Michael Kevane

Le Congrès pour la démocratie et le progrès (CDP) a réuni ses militants pour sa première session du Conseil national samedi à Ouagadougou alors que la classe politique burkinabè est profondément divisée sur des questions telles que le référendum et la mise en place du Sénat. Le parti au pouvoir a lancé un appel au président Blaise Compaoré pour la tenue d’un référendum sur l’article 37 de la Constitution qui l’empêche lui-même de briguer un autre mandat.

Les responsables du bureau exécutif national du parti au pouvoir, le CDP, ont officiellement demandé au président Blaise Compaoré de mettre en place le Sénat et de convoquer le corps électoral pour la tenue d’un référendum sur l’article 37 de la Constitution qui limite à deux le nombre de mandats présidentiels au Burkina Faso.

« Nous lançons un vibrant appel au président du Faso, à user de ses prérogatives constitutionnelles pour convoquer le corps électoral pour un scrutin référendaire relatif à la révision de l’article 37 », a déclaré Jocelyne Vokouma, secrétaire exécutive nationale adjointe chargée de la communication du Congrès pour la démocratie et le progrès.

via Burkina Faso: le CDP veut un référendum pour modifier la Constitution – afrique – RFI.

A couple days ago on the blog I remarked on the issue of authenticity in my short review of Tarquin Hall’s Indian detective story. I wish I could say I inspired Tyler Cowen of Marginal Revolution to then do the work of finding an academic study… but I think it was just coincidence that he posted a link to this study.

We present two studies that together test a fundamental yet rarely examined assumption underlying the contemporary appeal of authenticity—namely, that consumers assign higher value ratings to organizations regarded as authentic. Study 1 conducts content analysis of unsolicited online restaurant reviews entered voluntarily by consumers in three major U.S. metropolitan areas from October 2004 to October 2011; the data contain information from 1,271,796 reviews written by 252,359 unique reviewers of 18,869 restaurants. The findings show that consumers assign higher ratings to restaurants regarded as authentic, even after controlling for restaurant quality in several ways. In addition, we find that consumers perceive independent, family-owned, and specialist single-category restaurants as more authentic than they do chain, non-family-owned, and generalist multiple-category restaurants. Study 2 reinforces these findings using an experimental design in which participants were presented with photos and minimal descriptions of fictitious restaurants and then asked to evaluate the likely authenticity, quality, and overall value of the restaurants in a predetermined sequence. Central to both studies is an authenticity scale that was developed through the use of an online survey that ascertains the specific language used by individuals in referencing authenticity in the restaurant domain. Taken together, these studies demonstrate that authenticity generates higher consumer value ratings of organizations; the studies also identify certain types of organizations that are more likely to receive authenticity attributions by consumers.

via Organization Science: INFORMS.

Hope it comes to Netflix soon… looks interesting.

Le 21 avril 1944, les femmes françaises accèdent au droit de vote. Elles ne pourront cependant l’exercer pleinement qu’à partir du 29 avril 1945, date des élections municipales, premier scrutin d’après-guerre.

Il y a seulement 70 ans, en vertu de l’ordonnance d’Alger du gouvernement provisoire du général de Gaulle, les femmes obtiennent le droit de voter et d’être élues. Dans les images d’archives en noir et blanc, la voix est nasillarde et le commentateur des actualités filmées présente fièrement la « bonne religieuse » et une « gentille maman » glissant leur bulletin dans l’urne.

Leur toute nouvelle carte électorale en poche, les Françaises votent pour la première fois le 29 avril 1945 lors du premier tour des élections municipales, premier scrutin depuis la Libération. Ce 29 avril, les Françaises se sont déplacées en masse. On enregistre dans plusieurs villes de France des taux de participation féminine identiques à ceux des hommes.

via France: il y a 70 ans, les femmes obtenaient le droit de vote – france – RFI.

