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Two-Sided Market Power in Firm-to-Firm Trade

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Monica Morlacco develop a quantitative theory of prices in firm-to-firm trade with bilateral negotiations and two-sided market power.

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In 2018, the United States introduced tariffs of 10-50% on various imports, igniting a trade war with key trading partners, which responded with retaliatory tariffs. This video video event highlighted a shift towards protectionist trade policies and raised concerns about their impact on consumer prices and welfare. Notably, tariffs imposed during the Trump administration resulted in price increases equating to the rate of the tariffs, meaning consumers suffered the full burden of these costs, contradicting traditional trade theories that suggested incomplete price pass-through.

Research aims to explore why the pass-through from these tariffs was so complete and why conventional theories failed to predict this outcome. The study emphasizes two-sided heterogeneity and market power in global production networks, noting that modern trade primarily involves firm-to-firm transactions rather than direct sales to consumers.

A novel theoretical framework is presented, focusing on price-setting dynamics in firm-to-firm relationships, where firms have substantial market power on both sides of the transaction. This video video approach leads to two main predictions: (1) bilateral markups vary significantly across relationships, and (2) the pass-through of tariffs may be complete for smaller importers while being incomplete for larger ones due to the dynamics of demand and marginal costs.

The research utilizes detailed data on firm-to-firm transactions, particularly in the context of Trump tariffs, to empirically test the model’s predictions. Results indicate that US importers generally exhibit greater bargaining power compared to foreign exporters, a departure from conventional assumptions, and that foreign exporters' marginal costs are responsive to output changes.

Lastly, the study concludes that the overall pass-through from Trump tariffs was around 40%, attributed mainly to adjustments in foreign exporters' marginal costs. This video video finding questions previous work suggesting higher pass-through rates and highlights the significance of analyzing price dynamics at the firm level to understand broader economic implications. The research contributes to the literature on trade policy and pricing, emphasizing the relevance of global value chains in current international trade scenarios.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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In 2018, the US implemented a series

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of import tires ranging from 10 to 50% on a series

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of products and countries.

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This move, initiated what's been known famous, as,

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the trade war between the US

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and major trade partners who responded

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by imposing retaliatory tires on US exports.

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Now, this historic event, while somewhat unexpected,

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reflected the shift in the global landscape

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where protection in trade policies are now gaining traction

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and may potentially, change the nature of trade

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as we've known them for decades.

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So it's now very important to understand

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and reevaluate perhaps, the effect of trade policies on,

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prices and consumer welfare.

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Now, a striking finding emerged from studies of, the,

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the trade war that is,

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that the Trump tariff had a complete pass through on prices.

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Put it simply, if the US imposed a tariff by say 10%,

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then the US import price as well as the consumer prices

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of the same product went up by 10%.

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This also meant that the US consumers board the entire cost

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of tariffs imposed during the Trump administration.

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Now, this is surprising from the point of view

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of conventional trade theories,

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which would've predicted the pass to be incomplete instead.

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So against this backdrop, our research seeks

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to answer two questions.

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The first is, why was the pass through

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of Trump party so high?

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If we can, explain why.

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And secondly, why did economists getting wrong?

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So, are conventional trade theories accurate

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or, able to predict the dynamics

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of prices following shocks or such as static shocks?

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Or is there need for reassessment prior literature

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as extensively dealt with?

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Um, trying to understand the pastoral shocks on prices,

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especially in the context of, in the, in the context

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of exchange rate shocks, we contribute to a smaller sort

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of subset of this literature that tries

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to understand the behavior of aggregate prices

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by looking at firm behavior.

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So what firms does at the much more granular level,

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and specifically our contribution

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to this literature is emphasizing the importance

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of two-sided heterogeneity

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and market power in the context

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of global production networks, which is something

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that prior literature has typically overlooked,

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but we deal them being important for understanding prices.

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Now, traditional trade theories,

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operate under the assumptions

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that exporters from one country sell directly

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to end consumers to in a destination country,

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and they sell final goods to final consumers.

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However, modern trade involves predominantly,

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a global value chains

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and the exchange of intermediate goods within firm

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to firm relationships rather than firm, firm

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to final consumers.

