Background Every year, more than 700,000 die due to suicide, one of the most common reasons for youth death. While many studies have revealed two main fac- tors for suicidal behavior: impulsive suicidal behavior due to mental illness and ﬁ- nancial stress, it is not clear what happens if individuals face deterioration of mental health and economic recession. This paper attempts to answer this question and how suicide rates are correlated with these factors. Methods We empirically investigate whether economic recessions and air pollu- tion trigger suicides by examining Japan, a country with one of the highest suicide rates, from 2014 to 2021. We take advantage of the characteristics of the COVID-19 *All remaining errors are our own. †correspondence to: Sunbin Yoo, sunbinyoo.kyushu@gmail.com 1 pandemic and the periods before the pandemic, when both economic recessions and reductions in air pollution occurred simultaneously. Using monthly and municipal- level data, we construct a triple diﬀerence model that takes air pollution and unem- ployment as treatments. Results Our ﬁndingsshow that high levels of air pollutionand unemployment have substantial impacts on the suicide rates of adults (22.9% in the short term) and chil- dren (42.7% in the short term, 36.0% in the long term), indicating that the increase in suicide rates among children is almost twice as high as that among adults. Our study ﬁnds that unemployment and air pollution alone are not associated with increased suicide rates but their simultaneous occurrence triggers suicides. Conclusions Our study urges suicide prevention, particularly among children, as an essential consideration for public health. Furthermore, our results indicate the need for the government to allocate resources to recover air quality and the econ- omy simultaneously during a recession to reduce suicide mortality. Keywords: public health; children suicides; suicides; pandemic; 2 List of Abbreviations AQI Air Quality Index GDP Gross Domestic Product G7 Group of Seven DV Domestic Violence USEPA United States Environmental Protection Agency 1 Introduction 1 Suicide is one of the most common reasons for youth death (age 15–29), resulting 2 in 700,000 deaths annually–and indeed, in addition to the number of suicide victims, 3 there are vast numbers of people who attempt to kill themselves ([1]). A large number of 4 studies have found that the two dominant reasons for suicidal behaviors are impulsive 5 suicidal attempts/behaviors due to mental illness ([2], [3], [4], [5]) and ﬁnancial stress 6 ([6], [7], [8]). Therefore, what happens if individuals face a recession and exacerbation 7 of the mental illness simultaneously? This study attempts to answer this question and 8 determine how suicide rates are associated with these variables. 9 While observing ﬁnancial stress is relatively simple, as we can examine macroeco- 10 nomic indices(i.e., income levels, GDP ,or unemployment rates), tracking individual level 11 data on mental illness and suicidal attempts is challenging. To overcome this problem, 12 we choose to employ air pollution concentration as a proxy of one of the dominant fac- 13 tors that triggers suicidal behaviors by aggravating mental illness. We refer to previous 14 research ([9], [10], [11], [12], [13], [14], [15]) showing that air pollution is positively associ- 15 ated with impulsive suicidal behaviors. We also refer to previous works showing that such 16 trends are stronger for people with a mental illness and children ([16], [17], [7], [18], [19]). 17 The background provided by previous works allows us to provide quantitative evidence 18 on the impacts of the economic downturn (represented as unemployment growth) and 19 the increased likelihood of the induction of impulsive suicidal behaviors (triggered by air 20 4 pollution)1. 21 To this end, we begin by establishing our empirical framework. First, we use air pollu- 22 tion andunemployment rates astwo main independent variablesin our empiricalframe- 23 work. Second, we set our study period from 2014 to 2021. Therefore, our study periods 24 cover the COVID-19 pandemic, which has limited people’s (economic) activities and re- 25 duced air pollution. The pandemic provides a natural experimental condition that al- 26 lows us to examine both reduction of the air pollution and the unemployment growth 27 at the same time. Notably, our study period also includes a period before the COVID- 28 19 pandemic. Therefore the implications from our study can be extended beyond the 29 pandemic situation. Finally, we analyze the impact of air pollution reductions and un- 30 employment growth on diverse subpopulations divided by gender and age group. 