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Estimating Economic Impacts of Irrigation Water Supply Policy Using Synthetic Control Regions: A Comparative Case Study

Cameron Speira* and Eric Stradleya,b

aNOAA, National Marine Fisheries Service Southwest Fisheries Science Center Fisheries Ecology Division 110 Shaffer Road Santa Cruz, CA 95060 bWelch Consulting 1090 Vermont Ave. NW, Suite 900 Washington, DC 20005 *Corresponding Author. Tel.: +1 831-420-3910; fax: +1 831-420-3977. Email address: cameron.speir@noaa.gov

**DRAFT** Please do not cite or distribute without permission from the corresponding author

April 2017

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Estimating Economic Impacts of Irrigation Water Supply Policy Using Synthetic Control Regions: A Comparative Case Study

Abstract

We evaluate whether reductions in irrigation water deliveries from large, centrally-

operated water projects to farms in the California’s San Joaquin Valley resulted in

adverse economic impacts on the local economy. These reduced water deliveries resulted

from both drought conditions and instream flow requirements for protected species

habitat. Climate change, increased municipal demand, and more acute needs for instream

flow for environmental purposes will force communities in the study area and elsewhere

to cope with reduced irrigation water supply. We employ synthetic control methods to

construct a control region in to compare employment and wage outcomes in affected

areas to unaffected areas. This comparison is used to generate empirical estimates of the

magnitude of employment and wage impacts resulting from reduced irrigation water

supply from large, government-run supply projects. Our results indicate some direct

impacts in the form of lower agricultural employment and wage income concentrated in

the two of the four affected counties. We are unable to detect evidence of impacts to the

wider, non-agricultural economy in terms of employment and wage income.

Key Words: drought; irrigation; water supply; economic impacts; endangered species;

synthetic control methods

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1. Introduction

Agricultural areas in the western United States and other arid regions face the prospect of

reduced irrigation water supply because of drought, increased competition from urban

uses, or greater emphasis on instream flow to meet environmental goals. Local

communities are concerned that reduced irrigation water will have significant economic

impacts by reducing productive acreage and in turn reducing associated economic

activity (e.g., demand for complementary inputs such as labor, supply of farm products to

processors, income spent in the wider community). In this article, we examine

employment and income effects to the local economy of reduced water deliveries from

federal and state water projects to irrigation districts in California in 2007, 2008, and

2009. We employ a quasi-experimental approach where we compare employment and

wage income outcomes in areas where water supply was restricted to a synthetic control

region constructed from counties that were not affected by such restrictions.

Farming communities are concerned about the impact of reduced irrigation water supply

on the local economy. For example, Hanak (2005) finds that 12 out of 34 counties

sampled in California enacted ordinances restricting or prohibiting sale of irrigation water

out of the county in reaction to implementation of a water market in the early 1990’s.

Sunding et al. (2002) present a conceptual framework of the economic impacts of

reduced water supply that describes profit maximizing production decisions by

agricultural producers that are then aggregated into the region of interest. Reducing

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irrigation water supply increases the severity of the water input constraint and induces

various production responses, including alternative crop mixes, obtaining water from

other sources (e.g. groundwater or transfers), or changing irrigation technology. These

responses change the regional output of agricultural goods and the regional demand for

inputs to agricultural production. To the extent that reduced irrigation water supply

reduces agricultural production and input demand (e.g. for labor and agricultural

services), then regional income and employment are reduced via multiplier effects.

Previous studies have attempted to estimate local economic impacts, in terms of

employment and income, of reduced irrigation water supply. Many of these studies

employ input-output models for a portion of the analysis (Llop 2013). Howe et al. (1990)

use an input-output model to estimate the economic impacts (employment and income

losses) from transfers of irrigation water from the Arkansas River basin in Colorado to

urban use. They estimate the number of acres potentially fallowed as a result of proposed

water transfers and evaluate several scenarios that differ according to assumptions made

about resultant changes in crop mix and acreage. Howe and Goemans (2003) perform a

similar analysis for water transfers in the South Platte and Arkansas basins in Colorado.

Later studies combine programming models, including the positive mathematical

programming approach (Howitt 1995), that determine optimal crop mix and input usage

with input-output models that calculate changes in income and employment. Other

studies construct production functions and simulate labor use and farm income (Maneta

et al. 2009, Iglesias 2003, Frisvold et al. 2012) or general equilibrium models (Seung et

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al. 2000). Cai et al. (2008) analyzes the substitutability of water and labor via a basin-

scale simulation model, as well as empirically estimated farm-level production functions.

The existing studies on the effects of reduced water supply on employment typically rely

on simulation-type models that specify a production function or use multipliers to relate

changes in factor availability to employment. These studies are generally performed ex

ante in order to evaluate the effects of a proposed policy. An alternative approach is to

estimate the effects of an implemented policy ex post by comparing the observed

outcomes of interest (generally employment and income) to outcomes in a control group.

Frondel and Schmidt (2005) and Greenstone and Gayer (2009) present examples of

quasi-experiments and recommend increased use of these techniques in the analysis of

environmental policy. A substantial literature on program evaluation has developed in

the last two decades that focuses on testing for significant effects from policy and is

based on econometric methods and observed data. In our study, we use the synthetic

control method proposed by Abadie et al. (2010) to estimate the economic effects of

reduced irrigation water supply in the San Joaquin Valley using other counties as control

groups. The San Joaquin Valley of California offers a particularly compelling setting in

which to study the effects of reduced irrigation water supply. The unusually dry three-

year period of 2007-2009 combined with new restrictions on irrigation water use in 2009

to protect habitat for threatened fish species to result in substantial reductions in water

delivered to farms. This triggered widespread concern over the economic impacts on

local communities and sparked debate over the role of federal and state endangered

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species acts. These issues are ongoing as California and national stakeholders debate a

$25 billion plan to upgrade water supply infrastructure and restore fish habitat.

We examine two research questions: 1) did changes in water supply induce a shift in the

demand for farm labor and, if so, what is a reasonable estimate of the magnitude of this

shift and 2) did water supply shocks result in indirect and induced effects to the wider

regional economy beyond the agricultural sector? We compare employment and wage

income in affected counties in the San Joaquin Valley to synthetic control regions

constructed from other areas in California. We find evidence of potential farm

employment impacts on the order of 5,500 jobs concentrated in two counties in 2009 as

well as potential farm wage income impacts in one county in all three years of the

irrigation water supply shock. We are unable to detect any significant impacts to the

wider, non-agricultural economy.

2. Description of the Study Area: California’s San Joaquin Valley

The San Joaquin Valley of California (SJV) is one of the world’s most productive

farming regions. Agricultural production is dependent on irrigation water, much of

which is imported over relatively long distances from other parts of the state. For three

years, 2007-2009, the SJV was subject to much lower allocations of irrigation water due

to an extended drought and, to a lesser extent, new restrictions designed to improve

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instream flow conditions for protected fish species. It is this period that is the subject of

our study. Figure 1 is a map of California with our study area highlighted.