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And these fundamental shifts in the nature

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of international trade, which has to do

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with the secular rise of global production networks

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and the secular fragmentation

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of production carries very important implication for our,

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our, our understanding of prices and price changes.

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Now, in, in, in conventional trade theories,

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when exporter export sell

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to price staking consumers in the destination countries

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and to many consumers in the destination countries,

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conversely, in firm

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to firm trade exporters from one countries only sell

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to a limited number

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of importers in the destination countries.

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And this has to do with the fact

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that global value chains involve high fixed costs

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and, search costs.

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And so these relationships tend to be, the network

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of relationships tend to be sparse,

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and relationships tend to be repeated.

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Uh, so the firms tend to operate on, tend

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to transact on a repeated basis for prices.

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This has important implications

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because the prices within firm to firm relationships tend

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to be negotiated on a bilateral basis,

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challenging the conventional market clearing condition on,

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on traditional price models.

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Secondly, a second important feature

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of global value chains is that, firms on both sides

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of the transaction are dominant players.

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They're very large, and both sides

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of the transaction have market power over prices.

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And so there's two sided market power

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and not just on on white side of the market as,

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as it conventionally believed.

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Now, we, the, the, the same features also

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affect the way we understand pass through.

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In conventional models, exporter sets prices

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and they set the markup over the marginal cost.

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When a tariff hits the, the price, the marginal cost

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of the exporter to shipping the goods to the destination,

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country increases.

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And the exporter has the power to adjust the markup downward

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to avoid large losses in demand in the destination country.

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And that's what leads to incomplete posturing,

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conventional trade theories.

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However, in a scenario

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where there's bilateral bargaining power

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and take the extreme case where the importer set prices,

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for simplicity, then the import,

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the importer would set the markup as low

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as possible, say a markup to zero.

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And then so prices equal marginal cost.

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So when the, the tariff hits the marginal cost

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of delivering the good increases, the price has

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to increase one-to-one,

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which can explain like why the pasture can be complete in a

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scenario where there's two sided

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or, you know, two sided bargaining power.

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Now our research sort of builds on this simple intuition

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and delves into the nuances of, of, of these dynamics.

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Uh, in order to explain, the dynamics

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of pasture within global value chains, the main contribution

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of this paper is to develop some novel theory

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of prices in firm to FM relationships.

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So we take the network of firm tofe relationship

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as given this is a primitive of the model.

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It's something that we can observe in the data.

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So we just take this snapshot of firm to FM relationship

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as given and focus instead on the price decision problem

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between a foreign exporter and a US importer.

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Now, US importers buy inputs from all the

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exporters in their network

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Network. They combine,

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combine these inputs in production,

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they produce a product, then they then sell downstream.

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On the exporter side,

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the exporters produce their differentiated variety

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of the foreign input.

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They sell this product

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to all their importers in their network.

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And another assumption that we make,

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which is actually generalizing what's been done

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traditionally in the literature, is

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to allow the marginal cost of the exporter

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to increasing total output, which is something

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that makes sense in the context of global value chains,

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where there's a lot of relationship, specific investment,

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meaning that a fraction

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of inputs in the short run is fixed due to these investments

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that take a long time to, to materialize.

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And so leading to marginal cost

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to be increasing in output in the short term,

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this is gonna be important later on

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to understand the results.

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We then take the, the problem of of price setting decision

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between the quarter

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and exporter, the price solve the, the Nash product of the,

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of despair, meaning they saw the, the,

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this Nash product is the product of the gains from,

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from trade for the importers

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and exporter as weighted

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by their bargaining power in the relationship

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as in a standard, nash, problem.

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And what, and the, the gains from trade is the extra profit

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that the importer and exporter make

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by participating in the relationship versus not

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participating in that relationship,

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but carrying transaction

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with all their existing counterparts.

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And, and another thing that is important to mention is

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that what makes the, an assumption

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that makes this problem tractable is to assume

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that the price is the n in Nash

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bargaining solution for despair.

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Meaning it's a NH solution for the bilateral pair, assuming

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that the spare is in a n equilibrium,

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meaning taking every other negotiated outcome

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elsewhere in the network.

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As, as exos as given,

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the model delivers two sets of predictions.

