Bureau of Economic Analysis’ API

Data - BEA

Author

Info

source	dataset	.html	.RData
bea	api	2024-06-17	NA

List of APIs

source	dataset	.html	.RData
bdf	api	2024-06-19	NA
bea	api	2024-06-17	NA
bis	api	2024-04-19	NA
bls	api	2024-06-17	NA
ecb	api	2024-06-19	NA
eurostat	api	2024-06-08	NA
imf	api	2024-06-18	NA
insee	api	2024-06-18	NA
oecd	api	2024-05-07	2024-04-16
rdb	api	NA	NA
wdi	api	2024-04-14	NA

LAST_COMPILE

LAST_COMPILE
2024-06-19

API Key

The BEA API key should be obtained from https://apps.bea.gov/API/signup/index.cfm. This data API key will be of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX, which you’ll declare as a string variable into bea_key.

NIPA

NIPA Website. html
NIPA Handbook. html
NIPA Glossary. pdf
Fundamental Concepts. pdf

List of Main Tables

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterValues&",
       "datasetname=NIPA&",
       "ParameterName=TableName&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "ParamValue") %>%
  select(TableName, Description) %>%
  mutate(N = 1:n()) %>%
  filter(TableName %in% (list.files(pattern = "\\.qmd$") %>% gsub(".qmd$", "", .))) %>%
  mutate(html = paste0('<a  target=_blank href=', TableName, '.html > html </a>')) %>%
  mutate(bea = paste0('<a  target=_blank href=https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=1&nipa_table_list=', N, '&categories=survey > bea </a>')) %>%
  mutate(Last_compile = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/bea/", TableName, ".html"))$mtime)) %>%
  select(-N) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

List of Other Tables

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterValues&",
       "datasetname=NIPA&",
       "ParameterName=TableName&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "ParamValue") %>%
  select(TableName, Description) %>%
  mutate(N = 1:n()) %>%
  filter(!(TableName %in% (list.files(pattern = "\\.qmd$") %>% gsub(".qmd$", "", .)))) %>%
  mutate(bea = paste0('<a  target=_blank href=https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=1&nipa_table_list=', N, '&categories=survey > bea </a>')) %>%
  select(-N) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

List of Parameters

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterList&",
       "DataSetName=NIPA&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "Parameter") %>% 
  select(ParameterName, ParameterDataType, ParameterDescription) %>%
  {if (is_html_output()) print_table(.) else .}

ParameterName	ParameterDataType	ParameterDescription
Frequency	string	A - Annual, Q-Quarterly, M-Monthly
ShowMillions	string	A flag indicating that million-dollar data should be returned.
TableID	integer	The standard NIPA table identifier
TableName	string	The new NIPA table identifier
Year	integer	List of year(s) of data to retrieve (X for All)

Ex 1: gdp_adjustment

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=NIPA&",
       "TableName=T10105&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  select(LineNumber, LineDescription, SeriesCode, date, DataValue) %>%
  arrange(date, LineNumber) %>%
  filter(date == as.Date("2019-01-01")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Standard NI underlying detail tables

Ex 1

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=NIUnderlyingDetail&",
       "TableName=U20405&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  select(LineNumber, LineDescription, SeriesCode, date, DataValue) %>%
  arrange(date, LineNumber) %>%
  filter(date == as.Date("2018-01-01")) %>%
  select(-date) %>%
  mutate(`Share (%)` = (100*DataValue / DataValue[1]) %>% round(., digits = 2)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex 2

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=NIUnderlyingDetail&",
       "TableName=U20405&",
       "Frequency=Q&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  mutate(DataValue = DataValue %>% gsub(",", "", .) %>% as.numeric) %>%
  mutate(year = substr(TimePeriod, 1, 4),
         qtr = substr(TimePeriod, 6, 6) %>% as.numeric,
         month = ((qtr-1)*3+1) %>% str_pad(., 2, pad = "0"),
         date = paste0(year, "-", month, "-01") %>% as.Date) %>%
  filter(LineNumber == 1,
         date >= as.Date("2006-01-01"), 
         date <= as.Date("2010-01-01")) %>%
  mutate(DataValue = DataValue/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = DataValue)) + theme_minimal() +
  scale_x_date(breaks = seq(1870, 2020, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = function(x) seq(1000*floor(min(x)/1000), max(x), 100),
                     labels = dollar_format(suffix = "Bn", prefix = "$")) + 
  xlab("") + ylab("Total Consumption") + 
  geom_vline(xintercept = as.Date("2008-09-15"), linetype = "dashed", color = viridis(3)[2])

Fixed Asset Tables

List of Main Tables

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterValues&",
       "datasetname=FixedAssets&",
       "ParameterName=TableName&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "ParamValue") %>%
  select(TableName, Description) %>%
  mutate(N = 1:n()) %>%
  filter(TableName %in% (list.files(pattern = "\\.qmd$") %>% gsub(".qmd$", "", .))) %>%
  mutate(html = paste0('<a  target=_blank href=', TableName, '.html > html </a>')) %>%
  mutate(bea = paste0('<a  target=_blank href=https://apps.bea.gov/iTable/iTable.cfm?reqid=10&step=3&isuri=1&table_list=', N, '&categories=survey > bea </a>')) %>%
  select(-N) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

List of Other Tables

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterValues&",
       "datasetname=FixedAssets&",
       "ParameterName=TableName&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "ParamValue") %>%
  select(TableName, Description) %>%
  mutate(N = 1:n()) %>%
  filter(!(TableName %in% (list.files(pattern = "\\.qmd$") %>% gsub(".qmd$", "", .)))) %>%
  mutate(bea = paste0('<a  target=_blank href=https://apps.bea.gov/iTable/iTable.cfm?reqid=10&step=3&isuri=1&table_list=', N, '&categories=survey > bea </a>')) %>%
  select(-N) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

