Evaluation of Brazil's Agricultural Cisterns and Technical Assistance P1+2 Program

Last registered on April 17, 2019


Trial Information

General Information

Evaluation of Brazil's Agricultural Cisterns and Technical Assistance P1+2 Program
Initial registration date
April 15, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
April 17, 2019, 8:30 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


Primary Investigator


Other Primary Investigator(s)

PI Affiliation
University of Mannheim

Additional Trial Information

On going
Start date
End date
Secondary IDs
The semi-arid region of Northeastern Brazil is characterized by high levels of water and food insecurity as well as gender inequality. Frequent and extended droughts threaten the livelihoods of rural households. To address this, local NGO ASA with financial backing from the Federal Brazilian government has targeted the area with several social programs, most recently the expansion of the Agricultural Cisterns program known as P1+2. The P1+2 program seeks to increase the resilience of poor households to climate shocks by providing them with water storage capacity (a large cistern), a plan to adapt their production strategies (technical assistance), and cash to implement that plan. Moreover, the program seeks to promote female empowerment by reducing the intra-household imbalance of incomes and decision-making. The latter goal is addressed by placing the cistern in the part of agricultural land that is traditionally under the female’s control (the plot of land next to the main residence).

The new rainwater fed cisterns have capacity to store 52,000 liters, substantially improving water supply for irrigation of small crops and livestock during the dry season. This research project will use a cluster-randomized controlled trial to evaluate an addition of 2,000 households to the program, distributed across 63 municipalities in nine Northeastern Brazilian states to measure the causal effect of P1+2 on a series of indicators of climate resilience, female empowerment, and well-being.

Beyond evaluating a large-scale social program with high transformative potential, the research project aims to extract lessons which can be generalized to other contexts in which climate vulnerability and gender imbalances are pervasive.
External Link(s)

Registration Citation

Gonzalez-Navarro, Marco and dimitri szerman. 2019. "Evaluation of Brazil's Agricultural Cisterns and Technical Assistance P1+2 Program." AEA RCT Registry. April 17. https://doi.org/10.1257/rct.4109-1.0
Former Citation
Gonzalez-Navarro, Marco and dimitri szerman. 2019. "Evaluation of Brazil's Agricultural Cisterns and Technical Assistance P1+2 Program." AEA RCT Registry. April 17. https://www.socialscienceregistry.org/trials/4109/history/45158
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Experimental Details


The intervention consists of the delivery of a rainwater cistern for farm production free of charge coupled with rural extension services and a cash grant to the household.

This expansion of P1+2 will directly benefit about 2,000 vulnerable families living in 63 municipalities of Northeast Brazil. With a budget of R$100 million (US$ 32 million) to finance the construction of cisterns and extension services , the project will be implemented by a network of 34 local NGOs coordinated by ASA. Each family will receive a 52,000-liter rain-fed cistern, a cash grant of R$ 1,500 or R$3,000, and technical assistance services that will enable them to improve their production systems. As part of the cisterns component, participant families will also receive training in water management, irrigation techniques, and sustainable agricultural practices.

The intervention aims to address intra-household gender imbalances by having the cistern built in a portion of the farm that is typically managed by women – the quintal, or yard. The quintal is a plot that is typically used to raise small animals (poultry), grow orchards, herbs, and vegetables. The goal is to benefit the production that is managed by women, so that income generated by them raises more than that generated by men.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Farm production, income, and profits
2. Total family consumption (durables and non-durables)
3. Total family income (cash and cash plus self-consumption valued at market prices)
4. Food security
5. Female empowerment
Primary Outcomes (explanation)
1. Farm production is measured by physical quantities produced over a given time period. For example, kilograms of corn over the growing season. Farm income is measured as the value of output. For example, the price of corn times the quantity of corn produced. Profits is farm income minus expenditures on inputs used in the productive process. Expenditures include items such as seeds, fertilizer, and labor. Family labor is valued at market prices.

2. Total family consumption is composed of consumption of durables (existence of a suite of items such as refrigerator, TV, vehicles, air conditioning, etc.). Consumption of nondurables is taken over a certain period, and main items include: Expenditures on food, transportation, electricity, fuel, housing, and leisure.

3. Total family income is measured by summing up cash incomes across sources and family members. We also an alternative income variable that adds to the cash sources all self-consumption production valued at market prices.