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The first set is on the behavior of price levels

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and in particular the markups.

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And the second set of prediction is on the behavior

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of price changes following a shop that is a past elasticity.

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In terms of markups, we find that the markup, first

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of all, the, the each exporter set a markup

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to individual importers.

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So the price, the markup is bilateral.

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It's not an individual markup

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to all the importers in the exporters network,

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but the price, the markup varies, across firm

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to firm pairs, which is something

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that we do observe in the data.

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So a prediction that is born by the data

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and that traditional model wouldn't,

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be able to rationalize.

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Um, we then, we find

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that this bilateral markup can be understood

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as a weighted average between

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what we call an oligopoly markup,

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which reflect exporting pricing behavior like unilateral

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pricing behavior, and an oligo markdown which reflect

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importers unilateral price setting behavior.

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And the weight attached

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to the oligopoly oligo forces is proportional

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to the bargaining power of the exporter

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and importer respectively.

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A similar related prediction is that the markup

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or price should increase in the exporter's market share

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as long as the exporter has positive bargaining power is in

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the negotiations and should decrease

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in the importer's market share.

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So the market should be lower for large importers as long

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as importers have some bargaining power in the negotiation,

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which is a prediction where we can directly

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test in the data.

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Now the same, the behavior of market levels

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and the price levels explain, sort

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of help us understanding what's going on when we

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think about price changes.

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Um, again, in order to understand pass through,

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let's start from the conventional case

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of exporter setting prices.

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When the exporter set prices,

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the conventional wi wisdom applies that there is a tar,

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increases the marginal cost.

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There is downward markup adjustment incomplete pass through,

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just like in a simple example.

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So, the we, we learn two sets of, findings.

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So, so we learn two new things from our theory.

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The first thing is that other things equal the pass

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through increases in the bargaining power of the importers.

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And here the intuition is just like the simple example

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mentioned earlier, the as importers at prices,

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the markup goes down there is lower than one

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markup adjustment possible.

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And so the pass through tend to be more incomplete.

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Other things equal second.

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And this is,

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where the model gets a little bit more complicated.

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We do find that the past through,

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even when importer importers set prices can be both

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complete and incomplete.

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It is complete for small importers, importers that account

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for a tiny share of the exporters output

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for these importers, marginal costs is somewhat constant,

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and so the standard intuition of marginal cost pricing

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and full pass through apply for them.

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However, when importers are large, they account

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for a large share of the exporter output.

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And so, if the, if the tariff increases the price,

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the importers demand goes down,

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and as the importer demand goes down, total output produced

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by the exporter also goes down

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and so does the marginal cost.

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And this pushes the pass through to be incomplete.

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So overall, in order to understand pass through,

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just like markups, there's a lot

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of heterogeneity across firm to firm pairs in

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what the pass through could be.

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And, and so the model say that in order

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to understand aggregate password, one needs

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to understand what's going on at the very granular level

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and take a bottom up approach where, you know, in order

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to under understanding the aggregate means understanding

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how we aggregate from firm level

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behavior to aggregate behavior.

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We just, sorry, on the data.

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Uh, the second contribution of this paper is

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that we can test this model prediction on,

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on the data directly.

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And to do so, we construct a novel dataset,

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that merges reach information on firm

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to firm freight transaction for US importers

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and foreign exporter.

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This comes from the lifted dataset of the US census,

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which has information on price

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and quantities for firm to firm transaction dating back

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to 92, to 2019 or 2020.

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We merged this information with,

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information on firm level characteristics, both

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for importers and US importers,

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which come from the US census, of manufacturers

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or the longitudinal business dynamics datasets with,

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and also information on foreign exporters,

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which we obtained from the orbit dataset.

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Uh, finally, we also integrate information on trade policy.

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In particular, we focus on Trump tar as our case study.

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Uh, we use tariff, data, from, PA Obama quarters

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that, that work on returns to protectionism.

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Our empirical analysis, does several things.

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First of all, we confirm, we try, try

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to validate the model's prediction in what's called a

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reduced form approach, meaning simple regressions

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of prices and market shares.

