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Field
Abstract
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Before
Connections between firms are critical for industrial upgrading and economic growth. One possible barrier to firm-to-firm matching is information frictions: firms may not know the potential suppliers and buyers, or the specific product they offer or demand. These frictions may be especially prominent for smaller firms run by lower-income owners, who lack business connections and networks.
In this project, we aim to understand whether trade fairs can reduce information frictions. By coordinating a centralized meeting point for buyers and suppliers in a specific industry, trade fairs aim to reduce search costs and facilitate matching among firms. To test the impact of attending trade fairs on firm-to-firm connections, we partner with FISA, the organizer of Aquasur 2024, the largest trade fair for the fishing industry in the southern hemisphere, which will be held in Puerto Montt, Chile. We will first solicit firms’ interest in participating in the fair via an online application to a raffle to win a subsidy to attend the fair. Among the firms that express interest, we will randomly provide a subsidy on the registration fee to encourage firms’ actual attendance at the fair. Finally, among participating firms, we will organize a matchmaking event at the fair to facilitate supplier-buyer connections.
We will measure impacts using a combination of original surveys of firms, collected both at the fair and after, with rich firm-to-firm transaction-level data from VAT tax records. First, we will measure how much reducing information frictions by bringing firms together at the fair facilitates new business connections. Second, we will assess whether the new supplier linkages substitute transactions from other suppliers, thereby testing the business-stealing effects. Third, we will study additional ripple effects of the new linkages to the economy, such as upstream and downstream propagation and firm-level quality upgrading. Finally, we will combine the reduced form findings from the experiment with a structural model, building on the approach in Baqaee, Burstein, Duprez, Farhi (2024), to quantify the value of new business linkages and how that impacts aggregate productivity and growth.
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After
Connections between firms are critical for industrial upgrading and economic growth. One possible barrier to firm-to-firm matching is information frictions: firms may not know the potential suppliers and buyers, or the specific product they offer or demand. These frictions may be especially prominent for smaller firms run by lower-income owners, who lack business connections and networks.
In this project, we aim to understand whether trade fairs can reduce information frictions. By coordinating a centralized meeting point for buyers and suppliers in a specific industry, trade fairs aim to reduce search costs and facilitate matching among firms. To test the impact of attending trade fairs on firm-to-firm connections, we partner with FISA, and conduct a two-sided experiment at two large trade fairs in Chile: (1) Aquasur 2024, a large trade fair for the fishing industry held in Puerto Montt, Chile; (2) Expomin 2025, a large mining trade fair held in Santiago, Chile. For each fair, we will first solicit firms’ interest in participating in the fair via an online application to a raffle to win a subsidy to attend the fair. Among the firms that express interest, we will randomly provide a subsidy on the registration fee to encourage firms’ actual attendance at the fair. Finally, among participating firms, we will organize a matchmaking event at the fair to facilitate supplier-buyer connections.
We will measure impacts using a combination of original surveys of firms, collected both at the fair and after, with rich firm-to-firm transaction-level data from VAT tax records. First, we will measure how much reducing information frictions by bringing firms together at the fair facilitates new business connections. Second, we will assess whether the new supplier linkages substitute transactions from other suppliers, thereby testing the business-stealing effects. Third, we will study additional ripple effects of the new linkages to the economy, such as upstream and downstream propagation and firm-level quality upgrading. Finally, we will combine the reduced form findings from the experiment with a structural model, building on the approach in Baqaee, Burstein, Duprez, Farhi (2024), to quantify the value of new business linkages and how that impacts aggregate productivity and growth.
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Last Published
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Before
March 15, 2024 06:51 PM
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After
April 20, 2025 01:08 PM
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Intervention (Public)
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Before
This project studies the impact of trade fairs on reducing search costs that may limit firm-to-firm connections. To test the impact of attending trade fairs on firm-to-firm connections, we partner with FISA, the organizer of Aquasur 2024, the largest trade fair for the fishing industry in the southern hemisphere, which will be held in Puerto Montt, Chile. We will first solicit firms’ interest in participating in the fair via an online application to a raffle to win a subsidy to attend the fair. Among the firms that express interest, we will randomly subsidize the registration fee to encourage firms’ actual attendance at the fair. Finally, we will organize a matchmaking event at the fair among participating firms to facilitate supplier-buyer connections.
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After
This project studies the impact of trade fairs on reducing search costs that may limit firm-to-firm connections. To test the impact of attending trade fairs on firm-to-firm connections, we partner with FISA, the organizer of Aquasur 2024 and Expomin 2025. We will first solicit firms’ interest in participating in the fairs via an online application to a raffle to win a subsidy to attend the fair. Among the firms that express interest, we will randomly provide a subsidy on the registration fee to encourage firms’ actual attendance at the fairs. Finally, among participating firms, we will organize a matchmaking event at the fairs to facilitate supplier-buyer connections.
