Subjective "ladder" measurements of socio-economic status (SES) are easy-to-administer tools that ask respondents to rate their own SES, allowing them to evaluate their own material resources and determine where it places them relative to their community. Here, we sought to compare the MacArthur Scale of Subjective Social status to the WAMI, an objective measure of SES that includes data on water and sanitation, asset ownership, education, and income. Leveraging a study of 595 tuberculosis patients in Lima, Peru, we compared the MacArthur ladder score to the WAMI score using weighted Kappa scores and Spearman's rank correlation coefficient. We identified outliers that fell outside the 95th percentile and assessed the durability of the inconsistencies between scores by re-testing a subset of participants. We then used Akaike information criterion (AIC) to compare the predictability of logistic regression models evaluating the association between the two SES scoring systems and history of asthma. The correlation coefficient between the MacArthur ladder and WAMI scores was 0.37 and the weighted Kappa was 0.26. The correlation coefficients differed by less than 0.04 and the Kappa ranged from 0.26 to 0.34, indicating fair agreement. When we replaced the initial MacArthur ladder scores with retest scores, the number of individuals with disagreements between the two scores decreased from 21 to 10 and the correlation coefficient and weighted Kappa both increased by at least 0.03. Lastly, we found that when we categorized WAMI and MacArthur ladder scores into three groups, both had a linear trend association with history of asthma with effect sizes and AICs that differed by less than 15% and 2 points, respectively. Our findings demonstrated fair agreement between the MacArthur ladder and WAMI scores. The agreement between the two SES measurements increased when they were further categorized into 3-5 categories, the form in which SES is often used in epidemiologic studies. The MacArthur score also performed similarly to WAMI in predicting a socio-economically sensitive health outcome. Researchers should consider subjective SES tools as an alternative method for measuring SES, particularly in large health studies where data collection is a burden.