《 ᠪᠥᠬᠥᠨ 》
ᠮᠢᠨᠦ ᠠᠵᠢᠯ ᠪᠣᠯ ᠬᠥᠭᠵᠢᠵᠦ ᠪᠠᠶᠢᠭᠠ ᠤᠯᠤᠰ ᠤᠨ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ ᠬᠥᠭᠵᠢᠯᠲᠡ ᠂ ᠲᠠᠷᠢᠶᠠᠯᠠᠩ ᠮᠠᠯ ᠠᠵᠤ ᠠᠬᠤᠶ ᠂ ᠴᠢᠨᠠᠷ ᠤᠨ ᠢᠯᠭᠠᠯ ᠤᠨ ᠬᠡᠮᠵᠢᠶᠡ ᠂ ᠬᠡᠮᠵᠢᠬᠦ ᠵᠡᠷᠭᠡ ᠶᠢᠨ ᠠᠭᠤᠯᠭᠠ ᠶᠢᠨ ᠠᠭᠤᠯᠵᠠᠷ ᠳᠡᠭᠡᠷᠡ ᠪᠠᠶᠢᠬᠤ ᠶᠤᠮ ᠃ ᠪᠢ ᠲᠣᠮᠣ ᠬᠡᠪᠴᠢᠶᠡᠲᠦ ᠭᠡᠷ ᠪᠦᠯᠢ ᠶᠢᠨ ᠪᠠᠶᠢᠴᠠᠭᠠᠯᠲᠠ ᠪᠠ ᠣᠷᠣᠨ ᠪᠠᠶᠢᠷᠢ ᠶᠢᠨ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠶᠢ ᠠᠰᠢᠭᠯᠠᠨ ᠂ ᠡᠷᠦᠬᠡ᠂ ᠲᠠᠷᠢᠶᠠᠯᠠᠩ ᠤᠨ ᠲᠠᠯᠠᠪᠠᠶ ᠂ ᠵᠠᠬᠠ ᠳᠡᠯᠭᠡᠭᠦᠷ ᠦᠨ ᠠᠵᠢᠯᠯᠠᠭᠠ ᠶᠢ ᠰᠤᠳᠤᠯᠬᠤ ᠂ ᠣᠨᠴᠠᠭᠠᠶᠢᠯᠠᠨ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠳᠤᠲᠠᠭᠳᠠᠵᠤ ᠪᠠᠶᠢᠬᠤ ᠪᠤᠶᠤ ᠴᠤᠭᠯᠠᠭᠤᠯᠬᠤ ᠳᠤ ᠬᠦᠴᠢᠷ ᠲᠡᠶ ᠪᠠᠶᠢᠷᠢᠨ ᠳᠤ ᠰᠤᠳᠤᠯᠤᠯᠲᠠ ᠬᠢᠵᠦ ᠥᠩᠭᠡᠷᠡᠵᠡᠢ ᠃ ᠪᠢ ᠵᠧᠨᠧᠧᠸᠠ ᠶᠢᠨ ᠰᠤᠳᠤᠯᠬᠤ ᠰᠤᠷᠤᠭᠴᠢᠳ ᠤᠨ ᠰᠤᠷᠭᠠᠭᠤᠯᠢ ᠠᠴᠠ ᠳ᠋ᠣᠺᠲ᠋ᠣᠷ ᠴᠣᠯᠠ ᠬᠦᠷᠲᠡᠵᠦ ᠂ IFPRI᠂ ᠢᠮᠹ ᠂ ᠳᠡᠯᠡᠬᠡᠢ ᠶᠢᠨ ᠪᠠᠩᠭᠢ ᠳᠤ ᠪᠠᠶᠢᠴᠠᠭᠠᠯᠲᠠ ᠶᠢᠨ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠶᠢᠨ ᠠᠵᠢᠯ ᠢ ᠬᠢᠵᠦ ᠥᠩᠭᠡᠷᠡᠵᠡᠢ ᠃
