{"id":3666,"date":"2026-10-14T07:30:11","date_gmt":"2026-10-14T07:30:11","guid":{"rendered":"https:\/\/www.ub.edu\/beat\/?p=3666"},"modified":"2026-10-15T06:58:07","modified_gmt":"2026-10-15T06:58:07","slug":"optimalagebasedpolicies","status":"publish","type":"post","link":"https:\/\/www.ub.edu\/beat\/optimalagebasedpolicies\/","title":{"rendered":"Optimal Age-Based Policies for Pandemics – Luiz Brotherhood"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” width=”1\/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]Luiz Brotherhood <\/a>(with Philipp Kircher, Cezar Santos, and Miche\u0300le Tertilt)\u00a0<\/span><\/p>\n

During the Covid-19 pandemic, governments worldwide deployed broad lockdowns that saved lives but imposed immense economic and social costs. In our article, Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond<\/i>, recently published in The Review of Economic Studies<\/i>, we integrate economic behavior into epidemiological models to examine how age-targeted policies should be designed, establishing general principles for managing future epidemics.\u00a0<\/span><\/p>\n

A Tale of Two Generations: Locking Down the Young to Liberate the Old<\/b>\u00a0<\/span><\/p>\n

A defining feature of Covid-19 was its steep age gradient: the infection fatality rate for older adults was roughly 39 times higher than for younger individuals. In our model, we divide the population into young agents (working-age adults aged 20\u201364) and old agents (retirees aged 65+). In normal times, young adults spend considerable time outside working and socializing, whereas retirees spend more hours at home but deeply value outside activities, suffering a substantial welfare loss when confined indoors.\u00a0<\/span><\/p>\n

When a pandemic breaks out without government intervention (laissez-faire), this biological difference creates a sharp behavioral divide. Facing minimal mortality risk, young people have little private incentive to stay home, continuing their routines and spreading the virus. This creates a tragic dilemma for older adults: to survive in an environment saturated with infection, retirees are forced into severe, defensive self-isolation.\u00a0<\/span><\/p>\n

Figure 1 (below): Time at home: normal times and Covid scenarios. <\/b>Weekly awake home hours for young (20\u201364) and older (65+) individuals across normal times, laissez-faire Covid, the benchmark policy, and the optimal lockdown.<\/span><\/p>\n[\/vc_column_text]

\"\"<\/a><\/div><\/div>[\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” width=”1\/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”]
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Here lies our first key result: a social planner seeking to maximize overall social welfare would design a lockdown that targets the young far more aggressively than uniform policies did, while granting greater freedom to the elderly.\u00a0<\/span><\/p>\n

As illustrated in Figure 1, in normal times young individuals spend about 58 hours per week awake at home, while older individuals spend 89 hours. Under laissez-faire Covid, young agents increase their weekly home hours only modestly (to 64.8 hours), whereas older agents increase their time indoors to 103.5 hours.\u00a0<\/span><\/p>\n

Under the benchmark policy implemented during the pandemic, young agents were confined to 75.0 hours at home per week, while older individuals spent 100.6 hours indoors. Under the optimal age-targeted lockdown, the planner shifts the burden more decisively onto the young: young individuals are mandated to spend 80.6 hours at home per week. By curbing transmission at its main demographic source, community viral prevalence falls rapidly. The resulting safer environment leads to the result that older individuals are able to spend <\/b>more<\/i><\/b> time outside their homes under the optimal lockdown (spending only 94.3 hours at home) than they did under the benchmark policy (100.6 hours)<\/b>.\u00a0<\/span><\/p>\n

Rather than confining everyone equally or shielding the vulnerable by forcing them into deeper seclusion, optimal policy curbs the behavior of those who generate the contagion externality, thereby liberating the vulnerable to safely enjoy life outside.\u00a0<\/span><\/p>\n

Infectiousness versus Lethality: A General Rule for Pandemic Policy<\/b>\u00a0<\/span><\/p>\n

A widespread intuition is that a virus\u2019s deadliness should dictate the severity of lockdowns: if a pathogen is deadly, governments should lock down hard; if it is less lethal, restrictions can be relaxed. Our framework reveals that this intuition does not hold in general: optimal policy calls for stricter lockdowns when a virus is highly infectious, but <\/b>less<\/i><\/b> strict lockdowns when a virus is highly lethal.<\/b>\u00a0<\/span><\/p>\n

