# Does quality affect patients' choice of doctors? Evidence from the UK - Discussion

Rita Santos, Hugh Gravelle, Carole Propper, Fabrice Etilé

► To cite this version:

Rita Santos, Hugh Gravelle, Carole Propper, Fabrice Etilé. Does quality affect patients' choice of doctors? Evidence from the UK - Discussion. European Workshop of Health Economics and econometrics, 2014, Munich, Germany. 13 p. hal-01607934

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Submitted on 5 Jun 2020

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# Does quality affect patients' choice of doctors?
## Evidence from the UK.

Rita Santos, Hugh Gravelle & Carol
Propper

Discussion by Fabrice Etilé
(INRA-ALISS and Paris School of Economics)
Main research question

* Are patients (consumers of health care) responsive to variations in doctors/practices quality?

- In a fixed price/perfect information setting, a positive quality elasticity is a necessary (but not sufficient) condition for competition to improve quality.

- The quality elasticity is expressed in a
# Method

* Random-utility choice model with three key ingredients:

- dij: distance from patient i to practice j.

- Qj: quality of practice j.

$$V_{i \in a, j \in C_a} = -\alpha_i t(d_{ij}) + \beta_i Q_j + \varepsilon_{ij}$$

- Ca: set of practices j available for patients i living in LSOA a. $\varepsilon_{ij}$ i.i.d.: type1Gumbel

$$\alpha_i = \alpha X_i + \tilde{\alpha}_i, \beta_i = \beta X_i + \tilde{\beta}_i$$

$$y_{i \in a, j \in C_a} = 1 \Leftrightarrow V_{i \in a, j} = \max_{j \in C_a} V_{i \in a, j \in C_a}$$

* Estimation:

- Conditional logit: $Var(\tilde{\alpha}_i) = Var(\tilde{\beta}_i) = 0$

- Mixed multinomial logit: more robust to violations of the IIA. assumption, especially when differences in individual characteristics are not well-controlled.
# Data (1)

* **Attribution Data Set (ADS):** numbers of patients by age/gender in each practice *j* (main id: *j*).

- ADS expanded to get a data-set of patients' choices (one line = *i* living in *a*, age/gender, with choice **yi**=j).

- Matched with aggregate neighbourhood statistics for *a* (*Xi* variable).

* Several data sets giving various measures of quality for j (**Qj**):

- QOF: an official and multi-dimensional measure of the quality of cares.

- Alternative quality measures for robustness tests:
Data (2)

· Distance *dij*?

- Approximated by the *distance between the nearest surgery of a practice and the LSOA centroid*.

<figure><img src="image_2.png" /></figure>

· Ca?

- All practices within D=10 km of the centroid.

- Play with *D* to test the robustness of the results.
# Main results

* +one std in QOF => +0.83 pp for the probability of being chosen (+15% patients) / +125 m in distance.

- Effects lower than the impact of +one std in the proportion of female doctors or EU-trained doctors within the practice.

- Relative effects of various characteristics robust to the specification of the distance function ($t(d)$), the potential endogeneity of $Q$, nonlinearities of the utility function and violations of the IIA assumption (mixed logit) etc.
# Discussion/Questions

* Difference between « objective » quality (QOF) and subjective perceptions of quality?

- Some qualitative evidence that an increase in QOF in a given practice may not correspond to an increase in quality as perceived by patients (Chew-Graham et al., 2013, *BMC Family Practice*) + some important quality aspects are learnt through experience (Gravelle and Masiero, JHE, 2000).

⇒ cross-sectional QOF elasticity may overestimate the true quality elasticity of demand in a dynamic setting
Discussion/Questions

* Difference between « objective » quality (QOF) and subjective perceptions of quality?

- Document the heterogeneity and non-linearity of the relationship between QOF and a subjective measure such as « Overall patient satisfaction 2009 » (the CS correlation is only 0.2).

- High-QOF practices may be more likely to publicize their performances => reduced
# Discussion/Questions

## Distance:

- Centroid OK if population uniformly distributed. Otherwise, the distance is measured with important errors: how does this affect the results?

Example: the practice in green has a larger market share because it settled in a densely

<figure><img src="image_3.png" /></figure>

- Hospitals seldom move while practices populated area: If the endogenously choose their location to reduce later are more likely to patients' transportation costs (railway station be closer to the etc.) and to differentiate from competitors:

- Discussion?
Discussion/Questions

- Modelling choices: why treating the data as micro-data and not aggregate (market-level) data?

- Market = LSOA*Gender*Age Band

- BLP (1995, Ecta): Mixed Multinomial Logit

- Identification through variations of the « market share » of each practice between markets, with distance *d* explicitly playing the role of a price.

- Identification of the variance of the distance and quality effects (*Var(αi)*, *Var(βi)*) also requires that many different practices are observed to compete on more than a single market: variations in *d* and variations in the choice sets C for observationally identical individuals.
# Discussion/Questions

* Modelling choices: why treating the data as micro-data and not aggregate (market-level) data?

- Would perhaps avoid to enter too much in the discussion about your approximation for *d*.

- You can easily instrument several variables, as estimation uses a GMM approach.

- See the good survey by Nevo (2000, JEMS)
# Discussion/Questions

* Various issues:

- Endogeneity of quality due to patient valuation of unobserved characteristics (ξij) that may be correlated with QOF (e.g. presence of a coffee machine, proximity to a railway station etc.).

* Instrument = average QOF of neighbouring practices => likely to be correlated with ξij if practices also compete on these unobserved quality aspects.

* I would at least take the average QOF of neighbouring practices *not in the same LSOA* (but not valid if the LSOA specific valuation of unobserved characteristics are correlated across
# Discussion/Questions

* Various issues:

- Distance: value of time? Interact with the average wage rate in the LSOA if available...

- Link between changes in quality and changes in distance: if QOF ↗ then demand/profit ↗ and probability to relocate closer to the centroid ↗?

* Attenuate the estimated distance/QOF tradeoffs?

* Document the QOF-distance relationship.

* Structural model for practices' strategic decisions?

- Cluster errors on LSOA