31 We choose Japan for the following three reasons. First, suicide is one of the main 32 causes of death in Japan, particularly for Japanese people aged between 15 and 39. Such 33 high suicide rates make Japan the only G7 country where suicide is the leading cause of 34 death for young people2. The suicide rate in Japan reached the peak in 2020, with the 35 rates increasing for the entire population, women, and people aged 0 to 19 by approxi- 36 mately 4%, 15%, and 44%, respectively (Figure 1). Second, the Japanese economy is ex- 37 periencing a historically unprecedented recession during the COVID-19 pandemic. The 38 Japanese GDP declined by 4.6% in 2020. Moreover, many people have started to lose their 39 1The impact of air pollution on triggering suicides has been discussed in previous works; please refer to [20, 21, 22, 23, 24] for more details We further discuss this issue in Section 2.1. 2Furthermore, the suicide rate itself is higher than that in other countries (16.3% in Japan, 7.4% in Ger- many, which has the lowest suicide rate among the G7 countries). 5 jobs. The annual unemployment rate increased (by 0.4%) in 2020, and this increase has 40 been for the ﬁrst time since 2009 (Lehman shock). The rise of the unemployment rate 41 could increase the level of extreme stress for Japanese because lifetime employment is 42 regarded as the standard for Japanese people ([25]). The year 2020 marked the ﬁrst time 43 young people were concerned about their occupation since the time they started work- 44 ing. Third, air pollution has been alleviated in Japan during the COVID-19 pandemic due 45 to the stay-home order and decrease in business activities. [26] shows that the surface 46 PM 2:5concentration decreased by 30%-50% in Japan during February and March 2020 47 compared to that in the same period in 2018 and 2019. 48 To this end, we build a triple diﬀerence model by using monthly and municipality 49 level data on suicide rates categorized by gender, age group, air quality data, and data on 50 unemployment rates covering the entire Japanese population in 47 municipalities from 51 2014 to 2021. Building a triple diﬀerence model requires the treatment variables to be 52 independent. Notably, we focus on the entire COVID-19 pandemic period rather than a 53 speciﬁc lockdown period. Therefore, it is less likely that the start of the COVID-19 pan- 54 demic is correlated with the rapid increase in unemployment rates and reductions in air 55 pollution. This research setting allows us to eliminate possible concerns about endo- 56 geneity, for example, lockdown causing the growth of unemployment. We examine the 57 short-term eﬀects of the pandemic by examining the ﬁrst waves of pandemic (February- 58 May 2020) and the long-term eﬀects by examining the second waves (July-September 59 2020). 60 6 (a) The number of suicides per year from 2014 to 2020 (b) The number of suicides among individuals aged 0 to 19 per year from 2014 to 2020 Figure 1: Suicide Trend 7 The remainder of this paper is structured as follows. Section 2 provides a literature re- 61 view and background. The data and model are presented in Section 3. Section 4 presents 62 the empirical results. We discuss and interpret the results in Section 5. In Section 6, we 63 provide policy implications and conclude by presenting the limitations of this study. 64 8 2 Background 65 2.1 Literature Review and Our Contribution 66 Many previous studies have revealed that ambient air pollution ([27, 20, 21, 22, 23, 67 24]) andeconomic recession ([28,29]) trigger suicide. For example, [30] ﬁnds thatteenagers 68 (younger than 18) have a more than 10 times higher risk of suicide-related emergency 69 ambulance dispatches for psychiatric emergencies after exposure to PM 2:5than adults 70 (age between 18 and 64). Regarding economic recession, [29] report a change in the child 71 suicide rate after Great Recession in 2007. 72 Our study is not the ﬁrst to examine the association between economic recession, 73 air pollution, and suicide rate. However, no study has examined both of them simulta- 74 neously. In this study, we try to decompose the impact of air pollution and economic 75 recession on the suicide rate. By doing so, we can determine which factor has a stronger 76 negative impact on children’s mental health, which would be helpful to design policy 77 measures to protect people from suicidal behavior. 