This paper examines the effect of ability, as measured by IQ, on individual wages while controlling for other factors. The authors analyze data from the National Longitudinal Survey of 1976 on over 5,000 individuals. Their statistical model finds that higher IQ has a small but statistically significant effect on higher wages. Education level and living in an urban area have larger effects on wages. Unexpectedly, the model shows higher parental education lowers wages and availability of newspapers/magazines does not affect wages. The authors note limitations around incomplete education data and issues with the parental education ranking variable. They conclude that while IQ impacts wages, other social and educational factors have relatively stronger influences.
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.
The Egg or the Chicken First? Saving-Growth Nexus in Lesothopaperpublications3
Abstract: The paper is motivated by the divergent views in literature pertaining to the direction of causality between savings and economic growth. Using the annual time series data for the period 1980 to 2010 the paper investigates the long-run and causal relationship between savings and economic growth in Lesotho using the ARDL bounds test approach. As per the cointegration results, there exists a long-run relationship between savings and economic growth in Lesotho. Granger causality results, however, indicate that savings precede and drive economic growth in Lesotho, both in the short-run and long-run, and not the other way round. Hence policies aimed at enhancing economic growth in Lesotho should stimulate and spur meaningful savings levels.
Measurement of attributes of organizational citizenship behavior in academiciansIAEME Publication
This document summarizes a research study that measured attributes of organizational citizenship behavior (OCB) in academics. The study surveyed 85 academics to examine the relationship between OCB attributes like altruism, conscientiousness, and civic virtue, as well as the influence of demographic variables. Statistical tests found positive relationships between OCB and its attributes. OCB levels differed by age but not gender. The study contributes to understanding how OCB attributes relate to each other and how demographics influence OCB in academics.
Implications of Complex Behavioral Economics for Post Keynesian Economicspkconference
This document discusses the implications of complex behavioral economics for Post Keynesian economics. It argues that many models based on concepts like black swan events, Knightian uncertainty, and complexity economics do not assume an ergodic system and thus allow for non-epistemological uncertainty. It reviews how Keynes described bounded rational economic agents using heuristics and conventions. It also discusses concepts from behavioral economics like loss aversion, hyperbolic discounting, and herd behavior. Finally, it concludes that complexity can provide an ontological foundation for uncertainty and bounded rationality, with evolutionary adaptation in such a world.
This document provides an abstract and introduction to a paper that investigates the effect of statewide minimum wages on US labor markets from 2002-2007. It reviews previous literature on the topic, provides a theoretical analysis of how minimum wages may impact employment levels, and outlines the estimation strategy that will be used, including fixed-effects panel generalized least squares to account for factors like heteroskedasticity. The major purpose is to empirically examine how sectoral composition and other state-level factors may determine the relationship between minimum wages and employment.
In the first half of 2014, for example, the unemployment rate for the first and last rotation groups in the CPS were 7.5 percent and 6.1
percent, respectively. The official BLS unemployment rate for this period was 6.5 percent.These differences raise the obvious question: What was the unemployment rate in the first half of
2014?
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.
This reviewer report summarizes a research paper that analyzes how information quality impacts the cost of equity capital through liquidity risk. The paper examines the relationship between information quality and liquidity risk of stocks from 1983 to 2008, controlling for other factors. The author finds that higher information quality is negatively related to liquidity risk and the cost of capital. The empirical model builds on past research on information quality, liquidity risk, and the cost of capital. The reviewer comments that while the paper contributes to understanding how information quality affects costs, it could provide more discussion of the mechanisms and evidence to support the theoretical framework.
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.
The Egg or the Chicken First? Saving-Growth Nexus in Lesothopaperpublications3
Abstract: The paper is motivated by the divergent views in literature pertaining to the direction of causality between savings and economic growth. Using the annual time series data for the period 1980 to 2010 the paper investigates the long-run and causal relationship between savings and economic growth in Lesotho using the ARDL bounds test approach. As per the cointegration results, there exists a long-run relationship between savings and economic growth in Lesotho. Granger causality results, however, indicate that savings precede and drive economic growth in Lesotho, both in the short-run and long-run, and not the other way round. Hence policies aimed at enhancing economic growth in Lesotho should stimulate and spur meaningful savings levels.
Measurement of attributes of organizational citizenship behavior in academiciansIAEME Publication
This document summarizes a research study that measured attributes of organizational citizenship behavior (OCB) in academics. The study surveyed 85 academics to examine the relationship between OCB attributes like altruism, conscientiousness, and civic virtue, as well as the influence of demographic variables. Statistical tests found positive relationships between OCB and its attributes. OCB levels differed by age but not gender. The study contributes to understanding how OCB attributes relate to each other and how demographics influence OCB in academics.
