Calculators for public statistics

Components

1. Calculate the maternal mortality ratio per 100,000 live births

   baltor_maternal_mortality_ratio

   Calculate the maternal mortality ratio per 100,000 live births with exact Poisson confidence bounds from death and birth counts, keeping late maternal deaths out of the ratio and adding companion rates when their denominators are supplied.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   period, maternal_deaths, late_maternal_deaths, live_births, confidence_level, women_reproductive_age_person_years, deaths_women_reproductive_age

   SkillMIT9 files, 43.4 KBreads files, runs commands

2. Calculate intentional homicide victims per 100,000 population by year and sex

   baltor_homicide_rate

   Calculate intentional homicide victims per 100,000 population by year and sex with exact Poisson bounds and a pooled multi-year rate, counting victims rather than incidents or offenders and keeping attempts and conflict deaths out.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   confidence_level, years

   SkillMIT9 files, 51.2 KBreads files, runs commands

3. Calculate the unadjusted gender pay gap in mean and median gross hourly earnings

   baltor_gender_pay_gap

   Calculate the unadjusted gender pay gap in mean and median gross hourly earnings from weighted unidentified employee records, overall and by declared breakdown groups, refusing mismatched pay and hours periods.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   currency, earnings_period, hours_period, breakdowns, rows

   SkillMIT9 files, 50.2 KBreads files, runs commands

4. Calculate Foster-Greer-Thorbecke poverty measures

   baltor_fgt_poverty_measures

   Calculate the Foster-Greer-Thorbecke poverty headcount, poverty gap and squared poverty gap at a declared poverty line from weighted unit records, with exact period conversion and additive group decomposition.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   welfare_concept, weight_basis, welfare_unit, poverty_line, groups, rows

   SkillMIT9 files, 48.9 KBreads files, runs commands

5. Calculate Alkire-Foster multidimensional poverty headcount

   baltor_alkire_foster_poverty

   Calculate Alkire-Foster multidimensional poverty headcount, intensity and adjusted headcount ratio from weighted unit deprivation profiles, with exact rational indicator weights, a declared poverty cutoff, censored headcounts and indicator contributions.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   weight_basis, indicators, poverty_cutoff, missing_policy, groups, rows

   SkillMIT9 files, 57.5 KBreads files, runs commands

6. Calculate a directly age-standardized rate with Fay-Feuer gamma confidence bounds

   baltor_direct_age_standardization

   Calculate a directly age-standardized rate with Fay-Feuer gamma confidence bounds from age-specific event counts, person-years and a caller-supplied standard population, refusing gaps, overlaps and mismatched age groups.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   rate_multiplier, confidence_level, age_groups

   SkillMIT9 files, 46.7 KBreads files, runs commands

7. Calculate administrative vaccination coverage against target populations

   baltor_vaccination_coverage_dropout

   Calculate administrative vaccination coverage for declared antigen doses against explicit target-population denominators, dose-to-dose dropout rates and zero-dose estimates, flagging coverage above 100 percent and negative dropout instead of hiding them.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   period, areas, dropout_pairs, zero_dose_reference

   SkillMIT9 files, 73.8 KBreads files, runs commands

8. Score minimum dietary diversity for women and young children

   baltor_dietary_diversity

   Score minimum dietary diversity for women (10 food groups, at least 5) or for children aged 6 to 23 months (8 food groups, at least 5) from weighted unidentified food-group recall records, keeping unknown answers apart from foods not consumed.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   scheme, groups, rows

   SkillMIT9 files, 54.2 KBreads files, runs commands

9. Calculate moderate-or-severe and severe food insecurity prevalence

   baltor_fies_prevalence

   Calculate moderate-or-severe and severe food insecurity prevalence from a weighted Food Insecurity Experience Scale raw-score distribution with caller-supplied per-raw-score probabilities or raw-score thresholds, keeping incomplete responses out of the denominator.

   Command

   python3 -B scripts/tool.py < examples/example.input.json

   Input fields

   method, raw_scores, incomplete_responses, probabilities, thresholds, population

   SkillMIT9 files, 46.1 KBreads files, runs commands

10. Calculate the prevalence of undernourishment

    baltor_undernourishment_prevalence

    Calculate the prevalence of undernourishment as the share of a lognormal habitual dietary energy intake distribution below a minimum dietary energy requirement, from caller-supplied mean intake, coefficient of variation and sex-age requirement groups.

    Command

    python3 -B scripts/tool.py < examples/example.input.json

    Input fields

    mean_intake_per_person_day, coefficient_of_variation, requirement, population, cv_sensitivity

    SkillMIT9 files, 44.6 KBreads files, runs commands

11. Calculate crop yield gaps between actual and potential or water-limited yields

    baltor_crop_yield_gap

    Calculate crop yield gaps between actual and potential or water-limited yields for zones of one crop, standardizing yield units and grain moisture, weighting by harvested area and reporting the exploitable gap at a declared attainable fraction.

    Command

    python3 -B scripts/tool.py < examples/example.input.json

    Input fields

    crop_id, standard_moisture_pct, attainable_fraction, zones

    SkillMIT9 files, 57.0 KBreads files, runs commands

12. Calculate heating and cooling degree days from bounded daily temperature extrema

    baltor_degree_day_coverage

    Calculate heating and cooling degree days from bounded daily temperature extrema while exposing calendar gaps and unit assumptions for energy-efficiency analysis.

    Command

    python3 -B scripts/tool.py < examples/example.input.json

    Input fields

    start_date, end_date, temperature_unit, base_temperature, observations

    SkillMIT9 files, 32.9 KBreads files, runs commands

13. Calculate the share of seats held by women in elected bodies

    baltor_women_representation_share

    Calculate the share of seats held by women in declared parliamentary or local government bodies from seat counts, checking that members, vacancies and statutory seats reconcile and keeping elected and appointed seats apart.

    Command

    python3 -B scripts/tool.py < examples/example.input.json

    Input fields

    as_of, target_share_pct, bodies

    SkillMIT9 files, 47.7 KBreads files, runs commands

14. Calculate social protection coverage shares for declared population groups

    baltor_social_protection_coverage

    Calculate social protection coverage shares for declared population groups from scheme beneficiary counts with explicit person denominators, refusing non-person count units and giving bounds when people may receive more than one benefit.

    Command

    python3 -B scripts/tool.py < examples/example.input.json

    Input fields

    reference_date, groups, schemes

    SkillMIT9 files, 43.4 KBreads files, runs commands