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# Construction of a household panel data set of at-home food purchases from 2002 to 2015

Céline Bonnet

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Céline Bonnet. Construction of a household panel data set of at-home food purchases from 2002 to 2015. 2026. hal-05474230

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Construction of a household panel data set of at-home food
purchases from 2002 to 2015*

Céline Bonnet†

January 23, 2026

Abstract

In order to analyse the evolution of food consumption at home and its implications for sustain-
ability (e.g. nutritional quality and environmental impact), we created a time-varying database
of annual food purchases for a representative sample of households from 2002 to 2015. We also
need to link food consumption with nutritional information, environmental impact and prices. To
achieve this, we used five datasets: the Kantar WorldPanel dataset (2002–2015), which records
at-home food purchases by a representative panel of French households; the ClasSFood classifi-
cation system; the CIQUAL database, which provides detailed nutritional information for over
3,000 food products; the Agribalyse dataset, which includes environmental indicators for over
2,000 food products, and the INCA2 Recipes database, which offers detailed ingredient composi-
tions for processed foods. Several issues were also addressed that arose from the aggregation and
time comparison, such as differences in the measurement units of products purchased, differences
in purchase recording over time, and the ingredient composition of pre-prepared meals.

<footnote>*We acknowledge funding from INRAE for the funding of the ULTRAPRO project through the metaprogram SYALSA, and from ANR under grant ANR-17-EURE-0010 (Investissements d'Avenir program). All remaining errors are ours.</footnote>

<footnote>†Toulouse School of Economics, INRAE, 1, Esplanade de l'Université 31000 Toulouse, France; celine.bonnet@tse-fr.eu; https://www.tse-fr.eu/people/celine-bonnet</footnote>
# 1 Data

In order to analyse the evolution of food consumption at home and its implications for sustainability (e.g. nutritional quality and environmental impact), we need to compile a time-varying database of food purchases for a representative sample of households. We also need to link food consumption with nutritional information, environmental impact and prices. To achieve this, we will use the following five datasets: The Kantar WorldPanel dataset (2002–2015), which records at-home food purchases by a representative panel of French households, ClasSFood, a food classification system, the CIQUAL database, which provides detailed nutritional information for over 3,000 food products, the Agribalyse dataset, which includes environmental indicators for more than 2,000 food products, and the INCA2 Recipes database, which offers detailed ingredient compositions for processed foods.

## 1.1 Kantar WorldPanel

The Kantar Worldpanel Homescan dataset comprises all food purchases made at home by a representative sample of French households. According to Table 1, the size of this sample increases over time. Starting with 8,625 households in 2002, it reached over 25,000 households by 2008. The number of food purchases also increased, from more than seven million in 2002 to more than 19 million in 2015.

