AGRICULTURE, DAMS, AND WEATHER
by
Seyedeh Soudeh Mirghasemi
=
BY:
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF ECONOMICS
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2015
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Seyedeh Soudeh Mirghasemi, entitled Agriculture, Dams, and
Weather and recommend that it be accepted as fulfilling the dissertation requirement
for the Degree of Doctor of Philosophy.
Date: 29 July 2015
Price Fishback
Date: 29 July 2015
Ashley Langer
Date: 29 July 2015
Derek Lemoine
Date: 29 July 2015
Jessamyn Schaller
Date: 29 July 2015
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
Date: 29 July 2015
Dissertation Director: Price Fishback
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advanced degree at the University of Arizona and is deposited in the University
Library to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission,
provided that accurate acknowledgment of source is made. This work is licensed
under the Creative Commons Attribution-No Derivative Works 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/bynd/3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San
Francisco, California, 94105, USA.
SIGNED:
Seyedeh Soudeh Mirghasemi
4
ACKNOWLEDGEMENTS
This journey would not have been possible without such helpful, supportive, and
patient committee members, such as Ashley Langer, Derek Lemoine, Jessamyn
Schaller, and my amazing adviser, Price Fishback. Thank you all for your guidance
and encouragement throughout my graduate studies. I am also grateful for the
invaluable support and advice from Sandy Dall’erba, Gautam Gowrisankaran,
Gary Richardson, Andrew Gahan, Francina Dominguez, the members of Arizona
History Workshop, and the Arizona Environmental Energy Group. Furthermore, I would like to thank the Economic History Association, the University
of Arizona, the Institute of the Environment, and the University of Arizona’s
Department of Economics for their grants and support. Finally, I would like
to thank all of my classmates and friends. Graduate life is far easier when you
are surrounded by great people and the support they provide. All errors are my own.
Special thanks to my love, Bardiya, my caring parents, Marzieh and Mahmoud,
and amazing family members. Thank you for being there and for believing in me
and my success.
5
DEDICATION
To my beloved Bardiya, and my lovely parents, Marzieh and Mahmoud
6
TABLE OF CONTENTS
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
CHAPTER 1 Philosopher’s Concrete: Dam Construction, Farmland
and Agricultural Production in the Western U.S., 1890 - 1920 . . .
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Historical Background . . . . . . . . . . . . . . . . . . . . . .
1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Census of Agriculture Data . . . . . . . . . . . . . . .
1.3.2 Major Dams . . . . . . . . . . . . . . . . . . . . . . . .
1.3.3 Presidential Elections . . . . . . . . . . . . . . . . . . .
1.3.4 Climate and Geographical Data . . . . . . . . . . . . .
1.3.5 Soil Data . . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . .
1.4.1 Empirical Model . . . . . . . . . . . . . . . . . . . . .
1.4.2 IV strategy . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CHAPTER 2 Politics and Dam Construction: Historical Evidence
Western U.S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Historical Background . . . . . . . . . . . . . . . . . . . . .
2.2.1 National Irrigation Congress Role . . . . . . . . . . .
2.2.2 Passage of Irrigation Bill . . . . . . . . . . . . . . . .
2.2.3 Bureau of Reclamation . . . . . . . . . . . . . . . . .
2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Major Dams . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Presidential Election . . . . . . . . . . . . . . . . . .
2.3.3 Geographic Characteristics . . . . . . . . . . . . . .
2.4 Empirical Strategies and Results . . . . . . . . . . . . . . . .
2.4.1 Bureau in 1910 . . . . . . . . . . . . . . . . . . . . .
2.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . .
Values
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TABLE OF CONTENTS – Continued
2.5
2.4.3 Army Corps of Engineers . . . . . . . . . . . . . . . . . . . . . 58
2.4.4 Corps versus Bureau . . . . . . . . . . . . . . . . . . . . . . . 61
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
CHAPTER 3 The Impact of Climate Change on Agriculture:
Accounting for Climate Zones in the Ricardian Approach .
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . .
3.2 The Ricardian Setting . . . . . . . . . . . . . . . . .
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Climate Regions . . . . . . . . . . . . . . . . .
3.4.2 The Results for the Past (1997-2007) . . . . .
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . .
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REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
8
LIST OF FIGURES
1.1
1.2
1.3
1.4
1.5
1.6
2.1
2.2
Number of Dams Constructed by Different Types of Owner . . . . .
Percentage of Dams Constructed by Different Types of Owner . . .
Mean of the Height of Dams Constructed by Different Types of Owner
in Each Decade . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mean of the Maximum Storage of Dams Constructed by Different
Types of Owner in Each Decade . . . . . . . . . . . . . . . . . . . .
Percent Change in Farm Value per Acre (1900-1910) . . . . . . . . .
Percent Change in Farm Value per Acre (1910-1920) . . . . . . . . .
. 42
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2.4
2.5
2.6
Dams - Bureau and Corps . . . . . . . . . . . . . . . . . . . . . . .
Dams Constructed by the Bureau: (a) One Purpose: Irrigation, (b)
Multi Purposes: Irrigation - Hydroelectric, (c) Multi Purposes: Irrigation - Recreation . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dams Constructed by the Corps: (a) One Purpose: Flood Control,
(b) Multi Purposes: Flood Control - Hydroelectric, (c) Multi Purposes: Flood Control - Recreation . . . . . . . . . . . . . . . . . . .
Dams Constructed by the Bureau and Corps . . . . . . . . . . . . .
Dams Constructed by the Corps . . . . . . . . . . . . . . . . . . . .
Dams Constructed by the Bureau . . . . . . . . . . . . . . . . . . .
3.1
3.2
Climate Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
2.3
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LIST OF TABLES
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.10
1.11
1.12
1.13
1.14
2.1
2.2
2.3
2.4
2.5
2.6
2.7
Summary Statistics, 1900 - 1920 . . . . . . . . . . . . . . . . . . .
Summary Statistics - 1900 . . . . . . . . . . . . . . . . . . . . . . .
Pre Trend Test, 1890-1900 . . . . . . . . . . . . . . . . . . . . . . .
Federal Major Dams in the West . . . . . . . . . . . . . . . . . . .
Primary Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Association of the % of Votes for Republican in Presidential Elections
and Dam Construction . . . . . . . . . . . . . . . . . . . . . . . . .
Fixed Effect Results: Impact of a Newly Constructed Dam on the
Natural Log of the Value of the Land per Acre 1900-1920 . . . . . .
Fixed Effect and Instrumental Variable Results, Impact of a Newly
Constructed Dam on the Natural Log of the Value of the Land per
Acre 1900-1920 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fixed Effect and Instrumental Variable Results, Impact of a Newly
Constructed Dam on the Natural Log of the Value of the Land per
Acre 1900-1920 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Area Irrigated, Capital Invested . . . . . . . . . . . . . . . . . . . .
Fixed Effect Results: Impact of a Newly Constructed Dam on Bushel
per Acre and Acres Planted of Major Crops 1900-1920 . . . . . . .
Instrumental Variable Results, Impact of a Newly Constructed Dam
on Bushel per Acre and Acres Planted of Major Crops 1900-1920 . .
Fixed Effect and Instrumental Variable Results, Impact of a Newly
Constructed Dam on the Share of Acre Improved, Log of the Livestock
per Acre, and Log of the Dairy Value per Acre 1900-1920 . . . . . .
Instrumental Variable Results, Impact of a Newly Constructed Dam
on the Share of Acre Improved, Log of the Livestock per Acre, and
Log of the Dairy Value per Acre 1900-1920 . . . . . . . . . . . . . .
Federal Dams in the West . . . . . . . . . . . . . . . . . . . . . . .
Primary Purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Primary Purposes - Pre and Post 1936 . . . . . . . . . . . . . . . .
Association of the % of Votes for Republicans in Presidential Election
and Dam Construction . . . . . . . . . . . . . . . . . . . . . . . . .
Montana and Idaho Dams . . . . . . . . . . . . . . . . . . . . . . .
Logit Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Purposes - Pre and Post 1936 . . . . . . . . . . . . . . . . . . . . .
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LIST OF TABLES – Continued
3.1
3.2
3.3
3.4
3.5
3.6
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3.11
3.12
3.13
3.14
15A
15B
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20B
Summary Statistics - Northwest . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - West . . . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - Southwest . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - West North Center . . . . . . . . . . . . . . . .
Summary Statistics - South . . . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - Southeast . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - Center . . . . . . . . . . . . . . . . . . . . . .
Summary Statistics - East North Center . . . . . . . . . . . . . . . .
Summary Statistics - Northeast . . . . . . . . . . . . . . . . . . . . .
Chow Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Control Only for Fixed Effects in the Model . . . . . . . . . . . . . .
Effect of Climate on Land Values . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: West4 . . . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: West4 . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: South . . . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: South . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: Southeast . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Southeast . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: Center . . . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Center . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: East North Center . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: East North Center . . . . . . . . .
Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: Northeast . . . . . . . . . . . . . . . . . . . . . .
Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Northeast . . . . . . . . . . . . . .
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ABSTRACT
The first chapter investigates whether construction of the Bureau of Reclamation
dams in the early twentieth century raised farm values and increased agricultural
output. I construct a new county-level panel data set from 1890 to 1920 with
information on geography, climate, politics, agriculture, and major dams and then
evaluate the effect of the Bureau of Reclamation dams on the value of farms and
on crop productivity. Using fixed effect panel estimation, I find that new federal
dam construction increased the average value of farmland by approximately 6.4
percent. When I apply an instrument to control for potential endogeneity, the effect
of Bureau dams on the farmland value increases in size, although the estimate is no
longer statistically significant. When examining the crop output, the only crop for
which the dams had effects was alfalfa.
In the second chapter I investigate the effect of the geographic, economic and
political factors on dam construction at the beginning of the Bureau of Reclamation’s
operation in the American West. Applying county-level data which have been linked
from various data sources for the time period of 1900 to 1910, I show that the
percentage of votes for Republicans in presidential elections has a significant and
positive effect on major dam construction.
The last chapter investigates the effect of climate change on U.S. agriculture
using county-level data from 1997 to 2007. Compared to previous contributions,
we pay particular attention to the spatial heterogeneity across the climate zones
and include the presence of extreme weather events. The lack of consideration for
both effects may have led previous works to generate biased estimates and incorrect
impact forecasts. Further, while the current approaches use projected climate variables derived from coarse resolution Global Climate Models (GCMs), we use data
at a much finer resolution by relying on dynamically downscaled simulation data.
12
Chow-Wald tests indicate the presence of significant heterogeneity across zones in
the effects of climate on land values.
13
CHAPTER 1
Philosopher’s Concrete: Dam Construction, Farmland Values and Agricultural
Production in the Western U.S., 1890 - 1920
“The philosopher’s stone is really the philosophical stone, for philosophy is truly
likened to a magic jewel whose touch transmutes base substances into priceless
gems like itself.”
Manly P. Hall - The Secret Teachings of all Ages
“We had pushed aside foreign countries and native peoples. Now we would push
aside the desert.”
Bruce Reichert - The Bureau that changed the West
1.1 Introduction
Dam construction played a major role in the development of water resources
during the early 20th century in the American West. Over the first half of the
20th century, the number of major dams in the west and their maximum capacity
increased nearly sixteen-fold and two hundred-fold, respectively. At the same time,
the rate of population growth in the West was at least double the rate in the rest
of the U.S.1 This was associated with a greater than four-fold increase in national
agricultural output from 1900 to 1950.
The Newland Reclamation Act of 1902 created the Bureau of Reclamation, which
built the lion’s share of dam capacity for irrigation in the West. The initial aim of
the act was to improve agricultural production by providing irrigation to arid areas.
However, the effect of dam construction on agricultural growth in the west remains
1
Except for the 1930s
14
controversial. I construct an extensive historical county-level dataset from 1890
to 1920 and examine the effect of the Bureau’s dam construction on the value of
farmland and crop production in the West. Using the the data, I estimate the
average gain in agricultural outcomes from treatment for those places that were
treated (i.e., treatment on treated) and provide direct evidence that dams had a
positive effect on some agricultural outcomes.
Whether large public dams tend to have net benefits has been a controversial
topic. Studies in the U.S. have found positive effects of hydroelectric dams on the
local population and employment growth (Severnini (2014)), on county income and
earnings growth (Aleseyed et al. (1998)), and on agricultural productivity (Hansen
et al. (2011)). Duflo and Pande (2007) find that dams have reduced poverty and
raised productivity in India in the modern era. On the other hand, Eckstein (1958)
shows that the benefit of water resource development varies by the location of the site
and as characteristics of the region change. Kitchens (2014) investigates the effect
of electrification of the Tennessee Valley Authority’s (TVA) large scale hydroelectric
dams on economics activities. Comparing the counties with or without hydroelectric dams2 , he finds the TVA had an insignificant effect on economic growth in the
Southeastern United States. Further, Howe (1968) finds that public investments
in waterway improvements do not lead to rapid local economic growth. Reisner
(1993), in the highly influential Cadillac Desert, states that due to political pressures and only a shallow understanding of land productivity, climate conditions, and
the economic environment, it was mostly political connections that influenced the
water projects authorized by Congress (Reisner (1993)). Therefore, the locations of
the irrigation projects were determined in great haste and without comprehensive
examination.3 One finding consistent with these views is the fact that the Bureau’s
share of capital invested in the irrigation projects was around 19 percent, while its
share of the total acres irrigated was only about seven percent in 1920.4
2
With potential to have hydropower dam
In fact, during the first four years of federal investment in irrigation, 27 projects were authorized, but four of them were abandoned later Widtsoe (1928).
4
U.S. Census of Agriculture (1920)
3
15
Factors influencing farm value have been studied extensively. The traditional
approach assumes that farm value measures the discounted anticipated returns to
agricultural production (Featherstone and Baker (1987); Burt (1986); Castle and
Hoch (1982)). However, some studies show that the market value of farmland might
exceed its agricultural production value as a result of urban proximity and potential for recreational use (Barnard (2000)). Other studies estimate farm value based
on the potential development and conversion to urban use (Plantinga et al. (2002);
Livanis et al. (2006)). Furthermore, studies have investigated the effect of infrastructure investments other than dam construction such as the effect of an expanded
railroad network on agricultural land values (Donaldson and Hornbeck (2013), Fogel
(1994); Atack and Margo (2011)). Donaldson and Hornbeck (2013) estimate that,
in the absence of railroad investments, farmland value in the U.S would have been
64 percent lower.
To advance the debate about the net benefits of major federal dams, I investigate
the impact of the Bureau of Reclamation’s dam projects on the local economy and
agricultural activity from 1890 to 1920. To the best of my knowledge, this is the first
quantitative study that attempts to assess the effect of the Bureau of Reclamation
dams on agricultural activities. I develop a new historical county-level panel dataset
for the census years 1890, 1900, 1910, and 1920 with information on geography,
climate, politics, agriculture, as well as with information on Bureau of Reclamation
dams and other major dams. I use the data to measure the effect of Bureau dams
on farmland value and crop production.
Using fixed effects panel estimation, I find that new federal dam construction
increased the average value of farmland in the county by approximately 6.4 percent.
The estimation results indicate that new dams constructed by agencies other than
the Bureau did not have statistically significant effects on the outcomes. This is
reasonable, as the Bureau projects entailed vast federal investments compared to
the dam construction by other entities. This can be explained by the much larger
maximum capacity of the Federal dams compared to other agencies’ dams. Furthermore, Bureau dams constructed in the previous decade (1890-1900) did not have
16
any effect on the value of farms.
One potential source of endogeneity is that counties that lobbied for the Bureau
dams might have anticipated that their agricultural sector would grow faster. I
test whether there were differential pre-trends in economic activities in the Bureau
counties before federal construction, and I do not find this to be the case. I find suggestive evidence that in the first 20 years of the governmental irrigation movement,
federal dams were located in less densely populated areas and in areas where farm
value per acre was decreasing. Nonetheless, as a way to further reduce endogeneity,
I develop an instrumental variable approach. The instrument is defined as whether
the county had the potential to have a Bureau dam, interacted with the political
strength of Republicans in the two presidential elections before the Reclamation Act
was passed. When I apply the instrument to control for endogeneity, the effect of
Bureau dams on farmland value increases in size, although larger standard errors
mean that it is no longer statistically significant at the ten percent level.
To examine whether the Bureau’s dam construction affected agricultural activities, I estimate models with the production per acre and the average number of acres
planted with important crops, the value of livestock and dairy, and the share of improved acres as outcome variables. Looking at the impact of dam construction on
the average crop production per bushel, alfalfa is the only crop with a positive and
statistically significant coefficient; however, this crop had been actively produced
before the dam construction. This verifies that the original dam’s site had low soil
quality in addition to being arid because in lands with low nitrogen levels, which result in low quality of most agricultural crops, it is necessary to first plant crops such
as alfalfa. These findings are consistent with the narratives of the projects for this
period. Christopher McCune, in the Belle Fourche Project Bureau of Reclamation
Report, states:
Beginning in 1915, farmers increasingly turned to stock operations,
mostly sheep, to try to turn a profit, as alfalfa became the primary
crop of the project...One of the first reports given on the project lands
stated that grain, hay, alfalfa, and perhaps small fruits will constitute
17
the main crops, which was not much different than what had already
been grown in the region for several years (McCune (2001)).
Furthermore, I do not find a statistically significant effect of dam construction
on the value of livestock and the value of dairy products.
In this paper, I focus specifically on the Bureau investments, as they were a
turning point in the roles played by the private sector and the federal government
in the West. The Bureau was created after the government passed the Reclamation
Act to allow the government to build larger projects due to lack of finances and
engineering skills in the private sector. My results support the fact that dams had
positive effects on the local economy, but only in limited ways. These effects might
not have been sufficient but they were potentially important for the West.
1.2 Historical Background
Most of the development of the U.S. occurred in the East, and the Western U.S.
was comparatively underdeveloped until the end of the 19th century. Although the
Western U.S. provided abundant land for raising crops and livestock, farmers found
the climate arid and sought new, large-scale irrigation methods to develop the land.
The Federal Desert Land Act, also known as the Carey Act, gave permission to
private companies in the U.S. to assemble irrigation systems in the Western states
and to profit from the sales of water to the irrigators. Congress passed this Act
on August 18, 1894, as the federal government decided that the task of irrigation
was too large for individual settlers. The new Act delineated a new approach for
the disposal of public desert land. The private sector attempted to evaluate these
lands in the Western U.S. to find an opportunity to establish an agricultural society.
Except for in Idaho and Wyoming, the Carey Act was not as successful as intended.5
In 1908, Idaho received an additional two million acres (8,000 km2 ) and Wyoming received
an additional one million acres (4,000 km2 ) of land to develop under the Carey Act. Today,
approximately six percent of the Carey Act lands irrigated in the United States are in Idaho.
5
18
Westerners argued for further action by the government to build larger projects due
to lack of finances and engineering skill.
In 1902, Congress passed the Reclamation Act, which made the federal government, in the form of the Bureau of Reclamation, responsible for irrigation in the
U.S. Western states6 (Miller and Miller (1992)). The bill’s goal was to convert arid
federal land into a suitable place for living, by constructing dams, power plants,
canals, lateral systems, pumping plants, and other water facilities. Building of a
dam required prerequisite construction, such as roads and railroad construction.
The water projects were to be financed through a Reclamation Fund, which was
funded by selling federal land and, later, by selling the water to the irrigators (Reisner 1986). To discover the feasibility of the water projects, the geological surveys
were prepared by the Bureau of Reclamation, which included all related factors,
such as the amount of water flow, elevation of the surface and the streams, and
their catchment areas for the dam construction (Newell (1905)).
Initially, the Bureau’s ambition was to boost agricultural activities and help
the local economy by constructing water projects and delivering water to the arid
areas. However, because of political pressure from Congressmen, Senators, and
state legislators to acquire water projects, dams might have been constructed in the
districts with little potential for agriculture. The water projects mostly had to be
authorized by Congress, but the President could veto the bill. According to the
history of the Bureau of Reclamation, Michael Robinson7 states:
Initially, little consideration was given to the hard realities of the irrigated agriculture. Neither aid nor direction was given to settlers in carrying out the difficult and costly work of clearing and leveling the land, digging irrigation ditches, building roads and houses, and transporting crops
to remote markets....The government was immediately flooded with requests for project investment as the Local chambers of commerce, real
6
Western states served by Reclamation are Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oklahoma, Oregon, South Dakota, Utah,
Washington and Wyoming. Texas was not included during the first few years.
7
The son-in-law of a Commissioner of Reclamation
19
estate interests and congressman were convinced their areas were ideal
for reclamation development (Reisner (1993)).
Twenty-four federal irrigation projects were authorized within four years of the
passage of the Act, four of which were abandoned later.
The financing of the Reclamation projects compelled the farmers to meet their
repayment obligation in ten years. This proved to be an unrealistic estimate, as 60
percent of the farmers delayed their payments. In some cases, the delays stretched
beyond 20 years from the passage of the first Reclamation law, and the repayment
period was extended to 40 or 50 years.
1.3 Data
The new county-level dataset is assembled from several sources for the 1890 through
1920 census years
8
for the Western U.S.9
1.3.1 Census of Agriculture Data
The US Census of Agriculture data are reported for the following: value of farms,
number of acres in farming, improved acres, value of dairy products, value of livestock, production of important crops,
10
and population.11
Table 1.1 shows the summary statistics of the data. For counties with or without
Bureau dams, Bureau counties, on average, were less densely populated and had
fewer farms compared to non-Bureau counties. Furthermore, the Bureau counties
had statistically significantly more production per acre and a higher average number
of acres planted with alfalfa, while the production per acre and the average number
of acres planted with cotton were statistically significantly larger in non-Bureau
counties.
8
The U.S. Census of Agriculture data are available for every ten years during this time period.
Due to the change of boundaries over the years, the 1900 shapefile is chosen as a base year,
and 1910 and 1920 weighted average values are calculated applying the Geographic Information
System (GIS).
10
Sugar beet, Cotton, Wheat, Alfalfa
11
All dollar values are in 1926 constant dollars.
9
20
1.3.2 Major Dams
Information on dams comes from the National Inventory of Dams, Water Control
Infrastructure for all the major dams constructed in the U.S. from 1800 to 2003. The
data include information on the name, national ID, latitude, longitude, owner name,
type of owner, year of completion, purposes and the primary purpose, capacity,
height, and some other characteristics for the major dams in the U.S. The dataset
includes 8,121 major dams. A major dam is 50 feet or more in height, has a normal
storage capacity of 5,000 acre-feet or more, or has a maximum storage capacity of
25,000 acre-feet or more.
The total number of dams constructed in each decade by different owners is
shown in Figure 1.1. There are five types of owners:
1. Federal: The dam is owned by a federal agency.
2. Local: The dam is owned by a county, city, regional, or other similar local
government or government agency.
3. Private: The dam is owned by an individual or individuals, or by a private
company.
4. State: The dam is owned by a state or by a state agency.
5. Public utilities: The dam is owned by a public utility, such as Southern California Edison Company, Pacific Gas and Electric Company.
Figure 1.1 indicates that between 1900 and 1920, the number of dams constructed
by the federal government increased, but that the number of private dams increased
more. Figure 1.3 and Figure 1.4 show two characteristics of dams used to compare
their size. Figure 1.3 compares the height of the dams constructed by various owners
from 1880 to 2000. The tallest dams belong to the public utilities. Another characteristic of size is the maximum storage capacity, which is the total storage space
in a reservoir below the maximum attainable water surface elevation, including any
21
surcharge storage. Clearly, federal dams have significantly larger maximum storages
compared to the dams constructed by private entities and other type of owners.
The summary statistics of the federal dams are presented in Table 1.4. Between
1900 and 1920, there were 66 major federal dams throughout the U.S., 54 of them
constructed by the Bureau. Most of the non-Bureau dams at that time had been
constructed by the Bureau of Indian Affairs. Idaho, followed by California, Montana
and Wyoming, were the states with the most Bureau dams.
The primary purposes of dam construction include flood control, debris control,
fish and wildlife protection, hydroelectric generation, irrigation, navigation, fire protection, recreation, water supply enhancement, and tailings control. Table 1.5 shows
the frequency of the primary purpose of the dam construction. Clearly, most of the
dams built by the Bureau were intended for irrigation and water supply.
Dams can be constructed for either single purpose or multiple purposes, with
different geographical and topographical preferences. For irrigation dams, the river
gradient should be neither steep nor flat, but dams for hydroelectric power need
a higher river gradient (Cech (2010)). According to a study by Duflo and Pande
(2007), “Low (but nonzero) river gradient areas are most suitable for irrigation dams
while very steep river gradient areas are suitable for hydroelectric dams.”
1.3.3 Presidential Elections
The political data come from the ICPSR United States Historical Election Returns database. The data include the state-level percentage of votes for Republicans
in presidential elections.
The summary statistics for Western states in Table 1.6 show a strong relationship
between Republican votes in the presidential elections of 1900, 1904, 1908 and the
location of dams. We choose these elections since this time period was crucial, as
the majority of the authorization dates of the early projects were during 1903-1908.
Columns (2) through (4) show the percentages of Republican votes for the president in 1900, 1904 and 1908, and column (5) shows the average of the three elections.
22
The dam column is an indicator of whether or not a dam was constructed in a state
during the relevant time period. South Dakota, Washington, Wyoming, Oregon and
California remained Republican during the first decade of the 20th century, and the
Bureau constructed there.
1.3.4 Climate and Geographical Data
The climate data are obtained from the U.S. Historical Climatology Network
for each weather station. The number of stations is limited, and the Geographic
Information System (GIS) software is applied to interpolate the climate data and
calculate values at the county level. Then, the average of the climate variables is
calculated per decade. Specifically, the data include averages of extreme events: hot
days that exceed 100 degrees Fahrenheit; hot days that exceed 90 degrees Fahrenheit;
cold days below 32 degrees Fahrenheit; cold days below 0 degrees Fahrenheit; and
total rainfall per year.
Looking at Table 1.1, on average, Bureau counties compared to non-Bureau
counties were located in higher altitudes and in mountainous areas and also had less
annual precipitation, which is statistically significant at the one-percent level.
1.3.5 Soil Data
The soil data are from the Web Soil Survey (WSS), which uses information from
the National Cooperative Soil Survey. The soil survey was developed for polygons
of areas with similar soil characteristics. The data are available for each state, and
a special program, STATSGO, is needed to open and observe the data. The data
are interpolated by the GIS software for the 1900 shapefile.
The data include the fraction of the land prone to floods, the soil erodibility
factor (K-Factor), slope steepness (S factor), wind erosion, the fraction of the land
occupied by wetland, salinity, permeability, moisture capacity, clay content, and
sand content.12
12
The fraction of the land prone to floods is reported as a frequency variable, as none, very rare,
rare, occasional, frequent, and very frequent, and it is not included in my analysis.
23
1.4 Empirical Strategy
Initially, the Bureau’s ambition was to boost agricultural activities and help the
local economy by constructing water projects and delivering water to arid areas.
These were the areas with poor soil and unsuitable climate condition where the
Bureau expected to have a substantial effect on the agricultural activities. The
treatment effect can be defined as whether or not the arid county received a dam.
I estimate the average gain in agricultural outcomes treatment in those places that
were treated (i.e., treatment on treated) (Imbens and Lancaster (1994), rather than
estimating the effect of dam construction randomly (i.e., average treatment effect)
(Holland (1986)). Estimating the average treatment effect is not applicable, as the
dams are more likely to be constructed in places with geographical and topographical
prerequisites and higher potential need for water. To the best of my knowledge,
no other study estimates the effect of Bureau Reclamation dam construction on
agricultural outcomes.
1.4.1 Empirical Model
I estimate the effect of dam construction on farmland values and crop production
using county-level data in the following regression:
Yit = β0 + β1 Damit + β2 Xit + δi + wst + εit
(1.1)
The main outcome variable is the log of the value of the farm per acre in county i in
census year t. The Dam variable is an indicator of whether the Bureau constructed
a dam in county i in decade t, and β1 is the coefficient of interest. Xit , is a vector of
control variables. Rainfall, and, hot and cold extreme weather events are included
to control for climate conditions, and they are interacted with soil characteristics to
control for the combined effect of soil and climate on outcomes.
It is plausible that the value of farmland may have increased because of land
speculation rather than improvement in agricultural activities. Therefore, I consider
24
the production per acre and the average number of acres of important crops planted,
the value of livestock per acre, dairy value per acre, and the share of improved acres
to reveal whether there was an effect on agricultural activities.
I also include county fixed effects to capture the time-invariant unobserved characteristics related to each county. Year-state fixed effects are included to control
for the shocks that occurred in the states in each year. εit is the unobserved error
component. The identification of the effect of the dams comes from changes over
time when a dam is added within the same county after controlling for the factors
listed above.
Figure 1.5 and Figure 1.6 show the variation of farm value growth rates at the
county level for the first and second time period, respectively. The geographical
figures also illustrate dams constructed by the Bureau in each period. Figure 1.5
illustrates the percent change in the farm value per acre from 1900 to 1910. The
value of farms per acre in the Central and Western U.S. increased significantly during
this period. The counties that received Bureau dams experienced increases in the
value of the farmland per acre. Figure 1.6 shows the percent change in the farm
value per acre during 1910 to 1920. Due to the effects of World War I and high
inflation, the value of the land per acre did not increase in most of the U.S. during
this period.
Endogeneity
To obtain an unbiased estimate when applying a fixed effects model, the unobserved
error term must be uncorrelated with dam construction in each county. There are
a few scenarios that could lead to the violation of this identification assumption.
Construction of a dam required the building of roads and railroads, as most of
the dam sites were in remote areas.13 This effect will be captured by the dam coefficient since the prerequisite constructions were part of the projects, and, therefore,
I consider these changes to be part of the treatment effect.
13
Such as Coulee Railroad between Coulee City and Coulee Dam in Washington, and road
construction along Buffalo Bill Reservoir near Cody, Wyoming.
25
It is possible that dams were located in counties with high potential for agricultural activity, therefore not controlling for climate conditions leads to an overestimation of the coefficient of interest. Including more controls, such as climate
variables, helps to reduce the endogeneity problem.
Educated and up-to-date farmers likely had access to newer and superior technology. At the same time, they could better lobby to bring dams to their areas.
Since farmers’ education and knowledge have a positive effect on the agricultural
outcome and are positively associated with having a dam constructed nearby, the
coefficient of interest would be biased upwards. In order to address this issue I can
control for general county education. Since these were rural areas, I find it is unlikely
that farmers’ education differed much from the general measure of education.
Some farms had access to groundwater to irrigate their lands. It is likely that
dams were constructed to irrigate the areas in which construction of wells and access to groundwater were not possible. Thus, not having access to ground water is
positively correlated with dam construction, while it is negatively correlated with
the productivity of the land. This leads to an underestimate of the coefficient of
interest. There is no solid evidence of any new developments in groundwater pumping technology between 1900 and 1920 (Hornbeck and Keskin (2012)). Therefore,
the groundwater variable is time-invariant and can be captured by the county fixed
effect.
Furthermore, some might think that federal spending on other industries in a
county might have attracted other industrial construction besides dams. However,
the federal government was not spending much in the West at this time on anything
other than dam projects. So it is unlikely that such factors would bias my estimates.
To ensure that no other factors were contributing to endogeneity, I assess whether
there were differential pre-trends in economic activities in Bureau counties versus
non-Bureau counties before federal construction. The pre-trend test in a standard
difference-in-difference procedure was applied for some of the important variables,
such as population, population density, farm value, and farm value per acre, for the
1890-1900 period.
26
Table 1.3 shows the result of these comparisons: there are no statistically significant differences between the mean changes in the population, farm value per
acre, and total farm value variables in non-Bureau and Bureau counties. There is
a statistically significant difference between the mean growth trends of population
density in Bureau and non-Bureau counties, but the mean growth trend is higher
in non-Bureau counties. This would bias my coefficients towards finding no positive
effect of Bureau dams.
When looking at the growth rates (average of the changes), the null hypothesis
of identical trends cannot be rejected for population and population density. For
farm value per acre and total farm value, there is a statistically significant difference
between the means. However, the Bureau counties had a more negative farm value
per acre growth rate compared to non-Bureau counties. Also, the total farm value
growth rate in non-Bureau counties was higher in Bureau counties. Hence, I find
no evidence that the Bureau constructed dams in the counties with more economic
activities prior to dam construction.
1.4.2 IV strategy
As a way to further reduce endogeneity, I have developed an instrumental variable
approach. The study of the political economy of dam locations, Mirghasemi (2013)
shows that the locations of the dams were strongly associated with the states’ average Republican vote share in presidential elections. According to the Bureau of
Reclamation records, President Theodore Roosevelt strongly supported the flourishing of the West and about 24 out of 27 projects were approved immediately after
the Reclamation Act was passed.14 The average of the percentage of votes for Republicans in the 1896 and 1900 presidential elections can be used as a measure of
the relative political power of the Republicans, who held the Presidency and had a
majority in both houses of Congress for 18 consecutive years (from 1896 till 1912).
The instrument is constructed based on geographical and political factors. The geographical factor involves the potential places where the Bureau could construct a
14
The authorization dates of the projects were during 1903-1908
27
dam, while the political factor captures political strength. More specifically, the
instrument is defined as follows:
Zi =


1I{Dam1960i } ∗
(%Repub1896s +%Repub1900s )
2

0
if t = 1910, 1920
if t = 1900
The first part of the instrument is a geographical factor for places with potential
to have a dam. Dam1960i shows whether the Bureau constructed a dam in county i
by 1960.15 The measure captures all of the locations where the Bureau might have
expected to locate a dam, given the technology available through 1960.
The second part of instrument captures the political strength of the Republicans
for the two presentational elections before the Act was passed. It is the average state
Republican vote share in the presidential elections of 1896 and 1900. Republican
states were rewarded more when the President was Republican and Congress was
dominated by Republicans. The Zi value is interacted with the years 1910 and 1920
as all the projects had been authorized in the first few years after the passage of the
Reclamation Act.
The identification assumption is that the instrument is not correlated with
the error term. This assumption makes sense, as the political component of the
instrument is used for the period before the dam projects were authorized. The
measure is based on voting information from periods of ten to 15 years before the
impact of the dams. It is unlikely that there is serial correlation that stretches so
many years back in time.
