# Load Table 1 data
table1 <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\table1.dta")
# Column 1: Tabulate true_nat
table(table1$true_nat)##
## - austria benelux britain canada
## 2 3 4 88 1
## czechoslovakia france germany germany/france india
## 1 9 225 1 1
## swi/france switzerland usa
## 1 49 240# Column 2: Count patents by country
cat("USA patents:", sum(table1$usa == 1, na.rm = TRUE), "\n")## USA patents: 289cat("Germany patents:", sum(table1$germany == 1, na.rm = TRUE), "\n")## Germany patents: 197cat("Other countries patents:", sum(table1$usa == 0 & table1$germany == 0, na.rm = TRUE))## Other countries patents: 139# Load main dataset
chem_patents <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\chem_patents_maindataset.dta")
# Data preparation steps
chem_patents <- chem_patents %>%
arrange(uspto_class, grntyr) %>%
group_by(uspto_class) %>%
mutate(ccc = cumsum(count)) %>%
ungroup()
# Replace count variables with NA where ccc == 0
count_vars <- c("count_usa", "count")
for(var in count_vars) {
chem_patents[[var]] <- ifelse(chem_patents$ccc == 0, NA, chem_patents[[var]])
}
# Create time dummies
for(x in 1876:1939) {
chem_patents[[paste0("td_", x)]] <- as.numeric(chem_patents$grntyr == x)
}
# Filter data
chem_patents <- chem_patents %>%
group_by(uspto_class) %>%
mutate(
aaa = ifelse(ccc == 0 & grntyr == 1919, 1, NA),
bbb = max(aaa, na.rm = TRUE)
) %>%
ungroup() %>%
filter(bbb != 1, ccc != 0) %>%
select(-aaa, -bbb, -ccc)
# Convert to panel data
pdata <- pdata.frame(chem_patents, index = c("class_id", "grntyr"))
# Model specifications for Table 6
models_table6 <- list()
# Model 1
models_table6[[1]] <- plm(count_usa ~ treat + count_for_2 + . - class_id - grntyr - uspto_class_class_class_class_class_class - count - count_usa - treat - count_for_2,
data = pdata[, c("count_usa", "treat", "count_for_2", grep("td_", names(pdata), value = TRUE))],
model = "within", effect = "individual")
# Model 2
models_table6[[2]] <- plm(count_usa ~ treat + count_for + . - class_id - grntyr - uspto_class_class_class_class_class_class - count - count_usa - treat - count_for,
data = pdata[, c("count_usa", "treat", "count_for", grep("td_", names(pdata), value = TRUE))],
model = "within", effect = "individual")
# Model 3
models_table6[[3]] <- plm(count_usa ~ treat + . - class_id - grntyr - uspto_class_class_class_class_class_class - count - count_usa - treat,
data = pdata[, c("count_usa", "treat", grep("td_", names(pdata), value = TRUE))],
model = "within", effect = "individual")
# Display results
stargazer(models_table6, type = "text", title = "Table 6 Results")##
## Table 6 Results
## ==============================================================
## Dependent variable:
## --------------------------------
## count_usa
## (1) (2) (3)
## --------------------------------------------------------------
## td_1876 -0.384*** -0.384*** -0.384***
## (0.069) (0.069) (0.069)
##
## td_1877 -0.504*** -0.504*** -0.504***
## (0.066) (0.066) (0.066)
##
## td_1878 -0.639*** -0.639*** -0.639***
## (0.065) (0.065) (0.065)
##
## td_1879 -0.604*** -0.604*** -0.604***
## (0.063) (0.063) (0.063)
##
## td_1880 -0.583*** -0.583*** -0.583***
## (0.063) (0.063) (0.063)
##
## td_1881 -0.613*** -0.613*** -0.613***
## (0.062) (0.062) (0.062)
##
## td_1882 -0.455*** -0.455*** -0.455***
## (0.062) (0.062) (0.062)
##
## td_1883 -0.392*** -0.392*** -0.392***
## (0.061) (0.061) (0.061)
##
## td_1884 -0.474*** -0.474*** -0.474***
## (0.061) (0.061) (0.061)
##
## td_1885 -0.329*** -0.329*** -0.329***
## (0.060) (0.060) (0.060)
##
## td_1886 -0.440*** -0.440*** -0.440***
## (0.060) (0.060) (0.060)
##
## td_1887 -0.483*** -0.483*** -0.483***
## (0.060) (0.060) (0.060)
##
## td_1888 -0.468*** -0.468*** -0.468***
## (0.060) (0.060) (0.060)
##
## td_1889 -0.391*** -0.391*** -0.391***
## (0.060) (0.060) (0.060)
##
## td_1890 -0.331*** -0.331*** -0.331***
## (0.059) (0.059) (0.059)
##
## td_1891 -0.380*** -0.380*** -0.380***
## (0.059) (0.059) (0.059)
##
## td_1892 -0.452*** -0.452*** -0.452***
## (0.059) (0.059) (0.059)
##
## td_1893 -0.359*** -0.359*** -0.359***
## (0.059) (0.059) (0.059)
##
## td_1894 -0.455*** -0.455*** -0.455***
## (0.059) (0.059) (0.059)
##
## td_1895 -0.391*** -0.391*** -0.391***
## (0.059) (0.059) (0.059)
##
## td_1896 -0.382*** -0.382*** -0.382***
## (0.059) (0.059) (0.059)
##
## td_1897 -0.346*** -0.346*** -0.346***
## (0.059) (0.059) (0.059)
##
## td_1898 -0.417*** -0.417*** -0.417***
## (0.059) (0.059) (0.059)
##
## td_1899 -0.239*** -0.239*** -0.239***
## (0.058) (0.058) (0.058)
##
## td_1900 -0.291*** -0.291*** -0.291***
## (0.058) (0.058) (0.058)
##
## td_1901 -0.277*** -0.277*** -0.277***
## (0.058) (0.058) (0.058)
##
## td_1902 -0.278*** -0.278*** -0.278***
## (0.058) (0.058) (0.058)
##
## td_1903 -0.219*** -0.219*** -0.219***
## (0.058) (0.058) (0.058)
##
## td_1904 -0.254*** -0.254*** -0.254***
## (0.058) (0.058) (0.058)
##
## td_1905 -0.248*** -0.248*** -0.248***
## (0.058) (0.058) (0.058)
##
## td_1906 -0.203*** -0.203*** -0.203***
## (0.058) (0.058) (0.058)
##
## td_1907 -0.088 -0.088 -0.088
## (0.058) (0.058) (0.058)
##
## td_1908 -0.173*** -0.173*** -0.173***
## (0.058) (0.058) (0.058)
##
## td_1909 -0.119** -0.119** -0.119**
## (0.058) (0.058) (0.058)
##
## td_1910 -0.172*** -0.172*** -0.172***
## (0.058) (0.058) (0.058)
##
## td_1911 -0.119** -0.119** -0.119**
## (0.058) (0.058) (0.058)
##
## td_1912 -0.079 -0.079 -0.079
## (0.057) (0.057) (0.057)
##
## td_1913 -0.149*** -0.149*** -0.149***
## (0.057) (0.057) (0.057)
##
## td_1914 -0.005 -0.005 -0.005
## (0.057) (0.057) (0.057)
##
## td_1915 0.076 0.076 0.076
## (0.057) (0.057) (0.057)
##
## td_1916 0.144** 0.144** 0.144**
## (0.057) (0.057) (0.057)
##
## td_1917 0.119** 0.119** 0.119**
## (0.057) (0.057) (0.057)
##
## td_1918 0.066 0.066 0.066
## (0.057) (0.057) (0.057)
##
## td_1919 0.033 0.033 0.033
## (0.057) (0.057) (0.057)
##
## td_1920 0.017 0.017 0.017
## (0.057) (0.057) (0.057)
##
## td_1921 0.089 0.089 0.089
## (0.057) (0.057) (0.057)
##
## td_1922 0.099* 0.099* 0.099*
## (0.057) (0.057) (0.057)
##
## td_1923 0.037 0.037 0.037
## (0.057) (0.057) (0.057)
##
## td_1924 0.112** 0.112** 0.112**
## (0.057) (0.057) (0.057)
##
## td_1925 0.129** 0.129** 0.129**
## (0.057) (0.057) (0.057)
##
## td_1926 0.170*** 0.170*** 0.170***
## (0.057) (0.057) (0.057)
##
## td_1927 0.076 0.076 0.076
## (0.057) (0.057) (0.057)
##
## td_1928 0.086 0.086 0.086
## (0.057) (0.057) (0.057)
##
## td_1929 0.188*** 0.188*** 0.188***
## (0.057) (0.057) (0.057)
##
## td_1930 0.188*** 0.188*** 0.188***
## (0.057) (0.057) (0.057)
##
## td_1931 0.493*** 0.493*** 0.493***
## (0.057) (0.057) (0.057)
##
## td_1932 0.574*** 0.574*** 0.574***
## (0.057) (0.057) (0.057)
##
## td_1933 0.526*** 0.526*** 0.526***
## (0.057) (0.057) (0.057)
##
## td_1934 0.544*** 0.544*** 0.544***
## (0.057) (0.057) (0.057)
##
## td_1935 0.470*** 0.470*** 0.470***
## (0.057) (0.057) (0.057)
##
## td_1936 0.552*** 0.552*** 0.552***
## (0.057) (0.057) (0.057)
##
## td_1937 0.473*** 0.473*** 0.473***
## (0.057) (0.057) (0.057)
##
## td_1938 0.528*** 0.528*** 0.528***
## (0.057) (0.057) (0.057)
##
## td_1939 0.672*** 0.672*** 0.672***
## (0.057) (0.057) (0.057)
##
## --------------------------------------------------------------
## Observations 223,048 223,048 223,048
## R2 0.061 0.061 0.061
## Adjusted R2 0.041 0.041 0.041
## F Statistic (df = 64; 218400) 222.734*** 222.734*** 222.734***
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01# Load primary classes dataset
primary_classes <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\chem_patents_primaryclassesdataset.dta")
# Create time dummies
for(x in 1876:1939) {
primary_classes[[paste0("td_", x)]] <- as.numeric(primary_classes$grntyr == x)
}
pdata_primary <- pdata.frame(primary_classes, index = c("class_id", "grntyr"))
# Models for Table 7
models_table7 <- list()
models_table7[[1]] <- plm(count_usa ~ treat + count_for + . - class_id - grntyr - count_usa - treat - count_for,
data = pdata_primary[, c("count_usa", "treat", "count_for", grep("td_", names(pdata_primary), value = TRUE))],
model = "within", effect = "individual")
models_table7[[2]] <- plm(count_usa ~ count_cl + count_for + . - class_id - grntyr - count_usa - count_cl - count_for,
data = pdata_primary[, c("count_usa", "count_cl", "count_for", grep("td_", names(pdata_primary), value = TRUE))],
model = "within", effect = "individual")
models_table7[[3]] <- plm(count_usa ~ year_conf + count_for + . - class_id - grntyr - count_usa - year_conf - count_for,
data = pdata_primary[, c("count_usa", "year_conf", "count_for", grep("td_", names(pdata_primary), value = TRUE))],
model = "within", effect = "individual")
stargazer(models_table7, type = "text", title = "Table 7 Results")##
