OLS Estimates Research Articles

Bangladesh's Experience With Social Media Adoption in Public Organizations

The main purpose of this research is to find out effective strategies and factors of Bangladesh's Experience with Social Media Adoption in Public Organizations through data from nationality representative surveys conducted on different city corporations in Bangladesh. A structured questionnaire method was conducted and data collected from 311 public organizations of different city corporations in Bangladesh. Technology acceptance, a multiple regression model is estimated by the OLS method to find out the significant factors of social media adoption in the public organization of Bangladesh. Moreover, the results of OLS estimation found that if the public organization of Bangladesh perceives that social media is a useful, improved and easier tool to find information, the level of social media adoption will be increased and if the service delivery system is improved by using social media, the level of social media adoption in the public organization of Bangladesh will be increased significantly.

Determinants of FDI inflows to West Africa: Prospects for regional development and globalization

Abstract This paper examines the determinants of foreign direct investment (FDI) inflow into West Africa. FDI is regarded as the central engine for growth. Such inflows are not often satisfactory, both in terms of their volume and in terms of their sectoral distribution, particularly in developing countries. The study carried out a unit root test using the Im-Pesaran-shin (IPS) method, which revealed that four out of many variables were stationary at first difference, while other variables were stationary at level. Consequently, the Kao co-integration test methodology was used to analyze the long-run relationship. Thus, the regression analysis was carried out using the Panel ARDL method in an equation with a 50-year observation period. Concerning the remaining seven equations with shorter time series observations, the Pooled OLS estimation method was used to analyze the factors determining the inflow of FDI. The results indicate that financial development has a negative effect on FDI flows (and hence on globalization processes) in West Africa, while trade openness, institutional composite index and control of corruption have positive effects on FDI and hence increase globalization tendency. Based on these findings, the study recommends, among other things, that the authorities in West African countries vigorously pursue trade liberalization policy as an effort to globalize the region through FDI inflows. The study examined the macroeconomic determinants on FDI alongside institutional and socio-political determinants that are difficult to study in the case of West Africa as a region. The use of a composite institutional quality index, which combines multiple indicators of institutional quality, is another novelty of this research. Another unique contribution of the study is the use of the Africa Infrastructure Development Index (AIDI), which serves as a composite infrastructure index, as an explanatory variable.

Citizens' Perception of COVID-19 Passport Usefulness: A Cross Sectional Study.

This paper assesses the influence on people’s perception of the utility of the immunity passport (IP) program by sociodemographic factors, infectivity status, and the objective of its use. The material of this paper is a cross-sectional survey of 400 residents in Spain. The relation between utility perception and input variables is fitted with ordinary least squares (OLS) regression and linear quantile regression (LQR). The principal explanatory variable of usefulness perception is being vaccinated, especially when the objective of the IP is regulating mobility. The OLS estimate of the coefficient regression is (cr) = 0.415 (p = 0.001). We also found a positive and significant influence of that factor in all LQRs (cr = 0.652, p = 0.0026 at level (τ) = 0.75; cr = 0.482, p = 0.0047 at τ = 0.5 and cr = 0.201, p = 0.0385 at τ = 0.25). When the objective of the IP is regulating leisure, being vaccinated is relevant only to explain the central measures of usefulness perception. If the IP is used to regulate traveling, variables related to interviewees’ infectivity have greater relevance than sociodemographic factors. When its objective is ruling assembly, the more important variables than being vaccinated are gender and age. To create an effective implementation of the IP, it is advisable to have a general agreement among the population on its convenience. Therefore, the findings in this study have important implications for public health decision-makers.

Industrial policy and governments’ cybersecurity capacity: a tale of two developments?

ABSTRACT Are industrial policies having an impact on countries’ cybersecurity capacity across the globe? When analysing the securitisation of countries’ cyberspace, the empirical assessment of industrial policies is still rather unexplored. In parallel, scholars in the field of development have already raised their concerns regarding the risk for developing countries falling further behind vis-à-vis developed countries as a consequence of the disruptive dynamics brought forth by new technologies. Still, empirical studies that contrast the dynamics that new technologies are posing among developed and developing countries are rather scant. This paper looks to contribute to the empirical literature by assessing the role of industrial policy in cyber-related challenges through a cross-country OLS estimation model. Results show that industrial policies are having a significant impact across countries’ cyber capacities and that, moreover, there is an interactive relationship between countries’ cybersecurity capacities and R&D efforts once development values are controlled.

