Comparing ACS Table 18120 with PUMS (2019 1 year estimates)

Hello,

I'd like to seek help with a couple of questions about comparing ACS Table and PUMS estimates.

My project is related to estimate the number of people with vision difficulty and then calculate the employment-population ratio. I used two ways. 

(1) Based on the ACS Table 18120 (2019 ACS 1 year estimates from data.census.gov), for people with vision difficulty (age 18-64), the numbers were: employed  (1,734,139), unemployed (161,777), and not in the labor force (1,859,756). Then, I calculated the employment-population ratio as 1,734,139 / (1,734,139 + 161,777 + 1,859,756) = 46.2%

(2) I also used the PUMS person data (2019 ACS PUMS 1 year) to make the calculation. I first selected people with vision difficulty (age 18-64) by using DEYE = "1". Then, I utilized PROC SURVEYMEANS in SAS. I set negative replicate weights as zero before running the following code. 

proc surveyfreq data=pums19_1864w varmethod=jackknife;
weight pwgtp;
repweights pwgtp1-pwgtp80 / jkcoefs=0.05;
table Deye*ESR/row chisq;
run;

Based on the SAS output, for people with vision difficulty (age 18-64), the weighted numbers were:

1 Civilian employed, at work 1,691,122
2 Civilian employed, with a job but not at work 52,231
3 Unemployed 164,666
4 Armed forces, at work 6,587
5 Armed forces, with a job but not at work 289
6 Not in labor force  2,039,589

Then, I calculated the employment-population ratio as (1,691,122 + 52,231 + 6,587 + 289) / (1,691,122 + 52,231 + 164,666 + 6,587 + 289 + 2,039,589) = 44.3%

My questions are: 

(1) Is there anything wrong with my calculation about the employment-population ratio using ACS Table or PUMS? Should I remove "Armed forces" records from my calculation? 

If I remove "Armed forces", I consider the employment-population ratio as (1,691,122 + 52,231) / (1,691,122 + 52,231 + 164,666  + 2,039,589) = 44.2%

(2) Why is there a difference between the ACS Table estimates and the PUMS estimates? Does 46.2% vs 44.3% (or 44.2%) look normal? 

I look forward to your insights. Thank you. 

Carol