<table><caption>Table 1: Household and food purchase information per year</caption><thead><tr><th rowspan="2">Year</th><th colspan="7">Number of households</th><th rowspan="2">Number of purchases</th></tr><tr><th>All</th><th>GC+VP</th><th>GC+FL</th><th>GC+VP+FL</th><th>GC</th><th>GC+PF</th><th>Annual Pond</th></tr></thead><tbody><tr><td>2002</td><td>11,048</td><td>5,151</td><td>3,354</td><td>0</td><td>105</td><td>-</td><td>5,347</td><td>7,833,651</td></tr><tr><td>2003</td><td>9,501</td><td>5,162</td><td>3,415</td><td>0</td><td>93</td><td>-</td><td>4,883</td><td>7,749,097</td></tr><tr><td>2004</td><td>9,383</td><td>5,177</td><td>3,270</td><td>4</td><td>82</td><td>-</td><td>4,652</td><td>7,432,407</td></tr><tr><td>2005</td><td>14,932</td><td>8,499</td><td>5,193</td><td>2</td><td>0</td><td>-</td><td>7,422</td><td>11,865,103</td></tr><tr><td>2006</td><td>15,411</td><td>8,606</td><td>5,365</td><td>0</td><td>0</td><td>-</td><td>7,434</td><td>11,599,719</td></tr><tr><td>2007</td><td>14,024</td><td>7,992</td><td>4,904</td><td>1</td><td>0</td><td>-</td><td>7,301</td><td>11,697,700</td></tr><tr><td>2008</td><td>26,471</td><td>0</td><td>85</td><td>11,982</td><td>8,912</td><td>-</td><td>10,361</td><td>18,288,449</td></tr><tr><td>2009</td><td>26,026</td><td>-</td><td>-</td><td>-</td><td>9,262</td><td>12,657</td><td>10,956</td><td>18,454,488</td></tr><tr><td>2010</td><td>25,766</td><td>-</td><td>-</td><td>-</td><td>9,043</td><td>12,448</td><td>11,344</td><td>18,372,456</td></tr><tr><td>2011</td><td>27,291</td><td>-</td><td>-</td><td>-</td><td>9,324</td><td>12,114</td><td>10,927</td><td>18,249,417</td></tr><tr><td>2012</td><td>26,785</td><td>-</td><td>-</td><td>-</td><td>9,240</td><td>12,346</td><td>11,316</td><td>19,207,488</td></tr><tr><td>2013</td><td>27,945</td><td>-</td><td>-</td><td>-</td><td>9,111</td><td>13,387</td><td>10,893</td><td>19,912,796</td></tr><tr><td>2014</td><td>28,375</td><td>-</td><td>-</td><td>-</td><td>9,051</td><td>13,395</td><td>10,806</td><td>19,477,822</td></tr><tr><td>2015</td><td>26,130</td><td>-</td><td>-</td><td>-</td><td>8,764</td><td>13,423</td><td>11,178</td><td>19,935,093</td></tr></tbody></table>

Note: The first column represents the total number of households. The second column represents the total number of households that declare purchases from the GC ("produits gencodés" - bar code products) and VP ("viande et poisson" - meat and fish) panels. The third column represents the total number of households that declare purchases from the GC and FL ("fruits et légumes" - fruit and vegetables) panels. The fourth column represents the total number of households that declare purchases from the GC, FL and VP panels. The fifth column represents the total number of households that declare purchases from the GC panel. The sixth column represents the total number of households that declare all purchases. The seventh column represents the total number of households with a non-zero annual weight. The eighth column represents the total number of purchases.
The Kantar Worldpanel dataset contains information about households (region of residence, income, socio-economic status, etc.) and their members (socio-professional category, level of education, age, sex, height, weight, etc.). It also contains detailed information on purchases, such as the store where they were made, the brand, the price and the quantity bought, as well as a set of product characteristics. Information is also available on the representativeness of the household with respect to the French population. Kantar Worldpanel provides annual weights for households that declare purchases during at least ten four-week periods over the course of a year. These annual weights enable the household sample to be adjusted so that it always represents the French population, while also accounting for inactive periods, i.e. periods during which the household does not declare any purchases. This information is only available for a subset of households, as shown in Table 1.

## 1.2 ClasSFood

ClasSFood is a food classification system based on the French food supply and consumption. Food categories are designed according to their nutritional, environmental, and processing characteristics (Spiteri et al., 2023). Thus, this classification is well-suited to relevant analyses of food sustainability, taking into account consumption patterns in the French population (i.e. substituability between food products). As a hierarchical scheme, foodstuffs can be described at different levels of detail, allowing analysis of the whole diet or of more specific categories, which are described below. The first level distinguishes food products into 11 aggregated categories: 1. Grain products; 2. Fruit, vegetables, legumes, root vegetables and nuts; 3. Meat, fish, seafood, shellfish, eggs and plant-based meat substitutes; 4. Dairy products and plant-based dairy substitutes; 5. Sweet products and snacks; 6. Oils and fats; 7. Drinks; 8. Ready-made meals and salty snacks; 9. Appetisers; 10. Sauces and condiments; 11. Other products. The second level splits the diet into 54 categories. The third and fourth levels contain 153 and 194 categories, respectively. Finally, the fifth and most detailed level distinguishes food products into 223 categories.