Federal and state government water projects capture and store water in northern

California, where runoff is relatively abundant, and convey it to the Sacramento-San

Joaquin River Delta (the Delta) in the center of the state. Water is then pumped from the

Delta and exported to the much drier south for use by farms in the SJV and urban users.

The Delta is the source of drinking water for more than 25 million people and provides

irrigation water for over 4 million acres of highly productive farmland (ICF International

2012). Two entities pump water from the Delta for delivery to a large portion of the

irrigated acreage in the SJV: the Central Valley Project (CVP), operated by the U.S.

Bureau of Reclamation (USBR), and the State Water Project (SWP), operated by the

California Department of Water Resources (DWR). The projects move water south for

delivery to irrigation districts within the SJV. Irrigation districts within these projects are

divided into classes based on the source of their water supply and the seniority of water

rights. Different classes of contractor receive different priority in times of shortage;

therefore the effects of water supply restrictions are unevenly distributed throughout the

region. Water supply reductions from 2007-2009 were concentrated in southern and

western portions of the SJV, particularly Fresno, Tulare, Kings, and Kern Counties,

which receive large quantities of water, have junior allocation priority according to water

law, and are particularly dependent on imported water.

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The Delta is also the largest estuary on the west coast of the United States and provides

critical habitat for several species of fish protected under federal and state endangered

species statutes, particularly Delta smelt, Chinook salmon, and steelhead trout. From

2007 through 2009, this region suffered an intense drought, with runoff in the Delta

watershed at less than two-thirds of long-run averages (DWR 2010). Two federal

agencies, the US Fish and Wildlife Service (FWS) and National Marine Fisheries Service

(NMFS), are responsible for monitoring the effects of export of water from the Delta and

for prescribing conditions to ensure adequate habitat for several species protected under

the Endangered Species Act (ESA). FWS issued new rules designed to protect Delta

smelt that limited the quantity of water available for irrigation and which first took effect

in time for the 2009 irrigation season. Water exports from the Delta were reduced by 40

percent (2.4 million acre-feet) relative to average exports from 2001-2006. The

Congressional Research Service estimated that about 20-25 percent of this reduction was

due to endangered species policy, with the remaining reduction proportion due to low

runoff and below average reservoir storage (Cody 2009).

Table 1 shows the magnitude of the water supply shock in our treatment areas in 2007,

2008 and 2009. For CVP contractors in our affected areas, primarily in Fresno and Kings

Counties, there were minimal changes in deliveries (compared to mean values for the

previous six years) in 2007, but substantial reductions in 2008 and 2009. For SWP

contractors, primarily located in Kern and Kings Counties, 2007 saw an approximately 20

percent reduction in deliveries followed by nearly 70 percent reductions in deliveries in

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2008 and 2009. For comparison, Table 1 also shows surface water deliveries by federal

projects to other parts of California that are potential control areas. Some of these control

areas, particularly the Sacramento Valley, were also subject to substantial reductions due

to dry conditions. However, reductions in the San Joaquin Valley were much greater

during the treatment period. Our strategy to identify the economic impacts of the water

supply shock in the SJV is to compare outcomes in the SJV to other areas. To the extent

that all control areas experience drier than normal conditions during the treatment period,

our estimates may understate the full effects of reduced deliveries relative to previous

year. However, the magnitude of the supply shock across treatment and potential control

areas indicates that we should be able to identify the effects of conditions unique to the

SJV including watershed-specific low runoff, low reservoir storage levels, and fish

habitat considerations. Note that in this study we do not estimate the impacts of low

runoff conditions and habitat protection separately. Rather, we are only able to estimate

the effect of the aggregate reduction in water supply in each year. Early ex ante estimates

of the economic impact of conditions in 2009 in the SJV ranged from 10,000 to 80,000

lost jobs, including indirect and induced impacts (for the earliest initial estimates see

Howitt 2009a). The economic impact of this water supply reduction has been the subject

of considerable debate, including congressional hearings, proposed laws to temporarily

suspend the ESA, and public outcry1.

Figure 2 plots project water deliveries (CVP and SWP) and farm employment in the four of

the SJV counties most affected by the water supply shock of 2007-2009: Fresno, Tulare,

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Kings, and Kern. USBR and DWR report deliveries at the irrigation district level. To

generate county-level deliveries we used GIS software to overlay CVP and SWP irrigation

districts on county maps. In cases where districts spanned multiple counties, we allocated

deliveries according to the proportion of irrigation district land area in each county.

Deliveries of project water to irrigation districts in all four counties dropped during the

study period. Large declines in farm employment are also evident in three of these

counties, though the declines are not exactly concurrent with water supply changes and are

preceded by large employment increases.

3. Synthetic Control Method

Abadie et al. (2010) and Abadie and Gardeazabal (2003) propose a synthetic control

method that constructs an estimate of the outcomes that would have occurred in the

absence of the policy (i.e, a credible counterfactual case) from a combination of multiple

control units. The effect of the policy is then estimated by comparing the outcome

estimated for the synthetic control unit to the observed outcome in the exposed unit. We

use this framework to assess whether counties that were affected by reduced water

exports from the Delta had lower employment and wage income in those years than other

similar counties that were not.

The synthetic control method is part of a larger set of techniques designed to estimate

policy impacts from natural or quasi-experiments (Greenstone and Gayer 2009) where

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outcomes in a treatment group are compared to a control group. A quasi-experimental

approach offers several advantages over a more structural approach, such as estimation of

a labor demand function. Given the small number of counties and relatively short time

series for most data, an estimated labor demand function would be forced to rely on

relatively few observations. Use of panel data would require data on wages, other input

prices, and water deliveries, all of which are difficult to obtain at the county level.

Moreover, there is some evidence of disequilibrium in the farm labor market in our study

area in recent years, which would make identification of the labor demand model difficult

(Michael 2009a, Hertz and Zahniser 2013). A quasi-experimental setup can control for

unobserved confounding variables by observing treatment and control groups that are

exposed to the same set of covariates. Also, the model does not require assumptions

regarding a specific functional form or market equilibrium.

The synthetic control approach maintains that a combination of units often provides a

better comparison for the treated unit than any single unit. In this way, it is similar to

other models, such as traditional regression-based difference-in-difference estimators,

that estimate average treatment effects using many individuals. However, synthetic

control methods have two significant advantages that make it useful for our particular

application. First, it systematically constructs a control county as a weighted average of

county from a “donor pool”, thereby removing the subjectivity involved in choosing

appropriate comparison counties. In our case, treatment and control counties will differ

in crop mix, climate, and other factors that may affect economic outcomes of interest.

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These differences would make choosing any one (or several) control units to represent

what would have occurred in the absence of the reduction in irrigation water supply

difficult. The synthetic control method generates a single unit for comparison that

matches, as closely as possible, pre-treatment outcomes in the affected areas. Second, the

synthetic control method better addresses the type of uncertainty present in our data.

