Social protection programs are crucial for stabilizing household income, especially during crises. Brazil's response to the pandemic, the Auxilio Emergencial (AE) program, demonstrated the value of a resilient social safety net and digital tools. This study assesses AE's effectiveness in income stabilization, poverty reduction, and inequality. Results show that the pre-pandemic social protection system would have only buffered about a quarter of income loss, with unemployment insurance more significant for higher-income households, and social safety net transfers crucial for lower-income households, especially those in informal employment. AE successfully supported lower-income households during the pandemic, but its generosity went beyond the stabilization of income, resulting in large fiscal costs.
Generative artificial intelligence (gen AI) holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality. This note discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains. Given the vast uncertainty about the nature, impact, and speed of developments in gen AI, governments should take an agile approach that prepares them for both business as usual and highly disruptive scenarios.
This paper investigates the impact of automation on the U.S. labor market from 2000 to 2007, specifically examining whether more generous social protection programs can mitigate negative effects. Following Acemoglu and Restrepo (2020), the study finds that areas with higher robot adoption reduced employment and wages, in particular for workers without collegue degree. Notably, the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance (UI) alleviated the negative effects on wages, especially for less-skilled workers. The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation, contributing valuable insights for policymakers.
This paper investigates the impact of automation on the U.S. labor market from 2000 to 2007, specifically examining whether more generous social protection programs can mitigate negative effects. Following Acemoglu and Restrepo (2020), the study finds that areas with higher robot adoption reduced employment and wages, in particular for workers without collegue degree. Notably, the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance (UI) alleviated the negative effects on wages, especially for less-skilled workers. The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation, contributing valuable insights for policymakers.
Generative artificial intelligence (gen AI) holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality. This note discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains. Given the vast uncertainty about the nature, impact, and speed of developments in gen AI, governments should take an agile approach that prepares them for both business as usual and highly disruptive scenarios.
Social protection programs are crucial for stabilizing household income, especially during crises. Brazil's response to the pandemic, the Auxilio Emergencial (AE) program, demonstrated the value of a resilient social safety net and digital tools. This study assesses AE's effectiveness in income stabilization, poverty reduction, and inequality. Results show that the pre-pandemic social protection system would have only buffered about a quarter of income loss, with unemployment insurance more significant for higher-income households, and social safety net transfers crucial for lower-income households, especially those in informal employment. AE successfully supported lower-income households during the pandemic, but its generosity went beyond the stabilization of income, resulting in large fiscal costs.
This paper assesses the additional spending required to make substantial progress towards achieving the SDGs in Pakistan. We focus on critical areas of human (education and health) and physical (electricity, roads, and water and sanitation) capital. For each sector, we document the progress to date, assess where Pakistan stands relative to its peers, highlight key challenges, and estimate the additional spending required to make substantial progress. The estimates for the additional spending are derived using the IMF SDG costing methodology. We find that to achieve the SDGs in these sectors would require additional annual spending of about 16 percent of GDP in 2030 from the public and private sectors combined.
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