The following is the first-stage equation:
Damit = β0 + β1 Xit + β2 Zit + δi + wst + εit
(1.2)
X is the vector of the economic and geographical characteristics from Equation
15
The year 1960 is chosen since, after this decade, the number of federal dams constructed started
decreasing. Changing the year still keeps the IV as an valid instrument and does not alter the
results.
28
1. Z is the instrumental variable. δi is the vector of county fixed effects to capture
the time-invariant unobserved characteristics related to each county. wst is the
year-state interaction fixed effects to control for the shocks that happened within
the states, and εist is the unobserved error component.
1.5 Results
Table 1.7 displays the regression results for the log of the value of the farm per acre
as an independent variable. The dam variable in the model is an indicator of whether
the Bureau constructed a dam in county i during the decade before census year t.16
The first column of the results shows the baseline model controlling for county and
time fixed effects. The dam coefficient is positive but not statistically significant.
The coefficient indicates that for each newly constructed dam in a county, there is
an increase in farm value by roughly six percent of the mean farmland value in the
same county. Adding climate controls in the second column increases the size of
the dam coefficient, but it remains statistically insignificant. Column 3 indicates
the results of the estimation after adding interactions between soil characteristics
and climate variables to the model. The dam coefficient increases to 15 percent of
the mean farmland value, and it becomes statistically significant at the one-percent
level. The change in the coefficient shows that both the dam location and the
farmland values are influenced by the interaction between weather and soil, in ways
that lead to negative omitted variable bias for coefficients in specifications (1) and
(2). When the state year fixed effects are added to the model in the last column,
the dam coefficient remains statistically significant and increases to 19 percent.
The results of the IV estimation are shown in Table 1.8. The first column is the
same as the fourth column of Table 1.7. The Kleibergen-Paap F statistic of 17.3 for
the instrument in the first stage shows that the instrument is strong. Applying the
IV in the second column makes the dam coefficient larger, but it is not statistically
significant.
16
I have reestimated the model if the dam variable is the number of new dams constructed in
each county instead of a binary variable. The results are robust.
29
Table 1.9 shows the Fixed Effects and IV estimation results after adding the lag
of the dam variable and non-Bureau dams separately to the model. The first and
second columns display the same specification as the fourth column of Table 1.7,
except that they control for the impact of the dam constructed in both the current
period and previous period by including the Lag Bureau in the model. The fixed
effect estimation results in column 1 show a statistically significant effect of Bureau
dams on the farmland value; however, the lag of Bureau dams variable does not
have a statistically positive effect on the lag of the value of the farm. The sign and
magnitude of the dam coefficient are similar to those in the last column of Table 1.7.
Applying the instrument in the second column increases the dam coefficient in size,
but it is no longer statistically significant. The third and fourth columns show the
same specification as the fourth column of Table 1.7, except that they control for
the impact of the dams constructed by other group, including other federal dams,
state dams, and private dams. The third column shows the result of the Fixed Effect
estimation, and the sign and magnitude of the Bureau dam coefficient are similar
to those in the last column of Table 1.7. The new non-Bureau dams do not seem
to have a significant impact on the value of the land. When I apply an instrument
to control for endogeneity, the effect of Bureau dams on farmland value increases
in magnitude, although larger standard errors mean that it is no longer statistically
significant at the 10-percent level.
This is reasonable, as the Bureau projects were vast federal investments compared to the dam construction by other entities. The narrative shows that land
speculation occurred in some cases and led to increases in the price of the land
without it being developed.17 However, the IV results here cannot reject the hy17
Belle Fourche Project report (McCune (2001)): “Land speculation was practiced by several
project settlers, who would buy, but not develop lands in anticipation of selling them for greater
profit in the future. This led to even more instances of farmers going broke and selling out. Newell,
in his report remarked that “few newcomers can handle effectively even forty acres. If they obtain
a larger area, they must struggle to pay taxes and water charges with the products of less than
forty acres or with the scanty returns of lands ineffectively tilled.”
Rio Grande Project report (Autobee (1994)): “Concurrent with the rise of the Elephant Butte
Dam, prices for unimproved land shot up. At the beginning of construction in 1906, land averaged
30
pothesis of zero effect.
Table 1.10 shows the total acres irrigated and the capital invested, as well as
Bureau’s share for 1900, 1920, 1930, and 1940.18 The Bureau had 18.6, 21.7, and
25.8 percent of capital investments, respectively, in 1920, 1930, and 1940. However,
the acres irrigated by the Bureau of Reclamation were only 2.7, 6.5, 7.6, and 8.7
percent in 1910, 1920, 1930, and 1940, respectively.
To examine whether Bureau dam construction affected agricultural activities, I
estimate models with the production per acre and the average number of acres of
the important crops planted, the value of livestock and dairy, and the number of
improved acres as outcome variables. The results of the estimation of the production
per acre and the average number of acres of important crops planted are displayed
in Tables 1.11 and 1.11. The fixed effect estimation of the dam coefficient in Table
1.11 is positive and statistically significant only for the alfalfa crops. Constructing
a Bureau dam in a county increases the alfalfa production per acre and the average
number of acres of alfalfa, respectively, by roughly six percent and one percent.
Using IV estimation in Table 1.11, the dam coefficient remains statistically significant only when the outcome variable is alfalfa production per acre. Alfalfa was a
crop that was actively produced in Bureau counties before dam construction as seen
in Table 1.2, and dam construction, led only to increases in the alfalfa production
per acre in these counties. These findings are consistent with the narratives for some
of the projects of this period. Christopher J. McCune, in the Belle Fourche Project
Bureau of Reclamation report, states:
Beginning in 1915, farmers increasingly turned to stock operations,
mostly sheep, to try to and turn a profit, as alfalfa became the primary
crop of the project....One of the first reports given on the project lands
stated that Grain, hay, alfalfa, and perhaps small fruits will constitute
$17.50 an acre. Seven years later, the value of the same unimproved ground was $50 to $75 an
acre. A few years later, developed orchard and garden tracts within 10 miles of El Paso sold for
$650 to $1,200 an acre.”
18
Unfortunately, the census data for the capital invested are not available for the 1900 census
year.
31
the main crops, which was not much different than what had already
been grown in the region for several years (McCune (2001)).
Table 1.11 indicates that Bureau dam construction did not have a statistically
significant effect on other crops, such as cotton, sugar beets, and wheat. Estimation
results applying the instrument in Table 1.11 show that Bureau dam construction
did not have statistically significant effect on sugar beets and wheat. Even though
the dam coefficient for cotton, the average number of acres planted, is statistically
significant, not much cotton was planted in the Bureau counties as shown in Table
1.2.
Furthermore, the results of the Fixed Effect and IV estimations in Tables 1.13
and 1.13 for the value of livestock per acre, dairy value per acre, and the share
of improved acres show that none of the estimations of the dam coefficients are
statistically significant.
1.6 Conclusion
Did the construction of the Bureau dams in the early 20th century cause the
desert to flower and increase the gold that could be earned from the land? In this
paper, I develop a new county-level panel dataset from 1890 to 1920, including
information on geography, climate, politics, and agriculture, and on the Bureau of
Reclamation dams and other major dams. I use the data to evaluate the effect of
the Bureau dams on the value of farms and crop productivity.
The results indicate that for each newly constructed dam in a county, there is an
increase in the value of the farm by 19.3 percent of the mean farmland value in the
same county (approximately 6.4 percent). When I apply an instrument to control for
potential endogeneity, the effect of Bureau dams on farmland value increases in size;
however, the estimates also become noisier and are no longer statistically significant.
The estimation results indicate that the new dams constructed by agencies other
than the Bureau and the already constructed dams by the Bureau did not have a
32
statistically significant effect on the farm value. Furthermore, I estimate that the
only crop that the dams affected was alfalfa, which had been actively produced
before.
In this study, I focus specifically on the Bureau investments, as they represent
a turning point in water investments that shifted the source of dam funding from
the private sector to the federal government in the West. The Bureau was created
after the passage of the Reclamation Act to take further action to enable the federal
government to build larger projects due to the of lack of financing and engineering
skills in the private sector. My results support the fact that dams had a positive
but limited effect on the local economy.
33
Table 1.1: Summary Statistics, 1900 - 1920
Population
Population density
Bureau counties
Mean
Std. Dev.
11,293.5
8,772.4
4.5
6.1
Non-Bureau counties
Mean
Std. Dev.
16,414.2
34,060.3
27.7
314.8
Difference
-5,120.7 ∗∗∗
-23.2 ∗∗∗
Farm (number)
Farm (acre)
Acres improved
1,021.1
399,798.8
122,609.8
782.9
301,466.8
116,088.4
1,394.9
416,488.8
172,211.3
1,267.9
307,142.4
162,201.5
-373.8 ∗∗∗
-16,690.0
-49,601.4 ∗∗∗
15,233,854.8
49.0
125,258.6
3,050,205.22
12,352,326.5
40.6
119,570.7
1,819,528.67
15,868,580.4
45.7
170,270.1
2,262,063.35
15,191,183.1
55.9
244,429.3
1,623,395.70
-634,725.6
3.3
-45,011.5 ∗∗∗
788,141.87 ∗∗∗
1,071.0
5,667.8
43.1
86.0
354,318.1
20,078.2
33,140.3
11,452.1
4,663.6
40,211.0
364.6
710.7
872,999.4
43,132.2
45,309.6
12,582.3
1,450.9
2,439.8
3,319.1
11,724.5
437,975.6
33,462.9
10,700.1
4,640.3
12,691.1
19,838.4
9,792.1
32,699.1
864,168.6
61,269.9
22,141.0
8,659.7
-380.0
3,228.0
-3,276.0 ∗∗∗
-11,638.5 ∗∗∗
-83,657.5
-13,384.7 ∗∗∗
22,440.2 ∗∗∗
6,811.8 ∗∗∗
1,648.7
3.8
1.6
6.5
12.8
1.6
2,272.3
9.4
2.9
20.7
6.5
0.3
1,811.9
9.9
5.9
29.1
11.4
1.2
960.2 ∗∗∗
-6.3 ∗∗∗
-2.2 ∗∗∗
-15.6 ∗∗∗
-0.2
0.2
Value
Value
Value
Value
of
of
of
of
farmland
farmland/acre
dairy
livestcok
Sugar beet (bushel/acre)
Sugar beet (acre)
Cotton (bushel/acre)
Cotton (acre)
Wheat (bushel/acre)
Wheat (acre)
Alfalfa (bushel/acre)
Alfalfa (acre)
Elevation
3,232.5
Precipitation
3.0
Days exceeds 100 F
0.8
Days exceeds 90 F
5.1
Days below 32 F
6.4
Days below 0 F
0.5
Number of observation
*** p<0.01 ** p<0.05 * p<0.1
78
2556
34
Table 1.2: Summary Statistics - 1900
Population
Population density
Farm (number)
Farm (acre)
Acres improved
Value
Value
Value
Value
of
of
of
of
farmland
farmland/acre
dairy
livestcok
Sugar beet (bushel/acre)
Sugar beet (acre)
Cotton (bushel/acre)
Cotton (acre)
Wheat (bushel/acre)
Wheat (acre)
Alfalfa (bushel/acre)
Alfalfa (acre)
Bureau counties
Mean
Std. Dev.
7,196.3
5,093.9
2.8
3.0
Non-Bureau counties
Mean
Std. Dev.
12,444.9
19,357.0
22.2
252.1
Difference
-5,248.7 ∗∗∗
-19.4 ∗∗∗
604.8
340,019.0
84,515.4
370.2
306,937.1
100,784.8
1,206.0
384,294.4
133,998.8
1,217.6
314,680.0
143,430.6
-601.3 ∗∗∗
-44,275.5
-49,483.3 ∗∗
8,884,533.8
33.8
99,011.5
2,993,432.6
6,736,996.4
17.8
83,020.5
1,983,555.7
10,202,826.5
33.2
154,162.0
2,213,805.9
10,909,325.7
36.7
181,670.5
1,666,527.1
-1,318,292.6
0.7
-55,150.5 ∗∗∗
779,626.7 ∗
357.2
71.7
0.0
0.0
346,158.5
19,115.8
13,558.7
5,117.6
1,276.0
273.6
0.0
0.0
877,050.2
47,253.8
15,169.5
4,523.6
619.0
73.3
3,024.6
8,452.2
324,205.0
25,961.8
5,629.5
2,254.4
5,763.8
613.9
9,894.5
25,720.0
734,053.6
53,120.3
14,430.9
5,838.2
-261.8
-1.6
-3,024.6 ∗∗∗
-8,452.2 ∗∗∗
21,953.4
-6,846.0
7,929.1 ∗∗
2,863.2 ∗∗∗
2,272.3
5.2
0.6
7.2
2.9
0.04
1,812.6
7.0
1.8
11.6
6.0
0.18
960.2 ∗∗∗
-4.0 ∗∗∗
-0.3 ∗∗
-5.3 ∗∗∗
-1.3 ∗
0.06
Elevation
3,232.5
1,670.5
Precipitation
1.1
2.1
Days exceeds 100 F
0.3
0.7
Days exceeds 90 F
1.8
3.5
Days below 32 F
1.6
3.4
Days below 0 F
0.09
0.31
Number of observation
26
*** p<0.01 ** p<0.05 * p<0.1
852
35
Table 1.3: Pre Trend Test, 1890-1900
Bureau counties
Mean
SE
Growth trend (change)
Population density
Population
Farm value per acre
Farm value
Non-Bureau counties
Mean
SE
Difference
t-test
.5933242
2036.63
-5.147041
-288794.6
.3931483
687.0943
1.337172
715602.7
3.609915
2448.817
-3.656948
522189.5
1.168711
187.8532
.466467
70920.24
-3.016591∗∗
-412.1879
-1.490094
-810984.1
2.446
0.578
1.052
1.127
Growth rate (% change)
Population density
.6177756
Population
.606753
Farm value per acre
-.3225601
Farm value
.7791656
*** p<0.01 ** p<0.05 * p<0.1
.1767042
.1771024
.0685111
.2556491
.6273785
.6181378
-.186341
1.444999
.0774735
.0770102
.0149981
.1927996
-.0096029
-.0113847
-.1362191∗
-.6658336∗∗
0.049
0.059
1.942
2.079
36
Table 1.4: Federal Major Dams in the West
Federal dams
Total
Bureau
Others
1902 - 1910
31
30
1
1910 - 1920
35
24
11
total
66
54
12
Note: Although Bureau is the major federal dam builder, most of the other dam construction
was led by The Bureau of Indian Affairs.
The major dams are defined based on the standard criterion by the National Inventory of Dams.
These are the major dams, which are higher than 50 feet or have a normal capacity of at least
5,000 acre-feet or a maximum storage capacity of 25,000 or more.
Table 1.5: Primary Purpose
Primary purpose
Total
Bureau
Irrigation or water supply
60
51
Flood control or navigation
2
1
Hydroelectric
1
1
Recreation
1
-
Others
2
1
Note: The table shows the primary purposes of the dams constructed by Bureau during
1902-1920. The Others category includes debris control, fish and wildlife ponds, tailings and fire
protection, stock, or small farm pond purposes.
37
Table 1.6: Association of the % of Votes for Republican in Presidential Elections
and Dam Construction
State
1900
1904
1908
Average
Dam
North Dakota
62.1
75.1
61
66.1
-
South Dakota
56.8
71.1
58.8
62.2
yes
Washington
53.4
70
57.8
60.4
yes
Wyoming
58.6
66.9
55.4
60.3
yes
Oregon
55.5
67.3
56.5
59.8
yes
California
54.5
61.9
55.5
57.3
yes
Kansas
52.6
64.9
52.5
56.7
-
Utah
50.6
61.5
56.2
56.1
-
Idaho
46.9
65.8
54.1
55.6
yes
Nebraska
50.5
61.4
47.6
53.2
-
Colorado
42
55.3
46.9
48.1
-
Montana
39.8
53.5
46.9
46.7
yes
Nevada
37.8
56.7
43.9
46.1
-
Texas
30.9
22
22.4
25.1
-
Arizona
-
-
-
-
-
New Mexico
-
-
-
-
yes
Oklahoma
-
-
43.5
-
-
Arizona, New Mexico and Oklahoma were territories that did not have voting representation in
the U.S. Congress and, therefore, had no electoral votes.
