Teaching Statement — Mario Rafael Silva

Good instruction serves not merely to inform — it inspires. My journey into economics began as a teenager captivated by Samuelson's Economics, where clarity, intuition, real-world application, and policy relevance combined to illuminate the subject. This experience instilled my guiding principle: cool heads at the service of warm hearts.

My overarching pedagogical approach is to distill challenging material into intuitive, accessible components, gradually building complexity. Two central tenets guide my teaching. First, echoing Dani Rodrik, the level of abstraction should fit the pedagogical context: I begin with simple concepts, then build up complexity in a logical sequence that supports robust understanding. Second, I aim to bridge theory, empirical evidence, and computation. In all courses, I work to inspire curiosity and independence, provide intellectual structure, and foster confidence with economic tools.

UC Irvine — Teaching Assistant

As a teaching assistant at UC Irvine, I led classes in introductory and intermediate economics and econometrics, emphasizing inclusive clarity and addressing common concerns with structured, general explanations. I had the privilege of instructing a diverse population, with an especially large fraction of Hispanics, Asian Americans, and international students. For a subset of the latter, language issues played a role in understanding course material. I patiently explained the intuition underlying problems from a variety of angles so that each inquiring student could obtain a solid understanding.

Another challenge arose from the fact that, to many undergraduate students, economics seemed esoteric and removed from practical concerns. To this end, I always used examples that students could most relate to. In discussing adverse selection in teaching game theory, I brought up the example of the Affordable Care Act and how the individual mandate was used to keep the pool of insured individuals healthier, allowing for lower premiums. In addition to regular office hours, I provided additional help to students desiring walk-in assistance at the UCI Economics Learning Center. My experience organizing discussions, office hours, and assessment design laid the groundwork for the student-centered philosophy I uphold today.

Tongji University — Full-Time Instructor

Serving as full-time instructor at Tongji University profoundly shaped my teaching. In my undergraduate Money and Banking course, I constructed a logical progression of topics, motivating core questions such as what imparts value to fiat money and how money's roles have evolved. I used historical examples — including Chinese coinage and the gold standard — to make abstract concepts more tangible. We explored welfare costs of inflation, intertemporal choice, and the impossible trinity, before expanding the model to include capital and banking, highlighting how liquidity and return trade-offs underpin financial intermediation.

Students learned not just theory, but its practical relevance: distinguishing between fiat and inside money, understanding central bank roles, and investigating the roots of financial crises. Using overlapping generations models inspired by Champ, Freeman, and Haslag, as well as Kiyotaki and Moore (1997), we examined how shocks and frictions can disrupt the payments system and lead to misallocation in the real economy, emphasizing macro-financial linkages.

At Tongji, I also taught a second-semester macroeconomics course that would shape my later teaching at Hong Kong Baptist University. This course focused on equipping students with analytic tools and computational intuition. We began with time series methods — covering VARs, Markov processes, and modern filtering techniques — and dynamic programming foundations, proving the Principle of Optimality and Contraction Mapping Theorem and applying them through hands-on coding assignments with platforms such as QuantEcon. This integration of computation and theory remains central to my teaching.

We then studied the real business cycle model, emphasizing calibration, log-linearization, and different solution methods. Students learned about the contribution of investment-specific shocks (Greenwood et al., 1988), asset pricing, and the role of precautionary savings in liquidity premia (Aiyagari, 1994). We progressed from real to monetary and financial frictions, focusing on the New Keynesian model, price stickiness, and financial accelerator mechanisms (Bernanke et al., 1999). Recognizing that many people have a visual learning style, I used diagrams to capture the underlying intuition whenever possible.

Another important module covered endogenous variety and business formation as in Bilbiie et al. (2012), then labor market search theory — especially through the Mortensen–Pissarides framework and modern treatments (Petrosky-Nadeau and Wasmer, 2017). We discussed job creation, wage bargaining, equilibrium adjustment, and computational solution methods. Assignments incorporated real-world data, computational modeling, and presentations, which fostered critical engagement and deeper understanding.

Hong Kong Baptist University — Assistant Professor

At Hong Kong Baptist University, I have adapted my approach in a Masters-level macroeconomics course, further integrating liquidity-based views of monetary policy transmission (Geromichalos and Herrenbrueck, 2021), which address both traditional real balances and the effects of open market operations.

I have also expanded curriculum content to include the Economics of Digital Currencies, co-taught with Kim-Sau Chung. Here, students analyze the potential for central bank digital currencies to enhance liquidity, lower transaction costs, and address financial inclusion, while engaging with concerns about disintermediation and competition in banking. Students present in groups at the end of the course on various key topics: financial inclusion, interaction of CBDC with monetary policy, and the role of electronic cash in mitigating firm price discrimination implemented using payments data. This exercise also encourages students to exercise their creativity and initiative.

On a different vein, I have also taught "Data Analytics for Business Decision Making," a course which integrates Python programming, inferential statistics, and research design. For the final project, students design a questionnaire related to a product idea, gather data from their peers, and analyze the data — formally testing hypotheses — with the goal of assessing the viability and design of a business opportunity. The teaching style is highly interactive through the use of Jupyter Notebooks and in-class exercises. I have adapted my approach to students outside of a standard economics track while also enhancing the course with economics examples.