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    <title>input-output linkages · Julian Hinz</title>
    <link>https://julianhinz.com/topics/input-output-linkages/</link>
    <description>input-output linkages by Julian Hinz.</description>
    <language>en</language>
    <managingEditor>mail@julianhinz.com (Julian Hinz)</managingEditor>
    <lastBuildDate>Mon, 01 Jul 2024 00:00:00 +0000</lastBuildDate>
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      <title>OSSIO</title>
      <link>https://julianhinz.com/data-code/ossio/</link>
      <pubDate>Mon, 01 Jul 2024 00:00:00 +0000</pubDate>
      <guid>https://julianhinz.com/data-code/ossio/</guid>
      <dc:creator>Aaron Lohmann</dc:creator>
      <dc:creator>Gábor Békés</dc:creator>
      <dc:creator>Julian Hinz</dc:creator>
      <dc:creator>Miklós Koren</dc:creator>
      <category>input-output linkages</category>
      <category>open-source software</category>
      <description>&lt;p&gt;The Open Source Software Input Output Tables (OSSIO) provide global input-output tables of open-source software flows between countries. The tables cover five programming languages and 15 countries. They are constructed by mapping the geographic locations of software developers and the dependency linkages between software projects, aggregating these into flows between countries.&lt;/p&gt;
&lt;p&gt;The dataset was created as part of the EU Horizon Europe project &lt;a href=&#34;https://rethink-gsc.eu&#34;&gt;RETHINK-GSC&lt;/a&gt;, which studies knowledge flows in global supply chains.&lt;/p&gt;
&lt;h3 id=&#34;access&#34;&gt;Access&lt;/h3&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Dataset (RADAR):&lt;/strong&gt; &lt;a href=&#34;https://doi.org/10.22000/SaNahyIFpqpJVFbb&#34;&gt;doi.org/10.22000/SaNahyIFpqpJVFbb&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Kiel Institute page:&lt;/strong&gt; &lt;a href=&#34;https://www.kielinstitut.de/publications/dataset-open-source-software-input-output-tables-ossio-18789/&#34;&gt;kielinstitut.de&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Data is released under CC BY 4.0.
&lt;br&gt;&lt;/p&gt;
</description>
    </item>
    <item>
      <title>Trade Sanctions: Evidence From Russia</title>
      <link>https://julianhinz.com/research/trade_sanctions/</link>
      <pubDate>Fri, 23 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://julianhinz.com/research/trade_sanctions/</guid>
      <dc:creator>Julian Hinz</dc:creator>
      <dc:creator>Matthieu Crozet</dc:creator>
      <dc:creator>Patrik Šváb</dc:creator>
      <category>input-output linkages</category>
      <category>sanctions</category>
      <category>sanctions coalitions</category>
      <category>firm-level trade</category>
      <category>Russia</category>
      <description>&lt;figure&gt;&lt;img src=&#34;FigRUSanctionsProdnb.svg&#34;
         alt=&#34;Number of exported products by French firms following the imposition of sanctions against Russia in 2022.&#34;/&gt;&lt;figcaption&gt;
            &lt;p&gt;Number of exported products by French firms following the imposition of sanctions against Russia in 2022.&lt;/p&gt;
        &lt;/figcaption&gt;
&lt;/figure&gt;

&lt;p&gt;We review the economics of trade sanctions and provide new macro and micro evidence with the Russia 2014 and 2022 episodes as central case studies. On the macro side, we calibrate a multi-country, multi-sector general-equilibrium trade model with input–output linkages to quantify welfare effects under alternative coalition and intensity scenarios. Relative to 2014, the 2022 measures imposed substantially larger costs on Russia (about &amp;ndash;2.6% of real income) while the average cost for EU/UK senders remained modest (around &amp;ndash;0.1%), with larger losses in highly exposed small economies. Coalitions amplify pressure on the target at limited additional cost for most senders; hypothetical extensions bound sanctions potential (e.g., global participation or embargoes).
On the micro side, using French customs data (monthly firm–product–destination flows, 2021–2023) and a triple-difference design, we show that exports to Russia fell by roughly three quarters after February 2022. The adjustment is dominated by the extensive margin, with smaller but significant intensive-margin declines among continuing firms. Targeted products contracted far more than non-targeted ones (dual-use goods most severely), and we document diversion consistent with rerouting to nearby non-sanctioning countries.
We further discuss financial channels that propagate losses beyond listed goods, limits of ``smart&#39;&#39; sanctions when governments shield strategic firms, and political responses—including rally-around-the-flag effects.&lt;/p&gt;
</description>
    </item>
    <item>
      <title>Tariffs Hit Differently: The Regional Impact of US Tariffs across Europe and the Role of the Single Market</title>
      <link>https://julianhinz.com/research/tariffs_hit_differently/</link>
      <pubDate>Fri, 12 Dec 2025 00:00:00 +0000</pubDate>
      <guid>https://julianhinz.com/research/tariffs_hit_differently/</guid>
      <dc:creator>Gabriel Felbermayr</dc:creator>
      <dc:creator>Julian Hinz</dc:creator>
      <dc:creator>Sebastian Krantz</dc:creator>
      <dc:creator>Hendrik Mahlkow</dc:creator>
      <dc:creator>Joschka Wanner</dc:creator>
      <category>economic geography</category>
      <category>input-output linkages</category>
      <category>trade policy</category>
      <category>tariffs</category>
      <category>EU single market</category>
      <category>US</category>
      <description>&lt;figure&gt;&lt;img src=&#34;map_labor_location_change.svg&#34;
         alt=&#34;Changes in labor location under U.S. tariff shocks.&#34;/&gt;&lt;figcaption&gt;
            &lt;p&gt;Changes in labor location under U.S. tariff shocks.&lt;/p&gt;
        &lt;/figcaption&gt;
&lt;/figure&gt;

