Alcohol, violence and injury-induced mortality:

Evidence from a modern-day prohibition*

Kai Barron1, Charles D.H. Parry2,4, Debbie Bradshaw2,3, Rob Dorrington3, Pam

Groenewald2, Ria Laubscher2, and Richard Matzopoulos2,3

1WZB Berlin

2South African Medical Research Council

3University of Cape Town

4Stellenbosch University

抽象的

This paper evaluates the impact of a sudden and unexpected nation-wide alcohol sales ban

in South Africa. We find that this policy causally reduced injury-induced mortality in the coun-

try by at least 14%. We argue that this estimate constitutes a lower bound on the true impact

of alcohol on injury-induced mortality. We also document a sharp drop in violent crimes, 印迪-

cating a tight link between alcohol and aggressive behavior in society. Our results underscore

the severe harm that alcohol can cause and point towards a role for policy measures that target

the heaviest drinkers in society.

JEL Codes: I18, I12, K42

关键词: Alcohol, mortality, 经济学, 健康, 犯罪, 南非, COVID-19, 暴力.

* We are grateful to Johannes Abeler, Peter Barron, Anna Bindler, Andrew Faull, Tilman Fries, Elisabeth Grewenig, Heather
Jacklin, Simas Kucinskas, Jan Marcus, Melissa Newham, Paul Rodriguez Lesmes, Julia Schmieder, Marica Valente, Corne van
Walbeek, Anna Wilke and Rocco Zizzamia for many valuable suggestions. We also thank the editor and two anonymous referees
for their helpful comments. The authorship order of this paper follows the conventions from the health sciences with Barron and
Matzopoulos occupying the two lead author positions, reflecting the interdisciplinary nature of this research, which straddles eco-
nomics and the health sciences. Author contributions: KB and RM conceptualized the study. KB performed the statistical analysis
and wrote the draft manuscript. RD, PG, RL and DB contributed to the collection of the mortality data. All authors contributed to
the revision of the manuscript. Barron gratefully acknowledges financial support from the German Science Foundation via CRC
TRR 190 (project number 280092119).

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1 介绍

Excessive alcohol consumption is common in many developing and developed countries (艾伦

等人。, 2017; Katikireddi et al., 2017; Rehm et al., 2018; WHO, 2019; Probst et al., 2020). 它有

been associated with numerous social harms, including motor vehicle collisions, 暴力, risky

sexual behavior, long-run adverse health effects, reduced productivity at work, mortality, and mor-

bidity (看, 例如, Carpenter and Dobkin, 2011; Rehm et al., 2017; Griswold et al., 2018; WHO,

2019). These harms are often borne by individuals other than the person consuming alcohol. 这些

externalities may be imposed either directly (as in the case of interpersonal violence) or indirectly

(as in the case of public health insurance).1 最后, questions regarding the morality and

correct societal regulation of alcohol have been debated in societies around the world for centuries,

with virtually all modern and past societies placing legal and religious constraints on alcohol con-

消费 (Phillips, 2014). It is crucial, 所以, to accumulate robust empirical evidence that

allows us to construct a clear picture of the true influence of alcohol in society. Despite this, 我们的

current understanding of the causal impact that alcohol has at a societal level is largely limited to

the estimates of theoretical models (看, 例如, Rehm et al., 2003, 2017; Probst et al., 2018; Shield

等人。, 2020). There is a scarcity of direct causal evidence.2 One reason for this is that it is rare to

observe an abrupt abatement in alcohol consumption in the entire population of a region or country.

Without an exogenous shift of this nature, it is difficult to parse the influence of alcohol consump-

tion on a particular outcome from the influence of the personal characteristics of the individuals

who choose to drink heavily.

The sudden and unexpected ban on the sale of alcohol in South Africa on July 13, 2020 亲-

vides a rare opportunity to understand how alcohol consumption influences behavior and outcomes

1Alcohol consumption may also lead individuals to harm themselves—intoxication can reduce

self-control, inducing myopic behavior that the individual would avoid if sober (O’Donoghue and

Rabin, 2001; Schilbach, 2019).

2The causal evidence that does exist typically focuses on specific segments of society, with eval-

uations of the impact of changes to the minimum legal drinking age providing the main example

of this (Carpenter, 2004; Carpenter and Dobkin, 2011, 2017).

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Review of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 国际的 (抄送 4.0) 执照.

at a societal level. Since research by health scientists has identified alcohol consumption as a ma-

jor risk factor for injury-related deaths globally, this points towards the hypothesis that reducing

alcohol consumption in an entire country could cause a large reduction in injury-related mortal-

性 (看, 例如, Rehm et al., 2003, 2017).3 It is of key importance to test this hypothesized causal

relationship by assessing how mortality is actually affected when a policy that drastically reduces

alcohol consumption is introduced.

This paper uses the exogenous variation provided by a natural experiment in the form of a

sudden and unexpected alcohol sales ban to study the causal impact of alcohol on mortality due to

unnatural causes at a societal level.4 We also present evidence on one central potential mechanism

underpinning this relationship by evaluating the impact that the ban had on aggressive behavior

in society (例如, homicides, assaults, reported rape cases). This analysis is valuable as it provides

policy-makers with robust evidence about the harm that alcohol consumption generates in society

and informs our understanding of whether reducing alcohol consumption is an effective way to

save lives and alleviate interpersonal violence. It therefore contributes evidence towards the wider

discussion regarding the aggregate costs and benefits of alcohol consumption to society, paving the

way for evidence-based policy making.

Our main analysis uses daily mortality data from South Africa from the period between January

1, 2017 and December 31, 2021. This allows us to use data from previous years to carefully

3例如, the WHO (2019) estimates that alcohol was responsible for 0.9 million of the

5.9 million global injury-related deaths in 2016, while Probst et al. (2018) use a comparative risk

assessment approach to estimate that over 12 000 of the approximately 50 000 injury-related deaths

in South Africa in 2015 were attributable to alcohol consumption.

4In the paper we use the terms “injury-induced mortality” and “unnatural mortality” inter-

changeably. We do this because we find that the former provides a more natural terminology and

is therefore more suitable for an interdisciplinary readership, while the latter corresponds to the

designation of these deaths in the National Population Register dataset and is used on abbreviated

death certificates in South Africa (Dorrington et al., 2020). Deaths due to unnatural causes include

deaths with an external cause, such as homicides, traffic injuries and suicides, while natural deaths

pertain to conditions resulting from aging and illness.

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control for temporal regularities in mortality observed over the course of the year in our analysis.

This is important because we show that there are highly regular, systematic patterns in the number

of unnatural deaths observed according to the day-of-the-week and day-of-the-month. Using a

difference-in-difference empirical strategy, we evaluate the change in mortality due to unnatural

causes that occurred as a result of the alcohol sales ban implemented in July 2020.

This policy shift serves as a good natural experiment for several reasons. 第一的, it was unex-

pected. The alcohol ban was announced in the evening of Sunday, 七月 12, 2020, and came into

immediate effect from Monday morning on July 13, 2020. 第二, it was implemented in the mid-

dle of the so-called “Level 3” COVID-19 policy response period during which time other policies

and regulations were largely held constant.5 One important exception to this is that the alcohol

ban was introduced together with a curfew, which operated between 9PM and 4AM. 然而,

in our analysis we demonstrate that this curfew had very little influence on mortality. 第一的, 我们

show that when the alcohol ban was lifted, but the curfew remained in place, unnatural mortality

jumped back up to the same level observed in previous years, indicating that the curfew alone did

not reduce unnatural mortality. 第二, we conduct a sensitivity analysis that makes use of a one

hour reduction in the length of the curfew (i.e. moving the start time from 9PM to 10PM) 哪个

occurred in the middle of the relevant period. This shift on the intensive margin during the alcohol

ban period had no effect on unnatural mortality.

Our main result is that the alcohol ban reduced the number of people dying from unnatural

causes in South Africa by at least 120 per week. This reflects the lowest estimate of the effect size

that we obtain across a range of different empirical specifications. It represents a substantial reduc-

tion in mortality due to unnatural causes, since it implies a 14% reduction in all unnatural deaths

in the country when compared to the average level during the five weeks immediately preceding

the July Alcohol Ban.

The analysis below reveals an extremely strong gender gradient in this effect—the observed

reduction in mortality is almost entirely confined to men. This is not entirely surprising for two

5The July Alcohol Ban was in force between July 13, 2020 and August 17, 2020. It therefore

divides the Level 3 时期, which spanned June 1, 2020 to August 17, 2020, neatly in half.

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原因. 第一的, in South Africa men are far more likely to die of unnatural causes than women

(大约 78% of the over 150 000 deaths from unnatural causes recorded in our dataset

之间 2017 和 2019 were males).6 This pattern is not unique to South Africa. 例如,

Gawryszewski and Rodrigues (2006) describe the gender distribution of injury-related mortality

in Brazil in 2003 and show that 84.3% of the people that died from injury-related causes (例如,

homicides, suicides, transport-related deaths) were men. 第二, as in many countries around the

世界 (例如, 巴西, 俄罗斯), in South Africa men are far more likely than women to engage in heavy

drinking (WHO, 2019). We find that the ban on alcohol reduced the number of men dying due to

unnatural causes by at least 120 per week, but find no evidence that it had a statistically significant

effect on injury mortality among women in the population as a whole. (重要的, this does not

imply that the absence of alcohol had no impact on other outcomes for women, such as gender-

based violence, which often does not result in death. 以下, we show that the number of reported

rape cases was reduced by the alcohol ban.) 更远, we provide evidence that approximately half

of the observed reduction in mortality is found amongst young men aged 15-34.

To provide support for the validity of these main results, we conduct several robustness ex-

ercises. These include running placebo regressions, varying the window size around the policy

change used for our analysis, and relaxing the assumptions made on the error structure (附录

Section C.2). We also address two key concerns regarding the quality of the natural experiment

and the assumptions underlying our ability to use it to identify the impact of alcohol on mortality

(Appendix Section C.1).

