Course Description: Although this course is called Political Economy of Social Welfare, you can think of it as “Econ 101” for social workers, with a bit of political science thrown in. That is, much of its content will be similar to what you’d learn if you were to take a principles of economics course in an economics department. The key difference is that I’ll teach economic concepts by relating them to political and policy issues I think social workers might be interested in. Another difference is that there won’t be as much math in this course as there would be in an economics department. But that doesn’t mean there will be no math. It’s almost impossible to teach economics without using any math at all.
The U.S. economy is often described as “capitalist.” A capitalist economy is one with three key features: 1) much of the production of goods and services is with the intention of selling them to make profits 2) there’s private ownership and control of the means of production and 3) many, if not most, adults must become wage laborers (or be attached to someone who is) in order to meet their, as well as their families,’ subsistence needs. Since your first policy course (701) also covered economic and policy issues, I’ll say a little about how this course will differ from that one.
First, there will be a big difference in emphasis. Your first policy course had public policy in the foreground and economic issues in the background. The emphasis in this one will be reversed. That is, this is first and foremost a course in economics. As I said above, the content will be similar to what you’d learn in an economics department. So, if you’re not interested in economics, you won’t like this course. Also, given that much of the content will mesh with politics, if you’re not interested in that, you won’t like it either. So, if economics and politics aren’t your thing, you should run away from this course as fast as possible.
A second difference is that this one will cover a broader range of issues than did 701. In that course, at least in the version I’ve taught in the past, there wasn’t much attention paid to transportation, environmental, drug, and central banking policy. This course will focus on these areas, as well as a host of others.
Course Description: Although this course is called “Social Policy and Planning II,” you can think of it as a course in “Microeconomic Tools for Public Policy.” That is, its content is like what you’d encounter in the public policy courses required of those working on PhDs in public policy or policy analysis. Those courses cover key economic concepts, as well as quantitative tools used in policy research and analysis. The reason I’ve decided to adopt this approach is simple.
For better or worse, economics, especially microeconomics and public economics, has come to dominate discussions of public policy, including those policies social workers often care about. That is, policy discussions, both within and outside of academia, have come to focus on questions concerning the nature of capitalism, the proper role of markets, the appropriate justification for government intervention into markets, the justification for some markets being outlawed altogether, how taxes should be designed, the effects of taxes on people’s behaviors, the effects of policy interventions on people’s well-being and behaviors, etc. Given that this is the case, I think social workers, as much as is possible in a single semester, should be exposed to what I call “the economic approach to policy analysis.” That’s precisely what this course is designed to do.
Course Description: Most people trained in the social sciences are required to do coursework in statistics, and most of those courses are based on a school of thought called “Frequentist” or “Classical” statistics. This course is different; it will introduce you to Bayesian statistics.
The key difference between Frequentist and Bayesian statistics, for our purposes, is the different ways they use probability theory. Although probability plays a role in Frequentist statistics, courses taken by social scientists typically downplay that role. That is, other than the topics of confidence intervals and null hypothesis testing, probability doesn’t come up very much. Things are different in Bayesian statistics, simply because Bayesian statistics is, largely, applied probability theory. That is, it’s mainly about how data are used to update starting probabilities (called “priors”) to obtain later probabilities (called posteriors”). This is why, if you look at the syllabus below, so much of the course is devoted to probability theory.
We’ll mainly do two things in the course: 1) review the probability theory you need to know to understand Bayesian statistics and 2) review many of the topics you likely learned in your first two statistics courses (e.g., t-tests, correlations, ANOVA, regression, etc.), but will do so from a Bayesian perspective. As far as prerequisites are concerned, the first two courses in a Frequentist statistics sequence, as well as basic algebra, are a must. Some familiarity with calculus, especially integral calculus, would be helpful but not necessary. This is because one of the books I’ve assigned uses integral notation but doesn’t require you to calculate any integrals.
Course Description: Social scientists and policy researchers are often interested in making causal inferences. That is, they’re often interested in trying to figure out what causes what. Does the minimum wage cause unemployment? Do charter schools, compared to non-charter schools, increase student achievement? Does poverty negatively affect cognitive ability? These are just a few of the many questions of causal inference which preoccupy social scientists and policy researchers.
The “gold standard” for drawing such inferences is the randomized controlled experiment (RCE), but often RCEs are neither ethical nor feasible. For example, we wouldn’t want to randomly assign some people to poverty just to see if it affects their cognitive ability. The inability to draw causal inferences from data collected by way of RCEs means that such inferences must often be draw from observational data. Some of the standard tools which have been developed to do so are found in a field at the intersection of economics and statistics called econometrics.
Economics students are required to take courses in econometrics but students in other social science departments may not face the same requirement. This is unfortunate because such students miss out on the chance to learn about tools that may be very useful, given their interests. Of course, social science students outside of economics could take econometrics courses in economics departments as electives. The problems this can present, though, is that econometrics courses offered by economics departments may require backgrounds in calculus and matrix algebra. Yet many social science students outside of economics departments don’t have such backgrounds.
This is an applied econometrics course for social science students, outside of economics, who’re interested in learning techniques for causal inference with observational data. The prerequisites are high school or college algebra and at least one course in statistics which covered inferential statistics as well as an introduction to regression models.
Course Description: If, as I do, you follow the so-called “culture war,” you’ll know that one of the major battles in that war is about something called “Critical Race Theory” (CRT). But what exactly is CRT? That’s the question we’ll answer in this course.
First, we’ll begin by discussing the context back in the 1980s within which CRT emerged. Next, we’ll move into discussing the core tenets of CRT, as well as critiques of those tenets. The third part of the course will focus on several social/public policy issues as well as what light a CRT lens may be able to shed on them.
There are no official prerequisites for this course. However, since CRT emerged from the field of law, many of the readings will be about key legal cases that have influenced the development of CRT. So, students who have no interest in the law and the role it has played and, according to many, continue to play in the racial landscape of our society may not be that interested in this course.