38
Table 1.7: Fixed Effect Results: Impact of a Newly Constructed Dam on the Natural
Log of the Value of the Land per Acre 1900-1920
Dam
(1)
(2)
(3)
(4)
0.059
(0.104)
0.081
(0.107)
0.155∗
(0.092)
0.193∗
(0.105)
Time fixed effect
Yes
Yes
Yes
County fixed effect
Yes
Yes
Yes
Controls
No
Yes
Yes
Soil*climate
No
No
Yes
State*year fixed effect
No
No
No
R2
0.25
0.26
0.31
N
2610
2610
2610
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Controls: Extreme cold and hot events, and precipitation
Yes
Yes
Yes
Yes
Yes
0.40
2610
Table 1.8: Fixed Effect and Instrumental Variable Results, Impact of a Newly
Constructed Dam on the Natural Log of the Value of the Land per Acre 1900-1920
Dam
(FE)
(IV)
0.193∗
(0.105)
0.818
(0.632)
Controls
Yes
Yes
Time fixed effect
Yes
Yes
County fixed effect
Yes
Yes
State*year fixed effect
Yes
Yes
R2
0.40
0.39
N
2610
2609
Kleibergen-Paap F statistic:
17.319
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
39
Table 1.9: Fixed Effect and Instrumental Variable Results, Impact of a Newly Constructed Dam on the Natural Log of the Value of the Land per Acre 1900-1920
Dam
Lag Dam
(lag Bureau)
(FE)
(lag Bureau)
(IV)
(Non-Bureau )
(EF)
(Non-Bureau )
(IV)
0.211∗∗
(0.102)
0.122
(0.145)
0.554
(0.361)
0.248
(0.211)
0.189∗∗ *
(0.092)
0.716
(0.474)
-0.097
(0.091)
-0.099
(0.094)
Non-Bureau Dam
Controls
Yes
Yes
Yes
Yes
Time fixed effect
Yes
Yes
Yes
Yes
County fixed effect
Yes
Yes
Yes
Yes
State*year fixed effect
Yes
Yes
Yes
Yes
R2
0.40
0.39
0.40
0.39
N
2610
2609
2610
2609
Kleibergen-Paap F statistic
19.730
19.545
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
Lag Dam variable that equals one if Bureau constructed dam in county i in period t − 1
Non-Bureau Dam variable that equals one if other agencies constructed dam in county i in period t
Table 1.10: Area Irrigated, Capital Invested
Year
1910
1920
1930
1940
14,433,285
19,191,716
19,547,544
21,003,739
2.7
6.5
7.6
8.7
Total capital invested
-
697,657,328
1,062,049,201
1,052,049,201
Bureau share (%)
-
18.6
21.7
25.8
Total acres irrigated
Bureau share (%)
40
Table 1.11: Fixed Effect Results: Impact of a Newly Constructed Dam on Bushel
per Acre and Acres Planted of Major Crops 1900-1920
Dam
Alfalfa
bushel
acre
Cotton
bushel
acre
Sugar Beet
bushel
acre
Wheat
bushel
acre
0.057∗∗
(0.024)
-0.000
(0.000)
-0.002
(0.003)
-0.229
(0.168)
0.013∗∗
(0.006)
-0.001
(0.001)
0.033
(0.040)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Time fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
County fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
State*year fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R2
0.24
0.33
0.20
0.34
0.16
0.16
0.47
N
2610
2610
2610
2610
2610
2610
2610
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Dependent variables are defined as “bushel per acre” and “total acre divided by farm land”.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
-0.006
(0.008)
Yes
Yes
Yes
Yes
0.49
2610
Table 1.12: Instrumental Variable Results, Impact of a Newly Constructed Dam on
Bushel per Acre and Acres Planted of Major Crops 1900-1920
Dam
Alfalfa
bushel
acre
Cotton
bushel
acre
0.079∗∗
(0.038)
0.000
(0.001)
0.000
(0.021)
-0.005∗
(0.003)
Sugar Beet
bushel
acre
Wheat
bushel
acre
0.051
(0.063)
-0.488
(0.369)
0.111
(0.087)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Time fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
County fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
State*year fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R2
0.23
0.32
0.20
0.34
0.12
0.12
0.47
N
2609
2609
2609
2609
2609
2609
2609
*** p<0.01 ** p<0.05 * p<0.1
Kleibergen-Paap F statistic: 19.548
Standard errors are in parentheses, clustered at county level.
Dependent variables are defined as “bushel per acre” and “total acres divided by farmland”.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
-0.003
(0.021)
Yes
Yes
Yes
Yes
0.48
2609
41
Table 1.13: Fixed Effect and Instrumental Variable Results, Impact of a Newly
Constructed Dam on the Share of Acre Improved, Log of the Livestock per Acre,
and Log of the Dairy Value per Acre 1900-1920
Dam
Acre improved
livestock
Dairy
0.021
(0.020)
0.079
(0.092)
0.134
(0.153)
Controls
Yes
Yes
Time fixed effect
Yes
Yes
County fixed effect
Yes
Yes
State*year fixed effect
Yes
Yes
N
2599
2599
R2
0.37
0.56
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
Yes
Yes
Yes
Yes
2599
0.27
Table 1.14: Instrumental Variable Results, Impact of a Newly Constructed Dam on
the Share of Acre Improved, Log of the Livestock per Acre, and Log of the Dairy
Value per Acre 1900-1920
Dam
Acre improved
livestock
Dairy
-0.003
(0.062)
0.060
(0.339)
0.202
(0.438)
Controls
Yes
Yes
Time fixed effect
Yes
Yes
County fixed effect
Yes
Yes
State*year fixed effect
Yes
Yes
N
2597
2597
R2
0.37
0.56
*** p<0.01 ** p<0.05 * p<0.1
Standard errors are in parentheses, clustered at county level.
Controls: Extreme cold and hot events, precipitation and soil-climate interactions
Yes
Yes
Yes
Yes
2597
0.27
42
Figure 1.1: Number of Dams Constructed by Different Types of Owner
Note: Only Western states are included.
Figure 1.2: Percentage of Dams Constructed by Different Types of Owner
Note: Only Western states are included.
43
Figure 1.3: Mean of the Height of Dams Constructed by Different Types of Owner
in Each Decade
Note: Only Western states are included.
Figure 1.4: Mean of the Maximum Storage of Dams Constructed by Different Types
of Owner in Each Decade
Note: Only Western states are included.
44
Figure 1.5: Percent Change in Farm Value per Acre (1900-1910)
Figure 1.6: Percent Change in Farm Value per Acre (1910-1920)
45
CHAPTER 2
Politics and Dam Construction: Historical Evidence from the Western U.S.
2.1 Introduction
At the end of the nineteenth century private enterprise had reclaimed some of the
arid west, however there was a demand for larger irrigation projects to provide water
to the area. In 1902, Congress passed the Newlands or Reclamation Act to build
irrigation projects. According to the first annual report of the Department of the
Interior ( Newell (1905)):
The act provides for the entry of the lands reclaimed in accordance with
the provisions of the homestead law.
Although the locations for potential water projects were investigated by the
Bureau of Reclamation (hereafter referred to as Bureau) commissioners, engineers,
and other experts, political pressure had significant influence on location decision for
constructing the dams. Since authorizing, initializing and completing of the major
national projects had to be passed through Congress, political power and the aim
of the congressmen and senators could be quite influential on the location and the
number of the dams in each district. According to the history of the Bureau of
Reclamation, Michael Robinson1 states:
Initially, little consideration was given to the hard realities of the irrigated agriculture. Neither aid nor direction was given to settlers in carrying out the difficult and costly work of clearing and leveling the land, digging irrigation ditches, building roads and houses, and transporting crops
to remote markets....The government was immediately flooded with requests for project investment as the Local chambers of commerce, real
1
The son-in-law of a Commissioner of Reclamation
46
estate interests, and congressman were convinced their areas were ideal
for reclamation development( Reisner (1993)).
During the New Deal the Flood Control Act of 1937 authorized another federal
agency, the Army Corps of Engineers (hereafter referred to as Corps) to be in charge
of the controlling flooding on the west. This created a competition between the two
agencies in the regions with navigable rivers and led to couple of agreements to solve
the conflict. Acquisition of the projects by each constructor was related to their
political power in the region, which was related to the support of the President,
congressmen on the related committees, and legislators because of these projects
potentially brought a great deal benefit to the district.
2
These major water projects had important effects on the local economy and
agriculture, therefore it is worth investigating the important factors affecting the
location and the timing of the major water projects. A study by Hansen et al.
(2011), identified the major factors in the construction of the water infrastructure
using a state-level data set. Their results show that there is a strong correlation
between the House committee session-seat representation and the number of dams
constructed in a state. This effect is more pronounced in the western states compared
to an all state sample.
In this paper, we investigate the effect of geographic, economic and political
factors on the locational Bureau dams in the early 1900s. Then, the dam construction purposes are compared before and after 1937 when the Corps also started to
construct the dams in the West. Finally, a hypothetical estimation of the Corps
proportion in dam construction will be conducted.
2.2 Historical Background
2.2.1 National Irrigation Congress Role
The first National Irrigation Congress was organized in Salt Lake City, Utah in
1891 and made irrigation a substantial national issue.
2
Pork-barrel projects
The key people in the
47
Congress were Senator Francis E. Warren (Republican from Wyoming); William
E. Smythe, the editor of the “Irrigation Age” ; Elwood Mead, an irrigation engineer
from Wyoming. After that, all Congresses were held in the Western States until
1900. The ninth annual National Irrigation Congress organized in Chicago on Nov.
24, 1900 gave the reasonable hope that national irrigation was not a dead issue
and would be solved soon by the support of the Federal government and the help
of engineers and scientists. This was the first National Irrigation Congress held in
the East and the Congress proposed that the irrigation problem was not only an
arid States problem but also a national concern. During the meeting, Congressman
Newlands of Nevada gave a speech about a great friendship for the irrigation issue
and he appreciated the amazing ideas proposed by George H. Maxwell.
George H. Maxwell was a lobbyist, publicist, and journalist and one of the most
influential people in water development in the arid Western States. He joined the
fifth Irrigation Congress in Kansas in 1899 and by the end of the Irrigation Congress
he became a national leader. Maxwell was not satisfied with the activity of the Irrigation Congress in promoting legislation. He and two others founded the National
Irrigation Association (later the National Reclamation Association) in the eighth
National Irrigation Congress in Kansas in 1899 and he became executive director of
the Association. He was sent to Washington, D.C., in 1900 by the Reclamation Association along with Nevada Representative Francis G. Newlands, Frederick H. Newell
of the United States Geological Survey and director of the Reclamation Service, Senator William M. Stewart of Nevada, and Gifford Pinchot, well-known supporter of
forestry management, to promote the legislations. Maxwell, Newlands, and Newell
prepared legislation that was proposed in the House by Newlands. Maxwell also
offered suggestions on Theodore Roosevelt’s 1901 congressional speech on forestry
and irrigation written by Newell and Pinchot (Maxwell (accessed Feb 3, 2014)).
At the end of the ninth Congress, the committee reported the following resolution:
We hale with satisfaction the fact that both the great political parties
in their platforms in the last campaign, declared in favor of the recla-
48
mation of “Arid America” that settlers might build homes on the public
domain and to that end we urge open Congress that national opportunities commensurate with the magnitude on the problem should be
made for the preservation of the forest and the reforestation of the denuded area as natural storage reservoirs, and for the construction by the
national government as a part of its policy of internal improvement of
storage reservoirs and other works for flood protection and to save us
in aid of navigation and irrigation, the water which now runs to waste,
and for the development of artesian and subterranean sources of water
supplies. The water of all streams should for ever remain subject to the
public control and the rights to the use of water for irrigation should
inhere in the land irrigated and beneficial use be the basis the measure
and the limit of the right. We commend the efficient work of the various
bureaus of the national Government in the investigation of the physical
and legal problems and other conditions relating to irrigation, and in
promoting the adoption of more effective laws, customs and methods
of irrigated agriculture and urge upon Congress the necessity of providing liberal appropriations for this important work (Los Angeles Times
(1900)).
Furthermore, the industrial and commercial importance of irrigation was discussed in the evening session by Tom L. Cannon, secretary of the St. Louis Manufacturers Association, Elliot Durand and B G. Chandler. MR. Cannon stated that:
I believe in internal improvement as I do in the forms of Republican government. I believe in the Federal government improving its own property
for the benefit of the people composing the government....If it is right for
the Federal government to build harbors along the sea coast and great
waterway channels in different sections, it is right for the Federal government to improve that great American Desert and reclaim arid America
through irrigation. I believe in making this country not only the greatest
49
agricultural country in the world, and the greatest manufacturing country in the world, but I believe in making it the seat of a financial empire
and becoming a creditor of all nations instead of a debtor... These things
are closely allied with the work of this convention today. If we build storage reservoirs in the mountains of the West and control the water supply
for irrigation purpose...(Los Angeles Times (1900)).
2.2.2 Passage of Irrigation Bill
The irrigation Bill was first introduced to the Congress by the Nevada Representative, MR. Newlands. After the campaign of 1900, however, even the supporters of
irrigations considered it as a dream. Newland believed that “The U.S. should cease
its irrigation of foreign lands and begin the irrigation of its arid lands”, and had an
influential role in the passage of the Act. The bill faced the opposition by the House
leaders in the fifty-sixth Congress.
During this time Theodore Roosevelt became the president, and one of his first
priorities was to improve the irrigation in the West. On Dec. 3, 1901, President
Roosevelt made some important recommendations to the Senate and House of Representatives regarding different issues. One of his concerns was national irrigation.
In his recommendations he stated that:
Great storage works are necessary to equalize the flow of streams and to
save the flood waters. Their construction has been conclusively shown
to be an undertaking too vast for private effort. Nor can it be best accomplished by the individual State acting alone. Far-reaching interstate
problems are involved and the recourses of single States would often be
inadequate. It is properly a national function, at least in some of its
features. It is as right for national government to make the streams and
rivers of the arid region useful by engineering works for water storage
as to make useful the rivers and harbors of the humid region by engineering works of another kind .... The government should construct and
50
maintain these reservoirs as it does other public works. Where their
purpose is to regulate the flow of streams, the water should be turned
freely into the channels in the dry season to take the same course under
the same laws as the natural flow... The reclamation of the unsettled
arid public lands presents a different problem. Here it is not enough to
regulate the flow of streams. The object of the government is to dispose
of the land to settlers who build homes upon it. To accomplish this
object water must be brought within their reach.... The pioneer settlers
on the arid public domain chose their hoes along streams from which
they could themselves divert the water to reclaim their holdings. Such
opportunities are practically gone. There remain, however, vast areas
of public land, which can be made available for homestead settlement,
but only by reservoirs an main-line canals impracticable for private enterprise. These irrigation works should be built by the government for
actual settlers, and the cost of construction should so far as possible be
repaid be the land reclaimed. The distribution of the water, the division
of the streams among irrigators, should be left to the settlers themselves
in conformity with State laws and without interference with those laws
or with vested rights. The policy of the national government should be
to aid to irrigation in the several States and Territories in such manner
as will enable the people in the local communities to help themselves,
and as well stimulate needed reforms in the State laws and regulations
governing irrigation (The Washington Post (1901)).
The second time the Bill was passed by the majority of votes in Senate. In the
House, however, there were some strong opposers, such as Representative Joseph
Gurney Cannon (leader of the Republican Party, Illinois), Representative John
Daizell (Republican from Pennsylvania), Representative William Peters Hepburn
(Republican from Iowa), Representative Frank Smith Payne (Republican from Iowa)
and some others. Mr. Cannon voted against the bill although he was in favor of the
pending amendment. He stated that:
51
The law appropriating $25,000 a year for each of the agricultural colleges
out of the proceeds of the sale of public lands was a perpetual charge
that fund.
Representative James Robinson (Democrat, Indiana) opposed the bill as he believed
that the majority proportion of the fund from the sale of public lands would be
depleted for irrigation purposes. Therefore, the agricultural and mechanical colleges
would have to rely on Public Treasury. Mr. Daizell did not agree with the Bill as he
thought that the benefits only went to the arid States while the other States were
paying the costs (New York Times (1901)).
Supporters of the Bill were led by Western Republican Frank Wheeler Mondell
(Republican, Wyoming), William Augustus Reeder (Republican, Texas), as well
as Newlands, John F. Shafroth (Silver Republican (1897-1903), Democratic (19031922), Colorado). Representative Newlands had secured the vote by the Democratic
Congressional Campaign Committee and therefore, passing the bill needed a sufficient number of Western Republicans. At the end of the session, Mr. Cowherd
(Democrat, Missouri) gave a speech about the benefits of irrigation to the Western
states from the passage of the bill (Los Angeles Times (1902)).
Eventually, in June 13, 1902 the House passed the irrigation bill by a vote of 16 to
55. The bill provided that the receipts from the sale of the public lands in Arizona,
California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, New Mexico,
North Dakota, Oklahoma, Oregon, South Dakota, Utah, Washington, and Wyoming
for all time would be devoted to works of irrigation. The arid land reclamation
fund was to be placed in the Treasury for the construction of irrigation works and
providing the water available to the settlers. The Secretary had the authority for
letting of contracts for the irrigation works whenever the money necessary for a
project was available in the Reclamation Fund.
Provision is made for the payment out of the Treasury of any deficiencies
in the allowances to agricultural colleges owing to this disposition of
public lands. The secretory of the Interior is authorized to examine,
52
survey, and construct the irrigation works, and report the cost thereof
to Congress at each session.(New York Times (1901)).
The law authorizes the Secretary of the Interior to withdraw from entry the lands
capable of being reclaimed and provides that each project shall be self-compensatory,
compelling the settlers to pay back into the fund in ten equal annual installments
their proportionate part of the cost of each construction. Also to prevent land
monopoly, section 5 of the bill provides that:
No right to the use of water for land in private ownership shall be sold
for a tract exceeding 160 acres to any one land owner, and no such right
shall permanently attach until all payments therefore are made to any
land owner unless he be an actual bona fide resident on such land, or
occupant thereof. residing in the neighborhood of such land (New York
Times (1901)).
