pandemic <-
data.frame(prov = sptk21$R101_NAMA,
urban = sptk21$R105,
sex = sptk21$B4K4,
age = sptk21$B4K5,
marital = sptk21$B4K6,
education = sptk21$B4K7,
length_stay = sptk21$LAMA_TINGG,
employment = sptk21$R601A,
sat_health = sptk21$R708,
sat_social = sptk21$R1005,
happy_during = sptk21$R1501,
happy_before = sptk21$R1502) %>%
mutate(happy_diff = happy_during-happy_before,
happy_increase = ifelse(happy_diff>0, 1, 0))
str(pandemic)
'data.frame': 74684 obs. of 14 variables:
$ prov : chr "Aceh" "Aceh" "Aceh" "Aceh" ...
$ urban : int 2 2 2 2 2 2 2 2 2 2 ...
$ sex : int 2 1 2 2 1 1 1 2 1 1 ...
$ age : int 95 45 70 60 33 30 43 32 43 36 ...
$ marital : int 4 2 4 4 2 2 2 3 2 2 ...
$ education : int 2 3 3 3 5 5 4 5 3 10 ...
$ length_stay : int 90 40 65 56 30 8 43 32 15 35 ...
$ employment : int 2 1 2 1 1 1 1 1 1 1 ...
$ sat_health : int 4 7 8 7 8 9 8 8 8 8 ...
$ sat_social : int 7 8 8 9 8 9 8 9 6 8 ...
$ happy_during : int 7 7 7 8 8 8 8 7 8 9 ...
$ happy_before : int 5 6 5 5 6 7 6 5 5 7 ...
$ happy_diff : int 2 1 2 3 2 1 2 2 3 2 ...
$ happy_increase: num 1 1 1 1 1 1 1 1 1 1 ...
# during - before
## if + = during surplus = happy increase
## if - = before surplus = happy decrease
dat <- pandemic %>% dummy_cols(c("urban","sex","marital","employment"))
dat <- dat %>% select(urban_1:sex_2, age, marital_1:marital_4, education:length_stay,
employment_1:employment_2, sat_health:sat_social, happy_during:happy_increase)
str(dat)
'data.frame': 74684 obs. of 19 variables:
$ urban_1 : int 0 0 0 0 0 0 0 0 0 0 ...
$ urban_2 : int 1 1 1 1 1 1 1 1 1 1 ...
$ sex_1 : int 0 1 0 0 1 1 1 0 1 1 ...
$ sex_2 : int 1 0 1 1 0 0 0 1 0 0 ...
$ age : int 95 45 70 60 33 30 43 32 43 36 ...
$ marital_1 : int 0 0 0 0 0 0 0 0 0 0 ...
$ marital_2 : int 0 1 0 0 1 1 1 0 1 1 ...
$ marital_3 : int 0 0 0 0 0 0 0 1 0 0 ...
$ marital_4 : int 1 0 1 1 0 0 0 0 0 0 ...
$ education : int 2 3 3 3 5 5 4 5 3 10 ...
$ length_stay : int 90 40 65 56 30 8 43 32 15 35 ...
$ employment_1 : int 0 1 0 1 1 1 1 1 1 1 ...
$ employment_2 : int 1 0 1 0 0 0 0 0 0 0 ...
$ sat_health : int 4 7 8 7 8 9 8 8 8 8 ...
$ sat_social : int 7 8 8 9 8 9 8 9 6 8 ...
$ happy_during : int 7 7 7 8 8 8 8 7 8 9 ...
$ happy_before : int 5 6 5 5 6 7 6 5 5 7 ...
$ happy_diff : int 2 1 2 3 2 1 2 2 3 2 ...
