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 = "")
サブグループ分析(Age,
Female, Income, Immig, Pol_know)
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*Immig +
Treat*Pol_know + Dem + Rep + State_dummy,
se_type = "stata", data = df)
summary(sub_all)
##
## Call:
## lm_robust(formula = Y ~ Treat * Female + Treat * Age + Treat *
## Income + Primary + Manufac + Treat * Immig + Treat * 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
## (Intercept) 3.62957 0.52308 6.93882 2.352e-11 2.60029 4.65885
## TreatMX -0.01880 0.50392 -0.03731 9.703e-01 -1.01038 0.97278
## TreatChina -0.64677 0.45708 -1.41499 1.581e-01 -1.54618 0.25264
## Female -0.21741 0.16292 -1.33446 1.830e-01 -0.53799 0.10317
## Age -0.17039 0.06833 -2.49348 1.318e-02 -0.30485 -0.03593
## Income 0.09415 0.04028 2.33747 2.006e-02 0.01489 0.17342
## Primary -0.48330 0.60195 -0.80290 4.227e-01 -1.66777 0.70116
## Manufac -0.11977 0.16687 -0.71774 4.735e-01 -0.44813 0.20859
## Immig 0.20081 0.07843 2.56028 1.094e-02 0.04648 0.35514
## Pol_know -0.29188 0.09236 -3.16025 1.734e-03 -0.47361 -0.11014
## Dem 0.13048 0.14358 0.90873 3.642e-01 -0.15205 0.41301
## Rep 0.31387 0.13332 2.35428 1.919e-02 0.05153 0.57620
## State_dummy3 -0.14469 0.44664 -0.32395 7.462e-01 -1.02356 0.73417
## State_dummy4 0.18370 0.42376 0.43349 6.650e-01 -0.65015 1.01755
## State_dummy5 -0.03344 0.40178 -0.08324 9.337e-01 -0.82403 0.75714
## State_dummy6 -0.60444 0.72034 -0.83911 4.021e-01 -2.02188 0.81299
## State_dummy7 0.28054 0.51046 0.54958 5.830e-01 -0.72390 1.28498
## State_dummy8 1.68801 0.44683 3.77775 1.900e-04 0.80877 2.56724
## State_dummy9 0.10098 0.40877 0.24705 8.050e-01 -0.70335 0.90532
## State_dummy10 0.06967 0.43596 0.15980 8.731e-01 -0.78819 0.92752
## State_dummy11 0.09520 0.49122 0.19380 8.465e-01 -0.87138 1.06177
## State_dummy13 -0.08897 0.45044 -0.19752 8.436e-01 -0.97530 0.79736
## State_dummy14 -0.22942 0.45259 -0.50692 6.126e-01 -1.11999 0.66114
## State_dummy15 -0.64731 0.69782 -0.92762 3.543e-01 -2.02043 0.72580
## State_dummy16 -0.28295 0.83311 -0.33963 7.344e-01 -1.92228 1.35638
## State_dummy17 -0.01731 0.49068 -0.03527 9.719e-01 -0.98282 0.94821
## State_dummy18 0.56590 0.48519 1.16633 2.444e-01 -0.38883 1.52062
## State_dummy19 1.04385 0.40992 2.54645 1.137e-02 0.23723 1.85047
## State_dummy20 -0.43141 0.73032 -0.59071 5.552e-01 -1.86848 1.00566
## State_dummy21 0.31708 0.54675 0.57993 5.624e-01 -0.75878 1.39293
## State_dummy22 -0.30340 0.45667 -0.66436 5.070e-01 -1.20200 0.59521
## State_dummy23 0.13273 0.45604 0.29104 7.712e-01 -0.76463 1.03008
## State_dummy24 0.71436 0.50617 1.41132 1.592e-01 -0.28164 1.71037
## State_dummy25 0.74079 0.42641 1.73726 8.334e-02 -0.09827 1.57985
## State_dummy26 -0.05270 0.41940 -0.12566 9.001e-01 -0.87796 0.77256
## State_dummy28 -0.03868 0.76010 -0.05089 9.594e-01 -1.53435 1.45698
## State_dummy29 0.02441 0.47574 0.05131 9.591e-01 -0.91172 0.96054
## State_dummy30 0.19132 0.45777 0.41795 6.763e-01 -0.70944 1.09209
## State_dummy31 -0.90267 0.91551 -0.98598 3.249e-01 -2.70413 0.89879
## State_dummy32 -0.45027 0.44219 -1.01827 3.094e-01 -1.32037 0.41983
## State_dummy33 -0.34435 0.46472 -0.74098 4.593e-01 -1.25880 0.57009
## State_dummy34 0.13705 0.44179 0.31021 7.566e-01 -0.73228 1.00638
## State_dummy35 0.14728 0.42495 0.34658 7.291e-01 -0.68891 0.98347
## State_dummy36 -0.94063 0.62505 -1.50489 1.334e-01 -2.17055 0.28929
## State_dummy37 0.75223 0.62580 1.20202 2.303e-01 -0.47918 1.98363
## State_dummy38 -0.18718 0.46147 -0.40561 6.853e-01 -1.09523 0.72087
## State_dummy40 -0.49619 0.48513 -1.02280 3.072e-01 -1.45078 0.45841
## State_dummy41 -0.26091 0.51427 -0.50734 6.123e-01 -1.27286 0.75104
## State_dummy42 -0.11963 0.57619 -0.20762 8.357e-01 -1.25342 1.01416
## State_dummy43 0.22251 0.41956 0.53035 5.963e-01 -0.60306 1.04809
## State_dummy44 0.75429 0.41313 1.82579 6.885e-02 -0.05864 1.56721
## State_dummy46 -0.33923 0.48073 -0.70566 4.809e-01 -1.28518 0.60672
## State_dummy47 0.36121 0.61295 0.58930 5.561e-01 -0.84490 1.56732
## State_dummy49 -0.24288 0.44059 -0.55126 5.819e-01 -1.10983 0.62407
## State_dummy51 1.22639 0.48965 2.50462 1.278e-02 0.26290 2.18989
## TreatMX:Female -0.16809 0.25758 -0.65258 5.145e-01 -0.67494 0.33875
## TreatChina:Female 0.02800 0.24376 0.11487 9.086e-01 -0.45165 0.50765
## TreatMX:Age 0.11194 0.10984 1.01908 3.090e-01 -0.10420 0.32807
## TreatChina:Age -0.01337 0.09960 -0.13423 8.933e-01 -0.20935 0.18261
## TreatMX:Income -0.17129 0.06477 -2.64459 8.600e-03 -0.29873 -0.04384
## TreatChina:Income -0.02709 0.05808 -0.46642 6.412e-01 -0.14138 0.08720
## TreatMX:Immig -0.05174 0.10847 -0.47698 6.337e-01 -0.26517 0.16170
## TreatChina:Immig 0.03462 0.10477 0.33044 7.413e-01 -0.17153 0.24077
## TreatMX:Pol_know 0.13726 0.15472 0.88718 3.757e-01 -0.16718 0.44170
## TreatChina:Pol_know 0.21651 0.13223 1.63740 1.026e-01 -0.04368 0.47670
## DF
## (Intercept) 307
## TreatMX 307
## TreatChina 307
## Female 307
## Age 307
## Income 307
## Primary 307
## Manufac 307
## Immig 307
## Pol_know 307
## Dem 307
## Rep 307
## State_dummy3 307
## State_dummy4 307
## State_dummy5 307
## State_dummy6 307
## State_dummy7 307
## State_dummy8 307
## State_dummy9 307
## State_dummy10 307
## State_dummy11 307
## State_dummy13 307
## State_dummy14 307
## State_dummy15 307
## State_dummy16 307
## State_dummy17 307
## State_dummy18 307
## State_dummy19 307
## State_dummy20 307
## State_dummy21 307
## State_dummy22 307
## State_dummy23 307
## State_dummy24 307
## State_dummy25 307
## State_dummy26 307
## State_dummy28 307
## State_dummy29 307
## State_dummy30 307
## State_dummy31 307
## State_dummy32 307
## State_dummy33 307
## State_dummy34 307
## State_dummy35 307
## State_dummy36 307
## State_dummy37 307
## State_dummy38 307
## State_dummy40 307
## State_dummy41 307
## State_dummy42 307
## State_dummy43 307
## State_dummy44 307
## State_dummy46 307
## State_dummy47 307
## State_dummy49 307
## State_dummy51 307
## TreatMX:Female 307
## TreatChina:Female 307
## TreatMX:Age 307
## TreatChina:Age 307
## TreatMX:Income 307
## TreatChina:Income 307
## TreatMX:Immig 307
## TreatChina:Immig 307
## TreatMX:Pol_know 307
## TreatChina:Pol_know 307
##
## Multiple R-squared: 0.33 , Adjusted R-squared: 0.1904
## F-statistic: NA on 64 and 307 DF, p-value: NA
モデル６（対中国貿易への支持）
