In Figs 6 and 7 , we present heat maps for the correlation between the IGPA CAR, the CASA and the CASVA reported in Table 3 . For both event windows, we observe significant and negative correlations between our sentiment proxies. As expected, since these measures tend to move in opposite directions in response to good or bad news, an increase (decrease) in the abnormal search activity is associated with a more pessimistic (optimistic) sentiment during the windows centered around lockdown announcements. The correlation between the abnormal stock returns and the sentiment proxies is low and statistically insignificant for either window. Interestingly, this result is consistent with the results in Kim et al [ 25 ]. These authors show that Google searches are not correlated with contemporary stock returns, nor can they predict future abnormal returns.
In Fig 5 we present a heat map for the correlation between the IGPA index, the Sentiment Score and the Average Search Volume Index—ASVI- series for our sample period. It is possible to observe a strong and negative correlation between the levels of the IGPA and the ASVI series, which suggests that during periods of high stock market valuation, markets concerns about the development of the pandemic are dimmed. Concerning the correlations between the stock index and the sentiment score and between this last variable and the ASVI, correlations are very low and statistically insignificant at any standard level of significance.
For both event windows and for any of the stock or market sentiment variables considered, we observe positive and negative abnormal reactions. This situation is most likely to arise from the fact that each lockdown announcement involves some municipalities going into lockdown and others going out of it. We hypothesize that the observed heterogeneity in stock market and sentiment reactions reflects the number of people going into lockdown and their SES.
In columns (1) and (2) in Table 3 , we report the stock market reactions to each of the government announcements relating to the dynamic quarantine scheme. In column (1), we report the IGPA index cumulative abnormal returns (CAR) for the (−1, 0) window. In column (2) we report the observed CARs for the (−1, +1) window. Since our results are qualitatively unchanged using any of the domestic indexes and either choice of the benchmark portfolio, we only report our results for the IGPA index using the S&P500 as the benchmark portfolio. In columns (3) and (4) we report the Cumulative Abnormal Sentiment Activity for both event windows, and in columns (5) and (6) we present the observed Cumulative Abnormal Search Volume Activity upon lockdown announcements.
In sum, the results in Table 4 provide evidence that stock market reactions to lockdown announcements depend on the SES of the population under lockdown. Recognizing that there might be several reasons why such a phenomenon could be observed, it strongly suggests a high level of wealth concentration among the richer population. In fact, according to the World Inequality Database ( https://wid.world/ ), as of year-end 2018, the top 10% wealthiest population accounts for 60.4% of the total income of the country. As richer cohorts are considered for the changes in the number of people under lockdown, the predicted abnormal returns in the stock market are higher in magnitude. Furthermore, changes in the total population cannot explain stock market reactions to such announcements. This result is significant because it validates our proposed wealth ranking, which will also be used to analyze sentiment responses to government announcements below.
The results presented in panel A of Table 4 are obtained for a sorting of the population based on the MPI of the corresponding municipalities. As a robustness check, we consider an alternative sorting, based on Municipal Income, available at https://observatoriofiscal.cl/Informate/Repo/BrechasentreMunicipios . Municipal income includes all sources of financing available to municipalities; collection of business licenses, income from land and property taxes, payment of road taxes, fines collected by the municipality, as well as transfers from the Central Government. However, it does not include factors related to health, education, and living standards that the MPI, our preferred sorting variable, does include. Results are presented in panel B of Table 4 . For the (−1, 0) window, we obtain similar results, both in magnitude and statistical significance, to those obtained for the MPI sorting. Even though the goodness-of-fit of the regressions is somewhat dimmed, the R2 coefficients remain high and the monotonically decreasing market reactions are still observed as we move from high to low-income municipalities. For the (−1, +1) window, point estimates are smaller than those presented in panel A and exhibit low statistical significance.
For the (−1, +1) window in columns (7) to (12), the market reactions are smaller in magnitude than for the (−1, 0) window, with lower R 2 coefficients. However, estimates remain significantly different from zero for the wealthiest municipalities. Again, the effect vanishes when changes in total population are considered. The higher fits and the higher point estimates observed for the (−1, 0) are consistent with the timing of the announcements, which are typically made before noon, and with market reactions taking place on that same day. In any case, the monotonically decreasing market reactions upon government announcements for the (−1, +1) window are still observed as we move from the wealthiest municipalities to the whole population.
