The relation between volunteer work and income:
Do we need money to work for nothing?
Abstract
In order to try answer the question how does the income of an individual influence the decision to do volunteer work, first scientific literature is examined. Previous literature on factors influencing the decision to do volunteer jobs examined factors such as being asked to, having a partner who does volunteer work and education. A windfall from these articles is that there is a positive relation between income and volunteering. This article will explicitly address the relationship between
income and the decision to volunteer. I adopt an econometric model using Current Population Survey (CPS) data from the USA. The results show that there is indeed a positive relation between income and volunteering but also that other incentives are important in determining the reasons to do volunteer work.
Bachelor Thesis Onno Nachtegaal 10193987
Universiteit van Amsterdam BSc Economie en Bedrijfskunde
Specialization: Economie en Financiering Supervisor: J. Zheng
2 Statement of originality
This document is written by Student Onno Nachtegaal who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Introduction
Doing volunteer work is very noble and selfless idea. By doing volunteer work you are giving up time and energy to help other people. However the decision to do volunteer work is one that everyone has to make themselves. Some will do it and some won't do it. Everyone has different reasons to do or not to do volunteer work. These reasons can be anything from really wanting to help others to not caring about volunteer work to not having enough time or money to do volunteer work. This last reason is the thing that is the focus of this thesis. The income of people and how this has an effect on doing volunteer work. This lead me to the following research question: how does the income of an individual influence the decision to do volunteer work?
To start this thesis I will first give my motivation to explain why this subject interests me. Then to answer the research question I will first look at the current scientific literature on the subject of volunteer work. I will look at different reasons to do volunteer work and see if and how these articles include income in their analysis. After this I will examine some theory, for example the theory of time constraint and opportunity costs. Then I will describe what data I will use and where this data came from. I will also explain what I will do with this data to get an appropriate answer to the
research question. Following this I will list my hypothesis which is that I expect a positive relation between income and doing volunteer work and reason why I think this relation is positive. Then I will do the empirical research and list the results of this. Hopefully these results will give a better view of the relation between income and doing volunteer work. Following these results I will pose some suggestions for further research.
Motivation
The idea of doing volunteer work is a very noble principle. If you are doing volunteer work you are giving up time and energy without any payment. The only thing you will get from doing volunteer work is a mental reimbursement. You will perhaps be happy that you could help the helpless or that you could contribute to a just cause. But all this time energy and time you spent on doing volunteer work you could also have spent on normal work and by doing so earning money. So volunteer work is definitely not something that everybody wants to do. However it can still be very important for certain people.
To show the size of volunteer work in an economy let's look at volunteer work as part of the Gross Domestic Product or GDP. Volunteer work as part of the GDP can be measured by examining the contribution of non-profit institutions or NPIs to GDP. In the USA the size of NPIs of the GDP was 6.6%1. Of this 6.6% of GDP 1% accounted for volunteer work the other 5.6% accounted for paid
workers. In 2012 the GDP of the USA was 16,163.152 billion dollars. 1% of this is 161.63 billion
1
http://ccss.jhu.edu/wp-content/uploads/downloads/2013/04/JHU_Global-Civil-Society-Volunteering_FINAL_3.2013.pdf (figure 4)
3 dollars. This is a very large amount, especially when you remember that this work was done by people who didn't receive wage for their work.
Even though volunteer work contributes for such a large amount to the GDP, you hear very little about it. Volunteer work is a sector that is often overlooked. This tends to happen because it is difficult to measure volunteer work and sometimes also difficult to obtain useful data about
volunteer work as has been shown (Carson, 1999). This is an older article but it shows that investigating volunteer work isn't easy. Luckily in recent years there has become a lot more useful data about volunteer work available.
I personally am a big supporter of volunteer work. However I don't do volunteer work myself. The reason for this is that I am student and I don't have a lot of money. So if I have the time to work I will chose to work at a normal job to get some money. If I wouldn't be a student and have a normal job or if I would have a lot of money, I would definitely do volunteer work. Luckily there are a lot of who do volunteer work. All these people have their own different motives and reasons that make them willing to do volunteer work. However I don't know a lot about these motives and how important income is for these people in their decision to do volunteer work or not. This got me interested in the reasons to do volunteer work and finding out how income is correlated with the willingness to do volunteer work. However I couldn't find a lot of articles that focus on this relation. There are plenty articles that examine the reasons to do volunteer work but almost none focussed on the relation between income and doing volunteer work. So I decided I wanted investigate this
relation myself. This and the fact that volunteer work is a large and often overlooked sector made me decide to write my thesis about volunteer work and focus especially on the relation between income and volunteering.
