Download Tables with comparative results for linear and logistic fixed effect

Electronic Supplementary Material
Tables with comparative results for linear and logistic fixed effect models
In the following tables (Tables S1 and S2) we provide a comparative analysis of the logistic models (used in this
study) and the linear model used by similar studies (as discussed in the ‘Method’ section of the paper).
As noted in the paper the logistic fixed effects model takes the form:
In the linear model the response
is replaced by
the square root of the population.
1
. Note the linear model is weighted by
Table S1 Result of the Aggregate State Level Data Analysis
Unemployment rate lag 0
Linear
Logistic
Unemployment rate lag 1
Linear
Logistic
Unemployment rate lag 2
Linear
Unemployment rate lag 3
Unemployment rate lag 4
Logistic
Linear
Logistic
Linear
Logistic
Males All ages
State
0.480
0.669**
0.630*
0.719**
0.518
0.679**
0.099
0.305
-0.191
-0.116
Unemployment
(0.266)
(0.239)
(0.270)
(0.239)
(0.270)
(0.236)
(0.267)
(0.240)
(0.255)
(0.234)
State per capita
0.047**
0.037**
0.048**
0.037**
0.045**
0.034**
0.042**
0.027**
0.041**
0.024*
GDP ($0,000)
(0.009)
(0.011)
(0.009)
(0.010)
(0.009)
(0.010)
(0.009)
(0.010)
(0.009)
(0.010)
R-square
0.984
0.979
0.985
0.979
0.984
0.979
0.984
0.978
0.984
0.977
Durbin Watson
2.177
2.087
2.183
2.082
2.192
2.135
2.168
2.086
2.168
2.082
Females All ages
State
0.547
0.617
0.797*
0.723*
0.511
0.544
-0.064
-0.005
-0.142
-0.248
Unemployment
(0.372)
(0.326)
(0.360)
(0.312)
(0.364)
(0.316)
(0.362)
(0.321)
(0.354)
(0.311)
State per capita
0.041**
0.032**
0.042**
0.032**
0.037**
0.027*
0.034**
0.021*
0.035**
0.021*
GDP ($0,000)
(0.011)
(0.012)
(0.010)
(0.011)
(0.010)
(0.011)
(0.010)
(0.011)
(0.010)
(0.010)
R-square
0.982
0.974
0.982
0.975
0.982
0.974
0.982
0.974
0.982
0.974
Durbin Watson
2.146
2.182
2.161
2.198
2.178
2.216
2.167
2.203
2.163
2.205
Combined Sexes All ages
State
0.505
0.662**
0.752**
0.759**
0.519
0.651**
-0.014
0.196
-0.291
-0.222
Unemployment
(0.279)
(0.253)
(0.273)
(0.246)
(0.272)
(0.244)
(0.269)
(0.249)
(0.256)
(0.240)
State per capita
0.042**
0.034**
0.044**
0.034**
0.040**
0.030**
0.036**
0.023*
0.036**
0.020*
GDP ($0,000)
(0.009)
(0.010)
(0.008)
(0.010)
(0.008)
(0.009)
(0.008)
(0.009)
(0.008)
(0.009)
R-square
0.987
0.981
0.988
0.981
0.987
0.981
0.987
0.980
0.987
0.980
Durbin Watson
2.139
2.078
2.156
2.089
2.173
2.134
2.150
2.095
2.150
2.104
These are the results of models where each lag of unemployment was fitted separately. The standard errors are provided in parentheses. Statistical significance is marked with
stars.* - significant at 5% level of significance and **- significant at 1% level of significance.
2
Table S2 Result of the Aggregate State Level data with combined Unemployment Rate effects.