For both event windows, we obtain a monotonically decreasing magnitude of market reactions upon announcements as we move from the wealthiest municipalities to the whole population. For the (−1, 0) window in column (1), an increase of one million people in the wealthiest segment of the population under lockdown produces a more negative CAR by close to 800 basis points. This effect is statistically significant and economically meaningful. In column (3), an increase of one million people in the ABC1 segment under lockdown, produces a more negative CAR in the (−1, 0) window by close to 450 basis points, significant at the 10% level and achieving an R 2 coefficient near to 0.24. When we consider the population belonging to the municipalities in the highest wealth quintile in column (5), i.e., the first 24 wealthiest municipalities out of the 120 considered in our sample, the effect on CARs drops to nearly half of what we obtained in column (1), but remains both economically and statistically significant at a 5% level, with an R 2 close to 0.2. For the changes in the total population under lockdown in column (6), the effect on stock market reactions declines even further and becomes statistically insignificant. Furthermore, R 2 declines drastically.
Each column in Table 4 presents the results for the estimation of Eq 5 for a specific cohort. The first six columns of the table report the results for the (−1, 0) window; the last six columns show the results obtained for the (−1, +1) window. In the analysis that follows, we resort to the Cribari-Neto HC4 heteroskedasticity-consistent covariance matrix estimators for making inferences. Cribari-Neto [ 51 ] shows that this estimator performs well in small samples, especially in the presence of influential observations.
Regarding stock market reactions to lockdown announcements, we estimate Eq 5 for each of the government announcements in Table 3 . For discussion and analysis, we consider particularly relevant the population belonging to the ABC1 segment when comparing the results between the wealthiest and total population. In any case, to show that our results are not driven by the selection of an arbitrary cohort of the population, we consider six cohorts based on the MPI sorting for the change in confined population (ΔPopulation i,t ). Results are presented in panel A of Table 4 . The first cohort is the population belonging to the five municipalities featuring the lowest MPI, i.e., the wealthiest municipalities of the country according to this sorting, comprising 4.68% of the population. The second cohort corresponds to the population belonging to the top ten municipalities according to the MPI sorting, with 9.57% of the population. The rest of the cohorts are defined likewise, except the last one, that corresponds to the whole population. The third cohort comprises 12.76% of the richest population, a figure that roughly corresponds to the ABC1 socioeconomic segment of the country.
4.3 Sentiment responses and SES
Just as in the case of the cumulative abnormal returns for the stock market, we observe positive and negative cumulative abnormal responses to our sentiment proxies. To analyze whether sentiment reactions depend on the number and the socioeconomic characteristics of the people under lockdown, we estimate regression equations Eqs (14) and (15), which relate our abnormal sentiment measures to the government announcements in Table 3 to the number of people under lockdown. In either equation, just as in the case of stock returns, ΔPopulationi,t refers to changes in the number of people from cohort “i” under lockdown at the announcement made at time t. For the controls in xt, we consider the cumulative abnormal returns of the IGPA index for the corresponding event window, and the prevalent value of the Stringency Index at the day of the announcement. The stock market’s abnormal returns are included to control for the possible effect that stock market performance might have on market-wide sentiment . We include the Stringency Index to control for the effect that policy responses to the pandemic might have on market sentiment and can be considered a proxy for the severity of the disease. This index, developed in  and available at , ranges from 1 to 100 and records the strictness of lockdown style policies implemented by governments around the globe. It has been widely used in the recent literature on the economic and social effects of the COVID-19 pandemic [54, 55].
Estimation results for the Cumulative Abnormal Sentiment Activity in Eq (14) are presented in Table 5. In columns (1) to (6) we present results for the (−1, 0) event window. In columns (7) to (12) we report the results for the (−1, +1) window. It should be noted that the cumulative abnormal return of the IGPA index, included as a control in all specifications, is a generated regressors. As such, the variability from the first stage estimation of the control should be considered when performing inferences for the estimated parameters of equation Eq (14) . To make sure our results are valid, we compute and report statistical significance using non-parametric bootstrapped standard errors alongside the significance obtained by means of the Cribari-Neto HC4 robust standard error estimator, as explained in Note 2 in Table 5.
For the (−1, +1) windows, we obtain negative and statistically significant coefficients for the change in the wealthiest cohorts of the population under lockdown. For the ABC1 socioeconomic segment in column (9), we obtain a point estimate of −3.29, significant at the 10% (5%) level when Cribari-Neto HC4 (bootstrapped) standard errors are used. This is a very large economic effect. An increase of one million people under lockdown in this segment produces a negative abnormal sentiment reaction close to 330% percent. Notably, the goodness-of-fit of these specifications is 73%.
