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How to measure the territorial economic impact of the COVID-19 pandemic in contexts with scarce regional data? A methodological proposal and application for Argentina (April-September 2020)

In this paper we propose an index to measure the territorial economic impact of the COVID-19 pandemic in contexts with scarce or outdated regional data, which is often the case in developing countries. This index is based on data that are usually available in most countries: a) the sectoral productive structure of the regions, b) the operational level of each sector, c) the mobility of workers in each region, and d) the possibility of remote work among sectors. The empirical application for Argentina describes the territorial economic impact during the second and third quarters of 2020, both for the provinces and labor market areas. Our results show that the regional impact of COVID-19 on private economic activity was highly heterogeneous and, in some cases, dissociated from the regional health impact. The proposed index is also highly correlated with sporadic official data coming from national agencies, while it has a wider geographical and temporal scope.


Introduction
The COVID-19 pandemic and the different regulations imposed by the governments to contain the spread of the virus have produced deep transformations as well as multiple social and economic costs (Baldwin and Weder di Mauro, 2020;Barua, 2020;Noguchi, 2020). The trade-off between epidemiological prevention and economic activity is one of the most pressing issues that governments and societies are facing (Kok, 2020). In addition, the economic impacts of the pandemic and mitigation measures have been highly uneven. Winners and losers can be identified between countries, regions, sectors, businesses, households, or workers (Adams-Prassl et al., 2020;Blundell et al., 2020;ECLAC, 2020a;2020b;Sokol and Pataccini, 2020).
Recent studies highlight that, unlike other crises such as that of 2008-2009, the impact of the pandemic has been regional rather than national, and in the case of developed countries, the territorial differences observed within them have been greater than those registered between nations . This is why the literature raises the need to adopt a regional perspective in the analysis of the economic impact of the COVID-19 pandemic, in order to understand and adequately manage the uneven impact of isolation and mobility restriction measures (Benedetti et al., 2020;Brinks and Ibert, 2020;Cerqua and Letta, 2020;Giannone et al., 2020;OECD, 2020).
However, the study of the regional economic impact often faces several limitations, in many cases due to the limited availability of updated information at the subnational level. The abundance of real-time epidemiological statistics for multiple geographical scales -countries, regions, cities, neighborhoods-contrasts with the scarcity of economic statistics, which in developing countries are often non-existent or very outdated. Moreover, the health impact may be poorly correlated with the economic impact, as it is the case of some cities with a limited spread of the virus but highly dependent on tourism. On the other hand, available estimates of the economic impact of COVID-19 are usually presented at an aggregate level, that is, by country or by sector.
In this paper we intend to make a contribution especially relevant for the developing world, which is often missing in the urban and regional economics literature (Castells-Quintana and Herrera-Idárraga, 2019). In particular, we wonder how we can measure the territorial economic impact of the COVID-19 pandemic in contexts with scarce or outdated regional data. To this end, we propose the calculation of an index that, with minimal adjustments or adaptations to each context, could be used to measure the regional economic impact of the pandemic and isolation measures, based on data or statistics that are usually available in most countries. Our index of territorial economic impact by COVID-19 (ITEI-COVID) takes into account: a) the sectoral production structure of the different regions, based on pre-pandemic data, b) the operational level of each sector, based on secondary post-pandemic information at the national level, c) the mobility of workers in each region, based on the easily accessible data from Google Mobility Reports, and d) the possibility of remote work across different activities or sectors, based on recent studies that have been carried out in many countries.
As an application, we will show the results of the ITEI-COVID for the 24 provinces and the main 85 Labor Market Areas (LMAs) of Argentina, according to the evolution of the national and regional restrictions imposed both on people's mobility and on different economic activities. In Argentina, the provinces are the first subnational politicaladministrative level, followed on a much smaller scale by the municipalities or local governments. Meanwhile, the LMAs are defined as the portion of territory delimited by workers' daily movements between their workplace and their home (Borello, 2002;Rotondo et al., 2016). In this sense, they are made up of a central city or node and a set of other localities linked in labor terms. A similar geographical unit has also been analyzed in other Latin American countries, such as Chile for example (Rowe et al., 2017).