##################################################################################

# Code for using World Development Indicators to understand missing women problem
# http://data.worldbank.org/data-catalog/world-development-indicators

# You have to set your directory below with setwd()

##################################################################################

# Preliminaries
# This clears any data in memory etc
rm(list=ls())

# This set of packages can be used to conduct the analysis
library(gmodels)
library(psych)
library(gdata)
library(pastecs)
library(doBy)
library(WDI)
library(ggplot2)
library(countrycode)
library(memisc)

# Set working directory
setwd()

#check the directory with
getwd()

# Create a list of variables to import
wdilist <- c(“NY.GDP.PCAP.PP.KD”, # GDP per capita, PPP (constant 2005 intl $)
“NV.AGR.TOTL.ZS”, # Agriculture, value added (% of GDP)
“NV.IND.TOTL.ZS”, # Industry, value added (% of GDP))
“SP.POP.GROW”, # Population growth (annual %)
“SP.POP.TOTL”, # Population, total
“SP.POP.TOTL.FE.ZS”, # Population, female (% of total)
“SP.URB.TOTL.IN.ZS”, # Urban population (% of total)
“SP.POP.BRTH.MF”, # Sex ratio at birth (females per 1,000 males)
“SP.DYN.LE00.IN”, # Life expectancy at birth, total (years)
“SP.DYN.LE00.FE.IN”, # Life expectancy at birth, female (years)
“SP.DYN.LE00.MA.IN”, # Life expectancy at birth, male (years),
“SP.DYN.SMAM.MA”, # Age at first marriage, male
“SP.DYN.SMAM.FE”, # Age at first marriage, female
“SP.DYN.IMRT.IN”, # Infant mortality rate
“SP.DYN.TFRT.IN” )# Fertility rate, total (births per woman)

# Extract latest version of desired variables from WDI.
# This takes a long time (5 minutes)
wdim <- WDI(country=”all”, indicator = wdilist, extra = TRUE, start = 1960, end = as.numeric(substr(Sys.Date(),1,4)))

# Rename the variables
wdim <- rename.vars(wdim, c(“NY.GDP.PCAP.PP.KD”,”SP.POP.TOTL”), c(“GDPpcUSDreal”,”population”))
wdim <- rename.vars(wdim, c(“SP.POP.TOTL.FE.ZS”,”SP.URB.TOTL.IN.ZS”), c(“femaleperc”,”urbanperc”))
wdim <- rename.vars(wdim, c(“SP.POP.BRTH.MF”,”SP.DYN.LE00.IN”), c(“sexratiobirth”,”lifeexp”))
wdim <- rename.vars(wdim, c(“SP.POP.GROW”), c(“popgrow”))
wdim <- rename.vars(wdim, c(“SP.DYN.LE00.FE.IN”,”SP.DYN.LE00.MA.IN”), c(“lifexpfem”,”lifeexpmale”))
wdim <- rename.vars(wdim, c(“SP.DYN.SMAM.MA”,”SP.DYN.SMAM.FE”), c(“smammale”,”smamfemale”))
wdim <- rename.vars(wdim, c(“NV.AGR.TOTL.ZS”,”NV.IND.TOTL.ZS”), c(“gdpagshare”,”gdpindshare”))
wdim <- rename.vars(wdim, c(“SP.DYN.IMRT.IN”,”SP.DYN.TFRT.IN”), c(“infmort”,”fertility”))

# make population be in units of millions; make GDPpc into log GDPpc
wdim$population = wdim$population/1000000
wdim$logGDPpc = log(wdim$GDPpcUSDreal)

# Check names
names(wdim)

# Tell R that dataframe wdi is the relevant one
attach(wdim)

# Take out the entries that are aggregates (eg East Asia) and not countries
wdim = subset(wdim, !( region==”Aggregates”) )
table(wdim$region)

# Table by region of % female, for two different years
summaryBy(femaleperc  ~ region,  data=subset(wdim, year==1985 ), FUN=c(mean, min, max),na.rm=TRUE)
summaryBy(femaleperc  ~ region,  data=subset(wdim, year==2010 ), FUN=c(mean, min, max),na.rm=TRUE)

