In this paper, I propose three strategies to advance institutional analysis in resource and development econometrics: (1) recognition of institutional variables; (2) use of multilevel thinking and estimation; and (3) the adoption of causal graphs. I illustrate each strategy with examples from three previously published studies: (a) biomass extraction from Ranthambhore National Park by Dayal (2006), (b) air pollution in Goa by Das et al. (2009), and (c) trade-offs and synergies between carbon storage and livelihood benefits from forest commons by Chhatre and Agrawal (2009). As I see it, while there is no explicit institutional content in studies (a) and (b), by recognizing caste as a social norm and the role of norms in household decisions it is possible to further integrate an institutional perspective into these studies. My analysis also demonstrates how multilevel thinking is intrinsic to institutional thinking. Since there can be data at different levels within a study, using study (c) where forests are nested in countries, I show how multilevel statistics can help unpack variation in the data at forest and country levels. Similarly, since causality is vital to policy though extremely difficult to establish with observational data, a phenomenon that leads to disagreements among scholars, using examples from the three studies I show how causal graphs can help separate the disagreements between scholars into disagreements about the underlying causal structure and the correspondence between an agreed to causal structure and the data on hand. The paper examines specific strategies to include institutional analysis in resource econometrics.