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Haqdarshak: Leveraging Technology to Promote Citizen Access to the State
Last registered on July 03, 2018


Trial Information
General Information
Haqdarshak: Leveraging Technology to Promote Citizen Access to the State
Initial registration date
June 29, 2018
Last updated
July 03, 2018 8:03 PM EDT
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
World Bank
PI Affiliation
PI Affiliation
Additional Trial Information
On going
Start date
End date
Secondary IDs
While the Indian government administers many welfare programs for the poor, program coverage is often low as eligible households lack the knowledge and the means to navigate the complex and costly application processes. In this evaluation, we investigate the potential of leveraging technology and private-public partnerships to improve access to state goods and services. Our interventions will build on an existing model called Haqdarshak, which comprises of paid door-to-door services offered by a community level facilitator to citizens to inform them of the programs they are eligible for and help them apply for these programs. We will offer this services to 10,000 vulnerable households in rural villages of Rajasthan.

In particular, we will randomize (1) the price of the services, (2) messages warning against the provision of false information in screening and application and (3) facilitator type. Key research questions of the study include: whether a non-state, private actor can play a sustainable role in promoting awareness of and access to government programs; how do citizens engage with this type of intermediary and what is the demand for the different types of services offered; and does the magnitude and composition of beneficiaries change using this model by bringing more vulnerable groups into state systems.
External Link(s)
Registration Citation
Bussell, Jennifer et al. 2018. "Haqdarshak: Leveraging Technology to Promote Citizen Access to the State." AEA RCT Registry. July 03. https://doi.org/10.1257/rct.3114-1.0.
Former Citation
Bussell, Jennifer et al. 2018. "Haqdarshak: Leveraging Technology to Promote Citizen Access to the State." AEA RCT Registry. July 03. http://www.socialscienceregistry.org/trials/3114/history/31457.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Take-up for screening service; take-up for assistance to apply to different programs; heterogeneous by income level
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Process in applying for each program, e.g. difficulty of forms, wait times, number of government visits, quality of interactions with state actors, other intermediaries use, etc; ultimate receipt of programs
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will randomly vary the price for screening and take-up services.
Experimental Design Details
- We will first collect a household listing and baseline survey. We will randomly assign 850 of those listed to the pure control group, and about 10,000 to the treatment groups. - Screening Experiments (first round randomization): The facilitators will approach the treatment households to offer them the service of being screened to determine their program eligibility., at different randomly selected price points. If an individual chooses to be screened, the facilitator will then conduct the screening and inform them of the services that they are eligible for using the app, as well as provide them with some basic information about these services. Next, we will conduct the assistance experiment with those who had chosen to be screened and were eligible for at least one service. - Assistance Experiments: For the individuals who take up the screening, we will randomize the prices of getting assistance to help apply for each service that they are eligible for within the mobile app. Screening and assistant offers are also made to other individuals in the household. For analysis: - We will then examine not just take-up of the assistance (and ultimately of each social program), but we will also examine what types of individuals take-up each service at each of the different price points. For example, we can see whether the price screens in those who need it the most or screens out the most vulnerable women and children.
Randomization Method
Randomization done within the mobile application
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
10,850 households
Sample size: planned number of observations
10,850 households
Sample size (or number of clusters) by treatment arms
- For the screening experiment: 3332 full subsidy, 3334 half subsidy, 3334 no subsidy
- For the assistance experiment: 333 no subsidy, 333 half subsidy, 333 full subsidy. This sample is derived from the screening experiment sample and may change based on takeup rates.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
MIT Committee on the Use of Humans as Experimental Subjects (COUHES)
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)