M. Jahangir Alam - Wilfrid Laurier University

About Me

Dr. M. Jahangir Alam is an economist specializing in monetary policy, applied macroeconomics, firm dynamics, and machine learning in economics. He is an Assistant Professor in the Department of Economics at Wilfrid Laurier University and holds a Ph.D. in Economics from the University of Calgary. His research integrates empirical macroeconomics, data science, and policy analysis.

Before joining Laurier, Dr. Alam held academic appointments as a Visiting Assistant Professor at Texas A&M University, Western Michigan University, and Truman State University, and served as an Instructor and Post-Doctoral Fellow at HEC Montréal. His work has been published in leading journals—including the Journal of Economic Dynamics and Control, Economic Modelling, the North American Journal of Economics and Finance, and Applied Economics—covering topics such as capital misallocation, productivity, synthetic business data, and the long-run effects of monetary policy.

Dr. Alam is also deeply committed to teaching innovation. He integrates artificial intelligence and modern computational tools into his courses, creating AI-enhanced learning environments that support coding, empirical analysis, and reproducible research. He developed PlusMind AI, a platform for generating quizzes and teaching materials; created AI-supported course websites and automated assessment tools; designed course-specific chatbots; and incorporated ChatGPT into instruction based on Python, R, and Stata. He also delivers faculty workshops on the effective and responsible use of generative AI in education.

In the classroom, Dr. Alam has taught a wide range of undergraduate and graduate courses, including Machine Learning in Economics, Financial Econometrics, Macroeconomic Theory, Industrial Organization, and Economic Growth. His teaching emphasizes applied data analysis, coding proficiency, and empirical research design. Dr. Alam is proficient in Python, R, MATLAB, Stata, SAS, SQL, Spark, and GitHub, reflecting his strong commitment to data-driven and computational approaches in economics.

Upcoming Talks

Research Interests

Monetary Policy
Applied Macroeconomics
Machine Learning in Economics
International Trade

Research

Featured Publications

Capital Misallocation: Cyclicality and Sources

Journal: Journal of Economic Dynamics and Control

Date: March 2020

Status: Published

Abstract: Capital misallocation can lower aggregate total factor productivity, but much less is known about its cyclicality. Using European firm-level data for 2005 to 2014, I establish that capital misallocation, as measured by the dispersion of returns to capital, is higher during recessions and lower during booms. This result is robust to using a much longer dataset from Compustat for the United States and Canada. I also find that firms' net worth, measured as the difference between total assets and liabilities, relative to sales, can explain more capital misallocation than all the other examined firm-level factors combined. Furthermore, my results suggest that firms' net worth explains approximately 10 percent of capital misallocation and 30 percent of its cyclicality.

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Figure: Capital misallocation patterns across European firms during business cycles, showing higher dispersion during recessions and lower dispersion during booms.

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Figure: Long-run effects of monetary policy on R&D investment and firm productivity, showing heterogeneous responses across firm characteristics.

The Long-Run Effects of Monetary Policy: The Role of R&D Investment in Economic Growth

Co-authors: Eskander Alvi

Journal: Economic Modeling

Date: August 2024

Status: Published

Abstract: This study examines the long-term economic effects of monetary policy, focusing on its impact on research and development (R&D) investment and firm productivity. Using data from Compustat, we analyze the response of productivity to monetary policy shocks, with an emphasis on the role of firm size, age, and leverage as indicators of credit constraints. We use high-frequency financial market data for identifying monetary policy shocks through a Local Projections-Instrumental Variables (LP-IV) approach. Our findings reveal that smaller, younger, and less leveraged firms are more adversely affected by contractionary monetary policies, suggesting a significant impact on their R&D investments and subsequent productivity, particularly in the manufacturing sector. The findings also highlight that tight monetary policy has a persistent negative impact on firm productivity via reduced R&D investment, underscoring the need for policies like R&D subsidies to support long-term economic growth.

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Figure: Machine learning methods for causal estimation, comparing neural networks and gradient boosting performance in propensity score estimation.

Applying Machine Learning Methods in Causal Estimation: Analyzing the Benefits of College Athletic Success

Co-authors: Gerard Tetegan

Journal: Journal of Comments and Replications in Economics (Under review)

Date: July 2024

Status: Under Review

Abstract: This study investigates the causal effect of college athletic success on institutional benefits, employing the dataset from Anderson (2017). We contribute to the literature by implementing machine learning algorithms for propensity score estimation, effectively capturing non-linear relationships without explicit specification. Our results demonstrate that machine learning models, particularly neural networks and gradient boosting, significantly outperform traditional logistic regression in handling non-linearities and reducing bias. Gradient boosting, in particular, provides the most robust estimates, enhancing the precision and reliability of causal inferences. The analysis reveals that athletic success decreases acceptance rates while boosting donations, applications, academic reputation, in-state enrollment, and incoming SAT scores. This research underscores the efficacy of machine learning in improving causal inference in observational studies, highlighting its potential for broader application in policy and institutional decision-making.

