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.gitignore

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 # ---> LaTex
+*.bcf
+*.run.xml
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CREP_Interaction.tex

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+
+
+% ======================== Set up the environment to read the SLV Hedonic paper
+\documentclass{article}
+% ------------------------------------------------- Packages & Setup
+
+\usepackage{svg}
+\usepackage{pdflscape,array}
+\usepackage{array}              % For inserting large multi-page tables
+\usepackage{longtable}
+\usepackage{calc}
+\usepackage{multirow}
+\usepackage{dcolumn}
+\usepackage{threeparttable} % to insert the notes
+\usepackage{float}
+% \usepackage{caption}
+\usepackage[tableposition=top]{caption}
+% \usepackage{subcaption}
+\usepackage{ragged2e}
+\usepackage{placeins}
+\usepackage{tabularx,booktabs}
+\usepackage{subfig}
+\usepackage{graphicx}
+\usepackage{setspace}
+\usepackage{amssymb}
+\usepackage{quoting}
+\usepackage[style=apa,natbib=true]{biblatex}
+\usepackage{tikz}
+\usepackage{pdflscape}          % For inserting landscape-mode objects
+\usepackage{amsmath}            % For matrices
+\usepackage{listings}           % For inserting programming code
+\usepackage{rotating}           % For inserting sideways tables and figures
+\usepackage{soul} % for the command \hl
+% To allow ifdef checking
+\usepackage{etoolbox}
+\usepackage{paralist}
+\usepackage[inline]{enumitem}
+\usepackage{blindtext}
+\usepackage[printonlyused]{acronym}
+\usepackage{nameref}            % For referencing chapters numbers in main abastract
+\usepackage{hyperxmp}
+\usepackage[hidelinks]{hyperref}           % For referencing though out the document. Can change, look at user manual how to change
+\usepackage{cleveref}
+% ========================Other formating
+\graphicspath{{Figures}}
+\setlength\bibitemsep{\baselineskip}
+\addbibresource{supporting-files/FinalThesis.bib}
+
+% ==========================Title Setup
+
+\title{Policy Interactions of Water Conservation Programs. Is Efficiency Always Efficient?}
+\author{Alexander Gebben}
+\author{Steven Smith}
+\date{\today}
+
+% =======================Documents Start
+\begin{document}
+\maketitle
+\begin{abstract}
+	\input{body/abstract}
+\end{abstract}
+\newpage
+\input{supporting-files/symbols-and-abbreviations}
+\newpage
+% ========================Main Body
+\input{body/Intro}
+\input{body/background}
+\input{Sections/Data.tex}
+\input{Sections/Strategy.tex}
+\input{Sections/Results.tex}
+\input{body/conclusion}
+% ==========================Appendices
+\newpage
+\printbibliography
+\newpage
+\input{appendix.tex}
+\end{document}

Sections/A_All_Years_2011_Reg.tex

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+\section{Yearly Response Since 2011}
+In this appendix the \ac{ATT} of \ac{SBD1} policies is provided as an event study, with the average response of wells in \ac{CREP} reported independently from other \ac{SBD1} wells\footnote{Wells are only eligible for \ac{CREP} if they are in \ac{SBD1}.}. This provides a picture of how \ac{CREP} wells responded to \ac{SBD1} policies. Since the response to the pumping fee of \ac{CREP} wells is significantly different from other \ac{SBD1} wells there is evidence that wells were not enrolled in \ac{CREP} randomly. Rather wells were selectively placed into \ac{CREP} based on groundwater yield after maximizing farm profits in response to the pumping fee\footnote{The probit model of \cref{REG:SELECT_PROBIT} finds the response to the pumping fee to be a significant contributing factor to \ac{CREP} enrollment after controlling for other factors which may induce enrollment, such as crop choice.}. 
+
+\cref{2011CREPVSBD1} provides the regression table of this exercise. \cref{FIG:SBD1ALL} and \cref{FIG:CREPALL} are graphical representation of the results from \cref{2011CREPVSBD1}. 
+
+\input{Tables/REG_PRE_CREP_EVENT.tex}
+
+\cref{FIG:SBD1ALL} is a coefficient plot which includes only the \ac{SBD1} year interactions, providing a graphic representation of how the average \ac{SBD1} well responded to the mix of conservation policies across time. 
+\begin{figure}
+    \centering
+    \includegraphics[width=0.7\textwidth]{PRE_CREP_SBD1.pdf}
+    \caption{Subdistrict policy effect on non-CREP wells}
+    \label{FIG:SBD1ALL}
+\end{figure}
+
+\FloatBarrier
+
+\cref{FIG:CREPALL} is a coefficient plot of only the additional response of \ac{CREP} wells. These are the yearly coefficients in \cref{2011CREPVSBD1} and are in addition to the \ac{SBD1} average effect of \cref{FIG:SBD1ALL}.
+\begin{figure}
+    \centering
+    \includegraphics[width=\textwidth]{PRE_CREP_CREP.pdf}
+    \caption{Subdistrict policy effect on CREP wells}
+    \label{FIG:CREPALL}
+\end{figure}
+
+Of note is the time path of \ac{CREP} extraction reductions. In every year with a pumping fee \ac{CREP} wells extract less water than other \ac{SBD1} wells. In 2011, the pumping fee came into effect at \$45 dollars per \ac{AF}. In this year \ac{CREP} wells pumped 12.21 fewer \ac{AF} than other \ac{SBD1} wells. In the following year, the pumping fee was raised to \$75 per \ac{AF} and \ac{CREP} wells further lowered pumping rates by 33 \ac{AF} compared with other \ac{SBD1} wells. On average the initial pumping fee reduced \ac{SBD1} well pumping by 0.13 \ac{AF} per dollar, and \ac{CREP} wells by 0.409 \ac{AF} per dollar. In 2012 the \ac{SBD1} wells reduced groundwater extraction by 0.578 \ac{AF} per dollar, and \ac{CREP} wells by 1.03 \ac{AF} per dollar. 
+
+The decline of water use continued after 2016, which is the first year of the \ac{CREP} program. By 2020, all \ac{CREP} wells are entered in the program and the water applied to land constitutes the allowable volumes for approved cover crops.

Sections/A_CREP_All_Reg.tex

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+\section{\ac{CREP} Program Yearly Estimates}
+To illustrate how the effects of the \ac{CREP} program policy have changed over time, the yearly estimates from an event study are provided in \cref{REG_CREP_PERIODS}. This helps assess pre-trend variation and provides a rich description of the dynamic policy outcomes.
+
+\input{Tables/REG_CREP_PERIODS.tex}

Sections/A_CREP_Goals.tex

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+\section{\label{A_CREP_GOALS}Goals of the Federal Fallowing Incentives CREP Program}
+The main goals of the Conservation Reserve Enhancement Program (CREP) within the subdistricts are (\cite{sbd12013a}):
+\begin{enumerate}[label=(\roman*)]
+    \item Enroll 40,000 acres of cropland.
+    \item Reduce irrigated water use by 60,060 acre-feet per year.
+    \item Reduce annual fertilizer and pesticide application from enrolled acres by approximately 3,650 tons per year.
+    \item Restore and enhance a minimum of 750 acres of degraded temporary and permanent wetlands.
+    \item Increase streamflows in streams associated with the watershed within the project area.
+    \item Reduce energy consumption over the 15-year term of the CREP contract's two hundred million kW-hours.
+    \item Reduce the percentage of groundwater test wells containing nitrogen (NO\textsubscript{3}) levels above EPA standards.
+    \item Enable recovery of the groundwater levels in the unconfined aquifer of the closed basin by reducing consumptive withdrawals.
+\end{enumerate}

Sections/A_CREP_To_Near_Reg.tex

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+\section{Number of Neighboring Wells to CREP Wells}
+%\cref{REG:FITNEIGHBOR} provides an analysis of how adding wells into \ac{CREP} changes the number of wells that are in the neighborhood of a \ac{CREP} well. This rate changes the cumulative \ac{CREP} neighborhood affect. The individual affect on a neighboring well is estimated in \cref{MAINREGTBL}. \cref{REG:FITNEIGHBOR} is a regression which predicts the number of wells within a half mile of a \ac{CREP} well in each year of \ac{CREP}. The results are reported without any transformation, as a log-linear model, and a log-log model. This rate combined with the individual neighboring well effects can be used to identified the total average affect on pumping from adding a new \ac{CREP} well.
+\cref{REG:FITNEIGHBOR} provides an analysis of how adding wells into \ac{CREP} changes the number of wells that are in the neighborhood of a \ac{CREP} well. This rate changes the cumulative \ac{CREP} neighborhood affect. The individual effect on a neighboring well is estimated in \cref{MAINREGTBL}. \cref{REG:FITNEIGHBOR} is a regression which predicts the number of wells within a half mile of a \ac{CREP} well in each year of \ac{CREP}. The results are reported in three formats: without any transformation, using a log-linear model, and using a log-log model. This rate, combined with the effects of individual neighboring wells, can be used to identify the total average effect on pumping from adding a new \ac{CREP} well.
+
+\input{Tables/NEIGHBOR_FIT.tex}
+
+These results indicate that on average, adding one new \ac{CREP} well induces 10.64 additional neighboring wells, or that a 1\% increase in the number of \ac{CREP} wells adds 1.3\% more neighborhood wells. This is completed to find the average additionality of the neighborhood effect from \ac{CREP} enrollment. However, the average effect differs from the marginal affect. These results are useful to explain the total policy effects from \ac{CREP} when including spillover effects. However, they cannot be used to estimate the marginal effect of adding a new \ac{CREP} well under current enrollment levels. For this, refer to \cref{FIG:BOX}.

Sections/A_Distance_Reg.tex

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+\section{Neighborhood Effect Estimates at Various Radii}
+\label{A:RAD}
+The neighborhood pumping effect of \ac{CREP} enrollment for various radii are estimated in \cref{REGRAD}\footnote{The year number is the time after a \ac{CREP} is first enrolled at the specified distance. For example, if a well is enrolled in \ac{CREP} in 2015, wells that are within a half mile of the \ac{CREP} well are in \(Year=0\) in 2015, \(Year=5\) in 2020, and \(Year=-5\) in 2010.}. This provides a robustness check of the radius selected. The values are reported as an event study, showing how the coefficients vary over time. 
+\input{Tables/REG_DIFF_RAD.tex}
+The results of \cref{REGRAD} suggest that the neighborhood effect is sensitive to the radius of impact selected. The half mile radius shows the most consistent policy effect, with a statistically significant decline in well extraction in the first year of the program. Generally, this effect peaks two years after the policy begins, and then dampens in magnitude and statistical significance after this point.
+
+It is somewhat surprising that the smaller quarter mile radius has a less consistent policy effect, with only the peak period of the policy effect (two years after enrollment) showing a significant negative neighborhood effect. This can be explained in a few ways. 
+
+From a data perspective, doubling the radius of impact includes four times the area\footnote{\(4=\frac{\pi\cdot (2r)^{2}}{\pi\cdot r^{2}}\)}. The small radius therefore reduces the data set of the neighborhood effect by more than half. This lower data availability results in more noise in the estimate, preventing the effect from being picked up. 
+
+There are also causal explanations of this result. For example, cones of depressions around a well are present at close distances. At the quarter mile radius, the reduction in pumping of \ac{CREP} wells will raise the water table by more than wells at one half mile. As a result, the cost of pumping groundwater decreases more for close wells, creating an incentive to increase pumping. This generates an incentive counter to the pro-social generalized pumping reductions seen at farther distances. On net, very close wells may reduce extraction rates by less than more distant wells, with closer wells pumping about the same as before \ac{CREP} enrollment but with a lower operating cost.
+
+A second causal explanation is that using too small of a radius shifts treated wells into the control group. If the neighborhood pumping effect is approximately constant across the effected radius, then using a small radius will bias the results towards zero. For example, if the driver of decreased pumping is having a line of sight to the \ac{CREP} well, then a well within a quarter mile will reduce pumping at a similar rate as a well at a half mile. In that case, going from a half mile radius down to a quarter mile radius shifts approximately four-fifths of treated wells to the control group. In turn, this attributes some of policy impact to year fixed effects and the results lose significance. Due to these factors, the lower magnitude of neighborhood effects at the quarter mile radius is not viewed as a major concern in the model outcomes. 
+
+At the one-mile radius, the average neighborhood effect appears to be close to a random process, which is expected when the radius of impact is identified correctly. There are four years at the two-mile radius where the results are significant, and generally positive.  Most years are not significant, these results are attributed partially to random variation. However, as can be seen in \cref{FIG:MAP}, nearly all wells in \ac{SBD1} are within two miles of a \ac{CREP} well. As a result, the two-mile year interaction coefficients represent the pumping change relative to a small set of wells that are at the far edges of \ac{SBD1} or at the center.

Sections/A_Fallow_Prog.tex

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+\section{Fallow Program Estimates}
+\label{A:FALPROG}
+This appendix presents event study estimates for the \ac{SBD1} four-year fallowing program, comparable to the results provided in \cref{FIG:EVENTCREP} and \cref{FIG:EVENTNEAR} for the \ac{CREP} policy outcomes.
+
+\cref{FIG:EVENTFAL} provides the coefficient plot of the four-year fallowing program for wells enrolled in the program. Pre-trend cannot be rejected, although there is a strong response to the policy initiation\footnote{Year=0}. It is not clear why pumping rates increase in the year following entrance into the program. The most likely explanation is that the farmers rotate fallowed fields. The program allows farmers to rotate which fields are fallowed. After a well is enrolled in the contract that well may be permitted to reactivate in the following year, so long as a new well is enrolled. This would explain the large confidence intervals in year one.
+\FloatBarrier
+\begin{figure}[h]
+    \centering
+    \includegraphics[width=\textwidth]{FALLOW_EVENT_STUDY.pdf}
+    \caption{Fallow program event study}
+    \label{FIG:EVENTFAL}
+\end{figure}
+
+\cref{FIG:FALPROGNEAR} provides the coefficient plot for the neighborhood spillover effects of the four-year fallowing program. These results are not significant, and the hypotheses that there is not a neighborhood spillover effect cannot be rejected. This can be explained by the shorter contract term. Neighboring wells should not adjust long-run expectations of neighboring pumping based on the entrance into this temporary program.
+
+\FloatBarrier
+\begin{figure}
+    \centering
+    \includegraphics[width=\textwidth]{CLOSE_FAL_EVENT_STUDY.pdf}
+    \caption{Fallow program neighbor well event study}
+    \label{FIG:FALPROGNEAR}
+\end{figure}
+\FloatBarrier

Sections/A_Policies_CI.tex

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+\begin{landscape}
+\section{Total Policy Estimates}
+\label{A:CI_TBL}
+The results of \cref{RESTBL} are provided in \cref{RESTBLCI} with the inclusion of 95\% confidence intervals. The confidence intervals provide context for the sensitivity of the policy conservation outcomes, but they are reported separately to improve legibility.
+\input{Tables/Policy_Estimates_CI.tex}
+\end{landscape}
+

Sections/Abstract.tex

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+\begin{abstract}
+	Previous studies of groundwater management through fallowing have focused on isolated effects and single fallowing contract types. We identify fallow spillover effects that adjust expected property right surety along with policy interactions at the local and federal level. We focus on the application of federal fallowing incentives (CREP) for farmers in San Luis Valley, Colorado. Farmers in this environmentally sensitive region had previously self-organized to impose pumping fees that curb pumping and reduce externalities. These pumping fees are found to be highly effective in groundwater consumption but also dampen the effect of CREP. Farmers with the largest response to the subdistrict policies are found to self-select into the CREP program. Consequently, CREP conserves 33.4\% less water while program costs rise by 46.7\%. Changes to spillover effects for neighboring wells due to long-run expectations are estimated by leveraging variations in fallow contract length.  Neighbors are found to reduce pumping rates the most if wells are permanently retired, with only one-fourth the reductions for wells with a 15-year contract, and insignificant increases in pumping when fallowing a four-year term.
+\end{abstract}

Sections/Data.tex

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+\FloatBarrier
+
+\section{Data}
+The data used in the empirical analysis come primarily from \ac{CDSS}, through the HydroBase \ac{API} tools. This dataset is provided by the Colorado Division of Natural Resources and includes well attributes and pumping records, as available. \ac{SLV} wells were required by law to install pump monitors starting in 2009. This means that the pumping records for each well in the \ac{SLV} is available with two years of pre-pumping fee data available. Irrigated crop parcels are provided in geospatial files as part of this system, as well as \ac{CSV} files with the attributes of those parcels. The crop parcel is not a spatially fixed unit. Instead, it is defined as a contiguous piece of land using a unique crop type and irrigation technology combination in a given year\footnote{For example, if a particular legal parcel grows alfalfa on one half of the field and small grains in another the data set includes two different parcels for that year. If the legal parcel switches from flooding to sprinklers the next year, this too creates new crop parcel entries.}. However, wells are linked to crop parcels so the crop choice of wells can be assessed. There is some error in these estimates since the percentage of water applied to each crop is assumed to equal the crop distribution of the parcel.
+
+Geospatial data is used to generate a distance matrix between each well, this is used to identify the distance of wells from those that serve parcels enrolled in \ac{CREP}. Maps of the \ac{SLV} were used to manually create geospatial shape files of the subdistrict boundaries. Overlaying this layer with the map of wells allows the subdistrict wells to be identified. The distance is used as a cutoff radius for determining if a well is a neighbor to \ac{CREP}, which is determined by the geology of the \ac{SLV}. \cref{FIG:MAP} provides a visual summary of the distances from \ac{CREP} calculated for each well.
+
+%\FloatBarrier%
+\begin{figure}
+	\centering
+    \includegraphics[width=0.85\textwidth]{CREP_DIS_MAP}
+    \caption{Distance of wells from an enrolled \ac{CREP} well}
+    \label{FIG:MAP}
+\end{figure}
+%\FloatBarrier%
+
+ A one-half mile distance was selected for the primary results. Previous analysis in Kansas had used a larger radius of two miles based on local geologic conditions \citep{rouhirad2021}. Using such a large distance is not feasible in the \ac{SLV} since nearly all subdistrict wells are within a two-mile radius. This could lead to downward bias in the neighborhood policy estimates since some of the \ac{CREP} spillover will be attributed to the general \ac{SBD1} effect. Mitigating this is the lower permeability and separate closed basin of the \ac{SLV}. Furthermore, any neighborhood effect should be strongest closer to the well. A robustness check running neighborhood effects at each considered radius is provided in appendix \cref{A:RAD}.
+
+%\FloatBarrier
+
+
+A list of wells tied to farms enrolled in \ac{CREP} is provided by the subdistrict, and the unique groundwater ID are used to link them with pumping data \citep{sbd12023}. The unit of investigation is the well, so well level fixed effects are included to remove confounding factors. In order to observe any potential selection issues, summary statistics for wells were acquired by linking permits in the \ac{CDSS} system to wells. These statistics are provided in \cref{SUMSTAT}.
+
+%\FloatBarrier
+\input{Tables/Summary_Stats_Wells.tex}
+\FloatBarrier
+
+Upon comparison, many attributes in \ac{CREP} and subdistrict wells are in the same range. The largest discrepancy is in the pumping rate, which is found by testing the capacity of the well. These tests are not uniform and can occur anywhere from before completion to months after operation. This contributes to the large standard deviation in the \ac{CREP} group. This may suggest that wells with higher operating costs were entered into \ac{CREP} before more productive wells.
+
+Comparing subdistrict wells to other control wells suggests that the control group uses older and deeper wells, with larger production zones. This could create concerns that the control can respond asymmetrically upwards compared to the smaller subdistrict wells. However, due to the policy change bringing wells within the prior appropriations system, the legal well permits tend to create the pumping ceiling and not the maximum pumping rate. While there is still some potential for error, well level fixed effects will remove the effect of this capacity difference on the average, and such errors likely only occur in years with extremely high pumping rates.

