Published


Publications


The economic impact of critical-habitat designation: Evidence from vacant-land transactions

Maximilian Auffhammer, Maya Duru, Edward Rubin, and David L. Sunding

The Endangered Species Act (ESA) requires the federal government to designate critical habitat for species listed as threatened or endangered. This provision of the ESA has proven to be one of its most controversial, as critical-habitat land designation entails special management—and potentially greater regulation. In this paper we measure the economic impact of critical-habitat designation by estimating its effect on the market value of vacant land. Using data from over 13,000 vacant-land transactions that occurred within or near critical habitat for two important species in California (red-legged frog and Bay checkerspot butterfly), we show that critical-habitat designation resulted in a large and statistically significant decrease in land value. The estimated impact of critical-habitat designation is heterogeneous: the largest decreases occur within designated urban-growth boundaries.

Forthcoming, Land Economics



Bringing Satellite-Based Air-Quality Estimates Down to Earth

Meredith Fowlie, Edward Rubin, and Reed Walker

We use state-of-the-art, satellite-based PM2.5 estimates to assess the extent to which the EPA’s existing, monitor-based measurements over- or under-estimate true exposure to PM2.5 pollution. Treating satellite-based estimates as truth implies a substantial number of “policy errors”—over-regulating areas that comply with air quality standards and under-regulating other areas that appear to violate standards. We investigate the health implications of these apparent errors and highlight the importance of accounting for prediction error in satellite-based estimates. Uncertainty in “policy errors” increases substantially when we account for these underlying prediction errors.

Published (P&P) version | NBER Working Paper No. 25560 | EI @ Haas Working Paper 300

Fowlie, Meredith, Edward Rubin, and Reed Walker. 2019. “Bringing Satellite-Based Air Quality Estimates Down to Earth.” AEA Papers and Proceedings, 109: 283-88.

Projects


Working Papers


Decomposing “the” Elasticity of Demand: Empirical and Policy Insights from 300 Million Natural Gas Bills

with Maximilian Auffhammer

Public policy typically employs time- and group-invariant policies—partially due to historical limits that prevented precise identification of the heterogeneity underlying key parameters. We consider an important market—natural gas—where these limits have been relaxed and harness 300 million residential bills to identify income- and season-specific own-price elasticities. Exploiting service-territory spatial discontinuities and household-specific exogenous time-series variation, we show this demand elasticity varies substantially across seasons, income groups, and their interaction—from 0.06 (summer) to –0.61 (winter). This heterogeneity suggests an unexplored, implementable, and generalizable policy avenue—shifting fixed costs of operation into summer months—that is potentially more efficient and progressive than prevailing practices.

NBER Working Paper No. 24295 | EI @ Haas Working Paper 287

draft | slides | map: service and study area | map: PRISM mean temperature | map: US natural gas pipeline


Are our hopes too high? Testing cannabis legalization’s potential to reduce criminalization

Cannabis legalization advocates often argue that cannabis legalization offers the potential to reduce the private and social costs related to criminalization and incarceration—particularly for marginalized populations. While this assertion is theoretically plausible, it boils down to an empirically testable hypothesis that remains untested: does legalizing a previously illegal substance (cannabis) reduce arrests, citations, and general law-enforcement contact? This paper provides the first causal evidence that cannabis legalization need not necessarily reduce criminalization—and under the right circumstances, may in fact increase police incidents/arrests for both cannabis products and non-cannabis drugs. First, I present a theoretical model of police effort and drug consumption that demonstrates the importance of substitution and incentives for this hypothesis. I then empirically show that before legalization, drug-incident trends in Denver, Colorado matched trends in many other US cities. However, following cannabis legalization in Colorado, drug incidents spike sharply in Denver, while trends in comparison cities (unaffected by Colorado’s legalization) remain stable. This spike in drug-related police incidents occurs both for cannabis and non-cannabis drugs. Synthetic-control and difference-in-differences empirical designs corroborate the size and significance of this empirical observation, estimating that Colorado’s legalization of recreational cannabis nearly doubled police-involved drug incidents in Denver.

new draft in preparation | slides | US legalization map | timeline | time series drug offenses


Do aerially applied pesticides affect local air quality? Empirical evidence from California’s San Joaquin Valley