# Assume normal percent female = .504
# Calculate missing women in each country = normal women – actual women
# = (.504/(1-.504))*(actual men) – femaleperc*population
wdim$fracti=(.504)/(1-.504)
wdim$actwomen=(.01)*wdim$femaleperc*wdim$population
wdim$missingw = (wdim$fracti)*((.01)*(100-wdim$femaleperc)*wdim$population) -wdim$actwomen

# Add up the missing women across all countries, by the region, by year
summaryBy(missingw[year==1960]  ~ region,  data=subset(wdim, year==1960 ), FUN=c(sum),na.rm=TRUE)
summaryBy(missingw[year==1985]  ~ region,  data=subset(wdim, year==1985 ), FUN=c(sum),na.rm=TRUE)
summaryBy(missingw[year==2010]  ~ region,  data=subset(wdim, year==2010 ), FUN=c(sum),na.rm=TRUE)

# Make a plot of GDP per capita and % of population that is female
with(subset(wdim, year==2010 ), plot(femaleperc ~ GDPpcUSDreal,  xlab=”GDP per capita”, ylab=”% female”,
ylim= c(46, 55), xlim=c(100,50000), col= “blue”,
main=”GDP per capita and % of population that is female”) )
with(subset(wdim, year==2010 ),abline(lm(femaleperc ~ GDPpcUSDreal ), col= “red”))

# Run four regressions
fit1 <-with(subset(wdim, year==2000 ), lm(femaleperc ~ logGDPpc + population))
fit2 <-with(subset(wdim, year==2000 ), lm(femaleperc ~ population+ infmort))
fit3 <-with(subset(wdim, year==2000 ), lm(femaleperc ~ population+ infmort+fertility))
fit4 <-with(subset(wdim, year==2000 ), lm(femaleperc ~ logGDPpc +fertility+region))

# Use mtable to display results
# This produces output in tab-delimited format:
# Notepad window opens up; The contents of this file can be pasted into Word or Excel
fitx <- mtable(“Model 1″=fit1,”Model 2″=fit2, “Model 3″=fit3, “Model 4″=fit4,
summary.stats=c(“R-squared”,”N”))
fitx <- relabel(fitx,
“(Intercept)” = “Constant”,
logGDPpc = “real log GDP per capita”,
population = “Total population”,
infmort = “Infant mortality rate”,
fertility = “Fertility rate”)
file123 <- “mtable123.txt”
write.mtable(fitx,file=file123)
file.show(file123)

detach(wdim)

# Thanks to Bill Sundstrom for the mtable formatting code

# input data into R
data <- read.table(“tenureinsecurity.csv”, header=TRUE, sep=”,”)

# Tell R to assume survey1 is dataset from now until detach
attach(data)

# Run two regressions
fit1 <- lm(yield ~  female+age+fieldsize1)
fit2 <- lm(yield ~  female+age+fieldsize1+tenure)

# Put regression results into a table and label appropriately
fitx <- mtable(“Model 1″=fit1,”Model 2″=fit2,summary.stats=c(“sigma”,”R-squared”,”F”,”p”,”N”) )
(fitx <- relabel(fitx,
“(Intercept)” = “Constant”,
tenure = “Tenure insecurity”,
fieldsize1 = “Field size – hect.”,
female = “Is female?”,
age = “Age of farmer”
))

# Produces output in tab-delimited format
# Notepad window opens up in Windows, in Mac an R window opens; The contents can be pasted into Word or Excel
file123 <- “mtable123.txt”
write.mtable(fitx,file=file123)
file.show(file123)

# input data into R
readincamp = read.csv(“competitivereadingcamp.csv”)

# Tell R to assume readincamp is dataset from now until detach
attach(readincamp )

# Summarize data with summarySE command
# Source; the excellent http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/
# To do this have to install.packages(“bear”)
# summarySE gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
# rc2 will be a new data frame.
# measurevar: the name of a column that contains the variable to be summariezed
# groupvars: a vector containing names of columns that contain grouping variables
# na.rm: a boolean option that indicates whether to ignore NA’s – missing values
# conf.interval: the percent range of the confidence interval (default is 95%)
install.packages(“bear”)
library(bear)
rc2 <- summarySE(readincamp, measurevar=”score1″, groupvars=c(“treatment”, “female”))