Traditional regression-based difference-in-differences estimators most often use data on a

large sample of disaggregated units (e.g., individuals) and generate standard errors that

reflect the uncertainty about aggregate values in the population. We are able to observe

aggregate outcomes (on employment and wage income at the county level), so this type

of uncertainty is not present. Our main source of uncertainty regards how well the

synthetic control group accurately mimics the behavior of the treatment units in the

absence of the reductions in irrigation water supply. We draw on the methods proposed

by Abadie et al. (2010) and Bertand et al. (2004) and use placebo tests for inference that

better reflects the type of uncertainty present in our data.

3.1 Conceptual Model Underlying Synthetic Control Approach

We use the model proposed by Abadie et al. (2010). There are J+1 counties, with county

1 being exposed to the treatment and all J other counties as potential controls. There are

T total time periods with T0 non-treatment time periods.

Yit = α1tDit + βtZi + λtμi + δt + εit (1)

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In equation (1), Yit is the outcome of interest (employment or wage income) in county i at

time t, Dit is 0/1 indicator of whether county i is exposed to the treatment at time t, Zi is a

(rx1) vector of exogenous covariates, λt is time-varying common factor, μi is county-

specific common factor, and δt is a common intercept term that varies with time. The

parameter α1t is the treatment effect – the amount by which the outcome is shifted in the

treated county in time periods when the treatment is present.

We estimate the treatment effect α1t as the difference between the observed outcome in

treated periods (t > T0) and the counterfactual case. Abadie et al. (2010) show that the

difference between the observed outcome and a weighted average of the outcomes for all

potential donor counties (i.e., a synthetic control county) is an unbiased estimator of α1t.

, –∑ ∗ (2)

Each weight w*j is an element in W* a (Jx1) vector of weights that is chosen such that the

resulting synthetic control is the convex combination of donor counties that most closely

matches the treatment county in terms of the outcome and matching variables.

Abadie et al. (2010) choose a W* that minimizes the distance between the observed

values of the treated county and the synthetic control county, in terms of outcome and

matching variables. Specifically, let X1 be a vector containing the observed covariates

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and the outcomes for the treatment county in the pre-treatment period: X1 = (Z1, Y11, ...,

Y1T0). Let X0 then be a matrix consisting of the observed covariates and pre-treatment

outcomes for all donor counties; X0 is a (r+T0, J) matrix where the jth column is (Zj, Yj1,

..., YjT0). The optimal vector of weights W* is chosen to minimize:

(3)

where V is any (r+T0 x r+T0) positive semi-definite matrix. Choose V such that the mean

squared error of the outcome variable is minimized in non-treatment periods.

3.2 Significance of Estimated Treatment Effects

We use cross sectional placebo tests to assess the significance of the estimated treatment

effects. These tests consist of sequentially applying the synthetic control algorithm

described above to all 25 counties in our donor pool. The difference between the

observed and synthetic outcome is a placebo test where a treatment effect is estimated

though no treatment is applied. A significant treatment effect should be large relative to

the placebo test results for the donor counties. We can generate a quantitative assessment

of our estimated treatment effect by interpreting the placebo run as a permutation test. If,

for example, our estimated treatment effect is larger than 95 percent of the placebo test

effects, then we interpret it to be significant at the 5 percent level. This follows the

permutation test methods laid out in Abadie et al.(2010) as well as Bertrand et al. (2004).

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4. Data and Construction of Synthetic Control Regions

4.1 Data

Our data are county-level panel data for the period 2001 through 2011. We consider the

four southernmost counties in the San Joaquin Valley to be treatment areas: Fresno, Kings,

Tulare, and Kern2. We estimate the impact in each county separately to account for

differences in localized impacts. Impacts are in terms of labor market outcomes: farm

employment, farm payroll, non-farm employment, and non-farm payroll. The measures of

agricultural labor employed are estimate of the direct effects of reduced irrigation water

supply on labor demand. Nonagricultural labor outcomes are a measure of the indirect

and/or induced effects that may be transmitted through the broader economy. One

important issue to consider is the role of undocumented labor in our data. According to the

U.S. Department of Labor’s National Agricultural Workers Survey, over half of California

farm workers do not have legal work authorization. If these workers are not counted in

employment surveys, then employment numbers underestimate the number of workers

employed and, therefore, the impacts of the water supply reductions. Further, it may be that

seasonal undocumented workers represent the marginal units of labor employed, as

permanent workers would seem more likely to have legal work status. This would mean

that changes in labor demand would disproportionately affect the undocumented portion of

the labor force. We believe that the employment and compensation numbers we use

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provide reasonable estimates of labor market impacts for two reasons. First, employment

and compensations estimates are generated in part from the Bureau of Economic Analysis’

(BEA) Current Employment Statistics (CES) Program. According to BEA, the survey

instrument should capture undocumented workers, but BEA does not know the extent to

which they are excluded3. Second, there is some evidence that workers without legal status

use fraudulent documents, which means that the status of these workers is assumed to be

legal by employers and that they are included in official counts of payroll records

(Hotchkiss and Quispe-Agnoli 2008, GAO 2005).

Synthetic control counties are constructed from a donor pool of 25 counties in California.

We use crop mix, the value of crop production per acre, three weather variables,

population density, and population. Crop mix values are the percent of county cropped

acreage in each of seven crop categories. Weights are chosen so that the synthetic control

has similar values to the treatment region in terms of these predictor variables.

Figure 3 shows employment and compensation trends for the four affected counties and

25 potential donor counties. The effects of the national recession can be seen in 2008

through 2010, particularly in the non-farm sector. These pronounced macroeconomic

effects played a role in the intense interest in the economic impacts from ESA habitat

protections in 2009. From the farm sector graphs in Figure 3 and the harvested acreages

and farm-gate value graphs in Figure 4 we see that pre-2007 trends in the donor counties

resemble those in the treatment counties, but do not match precisely. Constructing

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synthetic control counties using pre-treatment trends and the values of matching variables

helps to construct a more plausible counterfactual. We also see a large drop in harvested

acres (though not crop value) in the affected areas relative to the donor counties in 2009.

This suggests that there may have been a strong effect from decreased water supply in the

affected area in 2009.

4.2 Synthetic Controls Weights and Predictor Variable Values

Table 2 displays the weights assigned to each control county in the constructed value of

the synthetic regions for farm employment and compensation. Table 2 shows that farm

employment for Fresno County, for example, was best reproduced by a linear

combination of Sacramento, Sutter, Monterey, Imperial, and Santa Barbara Counties.

Table 3 shows the resulting values of the predictor variables used to construct the

synthetic controls for farm employment and farm compensation. The values in Table 3

serve as one measure the analyst can use to assess how closely the synthetic control

matches the treatment county. Table 4 displays the weights of each control county in the

constructed value of the synthetic region for non-farm employment and non-farm

compensation. Table 5 shows the resulting values of the predictor values used to

construct the synthetic controls for non-farm employment and non-farm compensation.

We use the “synth” module written for Stata to implement the model (Abadie et al.