Sections/Results.tex

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+
+\section{Results}
+
+We cover the results sequentially in time, first estimating the response of \ac{CREP} wells to the pumping fee that begins in 2011 in \cref{SEC2011} then estimating the water reductions of \ac{CREP} in \cref{SECCREP}. 
+
+\subsection{2011 Pumping Fee and Subdistrict Policies}
+\label{SEC2011}
+The results of the \ac{DID} for the pumping rate effect in 2011 are provided in \cref{REG2011}. These results provide strong statistical evidence that wells which eventually enroll in \ac{CREP} have a heightened response to the pumping fee. On average, wells in the subdistrict reduce output by 30.9 \ac{AF} per year while wells in \ac{CREP} reduce groundwater use by 62.0 \ac{AF} per year, more than double that of other wells.  
+\newpage
+\input{Tables/REG2011.tex}
+
+Given the safeguards intended to prevent entry by farmland with low levels of water use, it is worth discussing how the \ac{CREP} wells could have lower levels of water use than other subdistrict wells. To be able to enter \ac{CREP} at least one-half \ac{AF} must be applied to the cropland for four out of the six years from 2008 until 2013. The pumping fee was \$45 per \ac{AF} in 2011 and raised to \$75 in 2012. This means that pumping choices optimized with the \$75 fee were only made for two of the six years. Each groundwater user faced higher cost for those two years, but conservation effort benefits and cost can vary across groundwater users due to aquifer, well, and land characteristics \citep{manning2019,rouhirad2020,guilfoos2013,ekpe2021}. Wells that became sub-economic to operate due to the higher pumping fee in 2012 can still be enrolled in \ac{CREP} since, prior to this, they pumped more than the average. According to the policy criteria, these wells are eligible for enrollment even though there is a reasonable expectation that the reduction in water use is a permanent shift caused by new pumping costs.
+
+The effectiveness of the pumping fee interacts with the \ac{CREP} effect, significantly reducing the net gains of the program. Taking the assumption that each \ac{CREP} well would have ended up under the same steady state water use, with or without the subdistrict policies, then the 62.0 \ac{AF} per year reduction is lost from the \ac{CREP} program. This assumption is within reason, the \ac{CREP} program requires the land be managed in a particular way, only allowing cover crop to be planted. The water required to maintain this type of fallowing is independent of historic pumping rates, so each \ac{CREP} well will reach the same final steady state. 
+
+Past research has identified pumping fees as being a more cost-efficient way to manage water than paying farmers to fallow \citep{rosenberg2020,hendricks2012}. This is a unique case where the water savings identified from \ac{CREP} enrollment can be estimated while a pumping fee is in place. It is in fact the effectiveness of the pumping fee that lowers the effectiveness of \ac{CREP}. If subdistrict wells had not responded to the pumping fee through significant reductions, then the \ac{CREP} program would have a large direct effect on conservation. This highlights the need for policymakers to not only consider the well-established benefits of conservation programs, but to include a broader policy overlap.
+
+These results also highlight the risk of overestimating policy gains by applying historic trends to the enrolled wells. In this case, there was a clear natural experiment where a policy shift created a sudden and widespread change in pumping costs. This makes the selection effect easy to identify empirically. However, this selection effect can be present even without a policy change. Whenever there are unobservable local cost changes then farms that were already on a trajectory to reduce water are more likely to enter the program. Any number of scenarios could arise that cause this local variation in costs. Opportunity costs may rise due to changes in input prices or alternative uses of land. Factors such as urbanization, soil quality, and changes to water stock have been found to significantly affect the enrollment into \ac{CREP} and \ac{CRP} \citep{parks1997,suter2008}. These factors can change at a farm level and on a yearly level. Farms that face higher production costs within two years of the program's start are more likely to enroll into \ac{CREP}, all else equal. In other settings there has been evidence of \ac{CREP} wells lowering water output before enrollment takes place \citep{rosenberg2020}. While these are much milder reduction than is seen in the \ac{SLV}, this farm level selection effect could explain the declines.
+
+Another policy factor worth considering is that the \ac{CREP} program does benefit farmers who face extreme adversity due to pumping costs. Even though the pumping fee is effective at reducing water use, there are uneven distributions of costs and equity concerns \citep{grabenstein2022,ekpe2021}. The selection effect found also means that farms bearing the highest cost of water reduction receive some compensation. The \ac{CREP} program allows for an off-ramp from farming that lets farm owners receive compensation for foregone returns. It would be difficult to craft a payment scheme that can equitably distribute funds based on the costs of the pumping fee. Self-reporting would lead to overestimation of damages, and paying farmers based on the fee paid would undo the fee. Farmers who enter \ac{CREP} must give up producing the land, and so the revealed preferences indicate that they were more affected by the program than other subdistrict users. Since they must fallow the land to enter \ac{CREP}, this compensation does not undo the pumping fee effects on groundwater extraction. While not a stated goal of \ac{CREP}, the compensation of farmers in this manner can even out the costs of the water reduction program.
+
+\subsection{CREP Effects}
+\label{SECCREP}
+
+Turning to the effects of the \ac{CREP} program, the estimates from \cref{EQ:SUNAB} are provided in \cref{REGCREP}, with yearly estimates provided graphically in \cref{FIG:EVENTCREP} and \cref{FIG:EVENTNEAR}\footnote{Regression results used to create these figures are provided in \cref{A_CREP_ALL_REG}.}.
+\input{Tables/REG_CREP.tex}
+The \ac{ATT} of wells that enroll in \ac{CREP} is a reduction of 38.7 \ac{AF} per year. This implies that only 38.4\% of the total well reductions are attributable to entering \ac{CREP}, with the other 61.6\% of reductions attributable to the subdistrict policies. \cref{FIG:EVENTCREP} presents the results as a response across time. There is an immediate reduction of groundwater use of 10 \ac{AF} in year one, but the major reductions do not occur until the second year of the program. Much of the initial program costs are subsidized through the \ac{FSA}, including the cost of planting new native crop cover. This one-year delay reflects the higher necessary water use as farmers are transferred to sustainable fallowing practices. The overall policy response remains large but does drift over time. 
+
+
+This can be explained in a few ways. First, as the subdistrict increases conservation efforts the difference between the subdistrict wells and the \ac{CREP} wells decreases. If the State of Colorado shut down all wells not enrolled in \ac{CREP} then the program would actually increase groundwater use, as \ac{CREP} wells can still apply small amounts of water to maintain crop cover. A second possibility is that farmers in \ac{CREP} are reallocating groundwater over time. While the rights must be retired on the field, adjacent fields may apply the groundwater from the \ac{CREP} wells to meet their appropriated water volume. We do not distinguish between these possibilities, but in either case the volume of water from the \ac{CREP} wells is maintained well below the counterfactual with the net volume of water dropping by over 95\% of historic levels.
+
+\begin{figure}
+    \includegraphics[width=0.95\textwidth]{Figures/CREP_EVENT_STUDY.pdf}
+    \caption{Event study of \ac{CREP} wells}
+    \label{FIG:EVENTCREP}
+\end{figure}
+
+Similar to \cite{rouhirad2021} evaluating \ac{CREP} in Kansas, we are able to identify a policy effect of \ac{CREP} causing neighboring wells to reduce output of pumping. The estimate of a reduction of 2.79 \ac{AF} per year equates to a 3\% reduction in water use of affected wells\footnote{Assuming the yearly average neighbor pumping rate of 92 \ac{AF} per year.}, with a total effect of 3,928 \ac{AF} per year. This compares with the direct \ac{CREP} effect on 5,960 \ac{AF} per year and 40\% of the overall reduction in groundwater pumping come from the spillover effects. 
+
+However, this estimate is an average over the entire \ac{CREP} program, but there is a dynamic component to the reduction outcome. \cref{FIG:EVENTNEAR} presents the neighborhood effects as an event study with adjustments in policy effects over time. Just as in the Kansas \ac{CREP} program, there is a clear decay of \ac{CREP} response with the estimate being statistically insignificant from zero after five years.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=0.95\textwidth]{Figures/CLOSE_CREP_EVENT_STUDY.pdf}
+    \caption{Event study of neighboring \ac{CREP} wells}
+    \label{FIG:EVENTNEAR}
+\end{figure}
+
+The direction of the effect on neighboring well pumping is not knowable a priori. \ac{CREP} retirements lead to higher water table levels, which in turn reduces pumping costs and creates an incentive to pump more water. This \emph{rebound effect} has been explored in groundwater settings and could cause the \ac{CREP} fallowing to increase the pumping rate of neighbors \citep{jevons1865,pfeiffer2014}. 
+
+Working in the other direction, lowering extraction pressure on a common-pool resource allows the remaining users to manage a large share of the resource, encouraging a Nash equilibrium with jointly lower extraction rates \citep{negri1989,provencher1993,libecap1984}. Another mechanism for lowered neighborhood groundwater use is social norms and ground up informal rule enforcement, leading to a cooperative equilibrium \citep{edwards2021,smith2018,javaid2015,ostrom1989}. The results in \cref{REGCREP} show that neighbors respond in-kind, lowering groundwater use after nearby \ac{CREP} wells stop pumping. This is evidence that the social norms, and cooperative equilibriums dominate these outcomes. However, these pro-social effects interact with the opportunity cost of pumping more water when pumping costs decrease. The decline in neighbor well response as seen in the event study is explained by the increasing opportunity cost of pumping. As the water table rises due to both \ac{CREP} and neighbor wells reduction in pumping, the financial benefit of pumping rises. This leads to users increasing pumping rates on the margin, even if not back to pre-\ac{CREP} levels. This interplay of incentives explains both the initial large decline in well usage and the gradual rebound. 
+
+\subsection{CREP Self-Selection}
+Next, the effect of the \ac{SBD1} pumping fee on enrollment numbers in \ac{CREP} is explored. By changing the incentives to farm, the pumping fee can induce additional enrollment in the program. The findings in \cref{SEC2011} and \cref{SECCREP} show that water conservation of each well enrolled in \ac{CREP} is 62\% less due to the prior reductions made to manage costs under the pumping fee. This is only one part of the overall effect of the pumping fee. Since the pumping fee increased fallowing in marginally economic farms, the pumping fee reduces the payment threshold needed to make a \ac{CREP} contract a viable alternative to farming. Farmers decide to enroll land into \ac{CREP} if the opportunity cost of farming is lower than the \ac{PES} amount. By increasing the cost of operating a farm, the fee can lower this opportunity cost, causing some farms to enter \ac{CREP} that would otherwise continue farming. On the other hand, the pumping fee can improve the profitability of farming by controlling externalities. Informed by the pumping statistics, it is argued that reduced water use correlates to reduced farm production and gross revenue. Reducing water as an input to crops means that fewer crops are grown, or less water intensive crops have been substituted. However, the total reduction due to the policy provides an estimate of relative value of water as an input, compared to the externality cost of pumping. Wells that reduce pumping less than the average are able to use an \ac{AF} of water to produce more profit than the typical well. The reduction of water applied after the pumping fee begins can be used to rank the relative costs of the fee to farms. While water intensive farmland that does not shift  water faces higher operating costs, these farms are not near the margin where a \ac{CREP} fee could induce fallowing. For other farms, a large response suggested the fee added a higher cost relative to farm productivity. 
+
+To predict the shift in \ac{CREP} enrollment due to the pumping fee, the reduction of groundwater extraction by a well after the pumping fee begins is used in a probit model. For each well, the average volume of water pumped between 2011 and 2013 is subtracted from the average water extracted in 2009 and 2010. This coefficient indicates if a strong response to the pumping fee drives membership into \ac{CREP}. Ditch fixed effect controls are included to capture the effect of access to surface water, and crop choice variables control for land quality. Crop choices are an indication of the water intensity required to optimize profits prior to the pumping fee implementation, and of soil characteristics. The water rights of wells are included, as possessing more water rights increases the value of a well. Wells providing water to marginally profitable cropland are expected to disproportionately enroll in \ac{CREP}, so water rights can reduce \ac{CREP} entrance. A probit model of \ac{CREP} enrollment is developed in \cref{REG:SELECT_PROBIT}. 
+
+\input{Tables/Probit_mod2.tex}
+
+The direction of each coefficient matches expectations. A larger reduction in water extraction after 2011 makes a well more likely to enter \ac{CREP}. While overall pumping rate after the fee is implemented makes a well less likely to join \ac{CREP}, as shown in model two of \cref{REG:SELECT_PROBIT}. Ownership of water rights is found to decrease the probability of a well joining \ac{CREP}. This variable captures both the effect of access to water rights and well capacity. Data was collected on well pumping tests, but it was not included because the well yield was nearly collinear with water rights. Since water rights are based on historic use, high water rights are strongly correlated with the capacity of wells. Compared to small wells, large wells with more access to water rights are more efficiently employed in areas with high crop density, have lower marginal operating costs, and provide farmers with a stronger legal claim to continue pumping in times of drought. Each of these factors make a well less likely to be a marginal producer that will join \ac{CREP} independent of the pumping fee rate. Similarly, deep wells are less impacted by declines in the water table and correlate to higher capital investment.
+
+The percentage of cropland applied to potatoes prior to the subdistrict formation is also found to significantly decrease the probability of a well entering \ac{CREP}. The excluded fixed effect in the model is \emph{small grains} which is primarily barley within \ac{SBD1}. Compared to small grains, potatoes are drought intolerant and require a more precise soil mineral content \citep{rosen2021}. Furthermore, \ac{SLV} is a major producer of potatoes accounting for 90\% of all potatoes produced in Colorado \citep{nationalagriculturalstatisticsservice2019a}. In 2011, the year the pumping fee began, average gross revenues per acre in the \ac{SLV} were \$4,165 for potatoes\footnote{Assuming a yield of \(\frac{375 cut}{Acre}\) \citep{nationalagriculturalstatisticsservice2019a} and a price of \$9.2 per cut \citep{nationalagriculturalstatisticsservice2013}.} and \$2,943 for barley\footnote{Assuming 115 \(\frac{Bushels}{Acre}\) \citep{nationalagriculturalstatisticsservice2012} at a price of \$25.59 per bushel \citep{internationalmonetaryfund2024}.}. Because there is a higher potential profit per acre of potatoes, higher quality parcels are more likely to grow these crops. While the profitability of small grain farmers and potato farms cannot be compared based on revenues alone, farmland with the lowest operating costs are best used for growing high yield crops. Small grains can be grown where potatoes are planted, but the reverse is not true. The fact that potatoes are selected as a crop indicates that the soil is amenable to producing the higher net value crop. In turn, potato farms are less likely to enter \ac{CREP}.
+
+One of the drivers of this selection effect can be demonstrated using the results from Chapter I. The hedonic model of farmland identifies that crop choice is predictive of the value of land. As the cost of operating a well increases with respect to the pumping fee sequentially higher yield land is taken out of production. Further diseconomies of scale are identified so small segments of large plots tend to be retired before small parcels.
+
+Using the hedonic model each farm parcel in \ac{SBD1} is assigned a per acre land value. The retirement path of crop land in \ac{SBD1} is assessed by removing the lowest marginal parcel from the production in five acre increments. Once a segment of land is removed the marginal value of the remaining land in the parcel increases. The removal of five acre plots is repeated until all land is dropped from production. This is used to present a plausible retirement path of land, showing how the mix of crop changes along this path in \cref{FIG:FEE_CHNG}.
+\begin{figure}
+    \includegraphics[width=\textwidth]{Figures/IRR_ACRES_EST_CHNG.pdf}
+    \caption{Expected retirements from pumping fees}
+    \label{FIG:FEE_CHNG}
+\end{figure}
+As more land is retired, the ratio of water-intensive potatoes changes relative to other crops. A heuristic for assessing the pumping fee is to assume that the inflection point of the least valuable land is the average operating cost of growing the crop. Applying this, the effect on crop mix and active farmland can be estimated using the average price of water applied to each acre of land. The vertical lines in \cref{FIG:FEE_CHNG} represent the average cost addition from the respective fee from the point of marginally profitable farms. The pumping fee retires higher rates of small grain and alfalfa, and these plots are enrolled in \ac{CREP} as the payments to fallow are now higher than the expected returns from irrigating the land.
+
+
+Due to this observed selection effect there is likely spatial clustering of \ac{CREP} wells that changes the total neighborhood pumping effect. Using the predicted change in groundwater use from \cref{REG2011}, the probit model is estimated under the counterfactual that pumping rates do not change after 2011. The expected number of wells enrolled in \ac{CREP} decreases by 26.07\%, from 154 to 122 wells. The \ac{SBD1} pumping fee induces enrollment into the \ac{CREP}, thereby increasing total water savings. Combining the effect of reduced per well savings and the increase in enrollment, the overall \ac{CREP} water savings are estimated to be 32\% lower than the counterfactual. Compared to the counterfactual, 29.5\% more wells are added to the program. While conservation is increased by this inducement effect the program costs rise by the number of wells added because of the fee, while net conservation is lower.
+
+From the findings in \cref{FIG:EVENTNEAR}, the addition of new wells in \ac{CREP} can create additional neighborhood spillover effects worth considering in the overall policy effectiveness. The addition of 32 new wells in the program provides additional neighborhood effects, but this adjustment changes in a nonlinear way. Two factors contribute to the non-linearity. First, \ac{CREP} wells are not randomly distributed across the subdistrict. For example, potato farmers are less likely to join \ac{CREP}, and land that is ideal for growing potatoes is more alkaline than alfalfa. It follows that CREP enrollment is affected by soil acidity which leads to spatial clustering. Second, even if \ac{CREP} was perfectly random across the subdistrict then the number of untreated wells (farther than a half mile from \ac{CREP}) declines with \ac{CREP} enrollment. On one extreme, if every well in the subdistrict was adjacent to a \ac{CREP} well, adding one more well to \ac{CREP} will not change the number of wells near a well enrolled in fallowing. This means there is a declining marginal spillover effects across \ac{CREP} enrollment. The first well enrolled induces more spillover effect than the final well.
+
+Because the effect depends on regional attributes, and previous enrollment level, the coefficient cannot be applied to estimate this indirect effect. Instead, a Monte Carlo simulation was run using the probit results. For this process, each \ac{CREP} well is randomly assigned a value between zero and one. Then, the predicted probability of each well being in \ac{CREP} is estimated using the results from \cref{REG:SELECT_PROBIT}. This probability is subtracted by the randomly generated number, and then wells are ranked from largest to smallest. For the baseline results, 32 wells are removed from the sample. Then a distance matrix between the wells is used to calculate the counterfactual number of nearby wells. This is repeated 10,000 times to acquire the expected marginal effect on the number of neighboring wells due to the pumping fee inducement of 32 additional \ac{CREP} wells. This leads to an estimate of a 3.27\% increase in the number of wells nearby \ac{CREP}. These results demonstrate the importance of accounting for local characteristics when estimating spillover effects from a \ac{PES} program. Due to the clustering of wells likely to join \ac{CREP}, the 29.5\% increase in enrollment leads to only a minor change in spillover estimates. A policy implication is that the payment for enrollment should vary based on current enrollment. When accounting for spillover effects, the addition of a unit in a \ac{PES} program with many neighbors is more valuable than an addition near other program participants. 
+
+To gain a better idea of how the nearby effects evolve with enrollment, the Monte Carlo is repeated assuming different starting enrollment levels of \ac{CREP}. This process is displayed in \cref{FIG:BOX} as a box plot. As enrollment increases, the rate of change of the number of added neighboring wells declines. If \ac{SBD1} had different characteristics, and was expected to have low enrollment absent intervention, the pumping fee would have had a more significant spillover effect than is estimated. For example, if only 14 wells were expected to enroll in \ac{CREP} prior to the pumping fee, the same addition of 32 wells would increase the number of neighboring wells by over 200. However, the actual addition of 32 wells is expected to add only 40 neighboring wells.
+
+
+\begin{figure}[!htp]
+    \includegraphics[width=0.95\textwidth]{Figures/BOX_PLOT.pdf}
+    \caption{Number of wells within a half mile of \ac{CREP} based on total enrollment}
+    \label{FIG:BOX}
+\end{figure}
+
+\cref{FIG:BOX} also demonstrates that the variance of outcomes is dependent on the number of enrolled wells. The general trend is for the spread of outcomes to converge as \ac{CREP} wells are added. However, low enrollment rates have less variance in outcome than moderate enrollment. When enrollment rates are low, any new addition is likely to pick up some new neighbors. As enrollment increases there is a higher chance that new additions to the program will be near existing \ac{CREP} wells, creating a downwards outlier. However, as enrollment becomes high, it becomes unlikely that a new addition will add any new wells creating a consistent set of outcomes. This evolution of variance is tracked through the box plot whiskers. 
+
+These results are compared to a counterfactual of random enrollment in \ac{CREP} shown in \cref{FIG:BOX_RAND}. This counterfactual presents the expected number of wells that would receive a neighborhood spillover effect from \ac{CREP} if wells were not enrolled in the program based on physical characteristics, or response to the pumping fee but instead were randomly enrolled. This removes the spatial clustering of \ac{CREP} wells increasing the total treatment effect. Compared to a random selection process, the spatial clustering of enrollment minimizes the conservation induced by neighborhood effects.
+\begin{figure}[!htp]
+    \includegraphics[width=0.95\textwidth]{Figures/BOX_PLOT_WITH_RANDOM.pdf}
+    \caption{Number of wells within a half mile of \ac{CREP} when randomly enrolled}
+    \label{FIG:BOX_RAND}
+\end{figure}
+
+
+\subsection{Contract Length}
+Finally, the effect of varying the length of \ac{CREP} fallowing contracts is estimated. For neighboring wells, the extractive equilibrium may change along the margin of contract length. Of importance for policymakers, changing the terms of the contract will change which farmland is brought into the program. Adjusting the contract length is one way to home in on the most cost-effective allocation of \ac{CREP} payments. \cref{MAINREGTBL} presents the results of estimating \cref{EQ:SUNAB} for wells tied to land that is entered into a permanent contract,  a 15-year \ac{CREP} contract, or a 4-year subdistrict fallowing contract. This is estimated for both the direct effect on wells in the retirement program and for neighboring wells within one-half mile. 
+\input{Tables/REG_ALL.tex}
+The direct effect of fallowing programs is to reduce groundwater extraction in associated wells. However, the permanently retired contracts are linked to wells with much lower reduction in pumping rates than the shorter 15-year and 4-year terms. This is driven primarily by the well's fixed effects which have different means in each group. Prior to 2011, the average yearly extraction rate was 64.9 \ac{AF} for wells that enroll in the permanent contract, 175 \ac{AF} for wells enrolled in the temporary contract, and 133 \ac{AF} for wells that enroll in the 4-year program. This integrates with the \ac{CREP} literature in multiple areas. First, the short-term contracts do attract wells with a higher-than-expected pumping rate based on entry requirements, as has been found in other settings \citep{rosenberg2020}. However, the permanently retired wells have a pre-policy extraction rate much lower than the average. In survey settings a preference for short-term conservation contracts were found that go beyond expected time value of money considerations \citep{yeboah2015}.  Theoretical models of \ac{CREP} incentives conclude that abatement costs increase when irreversibility is added to the \ac{CREP} terms \citep{yang2004}. The present analysis provides empirical justification that long-term contracts induce entrance by users that differ in economic incentives from those that enter short-term contracts. The fully irreversible contract was only entered into by farms that relied on wells with low productivity.
+
+This result is intuitive given the options available to farmers. There are long-run uncertainties about crop prices, input prices and legal threats. Fields that use large amounts of groundwater to grow crops have a disproportionately large added cost due to the pumping fees. However, such wells also have a larger uncertainty cost of being retired. If prices for crops which require a large volume of water\footnote{Such as potatoes.} rise, then profits of these fields will also increase. Under these uncertainties, permanent land retirement bears a larger cost to high-rate wells. In the short run the cost structure is more well known, and the risk of foregone profits is lower than over the long run. It is not a surprise then that the \ac{CREP} fee can induce large wells to enter into the short-term contracts when faced with higher water extraction costs, but not to enter into contracts that eliminate the option of ever restarting production. The irreversibility of the permanent contract matters more to farms that could foreseeably begin producing water rich crops in the future.
+
+The contract length may also affect the response of neighbor wells, although these results are less robust than the direct effects. The estimated water savings decrease along the margin of contract length. This is consistent with the theory that well neighbors optimize based on the expected game theoretic outcomes. If a field is permanently retired, neighbors can be assured that there will not be a rebound effect when the well enters back into production. More of the common-pool resource is captured by the remaining neighbor wells and a lower pumping rate equilibrium is achievable. In the short-term contracts of four years, neighboring well owners cannot rely on rules of \ac{CREP} to ensure a long-term non-prisoner dilemma outcome. The rebound effect may dominate at these shorter contract lengths since the higher water table provides an incentive to pump now, and nearby wells expect the tragedy of the commons equilibrium state to return once the well enters production. However, it should be noted that the results of the 15 and 4-year contract neighborhood effects are not robust to changes in the model specification\footnote{The 15-year contract has signs of pretend that suggest there could be a larger neighbor effect when including a year anticipation term.}\textsuperscript{, }\footnote{The four-year contract has noisy residuals that are sensitive to the number of lags the policy starts at. There are only two years of data for this contract type, so it is safer to say that there is no evidence of neighborhood pumping declines than to suggest the positive coefficient is definitive.}.