The results of the ITEI-COVID shown in this paper cover the six months -or two quarters-of greatest economic contraction in Argentina, from April to September 2020. According to official indicators from the National Institute of Statistics and Censuses (INDEC, in Spanish), the year-on-year fall in (national) GDP in the second quarter of 2020 was 19.1%, exceeding the 16.3% fall recorded in the first quarter of 2002, at the epicenter of the convertibility crisis. The year-on-year drop in the monthly economic activity estimator (EMAE, in Spanish) was above 25% in April, 20% in May, around 12-13% from June to August, and about 7% in September.
The paper is structured as follows. After a brief review of recent literature (section 2), we contextualize the Argentinean case (section 3) and present both the methodology and data used for the calculation of the index (section 4). In section 5 we firstly show the results obtained for the different provinces and LMAs, and then we present some validation exercises, comparing these results with regional official indicators that have been published discontinuously. Finally, we close with some conclusions.

The regional economic impact of the pandemic across the literature
In the same way that the pandemic increases individual and sectoral inequalities -either between workers in essential and non-essential sectors, between activities that can be carried out remotely and those that cannot, between formal and informal wage earners, or between companies that have invested in new technologies and the ones that do not find resources to do so in this context-, it is also expected to affect regional inequalities. This uneven territorial impact is to some extent predictable. It is due in part to the different speeds of regional circulation of the virus, but also to differences in terms of the timing of public policies, the intensity and duration of quarantine or isolation measures, the restrictions on mobility within and outside each region, the composition of local production structures and other characteristics of the regions, such as labor and income inequalities among the population or the regional dependence on international trade and global value chains (Aalbers et al., 2020;Ascani et al., 2020;Beyer et al., 2020;Bonaccorsi et al., 2020;Cerqua and Letta, 2020;Inoue et al., 2020;Kapitsinis, 2020;Ponce et al., 2020;Shen et al., 2020).
The study of the regional economic impact of the COVID-19 pandemic is relevant for several reasons. First, it is a basic input for designing and executing place-based responses Rahman et al., 2020), rather than centralized (one-size-fits-all) policies that have failed in many countries (Bailey and Tomlinson, 2020;Benedetti et al., 2020;Giannone et al., 2020;Morrison and Doussineau, 2019;OECD, 2020). As highlighted by Giannone et al. (2020), isolation measures established evenly at the national level can be very early in some cities -mainly small, where the virus takes longer to spread-or very late in others -such as large urban centers-. The possibility of mitigating the direct economic impact and the indirect effects of the recession depends crucially on the existence of place-based policies and targeted instruments, which generally imply a greater decentralization of functions, powers, and resources at the regional level. Second, the economic problems caused by the pandemic also tend to be region-specific, such as higher unemployment and poverty rates, business closures, multiple impacts on local production systems, among others. Finally, the systematization of empirical evidence in different countries will allow us to better understand the regional patterns, whose stylized features are still unknown . In this sense, the analysis of the short-term impact of the pandemic is a necessary starting point for future studies regarding the expected effects in the medium and long term, such as changes in the configuration of global value chains, impacts on internal migration, greater diseconomies of agglomeration, changes in values of the real estate, geography of discontent, among others.
Despite the short time since the outbreak of the pandemic, we can already find several papers that analyze its regional economic impact. Given the global scope of the pandemic, it is possible to identify studies for the United States (Barrot et al., 2020;Chetty et al., 2020;Muro et al., 2020), for different countries or regions in Europe (Bachtrögler et al., 2020;Bustos Tapetado and Solla Navarro, 2020;Cerqua and Letta, 2020;De la Fuente, 2020;Gombos et al., 2020;González Laxe et al., 2020;Kitsos, 2020;Pérez and Maudos, 2020;Prades Illanes and Tello Casas, 2020), for China and India (Beyer et al., 2020;Gong et al., 2020;Huang et al., 2020), for Colombia and Brazil (Bonet-Morón et al., 2020;Hernández-Díaz and Quintero, 2020;Porsse et al., 2020), among others. For Argentina, the few studies on the territorial economic impact of the pandemic are based on national and sectoral surveys with highly aggregated geographical units, such as the five or six geographical macro-regions in which the 24 provinces are usually grouped (FOP, 2020a;2020b;2020c;SPE, 2020;UIA, 2020). Other studies estimate the impact on the GDP of a single province, such as Santa Fe (BCSF, 2020), or at best of the different municipalities within Buenos Aires Province .
