I am a postdoc at the department of Spatial Economics at the Vrije Universiteit Amsterdam (VU). Before, I was a PhD candidate at the department of Spatial Economics and the Tinbergen Institute, supervised by Carolyn Fischer and Henri de Groot. I expect to defend my PhD dissertation in 2025Q1.
I do research on clean innovation, technology adoption, and energy and water consumption. I work with data and apply econometric methods to answer my research questions.
I also teach at the Bachelor and Master level, and I supervise Master theses. I am further enthusiastic about coding, which find their work in my research and teaching.
Published in Energy Economics (2024, open access) (link).
with Sacha den Nijs and Henri de Groot.
Seed money funding by Amsterdam Sustainability Institute (ASI) (€10,000).
In the news (BNR).
Abstract: This study investigates the energy efficiency (EE) gap, referring to private agents who are not making seemingly profitable investments to reduce energy use. We deploy a questionnaire among firms in The Netherlands in which we ask them about investment behavior and barriers to investing in EE. A set of 16 barriers is constructed based on the literature. We find that most firms (70%) have made EE investments in the past five years, and that the median firm has saved 10% of its energy use. The remaining profitable EE investment opportunities still leave room for another 15% of energy savings at the median firm. We find that uncertainty about future policies ranks as the leading barrier to EE investments, followed by lock-ins in current equipment, and energy price uncertainty. Especially energy-intensive firms indicate the importance of policy uncertainty. Additionally, we find that a firm’s network can be an important channel for obtaining EE investment knowledge.
R&R at Energy Economics.
Available as Tinbergen Institute discussion paper (link).
with Konstantin Sommer.
Abstract: We study the effects of the EU Emissions Trading System on the economic performance and investments of Dutch manufacturing firms. Motivated both by sizable differences between firms that became regulated in different phases and by a gradual increase in regulatory stringency, we pay close attention to the staggered design of the ETS as well as to potential treatment effect heterogeneity. We base our estimation on recent advances in the difference-in-differences literature and make use of administrative microdata. Our results align with those of the previous literature. Even when studying the more stringent third phase and when using estimators appropriate for the staggered ETS setting, there seems to be no discernible effects of the ETS on firms' economic performance. We also do not find any statistically significant effect on the investment behavior of regulated firms.
Available on my website (link).
Abstract: As directed technical change has the potential to delink economic activity from environmental degradation, understanding the drivers of environment-related innovation is valuable to policy makers. This chapter empirically investigates two drivers of clean innovation, energy prices and market competition, grounded in the induced innovation and inverted-U literature, respectively. Using a self-constructed dataset, I find evidence of the induced innovation hypothesis, but only when studying energy price changes, and not when studying energy price levels. I also consistently find an inverted-U relationship between competition and innovation, although the relationship is statistically uncertain. When combining the two hypotheses, the results are inconclusive. I do not find consistent and significant evidence that market competition affects the induced innovation effect. While the induced innovation findings are robust against different econometric specifications and estimation methods, they vary depending on whether energy prices are first-differenced or not. The literature would benefit from clarifying which specification is preferred.
Available as Tinbergen Institute discussion paper (link).
Abstract: When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps, namely (1) cleaning, (2) similarity scoring, (3) a decision rule based on supervised machine learning, and (4) group identification using community detection. The algorithm is applied to merging firms in the Amadeus Financials and Subsidiaries databases, containing firm-level business and ownership information, to applicants in PATSTAT, a worldwide patent database. For the application the algorithm vastly outperforms an exact string match by increasing the number of matched firms in the Amadeus Financials (Subsidiaries) database with 116% (160%). 53% (74%) of this improvement is due to cleaning, and another 41% (50%) improvement is due to similarity matching. 25.5% (7%) of all patent applications (applicants) since 1950 involving firms are matched to firms in the Amadeus databases, compared to 3.6% (1.3%) for an exact name match.
with Erik Ansink, Carmine Ornaghi and Mirco Tonin.
Research idea: We estimate the effects of home visits on households' water consumption. We use high-frequency consumption data and estimate the dynamic treatment effects using the new staggered difference-in-differences methods.
with Erik Ansink, Adria Rubio-Martin and Hector Macian-Sorribes.
Research idea: We study how alternative economic water policies benefit different sectors in the Júcar river basin. We use a theoretical framework to compare policy interventions and we simulate welfare outcomes using a hydro-economic model.
I am enthusiastic about teaching. In the classroom I invite participation and discussion. For the economics courses, I want my students to both gain a technical and an intuitive understanding of the materials. And ideally the student can link these two worlds. In my tutorials and lectures I often highlight the parallels, by working out economic models both mathematically as well as graphically. And where needed I provide students with extra material, like Python notebooks that further link up the theoretical model with the graphical solution (see for example this notebook).