Sections/Strategy.tex

@@ -0,0 +1,69 @@
+\section{Strategy}
+
+\subsection{Pumping Fee and Subdistrict Policies} 
+To assess the outcome of the subdistrict's water conservation efforts, a \ac{DID} applying well fixed effects is employed. In this model, wells that join \ac{CREP} are treated as having a different response to the subdistrict policies than other wells in \ac{SBD1}. The \Ac{SBD1} policy interactions with the \ac{CREP} payments are of interest. Heterogeneous response to the pumping fee has been identified in the subdistrict \citep{ekpe2021}, this could change the effectiveness of \ac{CREP}. If the wells that enter \ac{CREP} faced the highest cost from the pumping fee, they may have already reduced water use prior to entering \ac{CREP}. In one extreme, if the pumping fee already induced the \ac{CREP} wells to shut off then the \ac{CREP} program will not reduce the water use of wells. In such a case, the program would provide welfare recovery to disproportionately affected farmers but would not directly assist in conservation efforts. Through a rebound effect, it is possible that \ac{CREP} wells will pump more than their counterpart subdistrict wells. The model estimates the \ac{ATE} of the pumping fee and other policies on both \ac{CREP} and subdistrict wells. This is expressed as a \ac{DID} model in \cref{EQ:2011DID}.
+\begin{equation}
+	\label{EQ:2011DID}
+	Y_{w,t}=\alpha+\phi_{w}+\eta_{t}+\omega_{d,t}(w)+\gamma_{s,t}(w)+Sbd1 \cdot Post+CREP\cdot Post+\epsilon_{w,t}
+\end{equation}
+The dependent variable \(Y_{w,t}\) is the volume of water pumped in acre-feet for a well \emph{w} in year \emph{t}. The dummy variable \emph{Sbd1} is one if a well is in \ac{SBD1} and zero otherwise. The dummy variable \emph{CREP} is one if a well has ever been enrolled in \ac{CREP} and zero otherwise. All wells in \ac{CREP} are also in \ac{SBD1}, so the \emph{Sbd1} coefficient is one for all wells where \emph{CREP} is one. The dummy variable \emph{Post} is zero before 2011 and is one from 2011 forward. The interaction between \emph{Sbd1} and \emph{CREP} with \emph{Post} are the estimates of note. This interaction captures the average effect of \ac{SBD1} policies on wells and the \emph{CREP} interaction expresses any heterogeneous impact on \ac{CREP} wells. If \ac{CREP} wells behave similarly to other wells in the subdistrict, then the \emph{CREP} and \emph{Post} interaction will be statistically insignificant from zero, otherwise there is evidence that \ac{CREP} wells were affected differently.
+
+Fixed effects include \(\phi_{w}\) well, \(\eta_{t}\) year,  \(\omega_{d,t}\) ditch-year, and \(\gamma_{s,t}\) subdistrict-year. The suite of fixed effects used removes much of the potentially omitted variable bias by controlling for time and space fixed attributes. The well's fixed effect absorbs any time invariant attributes, such as capacity, well appropriation date, and perforation depth. Importantly, they also remove unobserved spatial components such as permeability or local geologic features. The year fixed effects account for variations that affect all \ac{SLV} farmers. These include crop prices, changes in local non-water input prices, federal and state policies, and generalized rainfall. Subdistrict-year effects further capture variations in individual subdistrict policies. All six subdistricts began employing water reduction policies during the explored time periods, this fixed effect captures such changes without explicit controls for pumping fee rate changes. Finally, the ditch year effects incorporate surface water access changes that impact certain users. Based on the interaction of the yearly snow runoff levels with ditch priority, ditch users will have variations in the accessibility of surface water in a given year. This is true even when accounting for yearly average snowmelt and precipitation captured in the year fixed effect. This ditch interaction accounts for this and other ditch time-varying factors affecting demand for groundwater.
+
+The yearly subdistrict policy effects are presented with an event study design to highlight the yearly changes in the policy suite. The changes to the pumping fee and the addition of other conservation policies suggest that the treatment effect will vary over time. \Cref{EQ:2011DID} is rewritten as an event study in \Cref{EQ:2011EVENT}.
+\begin{equation}
+	\label{EQ:2011EVENT}
+	Y_{w,t}=\alpha+\phi_{w}+\eta_{t}+\omega_{d,t}+\gamma_{s,t}+\sum_{t\not=2010}\left(\beta_{t}\cdot\rho_{w,t}\right)\left[Sbd1+CREP\right]+\epsilon_{w,t}
+\end{equation}
+
+Where \(\rho_{w,t}\) is an indicator variable for a well \emph{w} being in year \emph{t}. We refer to the estimates as representing the subdistrict policies in general since there are many policy changes occurring simultaneously. The pumping fee is the spearhead policy, but the investment in well purchases, land expansion, and short-term fallowing all contribute to the subdistrict effect.
+
+\subsection{CREP Choice}
+The decision of farmers to enroll their land in \ac{CREP} is treated as a simple comparison between the option values of the land. With land being enrolled if the condition in \cref{EQ:CREPCHOICE} is met.
+\begin{equation}
+	\label{EQ:CREPCHOICE}
+	\theta_{0}+\sum_{t=1}^{T} \frac{\theta_{CREP,t}\cdot A_{i}}{\left(r+1\right)^{t}} \ge \sum_{t=1}^{T} \frac{P_{\gamma}\cdot \gamma_{t}\cdot Q_{t,i}-C_{t,i}}{\left(r+1\right)^{t}}
+\end{equation}
+\(\theta_{0}\) being the initial sign up bonus payment per acre of \ac{CREP}, \emph{T} is the length of a \ac{CREP} contract, \(\theta_{CREP,t}\) is the yearly payment rate per acre, \(A_{i}\) is the area enrolled in \ac{CREP}, \(\gamma_{t}\) is the crop mix grown in a given year, \(P_{\gamma}\) is the weighted average price of the crop mix, \(C_{t,i}\) is the cost to operate the parcel, and \emph{r} is the discount rate.
+
+It follows from this model that the choice to enroll in \ac{CREP} depends on the relative attributes of soil. These attributes affect the crop choice \(\gamma_{t}\) and total yield \(Q_{t,i}\).  In the probit model of \ac{CREP} selection, pre-policy crop choice is included to account for these parcel quality characteristics. The pumping fee affects the cost of operating \(C_{t,i}\), the magnitude of water reductions from the fee is used to capture the relative cost increase from the pumping fee. 
+\subsection{CREP and Spillover Effects}
+The possibility of time varying heterogeneous group treatment effects should be considered when selecting an empirical strategy for evaluating the effects of \ac{CREP}. Since \ac{CREP} has staggered treatment periods, using treatment lags will bias the regression unless the cohort responses are identical, and there is no pre-trend \citep{sun2021,callaway2021,borusyak2021,goodman-bacon2021,dechaisemartin2020,gardner2022}. The complex nature of the policy implementations of \ac{SBD1} make this a potential issue. Ideally, if the \ac{CREP} participants were drawn randomly then such confounding interactions would be avoided \citep{athey2022}. This is unlikely to be the case since farmers can opt into the \ac{CREP} program, and the period farmers enter \ac{CREP} is contingent on subdistrict policies. \cref{FIG:CREP_GROUP_CHNG} groups wells that entered \ac{CREP} (treated wells) by the start of the \ac{CREP} contract year. The average pumping rate of these wells is calculated from 2009-2010, the range where no subdistrict policies had been implemented. If there are no group selection effects then the outcome variable (pumping) should be consistent prior to any treatment. 
+
+\FloatBarrier
+\begingroup
+\begin{figure}[h]
+	\centering
+	\includegraphics[width=.8\textwidth]{CREP_GROUP_TRENDS.jpeg}
+	\caption{Pre-2011 groundwater use of wells grouped by \ac{CREP} start year}
+	\label{FIG:CREP_GROUP_CHNG}
+	% I THINK IS A CUT/PASTE ERROR- \label{FIG:STOR}
+\end{figure}
+\endgroup
+\FloatBarrier
+
+There are large variations in average pre-pumping fee groundwater extraction rates across treatment cohorts. This suggests that an assumption of homogeneous group-time treatment effects would be violated. This is plausible in this dynamic policy scenario. Wells that entered the program at the initial sign-up would include any wells that are marginally profitable under earlier conditions. These wells entered when there was a higher water table, and when the fee was anywhere from \$45-\$75 per \ac{AF}. In this first wave of sign-ups some wells would have been entered even if there was not a pumping fee, while others were induced by higher prices. The pumping fee was later raised to \$150, at about this time the average pumping rate of wells entered into \ac{CREP} increases. The marginal cost of pumping being raised provides a new set of economic signals for farmers deciding if the \ac{CREP} lump sum is worth foregoing crop production. It is not surprising that the well attributes shift with this policy. Another possibility is that farmers learn from experience about the profitability of \ac{CREP}, and this new knowledge changes the type of farmers willing to enroll. 
+
+Given that the well attributes change over time, the possibility of heterogeneous treatment effects cannot be eliminated. To correct for this, a cohort weighted regression is used that results in an \ac{ATT} outcome \citep{sun2021}. The final equation to be estimated is presented in \cref{EQ:SUNAB}, but \cref{EQ:STG1} is the first step that accounts for covariates. This estimates the \ac{CREP} cohort\footnote{The cohorts in this case are the groups of wells that start a \ac{CREP} contract in a given year, there are unique cohorts from 2014-2021} \ac{ATE}.
+\begin{equation}
+	\label{EQ:STG1}
+	Y_{w,d,t,s}=\alpha+\phi_{w}+\eta_{t}+\omega_{d,t}+\gamma_{s,t}+\sum_{e\not= \infty} \sum_{\ell\not=-1}\delta_{\ell,e}(\theta_{w,e}\cdot \rho_{w,t}^{\ell})+\epsilon_{w,d,t,s}
+\end{equation}
+
+Where \(\rho_{w,t}^{\ell}\) is an indicator variable for a well \emph{w} being \(\ell\) periods away from treatment in the year \emph{t}, \(e\in E \) is the treatment cohort in this case the year a well enters \ac{CREP}, \(\theta_{w,e}\) is an indicator variable that is one if a well is in the \ac{CREP} treatment cohort \emph{e}. \(\delta_{\ell,e}\) is the coefficient of interest, being the treatments effect on the cohort \emph{e} with lag \(\ell\). The reference cohort is the never treated group \(e=\infty\), and the reference lag period is one year prior to treatment \(\ell=-1\).
+
+Next, weights are estimated which are used to predict the \ac{ATE} of \ac{CREP} from the cohort coefficients \(\delta_{\ell,e}\). These weights are the sample shares of cohorts in each lag period, as performed in \cref{EQ:STG2}.
+\begin{equation}
+	\label{EQ:STG2}
+	\Phi_{e,\ell}=Pr \left\{E_{w}=e\ |\ E_{w} \in \left[-\ell,T-\ell \right] \right\}
+\end{equation}
+\Cref{EQ:STG2} calculates the probability that the treatment cohort of a well \(E_{w}\) is in the sample of wells treated after a number of lags \(\ell\). If \(\ell=0\) then this is the probability that the cohort of the well was ever treated, and if \(\ell=-2\) then this is the probability that the cohort of the well was treated in the range of two years prior to the first treatment of any well, and at least two years before the end of the sample period.
+
+With these weights, the coefficients of interests can be calculated with \cref{EQ:SUNAB}.
+\begin{equation}
+	\label{EQ:SUNAB}
+	\widehat{CREP\ ATT}_{g}=\frac{1}{|g|}\sum_{\ell \in g} \sum_{e}\hat{\Phi}_{e,\ell}\cdot\hat{\delta}_{\ell,e}
+\end{equation}
+Where \emph{g} is the set of all lags \(\ell\). The final equation estimates the \ac{ATT}, by the sum of cohort treatment effects estimated in \cref{EQ:STG1} weighted by the cohort sample share in \cref{EQ:STG2} and scaled by the number of periods in the set \(|\)\emph{g}\(|\). This provides  consistent coefficient estimates under time and group varying treatment effects, as is the case for the \ac{CREP} program.
+
+The previous equations are written with regard to the direct effect of \ac{CREP} on wells that are in the program. However, \cref{EQ:SUNAB} is applicable to neighborhood effects of \ac{CREP}. These spillover effects that capture the neighboring well responses to hydrologic shifts, and social norms driven by \ac{CREP} also utilize this model. In such a case the first treatment period is expressed as the first year that a well was within one-half mile of a well that entered \ac{CREP}. Furthermore, all wells in \ac{CREP} are removed from the dataset to avoid attributing direct \ac{CREP} effects to spatial overlap between \ac{CREP} wells.