2011). Note that in creating the synthetic controls, we normalize the outcomes by the

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mean number of cropped acres (2001-2006) in the case of farm employment and

compensation or by year 2000 population in the case of non-farm employment and

compensation. This controls for differences in the size of the counties.

5. Results

Our estimates of the effect of reduced water supply in 2007, 2008, and 2009 are the

difference between observed outcomes in each treatment county and its synthetic control.

5.1 Farm Employment and Compensation

Table 6 reports our estimates of farm employment and compensation losses for each year

of the water supply shock. Figure 5 graphs the observed values and their synthetic

controls for farm employment over time for each of the four most affected counties in our

treatment area. Farm employment losses are observed in 2009 for Fresno County and

Kern County, two of the counties most affected by reduced irrigation water supply in that

year. Farm job losses are also observed in Kings County in 2008.

The Fresno and Tulare County results require some explanation. In both cases, the

optimal synthetic control, as derived by the method proposed by Abadie et al. (2010) was

lower than the observed values for all years. In these two cases, we shift the calculated

synthetic control upward by the amount of the Root Mean Squared Error for the non-

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treatment periods. This makes the estimate of job losses in Fresno and Tulare Counties

difference-in-differences (DD) estimators, as we are not comparing observed versus

synthetic outcomes directly, but rather comparing the difference in the treatment and

control values before and after the irrigation water supply shock.

Figure 6 plots the estimated treatment effect (the difference between observed farm

employment and the synthetic control value) for the treatment county and placebo effects

for the 25 donor counties. If the placebo results are similar to the treatment effects, we

would conclude that our estimated results are due to chance rather than a real change in

employment due to the water supply shock. Table 6 presents the rank order of the

magnitude of the estimate of treatment effects for each treated county. The estimated job

losses in 2009 in Fresno (rank of 5 out of 25 counties) and Kern Counties (rank of 4 out of

25 counties) are significant at the 20 percent and 16 percent levels. These would be treated

as not different from zero in conventional significant tests with 5 percent or 10 percent as

the cutoff values. For our purposes, however, we will interpret these results as evidence

that Delta export restrictions did induce some reduced level of agricultural employment in

portions of our study area because the plotted time series do seem to show a sharp break in

employment trends at that year. The inference results indicate that these numbers are

uncertain and that agriculture employment numbers are generated by a noisy process, which

make assigning causality to particular events difficult. Table 6 and Figure 8 show

significant farm compensation losses ($19 million – $35 million) in 2007, 2008, and 2009

for Kern County. No other significant farm compensation losses are observed.

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Though the estimated treatment effect for Kings County show no farm employment impact,

examination of the absolute level of farm employment reveals difficult to interpret

employment trends. In 2005, 2006, and 2007 farm employment in Kings County increased

substantially, eventually being about 25 percent higher than in 2001-2004. In 2008 farm

employment dropped precipitously, but back down to levels near the trend path traced by

the synthetic control. Figure 2 shows that this decline coincided with the large drop in

deliveries in 2008, indicating some negative impacts. The large run-up in employment,

however, complicates our ability to assign causality and our synthetic control is unable to

generate a significant negative impact.

5.2 Non-farm Employment and Compensation

Table 7 reports our estimates of non-farm employment and compensations losses for each

year of the water supply shock. Figure 9 graphs the observed values and their synthetic

controls for non-farm employment by county. In the case of Kings County, we construct a

difference-in-differences (DD) estimator by shifting the calculated synthetic control

downward by the amount of the Root Mean Squared Error for the non-treatment periods.

We observe non-farm employment levels in that are lower than the synthetic control in

Fresno and Kings Counties for portions of the treatment period. However, these differences

are not significant according to our placebo tests (results are plotted in Figure 10). Figure

11 graphs the observed values and their synthetic controls for non-farm compensation and

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shows small losses in 2009 in Fresno and Kern Counties. The size of the losses is not

significantly different from zero, according to our placebo tests (Figure 12).

5.3 Comparison of Results to Other Estimated Impacts

As noted previously, the economic impacts of the water supply reductions in the San

Joaquin Valley in 2009 were the subject of intense concern and debate. Three sets of

authors produced at least eight different estimates of employment and income losses, both

ex ante and ex post. Howitt et el. released three ex ante estimate of job and income losses

between January and September 2009 that combined a hydrologic, economic optimization,

and input-output models (Howitt et al. 2009a, 2009b, 2009c). The final, corrected and

updated, version estimated job losses of 6,400 in agriculture and almost 15,000 in non-

agricultural industries. Using several sets of multipliers, Michael (2009a) made an initial

ex ante forecast of 5,000-6,500 agricultural job losses due to the water supply shock with an

additional 5,000-6,000 jobs lost in non-agricultural industries. Michael (2009a) also

proposed that labor shortages in the region may have resulted in even lower job losses, with

a lower bound at zero. Michael et al. (2010) generated ex post economic impact estimates

using similar methods as the earlier studies by Howitt et al. and Michael, but using updated

data on water availability and cropping patterns. This report estimated approximately 4,500

agricultural jobs and between 1,000-3,000 non-agricultural jobs lost. Sunding et al. (2011)

used farm payroll data similar to ours and a simple econometric time series model to

generate an ex post estimate of 5,000 agricultural jobs lost.

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Our estimate of 5,500 jobs lost in agriculture is similar in magnitude to the ex post

estimates. Also, our results indicating that these impacts are confined to two counties,

Fresno and Kern is consistent with the results indicating intra-regional differences in

impacts in Michael et al. (2010). These differences are due to institutional and engineering

features of the project water supply system in the SJV that give lower priority to CVP

contractors on the west side of Fresno County and SWP contractors in southern/western

Kern County. In contrast to these other studies, however, we find no detectable

employment impacts in non-agricultural industries.

6. Conclusion

We examined whether reductions in water exports from the Sacramento-San Joaquin River

Delta from 2007 to 2009 caused adverse impacts the local economy in California’s San

Joaquin Valley. We compared employment and wage income data from four of the most

affected counties to corresponding synthetic control units. Our results indicate employment

losses of 5,500 agricultural jobs, concentrated in two counties in 2009. In addition, we find

wage income losses to farm employees of between approximately $20 million and $35

million in Kern County in 2007-2009, with the largest income losses occurring in 2009.

We did not detect any job or employee income effects in the wider, non-agricultural

economy. Our estimates of agricultural labor impacts are similar to ex post estimates

generated via combinations of positive math programming, input-output, and simple

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regression models.

Our estimate of no detectable impacts to the non-agricultural labor market is in contrast to

estimates that conclude small impacts of 2,000 to 3,000 jobs. The treatment period

occurred during the U.S. recession of 2007-2009 and broader macroeconomic events may

have swamped the effects of water supply reductions. It may also indicate that multiplier

effects from water supply reductions are small.