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Intervention End Date
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March 21, 2024
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After
April 26, 2025
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Primary Outcomes (Explanation)
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Before
To measure these outcomes, we will use comprehensive Value Added Tax (VAT) administrative data provided to the research team by the Central Bank of Chile. We will measure the impact of the experiment on the number of new buyers or suppliers, on the extensive margin, and the quantity sold or purchased, product sold, and prices, on the intensive margin. These data will provide us with detailed and accurate measures of whether transactions took place between firms that previously did not have commercial transactions. We will also supplement this data with other firm-level administrative data, which records firm-level sales, employment, profit, etc.
We will complement the administrative data with original firm surveys conducted during and after the trade fair to measure – on a buyer-supplier pair level – new introductions made and specifics discussed (e.g., samples shared, initial contracts signed, etc.). Specifically, we will conduct the following set of surveys:
1. Daily stand observation: This is a survey to be run continuously, over a 30-minute block, each day with each stand from our supplier experiment sample that is present at the fair, along with a random sample of a small number of other stands at the fair. Upon receiving consent from both the representatives and the visitors, enumerators will ask questions about the interaction between the two parties and ask several follow-up questions to both parties at the end of their interactions. Enumerators will also take a picture of the stand at the beginning of the fair, showing the setup of the stand (including banner, poster, and marketing materials) but without any person in the photo.
2. Daily stand survey: On each day of the fair, we will conduct a 30-minute daily stand survey with all suppliers participating in the experiment who are present at the fair. With this survey, we aim to record the firm’s activity during the fair. In addition to the experimental sample of suppliers, we will also survey the randomly sampled a small number of stands to administer the daily stand survey. This will inform us whether the activities of suppliers participating in the experiment are different from general suppliers at the fair.
3. Visitor survey: we will conduct a brief (approximately 5-10 minutes) survey with visitors at the fair to get some basic information about their activities and their purpose of attending the trade fair. We will administer the visitor survey with a random sample of visitors every day at the fair, randomly sampled at the main exit out of the fair.
4. A month after the event, we will conduct a phone survey with all firms we surveyed at the fair (including 1-3 above). The purpose of this survey is to (i) for those who attended the fair, record the progress a firm achieved with the contacts established during the fair and (ii) get more information on overall relationships between the firm and its suppliers and buyers. This survey will be conducted by phone by enumerators from a local survey agency.
5. A year after the event, we will conduct a survey with all firms in our sample The purpose of the survey is similar to the one-month follow-up survey above, i.e., (i) record the progress a firm achieved with the contacts established during the fair (for treated firms) and (ii) get more information on overall relationships between the firm and its suppliers and buyers.
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After
To measure these outcomes, we will use comprehensive Value Added Tax (VAT) administrative data provided to the research team by the Central Bank of Chile. We will measure the impact of the experiment on the number of new buyers or suppliers, on the extensive margin, and the quantity sold or purchased, product sold, and prices, on the intensive margin. These data will provide us with detailed and accurate measures of whether transactions took place between firms that previously did not have commercial transactions. We will also supplement this data with other firm-level administrative data, which records firm-level sales, employment, profit, etc.
We will complement the administrative data with original firm surveys conducted during and after the trade fair to measure – on a buyer-supplier pair level – new introductions made and specifics discussed (e.g., samples shared, initial contracts signed, etc.). Specifically, we will conduct the following set of surveys:
1. Daily stand observation (only for Aquasur): This is a survey to be run continuously, over a 30-minute block, each day with each stand from our supplier experiment sample that is present at the fair, along with a random sample of a small number of other stands at the fair. Upon receiving consent from both the representatives and the visitors, enumerators will ask questions about the interaction between the two parties and ask several follow-up questions to both parties at the end of their interactions. Enumerators will also take a picture of the stand at the beginning of the fair, showing the setup of the stand (including banner, poster, and marketing materials) but without any person in the photo.
2. Daily stand survey (for both Aquasur and Expomin): On each day of the fair, we will conduct a 30-minute daily stand survey with all suppliers participating in the experiment who are present at the fair. With this survey, we aim to record the firm’s activity during the fair. In addition to the experimental sample of suppliers, we will also survey the randomly sampled a small number of stands to administer the daily stand survey. This will inform us whether the activities of suppliers participating in the experiment are different from general suppliers at the fair.
3. Visitor survey (only for Aquasur): we will conduct a brief (approximately 5-10 minutes) survey with visitors at the fair to get some basic information about their activities and their purpose of attending the trade fair. We will administer the visitor survey with a random sample of visitors every day at the fair, randomly sampled at the main exit out of the fair.
