Multilevel modeling (MLM, also known as hierarchical linear modeling, HLM) is a methodological framework widely used in the social sciences to analyze data with a hierarchical structure, where lower units of aggregation are 'nested' in higher units, including longitudinal data. In economics, however, MLM is used very rarely. Instead, economists use separate econometric techniques including cluster-robust standard errors and fixed effects models. In this paper, we review the methodological literature and contrast the econometric techniques typically used in economics with the analysis of hierarchical data using MLM. Our review suggests that economic techniques are generally less convenient, flexible, and efficient compared to MLM. The important limitation of MLM, however, is its inability to deal with the omitted variable problem at the lowest level of data, while standard economic techniques may be complemented by quasi-experimental methods mitigating this problem. It is unlikely, though, that this limitation can explain and justify the rare use of MLM in economics. Overall, we conclude that MLM has been unreasonably ignored in economics, and we encourage economists to apply this framework by providing 'when and how' guidelines.