$ happy_increase: num 1 1 1 1 1 1 1 1 1 1 ...
stargazer(dat, type="latex")
% Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
% Date and time: Sun, Apr 23, 2023 - 01:52:07
\begin{table}[!htbp] \centering
\caption{}
\label{}
\begin{tabular}{@{\extracolsep{5pt}}lccccc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\
\hline \\[-1.8ex]
urban\_1 & 74,684 & 0.431 & 0.495 & 0 & 1 \\
urban\_2 & 74,684 & 0.569 & 0.495 & 0 & 1 \\
sex\_1 & 74,684 & 0.489 & 0.500 & 0 & 1 \\
sex\_2 & 74,684 & 0.511 & 0.500 & 0 & 1 \\
age & 74,684 & 47.430 & 13.500 & 14 & 98 \\
marital\_1 & 74,684 & 0.024 & 0.154 & 0 & 1 \\
marital\_2 & 74,684 & 0.813 & 0.390 & 0 & 1 \\
marital\_3 & 74,684 & 0.033 & 0.179 & 0 & 1 \\
marital\_4 & 74,684 & 0.130 & 0.336 & 0 & 1 \\
education & 74,684 & 4.007 & 1.957 & 1 & 10 \\
length\_stay & 74,684 & 30.384 & 19.515 & 0 & 98 \\
employment\_1 & 74,684 & 0.715 & 0.452 & 0 & 1 \\
employment\_2 & 74,684 & 0.285 & 0.452 & 0 & 1 \\
sat\_health & 74,684 & 7.651 & 1.496 & 0 & 10 \\
sat\_social & 74,684 & 7.974 & 1.371 & 0 & 10 \\
happy\_during & 74,684 & 7.761 & 1.333 & 0 & 10 \\
happy\_before & 74,684 & 8.052 & 1.410 & 0 & 10 \\
happy\_diff & 74,684 & $-$0.291 & 1.164 & $-$10 & 10 \\
happy\_increase & 74,684 & 0.111 & 0.314 & 0 & 1 \\
\hline \\[-1.8ex]
\end{tabular}
\end{table}
pandemic2 <-
data.frame(province = sptk21$R101_NAMA,
urban = ifelse(sptk21$R105==1, 1, 0),
female = ifelse(sptk21$B4K4==2, 1, 0),
age = sptk21$B4K5,
marital = sptk21$B4K6,
education = sptk21$B4K7,
length_stay = sptk21$LAMA_TINGG,
unemployed = ifelse(sptk21$R601A==2, 1, 0),
sat_health = sptk21$R708,
sat_social = sptk21$R1005,
happy_during = sptk21$R1501,
happy_before = sptk21$R1502) %>%
mutate(happy_diff = happy_during-happy_before,
happy_increase = ifelse(happy_diff>0, 1, 0),
marital = factor(recode(marital, `1`="not married yet", `2`="married", `3`="divorced", `4`="widowed"),
levels=c("not married yet","married","divorced","widowed")))
#,"sat_health","sat_social"
pandemic3 <- pandemic2 %>% select(!province)
happy_during <- lm(happy_during~., pandemic3 %>% select(!c("happy_before","happy_diff","happy_increase")))
happy_before <- lm(happy_before~., pandemic3 %>% select(!c("happy_during","happy_diff","happy_increase")))
happy_diff <- lm(happy_diff~., pandemic3 %>% select(!c("happy_during","happy_before","happy_increase")))
happy_increase <- glm(happy_increase~., family=binomial, data=pandemic3 %>% select(!c("happy_during","happy_before","happy_diff")))
summary(happy_during)
Call:
lm(formula = happy_during ~ ., data = pandemic3 %>% select(!c("happy_before",
"happy_diff", "happy_increase")))
Residuals:
Min 1Q Median 3Q Max
-9.2122 -0.6399 0.0626 0.7262 5.6659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1149093 0.0463708 67.174 < 2e-16 ***
urban -0.0454867 0.0093076 -4.887 1.03e-06 ***
female 0.0855087 0.0098220 8.706 < 2e-16 ***
age 0.0057016 0.0004359 13.079 < 2e-16 ***
maritalmarried 0.2559212 0.0285139 8.975 < 2e-16 ***
maritaldivorced -0.2007229 0.0370534 -5.417 6.08e-08 ***
maritalwidowed 0.0330822 0.0320129 1.033 0.301