As we show in the next section, the ITEI-COVID combines some topics that come from different strands of literature. For example, the analysis and definition of operational or vulnerability levels for the different economic sectors has been a common step in several of the papers mentioned (Bachtrögler et al., 2020;Bonet-Morón et al., 2020;Bustos Tapetado and Solla Navarro, 2020;González Laxe et al., 2020;Hernández-Díaz and Quintero, 2020;Lódola and Picón, 2020;Pérez and Maudos, 2020;Prades Illanes and Tello Casas, 2020).
Another line of research that has quickly become popular is the estimation of models that relate local epidemiological statistics with data on people's mobility from the location of their mobile devices (Badr et al., 2020;Kraemer et al., 2020;Lai et al., 2020;Weill et al., 2020). The use of mobility data, from Google Mobility or similar sources, has also been a frequent input in several papers that analyze the regional impacts of the pandemic (Bonacorsi et al., 2020;Campos-Vazquez and Esquivel, 2020;Chetty et al., 2020;Marcén and Morales, 2020), as well as in some cross-country studies (Askitas et al., 2020;Chen et al., 2020;König and Winkler, 2020;Maloney and Taskin, 2020;Sampi and Jooste, 2020).
Finally, as we have mentioned, the analysis of the potential for remote work or teleworking, as a possible response of certain economic activities and especially of some types of workers to mobility restrictions, has been the subject of numerous international studies (Crowley and Doran, 2020;Delaporte and Peña, 2020;Del Río-Chanona et al., 2020;Dingel and Neiman, 2020;Garrote Sanchez et al., 2020;Hatayama et al., 2020;Saltiel, 2020). In the particular case of Argentina, we can also find some specific studies on this topic Bonavida Foschiatti andGasparini, 2020, Red ISPA, 2020).

The COVID-19 pandemic and isolation measures in Argentina
The first imported case of COVID-19 in Argentina was confirmed on March 3th. A few days later, the national government established a mandatory quarantine for travelers entering or returning to the country -and then the closure of national borders-, the suspension of all artistic and sports shows, as well as classes at all educational levels. On March 19th, when confirmed cases in the country were barely 130 and there were still no signs of community circulation -80% of cases were imported and the remaining 20% were close contacts-, the President announced the beginning of a strict and mandatory quarantine for the entire country, the phase 1 of the Preventive and Compulsory Social Isolation (ASPO, in Spanish). Only those activities and workers considered essential were exempted, such as medical services and supplies, security personnel, food production, pharmacies, local food and cleaning supplies stores, public services, public transportation for essential workers, fuel dispensing, among others. It is worth noting that on the day of the announcement, about half of the 24 Argentinean provinces had not yet registered any positive cases. Moreover, in more than half of the provinces with cases, there were only one or two infected people. In most cities, there were no confirmed cases for several weeks or even months. However, during the first phases of strict quarantine and isolation, no territorial criteria were taken into account.
During this first stage and phase 2 of administrative isolation -end of March and practically all April-, the restrictions and exceptions to economic activity were raised at the level of sectors. While the economic activities considered essential continued in a relatively normal way (food and beverage processing, health services), there were others whose operation was significantly reduced (transportation) or indefinitely suspended (tourism, recreation, services cultural). On the other hand, despite the restrictions on mobility, some activities could be adapted and carried out remotely (various professional services, education), but others that require a physical presence in the workplace (manufacturing, construction) were naturally much more affected Bonavida Foschiatti and Gasparini, 2020;.
