Weather, Lockdown, and the Pandemic:
Evidence from the Philippines
Marjorie C. Pajaron* and Glacer Niño A. Vasquez
School of Economics, University of the Philippines, Diliman, Quezon City 1101 Philippines As the landscape of the COVID-19 pandemic continues to evolve, there is a need to better understand the factors that affected COVID-19 health outcomes using a more appropriate dataset and comprehensive variables. This paper constructs a novel daily provincial panel dataset (N = 14,507) during the nascent and important period of the pandemic (April–September 2020) to analyze both the socioeconomic (lockdowns or ECQ, mobility of individuals, health care capacity, and trends in transmission) and environmental factors (rainfall shocks, temperature in Celsius, average relative humidity, and wind speed) that affect COVID-19 health outcomes. A panel dataset is more apt than the other types of datasets since it addresses both spatial and time variations, as well as the time-invariant unobserved heterogeneity that, if ignored, would have resulted in biased estimates and findings. In addition, using a more complete list of explanatory variables could address omitted variable bias, which leads to proper identification and a more reliable set of findings that could aid the government in formulating optimal, multi-faceted, and timely policies for future health crises. Using fixed effects on panel data, our results, which are robust across the different lag structures and time periods used, are consistent with the existing literature with caveats. First, while ECQ is effective in stemming COVID-19 cases, it is ineffective in reducing COVID-19 deaths. Second, exogenous weather variables have heterogenous effects on COVID-19 health outcomes contingent on the period of analysis and the type of health outcome analyzed. Third, public behavior, which is only partially correlated with public policy (ECQ), matters in curtailing viral transmission. We conjecture that individuals voluntarily avoid infection for their own well-being, resulting in positive externalities, or they stay at home due to weather shocks.
Keywords: community quarantine, COVID-19, health care capacity, mobility, trends in transmission, weather parameters
*Corresponding author: [email protected]
INTRODUCTION
The current COVID-19 pandemic has resulted in catastrophic morbidity and mortality around the globe since it first appeared in December 2019. The Philippines is not inured to its devastating impact on health and the economy.
The country reported its first case on 30 Jan 2020 and the first death attributed to COVID-19 on 01 Feb 2020, which interestingly and unfortunately was the first ever recorded
mortality outside China (DOH 2020). On 07 Mar 2020, the DOH (Department of Health) officially confirmed community transmission and about eight months later, the number of COVID-19 cases and deaths in the country dramatically rose to about 385,000 and 7,000, respectively.
One of the government’s immediate responses to curb COVID-19 transmission during the first wave of the pandemic was to impose different types of community quarantine (CQ) or lockdowns in different parts of the ISSN 0031 - 7683
Date Received: 04 Nov 2022
country at different time periods. Despite these efforts, the Philippines had the second highest mortality among the ASEAN countries, next to Indonesia in early 2020 (WHO 2020). This statistic could be attributed to the limited healthcare capacity of the country and the implementation of lockdowns. However, meteorological factors could also explain COVID-19 incidence and mortality. Weather parameters, which could either prevent or hasten the spread of the virus, are particularly important in the study of COVID-19 in the Philippines given that the country has different climate types and experiences extreme weather variation due to its location within the Pacific Ring of Fire and along a typhoon belt.
The main purpose of this paper is to examine whether the different environmental (rainfall deviation, temperature, relative humidity, and wind speed) and non-environmental factors (lockdowns, health care capacity, trends in transmission, and mobility of individuals) affected COVID-19 daily new and vulnerable cases and deaths in the Philippines at the provincial/district level using the novel daily panel data that we constructed during the nascent period of the pandemic.
This paper adds to the existing literature on the determinants of COVID-19 health outcomes in the following ways. First, to the best of our knowledge, this is the first paper using the Philippine data that examines the impact of both the meteorological and socio-economic factors on COVID-19 daily cases and deaths during the first wave of the pandemic.
Using a more complete list of explanatory variables addresses omitted variable bias, which then leads to the proper identification of parameters and coefficients and, consequently, a more reliable and accurate set of findings.
Second, we constructed and used new daily panel data (N
= 14,507) from an assemblage of datasets and a myriad of sources from 13 April–30 September 2020 across the 85 provinces and districts in the Philippines.
For example, since there was no dataset for the different lockdowns imposed by both the national and local government units (LGUs), we had to compile and cull the information from different sources (news, executive orders or EOs, government resolutions, and Inter-Agency Task Force or IATF guidelines) and create the first lockdown daily panel dataset in the country at the provincial and district level. In addition, we also constructed daily provincial panel data on mobility using Google Mobility, weather variables using data from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), and health-related variables (COVID-19 cases and deaths, health care capacity, and trends in COVID-19 transmission) from the DOH.
Longitudinal data analysis is advantageous in several ways. First, compared to simple cross-sectional or time
series datasets, a panel dataset allows for a more rigorous and telling analysis. Second, we can use a model that eliminates time-invariant fixed effects (FE) or unobserved provincial/district effects (for example, predilection to use face masks or social distance), thereby addressing potential biases in our estimated coefficients.
Using the FE regression method on our panel data, our results, which are robust across the different lag structures and time periods we use, support the literature with caveats. First, although the lockdowns decreased COVID-19 cases, they did not prevent COVID-19 deaths [as in Gibson (2022) and Meo et al. (2020)] during the initial period of the pandemic. We can conjecture that one of the factors that contributed to the limitation of the efficacy of the lockdowns was their implementation.
