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Understanding Measurement Error in Agriculture
Last registered on June 15, 2020


Trial Information
General Information
Understanding Measurement Error in Agriculture
Initial registration date
Not yet registered
Last updated
June 15, 2020 2:54 AM EDT
Primary Investigator
Gothenburg University
Other Primary Investigator(s)
PI Affiliation
The University of Melbourne
Additional Trial Information
On going
Start date
End date
Secondary IDs
While measurement of agricultural production is critical to empirical development analyses and answering fundamental questions on the role of agriculture in household and individual welfare, there is a lack of understanding about the accuracy of farmer reported crop production and area measurement. In this project, we conduct a large, state-representative survey in Odisha, India containing self-reported and objective measures of agricultural variables to examine the extent of misreporting and measurement error in agricultural area, production, and yield. Using rich data on farmer characteristics, their psychological state, and personality traits, we explore whether misreporting and measurement error are systematically related to these characteristics. Finally, we conduct priming experiments to test if measurement error is affected by incentives to misreport.
External Link(s)
Registration Citation
Baranov, Victoria and Joe Vecci. 2020. "Understanding Measurement Error in Agriculture." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.5760-1.1.
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Experimental Details
The project consists of priming experiments that aim to test the extent to which measurement error in farmer reported production/area/yield is due to intentional misreporting.
Experiment 1: Financial Incentives.
The lower bound treatment group prior to asking questions related to agricultural activity were read the following preface:“We will now ask you some questions about your land holdings and agricultural production. This information will remain anonymous and it will not influence your chances of receiving benefits from any organisation including the government or IRRI.”

The upper bound treatment group were told the following preface.“We will now ask you some questions about your land holding and agricultural production. This information may influence your chances of receiving benefits from the government and organisations including IRRI in the future.” This did not involve deception as we will randomly select 25% of these households to receive a cash grant to be distributed between 2 and 6 months after the endline.

The control were not given any preface prior to eliciting agricultural activity.

Experiment 2: Lying (misreporting for any reason)
In this experiment subjects are given a prime prior to eliciting agricultural information.

In the honest prime treatment prior to eliciting agricultural information, farmers are given a list of words that are related to honesty such as truth and integrity, subjects must then list synonyms related to each of the listed words.

In the neutral prime treatment the word list is unrelated to honesty such as home and music.

A separate control contains no prime.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Are key outcomes are the following:
1. Measurement error in production: self-reported production based of the crop cut plot minus the plot production derived from the crop cut experiment.
2. Measurement error in area: self-reported area of the crop cut plot minus the area for the crop cut plot taken from GPS measures
3. Measurement error in yield: Calculated based on self-reported production and area, yield=production/area, minus yield calculated using the above mentioned objective measures of production and area.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The study was conducted in Odisha, India. The sample was selected as follows. First, we randomly selected 15 out of the 30 districts in the state. We then randomly selected 300 villages, from which we undertook a full village census. From the census we randomly selected 10 farmers in each village combining for a total sample of 3000 households prior to harvest. The sample is representative of farmers in the state.

Prior to harvest (Nov-Dec, 2019), trained enumerators conducted crop cutting of the largest plot of each farmer. Crop cutting (CC) has been recognised as the gold standard for objective agricultural production measurement since the 1950s by the Food and Agriculture Organization of the United Nations (FAO). At this time, enumerators also objectively measured plot area. In particular, a polygon of the plot is drawn using a GPS device by walking along the boundary of the plot, using geospatial software a complete polygon of the plot is then created.

After harvest (February-June), enumerators return to households and elicit farmer self reported measures of production, area and yield of the largest plot.
Experimental Design Details
Not available
Randomization Method
Randomisation took place via computer and was conducted by the International Rice Research Institute
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We expect a sample size of 2400 participants after subjects drop out in part due to crop failure and migration
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Experiment 1
Lower Bound: 540
Upward Bound: 420

Experiment 2:
Honesty Prime: 540
Neutral Prime: 540

Pure Control: 650
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We expect a sample size of 2400 participants after subjects drop out in part due to lack of harvest, crop failure or migration. Based on the sample size, we should be able to detect a minimum effect size of 0.163-0.176 SD with 80% power at 5% significance for the four main treatments (upward bias (0.163), downward bias (0.176), honesty prime (0.163), neutral prime (0.210)). We expect that neutral prime would not be different to control, but it was included as an “active” control to ensure that the simple act of reading word lists does not impact reporting. If this is confirmed in the data that reporting under the neutral prime and control is the same for all three measures (using the difference in means), then we will pool the neutral prime with the control.
IRB Name
International Rice Research Insitute Research Ethics Committee
IRB Approval Date
IRB Approval Number