|Fish<br>56.1<br>64.0<br>57.7|
|---|
|Fresh Fruits – Vegetables<br>72.2<br>77.1<br>74.8|
|Legumes and pulses<br>67.2<br>78.9<br>44.0|
|Meat<br>70.0<br>74.3<br>78.0|
|Nuts and derived products<br>75.8<br>68.9<br>89.6|
|Oil-bearing<br>49.6<br>55.8<br>58.5|
|Stimulant crops<br>24.1<br>42.8<br>54.1|
|Sugar<br>48.8<br>37.1|
|Vegetable and animal oils<br>78.5<br>71.5<br>75.3|
|_Source: _Espitia, Rocha, Ruta (2020).“Database on COVID-19 trade flows and policies”.|
|Note: The concentration of imports is calculated as the simple average by product group, across all import food dependent<br>countries of that group, of the sum of the import shares from top-3 exporters that are most affected by covid-19.<br> <br> <br><br><br><br>|
|I<br>d dFddIcIpFdi= 100 ∗(∑<br>∑<br>pIdiiTFI pIdi)/N<br>⁄<br>K=3<br>i=1<br>N<br>i=1<br>, where i, j, k, and n are respectively importer, exporter,<br>exporter rank and product. For definition of import food dependence see footnote 11. Low, medium and high import food|
|<br> <br><br><br>dependence corresponds to shares of import food dependence between 0 and 20, 20 and 50 and more than 50 respectively.|

# 5. **Effects of Covid-19 and export restrictions**

In this section we estimate the potential impact on food prices across products as a result of disruptions in supply in the top-50 countries most affected by Covid-19. We also estimate how the impact on prices could potentially be magnified as a result of uncooperative trade policy across exporting countries.

The initial shock on food export supply is captured through the negative impact that Covid-19 has on the food supply chain as a result of workers’ morbidity and social distancing practices applied by countries. These effects are difficult to quantify ex-ante, but they are likely to vary across products and countries according to factors such as the intensity of labor in production, as containment policies may limit the mobility of seasonal workers and activities that require larger numbers of individuals working in close proximity may be more affected (World Bank, 2020a). As we are ultimately interested in the trade dimension, we use recent data from Aguiar et al. (2019) to calculate this intensity as the share of unskilled labor in total value added of exports. The data are indicative of large variations across countries and products. For instance, production of animal-based products such as dairy is usually automated across countries and therefore has low levels of low-skill labor intensity. In contrast, cereals such as paddy rice and wheat on average involve significant participation of low-skilled workers in their production processes in developing countries, which is reflected in their high shares of low-skilled labor intensity (Appendix Table 6).

To account for this heterogeneity, we assume that decreases in export supply of a specific country-product depend on the share of low-skill workers in the total value added in exports. Other factors such as delays at the border and disruptions in trade logistics associated to Covid-19 may also contribute to reduce export supply, but their impact is more uniform across country-products. Informed by the magnitudes of decline in food exports in China in January and February 2020 (Section 3), we assume that exports in the most affected Covid-19 countries would decline in the short term (i.e. over the quarter following the outbreak of the pandemic) by a range between 5 percent for the least unskilled intensive country-product and 25 percent for the most unskilled intensive country-product.<sup>14</sup> To match the observed decline in food

> 14 Each country-product is assigned to a quintile in the overall distribution of unskilled labor value added over total value added. Decreases in export supply are assumed to be respectively 25, 20, 15, 10 and 5 for quintiles q5 (top 20 percent of the distribution), q4, q3, q2 and q1 (bottom 20 percent of the distribution). An important caveat to this analysis is that it does not explicitly consider other characteristics that vary by country-product, such as the seasonality in agricultural production. The sensitivity analysis in the next section partially accounts for this concern by allowing for smaller and larger initial shocks.

11