The Philippines implemented one of the world’s strictest and longest CQs of varying degrees, at different time periods and in different parts of the country, which then led to confusing variations in rules across the different LGUs (Reuters 2020). This implies that in addition to the associated opportunity costs of the lockdowns (given the closures and shutdowns of several businesses), their intended benefits were also not fully realized making these non-pharmaceutical interventions all the more costly.
Second, unlike quarantines and lockdowns, the different weather parameters and COVID-19 deaths had an inverse correlation. Heterogeneity in the impact of the weather parameters on COVID-19 contingent on the period of analysis and the type of health outcome also existed. Third, we find that increased mobility in residential areas was only partially correlated with ECQ during the first phase of the pandemic and it helped reduce both COVID-19 cases and deaths, whereas trends in COVID-19 transmission only decreased COVID-19 cases. Individuals either opted to stay at home voluntarily to avoid infection for their own welfare, leading to positive externalities, or they did not go out due to weather shocks.
The results of this comprehensive study could aid the policymakers in understanding the factors that affected COVID-19 health outcomes and in crafting policies to prepare the country for other possible health crises. Such preventive and preparatory measures could save the country trillions of pesos as lockdowns and quarantines resulted in both short-term and long-term economic and social costs (NEDA 2021).
METHODOLOGY
We assume a basic production function of health wherein the COVID-19 health outcomes (HO), measured as daily new cases and mortality, is a function of weather
parameters, lockdown (ECQ), mobility of individuals, health system capacity, and previous time period’s COVID-19 cases, which measure the epidemiological trend of the virus:
HO = HO (weather, ECQ, mobility, health system capacity, trends in transmission)
(1)
all other PPE (personal protective equipment) available for use by health care workers also in log form. Laggcasept is the lagged aggregate COVID-19 cases in log form and in moving average. �, 𝜆, and � are simply the associated coefficients of these three variables and specifically refer to the impacts of COVID testing capacity, available PPE, and previous aggregate cases, respectively, on COVID-19 health outcomes.
Our null hypotheses that we want to test are as follows.
First, we want to examine whether our five daily weather
variables affect COVID-19 health outcomes (H₀: 𝛿 = 0,
� = 0, 𝜎 = 0, 𝜏 = 0, 𝜓 = 0). For example, temperature and COVID-19 daily new cases could have a negative relationship given that faster evaporation and lower virus survival time in the atmosphere could shorten the duration of the period of contagiousness (Demongeot et al. 2020).
Tong et al. (2022), on the other hand, found a positive association between ARH and daily new cases. They posited that during winter, the coastal city of Shanghai had a high ARH in the air, resulting in the propagation of pathogens. Specifically, when ARH increased to more than 70%, the viral activity increased significantly. High wind speed, on the other hand, helps dilute and remove air droplets, which reduces the transmission rate (Rosario et al. 2020). The rainfall variable has conflicting effects on COVID-19 health outcomes (Hossain et al. 2021; Bilal et al.
2021). We aim to contribute to the literature by using daily rainfall shock or daily rainfall deviation instead, which we hypothesize to be more relevant. This method has been used in the previous literature. For example, Maccini and Yang (2009) found a statistically significant relationship between rainfall shocks, and health and schooling in Indonesia, whereas Pajaron (2017) found that rainfall shocks affected ex-post risk-coping mechanisms of Filipinos.
Second, we also want to test whether ECQ decreases COVID-19 incidence and mortality (H₀: 𝛾 = 0) since based on the literature and health guidelines the purpose of a strict social distancing measure, while waiting for the development of a new pandemic vaccine or as the pandemic evolves with new and more virulent strains of the virus, is to limit and control for the spread of the pandemic virus [as in Fung et al. (2015)].
Third, we expect that keeping everything else constant, increased mobility in residential areas would decrease the number of cases and mortality (H₀: 𝜃 = 0), as in Kraemer et al. (2020), Caldwell et al. (2021), and Nouvellet et al.
To test this empirically, we use the following base model:
(2)
where 𝐿𝑌hpt + n is the health outcome (in logarithmic form) n days {n = 7, 14} after the lockdown was imposed and other control variables occurred (all logarithmic transformations used in the paper are of the form log (y + 1) to allow for zero values). We consider four daily new COVID-19 health outcomes {ℎ = 1, 2,3,4} – total cases, vulnerable cases for those aged 0–4 or 60 and above, total deaths, and vulnerable deaths for the province or district p = 1, 2, …, 85 and t = 1, 2, …, 171 (daily time period from 13 Apr–30 Sep 2020). 𝛼p refers to unobserved time- invariant provincial FEs while 𝛽t pertains to time effect.
�pt is the unobservable error term that varies across time and space.
The weather variables we use are as follows: [1] rainfall deviation or rainfall shock (in millimeters or mm), which we measure as daily rainfall minus the historical mean 1978–2018 (𝑅𝐹 _shockpt); [2] temperature in °C (temperaturept); [3] average relative humidity or ARH in % (𝐴𝑅𝐻pt); [4] wind speed in m/s (wind_speedpt);
and [5] the interaction of rainfall deviation and wind speed to measure more intense weather (𝑅𝐹 _shockpt × wind_speedpt). The coefficients of these weather variables capture their effects on health outcomes: 𝛿 , �, 𝜎 , �, and 𝜓 pertain to the impacts of rainfall shocks, temperature, relative humidity, wind speed, and the interaction of rainfall deviation and wind speed, respectively.