78 Several studies have examined the trend of the suicide rate during the COVID-19 pan- 79 demic ([31, 32]), and a few studies have investigated the impact of air quality and eco- 80 nomic conditions during pandemic on the suicide rate. During the global COVID-19 81 pandemic, air quality in many areas has improved ([33]), although the pandemic has had 82 a severe negative impact on the global economy (according to International Monetary 83 Fund, the growth in the real-world gross domestic product (GDP) decreased by -3.3% in 84 9 2020 compared to that 2019 ([34]). Therefore, it is not clear whether the number of sui- 85 cides decreased during the pandemic due to the reduction of air pollutants or increased 86 because of economic recession. Thus, we quantify the impacts of economic recession 87 and air pollution on the suicide rate during the COVID-19 pandemic. 88 Several studies have shown the impact of air pollution and economic recession on 89 children’s psychological and physical health by focusing on hospital visits ([30, 35, 36]). 90 The ﬁndings of these works on the increase in hospital visits are worth examining. How- 91 ever, we identify a research gap because the increase in hospital visits does not indicate 92 that air pollution and recession are adversely associated with the children’s health, as 93 they might merely show an increase in the number of hospital visits. Thus, we choose 94 to examine the suicide rates of children, which represent an obviously adverse health 95 outcome. Therefore, to resolve this gap, we scrutinize the relationship between the sui- 96 cide rate, air pollution, and recession to examine whether the latter two variables lead to 97 serious health problems. To this end, we start from the previous works ([37], [18], [16]) 98 show that children are more vulnerable to air pollution, as they are more likely to behave 99 impulsively, which results in suicidal behaviors. 100 2.2 Unexpected outcomes of social distancing 101 Previous studies have shown the eﬀectiveness of social distancing in diverse aspects. 102 [38, 39, 40] express the eﬀectiveness of social distancing in suppressing infections in 103 terms of monetary value. However, other strands of literature have shown that social dis- 104 10 tancing, which includes case isolation and quarantine, triggered mental depression and 105 anxiety during the SARS epidemic ([41]). While it is evident that depression is critically 106 connected to suicide attempts, the question of whether social distancing is also inter- 107 connected with suicide rates has not yet been clearly investigated. Thus, we contribute 108 to the literature by showing evidence on the unexpected outcomes of social distancing 109 that may increase the risk of suicides. 110 In this study, we additionally examine teenagers because some literature has pointed 111 out the negative impact of social distancing on teenagers’ mental health ([42]). Given 112 that depression is closely connected to suicide attempts, social distancing might have a 113 positive impact on suicide growth (especially for children). 114 Because we employ a triple diﬀerence approach, which requires a randomized con- 115 trolled group (thus, the treatment groups should not have correlations), we ﬁrst provide 116 graphical evidence that shows the absence of a correlation between air pollution and un- 117 employment rates during the ﬁrst and second waves of the pandemic. Figure 2 shows the 118 time trend of PM 2:5and unemployment rate. Panel (a) presents the change in the average 119 concentration of PM 2:5over time in Japan. While it ﬂuctuates starting in 2014, it starts to 120 show a gradual decrease from a value of approximately 60 on the air quality index. On 121 the other hand, panel (b) shows the change over time in unemployment in Japan dur- 122 ing the study period. The unemployment trend shows a gradual decline from 2014 until 123 2019. However, the unemployment rate increases starting in 2020. Compared to Panel 124 (a) and (b), during the ﬁrst wave of the COVID-19 pandemic, the PM 2:5concentration 125 11 ﬂuctuates from 30.3 to 75.4, while the unemployment rate continues to increase during 126 this period. In the second wave, the concentration of PM 2:5increases from 37.8 to 43.3 127 and immediately drops to 33.4. However, the unemployment rate continues to increase 128 from 1.99. 