Question
Paper Details
ECON 326: Format and Content of Final Paper
• Paper due: Via Blackboard by 11:59PM on May 8 (Note that the link to submit the paper will disappear after this time.)
Format Requirements
– Use 11 or 12 point font, double-spacing, and one-inch margins.
– Use page numbering starting with the second page (not the title page).
– Put tables and figures at the end. Your paper should be 8-10 pages in length excluding the title page, bibliography, and tables and figures. I give an idea of the relative length of each section below. Don’t treat these as strict minimums/maximums, though. I prefer well-written concise language to wordy prose for the purpose of filling space.
– You should have (at least) 2 tables. See table formatting guidelines at the end of this document.
Content Requirements
0. Title Page (not to be included in the page count)
I. Introduction / Motivation (1-2 pages)
-What is your general topic, and why is it important? This is a research paper. You should avoid the use of personal commentary such as “I have always loved football, so I decided to study…†Instead, find statistics to use as evidence of football’s importance to U.S. (or worldwide) culture and economy.
-What is the specific research question(s) you tackle, and why should this question(s) be studied?
II. Background (2-3 pages)
– Are there facts the reader needs to understand about your topic before understanding the key set-up of your study and regressions? [Only if these facts are not common knowledge.]
– Briefly discuss the most important previous studies of your topic: What do they find? What questions do they leave unanswered? How does your approach differ? Refer to studies by authors (year), e.g. Dellavigna and Malmendier (2005) find that … Make sure each study you refer to is included in your bibliography.
III. Data (2-3 pages)
– Is your data a cross-section, time series, pooled cross-sections, or panel?
– Give a detailed explanation of where your data come from, how they were collected, and what the unit of observation is (i.e. from what population could they be considered a random sample?) If they are not a random sample, that’s okay, but be explicit about that, and discuss in later section whether it poses a problem.
– Introduce the key variables of interest, and clearly explain how they are defined.
– Discuss the other variables that you will use as controls in the regression.
– Provide a table of summary statistics, e.g. the mean values of key variables and important control variables. This helps the reader familiarize herself with your data and population.
IV. Population Model and Assumptions (1-2 pages)
– What is the dependent variable? Is it in levels or log form?
– Specify the population regression model you propose, and discuss the assumptions that may pose a challenge, for example:
– What type of factors does the error term include? Are you worried that any of these are correlated with your main explanatory variable?
– If your sample is not truly random, how might selection into the sample bias your estimates?
– What signs do you hypothesize for the parameters of interest?
V. Regression Model(s) and Interpretation of Results (2-3 pages)
-Define the main regression model(s) and the parameters you will estimate.
-Refer to a table of your coefficient estimates and their standard errors, with different regressions represented as different columns. The Table will appear at the end of your paper, but you will describe the results in this section.
– You will lose points for pasting Stata output into your paper. That is not what I mean by a table. See “Table Format for Regression Output†below.
-To keep your tables clean and concise, only show the estimated coefficients for variables that you will discuss in the text. If you include extra variables as “controls†but don’t care much about their coefficients, make it clear in the table footnotes that you are including those controls, but do not show their coefficient estimates or standard errors.
– In the text (not in the table), explain how to interpret the coefficient estimates of interest. Put the effect in words, as you’ve practiced many times in homework questions. Discuss both statistical significance and economic significance. If there is reason to believe your results may be biased by omitted variables (this is true in almost all papers—think hard about it!), then discuss the concerns, and explain whether it is likely your estimates are over- or under-estimating the true effect.
– The best papers will also use some of the techniques we talked about in class that go beyond a basic regression model, examples include:
i. Models might require squares, interactions and dummies variables,
ii. Control for time effects,
iii. Test for heteroskedusticity, robust standard errors, or weighted least squares,
iv. Time series models: MA AR, 1st differencing,
v. Panel Models: Diff-diff, Fixed effects,
vi. F-tests
VI. Limitations (1/2-1 page)
– By now you have explained the estimated effects you find with your regressions. Here, you should discuss the question: Can we interpret the effects as causal? This is a very important issue. There are probably good reasons to believe that your effects are not causal—so think carefully about what those reasons are, and discuss. I prefer you to err on the side of caution than to oversell your results! One approach is to mention the drawbacks of your dataset, and describe the type of additional data you would need to be able to estimate a more plausibly causal effect.