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We do see that bilateral prices in particular bilateral

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markups tend

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to be higher in relationships involving exporters

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with higher market share

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and tend to be lower in relationship is involving importers

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with a larger buyer share, as predicted by a model

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where there's both bilateral bargaining.

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So both sides of the market has some bargaining power in

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negotiations as well as a model where the, on average,

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the marginal cost of the exporters tend

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to increase in total output.

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We then validate this prediction structurally.

13:12.905 --> 13:15.405
So we structurally estimate the model in the model scale

13:15.405 --> 13:18.085
elasticities and, and,

13:18.265 --> 13:20.605
and we find something which is kind of surprising,

13:20.605 --> 13:21.685
well, not surprising, but novel.

13:22.025 --> 13:23.205
The first thing we find is

13:23.205 --> 13:26.685
that on average in US importers ha the,

13:26.685 --> 13:29.125
the US importers bargaining power is about four times

13:29.225 --> 13:32.285
as large as the bargaining power, of foreign exporters.

13:32.705 --> 13:35.565
And again, remember that in, in conventional trade theories,

13:35.565 --> 13:38.285
the assumption is that foreign exporters have full

13:38.285 --> 13:41.165
bargaining power and importers have zero bargaining power.

13:41.225 --> 13:44.965
So what we estimate in our model is very different than

13:45.115 --> 13:47.485
what traditional theories, assume.

13:48.065 --> 13:49.725
And secondly, we also find

13:49.725 --> 13:52.965
that on average the exporter's marginal costs are very

13:52.965 --> 13:54.605
elastic to total output.

13:55.025 --> 13:57.085
Um, and, which implies

13:57.085 --> 13:59.525
that the exporter supply elasticity is,

13:59.665 --> 14:00.885
is quite, is is large.

14:01.145 --> 14:04.245
And, again, which is something that conventional models,

14:04.705 --> 14:07.485
have typically overlooked with without.

14:07.515 --> 14:11.485
With these estimates, we then use our model to try speak to,

14:11.705 --> 14:13.765
the, the consequences,

14:13.945 --> 14:16.325
of t both at the bilateral pair level

14:16.345 --> 14:17.365
and at the aggregate level.

14:17.785 --> 14:20.965
The first exercise that we do is a sort of a goodness

14:20.965 --> 14:22.765
of fit test of the model.

14:23.465 --> 14:27.925
Um, so we take, we start in 2017, we observe the network

14:27.985 --> 14:29.285
of firm to firm relationships,

14:29.585 --> 14:32.205
and then we hit the economy with the Trump tariffs.

14:32.465 --> 14:35.165
And we use the model to predict what's the effect

14:35.185 --> 14:39.045
of the Trump tariffs on the, on the prices at, at the firm

14:39.065 --> 14:40.445
to firm level.

14:41.025 --> 14:43.885
And so what the model predicts should be the change in price

14:44.025 --> 14:45.325
in, in its relationship

14:45.625 --> 14:47.725
and compare the model predicted price

14:47.755 --> 14:48.765
with the observed price.

14:48.785 --> 14:50.885
So press price change between 2017

14:50.945 --> 14:53.885
And 2018, and we cannot reject the null

14:53.995 --> 14:55.845
that the model predicted price change

14:55.905 --> 14:58.685
and the observed price change are equal to each other,

14:58.935 --> 15:02.645
which lends credibility to the model forecasting ability.

15:03.425 --> 15:05.605
So then the last thing we do is to say, okay,

15:05.625 --> 15:08.205
we take the model, what's the aggregate pass through

15:08.265 --> 15:10.085
of Trump tires bottom up.

15:10.585 --> 15:12.485
And the model says, well, the aggregate pass

15:12.485 --> 15:13.845
through is about 40%,

15:13.895 --> 15:15.805
which is much lower than a hundred percent.

15:15.875 --> 15:16.885
It's very incomplete.

15:17.145 --> 15:19.645
And the model attributes about a hundred percent

15:19.665 --> 15:23.125
of this incompleteness to downward cost adjustment

15:23.705 --> 15:26.005
of foreign, of foreign exporting, meaning

15:26.585 --> 15:28.365
the US importers on the one hand,

15:28.485 --> 15:30.125
a very large bargaining power.

