This document examines potential socioeconomic factors that could explain differences in Olympic medal counts between countries. It reviews previous research finding correlations between medal counts and variables like GDP, population size, healthcare spending, and education levels. The authors develop a model using multiple linear regression to analyze the relationship between three independent variables - GDP per capita, population size, and literacy rate - and the dependent variable of total medals won, using data from 33 countries in the 2012 London Olympics. While all three independent variables had positive coefficients, only population size showed a statistically significant correlation with total medals won. The authors note the small sample size could limit significance.
The report summarizes the findings of The Economist Intelligence Unit's 2019 Worldwide Cost of Living Survey. For the first time, Singapore, Paris and Hong Kong share the title of most expensive cities in the world. Overall, Asian and European cities dominate the top ten most costly cities. Currency fluctuations were a major driver of changes in city rankings from last year. Cities like Istanbul and Buenos Aires saw dramatic drops due to currency crises in Turkey and Argentina.
IOSR Journal of Humanities and Social Science is a double blind peer reviewed International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
This document analyzes the impacts of hosting mega sporting events like the Olympics and FIFA World Cup on government spending patterns in education and healthcare. It finds that hosting an event negatively impacts short-term education spending and positively impacts long-term healthcare spending, especially in developing countries. The paper reviews literature on the economics of mega events and mechanisms for why countries host them, finding that while politicians claim economic benefits, happiness is a stronger motivator. Countries must cut public services like education and healthcare to fund the expensive events when private funding is insufficient.
Brazil is predicted to win 19 medals at the 2016 Rio Olympics based on a top-down quantitative model that uses macro-level factors like GDP, population size, and historical performance. A bottom-up analysis finds that while Brazil will not see as large of an outlier boost as host China did in 2008, they could potentially add a few medals from new sports and strategic investment, bringing their total to around 21 medals. Both top-down and bottom-up analyses indicate Brazil is unlikely to greatly outperform the 19 medal prediction from following general country trends.
This document analyzes whether hosting the Summer Olympic Games provides a lasting legacy for improved sports performance. The authors test if hosting leads to increased medal counts in subsequent Olympic Games. They find that while hosting countries see a significant boost in medals during the Games they host, this effect fades away immediately afterward and there is no significant difference in medal counts before and after hosting. To address concerns about endogeneity in host selection, they also compare hosting countries' performance to unsuccessful bidder countries, finding no difference. This suggests hosting the Olympics does not create a lasting structural improvement in a country's sports success based on medal counts.
Downloaded by Asma Aldarwish on 4142016. University of Color.docxelinoraudley582231
The document summarizes the plans for the 2012 London Olympic Games. It discusses how London was selected to host over other candidate cities like Paris. It also outlines the challenges of planning infrastructure for the Games, including building new venues and upgrading transportation. Finally, it provides context on the economics of the Olympics and responsibilities of the organizing committee.
The document is a research paper that examines the determinants of international soccer performance in the FIFA World Cup. It analyzes the relationship between a country's success in the World Cup and factors like wealth, culture, and the popularity of football. The researchers find that cultural predisposition toward football as the main sport and popularity of the sport within a nation are more statistically significant determinants of success than per capita wealth, contrary to some previous research. The paper reports on data collected on World Cup matches, goals, points, appearances, top goal scorers, viewership, temperature, population, and wealth for 40 countries in order to analyze which factors best predict winning percentage.
The report summarizes the findings of The Economist Intelligence Unit's 2019 Worldwide Cost of Living Survey. For the first time, Singapore, Paris and Hong Kong share the title of most expensive cities in the world. Overall, Asian and European cities dominate the top ten most costly cities. Currency fluctuations were a major driver of changes in city rankings from last year. Cities like Istanbul and Buenos Aires saw dramatic drops due to currency crises in Turkey and Argentina.
IOSR Journal of Humanities and Social Science is a double blind peer reviewed International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
This document analyzes the impacts of hosting mega sporting events like the Olympics and FIFA World Cup on government spending patterns in education and healthcare. It finds that hosting an event negatively impacts short-term education spending and positively impacts long-term healthcare spending, especially in developing countries. The paper reviews literature on the economics of mega events and mechanisms for why countries host them, finding that while politicians claim economic benefits, happiness is a stronger motivator. Countries must cut public services like education and healthcare to fund the expensive events when private funding is insufficient.
Brazil is predicted to win 19 medals at the 2016 Rio Olympics based on a top-down quantitative model that uses macro-level factors like GDP, population size, and historical performance. A bottom-up analysis finds that while Brazil will not see as large of an outlier boost as host China did in 2008, they could potentially add a few medals from new sports and strategic investment, bringing their total to around 21 medals. Both top-down and bottom-up analyses indicate Brazil is unlikely to greatly outperform the 19 medal prediction from following general country trends.
This document analyzes whether hosting the Summer Olympic Games provides a lasting legacy for improved sports performance. The authors test if hosting leads to increased medal counts in subsequent Olympic Games. They find that while hosting countries see a significant boost in medals during the Games they host, this effect fades away immediately afterward and there is no significant difference in medal counts before and after hosting. To address concerns about endogeneity in host selection, they also compare hosting countries' performance to unsuccessful bidder countries, finding no difference. This suggests hosting the Olympics does not create a lasting structural improvement in a country's sports success based on medal counts.
Downloaded by Asma Aldarwish on 4142016. University of Color.docxelinoraudley582231
The document summarizes the plans for the 2012 London Olympic Games. It discusses how London was selected to host over other candidate cities like Paris. It also outlines the challenges of planning infrastructure for the Games, including building new venues and upgrading transportation. Finally, it provides context on the economics of the Olympics and responsibilities of the organizing committee.
The document is a research paper that examines the determinants of international soccer performance in the FIFA World Cup. It analyzes the relationship between a country's success in the World Cup and factors like wealth, culture, and the popularity of football. The researchers find that cultural predisposition toward football as the main sport and popularity of the sport within a nation are more statistically significant determinants of success than per capita wealth, contrary to some previous research. The paper reports on data collected on World Cup matches, goals, points, appearances, top goal scorers, viewership, temperature, population, and wealth for 40 countries in order to analyze which factors best predict winning percentage.
