Federal Independent Agencies
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Federal Independent Agencies
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SBA Issues Guidance to Prioritize Defense-Critical Firms as New 8(a) Program Rules Take Effect
WASHINGTON, Sept. 11 -- The Small Business Administration issued the following news release on Sept. 10, 2026:
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SBA Issues Guidance to Prioritize Defense-Critical Firms as New 8(a) Program Rules Take Effect
Agency Advances the Smaller War Plants Commission's effort to prioritize domestic production capacity and military readiness
WASHINGTON -- Today, the U.S. Small Business Administration (SBA) announced new guidance relating to recent reforms of the 8(a) Business Development Program for individually-owned firms. In support of President Donald J. Trump's agenda to expand the Arsenal of ... Show Full Article WASHINGTON, Sept. 11 -- The Small Business Administration issued the following news release on Sept. 10, 2026: * * * SBA Issues Guidance to Prioritize Defense-Critical Firms as New 8(a) Program Rules Take Effect Agency Advances the Smaller War Plants Commission's effort to prioritize domestic production capacity and military readiness WASHINGTON -- Today, the U.S. Small Business Administration (SBA) announced new guidance relating to recent reforms of the 8(a) Business Development Program for individually-owned firms. In support of President Donald J. Trump's agenda to expand the Arsenal ofFreedom here at home, and the Smaller War Plants Commission's effort to strengthen critical supply chains within the defense industrial base, SBA will prioritize processing for 8(a) applicants in defense-critical industries. Additionally, the agency will reinstate merit-based "potential for success" reviews for prospective participants consistent with the statue and purpose of the program.
"Under President Trump's leadership and our partnership with Secretary Hegseth on the Smaller War Plants Commission, the SBA is leveraging the 8(a) Program to reindustrialize America and build out the network of small manufacturers and suppliers that equip our warfighters," said SBA Administrator Kelly Loeffler. "Instead of the Biden-era discriminatory DEI admissions framework and race-based preferences, we are putting merit first - empowering capable small businesses to build the technology and infrastructure that keeps our nation strong. By fast-tracking defense-critical firms and enforcing the rigorous statutory standards of the 8(a) Program, the SBA will strengthen domestic supply chains, expand production capacity, and ensure that the world's strongest military is backed by the world's most resilient industrial base."
Last month, SBA issued a final rule to end racial discrimination in the 8(a) Program and dismantle the race-based admissions framework that effectively barred Americans of certain races from accessing 8(a) set-aside and sole-source contracting opportunities. Under the new rule, individuals are no longer presumed "socially disadvantaged," and therefore eligible for the 8(a) Program, simply because they are a member of a racial minority group. Likewise, no individual may be barred from the 8(a) Program simply because they are white. Instead, all applicants will be required to prove their social disadvantage status by submitting verifiable, fact-based evidence.
Today's guidance further refines the program to ensure it delivers measurable results for American taxpayers while advancing key strategic priorities. It supports the Smaller War Plants Commission, a recent partnership between the SBA and the U.S. Department of War to expand American military capacity, capability, and resilience by investing in small manufacturers, who comprise 70% of the defense industrial base. Moving forward, the agency will prioritize reviewing and processing 8(a) applications for small businesses operating in the following defense-critical NAICS codes:
* NAICS 332992: Small Arms Ammunition Manufacturing
* NAICS 332993: Ammunition (except Small Arms) Manufacturing
* NAICS 336414: Guided Missile and Space Vehicle Manufacturing
* NAICS 336413: Other Aircraft Parts and Auxiliary Equipment Manufacturing
* NAICS 334511: Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing
* NAICS 334419: Other Electronic Component Manufacturing
* NAICS 331110: Iron and Steel Mills and Ferroalloy Manufacturing
* NAICS 332710: Machine Shops
* NAICS 332999: All Other Miscellaneous Fabricated Metal Product Manufacturing
* NAICS 336611: Ship Building and Repairing
In addition to fast-tracking consideration of defense-critical manufacturers, the SBA is officially restoring the "potential for success" review requirement. While required by statute and regulation, the Biden Administration's waivers led to fewer than half of 8(a) graduates achieving long-term commercial viability after leaving the program. The re-establishment of comprehensive financial and business document evaluations ensures participating firms possess the capabilities needed to successfully deliver on federal contracts, in line with the agency's broader effort to return merit to the 8(a) Program as the law intended.
On September 10th, when the final rule becomes effective, pending individually-owned 8(a) applications will be temporarily returned via the "Return to Business" system to allow applicants to align with the new standards and submit updated financial records. Applicants will have 45 calendar days to complete their updates and resubmit their applications for review.
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About the U.S. Small Business Administration
The U.S. Small Business Administration helps power the American dream of entrepreneurship. As the leading voice for small businesses within the federal government, the SBA empowers job creators with the resources and support they need to start, grow, and expand their businesses or recover from a declared disaster. It delivers services through an extensive network of SBA field offices and partnerships with public and private organizations. To learn more, visit www.sba.gov.
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Original text here: https://legacy.sba.gov/article/2026/09/10/sba-issues-guidance-prioritize-defense-critical-firms-new-8a-program-rules-take-effect
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SBA Issues Guidance to Prioritize Defense-Critical Firms as New 8(a) Program Rules Take Effect
Agency Advances the Smaller War Plants Commission's effort to prioritize domestic production capacity and military readiness
WASHINGTON -- Today, the U.S. Small Business Administration (SBA) announced new guidance relating to recent reforms of the 8(a) Business Development Program for individually-owned firms. In support of President Donald J. Trump's agenda to expand the Arsenal of ... Show Full Article WASHINGTON, Sept. 11 -- The Small Business Administration issued the following news release on Sept. 10, 2026: * * * SBA Issues Guidance to Prioritize Defense-Critical Firms as New 8(a) Program Rules Take Effect Agency Advances the Smaller War Plants Commission's effort to prioritize domestic production capacity and military readiness WASHINGTON -- Today, the U.S. Small Business Administration (SBA) announced new guidance relating to recent reforms of the 8(a) Business Development Program for individually-owned firms. In support of President Donald J. Trump's agenda to expand the Arsenal ofFreedom here at home, and the Smaller War Plants Commission's effort to strengthen critical supply chains within the defense industrial base, SBA will prioritize processing for 8(a) applicants in defense-critical industries. Additionally, the agency will reinstate merit-based "potential for success" reviews for prospective participants consistent with the statue and purpose of the program.
"Under President Trump's leadership and our partnership with Secretary Hegseth on the Smaller War Plants Commission, the SBA is leveraging the 8(a) Program to reindustrialize America and build out the network of small manufacturers and suppliers that equip our warfighters," said SBA Administrator Kelly Loeffler. "Instead of the Biden-era discriminatory DEI admissions framework and race-based preferences, we are putting merit first - empowering capable small businesses to build the technology and infrastructure that keeps our nation strong. By fast-tracking defense-critical firms and enforcing the rigorous statutory standards of the 8(a) Program, the SBA will strengthen domestic supply chains, expand production capacity, and ensure that the world's strongest military is backed by the world's most resilient industrial base."
Last month, SBA issued a final rule to end racial discrimination in the 8(a) Program and dismantle the race-based admissions framework that effectively barred Americans of certain races from accessing 8(a) set-aside and sole-source contracting opportunities. Under the new rule, individuals are no longer presumed "socially disadvantaged," and therefore eligible for the 8(a) Program, simply because they are a member of a racial minority group. Likewise, no individual may be barred from the 8(a) Program simply because they are white. Instead, all applicants will be required to prove their social disadvantage status by submitting verifiable, fact-based evidence.
Today's guidance further refines the program to ensure it delivers measurable results for American taxpayers while advancing key strategic priorities. It supports the Smaller War Plants Commission, a recent partnership between the SBA and the U.S. Department of War to expand American military capacity, capability, and resilience by investing in small manufacturers, who comprise 70% of the defense industrial base. Moving forward, the agency will prioritize reviewing and processing 8(a) applications for small businesses operating in the following defense-critical NAICS codes:
* NAICS 332992: Small Arms Ammunition Manufacturing
* NAICS 332993: Ammunition (except Small Arms) Manufacturing
* NAICS 336414: Guided Missile and Space Vehicle Manufacturing
* NAICS 336413: Other Aircraft Parts and Auxiliary Equipment Manufacturing
* NAICS 334511: Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing
* NAICS 334419: Other Electronic Component Manufacturing
* NAICS 331110: Iron and Steel Mills and Ferroalloy Manufacturing
* NAICS 332710: Machine Shops
* NAICS 332999: All Other Miscellaneous Fabricated Metal Product Manufacturing
* NAICS 336611: Ship Building and Repairing
In addition to fast-tracking consideration of defense-critical manufacturers, the SBA is officially restoring the "potential for success" review requirement. While required by statute and regulation, the Biden Administration's waivers led to fewer than half of 8(a) graduates achieving long-term commercial viability after leaving the program. The re-establishment of comprehensive financial and business document evaluations ensures participating firms possess the capabilities needed to successfully deliver on federal contracts, in line with the agency's broader effort to return merit to the 8(a) Program as the law intended.
On September 10th, when the final rule becomes effective, pending individually-owned 8(a) applications will be temporarily returned via the "Return to Business" system to allow applicants to align with the new standards and submit updated financial records. Applicants will have 45 calendar days to complete their updates and resubmit their applications for review.
* * *
About the U.S. Small Business Administration
The U.S. Small Business Administration helps power the American dream of entrepreneurship. As the leading voice for small businesses within the federal government, the SBA empowers job creators with the resources and support they need to start, grow, and expand their businesses or recover from a declared disaster. It delivers services through an extensive network of SBA field offices and partnerships with public and private organizations. To learn more, visit www.sba.gov.
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Original text here: https://legacy.sba.gov/article/2026/09/10/sba-issues-guidance-prioritize-defense-critical-firms-new-8a-program-rules-take-effect
Inter-American Development Bank: 'Geographic and Sectoral Impact of Productivity'
WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "The Geographic and Sectoral Impact of Productivity."
Here are excerpts:
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Abstract
To understand the geographic and sectoral impact of productivity, we build the first input-output matrix disaggregated at the geographic level within a country using unique administrative data harmonized to match national accounts. We use this data from Chile to calibrate a state-of-the-art general equilibrium quantitative trade model with production networks, labor mobility, firm ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "The Geographic and Sectoral Impact of Productivity." Here are excerpts: * * * Abstract To understand the geographic and sectoral impact of productivity, we build the first input-output matrix disaggregated at the geographic level within a country using unique administrative data harmonized to match national accounts. We use this data from Chile to calibrate a state-of-the-art general equilibrium quantitative trade model with production networks, labor mobility, firmselection, international and domestic trade, congestion of fixed factors, and knowledge diffusion. We consider two applications. First, we study the aggregate effects of local productivity shocks.
