UCSD MGT 100 Week 04

Kenneth C. Wilbur and Dan Yavorsky

- Theory
- Challenges
- What firms do

Useful theoretical concept that summarizes market response to price

`- Why is it useful?`

Often taught with perfect competition

`- Typically assumes stable, competitive, frictionless markets w free entry, full information, no differentiation - Model predicts zero LR economic profits - Any evidence?`

Also, taught with monopoly i.e. market power

`- What is market power? How would we measure it?`

Idealized demand curves are hard to estimate (why?)

Where do product attributes come in?

Many things can predict demand

`- preferences, information, advertising, quality, match value, costs, complements, substitutes, competitor prices, entry, policy, taxes, retail distribution, nature of equilibrium, stockpiling, other consumer behaviors...`

What do we need to estimate Demand?

Market research

`- Typically, conjoint analysis, customer interviews or simulated purchase environments`

Expert judgments, e.g. salesforce input

Cost-driven price adjustments

Demand modeling with archival data

Price experiments

`- Typically, market tests, digital experiments, bandits, coupons or other targeted price promotions`

Best practice: triangulation

Ideally, the best way to learn demand, because you create exogenous price variation; but…

Competitors & consumers can observe price variation

May change purchase timing, stockpiling or

reference pricesCompetitors, distribution partners or suppliers may react

Hence, experimenting to learn demand may change future demand

Relatively inexpensive for large organizations

Confidential

Depends on real consumer choices, i.e. revealed preferences

Enables counterfactual demand predictions at unobserved prices

Enables predictions of competitor pricing response

Evaluable after price changes or competitor price changes

Requires data, exogenous price variation, time, effort, training, commitment, trust, organizational buy-in

Always subject to untestable modeling assumptions

Requires the near future to resemble the recent past

`- To be fair, all predictive analytic techniques require these 3`

Evidence is scarce but supportive

`- Informally, I know several people who maintain demand models in large orgs - Formal evidence requires researchers and firm to collaboratively (a) estimate demand, (b) act on demand estimates, (c) observe how actions affect outcomes, & (d) report the results publicly. Hard to do & incentives conflict - Demand modeling can also go badly, e.g. due to price endogeneity`

Misra & Nair (2011) : B2B sales & salesforce compensation

Nair et al. (2017) : Casino loyalty rewards

Pathak and Shi (2021) : School choice

Dube and Misra (2023) : ZipRecruiter ad pricing

- history, math, discussion

- Old, famous, popular demand model
- Ported to econ from stats & psych by McFadden (1974, 1978, 1981; “Logistic regression’’)

Let \(i\) index consumers, \(j=1,...,J\) products, and \(t\) index choice occasions

Assume each \(i\) gets indirect utility \(u_{ijt}\) from product \(j\) in market \(t\):

\[u_{ijt}=x_{jt}\beta-\alpha p_{jt}+\epsilon_{ijt}\]

- Then, assuming each \(i\) picks the \(j\) that maximizes \(u_{ijt}\), has unit demand, and that \(\epsilon_{ijt}\sim\)
*i.i.d.*\(EV_1(0,1)\), market share is

\[Prob.\{u_{ijt}>u_{ikt}\forall{k\ne j}\}\equiv s_{jt}=\frac{e^{x_{jt}\beta-\alpha p_{jt}}}{\sum_{k=1}^J e^{x_{kt}\beta-\alpha p_{kt}}}\]

With \(N_t\) consumers, \(q_{jt}(\vec{x}; \vec{p})=N_t s_{jt}\). See Train 2009 sec 3.10 for proof

- Estimating \(\alpha\) and \(\beta\) enables us to predict
*every product’s quantity response*to a change in*any product’s attributes*\(x_{jt}\) or price \(p_{jt}\)

- Suppose \(\gamma_{t}\) indicates how popular the category is in market \(t\), so utility is \(u_{ijt}=\gamma_{t}+x_{jt}\beta-\alpha p_{jt}+\epsilon_{ijt}\). Then market share becomes

\[s_{jt}=\frac{e^{\gamma_{t}+x_{jt}\beta-\alpha p_{jt}}}{\sum_{k=1}^J e^{\gamma_{t}+x_{kt}\beta-\alpha p_{kt}}}\]

\[s_{jt}=\frac{e^{\gamma_{t}} e^{x_{jt}\beta-\alpha p_{jt}}}{e^{\gamma_{t}}\sum_{k=1}^J e^{x_{kt}\beta-\alpha p_{kt}}}\]

\[s_{jt}=\frac{e^{x_{jt}\beta-\alpha p_{jt}}}{\sum_{k=1}^J e^{x_{kt}\beta-\alpha p_{kt}}}\]

- Similar exercise applies to individual-specific intercepts \(\gamma_i\), or individual-time interactions \(\gamma_{it}\). Only differences across products affect predicted market shares

- We set product \(j=1\) utility to \(u_{i1t}=\epsilon_{i1t}\) to normalize utility, i.e.

\[s_{1t}=\frac{1}{\sum_{k=1}^J e^{x_{kt}\beta-\alpha p_{kt}}}\]

- Hence for every \(j\ne 1\), consider an affine transformation of market shares:

\[ln(s_{jt})-ln(s_{1t})=x_{jt}\beta-\alpha p_{jt}\]

- Looks like a regression equation… we sometimes add an error \(\xi_{jt}\) to represent unobserved product attributes:

\[ln(s_{jt})-ln(s_{1t})=x_{jt}\beta-\alpha p_{jt}+\xi_{jt}\]

- It’s the \((J-1)T\)
*differences*in market shares that identify demand parameters

Define \(y_{ijt} \equiv 1\{i \text{ chose } j \text{ in } t\}\). I.e., \(y_{ijt}=1\) iff \(i\) choose \(j\) at \(t\); otherwise \(y_{ijt}=0\).

