OLS Estimates Research Articles

Expenditure on Human Capital and Economic Growth: Evidences form Nepal

Human capital expenditure is critical to utilizing and profiting from all of the country's resources. Economists have acknowledged that human capital improvement and usage are critical to a nation's economic prosperity. The research article investigates the relationship between human capital spending and Nepal's economic growth. The study analyzed data from 1985 to 2022 to investigate experimentally the relationship between Nepal's human capital spending and GDP. The study uses a variety of macroeconomic indicators, such as GDP as a measure of economic growth, capital expenditure on education, recurrent expenditure on education, health expenditure, higher education enrollment, and school enrollment. LNRGDP serves as both the explanatory variable and the dependent variable in the study's use of regression analysis. The Unit Root Test, OLS Estimation, Unit Root Residual and Error Correction Approach, and Econometric Analysis are all included. The results of the regression show that higher enrollment has a positive effect while capital expenditure on education has a negative impact on GDP. Spending on ongoing education, health, and school enrollment has no statistically significant effect on GDP. A long-term, positive correlation between the investment in human capital and economic growth is found by the cointegration test. According to the findings, funding higher education is essential for fostering Nepal's economy. In addition, the study highlights the significance of policymakers carefully considering resource allocation for education and human capital development in order to ensure the nation's sustainable growth

Economic Policy Responses to Covid-19 and Expenditures on Domestic Travel: International Evidence

In response to the unprecedented circumstances resulting from the COVID-19 pandemic, various national government policies were implemented to reduce the serious economic damage caused by the pandemic and attempts to control it. These policies generally aimed to kick-start domestic-led recoveries in many sectors. This study examines how different types of fiscal measures adopted by governments in response to this pandemic are associated with the growth of expenditure on domestic travel in 2020. Utilizing data from 76 countries and applying OLS estimator and 2SLS regression (which mitigates the potential endogeneity problem), the study shows that governments’ additional spending and tax reductions (especially in non-health sectors) positively contributed to rising expenditure in domestic travel destinations. Similar results are found for countries that are very reliant on tourism. Our findings lend empirical support for Keynesian theory’s prediction concerning the effect of government spending and tax reduction on household consumption behavior in the short run, particularly during periods of economic stress and uncertainty.

The Returns to Education in China: An Analysis of Demographic and Degrees Heterogeneity

Currently, education is pivotal for Chinas future development in economy and technology. This study used the OLS estimation to evaluate the private return to education in China. The results show that an extra year of schooling will increase ones yearly nominal income by around 6.95 percent. Moreover, the heterogeneous analysis indicates that the return rate is higher for male employees and workers who live in urban areas. This research supplements previous literature in two directions. First, it uses CFPS2020, which is relatively new data, to do the estimation. Second, interaction terms of years of schooling and the highest degree of education are created to figure out the various return to education of different degree holders. The regression results indicate that employees who have higher degrees of education will enjoy a higher return for one more year of schooling.

Least Trimmed Squares for Regression Models with Stable Errors

Least Trimmed Squares (LTS) is a robust regression method with respect to outliers. It is based on performing Ordinary Least Squares (OLS) estimates on sub-datasets and determining the optimal solution corresponding to the minimum sum of squared residuals. Since the method of LTS is based on OLS, errors in regression models have finite variance. This work aims to generalize LTS for heavy tail data with infinite variance. When errors have infinite variance, it is impossible to benefit from OLS estimates. We use the property of variance existence of most ordered errors to find an initial robust OLS estimate. We polish the LTS method with a maximum likelihood estimator based on the density function of order statistics and determine the optimal solution for stable regression models. The proposed algorithm is implemented for linear regression models.