129 Second, we present the correlation matrix of unemployment rate and concentration 130 value of PM 2:5(Table 1). The correlation coeﬃcient between unemployment rate and 131 PM 2:5is 0.1994 during the ﬁrst wave and 0.060 during the second wave. This indicates 132 that there is only a weak statistical correlation between unemployment and PM 2:5. 133 Both the graphical evidence and correlation matrix show that the correlation between 134 the air pollution and the unemployment rates is unlikely to exist, and thus, we can con- 135 duct empirical analysis using a triple diﬀerences approach. 136 Table 1: Pearson Correlation Matrix Variables UE (1st wave) PM 2:5(1st wave) UE (2nd wave) PM 2:5(2nd wave) UE (1st wave) 1.000 PM 2:5(1st wave) 0.1994 1.000 UE (2nd wave) 1.000 PM 2:5(2nd wave) 0.060 1.000 Note that UEis abbreviation for unemployment rate. 3 Empirical Strategy 137 3.1 Data 138 In this section, we introduce three datasets used in this study. First, we collect data 139 on the number of suicides divided by sociodemographic information in Japan. Second, 140 12 (a) Trend in the PM 2:5concentration (b) Trend in the unemployment rate Figure 2: Change in the concentration of PM 2:5and unemployment rate over time 13 we use the PM 2:5air quality index (AQI), which shows the concentration of air pollutants 141 and their eﬀect on people’s health. Third, we employ the data on the unemployment rate, 142 which represent economic situations such as recessions. 143 After matching these datasets according to prefecture, year, and month, we merge 144 them into one dataset and proceed to our empirical analysis. Consequently, we acquire 145 3,996 samples from 47 prefectures from 2014 January to 2021 March. 146 3.1.1 Data on Suicide 147 The Ministry of Health, Labor, and Welfare provides data on suicides in Japan since 148 January 1, 2006, and we collect the data from January 1, 2014, to March 31, 2021 ([43]). 149 This aggregated dataset includes information such as the number of suicides by age, sex, 150 employment status, site, and reason, as well as the number of attempted suicides. The 151 dataset includes information on 150,668 suicides in 47 prefectures based on the numbers 152 reported at the municipality level. Children and teenagers (aged between 0 and 19 years 153 old) account for 2.77% of the total suicides, and adults (aged between 20 and 69 years old) 154 make up 72.1% of this total. Figure 3, shows the suicide rates by reason from February 155 to May 2019 and 2020. We ﬁnd that the rate of suicides due to economic factors, labor, 156 and school decreases over time. In contrast, the suicide rate caused by gender and home 157 problems increases over time. 158 14 Figure 3: Suicide rate by reason between February and May in 2019 and 2020 3.1.2 Data on air pollution 159 Data on the concentration of particular matter with an aerodynamic diameter 2:5 160 µm(PM 2:5) were acquired for the study period (January 1, 2014 - March 31, 2021) in all 161 prefectures in Japan from the World Air Quality Index project ([44]). The AQI is an in- 162 dex for reporting daily air quality based on the concentration of air pollutants. We use 163 AQI data that are estimated through a method used by the US Environmental Protection 164 Agency (USEPA). The AQI is an important index for the public to understand how good 165 or bad the air quality is for their health. According to the USEPA, the AQI has an esti- 166 mated value of 0–500 ([45]). The higher the value of the AQI is, the greater the level of air 167 pollution, and the higher the health risk. For instance, an AQI value smaller than 50 is 168 15 categorized as good with little health risk. 169 3.1.3 Data on unemployment rate 170 We obtain data on the unemployment rate from the Monthly Labor Survey from the 171 Statistic Bureau of Japan, which is provided by the Ministry of Health, Labor, and Welfare 172 ([46]). These data provide the quarterly average number of working and employed peo- 173 ple and the unemployment rate in 47 diﬀerent prefectures in Japan from January 1997 to 174 March 2021. 175 3.2 Study Design 176 Figure 2 demonstrates that the COVID-19 pandemic does not directly aﬀect the PM 2:5177 concentration or the unemployment rate. The concentration of PM 2:5continues to ﬂuc- 178 tuate after the outbreak of COVID-19, similar to the previous period. In addition, the 179 unemployment rate begins to rise at the end of 2019 (before the government conﬁrmed 180 the ﬁrst case of COVID-19 infections). Even though the unemployment rate continues 181 to increase, the slope of a line stays the same as that at the end of 2019. Therefore, there 182 is no direct impact of the COVID-19 pandemic on the unemployment rate. Moreover, 183 since we focus not on the lockdown or COVID-19 itself but on the COVID-19 pandemic 184 phase, we assume that we can conduct empirical analysis about the unemployment rate 185 and air pollution during the COVID-19 pandemic. Therefore, these facts validate our use 186 of triple diﬀerence analysis (eq. 1). 