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Regional analysis of the wage discrimination in the indigenous workers in Mexico

Christian De la Luz-Tovar SIBYL ITALIA PINEDA SALAZAR (2023, [Artículo, Artículo])

The objective of this research is to estimate and decompose the wage gap between indigenous and non-indigenous workers by region in Mexico, to examine whether there are regional differences in the existing wage inequality that a priori affects the indigenous population and whether these differences can be attributed to the job profile of this group or by systematic labor discrimination against them. Using the data from the 2018 National Household Expenditure Revenue Survey (ENIGH-N) and the Oaxaca-Blinder decomposition, it was found that indigenous workers face a wage gap in all regions of the county. But, this gap is more pronounced in the center and south regions, where, on average, the associated component with labor discrimination has a percentage greater than 56. In contrast, in the north-central and northern regions of Mexico, the residual component is on average less than 33%, which suggests that the wage gap is explained by differences in productivity between groups.                         

Labor economics Ethnicity wage gap Indigenous population Regions Oaxaca-Blinder decomposition Economía laboral Brecha salarial étnica Población indígena Regiones Descomposición de Oaxaca-Blinder CIENCIAS SOCIALES CIENCIAS SOCIALES

Regional analysis of the wage discrimination in the indigenous workers in Mexico

Christian De la Luz-Tovar SIBYL ITALIA PINEDA SALAZAR (2023, [Artículo, Artículo])

The objective of this research is to estimate and decompose the wage gap between indigenous and non-indigenous workers by region in Mexico, to examine whether there are regional differences in the existing wage inequality that a priori affects the indigenous population and whether these differences can be attributed to the job profile of this group or by systematic labor discrimination against them. Using the data from the 2018 National Household Expenditure Revenue Survey (ENIGH-N) and the Oaxaca-Blinder decomposition, it was found that indigenous workers face a wage gap in all regions of the county. But, this gap is more pronounced in the center and south regions, where, on average, the associated component with labor discrimination has a percentage greater than 56. In contrast, in the north-central and northern regions of Mexico, the residual component is on average less than 33%, which suggests that the wage gap is explained by differences in productivity between groups.                         

Labor economics Ethnicity wage gap Indigenous population Regions Oaxaca-Blinder decomposition Economía laboral Brecha salarial étnica Población indígena Regiones Descomposición de Oaxaca-Blinder CIENCIAS SOCIALES CIENCIAS SOCIALES

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE