Effect Estimation Research Articles

A path towards environmental sustainability: exploring the effects of technological innovation and investment freedom on load capacity factor

ABSTRACT This study examines the impact of investment freedom, technological innovation, renewable energy, and economic growth on load capacity factor (LCF) within the context of Sustainable Development Goals (SDGs) 7 and 13 for a group of high investment freedom countries (Luxembourg, Germany, Austria, Australia, Canada, Denmark, Netherlands, United Kingdom, Chile, Singapore, New Zealand, United States of America, Belgium, Finland, Uruguay, Latvia, Spain, Sweden, and Switzerland). Furthermore, this research assesses the impacts of achieving carbon neutrality by 2030. The data set covers the years between 1995 and 2019. Moreover, the validity of the load capacity curve (LCC) hypothesis is analyzed in all countries. Long-run coefficients are estimated using the Regularized Common Correlated Effects (rCCE) estimator, and the robustness analysis is performed using Common Correlated Effects (CCE) estimators. The overall assessment of the panel reveals that the LCC hypothesis is invalid in the selected nations, with the exception of Belgium. Other findings indicate that investment freedom reduces the LCF for New Zealand. However, investment freedom increases LCF, improving environmental quality in Latvia. Technological innovation decreases LCF for Singapore and increases it for Germany. Renewable energy increases LCF for the UK and Spain. Finally, policy implications for improving environmental quality are discussed.

Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose.

Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.

The Investment development path theory: Evidence from developing countries' agricultural sector.

We examined the investment development path (IDP) through the perspective of developing countries' agricultural sector. Our analytical approach indirectly accounts for interactions among countries regarding cross-border resource transfers. Aside from providing knowledge on testing the IDP by inferential statistics, the information would be relevant for policymaking. Identifying the stage(s) in the IDP not only highlights the global appeal of agriculture but also guides firms seeking to expand beyond borders. This information is essential for developing an effective economic strategy. We employed data from 1991 to 2021 for 55 countries from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and applied a fixed effects estimator corrected for serial correlation and non-constant variances. We found that agriculture in developing countries is currently in stages I and II of the IDP. Broadly, agricultural production requires policies that would increase outward foreign direct investment and inward foreign direct investment. Domestic agricultural businesses in developing countries must develop capacity by learning from foreign multinationals. This would enable agricultural businesses to invest abroad. Such a move would lead to an increase in outward FDI. As this would have resulted from increased GDP per capita, it will lead to movement from the existing stage to higher ones.

Measuring returns to experience using supervisor ratings of observed performance: The case of classroom teachers

AbstractWe study the returns to experience in teaching, estimated using supervisor ratings from classroom observations. We describe the assumptions required to interpret changes in observation ratings over time as the causal effect of experience on performance. We compare two difference‐in‐differences strategies: the two‐way fixed effects estimator common in the literature, and an alternative which avoids potential bias arising from effect heterogeneity. Using data from Tennessee and Washington, DC, we show empirical tests relevant to assessing the identifying assumptions and substantive threats—e.g., leniency bias, manipulation, changes in incentives or job assignments—and find our estimates are robust to several threats.

Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia

Brain functional network analysis has become a popular method to explore the laws of brain organization and identify biomarkers of neurological diseases. However, it is still a challenging task to construct an ideal brain network due to the limited understanding of the human brain. Existing methods often ignore the impact of temporal-lag on the results of brain network modeling, which may lead to some unreliable conclusions. To overcome this issue, we propose a novel brain functional network estimation method, which can simultaneously infer the causal mechanisms and temporal-lag values among brain regions. Specifically, our method converts the lag learning into an instantaneous effect estimation problem, and further embeds the search objectives into a deep neural network model as parameters to be learned. To verify the effectiveness of the proposed estimation method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database by comparing the proposed model with several existing methods, including correlation-based and causality-based methods. The experimental results show that our brain networks constructed by the proposed estimation method can not only achieve promising classification performance, but also exhibit some characteristics of physiological mechanisms. Our approach provides a new perspective for understanding the pathogenesis of brain diseases. The source code is released at https://github.com/NJUSTxiazw/CTLN.

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Empirical Evidence of ESG Disclosure's Impact on Corporate Investment Decision in Indonesia

The purpose of this study is to investigate the impact of environmental, social, and governance (ESG) disclosure on corporate investment decisions. Data were collected from annual reports of firms listed in Indonesia Stock Exchange between 2015 and 2020 (378 firm-year observations). Using Ordinary Least Square (OLS) and fixed effect estimation, this study found that the dimension of ESG practices positively affects corporate investment decisions. In addition, the presidential election periods increase the effectiveness of corporate investment decisions. The study results reveal that uncertainty in the economic and business environment over the impact of general elections increases the company's ability to generate higher investments. This study contributes to the company strategic alignment to justify management about investment prioritize in the stakeholder wealth through ESG disclosure.

