Ordinary Least-squares Method Research Articles

The Impact of Foreign Direct Investment, Trade Value on the Economic Growth of Greater Mekong Subregion (GMS)

In the study entitled “The impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries” which aims to study the changes of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS) from 2005–2022, and analyze the impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS), The data used in the study is geographical data. This study uses descriptive statistics to analyze changes and uses the estimation method of ordinary least squares (OLS) through a statistical program. Through the analysis of changes in foreign direct investment, trade value, and gross domestic product of GMS, it was found that, since GMS was established in 1992, the condition of foreign direct investment, trade value, and gross domestic product of GMS has been changing continuously due to the influence of Chinese investment that has come to invest in the region in terms of transportation infrastructure and electricity. In terms of trade, there are trade relations among the group of GMS countries, focusing on facilities for transportation and trade in the region. Cooperation in the region, contributing to economic growth and reducing poverty, as well as responding to the demand for products in the region, developing agriculture in urban areas, promoting GMS as a tourist source, and making the total domestic product of the countries in the region grow. A temporary drop in foreign direct investment, trade value, and gross domestic product of the GMS is due to the Mekong Delta member countries being affected by the financial crisis in Asia, political unrest, the financial crisis in China, and the spread of the COVID-19 disease. Analysis of the effect of foreign direct investment and trade value on the economic growth of the GMS countries by testing the relationship found that both the value of foreign direct investment and the value of international trade are related in the same direction to economic growth: if the value of foreign direct investment in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.1225%, and if the value of trade in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.7566% through statistical significance of 0.01.

Machine learning models applied to altimetry era tide gauge and grid altimetry data for comparative long-term trend estimation: A study from Shikoku Island, Japan

Estimation of sea level trends is essential for understanding sea level rise dynamics. In this study, the performance of traditional Ordinary Least Squares (OLS) linear trend forecasting is compared with modern machine learning techniques, specifically Random Forests (RF) and Least Squares Support Vector Machines (LS-SVM).These methods are applied to 50 years of long-term tide gauge (TG) data from six tide gauge stations off the coast of Shikoku Island, Japan, and CMEMS Grid Altimetry data from 1993 to the present. The analysis uses OLS, RF, and LS-SVM to estimate trends from both data sets and compares the results. The objective is to determine the consistency and accuracy of RF and LS-SVM methods compared to the OLS method. The results indicate that machine learning algorithms (LS-SVM) effectively estimate sea level trends, offering potential improvements in precision for both long-term and medium-term analyses. Shikoku Island's coastal sea level trend is determined as 2.91±1.44 mm/yr using TG data and 3.00±1.52 mm/yr using CMEMS Grid Altimeter data with the OLS approach. Using the LS-SVM approach, the trend is found as 2.96±1.58 mm/yr with TG data and 3.02±1.60 mm/yr with CMEMS Grid Altimetry data. The novelty of this study lies in its thorough comparison of traditional and machine learning approaches for sea level trend estimation, providing valuable insights for future predictions of the sea level rise.

Spatial Big Data Analytics of Inequity in Access to Live Music Among Disadvantaged Populations

In recent years, music cities around the world have developed various supportive policy frameworks ensuring live music is accessible to large populations. The question of whether live music is equitably distributed by socioeconomic status remains unanswered, however. This study investigated the accessibility of music events in socially and economically disadvantaged communities. The level of accessible concerts varies substantially across U.S. counties, revealing significant inequity in access to concerts. Almost 80 percent of concerts are located in 5 percent of counties. Ordinary least squares (OLS) regression, geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) methods were then employed to explore associations between accessible concerts and the socioeconomic characteristics of communities where these concerts are located. The regression models revealed that areas with a higher proportion of residents without a high school degree, higher unemployment rate, and higher poverty rate were less likely to have access to concerts, yet positive associations were found between accessible concerts and mean household income. Local R 2 values further confirmed the robustness of regression results of both GWR and MGWR and showed significant spatial nonstationarity of model performance. GWR and MGWR methods improved model performance significantly over the OLS method and proved how local conditions influence the OLS regression. The results from this study can guide decision makers and stakeholders in making appropriate policies to achieve equitable access to music events, especially for disadvantaged communities.

