The fourth part of the graduate course on inequalities and povertywill be based on the slides below
The third part of the graduate course on inequalities and povertywill be based on the slides below
The second part of the graduate course on inequalities and poverty will be based on the slides below
Here are some reading for the course on inequalities,
- Zheng B. (2000) Poverty orderings. Journal of Economic Surveys 14(4), 427-466.
- Pollak R.A. and T.J. Wales (1979) Welfare comparisons and equivalence scales. American Economic Review 69, 216-221.
- Kakwani N., A. Wagstaff and E. van Doorslaer (1997) Socioeconomic inequalities in health: Measurement, computation and statistical inference. Journal of Econometrics 77, 87-103.
- Cutler and Katz (1992) Rising inequality. American Econmic Review 82 (Proc), 546-551.
For the fourth cours on Inequalities, we will get back on the quantile regression, and discuss welfare functions as well as poverty indices. Slides are now online
To illustrate, we will use the following datasets
uk88 <- read.csv("http://www.vcharite.univ-mrs.fr/pp/lubrano/cours/fes88.csv",sep=";",header=FALSE)$V1 uk92 <- read.csv("http://www.vcharite.univ-mrs.fr/pp/lubrano/cours/fes92.csv",sep=";",header=FALSE)$V1 uk96 <- read.csv("http://www.vcharite.univ-mrs.fr/pp/lubrano/cours/fes96.csv",sep=";",header=FALSE)$V1 cpi <- c(421.7, 546.4, 602.4) y88 <- uk88/cpi y92 <- uk92/cpi y96 <- uk96/cpi
and for the part on applications of quantile regression
salary <- read.table("http://data.princeton.edu/wws509/datasets/salary.dat",header=TRUE)
Last week, in our Inequality course, we’ve been looking at data. We started with some simulated data, only a few of them
> library("ineq") > load(url("http://freakonometrics.free.fr/income_5.RData")) > (income=sort(income))  19233 23707 53297 61667 218662
How could we say that there is inequality in this sample? If we look at the wealth owned by the poorest, the poorest person (1 out of 5) owns 5% of the wealth; the bottom two (2 out of 5) own 11%, etc
> income/sum(income)  0.05107471 > sum(income[1:2])/sum(income)  0.1140305 > sum(income[1:3])/sum(income)  0.2555648 > sum(income[1:4])/sum(income)  0.4193262
If we plot those values, we get Lorenz curve
As mentioned last week, tomorrow, we will work with datasets, and start the part on the econometrics of inequality. I have uploded some slides, but additional concepts and tools will be mentioned on the black board (or on the computer, while coding). We will use some simulated data (mentioned in the slides), as well as us_income, on binned data.
The afternoon, we will have the first course on Inequalities. Slides are available from here.