Evidence of grade inflation in bachelor of business administration degrees in Turkey for the period from 2002 to 2022

The study analysed the change in the proportion of business management undergraduate programme graduates in Turkey who received honours (>2.99) over the past two decades. It also investigated the factors that contribute to grade inflation and the distribution of course grades. The research employed various statistical methods, including the random effects estimator, ANOVA, t-test, and ANCOVA, to examine the GPAs and 515,739 student grades from 12,579 courses taken by 46,416 graduates of business management undergraduate programmes at 40 universities between 2002 and 2022. According to our findings, the percentage of undergraduate business management students graduating with honours (>2.99) has increased from 11% in 2002 to 56% in 2022. The most significant increases occurred in 2021 and 2022, which coincided with the COVID-19 pandemic. Our research also revealed grade inflation, with a significant increase of 28% after controlling for variables that may affect graduation grades in Turkish undergraduate business management programmes. The factors that affect course grades include the student’s gender, grade level, language of instruction, instructor title, and course content. According to our findings, it can be concluded that the rapid expansion of undergraduate business management programmes in Turkey has resulted in a decline in qualifications. This, in turn, has led to a rise in grades.

Does economic self‐interest determine public attitudes toward immigrants? An econometric case study in Japan

AbstractWe examine two economic self‐interest hypotheses regarding the determinants of public attitudes toward immigrants: (1) the labor market hypothesis, which states that the employment and wage impacts of immigration determine the public attitudes, and (2) the welfare state hypothesis, which states that natives hold negative perceptions of immigrants due to concerns that they may strain the country's welfare budget. The first hypothesis predicts that natives' education will affect pro‐immigrant attitudes more positively when immigrants come from lower income countries. The second hypothesis predicts that natives' income affects the pro‐immigrant attitudes more negatively when immigrants come from lower income countries. We use the Japanese General Social Survey, which asks respondents' tolerance toward immigrants from different countries, allowing us to control for unobserved individual characteristics through a method akin to the fixed effect estimation. Our results indicate no difference in the effects of education and income on pro‐immigrant attitudes regardless of whether immigrants come from high‐ or low‐income countries. We conclude that economic self‐interest does not explain Japanese public attitudes toward immigrants. We discuss the policy implications about how to improve public attitudes toward immigrants.

What makes forest-based heterogeneous treatment effect estimators work?

What makes forest-based heterogeneous treatment effect estimators work?

The story of dividend payment and corporate cash flow in Ghana

AbstractThe thorny issue and contradictory conclusions on dividend payments and firm value are not ending any time soon. The existence of a divergence of findings in the literature suggests the need for further empirical studies on this subject matter. Therefore, this study focuses on how a firm's cash flow influences dividend payments in Ghana. Using data from 2015 to 2020, pooled ordinary least squares and random effects estimation, this study provides evidence of dividend payments in Ghana. The results show that dividend payments are influenced by a firm's net cash flow from operations, free cash flow to equity, firm size, and age. The relevant macroeconomic variables for dividend payments are inflation and gross domestic product growth. Another finding is that the more nonfinancial firms take on the characteristics of financial firms, the less dividends they pay. Managers of listed firms in Ghana should grow the size of their firms; however, this should be done in a manner not to expropriate value to managers as predicted in the Jensen agency problem associated with free cash flow.

Causal Effect Estimation With Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand

Causal Effect Estimation With Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand

DEVELOPMENT OF RETAIL BANKING SERVICES IN THE CONTEXT OF DIGITAL TRANSFORMATION

The article examines retail banking services, their importance and how they are improving in the context of digital transformation. In the article, using the data of 14 commercial banks in Uzbekistan, the number of observations is 280. The dependent variable is the deposit practice of commercial banks, the independent variables are the credit practice of commercial banks, the remote services of commercial banks and the practice of commercial banks with plastic cards, analyzed through several models.The research shows that when we analyze 3 models based on panel data, the Random effects estimator (REE) model econometric equation of the impact of retail banking services development practices in the national economy today is Y=0.59x1+0.08x2+0.22x3-1.66 is the most optimal model. Accordingly, regarding the choice between the Fixed effects estimator (FEE) model and the Random effects estimator (REE) model, the Hausman test is used to determine which model is more suitable. The test compares the estimates from two models and tests whether the difference between them is statistically significant. If the p-value of the test is greater than the significance level (usually 0.05), then the Random effects estimator (REE) model is preferred because it allows for unobserved heterogeneity not accounted for in the Fixed effects estimator (FEE) model.In this case, the p-value of the Hausman test is equal to 0.36, which is greater than 0.05. We use the Random effects estimator (REE) model to analyze the impact of the practice of developing retail banking services in the national economy today based on panel data. We came to the conclusion that it is suitable.

