The social stratification of internal migration and daily mobility during the COVID-19 pandemic

In a study of mobility patterns across different economic deciles, our findings underscore a dynamic shift between the years 2017 and 2020. For the year 2017, the Pearson correlation coefficient between mobility (measured as percentage points of mean reduction) and economic decile (with higher values indicating greater affluence) was a marginal -0.023, with a coefficient of determination ($R^{2}$) of only 0.0005 (Figure 2(e)). This suggests that the linear relationship between mobility and economic decile was notably weak, with economic decile explaining less than 0.1% of the variability in mobility. However, the landscape dramatically changed by 2020. The Pearson correlation deepened to -0.69, revealing a more pronounced inverse relationship. Furthermore, the $R^{2}$ value surged to 0.48, indicating that the economic decile accounted for approximately 48% of the variance in mobility (Figure 2(f)). This stark contrast between the two years highlights the impact of the association between economic conditions and mobility patterns. Specifically, in an examination of mobility patterns across different economic strata, a distinct temporal evolution was observed from 2017 to 2020. In 2017, the uppermost 20% of economic deciles accounted for nearly 19% of the total mobility change. However, by 2020, this contribution surged to approximately 41.51%. This indicates a pronounced increase in the concentration of mobility dynamics within the more affluent segments of the population. In a comparative analysis of mobility patterns between the richest and poorest economic segments, we employed independent two-sample t-tests to discern any significant differences in mobility reduction. For 2017, the results yielded no statistically significant difference in mobility behaviors between the 20% richest deciles and the 80% poorest deciles. In stark contrast, by 2020, the p-value sharply declined ($p=0.001$), indicating a prominent and statistically significant divergence in mobility patterns between these economic segments, where more affluent comunas moved much less than less affluent ones. The analyses, together with a more fine-grained study of mobility, both internal (within the areas of interest) and external (between areas of interest), can be found in Figure 3.

In our examination of migration patterns in Santiago de Chile (SCL) over the period from 2017 to 2022, we noted clear variations. In 2017, SCL saw an estimated net migration increase, with roughly 130K individuals arriving compared to about 95K departing. By 2020, this trend appeared to reverse, with the city estimated to experience a net outflow of around 35K people as emigration rose to nearly 160K, overshadowing the approximately 125K arrivals. Note that the number of people entering Santiago in 2020 is close to 2017. It is the emigration that exploded in 2020, thus causing the negative net migration. Intriguingly, by 2022, the dynamics seemed to revert to a net migration gain, with an estimated 150K individuals entering SCL, surpassing the expected 130K emigrants. In this case, although emigration remains relatively high compared to 2017, it is the significant jump in immigration that is driving the reversal in net migration. This shift hints at a renewed allure or other potential factors drawing people post-pandemic and some returning.

In general, we see a consistent change in the migration flow pattern from most comunas in Santiago in 2020 compared to 2017. The net migration rate for the individual comunas in SCL shows that there was an exodus from the city during COVID-19, with many comunas getting into negative values (see Figure 2(b)). Furthermore, by using 2017 as the baseline (Figure 2(a)), we see that, even in comunas with a positive net migration, the values were still below those from 2017.

Furthermore, in Figure 2(d), we show the net migration rate with comunas of SCL sorted from left to right by an increasing income average decile. Results show that the exodus from Santiago during 2020 was most significant for richer comunas like Las Condes and Providencia, which significantly increased their population contribution to multiple other regions. Similar analyses for 2022 show that the net migration rate has since reverted, and it is somewhat coming back to pre-pandemic values in 2022 (Figures 2(c) and 2(d)).

The emigration trend (people leaving Santiago) was particularly pronounced in comunas with higher average household incomes, where the percentage of people leaving almost doubled in 2020 compared to 2017. Interestingly, the average socioeconomic level for 2017 showed only a weak correlation with the percentage of the population migrating from each comuna (Figure 2(g)). However, in 2020, the average household income decile of the comuna alone explains 57% of the variance (see Figure 2(h)), hinting at a strong economic effect. This aligns with our regression analysis, which showed a higher $R^{2}$ value and coefficient for 2020 compared to 2017. In 2022, we observed a slight recovery after the pandemic, with income still being a much stronger predictor than it was in 2017 (Figure 2(i)). This is consistent with our regression analysis for 2022, which showed a significant relationship between the dependent variable and home income decile, although slightly weaker than in 2020. Notably, emigration from low-income comunas tended to increase in 2022 compared to both previous periods.

Overall, our findings suggest that income played a significant role in migration patterns in Santiago during the period from 2017 to 2022. The relationship between income and migration strengthened significantly in 2020, likely due to the economic impact of the pandemic, and remained strong in 2022, even as the situation began to recover.

We analyzed the difference in the percentage of migration at the comuna-comuna level comparing the years 2017 and 2020 for people leaving Santiago (see Figure 4(a)). The origin-destination matrix, sorted from bottom to top by ascending average income decile of the destination, and left to right by average income decile of the origin, shows the effect of the economic dimension on the availability of destinations for long-term relocation. People emigrating from lower-income comunas in Santiago seem to have distributed over a reduced number of destinations and tended to avoid the more expensive comunas. This is shown in the graph by a predominance of blue cells as we move to the top-left corner of the matrix (which signifies that fewer people moved between these comunas in 2020 compared to 2017). As we move to the higher-income origins (right in the matrix), we see a relative upsurge in migration over an increasing range of destinations (signaled by a predominance of red tones for the entire columns).

