Automated Assignment and Bureaucrat Productivity: Evidence from a Field Experiment in Peru

Last registered on June 03, 2026

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Pre-Trial

Trial Information

General Information

Title

Automated Assignment and Bureaucrat Productivity: Evidence from a Field Experiment in Peru

RCT ID

AEARCTR-0018756

Initial registration date

May 25, 2026

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

June 03, 2026, 8:30 AM EDT

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

Locations

Country

Peru

Region

Primary Investigator

Name

Alipio Ferreira

Affiliation

Southern Methodist University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Sandra Cermeno Leon

PI Affiliation

Innovations for Poverty Action

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2023-08-01

End date

2024-04-01

Keywords

Environment & Energy, Firms & Productivity, Governance

Additional Keywords

algorithm, bureaucratic productivity, managerial discretion, environmental enforcement

JEL code(s)

D73, H83, Q58, M11, C93

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Governments increasingly use algorithmic tools to manage bureaucratic production, but these tools differ in whether they replace or support human discretion. We study this distinction in a pre-registered field experiment with Peru's national environmental enforcement agency. Analysts were randomly assigned to receive enforcement case lists generated either by managers or by an optimization algorithm. Independently, suggested completion dates were randomized at the case level. Replacing managerial assignment with algorithmic assignment reduced timely case completion by approximately 5 percentage points relative to a control mean of 40 percent, with no effects on downstream enforcement outcomes such as fines, appeal rates, or appeal outcomes. In contrast, suggested completion dates increased timely completion by approximately 4 percentage points, with effects driven by analysts with discretionary case assignment. We provide evidence that managers use discretion to match complex cases to experienced analysts, and the automated assignment reduces productivity by breaking this link.

External Link(s)

Registration Citation

Citation

Cermeno Leon, Sandra and Alipio Ferreira. 2026. "Automated Assignment and Bureaucrat Productivity: Evidence from a Field Experiment in Peru." AEA RCT Registry. June 03. https://doi.org/10.1257/rct.18756-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

This paper evaluates two distinct automation treatments, implemented as a cross-randomized field experiment within OEFA's sanctioning unit. The first, which we call hard automation, replaces managerial discretion in case assignment: instead of team leaders allocating cases to analysts, an optimization algorithm generates the assignment list. The second, which we call soft automation, preserves the assignment decision but structures the information available to analysts: the algorithm produces suggested milestone dates for completing each procedural stage, displayed for a randomly selected subset of cases. These two treatments differ fundamentally in the degree to which they displace human judgment. Hard automation eliminates the team leader's role in matching cases to workers. Soft automation leaves that matching unchanged while providing analysts with scheduling information intended to help them organize their work.

Intervention (Hidden)

Intervention Start Date

2023-08-01

Intervention End Date

2023-12-31

Primary Outcomes

Primary Outcomes (end points)

Probability of concluding a case before December 31st (and alternative dates)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Number of days spent on a case, probability of starting a case, fines and penalties, probability of appeals, number of infractions, number of appeals, change in fine after appeal

Secondary Outcomes (explanation)

Experimental Design

The hard automation system generated case allocations using codified information on case characteristics and expected processing times. Relative to discretionary allocation, the algorithmic approach is transparent, generates a more even distribution of workload across workers, and is intended to reduce bias arising from poor managerial decisions. In contrast, discretionary assignment solves the same objective function as the algorithm but also incorporates soft information about worker-case fit, such as sector familiarity, writing quality, and reliability under time pressure. Both selection methods aim at maximizing output and preventing expiration risk by assigning cases under imperfect information about worker capacity and case complexity, but they differ in how they leverage that information. It is worth noting that the hard automation treatment is bundled: it simultaneously removes managerial judgment from case matching, and imposes workload equalization. The experiment therefore identifies the joint effect of these components rather than the effect of any single feature in isolation. However, we try to disentangle these two components in the mechanism analysis by providing evidence on how managers use discretion to match cases to workers.

Besides assigning cases to workers, the algorithm also suggested completion dates. This additional information could act as a nudge by helping analysts organize their work, regardless of how their cases were assigned. To assess the role of these dates, we randomized their display at the case level for 35% of control-group cases and for 50% of the cases handled by 22 randomly selected treated analysts. The remaining 34 treated analysts saw suggested dates for all of their cases. All suggested dates for conclusion lie before December 31, 2023, reflecting OEFA's intention to execute all the assigned cases in the same year.

Experimental Design Details

Randomization Method

Randomization done in office by a computer.

Randomization Unit

For the first randomization (hard automation): analyst level.
For the second randomization (nudge/soft automation): case level.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Treatment was clustered at two levels. The hard automation treatment (algorithmic vs. managerial case assignment) was randomized at the analyst level (95 analysts), with analysts stratified by economic sector (8 strata). The soft automation treatment (suggested completion dates) was randomized at the case level, within analyst lists. For treated analysts, a random subset of 50% of cases displayed suggested dates; for control analysts, a random 35% of cases displayed suggested dates; a randomly selected group of treated analysts had 100% of their cases display suggested dates.

Sample size: planned number of observations

1405 cases.

Sample size (or number of clusters) by treatment arms

Intervention 1 (hard automation)
Control: 572 cases with 35 analysts.
Treatment: 833 cases with 56 analysts.

Intervention 2:
Cases with no suggested date: 530
Cases with suggested date: 875

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB