Last updated: November 28, 2022

This page contains semiconductor trade data for use in forecasting the following questions on INFER:

Data is updated automatically at regular intervals and is sourced from COMTRADE.

Important Note: The annual data for US exports to China is a summation of monthly data, so data for the current year is to-date and is not complete.

Click the “Code” buttons to see the R code used on this page. An R Markdown file of this page is available here for anyone who wishes to download and run or modify it themselves.

library(jsonlite)
library(tidyverse)
library(lubridate)
library(scales)
library(zoo)
library(tidyr)
library(kableExtra)

#
#
#
#US exports of SME to China
#
#
#

us_sme_china <- read.csv(url("https://comtrade.un.org/api/get?type=C&freq=M&px=HS&ps=all&r=842&p=156&rg=2&cc=8486%2C903082%2C903141%2C854311%2C901041&fmt=csv"))

us_sme_china <- select(us_sme_china,"Period","Trade.Value..US..")

us_sme_china <- cbind("Date" = as.Date(parse_date_time(us_sme_china$Period,"ym")),us_sme_china)

us_sme_china <- us_sme_china %>% group_by(Date) %>% summarise(Value = sum(Trade.Value..US..)) %>% ungroup()

us_sme_china <- transform(us_sme_china,Rolling.Sum = rollapply(Value,12,sum, fill = NA, align = "left"))

us_sme_china <- transform(us_sme_china,Rolling.Average = rollapply(as.numeric(Value),6,mean, fill = NA, align = "right"))

us_sme_china_annual <- us_sme_china

us_sme_china_annual$DateFloor <- floor_date(us_sme_china_annual$Date,"year")

us_sme_china_annual <- us_sme_china_annual %>% group_by(DateFloor) %>% mutate(Annual.Sum = sum(Value)) %>% ungroup()

us_sme_china_annual$Annual.Sum[duplicated(us_sme_china_annual$Annual.Sum)] <- NA

us_sme_china_annual <- select(us_sme_china_annual,"Date","Rolling.Sum","Annual.Sum")

us_sme_china_annual_long <- gather(us_sme_china_annual,Type,Sum,Rolling.Sum,Annual.Sum)

us_sme_china_annual_long <- na.omit(us_sme_china_annual_long)

us_sme_china <- select(us_sme_china,"Date","Value","Rolling.Average")

us_sme_china_long <- gather(us_sme_china,Type,Result,Value,Rolling.Average)

us_sme_china_monthly_plot <- ggplot(us_sme_china_long, aes(Date,Result)) +
  geom_line(size=0.75, aes(color=Type)) +
  labs(x="Month", y="Export Value (Millions of Dollars)", title="US to China Monthly Semiconductor Equipment Exports",color = "Legend") +
  scale_color_hue(labels = c("6 Month Rolling Average", "Monthly Value")) +
  scale_y_continuous(labels = label_dollar(scale = 1e-6), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%b/%y")) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_line(colour = "gray90")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

#Expand limits: https://stackoverflow.com/questions/27028825/ggplot2-force-y-axis-to-start-at-origin-and-float-y-axis-upper-limit/59056123#59056123

us_sme_china_annual_plot <- ggplot(us_sme_china_annual_long, aes(Date,Sum)) +
  geom_line(size=0.75, aes(color=Type)) +
  geom_point(data = us_sme_china_annual_long[us_sme_china_annual_long$Type == "Annual.Sum",], color = "red", size=2) +
  labs(x="Year", y="Export Value (Billions of Dollars)", title="US to China Annual Semiconductor Equipment Exports", color = "Legend") +
  scale_color_hue(labels = c("Annual", "12-Month Rolling Sum")) +
  scale_y_continuous(labels = label_dollar(scale = 1e-9), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%Y")) +
  expand_limits(y = c(0,NA)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_line(colour = "gray90")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

us_sme_china_combined <- us_sme_china

us_sme_china_combined <- transform(us_sme_china_combined,Rolling.Sum = rollapply(Value,12,sum, fill = NA, align = "right"))

us_sme_china_combined$DateCeiling <- floor_date(rollback(ceiling_date(us_sme_china_combined$Date,"year")),"month")

us_sme_china_combined <- us_sme_china_combined %>% group_by(DateCeiling) %>% mutate(Annual.Sum = sum(Value)) %>% ungroup()

us_sme_china_combined <- us_sme_china_combined[,c("Date","Value","Rolling.Average","Rolling.Sum","Annual.Sum")]

us_sme_china_combined[,2:5] <- us_sme_china_combined[,2:5]/1000000


us_sme_china_combined <- us_sme_china_combined %>% arrange(desc(Date))

#number formatting discussion: https://stackoverflow.com/questions/3443687/formatting-decimal-places-in-r
us_sme_china_combined_formatted <- us_sme_china_combined %>% mutate_if(is.numeric,round,digits=0) %>% mutate_if(is.numeric,format,nsmall=0,big.mark=",")

us_sme_china_monthly_table <- kbl(us_sme_china_combined_formatted,col.names = c("Date","Monthly Export","6 Month Rolling Average","Rolling Annual Total","Total by Year")) %>%
  add_header_above(c(" " = 1,"Monthly Values (Millions of $)" = 2,"Annual Values (Millions of $)" = 2)) %>%
  kable_minimal(full_width = F) %>%
  column_spec(1,width_min = "6em") %>%
  scroll_box(width = "100%", height = "500px")
#
#
#
#US exports of chips to China
#
#
#
us_chips_china <- read.csv(url("https://comtrade.un.org/api/get?type=C&freq=M&px=HS&ps=all&r=842&p=156&rg=2&cc=8542&fmt=csv"))

us_chips_china <- select(us_chips_china,"Period","Trade.Value..US..")

us_chips_china <- cbind("Date" = as.Date(parse_date_time(us_chips_china$Period,"ym")),us_chips_china)

us_chips_china <- us_chips_china %>% group_by(Date) %>% summarise(Value = sum(Trade.Value..US..)) %>% ungroup()

us_chips_china <- transform(us_chips_china,Rolling.Sum = rollapply(Value,12,sum, fill = NA, align = "left"))

us_chips_china <- transform(us_chips_china,Rolling.Average = rollapply(as.numeric(Value),6,mean, fill = NA, align = "right"))

us_chips_china_annual <- us_chips_china

us_chips_china_annual$DateFloor <- floor_date(us_chips_china_annual$Date,"year")

us_chips_china_annual <- us_chips_china_annual %>% group_by(DateFloor) %>% mutate(Annual.Sum = sum(Value)) %>% ungroup()

us_chips_china_annual$Annual.Sum[duplicated(us_chips_china_annual$Annual.Sum)] <- NA

us_chips_china_annual <- select(us_chips_china_annual,"Date","Rolling.Sum","Annual.Sum")

us_chips_china_annual_long <- gather(us_chips_china_annual,Type,Sum,Rolling.Sum,Annual.Sum)

us_chips_china_annual_long <- na.omit(us_chips_china_annual_long)

us_chips_china <- select(us_chips_china,"Date","Value","Rolling.Average")

us_chips_china_long <- gather(us_chips_china,Type,Result,Value,Rolling.Average)

us_chips_china_monthly_plot <- ggplot(us_chips_china_long, aes(Date,Result)) +
  geom_line(size=0.75, aes(color=Type)) +
  labs(x="Month", y="Export Value (Millions of Dollars)", title="US to China Monthly Semiconductor Chips Exports",color = "Legend") +
  scale_color_hue(labels = c("6 Month Rolling Average", "Monthly Value")) +
  scale_y_continuous(labels = label_dollar(scale = 1e-6), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%b/%y")) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_line(colour = "gray90")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

#Expand limits: https://stackoverflow.com/questions/27028825/ggplot2-force-y-axis-to-start-at-origin-and-float-y-axis-upper-limit/59056123#59056123

us_chips_china_annual_plot <- ggplot(us_chips_china_annual_long, aes(Date,Sum)) +
  geom_line(size=0.75, aes(color=Type)) +
  geom_point(data = us_chips_china_annual_long[us_chips_china_annual_long$Type == "Annual.Sum",], color = "red", size=2) +
  labs(x="Year", y="Export Value (Billions of Dollars)", title="US to China Annual Semiconductor Chips Exports", color = "Legend") +
  scale_color_hue(labels = c("Annual", "12-Month Rolling Sum")) +
  scale_y_continuous(labels = label_dollar(scale = 1e-9), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%Y")) +
  expand_limits(y = c(0,NA)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_line(colour = "gray90")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

us_chips_china_combined <- us_chips_china

us_chips_china_combined <- transform(us_chips_china_combined,Rolling.Sum = rollapply(Value,12,sum, fill = NA, align = "right"))

us_chips_china_combined$DateCeiling <- floor_date(rollback(ceiling_date(us_chips_china_combined$Date,"year")),"month")

us_chips_china_combined <- us_chips_china_combined %>% group_by(DateCeiling) %>% mutate(Annual.Sum = sum(Value)) %>% ungroup()

us_chips_china_combined <- us_chips_china_combined[,c("Date","Value","Rolling.Average","Rolling.Sum","Annual.Sum")]

us_chips_china_combined[,2:5] <- us_chips_china_combined[,2:5]/1000000


us_chips_china_combined <- us_chips_china_combined %>% arrange(desc(Date))
#number formatting discussion: https://stackoverflow.com/questions/3443687/formatting-decimal-places-in-r
us_chips_china_combined_formatted <- us_chips_china_combined %>% mutate_if(is.numeric,round,digits=0) %>% mutate_if(is.numeric,format,nsmall=0,big.mark=",")

us_chips_china_monthly_table <- kbl(us_chips_china_combined_formatted,col.names = c("Date","Monthly Export","6 Month Rolling Average","Rolling Annual Total","Total by Year")) %>%
  add_header_above(c(" " = 1,"Monthly Values (Millions of $)" = 2,"Annual Values (Millions of $)" = 2)) %>%
  kable_minimal(full_width = F) %>%
  column_spec(1,width_min = "6em") %>%
  scroll_box(width = "100%", height = "500px")
#
#
#
#china chips imports total
#
#
#
china_chip_imports <- read.csv(url("https://comtrade.un.org/api/get?max=100000&type=C&freq=A&px=HS&ps=all&r=156&p=0&rg=1&cc=8542&fmt=csv"))

china_chip_imports <- select(china_chip_imports,"Period","Trade.Value..US..")

china_chip_imports <- cbind("Date" = as.Date(parse_date_time(china_chip_imports$Period,"y")),china_chip_imports)

china_chip_imports <- china_chip_imports %>% group_by(Date) %>% summarise(Value = sum(Trade.Value..US..)) %>% ungroup()

china_chip_imports_annual_plot <- ggplot(china_chip_imports, aes(Date,Value)) +
  geom_line(size=0.75, color = "red") +
  geom_point(color = "red", size=2) +
  labs(x="Year", y="Import Value (Billions of Dollars)", title="China Annual Semiconductor Chip Imports") +
  scale_y_continuous(labels = label_dollar(scale = 1e-9), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%Y")) +
  expand_limits(y = c(0,NA)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

china_chip_imports_formatted <- china_chip_imports

china_chip_imports_formatted$Date <- format(as.Date(china_chip_imports_formatted$Date),"%Y")
china_chip_imports_formatted[,2] <- china_chip_imports_formatted[,2]/1000000

china_chip_imports_formatted <- china_chip_imports_formatted %>% arrange(desc(Date))

#number formatting discussion: https://stackoverflow.com/questions/3443687/formatting-decimal-places-in-r
china_chip_imports_formatted <- china_chip_imports_formatted %>% mutate_if(is.numeric,round,digits=0) %>% mutate_if(is.numeric,format,nsmall=0,big.mark=",")

china_chip_imports_table <- kbl(china_chip_imports_formatted,col.names = c("Date","Semiconductor Imports (Millions of $)")) %>%
  kable_minimal(full_width = F) %>%
  column_spec(1,width_min = "6em") %>%
  scroll_box(width = "100%", height = "500px")
#
#
#
#china sme imports total
#
#
#

china_sme_imports <- read.csv(url("https://comtrade.un.org/api/get?max=100000&type=C&freq=A&px=HS&ps=all&r=156&p=0&rg=1&cc=8486%2C903082%2C903141%2C854311%2C901041&fmt=csv"))

china_sme_imports <- select(china_sme_imports,"Period","Trade.Value..US..")

china_sme_imports <- cbind("Date" = as.Date(parse_date_time(china_sme_imports$Period,"y")),china_sme_imports)

china_sme_imports <- china_sme_imports %>% group_by(Date) %>% summarise(Value = sum(Trade.Value..US..)) %>% ungroup()

china_sme_imports_annual_plot <- ggplot(china_sme_imports, aes(Date,Value)) +
  geom_line(size=0.75, color = "red") +
  geom_point(color = "red", size=2) +
  labs(x="Year", y="Import Value (Billions of Dollars)", title="China Annual Semiconductor Manufacturing Equipment Imports") +
  scale_y_continuous(labels = label_dollar(scale = 1e-9), limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +
  scale_x_date(breaks="1 year",labels = date_format("%Y")) +
  expand_limits(y = c(0,NA)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme(panel.grid.major = element_line(colour = "grey")) +
  theme(legend.position = "top") +
  theme(legend.title = element_blank())

china_sme_imports_formatted <- china_sme_imports

china_sme_imports_formatted$Date <- format(as.Date(china_sme_imports_formatted$Date),"%Y")
china_sme_imports_formatted[,2] <- china_sme_imports_formatted[,2]/1000000

china_sme_imports_formatted <- china_sme_imports_formatted %>% arrange(desc(Date))

#number formatting discussion: https://stackoverflow.com/questions/3443687/formatting-decimal-places-in-r
china_sme_imports_formatted <- china_sme_imports_formatted %>% mutate_if(is.numeric,round,digits=0) %>% mutate_if(is.numeric,format,nsmall=0,big.mark=",")

china_sme_imports_table <- kbl(china_sme_imports_formatted,col.names = c("Date","Semiconductor Equipment Imports (Millions of $)")) %>%
  kable_minimal(full_width = F) %>%
  column_spec(1,width_min = "6em") %>%
  scroll_box(width = "100%", height = "500px")

US Semiconductor Manufacturing Equipment Exports to China

us_sme_china_monthly_plot

us_sme_china_annual_plot

us_sme_china_monthly_table
Monthly Values (Millions of $)
Annual Values (Millions of $)
Date Monthly Export 6 Month Rolling Average Rolling Annual Total Total by Year
2022-08-01 467 549 6,727 4,309
2022-07-01 598 553 6,811 4,309
2022-06-01 438 541 7,032 4,309
2022-05-01 554 569 7,197 4,309
2022-04-01 600 570 7,266 4,309
2022-03-01 635 591 7,290 4,309
2022-02-01 494 573 7,330 4,309
2022-01-01 524 582 7,181 4,309
2021-12-01 605 631 7,254 7,254
2021-11-01 565 631 7,151 7,254
2021-10-01 725 641 7,036 7,254
2021-09-01 522 624 6,776 7,254
2021-08-01 552 649 6,895 7,254
2021-07-01 819 615 6,862 7,254
2021-06-01 603 578 6,517 7,254
2021-05-01 623 561 6,383 7,254
2021-04-01 624 532 6,202 7,254
2021-03-01 675 506 5,957 7,254
2021-02-01 345 500 5,794 7,254
2021-01-01 597 529 5,714 7,254
2020-12-01 502 508 5,466 5,466
2020-11-01 450 503 5,499 5,466
2020-10-01 465 501 5,346 5,466
2020-09-01 641 487 5,239 5,466
2020-08-01 519 466 4,863 5,466
2020-07-01 473 423 4,695 5,466
2020-06-01 469 403 4,559 5,466
2020-05-01 442 414 4,505 5,466
2020-04-01 379 390 4,496 5,466
2020-03-01 512 386 4,557 5,466
2020-02-01 265 345 4,349 5,466
2020-01-01 350 359 4,248 5,466
2019-12-01 535 357 4,092 4,092
2019-11-01 297 337 3,877 4,092
2019-10-01 357 360 3,729 4,092
2019-09-01 266 374 3,625 4,092
2019-08-01 350 380 3,655 4,092
2019-07-01 338 349 3,636 4,092
2019-06-01 415 325 3,869 4,092
2019-05-01 432 309 3,961 4,092
2019-04-01 441 262 3,962 4,092
2019-03-01 303 230 3,954 4,092
2019-02-01 164 229 4,010 4,092
2019-01-01 194 257 4,081 4,092
2018-12-01 320 320 4,115 4,115
2018-11-01 149 351 4,224 4,115
2018-10-01 253 399 4,237 4,115
2018-09-01 296 429 4,220 4,115
2018-08-01 330 439 4,099 4,115
2018-07-01 572 423 3,910 4,115
2018-06-01 506 366 3,513 4,115
2018-05-01 433 353 3,259 4,115
2018-04-01 434 308 3,097 4,115
2018-03-01 359 275 2,985 4,115
2018-02-01 235 244 2,890 4,115
2018-01-01 228 228 2,888 4,115
2017-12-01 428 220 2,888 2,888
2017-11-01 162 190 2,621 2,888
2017-10-01 236 209 2,588 2,888
2017-09-01 174 223 2,513 2,888
2017-08-01 142 238 2,475 2,888
2017-07-01 175 253 2,519 2,888
2017-06-01 253 262 2,535 2,888
2017-05-01 271 246 2,450 2,888
2017-04-01 321 223 2,404 2,888
2017-03-01 264 196 2,492 2,888
2017-02-01 234 175 2,626 2,888
2017-01-01 227 167 2,506 2,888
2016-12-01 161 161 2,428 2,428
2016-11-01 129 162 2,418 2,428
2016-10-01 162 178 2,351 2,428
2016-09-01 136 219 2,294 2,428
2016-08-01 185 263 2,379 2,428
2016-07-01 192 251 2,521 2,428
2016-06-01 168 244 2,524 2,428
2016-05-01 225 241 2,490 2,428
2016-04-01 409 214 2,419 2,428
2016-03-01 398 163 2,179 2,428
2016-02-01 114 134 2,008 2,428
2016-01-01 150 169 2,084 2,428
2015-12-01 151 177 2,060 2,060
2015-11-01 62 174 2,001 2,060
2015-10-01 105 189 2,003 2,060
2015-09-01 221 200 2,192 2,060
2015-08-01 328 201 2,089 2,060
2015-07-01 195 178 1,834 2,060
2015-06-01 134 167 1,742 2,060
2015-05-01 154 160 1,738 2,060
2015-04-01 169 145 1,662 2,060
2015-03-01 228 165 1,586 2,060
2015-02-01 190 147 1,511 2,060
2015-01-01 126 128 1,468 2,060
2014-12-01 92 124 1,525 1,525
2014-11-01 64 130 1,800 1,525
2014-10-01 294 132 1,879 1,525
2014-09-01 118 99 1,660 1,525
2014-08-01 73 105 1,599 1,525
2014-07-01 103 117 1,644 1,525
2014-06-01 130 130 1,623 1,525
2014-05-01 77 170 1,579 1,525
2014-04-01 92 181 1,575 1,525
2014-03-01 153 178 1,561 1,525
2014-02-01 147 162 1,514 1,525
2014-01-01 182 157 1,407 1,525
2013-12-01 367 140 1,304 1,304
2013-11-01 143 93 992 1,304
2013-10-01 74 82 881 1,304
2013-09-01 57 82 886 1,304
2013-08-01 117 91 923 1,304
2013-07-01 82 78 947 1,304
2013-06-01 85 77 1,002 1,304
2013-05-01 74 72 1,037 1,304
2013-04-01 78 65 1,018 1,304
2013-03-01 106 65 1,032 1,304
2013-02-01 39 63 997 1,304
2013-01-01 79 80 1,034 1,304
2012-12-01 55 90 1,040 1,040
2012-11-01 32 101 1,108 1,040
2012-10-01 80 105 1,187 1,040
2012-09-01 94 107 1,221 1,040
2012-08-01 141 103 1,250 1,040
2012-07-01 138 92 1,255 1,040
2012-06-01 120 83 1,237 1,040
2012-05-01 54 84 1,270 1,040
2012-04-01 93 93 1,372 1,040
2012-03-01 71 97 1,457 1,040
2012-02-01 76 105 1,588 1,040
2012-01-01 86 117 1,611 1,040
2011-12-01 123 123 1,677 1,677
2011-11-01 111 128 1,974 1,677
2011-10-01 113 135 2,029 1,677
2011-09-01 124 146 2,114 1,677
2011-08-01 145 159 2,200 1,677
2011-07-01 120 151 2,245 1,677
2011-06-01 153 157 2,330 1,677
2011-05-01 157 201 2,331 1,677
2011-04-01 178 203 2,319 1,677
2011-03-01 202 206 2,242 1,677
2011-02-01 98 208 2,164 1,677
2011-01-01 152 223 2,146 1,677
2010-12-01 420 232 2,094 2,094
2010-11-01 167 187 NA 2,094
2010-10-01 198 184 NA 2,094
2010-09-01 210 167 NA 2,094
2010-08-01 190 153 NA 2,094
2010-07-01 205 135 NA 2,094
2010-06-01 154 117 NA 2,094
2010-05-01 145 NA NA 2,094
2010-04-01 101 NA NA 2,094
2010-03-01 124 NA NA 2,094
2010-02-01 80 NA NA 2,094
2010-01-01 100 NA NA 2,094

US Semiconductor Chips Exports to China

us_chips_china_monthly_plot

us_chips_china_annual_plot

us_chips_china_monthly_table
Monthly Values (Millions of $)
Annual Values (Millions of $)
Date Monthly Export 6 Month Rolling Average Rolling Annual Total Total by Year
2022-08-01 768 804 10,055 6,385
2022-07-01 695 791 10,179 6,385
2022-06-01 894 820 10,573 6,385
2022-05-01 857 821 11,033 6,385
2022-04-01 754 838 11,410 6,385
2022-03-01 857 858 11,723 6,385
2022-02-01 687 871 12,047 6,385
2022-01-01 871 906 12,282 6,385
2021-12-01 897 942 12,265 12,265
2021-11-01 961 1,018 12,346 12,265
2021-10-01 871 1,063 12,256 12,265
2021-09-01 941 1,096 12,266 12,265
2021-08-01 893 1,136 12,131 12,265
2021-07-01 1,088 1,141 12,046 12,265
2021-06-01 1,354 1,102 11,895 12,265
2021-05-01 1,234 1,040 11,371 12,265
2021-04-01 1,068 979 11,029 12,265
2021-03-01 1,181 948 10,845 12,265
2021-02-01 923 886 10,500 12,265
2021-01-01 855 866 10,311 12,265
2020-12-01 978 880 10,162 10,162
2020-11-01 872 856 9,929 10,162
2020-10-01 881 859 9,752 10,162
2020-09-01 806 859 9,571 10,162
2020-08-01 808 864 9,419 10,162
2020-07-01 937 852 9,302 10,162
2020-06-01 830 813 8,895 10,162
2020-05-01 892 799 8,674 10,162
2020-04-01 884 766 8,421 10,162
2020-03-01 836 736 8,234 10,162
2020-02-01 733 705 8,186 10,162
2020-01-01 706 698 8,295 10,162
2019-12-01 745 669 8,149 8,149
2019-11-01 695 646 8,067 8,149
2019-10-01 700 637 8,005 8,149
2019-09-01 654 637 7,919 8,149
2019-08-01 691 659 7,776 8,149
2019-07-01 531 684 7,656 8,149
2019-06-01 608 689 7,541 8,149
2019-05-01 639 698 7,371 8,149
2019-04-01 697 697 7,150 8,149
2019-03-01 788 683 6,885 8,149
2019-02-01 842 637 6,579 8,149
2019-01-01 560 592 6,151 8,149
2018-12-01 663 568 6,097 6,097
2018-11-01 632 530 5,862 6,097
2018-10-01 614 495 5,734 6,097
2018-09-01 511 464 5,666 6,097
2018-08-01 571 459 5,716 6,097
2018-07-01 415 433 5,620 6,097
2018-06-01 438 448 5,569 6,097
2018-05-01 419 447 5,559 6,097
2018-04-01 431 461 5,527 6,097
2018-03-01 482 480 5,462 6,097
2018-02-01 415 493 5,370 6,097
2018-01-01 506 503 5,363 6,097
2017-12-01 428 480 5,286 5,286
2017-11-01 505 480 5,362 5,286
2017-10-01 546 460 5,263 5,286
2017-09-01 561 430 5,220 5,286
2017-08-01 475 402 5,116 5,286
2017-07-01 364 391 5,114 5,286
2017-06-01 428 401 5,173 5,286
2017-05-01 387 414 5,129 5,286
2017-04-01 366 417 5,237 5,286
2017-03-01 390 440 5,262 5,286
2017-02-01 408 451 5,384 5,286
2017-01-01 429 462 5,280 5,286
2016-12-01 504 461 5,179 5,179
2016-11-01 405 441 5,137 5,179
2016-10-01 502 456 5,177 5,179
2016-09-01 457 437 5,083 5,179
2016-08-01 473 447 5,047 5,179
2016-07-01 424 418 4,994 5,179
2016-06-01 384 402 5,074 5,179
2016-05-01 496 415 5,094 5,179
2016-04-01 390 407 4,955 5,179
2016-03-01 513 410 4,975 5,179
2016-02-01 304 395 4,867 5,179
2016-01-01 328 414 4,930 5,179
2015-12-01 461 443 5,006 5,006
2015-11-01 445 434 5,034 5,006
2015-10-01 409 419 4,973 5,006
2015-09-01 421 419 4,970 5,006
2015-08-01 420 417 4,960 5,006