Investigating the environmental Kuznets curve modified with HDI: evidence from a panel of eco-innovative countries

AbstractConsidering the increase in polluting emissions, the importance of determining the relationship between economic development and environmental quality is a crucial issue in achieving sustainable development goals. This study explores and empirically tests the validity of the Environmental Kuznets Curve (EKC) hypothesis in a panel data of 39 countries is assembled over the period 1993–2018. For this purpose, the Human Development Index is used as a measure of development, contrary to classical studies in which GDP growth was determined as an indicator of economic growth. Also using by two different measures of pollutant emissions, such as methane ($${{\text{CH}}}_{4}$$ CH 4 ) and carbon ($${{\text{CO}}}_{2}$$ CO 2 ) emissions, examining to the validity of the EKC hypothesis. The analysis method consists of the fixed effect and robustness estimation of the panel data model, and employed the U-test approach in the study. Empirical findings in this study show that strong evidence to support an inverted U-shaped relationship between CO2 emissions and human development, although the U-test results point at mixed results for CH4 emissions. In addition, it is detected that negative relationship between environmental related technological innovation and pollutants emission such as $${{\text{CH}}}_{4}$$ CH 4 and $${{\text{CO}}}_{2}$$ CO 2 , which highlights the importance of promoting innovative technologies in struggle global warming.

The Investment development path theory: Evidence from developing countries’ agricultural sector

Background We examined the investment development path (IDP) through the perspective of developing countries’ agricultural sector. Our analytical approach indirectly accounts for interactions among countries regarding cross-border resource transfers. Aside from providing knowledge on testing the IDP by inferential statistics, the information would be relevant for policymaking. Identifying the stage(s) in the IDP not only highlights the global appeal of agriculture but also guides firms seeking to expand beyond borders. This information is essential for developing an effective economic strategy. Methods We employed data from 1991 to 2021 for 55 countries from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and applied a fixed effects estimator corrected for serial correlation and non-constant variances. Results and conclusions We found that agriculture in developing countries is currently in stages I and II of the IDP. Broadly, agricultural production requires policies that would increase outward foreign direct investment and inward foreign direct investment. Domestic agricultural businesses in developing countries must develop capacity by learning from foreign multinationals. This would enable agricultural businesses to invest abroad. Such a move would lead to an increase in outward FDI. As this would have resulted from increased GDP per capita, it will lead to movement from the existing stage to higher ones.

Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia

Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.

The clarity of monetary policy communication and financial market volatility in developing economies

This study investigates the effect of the clarity of monetary policy statements (MPSs) on financial market volatilities by employing linguistic analysis and a panel fixed effects estimator for a newly built dataset drawn from the MPSs of 21 developing countries. Our results show that MPS clarity, measured by complexity and readability indices, negatively affects foreign exchange rate volatility, a key policy variable in developing economies, suggesting that clear central bank communication can reduce financial market volatility. Given the low literacy levels in developing countries, their central banks should make an extra effort to write statements that are easy to comprehend.

Development effects of industrialization, labor force participation and electrification on productive capacity in Africa: A panel data investigation

In our study spanning 2010–2022 across 50 African economies, we investigated the effects of industrialization, labor force participation, and electrification on Africa’s productive capacity. We also examined how electrification moderates the effect of industrialization and labor force participation on productive capacity. We adopted the pooled OLS, the fixed effect estimator, and the dynamic system GMM estimation technique for our analysis. Our findings indicate industrialization, labor force participation, and electrification as potent channels for enhancing Africa's productive capacity. In tandem with Africa’s reality, we find that electrification isn’t moderating the positive impact of industrialization and labor force participation on productivity. Our findings also revealed human capital development, government health expenditure, and institutional quality as pivotal drivers of productive capacity in Africa, while factors like global economic policy uncertainty and natural resource endowment exhibit predominantly negative roles. These results underscore the need for African policymakers and leaders to foster an enabling environment that facilitates industrialization, increased labor force participation, and improved electricity access to promote economic performance and productivity.

The multidimensional Mundlak estimator

Mundlak (1978) shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the multidimensional error component model. This is a generalization of Baltagi (2023) from the two-way Mundlak model to higher order multidimensional error components model, see Balazsi et al. (2024a) for the multidimensional fixed effects model and Balazsi et al. (2024b) for the multidimensional random effects model. The F test suggested by Mundlak (1978) to test for this correlation between the random effects and the regressors generate Hausman (1978) type tests that are easily generalizable to the multi-dimensional Mundlak regression.

Assessing the impact of financing decisions and ownership structure on green accounting disclosure: Evidence from developing economies

This study examines the impact of financing decisions and ownership structure on green accounting disclosure (GAD) in developing economies, where sustainability practices have not been extensively integrated into business models. We conducted empirical analysis considering 172 manufacturing companies from 2001 to 2022, utilizing both fixed effect and random effect estimation techniques. The findings revealed that firms that rely primarily on debt financing tend to have an inverse relationship with the levels of green accounting disclosure. However, firms that depend mainly on equity financing tend to have higher levels of green accounting disclosure. In addition, the results of the estimation analysis showed a favorable association between ownership concentration and disclosure of green accounting practices. The findings suggest that policymakers should consider incentivizing firms to prioritize equity financing over debt financing to promote higher levels of green accounting disclosure. Additionally, policies should aim at encouraging ownership concentration within firms to enhance the transparency and accountability of environmental reporting practices, ultimately advancing the achievement of Sustainable Development Goals 12 and 13.