187 16 (1)Yjym= + 1First ym+ 2Second ym+ 3APjym+ 4UEjym + 5(First ymAPjym) + 6(First ymUEjym) + 7(Second ymAPjym) + 8(Second ymUEjym) + 9(First ymAPjymUEjym) + 10(Second ymAPjymUEjym) +j+y+ m+jm+ jy+jym whereYjymis a logged suicide rate of prefecture jin monthmin yeary.First ymis a 188 dummy variablethat takes avalue of 1 ifthe periods ofobservations correspond toFebru- 189 ary 2020 to May 2020. Second ymis also a dummy variable that takes a value of 1 from July 190 2020 to September 2020. APjymis a dummy variable that takes a value of 1 if the AQI 191 PM2.5 value is in the top 50th percentile of each period. It takes 1 if the AQI is 50 or more 192 during the periods except for the ﬁrst and second waves, or if the AQI is 42 or more during 193 the ﬁrst wave, and 38 or more during the second wave. UEjymis a dummy valuable that 194 takes a value of 1 if the unemployment rate is in the top 50 percentile of respective peri- 195 ods. It takes 1 if the unemployment rate is 2.5 or more during the time except for the ﬁrst 196 and second waves, or if the unemployment rate is 2.2 or more during the ﬁrst wave, and 197 2.4 or more during the second wave. In our model, 9and 10are the parameters of inter- 198 est that denote the impact of the COVID-19 pandemic, air pollution, and unemployment 199 on the suicide rate. 200 We includeseveral types ofﬁxed eﬀects. First, we employinteraction terms withprefecture- 201 month (jm) and prefecture-year ( jy), which controls for yearly and monthly-speciﬁc 202 shocks, respectively, in each prefecture, such as seasonality in the suicide rate, monthly 203 local events, and climatic conditions. By including yearly interactions with the prefec- 204 ture, wecan control for macroeconomictrends, industrial orpopulation structural changes, 205 17 or suicide trends of each prefecture. Second, we include prefecture ( j), year (y) and 206 monthly ( m) dummy variables. All methods were carried out in accordance with rele- 207 vant guidelines and regulations. 208 4 Result 209 Table 2 represents the results of the triple diﬀerence model, and the main results of 210 this study3. All the independent variables we use in this study are dummy variables. The 211 ﬁrst to sixth columns refer to the relationship between dependent variables (suicide rate 212 of adults, children, male adults, male children, female adults, and female children) and 213 each independent variable. The interpretation of the results can be made as follows: the 214 coeﬃcient of column (1) in the ﬁrst row is -0.0911, revealing that the logarithmic value 215 of the adult suicide rate during the ﬁrst wave decreases by 9.11%. The coeﬃcients of the 216 tenth and eleventh rows are our main parameters of interest. 217 Our results indicate that during the ﬁrst wave, suicide rates among adults and chil- 218 dren increase by 22.91% and 42.66%, respectively, if they lived in areas with high un- 219 employment and expose to high air pollution. This shows that the increase in suicide 220 rates among children is double that of adults. Furthermore, males are more vulnerable 221 than females. The suicide rates among male adults and children increase by 24.06% and 222 58.70%, respectively, while suicide rates among females show a nonsigniﬁcant change. 223 During the second wave, the suicide rate among children increases by 35.96% for those 224 3We additionally conduct diﬀerence-in-diﬀerence estimations to check the robustness of our main re- sults and results are displayed in appendix. 18 living in areas experiencing high unemployment rates and air pollution. 