This document summarizes a study examining the racial wage gap in the United States using data from the 2014 Current Population Survey. A regression analysis found that black and American Indian workers earned significantly less than white workers with the same education and experience levels, indicating a persisting racial wage gap. Specifically, the analysis found that black workers earned 21.6% less and American Indian workers earned 58% less than white workers on average. The study concludes that racial discrimination continues to negatively impact minorities' wages in the U.S. labor market.
Abstract:
Data from 70 large export-oriented garment manufacturers in Bangladesh show that gender wage gaps are similar to those found in higher-income countries. Women’s wages are 20 percent lower than men’s and are 8 percent lower among narrowly-defined production workers; a gap remains even after controlling for very precisely measured skills. Longer careers of men in the sector explain around half of the wage gap, with the other half due in roughly equal parts to differences in internal and across-factory promotions. Our results are most consistent with broader gender norms, beyond gendered household responsibilities, driving the gap.
The document analyzes data from the 2009 ISSP survey on social inequality in Switzerland to examine factors influencing income levels. A structural equation model is used with income as the dependent variable, and factors like parents' jobs, education levels, and gender as predictors. The model finds the predictors have little significant effect on income. Most fit indexes show the model is not a good match for the data. The hypotheses and relationships between variables are rejected due to lack of evidence.
El proyecto de vida es el plan que una persona se traza para alcanzar sus objetivos a lo largo de su vida, dándole coherencia y dirección a través de los valores que ha integrado. El proyecto de vida marca el estilo de una persona en cómo actúa, se relaciona y ve la vida, sirviendo como una carta de navegación a través de las diferentes etapas de la vida de una persona.
Robinson es un educador británico considerado un experto en creatividad y calidad educativa. En su charla "Cómo escapar del valle de la muerte de la educación", critica el sistema educativo estadounidense por conducir a altos índices de deserción escolar al contradecir principios del desarrollo humano. Propone que la educación debe personalizarse, valorar a los docentes y dar autonomía a las escuelas, como ocurre en Finlandia.
Este documento define las habilidades cognitivas como un conjunto de operaciones mentales cuyo objetivo es integrar la información adquirida a través de los sentidos en una estructura de conocimiento significativa. Explica que las habilidades cognitivas incluyen el lenguaje, la atención, la comprensión, la elaboración, la memorización y la percepción. Además, destaca la importancia de las habilidades cognitivas para el desarrollo de las capacidades y el aprendizaje de los estudiantes.
El documento proporciona información sobre los arándanos. Explica que los arándanos son ricos en antioxidantes y vitaminas y que tienen beneficios para la salud como ayudar a bajar de peso. Los arándanos contienen fibra, antioxidantes y tienen pocas calorías, lo que ayuda a acelerar el metabolismo y quemar grasas. También actúan como diuréticos para eliminar líquidos. El documento incluye detalles sobre la producción y exportación de arándanos.
This document is a curriculum vitae for Hafiz Muhammad Rafique. It provides his personal details including name, address, contact information, date of birth, marital status, and religion. It lists his academic qualifications including a B.A degree, F.A and matriculation certificates. It details his computer skills and other skills like English shorthand and typing. It provides his employment experience working as an assistant account officer and operation clerk from January 2015 to June 2016. It lists his attributes, languages, and objective of working in an environment where his skills are appreciated.
Abstract:
Data from 70 large export-oriented garment manufacturers in Bangladesh show that gender wage gaps are similar to those found in higher-income countries. Women’s wages are 20 percent lower than men’s and are 8 percent lower among narrowly-defined production workers; a gap remains even after controlling for very precisely measured skills. Longer careers of men in the sector explain around half of the wage gap, with the other half due in roughly equal parts to differences in internal and across-factory promotions. Our results are most consistent with broader gender norms, beyond gendered household responsibilities, driving the gap.
The document analyzes data from the 2009 ISSP survey on social inequality in Switzerland to examine factors influencing income levels. A structural equation model is used with income as the dependent variable, and factors like parents' jobs, education levels, and gender as predictors. The model finds the predictors have little significant effect on income. Most fit indexes show the model is not a good match for the data. The hypotheses and relationships between variables are rejected due to lack of evidence.
El proyecto de vida es el plan que una persona se traza para alcanzar sus objetivos a lo largo de su vida, dándole coherencia y dirección a través de los valores que ha integrado. El proyecto de vida marca el estilo de una persona en cómo actúa, se relaciona y ve la vida, sirviendo como una carta de navegación a través de las diferentes etapas de la vida de una persona.
Robinson es un educador británico considerado un experto en creatividad y calidad educativa. En su charla "Cómo escapar del valle de la muerte de la educación", critica el sistema educativo estadounidense por conducir a altos índices de deserción escolar al contradecir principios del desarrollo humano. Propone que la educación debe personalizarse, valorar a los docentes y dar autonomía a las escuelas, como ocurre en Finlandia.
Este documento define las habilidades cognitivas como un conjunto de operaciones mentales cuyo objetivo es integrar la información adquirida a través de los sentidos en una estructura de conocimiento significativa. Explica que las habilidades cognitivas incluyen el lenguaje, la atención, la comprensión, la elaboración, la memorización y la percepción. Además, destaca la importancia de las habilidades cognitivas para el desarrollo de las capacidades y el aprendizaje de los estudiantes.
