Socioeconomic and Housing Conditions in Rural Bolivia: A Privacy-First Reproducible Survey Analysis

1. Introduction

Housing is multidimensional: construction materials, basic services, crowding and household resources jointly shape wellbeing. Survey analysis often compresses these dimensions into percentages without reporting denominators, uncertainty, missingness or the assumptions behind composite measures. These omissions weaken both scientific interpretation and operational decision-making.

A second challenge is disclosure. Household microdata may combine names, ages, occupations, income, health information and property characteristics. Removing a name column is insufficient when rare combinations can re-identify respondents. This project therefore treats privacy architecture as part of statistical quality rather than as an administrative appendix.

1.1 Research questions

1. How can adequate housing be operationalised transparently from multiple components?
2. Which socioeconomic characteristics are conditionally associated with adequate housing in a small synthetic cross-sectional sample?
3. How should missingness and sampling uncertainty be quantified and communicated?
4. How can survey analysis remain computationally reproducible without exposing confidential source records?

2. Methods

2.1 Study design and reporting framework

This simulation-based methodological study follows the Introduction-Methods-Results-Discussion structure and is mapped to the STROBE checklist for cross-sectional studies [1,2]. It is not a registered observational study and does not estimate population parameters for a real geographic area.

2.2 Data generation and units of analysis

A fixed pseudorandom seed generated 60 households and 271 nested members. Aggregate zones are fictional. No value was copied, perturbed or sampled from a real respondent. Household and person denominators are kept separate throughout.

2.3 Privacy and quality controls

Automated tests reject direct-identifier columns, duplicate identifiers, orphaned member records, invalid binary outcomes and impossible ages. Public file rules exclude SPSS, legacy Word and private raw-data directories from version control.

2.4 Estimands and uncertainty

For survey weights w_i and outcome y_i, the weighted mean is:

The 95% interval uses the 2.5th and 97.5th percentiles of 2,000 household bootstrap estimates:

2.5 Housing and vulnerability measures

The housing score is the unweighted share of six documented components: electricity, improved water, improved sanitation, quality walls, quality floor and absence of severe crowding. Adequate housing requires a score of at least two thirds. The vulnerability index combines housing, head education and capped income:

2.6 Exploratory model

A logistic generalised linear model estimates conditional associations with adequate housing:

Education, log income and household size are standardised. Missing education and income are median-imputed for computational completeness; empirical analysis should use design-aware multiple imputation and sensitivity analysis [5]. No coefficient is interpreted causally.

3. Results

3.1 Housing adequacy and services

The weighted adequate-housing estimate was 37.9% (95% bootstrap interval 26.7%-51.7%). Wide zone-specific intervals reflect the small number of households and discourage unstable rankings.

3.2 Socioeconomic patterning

Median synthetic monthly household income was BOB 2210. The mean vulnerability index was 44.6 on a 0-100 scale. Correlations describe co-movement only and do not establish pathways of causation.

3.3 Exploratory regression

4. Discussion

This analysis integrates disclosure controls, explicit denominators, uncertainty, documented measurement and automated reproduction. The wide intervals are analytically important: they show why complex machine learning and detailed subgroup ranking would be inappropriate for a 60-household study.

For a real survey, scientific interpretation would depend on information that cannot be reconstructed from the data alone: target population, sampling frame, selection probabilities, field dates, non-response process, questionnaire, interviewer procedures and ethics approval. These elements should be documented before substantive re-analysis.

5. Limitations

• Synthetic observations cannot validate or reproduce the original empirical findings.
• The synthetic weights do not represent a real sampling design.
• The housing threshold and vulnerability coefficients require substantive validation.
• Single median imputation understates missing-data uncertainty and is retained solely for computational completeness.
• Binary gender measurement is an artificial simplification.
• Cross-sectional associations cannot identify causal effects.

6. Conclusion

Strong research communication is not the accumulation of charts or equations. It is the alignment of a meaningful question, appropriate estimand, transparent method, uncertainty, ethical data handling and restrained conclusion. This workflow provides a publishable foundation once authorised, anonymised source data and a complete survey design are available.