Category: Data

  • CONFLICT RISK ASSESSMENT FOR BOSNIA AND HERZEGOVINA

    CONFLICT RISK ASSESSMENT FOR BOSNIA AND HERZEGOVINA

    Political Instability Task Force (PITF) Model Analysis

    Analysis Date: February 5, 2026
    Country: Bosnia and Herzegovina
    Assessment Period: 2024-2026
    Analyst: Conflict Prediction Study

    Author: Vehid Džiho

    EXECUTIVE SUMMARY

    This analysis applies the Political Instability Task Force (PITF) model to assess the risk of armed conflict in Bosnia and Herzegovina. Based on verified data from V-Dem, World Bank, UCDP, and the 2013 census, Bosnia faces MODERATE TO ELEVATED conflict risk due to its status as an anocracy with declining democratic quality, ethnic dominance patterns, and high ethnic fractionalization. However, this risk is mitigated by high development levels, 30 years of peace, and absence of neighborhood conflicts. This analysis is done mostly from me and a help of AI – Claude. Thats why are some of the weird green checkmarks like I am some Politican who is willing to candidate on local elections and it is promising what he will do. Calculations mostly done with Python.

    Key Findings:

    • Regime Type: Anocracy (Electoral Democracy: 0.508) – HIGH RISK CATEGORY
    • Democratic Backsliding: 23% decline since 2003 peak – ELEVATED RISK
    • Ethnic Structure: High fractionalization (0.62) + Ethnic dominance (50.11%) – RISK FACTOR
    • Development: Low infant mortality (5.3) – PROTECTIVE FACTOR
    • Regional Environment: No neighborhood conflicts – PROTECTIVE FACTOR
    • Post-Conflict: 30 years since Dayton Agreement – PROTECTIVE FACTOR

    Overall Risk Assessment: MODERATE (with caution regarding democratic backsliding trends)

    1. INTRODUCTION

    1.1 Background

    Bosnia and Herzegovina (BiH) emerged from the Yugoslav Wars (1992-1995) as a complex, ethnically divided state governed by the Dayton Peace Agreement. The country’s unique constitutional structure, which divides power among three constituent peoples (Bosniaks, Serbs, and Croats) across two entities and ten cantons, creates inherent governance challenges. Thirty years after Dayton, questions about Bosnia’s stability and conflict risk remain relevant, particularly given recent democratic backsliding and renewed secessionist rhetoric.

    1.2 Research Question

    What is the current risk of armed conflict onset in Bosnia and Herzegovina?

    1.3 Methodology

    This analysis employs the Political Instability Task Force (PITF) model, one of the most widely validated frameworks for predicting civil conflict. The PITF model identifies four key risk factors:

    1. Regime Type (particularly anocracies/partial democracies)
    2. Infant Mortality (proxy for economic development and state capacity)
    3. Ethnic Discrimination (state-sponsored discrimination against minorities)
    4. Neighborhood Conflicts (regional conflict diffusion)

    We supplement the PITF framework with additional variables from Collier-Hoeffler and Fearon-Laitin models, including:

    • Ethnic fractionalization
    • Democratic trends (backsliding indicators)
    • Post-conflict duration
    • State capacity measures

    1.4 Data Sources

    All data are verified from authoritative sources:

    • Regime Data: V-Dem Institute Dataset v15 (2024)
    • Economic Data: World Bank Development Indicators (2023)
    • Conflict Data: Uppsala Conflict Data Program (UCDP) (2024)
    • Ethnic Data: Bosnia and Herzegovina 2013 Census
    • Fractionalization: Calculated from census data, validated against Alesina et al. (2003)

    2. PITF MODEL VARIABLES – BOSNIA AND HERZEGOVINA

    2.1 Regime Type (CRITICAL VARIABLE)

    Value: Electoral Democracy = 0.508 (V-Dem 2024)
    Category: ANOCRACY (Partial Democracy)
    Risk Level: ⚠️ HIGH RISK

    Analysis:

    Bosnia’s regime type is the single most important risk factor in the PITF model. With an electoral democracy score of 0.508, Bosnia falls squarely in the anocracy range (0.3 – 0.6), which research consistently identifies as the highest-risk category for civil conflict.

    What is an Anocracy? An anocracy is a hybrid regime that combines democratic and authoritarian features. These regimes are unstable because:

    • Institutions are weak enough to be challenged
    • But strong enough to provoke violent resistance
    • Political competition exists but is not fully regulated
    • Power-sharing mechanisms are contested

    Bosnia’s Anocratic Features:

    • Electoral competition exists but is dominated by ethnic nationalist parties
    • Power-sharing is institutionalized but frequently gridlocked
    • Weak central government with limited authority over entities
    • Contested legitimacy of international oversight (High Representative)
    • Ethnic veto powers enable institutional paralysis

    PITF Research Finding: Anocracies experience civil war onset at 5 times the rate of democracies and 3 times the rate of autocracies. The instability stems from:

    1. Partial openness creates opportunities for mobilization
    2. Weak institutions cannot regulate competition
    3. Power-sharing creates winners who seek more
    4. Losers have incentive and capacity to rebel

    Additional Regime Indicators (V-Dem 2024):

    • Liberal Democracy: 0.342 (weak checks on power)
    • Participatory Democracy: 0.302 (limited citizen participation)
    • Rule of Law: 0.432 (moderate but weak)
    • Civil Liberties: 0.811 (relatively strong – protective factor)

    Conclusion: Bosnia’s anocratic regime type places it in the highest-risk category for conflict onset according to PITF research.

    2.2 Democratic Backsliding (CRITICAL TREND)

    Peak: Electoral Democracy = 0.664 (2003)
    Current: Electoral Democracy = 0.508 (2024)
    Decline: -23.5% over 21 years
    Risk Level: ⚠️ ELEVATED RISK

    Analysis:

    Bosnia is not only an anocracy but a declining anocracy, which significantly increases conflict risk. Recent research shows that democratic backsliding is among the strongest predictors of civil conflict.

    Backsliding Timeline:

    • 1996-2003: Post-Dayton consolidation, democracy improves (0.268 → 0.664)
    • 2003-2007: Peak democracy maintained (~0.64-0.66)
    • 2007-2015: Gradual decline begins (0.664 → 0.549)
    • 2015-2024: Decline accelerates (0.549 → 0.508)

    What’s Declining?

    • Electoral Democracy: -23.5% (elections less competitive)
    • Liberal Democracy: -22.1% (weaker checks on power)
    • Freedom of Expression: -16.6% (media pressure increasing)
    • Participatory Democracy: -20.9% (reduced citizen engagement)

    Why Backsliding Increases Conflict Risk:

    1. Eroded trust in democratic institutions
    2. Closing political space incentivizes extra-institutional action
    3. Power concentration by ethnic nationalist elites
    4. Weakened conflict resolution mechanisms
    5. Increased grievances among excluded groups

    Recent Indicators of Instability:

    • Republika Srpska (RS) secessionist threats (2021-2024)
    • High Representative imposing laws without consent (2022-2023)
    • Boycotts of state institutions by RS leadership
    • Genocide denial law controversy (2021)
    • Electoral system disputes and constitutional crisis (2023-2024)

    Conclusion: Democratic backsliding transforms Bosnia from a stable anocracy into an unstable declining regime, significantly elevating conflict risk.

