metadata: authors: 0: author: Christian,Paul J.
1: author: Kondylis,Florence
2: author: Mueller,Valerie
3: author: Zwager,Astrid Maria Theresia
4: author: Siegfried,Tobias
count: Mozambique
docna: 0: docna: Water when it counts : reducing scarcity
            through irrigation monitoring in Central Mozambique
docty: Policy Research Working Paper
abstracts: cdata!: Management of common-pool resources in
            the absence of individual pricing can lead to suboptimal
            allocation. In the context of irrigation schemes, this can
            create water scarcity even when there is sufficient water to
            meet the total requirements. High-frequency data from three
            irrigation schemes in Mozambique reveal patterns consistent
            with inefficiency in allocations. A randomized control trial
            compares two feedback tools: i) general information,
            charting the water requirements for common crops, and ii)
            individualized information, comparing water requirements
            with each farmer's water use in the same season of the
            previous year. Both types of feedback tools lead to higher
            reported and observed sufficiency of water relative to
            recommendations, and nearly eliminate reports of conflicts
            over water. The experiment fails to detect an additional
            effect of individualized comparative feedback relative to a
            general information treatment.
geo_reg_key: 517195,517191
repnme: repnme: Water when it counts : reducing scarcity
            through irrigation monitoring in Central Mozambique
display_title: Water when it counts : reducing
            scarcity through irrigation monitoring in Central Mozambique
listing_relative_url: /research/2018/02/29610607/water-counts-reducing-scarcity-through-irrigation-monitoring-central-mozambique
topicv3: Agriculture and Food Security m1326205 435,Water m1326522 137,Crop Production m1326627 174,Water Allocation and Water Economics
            m1326745 427,Water Scarcity m1504471 3390
publishtoextweb_dt: 2018-02-20T00:00:00Z
no_of_pages: 54
author_keys: 0: author_key: 1209901
1: author_key: 797414
2: author_key: NA
3: author_key: 1149699
4: author_key: NA
model_extractions: Policy Research Working Paper 8345

# Water When It Counts

Reducing Scarcity through Irrigation Monitoring in Central Mozambique _Paul Christian Florence Kondylis Valerie Mueller Astrid Zwager Tobias Siegfried_ Development Research Group Impact Evaluation Team February 2018 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 8345

# **Abstract**

Management of common-pool resources in the absence of individual pricing can lead to suboptimal allocation. In the context of irrigation schemes, this can create water scarcity even when there is sufficient water to meet the total requirements. High-frequency data from three irrigation schemes in Mozambique reveal patterns consistent with inefficiency in allocations. A randomized control trial compares two feedback tools: i) general information, charting the water requirements for common crops, and ii) individualized information, comparing water requirements with each farmer’s water use in the same season of the previous year. Both types of feedback tools lead to higher reported and observed sufficiency of water relative to recommendations, and nearly eliminate reports of conflicts over water. The experiment fails to detect an additional effect of individualized comparative feedback relative to a general information treatment.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pchristian@worldbank.org, fkondylis@worldbank.org, and vmuelle1@asu.edu. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Water When It Counts: Reducing Scarcity through Irrigation Monitoring in Central Mozambique**

_By_ PAUL CHRISTIAN FLORENCE KONDYLIS VALERIE MUELLER ASTRID ZWAGER TOBIAS SIEGFRIED<sup>*</sup> _Keywords: water, irrigation, information, experiment, productivity, Africa_ _JEL Codes: O13, Q15, Q16, Q25, D83_

## **I. Introduction**

Irrigation systems are a common pool resource. When farmers fail to internalize the cost of their use on others, the allocation of water within the irrigation scheme may not only be inefficient but also affect water availability in the long term. Collective action over the management of a scheme is then necessary to ensure the sustainability of irrigation supply. Decentralized models of governance have evolved to formalize the delivery of water resources (Ostrom, 1990; Ostrom and Schlager, 1992). Yet, local institutions like water user associations may not yield efficient resource sharing if the main constraint users face is limited knowledge of water management (Plusquellec, 2002; Meinzen-Dick, 2007).

> * Author affiliations are: Christian: World Bank; Kondylis: World Bank; Mueller: Arizona State University, International Food Policy Research Institute; Zwager: World Bank; Siegfried: Hydrosolutions, Ltd. Corresponding authors are: Christian, `pchristian@worldbank.org` ; Kondylis, `fkondylis@worldbank.org` ; and Mueller, `Valerie.Mueller.1@asu.edu` . Luiza Cardoso de Andrade provided superb research assistance. We thank Mario Chilundo, Steven Glover, Robert Naudascher, and Euclides Tiago for excellent field assistance. We appreciate valuable comments received from Dawit Mekonnen and seminar audiences at IFPRI and the AAEA meetings. The team is grateful for the support of Pedro Arlindo, Mark Austin, Aniceto Bila, Mark Lundell, Paiva Munguambe, Eugenio Nhone, and Antonio Samuel. This project benefited from funding provided by the USAID mission and World Bank country offices in Mozambique, the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI), as well as the i2i trust fund financed by UKAID.

_1_
_2_ _WATER WHEN IT COUNTS_ To understand the origins of irrigation management problems in central Mozambique, we designed an intensive user-based water monitoring protocol. Plot-level water use data was collected three times per day for 146 consecutive weeks covering 159 plots in three irrigation schemes. We paired this high-frequency time series with household surveys on agricultural practices and production to understand sources of inefficiency in water allocation in a region suffering from low agricultural productivity and periodic droughts. This data system adds to previous demonstrations of the feasibility of user-based systems for monitoring water management (Fraternali et al., 2012; Lowry and Fienen, 2013; Buytaert and et al., 2014; Little, Hayashi and Liang, 2016).

Two stylized facts emerge from the monitoring phase of our study. First, water allocations are inefficient in a context lacking penalties for overuse. The recommended water requirements of crops on 20-40% of the plots were not met in most weeks over the course of one year. This occurs despite all three schemes having sufficient water to meet each crop’s weekly water requirement in the same weeks. Scarcity in this context is, therefore, purely an allocation problem. If users could be induced to more efficiently allocate water, scarcity could be eliminated under a water neutral manner. Second, we observe that water scarcity arises from a particular allocation pattern. Although water requirements vary widely across the crop cycle, water applications are inflexible. As farmers have staggered planting times within a given scheme, this inflexible water application fails to take advantage of efficiency gains available through a simple reallocation of water across plots and planting stages.

These stylized facts are consistent with a model of behavior where farmers rely on inefficient rules of thumb. They choose a fixed level of water to deliver to their plots upon planting and stick to this rule until harvesting. In such a model, scarcity can be
_3_ _WATER WHEN IT COUNTS_ reduced by simple re-allocations. If farmers can be convinced to use smaller quantities of water during less water-intensive growth stages, more water is made available for farmers whose crops are in water-intensive stages without making anyone worse off.

In this setting, we ask whether informing farmers of their water requirements can yield efficiency gains and reduce observed scarcity and conflicts over water. A growing literature examines supply-side constraints in agricultural extension services (Beaman et al., 2015; Cole and Fernando, 2016; Kondylis, Mueller and Zhu, 2017; BenYishay and Mobarak, Forthcoming), as well as demand-side constraints that may affect a farmer’s responsiveness to an extension message (Aker, 2011; Beaman et al., 2015; Aker, Ghosh and Burrell, 2016). In particular, evidence on the role of tailored vs general information in changing adoption behavior emphasizes a need to balance two trade-offs.

On the one hand, recommendations that are tailored to each user may have the greatest impact. Recent evidence from utilities in developed countries demonstrates that delivering information to individuals on their consumption relative to others can significantly enhance conservation behavior (Ferraro and Price, 2013; Alcott and Rogers, 2014). In a context of low productivity agriculture, Hanna, Mullainathan and Schwartzstein (2014) show that due to the difficulty of learning about optimal strategies in agricultural environments, seaweed farmers in Indonesia only respond to extension information when that information includes feedback on their own practices. This would suggest that, in the context of irrigation, providing water users with metered information on their usage relative to the optimal water requirement for their crop at each growth cycle may be necessary to curb inefficiencies in water use.

However, the cost of tailoring recommendations to observations of individual behav-

> 0This is echoed in other settings (Aker, Ghosh and Burrell, 2016; Deichmann, Goyal and Mishra, 2016).
_4_ _WATER WHEN IT COUNTS_ ior can be substantial. In our context, tailored recommendations rely on the collection of high-frequency behavioral and water allocation data. Technical expertise is required to translate observed behavior into recommendations. Both factors may preclude the scalability of this type of feedback mechanism as an extension modality. In contrast, highlighting the relative water requirements over the life cycle of primary crops has the benefit of reaching a broader base of beneficiaries, since the main cost of producing the information is deciphering the primary crops grown in the region.

The competing demands of scalability and direct relevance for individual farmers create conflicting pressures on agricultural extension systems. Information disseminated through public extension must be cheap enough to generate that it can be scaled up and made available to farmers across remote areas. Yet, if generalized information is not being used by farmers for lack of relevance, providing general information alone may not be a panacea.

To test these ideas, we run a field experiment in which we assess whether individual feedback on usage of irrigation water is necessary to influence behavior beyond general, lower cost reminders. In practice, we examine the role of (1) providing farmers with general recommendations on water requirements, and (2) juxtaposing individual use relative to the general recommendations in curbing the inefficient water allocation patterns observed at baseline. Use of high-frequency water data permits an event-study design to quantify the effect of any feedback on water sufficiency. Within this event-study design, we randomize a subset of farmers to receive individualized feedback to assess any additional benefit of tailoring recommendations on water allocations.

Two months after feedback provision, we observe a marked drop in both water-related conflicts and plot-level water scarcity. Specifically, the share of plots receiving insuffi-
_WATER WHEN IT COUNTS_ _5_ cient water relative to their crop requirements decreased by 14.0 percentage points (from a pre-information weekly average of 27.48% of plots). High-frequency data on plot-level water availability suggests that these efficiency gains are achieved as farmers dynamically adjust their use to closely fit the water requirements over the crop cycle.

Farmers who were randomized into receiving additional, individualized information on water allocations over the crop life cycle were no more likely to change their behavior. Taken together, the findings suggest that providing relatively cheap information on general water requirements leads to water-conserving practices, with no additional benefits to providing more expensive individual feedback.

The remainder of the paper is organized as follows. Section II summarizes the context, data innovations, and establishes stylized facts from the one-year water monitoring phase of the study. Section III provides a theoretical framework for inefficiency arising from rules of thumb motivated by the baseline stylized facts. Section IV describes a field experiment that tests the impact of generalized vs. individualized feedback tools on the efficiency of water allocation, while Section V outlines the empirical methodology and main experimental results. The final section concludes.

# **II. Irrigation water use in Central Mozambique**

# _A. Study background_

Mozambique’s population primarily lives off low-productivity agriculture: 80% of workers are engaged in the agricultural sector, while its contribution to GDP is merely 25% (World Bank, 2011). In an effort to transform the agricultural sector, government and aid resources have been designated to support diversification of production into horticulture and other export-oriented crops. One of the main challenges farmers face is
_6_ _WATER WHEN IT COUNTS_ the reliance on rainfall for production. Sixty to eighty percent of annual precipitation falls during the region’s single rainy season, meaning that farming is not viable during most of the year (World Bank, 2007). In recognition of this constraint, the Government of Mozambique (GoM) has dramatically increased its investment in the water sector, spending 2.3 billion USD in 2008 (World Bank, 2010).

However, given the high incidence of droughts in recent years, any planned expansion of irrigation risks exacerbating water stress. Among African countries, Mozambique is highly vulnerable to natural disasters and extreme weather (GFDRR, 2017), including both droughts and floods. The water scarcity created by droughts has been estimated to impose a drag on GDP growth of 1.1% on average (World Bank, 2007). In 2016, a drought-induced water scarcity led to food shortages affecting an estimated 400,000-1.8 million people (Vidal, 2016). These periods of extreme water scarcity imply that water overuse under ‘normal’ climate conditions may come at a high cost during times of water scarcity.

Improving the performance and sustainability of irrigation schemes requires overcoming weak management by local institutions. Despite substantial investment, Mozambique’s coverage of irrigation has actually declined in recent decades. At the start of the 1980s, 120,000 hectares were irrigated, and 50,000 hectares have since been rehabilitated (FAO, 2016). These figures suggest a large margin for improvement in the sustainability of irrigation schemes.

We conducted a study of three irrigation sites in Central Mozambique to i) understand whether water is currently used efficiently in a context of weak local oversight, and ii) to test strategies for alleviating these inefficiencies. Two of these schemes consist of canal irrigation, while a third, employs both canals and sprinklers. Water for all three schemes
_WATER WHEN IT COUNTS_ _7_ is sourced from rivers or springs. This type of gravity-fed surface irrigation is the most common type of irrigation in Mozambique (FAO, 2016). All three schemes were rehabilitated as part of a World Bank-supported project called the Sustainable Irrigation Project (hereafter, PROIRRI), and are primarily devoted to production of horticultural crops through a contract farming arrangement with a local buyer. The PROIRRI project focused on horticultural production given its perceived commercial value. The crop portfolios in these schemes are representative of those that will be practiced under future irrigation expansions.

Farmers benefiting from schemes constructed under direct support from the GoM are required to organize themselves into water users associations. Water pricing has not been established to regulate use. Given the lack of incentives to monitor or conserve water in this context, it was not previously known whether farmers waste water and whether there are potential gains from reallocating the quantities of water currently used. Collecting water use data at high temporal and spatial resolution is therefore essential to shed light on the pattern of water use and formulate viable policy actions to improve the efficiency of water allocation.

# _B. Baseline data on water use_

At the heart of our contribution is a high temporal and spatial resolution water measurement system, which tracks weekly water availability at the plot level. We combined these with agricultural production data collected every four months. Water measurement and production surveys were collected between August, 2015 and July, 2017. We now describe the sampling strategy and detail the data collection protocols.

_Study Sample_
_8_ _WATER WHEN IT COUNTS_ To set up the measurement system, we fully mapped all plots within the three study schemes. During this exercise, we identified all plots that draw water from sprinklers, canals, or hydrants connected to the scheme; the source of water for these plots; and information on the household of the person in charge of cultivating them. During the initial contact with the water users, we identified 153 plots belonging to 120 households across the three schemes.