sub_china<- lm_robust(Y_China ~ Treat*Female + Treat*Age +
Treat*Income + Primary + Manufac +
Treat*Immig + Treat*Pol_know + Dem +
Rep + 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 * Immig + Treat *
## 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
## (Intercept) 1.67123 0.62458 2.67576 0.0078561 0.442228 2.900236
## TreatMX 1.41870 0.60017 2.36384 0.0187092 0.237737 2.599664
## TreatChina 0.53430 0.60883 0.87760 0.3808496 -0.663697 1.732302
## Female 0.69002 0.22378 3.08341 0.0022323 0.249674 1.130367
## Age -0.33308 0.08830 -3.77238 0.0001940 -0.506825 -0.159343
## Income 0.09699 0.05204 1.86368 0.0633213 -0.005415 0.199403
## Primary 0.22307 0.51763 0.43095 0.6668078 -0.795481 1.241626
## Manufac -0.02514 0.17506 -0.14361 0.8858989 -0.369600 0.319319
## Immig 0.27193 0.09874 2.75412 0.0062359 0.077646 0.466215
## Pol_know -0.19887 0.13679 -1.45380 0.1470228 -0.468044 0.070301
## Dem 0.34694 0.17379 1.99630 0.0467847 0.004966 0.688913
## Rep 0.31637 0.17999 1.75772 0.0797922 -0.037798 0.670534
## State_dummy3 -0.23735 0.46693 -0.50833 0.6115896 -1.156151 0.681441
## State_dummy4 0.13694 0.51674 0.26501 0.7911833 -0.879858 1.153734
## State_dummy5 -0.09363 0.42822 -0.21866 0.8270598 -0.936243 0.748975
## State_dummy6 -0.50858 0.92183 -0.55171 0.5815511 -2.322479 1.305323
## State_dummy7 -0.65831 0.69836 -0.94266 0.3465960 -2.032482 0.715858
## State_dummy8 -1.04817 0.47286 -2.21665 0.0273800 -1.978628 -0.117707
## State_dummy9 0.48699 0.44775 1.08762 0.2776144 -0.394065 1.368036
## State_dummy10 0.19343 0.54899 0.35233 0.7248293 -0.886824 1.273677
## State_dummy11 0.19862 0.44692 0.44441 0.6570578 -0.680796 1.078029
## State_dummy13 0.05018 0.44045 0.11392 0.9093743 -0.816497 0.916850
## State_dummy14 0.68992 0.48054 1.43572 0.1520999 -0.255648 1.635495
## State_dummy15 0.93493 0.59676 1.56669 0.1182173 -0.239318 2.109184
## State_dummy16 -1.01958 0.61678 -1.65306 0.0993414 -2.233239 0.194080
## State_dummy17 0.24101 0.66692 0.36137 0.7180726 -1.071314 1.553324
## State_dummy18 1.27377 0.47960 2.65590 0.0083227 0.330051 2.217481
## State_dummy19 -1.28428 0.43460 -2.95507 0.0033680 -2.139452 -0.429102
## State_dummy20 -0.39558 1.06450 -0.37162 0.7104350 -2.490219 1.699050
## State_dummy21 0.55145 0.68223 0.80830 0.4195456 -0.790996 1.893887
## State_dummy22 -0.62594 0.57041 -1.09735 0.2733496 -1.748340 0.496468
## State_dummy23 0.16482 0.73338 0.22474 0.8223344 -1.278269 1.607902
## State_dummy24 0.40066 1.01562 0.39450 0.6934848 -1.597794 2.399121
## State_dummy25 0.55073 0.53640 1.02671 0.3053669 -0.504762 1.606218
## State_dummy26 0.11276 0.94477 0.11935 0.9050729 -1.746285 1.971810
## State_dummy28 -0.05219 0.66126 -0.07893 0.9371401 -1.353366 1.248980
## State_dummy29 1.30257 0.52907 2.46201 0.0143653 0.261513 2.343620
## State_dummy30 0.03649 0.50157 0.07274 0.9420575 -0.950472 1.023445
## State_dummy31 -0.26481 0.47497 -0.55752 0.5775756 -1.199405 0.669795
## State_dummy32 -0.27958 0.47867 -0.58407 0.5596002 -1.221467 0.662311
## State_dummy33 -0.49089 0.46455 -1.05669 0.2914840 -1.405005 0.423224
## State_dummy34 1.27861 0.47315 2.70233 0.0072686 0.347581 2.209642
## State_dummy35 0.22676 0.49540 0.45773 0.6474732 -0.748045 1.201556
## State_dummy36 -1.19002 0.46633 -2.55188 0.0111987 -2.107629 -0.272410
## State_dummy37 0.93447 0.85906 1.08778 0.2775468 -0.755925 2.624857
## State_dummy38 -0.37573 0.46379 -0.81013 0.4184954 -1.288344 0.536884
## State_dummy40 1.24548 0.52007 2.39484 0.0172270 0.222130 2.268836
## State_dummy41 0.06344 0.82156 0.07722 0.9385028 -1.553173 1.680048
## State_dummy42 0.01509 0.62881 0.02399 0.9808752 -1.222231 1.252403
## State_dummy43 0.39253 0.46813 0.83850 0.4024018 -0.528625 1.313684
## State_dummy44 1.72561 0.50390 3.42447 0.0006998 0.734062 2.717150
## State_dummy46 -0.07134 0.67660 -0.10544 0.9160936 -1.402697 1.260013
## State_dummy47 1.49104 0.46606 3.19925 0.0015219 0.573967 2.408120
## State_dummy49 0.12348 0.50769 0.24322 0.8079973 -0.875509 1.122469
## State_dummy51 1.78509 0.53781 3.31915 0.0010116 0.726819 2.843356
## TreatMX:Female -0.75486 0.34090 -2.21432 0.0275414 -1.425663 -0.084065
## TreatChina:Female -0.41373 0.32662 -1.26670 0.2062223 -1.056436 0.228969
## TreatMX:Age 0.15392 0.13209 1.16527 0.2448129 -0.105997 0.413844
## TreatChina:Age -0.06833 0.12867 -0.53102 0.5957867 -0.321512 0.184859
## TreatMX:Income -0.14578 0.07647 -1.90647 0.0575225 -0.296253 0.004684
## TreatChina:Income -0.05531 0.07474 -0.74001 0.4598623 -0.202373 0.091758
## TreatMX:Immig -0.16036 0.13082 -1.22585 0.2211929 -0.417774 0.097049
## TreatChina:Immig -0.04097 0.13267 -0.30879 0.7576935 -0.302020 0.220088
## TreatMX:Pol_know -0.10466 0.20808 -0.50300 0.6153249 -0.514098 0.304773
## TreatChina:Pol_know 0.07235 0.19578 0.36956 0.7119688 -0.312888 0.457591
## DF
## (Intercept) 307
## TreatMX 307
## TreatChina 307
## Female 307
## Age 307
## Income 307
## Primary 307
## Manufac 307
## Immig 307
## Pol_know 307
## Dem 307
## Rep 307
## State_dummy3 307
## State_dummy4 307
## State_dummy5 307
## State_dummy6 307
## State_dummy7 307
## State_dummy8 307
## State_dummy9 307
## State_dummy10 307
## State_dummy11 307
## State_dummy13 307
## State_dummy14 307
## State_dummy15 307
## State_dummy16 307
## State_dummy17 307
## State_dummy18 307
## State_dummy19 307
## State_dummy20 307
## State_dummy21 307
## State_dummy22 307
## State_dummy23 307
## State_dummy24 307
## State_dummy25 307
## State_dummy26 307
## State_dummy28 307
## State_dummy29 307
## State_dummy30 307
## State_dummy31 307
## State_dummy32 307
## State_dummy33 307
## State_dummy34 307
## State_dummy35 307
## State_dummy36 307
## State_dummy37 307
## State_dummy38 307
## State_dummy40 307
## State_dummy41 307
## State_dummy42 307
## State_dummy43 307
## State_dummy44 307
## State_dummy46 307
## State_dummy47 307
## State_dummy49 307
## State_dummy51 307
## TreatMX:Female 307
## TreatChina:Female 307
## TreatMX:Age 307
## TreatChina:Age 307
## TreatMX:Income 307
## TreatChina:Income 307
## TreatMX:Immig 307
## TreatChina:Immig 307
## TreatMX:Pol_know 307
## TreatChina:Pol_know 307
##
## Multiple R-squared: 0.3726 , Adjusted R-squared: 0.2418
## F-statistic: NA on 64 and 307 DF, p-value: NA
モデル９（対ラテンアメリカ貿易への支持）
sub_LA<- lm_robust(Y_LA ~ Treat*Female + Treat*Age +