The height premium: a literature review and meta-analysis

The association between adult height and labor-market wages, or the “height premium” (HP), is an important input for quantifying potential economic benefits of nutritional interventions promoting growth. A large economics literature has evaluated this association; however, HP estimates differ greatly depending on the study populations and statistical methodologies used. We conducted a meta-analysis of HP estimates to describe the differences in estimates with different statistical methodologies and to examine potential effect modification of the HP by sex and country income category. We performed meta-analyses for studies using instrumental variables (IV) and ordinary least squares (OLS) methods, separately. OLS estimates were separated into those that were “low-adjusted” for confounding variables and “high-adjusted” for at least one common mediator variable, specifically cognition or schooling. Overall, in a total of 12 studies, the pooled estimates for IV studies indicated that each centimeter increase in height was associated with 3.58% greater wages (95% CI: 1.62-5.54%; I2=97.5%, p<0.001)). In the 24 total OLS studies, low-adjusted estimates indicated an HP of 1.06% (95% CI: 0.85-1.28%, I2=95.5%, p<0.001), while for high-adjusted estimates the HP was only 0.57% (95% CI: 0.41-0.73%, I2=95.8%, p<0.001). Further, the meta-analysis found evidence of effect modification by sex in OLS estimates but not IV, and for both IV and OLS for country income category. Overall, the literature suggests a robust association between adult height and wages; however, the magnitude of the estimate appears to be dependent on statistical methods and covariates selected for multivariable models. Our findings also suggest there may be differences by sex and country income category. Additional analyses are needed taking into account a causal inference framework and, if adult height is being used to capture the cumulative effect on wages of nutritional exposures from conception through adulthood, studies should not adjust for potential mediators including cognition and schooling.

Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics

Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation. To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when computing test statistics. To asymptotically justify this method, we prove bootstrap consistency for the self-normalized test statistics under mild conditions. In addition, the underlying bootstrap invariance principle allows us to prove bootstrap consistency also for traditional test statistics based on popular modified OLS estimators. Simulation results show that using bootstrap critical values instead of asymptotic critical values reduces size distortions associated with traditional test statistics considerably, but combining the VAR sieve bootstrap with self-normalization can lead to even less size distorted tests at the cost of only small power losses. We illustrate the usefulness of the VAR sieve bootstrap in empirical applications by analyzing the validity of the Fisher effect in 19 OECD countries.

The Impact of Internal Control Mechanisms on Fraud Detection and Prevention in Nigeria Deposit Money Banks

This study examined and assessed the impact of internal control mechanisms on fraud detection and prevention in Nigeria's deposit money banks. Specifically, the study evaluated the impact of control environment risk assessment, control activities, and monitoring activities on fraud detection and prevention in Nigerian deposit money banks. The descriptive research design was used in the study. Primary data was obtained from the management staff of FUGAZ Banks. Data gathered was estimated using descriptive statistics of respondents’ demographics, correlation analysis, Multiple OLS estimation and post-estimation tests. Discoveries from the study revealed that the control environment exerts a positive significant relationship with fraud detection and prevention in Nigeria DMBs with a coefficient estimate of 0. 03483 (p=0.0054>0.05); risk assessment exerts a positive significant relationship with agricultural investment in Nigeria with a coefficient estimate of 0.4082 (p=0.0199<0.05); control activities exert positive insignificant impact on fraud detection and prevention in Nigeria DMBs with a coefficient estimate of 0.3058 (p=0.0992>0.05) and monitoring activities exert a positive significant impact on fraud detection and prevention in Nigeria DMBs with coefficient estimate of 0.0307 (p=0.0215>0.05). Hence, the study suggested that the management of DMBs should adopt proactive actions towards its internal control; risk assessment of DMBs should be constantly carried out by the management and those charged with governance; management of DMBs should frequently modify the modalities of its operation per time and DMBs management staffs should on a constant basis monitor its internal control system to improve its soundness and control fraudulent activities.