VII. Conclusion (1/2 page)
Keep it brief (half a page), succinctly summarize what the reader should take away from your analysis: e.g. key results, and possible challenges in interpreting them.
VIII. Bibliography
In a consistent format, alphabetized by author, provide citations for all the works you cite and the source(s) of the data. Use any of the standard bibliography formats
IX. Tables and Figures
It is a common practice to put tables at the end so the reader knows exactly where to find them, and can flip easily between each table and the text that describes it. In this section, include all Tables and Figures (Graphs) in a clearly numbered fashion. In earlier sections of text, you should refer to the content of these tables, e.g. “the results of regression (1) are shown in Column (1) of Table 2.â€
Format: Table of Regression Results
There are 2 main ways to create tables:
– 1st you can create the table in excel by hand by entering the estimated results from stata, save it as a pdf or png file, and then include it in the text of your word or latex document
– 2nd you can have stata export the regression results directly into excel (you can still edit it by hand), then save it as above as a pdf or png, then include it in the text. We will learn in an upcoming Stata lab, how to easily export regression results into an Excel table.
General Comments on Tables:
1. Refer to any of the papers we read in class for examples of good tables.
2. The leftmost column will list the important X variables. You don’t need to report the coefficients of all control variables, only those that are important enough that you discuss them in the text.
3. Number the rest of the columns, which should each show results from a different regression. These regressions might vary in the controls/ X variables included (as in Example A below) or in the dependent variable used (as in Example B below).
a. Note at the top of the table (or at the top of each column) what is the dependent variable used.
b. Note that in Example B, Columns 1 and 2 use different sets of control variables, but the coefficients of the additional controls are not shown. Instead a row reads: Maternal characteristics, with “Yes†or “No†marked in each column. This is a good approach when you add a lot of controls (or fixed effects) and we aren’t interested in seeing their own effects on Y, just in seeing whether they change the effect of your main X on Y.
4. Include the R^2 and the number of observations in each regression.
5. If you do an F-test of joint significance for some of the variables you add, list the F-test statistic as shown in Example A, Column 4.
6. For each coefficient, report the estimate (rounded to two or at most three non-zero digits) and below, the standard errors (rounded to the same number of decimal places).
7. Include stars to indicate the statistical significance levels, as seen in example B: * for 10%, ** for 5%, and *** for 1%.
8. Use sensible variable names that can be understood.
9. Include a “Notes†area beneath your table. If you include other control variables that aren’t shown, list these here. If you are showing categorical variables such as region, mention which one is the omitted category. If the definition of any of the X variables is non-obvious, provide it here. Also mention whether the standard errors are using the robust formula.
Please read the milestone 1 and milestone 2 first. And then write the final paper. Please use the professional language and knowledge.
| Subject | Career Development | Pages | 19 | Style | APA |
|---|
Answer
The effect of parents’ present career on the choice of career of their children
Introduction
Education is considered to be one of the ways to eradicate ignorance, joblessness, corrupt systems of government, poor communication and poverty among many others. It is, therefore, the desire of every nation through its government to improve the quality of life and social status of its citizenry. In order to achieve this, different forms of interventions have to be undertaken across the nation considering the diversity and differences prevalent in the country. These interventions target the populace that is studying and at a critical point in their life as they make choices of future involvements. Umar (2014) postulates that choice of a career in a child’s life is an important event. As such all necessary support should be accorded to ensure that they make choices that will benefit them and eventually benefit the nation. According to social norms, a child’s choice of career is informed by present opportunities, present environment such as parents’ daily careers, role model careers and from friends and other family members. However, over the growing period the child’ world view changes and so does the preference for occupations (Devney, Devasmita & Robert, 2013).
As such guidance in determining the children’s future plans in making career choices require careful attention through analysis and counselling. As these analyses are being done, it is imperative to understand that career choice decision have far reaching effect throughout the learners’ lives. Many learners have ambitious future filled with glorious adventures where they aspire to work in the lucrative public or private establishments as soon as they complete their education (Farrington et al., 2011). Certain professions such as lawyers, engineers, medical doctors, accountants and so on available are considered lucrative and as such learners try as much as they can to see that they land in any of these professions. In this rapid and fast changing society, many learners are worried about what they will do with their lives and what kind of adult they will become. Their concerns extend to what occupation they will land into early in their working life and whether that will really be their career or it will just be a stepping stone to a better career (Umar, 2014). The effect of parent’s present career have received little attention in influencing the choice of career their children make. Therefore, this study seeks to fill the information gap by undertaking a study on the effect of parents’ present career on the choice of career of their children. To be able to undertake this research the following research question and hypothesis were used:
Question: Does the parents’ present career have an effect on the choice of career of their children?