ECQpt is the enhanced CQ imposed on province p at time t, whereas mobilitypt is the relative mobility of individuals in residential areas based on Google Mobility trends. Their associated coefficients, 𝛾 and 𝜃 , measure the relationship between ECQ and residential mobility with health outcomes, respectively.
LCOVID_testpt pertains to the daily number of unique individuals tested for COVID-19 in log form, whereas LPPEpt refers to the daily number of masks, gloves, and
(2021). Staying at home, whether voluntary or involuntary, has the classic positive externalities effect.
Fourth, we want to test whether a stronger health system can improve health outcomes and, therefore, decrease COVID-19 incidence and mortality [as in Haldar and Sethi (2020) and Cifuentes-Faura 2021)]. Thus, we test the following null hypotheses (H₀: � = 0, 𝜆 = 0). Fifth, we expect the trend in the transmission of the virus to have a positive correlation with future caseloads and deaths (H₀: � = 0). This follows from a standard epidemiological model, as in Geoffard and Philipson (1996), wherein infection hazard is an increasing function of prevalence.
To reiterate, all our explanatory variables are lagged by 1–2 wk [as in He et al. (2021), Hossain et al. (2021), and Tong et al. (2022)] to account for their delayed impact on health outcomes, which could be attributed to delays in reporting, the incubation period of the virus, or delays in the full implementation of health policies.
To test Equation 2 and to properly identify the impact of the different socioeconomic and environmental variables on COVID-19 cases and deaths in the Philippines, we use FE regression analysis on the longitudinal daily data that we constructed. FE model allows us to incorporate and eliminate unobserved time-invariant provincial FEs (𝛼p).
For example, one province may have social distancing measures already ingrained in its culture compared to another province. We also want to control for the time effect (𝛽t) that does not vary across provinces such as the initial price inflation of PPE (face masks and face shields) when the national government required their use in public places.
DATA DESCRIPTION
The novel daily longitudinal dataset that we constructed and used in this research covers 81 provinces and 4 districts of Metro Manila and spans from 13 Apr–30 Sep
2020 (N = 14,507).
COVID-19 Incidence and Mortality Dataset
Our daily COVID-19 health outcomes, which are our dependent variables, were all derived and generated using the daily COVID-19 updates from the DOH for the period of 13 Apr 2020–30 Sep 2020 and assigned to 85 provinces and districts, which were, in turn, derived from the Philippine Standard Geographic Codes of the Philippine Statistics Authority.
Table 1 below shows that from April–July 2020 (our initial period of analysis), the highest number of confirmed COVID-19 daily cases was 977, which was recorded by the southern Manila district (or the fourth district) composed of the cities of Las Piñas, Makati, Muntinlupa, Parañaque, Pasay, and Taguig and the municipality of Pateros on 31 July. Cebu (in the southern part of the Philippines), on the other hand, had the highest number of daily cases among the vulnerable group (165) on 30 July, the highest total daily deaths (28) recorded on 14 June and 01 Jul 2020, and the highest daily new vulnerable deaths (17) on 10 July.
From April–September 2020 (extended period of analysis), the Eastern Manila district (or the second district) – composed of the cities of Quezon, Mandaluyong, Marikina, Pasig, and San Juan – recorded the highest number of daily new total cases (1,479) and vulnerable cases (215) last 10 August, as well as daily new vulnerable deaths (17) on 06 August (Table 1).
Weather Parameters
Our weather parameters were derived from the data provided by PAGASA (2021). Our choice of these weather variables was contingent on data availability and the findings in the literature regarding which weather factors contribute to viral transmission and which suppress the spread of the virus.
Table 1. Summary statistics of health outcomes (dependent variables).
Period April–July 2020 April–September 2020
(1) (2) (3) (4)
Variable Mean
(SD) Max
(Min) Mean
(SD) Max
(Min)
Daily new cases 7.17
(34.94) 977
(0) 18.77
(78.23) 1,479
(0) Daily new vulnerable cases 1.05
(5.55) 165
(0) 2.55
(10.82) 215
(0)
Daily new deaths 0.33
(1.73) 28
(0) 0.48
(1.88) 28
(0) Daily new vulnerable deaths 0.2
(1.07) 17
(0) 0.3
(1.22) 17
(0) Notes: N = 9,349 for April–July 2020; N = 14,507 for April–September 2020
Our first weather variable is rainfall deviation or rainfall shock (in mm), which we computed as the difference between the daily rainfall and the historical average from 1974–2018 per weather station and then assigned to the province where the synoptic station was located. For provinces without any PAGASA weather station, we used weather data from the nearest province with a weather station identified by calculating the vector of pairwise great circle distances among provinces (for provinces with more than 1 weather station, we averaged their data).
Northern Samar recorded the highest rainfall deviation (301.14 mm) last 14 May (Table 2 Columns 2 and 4).
We also consider temperature (in °C), defined by PAGASA as “the highest thermometer reading obtained from the six-hourly time interval” (Villafuerte et al. 2021).
The province of Cagayan had the highest maximum temperature (40 °C) on 04 May, whereas La Union, Nueva Vizcaya, Benguet, and Ifugao provinces had the lowest maximum temperature (19.5 °C) on 13 July.
Our third weather parameter is ARH, which is defined by PAGASA as “the ratio of actual vapor pressure and saturation vapor pressure of the air for the prevailing temperature at a height of 1.25–2.00 m above the ground.”
