<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel rdf:about="https://hdl.handle.net/10419/109225">
    <title>EconStor Collection:</title>
    <link>https://hdl.handle.net/10419/109225</link>
    <description />
    <items>
      <rdf:Seq>
        <rdf:li rdf:resource="https://hdl.handle.net/10419/336548" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/336547" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/336549" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/336545" />
      </rdf:Seq>
    </items>
    <dc:date>2026-04-28T15:29:40Z</dc:date>
  </channel>
  <item rdf:about="https://hdl.handle.net/10419/336548">
    <title>Workers' exposure to artificial intelligence across development stages</title>
    <link>https://hdl.handle.net/10419/336548</link>
    <description>Title: Workers' exposure to artificial intelligence across development stages
Authors: Lewandowski, Piotr; Madon, Karol; Park, Albert
Abstract: This paper develops a task-adjusted, country-specific measure of workers' exposure to artificial intelligence (AI) across 108 countries. Building on Felten et al. (2021), we adapt the artificial intelligence occupational exposure (AIOE) index to worker-level data from the Programme for the International Assessment of Adult Competencies (PIAAC) and extend it globally using comparable surveys and regression-based predictions, covering about 89% of global employment. Accounting for country-specific task structures reveals substantial cross-country heterogeneity: workers in low-income countries exhibit AI exposure levels roughly 0.8 United States (US) standard deviations below those in high-income countries, largely due to differences in within-occupation task content. Regression decompositions attribute most cross-country variation to information and communications technology intensity and human capital. Highincome countries employ the majority of workers in highly AI-exposed occupations, while lowincome countries concentrate in less exposed ones. Using two PIAAC cycles, we document rising AI exposure in high-income countries, driven by shifts in within-occupation tasks rather than employment structure.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://hdl.handle.net/10419/336547">
    <title>Heat stress, air pollution risk, and population exposure: Evidence from selected Asian countries</title>
    <link>https://hdl.handle.net/10419/336547</link>
    <description>Title: Heat stress, air pollution risk, and population exposure: Evidence from selected Asian countries
Authors: Mahmud, Minhaj Uddin; Zhang, Yujie
Abstract: This study examines the interplay between extreme temperatures and air pollution risks, the geographic and temporal distribution, as well as the population burden of climate shocks in Bangladesh, Indonesia, Pakistan, Thailand, and Viet Nam-countries severely impacted by climate change. Using ERA5-HEAT temperature data and PM2.5 pollution data, we first identify "hotspots" within and across the countries by analyzing district level trends in heat stress and pollution exposure. We further explore the correlation between temperature and pollution shocks. Finally, jointly considering the spatial distribution of populations and key climate and pollution hazards, we highlight the most vulnerable groups with population weighted exposure measures. Our findings reveal distinct country-specific patterns in both the correlation between heat stress and air pollution risk, and the population exposure to the hazards across demographic profiles. These results emphasize targeted policies to mitigate the compounded effects of climate and air pollution hazards on vulnerable populations across Asia.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://hdl.handle.net/10419/336549">
    <title>Private sector development and policy actions in Timor-Leste: Lessons from a business mapping survey</title>
    <link>https://hdl.handle.net/10419/336549</link>
    <description>Title: Private sector development and policy actions in Timor-Leste: Lessons from a business mapping survey
Authors: Shinozaki, Shigehiro
Abstract: This paper empirically investigates what drives the growth of private businesses in Timor-Leste and the policy actions needed to facilitate growth amid global uncertainty, especially for micro, small, and medium-sized enterprises (MSMEs). It uses the linear probability model (LPM) to identify the MSME growth structure and the Oaxaca decomposition to reveal detailed growth factors by firm type in determining which policy areas need to be strengthened. Estimation results show that four types of MSMEs-those formalized, digitalized, internationalized, and women-led -exhibit different business characteristics and performance results compared with control groups. Accordingly, policy support areas differ by firm type, with a focused approach critical in designing and delivering policy assistance to MSMEs in Timor-Leste. The study suggests policy implications to consider when designing an evidence-based MSME policy framework that can maximize the benefits from the country's recent accession to Association of Southeast Asian Nations membership.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://hdl.handle.net/10419/336545">
    <title>Long-term outcomes of multi-context childhood poverty: Evidence from long panel data from Indonesia</title>
    <link>https://hdl.handle.net/10419/336545</link>
    <description>Title: Long-term outcomes of multi-context childhood poverty: Evidence from long panel data from Indonesia
Authors: Chen, Jiaying; Molato, Rhea; Park, Albert; Tan, Donnie-Paul
Abstract: Which poverty context matters for long-term outcomes-family or community, economic or social? We construct a measure of poverty along these dimensions and analyze individual-level longitudinal data spanning 21 years in Indonesia to examine the long-term outcomes associated with different types of deprivation experienced in childhood. We find that adverse outcomes in adulthood are associated not only with growing up in a poor family but also in a poor community. Family poverty generally has a stronger influence than community poverty, except for health and life satisfaction, which are shaped more by the community. We also find that both economic and social domains matter, with the economic domain's influence being stronger. Girls' education suffers more from exposure to early deprivations, whereas boys' health is hit harder. Our findings highlight the importance of accounting for different dimensions of childhood deprivation, and they have strong policy implications for addressing inequality of opportunity.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
</rdf:RDF>