## 1.3 CIQUAL

The CIQUAL database contains macronutrient and micronutrient values for over 3,185 food and drink products that are representative of consumption in France. Macronutrients include carbohydrates, dietary fibre, fats and proteins. Micronutrients include various vitamins and minerals. The products are divided into numerous categories and subcategories according to their content. This publicly available database is managed by Ciqual, which is part of the French governmental agency Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (Anses).<fnref n="1" />

<footnote n="1">¹ANSES. (2020). CIQUAL food composition table 2020, French version [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4770600</footnote>
We use the 2020 version of the database.<fnref n="2" />

## 1.4 Agribalyse

Agribalyse is a farm-to-fork Life Cycle Inventory (LCI) database that provides information on the environmental impact of over 2,400 French agricultural and food products.<fnref n="3" /> These data are based on the Life Cycle Assessment (LCA) method, which provides indicators of the environmental impact of products at every stage of production, distribution and consumption. There are different indicators of environmental impact, such as climate change, land use, marine eutrophication, acidification and resource use. The climate change indicator refers to the increase in average global temperatures resulting from CO₂ emissions. Land use refers to the loss of soil organic matter. The marine eutrophication indicator refers to the potential impact of substances that contribute to marine eutrophication. The idea behind the resource use indicator is that extracting resources today reduces the amount available for future generations. This database is related to the CIQUAL database thanks to the shared identification key for food products.<fnref n="4" />

## 1.5 INCA2 Recipes

The INCA2 recipe database was created in 2006–07 as part of the French national INCA2 survey of individual food intake. It uses a nomenclature of 1,280 food products. Of these, 654 are raw or minimally processed foods, while 626 are processed foods (ready meals, etc.) that can be broken down into their ingredients. This decomposition is very precise, as the database considers 760 ingredients and estimates the percentage of each ingredient. The INCA2 database is also related to the CIQUAL database, as it provides the CIQUAL identification key for each product.<fnref n="5" />

<footnote n="2">²Since we only use a unique database for nutritional information, we assume that the nutritional quality of products on the market remains constant over time.</footnote>

<footnote n="3">³Asselin-Balençon A.; Broekema R.; Teulon H.; Gastaldi G.; Houssier J.; Moutia A.; Rousseau, V.; Wermeille A.; Colomb V., 2024, "AGRIBALYSE® versions 3.0 and 3.0.1", https://doi.org/10.57745/IX0U1R, Recherche Data Gouv, V1</footnote>

<footnote n="4">⁴Since we only use a unique database for environmental information, we assume that the nutritional quality of products on the market remains constant over time.</footnote>

<footnote n="5">⁵Since we only use a unique database for recipes, we assume that the recipes of products offered on the market are constant over time.</footnote>
# 2 Data generating process

## 2.1 Data merger

In order to analyse the evolution of food consumption at home, we need to generate a household panel dataset of the yearly food purchases of French households. Figure 1 illustrates the interrelationship of the data. First, the three databases (CIQUAL, Agribalyse and INCA2 recipes) are linked by a shared identification key relating to product identification. We allocate each food product to one of the 223 ClasSFood categories and calculate the simple average for each nutritional and environmental indicator.<fnref n="6" /> Secondly, we classify all food purchases in the Kantar World Panel database using the ClasSFood classification system (223 food categories). This classification then becomes the new identification key that allows us to merge the four databases. We can then calculate the total quantities and expenditure for each household and each year. For quantities, we need to address the issue of the measurement units of the products purchased. Kantar Worldpanel collects quantities in the unit used during sale: liquid products are measured in litres or millilitres, solid products in kilograms or grams, and some products (such as salads and avocados) are sold by the piece. We have therefore created a dataset in which all products are converted to kilograms. This database provides an equivalent weight in kilograms. For example, we estimate that an egg weighs an average of 60 grams.