15-34
Linear
Logistic
35-44
Linear
Logistic
45-54
Linear
Logistic
55-64
Linear
65 and above
All Ages
Logistic
Linear
Logistic
Linear
Logistic
Males
Combined State
-0.834
-0.040
1.668
1.743
-0.279
0.385
-0.711
-0.437
0.392
0.213
0.705*
0.918**
Unemployment
(0.798)
(0.731)
(1.204)
(0.977)
(0.911)
(0.688)
(0.432)
(0.347)
(0.471)
(0.491)
(0.319)
(0.276)
State per capita
0.136**
0.213**
0.077**
0.077**
0.033
0.029
-0.008
-0.009
0.001
-0.021
0.049**
0.040**
GDP ($0,000)
(0.041)
(0.050)
(0.028)
(0.030)
(0.023)
(0.023)
(0.016)
(0.017)
(0.009)
(0.011)
(0.009)
(0.011)
R-square
0.914
0.930
0.734
0.824
0.910
0.946
0.983
0.990
0.961
0.972
0.985
0.979
Durbin Watson
1.974
2.079
2.112
2.190
1.982
1.982
2.222
2.276
2.325
2.291
2.187
2.114
Females
Combined State
-0.637
0.352
0.368
0.609
0.468
0.696
-0.727
-1.082
0.012
-0.457
0.791
0.796*
Unemployment
(1.326)
(1.166)
(2.032)
(1.691)
(1.563)
(1.188)
(1.151)
(0.916)
(0.315)
(0.386)
(0.440)
(0.375)
State per capita
0.232**
0.270**
0.055
0.050
-0.003
0.001
-0.039
-0.030
0.010
-0.005
0.042**
0.034**
GDP ($0,000)
(0.059)
(0.065)
(0.033)
(0.035)
(0.028)
(0.028)
(0.022)
(0.021)
(0.009)
(0.011)
(0.011)
(0.012)
R-square
0.778
0.829
0.634
0.724
0.849
0.905
0.951
0.971
0.887
0.901
0.982
0.975
Durbin Watson
2.061
2.134
2.150
2.126
2.263
2.313
2.186
2.104
2.266
2.215
2.160
2.194
Combined Sexes
Combined State
-0.926
0.104
2.171
2.079
0.142
0.652
-1.037
-0.730
0.381
-0.201
0.698*
0.871**
Unemployment
(0.830)
(0.751)
(1.232)
(1.021)
(0.781)
(0.626)
(0.481)
(0.382)
(0.456)
(0.525)
(0.325)
(0.287)
State per capita
0.165**
0.240**
0.072**
0.078**
0.019
0.020
-0.021
-0.017
0.007
-0.011
0.044**
0.036**
GDP ($0,000)
(0.039)
(0.046)
(0.022)
(0.025)
(0.016)
(0.018)
(0.014)
(0.014)
(0.008)
(0.010)
(0.009)
(0.010)
R-square
0.914
0.930
0.791
0.858
0.950
0.967
0.985
0.991
0.947
0.957
0.988
0.981
Durbin Watson
1.945
2.063
2.078
2.075
2.034
2.024
2.089
2.046
2.280
2.226
2.158
2.098
The models were fitted with unemployment rates at lags 0, 1 and 2 taken together. The coefficients are the sum of the coefficients of the three lags. The standard errors are
provided in parentheses. Statistical significance is marked with stars.* - significant at 5% level of significance and **- significant at 1% level of significance.
3
Age-Specific Analysis
The results of the age level analysis are provided in Table S3, where, for brevity, only the results for the
unemployment rates are presented. In the analysis, for changes in the current level of unemployment rate,
mortality is found to be significantly counter-cyclical in nature for males aged 35-44 and combined sexes in the
same age group. Furthermore, using the previous year’s unemployment rate (lag 1); the analysis shows that
mortality is pro-cyclical for males and combined sexes aged 55-64. (Note that only four (out of 45) results were
significant, hence we have to be careful about drawing any conclusions from this analysis.)
Table S3 Result of the Aggregate State Level data (Age-Specific)
15-34
35-44
45-54
55-64
65 and above
Males
Unemployment
0.019
1.892*
0.317
-0.211
0.225
Rate, Lag 0
(0.651)
(0.833)
(0.595)
(0.287)
(0.293)
Unemployment
0.481
0.876
0.103
-0.795**
-0.061
Rate, Lag 1
(0.608)
(0.809)
(0.573)
(0.277)
(0.289)
Unemployment
-0.196
0.564
0.253
0.027
0.082
Rate, Lag 2
(0.576)
(0.809)
(0.570)
(0.274)
(0.283)
Females
Unemployment
0.268
0.049
0.712
-0.230
-0.383
Rate, Lag 0
(1.021)
(1.236)
(0.934)
(0.613)
(0.247)
Unemployment
0.296
0.331
-0.086
-0.705
-0.009
Rate, Lag 1
(0.980)
(1.258)
(0.912)
(0.599)
(0.240)
Unemployment
0.250
0.606
0.540
-0.504
-0.198
Rate, Lag 2
(0.957)
(1.257)
(0.897)
(0.594)
(0.233)
Combined Sexes
Unemployment
0.129
2.175*
0.660
-0.348
-0.071
Rate, Lag 0
(0.675)
(0.851)
(0.545)
(0.310)
(0.314)
Unemployment
0.307
1.097
0.527
-0.807**
-0.102
Rate, Lag 1
(0.635)
(0.847)
(0.528)
(0.300)
(0.303)
Unemployment
0.042
0.742
0.299
-0.282
-0.106
Rate, Lag 2
(0.606)
(0.849)
(0.527)
(0.294)
(0.293)
These are the results of models where each lag of unemployment was fitted separately. Only the coefficients of
the unemployment rates are provided here (all values are in percentage points). The standard errors are provided
in parenthesis. Statistical significance is marked with stars.* - significant at 5% level of significance and **significant at 1% level of significance.