# _Irrigation water availability_

We employ a low-cost, community-centered approach to monitor water supply at the plot level. As part of the initial scheme listing, all water diversion points and their water catchment areas were mapped, and water measurement points were identified and calibrated. This calibration allowed us to convert simple measurements, such as water depth in a canal (measured with a ruler) or number of sprinklers on a plot, into volume of water diverted to the plots from that point. Each scheme was assigned a dedicated data collector who took the established measurements at each point three times a day.<sup>1</sup> These measures were then converted to flows through canals, sprinklers, and hydrants. From these, we estimated the volume of water delivered to each plot in a week. We obtain our measure of water availability by dividing that volume by the area of the plot. Water availability is therefore reported as a a depth measured in mm/week.<sup>2</sup><sup>_,_3</sup>

# _Agriculture surveys_

We additionally conduct household surveys every four months to capture detailed

> 1These data collectors were farmers from the schemes who received training and a nominal payment to collect measurements.

> 2This depth is interpretable as the maximum depth of water that could cover a plot if the full amount of water delivered in that week is distributed equally across the area of a cultivated plot.

> 3A detailed description of the monitoring methods and imputation protocols for translating measurements into estimates of volumetric flows is included in appendix A.A2. A full description of measurement methods being developed and tested around the world by technical partners contributing to the study is available at: http://www.imomohub.org/.
_9_ _WATER WHEN IT COUNTS_ information on agricultural production and irrigation practices in the study area. Each household is asked about the plots they cultivate in the scheme, as per the listing.<sup>4</sup> Table 1 shows the number of plots and households surveyed at every round.<sup>5</sup> TABLE 1—NUMBER OF OBSERVATIONS PER ROUND

|||||Round||||
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|
|Number of listed households|72|120|121|123|124|135|138|
|Number of listed plots|90|153|156|158|158|158|157|
|Average number of plots farmed per household|1.25|1.27|1.34|1.39|1.45|1.41|1.43|
|Share of listed households surveyed|90.3%|86.7%|91.7%|84.6%|86.3%|86.7%|74.6%|

Notes: Two of the three schemes were included in round one, with the third added for round 2, causing the substantial increase in households and plots from round 1 to 2. During each round, households that weren’t surveyed in any previous round are also asked about missed rounds retroactively, so reponse rates are expected to be higher for earlier rounds, as there were multiple occasions for households to be asked about their respective periods.

We collect detailed information on the cultivation patterns on all plots farmed by that family. The frequent surveys are timed roughly with the length of cropping cycles for typical crops. We use the planting and harvest dates to estimate the growth stage in which the crops are each week, using information on average growth stage length for each crop in the region. Combining this with information on weekly water requirements in each growth stage allows us to calculate an estimate of the weekly water requirements (mm/week) for all plots. We further exploit questions in which we ask farmers to declare whether they perceive water to be sufficient for production and whether they had a dispute concerning water for each of their plots with another farmer over the period of months between the survey rounds. A total of seven rounds of survey data were collected

> 4Prior to each round of agriculture surveys, the listing is updated to track any changes of plot ownership within the scheme as well as in and out migration. In limited occasions plots are divided up or merged leading to small changes in total number of plots across survey rounds. The numbers listed refer to the number of households and plots in round 2,

> 5We addressed non-response in a given round by re-surveying these same households in subsequent survey rounds and collecting retrospective data corresponding to the previously missed interview. The main reason for non-response are people farming their rain-fed plots during the rainy season, which can be far from the irrigation schemes.
_10_ _WATER WHEN IT COUNTS_ between August 2015 and July 2017.<sup>6</sup>

# _Precipitation and water requirements_

Finally, weekly precipitation data (mm/week) were extracted from the NOAAs Climate Prediction Centre (CPC) CMORPH product (Climate Prediction Center, 2015).<sup>7</sup> Crop specific water requirements are from the FAO Cropwat 8.0 tool (Doorenbos and Kassam, 1979; Allen et al., 1998; FAO, 201). These data allow us to estimate the quantity of rain that falls on these schemes in every day, so that we can assess total water available to a plot on a given day as the depth of rain that falls on a plot plus the depth of water that could be applied from irrigation.

## _C. Descriptive Statistics_

The first step in our study is to use our novel high-frequency irrigation monitoring system to assess irrigation and cultivation strategies in the absence of pricing or feedback interventions for one full year with the goal of uncovering whether farmers in these schemes are using water efficiently. If we find that farmers are using water inefficiently, the next step is to identify ways in which water could be more effectively allocated. These strategies underlie the content for the extension tools we use to test whether individual feedback is necessary to improve the efficiency of allocations. This sub-section summarizes findings from one full year of water monitoring to uncover what we believe to be a driving source of inefficiency: a particular type of dynamic inflexibility of allocations to time-specific requirements that we call rules of thumb.

_Cultivation Strategies_

> 6A detailed time-line of data collection activities can be found in the appendix in Figure A1.

> 7Rainfall data were extracted for the smallest geographical unit available in these data. Since the schemes are near to each other, this unit covers all three schemes, so precipitation data do not vary across schemes within a time period.
_11_ _WATER WHEN IT COUNTS_ Table 2 presents basic baseline descriptives of the plots collected in our sample.<sup>8</sup> Of the 120 farmers originally listed at the start of our study, the average household in a given scheme cultivated between 1.1 and 1.5 plots, with average plot sizes ranging across schemes from 0.4 to 0.9 hectares. Most of these plots were under active cultivation at baseline (67.2% to 77.4% across schemes). Typical crops include maize and horticultural crops such as cabbage, collard greens, and piri-piri, a variety of red chili pepper. Baby corn, a miniature ear of corn cultivated for export, is the most frequently cultivated crop. Between 53% and 81% of farmers cultivated baby corn in the irrigation schemes.

# _Timing of Rainfall and Role for Irrigation_

Among rainfed plots in our study region, the main production cycle takes place during the wet season (October through April). Irrigation allows farmers to reliably cultivate crops during both the wet and dry seasons, with the dry season occurring in May through September. In this context, the primary function of irrigation is to smooth water availability between rain events. The predictable schedule of water availability facilitated by irrigation allows farmers to stagger planting relative to the rainy season.<sup>9</sup>

# _Self-reported Water Sufficiency and Conflicts Over Water_

To motivate the value of information related to crop water requirements, Figure 1 highlights the water stress reported by farmers in these schemes prior to the intervention. The bars show the proportion of plots whose cultivators reported disputes over water (blue bars) and the proportion of plots whose owners reported that water was sufficient to meet their crops’ requirements (red bars). Unsurprisingly, farmers clearly feel they do not have sufficient access to water by the end of the dry season. It is important to emphasize, how-

> 8Note the scheme that entered the sample in round 2 uses retroactive data to formulate these descriptive statistics. 9The annual distribution of precipitation shown in Figure A6 highlights the role for irrigation. Clearly the months of May through September (the dry season) reflect a period of water scarcity. Water availability spikes during rain events as shown in Figure A7, but irrigation maintains a relatively smooth average level of water availability between events.
_12_ _WATER WHEN IT COUNTS_ TABLE 2—DESCRIPTIVE STATISTICS

|Scheme 1<br>_Sample sizes in baseline_|Scheme 2|Scheme 3|
|---|---|---|
|Number of listed households<br>47|31|53|
|Number of listed plots<br>62|31|63|
|Average number of plots farmed per household<br>1.472|1.093|1.245|
|Share of listed households surveyed<br>92.0%|88.4%|87.3%|

_General scheme characteristics in baseline_

|Irrigation type<br>Canal|Sprinkler|Canal|
|---|---|---|
|Average household landholding (ha)<br>1.528|1.642|1.832|
|Average plot size (ha)<br>0.422|0.622|0.890|
|Share of cultivated plots<br>67.2%|77.4%|72.2%|
|_Share of households that planted common crops_<br>Baby corn<br>53.2%|_in baseline_<br>80.6%|66.0%|
|Collard greens<br>23.4%|3.2%|17.0%|
|Maize<br>48.9%|22.6%|17.0%|
|Piri-Piri<br>29.8%|3.2%|13.2%|
|Cabbage<br>23.4%|32.3%|39.6%|

