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)
![](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 (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))
![](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 3.13 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))
![](data:image/png;base64,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)
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))
![](data:image/png;base64,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)
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 = "")
![](data:image/png;base64,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)
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