Our data suggest that the ARH in the entire country over our period of analysis was about 80%, which is considered high in the literature. Surigao del Norte and Occidental Mindoro recorded the highest ARH (99%) on 20 April
and 17 September, respectively, whereas Bulacan and all four Metro Manila districts recorded the lowest ARH (50%) on 24 April.
Our fourth weather variable is wind speed (in m/s), which is the average value in a 10-min period. The province of Batanes recorded the highest wind speed (8 m/s) both on 08 June and 23 August.
Lockdown Dataset
In the Philippines, the government’s initial response to address the first community transmission of COVID-19 in the country was to impose a CQ on Metro Manila on 15 Mar 2020. Since then, different parts of the country were placed under different types of CQs (Table 2).
On 17 Mar 2020, the entire island of Luzon – which is composed of eight administrative regions, including the National Capital Region (NCR), and home to more than half of the country’s population – was placed under total lockdown (ECQ) from 17 Mar–12 Apr 2020, which was later extended to 30 Apr 2020 and eventually extended again until 15 May 2020 through EO 112. Meanwhile, provinces outside Luzon, or those in Visayas and Mindanao, also adopted similar social distancing protocols and were placed under ECQ (such as Iloilo, Cebu, Negros Occidental, and Davao, to name a few).
Table 2. Summary statistics of weather, lockdown, health capacity variables, and human mobility (independent variables).
Period April–July 2020 April–September 2020
(1) (2) (3) (4)
Variable Mean
(SD) Max
(Min) Mean
(SD) Max
(Min) Rainfall (deviation from average 1974–2018, mm) –0.74
(15.12) 301.14
(–27.08) –1.49
(14.9) 301.14
(–31.38)
Temperature (max, °C) 32.62
(2.6) 40
(19.5) 32.37
(2.59) 40
(19.5)
Average relative humidity (%) 79.85
(7.12) 99
(50) 80.85
(6.84) 99
(50)
Wind speed (m/s) 1.92
(0.85) 8
(0) 1.88
(0.83) 8
(0)
ECQ 0.17
(0.38) 1
(0) 0.11
(0.31) 1
(0)
MECQ 0.02
(0.14) 1
(0) 0.02
(0.14) 1
(0)
Individuals tested 152.4
(676.25) 9,578
(0) 242.18
(961.83) 9,859
(0)
PPE available (in '000) 384
(985) 13,800
(0) 394
(943) 13,800
(0) Residential mobility (weighted, % change from
baseline) 4.63
(3.51) 25.68
(0.06) 4.36
(3.3) 25.68
(0.05) Notes: N = 9,349 for April–July 2020; N = 14,507 for April–September 2020
On 15 May, the IATF for the Management of Emerging Infectious Diseases issued Resolution No. 37, which placed various areas in the country under different forms of CQ until 31 May (Official Gazette 2020). For example, all highly urbanized cities in the NCR and other transitioning high-risk areas were placed under MECQ (or modified ECQ), which is a transition phase between the strictest type of CQ (ECQ) and GCQ (general CQ), until 31 May. All other LGUs in the country were placed under GCQ. On 29 May, the IATF issued Resolution No.
41, which set the CQ guidelines for the entire country from 01–15 Jun 2020. For this period, only two kinds of CQs were imposed – namely, MGCQ (modified GCQ) and GCQ.
We constructed the first lockdown dataset in the country from March–September 2020 using EOs, news outlets, government pronouncements, and resolutions from the IATF.
The most stringent quarantine type is ECQ followed by MECQ, GCQ, and MGCQ. From 13 April–31 July 2020 17.33% of the 9,349 daily province observations were placed under ECQ, whereas 1.88% were under MECQ (Table 2 Column 1). On the other hand, from 13 Apr–30 Sep 2020, 11.17% of the 14,507 daily province observations were placed under ECQ, whereas 2.25%
were placed under MECQ (Table 2 Column 3).
Health Care Capacity Dataset
The healthcare capacity of a country during pandemic measures how equipped a country is in handling the surge in capacity and it also affects how fast the virus is transmitted. We include time-variant healthcare capacity variables available daily during COVID-19 (testing capacity and PPE available), taken from DOH Data Drop as reported by the different health facilities that conducted COVID-19 tests and/or had COVID-19 patients.
Metro Manila had the most number of health facilities for testing COVID-19 and the most number of individuals tested, suggesting better health care capacity in the region.
It also had the highest number of available PPE (gowns, goggles, gloves, shoe covers, head covers, face shields, surgical masks, N95 masks, and coveralls) for healthcare workers’ use (daily provincial data on PPE were derived by averaging the weekly data by facilities from DOH and assigning these to their respective province/district).
In particular, the Eastern Manila district had the most number of individuals tested (9,578 last 29 Jul and 9,859 on 17 September), whereas the Southern Manila district had had the highest total number of available PPE for health care workers (about 14 million for the week of 04 May) (Table 2).
This is not surprising given that it is common information that there is a skewed distribution of health labor and
capital across the country. For example, in 2017, DOH reported that Metro Manila also had the most number of doctors (about 7,000) and health workers (about 11,000) or about 15 doctors and 23 other health workers per 10,0000 population, whereas Siquijor had the least number of doctors (8) and other health workers (85).
Human Mobility Dataset
We consider human mobility in residential areas since it can affect the spatial and temporal distribution of COVID-19 cases and deaths. We measure mobility as the change in visits and length of stay (relative to a baseline) in residential areas, which we derived from the Google COVID-19 Community Mobility Report (Google 2021).
