Effect Estimation Research Articles

How do product responsibility and corporate philanthropy affect firm value?

Satisfying the consumer and contributing to societal well-being have been globally acknowledged, and these developments consequently boost corporate image, attract investors, increase stock prices, enhance firm value, and enable industrial and other firms to contribute to national development. This paper examines how product responsibility and philanthropy affect the performance of industrial goods firms in Nigeria. A sample of 7 firms was selected from 24 listed firms after employing a judgmental sampling technique and using secondary data and a quantitative research method. Data validation and analysis were aided by econometric views statistical software, panel data regression, fixed and random effects estimators, stationarity test, cross-section dependence test, Durbin-Watson test, and Hausman test. The study revealed that investment in product responsibility, as evidenced by the rising stock turnover rate, is value-enhancing in Nigeria {B1 = 0.076807, P = 0.0171 or P < 0.05}, while philanthropic donation is value destroying {B1 = –0.369535, P = 0.5817 or P > 0.05}. It was concluded that consumers’ confidence in corporate institutions can enhance corporate value, while investment in philanthropy is not usually value-enhancing when done irresponsibly and non-strategically. The study, therefore, recommended that investment in product responsibility should be consolidated to sustain the rising stock turnover rate, while investment in philanthropy should be done strategically and responsibly to make it value-enhancing. AcknowledgmentThis research was based on Nnamdi Azikiwe University Ph.D. Dissertation funded by the Tertiary Education Trust Fund (Tetfund), Nigeria. University of Calabar in Nigeria is highly acknowledged for funding the PhD dissertation through its Tetfund platform.

Hockey babies: National Hockey League outcomes and short-term fertility spikes in Canada

Previous research shows that mood-altering events, such as sports results, elections outcomes and natural disasters, impact fertility, crime rate, and investor behaviour. In this paper, we use recent Canadian Birth Statistics and the National Hockey League (NHL) results to examine the link between sports events and short-term fertility spikes. In addition, using betting odds, we differentiate among unexpected wins, unexpected losses, and expected outcomes, as a test for the relevance of the Prospect Theory to this context. Our dataset is a daily panel, following all the seven Canadian Census Metropolitan Areas with a team in the NHL, from 2008 to 2019. In our panel fixed effects estimations, we account for the day of the week and all statutory federal and provincial holidays of Canada. In the estimations with raw NHL results, the coefficients of interest ultimately lose their statistical significance. In the specifications accounting for expectations, we find a statistically significant association with fertility for unexpected wins, and statistically insignificant coefficients for unexpected losses as well as expected NHL outcomes. In light of the results, we propose the greater likelihood of pre-game social gatherings on sports nights and celebratory sex after the euphoria of an unexpected win, as the possible channels of impact.

Variance estimation for average treatment effects estimated by g-computation

AbstractThe average treatment effect is used to evaluate effects of interventions in a population. Under certain causal assumptions, such an effect may be estimated from observational data using the g-computation technique. The asymptotic properties of this estimator appears not to be well-known and hence bootstrapping has become the preferred method for estimating its variance. Bootstrapping is, however, not an optimal choice for multiple reasons; it is a slow procedure and, if based on too few bootstrap samples, results in a highly variable estimator of the variance. In this paper, we consider estimators of potential outcome means and average treatment effects using g-computation. We consider these parameters for the entire population but also in subgroups, for example, the average treatment effect among the treated. We derive their asymptotic distributions in a general framework. An estimator of the asymptotic variance is proposed and shown to be consistent when g-computation is used in conjunction with the M-estimation technique. The proposed estimator is shown to be superior to the bootstrap technique in a simulation study. Robustness against model misspecification is also demonstrated by means of simulations.

Revisiting openness-growth nexus: Panel data estimates for selected developing Asian countries

Trade openness is viewed as a way for Asian countries to progress economically and socially. Therefore, determining how trade affects economic growth is crucial for society, particularly for emerging economies. This paper provides empirical evidence on the issue of how openness to trade affects economic growth. A sample of 22 selected Asian countries has been taken as a balanced panel. To carry out the analysis, Auto Regressive Distributive Lag Model (ARDL) on annual data from 1990 to 2020 is used. To select between Pooled Mean Group (PMG) Mean Group (MG) and Dynamic Fixed Effect (DFE) estimators of ARDL, the Hausman test is used. PMG turns out as a better estimator. PMG estimation confirms that trade openness and economic growth exhibit a positive and statistically significant relation in the long run, however, short-run results lack significance. The study concluded with the recommendation that policymakers should adopt and implement an optimal trade policy. The optimal trade policy suggests the sample countries be more open to trade because it may result in higher long-run economic growth.

Guidelines for the prevention and treatment of glucocorticoid-induced osteoporosis in pediatrics.

Objective. To provide a framework for healthcare professionals managing pediatric patients who are on active glucocorticoid (GC) therapy and to develop recommendations for the prevention and treatment of GC-induced osteoporosis in the pediatric population. Methods. A panel of experts on bone and pediatric diseases developed a series of PICO questions that address issues related to the prevention and treatment of osteoporosis in patients on GC therapy. In accordance with the GRADE approach, we conducted a systematic review of the literature, summarized effect estimations, and classified the quality of the evidence. Then, voting and the formulation of recommendations followed. Results. Seven recommendations and six general principles were developed for GC-induced osteoporosis in the pediatric population. Conclusion. These recommendations provide guidance for clinicians who must make decisions concerning pediatric patients undergoing treatment with GC.

Minimax rates for heterogeneous causal effect estimation

Minimax rates for heterogeneous causal effect estimation

Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models.

We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.

Effect of Financial Depth on Output and Economic Growth

A lot of theoretical insights have been provided on the relationship between the financial and real sectors of the economy. The emerging theoretical postulations, which are devoid of consensus, have since been the subject of empirical investigations in respect of which, again, there is no consensus, not to talk of unanimity. Thus, this state of lack of consensus in the findings of the existing empirical studies calls for further studies that are aimed at resolving the contradictions. This informs the objective of the study, which is to examine the effect of financial depth on output and economic growth across African countries spanning from 1980 to 2019. The theoretical background rests on neo-classical theory and the supply-leading hypothesis. The study employed the dynamic fixed effect (DFE) estimation technique. The result reported that the three alternative proxies for the level of financial depth have positive effects on both overall economic growth and sectoral output growth. Also, each of the seven conditioning variables has the expected effects on economic growth. Based on the findings, the study therefore recommends that policymakers take measures aimed at deepening the level of the different forms of finance in order to promote economic growth.

The Role of Digitalization of Financial Inclusion in Reducing Income Gaps: an Empirical Study at 33 Provinces in Indonesia

One of the problems faced by developing countries, including Indonesia, is income inequality. It is hoped that digitalization of financial inclusion can solve this problem. Increasing financial inclusion is expected to make financial services more accessible to everyone, especially for poor groups who previously did not have access to financial services. This research aims to analyze the role of digitalization of financial inclusion in reducing income gaps using empirical evidence from 33 provinces in Indonesia during the 2013-2022 period, using the fixed effects estimation method. The results of this study indicate that financial inclusion has a negative and significant impact on the Williamson index. This means that increasing financial inclusion can reduce income inequality. The decomposition results for each dimension show that the access dimension has a significant effect on reducing income inequality in Indonesia, while the usage dimension shows a positive influence on income inequality, but the presence of the SNKI policy in Indonesia in 2016-2022 has a negative influence on income inequality in Indonesia.

BOARD CHARACTERISTICS AND AUDIT QUALITY: EVIDENCE FROM VIETNAM

This study investigates the relationship between key director characteristics, board composition, and audit quality in the context of Vietnamese listed firms on the Ho Chi Minh City Stock Exchange over the period 2015 to 2020. Based on the study of Mustafa et al (2017), we employ a comprehensive dataset comprising 415 firms, we utilize a random effects estimation model to analyze the nuances of these associations. Our findings reveal a noteworthy positive relationship between directors aged 36-55 and 46-55 years old and audit quality, suggesting that the experience and expertise of directors within these age brackets contribute positively to the enhancement of audit processes. Furthermore, our study identifies a significant positive impact on audit quality associated with interlocking directorships, emphasizing the importance of interconnected networks within corporate governance structures. In addition, our results highlight the influential role of boards populated by members holding Master's degrees, indicating a positive correlation between educational qualifications and clients' demand for high audit quality. This insight underscores the significance of academic prowess and specialized knowledge in fostering a climate conducive to rigorous audit practices.