Implications of Complex Behavioral Economics for Post Keynesian Economicspkconference
This document discusses the implications of complex behavioral economics for Post Keynesian economics. It argues that many models based on concepts like black swan events, Knightian uncertainty, and complexity economics do not assume an ergodic system and thus allow for non-epistemological uncertainty. It reviews how Keynes described bounded rational economic agents using heuristics and conventions. It also discusses concepts from behavioral economics like loss aversion, hyperbolic discounting, and herd behavior. Finally, it concludes that complexity can provide an ontological foundation for uncertainty and bounded rationality, with evolutionary adaptation in such a world.
This document provides an abstract and introduction to a paper that investigates the effect of statewide minimum wages on US labor markets from 2002-2007. It reviews previous literature on the topic, provides a theoretical analysis of how minimum wages may impact employment levels, and outlines the estimation strategy that will be used, including fixed-effects panel generalized least squares to account for factors like heteroskedasticity. The major purpose is to empirically examine how sectoral composition and other state-level factors may determine the relationship between minimum wages and employment.
In the first half of 2014, for example, the unemployment rate for the first and last rotation groups in the CPS were 7.5 percent and 6.1
percent, respectively. The official BLS unemployment rate for this period was 6.5 percent.These differences raise the obvious question: What was the unemployment rate in the first half of
2014?
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.
This reviewer report summarizes a research paper that analyzes how information quality impacts the cost of equity capital through liquidity risk. The paper examines the relationship between information quality and liquidity risk of stocks from 1983 to 2008, controlling for other factors. The author finds that higher information quality is negatively related to liquidity risk and the cost of capital. The empirical model builds on past research on information quality, liquidity risk, and the cost of capital. The reviewer comments that while the paper contributes to understanding how information quality affects costs, it could provide more discussion of the mechanisms and evidence to support the theoretical framework.
What Causes Economic Growth? A Breakdown of The Solow Growth ModelJaredBilberry1
The document summarizes an empirical study examining the Solow growth model and the augmented Solow model developed by Mankiw, Romer and Weil. The study uses data from 1960-1985 for non-oil producing countries to test the relationship between GDP per capita in 1985 and variables for investment, population growth, and secondary education. Descriptive statistics show average GDP increased from 1960 to 1985 while population and investment levels also rose. Correlation analysis found GDP correlated positively with investment and education, but negatively with population growth, supporting the models' predictions.
There are several statistical tests that can be used to investigate correlations between variables based on the type of data and study design:
- A z-test can compare a sample proportion to a population proportion to see if they are significantly different, as when comparing PKU rates.
- Spearman's rank correlation or Pearson's correlation can measure the strength and direction of relationships between ordinal or interval/ratio variables.
- A t-test can analyze differences between repeated measures before and after an intervention to see if they are statistically significant.
- A chi-square test can determine if there is a relationship between categorical variables, such as student responses on a Likert scale. The appropriate test depends on the data
A Correlation Between Emotion-Focused Coping With Test...Stephanie King
This document describes a planned study to examine the relationship between amount of sleep and grade point average (GPA) in graduate students at Ohio University. The author hypothesizes that students who receive less sleep may see a decline in GPA compared to those who receive more sleep, due to the mental impacts of lack of sleep. Statistical analysis of sleep hours and GPA data from 40 graduate students will be conducted using correlation to determine if a relationship exists between the variables. Descriptive statistics and tests of normality will also be reported.
Impact of the income on happiness according to the social contextClmentRieux
Most of welfare economics studies focus on the relation between utility and quantities of consumption goods. Our micro-economic approach is not common as we do not only focus on a quantitative analysis, we include the social dimension which is qualitative. The aim of this paper is to study the impact of an income increase on welfare. It focuses on the evaluation of the individual well-being : not too happy, pretty happy, very happy. This paper also shows how the welfare of a person is likely to change to an income increase depending on her social characteristics (age, gender, religion...), we focused on some of them : marital status, labor force status and education. Increasing income leads to a better life evaluation until the income reached approximately 87,000$. Throughout, in order to provide rigorous results, as the outcome is an ordered categorical variable we used an ordered logistic regression model.
The document discusses a case study and survey conducted by an employee attitudes task force at Pluto Candy Company. The task force analyzed survey results to assess employee satisfaction and attitudes across the company's five divisions. They found that the manufacturing division reported significantly lower job satisfaction than other divisions, possibly due to limited job complexity. In contrast, the R&D division reported higher satisfaction likely because of more varied and meaningful work. The task force used various statistical analyses to compare employee attitudes across divisions.
HLEG thematic workshop on "Multidimensional Subjective Well-being", Andrew ClarkStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
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.
Mr 4. quantitative research design and methodsS'Roni Roni
1. This document discusses quantitative research design and measurement fundamentals. It describes exploratory, descriptive, and causal research designs.
2. Measurement in social sciences often involves concepts that are ill-defined compared to natural sciences. The goal is for measurements to be valid and match the real world.
3. Variables must be operationalized by connecting concepts to observable measurements. This impacts the level of measurement as nominal, ordinal, interval, or ratio.
CLA 2 Presentation
BUS 606 Advanced Statistical Concepts And Business Analytics
Agenda
Introduction
Multiple linear regression is the most appropriate statistical technique in predicting the outcome of a dependent variable at different values (Keith, 2019).