Tables/CREP_Reductions.tex

@@ -0,0 +1,27 @@
+% latex table generated in R 4.3.0 by xtable 1.8-4 package
+% Wed Jul  5 15:21:38 2023
+\FloatBarrier
+\begin{table}[ht]
+\centering
+\caption{Estimated CREP reductions} 
+\label{CREPRED}
+\begin{tabular}{lccccc}
+  \hline
+Year & Wells in CREP & Sbd.1 Policies & CREP & Total & Well \\ 
+  \hline
+2009 & 0 & 0 & 0 & 0 & 0 \\ 
+  2010 & 0 & 0 & 0 & 0 & 0 \\ 
+  2011 & 0 & -2835 & 0 & -2835 & -18 \\ 
+  2012 & 0 & -11860 & 0 & -11860 & -77 \\ 
+  2013 & 0 & -13263 & 0 & -13263 & -86 \\ 
+  2014 & 33 & -11750 & -355 & -12105 & -79 \\ 
+  2015 & 69 & -13262 & -1695 & -14958 & -97 \\ 
+  2016 & 104 & -10912 & -3630 & -14542 & -94 \\ 
+  2017 & 108 & -10274 & -5042 & -15316 & -99 \\ 
+  2018 & 129 & -15512 & -5691 & -21203 & -138 \\ 
+  2019 & 135 & -11436 & -5873 & -17309 & -112 \\ 
+  2020 & 144 & -14411 & -6051 & -20461 & -133 \\ 
+  2021 & 154 & -2906 & -5563 & -8468 & -55 \\ 
+   \hline
+\end{tabular}
+\end{table}

Tables/NEIGHBOR_FIT.tex

@@ -0,0 +1,76 @@
+\FloatBarrier
+
+\begingroup
+\begin{longtable}{lccc}
+   \captionsetup{justification=raggedright,singlelinecheck=false}
+   \caption{Affected neighbors by adding CREP wells}\label{REG:FITNEIGHBOR}\\
+   % \hline
+   % \hline
+   % Dependent Variable: & AF\\  
+   % Model:              & (1)\\  
+   \endfirsthead
+   
+   % At top of each page
+   % \multicolumn{2}{c}%
+   % {{\bfseries Table \thetable{} continued from previous page}} \\
+   % \hline
+
+   % End of table block
+   % Col 1 & Col 2 \\
+   % \hline
+
+% MUST BE HERE- tells macro to update name for subsequent pages
+   \endhead
+   
+   % At end of page when a new one will start
+   % \hline \multicolumn{2}{|r|}{{Continued on next page}} \\ \hline
+   % \endfoot
+   
+   % At end of etire table
+   % \hline
+   %  \multicolumn{2}{l}{
+   %    {End of table}} \\ \hline    % |r|
+   % \endlastfoot
+   
+   % \tabularnewline % \midrule \midrule
+ 
+   Dependent Variables: & \multicolumn{3}{c}{Number of Neighboring Wells} \\
+   & Linear        & Log-Linear       & Log-Log \\   
+Model:         & (1)           & (2)              & (3)\\  
+\midrule
+\emph{Variables}\\
+Num. CREP Wells      &  10.64$^{***}$& 1,133.9$^{***}$  & 1.300$^{***}$\\   
+   &  (1.099)      & (105.3)          & (0.1275)\\   
+Constant       & -197.9        & -4,316.1$^{***}$ & 0.7465\\   
+   & (135.6)       & (501.9)          & (0.6081)\\   
+
+\midrule
+\emph{Fit statistics}\\
+Observations   & 7             & 7                & 7\\  
+R$^2$          & 0.94935       & 0.95869          & 0.95412\\  
+Adjusted R$^2$ & 0.93922       & 0.95043          & 0.94494\\  
+
+   % Footnotes
+   \midrule \midrule
+   \multicolumn{4}{l}{\emph{a) IID standard errors in parentheses}}\\
+   \multicolumn{4}{l}{\emph{b) Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   % \tabularnewline
+   \multicolumn{4}{l}{\emph{c) One-half mile radius used}}\\
+
+   % \multicolumn{4}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+   % \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   % \tabularnewline
+
+   % \multicolumn{4}{l}{\emph{Note:} a) Any data entry where a CREP well was in the CREP program was dropped.} \\
+   % \multicolumn{4}{l}{\hspace{1.46cm}  A CREP well that entered in 2015 has data from 2009-2014.} \\
+
+   % \multicolumn{4}{l}{\hspace{1.0cm} b) Each of the 10 largest ditches are included in the Ditch-Year fixed effect.} \\
+
+   % \multicolumn{4}{l}{\hspace{1.0cm} c) Subdistricts One through Six included in the Subdistrict-Year fixed effect.} \\
+
+   % \multicolumn{4}{l}{\hspace{1.0cm} d)  CREP wells are removed once in the CREP program.} \\
+
+\end{longtable}
+\endgroup
+
+\FloatBarrier

Tables/Policy_Effects.tex

@@ -0,0 +1,53 @@
+\FloatBarrier
+
+\begin{table}[H]
+  \caption{Policy responses}\label{REGPOLICYRESPONSE}
+\begingroup
+\centering
+\begin{tabular}{lccc}
+   \tabularnewline \midrule \midrule
+   Dependent Variable: & \multicolumn{3}{c}{AF}\\
+                           & \textbf{2011 Policy Model}  & \textbf{Direct Effect}  & \textbf{Neighbor Response} \\    
+   Model:                  & (1)                         & (2)                      & (3)\\  
+   \midrule
+   \emph{Variables}\\
+   Sbd.1-Post 2011         & -30.87$^{***}$              &                          &   \\   
+                           & (8.477)                     &                          &   \\   
+   In CREP-Post 2011       & -31.17$^{***}$              &                          &   \\   
+                           & (7.072)                     &                          &   \\   
+   Near to CREP-Post 2011  & -5.407$^{**}$               &                          &   \\   
+                           & (2.218)                     &                          &   \\   
+   CREP Effect (ATT)  &   & -38.70$^{***}$ & -2.788$^{**}$\\  
+		      &                           & (2.905)        & (1.021)\\
+  \midrule
+   \emph{Fixed effects}\\
+   Near CREP-Post CREP     & $\checkmark$                &                          & \\  
+   In Fallow Program       & $\checkmark$                &                          & \\  
+   Well                    & $\checkmark$                & $\checkmark$             & $\checkmark$\\   
+   Year                    & $\checkmark$                &                          & \\  
+   Subdistrict-Year        & $\checkmark$                & $\checkmark$             & $\checkmark$\\   
+   Year-Ditch              & $\checkmark$                & $\checkmark$             & $\checkmark$\\   
+   In Fallow Program       &                             & $\checkmark$             & $\checkmark$\\   
+   Subdistrict-Year        &                             & $\checkmark$             & $\checkmark$\\   
+   In CREP After Treatment &                             &                          & $\checkmark$\\   
+   In CREP-Year            &                             &                          & $\checkmark$\\   
+   \midrule
+   \emph{Fit statistics}\\
+   Observations            & 48,563                      & 49,439                   & 49,439\\  
+   R$^2$                   & 0.74535                     & 0.74748                  & 0.74705\\  
+   Within R$^2$            & 0.00271                     & 0.01114                  & 0.00174\\  
+   \midrule \midrule
+   \multicolumn{4}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+   \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+\end{tabular}
+ 
+\par \raggedright 
+In model 1, all active CREP wells are removed. So a well that enters CREP in 2014 has data from 2009-2013.\\
+A well is considered a neighbor to a CREP or Fallow Program well if they are within one-half mile.\\
+Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+CREP wells are removed from the dataset when estimating neighbor responses.
+\par\endgroup
+\end{table}
+
+

Tables/Policy_Estimates.tex

@@ -0,0 +1,29 @@
+% latex table generated in R 4.3.0 by xtable 1.8-4 package
+% Sat Jul  1 18:41:03 2023
+\FloatBarrier
+\begin{table}[ht]
+\centering
+\caption{Estimated policy effects}
+\label{RESTBL}
+\begin{tabular}{lcccccccc}
+  \hline
+  & &\multicolumn{3}{c}{\textbf{CREP}}&  &\multicolumn{2}{c}{\textbf{Subdistrict One}}  & \\ \cmidrule{3-5} \cmidrule{7-8}
+
+	Year &No Policies& Direct & Spillover & Expected && Fallow & Other Policies & Total \\ 
+  \hline
+	2009 & 262016 & 0 & 0 & 0 && 0 & 0 & 0 \\ 
+	2010 & 302356 & 0 & 0 & 0 && 0 & 0 & 0 \\ 
+	2011 & 343124 & 0 & 0 & 0 && 0 & -26854 & -26854 \\ 
+	2012 & 383469 & 0 & 0 & 0 && 0 & -129790 & -129790 \\ 
+	2013 & 371124 & 0 & 0 & 0 && 0 & -147182 & -147182 \\ 
+	2014 & 354422 & -355 & -1010 & -2047 && 0 & -120684 & -122049 \\ 
+	2015 & 344911 & -1695 & -2271 & -4280 && 0 & -139511 & -143477 \\ 
+	2016 & 324888 & -3630 & -3824 & -6452 && 0 & -84525 & -91979 \\ 
+	2017 & 304553 & -5042 & -4062 & -6700 && 0 & -63698 & -72802 \\ 
+	2018 & 411357 & -5691 & -4524 & -8002 && 0 & -145097 & -155312 \\ 
+	2019 & 303262 & -5873 & -3473 & -8375 && 0 & -85370 & -94715 \\ 
+	2020 & 349048 & -6051 & -2768 & -8933 && -460 & -102379 & -111658 \\ 
+	2021 & 283098 & -5563 & -3213 & -9553 && -630 & -69106 & -78512 \\ 
+   \hline
+\end{tabular}
+\end{table}

Tables/Policy_Estimates_CI.tex

@@ -0,0 +1,60 @@
+\FloatBarrier
+
+\begingroup
+\begin{longtable}{lccc|ccccc|cc}
+   \captionsetup{justification=raggedright,singlelinecheck=false}
+   \caption{Estimated policy effects with confidence intervals}
+   \label{RESTBLCI}\\
+   % \hline
+   % \hline
+   % Dependent Variable: & AF\\  
+   % Model:              & (1)\\  
+   \endfirsthead
+   
+   % At top of each page
+   % \multicolumn{2}{c}%
+   % {{\bfseries Table \thetable{} continued from previous page}} \\
+   % \hline
+
+   % End of table block
+   % Col 1 & Col 2 \\
+   % \hline
+
+% MUST BE HERE- tells macro to update name for subsequent pages
+   \endhead
+   
+   % At end of page when a new one will start
+   % \hline \multicolumn{2}{|r|}{{Continued on next page}} \\ \hline
+   % \endfoot
+   
+   % At end of etire table
+   % \hline
+   %  \multicolumn{2}{l}{
+   %    {End of table}} \\ \hline    % |r|
+   % \endlastfoot
+   
+   % \tabularnewline % \midrule \midrule
+ 
+   \hline
+   & &\multicolumn{5}{c}{\textbf{CREP}}&  &\multicolumn{2}{c}{\textbf{Subdistrict One}}  & \\ \cmidrule{3-6} \cmidrule{8-10} 
+ Year & A.F & Direct & CI 95\% & Spillover & CI 95\% && Fallow & CI 95\% & Other Policies & Total \\ 
+   \hline
+ 2009 & 262016 & 0 & 0,0 & 0 & 0,0 && 0 & 0,0 & 0 & 0 \\ 
+   2010 & 302356 & 0 & 0,0 & 0 & 0,0 && 0 & 0,0 & 0 & 0 \\ 
+   2011 & 343124 & 0 & 0,0 & 0 & 0,0 && 0 & 0,0 & -26854 & -26854 \\ 
+   2012 & 383469 & 0 & 0,0 & 0 & 0,0 && 0 & 0,0 & -129790 & -129790 \\ 
+   2013 & 371124 & 0 & 0,0 & 0 & 0,0 && 0 & 0,0 & -147182 & -147182 \\ 
+   2014 & 354422 & -355 & -155,-555 & -1010 & -238,-1781 && 0 & 0,0 & -120684 & -122049 \\ 
+   2015 & 344911 & -1695 & -1236,-2155 & -2271 & -627,-3914 && 0 & 0,0 & -139511 & -143477 \\ 
+   2016 & 324888 & -3630 & -2874,-4386 & -3824 & -1311,-6337 && 0 & 0,0 & -84525 & -91979 \\ 
+   2017 & 304553 & -5042 & -4182,-5901 & -4062 & -1209,-6915 && 0 & 0,0 & -63698 & -72802 \\ 
+   2018 & 411357 & -5691 & -4671,-6711 & -4524 & -1024,-8024 && 0 & 0,0 & -145097 & -155312 \\ 
+   2019 & 303262 & -5873 & -4810,-6936 & -3473 & 277,-7223 && 0 & 0,0 & -85370 & -94715 \\ 
+   2020 & 349048 & -6051 & -4903,-7198 & -2768 & 1717,-7254 && -460 & -231,-688 & -102379 & -111658 \\ 
+   2021 & 283098 & -5563 & -4297,-6829 & -3213 & 1814,-8239 && -630 & 335,-1596 & -69106 & -78512 \\ 
+    \hline
+
+\end{longtable}
+\endgroup
+
+\FloatBarrier

Tables/Policy_Per_Est.tex

@@ -0,0 +1,28 @@
+% latex table generated in R 4.3.0 by xtable 1.8-4 package
+% Sat Jul  1 19:55:25 2023
+\FloatBarrier
+\begin{table}[ht]
+\centering
+\caption{Well level intensity of reduction} 
+\label{PERTBL}
+\begin{tabular}{lccccccc}
+  \hline
+  &\multicolumn{3}{c}{\textbf{Subdistrict One Policy Effects} }&  &\multicolumn{2}{c}{\textbf{CREP Policy Effects}}  & \\ \cmidrule{2-4} \cmidrule{6-7}
+  Year & Sbd1. Wells & CREP Wells & Fallow Prog. && CREP Wells & Close Wells & CREP Total\\ 
+  \hline
+  2009 & 0 & 0 & 0 && 0 & 0 & 0 \\ 
+  2010 & 0 & 0 & 0 && 0 & 0 & 0 \\ 
+  2011 & -0.08 & -0.14 & 0 && 0 & 0 & -0.14 \\ 
+  2012 & -0.34 & -0.53 & 0 && 0 & 0 & -0.53 \\ 
+  2013 & -0.40 & -0.61 & 0 && 0 & 0 & -0.61 \\ 
+  2014 & -0.34 & -0.53 & 0 && -0.05 & -0.03 & -0.58 \\ 
+  2015 & -0.41 & -0.55 & 0 && -0.10 & -0.03 & -0.66 \\ 
+  2016 & -0.27 & -0.40 & 0 && -0.18 & -0.04 & -0.58 \\ 
+  2017 & -0.22 & -0.41 & 0 && -0.22 & -0.04 & -0.62 \\ 
+  2018 & -0.36 & -0.52 & 0 && -0.17 & -0.05 & -0.69 \\ 
+  2019 & -0.29 & -0.57 & 0 && -0.15 & -0.04 & -0.73 \\ 
+  2020 & -0.30 & -0.72 & -0.74 && -0.10 & -0.03 & -0.82 \\ 
+  2021 & -0.25 & 0 & -0.46 && -0.34 & -0.03 & -0.59 \\ 
+   \hline
+\end{tabular}
+\end{table}

Tables/Probit.tex

@@ -0,0 +1,70 @@
+\FloatBarrier
+
+\begingroup
+\begin{longtable}{lc}   % |c|c|
+   \caption{CREP self-selection -- NEED A CAPTION FOR THIS TABLE!}\label{tab:ProbitCREP}\\
+   \hline
+   \hline
+   Dependent Variable:  & In CREP\\  
+                        & Probit \\   
+   Model:               & (1)\\  
+   \endfirsthead
+   
+   % At top of each page
+   % \multicolumn{2}{c}%
+   % {{\bfseries Table \thetable{} continued from previous page}} \\
+   % \hline
+
+   % End of table block
+   % Col 1 & Col 2 \\
+   % \hline
+   \endhead
+   
+   % At end of page when a new one will start
+   % \hline \multicolumn{2}{|r|}{{Continued on next page}} \\ \hline
+   % \endfoot
+   
+   % At end of etire table
+   % \hline
+   %  \multicolumn{2}{l}{
+   %    {End of table}} \\ \hline    % |r|
+   % \endlastfoot
+   
+   \midrule
+   \emph{Variables}\\
+    Pumping Change (A.F) & -0.0059$^{***}$\\   
+                          & (0.0017)\\   
+    Capacity (A.F)       & -0.0008\\   
+                          & (0.0009)\\   
+    Crop Area (Acres)    & 0.0002\\   
+                          & (0.0019)\\   
+    Alfalfa (\%)         & -0.4115$^{*}$\\   
+                          & (0.2448)\\   
+    Small Grains (\%)    & -0.3459\\   
+                          & (0.2336)\\   
+    Potatoes (\%)        & -1.669$^{***}$\\   
+                          & (0.3078)\\   
+    \midrule
+    \emph{Fixed Effects}\\
+    Ditch                & $\checkmark$\\   
+    \midrule
+    \emph{Fit statistics}\\
+    Observations         & 2,456\\  
+    Squared Correlation  & 0.13569\\  
+    Pseudo R$^2$         & 0.19602\\  
+    BIC                  & 967.27\\  
+    \midrule \midrule
+    \multicolumn{2}{l}{\emph{a) Heteroscedasticity-robust standard errors in parentheses}}\\
+    \multicolumn{2}{l}{\emph{b) Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+
+   \multicolumn{2}{l}{\emph{c) Ditch fixed effects includes indicators for the ten largest ditch systems}}\\
+   \multicolumn{2}{l}{\emph{d) All Crop variables taken as the average from 2009 and 2010}}\\
+   \multicolumn{2}{l}{\emph{e) \emph{Other Crops} excluded to avoid collinearity}}\\
+   \multicolumn{2}{l}{\emph{f) \emph{Pumping Change} is the average extraction rate of a Well}}\\
+   \multicolumn{2}{l}{\emph{    from 2011 to 2013 minus the average pumping rate from 2009 to 2010}}\\
+   
+   \tabularnewline
+\end{longtable}
+\endgroup
+
+\FloatBarrier

