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_us_NA.csv")
処置変数をFactor変数に変換
df <- df |>
mutate(Treat = factor(Treat,
levels = c("Control", "MX", "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, Q30: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 free trade (highe scores means greater support: 1 = very bad, 2 = bad, 3 = neither good or bad, 4 = good, 5 = very good)
Y = 6 - Q4,
# Outcome Variable: Attitude toward free trade by partners
Y_EU = 6 - Q5_1,
Y_CA = 6 - Q5_2,
Y_China = 6 - Q5_3,
Y_LA = 6 - Q5_4,
Y_TPP = 6 - Q5_5,
# Job types (primary sector including agriculture, fishery, forestry, mmanufacturing industry)
Primary = ifelse(Q39 == 1, 1, 0),
Manufac = ifelse(Q39 == 3, 1, 0),
# Political knowledge
Pol_know1 = ifelse(Q17 == 3, 1, 0),
Pol_know2 = ifelse(Q18 == 3, 1, 0),
Pol_know3 = ifelse(Q19 == 3, 1, 0),
Pol_know = Pol_know1 + Pol_know2 + Pol_know3,
# Lived in a foreign countries
Live_foreign = 4 - Q20_4,
# Evaluation of government
Eval_gov = 5 - Q21,
# Partisanship
Dem = ifelse(Q25 == 1, 1, 0),
Rep = ifelse(Q25 == 2, 1, 0),
# Immigration related questions
Immig = 6 - Q9,
# Perception on immigration
Immig_neigh = 2 - Q30,
# Immigrants in the neighborhood or place of work
Immig_self = 2 - Q31,
# Respondent is immigrant
# Ethnicity/race
White = ifelse(Q32 == 1, 1, 0),
# Non-Hispanic White
Black = ifelse(Q32 == 2, 1, 0),
# Black, Afro-Caribbean, or African American
Latino = ifelse(Q32 == 4, 1, 0),
# Latino or Hispanic American
East_Asian = ifelse(Q32 == 4, 1, 0),
# East Asian American
# Living location
Urban = ifelse(Q36 == 1, 1, 0),
Rurul = ifelse(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_LA, Y_China, Female, Age, Income, Primary,
Manufac, Immig, Pol_know, Dem, Rep,
State_dummy)
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",
"Immig", "Primary", "Manufac",
"Pol_know", "Dem", "Rep")) |>
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 (1)アメリカ
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 3.77 drop
## 2 MX 3.58 drop
## 3 China 3.64 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 3.13 drop
## 2 MX 3.12 drop
## 3 China 3.16 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))

対ラテンアメリカ貿易への支持
Support_LA.df <- df |>
group_by(Treat) |>
summarise(Y_LA = mean(Y_LA, na.rm = TRUE), # remove NAs
group = "drop")
Support_LA.df
## # A tibble: 3 × 3
## Treat Y_LA group
## <fct> <dbl> <chr>
## 1 Control 3.34 drop
## 2 MX 3.31 drop
## 3 China 3.41 drop
Support_LA.df |>
ggplot() +
geom_bar(aes(x = Treat, y = Y_LA), stat = "identity") +
geom_label(aes(x = Treat, y = Y_LA,
label = round(Y_LA, 3)), label.size = 1) +
labs(x = "Treatment", y = "Support for International Trade
with Latin America") +
coord_cartesian(ylim = c(0, 4.5))

単回帰分析
推定結果は、「補填 表4
A-1 アメリカ: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) 3.7680 0.04948 76.151 0.00000 3.6709 3.86508 947