2.2.3 Bureau of Reclamation
In 1902, the Reclamation Act created the Bureau to help provide irrigation in the
Western States (Miller and Miller (1992)). The bill was designated to convert arid
federal lands into suitable places for agriculture. The irrigation projects included
construction of dams, power plants, canals, and other water facilities. They were
to be financed through a Reclamation Fund which was provided from selling the
Federal land and later on by selling the water to the irrigators.
To determine the feasibility of the water projects, geological surveys were prepared by the Bureau that considered all related factors for dam construction such
as the amount of the water flow in the river, elevation of the surface, the streams,
and their catchment areas (Newell (1905)).
The Bureau’s primary purpose was to help improve the agriculture in the western U.S. However, because of the political pressure by the congressmen, and state
governments to acquire the water projects, dams might have been constructed in
the districts without having enough potential for agriculture. The water projects
53
mostly had been authorized by Congress; however, the Presidents were able to veto
the bill. According to the history of the Bureau of Reclamation, Michael Robinson3
states:
Initially, little consideration was given to the hard realities of the irrigated agriculture. Neither aid nor direction was given to settlers in carrying out the difficult and costly work of clearing and leveling the land, digging irrigation ditches, building roads and houses, and transporting crops
to remote markets....The government was immediately flooded with requests for project investment as the Local chambers of commerce, real
estate interests, and congressmen were convinced their areas were ideal
for reclamation development. (Reisner (1993))
The financing of the Reclamation projects obligated the farmers to meet their
repayment obligation in ten years. This proved to be an unrealistic estimate as sixty
percent of the farmers delayed their payments. In some cases the delays stretched
beyond twenty years from the passage of the first Reclamation law and the repayment period was extended to forty or fifty years.
2.3 Data
To understand the critical factors affecting the decisions of the location of dams,
a new dataset is assembled from several different datasets. The following section
includes the summary statistics and the sources of the combined datasets.
2.3.1 Major Dams
The dam dataset
4
comes from the National Atlas of the United State. The data
include information on the name, national ID, latitude, longitude, owner name, type
of owner, year of completion, all purposes, primary purpose, capacity, height, and
3
The son-in-law of a Commissioner of Reclamation
Source: National Atlas of the United States, 200603, Major Dams of the United States: National Atlas of the United States, Reston, VA.
4
54
some other characteristics for the major dams in the U.S. The dataset includes 8,121
dams considered to be “major”. A major dam is 50 feet or more in height or has
a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage
capacity of 25,000 acre-feet or more.
The summary statistics of the Federal dams are presented in Table 2.1. There
are 1,279 major Federal dams throughout the U.S. of which 371 were constructed
by the Bureau (Figure 2.4) and 615 by the Corps (Figure 2.5). The shares built by
other agencies- the Tennessee Valley Authority (TVA), The Bureau of Indian Affairs,
U.S. Forest Service, and U.S. Fish and Wildlife Service- are not large. Furthermore,
Figure 2.1 illustrates the histogram of the dams’ completion years for the Bureau
and the Corps.
The primary purposes of dam construction include flood control, debris control,
fish and wildlife protection, hydroelectric generation, irrigation, navigation, fire protection, recreation, water supply enhancement, and tailings control. Table 2.2 shows
the frequency of the primary purpose of the dams constructed by the Bureau and the
Corps in the West. Clearly, most of the dams constructed by the Bureau were based
on the primary purpose of irrigation and water supply. Furthermore, flood control
and navigation were main primary purposes of dam construction by the Corps in
the American West.
Table 2.3 illustrates the primary purposes of the dams constructed by the Bureau
and the Corps before and after the competition. Started in 1936, in the Pre and
Post periods, the Bureau has the majority of the dams with a primary purpose
of irrigation and water supply and the Corps has the majority of the dams with
primary purpose of flood control and navigation. However, in some cases both
agencies constructed dams with primary purposes that were not related to their
original mission.
Furthermore, the number of dams built just for one purpose of flood control or
irrigation decreases over time; however, the number of the dams constructed with
multi purposes and primary purposes of flood control or irrigation increases during
1950 and 1960. This is illustrated in Figures 2.2 and 2.3.
55
2.3.2 Presidential Election
The political data come from the ICPSR’s United States Historical Election Returns
(ICPSR, 1824-1968). The data include the percentage of votes for Republicans in
presidential elections at the state level. Since we are interested in the water infrastructures constructed during 1902 to 1910, the percentage of votes for Republicans
of the 1900, 1904, 1908 elections are chosen.
The summary statistics of the elections for the western states are shown in Table
2.4. The first three columns are the percentages of the republicans vote, and the
average for the three elections is shown in the fourth column. The Dam column is an
indicator whether a dam has been constructed in a state or not during the mentioned
time period. South Dakota, Washington, Wyoming, Oregon, and California had
remained Republican during the first decade of the twentieth century and dam
projects were constructed by the Bureau there. Idaho and Montana, which were
Democrat, had no dams constructed until they had voted more than 50 percent
Republican in 1904 (Table 2.5). The comparisons are suggestive that the political
power of the Republican party contributed to the construction of major dams in
Republican states.5 Therefore, it is reasonable to consider the percentages of votes
for Republican Presidential candidate as a political variable that had a significant
influence on the location of the dams.
2.3.3 Geographic Characteristics
The climate data are obtained from the U.S Historical Climatology Network for each
weather station and Geographic Information Systems software is used to interpolate
and obtain the county level climate data. Specifically, the data include the average
of extreme events (hot days that exceed 90 degrees Fahrenheit and cold days, below
32 degrees Fahrenheit for each year) and total rainfall is calculated for the 1900-1910
time period.
To have a proxy for the size of the rivers, an indicator is constructed from the
5
Arizona, New Mexico and Oklahoma were territories which did not have voting representation
in the U.S. Congress, and therefore did not have electoral votes for presidential election.
56
U.S. Geological Survey Geographic Names Information System. The indicators are
as follows: A small river is defined as passing through 5 to 10 counties. A medium
river passes through 11 to 50 counties, and finally a large river passes through more
than 50 counties. These are three variables that show the number of rivers of each
size that pass through or along the county.
2.4 Empirical Strategies and Results
2.4.1 Bureau in 1910
Using the county level dataset, I estimate the effect of the geographic, economic,
and political factors on the location of the dams constructed by the Bureau of Reclamation between 1902 and 1910. The sample includes the counties on the seventeen
Western States and the dependent variables is whether the dam has been located
in the county during 1902-1910. We restrict our analysis to this time period as we
aim to find out whether the Bureau paid appropriate attention to the agricultural
economics, soil science, climate condition, and other relevant factors in placing the
water projects in a county. Historical narratives suggest that the dams were constructed with little consideration for the need of areas irrigation for agriculture as
the political pressures led the proposed projects to have superficial conception of
the regions agriculture, soil quality, and market access (Reisner (1993)).
We estimate the following model by two logistic regressions, in one of which we
control for the political variable. The DAMist variable is an indicator of whether
the dam is constructed in state s, county i during the time period 1900-1910.
Damist = β0 + β1 Economicist + β2 Geographicist + β3 P oliticalst + ist
(2.1)
The economic variables include the population density of the year 1900. The
geographic variables are the yearly average of the following over 1900 to 1910: the
days that the temperature exceeds 90 degrees Fahrenheit, the days that the temperature decreases below 32 degrees Fahrenheit, and total precipitation. Other
57
geographic variables include the elevation of the surface and presence of rivers of
different sizes in the county. The political factor is the average of the percentage of
votes for Republicans in the presidential elections of 1900, 1904, and 1908. Finally,
the unobserved error component is it .
2.4.2 Results
The results of the Logit estimations of Equation 1 are presented in Table 2.6. The
first and second specifications are similar except model 2 incorporates the Republican voting measure.
The coefficient estimates in the two models have the expected signs. The population density has a negative correlation with the dams construction in the county. In
the beginning of the Bureau operation most of the dam were constructed in unpopulated areas where private dams are unlikely to locate. Furthermore, the counties
with large rivers were more likely to have dams in contrast with the counties with
small rivers. Extreme weather also has negative effect on the water projects’ construction.Total precipitation has a positive correlation with dam construction and
raises the probability that a county has a dam.
Finally, the results show that the political variable is statistically significant at
the 5 percent level and has a positive effect. This indicates that on average, a one
unit increase in the percentage of votes for Republicans increases the probability
of having a dam by 0.3 percent. Therefore the results support the hypothesis that
support for the Republican administration had a strong impact on the location of
major water projects at the beginning of the operation of the Bureau.
Concerns
Since the major national projects in the United States had to be passed through a
process involving the Congress, the Senate, and the House of Representatives, thus
being influenced by the political power of the mentioned members, it is necessary to
consider this political power in our model as the number of seats in the Senate and
58
the House or the seniority of the Congressman and Senators in relevant committees.
Although data are available for the mentioned factors individually, processing and
matching of the appropriate variables was not in the context of this paper within
the current time frame and will be added to the model in future editions.
Applying the average of the percentage of votes for Republican in presidential
elections is not a precise variable to evaluate the political power of the lower house
and upper house of the Congress; however, we suggest that it can be used as a
representative of the relative power balance in the era mainly due to the fact that
the majority of the Congress were Republican for consecutive years in the beginning
of the 20th century and they were not opposed to the president. Considering this
fact, the average of the percentage of the presidential votes in each state can be a
representative of the power of the congress and subcommittees related to the same
state.
2.4.3 Army Corps of Engineers
Organized in 1815, the Corps was exclusively in charge of the maintenance of the nation’s rivers and flood control. However, after the colossal failure of the “levees only”
policy and construction of a flood wall to control flooding on the river in the 1927
Mississippi flood, Congress authorized the Corps and the Federal Power Commission
to prepare a survey called “308 Reports” about possible improvements to the navigable streams for water power, flood control, and irrigation, in the River and Harbor
Act of 1927 (United States House of Representatives (1926)). After the disastrous
northeastern floods of March 1936, Congress passed the Flood Control Act of 1937
which made flood control an official activity of the Federal Government. Therefore, the Corps officially became a nationwide planning and construction agency
for flood control projects. This was a turning point in the history of the Corps as
it dramatically increased the agency’s scope by empowering it to construct flood
control reservoirs upon approval of Congress (Reuss and Walker (1983)). The Act
authorized the development of the dams for flood-control purposes alone; however,
anticipation of the potential possibility of hydro-power was important and included
59
in the bill (Billington et al. (2005)).
The act set up a rivalry between the Bureau and the Corps in the areas that had
navigable river basins since the Corps had the authority to improve the river for flood
control and navigation. The rivalry between the Corps and the Bureau took place in
three river basins:The Colombia, Missouri, and California Central Valley. There was
also some competition on non-navigable rivers such as the Colorado River, the Slat,
and the Gila Rivers. In general, a dam constructed by the Corps was more favorable
in the eyes of the farmers since the water was free and the Federal government would
have subsidized the dams with the purpose of flood control. (Reisner (1993))
Several agreements were negotiated to prevent the conflict between the Corps
and Bureau in the mentioned areas. The first agreement was the Pick-Sloan Missouri
Basin Program 6 in 1944. 7 Five years after the establishment of the Bureau in 1902,
nine projects were constructed on the river in response to political pressure from the
Missouri Basin states.
8
However, the repayments were delayed to as late as 1944.
As a solution to overcome the financial difficulties, it was suggested to build high
dams along upper tributaries to create hydropower revenue. On the other hand, in
March and May of 1943 two major floods occurred between Sioux City and Kansas
City and between Jefferson City and the mouth of the Missouri. As a result, the
Corps proposed construction of five monstrous dams in the mainstream of the Basin
in order to control the flooding. When Congress failed to come up with a solution
to reconcile the two proposals, President Roosevelt proposed that a single regional
authority such as the Tennessee Valley Authority solve the conflict between the
agencies (Billington et al. (2005)). The threat of losing both projects led to the
Sloan Plan and the Pick Plan, which established that the Corps would construct
the dam on the mainstream of the river and focus on the lower tributaries while the
Bureau would be in charge of upper tributaries. Any irrigation development by the
Corps was to be done under Reclamation law.
6
Flood Control Act 1944
The Missouri River is the longest river in the American West and the longest tributary in the
United States.
8
Cadillac Desert, pg 163
7
60
The second reconciliation was the Newell-Weaver Plan of 1949. The Corps and
Bureau agreed to take the development of the Colombia River Basin out of the
Colombia Valley Authority
9
in a way similar to the Pick-Sloan Plan that tried to
block out the Missouri Valley Authority. Under this agreement the Corps controlled
water development of the main stream of the Colombia river and the lower part of
the Snake River. The Bureau took control of the upper Snake River basin along the
Oregon-Idaho state lines and everything from there to the Continental Divide.
The same competition occurred in California. On November 1935 the secretary
of the Interior submitted a report to the President for the approval of the development of the Central Valley as a Federal reclamation project. The report was
approved by the President on December 2, 1935. The Bureau had just started the
investigation phase of their projects on the King and Kern River when the Corps
asked the House Flood Control and Appropriation Committees for permission to
do similar investigations on the same rivers (Reisner (1993)). Consequently, by
1940 Congress received two plans from the two agencies. Four years later, the 1944
Flood Control Act allowed the Corps to construct flood control purpose dams on
the American, Kern, and Kings Rivers. They started constructing the Folsom Dam
on the American River near the Sacramento, California in 1948. In 1949 the Folsom
Formula settled the competition between the two competitors as follows:
Multiple-purpose dams are the responsibility of the Bureau of Reclamation, and dams and the works exclusively for flood control are the
responsibility of the Corps of Engineers (Graham (1950)).
Under the Folsom Formula the Folsom Dam was transfered to the Bureau for
coordinated operation as an integral part of the Central Valley Project. Dam operations for the Pine Flat Dam constructed by the Corps on the Kings River in the
Southern San Joaquin Valley for the primary purpose of flood control were taken
over by Reclamation.
9
The Pittsburgh Press, Jun 9, 1949
61
2.4.4 Corps versus Bureau
The Flood Control Act of 1936 gave the authority to Corps to be in charge of flood
control throughout the U.S. Involving the Corps in the construction of the dams
in the West resulted in a fierce competition between the Bureau and the Corps
over dam locations and the appropriate construction sites in the areas where both
agencies could have water projects. The Corps was constructing the dams more for
the purpose of flood control at the beginning but by this time, the number of single
purpose dams had declined and the number of multi-purpose dams had increased.
This is illustrated in Figure 2.5. The multi purpose dams with the primary purpose
of flood control dramatically increased during the 1950s and 1960s. Dams with
recreation and hydropower purposes were potential sources of generating revenue.
We are interested in finding out hypothetically what would be the Corps dam
construction proportion if the Corps were just allowed to construct dams for flood
control purposes. In order to find that, we estimate the probability that the Corps
constructed a dam, in a sample including only the counties where Bureau or Corps
constructed dams after 1937, while controlling for different construction purposes.
Corpsist = β0 + β1 F lood P urposeist + β2 Irrigation P urposeist + β3 Economicist
+β4 Geographicist + αst + ist
(2.2)
The Corpsist variable is an indicator whether the dam was constructed by the Corps
in state s and county i during time period t. The purposes of the construction of
the dam are categorized as: 1) Flood control and navigation, 2) Irrigation and water
supply, 3) Others . The Flood Purpose is an indicator if the county has a dam with
flood control and navigation purposes at time t. The Irrigation Purpose is an indicator if the county has a dam with irrigation and water supply at time t. Therefore, β1
and β2 are the coefficients of interest, which respectively measures how much flood
control and irrigation purposes an influence on choosing dam construction agents
between Corps and Bureau. αst is year state fixed effects to capture the shocks that
62
happened within the states in each year.
2.5 Conclusion
After establishing the Bureau of Reclamation in the early twentieth century, many
major federal dams were constructed to improve the arid land and agriculture in
the western U.S. Geological surveys were prepared to locate areas with the potential
for planting valuable corps, appropriate soil quality, and accessibility to markets.
However, several histories claim that these factors were frequently disregarded as a
results of the political pressure by the powerful constituencies.
Assembling several different datasets for the beginning of the Bureau of Reclamation’s operation, this study investigates the extent to which geographic, economic,
and political factors had influence on the locations of dam construction. The estimation results show that the percentage of votes for Republicans in presidential
elections had a positive and statistically significant effect on the dam locations. On
average, a one unit increase in the percentage of votes for Republicans increases the
probability of having a dam by 0.3 percent. Furthermore, entering the Army Corps
of Engineers as another federal agency in constructing dams in the western U.S. led
to a rivalry between the Corps and the Bureau and changed the primary purpose of
constructing dams by the two agencies.
It is worthwhile to expand the datasets and perform the analysis beyond the first
ten years of the Bureau’s operation and include the 1920s and 1930s . Furthermore,
it might be interesting to investigate the effect of competition in the competitive
areas that reconciliation between two agencies occurred. However, this requires
identifying the competitive dam sites more precisely.