Tables/Probit_mod2.tex

@@ -0,0 +1,51 @@
+
+\begin{table}  %[h]
+   \caption{Probit model of selection into CREP}\label{REG:SELECT_PROBIT}
+   \centering
+   \begin{tabular}{lcc}
+      \tabularnewline \midrule \midrule
+      Dependent Variable: & \multicolumn{2}{c}{Well Enters CREP}\\
+      Model:                          & (1)             & (2)\\  
+      \midrule
+      \underline{\emph{Variables}}\\
+      Change in Avg. Water Use (AF)   & -0.0050$^{***}$ &   \\   
+                                      & (0.0013)        &   \\   
+Pre-Fee pumping (AF/year)  &                 & 0.0049$^{***}$\\   
+                                      &                 & (0.0012)\\   
+      Post-Fee Pumping (AF/year) &                 & -0.0055$^{***}$\\   
+                                      &                 & (0.0018)\\   
+      Water Rights (AF)               & -0.1928$^{***}$ & -0.1871$^{***}$\\   
+                                      & (0.0552)        & (0.0536)\\   
+      Well Depth (log feet)           & 0.1875$^{*}$    & 0.2213$^{**}$\\   
+                                      & (0.1030)        & (0.1068)\\   
+      Potatoes (\%)                   & -1.267$^{***}$  & -1.257$^{***}$\\   
+                                      & (0.2452)        & (0.2445)\\   
+      Alfalfa (\%)                    & -0.1998         & -0.1781\\   
+                                      & (0.1667)        & (0.1680)\\   
+      Other Crops (\%)                & 0.3271          & 0.3073\\   
+                                      & (0.2455)        & (0.2481)\\   
+      \midrule
+      \underline{\emph{Fixed effects}}\\
+      Ditch                           & $\checkmark$    & $\checkmark$\\   
+      
+      \midrule
+      \underline{\emph{Fit statistics}}\\
+      Observations                    & 2,149           & 2,149\\  
+      Squared Correlation             & 0.14192         & 0.14612\\  
+      Pseudo R$^2$                    & 0.20896         & 0.20959\\  
+      BIC                             & 821.08          & 828.17\\  
+      \midrule \midrule
+      
+      \multicolumn{3}{l}{\emph{Heteroscedasticity-robust standard errors in parentheses}}\\
+      \multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+      \midrule
+   \end{tabular}
+   
+   \par \raggedright 
+   \hspace{2cm}Pre-Fee pumping rate is the average yearly AF extracted from 2009 to 2010.\\
+   \hspace{2cm}Post-Fee pumping rate is the average yearly AF extracted from 2011 to 2013.\\
+   \hspace{3cm}This excludes all years after \ac{CREP} is active (2014-2024)\\
+   \hspace{2cm}\emph{Change in Avg. Water Use} is calculated by subtracting the Post-Fee variable\\
+   \hspace{3cm} by the Pre-Fee variable.
+
+\end{table}

Tables/REG2011.tex

@@ -0,0 +1,76 @@
+\FloatBarrier
+
+\begingroup
+\begin{longtable}{lc}   % |c|c|
+   \caption{Response to 2011 pumping fee}\label{REG2011}\\
+   \hline
+   \hline
+   Dependent Variable:    & AF\\  
+   Model:                 & (1)\\  
+   \hline
+   \endfirsthead
+   
+   % At top of each page
+   % \multicolumn{2}{c}%
+   % {{\bfseries Table \thetable{} continued from previous page}} \\
+   % \hline
+
+   % End of table block
+   % Col 1 & Col 2 \\
+   % \hline
+   \endhead
+   
+   % At end of page when a new one will start
+   % \hline \multicolumn{2}{|r|}{{Continued on next page}} \\ \hline
+   % \endfoot
+   
+   % At end of etire table
+   % \hline
+   %  \multicolumn{2}{l}{
+   %    {End of table}} \\ \hline    % |r|
+   % \endlastfoot
+   
+   \midrule
+   \emph{Variables}\\
+   Sbd.1-Post 2011        & -30.87$^{***}$\\   
+                          & (8.477)\\   
+   In CREP-Post 2011      & -31.17$^{***}$\\   
+                          & (7.072)\\   
+   Near to CREP-Post 2011 & -5.407$^{**}$\\   
+                          & (2.218)\\   
+   \midrule
+   \emph{Fixed Effects}\\
+   Subdistrict-Year       & $\checkmark$\\   
+   Ditch-Year             & $\checkmark$\\   
+   Near CREP-Post CREP    & $\checkmark$\\   
+   In Fallow Program      & $\checkmark$\\   
+   Well                   & $\checkmark$\\   
+   Year                   & $\checkmark$\\   
+   \midrule
+   \emph{Fit statistics}\\
+   Observations           & 48,563\\  
+   R$^2$                  & 0.74535\\  
+   Within R$^2$           & 0.00271\\  
+   \midrule \midrule
+   \multicolumn{2}{l}{\emph{a) Clustered (Well \& Year) standard errors in parentheses}}\\
+   \multicolumn{2}{l}{\emph{b) Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+
+
+   \multicolumn{2}{l}{\emph{c) CREP wells are only included before entering CREP. A well}}\\
+   \multicolumn{2}{l}{\emph{   that enters CREP in 2015 will have data from 2009-2014.}}\\
+
+   \multicolumn{2}{l}{\emph{d) The Sbd.1 dummy is 1 for all CREP wells, so the "In CREP" }}\\
+   \multicolumn{2}{l}{\emph{    coefficient is added to the "Sbd.1" coefficient term for}}\\
+   \multicolumn{2}{l}{\emph{    the full effect on CREP wells.}}\\
+
+   \multicolumn{2}{l}{\emph{e) A well is "close" if it is within one-half mile of a CREP well.}}\\
+   \multicolumn{2}{l}{\emph{f) Each of the 10 largest ditches are included in the Ditch-Year}}\\
+   \multicolumn{2}{l}{\emph{    fixed effect.}}\\
+   \multicolumn{2}{l}{\emph{g) Subdistricts two through six included in the Subdistrict-Year}}\\
+   \multicolumn{2}{l}{\emph{    fixed effect.}}\\
+
+   \tabularnewline
+\end{longtable}
+\endgroup
+
+\FloatBarrier

Tables/REG_ALL.tex

@@ -0,0 +1,61 @@
+\FloatBarrier
+\begingroup
+\begin{table}[ht]
+   \caption{Policy direct effects and spillover effects}\label{MAINREGTBL}
+   \centering
+   %\begin{adjustbox}{width = \textwidth, center}
+      \begin{tabular}{lccc|ccc}
+         \tabularnewline \midrule \midrule
+         Dependent Variable: & \multicolumn{6}{c}{AF}\\
+          & \multicolumn{3}{c}{\textbf{Direct Effect}} & \multicolumn{3}{c}{\textbf{Neighbor Response}} \\ 
+         Contract Length:& Perm.          & 15-Year        & 4-Year        & Perm.          & 15-Year       & 4-Year \\   
+         Model:                  & (1)            & (2)            & (3)           & (4)            & (5)           & (6)\\  
+         \midrule
+         \emph{Variables}\\
+         \textbf{ATT}                     & -16.08$^{***}$ & -64.78$^{***}$ & -49.54$^{**}$ & -3.010$^{***}$ & -0.8047       & 2.062$^{*}$\\   
+                                 & (2.308)        & (4.797)        & (22.17)       & (0.9125)       & (1.001)       & (1.033)\\   
+         \midrule
+         \emph{Fixed Effects}\\
+         Subdistrict-Year        & $\checkmark$   & $\checkmark$   & $\checkmark$  & $\checkmark$   & $\checkmark$  & $\checkmark$\\   
+         Ditch-Year              & $\checkmark$   & $\checkmark$   & $\checkmark$  & $\checkmark$   & $\checkmark$  & $\checkmark$\\   
+         In Fallow Program       & $\checkmark$   & $\checkmark$   &               & $\checkmark$   & $\checkmark$  & \\  
+         Well                    & $\checkmark$   & $\checkmark$   & $\checkmark$  & $\checkmark$   & $\checkmark$  & $\checkmark$\\   
+         In CREP-Year            &                &                & $\checkmark$  & $\checkmark$   & $\checkmark$  & $\checkmark$\\   
+         In CREP After Treatment &                &                & $\checkmark$  & $\checkmark$   & $\checkmark$  & $\checkmark$\\   
+         \midrule
+         \emph{Fit statistics}\\
+         Observations            & 48,594         & 48,282         & 49,439        & 47,437         & 47,437        & 48,563\\  
+         R$^2$                   & 0.74727        & 0.74580        & 0.74674       & 0.74495        & 0.74489       & 0.74605\\  
+         Within R$^2$            & 0.00149        & 0.01320        & 0.00106       & 0.00121        & 0.00096       & 0.00063\\  
+         \midrule \midrule
+       %  \multicolumn{7}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+       %  \multicolumn{7}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+         \tabularnewline
+      \end{tabular}
+      
+   %\end{adjustbox}
+   
+   %   \par \raggedright 
+   %%   \tiny
+   %   A well is considered a neighbor to a CREP or Fallow Program well if they are within one-half mile.\\
+   %   Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+   %   Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+   %   CREP wells are removed from the dataset when estimating neighbor responses.\\
+   %   When estimating the direct effect of permanent/temporary CREP contracts, wells under the other contract type are dropped from the dataset.
+
+
+      \par \raggedright%
+      %  \tiny
+      \noindent\hspace*{1cm}\begin{minipage}{\dimexpr\textwidth-1cm}
+      %  Notes:\\
+        a) Clustered (Well \& Year) standard errors in parentheses.\\
+        b) Signif. Codes: ***: 0.01, **: 0.05, *: 0.1\\
+        c) A well is considered a neighbor to a CREP or Fallow Program well if they are within one-half mile.\\
+        d) Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+        e) Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+        f) CREP wells are removed from the dataset when estimating neighbor responses.\\
+        g) When estimating the direct effect of permanent/temporary CREP contracts, wells under the other contract type are dropped from the dataset.
+
+      \end{minipage}
+\end{table}
+\endgroup

Tables/REG_CREP.tex

@@ -0,0 +1,47 @@
+\FloatBarrier
+\begingroup
+\begin{table}[h]
+   \centering
+   \caption{Estimated CREP treatment effect}\label{REGCREP}
+\label{A_CREP_ALL_REG}
+   \begin{tabular}{lcc}
+      \tabularnewline \midrule \midrule
+      Dependent Variable: & \multicolumn{2}{c}{AF}\\
+                              & CREP Wells     & Neighbor Wells \\   
+      Model:                  & (1)            & (2)\\  
+      \midrule
+      \emph{Variables}\\
+      ATT                     & -38.70$^{***}$ & -2.788$^{**}$\\   
+                              & (2.905)        & (1.021)\\   
+      \midrule
+      \emph{Fixed Effects}\\
+      Subdistrict-Year        & $\checkmark$   & $\checkmark$\\   
+      Ditch-Year              & $\checkmark$   & $\checkmark$\\   
+      In Fallow Program       & $\checkmark$   & $\checkmark$\\   
+      Well                    & $\checkmark$   & $\checkmark$\\   
+      In CREP After Treatment &                & $\checkmark$\\   
+      In CREP-Year            &                & $\checkmark$\\   
+      \midrule
+      \emph{Fit statistics}\\
+      Observations            & 49,439         & 49,439\\  
+      R$^2$                   & 0.74748        & 0.74705\\  
+      Within R$^2$            & 0.01114        & 0.00174\\  
+      \midrule \midrule
+    %  \multicolumn{3}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+    %  \multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+      \tabularnewline
+   \end{tabular}
+   
+   \par \raggedright 
+ %  \tiny
+ \noindent\hspace*{2cm}\begin{minipage}{\dimexpr\textwidth-2cm}
+ %  Notes:\\
+   a) Clustered (Well \& Year) standard errors in parentheses\\
+   b) Signif. Codes: ***: 0.01, **: 0.05, *: 0.1\\
+   c) A well is considered a neighbor to a CREP or Fallow Program well if they are within one-half mile.\\
+   d) Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+   e) Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+   f) CREP wells are removed from the dataset when estimating neighbor responses.\\
+ \end{minipage}
+\end{table}
+\endgroup

Tables/REG_CREP_CLOSE_COHORT.tex

@@ -0,0 +1,54 @@
+
+\FloatBarrier
+\begin{table}[H]
+   \caption{Neighbor CREP treatment effect by cohort}\label{REGCLOSECOHORT}
+   \centering
+   \begin{tabular}{lccc}
+      \tabularnewline \midrule \midrule
+      Dependent Variable: & \multicolumn{3}{c}{AF}\\
+                              & All CREP Wells & Permanent Contract & 15-Year Contract \\   
+      Model:                  & (1)            & (2)                & (3)\\  
+      \midrule
+      \emph{Variables}\\
+      cohort $=$ 2014         & -0.6622        & 2.736$^{**}$       & 0.5594\\   
+                              & (1.423)        & (1.184)            & (1.585)\\   
+      cohort $=$ 2015         & -8.599$^{***}$ & -9.689$^{*}$       & -5.288$^{***}$\\   
+                              & (1.583)        & (5.200)            & (1.273)\\   
+      cohort $=$ 2016         & 0.9955         & -9.086$^{***}$     & 8.166$^{**}$\\   
+                              & (1.685)        & (1.087)            & (2.842)\\   
+      cohort $=$ 2017         & 0.2011         &                    & 1.266\\   
+                              & (1.889)        &                    & (1.831)\\   
+      cohort $=$ 2018         & -5.207$^{**}$  &                    & 1.379\\   
+                              & (1.802)        &                    & (1.363)\\   
+      cohort $=$ 2019         & -7.017$^{**}$  &                    & -5.941$^{*}$\\   
+                              & (3.046)        &                    & (2.729)\\   
+      cohort $=$ 2020         & 3.412          & 1.108              &   \\   
+                              & (2.165)        & (1.358)            &   \\   
+      cohort $=$ 2021         & -15.70$^{***}$ & -3.931             &   \\   
+                              & (3.969)        & (2.279)            &   \\   
+      \midrule
+      \emph{Fixed Effects}\\
+      Subdistrict-Year        & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      Ditch-Year              & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      In Fallow Program       & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      In CREP After Treatment & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      Well                    & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      In CREP-Year            & $\checkmark$   & $\checkmark$       & $\checkmark$\\   
+      \midrule
+      \emph{Fit statistics}\\
+      Observations            & 49,439         & 47,437             & 47,437\\  
+      R$^2$                   & 0.74705        & 0.74495            & 0.74489\\  
+      Within R$^2$            & 0.00174        & 0.00121            & 0.00096\\  
+      \midrule \midrule
+      \multicolumn{4}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+      \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   \end{tabular}
+   
+   \par \raggedright 
+   A well is considered a neighbor to a CREP or Fallow Program well if they are within one-half mile.\\
+   Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+   Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+   CREP wells are removed from the dataset when estimating neighbor responses.
+\end{table}
+
+

Tables/REG_CREP_COHORT.tex

@@ -0,0 +1,50 @@
+
+\begin{table}[H]
+   \caption{CREP treatment effect by cohort}\label{REGCREPCOHORT}
+   \centering
+   \begin{tabular}{lccc}
+      \tabularnewline \midrule \midrule
+      Dependent Variable: & \multicolumn{3}{c}{AF}\\
+                        & All CREP Wells & Permanent Contracts & 15-Year Contract \\   
+      Model:            & (1)            & (2)                 & (3)\\  
+      \midrule
+      \emph{Variables}\\
+      cohort $=$ 2014   & -23.34$^{***}$ & -5.335$^{**}$       & -51.72$^{***}$\\   
+                        & (3.576)        & (2.023)             & (3.874)\\   
+      cohort $=$ 2015   & -36.66$^{***}$ & -27.48$^{***}$      & -40.23$^{***}$\\   
+                        & (5.398)        & (5.252)             & (8.487)\\   
+      cohort $=$ 2016   & -39.24$^{***}$ & -21.80$^{***}$      & -89.55$^{***}$\\   
+                        & (6.607)        & (5.129)             & (14.31)\\   
+      cohort $=$ 2017   & -195.0$^{***}$ &                     & -195.2$^{***}$\\   
+                        & (28.43)        &                     & (28.51)\\   
+      cohort $=$ 2018   & -39.53$^{***}$ & -2.431              & -62.04$^{***}$\\   
+                        & (11.68)        & (1.564)             & (16.33)\\   
+      cohort $=$ 2019   & -134.0$^{***}$ &                     & -134.2$^{***}$\\   
+                        & (20.66)        &                     & (20.66)\\   
+      cohort $=$ 2020   & -32.75$^{***}$ & -31.23$^{***}$      & -44.29$^{***}$\\   
+                        & (7.551)        & (8.479)             & (0.6819)\\   
+      cohort $=$ 2021   & -3.927         & -3.956              &   \\   
+                        & (11.75)        & (11.77)             &   \\   
+      \midrule
+      \emph{Fixed Effects}\\
+      Subdistrict-Year  & $\checkmark$   & $\checkmark$        & $\checkmark$\\   
+      Ditch-Year        & $\checkmark$   & $\checkmark$        & $\checkmark$\\   
+      In Fallow Program & $\checkmark$   & $\checkmark$        & $\checkmark$\\   
+      Well              & $\checkmark$   & $\checkmark$        & $\checkmark$\\   
+      \midrule
+      \emph{Fit statistics}\\
+      Observations      & 49,439         & 48,594              & 48,282\\  
+      R$^2$             & 0.74748        & 0.74727             & 0.74580\\  
+      Within R$^2$      & 0.01114        & 0.00149             & 0.01320\\  
+      \midrule \midrule
+      \multicolumn{4}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+      \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   \end{tabular}
+   
+   \par \raggedright 
+   Each of the 10 largest ditches are included in the Ditch-Year fixed effect.\\
+   Subdistricts One through Six included in the Subdistrict-Year fixed effect.\\
+   CREP wells are removed from the dataset when estimating neighbor responses.
+\end{table}
+
+