## TreatMX -0.1898 0.07313 -2.596 0.00958 -0.3334 -0.04633 947
## TreatChina -0.1260 0.07307 -1.724 0.08496 -0.2694 0.01739 947
##
## Multiple R-squared: 0.006984 , Adjusted R-squared: 0.004887
## F-statistic: 3.551 on 2 and 947 DF, p-value: 0.02909
モデル4(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) 3.13183 0.06783 46.1732 2.269e-246 2.9987 3.2649 963
## TreatMX -0.01668 0.09231 -0.1807 8.566e-01 -0.1978 0.1645 963
## TreatChina 0.03124 0.09372 0.3334 7.389e-01 -0.1527 0.2152 963
##
## Multiple R-squared: 0.0002952 , Adjusted R-squared: -0.001781
## F-statistic: 0.1449 on 2 and 963 DF, p-value: 0.8651
モデル7(対ラテンアメリカ貿易への支持)
Support_LA.fit <- lm_robust(Y_LA ~ Treat, se_type = "stata",
data = df)
summary(Support_LA.fit)
##
## Call:
## lm_robust(formula = Y_LA ~ Treat, data = df, se_type = "stata")
##
## Standard error type: HC1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 3.33898 0.05391 61.9345 0.0000 3.23318 3.4448 909
## TreatMX -0.02608 0.07516 -0.3470 0.7287 -0.17358 0.1214 909
## TreatChina 0.07470 0.07662 0.9749 0.3299 -0.07568 0.2251 909
##
## Multiple R-squared: 0.002116 , Adjusted R-squared: -7.942e-05
## F-statistic: 0.9475 on 2 and 909 DF, p-value: 0.3881
重回帰分析
推定結果は、「補填 表4
A-1 アメリカ: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 + Immig +
Pol_know + Dem + Rep +
State_dummy,se_type = "stata",
data = df)
summary(Support.fit)
##
## Call:
## lm_robust(formula = Y ~ Treat + Female + Age + Income + Primary +
## Manufac + Immig + Pol_know + Dem + Rep + 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) 3.684595 0.41627 8.851541 6.216e-17 2.86560 4.50359 317
## TreatMX -0.193994 0.11947 -1.623786 1.054e-01 -0.42905 0.04106 317
## TreatChina -0.141888 0.11599 -1.223239 2.221e-01 -0.37010 0.08633 317
## Female -0.267341 0.11270 -2.372101 1.828e-02 -0.48908 -0.04560 317
## Age -0.145924 0.04336 -3.365617 8.576e-04 -0.23123 -0.06062 317
## Income 0.027705 0.02470 1.121608 2.629e-01 -0.02089 0.07631 317
## Primary -0.444907 0.56683 -0.784908 4.331e-01 -1.56012 0.67031 317
## Manufac -0.134151 0.15938 -0.841704 4.006e-01 -0.44773 0.17943 317
## Immig 0.194880 0.04573 4.261317 2.683e-05 0.10490 0.28486 317
## Pol_know -0.188070 0.05884 -3.196048 1.534e-03 -0.30385 -0.07229 317
## Dem 0.101683 0.14172 0.717490 4.736e-01 -0.17715 0.38051 317
## Rep 0.247408 0.13788 1.794382 7.370e-02 -0.02387 0.51868 317
## State_dummy3 -0.230022 0.43478 -0.529056 5.971e-01 -1.08544 0.62539 317
## State_dummy4 0.078705 0.50217 0.156729 8.756e-01 -0.90931 1.06672 317
## State_dummy5 -0.102343 0.39280 -0.260545 7.946e-01 -0.87518 0.67049 317
## State_dummy6 -0.717446 0.79255 -0.905235 3.660e-01 -2.27677 0.84188 317
## State_dummy7 0.222789 0.49433 0.450692 6.525e-01 -0.74979 1.19537 317
## State_dummy8 1.381883 0.40077 3.448059 6.409e-04 0.59338 2.17039 317
## State_dummy9 -0.023594 0.39850 -0.059206 9.528e-01 -0.80763 0.76044 317
## State_dummy10 0.007681 0.43925 0.017487 9.861e-01 -0.85652 0.87189 317
## State_dummy11 -0.171902 0.40035 -0.429383 6.679e-01 -0.95958 0.61577 317
## State_dummy13 -0.162893 0.44043 -0.369850 7.117e-01 -1.02943 0.70364 317
## State_dummy14 -0.315255 0.43208 -0.729626 4.662e-01 -1.16536 0.53485 317
## State_dummy15 -0.793352 0.65664 -1.208193 2.279e-01 -2.08528 0.49858 317
## State_dummy16 -0.321327 0.75829 -0.423753 6.720e-01 -1.81324 1.17059 317
## State_dummy17 -0.183015 0.49799 -0.367509 7.135e-01 -1.16280 0.79676 317
## State_dummy18 0.449253 0.46608 0.963905 3.358e-01 -0.46774 1.36625 317
## State_dummy19 0.923787 0.38245 2.415469 1.628e-02 0.17133 1.67624 317
## State_dummy20 -0.803844 0.70199 -1.145093 2.530e-01 -2.18499 0.57730 317
## State_dummy21 0.161535 0.52085 0.310135 7.567e-01 -0.86323 1.18630 317
## State_dummy22 -0.383422 0.44550 -0.860646 3.901e-01 -1.25994 0.49310 317
## State_dummy23 0.128661 0.48685 0.264271 7.917e-01 -0.82921 1.08653 317
## State_dummy24 0.607601 0.46118 1.317488 1.886e-01 -0.29976 1.51496 317
## State_dummy25 0.532867 0.38186 1.395464 1.639e-01 -0.21843 1.28416 317
## State_dummy26 0.062745 0.40005 0.156842 8.755e-01 -0.72434 0.84983 317
## State_dummy28 -0.241716 0.71777 -0.336760 7.365e-01 -1.65391 1.17048 317
## State_dummy29 0.212426 0.40962 0.518595 6.044e-01 -0.59349 1.01834 317
## State_dummy30 0.093103 0.45773 0.203403 8.390e-01 -0.80747 0.99367 317
## State_dummy31 -1.060067 0.88707 -1.195018 2.330e-01 -2.80536 0.68523 317
## State_dummy32 -0.565206 0.42531 -1.328941 1.848e-01 -1.40198 0.27157 317
## State_dummy33 -0.543227 0.48262 -1.125579 2.612e-01 -1.49277 0.40632 317
## State_dummy34 -0.072329 0.42280 -0.171071 8.643e-01 -0.90418 0.75953 317
## State_dummy35 0.002196 0.41833 0.005249 9.958e-01 -0.82086 0.82525 317
## State_dummy36 -1.183397 0.57488 -2.058516 4.036e-02 -2.31446 -0.05234 317
## State_dummy37 0.671173 0.54187 1.238630 2.164e-01 -0.39494 1.73728 317
## State_dummy38 -0.290606 0.46124 -0.630059 5.291e-01 -1.19807 0.61686 317
## State_dummy40 -0.703485 0.44005 -1.598665 1.109e-01 -1.56926 0.16229 317
## State_dummy41 -0.188868 0.43629 -0.432894 6.654e-01 -1.04726 0.66952 317
## State_dummy42 -0.335421 0.56337 -0.595387 5.520e-01 -1.44383 0.77299 317
## State_dummy43 0.023265 0.38886 0.059830 9.523e-01 -0.74180 0.78833 317
## State_dummy44 0.621034 0.40152 1.546707 1.229e-01 -0.16895 1.41102 317
## State_dummy46 -0.393445 0.47203 -0.833527 4.052e-01 -1.32214 0.53525 317
## State_dummy47 0.161940 0.61604 0.262871 7.928e-01 -1.05011 1.37399 317
## State_dummy49 -0.389407 0.42688 -0.912228 3.623e-01 -1.22927 0.45046 317
## State_dummy51 0.770536 0.46723 1.649149 1.001e-01 -0.14873 1.68980 317
##
## Multiple R-squared: 0.3007 , Adjusted R-squared: 0.1816
## F-statistic: NA on 54 and 317 DF, p-value: NA
モデル5(対中国貿易への支持)
Support_China.fit <- lm_robust(Y_China ~ Treat + Female + Age + Income + Primary + Manufac +
Immig + Pol_know + Dem + Rep +
State_dummy, se_type =
"stata", data = df)
summary(Support_China.fit)