Binary outcomes, OLS, 2SLS and IV probit

For a binary outcome Y, generated by a simple threshold crossing model with a single exogenous normally distributed explanatory variable X, the OLS estimator of the coefficient on X in a linear probability model is a consistent estimator of the average partial effect of X. Even in this very simple setting, we show that when allowing for X to be endogenously determined, the 2SLS estimator, using a normally distributed instrumental variable Z, does not identify the same causal parameter. It instead estimates the average partial effect of Z, scaled by the coefficient on Z in the linear first-stage model for X, denoted γ 1, or equivalently, it estimates the average partial effect of the population predicted value of X, These causal parameters can differ substantially as we show for the normal Probit model, which implies that care has to be taken when interpreting 2SLS estimation results in a linear probability model. Under joint normality of the error terms, IV Probit maximum likelihood estimation does identify the average partial effect of X. The two-step control function procedure of Rivers and Vuong can also estimate this causal parameter consistently, but a double averaging is needed, one over the distribution of the first-stage error V and one over the distribution of X. If instead a single averaging is performed over the joint distribution of X and V, then the same causal parameter is estimated as the one estimated by the 2SLS estimator in the linear probability model. The 2SLS estimator is a consistent estimator when the average partial effect is equal to 0, and the standard Wald test for this hypothesis has correct size under strong instrument asymptotics. We show that, in general, the standard weak instrument first-stage F-test interpretations do not apply in this setting.

The Sell-in-May effect in ESG indices

PurposeThis study explores the “Sell-in-May” effect in environmental, social and governance (ESG) indices and compares the seasonal effects in ESG equity indices with conventional equity indices.Design/methodology/approachThe authors use ordinary least squares (OLS) models and M-estimation as a robustness check, as OLS estimates may be sensitive to outliers. The authors also employ bootstrap simulations to use the data efficiently and to test whether seasonal trading strategies can produce abnormal returns.FindingsThe regression results reveal that seasonal effects in USA ESG equity indices are similar to those in conventional equity indices. Higher returns are noticeable from November through April, mainly in ESG indices including small and medium capitalization stocks. When the authors extend the Sell-in-May strategy from October through April, the authors find that the seasonal effect is significant for multiple ESG indices, even after accounting for the January effect. Bootstrap simulations show that the Sell-in-May and Extended Sell-in-May strategies appear to beat a buy-and-hold strategy on a risk-adjusted basis and that this result is stronger in medium and small capitalization ESG indices.Originality/valueAlthough previous research has considered the effectiveness of seasonal equity trading strategies and the general performance of ESG stocks, this is the first study to specifically examine the “Sell in May” effect in ESG indices. The authors also consider an “Extended” Sell-in-May strategy where stocks are purchased one month earlier and show that the strategy produces higher returns.

Foreign direct investment inflow: The drivers and motivations in MENA Region

Purpose ― Reasons why Multinational Enterprise (MNEs) engage in foreign direct investment (hereafter referred to as FDI) abroad have been of great interest to policy markets, academia and international portfolio investors. This examines FDI inflow motives to the Middle East and North Africa (MENA) region for the period 2005 to 2019. Design/methodology/approach ― This research paper applies both the static and dynamic panel methodologies such as SYS-GMM, fixed effects, and pooled OLS estimators to investigate the motivational factors of MNEs FDI inflows to MENA countries. Findings ― Although specificity applies to countries, estimated results suggest that MNEs in the MENA region are predominantly interested in serving both home and host markets. Other motives such as efficiency-seeking FDI vary across countries, indicating that FDI motives are not homogeneous among region members. This paper provides useful insight for both firms and host countries in the region. Originality/value ― This research paper investigates the factors that motivate MNEs to consider FDI decisions in MENA countries. Rather than investigate the individual countries within the region as done in existing literature, this research paper simultaneously examines MNEs' investment motivations in the MENA region. The findings are significant, plausible and in line with the economic development of most countries in the region.

Inference in Experiments Conditional on Observed Imbalances in Covariates

Double-blind randomized controlled trials are traditionally seen as the gold standard for causal inferences as the difference-in-means estimator is an unbiased estimator of the average treatment effect in the experiment. The fact that this estimator is unbiased over all possible randomizations does not, however, mean that any given estimate is close to the true treatment effect. Similarly, while predetermined covariates will be balanced between treatment and control groups on average, large imbalances may be observed in a given experiment and the researcher may therefore want to condition on such covariates using linear regression. This article studies the theoretical properties of both the difference-in-means and OLS estimators conditional on observed differences in covariates. By deriving the statistical properties of the conditional estimators, we can establish guidance for how to deal with covariate imbalances.