<figure><img src="image_3.png" /><figcaption>Figure 1: Relationships between databases</figcaption></figure>

The generated household panel dataset on the yearly at-home consumption of 223 food categories

<footnote n="6">⁶We acknowledge that the simple average does not accurately reflect true consumption behaviour within each of the 223 ClasSFood categories. However, merging the Kantar World Panel food references (around 200,000 per year) with the 3,185 food products in the CIQUAL database would require a significant amount of work. This step will be the next one.</footnote>
then contains the following:

* Food characteristics: quantities, expenditure, nutrient content, environmental impact for all food products, animal versus plant-based products, processing (NOVA) level, etc

* Household demographics: location, income, socio-economic category, adult male equivalent consumption unit (FAO/WHO, 2004)<fnref n="7" />, annual weighting, etc (see Table 2 for a complete list of household demographics)

* Individual information: age, gender, socio-occupational category, education, body height, body weight, etc (see Table 2 for a complete list of individual demographics)

<footnote n="7">⁷The Adult Male Equivalents (AMEs) (FAO/WHO, 2004) provide coefficients for each household member representing an adult male's food consumption, which varies according to age and gender. These coefficients enable the allocation of household food consumption to individuals to be approximated and allow the direct comparison of food or energy intake between households of different sizes and compositions. They consider moderate physical activity for all household members (FAO/WHO, 2004; Fiedler and Mwangi, 2016; Bermudez et al., 2012).</footnote>
<table><caption>Table 2: Household and individual characteristics</caption><thead><tr><th>Variable</th><th>Description</th></tr></thead><tbody><tr><td><strong>Household variables</strong></td><td></td></tr><tr><td>nopnltNF</td><td>Identification number of the household</td></tr><tr><td>Pond</td><td>Annual weight of the household</td></tr><tr><td>Region</td><td>Kantar region of residence in 8 modalities: (Paris, East, West, Central East, Central West, North, South East, South West)</td></tr><tr><td>Department</td><td>County of residence of the household</td></tr><tr><td>CSP</td><td>The socio-professional category of the panelist</td></tr><tr><td>Inactivity</td><td>At least one person in the household is either retired or unemployed</td></tr><tr><td>Nf</td><td>Number of persons in the household</td></tr><tr><td>Unit consumption</td><td>The sum of consumption units in the household according to the FAO definition</td></tr><tr><td>Number of at-home meals</td><td>The total number of at-home meals for a week during the year on average (two meals per person per day)</td></tr><tr><td>Education</td><td>The education level of the panelist:<br>High (more than two years of tertiary education)<br>Middle (completed secondary education and up to two years of tertiary education)<br>Low (primary or secondary education)</td></tr><tr><td>Rural</td><td>Rural commune defined as having less than 2,000 inhabitants</td></tr><tr><td>Socio-economic class</td><td>Socio-economic class based on income and household size: (Modest, Middle inferior, Middle superior, Rich)</td></tr><tr><td>Income</td><td>Income classes:<br>&lt; 2KF, [2KF;3KF], [3KF;4KF], [4KF;5KF], [5KF;6KF], [6KF;7KF], [7KF;8KF], [8KF;9KF], [9KF;10KF], [10KF;12.5KF], [12.5KF;15KF], [15KF;17.5KF], [17.5KF;20KF], [20KF;25KF], [25KF;30KF], [30KF;35KF], [35KF;45KF], ≥ 45KF</td></tr><tr><td>Auto</td><td>Number of cars in the household</td></tr><tr><td>Naip</td><td>Year of birth of the panelist</td></tr><tr><td>Leg</td><td>Dummy variable: household benefits from the production of vegetables (from their own main residence, secondary residence, or another place)</td></tr><tr><td>Fruit</td><td>Dummy variable: household benefits from the production of fruits (from their own main residence, secondary residence, or another place)</td></tr><tr><td>Panel BMI</td><td>Body Mass Index of the panelist</td></tr><tr><td>BMI class</td><td>Ponderal status of the panelist (Normal, Overweight, Obese)</td></tr><tr><td>Age</td><td>Age group of the panelist (&lt;25, 25-44, 45-64, ≥65)</td></tr><tr><td>Child 6</td><td>Dummy variable for the presence of at least one child under six years old</td></tr><tr><td>Child 16</td><td>Dummy variable for the presence of at least one child under sixteen years old</td></tr><tr><td>Nb child</td><td>Number of children under sixteen years old</td></tr><tr><td>Single</td><td>Dummy variable: household composed of a single adult</td></tr><tr><td><strong>Individual variables</strong></td><td></td></tr><tr><td>Age<sub>i</sub></td><td>The age of individual <i>i</i> within the household</td></tr><tr><td>Gender<sub>i</sub></td><td>The gender of individual <i>i</i> within the household</td></tr><tr><td>Education<sub>i</sub></td><td>The education level of individual <i>i</i> within the household</td></tr><tr><td>BMI<sub>i</sub></td><td>The Body Mass Index of individual <i>i</i> within the household</td></tr><tr><td>CSP<sub>i</sub></td><td>The socio-occupational category of individual <i>i</i> within the household</td></tr></tbody></table>

## 2.2 Difference in purchase recording

In order to compare food consumption across households, it is necessary to record all food product purchases. However, Kantar WorldPanel does not record all food purchases from every household. This issue has varied over time. Since 2008, only some of the households in the sample have provided details of all their food purchases. As shown in Table 1, some households only declare GC products, meaning they only declare products with a barcode. For around 9,000 households, it is not possible to determine the purchases of all fresh products without a barcode, such as a large proportion of fruit and vegetables, meat products, and other artisanal food products. Only the 'GC+PF' panel
declared all food products from 2009 to 2019, and only the 'GC+VP+FL' panel declared all food products in 2008. We will therefore keep those households.

The problem was more complex before 2008. There were three panels: the GC panel, which recorded purchases with a barcode; the VP panel, which recorded meat and fish product purchases without a barcode; and the FL panel, which recorded fruit and vegetable purchases without a barcode. As shown in Table 1, purchases of barcoded products are consistently recorded, whereas purchases of unbarcoded products (such as fruit, vegetables, meat and fish) are not. Of the 223 food categories, 27 contain products with and without barcodes. Three are only collected by GC-FL and 24 by GC-VP. Consider a food category containing products with a barcode (GC) and fruit and vegetables without a barcode (FL). This food category is therefore GC-FL, for which we observe full purchase quantities and expenses for households belonging to the GC-FL panel. For households belonging to the GC-VP panel, however, we only observe purchase quantities and expenses for products with a barcode (GC), not for fruits and vegetables without a barcode (FL). We therefore need to estimate FL for the GC-VP households. A similar problem arises for food categories containing products with a barcode (GC) and meat and fish without a barcode (VP). We need to estimate VP for the GC-FL households.

To address the issue of missing data for certain food categories within some households, we have implemented an imputation procedure at the household level, as described in Bonnet et al. (2014).<fnref n="8" /> The objective is to perform Propensity Score Matching (PSM) on the consumption quantities and expenditures of the unobserved food category, which is classified as either GC-FL or GC-VP. The PSM process can be explained in the following steps.

* **Calculation of the Propensity Score:** Performing a probit regression to estimate the propensity score:

$$S_{ht} = X_{ht} + \varepsilon, \qquad (1)$$

where $S_{ht}$ represents the household panel of the food category (GC-FL or GC-VP), and $X_{ht}$ corresponds to the demographic variables used for calculating the score. Following Bonnet et al. (2014), we use a set of observed household characteristics that includes: Household size, Socio-economic class group, Age range, Socio-professional category of the panelist, Region of residence, Number of children (See Table 2).

* **Estimation of the Means:** Group the data into quantiles of 200 (200 equal parts) based on the propensity score of the previous step, then calculating the means of food consumption

<footnote n="8">⁸It should be noted that missing information is not systematically related to household consumption behaviour, and concerns an average of only a small percentage of household consumption.</footnote>
(Quantities and Expenditures) for each food category across households of the panel FL (VP) within each quantile: $\hat{C}_{ht}^{GC-FL}$

* Perform Propensity Score Matching: Match observations from the GC-FL and GC-VP panels based on their propensity scores.