Literature review
To answer the question how does the income of an individual influence the decision to do volunteer work, first a precise definition of volunteer work is needed. There are many slightly different
definitions of volunteer work such as doing work without monetary payment, work by which someone offers himself for a job or service or doing helpful or charitable work for other. Some of these definitions focus on not getting payment and other focus on helping others. However all of these come down to in the most basic idea of unpaid work to help others. I define volunteers in this article as "individuals who freely contribute their services, without remuneration, to public or voluntary organizations engaged in all types of social welfare activities" (Sieder & Kirshbaum, 1977, p. 1582). The opposite of volunteer work is normal or paid work. This is work for which individuals do receive a monetary reimbursement for their time and energy. In the rest of this paper I will refer to this kind of work as normal work.
Different reasons to do volunteer work are discovered in the related literature. Some of these reasons are economic, others are more from a sociological or psychological background. Though most of these articles don't focus on the relation between income and volunteer work, a lot of articles do include volunteer work in their reasoning or regressions. Therefore I will also make a short combined conclusion about the relation between income and volunteer work from all these articles.
One of the biggest influence on the decision to do volunteer work seems to be the social environment of someone. Mustillo, Wilson and Lynch (2004 pp.538-539) found that mothers have a large influence on their daughters concerning volunteer work. That is, if a mother does volunteer work this increases the chance that her daughter will also do volunteer work.
4 Rotolo and Wilson (2006) found a similar influence from partners. They found that if someone is married and their partner does volunteer work this increases the chance that they will also do volunteer work. In this study the influence of the women on the man is larger than the other way around. Both these results can be explained by the complementary theory. This theory states on volunteer work that if someone in the household does volunteer work, this has a positive effect on the decision to do volunteer work of the other members of the household. This makes sense because if you are confronted with someone who does volunteer work, you will hear and learn a lot more about it. There might also be social pressure in the household from the member who does volunteer work. He or she might want his or her spouse and children to also do volunteer work. This might lead to someone deciding to do volunteer work. If someone like this is absent from the household, it makes sense that these people know less about it and will therefore be less likely to do volunteer work.
Freeman (1997) also find evidence that supports this sociological view. They find that being asked to do volunteer work has a lot of influence on the decision to do volunteer work. This conclusion is backed up by the data that 44% of the population was asked to do volunteer work and that 89% of them did it last year. Whereas from the 56% of the population that weren't asked to do volunteer work only 29% did volunteer work last year. This again shows the influence of social pressure on the decision to do volunteer work.
Other factors that influence the decision to do volunteer work such as the government are also examined. Day and Devlin (1996) show that the decisions of the government can have different influences on the number of volunteers and thus on the decision to do volunteer work. For example a cut in healthcare or educational expenditure increases the number of volunteers. Whereas a cut in protection or economic areas decreases the number of volunteers. This shows a different motive to do or don't do volunteer work.
Education and religion are other influences on the decision to do volunteer work. "The odds of volunteering in a given year increased by 5% for each month the respondent was in school that year (Oesterle, Johnson, & Mortimer, 2004, p. 1138). This article focuses on the young adults in their early twenties and examines if they do volunteer work. As quoted there is quite a big difference in the volunteering between young adults if they still go to school or if they already have a job. So education has especially for young adults a positive influence to do volunteer work. Another example, Musick, Wilson, and Bynum Jr. (2000) find that for black Americans the church is an important motivator to do volunteer work. The church is where most black Americans come into contact with volunteer work. One of the reasons for this is the important role of the black church for black Americans. Thus for certain races religion can be an important motivator to do volunteer work. Musick, et al. (2000) also find that there are differences in volunteering between races. They find that white Americans do more volunteer work than black Americans. The main reason for this is the difference in human capital, white Americans have more human capital than black Americans. Wage is part of the human capital and thus according to this article there is a positive relation between wage and volunteering. This article isn't the only one that finds this positive relation. Vaillancourt (1994) finds a lot of factors that influence the decision to do volunteer work, one of these is the positive relation between income and volunteering. In table 1 (Vaillancourt, 1994, p. 820) it can be found that as income increases so does the coefficient of income that describes the chance that someone does volunteer work. For income levels of 10,000 to 14,999 dollar the coefficient is 0.0313. This coefficient will keep increasing as income increases. For income of 60,000 dollar and higher they find a coefficient of 0.3774. This is significantly higher than a coefficient of 0.0313. Day
5 and Devlin (1996) find a similar relation between income and doing volunteer work. Income of 5,000 to 10,000 dollars has a coefficient of 0.01741 for doing volunteer work. This coefficient again keeps increasing as income increases, as for income of 60,000 dollars and more the coefficient for doing volunteer work is 0.41649. This is an increase of 0.39908 which is again a large increase. Freeman (1997) finds that the coefficient of the logarithm of hourly earnings is 0.056, again showing a positive relation between income and volunteer work. However Freeman (1997, p.146) also finds this positive relation in a different way. Using data from two different surveys they compare people who do volunteer work and people who don't do volunteer work. One of the characteristics that is compared is mean hourly wage. One survey shows that the mean hourly wage of people who do volunteer work is 11.81 dollars and of people who don't do volunteer work is 9.80 dollars. The other survey finds 14.40 dollars and 10.56 dollars respectively. This again shows the positive relation between income and the willingness to do volunteer work.
