Mohammad H. Seyedsalehi — economist

I am a PhD candidate in Economics at the University of Toronto. My research focuses on environmental economics, empirical industrial organization, and econometrics. I study how firms’ pricing decisions and consumer choices shape the effectiveness of environmental incentives, with implications for policy design.

I will be on the 2026–2027 economics job market.

01 / Working papers

Original conceptual illustration of ethanol and gasoline markets connected by carbon-credit incentives.

Job Market Paper

The Carbon Paradox of Clean Incentives: Evidence from Ethanol

Abstract

When carbon pricing faces implementation barriers, promoting low-carbon production offers an alternative; it aims to reduce emissions by making cleaner substitutes more affordable. This paper develops a dynamic framework to assess whether incentives for cleaner production necessarily reduce emissions, considering both substitution toward cleaner goods and rebound effects from increased total consumption. I apply it to Brazil, the world’s second-largest ethanol market, where RenovaBio uses incentives funded by fossil fuel distributors to promote ethanol, a cleaner substitute for gasoline. The estimated unified equilibrium model combines supply from heterogeneous ethanol producers, differentiated fuel demand under oligopolistic pricing, and dynamic carbon-credit banking. The answer is no: RenovaBio increases ethanol production but displaces very little gasoline, expanding total fuel consumption and raising emissions by 0.22% relative to policy removal over 2020–2024. In contrast, replacing RenovaBio with an ethanol blending mandate about one percentage point higher generates essentially the same ethanol production, with 1.20% lower emissions than RenovaBio and smaller fuel-price distortions.

Paper
Conceptual illustration of geographic neighborhoods surrounding a target area.

Empirical Bayes with Side Information: Incorporating Geographic Relevance into Neighborhood Impact Estimation

Second revise and resubmit, Journal of Applied Econometrics

Abstract

This paper introduces a novel methodology for estimating neighborhood impacts on intergenerational mobility. The approach employs a nonparametric empirical Bayes framework that addresses geographic heterogeneity in the underlying data-generating process. First, we propose a theoretical framework to address the long-standing yet unresolved question of how to make better use of highly relevant samples—in this context, data from geographically closer regions—when designing a shrinkage estimator for a target area. We demonstrate, both theoretically and empirically, how neglecting this factor can lead to systematically distorted estimates. Second, using publicly available data, we demonstrate that our relevance-based empirical Bayes method reduces Mean Squared Error (MSE) by 26% to 34% relative to benchmark estimates.

02 / Publications

The Effects of Order Flow Imbalance on Stock Prices in Tehran Stock Exchange

With Mahdi Barakchian

Financial Management Perspective, 2022

Abstract

We investigate the effects of order-book events on the prices of the 30 largest stocks on the Tehran Stock Exchange during 2020. We measure the sensitivity of prices to changes in supply and demand volumes and identify the factors affecting this sensitivity. Following the approach of Cont et al. (2014), we conduct approximately 30,000 OLS regressions and show that, in a low-depth market, mid-price returns are significantly explained by order flow imbalance, which captures the net change in demand—that is, the difference between event volumes on the two sides of the order book. We also show that incorporating order flow imbalance from the first three levels of the order book increases the model’s explanatory power for changes in stock mid-prices, indicating that deeper levels of the order book also significantly affect price changes. Moreover, the results confirm that market depth reduces the price impact of order-book events. Our findings are robust across months and stocks.

Paper

03 / Work in progress

Who Benefits from Green Subsidies? Labor Standards and the Distribution of Gains

With Zahra Sedaghat

Abstract

How are the gains from green investment subsidies distributed between firms and workers? This project examines whether labor conditions attached to these subsidies increase workers’ share of the benefits, using Canada’s Clean Technology Investment Tax Credit. The policy offers a larger credit to firms meeting prevailing-wage and apprenticeship requirements, linking investment support to employment practices. The analysis links firms’ investment and contractor choices to worker earnings. Firms can qualify for additional support without raising existing workers’ pay if they already meet the standards or hire contractors that do. Higher wages at subsidized projects therefore need not reflect gains caused by the policy. Comparing subsidies with and without labor conditions at the same fiscal cost would assess whether these requirements increase worker earnings and training opportunities, and how they affect investment and hiring.

One Carbon Market or Many? Optimal Integration of Sectoral Carbon Markets

Abstract

This paper asks whether carbon credit markets that jointly penalize dirty activity and reward clean activity should be organized separately by sector or integrated into a common market. Integration can establish a common carbon price, equalizing marginal abatement costs across sectors and shifting emissions reductions toward lower-cost opportunities, while also allowing payments from dirty activity in one sector to finance clean investment in another. Separation, however, preserves sector-specific certificate prices, allowing clean incentives to respond independently to differences in innovation and adoption frictions. I study a market design in which clean activities generate tradable certificates and dirty activities create demand for the same asset, with sector-specific crediting and obligation rates determining the effective clean subsidy and carbon penalty. I characterize when a unified market can achieve the efficient combination of a common carbon price and differentiated clean-technology support, when separate sectoral markets perform better, and how this tradeoff depends on abatement costs and clean-technology spillovers across sectors.

Euler Equations for Dynamic Games

With Victor Aguirregabiria

Abstract

This project develops Euler equations for dynamic games to facilitate the estimation of firms’ dynamic strategic decisions without repeatedly solving their full optimization problems.

04 / Teaching

I have served as a teaching assistant for more than 2,000 assigned hours during my PhD at the University of Toronto, across undergraduate and graduate courses, with a focus on machine learning, data science, and finance. Before my PhD, I spent seven years teaching mathematics to International Mathematical Olympiad students.

Teaching experience
  • Machine Learning Applications in Economics and Finance (5 semesters)
  • Financial Economics, Banking, and Corporate Finance (3 semesters)
  • Financial Risk Management (3 semesters)
  • Microeconomics and macroeconomics
  • Environmental and climate economics
  • Data analysis and programming
  • International trade and economics for public policy