Our results also suggest that economic impacts from water supply reduction may be subject

to a threshold effect. Substantial reductions in project deliveries were observed in the study

area in 2008, but significant employment effects were only detected in 2009. Fresno and

Kerns County actually experience absolute employment increases in 2008 and the estimated

employment effects relative to the control counties is positive. In the future, periods of

low-runoff or water storage will coincide with strong requirements for instream flow for

habitat protections. The existence of non-linear employment impacts would suggest that a

precautionary water storage management system with lower mean and variance in

deliveries could mitigate some of these impacts.

Water exports from the Sacramento-San Joaquin Delta are a controversial and ongoing

policy issue as habitat considerations for Delta smelt, Chinook salmon, and steelhead will

continue to constrain Delta exports for the foreseeable future. These restrictions are

necessary to protect a critically impaired ecosystem, but policy makers will want to know

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the consequences for a farm economy that produces over $25 billion in output per year. As

climate change, growing demand from municipal users, and increased allocation to

environmental quality, agricultural communities will be faced with reduced quantities of

irrigation water throughout many arid and semi-arid regions. These results can help

stakeholders better understand the economic effects that occurred in this case, the first year

that new habitat considerations were a binding constraint on irrigation water supply. The

synthetic control method proposed by Abadie et al. (2010) and applied here offers a

promising method for policy evaluation of this type.

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Footnotes

1. See items in the popular press, for example,

http://online.wsj.com/article/SB10001424052970204731804574384731898375624.html,

http://naturalresources.house.gov/news/documentsingle.aspx?DocumentID=23016, and

http://www.economist.com/node/14699639.

http://www.nytimes.com/gwire/2009/05/12/12greenwire-calif-water-agency-changes-

course-on-delta-sme-10572.html

2. Three other SJV counties (San Joaquin, Stanislaus, and Merced) also receive CVP water.

We do not include these as treatment counties because a smaller proportion of their

deliveries are subject to Delta export restrictions and because preliminary analysis indicated

no significant effects in these counties.

3. See information at the Bureau of Labor Statistics website: www.bls.gov/ces/cesfaq.htm.

26

References

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control methods in comparative case studies. Journal of Statistical Software

42(13), 1-7.

Abaide. A. and Gardeazabal, J. 2003. The economic costs of conflict: A case study of the

Basque Country. American Economic Review 93(1), 112-132.

ICF International. 2012. Environmental Impact Report / Environmental Impact Statement

for the Bay Delta Conservation Plan, Appendix 1A: Primer on California Water

Delivery 3 Systems and the Delta.

Bertrand, M., Duflo, E., and Mullainathan, S. 2004. How much should we trust

Differences-In-differences estimates? The Quarterly Journal of Economics

119(1),249–275.

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inputs: Implications for water conservation in a River Basin context. Ecological

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Cody, B.A., P. Folger, and C. Brougher. 2009. California Drought: Hydrological and

Regulatory Water Supply Issues. Congressional Research Service Report

R40979.

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Department of Water Resources (DWR). 2010. California’s Drought of 2007–2009: An

Overview.

Frisvold, G. B. and Konyar, K. 2012. Less water: How will agriculture in southern

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Howe, C. W. and Goemans, C. 2003. Water transfers and their impacts: Lessons from

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Howitt, R. E. 1995. Positive mathematical programming. American Journal of

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Howitt, R., MacEwan, D. and Medellín-Azuara, J. 2009a. Economic impacts of

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University of California Giannini Foundation of Agricultural Economics.

Howitt, R., MacEwan, D., Medellín-Azuara, J., and S. Hatchett. 2009b. Economic

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30

Table 1 Changes in Water Deliveries from State and Federal Water Projects to Affected and Potential Control Areas. Affected Areas Potential Control Areas

CVP

South of Delta Contractorsa

SWPSan Joaquin Valley

Contractorsb

CVP Sacramento Valley

Contractorsc

USBRColorado Aqueductd

USBRKlamath Projecte,f

Mean Deliveries (Acre-feet) 2001-2006

1,036,547 1,083,080 562,625 4,268,676 365,005

Coefficient of Variation 2001-2006

0.13 0.36 0.10 0.05 0.10

Percent Change 2007

0.00 -0.21 -0.20 -0.02 0.29

Percent Change 2008

-0.55 -0.69 -0.24 -0.04 0.29

Percent Change 2009

-0.71 -0.68 -0.35 -0.12 0.24

a. Source USBR, CVP Schedule of Historical and Projected Irrigation Water Deliveries (Schedule A-14).Includes CVP contractors in the Delta-Mendota Pool and San Luis Canal operational units. Primarily Fresno and Kings Counties. b. Source: DWR SWP Delivery Reliability Reports 2011-2013 (Appendix A, Historical SWP Delivery Tables). Primarily Kern and Kings Counties. c. Source USBR, CVP Schedule of Historical and Projected Irrigation Water Deliveries (Schedule A-14).Includes CVP contractors in the Tehama-Colusa Canal and Sacramento River operational Units. Districts are in Shasta, Tehama, Glenn, Butte, Colusa, Sutter, Yolo, and Sacramento Counties. d. Source: USBR Colorado River Accounting and Water Use Reports 2001-2011 (Diversions from Mainstream-Available Return Flow and Consumptive Use of Such Water Tables). Includes annual diversions by Palo Verde, Imperial, and Coachella Valley Irrigation Districts. Primarily Imperial and Riverside Counties. e. Source: USBR Klamath Project annual Operations Plans. Includes estimated deliveries to irrigation districts in the Upper Klamath Lake and East Side delivery areas. Districts are located in Modoc and Sisikiyou Counties in California as well as Oregon. f. Note that these figures are pre-season forecasts of delivered water and that data are available beginning 2003.

31

Table 2 Donor County Weights in Each Synthetic Control Unit: Farm Employment and Compensation Farm Employment Farm Compensation

Donor County Fresno Tulare Kings Kern Fresno Tulare Kings Kern

Sacramento 0.145 - 0.078 0.006 0.407 0.053 0.254 0.083Yolo - - - 0.174 - 0.299 - - Sutter 0.219 - 0.24 - - - 0.169 - Glenn - - 0.12 - - - 0.028 - Monterey 0.443 - - - 0.143 0.31 - - Imperial 0.096 0.115 0.272 0.075 0.266 0.246 0.302 0.121Santa Clara - 0.138 - - - - - - San Benito - - 0.012 - - - - - Tehama - - - - - - - 0.171Butte - 0.028 - - - - - 0.096Lake - 0.240 - 0.156 0.184 - - 0.24Lassen - - 0.056 - - - 0.247 - San Bernardino - - 0.149 0.121 - 0.091 - - San Luis Obispo - - 0.073 - - - - - Santa Barbara 0.096 0.480 - 0.468 - - - 0.289

32

Table 3 Predictor Variables with Observed and Synthetic Values: Farm Compensation and Employment. Fresno Tulare Kings KernVariable

Observed Farm

Employment Synthetic

Farm Compensation

SyntheticObserved

Farm Employment

Synthetic

Farm Compensation

SyntheticObserved

Farm Employment

Synthetic

Farm Compensation

SyntheticObserved

Farm Employment

Synthetic

Farm Compensation

Synthetic Population density (per sq. mile)

