Valuing Formal Jobs and Formality in Labor Market Trajectories Using Discrete Choice Experiments: Evidence from Mexico

This study uses a survey-based discrete choice experiment (DCE) to elicit worker preferences for formal employment attributes and formal labor market trajectories in Mexico. The experimental variation comes from randomly generated hypothetical scenarios presented to respondents.

The survey contains two conjoint modules:

JOB CONJOINT: Respondents are asked to choose between pairs of hypothetical job offers. Each job is described by 11 attributes, with levels drawn randomly and independently for each profile:
(1) Salary: anchored to the respondent's own wage, varying from 60% less to 60% more (with a distribution that overweighs levels near the respondent's current wage);
(2) Health insurance: none, free IMSS coverage, or IMSS plus private major medical insurance;
(3) Retirement plan: no plan, or employer contributions of 1-5% of monthly salary to individual account;
(4) Workplace injury insurance: none, half salary during partial disability, or full salary during permanent disability;
(5) Housing fund contributions: none, or employer contributions of 1-5% to housing savings account;
(6) Subsidized mortgage: none, or INFONAVIT preferential interest rate;
(7) Life insurance: none, or beneficiaries receive full retirement fund upon death;
(8) Severance pay: none, or 1-3 months' salary upon dismissal;
(9) Childcare: none, or free IMSS daycare;
(10) End-of-year bonus (aguinaldo): none, 15 days, or 30 days;
(11) Protection against unjust dismissal: cannot dispute, or can dispute.

Each respondent completes 8 choice tasks (rounds 1-7 with unique profiles; round 8 is a consistency/reliability check that reverses the column order of round 2). Attribute order is randomized across respondents but fixed within respondent. For selected rounds (5, 6, and 7), follow-up questions ask which job has lower dismissal risk, more task independence, and/or more schedule flexibility.

LIFE-HISTORY CONJOINT: Respondents compare pairs of imaginary persons' complete labor market biographies spanning ages 25-70. Each life history is described by 10 attributes:
(1) Number of jobs (5, 10, 15, 22, or 30);
(2) Job type descriptions (informal, formal, or mixed sector jobs);
(3) Formality share (0%, 25%, 50%, 75%, or 100% formal);
(4) Employment continuity (never unemployed, short unemployment spells, or significant unemployment);
(5) Income trajectory (start and end monthly income, varying by formality);
(6) weeks contributed to social security and retirement fund accumulation;
(7) Health event (workplace injury at 50, diabetes diagnosis at 45, or childbirth complication at 35);
(8) IMSS coverage during health event (covered or paid out-of-pocket);
(9) Total retirement savings at age 70;
(10) Monthly retirement income.

IMSS coverage is linked to formality: fully informal profiles never have IMSS. Row order is randomized across respondents but fixed within respondent. Each respondent completes 8 choice tasks (7 unique + 1 reliability check).

Respondents are randomly assigned to one of three arms: Job Conjoint only (40% of sample), Life-History Conjoint only (40%), or both conjoints in sequence (20%). All respondents also answer background questions on employment, wages, benefits, health, retirement expectations, and demographics.

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(1) Binary choice (selected job or life history) in each conjoint task — the dependent variable for estimating average marginal component effects (AMCEs) and marginal willingness to pay.

(2) For the Job Conjoint: the marginal rate of substitution (MRS) between each nonwage benefit attribute and salary, interpreted as the respondent's implicit willingness to pay for that benefit.

(3) For the Life-History Conjoint: the marginal rate of substitution between formality-related attributes (formality share, IMSS coverage, health-event coverage) and two numeraires: (i) the present discounted value of lifetime income, computed under a linear trajectory assumption between the randomized start and end monthly income levels, and (ii) monthly retirement income, which is independently randomized. These MRS estimates capture the willingness to pay for formal labor market trajectories in both lifetime-income and retirement-peso units.

(4) Perceived job security: binary choice indicating which job in the pair has lower dismissal risk (asked twice across rounds 5-7 of the Job Conjoint).

(5) Perceived task independence: binary choice indicating which job offers more freedom in choosing tasks and how to perform them (asked for rounds 5 and 7).

(6) Perceived schedule flexibility: binary choice indicating which job offers more freedom in choosing work hours (asked for rounds 6 and 7).

The primary outcome variable is a binary indicator equal to 1 if the respondent chose Profile B (or the right-column profile) in a given choice task, and 0 otherwise. This variable is regressed on indicator variables for the attribute levels of both profiles using linear probability models (OLS) or conditional logit, following the standard conjoint analysis framework (Hainmueller, Hopkins, and Yamamoto, 2014).

AMCEs are estimated as the coefficient on each attribute level relative to a reference category. The MRS between a nonwage attribute and salary is computed as the ratio of the AMCE of that attribute to the AMCE of a unit change in salary (in pesos), yielding a peso-denominated willingness to pay. In the Life-History conjoint, the MRS is estimated using i) the present discounted value of wages over the employment history (25-70 years old) and ii) the monthly retirement income.

For the perceived job security, task independence, and schedule flexibility outcomes, the same regression framework is applied with the respective binary choice as the dependent variable, to identify which job attributes drive perceptions of these job qualities.

(1) Reservation wage for self-employed workers: the monthly salary that would induce the respondent to close their business and accept salaried employment.

(2) Reservation pension: the monthly pension amount that would induce the respondent to stop working at age 60.

(3) Willingness to pay for an annuity: the maximum lump sum the respondent would pay at age 60 for a lifetime monthly income stream equal to their stated reservation pension.