225 Table 2: Triple Diﬀerence Estimation (1) (2) (3) (4) (5) (6) Adult SR Child SR Male Adult SR Male Child SR Female Adult SR Female Child SR First -0.0911-0.1249 -0.0158 -0.0397 -0.2656-0.2139 (0.0509) (0.1278) (0.0589) (0.1335) (0.0870) (0.1687) Second -0.0041 0.1407 0.0075 0.0413 0.1317 -0.0695 (0.0556) (0.1162) (0.0644) (0.1191) (0.0974) (0.1301) AP 0.0041 0.0076 -0.0036 -0.0266 -0.0141 0.0428 (0.0175) (0.0409) (0.0203) (0.0431) (0.0296) (0.0505) UE 0.03220.0308 0.0011 -0.0294 0.06360.0215 (0.0179) (0.0401) (0.0207) (0.0425) (0.0302) (0.0480) APUE -0.0067 -0.0490 0.0150 0.0130 -0.0022 -0.0817 (0.0210) (0.0468) (0.0243) (0.0489) (0.0352) (0.0569) FirstAP -0.1300-0.1069 -0.1601-0.2861 -0.0473 0.1481 (0.0675) (0.1663) (0.0781) (0.1827) (0.1147) (0.1995) Second AP 0.0560 -0.2667 0.1090 -0.0603 -0.1384 -0.1941 (0.0742) (0.1674) (0.0859) (0.1799) (0.1279) (0.2017) FirstUE -0.2244-0.1557 -0.1925-0.1417 -0.20590.0233 (0.0633) (0.1565) (0.0733) (0.1699) (0.1081) (0.1939) Second UE 0.0404 0.0430 0.0080 0.0415 0.0066 0.2451 (0.0732) (0.1501) (0.0847) (0.1508) (0.1245) (0.1574) FirstAPUE 0.22910.42660.24060.58700.1673 0.1170 (0.0893) (0.2075) (0.1033) (0.2289) (0.1518) (0.2411) Second APUE -0.0091 0.3596-0.0412 0.2067 0.0526 0.0801 (0.1018) (0.2125) (0.1179) (0.2214) (0.1715) (0.2367) _cons -11.0599-12.5613-10.6875-12.0909-11.6926-12.2826 (0.0124) (0.0300) (0.0144) (0.0323) (0.0213) (0.0376) Observations 3995 2014 3992 1620 3813 1000 R20.246 0.565 0.226 0.674 0.119 0.784 Standard errors in parentheses p <0:1,p <0:05,p <0:01 Adult SR: log of suicide rate among adults Child SR: log of suicide rate among children Male Adult SR: log of suicide rate among male adults Male Child SR: log of suicide rate among male children Female Adult SR: log of suicide rate among female adults Female Child SR: log of suicide rate among female children 19 5 Discussion 226 Our study shows the importance of focusing on vulnerabilities associated with expo- 227 sure to both air pollution and unemployment. Our result supports that unemployment 228 is positively associated with adult suicide rates, and we conﬁrm that suicide rates tem- 229 porarily decreased if a person was unemployed during the ﬁrst wave. On the other hand, 230 our results do not show a positive interplay between exposure to high air pollution and 231 suicide rates; again, our results show that during the ﬁrst wave, suicide rates decreased in 232 the regions where air pollution concentration was high. Nevertheless, we provide clear 233 empirical evidence that a high unemployment rate and air pollution increased the sui- 234 cide rate if they occurred simultaneously during the pandemic. 235 This study enables policymakers to move beyond simply preventing suicides caused 236 by dominant factors to actively monitor those who are unemployed as well as exposed to 237 air pollution. Our results inspire the design of eﬀective policy instruments that prevent 238 suicides. Even though our research is not based on individual-level data, it is still help- 239 ful for policymakers. Our results indicate that ﬁrst wave dummy variables are negatively 240 correlated with the suicide rate of several groups, which is consistent with the previous 241 study [47]. In addition, much research suggests that suicide deaths decreased in the ini- 242 tial stage of COVID-19 outbreaks in many other countries such as Norway, the UK ([48]), 243 Germany, and Peru ([49], [50], [51]). Therefore, the decrease in the suicide rate in the 244 initial stage of the public health crisis is not surprising. 245 20 5.1 Child Suicide Rate 246 Our results show that air pollution and unemployment rates alone do not have any 247 relationship with the child suicide rate. However, a high unemployment rate and high 248 air pollution increase the child suicide rate if they happen simultaneously. Our ﬁnd- 249 ings show that high air pollution and unemployment rates increased the child suicide 250 rate (42.66% in the short term, 35.96% in the long term) and the male child suicide rate 251 (58.7% in the short term). Our results show that the increase in the child suicide rate was 252 higher than the increase in the adult suicide rate (22.91% in the short term). We identify 253 several possibilities why children were more vulnerable in the ﬁrst wave. While the aver- 254 age suicide rate due to economic (ﬁnancial), school, and labor problems declined from 255 2019 to 2020 during February to May, the suicide rate due to home and gender prob- 256 lems increased from February to May 2019 to February to May 2020 (Figure 3). This in- 257 dicates that during the ﬁrst wave, children may have committed suicide due to home 258 and gender-related problems because they spent more time with their families under 259 the stay-home order. Moreover, we ﬁnd that domestic violence (DV) help calls increased 260 by 47.5% from April to May in 2019 and 2020 (Figure 4). This increase in DV might have 261 increased the child suicide rate more than the adult suicide rate during the ﬁrst and sec- 262 ond waves. 