The study assessed the relationship between the cost of constructing an LWR Plant and the three predictor variables S, N, and CT.
We assessed the association between the two-test used to examine the employee performance.
Assumption of Regression Analysis
Multicollinearity
Multicollinearity is the condition where the predictor variables are highly correlated (Alin, 2010).
Correlation Analysis
4
Assumption of Regression Analysis Cont’
Normality test
The normality assumption is not violated after transforming the outcome variable C, using natural log (C) (Shapiro-Wilk = 0.967, p = 0.414).
5
Results and Discussion – Regression Analysis
Use Residual Analysis and R2 to Check Your Model
The R-Squared of 0.232 indicates that the model can explain about 23.2% of ln(C)
The low R-Square indicated that the model does not fit the data well (Brown, 2009).
6
Results and Discussion Cont’
State which Variables are Important in predicting the cost of constructing an LWR plant?
S is a significant contributing factor in predicting ln(C)(p = 0.021), but N and CT have no significant effect in predicting (p > 0.05)
7
Results and Discussion Cont’
State a prediction equation that can be used to predict ln(C).
After dropping N and CT from the model since they do not have a significance effect in predicting ln(C), the prediction equation is given by:
Does adding CT improve R2? If so, by what amount?
Adding CT in the model changes R-Square by 0.001 from 0.232 to 0.234 which is not significant different from zero (p > 0.05).
8
Results and Discussion Cont’ - Correlational Analysis
Evaluate the correlation between the two scores and state if there seems to be any association between the two.
There was a weak positive correlation between the two tests (r = 0.187). This suggested that the two test scores were not correlated.
9
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the Bayes’ theorem.
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
10
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the naïve version of the Bayes’ theorem
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
11
Results and Discussion Cont’
Compare your results in parts b and c and explain the difference or indifference based on observed probabilities
Naïve version and Bayes theorem have similar probabilities.
We have only one predictor in each sample division
This is because Naïve is a ...
Introduction to Factor Analysis for and With Mixed Methods: British Academy ...Wendy Olsen
In this presentation, we set up the aims and mechanisms of the Workshop on Integrated Mixed Methods Research held at University of Manchester (Nov. 3, 2014); it specifically focuses on Factor Analysis, which creates a scale for a gender norm about labour markets. We show how a classical scale and a factor are similar, how they relate to regression and to labour supply, and how NVIVO can be used to follow up a mixed methods workshop or focus group. This creates a mixed-methods approach to gender norms in the labour market. Quite original and very promising. The workshop was a huge success running from 10 am to 3 pm following by an extra hour discussing how this leads to possible research opportunities.
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 paper analyzes how attitudes toward redistribution in the US have changed over time from 1972 to 2010 using data from the General Social Survey. The authors find that while overall support for redistribution has remained flat, the determinants of those attitudes have changed significantly. Younger people and those with lower incomes or less education now support redistribution more, while the attitudes of older people and those with higher incomes or more education have become more polarized. Non-white groups also showed higher support for redistribution, but this difference has decreased over time. These results suggest preferences around redistribution in the US have become more stratified along socioeconomic lines in recent decades.
This document summarizes the literature on the labor market effects of education. It finds:
1) Simple estimates of the return to an additional year of schooling using a Mincer earnings equation are around 13-15%, but these are not causal effects and do not account for factors like ability.
2) Estimates that control for observable factors like ability using regression find smaller effects, around 3.5-5.6% for an additional year of college.
3) Estimates using instrumental variables techniques to account for unobservable factors find effects in the range of 10-11% per year of schooling, larger than OLS estimates but still smaller than simple Mincer returns.
The document provides an overview of examining differences and determining statistical significance. It discusses key concepts like random sampling, statistical significance, the null and alternative hypotheses, and hypothesis testing processes. It explains that statistical tests can use observed variation to provide results that allow decisions to be made with a known probability of being wrong, improving on typical managerial decision making. The document then delves into more specific statistical concepts like descriptive vs inferential statistics, random sampling, margin of error, the normal distribution, sampling distributions, and the t-test and F-test approaches to examining differences in means and variances.
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu.
Between Black and White Population1. Comparing annual percent .docxjasoninnes20
Between Black and White Population
1. Comparing annual percent of Medicare enrollees having at least one ambulatory visit between B and W
2. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c between B and W
3. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between B and W
4. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having
Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.
Here are the main steps for this assignment.
Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.
Step 2: Develop the research question and
Step 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.
The Report Structure:
Start with the
1.Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
· Title, this is the approved topic by your instructor.
· Student name
· Class name
· Instructor name
· Date
2.Introduction
Introduce the problem or topic being investigated. Include relevant background information, for example;
· Indicates why this is an issue or topic worth researching;
· Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
· Specify how others have operationalized this concept and measured these phenomena
Note: Introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3.Research Question or Research Hypothesis
What is the Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents' answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer o ...
