The Winter Olympics have been held since 1924, and each time host countries spend billions on organization, so it is important for them to know if this pays off in the future. This paper examines whether hosting the Winter Olympic Games yields long-term economic benefits. To achieve this, the difference-in-difference model for relative changes in Gross Domestic Product per capita was estimated. A difference-in-difference estimator examines post-Olympic impacts for host countries between 1972 and 2014. Regression results provide no additional long term impacts of hosting the Winter Olympics on GDP per capita.
{"title":"Economic impacts of hosting the winter olympics on GDP per capita","authors":"Aistė Žečkytė","doi":"10.15388/ljs.2021.26447","DOIUrl":"https://doi.org/10.15388/ljs.2021.26447","url":null,"abstract":"The Winter Olympics have been held since 1924, and each time host countries spend billions on organization, so it is important for them to know if this pays off in the future. This paper examines whether hosting the Winter Olympic Games yields long-term economic benefits. To achieve this, the difference-in-difference model for relative changes in Gross Domestic Product per capita was estimated. A difference-in-difference estimator examines post-Olympic impacts for host countries between 1972 and 2014. Regression results provide no additional long term impacts of hosting the Winter Olympics on GDP per capita.","PeriodicalId":30757,"journal":{"name":"Lithuanian Journal of Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41785300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this article is to present a methodology and results for nowcasting poverty and inequality indicators during economic growth and the Covid-19 pandemic in Lithuania. Nowcasting combines the techniques of tax-benefit microsimulation and calibration of the survey weights. For the microsimulation, the tax-benefit microsimulation model EUROMOD is used together with its additional components for Lithuania, which were developed by the Ministry of Social Security and Labour of the Republic of Lithuania. Three economic forecasts, developed by the Bank of Lithuania for 2020, are used: the rapid V-shaped recovery scenario, intermediate U-shaped recovery scenario and a slow extended U-shaped recovery scenario. The results show Lithuania's favourable tendencies in reducing poverty and inequality in the general population and by age groups in the context of rapid economic growth and improving the improved labour-market situation in 2018–2019. The results of 2020 suggest that relative at-risk-of-poverty rates and inequality in the country are likely to decline. The foreseen decrease in the at-risk-of-poverty rate is primarily due to reducing poverty risk among older people and children. The most vulnerable age groups include youth (18–24 years) and the elder working-age population (50–64 years). Poverty rates for these groups are likely to increase in 2020. However, it should be noted that the at-risk-of-poverty rates had also declined in Lithuania during the first years of the previous economic crisis. Decomposition of demographic/employment changes and policy effects for 2019–2020 show that due to demographic and employment changes, poverty and inequality is likely to increase in Lithuania in 2020. The impact of the policy effect is progressive, more favourable to the less well-off, leading to a reduction in poverty. Progressiveness is due to the fact that during the quarantine period, flat benefits were provided to a large part of the society: children, pensioners, job-seekers, self-employed.
{"title":"Nowcasting poverty and inequality in the context of economic growth and Covid-19 pandemic in Lithuania","authors":"Vitalija Gabnytė, Aušra Čižauskaitė, Jekaterina Navickė","doi":"10.15388/ljs.2021.26443","DOIUrl":"https://doi.org/10.15388/ljs.2021.26443","url":null,"abstract":"The purpose of this article is to present a methodology and results for nowcasting poverty and inequality indicators during economic growth and the Covid-19 pandemic in Lithuania. Nowcasting combines the techniques of tax-benefit microsimulation and calibration of the survey weights. For the microsimulation, the tax-benefit microsimulation model EUROMOD is used together with its additional components for Lithuania, which were developed by the Ministry of Social Security and Labour of the Republic of Lithuania. Three economic forecasts, developed by the Bank of Lithuania for 2020, are used: the rapid V-shaped recovery scenario, intermediate U-shaped recovery scenario and a slow extended U-shaped recovery scenario. The results show Lithuania's favourable tendencies in reducing poverty and inequality in the general population and by age groups in the context of rapid economic growth and improving the improved labour-market situation in 2018–2019. The results of 2020 suggest that relative at-risk-of-poverty rates and inequality in the country are likely to decline. The foreseen decrease in the at-risk-of-poverty rate is primarily due to reducing poverty risk among older people and children. The most vulnerable age groups include youth (18–24 years) and the elder working-age population (50–64 years). Poverty rates for these groups are likely to increase in 2020. However, it should be noted that the at-risk-of-poverty rates had also declined in Lithuania during the first years of the previous economic crisis. Decomposition of demographic/employment changes and policy effects for 2019–2020 show that due to demographic and employment changes, poverty and inequality is likely to increase in Lithuania in 2020. The impact of the policy effect is progressive, more favourable to the less well-off, leading to a reduction in poverty. Progressiveness is due to the fact that during the quarantine period, flat benefits were provided to a large part of the society: children, pensioners, job-seekers, self-employed.","PeriodicalId":30757,"journal":{"name":"Lithuanian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45629214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A short message is dedicated to professor Gunnar Kulldorff who was the founder of the Baltic-Nordic-Ukrainian Network on Survey Statistics, and the President of the International Statistical Institute.