##
## Call:
## lm_robust(formula = Y_China ~ Treat + Female + Age + Income +
## Primary + Manufac + Immig + Pol_know + Dem + Rep + 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) 2.24081 0.47808 4.68711 4.121e-06 1.30020 3.18142 317
## TreatMX 0.20225 0.16136 1.25338 2.110e-01 -0.11523 0.51972 317
## TreatChina 0.34984 0.15402 2.27140 2.379e-02 0.04681 0.65288 317
## Female 0.30394 0.14356 2.11716 3.502e-02 0.02149 0.58640 317
## Age -0.31173 0.05644 -5.52326 6.942e-08 -0.42278 -0.20069 317
## Income 0.02541 0.03214 0.79054 4.298e-01 -0.03783 0.08864 317
## Primary 0.27186 0.53868 0.50468 6.141e-01 -0.78798 1.33169 317
## Manufac -0.02531 0.16917 -0.14959 8.812e-01 -0.35815 0.30753 317
## Immig 0.20158 0.05759 3.50057 5.308e-04 0.08828 0.31488 317
## Pol_know -0.22832 0.08437 -2.70635 7.171e-03 -0.39431 -0.06234 317
## Dem 0.26431 0.17144 1.54167 1.242e-01 -0.07300 0.60162 317
## Rep 0.21227 0.18139 1.17026 2.428e-01 -0.14461 0.56914 317
## State_dummy3 -0.19477 0.45851 -0.42479 6.713e-01 -1.09687 0.70733 317
## State_dummy4 0.17143 0.57893 0.29611 7.673e-01 -0.96760 1.31046 317
## State_dummy5 -0.04755 0.40953 -0.11611 9.076e-01 -0.85328 0.75818 317
## State_dummy6 -0.51984 0.99953 -0.52009 6.034e-01 -2.48639 1.44671 317
## State_dummy7 -0.50315 0.57958 -0.86812 3.860e-01 -1.64345 0.63716 317
## State_dummy8 -1.30976 0.43212 -3.03100 2.638e-03 -2.15995 -0.45957 317
## State_dummy9 0.48059 0.43582 1.10273 2.710e-01 -0.37687 1.33806 317
## State_dummy10 0.25894 0.53937 0.48009 6.315e-01 -0.80225 1.32014 317
## State_dummy11 0.13310 0.40702 0.32701 7.439e-01 -0.66771 0.93391 317
## State_dummy13 0.07852 0.42232 0.18592 8.526e-01 -0.75239 0.90943 317
## State_dummy14 0.71939 0.47736 1.50701 1.328e-01 -0.21981 1.65860 317
## State_dummy15 0.83538 0.56281 1.48431 1.387e-01 -0.27193 1.94269 317
## State_dummy16 -0.94515 0.62997 -1.50032 1.345e-01 -2.18459 0.29429 317
## State_dummy17 0.11985 0.67927 0.17643 8.601e-01 -1.21661 1.45630 317
## State_dummy18 1.18964 0.46671 2.54898 1.127e-02 0.27140 2.10789 317
## State_dummy19 -1.24852 0.40428 -3.08824 2.192e-03 -2.04393 -0.45310 317
## State_dummy20 -0.29927 1.03891 -0.28806 7.735e-01 -2.34330 1.74477 317
## State_dummy21 0.56171 0.65018 0.86392 3.883e-01 -0.71751 1.84093 317
## State_dummy22 -0.57890 0.56941 -1.01667 3.101e-01 -1.69919 0.54139 317
## State_dummy23 0.24429 0.68036 0.35906 7.198e-01 -1.09429 1.58287 317
## State_dummy24 0.30694 0.96360 0.31853 7.503e-01 -1.58893 2.20281 317
## State_dummy25 0.30241 0.50288 0.60136 5.480e-01 -0.68699 1.29181 317
## State_dummy26 0.18370 0.83796 0.21922 8.266e-01 -1.46497 1.83237 317
## State_dummy28 -0.15886 0.58415 -0.27195 7.858e-01 -1.30817 0.99045 317
## State_dummy29 1.53576 0.45914 3.34489 9.220e-04 0.63242 2.43910 317
## State_dummy30 0.06800 0.49299 0.13793 8.904e-01 -0.90195 1.03795 317
## State_dummy31 -0.34046 0.47516 -0.71652 4.742e-01 -1.27532 0.59440 317
## State_dummy32 -0.25854 0.46200 -0.55962 5.761e-01 -1.16751 0.65042 317
## State_dummy33 -0.59766 0.48856 -1.22331 2.221e-01 -1.55889 0.36357 317
## State_dummy34 1.27307 0.43989 2.89405 4.067e-03 0.40759 2.13855 317
## State_dummy35 0.12714 0.48413 0.26262 7.930e-01 -0.82537 1.07965 317
## State_dummy36 -1.14931 0.45175 -2.54409 1.143e-02 -2.03812 -0.26049 317
## State_dummy37 1.02626 0.78037 1.31508 1.894e-01 -0.50911 2.56163 317
## State_dummy38 -0.37957 0.49445 -0.76766 4.433e-01 -1.35238 0.59325 317
## State_dummy40 1.19700 0.47179 2.53713 1.166e-02 0.26876 2.12523 317
## State_dummy41 0.15309 0.84031 0.18219 8.556e-01 -1.50019 1.80637 317
## State_dummy42 -0.15621 0.59515 -0.26248 7.931e-01 -1.32715 1.01473 317
## State_dummy43 0.34645 0.44697 0.77509 4.389e-01 -0.53297 1.22586 317
## State_dummy44 1.87577 0.50580 3.70850 2.461e-04 0.88061 2.87093 317
## State_dummy46 -0.02035 0.66393 -0.03065 9.756e-01 -1.32660 1.28591 317
## State_dummy47 1.44213 0.44689 3.22701 1.382e-03 0.56288 2.32138 317
## State_dummy49 0.17710 0.48190 0.36751 7.135e-01 -0.77102 1.12523 317
## State_dummy51 1.32811 0.48120 2.76003 6.116e-03 0.38137 2.27485 317
##
## Multiple R-squared: 0.3457 , Adjusted R-squared: 0.2342
## F-statistic: NA on 54 and 317 DF, p-value: NA
モデル8(対ラテンアメリカ貿易への支持)
Support_LA.fit <- lm_robust(Y_LA ~ Treat + Female + Age +
Income + Primary + Manufac +
Immig + Pol_know + Dem + Rep +
State_dummy, se_type = "stata",
data = df)
summary(Support_LA.fit)
##
## Call:
## lm_robust(formula = Y_LA ~ Treat + Female + Age + Income + Primary +
## Manufac + Immig + Pol_know + Dem + Rep + 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) 3.404666 0.38735 8.789661 1.089e-16 2.642471 4.16686 307
## TreatMX 0.034802 0.12728 0.273415 7.847e-01 -0.215659 0.28526 307
## TreatChina 0.143430 0.12636 1.135053 2.572e-01 -0.105219 0.39208 307
## Female 0.147323 0.10781 1.366503 1.728e-01 -0.064817 0.35946 307
## Age -0.104987 0.04375 -2.399491 1.701e-02 -0.191082 -0.01889 307
## Income 0.054068 0.02597 2.081902 3.818e-02 0.002965 0.10517 307
## Primary 0.311484 0.43245 0.720280 4.719e-01 -0.539454 1.16242 307
## Manufac -0.038937 0.16879 -0.230677 8.177e-01 -0.371074 0.29320 307
## Immig 0.160681 0.04400 3.652235 3.056e-04 0.074110 0.24725 307
## Pol_know -0.040195 0.07508 -0.535387 5.928e-01 -0.187923 0.10753 307
## Dem -0.006015 0.12061 -0.049872 9.603e-01 -0.243337 0.23131 307
## Rep -0.136075 0.13860 -0.981749 3.270e-01 -0.408811 0.13666 307
## State_dummy3 -0.685970 0.40833 -1.679927 9.399e-02 -1.489456 0.11752 307
## State_dummy4 -0.444290 0.48332 -0.919255 3.587e-01 -1.395320 0.50674 307
## State_dummy5 -0.677800 0.37580 -1.803616 7.227e-02 -1.417271 0.06167 307
## State_dummy6 -0.437443 0.58444 -0.748483 4.547e-01 -1.587457 0.71257 307
## State_dummy7 -0.002076 0.45848 -0.004528 9.964e-01 -0.904238 0.90009 307
## State_dummy8 0.510802 0.38744 1.318393 1.884e-01 -0.251578 1.27318 307
## State_dummy9 -0.236623 0.37085 -0.638052 5.239e-01 -0.966357 0.49311 307
## State_dummy10 -0.882747 0.50215 -1.757929 7.976e-02 -1.870841 0.10535 307
## State_dummy11 -1.483863 1.22813 -1.208232 2.279e-01 -3.900476 0.93275 307
## State_dummy13 -0.403032 0.38484 -1.047283 2.958e-01 -1.160281 0.35422 307
## State_dummy14 -0.357823 0.41136 -0.869853 3.851e-01 -1.167264 0.45162 