Switzerland being surrounded by four countries from the Euro zone (Austria, France, Germany, and Italy), the exchange rate shock might impact the diet quality of people through two main paths. First, assuming Swiss residents wage is labelled in Swiss francs, food items bought in the Euro countries become much cheaper. Cross-border shopping therefore becomes more appealing in particular for people living close to the border. We can expect the shopping effort to increase to access cheaper food prices (note that some food items such as meat have quantity restrictions so that individuals can only buy a limited amount on any given day, which can mitigate the phenomena).
The second path is through imports for Swiss retailers. Goods imported from the Euro area are cheaper. If the invoice is labelled in Euro, the importer has a large increase in his margin. The price paid by the Swiss consumer will probably partly capture this surplus in the margin. In addition, the pressure on Swiss goods will probably increase with the competition of goods coming from the Euro area. The price of goods in Swiss retails will also decrease after the exchange rate shock.
4.3 Literature review
4.3.1 Impact of exchange rate on prices
The exchange rate shock pass through (ER pass through) (i.e. the extent to which the change in exchange rate is reflected in prices) might take time and vary across products. The relationships between exchange rates and prices have been extensively studied in the United States. In terms of timing Gopinath and Rigobon (2008) found that the food items’ prices take between 1 and 5 months to pass-through after an exchange rate shock in the US. And the ER pass through is low at 22% for US imports.[235] The ER pass through might also differ depending on the market structure. Auer and Schoenle (2015) and Devereux and al. (2017) studied this price stickiness at the border. The price response to the exchange rate has an inversed U-shaped relationship in the importer market share. Small or large importing firms have a lower ER pass through.
Foods and beverages seem to have a partial ER pass through.[236, 237] Assuming a similar situation in Switzerland, the ER pass through is expected to be slow and partial since the market is dominated by two actors with more than 60% of the Swiss market share.
4.3.2 Impact of the 2015 EUR-CHF Exchange rate shock on economic outcomes
In Switzerland, several studies explored the effects of the discontinuation of the minimum exchange rate.
Bonadio and al. (2019) showed that the price of goods invoiced in Euro adapted instantaneously. Goods labelled in Swiss francs adapted rarely instantaneously and more than ten days were needed to pass through.[238] Using transaction-level data, Lein and al (2018) found similar results. EUR-invoiced goods’
prices of retailers and importing firms fell more at the shock than CHF-invoiced goods’ prices. Domestic goods’ prices fell more if similar EUR-invoiced goods fell more due to increase in competition. Swiss retailers also increased their part of imports after the ER shock. Finally, price adjustments increased in size and frequency after the shock, especially for imported goods.[239] Biello Pierra (2017) documents an increase of cross-border travel after the ER shock in the border region of South Switzerland. Italian supermarkets bordering Switzerland increased their sales.[240] The ER pass through heterogeneity in goods and speed might change the exposition to the shock for Swiss citizens.
4.3.3 Impacts of price changes on dietary intakes
Several manuscripts look at the change in diet during the last financial crisis. Griffith and al. (2015) studied the large inflation for foods prices in UK between 2007 and 2009. Households were able to maintain the energy intake and the diet quality by increasing shopping effort and slightly changing their preferences.[149, 152] Alves and al. (2018) explored the change of diet before and after the Great Recession in Portugal. The observed changes didn’t seem to be linked to the economic crisis.[241] Bartoll and al. (2015) showed that Spanish households reduced fruits, meat and cold meat consumption during the Financial crisis.[242]
The response of households is also studied in other settings. In developing countries, Alem and Söderbom (2012) looked at the response to a food price inflation between 2004 and 2008 in Ethiopia. Low asset and income households were particularly hit in this African country. Higher income households could better absorb the shock.[243] Ruhm (2000) showed that fruits and vegetable consumption increased, and fat consumption reduced when economic conditions worsened.[244] Studying gasoline price changes, Gicheva and al. (2010) showed that US households buy proportionally more sales food items when gasoline price rises.[245] Other types of shocks are also studied such as the anticipated shock at retirement. Aguiar and Hurst (2005) showed that people are able to smooth quality and quantity intake for retirement.[133]
4.3.4 Impact of policies on diets and prices
Some policies modify the prices of food items for selected groups of the population which provides opportunities to explore households’ responses. The US Supplemental Nutrition Assistance Program (SNAP) provides such a framework. The goal of this program is to reduce poverty and food insecurity by providing support to low-income households. The program seems effective against food insecurity.[246, 247] In a systematic review, Andreyeva and al. (2015) showed that the impact on diet quality appears to be limited.[248] Griffith and al. (2018) showed that a UK policy introducing vouchers for fruit, vegetables and milk have a positive effect on diet quality. The use of vouchers is also more effective than a transfer in cash.[192] Using an experimental design, Muller and al. (2017) showed that taxing unhealthy foods and subsidizing healthy foods tends to be regressive.[249] The diet quality of high-income women before the experiment is higher than for low-income women. Hence the tax will impact more low income than high income women. In addition, the response of the high-income women tends to be larger. Sugar taxation is one of the most advanced policy trying to influence the food choices of individuals. The tax should increase
to limit the transmission of the tax to the prices, with only 43% of the tax passing through the prices. Using the same methodology, Cawley and al. (2018) found a higher pass-through of 93% in Philadelphia.[251]
4.3.5 Heterogeneity in dietary intakes in the population
Darmon and Drewnowski (2008) uses 4 main reasons to explain the variation of nutrition in the population.[252] Education and culture are one potential factor influencing the diet. Cooking skills, motivation or nutrition knowledge are the underlying mechanism impacting the diet. These explanations probably did not vary in the case studied hereafter. The second explanation is access to food. Restrictive choice of food could push individuals to unhealthy processed food (longer life cycle). Alcott and al. (2018) find that more than 90% of the diet difference between high and low income is driven by the demand in the United States.[253] This result limits the restrictive offer explanation in the results obtained. In addition, Switzerland is a smaller country hence all consumers can access easily to supermarkets. Thirdly the budget constraint of households and relative food prices might drive the consumption to unhealthy food items.