15:30.225 --> 15:33.565
So they set prices, which other things equal push pass

15:33.565 --> 15:35.445
through to be larger than expected.

15:35.865 --> 15:39.165
But on the other hand, they also have a lot of oligo power,

15:39.235 --> 15:42.405
meaning they face a very elastic marginal cost schedule.

15:42.705 --> 15:45.005
And has the demand of US importers

15:45.605 --> 15:46.765
decrease due to the tariff?

15:46.765 --> 15:50.125
The marginal cost should also adjust downward, which is

15:50.125 --> 15:53.085
what explain the very incomplete passthrough.

15:53.175 --> 15:55.205
Where do we stand? Uh, what are we learn

15:55.425 --> 15:57.565
and what does our model have have

15:57.565 --> 15:59.885
to say about the incidents of Trump tariff?

16:00.185 --> 16:03.485
So on the one hand, our research proposes a new pricing

16:03.585 --> 16:05.725
theory of global value chains,

16:06.065 --> 16:10.445
and we show that the model is able to forecast the, the,

16:10.445 --> 16:11.725
the show the effect

16:11.725 --> 16:14.925
of trade policy on prices at the firm to firm level.

16:15.385 --> 16:18.125
So this is a, a price theory of global value chains,

16:18.125 --> 16:20.525
which is important, and as global value chains are gaining

16:20.765 --> 16:21.965
traction is, and they account

16:21.985 --> 16:24.085
for a large share of international trade.

16:24.275 --> 16:27.365
However, we do acknowledge that there is a discrepancy,

16:27.365 --> 16:29.125
which we, we between what the,

16:29.465 --> 16:31.285
our model say the aggregate incidents

16:31.285 --> 16:32.885
of Trump tariff should be,

16:32.985 --> 16:36.445
and what other studies of aggregate price changes have,

16:36.445 --> 16:38.965
have, have shown the about pass through.

16:38.965 --> 16:41.525
So we, we predict that the pass is very incomplete.

16:41.815 --> 16:43.765
These existing, studies show

16:43.765 --> 16:45.405
that the password was actually very complete.

16:45.945 --> 16:48.085
We do attribute this discrepancy

16:48.185 --> 16:50.605
to different sample selection in our studies

16:50.825 --> 16:52.205
for estimation purposes.

16:52.425 --> 16:55.005
Uh, we need to focus on a restricted sample

16:55.105 --> 16:57.285
of importers on a restricted sample of

16:57.845 --> 16:59.925
relationships which are repeated.

17:00.265 --> 17:04.525
And also and importantly, we do have to drop relationship

17:04.525 --> 17:08.645
between related party transaction means involving,

17:08.685 --> 17:10.845
relationship between firms that are related

17:10.945 --> 17:13.165
by a business relationship, which account

17:13.165 --> 17:14.925
for about 50% of total import.

17:14.925 --> 17:17.005
So we are dropping a, a big chunk

17:17.065 --> 17:18.965
of total imports in the United States.

17:19.265 --> 17:21.125
And the reason why we dropped this is be

17:21.125 --> 17:24.285
because we do believe that our theory applies for arms,

17:24.355 --> 17:26.885
arms length relationship rather than related party trade,

17:26.935 --> 17:30.125
where prices are not set by these sort of dynamics,

17:30.125 --> 17:31.765
but may reflect transfer pricing

17:31.905 --> 17:34.405
and other forces that we, that our model silent about.

17:34.985 --> 17:37.925
We are currently working on trying to pin down, which

17:37.965 --> 17:39.845
of this feature is most important

17:40.185 --> 17:41.965
for explaining the discrepancy.

17:42.465 --> 17:45.445
Uh, but we do believe it has to do with sample selection.

17:45.665 --> 17:48.685
But overall, our study contributes to the literature of pass

17:48.685 --> 17:50.805
through, providing a comprehensive understanding

17:50.805 --> 17:52.565
of prices in firm to term relationship.

17:52.565 --> 17:53.885
And we believe it,

17:54.025 --> 17:57.685
it is an important study helping policy makers understand

17:57.685 --> 18:00.485
the consequences of trade policy in the context of,

18:00.485 --> 18:01.565
of global value chains.

