Regression Estimates Research Articles

Timing the transition: gender diversity’s role in family IPOs

PurposeThis study investigates the relationship between board gender diversity (BGD) and the time to Initial Public Offering (IPO), which stands as an entrepreneurially risky choice, particularly challenging in family firms. We also investigate the moderating role of family ownership dispersion (FOD).Design/methodology/approachWe draw on an integrated theoretical framework bringing together the upper echelons theory and the socio-emotional wealth (SEW) perspective and on hand-collected data on a sample of Italian family IPOs that occurred in the period 2000–2020. We employ ordinary least squares (OLS) regression and alternative model estimations to test our hypotheses.FindingsBGD positively affects the time to IPO, thus, it increases the time required to go public. FOD negatively moderates this relationship. Our findings remain robust with different measures for BGD, FOD, and family business definition as well as with different econometric models.Originality/valueThe article develops literature on family firms and IPO and it enriches the academic debate about gender and IPOs in family firms. It adds to studies addressing the determinants of the time to IPO by incorporating gender diversity and the FOD into the discussion. Finally, it contributes to research on women and outcomes in family firms.

A Study on Entrepreneurship and Growth Nexus in High and Low-Income Countries : The Application of Panel Regression Estimation

While common sense would suggest that entrepreneurship and economic growth are positively related, it remains unclear whether entrepreneurship is a primary predictor of economic growth conceptually and empirically. Evidence from the literature has revealed a mixed result. Some authors conclude that entrepreneurship drives economic growth positively and significantly. However, others found an inverse relationship between entrepreneurship and growth. Within the paper's framework, entrepreneurship's actual impact on growth across some selected high- and low-income countries has been brought to light. The discussion starts by measuring the degree of association among the variables across the selected clusters of countries. A panel estimation technique, more specifically the Generalized Methods of Moments (GMM) technique, is adopted to make the comparison. Data on 39 high-income countries as well as 24 low-income countries from the period of 1999 to 2019 were considered. It was observed that entrepreneurship positively impacts growth across high-income countries. However, entrepreneurship does not necessarily aid growth within low-income countries.

Oil rent, corruption and economic growth relationship in Nigeria: evidence from various estimation techniques

PurposeDespite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.Design/methodology/approachVarious estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.FindingsThe bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.Originality/valueExisting studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

Atmospheric teleconnections between the Arctic and the Baltic Sea region as simulated by CESM1-LE

Abstract. This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing. The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas. The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, R2, was highest for winter: for T2 m, R2=0.64; for sea level pressure (SLP), R2=0.44; and for precipitation (PREC), R2=0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest R2=0.09 being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.

Cost and returns analysis of upland and lowland rice production among farmers in North Central Nigeria

Sustainability of the rice production and farmers’ income in Nigeria raises a serious concern due to the rising costs of production inputs and other factors beyond farmers’ control. As a result, the study compared the cost and returns of rice production in North central, Nigeria. A multistage sampling technique was used to select 387 rice farmers in the study area. Descriptive statistics, gross margin analysis and seeming unrelated regression were used to analyze the dataset collected. The findings revealed that most of the rice farmers were male, averagely young and have at least 9 family members in their respectively households. Results from Kwara State showed that total cost is higher among the upland rice farmers compared to their lowland counterparts, but reverse was the case in term of net profit, as this favoured the lowland rice farmers. For Niger State and pooled data, the lowland rice farmers had higher net profit than the upland rice farmers. Seemingly unrelated regression estimates indicated that costs associated with fertilizer, herbicide, seed, pesticide and labour used were significant and influencing factors affecting the revenue of rice farmers in the study area. While inaccessibility to cheap farm inputs was the major constraint affecting the rice farmers, other challenges include lack of storage, inadequate rainfall and inadequate credit facilities. The study recommended seamless access to improved rice varieties by the farmers as this can scale up adoption of improved technologies, boost production and ultimately the net revenue of rice farmers. Farmers are also advised to join and/or form a registered occupational groups through which credit facilities can easily be accessed, to expand the farming operations because of the positive and complementary role of social group and access to credit.

