注意：R/RStudio上で行った分析は、文字化けを避けるために英語で実施した。論文執筆の段階で、分析に用いた変数や分析結果を日本語へ訳した。
分析準備
分析に必要なパッケージをインストール
pacman::p_load(tidyverse, summarytools, DT, devtools, estimatr,
jtools, broom.mixed, ggstance)
library(tidyverse)
library(summarytools)
library(DT)
library(devtools)
library(estimatr)
library(jtools)
library(broom.mixed)
library(ggstance)
devtools::install_github("JaehyunSong/BalanceR")
library(BalanceR)
データを読み込む
df <- read_csv("cleaned_mx_NA.csv")
処置変数をFactor変数に変換
df <- df |>
mutate(Treat = factor(Treat,
levels = c("Control", "US", "China")))
データのクリーニング
df <- df |>
select(RESPID, Treat, Q1, Q2, Q4, Q5_1:Q5_5, Q9, Q17:Q19, Q20_4, Q21, Q25, Q32, Q35:Q37, Q39, Q40, Treat, Q3S)
df <- df |>
mutate(Female = if_else(Q2 == 2, 1, 0),
# Binary (female = 1, male =0)
# Age divided by 10 (centered by median)
Age = (Q1 - median(Q1)) / 10,
# Feeling thermometer (Experiments_priming)
Thermo = Q3S,
# Outcome Variable: Attitude toward international trade (high scores means greater support: 1 = very bad, 2 = bad, 3 = neither good or bad, 4 = good, 5 = very good)
Y = 6 - Q4,
# Outcome Varialbe: Attitude toward international trade by partners
Y_EU = 6 - Q5_1,
Y_US = 6 - Q5_2,
Y_China = 6 - Q5_3,
Y_Mercosur = 6 - Q5_4,
Y_TPP = 6 - Q5_5,
# Perception on emigration
Emig = 6 - Q9,
# Job types (primary sector including agriculture, fishery, forestry, mmanufacturing industry)
Primary = if_else(Q39 == 1, 1, 0),
Manufac = if_else(Q39 == 3, 1, 0),
# Political knowledge
Pol_know1 = if_else(Q17 == 2, 1, 0),
Pol_know2 = if_else(Q18 == 3, 1, 0),
Pol_know3 = if_else(Q19 == 3, 1, 0),
Pol_know = Pol_know1 + Pol_know2 + Pol_know3,
# Evaluation of government
Eval_gov = 5 - Q21,
# Partisanship
PRI = if_else(Q25 == 1, 1, 0),
PAN = if_else(Q25 == 2, 1, 0),
PRD = if_else(Q25 == 3, 1, 0),
MORENA = if_else(Q25 == 4, 1, 0),
# Demographic attributes
Emigration = 4 - Q20_4, # Emigrate to work
# Ethnicity/race
Indigenous = if_else(Q32 == 1, 1, 0),
Mestizo = if_else(Q32 == 3, 1, 0),
White = if_else(Q32 == 4, 1, 0),
# Living location
Urban = if_else(Q36 == 1, 1, 0),
Rurul = if_else(Q36 == 3, 1, 0),
# Education level centered by median
Edu = Q37 - 3.5,
# Income level centered by median
Income = Q40 - 5.5
)
# 州ダミーを作成
df <- df %>%
mutate(State_dummy = factor(Q35))
記述統計
df2 <- df |>
select(Y, Y_US, Y_China, Female, Age, Income, Primary,
Manufac, Emigration, Pol_know, PRI, PAN, PRD, MORENA)
print(dfSummary(df2,
style = "grid", plain.ascii = FALSE,
graph.magnif = 0.85),
method = "render", heading = FALSE)
バランス・チェック
Author/Maintainer: Jaehyun Song (https://www.jaysong.net / tintstyle@gmail.com)
BC <- BalanceR(data = df, group = "Treat",
cov = c("Female", "Age", "Income",
"Emigration", "Primary",
"Manufac","Pol_know", "PRI", "PAN",
"PRD", "MORENA")) |>
plot()
print(BC, digit = 3)
処置効果の比較
図4.4.
自由貿易に対する態度 (2)メキシコ
推定値をRで計算した後、エクセルで図４を作成した。
国際貿易一般への支持
Support.df <- df |>
group_by(Treat) |>
summarise(Y = mean(Y, na.rm = TRUE), # remove NAs
group = "drop")
Support.df
## # A tibble: 3 × 3
## Treat Y group
## <fct> <dbl> <chr>
## 1 Control 4.33 drop
## 2 US 4.25 drop
## 3 China 4.41 drop
Support.df |>
ggplot() +
geom_bar(aes(x = Treat, y = Y), stat = "identity") +
geom_label(aes(x = Treat, y = Y,
label = round(Y, 3)), label.size = 1) +
labs(x = "Treatment", y = "Support for
International Trade") +
coord_cartesian(ylim = c(0, 4.5))
対アメリカ貿易への支持
Support_US.df <- df |>
group_by(Treat) |>
summarise(Y_US = mean(Y_US, na.rm = TRUE), # remove NAs
group = "drop")
Support_US.df
## # A tibble: 3 × 3
## Treat Y_US group
## <fct> <dbl> <chr>
## 1 Control 3.99 drop
## 2 US 3.92 drop
## 3 China 3.85 drop
Support_US.df |>
ggplot() +
geom_bar(aes(x = Treat, y = Y_US), stat = "identity") +
geom_label(aes(x = Treat, y = Y_US,
label = round(Y_US, 3)), label.size = 1) +
labs(x = "Treatment", y = "Support for International Trade
with US") +
coord_cartesian(ylim = c(0, 4.5))
対中国貿易への支持
Support_China.df <- df |>
group_by(Treat) |>
summarise(Y_China = mean(Y_China, na.rm = TRUE),
# remove NAs
group = "drop")
Support_China.df
## # A tibble: 3 × 3
## Treat Y_China group
## <fct> <dbl> <chr>
## 1 Control 3.99 drop
## 2 US 3.81 drop
## 3 China 4.03 drop
Support_China.df |>
ggplot() +
geom_bar(aes(x = Treat, y = Y_China), stat = "identity") +
geom_label(aes(x = Treat, y = Y_China,
label = round(Y_China, 3)), label.size = 1) +