263 21 Figure 4: The trend in the number of DV help calls 5.2 Adult Suicide Rate 264 We ﬁnd that high unemployment in the ﬁrst wave decreased the suicide rates among 265 adults, male adults, and female adults by 22.44%, 19.25%, and 20.59%, respectively. Even 266 though there are complex reasons behind suicide, there are many possibilities that could 267 explain our results for adults. The reason why the high unemployment rate decreased 268 the suicide rate during the ﬁrst wave can be partially explained by emergency subsidies 269 provided by the Japanese government. Approximately 80% of cash was distributed to all 270 citizens (all Japanese people were eligible to gain 100,000 yen (940 USD)) by June. This 271 cash beneﬁt may have relieved ﬁnancial stress for Japanese people. In addition, the aver- 272 age suicide rate due to economic and ﬁnancial problems and labor problems decreased 273 22 from February to May 2019 to February to May 2020, while the suicide rate due to other 274 reasons increased from 2019 to 2020 (Figure 3). 275 During the ﬁrst wave, we ﬁnd that the suicide rate for adults, especially males, de- 276 creased by 13.00% and 16.01%, respectively, with exposure to higher air pollution. This 277 may be because of the strict stay-home orders from the Japanese government during the 278 ﬁrst wave. On April 7, the government declared a state of emergency as of May 6 for seven 279 prefectures experiencing severe situations, including Tokyo. As the number of infections 280 increased, the government expanded the state of emergency to all 47 prefectures within 281 the country on April 16. The state of emergency required people to stay at home and work 282 from home. It also requested that many stores and schools to be closed (e.g., restaurants, 283 gyms, department stores). During the ﬁrst wave, many workers worked from home and 284 many companies request that their employees reduce their commuting days. This may 285 have reduced the suicide rate among adults and male adults even if the air pollution con- 286 centration was high during the ﬁrst wave. We infer that ambient air pollution does not 287 deteriorate people’s mental health compared with that during the pre-COVID-19 period 288 because during the ﬁrst wave, people, especially adults and male adults who were em- 289 ployed, spent less time outdoors. 290 We alsoﬁnd that ahigher unemployment rate increasedthe suicide rateamong adults, 291 especially female adults, by 3.22% and 6.36%, respectively, which is consistent with pre- 292 vious work that suggests the rates of suicide attempts of unemployed women are higher 293 than those of employed women ([52]). Moreover, female adults may be more likely to ex- 294 23 perience psychological harm when they are unemployed than male adults because the 295 number of employed women is smaller than that of male workers in Japan4. 296 5.3 Government’s Measures’ 297 Given that a higher level of air pollution indicates a higher level of economic activity, 298 the results of this research provide supporting evidence that social and governance mea- 299 sures may produce ‘unexpected’ outcomes ([54]). We ﬁnd that the suicide rate among 300 children and adults increased when the unemployment rate and level of air pollution 301 were high during the ﬁrst wave. This ﬁnding indicates that the suicide rate could increase 302 when government measures are strict and the unemployment rate is high while the level 303 of economic activity is still high. However, our triple diﬀerence estimations show that 304 the suicide rate does not change when economic activity is accelerated and the unem- 305 ployment rate is high. This reveals that suicide rates do not increase even if the unem- 306 ployment rate and the level of economic activity are high without government measures. 307 Therefore, strict government measures, such as lockdowns or the declaration of a state 308 of emergency could increase the suicide rate signiﬁcantly during an economic recession 309 if the level of economic activity is high. 310 4[53] ﬁnds that women are more likely to be mentally vulnerable than men. 24 6 Conclusion 311 In this study, we examine whether economic recession and air pollution trigger sui- 312 cide by investigating the suicide rate in Japan, where suicide is one of the main causes of 313 death. Speciﬁcally, we conﬁgure our data period from 2014 to 2021 to cover the COVID- 314 19 pandemic period, when an economic recession and the improvement of air quality 315 occurred simultaneously. The results of the triple diﬀerence model suggest that eco- 316 nomic recession and air pollution trigger suicide among adults and