Darmon and al (2004) finds a lower diet cost for energy-dense food.[116] Nonetheless Gao and al. (2013) find that own-price elasticities of demand for diet quality are inelastic.[120] This result indicate that the budget constraint might trigger the poor food diet behavior of some Swiss residents. A higher change in the diet for low income households after the shock might indicate this budget constraint. Lastly the stickiness of the preferences might play a great role and potentially offsetting the effect through the price change.
4.4 Data and methods
4.4.1 Data: MenuCH
The dataset includes two non-consecutive 24-hour dietary recalls (first by face-to-face interview and the second by telephone interview, both with a certified dietician) from more than 2000 participants aged 18 to 75 years from the three main linguistic regions of Switzerland.[178]
Participants were recruited from the national sampling frame for person and household surveys. The survey population was intended to be representative of the Swiss population in terms of age and place of residency across all seven major areas of Switzerland, but did not survey people from every canton. A total of 5496 eligible people reachable by phone were invited to participate, of whom 2086 (38%) responded [179].
Participants and non-participants had similar age and marital status but participants were more frequently women and Swiss nationals than non-participants. Survey sampling weights were derived to adjust statistical analysis to be more representative of the Swiss adult population aged 18 to 75 years and to account for non-response.
4.4.2 Empirical approach
Using the unexpected removal of the EURCHF lower bound as a natural experiment we implement a regression discontinuity design (RDD) approach. The general idea is to compare dietary choices of individuals in a restricted window before and after the exchange rate shock. Under several assumption (see below), any observed change in dietary patterns can be attributable to the shock as individuals should be comparable in any other characteristics just before and just after the shock. One key assumption is that individuals cannot manipulate or anticipate the shock. In our case, individuals could not manipulate their exposition to the shock or the timing of the shock since only the members of the Swiss National Bank board took this decision. In addition, as Figure 4.1 shows the shock was largely unexpected by financial markets therefore Swiss households could probably not anticipate this decision and change their behaviour ex ante.
Equation 1 shows how this is implemented in practice.
𝑦 = 𝛽 + 𝛽 𝑃𝑜𝑠𝑡 + 𝛽 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑠ℎ𝑜𝑐𝑘 + 𝛽 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑠ℎ𝑜𝑐𝑘 × 𝑃𝑜𝑠𝑡 + 𝜀 (1) The dependent variables of interest (yi) are the different diet quality indicators, an overall healthy heating index (HEI), total calories, and food expenditures. Post is a variable indicating whether the observation is before or after the shock. Distance to shock reflects the time difference between a specific observation and the shock, in days (here, a linear relationship is assumed). 1 measures the impact of the shock on the dependent variable.
4.4.3 Sample selection
To implement the regression discontinuity design (RDD) strategy, we focused on the period ranging from the December 1st, 2014 to February 19th, 2015. The sample is composed of 756 dietary recalls (see Table 4.1 for descriptive statistics). The average age of the subset of selected participants was 46 years old and ranged from 19 to 76 years old. Individuals had a mean BMI of 24.6 kg/m2. Male composed 43% of the sample. The subsample contained mainly workers, retirees, student and full-time fathers or mothers. More than 81% had Swiss nationality. The subsample had high proportion of highly educated individuals, i.e.
32% achieved a university degree or equivalent. The proportion of student (8%) was also high. More than 80 % of the subsample reported having a good or very good health.