- Maximum likelihood: Assume the probability of observation \(\{i,j,t\}\) is \(s_{jt}^{y_{ijt}}\);

then lik. is \(L(\beta )=\prod\limits_{\forall i,j,t} s_{jt}^{y_{ijt}}\); & choose parameters to maximize log lik:

\[\sum_{\forall i,j,t} y_{ijt}\ln s_{jt}\]

- Method of Moments: Choose \(\alpha\) and \(\beta\) to solve some vector of “moments,” i.e. interactions between observables \(z\) and error terms, e.g.

\[ \sum_{\forall j,t}z_{jt} (\frac{\sum_{\forall i}y_{ijt}}{N_t} - s_{jt})=0\]

- Linearize the model and choose parameters to minimize the sum of square errors

Discrete choice models predict

*choice probabilities*rather than*choices*, because utility is always unobserved; hence nonstandard fit statisticsPredicted outcomes are inherently stochastic, so limited predictive ability

- Likelihood Ratio Test: (not R-sq)

\[\rho=1-\frac{ln L(\hat\beta)}{ln L(0)}\]

As \(L(\hat\beta)\to 1\), \(ln L(\hat\beta)\to 0\), \(\rho\to 1\)

As \(ln L(\hat\beta)\to ln L(0)\), \(\rho\to 0\)

`- Heuristic: 0.2-0.4 is pretty good`

Hit Rate: % of individuals for whom most-probable choice was actually chosen

R-sq using prediction errors at the \(jt\) level

Microfounded, i.e. behavioral predictions are consistent with a clearly specified theory of consumer choice

`- Economists widely believe that microfounded models are more generalizable than purely statistical models*`

Extensions accommodate preference heterogeneity

`- We'll cover 3 types of extensions when we cover heterogeneous demand modeling`

Likelihood function is globally concave in the parameters, ensuring fast and reliable estimation

Assuming \(\epsilon_{ijt}\sim\)

*i.i.d.*\(EV_1(0,1)\) is convenient but unrealistic`- Alternatives exist but can be computationally expensive`

Analyst selects the choice set \(J\), market size \(N_t\), attributes \(x_{jt}\), and price structure \(p_{jt}\).

`- What's a j? What's a t? What's in x? How do we measure p? Who's in N?`

Market share derivatives depend on market shares alone (IIA; see Train Sec. 3.6)

Price Endogeneity

`- Affects all demand models`

- Famous example from McFadden (1974)
- Suppose you estimate demand for transportation with three options: {Blue Bus, Red Bus, Car}, each with 33% market share
- Now suppose you painted the red buses blue and want to predict market shares in the new choice set: {Blue Bus, Car}
- MNL will predict Blue Bus and Car shares of 50%, not 67% and 33% (why?)

IIA is testable & usually rejected by data

3 common remedies:

Impose structure on choice set,

e.g. Nested Logit or Ordered LogitRelax i.i.d. assumption, e.g. Multivariate Probit

Change model structure so IIA does not hold, e.g. heterogeneous logit

Fundamental issue: Demand model should be a causal relationship but prices are usually nonrandom. We have to distinguish correlation from causation

`- Prices may covary with unobserved demand shifters, unmeasured product attributes or other missing variables - Price endogeneity is a measurement problem, not a modelling problem - Hard to verify empirically--needed data is missing--but widely believed important`

\(\hat{\alpha}\) will be biased when prices depend on unmodeled demand factors (e.g., image, reliability). Implies wrong demand slope, biased demand predictions

Typically, bias will be toward zero (“too flat”), hence underestimating price sensitivity and overestimating price change

Affects all demand models, not just MNL

Common solutions: Experiments or quasi-experiments (Instrumental variables, regression discontinuities, natural experiments, dfce-in-dfce, synthetic control, double/debiased machine learning)

- Berry (1994): Linearize MNL and use instruments

\(ln(s_{jt})-ln(s_{1t})=x_{jt}\beta-\alpha p_{jt}+\xi_{jt}\)

Valid instruments will (1) predict \(p_{jt}\) and (2) don’t predict \(\xi_{jt}\)

`(1) is testable; (2) is not Classic instruments are input prices`

In general price endogeneity requires careful attention

`- We set up our smartphone sales data with exogenous price variation in promotions, so this class will disregard it for simplicity - Price endogeneity leads to biased demand slope, which could lead to biased price recommendation, which could lead to suboptimal outcomes - Endogeneity is a major topic in graduate study`

- T/F: The MNL assumes all consumers have the same preference parameters.

- Wrangle data
- Estimate MNL
- Interpret parameters and SEs
- Assess model fit

- Let’s take a look

- Demand modeling enables data-driven sales predictions for counterfactual prices and attributes
- MNL is popular bc it is powerful, microfounded and tractable
- MNL has some important limitations (eg, IIA) which can be alleviated by modeling heterogeneity
- Price endogeneity is a measurement problem that biases demand parameter estimates when price is correlated with unobserved demand shifters. Can often be resolved through experimental or quasi-experimental techniques