We show that location-sector interactions are crucial: locations and sectors separately account for less than half of the dispersion in GDP elasticities from location-sectorspecific productivity shocks. Geography-specific input-output linkages explain 16% of the dispersion, due to the role played by small and influential markets. Second, we analyze the exit of a large steel plant. We show that geographically disaggregated production linkages substantially increase the propagation of the plant exit.
* * *
Introduction
Understanding how local productivity shocks propagate through the economy is ubiquitous in policy debates. Some of these shocks arise from policy interventions, such as place-based initiatives aimed at boosting productivity in lagging regions or industrial policies targeting specific sectors. Other shocks are due to adverse events, including natural disasters, social conflicts, or the exit of large firms; or positive events such as increases in global demand for commodities that are geographically concentrated. Each of these episodes initially impacts a specific market defined by its sector and location. More importantly, these shocks can then propagate indirectly across locations and sectors through multiple channels, such as upstream and downstream propagation through supply chains, potentially generating uneven impacts, with winners and losers across locations and sectors. Disaggregated quantitative analysis has been constrained by the lack of geographically disaggregated economic accounts, particularly input-output tables, which are crucial for understanding the propagation of such shocks.
We construct novel geographically and sectorally disaggregated economic accounts that characterize economic activity across location and sector pairs in Chile, including geographic and sectoral input-output matrices, using unique administrative data and survey sources. The resulting disaggregated accounts are disciplined to match national accounts and aggregate identities. We combine this new dataset with a state-of-the-art general equilibrium quantitative trade model (Caliendo et al., 2018) to study how local productivity changes are transmitted across all locations and sectors of the economy.
These shocks operate through multiple mechanisms, including backward and forward input-output linkages, domestic and international trade, labor migration, congestion in fixed factors such as land and infrastructure, selection and extensive-margin adjustments of firms, and knowledge diffusion.
The disaggregated economic accounts link national accounts aggregates to detailed information disaggregated along both sectoral and location dimensions. Specifically, we build on national accounts built by the Central Bank of Chile (CBC), which provides estimates of regional gross domestic product (GDP), and extend them by geographically disaggregating the input-output matrices. The resulting dataset is internally consistent: aggregating sectoral and spatial flows exactly reproduces national totals and satisfies all accounting identities. Such disaggregated measures of economic activity and interconnections through supply chains remain scarce, both for Chile and the rest of the world.
To implement this measurement, we combine a wide range of administrative data covering the universe of formal firms in Chile between 2018 and 2023. First, we use standard administrative tax records from the Chilean Internal Revenue Service to construct firmlevel measures. This data provides direct measures of total, domestic, and international sales; total, domestic, and imported material expenditures; value added; employment; wages; capital stocks and investment. We start from national accounts regional GDP estimates and disaggregate those at the location-sector level using shares from firms' tax records. Second, and more novel, we exploit firm-to-firm value-added tax records to directly measure production linkages across locations and sectors. Existing work typically infers these linkages from surveys or national input-output tables and spatially allocates them using indirect methods, such as gravity equations or proportionality assumptions. In contrast, the Chilean firm-to-firm data report the origin and destination of each transaction for the universe of formal firms, allowing us to observe trade flows across locations and sectors rather than imputing them. This feature enables us to recover the production network at a fine geographic and sectoral resolution without imposing assumptions on the data.
Taken together, this dataset delivers a comprehensive coverage of economic activity across the Chilean territory and provides a direct, measurement-based characterization of geographically and sectorally disaggregated input-output linkages.
Using the model calibrated with the disaggregated economic accounts, we implement two applications to understand how location-sector-specific productivity shocks propagate throughout the economy. The first application studies the aggregate effects of local productivity shocks across all location-sector pairs in the economy. The second application analyzes the exit of a large steel plant in the south of Chile, quantifies its local and aggregate effects, and identifies winners and losers across locations and sectors.
The first application delivers five results. First, when shocks are allowed to vary only by location, or only by sector, we recover the broad qualitative patterns emphasized in the quantitative trade literature. We document the elasticity of GDP to productivity shocks, which we refer to GDP elasticities. We scale these elasticities by the share of GDP that is exposed to the shock, that is, the Domar weight of the shock. We scale by the Domar weight in order to control for mechanical heterogeneity driven by the magnitude of the shock. Regional GDP elasticities are largest in economically central and diversified areas, while sectoral GDP elasticities are strongest in business and social services and comparatively muted in mining. Second, once shocks are disaggregated at the location sector level, what matters is the dispersion rather than the average effects. Average GDP elasticities remain close to those obtained under one-dimensional shocks, but heterogeneity rises sharply. Furthermore, geography accounts for 12% of the dispersion in aggregate GDP elasticities, sectors account for 16%, and 73% is accounted for by the interaction between the two. Where a shock occurs and which sector is hit are therefore not sufficient statistics for understanding the impact of location-sector-specific shocks. What matters is the interaction between sectors and locations. This underscores the relevance of including geography when measuring economic accounts and the propagation of productivity shocks.
Third, standard sufficient statistics (Baqaee & Farhi, 2020) remain informative but incomplete. If Domar weights were sufficient statistics, all GDP elasticities should be equal to one. The fact that there is significant dispersion means that those sufficient statistics are not sufficient in this framework. As shown by Baqaee and Farhi (2019, 2020, 2024), the difference between these sufficient statistics and the model can be driven by both non-linearities and inefficiencies of the model.
Fourth, trade and input-output linkages shape propagation through distinct channels.
Shutting down production networks reduces the dispersion of impacts by 44%. Furthermore, without input-output linkages, sectors become more important. Shutting down trade has an even larger effect, accounting for about 54% of the dispersion of GDP elasticities.
Contrary to input-output linkages, without trade, dispersion of GDP elasticities becomes much more geographic and the location-sector interaction falls sharply. These channels are therefore complementary: trade redistributes shocks across sector, while production networks redistribute shocks across space.
Finally, geographically disaggregated input-output tables matter mainly for heterogeneity among smaller and influential markets rather than for average aggregate effects.
Replacing the geography-specific input-output matrix with the standard national inputoutput table leaves average GDP elasticities largely unchanged, but removes about 16% of the overall dispersion of GDP elasticities across location-sectors. This reduction is driven by small markets that have high GDP elasticities in the benchmark model. Furthermore, eliminating the geography aspect of the input-output matrix increases the relevance of sectors in GDP elasticity dispersion by about 19%, highlighting the relevance of appropriately measuring the spatial dimension of input-output tables.
The second application illustrates these insights in a concrete and policy-relevant setting: the exit of a large plant in a location that relies substantially on the economic activity of that plant. Although the plant that exited, Huachipato, accounts for only a negligible share of national GDP, its exit generates aggregate losses several times larger than its direct contribution. About three-quarters of the national GDP and employment losses occur outside the location-sector of the plant, showing that localized firm exits can have substantial national consequences once labor mobility, trade and production networks are accounted for.
This case study also highlights the value of geographically disaggregated inputoutput data. Replacing geography-specific production linkages with national input-output coefficients lowers the GDP elasticity of the shock from 3.41 to 1.51 This comparison shows that spatial detail in production linkages matters quantitatively for the aggregate strength of propagation. This case study reinforces our central message: localized productivity shocks propagate through the economy not because of geography or sector alone, but because of how the two interact within the economy's spatial and production network structure.
Related Literature. We relate to three strands of the literature. First, we contribute to the literature on the construction of disaggregated economic accounts. A prominent set of contributions constructs cross-region and international trade matrices, often by combining shipment data, international trade statistics, input-output tables, and regional labor or output shares as in Leontief and Strout (1963), Caliendo and Parro (2015), and Caliendo et al. (2018, 2019). Because disaggregated and domestic trade data are typically unavailable, these approaches rely on proportionality assumptions or gravity-based allocations to spatially disaggregate producer flows. For instance, Rodriguez-Clare et al. (2025) recover industry-level trade flows across U.S. region pairs using a gravity-based framework, while Andersen et al. (2026) apply a similar approach to Denmark for firm's transactions. On the other hand, Caliendo et al. (2018) measures the geographical aspect of input-output linkages through proportionality assumptions. While these methods have substantially advanced the measurement of spatial trade linkages, they recover cross-location flows indirectly and do not identify the full geography-sector input-output structure of the economy. A closely related strand uses administrative tax records to measure income flows and production linkages at a high level of granularity as in Card et al. (2013), Huneeus (2018), Dhyne et al. (2021), Adao et al. (2022), Bernard et al. (2022), and Atkin et al. (2025).
In particular, Atkin et al. (2025) exploits Chilean administrative data to measure detailed bilateral transactions between individuals, firms, and the government, highlighting how distortions influence real income across individuals, but they do not focus on the spatial aspect of the production network let alone its role on propagating productivity shocks.
Our paper contributes to this literature by constructing novel geographically and sectorally disaggregated accounts for Chile that jointly characterize economic activity and production linkages at the location-sector level for an entire country using detailed administrative data and without imposing additional assumptions or structure on the data. Furthermore, all bilateral flows are disciplined to exactly match Chile's national accounts and input-output tables, ensuring national aggregation, internal consistency, and compliance with accounting identities.
Second, we contribute to the literature of propagation through production networks that studies how local productivity shocks impact aggregates and propagate through the economy. A classical starting point is the insight that, first-order aggregate responses can be summarized by Domar weights, as formalized by Hulten (1978). Building on this foundation, a large literature studies shock propagation through input-output linkages and network structure, from early multisector contributions such as Long and Plosser (1983) and Horvath (2000) to modern network-based analyses in Acemoglu et al. (2012, 2016), and nonlinear general-equilibrium frameworks in Baqaee and Farhi (2019, 2020, 2024). This strand of the literature typically abstracts from studying the role of geography.
We contribute to this literature by showing the role of geography for the propagation of shocks. In particular we bring to this literature the frameworks from the quantitative spatial literature (Caliendo et al., 2018) which include trade frictions related to geography and we measure the role of geography by building disaggregated economic accounts across space in Chile.
Finally, our paper contributes to the literature of quantitative spatial economics. This literature has developed general equilibrium frameworks to quantify how productivity and trade shocks impact economic activity across locations and affect aggregate welfare through trade costs and geography, starting from the Ricardian structure in Eaton and Kortum (2002) and its counterfactual implementation in Dekle et al. (2008). This approach is extended to multi-sector models with input-output linkages in Caliendo and Parro (2015) and to fully specified spatial equilibria in Caliendo et al. (2018) and Caliendo et al. (2019).1 Our contribution to this literature is to provide a unified measurement and quantitative framework to study the geographic and sectoral propagation of productivity shocks through empirically identified production networks embedded in a spatial general equilibrium. The closest paper to our approach is Caliendo et al. (2018), which constructs spatial trade linkages by combining data on cross-region shipments, input-output trade between producers, and regional labor or output shares. We depart from this approach by directly measuring bilateral geography-sector input flows using administrative firm-to-firm transaction data, thereby identifying the full origin-destination structure of production without relying on proportional or gravity-based allocations. This richer measurement is especially important for service sectors, whose geographic flows have been largely absent from existing spatial analyses due to their non-physical nature and the lack of survey data. We apply our framework to the Chilean economy, whose pronounced geographic heterogeneity provides a natural setting to study how productivity shocks propagate across sectors and locations.
