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Comparative Socioeconomic Analysis

Comparative statistical analysis of municipalities in Laguna Province covering population, households, and social welfare programs. Supporting MSWDO planning, assessment, and decision-making.

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Liliw • Magdalena • Majayjay
Laguna Province
1. Dataset
Population & Households Social Welfare Programs All Data
Viewing: All Data · 2024 · Liliw, Magdalena, Majayjay
2. Year
2015 2020 2021 2022 2023 2024 2025 2026
Descriptive Analysis Population Growth Household vs Population Program Beneficiaries Report & Download Key Insights Recommendations

Descriptive Analysis

Total Population (2024)
96,520
Highest: Liliw
Total Households (2024)
25,962
Highest: Liliw
Total Beneficiaries (2024)
10,890
Highest: Majayjay
Population, Households & Beneficiaries per Municipality

Grouped bar chart 2024

Key Differences
Municipality Population Benef. %
Liliw 39,976 5.5%
Magdalena 28,131 14.6%
Majayjay 28,413 16.2%

Liliw leads in population. Majayjay has the most welfare beneficiaries. Beneficiary rates range from 5.5% to 16.2%.

Population Growth Analysis

Population per Year per Municipality

Line chart all recorded years

Growth Rates
Liliw +8.31%
Avg. yearly growth
Magdalena +9.58%
Avg. yearly growth
Majayjay +0.36%
Avg. yearly growth

Magdalena shows the fastest average population growth among the municipalities.

Key Finding

Magdalena shows the highest average yearly population growth rate at +9.58%, based on the available population records.

Explanation of Growth Trend
Young Population Structure
Higher birth rates due to lower median age contribute to sustained natural population increase.
Sustained Growth Trend
Continuous increase based on census data reflects a long-term upward demographic trajectory.
Resource Availability & Settlement Expansion
Access to water and land in the Santa Cruz watershed supports population growth, leading to expansion of settlements and built-up areas.
Increasing Human Activities & Environmental Interaction
Rising population leads to increased land use, water consumption, and environmental changes, reflecting strong interaction between people and natural resources.
Supporting Evidence
1
PhilAtlas (2020)
Magdalena, Laguna Population Data
View Source
2
Magpantay & Sanchez (2023)
JESAM Environmental & Socio-demographic Study
View Source
3
Sandoval et al. (2023)
Water Quality Assessment Santa Cruz River, Laguna
View Source
Conclusion

The rapid population increase in Magdalena is driven by a combination of demographic factors and environmental-resource dynamics. The availability of water and land supports continuous settlement expansion, while increasing human activities further accelerate growth.

However, this growth also places pressure on natural resources, particularly water systems and land use. As population increases, environmental impacts such as changes in land cover and water quality become more evident. Therefore, sustainable resource management is essential to balance population growth with environmental protection.

Household vs Population Analysis

Population vs Households Combo Chart

Bars = Population, Line = Households 2024

Average Household Size
Liliw 3.73 persons/hh
10,707 households · 39,976 pop.
Magdalena 3.91 persons/hh
7,203 households · 28,131 pop.
Majayjay 3.53 persons/hh
8,052 households · 28,413 pop.

Program Beneficiaries Analysis

Senior Citizen Pension
Highest Demand Program
7,978 total beneficiaries
Liliw
2,191
Total Beneficiaries
Magdalena
4,100
Total Beneficiaries
Majayjay
4,599
Total Beneficiaries
Programs per Municipality (Stacked)

4Ps, AICS, PWD Assistance, Senior Citizen Pension, Solo Parent 2024

Beneficiaries Trend (Yearly)

Total beneficiaries over all years

Note: Only usable annual counts are shown. Missing records and stored 0 are N/A, not zero beneficiaries.
Program LiliwMagdalenaMajayjay Total Highest
4Ps 300 N/A N/A 300 Liliw
AICS N/A N/A N/A N/A N/A
PWD Assistance 864 690 587 2,141 Liliw
Senior Citizen Pension 950 3,280 3,748 7,978 Majayjay
Solo Parent 77 130 264 471 Majayjay

Totals use known annual counts only. Any N/A cell (missing record or stored 0) means the total does not represent all three municipalities.

Analysis Report & Download

Social Welfare Programs Analysis Report
Reporting period
Reporting year 2024; program records available 2015 to 2026
Dataset
All Data (programs report)
Municipalities
Liliw, Magdalena, Majayjay
Recorded beneficiaries
10,890 (3 of 3 municipalities have records)
The report contains
  • Title, reporting period, dataset, municipalities and date generated
  • Data summary and tables: beneficiaries by program, municipality summary, yearly totals
  • Methodology, AHP-weighted ranking, recommendations, and limitations / missing-data notes

Report for 2024

Read it here, or download a copy. Every format matches the selected dataset and year.