(4) Subjective probability of main health risk: the respondent's perceived probability of their self-identified main health risk occurring.

(5) Expected medical costs: the respondent's estimate of the annual cost of treating their main health risk, a heart attack, a car/workplace accident, and major organ problems, for an uninsured person.

(6) IMSS service quality expectations: ordinal measure of whether the respondent believes IMSS would provide timely and effective treatment in a serious health emergency.

(7) Consistency/reliability: agreement rate between the original choice task and the reversed-order reliability check (round 8 in both conjoints).

Reservation wages and pensions are elicited as continuous variables (in pesos). The willingness to pay for the annuity is elicited using a multiple-price-list-style slider in millions of pesos. Health risk probabilities are elicited on a visual probability scale. Expected medical costs are entered as continuous peso amounts.

The reliability rate is the fraction of respondents whose choice in round 8 (reversed column order) is consistent with their choice in round 2 (same profiles, original order). This measures attentiveness and stability of preferences.

These secondary outcomes will be analyzed descriptively and used to explore heterogeneity in the primary conjoint estimates (e.g., whether WTP for health insurance varies by health risk perceptions or current IMSS enrollment).

DESIGN OVERVIEW

This is a survey-based discrete choice experiment with three experimental arms, implemented as an online survey on Qualtrics and distributed through the Netquest panel in Mexico.

TREATMENT ARMS AND ASSIGNMENT

Respondents are randomly assigned to one of three arms upon entering the survey:
Arm 1 — Job Conjoint only (40% of sample, ~1,600 respondents)
Arm 2 — Life-History Conjoint only (40% of sample, ~1,600 respondents)
Arm 3 — Both conjoints (20% of sample, ~800 respondents)

Assignment is performed by the Qualtrics randomizer at the block level.

WITHIN-RESPONDENT RANDOMIZATION

Within each conjoint, every choice task presents a fresh pair of randomly generated profiles. Attribute levels are drawn independently for each profile using JavaScript executed in the respondent's browser at the start of the survey. For the Job Conjoint, salary levels are anchored to the respondent's own reported wage and drawn from a distribution that overweighs values near the current wage (using repeated entries). For the Life-History Conjoint, profiles are constrained so that fully informal trajectories cannot have IMSS coverage, and income trajectories are matched to the formality category.

The order of attribute rows is randomized once per respondent (Fisher-Yates shuffle) and held fixed across all choice tasks for that respondent.

NUMBER OF CHOICE TASKS

Job Conjoint: 8 rounds per respondent (7 unique pairs + 1 reliability check). Rounds 5, 6, and 7 include follow-up questions on perceived job security, task independence, and schedule flexibility.
Life-History Conjoint: 8 rounds per respondent (7 unique pairs + 1 reliability check).
Combined arm: 4 job rounds + 4 life-history rounds.

The reliability check (round 8) presents the same two profiles from round 2 but with swapped column positions.

SURVEY STRUCTURE

All respondents complete the following modules before the conjoint tasks: informed consent, age screening, employment screening (based on ENOE methodology), job characteristics, industry, wages/income, informality and benefits, secondary employment, demographics, household composition, retirement expectations, and health status/risk perceptions. Three attention checks are embedded throughout the survey.

TARGET SAMPLE AND ELIGIBILITY

Target: 4,000 completed surveys.
Eligibility: Adults aged 25-65, currently working at least 30 hours per week in Mexico (either salaried or self-employed), recruited from the Netquest online panel.
Quota controls enforce balance on sex (max 2,460 men / 1,640 women), age (max 2,050 per age group: 25-40 and 41-64), and socioeconomic level (AB: 482; C+: 766; C: 993; C-: 1,092; D+: 437; D/E: 330).

Not available

Randomization is performed by computer using two mechanisms:
(1) Assignment to experimental arm (Job Conjoint, Life-History Conjoint, or both) is implemented via the Qualtrics survey flow randomizer, which assigns respondents to blocks with pre-specified probabilities (40%, 40%, 20%).
(2) Within each conjoint, attribute profiles are generated by JavaScript code executed in the respondent's browser. The code uses a Fisher-Yates shuffle algorithm and Math.random() to independently draw attribute levels for each profile in each choice task. A check ensures that the two profiles in any pair are not identical across all attributes. Attribute row order is also randomized once per respondent using the same shuffle algorithm.

Individual respondent (for arm assignment) and individual choice task (for profile generation within arms)

No

N/A — randomization is at the individual level.

4,000 respondents. At the choice-task level: approximately 1,600 x 8 = 12,800 observations for Arm 1 (Job Conjoint), 1,600 x 8 = 12,800 for Arm 2 (Life-History Conjoint), and 800 x 8 = 6,400 for Arm 3 (4 job + 4 life-history rounds). Total choice-task observations: approximately 32,000.

Arm 1 (Job Conjoint only): ~1,600 respondents (40%)
Arm 2 (Life-History Conjoint only): ~1,600 respondents (40%)
Arm 3 (Both conjoints): ~800 respondents (20%)

The MDE for an AMCE in a forced-choice conjoint is approximately 2.8 * sqrt(p(1-p) / (N*T*J)), where N is the number of respondents, T is the number of tasks per respondent, J is the number of profiles per task, and p is the baseline choice probability. With N = 2,400, T = 8, J = 2, and p = 0.5, the minimum detectable AMCE is approximately 0.013 (1.3 percentage points) for binary attributes at the 5% significance level with 80% power. For heterogeneous effects estimated on half-samples (e.g., by gender or formality status), the MDE is approximately 0.026 (2.6 percentage points).