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Impact of trade digitalization on trade, wages and producer prices : global, regional and national estimates
No full textTrade digitalization, or the use of electronic data, documents and systems to conduct international trade, has been instrumental in facilitating trade and reducing trade costs globally. UN ESCAP has a long-standing support programme in this area, spanning research, capacity building and intergovernmental cooperation. On the research side, UN ESCAP work has focused on the development of regional and global databases for evidence-based policy making, including the ESCAP-World Bank bilateral trade cost database, the UN Global Survey on Digital and Sustainable Trade Facilitation (www.untfsurvey.org), and the UN Trade Digitalization Index (TDI) (tdi.digitalizetrade.org). These databases provide a wealth of data that have been used by ESCAP as well as other organizations and individual experts to evaluate the impact of trade digitalization for many years. A plethora of reports and analyses have been released over time, using slightly different datasets and methodologies.
In this context, the purpose of this report is to provide easier access to our most recent estimates of the impact of trade digitalization at the global and regional levels, as well as to our previously unpublished national level estimates. Importantly, these estimates consider some of the most recent advances in trade digitalization efforts, and as such are often lower – but arguably, more realistic and up to date – than those found in older studies and reports. We also go to great length in trying to isolate the specific impact of trade digitalization, such that the estimates provided here may generally be interpreted as lower-bound estimates of the impacts.
The estimates presented cover the impacts of trade digitalization on trade costs, trade, wages and producer prices. Trade digitalization is expected to make engaging in trade easier and more efficient, resulting in lower trade costs. Lower trade costs result in increased competitiveness and growth in exports. Such improvements in trade efficiency and trade volumes may in turn allow for higher labor wages and reduced producer prices, as confirmed by the results presented here.
Readers and users are reminded that the results are from two different econometric models designed to evaluate the impact associated with implementation of the paperless trade and cross-border paperless trade measures included in the UN TDI. The trade costs impact estimates are based on a partial equilibrium model (i.e., not accounting for feedback effects), while trade, wages and producer prices estimates are based on a general equilibrium structural gravity model. The general equilibrium model specification is provided in Annex. Additional methodological details and models are available in UNESCAP (2023) and Duval and Utoktham (2024a).Introduction................................................................................................................................1
Global and Regional Overview..................................................................................................2
Developed Economies.................................................................................................................5
Latin America and the Caribbean...............................................................................................5
Middle East and North Africa.....................................................................................................5
South-East and East Asia.............................................................................................................5
South Asia......................................................................................................................................6
Pacific Islands...............................................................................................................................6
South and East Europe, Caucasus and Central Asia ................................................................6
Sub-Saharan Africa ......................................................................................................................6
Conclusion, limitations and way forward ..................................................................................7
References..................................................................................................................................9
Annex: Methodology ...............................................................................................................1
Big data for official statistics : strategic considerations and recommendations on data infrastructure and governance
No full textThis working paper provides guidance for Senior Leaders of National Statistical Offices (NSOs) seeking to modernise their data infrastructure in order to harness big data and other non-traditional data sources for official statistics. Drawing on country experiences from Asia and the Pacific, the paper outlines key infrastructure components, highlights best practices, and presents recommendations tailored to different levels of institutional readiness.