Tables/REG_CREP_PERIODS.tex

@@ -0,0 +1,87 @@
+%\FloatBarrier
+
+\begingroup
+\begin{longtable}{>{\raggedright}p{0.27\textwidth}>{\centering}p{0.2\textwidth}p{0.2\textwidth}}
+  % Start table header
+  \caption{Estimated CREP treatment effect, no aggregation}\label{REG_CREP_PERIODS} \\  % Not used->
+  \hline
+  \hline
+    Dependent Variable: & \multicolumn{2}{c}{AF}\\
+                        & CREP Wells     & Neighbor Wells \\
+    Model:              & (1)            & (2)\\  
+  \endfirsthead
+  \hline
+  \endhead
+  \hline
+
+\emph{Variables} & &\\
+Year $=$ -12          & 60.03$^{***}$  & 4.022\\   
+  & (7.109)        & (7.607)\\   
+Year $=$ -11            & 43.44$^{***}$  & 21.13$^{***}$\\   
+  & (6.172)        & (4.051)\\   
+Year $=$ -10            & 51.01$^{***}$  & 10.61$^{**}$\\   
+  & (6.242)        & (3.994)\\   
+Year $=$ -9             & 58.42$^{***}$  & 9.114$^{*}$\\   
+  & (13.51)        & (4.270)\\   
+Year $=$ -8             & 50.33$^{***}$  & 8.025$^{*}$\\   
+  & (13.58)        & (4.017)\\   
+Year $=$ -7             & 40.78$^{***}$  & 4.332$^{*}$\\   
+  & (6.726)        & (2.137)\\   
+Year $=$ -6             & 25.67$^{***}$  & 0.5265\\   
+  & (5.526)        & (1.831)\\   
+Year $=$ -5             & 25.83$^{***}$  & 3.507$^{**}$\\   
+  & (4.107)        & (1.385)\\   
+Year $=$ -4             & 12.29$^{***}$  & 2.248\\   
+  & (3.712)        & (1.374)\\   
+Year $=$ -3             & 9.242$^{**}$   & 2.150$^{*}$\\   
+  & (3.169)        & (1.063)\\   
+Year $=$ -2             & 4.613$^{*}$    & 1.347\\   
+  & (2.290)        & (0.7693)\\   
+Year $=$ 0              & -10.77$^{***}$ & -2.040$^{**}$\\   
+  & (2.783)        & (0.7153)\\   
+Year $=$ 1              & -39.63$^{***}$ & -3.153$^{***}$\\   
+  & (3.352)        & (1.021)\\   
+Year $=$ 2              & -55.36$^{***}$ & -4.441$^{***}$\\   
+  & (3.905)        & (1.238)\\   
+Year $=$ 3              & -49.05$^{***}$ & -3.191$^{**}$\\   
+  & (3.798)        & (1.155)\\   
+Year $=$ 4              & -48.58$^{***}$ & -3.818$^{**}$\\   
+  & (3.723)        & (1.531)\\   
+Year $=$ 5              & -39.07$^{***}$ & -1.271\\   
+  & (3.578)        & (1.362)\\   
+Year $=$ 6              & -37.89$^{***}$ & -0.7069\\   
+  & (3.794)        & (1.767)\\   
+Year $=$ 7              & -24.56$^{***}$ & -2.999\\   
+  & (4.338)        & (1.917)\\   
+   \hline 
+   \emph{Fixed Effects}    &                & \\
+   Subdistrict-Year        & $\checkmark$   & $\checkmark$\\   
+   Ditch-Year              & $\checkmark$   & $\checkmark$\\   
+   In Fallow Program       & $\checkmark$   & $\checkmark$\\   
+   Well                    & $\checkmark$   & $\checkmark$\\   
+   In CREP After Treatment &                & $\checkmark$\\   
+   In CREP-Year            &                & $\checkmark$\\   
+   \midrule
+   \emph{Fit statistics}\\
+   Observations            & 49,439         & 49,439\\  
+   R$^2$                   & 0.74748        & 0.74705\\  
+   Within R$^2$            & 0.01114        & 0.00174\\  
+   \midrule \midrule
+   \multicolumn{3}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+   \multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   % Notes...
+   \hline
+   \multicolumn{3}{l}{\emph{Note:} a) A well is considered a neighbor to a CREP or} \\
+   \multicolumn{3}{l}{\hspace{1.46cm} Fallow Program well if they are within one-half mile.} \\
+
+   \multicolumn{3}{l}{\hspace{1.0cm} b) Each of the 10 largest ditches are included in the} \\
+   \multicolumn{3}{l}{\hspace{1.46cm} Ditch-Year fixed effect.} \\
+
+   \multicolumn{3}{l}{\hspace{1.0cm} c) Subdistricts One through Six included in the} \\
+   \multicolumn{3}{l}{\hspace{1.46cm} Subdistrict-Year fixed effect.} \\
+
+\multicolumn{3}{l}{\hspace{1.0cm} d) CREP wells are removed from the dataset when} \\ 
+\multicolumn{3}{l}{\hspace{1.46cm} estimating neighbor responses.} \\ 
+ \hline % \\[-1.8ex]
+ \end{longtable}
+ \endgroup

Tables/REG_DIFF_RAD.tex

@@ -0,0 +1,83 @@
+\FloatBarrier
+
+\begin{longtable}{>{\raggedright}p{0.21\textwidth}>{\centering}p{0.16\textwidth}>{\centering}p{0.16\textwidth}>{\centering}p{0.16\textwidth}p{0.16\textwidth}}
+   \endfirsthead 
+   \hline
+   \hline
+%   Country & Capital & Continent\\\hline
+   \endhead
+   \captionsetup{justification=raggedright,singlelinecheck=false}
+   \caption{Neighbor response with changes in radius cutoff}\label{REGRAD} \\
+   % \tabularnewline
+    \midrule \midrule
+   Dependent Variable: & \multicolumn{4}{c}{AF}\\
+                           & \textbf{One-Quarter Mile}  & \textbf{One-Half Mile}  & \textbf{One Mile}  & \textbf{Two Miles} \\    
+   Model:                  & (1)                               & (2)                     & (3)                & (4)\\  
+   \midrule
+   \emph{Variables} & & & & \\
+   Year $=$ -12            & -3.662                      & 4.022                 & 4.158              &   \\   
+   & (4.846)                     & (7.607)               & (4.559)            &   \\   
+Year $=$ -11            & 11.81$^{**}$                & 21.13$^{***}$         & 11.38$^{***}$      & -8.510\\   
+   & (4.059)                     & (4.051)               & (3.283)            & (5.855)\\   
+Year $=$ -10            & 3.620                       & 10.61$^{**}$          & 2.824              & -19.84\\   
+   & (5.905)                     & (3.994)               & (4.242)            & (12.84)\\   
+Year $=$ -9             & -0.3975                     & 9.114$^{*}$           & 9.464$^{**}$       & 32.56$^{**}$\\   
+   & (6.802)                     & (4.270)               & (4.307)            & (12.35)\\   
+Year $=$ -8             & 6.122                       & 8.025$^{*}$           & -5.635             & 18.01\\   
+   & (5.372)                     & (4.017)               & (4.751)            & (15.49)\\   
+Year $=$ -7             & -0.6433                     & 4.332$^{*}$           & -0.0490            & 28.65$^{***}$\\   
+   & (2.946)                     & (2.137)               & (2.309)            & (4.396)\\   
+Year $=$ -6             & -0.6307                     & 0.5265                & -1.245             & 19.88$^{*}$\\   
+   & (2.661)                     & (1.831)               & (2.250)            & (9.392)\\   
+Year $=$ -5             & -2.513                      & 3.507$^{**}$          & 4.726$^{**}$       & 60.97$^{***}$\\   
+   & (2.327)                     & (1.385)               & (2.163)            & (16.01)\\   
+Year $=$ -4             & -1.223                      & 2.248                 & 3.205              & 29.33$^{*}$\\   
+   & (1.815)                     & (1.374)               & (2.105)            & (14.96)\\   
+Year $=$ -3             & -2.271                      & 2.150$^{*}$           & -0.3262            & -20.77$^{**}$\\   
+   & (1.491)                     & (1.063)               & (1.832)            & (7.205)\\   
+Year $=$ -2             & -2.238$^{*}$                & 1.347                 & 1.895              & -20.43$^{**}$\\   
+   & (1.231)                     & (0.7693)              & (1.627)            & (6.959)\\   
+Year $=$ 0              & 0.3256                      & -2.040$^{**}$         & -0.3629            & 6.583$^{*}$\\   
+   & (1.149)                     & (0.7153)              & (0.9964)           & (3.186)\\   
+Year $=$ 1              & 0.0610                      & -3.153$^{***}$        & -2.008             & 22.02\\   
+   & (1.285)                     & (1.021)               & (1.603)            & (14.07)\\   
+Year $=$ 2              & -3.699$^{**}$               & -4.441$^{***}$        & 1.670              & 11.34\\   
+   & (1.621)                     & (1.238)               & (1.862)            & (14.72)\\   
+Year $=$ 3              & -1.135                      & -3.191$^{**}$         & 3.513              & 20.60$^{***}$\\   
+   & (1.374)                     & (1.155)               & (2.037)            & (6.162)\\   
+Year $=$ 4              & 0.2532                      & -3.818$^{**}$         & 5.430$^{**}$       & 13.63$^{*}$\\   
+   & (1.695)                     & (1.531)               & (2.219)            & (6.771)\\   
+Year $=$ 5              & 1.730                       & -1.271                & -0.3170            & 43.53$^{**}$\\   
+   & (1.922)                     & (1.362)               & (2.078)            & (15.40)\\   
+Year $=$ 6              & 2.777                       & -0.7069               & 1.592              & 3.543\\   
+   & (2.545)                     & (1.767)               & (2.490)            & (6.994)\\   
+Year $=$ 7              & 5.904$^{*}$                 & -2.999                & -7.270$^{**}$      & -2.339\\   
+   & (2.982)                     & (1.917)               & (2.762)            & (5.597)\\   
+\midrule
+\emph{Fixed Effects} & & & & \\
+Subdistrict-Year        & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+Ditch-Year              & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+In Fallow Program       & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+In CREP After & & & & \\
+Treatment               & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+Well                    & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+In CREP-Year            & $\checkmark$                & $\checkmark$          & $\checkmark$       & $\checkmark$\\   
+\midrule
+\emph{Fit statistics} & & & & \\
+Observations            & 49,439                      & 49,439                & 49,439             & 49,439\\  
+R$^2$                   & 0.74701                     & 0.74705               & 0.74722            & 0.74768\\  
+Within R$^2$            & 0.00158                     & 0.00174               & 0.00238            & 0.00420\\  
+\midrule \midrule
+\multicolumn{5}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+\multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+
+\multicolumn{5}{l}{\emph{Note:} a) All wells within the given radius are included, so the two-mile cutoff includes} \\
+\multicolumn{5}{l}{\hspace{1.46cm}  all quarter-mile wells.} \\
+
+\multicolumn{5}{l}{\hspace{1.0cm} b) Each of the 10 largest ditches are included in the Ditch-Year fixed effect.} \\
+
+\multicolumn{5}{l}{\hspace{1.0cm} c) Subdistricts Two through Six included in the Subdistrict-Year fixed effect.} \\
+
+\end{longtable}
+
+\FloatBarrier

Tables/REG_PRE_CREP_EVENT.tex

@@ -0,0 +1,110 @@
+\FloatBarrier
+
+\begingroup
+\begin{longtable}{lc}   % |c|c|
+   \captionsetup{justification=raggedright,singlelinecheck=false}
+   \caption{Pumping fee response of Subdistrict One and CREP wells}\label{2011CREPVSBD1}\\
+   \hline
+   \hline
+   Dependent Variable: & AF\\  
+   Model:              & (1)\\  
+   \endfirsthead
+   
+   % At top of each page
+   % \multicolumn{2}{c}%
+   % {{\bfseries Table \thetable{} continued from previous page}} \\
+   % \hline
+
+   % End of table block
+   % Col 1 & Col 2 \\
+   % \hline
+
+% MUST BE HERE- tells macro to update name for subsequent pages
+   \endhead
+   
+   % At end of page when a new one will start
+   % \hline \multicolumn{2}{|r|}{{Continued on next page}} \\ \hline
+   % \endfoot
+   
+   % At end of etire table
+   % \hline
+   %  \multicolumn{2}{l}{
+   %    {End of table}} \\ \hline    % |r|
+   % \endlastfoot
+   
+   % \tabularnewline % \midrule \midrule
+   \midrule
+   \emph{Variables}\\
+   In CREP:2011        & -12.21$^{***}$\\   
+                       & (0.9757)\\   
+   In CREP:2012        & -33.64$^{***}$\\   
+                       & (5.930)\\   
+   In CREP:2013        & -36.40$^{***}$\\   
+                       & (6.479)\\   
+   In CREP:2014        & -35.62$^{***}$\\   
+                       & (6.741)\\   
+   In CREP:2015        & -37.95$^{***}$\\   
+                       & (6.835)\\   
+   In CREP:2016        & -43.42$^{***}$\\   
+                       & (12.51)\\   
+   In CREP:2017        & -47.27$^{***}$\\   
+                       & (10.68)\\   
+   In CREP:2018        & -52.00$^{***}$\\   
+                       & (6.399)\\   
+   In CREP:2019        & -48.52$^{***}$\\   
+                       & (8.736)\\   
+   In CREP:2020        & -62.83$^{***}$\\   
+                       & (9.969)\\   
+   Sbd.1:2011          & -6.200\\   
+                       & (6.310)\\   
+   Sbd.1:2012          & -43.37$^{***}$\\   
+                       & (8.328)\\   
+   Sbd.1:2013          & -49.72$^{***}$\\   
+                       & (8.453)\\   
+   Sbd.1:2014          & -40.68$^{***}$\\   
+                       & (8.560)\\   
+   Sbd.1:2015          & -48.17$^{***}$\\   
+                       & (5.237)\\   
+   Sbd.1:2016          & -27.44$^{***}$\\   
+                       & (5.802)\\   
+   Sbd.1:2017          & -19.45$^{*}$\\   
+                       & (9.732)\\   
+   Sbd.1:2018          & -48.73$^{***}$\\   
+                       & (10.51)\\   
+   Sbd.1:2019          & -25.74$^{***}$\\   
+                       & (6.772)\\   
+   Sbd.1:2020          & -30.75$^{**}$\\   
+                       & (10.98)\\   
+   Sbd.1:2021          & -18.87\\   
+                       & (11.57)\\   
+   \midrule
+   \emph{Fixed Effects}\\
+   Subdistrict-Year    & $\checkmark$\\   
+   Ditch-Year          & $\checkmark$\\   
+   Year                & $\checkmark$\\   
+   Well                & $\checkmark$\\   
+   \midrule
+   \emph{Fit statistics}\\
+   Observations        & 48,563\\  
+   R$^2$               & 0.74589\\  
+   Within R$^2$        & 0.00486\\ 
+
+   % Footnotes
+   \midrule \midrule
+   \multicolumn{2}{l}{\emph{Clustered (Well \& Year) standard errors in parentheses}}\\
+   \multicolumn{2}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
+   \tabularnewline
+
+   \multicolumn{2}{l}{\emph{Note:} a) Any data entry where a CREP well was in the CREP program was dropped.} \\
+   \multicolumn{2}{l}{\hspace{1.46cm}  A CREP well that entered in 2015 has data from 2009-2014.} \\
+
+   \multicolumn{2}{l}{\hspace{1.0cm} b) Each of the 10 largest ditches are included in the Ditch-Year fixed effect.} \\
+
+   \multicolumn{2}{l}{\hspace{1.0cm} c) Subdistricts One through Six included in the Subdistrict-Year fixed effect.} \\
+
+   \multicolumn{2}{l}{\hspace{1.0cm} d)  CREP wells are removed once in the CREP program.} \\
+
+\end{longtable}
+\endgroup
+
+\FloatBarrier

Tables/Summary_Stats_Wells.tex

@@ -0,0 +1,78 @@
+\begingroup
+% \vspace*{-\baselineskip}
+\begin{longtable}{lccc}
+	\caption{Summary statistics: well attributes}\label{SUMSTAT}\\
+
+	\hline\hline
+%	Column 1 & Column 2 & Column 3 & Column 4 \\ \hline
+& Mean & S.d & Obs. \\ 
+ \hline
+	\endfirsthead
+	
+	% \multicolumn{4}{c}%
+	% {{\bfseries Table \thetable\ continued from previous page}} \\
+	% \hline
+	% Column 1 & Column 2 & Column 3 & Column 4 \\ \hline
+	\endhead
+	
+	% \hline \multicolumn{4}{|r|}{{Continued on next page}} \\ \hline
+	% \endfoot
+	
+	% \hline \hline
+	% \endlastfoot
+
+	% \hline
+    % \multicolumn{4}{l}
+	% {
+	% 	{\emph{Notes:} When multiple permits exist for a single well the max value of the field is used, except for \emph{First Completed Date}, and \emph{Static Water Level} where the minimum value is used. The \emph{Producing Zone} was calculated by subtracting the maximum listed bottom perforation from the minimum upper perforation value.}
+	% 	} \\ \hline
+    % \endlastfoot
+	
+	& \multicolumn{3}{c}{CREP}  \\ \cmidrule{2-4}
+	Number of Wells & & & 154\\
+	Static Water Level (ft.) & 31.5&  2.1 & 2\\ 
+	Pumping Rate (AF/Year) &  663 & 418 & 74\\ 
+	Total Depth (ft.) & 130& 183& 152\\ 
+	Bottom Perforation (ft.) & 124& 182& 152\\ 
+	Producing Zone (ft.) &  87& 106& 152\\ 
+	Elevation (ft.) & 7570&   99 & 2\\ 
+	First Completed Date (M-Y) & 04-1977 & &78 \\ 
+	Last Completed Date (M-Y) &03-1980 &  & 78\\ 
+  \hline
+ & \multicolumn{3}{c}{Subdistrict One}  \\ \cmidrule{2-4}
+	Number of Wells & & & 2490 \\
+	Static Water Level (ft.) & 27.1 & 14.4 & 58\\ 
+	Pumping Rate (AF/Year) &  812& 386 & 985\\ 
+	Total Depth (ft.) & 118& 160 & 2462\\ 
+	Bottom Perforation (ft.) & 116 & 164& 2464\\ 
+	Producing Zone (ft.) &  79 &  80 & 2457\\ 
+	Elevation (ft.) & 6915 & 2218 & 95\\ 
+	First Completed Date (M-Y) & 11-1974 & & 1019\\ 
+	Last Completed Date (M-Y) &  11-1977 & & 1019\\ 
+  \hline
+ & \multicolumn{3}{c}{Other Wells}  \\ \cmidrule{2-4}
+	Number of Wells & & &1159\\
+ Static Water Level (ft.) & 50.8 & 48.8 & 25 \\ 
+ Pumping Rate (AF/Year)& 1067& 666 & 305\\ 
+ Total Depth (ft.) & 417& 414&1090\\ 
+  Bottom Perforation (ft.) & 412& 414 &1114 \\ 
+  Producing Zone (ft.) & 247& 259& 1107 \\ 
+  Elevation (ft.) & 6995& 2142& 36\\ 
+  First Completed Date (M-Y) & 03-1968 & &318 \\ 
+  Last Completed Date (M-Y) & 07-1971 &  &318\\ 
+   \hline
+   \hline
+\end{longtable}
+
+\vspace*{-1.5\baselineskip}
+\begin{tablenotes}
+	\begin{singlespace}
+		\item{\emph{Notes:} When multiple permits exist for a single well the max value of the field is used, except for \emph{First Completed Date}, and \emph{Static Water Level} where the minimum value is used. The \emph{Producing Zone} was calculated by subtracting the maximum listed bottom perforation from the minimum upper perforation value.}
+	\end{singlespace}
+\end{tablenotes}
+\vspace*{\baselineskip}
+\endgroup
+
+
+% latex table generated in R 4 3 0 by xtable 1 8-4 package
+% Sun Jul  2 14:30:35 2023

