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This paper presents descriptive statistics from the first wave of the Syrian Refugee Life Study (S-RLS), which was launched in 2020. S-RLS is a longitudinal study that tracks a representative sample of 2,500 registered Syrian refugee households in Jordan. It collects comprehensive data on socio-demographic variables as well as information on health and well-being, preferences, social capital, attitudes, and safety and crime perceptions. This study uses these novel data to document the socio-demographic characteristics of Syrian refugees in Jordan, and compare them to those of the representative Jordanian and non-Jordanian populations interviewed in the 2016 Jordan Labor Market Panel Survey. The findings point to lags in basic service access, housing quality, and educational attainment for the Syrian refugee population, relative to the non-refugee population. The impacts of the pandemic may serve to partially explain these documented disparities. The data also illustrate that most Syrian refugees have not recovered economically from the shock of COVID-19 and that this population has larger gender disparities in terms of income, employment, prevalence of child marriage, and gender attitudes than their non-refugee counterparts. Finally, mental health problems are common for Syrian refugees in 2020, with depression indicated among over 61 percent of the population.

Mental health.

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Policies aimed at raising agricultural productivity have been a centerpiece in the fight against global poverty. Their impacts are often measured using field or quasi-experiments that provide strong causal identification, but may be too small-scale to capture the general equilibrium (GE) effects that emerge once the policy is scaled up. We propose a new approach for quantifying large-scale GE policy counterfactuals that can both complement and be informed by evidence from field and quasi-experiments. We develop a quantitative model of farm production, consumption and trading that captures important features of this setting, and propose a new solution method that relies on rich but widely available microdata. We showcase our approach in the context of a subsidy for modern inputs in Uganda, using variation from field and quasi-experiments for parameter estimation. We find that both the average and distributional impacts of the subsidy differ meaningfully when comparing a local intervention to one at scale, even for the same sample of farmers, and quantify the underlying mechanisms. We further document new insights on how GE forces differ as a function of saturation rates at different geographical scales, and on the importance of capturing a granular economic geography for counterfactual analysis.