education 0.0715472 0.0024724 28.938 < 2e-16 ***
length_stay -0.0013099 0.0002859 -4.582 4.61e-06 ***
unemployed 0.0784491 0.0104993 7.472 7.99e-14 ***
sat_health 0.2716369 0.0031148 87.207 < 2e-16 ***
sat_social 0.2255160 0.0032723 68.917 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.184 on 74672 degrees of freedom
Multiple R-squared: 0.2111, Adjusted R-squared: 0.211
F-statistic: 1816 on 11 and 74672 DF, p-value: < 2.2e-16
Call:
lm(formula = happy_before ~ ., data = pandemic3 %>% select(!c("happy_during",
"happy_diff", "happy_increase")))
Residuals:
Min 1Q Median 3Q Max
-8.9686 -0.6139 0.1010 0.8173 5.4675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.4295006 0.0496021 69.140 < 2e-16 ***
urban 0.1757702 0.0099562 17.654 < 2e-16 ***
female 0.1266096 0.0105064 12.051 < 2e-16 ***
age 0.0040274 0.0004663 8.637 < 2e-16 ***
maritalmarried 0.2178123 0.0305009 7.141 9.34e-13 ***
maritaldivorced -0.1747493 0.0396355 -4.409 1.04e-05 ***
maritalwidowed -0.0341905 0.0342437 -0.998 0.318067
education 0.0663321 0.0026447 25.081 < 2e-16 ***
length_stay -0.0011691 0.0003058 -3.823 0.000132 ***
unemployed 0.0493670 0.0112309 4.396 1.11e-05 ***
sat_health 0.2445975 0.0033319 73.411 < 2e-16 ***
sat_social 0.2518427 0.0035003 71.949 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.266 on 74672 degrees of freedom
Multiple R-squared: 0.1933, Adjusted R-squared: 0.1932
F-statistic: 1627 on 11 and 74672 DF, p-value: < 2.2e-16
Call:
lm(formula = happy_diff ~ ., data = pandemic3 %>% select(!c("happy_during",
"happy_before", "happy_increase")))
Residuals:
Min 1Q Median 3Q Max
-9.8036 -0.6340 0.1908 0.3891 10.5309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3145913 0.0453839 -6.932 4.19e-12 ***
urban -0.2212570 0.0091095 -24.289 < 2e-16 ***
female -0.0411009 0.0096129 -4.276 1.91e-05 ***
age 0.0016741 0.0004266 3.924 8.72e-05 ***
maritalmarried 0.0381089 0.0279071 1.366 0.17208
maritaldivorced -0.0259736 0.0362648 -0.716 0.47386
maritalwidowed 0.0672727 0.0313316 2.147 0.03179 *
education 0.0052150 0.0024198 2.155 0.03115 *
length_stay -0.0001408 0.0002798 -0.503 0.61490
unemployed 0.0290821 0.0102758 2.830 0.00465 **
sat_health 0.0270394 0.0030486 8.870 < 2e-16 ***
sat_social -0.0263268 0.0032026 -8.220 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.159 on 74672 degrees of freedom
Multiple R-squared: 0.01028, Adjusted R-squared: 0.01013
F-statistic: 70.5 on 11 and 74672 DF, p-value: < 2.2e-16
Call:
glm(formula = happy_increase ~ ., family = binomial, data = pandemic3 %>%
select(!c("happy_during", "happy_before", "happy_diff")))
Deviance Residuals:
Min 1Q Median 3Q Max
-0.8054 -0.5097 -0.4710 -0.4321 2.3774
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.9894196 0.1245823 -7.942 1.99e-15 ***
urban -0.2949294 0.0256084 -11.517 < 2e-16 ***
female -0.1311063 0.0266211 -4.925 8.44e-07 ***
age -0.0026332 0.0011724 -2.246 0.02471 *
maritalmarried 0.1620582 0.0809362 2.002 0.04525 *
maritaldivorced -0.0030579 0.1062969 -0.029 0.97705
maritalwidowed 0.2447544 0.0900995 2.716 0.00660 **
education 0.0032011 0.0067282 0.476 0.63423