Even though the articles in the paragraph above all find a positive relation between volunteering and income, none of these articles focuses on this relation. They find this relation because they include income as a control variable. I believe however that income can be very important on the decision to do volunteer work, so I will focus on this relation.
If the decision of someone on how he spends his time is examined, one of the first theories that comes to mind is the theory of opportunity costs. The theory of opportunity costs looks at what also could have been done with the time, capital, land or another asset as demonstrated by Krugman, Obstfeld, and Melitz (2012, p.55). So this theory looks at what also could have been earned by doing something else. These not received earnings should be a part of the decision making process and should be viewed as costs. If for example someone has a wage of 20 dollars an hour, the opportunity cost of choosing one hour of leisure instead of working one hour will be 20 dollars. This is similar to when someone chooses to do volunteer work instead of normal work. He will also have opportunity costs equal to what he could have earned doing his normal work.
The fact that someone has to choose between work and leisure is because these are
substitutes. A person has limited amount of time every day, a time constraint. Every day this person has to choose what he wants to do. He can't do two things at the same time, so he can't do normal work and volunteer work at the same time. He will have to chose between different activities e.g. normal work, volunteer work, leisure. All these activities are substitutes. Someone can chose what he wants to do and substitute between his options to maximize his utility. This might be difficult
considering volunteer work but still doable (Montmarquette & Monty, 1987, p.146).
Now let's compare the opportunity costs of two different individuals. For example person A has an income of 10 dollars per hour and person B has an income of 40 dollars per hour. The opportunity costs for persons A and B will be 10 dollars and 40 dollars per hour respectively. This shows that the opportunity costs of normal work go up if the income increases. This means that if someone does volunteer work the income he will miss is higher if his wage is higher. I assume here that the relationship between opportunity costs and doing volunteer work is linear. This means that in this example if the wage increases with 1% the chance that someone does volunteer work decreases with also 1%. Assuming this, this implies that according to the theory about opportunity costs person A will do more volunteer work because his wage and thus his opportunity costs are lower than those of person B.
6 Data description
The data I will use in this thesis is data from the Current Population survey or CPS. The CPS is a monthly survey conducted in the USA. The CPS is conducted by the Census Bureau commissioned by the Bureau of Labor Statistics. Every month about 60,000 households are questioned by the survey. Only one adult person of the household answers the questions, he or she does this for the entire household. To be eligible for the survey the person who answers the questions has to belong to the civilian noninstitutional population. This means people of at least sixteen years old who are not in active duty in the military, live in one of the states of the USA and who are not inmates of an institution. A household is question for four successive months, then is isn't questioned for eight months and after those eight months it is returned to the sampling scheme so it can be questioned again.
The CPS contains a set of basic questions that are the same each month. But some months there are supplements which focus on a specific subject. The main focus of the basic questions is on employment and unemployment. However there are a thousand different variables as a result of the many different questions. Some of the subjects of the basic questions are household, labor force, demographic, earnings, geographic and basic CPS school enrolment. Some subjects of the different monthly supplements are veterans, school enrolment, fertility, food security and volunteering.
For this research I will use the months that have the supplement of volunteering. This supplement started in September 2002 and has continued until now. The volunteering supplement stayed in the month September. So I will use data from the month September of the past thirteen years. That is September 2002 up and including September 2014. I will focus on volunteering and on earnings but I will also include some other basic variables that I will use as control variables. This means that most variables I will use are from the basic supplement.