# In the new data frame rc2, make treatment and female into factor rather than numeric variables
rc$treatment2 <- factor(rc$treatment)
rc2$female2 <- factor(rc$female)

# Now use ggplot to make the bar plot
# need install.packages(“ggplot2”) and then library(“ggplot2″)
# Error bars represent standard error (se) or confidence interval (ci) of the mean
ggplot(rc2, aes(x=treatment2, y=score1, fill=female2)) +
geom_bar(position=position_dodge(), stat=”identity”)  + # Thinner lines
geom_errorbar(aes(ymin=score1-ci, ymax=score1+ci),
size=.3,    # Thinner lines
width=.2,
position=position_dodge(.9)) +
xlab(“Treatment”) +
ylab(“Score 1″) +
scale_fill_hue(name=”Gender”, # Legend label
breaks=c(“0”, “1”),
labels=c(“Male”, “Female”)) +
ggtitle(“The Effect of Treatment on Test Score1 (with confidence intervals)”) +
scale_y_continuous(breaks=0:20*2) + #control ticks on y axis
theme_bw() #make  background white

# make a boxplot of the means
# Here is some great help http://www.r-bloggers.com/box-plot-with-r-tutorial/
boxplot(score1  ~ female*competitive, main=”Scores on reading test”,
xlab=””, ylab=”Score 1″,
col=(c(“white”,”gray”)), las = 2,
at =c(1,2,4,5),par(mar = c(8, 5, 4, 2)+ 0.1),
names = c(“men, noncomp”,”men, comp”,”women, noncom”,”women, comp”))

# These are some of the different ways to do the calculation that social scientists probably
# do most frequently; Calculate means for different groups or conditions
# input data into R (the data is on some reading camps run in Ghana)
readincamp = read.csv(“competitivereadingcamp.csv”)

# Tell R to assume readincamp is dataset from now until detach
attach(readincamp )

# Find the means of score1 initial scores, by gender and type of reading camp
summaryBy(score1  ~ female+competitive, data=readincamp , FUN=c(mean),na.rm=TRUE)
summaryBy(score1  ~ female+africanbooks, data=readincamp , FUN=c(mean),na.rm=TRUE)
# Another way to do it
print(tapply(X=score1, INDEX=list(africanbooks, female), FUN=mean , na.rm = TRUE))
print(tapply(X=score1, INDEX=list(competitive, female), FUN=mean , na.rm = TRUE))

# Another way: Summarize data with summarySE command

# Source: the excellent http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
# To do this have to install.packages(“bear”)
# summarySE gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
# rc2 will be a new data frame.
# measurevar: the name of a column that contains the variable to be summariezed
# groupvars: a vector containing names of columns that contain grouping variables
# na.rm: a boolean option that indicates whether to ignore NA’s – missing values
# conf.interval: the percent range of the confidence interval (default is 95%)
install.packages(“bear”)
library(bear)
rc2

# I find it helpful to have all the packages I use in a single R file rather than
# having them in each script
# I am sure that will change as i start using ever more packages
# Install files to be run at beginning when install R on your laptop

# To find R version
sessionInfo()
# You should have R version 3.0.3; if not download latest version

install.packages(“sandwich”)
install.packages(“Hmisc”)
install.packages(“gmodels”)
install.packages(“psych”)
install.packages(“gdata”)
install.packages(“pastecs”)
install.packages(“doBy”)
install.packages(‘WDI’)
install.packages(‘ggplot2’)
install.packages(‘countrycode’)
install.packages(‘RCurl’)
install.packages(‘dplyr’)
install.packages(“plyr”)
install.packages(‘memisc’)
install.packages(“bear”)

library(“bear”)
library(“sandwich”)
library(“Hmisc”)
library(“gmodels”)
library(“psych”)
library(“gdata”)
library(“pastecs”)
library(“doBy”)
library(‘WDI’)
library(‘ggplot2’)
library(‘countrycode’)
library(‘RCurl’)
library(‘dplyr’)
library(‘memisc’)