The baseline was computed by Google as the median value from 03 Jan–06 Feb 2020 for the corresponding day of the week. The mobility variable was reported by Google at the regional level; hence, we distributed it to the corresponding provinces using population as weights.
Figure 1 below shows the population-weighted mobility trend in residential areas and in other areas, averaged across the provinces in the Philippines. After the imposition of Luzon-wide ECQ on 17 Mar 2020 on about half of the total provinces/districts in the Philippines, there was an expected decrease in mobility, on average, relative to the baseline, in the following areas: retail and recreation, grocery and pharmacy, parks, transit stations, and workplace, whereas there was an observed increase in the length of stay in residential areas on average.
Table 2 (Columns 2 and 4) reveals that Cebu recorded the highest percent change in mobility in residential areas relative to the baseline (25.68%) on 01 May.
RESULTS AND DISCUSSION
The discussion of regression results is divided into three subsections. The first and second subsections focus on the lagged impact of socioeconomic and environmental factors on COVID-19 cases and deaths (in log form) using FEs on April–July 2020 panel dataset. The third subsection presents our results when we extend our analysis to September 2020 to allow for more variation in our weather variables (more rainy days and lower temperature, on average, for example) and in our lockdown measures (MECQ in addition to ECQ). All the health-related variables (both dependent and independent) are in logarithmic form, as discussed in the methodology section.
Impact of Environmental and Socioeconomic Factors on Daily COVID-19 Total New Cases and Vulnerable Cases a Week to Two Weeks Later Table 3 Column 1 below shows the results of our FEs regressions and we find that weather variables a week prior affect the number of daily new cases (in logarithmic form) during the nascent period of the pandemic (April–
July 2020). We first examine the period of April–July 2020 when the first full lockdown (ECQ) was in effect in the country. We later extend this to September to have a longer and more comprehensive analysis of the factors affecting the COVID-19 health outcomes in the country.
In terms of our weather variables, we find that ARH a week prior is positively associated with daily new cases, consistent with the findings in the literature [He et al.
(2021), for example]. Our results suggest that a 1%-point increase in ARH (from an average of about 80%) is associated with a 1% increase in daily new COVID-19 cases. In the literature, it is found that high humidity in the air (above 70%) could aid in the propagation of pathogens and viral activity.
On the other hand, our post-estimation test reveals that as windspeed increases by 1 m/s, daily COVID-19 new cases decreased by 4% a week later, keeping everything constant (our post-estimation test for windspeed is conducted by testing the joint significance of the coefficients of windspeed, and the interaction of windspeed and rainfall).
We also find that lockdown (ECQ) and increased mobility in residential areas are negatively correlated with
COVID-19 incidence a week later. Provinces under ECQ have 33% lower COVID-19 daily new cases compared to those not under ECQ, whereas a 1%-point increase in residential mobility results in a 10% decrease in the same health outcome. However, the transmission trend, measured by aggregate cases in moving average, as well as the number of individuals tested (both in logarithmic form and lagged by one week) are positively correlated with COVID-19 incidence. Both results are intuitive and the finding on trends in transmission is consistent with that in the literature that posits that infection hazard and prevalence are positively correlated, as discussed in the methodology section.
We consider disaggregating daily new cases by age group since individuals aged four years and below, and 60 and above are more likely to be infected due to their vulnerability to the disease, which can be attributed to their relatively weaker health status and existing co-morbidities (Chinnadurai et al. 2020; Shah and Saugstad 2021). The results are robust when we analyze the factors that affect the COVID-19 vulnerable cases, a subset of COVID-19 new cases (Table 3 Column 2). That is, almost all the factors that affect COVID-19 new cases significantly – except trends in transmission – also affect vulnerable cases a week later. However, although the signs and statistical significance of these coefficients are similar to those in Column 1, their impacts are smaller.
We also consider a different lag structure such as using independent variables (trends in transmission, mobility,
Figure 1. Mobility trends in the Philippines (15 Feb–30 Sep 2020).
lockdown, weather variables, and health care capacity) 2 wk prior, and we find that the results on total COVID-19 daily new cases are robust (Appendix I Column 1). That is, the impact of the factors affecting COVID-19 daily new cases persists 2 wk later except for aggregate cases. As to COVID-19 daily new vulnerable cases, we find that the impact of the following factors on COVID-19 vulnerable
cases persist over time – lockdown, rainfall deviation, wind speed, residential mobility, and health care capacity.
(Appendix I Column 2). We also find that temperature has a delayed negative correlation with vulnerable cases, whereas ARH is not statistically significant anymore.
Table 3. The impact of environmental and socio-economic factors a week prior on daily COVID–19 total and vulnerable cases (April–July 2020), fixed effects.
