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Field
Trial Title
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Before
The Hidden Costs of Poverty:
Mental Load and Technology Learning
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After
Financial Distress and the Cost of Learning:
Evidence from a Field Experiment
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Abstract
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Before
Many economically valuable technologies require upfront learning investments, yet adoption decisions are often made under conditions that distort the ability to undertake such investments. This paper studies whether financial distress reduces technology adoption by limiting the cognitive resources required to make such learning investments. We implement a field experiment with workers during the scarce season in rural Haiti. Workers are randomly assigned to receive part of their expected earnings before the workday or to receive all compensation afterward. At the worksite, workers choose between an immediately productive manual task and a higher-return technology that yields earnings only after an upfront learning stage. This design allows us to distinguish between the decision to attempt the technology and successful completion of the learning process conditional on attempting.
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After
This randomized field experiment studies whether the timing of payment for previously earned wages affects workers’ willingness to enter and persist in a learning-intensive production task. The study takes place in a maize-processing firm in rural Haiti. Participants first complete a paid manual corn-shelling task and earn a fixed payment. The experiment then randomizes whether those previously earned wages are paid immediately or deferred until after the next work session, holding total compensation fixed. In the next session, participants receive standardized instruction on a higher-throughput mechanical sheller that requires self-assembly and then face production choices involving the familiar manual method and the mechanical sheller. Within the firm’s real production need for both manual- and machine-shelled corn, the design also randomizes the initial production method, allowing the study to distinguish initial entry into the learning-intensive technology from subsequent switching, escape from the familiar low-productivity task, and retention on the unfamiliar technology. The primary outcomes are initial choice of the mechanical sheller and post-assignment machine use. The unit of randomization is the individual worker. The planned sample is 400 eligible workers recruited from the firm’s labor pool. Treatment assignment is implemented using computer-generated individual-level randomization, stratified by work session.
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Trial Start Date
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Before
July 01, 2026
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After
June 19, 2026
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Last Published
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Before
April 29, 2026 03:48 PM
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After
June 19, 2026 02:05 PM
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Primary Outcomes (End Points)
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Before
H1 (confirmatory, primary): D_i — binary indicator equal to 1 if the worker chooses the mechanical corn sheller (machine adoption attempt). Tested at alpha = 0.05, Step 1 of fixed-sequence Holm-Bonferroni procedure.
H2 (confirmatory, secondary): D*_i — binary indicator equal to 1 if the worker both chooses the machine (D_i = 1) and successfully completes assembly without abandoning (A_i = 0). This is the composite successful adoption indicator. Tested at alpha = 0.05, Step 2 of fixed-sequence Holm-Bonferroni, conditional on H1 being confirmed.
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After
Primary outcome 1: Initial entry into the learning-intensive technology. This is an indicator equal to one if the participant chooses the mechanical sheller rather than manual shelling at the initial task-choice point in the second work session.
Primary outcome 2: Post-assignment machine use. This is an indicator equal to one if the participant uses or chooses to continue using the mechanical sheller at the pre-specified post-assignment choice point after randomized initial production-method assignment. This outcome will also be decomposed by randomized starting method: switching from manual start to machine use, and retention among participants randomly assigned to start with the machine.
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Primary Outcomes (Explanation)
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Before
D_i is recorded by a trained enumerator at the technology choice station on the session day. Workers indicate their choice on a private paper form; enumerators record the choice and the time. D*_i is the product of D_i and (1 - A_i), where A_i is the abandonment indicator. A_i = 1 if the worker chose the machine but stopped the assembly process before completion and switched to manual shelling. Assembly completion is confirmed by the enumerator upon successful operation of the machine. The confirmatory analysis uses a probit regression specification: D_i = alpha_0 + alpha_1*Cash_i + alpha_2'X_i + error, where Cash_i = 1 for cash-relief workers. Confirmation of H1 requires alpha_1 > 0 at alpha = 0.05. Complementary logistic regression and chi-square tests of proportions are also reported. H2 mirrors this specification with D*_i as the outcome.
