Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines
Fall 2025
Meeting Time: TuTh 1:00–2:15 p.m.
Location: Holdsworth Hall, Room 305
Instructor & TA
Instructor
Tihitina Andarge
Pronouns: she/her/hers
Pronunciation: Ti (as in Tin) – hi (as in hint) – ti (as in Tin) – na (as in sonar)
Last name pronunciation: Ahn-dahr-gay
(Please feel free to call me Tina if you would like.)
Email: [email protected]
Office: 306D Stockbridge Hall
Office Hours: W 2:00–3:00 p.m. in my office and Zoom, or by appointment
Teaching Assistant
Sparshi Srivastava
Pronouns: she/her/hers
Pronunciation: Sp (as in Space) – ar (as in later) – shi (as in sushi)
Last name pronunciation: Shrih-vahs-TAH-vuh
Email: [email protected]
Office Hours: Tu 3:00–4:00 p.m. in Stockbridge 410 and Zoom
Communication
I will make class announcements using Canvas. Please make sure that your account is activated. The best way to contact me is by e-mail. I will do my best to respond within one business day. Please feel free to contact me again if you have not received a response within this time frame.
Overview
This course will explore environmental and resource issues through the lens of empirical economics. We will focus on statistical and econometric tools typically used to analyze these issues, with a particular emphasis on causal inference. This will provide a foundation for implementing these techniques (using the programming language R) and reading and discussing scholarly work in environmental and resource economics. We will cover topics related to air and water pollution, water use, deforestation, monitoring and enforcement, health, and political economy as it relates to the environment. Students will regularly submit problem sets, read and respond to articles, and participate in class discussions.
ECHO360 Lecture Recordings
Lectures will be video and audio recorded and distributed on Canvas through ECHO360. This will help you catch up if you are unable to attend class for any reason. The system is designed to capture the instructor and the front of the classroom and should avoid capturing students’ likeness. However, students’ participation may be recorded. The recordings will not be made available to anyone outside of the class. Do note that there is no guarantee that the recordings will be of a sufficiently good quality as to be audible, clear or useful. Technical malfunctions are likely to occur from time to time. I recommend that you attend our class meetings rather than rely on the availability of recordings.
Learning Objectives
Students will:
- Understand causal inference.
- Become familiar with empirical analysis of environmental issues.
- Become familiar with implementing different econometric techniques using the programming language R.
- Sharpen critical reasoning through reflection on assigned articles.
- Critically engage with cutting edge research in economics.
Department of Resource Economics Student Learning Objectives (SLOs)
This course contributes to the following student learning objectives for undergraduate students in the Department of Resource Economics:
| Student Learning Objective (SLO) | Component(s) of the Course that Meet the Objective |
|---|---|
| SLO 3: Achieve proficiency in supporting disciplines (macroeconomics, mathematics, statistics, finance). | Students will learn statistical and econometric techniques and implement them in R. Students will become familiar with data wrangling and working with different types of data sets (panel data, geospatial data, etc.). |
| SLO 5i: Communicate effectively (orally). | Presentations, discussion facilitator task. |
| SLO 5ii: Communicate effectively (in writing). | Written responses to journal articles, reflections, problem sets. |
| SLO 5iii: Communicate effectively using current digital and multimedia technology. | Presentations, problem sets (RMarkdown submissions). |
| SLO 6: Integrate theoretical principles with quantitative techniques to promote decision-making. | Problem sets. |
| SLO 8: Consistently foster safe, fair, open, and diverse professional and social environments. | Classroom environment. |
| SLO 9: Continually integrate new knowledge from a variety of sources and discern source quality to make well-informed decisions. | Reflections. |
| Experiential Goal | Component(s) of the Course that Meet the Objective |
|---|---|
| EG 1a: Enhance teamwork/collaborative skills through group work, activities, assignments, etc. | Problem sets |
| EG 2: Experience active learning strategies (flipped classrooms, debate, field trips, experiments/games, presentations, student-response systems). | Discussion facilitator tasks |
| EG 3: Engage in non-economics aspects of career preparation. | Students synthesize information from disparate sources and apply analytical tools to understand environmental and natural resource challenges. |
Prerequisites
Pre-requisites include RES-ECON 202 or ECON 203 and RES-ECON 212 or STATISTICS 240. Students are expected to be proficient in microeconomic analysis, statistics, and calculus at the time of enrollment. Prior knowledge of hypothesis testing, regression modeling, marginal analysis, optimization, and differentiation is crucial for this course.