Agricultural Price Fluctuation and Its Effects on Small-Scale Farmer Income: A Case of Maize Crop in Kilolo District

This study examined the agriculture crop price fluctuation and its effects on small-scale farmer income in Tanzania, specifically in Kilolo District in Iringa Region. The study sampled 90 respondents, where 84 respondents were for structured questionnaire and 6 for interviews. Simple random sampling and purposive sampling techniques were used to collect data. The mixed-methods data were analysed using linear regression estimation techniques, estimated Ordinary Least Squares estimator, and content analysis for qualitative data. The study revealed that Agriculture cash crops positively influence the yearly income of small-scale farmers, signifying that increased engagement in cash crop cultivation contributes to higher income levels for these farmers. Conversely, the study unveiled the negative implications of crop price fluctuations on small-scale farmers’ yearly income, demonstrating that erratic price variations can substantially challenge their economic stability. These findings were robustly supported by statistical analyses, including the t-test and regression analysis, which underscored the significance of Agriculture cash crops and crop price fluctuations as determinants of small-scale farmers’ yearly income. The adjusted R² value of 0.607 further substantiated these results, signifying that the independent variables, Agriculture cash crops, and crop price fluctuations account for approximately 60.7% of the variation observed in the dependent variable. In such a sense, small-scale farmers’ yearly income is determined by agricultural cash crops done by small-scale farmers and the crop prices fluctuation in the markets. There is a need for marketing officers to provide marketing information to small-scale farmers from time to time for small-scale farmers to be aware of cash crop price fluctuations and what kind of cash crops will have a higher market value, hence lowering the level of price fluctuation in the markets. Such a move would increase small-scale farmers’ yearly income in Kilolo District

DETERMINANTS OF FINANCIAL DISTRESS: REVIEW OF THE ASPECTS OF PROFITABILITY, LIQUIDITY, LEVERAGE, AND ACTIVITY

This research aims to determine the determinants of financial distress in wholesale and retail trade subsector companies listed on the Indonesian Stock Exchange. In this research, financial distress is measured by Zscore, profitability is measured by Return on Assets, liquidity is measured by Current Assets, Debt measures leverage to Assets Ratio, and Total Assets Turnover measures activity. The research methods used in this research are descriptive and verification methods. The sample for this research was 29 companies obtained based on a purposive sampling technique with a five-year research period of 5 years, so the observation data totalled 145. The statistical analysis used is a linear regression analysis of panel data with time lag using the Eviews 10—Tektik program panel data linear regression estimation using the Fixed Effect Model. The coefficient test results show that profitability hurts financial distress, liquidity does not affect financial distress, leverage hurts negatively, and activity positively affects financial distress.

Rising Bribes: Accessing Public Health Services in Northern Cyprus during COVID-19

Objective: This study investigates the impact of the COVID-19 pandemic on informal payments (bribes) in accessing public health services. It assesses whether the pandemic increased informal payments and whether vulnerable populations, namely women, migrants, and low-income individuals, were more susceptible to bribery.
 
 Methods: Data from pre-pandemic (2019) and pandemic (2021) Global Corruption Barometer surveys in Northern Cyprus were statistically analyzed to examine changes in informal payments and their associations with gender, income, and immigrant status.
 
 Results: The study revealed a significant surge in informal payments for accessing public health services during the COVID-19 pandemic. In 2019, 4.70% of respondents reported paying bribes, but in 2021, this figure spiked to 17.85%, with an odds ratio of 4.391 underscoring the magnitude of the increase. Notably, gender and socioeconomic disparities were evident. Women reported higher bribe rates (20.3%) compared to men (13.7%), migrants had substantially higher bribe rates (25.7%) than natives (15.7%), and individuals with lower incomes (25.6%) were more inclined to engage in bribery. Logistic regression estimation results showed that migrants had a 9.48% higher likelihood of bribery, low-income individuals had a 14.27% higher probability, and being female was associated with a 9.28% higher chance of engaging in bribery.
 
 Conclusion: This study highlights a troubling surge in bribery within the public health sector during the COVID-19 pandemic. Vulnerable populations, especially women, migrants, and low-income individuals, face increased risks. Urgent action is needed to combat corruption in healthcare during pandemics and ensure equitable access to healthcare services.
 
 Key Words
 COVID-19, bribes, public health, vulnerable groups, Cyprus

Effect of Increasing Sample Size on Multi-Collinearity in Multilevel Non-Linear Model

A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.