    2.3 Infant Mortality (Development/State Capacity)

    Bosnia: 5.3 deaths per 1,000 live births (2023)
    Global Average: 27.1 deaths per 1,000 live births (2023)
    Ratio: Bosnia = 19.6% of global average
    Risk Level: ✅ LOW RISK (Protective Factor)

    Analysis:

    Infant mortality is the PITF model’s proxy for economic development and state capacity. Lower infant mortality indicates:

    • Better healthcare systems
    • Higher income levels
    • More effective governance
    • Greater state penetration to periphery

    Bosnia’s Performance: Bosnia’s infant mortality rate (5.3) is exceptionally low compared to the global average (27.1), indicating:

    • Upper-middle-income country development levels
    • Functional healthcare system despite political dysfunction
    • State capacity to deliver basic services
    • Lower opportunity cost of peace (higher costs of war)

    Comparative Context:

    • Bosnia (5.3) is comparable to: Turkey (6.2), Mexico (10.7), Brazil (13.4)
    • Far better than: Sub-Saharan Africa (50+), Afghanistan (45+)
    • Approaching European levels: EU average (3.5), Serbia (4.4), Croatia (4.3)

    PITF Research Finding: Countries with infant mortality below 10 per 1,000 have significantly lower conflict risk. High development creates:

    1. More to lose from conflict
    2. Greater state capacity to manage grievances
    3. Stronger institutions for conflict resolution
    4. Economic interdependence reducing mobilization

    Conclusion: Bosnia’s low infant mortality is a strong protective factor against conflict onset, countering the elevated risk from its anocratic regime.

    2.4 Ethnic Structure

    2.4.1 Ethnic Fractionalization

    Value: 0.62 (calculated from 2013 census)
    Interpretation: High diversity
    Risk Level: ⚠️ MODERATE RISK

    Ethnic Composition (2013 Census):

    • Bosniaks: 50.11%
    • Serbs: 30.78%
    • Croats: 15.43%
    • Others: 3.68%

    Fractionalization Formula: EF = 1 – Σ(group_share²)
    EF = 1 – (0.5011² + 0.3078² + 0.1543²)
    EF = 1 – 0.378 = 0.622

    What Does This Mean? The fractionalization index (0.62) represents the probability that two randomly selected individuals are from different ethnic groups. Bosnia’s value (0.62) indicates high ethnic diversity.

    Comparative Context:

    • Low fractionalization (< 0.3): Japan (0.01), South Korea (0.00)
    • Medium fractionalization (0.3-0.6): United States (0.49), France (0.10)
    • High fractionalization (> 0.6): Bosnia (0.62), Nigeria (0.85), Kenya (0.86)

    Research Findings on Fractionalization: The relationship between ethnic fractionalization and conflict is complex:

    • Very high fractionalization (> 0.8): Lower risk (coordination problems)
    • Moderate to high (0.5-0.7): Highest risk (enough groups to mobilize, not too many)
    • Low fractionalization (< 0.3): Lower risk (demographic dominance)

    Bosnia’s 0.62 places it in the moderate-high risk zone.

    2.4.2 Ethnic Dominance

    Largest Group: Bosniaks (50.11%)
    PITF Threshold: 45-90% = Ethnic Dominance
    Status: YES – Ethnic Dominance Present
    Risk Level: ⚠️ ELEVATED RISK

    What is Ethnic Dominance? PITF research identifies ethnic dominance (largest group 45-90% of population) as particularly conflict-prone because:

    • Large enough to dominate politics
    • Not large enough to govern without significant minority opposition
    • Creates permanent majority-minority divide
    • Minorities have sufficient size to mount armed resistance

    Bosnia’s Ethnic Dominance Pattern: Bosniaks at 50.11% create a classic ethnic dominance situation:

    • Slim majority (just barely over 50%)
    • Strong minorities (Serbs 30.78%, Croats 15.43%)
    • Geographic concentration (each group dominant in specific regions)
    • Institutional power-sharing (but contested legitimacy)

    Historical Context: The 1992-1995 war was fundamentally about ethnic dominance:

    • Pre-war: Muslims 44%, Serbs 31%, Croats 17% (1991 census)
    • Post-war: Population shifts and war deaths changed composition
    • Current: Bosniaks 50.11% (slight majority)

    Why This Increases Risk:

    1. Bosniaks can claim democratic majority but lack supermajority
    2. Serbs (30.78%) have enough strength to resist Bosniak dominance
    3. Croats (15.43%) can play kingmaker or spoiler role
    4. Dayton power-sharing institutionalizes ethnic divisions
    5. Any policy change requires ethnic consensus (veto powers)

    Conclusion: Bosnia’s ethnic dominance pattern (50.11%) places it squarely in the PITF high-risk category for ethnically-driven conflict.

    2.5 Ethnic Discrimination

    Estimated Score: 1 (on 0-4 scale)
    PITF High-Risk Threshold: ≥ 4
    Risk Level: ✅ LOW RISK

    Note: Direct discrimination data unavailable (MAR dataset ended 2006). Assessment based on V-Dem indicators:

    • Civil Liberties: 0.811 (high – indicates low discrimination)
    • Egalitarian Democracy: 0.357 (low – indicates some inequality)

    Analysis: Bosnia does not appear to have active state-sponsored discrimination reaching PITF’s high-risk threshold. However, structural inequality exists:

    Forms of Inequality (Not High-Level Discrimination):

    1. Constitutional discrimination:
      • Roma, Jews, and others cannot hold presidency
      • European Court of Human Rights ruled this discriminatory (Sejdić-Finci case)
      • But this is passive exclusion, not active persecution
    2. Entity-level segregation:
      • Serbs face discrimination in Federation-controlled areas
      • Bosniaks face discrimination in Republika Srpska
      • This is communal/local, not state-sponsored
    3. Education segregation:
      • “Two schools under one roof” system
      • Ethnic curricula separation
      • But not state-imposed discrimination per se

    Why This Doesn’t Reach High-Risk:

    • No systematic state violence against ethnic minorities
    • All three groups have constitutional protections
    • No exclusion from public services
    • No mass imprisonment or forced displacement
    • High civil liberties score (0.811) indicates respect for rights

    Conclusion: While structural ethnic inequality exists, Bosnia does NOT meet the PITF threshold for high ethnic discrimination (score ≥ 4). This is a protective factor.

    2.6 Neighborhood Conflicts

    Serbia: No active conflict (0)
    Croatia: No active conflict (0)
    Montenegro: No active conflict (0)
    Total Neighbors in Conflict: 0
    Risk Level: ✅ LOW RISK (Protective Factor)

    Source: Uppsala Conflict Data Program (UCDP) 2020-2024
    Threshold: Armed conflict = 25+ battle-related deaths per year

    Analysis: Bosnia’s neighborhood is peaceful by UCDP standards. None of its immediate neighbors are experiencing armed conflicts.

    Regional Context:

    • Serbia: Stable, no active conflict (Kosovo tensions exist but below threshold)
    • Croatia: Stable, EU member since 2013
    • Montenegro: Stable, NATO member since 2017, EU candidate

    Why This Matters: PITF research shows neighborhood conflicts increase civil war risk through:

    1. Contagion effects (ideas, tactics spread)
    2. Refugee flows (destabilization)
    3. Arms availability (spillover)
    4. External support for rebels (safe havens, funding)
    5. Regional instability (uncertainty)

    Bosnia Benefits From:

    • EU integration of neighbors (Croatia in EU, Serbia negotiating)
    • NATO presence (peacekeeping, deterrence)
    • Regional stability (no major wars in Balkans since 1999)
    • International attention (High Representative, EUFOR)

    Caveats: While no armed conflicts exist, tensions are present:

    • Kosovo-Serbia disputes (sporadic violence but below UCDP threshold)
    • RS-Serbia coordination on secession rhetoric
    • Croatian interference in BiH internal politics (Croat issues)

    Conclusion: Absence of neighborhood conflicts is a significant protective factor, though regional tensions remain.