This document is the thesis of Jose Luis Contreras Biekert for obtaining a Doctorate in Economics. It consists of three essays in microeconomics. The first essay empirically analyzes the impact of hosting the Summer Olympic Games on a country's future performance and finds no lasting legacy for sports. The second essay develops a general equilibrium model to analyze mitigation of negative externalities through public good production and income taxes. It finds conditions for a Pareto optimal equilibrium and that eco-friendly technological changes may reduce welfare. The third essay considers coalition formation and models how it reduces rivalry in consumption, finding it can impact inequality.
The document discusses the impacts of hosting mega sporting events like the Olympics or FIFA World Cup. It provides background on the bidding process and factors considered in selecting host countries. The economic impacts are analyzed, including both potential positive and negative effects. Specifically, significant public money is spent on new venues and infrastructure, which can create jobs but may also be viewed as a transfer of short-term spending. The success of events depends on the long-term use of venues and infrastructure after the event. Developing countries in particular may see few economic benefits and waste money that could be spent on other priorities like education and healthcare.
In this paper, we analyze the demographic and economic consequences of endogenous migrations flows over the coming decades in a multi-regions overlapping generations general equilibrium model (INGENUE 2) in which the world is divided in ten regions. Our analysis offers a global perspective on the consequences of international migration flows. The value-added of the INGENUE 2 model is that it enables us to analyze the effects of international migration on both the destination and the origin regions. A further innovation of our analysis is that international migration is treated as endogenous.
In a first step, we estimate the determinants of migration in an econometric model. We show, in particular, that the income differential is one of the key variables explaining migration flows. In a second step, we endogenize migration flows in the INGENUE 2 model. In order to do so, we use the econometrically estimated relationships between demographic and income developments in the INGENUE model, which enables us to project long-run migration flows and to improve on projections of purely demographic models.
Authored by: Vladimir Borgy, Xavier Chojnicki, Gelles Le Garrec, Cyrille Schwellnus
Published in 2009
How To Avoid During The London 2012 OlympicsJill Crawford
The document discusses the benefits of hosting the 2012 Olympic Games in London for the UK. It notes that hosting the Games would provide a massive economic boost through tourism and infrastructure spending. Over £9 billion was invested in constructing venues and transportation links. Hosting the Olympics could also help the government meet public health goals by inspiring greater sports participation. The tourism generated during the Games also provides economic benefits. Overall, the net benefits of hosting the Olympics are expected to outweigh the costs of bidding and construction.
London 2012 porto allegre [compatibility mode]Iain Macrury
The document discusses plans to evaluate the social and economic legacy of the 2012 London Olympics through quantitative indicators and qualitative community engagement. Key areas of focus include the economy, employment, education, health, and sports development. Quantitative data will be collected from various sources to measure factors like population, employment, health, housing, and more over time. Qualitative research will capture local experiences of regeneration and the Olympics through group interviews and oral history to provide context. The goal is to assess the impact on local communities and progress toward the promises made for the Games.
This document summarizes an economic analysis of the size and scope of the sports industry in the United States. It estimates the industry ranges from $44 to $73 billion in 2005 based on aggregate supply and demand. The analysis considers three main components: sports participation, spectator sports viewing, and following sports media. It acknowledges difficulties in precisely defining "sport" and includes borderline activities like exercise, recreation, and games.
Brazil is hosting the 2014 World Cup and 2016 Olympics, hoping to boost its economy and global reputation. However, mega sports events have had mixed legacies. Some forecasts predict large economic and tourism gains for Brazil, while risks include economic losses, human rights violations from forced evictions, and failure to generate benefits for the public. Assessing the potential legacy depends on Brazil's ability to learn from mistakes of other host countries and ensure gains are inclusive and sustainable.
This document provides an overview of the history and development of Rio de Janeiro, Brazil from its discovery in the 1500s to modern times. Key events included the Portuguese founding the city in 1565, the discovery of gold and diamonds which led to growth in the late 1600s, an influx of Portuguese and African populations in the 1800s, Brazil gaining independence in 1822 with Rio de Janeiro as the capital, and infrastructure developments in the early 1900s as the city expanded southward. Rio lost its status as national capital between 1960-1975 but remains an economically and culturally important city.
This document discusses government expenditure on sport and its economic and social impacts. It notes that both direct benefits like economic impact and indirect benefits like national pride and reduced crime can justify public funding of sport. However, it also acknowledges potential negative impacts like an over-reliance on importing foreign players rather than developing domestic talent. The document uses the example of UK government funding for the Olympic team to show a strong correlation between money spent and medals won. It argues that major sporting events can have long-term positive impacts through urban regeneration and infrastructure development, using the 2012 London Olympics and regeneration of East London as a key case study, though some studies question whether benefits outweigh substantial costs.
IntroductionThe physical to human capital proportion is compos.docxmariuse18nolet
Introduction:
The physical to human capital proportion is composed in expanding work Solow sort demonstrate as opposed to level of them. It is accepted that the development of yield relies on upon the physical to human capital proportion where human capital is most certainly not just measured by instruction additionally by wellbeing or sustenance status. Accordingly, human capital is characterized in this study with individual instruction level and dietary status. In the event that there is an irregularity impact in the middle of physical and human capital, the awkwardness impact that is a symmetric condition on the physical to human capital proportion will be fundamentally beneath or over its enduring state esteem. The uneven unevenness impact is exhibited by a kilter U-molded reliance or if the every capita yield development depends just as decidedly or just as adversely on the physical to human degree. They generally state prove that development depends absolutely on the physical to human capital degree. Islam (1995) discovers negative human capital coefficients. This negative sign of human capital coefficients is considered by us to be an evidence for the importance of physical to human capital degree in development study. They draw thoughtfulness regarding the complementarities between human capital and physical capital. The complementarities between human capital and physical capital are the regular of the generation technique since gear needs qualified laborers to oversee them and qualified mechanics to settle them. Also, even advanced agrarian creation obliges a generally educated horticulture workforce where specialists who can understand directions on a compost pack, get it data contained in manual conveyed by augmentation specialists and get it the substance of a repair manual for agrarian supplies and up-to-the-moment administrations need individuals who can make basic estimations quickly and exactly. On the off chance that nations concentrate on physical capital while overlooking their human capital, they will before long get to be cognizant that the comes back to physical capital are more terrible than they need to be then they will have poorer yield. They likewise uncovered that presenting unrivaled strategies for creation, better approaches for doing things and presenting more compound and entangled items are simpler said than done on the off chance that purchasers, laborers and buyers have unacceptable preparing and instruction to empower them to comprehend the new engineering.