Hypothesis:
Null Hypothesis (H0):H0:µ=0. The parents’ present occupation has no effect on the choice of career of their children.
Alternate Hypothesis (HA): HA: µ≠0. The parents’ present occupation has an effect on the choice of career of their children.
Rationale
Given that the kind of career pursued affects the life of the youths throughout their lifetime by determining location of living, type of friends, education attainment levels, income levels among others, it is imperative to ensure that career chosen matches learners’ uniqueness and not to parents’ preferences (Umar, 2014). This is because it will increase the ability of a learner to meet life’s challenges and problems when they are out of school when they are far from their parents. This should, therefore, be included in the planning of a leaners future which has to be a combination of parents, teachers and school counselors.
Motivation
To understand the effect of environment on career choices is important to assist learners make informed choices about tomorrow, despite their present predicament and the predicament of their parents.
Background
The ability of learners to meet future challenges and problems is hinged on proper planning. This planning is a combined role of parents, teachers and school counselors who should give learners a wide view and exposure on life and its expectation resulting into learners making wise choices. Some aspects such as the effect of career on location of living, type of friends, education attainment levels, income levels among others require the input of parents, teachers and school counselors (Umar, 2014). However, career choices should be matched to a learners’ personality that is seen in differences in desires in life and from career choices.
Career choices are influenced by factors such as the environment, opportunity and personality. The level and background of the parents’ education may have an effect on the learner’s views on whether or not to continue their education. A good assessment of oneself also affects what career one will choose while availability of opportunities will influence education and career choices. Various Scholars have undertaken research to find out the effect of different aspects on career choice. According to Draper and Louw (2007), the role of family members in influencing a learner’s career choice is important. In their study they found out that learners were influenced by their family desire especially for the medical profession where learners with a doctor in the family were most likely to choose a career in the medical profession.
In another study by Kiolbassa et al (2011), the results showed that most general practitioners were influenced by their family, friendly conditions and sizeable income to choose a career in medicine. With regards to gender, the study found out that female practitioners were more likely to be influenced this way than their male counterparts. For the males, they were mainly influenced by the status that comes with being a general practitioner. According to Kiolbassa et al., (2011), males and females have different reasons for their choice of careers. Among the reasons provided were personal ambition and the need to help patients. The study also found out that as learners grew in the field of study in school, they desired to make changes. While they initially wanted to specialize later they changed and wanted to be general practitioners.
A study by Farrington et al. (2012) found out that attitude difference toward a career between male and female made a vast difference of careers chosen by females. However, the study also points out that attitude comes about due to the type of influence a person has received throughout their development making it important to study the nature of upbringing on attitude. A research by Gottfredson (2004) found out that gender type and the prestige the career comes with influence how learners make their career choice. In support of this, Bucak and Kadirgan (2011) explains that female prefer those careers which are perceived to be good for females while male undertake those that are perceived to be good for males. This shows that women makes choices in consideration with family factors resulting into choices that are based on putting their families first while men don’t focus on family factors but on the prestige attached to the career.
According to a study by Farrington et.al (2012) in South Africa, whites are more likely to enter the field of entrepreneurship than are learners form other ethnic groups. According to the authors, this can be attributed to existence of barriers to entry in some career fields, such as entrepreneurship which is caused by the differences in perceptions held by each ethnic group.
Data
This study used a cross-sectional data from a time series High School Longitudinal Survey 2009 dataset. The data collection for High School Longitudinal Survey 2009 (HSLS:09) was conducted in the 2009-10 school year, with a randomly selected sample of fall-term 9th-graders in more than 900 public and private high schools with both a 9th and an 11th grade. The survey involved a mathematics assessment and online survey. In addition, the survey involved mathematics and science teachers, principals, the surveyed learners’ parents, and the school’s lead counselor which was done via phone or on the Web. First follow-up of HSLS: 09 took place in the spring of 2012 when most sample members were in the spring of their 11th grade. The survey also followed up on dropouts and transfer learners and those who remained in the base-year school. In the summer of 2013, a postsecondary update took place to learn about the cohort’s post secondary plans and decisions. High school transcripts were collected in the fall of 2013.