Many policymakers, public-health advocates, and citizen groups question whether current pesticide regulations properly equate the marginal social costs of pesticide applications to their marginal social benefits—with particular concern for negative health effects stemming from pesticide exposure. Additionally, recent research and policies in public health, epidemiology, and economics emphasize how fine particulate matter (PM2.5) concentrations harm humans through increased mortality, morbidity, mental health issues, and a host of socioeconomic outcomes. This paper presents the first empirical evidence that aerially applied pesticides increase local PM2.5 concentrations. To causally estimate this effect, I combine the universe of aerial pesticide applications in the five southern counties of California’s San Joaquin Valley (1.8M reports) with the U.S. EPA’s PM2.5 monitoring network—exploiting (1) spatiotemporal variation in aerial pesticide applications and (2) variation in local wind patterns. I find significant evidence that (upwind) aerial pesticide applications within 1.5km increase local PM2.5 concentrations. The magnitudes of the point estimates suggest that the top decile of aerial applications may sufficiently increase local PM2.5 to warrant concern for human health.


In Progress


Mismeasurement in exposure and access: Insights from cellphone data


How salient are environmental risks? The short- and long-run effects of lead exposure in piped water


Irrigation and climatic effects on water levels in the U.S. High Plains Aquifer

with Lilyan Fulginiti and Richard Perrin


Is “Michelle” less productive than “Michael”? A field experiment on consumer-based gender discrimination in the marketplace

with Erin Kelley and Matthew Pecenco


Presentations


Mismeasurement in exposure and access: Insights from cellphone data
Western Economic Association Annual Conference, June 2019

The economic impact of critical-habitat designation: Evidence from vacant-land transactions
The Occasional Workshop (UCSB), November 2018

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
World Congress of Environmental Economics (WCERE), June 2018

Is “Michelle” less productive than “Michael”? A field experiment on consumer-based gender discrimination in the marketplace
UC Berkeley, Computational Text Analysis Working Group, April 2018

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
University of Oregon, Economics, February 2018

Is “Michelle” less productive than “Michael”? A field experiment on consumer-based gender discrimination in the marketplace
IGC-PEDL Workshop at Oxford, December 2017

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
Heartland Environmental and Resource Economics Workshop, September 2017

Is “Michelle” less productive than “Michael”? A field experiment on consumer-based gender discrimination in the marketplace
Berkeley Development Economics Lunch, September 2017

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
Camp Resources, August 2017

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
AAEA Annual Meeting, August 2017

Do marijuana stores increase or reduce neighborhood crime? Evidence from Denver, Colorado
UC Berkeley, Summer Research Seminar, July 2017

Natural gas elasticities and optimal cost recovery under heterogeneity: Evidence from 300 million natural gas bills in California
AERE Annual Summer Conference, June 2017

Summertime, and pass-through is easier: Chasing down price elasticities for residential natural gas demand in 275 million bills
22nd Annual POWER Conference on Energy Research and Policy, March 2017

Natural gas elasticities, seasonal heterogeneity, and consumer behavior: Evidence from 300M+ bills
UC Berkeley, Environmental and Resource Economics Seminar, November 2016

(Mile-) High Quandaries: Evidence from Denver that Marijuana Legalization May Increase Drug Arrests
UC Berkeley, Environmental and Resource Economics Seminar, April 2015

Irrigation and Climatic Effects on Water Levels in the U.S. High Plains Aquifer Along the 41st Parallel in Nebraska (and Several Questions about Model Complexity)
University of Nebraska-Lincoln, Statistics Departmental Seminar, March 2013

Irrigation and Climatic Effects on Water Levels in the U.S. High Plains Aquifer
International Conference of Agricultural Economists, poster, August 2012

Reproductive Ecology of Western Painted Turtles (Chrysemys picta)
Midwest Fish and Wildlife Conference, poster, December 2006

R scripts

Run R code in terminal without entering R or saving a script. (The -e option evaluates the given R expression.)

Create a nice timeline (shown below) in R’s ggplot2.