To better understand what is driving this drop in mortality due to unnatural causes, we aug-

ment our main results by conducting an additional analysis that examines police crime data on

6While detailed cause-of-death data is not yet available in South Africa for 2020, Matzopoulos

等人. (2015) report that for 2009, the three leading causes of unnatural mortality in South Africa

were homicides, road-traffic injuries and suicide. Homicides constituted 36% of unnatural deaths,

和 86% of these being male deaths. Road-traffic injuries resulted in 33% of unnatural deaths,

和 76% of these being male deaths. Suicides made up 12.3% of unnatural deaths, 和 82% 的

these being male deaths.

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homicides, assaults, and reported rape cases during the period of interest. We document evidence

indicating that the alcohol ban resulted in a sharp drop in all of these outcomes, with at least 77

fewer homicides, 790 fewer assaults and 105 fewer rape cases reported per week during the alcohol

ban period in comparison to the preceding five weeks. This constitutes a drop in each outcome of

21%, 33% 和 19% 分别.

To illustrate the dynamic effects of the alcohol ban, we also report the results from event study

analyses that examine the evolution in unnatural mortality and the three violent crime outcomes

随着时间的推移. The general pattern that emerges is that the effect of the ban appears to have been

strongest in the first few weeks. One speculative potential reason for this is that black market trade

and production may have reduced the effectiveness of the ban over time. 全面的, the results we

document provide compelling evidence that alcohol is causally responsible for inducing aggressive

behavior in society at a significant scale, resulting in substantial harm.

What lessons can be drawn from these results? 第一的, these findings are highly informative

for policy discussions within South Africa as they provide clear causal evidence of a strong re-

lationship between alcohol consumption and both interpersonal violence and unnatural mortality.

This evidence therefore helps to support the conclusions drawn from comparative risk assessment

(CRA) analyses by health scientists (看, 例如, Probst et al., 2018; Matzopoulos et al., 2021).

第二, this paper provides a valuable contribution to the collective global effort to better

understand the relationship between alcohol, violence and injury-related outcomes more generally.

This is an extremely important endeavour, since alcohol is estimated to have been responsible for

5.3% of all deaths worldwide in 2016 (3 百万), 和 0.9 million of those being injury-related

deaths (WHO, 2019; Shield et al., 2020). While it is essential to acknowledge that any evidence

collected within a single country relates to behavior that occurs within a particular societal context,

collecting rigorous evidence across a range of contexts makes it possible to aggregate the evidence

and identify which alcohol-driven relationships occur systematically across contexts, and which are

context-specific (IE。, mediated by an interaction between alcohol consumption and other societal

因素). The evidence presented here is particularly useful for this exercise since South Africa

is part of a class of countries that make up a large part of the world’s population, but tend to be

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underrepresented as the focus of academic research relative to more developed nations due partially

to constraints on the availability of highly detailed data. This evidence from South Africa provides

an informative benchmark for countries characterised by high levels of injury-related deaths, A

sizable fraction of the population that drinks excessively, a strong asymmetry in drinking patterns

between men and women, and high levels of poverty and inequality. This set of characteristics is

reflective of several countries in Eastern Europe and South America, such as Brazil (在哪里 32.6%

of men and 6.9% of women were heavy episodic drinkers [HEDs] 在 2016, which is nearly identical

to the pattern in South Africa, 在哪里 30.6% of men and 6.5% of women were HEDs, 根据

WHO (2019)) and Russia (在哪里 48.4% of men and 24.2% of women were HEDs in 2016). 两个都

Brazil and Russia also share many other structural similarities with South Africa that could interact

with alcohol consumption in influencing behavior, such as suffering from social issues including

贫困, inequality and high levels of violence. Both countries are also characterized by a strong

gender asymmetry in unnatural deaths, similar to South Africa (看, 例如, Starodubov et al., 2018;

Gawryszewski and Rodrigues, 2006).

第三, our results provide a society-level demonstration of the way in which alcohol can act

as a catalyst in inducing violence. While contextual factors in different countries may shape the

way in which excessive alcohol consumption manifests in behavior, the growing body of evidence

of a deep link between excessive alcohol consumption and aggressive behavior is important for

all countries. The evidence discussed in this paper complements a large body of existing work

showing that there is a strong association between alcohol consumption and aggressive behavior

across a range of domains (for a review of this evidence, see Tomlinson et al., 2016). More specif-

ically, our causal evidence contributes to the existing literature that documents a strong association

between homicides and alcohol, finding that a high fraction of homicide offenders (and victims)

were under the influence of alcohol at the time of the offence (for systematic reviews, see Darke,

2010; Kuhns et al., 2011, 2014).7

7例如, Kuhns et al. (2011) reports that from over 70 000 toxicology test results from

13 国家 (predominantly from the United States), 48% of homicide victims tested positive for

alcohol, while Kuhns et al. (2014) reports that from almost 30 000 homicide offenders across nine

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所以, in summary, the evidence discussed in this paper is highly informative for local pol-

icy discussions, but also helps to advance the wider global effort of constructing a clear evidence-

based understanding of the relationship between alcohol, aggressive behaviour and harmful out-

来了.

Relation to the literature. This paper contributes to several strands of the literature. It re-

lates most closely to the body of work that studies the short-run relationship between alcohol and

harmful behavior, such as violence, suicide and crime (Carpenter, 2004, 2005A, 2007; Biderman

等人。, 2010; Rossow and Norstr¨om, 2012; Wilkinson et al., 2016), road traffic collisions (Baugh-

man et al., 2001; Chikritzhs and Stockwell, 2006), risky sexual behavior (Carpenter, 2005乙), 和

outcomes such as mortality and morbidity (Matzopoulos et al., 2006; Carpenter and Dobkin, 2009;

Marcus and Siedler, 2015; Carpenter and Dobkin, 2017; Sanchez-Ramirez and Voaklander, 2018;

Nakaguma and Restrepo, 2018). There are two main empirical approaches that have been em-

ployed in this literature to provide this type of causal evidence: (我) using changes in underage

drunk driving laws or minimum drinking age laws (看, 例如, Wagenaar and Toomey, 2002; Car-

penter and Dobkin, 2009, 2011, 2017), 或者 (二) using changes in the alcohol trading hour regulations

(看, 例如, Biderman et al., 2010; Green et al., 2014; Marcus and Siedler, 2015; Wilkinson et al.,

2016; Sanchez-Ramirez and Voaklander, 2018).8 Each of these approaches generates valuable in-

sights regarding the influence of an important alcohol control policy margin (IE。, restrictions on

young adults on the verge of legal adulthood, or restrictions on late-night on-premise drinking

or late-night purchases). Collectively, this evidence points towards alcohol control policies being

effective in reducing short-run social harms on these margins.

据我们所知, we are the first to document causal evidence of the short-run

国家 (mostly Australia, the United States and Europe), 48% were reported to be under the

influence of alcohol. In recent work that explores the causal role of alcohol in victimization more

宽广地, Bindler et al. (2021) show that obtaining increased access to alcohol at ages 16 和 18 在

the Netherlands results in sharp discontinuous increases in the risk of being a crime victim.

8An exception to this is Nakaguma and Restrepo (2018), who study the impact of a single-

day alcohol sales ban during the 2012 municipal elections in Brazil and find that motor vehicle

collisions and traffic-related hospitalizations were reduced by 19% 和 17% 分别.

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impact that alcohol consumption has at a societal level in contemporary times. 在这个, our paper

joins a long history of research trying to understand the relationship between alcohol and mortality

and morbidity more broadly (看, 例如, Bates, 1918; Emerson, 1932; Warburton et al., 1932, 为了

some early contributions). This work emanates from the contentious social debates of the late

nineteenth and early twentieth century in many Western societies, including the United States,

about whether allowing alcohol consumption is good for society (Blocker, 2006). A set of more

recent studies have tried to estimate the effect that state and federal prohibition statutes enacted in

the United States during the early decades of the twentieth century had on mortality and morbidity

(Miron and Zwiebel, 1991; Miron, 1999; Dills and Miron, 2004; Owens, 2011; 利文斯顿, 2016;

Law and Marks, 2020). This literature portrays a highly ambiguous picture regarding the health and

safety impacts of alcohol prohibition. 然而, in a recent contribution, Law and Marks (2020)

argue that they overcome several empirical challenges faced by the prior work and conclude that

early prohibition laws enacted between 1900 和 1920 significantly reduced mortality rates in the

United States.9

Our results are in line with the conclusions of Law and Marks (2020). 然而, 我们的研究

differs from the research examining the United States Prohibition era in several important ways.

The Prohibition research typically considers a substantially longer time horizon, often using yearly

数据. This implies that it is evaluating the composite effect of prohibition laws, along with all

the social changes that occur as society shifts to a new equilibrium. 此外, 下列

considerations suggest that these evaluations are likely to be measuring the influence of alcohol

together with other social changes: (我) endogenous community characteristics influenced where

dry laws were passed prior to 1920, and the degree to which they were enforced after National

9Bhattacharya et al. (2013) reach a similar conclusion in their insightful analysis of the 1985-

1988 Gorbachev Anti-Alcohol campaign, showing that the campaign was associated with a marked

reduction in mortality during the late 1980s, while the demise of the campaign saw increased

mortality in the early 1990s. 有趣的是, much of this effect was lagged due to the delayed effect

of alcoholism on several health outcomes leading to mortality, 例如. liver cirrhosis and heart disease.

Our paper complements their work by providing an analysis of the short-term behavioral impact.

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Prohibition came into force in 1920, (二) the first decades of the twentieth century constituted a

period of substantial turbulence in the prevailing social norms regarding alcohol, 和 (三、) the gap

between prohibition laws being enacted and becoming effective was up to two years (Blocker,

2006; Law and Marks, 2020). 相比之下, we use daily mortality data to study the impact of an

immediate and unanticipated five-week drop in alcohol consumption. 所以, the interpretation

of our results is complementary but different: our results examine the short-run influence of alcohol

on mortality in society as it currently is, rather than the influence of alcohol prohibition policies

on medium and long-run mortality after adjusting to the new equilibrium. 此外, society has

changed in the last hundred years, which makes it useful to document modern evidence.