## Table 7 Results
## ==============================================================
## Dependent variable:
## --------------------------------
## count_usa
## (1) (2) (3)
## --------------------------------------------------------------
## td_1876 0.002 0.002 0.002
## (0.012) (0.012) (0.012)
##
## td_1877 -0.004 -0.004 -0.004
## (0.012) (0.012) (0.012)
##
## td_1878 -0.010 -0.010 -0.010
## (0.012) (0.012) (0.012)
##
## td_1879 -0.007 -0.007 -0.007
## (0.012) (0.012) (0.012)
##
## td_1880 -0.0002 -0.0002 -0.0002
## (0.012) (0.012) (0.012)
##
## td_1881 -0.006 -0.006 -0.006
## (0.012) (0.012) (0.012)
##
## td_1882 0.021* 0.021* 0.021*
## (0.012) (0.012) (0.012)
##
## td_1883 0.036*** 0.036*** 0.036***
## (0.012) (0.012) (0.012)
##
## td_1884 0.018 0.018 0.018
## (0.012) (0.012) (0.012)
##
## td_1885 0.047*** 0.047*** 0.047***
## (0.012) (0.012) (0.012)
##
## td_1886 0.026** 0.026** 0.026**
## (0.012) (0.012) (0.012)
##
## td_1887 0.010 0.010 0.010
## (0.012) (0.012) (0.012)
##
## td_1888 0.011 0.011 0.011
## (0.012) (0.012) (0.012)
##
## td_1889 0.027** 0.027** 0.027**
## (0.012) (0.012) (0.012)
##
## td_1890 0.044*** 0.044*** 0.044***
## (0.012) (0.012) (0.012)
##
## td_1891 0.032*** 0.032*** 0.032***
## (0.012) (0.012) (0.012)
##
## td_1892 0.018 0.018 0.018
## (0.012) (0.012) (0.012)
##
## td_1893 0.028** 0.028** 0.028**
## (0.012) (0.012) (0.012)
##
## td_1894 0.010 0.010 0.010
## (0.012) (0.012) (0.012)
##
## td_1895 0.016 0.016 0.016
## (0.012) (0.012) (0.012)
##
## td_1896 0.031*** 0.031*** 0.031***
## (0.012) (0.012) (0.012)
##
## td_1897 0.030** 0.030** 0.030**
## (0.012) (0.012) (0.012)
##
## td_1898 0.017 0.017 0.017
## (0.012) (0.012) (0.012)
##
## td_1899 0.063*** 0.063*** 0.063***
## (0.012) (0.012) (0.012)
##
## td_1900 0.042*** 0.042*** 0.042***
## (0.012) (0.012) (0.012)
##
## td_1901 0.049*** 0.049*** 0.049***
## (0.012) (0.012) (0.012)
##
## td_1902 0.058*** 0.058*** 0.058***
## (0.012) (0.012) (0.012)
##
## td_1903 0.067*** 0.067*** 0.067***
## (0.012) (0.012) (0.012)
##
## td_1904 0.062*** 0.062*** 0.062***
## (0.012) (0.012) (0.012)
##
## td_1905 0.057*** 0.057*** 0.057***
## (0.012) (0.012) (0.012)
##
## td_1906 0.083*** 0.083*** 0.083***
## (0.012) (0.012) (0.012)
##
## td_1907 0.100*** 0.100*** 0.100***
## (0.012) (0.012) (0.012)
##
## td_1908 0.082*** 0.082*** 0.082***
## (0.012) (0.012) (0.012)
##
## td_1909 0.096*** 0.096*** 0.096***
## (0.012) (0.012) (0.012)
##
## td_1910 0.088*** 0.088*** 0.088***
## (0.012) (0.012) (0.012)
##
## td_1911 0.088*** 0.088*** 0.088***
## (0.012) (0.012) (0.012)
##
## td_1912 0.109*** 0.109*** 0.109***
## (0.012) (0.012) (0.012)
##
## td_1913 0.091*** 0.091*** 0.091***
## (0.012) (0.012) (0.012)
##
## td_1914 0.131*** 0.131*** 0.131***
## (0.012) (0.012) (0.012)
##
## td_1915 0.168*** 0.168*** 0.168***
## (0.012) (0.012) (0.012)
##
## td_1916 0.196*** 0.196*** 0.196***
## (0.012) (0.012) (0.012)
##
## td_1917 0.192*** 0.192*** 0.192***
## (0.012) (0.012) (0.012)
##
## td_1918 0.178*** 0.178*** 0.178***
## (0.012) (0.012) (0.012)
##
## td_1919 0.155*** 0.155*** 0.155***
## (0.012) (0.012) (0.012)
##
## td_1920 0.161*** 0.161*** 0.161***
## (0.012) (0.012) (0.012)
##
## td_1921 0.178*** 0.178*** 0.178***
## (0.012) (0.012) (0.012)
##
## td_1922 0.196*** 0.196*** 0.196***
## (0.012) (0.012) (0.012)
##
## td_1923 0.183*** 0.183*** 0.183***
## (0.012) (0.012) (0.012)
##
## td_1924 0.207*** 0.207*** 0.207***
## (0.012) (0.012) (0.012)
##
## td_1925 0.203*** 0.203*** 0.203***
## (0.012) (0.012) (0.012)
##
## td_1926 0.219*** 0.219*** 0.219***
## (0.012) (0.012) (0.012)
##
## td_1927 0.179*** 0.179*** 0.179***
## (0.012) (0.012) (0.012)
##
## td_1928 0.164*** 0.164*** 0.164***
## (0.012) (0.012) (0.012)
##
## td_1929 0.204*** 0.204*** 0.204***
## (0.012) (0.012) (0.012)
##
## td_1930 0.213*** 0.213*** 0.213***
## (0.012) (0.012) (0.012)
##
## td_1931 0.301*** 0.301*** 0.301***
## (0.012) (0.012) (0.012)
##
## td_1932 0.318*** 0.318*** 0.318***
## (0.012) (0.012) (0.012)
##
## td_1933 0.288*** 0.288*** 0.288***
## (0.012) (0.012) (0.012)
##
## td_1934 0.281*** 0.281*** 0.281***
## (0.012) (0.012) (0.012)
##
## td_1935 0.270*** 0.270*** 0.270***
## (0.012) (0.012) (0.012)
##
## td_1936 0.273*** 0.273*** 0.273***
## (0.012) (0.012) (0.012)
##
## td_1937 0.252*** 0.252*** 0.252***
## (0.012) (0.012) (0.012)
##
## td_1938 0.272*** 0.272*** 0.272***
## (0.012) (0.012) (0.012)
##
## td_1939 0.312*** 0.312*** 0.312***
## (0.012) (0.012) (0.012)
##
## --------------------------------------------------------------
## Observations 367,640 367,640 367,640
## R2 0.025 0.025 0.025
## Adjusted R2 0.010 0.010 0.010
## F Statistic (df = 64; 361920) 145.452*** 145.452*** 145.452***
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01# Load indigo dataset
indigo_data <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\chem_patents_indigodataset.dta")
# Create time dummies
for(x in 1876:1939) {
indigo_data[[paste0("td_", x)]] <- as.numeric(indigo_data$grntyr == x)
}
pdata_indigo <- pdata.frame(indigo_data, index = c("class_id", "grntyr"))
# Models for Table 8
models_table8 <- list()
models_table8[[1]] <- plm(count_usa ~ treat + count_for + . - class_id - grntyr - count_usa - treat - count_for,
data = pdata_indigo[, c("count_usa", "treat", "count_for", grep("td_", names(pdata_indigo), value = TRUE))],
model = "within", effect = "individual")
models_table8[[2]] <- plm(count_usa ~ count_cl + count_for + . - class_id - grntyr - count_usa - count_cl - count_for,
data = pdata_indigo[, c("count_usa", "count_cl", "count_for", grep("td_", names(pdata_indigo), value = TRUE))],
model = "within", effect = "individual")
models_table8[[3]] <- plm(count_usa ~ year_conf + count_for + . - class_id - grntyr - count_usa - year_conf - count_for,
data = pdata_indigo[, c("count_usa", "year_conf", "count_for", grep("td_", names(pdata_indigo), value = TRUE))],
model = "within", effect = "individual")
stargazer(models_table8, type = "text", title = "Table 8 Results")##
## Table 8 Results
## ==========================================================
## Dependent variable:
## -----------------------------
## count_usa
## (1) (2) (3)
## ----------------------------------------------------------
## td_1876 0.001 0.001 0.001
## (0.007) (0.007) (0.007)
##
## td_1877 0.001 0.001 0.001
## (0.007) (0.007) (0.007)
##
## td_1878 -0.001 -0.001 -0.001
## (0.007) (0.007) (0.007)
##
## td_1879 0.000 0.000 0.000
## (0.007) (0.007) (0.007)
##
## td_1880 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1881 0.003 0.003 0.003
## (0.007) (0.007) (0.007)
##
## td_1882 0.003 0.003 0.003
## (0.007) (0.007) (0.007)
##
## td_1883 -0.001 -0.001 -0.001
## (0.007) (0.007) (0.007)
##
## td_1884 0.000 0.000 0.000
## (0.007) (0.007) (0.007)
##
## td_1885 0.003 0.003 0.003
## (0.007) (0.007) (0.007)
##
## td_1886 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1887 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1888 0.006 0.006 0.006
## (0.007) (0.007) (0.007)
##
## td_1889 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1890 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1891 -0.001 -0.001 -0.001
## (0.007) (0.007) (0.007)
##
## td_1892 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1893 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1894 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1895 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1896 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1897 0.006 0.006 0.006
## (0.007) (0.007) (0.007)
##
## td_1898 -0.001 -0.001 -0.001
## (0.007) (0.007) (0.007)
##
## td_1899 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1900 0.001 0.001 0.001
## (0.007) (0.007) (0.007)
##
## td_1901 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1902 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1903 0.000 0.000 0.000
## (0.007) (0.007) (0.007)
##
## td_1904 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1905 0.004 0.004 0.004
## (0.007) (0.007) (0.007)
##
## td_1906 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1907 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1908 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1909 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1910 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1911 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1912 0.003 0.003 0.003
## (0.007) (0.007) (0.007)
##
## td_1913 -0.003 -0.003 -0.003
## (0.007) (0.007) (0.007)
##
## td_1914 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1915 0.003 0.003 0.003
## (0.007) (0.007) (0.007)
##
## td_1916 0.004 0.004 0.004
## (0.007) (0.007) (0.007)
##
## td_1917 0.010 0.010 0.010
## (0.007) (0.007) (0.007)
##
## td_1918 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1919 -0.004 -0.004 -0.004
## (0.007) (0.007) (0.007)
##
## td_1920 0.013* 0.013* 0.013*
## (0.007) (0.007) (0.007)
##
## td_1921 0.016** 0.016** 0.016**
## (0.007) (0.007) (0.007)
##
## td_1922 0.010 0.010 0.010
## (0.007) (0.007) (0.007)
##
## td_1923 0.014** 0.014** 0.014**
## (0.007) (0.007) (0.007)
##
## td_1924 0.004 0.004 0.004
## (0.007) (0.007) (0.007)
##
## td_1925 0.000 0.000 0.000
## (0.007) (0.007) (0.007)
##
## td_1926 0.021*** 0.021*** 0.021***
## (0.007) (0.007) (0.007)
##
## td_1927 0.031*** 0.031*** 0.031***
## (0.007) (0.007) (0.007)
##
## td_1928 0.020*** 0.020*** 0.020***
## (0.007) (0.007) (0.007)
##
## td_1929 -0.001 -0.001 -0.001
## (0.007) (0.007) (0.007)
##
## td_1930 0.014** 0.014** 0.014**
## (0.007) (0.007) (0.007)
##
## td_1931 0.053*** 0.053*** 0.053***
## (0.007) (0.007) (0.007)
##
## td_1932 0.038*** 0.038*** 0.038***
## (0.007) (0.007) (0.007)
##
## td_1933 0.020*** 0.020*** 0.020***
## (0.007) (0.007) (0.007)
##
## td_1934 0.068*** 0.068*** 0.068***
## (0.007) (0.007) (0.007)
##
## td_1935 0.044*** 0.044*** 0.044***
## (0.007) (0.007) (0.007)
##
## td_1936 0.135*** 0.135*** 0.135***
## (0.007) (0.007) (0.007)
##
## td_1937 0.089*** 0.089*** 0.089***
## (0.007) (0.007) (0.007)
##
## td_1938 0.040*** 0.040*** 0.040***
## (0.007) (0.007) (0.007)
##
## td_1939 0.094*** 0.094*** 0.094***
## (0.007) (0.007) (0.007)
##
## ----------------------------------------------------------
## Observations 45,760 45,760 45,760
## R2 0.036 0.036 0.036
## Adjusted R2 0.020 0.020 0.020
## F Statistic (df = 64; 44992) 26.564*** 26.564*** 26.564***
## ==========================================================
## Note: *p<0.1; **p<0.05; ***p<0.01# Load DuPont data
dupont_data <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\dupont_data.dta")
# Create time dummies (assuming similar time period)
for(x in 1876:1939) {
dupont_data[[paste0("td_", x)]] <- as.numeric(dupont_data$grntyr == x)
}
pdata_dupont <- pdata.frame(dupont_data, index = c("class_id", "grntyr"))
# Models for Table 9
models_table9 <- list()
models_table9[[1]] <- plm(patents ~ treat_NO_dupont + treat_dupont + count_for + . - class_id - grntyr - patents - treat_NO_dupont - treat_dupont - count_for,
data = pdata_dupont[, c("patents", "treat_NO_dupont", "treat_dupont", "count_for", grep("td_", names(pdata_dupont), value = TRUE))],
model = "within", effect = "individual")
# Additional models would follow similar pattern...
stargazer(models_table9, type = "text", title = "Table 9 Results")##
## Table 9 Results
## =========================================
## Dependent variable:
## ----------------------------
## patents
## -----------------------------------------
## td_1889 -0.257***
## (0.005)
##
## td_1891 -0.257***
## (0.005)
##
## td_1896 -0.256***
## (0.005)
##
## td_1898 -0.257***
## (0.005)
##
## td_1906 -0.256***
## (0.005)
##
## td_1907 -0.252***
## (0.005)
##
## td_1908 -0.253***
## (0.005)
##
## td_1909 -0.248***
## (0.005)
##
## td_1910 -0.253***
## (0.005)
##
## td_1911 -0.253***
## (0.005)
##
## td_1912 -0.254***
## (0.005)
##
## td_1913 -0.254***
## (0.005)
##
## td_1914 -0.255***
## (0.005)
##
## td_1915 -0.254***
## (0.005)
##
## td_1916 -0.254***
## (0.005)
##
## td_1917 -0.253***
## (0.005)
##
## td_1918 -0.254***
## (0.005)
##
## td_1919 -0.250***
## (0.005)
##
## td_1920 -0.249***
## (0.005)
##
## td_1921 -0.255***
## (0.005)
##
## td_1922 -0.253***
## (0.005)
##
## td_1923 -0.252***
## (0.005)
##
## td_1924 -0.253***
## (0.005)
##
## td_1925 -0.249***
## (0.005)
##
## td_1926 -0.250***
## (0.005)
##
## td_1927 -0.243***
## (0.005)
##
## td_1928 -0.242***
## (0.005)
##
## td_1929 -0.234***
## (0.005)
##
## td_1930 -0.224***
## (0.005)
##
## td_1931 -0.207***
## (0.005)
##
## td_1932 -0.157***
## (0.005)
##
## td_1933 -0.107***
## (0.005)
##
## td_1934 -0.093***
## (0.005)
##
## td_1935 -0.074***
## (0.005)
##
## td_1936 0.028***
## (0.005)
##
## td_1937 -0.027***
## (0.005)
##
## td_1938 -0.029***
## (0.005)
##
## td_1939 0.030***
## (0.005)
##
## -----------------------------------------
## Observations 222,924
## R2 0.097
## Adjusted R2 0.073
## F Statistic 613.315*** (df = 38; 217170)
## =========================================
## Note: *p<0.1; **p<0.05; ***p<0.01## Figure 1
fig1 <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\fig1.dta")
# 直接使用count_germany作为count_ger
fig1$count_ger <- fig1$count_germany
# 提取时间虚拟变量
td_vars <- grep("^td_", names(fig1), value = TRUE)
cat("Running regression models...\n")## Running regression models...cat("Time period covered:", length(td_vars), "years from 1875 to 1939\n")## Time period covered: 65 years from 1875 to 1939cat("Sample sizes:\n")## Sample sizes:cat(" licensed_class == 0:", sum(fig1$licensed_class == 0), "observations\n")## licensed_class == 0: 65 observationscat(" licensed_class == 1:", sum(fig1$licensed_class == 1), "observations\n")## licensed_class == 1: 65 observations# 对licensed_class == 0的样本回归 (无常数项)
model_non_licensed <- lm(
reformulate(td_vars, "count_ger", intercept = FALSE),
data = fig1[fig1$licensed_class == 0, ]
)
# 对licensed_class == 1的样本回归 (无常数项)
model_licensed <- lm(
reformulate(td_vars, "count_ger", intercept = FALSE),
data = fig1[fig1$licensed_class == 1, ]
)
# 输出结果
cat("\n=== Regression results for non-licensed classes (licensed_class == 0) ===\n")##
## === Regression results for non-licensed classes (licensed_class == 0) ===print(summary(model_non_licensed))##
## Call:
## lm(formula = reformulate(td_vars, "count_ger", intercept = FALSE),
## data = fig1[fig1$licensed_class == 0, ])
##
## Residuals:
## ALL 65 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## td_1875 3 NaN NaN NaN
## td_1876 3 NaN NaN NaN
## td_1877 11 NaN NaN NaN
## td_1878 8 NaN NaN NaN
## td_1879 11 NaN NaN NaN
## td_1880 18 NaN NaN NaN
## td_1881 28 NaN NaN NaN
## td_1882 32 NaN NaN NaN
## td_1883 42 NaN NaN NaN
## td_1884 29 NaN NaN NaN
## td_1885 47 NaN NaN NaN
## td_1886 40 NaN NaN NaN
## td_1887 48 NaN NaN NaN
## td_1888 66 NaN NaN NaN
## td_1889 74 NaN NaN NaN
## td_1890 76 NaN NaN NaN
## td_1891 81 NaN NaN NaN
## td_1892 80 NaN NaN NaN
## td_1893 105 NaN NaN NaN
## td_1894 108 NaN NaN NaN
## td_1895 79 NaN NaN NaN
## td_1896 105 NaN NaN NaN
## td_1897 117 NaN NaN NaN
## td_1898 143 NaN NaN NaN
## td_1899 160 NaN NaN NaN
## td_1900 229 NaN NaN NaN
## td_1901 178 NaN NaN NaN
## td_1902 156 NaN NaN NaN
## td_1903 170 NaN NaN NaN
## td_1904 150 NaN NaN NaN
## td_1905 144 NaN NaN NaN
## td_1906 124 NaN NaN NaN
## td_1907 149 NaN NaN NaN
## td_1908 147 NaN NaN NaN
## td_1909 138 NaN NaN NaN
## td_1910 167 NaN NaN NaN
## td_1911 191 NaN NaN NaN
## td_1912 174 NaN NaN NaN
## td_1913 173 NaN NaN NaN
## td_1914 161 NaN NaN NaN
## td_1915 128 NaN NaN NaN
## td_1916 89 NaN NaN NaN
## td_1917 71 NaN NaN NaN
## td_1918 40 NaN NaN NaN
## td_1919 30 NaN NaN NaN
## td_1920 25 NaN NaN NaN
## td_1921 57 NaN NaN NaN
## td_1922 118 NaN NaN NaN
## td_1923 98 NaN NaN NaN
## td_1924 120 NaN NaN NaN
## td_1925 177 NaN NaN NaN
## td_1926 160 NaN NaN NaN
## td_1927 156 NaN NaN NaN
## td_1928 208 NaN NaN NaN
## td_1929 309 NaN NaN NaN
## td_1930 323 NaN NaN NaN
## td_1931 445 NaN NaN NaN
## td_1932 591 NaN NaN NaN
## td_1933 592 NaN NaN NaN
## td_1934 480 NaN NaN NaN
## td_1935 415 NaN NaN NaN
## td_1936 401 NaN NaN NaN
## td_1937 446 NaN NaN NaN
## td_1938 478 NaN NaN NaN
## td_1939 535 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 65 and 0 DF, p-value: NAcat("\n=== Regression results for licensed classes (licensed_class == 1) ===\n")##
## === Regression results for licensed classes (licensed_class == 1) ===print(summary(model_licensed))##
## Call:
## lm(formula = reformulate(td_vars, "count_ger", intercept = FALSE),
## data = fig1[fig1$licensed_class == 1, ])
##
## Residuals:
## ALL 65 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## td_1875 1 NaN NaN NaN
## td_1876 0 NaN NaN NaN
## td_1877 2 NaN NaN NaN
## td_1878 3 NaN NaN NaN
## td_1879 0 NaN NaN NaN
## td_1880 8 NaN NaN NaN
## td_1881 18 NaN NaN NaN
## td_1882 30 NaN NaN NaN
## td_1883 8 NaN NaN NaN
## td_1884 5 NaN NaN NaN
## td_1885 10 NaN NaN NaN
## td_1886 5 NaN NaN NaN
## td_1887 10 NaN NaN NaN
## td_1888 15 NaN NaN NaN
## td_1889 21 NaN NaN NaN
## td_1890 20 NaN NaN NaN
## td_1891 26 NaN NaN NaN
## td_1892 19 NaN NaN NaN
## td_1893 27 NaN NaN NaN
## td_1894 27 NaN NaN NaN
## td_1895 31 NaN NaN NaN
## td_1896 35 NaN NaN NaN
## td_1897 26 NaN NaN NaN
## td_1898 41 NaN NaN NaN
## td_1899 54 NaN NaN NaN
## td_1900 90 NaN NaN NaN
## td_1901 66 NaN NaN NaN
## td_1902 57 NaN NaN NaN
## td_1903 72 NaN NaN NaN
## td_1904 60 NaN NaN NaN
## td_1905 60 NaN NaN NaN
## td_1906 44 NaN NaN NaN
## td_1907 65 NaN NaN NaN
## td_1908 77 NaN NaN NaN
## td_1909 77 NaN NaN NaN
## td_1910 86 NaN NaN NaN
## td_1911 103 NaN NaN NaN
## td_1912 105 NaN NaN NaN
## td_1913 84 NaN NaN NaN
## td_1914 72 NaN NaN NaN
## td_1915 66 NaN NaN NaN
## td_1916 70 NaN NaN NaN
## td_1917 35 NaN NaN NaN
## td_1918 25 NaN NaN NaN
## td_1919 2 NaN NaN NaN
## td_1920 8 NaN NaN NaN
## td_1921 18 NaN NaN NaN
## td_1922 43 NaN NaN NaN
## td_1923 33 NaN NaN NaN
## td_1924 28 NaN NaN NaN
## td_1925 58 NaN NaN NaN
## td_1926 38 NaN NaN NaN
## td_1927 36 NaN NaN NaN
## td_1928 56 NaN NaN NaN
## td_1929 97 NaN NaN NaN
## td_1930 103 NaN NaN NaN
## td_1931 128 NaN NaN NaN
## td_1932 201 NaN NaN NaN
## td_1933 187 NaN NaN NaN
## td_1934 109 NaN NaN NaN
## td_1935 117 NaN NaN NaN
## td_1936 123 NaN NaN NaN
## td_1937 118 NaN NaN NaN
## td_1938 137 NaN NaN NaN
## td_1939 139 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 65 and 0 DF, p-value: NA# 可选：提取系数用于绘图或其他分析
coef_non_licensed <- coef(model_non_licensed)
coef_licensed <- coef(model_licensed)
cat("\nNumber of coefficients for non-licensed classes:", length(coef_non_licensed), "\n")##
## Number of coefficients for non-licensed classes: 65cat("Number of coefficients for licensed classes:", length(coef_licensed), "\n")## Number of coefficients for licensed classes: 65# Load main dataset for figures
chem_figures <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\chem_patents_maindataset.dta")
# Figure 2: Count CL in 1930
fig2_data <- chem_figures %>% filter(grntyr == 1930) %>% select(count_cl)
print("Figure 2 - Count CL in 1930:")## [1] "Figure 2 - Count CL in 1930:"print(summary(fig2_data))## count_cl
## Min. : 0.00000
## 1st Qu.: 0.00000
## Median : 0.00000
## Mean : 0.08416
## 3rd Qu.: 0.00000
## Max. :15.00000# Figure 3: Year conf in 1930
fig3_data <- chem_figures %>% filter(grntyr == 1930) %>% select(year_conf)
print("Figure 3 - Year conf in 1930:")## [1] "Figure 3 - Year conf in 1930:"print(summary(fig3_data))## year_conf
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 0.000
## Mean : 1.076
## 3rd Qu.: 0.000
## Max. :161.000# Create time dummies and interaction terms
for(x in 1875:1918) {
chem_figures[[paste0("td_", x)]] <- as.numeric(chem_figures$grntyr == x)
chem_figures[[paste0("treat_", x)]] <- ifelse(chem_figures$grntyr == x, chem_figures$treat, 0)
}
# Remove specific variables as in Stata code
chem_figures <- chem_figures %>% select(-td_1900, -treat_1900)
pdata_fig4 <- pdata.frame(chem_figures, index = c("class_id", "grntyr"))
# Run regression
fig4_model <- plm(count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class - count - count_usa,
data = pdata_fig4[, c("count_usa", "count_for",
grep("treat_", names(pdata_fig4), value = TRUE),
grep("td_", names(pdata_fig4), value = TRUE))],
model = "within", effect = "individual")
summary(fig4_model)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class -
## count - count_usa, data = pdata_fig4[, c("count_usa", "count_for",
## grep("treat_", names(pdata_fig4), value = TRUE), grep("td_",
## names(pdata_fig4), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 7248, T = 65, N = 471120
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.336713 -0.264930 -0.036121 0.141257 56.615204
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## count_for 0.3168408 0.0029212 108.4611 < 2.2e-16 ***
## td_1875 -0.4308885 0.0107895 -39.9358 < 2.2e-16 ***
## td_1876 -0.4265404 0.0107893 -39.5337 < 2.2e-16 ***
## td_1877 -0.4289897 0.0107890 -39.7618 < 2.2e-16 ***
## td_1878 -0.4444259 0.0107892 -41.1916 < 2.2e-16 ***
## td_1879 -0.4359046 0.0107887 -40.4037 < 2.2e-16 ***
## td_1880 -0.4271620 0.0107887 -39.5935 < 2.2e-16 ***
## td_1881 -0.4337887 0.0107871 -40.2136 < 2.2e-16 ***
## td_1882 -0.3974373 0.0107862 -36.8468 < 2.2e-16 ***
## td_1883 -0.3765343 0.0107868 -34.9071 < 2.2e-16 ***
## td_1884 -0.3968117 0.0107864 -36.7883 < 2.2e-16 ***
## td_1885 -0.3532913 0.0107866 -32.7527 < 2.2e-16 ***
## td_1886 -0.3818181 0.0107871 -35.3958 < 2.2e-16 ***
## td_1887 -0.3957858 0.0107866 -36.6923 < 2.2e-16 ***
## td_1888 -0.3948037 0.0107850 -36.6066 < 2.2e-16 ***
## td_1889 -0.3715498 0.0107845 -34.4523 < 2.2e-16 ***
## td_1890 -0.3524282 0.0107839 -32.6810 < 2.2e-16 ***
## td_1891 -0.3695596 0.0107837 -34.2701 < 2.2e-16 ***
## td_1892 -0.3939513 0.0107845 -36.5294 < 2.2e-16 ***
## td_1893 -0.3665626 0.0107827 -33.9954 < 2.2e-16 ***
## td_1894 -0.4041571 0.0107826 -37.4825 < 2.2e-16 ***
## td_1895 -0.3787694 0.0107835 -35.1250 < 2.2e-16 ***
## td_1896 -0.3773788 0.0107831 -34.9973 < 2.2e-16 ***
## td_1897 -0.3668495 0.0107816 -34.0255 < 2.2e-16 ***
## td_1898 -0.4035454 0.0107800 -37.4346 < 2.2e-16 ***
## td_1899 -0.3369571 0.0107779 -31.2638 < 2.2e-16 ***
## td_1901 -0.3499862 0.0107787 -32.4701 < 2.2e-16 ***
## td_1902 -0.3498113 0.0107788 -32.4537 < 2.2e-16 ***
## td_1903 -0.3206698 0.0107788 -29.7499 < 2.2e-16 ***
## td_1904 -0.3313110 0.0107803 -30.7329 < 2.2e-16 ***
## td_1905 -0.3282087 0.0107805 -30.4447 < 2.2e-16 ***
## td_1906 -0.3045231 0.0107810 -28.2463 < 2.2e-16 ***
## td_1907 -0.2504626 0.0107796 -23.2348 < 2.2e-16 ***
## td_1908 -0.2950662 0.0107794 -27.3733 < 2.2e-16 ***
## td_1909 -0.2655954 0.0107796 -24.6387 < 2.2e-16 ***
## td_1910 -0.2961085 0.0107791 -27.4707 < 2.2e-16 ***
## td_1911 -0.2677443 0.0107791 -24.8392 < 2.2e-16 ***
## td_1912 -0.2455545 0.0107790 -22.7808 < 2.2e-16 ***
## td_1913 -0.2817091 0.0107802 -26.1321 < 2.2e-16 ***
## td_1914 -0.1987952 0.0107797 -18.4415 < 2.2e-16 ***
## td_1915 -0.1478176 0.0107799 -13.7124 < 2.2e-16 ***
## td_1916 -0.1039339 0.0107801 -9.6413 < 2.2e-16 ***
## td_1917 -0.1134728 0.0107809 -10.5253 < 2.2e-16 ***
## td_1918 -0.1416648 0.0107821 -13.1389 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 400810
## Residual Sum of Squares: 373350
## R-Squared: 0.068524
## Adj. R-Squared: 0.053882
## F-statistic: 775.494 on 44 and 463828 DF, p-value: < 2.22e-16fig5 <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\fig5.dta")
# Create bins and summarize
bins <- seq(0, 1, by = 0.1)
for(i in 1:(length(bins)-1)) {
cat(paste0("Share ", bins[i], "-", bins[i+1], " (non-licensed): "))
print(summary(fig5$share[fig5$licensed_class == 0 & fig5$share >= bins[i] & fig5$share < bins[i+1]]))
cat(paste0("Share ", bins[i], "-", bins[i+1], " (licensed): "))
print(summary(fig5$share[fig5$licensed_class == 1 & fig5$share >= bins[i] & fig5$share < bins[i+1]]))
}## Share 0-0.1 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0000000 0.0000000 0.0003737 0.0000000 0.0909091
## Share 0-0.1 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.00486 0.00000 0.09091
## Share 0.1-0.2 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1000 0.1250 0.1559 0.1482 0.1667 0.1905
## Share 0.1-0.2 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1000 0.1111 0.1250 0.1333 0.1667 0.1818
## Share 0.2-0.3 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2000 0.2000 0.2500 0.2414 0.2500 0.2941
## Share 0.2-0.3 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2000 0.2375 0.2500 0.2408 0.2500 0.2857
## Share 0.3-0.4 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3000 0.3333 0.3333 0.3364 0.3333 0.3846
## Share 0.3-0.4 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3077 0.3333 0.3333 0.3426 0.3458 0.3846
## Share 0.4-0.5 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4000 0.4000 0.4189 0.4230 0.4286 0.4857
## Share 0.4-0.5 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4000 0.4214 0.4286 0.4373 0.4644 0.4783
## Share 0.5-0.6 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5000 0.5000 0.5000 0.5124 0.5000 0.5952
## Share 0.5-0.6 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5000 0.5000 0.5000 0.5194 0.5455 0.5833
## Share 0.6-0.7 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6000 0.6250 0.6667 0.6532 0.6667 0.7000
## Share 0.6-0.7 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6000 0.6667 0.6667 0.6620 0.6667 0.7000
## Share 0.7-0.8 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.7027 0.7241 0.7500 0.7453 0.7600 0.7949
## Share 0.7-0.8 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.7500 0.7639 0.7778 0.7712 0.7817 0.7857
## Share 0.8-0.9 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8000 0.8235 0.8571 0.8506 0.8772 0.9000
## Share 0.8-0.9 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8000 0.8032 0.8406 0.8431 0.8778 0.8994
## Share 0.9-1 (non-licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9022 0.9167 0.9333 0.9346 0.9488 0.9859
## Share 0.9-1 (licensed): Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9118 0.9118 0.9118 0.9118 0.9118 0.9118# Create time-varying treatment variables
for(var in c("treat", "count_cl", "year_conf")) {
for(x in 1919:1939) {
chem_figures[[paste0(var, "_", x)]] <- ifelse(chem_figures$grntyr == x, chem_figures[[var]], 0)
}
}
pdata_figures <- pdata.frame(chem_figures, index = c("class_id", "grntyr"))
# Figure 7
fig7_model <- plm(count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class - count - count_usa,
data = pdata_figures[, c("count_usa", "count_for",
grep("treat_", names(pdata_figures), value = TRUE),
grep("td_", names(pdata_figures), value = TRUE))],
model = "within", effect = "individual")
# Figure 8
fig8_model <- plm(count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class - count - count_usa,
data = pdata_figures[, c("count_usa", "count_for",
grep("count_cl_", names(pdata_figures), value = TRUE),
grep("td_", names(pdata_figures), value = TRUE))],
model = "within", effect = "individual")
# Figure 9
fig9_model <- plm(count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class - count - count_usa,
data = pdata_figures[, c("count_usa", "count_for",
grep("year_conf_", names(pdata_figures), value = TRUE),
grep("td_", names(pdata_figures), value = TRUE))],
model = "within", effect = "individual")
summary(fig7_model)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class -
## count - count_usa, data = pdata_figures[, c("count_usa",
## "count_for", grep("treat_", names(pdata_figures), value = TRUE),
## grep("td_", names(pdata_figures), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 7248, T = 65, N = 471120
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.349687 -0.262319 -0.035918 0.140264 56.660106
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## count_for 0.3051794 0.0029510 103.4173 < 2.2e-16 ***
## treat_1919 -0.1390698 0.0495215 -2.8083 0.004981 **
## treat_1920 -0.1218695 0.0495211 -2.4610 0.013857 *
## treat_1921 -0.0807625 0.0495195 -1.6309 0.102907
## treat_1922 -0.1440333 0.0495176 -2.9087 0.003629 **
## treat_1923 -0.0886856 0.0495187 -1.7910 0.073302 .
## treat_1924 -0.1258678 0.0495179 -2.5419 0.011027 *
## treat_1925 -0.1588739 0.0495186 -3.2084 0.001335 **
## treat_1926 -0.0244829 0.0495176 -0.4944 0.621004
## treat_1927 0.0113855 0.0495176 0.2299 0.818147
## treat_1928 -0.0096186 0.0495209 -0.1942 0.845994
## treat_1929 -0.0885813 0.0495306 -1.7884 0.073710 .
## treat_1930 -0.0808124 0.0495302 -1.6316 0.102769
## treat_1931 0.1206270 0.0495648 2.4337 0.014945 *
## treat_1932 0.3647623 0.0496911 7.3406 2.13e-13 ***
## treat_1933 0.4315286 0.0496237 8.6960 < 2.2e-16 ***
## treat_1934 0.4253747 0.0495455 8.5855 < 2.2e-16 ***
## treat_1935 0.4596614 0.0495637 9.2742 < 2.2e-16 ***
## treat_1936 0.6973511 0.0495664 14.0690 < 2.2e-16 ***
## treat_1937 0.5400754 0.0495554 10.8984 < 2.2e-16 ***
## treat_1938 0.6312583 0.0495832 12.7313 < 2.2e-16 ***
## treat_1939 0.9173378 0.0495652 18.5077 < 2.2e-16 ***
## td_1875 -0.4256610 0.0107909 -39.4464 < 2.2e-16 ***
## td_1876 -0.4212952 0.0107906 -39.0426 < 2.2e-16 ***
## td_1877 -0.4237204 0.0107904 -39.2684 < 2.2e-16 ***
## td_1878 -0.4391774 0.0107906 -40.7000 < 2.2e-16 ***
## td_1879 -0.4306143 0.0107901 -39.9082 < 2.2e-16 ***
## td_1880 -0.4218685 0.0107901 -39.0978 < 2.2e-16 ***
## td_1881 -0.4283681 0.0107886 -39.7055 < 2.2e-16 ***
## td_1882 -0.3919379 0.0107878 -36.3316 < 2.2e-16 ***
## td_1883 -0.3710831 0.0107883 -34.3968 < 2.2e-16 ***
## td_1884 -0.3913251 0.0107879 -36.2743 < 2.2e-16 ***
## td_1885 -0.3478273 0.0107882 -32.2415 < 2.2e-16 ***
## td_1886 -0.3763959 0.0107886 -34.8882 < 2.2e-16 ***
## td_1887 -0.3903217 0.0107882 -36.1805 < 2.2e-16 ***
## td_1888 -0.3891981 0.0107867 -36.0812 < 2.2e-16 ***
## td_1889 -0.3658911 0.0107862 -33.9221 < 2.2e-16 ***
## td_1890 -0.3467115 0.0107857 -32.1456 < 2.2e-16 ***
## td_1891 -0.3638268 0.0107855 -33.7329 < 2.2e-16 ***
## td_1892 -0.3882959 0.0107862 -35.9992 < 2.2e-16 ***
## td_1893 -0.3607269 0.0107846 -33.4482 < 2.2e-16 ***
## td_1894 -0.3983037 0.0107845 -36.9331 < 2.2e-16 ***
## td_1895 -0.3730125 0.0107853 -34.5852 < 2.2e-16 ***
## td_1896 -0.3715817 0.0107850 -34.4537 < 2.2e-16 ***
## td_1897 -0.3608916 0.0107836 -33.4666 < 2.2e-16 ***
## td_1898 -0.3973895 0.0107822 -36.8560 < 2.2e-16 ***
## td_1899 -0.3304794 0.0107804 -30.6555 < 2.2e-16 ***
## td_1901 -0.3436485 0.0107811 -31.8750 < 2.2e-16 ***
## td_1902 -0.3434801 0.0107812 -31.8593 < 2.2e-16 ***
## td_1903 -0.3143498 0.0107812 -29.1571 < 2.2e-16 ***
## td_1904 -0.3251986 0.0107825 -30.1598 < 2.2e-16 ***
## td_1905 -0.3221140 0.0107826 -29.8734 < 2.2e-16 ***
## td_1906 -0.2984927 0.0107831 -27.6815 < 2.2e-16 ***
## td_1907 -0.2442536 0.0107819 -22.6541 < 2.2e-16 ***
## td_1908 -0.2888203 0.0107817 -26.7881 < 2.2e-16 ***
## td_1909 -0.2593881 0.0107819 -24.0577 < 2.2e-16 ***
## td_1910 -0.2898191 0.0107814 -26.8814 < 2.2e-16 ***
## td_1911 -0.2614629 0.0107814 -24.2512 < 2.2e-16 ***
## td_1912 -0.2392570 0.0107814 -22.1917 < 2.2e-16 ***
## td_1913 -0.2755790 0.0107824 -25.5582 < 2.2e-16 ***
## td_1914 -0.1926039 0.0107820 -17.8635 < 2.2e-16 ***
## td_1915 -0.1416441 0.0107821 -13.1370 < 2.2e-16 ***
## td_1916 -0.0977860 0.0107823 -9.0691 < 2.2e-16 ***
## td_1917 -0.1074360 0.0107830 -9.9634 < 2.2e-16 ***
## td_1918 -0.1357583 0.0107840 -12.5888 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 400810
## Residual Sum of Squares: 372470
## R-Squared: 0.070722
## Adj. R-Squared: 0.056072
## F-statistic: 543.043 on 65 and 463807 DF, p-value: < 2.22e-16summary(fig8_model) ## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class -
## count - count_usa, data = pdata_figures[, c("count_usa",
## "count_for", grep("count_cl_", names(pdata_figures), value = TRUE),
## grep("td_", names(pdata_figures), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 7248, T = 65, N = 471120
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.356747 -0.258682 -0.038511 0.136906 56.658536
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## count_for 0.3040731 0.0029529 102.9751 < 2.2e-16 ***
## count_cl_itt 0.0713974 0.0025820 27.6524 < 2.2e-16 ***
## count_cl_2 -0.0052174 0.0013165 -3.9631 7.400e-05 ***
## count_cl_1919 -0.1232106 0.0229124 -5.3775 7.558e-08 ***
## count_cl_1920 -0.1296104 0.0229123 -5.6568 1.543e-08 ***
## count_cl_1921 -0.0831446 0.0229121 -3.6289 0.0002847 ***
## count_cl_1922 -0.1248014 0.0229127 -5.4468 5.130e-08 ***
## count_cl_1923 -0.1207520 0.0229122 -5.2702 1.363e-07 ***
## count_cl_1924 -0.1408728 0.0229122 -6.1484 7.835e-10 ***
## count_cl_1925 -0.1569204 0.0229136 -6.8483 7.480e-12 ***
## count_cl_1926 -0.0954199 0.0229125 -4.1645 3.120e-05 ***
## count_cl_1927 -0.0873293 0.0229125 -3.8114 0.0001382 ***
## count_cl_1928 -0.0917152 0.0229154 -4.0023 6.273e-05 ***
## count_cl_1929 -0.1280222 0.0229197 -5.5857 2.329e-08 ***
## count_cl_1930 -0.1274135 0.0229194 -5.5592 2.712e-08 ***
## count_cl_1931 -0.0764718 0.0229408 -3.3334 0.0008578 ***
## count_cl_1932 0.0089664 0.0229964 0.3899 0.6966082
## count_cl_1933 0.0255836 0.0229687 1.1138 0.2653468
## count_cl_1934 0.0443935 0.0229289 1.9361 0.0528519 .
## count_cl_1935 0.1275841 0.0229332 5.5633 2.649e-08 ***
## count_cl_1936 0.2050899 0.0229332 8.9429 < 2.2e-16 ***
## count_cl_1937 0.1243849 0.0229289 5.4248 5.804e-08 ***
## count_cl_1938 0.1395484 0.0229352 6.0845 1.170e-09 ***
## count_cl_1939 0.2084023 0.0229360 9.0863 < 2.2e-16 ***
## td_1875 -0.4079394 0.0107990 -37.7756 < 2.2e-16 ***
## td_1876 -0.4035720 0.0107988 -37.3719 < 2.2e-16 ***
## td_1877 -0.4059949 0.0107985 -37.5972 < 2.2e-16 ***
## td_1878 -0.4214539 0.0107988 -39.0279 < 2.2e-16 ***
## td_1879 -0.4128869 0.0107983 -38.2363 < 2.2e-16 ***
## td_1880 -0.4041407 0.0107983 -37.4265 < 2.2e-16 ***
## td_1881 -0.4106283 0.0107969 -38.0321 < 2.2e-16 ***
## td_1882 -0.3741906 0.0107961 -34.6598 < 2.2e-16 ***
## td_1883 -0.3533404 0.0107966 -32.7271 < 2.2e-16 ***
## td_1884 -0.3735790 0.0107962 -34.6028 < 2.2e-16 ***
## td_1885 -0.3300833 0.0107964 -30.5733 < 2.2e-16 ***
## td_1886 -0.3586559 0.0107969 -33.2185 < 2.2e-16 ***
## td_1887 -0.3725778 0.0107964 -34.5093 < 2.2e-16 ***
## td_1888 -0.3714407 0.0107951 -34.4084 < 2.2e-16 ***
## td_1889 -0.3481287 0.0107946 -32.2503 < 2.2e-16 ***
## td_1890 -0.3289436 0.0107941 -30.4745 < 2.2e-16 ***
## td_1891 -0.3460574 0.0107939 -32.0604 < 2.2e-16 ***
## td_1892 -0.3705337 0.0107946 -34.3258 < 2.2e-16 ***
## td_1893 -0.3429477 0.0107931 -31.7748 < 2.2e-16 ***
## td_1894 -0.3805228 0.0107929 -35.2566 < 2.2e-16 ***
## td_1895 -0.3552408 0.0107937 -32.9118 < 2.2e-16 ***
## td_1896 -0.3538062 0.0107934 -32.7799 < 2.2e-16 ***
## td_1897 -0.3431008 0.0107922 -31.7917 < 2.2e-16 ***
## td_1898 -0.3795799 0.0107909 -35.1761 < 2.2e-16 ***
## td_1899 -0.3126393 0.0107892 -28.9770 < 2.2e-16 ***
## td_1901 -0.3258217 0.0107899 -30.1970 < 2.2e-16 ***
## td_1902 -0.3256539 0.0107899 -30.1814 < 2.2e-16 ***
## td_1903 -0.2965247 0.0107899 -27.4816 < 2.2e-16 ***
## td_1904 -0.3073931 0.0107911 -28.4858 < 2.2e-16 ***
## td_1905 -0.3043102 0.0107912 -28.1998 < 2.2e-16 ***
## td_1906 -0.2806950 0.0107917 -26.0104 < 2.2e-16 ***
## td_1907 -0.2264390 0.0107905 -20.9850 < 2.2e-16 ***
## td_1908 -0.2710021 0.0107903 -25.1153 < 2.2e-16 ***
## td_1909 -0.2415736 0.0107905 -22.3875 < 2.2e-16 ***
## td_1910 -0.2719969 0.0107901 -25.2080 < 2.2e-16 ***
## td_1911 -0.2436414 0.0107901 -22.5800 < 2.2e-16 ***
## td_1912 -0.2214340 0.0107901 -20.5220 < 2.2e-16 ***
## td_1913 -0.2577718 0.0107910 -23.8876 < 2.2e-16 ***
## td_1914 -0.1747910 0.0107906 -16.1984 < 2.2e-16 ***
## td_1915 -0.1238328 0.0107907 -11.4758 < 2.2e-16 ***
## td_1916 -0.0799772 0.0107909 -7.4115 1.250e-13 ***
## td_1917 -0.0896377 0.0107916 -8.3062 < 2.2e-16 ***
## td_1918 -0.1179724 0.0107925 -10.9309 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 400810
## Residual Sum of Squares: 372020
## R-Squared: 0.071846
## Adj. R-Squared: 0.057209
## F-statistic: 535.848 on 67 and 463805 DF, p-value: < 2.22e-16summary(fig9_model)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_usa ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class -
## count - count_usa, data = pdata_figures[, c("count_usa",
## "count_for", grep("year_conf_", names(pdata_figures), value = TRUE),
## grep("td_", names(pdata_figures), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 7248, T = 65, N = 471120
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.358232 -0.258342 -0.038197 0.136791 56.663836
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## count_for 3.0271e-01 2.9512e-03 102.5719 < 2.2e-16 ***
## year_conf_2 -5.0658e-05 1.0787e-05 -4.6965 2.648e-06 ***
## year_conf_itt 9.0640e-03 3.1781e-04 28.5200 < 2.2e-16 ***
## year_conf_1919 -1.1926e-02 1.8574e-03 -6.4208 1.357e-10 ***
## year_conf_1920 -1.1626e-02 1.8574e-03 -6.2596 3.862e-10 ***
## year_conf_1921 -8.2219e-03 1.8573e-03 -4.4267 9.569e-06 ***
## year_conf_1922 -1.1105e-02 1.8574e-03 -5.9791 2.246e-09 ***
## year_conf_1923 -1.0471e-02 1.8573e-03 -5.6374 1.727e-08 ***
## year_conf_1924 -1.2470e-02 1.8573e-03 -6.7140 1.896e-11 ***
## year_conf_1925 -1.3693e-02 1.8574e-03 -7.3722 1.681e-13 ***
## year_conf_1926 -9.4043e-03 1.8573e-03 -5.0633 4.121e-07 ***
## year_conf_1927 -7.9967e-03 1.8573e-03 -4.3055 1.667e-05 ***
## year_conf_1928 -7.6438e-03 1.8575e-03 -4.1151 3.870e-05 ***
## year_conf_1929 -1.0318e-02 1.8577e-03 -5.5543 2.789e-08 ***
## year_conf_1930 -1.1032e-02 1.8577e-03 -5.9382 2.883e-09 ***
## year_conf_1931 -6.2165e-03 1.8592e-03 -3.3436 0.000827 ***
## year_conf_1932 4.5048e-04 1.8629e-03 0.2418 0.808920
## year_conf_1933 3.2627e-03 1.8613e-03 1.7529 0.079617 .
## year_conf_1934 4.5267e-03 1.8583e-03 2.4359 0.014857 *
## year_conf_1935 8.3580e-03 1.8587e-03 4.4968 6.900e-06 ***
## year_conf_1936 1.5788e-02 1.8587e-03 8.4941 < 2.2e-16 ***
## year_conf_1937 9.3639e-03 1.8585e-03 5.0384 4.696e-07 ***
## year_conf_1938 1.1363e-02 1.8590e-03 6.1123 9.827e-10 ***
## year_conf_1939 1.7624e-02 1.8590e-03 9.4800 < 2.2e-16 ***
## td_1875 -4.0743e-01 1.0797e-02 -37.7348 < 2.2e-16 ***
## td_1876 -4.0307e-01 1.0797e-02 -37.3308 < 2.2e-16 ***
## td_1877 -4.0549e-01 1.0797e-02 -37.5559 < 2.2e-16 ***
## td_1878 -4.2095e-01 1.0797e-02 -38.9871 < 2.2e-16 ***
## td_1879 -4.1237e-01 1.0797e-02 -38.1948 < 2.2e-16 ***
## td_1880 -4.0363e-01 1.0797e-02 -37.3848 < 2.2e-16 ***
## td_1881 -4.1010e-01 1.0795e-02 -37.9891 < 2.2e-16 ***
## td_1882 -3.7365e-01 1.0794e-02 -34.6155 < 2.2e-16 ***
## td_1883 -3.5281e-01 1.0795e-02 -32.6830 < 2.2e-16 ***
## td_1884 -3.7304e-01 1.0795e-02 -34.5585 < 2.2e-16 ***
## td_1885 -3.2955e-01 1.0795e-02 -30.5287 < 2.2e-16 ***
## td_1886 -3.5813e-01 1.0795e-02 -33.1748 < 2.2e-16 ***
## td_1887 -3.7205e-01 1.0795e-02 -34.4653 < 2.2e-16 ***
## td_1888 -3.7089e-01 1.0793e-02 -34.3628 < 2.2e-16 ***
## td_1889 -3.4757e-01 1.0793e-02 -32.2038 < 2.2e-16 ***
## td_1890 -3.2838e-01 1.0792e-02 -30.4270 < 2.2e-16 ***
## td_1891 -3.4549e-01 1.0792e-02 -32.0130 < 2.2e-16 ***
## td_1892 -3.6998e-01 1.0793e-02 -34.2796 < 2.2e-16 ***
## td_1893 -3.4237e-01 1.0791e-02 -31.7262 < 2.2e-16 ***
## td_1894 -3.7994e-01 1.0791e-02 -35.2084 < 2.2e-16 ***
## td_1895 -3.5467e-01 1.0792e-02 -32.8643 < 2.2e-16 ***
## td_1896 -3.5323e-01 1.0792e-02 -32.7319 < 2.2e-16 ***
## td_1897 -3.4251e-01 1.0791e-02 -31.7418 < 2.2e-16 ***
## td_1898 -3.7897e-01 1.0789e-02 -35.1245 < 2.2e-16 ***
## td_1899 -3.1199e-01 1.0788e-02 -28.9209 < 2.2e-16 ***
## td_1901 -3.2519e-01 1.0788e-02 -30.1427 < 2.2e-16 ***
## td_1902 -3.2502e-01 1.0788e-02 -30.1271 < 2.2e-16 ***
## td_1903 -2.9589e-01 1.0788e-02 -27.4270 < 2.2e-16 ***
## td_1904 -3.0678e-01 1.0790e-02 -28.4336 < 2.2e-16 ***
## td_1905 -3.0370e-01 1.0790e-02 -28.1478 < 2.2e-16 ***
## td_1906 -2.8010e-01 1.0790e-02 -25.9588 < 2.2e-16 ***
## td_1907 -2.2582e-01 1.0789e-02 -20.9306 < 2.2e-16 ***
## td_1908 -2.7038e-01 1.0789e-02 -25.0612 < 2.2e-16 ***
## td_1909 -2.4095e-01 1.0789e-02 -22.3334 < 2.2e-16 ***
## td_1910 -2.7137e-01 1.0788e-02 -25.1534 < 2.2e-16 ***
## td_1911 -2.4301e-01 1.0789e-02 -22.5251 < 2.2e-16 ***
## td_1912 -2.2080e-01 1.0788e-02 -20.4667 < 2.2e-16 ***
## td_1913 -2.5716e-01 1.0789e-02 -23.8346 < 2.2e-16 ***
## td_1914 -1.7417e-01 1.0789e-02 -16.1436 < 2.2e-16 ***
## td_1915 -1.2322e-01 1.0789e-02 -11.4205 < 2.2e-16 ***
## td_1916 -7.9365e-02 1.0789e-02 -7.3559 1.900e-13 ***
## td_1917 -8.9038e-02 1.0790e-02 -8.2519 < 2.2e-16 ***
## td_1918 -1.1739e-01 1.0791e-02 -10.8784 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 400810
## Residual Sum of Squares: 371910
## R-Squared: 0.07211
## Adj. R-Squared: 0.057478
## F-statistic: 537.975 on 67 and 463805 DF, p-value: < 2.22e-16## Figure 10
fig10 <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\fig10.dta")
# 首先创建时间变量 - 从td_*变量推断年份
cat("Creating year variable from time dummies...\n")## Creating year variable from time dummies...td_vars <- grep("^td_1[0-9]{3}$", names(fig10), value = TRUE)
years <- as.numeric(gsub("td_", "", td_vars))
# 为每个观测值分配年份
fig10$year <- NA
for(i in seq_along(td_vars)) {
fig10$year[fig10[[td_vars[i]]] == 1] <- years[i]
}
# 检查年份分配情况
cat("Year distribution:\n")## Year distribution:year_table <- table(fig10$year, useNA = "always")
print(year_table)##
## 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
## 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496
## 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901
## 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496
## 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914
## 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496
## 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927
## 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496
## 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 <NA>
## 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496 14496# 移除年份为NA的观测值
fig10_clean <- fig10[!is.na(fig10$year), ]
cat("\nRemoved", sum(is.na(fig10$year)), "observations with missing year\n")##
## Removed 14496 observations with missing yearcat("Remaining observations:", nrow(fig10_clean), "\n")## Remaining observations: 927744# 检查class_id的分布
cat("\nClass_id distribution:\n")##
## Class_id distribution:class_table <- table(fig10_clean$class_id)
cat("Number of unique class_id:", length(unique(fig10_clean$class_id)), "\n")## Number of unique class_id: 7248cat("Observations per class_id:", mean(table(fig10_clean$class_id)), "\n")## Observations per class_id: 128# 运行面板回归
cat("\nRunning panel regression...\n")##
## Running panel regression...# 方法1：使用明确的变量列表（更稳定）
fig10_model <- plm(
y ~ usa_treat_td1919 + usa_treat_td1920 + usa_treat_td1921 + usa_treat_td1922 + usa_treat_td1923 +
usa_treat_td1924 + usa_treat_td1925 + usa_treat_td1926 + usa_treat_td1927 + usa_treat_td1928 +
usa_treat_td1929 + usa_treat_td1930 + usa_treat_td1931 + usa_treat_td1932 + usa_treat_td1933 +
usa_treat_td1934 + usa_treat_td1935 + usa_treat_td1936 + usa_treat_td1937 + usa_treat_td1938 +
usa_treat_td1939 +
usa_td1919 + usa_td1920 + usa_td1921 + usa_td1922 + usa_td1923 + usa_td1924 + usa_td1925 +
usa_td1926 + usa_td1927 + usa_td1928 + usa_td1929 + usa_td1930 + usa_td1931 + usa_td1932 +
usa_td1933 + usa_td1934 + usa_td1935 + usa_td1936 + usa_td1937 + usa_td1938 + usa_td1939 +
treat_td1919 + treat_td1920 + treat_td1921 + treat_td1922 + treat_td1923 + treat_td1924 +
treat_td1925 + treat_td1926 + treat_td1927 + treat_td1928 + treat_td1929 + treat_td1930 +
treat_td1931 + treat_td1932 + treat_td1933 + treat_td1934 + treat_td1935 + treat_td1936 +
treat_td1937 + treat_td1938 + treat_td1939 +
usa + treat,
data = fig10_clean,
index = c("class_id", "year"),
model = "within",
effect = "individual"
)
# 输出结果
cat("\n=== Figure 10 Regression Results ===\n")##
## === Figure 10 Regression Results ===print(summary(fig10_model))## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = y ~ usa_treat_td1919 + usa_treat_td1920 + usa_treat_td1921 +
## usa_treat_td1922 + usa_treat_td1923 + usa_treat_td1924 +
## usa_treat_td1925 + usa_treat_td1926 + usa_treat_td1927 +
## usa_treat_td1928 + usa_treat_td1929 + usa_treat_td1930 +
## usa_treat_td1931 + usa_treat_td1932 + usa_treat_td1933 +
## usa_treat_td1934 + usa_treat_td1935 + usa_treat_td1936 +
## usa_treat_td1937 + usa_treat_td1938 + usa_treat_td1939 +
## usa_td1919 + usa_td1920 + usa_td1921 + usa_td1922 + usa_td1923 +
## usa_td1924 + usa_td1925 + usa_td1926 + usa_td1927 + usa_td1928 +
## usa_td1929 + usa_td1930 + usa_td1931 + usa_td1932 + usa_td1933 +
## usa_td1934 + usa_td1935 + usa_td1936 + usa_td1937 + usa_td1938 +
## usa_td1939 + treat_td1919 + treat_td1920 + treat_td1921 +
## treat_td1922 + treat_td1923 + treat_td1924 + treat_td1925 +
## treat_td1926 + treat_td1927 + treat_td1928 + treat_td1929 +
## treat_td1930 + treat_td1931 + treat_td1932 + treat_td1933 +
## treat_td1934 + treat_td1935 + treat_td1936 + treat_td1937 +
## treat_td1938 + treat_td1939 + usa + treat, data = fig10_clean,
## effect = "individual", model = "within", index = c("class_id",
## "year"))
##
## Balanced Panel: n = 7248, T = 64, N = 927744
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -7.121666 -0.212323 -0.032636 0.128334 62.514239
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## usa_treat_td1919 -0.2357866 0.0579182 -4.0710 4.681e-05 ***
## usa_treat_td1920 -0.1804997 0.0579182 -3.1165 0.0018304 **
## usa_treat_td1921 -0.1550781 0.0579182 -2.6775 0.0074168 **
## usa_treat_td1922 -0.2180327 0.0579182 -3.7645 0.0001669 ***
## usa_treat_td1923 -0.1424706 0.0579182 -2.4599 0.0138994 *
## usa_treat_td1924 -0.2554625 0.0579182 -4.4107 1.030e-05 ***
## usa_treat_td1925 -0.2514942 0.0579182 -4.3422 1.411e-05 ***
## usa_treat_td1926 -0.1580336 0.0579182 -2.7286 0.0063612 **
## usa_treat_td1927 -0.0639942 0.0579182 -1.1049 0.2692006
## usa_treat_td1928 -0.0856336 0.0579182 -1.4785 0.1392675
## usa_treat_td1929 -0.1507171 0.0579182 -2.6022 0.0092619 **
## usa_treat_td1930 -0.1989355 0.0579182 -3.4348 0.0005931 ***
## usa_treat_td1931 -0.2341951 0.0579182 -4.0435 5.265e-05 ***
## usa_treat_td1932 -0.1141141 0.0579182 -1.9703 0.0488087 *
## usa_treat_td1933 0.0299005 0.0579182 0.5163 0.6056780
## usa_treat_td1934 -0.0586619 0.0579182 -1.0128 0.3111371
## usa_treat_td1935 -0.1034287 0.0579182 -1.7858 0.0741366 .
## usa_treat_td1936 0.0651600 0.0579182 1.1250 0.2605746
## usa_treat_td1937 -0.0133576 0.0579182 -0.2306 0.8176028
## usa_treat_td1938 0.1386554 0.0579182 2.3940 0.0166666 *
## usa_treat_td1939 0.2524120 0.0579182 4.3581 1.312e-05 ***
## usa_td1919 0.1782398 0.0090209 19.7585 < 2.2e-16 ***
## usa_td1920 0.1705720 0.0090209 18.9085 < 2.2e-16 ***
## usa_td1921 0.2165789 0.0090209 24.0085 < 2.2e-16 ***
## usa_td1922 0.2319146 0.0090209 25.7085 < 2.2e-16 ***
## usa_td1923 0.1890905 0.0090209 20.9613 < 2.2e-16 ***
## usa_td1924 0.2395824 0.0090209 26.5585 < 2.2e-16 ***
## usa_td1925 0.2534713 0.0090209 28.0982 < 2.2e-16 ***
## usa_td1926 0.2850106 0.0090209 31.5944 < 2.2e-16 ***
## usa_td1927 0.2118046 0.0090209 23.4793 < 2.2e-16 ***
## usa_td1928 0.2304678 0.0090209 25.5481 < 2.2e-16 ***
## usa_td1929 0.3015037 0.0090209 33.4227 < 2.2e-16 ***
## usa_td1930 0.3169840 0.0090209 35.1388 < 2.2e-16 ***
## usa_td1931 0.5099817 0.0090209 56.5332 < 2.2e-16 ***
## usa_td1932 0.5565673 0.0090209 61.6974 < 2.2e-16 ***
## usa_td1933 0.5554099 0.0090209 61.5691 < 2.2e-16 ***
## usa_td1934 0.5755199 0.0090209 63.7983 < 2.2e-16 ***
## usa_td1935 0.5399296 0.0090209 59.8530 < 2.2e-16 ***
## usa_td1936 0.6124122 0.0090209 67.8880 < 2.2e-16 ***
## usa_td1937 0.5778347 0.0090209 64.0549 < 2.2e-16 ***
## usa_td1938 0.6222502 0.0090209 68.9786 < 2.2e-16 ***
## usa_td1939 0.7495650 0.0090209 83.0918 < 2.2e-16 ***
## treat_td1919 0.1418686 0.0407019 3.4856 0.0004911 ***
## treat_td1920 0.1150829 0.0407019 2.8275 0.0046920 **
## treat_td1921 0.1002020 0.0407019 2.4619 0.0138224 *
## treat_td1922 0.1299639 0.0407019 3.1931 0.0014077 **
## treat_td1923 0.1180591 0.0407019 2.9006 0.0037248 **
## treat_td1924 0.1597258 0.0407019 3.9243 8.699e-05 ***
## treat_td1925 0.1597258 0.0407019 3.9243 8.699e-05 ***
## treat_td1926 0.1418686 0.0407019 3.4856 0.0004911 ***
## treat_td1927 0.1478210 0.0407019 3.6318 0.0002815 ***
## treat_td1928 0.1924639 0.0407019 4.7286 2.261e-06 ***
## treat_td1929 0.1656781 0.0407019 4.0705 4.691e-05 ***
## treat_td1930 0.2043686 0.0407019 5.0211 5.138e-07 ***
## treat_td1931 0.3561543 0.0407019 8.7503 < 2.2e-16 ***
## treat_td1932 0.6388924 0.0407019 15.6969 < 2.2e-16 ***
## treat_td1933 0.4692496 0.0407019 11.5289 < 2.2e-16 ***
## treat_td1934 0.3680591 0.0407019 9.0428 < 2.2e-16 ***
## treat_td1935 0.5317496 0.0407019 13.0645 < 2.2e-16 ***
## treat_td1936 0.5347258 0.0407019 13.1376 < 2.2e-16 ***
## treat_td1937 0.4632972 0.0407019 11.3827 < 2.2e-16 ***
## treat_td1938 0.4216305 0.0407019 10.3590 < 2.2e-16 ***
## treat_td1939 0.4275829 0.0407019 10.5052 < 2.2e-16 ***
## usa 0.1765944 0.0017242 102.4199 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 550390
## Residual Sum of Squares: 506230
## R-Squared: 0.080238
## Adj. R-Squared: 0.072933
## F-statistic: 1254.64 on 64 and 920432 DF, p-value: < 2.22e-16# 模型信息
cat("\nModel information:\n")##
## Model information:cat("Number of observations:", nobs(fig10_model), "\n")## Number of observations: 927744if(nobs(fig10_model) > 0) {
cat("Number of groups (class_id):", length(unique(index(fig10_model)[[1]])), "\n")
cat("Time periods:", length(unique(index(fig10_model)[[2]])), "\n")
}## Number of groups (class_id): 7248
## Time periods: 64# Count France regression
fig11_model <- plm(count_france ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class - count - count_france,
data = pdata_figures[, c("count_france",
grep("treat_", names(pdata_figures), value = TRUE),
grep("td_", names(pdata_figures), value = TRUE))],
model = "within", effect = "individual")
summary(fig11_model)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_france ~ . - class_id - grntyr - uspto_class_class_class_class_class_class_class -
## count - count_france, data = pdata_figures[, c("count_france",
## grep("treat_", names(pdata_figures), value = TRUE), grep("td_",
## names(pdata_figures), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 7248, T = 65, N = 471120
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.4217491 -0.0144159 -0.0018934 0.0019345 16.6279431
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## treat_1919 -0.01280783 0.00707564 -1.8101 0.0702763 .