In columns (12), the effect of total population changes under lockdown on abnormal sentiment is also negative, but the point estimate of −1.72 is nearly half of the one obtained for the ABC1 segment. It is still significant at the 15% or 10% level, depending on the estimator used to compute standard errors, and the specification features a rather large R2 coefficient close to 0.55. Since we are interested in assessing whether market sentiment responds differently to different socioeconomic cohorts of the population under lockdown, we perform a Wald test for the equality of the population variable coefficient between specifications (9) and (12). The (unreported) test rejects the null hypothesis of equality of coefficients at any standard level of significance, using either robust HC4 or bootstrapped errors for the covariance matrix.
Similarly to the phenomenon observed for the abnormal returns of the stock market, we obtain a nearly monotonically decreasing magnitude of market sentiment responses to government announcements as we move from the wealthiest municipalities in column (7) to the whole population in column (12). For instance, for the richest 4.68% of the population in column (7), the point estimate of the change in population variable reaches a value of −4.4, two and half times bigger than the coefficient obtained for the total population in column (12), though it is only significant at the 15% level using bootstrapped errors. In column (11), when we consider the municipalities from the first wealth quintile that comprises 22.37% of the country’s population, the point estimate drops to −2.77, but remains both economically and statistically significant at the 10% level, with an R2 close to 0.65.
For the (−1, 0) window, all specifications return statistically insignificant coefficients, regardless of the type of standard errors used for inference, and exhibit lower fits than for the (−1, +1) window.
The results in Table 5 constitute novel evidence on the SES of Twitter users. The observed relationship between Abnormal Sentiment Activity and changes in the characteristics of people under lockdown suggest a socioeconomic segregation among the users of the platform. Our results are in line with the results of a survey carried out by the Pew Research Center in the US, in which Twitter users appear to be more highly educated and having higher incomes than the rest of the population.
The estimation results of Eq 15 for the Cumulative Abnormal Search Volume Activity of Google Trends are presented in Table 6. For the controls in xt, we consider again the cumulative abnormal returns of the IGPA index for the corresponding event window, and the prevalent value of the Stringency Index at the day of the announcement. Since the CARs of the IGPA index are generated regressors, we report significance both for bootstrapped standard errors and HC4 robust standard errors, as explained in Note 2 of the table.
For the (−1, 0) event window, when we consider changes in the total population under lockdown, we obtain a positive effect of those changes on abnormal search activities. In column (6), an increase of one million people in the total population under lockdown increases the abnormal volume of search activity by nearly 15%, a rather large effect considering the magnitudes of the reactions presented in column (5) of Table 3. For the rest of the cohorts in columns (1) to (5), the estimated coefficients are statistically insignificant, with much smaller R2 coefficients. For the (−1, +1) window in columns (7) to (12), our point estimators are similar to the ones we obtained for the (−1, 0) window. Still, they all turn out to be statistically insignificant.
Based on the evidence presented in Table 6, among the users of Google queries, there seems to be no socioeconomic segregation, as measured by a truncation of municipalities based on the MPI. When the total number of people under lockdown increases, an abnormal increase in the pandemic-related Google searches is observed. Such an effect is smaller in magnitude and statistically insignificant when changes in the wealthiest populationidered. In any case, this evidence should be taken with caution. Google Trends counts aggregate “searches” and not the people who perform them. A priori, it does not reveal whether a spike in the relative proliferation of a search term is due to a few power users or many infrequent users. Finally, there is some evidence that abnormal search activity appears to be concentrated in the shorter (-1,0) window, a phenomenon that suggests instantaneity and the short life of Google queries in pandemic-related news.
As an alternative approach to analyze the impact of lockdown announcements on market sentiment, we consider a panel data regression similar to the Difference in Difference (DiD) methodology, which allows us to take advantage of the panel structure of our data, increasing considerably the sample size for the estimation. It should be noted, however, that the sample size achieved is still rather small, and results should be interpreted in light of this limitation. As explained in the Methods section, we estimate the specification presented in Eq (17) for the Abnormal Sentiment Activity index and the Abnormal Search Volume Activity index. To make results comparable to our previous results, we consider the three days in the (−1,1) window centered around each announcement day. For this window, equation Eq (17) can be written as:
To see what effects the parameters in Eq (18) capture, let’s assume that we are interested in the sentiment responses on the day after government announcements are made. It is straightforward to see that the expected difference in sentiment between locked down and not locked down wealthy municipalities is given by δ+1+β+1. Also, the expected difference in sentiment between locked down and not locked down, non-wealthy municipalities is δ+1. The parameter β+1 is then analogous to the standard difference-in-difference estimator; i.e., it is the average difference in the expected outcome between confined and not confined wealthy municipalities and between locked down and not locked down non-wealthy municipalities, where the average is taken for all the days in the sample that correspond to the day after the government makes an announcement.