appendix.tex

@@ -0,0 +1,17 @@
+\appendix
+% ======================== Insert all of the appendix files
+\FloatBarrier
+\input{Sections/A_CREP_Goals.tex}
+\FloatBarrier
+\input{Sections/A_CREP_All_Reg.tex}
+\FloatBarrier
+\input{Sections/A_Distance_Reg.tex}
+\FloatBarrier
+\input{Sections/A_All_Years_2011_Reg.tex}
+\FloatBarrier
+\input{Sections/A_Policies_CI.tex}
+\FloatBarrier
+\input{Sections/A_Fallow_Prog.tex}
+\FloatBarrier
+\input{Sections/A_CREP_To_Near_Reg.tex}
+\FloatBarrier

body/Intro.tex

@@ -0,0 +1,43 @@
+\section{Introduction}
+Economists are interested in identifying the socially optimal management strategy of common-pool resources. In many cases, the theoretically preferred policy is not politically feasible, and conservation policies are evaluated in conjunction with political feasibility \citep{walter2020,barragan-beaud2018,lipsey1956}. This paper evaluates a case where a first-best policy, a tax on externalities, is followed by a politically feasible but second-best alternative, a \ac{PES}. Econometric results show that the existence of the fee substantially reduces the expected gains from the second-best alternative. The joint outcome of the two policies is not the sum of the expected resource conservation of each policy, as estimated independently. Rather, the pumping tax decreases the total direct water conserved by the \ac{PES} by 32\%. This conservation reduction comes from the interaction of two effects from the pumping fee, each enrolled well in the program already reduced water usage by 62 \(\frac{\ac{AF}}{Year}\), and the added cost of pumping encouraged 29.52\% more wells to enroll. This case study is relevant to policy design, economic outcomes of policies must be estimated with consideration to the existing resource management efforts.
+
+Groundwater resources have lacked clearly defined property rights resulting in externality costs of extraction. These include the cost to downstream surface water holders \citep{cobourn2015}, the common-pool resource management loss in a tragedy of the commons \citep{gisser1980,brownjr.1972}, local pumping externalities \citep{brozovic2010}, and added legal threat of neighbors when water rights could not be fulfilled. The estimates of these costs have historically been difficult to assess. Understanding and managing these costs is becoming progressively more important to economists and policymakers as arid regions face increased resource scarcity.
+
+Theory and empirical research contribute to our understanding of the effectiveness of groundwater programs. The usual \emph{first-best} policies of pumping fees and quotas have been tried \citep{schuerhoff2013,drysdale2018,smith2017,smith2018}. A pumping fee can be economically efficient\footnote{When the rate is property set, and is assessed on the party with the lowest abatement costs.} because the creator of the externality bears the cost of the decision to pump water \citep{pigou1924}. The groundwater user is able to determine their own cost and benefits inclusive of social costs, which brings their private benefit in line with the socially optimal outcomes. A common hurdle to initiating a Pigouvian tax is that the interests of the existing industry are harmed by adding the tax \citep{jenkins2014}. Also, the policy outcomes of pumping fees sometimes fall short of expectations, with institutional enforcement, and political influences of competing interest groups identified as hindering conservation \citep{yang2003,schuerhoff2013}. Both the institutional rigidity and reduced conservation can be over-improved through collective action \citep{huang2013,cody2018,ostrom1990}. In the case study elaborated in this paper, groundwater users in the \ac{SLV}, were able to take collective action and employ a self-imposed pumping fee to mitigate externality costs of pumping. The result was a 33\% reduction in groundwater extraction \citep{smith2017}. 
+
+The ability to self-organize is not a given, as is evidenced by the scarce case studies of a pumping fee adoption. It can be easier to induce political change by paying users to reduce an externality rather than charging them for generating the costs. One common and growing alternative following this method is a \ac{PES} \citep{wunder2008,engel2008}. The \ac{PES} of study is \ac{CREP}, which is a federal program that pays farmers to fallow land and to grow vegetation that improves local environmental quality. 
+
+There are a number of studies that evaluate the factors that contribute to enrollment in \ac{CREP}. Payment rates are consistently found to drive enrollment \citep{monger2018,suter2004,suter2008}. Other enrollment factors are centered on opportunity costs, with land quality and urbanization effecting enrollment numbers \citep{parks1997,plantinga2001}. Some benefits identified in specific applications of \ac{CREP} include increasing the water table by 15\% \citep{manning2020}, decreasing water intensity by 1.29 \(\frac{\ac{AF}}{acre}\) \citep{rosenberg2020} and creating cooperative norms where neighboring wells reduce pumping rates by 9.6 \(\frac{\ac{AF}}{year}\) \citep{rouhirad2021}. Due to the distortions created in \emph{second-best} policy outcomes, some inefficiencies are identified. Such programs offer a lump sum payment for enrollment. As a result land is enrolled based on opportunity costs, rather than targeting land with a high return for water conservation\citep{wanhongyang2005}. For example, regions with high vacant land values due to growing urban development will require a higher \ac{CREP} payment to enter the program than equivalent parcels in rural areas, a factor unrelated to total water use of the parcel. A related program, \ac{CRP}, has been identified as suffering from a rebound effect where non-farmland is converted into farmland, leading to a 9\% slippage in conservation \citep{wu2000}.
+
+The current analysis identifies how conservation outcomes of groundwater management programs change when alternative water conservation polices are in place. This is an important question to answer because almost all farmland in the United States will qualify for some form of \ac{PES} and the addition of a pumping fee or quota system should account for the combined policy outcome.
+
+There are multiple mechanisms by which a pumping fee can change the water conservation outcomes of a \ac{PES}. Both direct and indirect consequences of the fee are examined using econometric methods. To highlight the different types of interactions between a \ac{PES} and pumping fee consider the model of \ac{PES} water conservation:
+\begin{equation}
+	Conservation = \sum_{w=1}^{W} \left( \theta_{w}\cdot I_{w}+\beta\left[1-\theta_{w}\right]\cdot\eta_{w}\right)
+\end{equation}
+Where \emph{w} is a well and \(w \in W\), \(I_{w}\) is the average water intensity of the well, \(\theta_{w}\) is a dummy variable indicating that a well is enrolled in the \ac{PES}, \(\beta\) is the change in pumping that results from a well being near a fallowed well, \(\eta_{w}\) is a dummy variable for a well being close to a well enrolled in the \ac{PES} program.
+%\begin{equation}
+%    \theta_{w}=
+%    \begin{cases}
+%	    1\text{ if } P_{PES}\cdot Area_{w} \ge\pi_{w}\  \\
+%	    \text{else}=0  \\
+%    \end{cases}
+%\end{equation}
+%Where \(P_{PES}\) is the price paid by the \ac{PES} program to retire an acre, \(Area_{w}\) is the area covered by a well and is assumed to be constant, and \(\pi_{w}\) is the total profit attributable to the operation of the well. 
+%and is defined as \(\eta_{w}=\max\left(\theta_{f\left(w,d\right)}\right)\). \emph{f} is a subset of wells that are within a distance (\(d)\)) of well \emph{w}.
+
+The \ac{PES} water conservation can be separated into direct and neighborhood spillover effects. The direct effect is captured in the term \(\theta_{w}I_{w}\) which is the amount of water saved by an enrolled well when it is shut off. The neighborhood effect is captured in \(\beta\left[1-\theta_{w}\right]\cdot\eta_{w}\), this is the change in water pumped by wells that are sufficiently near to a \ac{PES} well from both pro-social and water table consideration.
+
+A marginal increase in the pumping fee influences each of these components through the equation:
+%\begin{equation}
+%	\frac{dConservation}{dFee} = \sum_{w=1}^{W} \left( \frac{d \theta_{w}}{d\ Fee}\cdot I_{w}+\frac{d\ I_{w}}{d\ Fee}\cdot \theta_{w}+\beta\left(\frac{\eta_{w}}{dFee}-\left[\theta_{w}\frac{\eta_{w}}{dFee}+\eta_{w}\frac{\theta_{w}}{dFee}\right]\right)\right)
+%\end{equation}
+\begin{equation}
+\frac{dConservation}{dFee} = \sum_{w=1}^{W} \left[\theta_{w}\frac{dI_{w}}{dFee}+I_{w}\frac{d\theta_{w}}{dFee}+\beta\left(\left[1-\theta_{w}\right]\frac{\eta_{w}}{dFee}-\eta_{w}\frac{\theta_{w}}{dFee}\right)\right]
+\end{equation}
+
+Four total effects from the pumping fee on the \ac{PES} are identified. First, the fee changes the intensity of groundwater pumped by wells in the program prior to enrollment based on the term \(\theta_{w}\frac{dI_{w}}{dFee}\). Each well enrolled in the \ac{PES} reduces output to zero, but the existence of the fee lowers the baseline pumping rate. Therefore, each well added to the \ac{PES} conserves less water under a pumping fee policy. Second, the pumping fee changes which wells enroll in the \ac{PES} through the term \(I_{w}\frac{d\theta_{w}}{dFee}\). The fee makes some wells more or less profitable to operate. This in turn changes which wells select into the \ac{PES} program. When this term is positive more wells are enrolled because of the fee which counteracts the reduced water saving per well enrolled in the term \(\theta_{w}\frac{dI_{w}}{dFee}\). Third, the number of wells that respond to neighborhood spillover effects adjust. Changing which wells enroll in the program also changes which wells are near enough to a fallowed \ac{PES} well to induced shifts in pumping behavior, this effect is \(\beta\left[1-\theta_{w}\right]\frac{\eta_{w}}{dFee}\). Fourth, the addition of wells to the \ac{PES} removes any potential neighborhood spillover effects that the well would have responded to, which is shown in the term \(-\beta \eta_{w} \frac{\theta_{w}}{dFee}\).
+
+These interactions are each evaluated empirically. First, the direct effect of the pumping rate of wells that eventually join \ac{CREP} and the magnitude of the neighborhood effect is found using a \ac{DID} study. An event study is developed that considers the institutional paths taken by farmers in the \ac{SLV}. In this setting, farmers self-organize and develop the self-imposed pumping fee. This fee began in 2011 and is treated as a natural experiment to develop a \ac{DID} estimate of the pumping effect replicating the work of \citep{smith2017} while distinguishing wells that join the \ac{PES} using \ac{CREP} as a separate treatment group. By 2014, farmland could begin active enrollment in \ac{CREP}. This is staggered treatment, so a weighted event study is used to estimate the amount of water conserved by the program \citep{sun2021}. A similar methodology is used to predict any neighborhood effects from \ac{CREP} enrollment as has been identified in Kansas \ac{CREP} \citep{rouhirad2021}.
+
+Second, a probit model is used to determine if the fee changes which wells enroll in the \ac{PES} and how much additional water is conserved through this process. To identify an indirect effect on neighboring wells the results of the probit are used in a Monte Carlo simulation. The Monte Carlo simulates which wells may have been added to \ac{CREP} as a result of the fee. In each simulation the number of neighboring wells is calculated providing a metric to assess the additionality of the fee on the spillover effects. These results are combined to estimate the total change on the \ac{PES} water conservation that result from the pumping fee.

body/abstract.tex

@@ -0,0 +1 @@
+Previous studies of groundwater management through \ac{PES} have focused on the isolated effects of the program. We evaluate the interaction of existing groundwater management programs with \ac{PES}, finding that existing conservation efforts can mitigate water conservation. We focus on the application of federal fallowing incentives using \ac{CREP} for farmers in \acl{SLV}, Colorado. Farmers in this environmentally sensitive region had previously self-organized to impose pumping fees that curb groundwater extraction and reduce externalities. These pumping fees are found to be highly effective in reducing groundwater consumption but have a second-order effect of dampening the conservation of \ac{CREP}. Farmers with the largest response to the subdistrict policies self-select into the \ac{CREP} program. Consequently, each well enrolled in \ac{CREP} conserves 62\% less water than would be expected without a pumping fee. Overall, the pumping fees reduce the conservation outcomes of \ac{CREP} by 32\%  while raising program costs by 29.5\%. These outcomes highlight the need to consider interactions between conservation efforts when designing policy. They also suggest that other metrics of success should be considered. \ac{CREP} is effective at compensating the farmers who are the most affected by drought and pumping fees. Furthermore, the program is found to encourage spillover effects, where neighboring wells outside the program cooperatively reduce water usage by 2.8 \ac{AF} per year. The findings provide evidence that the interaction of policy, regional attributes, and community create complexities for \ac{PES} design.