Integrative Experience
This course will satisfy the Integrative Experience requirement for Resource Economics majors when taken with RES-ECON 394LI and RES-ECON 471.
The Integrative Experience (IE) requirement at UMass Amherst addresses the challenges associated with educa- tional fragmentation. Positioned in the upper-division, the IE provides students with a structured opportunity to look back on their early college learning experiences, reflect upon and make connections between those earlier experiences and the more advanced work in their major, and use their integrated learning to prepare for the demands of the world beyond the University.
In line with these goals, this course will require students to synthesize information from disparate sources and apply a wide variety of analytical tools to understand environmental and natural resource challenges.
Course Materials
“Mastering ‘Metrics: The Path from Cause to Effect” by Angrist and Pischke (ISBN: 0691152845). Bring this text with you to each class meeting. You should read the textbook chapters and journal articles closely and be prepared to discuss them in class.
In addition to this text, you will also be required to read assigned journal articles that apply the tools discussed in Mastering ‘Metrics to environmental and resource economics questions. As you read the journal articles, make sure that you understand the overall economic insights of the paper (big picture) as well as the empirical strategy for identifying causality (details). A full list of articles is at the end of this syllabus. All articles will be available on Canvas.
Grading
Problem Sets (50%): You will be assigned 6 problem sets over the course of the semester. The problem sets will consist of problems related to the textbook and/or programming in R. I encourage you to work together but require that you submit your own problem set written in your own words and code. Your lowest problem set grade will be dropped.
Quizzes (20%): We will have 6 take-home quizzes. The quizzes will be based on the readings and will not cover programming in R. Your lowest quiz grade will be dropped.
Reflections (20%)
- Reflection a: A one paragraph response to the journal articles will be due at 10 a.m. the day we plan to discuss the article. For each chapter in Mastering ‘Metrics, pick at least one of the two journal articles to reflect on. During class, you will share your responses in smaller groups of two or three and then with the larger class. I will provide a rubric.
- Reflection b: A one paragraph follow-up reflection on your written response to the journal article will be due at 11:59 p.m. the following class day. In this exercise, you will reflect on what you learned after the class discussion of journal article and the concepts in Mastering ‘Metrics.
Discussion Facilitator Task (10%): This task involves presenting and discussing a peer-reviewed article of your choosing from a well-regarded journal. The article must use the econometric method we are covering during the class in which your presentation occurs. During the second week of the semester, I will send out a survey to gather your topic preferences. I will use the survey to assign students to presentation dates and groups. Once you’re assigned a presentation date, please email me your proposed article for my approval at least 1 week prior to your presentation date (the earlier the better). A rubric is available on Canvas.
Grading Scale
| Course Grade | Letter Grade | Grade Points |
|---|---|---|
| < 60 | F | 0 |
| 60 | D | 1 |
| 63 | D+ | 1.3 |
| 67 | C- | 1.7 |
| 70 | C | 2 |
| 73 | C+ | 2.3 |
| 77 | B- | 2.7 |
| 80 | B | 3 |
| 83 | B+ | 3.3 |
| 87 | A- | 3.7 |
| 90 | A | 4 |
Any dispute over grading must be made to me in writing via e-mail within one week of the assigned grade. After one week, your grade will not be changed.
Deadlines, Late Submissions, and Absences
Quizzes: Quizzes will be given for each unit we cover. Quizzes submitted late will be penalized 25 percentage points for each 24 hours that it is late. For example, if the assignment is submitted two days late, you will be penalized 50 percentage points. After 4 days, you will receive zero credit.
Reflections: A late submission will be penalized 25 percentage points for each 24 hours that it is late. For example, if the assignment is submitted two days late, you will be penalized 50 percentage points. After 4 days, you will receive zero credit.
Problem sets: The due dates for each assignment are given in the calendar below. Unless otherwise specified, these should be submitted by 11:59 p.m. on the specified date. Submissions after that time will be deemed late. A late submission will be penalized 10 percentage points for each day that it is late. For example, if the assignment is submitted 4 days late, you will be penalized 40 percentage points. After 10 days, you will receive zero credit.