Empirical Analysis on Economic Sustainable of Rice Rain-Fed Area in Rural Cambodia

This paper investigates the economic sustainability of Cambodian rural rice farmers. Rice in Cambodia is a stable crop just as in many other Southeast Asian countries. The rice plants not only provide food to people but also a source of feed for livestock. Based on survey about of 6 districts of the rural household in Battambang of Cambodia, a total 204 rice farmers were interviewed and multiple regression models was applied using the method of Ordinary Least Squares (OLS) to determine the factors affecting the Crop-livestock farmers’ disposable income. The study aggregated the livestock of 3 species including cattle, pig and chicken in cattle unit standard known as Livestock Unit (LU). The result of the empirical analysis shows that many household demography factors as well as the socioeconomic factors influences the disposable income. The findings in the study provide an insight to the government and extension offices on effective development of economic sustainability of rice farming and livestock production system in the regions; investing more in public animal healthcare; ensure stable market prices for both rice and livestock, paying more attention on the factors limiting income in particular to poorer provinces and districts. Provide irrigation facilities accompanied with high technological varieties to encourage the double or even triple rice seasons. In addition, government (especially the ministry of agriculture) should embark on technological transfer among provinces and countries with successful agricultural system, orient farmers on the potential benefits of crop-livestock farming, provide training ground and scheme to local farmers.

PANDEMIC’S IMPACT ON BURSA MALAYSIA SECTORS: A COMPREHENSIVE STUDY UTILIZING OLSM AND ESM

The pandemic has the potential to induce significant stock market volatility as a consequence of investor decisions, while movement restrictions may force the closure of many small-to-medium enterprises (SMEs). Despite this, there is a noticeable absence of comprehensive perspectives on the pandemic's impact across all sectors in Bursa Malaysia. Consequently, this paper endeavors to scrutinize the sector within Bursa Malaysia most profoundly affected by Covid-19. Employing the Ordinary Least Squares method (OLSM) method and the Event Study Method (ESM), the study compares stock returns and risks. The independent variables encompass daily counts of Covid-19 cases and deaths in three countries—Malaysia, the United States, and China—as well as the Brent oil price. The study focuses on 13 sectorial indices, including construction, consumer products, energy, finance, healthcare, industrial products, plantations, property, real estate investment trusts, technology, telecommunications and media, transportation and logistics, and utilities. Utilizing the ESM, the study investigates the daily average return, abnormal return, and expected return of each sector to gauge the impact of Covid-19. Historical data from March 18 to May 3 over three years (2019 to 2021) is employed. Results indicate that an increase in Covid-19 cases in Malaysia correlates with a decrease in the performance of these 13 sectors. Consequently, this study contributes to enhancing investor comprehension of sectorial indices' volatility and significance during the pandemic, aiding them in monitoring sectoral performance in the securities market.

Reilly’s gravity law adapted to finance

The present paper proposes new indicators that explain the pricing of the unitary pure interest rates and interests by adapting previous Reilly’s Law (RL) models of urban economics and regional economics and Financial Gravity (FG) models for financial services. A panel dataset of 114 countries for 1967-2021 is handled, applying the OLS methodology. The results show the higher explanatory power of the adaptation of the FG models rather than the proposed adaptation of the RL ones. Purpose: This paper tries to adapt previous Gravity Equations from Urban Economics to Finance and empirically checks whether they behave better than the recently found Financial Gravity (FG) models. For that, new indicators are provided jointly with their adaptations to Ordinary Least Square (OLS) methods. Additionally, an economic perspective of the bid-asks of financial products is provided. Design/methodology/approach: OLS methods are used to check the desirability and accuracy of the new indicators. A panel dataset of 114 countries for 1967-2021 is handled, applying the OLS methodology. Findings: The most adequate are the FG models after the empirical check, providing the desirable properties of those models. The results show the higher explanatory power of the adaptation of the FG models rather than the proposed adaptation of the RL ones. Originality: This is the first paper, to the author’s knowledge, on providing an adaptation of Reilly’s Law from urban economics models to finance, also providing empirical evidence after adapting these models to linear expressions for applying OLS methods. We also provide evidence on the statistical equality between the unitary pure interest of loan and deposit interests, confirming the good properties of the FG models.

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Evaluating the Characteristics of Spatial Variability of Soil in Vertical Direction Highly Heterogeneous Region Based on Cone Penetration Test

Abstract Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

The Impact of Female Entrepreneurship on Economic Growth in Developing and Developed Economies

Abstract Significance. The development of women’s entrepreneurship is still a largely untapped potential for growth and prosperity. The economic role of women is crucial for growth and a more equitable distribution of wealth. Economic opportunities are much more important for women today than for the previous generation, especially for developing countries. In this regard, the impact of women’s entrepreneurship on economic growth is an important area of research reflecting broader interests in gender equality, economic development and human resource optimization. Objectives. Analysis of the impact of women’s entrepreneurship on the economies of developed and developing countries and the development of some recommendations for the development of women’s entrepreneurship. Methods. For this study, the method of ordinary least squares (OLS) was used, which analyses the impact of women’s entrepreneurship on economic growth in developed and developing countries in the period 2012-2022. Results. The under-development of women’s entrepreneurship represents untapped potential for growth and prosperity in many countries. In this regard, there is an urgent need to create more favourable conditions for the development and strengthening of women’s entrepreneurship, more thoughtful policies and decisive measures aimed at empowering women are crucial. The results obtained in the course of this study are important for the development of theoretical and methodological foundations for the study of women’s entrepreneurship. The results of the study may be useful for women entrepreneurs, NGOs/NGOs, development institutions and government agencies.

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans

To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard. Retrospective cohort study. Glaucomatous and healthy eyes with ≥5 reliable peripapillary OCT (Spectralis, Heidelberg Engineering) circle scans were included. A weakly supervised time-series learning model, called noise positive-unlabeled (Noise-PU) DL was developed to classify whether sequences of OCT B-scans showed glaucoma progression. The model used 2 learning schemes, one to identify age-related changes by differentiating test sequences from glaucoma vs healthy eyes, and the other to identify test-retest variability based on scrambled OCTs of glaucoma eyes. Both models' bases were convolutional neural networks (CNN) and long short-term memory (LSTM) networks which were combined to form a CNN-LSTM model. Model features were combined and jointly trained to identify glaucoma progression, accounting for age-related loss. The DL model's outcomes were compared with ordinary least squares (OLS) regression of retinal nerve fiber layer (RNFL) thickness over time, matched for specificity. The hit ratio was used as a proxy for sensitivity. Eight thousand seven hundred eighty-five follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. The mean follow-up time was 3.5 ± 1.6 years. In the test sample, the hit ratios of the DL and OLS methods were 0.498 (95%CI: 0.470-0.526) and 0.284 (95%CI: 0.258-0.309) respectively (P < .001) when the specificities were equalized to 95%. A DL model was able to identify longitudinal glaucomatous structural changes in OCT B-scans using a surrogate reference standard for progression.

A Remedial Measure of Multicollinearity in Multiple Linear Regression in the Presence of High Leverage Points

The ordinary least squares (OLS) is the widely used method in multiple linear regression model due to tradition and its optimal properties. Nonetheless, in the presence of multicollinearity, the OLS method is inefficient because the standard errors of its estimates become inflated. Many methods have been proposed to remedy this problem that include the Jackknife Ridge Regression (JAK). However, the performance of JAK is poor when multicollinearity and high leverage points (HLPs) which are outlying observations in the X- direction are present in the data. As a solution to this problem, Robust Jackknife Ridge MM (RJMM) and Robust Jackknife Ridge GM2 (RJGM2) estimators are put forward. Nevertheless, they are still not very efficient because they suffer from long computational running time, some elements of biased and do not have bounded influence property. This paper proposes a robust Jackknife ridge regression that integrates a generalized M estimator and fast improvised Gt (GM-FIMGT) estimator, in its establishment. We name this method the robust Jackknife ridge regression based on GM-FIMGT, denoted as RJFIMGT. The numerical results show that the proposed RJFIMGT method was found to be the best method as it has the least values of RMSE and bias compared to other methods in this study.

Life Function Analysis of a Two-Parameter Exponential Distribution Using Robust Rank Regression

In this research, a proposed method is presented to estimate the life function of the two-parameter exponential distribution by using the robust rank regression on the dependent variable, which depends on the robust variance-covariance matrix (the FMCD and OGK method) and the robust mean vector through which the rank regression coefficients are estimated using the method of ordinary least squares and employing it in estimating the scale and location parameters of the exponential distribution and thus analyzing the life function on its basis. A comparison was also made between the proposed method and the classical method of rank regression on the dependent variable of life data at different sample sizes (from 10 to 25) to show the efficiency of the proposed method based on the mean square error of the estimated parameters, through a program prepared by the researcher in MATLAB language dedicated to this purpose. The practical application results concluded that the proposed robust method of rank regression on the dependent variable in estimating the parameters of the exponential distribution was more efficient than the classical method. Paper type: Research paper.

Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry

One of the main effects of climate change is rising sea levels, which presents challenges due to its geographically heterogenous nature. Often, contradictory results arise from examining different sources of measurement and time spans. This study addresses these issues by analysing both long-term (1995–2022) and decadal (2000–2009 and 2010–2019) sea-level trends in the Baltic Sea. Two independent sources of data, which consist of 13 tide gauge (TG) stations and multi-mission along-track satellite altimetry (SA), are utilized to calculate sea-level trends using the ordinary least-squares method. Given that the Baltic Sea is influenced by geographically varying vertical land motion (VLM), both relative sea level (RSL) and absolute sea level (ASL) trends were examined for the long-term assessment. The results for the long-term ASL show estimates for TG and SA to be 3.3 mm/yr and 3.9 mm/yr, respectively, indicating agreement between sources. Additionally, the comparison of long-term RSL ranges from −2 to 4.5 mm/yr, while ASL varies between 2 and 5.4 mm/yr, as expected due to the VLM. Spatial variation in long-term ASL trends is observed, with higher rates in the northern and eastern regions. Decadal sea-level trends show higher rates, particularly the decade 2000–2009. Comparison with other available sea-level datasets (gridded models) yields comparable results. Therefore, this study evaluates the ability of SA as a reliable source for determining reginal sea-level trends in comparison with TG data.

Union membership and the wage gap between the public and private sectors: evidence from China

As trade unions are active in corporations worldwide, their effects on the labor market have attracted global attention. However, there is scarce empirical evidence regarding how trade unions’ effects on the wage gap differ between the public and private sectors, especially in the Chinese context. Using national longitudinal survey data from the China Family Panel Studies for the years 2010, 2012, 2014, 2016, and 2018 and the Blinder-Oaxaca decomposition method, this study estimates unions’ effect on the wage gap between the public and private sectors in China. The results from the Ordinary Least Squares (OLS) method indicate the existence of a significant positive union wage premium nationwide. Additionally, the premium in the public sector is greater than that in the private sector. However, this effect becomes insignificant after accounting for individual heterogeneity using the fixed effects model. The decomposition results based on the OLS method indicate that the union coverage difference (the endowment effect) widens the wage gap between the public and private sectors; conversely, the union wage premium difference (the price effect) narrows the wage gap. These results indicate that a policy expanding union coverage in the private sector may effectively narrow the wage gap between both sectors.

Evaluating the Impact of Green Gentrification -A Case Study of London

In this research, we utilize the Ordinary Least Squares (OLS) regression method to explore the relationship between green gentrification and key socioeconomic factors within London's urban landscape. Our aim is to delve into the impact of introducing or expanding green spaces on residents' lives, particularly in relation to income, life expectancy, well-being, housing costs, and unemployment rates. Through the use of OLS regression and an extensive dataset sourced from empirical evidence, we quantitatively analyze the connections between green gentrification and variations in these socioeconomic variables across different neighborhoods. Our findings reveal noteworthy associations, highlighting positive correlations between green spaces and well-being, life expectancy, and house prices, while also noting a negative correlation with the unemployment rate. These insights contribute to a comprehensive understanding of the complex dynamics between urban development, environmental enhancements, and societal well-being, emphasizing the potential advantages of green gentrification and endorsing inclusive policies. Grounded in the OLS method, our research strengthens the empirical foundation for informed decision-making among policymakers, urban planners, and researchers dedicated to cultivating sustainable and equitable urban environments.

Transfer learning across datasets with different input dimensions: An algorithm and analysis for the linear regression case

With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help to improve the accuracy of a model. On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail. In this work, we propose a transfer learning algorithm that combines new and historical data with different input dimensions. This approach is easy to implement, efficient, with computational complexity equivalent to the ordinary least-squares method, and requires no hyperparameter tuning, making it straightforward to apply when the new data is limited. Different from other approaches, we provide a rigorous theoretical study of its robustness, showing that it cannot be outperformed by a baseline that utilizes only the new data. Our approach achieves state-of-the-art performance on 9 real-life datasets, outperforming the linear DSFT, another linear transfer learning algorithm, and performing comparably to non-linear DSFT.11The code for reproducing the results of this paper is available at https://github.com/lpsilvestrin/incremental_input_tl.