Understanding the Impact of Trump's Tariffs

00:00:00 - In 2018, the US implemented a series of import tires ranging from 10 to 50% on a series of products and countries. This move, initiated what's been known famous, as, the trade war between the US and major trade partners who responded by imposing retaliatory tires on US exports. Now, this historic event, while somewhat unexpected, reflected the shift in the global landscape where protection in trade policies are now gaining traction
00:00:35 - as we've known them for decades. So it's now very important to understand and reevaluate perhaps, the effect of trade policies on, prices and consumer welfare. Now, a striking finding emerged from studies of, the, the trade war that is, that the Trump tariff had a complete pass through on prices. Put it simply, if the US imposed a tariff by say 10%, then the US import price as well as the consumer prices
00:01:04 - This also meant that the US consumers board the entire cost of tariffs imposed during the Trump administration. Now, this is surprising from the point of view of conventional trade theories, which would've predicted the pass to be incomplete instead. So against this backdrop, our research seeks to answer two questions. The first is, why was the pass through of Trump party so high? If we can, explain why. And secondly, why did economists getting wrong?
00:01:37 - or, able to predict the dynamics of prices following shocks or such as static shocks? Or is there need for reassessment prior literature as extensively dealt with? trying to understand the pastoral shocks on prices, especially in the context of, in the, in the context of exchange rate shocks, we contribute to a smaller sort of subset of this literature that tries to understand the behavior of aggregate prices by looking at firm behavior.
00:02:08 - and specifically our contribution to this literature is emphasizing the importance of two-sided heterogeneity and market power in the context of global production networks, which is something that prior literature has typically overlooked, but we deal them being important for understanding prices. Now, traditional trade theories, operate under the assumptions that exporters from one country sell directly to end consumers to in a destination country,
00:02:39 - However, modern trade involves predominantly, a global value chains and the exchange of intermediate goods within firm to firm relationships rather than firm, firm to final consumers. And these fundamental shifts in the nature of international trade, which has to do with the secular rise of global production networks and the secular fragmentation of production carries very important implication for our, our, our understanding of prices and price changes.

Dynamics of Firm-to-Firm Trade Relationships

00:03:11 - when exporter export sell to price staking consumers in the destination countries and to many consumers in the destination countries, conversely, in firm to firm trade exporters from one countries only sell to a limited number of importers in the destination countries. And this has to do with the fact that global value chains involve high fixed costs and, search costs. And so these relationships tend to be, the network of relationships tend to be sparse, and relationships tend to be repeated.
00:03:42 - to transact on a repeated basis for prices. This has important implications because the prices within firm to firm relationships tend to be negotiated on a bilateral basis, challenging the conventional market clearing condition on, on traditional price models. Secondly, a second important feature of global value chains is that, firms on both sides of the transaction are dominant players. They're very large, and both sides of the transaction have market power over prices.
00:04:13 - and not just on on white side of the market as, as it conventionally believed. Now, we, the, the, the same features also affect the way we understand pass through. In conventional models, exporter sets prices and they set the markup over the marginal cost. When a tariff hits the, the price, the marginal cost of the exporter to shipping the goods to the destination, country increases. And the exporter has the power to adjust the markup downward to avoid large losses in demand in the destination country.
00:04:45 - conventional trade theories. However, in a scenario where there's bilateral bargaining power and take the extreme case where the importer set prices, for simplicity, then the import, the importer would set the markup as low as possible, say a markup to zero. And then so prices equal marginal cost. So when the, the tariff hits the marginal cost of delivering the good increases, the price has to increase one-to-one, which can explain like why the pasture can be complete in a scenario where there's two sided
00:05:17 - Now our research sort of builds on this simple intuition and delves into the nuances of, of, of these dynamics. in order to explain, the dynamics of pasture within global value chains, the main contribution of this paper is to develop some novel theory of prices in firm to FM relationships. So we take the network of firm tofe relationship as given this is a primitive of the model. It's something that we can observe in the data. So we just take this snapshot of firm to FM relationship
00:05:48 - between a foreign exporter and a US importer. Now, US importers buy inputs from all the exporters in their network Network. They combine, combine these inputs in production, they produce a product, then they then sell downstream. On the exporter side, the exporters produce their differentiated variety of the foreign input. They sell this product to all their importers in their network. And another assumption that we make, which is actually generalizing what's been done

