Geographic and gender disparities in global education achievement during the COVID-19 pandemic.

School closures induced by the COVID-19 pandemic have negatively impacted on 1.7 billion children, resulting in losses of learning time and a decline of learning scores. However, the learning losses of students exposed to the COVID-19 pandemic at the country level have been quantitatively unaddressed. Here we model the global learning losses of students due to the COVID-19 in 2020. Our results reveal a global average Harmonized Test Scores (HTS) loss of 2.26 points. Learning continuity measures reduce the global average HTS loss by 1.64 points. South Asia and Sub-Saharan Africa have high HTS losses (5.82 and 2.94 points), while Europe & Central Asia and North America have low HTS losses (0.85 and 0.93 points). Compared with South Asia and Sub-Saharan Africa, North America and Europe & Central Asia implement more effective learning continuity measures. HTS losses in low-income and lower-middle-income countries are higher (3.35 and 3.13 points) than those in high-income and upper-middle-income countries (0.99 and 2.31 points). Learning losses of global female students are higher than their male counterparts, and there is significant heterogeneity across national regions. Our results reveal both global learning losses and gender inequality in learning scores due to the COVID-19 pandemic. Global disparities highlight the importance of the need to mitigate education inequality.

The real estate market in Ghana: the nexus between price and income levels

Purpose: This study aimed to identify the link between the income levels of government workers and the prices of real estate houses in Ghana to identify the prevailing mortgage gaps and to stimulate both reactive and proactive government policies backed by continuous stakeholder engagements under the new normal. Design/methodology/approach: The quantitative approach was used for this study. Two data collection methods were used to achieve the objectives of the study: the survey method, using a questionnaire to collect the primary data, and the use of documentary information as the source of secondary data. For the primary data, prices of two-bedroom and three-bedroom houses were collected. The secondary data collected were: (1) salary levels of government employees and (2) mortgage values prevailing. The three data sets were analysed and structured to identify the relationship between income levels and the prices of real estate houses within the prevailing mortgage system. Findings: It will require a quadrupling of the salaries of only the highest income earners of government employees to afford the average price of a basic two-bedroom and three-bedroom housing in Ghana. Largely, government employees cannot afford these houses with the current price levels and the mortgage systems available. The real estate market in Ghana has not focused on lower-earning groups. The effects of the new normal resulting from the effects of Covid-19 require a paradigm change. Originality/value: The paper established the relationship between salary levels of government employees and the process of basic accommodation types on offer in the Ghanaian market by the real estate industry: two- and three-bedroom houses. The findings will help real estate developers to consider their approach to housing designs and construction methods and the pricing to ensure that they meet the needs of the public sector workers who could form a large customer base. © 2022, Emerald Publishing Limited.

The real estate market in Ghana: the nexus between price and income levels

PurposeThis study aimed to identify the link between the income levels of government workers and the prices of real estate houses in Ghana to identify the prevailing mortgage gaps and to stimulate both reactive and proactive government policies backed by continuous stakeholder engagements under the new normal.Design/methodology/approachThe quantitative approach was used for this study. Two data collection methods were used to achieve the objectives of the study: the survey method, using a questionnaire to collect the primary data, and the use of documentary information as the source of secondary data. For the primary data, prices of two-bedroom and three-bedroom houses were collected. The secondary data collected were: (1) salary levels of government employees and (2) mortgage values prevailing. The three data sets were analysed and structured to identify the relationship between income levels and the prices of real estate houses within the prevailing mortgage system.FindingsIt will require a quadrupling of the salaries of only the highest income earners of government employees to afford the average price of a basic two-bedroom and three-bedroom housing in Ghana. Largely, government employees cannot afford these houses with the current price levels and the mortgage systems available. The real estate market in Ghana has not focused on lower-earning groups. The effects of the new normal resulting from the effects of Covid-19 require a paradigm change.Originality/valueThe paper established the relationship between salary levels of government employees and the process of basic accommodation types on offer in the Ghanaian market by the real estate industry: two- and three-bedroom houses. The findings will help real estate developers to consider their approach to housing designs and construction methods and the pricing to ensure that they meet the needs of the public sector workers who could form a large customer base.

Mortality in Four Waves of COVID-19 Is Differently Associated with Healthcare Capacities Affected by Economic Disparities.

BACKGROUND: The greatest challenges are imposed on the overall capacity of disease management when the cases reach the maximum in each wave of the pandemic. METHODS: The cases and deaths for the four waves of COVID-19 in 119 countries and regions (CRs) were collected. We compared the mortality across CRs where populations experience different economic and healthcare disparities. FINDINGS: Among 119 CRs, 117, 112, 111, and 55 have experienced 1, 2, 3, and 4 waves of COVID-19 disease, respectively. The average mortality rates at the disease turning point were 0.036, 0.019. 0.017, and 0.015 for the waves 1, 2, 3, and 4, respectively. Among 49 potential factors, income level, gross national income (GNI) per capita, and school enrollment are positively correlated with the mortality rates in the first wave, but negatively correlated with the rates of the rest of the waves. Their values for the first wave are 0.253, 0.346 and 0.385, respectively. The r value for waves 2, 3, and 4 are -0.310, -0.293, -0.234; -0.263, -0.284, -0.282; and -0.330, -0.394, -0.048, respectively. In high-income CRs, the mortality rates in waves 2 and 3 were 29% and 28% of that in wave 1; while in upper-middle-income CRs, the rates for waves 2 and 3 were 76% and 79% of that in wave 1. The rates in waves 2 and 3 for lower-middle-income countries were 88% and 89% of that in wave 1, and for low-income countries were 135% and 135%. Furthermore, comparison among the largest case numbers through all waves indicated that the mortalities in upper- and lower-middle-income countries is 65% more than that of the high-income countries. INTERPRETATION: Conclusions from the first wave of the COVID-19 pandemic do not apply to the following waves. The clinical outcomes in developing countries become worse along with the expansion of the pandemic.

Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.