Data Notes & Limitations
  • Figures are annual beneficiary counts (month IS NULL). Stored 0, NULL, and missing annual records are shown as N/A because they mean data have not yet been entered. They are not treated as verified zero beneficiaries.
  • Monthly program rows are not added to annual municipality totals. Soft-deleted records are excluded.
  • No budget, unit cost, eligibility, need or outcome data are used, so the counts describe recorded reach only and do not measure program effectiveness.
  • The records do not state whether a person who receives more than one program is counted once or once per program, so the total across programs may not equal the number of unique persons.
  • Liliw: N/A for AICS in 2024; the total covers known programs only and does not represent every program.
  • Magdalena: N/A for 4Ps, AICS in 2024; the total covers known programs only and does not represent every program.
  • Majayjay: N/A for 4Ps, AICS in 2024; the total covers known programs only and does not represent every program.
  • Years without usable annual counts appear as N/A. Yearly totals may cover different municipalities and program types, so compare years with care. A change from N/A to a positive count is not a confirmed increase.
  • Comparative ranking covers Liliw, Magdalena, and Majayjay only.
AHP-Weighted Comparative Ranking
AHP: Computed

AHP-weighted comparative score based on beneficiary counts

The score is an AHP-weighted comparative score based on beneficiary counts using approved AHP criteria weights. It does not measure poverty severity, program effectiveness, or unmet need.

Rank Municipality Score
1 Majayjay 67.29
2 Magdalena 51.76
3 Liliw 32.71
Approved criteria weights
  • Senior Citizen Pension: raw 0.3737, effective 0.373737
  • PWD Assistance: raw 0.3271, effective 0.327133
  • Solo Parent: raw 0.2991, effective 0.299130
AHP calculation details
Methodology
  1. The overall ranking uses approved AHP criteria weights from pairwise comparisons of 10 respondents. Weights are not recalculated from beneficiary counts.
  2. Approved raw weights: Senior Citizen Pension raw 0.3737, effective 0.373737; PWD Assistance raw 0.3271, effective 0.327133; Solo Parent raw 0.2991, effective 0.29913. Each raw weight is divided by the total (0.9999) so effective weights sum to 1.0.
  3. AICS and other programs may appear in descriptive tables when annual data exist, but they are excluded from the three-criterion AHP score.
  4. Only annual municipality-level records are used (month IS NULL, not soft-deleted). Monthly slices are never added to annual totals.
  5. A stored beneficiary_count of 0, NULL, or a missing annual record is N/A (data not yet entered) and is never treated as zero beneficiaries.
  6. N(i,j) = (X(i,j) - min(X(j))) / (max(X(j)) - min(X(j))) across Liliw, Magdalena, and Majayjay. Score(i) = Σ effectiveWeight(j) × N(i,j), displayed on a 0–100 scale.
  7. If all three valid values for a criterion are equal, each municipality receives a normalized value of 1 for that criterion so no municipality gains an artificial advantage.
  8. Duplicate active annual rows for the same municipality, year, and program are not summed. The row with the highest id is used.
  9. A complete overall ranking requires a valid annual count greater than zero for Senior Citizen Pension, PWD Assistance, and Solo Parent in every municipality. Missing values are not replaced with zero and weights are not renormalized over a subset of criteria.

Key Insights

Finding 1

AHP-weighted comparative ranking for 2024: Majayjay ranks 1 with a score of 67.29 out of 100. This is a comparative score based on beneficiary counts, not a measure of poverty, effectiveness, or unmet need.

Finding 2

Majayjay has the most registered beneficiaries (4,599) - 16.2% of its population.

Finding 3

The Senior Citizen Pension program has the highest total beneficiaries (7,978) across municipalities with data for 2024.

Finding 4

Liliw has the highest population (39,976) while Magdalena has the lowest.

Finding 5

PWD Assistance in 2024: highest known count is Liliw (864); lowest known count is Majayjay (587).

Finding 6

Senior Citizen Pension in 2024: highest known count is Majayjay (3,748); lowest known count is Liliw (950).

Finding 7

Solo Parent in 2024: highest known count is Majayjay (264); lowest known count is Liliw (77).

Finding 8

Liliw / 4Ps decreased from 350 in 2023 to 300 in 2024. Validate the records before treating this as a confirmed program change.

Finding 9

Liliw / PWD Assistance increased from 445 in 2023 to 864 in 2024. Validate the records before treating this as a confirmed program change.

Finding 10

Liliw / Senior Citizen Pension increased from 800 in 2023 to 950 in 2024. Validate the records before treating this as a confirmed program change.

Recommendations

Recommendation 1

The known Senior Citizen Pension counts in 2024 differ substantially across municipalities. Review the source records and confirm they are comparable before drawing conclusions.

Recommendation 2

The known Solo Parent counts in 2024 differ substantially across municipalities. Review the source records and confirm they are comparable before drawing conclusions.

Recommendation 3

Where year-to-year changes appear between 2023 and 2024, validate the records and check documented program changes before treating the difference as a confirmed increase or decrease in beneficiaries.

Recommendation 4

Majayjay (rank 1, score 67.29): the largest criterion contribution is Senior Citizen Pension. Use this as a comparative profile of recorded beneficiary counts, not as evidence of greater poverty or better program performance.

Recommendation 5

Magdalena (rank 2, score 51.76): the largest criterion contribution is Senior Citizen Pension. Use this as a comparative profile of recorded beneficiary counts, not as evidence of greater poverty or better program performance.

Recommendation 6

Liliw (rank 3, score 32.71): the largest criterion contribution is PWD Assistance. Use this as a comparative profile of recorded beneficiary counts, not as evidence of greater poverty or better program performance.