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。

1 分析準備

1.1 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)

1.2 データを読み込む

df <- read_csv("cleaned_us_NA.csv")

1.3 処置変数をFactor変数に変換

df <- df |>  
  mutate(Treat = factor(Treat,
                        levels = c("Control", "MX", "China")))

2 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))

3 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)

Data Frame Summary

df2

Dimensions: 1050 x 13
Duplicates: 0

Variable

Stats / Values

Freqs (% of Valid)

Graph

Valid

Missing

Y [numeric]

Mean (sd) : 3.7 (0.9)

min ≤ med ≤ max:

1 ≤ 4 ≤ 5

IQR (CV) : 1 (0.3)

1	:	18	(	1.9%	)
2	:	78	(	8.2%	)
3	:	290	(	30.5%	)
4	:	386	(	40.6%	)
5	:	178	(	18.7%	)

950 (90.5%)

100 (9.5%)

Y_LA [numeric]

Mean (sd) : 3.4 (0.9)

min ≤ med ≤ max:

1 ≤ 3 ≤ 5

IQR (CV) : 1 (0.3)

1	:	28	(	3.1%	)
2	:	122	(	13.4%	)
3	:	347	(	38.0%	)
4	:	328	(	36.0%	)
5	:	87	(	9.5%	)

912 (86.9%)

138 (13.1%)

Y_China [numeric]

Mean (sd) : 3.1 (1.2)

min ≤ med ≤ max:

1 ≤ 3 ≤ 5

IQR (CV) : 2 (0.4)

1	:	96	(	9.9%	)
2	:	198	(	20.5%	)
3	:	262	(	27.1%	)
4	:	298	(	30.8%	)
5	:	112	(	11.6%	)

966 (92.0%)

84 (8.0%)

Female [numeric]

Min : 0

Mean : 0.5

Max : 1

0	:	525	(	50.0%	)
1	:	525	(	50.0%	)

1050 (100.0%)

0 (0.0%)

Age [numeric]

Mean (sd) : 0 (1.3)

min ≤ med ≤ max:

-2.2 ≤ 0 ≤ 2.3

IQR (CV) : 2.3 (-99.3)

46 distinct values

1050 (100.0%)

0 (0.0%)

Income [numeric]

Mean (sd) : -0.1 (2.1)

min ≤ med ≤ max:

-4.5 ≤ -0.5 ≤ 4.5

IQR (CV) : 3 (-22.7)

10 distinct values

989 (94.2%)

61 (5.8%)

Primary [numeric]

Min : 0

Mean : 0

Max : 1

0	:	660	(	98.5%	)
1	:	10	(	1.5%	)

670 (63.8%)

380 (36.2%)

Manufac [numeric]

Min : 0

Mean : 0.1

Max : 1

0	:	603	(	90.0%	)
1	:	67	(	10.0%	)

670 (63.8%)

380 (36.2%)

Immig [numeric]

Mean (sd) : 3.4 (1.3)

min ≤ med ≤ max:

1 ≤ 4 ≤ 5

IQR (CV) : 1 (0.4)

1	:	143	(	14.0%	)
2	:	63	(	6.2%	)
3	:	298	(	29.3%	)
4	:	281	(	27.6%	)
5	:	233	(	22.9%	)

1018 (97.0%)

32 (3.0%)

Pol_know [numeric]

Mean (sd) : 1.9 (0.8)

min ≤ med ≤ max:

0 ≤ 2 ≤ 3

IQR (CV) : 0 (0.4)

0	:	32	(	5.7%	)
1	:	93	(	16.7%	)
2	:	306	(	54.9%	)
3	:	126	(	22.6%	)

557 (53.0%)

493 (47.0%)

Dem [numeric]

Min : 0

Mean : 0.4

Max : 1

0	:	552	(	57.1%	)
1	:	415	(	42.9%	)

967 (92.1%)

83 (7.9%)

Rep [numeric]

Min : 0

Mean : 0.3

Max : 1

0	:	644	(	66.6%	)
1	:	323	(	33.4%	)

967 (92.1%)

83 (7.9%)

State_dummy [factor]

1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9
10. 10
[ 40 others ]

20	(	1.9%	)
4	(	0.4%	)
34	(	3.2%	)
8	(	0.8%	)
101	(	9.6%	)
9	(	0.9%	)
14	(	1.3%	)
3	(	0.3%	)
80	(	7.6%	)
40	(	3.8%	)
737	(	70.2%	)

1050 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.1)
2023-07-12

4 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)

5 処置効果の比較

5.1 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。

5.1.1 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))

5.1.2 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))

5.1.3 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))

6 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

6.1 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

6.2 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

6.3 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

7 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

7.1 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

7.2 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

7.3 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

7.4 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 = "")

分析再現用コード（アメリカ分析）：第４章「自由貿易の国内基盤に関する比較分析　アメリカとメキシコにおけるサーベイ実験」久米郁男編『なぜ自由貿易は支持されるのか』（有斐閣、2023年）

高橋百合子

July 13, 2023

1 分析準備

1.1 分析に必要なパッケージをインストール

1.2 データを読み込む

1.3 処置変数をFactor変数に変換

2 データのクリーニング

3 記述統計

Data Frame Summary

df2

4 バランス・チェック

5 処置効果の比較

5.1 図4.4. 自由貿易に対する態度 (1)アメリカ

5.1.1 国際貿易一般への支持

5.1.2 対中国貿易への支持

5.1.3 対ラテンアメリカ貿易への支持

6 単回帰分析

6.1 モデル１（国際貿易一般への支持）

6.2 モデル４（対中国貿易への支持

6.3 モデル７（対ラテンアメリカ貿易への支持）

7 重回帰分析

7.1 モデル２（国際貿易一般への支持）

7.2 モデル５（対中国貿易への支持）

7.3 モデル８（対ラテンアメリカ貿易への支持）

7.4 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）

分析再現用コード（アメリカ分析）：第４章「自由貿易の国内基盤に関する比較分析 アメリカとメキシコにおけるサーベイ実験」久米郁男編『なぜ自由貿易は支持されるのか』（有斐閣、2023年）

高橋百合子

July 13, 2023

1 分析準備

1.1 分析に必要なパッケージをインストール

1.2 データを読み込む

1.3 処置変数をFactor変数に変換

2 データのクリーニング

3 記述統計

Data Frame Summary

df2

4 バランス・チェック

5 処置効果の比較

5.1 図4.4. 自由貿易に対する態度 (1)アメリカ

5.1.1 国際貿易一般への支持

5.1.2 対中国貿易への支持

5.1.3 対ラテンアメリカ貿易への支持

6 単回帰分析

6.1 モデル１（国際貿易一般への支持）

6.2 モデル４（対中国貿易への支持

6.3 モデル７（対ラテンアメリカ貿易への支持）

7 重回帰分析

7.1 モデル２（国際貿易一般への支持）

7.2 モデル５（対中国貿易への支持）

7.3 モデル８（対ラテンアメリカ貿易への支持）

7.4 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）

分析再現用コード（アメリカ分析）：第４章「自由貿易の国内基盤に関する比較分析　アメリカとメキシコにおけるサーベイ実験」久米郁男編『なぜ自由貿易は支持されるのか』（有斐閣、2023年）

7 重回帰分析　　　　　　　　　