Incidence of the Digital Economy and Frictional Unemployment: International Evidence

ABSTRACT This paper is the first to quantify the relationship between the incidence of the digital economy and long-term frictional unemployment across countries. This paper contributes a novel approach to the literature that had been missing by focusing on the impact of the digital economy on unemployment across a global sample of countries . The resulting evidence indicates that there is a robust, negative partial correlation between national unemployment rates and the incidence of the digital economy, proxied by the share of the adult population that reports using the internet to pay bills. Further, the absolute values of OLS estimates of the partial correlation suggest that it might be higher for developing than high-income economies. Controlling for informal employment appears to be key for removing a positive omitted-variable bias in the estimate of the partial correlation between unemployment and the digital economy, which is due to a negative bivariate correlation between unemployment and informality, and a negative bivariate correlation between informality and the incidence of digital payment. The results from IV estimations suggest that the partial correlation between unemployment and digital payments is negative, with the absolute value of the estimates being larger than the absolute value of the OLS estimates.

Technological change and unemployment nexus from a gender perspective: empirical evidence from a panel cointegration approach

This study investigates the long-run relationship between technological change and unemployment, focusing on a gender perspective in developed economies. Considering the obstacles women face in accessing labor markets, this study aims to empirically combine the technological change and unemployment nexus with a gender perspective in 20 OECD economies from 1985 to 2019 by using multifactor productivity (MFP) as a proxy for technological change. The findings from Westerlund Panel Cointegration Test and Pedroni's Panel-Dynamic Ordinary Least-Squares (PDOLS) estimator indicate the presence of a long-run relationship between MFP and unemployment rate with diversified gender effects. Even though MFP affects total and male unemployment significantly and negatively in the long run, there is no significant effect on female unemployment for the whole panel. However, the findings by economies are diversified and they indicate the presence of technological unemployment for women in some economies. Thus, the empirical results clearly show that the long-run relationship between MFP and unemployment is affected by gender differences.

Economic and non-economic determinants of economic growth in Bangladesh: multivariate regression analysis

This study focuses on the scenario of the economic development of Bangladesh. The major objective of the study is to examine the economic and non-economic determinants of the economic growth of the country. This study employs the Multivariate OLS regression and GLM technique to explore the influences of those variables to the economic growth and development of the country. The empirical results show that agriculture, industry, and service sector contribution to real GDP are positive where industry and service sectors are statistically significant. The results of the economic determinant model illustrate that the capital, labor forces, imports, and total reserve of the country positively influence the economic development of the country which are significant also. The OLS estimation shows that coefficients of non-economic determinants such as control of corruption and bureaucratic quality are positively significant to influence the economy. The results also present that internal conflict, democratic accountability, and rule and order situation negatively affect the economic growth in Bangladesh. The GLM estimation shows the control of corruption, rule, and order also a positive effect on economic growth. These empirical findings are consistent with the exploratory analysis and practices.

The relationship between knowledge-based economies and economic growth: an empirical analysis on the Asia-Pacific region 2011–2018

PurposeThis paper aims to examine the relationship between knowledge-economy and economic growth in 16 Asia-Pacific (AP) countries during the period 2011–2018. The study also aims to investigate a diversity of knowledge-economy pillars, including tertiary education, domestic innovation, foreign innovation, economic incentives and institutional regime and information and communications technologies (ICTs) and their relation to economic growth.Design/methodology/approachThe study applies a comparative empirical analysis using pooled ordinary least squares (OLS), one-step difference generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVc) estimators to test this relationship.FindingsPooled OLS estimators deemed suboptimal to the panel data under study, while GMM results reveal a significant relationship between tertiary education, domestic and foreign innovation, government expenditure and investments with economic growth. Of these results, domestic innovation, investments and government consumption are positively correlated with economic growth, whereas tertiary education and foreign innovation show a negative relation. Meanwhile, institutions and ICT have insignificant relationships with economic growth. LSDVc results coincide with GMM results with respect to tertiary education, whereas institutions is the only additional significant and negatively correlated variable with economic growth.Research limitations/implicationsThe main limitation of this research lies in the unavailability of proxy data for knowledge economy pillars in monetary terms, and hence, the paper relies on indices.Originality/valueThe novelty of the study lies in its aim to investigate economic growth in the AP region that is enhanced by domestic innovation, foreign innovation or both – an area which is empirically understudied in the knowledge-economy context. Further, the paper’s novelty lies in its application of a comparative empirical analysis between the most popular dynamic panel estimators – dynamic GMM and bias-corrected LSDVc for AP countries.

