Nicole Robiro
- Ph.D. in Economics, Goethe University Frankfurt, GSEFM program (2020 - Present)
- M.Sc. in International Economics and Economic Policy, Goethe University Frankfurt (2017 - 2019)
- Academic Stay Abroad, Universidade Católica Portuguesa (2016)
- B.Sc. in Economics and Business Administration, Goethe University Frankfurt (2014 - 2018)
- Development Economics
- Applied Microeconomics
- Agricultural Economics
- Environmental Economics
- Health Economics
- Courses
- Development Microeconomics (Ph.D. level, teaching assistant, 2022 - 2026)
- Seminar Research Methods (Undergraduate level, Co-Instructor, 2023)
Thesis 2021 - present:
- Supervision Bachelor theses (15)
- Supervision Master theses (4)
Click here to download the CV.
Prof. Matthias Schündeln, Ph.D (1. Supervisor)
Chair of Development Economics
Goethe University Frankfurt
schuendeln@wiwi.uni-frankfurt.de
Prof. Dr. Reyn van Ewijk (2. Supervisor)
Chair of Statistics and Econometrics
Johannes Gutenberg Universität Mainz
vanewijk@uni-mainz.de
Prof. Dr. Manuel Linsenmeier
Center for Critical Computational Studies
Goethe University Frankfurt
linsenmeier@c3s.uni-frankfurt.de
The Productivity Effects of Irrigation Aid: Evidence from Border Difference-in-Differences in Sub-Saharan Africa (Job Market Paper)
Abstract: This paper examines the microlevel effectiveness of irrigation-focused development aid in sub-Saharan Africa between 2005 and 2020 using georeferenced project data and high-resolution remote sensing. Employing a localized difference-in-differences strategy near country borders, the study finds evidence that irrigation aid raises the production value of cash crops, while staple crop yields remain largely unresponsive. This pattern indicates that the gains are concentrated in market-oriented production rather than extending broadly across crop categories. The study contributes to the literature on foreign aid and agricultural development by introducing a high-resolution empirical framework that leverages border matching and grid-level data to identify local treatment effects. This approach provides an alternative identification strategy to commonly used instrumental-variable and shift-share designs, expanding the scope for evaluating geographically targeted development interventions.
Predictable Patterns of Protest: The Impact of Agricultural Cycles on Social Unrest in Kenya (with S. Seelos). Food Policy, 2026, doi.org/10.1016/j.foodpol.2026.103136
Abstract: This paper investigates how the agricultural cycle influences protest behavior in Kenya from 2000 to 2019. Using satellite data to identify the crop cycle and local harvest periods at the lowest available administrative level (wards) and geocoded protest records, we find that protest activity does not decline during the harvest period but increases with a temporal lag. A dynamic difference-in-differences specification shows that protest activity gradually rises in the two to three months following harvest, peaking around the onset of the subsequent agricultural cycle. These patterns are consistent with theories of opportunity costs and seasonal scarcity. While the opportunity costs of protest participation are highest during harvest periods, they decline thereafter and coincide with the lean season, when household food stocks become exhausted. These findings are robust across empirical specifications and contribute to a deeper understanding of the temporal dynamics of unrest in agrarian settings. They also highlight the importance of timing in the design of rural development and conflict prevention policies.
Can financing Protected Areas save forest cover and biodiversity? Results from a geospatial matching approach (Work in Progress) (with Y. Eilers, University of Oxford; J. Kluve, Humbold-Universität zu Berlin, KfW Development Bank, and IZA; J. Schielein, KfW Development Bank; M. Wong ,KfW Development Bank)
Abstract: We study the effectiveness of financing protected areas in reducing forest cover loss. Specifically, we construct a unique georeferenced database comprising 398 PAs in Latin America and the Caribbean (LAC) (co-) financed by KFW Development Bank, one of the largest conservation financiers worldwide, and covering the time period 2000-2020. The empirical analysis identifies treatment effects using a geospatial statistical matching approach: first, we augment the database with key covariates granularily characterizing the PAs, obtained from several data sources including satellite imagery on forest coverage. Second, given that the selection of a given area to be protected is non-random, we fractionize each PA into cells of 500 ha size, then match non-treated cells using a CEM (Coarsened Exact Matching) algorithm conditional on covariates, country, and most importantly pre-treatment trends in forest cover during all years preceding the start of protection. Third, treatment effects are obtained at the project level using difference-in-differences for the matched treatment-control samples over the full 21 years sampling period, with flexible pre- and post-treatment periods. The findings indicate that financing PAs is effective: in more than half of the projects 17 out of 32 there is a statistically significant causal reduction of forest cover loss. The effect is sizeable, as indicated by an aggregate average effect of 8 % conserved tree cover compared to non-PAs for the average project period of ten years.
Prenatal Exposure and the Pandemic: Assessing the Effects of COVID-19–Driven Air Quality Improvements in India (Work in Progress) (with N. Galinski, Goethe University)
Abstract: We study the effects of prenatal exposure to fine particulate matter (PM2.5) on fetal and neonatal health using geo-referenced DHS data for children born between 2015 and 2020 linked to high-resolution satellite PM2.5 estimates. We pursue two complementary approaches. First, we instrument pollution exposure with thermal inversions for children born before the pandemic. Second, we exploit the sharp, lockdown-induced decline in air pollution in 2020 in a cohort event study design that compares children in-utero during the first COVID lockdown with an earlier cohort born in the same calendar months. Unlike existing studies for India, we include fetal deaths, which avoids understating effects through selective survival. Preliminary results show that higher prenatal PM2.5 exposure increases the probability of fetal death. Cohorts exposed in-utero during the lockdown, however, show no significant health improvements, suggesting that the benefits of cleaner air were offset by income losses, disrupted health care and COVID infections.