In the models with all the lags taken together the study does not find any significant cyclical mortality for the
age level analysis. The results are provided in Table S4.
4
Table S4 Result of the Aggregate State Level data with combined Unemployment Rate effects (Age-Specific)
15-34
35-44
45-54
55-64
65 and above
Males
Combined State
-0.040
1.743
0.385
-0.437
0.213
Unemployment
(0.731)
(0.977)
(0.688)
(0.347)
(0.491)
State per capita
0.213**
0.077**
0.029
-0.009
-0.021
GDP ($0,000)
(0.050)
(0.030)
(0.023)
(0.017)
(0.011)
R-square
0.930
0.824
0.946
0.990
0.972
Durbin Watson
2.079
2.190
1.982
2.276
2.291
Females
Combined State
0.352
0.609
0.696
-1.082
-0.457
Unemployment
(1.166)
(1.691)
(1.188)
(0.916)
(0.386)
State per capita
0.270**
0.050
0.001
-0.030
-0.005
GDP ($0,000)
(0.065)
(0.035)
(0.028)
(0.021)
(0.011)
R-square
0.829
0.724
0.905
0.971
0.901
Durbin Watson
2.134
2.126
2.313
2.104
2.215
Combined Sexes
Combined State
0.104
2.079
0.652
-0.730
-0.201
Unemployment
(0.751)
(1.021)
(0.626)
(0.382)
(0.525)
State per capita
0.240**
0.078**
0.020
-0.017
-0.011
GDP ($0,000)
(0.046)
(0.025)
(0.018)
(0.014)
(0.010)
R-square
0.930
0.858
0.967
0.991
0.957
Durbin Watson
2.063
2.075
2.024
2.046
2.226
The models were fitted with unemployment rates at lags 0, 1 and 2 taken together. The coefficients are the sum
of the coefficients of the three lags. The standard errors are provided in parenthesis. Statistical significance is
marked with stars.* - significant at 5% level of significance and **- significant at 1% level of significance.
References (electronic supplement only)
Guest, R. S. (2005). A Life Cycle Analysis of Housing Affordability Options for First Home Owner-Occupiers
in Australia, Economic Record, 81, 237-248.
Ruhm, C. J. (2000). Are Recessions Good for Your Health?, Quarterly Journal of Economics, 115, 617-650.
Worthington, A. C. (2009). The Usage and Understanding of Australian Household Mortgages, International
Journal of Housing Markets and Analysis, 2(4), 347-362.