Sample and weights
The survey of HSLS: 09 involved a two-stage process for the learners in the base year. First, the survey used stratified random sampling and school recruitment which resulted in the identification of 1,889 eligible schools. From these schools, a total of 944 of these schools participated in the study, which was 55.5 percent (weighted) or 50.0 percent unweighted response rate. In the second stage and final stage of sampling, ninth-grade enrollment lists were used to select learners randomly from the sampled school, resulting into about 25,206 eligible selections (or about 27 per school).
Due to the complex survey design used, the survey use weights. The analytic weights were used in combination with software that accounts for HSLS: 09 complex survey designs to produce estimates for the target population, with appropriate standard errors. There were five analytic weights sets for HSLS:09 survey: a school-level weight, a learner-level weight, two learner-level weights associated with contextual data from science and mathematics courses, and a learner-level weight for use with parent-supplied family and home contextual data.The school-level weight is important when undertaking school-level analyses and will involve the school administrator and counselor questionnaires. On the other hand, the learner-level weight is important when undertaking learner-level analyses. The data also contain special learner weights because of the comparatively low unit response rates for parents and teachersadjusted for parent, mathematics teacher, and science teacher nonresponse. These special weights are created in the assumption that parents and teachers provide contextual data for participating learners based on learner as the unit of analysis.
Mode of Data Collection
Data was collected using computer-assisted telephone interview (CATI), on-site questionnaire, telephone interviews and web-based surveys.
Description of Variables
Arts related variables include learner participation in outside of schools arts activities, credit hours of arts classes taken, GPA from arts classes, and parent led arts experiences. Data was also collected on other variables by respondent type as follows: with regard to the learner, learner’s interests and goals in regards to school generally and to STEM specifically; Learner’s identity formation; Learner’s academic behavior (such as attendance, study habits); Learner’s attitudes and beliefs (such as self-efficacy); Learner’s social and cultural experiences; Learner’s exposure to STEM through school or home activities; Learner’s negative school and STEM experiences. With regard to the parents, parents’ demographics; Parents’ sources, and quality of information; Parents’college planning and financing; Parents’educational expectations; Parents’discussions about courses, postsecondary options, careers; Parents’support and resources for academic pursuits at home; school involvement
With regard to the teachers, teacher’s demographics; teacher’s professional preparation and experience; Teacher’s perceptions of parental involvement; Teacher’s perceptions of educational leadership; Teacher’s math and science richness to school; work-related attitudes (such as efficacy). As for the administrators data was collected on: Outreach and transition programs for 8th graders; course availability and selection processes; planning for transition to postsecondary. In relation to counselors, data was collected on caseload; duties; how learners enter pathways for postsecondary education and/or the workforce; course placement and advising; supports for struggling and excelling learners.
Variable Description
Key variables of the study are; the number of persons in the household determines the income requirement and the risk associated with work availability. The more the number the higher the household demand. Occupation of the parent may have a positive and negative effect on the choice of occupation of their children (Devney, Devasmita & Robert, 2013). Moreover, the numbers of hours worked per week denotes the nature and demand of the work and also whether it pays better or not. Different careers require different skills and educational attainments such as the level of education attained, is expected to have an effect on the career chosen (Umar, 2014). These variables use different units of observation as follows; Learner occupation is binary as a choice between Health occupation and Life, physical sciences and engineering occupations, Household size is measured in a continuous manner as the number of people in a household, Parent occupation is binary as a choice between health occupation and Life and physical sciences and engineering occupations, education is also measured in terms of the level attained with categories (less than high school, high school, associate degree, bachelors, masters and PhD) while learners’ gender is binary for male or female. Work is measured in three categories namely non employed, working less than 35 hours per week, and working more than 35 hours per week. The dataset includes 880 observations whose parents’ occupation and the learners occupation are available from the High School Longitudinal Survey of 2009.
Population Model and Assumptions
A model is used to develop and show the relationship that exits within different variables facing a subject of interest. A model determines the nature of relationship that exist. The dependent variable which is the outcome variable determines the choice of the model. With continuous dependent variable, the model is chosen such that it is able to explain the variation in the outcome variable due to the changes in the independent variables. With a categorical dependent variable the model is chosen so as to show the likelihood of being in one category as compared to being in the other category and how that likelihood changes with changes in the independent variables. This study uses a logit model given that the dependent variable is binary variable with two categories.