_Average share of plot area dedicated to most common crops in baseline_

|Baby corn|43.6%|43.4%|22.0%|
|---|---|---|---|
|Collard greens|23.0%|4.0%|8.3%|
|Maize|64.6%|53.5%|26.8%|
|Piri-Piri|51.9%|18.0%|23.0%|
|Cabbage|43.8%|17.8%|22.4%|

_Notes_ : Data refers to rounds 1-3 (rounds pre-feedback experiment). The number of listed households and plots indicates the total number of unique observations in those rounds. All other variables are averaged across the 3 rounds.

ever, that water stress is not unique to the dry season. Insufficiency is reported for some plots in every month, and disputes are common in most months of the year. Reports of conflict coincide with reports of water insufficiency; the slope of a regression of monthly
<!-- Start of picture text --> S) 98.1 Regrenough water<br>S 96.9 33. me 98:2 Slope = 473<br>89.5 R°=.261<br>83.1<br>89 80.8<br>£ 67.3 67.3 67.3<br>D 63.5 65.4<br>2£& 59.6<br>© 462 49.7<br>D 415° 40.<br>£¢ 36. 5 4<br>® 29. 30.<br>5 6.0<br>a oO 19.<br>N<br>10. 11. 25<br>7. 5d 8.4 8.7<br>15, 19 2.8<br>oO % © © © © © © © © © 0 .© © 2. 0. WO ©<br>x Ss s s RS s RS s AS x x Ss ss e) s AS RS<br>SPB DH GM Pia MOS WP TWN PM OS<br>MEE Proportion of households that report conflict<br>ME Proportion that report enough water<br><!-- End of picture text -->
_14_ _WATER WHEN IT COUNTS_ this volume is divided by area, our measure of water availability is interpretable as the depth of water that could be applied to a plot if the volume of water is evenly distributed across a plot. This measure allows us to benchmark the degree to which a plot has enough water to meet recommended requirements.

To summarize the degree to which a plot is being allocated sufficient water to meet the cultivated crop’s requirements, we compute the water gap as water availability minus growth stage specific water requirements. Growth stages are defined by the planting dates reported by farmers in the follow-up surveys, and the average length of the growth stages according to the FAO Cropwat 8.0.<sup>10</sup> The gap is defined in mm/day for each crop-specific growth stage. A negative water gap means that there is not enough water available to the plot to meet the recommended watering requirements given its growth stage. A positive gap means that sufficient water is available for the plot to meet its requirements. A plot whose water gap is larger in absolute value is applying a level of water that is further away from the recommendation.

Using the water gap to measure proximity to the water requirements reveals interesting patterns of misallocation. Figure 2 shows the average water available to plots observed with crops in growth stages 1, 2, 3, or 4 (gray bars) along with the recommended requirements for the three most common crops (lines). What emerges is a striking pattern between water availability and requirements across crop growth stage. While water _requirements_ vary greatly by growth stage, water availability does not. When crops are at growth stage 3, the available volume of water to the average plot is close to (just above) the requirement for crops. But in growth stages 1 and 2, we do not observe water quanti-

> 10For the remainder of the paper, we will refer to four main growth stages, labeled 1-4, which correspond to these stages as follows: 1-Initial stage, 2-Vegetative Stage, 3-Reproduction Stage, and 4-Maturation Stage. The implications of water stress on total production vary depending on the timing of the stress in the crop cycle. Water shortages during the initial and reproduction stages typically can lead to irreversible damages (Varzi, 2016).
<!-- Start of picture text --> ©<br><e)<br>><br>S<br>£<br>$4 ZS<br>. Zz<br>oO<br>1 2 3 4<br>Growth stage<br>Av. water availability == Maize<br>Cabbage =———= Baby corn<br><!-- End of picture text -->
<!-- Start of picture text --> Schemes pooles Scheme 1<br>© ©<br>ze Be<br>fa]25a25<br>*x<br>“n<br>°°<br>20 2 4 2 0 2 4<br>mmiday (log) mmiday (log)<br>1 — 2 —_ 3 —4 1 — 2 — 3 — 4<br>Scheme 2 Scheme 3<br>~ ©<br>rdapo55aa>o<br>+~<br>“n<br>°°<br>20) mmiday (log) 2 4 2 0 mmiday (log) 2 4<br>1 — 2 — 3 —4 1 — 2 — 3 — 4<br><!-- End of picture text -->
<!-- Start of picture text --> Pooled schemes Scheme 1<br>2222<br>©©<br>3©lg>e5 ©4=e5<br>Se22€ Sy =<br>oe£<5=g@8 <5=$<br>~.Nzy.NF]<br>°°<br>Jul 15 Jan 16 Jul 16 Jan 17, Jul 15 Jan 16 Jul 16 Jan 17,<br>1 Share of plots in deficit — voareeemeGaily 1 Share of plots in deficit — yoereaem®Gaily<br>Scheme 2 Scheme 3<br>2222<br>©©<br>©>©><br>ee<br>8 8<br>3 —e & =<br>& “3 8 +5<br>6 3G 3<br>“0- N4= “0.N8=<br>° °° °<br>Jul 15 Jan 16 Jul 16 Jan 17, Jul 15 Jan 16 Jul 16 Jan 17,<br><!-- End of picture text -->
_18_ _WATER WHEN IT COUNTS_ thumb interact with dynamic requirements to create inefficiencies. We use this model to motivate what mechanism can be used to improve overall water efficiency. Ultimately, we cannot disentangle what drives current water applications.<sup>13</sup> We then use this model to inform the design of an information experiment to combat these observed inefficiencies.

# **III. Conceptual Framework for Efficiency Improvements**

We build a simple model of water balance to show that following fixed rules without taking advantage of crop dynamics can create a simultaneous situation of wasted water at a scheme level and water scarcity at a plot level. This model helps us to conceptualize where the gains from water allocation might come from and how information can achieve improvements.

For each plot _i_ in a scheme, a farmer chooses how much water to use in each growth stage and the investment of other inputs so as to maximize her production, subject to a water balance constraint at the scheme level. We can write this constraint as follows: In equation (1), _π_ () is a concave crop production (or revenue<sup>14</sup> ) function concave in water delivered per growth stage, _wig_ , and in other inputs, _xi_ . The water balance constraint requires that the sum of all water allocations across _N_ plots is less than or equal to the

> 13It should be noted that we cannot disentangle precisely what motivates these rules of thumb. A few hypotheses come to mind. One possibility is that because outcomes from different watering levels are noisy, farmers are imperfectly informed about the true relationship between water and yields. If crops are especially sensitive to water in growth stage 3, it may be that these rules of thumb arise from testing different quantities of water and settling on the level that is required at the most intensive stage. Alternatively, the rules could arise from coordination costs, wherein farmers know that revising water allocation requests upward is costly. Thus, they request the maximum quantity they need for that crop over the life cycle, at planting, in order to avoid having to request an increased allotment as the crop develops.

> 14We refer to farmers maximizing production. Realistically, they maximize profits, accounting for the cost of inputs. Since water is free, we abstract away from other inputs by assuming that the _π_ () function includes the net cost of inputs that would be optimally chosen for the given set of water choices. Farmers then simply choose the level of water that maximizes their harvest given an optimally chosen set of complementary inputs on a given plot.
_19_ _WATER WHEN IT COUNTS_ total availability of water in the system in week _t_ , given by _Wt_ .