More Related Content
Similar to Effects of ability on determining wages of individuals
What Causes Economic Growth? A Breakdown of The Solow Growth ModelJaredBilberry1
The document summarizes an empirical study examining the Solow growth model and the augmented Solow model developed by Mankiw, Romer and Weil. The study uses data from 1960-1985 for non-oil producing countries to test the relationship between GDP per capita in 1985 and variables for investment, population growth, and secondary education. Descriptive statistics show average GDP increased from 1960 to 1985 while population and investment levels also rose. Correlation analysis found GDP correlated positively with investment and education, but negatively with population growth, supporting the models' predictions.
There are several statistical tests that can be used to investigate correlations between variables based on the type of data and study design:
- A z-test can compare a sample proportion to a population proportion to see if they are significantly different, as when comparing PKU rates.
- Spearman's rank correlation or Pearson's correlation can measure the strength and direction of relationships between ordinal or interval/ratio variables.
- A t-test can analyze differences between repeated measures before and after an intervention to see if they are statistically significant.
- A chi-square test can determine if there is a relationship between categorical variables, such as student responses on a Likert scale. The appropriate test depends on the data
A Correlation Between Emotion-Focused Coping With Test...Stephanie King
This document describes a planned study to examine the relationship between amount of sleep and grade point average (GPA) in graduate students at Ohio University. The author hypothesizes that students who receive less sleep may see a decline in GPA compared to those who receive more sleep, due to the mental impacts of lack of sleep. Statistical analysis of sleep hours and GPA data from 40 graduate students will be conducted using correlation to determine if a relationship exists between the variables. Descriptive statistics and tests of normality will also be reported.
Impact of the income on happiness according to the social contextClmentRieux
Most of welfare economics studies focus on the relation between utility and quantities of consumption goods. Our micro-economic approach is not common as we do not only focus on a quantitative analysis, we include the social dimension which is qualitative. The aim of this paper is to study the impact of an income increase on welfare. It focuses on the evaluation of the individual well-being : not too happy, pretty happy, very happy. This paper also shows how the welfare of a person is likely to change to an income increase depending on her social characteristics (age, gender, religion...), we focused on some of them : marital status, labor force status and education. Increasing income leads to a better life evaluation until the income reached approximately 87,000$. Throughout, in order to provide rigorous results, as the outcome is an ordered categorical variable we used an ordered logistic regression model.
The document discusses a case study and survey conducted by an employee attitudes task force at Pluto Candy Company. The task force analyzed survey results to assess employee satisfaction and attitudes across the company's five divisions. They found that the manufacturing division reported significantly lower job satisfaction than other divisions, possibly due to limited job complexity. In contrast, the R&D division reported higher satisfaction likely because of more varied and meaningful work. The task force used various statistical analyses to compare employee attitudes across divisions.
HLEG thematic workshop on "Multidimensional Subjective Well-being", Andrew ClarkStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
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.
Mr 4. quantitative research design and methodsS'Roni Roni
1. This document discusses quantitative research design and measurement fundamentals. It describes exploratory, descriptive, and causal research designs.
2. Measurement in social sciences often involves concepts that are ill-defined compared to natural sciences. The goal is for measurements to be valid and match the real world.
3. Variables must be operationalized by connecting concepts to observable measurements. This impacts the level of measurement as nominal, ordinal, interval, or ratio.
CLA 2 Presentation
BUS 606 Advanced Statistical Concepts And Business Analytics
Agenda
Introduction
Multiple linear regression is the most appropriate statistical technique in predicting the outcome of a dependent variable at different values (Keith, 2019).
The study assessed the relationship between the cost of constructing an LWR Plant and the three predictor variables S, N, and CT.
We assessed the association between the two-test used to examine the employee performance.
Assumption of Regression Analysis
Multicollinearity
Multicollinearity is the condition where the predictor variables are highly correlated (Alin, 2010).
Correlation Analysis
4
Assumption of Regression Analysis Cont’
Normality test
The normality assumption is not violated after transforming the outcome variable C, using natural log (C) (Shapiro-Wilk = 0.967, p = 0.414).
5
Results and Discussion – Regression Analysis
Use Residual Analysis and R2 to Check Your Model
The R-Squared of 0.232 indicates that the model can explain about 23.2% of ln(C)
The low R-Square indicated that the model does not fit the data well (Brown, 2009).
6
Results and Discussion Cont’
State which Variables are Important in predicting the cost of constructing an LWR plant?
S is a significant contributing factor in predicting ln(C)(p = 0.021), but N and CT have no significant effect in predicting (p > 0.05)
7
Results and Discussion Cont’
State a prediction equation that can be used to predict ln(C).
After dropping N and CT from the model since they do not have a significance effect in predicting ln(C), the prediction equation is given by:
Does adding CT improve R2? If so, by what amount?
Adding CT in the model changes R-Square by 0.001 from 0.232 to 0.234 which is not significant different from zero (p > 0.05).
8
Results and Discussion Cont’ - Correlational Analysis
Evaluate the correlation between the two scores and state if there seems to be any association between the two.