(1) (2)
Dependent variable Daily new cases Daily new vulnerable cases
Weather variables
Rainfall (deviation from average 1974–2018, mm) –0.00 –0.00
(0.00) (0.00)
Temperature (max, °C) –0.01 –0.01
(0.01) (0.01)
Average relative humidity (%) 0.01** 0.00*
(0.00) (0.00)
Wind speed (m/s) –0.04*** –0.03***
(0.01) (0.01)
Rainfall (deviation from average 1974–2018, mm)
x wind speed (m/s) 0.00 –0.00
(0.00) (0.00)
Socio-economic variables
ECQ –0.33*** –0.20***
(0.06) (0.05)
Residential mobility (weighted,% change from baseline) –0.10*** –0.06**
(0.02) (0.02)
Aggregate cases (MA, prior week) 0.10*** –0.00
(0.03) (0.02)
Individuals tested (MA, prior week) 0.13*** 0.06**
(0.03) (0.02)
PPE –0.01 0.00
(0.01) (0.01)
Constant 1.16** 0.75**
(0.48) (0.35)
Observations 9,349 9,349
R-squared 0.41 0.20
No. of provinces/districts 85 85
Post-estimation test
Rainfall (RF + RF * WS) –0.0002 –0.0003
(0.0005) (0.0003)
Windspeed (WS + RF * WS) –0.0421*** –0.0302***
(0.0127) (0.01)
Notes: robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1; time dummies included in all specifications; all health-related variables are in log form; [MA] moving average
Impact of Environmental and Socioeconomic Factors on Daily COVID-19 Total New Deaths and Vulnerable Deaths a Week to Two Weeks Later We find interesting results when we analyze the impact of the different environmental and non-environmental factors on COVID-19 total new deaths and vulnerable deaths using FEs regressions and lagging independent variables by 1–2 wk.
First, for our weather variables, we find that rainfall deviation (measured as deviation from historical mean 1974–2018), temperature and wind speed significantly affect COVID-19 total deaths a week later from April–
July 2020. Interestingly, our post-estimation test reveals that the impact of rainfall deviation is negative. This is consistent with the findings of Bilal et al. (2021) using the US data. We conjecture that during the rainy season – keeping everything else constant – individuals tend to stay at home, thereby maintaining social distancing and suppressing viral transmission. In addition, as discussed above, rainfall could contribute to the washout process of microbial bioaerosols, resulting in shorter residence times of viruses in the atmosphere and, therefore, precluding further dispersion.
Wind speed is also found to be inversely correlated with COVID-19 deaths. As it increases by 1 m/s, total new deaths decrease by 2% (post-estimation test). However, when we consider the interaction of rainfall and wind speed [as in Qiu et al. (2020)] to determine their combined impact, we find that this interaction term positively affects COVID-19 deaths. A possible explanation is that this interaction term captures the adverse impact of an intense weather parameter. For example, excess rainfall with strong wind could result in flooding and displacement of households to evacuation areas where social distancing protocols are less likely followed. We also find the temperature to be inversely correlated with COVID-19 deaths, which is consistent with the findings of Bilal et al.
(2021). As the maximum temperature increases by 1 °C, daily new total deaths decrease by about 1% a week later.
Second, although lockdown (ECQ) a week prior is inversely correlated with COVID-19 cases (as discussed above), it did not affect daily new total deaths attributed to COVID-19 (Table 4 Columns 1). Third, staying at home decreases new total COVID-19 deaths, whereas the number of individuals tested increases mortality;
both these findings are consistent with the results when COVID-19 incidence is analyzed. Upon further examination, we find that ECQ and mobility in residential areas are only moderately or partially correlated (0.3). In fact, graphically, we find that although the imposition of ECQ was associated with increased relative mobility in residential areas at the beginning of the pandemic, many Filipinos still chose to stay at home, even with the lifting
of ECQ (Appendix II). Meaning, there are other factors that induce individuals to stay at home, independent of the imposition of ECQ. In the literature, as discussed above, individuals stay at home in the face of rainfall shocks.
Filipinos may also choose to socially distance themselves voluntarily for their well-being, which then has a positive externality effect on the community.
When we examine the factors that affect COVID-19 daily new vulnerable deaths, we find that almost all the results are robust except that ARH is statistically significant and positively correlated with vulnerable deaths while both rainfall deviation and number of individuals tested are statistically insignificant.
When we consider a 14-d lag structure in the independent variables, we find that only the rainfall variables (deviation and interaction with windspeed) are not robust. Or another way of putting this is that while the impact of the three other weather variables (temperature, ARH, and windspeed) and socioeconomic factors (lockdown, aggregate cases, staying at home, and health care capacity) on COVID-19 deaths persist for 2 wk, the impact of rainfall variables is only significant for 1 wk (Appendix I Column 3). The persistence of wind speed and temperature may have something to do with their direct effect on COVID-19. For example, a relatively high temperature (as experienced in the Philippines) leads to the inactivation of the viral lipid membrane, as well as a decrease in viral stability and transmission rate (de Ángel Solá et al. 2020).
High wind speed, on the other hand, helps dilute and remove air droplets, which results to a decrease in viral concentration in the air and viral dispersion (Rosario et al. 2020). On the other hand, the impact of rainfall on COVID-19 partially relies on people staying at home and the washout process (Hossain et al. 2021). We also find that in the literature, relatively, the rainfall variable has conflicting results while temperature and windspeed have more consistently demonstrated negative effects on COVID-19 incidence and mortality (Demongeot et al.
2020; Bilal et al. 2021; de Ángel Solá et al. 2020; Rosario et al. 2020; Hossain et al. 2021).
Extending this analysis to vulnerable deaths, we find that the results for lockdown, temperature, windspeed staying at home, and available PPE are robust (Appendix I Column 4).
Extending the Period of Analysis to September 2020 We extend the period of analysis to July–September 2020 to see whether the weather variables will have a differential impact given that the rainy period is more captured here. We also want to know whether other types of lockdowns imposed after July affect the results. Since ECQ was no longer implemented in August and September 2020, we created a binary variable with a value of 1 if a
Table 4. The impact of environmental and socio-economic factors a week prior on daily COVID–19 total and vulnerable deaths (April–July 2020), fixed effects.