The Price of Losing Autonomy: Assessing the Economic Impact of County-to-District Mergers in China

Amalgamations in democratic settings often result in disadvantages for small and peripheral units, as their political power diminishes in the merged jurisdictions. This article extends the discussion to China by assessing the economic outcomes of county-level units after their conversion into districts directly controlled by city authorities. Leveraging a large county-year panel dataset and employing an advanced causal effect estimator, my analysis reveals a generally less optimistic outlook for these county-turned districts following the mergers. The negative impacts are initially evident in microeconomic indicators, especially in resident deposits, and later materialize in the long-term trajectories of macroeconomic indicators. Furthermore, heterogeneous analysis and a comparative case study in Hangzhou city link the problem to the loss of administrative autonomy. Notably, the negative impacts are less likely to manifest in county-turned districts that are under provincial protection and therefore maintain a certain degree of administrative autonomy after the mergers.

Digital transformation and governance heterogeneity as determinants of CSR disclosure: insights from Chinese A-share companies

PurposeIn this fast-changing world, digitization has become crucial to organizations, allowing decision-makers to alter corporate processes. Companies with a higher corporate social responsibility (CSR) level not only help encourage employees to focus on their goals, but they also show that they take their social responsibility seriously, which is increasingly important in today’s digital economy. So, this study aims to examine the relationship between digital transformation and CSR disclosure of Chinese A-share companies. Furthermore, this research investigates the moderating impact of governance heterogeneity, including CEO power and corporate internal control (INT) mechanisms.Design/methodology/approachThis study used fixed effect estimation with robust standard errors to examine the relationship between digital transformation and CSR disclosure and the moderating effect of governance heterogeneity among Chinese A-share companies from 2010 to 2020. The whole sample consists of 17,266 firms, including 5,038 state-owned enterprise (SOE) company records and 12,228 non-SOE records. The whole sample data is collected from the China Stock Market and Accounting Research, the Chinese Research Data Services and the WIND databases.FindingsThe regression results lead us to three conclusions after classifying the sample into non-SOE and SOE groups. First, Chinese A-share businesses with greater levels of digitalization have lower CSR disclosures. Both SOE and non-SOE are consistent with these findings. Second, increasing CEO authority creates a more centralized company decision-making structure (Breuer et al., 2022; Freire, 2019), which improves the negative association between digitalization and CSR disclosure. These conclusions, however, also apply to non-SOE. Finally, INT reinforces the association between corporate digitization and CSR disclosure, which is especially obvious in SOEs. These findings are robust to alternative HEXUN CSR disclosure index. Heterogeneity analysis shows that the negative relationship between corporate digitalization and CSR disclosures is more pronounced in bigger, highly levered and highly financialized firms.Originality/valueDigitalization and CSR disclosure are well studied, but few have examined their interactions from a governance heterogeneity perspective in China. Practitioners and policymakers may use these insights to help business owners implement suitable digital policies for firm development from diverse business perspectives.

Relationships among gut microbiota, plasma metabolites, and juvenile idiopathic arthritis: a mediation Mendelian randomization study.

The objective of this study is to investigate the causal relationship between gut microbiota and juvenile idiopathic arthritis, and to identify and quantify the potential role of plasma metabolites as mediators. Using summary-level data from genome-wide association studies, a two-sample Mendelian randomization was conducted involving 131 gut microbiota genus, 1,400 plasma metabolites, and juvenile idiopathic arthritis. Additionally, a two-step approach was employed to quantify the proportion of the effect of gut microbiota on juvenile idiopathic arthritis mediated by plasma metabolites. Effect estimation primarily utilized Inverse Variance Weighting, with further validation using Bayesian weighted Mendelian randomization. In our MR analysis, a positive correlation was observed between Rikenellaceae and the risk of juvenile idiopathic arthritis, while Dorea showed a negative correlation with juvenile idiopathic arthritis risk. Mediation analysis indicated that Furaneol sulfate levels acted as a mediator between Dorea and juvenile idiopathic arthritis, with an indirect effect proportion of 19.94, 95% CI [8.86-31.03%]. Our study confirms a causal relationship between specific microbial genus and juvenile idiopathic arthritis, and computes the proportion of the effect mediated by plasma metabolites, offering novel insights for clinical interventions in juvenile idiopathic arthritis.