This paper also relates to the small body of literature that studies the impact of curfews on

犯罪, which documents mixed results.10 Last, our results add to the recent work studying the

impact of COVID-19 policy responses on crime, 暴力, morbidity and mortality in South Africa

and other countries (例如. Poblete-Cazenave, 2020; Leslie and Wilson, 2020; Abrams, 2021; Bullinger

等人。, 2021; Nivette et al., 2021; Navsaria et al., 2021; Moultrie et al., 2021; Chu et al., 2022).11

The remainder of the paper is organized as follows: 部分 2 describes the data and policy

background, 部分 3 outlines the empirical strategy we adopt, 部分 4 reports the main re-

sults and robustness exercises, 部分 5 presents an event-study analysis, 部分 6 描述了

additional results on interpersonal violence, and Section 7 concludes.

10Kline (2012) shows that the introduction of a juvenile curfew in Dallas reduced the arrest rate

of individuals below the statutory curfew age for both violent and property crimes. 相比之下,

Carr and Doleac (2018) use variation in the timing of the onset curfews in Washington DC to

provide evidence that gunfire increased by 150% during the marginal hour (IE。, the first hour of the

curfew). 所以, the existing evidence regarding the effectiveness of curfews is ambiguous—it

is not well established whether they increase or decrease crime rates.

11In an earlier version of this paper, we also devoted more space to describing the evolution of

unnatural mortality during other phases of the policy response to COVID-19 in South Africa (看,

例如, Appendix C in Barron et al., 2020).

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2 Data and the Policy Landscape

2.1 Policy Timeline

The policy change studied in this paper is the introduction of a complete ban on all alcohol sales

in South Africa. This change was announced on the evening of Sunday, 七月 12, 2020 and came

into force immediately the following morning on Monday, 七月 13, 2020 (Government Gazette,

2020乙). The explanation provided by the South African government for implementing this policy

was to try to free up hospital resources in order to be prepared for potential COVID-19 related hos-

pitalizations (Ramaphosa, 2020). The underlying idea circulating amongst medical professionals

was that alcohol-related injuries are responsible for a substantial number of hospital admissions

every week in South Africa and, 所以, making alcohol unavailable would reduce the number

of such injuries, thereby freeing up hospital resources in the short-run. The ban was unexpected

and represented a deviation from the South African government’s carefully constructed COVID-19

response plan, which involved a cautious step-by-step scaling back of restrictions from the most

extreme policy bundle (等级 5) to the least extreme (等级 1). The alcohol ban was implemented

in the middle of the Level 3 时期.

To properly interpret the results below, it is important to fully understand the context and policy

background. 期间 2020, 南非, like the rest of the world, faced the challenge of having to

rapidly develop a policy response to try to ameliorate the impact of the COVID-19 pandemic. 这

South African government’s initial response was swift and decisive: on March 27, 2020, 南

Africa entered a stringent lockdown period that included strict stay-at-home orders (政府

Gazette, 2020A). After an initial period of high uncertainty, the government developed a policy

response plan that involved a gradual step-by-step relaxation of the strict policy response measures

from Level 5 to Level 1. 数字 1 provides an overview of the timeline of policy changes during the

period of interest for this paper and Table 15 in the Appendices summarizes the main regulatory

changes during each period.

After the initial period of extremely strict Level 5 措施, there was a slight relaxation of

policy measures to Level 4 on May 1, 2020, but for much of the general population, this still

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involved a continuation of the state of lockdown. On June 1, 2020, the country entered Level 3,

which is the key period of interest for this paper. 等级 3 involved a further relaxation of policy

restrictions on daily life. The key restrictions in place during Level 3 were the following: (我) off-

premises and e-commerce alcohol sales were only permitted from Monday to Thursday between

9AM and 5PM,12 (二) there was no official curfew, but individuals were only permitted to leave

their house when they had a valid reason (例如. exercise between 6AM and 6PM, going to work),

(三、) gathering in groups was still forbidden, with some exemptions for work or specific religious

事件. In practise, these restrictions were not easy to regulate and enforce, particularly in areas

with informal housing and high-density living conditions.

数字 1: Timeline of policy events

Control A

Treatment

Full Lockdown

July Alcohol Ban

等级 5

等级 4

等级 3

等级 2

27 行进 2020

1 可能 2020

1 六月 2020

13 七月 2020

18 八月 2020

31 七月 2020
(Curfew shift)

In the middle of the Level 3 时期, on July 13, 2020, the government abruptly introduced a

complete ban on the sale of alcohol. Along with this alcohol ban, a curfew from 9PM to 4AM was

introduced. Below we provide evidence showing that the curfew is unlikely to have been a key

determinant of any changes observed in mortality due to unnatural causes during this period.

We therefore consider the July Alcohol Ban period as our treatment period and evaluate how

the level of unnatural mortality was shifted by the introduction of the alcohol ban.

2.2 数据

This paper uses outcome data from two sources. 第一的, in our main analysis, we use national daily

mortality data from January 1, 2017 to September 13, 2020. This dataset is collected by the De-

12These sales were permitted for businesses holding either an on-premises or off-premises con-

sumption liquor license.

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partment of Home Affairs and curated by the South African Medical Research Council. 它包含

a record of all deaths of persons with a valid South African identity document (Dorrington et al.,

2020). We focus on mortality due to unnatural causes. This includes deaths precipitated by road

traffic injuries, interpersonal violence, and suicide, but excludes all deaths due to natural causes,

such as illness (例如, COVID-19). Unnatural deaths, 所以, are often caused by risky behavior

with immediate consequences. 像这样, the data allow us to examine how policy changes imple-

mented during 2020 influenced short-run mortality through changes in behavior. In the remainder

of the article, all references to mortality refer to mortality due to unnatural causes unless otherwise

specified.

第二, in Section 6, we augment our main analysis by investigating interpersonal violence as

a potential mechanism driving our unnatural mortality results. We do this using data obtained from

the South African Police Services (SAPS) on homicides, assaults and reported rape cases. 这

data is described in more detail in Section 6.

数字 2 provides a descriptive illustration of our unnatural mortality data. The bold black line

denotes weekly mortality levels due to unnatural causes between March 2020 and February 2021,

while the grey lines reflect the same measure for each of the previous three years. The shaded

vertical bars denote the three periods during which alcohol bans were implemented, with the July

Alcohol Ban the second of these three alcohol bans. The thick dark bar at the bottom of the figure

indicates when a curfew was in place (见表 15 for details regarding the curfew).

The figure reveals several interesting features in the data. 第一的, it is striking how regular mor-

tality patterns are from year to year (prior to 2020). The three grey lines (reflecting 2017, 2018

和 2019) all appear to follow a similar trajectory. 第二, the strong Level 5 and Level 4 政策

responses, which included a full lockdown as well as an alcohol ban, were associated with a large

drop in unnatural mortality in 2020 relative to previous years. 第三, a visual inspection of the

graph suggests that the introduction of the Level 3 period brought mortality numbers back up to a

level slightly below that observed in previous years. The figure also provides suggestive evidence

that the introduction of the July Alcohol Ban then reduced the rate of unnatural mortality again.

(The aim of the analysis below is to evaluate whether this visual pattern in the raw data reflects a

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robust statistical relationship when subjected to a more rigorous analysis.) 最后的, the figure suggests

that during the first week following each of the alcohol bans, there was a sharp rise in unnatural

mortality. 例如, this appears to have occurred at the end of the Level 3 period—mortality

jumped upwards despite the curfew remaining in place when the alcohol ban was rescinded. 这

increase in mortality during the first part of the Level 2 period suggests that the curfew was not a

crucial reason for the lower mortality levels observed during the July Alcohol Ban.

Our main analysis uses three versions of this unnatural mortality data. The first contains a

record of daily mortality levels in the country as a whole.13 The second dataset is similar, 除了

that it is disaggregated by gender: it contains two observations for every day—one for men and

one for women. The third dataset contains unnatural mortality data for the sub-population of

individuals aged 15-34 (for completeness, we also report the corresponding results for other age

团体). The main reason for examining this sub-population is that young adults are typically

viewed as being the group that is most prone to risky behavior and therefore potentially the most

affected by the short-run negative outcomes associated with alcohol.

13To facilitate the interpretation of the analysis below, it is important to take note of some other

empirical regularities observed in the data. In Appendix D, we show that unnatural mortality dis-

plays the following patterns. 第一的, the number of daily deaths due to unnatural causes is markedly

different for men and women. 之间 2017 和 2019, the daily average number of deaths due to

unnatural causes was 31 for women and 109 for men. 第二, unnatural mortality in South Africa

follows a strong and systematic weekly pattern: Mortality is at least 50% higher on Saturdays and

Sundays in comparison to weekdays for men, and at least 25% higher for women. 第三, 那里

is also variation in mortality according to the day of the month, with higher mortality levels ob-

served at the beginning and end of the month. One potential explanation for these monthly peaks

is that they are associated with wage payment days. This monthly cycle is the reason why Figure

2 displays a zigzag pattern in weekly mortality. 第四, there is some heterogeneity in mortality

observed across different months of the year, with the main outlier being December, where higher

levels of mortality are observed. In our analysis below, the detailed data that we have from previous

years allows us to control for these systematic patterns in mortality.

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3 Empirical Strategy

Our empirical strategy utilizes the sudden implementation of the July Alcohol Ban as a natural

实验. In combination with the observation that unnatural mortality follows a highly regular

temporal pattern, this allows us to employ a difference-in-difference style estimation approach.

本质上, our main analysis conducts a comparison of the number of unnatural deaths observed

during the alcohol ban period (in the second half of Level 3) with the number observed during the

period that immediately preceded it (in the first half of Level 3).

