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)
![](data:image/png;base64,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)
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))
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)
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))
![](data:image/png;base64,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)
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))
![](data:image/png;base64,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)
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 = "")
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)
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