If you are unable to submit an assignment or take an exam at the scheduled time, please inform me and the TAs as soon as possible. Your physical and mental health are important to me and we will work together to reach a fair resolution. If you are struggling, I highly encourage you to use the resources available to you on campus.
Personal Wellness Statement
I want to emphasize that your physical and mental health are important to me. Family emergencies, physical or mental illness, personal crises, or childcare issues can significantly affect your academic performance. If you encounter any issues that severely affect your ability to engage in this course, please contact me and we will work together to reach a fair resolution. You do not need to tell me all the details of your situation. You may also speak with someone from student services who can help me determine adequate accommodations without revealing sensitive information to me. There are also a host of resources available to you on campus that may be helpful for your physical and mental health: Student Life Single Stop Resources, Center for Counselling and Psychological Health (CCPH), and the Office of Equity and Inclusion.
Diversity and Inclusion Statement
I am committed to fostering a safe and inclusive learning environment in which everyone feels valued, respected, and comfortable sharing their thoughts and experiences, regardless of their sex, gender, race, ethnicity, socioeco- nomic status, sexual orientation, political beliefs, physical ability, mental ability, and/or religious/non-religious background. Creating such an environment is a team effort; I expect you to treat all members of our community with the utmost respect. Please see the Guidelines for Classroom Civility and Respect.
Academic Alerts
I have partnered with Student Success and your academic advisors to assist you on your path to success. Throughout the semester, I will communicate with Student Success and academic advisors regarding your progress in the course. If you are contacted, please consider scheduling appointments such as tutoring or aca- demic advising and with me. Referrals are not punitive and are meant to assist you in connecting with resources at UMass. Please email @email if you have any questions or need assistance connecting with resources.
Accommodation Statement
Your success in this course is important to me. We all learn differently and bring different strengths and needs to the class. If there are aspects of the course that prevent you from learning or make you feel excluded, please let me know as soon as possible. Together we will develop strategies to meet both your needs and the requirements of the course.
The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements. For further information, please visit Disability Services.
There are also a range of resource on campus, including:
- Student Success
- Center for Counseling and Pyschological Health (CCPH)
- English as a Second Language (ESL) Program
Academic Honesty Statement
UMass Amherst is strongly committed to academic integrity, which is defined as completing all academic work without cheating, lying, stealing, or receiving unauthorized assistance from any other person, or using any source of information not appropriately authorized or attributed. As a community, we hold each other accountable and support each other’s knowledge and understanding of academic integrity. Academic dishonesty is prohibited in all programs of the University and includes but is not limited to: Cheating, fabrication, plagiarism, lying, and facilitating dishonesty, via analogue and digital means. Sanctions may be imposed on any student who has committed or participated in an academic integrity infraction. Any person who has reason to believe that a student has committed an academic integrity infraction should bring such information to the attention of the appropriate course instructor as soon as possible. All students at the University of Massachusetts Amherst have read and acknowledged the Commitment to Academic Integrity and are knowingly responsible for completing all work with integrity and in accordance with the policy.
Generative AI (e.g., ChatGPT, GitHub Copilot, etc.) may be useful tools for coding. You may use generative AI only for troubleshooting code. You may not upload any of the course materials to get an answer (e.g., upload all or part of the problem set and data). However, you may describe the error you’re getting to troubleshoot using generative AI. If you decide to use generative AI in this manner, you must include all prompts that you used to assist you as part of your problem set submission. You may not use generative AI for any other purpose (e.g., generating code from scratch, summarizing readings, writing reflections, quizzes, etc.).
I strongly encourage you to work with other students in the course on the weekly problem sets. However, all submitted work must be your own.
Title IX Statement
In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual as- sault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found here. You do not need to make a formal report to access them. If you need immediate support, you are not alone. Free and confidential support is available 24 hours a day / 7 days a week / 365 days a year at the SASA Hotline 413-545-0800.
Copyright and Sharing of Material
As a student in this course, you will have access to relevant materials, including but not limited to, videos, all course recordings, problem sets and solutions, lecture slides, in-class demonstrations and code, and quizzes. These materials are protected by U.S. copyright laws and by University policy. You may take notes and make copies of course materials for your own use in this class. You may also share those materials with another student who is registered and enrolled in this course.
You may not reproduce, distribute, upload, or display any lecture notes or recordings or course materials in any other way — whether or not a fee is charged — without the express written consent of the owner(s). If you do so, you may be subject to disciplinary action under the UMass Code of Student Conduct.