Data:
We have utilized information for 1990 and 2000 for 36 nations where 1980' every capita GDP level information is gotten for beginning starting salary level. The physical cash flow to education*worker exertion level information is computed as electric power utilization (kwh) to Education (E) information times the specialist exertion record (e) (EL/E*e). EL/E*e electric force utilization (kwh) demonstrated as (EL) stands for physical capital inter.
This document provides an introduction to a dissertation that examines the relationship between trade liberalization, government debt, human capital, and income inequality using panel data econometrics. It aims to understand the determinants of global income inequality and account for rising inequality in developed countries. The analysis uses the consistent EHII index to measure household income inequality across 136 countries from 1968-2008. Static and dynamic panel techniques are employed to explore how macroeconomic variables like human capital, trade openness, government debt, inflation, and growth impact inequality. It also considers whether effects differ between developed and developing countries. The results seek to inform policy to reduce inequality and its associated social and economic issues.
Ssd 2012 poster schmitt olympic medals april 9 2012Erika Schmitt
This study examined the relationship between a nation's level of gender equality and women's participation and success in the Summer Olympic Games from 1996-2008. The researchers found a modest but significant positive relationship between a nation's Gender Inequality Index score and the percentage of its Olympic participants and medalists that were women. However, the relationship was not strong and many nations did not fit the pattern. While previous research has linked greater gender equality with improved opportunities and outcomes for women in various areas, this represents the first cross-national study to examine its connection to athletic participation and success specifically.
Remittance inflow and economic growth the case of georgiaAzer Dilanchiev
Abstract:
Remittance inflow become one of the main source of capital flows in the world. It is noted that remittance is
very effective in promoting household welfare and as an alternative source of capital inflow. However in it
uncertain whether or not it leads to economic growth. This article examines the effects of remittances inflow
on economic growth in Georgian republic. The impact of remittance inflow on GDP growth was analyzed and
tested by Unit Root Test, Johansen Co-integration and VAR Granger Causality/Block Exogeneity Wald Tests.
In the paper the quarterly data interval from the first quarter of 1999 to third quarter of 2015 was used. As a
result it was found out that that there is a nexus between remittance and GDP and it is concluded that
remittance leads to increase in GDP growth.
This document presents the results of a statistical analysis of the relationship between migration and development in developing countries. It finds that there is no significant relationship between labor migration and GDP growth rates, but there is a strong negative relationship between labor migration and poverty levels. When controlling for education, net migration, and unemployment, about 63% of the variation in poverty can be explained by labor migration. While migration may decrease the labor force, remittances sent home can contribute to reducing poverty.
2012 - Charting international labor comparisonsRichard Han
This document is the 2012 edition of Charting International Labor Comparisons published by the U.S. Bureau of Labor Statistics. It features labor market data from 2010 and earlier years for various countries. The document contains four sections covering gross domestic product, the labor market, manufacturing competitiveness, and consumer prices. Each section includes 10-12 charts comparing indicators such as GDP, labor force participation, hourly compensation costs, and inflation rates across countries.
Tourism has been found to contribute to decreasing income inequality according to some studies. The author analyzes the impact of tourism on income inequality using panel data and cross-country regression. Variables like the Gini coefficient, tourism's contribution to GDP, education levels, economic factors, and others are used. Fixed effects regression shows that as tourism's contribution to GDP increases by 1%, the Gini coefficient decreases by around 0.266, indicating tourism is associated with reduced income inequality. Some variables like inflation and female labor participation were also found to significantly impact income inequality.
FIFA World Cup 2014: Social Impacts and Policy StrategiesFlavio Kleijssen
The main aim of this study is to analyze ex-ante the likely socio- economic impact of the 2014 FIFA World Cup that will take place in Brazil. The recent trends of highly competitive bids to be designated host country of a sport mega-event show us the bidders believe such events to generate positive impacts. In this paper, we analyze through a descriptive approach the main aspects of potential economic and social effects through the organization of the World Cup, while contrasting it with the major cost and risks Brazil is bearing.
Our conclusions show that organizing a major sporting event is a unique opportunity for economic and social development, can accelerate infrastructure improvement and it is a major factor for gaining in international reputation, and, therefore, increasing in the long run a country’s soft power. However, in the case of Brazil, the high cost, widespread popular discontent, safety issues, lack of planning and coordination may have an important negative impact, which is likely to offset the benefits. It has been said that Brazil is the country of the future, and always will be. The 2014 World Cup, and 2016 Olympic Games will give a strong indication whether this still holds true.
Growing wage dispersion, rather than declining labor share of income, has been the primary driver of rising income inequality according to a new IMF study. Using household surveys and macroeconomic data from 81 countries over 40 years, the study found that the largest factor contributing to increasing income inequality was growing disparity in wages, particularly at the top of the wage distribution. Rising financial globalization, decreasing unionization, and a shrinking state sector were also found to associate with wider wage dispersion. To curb undesired distributional effects, policymakers need to focus on labor market outcomes and wage differences through policies that promote inclusive growth and minimize market distortions.
Global Wealth Databook 2019 - Credit Suisse Research InstituteCarlosLazzarini3
O arquivo "Global Wealth Databook 2019" é uma publicação do Credit Suisse Research Institute que detalha a distribuição e os níveis de riqueza das famílias em todo o mundo, desde o ano 2000 até meados de 2019. Ele oferece uma análise abrangente da riqueza global, abrangendo tanto ativos financeiros quanto não financeiros, menos dívidas, para mais de 200 países. Destaca-se a distribuição desigual da riqueza, com menos de 1% da população adulta possuindo 44% da riqueza global. O relatório também aborda a crescente importância da China e de outras economias emergentes na criação de riqueza global, especialmente após a crise financeira global. Além disso, examina as tendências na composição das carteiras de riqueza e fornece detalhes específicos por região e país.
2011 - Charting international labor comparisonsRichard Han
The document is a report published by the U.S. Bureau of Labor Statistics that provides international labor comparisons data for 2009 and earlier years. It features data on gross domestic product, labor force indicators, manufacturing costs and productivity, and consumer prices for various countries. The report aims to improve comparability of cross-country labor statistics by adjusting data to a common conceptual framework. It contains charts and tables presenting key labor market indicators for different economies.
