Chapter 7: Crisis Economics – Shocks, Speculation & Emergency Behavior

 

Chapter 7: Crisis Economics – Shocks, Speculation & Emergency Behavior

Objective:

To understand how crises—be they pandemics, wars, inflation shocks, or policy upheavals—alter market behavior through speculation, hoarding, panic-buying, and black marketing. This chapter explores these disruptions using analytical models, real-world case studies, and graphical insights.

 

1. Introduction: When Logic Breaks and Emotion Buys

Markets function on signals: price indicates scarcity, cost reflects effort, and consumers optimize utility. But during crises, this neat logic collapses. Crises incite emergency behavior, where fear outweighs rational decision-making. Buyers hoard, speculators hype, and sellers shift to informal channels where prices are unregulated.

What emerges is a fragile ecosystem of:

·         Speculative Demand – Driven by fear or expectations

·         Hoarding – Suppliers reduce supply intentionally or due to fear of shortage

·         Black Markets – Arise due to legal price controls or bans

·         Price Volatility – As expectations and reality move apart

These distortions are not outliers—they are part of the crisis economics framework, a necessity to understand how the market fails and how it can be rescued.

 

2. Crisis Shocks: Categories and Implications

Economic crises can arise from internal or external shocks. Each affects market behavior differently:

Shock Type	Examples	Market Impact
Supply Shocks	War, natural disasters, factory closures	Supply ↓, Prices ↑
Demand Shocks	Pandemic panic, oil demand crash	Demand ↑ or ↓, Supply lagging
Currency Shocks	Rupee devaluation, capital outflows	Import prices ↑, Cost-push inflation
Policy Shocks	Bans, sudden taxation, lockdowns	Legal markets paralyzed, informal sectors grow

: Crises create ripple effects where rational economic agents behave unpredictably, pushing systems into chaos if not corrected with intelligent policy.

 

3. Rewriting the Equations of Demand and Supply Under Stress

Modified Demand Equation in a Crisis:

Qd = α – βP + γE + δF + θM

Where:

·         P = Price

·         E = Expectation of future price rise

·         F = Fear/Panic Index (ranging 0–1)

·         M = Media/Information pressure

·         γ, δ, θ are parameters measuring how external shocks distort logical demand

During crises, γE + δF + θM dominate the equation—people buy more because they’re scared, not because of price or need.

Modified Supply Equation in a Crisis:

Qs = a + bP – cH + dT – φR

Where:

·         H = Hoarding impact (intended or unintended supply withdrawal)

·         T = Delay due to logistical constraints

·         R = Regulatory bottlenecks

·         φ = Negative elasticity due to interference

Interpretation: Supply doesn’t just respond to price but is blocked or slowed by fear, hoarding, or poor governance. This results in the supply curve shifting leftward during crisis.

 

4. Graphical Insight: Panic Buying and Hoarding Converge

Below is a visual representation of how markets behave during crises.

 

 

Key Observations:

·         Demand shifts right (from D1 to D2) due to panic, expectation, and hype.

·         Supply shifts left (from S1 to S2) due to hoarding and delays.

·         Price increases from P1 (₹66.67) to P2 (₹76.67).

·         Quantity available falls from Q1 (2333 units) to Q2 (2233 units).

The gap between official and black market price widens—fertile ground for profiteers and economic inequality.

 

5. Case Studies of Crisis-Induced Market Behavior

Case 1: COVID-19 and Sanitizer Panic (2020)

In March–April 2020, a 100ml sanitizer bottle that cost ₹50 before the pandemic was selling at ₹300–₹500 in several regions of India.

·         Hoarding by distributors reduced available supply.

·         Fearful consumers overbought (panic multiplier).

·         Government interventions came late, allowing black markets to flourish.

The modified equations in action:

Qd = 3000 – 10P + 400 (panic)
Qs = 1000 + 20P – 300 (hoarding)

New Equilibrium:
3000 – 10P = 700 + 20P → P = ₹76.67, Q = 2233 units

Compared to the normal: P = ₹66.67, Q = 2333 units.

This is not just inflation—it’s distortion.

 

Case 2: Russia-Ukraine War and Wheat Prices (2022)

Ukraine and Russia accounted for over 30% of global wheat exports. The war led to:

·         Export bans

·         Transportation disruptions

·         Surge in futures buying by speculators

·         Domestic hoarding by governments and households

Prices increased by over 40% in global markets. Even non-importing countries saw spikes due to expectation-based demand and supply chain mimicry.

 

Case 3: Bitcoin and Cryptocurrency Boom-Bust Cycle (2020–22)

Unlike essential goods, cryptocurrencies exhibited pure speculative demand.

·         Demand driven not by use, but by media, influencer hype, and fear of missing out (FOMO).

·         Price surged from $8000 to $64000 in 18 months.

·         Crashed below $20000 in early 2022 as fear reversed.

This reflects the γE + θM term dominating the demand equation.

 

6. Numerical Caselet: Crisis Price Prediction

Scenario:

During a sudden disease outbreak, consumers start panic-buying medical gloves. Government data shows:

·         Pre-crisis: Qd = 4000 – 15P, Qs = 1500 + 25P

·         In-crisis: Panic index (F) = 0.75, Hoarding index (H) = 40 units

Modified:

·         Qd = 4000 – 15P + 500F = 4000 – 15P + 375 = 4375 – 15P

·         Qs = 1500 + 25P – 40×10 = 1100 + 25P

Find Equilibrium:

4375 – 15P = 1100 + 25P
⇒ 3275 = 40P
⇒ P = ₹81.88
⇒ Q = 1100 + 25×81.88 = 1100 + 2047 = 3147 units

Result:

·         Price increase of 30%

·         Quantity drop of 400–500 units

·         Panic increased demand even as hoarding reduced supply

 

7. Emergency Behavior: Irrationality is Rational in Fear

Crises disrupt assumptions of rationality.