In doing this, it is important to isolate the effect of the alcohol ban from unrelated weekly and

seasonal changes in behavior. Using detailed mortality data from four years (i.e. 2017, 2018, 2019

和 2020), we do this in three main ways: (1) we control for the systematic variation in mortality

using day-of-the-week, day-of-the-month and year fixed effects, (2) we control directly for the

baseline mortality level observed during the Level 3 calendar period and alcohol ban calendar

period in the preceding three years, 和 (3) to account for the role played by weekends and the

systematic way in which the first and last weekends of the month are characterized by higher

levels of unnatural mortality, we flexibly control for weekend effects. 此外, to allow for the

fact that the systematic patterns in behavior may have changed in 2020, we interact these weekend

effects with 2020 indicator variables in our main analysis. 以下, we conduct several robustness

exercises to ensure that our results are not driven by our empirical specification.

Our main specification, 所以, removes weekly, monthly, seasonal and yearly time trends

that may play a role, allowing us to focus on the difference in mortality observed within the Level 3

period in 2020 before and after the implementation of the alcohol ban.14 We estimate the following

14Difference-in-difference studies typically use a control group that follows the same time tra-

jectory as the treatment group, but that are not affected by the intervention or natural experiment

(often due to being in a different geographical location). 这里, we instead use detailed information

on outcomes observed in previous years in the same geographical location as our control. 这

approach has also been used in other previous (例如. Caliendo and Wrohlich, 2010; Sch¨onberg and

Ludsteck, 2014) and contemporary (例如. Leslie and Wilson, 2020; Abrams, 2021) 工作.

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model using Ordinary Least Squares:

我的,t,g =α0 + α1 · L3y,t + α2 · Ty,t + α3 · L3y,t × Y2020 + β · Ty,t × Y2020 + λy,t + ϵy,t,G

(1)

where My,t,g refers to the number of daily unnatural deaths in year y on day-of-the-year t in group

G (i.e. for a specific gender or age group) and λy,t is a vector of time-related fixed effects that vary

across specifications.15 To control for seasonal mortality, we include two calendar period indicator

变量: L3y,t, which corresponds to the Level 3 calendar period, and Ty,t, which corresponds to

the July Alcohol Ban calendar period. 重要的, both these variables take a value of 1 为了

relevant calendar periods in all years in our data (IE。, in the years between 2017 和 2020). 我们

then interact each of these two variables with an indicator variable that takes a value of 1 if the year

是 2020. The first interaction variable, L3y,t × Y2020, is crucial for our identification as it controls

for the influence of the basket of Level 3 policies that were in place throughout the Level 3 时期

(which encompasses the July Alcohol Ban period). Controlling for the baseline level of mortality

during the Level 3 period in 2020 allows us to use the second interaction variable, Ty,t × Y2020, 到

examine the shift in unnatural mortality that occurred when the July Alcohol Ban was introduced.

Our main coefficient of interest, β, provides an estimate of the impact of the alcohol ban on

15The vector λy,t varies across the empirical specifications that we use.

In the specification

associated with columns (*A) of our results, it is an empty vector. In columns (*乙), it includes

a set of weekend controls that contains an indicator variable for being a weekend day, the first

weekend of the month, the last weekend of the month, and also interactions of each of these

weekend variables with a 2020 indicator variable. In our preferred specification, usually reported

in columns (*C) of our results tables, λy,t includes day-of-the-week, day-of-the-month and year

fixed effects in addition to the weekend controls. Due to the substantial systematic weekly and

monthly heterogeneity in mortality described in Appendix Section D, the inclusion of these fixed

effects should improve the precision of the estimates. As an illustration, 桌子 5 in the Appendices

provides an example of the results that report the coefficients for the weekend variables in full.

笔记, in column (*C), the reason that the weekend day variable is omitted is to avoid collinearity

with the day-of-the-week fixed effects.

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mortality by estimating the shift in unnatural mortality that occurred when the alcohol ban was

introduced. 具体来说, β reports the difference between the first and second halves of the Level

3 period in 2020, controlling for the corresponding difference observed in pre-2020 years.16

4 结果

4.1 The impact of the alcohol ban on the population as a whole

桌子 1 reports our main results. The main coefficient of interest, β, is associated with the in-

teraction variable, Alcohol Ban Period × Year=2020, and is reported in bold in the table. 我们的

preferred specification is reported in column (1C) and includes the full set of fixed effects. 这

results indicate that the alcohol ban reduced unnatural mortality by 21.99 deaths per day (95% CI:

11.39–32.58). Our estimates of the magnitude of the impact of the alcohol ban are similar across

the different specifications, but the inclusion of fixed effects substantially improves the precision.

When interpreting these results, there are two additional important considerations to keep in

头脑. 第一的, it is also worth noting that there is a large estimated relationship between weekends

and mortality. 桌子 5 in the Appendices reports the coefficients for the full set of weekend con-

巨魔. These results show that: (我) substantially more individuals die from unnatural causes on

Saturdays and Sundays in comparison to other days of the week, (二) this weekend effect is even

more pronounced on the first and last weekend of the month, 和 (三、) these weekend effects were

dampened during 2020 (as can also be seen in Figure 15 in the appendices). 然而, controlling

for this weekend effect does not affect the estimated impact of the alcohol ban much. 第二,

16The interpretation of the other coefficients is as follows: α1 reports the average difference in

unnatural mortality between the first half of the Level 3 calendar period and the rest of the year

in pre-2020 years, while α2 reports the average difference in unnatural mortality between the first

and second halves of the Level 3 calendar period in pre-2020 years. The corresponding interaction

coefficients for 2020, α3 and β, report the change in the corresponding objects for 2020 关系到

pre-2020 years: α3 is the additional difference in unnatural mortality between the first half of the

等级 3 period and the rest of the year in 2020 relative to pre-2020 years.

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数字 2 showed that there was a spike in unnatural mortality during the first week of the Level 3

时期 (IE。, the first week of June 2020). This week forms part of our control period and since this

one-week spike in unnatural mortality may have been a reaction to the end of the previous alcohol

禁止, it could be argued that it is appropriate to also consider an empirical specification that omits

this week from the control period. 为此原因, we also conduct this exercise, replicating Table

1, but essentially reducing the Level 3 period by one week in our estimation by omitting the first

week of June 2020 from the Level 3 period variable. These results are reported in Table 6 在里面

Appendices. 正如预期的那样, this slightly reduces the magnitude of our estimate for β, with these

results indicating that the reduction in unnatural mortality due to the alcohol ban was 17.96 deaths

per day (95% CI: 7.60–28.33). We view it as reassuring that the results are not very sensitive to

the inclusion or exclusion of this week and also yield highly consistent estimates across the range

of different exercises we conduct (下面讨论).

4.2 Heterogeneity by gender

下一个, we consider heterogeneity by gender. There are two reasons for this. 第一的, unnatural mortal-

ity levels of men and women are very different, with approximately 3.5 men dying from unnatural

causes for every 1 woman (看, 例如, 数字 16 in the Online Appendix). 第二, the cause-of-

death distribution is different for men and women. 例如, the ratio of men to women dying

from homicides is higher than the ratio of men to women dying from road-traffic injuries (看, 例如,

Matzopoulos et al., 2015). 第三, we know from the existing literature that men and women dis-

play markedly different patterns of drinking behavior in many countries, including South Africa.

例如, the WHO (2019) reports that heavy episodic drinking was five times higher amongst

men in comparison to women in South Africa in 2016. 一起, these factors could lead to a

differential effect of the alcohol sales ban by gender.

桌子 3 in the Online Appendix reports the estimated impact of the alcohol ban on the unnatural

mortality of men and women. For men, the pattern is similar to that observed in the population as

a whole, with the estimates indicating that the alcohol ban reduced mortality by approximately 21

deaths per day—our preferred specification in column (1C) reports a reduction of 21.43 (95% CI:

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12.13–30.74). For women, we find no significant impact of the alcohol ban on mortality. As above,

we also replicate the results when omitting the first week of June 2020 from the control period (看

桌子 7) and under this specification estimate that the alcohol ban reduced mortality amongst men

经过 18.05 per day (95% CI: 8.95–27.15).

4.3 Focusing on younger adults

Young adults comprise a group that is of particular interest when studying the impact of alcohol

on short-run outcomes. The reasons for this is that they are typically more likely to engage in risky

行为 (例如. risky drinking). We therefore estimate the impact of the alcohol ban on the sub-

population of younger adults between the ages of 15 和 34 年. 桌子 4 in the Online Appendix

reports these results. We find that the alcohol ban reduced mortality amongst men in this age-group

by approximately 12 deaths per day, with an estimated reduction of 11.78 (95% CI: 5.49–18.07) 在

柱子 (1C), and may have had a small impact on the mortality of younger women. An important

implication of these results is that the reduction in mortality observed for men of all ages does

not seem to be completely due to a reduction in risky behavior by young adults. 这 12 lives of

younger men saved per day by the alcohol ban is only slightly over half of the 21 male lives of all

ages saved per day.17 When we omit the first week of Level 3 from our control period (见表 8

in the Appendices), we obtain an estimated reduction in the mortality of young men of 10.24 (95%

CI: 3.90–16.58), and no significant impact for young women.

To investigate the relationship between age and the impact of the alcohol ban further, 图中

17然而, an important caveat to keep in mind is that the victims of alcohol-related deaths

are often not the users themselves (as in the case of interpersonal violence and motor vehicle

碰撞). 所以, the demographic characteristics of the individuals engaging in the risky

behavior may not always correspond to the demographic characteristics of the individuals who

are affected by the behaviour. Examining the change in mortality amongst young adults may not

reflect the true aggregate impact of any change in the behaviour of young adults. This externality

of alcohol consumption illustrates the importance of examining the impact of changes in alcohol

consumption on society as a whole, as opposed to focusing on the particular sub-population.

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6 in the Appendices, we compare the age distribution of unnatural mortality during the 5 weeks

preceding the alcohol ban with the age distribution during the 5 weeks of the alcohol ban. 这

figure suggests that the majority of the decrease in unnatural mortality due to the alcohol ban

was observed for individuals between 18 和 35 年龄. 然而, it appears to show that

there was also a decrease in unnatural mortality for individuals over 35. Tables 9, 10 和 11 在

the Appendices provide further evidence by replicating the empirical analysis used in Table 4 为了

other age groups. These results are largely in line with the suggestive evidence from Figure 6:

(我) for individuals aged 14 or younger, we find no effect of the alcohol ban on mortality, (二) 为了

individuals between 35 和 54 年, we estimate that the alcohol ban resulted in 6.7 fewer male

deaths per day, with a possible smaller reduction in the mortality of women by 1.3 deaths per day,

(三、) for individuals over 55 年, we estimate that there were 2.5 fewer male deaths per day, 和

1.6 additional female deaths per day during the alcohol ban period.18 Overall, the results indicate

that most of the effect of the alcohol ban on unnatural mortality was concentrated amongst younger

male adults, with a smaller, but still sizable, impact also observed amongst middle aged men.

4.4 Robustness exercises

To provide support for the validity of these findings, we conduct several robustness exercises.

These exercises, and the associated results, are discussed in detail in Section C of the appendices.

The first two exercises address concerns regarding the suitability of the natural experiment for

providing causal evidence on the impact of reducing alcohol consumption (see Section C.1). 要做的事

这, we show that the primary candidate confounding factors were unlikely to have contributed to

the observed reduction in unnatural mortality. Aside from the alcohol ban, the two main sources of

behavioral change in society during the period we are studying were: (我) the COVID-19 pandemic

和 (二) the associated changes in regulation. We reason that fear of COVID-19 was unlikely to

have caused a reduction in unnatural mortality during the period of the July Alcohol Ban since the

18It is unclear to us why the alcohol ban may have resulted in an increase in mortality due to

unnatural causes amongst women over 55 年龄. One speculative potential explanation is

that some COVID-19 related deaths were misclassified as unnatural deaths.

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number of daily confirmed COVID-19 cases was dropping rapidly. We also evaluate the possibility

that the main contemporaneous regulatory change, namely the introduction of a curfew, influenced

unnatural mortality. 要做到这一点, we make two observations. 第一的, we note that when the July Al-

cohol Ban ended, the curfew remained in place. 数字 2 shows that unnatural mortality increased

sharply at this point in time and remained at pre-2020 levels despite the ongoing curfew. 第二,

we estimate the impact of a one hour reduction in the curfew length which occurred in the middle

of the July Alcohol Ban period. We show that it did not have a statistically significant impact on

unnatural mortality. These observations on both the extensive and intensive margin support our

assessment that the curfew was unlikely to have been a key factor in reducing mortality during the

July Alcohol Ban.

The next four exercises check that our results are not driven by the particular empirical strategy

that we adopt nor by anomalies in the data (see Section C.2). 第一的, we run a set of placebo

回归. 本质上, this involves replicating our main analysis, but replacing 2020 和 2019

as our treatment year and using 2016 到 2018 as our comparison years. 正如预期的那样, the coefficients

associated with the interaction term of interest, Alcohol Ban Period × Year=2019, are no longer

statistically significant.

第二, we examine whether our results are sensitive to the precise choice of time window

used for our estimation. 要做到这一点, we conduct an additional robustness exercise where we vary

the length of the treatment-control time window around the introduction of the alcohol ban used

in our analysis. Instead of including a indicator variable for the entire Level 3 时期, we consider

time windows of between 2 weeks and 5 weeks in length. We find that the estimated impact of the

alcohol ban remains fairly stable when considering windows of 5 weeks, 4 weeks and 3 weeks in

length. The only exception to this is that when we use a very narrow window of only 2 weeks in

length, we no longer observe a significant coefficient estimate.19

19In Section C.2.2, we discuss several potential explanations for this, including the important

consideration that the two week period prior to July 13 normally includes a payday weekend (和

the associated inflated mortality levels), while the two week period afterwards does not. Given the

very short time window being considered, this imbalance between the treatment and control period

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第三, we consider alternative approaches to calculating the standard errors and drawing in-

ference from our regressions. 具体来说, we conduct a series of exercises that reproduce the

results from Table 3 but relax the assumptions on the error structure to allow for serial correlation

in the error term. The results from these exercises are discussed in detail in Section C.2.3 and

报告 (我) standard errors estimated by clustering at the calendar-week level, (二) standard errors

calculated using the Newey-West (1987) variance estimator that allows for autocorrelation up to a

pre-specified lag length, 和 (三、) p-values for the main coefficient of interest, calculated using the

wild cluster bootstrap to correct for the small number of clusters when clustering at the year level

(Cameron and Miller, 2015; Roodman et al., 2019). These exercises yield estimates that are all

consistent with the findings reported above.

最后的, we replicate our main results, but restrict the dataset to only contain observations during

the Level 3 calendar period. 所以, we use data from the years 2017 到 2020, 之间 1 六月

和 17 August of each year, and estimate the following simplified version of our main estimation

方程:

我的,t,g =α0 + α1 · Ty,t + β · Ty,t × Y2020 + λy,t + ϵy,t,G

(2)

The point estimates from our preferred specification, which includes fixed effects, are very close

to those in our main results.20

Collectively, we view these six exercises as providing strong support for the validity of the

results discussed above.

could provide an explanation for absence of a significant estimate if our weekend and fixed effect

controls are not perfectly accounting for this imbalance.

20The results from the other specifications are also largely in line with our main results, 但是

point estimates are less stable across specifications. The main difference between these results and

the results from our main estimation approach is that we observe a significant impact of the alcohol

ban on female mortality under specifications that don’t include fixed effects. For the reasons dis-

cussed above, we view the results with fixed effects as being more trustworthy. See Section C.2.4

for further details.

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5 Event study analysis

在这个部分, we augment our main analysis by using an event-study design to examine how

unnatural mortality levels evolved week-by-week in the period before and after the implementa-

tion of the July Alcohol Ban. This generalization of the difference-in-difference style empirical

approach used above involves using indicator variables for each of the lag and lead weeks around

the event of interest (看, 例如, Schmidheiny and Siegloch, 2019; Clarke and Tapia-Schythe, 2021).

One benefit of the event-study design is that it can help to provide an informative illustration of

where the estimated aggregate effect is coming from. 例如, it can show whether the effect

is driven by a large shift in mortality in a single week or a smaller shift spread across several weeks.

It can also provide an indication of whether any dynamic effects are present. 以下, we provide

estimates for both the transition into the July Alcohol Ban on July 13, 2020, and also the transition

out of the July Alcohol Ban on August 18, 2020.

For the transition into the alcohol ban, which is our primary focus, our event-study analysis

considers the eleven weeks of the Level 3 时期, comprising the six weeks before the July Alco-

hol Ban and the five weeks in which the July Alcohol Ban was in force. Following Schmidheiny

and Siegloch (2019), we refer to this eleven week period as the effect window. The central idea

is to fix one of the weeks as the benchmark, and to compare the level of the outcome of interest

(unnatural mortality, 我的,t,G) in each of the other nearby weeks to the benchmark level. To esti-

mate our event-study, we therefore adjust the empirical strategy described above in equation 1 经过

including indicator variables for each of the weeks in the effect window to arrive at the following

specification:

我的,t,g =α0 +

j
(西德:88)

j=j

βjbj

y,t + µy,t + λy,t + ϵy,t,G

(3)

where λy,t is a vector of time-related fixed effects (as in equation 1 多于), µy,t contains indicator

variables for the Level 3 calendar period (L3y,t) and the July Alcohol Ban calendar period (Ty,t) 在

all years (IE。, 不仅 2020), and bj

y,t is a treatment indicator that takes a value of one when the

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event of interest, e, namely the start of the July Alcohol Ban, takes place j ∈ [j, j] weeks away

from w(t), where w(t) refers to the week in which day t occurs.

bj
y,t =






if y ≤ 2019 & j = j

1[w(t) ≤ e + j]

if y = 2020 & j = j

1[w(t) = e + j]

if y = 2020 & j < j < j 1[w(t) ≥ e + j] if y = 2020 & j = j (4) Following the standard event-study approach, the indicator variables at the endpoints of the ef- fect window serve to bin together all observations that occur outside the effect window (see, e.g., Schmidheiny and Siegloch, 2019). Put simply, in our setting, all observations that occurred seven weeks or more before the July Alcohol Ban (including all observations from 2019 or earlier) are binned together, with b j y,t = b−7 y,t = 1 for these observations. Similarly, all observations that oc- curred six weeks or more after the July Alcohol Ban are binned together and bj y,t = b6 y,t = 1 for these observations. For the weeks within the eleven week effect window, bj y,t = 1 when an obser- vation occurs in a week that is j weeks away from the onset of the July Alcohol Ban (with negative values denoting weeks before the ban, and positive values denoting weeks after the ban). Impor- tantly, since the transition into the July Alcohol Ban occurred between two weeks (i.e., between Sunday and Monday), we define j = {−7, ..., −1, 1, ..., 6}, with j = −1 denoting the last week before the ban and j = 1 the first week after the onset of the ban, so there is no period j = 0. In our analysis of the transition into the ban, the benchmark week is j = −1.21 21For all the observations in 2020, using the normalization that the event of interest occurs in week 0 (i.e., e = 0), we can rewrite equation 4 more simply as follows: l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / d o i / . / 1 0 1 1 6 2 / r e s t _ a _ 0 1 2 2 8 2 0 4 1 4 5 8 / r e s t _ a _ 0 1 2 2 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 1[w(t) ≤ −7] bj y,t = 1[w(t) = j] 1[w(t) ≥ 6]    if j = −7 if − 7 < j < 6 if j = 6 23 Review of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The results from our event study analysis for the transition into the July Alcohol Ban are dis- played in the left-hand panels of Figures 4 and 5 in the Online Appendices.22 Figure 4 reports the results corresponding to specification (*b) of our main regressions, including a range of weekend controls. Figure 5 corresponds to specification (*c), which additionally includes a full set of calen- dar fixed effects (see footnote 15 for details). In both figures, the top-left panel reports the results for men, while the bottom-left panel reports the results for women. These results for the transition into the alcohol ban reveal several insights. First, the high coefficient estimate for men in week j = −6 is consistent with the observation that there was a jump in unnatural mortality during the first week of June 2020 (recall that j = −6 is the first week after Alcohol Ban 1, which lasted over two months). This jump in unnatural mortality can also be seen clearly in the raw data in Figure 2. Second, for men, all the point estimates in weeks during the July Alcohol Ban (i.e., j > 0) in both specifications are negative.23 Therefore, these results are

consistent with the aggregate level results above that pool the weeks together. 第三, the lower left-

hand panels of both figures show that the coefficient estimates for women are almost completely

flat and close to 0 (only β−2 in Figure 5 is statistically different from zero at the 10% 等级). 这

22笔记, the figures report the coefficient estimates for the weeks within the event window. 我们

exclude the coefficients for the two endpoints (j = −7 and j = 6) from the figures as they are

not very informative for evaluating the event-study transition. The interpretation of the binned

coefficient at the start of the event window is the difference in unnatural mortality between the

benchmark week (j = −1) and the average level in all weeks prior to the event window. 这

interpretation of the binned coefficient at the end is similar.