5
R code used in the paper [Code for paper was run on R version 2.15]
# initial stuff
options(contrasts=c(factor="contr.sum", ordered="contr.poly"))
#this sets the default contrasts in R to sum contrasts
library(MASS)
library(car) #need to install the package first
library(lmtest) #need to install the package first
library (stats)
# the following function creates the coefficients output in a
desired format (as presented in the papers). Obtained from Dave
Armstrong's website <http://www.quantoid.net/>
shuffle <- function(b,se,p, df.res, two.sided=T, digits, sep= ",",
apa=T){
nb <- names(b)
nse <- sapply(1:length(b), function(x)paste(rep("", each=x),
collapse=""))
stars <- sapply(1:length(p), function(x)paste(rep("*", x),
collapse=""))
stars <- c("", stars)
sig <- do.call(cbind, lapply(p,
function(x)(2^(two.sided)*pt(abs(b/se), df.res, lower.tail=F)) < x))
sig <- apply(sig, 1, sum)+1
b <- sprintf(paste("%.", digits, "f", sep=""), b)
se <- sprintf(paste("%.", digits, "f", sep=""), se)
if(apa){
newb <- gsub("^-0", "-", gsub("^0", "", b))
} else{
newb <- b
}
newb <- paste(newb, stars[sig], sep="")
if(apa){
newse <- gsub("^-0", "-", gsub("^0\\.", "\\.", se))
} else{
newse <- se
}
newse <- paste("(", newse, ")", sep="")
mat <- matrix(rbind(newb, newse), ncol=1)
rownames(mat) <- as.vector(rbind(nb, nse))
mat <- cbind(sep, mat)
colnames(mat) <- c("", "")
noquote(mat)
}
# Table 1 - descriptive statistics (getting the means and standard
deviations of the variables used in the study)
a<-c("./mdatay/Statewn/mnt6.csv", "./mdatay/Statewn/fnt6.csv" ,
"./mdatay/Statewn/bnt6.csv")
6
vmn<-matrix(nrow=8,ncol=3)
vsd<-matrix(nrow=8,ncol=3)
for( i in 1:3){
mean1<-vector(length=8)
sd1<-vector(length=8)
ausm1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
attach(ausm1)
mr<-(Deaths/Pop*100000)
GDP=GDPPP/1000
MED=MEDPP/100
mean1[1]<-mean(mr)
mean1[2]<-mean(U0)*100
mean1[3]<-mean(GDP)
mean1[4]<-mean(MED)
mean1[5]<-mean(ABP)*100
mean1[6]<-mean(EDUP)*100
mean1[7]<-mean(p5)*100
mean1[8]<-mean(p65)*100
sd1[1]<-sd(mr)
sd1[2]<-sd(U0)*100
sd1[3]<-sd(GDP)
sd1[4]<-sd(MED)
sd1[5]<-sd(ABP)*100
sd1[6]<-sd(EDUP)*100
sd1[7]<-sd(p5)*100
sd1[8]<-sd(p65)*100
vmn[,i]<-mean1
vsd[,i]<-sd1
detach(ausm1)
}
round(vmn,digits=2) # this gives the means used in the table
round(vsd,digits=2) # this gives the standard deviations used in the
table
# Table 2 - output of the regressions with separate model for each
unemployment rate
a<-c("./mdatay/Statewn/mnt6.csv", "./mdatay/Statewn/fnt6.csv" ,
"./mdatay/Statewn/bnt6.csv")
result1<- matrix(nrow=14,ncol=9)
rs1<- vector(length=9)
rs2<- vector(length=9)
for( i in 1:3){
j=(i*3)-2
k=j+1
l=j+2
ma1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
7
#the model used by other researchers takes the form(not fitted)
#mr<-(Deaths/Pop*100000)
#weight=sqrt(Pop)
#fixed1<lm(log(mr)~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed2<lm(log(mr)~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed3<lm(log(mr)~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
log1<glm(dr~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log2<glm(dr~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log3<glm(dr~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
d1<-shuffle(log1$coef,sqrt(diag(vcov(log1))),
c(0.1,.05,.01),log1$df.residual, two.sided=T, digits=3, apa=F)
d2<-shuffle(log2$coef,sqrt(diag(vcov(log2))),
c(0.1,.05,.01),log2$df.residual, two.sided=T, digits=3, apa=F)
d3<-shuffle(log3$coef,sqrt(diag(vcov(log3))),
c(0.1,.05,.01),log3$df.residual, two.sided=T, digits=3, apa=F)
result1[,j]<-d1[3:16,2]
result1[,k]<-d2[3:16,2]