4. Phone follow-up survey (for both Aquasur and Expomin): A month after the event, we will conduct a phone survey with all firms we surveyed at the fair (including 1-3 above). The purpose of this survey is to (i) for those who attended the fair, record the progress a firm achieved with the contacts established during the fair and (ii) get more information on overall relationships between the firm and its suppliers and buyers. This survey will be conducted by phone by enumerators from a local survey agency.
5. Endline survey (for both Aquasur and Expomin): A year after the event, we will conduct a survey with all firms in our sample (both treated and control). The purpose of the survey is to (i) record the progress a firm achieved with the contacts established during the fair (for treated firms) and (ii) get more information on overall relationships between the firm and its suppliers and buyers.
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Planned Number of Clusters
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Before
For our main experiment to encourage suppliers to attend at the fair: We expect to have about 220 SME suppliers in total, which constitutes our clusters.
For our sub-experiment of matchmaking program: We expect to have about 30-100 buyer-product pairs and 65-130 suppliers. For the outcome variables defined at the buyer-product and supplier pairs (e.g., whether a new transaction is initiated between the matched pair), we cluster our analysis at the combination of buyer-product and supplier level. For the outcome variables that are defined at the buyer-pair (e.g., the presence of a transaction with the preexisting supplier of the buyer firm within the same product category), we cluster our analysis at the buyer-product level.
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After
For our main experiment to encourage suppliers to attend at the fair: We expect to have about 220 SME suppliers from Aquasur and 420 SME suppliers from Expomin, which constitutes our clusters.
For our sub-experiment of matchmaking program: We expect to have about 30-100 buyer-product pairs and 65-130 suppliers for Aquasur, and 80-250 buyer-product pairs and 100-125 suppliers for Expomin. For the outcome variables defined at the buyer-product and supplier pairs (e.g., whether a new transaction is initiated between the matched pair), we cluster our analysis at the combination of buyer-product and supplier level. For the outcome variables that are defined at the buyer-pair (e.g., the presence of a transaction with the preexisting supplier of the buyer firm within the same product category), we cluster our analysis at the buyer-product level.
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Planned Number of Observations
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Before
For our main experiment to encourage suppliers to attend at the fair: We expect to draw our treatment suppliers from about 220 SME suppliers in total. For outcome variables that are defined at the supplier-buyer-pair level (e.g., whether there is a new trade relationship, volume of transactions, prices), our observation is 220 SME suppliers times the number of potential buyers that appear in the trade fair (around 20 major salmon firms, plus some small-scale buyers). In this case, we cluster standard errors at the supplier-level. For secondary outcome variables that are defined at the supplier level (e.g., sales, employment, profit), the unit of the observation corresponds to the number of suppliers participated in the randomization (about 220).
For our sub-experiment of matchmaking program: We expect to assign treatment matches from about 30-100 buyer-product and 65-130 suppliers. For the outcome variables defined at the buyer-product and supplier pairs (e.g., whether a new transaction is initiated between the matched pair), the unit of observations is the potential number of supplier-buyer matches. For outcome variables defined at the buyer-product-level (e.g., purchases of these products from new suppliers and preexisting suppliers), the unit of the observation corresponds to the number of buyer-product combination, which we expect to be about 30-100 as we mentioned above.
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After
For our main experiment to encourage suppliers to attend at the fair: We expect to draw our treatment suppliers from about 220 SME suppliers for Aquasur and 420 for Expomin. For outcome variables that are defined at the supplier-buyer-pair level (e.g., whether there is a new trade relationship, volume of transactions, prices), our observation is the number of SME suppliers times the number of potential buyers that appear in each of the trade fairs. In this case, we cluster standard errors at the supplier-level. For secondary outcome variables that are defined at the supplier level (e.g., sales, employment, profit), the unit of the observation corresponds to the number of suppliers participated in the randomization.
For our sub-experiment of matchmaking program: We expect to assign treatment matches from about 30-100 buyer-product and 65-130 suppliers at Aquasur, and 80-250 buyer-product pairs and 100-125 suppliers for Expomin. For the outcome variables defined at the buyer-product and supplier pairs (e.g., whether a new transaction is initiated between the matched pair), the unit of observations is the potential number of supplier-buyer matches. For outcome variables defined at the buyer-product-level (e.g., purchases of these products from new suppliers and preexisting suppliers), the unit of the observation corresponds to the number of buyer-product combination.
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Sample size (or number of clusters) by treatment arms
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Before
We expect to recruit 220 SME suppliers to participate in the study, among which 110 will be randomly selected to receive the financial subsidy to attend the fair and we expect 65 of them to eventually take up the subsidy and attend the fair. The sub-experiment of matchmaking program will involve approximately 65-130 suppliers (those from the treatment group who take up the subsidy and some other suppliers who sign up on their own at full cost) and approximately 30-100 buyer-products who register (on their own) to attend the fair and express interest in the program.