From the beginning of May, with the passage to phase 3 of geographic segmentation, the quarantine administration and especially the excepted activities began to take into account the context and epidemiological evolution of each region. The latter was deepened when some parts of the country advanced to phase 4, of progressive re-opening. During June, the isolation measures were further relaxed in many regions, and even several cities moved to phase 5 of social distancing (called DISPO, or new normality), in which the circulation and development of a large number of additional activities were allowed. On the other hand, other cities with a marked community circulation of the virus -such as the Metropolitan Area of Buenos Aires, Resistencia, or San Salvador de Jujuy, among otherscontinued under the ASPO measures and even went back in phases by the end of June or the beginning of July.
Until June the vast majority of cases were concentrated in the city of Buenos Aires (CABA, in Spanish) and its surroundings (Figure 1), which explains the gradual relaxation of the restrictions on mobility and on economic activities in different parts of the country. Since July and especially during August and September, the epidemiological situation in many cities became more complicated and complex, but despite the setbacks in ASPO phases and the re-imposition of de jure restrictions, the levels of de facto mobility did not necessarily respond in the same way (Levy Yeyati and Sartorio, 2020). This evolution allows us to anticipate an unequal regional impact of the pandemic and the consequent isolation measures. On the one hand, when the exemptions were raised at the sectoral level (phases 1 and 2), the territorial impact could be conditioned by the heterogeneous sectoral distribution of production and employment in the country, which is reflected in different regional productive specializations. On the other hand, in the later stages of ASPO, the unequal health impact of the virus in the different regions was an extra source of heterogeneity and, associated with this, the advances and setbacks in phases, as well as the tension between de jure restrictions and de facto mobility.

Data and methods
During the month t of April (phases 1 and 2 of ASPO, with restrictions and exceptions defined at the sectoral level), the ITEI-COVID in region j is calculated as: where Sij is the weight of sector i in region j and OPit is the operational level of sector i in the country in this month. Meanwhile, for the months t from May to September, where mobility restrictions were relaxed or re-imposed with different (de jure or de facto) intensities, according to the regional context, the ITEI for each region j is obtained as follows: where we distinguish, on the one hand, the k sectors that showed a high operational level during April -the stage of greatest restrictions-and therefore also in the following months regardless of the regional context, and on the other hand, the rest of the sectors whose operational level effectively depended on the flexibility or not of labor mobility in each region. In this sense, LMIjt is an index of people's mobility to their workplace -or labor mobility indexin region j, based on Google Mobility Reports, during the working days of month t. RWIj is a remote work index, which reflects in what proportion the workers in region j could carry out their work activities from their home, so they would not need to go to their workplace.
Given that in Argentina we do not have complete, homogeneous, and updated sectoral value-added statistics at the territorial level, we use data on formal salaried employment in the private sector to define the sectoral weights (Sij). This information comes from the databases of provinces and LMAs elaborated by the Employment and Business Dynamics Observatory (EBDO), under the Ministry of Labor, Employment, and Social Security. In particular, we use average employment data from the year 2019 and we calculate the weight of formal private employment in each sector (ISIC at 2 digits) over the total formal private employment in each province or LMA.
It is worth noting that the regional data offered by EBDO cover the entire universe of formal salaried employment in the private sector in each province or LMA 2 , based on the crossing of administrative records of the Argentinean Integrated Pension System and the Federal Administration of Public Revenues (AFIP, in Spanish). It is not an estimate or projection according to sample data, as it happens with the National Population Survey (NHS) of INDEC. Obviously, the limitation of using data on formal salaried employment in the private sector to describe the regional (private) production structure is that informal salaried employment and self-employed workers are not considered 3 . However, in a previous working paper (Niembro and Calá, 2020) we show that the general patterns for April remain relatively unchanged when we incorporate data on informality and self-employment using information from NHS.