63
Table 2.1: Federal Dams in the West
Federal dams
Total
Bureau
Corps (West)
Corps
TVA
Others
1279
371
190
615
60
233
Note: The Corps and the Bureau were the major federal dam builders. The
shares by another agencies, The Tennessee Valley Authority (TVA), The Bureau of
Indian Affairs, U.S. Forest Service, and U.S. Fish and Wildlife Service are not
considerable.
Major dams: height greater that 50 feet or a normal capacity of at least 5,000
acre-feet or a maximum storage capacity of 25,000 or more:
“http://nationalatlas.gov/mld/dams00x.html”
64
Table 2.2: Primary Purposes
Primary Purpose
Total
Bureau
Corps (West)
Irrigation or Water Supply
353
309
19
Flood control or Navigation
537
29
141
Hydroelectric
51
24
13
Recreation
18
1
17
Others
22
4
6
Note: The table shows primary purposes of dams constructed by the Bureau and
the Corps.
Others category includes debris control, fish and wildlife pond, tailings and fire
protection, stock, or small farm pond purposes.
65
Table 2.3: Primary Purposes - Pre and Post 1936
Primary purposes
Bureau-Pre
Bureau-Post
Corps-Pre
Corps-Post
Irrigation or water supply
89
216
1
18
Flood Control or navigation
2
25
1
140
Hydroelectric or recreation
2
23
-
24
Note: The table includes number of dams constructed with different primary
purposes by the Bureau and the Corps before and after 1936, the year that
Congress passed the Flood Control Act.
66
Table 2.4: Association of the % of Votes for Republicans in Presidential Election
and Dam Construction
State
1900
1904
1908
Average
Dam
North Dakota
62.1
75.1
61
66.1
-
South Dakota
56.8
71.1
58.8
62.2
yes
Washington
53.4
70
57.8
60.4
yes
Wyoming
58.6
66.9
55.4
60.3
yes
Oregon
55.5
67.3
56.5
59.8
yes
California
54.5
61.9
55.5
57.3
yes
Kansas
52.6
64.9
52.5
56.7
-
Utah
50.6
61.5
56.2
56.1
-
Idaho
46.9
65.8
54.1
55.6
yes
Nebraska
50.5
61.4
47.6
53.2
-
Colorado
42
55.3
46.9
48.1
-
Montana
39.8
53.5
46.9
46.7
yes
Nevada
37.8
56.7
43.9
46.1
-
Texas
30.9
22
22.4
25.1
-
Arizona
-
-
-
-
New Mexico
-
-
-
-
yes
Oklahoma
-
-
43.5
-
-
Note: Arizona, New Mexico, and Oklahoma were territories which did not have
voting representation in the U.S. Congress, and therefore did not have electoral
votes for presidential election.
67
Table 2.5: Montana and Idaho Dams
Dam Name
State
Starting
Completion
Como
MT
1908
1910
Willow Creek Bor MT
MT
1907
1911
Boise River Diversion
ID
1906
1908
Deer Flat Lower
ID
1906
1908
Deer Flat Upper
ID
1905
1908
Reservoir A
ID
1907
1907
Minidoka
ID
1904
1906
Note: Dams were constructed by the Bureau during 1902 to 1910 in Montana and
Idaho. These states were Democratic and became Republican in 1904.
68
Table 2.6: Logit Estimation
Model 1
Variables
Model 2
Coef.
Std. Error
Coef.
Std. Error
Mar. eff. (%)
Population Density
-0.124***
0.061
-0.107**
0.061
-0.26
Small River
-1.211**
0.647
-1.250**
0.651
-3.01
Large River
0.410
1.177
0.648
1.258
1.56
Hot days
-0.031***
0.011
-0.013
0.013
-0.03
Cold days
-0.026**
0.015
-0.039***
0.017
-0.09
0.017
0.041
0.004
0.041
0.01
-
-
0.125 ***
0.059
0.30
Constant
-1.214
1.142
-8.732***
3.725
-21.03
Log Likelihood
-75.353
-71.268
0.128
0.175
813
813
Rainfall
% of Votes for Republicans
Pseudo R2
Total # of obs.
LR test
8.171***
p-value
0.004
Note: *** significant at 5% ; ** significant at 10%
69
Table 2.7: Purposes - Pre and Post 1936
Purposes
Bureau-Pre
%
Bureau-Post
%
Corps-Post
%
Irrigation
98
97
226
84
41
22
Water supply
16
16
88
33
58
31
Flood control
18
18
115
43
155
82
-
-
4
1
20
11
Hydroelectric
24
24
83
31
50
27
Recreation
29
29
114
42
123
65
Navigation
Total
101
270
188
Note: The table includes the number of dams constructed by the Bureau and the
Corps and the percentage of dams constructed with different purposes before and
after 1936.
70
Figure 2.1: Dams - Bureau and Corps
Note: The horizontal axis is the year of completion of dams. The right hand side
graph shows the number of dams constructed in each year by the Corps in the West.
Except for two dams that were completed in 1916 and 1923, the completion year
starts in 1937.
71
Figure 2.2: Dams Constructed by the Bureau: (a) One Purpose: Irrigation, (b)
Multi Purposes: Irrigation - Hydroelectric, (c) Multi Purposes: Irrigation - Recreation
(a)
(c)
(b)
72
Figure 2.3: Dams Constructed by the Corps: (a) One Purpose: Flood Control, (b)
Multi Purposes: Flood Control - Hydroelectric, (c) Multi Purposes: Flood Control
- Recreation
(a)
(c)
(b)
73
Figure 2.4: Dams Constructed by the Bureau and Corps
Note: Total number of Federal dams constructed by the Bureau and the Corps.
The black dots are dams constructed by the Corps and the Red dots are are the
dams constructed by the Bureau. Major rivers in the USA are illustrated with the
blue color.
74
Figure 2.5: Dams Constructed by the Corps
Note: Total number of Federal dams constructed by the Corps. Major rivers in the
U.S. are illustrated with blue color.
75
Figure 2.6: Dams Constructed by the Bureau
Note: Total number of Federal dams constructed by the Bureau. Major rivers in
the U.S. are illustrated with blue color.
76
CHAPTER 3
The Impact of Climate Change on Agriculture:
Accounting for Climate Zones in the Ricardian Approach
Soudeh Mirghasemi
1
Sandy Dall’erba2
Francina Dominguez3
3.1 Introduction
Although small in terms of employment, agriculture is one of the most important
sectors of the U.S. economy. The market value of agricultural products sold was
about $395 billion in 2012.4 Roughly 52 percent of this value comes from just nine
states: California, Iowa, Texas, Nebraska, Minnesota, Kansas, Illinois, North Carolina, and Wisconsin. The United States controls almost half of the world’s grain
exports and is the world’s largest producer and exporter of agricultural goods. In
2011, the U.S. produced 18.5% of the total grain production of the world.5 Furthermore, agriculture is linked to other sectors of the economy such as transportation,
manufacturing, marketing, and utilities through its supply and purchase linkages.
Consequently, understanding the effect of climate change on agriculture plays a
major role on the price of the agricultural products in the U.S. and worldwide.
1
University of Arizona, Department of Economics, University of Arizona, AZ 85721, USA.
E-mail: [email protected]
2
Department of Agricultural and Consumer Economics and Regional Economics Applications Laborator, University of Illinois at Urbana-Champaign, IL 61801, USA. E-mail:
[email protected]
3
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, IL 61801,
USA. E-mail: [email protected]
4
This was $97 billion, or 33 percent, more than in 2007, at the time of the last agriculture
census. Source: http://www.agcensus.usda.gov
5
40.5% of corn, 35% of soybean, 12% of cotton, and 8% of wheat
77
Since agriculture uses over 42% of the land of this country, one should not expect
the effect of climate on agriculture to be the same throughout the U.S. One example
is the drought of the summer of 2012 which was the most severe drought after
the 1950s. Indeed, its effect was more severe in some regions such as the Corn
Belt compared to the other parts of the country. The U.S. export prices for corn
increased roughly about 130 percent above the historical average 6 , as a consequence
of drought-related crop damage. The drought led also to an increase in the price
of the products derived from corn, such as ethanol (Adonizio et al. (2012)). The
attention generated by the impact of the summer of 2012 drought on the Corn
Belt illustrates how vital it is for the U.S. agricultural sector to understand climate
change in different regions and how to mitigate and/or adapt to it.
Beyond the case of extreme events, the slow process of climate change is also
very likely to bring more dramatic transformations in some parts of the country
than others. For instance, a recent report by Jardine et al. (2013) highlights how
specifically the Southwestern U.S. region is likely to be challenged by future climate
conditions. In addition to already being the hottest7 and driest region of the country,
the Southwest climate zone is warming and experiencing more drought than in the
past century. It is also encountering a reduction in streamflows from its four major
drainage basins. The projected climate conditions compiled in this report indicate
more frequent heat waves in summer, a decrease in precipitation, more frequent
precipitation extremes in winter, a decline in river flows and soil moisture, and more
severe extremes (droughts and/or floods) in parts of the Southwest. Measuring
the impact of future climate conditions on agriculture has attracted the attention of
many scholars over the last two decades. According to previous studies, some regions
of the US will be winners and others losers, but it is still unclear whether climate
change will bring a net gain or a net loss for U.S. agriculture as a whole. Deschenes
and Greenstone (2007) found that U.S. agriculture will benefit from the climate
change and the annual profits will increase by $1.3 billion. Mendelsohn et al. (1994)
6
7
In the last 20 years: October 1992 to September 2012. Source: http://www.bls.gov
Based on July maximum temperatures
78
estimated that global warming may have benefits of about $1 − $2 billion per year,
even without CO2 fertilization. Massetti and Mendelsohn, 2011 showed that the
U.S. will benefit from climate change by 14.8-15.2 billion. In contrast, some other
studies such as Schlenker et al. (2006) and Schlenker et al. (2005) lsuggest that there
will be an annual loss of $3.1−$7.2 billion and $5−$5.3 billion respectively. So there
is significant uncertainty about the impact of future climate changes on agriculture.
There are several reasons why previous studies generate contradictory results. Some
use a cross-sectional approach while some use panel data estimation with different
sets of geographic fixed effects; there are differences in the discount factor used for
actualization; and different specifications of temperature and precipitation data are
used. The way that heterogeneity is defined is also different in previous studies.
For example, Deschenes and Greenstone (2007) divided counties into irrigated vs.
non-irrigated counties. Schlenker et al. (2006) and Schlenker and Roberts (2009)
simply focus on the counties east of the 100th meridian, the historical boundary of
non-irrigated agriculture.8 Yet, the econometric literature has highlighted numerous
times that an improper consideration of heterogeneity in coefficients across crosssectional observations may lead to biased and inconsistent estimates and hence to
improper conclusions on the marginal effects of the estimates (Anselin and Le Gallo
(2006); Durlauf and Johnson (1995)).
Furthermore, preceding studies may suffer from an omitted variable bias for their
lack of consideration of extreme climate events, which have increased in frequency
and intensity over the last decades and are expected to increase even further in
the future (Trenberth (1999)). Schlenker and Roberts (2009) consider the extreme
climate events in their analysis and state that:
Holding the average temperature constant, days with more variation
will include more exposure to extreme outcomes, which can critically
influence yields.
Researchers have taken different approaches to studying the effect of climate
8
100th meridian is one hundred degrees of longitude west of Greenwich. To the east of the
100th Meridian, average annual precipitation is in excess of twenty inches.
79
change on agriculture and crop production. These approaches can be summarized
in three categories. The first one is the crop growth simulation model used by Nordhaus (1991), Richard (1995), Neumann and Mendelsohn (1999), Reilly et al. (2003),
and Nelson et al. (2009). Based on agronomic (biophysical) models, their strength
lies in their capacity to simulate crop growth over the life cycle of a plant exposed
to the full range of weather outcomes, including extreme events. They also simulate how changes in climate modify a crop’s input requirements such as fertilizers
and irrigation. However, this approach has been criticized for the large number of
calibrated parameters it relies on, its lack of validity on a global basis, and for not
accounting for the farmers’ capacity to adapt their crop choice to climate (Antle and
Capalbo (2010); Hertel et al. (2010)). It should be noted that the Intergovernmental
Panel on Climate Change (IPCC) uses the crop model approach in conjunction with
the Basic Linked System (BLS) of National Agricultural Policy models, a world-level
equilibrium model system (Fisher (2001)), to estimate the impact of climate change
on food production (Parry et al. (2004)). This approach is very complex and requires coordination among several groups with large computational capacity and is
beyond the goals of the present study.
The second type of model uses regression analysis to estimate the impact of
climate and other exogenous inputs (such as soil quality) on one type of crop (Adams
(1989), Lobell and Burke (2008), and Schlenker and Roberts (2009)).9 This approach
requires significantly less data and computational resources than the simulated crop
growth models, but does not consider the ability of farmers to adapt their choice
of inputs and crops to changing climate conditions. This framework is particularly
relevant in developing countries where it is difficult to assume that farmers have the
private capital or government support necessary to adapt their farming practices
(Lobell and Field (2007); Parry et al. (2004)).
However, when it comes to the U.S., empirical evidence clearly demonstrates
that adaptation at the farm level is already taking place. The 1996 report of Schimmelpfennig et al. indicates that:
9
Production function approach
80
Some of the alternatives considered are adoption of later maturing cultivars, change in crop mix, and a timing shift of field operations to take
advantage of longer growing seasons.
Adaptation does not limit itself to crop-producers. Schimmelpfennig et al. (1996)
also reports that:
The growth of dairy in the South is a testament to the creativity of farmers in finding ways to cool animals in hot climates 10 . Other adaptations
include herd reduction in dry years, shifting to heat-resistant breeds, and
replacing cattle with sheep.
Another reason that makes it difficult to justify a crop-production approach in
the U.S. is that the regression framework used in this literature treats each crop
individually. By definition it does not account for the fact that crops are mutually
dependent through factors such as crop rotation11 (Padgitt et al. (2000) or access to
inputs (land, water, fertilizers, and government subsidies) whether they share the
latter or compete for them. Finally, we believe that the prospect of an uncertain
climate drives the desire to make public and private investment decisions that mitigate the impact of climate and increase the chance of adaptation. For instance,
Antle and Capalbo (2010) indicate that mitigation policies, such as cap and trade
on CO2 emissions linked with agricultural offsets, are already under consideration
in the U.S.
Based on these observations, a third theoretical framework called the Ricardian approach was initiated by Mendelsohn et al. (1994). Their regression model
approach relies on the assumption that landowners, well aware of their local production conditions, allocate their land to the most rewarding use. This framework has
attracted much attention when analyzing the US agricultural sector (e.g. Schlenker
et al. (2006), Schlenker et al. (2005), and Deschenes and Greenstone (2007)), partly
because decisions on which crop to plant, how much of each input (fertilizers, herbicides, etc.) to use and what tillage/management technique to adopt are determined
10
11
For example, shading, wetting, circulating air, and air conditioning.
Practiced for 85 percent of the corn and 75 percent of the wheat of the U.S. over 1990-1997.
81
endogenously and will be reflected in the value of farmland and/or agricultural profits, the usual dependent variables in a Ricardian regression framework (Kelly et al.
(2005)).
The estimated impact of climate change on agriculture by applying the Ricardian
approach is found to be smaller than the former estimates as a result of taking
adaptation into account.12 The main hypothesis in the Ricardian approach is that as
the climate conditions change, the farmer adapts to the new conditions and adjusts
the input or output. In principle, the farmer, in response to a change in weather,
might modify the amount of the irrigated water or fertilizer, modify combination of
the crops, or switch to grow another crop in order to maximize his profit. Therefore,
in a well-functioning market, land prices are representative of the discounted value
of land rent.
While we also adopt a Ricardian framework in this paper, our work departs
from previous contributions on a number of important points. First, we pay particular attention to the spatial heterogeneity among the climate zones. Previous
contributions about the U.S. have overlooked the fact that the role of climate conditions on agriculture is expected to vary across climate zones, which raises concerns
about the accuracy of coefficient estimates measured on the entire sample of U.S.
counties. Even within one climate zone such as the Southwest, the large variety of
the Southwest’s landscapes (mountains, valleys, plateaus, canyons, and plains) and
associated elevation leads to diverse climates.13 Waldinger (2014) examines the economic effects of long term climate change during the Little Ice Age in Europe. She
investigates economic and climatological heterogeneities in the effect of temperature
on the size of city. Her findings show that cities that were larger and had better
access to trade were less affected by climate change. Further, temperature changes
affected cities differently depending on the climate zone where the city was located.
While relatively warm cities were negatively affected by increases in temperature,
relatively cold cities benefited from temperature increases.
12
13
The first two approaches overestimate the damage from climate change.
Pinal, AZ, is 713 meters above sea level while Hinsdale, CO, is 3311 meters high.
82
We aim to obtain the effect of climate change on the U.S. agricultural farm
value in years 1997, 2002, 2007 for the nine different climate zones in Figure 3.1
throughout the U.S. Secondly, we include extreme events (heat and cold waves
as well as heavy precipitation) because several global and regional climate models
suggest that extreme events will occur more often in the future (Tebaldi et al. (2006)
and Dominguez et al. (2012)). Unlike work using global climate models (GCMs) data
or statistically downscaled GCMs, we use dynamically downscaled data that allow
us to explicitly account for changes in the intensity and frequency of extreme events
at the local scale (a spatial resolution of 35-50km while GCMs used in Schlenker
et al. (2006) have a spatial resolution of about 200-300km). This level of detail is
applied to future climate projections, which in our case come from seven regional
climate model (RCM) simulations. Such a variety of projections allow us to account
for model uncertainty for future climate projections and will allow us to provide an
envelope of likely future farmland values. This approach improves upon the usual
projections based on a single climate model.