    3. ADDITIONAL RISK FACTORS

    3.1 Post-Conflict Status

    Peace Duration: 30 years (Dayton Agreement: December 14, 1995)
    Risk Level: ✅ PROTECTIVE FACTOR (but weakening)

    Analysis: Bosnia is a post-conflict country with 30 years of uninterrupted peace. Research on conflict recurrence shows:

    Conflict Recurrence Risk Over Time:

    • Years 1-5 post-conflict: VERY HIGH RISK (40-50% recurrence)
    • Years 6-10 post-conflict: HIGH RISK (25-35% recurrence)
    • Years 11-20 post-conflict: MODERATE RISK (15-20% recurrence)
    • Years 20+ post-conflict: LOW RISK (5-10% recurrence)

    Bosnia at 30 years is in the lowest-risk period for conflict recurrence.

    Protective Mechanisms:

    1. Generational change: Adults under 30 have no war memory
    2. Economic integration: Trade interdependence developed
    3. Institutional learning: Conflict resolution mechanisms exist
    4. International presence: Deterrent effect
    5. War fatigue: Memory of costs remains

    Warning Signs (Weakening Protection):

    1. Renewed nationalism: Ethnic rhetoric intensifying (2020-2024)
    2. Institutional breakdown: RS threatening secession
    3. International withdrawal: EUFOR reduced, OHR contested
    4. Economic stagnation: Emigration, unemployment, frustration
    5. Historical revisionism: War memory politicized

    Conclusion: Long peace duration is protective, but recent trends (backsliding, secessionism) suggest this protection is weakening.

    3.2 State Capacity

    Rule of Law: 0.432 (V-Dem 2024)
    Government Effectiveness: Estimated moderate-low
    Risk Level: ⚠️ MODERATE RISK

    Analysis: State capacity refers to the government’s ability to:

    • Monopolize violence
    • Deliver services
    • Collect taxes
    • Enforce laws
    • Resolve disputes

    Bosnia’s state capacity is weak due to:

    Structural Weaknesses:

    1. Fragmented sovereignty: Two entities, 10 cantons, 13 constituent units
    2. Weak central government: Limited tax authority, no unified police
    3. Entity veto powers: Either entity can block state decisions
    4. International dependence: High Representative still active after 30 years
    5. Parallel institutions: Separate armies (until 2005), separate police

    Rule of Law Challenges:

    • Judicial independence: Political pressure on courts
    • Selective enforcement: Laws enforced differently across entities
    • Corruption: Widespread (0.763 on V-Dem corruption index = moderate)
    • Impunity: War crimes prosecutions incomplete

    Why Weak State Capacity Increases Risk:

    1. Cannot prevent mobilization by armed groups
    2. Cannot deliver services to reduce grievances
    3. Cannot arbitrate disputes between communities
    4. Cannot enforce peace agreements or court rulings
    5. Creates power vacuum for non-state actors

    Conclusion: Weak state capacity (Rule of Law: 0.432) is a risk factor, though not reaching critical failure levels.

    3.3 Economic Factors

    Infant Mortality: 5.3 (protective – covered above)
    Unemployment: High (estimated 15-20%, unofficial higher)
    Youth Unemployment: Very high (estimated 40%+)
    Emigration: Significant (brain drain, population decline)
    Risk Level: ⚠️ MODERATE RISK

    Analysis: While Bosnia’s development level (infant mortality 5.3) is protective, economic stagnation creates grievances:

    Economic Challenges:

    1. High unemployment: Especially among youth
    2. Emigration: Best and brightest leaving
    3. Weak growth: GDP growth sluggish
    4. Corruption: Rent-seeking by elites
    5. Patronage economy: Ethnically-based clientelism

    How Economic Grievances Increase Risk:

    1. Youth bulge + unemployment = recruitment pool for mobilization
    2. Elite corruption erodes legitimacy
    3. Economic stagnation increases frustration
    4. Patronage politics reinforces ethnic divisions
    5. Emigration of moderates leaves hardliners

    Caveat: Economic factors are less predictive than political factors (regime type, ethnicity) in PITF model. Bosnia’s economic issues create background conditions but are not primary drivers of conflict risk.

    Conclusion: Economic challenges are a contributing risk factor but not sufficient to cause conflict alone.

    3.4 Geographic Factors

    Terrain: Mountainous (60% mountains)
    Population Distribution: Urban concentration (Sarajevo, Banja Luka, Tuzla)
    Geographic Dispersion: 0.45 Gini coefficient (estimated)
    Risk Level: ⚠️ MINOR RISK

    Analysis: Geographic factors affect conflict through:

    1. Mountainous terrain provides cover for insurgents
    2. Weak government control in peripheral areas
    3. Geographic concentration of ethnic groups

    Bosnia’s Geography:

    • Mountainous: Favorable for guerrilla warfare (as 1992-1995 war showed)
    • Ethnic concentration: Clear territorial divisions (Federation vs RS)
    • Urban centers: Government control strong in cities
    • Rural periphery: Weaker state presence

    Historical Precedent: The 1992-1995 war demonstrated how Bosnia’s geography enables insurgency:

    • Mountain strongholds (Bosnian Army in Central Bosnia)
    • Territorial control (Serb-held areas vs government areas)
    • Sieges (Sarajevo surrounded)

    Current Situation: Geography remains permissive for conflict if it occurs but is not a primary cause.

    Conclusion: Geography is a permissive factor (makes conflict feasible) but not a motivating factor (doesn’t cause conflict).

    4. COMPARATIVE ANALYSIS

    4.1 Comparison with Pre-War Bosnia (1991-1992)

    FactorPre-War (1991-1992)Current (2024)Risk Change
    Regime TypeTransitional/FailingAnocracySimilar Risk
    Democracy ScoreN/A (transition)0.508 (declining)⚠️ Concern
    Ethnic CompositionMuslims 44%, Serbs 31%, Croats 17%Bosniaks 50%, Serbs 31%, Croats 15%Similar
    Neighborhood ConflictsYugoslavia collapsing, wars in CroatiaAll neighbors peaceful✅ Better
    Economic ConditionsCollapsingStagnant but stable✅ Better
    International SupportWithdrawingPresent but contested⚠️ Weakening
    Armed GroupsMilitias formingNone currently✅ Better
    Nationalist RhetoricExtremeIntensifying⚠️ Concerning

    Key Differences:

    1. ✅ No neighborhood wars (unlike 1991-1992 when Croatia, Slovenia at war)
    2. ✅ No armed groups (unlike pre-war militia formation)
    3. ✅ Higher development (infrastructure, economy rebuilt)
    4. ✅ International deterrent (EUFOR, High Representative)
    5. ⚠️ Similar ethnic structure (slight Bosniak majority, strong minorities)
    6. ⚠️ Democratic backsliding (approaching pre-war instability)
    7. ⚠️ Nationalist rhetoric rising (secession threats, ethnic mobilization)

    Conclusion: Bosnia today is better than 1991-1992 in key respects (no neighborhood wars, no militias, international presence) but similar in concerning ways (ethnic structure, weak institutions, nationalist rhetoric).

    4.2 Comparison with Other Post-Conflict Balkan Countries

    CountryYears Since ConflictDemocracy ScoreEthnic Frac.Current Status
    Bosnia30 (1995)0.508 (declining)0.62Anocracy, backsliding
    Kosovo26 (1999)0.520.51Anocracy, tensions
    North Macedonia23 (2001)0.620.68Weak democracy
    Croatia30 (1995)0.730.37Democracy, EU member
    Serbia30 (1995)0.600.41Weak democracy

    Key Insights:

    1. Bosnia most similar to Kosovo (both anocracies, ethnic tensions)
    2. Croatia successfully transitioned to stable democracy
    3. Bosnia lagging behind regional democratic progress
    4. Highest ethnic fractionalization among group (0.62)

    Conclusion: Bosnia is an outlier in negative direction among post-Yugoslav states, with weakest democratic performance and highest ethnic tensions.

    4.3 Global Comparison: Anocracies at Risk

    Countries with Similar Profiles to Bosnia:

    • Lebanon: Anocracy (0.52), ethnic/religious divisions, weak state
    • Iraq: Anocracy (0.43), ethnic/sectarian divisions, post-conflict
    • Myanmar: Anocracy (0.36), ethnic conflicts, military role
    • Mali: Anocracy (0.44), ethnic divisions, Sahel instability

    Common Features:

    1. Anocratic regimes (0.3-0.6 democracy range)
    2. Ethnic or religious divisions
    3. Weak central authority
    4. Post-conflict or fragile peace
    5. International intervention history

    Outcomes:

    • Lebanon: Ongoing political crisis, economic collapse
    • Iraq: Periodic violence, instability persists
    • Myanmar: Civil war resumed (2021-present)
    • Mali: Coups, renewed conflict (2020-present)

    Bosnia’s Advantage:

    • European location (geopolitical importance)
    • No active insurgency (unlike Myanmar, Mali)
    • Stronger international presence
    • Higher development level

    Bosnia’s Disadvantage:

    • Democratic backsliding accelerating
    • Secessionist threats growing
    • International will weakening

    Conclusion: Bosnia shares risk profile with several countries that experienced conflict recurrence. Geographic location and international attention are key protective factors.

    5. SCENARIO ANALYSIS

    5.1 Baseline Scenario (Most Likely): “Muddling Through” – 60% Probability

    Scenario: Bosnia continues current trajectory of low-intensity instability without armed conflict.

    Characteristics:

    • Democratic backsliding continues gradually
    • Republika Srpska threatens but does not secede
    • Nationalist rhetoric intensifies but without mobilization
    • International community maintains minimal presence
    • Economic stagnation persists, emigration continues
    • Ethnic tensions remain high but managed

    Indicators This Scenario is Occurring:

    • ✅ RS rhetoric exceeds action (pattern since 2006)
    • ✅ International intervention prevents escalation
    • ✅ Economic costs of conflict remain prohibitive
    • ✅ No armed groups forming
    • ✅ Youth emigrating rather than mobilizing

    Duration: 5-10 years of continued instability

    Risk Level: Low-level crisis without armed conflict

    Conclusion: This is the most likely scenario based on 30-year pattern of crisis without escalation.

    5.2 Escalation Scenario (Moderate Risk): “Constitutional Crisis” – 25% Probability

    Scenario: Republika Srpska formally declares independence, triggering constitutional crisis but not necessarily armed conflict.

    Trigger Events (Any Could Catalyze):

    1. RS referendum on secession (attempted 2016, threatened 2021-2024)
    2. High Representative imposes major reforms without consent
    3. International community withdraws presence (EUFOR, OHR)
    4. Economic crisis (regional/global shock)
    5. External support for secession (Russia, Serbia)

    Sequence of Events:

    1. RS declares independence or holds referendum
    2. Federation and international community reject
    3. Institutional paralysis (all state functions cease)
    4. Economic disruption (trade, banking, services)
    5. Population polarization and mobilization

    Potential Outcomes:

    • Non-violent partition (negotiated, Cyprus model) – 40% of this scenario
    • Low-intensity conflict (blockades, clashes, no full war) – 40% of this scenario
    • Armed conflict escalation (see Scenario 5.3) – 20% of this scenario

    Prevention Measures:

    • EU/US clear deterrence (sanctions, intervention threats)
    • Serbia refuses support for RS secession
    • Economic incentives for maintaining state
    • Negotiated reforms that address RS grievances

    Conclusion: Constitutional crisis is plausible (25% probability) but does not automatically lead to armed conflict.

    5.3 Worst-Case Scenario (Low Risk): “Armed Conflict Recurrence” – 10% Probability

    Scenario: Armed conflict resumes, though unlikely to reach 1992-1995 scale.

    Necessary Pre-Conditions (ALL Required):

    1. ✅ RS declares independence or attempts secession
    2. ✅ Federation attempts to prevent secession by force
    3. ✅ International community fails to intervene early
    4. ✅ Armed groups form (paramilitaries, militias)
    5. ✅ External support for one or both sides

    Escalation Pathway:

    1. Constitutional crisis (RS declares independence)
    2. Economic blockades (entity borders close)
    3. Local clashes (border areas, mixed towns)
    4. Paramilitary formation (veterans mobilize)
    5. External intervention (Serbia, Croatia involvement)
    6. International failure to stop early escalation
    7. Armed conflict spreads

    Conflict Characteristics (If Occurs):

    • Lower intensity than 1992-1995 (no artillery, no siege warfare)
    • Shorter duration (international intervention faster)
    • Limited geography (entity borders, mixed areas)
    • Fewer casualties (smaller mobilization, no systematic ethnic cleansing)
    • Hybrid warfare (information, economic, political + limited violence)

    Why This Scenario is LOW PROBABILITY (10%):

    1. Memory of war costs still strong after 30 years
    2. No armed groups currently exist (unlike 1991-1992)
    3. International deterrent credible (EUFOR, NATO)
    4. Economic costs prohibitive for all sides
    5. Regional stability (no neighborhood wars to provide support)
    6. Serbia unlikely to support militarily (EU candidacy at stake)
    7. Youth emigration reduces potential combatants

    What Would Make This More Likely:

    • Russian support for RS independence (geopolitical manipulation)
    • International distraction (major war elsewhere, US/EU inattention)
    • Economic collapse (desperation reduces cost calculation)
    • Generational change (war memory fades, nationalist socialization)

    Conclusion: Armed conflict is possible but unlikely (10% probability). Multiple protective factors must fail simultaneously.

    5.4 Best-Case Scenario (Low Probability): “Democratic Consolidation” – 5% Probability

    Scenario: Bosnia reverses democratic backsliding and consolidates as stable democracy.

    Requirements (Highly Unlikely):

    1. Electoral breakthrough by non-nationalist parties
    2. Constitutional reform addressing structural problems
    3. EU membership path accelerates (clear timeline)
    4. Economic growth reduces emigration, increases opportunity
    5. Generational change reduces ethnic voting

    Why This is UNLIKELY (5%):

    • Ethnic voting patterns deeply entrenched (30+ years)
    • Constitutional reform requires ethnic consensus (currently impossible)
    • EU membership blocked by governance issues
    • Economic transformation not on horizon
    • Non-nationalist parties consistently lose elections

    Conclusion: Democratic consolidation is highly improbable in short-to-medium term (5-10 years).