This document is the thesis of Jose Luis Contreras Biekert for obtaining a Doctorate in Economics. It consists of three essays in microeconomics. The first essay empirically analyzes the impact of hosting the Summer Olympic Games on a country's future performance and finds no lasting legacy for sports. The second essay develops a general equilibrium model to analyze mitigation of negative externalities through public good production and income taxes. It finds conditions for a Pareto optimal equilibrium and that eco-friendly technological changes may reduce welfare. The third essay considers coalition formation and models how it reduces rivalry in consumption, finding it can impact inequality.
The document discusses the impacts of hosting mega sporting events like the Olympics or FIFA World Cup. It provides background on the bidding process and factors considered in selecting host countries. The economic impacts are analyzed, including both potential positive and negative effects. Specifically, significant public money is spent on new venues and infrastructure, which can create jobs but may also be viewed as a transfer of short-term spending. The success of events depends on the long-term use of venues and infrastructure after the event. Developing countries in particular may see few economic benefits and waste money that could be spent on other priorities like education and healthcare.
In this paper, we analyze the demographic and economic consequences of endogenous migrations flows over the coming decades in a multi-regions overlapping generations general equilibrium model (INGENUE 2) in which the world is divided in ten regions. Our analysis offers a global perspective on the consequences of international migration flows. The value-added of the INGENUE 2 model is that it enables us to analyze the effects of international migration on both the destination and the origin regions. A further innovation of our analysis is that international migration is treated as endogenous.
In a first step, we estimate the determinants of migration in an econometric model. We show, in particular, that the income differential is one of the key variables explaining migration flows. In a second step, we endogenize migration flows in the INGENUE 2 model. In order to do so, we use the econometrically estimated relationships between demographic and income developments in the INGENUE model, which enables us to project long-run migration flows and to improve on projections of purely demographic models.
Authored by: Vladimir Borgy, Xavier Chojnicki, Gelles Le Garrec, Cyrille Schwellnus
Published in 2009
How To Avoid During The London 2012 OlympicsJill Crawford
The document discusses the benefits of hosting the 2012 Olympic Games in London for the UK. It notes that hosting the Games would provide a massive economic boost through tourism and infrastructure spending. Over £9 billion was invested in constructing venues and transportation links. Hosting the Olympics could also help the government meet public health goals by inspiring greater sports participation. The tourism generated during the Games also provides economic benefits. Overall, the net benefits of hosting the Olympics are expected to outweigh the costs of bidding and construction.
London 2012 porto allegre [compatibility mode]Iain Macrury
The document discusses plans to evaluate the social and economic legacy of the 2012 London Olympics through quantitative indicators and qualitative community engagement. Key areas of focus include the economy, employment, education, health, and sports development. Quantitative data will be collected from various sources to measure factors like population, employment, health, housing, and more over time. Qualitative research will capture local experiences of regeneration and the Olympics through group interviews and oral history to provide context. The goal is to assess the impact on local communities and progress toward the promises made for the Games.
This document summarizes an economic analysis of the size and scope of the sports industry in the United States. It estimates the industry ranges from $44 to $73 billion in 2005 based on aggregate supply and demand. The analysis considers three main components: sports participation, spectator sports viewing, and following sports media. It acknowledges difficulties in precisely defining "sport" and includes borderline activities like exercise, recreation, and games.
Brazil is hosting the 2014 World Cup and 2016 Olympics, hoping to boost its economy and global reputation. However, mega sports events have had mixed legacies. Some forecasts predict large economic and tourism gains for Brazil, while risks include economic losses, human rights violations from forced evictions, and failure to generate benefits for the public. Assessing the potential legacy depends on Brazil's ability to learn from mistakes of other host countries and ensure gains are inclusive and sustainable.
This document provides an overview of the history and development of Rio de Janeiro, Brazil from its discovery in the 1500s to modern times. Key events included the Portuguese founding the city in 1565, the discovery of gold and diamonds which led to growth in the late 1600s, an influx of Portuguese and African populations in the 1800s, Brazil gaining independence in 1822 with Rio de Janeiro as the capital, and infrastructure developments in the early 1900s as the city expanded southward. Rio lost its status as national capital between 1960-1975 but remains an economically and culturally important city.
This document discusses government expenditure on sport and its economic and social impacts. It notes that both direct benefits like economic impact and indirect benefits like national pride and reduced crime can justify public funding of sport. However, it also acknowledges potential negative impacts like an over-reliance on importing foreign players rather than developing domestic talent. The document uses the example of UK government funding for the Olympic team to show a strong correlation between money spent and medals won. It argues that major sporting events can have long-term positive impacts through urban regeneration and infrastructure development, using the 2012 London Olympics and regeneration of East London as a key case study, though some studies question whether benefits outweigh substantial costs.
IntroductionThe physical to human capital proportion is compos.docxmariuse18nolet
Introduction:
The physical to human capital proportion is composed in expanding work Solow sort demonstrate as opposed to level of them. It is accepted that the development of yield relies on upon the physical to human capital proportion where human capital is most certainly not just measured by instruction additionally by wellbeing or sustenance status. Accordingly, human capital is characterized in this study with individual instruction level and dietary status. In the event that there is an irregularity impact in the middle of physical and human capital, the awkwardness impact that is a symmetric condition on the physical to human capital proportion will be fundamentally beneath or over its enduring state esteem. The uneven unevenness impact is exhibited by a kilter U-molded reliance or if the every capita yield development depends just as decidedly or just as adversely on the physical to human degree. They generally state prove that development depends absolutely on the physical to human capital degree. Islam (1995) discovers negative human capital coefficients. This negative sign of human capital coefficients is considered by us to be an evidence for the importance of physical to human capital degree in development study. They draw thoughtfulness regarding the complementarities between human capital and physical capital. The complementarities between human capital and physical capital are the regular of the generation technique since gear needs qualified laborers to oversee them and qualified mechanics to settle them. Also, even advanced agrarian creation obliges a generally educated horticulture workforce where specialists who can understand directions on a compost pack, get it data contained in manual conveyed by augmentation specialists and get it the substance of a repair manual for agrarian supplies and up-to-the-moment administrations need individuals who can make basic estimations quickly and exactly. On the off chance that nations concentrate on physical capital while overlooking their human capital, they will before long get to be cognizant that the comes back to physical capital are more terrible than they need to be then they will have poorer yield. They likewise uncovered that presenting unrivaled strategies for creation, better approaches for doing things and presenting more compound and entangled items are simpler said than done on the off chance that purchasers, laborers and buyers have unacceptable preparing and instruction to empower them to comprehend the new engineering.