The next section extends the Ricardian framework to a setting allowing for crosssectional heterogeneity. Section 3 presents the dataset and data sources which report
climate data at a much finer scale than in any of the previous Ricardian papers. Section 4 displays and discusses the estimation results first and then carries on with a
set of predictions on future agricultural land values considering cross-sectional heterogeneity. Finally, section 5 summarizes our results and provides some concluding
remarks.
3.2 The Ricardian Setting
In the traditional Ricardian setting a single farmer puts his land to its most profitable use given a set of local conditions. The farmer responds to local changes
in weather by modifying the amount of irrigated water, fertilizer, land planted for
the crop or switching to another crop. In a well-functioning market land prices
are representative of the discounted value of land rent. This assumption allows us
83
to measure the effect of the climate change on farmland value. In the absence of
data at the individual farm level, a Ricardian model is estimated on a sample of
geographical units and its reduced form is as follows:
Yizt = Xizt β + εit
(3.1)
where the dependent variable Y, farmland value per acre, is a function of climate,
land and human variables. One caveat of Equation (3.1) is that it does not consider
the spatial heterogeneity across the climate zones. The parameter estimate equals
the average marginal effect of the zth climate variable on the dependent variable y
as if there is no different impact across the climate regions.
∂yizt
=β
∂xizt
(3.2)
However, we believe that the marginal effect of the climate variable on the land
value is different in each climate zone. Therefore, we estimate the effect of climate
on farm land values using county level data:
Yizt =
9
X
Xizk t βzk + wst + δi + εit
(3.3)
k=1
The main outcome variable is the log of the value of the farm per acre in county i,
in climate zone z, and in census year t. Xizt includes measures of income per capita,
income per capita squares, population density, and population density squares as
a proxy for demand. It also includes climate variables, rainfall, and hot and cold
extreme events to control for climate conditions. βz1 to βz9 are the coefficients
estimates that are assumed to be different in each of the nine climate zones. The
coefficient of interest are the Hot and Cold Events, which are the new factors we
incorporate.
We also include county fixed effects to capture the time-invariant unobserved
characteristics related to each county. Year-state fixed effects are included to control
84
for the shocks that happened within the states in each year. εit is the unobserved
error component. The identification of the effect of extreme events comes from
changes over time within the same county after controlling for shocks common to
the counties in each state-year and for the factors listed above.
3.3 Data
We follow the traditional Ricardian (Hedonic) approach and perform our analysis
at the county level.14 The new county level dataset is assembled from several sources
for the 1992 through 2007 agricultural census years for the whole United States.
15
The dependent variable is the log of the estimated value of the land and buildings per acre provided by the US department of Agriculture (USDA). Independent
variables can be divided into three different categories: climate data, soil data and
socio-economic characteristics.
When it comes to climate data, we rely on a finer scale to avoid any errors
(noise) created by coarse measurements commonly used in both past observations
and future projections. Unlike work using global climate models (GCMs) data or
statistically downscaled GCMs, the use of dynamically downscaled data will allow us to explicitly account for changes in the intensity and frequency of extreme
events at the local scale. This technique is applied to both past observations and
to projections. While current approaches use projected climate variables derived
from global climate models (GCMs) with a spatial resolution of about 200-300km
(as in Schlenker et al. (2006)), we use data provided by regional climate model
(RCM) simulations, driven by GCMs at their boundaries, with a spatial resolution
of 35-50km. Our projections are performed over the 2038-2080 period defined by
NARCCAP. This ensemble of RCM-GCM simulations provides us with an envelope
of future climate scenarios that is then used in the econometric model. This helps
to compare the results in the historical period with those obtained with gridded
14
Specifically, we focus on the 3,076 counties of the conterminous U.S. States. Alaska and remote
islands such as Hawaii and Puerto Rico are thus excluded.
15
The U.S. Census of Agriculture data are available for every 5 years in this time period.
85
observations from the North American Regional Reanalysis (NARR, Mesinger et al.
(2006)), available from 1979 to the present.
To control for the climate conditions, we collected temperature and precipitation
data from the North American Regional Reanalysis (NARR). The NARR assimilated dataset covers the contiguous United States and provides data at a 32km
spatial resolution and 3-hourly temporal resolution from 1979 to the present. Thus,
we obtain accurate estimates of climate variables based only on agricultural plots
within each county for all the U.S. counties. We prefer NARR data over the popular PRISM data (Deschenes and Greenstone (2007), Schlenker et al. (2006) and
Schlenker and Roberts (2009)) since the latter is a monthly dataset that will not
provide information on extreme events and cannot capture how the precipitation or
Hot/Cold Events are being experienced by the plants. In contrast, NARR is available on a daily time scale permitting us to include extreme precipitation as well as
Hot and Cold Events.16
Specifically, the climate variables that will be put in the model are:
1. Monthly temperature of January, April, July, and October.
2. Monthly average precipitation of January, April, July, and October.
3. Extreme Hot Event: number of events per year.
4. Extreme Cold Event : number of events per year.
We calculate extreme high and cold temperatures (extreme events) based on two
thresholds, below 8◦ C as a Cold threshold, above 32◦ C as a Hot threshold. The Hot
and Cold Events are defined as the number of times that temperature was below or
above the threshold for more than three consecutive days in a year.
16
Since the climate data are provided as points, Inverse Distance Weighting (IDW) interpolation
method was used to make continuous climate variation on continental U.S. counties by using
Geographical Information System (GIS) software. Zonal Sum analysis with respect to counties
was also used to calibrate each county’s climate conditions. The IDW (Inverse Distance Weighted)
tool uses a method of interpolation that estimates cell values by averaging the values of sample
data points in the neighborhood of each processing cell. The closer a point is to the center of the
cell being estimated, the more influence, or weight, it has in the averaging process. Special thanks
to Dongwoo Kang for helping us with this.
86
In future work when we develop projections, while current approaches use projected climate variables derived from coarse resolution GCMs, we will use data at
a much finer resolution by using dynamically downscaled simulations.17 A dynamically downscaled model is used to capture the future impact of the climate change on
yield production. These models provide more realistic precipitation and temperature
data with high resolution.18 To date, all of the Ricardian studies have used only the
coarse global climate models (GCM) with a spatial resolution of about 200-300 km.
While GCMs generally do not represent climate variables due to their coarse spatial resolution and physical parameterizations, using these downscaled simulations
datasets will help to address the issue of uncertainty in the future climate. Figure
3.2 shows the difference in aggregation between the two sets of spatial models.19
The variables capturing human intervention include population density, which
acts as a proxy for demand and for the potential effect of development upon farmland
value as well as per capita income. They come from the Regional Economic Accounts
developed by the Bureau of Economic Analysis.
17
Downscaled simulations were generated by Francina Dominguez and Yolande Serra at the University of Arizona, department of atmospheric science using the Weather Research and Forecasting
(WRF) model driven by the GCM : Hadley Centre coupled model, version 3 , HadCM3.
18
Dominguez et al. (2012): “GCMs generally do not realistically represent precipitation due to
their coarse spatial resolution and physical parameterizations, especially in complex terrain. Higher
spatial resolution, improved representation of orography and land-surface heterogeneity, and hence
a better representation of precipitation, are most practically achieved with the use of regional
climate models (RCMs), as GCMs are presently too computationally expensive for multidecade
climate change projection-type integrations with equivalent resolution. RCMs generally better
capture mean and extreme precipitation at the regional scale (Diffenbaugh et al. (2005); Leung
and Qian (2009)). We refer to the process of using RCMs forced at their lateral boundaries by
GCMs as Dynamical Downscaling.”
19
Since soil properties of land have an important role in agricultural production, soil properties
of counties were also considered as an explanatory variables in the model. The Natural Resource
Conservation Service of U.S. Department of Agriculture (USDA) provides U.S. General Soil Map
(STATGO2) containing various soil properties including flood frequency, soil erodibility factor (K
factor), representative slope, wind erodibility index, hydric rating, electrical conductivity, permeability(K sat), salinity, available water capacity, percent clay, and percent sand. These properties
are provided as polygon data which do not match with counties boundaries. By using Intersect
analysis of GIS software, each counties’ weighted average soil properties were recalculated. The
Flood Frequency ratio, Slope Steepness, Moisture Capacity, and Clay Content are used as a control
for soil characteristics.
87
3.4 Results
3.4.1 Climate Regions
To test if we can pool some of the climate zones into one climate zone, we perform
Chow tests for each climate zone with its neighbors. Table 3.10 illustrates the F
test results. Check marks indicate that coefficient estimates of the two zones in the
pooled model do not statistically differ from each other. Notice that we did the
comparison only for the climate zones that are adjacent to each other. Table 3.10
shows the null hypothesis is rejected for climate regions such as South, Southeast,
Northeast, Center, and East North Central. However, looking at the west side,
the West climate zone has similar effect on the land values as the Northwest and
Southwest. Furthermore, the Northwest region has similar effect on land value as
the West, Southwest, and West North Center.
After doing the pairwise comparison, we merge two of the similar zones and
again compare them with their neighbors. We combine the West and Northwest
regions and compare them with Southwest region. The F test indicates that we
can combine these three regions and consider them as one. Finally, we combine
the West, Northwest, and Southwest and compare them with the Northwest Center
region. The Chow test shows that we can merge all these four regions to one climate
zone which we call “West 4” accordingly.
Therefore, the number of climate zones is reduced to six instead of nine. We do
the rest of the analysis based on six climate regions.
3.4.2 The Results for the Past (1997-2007)
Table 3.11 shows the within R2 estimates when estimating the log of the farmland
values as a function of different types of fixed effects. This is done to examine
whether there is still much variation to be explained after controlling for fixed effects.
The first column only controls for the climate zone fixed effects and has a low
within R2 of zero. Controlling for the year and year-state fixed effects in the second
and third columns increases the within R2 to 0.38 and 0.56.
88
Table 3.12 illustrates the effect of various measures of climate on the log of the
farmland value per acre. For the moment, we focus on the Hot and Cold Events,
which are the new factors that we incorporated. In the first column, the extreme
event parameters are identified from variation within counties across time, across
counties at the same time, and across time and space. The Hot and Cold Event
estimates both have an expected negative impact on the farmland values; however,
the Hot Event coefficient is not statistically significant. The second specification’s
identifying variation is within county across time. A one unit increase in Cold Events
decreases the farmland value per acre by 0.86 percent. The Hot Event’s coefficient
is positive and statistically significant. We examine the variation within the same
state in the same year in the third column using county fixed effects. The extreme
weather parameters both have the expected sign, but only the Cold Event coefficient
is statistically significant. In the last column, the extreme events parameters are
identified from within county comparisons across time controlling for shocks in each
state-year. The parameters both have statistically negative impact on the land
values. A one unit increase in Hot Events and Cold Events reduce the land values
per acre by 0.22 and 0.16 percent. The Hot Event estimate is the most negative
compared to the other specifications in the table. The last specification has the
smallest effect of the Cold Event on the land value.
We estimate separate marginal effects of the explanatory variables across climate
zones to be able to distinguish each zone’s marginal effects on the farm value per
acre. We interact the zone dummies with all the control variables and run Equation
3 again. Table 3.13 indicates the estimation result. The statistical significance and
magnitude of the marginal effects of the control variables are different among climate
zones. For example, an increase in population density in the West4 has larger effect
on the land value compared to the rest of the U.S. Further, the average temperature
and precipitation in July have statistically negative effects on the land values in the
West4, South, and Southeast regions. Also, although in a pooled model the Hot
and Cold Events have statistically negative effects on farmland value, here we see
that the coefficients are statistically positive in the Center region. Finally, to see
89
if the structural differences between the marginal effects are statistically significant
across climate zones, we run different regressions considering each climate zone as
a base group in each regression. In the first six sets of regressions, we control
only for state year fixed effects (Tables 15A to 20A). In the second six sets of
regressions we control for both county and state year fixed effects (Tables 15B to
20B). The reference group is changing in each table. The stars show the level of
the significance. The important point is that no matter which variation we apply,
some of the coefficients are statistically different from the one in the reference group.
This shows how important it is to consider the differences in marginal effects for
each climate zone separately instead of treating them as identical across the country.
3.5 Conclusion
This study shows the importance of considering spatial heterogeneity in estimating the impact of climate change on U.S. agriculture based on the Ricardian
approach. We investigates the effect of climate change on U.S. agriculture using
county-level data for the 1997, 2002, and 2007 census years.
Compared to
previous contributions, we pay particular attention to the spatial heterogeneity
among the climate zones and include the presence of extreme weather events.
We test if climate has different marginal effects on the land values in the various
climate zones.
Furthermore, while current approaches use projected climate
variables derived from coarse resolution generation global climate models (GCMs),
we use data at a much finer resolution by using dynamically downscaled simulations which are the combination of the GSMs and Regional climate models (RCMs).
We estimate separate marginal effects of the explanatory variables across
climate zones to be able to distinguish each zone’s marginal effects on the farm
value per acre. The statistical significance and magnitude of the marginal effects of
the control variables are different among climate zones. Although in a pooled model
the Hot and Cold Events have statistically negative effects on farmland value, here
90
we see that the coefficients are statistically positive in the Center region.
Finally, to examine whether the marginal effects vary in a statistically significant
fashion across climate zones, we run different regressions considering each climate
zone as a base group in each regression. In the first six sets of regressions, we control
only for state year fixed effects . In the second six sets of regressions we control for
both county and state year fixed effects. The important point is that no matter
which variation we apply, some of the coefficients are statistically different from the
one in the reference group. This shows how important it is to consider the marginal
effect for each climate zone separately instead of assuming the same marginal effect
throughout the U.S.
91
Table 3.1: Summary Statistics - Northwest
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
N
Note: values in 2007 constant dollar
Mean
Std. Dev.
59339.605
94845.217
44.281
72.266
180.733
128.637
3.914
3.592
189.059
128.809
2.177
1.439
202.508
128.715
0.728
0.663
191.268
129.159
2.294
2.506
0.98
1.369
10.83
2.979
387708.723 378476.139
670855.782 566314.092
3264.336
3076.488
7.702
0.883
339
Table 3.2: Summary Statistics - West
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: values in 2007 constant dollar
Mean
206855.6
69.946
187.012
2.742
194.501
0.901
207.07
0.194
197.227
0.664
3.188
9.898
513319.148
1865789.746
4213.164
7.913
Std. Dev.
382482.391
86.256
129.085
3.076
128.682
0.798
128.352
0.19
128.678
1.007
2.508
2.151
545410.853
2494569.288
4869.8
0.971
N
185
185
185
185
185
185
185
185
185
185
185
185
176
185
183
183
92
Table 3.3: Summary Statistics - Southwest
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
73311.188
29.829
179.706
0.807
190.58
1.093
204.744
1.478
192.674
0.921
2.466
7.59
815238.53
562565.136
1503.412
6.819
Std. Dev.
242059.756
67.77
128.852
0.691
127.786
0.857
128.693
0.746
129.972
0.559
2.66
1.91
848288.351
503668.13
3489.254
0.916
N
393
393
393
393
393
393
393
393
393
393
393
393
372
389
386
386
Table 3.4: Summary Statistics - West North Center
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farm value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
46835.14
65.413
183.665
2.129
194.085
2.638
207.298
2.811
197.088
2.648
3.009
10.402
318911.791
507217.709
2455.807
7.553
Std. Dev.
98146.979
77.941
127.988
1.686
127.467
1.413
128.34
1.643
129.774
1.527
2.73
3.157
390442.044
558760.413
2104.989
0.751
N
8509
8509
8509
8509
8509
8509
8509
8509
8509
8509
8509
8509
8461
8501
8494
8494
93
Table 3.5: Summary Statistics - South
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
34915.899
44.102
188.706
2.257
198.723
3.123
209.853
3.448
200.913
3.366
5.489
10.784
380163.144
435447.563
1505.015
7.148
Std. Dev.
55283.007
58.897
127.703
1.83
126.889
1.655
128.582
2.087
129.407
1.868
2.683
2.884
292894.274
273935.256
842.513
0.618
N
1907
1907
1907
1907
1907
1907
1907
1907
1907
1907
1907
1907
1901
1906
1904
1904
Table 3.6: Summary Statistics - Southeast
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
52111.258
93.515
190.312
3.09
198.045
2.63
208.694
3.952
201.385
3.208
3.294
12.137
101119.077
316950.833
3368.493
8.012
Std. Dev.
66746.392
81.986
128.867
1.095
127.152
1.148
128.655
1.228
128.617
1.115
2.473
3.57
78966.049
310144.239
1867.119
0.453
N
1461
1461
1461
1461
1461
1461
1461
1461
1461
1461
1461
1461
1455
1459
1459
1459
94
Table 3.7: Summary Statistics - Center
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
39964.056
85.321
183.231
2.494
193.887
3.125
207.062
2.635
197.175
2.617
1.731
12.216
177588.156
495801.603
2750.748
7.846
Std. Dev.