Results for the ASA index are presented in columns (1) to (4) of Table 7 and in Fig 8. Results for the ASVA index are reported in columns (5) to (8) of the same table. Specifications differ in the inclusion of controls. We consider the abnormal return, AR, of the IGPA index, and the value of the Stringency Index as control variables. Given our sample size, we present standard errors computed using White HC0 robust standard errors. Since the abnormal return of the IGPA index is a generated regressor, we report significance both for bootstrapped standard errors and HC0 robust standard errors, as explained in Note 2 of the table. For estimation, the 15 municipalities with the lowest MPI are considered wealthy. They correspond to the ABC1 socioeconomic segment that comprises 12.76% of the total population.
For the ASA index, the results in panel A of Table 7 show that the largest difference in social sentiment responses between wealthy and non-wealthy municipalities occurs the day after government announcements are made. In column (1), when no controls are included, the parameter estimate for β+1 is negative and statistically significant at the 1% level. The effect is also economically meaningful. The expected abnormal sentiment response upon lockdown is more than 4 times more negative for wealthy municipalities than for non-wealthy municipalities (). This result is almost identical for all specifications, regardless of the inclusion of controls.
For the days preceding announcement days, the estimator β−1 is relatively small and it is statistically insignificant for all specifications at the 10% level, except for the estimation in column (3). For the announcement day, the β0 is negative and statistically significant in columns (1) and (2), but loses significance whenever the Stringency Index is included in specifications (3) and (4). In fact, when both controls are included in the later specification, only the estimate for β+1 is statistically significant at the 10% level. These results are in line with those presented in Table 5 where we document that, controlling for stock market performance and the level of the Stringency Index, changes in the number of people from the wealthiest municipalities under lockdown have explanatory power over the cumulative abnormal sentiment activity variable, CASA, but only for the (−1, +1) window.
Regarding the controls, the Stringency Index estimate is negative and significant, which suggests that the strictness of lockdown policies implemented by the government, or the severity of the pandemic to which lockdown policies respond, has a negative impact on overall market sentiment. The stock market’s abnormal performance, proxied by the IGPA AR, is not significant in our estimations.
For the ASVA index, the results in Table 7 provide no evidence of a statistically significant difference in the abnormal volume of pandemic-related searches in response to government announcements between wealthy and non-wealthy municipalities. All the estimators in Panel A turn out to be statistically insignificant, with the exception of those corresponding to specification (6). In any case, whenever the Stringency Index is included as a control, all estimated coefficients result statistically insignificant at the 10% level. Interestingly, the δ parameters in Panel B, that in this case capture the effects of quarantine announcements on the ASVA index for non-wealthy municipalities, are positive and highly significant for the same day and the day preceding government announcements, but not for the day after them. These results seem to reflect the high levels of anxiety regarding potential lockdown measures that government announcements produce on the population. Furthermore, the results presented in Table 7 are consistent with our previous results presented in Table 6, in which increases in the total population under lockdown increases the abnormal volume of search activity, but with statistical significance only for the (−1, 0) window.
The controls included in the empirical specification have the expected signs and are highly significant across the different specifications. The Stringency Index exhibits a positive sign, which suggests that the prevalence of stricter lockdown policies produces more pandemic-related internet searches. For the performance of the stock market, we observe that the higher the abnormal returns of the stock market surrounding announcements, the lower the volume of pandemic-related queries. This is in line with the correlations presented in Fig 5, where we document a strong and negative correlation between the levels of the IGPA and the ASVI series, which suggests that during periods of high stock market valuation, market participants care less about the development of the pandemic. Even though recent financial literature shows that causality might run in the opposite direction, with Google search volumes being able to predict stock returns [18, 25, 58], we acknowledge that in our case it is hard to claim that causality actually runs in that direction. We consider a short time span around an impactful event, the government announcement, that is likely to affect both the stock market and Google search volumes at the same time.