Data:
We have utilized information for 1990 and 2000 for 36 nations where 1980' every capita GDP level information is gotten for beginning starting salary level. The physical cash flow to education*worker exertion level information is computed as electric power utilization (kwh) to Education (E) information times the specialist exertion record (e) (EL/E*e). EL/E*e electric force utilization (kwh) demonstrated as (EL) stands for physical capital inter.
This document provides an introduction to a dissertation that examines the relationship between trade liberalization, government debt, human capital, and income inequality using panel data econometrics. It aims to understand the determinants of global income inequality and account for rising inequality in developed countries. The analysis uses the consistent EHII index to measure household income inequality across 136 countries from 1968-2008. Static and dynamic panel techniques are employed to explore how macroeconomic variables like human capital, trade openness, government debt, inflation, and growth impact inequality. It also considers whether effects differ between developed and developing countries. The results seek to inform policy to reduce inequality and its associated social and economic issues.
Ssd 2012 poster schmitt olympic medals april 9 2012Erika Schmitt
This study examined the relationship between a nation's level of gender equality and women's participation and success in the Summer Olympic Games from 1996-2008. The researchers found a modest but significant positive relationship between a nation's Gender Inequality Index score and the percentage of its Olympic participants and medalists that were women. However, the relationship was not strong and many nations did not fit the pattern. While previous research has linked greater gender equality with improved opportunities and outcomes for women in various areas, this represents the first cross-national study to examine its connection to athletic participation and success specifically.
Remittance inflow and economic growth the case of georgiaAzer Dilanchiev
Abstract:
Remittance inflow become one of the main source of capital flows in the world. It is noted that remittance is
very effective in promoting household welfare and as an alternative source of capital inflow. However in it
uncertain whether or not it leads to economic growth. This article examines the effects of remittances inflow
on economic growth in Georgian republic. The impact of remittance inflow on GDP growth was analyzed and
tested by Unit Root Test, Johansen Co-integration and VAR Granger Causality/Block Exogeneity Wald Tests.
In the paper the quarterly data interval from the first quarter of 1999 to third quarter of 2015 was used. As a
result it was found out that that there is a nexus between remittance and GDP and it is concluded that
remittance leads to increase in GDP growth.
This document presents the results of a statistical analysis of the relationship between migration and development in developing countries. It finds that there is no significant relationship between labor migration and GDP growth rates, but there is a strong negative relationship between labor migration and poverty levels. When controlling for education, net migration, and unemployment, about 63% of the variation in poverty can be explained by labor migration. While migration may decrease the labor force, remittances sent home can contribute to reducing poverty.
2012 - Charting international labor comparisonsRichard Han
This document is the 2012 edition of Charting International Labor Comparisons published by the U.S. Bureau of Labor Statistics. It features labor market data from 2010 and earlier years for various countries. The document contains four sections covering gross domestic product, the labor market, manufacturing competitiveness, and consumer prices. Each section includes 10-12 charts comparing indicators such as GDP, labor force participation, hourly compensation costs, and inflation rates across countries.
Tourism has been found to contribute to decreasing income inequality according to some studies. The author analyzes the impact of tourism on income inequality using panel data and cross-country regression. Variables like the Gini coefficient, tourism's contribution to GDP, education levels, economic factors, and others are used. Fixed effects regression shows that as tourism's contribution to GDP increases by 1%, the Gini coefficient decreases by around 0.266, indicating tourism is associated with reduced income inequality. Some variables like inflation and female labor participation were also found to significantly impact income inequality.
FIFA World Cup 2014: Social Impacts and Policy StrategiesFlavio Kleijssen
The main aim of this study is to analyze ex-ante the likely socio- economic impact of the 2014 FIFA World Cup that will take place in Brazil. The recent trends of highly competitive bids to be designated host country of a sport mega-event show us the bidders believe such events to generate positive impacts. In this paper, we analyze through a descriptive approach the main aspects of potential economic and social effects through the organization of the World Cup, while contrasting it with the major cost and risks Brazil is bearing.
Our conclusions show that organizing a major sporting event is a unique opportunity for economic and social development, can accelerate infrastructure improvement and it is a major factor for gaining in international reputation, and, therefore, increasing in the long run a country’s soft power. However, in the case of Brazil, the high cost, widespread popular discontent, safety issues, lack of planning and coordination may have an important negative impact, which is likely to offset the benefits. It has been said that Brazil is the country of the future, and always will be. The 2014 World Cup, and 2016 Olympic Games will give a strong indication whether this still holds true.
Growing wage dispersion, rather than declining labor share of income, has been the primary driver of rising income inequality according to a new IMF study. Using household surveys and macroeconomic data from 81 countries over 40 years, the study found that the largest factor contributing to increasing income inequality was growing disparity in wages, particularly at the top of the wage distribution. Rising financial globalization, decreasing unionization, and a shrinking state sector were also found to associate with wider wage dispersion. To curb undesired distributional effects, policymakers need to focus on labor market outcomes and wage differences through policies that promote inclusive growth and minimize market distortions.
Global Wealth Databook 2019 - Credit Suisse Research InstituteCarlosLazzarini3
O arquivo "Global Wealth Databook 2019" é uma publicação do Credit Suisse Research Institute que detalha a distribuição e os níveis de riqueza das famílias em todo o mundo, desde o ano 2000 até meados de 2019. Ele oferece uma análise abrangente da riqueza global, abrangendo tanto ativos financeiros quanto não financeiros, menos dívidas, para mais de 200 países. Destaca-se a distribuição desigual da riqueza, com menos de 1% da população adulta possuindo 44% da riqueza global. O relatório também aborda a crescente importância da China e de outras economias emergentes na criação de riqueza global, especialmente após a crise financeira global. Além disso, examina as tendências na composição das carteiras de riqueza e fornece detalhes específicos por região e país.