result1[,l]<-d3[3:16,2]
rs1[j]<-1-(log1$deviance/log1$null.deviance)
rs2[j]<-durbinWatsonTest(log1)$dw
rs1[k]<-1-(log2$deviance/log2$null.deviance)
rs2[k]<-durbinWatsonTest(log2)$dw
rs1[l]<-1-(log3$deviance/log3$null.deviance)
rs2[l]<-durbinWatsonTest(log2)$dw
detach(ma1)
}
result1 # Coefficients, first 3 are males, next 3 are females and
last 3 are individuals
round(rs1,digits=3) #rsquared , first 3 are males, next 3 are
females and last 3 are individuals
round(rs2,digits=3) #dwstatistic, first 3 are males, next 3 are
females and last 3 are individuals
#Table 3 - estimating the effect of combined lags of unemployment
rate on mortality using a distributed lag approach (in a fixed
effects context)
a<-c("./mdatay/Statewn/mnt6.csv", "./mdatay/Statewn/fnt6.csv" ,
"./mdatay/Statewn/bnt6.csv")
est2<- vector(length=3)
8
se2<- vector(length=3)
pt2<- vector(length=3)
rs2<-vector(length=3)
dw2<-vector(length=3)
result2<- matrix(nrow=12,ncol=3)
for( i in 1:3){
ma1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
logi<glm(dr~U0+U1+U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),f
amily=quasibinomial(logit))
d1<-shuffle(logi$coef,sqrt(diag(vcov(logi))),
c(.1,.05,.01),logi$df.residual, two.sided=T, digits=3, apa=F)
result2[,i]<-d1[9:20,2]
est2[i]<-sum(logi$coef[2:4])
af<-subset(vcov(logi), select = U0:U2)
af2<-af[2:4,]
se2[i]<-sqrt(sum(af2))
pt2[i]<-2*(1-pnorm(abs(est2[i]/se2[i]),mean=0,sd=1))
rs2[i]<-1-(logi$deviance/logi$null.deviance)
dw2[i]<-durbinWatsonTest(logi)$dw
detach(ma1)
}
round(est2,digits=3) # provides the estimate for unemployment
round(se2,digits=3) # provides the standard error for unemployment
round(pt2,digits=3) # provides the probability value for
unemployment coefficient (used for finding significance)
round(rs2,digits=3) # R-squared
round(dw2,digits=3) # Dw Statistic
result2
# other coeffients - GDP, Medical EXP, Education,
ABP, P5 and P65
# Table 5 - output of the regressions with separate model for each
unemployment rate
### Expansion###
a<-c("./mdatay/Statewn/mnt6c.csv", "./mdatay/Statewn/fnt6c.csv" ,
"./mdatay/Statewn/bnt6c.csv")
result1<- matrix(nrow=14,ncol=9)
rs1<- vector(length=9)
rs2<- vector(length=9)
for( i in 1:3){
j=(i*3)-2
k=j+1
l=j+2
maa1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
9
ma1<-subset(maa1,Growth==1)
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
#the model used by other researchers takes the form(not fitted)
#mr<-(Deaths/Pop*100000)
#weight=sqrt(Pop)
#fixed1<lm(log(mr)~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed2<lm(log(mr)~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed3<lm(log(mr)~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
log1<glm(dr~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log2<glm(dr~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log3<glm(dr~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
d1<-shuffle(log1$coef,sqrt(diag(vcov(log1))),
c(0.1,.05,.01),log1$df.residual, two.sided=T, digits=3, apa=F)
d2<-shuffle(log2$coef,sqrt(diag(vcov(log2))),
c(0.1,.05,.01),log2$df.residual, two.sided=T, digits=3, apa=F)
d3<-shuffle(log3$coef,sqrt(diag(vcov(log3))),
c(0.1,.05,.01),log3$df.residual, two.sided=T, digits=3, apa=F)
result1[,j]<-d1[3:16,2]
result1[,k]<-d2[3:16,2]
result1[,l]<-d3[3:16,2]
rs1[j]<-1-(log1$deviance/log1$null.deviance)
rs2[j]<-durbinWatsonTest(log1)$dw
rs1[k]<-1-(log2$deviance/log2$null.deviance)
rs2[k]<-durbinWatsonTest(log2)$dw
rs1[l]<-1-(log3$deviance/log3$null.deviance)
rs2[l]<-durbinWatsonTest(log2)$dw
detach(ma1)
}
result1 # Coefficients, first 3 are males, next 3 are females and