(from Guide to R For SCU Economics Students Version 2.2, March 28, 2014 © William A. Sundstrom, 2012, 2013, 2014 licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license: http://creativecommons.org/licenses/by-nc-sa/3.0/)

To get started, you need to download and install two different free software packages: R, and RStudio. You will actually be interacting with RStudio, even though R does all the number crunching. Links to the web sites for downloads are provided here and on the ECON 41/42 course page on Canvas (Camino).

R is maintained by CRAN, the “Comprehensive R Archive Network.” CRAN is a collection of sites that carry identical material, including downloads of R, extensions, documentation, and other R related files. R is available for many platforms, and below are download instructions for Mac and Windows.

1. To download R, visit the website www.R-project.org (you can link from Canvas). There is a space on the website titled “Getting Started”. In it, click on the link that says “download R”.
2. Next, you will be asked to choose a CRAN “Mirror” from a long list. A mirror is a server that maintains everything R related. It does not matter which one you pick.
3. After arriving at the download page, click on the link corresponding to the operating system that you will be using to run R—for most of you, that’s Mac OS X or Windows.
4. Follow the instructions on the next screen:

MAC users:

Basically, if you are running Mac OS X release 10.6 or higher, click on the link to the latest version of R. If you are running an older version of Mac OS, read the instructions and download the correct version of R.

After the install package has been downloaded, find it (probably in your Downloads folder) and double click. Let Installer walk you through installation. No customization is necessary. After installation is done, R is now on your system.

Windows users:

Click the link that says “install R for the first time”. On the next screen, click the link to download R for Windows.

After downloading the install package, which probably can be found in your Downloads folder, double click it and follow the installer’s instructions. Typically nothing needs to be done other than clicking either ‘next’ or ‘I Agree’ every time, accepting the default settings.

1. Mac and Windows users: Now that you have installed R, you could access and run it by clicking on the icon that should now be on your desktop or among your programs. But as mentioned above, we will be interacting with R through the RStudio interface, so let’s install that now.

2. Install RStudio

Now that you have R installed on your machine, let’s download and install RStudio, which provides a more user-friendly interface and script editor for working with R. Downloading is similar for Mac and Windows.

1. Visit www.Rstudio.com
2. Click the “Download now” button at the bottom left.
3. On the next page, click on the “Download RStudio Desktop” button.
4. On the next screen, you should find your correct system under the label that says ‘Recommended For Your System’. Click that link. If it does not recommend your correct system, find it in the list and click the link.
5. After the download, go into your Downloads folder and double click on the downloaded package to install.
6. If you have an older Mac (running OS X 10.5), please see the professor: You may need to install a different version of RStudio.

3. Create a working directory (folder)

You will need to have all your scripts and data in a single working directory (folder). You should create this new folder in whatever folder you use for course work. Once you create the working directory, you will need to know its full “path” name. For example, on my Mac it might be: /Users/mkevane/Documents/Courses/econ_135

You can find the full path using Finder on a Mac or Explorer in Windows.

Another high school student who survived reported that a crew member named Park Ji-young, 22, had helped teenagers to get life jackets and escape by urging them to jump into the frigid waters of the Yellow Sea where rescue boats were waiting. She stayed behind without a life jacket for herself despite the youngsters’ entreaties to jump with them. “After saving you, I will get out,” she said. “The crew goes out last.”

She was later found dead, floating in the sea.

via In Sad Twist on Proud Tradition, Captains Let Others Go Down With Ship – NYTimes.com.

So… I’m like… I want to adopt Mishkin’s new macroeconomics textbook for my MBA macro class this summer and in the fall… Why? Because I reviewed a chapter of the new edition for Pearson and I liked the approach.  Sure there were some things that annoyed me, but in macro it is all about the lesser annoyance, and Mankiw is a major annoyance.  But then a colleague and I were talking, and he reminded me about Inside Job… see below… oh does Mishkin look bad….