Table 1: Increase in home hours, first year, young agents<\/b>\u00a0<\/span><\/p>\n\n\n\n\n\n\n\n\n
\u00a0<\/span><\/td>\nPanel A.<\/b><\/span>
\nNo lockdown<\/b>\u00a0<\/span><\/td>\n		Panel B.<\/b><\/span>
\nOptimal lockdown<\/b>\u00a0<\/span><\/td>\n<\/tr>\n
\u00a0<\/span><\/td>\nChange in home hours rel. to no-disease<\/i>\u00a0<\/span><\/td>\nChange in home hours rel. to no-lockdown<\/i>\u00a0<\/span><\/td>\n<\/tr>\n
\u00a0<\/span><\/td>\nLess lethal<\/b>\u00a0<\/span><\/td>\nCovid<\/b>\u00a0<\/span><\/td>\nMore lethal<\/b>\u00a0<\/span><\/td>\nLess lethal<\/b>\u00a0<\/span><\/td>\nCovid<\/b>\u00a0<\/span><\/td>\nMore lethal<\/b>\u00a0<\/span><\/td>\n<\/tr>\n
Less infectious\u00a0<\/span><\/td>\n0.19\u00a0<\/span><\/td>\n0.32\u00a0<\/span><\/td>\n0.41\u00a0<\/span><\/td>\n1.89\u00a0<\/span><\/td>\n2.17\u00a0<\/span><\/td>\n2.46\u00a0<\/span><\/td>\n<\/tr>\n
Covid\u00a0<\/span><\/td>\n3.67\u00a0<\/span><\/td>\n6.87\u00a0<\/span><\/td>\n9.20\u00a0<\/span><\/td>\n18.36\u00a0<\/span><\/td>\n15.76\u00a0<\/span><\/td>\n13.88\u00a0<\/span><\/td>\n<\/tr>\n
More infectious\u00a0<\/span><\/td>\n4.11\u00a0<\/span><\/td>\n8.50\u00a0<\/span><\/td>\n12.23\u00a0<\/span><\/td>\n27.73\u00a0<\/span><\/td>\n23.50\u00a0<\/span><\/td>\n20.54\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Note: <\/i>Weekly increase in awake home hours for young agents (20\u201364) in the first year. The left panel shows voluntary hours relative to no-disease; the right panel shows additional hours imposed by optimal lockdown relative to no-lockdown. R<\/i>0 and CFR are scaled relative to Covid values (0.5\u00d7, 1.0\u00d7, 1.5\u00d7).\u00a0<\/span><\/p>\n

Each cell in Table 1 represents a distinct virus scenario, defined by combining one level of infectiousness (a row) with one level of lethality (a column): the virus may be less infectious, as infectious as Covid, or more infectious, and independently less lethal, as lethal as Covid, or more lethal. The Covid benchmark is the central cell in each panel: find the middle row, labeled \u201cCovid\u201d (Covid-level infectiousness), and the middle column, also labeled \u201cCovid\u201d (Covid-level lethality). Their intersection reports the results for a virus with both characteristics at the Covid benchmark. Panel A reports the change in young agents\u2019 weekly awake home hours in the pandemic scenario without lockdown, relative to the no-pandemic baseline. In the first year of the Covid pandemic, young agents voluntarily spend, on average, 6.87 more hours per week awake at home than they did before the pandemic. Panel B reports the optimal-lockdown scenario and shows the additional change relative to the pandemic scenario without lockdown: young agents spend 15.76 more hours per week awake at home.\u00a0<\/span><\/p>\n

If we increase only infectiousness<\/b> (moving down to the \u201cMore infectious\u201d row), individual outings generate exponential contagion externalities that people do not internalize; voluntary hours rise to 8.50, but optimal policy escalates to 23.50 additional hours to contain community spread. In contrast, if we increase only lethality<\/b> (moving right to the \u201cMore lethal\u201d column), self-preservation takes over: fear drives voluntary hours up to 9.20, allowing the planner to impose fewer<\/i> additional hours (13.88 instead of 15.76).\u00a0<\/span><\/p>\n

Looking across the entire table, when infectiousness is low (top row), higher lethality still leads to slightly more restrictions (1.89 to 2.46 hours) because initial voluntary caution is too small to curb spread. However, for Covid-level infectiousness and above, a pattern emerges that may seem counterintuitive at first glance: higher lethality consistently generates more voluntary self-protection, enabling governments to implement milder lockdowns. Policymakers can rely on private vigilance against deadly diseases, but must intervene aggressively against highly contagious ones.\u00a0<\/span><\/p>\n

Policy Implications and Takeaways<\/b>\u00a0<\/span><\/p>\n

Our findings highlight three key lessons for future epidemics:<\/span><\/p>\n

1)Age-targeted policies avoid needless economic costs. <\/b>Uniform lockdowns ignore demographic differences. Restricting the young (who drive transmission) protects the vulnerable while allowing the elderly to enjoy more outside life than under unregulated contagion.<\/span><\/p>\n

2)Optimal restrictions scale with contagion, not lethality. <\/b>Given that individuals already curb their outings when faced with fatal risks, the social planner\u2019s role is to address the transmission externality. Consequently, the optimal policy mandates stricter lockdowns for highly infectious pathogens than for highly lethal ones.<\/span><\/p>\n

3)Behavioral responses are central to epidemic policy. <\/b>Models that assume fixed contact rates misjudge the need for intervention. Incorporating voluntary behavior ensures that public health measures are neither overly punitive nor dangerously lax.<\/span><\/p>\n <\/div>[\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” width=”1\/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” width=”1\/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]Luiz Brotherhood (with Philipp Kircher, Cezar Santos, and Miche\u0300le Tertilt)\u00a0 During the Covid-19 pandemic,…<\/p>\n","protected":false},"author":2,"featured_media":3671,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[72],"tags":[],"class_list":["post-3666","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/posts\/3666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/comments?post=3666"}],"version-history":[{"count":14,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/posts\/3666\/revisions"}],"predecessor-version":[{"id":3698,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/posts\/3666\/revisions\/3698"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/media\/3671"}],"wp:attachment":[{"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/media?parent=3666"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/categories?post=3666"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ub.edu\/beat\/wp-json\/wp\/v2\/tags?post=3666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}