ᠪᠢ Dollar Scholarsᠶᠢ ᠡᠭᠦᠳᠦᠨ ᠪᠠᠶᠢᠭᠤᠯᠤᠵᠠᠢ ᠂ ᠨᠢᠭᠡ ᠰᠠᠭᠤᠷᠢ ᠮᠥᠩᠭᠥᠨ ᠦ ᠰᠠᠭᠤᠷᠢ ᠮᠥᠩᠭᠥᠨ ᠦ ᠪᠦᠬᠦᠯᠢ ᠡᠳᠦᠷ ᠨᠢ ᠶᠡᠷᠦ ᠡᠴᠡ ᠣᠯᠵᠤ ᠦᠵᠡᠬᠦ ᠨᠢ ᠨᠠᠰᠤᠲᠠᠨ ᠳᠤ ᠦᠵᠡᠵᠦ ᠪᠠᠶᠢᠬᠤ ᠦᠭᠡᠢ ᠨᠠᠰᠤᠲᠠᠨ ᠳᠤ ᠭᠡᠷᠡᠴᠢ ᠮᠠᠲ᠋ᠧᠷᠢᠶᠠᠯ ᠤᠨ ᠡᠳ᠋ ᠦᠨ ᠵᠠᠰᠠᠭ ᠤᠨ ᠬᠥᠮᠥᠵᠢᠯ ᠢ ᠬᠠᠩᠭᠠᠳᠠᠭ ᠃ ᠪᠢ ᠪᠠᠰᠠ ᠲᠡᠳᠡᠨ ᠦ ᠰᠦᠯᠵᠢᠶᠡᠨ ᠥᠷᠲᠡᠭᠡ ᠪᠠ ᠣᠯᠠᠨ ᠬᠡᠯᠡ ᠬᠤᠪᠢᠶᠠᠭᠰᠠᠨ ᠰᠢᠯᠭᠠᠯᠲᠠ ᠶᠢᠨ ᠬᠦᠳᠡᠯᠭᠡᠭᠡᠨ ᠢ ᠪᠠᠢ᠌ᠭᠤᠯᠵᠤ
ᠠᠵᠤ ᠠᠬᠤᠢ ᠶᠢᠨ ᠤᠬᠠᠭᠠᠨ ᠤ ᠡᠮᠦᠨᠡ ᠂ ᠪᠢ ᠠᠵᠤ ᠠᠬᠤᠢ ᠶᠢᠨ ᠤᠬᠠᠭᠠᠨ ᠤ ᠡᠯᠧᠺᠲ᠋ᠷᠣᠨ ᠤ ᠰᠢᠭᠦᠮᠵᠢᠯᠡᠭ᠍ᠴᠢ ᠪᠣᠯᠵᠤ ᠲᠤᠯᠤᠭᠠᠨ ᠵᠢᠯ ᠪᠣᠯᠵᠠᠢ ᠂ 2004 ᠣᠨ ᠠᠴᠠ 2011 ᠣᠨ ᠳᠤ ᠰᠸᠦ ᠡᠴᠡ ᠮᠣᠠ ᠽᠠ ᠬᠦᠷᠬᠦ ᠳᠦ ᠭᠡᠳᠡᠷᠭᠦ ᠳᠠᠭᠠᠷᠢᠬᠤ ᠲᠡᠮᠡᠴᠡᠭᠡᠨ ᠂ ᠸᠢᠷᠤᠧᠲ᠋ ᠭᠤᠷᠪᠠᠳᠤᠭᠠᠷ ᠤᠷᠤᠯᠳᠤᠭᠠᠨ ᠢ ᠲᠠᠭᠠᠯᠠᠵᠠᠢ ᠃ ᠪᠢ ᠣᠳᠣ ᠪᠠᠰᠠ ᠡᠰᠡᠷᠭᠦᠴᠡᠭᠰᠡᠨ ᠴᠣᠬᠢᠯᠲᠠ 2 ᠪᠣᠯᠣᠨ 11 ᠣᠨ ᠤ ᠪᠠᠵᠢᠩ ᠢ ᠨᠠᠭᠠᠳᠴᠤ ᠂ ᠡᠭᠦᠷᠢᠳᠡ ᠡᠷᠢᠵᠦ ᠶᠠᠪᠤᠬᠤ ᠳᠤ ᠪᠠᠵᠢ ᠶᠢᠨ ᠬᠤᠪᠢ ᠪᠠᠷ ᠳᠠᠭᠠᠷᠢᠯᠲᠠ ᠬᠢᠵᠡᠢ ᠃
ᠰᠢᠨᠵᠢᠯᠡᠬᠦ ᠤᠬᠠᠭᠠᠨ ᠤ ᠰᠤᠳᠤᠯᠭᠠᠨ
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ᠠᠵᠢᠯᠯᠠᠬᠤ ᠴᠠᠭᠠᠰᠤ
Spreading the love around: how does agricultural labor demand respond to temporary weather shocks?