23It is worth noting that in specification (*C), but not specification (*乙), the point estimate for β−2

also appears to be negative (虽然 0 still lies within the 95% CI). One potential reason for this

negative point estimate two weeks prior to the July Alcohol Ban is that this week includes the first

weekend of the month. If our calendar fixed effects do not perfectly account for all the variation

in unnatural mortality due to payday effects, this could make the estimates for payday weekends

more sensitive to the choice of specification. This is perhaps supported by the observation that for

women in the lower-left panel of Figure 5 the only coefficient with a 95% confidence interval lying

fully below zero is also week j = −2.

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null result for women is highly informative as it shows that there was no change in the causes of

unnatural mortality that affected women when the July Alcohol Ban was introduced. This implies

that the results that we observe for men must be driven by some change in the causes of unnatural

mortality that affect men differently from women. Since alcohol consumption patterns in South

Africa are highly gendered, this provides further support for the idea that the reduction in alcohol

consumption is the likely explanation for the effect we observe.

The right-hand panels of Figures 4 和 5 repeat the event-study analysis for the transition out

of the July Alcohol Ban. 这里, we define the effect window as the five weeks of the July Alcohol

Ban plus the five weeks following the alcohol ban. We now assign the benchmark period to be

the first week after the end of the alcohol ban (j = 1), so that it is again the closest week lying

outside the alcohol ban period. Before discussing these results, it is important to re-iterate that our

main source of identification in this paper comes from the transition into the alcohol ban, 自从

this was unexpected and occurred in the absence of other substantial policy changes. 相比之下,

the transition out coincided with other changes to regulation (IE。, the relaxation from Level 3 到

等级 2: 见表 15 欲了解详情). 尽管如此, we view this exercise of examining the transition

out of the ban as being useful for the following reasons. 第一的, even though other regulations

changed when the alcohol ban was lifted, it still provides a useful check of whether unnatural

mortality increased when alcohol became available again. This is informative as it indicates that

any unknown change in society influencing mortality that may have occurred when the alcohol ban

was initiated would have also needed to be reversed at the same time as the alcohol ban ended to

generate the down-up pattern in unnatural mortality that we observe. 第二, the curfew that was

initiated at the same time as the July Alcohol Ban remained in place when the alcohol ban was

lifted, so the transition out provides a test of whether the curfew alone reduced unnatural mortality.

If it did, then one would expect unnatural mortality to remain low when the alcohol ban was lifted

and the curfew remained in place.

Examining the top right-hand panels of Figures 4 和 5 shows that for men the transition out

of the July Alcohol Ban was associated with a step-up in unnatural mortality. These results are

consistent with our main results, and also provide evidence that the curfew alone was not the main

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driver of the reduction in unnatural mortality observed during the July Alcohol Ban. Turning to the

bottom-right panels of the two figures, we see that for women in contrast to the transition into the

alcohol ban, which was not associated with a change in mortality, the transition out was associated

with an increase in unnatural mortality. This is consistent with the idea that the change in unnatural

mortality when transitioning out of the alcohol ban was driven by both the re-availability of alcohol

and also by the relaxation of other regulations that occurred when moving from Level 3 to Level

2. This is also consistent with the observation that the event-study coefficient estimates for men in

人物 4 和 5 are larger in magnitude (more negative) during the transition out in comparison to

the transition in. Examining the raw data in Figure 2 shows this even more clearly.

全面的, we interpret the results of this event-study analysis to be highly supportive of our main

结果. 然而, as a caveat, it is important not to place too much weight on the estimates for each

individual week in our event-study analysis because: (我) the coefficient for each week is estimated

from a small number of observations, 和 (二) as we see from comparing Figures 4 和 5, 有

some sensitivity in the estimates to the precise choice of empirical specification.

6 犯罪: interpersonal violence as a mediator

One of the primary causes of injury-induced mortality is interpersonal violence. 所以, to shed

light on one potential mechanism that could be driving our main results, in this section we examine

the impact of the July Alcohol Ban on three outcomes related to interpersonal violence. 要做到这一点,

we use data collected by the South African Police Service (SAPS). This data contains the daily

number of reported contact crimes in South Africa during the three month period between June

1, 2020 and August 31, 2020 in three categories: homicides, assault with intent to inflict grievous

bodily harm (GBH), and rape. 数字 7 in the Online Appendices displays the raw data. This figure

reveals that each of these three outcomes followed a very similar week-by-week trajectory around

the July Alcohol Ban to that observed for unnatural mortality. 具体来说, in the very first week

of June, which is also the first week following Alcohol Ban 1, we observe an elevated level of each

of the three outcomes. 此后, in the next five weeks (which immediately precede the July

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Alcohol Ban) each of the outcomes stays relatively flat, with a slight increase around the change

of month between June and July.24 At the onset of the July Alcohol Ban, all three outcomes drop

to a lower level for the duration of the ban, and when the ban is lifted, we again see a large jump

upwards in all three outcomes. 所以, the raw data suggests that the July Alcohol Ban affected

each of these three outcomes similarly to how it affected injury-induced mortality.

To assess whether the pattern observed in the raw data reflects a statistically significant shift

in each of the outcomes at the onset of the July Alcohol Ban, we use a simplified version of our

empirical strategy from above (simplified due to the reduced data availability).25 Using only the

eleven weeks and one day of the Level 3 period between June 1, 2020 and August 17, 2020, 我们

estimate the following specification for each of our three contact crime outcomes and also for

unnatural mortality:

Zt =α0 + β · Tt + κt + ϵt

(5)

where Zt refers to outcome Z at time t, Tt is an indicator variable that takes a value of one during

the July Alcohol Ban, and κt is a vector of indicator variables that control for the effect of weekends

on each of the outcomes.26

24To check more formally for the presence of a statistically meaningful pre-trend, we conduct

an additional analysis using only data from this five-week period before the July Alcohol Ban in

which we regress each of the outcomes on a week counter that takes values from -5 (the fifth week

before the ban) 到 -1 (the week before the ban). These results are reported in Table 12 的

Appendices. If the drop in the outcomes observed at the start of the July Alcohol Ban were part of

an existing pre-trend, we would expect to see a negative coefficient on the Week Counter variables

表中 12. 然而, we do not observe a statistically significant negative coefficient for any of

the four outcomes. This evidence suggests that the estimated drop in all four outcomes at the start

of the July Alcohol Ban is not a continuation or exacerbation of an existing pre-trend.

25The reason for not including the full set of fixed effects considered above is to avoid over-

fitting, since here we use a smaller sample consisting of 78 observations (天).

26In specification (*2) 表中 2, κt contains three indicator variables, Weekend Day, 哪个

takes a value of one for Saturdays and Sundays, First Weekend of Month and Last Weekend of

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The results are reported in Table 2. They show that there was a statistically significant drop in

all of our outcomes of interest when comparing the July Alcohol Ban period to the preceding six

weeks. 然而, as discussed in the main results above, one important caveat to these results is

that this six week comparison period includes the first week of June, which saw a jump in all of our

outcomes of interest. 所以, we also replicate the analysis reported in Table 2, excluding this

first week of June. These replication results are reported in Table 13 in the Online Appendices and

also show a statistically significant drop in all four outcomes, but of a slightly smaller magnitude.

The first two columns of Tables 2 和 13, labelled (H*), estimate that the number of homicides was

reduced by 11 [15] per day, depending on the specification, during the July Alcohol Ban period in

comparison to the five [six] week preceding period. This reduction represents a substantial fraction

的 54 [58] daily homicides that occurred on average during the five [six] weeks leading up to

the July Alcohol Ban. It also suggests that a large proportion of the 18 [22] fewer daily unnatural

deaths during the July Alcohol Ban period can be attributed to this reduction in interpersonal

暴力.

The next four columns, (A*) 和 (R*), show that there was also a sharp drop in assaults (和

intent to cause GBH) 的 113 [142] per day, and in reported rape cases of 15 [19] per day. The final

two columns, (U*), provide another robustness check for unnatural mortality, since the specifica-

tion here is the simplest one used in the paper, making use of data from only a 10 [11] week period

during Level 3 在 2020. It is therefore reassuring that the results we observe from this simple

specification are highly consistent with the main results reported above.

To illustrate the dynamic effects in each of these four outcomes around the onset of the July

Alcohol Ban, we adjust the specification in equation 5 by replacing Tt with indicator variables for

each of the eleven weeks in the Level 3 时期. This allows us to conduct a simple version of the

event-study analysis that we used above.27 Figure 3 displays the coefficients from this analysis for

Month, which indicate whether it is the first or last weekend in the month. In specification (*1), κt

is an empty vector.