I choose to use the data from the CPS because it was the best database I could find. The CPS has a lot of data that I can use so it will be representative for the population. The large amount of different data also gives me the opportunity to make exactly the model I want to make. Another reason to use the CPS is because it gets data from the USA and there is a lot of volunteer work in the USA. I looked at data about the Netherlands and Europe but the databases that I found were either too small or didn't have the right data for my research.
From the data available on dataferret, the website that lets you download data from the CPS, I created my own microdataset. I will now describe the variables that I will use in my regression. The main focus is still on volunteering and income, but there are plenty of other variables that I will use. These variables might also explain why someone will do volunteer work, so these variables are included as a set of control variables. Some of the variables can't use continuous values such as sex, this can only be male or female. These variables will be turned into dummy variables. Dummy variables can only have two values, in this thesis either zero or one. Zero stands for no and one stand for yes. Every variable that is a dummy variable will be mentioned and explained what its values mean.
The variables that I will use in my regression are: volunteer, income, male, age, highschool, marriage, children, familyincome, employment and school. A more detailed description of these variables can be found in the appendix.
Using these variables I will construct a model. Year fixed effect variables are included to take into account of different effect of years. For example the economic crisis that started in 2008, this might have an effect on the amount of volunteer work. To account for these possible differences between the years I will include twelve dummy variables. The dummy variables will be year2003,
7 year2004, year2005, year2006, year2007, year2008, year2009, year2010, year2011, year2012, year2013 and year2014. These dummy variables will have a value of one if the respondent filled in the CPS in that year and a zero if he filled it in in another year. If all these dummy variables are zero, the respondent filled in the CPS in the year 2002.
Two other variables that I will add to the model are income2 and income3. To get these
variables I will generate them using stata. Income2 is income times income and income3 is income
times income times income. The reason that I include these variables is that income might have an unlinear relation with doing volunteer work and by including these two variables I account for that possible unlinear relation. For example income might increase the chance that someone does volunteer work but after a certain amount of income read the threshold, the next increase in income might decrease the chance that someone does volunteer work. This could also apply to a higher threshold that in turn might increase again the chance that someone does volunteer work.
Using all these variables I created the following formula: Volunteer=
α+β1*income+β2*income2+β3*income3+θ*C. Here θ is the coefficient matrix and C is the set of
control variables. I added the alpha as a constant because even if all things are zero there is still a chance that someone will do volunteer work.
The regression technique I will use is the probit regression. I can't use the most standard regression, the OLS regression. Because the dependent variable has only two possible values I will have to use non-linear regression. This can either be a probit regression, a logit regression or a linear probability regression. I chose to use the probit regression instead of the linear probability regression because this regression is less bias than the linear probability regression.
Hypothesis
The research question can have a lot of different outcomes depending on the size of the effect of income on the decision to do volunteer work. However it can only have three basic outcomes, namely that income has a positive effect, a negative effect or no significant effect at all on the decision to do volunteer work. I expect that income will have a significant positive effect.
My expectations are the opposite of what the theory of opportunity costs predicts. I believe that if income increases so does the willingness to do volunteer work. The reason that the theory of opportunity costs does not apply to volunteer work is that I believe that the incentive theory is stronger under such circumstances, especially when the very nature of volunteering work stimulates intrinsic motivations out of people. This theory states that behaviour is motivated by internal desires and wishes i.e. non-monetary benefits. Examples of this are satisfaction to help others, gaining praise from your peers or honour. This in contradiction with the theory of opportunity costs that believes that people focus only on monetary benefits and costs. I believe that in the case of volunteer work people value intrinsic incentives higher than monetary incentives. So as wage increases people will have to work less to have the same amount income. This means that as wage increases people will have more time to satisfy their internal wishes and desires, in this case doing volunteer work.
The fact that as income increases so does the willingness to do volunteer work also applies to me personally, I would like to do volunteer work but I don't do it because I have not enough money and my wage is too low. My prediction however is in line with earlier results found in scientific articles. That is that if income increases so does the chance that someone does volunteer work.