The remainder of the paper is organized as follows. Section 2 describes the construction of the disaggregated economic accounts, combining state-of-the-art administrative records and survey sources and making them consistent with national accounts. Section 3 presents descriptive evidence on the economic composition of Chile and the geographic structure of the production network. Section 4 introduces the quantitative trade model and defines the sufficient statistics used to measure the aggregate effects of location-sector shocks and the geographic, sectoral, and geography-sector exposures that govern their propagation. Section 5 presents the model calibration. Section 6 reports the results from the quantitative exercises and their applications. Section 7 concludes.
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View full text here: https://publications.iadb.org/en/geographic-and-sectoral-impact-productivity
[Category: IADB]
Here are excerpts:
* * *
Abstract
To understand the geographic and sectoral impact of productivity, we build the first input-output matrix disaggregated at the geographic level within a country using unique administrative data harmonized to match national accounts. We use this data from Chile to calibrate a state-of-the-art general equilibrium quantitative trade model with production networks, labor mobility, firm ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "The Geographic and Sectoral Impact of Productivity." Here are excerpts: * * * Abstract To understand the geographic and sectoral impact of productivity, we build the first input-output matrix disaggregated at the geographic level within a country using unique administrative data harmonized to match national accounts. We use this data from Chile to calibrate a state-of-the-art general equilibrium quantitative trade model with production networks, labor mobility, firmselection, international and domestic trade, congestion of fixed factors, and knowledge diffusion. We consider two applications. First, we study the aggregate effects of local productivity shocks.
We show that location-sector interactions are crucial: locations and sectors separately account for less than half of the dispersion in GDP elasticities from location-sectorspecific productivity shocks. Geography-specific input-output linkages explain 16% of the dispersion, due to the role played by small and influential markets. Second, we analyze the exit of a large steel plant. We show that geographically disaggregated production linkages substantially increase the propagation of the plant exit.
* * *
Introduction
Understanding how local productivity shocks propagate through the economy is ubiquitous in policy debates. Some of these shocks arise from policy interventions, such as place-based initiatives aimed at boosting productivity in lagging regions or industrial policies targeting specific sectors. Other shocks are due to adverse events, including natural disasters, social conflicts, or the exit of large firms; or positive events such as increases in global demand for commodities that are geographically concentrated. Each of these episodes initially impacts a specific market defined by its sector and location. More importantly, these shocks can then propagate indirectly across locations and sectors through multiple channels, such as upstream and downstream propagation through supply chains, potentially generating uneven impacts, with winners and losers across locations and sectors. Disaggregated quantitative analysis has been constrained by the lack of geographically disaggregated economic accounts, particularly input-output tables, which are crucial for understanding the propagation of such shocks.
We construct novel geographically and sectorally disaggregated economic accounts that characterize economic activity across location and sector pairs in Chile, including geographic and sectoral input-output matrices, using unique administrative data and survey sources. The resulting disaggregated accounts are disciplined to match national accounts and aggregate identities. We combine this new dataset with a state-of-the-art general equilibrium quantitative trade model (Caliendo et al., 2018) to study how local productivity changes are transmitted across all locations and sectors of the economy.
These shocks operate through multiple mechanisms, including backward and forward input-output linkages, domestic and international trade, labor migration, congestion in fixed factors such as land and infrastructure, selection and extensive-margin adjustments of firms, and knowledge diffusion.
The disaggregated economic accounts link national accounts aggregates to detailed information disaggregated along both sectoral and location dimensions. Specifically, we build on national accounts built by the Central Bank of Chile (CBC), which provides estimates of regional gross domestic product (GDP), and extend them by geographically disaggregating the input-output matrices. The resulting dataset is internally consistent: aggregating sectoral and spatial flows exactly reproduces national totals and satisfies all accounting identities. Such disaggregated measures of economic activity and interconnections through supply chains remain scarce, both for Chile and the rest of the world.
To implement this measurement, we combine a wide range of administrative data covering the universe of formal firms in Chile between 2018 and 2023. First, we use standard administrative tax records from the Chilean Internal Revenue Service to construct firmlevel measures. This data provides direct measures of total, domestic, and international sales; total, domestic, and imported material expenditures; value added; employment; wages; capital stocks and investment. We start from national accounts regional GDP estimates and disaggregate those at the location-sector level using shares from firms' tax records. Second, and more novel, we exploit firm-to-firm value-added tax records to directly measure production linkages across locations and sectors. Existing work typically infers these linkages from surveys or national input-output tables and spatially allocates them using indirect methods, such as gravity equations or proportionality assumptions. In contrast, the Chilean firm-to-firm data report the origin and destination of each transaction for the universe of formal firms, allowing us to observe trade flows across locations and sectors rather than imputing them. This feature enables us to recover the production network at a fine geographic and sectoral resolution without imposing assumptions on the data.
Taken together, this dataset delivers a comprehensive coverage of economic activity across the Chilean territory and provides a direct, measurement-based characterization of geographically and sectorally disaggregated input-output linkages.
Using the model calibrated with the disaggregated economic accounts, we implement two applications to understand how location-sector-specific productivity shocks propagate throughout the economy. The first application studies the aggregate effects of local productivity shocks across all location-sector pairs in the economy. The second application analyzes the exit of a large steel plant in the south of Chile, quantifies its local and aggregate effects, and identifies winners and losers across locations and sectors.
The first application delivers five results. First, when shocks are allowed to vary only by location, or only by sector, we recover the broad qualitative patterns emphasized in the quantitative trade literature. We document the elasticity of GDP to productivity shocks, which we refer to GDP elasticities. We scale these elasticities by the share of GDP that is exposed to the shock, that is, the Domar weight of the shock. We scale by the Domar weight in order to control for mechanical heterogeneity driven by the magnitude of the shock. Regional GDP elasticities are largest in economically central and diversified areas, while sectoral GDP elasticities are strongest in business and social services and comparatively muted in mining. Second, once shocks are disaggregated at the location sector level, what matters is the dispersion rather than the average effects. Average GDP elasticities remain close to those obtained under one-dimensional shocks, but heterogeneity rises sharply. Furthermore, geography accounts for 12% of the dispersion in aggregate GDP elasticities, sectors account for 16%, and 73% is accounted for by the interaction between the two. Where a shock occurs and which sector is hit are therefore not sufficient statistics for understanding the impact of location-sector-specific shocks. What matters is the interaction between sectors and locations. This underscores the relevance of including geography when measuring economic accounts and the propagation of productivity shocks.
Third, standard sufficient statistics (Baqaee & Farhi, 2020) remain informative but incomplete. If Domar weights were sufficient statistics, all GDP elasticities should be equal to one. The fact that there is significant dispersion means that those sufficient statistics are not sufficient in this framework. As shown by Baqaee and Farhi (2019, 2020, 2024), the difference between these sufficient statistics and the model can be driven by both non-linearities and inefficiencies of the model.
Fourth, trade and input-output linkages shape propagation through distinct channels.
Shutting down production networks reduces the dispersion of impacts by 44%. Furthermore, without input-output linkages, sectors become more important. Shutting down trade has an even larger effect, accounting for about 54% of the dispersion of GDP elasticities.
Contrary to input-output linkages, without trade, dispersion of GDP elasticities becomes much more geographic and the location-sector interaction falls sharply. These channels are therefore complementary: trade redistributes shocks across sector, while production networks redistribute shocks across space.
Finally, geographically disaggregated input-output tables matter mainly for heterogeneity among smaller and influential markets rather than for average aggregate effects.
Replacing the geography-specific input-output matrix with the standard national inputoutput table leaves average GDP elasticities largely unchanged, but removes about 16% of the overall dispersion of GDP elasticities across location-sectors. This reduction is driven by small markets that have high GDP elasticities in the benchmark model. Furthermore, eliminating the geography aspect of the input-output matrix increases the relevance of sectors in GDP elasticity dispersion by about 19%, highlighting the relevance of appropriately measuring the spatial dimension of input-output tables.
The second application illustrates these insights in a concrete and policy-relevant setting: the exit of a large plant in a location that relies substantially on the economic activity of that plant. Although the plant that exited, Huachipato, accounts for only a negligible share of national GDP, its exit generates aggregate losses several times larger than its direct contribution. About three-quarters of the national GDP and employment losses occur outside the location-sector of the plant, showing that localized firm exits can have substantial national consequences once labor mobility, trade and production networks are accounted for.
This case study also highlights the value of geographically disaggregated inputoutput data. Replacing geography-specific production linkages with national input-output coefficients lowers the GDP elasticity of the shock from 3.41 to 1.51 This comparison shows that spatial detail in production linkages matters quantitatively for the aggregate strength of propagation. This case study reinforces our central message: localized productivity shocks propagate through the economy not because of geography or sector alone, but because of how the two interact within the economy's spatial and production network structure.
Related Literature. We relate to three strands of the literature. First, we contribute to the literature on the construction of disaggregated economic accounts. A prominent set of contributions constructs cross-region and international trade matrices, often by combining shipment data, international trade statistics, input-output tables, and regional labor or output shares as in Leontief and Strout (1963), Caliendo and Parro (2015), and Caliendo et al. (2018, 2019). Because disaggregated and domestic trade data are typically unavailable, these approaches rely on proportionality assumptions or gravity-based allocations to spatially disaggregate producer flows. For instance, Rodriguez-Clare et al. (2025) recover industry-level trade flows across U.S. region pairs using a gravity-based framework, while Andersen et al. (2026) apply a similar approach to Denmark for firm's transactions. On the other hand, Caliendo et al. (2018) measures the geographical aspect of input-output linkages through proportionality assumptions. While these methods have substantially advanced the measurement of spatial trade linkages, they recover cross-location flows indirectly and do not identify the full geography-sector input-output structure of the economy. A closely related strand uses administrative tax records to measure income flows and production linkages at a high level of granularity as in Card et al. (2013), Huneeus (2018), Dhyne et al. (2021), Adao et al. (2022), Bernard et al. (2022), and Atkin et al. (2025).
In particular, Atkin et al. (2025) exploits Chilean administrative data to measure detailed bilateral transactions between individuals, firms, and the government, highlighting how distortions influence real income across individuals, but they do not focus on the spatial aspect of the production network let alone its role on propagating productivity shocks.
Our paper contributes to this literature by constructing novel geographically and sectorally disaggregated accounts for Chile that jointly characterize economic activity and production linkages at the location-sector level for an entire country using detailed administrative data and without imposing additional assumptions or structure on the data. Furthermore, all bilateral flows are disciplined to exactly match Chile's national accounts and input-output tables, ensuring national aggregation, internal consistency, and compliance with accounting identities.
