 in PRS analyses. We found little evidence of associations between rheumatoid arthritis PRSs and autism-related phenotypes in the offspring (maternal PRS on autism: RR 0.89, 95%CI 0.73–1.07, p = 0.21; offspring’s own PRS on autism: RR 1.11, 95%CI 0.88–1.39, p = 0.39). MR results provided little evidence for a causal effect (IVW OR 1.01, 95%CI 0.98–1.04, p = 0.56). There was little evidence for associations between genetic liability for rheumatoid arthritis on autism-related outcomes in offspring. Lifetime risk for rheumatoid arthritis has no causal effects on autism.

What Is Going on with Visual Attention in Reading and Dyslexia? A Critical Review of Recent Studies

This critical review examined current issues to do with the role of visual attention in reading. To do this, we searched for and reviewed 18 recent articles, including all that were found after 2019 and used a Latin alphabet. Inspection of these articles showed that the Visual Attention Span task was run a number of times in well-controlled studies and was typically a small but significant predictor of reading ability, even after potential covariation with phonological effects were accounted for. A number of other types of tasks were used to examine different aspects of visual attention, with differences between dyslexic readers and controls typically found. However, most of these studies did not adequately control for phonological effects, and of those that did, only very weak and non-significant results were found. Furthermore, in the smaller studies, separate within-group correlations between the tasks and reading performance were generally not provided, making causal effects of the manipulations difficult to ascertain. Overall, it seems reasonable to suggest that understanding how and why different types of visual tasks affect particular aspects of reading performance is an important area for future research.

Non-Causal Effects of Asthma on COVID-19 Susceptibility and Severity

Background: Asthma is observationally associated with an increased risk of COVID-19, but the causality remains unclear. We aim to determine whether there is a casual role of asthma in susceptibility to SARS-CoV-2 infection or COVID-19 severity.Methods: Instrumental variables (IVs) for asthma and moderate-to-severe asthma were obtained from publicly available summary statistics from the most recent and largest genome-wide association study (GWAS), including 394 283 and 57 695 participants of European ancestry, respectively. The corresponding data for COVID-19 susceptibility, hospitalization and severe-disease were derived from the COVID-19 Host Genetics Initiative GWAS meta-analysis of up to 1 683 768 individuals of European descent. Causality was inferred between correlated traits by Mendelian Randomization analyses. Inverse-variance weighted method was used as the primary MR estimates and multiple alternate approaches and several sensitivity analyses were also conducted.Results: Our MR analysis revealed no causal effects of asthma on COVID-19 susceptibility, hospitalization or severe disease, with odds ratio (OR) of 0.994 (95% CI: 0.962–1.027), 1.020 (95% CI: 0.955–1.089), and 0.929 (95% CI: 0.836–1.032), respectively. Furthermore, using genetic variants for moderate-to-severe asthma, a similar pattern of results was observed for COVID-19 susceptibility (OR: 0.988, 95% CI: 0.946–1.031), hospitalization (OR: 0.967, 95% CI: 0.906–1.031), and severe disease (OR: 0.911, 95% CI: 0.823–1.009). The association of asthma and moderate-to-severe asthma with COVID-19 was overall robust to sensitivity analyses.Conclusion: Genetically predicted asthma was not associated with susceptibility to, or severity of, COVID-19 disease, indicating that asthma is unlikely to be a causal factor in the development of COVID-19.

Identification of causal effects in case-control studies

Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities.