Last updated: April 03, 2023
This page contains semiconductor trade data for use in forecasting the following questions on INFER:
- What will be the value, in dollars, of U.S. exports of semiconductor chips to China in 2022?
- What will be the value, in dollars, of all Chinese imports of semiconductor manufacturing equipment in 2022?
- What will be the value, in dollars, of all Chinese imports of semiconductor chips in 2022?
- What will be the value, in dollars, of U.S. exports of semiconductor manufacturing equipment to China in 2022?
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| Date | Monthly Export | 6 Month Rolling Average | Rolling Annual Total | Total by Year |
|---|---|---|---|---|
| 2022-12-01 | 364 | 437 | 5,866 | 5,866 |
| 2022-11-01 | 288 | 449 | 6,107 | 5,866 |
| 2022-10-01 | 439 | 494 | 6,385 | 5,866 |
| 2022-09-01 | 466 | 520 | 6,670 | 5,866 |
| 2022-08-01 | 467 | 549 | 6,727 | 5,866 |
| 2022-07-01 | 598 | 553 | 6,811 | 5,866 |
| 2022-06-01 | 438 | 541 | 7,032 | 5,866 |
| 2022-05-01 | 554 | 569 | 7,197 | 5,866 |
| 2022-04-01 | 600 | 570 | 7,266 | 5,866 |
| 2022-03-01 | 635 | 591 | 7,290 | 5,866 |
| 2022-02-01 | 494 | 573 | 7,330 | 5,866 |
| 2022-01-01 | 524 | 582 | 7,181 | 5,866 |
| 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| Date | Monthly Export | 6 Month Rolling Average | Rolling Annual Total | Total by Year |
|---|---|---|---|---|
| 2022-12-01 | 586 | 749 | 9,418 | 9,418 |
| 2022-11-01 | 784 | 801 | 9,729 | 9,418 |
| 2022-10-01 | 784 | 813 | 9,906 | 9,418 |
| 2022-09-01 | 879 | 808 | 9,993 | 9,418 |
| 2022-08-01 | 768 | 804 | 10,055 | 9,418 |
| 2022-07-01 | 695 | 791 | 10,179 | 9,418 |
| 2022-06-01 | 894 | 820 | 10,573 | 9,418 |
| 2022-05-01 | 857 | 821 | 11,033 | 9,418 |
| 2022-04-01 | 754 | 838 | 11,410 | 9,418 |
| 2022-03-01 | 857 | 858 | 11,723 | 9,418 |
| 2022-02-01 | 687 | 871 | 12,047 | 9,418 |
| 2022-01-01 | 871 | 906 | 12,282 | 9,418 |
| 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 |