307
## State_dummy15 -0.487484 0.84349 -0.577935 5.637e-01 -2.147243 1.17227 307
## State_dummy16 -1.113992 0.75219 -1.480993 1.396e-01 -2.594097 0.36611 307
## State_dummy17 -0.451901 0.44979 -1.004694 3.158e-01 -1.336961 0.43316 307
## State_dummy18 0.392252 0.77065 0.508992 6.111e-01 -1.124163 1.90867 307
## State_dummy19 -2.141947 0.35746 -5.992123 5.797e-09 -2.845330 -1.43856 307
## State_dummy20 -1.240526 0.58172 -2.132499 3.376e-02 -2.385196 -0.09586 307
## State_dummy21 -0.040529 0.46347 -0.087447 9.304e-01 -0.952514 0.87146 307
## State_dummy22 -0.639475 0.51326 -1.245915 2.137e-01 -1.649422 0.37047 307
## State_dummy23 -0.467836 0.41862 -1.117557 2.646e-01 -1.291570 0.35590 307
## State_dummy24 0.722191 0.37134 1.944805 5.271e-02 -0.008510 1.45289 307
## State_dummy25 -0.365674 0.42728 -0.855819 3.928e-01 -1.206441 0.47509 307
## State_dummy26 -0.071536 0.36247 -0.197356 8.437e-01 -0.784780 0.64171 307
## State_dummy28 -0.608374 0.84961 -0.716065 4.745e-01 -2.280163 1.06342 307
## State_dummy29 0.818044 0.43138 1.896325 5.886e-02 -0.030799 1.66689 307
## State_dummy30 -0.691000 0.45392 -1.522307 1.290e-01 -1.584180 0.20218 307
## State_dummy31 -0.086746 0.36115 -0.240196 8.103e-01 -0.797382 0.62389 307
## State_dummy32 -0.711271 0.40818 -1.742558 8.241e-02 -1.514448 0.09191 307
## State_dummy33 -0.556327 0.40878 -1.360956 1.745e-01 -1.360685 0.24803 307
## State_dummy34 -0.022889 0.39090 -0.058556 9.533e-01 -0.792062 0.74628 307
## State_dummy35 -0.549551 0.43228 -1.271279 2.046e-01 -1.400161 0.30106 307
## State_dummy36 -0.358816 0.61159 -0.586699 5.578e-01 -1.562246 0.84461 307
## State_dummy37 0.281028 0.46552 0.603685 5.465e-01 -0.634988 1.19704 307
## State_dummy38 -0.723930 0.45130 -1.604085 1.097e-01 -1.611971 0.16411 307
## State_dummy40 -0.028130 0.35523 -0.079187 9.369e-01 -0.727129 0.67087 307
## State_dummy41 -0.043706 0.36969 -0.118223 9.060e-01 -0.771151 0.68374 307
## State_dummy42 -0.174591 0.43940 -0.397342 6.914e-01 -1.039204 0.69002 307
## State_dummy43 -0.650075 0.38362 -1.694565 9.117e-02 -1.404939 0.10479 307
## State_dummy44 -1.062700 0.39201 -2.710873 7.088e-03 -1.834074 -0.29133 307
## State_dummy46 -0.980219 0.43267 -2.265525 2.418e-02 -1.831588 -0.12885 307
## State_dummy47 -0.471325 0.70327 -0.670193 5.032e-01 -1.855160 0.91251 307
## State_dummy49 -0.562575 0.38496 -1.461394 1.449e-01 -1.320065 0.19491 307
## State_dummy51 0.747341 0.42344 1.764943 7.857e-02 -0.085864 1.58055 307
##
## Multiple R-squared: 0.2268 , Adjusted R-squared: 0.09078
## F-statistic: NA on 54 and 307 DF, p-value: NA
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
(1)アメリカ)
plot_summs(Support.fit, Support_China.fit, Support_LA.fit,
scale = TRUE, robust = TRUE,
coefs = c("処置:メキシコ"="TreatMX",
"処置:中国"="TreatChina", "性別"=
"Female", "年齢"= "Age",
"所得"="Income", "一次産業"="Primary",
"製造業"= "Manufac", "移民認識"="Immig",
"政治知識"="Pol_know",
"民主党支持"= "Dem", "共和党支持"="Rep"),
model.names = c("国際貿易一般", "対中国貿易",
"対ラテンアメリカ貿易"),
legend.title = "")