The assumptions of a logit model are as follows. Firstly, the categories must represent discrete units that are mutually exclusive and exhaustive such as yes or No. Secondly, dependent variable categories must be logically ordered. Thirdly, there should be no outliers which are observations lying more than three standard deviations from the mean. Fourthly, dependent variable must have a meaningful base/reference category. Fifthly, that data must have an adequate cell sample sizes. Additionally, there shall be no correlation between independent variables commonly known as multi-collinearity. Finally, there shall be a linear relationship between dependent and independent variables and the error terms must be uncorrelated.
Regression Model(s) and Interpretation of Results
Model
This paper uses a linear model to conceptualize and model the effect of a parent occupation on the career their children are pursuing. The main model can be illustrated as follows:
Where P(occ=1) refers to the probability that the learner will choose a health related occupation, HH size is the number of members in the students household, Pocc= represents the parents occupation, Pemp= the parents employment category including in terms of hours worked a week, educ is the parents education level and Ssex is the learners gender.
The model was run in STATA and the results are presented in the appendix Table 2. The results show that R-squared is 0.1577 and Chi squared is 165.1 with a p-value of 0.0000. According to the results, gender of the learners, and parents’ current occupation had a positive influence on the choice of career, while education of the parent had a negative influence on the learners’ choice of career. This is consistent with Bucak and Kadirgan’s (2011) and Farrington’s (2012) assertions. The results show that male learners were more likely to select a health related career compared to the female learners. Moreover, the results show that parental occupation had a positive influence in the selection of career with parents whose occupation was health related having their children choose a health related occupation. This is consistent with Umar (2014). Learners whose parents had an associate degree were less likely to choose health related occupation in comparison with learners whose parents had high school education. In addition, Learners whose parents had a PhD were less likely to choose health related occupation in comparison with learners whose parents had high school education.
Limitations
This study faced the limitations of narrow scope as the research work is limited to two general occupations both for the learners and parents namely Health occupation and life and physical sciences occupations. In addition due to the nature of the dependent and other variables in the model we cannot interpret the effect as causal but as likelihood of the event occurring in case the independent variable changes from 1 to 0.
Conclusion
This study sought to undertake a survey on the effect of parents’ present career on the choice of career of their children. To be able to undertake this research the following research question and hypothesis were set. Question: Does the parents’ present career have an effect on the choice of career of their children? The hypothesis stated that the parents’ present occupation have no effect on the choice of career of their children. The study used a logit model to determine whether there was any influence of the parent’s current occupation on the choice of career by their children. The results of the study who that there is a high relationship between the parents’ current occupation and the choice of career by the learners. In this study those parents whose careers were health related had their children seeking topursue career in health too.
References
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Bucak.S and Kadirgan.N. (2011). Influence of gender in choosing a career amongst engineering field: A survey study from Turkey. European Journal of engineering education, (5), 449-460. Devney, Katherine P., Devasmita Chakraverty, & Robert H. Tai. (2013). “The association of family influence and initial interest in science.” Science Education 97.3 (2013): 395-409. Draper, C & Louw, G. (2007). Choosing a career in medicine: the motivations of medical learners from the University of Cape Town. Journal of Education for primary care, 18, 338-455. Farrington. S., Gray. B., & Gary. S., (2012).The influence of gender and ethnicity on perceptions of an entrepreneurial career in the South African context. Journal of entrepreneurship and small business management, (5), 118. Retrieved from: www.sajesbm.co.za/index.php/sajesbm/article/download/24/28. Gottfredson, L.S. (2004). Using Gottfredson’s Theory of Circumscription and Compromise in Career Guidance and Counseling. Delaware: www.googlescholar.co.za Kiolbassa, K., Miksch, A., Hermann, K., Loh, A., Szecsenyi, J., Joos, S & Goetz, K. (2011). Becoming a general practitioner – Which factors have most impact on career choice of medical learners? BMC Family Practice, No Vol, 12-25. DOI: 10.1186/1471-2296-12-25. Umar, I. (2014) “Factors influencing learners career choice in accounting: The case of Yobe State University” Research journal of Finance and Accounting 5.17: 59-62.
Appendix 1: Table 1: Summary statistics
Table 2: Logit regression results
Standard errors in parentheses * p< 0.1, ** p< 0.05, *** p< 0.01 The categories recoded and the lowest selected as the based category. Education of the parent we have high school, employment we have no work.
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