* Fit an OLS model to predict to predict $\hat{C}_{ht}^{GC}$: Estimating a linear prediction of the food consumption of bar code products for households of the panel VP (FL) regressing $C_{ht}^{GC}$ on $X_{ht}$ to predict $\hat{C}_{ht}^{GC}$

* Calculate $\hat{C}_{ht}^{FL} = \hat{C}_{ht}^{GC-FL} - \hat{C}_{ht}^{GC}$ ($\hat{C}_{ht}^{VP} = \hat{C}_{ht}^{GC-VP} - \hat{C}_{ht}^{GC}$)

* Computing for the households of the panel VP (FL)<fnref n="9" />

$$ \hat{C}_{ht}^{GC-FL} = \begin{cases} C_{ht}^{GC} + \hat{C}_{ht}^{FL} & \text{if } \hat{C}_{ht}^{FL} > 0 \\ C_{ht}^{GC} & \text{if } \hat{C}_{ht}^{FL} < 0 \end{cases} \quad (2) $$

$$ \left( \hat{C}_{ht}^{GC-VP} = \begin{cases} C_{ht}^{GC} + \hat{C}_{ht}^{VP} & \text{if } \hat{C}_{ht}^{VP} > 0 \\ C_{ht}^{GC} & \text{if } \hat{C}_{ht}^{VP} < 0 \end{cases} \right) \quad (3) $$

<footnote n="9">⁹This last step includes a restriction of only adding the estimated $\hat{C}_{ht}^{FL}$ ($\hat{C}_{ht}^{VP}$) if it is non-negative. This restriction is arbitrary and is used to avoid having households with a negative estimated $\hat{C}_{ht}^{GC-FL}$ ($\hat{C}_{ht}^{GC-VP}$), which is not coherent since households cannot have negative food consumption. This restriction does not appear to lead to any significant overestimation of the estimated $\hat{C}_{ht}^{GC-FL}$ ($\hat{C}_{ht}^{GC-VP}$). Indeed, only 0.001% of observations over the whole period of 2002 to 2007 for food quantities of the GC-FL food categories, 0.1% for food quantities of the GC-VP food categories, 0.0001% for food expenses of the GC-FL food categories, and 0.08% for food expenses of the GC-VP food categories had a negative estimate.</footnote>
## 2.3 Ingredient decomposition of pre-prepared meals

In order to address the issue of consumption trends for certain ingredients, such as fruit, vegetables, meat and fish, it is necessary to break down pre-prepared meals into their constituent parts. For this purpose, we use the INCA2 recipe database, which contains the percentage of each ingredient and enables us to calculate the total quantity of each one. However, this method cannot be applied to expenditure, nutrient intake or environmental impact. This is because the price of a meal is not proportional to the quantity of each ingredient it contains. For example, we know that meat and fish ingredients are more expensive than starchy foods. To solve this problem, we associate each ingredient with an equivalent fresh food product that is already present in the ClasSFood database, which contains 223 food categories. We then construct price, nutrient and environmental impact indexes. Taking the example of expenditure, we proceed as follows:

* Compute the share $S_{kit}$ of expenditures of each ingredient $k$ of a pre-prepared meal $i$:

$$S_{kit} = \frac{Q_{ki}P_{kt}}{\sum_{j=1}^{K_i} Q_{ji}P_{jt}}$$

where $K_i$ is the total number of ingredients in the pre-prepared meal $i$, $Q_{ki}$ the quantity of ingredient $k$ in the pre-prepared meal $i$, and $P_{kt}$ the price of ingredient $k$ of the year $t$ recovered by the ratio between the total expenditure and the total quantity of the ingredient $k$ over all households in the year $t$.

* Distribute the total expenditures of the pre-prepared meal $i$ across ingredients and compute the expenditure related to the ingredient $k$ of the pre-prepared meal $i$ in the year $t$ for each household $h$:

$$d_{kiht} = D_{iht}S_{kit}$$

This process is applied each year for each category of pre-prepared meal in the ClasSFood classification. A similar approach is used for energy and nutrient intake, as well as environmental impact.
References

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BONNET, C., P. DUBOIS, AND V. OROZCO (2014): “Household Food Consumption, Individual Calories Intake and Obesity in France,” *Empirical Economics*, 46, 1143–1166.

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