(1) (2)
Dependent variable Daily new deaths Daily new vulnerable deaths
Weather variables
Rainfall (deviation from average 1974–2018, mm) –0.00** –0.00*
(0.00) (0.00)
Temperature (max, °C) –0.01* –0.01*
(0.00) (0.00)
Average relative humidity (%) 0.00 0.00*
(0.00) (0.00)
Wind speed (m/s) –0.02** –0.02**
(0.01) (0.01)
Rainfall (deviation from average 1974–2018, mm) x wind speed (m/s) 0.00** 0.00*
(0.00) (0.00)
Socio-economic variables
ECQ –0.01 –0.01
(0.05) (0.05)
Residential mobility (weighted,% change from baseline) –0.03* –0.03*
(0.02) (0.02)
Aggregate cases (MA, prior week) –0.01 –0.00
(0.03) (0.02)
Individuals tested (MA, prior week) 0.02* 0.01
(0.01) (0.01)
PPE –0.00 0.00
(0.00) (0.00)
Constant 0.47** 0.38**
(0.21) (0.17)
Observations 9,349 9,349
R-squared 0.09 0.08
No. of provinces/districts 85 85
Post-estimation test
Rainfall (RF + RF * WS) –0.0004** –0.0002
(0.0002) (0.0001)
Windspeed (WS + RF * WS) –0.0241** –0.021**
(0.01) (0.0099)
Notes: robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1; time dummies included in all specifications; all health-related variables are in log form;
[MA] moving average
province or district p was placed under ECQ or MECQ, the two most stringent forms of lockdowns in the country, at time period t.
Interestingly, we find that the results for lockdowns are consistent with our previous analysis – while they negatively affect COVID-19 incidence, they do not matter in determining COVID-19 deaths (Appendix III
Columns 1–4). We also find that staying at home does not matter in the extended period of analysis. Finally, different weather parameters affect our health outcomes this time – temperature is negatively correlated with all COVID-19 health outcomes (total and vulnerable cases and deaths), ARH does not matter anymore in determining COVID-19 incidence and mortality, and rainfall deviation
negatively affects COVID-19 vulnerable deaths. We can conjecture that since the extended period of analysis has more rainy days (higher rainfall deviation) and lower temperature, on average, then these weather parameters impact the COVID-19 health variables more than when we analyze a shorter period.
CONCLUSION
This paper aims to contribute to the growing literature on the determinants of COVID-19 incidence and mortality by constructing and using the novel daily provincial/
district longitudinal data from the Philippines, as well as including a more comprehensive list of possible factors during the nascent period of the pandemic in the country (April–September 2020). To the best of our knowledge, this is the first empirical study in the Philippines that examines the impact of both the environmental and socioeconomic factors (weather parameters, lockdown, mobility of individuals, health care capacity, and trends in transmission) on COVID-19 daily cases and deaths.
It is important to include the different meteorological variables in analyzing COVID-19 in the Philippines given that the country is located in the Pacific Ring of Fire and along a typhoon belt, which makes it susceptible to extreme weather variation. In addition, it is crucial to analyze a more comprehensive list of factors that affect COVID-19 incidence and mortality to prevent future outbreaks as well as to prepare the country for future health crises. The lack of preparatory measures could be very costly to the country. For example, the National Economic and Development Authority (NEDA 2021) estimates that the country will lose about PHP 37 trillion in the next few decades in addition to the loss of PHP 4 trillion in 2020 due to the closures and shutdowns of schools and businesses, as well as to the near standstill of other productive economic activities.
Our FEs regression results are consistent with those in the literature as discussed in this paper, with caveats. First, weather parameters affect COVID-19 cases and deaths;
however, these meteorological variables have differential impacts on COVID-19 health outcomes contingent on the period of analysis and the type of health outcome.
For example, average relative humidity only positively affects COVID-19 cases and deaths during the early period of the pandemic in the Philippines (April–July 2020), encompassing summer and early months of the rainy season. We also find that although rainfall deviation and wind speed, individually, are inversely correlated with COVID-19 deaths, their interaction positively affects COVID-19 deaths, indicating that the severity of a weather parameter also matters.
Second, while lockdown is negatively correlated with daily new cases of COVID-19 [as in Alfano and Ercolano (2020)], it does not affect daily deaths attributed to COVID-19. These results are robust across the different lag structures and time periods used. The results suggest that the lockdown is ineffective in terms of lives saved and may imply that the economic costs of the lockdown exceed the benefits. Third, staying at home during the initial phase of the pandemic (April–July) contributes to reducing COVID-19 cases and deaths. We find that the decision to stay at home is independent of ECQ and that many Filipinos remained at home even after the ECQ was lifted. Individuals may have opted to socially distance themselves for their own well-being, thereby, generating positive externalities effects, and/or they stayed home due to weather shocks.
In summary, our results suggest that while the lockdowns imposed by the government during the initial period of the pandemic in the Philippines were effective in reducing COVID-19 daily cases, they were ineffective in reducing COVID-19 deaths, as in the case of New Zealand (Gibson 2022). However, staying at home as well as the weather parameters (rainfall deviation, temperature, wind speed, and severity of the weather) were important factors in terms of saved lives.
