This paper reviews 70 recent empirical and theoretical studies that analyse land-use change at the farm-household level. The review builds on a conceptual framework of land-use change drivers and conducts a meta-analysis. It turns out that the most frequently analysed scenario is the conversion of non-used forests or forested areas into land used for agricultural purposes about a third of all considered scenarios. The second largest share is accounted for by studies that look into the conver-sion of non-used forests or forested areas into ranching. Most studies analyse land-use change using household and/or village data and, in doing so, often rely on relatively small samples of 100-200 ob-servations. There is a clear regional concentration of studies on Central and South America and some studies on African countries, with only few studies on Asian countries. This is surprising, since evi-dence hints at high deforestation rates in South-East Asia due to logging activities and plantation agri-culture. We find that a number of studies face problems of internal validity because of endogeneity (simultaneity and reverse causality) and omitted variable bias that are not adequately addressed. De-spite these weaknesses, the literature points at micro-level economic growth, for example in income and capital endowments, as a strong catalyst of human induced land-use change. The rich reviewed empirical literature illustrates the complexity of micro-level land-use change processes, in particular the inter-relationships between household-level characteristics, factor market conditions, and land-use change. These are conditioned by institutions and policies. In particular, the market-oriented re-forms adopted by many developing countries in the 1980s and 1990s seem to have had an important role in altering land use, while impacts of more recent policies, like PES or REDD+, still need to be bet-ter explored. However, the empirical designs of many reviewed studies fail to properly account for this complexity. Finally, the review reveals a lack of interdisciplinary work that uses integrated data and models to analyse land-use change.