27笔记, in contrast to the specification used in our event study in Section 5, here we only use data

from the eleven weeks, so there are no binned categories at the end points. 我们, 所以, 包括

the indicators for weeks j = −6 to j = −2, and j = 1 to j = 5, with week j = −1 serving as the

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each of the four outcomes. 全面的, the figure shows a relatively sharp drop for all four outcomes

at the onset of the July Alcohol Ban, providing support for our main findings.

7 Concluding discussion

In this paper we have documented evidence that a five-week-long nationwide ban on the sale and

transport of alcohol resulted in a reduction of at least 14% of all unnatural deaths during that period.

This is a large and meaningful number of lives saved. We have also shown that the alcohol ban led

to a sharp drop in violent crimes, suggesting that the relationship between alcohol and aggressive

behavior is one of the key mechanisms driving our mortality results. Our findings provide unique

causal evidence on the impact that a short-term absence of alcohol can have in a society; 或者更确切地说,

perhaps much more importantly, they provide a clear illustration of the impact that the presence

of alcohol has on society every day. They demonstrate that alcohol can substantially increase the

amount of behavior-induced harm observed in the population.

There are several important considerations that should be kept in mind when interpreting our

结果. 第一的, it is important not to extrapolate from these results to try to infer the impact that

a longer ban on alcohol would have on mortality. The alcohol ban that we evaluate lasted only

five weeks. In the presence of a longer ban, society would shift to a new equilibrium, 这可能

involve legally acquired alcohol being replaced by illegally acquired or homemade alcohol, 或一个

substitution to other recreational drugs. 所以, our results should not be taken as evidence

that prohibition works well, but rather as evidence of the magnitude of harm generated by alcohol

in society. They illuminate the substantial benefits to society that can be achieved by carefully

implementing policies that will successfully curb alcohol consumption in the long-run—policies

other than a complete prohibition on alcohol sales may well be more effective avenues for pursuing

this objective.28

omitted benchmark week (IE。, we include indicator variables for 10 weeks, and omit an indicator

for one week to serve as the benchmark).

28The World Health Organization has proposed five such intervention strategies as part of its

SAFER initiative (WHO, 2018).

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第二, our estimates of the impact of the alcohol sales ban likely constitute a lower bound

on the true impact of alcohol on short-run unnatural mortality in South Africa. The main reason

for this is that the July Alcohol Ban occurred against the backdrop of COVID-19 which implies

that during our control period (the first half of the Level 3 时期), people were more likely to be

at home and less likely to be going out to bars and restaurants in comparison to the same period

in previous years (例如, see the Google mobility trends in Figure 10 in the Appendices, which also

shows that there was not a sharp change in these trends at the onset of the alcohol ban). 这

depressed level of social activity translated into a lower benchmark level of unnatural mortality

in our main control period in comparison to the same period in previous years (见图 2).

所以, we are comparing the outcomes observed during the July Alcohol Ban period to an

already lowered base level of these outcomes, which suggests that our estimated effect sizes are

likely to be smaller than they would be if an alcohol ban were implemented during a year with

more social activity.29 Importantly, since the effect sizes that we observe are still so large, 这些

lower bound estimates provide valuable information, pointing towards an even larger influence of

alcohol when social activity is at normal pre-COVID levels.30

29An additional reason why our estimates likely constitute a lower bound on the true effect

size of the presence of alcohol in society is that, according to media reports, compliance with

the alcohol sales ban was imperfect. Some examples of the media reports include articles in the

Guardian (2020) and the Economist (2020).

30One concern that can be raised is that alcohol consumption may have been higher-than-normal

during the COVID-19 pandemic outside of the alcohol bans, which would potentially increase the

effectiveness of an alcohol ban relative to other years. There are two pieces of evidence that suggest

that this was not the case. 第一的, 人物 8 和 9 in the Appendices show that monthly alcohol

production and sales were at approximately the same level in 2020 如 2019 during the months

when the alcohol bans were not in place and dropped during the bans. This is suggestive evidence

that alcohol consumption was roughly normal during 2020 outside of the ban periods. 第二, 如果

alcohol consumption were inflated during 2020 then we would expect to observe inflated levels of

unnatural mortality during our control period relative to previous years. Instead we observe the

reverse (见图 2).

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第三, the absence of an estimated impact of alcohol on female mortality should not be taken

as evidence that women are less affected by the presence of alcohol in society than men. 尽管

women drink substantially less than men in many societies around the world (including South

非洲) they are often the victims of alcohol-related harms. Our results showing that the number

of reported rape cases dropped during the July Alcohol Ban illustrate this. It should also be kept

in mind that there are many other forms of gender-based violence that do not result in either death

or a reported rape case that fall outside the scope of this paper.

第四, while the evidence that we report on homicides and assaults suggests that a large part of

the influence that alcohol has on mortality due to unnatural causes is mediated by alcohol-induced

aggressive behavior, it is important not to neglect road-traffic collisions as another important po-

tential channel through which alcohol can induce injury and death. 很遗憾, 在那个时间

writing this paper, it was not possible for us to obtain data that would allow us to study this chan-

nel directly. 然而, in the future more detailed cause-of-death data may become available and

could be used retrospectively to provide direct evidence on the effect that the ban had on road-

traffic collision mortality. Given the background pandemic context, an important consideration to

keep in mind when thinking about road-traffic collision deaths during this period is that it is not

entirely clear a priori whether the lower-than-usual level of traffic volume and road congestion

would increase or decrease the number of road-traffic collision fatalities. It is plausible that emp-

tier roads can result in more speeding and more deaths (例如, in the United States there

were more motor vehicle deaths and lower traffic volumes during the period from June 2020 到

十二月 2020 in comparison to the same period in 2019, according to preliminary estimates by

the National Safety Council).31 然而, in the South African context there are two reasons to

believe that road-traffic fatalities were lower than usual during our control period. 第一的, Navsaria

31For further details, see Bolotnikova (2022) and National Safety Council (2022). 然而, 它

is also worth noting that this pattern of behavior in the United States is not representative of the

evidence from around the world during the pandemic. Yasin et al. (2021) review the evidence

on traffic volumes and fatalities during April 2020 从 36 countries and find that 32 of those

countries experienced a reduction in road deaths in comparison to 2019.

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等人. (2021) show that during June 2020 (the first four weeks of our control period) 的数量

trauma patients admitted due to a road traffic collisions was 32% lower than pre-COVID levels in

a large tertiary urban trauma centre in Cape Town, 南非. 第二, 数字 2 shows that the

level of unnatural mortality was lower-than-usual during our control period (relative to previous

年), indicating that if road-traffic fatalities were higher-than-usual, there would have needed to

be a much larger drop in some other cause of unnatural mortality. 合在一起, the available evi-

dence suggests that road-traffic fatalities were likely lower-than-usual in our control period, 哪个

implies that there was less scope for reducing road-traffic fatalities by banning alcohol than would

normally be the case. This further supports the idea that we are estimating a lower bound on the

impact of alcohol on unnatural mortality.

The results discussed above raise important questions regarding the optimal design of alcohol

control policy. 尤其, since several of the social harms we study (例如, homicide, assault,

强奸) involve the cost of an action being borne by another individual in society, there seems to be a

potential mandate for policy intervention. 然而, policy discussions surrounding optimal alco-

hol control are complex as they involve taking a global perspective and balancing all of the social

benefits of alcohol consumption (which are non-trivial to measure) against the large set of poten-

tial short-term and long-term social costs (which are also typically difficult to causally estimate).32

Welfare analysis is further complicated by the fact that some of the costs of alcohol consumption

are borne by the individual themselves, such as the influence on their long-term health and cog-

nitive functioning, and on the quality of their short-term decision-making. A fully rational model

would ascribe a limited role for policy intervention if these were the only costs, since the individ-

ual consuming alcohol is assumed to be factoring in these costs when they maximize their own

32例如, the range of potential social harms includes outcomes such as the emotional

abuse of family-members and the loss of utility due to poor decision-making (which are difficult

to fully measure) as well as long-run outcomes, such as liver cirrhosis, cardiovascular diseases,

cancers and mental health outcomes (for which it is challenging to construct a precise causal attri-

bution to alcohol consumption). This makes it very difficult to fully evaluate the aggregate social

cost of alcohol consumption.

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lifetime utility and decide to consume alcohol. 然而, a behavioral model that allows for hyper-

bolic discounting, (non-rational) addiction, bounded rationality or imperfect foresight regarding

the future costs of consuming alcohol would permit a role for welfare-enhancing intervention that

assists individuals in overcoming their own behavioral biases (看, 例如, 西蒙, 1984; Ainslie,

1991; Orphanides and Zervos, 1995; Laibson, 1997; Rubinstein, 2003). 最后, there is an

entire literature devoted to the design of optimal alcohol control (see the Handbook chapter by

Cawley and Ruhm, 2011, for a discussion of traditional and behavioral economics models of risky

行为, and their implications for the design of policy interventions). In relation to the current

纸, the discussion in Carpenter and Dobkin (2011) provides the most useful benchmark case of

welfare analysis and their discussion highlights the numerous challenges faced in making progress

when taking a general perspective that weighs up all the costs and benefits in an exercise of this

自然.

这里, we have opted not to try to take a general perspective that considers all the costs and

benefits, but rather highlight what we view as the main policy lessons of our results. 中的一个

key take-aways from our results is that alcohol consumption can play a pivotal role in inducing

aggressive behavior in society at a significant scale, resulting in substantial harm.33 When thinking

about the policy implications of this, it is important to consider that the evidence from the existing

文学, along with the pattern of drinking observed in South Africa (where heavy drinking is

33One way to think about the magnitude is in terms of the value of a statistical life (VSL) 作为

discussed by Viscusi and Aldy (2003) (bearing in mind all the substantial caveats that are implicit

in assigning a monetary value to a human life). Converting the estimates from Viscusi and Aldy

(2003) of the VSL for the United States to 2020 US$ gives $10.52 百万. Since the per capita

average income is approximately 11 times lower in South Africa, one perspective is to view $1 million as the appropriate value for thinking about the monetary VSL in our paper. This implies a cost to society, in South Africa, of at least $115 million per week due to deaths resulting from

alcohol consumption. This estimate does not account for the costs due to any of the other harms

not resulting in death (例如, assaults, 强奸, long-run deleterious health outcomes). 此外, 它

is important to keep in mind that short-run alcohol-related deaths tend to be amongst younger

个人.