If a scheme only has one plot, then water allocation for each growth stage solves: For plot _i_ under cultivation in growth stage _g_ during week _t_ , call the level of water that solves equation (2) _w_<sup>_∗_</sup> _igt_<sup>. This is the optimal water allocation given perfect information. If</sup> a farmer delivers more water than _w_<sup>_∗_</sup> _igt_<sup>to her plot, overwatering causes yields to be lower</sup> than potential given a choice of inputs _xi_ through waterlogging. If a farmer delivers less than _w_<sup>_∗_</sup> _igt_<sup>toherplot,waterstresscausesyieldstobebelowpotential.Aparameterof</sup> primary interest then is the water gap for a given growth stage as _wigt − w_<sup>_∗_</sup> _igt_<sup>. A positive</sup> water gap in a growth stage _g_ observed in week _t_ means too much water is being delivered to a plot, while a negative one means too little water is being delivered. In a single-plot scheme with no cost to water and perfect information, a positive water gap would never be observed, because excess water would be allowed to flow out of the scheme rather than being allocated as availability to the plot, and a negative one would be observed only when _wigt < Wt_ .

Introducing multiple farmers under perfect information yields a water availability constraint of ∑<sup>_N_</sup> _i_ =1<sup>_w∗_</sup> _igt_<sup>_≤Wt_.Allfarmerschoose</sup><sup>_wigt_=</sup><sup>_w∗_</sup> _igt_<sup>.If∑</sup><sup>_N_</sup> _i_ =1<sup>_w∗_</sup> _igt_<sup>_> Wt_,sothatitis</sup> not possible to fully satisfy every farmer’s desired water demand, then the cooperative solution will be the set of water allocations per growth stage that solves:
_20_ _WATER WHEN IT COUNTS_ If _π_ () is concave with regard to _wg_ , the set of water availability allocations with full information that solves (3) will always have _wigt ≤ w_<sup>_∗_</sup> _igt_<sup>_._Thus, under perfect information</sup> and coordination, no one overwaters their crops, and underwatering will only occur when there is not sufficient water to meet every farmer’s demand in all growth stages. As a result, is not possible to improve any farmer’s water usage without making another farmer worse off given overall constraints on the total quantity of water available at the scheme level.

Farmers may be misinformed about the true return of water on production. If they are using rules of thumb in which they believe that crop requirements do not vary by growth stage, then farmers act as though the following is true: Under these beliefs, farmers will choose a water allocation as for all growth stages to solve: Since equation (5) is fixed for all growth stages, water use under rules of thumb will also not vary by growth stage, implying that _wigt_ = _w_ ̄ for all _i, g_ , where _w_ ̄ is a fixed constant. If true water requirements for plants do vary by growth stage, _w_<sup>_∗_</sup> _i,g_<sup>=</sup><sup>_w_ ̄15for</sup>

> stage. This could arise from each farmer setting the rule of thumb at a different level so that15In fact we observe substantial variation in water availability across plots for plots with _w_  ̄ would be indexed by plotcrops in the same growth _i_ . Alternatively variation could arise from all farmers having the same fixed rule, but having different bargaining position to negotiate the scarcity that arises from everyone following the inefficient rule. For example, imagine that there are only two farmers, each with one plot in the scheme, and the upstream farmer freely chooses water availability subject only to will choosethe total water constraint, while the downstream farmer chooses water availability subject to the upstream farmer’s choiceand the constraint. Then if 2 _w jt_ = _Wt − w_  ̄. This bargaining would also generate variation in observed allocations even if everyone is trying _∗ w_  ̄ _> Wt_ for a period _t_ , the upstream farmer will choose _wit_ = _w_  ̄ and the downstream farmer
_21_ _WATER WHEN IT COUNTS_ some growth stages. With this behavior there is scope for improving farmers’ water use without making other farmers worse off, because some farmers will have a non-zero water gap even if ∑<sup>_N_</sup> _i_ =1<sup>_w∗_</sup> _igt_<sup>_≤Wt_.</sup> Improvements in water allocation are possible without changes in total water at the scheme level if different farmers are in different growth stages at the same time. The following holds for some farmer _i_ in growth stage _g_ and another farmer _j_ in growth stage _h_ the following for at least one week _t_ : Equation (6) indicates that if there are growth stages in which the requirements are less than the rules of thumb and growth stages in which the requirements are greater than the rules of thumb, there is scope for efficiency improving reallocation. By taking ̄ water of the quantity _w − w_<sup>_∗_</sup> _jh_<sup>andgivingitto</sup><sup>_w_ ̄</sup><sup>_−w∗_</sup> _ig_<sup>,farmer</sup><sup>_i_canbemadebetteroff</sup> without making farmer _j_ worse off or changing the total quantity of water available to the scheme.

Equation (6) shows a role for information in water management where farmers are following rules of thumb. Any reminder or feedback system that induces farmer _j_ to shift from a water allocation of _w_ ̄ to _w_<sup>_∗_</sup> _jh_<sup>willimproveoverallwaterallocationsinthe</sup> scheme.

Several parameters of interest for evaluating the value of information emerge from the simple model above. The first is the water gap for a plot in a given week. Given an estimate of true water requirements, the observed water gap on a plot is defined above as to follow the same rule. Since we cannot distinguish variation from bargaining or each farmer having a different rule of thumb in the data, for readability we do not index _w_ by plots _i_ or model the bargaining that resolves overuse.
_22_ _WATER WHEN IT COUNTS_ _wit − w_<sup>_∗_</sup> _it_ 16 At a scheme level, the aggregate water gap is _Wit −_ ∑ _Ni_ =1<sup>_π_(</sup><sup>_w∗_</sup> _igt_<sup>).17In weeks</sup> when the aggregate water gap is positive, information should weakly decrease the proportion of people who are experiencing a negative water gap. We will use the proportion of plot-weeks where we observe a negative water gap as our primary indicator of the value of information. If providing feedback reduces the share of plots where there is less water available than is required to meet recommendations, we will say that feedback is welfare enhancing.

At a scheme level, water savings attributable to the information intervention is computed using the following: If all farmers are following rules of thumb, (7) the total quantity of water used by all farmers between period 1 and period T is the sum of the _w_ ̄ over all cultivated plots in the period. The prediction for whether a scheme in total will use less water when feedback is provided is ambiguous. If rules of thumb are “mostly” higher than optimal, information that shifts farmers toward the optimal quantity will reduce the total quantity of water used. If rules of thumb are mostly lower than optimal quantities, farmers may respond to the information by trying to increase water uptake overall, subject to the constraint that the sum of all water used is less than the total water available to the scheme.

It is not clear _ex ante_ what types of reminders will achieve feasible improvements

> 16The water gap is interpreted in the same way as the observed water gap described in section II. It is the difference between the farmer’s chosen water allocation and the recommended volume applied given inputs and the crop cultivated on that farmer’s plot.

> 17The aggregate water gap cannot be changed by farmers’ behavior subject to choices of crops and planting times, since it is a function of only crop requirements and total water available in the scheme, but it is a measure indicating whether farmers are making constrained choices since it is positive if and only if there is enough water to meet each farmers optimum water demand given optimal allocations.
_23_ _WATER WHEN IT COUNTS_ in water allocations. It may be sufficient to remind farmers that crop requirements are not constant, allowing them to adjust the rules of thumb accordingly. But farmers may be misinformed of how their own water allocations actually relate to requirements. The literature on energy metering suggests a key role for individualized feedback to align users with allocations. Since information on individual metered information is expensive to collect and implement, we test whether individual reminders are needed to achieve the allocation gains identified in equation (6).