Treat*Income + Primary + Manufac +
Treat*Immig + Treat*Pol_know + Dem + Rep +
State_dummy, se_type = "stata", data = df)
summary(sub_LA)
##
## Call:
## lm_robust(formula = Y_LA ~ Treat * Female + Treat * Age + Treat *
## Income + Primary + Manufac + Treat * Immig + Treat * 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
## (Intercept) 3.83651 0.48084 7.97876 3.239e-14 2.89022 4.782792
## TreatMX -0.53770 0.51214 -1.04989 2.946e-01 -1.54558 0.470192
## TreatChina -0.64049 0.47826 -1.33920 1.815e-01 -1.58171 0.300727
## Female 0.27809 0.16147 1.72228 8.606e-02 -0.03967 0.595861
## Age -0.04986 0.06841 -0.72888 4.667e-01 -0.18448 0.084763
## Income 0.12199 0.04344 2.80815 5.313e-03 0.03650 0.207486
## Primary 0.38279 0.44912 0.85230 3.947e-01 -0.50108 1.266657
## Manufac -0.01456 0.16999 -0.08566 9.318e-01 -0.34911 0.319986
## Immig 0.06240 0.06143 1.01587 3.105e-01 -0.05849 0.183298
## Pol_know -0.16987 0.12621 -1.34585 1.794e-01 -0.41825 0.078523
## Dem 0.02263 0.13045 0.17345 8.624e-01 -0.23410 0.279347
## Rep -0.10761 0.14238 -0.75581 4.504e-01 -0.38782 0.172590
## State_dummy3 -0.67310 0.41650 -1.61610 1.071e-01 -1.49276 0.146558
## State_dummy4 -0.36904 0.46974 -0.78564 4.327e-01 -1.29348 0.555388
## State_dummy5 -0.63180 0.38164 -1.65550 9.888e-02 -1.38286 0.119254
## State_dummy6 -0.34314 0.58315 -0.58843 5.567e-01 -1.49078 0.804491
## State_dummy7 0.06146 0.48244 0.12739 8.987e-01 -0.88798 1.010899
## State_dummy8 0.85395 0.41342 2.06558 3.974e-02 0.04035 1.667559
## State_dummy9 -0.16402 0.36773 -0.44602 6.559e-01 -0.88770 0.559669
## State_dummy10 -0.87897 0.48984 -1.79440 7.377e-02 -1.84296 0.085026
## State_dummy11 -1.32531 1.23368 -1.07427 2.836e-01 -3.75318 1.102565
## State_dummy13 -0.37861 0.39856 -0.94995 3.429e-01 -1.16297 0.405745
## State_dummy14 -0.30485 0.40740 -0.74828 4.549e-01 -1.10661 0.496905
## State_dummy15 -0.37052 0.79588 -0.46555 6.419e-01 -1.93681 1.195767
## State_dummy16 -1.16769 0.74400 -1.56949 1.176e-01 -2.63186 0.296481
## State_dummy17 -0.22626 0.44412 -0.50946 6.108e-01 -1.10029 0.647761
## State_dummy18 0.55125 0.81177 0.67907 4.976e-01 -1.04631 2.148809
## State_dummy19 -2.14311 0.37087 -5.77866 1.901e-08 -2.87297 -1.413253
## State_dummy20 -0.87597 0.56903 -1.53942 1.248e-01 -1.99581 0.243863
## State_dummy21 0.06355 0.50945 0.12474 9.008e-01 -0.93903 1.066125
## State_dummy22 -0.54683 0.50303 -1.08708 2.779e-01 -1.53678 0.443116
## State_dummy23 -0.41663 0.42778 -0.97393 3.309e-01 -1.25850 0.425238
## State_dummy24 0.93714 0.40543 2.31144 2.149e-02 0.13925 1.735023
## State_dummy25 -0.25507 0.44503 -0.57314 5.670e-01 -1.13088 0.620747
## State_dummy26 -0.08650 0.37378 -0.23143 8.171e-01 -0.82209 0.649083
## State_dummy28 -0.48024 0.86474 -0.55536 5.791e-01 -2.18204 1.221558
## State_dummy29 0.90514 0.47019 1.92504 5.518e-02 -0.02019 1.830475
## State_dummy30 -0.69333 0.46743 -1.48327 1.391e-01 -1.61323 0.226571
## State_dummy31 -0.03426 0.37678 -0.09094 9.276e-01 -0.77576 0.707231
## State_dummy32 -0.61593 0.41892 -1.47028 1.425e-01 -1.44036 0.208497
## State_dummy33 -0.46906 0.40381 -1.16160 2.463e-01 -1.26375 