2011 - Charting international labor comparisonsRichard Han
The document is a report published by the U.S. Bureau of Labor Statistics that provides international labor comparisons data for 2009 and earlier years. It features data on gross domestic product, labor force indicators, manufacturing costs and productivity, and consumer prices for various countries. The report aims to improve comparability of cross-country labor statistics by adjusting data to a common conceptual framework. It contains charts and tables presenting key labor market indicators for different economies.
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Abstract
In this paper, we attempt to examine potential socioeconomic factors that would explain
the unequal distribution of medals won by participating countries in the Olympics. We attempt to
identify a relationship between our dependent variable, total medals won, and our three
independent variables, a country’s GDP per capita, population size, and literacy rate. While our
results showed a positive coefficient for all three variables, the only statistically significant
correlation was in the relationship between total medals won and population size. We conclude
that the lack of significance in the results could be explained by the small sample size of 32
countries, which is only a fraction of the 203 countries that competed in the 2012 Olympic
Games.
Background
Since the founding of the modern Olympics, there has been an emphasis on the individual
competition among athletes, as opposed to the contest between countries. Although the spirit of
the Olympic Games has naturally valued participation over holistic success, results often rank
nations in accordance with the number of medals its athletes have accumulated. Medal counts by
country are widely reported by media outlets both during and after the Olympic Games.
Common Olympic knowledge and a quick overview of past results clearly indicate that medals
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are not spread out evenly among competing nations. At the 2004 Summer Olympics in Athens,
Greece, there were a total of 199 countries in participation1. Out of these 199 countries, 124 of
them did not win a single medal. On the other end of the spectrum, the 10 winningest countries
collected a total of 514 medals, which totaled to more than half of the available medals (Bian,
2005). This leads to an overt conclusion that the countries involved in the Olympic Games do not
have an equal capability to win medals. Thus, begging the question: What socioeconomic factors
give a country an advantage in acquiring Olympic medals?
This proposed question comes of importance when considering the large-scale impact the
Olympic Games have on the participating countries. The Olympics have long been considered a
prestigious event, where nations send their most qualified citizen-athletes to represent their
homeland during bouts of pure athletic competition. Therefore, the accomplishments of a
country’s athletes are typically regarded as a status of national prominence. Countries have the
ability to display their prowess on a worldwide scale, and thus, approach the Olympics Games
with the utmost seriousness. Furthermore, a nation that is successful in the Olympic Games will
attract attention from all across the globe. This publicity could provide a variety of economic
impacts, such as an increase in tourism and international trade. On the political side, success in
the Olympics could boost a country’s reputation and demand respect from other nations, as it
displays progress and capability. There are a multitude of benefits that stem from a country’s
Olympic accomplishments, and thus, it is certainly worthwhile to investigate the causes that
increase a country’s ability to win more medals than others.
1 It should be noted that although there are currently only 196 countries in the world, there are 206 National
Olympic Committees, representing nation-states and still disputed regions such as Kosovo.
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The uneven circulation of Olympic medals among participating countries might
potentially be explained by the varying strengths of countries in different sports i.e countries
with heavy annual snowfall will most likely be better in Winter Olympics sports such as cross
country skiing since they have access the necessary facilities. However, throughout the field of
economics, a wealth of research has been conducted to determine how a range of socioeconomic
factors may influence a country’s success in the Olympics. A 2010 research article in the
“International Journal of Sports Science and Engineering”, written by Yong Jiang, Tingting Ma,
and Zhe Huang, sought to determine the economic factors that influenced the medal count of 15
countries in the 2008 Beijing Summer Olympics. Their research concluded that the prominent
economic factors influencing a country’s Olympic results were the average annual GDP growth
rate over the three years prior to the Olympics (2005-2008), the average proportion of GDP
expended on health care between 2005-2008, and the education index of a country, which is
“measured by adult literacy rate (2/3 weight) and elementary school, high school, and college
comprehensive enrollment rate (1/3 weight)” (Jiang, Ma, & Huang, 2010). Additionally, they
researchers found Olympic results were related, with slightly lower correlation, to the variables
of GDP per capita growth rate from 2005-2008, total industrial production index, agricultural
production index per capita in the host year (2008), and population density (Jiang, Ma, & Huang,
2010). For the sake of accurate results, it should certainly be noted that this source only analyzed
the variables’ effects on a small sample of 15 countries, yet, this is promising evidence to support
the theory that economic variables can influence the success of a country at the Olympics.
Another article, published in 2014 in the “Journal of Statistics Education” and written by
Nancy Carter, Nathan Felton, and Neil Schwertman, investigated the effect of population size
and income levels on the medal count of all 203 nations in the 2012 London Summer Olympics.
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A regression analysis of total medals vs. income levels and population size produced an adjusted
R-squared value of 25.51%, showing slight correlation between medal count and the independent
variables (Carter, Felton, & Schwertman, 2014). However, when the regression analysis of total
medals vs. income levels, population size, and the added variable of GDP was performed, the
adjusted R-squared value spiked to 70.25 %, showing strong correlation between total medal
count and these three variables (Carter, Felton, & Schwertman, 2014). This drastic change
created by the addition of one variable shows that GDP was the most influential economic factor
of their model. It’s not a surprise to see GDP having a large influence of total medal counts,
since countries that have a higher GDP are more developed and boast a population with higher
levels of per capita income. This extra per capita income would then translate into better access
to youth sports, more advanced facilities and training programs and above all give the citizen-
athletes a greater amount of leisure time to practice and hone their sporting skills. This source
was highly successful in providing evidence that economic factors can influence medal count,
especially when considering that the source used information from all 203 countries that
participated in the 2012 Summer Olympics in London.
There is proven regression evidence that a variety of socioeconomic factors could be used
to predict a country’s success at the Olympics. Therefore, to answer our proposed question of:
“What socioeconomic factors give a country an advantage in winning Olympic medals?”, we
will examine three independent variables to test through regression analysis. We hope to develop
a model that will help explain the uneven balance of medals won across participating countries at
the Olympics using these variables.