## treat_1920 -0.01280783 0.00707564 -1.8101 0.0702763 .
## treat_1921 -0.00983164 0.00707564 -1.3895 0.1646799
## treat_1922 0.04969217 0.00707564 7.0230 2.175e-12 ***
## treat_1923 -0.01578402 0.00707564 -2.2308 0.0256979 *
## treat_1924 -0.00387926 0.00707564 -0.5483 0.5835167
## treat_1925 -0.00983164 0.00707564 -1.3895 0.1646799
## treat_1926 -0.00387926 0.00707564 -0.5483 0.5835167
## treat_1927 -0.02471259 0.00707564 -3.4926 0.0004783 ***
## treat_1928 -0.00387926 0.00707564 -0.5483 0.5835167
## treat_1929 -0.00387926 0.00707564 -0.5483 0.5835167
## treat_1930 -0.02471259 0.00707564 -3.4926 0.0004783 ***
## treat_1931 -0.00090307 0.00707564 -0.1276 0.8984412
## treat_1932 -0.01578402 0.00707564 -2.2308 0.0256979 *
## treat_1933 0.00207312 0.00707564 0.2930 0.7695270
## treat_1934 -0.01578402 0.00707564 -2.2308 0.0256979 *
## treat_1935 0.01993026 0.00707564 2.8167 0.0048516 **
## treat_1936 0.01695407 0.00707564 2.3961 0.0165702 *
## treat_1937 0.01993026 0.00707564 2.8167 0.0048516 **
## treat_1938 0.01100169 0.00707564 1.5549 0.1199782
## treat_1939 -0.02173640 0.00707564 -3.0720 0.0021264 **
## td_1875 -0.01086426 0.00154017 -7.0539 1.742e-12 ***
## td_1876 -0.01045036 0.00154017 -6.7852 1.161e-11 ***
## td_1877 -0.01141614 0.00154017 -7.4123 1.244e-13 ***
## td_1878 -0.01045036 0.00154017 -6.7852 1.161e-11 ***
## td_1879 -0.00810488 0.00154017 -5.2623 1.423e-07 ***
## td_1880 -0.01196802 0.00154017 -7.7706 7.829e-15 ***
## td_1881 -0.00769097 0.00154017 -4.9936 5.929e-07 ***
## td_1882 -0.00672519 0.00154017 -4.3665 1.263e-05 ***
## td_1883 -0.00658722 0.00154017 -4.2769 1.895e-05 ***
## td_1884 -0.00658722 0.00154017 -4.2769 1.895e-05 ***
## td_1885 -0.01100223 0.00154017 -7.1435 9.111e-13 ***
## td_1886 -0.00962254 0.00154017 -6.2477 4.169e-10 ***
## td_1887 -0.00796691 0.00154017 -5.1727 2.308e-07 ***
## td_1888 -0.00175830 0.00154017 -1.1416 0.2536092
## td_1889 -0.00148237 0.00154017 -0.9625 0.3358153
## td_1890 0.00072514 0.00154017 0.4708 0.6377712
## td_1891 0.00017326 0.00154017 0.1125 0.9104299
## td_1892 0.00127702 0.00154017 0.8291 0.4070262
## td_1893 0.00927922 0.00154017 6.0248 1.694e-09 ***
## td_1894 0.00941719 0.00154017 6.1144 9.701e-10 ***
## td_1895 0.00541609 0.00154017 3.5165 0.0004372 ***
## td_1896 0.00403640 0.00154017 2.6207 0.0087741 **
## td_1897 0.00955516 0.00154017 6.2040 5.511e-10 ***
## td_1898 0.01797128 0.00154017 11.6684 < 2.2e-16 ***
## td_1899 0.01134876 0.00154017 7.3685 1.728e-13 ***
## td_1901 -0.00079252 0.00154017 -0.5146 0.6068566
## td_1902 0.00307061 0.00154017 1.9937 0.0461873 *
## td_1903 -0.00658722 0.00154017 -4.2769 1.895e-05 ***
## td_1904 -0.00465565 0.00154017 -3.0228 0.0025045 **
## td_1905 -0.00493159 0.00154017 -3.2020 0.0013650 **
## td_1906 -0.00382784 0.00154017 -2.4853 0.0129434 *
## td_1907 -0.00493159 0.00154017 -3.2020 0.0013650 **
## td_1908 -0.00493159 0.00154017 -3.2020 0.0013650 **
## td_1909 -0.00286206 0.00154017 -1.8583 0.0631312 .
## td_1910 -0.00368987 0.00154017 -2.3958 0.0165867 *
## td_1911 -0.00106846 0.00154017 -0.6937 0.4878541
## td_1912 -0.00203424 0.00154017 -1.3208 0.1865724
## td_1913 -0.00782894 0.00154017 -5.0832 3.713e-07 ***
## td_1914 -0.00644925 0.00154017 -4.1874 2.823e-05 ***
## td_1915 -0.00286206 0.00154017 -1.8583 0.0631312 .
## td_1916 -0.00175830 0.00154017 -1.1416 0.2536092
## td_1917 -0.00631128 0.00154017 -4.0978 4.172e-05 ***
## td_1918 -0.00713910 0.00154017 -4.6353 3.566e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7622.2
## Residual Sum of Squares: 7605
## R-Squared: 0.0022575
## Adj. R-Squared: -0.01347
## F-statistic: 16.397 on 64 and 463808 DF, p-value: < 2.22e-16# Load indigo dataset for Figure 13
indigo_fig13 <- read_dta("C:\\Users\\wang\\Desktop\\Moser and Voena (2012) replication package\\compulsory_licensing_replication\\chem_patents_indigodataset.dta")
# Create time dummies and treatment interactions
for(x in 1876:1939) {
indigo_fig13[[paste0("td_", x)]] <- as.numeric(indigo_fig13$grntyr == x)
}
for(x in 1919:1939) {
indigo_fig13[[paste0("treat_", x)]] <- ifelse(indigo_fig13$grntyr == x, indigo_fig13$treat, 0)
}
pdata_indigo_fig13 <- pdata.frame(indigo_fig13, index = c("class_id", "grntyr"))
fig13_model <- plm(count_usa ~ . - class_id - grntyr - count_usa,
data = pdata_indigo_fig13[, c("count_usa", "count_for",
grep("treat_", names(pdata_indigo_fig13), value = TRUE),
grep("td_", names(pdata_indigo_fig13), value = TRUE))],
model = "within", effect = "individual")
summary(fig13_model)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = count_usa ~ . - class_id - grntyr - count_usa,
## data = pdata_indigo_fig13[, c("count_usa", "count_for", grep("treat_",
## names(pdata_indigo_fig13), value = TRUE), grep("td_",
## names(pdata_indigo_fig13), value = TRUE))], effect = "individual",
## model = "within")
##
## Balanced Panel: n = 704, T = 65, N = 45760
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.7378213 -0.0185688 -0.0036886 0.0099155 4.7424259
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## count_for 0.03227848 0.00311596 10.3591 < 2.2e-16 ***
## treat_1919 0.00073284 0.01326601 0.0552 0.9559461
## treat_1920 0.01841459 0.01326676 1.3880 0.1651366
## treat_1921 0.02303356 0.01326538 1.7364 0.0825059 .
## treat_1922 0.01054275 0.01326522 0.7948 0.4267537
## treat_1923 0.00629578 0.01326572 0.4746 0.6350816
## treat_1924 0.00828410 0.01326529 0.6245 0.5323064
## treat_1925 0.01452804 0.01326605 1.0951 0.2734655
## treat_1926 0.02579743 0.01326551 1.9447 0.0518173 .
## treat_1927 0.01891697 0.01326890 1.4257 0.1539728
## treat_1928 0.07554848 0.01326599 5.6949 1.242e-08 ***
## treat_1929 0.01271752 0.01326659 0.9586 0.3377590
## treat_1930 0.00532536 0.01326522 0.4015 0.6880889
## treat_1931 0.07159759 0.01326525 5.3974 6.796e-08 ***
## treat_1932 0.08890056 0.01326528 6.7017 2.084e-11 ***
## treat_1933 0.04078277 0.01327324 3.0726 0.0021236 **
## treat_1934 0.11254285 0.01326779 8.4824 < 2.2e-16 ***
## treat_1935 0.11189463 0.01326553 8.4350 < 2.2e-16 ***
## treat_1936 0.13249482 0.01326519 9.9882 < 2.2e-16 ***
## treat_1937 0.02701859 0.01326521 2.0368 0.0416759 *
## treat_1938 0.08498168 0.01326771 6.4051 1.517e-10 ***
## treat_1939 0.02315979 0.01326608 1.7458 0.0808543 .
## td_1876 0.00151216 0.00717637 0.2107 0.8331123
## td_1877 0.00146630 0.00717637 0.2043 0.8381013
## td_1878 -0.00137460 0.00717637 -0.1915 0.8480988
## td_1879 0.00009170 0.00717637 0.0128 0.9898049
## td_1880 -0.00467402 0.00717648 -0.6513 0.5148586
## td_1881 0.00229071 0.00717657 0.3192 0.7495819
## td_1882 0.00247411 0.00717646 0.3448 0.7302814
## td_1883 -0.00146630 0.00717637 -0.2043 0.8381013
## td_1884 -0.00036680 0.00717646 -0.0511 0.9592367
## td_1885 0.00261166 0.00717640 0.3639 0.7159172
## td_1886 -0.00302431 0.00717639 -0.4214 0.6734469
## td_1887 -0.00329941 0.00717651 -0.4598 0.6456967
## td_1888 0.00545257 0.00717640 0.7598 0.4473835
## td_1889 -0.00458231 0.00717644 -0.6385 0.5231370
## td_1890 -0.00476572 0.00717654 -0.6641 0.5066495
## td_1891 -0.00151216 0.00717637 -0.2107 0.8331123
## td_1892 -0.00471987 0.00717651 -0.6577 0.5107453
## td_1893 -0.00536177 0.00717716 -0.7471 0.4550312
## td_1894 -0.00325356 0.00717648 -0.4534 0.6502886
## td_1895 -0.00549932 0.00717736 -0.7662 0.4435597
## td_1896 -0.00430721 0.00717637 -0.6002 0.5483801
## td_1897 0.00559012 0.00717637 0.7790 0.4360067
## td_1898 -0.00210821 0.00717668 -0.2938 0.7689442
## td_1899 -0.00357451 0.00717672 -0.4981 0.6184369
## td_1900 -0.00013845 0.00717795 -0.0193 0.9846113
## td_1901 -0.00490416 0.00717913 -0.6831 0.4945385
## td_1902 -0.00618707 0.00717878 -0.8619 0.3887716
## td_1903 -0.00197155 0.00717889 -0.2746 0.7836001
## td_1904 -0.00577442 0.00717786 -0.8045 0.4211260
## td_1905 0.00316096 0.00717716 0.4404 0.6596353
## td_1906 -0.00380376 0.00717697 -0.5300 0.5961178
## td_1907 -0.00545347 0.00717729 -0.7598 0.4473648
## td_1908 -0.00522422 0.00717697 -0.7279 0.4666702
## td_1909 -0.00527007 0.00717703 -0.7343 0.4627721
## td_1910 -0.00458231 0.00717644 -0.6385 0.5231370
## td_1911 -0.00513252 0.00717686 -0.7151 0.4745216
## td_1912 0.00201561 0.00717681 0.2808 0.7788268
## td_1913 -0.00421641 0.00717760 -0.5874 0.5569108
## td_1914 -0.00527007 0.00717703 -0.7343 0.4627721
## td_1915 0.00256581 0.00717642 0.3575 0.7206943
## td_1916 0.00361946 0.00717664 0.5043 0.6140253
## td_1917 0.00998903 0.00717637 1.3919 0.1639494
## td_1918 -0.00421551 0.00717637 -0.5874 0.5569275
## td_1919 -0.00439565 0.00757689 -0.5801 0.5618240
## td_1920 0.00918058 0.00757708 1.2116 0.2256623
## td_1921 0.01131266 0.00757688 1.4931 0.1354308
## td_1922 0.00704849 0.00757747 0.9302 0.3522775
## td_1923 0.01254657 0.00757717 1.6558 0.0977617 .
## td_1924 0.00205565 0.00757722 0.2713 0.7861671
## td_1925 -0.00293720 0.00757704 -0.3876 0.6982807
## td_1926 0.01580028 0.00757740 2.0852 0.0370582 *
## td_1927 0.02617893 0.00757747 3.4548 0.0005511 ***
## td_1928 0.00530936 0.00757747 0.7007 0.4835082
## td_1929 -0.00563065 0.00757825 -0.7430 0.4574850
## td_1930 0.01226588 0.00757747 1.6187 0.1055123
## td_1931 0.03801602 0.00757796 5.0167 5.278e-07 ***
## td_1932 0.01821085 0.00758553 2.4007 0.0163663 *
## td_1933 0.00856197 0.00758228 1.1292 0.2588160
## td_1934 0.04508371 0.00758228 5.9459 2.769e-09 ***
## td_1935 0.02247612 0.00757778 2.9661 0.0030180 **
## td_1936 0.10915196 0.00757825 14.4033 < 2.2e-16 ***
## td_1937 0.08334568 0.00757778 10.9987 < 2.2e-16 ***
## td_1938 0.02112884 0.00758094 2.7871 0.0053204 **
## td_1939 0.08895603 0.00757728 11.7398 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 854.74
## Residual Sum of Squares: 815.22
## R-Squared: 0.046234
## Adj. R-Squared: 0.0295
## F-statistic: 25.3479 on 86 and 44970 DF, p-value: < 2.22e-16