  Despite impressive advances in object-recognition, deep learning systems'
performance degrades significantly across geographies and lower income levels
raising pressing concerns of inequity. Addressing such performance gaps remains
a challenge, as little is understood about why performance degrades across
incomes or geographies. We take a step in this direction by annotating images
from Dollar Street, a popular benchmark of geographically and economically
diverse images, labeling each image with factors such as color, shape, and
background. These annotations unlock a new granular view into how objects
differ across incomes and regions. We then use these object differences to
pinpoint model vulnerabilities across incomes and regions. We study a range of
modern vision models, finding that performance disparities are most associated
with differences in texture, occlusion, and images with darker lighting. We
illustrate how insights from our factor labels can surface mitigations to
improve models' performance disparities. As an example, we show that mitigating
a model's vulnerability to texture can improve performance on the lower income
level. We release all the factor annotations along with an interactive
dashboard to facilitate research into more equitable vision systems.