&lt;p&gt;We study how recent U.S. tariffs ripple through European regions and how the EU Single Market cushions these shocks. Using a regionalized inter-country input–output framework with labor mobility, we embed 288 European NUTS2 regions in a global production network and quantify the heterogeneous effects of U.S. import protection.&lt;/p&gt;
&lt;p&gt;Our counterfactuals compare outcomes under an integrated versus a fragmented Europe. Integration allows firms and workers to substitute along intra-EU value chains, which substantially lowers both average income losses and the dispersion of outcomes across regions. The results highlight how deep integration shapes the geography of trade policy exposure.&lt;/p&gt;
</description>
    </item>
    <item>
      <title>Brothers in arms: The value of coalitions in sanctions regimes</title>
      <link>https://julianhinz.com/research/sanctions_coalitions/</link>
      <pubDate>Mon, 15 Apr 2024 00:00:00 +0000</pubDate>
      <guid>https://julianhinz.com/research/sanctions_coalitions/</guid>
      <dc:creator>Sonali Chowdhry</dc:creator>
      <dc:creator>Julian Hinz</dc:creator>
      <dc:creator>Katrin Kamin</dc:creator>
      <dc:creator>Joschka Wanner</dc:creator>
      <category>quantitative trade models</category>
      <category>input-output linkages</category>
      <category>econometrics</category>
      <category>sanctions</category>
      <category>sanctions coalitions</category>
      <category>Russia</category>
      <description>&lt;figure&gt;&lt;img src=&#34;cost_contribution_russia.png&#34;
         alt=&#34;Incurred and imposed welfare costs of the 2014 Russia sanctions.&#34;/&gt;&lt;figcaption&gt;
            &lt;p&gt;Incurred and imposed welfare costs of the 2014 Russia sanctions.&lt;/p&gt;
        &lt;/figcaption&gt;
&lt;/figure&gt;

&lt;p&gt;This paper examines the impact of coalitions on the economic costs of the 2012 Iran and 2014 Russia sanctions. By estimating and simulating a quantitative general equilibrium trade model under different coalition setups, we (i) dissect welfare losses for sanctions senders and target; (ii) compare prospective coalition partners; (iii) investigate “optimal” coalitions that maximise payoff from sanctions; (iv) provide bounds for sanctions potential, i.e. the maximum welfare change attainable when sanctions are scaled vertically up to an embargo, and horizontally up to a global regime. Relative to unilateral action, we find that coalitions magnify welfare losses imposed while their impact on domestic welfare loss incurred depends on the design and sectoral dimension of sanctions. Hypothetical cooperation of large developing economies such as China additionally raises the deterrent force of coalitions. Additionally, we quantify transfers that equalise welfare losses across coalition members to further demonstrate asymmetries in the relative economic burden of sanctions. In all scenarios, we implement a novel Bayesian bootstrap procedure that generates confidence bands for simulation outcomes.&lt;/p&gt;
</description>
    </item>
    <item>
      <title>Bearing the cost of politics: Consumer prices and welfare in Russia</title>
      <link>https://julianhinz.com/research/prices_in_russia/</link>
      <pubDate>Tue, 08 Mar 2022 00:00:00 +0000</pubDate>
      <guid>https://julianhinz.com/research/prices_in_russia/</guid>
      <dc:creator>Julian Hinz</dc:creator>
      <dc:creator>Evgenii Monastyrenko</dc:creator>
      <category>input-output linkages</category>
      <category>sanctions</category>
      <category>prices</category>
      <category>Russia</category>
      <description>&lt;figure&gt;&lt;img src=&#34;treatment_control.svg&#34;
         alt=&#34;Evolution of average prices of embargoed and non-embargoed products.&#34;/&gt;&lt;figcaption&gt;
            &lt;p&gt;Evolution of average prices of embargoed and non-embargoed products.&lt;/p&gt;
        &lt;/figcaption&gt;
&lt;/figure&gt;

&lt;p&gt;In August 2014, the Russian Federation implemented an embargo on select food and agricultural imports from Western countries in response to previously imposed economic sanctions. In this paper we quantify the effect of this embargo on consumer prices and welfare in Russia.
We provide evidence for the direct effect on monthly consumer prices with a difference-in-differences approach. The embargo caused prices of embargoed goods to rise by up to 7.7% &amp;ndash; 14.9% in the short run and by on average 2.6% &amp;ndash; 8.1% until at least 2016. The results further suggest the shock was transmitted to non-embargoed sectors through domestic input-output linkages.
We then construct a general equilibrium Ricardian model of trade with input-output linkages and goods that are tradable, non-tradable or &lt;em&gt;embargoed&lt;/em&gt;. The model-based counterfactual analysis predicts the overall price index in Russia to have increased by 0.33% and welfare to have declined by 1.84% due to the embargo.&lt;/p&gt;
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