There was a weak positive correlation between the two tests (r = 0.187). This suggested that the two test scores were not correlated.
9
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the Bayes’ theorem.
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
10
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the naïve version of the Bayes’ theorem
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
11
Results and Discussion Cont’
Compare your results in parts b and c and explain the difference or indifference based on observed probabilities
Naïve version and Bayes theorem have similar probabilities.
We have only one predictor in each sample division
This is because Naïve is a ...
Introduction to Factor Analysis for and With Mixed Methods: British Academy ...Wendy Olsen
In this presentation, we set up the aims and mechanisms of the Workshop on Integrated Mixed Methods Research held at University of Manchester (Nov. 3, 2014); it specifically focuses on Factor Analysis, which creates a scale for a gender norm about labour markets. We show how a classical scale and a factor are similar, how they relate to regression and to labour supply, and how NVIVO can be used to follow up a mixed methods workshop or focus group. This creates a mixed-methods approach to gender norms in the labour market. Quite original and very promising. The workshop was a huge success running from 10 am to 3 pm following by an extra hour discussing how this leads to possible research opportunities.
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 paper analyzes how attitudes toward redistribution in the US have changed over time from 1972 to 2010 using data from the General Social Survey. The authors find that while overall support for redistribution has remained flat, the determinants of those attitudes have changed significantly. Younger people and those with lower incomes or less education now support redistribution more, while the attitudes of older people and those with higher incomes or more education have become more polarized. Non-white groups also showed higher support for redistribution, but this difference has decreased over time. These results suggest preferences around redistribution in the US have become more stratified along socioeconomic lines in recent decades.
This document summarizes the literature on the labor market effects of education. It finds:
1) Simple estimates of the return to an additional year of schooling using a Mincer earnings equation are around 13-15%, but these are not causal effects and do not account for factors like ability.
2) Estimates that control for observable factors like ability using regression find smaller effects, around 3.5-5.6% for an additional year of college.
3) Estimates using instrumental variables techniques to account for unobservable factors find effects in the range of 10-11% per year of schooling, larger than OLS estimates but still smaller than simple Mincer returns.
The document provides an overview of examining differences and determining statistical significance. It discusses key concepts like random sampling, statistical significance, the null and alternative hypotheses, and hypothesis testing processes. It explains that statistical tests can use observed variation to provide results that allow decisions to be made with a known probability of being wrong, improving on typical managerial decision making. The document then delves into more specific statistical concepts like descriptive vs inferential statistics, random sampling, margin of error, the normal distribution, sampling distributions, and the t-test and F-test approaches to examining differences in means and variances.
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu.
Between Black and White Population1. Comparing annual percent .docxjasoninnes20
Between Black and White Population
1. Comparing annual percent of Medicare enrollees having at least one ambulatory visit between B and W
2. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c between B and W
3. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between B and W
4. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having
Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.
Here are the main steps for this assignment.
Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.
Step 2: Develop the research question and
Step 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.
The Report Structure:
Start with the
1.Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
· Title, this is the approved topic by your instructor.
· Student name
· Class name
· Instructor name
· Date
2.Introduction
Introduce the problem or topic being investigated. Include relevant background information, for example;
· Indicates why this is an issue or topic worth researching;
· Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
· Specify how others have operationalized this concept and measured these phenomena
Note: Introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3.Research Question or Research Hypothesis
What is the Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents' answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer o ...
Similar to Effects of ability on determining wages of individuals (17)
Between Black and White Population1. Comparing annual percent .docx
Effects of ability on determining wages of individuals
1. ECONOMETRICS
(ECON 330)
RESEARCH PAPER
Effects of ability
on determining wages of individuals
Arsalan Anwar 13020392
Gurbux Lohana 13020464
Nabeel Muhammad 13020313
Muhammad Farhan Anwar 14020391
We greatly appreciate the research assistance provided by TAs. The findings and conclusions of
this paper are those of the authors and may not reflect the viewpoint(s) of others.
2. 1
TABLE OF CONTENTS
1. Abstract ………………………………..………………. Page 2
2. Introduction and Literature Review …………………… Page 3
3. Data Analysis ………………………………………….. Page 4
4. Statistical Model ………………………………………. Page 5
5. Sensitivity Analysis …….……………………………… Page 7
6. Limitations, Issues and Remedies ……………………... Page 7
7. Conclusion ……………………………………………... Page 8
8. Bibliography ……………………………………………. Page 9
9. Appendix …….……….………………………………… Page10
3. 2
Abstract
This paper delves into discerning the effect of ability on wage, using IQ as a proxy for the
former, while controlling for other factors that could potentially influence wage and be correlated
with ability in some respect. The paper has been segmented into different parts to explain this
relationship. The first part is based on the introduction & literature review where we have
detailed our hypothesis and referenced previous studies that provide past findings pertaining to
our study. The following section on data description explains the data we have used for our
study. Next, we have formed a statistical model to gauge the causal relationship between wage
and ability while using several econometric tests to derive meaningful conclusions, before we
move to our sensitivity analysis. In the penultimate section, we provide data limitations, issues
and remedies. Finally, we conclude by providing our take on our model and the extent its
conformity to logic and relevance to previous studies.