labs(x = "Treatment", y = "Support for International Trade
with China") +
coord_cartesian(ylim = c(0, 4.5))
単回帰分析
推定結果は、「補填 表4
A-2 メキシコ：貿易相手別の支持」のモデル1，4，7に相当
モデル１（国際貿易一般への支持）
Support.fit <- lm_robust(Y ~ Treat, se_type = "stata",
data = df)
summary(Support.fit)
##
## Call:
## lm_robust(formula = Y ~ Treat, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 4.32948 0.03801 113.911 0.0000 4.25490 4.40406 1038
## TreatUS -0.07661 0.05477 -1.399 0.1622 -0.18407 0.03086 1038
## TreatChina 0.07974 0.05455 1.462 0.1441 -0.02731 0.18679 1038
##
## Multiple R-squared: 0.007747 , Adjusted R-squared: 0.005835
## F-statistic: 3.961 on 2 and 1038 DF, p-value: 0.01933
モデル４（対アメリカ貿易への支持
Support_US.fit <- lm_robust(Y_US ~ Treat, se_type = "stata",
data = df)
summary(Support_US.fit)
##
## Call:
## lm_robust(formula = Y_US ~ Treat, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 3.99135 0.05234 76.2509 0.00000 3.8886 4.094068 1043
## TreatUS -0.06872 0.07325 -0.9381 0.34840 -0.2125 0.075019 1043
## TreatChina -0.14564 0.07701 -1.8913 0.05887 -0.2967 0.005467 1043
##
## Multiple R-squared: 0.003554 , Adjusted R-squared: 0.001643
## F-statistic: 1.789 on 2 and 1043 DF, p-value: 0.1677
モデル７（対中国貿易への支持）
Support_China.fit <- lm_robust(Y_China ~ Treat, se_type =
"stata", data = df)
summary(Support_China.fit)
##
## Call:
## lm_robust(formula = Y_China ~ Treat, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 3.98559 0.04973 80.1439 0.00000 3.88801 4.08317 1039
## TreatUS -0.17690 0.07597 -2.3286 0.02007 -0.32596 -0.02783 1039
## TreatChina 0.04869 0.06684 0.7286 0.46644 -0.08246 0.17985 1039
##
## Multiple R-squared: 0.01037 , Adjusted R-squared: 0.008469
## F-statistic: 4.986 on 2 and 1039 DF, p-value: 0.006998
重回帰分析
推定結果は、「補填 表4
A-2 メキシコ：貿易相手別の支持」のモデル2，5，8に相当
モデル２（国際貿易一般への支持
Support.fit <- lm_robust(Y ~ Treat + Female + Age + Income +
Primary + Manufac + Emigration +
Pol_know + PRI + PAN + PRD + MORENA +
State_dummy,
se_type = "stata", data = df)
summary(Support.fit)
##
## Call:
## lm_robust(formula = Y ~ Treat + Female + Age + Income + Primary +
## Manufac + Emigration + Pol_know + PRI + PAN + PRD + MORENA +
## State_dummy, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 4.713507 0.29039 16.23147 7.291e-51 4.143409 5.28361 735
## TreatUS -0.092687 0.06175 -1.50103 1.338e-01 -0.213913 0.02854 735
## TreatChina -0.024345 0.06462 -0.37675 7.065e-01 -0.151205 0.10251 735
## Female -0.058442 0.05346 -1.09311 2.747e-01 -0.163402 0.04652 735
## Age 0.031711 0.02140 1.48213 1.387e-01 -0.010293 0.07372 735
## Income 0.030106 0.01451 2.07438 3.839e-02 0.001614 0.05860 735
## Primary -0.070642 0.15874 -0.44501 6.564e-01 -0.382284 0.24100 735
## Manufac -0.066369 0.10711 -0.61961 5.357e-01 -0.276653 0.14392 735
## Emigration 0.097297 0.04307 2.25914 2.417e-02 0.012746 0.18185 735
## Pol_know -0.137382 0.06450 -2.12998 3.350e-02 -0.264007 -0.01076 735
## PRI 0.189167 0.10098 1.87335 6.142e-02 -0.009072 0.38741 735
## PAN 0.173156 0.08094 2.13921 3.275e-02 0.014247 0.33206 735
## PRD -0.202818 0.21813 -0.92982 3.528e-01 -0.631041 0.22541 735
## MORENA 0.090158 0.07674 1.17480 2.405e-01 -0.060504 0.24082 735
## State_dummy2 0.011292 0.24735 0.04565 9.636e-01 -0.474300 0.49689 735
## State_dummy3 0.157078 0.29077 0.54022 5.892e-01 -0.413756 0.72791 735
## State_dummy4 -0.106564 0.32861 -0.32429 7.458e-01 -0.751692 0.53856 735
## State_dummy5 -0.094531 0.21281 -0.44420 6.570e-01 -0.512321 0.32326 735
## State_dummy6 0.233956 0.43935 0.53250 5.945e-01 -0.628577 1.09649 735
## State_dummy7 -0.018388 0.26898 -0.06836 9.455e-01 -0.546453 0.50968 735
## State_dummy8 0.067161 0.27415 0.24498 8.065e-01 -0.471054 0.60538 735
## State_dummy9 -0.029232 0.26345 -0.11096 9.117e-01 -0.546435 0.48797 735
## State_dummy10 -0.164604 0.32807 -0.50174 6.160e-01 -0.808665 0.47946 735
## State_dummy11 0.524118 0.27631 1.89682 5.824e-02 -0.018342 1.06658 735
## State_dummy12 0.056846 0.25154 0.22600 8.213e-01 -0.436972 0.55067 735
## State_dummy13 -0.026358 0.30331 -0.08690 9.308e-01 -0.621808 0.56909 735
## State_dummy14 -0.011629 0.22566 -0.05153 9.589e-01 -0.454644 0.43139 735
## State_dummy15 0.037296 0.21521 0.17330 8.625e-01 -0.385205 0.45980 735
## State_dummy16 0.264115 0.24291 1.08728 2.773e-01 -0.212773 0.74100 735
## State_dummy17 -0.006792 