Understanding Price Dynamics in Global Trade

00:06:18 - to allow the marginal cost of the exporter to increasing total output, which is something that makes sense in the context of global value chains, where there's a lot of relationship, specific investment, meaning that a fraction of inputs in the short run is fixed due to these investments that take a long time to, to materialize. And so leading to marginal cost to be increasing in output in the short term, this is gonna be important later on to understand the results. We then take the, the problem of of price setting decision
00:06:49 - and exporter, the price solve the, the Nash product of the, of despair, meaning they saw the, the, this Nash product is the product of the gains from, from trade for the importers and exporter as weighted by their bargaining power in the relationship as in a standard, nash, problem. And what, and the, the gains from trade is the extra profit that the importer and exporter make by participating in the relationship versus not participating in that relationship,
00:07:20 - with all their existing counterparts. And, and another thing that is important to mention is that what makes the, an assumption that makes this problem tractable is to assume that the price is the n in Nash bargaining solution for despair. Meaning it's a NH solution for the bilateral pair, assuming that the spare is in a n equilibrium, meaning taking every other negotiated outcome elsewhere in the network. As, as exos as given, the model delivers two sets of predictions.
00:07:52 - and in particular the markups. And the second set of prediction is on the behavior of price changes following a shop that is a past elasticity. In terms of markups, we find that the markup, first of all, the, the each exporter set a markup to individual importers. So the price, the markup is bilateral. It's not an individual markup to all the importers in the exporters network, but the price, the markup varies, across firm to firm pairs, which is something that we do observe in the data.
00:08:22 - and that traditional model wouldn't, be able to rationalize. we then, we find that this bilateral markup can be understood as a weighted average between what we call an oligopoly markup, which reflect exporting pricing behavior like unilateral pricing behavior, and an oligo markdown which reflect importers unilateral price setting behavior. And the weight attached to the oligopoly oligo forces is proportional to the bargaining power of the exporter and importer respectively.
00:08:55 - or price should increase in the exporter's market share as long as the exporter has positive bargaining power is in the negotiations and should decrease in the importer's market share. So the market should be lower for large importers as long as importers have some bargaining power in the negotiation, which is a prediction where we can directly test in the data. Now the same, the behavior of market levels and the price levels explain, sort of help us understanding what's going on when we think about price changes.

Understanding Price Pass-Through Dynamics

00:09:26 - let's start from the conventional case of exporter setting prices. When the exporter set prices, the conventional wi wisdom applies that there is a tar, increases the marginal cost. There is downward markup adjustment incomplete pass through, just like in a simple example. So, the we, we learn two sets of, findings. So, so we learn two new things from our theory. The first thing is that other things equal the pass through increases in the bargaining power of the importers.
00:09:57 - mentioned earlier, the as importers at prices, the markup goes down there is lower than one markup adjustment possible. And so the pass through tend to be more incomplete. Other things equal second. And this is, where the model gets a little bit more complicated. We do find that the past through, even when importer importers set prices can be both complete and incomplete. It is complete for small importers, importers that account for a tiny share of the exporters output
00:10:28 - and so the standard intuition of marginal cost pricing and full pass through apply for them. However, when importers are large, they account for a large share of the exporter output. And so, if the, if the tariff increases the price, the importers demand goes down, and as the importer demand goes down, total output produced by the exporter also goes down and so does the marginal cost. And this pushes the pass through to be incomplete. So overall, in order to understand pass through,
00:10:58 - of heterogeneity across firm to firm pairs in what the pass through could be. And, and so the model say that in order to understand aggregate password, one needs to understand what's going on at the very granular level and take a bottom up approach where, you know, in order to under understanding the aggregate means understanding how we aggregate from firm level behavior to aggregate behavior. We just, sorry, on the data. the second contribution of this paper is
00:11:30 - on the data directly. And to do so, we construct a novel dataset, that merges reach information on firm to firm freight transaction for US importers and foreign exporter. This comes from the lifted dataset of the US census, which has information on price and quantities for firm to firm transaction dating back to 92, to 2019 or 2020. We merged this information with, information on firm level characteristics, both
00:12:00 - which come from the US census, of manufacturers or the longitudinal business dynamics datasets with, and also information on foreign exporters, which we obtained from the orbit dataset. finally, we also integrate information on trade policy. In particular, we focus on Trump tar as our case study. we use tariff, data, from, PA Obama quarters that, that work on returns to protectionism. Our empirical analysis, does several things.