Price Dispersion and Trade Policy

Journal: Review of International Economics (R&R)

Date: June 2024

Status: Revise & Resubmit

Abstract: This study examines the impact of the Canada-United States Free Trade Agreement (FTA) on producer prices at the product level in Canada. The effect of trade reforms on producer price dispersion is not straightforward: while increased competition may compress the price distribution among producers, access to new niche markets and product differentiation may lead to more dispersed prices. The study uses newly compiled Canadian product-level data and observes a significant increase in product price dispersion in Canada due to the FTA. To establish a causal relationship, the study employs a difference-in-differences (DiD) approach, treating the FTA as a quasi-experiment. The results show that export-oriented products contributed the most to Canada's price dispersion rise. The study also found that trade exposure decreased the number of products produced by a plant and increased the likelihood of discontinuing a high-priced product.

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Figure: Impact of Canada-US Free Trade Agreement on producer price dispersion at the product level in Canadian markets.

Current Projects

Federal Statistical Research Data Center Projects

Project with Pedro Bento and Tatevik Sekhposyan: This research project, using the confidential Quarterly Financial Report (QFR) database from the Census Bureau, aims to shed light on the supply-side effects of monetary policy interventions. It will address three key research topics on the misallocation channel of monetary shocks: 1) provide empirical evidence on the sources of capital misallocation; 2) develop a theoretical model incorporating firm size-dependent financial frictions to understand the heterogeneous effects of monetary shocks on firm dynamics related to capital misallocation; and 3) examine the short-term distributional impacts of monetary shocks in relation to capital misallocation.

International Monetary Policy and Trade Dynamics

Project with Matthew Schaffer: This research project focuses on three key areas of international monetary policy and trade dynamics. The first study analyzes the impact of US Federal Reserve policy on US firms' export-import activities using the Longitudinal Foreign Trade Transactions Database (LFTTD). The second study explores how US monetary policy influences global trade flows, with a focus on country-specific impacts. The third project examines the effects of European Central Bank (ECB) policy on trade between the US and euro area countries, highlighting ECB policy spillovers to the US.

Inflation Expectations Through the Lens of Artificial Intelligence

Project with Huiyu Li and Tatevik Sekhposyan: This project leverages OpenAI's Large Language Model, ChatGPT-4, to generate real-time forecasts for key economic indicators, such as the Consumer Price Index (CPI) inflation, Unemployment Rate, Federal Funds Rate, and GDP Growth Rate for the United States, Canada, and globally. By integrating traditional economic data with advanced AI techniques, our research aims to provide a novel perspective on the formation and evolution of inflation expectations and other macroeconomic indicators.

Work in Progress

Differential Effects of Monetary Policy on Capital Misallocation

with Pedro Bento and Tatevik Sekhposyan

Monetary Policy

Work in Progress

ChatMacro: Evaluating Inflation Forecasts of Generative AI

with Shane Boyle, Huiyu Li, and Tatevik Sekhposyan

AI & Applied Macroeconomics

Work in Progress

Dispersion of News Sentiment and Stock Price Return: Applying Deep Learning Approach

with Maksim Isakin

Machine Learning

Work in Progress

Applying ML Methods in Causal Estimation: Analyzing the Benefits of College Athletic Success

with Gerard Tetegan

Machine Learning

Work in Progress

Monetary Policy Shocks and Allocative Efficiency across U.S. Firms

with Eskander Alvi

Monetary Policy

Work in Progress

Misallocation and Trade Policy

with Huju Liu

International Trade

Work in Progress

Productivity Gains from International Trade in Young and Old Economies

with Maksim Isakin

International Trade

Work in Progress

Identifying Macro Determinants of Natural Disaster: Applying Machine Learning Approach

with Pallab Mozumder

Machine Learning

Work in Progress

The Lasting Impact of COVID-19 on Household Spending Patterns

with Nazneen Ahmad

Applied Macroeconomics

Journal Publications

Published

Productivity Responses of High-Tech Firms to Monetary Policy

North American Journal of Economics and Finance

2025

Monetary Policy

Published

Spatial Misallocation in Canada

with Herb Emery

Review of Canadian Studies and Research

2025

Applied Macroeconomics

Published

Place-based Scholarships: An Updated Assessment of Property Value Impacts from the Kalamazoo Promise

with W. Jason Beasley

Applied Economics

2025

Applied Microeconomics

Published

The Long-Run Effects of Monetary Policy: The Role of R&D Investment in Economic Growth

with Eskander Alvi

Economic Modelling

2024

Monetary Policy

Published

Capital Misallocation: Cyclicality and Sources

Journal of Economic Dynamics and Control

2020

Applied Macroeconomics

Published

Applying Data Synthesis for Longitudinal Business Data across Three Countries

with Benoit Dostie, Jörg Drechsler, and Lars Vilhuber

Statistics in Transition

2020

Data Science

Revise & Resubmit (R&R)