0.28988 -0.02343 9.813e-01 -0.575875 0.56229 735
## State_dummy19 -0.100445 0.22778 -0.44098 6.594e-01 -0.547619 0.34673 735
## State_dummy20 0.003436 0.29347 0.01171 9.907e-01 -0.572694 0.57957 735
## State_dummy21 -0.250277 0.24543 -1.01974 3.082e-01 -0.732111 0.23156 735
## State_dummy22 -0.379623 0.26412 -1.43730 1.511e-01 -0.898145 0.13890 735
## State_dummy23 0.079531 0.24105 0.32994 7.415e-01 -0.393693 0.55275 735
## State_dummy24 0.134118 0.27150 0.49399 6.215e-01 -0.398889 0.66712 735
## State_dummy25 -0.075718 0.30990 -0.24433 8.070e-01 -0.684109 0.53267 735
## State_dummy26 0.040002 0.28222 0.14174 8.873e-01 -0.514060 0.59406 735
## State_dummy27 -0.046953 0.28323 -0.16577 8.684e-01 -0.602991 0.50909 735
## State_dummy28 0.571136 0.22219 2.57046 1.035e-02 0.134929 1.00734 735
## State_dummy29 -0.165187 0.28894 -0.57171 5.677e-01 -0.732425 0.40205 735
## State_dummy30 0.078147 0.23675 0.33008 7.414e-01 -0.386649 0.54294 735
## State_dummy31 -0.072598 0.27976 -0.25950 7.953e-01 -0.621829 0.47663 735
## State_dummy32 -0.359936 0.45723 -0.78722 4.314e-01 -1.257561 0.53769 735
##
## Multiple R-squared: 0.06882 , Adjusted R-squared: 0.01434
## F-statistic: 16.26 on 43 and 735 DF, p-value: < 2.2e-16
モデル５（対アメリカ貿易への支持）
Support_US.fit <- lm_robust(Y_US ~ Treat + Female + Age +
Income + Primary + Manufac +
Emigration + Pol_know + PRI + PAN +
PRD + MORENA + State_dummy,
se_type = "stata", data = df)
summary(Support_US.fit)
##
## Call:
## lm_robust(formula = Y_US ~ Treat + Female + Age + Income + Primary +
## Manufac + Emigration + Pol_know + PRI + PAN + PRD + MORENA +
## State_dummy, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 4.428804 0.45408 9.75333 3.209e-21 3.537357 5.32025 737
## TreatUS -0.129978 0.08683 -1.49701 1.348e-01 -0.300432 0.04048 737
## TreatChina -0.203467 0.08922 -2.28038 2.287e-02 -0.378632 -0.02830 737
## Female -0.074197 0.07411 -1.00114 3.171e-01 -0.219693 0.07130 737
## Age 0.061256 0.02971 2.06205 3.955e-02 0.002937 0.11958 737
## Income 0.005149 0.01854 0.27776 7.813e-01 -0.031244 0.04154 737
## Primary -0.308600 0.24447 -1.26231 2.072e-01 -0.788545 0.17134 737
## Manufac 0.160315 0.12394 1.29353 1.962e-01 -0.082995 0.40362 737
## Emigration 0.108039 0.06466 1.67091 9.516e-02 -0.018898 0.23498 737
## Pol_know -0.184634 0.10545 -1.75093 8.037e-02 -0.391649 0.02238 737
## PRI 0.425337 0.13997 3.03870 2.460e-03 0.150544 0.70013 737
## PAN 0.358257 0.11041 3.24492 1.228e-03 0.141510 0.57500 737
## PRD 0.083153 0.24277 0.34252 7.321e-01 -0.393449 0.55976 737
## MORENA 0.166320 0.10488 1.58576 1.132e-01 -0.039586 0.37223 737
## State_dummy2 -0.330958 0.37409 -0.88470 3.766e-01 -1.065368 0.40345 737
## State_dummy3 0.082274 0.42868 0.19193 8.479e-01 -0.759295 0.92384 737
## State_dummy4 -0.845219 0.54595 -1.54817 1.220e-01 -1.917017 0.22658 737
## State_dummy5 -0.166525 0.33789 -0.49284 6.223e-01 -0.829869 0.49682 737
## State_dummy6 0.190866 0.63865 0.29886 7.651e-01 -1.062923 1.44466 737
## State_dummy7 0.074285 0.43224 0.17186 8.636e-01 -0.774287 0.92286 737
## State_dummy8 -0.076763 0.43093 -0.17813 8.587e-01 -0.922767 0.76924 737
## State_dummy9 0.125719 0.39003 0.32233 7.473e-01 -0.639990 0.89143 737
## State_dummy10 -0.180788 0.53032 -0.34091 7.333e-01 -1.221898 0.86032 737
## State_dummy11 0.366904 0.40554 0.90474 3.659e-01 -0.429238 1.16305 737
## State_dummy12 -0.213128 0.41332 -0.51564 6.063e-01 -1.024562 0.59831 737
## State_dummy13 -0.045141 0.41644 -0.10840 9.137e-01 -0.862698 0.77242 737
## State_dummy14 -0.068760 0.34738 -0.19794 8.431e-01 -0.750735 0.61321 737
## State_dummy15 -0.324440 0.34672 -0.93573 3.497e-01 -1.005126 0.35625 737
## State_dummy16 0.373701 0.36230 1.03147 3.027e-01 -0.337564 1.08497 737
## State_dummy17 -0.100681 0.45742 -0.22011 8.259e-01 -0.998684 0.79732 737
## State_dummy19 -0.089694 0.35694 -0.25128 8.017e-01 -0.790438 0.61105 737
## State_dummy20 -0.643816 0.38128 -1.68858 9.172e-02 -1.392333 0.10470 737
## State_dummy21 0.012822 0.36368 0.03526 9.719e-01 -0.701159 0.72680 737
## State_dummy22 -0.786130 0.41093 -1.91303 5.613e-02 -1.592873 0.02061 737
## State_dummy23 0.102655 0.41301 0.24855 8.038e-01 -0.708167 0.91348 737
## State_dummy24 -0.041909 0.38808 -0.10799 9.140e-01 -0.803788 0.71997 737
## State_dummy25 0.070251 0.40129 0.17506 8.611e-01 -0.717551 0.85805 737
## State_dummy26 0.283864 0.38349 0.74021 4.594e-01 -0.469007 1.03674 737
## State_dummy27 -0.303569 0.40515 -0.74928 4.539e-01 -1.098952 0.49181 737
## State_dummy28 -3.062538 0.34511 -8.87406 5.291e-18 -3.740056 -2.38502 737
## State_dummy29 0.041666 0.42661 0.09767 9.222e-01 -0.795849 0.87918 737
## State_dummy30 -0.014810 0.36803 -0.04024 9.679e-01 -0.737314 0.70769 737
## State_dummy31 0.167847 0.38786 0.43275 6.653e-01 -0.593601 0.92930 737
## State_dummy32 -0.166592 0.48121 -0.34620 7.293e-01 -1.111291 0.77811 737
##
## Multiple R-squared: 0.09351 , Adjusted R-squared: 0.04062
## F-statistic: 204.4 on 43 and 737 DF, p-value: < 2.2e-16
対モデル８（中国貿易への支持）
Support_China.fit <- lm_robust(Y_China ~ Treat + Female + Age
+ Income + Primary + Manufac +
Emigration + Pol_know + PRI + PAN +
PRD + MORENA + State_dummy,
se_type = "stata", data = df)
summary(Support_China.fit)
##
## Call:
## lm_robust(formula = Y_China ~ Treat + Female + Age + Income +
## Primary + Manufac + Emigration + Pol_know + PRI + PAN + PRD +
## MORENA + State_dummy, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 4.165324 0.29422 14.15731 2.038e-40 3.58772 4.742928 736
## TreatUS -0.291165 0.08568 -3.39847 7.141e-04 -0.45936 -0.122968 736
## TreatChina -0.054089 0.07728 -0.69991 4.842e-01 -0.20581 0.097628 736
## Female 0.001087 0.06743 0.01611 9.871e-01 -0.13129 0.133464 736
## Age -0.020703 0.02893 -0.71557 4.745e-01 -0.07750 0.036097 736
## Income -0.018382 0.01680 -1.09413 2.743e-01 -0.05136 0.014601 736
## Primary -0.020314 0.19493 -0.10421 9.170e-01 -0.40300 0.362377 736
## Manufac -0.067534 0.12771 -0.52882 5.971e-01 -0.31825 0.183180 736
## Emigration 0.026622 0.05338 0.49876 6.181e-01 -0.07816 0.131408 736
## Pol_know -0.065844 0.07866 -0.83703 4.028e-01 -0.22028 0.088590 736
## PRI 0.463441 0.13508 3.43093 6.351e-04 0.19826 0.728624 736
## PAN 0.198771 0.11198 1.77512 7.629e-02 -0.02106 0.418603 736
## PRD 0.290429 0.19502 1.48924 1.369e-01 -0.09243 0.673287 736
## MORENA 0.416678 0.09930 4.19599 3.049e-05 0.22173 0.611631 736
## State_dummy2 -0.235335 0.22723 -1.03567 3.007e-01 -0.68143 0.210761 736
## State_dummy3 0.299151 0.42205 0.70881 4.787e-01 -0.52941 1.127714 736
## State_dummy4 -0.450586 0.20042 -2.24819 2.486e-02 -0.84405 -0.057119 736
## State_dummy5 -0.240958 0.14899 -1.61724 1.063e-01 -0.53346 0.051544 736
## State_dummy6 -0.319852 0.28761 -1.11211 2.665e-01 -0.88448 0.244780 736
## State_dummy7 -0.579897 0.34724 -1.67002 9.534e-02 -1.26159 0.101800 736
## State_dummy8 -0.072566 0.27416 -0.26469 7.913e-01 -0.61079 0.465656 736
## State_dummy9 -0.072294 0.23603 -0.30629 7.595e-01 -0.53567 0.391085 736
## State_dummy10 -0.436022 0.39578 -1.10167 2.710e-01 -1.21302 0.340979 736
## State_dummy11 0.458076 0.35323 1.29682 1.951e-01 -0.23538 1.151535 736
## State_dummy12 -0.377063 0.26704 -1.41199 1.584e-01 -0.90132 0.147196 736
## State_dummy13 -0.581105 0.37353 -1.55572 1.202e-01 -1.31441 0.152203 736
## State_dummy14 -0.057610 0.17205 -0.33485 7.378e-01 -0.39537 0.280149 736
## State_dummy15 -0.486043 0.16529 -2.94054 3.379e-03 -0.81054 -0.161547 736
## State_dummy16 0.105415 0.22342 0.47182 6.372e-01 -0.33320 0.544031 736
## State_dummy17 -0.084177 0.32755 -0.25699 7.973e-01 -0.72723 0.558874 736
## State_dummy19 -0.029126 0.16081 -0.18112 8.563e-01 -0.34482 0.286568 736
## State_dummy20 -0.969073 0.40553 -2.38963 1.712e-02 -1.76521 -0.172935 736
## State_dummy21 -0.404981 0.20125 -2.01228 4.455e-02 -0.80008 -0.009879 736
## State_dummy22 -0.832088 0.29156 -2.85390 4.440e-03 -1.40448 -0.259695 736
## State_dummy23 0.218747 0.18612 1.17528 2.403e-01 -0.14665 0.584142 736
## State_dummy24 -0.202196 0.44192 -0.45754 6.474e-01 -1.06977 0.665377 736
## State_dummy25 0.105334 0.26016 0.40488 6.857e-01 -0.40541 0.616078 736
## State_dummy26 0.326066 0.23364 1.39558 1.633e-01 -0.13262 0.784751 736
## State_dummy27 -0.081308 0.31137 -0.26113 7.941e-01 -0.69258 0.529967 736
## State_dummy28 -0.375791 0.17247 -2.17889 2.966e-02 -0.71438 -0.037200 736
## State_dummy29 0.269151 0.23280 1.15615 2.480e-01 -0.18788 0.726179 736
## State_dummy30 -0.485246 0.23369 -2.07648 3.820e-02 -0.94402 -0.026474 736
## State_dummy31 -0.179804 0.28836 -0.62354 5.331e-01 -0.74591 0.386299 736
## State_dummy32 -0.476943 0.40941 -1.16495 2.444e-01 -1.28070 0.326811 736
##
## Multiple R-squared: 0.1271 , Adjusted R-squared: 0.07609
## F-statistic: 2.929 on 43 and 736 DF, p-value: 4.048e-09
モデル2, 5,