6 ᠤᠯᠤᠰ ᠤᠨ ᠬᠡᠪᠴᠢᠶᠠᠨ ᠳᠤ ᠲᠠᠷᠢᠶᠠᠴᠢᠣᠠ ᠤᠨ ᠬᠡᠷ ᠪᠤᠢᠷᠢ ᠶᠢᠨ ᠡᠷᠣᠭᠡ ᠭᠠᠩ ᠬᠠᠭᠰᠠᠷᠭᠠᠨ ᠪᠠ ᠬᠡᠪ ᠤᠨ ᠪᠤᠰᠤ ᠪᠤᠷᠣᠭᠠᠨ ᠤ ᠴᠠᠭ ᠬᠤᠭᠤᠴᠠᠭᠠᠨ ᠳᠤ ᠬᠡᠷ ᠪᠤᠢᠯᠢ ᠶᠢᠨ ᠬᠦᠢᠴᠣᠠ ᠤ ᠬᠦᠢᠴᠣᠠ ᠤ ᠬᠦᠢᠲᠠᠯᠮᠣᠷᠢ ᠶᠢ ᠨᠠᠮᠠᠭᠲᠠᠭᠦᠯᠵᠤ ᠪᠠᠢᠢᠨᠠ ᠃ ᠡᠮᠡᠭᠲᠠᠢ ᠠᠢᠢᠮᠠᠬᠲᠠᠨ ᠪᠠᠢᠢᠭᠠ ᠡᠷᠣᠭᠡ ᠡᠢᠯᠠᠣᠠᠲᠠ ᠬᠢᠵᠠᠭᠠᠷᠯᠠᠯ ᠲᠠᠢ ᠪᠠᠢᠢᠨᠠ ᠃
ᠬᠡᠶᠢᠰᠪᠦᠷᠢ ᠭᠠᠷᠭᠠᠬᠤ
This is the first study to separately identify labor demand responses to both unanticipated droughts and periods of above normal rainfall for three forms of agricultural labor that are central to agricultural production: family, hired, and mutual exchange labor. I use a simple parcel‑level model with three labor types and monitoring costs to study how family labor is reallocated across parcels during drought. In six countries I find small but statistically significant decreases in the share of hired labor during droughts and during periods of above-normal rainfall, indicating a strong reliance on family labor during shocks. I find that farm households appear to lean heavily on exchange labor by using exchange labor more frequently during periods of above-normal rainfall. Due to the heavy reliance on family labor, household size and the gender of the household head are important moderating factors. Women-headed households appear to be significantly more constrained in how they apply labor on family farms. My findings underscore that these labor types are distinct in how they respond to temporary weather shocks. The results of my analysis have important implications for policies targeted at stimulating agricultural labor markets.
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ᠠᠵᠢᠯ ᠢ ᠶᠠᠪᠤᠭᠳᠠᠭᠤᠯᠵᠤ ᠪᠠᠶᠢᠨᠠ
Assessing land area from above: evidence from Armenia
A randomized trial within a household survey: tracing fields on satellite imagery took 42% less time than measuring with GPS and improved on self-reported estimates, though it was less accurate than GPS.
ᠬᠡᠶᠢᠰᠪᠦᠷᠢ ᠭᠠᠷᠭᠠᠬᠤ
Using primary data collected by the World Bank in Armenia as part of a randomized controlled trial, we evaluate the use of a satellite image tracing to measure agricultural land in the context of a household survey. Satellite tracing offers several benefits, including a large reduction in the cost of measuring land, the potential to measure more parcels of land, and improved security for survey enumerators. We find the satellite tracing approach yields consistent results, and requires 42% less time than measuring with GPS, and provides value-added over self-reported estimates. Due to the potential cost savings, and other non-trivial benefits, we suggest that land measurement via satellite image tracing offers significant value, though is not as accurate as land area estimates conducted with a handheld GPS.
ᠪᠢᠴᠢᠭ᠌ ᠵᠢᠭᠠᠬᠤ
ᠲᠡᠷᠡ ᠦᠨᠡᠬᠡᠷ ᠮᠢᠨᠦ ᠠᠮᠧᠷᠢᠺᠠ ᠶᠢᠨ ᠶᠡᠬᠡ ᠰᠤᠷᠭᠠᠭᠤᠯᠢ ᠳᠤ ᠰᠤᠷᠤᠯᠴᠠᠵᠤ ᠥᠩᠭᠡᠷᠡᠭ᠍ᠰᠡᠨ ᠣᠬᠢ ᠫᠷᠣᠹᠧᠰᠰᠣᠷ ᠤᠨ ᠳᠣᠲᠣᠷᠠᠬᠢ ᠬᠠᠮᠤᠭ ᠰᠠᠢ᠌ᠨ ᠫᠷᠣᠹᠧᠰᠰᠣᠷ ᠤᠨ ᠳᠣᠲᠣᠷᠠᠬᠢ ᠨᠢᠭᠡ ᠃ ᠲᠡᠷᠡ ᠠᠯᠢ ᠪᠣᠯᠬᠤ ᠪᠠᠷ ᠠᠩᠭᠢ ᠳᠠᠭᠠᠨ ᠪᠣᠳᠠᠲᠤ ᠳᠡᠯᠡᠬᠡᠢ ᠶᠢᠨ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠶᠢᠨ ᠵᠢᠱᠢᠶᠡ ᠬᠡᠷᠡᠭᠯᠡᠭᠦᠯᠦᠨᠡ ᠂ ᠬᠠᠷᠢᠨ ᠵᠥᠪᠬᠡᠨ 〈 ᠰᠠᠷᠢ 4 ᠠᠯᠮᠤᠷᠠᠳ ᠲᠠᠢ ᠂ ᠳ᠋ᠠᠧ ᠸᠧ 15 ᠵᠦᠷᠵᠢ ᠲᠡᠢ 〉 ᠮᠠᠶᠢᠭ ᠤᠨ ᠠᠰᠠᠭᠤᠳᠠᠯ ᠲᠠᠢ ᠪᠢᠰᠢ ᠁ ᠲᠡᠷᠡ ᠪᠣᠯ ᠶᠡᠬᠡ ᠰᠤᠷᠭᠠᠭᠤᠯᠢ ᠶᠢᠨ ᠫᠣᠹᠧᠰᠰᠣᠷ ᠤᠨ ᠶᠣᠰᠣᠲᠠᠢ ᠠᠯᠢᠪᠠ ᠪᠥᠬᠥᠨ ᠢ ᠬᠠᠮᠤᠭ ᠠᠴᠠ ᠢᠯᠡᠭᠦᠦ ᠬᠢᠵᠦ ᠴᠢᠳᠠᠨᠠ ᠃
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2021 – 2024
ᠬᠠᠪᠰᠤᠷᠤᠭᠴᠢ ᠹᠣᠹᠧᠰᠰᠣᠷ · ᠠᠮᠧᠷᠢᠺᠠ ᠶᠢᠨ ᠶᠡᠬᠡ ᠰᠤᠷᠭᠠᠭᠤᠯᠢ
ᠪᠣᠳᠠᠲᠤ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠪᠣᠯᠤᠨ ᠪᠣᠳᠠᠲᠤ ᠳᠡᠯᠡᠬᠡᠶ ᠶᠢᠨ ᠬᠡᠪ ᠵᠠᠭᠪᠤᠷ ᠢᠶᠠᠷ ᠵᠢᠭᠠᠬᠤ ᠮᠠᠲ᠋ᠧᠷᠢᠶᠠᠯ ᠤᠨ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ ᠬᠥᠭᠵᠢᠯᠲᠡ᠂ ᠬᠡᠷᠡᠭᠯᠡᠭᠡᠨ ᠦ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ ᠤᠬᠠᠭᠠᠨ᠂ ᠳᠣᠲᠣᠭᠠᠳᠤ ᠶᠢᠨ ᠮᠡᠷᠭᠡᠵᠢᠯ ᠦᠨ ᠳᠣᠤᠷᠠ ᠠᠴᠠ ᠬᠢᠴᠢᠶᠡᠯᠯᠡᠬᠦ ᠪᠢᠴᠢᠯ ᠪᠠ ᠬᠣᠷᠸᠠ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ ᠤᠬᠠᠭᠠᠨ ᠤ ᠮᠡᠷᠭᠡᠵᠢᠯ᠃
