metadata: authors: 0: author: Vagliasindi,Maria
count: World
docna: 0: docna: Measuring the Economic Impact of COVID-19
            with Real-Time Electricity Indicators
docty: Policy Research Working Paper
subtopic: Energy Policies & Economics,Health Care Services Industry,Public Health Promotion,Consumption,Fiscal & Monetary Policy
teratopic: Macroeconomics and Economic Growth,Industry,Health, Nutrition and Population,Energy
abstracts: cdata!: The COVID-19 pandemic is posing
            unprecedented challenges, making it difficult for policy
            makers to design appropriate policies.  In this context,
            real-time information can play a most valuable role for
            policy makers in developing countries, particularly since
            official economic indicators, such as the evolution of GDP
            and unemployment, not only are released with considerable
            delays, but also are not always fully reliable. This paper
            follows the literature by using the dependent variable
            electricity consumption per capita as a proxy measure of
            economic activity in the short run. Based on this method, it
            examines the short-run economic impact of the pandemic
            itself, as well as the public health restrictions that were
            adopted to control the outbreak and the macro-economic
            measures applied to revive the economy. The analysis
            confirms the significant cost of lockdown measures in terms
            of reduction in economic activity but finds that the spread
            of the disease itself had an economic impact distinct from
            that of the lockdown measures. The analysis shows that the
            use of expansionary fiscal and monetary policies also played
            a key role in mitigating such an impact, driving some
            initial recovery. Finally, the evidence points to a complete
            structural break in economic activity at the onset of the
            lockdown period.
repnme: repnme: Measuring the Economic Impact of COVID-19
            with Real-Time Electricity Indicators
display_title: Measuring the Economic Impact of
            COVID-19 with Real-Time Electricity Indicators
listing_relative_url: /research/2021/10/33488583/measuring-economic-impact-covid-19-real-time-electricity-indicators
topicv3: Power and Electricity Sector m1327094 515,Macroeconomic and Structural Policies
            m1327380 439,Pandemics m1327520 2167,Health m1504661 3480,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-10-14T00:00:00Z
dois: 10.1596/1813-9450-9806
historic_topic: Macroeconomics and Economic Growth,Industry,Health, Nutrition and Population,Energy
origu: Chief Economist Infrastructure (INFCE)
no_of_pages: 32
author_keys: 0: author_key: 619680
model_extractions: Policy Research Working Paper 9806 Measuring the Economic Impact of COVID-19 with Real-Time Electricity Indicators _Maria Vagliasindi_ Infrastructure Chief Economist Office October 2021
Policy Research Working Paper 9806

# **Abstract**

The COVID-19 pandemic is posing unprecedented challenges, making it difficult for policy makers to design appropriate policies. In this context, real-time information can play a most valuable role for policy makers in developing countries, particularly since official economic indicators, such as the evolution of GDP and unemployment, not only are released with considerable delays, but also are not always fully reliable. This paper follows the literature by using the dependent variable electricity consumption per capita as a proxy measure of economic activity in the short run. Based on this method, it examines the short-run economic impact of the pandemic itself, as well as the public health restrictions that were adopted to control the outbreak and the macro-economic measures applied to revive the economy. The analysis confirms the significant cost of lockdown measures in terms of reduction in economic activity but finds that the spread of the disease itself had an economic impact distinct from that of the lockdown measures. The analysis shows that the use of expansionary fiscal and monetary policies also played a key role in mitigating such an impact, driving some initial recovery. Finally, the evidence points to a complete structural break in economic activity at the onset of the lockdown period.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mvagliasindi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Measuring the Economic Impact of COVID-19 with Real-Time Electricity Indicators**

Maria Vagliasindi*

## **Keywords:** Power sector, COVID 19, electricity demand, real time indicators

**JEL:** D14, E21, G51

*Special thanks go to Luis Andres, Vivien Foster and Tatyana Kramskaya, for the excellent comments and insightful suggestions on a previous draft of the paper, Robert Beyer, Tito Cordella, Mark Roberts, and participants to a seminar which was held on 4 November 2020 at the World Bank. Liliana Benitez provided invaluable support to the collection of data and information.

** Maria Vagliasindi is Lead Economist in the Chief Economist Office of the Infrastructure Practice Group.
# **_1. Introduction_**

The COVID-19 pandemic is posing unprecedented challenges, making it difficult for policy makers to design appropriate policies. The size of the economic shock triggered by the pandemic and the associated lockdown has not been fully assessed because the official economic indicators, such as evolution of GDP and unemployment, are not only released with considerable delay, but are also not always fully reliable, and often revised downwards. In this context, real-time information can play a most valuable role for policy makers in developing countries.