length_stay -0.0023426 0.0007719 -3.035 0.00241 **
unemployed 0.0359051 0.0286366 1.254 0.20991
sat_health -0.0169927 0.0082930 -2.049 0.04046 *
sat_social -0.0972900 0.0082010 -11.863 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 52070 on 74683 degrees of freedom
Residual deviance: 51716 on 74672 degrees of freedom
AIC: 51740
Number of Fisher Scoring iterations: 5
stargazer(happy_before, happy_during, happy_diff, happy_increase)
% Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
% Date and time: Sun, Apr 23, 2023 - 01:52:08
\begin{table}[!htbp] \centering
\caption{}
\label{}
\begin{tabular}{@{\extracolsep{5pt}}lcccc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
& \multicolumn{4}{c}{\textit{Dependent variable:}} \\
\cline{2-5}
\\[-1.8ex] & happy\_before & happy\_during & happy\_diff & happy\_increase \\
\\[-1.8ex] & \textit{OLS} & \textit{OLS} & \textit{OLS} & \textit{logistic} \\
\\[-1.8ex] & (1) & (2) & (3) & (4)\\
\hline \\[-1.8ex]
urban & 0.176$^{***}$ & $-$0.045$^{***}$ & $-$0.221$^{***}$ & $-$0.295$^{***}$ \\
& (0.010) & (0.009) & (0.009) & (0.026) \\
& & & & \\
female & 0.127$^{***}$ & 0.086$^{***}$ & $-$0.041$^{***}$ & $-$0.131$^{***}$ \\
& (0.011) & (0.010) & (0.010) & (0.027) \\
& & & & \\
age & 0.004$^{***}$ & 0.006$^{***}$ & 0.002$^{***}$ & $-$0.003$^{**}$ \\
& (0.0005) & (0.0004) & (0.0004) & (0.001) \\
& & & & \\
maritalmarried & 0.218$^{***}$ & 0.256$^{***}$ & 0.038 & 0.162$^{**}$ \\
& (0.031) & (0.029) & (0.028) & (0.081) \\
& & & & \\
maritaldivorced & $-$0.175$^{***}$ & $-$0.201$^{***}$ & $-$0.026 & $-$0.003 \\
& (0.040) & (0.037) & (0.036) & (0.106) \\
& & & & \\
maritalwidowed & $-$0.034 & 0.033 & 0.067$^{**}$ & 0.245$^{***}$ \\
& (0.034) & (0.032) & (0.031) & (0.090) \\
& & & & \\
education & 0.066$^{***}$ & 0.072$^{***}$ & 0.005$^{**}$ & 0.003 \\
& (0.003) & (0.002) & (0.002) & (0.007) \\
& & & & \\
length\_stay & $-$0.001$^{***}$ & $-$0.001$^{***}$ & $-$0.0001 & $-$0.002$^{***}$ \\
& (0.0003) & (0.0003) & (0.0003) & (0.001) \\
& & & & \\
unemployed & 0.049$^{***}$ & 0.078$^{***}$ & 0.029$^{***}$ & 0.036 \\
& (0.011) & (0.010) & (0.010) & (0.029) \\
& & & & \\
sat\_health & 0.245$^{***}$ & 0.272$^{***}$ & 0.027$^{***}$ & $-$0.017$^{**}$ \\
& (0.003) & (0.003) & (0.003) & (0.008) \\
& & & & \\
sat\_social & 0.252$^{***}$ & 0.226$^{***}$ & $-$0.026$^{***}$ & $-$0.097$^{***}$ \\
& (0.004) & (0.003) & (0.003) & (0.008) \\
& & & & \\
Constant & 3.430$^{***}$ & 3.115$^{***}$ & $-$0.315$^{***}$ & $-$0.989$^{***}$ \\
& (0.050) & (0.046) & (0.045) & (0.125) \\
& & & & \\
\hline \\[-1.8ex]
Observations & 74,684 & 74,684 & 74,684 & 74,684 \\
R$^{2}$ & 0.193 & 0.211 & 0.010 & \\
Adjusted R$^{2}$ & 0.193 & 0.211 & 0.010 & \\
Log Likelihood & & & & $-$25,858.220 \\
Akaike Inf. Crit. & & & & 51,740.440 \\
Residual Std. Error (df = 74672) & 1.266 & 1.184 & 1.159 & \\
F Statistic (df = 11; 74672) & 1,626.859$^{***}$ & 1,816.459$^{***}$ & 70.496$^{***}$ & \\
\hline
\hline \\[-1.8ex]
\textit{Note:} & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
\end{tabular}
\end{table}