# Setup ----
# Packages
library(ggplot2)
# Define colors (from https://www.materialpalette.com)
dark_primary_color   <- "#C2185B"
primary_color        <- "#E91E63"
light_primary_color  <- "#F8BBD0"
text_primary_color   <- "#FFFFFF"
accent_color         <- "#9E9E9E"
primary_text_color   <- "#212121"
secondary_text_color <- "#757575"
divider_color        <- "#BDBDBD"

# Plot the timeline ----
ggplot() +
  # Two rectangles
  geom_rect(aes(xmin = 2009, xmax = 2011+4/12, ymin = 0, ymax = 0.35),
    fill = divider_color) +
  geom_rect(aes(xmin = 2011+4/12, xmax = 2014, ymin = 0, ymax = 0.35),
    fill = light_primary_color) +
  geom_rect(aes(xmin = 2014, xmax = 2017, ymin = 0, ymax = 0.35),
    fill = primary_color) +
  # Label rectangles (periods)
  annotate(geom = "text", x = 2010, y = -0.15, label = "Period A",
    color = divider_color) +
  annotate(geom = "text", x = 2015.5, y = -0.15, label = "Period B",
    color = primary_color) +
  # Add ellipses
  annotate(geom = "point", x = seq(2009-0.15, 2009-0.45, -0.15), y = 0.175,
    size = 1.5, color = divider_color) +
  annotate(geom = "point", x = seq(2017+0.15, 2017+0.45, 0.15), y = 0.175,
    size = 1.5, color = primary_color) +
  # Time axis
  geom_hline(yintercept = 0, size = 1, color = primary_text_color) +
  # Label time
  annotate(geom = "text", x = 2009:2017, y = -0.05, label = 2009:2017,
    size = 4, color = secondary_text_color) +
  # Points for events
  annotate(geom = "point", x = c(2011+4/12, 2012+7/12, 2014), y = 0,
    size = 4, color = primary_text_color) +
  # Label events:
  # First event
  annotate(geom = "text", x = 2011+4/12+0.15, y = 0.9, hjust = 0,
    label = "An event triggers a change", color = primary_text_color) +
  geom_segment(aes(x = 2011+4/12, xend = 2011+4/12,
    y = 0, yend = 0.9), color = primary_text_color) +
  geom_segment(aes(x = 2011+4/12, xend = 2011+4/12+0.1,
    y = 0.9, yend = 0.9), color = primary_text_color) +
  # Intermediate event
  annotate(geom = "text", x = 2012+7/12+0.15, y = 0.7, hjust = 0,
    label = "A relevant intermediate event", color = primary_text_color) +
  geom_segment(aes(x = 2012+7/12, xend = 2012+7/12,
      y = 0, yend = 0.7), color = primary_text_color) +
  geom_segment(aes(x = 2012+7/12, xend = 2012+7/12+0.1,
      y = 0.7, yend = 0.7), color = primary_text_color) +
  # Period B begins
  annotate(geom = "text", x = 2014+0.15, y = 0.5, hjust = 0,
    label = "The new period officially begins", color = primary_text_color) +
  geom_segment(aes(x = 2014, xend = 2014,
      y = 0, yend = 0.5), color = primary_text_color) +
  geom_segment(aes(x = 2014, xend = 2014+0.1,
      y = 0.5, yend = 0.5), color = primary_text_color) +
  # Theme stuff
  theme_bw() +
  ylim(c(-0.15,1)) +
  theme(axis.text = element_blank(), axis.title = element_blank(),
    axis.ticks = element_blank(), panel.grid = element_blank(),
    panel.border = element_blank())

LaTeX

My LaTeX (beamer) template: .tex file | example PDF

Example of my slide template.

Example of my slide template.

Bash

Pretty CSV scrolling

Hat tip (and more): Stefaan Lippens.

For a bit more raw view:

Git

Remove a file (your.file) from Git’s cache

Begin tracking a file extension (.eg) with Git’s Large File Storage (LFS) and check the .gitattributes file for success.

Check which files LFS currently tracks.

Pandoc

Convert a Markdown (.txt) file to Word (.docx).

Convert a Markdown (.txt) file to PDF (.pdf).

Renaming

Place a “1” between the first four characters and the subsequent characters in each filename in a folder.

Sleeping and shutdown

Tell the computer to wait a bit and then go to sleep.

Tell the computer to shutdown now.

Shut it down at a specific time (here: 2:30pm on 01 January 2016)

Shut it down after a specified amount of time (here: 30 minutes)

Miscellany

OSX: Show only active apps in the terminal. h/t

Kill all processes named “rsession”.

Re-index the Spotlight utility.

Linux code for playing hide and seek.
Warning You might not want HAL to find you.