# **IV. Field Experiment**

Section III shows that efficiency-improving reallocations are possible given the type of practices observed in Section II.D. One hypothesis is that providing information on water requirements may lead to a redistribution of water from plots that over-watered in the early growth stages to plots that under-watered in the later growth stages. It is not clear what kind of information is crucial to achieve this redistribution process.

We designed and ran a field experiment to test the impact of providing information on water recommendations on water allocation. We implement two different feedback tools, one providing general watering recommendations across growth stages, and a second that adds individualized feedback on a farmer’s own actual water use to the general watering recommendations.

We randomly assign all households in our study area to one of two treatments, general recommendation (general feedback treatment) or general recommendation plus individualized feedback (individualized feedback treatment). The first round of feedback tools was distributed in December 2016. For the randomization, we used the latest household listing available from the July 2016 survey. We expect that improvements in allocation
_24_

# _WATER WHEN IT COUNTS_

that arise from the provision of information would come from high water users redistributing water to low users. To ensure an even mix of treated households who are high or low users at baseline, we randomized the treatment in matched pairs. Within a scheme, each farmer was ranked by their mean water gap across all crops. The initial pair-wise randomization was done after round 3 when 66 households were assigned to the general feedback treatment and 63 to the individual feedback treatment. Table A1 reports observable characteristics are balanced across treatment assignment at the 5 percent critical level. Farmers who join after this round are pooled together and randomly assigned to either treatment each round. Across all rounds, 75 households were assigned to general feedback and 72 to individual feedback.

Figure 5 illustrates a sample of a chart provided to a farmer assigned to the general feedback treatment in Scheme 1. The sheet shows his primary crop is red pepper on plot 1010202, and includes a chart highlighting both the water requirements and length in days of each growth stage. The chart is meant to communicate the general pattern of relatively minimal amounts of water are required in growth stages 1 and 2. Higher applications of water are necessary at later stages of the crop cycle.

Farmers assigned to the individualized feedback treatment receive information about their actual water use in the same season of the previous year in addition to the information about their primary crop’s water requirements. Figure 6 shows both the water requirement and water use information for a sample producer of tomatoes in Scheme 1. For this specific case, we witness the water applications exceed the requirements at every growth stage. Moreover, tomato production demands more water at later stages of growth. The water use pattern is the reverse of the water required. Feedback is specific
_25_ _WATER WHEN IT COUNTS_ to tomato planted on plot 1010101.<sup>18</sup> The information treatments were delivered by our enumeration team during each agriculture survey. The feedback was provided prior to starting the survey. The information experiment began in December 2016 (Figure A1). The information treatment was repeated for the same farmers two additional times in April 2017 and July 2017 to ensure that farmers received feedback in each of the relevant seasons. Treatment is assigned only once, so that a farmer once assigned always receives the same type of feedback, but the crop they are informed about can vary depending on which crop was most relevant in that same upcoming period last year.

Water measurement and agriculture surveys continued for three rounds after the start of the treatment, following the schedule described in section 2.2. A total of 157 households were listed as part of the schemes, of which: 125 were surveyed in at least one of the prefeedback rounds (rounds 1-4), and 129 responded to at least one of three post-feedback surveys (rounds 5-7). Finally, 115 households responded to at least one pre-feedback and one post-feedback round. Since feedback was delivered during the survey, delivery of feedback is conditional on finding the household for that survey round. Out of the 75 and 72 assigned to the general guidance and individual feedback, respectively, 66 and 62 received at least one round of feedback.<sup>19</sup> Scheduling of water allocations is typically arranged by heads of water user associations, and is fixed according to the days of the week and time of day. The fact that the generalized feedback communicates to farmers that they should vary their water use throughout the crop season runs in direct contrast to the idea of a fixed schedule. We

> 18The farmers are reminded of the plot the information relates to using a map of the scheme. The feedback tools displayed here are shown with English translations. The versions used in the field were written in Portuguese. 198 households that joined the schemes after round 4 did not receive any treatment. New households that joined after the original matched pairs randomization were individually assigned a feedback treatment status.
Scheme 1 <!-- Start of picture text --> Farmer's name:<br>Plot ID: 1010201<br>Irrigation type: Canal<br>Crop: Piri-piri<br>30<br>24.82<br>25<br>= 20<br>g 1771<br>a<br>a<br>§ 15<br>3<br>E 10.43<br>—€ 10<br>6.19<br>|0 L<br>1 2 3 4<br>Growth stage<br>Numbcamperfd ores 28 22 20 5<br>in each stage:<br><!-- End of picture text -->
Scheme 1 <!-- Start of picture text --> Farmer's name:<br>Plot ID: 1010102<br>Irrigation type: Canal<br>Crop: Cabbage<br>45<br>40 38.87<br>35,<br>y 30 27.46: 28.70<br>:<br>235<br>s 20.23<br>2 70 17.69 17-70<br>o<br>E<br>15 12.43 10.87<br>10tt] i<br>1 2 3 4<br>Growth stages<br>Number of days<br>: 25 35 25 10<br>in each stage:<br><!-- End of picture text -->
_28_ _WATER WHEN IT COUNTS_ growth, we expect that farmers will choose to use less water during early growth stages, allowing them to reallocate water to the later growth stages. If this hypothesis is correct, Since both treatments receive feedback, we start by using an event study design to draw comparisons in the incidence of water-related conflicts before and after the introduction of feedback (Figure 7). In the month immediately before the first distribution of feedback (December), the incidence of conflict was similar to that reported in the same month of the previous year. One month after farmers started receiving feedback (January), reports of conflict feel to zero and remained there from January to May. Over the dry period (July-November), conflicts are again reported, but their incidence remains substantially below that of the previous year. These findings are largely corroborated with our plotlevel measurements of water sufficiency (Figure 8). Overall, these results are consistent with the idea that farmers quickly began to use water more efficiently in accordance with the recommendations they received.