last 3 are individuals
round(rs1,digits=3) #rsquared , first 3 are males, next 3 are
females and last 3 are individuals
round(rs2,digits=3) #dwstatistic, first 3 are males, next 3 are
females and last 3 are individuals
10
### Contaction ###
a<-c("./mdatay/Statewn/mnt6c.csv", "./mdatay/Statewn/fnt6c.csv" ,
"./mdatay/Statewn/bnt6c.csv")
result1<- matrix(nrow=14,ncol=9)
rs1<- vector(length=9)
rs2<- vector(length=9)
for( i in 1:3){
j=(i*3)-2
k=j+1
l=j+2
maa1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
ma1<-subset(maa1,Growth==0)
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
#the model used by other researchers takes the form(not fitted)
#mr<-(Deaths/Pop*100000)
#weight=sqrt(Pop)
#fixed1<lm(log(mr)~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed2<lm(log(mr)~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
#fixed3<lm(log(mr)~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),wei
ghts=weight)
log1<glm(dr~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log2<glm(dr~U1+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
log3<glm(dr~U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
d1<-shuffle(log1$coef,sqrt(diag(vcov(log1))),
c(0.1,.05,.01),log1$df.residual, two.sided=T, digits=3, apa=F)
d2<-shuffle(log2$coef,sqrt(diag(vcov(log2))),
c(0.1,.05,.01),log2$df.residual, two.sided=T, digits=3, apa=F)
d3<-shuffle(log3$coef,sqrt(diag(vcov(log3))),
c(0.1,.05,.01),log3$df.residual, two.sided=T, digits=3, apa=F)
result1[,j]<-d1[3:16,2]
result1[,k]<-d2[3:16,2]
result1[,l]<-d3[3:16,2]
rs1[j]<-1-(log1$deviance/log1$null.deviance)
rs2[j]<-durbinWatsonTest(log1)$dw
rs1[k]<-1-(log2$deviance/log2$null.deviance)
rs2[k]<-durbinWatsonTest(log2)$dw
11
rs1[l]<-1-(log3$deviance/log3$null.deviance)
rs2[l]<-durbinWatsonTest(log2)$dw
detach(ma1)
}
result1 # Coefficients, first 3 are males, next 3 are females and
last 3 are individuals
round(rs1,digits=3) #rsquared , first 3 are males, next 3 are
females and last 3 are individuals
round(rs2,digits=3) #dwstatistic, first 3 are males, next 3 are
females and last 3 are individuals
### Distributed lag models ###
#Table 5 - estimating the effect of combined lags of unemployment
rate on mortality using a distributed lag approach (in a fixed
effects context)
### Expansion ###
a<-c("./mdatay/Statewn/mnt6c.csv", "./mdatay/Statewn/fnt6c.csv" ,
"./mdatay/Statewn/bnt6c.csv")
est2<- vector(length=3)
se2<- vector(length=3)
pt2<- vector(length=3)
rs2<-vector(length=3)
dw2<-vector(length=3)
result2<- matrix(nrow=12,ncol=3)
for( i in 1:3){
maa1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
ma1<-subset(maa1,Growth==1)
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
logi<glm(dr~U0+U1+U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),f
amily=quasibinomial(logit))
d1<-shuffle(logi$coef,sqrt(diag(vcov(logi))),
c(.1,.05,.01),logi$df.residual, two.sided=T, digits=3, apa=F)
result2[,i]<-d1[9:20,2]
est2[i]<-sum(logi$coef[2:4])
af<-subset(vcov(logi), select = U0:U2)
af2<-af[2:4,]
se2[i]<-sqrt(sum(af2))
pt2[i]<-2*(1-pnorm(abs(est2[i]/se2[i]),mean=0,sd=1))
rs2[i]<-1-(logi$deviance/logi$null.deviance)
dw2[i]<-durbinWatsonTest(logi)$dw
detach(ma1)
}
round(est2,digits=3) # provides the estimate for unemployment
12
round(se2,digits=3) # provides the standard error for unemployment
round(pt2,digits=3) # provides the probability value for
unemployment coefficient (used for finding significance)
round(rs2,digits=3) # R-squared
round(dw2,digits=3) # Dw Statistic
result2
# other coeffients - GDP, Medical EXP, Education,
ABP, P5 and P65
