Category Archives: Education

Adding a University to Your Hub

The following comes from David Card (1995) using NLSY for men. We consider the relationship of wages (log wages) to education and other controls. Of course, there is the confounding omitted variable “ability” of which little is known that’ll make our results biased. This is such an important empirical question it is part of the active research agenda till today.

Card makes the following argument, we can assume that some of the exogenous variation that education has on earnings can come from the difference in groups which leave near 4-year institutions and those who live far away from them. I have recreated the following from Card’s paper to make the case for the 4-year college distance treatment. Particularly due to it’s effect on poor and low educated households.

Educ_Distance_Quantile

This is what what I want to note ever so slightly. Where colleges exist in cities it makes a difference in the lower quantiles of education fulfilment. Intuitively this makes a lot of sense. Costs to low income households are down if their children attend local universities. Running a regression with respect to the indicator variable for nearby 4-year colleges also shows a positive relation. Thus, we have a (positive) causal relationship between years of education and nearby colleges. This has one important implication in my mind, recall from my past post I mentioned the same technology hubs over and over. They came up in Enrico Moretti’s work. These are Austin, New York, Seattle, Bay Area, etc. All of them have large and highly reputable colleges. More than that they have several large and highly reputable colleges.

Thus, when we discuss the effects of human capital externalities we can probably see from the graph above a positive generational effect. If in San Francisco day labourers are attracted to the hustle and bustle of a burgeoning city their kids will have an easier time affording San Francisco University. This might be the machinery of innovative cities so many urban planners and developmental economists discuss. Don’t take my statement as definitive proof, just food for thought.

I have a STATA do. file here and Card’s data here (as a STATA .dta) for anyone interested in the IV regressions since I didn’t reproduce them in this post.

PS I remembered this paper only because I had a chance to read it as part of my advanced econometrics class. Just goes to show you it pays to read the suggested readings no matter how overwhelming the number is. Frankly, I sometimes felt like I spent more time reading them and not enough drilling some basic concepts into my head.

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Geography & Labour

Enrico Moretti and other economists discussing spatial and urban economics have documented in the United States a divergence in earnings based on location. Hubs where there exists a great concentration of high skilled workers tend to be areas where all residents have high earnings relative to other regions.

This comes from the following two papers by Moretti: 2013 (with Thulin) and 2003. With this we know that high skilled labour in small cities transfers to these hubs. And married couples with high investments in human capital match up with each other and with high education hubs (Silicon Valley, Seattle, Los Angeles, Boston, etc).
Screen Shot 2017-05-29 at 4.04.34 AMIt then seems important in the United States if this self-sorting has any consequences on the political objectives of cities and their citizens. The recommendations Moretti makes is increased investment in education. Competition for headquarters of a large internet firm or medical research company will result in outcomes that don’t benefit the residents.

Taking this one more step (maybe a step too far) we may find the voting results of residents different as the self-sorting takes place. If the country as a whole needs greater investment in education in “knowledge” sector the voting system may limit this. The electoral college will give the rural and low density populations a disproportionate voice. As these actors attempt to protect their fortunes, they’ll election politicians who’ll save their local industries. They’ll do so with protectionist policies, subsidies, tax breaks and so on. The point is the government’s budget is constrained, it can not give rural America tax breaks for their manufacturing jobs and institute a dramatic investment in high level education. But the voting system favors the local population in low density areas, so they’ll keep voting for immediate solutions that don’t harm their welfare in the near term.

That’s not to say that proportional representation is correct form of governance but rather that the prior uncertainty (belief) in a human capital investment by rural residents is very high. This is due to the natural uncertainty anyone can have in an investment and lack of smoothing by agents (contrary to theory).

I have discussed at length on this blog last year the ways in which students don’t anticipate debt burden in a manner similar to the consumption cycle in labour economics. I think an assumption can be made for the rural voters (or voters in general) when they vote in investments in education. However, we further assume that this isn’t shown by compensating for the investment with higher taxes or certain reforms but rather compensated by lack of belief in the investment itself.

How can we model such actors? We can give them imperfect memory or a bias to their immediate welfare changes. So, a off-shoring jobs to a third world country pains them much more than the change 10 years from said off-shore in earnings after re-training. Their posterior beliefs will change but that’ll take time and experience. Again, expectations in returns to education I have discussed previously are important research interest of mine because it is surprisingly important and could be informative to many issues.

Research on Scholarships in Higher Education

FINANCIAL INCENTIVES AND EDUCATIONAL INVESTMENT: THE IMPACT OF PERFORMANCE-BASED SCHOLARSHIPS ON STUDENT TIME USE (2016)

Although education policymakers have become increasingly interested in using incentives to improve educational outcomes, the evidence continues to generate, at best, small impacts, leading to the question of whether such incentives can actually change student effort toward their educational attainment as suggested by Becker’s model of individual decision making. As a whole, we find evidence consistent with this model: students eligible for performance-based scholarships increased effort in terms of the amount and quality of time spent on educational activities and decreased time spent on other activities. Further, it appears that such changes in behavior do not persist beyond eligibility for the performance-based scholarship suggesting that such incentives do not permanently change their cost of effort or their ability to transform effort into educational outcomes. And, students expected to be most responsive to the incentive – such as those with fewer time constraints and those who may be more myopic in their time preferences – likely were.

