# Error in Calculating Total Population for 1990 and 2000 Samples

I am working with time series ACS data using the default sample for each year to determine the overall population of Michigan over time. For 1990 and 2000, my calculation (sum(PERWT)) is giving me a figure nearly double the size of the census reported population for both years. The result for every other year seems completely accurate.

My R code is attached below:

ACS_Time ← left_join(ACS_Time, State_Labels)
ACS_Time.MI ← ACS_Time[ACS_Time\$STATEFIP == 26]

ACS_Time.MI\$WorkingAGE<-with(ACS_Time.MI,as.numeric(AGE>=16 & AGE<=64))
ACS_Time.MI\$Immigrant<-with(ACS_Time.MI,as.numeric(CITIZEN>1))

MI_Pop.Time ← as.data.table(ACS_Time.MI[,list(
“Under 18” = sum(PERWT[AGE <18]),
“18-24” = sum(PERWT[AGE >= 18 & AGE <= 24]),
“25-54” = sum(PERWT[AGE >= 25 & AGE <= 54]),
“55 - 64” = sum(PERWT[AGE >= 55 & AGE <= 64]),
“65+” =sum(PERWT[AGE >= 65])
), by = list(YEAR, Immigrant)][order(Immigrant, YEAR)])

I reviewed your data extracts and it appears that you requested an extract with two 1990 samples (5% state and 1% metro) and two 2000 samples (5% and 1%). Each of the weighted samples is intended to represent the US population, so an extract that includes two samples would result in counts that are about twice the actual values. When grouping cases by SAMPLE rather than YEAR, the sum of PERWT for Michigan respondents is about 9.3 million for each of the 1990 samples and 9.9 million for the 2000 samples. I hope this solves the issue that you’re encountering.