Second, we contribute to the literature of propagation through production networks that studies how local productivity shocks impact aggregates and propagate through the economy. A classical starting point is the insight that, first-order aggregate responses can be summarized by Domar weights, as formalized by Hulten (1978). Building on this foundation, a large literature studies shock propagation through input-output linkages and network structure, from early multisector contributions such as Long and Plosser (1983) and Horvath (2000) to modern network-based analyses in Acemoglu et al. (2012, 2016), and nonlinear general-equilibrium frameworks in Baqaee and Farhi (2019, 2020, 2024). This strand of the literature typically abstracts from studying the role of geography.
We contribute to this literature by showing the role of geography for the propagation of shocks. In particular we bring to this literature the frameworks from the quantitative spatial literature (Caliendo et al., 2018) which include trade frictions related to geography and we measure the role of geography by building disaggregated economic accounts across space in Chile.
Finally, our paper contributes to the literature of quantitative spatial economics. This literature has developed general equilibrium frameworks to quantify how productivity and trade shocks impact economic activity across locations and affect aggregate welfare through trade costs and geography, starting from the Ricardian structure in Eaton and Kortum (2002) and its counterfactual implementation in Dekle et al. (2008). This approach is extended to multi-sector models with input-output linkages in Caliendo and Parro (2015) and to fully specified spatial equilibria in Caliendo et al. (2018) and Caliendo et al. (2019).1 Our contribution to this literature is to provide a unified measurement and quantitative framework to study the geographic and sectoral propagation of productivity shocks through empirically identified production networks embedded in a spatial general equilibrium. The closest paper to our approach is Caliendo et al. (2018), which constructs spatial trade linkages by combining data on cross-region shipments, input-output trade between producers, and regional labor or output shares. We depart from this approach by directly measuring bilateral geography-sector input flows using administrative firm-to-firm transaction data, thereby identifying the full origin-destination structure of production without relying on proportional or gravity-based allocations. This richer measurement is especially important for service sectors, whose geographic flows have been largely absent from existing spatial analyses due to their non-physical nature and the lack of survey data. We apply our framework to the Chilean economy, whose pronounced geographic heterogeneity provides a natural setting to study how productivity shocks propagate across sectors and locations.
The remainder of the paper is organized as follows. Section 2 describes the construction of the disaggregated economic accounts, combining state-of-the-art administrative records and survey sources and making them consistent with national accounts. Section 3 presents descriptive evidence on the economic composition of Chile and the geographic structure of the production network. Section 4 introduces the quantitative trade model and defines the sufficient statistics used to measure the aggregate effects of location-sector shocks and the geographic, sectoral, and geography-sector exposures that govern their propagation. Section 5 presents the model calibration. Section 6 reports the results from the quantitative exercises and their applications. Section 7 concludes.
* * *
View full text here: https://publications.iadb.org/en/geographic-and-sectoral-impact-productivity
[Category: IADB]
Inter-American Development Bank: 'Formal Credit and SME Performance: Reviewing Evidence From Impact Evaluations'
WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Formal Credit and SME Performance: Reviewing Evidence from Impact Evaluations."
Here are excerpts:
* * *
Abstract
This paper reviews the impact evaluation evidence on how expanding formal credit affects small and medium enterprise (SME) performance, focusing on developing countries, particularly Latin America, while using evidence from advanced economies to sharpen mechanisms and external validity. Across the core evidence base, the metaanalytic mean effects of formal ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Formal Credit and SME Performance: Reviewing Evidence from Impact Evaluations." Here are excerpts: * * * Abstract This paper reviews the impact evaluation evidence on how expanding formal credit affects small and medium enterprise (SME) performance, focusing on developing countries, particularly Latin America, while using evidence from advanced economies to sharpen mechanisms and external validity. Across the core evidence base, the metaanalytic mean effects of formalloans are economically meaningful: employment rises by about 12% on average, sales by about 18%, and profits by about 18%, albeit with substantial heterogeneity across settings, programs, and outcome horizons. The effects are larger in some large-scale public interventions and in programs explicitly targeting constrained firms but smaller or statistically indistinguishable from zero in others.
Taken together, the evidence supports the view that relaxing financial constraints can raise SME scale and performance, but it also underscores that credit expansions are not automatically welfare improving, can create distributional and competitive spillovers, and are sensitive to program design, targeting, and local financial architecture.
* * *
Introduction
Policy interest in small and medium enterprise (SME) finance rests on a straightforward development narrative: SMEs are thought to be important engines of job creation, innovation, and local economic dynamism but are hindered by frictions that limit their access to external finance. If credit constraints bind, expanding access to formal loans should allow high-return projects to be financed, raise capital accumulation, increase working capital, enable the adoption of productivity-enhancing technologies, and ultimately increase firm growth and employment. Yet two decades of empirical work have produced a more nuanced picture. In many environments, lending interventions deliver meaningful gains in some outcomes and some firm segments but not others; effects can be short-lived; and in settings with weak screening, volatile macro conditions, or intense local competition, credit can generate limited net gains--or even losses--for average borrowers.
This paper synthesizes the impact evaluation evidence on SME credit expansions, with a focus on developing countries. It is anchored in the set of evaluations consolidated by Bruhn et al. (2025b), who offer a rare apples-to-apples meta-analytic mapping of impacts on employment, sales, and profits across a broad set of interventions. The evidence base includes randomized controlled trials (RCTs), regression discontinuity designs (RDDs) and difference-in-differences (DiD) designs exploiting eligibility thresholds or staggered rollouts, and quasi-experimental evaluations leveraging credit supply shocks through banks and public programs. The resulting literature is unusually well suited to answering a practical policy question: if a government, donor, or financial intermediary expands formal loans to SMEs, what should be expected to happen to jobs, revenues, and profits?
The paper also extends the evidence base in five directions, drawing on five newer contributions. First, it adds macro-policy-driven credit easing in Iran during 2005-2013 (Amini and Salehi Esfahani, 2025), which provides insight into credit expansions embedded in broader expansionary policy shifts. Second, it adds new RCT evidence on joint "training plus loans" interventions among Kenyan SMEs (Ashraf and Lyons, 2026), sharpening the question of complementarities between finance and managerial capital. Third, it adds evidence on fintech-enabled lending and collateral valuation (Beaumont et al., 2025), which speaks to whether new data and lending technologies can relax collateral constraints and change the composition of borrowers and credit terms. Fourth, it adds evidence on movable collateral registries from Malawi (Pendame and Akotey, 2023), which is directly relevant to a prominent reform agenda in low-income countries aimed at unlocking credit using movable assets.
Fifth, it adds evidence on credit guarantees from China (Yu et al., 2022), which quantifies how guarantees translate into borrowing, investment, innovation, and productivity.
Two themes motivate the structure of the paper. The first is identification: credible evaluation of credit requires separating changes in credit supply from changes in credit demand and addressing endogenous matching between firms and lenders. The corporate credit supply literature emphasizes precisely these challenges and develops empirical strategies that the SME finance evaluation literature increasingly shares, especially as credit registry data and matched bank-firm panels become more common (G~uler et al., 2021). The second theme is heterogeneity: the effects of credit expansions depend on which firms receive loans, what terms they receive, whether loans crowd out other funding, how competitive local markets are, and what complementary constraints (skills, demand, infrastructure, governance) remain binding.
The remainder of the paper proceeds as follows. Section 2 develops a conceptual framework linking credit access to SME performance and clarifies why impacts can be positive, small, or negative. Section 3 describes the evaluation landscape and the main identification strategies used in impact evaluations of SME credit access, adapting organizing principles from the corporate credit supply literature. Section 4 synthesizes average impacts on employment, sales, and profits and interprets their economic significance. Section 5 reviews heterogeneity by intervention type, institutional channel, and firm segment, emphasizing developing country evidence while using advanced economy studies to illuminate mechanisms and external validity. Section 6 focuses on mechanisms, complementary constraints, macroeconomic context, and market-level spillovers. Section 7 consolidates policy implications and priorities for future research, and Section 8 concludes.
* * *
View full text here: https://publications.iadb.org/en/formal-credit-and-sme-performance-reviewing-evidence-impact-evaluations
[Category: IADB]
Here are excerpts:
* * *
Abstract
This paper reviews the impact evaluation evidence on how expanding formal credit affects small and medium enterprise (SME) performance, focusing on developing countries, particularly Latin America, while using evidence from advanced economies to sharpen mechanisms and external validity. Across the core evidence base, the metaanalytic mean effects of formal ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Formal Credit and SME Performance: Reviewing Evidence from Impact Evaluations." Here are excerpts: * * * Abstract This paper reviews the impact evaluation evidence on how expanding formal credit affects small and medium enterprise (SME) performance, focusing on developing countries, particularly Latin America, while using evidence from advanced economies to sharpen mechanisms and external validity. Across the core evidence base, the metaanalytic mean effects of formalloans are economically meaningful: employment rises by about 12% on average, sales by about 18%, and profits by about 18%, albeit with substantial heterogeneity across settings, programs, and outcome horizons. The effects are larger in some large-scale public interventions and in programs explicitly targeting constrained firms but smaller or statistically indistinguishable from zero in others.
Taken together, the evidence supports the view that relaxing financial constraints can raise SME scale and performance, but it also underscores that credit expansions are not automatically welfare improving, can create distributional and competitive spillovers, and are sensitive to program design, targeting, and local financial architecture.
* * *
Introduction
Policy interest in small and medium enterprise (SME) finance rests on a straightforward development narrative: SMEs are thought to be important engines of job creation, innovation, and local economic dynamism but are hindered by frictions that limit their access to external finance. If credit constraints bind, expanding access to formal loans should allow high-return projects to be financed, raise capital accumulation, increase working capital, enable the adoption of productivity-enhancing technologies, and ultimately increase firm growth and employment. Yet two decades of empirical work have produced a more nuanced picture. In many environments, lending interventions deliver meaningful gains in some outcomes and some firm segments but not others; effects can be short-lived; and in settings with weak screening, volatile macro conditions, or intense local competition, credit can generate limited net gains--or even losses--for average borrowers.
This paper synthesizes the impact evaluation evidence on SME credit expansions, with a focus on developing countries. It is anchored in the set of evaluations consolidated by Bruhn et al. (2025b), who offer a rare apples-to-apples meta-analytic mapping of impacts on employment, sales, and profits across a broad set of interventions. The evidence base includes randomized controlled trials (RCTs), regression discontinuity designs (RDDs) and difference-in-differences (DiD) designs exploiting eligibility thresholds or staggered rollouts, and quasi-experimental evaluations leveraging credit supply shocks through banks and public programs. The resulting literature is unusually well suited to answering a practical policy question: if a government, donor, or financial intermediary expands formal loans to SMEs, what should be expected to happen to jobs, revenues, and profits?