The performance of human capital in relationship with economic growth

In this paper I examine the impact of human capital on the economic growth for the OECD countries during the period 1990-2022 employing fixed effects estimator. My contribution to the literature comes from the chosen variables, as the proxies selected were not yet used by other researchers in the same formula. The indicators selected are tertiary enrollment, spending on health, life expectancy and population growth. The data was constructed into a panel of 38 countries over the period 1990-2022. Results surprisingly show that only tertiary enrollment, life expectancy and population growth have a significant impact on economic growth. Therefore, my recommendations are focusing on improving the quality of life.

Analisis Ekonomi Pekerja Migran Indonesia dalam Memilih Jenis Pekerjaan pada BP3MI Provinsi Jawa Barat

This article presents the results of economic research on Indonesian migrant workers in choosing the type of work. Primary data were obtained through a survey of prospective migrant workers managed by the Indonesian Migrant Workers Protection Agency of West Java Province. The data explored represent the economic, social, demographic, and institutional aspects of migration, with mixed data properties, namely nominal, ordinal and multinomial. The qualitative limited dependent variable (QLDV) econometric method was used to estimate the multinomial logit equation. The results of the marginal effects estimation found that education level, wage level in the destination country, lack of job opportunities in the place of residence, experience working as a migrant worker, domestic wages, and the existence of income from household members other than husband and wife explained the decision to choose the type of job to work. The probability of choosing the operator/factory occupation is 0.3954, which is greater than other occupations. With a high school education level and an overseas wage of IDR 15.26 million per month.

Doubly robust proximal synthetic controls.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance.

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.

Robustness of response-adaptive randomization.

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Comparative Efficacy of Different Protein Supplements on Muscle Mass, Strength, and Physical Indices of Sarcopenia among Community-Dwelling, Hospitalized or Institutionalized Older Adults Undergoing Resistance Training: A Network Meta-Analysis of Randomized Controlled Trials.

Aging-related sarcopenia exerts harmful impacts on muscle mass, strength, and physical mobility. Protein supplementation has been demonstrated to augment efficacy of resistance training (RT) in elderly. This study compared the relative effects of different protein supplements on muscle mass, strength, and mobility outcomes in middle-aged and older individuals undergoing RT. A comprehensive search of online databases was performed to identify randomized controlled trials (RCTs) examining the efficacy of protein supplement plus RT in untrained community-dwelling adults, hospitalized, or institutionalized residents who suffered acute or chronic health conditions. Network meta-analysis (NMA) was performed using a frequentist method for all analyses. Treatment effects for main outcomes were expressed as standard mean difference (SMD) with 95% confidence interval (CI). We used the surface-under-the cumulative-ranking (SUCRA) scores to rank probabilities of effect estimation among all identified treatments. Meta-regression analyses were performed to identify any relevant moderator of the treatment efficacy and results were expressed as β with 95% credible interval (CrI). We finally included 78 RCTs (5272 participants) for analyses. Among the six protein sources identified in this NMA, namely whey, milk, casein, meat, soy, and peanut, whey supplement yielded the most effective treatments augmenting efficacy of RT on muscle mass (SMD = 1.29, 95% CI: 0.96, 1.62; SUCRA = 0.86), handgrip strength (SMD = 1.46, 95% CI: 0.92, 2.00; SUCRA = 0.85), and walking speed (SMD = 0.73, 95% CI: 0.39, 1.07; SUCRA = 0.84). Participant's health condition, sex, and supplementation dose were significant factors moderating the treatment efficacy on muscle mass (β = 0.74; 95% CrI: 0.22, 1.25), handgrip strength (β = -1.72; 95% CrI: -2.68, -0.77), and leg strength (β = 0.76; 95% CrI: 0.06, 1.47), respectively. Our findings suggest whey protein yields the optimal supplements to counter sarcopenia in older individuals undergoing RT.

Adversarially Balanced Representation for Continuous Treatment Effect Estimation

Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates. However the existing methods mostly consider the scenario of binary treatments. In this paper, we consider the more practical and challenging scenario in which the treatment is a continuous variable (e.g. dosage of a medication), and we address the two main challenges of this setup. We propose the adversarial counterfactual regression network (ACFR) that adversarially minimizes the representation imbalance in terms of KL divergence, and also maintains the impact of the treatment value on the outcome prediction by leveraging an attention mechanism. Theoretically we demonstrate that ACFR objective function is grounded in an upper bound on counterfactual outcome prediction error. Our experimental evaluation on semi-synthetic datasets demonstrates the empirical superiority of ACFR over a range of state-of-the-art methods.