So I dunno… I tend to think that there is an ethical problem here. Perhaps I am unaware of the facts. But Mishkin’s rebuttal to set the record straight (and maybe there are others) seems unconvincing.  It seems pretty obvious he did a fistpump… at the prospect of receiving $124,000 for a puff-piece article (though I wonder about the conflicts of interest at being paid such a large sum while also being an employee…where exactly? Columbia University?)… and then of course will have to live it down for a long time… probably he hoped nobody would ever even know… and perhaps he wrote and took money for dozens of similar articles… So why should I contribute indirectly to his wealth?  Should I ask him to contribute any royalties to Friends of African Village Libraries? (Where I am a slave labor volunteer for the past 13 years…)

So I think I have decided, dear reader, to adopt the text provisionally, and read it carefully, and see what my MBA students think of the ethical issues.  So stay tuned later this summer when I come back to the issue.

From our Faculty Senate president:

Ballot Measure #1 asked faculty members to approve or disapprove of the following statement:

“The Trustees’ declaration that something as all-encompassing as identity and mission is outside of the shared governance system would break the rules, the culture and the meaning of shared governance at SCU. The issue of shared governance is independent of perspectives on abortion policy. If implemented, the Trustees’ declaration would be incompatible with a meaningful vision of shared governance going forward.”

Ballot Measure #2 asked faculty whether the Faculty Senate should ask for enhanced representation by faculty and staff on the Board of Trustees. The ballot measure stated:
“The Faculty Senate shall ask the Trustees to add some faculty (in addition to the President and the Rector of the Jesuit Community) and staff voices to the Board of Trustees, as was the case 25 years ago in the case of faculty.”

Measure #1 passed 70% to 30%, Measure #2 passed 80%-20%.

On Tuesday, India’s Supreme Court ruled that the government had to recognize a third gender, one neither male nor female, as an official category. According to the Associated Press, this means access to social welfare services and minority rights protections for “people who have acquired the physical characteristics of the opposite sex or present themselves in a way that does not correspond with their sex at birth.”

via There is a third gender in India, and the law finally recognized them – Vox.

So if they succeed Compaoré would have been in power from 1983 (when he co-led the coup with Sankara that brought the two of them into power) until 2022 (if he takes a six year term from 2016-2022. Let’s see… that would be 39 years…pretty much the entire lifespan of 75% of all Burkinabè who will be alive then.

I would much rather see a contest between Roch Marc Christian Kaboré and Zépherin Diabré… they share similar technocratic tendencies… have been part of the regime and certainly are not opponents of basic crony capitalism policies…. they might be far less involved in foreign affairs…. but most important they would likely consolidate mechanisms for peaceful and regular transfers of power.  Exactly the thing that a Compaoré re-election will not get the country.

« Quand il y a un litige politique dans un pays, que certains tirent à gauche, d’autres tirent à droite… Quand il y en a qui veulent appliquer la Constitution et d’autres qui sont rébellion avec la Constitution, qu’est-ce que l’on fait ? Si on laisse les gens se tirailler, on va vers le coup d’Etat. Alors ? Alors, allons vers le peuple. C’est le peuple qui va trancher », a lancé Hermann Yaméogo, président de l’Union nationale pour la démocratie et le développement. Assimi Kouanda, secrétaire exécutif du Congrès pour la démocratie et le progrès, le parti au pouvoir, également ministre d’Etat chargé de mission à la présidence, a réaffirmé la volonté du président Blaise Compaoré d’organiser un référendum pour le « déverrouillage » de l’article 37 de la Constitution. « Le président du Faso pense qu’il n’y a pas d’autre solution », a-t-il affirmé. « C’est un démocrate, un républicain, a-t-il insisté. Son intention est de vous consulter à travers un référendum. Et vous allez vous exprimer à travers de ce référendum. »

via Burkina Faso: les partisans de Compaoré mobilisent – RFI.