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2015 – 2018
ᠵᠧᠨᠧᠧᠸᠠ ᠶᠢᠨ ᠰᠤᠳᠤᠯᠬᠤ ᠰᠤᠷᠤᠭᠴᠢ
ᠣᠯᠠᠨ ᠤᠯᠤᠰ ᠤᠨ ᠶᠠᠪᠤᠳᠠᠯ ᠤᠨ ᠠᠵᠤ ᠠᠬᠤᠶ ( 2015 ︱ 2016 ᠣᠨ ) ᠪᠣᠯᠤᠨ ᠬᠣᠷᠸᠠ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ A ( 2017 – 2018 ᠣᠨ ) ᠰᠢᠯᠭᠠᠷᠠᠭᠤᠯᠤᠨ ᠲᠣᠭᠲᠠᠭᠠᠬᠤ ᠬᠤᠷᠠᠯ ᠃
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ᠨᠡᠶᠢᠭᠡᠮ ᠲᠣᠭᠣᠷᠢᠭ
Economics Discord
300 ᠭᠠᠷᠤᠶ ᠤᠯᠤᠰ ᠠᠴᠠ ᠢᠷᠡᠭᠰᠡᠨ ᠠᠵᠤ ᠠᠬᠤᠢ ᠶᠢᠨ ᠰᠤᠷᠤᠭᠴᠢ ᠶᠢᠨ ᠰᠤᠳᠤᠯᠭᠠᠨ ᠤ ᠰᠤᠳᠤᠯᠭᠠᠨ ᠤ ᠠᠵᠢᠯᠲᠠᠨ ᠂ ᠠᠵᠤ ᠠᠬᠤᠶ ᠶᠢᠨ ᠤᠬᠠᠭᠠᠨᠲᠠᠳ ᠢ ᠬᠠᠪᠰᠤᠷᠴᠤ ᠰᠤᠷᠤᠯᠴᠠᠭᠰᠠᠨ 5ᠣ1（ᠴ８） 5ᠣ1（ᠴ） 301（ᠭᠤᠷᠪᠠ） ᠰᠤᠷᠭᠠᠨ ᠬᠥᠮᠦᠵᠢᠯ ᠦᠨ ᠠᠰᠢᠭ ᠬᠣᠨᠵᠢᠪᠤᠷᠢ ᠪᠤᠰᠤ ᠪᠠ ᠬᠠᠮᠵᠢᠯᠴᠠᠭᠠᠨ ᠤ ᠡᠵᠡᠩᠨᠡᠯᠲᠡ ᠶᠢᠨ Discord ᠦᠢᠯᠡᠴᠢᠯᠡᠭᠡᠨ ᠦ ᠮᠠᠰᠢᠨ ᠃
Dollar Scholars
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ᠡᠭᠦᠳᠦᠨ ᠪᠦᠲᠦᠭᠡᠭᠴᠢ
Dollar Scholars
ᠨᠠᠰᠤᠨ ᠳᠤ ᠬᠦᠷᠦᠭ᠍ᠰᠡᠳ ᠡᠳ᠋ ᠦᠨ ᠶᠠᠪᠤᠳᠠᠯ ᠤᠨ ᠬᠥᠮᠥᠵᠢᠯ ᠦᠨ ᠰᠠᠭᠤᠷᠢ ᠂ ᠬᠥᠮᠥᠨ ᠦ ᠬᠥᠮᠥᠵᠢᠯ ᠢ ᠬᠥᠮᠥᠵᠢᠭᠦᠯᠦᠨ ᠂ ᠰᠤᠳᠤᠯᠭᠠᠨ ᠤ ᠰᠠᠭᠤᠷᠢ ᠂ ᠤᠴᠢᠷ ᠨᠢ ᠢᠲᠡᠭᠡᠮᠵᠢ ᠶᠢᠨ ᠬᠡᠷᠡᠭ᠍ᠯᠡᠬᠦ ᠦᠭᠡᠢ ᠬᠥᠮᠥᠰ ᠢ ᠬᠠᠪᠰᠤᠷᠬᠤ ᠦᠭᠡᠢ ᠃
ᠲᠣᠭᠯᠠᠯᠲᠠ ᠨᠡᠪᠲᠡᠷᠡᠭᠦᠯᠬᠦ
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ເວັບ ᠨᠡᠭᠡᠭᠡᠨ ᠬᠥᠭᠵᠢᠭᠦᠯᠬᠦ
dollarscholars.org