The paper also extends the evidence base in five directions, drawing on five newer contributions. First, it adds macro-policy-driven credit easing in Iran during 2005-2013 (Amini and Salehi Esfahani, 2025), which provides insight into credit expansions embedded in broader expansionary policy shifts. Second, it adds new RCT evidence on joint "training plus loans" interventions among Kenyan SMEs (Ashraf and Lyons, 2026), sharpening the question of complementarities between finance and managerial capital. Third, it adds evidence on fintech-enabled lending and collateral valuation (Beaumont et al., 2025), which speaks to whether new data and lending technologies can relax collateral constraints and change the composition of borrowers and credit terms. Fourth, it adds evidence on movable collateral registries from Malawi (Pendame and Akotey, 2023), which is directly relevant to a prominent reform agenda in low-income countries aimed at unlocking credit using movable assets.
Fifth, it adds evidence on credit guarantees from China (Yu et al., 2022), which quantifies how guarantees translate into borrowing, investment, innovation, and productivity.
Two themes motivate the structure of the paper. The first is identification: credible evaluation of credit requires separating changes in credit supply from changes in credit demand and addressing endogenous matching between firms and lenders. The corporate credit supply literature emphasizes precisely these challenges and develops empirical strategies that the SME finance evaluation literature increasingly shares, especially as credit registry data and matched bank-firm panels become more common (G~uler et al., 2021). The second theme is heterogeneity: the effects of credit expansions depend on which firms receive loans, what terms they receive, whether loans crowd out other funding, how competitive local markets are, and what complementary constraints (skills, demand, infrastructure, governance) remain binding.
The remainder of the paper proceeds as follows. Section 2 develops a conceptual framework linking credit access to SME performance and clarifies why impacts can be positive, small, or negative. Section 3 describes the evaluation landscape and the main identification strategies used in impact evaluations of SME credit access, adapting organizing principles from the corporate credit supply literature. Section 4 synthesizes average impacts on employment, sales, and profits and interprets their economic significance. Section 5 reviews heterogeneity by intervention type, institutional channel, and firm segment, emphasizing developing country evidence while using advanced economy studies to illuminate mechanisms and external validity. Section 6 focuses on mechanisms, complementary constraints, macroeconomic context, and market-level spillovers. Section 7 consolidates policy implications and priorities for future research, and Section 8 concludes.
* * *
View full text here: https://publications.iadb.org/en/formal-credit-and-sme-performance-reviewing-evidence-impact-evaluations
[Category: IADB]
Inter-American Development Bank: 'Are the Vulnerable Non-Poor Different? Heterogeneous Responses to Macroeconomic Fluctuations in Latin America'
WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Are the Vulnerable Non-Poor Different? Heterogeneous Responses to Macroeconomic Fluctuations in Latin America."
Here are excerpts:
* * *
Abstract
This paper studies the dynamics of vulnerable non-poor households in Latin America, focusing on how they compare with the poor and how both groups respond to macroeconomic fluctuations. Using harmonized household survey microdata from 15 countries over more than three decades (1992-2024), complemented with longitudinal data ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Are the Vulnerable Non-Poor Different? Heterogeneous Responses to Macroeconomic Fluctuations in Latin America." Here are excerpts: * * * Abstract This paper studies the dynamics of vulnerable non-poor households in Latin America, focusing on how they compare with the poor and how both groups respond to macroeconomic fluctuations. Using harmonized household survey microdata from 15 countries over more than three decades (1992-2024), complemented with longitudinal datafor four of the region's largest economies, we examine how the size of socioeconomic groups evolves with long-run economic growth and cyclical fluctuations and whether labor market responses to macroeconomic conditions differ systematically between poor and vulnerable individuals. We find that the share of vulnerable non-poor individuals has risen modestly, reflecting higher inflows from poverty than outflows to richer groups. While labor market outcomes are strongly procyclical for both poor and vulnerable individuals, a striking pattern emerges across countries: In less developed economies, vulnerable individuals experience significantly stronger cyclical changes in labor market outcomes than the poor, whereas the opposite pattern arises in more developed economies, particularly along employment margins. Exploiting longitudinal data, we further show that transitions into and out of vulnerability are driven primarily by changes in labor income, especially through employment and hourly earnings adjustments. Overall, the results highlight that the relationship between vulnerability and macroeconomic fluctuations depends critically on labor market structure, which shapes the adjustment margins available to different groups of workers
Introduction
Over the last three decades, Latin America has experienced a substantial decline in poverty (Chang et al., 2025; Gasparini et al., 2023), yet a large share of the population remains concentrated just above the poverty line. These vulnerable non-poor households are not classified as poor under conventional definitions, but they still face a substantial risk of falling into poverty in the face of adverse shocks. Understanding the dynamics of this group is particularly relevant in a region characterized by recurrent macroeconomic instability, widespread labor market informality, and limited social protection systems (Levy and Schady, 2013; OECD, 2025).
While a large literature has examined poverty dynamics and labor market adjustment over the business cycle, much less is known about how vulnerable non-poor households respond to macroeconomic fluctuations and whether their responses differ systematically from those of the poor. This distinction is potentially important. Although both groups face economic insecurity, they may occupy different positions within the labor market, differ in their attachment to employment margins, and therefore adjust differently to changes in aggregate economic conditions. Understanding these heterogeneous responses is key to characterizing the nature of vulnerability in Latin America and the mechanisms through which macroeconomic fluctuations translate into changes in socioeconomic status.
This paper studies the dynamics of poor and vulnerable non-poor groups in Latin America, with a particular focus on their relationship with macroeconomic fluctuations. To this end, we draw on the Socio-Economic Database for Latin America and the Caribbean (SEDLAC, 2024), a large database of harmonized household surveys developed by CEDLAS and the World Bank, covering 15 Latin American countries over the period 1992-2024. Throughout the paper, we classify individuals into three groups based on daily household per capita income measured in 2017 PPP US dollars: the poor, with incomes below USD 6.85; the vulnerable non-poor, with incomes between USD 6.85 and USD 14; and the non-vulnerable, with incomes above USD 14.
The analysis combines repeated cross-sectional information for the full sample of countries with longitudinal data for four of the region's largest economies--Argentina, Brazil, Chile, and Peru-- allowing us to examine both aggregate changes in the size of socioeconomic groups and individual labor market responses over the business cycle.
We first examine how the size of these groups evolved over the last three decades and how these changes correlate with long-run economic growth and cyclical economic fluctuations. To distinguish between long-run and cyclical movements, we decompose GDP per capita into trend and cyclical components using a Hodrick-Prescott filter (Hodrick and Prescott, 1997). We document two main patterns. First, the share of vulnerable non-poor individuals increased, driven by a greater incidence of transitions from poverty into vulnerability than from vulnerability into the non-vulnerable. This increase reinforces the view of vulnerability as a quantitatively important and persistent socioeconomic condition in Latin America. Second, while long-run economic growth is associated with a moderate expansion of the vulnerable group, negative cyclical fluctuations tend to increase its size, suggesting that macroeconomic conditions induce substantial movements across socioeconomic groups. These patterns are consistent with the idea that the vulnerable non-poor function as a transition group within the region's income distribution.
We then examine whether labor market outcomes of the poor and the vulnerable non-poor respond differently to macroeconomic conditions. A central empirical challenge is that labor market outcomes and socioeconomic status may be jointly determined by contemporaneous economic conditions. To address this issue, we implement two complementary strategies. First, for a subset of countries with longitudinal data, we classify individuals into socioeconomic groups based on lagged household income and examine how subsequent labor market outcomes evolve with macroeconomic conditions. Second, using repeated cross sections for the full sample of countries, we classify individuals according to predicted household income constructed from predetermined characteristics. Both approaches yield similar patterns. Across all countries, labor market outcomes are strongly procyclical for both groups: Periods of above-trend economic activity are associated with higher employment, lower unemployment and informality, and higher wages and earnings. However, the relative magnitude of these responses differs systematically across countries with different labor market structures.
In economies with higher levels of poverty, informality, and rurality, vulnerable individuals tend to experience stronger labor market improvements during economic expansions than the poor.
In contrast, in more developed economies, the poor often exhibit larger cyclical responses, particularly along employment margins. These patterns suggest that the relationship between vulnerability and macroeconomic fluctuations depends critically on the structure of labor markets. In less developed economies, poor individuals are more likely to be concentrated in low-productivity activities with limited scope for cyclical adjustment, while vulnerable individuals appear better positioned to benefit from economic expansions. By contrast, in more developed economies, poor individuals exhibit stronger attachment to the employment and unemployment margins, making their labor market outcomes more responsive to cyclical fluctuations.
Finally, exploiting the longitudinal data available for a subset of countries, we examine transitions into and out of vulnerability, thereby linking the cyclical labor market responses with changes in socioeconomic status. We document substantial movement into and out of vulnerability over the economic cycle and show that transitions are driven primarily by changes in labor income rather than other income sources or household composition. We further show that, within labor income, both the employment and earnings margins matter, with changes in hourly labor income playing a particularly important role in transitions into and out of vulnerability.
This paper relates to several strands of the literature. First, it contributes to work on vulnerability and the vulnerable non-poor in developing countries, which emphasizes that a large share of households above the poverty line remain exposed to adverse shocks and cannot be considered economically secure (Banerjee and Duflo, 2008; Birdsall et al., 2014; Lopez-Calva and Ortiz-Juarez, 2014; OECD, 2019). Second, it relates to the literature examining labor market adjustment over the business cycle in Latin America, particularly the role of the informality and employment margins in shaping responses to macroeconomic fluctuations (Bosch and Maloney, 2010; Fernandez and Meza, 2015; Coskun, 2022). Our contribution is to bring these strands of the literature together by examining how labor market responses to cyclical fluctuations differ between poor and vulnerable non-poor individuals, how these differences vary across labor market structures in the region, and how they translate into transitions across socioeconomic groups over the cycle.
The rest of the paper is organized as follows. Section 2 describes the main data sources and the construction of the harmonized database used throughout the analysis. Section 3 discusses the definition and operationalization of the vulnerable non-poor group and presents its main demographic, educational, and labor market characteristics. Section 4 examines the evolution of socioeconomic groups and their relationship with long-run economic growth and cyclical fluctuations. Section 5 analyzes how labor market outcomes of poor and vulnerable individuals respond to macroeconomic conditions and whether these responses differ systematically across socioeconomic groups and countries. Section 6 exploits longitudinal data to study transitions across socioeconomic groups over the economic cycle and the labor market mechanisms underlying those transitions. Section 7 concludes.
* * *
View full text here: https://publications.iadb.org/en/are-vulnerable-non-poor-different-heterogeneous-responses-macroeconomic-fluctuations-latin-america
[Category: IADB]
Here are excerpts:
* * *
Abstract
This paper studies the dynamics of vulnerable non-poor households in Latin America, focusing on how they compare with the poor and how both groups respond to macroeconomic fluctuations. Using harmonized household survey microdata from 15 countries over more than three decades (1992-2024), complemented with longitudinal data ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "Are the Vulnerable Non-Poor Different? Heterogeneous Responses to Macroeconomic Fluctuations in Latin America." Here are excerpts: * * * Abstract This paper studies the dynamics of vulnerable non-poor households in Latin America, focusing on how they compare with the poor and how both groups respond to macroeconomic fluctuations. Using harmonized household survey microdata from 15 countries over more than three decades (1992-2024), complemented with longitudinal datafor four of the region's largest economies, we examine how the size of socioeconomic groups evolves with long-run economic growth and cyclical fluctuations and whether labor market responses to macroeconomic conditions differ systematically between poor and vulnerable individuals. We find that the share of vulnerable non-poor individuals has risen modestly, reflecting higher inflows from poverty than outflows to richer groups. While labor market outcomes are strongly procyclical for both poor and vulnerable individuals, a striking pattern emerges across countries: In less developed economies, vulnerable individuals experience significantly stronger cyclical changes in labor market outcomes than the poor, whereas the opposite pattern arises in more developed economies, particularly along employment margins. Exploiting longitudinal data, we further show that transitions into and out of vulnerability are driven primarily by changes in labor income, especially through employment and hourly earnings adjustments. Overall, the results highlight that the relationship between vulnerability and macroeconomic fluctuations depends critically on labor market structure, which shapes the adjustment margins available to different groups of workers
Introduction
Over the last three decades, Latin America has experienced a substantial decline in poverty (Chang et al., 2025; Gasparini et al., 2023), yet a large share of the population remains concentrated just above the poverty line. These vulnerable non-poor households are not classified as poor under conventional definitions, but they still face a substantial risk of falling into poverty in the face of adverse shocks. Understanding the dynamics of this group is particularly relevant in a region characterized by recurrent macroeconomic instability, widespread labor market informality, and limited social protection systems (Levy and Schady, 2013; OECD, 2025).
While a large literature has examined poverty dynamics and labor market adjustment over the business cycle, much less is known about how vulnerable non-poor households respond to macroeconomic fluctuations and whether their responses differ systematically from those of the poor. This distinction is potentially important. Although both groups face economic insecurity, they may occupy different positions within the labor market, differ in their attachment to employment margins, and therefore adjust differently to changes in aggregate economic conditions. Understanding these heterogeneous responses is key to characterizing the nature of vulnerability in Latin America and the mechanisms through which macroeconomic fluctuations translate into changes in socioeconomic status.
This paper studies the dynamics of poor and vulnerable non-poor groups in Latin America, with a particular focus on their relationship with macroeconomic fluctuations. To this end, we draw on the Socio-Economic Database for Latin America and the Caribbean (SEDLAC, 2024), a large database of harmonized household surveys developed by CEDLAS and the World Bank, covering 15 Latin American countries over the period 1992-2024. Throughout the paper, we classify individuals into three groups based on daily household per capita income measured in 2017 PPP US dollars: the poor, with incomes below USD 6.85; the vulnerable non-poor, with incomes between USD 6.85 and USD 14; and the non-vulnerable, with incomes above USD 14.
The analysis combines repeated cross-sectional information for the full sample of countries with longitudinal data for four of the region's largest economies--Argentina, Brazil, Chile, and Peru-- allowing us to examine both aggregate changes in the size of socioeconomic groups and individual labor market responses over the business cycle.
We first examine how the size of these groups evolved over the last three decades and how these changes correlate with long-run economic growth and cyclical economic fluctuations. To distinguish between long-run and cyclical movements, we decompose GDP per capita into trend and cyclical components using a Hodrick-Prescott filter (Hodrick and Prescott, 1997). We document two main patterns. First, the share of vulnerable non-poor individuals increased, driven by a greater incidence of transitions from poverty into vulnerability than from vulnerability into the non-vulnerable. This increase reinforces the view of vulnerability as a quantitatively important and persistent socioeconomic condition in Latin America. Second, while long-run economic growth is associated with a moderate expansion of the vulnerable group, negative cyclical fluctuations tend to increase its size, suggesting that macroeconomic conditions induce substantial movements across socioeconomic groups. These patterns are consistent with the idea that the vulnerable non-poor function as a transition group within the region's income distribution.
We then examine whether labor market outcomes of the poor and the vulnerable non-poor respond differently to macroeconomic conditions. A central empirical challenge is that labor market outcomes and socioeconomic status may be jointly determined by contemporaneous economic conditions. To address this issue, we implement two complementary strategies. First, for a subset of countries with longitudinal data, we classify individuals into socioeconomic groups based on lagged household income and examine how subsequent labor market outcomes evolve with macroeconomic conditions. Second, using repeated cross sections for the full sample of countries, we classify individuals according to predicted household income constructed from predetermined characteristics. Both approaches yield similar patterns. Across all countries, labor market outcomes are strongly procyclical for both groups: Periods of above-trend economic activity are associated with higher employment, lower unemployment and informality, and higher wages and earnings. However, the relative magnitude of these responses differs systematically across countries with different labor market structures.
In economies with higher levels of poverty, informality, and rurality, vulnerable individuals tend to experience stronger labor market improvements during economic expansions than the poor.
In contrast, in more developed economies, the poor often exhibit larger cyclical responses, particularly along employment margins. These patterns suggest that the relationship between vulnerability and macroeconomic fluctuations depends critically on the structure of labor markets. In less developed economies, poor individuals are more likely to be concentrated in low-productivity activities with limited scope for cyclical adjustment, while vulnerable individuals appear better positioned to benefit from economic expansions. By contrast, in more developed economies, poor individuals exhibit stronger attachment to the employment and unemployment margins, making their labor market outcomes more responsive to cyclical fluctuations.
Finally, exploiting the longitudinal data available for a subset of countries, we examine transitions into and out of vulnerability, thereby linking the cyclical labor market responses with changes in socioeconomic status. We document substantial movement into and out of vulnerability over the economic cycle and show that transitions are driven primarily by changes in labor income rather than other income sources or household composition. We further show that, within labor income, both the employment and earnings margins matter, with changes in hourly labor income playing a particularly important role in transitions into and out of vulnerability.
This paper relates to several strands of the literature. First, it contributes to work on vulnerability and the vulnerable non-poor in developing countries, which emphasizes that a large share of households above the poverty line remain exposed to adverse shocks and cannot be considered economically secure (Banerjee and Duflo, 2008; Birdsall et al., 2014; Lopez-Calva and Ortiz-Juarez, 2014; OECD, 2019). Second, it relates to the literature examining labor market adjustment over the business cycle in Latin America, particularly the role of the informality and employment margins in shaping responses to macroeconomic fluctuations (Bosch and Maloney, 2010; Fernandez and Meza, 2015; Coskun, 2022). Our contribution is to bring these strands of the literature together by examining how labor market responses to cyclical fluctuations differ between poor and vulnerable non-poor individuals, how these differences vary across labor market structures in the region, and how they translate into transitions across socioeconomic groups over the cycle.
The rest of the paper is organized as follows. Section 2 describes the main data sources and the construction of the harmonized database used throughout the analysis. Section 3 discusses the definition and operationalization of the vulnerable non-poor group and presents its main demographic, educational, and labor market characteristics. Section 4 examines the evolution of socioeconomic groups and their relationship with long-run economic growth and cyclical fluctuations. Section 5 analyzes how labor market outcomes of poor and vulnerable individuals respond to macroeconomic conditions and whether these responses differ systematically across socioeconomic groups and countries. Section 6 exploits longitudinal data to study transitions across socioeconomic groups over the economic cycle and the labor market mechanisms underlying those transitions. Section 7 concludes.
* * *
View full text here: https://publications.iadb.org/en/are-vulnerable-non-poor-different-heterogeneous-responses-macroeconomic-fluctuations-latin-america
[Category: IADB]
Inter-American Development Bank: 'AI and Judicial Productivity: The Impact of MIDAS on the Courts of Fortaleza, Brazil'
WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "AI and Judicial Productivity: The Impact of MIDAS on the Courts of Fortaleza, Brazil."
Here are excerpts:
* * *
Abstract
This paper presents preliminary results from a pilot study conducted in the courts of Ceara, Brazil. The study evaluates the impact of introducing a tool that uses natural language processing and machine learning techniques to cluster judicial acts by textual similarity on clerk productivity, measured as the number of case files a clerk can produce ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "AI and Judicial Productivity: The Impact of MIDAS on the Courts of Fortaleza, Brazil." Here are excerpts: * * * Abstract This paper presents preliminary results from a pilot study conducted in the courts of Ceara, Brazil. The study evaluates the impact of introducing a tool that uses natural language processing and machine learning techniques to cluster judicial acts by textual similarity on clerk productivity, measured as the number of case files a clerk can producein a day.
Estimates indicate that treatment-group clerks produced approximately 10 more case files per day than control-group clerks, a statistically significant difference equivalent to a 37% increase relative to the control group mean. The results are robust to the exclusion of outlier observations and exceptionally productive clerks.
* * *
Introduction
Estimates from 2019 indicated that around 1.5 billion individuals were unable to resolve their legal problems, despite living in contexts with a functioning justice system and institutions (World Justice Project, 2019). In Brazil, the State Justice system closed 2025 with nearly 58 million pending judicial processes, despite having adjudicated and closed more than 30 million during that year1. In the state of Ceara alone, the state Court of Justice (TJCE) received 692,630 new judicial processes and ended the year with more than one million pending.2
These backlogs are not merely an administrative problem, as the efficiency of the judicial system has significant economic repercussions. The literature shows that delays in resolving judicial proceedings hamper contract enforcement and insolvency resolution, generating a negative effect on firms' credit and investment (Djankov et al., 2008; Ponticelli and Alencar, 2016; Visaria, 2009; Chemin, 2012). Similarly, reductions in judicial processing times have been associated with greater entrepreneurship (Chemin, 2009), firm performance (Chakraborty, 2016), and the country's economic growth and development (Amirapu, 2021; Djankov et al., 2025). The need to increase judicial productivity becomes even more relevant in a context in which artificial intelligence tools are also reducing the costs of access to justice and potentially increasing the volume of litigation faced by courts (Shah and Levy, 2026).
This paper analyzes whether providing judicial clerks with a tool based on natural language processing (NLP) that clusters similar rulings, allowing them to process cases in batches rather than one by one, increases their daily productivity relative to the traditional workflow. To this end, we carried out a pilot study to examine the consequences of introducing the MIDAS system (Mecanismo Identificador de Actos Similares -- Similar Acts Identification Mechanism) at the First-Degree Judicial Secretariat (SEJUD) of the Court of Justice of Ceara (TJCE), Brazil3 .
MIDAS is a tool that uses NLP and machine learning techniques to cluster judicial rulings by textual similarity. Whereas in the traditional workflow clerks must prepare each case file one by one, with MIDAS clerks can prepare multiple case files at once, thereby reducing the average time per case and increasing total output volume. This increase in productivity should, in principle, contribute to reducing the backlog, to the extent that it does not simply shift the bottleneck to another stage of the judicial process.