List of Parameters

What is the list of parameters in the Fixed Asset Tables? The answer is found by typing:

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetParameterList&",
       "DataSetName=FixedAssets&") %>%
  fromJSON(.) %>%
  pluck("BEAAPI", "Results", "Parameter") %>% 
  select(ParameterName, ParameterDataType, ParameterDescription) %>%
  {if (is_html_output()) print_table(.) else .}

ParameterName	ParameterDataType	ParameterDescription
TableName	string	The new Fixed Assets identifier
Year	integer	List of year(s) of data to retrieve (X for All)

Ex 1

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=FixedAssets&",
       "TableName=FAAt101&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  select(LineNumber, LineDescription, SeriesCode, date, DataValue) %>%
  arrange(date, LineNumber) %>%
  filter(date == as.Date("2018-01-01")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex 2A

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=FixedAssets&",
       "TableName=FAAt101&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  arrange(date, LineNumber) %>%
  left_join(gdp_adjustment, by = "date") %>%
  mutate(value = 1000 * `Real GDP / Real GDP Trend (Log Linear)` * DataValue / GDP) %>%
  filter(!is.na(value)) %>%
  filter(date == as.Date("2018-01-01")) %>%
  mutate(value = (100*value) %>% round(., 1),
         year = date %>% year) %>%
  select(LineNumber, LineDescription, year, value) %>%
  spread(year, value) %>%
  mutate_at(vars(-LineDescription, -LineNumber), funs(paste0(., "%"))) %>%
  as.data.frame %>%
  arrange(LineNumber) %>%
  filter(LineNumber <= 15) %>%
  select(-LineNumber) %>%
  knitr::kable(booktabs = T, linesep = "") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position", "repeat_header")) %>%
  add_indent(c(3:14)) %>%
  add_indent(c(4:8, 10:14)) %>%
  add_indent(c(5:7, 11:13))

LineDescription	2018
Fixed assets and consumer durable goods	265332.3%
Fixed assets	244061.2%
Private	185131.7%
Nonresidential	98072.5%
Equipment	27549.1%
Structures	57434.5%
Intellectual property products	13088.9%
Residential	87059.1%
Government	58929.5%
Nonresidential	57184.3%
Equipment	4063.7%
Structures	48280.8%
Intellectual property products	4839.7%
Residential	1745.3%
Consumer durable goods	21271.2%

Ex 2B

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=FixedAssets&",
       "TableName=FAAt101&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  arrange(date, LineNumber) %>%
  left_join(gdp_adjustment, by = "date") %>%
  mutate(value = 1000 * `Real GDP / Real GDP Trend (Log Linear)` * DataValue / GDP) %>%
  filter(!is.na(value)) %>%
  filter(date == as.Date("2018-01-01")) %>%
  mutate(value = (100*value) %>% round(., 1),
         year = date %>% year) %>%
  select(LineNumber, LineDescription, year, value) %>%
  spread(year, value) %>%
  mutate_at(vars(-LineDescription, -LineNumber), funs(paste0(., "%"))) %>%
  as.data.frame %>%
  arrange(LineNumber) %>%
  filter(LineNumber <= 15) %>%
  select(-LineNumber) %>%
  knitr::kable(booktabs = T, linesep = "") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position", "repeat_header")) %>%
  add_indent(c(3:14)) %>%
  add_indent(c(4:8, 10:14)) %>%
  add_indent(c(5:7, 11:13)) %>%
  row_spec(1, bold = T, color = "#000000BB", background = "#44015422") %>%
  row_spec(c(2, 15), bold = T, color = "#000000BB", background = "#21908C22") %>%
  row_spec(c(3, 9), bold = T, color = "#000000BB", background = "#35B77922") %>%
  row_spec(c(4, 8, 10, 14), bold = T, color = "#000000BB", background = "#FDE72522")

LineDescription	2018
Fixed assets and consumer durable goods	265332.3%
Fixed assets	244061.2%
Private	185131.7%
Nonresidential	98072.5%
Equipment	27549.1%
Structures	57434.5%
Intellectual property products	13088.9%
Residential	87059.1%
Government	58929.5%
Nonresidential	57184.3%
Equipment	4063.7%
Structures	48280.8%
Intellectual property products	4839.7%
Residential	1745.3%
Consumer durable goods	21271.2%

Ex 3

Code

paste0("https://apps.bea.gov/api/data/?&",
       "UserID=", bea_key, "&",
       "method=GetData&",
       "DataSetName=FixedAssets&",
       "TableName=FAAt101&",
       "Frequency=A&",
       "Year=ALL&",
       "ResultFormat=JSON") %>%
  fromJSON(.) %>% 
  pluck("BEAAPI", "Results", "Data") %>% 
  select(TableName, LineDescription, TimePeriod, SeriesCode, DataValue, LineNumber) %>%
  mutate_at(vars(DataValue, LineNumber), funs(gsub(",", "", .) %>% as.numeric)) %>%
  mutate(date = TimePeriod %>% paste0("-01-01") %>% as.Date) %>%
  arrange(date, LineNumber) %>%
  filter(LineNumber %in% c(5, 6, 8, 15)) %>%
  left_join(gdp_adjustment, by = "date") %>%
  mutate(value = 1000 * `Real GDP / Real GDP Trend (Log Linear)` * DataValue / GDP) %>%
  ggplot + geom_line(aes(x = date, y = value, color = LineDescription)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y"),
               limits = c(1928, 2019) %>% paste0("-01-01") %>% as.Date) + 
  scale_y_continuous(breaks = 0.01*seq(0, 160, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.2, 0.3),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))