2015-07-01 503 408 4,963 5,006
2015-06-01 404 391 4,814 5,006
2015-05-01 357 405 4,792 5,006
2015-04-01 410 410 4,780 5,006
2015-03-01 405 409 4,716 5,006
2015-02-01 366 410 4,603 5,006
2015-01-01 404 419 4,575 5,006
2014-12-01 489 411 4,476 4,476
2014-11-01 384 393 4,309 4,476
2014-10-01 405 387 4,236 4,476
2014-09-01 412 377 4,294 4,476
2014-08-01 423 357 4,182 4,476
2014-07-01 355 343 4,101 4,476
2014-06-01 382 335 4,091 4,476
2014-05-01 345 325 4,022 4,476
2014-04-01 346 319 4,025 4,476
2014-03-01 292 339 3,954 4,476
2014-02-01 338 340 3,999 4,476
2014-01-01 306 341 3,926 4,476
2013-12-01 322 347 3,885 3,885
2013-11-01 311 346 3,835 3,885
2013-10-01 463 352 3,763 3,885
2013-09-01 299 320 3,559 3,885
2013-08-01 342 327 3,520 3,885
2013-07-01 345 314 3,443 3,885
2013-06-01 313 300 3,357 3,885
2013-05-01 347 294 3,372 3,885
2013-04-01 275 276 3,346 3,885
2013-03-01 337 273 3,303 3,885
2013-02-01 265 260 3,212 3,885
2013-01-01 264 260 3,138 3,885
2012-12-01 272 259 3,153 3,153
2012-11-01 239 268 3,151 3,153
2012-10-01 259 282 3,252 3,153
2012-09-01 260 278 3,314 3,153
2012-08-01 265 275 3,432 3,153
2012-07-01 259 263 3,481 3,153
2012-06-01 328 266 3,557 3,153
2012-05-01 322 257 3,558 3,153
2012-04-01 232 260 3,568 3,153
2012-03-01 246 275 3,604 3,153
2012-02-01 192 297 3,725 3,153
2012-01-01 279 317 3,846 3,153
2011-12-01 271 327 3,985 3,985
2011-11-01 340 336 4,179 3,985
2011-10-01 321 335 4,281 3,985
2011-09-01 378 326 4,353 3,985
2011-08-01 314 324 4,402 3,985
2011-07-01 335 324 4,517 3,985
2011-06-01 329 338 4,644 3,985
2011-05-01 332 360 4,868 3,985
2011-04-01 267 379 5,092 3,985
2011-03-01 367 400 5,324 3,985
2011-02-01 313 410 5,471 3,985
2011-01-01 417 429 5,591 3,985
2010-12-01 466 436 5,667 5,667
2010-11-01 442 451 NA 5,667
2010-10-01 394 470 NA 5,667
2010-09-01 427 488 NA 5,667
2010-08-01 429 502 NA 5,667
2010-07-01 461 503 NA 5,667
2010-06-01 554 508 NA 5,667
2010-05-01 555 NA NA 5,667
2010-04-01 500 NA NA 5,667
2010-03-01 514 NA NA 5,667
2010-02-01 432 NA NA 5,667
2010-01-01 493 NA NA 5,667

China Semiconductor Manufacturing Equipment Imports

china_sme_imports_annual_plot

china_sme_imports_table
Date Semiconductor Equipment Imports (Millions of $)
2021 44,447
2020 33,865
2019 28,317
2018 32,663
2017 20,821
2016 15,047
2015 13,284
2014 12,151
2013 9,122
2012 7,964
2011 18,436
2010 12,729
2009 5,229
2008 6,972
2007 6,723
2006 1,126
2005 729
2004 978
2003 479
2002 367
2001 253
2000 228
1999 89
1998 46
1997 35
1996 22

China Semiconductor Chips Imports

china_chip_imports_annual_plot

china_chip_imports_table
Date Semiconductor Imports (Millions of $)
2021 433,727
2020 350,770
2019 306,397
2018 312,952
2017 261,161
2016 227,617
2015 230,657
2014 218,520
2013 232,078
2012 192,967
2011 171,142
2010 158,010
2009 120,751
2008 130,583
2007 128,664
2006 107,152
2005 82,202
2004 61,707
2003 41,834
2002 26,374
2001 16,998
2000 13,800
1999 7,924
1998 4,879
1997 3,642
1996 2,725
1995 2,366
1994 1,634
1993 1,165
1992 863