Estimating the Returns to Education Using a Machine Learning Approach – Evidence for Different Regions

Abstract This article revisits the Mincer earnings function and presents comparable estimates of the average monetary returns associated with an additional year of education across different regions worldwide. In contrast to the traditional Ordinary Least Squares (OLS) method commonly employed in the literature, this study applied a cutting-edge approach known as Support Vector Regression (SVR), which belongs to the family of machine learning (ML) algorithms. SVR is specifically chosen to address the bias arising from underfitting inherent in OLS. The analysis focuses on recent data spanning from 2010 to 2018, ensuring temporal homogeneity across the examined regions. The findings reveal that each additional year of education, on average, yields a private rate of returns of 10.4%. Notably, Sub-Saharan Africa exhibits the highest returns to education at 17.8%, while Europe demonstrates the lowest returns at 7.2%. Moreover, higher education is associated with the highest returns across the regions, with a rate of 12%, whereas primary education yields returns of 10%. Interestingly, women generally experience higher returns than men, with rates of 10.6 and 10.1%, respectively. Over time, the returns to education exhibit a modest decline, decreasing at a rate of approximately 0.1% per year, while the average duration of education demonstrates an increase of 0.16 years per year (1% per year). The application of the state-of-the-art ML technique, SVR, not only improves the accuracy of estimates but also enhances predictive performance measures such as the coefficient of determination (R 2) and Root Mean Square Error (RMSE) when compared to the OLS method. The implications drawn from these findings emphasize the need for expanding university education, as well as investments in primary education, along with significant attention toward promoting girls’ education. These findings hold considerable importance for policymakers who are tasked with making informed decisions regarding education expenditure and the implementation of education financing programs.

Financial distress and stock price crash risk in Egyptian firms

Economic policy uncertainty intensified as a result of the global financial crisis. To overcome these obstacles, firms handle issues with financial distress and crash risk more proactively. This paper offers new insights into the relationship between financial distress and crash risk on the Egyptian stock market during the period of 2014–2021 and presents how managers strengthen the bad news hoarding mechanism to their advantage. Data were collected via financial statements and reports obtained from the Thomson Reuters database using 824 annual observations of 103 Egyptian firms via the generalized method of moments and ordinary least squares. Results show a strong positive impact of financial distress on crash risk using OLS and GMM. Results support the role of managerial opportunism to cover up bad news that undermines a firm’s economic fundamentals. The findings support an agency theory of how financial distress affects crash risk. The findings support conducting robust tests for alternative financial distress and crash risk measures.

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.

Effect of government intervention in relation to COVID-19 cases and deaths in Malawi

The COVID-19 pandemic affected the world in various ways. In response to this, countries set up various interventions such as lockdowns, physical distancing, and mandatory face covering, among others. Governments also put in place measures to ensure compliance. However, the extent to which the various responses impacted the deaths and confirmed cases remains debatable. This paper explores this question by looking at how government stringency measures impacted deaths and cases in Malawi. We employ an instrumental variable (IV) approach to assess the impact of government action on confirmed COVID-19 cases and deaths. We measure government policy by the stringency index. By leveraging the IV approach, we circumvent potential endogeneity concerns between our main policy variable and the outcome variables. Our data comes from the University of Oxford COVID-19 project and spans a daily frequency from 20 February 2020 to 25 April 2022, covering multiple waves of the pandemic. Overall, the findings show that despite Malawi never having implemented a full lockdown, the government policies may have helped to reduce both cases and deaths related to COVID-19. Specifically, the IV shows that a unit increase in the government stringency index results in a drop of 179 cases and 6 deaths. All the results are statistically significant at 1% level and remain robust to the use of the ordinary least-squares method. This study demonstrates the efficacy of non-pharmaceutical tools implemented by the government to fight COVID-19. Thus, policymakers need to place more emphasis on the need for the public to adhere to these stringency measures in the event of new waves of the pandemic or similar outbreaks.