absence of such data sources for Afghan refugees and Iranians, we focus on a specific dimension of social cohesion that can be measured using items in HEIS. In particular, we look into assistance received by Afghan households from other households as a measure of trust between two communities. Pham et al. (2021) use participatory research methods to identify the elements of the predominant understanding of social cohesion in the Democratic Republic of the Congo. Their study suggests that helping each other and sharing (generosity and charity) represent strong indicators of social cohesion. Accordingly, although our measure of social cohesion departs from its general definition, it captures one of the most important aspects of a broad concept.

Having our definition of social cohesion in mind, we compute different variables using HEIS to measure trust between the communities. **Aid** is an unearned income item corresponding to aids received from NGOs and charity institutions. **Transfer** is another item in the unearned income table that comes from direct monetary transfers from other households. Moreover, in HEIS expenditure tables, in addition to the quantity and value of the items, their method of obtaining (purchase, home production, from work, free) is also inquired. Using this information, we compute **free items** as the sum of all expenditure items that their method of obtaining is mentioned as ‘free’. Finally, we sum over household expenditure items for charitable giving and build variable **donation** that measures the transfers and contributions that are given to other households. According to rows (16) to (18), the means of aid, transfer, and free items are significantly higher among Afghan households, and it raises above their monthly expenditure per capita. However, the share of Afghan households who receive aid, transfer, and free goods are respectively 3.2%, 6.6%, and 14.4%, which is significantly lower than these shares for Iranians.

## **4.2.4. Measuring economic shock**

In order to measure shocks, we need a variable that both reflects the exogenous nature of macro shocks in Iran’s economy and has at least a monthly frequency to be merged with HEIS data. In this regard, our best candidate is the exchange rate (US dollar to IR rial) which is available daily and, as shown in Figure 3, quickly reveals the effect of sanctions on Iran’s economy. In addition, since we aim to study the effect of shock on social cohesion, it is also important that exchange rate shock captures not only the direct economic impact of exogenous shocks but also people’s perceptions and expectations about the status of the economy. Besides, many Afghan refugees send remittances back home and regularly consider returning home or remigrating to other places. Such considerations are mostly affected by the exchange rate that is instantly visible to them. In contrast, other shock measures like GDP growth capture the economic shocks to all sectors of the economy, many of which are irrelevant to work for refugees.

Since the recall period for most of our dependent variables is the past year, our primary variable to − measure monthly shock is the year-on-year difference in log of USD to IRR rate, i.e. ( log Xrate�,� log Xrate�,��� ). However, because the exchange rate in Iran jumps in bad economic times and remains almost constant in good times, which is evident in Figure 3, the variation in this measure of shock mainly reflects bad economic shocks. For this reason, when we examine the asymmetric effect of good and bad shocks in section 5.4, we apply a Hodrick-Prescott filter to extract the cyclical component of the log of the exchange rate.

# **4.3. Identification strategy**

In order to examine the unequal effect of economic shocks in Iran on Afghan refugees and natives, we utilize two approaches. The first approach is based on estimating the following regression y�� = α� + α�Afghan� + α� Shock� + α� Shock� × Afghan� + γX�� + T� + ε�� (1) where y�� is one of the dependent variables listed in Table 2, Afghan� is the dummy of being a refugee for household or individual _i_ , Shock� is the index of economic shock at time _t_ , X�� is a vector of control variables, T� is a vector of year and month fixed effects, and ε�� is the error term. We estimate this regression with OLS method when the dependent variable is continuous and with Logit when the dependent variable 8