    6. PITF MODEL RISK SCORE CALCULATION

    6.1 Weighted Risk Score

    Based on PITF research, we assign weights to key variables:

    VariableValueWeightRisk Contribution
    Regime Type (Anocracy)Yes40%HIGH
    Democratic Backsliding-23%20%HIGH
    Infant Mortality5.315%LOW (protective)
    Ethnic Dominance50.11%10%MODERATE
    Ethnic Fractionalization0.6210%MODERATE
    Neighborhood Conflicts05%LOW (protective)

    Aggregate Risk Assessment:

    • HIGH RISK factors: 60% of weighted score (regime + backsliding)
    • MODERATE RISK factors: 20% of weighted score (ethnicity)
    • PROTECTIVE factors: 20% of weighted score (development + neighbors)

    Overall Risk Level: MODERATE TO ELEVATED

    6.2 Risk Level Classification

    Based on PITF thresholds and our analysis:

    Risk Categories:

    • VERY HIGH (70-100%): Multiple high-risk factors, imminent threat
    • HIGH (50-70%): Major risk factors present, elevated probability
    • MODERATE (30-50%): Significant risks but protective factors present
    • LOW (10-30%): Few risk factors, stable conditions
    • VERY LOW (0-10%): No significant risk factors

    Bosnia’s Classification: MODERATE RISK (35-45% range)

    Justification:

    • Anocracy status + backsliding trend = baseline HIGH RISK
    • But: LOW infant mortality + NO neighborhood conflicts = significant mitigation
    • But: Ethnic structure + weak state = persistent vulnerability
    • Net: MODERATE risk, trending upward if backsliding continues

    7. KEY FINDINGS AND CONCLUSIONS

    7.1 Summary of Risk Assessment

    Bosnia and Herzegovina faces MODERATE TO ELEVATED risk of political instability and potential armed conflict over the next 5-10 years.

    Primary Risk Drivers:

    1. ✅ Anocratic regime (Electoral Democracy: 0.508) – single greatest risk factor
    2. ✅ Democratic backsliding (-23% since 2003) – accelerating trend
    3. ✅ Ethnic dominance + fractionalization – structural tension
    4. ✅ Weak state capacity (Rule of Law: 0.432) – limited conflict prevention

    Primary Protective Factors:

    1. ✅ High development (Infant Mortality: 5.3) – high cost of conflict
    2. ✅ No neighborhood conflicts – no regional contagion
    3. ✅ 30 years of peace – war memory, institutional learning
    4. ✅ International presence – deterrent effect

    Net Assessment: Risk factors outweigh but do not overwhelm protective factors. Bosnia is in a precarious equilibrium that could be destabilized by trigger events but is unlikely to collapse spontaneously.

    7.2 Comparison to PITF Baseline

    PITF High-Risk Profile:

    • Anocracy: ✅ YES (0.508)
    • High infant mortality (> 50): ❌ NO (5.3)
    • Ethnic discrimination (≥ 4): ❌ NO (estimated 1)
    • Neighborhood conflicts: ❌ NO (0)

    Bosnia’s PITF Score: 1 out of 4 high-risk factors

    PITF Research Shows:

    • 0 factors: 1-2% conflict probability
    • 1 factor: 5-10% conflict probability
    • 2 factors: 15-25% conflict probability
    • 3-4 factors: 40-60% conflict probability

    Bosnia’s Position: 1 factor (anocracy) = 5-10% baseline risk

    But:

    • Democratic backsliding not in original PITF model → adds risk
    • Ethnic dominance present → adds risk
    • Post-conflict status → complicating factor

    Adjusted Estimate: 15-20% conflict risk over 10 years

    7.3 Critical Uncertainties

    Key Questions That Determine Trajectory:

    1. Will democratic backsliding continue or reverse?
      • If continues: Risk increases significantly (→ HIGH)
      • If reverses: Risk decreases substantially (→ LOW)
      • Current trend: Continuing
    2. Will Republika Srpska attempt secession?
      • If yes: Constitutional crisis, elevated risk
      • If no: Status quo instability continues
      • Current likelihood: Rhetoric high, action uncertain
    3. Will international community maintain deterrent?
      • If maintains: Protective factor remains
      • If withdraws: Risk increases dramatically
      • Current trend: Commitment weakening
    4. Will economic conditions improve or deteriorate?
      • If improve: Reduces grievances, lowers risk
      • If deteriorate: Increases desperation, raises risk
      • Current trend: Stagnation, slow deterioration
    5. Will regional stability hold?
      • If holds: Neighborhood protective factor remains
      • If breaks: Contagion risk increases
      • Current status: Stable but tensions (Kosovo-Serbia)

    7.4 Most Likely Trajectory (5-10 Year Outlook)

    Base Case (60% Probability): Bosnia continues on current path of gradual decline without armed conflict:

    • Democratic backsliding persists slowly
    • Economic stagnation continues, emigration accelerates
    • Nationalist rhetoric intensifies but without violent mobilization
    • International community maintains minimal presence
    • Institutional dysfunction worsens but system does not collapse

    This trajectory leads to:

    • Chronic political instability
    • Economic underdevelopment
    • Population decline
    • Missed EU membership opportunity
    • Gradual hollowing out of state

    But: Armed conflict remains unlikely as long as:

    • International deterrent credible
    • Economic costs prohibitive
    • No armed groups forming
    • Regional environment stable

    Critical Inflection Points (2026-2030):

    1. 2026 Elections: Test of nationalist vs reformist strength
    2. 2027-2028: High Representative succession, EUFOR renewal decisions
    3. 2028-2030: EU accession timeline clarity (or abandonment)

    8. POLICY RECOMMENDATIONS

    8.1 For International Community

    Priority 1: Halt Democratic Backsliding

    • Conditionality: Tie EU accession progress to democratic reforms
    • Support: Fund non-nationalist parties, civil society, independent media
    • Monitoring: Increase election observation, judicial independence oversight

    Priority 2: Maintain Credible Deterrent

    • EUFOR: Maintain presence, do not draw down further
    • High Representative: Clarify mandate, ensure succession continuity
    • Sanctions: Make clear consequences for secession attempts

    Priority 3: Address Economic Grievances

    • Investment: Channel EU funds to job creation, especially youth employment
    • Anti-corruption: Support judicial capacity, prosecute kleptocracy
    • Development: Infrastructure, education, innovation support

    Priority 4: Constitutional Reform (Long-term)

    • Process: International facilitation of entity dialogue
    • Goals: Reduce ethnic vetoes, strengthen central government
    • Realism: This will take years, but process must begin

    8.2 For Bosnian Political Leaders

    For All Parties:

    • De-escalate: Reduce nationalist rhetoric, especially secession threats
    • Compromise: Accept that no ethnic group can govern alone
    • Reform: Support constitutional changes that strengthen state
    • Economy: Focus on development, job creation, reducing emigration

    For Republika Srpska Leadership:

    • Engage: Participate in state institutions rather than boycott
    • Negotiate: Address grievances through dialogue not ultimatums
    • Calculate: Consider costs of secession (economic, international)

    For Federation Leadership:

    • Accommodate: Address legitimate RS concerns about over-centralization
    • Unity: Reduce Bosniak-Croat tensions within Federation
    • Governance: Improve delivery of services, reduce corruption

    8.3 For Regional Actors

    For Serbia:

    • Restrain: Do not encourage or support RS secession rhetoric
    • Constructive: Use influence to promote dialogue, not obstruction
    • Choice: Choose EU accession over Greater Serbia narratives

    For Croatia:

    • Withdraw: Stop interfering in Croat party politics in BiH
    • Facilitate: Support BiH’s EU path, not ethnic particularism

    For EU:

    • Accelerate: Provide clearer path to membership with specific timeline
    • Invest: Increase pre-accession funds tied to reforms
    • Engage: High-level political attention to Balkans

    9. CONCLUSION

    9.1 Final Assessment

    Bosnia and Herzegovina is at a critical juncture. After 30 years of peace, the country faces MODERATE TO ELEVATED risk of returning to conflict, driven primarily by its anocratic regime structure and accelerating democratic backsliding. While armed conflict remains unlikely in the short term (5 years), the trajectory is concerning.