4. 3
Introduction and Literature Review
(IQ as a proxy for ability)
In the past, a lot of research has been done on the causal effect of ability on wage.
Furthermore, different economists have used different variables to explain this relation. Some of
these variables include like IQ, interpersonal skills, level of education, sociability, and
experience. We believe IQ is the most suitable proxy amongst these, which led us to use it for
ability.
The model that we have formed in this paper is based on the data given to us. Variables
are included in wage regression equation based on intuition and analysis of past studies. Since
some of the variables in our data were irrelevant for this case, we ignored them altogether.
Econometric analysis part of the paper will further explain the variables for our model in detail.
A paper by (Cohan & Kiker, 1986) has attempted to establish a relationship between
wage, IQ and other contextual phenomenon. By accounting for IQ, they have controlled for
variables such as characteristics of family, high school friends, high school peers and their
families, and high schools to earnings at ages 35 and 53. According to them, IQ is not a
significant factor in explaining wage. Other factors count much more. Similarly, another study
by Cohn and Kiker in 1981, has measured cognitive ability for explaining variability in income
by establishing their model on Panel Study of Income Dynamics (PSID). Their findings also
concur that cognitive ability has negligible effects on earnings.
However, other studies have shown significant effects of IQ on wage. A study by
(Murnane, Willet & Levy, 1995) estimates that differences of mathematics achievement scores
have large effects on wage. This study is pertinent to our case because a large part of IQ tests
detects quantitative abilities. In addition, (Altonji, 1992) estimates augmented returns of 15
percent in wage for each standard deviation of IQ. He conducted tests of 692 individuals enrolled
in the Kalamzoo, Michigan school district between the years of 1928 and 1952.
Since our paper is based on effect of IQ on wage, variables which have a correlation with
IQ and an effect on wage, besides IQ itself, are also included in the regression model. A study
carried out in 1969 has shown that variables like parents’ education and good schooling have
positive effects on IQ. “Environment acts as a threshold variable to influence IQ” (Jensen,
1969).
The researches by (Datcher, 1982) and (Hauser and Megan, 1997) show that positive
environment and facilities provided to children at an early age play a significant role in
improving the performance on general IQ tests. Therefore, we have introduced variables such as
parents’ education, residence in metropolitan area besides access to newspapers, library and
magazines at an early age, in our model.
5. 4
Data Analysis
The data provided to us is cross sectional in nature. It was obtained from the national
longitudinal survey of 1976 of youth in 1966; family demographics, locality and education level
of 5226 individuals ranging from 14-24 years old age group were included therein. Our
hypothesis states that, there exists a causal relationship between IQ and the wage of an
individual. The dependent variable, wage76, contains values ranging from 0 to 3.179, with a
mean of 1.65 which is irrational. So, we have introduced a new variable wages76 (i.e.
10,000*wage76) using Consumer income report of 1978, which says in 1976, on average, an
individual earns 15,000 dollars per annum. We have used wage instead of Log (wage) as our
dependent variable because the kdensity function of wage is placed rather normally around the
mean relative to that of log (wage). Our variable of interest (IQ) has values ranging from 50-158,
having a mean of about 101.
To examine the unbiased causal effects of IQ on wage, we have controlled for race,
parents’ education, educational level at the age of 25, number of siblings, residence of urban
area, and availability of magazines, library and newspapers.
There may be many other unobserved variables in our error term which affect wage but
do not affect our exogenous variables thus implying that, our OLS model assumes zero mean
conditionality of the error term. We have not used the simple regression model basing wage on
only IQ, so as to avoid the omitted variable bias.
Variable Description
Wage76 The wage of the individual in 1976
IQ Intelligence Quotient in 1976
Black =1, if the observed individual is black, and =0 otherwise
g25 Education level at the age of 25
Famed Father’s and Mother’s education level ranked from 1-9 in decreasing order,
1=highest…. 9=lowest
smsa66 =1 If living in a metropolitan area, =0 otherwise
num_sib number of siblings in 1966
mag_14 =1 if magazine was available at the age of 14, =0 otherwise
news_14 =1 if newspaper was available at the age of 14, =0 otherwise
lib_14 =1 if library access was available at the age of 14, =0 otherwise
The wages of a particular individual are significantly influenced by aforesaid variables,
all of which are factors that are correlated with IQ hence making it imperative for us to control
for them. The assumption in our model is that the individuals observed do not attain education
after the age of 25, the rationale behind which is explained later. We have included the dummy
variable black in our model to control for the changes in the wages due to race differences; this
data was collected in 1976 when concerns over racial discrimination were still very much
prevalent in the US society. It is also plausible to believe that the area of residence bears impact
6. 5
on the development of an individual justifying the inclusion of smsa66 to check the impact on
wage. In addition to these, we have used other dummy variables like availability of magazine,
newspaper and library at the age of 14 because numerous researches like Altonji, J. G. (1992)
and Cohan, E., & Kiker, B. F. (1986) show the existence of a causal relation of such
environmental factors with wages. To ascertain the ceteris paribus effect of IQ on wages, we
have controlled for the above mentioned variables. We have deleted some values of lib_14,
mag_14 and news_14, which have values other then 0 and 1.
