Chapter 11: Suggestions, Recommendations, and Limitations

 

Chapter 11: Suggestions, Recommendations, and Limitations

Objective

This chapter aims to consolidate key insights from earlier discussions, draw actionable recommendations for various stakeholders—economists, businesses, policymakers, educators, and students—and highlight the limitations of the study. It advocates for a contextual, data-driven, and dynamic understanding of demand and supply across sectors, while also recognizing gaps in models, data, and experimentation.

 

Part A: Key Suggestions and Observations

The following observations arise from real-time data analysis, cross-sectoral elasticity interpretations, behavioral influences, and sector-based demand-supply behavior.

 

1. Contextualize Demand-Supply Models by Sector

Standard models, such as:

·         Demand function: Qd = a - bP

·         Supply function: Qs = c + dP

... are limited in real-world application across diverse sectors.

Different sectors require context-based demand equations. For instance:

·         Agriculture (weather-sensitive):
Qd = f(P, Y, S, W)
Where:
P = Price,
Y = Income,
S = Seasonal index,
W = Weather pattern or rainfall.

·         Consumer Electronics (promotion-sensitive):
Qd = f(P, A, D)
Where:
A = Advertisement effectiveness,
D = Discount schemes or digital engagement.

This demonstrates the importance of tailoring models to sectoral realities, such as perishability in food or brand loyalty in tech products.

 

2. Integrate Behavioral Economics into Demand Models

Traditional models ignore consumer psychology, even though behavior deeply influences demand.

We propose incorporating behavioral elements like perceived value, peer pressure, and fear of missing out (FOMO) into the demand equation:

·         Adjusted Demand Model:
Qd = α - βP + γ1(Sp) + γ2(H)

Where:
Sp = Perceived status or social utility,
H = Herd behavior score from digital platforms.

For example, luxury items like watches or branded clothes may see higher demand not due to need but due to psychological drivers.

 

3. Real-Time Elasticity Monitoring

Price elasticity of demand (PED) is traditionally calculated as:

·         PED = (% Change in Quantity Demanded) ÷ (% Change in Price)

However, this elasticity varies based on time, events, or crises. For example:

·         Normal vitamin demand: PED ≈ -1.2

·         During COVID-19: PED ≈ -0.2

Recommendation: Develop dashboards for industries to track real-time elasticity changes during events like:

·         Natural disasters

·         Festival seasons

·         Budget announcements

·         Global conflicts

This would help businesses and governments act with precision, using elasticity trends to predict demand shifts.

 

4. Use of Digital Data in Demand Forecasting

Demand can now be predicted through digital footprints. We recommend using indicators such as:

·         Google Trends search data (G)

·         E-commerce click-through rate (C)

·         Online review scores (R)

Suggested model:
Qd = δ1(P) + δ2(G) + δ3(C) + δ4(R)

Example: OTT platforms like Netflix or Disney+ can estimate likely demand for a genre based on increased searches and trailer clicks.

In the fashion sector, a spike in online searches for “ethnic wear for Diwali” may forecast a 20–25% surge in demand within the following 7–10 days.

 

5. Introducing Sector Elasticity Matrix (SEM) in Education

A Sector Elasticity Matrix (SEM) should become part of university economics and business curricula.

This matrix includes:

·         Price Elasticity of Demand (Ep)

·         Income Elasticity of Demand (Ei)

·         Seasonal Index (S)

Example:

Sector	Ep	Ei	S (Seasonal Index)
Agriculture	-0.3	0.2	High
Smartphones	-1.5	1.4	Moderate
Travel Tickets	-1.2	1.6	Very High

Incorporating SEM in classroom simulations and case studies can enhance understanding of practical demand forecasting.

 

6. Elasticity as a Tool for Public Policy

Elasticity data should guide taxation and subsidies. For instance:

·         If a 10% increase in petrol price reduces usage by only 1%, then PED = -0.1 (inelastic), suggesting revenue can increase without drastically affecting consumption.

·         In contrast, doubling tax on cold drinks may cut demand by 30%, implying PED = -1.5 (elastic), which could harm small retailers.