El documento proporciona información sobre los arándanos. Explica que los arándanos son ricos en antioxidantes y vitaminas y que tienen beneficios para la salud como ayudar a bajar de peso. Los arándanos contienen fibra, antioxidantes y tienen pocas calorías, lo que ayuda a acelerar el metabolismo y quemar grasas. También actúan como diuréticos para eliminar líquidos. El documento incluye detalles sobre la producción y exportación de arándanos.
This document is a curriculum vitae for Hafiz Muhammad Rafique. It provides his personal details including name, address, contact information, date of birth, marital status, and religion. It lists his academic qualifications including a B.A degree, F.A and matriculation certificates. It details his computer skills and other skills like English shorthand and typing. It provides his employment experience working as an assistant account officer and operation clerk from January 2015 to June 2016. It lists his attributes, languages, and objective of working in an environment where his skills are appreciated.
La administración es la ciencia que estudia la planificación, organización, dirección y control de los recursos de una organización para lograr sus objetivos de manera eficiente. Las funciones de la administración incluyen planificar, organizar, dirigir, integrar el personal y controlar. Sus objetivos son alcanzar los objetivos de la organización de forma eficiente y eficaz y asegurar que produzca o preste sus servicios. La administración se caracteriza por ser universal, específica, tener una unidad temporal y jerárquica, ser un medio instrumental, tener amplitud
Wei Chen has a Bachelor's degree in Business Administration from George Washington University and relevant experience in marketing and student representation. She held internships with property development and marketing firms in China, coordinating events and developing customer relations. At GW, she volunteers mentoring underprivileged youth, helping increase graduation rates. Chen is fluent in English, Mandarin and Cantonese with basic Korean skills and proficiency in Microsoft Office and design software.
Genevieve Adams has over 15 years of experience in customer support roles. She has strong communication, problem-solving, and relationship building skills. Her most recent role was as a Senior Biller Specialist at CheckFreePay/Fiserv, where she coordinated billing functions and ensured accurate customer information over 8 years. Prior to that, she held roles with increasing responsibility in payment research, customer care, banking, and telecommunications. Adams has education in general studies and seeks to apply her skills and experience in a customer support position.
El documento describe cinco tipos de aprendizaje: aprendizaje memorístico, aprendizaje receptivo, aprendizaje por descubrimiento, aprendizaje significativo y aprendizaje visual. Se define cada tipo de aprendizaje y se proporciona un ejemplo para ilustrarlo.
El documento lista tres revistas médicas peruanas especializadas en psicología y ginecología, e incluye información sobre cáncer entre 2012-2013. Además, proporciona ejemplos del uso de los operadores booleanos AND, OR y AND NOT en búsquedas relacionadas con temas médicos.
A ASSERTI é uma associação de empresas de serviços de tecnologia da informação fundada em 2012 com 30 empresas e atualmente com 36 associadas. A associação tem como objetivos representar o setor de TI regionalmente, melhorar a capacitação profissional, fortalecer as empresas associadas e promover o desenvolvimento do setor.
El documento trata sobre el transporte fluvial. Explica que el transporte fluvial consiste en el traslado de productos o pasajeros a través de ríos con profundidad adecuada. Es una importante vía de comercio interior y externo. Además, describe que el transporte fluvial tiene miles de años de antigüedad y ha sido una forma importante de transporte y comercio.
1) O documento descreve a entrada em vigor do novo Plano Diretor Municipal do Nordeste após um processo de revisão.
2) A revisão foi necessária para incluir mais áreas para construção e urbanização na sede do concelho e em algumas freguesias devido a mudanças socioeconômicas.
3) Uma parte significativa das novas áreas disponíveis para construção estava classificada como reserva agrícola, encontrando-se desajustada da atividade econômica atual.
Este documento presenta un sistema de triaje para clasificar pacientes en una sala de emergencias en tres niveles de prioridad (R1, R2, R3) dependiendo de la gravedad de sus síntomas y la atención médica requerida de manera urgente.
Este documento presenta a los miembros de la familia Maluzin Brito. Describe a cada miembro, incluyendo sus edades, personalidades y cualidades admirables. Resalta que a pesar de los altibajos, la familia siempre se ha apoyado unos a otros y son un ejemplo a seguir por su fuerza y espíritu de lucha.
Este documento describe los fundamentos básicos de la comunicación corporativa. Explica que la comunicación corporativa se compone de elementos internos y externos que proyectan la imagen de una institución de manera eficiente. También define los tipos de comunicación como auditiva, visual y táctil, así como las formas de comunicación directa e indirecta. Además, señala que la comunicación corporativa envía mensajes a públicos objetivo para dar a conocer la misión y visión de una organización.
Gender Differences in Returns on Education I. Int.docxhanneloremccaffery
Gender Differences in Returns on Education
I. Introduction
For a society that claims to value equality in the workplace, the gender gap in wages in
America seems awfully persistent. This paper investigates the differences in wages between men
and women at different levels of education using data from a sub sample of the Current
Population Survey (2012). Such analysis will help reveal the nature of the gender gap, and may
help identify the segments in which discrimination in the workforce may exist. Using linear
regressions, I first confirm the wage gap in the data and that returns to education are positive.
Next, I use interaction variables to illuminate gender differences on returns at the different levels
of education (high school, bachelor’s, and master’s). Overall, I find that females see higher
returns than men for completing high school and college, but not for graduate school.