4. Food security is measured using a Brazilian version of the FAO Household Food Insecurity Access Scale (HFIAS).

5. Female empowerment is measured using a locally adapted version of IFPRI’s Women’s Empowerment in Agriculture Index (WEAI)

Secondary Outcomes

Secondary Outcomes (end points)
1. Farm investment
2. Marketable production
3. Labor income
4. Propensity to migrate
Secondary Outcomes (explanation)
1. Farm investment is measured by changes in an index of total assets used in agricultural production and marketing -- e.g., machines, physical structures such as fencing and irrigation equipment, vehicles, greenhouses, etc.
2. Marketable production is the difference between total farm production (see primary outcomes) and physical quantities consumed within the households. Alternatively, we can measure marketable production as the production that is traded in markets.
3. Labor income: income earned by household members in work outside of the household.
4. Propensity to migrate is measured by the number of household members who emigrate to urban areas, or to other rural areas in search of work within a specified period of time.

Experimental Design

Experimental Design
Research design:
This trial employs a cluster-randomized design, where the unit of analysis is a household, and the cluster is a village. The intervention (henceforth the “treatment”) is the delivery of a rainwater cistern coupled with rural extension services and a cash grant of either R$ 1,500 or R$ 3,000 to the household. The choice between the two cash grant levels depends on the household’s poverty profile and the intervention’s budget.

Selecting Treatment and Control Villages
In each of the 63 municipalities selected by ASA for the project implementation, a Municipal Committee (MC) formed by local leaders selects between 6 and 12 villages that will enter a lottery. Typically, MCs select the neediest villages in the municipality – those with higher rates of poverty, lowest water availability, etc. Selected villages enter a lottery that randomly allocates three villages to control status and the rest into treatment. No households in control villages will receive P1+2 for the next two years, at least. The lottery is done electronically through a purpose-built website (http://impactocisternas.org) during a meeting organized by the member of the implementing agency, who was previously trained to use the website and explain the procedure to the members of the MC.

The completion of the 63 lotteries yielded a sample frame of 565 villages, split in control (N = 171) and treatment (N = 394) villages.

Eligibility of Households
To be eligible, households must meet the following criteria:

1) Be a beneficiary of the Primeira Agua program, which delivered a 16,000-liter cistern for human consumption.

2) Be enrolled in Cadastro Unico, a national registry of vulnerable households, maintained by MDS. Cadastro Unico is the main gateway for vulnerable households to enter many of the federal government’s social policies, such as Bolsa Família. It contains detailed information on household composition and demographics, income, dwelling characteristics, location, etc.

Selecting Eligible Households

1) MDS develops a list of all eligible households in the 63 municipalities, using data from the Cadastro Unico joined with the list of past beneficiaries of the Primeira Agua program.

2) The research team then randomly chooses and ranks 20 households and provides this list to the implementer, who must go down the list in that order until six households receive the treatment. The list has up to 20 household names to allow for substitution of households that cannot be reached (e.g., in cases of death or migration) due to outdated Cadunico records.

The research team can then apply steps 1 and 2 to select comparable households in control villages to be interviewed (see below for details on data collection), thus avoiding differential selection of households in control and treatment villages.

Selection of Individuals Within Households

To increase the ability to test for program effects on intra-household bargaining power, the research design introduces intra-household variation in the assignment of cisterns to the man or woman. Although the unit of analysis is a household, treatment is awarded to one individual within the household. This is the individual that receives training and signs documents under his or her name.

The research design introduces intra-household gender variation in the assignment of treatment using Cadastro Unico’s list of household members. The research team randomly allocates the program to the man or woman identified as the household head or his/her spouse. This works only as a nudge since implementers are free to enroll anyone in the household into treatment. Whether or not this is nudge is powerful enough to create intra-household variation, is a question that will have to wait until implementers finish enrolling households into the program. If powerful, it will provide additional variation in treatment assignment, which can be used to test the hypotheses that P1+2 attenuates intra-household gender imbalances, identifies factors that may prevent or promote effects on reducing gender inequality, and the trade-offs (if any) between the goals of improving climate adaptation and reducing gender imbalances.