0.325623
## State_dummy34 0.20788 0.41868 0.49652 6.199e-01 -0.61607 1.031833
## State_dummy35 -0.40424 0.43544 -0.92834 3.540e-01 -1.26118 0.452708
## State_dummy36 -0.13548 0.61794 -0.21925 8.266e-01 -1.35158 1.080622
## State_dummy37 0.33119 0.47974 0.69035 4.905e-01 -0.61293 1.275311
## State_dummy38 -0.58250 0.45201 -1.28869 1.985e-01 -1.47206 0.307049
## State_dummy40 0.13251 0.38397 0.34511 7.303e-01 -0.62313 0.888151
## State_dummy41 -0.10902 0.37827 -0.28821 7.734e-01 -0.85346 0.635410
## State_dummy42 -0.08891 0.44616 -0.19929 8.422e-01 -0.96695 0.789126
## State_dummy43 -0.50223 0.40540 -1.23885 2.164e-01 -1.30004 0.295588
## State_dummy44 -1.02589 0.38021 -2.69820 7.370e-03 -1.77415 -0.277638
## State_dummy46 -0.96176 0.42123 -2.28321 2.312e-02 -1.79073 -0.132784
## State_dummy47 -0.38183 0.64026 -0.59638 5.514e-01 -1.64185 0.878180
## State_dummy49 -0.49265 0.39095 -1.26014 2.086e-01 -1.26202 0.276726
## State_dummy51 0.82091 0.45498 1.80426 7.220e-02 -0.07449 1.716303
## TreatMX:Female -0.16926 0.25612 -0.66086 5.092e-01 -0.67330 0.334777
## TreatChina:Female -0.26112 0.24994 -1.04475 2.970e-01 -0.75300 0.230753
## TreatMX:Age -0.04828 0.11291 -0.42763 6.692e-01 -0.27049 0.173920
## TreatChina:Age -0.11255 0.10555 -1.06625 2.872e-01 -0.32028 0.095183
## TreatMX:Income -0.12640 0.06727 -1.87899 6.123e-02 -0.25880 0.005987
## TreatChina:Income -0.08067 0.06222 -1.29653 1.958e-01 -0.20312 0.041778
## TreatMX:Immig 0.09432 0.09814 0.96114 3.373e-01 -0.09881 0.287458
## TreatChina:Immig 0.15095 0.09071 1.66416 9.713e-02 -0.02756 0.329454
## TreatMX:Pol_know 0.19337 0.21229 0.91085 3.631e-01 -0.22442 0.611160
## TreatChina:Pol_know 0.18694 0.17374 1.07599 2.828e-01 -0.15498 0.528863
## DF
## (Intercept) 297
## TreatMX 297
## TreatChina 297
## Female 297
## Age 297
## Income 297
## Primary 297
## Manufac 297
## Immig 297
## Pol_know 297
## Dem 297
## Rep 297
## State_dummy3 297
## State_dummy4 297
## State_dummy5 297
## State_dummy6 297
## State_dummy7 297
## State_dummy8 297
## State_dummy9 297
## State_dummy10 297
## State_dummy11 297
## State_dummy13 297
## State_dummy14 297
## State_dummy15 297
## State_dummy16 297
## State_dummy17 297
## State_dummy18 297
## State_dummy19 297
## State_dummy20 297
## State_dummy21 297
## State_dummy22 297
## State_dummy23 297
## State_dummy24 297
## State_dummy25 297
## State_dummy26 297
## State_dummy28 297
## State_dummy29 297
## State_dummy30 297
## State_dummy31 297
## State_dummy32 297
## State_dummy33 297
## State_dummy34 297
## State_dummy35 297
## State_dummy36 297
## State_dummy37 297
## State_dummy38 297
## State_dummy40 297
## State_dummy41 297
## State_dummy42 297
## State_dummy43 297
## State_dummy44 297
## State_dummy46 297
## State_dummy47 297
## State_dummy49 297
## State_dummy51 297
## TreatMX:Female 297
## TreatChina:Female 297
## TreatMX:Age 297
## TreatChina:Age 297
## TreatMX:Income 297
## TreatChina:Income 297
## TreatMX:Immig 297
## TreatChina:Immig 297
## TreatMX:Pol_know 297
## TreatChina:Pol_know 297
##
## Multiple R-squared: 0.2529 , Adjusted R-squared: 0.09196
## F-statistic: NA on 64 and 297 DF, p-value: NA