II. Data
The data set consists of 999 observations of working individuals between the ages of 18
and 54:
The average age in the sample is 39.11 years old. On average, individuals made $16.92
an hour with a standard deviation of $9.80. The average highest grade completed, 13.28, shows
that most graduated high school. 88% of the sample have high school diplomas, 24% hold a
bachelor's degree, and 7.4% have completed at least a master’s. A majority was white (81.6%).
10% of the individuals were black, 9% were other races. 22.7% of the workers were parttime.
Approximately half of the sample was female. The following histogram shows the distribution of
education level:
Most of the data lies on the milestone years. The 12, 14, 16, and 16 areas represent high school
diplomas, associate's, bachelor’s, and master’s degrees. However there is some ambiguity at the
14th grade level: these observations could be both associate’s degree holders or four year college
dropouts.
III. Empirical Methodology
To compare gender differences in the returns on wages at different levels of education I
run a linear regression on log wages:
The particular variables of interest are B9, B10, and B11. These interaction variables will show
the additional percentage point increase or decrease in wages that females accrue at the different
levels of education.
Because the distribution of wages is skewed right, I choose to use log wages, which are
more normally distributed and thus may increase the goodness of fit. Based on prior research, I
expect to see positive, though diminishing, returns to age. Thus, one would expect B1 to be
positive and B2 to be negative. Income inequality between whites and blacks is well established
in economic literature, so I expect B3 to be negative. B4 is also likely negative since many of the
higher paying jobs would be full time. I expect a negative coefficient on the female variable,
matching my hypothesis that the wage gap is present in the data. Lastly, the coeffic ...
This document summarizes a regression analysis of factors that influence wages. The analysis finds that on average, women earn $15,038 less per year than men, which is statistically significant. Women also work on average 9.6 fewer hours per week than men. A t-test shows no statistically significant difference in wages between black and white individuals. When factors like age, education level, hours worked, and number of children are controlled for in a regression model, women earn 28% less than men on average and individuals with a college degree earn 51% more than those without a college degree.
This study examines factors that contribute to differences in wages across professions using data from the 2006 and 2011 Current Population Survey. The dependent variable is salary. Independent variables are education, experience (measured by age), occupation, geography, gender, and race. Descriptive statistics show average salary was $40,591 in 2006 and $44,449 in 2011, with average education being some college for both years. Regression analysis will determine how these independent variables impact salary and if their effects differed before and after the recession.
CSE 578 Data Visualization Systems Documentation RepoMargenePurnell14
This document summarizes a data visualization project conducted by Team 44 for XYZ corporation. The team analyzed US census data to determine factors correlated with annual income and classify individuals as earning over or under $50,000. They explored relationships between income and variables like occupation, education, capital gain, work hours, and developed visualizations to analyze correlations. The team will use their findings to predict income and help UVW College increase enrollment by targeting specific demographic groups.
The document analyzes a statistical model to examine factors contributing to the gender pay gap using data from the 1985 Current Population Survey. Key findings:
- The final model found that gender was the most significant predictor of wages, with women earning on average 20.8% less than men, all else being equal.
- Other factors like education, experience, union membership, region, and occupation also influenced wages. Having a job in management led to 20.5% higher wages while service work led to lower wages.
- While the gender pay gap of 20.8% was significant, the model explained only 34.85% of variation in wages, so other unspecified factors also contribute to differences in pay.
How does Age Influence IncomeECO490Danni SongNanc.docxwellesleyterresa
- The study examines the relationship between age and income using data from 350 US workers over 15 years.
- Regression analysis finds that income increases with age up to age 40-69 but then declines, supporting human capital theory. Age 15-39 and age 40-69 are significant predictors of income.
- Additional regressions including control variables for race show age remains the strongest predictor of income, though being black is also significantly associated with lower income.
Understanding Gender Based Wage Gap .docxmarilucorr
Understanding Gender Based Wage Gap
Smaher ali Alharbi
Virginia State University
Understanding Gender Based Wage Gap
If we look across the industries, wage rates differ drastically, and these differences are attributed to how much people value certain occupation or the goods and services produced within certain industry. While it’s common for sportsmen to have millions of dollars of revenue annually, common labor often has to work for minimum wage. To explain these differences one can adopt social, cultural or economic framework and conclude that certain jobs require minimum skill and education, while others require more dedication and investment. Additionally, we can say that some give consumers higher value, and thus people are ready to pay more to get access to those goods and services. While some of the differences in wage rates can seem quite unfair, the fact that wages for basketball player and a manufacturer are different, can be explained by common sense, however, when it comes to more complicated issues, like gender-based or race-based pay gaps, explaining those becomes much harder. This becomes even more confusing when we control for education, age, experience, and industry and observe that a man and a woman with similar background, working on the same position and doing same job have different wage rates.
The goal of this research paper is to evaluate gender gap, understand some of the variables which can explain it and suggest policies that will decrease gender-based wage gap. To accomplish this, I will rely on existing literature that addresses the topic and make conclusions based on existing scientific knowledge about this topic. While this paper won’t conduct regression analysis, it will discuss a potential model for building one; based on this model, one could assess how various independent variables affect gender based wage differences and what needs to be done to change that.
“While the gender gap in the performance of housework has narrowed in many countries for which data are available, it remains universal and large.” (Sayer, 2010). This existing gap makes the problem extremely important. Another reason why this topic is important is that by classical theory about labor market, we expect that wage rate within the industry, for the same position should be the same, because market regulated the wage and workers and employers simply have to accept it; however, in real like employers have higher negotiating power, and they can pretty much set wages as they prefer. What’s interesting is that, for some reason sometimes women are offered a different rate than men. This happens even then we control for various variables. So, what causes women to get lower wages? Are they worse employees? Are they less skilled? Or is it employer who simply likes to discriminate against women?
