There are a number of differences between the CPS and ACS that result in expected discrepancies in estimates for poverty rates between the two surveys. These include differences in misreporting of income by respondents across surveys (see this working paper by Rothbaum), differences in reference periods for income (ACS asks about income in the past 12 month, while the ASEC asks about the previous calendar year), and the lack of questions regarding taxes in the ACS (see Fox, Glassman, and Pacas) for SPM calculations, among others. Additional differences are summarized in this fact sheet comparing ACS and the CPS ASEC. Considering these significant differences between the surveys, the discrepancy that you are noting is relatively small. For comparison, the reported margin of error for poverty estimates in the 2023 ASEC is 0.3 percentage points, which is without considering the differences in methods that the ACS introduces.
To replicate official poverty statistics, the Poverty in the United States report “recommends that people use the CPS ASEC for timely and thorough estimates of national poverty”, but that the ACS “can be used for subnational poverty estimates” due to its larger sample size. The Census Bureau presents annual estimates of poverty by state and other smaller geographic units based on data collected in the ACS. Single-year estimates from the ACS are available for geographic units with populations of 65,000 or more. Estimates of income and poverty for all geographic units, including census tracts and block groups, are available by pooling 5 years of ACS data. We provide ACS summary tables of poverty for states and other smaller geographic areas on IPUMS NHGIS.
In order to use the microdata on IPUMS CPS to replicate national estimates, you should run the following specification in SDA:
IPUMS CPS:
Row = offpov
Weight = sdawt
Filter = year(2016), race(200), offpov(1-2)
While SPMPOV, which you used in the screenshots you shared, reports poverty status using the supplemental poverty measure, OFFPOV provides the official poverty status that is used in the report. Additionally, the report uses the 2016 ASEC to estimate poverty for 2015 since income questions in the survey reference the preceding calendar year. When I do this, I am able to exactly replicate the report’s estimate of 24.1%.
If you plan to use IPUMS USA ACS data to match published state-level poverty estimates within the margin of error, you should run the following specification in SDA:
IPUMS USA:
Row = statefip
Column = cbpoverty(r:1-99; 100-501)
Weight = perwt
Filter = year(2016), race(2), gq(0-2,5), cbpoverty(1-501)
CBPOVERTY uses the Census Bureau’s family definitions to report each person’s total family income expressed as a percentage of the poverty threshold for their specific family situation. It is provided so that users can replicate published Census Bureau poverty estimates. You should also include respondents in GQ = 5 in your analysis.