Revise & Resubmit

Price Dispersion and Trade Policy: Evidence from the Canada–U.S. Free Trade Agreement

Review of International Economics

International Trade

Revise & Resubmit

The Convenience Yield Channel of Monetary Policy and International Stock Prices

with Matthew Schaffer and Rashed Sardar

Journal of International Financial Markets, Institutions & Money

International Finance

Revise & Resubmit

Differences in Firm Growth across Countries: Does it Explain GDP Differences?

Journal of Comparative Economics

Applied Macroeconomics

Textbooks

Data Science with Generative AI for Economic and Social Issues | Download PDF

Type: Educational Textbook

Subject: Data Science and Generative AI

Status: Available

Description: This comprehensive textbook applies machine learning techniques to causal estimation methods, forecasting, and practical Python exercises for data analysis. Learning aids include summaries, exercises, replication codes, LaTeX slides, and Python code access via Google Colab and GitHub. The textbook incorporates AI tools like ChatGPT and ChatBot for interactive Q&A, coding assistance, and personalized learning experiences.

Data Science with Generative AI Textbook

Children in the slums of Dhaka: Diarrhoea Prevalence and its Implications

Book Chapter • Environmental Valuation in South Asia • 2011

Published: 05 November 2012

Editors: A. K. Enamul Haque, M. N. Murty, Priya Shyamsundar

Summary: Diarrhoeal disease is a leading cause of child mortality (ages 0-5). While global deaths declined from 4.6M (1980s) to 2.5M (2000), morbidity remains high at 3.2 episodes per child annually. In Bangladesh, 125,000 children under five die yearly from diarrhoea.

Children in the slums of Dhaka: Diarrhoea Prevalence and its Implications

Teaching

Teaching Philosophy

My teaching philosophy is to create a supportive, engaging environment where students learn to think critically and apply economic principles to real-world problems. I integrate traditional instruction with modern AI tools to enhance learning, especially in quantitative and coding-based courses. I teach students how to use platforms like ChatGPT effectively and ethically—to improve analysis, strengthen research, and develop responsible data-driven decision-making skills. My goal is to equip students with the confidence, curiosity, and practical abilities needed to succeed as economists and informed professionals.

AI-Integrated Courses

Economic Growth and Development

Objectives:

Understand key theories, models, and empirical facts of economic growth.
Analyze cross-country differences in productivity, GDP, and development.
Apply growth models (Solow, Romer, Schumpeterian) to interpret real data and policy.

Course Outline:
🔗 Download Course Outline (Fall 2025)

Chatbot:
🔗 Economics of Growth Chatbot

AI-Integrated Course Website:
🔗 Economics of Growth Portal

Economics Paper & Seminar, Capstone

Objectives:

Apply economic theory and empirical methods to a focused research question.
Design, execute, and present an independent research project using real data.
Develop professional research skills: proposal writing, data management, coding, and communication.

Course Outline:
🔗 Download Course Outline (Fall 2025)

Chatbot:
🔗 Capstone Project Chatbot

AI-Integrated Course Website:
🔗 Capstone Project Portal

Intermediate Macroeconomics

Applied Econometrics

Advanced Macroeconomics

Machine Learning in Economics

Principles of Macroeconomics

Financial Econometrics

AI in Education

I use artificial intelligence tools, including ChatGPT, to support learning, strengthen critical thinking, and create high-quality instructional materials. To address the limitations of general AI platforms, our team developed PlusMind AI, which generates quizzes, slides, and course-specific learning resources now used for educational case studies at multiple institutions.