Figures 7 and 8 show that households were more likely to perceive water as sufficient and were consequently less likely to report conflicts over water in the months following the introduction of feedback than they were before feedback started. As Sections II.D and III suggest, the main gains from reallocation would come from reallocating water across planting stages. If farmers respond to reminders and there is any scope to increase watering, they should increase watering only in the most water-intensive (late) growth stages, when crops are not getting enough water. Instead, watering in the early growth stages should decline, as there is already more than sufficient water. This general pattern of reallocation is observed in Figure 9 among both treatment groups. In both groups, the distribution of water use in the first growth stage shifts left (or closer to requirements)
<!-- Start of picture text --> (oo)<br>© 56.2<br>50.9 50.9<br>no<br>ae}<br>£ (oo)<br>oT 36.4<br>a ;<br>ro) 4.0<br>x= 2.4 2.4<br>We} 29.<br>&<br>oO 24.5<br>Sc 21!<br>® 19.2<br>oa<br>oO<br>10.6<br>8.4 7.6<br>5.6 5.9<br>1.9 2.8<br>0.0 0.0 0.0 0.0 0.0 0.8<br>oO<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br>MM sPre-feedback [ENN Post-feedback<br><!-- End of picture text -->
<!-- Start of picture text --> oO 100 100 100 100 100 100 99<br>oO 98 96 35<br>— 93 92 94<br>87<br>81 81<br>3s © 73<br>g 67<br>oO 63<br>36 62 60<br>Lo<br>ra<br>[o}<br>©<br>1o))<br>Bo<br>oY 34 34<br>oO—<br>®<br>o<br>21<br>oO<br>N<br>oO<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br>MMM Pre-feedback [EM Post-feedback<br><!-- End of picture text -->
<!-- Start of picture text --> General Feedback: Pre Individual Feedback: Pre<br>©©<br>©©<br>z 2<br>fa]et5 aey5<br>“0 a<br>°°<br>20 2 4 6 2 0 2 4 6<br>mmiday (log) mmiday (log)<br>General Feedback: Post Individual Feedback: Post<br>©©<br>©©<br>2 2<br>a2s55aax<br>“n<br>°°<br>2 0 2 4 6 2 0 2 4 6<br>mmiday (log) mmiday (log)<br><!-- End of picture text -->
_32_ _WATER WHEN IT COUNTS_ feedback causes farmers to redistribute water from early to later growth stages, we expect there to be a reduced proportion of farmer-crops suffering from a lack of water to meet crop requirements, or _γ <_ 0, following the receipt of feedback. The additional effect of individualized feedback, to inform farmers of how their own usage compared to the generalized recommendation in the previous year, is measured by _δ_ . We hypothesize _δ ≤_ 0. While the literature on energy use provides empirical support for individualized feedback on resource-conserving behavior, it is possible that tailored-information provides no additional insight to the farmer, in this context. We condition measurement of these two effects on _Xijgt_ , which includes stratified pair fixed effects and month of planting fixed effects. Standard errors are clustered at the household level to account for autocorrelation.

Table 3 shows that the results of information provision accord with predictions. In columns 1 and 2, the share of plot-crop-week observations where the farmer has a negative water gap falls by 14% after treatment begins. The difference is significant at the 1% level. When we interact the post and individual feedback dummies with an indicator for whether a household was below the median water gap in rounds prior to the feedback experiment, the regression results show that the post-information treatment is associated with a reduction in the number of people who have a below-zero water gap of 19.8%, which is significant at the 1% level. There is no evidence that individualized information has any additional effect.<sup>20</sup>

> 20We also evaluated generalized feedback effects on the natural log of revenue per hectare. We find no evidence that improved water allocations lead to significant improvements in farmer revenue (Table A2).
_33_ _WATER WHEN IT COUNTS_ TABLE 3—PLOT-CROPS MOVED FROM INSUFFICIENT TO SUFFICIENT WATER AVAILABILITY

||(1)|(2)|
|---|---|---|
|Post|-0.140<sup>_∗∗∗_</sup>|-0.198<sup>_∗∗∗_</sup>|
||(0.023)|(0.028)|
|Individual feedback|-0.0216|-0.0369|
||(0.022)|(0.028)|
|Individual feedback * post|0.00818|0.0177|
||(0.034)|(0.043)|
|High water gap||-0.155<sup>_∗∗∗_</sup><br>(0.038)|
|Post * High water gap||0.136<sup>_∗∗∗_</sup><br>(0.043)|
|Individual feedback * High water gap||0.0455<br>(0.049)|
|Individual feedback * Post * High water gap||-0.0344<br>(0.066)|
|Observations|3724|3709|
|Adjusted_R_<sup>2</sup>|0.201|0.208|
|Randomization pairs fxed-effects|Yes|Yes|
|Month ofplantingfxed-effects|Yes|Yes|

Notes: Observations at are at household-round-plot-crop-growth stage level. Sample is restricted to plots that were cultivated. Standard errors are clustered by household to account for autocorrelation.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01

# _B. Robustness_

In the previous section, we find that the share of people with a negative water gap falls after the initiation of information provision, but that there is no additional gain from individualized feedback. However, because everyone receives general information, we cannot rule out that a factor other than information caused the improvement in people meeting requirements. The most obvious candidate for an external factor would be increased rainfall. Figure 10 shows the share of plots under cultivation with a negative
_34_ _WATER WHEN IT COUNTS_ water gap, along with rainfall. Information provision coincided with the start of the rainy season, and it is clear that rainfall was both more intense and consistent during the rainy season following the information treatment. However, for the last several months of the study, we observe the dry season when rain has stopped. In the dry season, the share of plots with negative water gap is higher than the rainy season, but it is much lower than in the previous year. This suggests that we cannot rule out that information contributed to the observed improvements in farmers’ ability to meet average requirements.

One concern in interpreting the result that individual feedback does not have an additional effect on water use relative to general feedback is that attrition from the survey is correlated with the treatment. Attrition appears to be balanced by feedback treatment status. Only 6.67% of households assigned to individual feedback do not appear in any post-feedback round, and only 9.23% of households assigned to general feedback do not appear in any post feedback round. The p-value for the difference in these rates of attrition is .559.<sup>21</sup> We additionally assess the relationship between water requirements and availability before and after the information treatment began to substantiate the claim made in this paper: the observed changes farmers make in response to any feedback is related to moving away from rules of thumb and towards an allocation where water available to plots more closely matches growth-stage-specific requirements. If farmers are following rules of thumb with no regard to requirements, the slope of a regression of availability on requirements would be zero. If they were allocating perfectly efficiently the slope should be one.

> 21We further investigate selective attrition by testing whether the pre-feedback values of observables used to test balance in A1 can predict attrition. We cannot reject that attrition is balanced on these variables. A joint test of their significance has an F-stat of 1.22.
<!-- Start of picture text --> a<br>Oo | fo)<br>oo mn | w<br>oD |<br>I<br>(0) a, | Tt<br>ot ) ©<br>© VY ot<br>c OE<br>s | E<br>==<br>2<br>3S | \} f | 4 ae<br>a i ) ! / ) 1 oS<br>: |! { : i<br>° 4 |<br>g<br>ro) I<br>Q 1<br>Jo) - oO<br>a<br>Jul/15 Jan/16 Jul/16 Jan/17 Jul/17 Jan/18<br>Rainfall — Individual feedback — General feedback<br><!-- End of picture text -->

Notes: Observations are plot-crop-week. Lines show the proportion of plots in the week (x-axis) where water gap is negative (water availability<water requirements) given the crop cultivated and that crop's growth stage in the week. If plots have multiple crops cultivated simultaneously, the plot is represented once for each crop. The grey dotted line indicates the week in December 2016 when all farmers had received feedback. Grey bars measure rainfall in the week.
_36_

## _WATER WHEN IT COUNTS_

p-value=0.031). In the post information period, the coefficient of availability to requirements is 1.727, and we reject that the slope is equal to 1 with a p-value of .023, suggesting that allocations as assessed by availability may have become _overly_ responsive to requirements in response to the feedback. If requirements are not correlated with rain, as we would expect in irrigation schemes where planting decisions need not be tied to rainfall, differences in rain in the pre- and post- feedback periods would be expected to shift the axis of the post-information line in Figure 11 rather than the slope. Since it is the slope that changes and not the constant, this is further evidence that it is the information rather than an external factor, such as increased rainfall, that improves the ability of farmers to at least meet minimum requirements. This improvement is observed among farmers who receive the general reminders as well as those who receive the individual feedback, suggesting that individualized feedback based on metering is not instrumental in achieving the improvement.