While theoretical work on the current COVID-19 pandemic is flourishing, still a relatively small part of the literature offers a comprehensive assessment of the economic impact. The current crisis encompasses both supply and demand shocks. Lockdowns and social distancing measures are forcing workers to stay at home, causing rationing in the consumption of some services, while consumers react to the pandemic by reducing consumption that requires interpersonal contact (Wren-Lewis, 2020). The recent paper by Guerrieri _et al._ (2020) models such impact triggered by the COVID-19 pandemic as a “Keynesian” supply shock, which ends up generating a much larger drop in aggregate demand larger than the supply shock itself. Social distancing measures and especially lockdowns have been subject to strong public debate, with many commentators claiming that the economic impact associated with them is more severe than the human cost these interventions are trying to prevent.

In this context, real-time information can play a most valuable role for policy makers in developing countries, particularly to address the questions highlighted in what follows. How has the magnitude of the economic impact been affected by the evolution of the pandemic and the stringency of lockdowns? What were the relative economic impacts of the disease versus the health policy remedy of lockdown? How effective were macroeconomic monetary and fiscal policy responses in shoring-up the economy?

As shown by recent studies (Arora and Lieskovsky, 2014; Cicala, 2020; Chen _et al._ , 2020, Demirgüç-Kunt _et al._ , 2020), electricity demand and consumption have been used as a relevant proxy for economic activity in the short run. The focus of these studies has been only on a selected number of countries, mostly the United States and developed countries in Europe (Cicala, 2020 for Europe and Janzen and Radulescu, 2020 for Switzerland), with the exception of Demirgüç-Kunt _et al._ which includes countries in Europe and Central Asia and Beyer _et al._ (2020) for India.

This paper proceeds as follows. Section 2 describes the data that we will subsequently use to provide a real-time estimation of economic activity. Section 3 provides estimates of the impact of the pandemic and lockdown evolution on activity measures. Section 4 concludes with a discussion on policy implications.

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<!-- Start of picture text --> Most stringent<br>16 1<br>1 lockdown<br>1<br>15 H I<br>1<br>14 !<br>1j<br>l<br>13 i]<br>12 ! ' |i]!I! "i | :<br>11 | |I1<br>10 2<br>| |<br>9\<br>8|‘<br>1<br>l<br>71<br>1<br>6<br>1-Jan 1-Feb ' 1-Mar 1-Apr 1-May 1-Jun 1-Jul<br><!-- End of picture text -->
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much later stage in response to rising cases. Although Singapore's lockdown seemingly had no effect on several large outbreaks among foreign worker dormitories that started in early April, it appears to have arrested the growth of the epidemic in the general population, using school closures and workplace distancing as recognized and effective attack rate reduction measures. Schools and businesses remained initially open, while businesses have moved to encourage working from home. In addition to other precautionary measures like regular temperature checks at work and enacting business continuity plans, the World Health Organization praised Singapore for its efforts at contact tracing and its measures to limit transmission. In Singapore, they introduced legal implications for individuals who defy the health precautions. In addition, harsh legal measures were also enforced for providing false information posing a risk to public health, and made such cases liable to a hefty fine (up to US$7,000) with the possibility of 6 months imprisonment.

Nearly 4 months after recording its first case, Singapore experienced its second wave of infections, triggering a lockdown effective April 28, 2020. While Singapore has implemented more restrictive measures, it has not gone into a total lockdown, scoring at the apex “85”, again according to the index we are following. Most recently, Singapore implemented more restrictions and those who fail to follow social distancing measures (such as maintaining social distancing in public areas) would now face fines or jail. Singapore is reporting equally high levels of COVID-19 cases but accompanied by much lower mortality rates.

In terms of economic impact, Figure 11 shows how in this case economic activity was initially not much affected by the lockdown measures, due to the limited severity of those, whereas a distinct impact is clearly discernible once Singapore entered Phase 3 where the number of deaths started to become significant.

# **_3. Results_**

In this section, we estimate a baseline model that relates the demand for electricity in a country, as a proxy of economic activity, to the evolution of the pandemic and the lockdowns, and a range of controls. Our model accounts for seasonality and weekly patterns in electricity consumption, as well as the changes in electricity demand during the national holidays. We also control for differences in electricity consumption related to heating and cooling degrees.<sup>3</sup>

# **_3.1 Econometric model and testable hypotheses_**

The basic specification of the model used is as follows:

> 3 To approximate the heating and cooling demand, we use the differential degrees when the daily average temperature is below 16 C and when the temperature exceeds 26 C.

