Thailand Province-Level Analysis

Author

Affiliation

Sasiton Treeprak, Sciences Manager

HS-TECH Engineering

This report summarizes two province-level scatter analyses relating Gross Provincial Product (GPP) per capita to two health outcomes: life expectancy at birth (2020) and suicide rate (2019). Both analyses cover all 77 Thai provinces.

1 GPP per Capita vs. Life Expectancy (2020)

Overall correlation: Pearson r ≈ -0.36 (n = 77 provinces) — a weak-to-moderate negative relationship. Higher GPP per capita is associated with slightly lower life expectancy across provinces.

Figure 1: GPP per capita (2020) vs. life expectancy (2020), clustered by region-group

1.1 By 8 Regions

Table 1: Mean GPP and Life Expectancy

Region group	Mean GPP/capita (Baht)	Life expectancy (yrs)
Bangkok	585,689	74.7
East	353,730	75
Central	214,621	75.1
North	105,101	75.7
Perimeter	277,842	76.1
Northeast	80,127	76.5
West	155,443	77
South	136,596	77.8

1.2 Key observations

The richest provinces — Rayong, Bangkok, Prachin Buri (all industrial / Eastern-seaboard) — sit at the lowest life-expectancy tier.
The South and Northeast — lower-GPP regions — have the highest average life expectancy.

1.3 Note

Important data limitation: life expectancy is only published at the regional level (8 groups) by NESDB/NSO for 2020, not by individual province. Every province in the same region-group shares an identical assigned value, so this correlation reflects a regional pattern rather than true province-specific variation.

2 GPP per Capita vs. Suicide Rate (2019)

Overall correlation: Pearson r ≈ -0.20 (log GPP, n = 77 provinces) — a weak negative relationship. Higher GPP per capita is associated with slightly lower suicide rate.

Figure 2: GPP per capita (2019, log scale) vs. suicide rate (2019)

2.1 By 6 regions

Table 2: Mean GPP and Suicide

Region	Mean GPP/capita (Baht)	Suicide rate (per 100,000)
Bangkok & Vicinities	352,508	4.11 (lowest)
Southern	162,237	6.1
Western	191,117	6.15
Eastern	395,695	6.9
Northeastern	79,897	6.94
Northern	109,503	9.33 (highest)

2.2 Key observations

Bangkok & Vicinities (highest GPP) has the lowest suicide rate, consistent with the overall negative trend.
The Northern region is a clear outlier: it has only middling GPP but the highest suicide rates — Nan, Phrae, and Lamphun all exceed 11 per 100,000.
The Northeast, despite being the poorest region by GPP, is mid-pack for suicide rate, not the worst — breaking a simple “poorer means higher risk” story.
Southern provinces — Narathiwat, Pattani, Yala — have the lowest suicide rates in the country (under 2.5), despite modest GPP, likely reflecting cultural/religious factors in this majority-Muslim region rather than economic conditions.
Suicide rate 2019 was computed from raw counts: (male deaths + female deaths) / (male population + female population) × 100,000, combining both sexes rather than averaging published sex-specific rates.

3 Combining the Two Analyses

Both relationships point in the same direction — provinces with higher GPP per capita trend toward a slightly lower suicide rate, but slightly lower (not higher) life expectancy. However, neither relationship is strong: r² is approximately 13% for life expectancy and 4% for suicide rate, meaning GPP per capita explains only a small share of the variation in either outcome.
The life-expectancy result is likely confounded by industrial and environmental exposure in high-GPP Eastern-seaboard provinces. The suicide-rate pattern is dominated by a distinct Northern-region effect that overwhelms any simple wealth-based explanation. GPP per capita should be treated as a weak, non-causal predictor for either outcome on its own — other factors (healthcare access, environmental exposure, culture, religion, urbanization, and reporting practices) likely play a larger role.

Note: Note: This is a descriptive, exploratory summary intended to surface patterns for further investigation — not a causal or policy analysis.

INFORMATION