The operational level of each sector in each month (OPit) ranges from a maximum of 100 (complete) to a minimum of 0 (null), going through intermediate values of 75 (high), 50 (medium), and 25 (low). In order to carry out a simple sensitivity analysis -and since we cannot affirm a specific and exact level-, we define for each sector a hypothesis of minimum operational level and another of maximum level, based on the search and interpretation of secondary information, such as recent statistics published by INDEC and other official agencies, reports from consultants and research centers, and information from various surveys and sectoral chambers. Annex 1 presents the list of the sectors considered -the k sectors of the second formula are highlighted-, the two possible hypotheses defined, and the sources reviewed in each case. As mentioned, the definition of operational or vulnerability levels for each sector has been common in recent studies on the economic impact of the pandemic.
To account for people's mobility to their workplace (LMIjt) in the different regions and months, we use data from Google Mobility Reports, which in the case of Argentina is published for the provinces and the main departments within them 4 . These calculations reflect how mobility and permanence in different places -shops and leisure spaces, supermarkets and pharmacies, parks, transport stations, residential areas and, what interests us here, workplaces-have changed in percentage terms with respect to a prepandemic baseline value (the median for each day of the week during the 5 weeks from January 3th to February 6th). As mentioned, the use of data from Google Mobility or similar sources has become very popular. First, we obtain for each province or department the average mobility to workplaces for the working days of each month, excluding weekends, holidays, and non-working days. Second, taking as a benchmark the value of April -mobility explained mainly by the sectoral restrictions and exceptions and the different regional production structures-, we obtain the differences in mobility from May to September, that is, the recovery of mobility depending on the different evolution of each region. Then, based on a correspondence table that we have prepared, we obtain the respective values for the different LMAs, weighing the departments according to their population when it is necessary to combine two or more departments. Finally, the values for each province and LMA are divided by the national value. In other words, the labor mobility index indicates the greater or lesser recovery in mobility (above or below 1, the national level) in the provinces and LMAs with respect to the whole country.
It should be taken into account that less territorial mobility to workplaces could reflect both less flexibility in isolation measures and a greater ability of workers in that region to perform their activities from home. Therefore, the last component of the ITEI (RWIj) accounts for the potential of remote work in each region, based on the Remote Work Indicator (RWI) proposed by  in the case of Argentina. In general, the methodology for the RWI calculation (inspired by Del Río-Chanona et al., 2020) consists of identifying the tasks performed by a worker in each of the occupational categories that companies declare for their employees, identifying which of them can be carried out under a telework modality. The RWI then indicates the possibility of a worker performing their activities from home, being 0 if none of the tasks can be carried out by teleworking, and 1 if all the tasks can be carried out under this modality. The RWI, which in principle characterizes each job position (accountant, mechanical engineer, waiter, bricklayer), can then be added to characterize the different sectors or Argentinean provinces . For the different LMAs, we obtain a local proxy of the RWIj based on the RWI for each sector and the respective sectoral weights (Sij).
As with the labor mobility index, the values for each province and LMA are divided by the national value 5 .
Due to its form of calculation, the ITEI must be interpreted as a negative index, that is, it takes higher values if the economic (private) activity has been greatly affected by the pandemic and isolation measures, and vice versa. As with any other index, the ITEI should be interpreted with some caution, prioritizing a relative comparison between regions and not an interpretation of the absolute values in each case. Table 1 shows the average values of the ITEI by province, for each month, quarter, and the whole semester. It is the average between the minimum and maximum values of the index, corresponding to the hypotheses of maximum and minimum operational level, respectively. Meanwhile, the quarterly and six-monthly values are obtained as a simple average of the respective monthly values. In line with the evolution of the EMAE, our index shows, in the aggregate of all provinces (last row), a very considerable negative impact in the first month, but also a sustained recovery in economic activity between April and June -a substantial fall in the index-. From June onwards, this value remains relatively stable in the range of 21-23 points.

Economic impact on Argentinean provinces and LMAs
In general, there is considerable stability in the relative position of the most and least affected provinces. For example, the 5 most affected provinces in the semester (from Jujuy to Chubut) were among the worst 10 positions in most of the months. At the other extreme, of the 10 least affected provinces in the semester (from Tucumán to Santiago del Estero), half of them never were in the top ten of the most affected, and the other half only appeared there in one of the six months analyzed. Figure 2 shows only the quarterly and six-monthly values of the ITEI in standardized values -minus the average of all provinces, divided by the standard deviation-. The marked stability of the ten least affected provinces is again evident, as they are consistently below the provincial average. It can also be seen that the most affected provinces tend to have ITEI values above the average, although the fluctuations between quarters are a little more marked -Jujuy is the case with the greatest variability-.
At this point, it is interesting to note that the economic impact of the pandemic can be dissociated from the health impact (which is exposed in Annex 2). The reason is that the regional economic impact depends both on the regional production structure -the relative importance of each sector-and people's mobility to their workplaces, which partly depends on the epidemiological evolution of each region. Thus, in some provinces -like CABA and Buenos Aires on one side, and Catamarca and San Luis on the other-, there was a clear alignment between health and economic impacts. However, the relatively high number of cases in Chaco does not seem to be reflected in its low economic impact, while provinces such as Chubut or San Juan exhibit a substantial economic impact along with a relatively low number of cases. There are also changes along the period, for example in Jujuy. Due to its agri-food production profile, the economic activity was little affected during the first months. However, when the epidemiological situation worsened and restrictions on mobility were reimposed, the economic activity collapsed.

9
Tabla 1: ITEI for provinces: monthly, quarterly, and six-monthly values and ranking ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank ITEI(mean) Rank  Figure 2: ITEI for provinces: standardized values per quarter and semester Figure 3 shows, for the whole semester, the lower and upper value of the ITEI and the average of both for the main 85 LMAs in the country. In a simple robustness analysis, we verify that the main results at the extremes of the distribution remain relatively unchanged even if we bring, on the one hand, the sectoral operational level closer to its maximum hypothesis for the most affected LMAs, and on the other hand, we bring the operational level to the minimum hypothesis for the least affected LMAs. The ITEI-Lower for the 8 most affected LMAs is on average 28.8, while the ITEI-Upper for the 8 least affected LMAs is on average 27.6. Figure 4 shows the quarterly and six-monthly standardized values of the ITEI for each LMA -analogous to Figure 2 for provinces-. Several of the above-mentioned fluctuations at the provincial level are also reflected in variations of the main LMAs in each province. For example, the situation within the province of Jujuy (San Salvador de Jujuy, San Pedro de Jujuy, Libertador General San Martín) worsened between the second and third quarter, mainly due to health problems, restrictions, and reductions in labor mobility. Another interesting issue in Figure 4 is that the variability is much lower among the least affected LMAs rather than the most affected ones, indicating that the situation of the former has barely changed along the semester.
As mentioned, although the evolution of the epidemiological situation is important, the regional production structure is also a central aspect. For example, touristic areas continuously appear among the most affected LMAs throughout the whole semester. On the other hand, among the least affected LMAs, there are some areas specialized in agri-food production but especially several areas with a more diversified agro-industrial profile. Finally, and as a kind of summary, the maps in Figure 5 show the provinces and LMAs distributed throughout the country, according to the average economic impact in the second and third quarters. Apart from emphasizing some of the previous results, such as the deteriorating situation in the northwest of the country (Jujuy and Salta), the figure highlights the heterogeneity among the LMAs within the provinces. This is evident not only in large and diverse provinces, such as Córdoba or Buenos Aires, but also in smaller ones, such as Misiones or Tierra del Fuego.

Comparison and validation against official indicators
As mentioned, few regional data are periodically produced in Argentina. However, given the severity of the crisis caused by the COVID-19 pandemic, some national agencies have sporadically calculated and published some indicators that could be taken as proxies of the regional economic impact of the pandemic and isolation measures. The comparison of these statistics with the ITEI values allows us to analyze their degree of correspondence and reliability (as in Fezzi and Fanghella, 2020).