The Impact of Age Difference on the Life Satisfaction of Wives in Specialized and Nonspecialized Households in Japan / Yaş Farkının Japonya’daki Uzmanlaşmış ve Uzmanlaşmamış Hanehalklarındaki Evli Kadınların Hayat Memnuniyetine Etkisi

Using panel data from the Keio Household Panel Survey (KHPS) between 2005 and 2018, this study examines the impact of age difference on wives' life satisfaction in Japan and how age difference affects wives' life satisfaction over the duration of marriage. Considering the specialization theory, the effect of age gap over time is also tested by dividing the sample by wives' employment status. The results of the pooled OLS estimation show that wives married to younger men are satisfied with their lives, while no significant finding is observed for wives married to older men. On the other hand, the fixed effects estimation findings reveal that life satisfaction of wives married to older men increases with years of marriage, while there is no significant finding for wives married to younger men. The results also show that housewives representing specialized households are more satisfied over time, while there are no significant results for working wives representing nonspecialized households. It is also shown that the life satisfaction of housewives married to older men increases as the length of the marriage increases and is always significant, even when alternative specification is used.

Selection ability and market timing skills of mutual fund and unit trust managers in a developing economy: evidence from Ghana

PurposeThe study assess the selection ability and market timing skills of mutual fund and unit trust managers in Ghana.Design/methodology/approachThe study uses an improved survivorship bias-free dataset of yearly after-fee returns of all mutual funds and unit trusts operating in Ghana from January 2011 to December 2019, cumulating in nine years of quantitative fund data. The authors assess Mutual funds and Unit trusts that ever existed, “alive” or “dead,” over the sample period in the study. The authors construct factor loadings to enable the application of multifactor models in the analysis. The authors apply the unconditional versions of the Jensen alpha, Fama-French three-factor, and Carhart four-factor models to determine the selection ability and market timing skills of 32 mutual funds and 17 unit trusts. The authors deploy HAC-consistent robust standard errors to the OLS estimations to subdue the effect of heterogeneity and autocorrelation.FindingsThe results indicate that, on average, mutual funds and unit trust managers possess market timing skills but no selection ability. When the results are decomposed into fund types, fixed-income and balanced mutual fund managers possess selection ability and market timing skills.Originality/valueTo the authors' best knowledge, this study is the earliest to examine the selection ability and market timing skills of both mutual fund and unit trust managers in Sub-Saharan Africa (SSA). It is also the earliest to construct factor loadings for the Ghana stock market.

Properties of the coefficient estimators for the linear regression model with heteroskedastic error term

In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic. For the heteroskedasticity of the changed segment type, using Monte-Carlo method, we investigate empirical properties of the proposed and ordinary least squares (OLS) estimators. The results show that the empirical covariance of the EGLS estimators is smaller than that of OLS estimators.

THE IMPACT OF ENVIRONMENTAL TAXES ON TRANSPORTATION AND STORAGE ENTERPRISES’ DEVELOPMENT – THE CASE OF BALKAN COUNTRIES

Sustainable development counteracts climate change and strives for a good quality of life. It is a development based on economic, social and environmental goals which should be evaluated. Environmental taxes have to ensure enterprises’ development follows sustainability principles. The basis of environmental taxes is a physical unit of harmful substance emissions with a proven negative impact on the environment. The article’s main objective is to assess the impact of environmental taxes (Envtax) on the sustainable development of the transportation and storage enterprises (Susd) (NACE Rev. 2: H) from 2008 to 2018. We created integrated indicators of sustainable development and its components, single-equation and multi-equation models (the OLS estimation) and Model Autoregressive Moving Average with eXogenous (ARMAX) to verify the research hypothesis. The research results indicate that energy (Entax t), transport (Trtax) and pollution taxes (Poltax) from current and previous period significantly affect the development of transportation and storage enterprises within sustainability principles (economic: Ed, social: Sd, and environmental: Envd). There is a strong variation in the strength and direction of the impact of environmental taxes on the development of the transportation and storage enterprises following sustainability principles in Bulgaria, Croatia, Romania, and Slovenia from 2008 to 2018. The results indicate that taxes are not effectively used and that implementing reforms in the European Union in this area is the right direction.