Statistical Model:
We have used the ordinary least squares (OLS) method for our regression model which
is:
Wage76= β0 + β1(IQ) + β2(famed) + β3(smsa66) + β4(g25) + β5(num_sib) + β6(black) + β7
(mag_14) + β8(news_14) + β9(lib _ 14) + û
To satisfy the five Gauss-Markov assumptions, we have used several tests at the 95%
significance level.
To check for misrepresentation in the model, we used the Ramsey Reset test (ovtest). The
probability of F value came out be 0.2893 (Check in Appendix), thus we failed to reject our null
hypothesis, implying our model has no misrepresentation.
To satisfy MLR 5 we checked for heteroskedasticity by using Breusch-Pagan test (Check
in Appendix). The probability of our chi-square value is 0.0081; so we reject our null hypothesis
which shows there is heteroskedasticity in our model. Therefore, we are using robust standard
errors to correct this issue.
To test perfect multicollinearity in independent variables, we have looked at Variance
Inflation Factor (VIF) (Check in Appendix). All values are less than 2 implying the absence of
perfect multicollinearity in the model.
The R-squared value is 0.1048, which shows that our model explains about 10.48% of the
total variation in wages. The model gives a modest account of the variation because much of the
unexplained variation is accounted by other variables such as experience and tenure which is not
included in our model. The F-test value shows that variables in the model are jointly significant.
After we run the regression the average returns to ability for wages, while controlling for
other factors, comes out to be 19.89 dollars. This shows that IQ, which we used as a proxy for
ability, has a significant impact on determining individuals’ wages. This finding is corroborated
by studies which show that “people with higher aptitudes for comprehending and quantitative
applications earn better wages“(Jensen, 1969)
7. 6
Coefficient of g25 indicates that, ceteris paribus, average returns of education on wages
are 233.3 dollars. This coefficient is highly economically significant. This is in accordance with
the intuition that wages increases with the level of education. A study by (Bartik, 2000) has
shown that there is a positive correlation between wages and level of education.
The co-efficient of famed is counter-intuitive, the data has accounted for parents’
education level by assigning 1 to the highest rank and 9 to the lowest; getting a significantly
positive coefficient of 78.97, as is our case, does not support intuition. The coefficient indicates
that, ceteris paribus, one rank decrease in the parents’ education will increase individuals’ wage
by 79 dollars. A study (Corcoran, Gordon, Laren & Solon, 1990) has shown that highly educated
parents are able to give more facilities and better nurturing environment to their children. This
leads us to believe highly educated parents’ children have higher chances of earning better
wages.
The co-efficient of black indicates that the difference of wage between blacks and whites,
on average, is -1637, which is highly significant. Controlling for everything else, Black
individuals earned $1637 less wage, on average, than their non-black counterparts. This relation
is acknowledged by a research carried by US Labor Bureau of Statistics ("Consumer income,"
1978).
The co-efficient of num_sibs is -170.9 indicating, an expected decrease in wage due to an
increase in the number of siblings by 1, ceteris paribus. A possible explanation for this could be:
as number of siblings increase, the resources and attention of parents per child decrease which
eventually lead to a decreased capability for earning wages ("Consumer income," 1978).
The co-efficient of smsa66 is 1388, very highly significant, is also in line with our
assumption that people living near the metropolitan areas, on average, earn higher than those of
non-metropolitan areas, ceteris paribus. The effect of this dummy variable may be accounted by
factors such as relatively higher employment opportunities in metropolitan areas and the
grooming effects of metropolitan areas ("Consumer income," 1978).
All above mentioned variables are statistically significant at 5% significance level.
Although the dummy variable news_14 is statistically insignificant, it bears a coefficient
of 467.1 in our model. We have included it in our model with the presumption that the
availability of newspaper develops the habit of reading newspaper, creating general as well as
job-related awareness among individuals since an early age, potentially enabling them to earn
higher wages in the future.
lib_14 has a negative co-efficient of 50.18 in our model, which is not plausible in theory.
We would like to believe that the availability of libraries should have a positive rather than
deterrent effect on wages. However, the statistical insignificance on lib_14’s co-efficient in the
model suggests library availability bears no significant effect, neither positive nor negative, on
one’s wages.
8. 7
mag_14 has a coefficient of 64.29 which is again statistically insignificant. It shows that
availability of magazine at age 14 does not affect the wage of an individual. Again our result of
coefficient is against our assumption for magazine availability’s effects, which should be
positive.