We hope that our paper could aid the policymakers in crafting preventive and preparatory health measures to preclude the further spread of the virus, as well as to avoid resorting to lockdowns, which so far had resulted in catastrophic financial and social losses both in the short and the long run.
ACKNOWLEDGMENTS
The authors would like to thank the participants of the 2021 Asian Econometric Society conference for their helpful comments.
NOTES ON APPENDICES
Appendix I reports the impact of environmental and socio-economic factors two weeks prior on daily COVID-19 cases and deaths (April–July 2020). Appendix II shows, graphically, the trend in residential mobility and lockdowns (February–September 20202). Appendix III presents the impact of environmental and socio-economic factors 1 wk prior on daily COVID-19 cases and deaths (April–September 2020). The complete appendices section of the study is accessible at https://philjournsci.
dost.gov.ph.
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Appendix I. The impact of environmental and socio-economic factors two weeks prior on daily COVID-19 cases and deaths (April–July 2020), fixed effects.
(1) (2) (3) (4)
Dependent variable Daily new
cases Daily new vulnerable
cases Daily new
deaths Daily new vulnerable deaths Weather variables
Rainfall (deviation from average 1974–2018, mm) –0.00 0.00 –0.00 –0.00**
(0.00) (0.00) (0.00) (0.00)
Temperature (max, °C) –0.01 –0.01* –0.01*** –0.01***
(0.01) (0.01) (0.00) (0.00)
Average relative humidity (%) 0.01** 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Wind speed (m/s) –0.03** –0.03** –0.03*** –0.02***
(0.01) (0.01) (0.01) (0.01)
Rainfall (deviation from average 1974-2018, mm)
x wind speed (m/s) 0.00 –0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Socio-economic variables
ECQ –0.22*** –0.17*** –0.05 –0.05
(0.06) (0.04) (0.06) (0.05)
Residential mobility (weighted, % change from
baseline) –0.13*** –0.07*** –0.04** –0.04**
(0.02) (0.03) (0.02) (0.02)
Aggregate cases (MA, prior week) 0.05 –0.02 –0.01 –0.01
(0.04) (0.03) (0.02) (0.02)
Individuals tested (MA, prior week) 0.13*** 0.07*** 0.03** 0.02*
(0.03) (0.02) (0.01) (0.01)
PPE –0.01 0.00 0.00 0.00
(0.01) (0.01) (0.00) (0.00)
Constant 1.07** 0.84** 0.76*** 0.57***
(0.50) (0.40) (0.23) (0.19)
Observations 9,349 9,349 9,349 9,349
R-squared 0.45 0.24 0.12 0.10
No. of provinces/districts 85 85 85 85
Post-estimation test
Rainfall (RF + RF * WS) 0.0001 0.0003 –0.0002 –0.0004**
(0.0006) (0.0004) (0.0002) (0.0002)
Windspeed (WS + RF * WS) –0.0263** –0.0318** –0.0264*** –0.0231***
(0.0119) (0.0127) (0.0086) (0.0085)
Notes: robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1; time dummies included in all specifications; all health-related variables are in log form;
[MA] moving average
APPENDICES
Appendix II. Residential mobility trend and lockdowns in the Philippines (15 Feb–30 Sep 2020).
Appendix III. The impact of environmental and socio-economic factors one week prior on daily COVID-19 cases and deaths (April–
September 2020), fixed effects.
(1) (2) (3) (4)
Dependent variable Daily new
cases Daily new vulnerable
cases Daily new
deaths Daily new vulnerable deaths Weather variables
Rainfall (deviation from average 1974–2018,
mm) –0.00 –0.00 –0.00 –0.00
(0.00) (0.00) (0.00) (0.00)
Temperature (max, °C) –0.02** –0.02** –0.01*** –0.01***
(0.01) (0.01) (0.00) (0.00)
Average relative humidity (%) 0.01 0.00 –0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Wind speed (m/s) –0.01 –0.03** –0.03*** –0.02***
(0.01) (0.01) (0.01) (0.01)
Rainfall (deviation from average 1974–2018,
mm) x wind speed (m/s) 0.00 0.00 –0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Socio-economic variables
ECQ/MECQ –0.23*** –0.17*** –0.07 –0.05
(0.08) (0.06) (0.05) (0.04)
Residential mobility (weighted, % change from
baseline) –0.05 –0.02 0.00 0.00
(0.04) (0.03) (0.02) (0.02)
Aggregate cases (MA, prior week) 0.11** 0.04 0.02 0.02
(0.05) (0.04) (0.03) (0.02)
Individuals tested (MA, prior week) 0.23*** 0.15*** 0.06*** 0.05***
(0.03) (0.03) (0.01) (0.01)
PPE –0.01 –0.00 –0.00 –0.00
(0.01) (0.01) (0.00) (0.00)
Constant 1.04* 0.70* 0.66*** 0.42**
(0.61) (0.41) (0.22) (0.18)
Observations 14,507 14,507 14,507 14,507
R-squared 0.50 0.34 0.15 0.12
No. of provinces/districts 85 85 85 85
Post-estimation test
Rainfall (RF + RF * WS) –0.0006 –0.0005 –0.0004 –0.0005*
(0.0006) (0.0004) (0.0003) (0.0003)
Windspeed (WS + RF * WS) –0.0116 –0.0316** –0.0264*** –0.0223***
(0.0141) (0.0123) (0.0078) (0.008)
Notes: robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1; time dummies included in all specifications; all health-related variables are in log form;
[MA] moving average