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(1) , ii,tt ii,tt ii,tt ii tt ii,tt ii,tt<sup>+ εε</sup> ii,tt where: YY = ββLL + ωωPP + +θθMM + θθWW + ΦΦC where:

- _Yc,t_ is the electricity consumption in country _i_ at time _t_ .

- _Li,t_ is the indicator capturing the severity of lockdown, capturing both the de jure and de facto indices, which are introduced separately in the regressions.

- _Pi,t_ is the indicator capturing the evolution of the pandemic through the daily number of infected cases and deaths per million due to COVID-19.

- _Mi,t_ are macroeconomic policies, including the level of policy rates and the fiscal measures to capture the policy responses.

- _Wi,t_ is equal to one if date _t_ is a working date (that is, excluding national holidays or nonworking days such as weekends).

- _CHi,ti,t_

- • , is an i.i.d. innovation term. • ii tt are the estimated parameters.

- εε ββ, ωω, θθ, θθ, ππ, a aa γγ

- _CHi,ti,t_ represents two variables for the heating and cooling days.

This model can be used to test the following hypotheses.

**Hypothesis 1:** _Countries where the lockdown measures were effectively enforced were characterized by a greater decline in economic activity._

This first hypothesis tests whether the economic impact reflects the severity of the lockdown measures is more severe depending on the evolution of the lockdown. In terms of equation (1), this translates into a coefficient value <0. Since the stringency of the national lockdowns can be measured both using de jure and de facto indices, as noted above, the econometric model can also be used to explore which of these has the most influence on economic ββ activity.

**Hypothesis 2:** _Countries where the pandemic moved from Phase 1 to Phase 2 and where loss of lives was experienced also display a greater decline in economic activity._

The second hypothesis is that evolution of the pandemic captured by the move from Phase 1 to Phase 2, when countries start registering actual cases of death for COVID-19, also has a distinct economic impact over and above the lockdown measures themselves. In terms of equation (1), this would imply a coefficient value . It is of interest to compare the adverse economic consequences of the pandemic itself relative to the associated lockdown ωω< 0 measures.

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**Hypothesis 3:** _Countries adopting expansionary monetary and fiscal policy responses have been effective in shoring-up the economy._

This third hypothesis examines whether countries that have started to implement expansionary monetary and fiscal policies have been showing sign of recovery in economic activity. We insert in the model a variable capturing implementation of reduction in policy rates by the Central Bank, the type of accommodating monetary policy as well as the announcement of fiscal stimulus packages. In terms of equation (1), this is captured by coefficient values . θθ> 0In addition, the following specification is also estimated. (2) ii,tt ii,tt ii,tt ii,tt ii,tt ii,tt<sup>+ ππDD</sup> tt<sup>+ ππEE</sup> ii<sup>+ εε</sup> ii,tt<sup>,</sup> where the additional variables are:YY = ββLL + ωωPP + +θθMM + θθWW + ΦΦC

- _Dt_ is a dummy for the month where all countries in the sample entered Phase 3.

- _Et_ is a vector of time invariant control variables, including the previous year annual GDP per capita, the electricity price, an indicator of affordability and the latitude of country _i_ .

This specification permits the testing of a fourth hypothesis.

**Hypothesis 4:** _The pandemic and the associated lockdown measures are associated with a structural break in economic activity across all countries that goes beyond individual countries’ specific lockdown measures._

This fourth hypothesis goes more in depth to determine whether the pandemic and associated lockdown measures caused a structural break in economic activity after a certain threshold for all countries in the sample. To test such a hypothesis, a dummy capturing the peak of the impact (towards the beginning of April, when most of the countries move from Phase 2 to Phase 3) is introduced and interacted with the right-hand side variables. This then provides an automatic test of whether the parameters differ before and after this structural break. In equation (2), this would signify a coefficient value of . ππ> 0 The full set of explanatory variables is presented in Table 1.