Sensitivity Analysis
For sensitivity analysis we introduced a new dummy variable named IQ2, which takes a
value of 1 when the observed individual has an IQ higher than or equal to the mean (101) and 0
otherwise; when we replace IQ in our original model by the new IQ2 variable, its coefficient is
530 which indicates that, after controlling other factors the individuals with higher than average
IQ will, on average, earn more than the individuals with below average IQ. The coefficient on IQ
tells us that the average returns to IQ are just 20 which are way less than 530. Most prominently,
the coefficient of IQ2 comes out to be significant.
We now test for the significance of racial discrimination prevalent in the returns of IQ on
wage structure according to our model. It is a statistically insignificant claim that the returns to
wages of IQ differ for blacks and non-blacks. To check for this, we introduce an interaction term
IQ*black, which tells us the difference in returns to IQ from black to white. But the coefficient
of this interaction term not only comes out to be insignificant but also increases multi-
collinearity.
We introduced another interaction term black*g25 to check whether economic returns to
education at age 25 differ from black to white, but its coefficient also comes out to be
insignificant.
Limitations, Issues and Remedies
The education level in 1966 is given for individuals for age group 14-24 but, later in
1976, many of the observed individuals would be more than 25 years of age rendering it
impossible for us to ascertain whether they continued or finished their education at 25 years of
age; we are only provided with g25 educational level of our observations. Therefore, we have
assumed that g25 is the terminal level of education, an assumption necessitated by a limitation of
our data.
Another problem in our data is that variable famed is ranked from 1 to 9 (1 for highest,
and 9 for lowest). This created a problem in regression because we do not know the method of
this ranking system. To counter this problem, we summed up the highest grade of mother and
father’s education. Though our assumption was valid for values greater than 30, but for the
values less than and equal to 30, there were multiple ranks given to a single summed value so we
had to use the ranking system used in the given data, although admittedly we were not sure of the
9. 8
ranking method. The remedy to this problem can simply be to rank parents according to the sum
of their highest level of education with maximum of 9 given to the best and 1 given to the lowest.
Conclusion
We had set out to determine the relationship between wage and ability, using IQ as a
proxy for the latter. Our model confirmed our hypothesis that each point increment in IQ causes
statistically significant positive wage differences. However, other variables have more
economically significant coefficients, thus creating relatively higher influence on wage than IQ
in terms of magnitude. These results lead us to conclude that other variables such as residence in
metropolitan areas, race, and years of education bear more impact on wages.
There were certain results in our model that worked against logic. According to the
model, a higher level of education for parents affected the wages negatively; we could not come
up with a logical explanation for this phenomenon. We are of the view that relatively higher
education for their parents should enable individuals to earn at least as high wages as their
counterparts, if not more because famed66 could not possibly deter wage rates. Similarly, the
availability of newspapers, and magazines was not a significant factor according to the model,
which came as a surprise since these can be taken as tools for a better nurturing which should
improve grooming and eventual wage earning for observed individuals.
Our OLS model proves that our hypothesis is consistent with the findings of the previous
studies conducted by several researchers as discussed earlier in the paper. We have concluded
from our findings that though IQ affects wage but there are also other variables that have more
economically significant impact on wages. We can further extend our research by taking another
hypothesis that whether after certain ideal level of IQ, does the effect of IQ on wages vary or
does not vary economically.
10. 9
Bibliography
Altonji, J. G. (1992). The effects of high school curriculum on education and labor market
outcomes. Journal of Human Resources, 409-12.
Cohan, E., & Kiker, B. F. (1986). Socioeconomic background, schooling, experience and
monetary rewards in the united states . Economica, 497-53.
Corcoran, M., Gordon, R., Laren, D., & Solon, G. (1990). The american economic
review. POVERTY AND THE UNDERCLAS, 80(2), 362-366.
Datcher, Linda (1982) "Effects of community and family background on achievement"
Review of Economics and Statistics, Vol. 64, February, 32-41.
Hauser, Robert M. and Megan, M. Sweeney (1997) "Does poverty in adolescence affect the
life chances of high school graduates" Consequences of Growing Up Poor, Duncan, Greg J.
and Jeanne Brooks-Gunn, eds., Russell Sage Foundation, New York, 541-595.
Jensen, A. R. (1969). How much can we boost iq and scholastic achievement?. Harvard
Educational Review, 111-13.
Murnane, R. J., Willet, J. B., & Levy, F. (1995). The growing importance of cognitive skills in
wage determination. Review of Economics and Statistics, 251-66.
US Census Bureau, (1978). Consumer income (Series P60, No.109)
11. 10
APPENDIX
GRAPH A (WAGES76):
GRAPH B (LWAGE):
0
.00002.00004.00006.00008
.0001
Density
0 10000 20000 30000 40000
wages76
Kernel density estimate
Normal density
kernel = epanechnikov, bandwidth = 779.8797
Kernel density estimate
0
.5
1
1.5
2
Density
7 8 9 10 11
lwage
Kernel density estimate
Normal density
kernel = epanechnikov, bandwidth = 0.0472
Kernel density estimate