The study was conducted between June and August 2025 within the framework of the Modernization Program for the Judiciary of the State of Ceara (PROMOJUD), with support from the Inter-American Development Bank (IDB). The study compared the daily productivity of 62 clerks assigned to two groups: a treatment group that processed case files with the support of MIDAS, and a control group that followed the traditional workflow. The central element of the design was randomization at the level of the ruling or judicial act: within each cluster generated by the system, rulings were randomly reordered using a Python script, and a fraction was extracted to be processed individually by the control group. This mechanism ensures that the judicial acts assigned to both groups come from the same pool of rulings and are comparable in their observable and unobservable characteristics in expectation.
The results indicate that treatment-group clerks produced on average 10 more case files per day than control-group clerks, a statistically significant difference equivalent to a 37% increase relative to the control group mean. Furthermore, these results are robust to the exclusion of potentially outlying observations, which reinforces the robustness of the conclusions. In terms of workload, our estimates suggest that access to the tool reduces case file preparation time from approximately 16 to 12 minutes per case file, a gain of 4 minutes per case that, accumulated over the workday, allows clerks to process one-third more case files in the same amount of time.
Furthermore, the tool appears to expand the right tail of the productivity distribution: days on which a clerk analyzes more than 100 case files are notably more frequent among those who used MIDAS. However, since the treatment group included both clerks with full adherence to the system and clerks with partial exposure, the estimated effects should be interpreted as a lower bound on the tool's true impact.
This paper contributes to the existing literature on the use of artificial intelligence to improve efficiency in the judicial sector (Aidid and Alarie, 2023; Casey and Niblett, 2019; Volokh, 2019).
Prior studies have shown how these tools can be used to automate routine tasks, such as analyzing and processing evidence, conducting investigations, and classifying legal documents (de Oliveira and Nascimento, 2021; Oliveira and Sperandio Nascimento, 2025; Razmetaeva and Razmetaev, 2021; Solovey et al., 2025), allowing court staff to devote more time to other activities (Alarie et al., 2018; Borgesano et al., 2025). The literature also highlights their potential to assist in judicial decision-making and, in some cases, to partially substitute judges' functions (Chen et al., 2022; Volokh, 2019). Some countries already report productivity gains derived from their use, including Argentina and Brazil (de Sousa et al., 2022; OECD, 2025). Our study contributes to this growing body of research by being, to our knowledge, the first to estimate the impact of an NLP-based clustering tool on judicial staff productivity through a pilot study with randomization at the level of the judicial act.
The remainder of the paper is organized as follows: Section 2 describes how the intervention works, the MIDAS system, and its position within the Judicial Secretariat's workflow. Section 3 then presents the study design, while Section 4 describes the data used. Section 5 presents the preliminary results of the pilot study, as well as the lessons that should be considered in future studies. Finally, Section 6 discusses the study's main methodological limitations and concludes the paper.
* * *
View full text here: https://publications.iadb.org/en/ai-and-judicial-productivity-impact-midas-courts-fortaleza-brazil
[Category: IADB]
Here are excerpts:
* * *
Abstract
This paper presents preliminary results from a pilot study conducted in the courts of Ceara, Brazil. The study evaluates the impact of introducing a tool that uses natural language processing and machine learning techniques to cluster judicial acts by textual similarity on clerk productivity, measured as the number of case files a clerk can produce ... Show Full Article WASHINGTON, Sept. 11 (TNSLrpt) -- The Inter-American Development Bank issued the following white paper in August 2026 entitled "AI and Judicial Productivity: The Impact of MIDAS on the Courts of Fortaleza, Brazil." Here are excerpts: * * * Abstract This paper presents preliminary results from a pilot study conducted in the courts of Ceara, Brazil. The study evaluates the impact of introducing a tool that uses natural language processing and machine learning techniques to cluster judicial acts by textual similarity on clerk productivity, measured as the number of case files a clerk can producein a day.
Estimates indicate that treatment-group clerks produced approximately 10 more case files per day than control-group clerks, a statistically significant difference equivalent to a 37% increase relative to the control group mean. The results are robust to the exclusion of outlier observations and exceptionally productive clerks.
* * *
Introduction
Estimates from 2019 indicated that around 1.5 billion individuals were unable to resolve their legal problems, despite living in contexts with a functioning justice system and institutions (World Justice Project, 2019). In Brazil, the State Justice system closed 2025 with nearly 58 million pending judicial processes, despite having adjudicated and closed more than 30 million during that year1. In the state of Ceara alone, the state Court of Justice (TJCE) received 692,630 new judicial processes and ended the year with more than one million pending.2
These backlogs are not merely an administrative problem, as the efficiency of the judicial system has significant economic repercussions. The literature shows that delays in resolving judicial proceedings hamper contract enforcement and insolvency resolution, generating a negative effect on firms' credit and investment (Djankov et al., 2008; Ponticelli and Alencar, 2016; Visaria, 2009; Chemin, 2012). Similarly, reductions in judicial processing times have been associated with greater entrepreneurship (Chemin, 2009), firm performance (Chakraborty, 2016), and the country's economic growth and development (Amirapu, 2021; Djankov et al., 2025). The need to increase judicial productivity becomes even more relevant in a context in which artificial intelligence tools are also reducing the costs of access to justice and potentially increasing the volume of litigation faced by courts (Shah and Levy, 2026).
This paper analyzes whether providing judicial clerks with a tool based on natural language processing (NLP) that clusters similar rulings, allowing them to process cases in batches rather than one by one, increases their daily productivity relative to the traditional workflow. To this end, we carried out a pilot study to examine the consequences of introducing the MIDAS system (Mecanismo Identificador de Actos Similares -- Similar Acts Identification Mechanism) at the First-Degree Judicial Secretariat (SEJUD) of the Court of Justice of Ceara (TJCE), Brazil3 .
MIDAS is a tool that uses NLP and machine learning techniques to cluster judicial rulings by textual similarity. Whereas in the traditional workflow clerks must prepare each case file one by one, with MIDAS clerks can prepare multiple case files at once, thereby reducing the average time per case and increasing total output volume. This increase in productivity should, in principle, contribute to reducing the backlog, to the extent that it does not simply shift the bottleneck to another stage of the judicial process.
The study was conducted between June and August 2025 within the framework of the Modernization Program for the Judiciary of the State of Ceara (PROMOJUD), with support from the Inter-American Development Bank (IDB). The study compared the daily productivity of 62 clerks assigned to two groups: a treatment group that processed case files with the support of MIDAS, and a control group that followed the traditional workflow. The central element of the design was randomization at the level of the ruling or judicial act: within each cluster generated by the system, rulings were randomly reordered using a Python script, and a fraction was extracted to be processed individually by the control group. This mechanism ensures that the judicial acts assigned to both groups come from the same pool of rulings and are comparable in their observable and unobservable characteristics in expectation.
The results indicate that treatment-group clerks produced on average 10 more case files per day than control-group clerks, a statistically significant difference equivalent to a 37% increase relative to the control group mean. Furthermore, these results are robust to the exclusion of potentially outlying observations, which reinforces the robustness of the conclusions. In terms of workload, our estimates suggest that access to the tool reduces case file preparation time from approximately 16 to 12 minutes per case file, a gain of 4 minutes per case that, accumulated over the workday, allows clerks to process one-third more case files in the same amount of time.
Furthermore, the tool appears to expand the right tail of the productivity distribution: days on which a clerk analyzes more than 100 case files are notably more frequent among those who used MIDAS. However, since the treatment group included both clerks with full adherence to the system and clerks with partial exposure, the estimated effects should be interpreted as a lower bound on the tool's true impact.
This paper contributes to the existing literature on the use of artificial intelligence to improve efficiency in the judicial sector (Aidid and Alarie, 2023; Casey and Niblett, 2019; Volokh, 2019).
Prior studies have shown how these tools can be used to automate routine tasks, such as analyzing and processing evidence, conducting investigations, and classifying legal documents (de Oliveira and Nascimento, 2021; Oliveira and Sperandio Nascimento, 2025; Razmetaeva and Razmetaev, 2021; Solovey et al., 2025), allowing court staff to devote more time to other activities (Alarie et al., 2018; Borgesano et al., 2025). The literature also highlights their potential to assist in judicial decision-making and, in some cases, to partially substitute judges' functions (Chen et al., 2022; Volokh, 2019). Some countries already report productivity gains derived from their use, including Argentina and Brazil (de Sousa et al., 2022; OECD, 2025). Our study contributes to this growing body of research by being, to our knowledge, the first to estimate the impact of an NLP-based clustering tool on judicial staff productivity through a pilot study with randomization at the level of the judicial act.
The remainder of the paper is organized as follows: Section 2 describes how the intervention works, the MIDAS system, and its position within the Judicial Secretariat's workflow. Section 3 then presents the study design, while Section 4 describes the data used. Section 5 presents the preliminary results of the pilot study, as well as the lessons that should be considered in future studies. Finally, Section 6 discusses the study's main methodological limitations and concludes the paper.
* * *
View full text here: https://publications.iadb.org/en/ai-and-judicial-productivity-impact-midas-courts-fortaleza-brazil
[Category: IADB]
IDB Lab Announces Winners of WeXchange Women STEMpreneurs 2026
WASHINGTON, Sept. 11 -- The Inter-American Development Bank issued the following news release on Sept. 10, 2026:
* * *
IDB Lab Announces Winners of WeXchange Women STEMpreneurs 2026
Five selected startups will present their solutions to investors in October during the IDB Group's Global Entrepreneurship and Technology Forum for Latin America and the Caribbean - GET Forum.
WASHINGTON -- IDB Lab, the innovation and venture arm of the Inter-American Development Bank Group (IDB Group), today announced the five women-led businesses selected as winners of the 2026 WeXchange Women STEMpreneurs competition. ... Show Full Article WASHINGTON, Sept. 11 -- The Inter-American Development Bank issued the following news release on Sept. 10, 2026: * * * IDB Lab Announces Winners of WeXchange Women STEMpreneurs 2026 Five selected startups will present their solutions to investors in October during the IDB Group's Global Entrepreneurship and Technology Forum for Latin America and the Caribbean - GET Forum. WASHINGTON -- IDB Lab, the innovation and venture arm of the Inter-American Development Bank Group (IDB Group), today announced the five women-led businesses selected as winners of the 2026 WeXchange Women STEMpreneurs competition.WeXchange is a platform that connects women entrepreneurs in science, technology, engineering, and mathematics (STEM) across Latin America and the Caribbean with investors and growth opportunities.
The 2026 edition received more than 350 applications from 35 countries, featuring solutions that promote economic or social development through edtech, healthtech, fintech, software as a service (SaaS), biotech, climatetech, agtech, and e-commerce, among other sectors. Colombia-based venture firm, EWA Capital led the selection process, which involved more than 80 judges from over 60 organizations, including 50 investment funds from across Latin America and the Caribbean.
The selected women-led startups are currently raising capital or planning to do so in the coming months. Their solutions will be showcased to venture capital investors and other innovation ecosystem stakeholders from Latin America and the Caribbean during WeXchange Demo Day 2026, which will take place on October 7 in Quito, Ecuador, as part of GET Forum.