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**Table 1 Explanatory Variables Influencing Electricity consumption per capita (MWh/person)**

|**Variables**|**Definition**|**Expected Sign**|
|---|---|---|
||**PANDEMIC EVOLUTION**||
|**Covid cases**|= number of (new) infected cases reported daily (per million)|**-**|
|**Covid deaths**|= number of (new) deaths from Covid-19 reported daily|**-**|
||**LOCKDOWN EVOLUTION**<br>||
|**Stringency index**|= average value of the government response stringency index based<br>on the policy decisions taken, with the index ranging from 0 (less<br>stringent)to 1(most stringent).|**-**|
|**Mobility (working time)**|= time spent in work places, compared to a baseline (Google<br>communitymobility)|**+**|
|**Mobility (transit time)**|= time spent in workplaces, compared to a baseline (Google<br>community mobility)|**+**|
|**Lockdown (April dummy)**|= time spent in public transport hubs such as subway, bus, and train<br>d bl Gl bl|**-**|
||stations compare to a aseine (ooge community moiity)||
||**MACROECONOMIC FISCAL AND MONETARY POLICIES**||
|**Policy rate**|= interest policy rates reduction implemented by the Central Banks|**+**|
|**Fiscal measures**|= announcement of fiscal stimulus packages|**+**|
||<br>**DAILY CONTROLS**<br>= 1 if ki d||
|**Working days**|worng ay<br>= 0 otherwise (weekends or national holidays)|**+**|
|**Cooling**|= Min (temp -26C, 0), where temp is the average daily temperature|**+**|
|**Heating**|= Max (16-temp, 0)|**+**|
||**COUNTRY CONTROLS**||
|**GDP per capita (US$)**|= GDP per capita (US$ thousand)|**+**|
|**Electricity prices**<br>**(US$per MWh)**|= average business and residential electricity price (US$c per MWh)|**-**|
|**Affordability**|= average electricity tariff (in terms of GDP)|**+**|
|**Distance from equator**|= distance of the capital from the equator (in miles)|**-**|

15
# **_3.2 Empirical results_**

Table 2 provides the results of the fixed and random effect regressions, estimating electricity consumption for the basic specification (1), which can be used to test the stated hypotheses. Hypotheses 1 and 2 are validated as the coefficients , which can be interpreted as estimates of the effect of the spread of the disease, and the coefficient , which estimates whether the effect of the lockdown measures are both ωω negative and significant at the 1 percent confidence level. It is also worth noting that is in absolute terms higher ββ than , confirming the higher impact of the lockdown measures, but implying a distinct but lower impact associated ββ to the spread of the disease across all specifications. Indeed, the elasticity of the marginal effect of the lockdown ωω measures estimated at the mean is equal to -0.06, twice as much as the elasticity of the marginal effect of the spread of the disease. If we include the interacted term, it is characterized by a positive and significant sign, due to the presence of some non-linearities, smoothening the negative impact of each of the measures. Hypothesis 3 is also confirmed across all specifications. All the coefficients capturing the policy rate interventions as well as expansionary fiscal policies are positively associated to a recovery. Namely, when Central Banks stop θθ reducing the policy interest rates, it signals expectations of future growth and recovery, which are materialized in higher recovery in economic activity. Since this type of accommodating monetary policy is done in coordination with the announcement of fiscal stimulus packages, we can also conclude the effectiveness of such expansionary policies. The elasticity of the marginal effect of the monetary policy estimated at the mean is equal to 0.23 significantly higher than the elasticity of the marginal effect of the fiscal policy, probably due to the immediate implementation of the policy rate change vis-à-vis the announcement of the fiscal stimulus package. If we include the interacted term, it is characterized by a negative and significant sign, due to the presence of some non-linearities, smoothening the positive impact of each of the policies.

Among the other controls that are included in Table 2, it is worth noting the expected positive sign of higher electricity demand during working days, and during extreme periods (either requiring additional heating or cooling). Last but not least, the overall explanatory power both under the fixed and random effect specifications is around 40 percent.

16
|PANDEMIC<br>&LOCKDOWNEVOLUTION|
|---|
<!-- Start of picture text --> PANDEMIC & LOCKDOWN EVOLUTION |<br>MACROECONOMIC POLICIES |<br>COUNTRY LEVEL CONTROLS |<br>SECTORAL CONTROLS |<br><!-- End of picture text -->
To start to test hypothesis 4 and identify signs of structural break in the data before and after a threshold date, we first introduce monthly dummies, and retain the month of April in column (b) of Table 3. The sign associated to the dummy variable capturing the month of April is negative and statistically significant at the 1 percent confidence level.

The dummy capturing the month of April is also reported in Table 4 where our model (1) specification is tested introducing country fixed effects. In this case the explanatory value of the regression is close to 100 percent. Whereas this result provides strong evidence on the presence of a structural break in the short term, we are not yet in a position to answer whether this structural break will persist in the long-run, 20
|PANDEMIC<br>&LOCKDOWNEVOLUTION|
|---|
To go further in identifying the structural break, Table 5 interacts all variables with a dummy for the break and interacting it with the right-hand side variables. Column (2.a) reports the coefficients associated to the right-hand side variables as in specification (2), whereas column (2.b) reports the coefficients associated to the interacted variable of the “break” dummy and the respective right-hand side variables.