### Contraction ###
a<-c("./mdatay/Statewn/mnt6c.csv", "./mdatay/Statewn/fnt6c.csv" ,
"./mdatay/Statewn/bnt6c.csv")
est2<- vector(length=3)
se2<- vector(length=3)
pt2<- vector(length=3)
rs2<-vector(length=3)
dw2<-vector(length=3)
result2<- matrix(nrow=12,ncol=3)
for( i in 1:3){
maa1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
ma1<-subset(maa1,Growth==0)
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
logi<glm(dr~U0+U1+U2+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),f
amily=quasibinomial(logit))
d1<-shuffle(logi$coef,sqrt(diag(vcov(logi))),
c(.1,.05,.01),logi$df.residual, two.sided=T, digits=3, apa=F)
result2[,i]<-d1[9:20,2]
est2[i]<-sum(logi$coef[2:4])
af<-subset(vcov(logi), select = U0:U2)
af2<-af[2:4,]
se2[i]<-sqrt(sum(af2))
pt2[i]<-2*(1-pnorm(abs(est2[i]/se2[i]),mean=0,sd=1))
rs2[i]<-1-(logi$deviance/logi$null.deviance)
dw2[i]<-durbinWatsonTest(logi)$dw
detach(ma1)
}
round(est2,digits=3) # provides the estimate for unemployment
round(se2,digits=3) # provides the standard error for unemployment
round(pt2,digits=3) # provides the probability value for
unemployment coefficient (used for finding significance)
round(rs2,digits=3) # R-squared
round(dw2,digits=3) # Dw Statistic
result2
# other coeffients - GDP, Medical EXP, Education,
ABP, P5 and P65
#Figures 1a and 1b
13
win.metafile(file="C:/Plots/Fig_%d.wmf")
par(mfrow=c(2,1))
ausm1 <read.csv(file="C:/mdatay/National/ausb6.csv",head=TRUE,sep=",")
attach(ausm1)
mar<- Deaths/Pop
#the following creates a scatterplot (with a lowess smoother) of
mortality rate vs unemployment rate
plot(U0,mar, ylab="Mortality Rate", xlab="Unemployment
Rate",main="Mortality Rate vs Unemployment Rate", sub="Raw
Series",pch="*")
lines(lowess(U0,mar,f=2/3))
cor.test(mar, U0)
#the following set of commands uses a cubic spline detrending on
unemployment rate and log of mortality rates series and plot them
togester in a time series plot
z<-ts(log(mar),start=1985,frequency=1)
x <- time(z)-1997
fit <- rlm(cbind(1,x,x^2,x^3),z)
summary(fit)
z2<-fit$residuals
z<-ts(U0,start=1985,frequency=1)
x <- time(z)-1997
fit <- rlm(cbind(1,x,x^2,x^3),z)
summary(fit)
z1<-fit$residuals
ts.plot(z1,z2/2,ylab="Standard Deviations from
Mean",main="Log(Mortality Rate) vs Unemployment
Rate",lty=1:2,pch="*")
legend("bottomright",legend=c("Unemployment rate","Log(Mortality
Rate)"),lty=1:2,bty="n")
detach(ausm1)
#Figure 2 - mortality rate at different levels of unemployment rate
using median values of other covariates
par(mfrow=c(1,1))
a<-c("C:/mdatay/Statewn/mnt6.csv", "C:/mdatay/Statewn/fnt6.csv" ,
"C:/mdatay/Statewn/bnt6.csv")
pop<-matrix(nrow=3,ncol=11)
for( i in 1:3){
ma1 <- read.csv(file=paste(a[i]),head=TRUE,sep=",")
attach(ma1)
GDP=GDPPP/10000
MED=MEDPP/1000
14
Pop1=round(Pop,digits=0)
dr=cbind(Deaths,Pop1)
logi<glm(dr~U0+GDP+MED+EDUP+ABP+p5+p65+factor(Time)+factor(STATE),family=
quasibinomial(logit))
g1<-median(GDP)
m1<-median(MED)
e1<-median(EDUP)
ab1<-median(ABP)
pl1<-median(p5)
ph1<-median(p65)
a2<-logi$coef[1:8]
for( j in 1:11){
k=(j-1)/100
a1<-cbind(1,k,g1,m1,e1,ab1,pl1,ph1)
b1<-a1%*%a2
pop[i,j]<-exp(b1)/(1+exp(b1))
}
detach(ma1)
}
ym<-pop[1,]
yf<-pop[2,]
yb<-pop[3,]
xnew<-0:10
plot(xnew, ym, ylim=c(0.0057,0.0069), sub="Lag 0",
xlab="Unemployment Rate (%)",
ylab="Mortality Rate",pch="*")
lines(xnew, ym,lty=1)
points(xnew,yf,pch="*")
lines(xnew, yf,lty=2)
points(xnew,yb,pch="*")
lines(xnew, yb,lty=3)
legend("topleft",legend=c("Males","Females","Combined"),lty=1:3,bty=
"n")
## The calculation below gives the value reported in the discussion
section
mean(diff(yb,lag=1))*100000
dev.off()
15