ᠪᠢ ᠰᠠᠭᠤᠷᠢ ᠮᠥᠩᠭᠥᠨ ᠦ ᠰᠠᠭᠤᠷᠢ ᠮᠥᠩᠭᠥᠨ ᠦ ᠰᠦᠯᠵᠢᠶᠡ ᠶᠢ ᠵᠢᠷᠤᠭᠯᠠᠨ ᠲᠥᠯᠥᠪᠯᠡᠵᠦ ᠂ ᠺᠣᠣᠳ᠋ᠯᠠᠬᠤ ᠪᠠ ᠥᠪᠡᠷ ᠢᠶᠡᠷ ᠣᠷᠴᠢᠭᠤᠯᠵᠤ ᠂ ᠲᠦᠶᠢᠶᠢᠸᠧᠨ ᠺᠣᠮᠠᠨ ᠤ ᠺᠣᠮᠢᠰ ᠢᠶᠠᠷ ᠵᠣᠬᠢᠶᠠᠭᠰᠠᠨ ᠂ ᠣᠨᠭᠧᠩ ᠺᠣᠮᠠᠨ ᠢᠶᠠᠷ ᠥᠪᠡᠷ ᠢᠶᠡᠨ ᠨᠠᠢ᠌ᠬᠢᠯᠭᠠᠭᠰᠠᠨ ᠂ ᠨᠢᠭᠡ ᠰᠠᠭᠤᠷᠢ ᠢᠨᠲ᠋ᠸᠷ ᠳᠡᠭᠡᠷᠡ ᠥᠪᠡᠷ ᠢᠶᠡᠨ ᠨᠠᠢ᠌ᠵᠢᠭᠤᠯᠤᠭᠰᠠᠨ ᠵᠧᠨᠭᠧᠦ ᠰᠦᠯᠵᠢᠶᠡᠨ ᠦ ᠥᠷᠲᠡᠭᠡ ᠂ ᠢᠨᠲ᠋ᠸᠷ ᠳᠠᠭᠠᠷᠢᠯᠲᠠ ᠶᠢᠨ ᠳᠠᠷᠠᠭᠠ ᠠᠮᠤᠷ ᠲᠦᠪᠰᠢᠨ ᠦ ᠲᠣᠬᠠᠢ ᠪᠣᠳᠣᠯᠬᠢᠯᠠᠵᠤ ᠳᠠᠬᠢᠨ ᠪᠠᠶᠢᠭᠤᠯᠬᠤ ᠃ 44 ᠬᠡᠯᠡᠨ ᠦ ᠬᠡᠪᠯᠡᠯ ᠲᠡᠢ ᠂ ᠡᠭᠦᠨ ᠳᠦ ᠪᠠᠷᠠᠭᠤᠨ ᠠᠴᠠ ᠵᠡᠭᠦᠨᠰᠢ 4 ᠬᠡᠯᠡ ( ᠠᠷᠠᠪ ᠂ ᠾᠧᠪᠧᠢ ᠂ ᠫᠧᠷᠰ ᠬᠡᠯᠡ ᠂ ᠦᠷᠳ᠋ ) ᠵᠡᠷᠭᠡ ᠳᠥᠷᠪᠡᠨ ᠵᠦᠢᠯ ᠦᠨ ᠬᠡᠯᠡ ᠪᠡᠷ ᠪᠠᠶᠢᠨᠠ ᠂ ᠦᠢᠯᠡᠴᠢᠯᠡᠨ ᠣᠷᠴᠢᠭᠤᠯᠤᠭᠴᠢ ᠳᠥᠭᠥᠮ CSV ᠴᠤᠪᠤᠷᠠᠯ ᠢᠶᠠᠷ ᠳᠠᠮᠵᠢᠨ ᠶᠠᠮᠠᠷᠪᠠ ᠬᠡᠪᠯᠡᠯ ᠢ ᠪᠠᠶᠢᠴᠠᠭᠠᠵᠤ ᠵᠠᠯᠠᠷᠠᠭᠤᠯᠵᠤ ᠪᠣᠯᠣᠨᠠ ᠃
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Financial Knowledge Study
A multilingual financial-knowledge survey and quiz in 31 languages, plus five playful ones such as Pirate Speak and 1930s Mobster. Questions are adapted from Lusardi & Mitchell (2014) and the FINRA Foundation's National Financial Capability Study; translations were drafted with Microsoft Copilot and Deep Translator and reviewed by volunteers where available.