Examining the relationship between tree canopy coverage and crime: An examination of within-neighborhood change

ABSTRACT A bourgeoning area of research suggests that tree canopy coverage can reduce crime in neighborhoods. However, nearly all prior studies have analyzed the effects of tree canopy coverage on crime across neighborhoods at a single timepoint. Thus, it is necessary to examine change within neighborhoods of the relationship between tree canopy coverage and aggregate crime outcomes. For the present study, we conduct a longitudinal block group analysis of tree canopy coverage, sociodemographic characteristics, and crime in Washington, DC. To model within-neighborhood change, we employ an unconditional Poisson-based regression estimator with dummy variables to represent fixed-effects. The findings show that tree canopy coverage can indeed reduce crime within neighborhoods over time, and with substantive magnitude. Yet, these salutary effects pertain to violent crimes only. In fact, we determine that tree canopy coverage leads to more motor vehicle thefts. The implications of these findings for criminology, urban studies, and public policy are discussed.

Effectiveness of the main national pro-poor targeted program in Armenia: evidence from the period 2004–2018

ABSTRACT Using the Armenian Household Survey data for 2004–2018 this study explores the causal effect of family benefits, the country’s main poverty targeted program, on multidimensional and monetary poverty. We find that only 40% of the total fund of this targeted program reaches the severe multidimensional poor households. Our regression estimation based on bias-corrected dynamic panel data models reveals that an increase in the proportion of households receiving family benefits mostly results in a decrease in the incidence of severe multidimensional poverty. However, it has no significant effect on moderately poor households and those who are vulnerable to poverty. This program significantly benefits only those households who are poor in terms of their health expenditure. We further observe that the impact of these benefits is significant on extreme monetary poor but the effect is inconclusive on those who live below the national upper poverty line. Our findings suggest that in the current development stage of the country the targeted poverty programs should focus on enhancing capabilities and human capital development. Also, to address the social assistance coverage gap, the government needs to enhance incentive mechanisms for the main actors in the social protection system.

Truncated composite quantile regression with covariates measurement errors

Truncated data and measurement error data are often encountered in practice. In this paper, we study two classes of truncated composite quantile regression with covariates measured with errors. We propose weighted composite quantile regression estimators of the slope parameters based on weights which are random quantities and determined by the product-limit estimates of the distribution function of truncated variable. However, the estimators are often biased when one treats mismeasured covariates as error-free. To eliminate the bias and obtain consistent estimators, a debiasing technique is proposed. The resulting estimates are proved to be asymptotically normal and root n consistent under some mild conditions. Simulation studies and a real data example show that the proposed methods work promisingly.

Climate finance and women-hunger alleviation in the global south: Is the Sub-Saharan Africa case any different?

To unearth the influence of climate finance (CF) on women-hunger alleviation in Sub-Saharan Africa (SSA), the study used unbalanced panel data for 43 SSA countries for the period 2006-2018. Data was analysed using system-GMM to deal with the endogeneity problem inherent in the model, among other panel regression estimators. Also, the sensitivity of the estimates was carried out using panel fixed effect quantile regression. The findings showed that CF and its components have a significant effect on women-hunger alleviation in SSA, apart from FDI. Further, control of corruption also showed a significant women-hunger alleviation impact. For the climate variables, areas in SSA with higher temperature are more likely to experience worsened women-hunger. Based on the findings, the study recommends that SSA countries need to strengthen their fight against corruption. More so, donors should extend CF as financial aid or support to government budget, due to their potential of alleviating women-hunger.

Multiple regimes in the preferences for redistribution

AbstractThis paper provides novel evidence of nonlinearities in the formation of preferences for redistribution by uncovering evidence of multiple regimes consistent with the presence of multiple equilibria and multiple steady states. Using threshold regressions that account for the endogeneity of the threshold variable, countries are classified into three groups that share common characteristics. Finally, our analysis reveals substantial evidence of parameter heterogeneity in the coefficient estimates of threshold regressions.