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Model
To investigate what factors would influence a country’s medal count at the Olympic
Games, we have developed a model that uses three independent variables. These variables are a
country’s GDP per capita, population size, and literacy rate at the year of the Olympics. We will
use a sample size of 33 countries and cross-sectional data from year 2012, in which the Summer
Olympics took place in London. We chose this sample size based on availability of data, but we
believe it is large enough to depict a feasible relationship between our independent variables and
dependent variables. The model we used is as follows:
E(Y) = B0 + B1 (GDP Per Capita) + B2 (Population Size) + B3 (Literacy Rate) + e
We chose these three independent variables because we believe they can each serve a
purpose in explaining the unequal balance of medals won by countries that participate in the
Olympic Games. They also are quite similar to the ones used in the previous scholarly work, so
we assume that they will produce homogenous results. The total medals (combination of gold,
silver, and bronze) won by the countries in our sample size represents our dependent variable,
shown in the equation as E(Y). We are running this specific multiple linear regression model to
determine the effect of the independent variables on the dependent variable. If an increase in the
independent variables leads to an increase in the dependent variable and a positive coefficient is
produced (or vice versa with a negative coefficient), we will have developed an explanation for
the uneven distribution of Olympic medals across competing countries in that socioeconomic
factors, such as the variables of GDP per capita, population, and literacy rate, can be a plausible
influence on and predictor of a country’s Olympic success. We expect our multiple linear
regression model to produce a positive coefficient between all of the independent variables and
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the dependent variable of total medals won, since as a country’s population becomes wealthier,
larger and better educated, it will be able to produce better athletes as well.
For our first independent variable, we chose GDP per capita. GDP per capita is gross
domestic product, the sum of gross value added from all producers within a country plus any
taxes on these products and minus any subsidies that are not included in the value of these
products, divided by the country’s midyear population. We saw this as a very basic, yet essential
measurement of a country’s economic status; how much income is each individual receiving
throughout a given year. We expect GDP per capita to have a positive coefficient in its
relationship with our dependent variable, total medals won. Not only did we notice this trend
throughout a multitude of research articles, but socioeconomic logic would lead one to support
this idea. GDP per capita shows economic progress for a country, and thus, the higher the GDP
per capita, the more financially stable its citizens and government are. This opens up
opportunities for a nation to focus on non-necessity activities, such as athletics. Due to the
country’s financial cushion, it will have both more time and monetary resources than less
economically stable countries to improve athletic progress. This can be done by building higher
quality sporting facilities, which would lead to better training for a nation’s athletes and also
increase general interest and participation among a country’s citizens, which would spur future
progress amongst the youth. Additionally, a country with better finances can afford to practice a
higher quality nutrition regiment, which would also increase the quality of their athletes’
performance. Lastly, economic stability allows citizens to progress athletically on a personal
level, at a faster rate due to the simple fact that the average person does not have to spend the
majority of his or her time working or searching for essential resources, as many people in less
wealthy countries must do to survive. Thus, the relationship between GDP per capita and
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Olympic success should have a positive coefficient, as a country with higher economic stability
can support the time, resources, and interest to progress athletically.
We chose a country’s overall population size as our second independent variable. We saw
population size as a factor that would certainly have easily accessible data for all the countries in
our sample size. More importantly, we believe a larger population would improve Olympic
success, thus producing a positive coefficient. The thought process behind this assumption is
based off the simple principle that the greater a country’s population is, the larger the pool of
capable athletes and there will be a greater variety of athletic events and programs that can be
explored within that country. For example, a country with a smaller population may only send a
handful of athletes to the Olympics in a narrower spectrum of events. However, if a country has
a greater population, there is a larger athlete pool with the potential of superior athletic traits,
which would produce a higher number of Olympic-caliber athletes. If a country has a larger
number of athletes, then there is also much higher likelihood that it will be able to compete in
many, if not all the Olympic events offered. A country with, for example, 10 citizens of
Olympic-caliber athleticism would most likely not develop 10 long distance runners.
Realistically, the 10 athletes would spread across a variety of events, such as different track and
field roles, or possibly just different sports in general. This theory is also supported in the fact
that only athletes/teams who qualify for a particular event can compete in the Olympics. Due to
the immense number of athletes from other countries attempting to qualify for a particular sport,
especially the more common ones, it would be in a country’s best interest to spread their athletes
across a wide variety of events to increase their likelihood of winning medals. Therefore, we
believe population size to be a strong indicator of Olympic success and expect a positive
coefficient between population size and total medals.
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Lastly, our third independent variable is a country’s literacy rate. Similar to the other
variables used, we noticed a common trend in previous regression articles that showed a higher
literacy rate related to more medals won by that country. Literacy rate is measured as a
percentage of a country’s population aged 15 years old or older that can both read and write a
short, simple statement about their everyday life with understanding. On a primary level, literacy
rate is indicative of a country’s educational progress. As more citizens are provided with
educational opportunities, more become capable of reading and writing, which increase the
literacy rate. However, one must consider how a country achieves educational progress. As with
many other national characteristics, educational attainment is achieved through economic
progress. A country that has greater financial stability is capable of allocating its monetary
resources to build schools, pay teacher salaries, and improve other factors, such as public
transportation, that will allow its citizens to take advantage of educational opportunities. When a
quality education system is in place and backed by adequate finances, the number of citizens able
to pursue an education will increase with time. This is explained previously in our analysis, as
economic stability allows people to pursue self-interests, rather than life’s necessities.
Therefore, a higher literacy rate is related with greater economic progress. And as described with
GDP per capita, economic stability allows citizens the opportunity to explore non-necessity
activities, such as athletics. In conclusion, literacy rate is a prominent indication of economic
status, which in turn, will promote athletic development. We expect our third variable, literacy
rate, to have a positive coefficient in its relationship with total Olympic medals won as well.
We hope that by implementing our three variables into the multiple linear regression
model, we will be able to show that they have a positive influence on a country’s success at the
Olympic Games, as measured by total medals won.
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Data
The data for this experiment was collected from a variety of quality sources which provided us
with a reliable data set of 32 countries who participated in the 2012 Olympic Games in London.
The 32 countries were chosen since they were the countries with the top 32 total medal counts
when the 2012 Games came to a close. To measure the dependent variable, total medal count, we
used data from the International Olympic Committee’s website, Olympics.org. The databases for
every Olympics’ total medal count can be found here under “the results” tab within the Olympic
Games section. This allowed us to measure the total number of medals earned and in what place
each competing country finished when the Games ended.