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ᠰᠤᠳᠤᠯᠬᠤ ᠺᠣᠮᠫᠢᠦᠢᠲ᠋ᠧᠷ ᠦᠨ ᠮᠡᠷᠭᠡᠵᠢᠯ
ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠬᠢᠭᠡᠳ ᠭᠠᠵᠠᠷ ᠵᠦᠢ ᠶᠢᠨ ᠵᠠᠳᠠᠯᠤᠯᠲᠠ
ᠭᠡᠷ ᠪᠦᠯᠢ ᠶᠢᠨ ᠪᠠᠶᠢᠴᠠᠭᠠᠯᠲᠠ ᠶᠢᠨ ᠵᠡᠷᠭᠡ ᠲᠣᠮᠣ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠳᠤ ᠬᠡᠷᠡᠭᠯᠡᠬᠦ Python ᠪᠣᠯᠣᠨ Stata ᠂ ᠳᠠᠭᠠᠭᠤᠯᠢ ᠣᠳᠣᠨ ᠤ ᠲᠣᠭᠠ ᠪᠠᠷᠢᠮᠲᠠ ᠳᠤ ᠬᠡᠷᠡᠭᠯᠡᠬᠦ GIS ( ArcGIS ) ᠪᠠᠭᠲᠠᠨᠠ ᠂ ᠡᠭᠦᠨ ᠳᠦ ᠲᠤᠰ ᠰᠦᠯᠵᠢᠶᠡ ᠳᠡᠭᠡᠷᠡᠬᠢ ᠸᠢᠶᠢᠷ ᠰᠥᠨᠢ ᠶᠢᠨ ᠳ᠋ᠧᠩ ᠦᠨ ᠨᠡᠶᠢᠯᠡᠮᠡᠯ ᠵᠢᠷᠤᠭ ᠢ ᠪᠠᠭᠲᠠᠭᠠᠨᠠ ᠃
ᠡᠯᠧᠺᠲ᠋ᠷᠣᠨ ᠲᠠᠮᠢᠷ ᠤᠨ ᠰᠢᠭᠦᠮᠵᠢ
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2004 – 2011
ᠡᠯᠢᠭᠡᠨ ᠦ ᠲᠠᠮᠢᠷ ᠤᠨ ᠰᠢᠭᠦᠮᠵᠢᠯᠡᠭᠴᠢ
ᠳ᠋ᠢᠯᠢᠶᠷ ᠲᠦ ᠲᠧᠯᠧᠢᠰ ᠂ ᠲᠧᠯᠸᠢᠰ ᠂ ᠲᠧᠯᠸᠢᠰ ᠂ ᠰᠣᠯᠣᠩᠭᠣᠰ ᠵᠡᠷᠭᠡ ᠨᠡᠪᠲᠡᠷᠡᠭᠦᠯᠭᠡ ᠶᠢᠨ ᠬᠣᠷᠢᠶᠠ ᠶᠢᠨ Alex “Vansilli” Nguyen᠂ Glenn “Boomerman” Lane ᠂ Zach “Hoo_man” Godfreyᠵᠡᠷᠭᠡ ᠨᠡᠪᠲᠡᠷᠡᠭᠦᠯᠭᠡ ᠶᠢᠨ ᠬᠣᠯᠪᠣᠭᠠᠯᠠᠭᠴᠢ ᠭᠡᠳᠡᠭ ᠃
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Press · MTV.com ( ᠳᠠᠩᠰᠠ ᠡᠪᠬᠡᠮᠡᠯ ᠳᠦ )
Video game play-by-play comes with a deadly challenge