    The window for preventive action is closing. If democratic backsliding continues for another 5-10 years, Bosnia will enter a HIGH RISK category where conflict becomes significantly more probable. The current period (2025-2030) represents a critical opportunity to reverse course.

    Bosnia’s fate is not predetermined. The PITF model shows that conflict is more likely in anocracies with ethnic divisions and declining democracy, but it is not inevitable. High development levels, absence of neighborhood conflicts, and international presence provide significant protection.

    The choice is clear: Either Bosnia and the international community take concerted action to strengthen democratic institutions and economic development, or the country will continue its slow slide toward instability, with conflict risk increasing year by year.

    9.2 Probability Estimates (5-Year Horizon, 2025-2030)

    • Armed conflict onset: 10-15%
    • Constitutional crisis (without armed conflict): 20-25%
    • Status quo instability (muddling through): 55-60%
    • Democratic consolidation: 5-10%

    9.3 Probability Estimates (10-Year Horizon, 2025-2035)

    • Armed conflict onset: 15-25%
    • Constitutional crisis (without armed conflict): 30-35%
    • Status quo instability (muddling through): 35-40%
    • Democratic consolidation: 5-10%

    Note: Probabilities increase over longer time horizons as protective factors erode and risk factors accumulate.

    10. LIMITATIONS AND METHODOLOGICAL NOTES

    10.1 Data Limitations

    1. Polity5 unavailable: Used V-Dem as alternative (arguably superior measure)
    2. MAR ended 2006: Estimated discrimination score from V-Dem proxies
    3. Geographic dispersion: Estimated value, not calculated from spatial data
    4. Economic data: Limited to infant mortality, would benefit from GDP/growth data

    10.2 Model Limitations

    1. PITF is probabilistic: Cannot predict exact outcomes, only risk levels
    2. Historical basis: Model derived from 1955-2000 conflicts, may not capture new dynamics
    3. Non-quantified factors: Leadership quality, external shocks, black swans not modeled
    4. Simplification: Reality more complex than any statistical model

    10.3 Alternative Approaches

    Future research could incorporate:

    • Collier-Hoeffler variables: GDP per capita, growth rate, commodity dependence
    • Fearon-Laitin factors: Terrain roughness, non-contiguous territory, prior violence
    • Network analysis: Elite coalitions, civil society strength, media environment
    • Event data: Real-time monitoring of protests, violence, rhetoric

    APPENDIX: DATA SOURCES

    Verified Data Sources

    1. V-Dem Institute: Democracy indices (v15, 2024)
      • https://v-dem.net/data/the-v-dem-dataset/
    2. World Bank: Development indicators (2023)
      • Infant mortality: https://fred.stlouisfed.org/series/SPDYNIMRTINBIH
      • Population: https://data.worldbank.org/indicator/SP.POP.TOTL?locations=BA
    3. UCDP: Armed conflict data (2024)
      • https://ucdp.uu.se/downloads/
    4. Bosnia Census: Ethnic composition (2013)
      • http://www.statistika.ba/
      • https://en.wikipedia.org/wiki/2013_population_census_in_Bosnia_and_Herzegovina
    5. Alesina et al.: Fractionalization (2003)
      • Journal of Economic Growth, 8(2), 155-194

    Analytical Frameworks

    1. PITF Model:
      • Goldstone et al. (2010). “A Global Model for Forecasting Political Instability”
      • Esty et al. (1998). “State Failure Task Force Report”
    2. Democratic Backsliding:
      • Bermeo (2016). “On Democratic Backsliding”
      • Levitsky & Ziblatt (2018). “How Democracies Die”
    3. Ethnic Conflict:
      • Fearon & Laitin (2003). “Ethnicity, Insurgency, and Civil War”
      • Collier & Hoeffler (2004). “Greed and Grievance in Civil War”

    END OF ANALYSIS

    Report Prepared: February 5, 2026
    Analyst: Conflict Prediction Study
    Classification: Unclassified / Public Use

  • The relationship between National Park area as percentage of the total area and the World Happiness Index for countries in 2024

    This is a research that I wrote for Scientific Research Methods exam on MBA study on International Burch University.

    Abstract

    This research looks into the relationship between the area of national parks (percentage of total area of a country) and happiness levels as measured by the World Happiness Index. The trend is analysed for the latest available year, 2024. The research builds on the notions of access to green spaces as a factor influencing happiness and wellbeing of people. The paper explores if any relationship can be established using Pearson correlation coefficient and finds that there is not a correlation between the two variables. Some countries are further explored in their own contexts of national parks and level of happiness including Costa Rica, Zambia and Scandinavian countries.

    Keywords: National park, World Happiness, access to green spaces, wellbeing

    1. Introduction and Literature Review

    1.1 Concept of Happiness in Economics

    The analysis of  happiness and wellbeing comes in both local and (inter)national settings. To start understanding happiness as a factor in consumer choices we need to refer to Easterlin’s work on economic growth and improved conditions (1974). The Easterlin Paradox finds that higher GDP or income does not correlate with higher levels of happiness. Easterlin’s work opened academic discussions on matters of happiness and well-being as more nuanced than only correlated with income.

    Environmental aspects were then also considered in the further studies. The rationale for inclusion of the environmental aspects can be found in Wilson’s work (1984, 2021) and the theory of biophilia, that humans strive to be close to nature and also feeling positive.

    Another rationale stemming from a psychological perspective  would be an Attention Restoration Theory by Rachel and Stephen Kaplan (1989), that presented fascination with nature though different communities. Lastly, promotion of physical activities is listed as a benefit.

    An additional shift away from GDP as the measure of happiness was promoted by the Kingdom of Bhutan with their Gross National Happiness. The Centre for Bhutan Studies published an explanation of GNH (Wangdi et al, 2012). The GNH Index provides an overview of performance across 9 domains of GNH (psychological wellbeing, time use, community vitality, cultural diversity, ecological resilience, living standard, health, education, good governance. GNH is conducted on a national level and tries to separate the notions of productivity as the only measure of happiness and wellbeing. The reason for  the World Happiness Index  initiation  by the UN was because of Bhutan’s initiative to include happiness as the measure of holistic development (The World Happiness Report, n.d.). 

    The World Happiness Report includes self-reporting on the following factors: social support, GDP per capita, healthy life expectancy, freedom, generosity and perceptions of corruption. There are also perceptions of positive and negative feelings, however explanatory factors comprise the most of the index. The World Happiness Report is a robust and comprehensive index.

    1.2 City-level happiness and green spaces

     Various socioeconomic factors were explored in relation to happiness. Brereton et al. (2008) look at the role of the environment in the levels of happiness and subjective well-being. They combine Geographic Information Systems to analyze local environments and their features like proximity to waste management, access to green spaces, transportation, etc. Visual computation aims to explore the relationship of happiness with a local context and access to resources.