New Approach to Measure Wellbeing: A Case Study on Bangladesh

Understanding wellbeing is fundamental to any effort to enhance it. Using the latest World Values Survey (WVS), this study examines the incidence, intensity, and determinants of wellbeing in Bangladesh by constructing a multidimensional wellbeing index (MDWI). The study also provides some insights into how wellbeing changed during 2002–2018 in Bangladesh. The MDWI is calculated using twenty-five indicators corresponding to seven domains. The results indicate that, on average, Bangladeshi people experience a moderate level of wellbeing, and the health domain is the main contributor to wellbeing followed by finance, culture, safety, governance, religion, and science and technology. Intertemporal analysis reveals that over time, people’s wellbeing has increased. Notable differences in wellbeing were observed among the social class groups and administrative divisions. Gender, education, and social class groups appeared to be significantly and positively associated with wellbeing in Bangladesh while age and political party affiliation have significant and negative associations with the MDWI. The regression estimation results based on MDWI appeared robust with those of the widely used self-rated life satisfaction. The results can be used as valuable policy guidelines that improve human wellbeing, which is critical for achieving sustainable development goals in Bangladesh and other developing countries.

Analyzing the Impact of Liability Insurance on the Financial Performance of Insurance Companies in Nigeria

The insurance sector reforms in Nigeria sought to create a strong, diverse, and trustworthy industry that protects insured premiums, actively contributes to the country's economic progress, and participates in worldwide financial systems. Despite this development, insurance business operations and survival have not been impressive, and performance remains alarmingly poor. Therefore, this study investigates the effect of workers' compensation written premiums and workers' compensation claims paid of the chosen insurance companies' return on assets (ROA) in Nigeria. This study is a longitudinal research design. Secondary data obtained from the financial statement of five selected insurance firms in Nigeria over the year 2013 to 2022 were analysed with correlation analysis and panel regression estimation technique. The result of the study, based on Hausman verification, showed that workers' compensation written premium has a significant positive effect on RoA and that workers' compensation claims paid has a significant negative effect on RoA in Nigeria. Consequently, this investigation established that liability insurance is an important factor that may have been disturbing the performance of insurance firms in Nigeria, and must be treated with utmost care in the process of enhancing performance. Therefore, this study recommended among others that insurance firms need to increase the rate of client application towards workers' compensation insurance to realize increased levels of premium and income for performance-enhancing operations.

Seroprevalence of Japanese encephalitis virus-specific antibodies in Australia following novel epidemic spread: protocol for a national cross-sectional study

IntroductionJapanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes encephalitis and other morbidity in Southeast Asia. Since February 2022, geographically dispersed JEV human, animal and vector detections occurred on...

Non-asymptotic bounds for the ℓ∞ estimator in linear regression with uniform noise

Non-asymptotic bounds for the ℓ∞ estimator in linear regression with uniform noise

Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity

Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity

Vaccination and its impact on healthcare utilization in two groups of vaccinated and unvaccinated patients with COVID-19: A cross-sectional study in Iran between 2021 and 2022.

One of the main responsibilities of health systems impacted by the global Coronavirus disease 2019(COVID-19) pandemic, where the first case was discovered in Wuhan, China, in December 2019, is the provision of medical services. The current study looked into the impact of vaccination on the utilization of services provided to COVID-19 patients. This study was conducted in Iran between 2021 and 2022, utilizing a cross-sectional research design. The research team collected data on the utilization of provided services and the number of COVID-19 vaccines administered to 1000 patients in Iran through a random sampling approach. The data wereanalyzed with statistical methods, including the mean difference test, and multiple linear regression. Regression estimates show that after controlling for confounding variables like age, type of admission, and comorbidities, vaccination reduces the utilization of healthcare services in the general majority of services. The study's results reveal a fall in COVID-19 patients' utilization of services, specifically in patients administered two or three doses of the vaccine. However, the reduction is not statistically significant. Regression models are in contrast to univariate analysis findings that vaccination increases the mean utilization of healthcare services for COVID-19 patients in general. Comorbidities are a crucial factor in determining the utilization of diagnostic and treatment services for COVID-19 patients. Full COVID-19 vaccination and other implementations, including investing in public health, cooperating globally, and vaccinating high-risk groups for future pandemics, are essential as a critical response to this pandemic as they reduce healthcare service utilization to alleviate the burden on healthcare systems and allocate resources more efficiently.