Gunnar Kuldorff教授是波罗的海-北欧-乌克兰调查统计网络的创始人，也是国际统计研究所的主席。
{"title":"Remembering Gunnar Kulldorff (1927–2015)","authors":"D. Pumputis, Andrius Čiginas","doi":"10.15388/ljs.2021.26446","DOIUrl":"https://doi.org/10.15388/ljs.2021.26446","url":null,"abstract":"A short message is dedicated to professor Gunnar Kulldorff who was the founder of the Baltic-Nordic-Ukrainian Network on Survey Statistics, and the President of the International Statistical Institute.","PeriodicalId":30757,"journal":{"name":"Lithuanian Journal of Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42863678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Academic institutions are seeking to attract the most capable and prospective students. Many research studies seek to identify factors determining a successful transition from secondary to tertiary education. One of the important issues is the predictive value of national testing/Matura examination results in relation to higher education institutions admission. The aim of our study is to quantify the odds to study at university and the results of centralised student assessments (the 10th grade and Matura) of mathematics and the Lithuanian language and literature for the period of five years (Matura examinations for the period 2014–2018), taking into account the student's gender, the location of the school attended, social support, and special needs indicators. We estimate that the Matura grade in mathematics is almost double more important when compared to the10th grade test results for odds of studying at higher education institutions. Grades of the Lithuanian language and literature obtained to be twice more import for males than females. From 2023 or 2024, we may observe the downward trend of people with tertiary educational attainment in Lithuania.
{"title":"The predictive value of the 10th grade and Matura examination achievements for higher education admission","authors":"A. Jakaitienė, R. Želvys","doi":"10.15388/ljs.2021.26445","DOIUrl":"https://doi.org/10.15388/ljs.2021.26445","url":null,"abstract":"Academic institutions are seeking to attract the most capable and prospective students. Many research studies seek to identify factors determining a successful transition from secondary to tertiary education. One of the important issues is the predictive value of national testing/Matura examination results in relation to higher education institutions admission. The aim of our study is to quantify the odds to study at university and the results of centralised student assessments (the 10th grade and Matura) of mathematics and the Lithuanian language and literature for the period of five years (Matura examinations for the period 2014–2018), taking into account the student's gender, the location of the school attended, social support, and special needs indicators. We estimate that the Matura grade in mathematics is almost double more important when compared to the10th grade test results for odds of studying at higher education institutions. Grades of the Lithuanian language and literature obtained to be twice more import for males than females. From 2023 or 2024, we may observe the downward trend of people with tertiary educational attainment in Lithuania.","PeriodicalId":30757,"journal":{"name":"Lithuanian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48891892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayes multiclass classification of spatial Gaussian data following the universal kriging model is considered. The closed-form expressions for the maximum likelihood (ML) estimator of regression parameters and the actual error rate (AER) in terms of semivariograms are derived.
{"title":"On the classification error rates in terms of semivariograms for Gaussian universal kriging models","authors":"K. Dučinskas, L. Dreiziene","doi":"10.15388/ljs.2021.26442","DOIUrl":"https://doi.org/10.15388/ljs.2021.26442","url":null,"abstract":"Bayes multiclass classification of spatial Gaussian data following the universal kriging model is considered. The closed-form expressions for the maximum likelihood (ML) estimator of regression parameters and the actual error rate (AER) in terms of semivariograms are derived.","PeriodicalId":30757,"journal":{"name":"Lithuanian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44482969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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