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After
Initial entry into the learning-intensive technology is measured after standardized instruction on the mechanical sheller and before the randomized initial production-method assignment is implemented. The variable equals one if the participant chooses the mechanical sheller and zero if the participant chooses manual shelling. For the intent-to-treat analysis over all randomized participants, participants who do not attend the second session will be coded as not entering the mechanical task, with attrition and bounding analyses reported separately.
Post-assignment machine use is measured after the participant has worked under the randomly assigned initial production method and reaches the pre-specified opportunity to continue or switch. The variable equals one if the participant is using or chooses the mechanical sheller at that point and zero otherwise. Because initial production method is randomized, this outcome allows separate estimation of two margins: escape from the familiar manual method among participants randomly assigned to start manually, and retention on the unfamiliar technology among participants randomly assigned to start mechanically.
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Experimental Design (Public)
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Before
Two-arm between-subject randomized controlled trial. Workers are randomized at the individual level to one of two arms with equal allocation (1:1 ratio). No clustering; randomization is by individual. All sessions take place during the lean season (April-May 2027) in rural northern Haiti, ensuring that confounds of seasonal comparison designs (temperature, agricultural labor demand, nutritional stock, worker composition) are held constant by construction.
Sample size: 400 total (200 per arm). Planned attrition rate: 15%. Required sample per arm for 80% power at the primary moderate effect (delta-p = 0.15, p_C = 0.30, p_T = 0.45, alpha = 0.05) is 163; 200 per arm provides a buffer. Sessions take place in groups of 20-25 workers at the firm's premises.
Eligibility: Adults aged 18-65 residing in study communities, primarily dependent on farming or farm-labor income, physically capable of four hours of manual labor, and available for a full-day session. Screening occurs at least one week before the session during a routine pre-employment visit.
Treatment assignment: Individual-level randomization using computer-generated assignment. Workers are informed of neither the study nor their arm assignment. The session is delivered by the company supervisor under normal operating conditions.
Multiple testing: Confirmatory hypotheses H1 and H2 are tested using a fixed-sequence Holm-Bonferroni procedure in the order H1 -> H2. H1 is tested at alpha = 0.05 (unadjusted). H2 is tested at alpha = 0.05 conditional on H1 being rejected. H3 is pre-classified as exploratory with no rejection threshold.
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After
This study is a randomized field experiment conducted with workers in a maize-processing firm in rural Haiti. The study examines whether earlier access to previously earned wages changes willingness to adopt a more productive but unfamiliar production technology. The experiment has two randomized components. First, after all participants complete an initial paid manual shelling task, participants are individually randomized to receive the wages earned in that task either immediately or after the next work session. Total compensation is held constant. Second, during the next work session, after standardized instruction on the mechanical sheller and measurement of initial task choice, participants are individually randomized to an initial production method, manual shelling or mechanical shelling, subject to the firm’s real production need for both types of output.
The design therefore creates a 2 by 2 structure: immediate payment plus manual start; immediate payment plus machine start; deferred payment plus manual start; deferred payment plus machine start. The first randomization identifies the effect of earned-wage payment timing on technology entry and post-assignment machine use. The second randomization identifies path dependence in production method by separating the effect of starting with a familiar low-productivity method from starting with the unfamiliar higher-throughput method. The primary sample consists of eligible workers recruited from the firm’s labor pool who complete the first paid work session and are randomized to payment timing. The planned sample is 300 workers. Enrollment will occur through work sessions organized with the firm. Randomization will be implemented by the research team using a computer-generated assignment list, stratified by work session.
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Randomization Method
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Before
Individual-level randomization using a computer-generated random assignment list, produced in advance of any data collection. Assignment is conducted in the study office prior to worker contact. Workers are assigned sequentially as they are enrolled through the firm's labor roster. The randomization list is generated using statistical software (Stata or R) with a fixed seed documented in the pre-analysis plan. Enumerators delivering the cash advance are aware of arm assignment for logistical purposes only; session supervisors are not told which workers received an advance.
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After
Randomization will be conducted by computer by the research team before each work session. The payment-timing assignment will be randomized at the individual-worker level using a reproducible randomization seed and stratification by work session. The initial production-method assignment in the second session will also be randomized at the individual-worker level, stratified by work session and subject to the firm’s operational requirement that both manual- and machine-shelled corn be produced. Assignment lists will be prepared before treatment revelation. Field staff will reveal assignments only at the relevant implementation point.
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Randomization Unit
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Before
Individual worker. No clustering. Each eligible worker is independently assigned to the cash-relief arm (Arm 1) or control arm (Arm 2) with equal probability (1:1 allocation).
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After
Individual worker. Payment timing is randomized at the individual-worker level. Initial production method in the second work session is also randomized at the individual-worker level. Work session fixed effects will be included in the main analysis to account for stratification and session-level conditions.
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Sample size (or number of clusters) by treatment arms
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Before
Arm 1 (Cash-relief): n = 200 workers
Arm 2 (Control): n = 200 workers
Total: N = 400 workers
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After
Planned assignment is 150 workers to immediate payment and 150 workers to deferred payment. Within the second work session, planned assignment is 200 workers to manual start and 200 workers to machine start, yielding approximately 100 workers in each of four cells: immediate payment plus manual start; immediate payment plus machine start; deferred payment plus manual start; deferred payment plus machine start. Actual cell sizes may differ slightly because of attendance, operational production constraints, or incomplete participation, all of which will be documented.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
Primary hypothesis H1 (entry margin, binary outcome D_i):
- Control-arm baseline attempt rate (p_C): 0.30
- Treatment-arm attempt rate (p_T): 0.45 (moderate scenario, delta-p = 0.15)
- Required sample per arm at 80% power, alpha = 0.05 (two-sided): 163
- Power at n = 200 per arm (moderate scenario): 0.91
- Power at n = 200 per arm (small effect, delta-p = 0.09): 0.48 (underpowered; reported as lower bound)
- Power at n = 200 per arm (large effect, delta-p = 0.19): 0.98
- Attrition adjustment: 15% assumed; 163/0.85 = 192 required; 200 recruited
Standard deviations are mechanically determined by the binary outcome: SD = sqrt(p*(1-p)).
At p_C = 0.30: SD = 0.458. At p_T = 0.45: SD = 0.497.
H2 (perseverance margin): Secondary confirmatory hypothesis. Power depends on attempt rate from H1. At baseline attempt rate of 0.50 and delta-p = 0.19 in perseverance, power at n = 100 attempters per arm is approximately 0.80.
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After
The planned sample is 400 completed behavioral observations. Participants are individually randomized to the earned-wage payment-timing treatment, with approximately 200 workers assigned to immediate payment of previously earned Day 1 wages and 200 workers assigned to deferred payment of the same earned wages after the Day 2 work session. In the second work session, participants are also individually randomized to the starting production method, with approximately 200 workers assigned to begin with manual shelling and 200 workers assigned to begin with the mechanical sheller. Under balanced assignment, this yields approximately 100 workers in each payment-timing-by-starting-method cell.
The study has two co-primary binary outcomes: initial machine preference before starting-method randomization, and post-assignment machine use after the revision opportunity. The confirmatory family contains two one-sided tests in the pre-specified positive direction. We will control the familywise error rate using the Holm step-down procedure. For conservative planning, power calculations use a one-sided alpha of 0.025 for each primary outcome.
For the first primary outcome, initial machine preference, the benchmark assumes a control-group machine-preference rate of 0.30 and a treatment-group rate of 0.45. This corresponds to a 15 percentage-point increase. With 200 workers per payment-timing arm and a one-sided alpha of 0.025, the design has approximately 88 percent power to detect this effect. The minimum detectable effect for 80 percent power is approximately 13.5 percentage points when the control-group mean is 0.30.
For the second primary outcome, post-assignment machine use, the benchmark assumes that immediate access to previously earned wages increases starting-method-standardized machine use by 15 percentage points. One illustrative case is a 15 percentage-point increase in manual-to-machine switching among participants randomly assigned to begin manually and a 15 percentage-point increase in machine retention among participants randomly assigned to begin with the machine. With approximately 100 workers in each payment-timing-by-starting-method cell, corresponding to 400 completed behavioral observations overall, the design has approximately 89 percent power to detect this starting-method-standardized 15 percentage-point effect using a conservative one-sided alpha of 0.025. The minimum detectable starting-method-standardized effect for 80 percent power is approximately 13.3 to 13.5 percentage points.