An interesting implication would be the consequences of the research on the return to education of students. If at every quarter/semester students determine to remain registered or not. Then certain incentives can for each period maximize an investment so that the next period a net positive outcome is reached. From this a bigger return to educational investment is attained.

Even More on Youth Co-residence

I have been trying to understand how to setup a panel on youth college financing, particularly a monthly panel as the yearly panel cannot give information on youth co-residence, at least I don’t think it can. Also, because the results were rubbish it is important to get the work right.

I ran into a job market paper (chapter 3) discussing almost exactly the same research but instead controls for different variables. I take into consideration family aid, grants and GPA.

I am still unsure about the estimation design using NLYS97. In particular, in Kaplan (2012) the discussion on sample design notes that co-residency question ends in 2002, at which point none of the cohorts from the survey were eligible to enter college. To replace this between 2003 and 2011 the respondents give the date of their first time moving out and first time they moved back with their parents. All these spells had to be at least three months, as noted by the question given to respondents. However, once I used these variables and followed a person for 1 year from their graduation I got incredibly imprecise results. I got no conclusions. Partly I think it’s due to the fact the once you restrict the data to a subset of college graduates you do end up with a lot of skipped responses (for valid reasons) coupled with plain missing values. Now with a better understanding of the survey I can formulate a proper panel, yearly or monthly depending on what I can actually use. It is a little difficult dealing with semester or term based variables (which are my main concern). I have to make assumption about the type of schools to make the work doable and efficient.

The job market paper is interesting. Isolating to just the effects of parental co-residence and student loans, one sees similar results to the papers discussed in the last post. What this says about using parents to smoothing consumption choices isn’t clear. Part of that is a discussion of expectations and human capital. Though the empirical evidence can be seen as an explanation of debt aversiveness or response to new budget constraints, which aren’t totally mutually exclusive from the argument of smoothing yet.

More on Parental Co-residence and Student Loan Debt

In my last blog post I had some doubts over the popular economic punditry’s claim that more young adults living at home is due to the increasing reliance on student loans. There are large scale changes in marital status and demographics that also important. I do think that reframing the question to whether student debt causes parental co-residence is valid. In fact, I think parental co-residence is part of the consumption smoothing used by college graduates who face borrowing constraints. Outside standard life-cycle model, I think credit constraints and debt averse behaviour are likely contributing factors.

There are some interesting papers on the subject. Looney and Yannelis (2015) at the Brookings Institute investigate the characteristics that drive default rates. In their analysis of course is the discussion on the burden 2-year community colleges and for-profit colleges share for the increased default rate since 2000. This isn’t really on topic but in Avery and Turner (2012) and a quick look through the Department of Education’s student loan data for default rates shows that the schools classified as for-profit tend to have exceptionally high default rates. Most statistical evidence shows public and private 4 year universities have a substantially smaller default rate. While the popular discussion ignores the difference amongst schools, it is very important for any future policy goal to understand difference amongst institutions. As an investment, attending a 4 year program (as discussed in the last post) remains a “good” deal (leaving this vague due to heterogeneity amongst students).

Back to the question at had, do student loans (or rather the debt) cause parental co-residence post graduation? Bleemer et al. (2015) find evidence of causality using the Federal Reserve Bank of New York’s Consumer Credit Panel. They use the panel and local factors such as housing price, employment rate and student debt in local college. Another paper by Dettling and Hsu (2016) is similar but for the consideration of credit risk and payment delinquency as part of the inquiry in place of considering geographical factors for individuals after a period of independent living. Dettling and Hsu are really interested in the consumption smoothing of young adults post-graduation. Dettling and Hsu find that change in debt portfolios of young adults contributed 32 percent of increases in co-residence with parents between 2005 and 2013.

This leads to me to question my results from my last post. There will be an addendum to them. I believe I already mentioned they were in hast. It’s a good time to remind any readers, that if anything, this blog is me thinking out loud. Nonetheless, the information above, particularly Bleemer et al (2015), is very important and I recommend reading it. On economic situations and living arrangements I recommend Kaplan (2012).

Update

 

Living Arrangments and Student Loans

The discussion surrounding student loans has always been focused on a supposed bubble. College students with debt in the U.S. are thought to be borrowing too much. The debt in popular media is characterized as extremely harmful to their living standards after they graduate. As well, college education is itself questioned when the heavy debt burdens are considered. Large-scale demographic changes are also linked to student loans at times.

