The Scenario
You are a program manager at a regional economic development nonprofit. Your organization is applying for a small-business support grant and the RFP requires market need data for 20 rural counties: the number of nonemployer firms in NAICS 72 (accommodation and food services) and the net job creation rate in the area.
Those two numbers come from two different Census programs: Nonemployer Statistics (NES) for self-employed firm counts, and Business Dynamics Statistics (BDS) for job creation and establishment entry/exit rates. You have eight days until the letter of intent deadline.
The bad version:
- You go to the Census website and find Nonemployer Statistics under one program page and BDS under a separate program page with a different API format
- You get NES working for three counties and realize BDS uses a different geographic identifier structure
- You build a VLOOKUP to join the two datasets and discover the county name formatting does not match
- You spend a day reconciling the geographic keys between the two Census programs
- You submit the letter of intent with a placeholder for the job creation rate and a promise to provide it in the full application.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your spreadsheet that knows both the NES and BDS datasets, their geographic formats, and how to join them on a common county identifier.
Open the SheetXAI sidebar and type:
For each county in my sheet, fetch Census Nonemployer Statistics for NAICS 72 — number of firms and receipts — and write them into columns C and D. Also query Census Business Dynamics Statistics for net job creation rate and establishment entry rate for NAICS sector 72 for the same counties and write them into columns E and F.
SheetXAI calls both endpoints, aligns the results by county FIPS, and writes the four indicators into columns C through F. Counties with no data in either dataset are flagged individually so you know exactly which data is missing.
What You Get
A grant-ready market need table, 20 counties complete:
- Column C — nonemployer firm count, NAICS 72
- Column D — nonemployer receipts, NAICS 72
- Column E — net job creation rate (BDS)
- Column F — establishment entry rate (BDS)
These are official Census Bureau statistics from two distinct programs, joined on county FIPS without a manual VLOOKUP. Your grant narrative can cite both datasets as authoritative sources.
What If the Data Is Not Quite Ready
Multi-dataset Census pulls at the county level have some reliable friction points.
When your county list uses names instead of FIPS codes
Your sheet has "Harlan County, KY" in column A rather than numeric state and county FIPS.
Convert county names in column A to state FIPS and county FIPS pairs. Then fetch Census Nonemployer Statistics for NAICS 72 — firms and receipts — and BDS net job creation rate for each county. Write results into columns C through F.
When you need BDS data at the state level instead of county
Your grant reviewer asks for state-level business dynamics as a benchmark comparison alongside your county data.
Query Census Business Dynamics Statistics for job creation, job destruction, and establishment entry/exit rates for NAICS sector 44-45 for all states in column A and write the results into my sheet. Label each column clearly.
When you want to compare NAICS 72 to another sector for context
Your narrative needs to show that NAICS 72 has a higher establishment volatility than the regional average for small businesses.
For each county in my sheet, fetch Census Nonemployer Statistics firm counts for NAICS 72 and NAICS 44 (retail). Write NAICS 72 firms into column C and NAICS 44 firms into column D. Compute the NAICS 72 share of total nonemployer firms across both sectors and write it into column E.
When you need NES plus BDS plus ACS income data in one combined market-need score
Your program officer wants a single composite index: self-employment density, job volatility, and area income all weighted together.
For each county in my sheet, fetch Census NES firm count for NAICS 72 into column C and NES receipts into column D. Fetch BDS net job creation rate for NAICS 72 into column E. Fetch ACS 5-year median household income for each county into column F. Compute a market need score as (NES firms per 1000 residents × 0.4) + (1 — job creation rate × 0.3) + ((1 — median income / 60000) × 0.3) and write it into column G. Rank counties by score descending into column H.
The pattern: three Census programs, one composite index, and the grant ranking already in the sheet before you open the RFP template.
Try It
Get the 7-day free trial of SheetXAI and open any sheet with a list of counties, then ask it to pull Census Nonemployer Statistics or Business Dynamics data for a target NAICS sector. The Census Bureau integration is included in every SheetXAI plan. For related workflows, see how to pull County Business Patterns by NAICS code or the Census Bureau in Google Sheets overview.