This paper explores how state and religious providers of education compete during the nation building process. Using novel administrative data, we characterize the evolution of Indonesia’s Islamic education system and religious school choice after the introduction of mass public primary schooling in the 1970s. Funded through informal taxation, Islamic schools competed with the state by entering in the same markets. While primary enrollment shifted towards state schools, religious education increased overall as Islamic schools absorbed growing demand for secondary education. In the short run, electoral support for the secular regime weakened in markets with greater public school construction. Over the long run, Islamic schools established at this juncture are more differentiated in terms of religious curriculum, and cohorts exposed to mass public schooling as children are more invested in religion than in the national identity. Our findings offer a new perspective on the political economy of education reforms and the emergence of parallel systems of public goods provision.

We use a natural experiment in Indonesia to provide causal evidence on the role of location-specific human capital and skill transferability in shaping the spatial distribution of productivity. From 1979–1988, the Transmigration Program relocated two million migrants from rural Java and Bali to new rural settlements in the Outer Islands. Villages assigned migrants from regions with more similar agroclimatic endowments exhibit higher rice productivity and nighttime light intensity one to two decades later. We find some evidence of migrants' adaptation to agroclimatic change. Overall, our results suggest that regional productivity differences may overstate the potential gains from migration.

How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These “best-case” scenarios with annual data fall short of workable early-warning systems.