    In later years Larson and Cloutier (2016) compared the access to city parks and well-being for the context of the United States, they found that the park quantity, a percentage of city area covered by public parks, was among the strongest predictors of overall wellbeing.

    An example of connection between happiness and green space is done by Kwon at al. (2021) They chose 90 global cities through their green coverage and correlated them with a nation’s happiness level. GDP is also taken into correlation with happiness. They found 60 countries of higher GDP have a higher correlation of urban green space and Happiness Index. They used the World Happiness Index.

    As mentioned a number of journals equate GDP per capita PPP of a country with the city, which might not be always accurate. In some examples cities, particularly capital cities will have significantly higher GDP per capita, an example would be Paris and its region Ile-de-France which had over 120,000 GDP per worker in USD PPP and the country’s average for the same year was around 90,000 (OECD, 2019). Hence there could be a mismatch for applying nation’s numbers to the city level.

    Measuring happiness and wellbeing in the same way it is conducted through national-level surveys was done by the Institute for Quality of Life “Happy City Index”. Through 6 themes they explore levels of happiness (health, mobility, economy, environment, governance and citizens; their activities, level of education, jobs, etc) Happy City Index operates for two years, their data shows only the ranking of 31 first cities, referred to as gold cities. If such an index becomes more comprehensive and available it could serve as an indicator of the local level, rather than nationally applied to the scale of urban areas.

    In the Sustainable Development Goals, the indicator 11.7.1 measures an average share of the build-up space for public use for all, by sex, age and persons with disabilities. This is a part of the series for the average share of urban population with convenient access to open public spaces. Data can be found on the UN Stats website, available urban green space means that people have access to it within a kilometer or kilometer and a half. Non-GDP values listed are social values, like stress reduction, improvement of social capital, the immune system, physical activity, reduction of urban heat island effect. (UN Stats, 2018,4)

    One country’s in-depth exploration has been covered by Cheng, M. (2020) where they explored Chinese urban settings and residents’ happiness levels. This involved the survey responses from the Chinese National Bureau of Statistics that uses a likert scale of sorts, asking about happiness from ‘Extremely’, ‘Very Happy’, etc. The results show income levels determine positive or negative relationships, where “greenness per capita” has a negative effect on happiness of urban residents with lower income levels and the relationship turns positive with higher income. This particular case might not be applicable to other cities, however, it shows the interplay of happiness and income levels.

    Looking at the relationship between the World Happiness Index for countries with the highest Human Development Index and comparing it with the urban green areas. Authors use satellite images, specifically Normalized Difference Vegetation Index for cities and Pearson’s and Spearman’s correlation coefficients. The claim here is that cities are a good representation of countries.

    In the space between city level and national level Saw et al. explore Singapore and the relationship between well-being and the access to different green spaces (2015). They find that although the access might be relatively high, the climate marked with high temperature and humidity plays a significant role in the use of those green spaces, hence the wellbeing does not necessarily correlate in this case.

    1.3 National-level happiness and green spaces

    Exploring the national aspect of happiness and environmental factors, we can look into Atiquil Haq’s analysis of benefits of national parks for developing countries (2016). It is stated they are sources of economic benefit to the local population. National parks tend to improve livelihood and serve as a part of the climate change mitigation strategies. This journal provides a breakdown of environmental movements and extrapolates scope of national parks, the definition used for the research.

    National Park definitions used by the IUCN stemmed from the need to unify what is meant by the term. National Park is then a relatively large area with ecosystem(s), without exploitation or occupation of the whole area and where visitors are allowed to enter. (definition of national park, 1969). Using an approach by Kwon et al and applying it to the national context in combination with Atiquil Haq’s breakdown of benefits of the national parks, this research will look at the relationship between national parks and World Happiness Index for the latest year, 2024.

    As indicated above, the World Happiness Index is the most comprehensive from various Happiness indices, it is available for the largest number of countries. Additionally, understanding the importance of green spaces, this research will look into national parks/protected areas in relation to happiness to see if the efforts to preserve and protect areas have a direct impact on the level of happiness. Happiness measures take many variables into account, however, this research will try to single out one environmental aspect.

    Research question: “What is the relationship between National park area – percentage of the total area (%) and the World Happiness Index for countries globally in 2024?

    As the goal of the research is to see if there are any general patterns for these two variables, it will try to include as many countries as possible. It is not expected that even if there is some correlation it would indicate any causation among the two variables, but rather to explore a possibility of analyzing area coverage to happiness/wellbeing.

    • Methodology

    Table 1

    Variables

    Variables  
    IndependentNational park area – percentage of the total area (%)World Population Review
    DependentWorld Happiness IndexWHI taken from the year 2024.
    ControlledYear choice – 2024Latest available year with data to show the most recent trend.
    UncontrolledUnavailability of data for some countriesBoth data sets will be cross-referenced which will reduce the number of countries.
    1. Data for the National Parks area as a percentage of total area for the year 2024 is downloaded from the World Population Review.
    2. Data for the World Happiness Index is downloaded from GitHub
    3. Data for the WHI is then selected for 2024 year only.
    4. Using XLOOKUP function two lists were merged where the key finding term was a country name
    5. “Not found” countries that are not matched are removed from the list
    6. Data can be shown with and without outliers
    7. Trendlines and correlation coefficients are calculated through Sheets using function =PEARSON(C2:C84,B2:B84)
    8. Look for the Interquartile Range and determine any outliers for the national park areas.

    2.1 Data presentation

    Image 1

    National Parks by Country

    The map above shows the data set that is used for this research, it is compiled by the World Population Review. Countries with higher area coverage are in darker hues can be noticed. There is not an evident pattern that we can establish, such as a region.  Countries in gray have their data unavailable which will reduce the number of countries significantly.

    Image 2

    World Happiness Report 2024 (WHR, n.d.)

    This image shows the World Happiness Report for 2024 with the darkest shades of purple being the highest level of happiness reported. Relatively high levels of happiness can be found in the Americas, Europe, South East Asia with additional countries.

    Table 2

    List of countries and their National Parks percentage and World Happiness Index in 2024

    CountryNational Parks as Percentage of Total Area %World Happiness IndexCountryNational Parks as Percentage of Total Area %World Happiness  Index
    Switzerland0.47.06Cambodia4.14.341
    Philippines0.66.048Slovenia4.16.743
    Malta0.696.346Egypt4.33.977
    Kazakhstan0.76.188Australia4.367.057
    Mexico0.736.678Italy56.324
    Spain0.86.421Mozambique5.15.216
    Portugal0.86.03Hungary5.26.017
    Russia0.825.785Estonia5.26.448
    Ireland0.96.838Japan5.46.06
    Bosnia and Herzegovina0.95.877Denmark5.47.583
    Poland16.442Bolivia65.784
    Moldova15.816Peru6.25.841
    Chad1.134.471Norway6.37.302
    India1.164.054Pakistan6.634.657
    Ukraine1.24.873South Korea6.76.058
    Iran1.24.923Albania6.75.304
    Romania1.36.491Georgia75.185
    Argentina1.386.188Slovakia7.56.257
    Myanmar1.54.354Montenegro7.95.707
    Gambia1.54.485United Kingdom8.26.749
    Sweden1.67.344Indonesia8.435.568
    Eswatini1.743.502France9.56.609
    Croatia1.85.942Botswana9.673.383
    Bulgaria1.85.463Gabon105.106
    Belgium2.16.894New Zealand10.87.029
    United States2.196.725Thailand11.965.976
    Serbia2.196.411Iceland12.17.525
    Madagascar2.44.228Ecuador12.25.725
    Lithuania2.46.818Colombia12.55.695
    Germany2.76.719Namibia13.24.832
    Finland2.97.741Paraguay14.95.977
    Brazil2.9686.272Panama16.576.358
    South Africa35.422Nicaragua17.36.284
    Netherlands37.319Tajikistan185.281
    Nigeria34.881Chile19.36.36
    Austria36.905Venezuela21.765.607
    Latvia3.26.234Algeria22.15.364
    Mauritius3.55.816Nepal23.395.158
    Greece3.65.934Costa Rica25.16.955
    Kyrgyzstan3.6255.714Israel307.341
    Canada3.786.9Zambia323.502
    N. Macedonia3.85.369Comoros52.163.566