Governments can apply the formula:

·         Impact Score = PED × (% Change in Price)

This can support better decisions for:

·         GST rates

·         Fuel price changes

·         Food subsidy budgets

 

7. Combining Classical Models with Experiments

Experiments bring theoretical assumptions to life. Recommended experimental techniques:

·         A/B testing: Two groups offered different prices for the same product.

·         Discount elasticity test: Vary discounts to measure change in demand.

·         Consumer trials in supermarkets or online platforms.

Findings can refine intercepts and slopes in models:

·         Revised demand: Qd = a' - b'P
(Where a’ and b’ are adjusted after experiment)

Example: In e-commerce, a ₹10 drop in price might raise daily orders by 20%, helping update the value of "b" in real-time.

 

Part B: Stakeholder-Specific Recommendations

Stakeholder	Recommendations
Policy Makers	Use real-time elasticity data before changing subsidy or price ceilings. Run policy simulations using behavioral indicators.
Businesses	Segment consumers by income and price sensitivity. Use seasonality and digital data to time offers and stock.
Educators	Replace blackboard theory with live data projects. Encourage students to use Excel, R, or Python for forecasting exercises.
Researchers	Use hybrid models combining regression, time series, and behavioral survey tools. Apply them to high-variability sectors.
Students	Apply concepts in live projects, such as demand for movie tickets during holidays or organic food post-COVID. Use open datasets from RBI, NSO, CMIE.

 Part C: Limitations of the Study and Book

Despite robust sectoral analysis and model integration, the following limitations are acknowledged:

 

1. Data Constraints

·         Many sectors lack updated public data, especially MSMEs.

·         Some data was approximated using third-party reports or indirect indicators.

·         Rural demand data is often underreported.

Example: Khadi product demand was modeled using only e-commerce and KVIC data, which misses small offline transactions.

 

2. Volatile Nature of Demand

Elasticity values change with:

·         Economic shocks

·         Cultural shifts

·         Policy changes

What holds today may not apply next quarter. Thus, models like:

·         Qd = a - bP

... require frequent recalibration.

 

3. Generalization Across Economies

This book uses India-centric data but recognizes:

·         Rural and urban consumption differ widely.

·         Demand in Tier 2/3 cities may not follow textbook models.

·         Informal sector behavior often deviates from formal market theories.

Hence, national-level elasticity numbers may have limited local relevance.

 

4. Behavioral Survey Bias

Perception surveys used in the book had limitations:

·         Respondents sometimes answered based on social norms.

·         Income misreporting and preference exaggeration occurred.

·         Sample sizes in a few sectors were small or skewed toward urban youth.

Thus, results may not fully capture diverse market behavior.

 

5. Limited Use of Field Experiments

While experiments are suggested, actual fieldwork in this book was limited due to:

·         Budget and time constraints

·         Institutional approvals

·         Lack of access to experimental platforms

Most findings are from secondary data and regression models, not direct consumer trials.

 

6. Seasonal Index Assumptions

Seasonal indices were calculated using:

·         Seasonal Index = (Monthly Demand) ÷ (Average Monthly Demand)

This assumes a stable repeating pattern. However, factors like pandemics or war disrupt seasonality.

Example: Travel demand fell to near zero during COVID-19, even during holiday seasons, making historical indices unreliable.

 

Conclusion

This chapter aims to offer both strategic direction and self-awareness. The suggestions offered build a bridge between theory and application, while the limitations section keeps the analysis grounded.

·         Sectoral elasticity must be integrated into pricing and forecasting.

·         Behavioral insights are not optional—they are essential.

·         Digital and real-time data have revolutionized demand analysis.

·         Textbook models need real-world testing, refinement, and updating.

By accepting the limitations and addressing gaps, we encourage future economists, researchers, and students to expand this work through:

·         Field experiments

·         Real-time dashboard building

·         AI-based prediction models

·         Cross-disciplinary collaborations

As the architecture of demand and supply evolves, so should our tools to study it.

"In the real world, economic models must breathe, adapt, and occasionally break—to be rebuilt better." – Anonymous Economist