Get an overview of the ESRU-EMOVI 2023 social mobility survey.

Returns information about available datasets, survey design, mobility dimensions, and key variables.

List available variables in the survey.

Args: dataset: Which dataset to list variables from. Options: entrevistado, hogar, inclusion_financiera. section: Filter by questionnaire section (optional). search: Search term to filter by variable name or description (optional).

Returns a list of variables with their labels.

Get detailed information about a specific variable.

Args: variable: The variable name (e.g., 'educ', 'ingc_pc', 'sexo').

Returns the variable label, value labels, dataset, and section.

Compute a weighted crosstab between two variables.

Args: row_var: Variable for rows (e.g., 'educ', 'region'). col_var: Variable for columns (e.g., 'sexo', 'cohorte'). filter: Optional filter expression (e.g., "sexo == 1", "cohorte == 3"). normalize: How to normalize: 'row' (default), 'col', 'all', or 'none'. dataset: Which dataset to use (default: entrevistado).

Returns a markdown table with weighted proportions.

Compute an intergenerational mobility transition matrix.

This is the core analysis tool for social mobility research.

Args: dimension: Type of mobility to analyze. - "education": Educational mobility (4x4 matrix). Origin = max(father, mother) education; Destination = respondent education. - "occupation": Occupational class mobility. Origin = father's class; Destination = respondent's class. - "wealth": Wealth quintile mobility (5x5 matrix). Based on PCA wealth index from household assets (origin vs current). filter: Optional filter expression. Examples: "sexo == 2" (women only), "cohorte == 1" (ages 25-34), "region_14 == 5" (Southern region of origin). by: Optional grouping variable to produce separate matrices. Examples: "sexo" (by gender), "region_14" (by region of origin), "cohorte" (by age cohort). origin_category: Optional origin quintile/category to filter. Example: 1 for Q1 (poorest) in wealth, or 1 for "Primaria o menos" in education. Returns only the destination distribution for that origin. include_se: If True, compute Taylor-linearized standard errors and 95% confidence intervals for each matrix cell.

Returns markdown transition matrix with row percentages (origin -> destination), summary statistics, formal mobility indices, and optionally standard errors.

Compute weighted descriptive statistics for a variable.

Args: variable: The numeric variable to analyze (e.g., 'ingc_pc', 'educ'). filter: Optional filter expression (e.g., "sexo == 1"). by: Optional grouping variable (e.g., "region", "sexo", "cohorte"). dataset: Which dataset to use (default: entrevistado).

Returns weighted mean, median, std, percentiles (25th, 75th), min, max, and sample sizes. If 'by' is specified, returns stats per group.

Compare a variable across groups defined by another variable.

Args: variable: The variable to compare (e.g., 'ingc_pc', 'educ'). group_var: The grouping variable (e.g., 'sexo', 'region', 'cohorte'). metric: Which metric to compute: 'mean', 'median', or 'distribution'. filter: Optional filter expression. dataset: Which dataset to use (default: entrevistado).

Returns a comparison table showing the metric for each group.

Extract a subset of raw data for specific variables.

Args: variables: List of variable names to include (e.g., ["sexo", "educ", "ingc_pc"]). filter: Optional filter expression (e.g., "sexo == 2 and cohorte == 1"). limit: Maximum number of rows to return (default: 20, max: 100). dataset: Which dataset to use (default: entrevistado).

Returns a markdown table with the requested data. Use this to inspect raw values or extract data for custom analysis.

Analyze financial inclusion from the ESRU-EMOVI 2023 inclusion module.

Args: dimension: Financial inclusion dimension to analyze. - "savings": Formal and informal savings behavior - "credit": Access to credit and debt - "banking": Banking services and financial products - "literacy": Financial education and knowledge - "discrimination": Discrimination in financial services filter: Optional filter expression (e.g., "sexo == 1"). by: Optional grouping variable (e.g., "sexo", "entidad").

Returns markdown summary with weighted proportions for each variable in the selected dimension.

Compare income between 2017 and 2023 for matched respondents.

Merges the 2017 income module with the 2023 respondent data on folio.

Args: metric: What to compute. - "change": Income change statistics (absolute and relative). - "poverty": Poverty transition rates using CEEY poverty lines. - "summary": Full summary with both income change and poverty. filter: Optional filter expression (e.g., "sexo == 1", "rururb == 1"). by: Optional grouping variable (e.g., "sexo", "rururb", "cohorte").

Returns markdown summary with weighted statistics on temporal income dynamics.

Generate a visualization of mobility transition matrix.

Args: dimension: Mobility dimension — "education", "occupation", or "wealth" chart_type: Type of chart — "heatmap", "sankey", or "prais_bar" filter: Optional filter expression (e.g., "sexo == 1") by: Optional grouping variable