Data collection:
This research project comprises two waves of primary data collection using household survey instruments developed by the research team. In each wave, 2,930 households from treatment and control villages will be interviewed. MDS held a public tender process to hire a specialized firm to administer the survey instruments. The first wave (baseline) took place between July and September of 2018. The second wave (endline) will reinterview the same households 18 months after the treatment is delivered.

There are two survey instruments. The first must be answered by the person who spends more time in rural activities within the household and contains questions on land use and crop choices; water usage; income; labor, farm production; technology use; household consumption; food security; and subjective well-being. The second must be answered by the female (head of household or spouse) and covers topics on female autonomy, social capital, and decision-making power.

Finally, we plan to use automated response mobile calls to collect high-frequency data on consumption patterns and food security. This is a cost-effective way to collect data on issues such as food consumption and reactions to weather shocks, which require high-frequency monitoring. Up to 85% of the target households use mobile phones, and cellphone coverage in the region is good.

Proposed data analysis:
To estimate the impact of the cisterns on outcome y (e.g., income, indexes of women empowerment, crop diversification, etc.), we will estimate the following population regression equation by ordinary least squares (OLS):

y_icmt=α_m+βT_icm+λy_(icmt-1)+ ϵ_icmt (1)

Where T_icmt takes the value of one if family i lives in a treatment village and zero otherwise, α_m is a municipality fixed-effect, θ_t is a time fixed-effect, and ϵ_icmt is the idiosyncratic error term. Standard errors will be clustered at the village level, c.

To test for effects of giving treatment to women vs. men on gender issues (bargaining power, autonomy, and resource allocation), we will estimate the following regression model:

y_icmt=κ_m+ϕT_icm+γT_icm×W_imct+ωy_(icmt-1)+ ξ_icmt (2)

where W_imct indicates that a woman was the individual assigned to receive treatment. In case this indicator does not follow the random assignment, we can still estimate intention-to-treat effects and local average treatment effects by applying an instrumental variables approach, using variation from the intra-household random assignment.
Experimental Design Details
Randomization Method
After the Municipal Committee selects between 6 and 12 villages, these villages enter a lottery that randomly allocates three villages to control status and the rest into treatment. The lottery is done electronically through a purpose-built website (http://impactocisternas.org).

The research team then randomly chooses six beneficiaries in each treatment village and provides this list of six households to the implementer. The actual list contains up to 20 households, to allow for substitution of households that cannot be reached (e.g., in cases of death or migration) or do not wish to participate in the program. The implementer proceeds numerically down the list until the six households receive the treatment.

The research team then randomly allocates the program to the man or woman identified as the household head or his/her spouse.

All the randomizations are done on a computer by the PIs.
Randomization Unit
Randomization first occurs at the village level and is then followed by randomizing individual households within the selected villages.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
565 clusters (villages)
Sample size: planned number of observations
996 households will be in control, with 1934 households receiving treatment.
Sample size (or number of clusters) by treatment arms
171 villages allocated into control and 394 villages allocated to treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We estimate the MDEs using a log transformation and a transformation in the form of log(x+1) for variables 1 to 3. The following minimum detectable sizes are calculated using a power of 0.8 and alpha of 0.05. For the log(x) transformations: 1) Log of Total farm income [income from land rents + total sales revenue]: MDE (in logarithmic points) : 0.553 | Percentage: 7.8% of the untreated mean | Mean: 7.129 | SD: 2.867 2) Log of Total Family Expenses: MDE (in logarithmic points): 0.109| Percentage: 1.7% of the untreated mean | Mean: 6.372 | SD: 0.835 3) Log of Total Family Income: MDE (in logarithmic points): 0.424 | Percentage: 6.1% of the untreated mean | Mean: 6.925 | SD: 2.665 For log(x+1) transformations: 1) Log(Total farm income + 1): MDE (in logarithmic points): 0.748 | Percentage: 17.7% of the untreated mean | Mean: 4.23 | SD: 5.14 2) Log(Total Family Expenses+1): MDE (in logarithmic points) : 0.109 | Percentage: 1.7% of the untreated mean | Mean: 6.374 | SD: 0.834 3) Log(Total Family Income+1): MDE (in logarithmic points) : 0.76 | Percentage: 12.6% of the untreated mean | Mean: 6.008 | SD: 4.263 Variable 4 has not been transformed in any way: 4) Food Security Index - standardized by the control: MDE: 0.2099 | Mean: 0.003 | SD: 1.182

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