The dataset is small so it is available directly with the package (no download required). It includes three variables:
nbf
: Index of net business formation (1967=100)nbi
: Number of new business incorporations (number)cbf
: Current liabilities of business failures, NSA (mil. $)Load it with:
entrydatar::scb
The data has both monthly and annual frequency component:
date_ym nbf_annual nbf nbi_annual nbi cbf_annual cbf 1: 194801 101.1 115.7 95462 9380 234.6 13.0 2: 194802 101.1 107.5 95462 8329 234.6 25.6 3: 194803 101.1 105.0 95462 8349 234.6 17.5 4: 194804 101.1 104.5 95462 8396 234.6 15.3 5: 194805 101.1 103.7 95462 8064 234.6 13.8 --- 560: 199408 125.5 125.8 741059 64844 28943.9 2106.8 561: 199409 125.5 125.3 741059 64564 28943.9 3434.0 562: 199410 125.5 124.6 741059 60488 28943.9 2023.1 563: 199411 125.5 127.9 741059 64542 28943.9 2511.8 564: 199412 125.5 127.3 741059 62908 28943.9 3108.0
There is also a function like the other in this package to access an interval:
entrydatar::get_nbf(1980, 1998)
Note that the data only extends to 1994 (included).
Quick summary stats:
skimr::skim(entrydatar::get_nbf(1980, 1998)[, lapply(.SD, as.numeric), .SDcols = c("nbi", "nbf", "cbf")]) Skim summary statistics n obs: 180 n variables: 3 Variable type: numeric variable missing complete n mean sd min p25 median p75 max hist 1 cbf 0 180 180 3276.13 2699.21 190.8 1614.83 2684 3941.58 15757.6 ▇▇▂▂▁▁▁▁ 2 nbf 0 180 180 121.34 4.42 112 118.5 121.1 124.53 137.9 ▂▃▇▅▃▁▁▁ 3 nbi 0 180 180 54048.79 5149.2 40648 50663.25 54809 57616.75 65691 ▁▂▃▆▇▇▂▂
From the original file, we have the sources of the information gathered here:
(c) Erik Loualiche
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