Michael Parkin Economics Ppt

Michael Parkin talked with Janet Currie about her work and the progress that economists have made in understanding how public policies can influence the distribution of income and economic well-being as well as the supply of labor and human capital. Professor Currie, what attracted you to economics? Read Free Michael Parkin Economics 9th Edition Issue and Economics in the News, this new edition uses real-world examples and applications to incorporate the latest developments in the Eurozone and UK policy. The leading economists in the news today started out like you, as students taking a course in the principles of economics.

Talking with Janet Currie

Janet Currie is a professor of economics at Columbia University. Born in Kingston, Canada, she attended the University of Toronto, where she received her B.A. and M.A. in economics before moving to Princeton University, where she completed her Ph.D. in 1988. Professor Currie's research examines a wide range of public programs -- medical, nutritional, educational, and housing -- on (mainly) poor families. She has provided valuable assessments of the short-term and long-term effects of the Head Start program (enriched preschool for children in poor households) and Medicaid (government provided health insurance for poor mothers and their children).
Michael Parkin talked with Janet Currie about her work and the progress that economists have made in understanding how public policies can influence the distribution of income and economic well-being as well as the supply of labor and human capital.
Professor Currie, what attracted you to economics?

I was attracted to economics because it addresses questions of broad human interest (such as poverty and inequality) with intellectual rigor. Some may view the economic paradigm as restrictive (does everyone really maximize utility all the time?), but it provides a set of tools that yield powerful predictions about human behavior, and it can be tested. For example, the 'Law of Demand' (people consume less of a good when the price goes up) can be adopted to think about why many eligible people do not participate in social programs that might benefit them.
Why are there still relatively few women in our field?

Like other scientific careers, that of an economist requires an initial investment in mathematical skills. Mathematics is the language of science, and it is difficult to become a scientist if you don't speak the language. Undergraduate programs in economics may be partially to blame for not preparing students adequately for graduate work in the field. Many programs are aimed more at preparing students for careers in law or business. While this may be what the majority of undergraduate economics students want, we should also serve those who may go on to study economics in graduate school.
Difficulties in combining work and family are also an issue, but economics is not unique in requiring women to devote a lot of time to their careers at precisely the point when traditionally women would spend most time with their families. However, supports such as maternity leave and child care are improving, and making it easier for younger women to 'do it all.'
Could you briefly describe Head Start and summarize your main conclusions about its effects?

Head Start is a preschool program for disadvantaged three- to five-year-old children. In a series of studies comparing Head Start children to siblings who did not attend, I find evidence of lasting effects of Head Start in terms of schooling attainment and reductions in criminal activity. These findings are important because, while everyone would like to believe that investments in children pay off, there was little prior evidence of longer-term effects of Head Start.
You asked in a recent paper 'Are public housing projects good for kids?' What's the answer?

The title of this paper is intentionally provocative. Given all the negative publicity about some housing projects, most people assumethat public housing must be bad for children. However, the key question is not whether kids in public housing do worse than other kids (they do) but whether they do better than they would have done in the absence of the program. For example, without the program, some children might have become homeless or had to move many times. It turns out that on average, public housing programs do improve the housing available to poor families, and have some positive effects in terms of schooling attainments.
In another paper, published in 1998, you summarized what we know and need to know about the effects of welfare programs on children. What, in a nutshell, do we know? What do we still need to know?

And how would we set about finding the needed answers? One striking conclusion from this review of welfare programs is that the available evidence suggests that in-kind programs are more effective than traditional welfare programs, which give cash to parents. This might account for the growing proportion of aid to poor families that is given in the form of specific in-kind benefits (e.g. Head Start, medical insurance, housing assistance). However, the evidence is far from complete. We need a lot more information about effects of programs on children, since anti-poverty programs are often justified in terms of their possible beneficial effects on children. We also need more information about longer-term effects of programs. For example, does it matter at what age the benefits are received?
In terms of how to find out what we need to know, I am a big supporter of social experiments, since a real random-assignment, treatment-control design provides more convincing answers than most statistical studies. On the other hand, it isn't possible to mount an experiment for every question. Much more could be done with existing data if more of it was made available to researchers, and if governments were more willing to allow linkages of different data sources.
What are some of the social experiments that have provided convincing answers? And how could economists do better work if governments were more willing to allow linkages of different data sources?

The great thing about a well-designed experiment is that anyone can understand the results. For example, in a drug trial, we randomly assign people to a treatment group that gets the drug, and a control group that gets a placebo. Because of the random assignment, the two groups are the same on average, so that any ex poste differences in how they do can be attributed to the treatment. Social experiments like the 'Moving to Opportunity' project also rely on random assignment. In this experiment, the treatment consisted of giving a voucher to families in public housing projects that allowed the families to move into a low-poverty neighborhood and gave them some assistance in relocating. The initial results indicate that the experiment had an effect on youth crime, as well as on criminal victimization. So far, there are no positive effects on schooling attainment or parental employment, but it is possible that these will emerge in the followup that is currently being conducted.
Another interesting experiment involves Early Head Start, a program that extends Head Start benefits to infants and toddlers. This program has demonstrated short-term effects of the program on cognitive test scores. Again, it will be necessary to do some long-term followup in order to see whether these benefits are retained.
The downside of experiments is that they are very expensive, relative to a statistical study, and cannot be used to answer questions other than those they were designed to address. Some of my work on the effects of Medicaid expansions provides an example of what can be done by linking various types of data. The U.S. government collects a good deal of survey data about health insurance and health care utilization. Because my co-author, Jon Gruber, was working in Washington, we were able to get state identifiers so that we could link information about Medicaid income cutoffs in each state to the individual-level records. This enabled us