The starting-method-specific decomposition estimates are not powered as separate confirmatory hypotheses. With approximately 100 treated and 100 control workers within a given starting-method subgroup, a 15 percentage-point subgroup effect has only about 59 to 62 percent power under a one-sided alpha of 0.025, depending on the baseline rate. The minimum detectable subgroup effect for 80 percent power is approximately 18 to 19 percentage points. For this reason, the manual-starter and machine-starter estimates will be reported as pre-specified decomposition analyses used to interpret the source of the pooled post-assignment effect, not as separate confirmatory tests.
If the scheduled lean-season field period ends before 400 completed behavioral observations are reached, the final analysis will report the realized sample size, the assignment-based study flow, treatment-arm attrition, and updated minimum detectable effects. The study will not report post-hoc power calculations.
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Secondary Outcomes (End Points)
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Before
H3 (exploratory, no pre-specified direction): A_i — binary assembly abandonment indicator, conditional on D_i = 1.
Additional secondary outcomes:
- Total piece-rate earnings ln(Y_i) over the four-hour session
- Assembly time in minutes (conditional on D*_i = 1)
- Pre-choice subjective probability of successful assembly, p-hat_i (0-100 scale)
- Pre-choice expected assembly time, t-hat_i (minutes)
- Pre-choice expected production, E-hat_i (earnings in USD)
- Morning Perceived Stress Scale (PSS-3) score — financial stress mediator
- Sleep quality, duration, and latency — sleep mediator
Ai ∈ {0,1}: Assembly abandonment, conditional on Di = 1 — whether the worker who attempted the machine failed to complete assembly and reverted to manual shelling (exploratory hypothesis H2).
ln(Yi): Natural log of total piece-rate earnings from the four-hour production session (secondary confirmatory hypothesis H3).
pi ∈ [0,1]: Worker's self-assessed probability of successfully completing assembly, elicited after viewing the instructional video but before the technology choice form is distributed (belief channel — auxiliary exploratory test).
t̂i: Worker's expected time (in minutes) to complete assembly, elicited after viewing the instructional video but before the technology choice form is distributed (belief channel — auxiliary exploratory test).
Êi: Worker's expected earnings from choosing the machine (in USD), elicited after viewing the instructional video but before the technology choice form is distributed (auxiliary exploratory test).
Sleep quality: Subjective sleep quality (0–10 scale), sleep duration (hours), and sleep latency (minutes), collected in the morning survey battery (mechanism analysis).
Morning PSS-3 score: Three-item Perceived Stress Scale sum (0–12) measuring financial preoccupation on arrival (first-stage manipulation check, H4).
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After
Secondary outcomes include: attendance at the second work session; completion of mechanical-sheller assembly; time required to complete assembly; abandonment during assembly; total output during the work session; productivity measured as output per unit of time; piece-rate or total earnings during the work session where applicable; time until first switch; self-reported perceived difficulty of the mechanical sheller; comprehension of standardized instructions; stated preference between manual and mechanical shelling after experience; and measures of short-run financial pressure, including debt repayment, urgent expenditures, and liquidity constraints measured before the second work session.
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Secondary Outcomes (Explanation)
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Before
H3 (abandonment) is estimated via linear probability model and logistic regression, conditional on D_i = 1, with inverse probability weighting (IPW) to correct for selection into machine adoption. No pre-specified rejection threshold; results in either direction are treated as theoretically informative, adjudicating between bandwidth-depletion (Mani et al., 2013) and distraction-facilitation (Dang et al., 2016) accounts of poverty and procedural learning.
Earnings are measured by weighing individual output at the end of the session and multiplying by the piece-rate. Assembly time is recorded from the start of the instructional video to confirmed machine operation. Pre-choice beliefs are elicited verbally by enumerators immediately after the instructional video, before workers submit their technology choice forms. PSS-3 is the three-item financial preoccupation subscale of the Perceived Stress Scale, administered on the morning of the session day and again post-session. Sleep measures are self-reported (quality 0-10, hours slept, minutes to fall asleep).
Causal mediation analysis (Imai et al., 2010) is used to estimate the average causal mediation effect (ACME) of financial stress and sleep on adoption outcomes, with 1,000 bootstrap iterations and sensitivity analysis via the rho-star critical value approach.
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After
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