[영문]Objectives. Income data are often missing for substantial proportions of surveyparticipants and these records are often dropped from analyses. To explore theimplications of excluding records with missing income, we examined characteristicsof survey participants with and without income information.Methods. Using statewide population-based postpartum survey data fromthe California Maternal and Infant Health Assessment, we compared the age,education, parity, marital status, timely prenatal care initiation, and neighborhoodpoverty characteristics of women with and without reported income data,overall, and by race/ethnicity/nativity.Results. Overall, compared with respondents who reported income, respondentswith missing income information generally appeared younger, lesseducated, and of lower parity. They were more likely to be unmarried, tohave received delayed or no prenatal care, and to reside in poor neighborhoods;and they generally appeared more similar to lower- than higher-incomewomen. However, the patterns appeared to vary by racial/ethnic/nativity group.For example, among U.S.-born African American women, the characteristicsof the missing-income group were generally similar to those of low-incomewomen, while European American women with missing income informationmore closely resembled their moderate-income counterparts.Conclusions. Respondents with missing income information may not be arandom subset of population-based survey participants and may differ on otherrelevant sociodemographic characteristics. Before deciding how to deal analyticallywith missing income information, researchers should examine relevantcharacteristics and consider how different approaches could affect study findings.Particularly for ethnically diverse populations, we recommend including amissing income category or employing multiple-imputation techniques ratherthan excluding those records.