Algorithmic Pricing &
Profitability
Variables and Identification Plan
This note specifies outcome variables, explanatory dimensions, and identification strategies to evaluate whether algorithmic pricing improves retailer profitability. Emphasis is placed on firm-level financial indicators available in the dataset and on causal methods suitable for staggered adoption across markets.
The study links retailer–product level evidence of algorithmic
pricing on PriceSpy to firm-year financial outcomes. Adoption is
detected at the firm level (AlgoRetailer = 0/1).
Profitability impacts are estimated by comparing adopters with matched
non-adopters using CEM/PSM, supplemented with DiD and SCM designs.
Winsorization:
To reduce the influence of extreme outliers, the following variables are
winsorized at the 1st/99th percentiles within each year. Winsorized
versions carry the _W suffix and are used in matching and
regressions:
ROA_WProfitMargin_WSalesPerTA_WAvgInv_WInvTurnover_WDIO_WSalesGrowth_WOpProfitGrowth_WROAGrowth_WProfitMarginGrowth_WSalesPerTAGrowth_WDebtRatio_WLogTA_WLogSales_WRationale: EBIT/OpProfit, ROA, and ProfitMargin are central profitability metrics. SalesPerTA and growth rates capture efficiency and dynamics of performance. Winsorization ensures these measures are not distorted by extreme values.
Treatment
AlgoRetailer (0/1): Firm classified as algorithmic
retailer (treatment group).onPSpy: Indicator of presence on the PriceSpy
platform.Market presence and portfolio
Market coverage: number of categories and products listed (if
aggregated).
Firm identifiers: StoreID, Identifier,
CompanyName.
Identifier: not used in analysis, but included for data
merging and tracking.StoreID: links firm data to PriceSpy records.CompanyName: unique firm identification
(canonical).Contextual dimensions: Country,
Industry, Sector, SectorNew.
Country: one of the seven major markets where PriceSpy
operates.Industry: fine-grained sub-industry classification
(Eikon) and 21 SNI codes (SHOF).Sector: contains empty strings for Swedish data (no
Sector column provided).SectorNew: coarser aggregation of Industry
used for grouping.Scale and structure
TA (total assets), Sales (turnover),
TCap (capital).DebtLT, DebtIntB,
DebtRatio (and DebtRatio_W).Cash,
AccRec, AccPay, Inv,
AvgInv (and AvgInv_W).COGS, Expenses,
Interest, Tax.Construct a control group of non-adopters matched on pre-treatment characteristics. Suggested coarsening:
Notes:
Difference-in-Differences (staggered adoption)
Synthetic Control (SCM) for targeted cases
Sensitivity
| Name | Definition / formula | Source |
|---|---|---|
| EBIT | Operating profit before interest & tax | Financial accounts |
| OpProfit | Operating profit (alt. measure) | Financial accounts |
| NetIncome | Profit after tax and financing | Financial accounts |
| Sales | Turnover | Financial accounts |
| TA | Total assets | Financial accounts |
| ROA | OpProfit ÷ TA | Derived |
| ProfitMargin | OpProfit ÷ Sales | Derived |
| SalesPerTA | Sales ÷ TA (asset turnover) | Derived |
| SalesGrowth | ΔSales / Sales (YoY) | Derived |
| ROAGrowth | ΔROA / ROA (YoY) | Derived |
| ProfitMarginGrowth | ΔProfitMargin / ProfitMargin (YoY) | Derived |
| SalesPerTAGrowth | ΔSalesPerTA / SalesPerTA (YoY) | Derived |
| DebtRatio | Debt ÷ TA | Financial accounts |
| AvgInv | Average inventory | Financial accounts |
| InvTurnover | Sales ÷ AvgInv | Derived |
| DIO | Days inventory outstanding | Derived |
| LogTA | log(TA) | Derived |
| LogSales | log(Sales) | Derived |
| *_W variables | Winsorized versions of above (see list) | Derived (trimmed) |
| AlgoRetailer | 0/1 adoption indicator | Platform data |
| onPSpy | Presence on PriceSpy | Platform data |
| Country, SectorNew | Firm context | Metadata |
We load the harmonized firm–year panel, inspect structure and coverage, and generate a few quick tables.
