to migration, job mobility, non-ownership of residence, death, etc. Row (6) of Table 2 shows that the replacement rate is much higher for Afghan refugees (12.9%) compared to Iranians (3.7%).

Next, we look into the change in the employment status of _active individuals_ (employed and unemployed) in the rotating panel sample. Figure 4 illustrates the transition rates from employment/unemployment into employment/unemployment/inactivity. We find that transition from unemployment to employment is much higher among Afghan individuals (62.4%) than Iranians (38.2%). The transition rate from employment to unemployment is slightly larger among Afghan individuals (16.5%) than Iranians (14.3%). Based on these transition rates, we define two variables for regression estimations: **unemployment to employment** is a dummy variable for unemployed individuals that becomes one if the status becomes employed in the next period. In a similar way, we define **employment to unemployment** dummy for unemployed individuals in each round of LFS surveys.

Using the information of the type of occupations and the transition between them, we can measure upward and downward occupational mobility. Figure 5 shows the transition between skilled and unskilled jobs for individuals who are _employed_ in two consecutive years. While 41% of Iranians with an unskilled job move to a skilled job next year, this rate for Afghan refugees is 28%. On the other hand, the skilled to unskilled transition is higher among Afghans (26.5%) than Iranians (7.1%). The transition rates for the matched sample of Iranians are between Afghan refugees and all Iranians. Hence, we can conclude that in terms of occupational mobility, Afghan refugees have lower upward mobility and higher downward mobility. In this regard, we define two dummy variables for employed individuals in two consecutive years: **upward mobility** is a dummy for unskilled workers that becomes one if they obtain a skilled job next year. **Downward mobility** is a dummy for skilled workers and is one if they have an unskilled job next year.

One important issue regarding the transition dummies is that due to refreshing the LFS rotating panel sample in 2013 and 2018 by SCI, we cannot observe the transition variables from 2012 to 2013, from 2017 to 2018, and from 2019 to 2020 (the data is not available yet). This is rather unfortunate for our study since, according to Figure 3, these years were important times in terms of macroeconomic shocks. Missing them reduces the variation of the independent variable in our estimations.

# **4.2.2. HEIS variables**

Rows (11) to (18) of Table 2 show variables computed from HEIS surveys. All of these variables are measured as the logarithm of values at constant 2011 prices. For measuring household per capita expenditure, we use the modified OECD adult equivalent scale, which gives value 1.0 to the first adult, 0.5 to each member aged 14 and over, and 0.3 to each child aged under 14. Since the HEIS recall period for the majority of expenditure items is past month, we measure per capita **total expenditure** and **food expenditure** on a monthly basis. Figure 6 shows the distribution of total expenditure per capita by nationality of the household which has a log-normal shape. We observe that Afghan households spend less on average, and their expenditure distribution has a shorter tail. The recall period of income tables is past year, and thus they have larger numbers. According to Table 2, Afghan refugees spend less overall and on food items and have lower **total income** . Their **earned income** , however, is larger than Iranians because of their higher labor force participation. The **wage rate** of the household’s head is almost equal in the two communities, even though, on average, Afghan households have higher wage rates than the matched sample of Iranians, suggesting that Afghan workers are more productive than their Iranian counterparts. It is to be noted that the wage rate is reported only for wage-earners and not self-employed individuals, and for this reason, it has fewer observations than earned income.

# **4.2.3. Measuring social cohesion**

Social cohesion is a multi-faceted phenomenon with a range of dimensions such as belonging, inclusion, participation, recognition, and legitimacy. In the forced displacement context, factors such as common identity, economic inclusion, and trust between two communities are critical dimensions to measure social cohesion (de Berry & Roberts, 2018). Measuring all dimensions of social cohesion needs a range of subjective and objective indicators concerning inter and intra-community relations and attitudes. In the 7