Literature Review
Considering that our primary emphasis is on existing wage gap in Unites States, looki ...
The gender pay gap statistic, which shows that women earn 77 cents for every dollar men earn, is often misunderstood and misused by both critics and supporters of gender equality. While the statistic does not account for all factors like occupation and experience, it still provides useful information about gender inequality in the workplace. The author analyzes additional data showing the pay gap varies in different situations but never disappears, suggesting discrimination remains an issue. More nuanced analysis is needed to fully understand the causes of the gender pay gap.
This document provides an overview of categorical data analysis techniques. It discusses chi-square tests for independence and their limitations in describing association strength. Better measures include comparing proportions, calculating odds ratios, and examining concordant/discordant pairs. Larger sample sizes can make weak associations appear statistically significant with chi-square tests, so other measures are preferable. The document also covers logistic regression and residual analysis for categorical data.
La administración es la ciencia que estudia la planificación, organización, dirección y control de los recursos de una organización para lograr sus objetivos de manera eficiente. Las funciones de la administración incluyen planificar, organizar, dirigir, integrar el personal y controlar. Sus objetivos son alcanzar los objetivos de la organización de forma eficiente y eficaz y asegurar que produzca o preste sus servicios. La administración se caracteriza por ser universal, específica, tener una unidad temporal y jerárquica, ser un medio instrumental, tener amplitud
Wei Chen has a Bachelor's degree in Business Administration from George Washington University and relevant experience in marketing and student representation. She held internships with property development and marketing firms in China, coordinating events and developing customer relations. At GW, she volunteers mentoring underprivileged youth, helping increase graduation rates. Chen is fluent in English, Mandarin and Cantonese with basic Korean skills and proficiency in Microsoft Office and design software.
Genevieve Adams has over 15 years of experience in customer support roles. She has strong communication, problem-solving, and relationship building skills. Her most recent role was as a Senior Biller Specialist at CheckFreePay/Fiserv, where she coordinated billing functions and ensured accurate customer information over 8 years. Prior to that, she held roles with increasing responsibility in payment research, customer care, banking, and telecommunications. Adams has education in general studies and seeks to apply her skills and experience in a customer support position.
El documento describe cinco tipos de aprendizaje: aprendizaje memorístico, aprendizaje receptivo, aprendizaje por descubrimiento, aprendizaje significativo y aprendizaje visual. Se define cada tipo de aprendizaje y se proporciona un ejemplo para ilustrarlo.
El documento lista tres revistas médicas peruanas especializadas en psicología y ginecología, e incluye información sobre cáncer entre 2012-2013. Además, proporciona ejemplos del uso de los operadores booleanos AND, OR y AND NOT en búsquedas relacionadas con temas médicos.
A ASSERTI é uma associação de empresas de serviços de tecnologia da informação fundada em 2012 com 30 empresas e atualmente com 36 associadas. A associação tem como objetivos representar o setor de TI regionalmente, melhorar a capacitação profissional, fortalecer as empresas associadas e promover o desenvolvimento do setor.
El documento trata sobre el transporte fluvial. Explica que el transporte fluvial consiste en el traslado de productos o pasajeros a través de ríos con profundidad adecuada. Es una importante vía de comercio interior y externo. Además, describe que el transporte fluvial tiene miles de años de antigüedad y ha sido una forma importante de transporte y comercio.
1) O documento descreve a entrada em vigor do novo Plano Diretor Municipal do Nordeste após um processo de revisão.
2) A revisão foi necessária para incluir mais áreas para construção e urbanização na sede do concelho e em algumas freguesias devido a mudanças socioeconômicas.
3) Uma parte significativa das novas áreas disponíveis para construção estava classificada como reserva agrícola, encontrando-se desajustada da atividade econômica atual.
Este documento presenta un sistema de triaje para clasificar pacientes en una sala de emergencias en tres niveles de prioridad (R1, R2, R3) dependiendo de la gravedad de sus síntomas y la atención médica requerida de manera urgente.
Este documento presenta a los miembros de la familia Maluzin Brito. Describe a cada miembro, incluyendo sus edades, personalidades y cualidades admirables. Resalta que a pesar de los altibajos, la familia siempre se ha apoyado unos a otros y son un ejemplo a seguir por su fuerza y espíritu de lucha.
Este documento describe los fundamentos básicos de la comunicación corporativa. Explica que la comunicación corporativa se compone de elementos internos y externos que proyectan la imagen de una institución de manera eficiente. También define los tipos de comunicación como auditiva, visual y táctil, así como las formas de comunicación directa e indirecta. Además, señala que la comunicación corporativa envía mensajes a públicos objetivo para dar a conocer la misión y visión de una organización.
Gender Differences in Returns on Education I. Int.docxhanneloremccaffery
Gender Differences in Returns on Education
I. Introduction
For a society that claims to value equality in the workplace, the gender gap in wages in
America seems awfully persistent. This paper investigates the differences in wages between men
and women at different levels of education using data from a sub sample of the Current
Population Survey (2012). Such analysis will help reveal the nature of the gender gap, and may
help identify the segments in which discrimination in the workforce may exist. Using linear
regressions, I first confirm the wage gap in the data and that returns to education are positive.
Next, I use interaction variables to illuminate gender differences on returns at the different levels
of education (high school, bachelor’s, and master’s). Overall, I find that females see higher
returns than men for completing high school and college, but not for graduate school.
II. Data
The data set consists of 999 observations of working individuals between the ages of 18
and 54:
The average age in the sample is 39.11 years old. On average, individuals made $16.92
an hour with a standard deviation of $9.80. The average highest grade completed, 13.28, shows
that most graduated high school. 88% of the sample have high school diplomas, 24% hold a
bachelor's degree, and 7.4% have completed at least a master’s. A majority was white (81.6%).
10% of the individuals were black, 9% were other races. 22.7% of the workers were parttime.
Approximately half of the sample was female. The following histogram shows the distribution of
education level:
Most of the data lies on the milestone years. The 12, 14, 16, and 16 areas represent high school
diplomas, associate's, bachelor’s, and master’s degrees. However there is some ambiguity at the
14th grade level: these observations could be both associate’s degree holders or four year college
dropouts.
III. Empirical Methodology
To compare gender differences in the returns on wages at different levels of education I
run a linear regression on log wages:
The particular variables of interest are B9, B10, and B11. These interaction variables will show
the additional percentage point increase or decrease in wages that females accrue at the different
levels of education.
Because the distribution of wages is skewed right, I choose to use log wages, which are
more normally distributed and thus may increase the goodness of fit. Based on prior research, I
expect to see positive, though diminishing, returns to age. Thus, one would expect B1 to be
positive and B2 to be negative. Income inequality between whites and blacks is well established
in economic literature, so I expect B3 to be negative. B4 is also likely negative since many of the
higher paying jobs would be full time. I expect a negative coefficient on the female variable,
matching my hypothesis that the wage gap is present in the data. Lastly, the coeffic ...
This document summarizes a regression analysis of factors that influence wages. The analysis finds that on average, women earn $15,038 less per year than men, which is statistically significant. Women also work on average 9.6 fewer hours per week than men. A t-test shows no statistically significant difference in wages between black and white individuals. When factors like age, education level, hours worked, and number of children are controlled for in a regression model, women earn 28% less than men on average and individuals with a college degree earn 51% more than those without a college degree.
This study examines factors that contribute to differences in wages across professions using data from the 2006 and 2011 Current Population Survey. The dependent variable is salary. Independent variables are education, experience (measured by age), occupation, geography, gender, and race. Descriptive statistics show average salary was $40,591 in 2006 and $44,449 in 2011, with average education being some college for both years. Regression analysis will determine how these independent variables impact salary and if their effects differed before and after the recession.
CSE 578 Data Visualization Systems Documentation RepoMargenePurnell14
This document summarizes a data visualization project conducted by Team 44 for XYZ corporation. The team analyzed US census data to determine factors correlated with annual income and classify individuals as earning over or under $50,000. They explored relationships between income and variables like occupation, education, capital gain, work hours, and developed visualizations to analyze correlations. The team will use their findings to predict income and help UVW College increase enrollment by targeting specific demographic groups.
The document analyzes a statistical model to examine factors contributing to the gender pay gap using data from the 1985 Current Population Survey. Key findings:
- The final model found that gender was the most significant predictor of wages, with women earning on average 20.8% less than men, all else being equal.
- Other factors like education, experience, union membership, region, and occupation also influenced wages. Having a job in management led to 20.5% higher wages while service work led to lower wages.
- While the gender pay gap of 20.8% was significant, the model explained only 34.85% of variation in wages, so other unspecified factors also contribute to differences in pay.
How does Age Influence IncomeECO490Danni SongNanc.docxwellesleyterresa
- The study examines the relationship between age and income using data from 350 US workers over 15 years.
- Regression analysis finds that income increases with age up to age 40-69 but then declines, supporting human capital theory. Age 15-39 and age 40-69 are significant predictors of income.
- Additional regressions including control variables for race show age remains the strongest predictor of income, though being black is also significantly associated with lower income.
Understanding Gender Based Wage Gap .docxmarilucorr
Understanding Gender Based Wage Gap
Smaher ali Alharbi
Virginia State University
Understanding Gender Based Wage Gap
If we look across the industries, wage rates differ drastically, and these differences are attributed to how much people value certain occupation or the goods and services produced within certain industry. While it’s common for sportsmen to have millions of dollars of revenue annually, common labor often has to work for minimum wage. To explain these differences one can adopt social, cultural or economic framework and conclude that certain jobs require minimum skill and education, while others require more dedication and investment. Additionally, we can say that some give consumers higher value, and thus people are ready to pay more to get access to those goods and services. While some of the differences in wage rates can seem quite unfair, the fact that wages for basketball player and a manufacturer are different, can be explained by common sense, however, when it comes to more complicated issues, like gender-based or race-based pay gaps, explaining those becomes much harder. This becomes even more confusing when we control for education, age, experience, and industry and observe that a man and a woman with similar background, working on the same position and doing same job have different wage rates.
The goal of this research paper is to evaluate gender gap, understand some of the variables which can explain it and suggest policies that will decrease gender-based wage gap. To accomplish this, I will rely on existing literature that addresses the topic and make conclusions based on existing scientific knowledge about this topic. While this paper won’t conduct regression analysis, it will discuss a potential model for building one; based on this model, one could assess how various independent variables affect gender based wage differences and what needs to be done to change that.
