Table 4.3: Covariates balance

Covariates P-value

Age at time of interview (years) 0.293

Education categories 0.605

Number of people living in HH 0.267

Number of cars 0.889

Net Income 0.467

Health status 0.345

Nationality 0.235

Gender 0.110

Type of interview 0.000

Linguistic region 0.009

Household status 0.839

Civil status 0.851

Activity (Worker, retiree...) 0.431

Smoking status 0.956

Recall day number 0.179

4.4.6 Robustness checks

As said in the previous section, there are two main threats to validity of the discontinuity due to the survey sampling. Namely, the sampling of individuals while random, the collection of the data over time was not, hence individuals sampled prior to the shock may differ to individuals sampled after the shock. Secondly, two surveys of diets were undertaken, the first a face to face interview and the second a phone call interview.

Given the sample exposed to the shock was composed of individuals right at the end of the study it is more likely that we have a higher proportion of second interviews in this sample. If the sampling method influences reporting of outcomes this could affect results. There may also be behavioural change linked to the first interview when a nutritionist was present either during the first interview or subsequently afterwards. Below we indicate how we allowed for these potential biases.

The two first robustness checks are usual in the RDD literature. First, we add controls, including the type of interview (first face to face or second/nutritionist) and a rich set of individual and household controls reported previously to see if it changes the results. Second, we change the functional form before and after the shock with a second and third order polynomial for potential non linearities linking the diet and time such as seasonality in the diets.

Thirdly we implement the inverse probability propensity score matching procedure. In the first step we calculate the probability of being observed after the shock based on the unbalanced controls using a logit model. We then weight the observations before and after the shock by the inverse of the probability to be in the group before the shock (1 – probability exposed to shock). It allows to increase the weight of observations close to the group after the shock. The weight for the group observed after the shock is the inverse of the probability of being in the second group to give more weight on observations close to the group before the shock. These weights should increase the comparability of the sample before and after the shock based on the controls used in the first logistic regression.

Fourthly, as the MenuCH survey had two interviews, we keep only individuals observed on both sides of the shock hence the population is similar on both sides. This implies identical samples before and after the shock, however, it reduces unfortunately the sample size by more than half limiting the power of the coefficients but increasing probably the accuracy. The only differences between the groups are potential time trends and the type of interviews, a face-to-face interview before the shock and phone interview after the shock, hence the estimates are unbiased only if we assume no effects of the survey methods and timing.

Finally, we implement some placebo tests at different dates where no such shock happened a priori and the seasonality of diets should not impact the estimates. Hence, we select dates during the summer 2014. The sample is not restricted to individuals observed on both side of the “placebo” shock. The time span before and after the shock is also 45 days and the “placebo” shock is placed the 15^th of the month as in the basic setup.

In addition, the distribution of the dependent variables is also explored. The diets measured at the food item level such as legumes show a large mass point at zero. Modelling this mass point with a two-part model

4.4.7 Heterogeneity of effects

We use the time cost to reach the border as a proxy of the degree of exposition to the shock. Individuals living near the border could easily go abroad and experience the full impact of the exchange rate shock assuming prices stayed unchanged in Switzerland. The effect of the border population is expected to be stronger where the probability and the frequency is higher. We calculate the travel time to reach the border using the geo-localisation of individuals’ houses and the Swiss border points. The border points are the main roads crossing the border. Due to data availability, we could not find small roads crossing the border. This travel time is then valued using the opportunity cost of time. We then create two groups. The border group consists of households having an estimated travel time cost below the mean and a non-border group having an estimated travel time cost above the mean. The resulting model is:

𝑦 = 𝛽 + 𝛽 𝑃𝑜𝑠𝑡 + 𝛽 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑠ℎ𝑜𝑐𝑘 + 𝛽 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑠ℎ𝑜𝑐𝑘 × 𝑃𝑜𝑠𝑡 + 𝛽 𝐵𝑜𝑟𝑑𝑒𝑟 + 𝛽 𝐵𝑜𝑟𝑑𝑒𝑟 × 𝑃𝑜𝑠𝑡 + 𝜀 (4)

5 is the coefficient of interest which measure the difference between the two groups living near or far from the border. 4 observations on 756 are lost due to the unavailability of the household location. This loss should not drive the result.

We finally also look at the response of lower income compared to higher income households. Food expenditures represent a higher proportion of household income/budgets for low-income households, hence they face tighter budget constraints and hence the effects of any price shock would on diet choices and food consumption is likely to be more significant.

4.5 Results

4.5.1 Descriptive analyses

Figure 4.2 shows sodium intake before and after the shock and fitted lines. A small upward trend is present on both sides of the cutoff. At the discontinuity a drop occurs.