Figure 1
Figure 1 represents a histogram of the data we obtained for the total medal counts for all
32 countries. As one can clearly see, the histogram is incredibly skewed to the right. This skew
to the right indicates that the vast majority of countries earn very little medals or no medals at all
and only a handful of countries dominate the competition. Which indicates that there must exist a
0
5
10
15
20
Frequencey
Total Medal Bin Range
Total Medal Counts 2012 Summer Olympics
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set of factors that allow only a relative few (10 or so out of 203) of the participating countries to
fall on the extreme right tail of the distribution. The purpose of our study is to hopefully identify
these factors that affect a country’s ability to win a large number of medals, as the independent
variables we have chosen: GDP per Capita, population size and literacy rate.
As for the independent variable’s data, we used the World Bank’s extensive economic
and social databases. For our first variable of GDP per Capita, we utilized the data set titled GDP
per capita (Current US $) in the World Bank’s economic indicators section. This gave us the
international data needed to measure each participating country’s GDP per Capita, high or small.
We used the World Bank’s databases again in order to obtain the population size, at the time of
the Olympics (2012), for the 32 countries included in our study. The data set was simply named
Population, total and was found under the “Climate Change” group of economic indicators.
Census data from every country is reported to the World Bank in order to create this data set.
Finally, the World Bank was also able to provide us with each participating countries’ literacy
rate (pretty impressive database). We specifically choose to use the data under the Literacy rate,
adult total (% of people ages 15 and above) heading because we believed this to be the all-
encompassing data set of the numerous other literacy rate measures, such as total female literacy
rate, etc.
We saw our three independent variables, GDP per capita, population size, and literacy
rate, as viable socioeconomic factors that could potentially explain the unequal distribution of
medals across countries in the Olympics throughout history. This skewedness is seen on a much
smaller scale in Figure 1, in which the countries in our sample certainly show an uneven spread
of medals across the 32 countries. We proceeded to run the regression tests to determine if our
variables could be considered significant influences on Olympic success.
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Results
After running our multiple regression test on our data set, we were able to find only one
significant correlation between our chosen independent variables and the amount of total medals
won, which was population size (X2). GDP per capita, just barely missed out on becoming
statistically significant, but literacy rate was quite far away from being significant.
To begin, we examined the coefficients of our model. All three of our coefficients carried
the sign that we had expected them to carry, which was positive. The coefficient for GDP per
capita was positive at .000365041, but extremely low. We believe the reason why the coefficient
is so small is because GDP per capita can increase by hundreds or even thousands of dollars a
year, which means a $1 increase will do little to increase total medal count. For population size,
the coefficient was again, as predicted, positive and extremely small at 6.952E-08. Very tiny
coefficient, but again, population size can fluctuate by even more so than GDP per capita,
sometimes in the hundreds of thousands. Finally, the literacy rate coefficient was again positive
as predicted, but this time it wasn’t extremely small at .298700618. While this might seem
relatively large, one must remember that it is quite difficult and time consuming for a country to
increase its literacy rate by 1% in order to gain the predicted .298700618 total medals.
We tested the effects of the independent variables on total medal counts in the Olympics
by running t-tests for each variable. The results of our hypothesis tests showed that only one of
our independent variables were significant, that being population size. The t* test statistic for
population size came out to be 4.9257 which was well above our tcv of 2.048, giving us the
necessary requirements to reject the null hypothesis of B2 being 0. We also used a p-value
hypothesis test on each of the variables and the results were similar. Population size was the only
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variable that showed statistical significant with a p-value of .00003397789, which is well above
the 95% confidence p-value level of .05. It is important to note that GDP per capita was just
barely under the cut off for being statistically significant and we believe had we collected just a
few more data points that GDP per capita could have been significant at the 95% level.
After considering the variables individually, we investigated our model as a whole. The
adjusted coefficient of determination or R2 ended up being .451471591, suggesting a moderately
strong relationship between our variables and total medal count. In general, the close the value is
to 1, the better the model is at explaining the variability in the data. A value of 0 indicates that
the model has no effect on the dependent variable. So our value of .451471591, being nearly in
the middle of the two, implies that our variables have a moderate effect on determining a
country’s total medal count. In order to confirm this, we ran an overall fit test of the model using
an F* statistic and the same results were found. Our F* of 9.50494 was well above the Fcv of
2.946, further proving the validity of our model.
Finally, we checked our model for any potential underlying problems by testing for
multicollinearity, heteroskedsticity, and autocorrelation. The only problem that seemed to appear
in our model was autocorrelation; two of the variables were found to be positively 1st order
autocorrelated and one was inconclusive. However, we believe these results should be nullified
since we used a cross-sectional data set and autocorrelation only really appears in time-series
data. We used a Pearson correlation test to test multicollinearity and our highest value of .39827
is low enough not to be considered problematic. Our Goldfeld-Quant test revealed there to be no
significant heteroscedasticity in the model and our Durbin-Watson test was thrown off by the
nature of our cross-sectional data.
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Conclusion
In conclusion, the multiple regression output exhibited, that for the set of countries that
we used, the factors of GDP per capita, population size and overall literacy rate have a
moderately significant impact on the total medals a country wins in an Olympics. Since one of
our variables was significant at the .01 level and another one was just barely above the .05
confidence level.
These results were pretty much in line with what we were expecting, since we believed
these variables would indicate a country’s base ability to produce Olympic-caliber athletes. We
were however surprised at the strength of the correlation, because we initially believed the
variables to have a very strong and significant relationship with total medal counts. One possible
reason for this was the size of our data set. We only chose to use 32 countries from the 203 that
participated in the 2012 Olympics, even though there were quite a few more, because we thought
any number over 30 would be sufficient and since a majority of competing nations do not win
any medals at all. Had we used the entire data set for the 2012 Olympics, we firmly believe that
our coefficients for all three variables would increase further and we would predict the GDP per
capita, that just barely missed out on being statistically significant, would become significant had
we added the rest of the participating countries. We also used a cross-sectional data set rather
than a time-series one and that could have affected our data, since there may have been an
underlying variable, present at the 2012 Olympics, which possible skewed our results.
Despite these possible anomalies in the data, we firmly believe that through the model we
constructed and tested, a country’s performance in the Olympics can be predicted by key
socioeconomic factors including GDP per capita, population size and adult literacy rates.
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