“While the gender gap in the performance of housework has narrowed in many countries for which data are available, it remains universal and large.” (Sayer, 2010). This existing gap makes the problem extremely important. Another reason why this topic is important is that by classical theory about labor market, we expect that wage rate within the industry, for the same position should be the same, because market regulated the wage and workers and employers simply have to accept it; however, in real like employers have higher negotiating power, and they can pretty much set wages as they prefer. What’s interesting is that, for some reason sometimes women are offered a different rate than men. This happens even then we control for various variables. So, what causes women to get lower wages? Are they worse employees? Are they less skilled? Or is it employer who simply likes to discriminate against women?
Literature Review
Considering that our primary emphasis is on existing wage gap in Unites States, looki ...
The gender pay gap statistic, which shows that women earn 77 cents for every dollar men earn, is often misunderstood and misused by both critics and supporters of gender equality. While the statistic does not account for all factors like occupation and experience, it still provides useful information about gender inequality in the workplace. The author analyzes additional data showing the pay gap varies in different situations but never disappears, suggesting discrimination remains an issue. More nuanced analysis is needed to fully understand the causes of the gender pay gap.
This document provides an overview of categorical data analysis techniques. It discusses chi-square tests for independence and their limitations in describing association strength. Better measures include comparing proportions, calculating odds ratios, and examining concordant/discordant pairs. Larger sample sizes can make weak associations appear statistically significant with chi-square tests, so other measures are preferable. The document also covers logistic regression and residual analysis for categorical data.
This document analyzes income inequality in relation to education level using data from the 2003 and 2013 Current Population Surveys. It finds:
1) The largest education group in both surveys was those with a high school degree or less, followed by some college, college graduate, and advanced degree.
2) Calculated Gini coefficients showed some education groups and gender combinations became more equal over time, while others increased in inequality.
3) Regression analysis rejected the hypothesis of no difference in earnings between similarly educated males and females, indicating income inequality.
Final Exam Due Friday, Week EightInstructions Each response is.docxmydrynan
Final Exam Due Friday, Week Eight
Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question.
Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements.
NO LATE EXAMS WILL BE ACCEPTED!
1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work?
2. How can team effort result in improved organizational profitability?
3. What is reverse discrimination and how does it influence the design of an executive compensation plan?
4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.)
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
This document analyzes the effects of language barriers on labor force status and occupation in the United States using data from the 2010-2011 American Community Survey. It begins with an introduction on the motivation and research question. Descriptive statistics are then provided on variables like age, gender, education level, English fluency, and whether individuals are in the labor force or what industry they work in. Conditional statistics examine how being out of the labor force or working in manual labor is affected by independent variables. Logistic regression models are then used to analyze the relationships between the variables.
This chapter discusses recent trends in the US labor market and their impact on women and men. It covers declining gender wage gaps as women have narrowed differences through improved qualifications and human capital investments. Specifically, women have increased their college degrees, job training, and experience while also overcoming wage discrimination. Additionally, returns to education have increased as demand has grown for skilled workers amid technological changes. However, wage inequality also widened between low- and high-earning individuals within gender groups. Non-standard work arrangements and self-employment among women have also risen.
Student response 1The sampling could lead to bias or error for s.docxhanneloremccaffery
Student response 1
The sampling could lead to bias or error for several reasons, the 3 main ones are: a) the data is not up-to-date: this census was taken in 2010 or before and we are in 2014, that means that these data are old and in some cases, outdated, and therefore may not be reliable depending on what the information is requires for. b) Not all of the persons within a specified area will provide the required information to satisfy the census criteria to be a true representative sample: Americans in general are very sensitive about giving out personal information to anyone, least the government and therefore, it can be expected that some of the information the census personnel received will contain errors. And c) The samplers / data gatherers will not get to meet everyone within their respective sample area to compile any data on then. They may visit a house and find that there is no one there to answer their questions (and while some of them get around this by asking the neighbors basic questions about their next door neighbors). They either leave without any information, limited or inaccurate information (what they get from the neighbors). Are they neighbors that are bias towards their neighbors? What about poll workers? The fact is, errors and biases can hardly be avoided in some cases.
Student response 2
The Census Bureau’s sampling process could lead to a bias or error in the data regarding the population of zip code 30331. The information provided by American FactFinder can be classified as secondary data. “For many years the census has been the backbone secondary data in the United States” (Burns & Bush, 2012). Secondary data provides a number of uses for marketing research and marketing researchers decide what to include or dispel from their studies. “Data needed for marketing management decisions can be grouped into two types: primary and secondary” (Burns & Bush, 2012). Data collected though secondary sources tend to be accessible, inexpensive and enhance primary research that has previously conducted (Burns & Bush, 2012).
Along with the advantages secondary data presents, market researchers must also be weary of the existing disadvantages. Problems associated with secondary information include outdated data, incompatible reporting units and unusable class definition. “These problems exist because secondary data has not been collected specifically to address the problem at hand…” (Burns & Bush, 2012). The American FactFinder shows the 2010 population for various zip codes and housing characteristics. Zip code data can be inadequate for researchers when attempting to ascertain specific information about the market. Furthermore, the census does not account for every individual in an area due to lack of data and incorrect data. Inaccurate data can be detrimental to marketing research efforts as conflicting numbers can result in the misrepresentation of facts.
Burns, A. C. & Bush, R. F. (2012). Basic marketing research usin.