As all variables and their respective interacted terms are significant and in almost all cases at the 1 percent confidence level, this means that the parameters differ significantly before and after the break, validating Hypothesis 2 and confirming the presence of a structural break.

In terms of interpreting the sign of the interacted terms, the impact of the pandemic evolution is somewhat smoothened after the structural break, and so is the impact of monetary intervention. On the other hand, the increase in the monthly production index is instead strengthened, confirming that the economy is starting to recover. Interestingly also the impact of the initial baseline in terms of GDP per capita, electricity prices and affordability are also smoothened. The smoothening impact is substantial in the case of affordability for which the overall impact is close to zero which may explain the insignificant value found in the previous regressions. As one would expect, during such a substantial recession affordability issues that may have not been so strong to affect consumption before the break, became very significant afterwards, posing significant challenges to recovery.

22
change compared to a baseline. Tables 6 and 7 replicate respectively Tables 2 and 3 using specifications (1) and (2).

As explained in Section 2, a lower value of the mobility index reflects the decreased mobility following the lockdown. Accordingly, we expect a positive sign associated to such indicators, which is confirmed. It is also worth noticing that the absolute value of the mobility indicators associated to the length of time spent commuting to work is higher than the one associated to public transit. All other results related to the tests of the various hypotheses are validated, so that we do not need to repeat them.

Last but not least, also the explanatory value of the regression is somewhat higher, reaching a value close to 70 percent in Table 7, but this may also be partially due to the reduction in the number of countries for which COVID19 Community Mobility Reports are available.

Some cautionary remarks are needed as mobility itself may have a direct effect on electricity consumption and reverse causality may occur. Therefore, the estimated coefficients may be biased. We report these results for illustration purposes, as this is the best proxy of _de facto_ more stringent lockdown.

24
<!-- Start of picture text --> PANDEMIC & LOCKDOWN EVOLUTION<br>MACROECONOMIC CONTROLS<br>SECTORAL CONTROLS<br><!-- End of picture text -->
|PANDEMIC & LOCKDOWN EVOLUTION<br>MACROECONOMIC POLICIES|
|---|
|COUNTRY<br>LEVEL CONTROLS|
|SECTORALCONTROLS|

<!-- Start of picture text --> PANDEMIC & LOCKDOWN EVOLUTION<br><!-- End of picture text --> <!-- Start of picture text --> SECTORAL CONTROLS<br><!-- End of picture text -->
# **_4. Conclusions_**

Empirical estimates of the size of the economic shocks triggered by the pandemic and lockdown evolution have been scarce since official economic indicators are only made available with a significant lag. In this paper, we provide an illustration by tracking the evolution of high-frequency variables, which proxy economic activity.

Proxy measures of economic activity allow us to investigate the economic impact of lockdowns, which have been the main public health policy implemented by governments around the world to contain the pandemic. It has been argued that these measures, which are useful in “flattening the curve” of health costs, may come at high economic costs.

The analysis in this paper also confirms the significant cost of such measures in terms of reduction in economic activity. Interestingly, it shows that beyond the impact of the lockdowns, also the spread of the disease itself has an economic impact distinct from that of lockdown measures, as the incidence of death per population increases the drop in activity associated with the spread of the disease, and is statistically highly significant, though the coefficient is lower than the one associated with lockdown measures. The results suggest that the de facto lockdown index has a much larger effect than the de jure lockdown index, pointing to variations in the seriousness with which the lockdown arrangements were enforced.

At a time when countries in the region are grappling with ways of relaxing lockdown measures, our results suggest that policy makers should be cautious in reopening their economies too fast. The drop in economic activity observed when lockdowns are in place is not solely explained by the lockdown restrictions themselves but is also associated with the behavioral response to the spread of the disease. Therefore, a fast reopening that generates a rebound in the spread of the disease can be damaging not only in human terms but also in economic ones. An unexpected increase in the infection rates or the number of deaths after opening up might slow down or even revert positive economic trends.

We also identify some emerging encouraging results linking expansionary policy together with accommodative monetary policy and expansionary fiscal policies bringing about recovery in economic activity. The result that the policy rate and the fiscal measures had a significant positive impact on electricity consumption point at the effectiveness of expansionary policies.

The applicability of such data would diminish with time when economic actors adjust their behavior to the new environment. Still, these data appear to be invaluable for short-term assessments. More effort should go into establishing and calibrating the relationships between these proxies and the actual economic output.

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