49727.896
79.172
127.872
0.947
127.29
1.218
128.339
0.98
129.896
1.082
1.575
2.353
109108.827
376332.839
1067.162
0.389
N
1879
1879
1879
1879
1879
1879
1879
1879
1879
1879
1879
1879
1877
1879
1879
1879
Table 3.8: Summary Statistics - East North Center
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland (acre)
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
38898.946
59.049
175.638
0.906
189.324
2.412
205.389
2.993
192.865
2.531
1.468
7.414
258554.965
671052.257
2646.751
7.810
Std. Dev.
45601.236
67.987
126.969
0.716
128.08
1.023
128.004
1.407
130.973
0.851
1.82
1.353
155865.322
429349.405
941.158
0.397
N
961
961
961
961
961
961
961
961
961
961
961
961
958
961
961
961
95
Table 3.9: Summary Statistics - Northeast
Variable
Population
Population density
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot event frequency
Cold event frequency
Farmland
Farmland value
Farmland value per acre
Log of farmland value per acre
Note: Values in 2007 constant dollar
Mean
85632.151
133.068
180.25
2.149
189.723
3.129
203.446
2.397
194.109
2.565
0.274
9.355
110903.417
371702.686
4038.252
8.1
Std. Dev.
66156.289
101.092
128.02
0.638
127.472
1.36
128.124
0.887
130.032
1.268
0.774
1.924
73976.955
300972.764
3953.638
0.588
N
517
517
517
517
517
517
517
517
517
517
517
517
516
516
516
516
96
Table 3.10: Chow Test
Climate Regions
Nw W SW WNCen S SE ENCen C NE
North West
X
X
X
West
X
X
South West
X
X
5
5
West North Center
X
5
5
5
South
5
5
5
5
South East
5
5
5
East North Center
5
5
Center
5 5
5
5
North East
5
5
The Chow test is done for only the climate regions that are adjacent.
Check marks: Cannot reject the null hypothesis, and so we can pool the two regions.
97
Table 3.11: Control Only for Fixed Effects in the Model
Fixed effects
climate zone
Year
Year-state
County
N
R2 (within)
(1)
yes
(2)
(3)
yes
(4)
8494
0.38
(6)
yes
yes
yes
8494
0.56
yes
yes
yes
8494
0.00
(5)
8494
0.56
yes
8494
0.38
Dependent variable: log of the value of the land per acre
Standard errors are in parentheses
8494
0.56
98
Table 3.12: Effect of Climate on Land Values
Population density
Population density square
January temperature
January precipitation
April temperature
April precipitation
July temperature
July precipitation
October temperature
October precipitation
Hot Event
Cold Event
Constant
(1)
.0108911∗∗∗
(.0003499)
-.0000205∗∗∗
(1.03e-06)
.0207079∗∗∗
(.0015374)
.0070436∗∗
(.0031685)
-.0285335∗∗∗
(.0039409)
-.0062709∗∗∗
(.0020935)
-.0200398∗∗∗
(.0028481)
-.0052232∗∗
(.0021624)
.0274608∗∗∗
(.001675)
.0146347∗∗∗
(.0022229)
-.0010818
(.0008374)
-.0061158∗∗∗
(.0011172)
7.613559∗∗∗
(.0509266)
(2)
.009503∗∗∗
(.0012022)
-.0000159∗∗∗
(2.21e-06)
.0246165∗∗∗
(.0017524)
-.0066635∗
(.0034151)
-.0120193∗∗∗
(.0038125)
-.006612∗∗∗
(.0020823)
-.0331554∗∗∗
(.0029059)
-.008637∗∗∗
(.0021606)
.0203432∗∗∗
(.0015695)
.002956
(.0022659)
.0025622∗∗∗
(.0008274)
-.0086834∗∗∗
(.001797)
7.94954∗∗∗
(.0833759)
Y
County FE
Year-state FE
N
8494
8494
R2
0.18
0.20
Dependent variable: log of the value of the land per acre
Standard errors are in parentheses
(3)
.007176∗∗∗
(.0003279)
-.0000139∗∗∗
(9.19e-07)
.0073757∗∗
(.0032901)
.0170788∗∗∗
(.0036607)
-.0118179∗
(.0060563)
.0059995∗∗
(.002759)
-.0566639∗∗∗
(.005609)
-.0055112∗∗
(.0024381)
.0209097∗∗∗
(.0046691)
.0215846∗∗∗
(.0028795)
-.00105
(.0011571)
-.0023965∗∗∗
(.0006508)
19.41571∗∗∗
(1.871941)
Y
8494
0.54
(4)
-.0018484
(.0015375)
2.15e-07
(2.66e-06)
-.0005569
(.0039477)
-.0028808
(.0038558)
-.0190122∗∗∗
(.0062151)
-.0018096
(.002645)
-.0144192∗∗
(.0063906)
-.0035824
(.0023981)
.0099965∗
(.0052864)
.005547∗
(.0029546)
-.0025514∗∗
(.0010826)
-.0016772∗∗∗
(.0005798)
12.10962∗∗∗
(1.972285)
Y
Y
8494
0.57
Population density
West4
South
South East
0.0140***
0.00817***
0.00463***
(0.00148)
(0.000695)
(0.000417)
Population density square -2.78e-05*** -1.83e-05*** -8.13e-06***
(3.75e-06)
(2.32e-06)
(1.30e-06)
January temperature
-0.00424
0.0619***
0.0251*
(0.00546)
(0.0100)
(0.0139)
January precipitation
0.0357***
0.0411***
-0.0195**
(0.00881)
(0.00720)
(0.00770)
April temperature
0.0229*
-0.101***
-0.0217
(0.0118)
(0.0155)
(0.0219)
April precipitation
0.0221**
0.00165
0.00226
(0.0108)
(0.00429)
(0.00717)
July temperature
-0.0840***
-0.0835***
-0.0575***
(0.00968)
(0.0156)
(0.0145)
July precipitation
-0.0312***
-0.00451
-0.0256***
(0.00866)
(0.00407)
(0.00652)
October temperature
0.0171*
0.0658***
-0.0758***
(0.0103)
(0.00809)
(0.0134)
October precipitation
0.0557***
0.0130**
0.0100
(0.00652)
(0.00514)
(0.00766)
Hot Event
0.000178
-0.00444*
0.000902
(0.00625)
(0.00230)
(0.00218)
Cold Event
-0.00150**
-0.00498**
0.00296
(0.000595)
(0.00231)
(0.00980)
N
8494
0.55
R2
The table shows estimation result for one regression.
Dependent variable: log of the value of the land per acre
Standard errors are in parentheses, clustered on county level.
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Center
0.00494***
(0.000373)
-9.62e-06***
(1.05e-06)
-0.0212***
(0.00767)
-0.0269***
(0.00645)
-0.0254
(0.0157)
-0.0285***
(0.00634)
-0.0145
(0.0141)
-0.00874
(0.00563)
-0.00330
(0.0103)
0.0171***
(0.00575)
0.00417***
(0.00160)
-0.00470*
(0.00280)
East North Center
0.00537***
(0.000687)
-1.14e-05***
(2.27e-06)
-0.0233*
(0.0128)
-0.0962***
(0.0180)
0.0384***
(0.0132)
0.00686
(0.00913)
-0.0193
(0.0145)
-0.00452
(0.00597)
0.0487***
(0.0145)
0.0113
(0.0116)
-0.0121***
(0.00341)
0.00128
(0.00116)
Table 3.13: Effect of Climate on Land Values - Including State-Year Fixed Effects
North East
0.00152
(0.00107)
6.02e-07
(2.73e-06)
0.00577
(0.0205)
0.00936
(0.0239)
0.0910**
(0.0363)
-0.00427
(0.0199)
-0.0708
(0.0633)
0.00669
(0.0218)
0.0875
(0.0744)
0.0472***
(0.0174)
-0.00165
(0.0151)
0.000931
(0.00430)
99
Population density
West4
South
South East
0.000389
0.00173
-0.00296
(0.00327)
(0.00625)
(0.00351)
Population density square
9.45e-07
-7.22e-06
2.51e-07
(6.57e-06) (1.15e-05)
(6.95e-06)
January temperature
0.0157***
-0.0250**
-0.0147
(0.00538)
(0.0124)
(0.0156)
January precipitation
0.00867
-0.0196
-0.0223*
(0.00934)
(0.0120)
(0.0120)
April temperature
-0.00674
-0.0420**
-0.0484
(0.0112)
(0.0202)
(0.0316)
April precipitation
0.00628
-0.00818
0.00106
(0.0104)
(0.0111)
(0.0130)
July temperature
-0.0189*
-0.0568***
-0.0177
(0.0104)
(0.0203)
(0.0265)
July precipitation
-0.0204**
0.0172*
-0.00347
(0.00810)
(0.00894)
(0.0106)
October temperature
0.0335***
-0.0193
-0.0990***
(0.0117)
(0.0146)
(0.0194)
October precipitation
0.0175**
-0.0264***
-0.0112
(0.00697)
(0.00887)
(0.0105)
Hot Event
-0.0177***
0.0154**
0.0219***
(0.00555)
(0.00605)
(0.00608)
Cold Event
-0.00157**
0.00459*
0.00174
(0.000694)
(0.00245)
(0.0103)
N
8494
0.59
R2
The table shows estimation result for one regression.
Dependent variable: log of the value of the land per acre
Standard errors are in parentheses, clustered on county level.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Center
-0.00289
(0.00360)
-1.16e-06
(7.22e-06)
-0.0394***
(0.0117)
-0.0347***
(0.0120)
-0.0188
(0.0196)
-0.0288**
(0.0123)
0.0600***
(0.0221)
0.0231**
(0.00984)
-0.0291*
(0.0170)
0.00567
(0.00908)
0.0208***
(0.00580)
0.000267
(0.00285)
East North Center
-0.000928
(0.00293)
-4.12e-06
(5.71e-06)
-0.0773***
(0.0143)
-0.0959***
(0.0204)
0.0362***
(0.0125)
0.0185*
(0.0101)
-0.0206
(0.0201)
-0.00840
(0.00581)
-0.000604
(0.0196)
0.0173
(0.0122)
-0.00387
(0.00364)
0.00114
(0.00108)
North East
-0.0176*
(0.00979)
2.55e-05
(1.57e-05)
-0.0501**
(0.0251)
0.00994
(0.0256)
-0.00455
(0.0363)
-0.0171
(0.0205)
-0.0211
(0.0731)
-0.0136
(0.0211)
0.0749
(0.0812)
0.0416***
(0.0157)
0.00370
(0.0127)
-0.000640
(0.00424)
Table 3.14: Effect of Climate on Land Values - Including County and State-Year Fixed Effects
100
101
Table 15A: Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: West4
Variables
Base
S
SE
C
ENCen NE
Population density
*** *** *** ***
***
***
Population density square ***
** *** ***
***
***
January temperature
*** **
*
January precipitation
***
*** ***
***
April temperature
*
***
*
**
*
April precipitation
**
*
***
July temperature
***
***
***
July precipitation
*** ***
**
**
October temperature
*
*** ***
*
October precipitation
*** *** *** ***
***
Hot event
*
Cold event
**
**
The table shows estimation results of one regression. West4 region is dropped.
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Table 15B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: West4
Variables
Base
S
SE
C
ENCen NE
Population density
*
Population density square
January temperature
***
**
***
***
**
January precipitation
*
***
***
April temperature
**
**
April precipitation
**
July temperature
*
***
***
July precipitation
**
*
**
October temperature
***
***
*
October precipitation
**
***
Hot event
***
** *** ***
**
Cold event
**
*
**
The table shows estimation results for one regression. West4 region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
102
Table 16A: Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: South
Variables
Base West4 SE
C
ENCen
Population density
***
***
*** ***
***
Population density square ***
**
*** ***
**
January temperature
***
***
** ***
***
January precipitation
***
*** ***
***
April temperature
***
***
*** ***
***
April precipitation
*
***
July temperature
***
***
***
July precipitation
***
***
October temperature
***
***
*** ***
October precipitation
**
***
Hot event
*
*
***
*
Cold event
**
**
The table shows estimation results of one regression. South region
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
NE
***
***
**
***
*
is dropped.
Table 16B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: South
Variables
Base West4 SE
C
ENCen NE
Population density
*
Population density square
*
January temperature
**
***
January precipitation
***
April temperature
***
**
***
April precipitation
***
*
July temperature
***
***
***
**
July precipitation
*
***
October temperature
***
October precipitation
***
***
**
***
Hot event
**
*
*
Cold event
*
The table shows estimation results of one regression. South region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
103
Table 17A: Effect of Climate on Land Values - Including State-Year Fixed Effects Reference Group: Southeast
Variables
Base West4
S
C
ENCen NE
Population density
*** ***
***
***
Population density square *** ***
***
***
January temperature
*
**
** ***
**
January precipitation
**
***
***
***
April temperature
*
***
**
***
April precipitation
***
July temperature
***
**
*
July precipitation
***
***
*
**
October temperature
*** ***
*** ***
***
**
October precipitation
***
*
Hot event
*
***
Cold event
The table shows estimation results of one regression. Southeast region is dropped.
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Table 17B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Southeast
Variables
Base West4
S
C
ENCen NE
Population density
**
Population density square
January temperature
***
*
January precipitation
*
*
***
April temperature
*
***
April precipitation
***
July temperature
**
July precipitation
***
*** ***
*
October temperature
*** ***
*** ***
***
*
October precipitation
*
**
Hot event
*
***
*
*
Cold event
The table shows estimation results of one regression. Southeast region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
104
Table 18A: Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: Center
Variables
Base West4
S
SE ENCen NE
Population density
*** ***
***
***
Population density square *** ***
***
***
January temperature
*** *
*** ***
January precipitation
*** ***
***
***
April temperature
**
***
***
***
April precipitation
*** ***
*** ***
***
July temperature
***
*** **
July precipitation
**
*
October temperature
*** ***
***
October precipitation
*** ***
Hot event
***
***
***
Cold event
*
**
The table shows estimation results of one regression. Center region is dropped.
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Table 18B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Center
Variables
Base West4
S
SE ENCen NE
Population density
*** ***
***
***
Population density square
January temperature
**
***
***
January precipitation
*** ***
***
April temperature
***
April precipitation
*** **
*** ***
***
July temperature
**
***
*** **
**
July precipitation
**
***
October temperature
*
***
October precipitation
***
***
*
Hot event
*
***
*
*
Cold event
The table shows estimation results of one regression. Center region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
105
Table 19A: Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: East North Center
Variables
Base West4
S
SE
C
NE
Population density
*** ***
***
***
Population density square *** ***
**
***
January temperature
*
*** **
January precipitation
*** ***
*** *** *** ***
April temperature
***
*** ** ***
April precipitation
***
July temperature
***
***
*
July precipitation
**
**
October temperature
*** *
*** ***
October precipitation
***
*
Hot event
*** *
*
*** ***
Cold event
**
**
**
The table shows estimation results of one regression. ENCen region is dropped.
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
Table 19B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: East North Center
Variables
Base West4
S
SE
C
NE
Population density
Population density square
*
January temperature
***
***
*** *** ***
January precipitation
***
***
*** *** *** ***
April temperature
***
**
*** *** ***
April precipitation
*
*
***
July temperature
**
**
July precipitation
*
October temperature
***
October precipitation
**
Hot event
**
*
*
Cold event
**
The table shows estimation results of one regression. ENCen region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
106
Table 20A: Effect of Climate on Land Values - Including State-Year Fixed Effects
- Reference Group: Northeast
Variables
Base West4
Population density
***
Population density square
***
January temperature
January precipitation
April temperature
**
*
April precipitation
July temperature
July precipitation
October temperature
October precipitation
***
Hot event
Cold event
The whole table shows estimation results of
Estimation includes state year fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
S
***
***
**
SE
***
***
C
***
***
***
***
***
*
**
*
ENCen
***
***
***
*
one regression. NE region is dropped.
Table 20B: Effect of Climate on Land Values - Including County and State-Year
Fixed Effects - Reference Group: Northeast
Variables
Base West4
S
SE C ENCen
Population density
*
*
*
Population density square
*
*
January temperature
**
**
*
January precipitation
***
April temperature
April precipitation
July temperature
July precipitation
October temperature
*
October precipitation
***
*** **
Hot event
Cold event
The table shows estimation results of one regression. NE region is dropped.
Estimation includes state year fixed effects and county fixed effects.
*** p <0.01, ** p<0.05, * p<0.1
107
Figure 3.1: Climate Regions
Note:
Through climate analysis, National Climatic Data Center scien-
tist have identified nine climatically consistents regions within the contiguous
United States. Source: http://www.ncdc.noaa.gov/monitoring-references/maps/usclimate-regions.php
108
Figure 3.2: Climate Data
Top: Coarse global climate model precipitation simulated over the U.S. (GCMs
with 200 - 300 km spatial resolution)
Bottom: Dynamically downscaling global fields using a regional climate model
will allow us to explicitly account for changes in the intensity and frequency of
extreme events at the local scale. (Spatial resolution of 35km - Dominguez et al.,
2012)
109
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