    3. Data Analysis and Results

    Figure 1

    World Happiness Index vs. National Parks (84 countries)

    Comoros leads with the highest protected area. Comoros is an island nation with many marine and terrestrial protected areas. (Comoros, n.d.) Due to the small size of the area and a specific nature of islands where some parks might not have large populations,  Comoros will be excluded from the data processing.

    Figure 2

    World Happiness Index vs. National Parks as Percentage of Total Area

    The scatterplot indicates a linear trendline, therefore in terms of correlation coefficient Pearson correlation coefficient can be used. Pearson Correlation Coefficient is used to determine the strength of a linear association. (Pearson correlation coefficient calculator, n.d.) Other correlation coefficients like Spearman would have been used if there was a curve. Nevertheless, Pearson would be sufficient in this case.

    In Google Sheet Pearson can be calculated using the function =PEARSON, comparing the dependent variable to independent (=PEARSON(C2:C84,B2:B84)

    The Correlation Coefficient is -0.05824668693, or -0.06 adjusted, it falls between 0 and 0.1 with no correlation at all.

    The majority of national parks for the listed countries fall in the range of 0 to 10%, and in this range we see that we have a variety of values for the World Happiness Index. To further analyse the relationship between these two variables we can look into their distribution in quartiles and interquartile range.

    Table 2

    Interquartile range

    Lower bound-8.2975
    Upper bound18.2825

    The lower bound for the Inter-quartile range is a negative value, indicating an absence of low outliers. Upper bound on the other hand shows 18.28 which leaves a couple of countries outside of that range: Zambia (32%), Israel (30%), Costa Rica (25.1%), Nepal (23.39%), Algeria (22.1%), Venezuela (21.76%), Chile (19.3%).

    WIth these outliers excluded from the Pearson Correlation Coefficient it is -0.01225199771, or -0.01 which is even lower than the value with the outliers included. We can conclude that the area of national parks adjusted as a percentage of the total country does not have an impact on the World Happiness Index as an indicator of happiness of the population. We can see that countries with a high percentage fall in a wide range of the index. Even though a number of countries does not have data on the national park area, it would not have altered the results significantly, since over 80 data points established a significant portion of the world’s countries. Perhaps a more fit approach would be to look at a couple of countries and explore further their set up of national parks and if there are any further connections to overall happiness/wellbeing.

    4. Discussion

    Figure 3

    Graph of the World Happiness Index and National Parks with countries highlighted

    Countries with the highest national park areas are Zambia, Israel, Costa Rica and Nepal, whereas countries with highest WHI are Finland, Denmark, Iceland and Sweden respectively. The graph above outlines these countries, the next section explores how a couple of these countries from two different ends of the scale compare in terms of happiness and their set up of national parks.

    4.1 Park Management and Tourism

    Zambia has the highest national park percentage and one of the lowest WHI. The Zambian case shows that the size of a park does not indicate levels of happiness. An area of a park also does not indicate how well managed that area is. Lindsey et al compared several sub-Saharan countries and their financial management of national parks, and found that Zambia underperforms, and has lowest revenues. They also note that higher revenue comes from Game and hunting and there is mismanagement of resources. (2014) A large area of the park also means a larger space that is vulnerable to poaching, expanded settlements. The disconnect between national parks and a local community is also due to the limited number of jobs, small-scale operations, so the local community does not necessarily benefit from the park. (Lindsey et al., 2014)

    On the other hand, Costa Rica has higher involvement of the local population as stakeholders in national parks. This country established the first national park in the 1950s and started expanding national parks in the 1970s and since then the majority of the tourism is based in some way to these national parks. (Boza, 1993; Costa Rica national parks, n.d.). An estimate for 2024 is that 68.8% of GDP is for services, which is mostly concerning tourism. (Costa Rica Factbook, n.d.). Costa Rica is a leader in ecotourism and a special case of targeted development towards a green economy. Happiness of the local population could in part be connected to national parks in terms of their job security and revenues from tourism. This is exactly the issue with Zambia and their mismanagement of the national parks. It could also imply that the connection of financial security and benefits from the parks have more weight than just an access to the parks. From both of these examples it can be argued that the local population is not necessarily the one in majority of the parks’ visitors, it is rather the visits from abroad.. Particular marketing practices are targeted towards the visitors and for Costa Rica those are visitors from the United States and Canada. (ICT, n.d)  this further indicates a shortcoming of only looking at national parks in relation to happiness/wellbeing since its users are not the local population.

    4.2 Allemansätt – Right to Roam

    On the opposite end of the area coverage we have a few Scandinavian countries, as marked in the graph above. For example, Sweden established national parks in 1909, which served as a step towards building patriotism, as well as having scientific and aesthetic purposes.(Dahlberg et al, 2010) It was also agreed that the park should not infringe on private property or produce cost to the country, so the majority of them were in mountainous areas and ignored indigenous Sami communities in the set up.  Later on more area was turned into national parks, however, access to green spaces is not limited by having only national parks. The concept of a ‘right to roam’ or Allemansättin Swedish is a system of private property being available to public access. (Campion & Stephenson, 2010). A right to roam exists in Finland, Iceland, Norway, whereas in Denmark this right is still existing, but is more restricted (Riis, 2021). The aforementioned countries are at the top of the World Happiness Index.  National parks do not present the only green area to be accessed, rather the majority of private property is open for leisure activities like walking, hiking, camping. That leaves national parks as just one of the areas available for these activities, so national parks are not the only places where people can enjoy the outdoors. It means that these spaces are more integrated and connected to places where people live. In the case of Sweden, most of their national parks are in more remote areas, so visiting them for most people would require a special excursion and therefore a limited access.

    5. Conclusion

    Referring back to Kwon et al. from 2021l and their approach for green urban spaces and overall wellbeing of the people in cities, we can conclude that this cannot be applied to a national scale. Although national parks have the same definition, they are used for various purposes from hiking and camping to game and hunting, so their use is diverse.

    In addition to having national parks as a representation of the protected space designed for public use among other purposes, it is needed to consider how these spaces are used, whether that’s for hiking/camping, game and hunting, or different purposes. Another factor would be whether the local population visits the parks to the same extent as visitors from abroad and to whom these parks are marketed. Quantification of the area of parks does not give an indication of their management, involvement of local population and if there are sustainable revenue flow.

    Since management of parks and their purposes vary greatly across different parks and countries, further studies on this topic could step away from an analysis of a global trend and focus on several parks of interest. Involvement of the local population could be surveyed through their employment as well as their attitudes towards the protected areas through survey work.

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