134.1 283.3 550.5 76.3 268.5 170.6 93.1 161.0 356.6 81.3 125.0 177.7

ln(Population) 13.6 12.6 12.7 12.8 12.5 12.6 11.8 12.0 11.9 13.4 12.6 12.0

Seasonal precipitation 77.8 191.1 223 220.7 223.1 171 141.3 165.0 185 134.6 200.3 275

Annual Cooling Degree Days

1,928.3 995.6 1,865 1,930.9 940.5 1,944 2,143.3 2,231.5 2,036 2,250.8 1,107.8 1,385

Annual Heating Degree Days

2,326.1 2,247.3 2,216 2,242.5 2,348.4 1,942 2,196.0 2,194.5 2,633 2,394.6 2,354.8 2,425

Field Crop Acreage % 15.0 5.9 11.2 19.0 2.2 8.3 41.6 7.2 9.6 7.8 4.5 3.9

Grains Acreage % 4.0 5.4 10.8 3.7 4.1 10.6 10.5 10.7 14.1 4.6 4.7 4.8

Orchard Acreage % 9.6 4.5 3.2 15.2 3.4 1.8 5.2 6.0 4.3 7.1 2.9 5.1

Rice Acreage % 0.3 7.4 1.5 0.0 0.6 2.5 0.0 10.1 6.7 0.0 1.4 2.3

Truck Crop Acreage % 12.7 13.9 9.4 0.8 7.9 14.6 4.8 7.4 7.9 3.2 8.5 5.7

Vegetable Acreage % 10.2 3.1 6.6 4.2 3.2 2.1 0.8 1.2 3.1 2.8 2.8 3.6

Pasture Acreage % 48.3 58.5 56.7 57.1 78.4 59.6 37.2 55.9 53.0 74.5 75.0 74.4

Value per cropped acre $ 1,538.9 $ 1,525.1 $ 1,176 $ 1,230.1 $ 1,033.3 $ 1,235 $ 805.5 $ 782.8 $ 855 $ 864.1 $ 864.0 $ 808

33

Table 4 Donor County Weights in Each Synthetic Control Unit: Non-farm Employment and Compensation Non-farm Employment Non-farm Compensation

Donor County Fresno Tulare Kings Kern Fresno Tulare Kings Kern

Sacramento 0.244 0.099 0.013 0.054 0.286 0.116 0.021 0.010Yolo 0.035 - - 0.009 - - 0.035 0.178Sutter 0.245 0.144 - 0.311 0.186 - 0.325 -Glenn - - 0.347 - - 0.682 - -Monterey 0.165 - - 0.028 - - - -Imperial 0.165 0.301 0.215 0.201 0.261 0.125 0.500 0.485Solano - - 0.130 - - - - -Lake - 0.408 0.041 - 0.266 - - 0.066Lassen - - 0.010 - - - 0.119 -San Bernardino 0.147 0.047 0.233 0.397 - 0.077 - -San Luis Obispo - - 0.010 - - - - 0.262

34

Table 5 Predictor Variables with Observed and Synthetic Values: Non-farm Compensation and Employment. Fresno Tulare Kings Kern

Variable Observed Non-farm

Employment Synthetic

Non-farm Compensation

SyntheticObserved

Non-farm Employment

Synthetic

Non-farm Compensation

SyntheticObserved

Non-farm Employment

Synthetic

Non-farm Compensation

SyntheticObserved

Non-farm Employment

Synthetic

Non-farm Compensation

Synthetic Population density (per sq. mile)

134.1 385.2 407.9 76.3 177.5 171.5 93.1 115.2 92.9 81.3 154.7 81.5

ln(Population) 13.6 12.8 12.1 12.8 11.7 11.2 11.8 12.0 11.6 13.4 12.8 12.0

Seasonal precipitation 77.8 182.1 244.9 220.7 255.9 252.7 141.3 197.7 141.7 134.6 142.3 134.7

Annual Cooling Degree Days

1,928 1,784 2,009 1,931 2,103 1,892 2,143 2,166 2,796 2,251 2,242 2,797

Annual Heating Degree Days

2,326 2,184 2,308 2,243 2,410 2,261 2,196 2,202 1,918 2,395 2,179 1,725

Field Crop Acreage % 15.0 9.3 10.6 19.0 7.2 8.5 41.6 6.0 8.5 7.8 6.0 7.8

Grains Acreage % 4.0 8.4 11.0 3.7 10.6 7.1 10.5 9.5 17.1 4.6 7.9 15.6

Orchard Acreage % 9.6 5.3 6.5 15.2 6.4 8.3 5.2 5.1 6.1 7.1 5.7 2.1

Rice Acreage % 0.3 8.8 6.8 0.0 4.8 13.5 0.0 6.7 10.5 0.0 9.9 1.4

Truck Crop Acreage % 12.7 9.7 7.3 0.8 6.8 3.7 4.8 5.1 11.7 3.2 6.9 11.5

Vegetable Acreage % 10.2 3.4 5.4 4.2 4.3 1.6 0.8 0.8 0.3 2.8 0.8 1.7

Pasture Acreage % 48.3 53.7 50.9 57.1 58.6 57.1 37.2 66.4 43.4 74.5 60.9 59.2

Value per cropped acre 1,539 1,078 $ 970 1,230 883 $ 678 805 605 $ 1,018 864 706 $ 913

Note: Values except for population are the mean of the pretreatment period, 2001-2006.

35

Table 6 Results: Estimated Farm Employment and Compensation Differences by Treatment County

County Year Observed

Farm Employment

Synthetic Farm

Employment

Difference

RankOrder

of Difference (out of 25)

Observed Farm

Compensation

Synthetic Farm

Compensation

Difference

RankOrder

of Difference (out of 25)

Fresno 2001-2006 46,733 46,409 321 $ 423,596 $ 425,372 $ -1,776 2007 48,100 47,060 1,040 22 454,596 $ 443,210 11,386 16 2008 48,900 48,158 742 21 500,437 474,339 26,098 19 2009 45,100 47,301 -2,201 5 535,483 518,228 17,255 17 Tulare 2001-2006 32,633 33,226 -593 344,557 346,217 -1,660 2007 35,000 34,086 914 21 359,250 332,470 26,780 21 2008 36,700 34,978 1,722 24 396,889 374,369 22,520 19 2009 36,400 35,051 1,349 21 421,875 418,741 3,134 16 Kings 2001-2006 7,383 7,318 66 108,170 111,003 -2,833 2007 9,300 6,991 2,309 24 118,354 111,560 6,794 18 2008 6,700 7,020 -320 4 130,547 121,819 8,728 18 2009 6,500 6,173 327 18 139,420 134,384 5,036 17 Kern 2001-2006 42,217 40,702 1,515 474,388 480,577 -6,189 2007 45,600 42,686 2,914 23 479,689 498,792 -19,103 4 2008 49,600 44,777 4,823 24 542,922 567,878 -24,956 3 2009 42,300 45,558 -3,258 5 625,167 660,639 -35,472 2

36

Table 7 Results: Estimated Non-Farm Employment and Compensation Differences by Treatment County

County Year Observed Non-farm

Employment

Synthetic Non-farm

Employment

Difference

RankOrder

of Difference (out of 25)

Observed Non-farm

Compensation (Thousand $)

Synthetic Non-farm

Compensation (Thousand $)

Difference

RankOrder

of Difference (out of 25)

Fresno 2001-2006 287,400 287,742 -342 $ 13,603,888 $ 13,590,215 $ 13,673 2007 306,400 306,890 -490 13 16,599,003 16,482,728 116,275 15 2008 303,000 302,393 607 15 17,019,764 16,968,505 51,260 12 2009 286,500 288,242 -1,742 11 16,366,771 16,473,854 -107,083 7 Tulare 2001-2006 105,150 106,007 -857 $ 4,858,595 $ 4,909,415 $ -50,820 2007 113,600 111,435 2,165 15 6,066,617 5,989,332 77,285 18 2008 113,600 110,621 2,979 15 6,300,385 6,214,013 86,373 16 2009 107,300 106,164 1,136 14 6,142,462 6,052,438 90,024 16 Kings 2001-2006 32,750 33,262 -512 $ 1,843,603 $ 1,858,627 $ -15,024 2007 35,600 35,770 -170 22 2,341,890 2,340,213 1,677 12 2008 37,400 35,206 2,194 23 2,473,840 2,422,913 50,927 19 2009 36,300 33,478 2,822 23 2,396,545 2,391,850 4,695 11 Kern 2001-2006 213,600 218,078 -4,478 $ 11,347,022 $ 11,616,342 $ -269,320 2007 238,700 238,001 699 11 14,657,950 14,444,850 213,100 17 2008 238,500 234,197 4,303 14 15,284,468 15,041,276 243,192 19 2009 228,100 223,050 5,050 17 14,697,859 14,720,648 -22,789 10

37

Fig. 1. Map of California with Study Area Highlighted

38

Fig. 2. Total Project (CVP and SWP) Water Deliveries and Total Agricultural Employment in the Four Affected Counties in the Study Area, 1981-2010

Sources: SWP deliveries: California Department of Water Resources, Table B-5B of Appendix B, Bulletin 132-12. http://www.water.ca.gov/swpao/docs/bulletin/12/Appendix_B.pdf. CVP deliveries: Schedule A-14 of CVP Ratebooks-Irrigation. http://www.usbr.gov/mp/cvpwaterrates/ratebooks/irrigation/2012/2012_irr_sch_a-14.pdf.

45,

000

46,

000

47,

000

48,

000

49,

000

200

400

600

800

1,0

001

,200

Wa

ter

De

live

ries

(Tho

usan

d A

cre

-fee

t)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Water Deliveries

Farm Employment

Fresno

30,

000

32,

000

34,

000

36,

000

38,

000

Far

m E

mpl

oym

ent

300

400

500

600

700

800

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Water Deliveries

Farm Employment

Tulare

6,0

007

,000

8,0

009

,000

10,

000

100

150

200

250

300

350

Wa

ter

De

live

ries

(Tho

usan

d A

cre

-fee

t)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Water Deliveries

Farm Employment

Kings

40,

000

42,

000

44,

000

46,

000

48,

000

50,

000

Far

m E

mpl

oym

ent

1,5

002

,000

2,5

003

,000

3,5

00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Water Deliveries

Farm Employment

Kern

39

Fig. 3. Employment and Compensation Outcomes in Affected Counties and Potential Donor Counties

Sources: California Employment Development Department (Employment) and U.S. Bureau of Economic Analysis (Compensation). Note the difference in scale for Non-farm employment and compensation.

100

,000

110

,000

120

,000

130

,000

140

,000

Job

s

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Affected Counties

All Donor Counties

Farm Employment

1,2

00,0

001

,400

,000

1,6

00,0

001

,800

,000

2,0

00,0

00

Wa

ge I

ncom

e

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Affected Counties

All Donor Counties

Farm Compensation

3,5

00,0

003

,600

,000

3,7

00,0

003

,800

,000

3,9

00,0

00

Job

s -

All

Do

nor

Co

untie

s

600

,000

620

,000

640

,000

660

,000

680

,000

700

,000

Job

s -

Aff

ect

ed C

ount

ies

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Affected Counties

All Donor Counties

Non-farm Employment

220

,000

,000

240

,000

,000

260

,000

,000

280

,000

,000

300

,000

,000

Wa

ge I

ncom

e -

All

Don

or C

ount

ies

25,

000,

000

30,

000,

000

35,

000,

000

40,

000,

000

45,

000,

000

Wa

ge I

ncom

e -

Aff

ecte

d C

oun

ties

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Affected Counties

All Donor Counties

Non-farm Compensation

40

Fig. 4. Total Harvested Acres and Farm-gate Value (2011 dollars) of All Crops

22

22

3B

illio

ns

of 2

011

Do

llars

(P

PI-

adj

ust

ed)

1,8

00,0

001

,850

,000

1,9

00,0

001

,950

,000

2,0

00,0

00H

arve

sted

Acr

es

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Harvested Acres

Value

Donor Counties

10

11

12

13

Bill

ion

s of

201

1 D

olla

rs (

PP

I-a

dju

sted

)

6,6

00,0

006

,800

,000

7,0

00,0

007

,200

,000

7,4

00,0

00H

arve

sted

Acr

es

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Harvested Acres

Value

Affected Counties - South SJV

41

Fig. 5. Results: Observed and Synthetic Control Values for Farm Employment

25,

000

30,

000

35,

000

40,

000

45,

000

50,

000

Far

m E

mpl

oym

ent

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno - Observed

Synthetic Control + Linear Shift (DD)

Synthetic Control

25,

000

30,

000

35,

000

40,

000

45,

000

50,

000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare - Observed

Synthetic + Linear Shift (DD)

Sythetic Control

5,0

006

,000

7,0

008

,000

9,0

00F

arm

Em

plo

yme

nt

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings - Observed

Synthetic Control

25,

000

30,

000

35,

000

40,

000

45,

000

50,

000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern - Observed

Synthetic Control

42

Fig. 6. Difference Between Observed and Synthetic Value: Farm Employment

-8,0

00

-6,0

00

-4,0

00

-2,0

00

2,0

004

,000

0E

stim

ated

Tre

atm

ent

Eff

ect

(F

arm

Jo

bs)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno

-8,0

00

-6,0

00

-4,0

00

-2,0

00

2,0

004

,000

0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare-3

,00

0-2

,00

0-1

,00

00

1,0

002

,000

Est

imat

ed T

reat

men

t E

ffe

ct (

Fa

rm J

obs

)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings

-15

,000

-10

,000

-5,0

00

05

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern

43

Fig. 7. Results: Observed and Synthetic Control Values for Farm Compensation

300

,000

400

,000

500

,000

Far

m W

age

Com

pen

satio

n

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno - Observed

Synthetic Control

300

,000

400

,000

500

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare - Observed

Synthetic Control8

0,00

01

00,0

001

20,0

001

40,0

00F

arm

Wa

ge C

omp

ensa

tion

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings - Observed

Synthetic Control

400

,000

500

,000

600

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern - Observed

Synthetic Control

44

Fig. 8. Difference Between Observed and Synthetic Control Values: Farm Compensation