# **VI. Conclusion**

Using high-frequency data collected in three irrigation schemes in central Mozambique reveals that a major source of water inefficiency comes from applications of rules of thumb. In this context, these rules of thumb are likely established to satisfy equitable decision rules pertaining to the scheduling of extracting water within a water user group. Yet, these rules fail to consider the precise water requirements of specific crops within the scheme over time. As a result, a significant portion of farmers tend to over-water at earlier stages of the crop cycle, jeopardizing the amount of water available at later stages of the crop cycle when water requirements are at their peak.

We show that providing farmers with an auxiliary rule of thumb related to the water
<!-- Start of picture text --> oO<br>N<br>Ss<br>Oo<br>xe}<br>€ Post-feedback<br>=E oe P-valueRegressionof coefficentcoefficent:= 1:1.7270.023<br>Cc<br>‘©<br>—<br>va<br>[o)<br>~ O<br>or<br>Cc<br>><br>ad<br>aaa<br>= Ww =_--" =<br>o _e-<br>> _--7 Pre-feedback<br>Oo =_--" Regression coefficent: 0.451<br>© -e--7 = P-value of coefficent = 1: 0.031<br>w =_--" --7<br>SO 4-7<br>0 2 4 6 8<br>Water requirement (mm/day)<br>——— Pre-feedback ——— Post-feedback<br>Pre-feedback 95%Cl Post-feedback 95%Cl<br><!-- End of picture text -->
_38_

## _WATER WHEN IT COUNTS_

in Mozambique. While generalized recommendations appear sufficient to reduce water scarcity in ways that are consistent with improved scheduling rules being the mechanism, we do not find that these improvements are associated with significantly higher revenue for farmers. Continued water monitoring will reveal whether profits may improve as farmers better optimize their cropping strategies to reflect the newly more efficient allocations of water. What we learn from this experiment is that information campaigns may be instrumental in achieving water savings over periodic dry spells.

# **REFERENCES**

- **Aker, Jenny C.** 2011. “Dial A for agriculture: a review of information and communication technologies for agricultural extension in developing countries.” _Agricultural Economics_ , 42(6): 631–647.

- **Aker, Jenny C., Ishita Ghosh, and Jenna Burrell.** 2016. “The promise (and pitfalls) of ICT for agriculture initiatives.” _Agricultural Economics_ , 47(S1): 35–48.

- **Alcott, H., and T. Rogers.** 2014. “The Short-Run and Long-Run Effects of Behavioral Interventions: ExExperiment Evidence from Energy Conservation.” _American Economic Review_ , 104(10): 3003–3037.

- **Allen, R., L. Pereira, D. Raes, and M. Smith.** 1998. “Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements.” Food and Agriculture Organization of the United Nations FAO Irrigation and Drainage Paper 56, Rome.

- **Beaman, L., A. BenYishay, J. Magruder, and A. M. Mobarak.** 2015. “Can Network Theory-based Targeting Increase Technology Adoption?” University of California, Berkeley Working Paper.

- **BenYishay, A., and A. M. Mobarak.** Forthcoming. “Social Learning and Incentives for
_39_ _WATER WHEN IT COUNTS_ Experimentation and Communication.” _Review of Economic Studies_ .

- **Braca, Giovanni.** 2008. “Stage-discharge relationships in open channels: Practices and problems.” _FORALPS Technical Report, 11_ , , (1): 44.

- **Buytaert, W., and et al.** 2014. “Citizen Science in Hydrology and Water Resources: Opportunities for Knowledge Generation, Ecosystem Service Management, and Sustainable Development.” _Frontiers in Earth Science_ , 2.

- **Climate Prediction Center, National Centers for Environmental Prediction, National Weather Service NOAA U.S. Department of Commerce.** 2015. “NOAA NCEP CPC CMORPH daily mean morphed cmorph: estimated precipitation data from 23 Feb 2005 to present.”

- **Cole, S., and A. N. Fernando.** 2016. “‘Mobile’izing Agricultural Advice: Technology Adoption, Diffusion and Sustainability.” Notre Dame University Working Paper.

- **Deichmann, Uwe, Aparajita Goyal, and Deepak Mishra.** 2016. “Will digital technologies transform agriculture in developing countries?” _Agricultural Economics_ , 47(S1): 21–33.

- **Doorenbos, J., and A. Kassam.** 1979. “Yield Response to Water.” Food and Agriculture Organization of the United Nations FAO Irrigation and Drainage Paper 33, Rome.

- **FAO.** 201. “Cropwat 8.0.”

**FAO.** 2016. “Aquastat: Mozambique.”

- **Ferraro, P., and M. Price.** 2013. “Using Nonpecuniary Strategies to Influence Behavior: Evidence from a Large-Scale Field Experiment.” _Review of Economics and Statistics_ , 95(1): 64–73.

- **Fraternali, P., A. Castelletti, R. Soncini-Sessa, C. Ruiz, and A. Rizzoli.** 2012. “Putting Humans in the Loop: Social Computing for Water Resources Management.” _Environ-_
_40_

# _WATER WHEN IT COUNTS_

_mental Modelling and Software_ , 37(C): 68–77.

- **Global Facility for Disaster Reduction and Recovery (GFDRR).** 2017. “Country Profile: Mozambique.” _Accessed online February 9, 2017 at: https://www.gfdrr.org/sites/gfdrr/files/region/MZ.pdf_ .

- **Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein.** 2014. “Learning through noticing: Theory and evidence from a field experiment.” _The Quarterly Journal of Economics_ , 129(3): 1311–1353.

- **Kondylis, F., V. Mueller, and J. Zhu.** 2017. “Seeing is Believing? Evidence from an Extension Network Experiment.” _Journal of Development Economics_ , 125: 1–20.

- **Little, K., M. Hayashi, and S. Liang.** 2016. “Community-Based Groundwater Monitoring Network Using a Citizen-Science Approach.” _Groundwater_ , 54(3): 317–324.

- **Lowry, C., and N. Fienen.** 2013. “CrowdHydrology: Crowdsourcing Hydrologic Data and Engaging Citizen Scientists’.” _Groundwater_ , 51(1): 151–156.

- **Meinzen-Dick, R.** 2007. “Beyond Panaceas in Water Institutions.” _Proceedings of the National Academy of Sciences_ , 104(39): 15200–15205.

- **Ostrom, E.** 1990. _Governing the Commons: The Evolution of Institutions for Collective Action._ Cambridge:Cambridge University Press.

- **Ostrom, E., and E. Schlager.** 1992. “Property-Rights Regimes and Natural Resources: A Conceptual Analysis.” _Land Economics_ , 68(3): 249–262.

- **Plusquellec, H.** 2002. “Is the Daunting Challenge of Irrigation Achievable?” _Irrigation and Drainage_ , 51(3): 185–198.

- **Varzi, M. M.** 2016. “Crop Water Production Functions–A Review of Available Mathematical Method.” _Journal of Agricultural Science_ , 8(4): 76–83.

- **Vidal, John.** 2016. “As Mozambique’s Rivers Dry Up, the Hopes of a Harvest Evaporate
_41_ _WATER WHEN IT COUNTS_ Too.” _The Guardian_ .

- **World Bank.** 2007. “Mozambique Country Water Resources Assistance Strategy: Making Water Work for Sustainable Growth and Poverty Reduction.” 56855, World Bank Group.

- **World Bank.** 2010. “Mozambique Public Expenditure Review for the Water Sector.” 54692-MZ, World Bank Group.

- **World Bank.** 2011. “Mozambique: Analysis of Public Expenditure in Agriculture.” 59918-MZ, World Bank Group.