8の推定結果を可視化（図4.5 自由貿易に対する支持の規定要因
(2)メキシコ）
plot_summs(Support.fit, Support_US.fit, Support_China.fit,
scale = TRUE, robust = TRUE,
coefs = c("処置：アメリカ"="TreatUS",
"処置：中国"="TreatChina", "性別"=
"Female", "年齢"= "Age",
"所得"="Income", "一次産業"="Primary",
"製造業"= "Manufac",
"移民経験"="Emigration",
"政治知識"="Pol_know", "PRI支持"= "PRI",
"PAN支持"="PAN", "PRD支持"="PRD",
"MORENA支持"="MORENA"),
model.names = c("国際貿易一般", "対アメリカ貿易",
"対中国貿易"),
legend.title = "")
サブグループ分析(Age,
Female, Income, Emigration, Pol_know)
推定結果は、「補填 表4
A-2 メキシコ：貿易相手別の支持」のモデル3，6，9に相当
モデル３（国際貿易一般への支持）
sub_all<- lm_robust(Y ~ Treat*Female + Treat*Age + Treat*Income
+ Primary + Manufac + Treat*Emigration +
Treat*Pol_know + PRI + PAN + PRD + MORENA
+ State_dummy,se_type = "stata", data = df)
summary(sub_all)
##
## Call:
## lm_robust(formula = Y ~ Treat * Female + Treat * Age + Treat *
## Income + Primary + Manufac + Treat * Emigration + Treat *
## Pol_know + PRI + PAN + PRD + MORENA + State_dummy, data = df,
## se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower
## (Intercept) 4.822062 0.45618 10.57045 2.185e-24 3.926464
## TreatUS -0.544145 0.59056 -0.92141 3.571e-01 -1.703549
## TreatChina 0.052730 0.54301 0.09711 9.227e-01 -1.013335
## Female 0.104788 0.09059 1.15678 2.477e-01 -0.073054
## Age 0.068259 0.03688 1.85101 6.458e-02 -0.004139
## Income 0.018406 0.02513 0.73249 4.641e-01 -0.030926
## Primary -0.092820 0.16409 -0.56567 5.718e-01 -0.414964
## Manufac -0.044338 0.10890 -0.40713 6.840e-01 -0.258142
## Emigration 0.136601 0.06815 2.00440 4.540e-02 0.002805
## Pol_know -0.244028 0.13921 -1.75300 8.002e-02 -0.517323
## PRI 0.186046 0.10241 1.81675 6.967e-02 -0.015002
## PAN 0.181803 0.08171 2.22495 2.639e-02 0.021385
## PRD -0.180169 0.22029 -0.81789 4.137e-01 -0.612642
## MORENA 0.102729 0.07681 1.33743 1.815e-01 -0.048069
## State_dummy2 0.058877 0.24547 0.23986 8.105e-01 -0.423037
## State_dummy3 0.182316 0.30489 0.59798 5.500e-01 -0.416248
## State_dummy4 -0.087606 0.33659 -0.26027 7.947e-01 -0.748411
## State_dummy5 -0.060098 0.20957 -0.28676 7.744e-01 -0.471537
## State_dummy6 0.274077 0.45610 0.60092 5.481e-01 -0.621353
## State_dummy7 -0.006236 0.27334 -0.02281 9.818e-01 -0.542874
## State_dummy8 0.128756 0.27284 0.47191 6.371e-01 -0.406893
## State_dummy9 0.022179 0.26291 0.08436 9.328e-01 -0.493985
## State_dummy10 -0.142658 0.31021 -0.45988 6.457e-01 -0.751670
## State_dummy11 0.630467 0.28941 2.17846 2.969e-02 0.062287
## State_dummy12 0.101314 0.25069 0.40415 6.862e-01 -0.390843
## State_dummy13 0.015975 0.29869 0.05348 9.574e-01 -0.570432
## State_dummy14 0.022681 0.22293 0.10174 9.190e-01 -0.414985
## State_dummy15 0.087045 0.21243 0.40977 6.821e-01 -0.330001
## State_dummy16 0.303731 0.24288 1.25057 2.115e-01 -0.173091
## State_dummy17 0.057944 0.28336 0.20449 8.380e-01 -0.498360
## State_dummy19 -0.062485 0.22536 -0.27727 7.817e-01 -0.504915
## State_dummy20 0.017738 0.27585 0.06431 9.487e-01 -0.523815
## State_dummy21 -0.206441 0.24215 -0.85252 3.942e-01 -0.681845
## State_dummy22 -0.347824 0.26247 -1.32522 1.855e-01 -0.863108
## State_dummy23 0.139897 0.24182 0.57851 5.631e-01 -0.334858
## State_dummy24 0.195033 0.25396 0.76796 4.428e-01 -0.303559
## State_dummy25 -0.076363 0.30554 -0.24993 8.027e-01 -0.676210
## State_dummy26 0.075858 0.26117 0.29045 7.716e-01 -0.436881
## State_dummy27 -0.038749 0.28972 -0.13375 8.936e-01 -0.607540
## State_dummy28 0.575628 0.23242 2.47672 1.349e-02 0.119341
## State_dummy29 -0.188646 0.28563 -0.66045 5.092e-01 -0.749411
## State_dummy30 0.089675 0.23265 0.38544 7.000e-01 -0.367078
## State_dummy31 -0.015072 0.27992 -0.05384 9.571e-01 -0.564623
## State_dummy32 -0.342324 0.47954 -0.71386 4.755e-01 -1.283771
## TreatUS:Female -0.226426 0.13055 -1.73445 8.326e-02 -0.482720
## TreatChina:Female -0.243728 0.13129 -1.85639 6.380e-02 -0.501486
## TreatUS:Age -0.067231 0.05132 -1.31002 1.906e-01 -0.167985
## TreatChina:Age -0.044928 0.05116 -0.87818 3.801e-01 -0.145368
## TreatUS:Income 0.042086 0.03912 1.07590 2.823e-01 -0.034710
## TreatChina:Income 0.000781 0.03151 0.02478 9.802e-01 -0.061086
## TreatUS:Emigration -0.077296 0.10104 -0.76497 4.445e-01 -0.275670
## TreatChina:Emigration -0.055704 0.10690 -0.52108 6.025e-01 -0.265574
## TreatUS:Pol_know 0.258116 0.18860 1.36862 1.715e-01 -0.112144
## TreatChina:Pol_know 0.037954 0.16409 0.23130 8.171e-01 -0.284193
## CI Upper DF
## (Intercept) 5.71766 725
## TreatUS 0.61526 725
## TreatChina 1.11880 725
## Female 0.28263 725
## Age 0.14066 725
## Income 0.06774 725
## Primary 0.22932 725
## Manufac 0.16947 725
## Emigration 0.27040 725
## Pol_know 0.02927 725
## PRI 0.38709 725
## PAN 0.34222 725
## PRD 0.25231 725
## MORENA 0.25353 725
## State_dummy2 0.54079 725
## State_dummy3 0.78088 725
## State_dummy4 0.57320 725
## State_dummy5 0.35134 725
## State_dummy6 1.16951 725
## State_dummy7 0.53040 725
## State_dummy8 0.66441 725
## State_dummy9 0.53834 725
## State_dummy10 0.46635 725
## State_dummy11 1.19865 725
## State_dummy12 0.59347 725
## State_dummy13 0.60238 725
## State_dummy14 0.46035 725
## State_dummy15 0.50409 725
## State_dummy16 0.78055 725
## State_dummy17 0.61425 725
## State_dummy19 0.37995 725
## State_dummy20 0.55929 725
## State_dummy21 0.26896 725
## State_dummy22 0.16746 725
## State_dummy23 0.61465 725
## State_dummy24 0.69362 725
## State_dummy25 0.52349 725
## State_dummy26 0.58860 725
## State_dummy27 0.53004 725
## State_dummy28 1.03192 725
## State_dummy29 0.37212 725
## State_dummy30 0.54643 725
## State_dummy31 0.53448 725
## State_dummy32 0.59912 725
## TreatUS:Female 0.02987 725
## TreatChina:Female 0.01403 725
## TreatUS:Age 0.03352 725
## TreatChina:Age 0.05551 725
## TreatUS:Income 0.11888 725
## TreatChina:Income 0.06265 725
## TreatUS:Emigration 0.12108 725
## TreatChina:Emigration 0.15417 725
## TreatUS:Pol_know 0.62837 725
## TreatChina:Pol_know 0.36010 725
##
## Multiple R-squared: 0.0812 , Adjusted R-squared: 0.01403
## F-statistic: 13.65 on 53 and 725 DF, p-value: < 2.2e-16
モデル６（対アメリカ貿易への支持）
sub_US<- lm_robust(Y_US ~ Treat*Female + Treat*Age +
Treat*Income + Primary + Manufac +
Treat*Emigration + Treat*Pol_know + PRI +
PAN + PRD + MORENA + State_dummy,
se_type = "stata", data = df)
summary(sub_US)
##
## Call:
## lm_robust(formula = Y_US ~ Treat * Female + Treat * Age + Treat *
## Income + Primary + Manufac + Treat * Emigration + Treat *
## Pol_know + PRI + PAN + PRD + MORENA + State_dummy, data = df,
## se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper
## (Intercept) 4.825009 0.65855 7.32674 6.284e-13 3.53213 6.11789
## TreatUS -1.012855 0.82329 -1.23025 2.190e-01 -2.62916 0.60346
## TreatChina -0.630699 0.87056 -0.72448 4.690e-01 -2.33981 1.07841
## Female -0.014705 0.12979 -0.11331 9.098e-01 -0.26950 0.24009
## Age 0.059584 0.04619 1.28993 1.975e-01 -0.03110 0.15027
## Income -0.002438 0.03549 -0.06871 9.452e-01 -0.07210 0.06723
## Primary -0.322831 0.25573 -1.26241 2.072e-01 -0.82488 0.17922
## Manufac 0.168144 0.12539 1.34093 1.804e-01 -0.07803 0.41432
## Emigration 0.029221 0.10729 0.27234 7.854e-01 -0.18142 0.23987
## Pol_know -0.314299 0.20233 -1.55343 1.208e-01 -0.71151 0.08291
## PRI 0.418682 0.14237 2.94086 3.377e-03 0.13918 0.69818
## PAN 0.362250 0.11078 3.27006 1.126e-03 0.14477 0.57973
## PRD 0.100218 0.24557 0.40811 6.833e-01 -0.38189 0.58233
## MORENA 0.166148 0.10560 1.57339 1.161e-01 -0.04117 0.37346
## State_dummy2 -0.280530 0.37897 -0.74025 4.594e-01 -1.02453 0.46347
## State_dummy3 0.129539 0.42886 0.30206 7.627e-01 -0.71241 0.97149
## State_dummy4 -0.855709 0.54629 -1.56640 1.177e-01 -1.92820 0.21679
## State_dummy5 -0.114854 0.34099 -0.33683 7.363e-01 -0.78430 0.55459
## State_dummy6 0.237573 0.63918 0.37169 7.102e-01 -1.01728 1.49242
## State_dummy7 0.119425 0.43893 0.27208 7.856e-01 -0.74230 0.98115
## State_dummy8 -0.031001 0.43136 -0.07187 9.427e-01 -0.87787 0.81587
## State_dummy9 0.186059 0.39722 0.46841 6.396e-01 -0.59377 0.96589
## State_dummy10 -0.196651 0.52116 -0.37734 7.060e-01 -1.21980 0.82650
## State_dummy11 0.439942 0.41766 1.05335 2.925e-01 -0.38002 1.25990
## State_dummy12 -0.170103 0.41540 -0.40950 6.823e-01 -0.98562 0.64542
## State_dummy13 0.007050 0.42512 0.01658 9.868e-01 -0.82756 0.84166
## State_dummy14 -0.019850 0.35159 -0.05646 9.550e-01 -0.71010 0.67041
## State_dummy15 -0.276857 0.35092 -0.78896 4.304e-01 -0.96578 0.41207
## State_dummy16 0.413457 0.36655 1.12796 2.597e-01 -0.30617 1.13308
## State_dummy17 -0.043420 0.46350 -0.09368 9.254e-01 -0.95337 0.86653
## State_dummy19 -0.051482 0.36151 -0.14241 8.868e-01 -0.76121 0.65824
## State_dummy20 -0.567802 0.40434 -1.40425 1.607e-01 -1.36162 0.22602
## State_dummy21 0.050820 0.36562 0.13900 8.895e-01 -0.66697 0.76861
## State_dummy22 -0.746919 0.41188 -1.81345 7.018e-02 -1.55553 0.06169
## State_dummy23 0.127737 0.41659 0.30663 7.592e-01 -0.69013 0.94560
## State_dummy24 0.023490 0.39906 0.05886 9.531e-01 -0.75996 0.80693
## State_dummy25 0.111605 0.40509 0.27551 7.830e-01 -0.68368 0.90689
## State_dummy26 0.310937 0.39559 0.78602 4.321e-01 -0.46569 1.08756
## State_dummy27 -0.234884 0.41188 -0.57027 5.687e-01 -1.04350 0.57373
## State_dummy28 -3.073452 0.36624 -8.39191 2.487e-16 -3.79247 -2.35444
## State_dummy29 0.080099 0.43451 0.18434 8.538e-01 -0.77294 0.93313
## State_dummy30 0.033801 0.37284 0.09066 9.278e-01 -0.69817 0.76577
## State_dummy31 0.226984 0.39204 0.57899 5.628e-01 -0.54267 0.99664
## State_dummy32 -0.137542 0.49483 -0.27796 7.811e-01 -1.10902 0.83393
## TreatUS:Female -0.054528 0.17581 -0.31016 7.565e-01 -0.39968 0.29062
## TreatChina:Female -0.097011 0.18371 -0.52806 5.976e-01 -0.45768 0.26366
## TreatUS:Age -0.036558 0.06842 -0.53431 5.933e-01 -0.17088 0.09777
## TreatChina:Age 0.034245 0.06967 0.49149 6.232e-01 -0.10254 0.17103
## TreatUS:Income 0.016608 0.04600 0.36101 7.182e-01 -0.07371 0.10692
## TreatChina:Income 0.007511 0.04542 0.16537 8.687e-01 -0.08166 0.09668
## TreatUS:Emigration 0.090566 0.14920 0.60702 5.440e-01 -0.20234 0.38347
## TreatChina:Emigration 0.149747 0.16500 0.90757 3.644e-01 -0.17418 0.47368
## TreatUS:Pol_know 0.282755 0.25741 1.09848 2.724e-01 -0.22259 0.78810
## TreatChina:Pol_know 0.096084 0.26430 0.36355 7.163e-01 -0.42279 0.61496
## DF
## (Intercept) 727
## TreatUS 727
## TreatChina 727
## Female 727
## Age 727
## Income 727
## Primary 727
## Manufac 727
## Emigration 727
## Pol_know 727
## PRI 727
## PAN 727
## PRD 727
## MORENA 727
## State_dummy2 727
## State_dummy3 727
## State_dummy4 727
## State_dummy5 727
## State_dummy6 727
## State_dummy7 727
## State_dummy8 727
## State_dummy9 727
## State_dummy10 727
## State_dummy11 727
## State_dummy12 727
## State_dummy13 727
## State_dummy14 727
## State_dummy15 727
## State_dummy16 727
## State_dummy17 727
## State_dummy19 727
## State_dummy20 727
## State_dummy21 727
## State_dummy22 727
## State_dummy23 727
## State_dummy24 727
## State_dummy25 727
## State_dummy26 727
## State_dummy27 727
## State_dummy28 727
## State_dummy29 727
## State_dummy30 727
## State_dummy31 727
## State_dummy32 727
## TreatUS:Female 727
## TreatChina:Female 727
## TreatUS:Age 727
## TreatChina:Age 727
## TreatUS:Income 727
## TreatChina:Income 727
## TreatUS:Emigration 727
## TreatChina:Emigration 727
## TreatUS:Pol_know 727
## TreatChina:Pol_know 727
##
## Multiple R-squared: 0.09735 , Adjusted R-squared: 0.03154
## F-statistic: 165.7 on 53 and 727 DF, p-value: < 2.2e-16
モデル９（対中国貿易への支持）
sub_China<- lm_robust(Y_China ~ Treat*Female + Treat*Age +
Treat*Income + Primary + Manufac +
Treat*Emigration + Treat*Pol_know + PRI
+ PAN + PRD + MORENA + State_dummy,
se_type = "stata", data = df)
summary(sub_China)
##
## Call:
## lm_robust(formula = Y_China ~ Treat * Female + Treat * Age +
## Treat * Income + Primary + Manufac + Treat * Emigration +
## Treat * Pol_know + PRI + PAN + PRD + MORENA + State_dummy,
## data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower
## (Intercept) 4.7084498 0.60043 7.841782 1.587e-14 3.529661
## TreatUS -1.6006260 0.74992 -2.134402 3.314e-02 -3.072893
## TreatChina -0.4174266 0.69484 -0.600749 5.482e-01 -1.781570
## Female 0.0644000 0.11384 0.565699 5.718e-01 -0.159098
## Age 0.0548637 0.04722 1.161923 2.456e-01 -0.037836
## Income -0.0003183 0.03185 -0.009992 9.920e-01 -0.062857
## Primary -0.0698236 0.20168 -0.346208 7.293e-01 -0.465771
## Manufac -0.0396789 0.12911 -0.307315 7.587e-01 -0.293161
## Emigration 0.0636617 0.08216 0.774851 4.387e-01 -0.097638
## Pol_know -0.2928860 0.20003 -1.464218 1.436e-01 -0.685590
## PRI 0.4559425 0.13762 3.313072 9.685e-04 0.185763
## PAN 0.2003025 0.11097 1.804999 7.149e-02 -0.017560
## PRD 0.2926160 0.20010 1.462350 1.441e-01 -0.100227
## MORENA 0.4274679 0.09906 4.315081 1.817e-05 0.232982
## State_dummy2 -0.2118285 0.23478 -0.902235 3.672e-01 -0.672761
## State_dummy3 0.3438605 0.45699 0.752451 4.520e-01 -0.553313
## State_dummy4 -0.3611706 0.23826 -1.515894 1.300e-01 -0.828923
## State_dummy5 -0.1894897 0.15576 -1.216522 2.242e-01 -0.495290
## State_dummy6 -0.2278943 0.29932 -0.761370 4.467e-01 -0.815533
## State_dummy7 -0.5460257 0.34743 -1.571618 1.165e-01 -1.228111
## State_dummy8 0.0222231 0.26247 0.084669 9.325e-01 -0.493067
## State_dummy9 -0.0280559 0.23524 -0.119263 9.051e-01 -0.489895
## State_dummy10 -0.3788555 0.40561 -0.934033 3.506e-01 -1.175169
## State_dummy11 0.5772261 0.38327 1.506039 1.325e-01 -0.175232
## State_dummy12 -0.2858001 0.27262 -1.048364 2.948e-01 -0.821009
## State_dummy13 -0.5425180 0.37282 -1.455175 1.461e-01 -1.274452
## State_dummy14 -0.0091662 0.17706 -0.051767 9.587e-01 -0.356786
## State_dummy15 -0.4003069 0.17123 -2.337861 1.967e-02 -0.736468
## State_dummy16 0.1893821 0.23388 0.809728 4.184e-01 -0.269787
## State_dummy17 0.0506373 0.31709 0.159693 8.732e-01 -0.571888
## State_dummy19 0.0285365 0.16662 0.171268 8.641e-01 -0.298575
## State_dummy20 -0.9632448 0.42424 -2.270530 2.347e-02 -1.796124
## State_dummy21 -0.3490376 0.20975 -1.664029 9.654e-02 -0.760835
## State_dummy22 -0.7708109 0.28798 -2.676585 7.606e-03 -1.336190
## State_dummy23 0.3368756 0.19834 1.698477 8.985e-02 -0.052512
## State_dummy24 -0.1353170 0.45012 -0.300626 7.638e-01 -1.019005
## State_dummy25 0.1131261 0.26238 0.431149 6.665e-01 -0.401993
## State_dummy26 0.4027023 0.23203 1.735548 8.307e-02 -0.052831
## State_dummy27 -0.0613834 0.31980 -0.191945 8.478e-01 -0.689222
## State_dummy28 -0.3985332 0.19215 -2.074029 3.843e-02 -0.775777
## State_dummy29 0.2636336 0.23110 1.140794 2.543e-01 -0.190064
## State_dummy30 -0.4203238 0.23881 -1.760106 7.881e-02 -0.889157
## State_dummy31 -0.0781501 0.28922 -0.270208 7.871e-01 -0.645962
## State_dummy32 -0.4769115 0.45545 -1.047121 2.954e-01 -1.371068
## TreatUS:Female -0.0154022 0.17180 -0.089650 9.286e-01 -0.352694
## TreatChina:Female -0.1331927 0.15179 -0.877478 3.805e-01 -0.431193
## TreatUS:Age -0.1283029 0.07226 -1.775614 7.622e-02 -0.270163
## TreatChina:Age -0.1024033 0.06494 -1.576867 1.153e-01 -0.229898
## TreatUS:Income 0.0445608 0.04438 1.004059 3.157e-01 -0.042569
## TreatChina:Income -0.0704867 0.03893 -1.810688 7.060e-02 -0.146912
## TreatUS:Emigration 0.0097834 0.13246 0.073861 9.411e-01 -0.250261
## TreatChina:Emigration -0.1348358 0.12335 -1.093119 2.747e-01 -0.377000
## TreatUS:Pol_know 0.4875166 0.24380 1.999650 4.591e-02 0.008878
## TreatChina:Pol_know 0.1622145 0.22203 0.730594 4.653e-01 -0.273685
## CI Upper DF
## (Intercept) 5.887238 726
## TreatUS -0.128359 726
## TreatChina 0.946717 726
## Female 0.287898 726
## Age 0.147564 726
## Income 0.062220 726
## Primary 0.326124 726
## Manufac 0.213804 726
## Emigration 0.224961 726
## Pol_know 0.099818 726
## PRI 0.726122 726
## PAN 0.418165 726
## PRD 0.685459 726
## MORENA 0.621953 726
## State_dummy2 0.249104 726
## State_dummy3 1.241034 726
## State_dummy4 0.106582 726
## State_dummy5 0.116311 726
## State_dummy6 0.359744 726
## State_dummy7 0.136060 726
## State_dummy8 0.537513 726
## State_dummy9 0.433783 726
## State_dummy10 0.417458 726
## State_dummy11 1.329685 726
## State_dummy12 0.249408 726
## State_dummy13 0.189415 726
## State_dummy14 0.338453 726
## State_dummy15 -0.064146 726
## State_dummy16 0.648551 726
## State_dummy17 0.673163 726
## State_dummy19 0.355648 726
## State_dummy20 -0.130365 726
## State_dummy21 0.062760 726
## State_dummy22 -0.205432 726
## State_dummy23 0.726264 726
## State_dummy24 0.748371 726
## State_dummy25 0.628245 726
## State_dummy26 0.858235 726
## State_dummy27 0.566455 726
## State_dummy28 -0.021289 726
## State_dummy29 0.717331 726
## State_dummy30 0.048509 726
## State_dummy31 0.489662 726
## State_dummy32 0.417245 726
## TreatUS:Female 0.321890 726
## TreatChina:Female 0.164808 726
## TreatUS:Age 0.013557 726
## TreatChina:Age 0.025091 726
## TreatUS:Income 0.131691 726
## TreatChina:Income 0.005938 726
## TreatUS:Emigration 0.269827 726
## TreatChina:Emigration 0.107329 726
## TreatUS:Pol_know 0.966156 726
## TreatChina:Pol_know 0.598114 726
##
## Multiple R-squared: 0.1464 , Adjusted R-squared: 0.08406
## F-statistic: 2.739 on 53 and 726 DF, p-value: 2.374e-09