A Gravity Model Analysis of Portuguese Foreign Direct Investment

Cultural and linguistic affinities have been studied in international business through the gravity model and economics of language. International investment theories enable the assessment of organisational, location, and internalisation motivations. The present study assesses the impact of transport costs, common language and market size based on the arguments of the gravity model, economics of language, and international investment theory. This investigation evaluates the relationship between the Portuguese FDI and the gravity model, using panel data between 2005 and 2020. The OLS estimator, PPML-Poisson Pseudo-Maximum-Likelihood estimator, and panel quantile regressions were used as an econometric methodology. Regarding research, we sought to understand to what extent cultural and linguistic issues, namely Portuguese-speaking countries, contribute to explaining Portuguese FDI. The economic dimension of the Portuguese economy and investor countries were introduced into the regression model. In addition, transportation and transaction costs were analysed across geographical distances. The econometric results show that the common language and cultural aspects are positively correlated with the Portuguese FDI, which allows us to conclude that the Portuguese language reduces the asymmetries between the home market and the host country. When panel quantile regressions are applied, it is possible to observe that the Portuguese economy has dimension enough to attract FDI, just as the countries investing in the Portuguese economy have a return on the investment made. Finally, geographical distance negatively impacts FDI, showing that geographical proximity increases the probability of attracting FDI.

Do Analyst Attention Help Improve the Growth of Enterprises: Perspective of Financial Constraint

Based on the data of Chinese listed companies obtained by CSMAR, this paper examines the impact of analyst concerns on the growth rate of enterprises' main businesses. The OLS estimation results show that both analysts' attention and research reports' attention have a significant positive impact on the growth rate of the main business of the enterprise. Heterogeneity analysis shows that analysts' concerns have a greater impact on SMEs with total assets below the 50th percentile that year. Mechanism analysis shows that the positive impact of analysts on enterprises is achieved by easing financing constraints. Therefore, this paper believes that analysts' attention is the key factor to promote the growth of SMEs, and investors can also pay more attention to these enterprises. For small and medium-sized enterprises, financing constraints are a very difficult problem. This paper provides a new perspective and empirical evidence to ease the financing constraints of enterprises, especially SMEs.

Does the individual effect of resource rents imperative in the attainment of environmental sustainability? Evidence of Southeast Asian economies.

This study investigates the impact of natural resource exploitation on environmental sustainability in Southeast Asian economies, while testing the Environmental Kuznets Curve (EKC) inverted U-shaped hypothesis, a model which suggests an initial increase in environmental degradation with economic growth followed by a decrease at a certain level of income. Utilizing World Development Indicators data from 1995 to 2018, the research dissects the long-term influence of various resource rents, namely coal, oil, and forest. The research highlights the indispensable role of renewable energy in maintaining ecological balance. Results indicate that while coal rent exacerbates environmental degradation, forest and oil rents prove eco-friendly, although this is only confirmed in fully modified OLS estimation. The study underscores the importance of forest rents in achieving environmental sustainability. Renewable energy emerges as vital for promoting sustainable low-carbon practices. In line with the EKC hypothesis, the study finds that economic growth initially increases carbon emissions, but eventually reduces them. It calls for appropriate measures to manage resource exploitation, ensure renewable energy availability, alleviate energy poverty, and curb deforestation, thereby mitigating ecological damage.

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden.

Impact of CSR on Organizational Resilience across Chinese Listed Firms: Combination of Process-oriented and Outcome-oriented Perspectives