-10

0,00

0-5

0,0

000

50,

000

100

,000

150

,000

Est

imat

ed T

reat

men

t E

ffe

ct (

Fa

rm C

om

pens

atio

n)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno

-10

0,00

0-5

0,0

000

50,

000

100

,000

150

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare-1

00,

000

-50

,000

05

0,00

01

00,0

001

50,0

00E

stim

ated

Tre

atm

ent

Eff

ect

(F

arm

Co

mpe

nsat

ion

)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings

-10

0,00

0-5

0,0

000

50,

000

100

,000

150

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern

45

Fig. 9. Results: Observed and Synthetic Control Values for Non-farm Employment

270

,000

280

,000

290

,000

300

,000

310

,000

Non

-far

m E

mpl

oym

ent

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno - Observed

Synthetic Control

100

,000

105

,000

110

,000

115

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare - Observed

Sythetic Control3

2,00

03

4,00

03

6,00

03

8,00

04

0,00

0N

on-f

arm

Em

plo

yme

nt

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings - Observed

Synthetic Control

Synthetic Control - Linear Shift (DD)

200

,000

210

,000

220

,000

230

,000

240

,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern - Observed

Synthetic Control

46

Fig. 10. Difference between Observed and Synthetic values: Non-farm Employment

-40

,000

-20

,000

02

0,00

04

0,00

0E

stim

ated

Tre

atm

ent

Eff

ect

(N

on-

farm

Jo

bs)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno

-40

,000

-20

,000

02

0,00

04

0,00

0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare-2

0,0

000

20,

000

Est

imat

ed T

reat

men

t E

ffe

ct (

No

n-fa

rm J

obs

)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings

-40

,000

-20

,000

02

0,00

04

0,00

0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern

47

Fig. 11. Results: Observed and Synthetic Control Values for Non-farm Compensation

12,

000,

000

14,

000,

000

16,

000,

000

Non

-far

m W

age

Com

pen

satio

n

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno - Observed

Synthetic Control

4,0

00,0

005

,000

,000

6,0

00,0

00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare - Observed

Synthetic Control

1,5

00,0

002

,000

,000

2,5

00,0

00N

on-f

arm

Wa

ge C

omp

ensa

tion

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings - Observed

Synthetic Control

10,

000,

000

12,

000,

000

14,

000,

000

16,

000,

000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern - Observed

Synthetic Control

48

Fig. 12. Difference between Observed and Synthetic values: Non-farm Compensation

-2,0

00,

000

-1,0

00,

000

01

,000

,000

2,0

00,0

00E

stim

ated

Tre

atm

ent

Eff

ect

(N

on-

farm

Co

mpe

nsat

ion

)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fresno

-2,0

00,

000

-1,0

00,

000

01

,000

,000

2,0

00,0

00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tulare-4

00,

000

-20

0,00

00

200

,000

400

,000

Est

imat

ed T

reat

men

t E

ffe

ct (

No

n-fa

rm C

om

pens

atio

n)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kings

-2,0

00,

000

-1,0

00,

000

01

,000

,000

2,0

00,0

00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Kern

49

Data Appendix 1. Employment and compensation data: Employment data are taken from the official estimates of employment by industry and county released by the California Employment Development Department (EDD). These estimates are generated from the Currently Employment Statistics program monthly surveys conducted by the Bureau of Labor Statistics and individual states. Data are benchmarked to payroll tax data from the Quarterly Census of Employment and Wages. Employment data may be downloaded from the EDD website (http://www.labormarketinfo.edd.ca.gov/LMID/Employment_by_Industry_Data.html). Wage income data are taken from the U.S. Bureau of Economic Analysis Table CA06N Compensation of Employees by NAICS Industry”. Industry compensation data can be downloaded from the BEA Regional Economic Accounts webpage (http://www.bea.gov/iTable/index_regional.cfm). Agricultural data are taken from the “Farm Employment” or “Farm Compensation” lines and includes employees and payroll of establishments reporting under NAICS sector 11. Non-agricultural employment data are taken from the “Nonfarm Employment” or “Nonfarm Compensation” lines and include employees and payroll of all other establishments. 2. County Crop Report Data: Crop acreage and value data by county are from county agricultural commissioner’s annual reports. Annual report data from 1980-2011 can be downloaded from USDA-NASS (http://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/Detail/index.asp) 3. Water Delivery Data: Annual water delivery data are generated from published reports by the U.S. Bureau of Reclamation (USBR) and the California Department of Water Resources (DWR). Water deliveries for the Central Valley Project are from the 2013 CVP Annual Ratebook Schedule A-14 “Schedule of Historical (1981-2011) & Projected (2012-2030) Irrigation Water Deliveries for Calculation of Individual Contractor Prorated Capital Costs.” Current year versions of this report can be downloaded from USBR (http://www.usbr.gov/mp/cvpwaterrates/ratebooks/). Annual water deliveries for the State Water Project are taken from Appendix B (Table B-5B) of Bulletin 132-12 “Management of the California State Water Project.” This report can be downloaed from DWR (http://www.water.ca.gov/swpao/bulletin_home.cfm). For each project, CVP and SWP, data are reported as the quantity of water delivered each year to each irrigation district contracting with the project. We allocate delivered water to each county in the study area by overlaying maps of all irrigation districts in California onto maps of counties. In cases where irrigation districts span multiple counties, we calculate the percentage of district land area in the two (or more counties) and allocate water deliveries for that district to each county according to the proportion of district land in the county. A full list of irrigation districts is given in the supplemental materials. Deliveries to Sacramento Valley Region control areas consist of deliveries to the following CVP operational units: Sacramento River – Willows, Tehama-Colusa Canal, and Black Butte Unit. Deliveries to San Joaquin Region affected areas consist of deliveries to SWP irrigation districts in Kings and Kern Counties and the following CVP operational units: Buchanan Unit, Cross Valley Canal, Delta-Mendota Canal, Delta-Mendota Pool, Friant Dam, Friant-Kern Canal, Madera Canal, New Melones Unit, and San Luis Canal. 4. Weather Data: Annual precipitation, seasonal cooling degree days, and seasonal heating degree days are from NOAA's National Climatic Data Center (www.ncdc.noaa.gov). County level readings are taken from a weather station within the arable portion of each county. Seasonal cooling and heating degree days are the sum of degree days from April – September. 5. Population and demographic data are from the 2000 U.S. Census.