*This paper explores the relationship between female genital cutting (FGC) and the marriage market. I develop a general equilibrium model of parents' decisions to cut their daughters, where the bride price, the groom’s traditional payment to the bride’s family upon marriage, is determined endogenously in the marriage market. The model predicts that in a context where FGC is a marker of chastity, an unobservable but valuable trait in the marriage market, the practice increases the marital surplus, the bride price. I use a difference-in-differences approach and test the model’s predictions on Egyptian data. I build a village-level dataset of the coverage of an anti-FGC campaign, using archive information on Egyptian radio transmitters and Irregular Terrain Model (ITM) software. I find that cohorts exposed to the campaign are 13% less likely to be cut and receive a 24% lower bride price. To further support my finding that FGC generates marriage market returns, I provide evidence suggesting that the scarcity of cut women led to an increase in their bride price. When investigating whether FGC is a marker of chastity, I find that the decline in FGC is substituted by an increase in pre-marital virginity testing and child marriage. Finally, to better understand whether these marriage market returns provide an incentive for parents to cut their daughters, I conduct a cross-Africa analysis. I find that the practice of bride price is associated with a 16% higher likelihood of a daughter being cut.

**The recent literature on income distribution analysis has paid increasing attention to the biases arising from applying the usual models for income distributions to data suffering from reporting or non-response errors. In particular, a large strain of this literature focuses on dealing with these biases through performing corrections or imputations to the data prior to the analysis. Most of these corrections, however, either rely on arbitrary choices for merging different data sources such as survey data and administrative tax records, or lack a formal ellicitation of the sources of biases they attempt to correct for. This presentation proposes a parametric framework for integrating formally-defined reporting and/or non-response processes to standard interpersonal income distribution models. By requiring an explicit income reporting function, relating observed incomes in data to (potentially unobserved) true incomes and to relevant individual characteristics, the reporting or measurement errors considered to affect the data are given formal treatment. Secondly, by requiring an explicit response probability function relating non-response probabilities to individuals' characteristics and incomes, the non-response biases considered to affect the data are also integrated to the income distribution model. Analytical expressions for deriving probability density functions (pdf), cumulative distribution functions (cdf), and Generalized Lorenz curves (GLC) will be covered. As illustration, the framework is applied to derive the pdf, cdf, and GLC of integrating to a Generalized Beta distribution of the second kind (GB2) model for income distribution a linear progressive under-reporting process on high-incomes and a truncation for missing top-incomes. This 'expanded' model is then taken to data on incomes from the European Union's Statistics on Income and Living Conditions (EU-SILC) to yield distributional estimates integrating both types of corrections.