| Country | Unique algos | Unique on PriceSpy | Unique firms |
|---|---|---|---|
| Denmark | 1 | 10 | 1234 |
| Finland | 1 | 12 | 3403 |
| France | 3 | 24 | 41188 |
| Norway | 2 | 35 | 6863 |
| Sweden | 22 | 374 | 974667 |
| UK | 10 | 50 | 14095 |
| Total | 33 | 493 | 1041439 |
Before turning to the descriptive comparison, we clarify the meaning of the key variables:
| Country | Group | Unique firms | ROA | Profit margin | Asset turnover | Log TA | Log Sales |
|---|---|---|---|---|---|---|---|
| Denmark | Non-algo | 10 | -0.109 (0.666) | -0.192 (1.175) | 3.382 (1.759) | 10.581 (1.254) | 11.485 (0.845) |
| Finland | Non-algo | 12 | 0.045 (0.104) | 0.007 (0.060) | 3.609 (2.377) | 9.642 (1.710) | 10.588 (1.317) |
| France | Non-algo | 22 | 0.061 (0.174) | -0.201 (1.403) | 2.214 (1.078) | 9.348 (2.215) | 9.660 (2.085) |
| France | Algo | 2 | 0.117 (0.031) | 0.025 (0.010) | 4.916 (0.730) | 6.725 (0.660) | 8.306 (0.794) |
| Norway | Non-algo | 34 | 0.110 (0.162) | 0.045 (0.076) | 2.947 (1.498) | 8.687 (1.686) | 9.570 (1.452) |
| Norway | Algo | 1 | 0.100 (0.155) | 0.010 (0.017) | 6.653 (4.151) | 7.756 (2.874) | 9.542 (2.206) |
| Sweden | Non-algo | 506 | 0.057 (0.211) | -0.026 (0.598) | 2.770 (1.683) | 8.887 (1.967) | 9.558 (1.931) |
| Sweden | Algo | 31 | 0.000 (0.294) | -0.123 (0.916) | 3.359 (1.659) | 8.588 (1.854) | 9.490 (1.780) |
| UK | Non-algo | 41 | 0.135 (0.285) | 0.001 (0.787) | 2.445 (1.634) | 9.848 (2.287) | 10.397 (1.752) |
| UK | Algo | 9 | 0.082 (0.264) | -0.026 (0.442) | 3.370 (1.193) | 7.190 (2.925) | 8.628 (2.995) |
Scope. Firm–year panel linking PriceSpy retailer presence and algo status to financials from multiple sources (Eikon, SHOF, Companies House/CH). Processing aims for harmonized identifiers, consistent industry/sector labels, clean accounting signs, derived ratios, and robust outlier handling.
Load main panel (APfullDATA.rds) and UK Companies
House long-format Excel.
Drop unused columns (after, CompanyID)
and IndustryGroup from CH.
Rename CH columns to align with the main schema (e.g.,
Identifier, StoreID, CompanyName,
Country, Industry, Sector,
TA, Year, Sales,
OpProfit, EBIT, NetIncome,
etc.).
Annotate CH rows: dataSource = "CH",
onPSpy = 1; cast Identifier to
character.
Row-bind main and CH data; keep only data in
environment.
Remove blank CompanyName; convert to
data.table (dt).
Normalize fields:
Country = "Sweden" where
dataSource == "SHOF".onPSpy = NA with 1 (assume NA = on
platform).Focus on Sweden & on-platform rows
(onPSpy == 1 & Country == "Sweden").
Prefer Eikon over SHOF when
duplicates exist in the same (CompanyName,
Year).
Standardize retailer names:
_scripts/standardize_company_names.R (manual
mapping/audit).CompanyName to catch residual
duplicates._scripts/standardize_names_shof_eikon.R to align
SHOF ↔︎ Eikon variants.Remove SHOF rows where an Eikon row exists for the same
CompanyName–Year.
Clean Industry: trim whitespace, map specific
numeric codes to ALL-CAPS labels; convert "" →
NA.
For CH rows with Industry == NA, set
Industry = "RETAILER" (temporary canonical label).
Build SectorNew:
Sector == "", set SectorNew = Industry;
otherwise SectorNew = Sector.SectorNew == "RETAILER" with
"Retail Trade".Expected quirk: Sector contains
empty strings for Swedish data; handled via
SectorNew.
Keep a safety backup (dt_backup).
Remove rows with missing key inputs unless
treated (AlgoRetailer == 1):
TA is NA (except treated), drop if
OpProfit is NA (except treated), drop if
Sales is NA (except treated).Enforce accounting sign: set negative COGS to
abs(COGS).
Profitability and efficiency:
ROA = OpProfit / TA,
ProfitMargin = OpProfit / Sales,
SalesPerTA = Sales / TA.Working capital & inventory dynamics:
AvgInv = (Inv + lag(Inv)) / 2 (by
CompanyName), InvTurnover = COGS / AvgInv,
DIO = 365 / InvTurnover.Growth rates (by CompanyName; first year naturally
NA):
SalesGrowth, OpProfitGrowth,
ROAGrowth, ProfitMarginGrowth,
SalesPerTAGrowth.Leverage & scale transforms:
DebtRatio = DebtLT / TA;
LogTA = log(TA + 1),
LogSales = log(Sales + 1).For non-key metrics when
onPSpy == 0, drop rows with COGS or
Inv missing.
Remove rows where any of the following are Inf,
-Inf, or NaN:
ROA, ProfitMargin, SalesPerTA,
AvgInv, InvTurnover, DIO,
DebtRatio.
(Growth variables keep expected first-year NAs.)
Replace any residual Inf/NaN in all
derived variables (incl. growths) with NA.
Year and create _W copies (preferred
for matching/estimation):ROA_W, ProfitMargin_W,
SalesPerTA_W, AvgInv_W,
InvTurnover_W, DIO_W,SalesGrowth_W, OpProfitGrowth_W,
ROAGrowth_W, ProfitMarginGrowth_W,
SalesPerTAGrowth_W,DebtRatio_W, LogTA_W,
LogSales_W.Country × Year for stricter
comparability.)Write intermediary backups to
_data/interim/backup_pre_final_clean.csv and
_data/interim/backup_final_clean.csv.
Sanity checks:
summary(dt); verify year coverage (2000–2024).Sector).Output: a harmonized, de-duplicated firm–year panel with derived and winsorized variables, ready for matching (CEM/PSM) and causal analysis.