Living arrangements broadly speaking are of concern. The number of adults who live with their parents has steadily increased for 50 years (figure 1). With increases in total student loan debt for college-aged individuals growing as well, many link the two to the trend in figure 1.

TotalwithParent

For young adults, there is also a similar trend (figure 2). For what we assume to be surely out of college population, there is an upward change in the number of young adults that co-reside with parents. For the in-college cohort there is less significant amount of variation (figure 3).

There could be many reasons for these living arrangements. This could represent the heavy burden of student loans. Students may not understand the true costs of attending college. The may in fact borrow too much, at least in comparison to the future earnings their major brings. Not only are they borrowing too much but they could also be substantially more debt averse than believed to be. The immediate impact of a heavy debt burden may influence their cohabitation preferences if they want to pay off the loans quickly.

There is some evidence to the debt averse behaviours of college students, one that doesn’t reflect life-cycle model predictions of smoothing. In Rothstein and Rouse (2011)  empirical investigation into an anonymous university’s “no-loans” policy revealed a natural experiment which shows that students do not follow standard model assumptions of consumption smoothing. Regular model assumptions would mean that student loans would impact career and major choices in a very limited manner. By looking at career choices in “public-interest” jobs and gifts to the university, they find the prevalence of student loan debt to have a negative effect on both. To counter their large debt burden, students chose jobs in finance amongst others. Moreover, Rothstein and Rouse argue that credit constraints are the main culprit behind the break down of the lifecycle model due to the effect of debt on defaults in pledges of alumni.

This brings into question whether or not all students have it in their best interest to take up the costs of college. For a while now giving college a try was considered to be a good investment for anyone. However, if college tends to just push students towards well paying jobs in the face of surprising debt tolls then maybe college is harmful to those who cannot make it into finance or other well-paying “knowledge” private sector jobs.

Avery,Turner 175

Avery and Turner (2012) produced the figure above assuming returns to college begin four years after entrance into a college. The opportunity cost in this case is very high. Of course, there is uncertainty these gains. Some, due to heterogeneity among students, earn a greater return to their education. Even among institutions, wages and graduation rates differ tremendously. Avery and Turner note the following:

If individuals can make accurate predictions about whether they will complete college individuals can make accurate predictions about whether they will complete college and what they would earn conditional on attaining a college degree, then most and what they would earn conditional on attaining a college degree, then most of the variation in lifetime earnings outcomes can be attributed to heterogeneity

In fact, Avery and Turner in a footnote reference Chen (2008) that attributes heterogeneity to the wage inequality amongst college graduates. So, one may say that the difference amongst graduates (and for that matter those who actually graduate or drop out) to be determined by aptitude in college. Thus, Avery and Turner conclude that there is no “student loan bubble” due to the gains possible from attending college (with 2-year community colleges and for-profit institutions being the exception) for those who graduate in good standing.

As part of a quick investigation, I looked through the National Longitudinal Survey of Youth from 1997 from around 2003 to 2010. Looking at college financing and living arrangements. The estimated the following latent model:

Screen Shot 2016-06-11 at 3.28.43 PM

By panel random effects logit, for R > 0. The time variant variables are owed college loans, total income, college grants, college family aid and the dependent variable for residence with parents. The time constant variables are race, phd, masters, professional degree and female. From this I found no significant effect, part of that might due to programming error since I am observing an unbalanced panel with limited information on durations.

Demographic choices are far more significant, and even then they aren’t very precise. I did have some difficulty navigating the NLS database to find some way to noting if an individual lived with their parents. I used the date they moved back in with parents but in the dataset there was some issues as it was unclear how long they moved back in and when they moved out. Many respondents validly and invalidly skipped the query. I do not think this result is well identified but my hunch is better data cleaning and choices would not change much. Neither would controlling for macroeconomic effects and marital status.

On living arrangements Pew Research Centre‘s investigation by Richard Fry (2016) points to demographic changes as the leading cause of increased parental residency. I find in general that result to be very complementing to the debt averseness story. There is more to the dynamics of human capital investments than is commonly believed in other discussions. Here, one can see that debt averseness can curb the assumed consumption smoothing but this is masked as young adults unable to get a start in the world. In fact, as has already been discussed, a college education has great returns on investment. For the most part, one must attend college to get a start in the economy.

Students do not enter college knowing exactly what their aims are or have perfect information about their chances at success. This may be the market failure in student loan market, students may not have perfect information. Nonetheless, college education as investment can be viewed as an option to some degree, thus the assumed relationship between living arrangements and student debt implicates student behaviours much more than the life cycle model. It is unlikely that students are so debt averse and ignorant of information they receive while attending college that they are saddled to living with their parents in droves, so much that they are responsible for the trend in the first figures from the Current Population Survey.

This is a very interesting subject. I will be revisiting it as I acclimate myself with NLSY97 dataset. Specifically, I think one could find better data or more information on specifications of the survey’s questions.

Misc:

Stata data file here

CPS data used here , graphs here and here.

Update

Update 2