children. Further- 317 more, we ﬁnd that children were more likely to be vulnerable to economic recession and 318 air pollution than adults during the ﬁrst wave of the pandemic (the coeﬃcient of the child 319 suicide rate in the triple diﬀerence model is twice that of adults). In addition, our results 320 of the triple diﬀerence model show positive and signiﬁcant associations with the suicide 321 rates of both children and adults during the ﬁrst wave but a positive and signiﬁcant rela- 322 tionship with the suicide rate of only children during the second wave. This ﬁning proves 323 that the impact of the COVID-19 pandemic is long term for children, while it is short term 324 for adults. 325 These resultssuggest some policyimplications for preventing suicidalbehaviors. First, 326 our results provide evidence of the need for air quality control during the recession to 327 prevent suicide. Our study shows that economic recession solely decreases the suicide 328 rate during recession. However, if a recession is accompanied by air pollution, the impact 329 of the recession on the suicide rate becomes signiﬁcantly positive. This result indicates 330 the need for the government to allocate resources to recover air quality and the economy 331 25 simultaneously during a recession to reduce suicide mortality. 332 Our results suggest to policymakers the need for suicide prevention for children dur- 333 ing the COVID-19 pandemic. We ﬁnd that the impact of the COVID-19 pandemic on 334 the child suicide rate is long term. Thus, the government should implement a policy to 335 protect children from suicidal behavior because there are no signs that the situation is 336 being brought under control in Japan. Previous studies have suggested several ways to 337 protect children from suicide ([55, 56]). To protect children from suicide mortality, the 338 government should implement a policy to save children who cannot seek help during 339 the pandemic. 340 We also identify suicide prevention needs for female adults. We ﬁnd that an increase 341 in the unemployment rate signiﬁcantly increases the suicide rate among female adults. 342 We conjecture that Japanese females are more vulnerable than other populations to eco- 343 nomic recession because Japanese women suﬀer from a considerable gender gap5. This 344 harmful gender gap for Japanese female adults may lead to suicide when they face men- 345 tal deterioration due to recession. Therefore, improvement of women’s social status and 346 a solution to the gender gap is needed to reduce suicide mortality for female adults. 347 While ouridentiﬁcation strategy usingtriple diﬀerence models wasappropriate, with- 348 out detailed individual data (i.e., whether a person was suﬀering from depression), we 349 cannot prove clear causality because our dataset is not at the individual level. The main 350 reason for our inability to demonstrate causality is that we cannot scrutinize whether 351 economic recessionand air pollutionimmediately trigger suicide. Nonetheless, ourstudy 352 5The global gender gap index raked Japan 120 place out of 156 countries ([57]) 26 is still valid because we ﬁnd a trend of economic recession and air pollution increasing 353 suicide rates. Future research should focus on individual suicide attempts to address 354 causality if individual-level suicide data are available. 355 Declarations 356 Ethics approval and consent to participate 357 Not applicable 358 359 Consent for publication 360 Not applicable 361 362 Availability of data and materials 363 The datasets generated and/or analysed during the current study are available in the 364 GitHub repository, https://github.com/akiokuyama/Children-Mirror-Adults-for-the-Worse/ 365 tree/main/data 366 367 Competing interests 368 On behalf of all authors, the corresponding author states that there are no conﬂicts of 369 interest. 370 371 Funding 372 27 This research is supported by JSPS KAKENHI Grant Number JP20H00648 and the Envi- 373 ronment Research and Technology Development Fund (JPMEERF20201001) of the En- 374 vironmental Restoration and Conservation Agency of Japan. 375 376 Authors’ contributions 377 Akihiro OKUYAMA: Conceptualization, Methodology, Formal analysis, Writing (Original 378 Draft); Sunbin YOO: Conceptualization, Methodology, Formal analysis, Writing (Review 379 and Editing); Shunsuke MANAGI: Resources and Supervision. 380 381 Acknowledgements 382 We gratefully acknowledge the participants of various conferences and seminars for pro- 383 viding constructive comments. 384