For the moment, the most interesting official statistic, and also the most comprehensive in territorial terms, is the percentage of companies with zero or minimum sales. This indicator was calculated for the 24 provinces and several cities in the country between April and August, based on data from all formal companies that pay taxes to AFIP (CEP XXI, 2020). In order to compare this indicator of cities with the ITEI for LMAs, we weight the data by the population of each city in those cases where one LMA covers more than one of these cities. It is worth noting that, in this way, we have information for only 50 of the 85 LMAs, showing the greater geographical and temporal coverage of the ITEI. Figure 6 contrasts the values of the ITEI and the percentage of companies with zero or minimum sales for the two months of greatest economic impact in the country (April and May) and the latest available (August). In all cases, there is a positive relationship between the two indicators. Higher levels of the ITEI, both for provinces and LMAs, generally coincide with higher percentages of companies in a critical situation. In dynamic terms, there is also a certain correspondence between these indicators, especially for the provinces. The shift, month by month, from the top to the bottom -i.e. reduction in the economic impact measured by the ITEI-corresponds to a shift from the right to the left -i.e. reduction in the percentage of companies with zero or minimum sales-. For the LMAs, the correspondence is a little weaker, above all in the comparison with August, showing a greater heterogeneity in the situation of the companies among the different localities. The previous linkages are also evident when computing Pearson's correlations between the two indicators, as can be seen, in particular, along the diagonals highlighted in bold in Table 2. These correlations are positive and significant in all months in the case of the LMAs, and in April, May, and August for the provinces. If instead of comparing the absolute values, we analyze the percentage changes with respect to April -that is, the recovery of both indicators against the month of greatest economic impact-, we can appreciate positive and significant correlations for all months, both for provinces and LMAs (Table 3).   Significance level: *p<0.10, **p<0.05, ***p<0.01.

Conclusions
In this paper we propose the construction of an index to analyze the territorial economic impact of the COVID-19 pandemic and the consequent isolation measures in contexts with scarce or outdated regional data. This can be particularly useful for developing countries, where not only national and regional statistical systems are usually weaker, but also tend to focus mainly on sectoral data. This sectoral bias is explained by the high degree of productive specialization of some regions -frequently related to the exploitation of natural resources-, and often leads to reducing the analysis of the territorial impact to what happens only in a few sectors in which each region is specialized. However, contexts as disruptive as the COVID-19 pandemic require both a comprehensive sectoral view -since the vast majority of the economic activities have been affected to some extent-and a recognition of territorial particularities in terms of the epidemiological situation and political management of the pandemic.
With minimal adjustments or adaptations to each context, the proposed index can be used to analyze the uneven territorial economic impact of the pandemic elsewhere, based on data or statistics that are usually available in most countries: a) the sectoral production structure of the different regions (pre-pandemic data), b) the operational level of each sector (post-pandemic data at the national level), c) the mobility of workers in each region (postpandemic data from Google Mobility Reports or other available sources), and d) the possibility of remote work among the different sectors (calculated by several recent studies).
In line with recent literature, the empirical application for Argentina showed the uneven impact of the COVID-19 pandemic on regional economic activity. In this sense, the ITEI revealed large disparities between the 24 provinces and the main 85 LMAs of the country, as well as the heterogeneity within some provinces, which revalues the use of smaller The results also showed that, although the economic impact of the pandemic has been decreasing over the months for the country as a whole, there is considerable stability in the relative position of the most and least affected regions. Besides, the economic impact in many regions has been dissociated from the health impact or the relative number of cases (in line with Cerqua and Letta, 2020). Finally, the comparison with sporadic official indicators of the regional impact of the pandemic has emphasized the validity of the proposed index, which also has a higher geographical and temporal coverage. Although in this paper we have proposed a relatively simple and descriptive exercise, the calculation of an index of territorial economic impact can be a relevant input for the design, implementation, and monitoring of targeted and place-based policies, which seek to mitigate the harmful economic impacts of the pandemic and isolation measures. In the future, the collection of evidence on the immediate or short-term impacts of the pandemic may give rise to other studies that analyze the medium-and long-term impacts, especially concerning the evolution of regional asymmetries. Likewise, economic impact indicators such as the ITEI can be the starting point -or the dependent variable-for future studies that seek to analyze with more detail the regional factors behind this phenomenon.