Beyond marginal: Estimating the demand for water quality

Using micro-level data across Wisconsin covering over 100 inland lakes, we recover first- and second-stage hedonic welfare estimates for non-marginal changes in water quality. We overcome longstanding endogeneity concerns with Rosen (1974)’s second stage hedonic framework and recover slope estimates for water quality demand using instruments based on sorting behavior. For near lake Wisconsin households, we find the slope of their water quality demand function is bounded by − 2087 when imperfect instruments are employed, which is significantly more price inelastic than the naïve OLS estimate of − 895. Applying these estimates to a hypothetical policy scenario where water quality reduces by 24.2% due to a 30-year continuation of current trends, we find welfare losses of at least $7554 per household. These losses are 22% ($1658) more than what is predicted from marginal willingness to pay estimates recovered from the first-stage hedonic. For policymakers, our results highlight the importance of recovering underlying demand functions when evaluating non-marginal water quality improvements.

An Analysis on the Economic Factors Affecting the Unemployment Rate in the Philippines from 1993-2018

The study aims to determine the factors affecting the unemployment rate from 1993-2018 using the OLS estimators. With 26 observations, the research utilized six independent variables, namely GDP growth rate, inflation rate, foreign direct investment, government spending, capital investment, and trade openness. The dataset was taken from The Global Economy and the Philippine Statistics Authority. Furthermore, as the dataset was processed, it was found that GDP growth rate, inflation rate, foreign direct investment, and trade openness have a significant relationship with the dependent variable. On the other hand, government spending and capital investment have no significant relationship with the unemployment rate.

COMPARISONS BETWEEN ROBUST REGRESSION APPROACHES IN THE PRESENCE OF OUTLIERS AND HIGH LEVERAGE POINTS

The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However, some fundamental assumptions must be fulfilled to provide good parameter estimates for the OLS estimation. The error term in the regression model must be identically and independently comes from a Normal distribution. The failure to fulfill the assumptions will result in a poor estimation of parameters. The violation of assumptions may occur due to the presence of unusual observations (which is known as outliers or high leverage points. Even in the case of only one single extreme value appearing in the set of data, the result of the OLS estimation will be affected. The parameter estimates may become bias and unreliable if the data contains outlier or high leverage point. In order to solve the consequences due to unusual observations, robust regression is suggested to help in reducing the effect of unusual observation to the result of estimation. There are four types of robust regression estimations practiced in this paper: M estimation, LTS estimation, S estimation, and MM estimation, respectively. Comparisons of the result among different types of robust estimator and the classical least square estimator have been carried out. M estimation works well when the data is only contaminated in response variable. But in the case of presence of high leverage point, M estimation cannot perform well.

Skilled Mutual Fund Selection: False Discovery Control Under Dependence

Selecting skilled mutual funds through the multiple testing framework has received increasing attention from finance researchers and statisticians. The intercept α of Carhart four-factor model is commonly used to measure the true performance of mutual funds, and positive α’s are considered as skilled. We observe that the standardized ordinary least-square estimates of α’s across the funds possess strong dependence and nonnormality structures, indicating that the conventional multiple testing methods are inadequate for selecting the skilled funds. We start from a decision theoretical perspective, and propose an optimal multiple testing procedure to minimize a combination of false discovery rate and false nondiscovery rate. Our proposed testing procedure is constructed based on the probability of each fund not being skilled conditional on the information across all of the funds in our study. To model the distribution of the information used for the testing procedure, we consider a mixture model under dependence and propose a new method called “approximate empirical Bayes” to fit the parameters. Empirical studies show that our selected skilled funds have superior long-term and short-term performance, for example, our selection strongly outperforms the S&P 500 index during the same period.