8 Results
Summary of probit regression analysis for income and control variables explaining volunteer work Table 1 Variable dy/dx SE Income 0.0043334*** 0.0011842 Income2 -0.0000316 0.0000222 Male Age -0. 0680841*** 0. 0001209 0.0055665 0.0004447 Highschool -0. 0070413 0.0073018 Marriage Children School Familyincome1 Familyincome2 Familyincome3 Familyincome4 Familyincome5 Familyincome6 Familyincome7 Familyincome8 Familyincome9 Familyincome10 Familyincome11 Familyincome12 Familyincome13 Constant 0. 0678325*** 0. 0033716 0. 157578*** -0. 013757 -0. 0201046 -0.0350116 -0.0371376 -0.0251821 -0.0281796 -0.0180711 -0.0192471 -0.0382262* -0.0075486 0.0241192 0.0422939** 0.0667338*** -0.2050428*** 0.0082182 0.003972 0.0066049 0.0218601 0.0228237 0.0204125 0.019918 0.0168518 0.0154674 0.015241 0.0151646 0.0162363 0.0135655 0.0135331 0.0128098 0.0111606 0.002736 * p<0.05. ** p<0.01. *** p<0.001
9 Table 2 Variable dy/dx SE Year2003 0.0395172** 0.0151421 Year2004 0.0180534 0.01531 Year2005 Year2006 0.0227141 0.0013321 0.0152436 0.01554 Year2007 -0.0105741 0.0154906 Year2008 Year2009 Year2010 Year2011 Year2012 Year2013 Year2014 -0.0085657 0.0127811 -0.0011344 -0.0052464 0.0093491 0.0085477 0.0009476 0.0158077 0.0162258 0.0163671 0.0165934 0.0166574 0.0138782 0.0138851 * p<0.05. ** p<0.01. *** p<0.001
In the tables the marginal effects from the probit regression are listed, these include the variable names, the coefficients, the standard errors of these variables and significance levels. As mentioned before the depended variable is volunteer so this one is not listed in the tables. Table 1 features the variables income and income2 as well as the control variables and the constant. Table 2 features the
dummy variables for the different years.
I decided to leave two variables out of the regression analyses, namely income3 and
employment. I left income3 out because it had a very small coefficient and when this variable was
included the main variable income was highly insignificant. But when income3 is omitted from the
regression income becomes significant. The variable employment is omitted because it was highly correlated with income, so much that the results for this variable weren't given by stata. Because of this I decided to leave this variable out of the regression. I believe that by omitting this variable the conclusion of this research won't be affected.
From the table it can be concluded that income indeed has a positive correlation with doing volunteer work. Income has a coefficient of 0.0043334, this means that if the hourly wage of someone increases with one dollar their willingness to do volunteer work will increase with 0.4%. Income2 has a negative correlation with doing volunteer work. However seeing how this coefficient
is insignificant there is no proof from this research that there is a nonlinear effect of income on doing volunteer work. This means that the only effect of income on doing volunteer work from this
10 Another interesting result is the difference in the coefficients of the variable family income. This starts out with negative coefficients and these become positive after the tenth category. So any family income lower than 50,000 dollar decreases the willingness to do volunteer work and a family income of 50,000 dollar or more increases the willingness to do volunteer work. A possible
explanation for this might be that when the family income is less than 50,000 dollar, the people who work in this family will have worries about having enough money to pay all their bills. These people will now chose normal work over volunteer work. When they reach an income of 50,000 dollar or more, these worries might be gone and now they will want to do volunteer work.
These results of a positive relation between income and volunteering can also be found in scientific articles. As mentioned in the literature review Vaillancourt (1994), Day and Devlin (1996) and Freeman (1997) all find this positive relation as well. However most of them use income as a control variable and income is often a categorical variable similar to the variable familyincome use in this thesis. Freeman (1997) uses a similar definition of income as I use namely hourly earnings. He finds that the coefficient of logarithm of hourly earnings is 5.6%, which is a lot larger than the 0,4% found in this thesis. A reason for this might be the use of the logarithm or the use of a different model. The negative coefficients of familyincome until it reaches 50,000 dollars a year is something that none of these articles found, a reason for this is that these articles use individual income and not family income. Another way that this research differs from the mentioned articles is that I included the term income2 which was not done before. The reason for this was to find out if there is a
nonlinear relation between income and volunteering. However income2 turned out be insignificant
and thus proving that in this research there is no nonlinear relation between income and volunteering.
Discussion and conclude
As found in the scientific literature sociological aspects also play a role in determining the willingness of people to do volunteer work. As found before women do more volunteer work as well as married people. A variable that had a surprising outcome is school. This variables indicates whether this person was enrolled in school, college or university in the past week. The coefficient of the variable school is 0. 157578 which is quite large. A reason for this could be that doing volunteer work is mandatory in some schools.
For further research on this subject the same research can be done on a different sample to see if the results are the same. Another possible way to continue this research is to look closer at the relation between income and family income. Another suggestion is to examine the thresholds of income and family income and try to answer why these threshold are at these amounts. One other aspect that might also need to be further investigated is the influence of volunteer work on income. As shown by Day and Devlin (1998) doing volunteer work increases the wage, to be more precise increases the wage by 7% on average. This could also be a motive to do volunteer work besides having a high enough wage. Which of these motives is stronger, is something that would be interesting to investigate.
