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Retail Grocery Evolution: What Can We Learn From Store Counts Data?
Nan Yang. Not indexed, 2015
Topic: Risk, prevention and care in population studies
I develop a dynamic oligopoly model to study the evolution of retail grocery: Grocery chains expand at the expense of independent local stores. Model estimation only requires panel data on store counts and market demand, yet it quantifies the determinants of chain stores' entry and the consequences for incumbent stores' profit and survival. A unique feature of the model is that any Markov-perfect equilibrium that survives some intuitive refinements can be quickly computed from low-dimensional contraction mappings. After computation, it is easy to check the uniqueness of the refined equilibrium. These results facilitate fast and reliable counterfactual simulations. I estimate the model using observations of grocery stores' entries and exits in the Netherlands. The average sunk cost of entry can be multiple times more than a store's expected discounted profit, which considerably deters chain stores' expansion. An entering chain store reduces an incumbent local store's net present value between 28% and 58%. A simulation study shows that, given the large sunk cost and demand uncertainty, Dutch chain stores will continue to progress slowly in the coming years. Relaxing restrictions on chain store entry will quicken the destruction of local stores, but, perhaps surprisingly, hurt chain stores' profits.
Key findings
- These results facilitate fast and reliable counterfactual simulations.
Why it matters
This economic modeling study examined how grocery chains expand at the expense of independent local stores in the Netherlands. It has no relevance to treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 12/100
Preprint — not yet peer-reviewed. It scored 12 on the same rubric, which is below the 16 cap we apply to non-peer-reviewed sources. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 11 of the 94 points we could judge it on, which is 12 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Not indexed) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 1 / 5 | 2015 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 13 year(s) since 2015 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Nan Yang: h-index 6 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | National University of Singapore: institution h-index 1,166 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 11 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 11 of the 94 points we could judge it on is 12 out of 100. |
| Total | 12 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. This record has no journal, so the citation leaves it out. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Yang, N. (2015). Retail Grocery Evolution: What Can We Learn From Store Counts Data? https://doi.org/10.2139/ssrn.2631502
Vancouver
Yang N. Retail Grocery Evolution: What Can We Learn From Store Counts Data? 2015. doi:10.2139/ssrn.2631502.
BibTeX
@article{yang2015-a9f30bfe,
author = {Yang, Nan},
title = {{Retail Grocery Evolution: What Can We Learn From Store Counts Data?}},
year = {2015},
doi = {10.2139/ssrn.2631502},
url = {https://doi.org/10.2139/ssrn.2631502}
}
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