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After
For Aquasur, we expect to recruit 220 SME suppliers to participate in the study, among which 110 will be randomly selected to receive the financial subsidy to attend the fair and we expect 65 of them to eventually take up the subsidy and attend the fair. The sub-experiment of matchmaking program will involve approximately 65-130 suppliers (those from the treatment group who take up the subsidy and some other suppliers who sign up on their own at full cost) and a subset of buyers who register (on their own) to attend the fair and express interest in the program – we expect that number to be 30-100 (maximum).
For Expomin, we expect to recruit 420 SME suppliers to participate in the study, among which 210 will be randomly selected to receive the financial subsidy to attend the fair and we expect 100-125 of them to eventually take up the subsidy and attend the fair. The sub-experiment of matchmaking program will involve approximately 100-125 suppliers and a subset of buyers who register (on their own) to attend the fair and express interest in the program – we expect that number to be 15 to 50 (with an average of 5 interested products per buyer firm).
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
We conduct power calculations for the probability that a supplier makes a connection to a major buyer of this industry, i.e., 17 major salmon producers. We assume that the baseline probability that a supplier has with a buyer is 0.001, with 0.017 of the inter-cluster correlation coefficient (ICC) among suppliers, based on our administrative VAT data. We assume a 60% take-up rate of our main experiment to encourage suppliers to attend the fair. Under these assumptions, we have the power to detect the treatment effect for the probability of forming a link by 0.0058 with 190 samples (95 treatment and 95 control suppliers). This treatment effect is equivalent to 0.1 (=0.0058 times 17) new buyer connections per supplier by attending the fair.
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After
We conduct power calculations for our main outcome variable of the presence of transaction between a supplier and a buyer in the fishing industry (the analysis is done prior to the first trade fair, Aquasur). In particular, we analyze the power for the probability that a supplier makes a connection to a major buyer of this industry, i.e., 17 major salmon producers. We assume that the probability that a supplier has with a buyer is 0.001, with 0.017 of inter-cluster correlation coefficient (ICC) among suppliers, based on our administrative VAT data. We assume 60% take-up rate of our main experiment to encourage suppliers to attend the fair. Under these assumptions, we have a power to detect the treatment effect for the probability of forming a link by 0.0058, which is equivalent to 0.1 (=0.0058 times 17) new buyer connections per supplier by attending the fair, with 190 samples (95 treatment and 95 control suppliers).
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Intervention (Hidden)
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Before
Our main experiment is to randomly provide a subsidy to encourage suppliers’ attendance at the fair. We recruit small and medium sized enterprises (SMEs) who are potentially interested in attending the trade fair and would like to bring their products and services to showcase at the fair. We will randomly provide a subset of the suppliers with a 60% subsidy on their registration fee to encourage actual attendance at the fair. Using the randomized subsidy as an “encouragement design,” we will study the impact of attending the fair on business connections and firm outcomes.
We will also run a sub-experiment of “matchmaking program” at the fair to randomly reduce information friction between particular pairs of suppliers and buyers. Among the SME suppliers who decide to attend fair (as well as among a wider set of suppliers at the fair) and potential buyers at the fair (i.e., firms who sign up as visitors to the fair and will be primarily looking for suppliers at the fair), we will elicit their interest in participating in a matchmaking program at the fair. Among those who express interest, we will randomly form matched pairs of potential suppliers and buyers, based on the product information provided by the firms, and invite the matched pairs to attend a series of matchmaking meetings happening at the fair. Leveraging this variation in matching at the buyer-product level will also allow us to assess whether the new supplier linkages substitute transactions from other suppliers, thereby testing for business-stealing effects.
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After
Our main experiment is to randomly provide a subsidy to encourage suppliers’ attendance at the fair. We recruit small and medium sized enterprises (SMEs) who are potentially interested in attending the trade fairs and would like to bring their products and services to showcase at the fair. We will randomly provide a subset of the suppliers with a 60% subsidy on their registration fee to encourage actual attendance at the fairs. Using the randomized subsidy as an “encouragement design,” we will study the impact of attending the fairs on business connections and firm outcomes.
At each fair, we will also run a sub-experiment of “matchmaking program” at the fair to randomly reduce information friction between particular pairs of suppliers and buyers. Among the SME suppliers who decide to attend fair (as well as among a wider set of suppliers at the fair) and potential buyers at the fair (i.e., firms who sign up as visitors to the fair and will be primarily looking for suppliers at the fair), we will elicit their interest in participating in a matchmaking program at the fair. Among those who express interest, we will randomly form matched pairs of potential suppliers and buyers, based on the product information provided by the firms, and invite the matched pairs to attend a series of matchmaking meetings happening at the fair. Leveraging this variation in matching at the buyer-product level will also allow us to assess whether the new supplier linkages substitute transactions from other suppliers, thereby testing for business-stealing effects.
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