body/background.tex

@@ -0,0 +1,82 @@
+\section{Background}
+
+\subsection{Subdistrict One}
+The \acf{SLV} is an agricultural region located in south-central Colorado. Farming practices started in 1630 with industrial farming becoming common by 1880 \citep{hearne1988}. The \ac{SLV} expanded rapidly at the turn of the twentieth century, with developments in rotary well technology spurring an explosion in groundwater use by 1940 \citep{cody2015}. Today 26\% of direct employment continues to come from the agricultural sector, median incomes are  \$47,599 which is 37\% lower than the Colorado average \citep{slvdev2024}.
+
+The valley has a multifaceted water system with the Rio Grande River providing surface water, an unconfined aquifer providing strong feedback to the river, and a lower confined aquifer supplying hydrologic pressure. Transmissivity in the region varies but ranges from 700-30,200 \(\frac{feet^2}{day}\) in the unconfined aquifer and 13,400 to 16,800 \(\frac{feet^2}{day}\) in the confined aquifer \citep{bexfield2010}. This connectivity makes the groundwater a common-pool resource creating a tragedy of the commons \citep{hardin1968}. From both a geologic and technical level, the linkages between groundwater and surface water were not well understood at the time farmers increased drilling rates in the 1940s. This type of uncertainty can lead to institutional misallocation of resources which are difficult to correct \citep{libecap2011}. Sunk capital in wells provided an incentive to continue groundwater extraction under drought conditions in the 1950s and 1960s \citep{loos2022,cody2015}. This came to a head in 1969 when the State of Colorado passed the \emph{Water Rights Administration and Determination Act of 1969}, placing groundwater use more directly under prior apportion orders \citep{cody2015}. Out of this, the conservation districts of Colorado were formed to manage agricultural water usage in six Colorado hydrologic basins. The \ac{SLV} falls into the \acl{RGWCD}.
+
+With the backdrop of this institutional development, a drought in the early 2000s created historically large declines in aquifer storage volumes (See \cref{FIG:STOR}). During this era, other wells in Colorado were required to retire due to pumping deemed out of priority \citep{loos2022}. Facing this legal uncertainty and increasing externalities from pumping, \ac{SBD1} of the \ac{RGWCD} was formed in 2006. The number of farmers in the \ac{SLV}, and their joint interest in preserving agricultural productivity, contributed to the ability to manage the groundwater common-pool resource collectively \citep{ostrom1990,walker1990,smith2018,cody2015}. 
+
+%\FloatBarrier
+\begin{figure}
+		\includegraphics[width=0.97\textwidth]{GW_LEVEL}
+	\caption{Storage levels of closed basin relative to 2000}
+	\label{FIG:STOR}
+\end{figure}
+
+Members in the subdistrict voted for enfranchisement under the authority of \ac{RGWCD}. Six total subdistricts have formed in the \ac{SLV}, each addressing water management locally. However, the region encompassing \ac{SBD1} was deemed to create the most injurious depletions to downstream rights holders. Given the limited resources of the \ac{RGWCD}, \ac{SBD1} was given priority over other subdistricts, becoming the first subdistrict to implement a water management plan.
+
+Leveraging the flexibility afforded to the subdistrict as a self-governed institution, different policies have been tried to reduce depletions. One policy that has met with interest from economists is a pumping fee. By charging groundwater users an additional cost above the electrical costs of pumping, the external costs can be internalized, allowing the socially optimal pumping rate to be achieved through pricing \citep{pigou1924}. Empirical evidence finds that the subdistrict pumping fee was able to reduce groundwater use by 33\% and promote institutional social norms that encourage water conservation \citep{smith2017,smith2018}. 
+
+Aquifer levels have declined since the policy's inception, despite the reduced water use from the pumping fees. Climate factors have been identified as contributing to these declines \citep{grabenstein2022,sbd12023,sbd12022a}. The pumping fee has been raised four times to address this issue and currently is set at \$500 per \ac{AF}, suggesting that the 63\% decline in groundwater pumping from 2011 levels has been insufficient to meet aquifer goals \citep{sbd12022a}.
+
+The subdistrict has taken a holistic approach to policy management, employing a range of regulatory options. The focus of the present paper is involvement in \cref{CREP}, a program that pays users to retire cropland (refer to \cref{bckgndcrep}). A related program was a temporary fallowing program that provided rental rates to fallow farms for a four-year window. By retiring land for four years, the farmers were able to receive a payment and had the flexibility to rotate which acreage was fallowed. This program was only offered for the 2020 and 2021 irrigation years. The subdistrict is active in purchasing wells and surface water rights in order to retire or provide augmentation. They have also used funds to purchase land on the outskirts of the subdistrict and acquiring the associated water rights. Such mixing of policies and repeated treatment can make isolating policy effects difficult and lead to reversals of cause-and-effect interpretation \citep{besley2000,callaway2021}. Much of the policy changes can be treated as exogenous since the unexpected drought was outside human control. With proper econometric methods, such dynamic policy interactions provide a rich pool of knowledge to assess groundwater management. 
+
+\subsection{CREP}
+\label{bckgndcrep}
+The \acf{CREP} is a program offered by the \acf{FSA}, which creates incentives to apply conservation practices on agricultural land within environmentally sensitive regions. \ac{CREP} is an offshoot of the \acf{CRP} program which provides more moderate incentives but covers a large portion of the country. \citep{fsa2023}
+
+Each local \ac{CREP} program has goals and requirements that are tailored to the region, with the Colorado Rio Grande \ac{CREP} program having additional entry requirements over \ac{CRP} or \ac{CREP} \citep{sbd12013}. The \ac{CREP} program as applied to the \ac{SLV} pays farmers to fallow land\footnote{The program allows for various forms of "fallowing" including planting cover crop or reintroducing wetland.}, requiring that no crops are planted over the contract term. The payment includes a sign-up bonus of \$300 per acre, with a yearly payment of \$288 \(\frac{acre}{year}\) over 15 years \citep{rgwcd2014,fsa2023a,sbd12011,rgwcd2023}\footnote{Federal, state, and local payments when land is not in the bonus payment region.}. The \ac{FSA} pays \$200 \(\frac{acre}{year}\) of this total with the caveat that a minimum of 20\% of funding must come from the State of Colorado or the subdistrict \citep{rgwcd2014}. The subdistrict funding of \ac{CREP} comes from self-imposed acreage fees that adjust to meet demand \citep{sbd12011}. As part of the fallowing incentive structure, the subdistrict provided resources to develop an additional \ac{CREP} contract option. Unlike other \ac{CREP} programs, \ac{SLV} participants can enter a permanent retirement contract \citep{sbd12011}. The subdistrict pays a one-time bonus of \(\frac{\$100}{acre}\) and a yearly bonus of \(\frac{\$22}{acre-year}\) for entering a permanent retirement contract\footnote{Amount above the 15-year contract, but the subdistrict supports both contract types.}\textsuperscript{,}\footnote{Payment only continues for 15 years, even if the contract is for permanent retirement.}. 
+
+There is a set of criteria for eligibility in \ac{CREP}. Three of the most restrictive being that all land must be in \ac{SBD1} \citep{sbd12013a}, the covered area must have been irrigated with at least half a \ac{AF} per acre for at least four of the six years between 2008 and 2013, and half a \ac{AF} per acre must have been applied to the land within two years of submitting the application \citep{rgwcd2015}\footnote{Other restrictions include the land must be capable of being irrigated and the cropland must have water rights.}. Notably, for the proceeding analysis, the subdistrict pumping fee discussed in \cite{smith2017} was set at \$45 in 2011 and raised to \$75 per \ac{AF} in 2012. The groundwater use of farms during the four-year window with a low pumping fee can be used to meet the \ac{CREP} eligibility requirements, allowing farms with low water use in 2012-2013 to enter the program. 
+
+The sign-up period began in 2013, placing the 2011 irrigation year within two years of sign-ups. 2011 happened to be the highest groundwater use year on record for \ac{SLV} farmers (see \cref{fig:AVPUMP}). While the \ac{SBD1} farmers began reducing water relative to other \ac{SLV} farms in 2011, the overall water use rate was high. For this reason, the pumping choices made in response to the \ac{SBD1} pumping fee do not significantly affect entry into \ac{CREP} based on either minimum irrigation requirements. 
+
+Within the subdistrict, the main goals of \ac{CREP} are to enroll 40,000 acres of cropland and reduce irrigated water use by 60,060 \ac{AF} per year\footnote{Other goals are included in \cref{A_CREP_GOALS} (\cite{sbd12013a}).}. Since 2013, the subdistrict has enrolled 10,868 acres of farmland. Engineering estimates of water consumption reduction due to \ac{CREP} are 14,775 \ac{AF} per year in 2023\footnote{14,666 in 2022 and 17,365 in 2021.} \citep{sbd12023,sbd12022,sbd12021}. Prior to the subdistrict pumping fee, wells that were enrolled in \ac{CREP} averaged 17,365 \ac{AF} per year in total pumping. 
+
+\subsection{Evidence of \ac{CREP} Outcome Changes}
+The existence of the pumping fee changes the incentives of farmers choosing to enter the \ac{CREP} program. The pumping fee has an effect on the amount of land enrolled in \ac{CREP}, and the amount of water saved per acre enrolled. The direction of these factors is ambiguous without further empirical analysis.
+
+The first adjustment is made along the intensity of groundwater use, which lowers the amount of water saved by \ac{CREP}.  The marginal cost of pumping increases because of the pumping fee. Increasing the cost of using groundwater reduces the quantity of groundwater applied by farmers\footnote{This is strictly non-increasing and could remain zero.}. Since all wells in the subdistrict decreased water use prior to the CREP program, \ac{CREP} induces less water conservation per well than if the pumping fee did not exist. Taking one extreme, if the pumping fee was high enough then all wells would be shut off, so the gains from \ac{CREP} would be zero. This implies that the marginal abatement cost of \ac{CREP} increases from the fee. Each enrolled well will cost the same amount as before the pumping fee, but the amount of water per retired well is lower.
+
+The fee can also interact in \ac{CREP} by changing the economics of entering the program. In other settings, \ac{PES} programs have been found to suffer from selection bias, with agents choosing to enter the program if they already meet the conservation standards \citep{daniels2010,martinpersson2013}. The design of the pumping fee is to alter groundwater extraction rates. By doing so, the fee increases the number of wells that would be in compliance with \ac{CREP}\footnote{Or close to compliance.} which may select into the program. In other groundwater management settings, the gains from the program were found to raise the value of farmland unevenly based on the hydraulic connectivity \citep{edwards2016}. This provides a mechanism by which the pumping fee has unevenly encouraged enrollment in \ac{CREP} based on spatial characteristics. This further reduces the per well intensity savings but will likely increase the amount of farmland enrolled in \ac{CREP}. This factor can increase overall water savings by inducing more enrollment but increase abatement costs through selection of wells that would have pumped less than the average subdistrict well.
+
+An evaluation of crop choice in the \ac{SLV} suggests that heterogeneous land attributes are significant in selecting into \ac{CREP}. \cref{FIG:CROP} shows the average acreage of crops grown by three user groups; farmland outside \ac{SBD1} ('\emph{Other} wells'), farmland in \ac{SBD1} \emph{Sbd.1}, and farmland that is enrolled in \emph{\ac{CREP}}. The crop choice is broken out into three periods: before the pumping fee (\emph{Pre-Policy}), after the pumping fee but before \ac{CREP} starts (\emph{post-2011}), and after \ac{CREP} begins (\emph{post-2014}). Wells that are entered into \ac{CREP} grow substantially more small grains and alfalfa compared to wells in \ac{SBD1}. These crops have been identified as seeing the largest intensive adjustments to the pumping fee \citep{smith2017}. This suggests that heterogeneous farm conditions lead to a selection of some farms into \ac{CREP}.
+
+
+% \FloatBarrier
+\begin{figure}
+	\subfloat[Crops of Other Wells]{
+	\begin{minipage}[c][0.32\linewidth]{0.32\textwidth}
+		\centering
+		\includegraphics[width=1\textwidth]{OTHER_CROP.jpeg}
+	\end{minipage}}
+	\hfill
+	\subfloat[Crops of Sbd.1 Wells]{
+	\begin{minipage}[c][0.32\linewidth]{0.32\textwidth}
+		\centering
+		\includegraphics[width=1\textwidth]{SBD1_CROP.jpeg}
+	\end{minipage}}
+	\hfill
+	\subfloat[Crops of \ac{CREP} Wells]{
+	\begin{minipage}[c][0.32\linewidth]{0.32\textwidth}
+		\centering
+		\includegraphics[width=1\textwidth]{CREP_CROP.jpeg}
+	\end{minipage}}
+  
+ 	\caption{Crop variations by groups (average acreage per well)}\label{FIG:CROP}
+ \end{figure}
+ % \FloatBarrier
+
+The well intensity margin also appears to be in play. The average pumping rate of wells in each of these three groups is provided in \cref{fig:AVPUMP}. \ac{CREP} wells and subdistrict wells have a nearly identical average prior to the pumping fee, suggesting the parallel trend assumption is valid. After the pumping fee starts, \ac{SBD1} wells, and wells that eventually join \ac{CREP} deviate from the control group, reducing average pumping. However, the \ac{CREP} wells reduce output even more than the subdistrict average while following the same yearly trend. The disproportionate response of \ac{CREP} wells is indicative of selection into \ac{CREP} by land that was most impacted by the pumping fee.
+
+% \FloatBarrier
+\begingroup
+\begin{figure}[h]
+	\includegraphics[width=\linewidth]{Pumping_Rates.jpeg}
+	\caption{Adjustments in average pumping by well group}
+	\label{fig:AVPUMP}
+\end{figure}
+\endgroup
+
+This preliminary evidence is used to inform the following empirical analysis. The intensive margin of \ac{CREP} wells is explored before and after the pumping fee using a \ac{DID} specification. Next, a probit model is applied to predict the likelihood of a well joining \ac{CREP} based on their response to the pumping fee. These can be used together to estimate the direct effect of \ac{CREP} on water savings and program abatement costs.

body/conclusion.tex

@@ -0,0 +1,40 @@
+\section{Discussion and Conclusion}
+\subsection{Water Conservation Outcomes} 
+Combining each of these empirical estimates a picture of the overall groundwater reduction in the subdistrict can be sketched. The interaction between the \ac{CREP} program and the existing pumping fee is the primary result, but the combined output is also relevant for policy choice. Overall effects are summarized by estimating each program using an event study and combining the effects.
+
+The estimated changes due to subdistrict and \ac{CREP} policies are summarized in \cref{RESTBL}, confidence intervals are suppressed for clarity but are provided in appendix \cref{A:CI_TBL}. The \emph{Direct} and \emph{Spillover} columns predict the amount of water saved as a result of the \ac{CREP} fallow program from the methods in \cref{SECCREP} using the well dataset\footnote{There is yearly variation because the number of wells in \ac{CREP} changes, and not all cohorts are in the same lag set. Estimates were made by summing the coefficient for each well in a given lag year and neighbor lag year.}. The \emph{Expected Column} is the total volume of water reduced by \ac{CREP} wells due to the Subdistrict One policies from \cref{REG2011}. This is referred to as \emph{expected} because a \ac{CREP} policy planners would expect that \ac{CREP} will induce this additional volume of water when not considering the interaction with the pumping fee. The total volume of water saved because of \ac{CREP} is the sum of the \emph{Direct} and \emph{Spillover} columns while the total volume of water reduced because of the \ac{CREP} wells fallowing is the sum of all three columns. Under the Subdistrict sections the Fallow column is the predicted direct effect of water savings from the four-year fallowing program as estimated in appendix \cref{A:FALPROG}\footnote{Neighborhood effects are assumed to be zero for this program.}. The \emph{Other Policies} column captures the response of wells not in \ac{CREP} relative to the control group\footnote{The model predicts a yearly event study of water savings of all wells in Subdistrict One compared to the control. These yearly net subdistrict savings are then subtracted by the other policy estimates. Leaving the reduction in water that cannot be accounted for by the \ac{CREP} program and short-term fallow programs.} estimating the yearly effect of all other policies including the pumping fee. Finally, the \emph{No Policies} column is the counterfactual volume expected if none of the water conservation efforts were made. 
+
+\input{Tables/Policy_Estimates.tex}
+
+These results help provide a benchmark of the overall savings. The pumping fee and other subdistrict policies consistently provided the most water savings. This is due to the directed cost of pumping and the fact that all wells within the subdistrict are affected. Over time the direct effect of \ac{CREP} has increased as enrollment enlarges, but the relative importance of the neighbor effects has declined. The neighborhood effect coefficients revert to zero over time, becoming insignificant in five years. The wells enrolled in \ac{CREP} from 2014-2016 have already been active for five years, thus making these cohorts' addition to the spillover effect negligible going forward. Also, as more wells are enrolled then there are fewer wells that are not neighbors to a \ac{CREP} well. Since there is some spatial clustering in the program enrollment, latter \ac{CREP} wells add a smaller number of wells to neighboring treatment group. The progression of the added effect of each policy outcome is provided in \cref{FIG:BAR}.
+%\FloatBarrier
+    \begin{figure}[h]%
+        \centering
+        \includegraphics[width=\textwidth]{Policy_Bar_Graph}
+	\caption{Water use and conservation in \ac{SBD1}}
+        \label{FIG:BAR}
+    \end{figure}%
+    While the overall water conservation of \ac{SBD1} is substantial the \ac{CREP} and resulting spillover effects are minor compared to the pumping fee and other \ac{SBD1} policies. This smaller \ac{CREP} impact is not due entirely to the original \ac{PES} design but is in part reduced precisely because the \ac{SBD1} polices are successful at reducing groundwater extraction, which adds complications for policy makers seeking to reach conservation targets.
+
+How the outcomes of \ac{CREP} may be different from policymaker expectations can also be found. The volume of groundwater saved by the \ac{CREP} program is found to be 32\% lower due to the existence of Subdistrict One policies. However, it does not appear that either the spillover effects or the policy slippage due to the pumping fee were accounted for in \ac{CREP} plans. There are two unknowns pushing in opposite directions. A policy planner making choices without this knowledge would overestimate the policy benefits of \ac{CREP} by 33\%\footnote{They would overestimate the direct reduction of \ac{CREP} wells by 61.6\%}. In other settings the gains from \ac{CREP} are likely to be underestimated, since there is evidence that it encourages neighbors to cooperate and reduce water output. However, it is important to consider local spatial attributes and overall enrollment levels, when estimating these effects in a \ac{PES} program. In this case study it was found that the effect of adding 32 wells to \ac{CREP} only increased neighborhood effects by 3.27\%.
+
+
+
+The \ac{CREP} benchmarks can also be compared to the stated goals of the program. Looking at the year with the largest \ac{CREP} reduction the \ac{CREP} policy has achieved 17\% of the 60,000 \ac{AF} per year savings target. With the acreage intensity falling -0.49 \(\frac{\ac{AF}}{acre}\) short of the rate needed to reach the goal after full enrollment of 40,000 acres. However, the overall efforts of the farms in \ac{CREP} are relevant to outcomes. When evaluating the total amount of water saved by wells in \ac{CREP} the intensity of savings is 0.80\(\frac{\ac{AF}}{acre}\) above the rate needed to reach the \ac{CREP} program goals. The owners of \ac{CREP} wells have contributed to a more stable aquifer even if much of the conservation efforts were made prior to the \ac{CREP} program starting. 
+
+
+
+%\FloatBarrier
+\begin{landscape}%
+        \centering
+    \begin{figure}[h]%
+        \caption{Cumulative effect of conservation policies }
+        \label{FIG:POLICYEFF}
+        \includegraphics[width=1.3\textwidth]{POLICY_COUNTER_FACT}
+    \end{figure}%
+\end{landscape}%
+%\FloatBarrier
+
+The net expected water savings due to conservation efforts are provided in \cref{FIG:POLICYEFF}. Despite the declining aquifer levels, the subdistrict has been successful in reducing the volume of water pumped. The upticks in water use after 2015 does physically lower the water table, but these cumulative results suggest that without the subdistrict policies an even more severe drawdown would have occurred. Comparable wells outside the subdistrict responded to climate, prices, and other factors during this period by increasing groundwater use. The marginal value of groundwater increased, partially reflected in the subdistrict's need to raise pumping fee rates.
+
+For policymakers this provides an important case study for developing \ac{PES} programs. While more conservation may always seem better, the existence of highly efficient water saving policies are identified as reducing \ac{PES} effectiveness. The Pigouvian tax on water use was found to increase enrollment in the \ac{PES} while also lowering overall program savings. Complications in the enrollment structure led to these results. Tailoring program goals, and enrollment criteria to the \ac{PES} region is required to avoid unintended effects from policy interaction.  Such programs have pro-social benefits of encouraging neighbors to conserve water, but once again complicating factors can arise. The spatial distribution of parcels is a major factor in outcomes of these neighborhood effects. Taken together, these outcomes provide insights into the complexities of conservation policy interactions. 

supporting-files/symbols-and-abbreviations.tex

@@ -0,0 +1,55 @@
+% You can add them any where in the text, but it is good to keep it in the same place.
+% >>> Symbols >> General Nomenclature
+\textbf{Acronyms}
+\begin{acronym}
+	\acro{AF}{acre-foot}
+	\acro{API}{application programming interface}
+	\acro{ATE}{average treatment effect}
+	\acro{ATT}{average treatment effect on the treated}
+	\acro{ARP}{Annual Replacement Plan}
+	\acro{FSA}{Farm Service Agency}
+	\acro{PES}{payment for environmental services}
+	\acro{BTU}{British thermal unit}
+	\acro{WTI}{West Texas Intermediate}
+	\acro{HH}{Henry Hub}
+	\acro{CRP}{Conservation Reserve Payments}
+	\acro{CREP}{Conservation Reserve Enhancement Program}
+	\acro{GASP}{Groundwater Appropriators of the South Platte River Basin, Inc}
+	\acro{GBM}{Generalized Boosted Model}
+	\acro{IPTW}{inverse probability of treatment weighting}
+	\acro{NPV}{net present value}
+	\acro{CPI}{consumer price index}
+	\acro{CSV}{Comma Separated Value file format}
+	\acro{CDSS}{Colorado Department of Support Services}
+	\acro{RGWCD}{Rio Grande Water Conservation District}
+	\acro{CSM}{Colorado School of Mines}
+	\acro{EC}{error correction}
+	\acro{USDA}{United States Department of Agriculture}
+	\acro{DID}[DiD]{difference-in-differences}
+	\acro{BTC}{Bitcoin}
+	\acro{BBL}{barrel of crude oil} \acro{ARDL}{autoregressive distributed lag model}
+	\acro{ADF}{augmented Dickey-Fuller}
+	\acro{NARDL}{nonlinear autoregressive distributed lag model}
+	\acro{IRF}{impulse response function}
+	\acro{SVAR}{structural vector autoregression}
+	\acro{VAR}{vector autoregression}
+	\acro{AIC}{Akaike information criterion}
+	\acro{ASIC}{application-specific integrated circuit}
+	\acro{PPI}{Producer Price Index}
+	\acro{PSS}{Pesaran, Shin and Smith}
+	\acro{ACF}{autocorrelation function}
+	\acro{PACF}{partial autocorrelation function}
+	\acro{JB}{Jarque-Bera}
+	\acro{LM}[LM]{Lagrange multiplier}
+	\acro{ARCH}[ARCH]{autoregressive conditional heteroscedasticity}
+	\acro{GOR}{gas-to-oil ratio}
+	\acro{POW}[PoW]{proof of work}
+	\acro{MMCF}{1,000,000 cubic feet of gas}
+	\acro{MCF}{1,000 cubic feet of gas}
+	\acro{TVD}{true vertical depth}
+	\acro{MD}{measured depth}
+	\acro{MTD}{measured total depth}
+	\acro{SLV}[San Luis Valley]{San Luis Valley}
+	\acro{SBD1}[Subdistrict One]{Subdistrict One of the Rio Grande Conservation District}
+	\acro{SBD2}[Subdistrict Two]{Subdistrict Two of the Rio Grande Conservation District}
+\end{acronym}