Here are some constructive ways to approach the topic of getting paid in full without coming across as angry or bitter:
Focus on professionalism. Explain that as service providers, it's important we conduct ourselves professionally by fulfilling contractual obligations and honoring agreements. Highlight how getting paid allows us to do our work and serve other clients.
Lead with understanding, not accusation. Acknowledge that non-payment can happen for various reasons outside others' control. Offer to have an open discussion to resolve issues respectfully.
Emphasize mutual benefit. Remind that getting proper compensation allows you to continue operating, which benefits all parties. Timely payment protects the business relationship.
Suggest practical solutions. Offer payment plans, discounts
This document discusses a study analyzing gender pay gap using statistical tools and sample data. The study aims to collect and analyze data to identify patterns and trends in salaries between men and women aged 50-60. The document outlines the methodology, including using a simple random sample of 50-60 year olds and collecting data via survey. It then categorizes the variables, presents some preliminary analysis using tables and graphs, and discusses discrete and continuous probability distributions and their use in further analyzing the data. Potential ethical issues with using real individual data are also addressed.
Proposal for Predicting Job Satisfaction and Success-James LiJames Li
This document summarizes a proposed study that will examine the relationship between race, education level, income, and job satisfaction. The study hypothesizes that people with higher education will have lower job satisfaction, African Americans will be more satisfied with their income, and white Americans will earn higher incomes on average. The study will survey 100 participants on their race, education level, income, and satisfaction with their income and job. It will use ANOVA analysis to test for main effects of race and education, and potential interactions between these variables, on the outcomes of income and job satisfaction. The results could imply the need to reform education to benefit all races equally financially.
You clearly understand the concepts of this assignment. You’ve don.docxjeffevans62972
The document provides feedback on a student's assignment. It praises the student's clear understanding of statistics concepts and application in answering problems correctly. It notes the student demonstrated understanding of formulas and explained results well. The feedback encourages the student to focus on academic writing style and using credible sources to back work. It says these skills will help the student succeed personally and professionally.
Non-wage income is a big component of total income in America, yet is almost never analyzed in terms of inequality and discrimination. Here we use the Tobit method to determine the likelihood of a person earning Non-Wage income.
2. 1) Introduction
Perhaps one of the most common questions about today’s labor market is if a
significant racial gap in wages exists in the United States, in other words, do White
workers earn more than minorities? This paper’s goal is to answer this question taking
into consideration the different levels of education, holding all else constant.
Consequently, this is a relevant question because if proven that there is a significant gap
in our collected data it opens room for a possible racial discrimination that employees are
subject to when firms are determining their wages. The data evaluated in this study is a
sub sample of the Current Population Survey 2014. The Analysis is based on a regression
via OLS, which accounts for factors used to determine wage based of different levels of
education, (high school, bachelor’s and masters), age and workers class and then factors
that shouldn’t be relevant upon determining wages such as gender and race. After
conducting this study and running several regressions it is safe to assume that the racial
gap on wages still exists in the American society, subsequently it is easy to illustrate my
statement by comparing the wages between Black and Whites workers.
2) Data
The data set consists of 1,199 observations of working individuals between the ages of 23
and 59
Table 1: Mean
Mean
Age 41.598
(10.75785)
Earnings per hour 22.65073
(13.89619)
Female .5029191
3. (.5002001)
White .8106756
(.3919296)
Black .1067556
(.3089312)
American Indian .0100083
(.0995813)
Asian .058382
(.2345622)
Mixed Races .0141785
(.1182756)
Hispanic .1351126
(.3419864)
High School .2710592
(.4446921)
Bachelor's Degree .2335279
(.4232518)
Master's Degree .0917431
(.2887834)
Part time .0075503
(.0866003)
Observations 1,199
Standard deviation in parentheses
The average age in the sample is 41.59 years old. On average, individuals made $
22.65 an hour with a standard deviation of $13.89. Approximately half of our sample was
female. The majority was White (81.06%). 10.67% of the individuals were Black, and
14.17% were mixed races, it is worth mentioning that 13.51% of individuals in our
4. sample were Hispanics. 27.1% of our sample holds a High School diploma, 23.35% have
a bachelor’s degree and 9.17% have completed a master’s degree. 0.755% of our sample
have a part time job.
Most of the data is located to the right side of the histogram and relies on the
milestone years. 13, 14, 16 and 18 areas represent high school diplomas, associates,
bachelors and master’s degrees. The gaps that exist in the graph represent areas where
there are no people in our sample that can be classified in those groups.
3) Empirical Model
To identify and compare racial differences in the returns on wages on different levels of
education I ran the following linear regression on log wages:
5. Log (wage) = β0 + β1 Age + β2 Age
2 + β3 Years of Education + β4 Black + β5 American Indian + β6
Asian/PacificIslands + β7 Other + β8 Hispanic + β9 female + β10 part-time + ɛi
I decided to use log wages since it makes it easier to read and interpret the results
of the regression. It is also important to remember that log wage are more normally
distributed and consequently it may increase the goodness of fit. Holding all else
constant, based on my previous research, I expect to see a positive, yet diminishing,
return to age. Therefore, I would expect β1 to be positive and β2 to be negative. I expect
β3 to be a positive coefficient because the more education you have more skills and
knowledge you will acquire and therefore increases your chances of having a higher
paying job position. To match my hypothesis I expect the coefficients β4, β5, β6, β7 and β8
to be negative. I also expect B9 to be negative due to income inequality between men and
women. Lastly I anticipate β10 to be negative since part time jobs pay less than full time
jobs.