Surface functionalized plant residue in cu2+ scavenging: Chemometrics of operational parameters for process economy validation

Wastes obtained from the nuts of dika fruits (Irvingia gabonensis) were treated with EDTA in surface functionalization for the adsorption of Cu2+. EDTA functionalized Irvingia gabonensis (EIG) showed strong stretching vibration of C=O. Morphological appearance of EIG showed fibrous appearance of agrowastes while the Branneur-Emmer-Teller (BET) surface area was low. Optimum pH was obtained to be 5 while adsorption equilibrium time was rapid. The adsorption data fitted best into the Freundlich adsorption isotherm and the Langmuir maximum monolayer adsorption capacity was 73.53 mg/g. The Pseudo-second order kinetics model best described the kinetics of the Cu2+-EIG adsorption system. The adsorption energy of the Dubinin-Radushkevich modeling was 5.51 kJ/mol, indicating that Cu2+ uptake onto EIG was a more of physical in nature. Correlation analysis validated the strength of relationship between concentration, equilibrium time and quantity adsorbed. The regression model estimated using the ordinary least square estimator (OLSE) indicates that every 1 unit temperature change will cause a 20.5 % uptake of Cu2+ thus presented an economical process.

The impact of IFRS adoption on foreign direct investment in CIS

Abstract Exploring the relationship between International Financial Reporting Standards (IFRS) and Foreign Direct Investment (FDI) inflows is the main objective of this paper. Although IFRS is identified as a determinant of FDI, a few studies have examined the impact of IFRS on FDI inflows, and it is unexplored as to whether IFRS impacts CIS countries. This paper covers ten (10) IFRS adopted CIS countries from 2000 to 2019 using Ordinary Least Square (OLS) and bias-corrected Least Square Dummy Variable (LSDVC). OLS estimator shows that IFRS positively impacts FDI inflows. However, according to the results of LSDVC, there is a negative relationship between IFRS adoption and FDI inflows. Generally, in developing countries implementing IFRS would lead to FDI enhancement. The negative relationship between IFRS adoption and FDI inflows in CIS countries shows that IFRS is not an essential factor of FDI inflows.

Value chain analysis of potato in Farta District of South Gondar Zone, Amhara National Regional State of Ethiopia

Potatoes are the world's most important root and tuber crop. It is grown in over 125 countries and consumed by over a billion people daily. Millions of people in impoverished countries rely on potatoes product to their survival. Today, developing countries are the world's major producers and distributors of potatoes and potato products. Potato can be effective produce for allowing smallholder families to achieve their food security. Ethiopia has probably the maximum prospective country for potato production. However, in Ethiopia, various factors influence potato production and commercialization. It is the main tuber crop produced in Farta District. Potato production and value chain status are not studied well. Hence the main aim of this study was to evaluate the value chains of potatoes in the Farta district, Ethiopia. 123 sample potato farmers were chosen using a two-stage random sampling technique in four kebeles. Descriptive, inferential, value chain approach and econometrics analysis were employed. In addition, Heckman's two-stage selection econometrics model was employed to analyze the determinants of potato market participation and sales quantity. Producers, traders, chain supports, chain enablers, and final consumers were the main value chain actors for potato in the study area. Wholesalers control the potato value chain due to their finances advantage. Probit model estimation result provides that: distance to the nearby market, family size, oxen owned by farmers, market information, land size allocated for potato production, and quantity of inorganic fertilizer were the significant variables affecting the decision to market participation positively except family size. The OLS estimation result provides that: the education level of the farmers, farming experience, the number of extension contact, the land size allocated for potatoes, and the quantity of inorganic fertilizer is the significant variables influencing the amount of potato market supply positively. The main constraints for potato production and commercialization in study area were: Shortage of improved seed, lack of capacity building training to the post-harvest management approach, price fluctuation, shortage of market information, absence of policy framework in price-setting strategy was produced and marketing constraints of potatoes. Therefore, the study suggests that; increasing access to farm inputs, introducing new and improved crop varieties, establishing suitable post-harvest management facilities. In addition, follow up misconduct practice for price-setting strategies, strengthening market information service, facilitating conditions that can promote the smallholder farmers for participating in the market and minimize those constraints which impede the complete value chain in potato production and development.

Panel cointegrating polynomial regression analysis and an illustration with the environmental kuznets curve

The analysis of cointegrating polynomial regressions, i.e, regressions that include an integrated process and its powers as explanatory variables is extended from the time series to the panel case by developing two estimators, a modified and a fully modified OLS estimator. As usual in the cointegration literature, the stationary errors are allowed to be serially correlated and the regressors are allowed to be endogenous. Both individual and time fixed effects are accommodated and the analysis uses an i.i.d. random linear process framework. The modified OLS estimator utilizes the large cross-sectional dimension that allows to consistently estimate and subtract an additive bias term without the need to also transform the dependent variable as required in fully modified OLS estimation. Both developed estimators have zero mean Gaussian limiting distributions and thus allow for standard asymptotic inference. A brief application to the environmental Kuznets curve illustrates the developed methods.