I regularly share practical strategies for integrating AI into teaching through invited workshops focused on responsible AI use, personalized materials, and course-specific chatbots. Presentation links are provided below:

Presentations on Generative AI in Teaching

Adapting AI in Education: Enhancing Learning through Personalized Teaching Materials
Presented at the Mathematics Department, Texas A&M University, 2023.
🔗 [Slides]
Adapting AI in Education: Enhancing Learning through Personalized Teaching Materials
Presented at the Center for Teaching Excellence, Texas A&M University, 2023.
🔗 [Slides]
Adapting AI in Education: The Role of Course-Specific Chatbots
Presented at the Transformational Teaching & Learning Conference (TTLC), Texas A&M University, 2024.
🔗 [Slides], 🔗 [Notes]
Adapting AI in Education: Using Course-Specific Chatbots
Faculty Development Workshops, Center for Teaching Excellence, Texas A&M University, 2024.
🔗 [Slides], 🔗 [Steps for Creating a New GPT], 🔗 [Creating a Sample GPT], 🔗 [Sample Knowledge Document]

Research Lab

PlusMind AI 🔗 Click here

AI-Generated Quiz Creation: PlusMind AI contributes to educational technology by automating the creation of multiple-choice questions and quizzes for platforms like Canvas and Blackboard, streamlining the process of test bank development.
Seamless Integration with Educational Platforms: PlusMind AI enhances educational workflows through its integration with Canvas, facilitated by a Chrome plugin. This allows for the immediate export and straightforward editing of mathematical equations, thereby improving course management.
Interactive and Personalized Learning: PlusMind AI's chatbot is used to develop interactive course content, promoting independent learning and self-guided study among students, contributing significantly to personalized education experiences.

aieconlab 🔗 Click here

Causal Machine Learning and Predictive Models: We explore the intersection of machine learning and econometrics, using causal techniques to gain insights from economic data. Our work includes developing advanced predictive models, like LSTM networks and sophisticated text analysis methods, to forecast economic trends and understand social phenomena.
Prompt Engineering with Python: We focus on the crucial role of prompt engineering to optimize AI models. Using Python, we develop and refine prompts to enhance the performance and accuracy of our machine learning analyses.
ChatBot Development for Economic and Financial Applications: We lead in using chatbots for data science in economics and finance. Our chatbots facilitate data analysis, provide economic insights, and support financial decision-making, making complex data accessible and actionable. They are also integral to our teaching, highlighting their utility in data preprocessing and model interpretation.

AI Inflation Expectations 🔗 Click here

AI-Generated Inflation Forecasts for the United States: AI models, including ChatGPT, contribute to economic forecasting by providing real-time, hourly updates on U.S. CPI inflation expectations, merging traditional economic indicators with AI insights.
AI-Generated Inflation Forecasts for the Rest of the World: Investigate how AI techniques are applied globally to predict inflation trends, showcasing the adaptability and scalability of AI models across varied economic environments.
AI-Generated Inflation Forecasts using RAG (Retrieval-Augmented Generation): Discuss the use of RAG methodology to improve inflation forecasts, which enhances the accuracy and reliability of predictions by integrating AI's generative capabilities with real-time data retrieval.

M. Jahangir Alam, PhD

Assistant Professor
Lazaridis School of Business and Economics
Department of Economics
Wilfrid Laurier University

75 University Ave W
Waterloo, ON N2L 3C5
Canada

Email: moalam@wlu.ca
Phone: (+1) 660-988-4273
Website: mjahangiralam.com

Quick Links

Academic Resources

Projects & Tools

AI Game Projects

Empirical Research Databases

Access an array of databases to enrich your empirical research.

Financial Databases

Labor and Population Data

Current Population Survey (CPS)

Development Data

World Development Indicators (WDI)

M. Jahangir Alam, PhD

About Me

Research

Featured Publications

Current Projects

Federal Statistical Research Data Center Projects

International Monetary Policy and Trade Dynamics

Work in Progress

ChatMacro: Evaluating Inflation Forecasts of Generative AI

Dispersion of News Sentiment and Stock Price Return: Applying Deep Learning Approach

Applying ML Methods in Causal Estimation: Analyzing the Benefits of College Athletic Success

Misallocation and Trade Policy

Productivity Gains from International Trade in Young and Old Economies

Identifying Macro Determinants of Natural Disaster: Applying Machine Learning Approach

The Lasting Impact of COVID-19 on Household Spending Patterns

Journal Publications

Revise & Resubmit (R&R)

Differences in Firm Growth across Countries: Does it Explain GDP Differences?

Textbooks

Data Science with Generative AI for Economic and Social Issues | Download PDF

Teaching

Teaching Philosophy

AI-Integrated Courses

Economic Growth and Development

Objectives:

Economics Paper & Seminar, Capstone

Objectives:

Intermediate Macroeconomics

Applied Econometrics

Advanced Macroeconomics

Machine Learning in Economics

Principles of Macroeconomics

Financial Econometrics

AI in Education

Presentations on Generative AI in Teaching

Research Lab

M. Jahangir Alam, PhD

Quick Links

Academic Resources

Projects & Tools

AI Game Projects

Empirical Research Databases

Financial Databases

Labor and Population Data

Development Data

Macroeconomic and International Data

AI Assistant