Observed Behaviors:

·         Stockpiling – Beyond logical use

·         Speculation – Buy now, sell at peak

·         Informal Markets – Legal channels can’t serve excess demand

These are rational strategies under uncertainty, but they create externalities that hurt the broader economy—especially the poor and marginalized.

 

8. Policy Intervention: Taming the Crisis Monster

Discussion Prompt:

Design a 4-point Market Stabilization Policy Toolkit for governments.

Toolkit:

1.      Real-Time Inventory Dashboards

o    Use AI and logistics to monitor stock movement across regions.

2.      Temporary Price Caps + Incentive Bonuses

o    Cap prices but reward efficient sellers with tax rebates or fuel subsidies.

3.      Information Campaigns

o    Reduce media-driven panic via expert-led communication.

4.      Enforcement Against Hoarding & Black Marketing

o    Use legal power to confiscate hoarded goods and redistribute them publicly.

 

9. Long-Term Institutional Measures

Crisis economics teaches us the need for proactive rather than reactive frameworks:

·         National Crisis Resilience Units

·         Digital Supply Chain Monitoring

·         Commodity Futures Regulation During Emergencies

·         Integration of Psychological Economics in Policy Design

Markets aren’t perfect. But systems can be.

 

10. Conclusion: The Invisible Hand Doesn’t Work When It's Tied

In times of peace and certainty, markets self-regulate well. But crises expose their soft underbelly. Demand overshoots, supply collapses, and equilibrium is broken not just economically but socially.

Speculation and hoarding aren't just strategies—they are symptoms of broken trust in institutions and systems.

Crisis economics is not about controlling every move—but about creating flexible safety nets, accurate real-time data, and a public psychology of stability.

 

Teaching Notes & Reflection Questions

1.      Why do people overbuy during a crisis? Does it make economic sense individually?

2.      Plot a graph showing black-market pricing. How does it hurt consumer surplus?

3.      If expectations can drive demand more than price, how can you regulate "expectation"?

4.      Is hoarding always unethical? What if it’s for personal safety or future scarcity?

5.      Design a simulation model to detect panic-driven buying in essential goods.

 

Case Study : The Onion Crisis in India – A Case of Speculation, Hoarding, and Policy Gaps

Background:

Onions are an essential staple in Indian kitchens, and their price volatility is a politically sensitive issue. In multiple years—2006, 2010, 2013, 2019, and 2023—India witnessed massive spikes in onion prices, often due to a mix of unseasonal rainfall, poor storage infrastructure, hoarding by traders, and policy failures.

 

Event Timeline (2019 Example):

·         June–July: Unseasonal rain in Maharashtra damaged standing crops.

·         August: Market arrivals declined sharply.

·         September: Prices began to climb rapidly, touching ₹100/kg in retail markets.

·         October: Traders and middlemen hoarded stocks in cold storages.

·         November: Government imposed export bans and capped stock limits.

·         December: Prices surged to ₹150/kg in major cities like Delhi and Mumbai.

 

Modified Demand and Supply Dynamics:

Let’s analyze this using our crisis model.

Normal Conditions:

·         Demand: Qd = 5000 – 12P

·         Supply: Qs = 2000 + 25P

Crisis Conditions:

·         Crop loss reduced supply by 600 units (weather shock)

·         Hoarding effect = 300 units

·         Panic buying added 400 units to demand

New Equations:

·         Qd = 5000 – 12P + 400 = 5400 – 12P

·         Qs = 2000 + 25P – 600 – 300 = 1100 + 25P

Equilibrium:
5400 – 12P = 1100 + 25P
⇒ 4300 = 37P
⇒ P = ₹116.22/kg, Q ≈ 3105 kg

 

Black Market Insight:

In cities where rationing was introduced, a parallel market emerged:

·         Official supply (rationed): 2 kg/person at ₹60/kg

·         Black market price: ₹140–₹160/kg

·         Traders sold stored onions discreetly to restaurants, hotels, and local vendors

 

Public Response:

·         Citizens protested outside mandis.

·         Memes and outrage flooded social media.

·         Politicians blamed each other; policy became reactive instead of preventive.

·         Imports from Egypt, Afghanistan, and Turkey were arranged late.

 

Teaching Notes:

Lessons Learned:

1.      Lack of preemptive action on weather data worsened the situation.

2.      Storage without monitoring enabled hoarding.

3.      Import decisions should have been made earlier.

4.      Price caps without real-time stock control pushed sales underground.

Reflection Questions:

·         What early-warning systems could prevent a repeat of the onion crisis?

·         Should essential commodities be placed under price surveillance like fuel?

·         How can supply chain transparency be improved at the mandi level?

·         Should India regulate speculative storage of food items?

 

Policy Suggestions (Post-Crisis Recommendations):

·         Smart mandi surveillance using RFID/barcodes for stock entry and exit

·         Buffer stock with public-private storage partnerships

·         Real-time price and volume dashboards at national and state levels

·         Seasonal crop insurance for volatile vegetables

 

This case study shows how Crisis Economics is not just about macro-level disruptions like pandemics or wars, but also local, seasonal events that affect millions of consumers. The onion crisis underscores the power of speculation, the fragility of supply chains, and the importance of proactive policy