So as a final conclusion I can say that according to this research income does have a positive relation with doing volunteer work. However income is not the only variable that will explain why people do volunteer work. I believe that everyone has different incentives to do volunteer work but that income is one of the most important ones.
11 References
Carson, E. D. (1999). Comment: on defining and measuring volunteering in the United States and abroad. Law and contemporary problems, 62(4), 67-71.
Day, K. M., & Devlin, R. A. (1998). The payoff of work without pay: volunteer work as an investment in human capital. The Canadian jounal of economics, 31(5), 1179-1191.
Day, K. M., & Devlin, R. A. (1996). Volunteerism and crowding out: Canadian econometric evidence.
The Canadian journal of economics, 29(1), 37-53.
Freeman, R. B. (1997). Working for nothing: the supply of volunteer labor. Journal of labor
economics, 15(1), 140-166.
Krugman, P. R., Obstfeld, M., Melitz, M. J. (2012). International economics theory & policy (9th e.d.) Harlow: Pearson
Montmarquette, C., & Monty, L. (1987). An empirical model of a household's choice of activities.
Journal of applied econometrics, 2(2), 145-158.
Musick, M. A., Wilson, J., & Bynum Jr., W. B. (2000). Race and formal volunteering: the differential effects of class and religion. Social forces 78(4), 1539-1570.
Mustillo, S., Wilson, J., & Lynch, S. M. (2004). Legacy volunteering: A test of two theories of intergenerational transmission. Journal of marriage and family, 66(2), 530-541.
.
Oesterle, S., Johnson, M. K., & Mortimer, J. T. (2004). Volunteerism during the transition to adulthood: a life course perspective. Social forces, 82(3), 1123-1149.
Rotolo, T., & Wilson, J. (2006). Substitute or complement? Spousal influence on volunteering. Journal
of marriage and family, 68(2), 305-319.
Sieder, V. M., & Kirshbaum, D. C. (1977). Volunteers. Encyclopedia of Social Work, 17, 1582-1591.
Vaillancourt, F. (1994). To volunteer or not. The Canadian journal of economics, 27(4), 813-826.
Appendix
Detailed description variables
Volunteer: Has the respondent done volunteer work in the past twelve months or not. This will be a dummy variables with one for volunteer work and zero for no volunteer work.
Income: Dataferret provides the hourly rate of pay, which is a good way of comparing income. The hourly rate of pay has range of 0 to 99.99. Higher hourly rates of pay are unlikely so these are excluded from the possible answers.
Male: Either the respondent is male or female. This will be a dummy variable with one for male and zero for female.
12 Age: The age of respondent, this variable has a range of 0 to 80. Respondents who are older than 80 are added to the group of 80.
Highschool: This variable measures whether the respondent completed high school or not. This is a dummy variable with one for high school completed and zero for high school not completed. Included in the group of those who completed high school are those who completed their GED. GED stands for General Education Development and is in most of the states in the United States of America an equivalent for a high school diploma.
Marriage: The relationship status of the respondent. This is a dummy variable. It will have a value of one if the respondent is married and a value of zero if the respondent is not married.
Children: The number of own children of the respondent of the age of 17 and younger.
Familyincome: The total combined income of all the family members of the respondent over the past twelve months. This is a categorical variable, this means that the variable has certain categories but that the order of the categories has no meaning. These categories will be transformed into dummy variables with a one if the respondent belongs to that category and a zero otherwise. This variable has fourteen categories of income in dollars: Zero less than 5,000; one 5,000 to 7,499; two 7,500 to 9,999; three 10,000 to 12,499; four 12,500 to 14,999; five 15,000 to 19,999; six 20,000 to 24,999; seven 25,000 to 29,999; eight 30,000 to 34,999; nine 35,000 to 39,999; ten 40,000 to 49,999; eleven 50,000 to 59,999; twelve 60,000 to 74,999; thirteen 75,000 or more. This means that this variables will be split into thirteen dummy variables, one for every category except for the first one. People who apply to this category simply have a zero for all other categories.
Employment: The respondent is either employed or unemployed. This is a dummy variable with one for employed and zero for unemployed.
School: If the respondent was enrolled in school, college or university last week? This is a dummy variable with one for enrolled in school, college or university and zero if they are not.