Omitted bias variable may occur in this model, because there is a lack of
geographical data in the model affecting the race variable. It is worth mentioning that
there is a significant Black population living down South, also the economy in that part of
the country is less developed than the Northeastern part of the United States,
subsequently this factor could affect people’s wages. For example, an employee that
works for a law firm in New York City, New York may earn more than an employee in
that works for a law firm in Jackson, Mississippi since the cost of living in New York is
higher than the cost of living in Mississippi due to the economic development of these
two regions. Consequently, this would mean that the error term is negatively correlated
6. with wages causing a downward or upward biased in some of the estimate of the race
coefficient.
4) Result
Table 2: RegressionCoefficient
(1) (2) (3)
Model Log Wage Alternative Log
Wage - White
Alternative Log
Wage - Black
Education 0.0931*** 0.0969*** 0.0960***
(0.00674) (0.00648) (0.00647)
Age 0.0471*** 0.0495*** 0.0471***
(0.0142) (0.0142) (0.0142)
Age2 -0.000449*** -0.000479*** -0.000447***
(0.000172) (0.000173) (0.000172)
Female -0.133*** -0.137*** -0.136***
(0.0356) (0.0358) (0.0357)
Black -0.216*** -0.204***
(0.0596) (0.0590)
American Indian -0.580***
(0.206)
Asian -0.0424
(0.0720)
Mixed 0.244*
(0.140)
Hispanic -0.0607
(0.0540)
White 0.123***
(0.0454)
Part- Time -0.419** -0.421** -0.420**
(0.206) (0.207) (0.207)
Constant 0.618** 0.390 0.565*
(0.296) (0.298) (0.295)
7. Observations 810 810 810
R2 0.280 0.265 0.269
Adjusted R2 0.271 0.259 0.264
Standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Table 2 presents 810 observations, the reason why it shows a lower number of
observations than Table 1 is due to the fact that among 1,199 people in our sample only
810 individuals had a job. The first column in our table (Model Log Wage) reveals that
the workers wage will increase on average by 9.31% for each additional year of
education, all else held constant. The effect of one additional year age is an average
increase in wages of 4.71% minus 0.0449% times current age, all else held constant. The
returns to ages are decreasing. In comparison to male, females earn on average 13.3%
less, all else held constant. In comparison to White workers, Black workers earn on
average 21.6% less, all else held constant. Also in comparison to White workers,
American Indian workers earn on average 58% less, all else held constant. Asian workers
earn on average 4.24% less than White workers, all else held constant. Mixed races
workers on average earn 24.4% more than White workers all else held constant.
Hispanics earn on average 6.07% less than white workers all else held constant. Part time
workers earn on average 41.9% less in comparison to full time workers. The R2 is 0.280
meaning 28% of the variation of the log wage is explained by the model. The variables
that are statistically significant at a 5% level are: Education, Age, Age2, Part -time,
Female, Black workers, and American Indian workers. Summing to the effect that if they
didn’t have an effect on the wage the chances that we get the data as extreme as we did it
will be less than 5%. I also conducted a T-test for the Black workers variable and I was to
reject the null hypothesis and prove that White workers earn more than Black workers.
8. I conducted an F test, I excluded race dummy variables to build s my restricted
model and I was able to reject the null, as the corresponding p-value is 0.0417. It can be
concluded that the majority of the race coefficients are jointly significant in determining
the log of wage.
The regression’s result predicts that a 40-year-old White male with 10 years
education working full time will get a log wage of 4.833. A Black male with the same
exact level of education, the same age and working on a full time job will get a log wage
of 4.617. Since the Black workers are earning less in comparison to the White workers
the result matches with my predictions.
To exemplify my thesis a little bit more I ran two other regressions with almost
same variables, (with an exception of Hispanic), that I used on the Model Log Wage
regression but I made some adjustments on the race dummy variables. In the Alternative
Log Wage – White regression I kept the White variable and excluded all the other race
dummy variables so I could have a better dimension of how much more White workers
earn in comparison to other races, and I concluded that White workers make on average
12.3% more than other races, all else held constant. The R2 is 0.265 meaning 26.5% of
the variation of the log wage is explained by the model. In the Alternative Log Wage –
Black regression I kept the Black variable and also excluded all the other race dummy
variables so I could have a better dimension of how much less Black workers earn in
comparison to other races and I concluded that Black workers make on average 20.4%
less than other races, all else held constant. The R2 is 0.269 meaning 26.9% of the
variation of the log wage is explained by the model. Taking into consideration both of the
R2 ‘s presented in the alternative models it is safe to assume that a model lacking any race
9. or ethnicity relatively does a poor job of explaining the variation in log wages when
compared to the model with all the races and ethnicities.
5) Conclusion
In today’s society higher education has a significant impact upon the
determination of wages, the more years of education a worker has the more likely he will
be compensated with a competitive salary. After all the highest paying jobs available in
the market require many skills that for the most part can be acquired through education.
Consequently it is understandable that a worker that has a master’s degree will be
receiving a higher salary that a worker that only has his/her high school diploma.
Although, according to my results, the racial gap is alive and well in the American
society in the year of 2014 despite all our best efforts to eradicate racial based
discrimination in the labor market. Thus minorities are still getting paid less than white
workers even if they have the same amount of education.