The selected startups are:
Kresko RNAtech (Argentina): A biotechnology company developing a new generation of functional ingredients based on bioactive ribonucleic acid (RNA). The company combines artificial intelligence tools, a proprietary stabilization method, and biological validation to identify and develop compounds found in foods and plants with applications in health, nutrition, and beauty.
GOW Credit (Mexico): A technology company developing AI-powered infrastructure to finance small and medium-sized enterprises (SMEs) in Latin America. Its technology enables financial institutions to identify creditworthy SMEs that are often excluded from traditional approval processes, creating new financing opportunities aligned with their risk policies.
Ciudata (Bolivia): A business intelligence platform that supports decision-making for companies and public-sector entities. Using vehicles equipped with cameras and computer vision technology, the company collects territorial information and transforms it into data on commercial establishments and economic activity.
Pilou (Mexico): A wealth management platform that enables individuals and businesses across Latin America to invest in the United States and Mexico. It currently serves more than 1,500 clients in 17 countries.
Salva Health (Colombia): A technology company that developed Julieta, a portable, fast, and noninvasive medical device that facilitates access to breast cancer screening. The solution aims to expand access to early detection tools, particularly in communities with limited access to these services.
In addition to presenting their solutions during WeXchange Demo Day, the winning entrepreneurs will participate in networking activities with investors and other stakeholders in the entrepreneurship and innovation ecosystem during GET Forum, expanding their opportunities to connect with networks across Latin America and the Caribbean.
WeXchange's activities are made possible by the support of the Women Entrepreneurs Finance Initiative (We-Fi).
* * *
About IDB Lab
IDB Lab is the innovation and venture arm of the Inter-American Development Bank Group. We focus on entrepreneurship and technology to co-create solutions to development challenges and activate new industries for growth in Latin America and the Caribbean. IDB Lab supports high-impact startups and the innovation ecosystems they need to thrive through flexible financing, practical knowledge, and global connections. www.idblab.org
* * *
About EWA Capital
EWA Capital is a gender-lens venture capital fund focused on backing early-stage technology companies that are building the future of Latin America. It invests in scalable and disruptive business models that drive progress, accelerate regional transformation, and generate sustainable impact. EWA Capital supports companies from the seed stage through Series A. www.ewa.capital
* * *
About We-Fi
The Women Entrepreneurs Finance Initiative (We-Fi) is an unprecedented partnership dedicated to mobilizing funding for women-owned and women-led businesses in developing countries. We-Fi includes 14 governments and six multilateral development banks as implementing partners, as well as public- and private-sector collaborators from around the world. www.we-fi.org
* * *
Original text here: https://www.iadb.org/en/news/idb-lab-announces-winners-wexchange-women-stempreneurs-2026
* * *
IDB Lab Announces Winners of WeXchange Women STEMpreneurs 2026
Five selected startups will present their solutions to investors in October during the IDB Group's Global Entrepreneurship and Technology Forum for Latin America and the Caribbean - GET Forum.
WASHINGTON -- IDB Lab, the innovation and venture arm of the Inter-American Development Bank Group (IDB Group), today announced the five women-led businesses selected as winners of the 2026 WeXchange Women STEMpreneurs competition. ... Show Full Article WASHINGTON, Sept. 11 -- The Inter-American Development Bank issued the following news release on Sept. 10, 2026: * * * IDB Lab Announces Winners of WeXchange Women STEMpreneurs 2026 Five selected startups will present their solutions to investors in October during the IDB Group's Global Entrepreneurship and Technology Forum for Latin America and the Caribbean - GET Forum. WASHINGTON -- IDB Lab, the innovation and venture arm of the Inter-American Development Bank Group (IDB Group), today announced the five women-led businesses selected as winners of the 2026 WeXchange Women STEMpreneurs competition.WeXchange is a platform that connects women entrepreneurs in science, technology, engineering, and mathematics (STEM) across Latin America and the Caribbean with investors and growth opportunities.
The 2026 edition received more than 350 applications from 35 countries, featuring solutions that promote economic or social development through edtech, healthtech, fintech, software as a service (SaaS), biotech, climatetech, agtech, and e-commerce, among other sectors. Colombia-based venture firm, EWA Capital led the selection process, which involved more than 80 judges from over 60 organizations, including 50 investment funds from across Latin America and the Caribbean.
The selected women-led startups are currently raising capital or planning to do so in the coming months. Their solutions will be showcased to venture capital investors and other innovation ecosystem stakeholders from Latin America and the Caribbean during WeXchange Demo Day 2026, which will take place on October 7 in Quito, Ecuador, as part of GET Forum.
The selected startups are:
Kresko RNAtech (Argentina): A biotechnology company developing a new generation of functional ingredients based on bioactive ribonucleic acid (RNA). The company combines artificial intelligence tools, a proprietary stabilization method, and biological validation to identify and develop compounds found in foods and plants with applications in health, nutrition, and beauty.
GOW Credit (Mexico): A technology company developing AI-powered infrastructure to finance small and medium-sized enterprises (SMEs) in Latin America. Its technology enables financial institutions to identify creditworthy SMEs that are often excluded from traditional approval processes, creating new financing opportunities aligned with their risk policies.
Ciudata (Bolivia): A business intelligence platform that supports decision-making for companies and public-sector entities. Using vehicles equipped with cameras and computer vision technology, the company collects territorial information and transforms it into data on commercial establishments and economic activity.
Pilou (Mexico): A wealth management platform that enables individuals and businesses across Latin America to invest in the United States and Mexico. It currently serves more than 1,500 clients in 17 countries.
Salva Health (Colombia): A technology company that developed Julieta, a portable, fast, and noninvasive medical device that facilitates access to breast cancer screening. The solution aims to expand access to early detection tools, particularly in communities with limited access to these services.
In addition to presenting their solutions during WeXchange Demo Day, the winning entrepreneurs will participate in networking activities with investors and other stakeholders in the entrepreneurship and innovation ecosystem during GET Forum, expanding their opportunities to connect with networks across Latin America and the Caribbean.
WeXchange's activities are made possible by the support of the Women Entrepreneurs Finance Initiative (We-Fi).
* * *
About IDB Lab
IDB Lab is the innovation and venture arm of the Inter-American Development Bank Group. We focus on entrepreneurship and technology to co-create solutions to development challenges and activate new industries for growth in Latin America and the Caribbean. IDB Lab supports high-impact startups and the innovation ecosystems they need to thrive through flexible financing, practical knowledge, and global connections. www.idblab.org
* * *
About EWA Capital
EWA Capital is a gender-lens venture capital fund focused on backing early-stage technology companies that are building the future of Latin America. It invests in scalable and disruptive business models that drive progress, accelerate regional transformation, and generate sustainable impact. EWA Capital supports companies from the seed stage through Series A. www.ewa.capital
* * *
About We-Fi
The Women Entrepreneurs Finance Initiative (We-Fi) is an unprecedented partnership dedicated to mobilizing funding for women-owned and women-led businesses in developing countries. We-Fi includes 14 governments and six multilateral development banks as implementing partners, as well as public- and private-sector collaborators from around the world. www.we-fi.org
* * *
Original text here: https://www.iadb.org/en/news/idb-lab-announces-winners-wexchange-women-stempreneurs-2026
FHLBanks Price $1 Billion 2-Year Global on September 10, 2026
WASHINGTON, Sept. 11 -- The Federal Home Loan Banks Office of Finance issued the following news release:
* * *
FHLBanks Price $1 Billion 2-Year Global on September 10, 2026
The FHLBanks have priced the $1 billion 2-year Global as follows:
Leads: Barclays Capital Inc., Deutsche Bank Securities Inc., Wells Fargo Securities, LLC
Distribution Group: 7 Firms
CUSIP: 3130BC4W0
Pricing Date: 9/10/2026
Settlement: 9/11/2026
Maturity: 9/1/2028
Treasury Yield: 4.535%
Spread: 3.0 bps
Yield: 4.565%
Price: 99.880
Coupon: 4.5%
Interest Payments: March 1 and September 1, beginning on March ... Show Full Article WASHINGTON, Sept. 11 -- The Federal Home Loan Banks Office of Finance issued the following news release: * * * FHLBanks Price $1 Billion 2-Year Global on September 10, 2026 The FHLBanks have priced the $1 billion 2-year Global as follows: Leads: Barclays Capital Inc., Deutsche Bank Securities Inc., Wells Fargo Securities, LLC Distribution Group: 7 Firms CUSIP: 3130BC4W0 Pricing Date: 9/10/2026 Settlement: 9/11/2026 Maturity: 9/1/2028 Treasury Yield: 4.535% Spread: 3.0 bps Yield: 4.565% Price: 99.880 Coupon: 4.5% Interest Payments: March 1 and September 1, beginning on March1, 2027 (short first coupon)
Preliminary distribution information will be available on www.fhlb-of.com.
This announcement is neither an offer to sell, nor a solicitation of offers to buy, these securities. Distribution estimates are based on order book composition for syndicated issues furnished to the Office of Finance by underwriters at the time of securities pricing and may not reflect the current distribution of securities. The Office of Finance does not independently validate submitted data.
* * *
Original text here: https://www.fhlb-of.com/news/fhlbanks-price-1-billion-2-year-global-on-september-10-2026/
* * *
FHLBanks Price $1 Billion 2-Year Global on September 10, 2026
The FHLBanks have priced the $1 billion 2-year Global as follows:
Leads: Barclays Capital Inc., Deutsche Bank Securities Inc., Wells Fargo Securities, LLC
Distribution Group: 7 Firms
CUSIP: 3130BC4W0
Pricing Date: 9/10/2026
Settlement: 9/11/2026
Maturity: 9/1/2028
Treasury Yield: 4.535%
Spread: 3.0 bps
Yield: 4.565%
Price: 99.880
Coupon: 4.5%
Interest Payments: March 1 and September 1, beginning on March ... Show Full Article WASHINGTON, Sept. 11 -- The Federal Home Loan Banks Office of Finance issued the following news release: * * * FHLBanks Price $1 Billion 2-Year Global on September 10, 2026 The FHLBanks have priced the $1 billion 2-year Global as follows: Leads: Barclays Capital Inc., Deutsche Bank Securities Inc., Wells Fargo Securities, LLC Distribution Group: 7 Firms CUSIP: 3130BC4W0 Pricing Date: 9/10/2026 Settlement: 9/11/2026 Maturity: 9/1/2028 Treasury Yield: 4.535% Spread: 3.0 bps Yield: 4.565% Price: 99.880 Coupon: 4.5% Interest Payments: March 1 and September 1, beginning on March1, 2027 (short first coupon)
Preliminary distribution information will be available on www.fhlb-of.com.
This announcement is neither an offer to sell, nor a solicitation of offers to buy, these securities. Distribution estimates are based on order book composition for syndicated issues furnished to the Office of Finance by underwriters at the time of securities pricing and may not reflect the current distribution of securities. The Office of Finance does not independently validate submitted data.
* * *
Original text here: https://www.fhlb-of.com/news/fhlbanks-price-1-billion-2-year-global-on-september-10-2026/