The firms are confronted with increasingly uncertain environment. Their ability to endure, recover and bounce back from a major disturbance is essential for firms’ survival and development. Therefore, how to cultivate organizational resilience has become an important issue. The paper operationalizes organizational resilience into two parts. One is the process of forming organizational resilience in pre-adversity period, another is the manifestation of organizational resilience in post-adversity period. Using the panel data from 2010 to 2019 and cross-sectional data in 2020 of listed firms in Chinese stock market, the paper employs fixed effect and OLS estimations to study the influence of corporate social responsibility (CSR) on organizational resilience by combining process-oriented and outcome-oriented perspectives. The results show that in context of China, CSR performance has mixed effects on organizational resilience. Specifically, CSR practice impacts organizational resilience through reducing financial volatility, instead of through improving economic strength in the process of formation. Furthermore, CSR practice shows no significant impact on the manifestation of organizational resilience when an adversity happens. The findings support the insurance-like effect of CSR practice. The study is the first attempt to interpret organizational resilience as a compound capability which is developed at tranquil period and manifested at adversity period. It is also one of the few empirical studies concerning the influence of CSR on organizational resilience with the background of a recently happened crisis. It contributes to the body of literatures on determinants of organizational resilience and suggests that CSR practice is a practical way to build up organizational resilience and maintain business sustainability.

Capital liberalization, growth and moral hazard: Lessons from the global financial crisis

The 2008 global crisis, initiated in the USA, a developed country, is significant as it's the last global crisis caused by capital flows. This study investigates the link between capital account liberalization and economic growth during the 2008 global crisis in 105 countries, including moral hazard. Furthermore, it considers portfolio equity and debt flows as asset characteristics and tests two moral hazard channels (i.e., sudden stop and credit booms) employing the OLS estimation technique. The findings show that capital inflows promote growth, with portfolio equity flows having more contribution and stability than portfolio debt flows. The hypothesis is verified that the excessive risk-taking behavior of financial institutions caused the 2008 crisis. Capital accumulation and credit expansion contribute to growth, but trade openness, sudden stop and labor force participation decrease growth. Decision-makers should evaluate capital inflows independently based on asset characteristics and implement distinct policies. Microeconomic strategies (such as revising limited liability) might mitigate the risks in capital account liberalization.

Pollutive cooking fuels and rural labor supply: Evidence from a large-scale population census in China

Pollutive cooking fuels are a major source of indoor and ambient air pollution. Health issues resulting from pollutive cooking can hinder the rural population from labor market participation and reduce labor supply. With a sizable rural population still reliant on solid fuels, China is experiencing a severe labor supply constraint and a quick switch to clean energy. This study examines the potential effects of using pollutive cooking fuels on the availability of labor in rural areas as well as the potential benefits of switching to clean energy. The results demonstrate that the working hours of rural employees are significantly reduced by the use of polluting cooking fuels, which is a reduction of 0.8% from OLS estimate, 14.8% from IV estimation, and 3.7% from MTE estimation. There is substantial heterogeneity across the white-collar workers and the blue-collar workers, males and females, households with independent kitchens and households without kitchens. The policy-related effects show that either encouraging clean fuel inclination or enhancing clean energy infrastructure can increase rural labor supply by 3%–5%. This study suggests that affordable and accessible clean energy is essential to sustain a sufficient labor supply and reduce labor market inequalities.

A Cautionary Note on the Use of Accounting Semi-Identity-Based Models

This study employs a Monte Carlo simulation to see whether accounting identity problems are present in the Fazzari, Hubbard, and Petersen model (1988). The Monte Carlo simulation generates 50,000 random cash flows, Tobin’s Q, and error term variables, which shape an investment variable that is dependent on them. Cash flows and investments are linked by a partial accounting identity, also known as an accounting semi-identity (ASI). An accounting identity is, for example, an equality between the left and right sides of a balance sheet. An ASI is not a complete one since one or more components of the accounting identity are missing. The estimated coefficients of an ASI do not represent reality, according to the OLS estimations. The regression tells us less about causality the closer the data are to the accounting identity. This is the first time that the biases of OLS estimations in an ASI-based model have been demonstrated.

Two-stage shrunken least squares estimator and its superiority

In linear regression model, the superiority of ordinary least squares estimator (OLSE) will be failed when there exist multi-collinearity problems. Based on the class of generalized shrunken least squares (GSLS) estimators suggested by Wang (1990), this article proposes a two-stage shrunken least squares estimator and discusses its superiority theoretically, and finally verifies the results by numerical simulations.