Model Validation of Importer-Exporter Price Dynamics

00:12:33 - to validate the model's prediction in what's called a reduced form approach, meaning simple regressions of prices and market shares. We do see that bilateral prices in particular bilateral markups tend to be higher in relationships involving exporters with higher market share and tend to be lower in relationship is involving importers with a larger buyer share, as predicted by a model where there's both bilateral bargaining. So both sides of the market has some bargaining power in
00:13:05 - the marginal cost of the exporters tend to increase in total output. We then validate this prediction structurally. So we structurally estimate the model in the model scale elasticities and, and, and we find something which is kind of surprising, well, not surprising, but novel. The first thing we find is that on average in US importers ha the, the US importers bargaining power is about four times as large as the bargaining power, of foreign exporters.
00:13:35 - the assumption is that foreign exporters have full bargaining power and importers have zero bargaining power. So what we estimate in our model is very different than what traditional theories, assume. And secondly, we also find that on average the exporter's marginal costs are very elastic to total output. and, which implies that the exporter supply elasticity is, is quite, is is large. And, again, which is something that conventional models,
00:14:07 - With these estimates, we then use our model to try speak to, the, the consequences, of t both at the bilateral pair level and at the aggregate level. The first exercise that we do is a sort of a goodness of fit test of the model. so we take, we start in 2017, we observe the network of firm to firm relationships, and then we hit the economy with the Trump tariffs. And we use the model to predict what's the effect
00:14:39 - to firm level. And so what the model predicts should be the change in price in, in its relationship and compare the model predicted price with the observed price. So press price change between 2017 And 2018, and we cannot reject the null that the model predicted price change and the observed price change are equal to each other, which lends credibility to the model forecasting ability. So then the last thing we do is to say, okay,
00:15:08 - of Trump tires bottom up. And the model says, well, the aggregate pass through is about 40%, which is much lower than a hundred percent. It's very incomplete. And the model attributes about a hundred percent of this incompleteness to downward cost adjustment of foreign, of foreign exporting, meaning the US importers on the one hand, a very large bargaining power. So they set prices, which other things equal push pass through to be larger than expected.

Trade Policy Impact on Pricing Dynamics

00:15:39 - meaning they face a very elastic marginal cost schedule. And has the demand of US importers decrease due to the tariff? The marginal cost should also adjust downward, which is what explain the very incomplete passthrough. Where do we stand? what are we learn and what does our model have have to say about the incidents of Trump tariff? So on the one hand, our research proposes a new pricing theory of global value chains,
00:16:10 - the show the effect of trade policy on prices at the firm to firm level. So this is a, a price theory of global value chains, which is important, and as global value chains are gaining traction is, and they account for a large share of international trade. However, we do acknowledge that there is a discrepancy, which we, we between what the, our model say the aggregate incidents of Trump tariff should be, and what other studies of aggregate price changes have,
00:16:38 - So we, we predict that the pass is very incomplete. These existing, studies show that the password was actually very complete. We do attribute this discrepancy to different sample selection in our studies for estimation purposes. we need to focus on a restricted sample of importers on a restricted sample of relationships which are repeated. And also and importantly, we do have to drop relationship between related party transaction means involving,
00:17:10 - by a business relationship, which account for about 50% of total import. So we are dropping a, a big chunk of total imports in the United States. And the reason why we dropped this is be because we do believe that our theory applies for arms, arms length relationship rather than related party trade, where prices are not set by these sort of dynamics, but may reflect transfer pricing and other forces that we, that our model silent about. We are currently working on trying to pin down, which of this feature is most important
00:18:00 - but we do believe it has to do with sample selection. But overall, our study contributes to the literature of pass through, providing a comprehensive understanding of prices in firm to term relationship. And we believe it, it is an important study helping policy makers understand the consequences of trade policy in the context of, of global value chains.

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