Utilizing the Index of Quality of Life in Urban Areas (ICVU) [34], we evaluated the willingness among high-income migrants to cede urban amenities (as measured by the ICVU) when leaving Santiago. Given that Santiago de Chile ranks among Chile’s most urbanized areas, it is noteworthy that most destination comunas registered lower ICVU scores in comparison to affluent comunas in SCL. Our analysis revealed heterogeneity in amenity trade-offs. For instance, Providencia (ranked second in ICVU) exhibited a statistically significant deviation from the expected amenity trade-offs with an average ICVU difference greater than predicted. Meanwhile, Vitacura (top-ranked) displayed less variation and stayed below the expected difference (i.e., migrants conceded less in quality of life).

Further quantification of these tendencies can be observed in the origin-destination matrices, segmented by average income decile (Figure 4(d)). Similar to the patterns observed in 2017, migration predominantly occurred towards comunas with elevated socioeconomic indices. However, in 2020, the data indicated an increased variation in amenity trade-offs across income deciles. For example, in 2020, origins with lower income deciles had a greater percentage of people moving to destinations with a closer average economic level.

To further measure the changes in the selection of destinations compared to pre-pandemic patterns, we calculate the 1-Wasserstein divergence [40] for each column in the origin-destination matrices of 2020 and 2022 against 2017 (see Figure 4(b)). We see that the divergence from 2017 is larger for comunas with lower income. Despite having the most significant increase in emigration, high-income comunas had the lowest variation in their selection of destinations. As observed before, 2022 shows again a move back to pre-pandemic patterns.

At the regional level, preferred destinations as per the hosted proportion of migrants from SCL stayed relatively stable, except for Valparaíso (a neighboring region and a popular vacation destination), which saw a statistically significant increase in immigration (see Figure 4(c)). This pattern coincides with findings in other cities (e.g., New York City), suggesting that many urban residents moved to neighboring areas, second residences, and holiday destinations [41]. Moreover, in Figure 4(c), we sorted the regions using the Gravity Model (i.e., the population at the destination over distance to SCL) [42]. The graph shows that higher values from the Gravity Model correlate to higher percentages of migration to the corresponding region. This indicates that, besides economic aspects, distance and population density may have been still meaningful determinants in the selection of the destination in 2020 [43].

The COVID-19 pandemic spurred distinctive migration trends globally. While some large metropolitan areas saw a surge in residents relocating to rural settings [44, 45], anecdotal evidence also highlighted urban relocations [46]. In Santiago de Chile (SCL), our analysis suggests that, contrary to some trends, people predominantly favored urban comunas over rural ones.

To quantify this preference, we analyzed the origin-destination migration matrices from 2020 and 2017, focusing on the difference in migration percentages at the comuna level. Sorting the rows (destinations) by increasing rurality percentages (Figure 5(a)) and columns by average income decile of SCL comunas, the visualization reveals a pronounced decrease in 2020 migrations to high urbanization comunas compared to 2017. However, there wasn’t a marked inclination for rural destinations. Notably, the wealthier comunas (as seen in the rightmost columns) showed increased migrations, irrespective of the destination’s rurality.

To gain additional quantitative insight into the change in preference for rural destinations, we calculated the difference against 2017 in average rurality of migration destinations for each SCL comuna $\Delta R_{x}$, weighted by the percentage of emigrants (see more details in the Methodology Section).

The rurality preferences across Santiago de Chile (SCL) comunas are quantitatively depicted in Figure 5(b). Notably, high-income comunas such as Vitacura exhibit a positive change in average destination rurality $\Delta R_{x}>0$, contrasting with over half of the comunas that either retained a similar preference $\Delta R_{x}\approx 0$ or skewed towards more urbanized destinations $\Delta R_{x}<0$. These findings were observed while sorting the SCL comunas by ascending average income decile. Despite this binary urban-rural classification, no statistically significant correlation was evident between the socioeconomic status of the origin comuna and the shift in average destination rurality.

To refine this analysis, we transitioned from a binary rurality measure to a continuous metric based on population density. Figure 5(d) delineates the relationship between the poverty rate in SCL comunas and the average population density of destination comunas. A stronger trend emerged in 2020, indicating migration from wealthier comunas to more densely populated areas, substantiated by an increase in the coefficient of determination $R^{2}$ from 0.17 in 2017 to 0.32 in 2020. Intriguingly, 2022 data revealed a further intensification of this pattern, as evidenced by an $R^{2}$ value of 0.41, deviating from other metrics that reverted to pre-pandemic levels.

Finally, at the regional level, we analyzed the potential impact for the hosting regions of this increased emigration from SCL compared to 2017. We quantified the difference in the percentage of population change for each destination region and sorted them using the Gravity Model (Figure 5(c)). We see that for neighboring regions such as Valparaiso and O’Higgins, despite already having relatively large populations, the emigration from SCL in 2020 still represents an increase of around 1.5% of their normal inflow of 1.5% - 2.0% seen in 2017. In other words, these regions saw an almost two-fold increase in their population growth compared to previous years. Most notably, the region of Aysén, in the south of Chile, witnessed the biggest increase with respect to its population, going from a new 1.3% population formed by migrants from SCL in 2017 to 3.2% in 2020.