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common amongst those who drink), points towards heavy drinking, as opposed to social drinking,

as a key driver of this aggressive behavior (Duke et al., 2011; Kuhns et al., 2014; Tomlinson et al.,

2016; Matzopoulos et al., 2021). This implies that a society that wishes to alleviate these short-run

harms from alcohol consumption should start by targeting a reduction in the alcohol consumption

of the heaviest drinkers in society. This provides a clear principle to guide which policy levers

to prioritize. 例如, one candidate policy lever that has been proposed to reduce heavy

drinking is the use of minimum unit pricing (MUP). The rationale for this is that the heaviest

drinkers typically spend the least per unit of pure alcohol and will be the group that reduces their

consumption most in response to an increase in the floor price of a standard drink (福尔摩斯等人。,

2014; O’Donnell et al., 2019; van Walbeek and Chelwa, 2021; Gibbs et al., 2021). As noted in the

introduction, South Africa is not at all unusual in terms of the proportion of the adult population

that engage in heavy episodic drinking, with many countries around the world observing a higher

proportion of the population engaging in ‘binge-drinking’. While other background social factors

may influence the way that intoxication manifests in behavior, potentially making South Africa a

better model for countries facing similar social issues (例如, 巴西, 俄罗斯), the results in this paper

should serve as a warning for all countries with substantial levels of heavy drinking.

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TABLES AND FIGURES IN PAPER

数字 2: Weekly mortality (unnatural deaths, all ages)

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Alcohol Ban 1July Alcohol BanAlcohol Ban 3Level 3 Period2505007501000125015001750Weekly Mortality (Unnatural)1 Mar 2020Level 5: 27/3等级 4: 1/5等级 3: 1/613 Jul 2020Level 2: 18/829 Dec 20201 Feb 2021Week of the yearAlcohol Ban2017/182018/19Curfew2019/202020/21Review of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 国际的 (抄送 4.0) 执照.

桌子 1: Impact of the alcohol ban on mortality (entire population)

等级 3 Period = 1

(1/6-17/8)

(1A)

6.88

(1乙)

3.00

(1C)

1.75

(5.14)

(2.80)

(2.20)

Alcohol Ban Period = 1

-2.17

-0.28

0.91

(13/7-17/8)

(6.88)

(3.31)

(2.73)

等级 3 Period x Year=2020

-13.46∗∗

-2.29

-1.78

(6.19)

(4.32)

(4.51)

Alcohol Ban Period x Year=2020

-20.93∗∗

-21.55∗∗∗

-21.99∗∗∗

(8.41)

(5.30)

(5.40)

持续的

136.96∗∗∗

115.14∗∗∗

130.76∗∗∗

(1.56)

(1.13)

(4.42)

Weekend Controls

Day of Week FEs

Day of Month FEs

Year FEs

观察结果

Adjusted R2

是

1460

0.008

1460

0.524

1460

0.599

Notes: (我) Each observation contains unnatural mortality data for a sin-

gle day, (二) Heteroskedasticity robust standard errors (Huber, 1967;

白色的, 1980, 1982) are reported in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01, (iii) The estimation uses data from 2017 to 2020, be- tween January, 1 and December, 31 of each year, excluding February, 29, (iv) All three columns report estimates of the impact on unnatural mortality, and differ only in their specifications, with column (*a) the simplest specification, (*b) adding controls for the weekend, and (*c) adding fixed effects. The weekend controls include an indicator vari- able for a weekend day, an indicator for the first weekend of the month, and one for the last weekend of the month. They also include all three variables interacted with a 2020 indicator variable. In column (*c), the weekend day indicator is excluded due to the Day of Week FEs. 42 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / d o i / / . 1 0 1 1 6 2 / r e s t _ a _ 0 1 2 2 8 2 0 4 1 4 5 8 / r e s t _ a _ 0 1 2 2 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Review of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. y t i l a t r o M l a r u t a n n U e p a R d e t r o p e R ) H B G ( t l u a s s A e d i c i m o H ) 2 U ( ) 1 U ( ) 2 R ( ) 1 R ( ) 2 A ( ) 1 A ( ) 2 H ( ) 1 H ( y t i l a t r o m d n a s e c n e f f o l a n i m i r c n o n a b l o h o c l a e h t f o t c a p m I : 2 e l b a T ) 8 6 . 3 ( ) 8 8 . 4 ( ) 1 0 . 4 ( ) 5 1 . 5 ( ) 9 2 . 8 1 ( ) 8 3 . 7 2 ( ) 5 4 . 2 ( ) 2 1 . 3 ( ∗ ∗ ∗ 8 2 . 2 2 - ∗ ∗ ∗ 0 1 . 3 2 - ∗ ∗ ∗ 8 2 . 9 1 - ∗ ∗ ∗ 3 2 . 0 2 - ∗ ∗ ∗ 5 1 . 2 4 1 - ∗ ∗ ∗ 4 9 . 8 4 1 - ∗ ∗ ∗ 6 0 . 5 1 - ∗ ∗ ∗ 9 5 . 5 1 - 1 = d o i r e P n a B l o h o c l A ∗ ∗ ∗ 4 1 . 3 2 ) 7 1 . 4 ( ∗ ∗ ∗ 4 0 . 9 1 ) 1 1 . 7 ( ∗ ∗ 5 7 . 4 1 ) 9 0 . 7 ( ∗ ∗ ∗ 3 8 . 6 2 ) 7 0 . 5 ( ∗ 7 8 . 7 1 ) 2 5 . 9 ( 8 0 . 3 - ) 3 2 . 9 ( ∗ ∗ ∗ 4 7 . 6 4 1 ) 7 3 . 7 1 ( ∗ ∗ ∗ 9 3 . 9 4 1 ) 1 3 . 7 4 ( 0 0 . 7 3 ) 7 4 . 5 4 ( ∗ ∗ ∗ 7 1 . 1 1 ) 4 5 . 2 ( ∗ ∗ ∗ 1 4 . 4 1 ) 2 7 . 3 ( ∗ ∗ 2 9 . 6 1 ) 4 4 . 6 ( 1 = h t n o M f o d n e k e e W t s r i F 1 = y a D d n e k e e W 1 = h t n o M f o d n e k e e W t s a L 43 ) 3 3 . 3 ( ) 3 8 . 3 ( ) 7 7 . 3 ( ) 3 9 . 3 ( ) 6 1 . 9 1 ( ) 2 6 . 4 2 ( ) 5 4 . 2 ( ) 1 7 . 2 ( ∗ ∗ ∗ 6 2 . 1 2 1 ∗ ∗ ∗ 8 3 . 0 3 1 ∗ ∗ ∗ 0 9 . 3 7 ∗ ∗ ∗ 2 1 . 3 8 ∗ ∗ ∗ 9 5 . 5 1 3 ∗ ∗ ∗ 0 5 . 3 7 3 ∗ ∗ ∗ 4 4 . 2 5 ∗ ∗ ∗ 1 8 . 7 5 t n a t s n o C 8 7 8 7 8 7 8 7 8 7 8 7 8 7 8 7 4 5 5 . 0 9 0 2 . 0 4 8 4 . 0 3 5 1 . 0 7 5 6 . 0 0 5 2 . 0 8 2 5 . 0 1 2 2 . 0 s n o i t a v r e s b O 2 R d e t s u j d A y t i c i t s a d e k s o r e t e H ) i i ( , y a d e l g n i s a r o f e m o c t u o t n a v e l e r e h t r o f s e s a c f o r e b m u n l a t o t e h t s n i a t n o c n o i t a v r e s b o h c a E ) i ( : s e t o N , 1 0 . 0 < p ∗ ∗ ∗ , 5 0 . 0 < p ∗ ∗ , 0 1 . 0 < p ∗ s e s e h t n e r a p n i d e t r o p e r e r a ) 2 8 9 1 , 0 8 9 1 , e t i h W ; 7 6 9 1 , r e b u H ( s r o r r e d r a d n a t s t s u b o r e h t n i d e b i r c s e d s i t s e r e t n i f o e m o c t u o e h T ) v i ( 7 1 , t s u g u A d n a 1 , e n u J n e e w t e b , 0 2 0 2 m o r f a t a d s e s u n o i t a m i t s e e h T ) i i i ( . s e m o c t u o r u o f e h t f o h c a e r o f d e t a m i t s e s n o i t a c fi i c e p s o w t h t i w , r e d a e h n m u l o c l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / d o i / / . 1 0 1 1 6 2 / r e s t _ a _ 0 1 2 2 8 2 0 4 1 4 5 8 / r e s t _ a _ 0 1 2 2 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Review of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Figure 3: Event study: Dynamics of crime and unnatural mortality l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / d o i / / . 1 0 1 1 6 2 / r e s t _ a _ 0 1 2 2 8 2 0 4 1 4 5 8 / r e s t _ a _ 0 1 2 2 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 44 -50-40-30-20-1001020304050Change in Daily Homicide Numbers-6-5-4-3-2-1+1+2+3+4+5Weeks From EventPoint Estimate95% Confidence IntervalHomicide-400-300-200-1000100200300400Change in Daily Assault Numbers-6-5-4-3-2-1+1+2+3+4+5Weeks From EventPoint Estimate95% Confidence IntervalAssaults (GBH)-50-40-30-20-1001020304050Change in Daily Reported Rape Cases-6-5-4-3-2-1+1+2+3+4+5Weeks From EventPoint Estimate95% Confidence IntervalReported Rape Cases-50-40-30-20-1001020304050Change in Daily Unnatural Mortality-6-5-4-3-2-1+1+2+3+4+5Weeks From EventPoint Estimate95% Confidence IntervalUnnatural MortalityReview of Economics and Statistics Just Accepted MS. https://doi.org/10.1162/rest_a_01228 © 2022 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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