Data that you enter when using generative AI to produce text, images, code, or video will become part of the model used to train that tool in the future. As the content of this course is copyrighted, you may not feed any of the materials to AI without my explicit, written consent.
Similarly, you own the copyright to your original work. If I am interested in posting your answers or papers on the course website, I will ask for your written permission.
Acknowledgements
This course design draws from a previous course taught by Professor Nathan Chan. I extend a big thank you to him!
Tentative Course Schedule*
| Week | Lecture | Date | Topics | Reading | Due* |
|---|---|---|---|---|---|
| 1 | 1 | Sept. 2 | Syllabus, Causality and Identification | ||
| 1 | 2 | Sept. 4 | Statistics Review, Causality and Identification | ||
| 2 | 3 | Sept. 9 | Statistics Review, Causality and Identification | MM Intro and Ch. 1 | |
| 2 | 4 | Sept. 11 | Causality and Identification, Randomized Controlled Trials | MM Ch. 1 | Class survey; sign up for discussion facilitator task |
| 3 | 5 | Sept. 16 | Introduction to R | Handout on Canvas | Quiz 0 |
| 3 | 6 | Sept. 18 | Introduction to R, Regressions | MM Ch. 2 (focus on 2.1 and 2.2) | |
| 4 | 7 | Sept. 23 | Regressions | MM Ch. 2 (focus on 2.1 and 2.2) | |
| 4 | 8 | Sept. 25 | Regressions (continued) | Bernedo Del Carpio et al. (2021); Jayachandran et al. (2017) | Reflection 1a |
| 5 | 9 | Sept. 30 | Regressions in R | Handout on Canvas | Quiz 1; Reflection 1b |
| 5 | 10 | Oct. 2 | Data Visualization | Handout on Canvas | |
| 6 | 11 | Oct. 7 | Instrumental Variables | MM Ch. 3 (focus on 3.1) | Problem Set 1 |
| 6 | 12 | Oct. 9 | Instrumental Variables | Moretti and Neidell (2011); Bondy et al. (2020) | Reflection 2a |
| 7 | 13 | Oct. 14 | Instrumental Variables in R | Handout on Canvas | Quiz 2; Reflection 2a |
| 7 | 14 | Oct. 16 | Instrumental Variables in R | Handout on Canvas | |
| 8 | 15 | Oct. 21 | Regression Discontinuity | MM Ch. 4 (focus on 4.1) | Problem Set 2 |
| 8 | 16 | Oct. 23 | Regression Discontinuity | Nataraj and Hanemann (2011); Pacca et al. (2021) | Reflection 3a |
| 9 | 17 | Oct. 28 | Regression Discontinuity in R | Handout on Canvas | Quiz 3; Reflection 3b |
| 9 | 18 | Oct. 30 | Difference-in-Differences | MM Ch. 5 | |
| 10 | — | Nov. 4 | No Class – Election Day | ||
| 10 | 19 | Nov. 6 | Difference-in-Differences | Palacios et al. (2020); Harlemann et al. (2022) | Reflection 4a |
| 11 | — | Nov. 11 | No Class – Veterans Day | ||
| 11 | — | Nov. 13 | No Class! | Problem Set 3 | |
| 12 | 20 | Nov. 18 | Difference-in-Differences in R | Handout on Canvas | Reflection 4b |
| 12 | 21 | Nov. 20 | Geospatial Data | Handout on Canvas | |
| 13 | — | Nov. 25 | Thanksgiving Break | ||
| 13 | — | Nov. 27 | Thanksgiving Break | ||
| 14 | 22 | Dec. 2 | Geospatial Data in R | Handout on Canvas | |
| 14 | 23 | Dec. 4 | Geospatial Data in R | Handout on Canvas | Problem Set 4 |
| 15 | 24 | Dec. 9 | Course Wrap-up | Quiz 5**; Problem Set 5**; Course Evaluations |
*“a” reflections are due at 10 a.m. the class day the article(s) will be discussed. For each unit, pick at least one article to write the reflection on. “b” reflections are due at 11:59 p.m. on the day noted in the schedule. Problem sets and quizzes are due at 11:59 p.m. on the day noted in the schedule.
**Quiz 5 and Problem Set 5 must be turned in by Dec. 14.