questionnaire does not include information about the nationality of the household members. Nevertheless, as described in Appendix A, we can identify the nationality of households from the expenditure items after 2011.

## **4.1.1. Matched samples**

As the impact of macroeconomic shocks can be highly heterogeneous across different segments of society, in addition to comparing Afghan refugees with all Iranians, we also make a comparison group of Iranians who have similar characteristics with Afghans. In this way, we can assume that in the absence of shocks, the trend of Afghan refugees’ outcomes would have been more similar to the matched sample rather than the whole population. For this purpose, first, we make a stratification based on survey round and location in which matching is exact. Second, we use a vector of covariates to match Afghan individuals in LFS and Afghan households in HEIS to their Iranian counterparts within each stratum.

To match Afghan individuals in LFS with Iranians, we exclude the population not participating in the labor force and apply exact matching on round, location, and employment status (employed/unemployed). We then use the following variables for matching at the individual level: gender, being household head, education, marital status, age, work experience, and household size. For employed individuals, we also include occupation code, job status code (self-employed, wage-earner, etc.), firm size, and having medical insurance via their job as additional covariates for matching. The covariates utilized for matching in HEIS include household size, number of students and workers, vehicle possession, household head’s gender, age, education, marital status, and occupation dummies. In both surveys, we achieve the best balance when using Mahalanobis pair matching with replacement. Appendix Table 2 shows the covariate balance between Afghan and Iranians for different matching techniques in HEIS.

# **4.2. Variables and summary statistics**

To measure inequality and social cohesion between and within refugees and host communities, we utilize the surveys to construct various variables. In below, we provide the definition of each variable and its summary statistics.

## **4.2.1. LFS variables**

Table 2 shows that the **working hours** per week is about 2 hours higher in Afghan individuals compared to Iranians, and the **unemployment rate** (percent of unemployed individuals to active population) is much less among Afghan refugees (4.8%) compared to Iranians (10.8%). Among unemployed individuals, being laid off is less common among Afghan refugees (8.5%) than Iranians (11%). The **duration of unemployment** also suggests a better image among Afghan refugees than Iranians. Table 2 shows that, on average, the unemployment length of Afghans takes 7.1 months, but this number is 19.3 months for Iranians. However, comparing the job class between Iranians and Afghan refugees shows much better conditions for Iranians. Based on the classification of occupation (ISCO-4), we distinguish between unskilled and skilled/semi-skilled occupations<sup>5</sup> and compute the share of unskilled workers among employed individuals. The fifth row of Table 2 shows a significant discrepancy in terms of unskilled jobs between Afghan refugees (50.8%) and Iranian workers (15.2%). Hence, although Afghan refugees have less and shorter unemployment than Iranians, they mainly work longer hours in low-paying unskilled jobs.

Using the rotating panel feature of LFS, we compute various transition variables for each subsample by comparing the average outcome of individuals present in the sample in the same season of two consecutive years. First, we define the **sample replacement** , which is a dummy in the LFS rotating panel sample and is one if the household is not responding next survey round and replaced by a new household. As the sample design in LFS and HEIS is based on households’ location, the sample replacement dummy indicates whether the household is present in its previous dwelling or replaced by new households residing there. If no one is present in the previously sampled location, the SCI officer chooses a nearby location within the substratum for sampling. Hence, sample replacement is an index of movement that can be due

> 5 The first digit of ISCO-4 corresponds to different skills and category 9 is for unskilled occupation in various sectors.

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