The development of this paper was supported by ESCAP’s project on Big Data for Official Statistics, funded by the 2030 Agenda Sub-Fund of the UN Peace and Development Trust Fund.Contents
- Figures and Boxes
Abstract
Executive summary
Introduction
Key concepts and definitions
Data Infrastructure
Distinguishing Data Infrastructure, Data Architecture, and IT Infrastructure
- Data governance
- Data Infrastructure components for an NSO
- Data Infrastructure: Strategic considerations
Recommendations
Annex 1: Country experiences
- Indonesia
- Uzbekistan
- Maldives
- Viet Nam
Annex 2: Glossary of terms and acronym
SATGPT for rapid flood mapping of the 2020 Poyang Lake flood in China
No full textThis working paper pioneers a flood mapping framework driven by an Large Language Models LLMs), integrating Google Earth Engine and ArcGIS to reconstruct the July 2020 flood event in Poyang Lake, China. The AI-driven tool, SATGPT, was benchmarked against Sentinel-1 SAR-derived water masks, quantifying a total inundation area of 2,579.08 km². Comparative validation achieved an Intersection over Union (IoU) of 0.776 (±0.032) and Dice coefficient of 0.874 (±0.019) across 30 validation tiles. Pixel-level classifications showed major water bodies were consistently detected by both algorithms (77.6% of the proportion). Key achievement of SATGPT’s ability to generate a stable flood delineation within 60 seconds, demonstrating its near real-time mapping capability. The paper establishes a benchmark for AI-driven disaster response tools, revealing that herbaceous wetlands bore 63.2% of the inundation impacts. Multicriteria impact analysis represented that herbaceous wetland experienced 63.2% (±2.1%) of inundation impacts. However, Sentinel-1’s 10-m resolution C-band SAR data detected 18.3% more ephemeral floods than SATGPT’s 30-m Landsat-based outputs (p<0.05), particularly in cloud-obscured riparian zones. To enhance future applications, the paper recommends transitioning to monthly water datasets, integrating SAR-optical data fusion, and adopting active learning to refine wetland segmentation. In conclusion, the working paper provides a replicable template for AI-driven disaster analytics supporting rapid response, insurance assessments, and climate-resilient planning in vulnerable delta regions.</p
Voluntary national review brief : Philippines DHS 2022
No full textLeave no one behind (LNOB) is the central, transformative promise of the 2030 Agenda for Sustainable
Development and its Sustainable Development Goals (SDGs). Between 2013 and 2022, Philippines has made
significant progress in enhancing equality of opportunity for all. Out of the 16 SDG indicators analyzed, 6
are either universally accessed or nearly universal. The challenge ahead is to close the gaps for the furthest
behind groups in remaining indicators such as completion of secondary and attendance to higher education
as well as access to clean fuels for cooking
Organizational Identity for Cross-border Digital Trade: Achieving Technical and Legal Interoperability Within the Model Law on the Use and Cross-border Recognition of Identity Management and Trust Services (MLIT) Using the vLEI
No full textIdentity management is a cornerstone of digital trade and a core component of trust services, starting with electronic signatures. However, there is limited awareness of its legal and technical implications. The United Nations Commission on International Trade Law (UNCITRAL) has prepared a Model Law on the Use and Cross-border Recognition of Identity Management and Trust Services to provide uniform guidance on how to establish an enabling legal environment for identity management and trust services. The Global Legal Entity Identifier Foundation (GLEIF) has developed the Legal Entity Identifier (LEI) and its digital counterpart the verifiable LEI (vLEI) as universal solutions for a secure and cost-effective persistent business identifier. This paper illustrates how the MLIT and the vLEI may interact to provide legal and operational certainty to identification needs, thus fostering global economic growth
Asia-Pacific perspectives on the Doha Programme of Action for the Least Developed Countries for the Decade 2022-2031
No full textBackground paper prepared for the Asia-Pacific Countries with Special Needs Development Report 2025This paper provides the first comprehensive review of the Doha Programme of Action (DPoA) in the Asia-Pacific region, as part of the biennial review mandated by the programme. The DPoA seeks to support least developed countries (LDCs) in achieving sustainable and irreversible graduation from the LDC category by building capacity and resilience to external shocks. This report proposes strategies and recommends 18 action items to address the challenges faced by the Asia-Pacific LDCs. Each action item is associated with an indicator to assess where each LDC stands and to reveal gaps against targets set for the end of DPoA in 2031
Evaluation of the project on Regional Inclusive Business Models in Agriculture and Food Systems
No full textBackground of the Evaluation
The United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), supported by
the Gates Foundation, implemented the “Regional Inclusive Business Models in Agriculture and Food
Systems” project from August 2021 to September 2024. With USD 1.5 million in funding, the project
targeted inclusive and innovative business practices within agriculture and food systems, aiming at
sustainable economic growth and poverty alleviation across India, Viet Nam, and Thailand.
Purpose and Scope
The evaluation, aligned with ESCAP’s Monitoring and Evaluation (M&E) guidelines and OECD-DAC
criteria, aimed at assessing the project’s relevance, effectiveness, efficiency, sustainability, and
integration of cross-cutting issues like gender mainstreaming and disability inclusion. It spanned the
entire implementation period and targeted key stakeholders involved in program activities, focusing
on accountability and providing actionable insights for future programming.
Methodology
A utilization-focused, mixed-methods approach was employed, involving extensive document
reviews, semi-structured interviews with 35 stakeholders from government, private sector, IBSOs, and
donors, as well as quantitative data analysis. The data collection combined both in-person and virtual
consultations, complemented by systematic analysis through triangulation and thematic coding
