Fekauʻaki mo
ʻOku tuʻu ʻeku ngaue ʻi he fetaulakiʻanga ʻo e fakalakalaka fakaʻekonomika, ngoue, tangata pe fefine, mo e fua ʻi he ngaahi fonua langalanga hake. ʻOku ou fakaʻaongaʻi ʻa e ngaahi savea lalahi ʻa e ngaahi famili mo e fakamatala fakasiospatial ke ako ʻaki ʻa e founga ngaue ʻa e ngaahi famili, faama mo e maketi, tautautefito ki he ngaahi feituʻu ʻoku siʻisiʻi pe faingataʻa ke tanaki ʻa e fakamatala. Naʻa ku maʻu hoku PhD ʻi he Graduate Institute ʻi Siniva pea kuo u ngaue mo e fakamatala ʻo e savea ʻi he IFPRI, IMF, mo e Pangike ʻa Mamani.
Naʻa ku fokotuʻu ʻa e Dollar Scholars, ko ha fakavaʻe ʻoku ne ʻomi ha ako fakapaʻanga ʻoku makatuʻunga ʻi he fakamoʻoni ki he kakai lalahi kuo teʻeki ai ke nau maʻu ia. Naʻa ku toe langa foki mo ʻene uepisaiti mo e sivi ʻi ha ngaahi lea kehekehe.
Kimuʻa ʻi he ʻekonomika, naʻa ku hoko ko ha tokotaha fakamatala fakapalofesinale ʻi he eSports ʻi ha taʻu ʻe fitu, ʻo ui ʻa e feʻauhi Counter-Strike mo e Warcraft III mei Seoul ki Monza ʻi he vahaʻa ʻo e 2004 mo e 2011. ʻOku ou kei vaʻinga pe ʻi he Counter-Strike 2 mo e Overwatch 2, pea ʻohofi ʻi he EverQuest.
Fekumi
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Pepa ngaue
Spreading the love around: how does agricultural labor demand respond to temporary weather shocks?
ʻI he ngaahi fonua ʻe ono, ʻoku fakafalala lahi ʻa e ngaahi famili faama ki he ngaue fakafamili lolotonga e laʻalaʻalaʻa mo e ngaahi vahaʻa taimi ʻuha ʻoku ʻikai angamaheni. ʻOku fakangatangata ange ʻa e ngaahi famili ʻoku taki ʻe he kakai fefine.
Abstract
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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Ngaue ʻoku lolotonga fakahoko
Assessing land area from above: evidence from Armenia
Naʻe siʻisiʻi ange ʻa e taimi ʻo hono fakatotoloʻi ʻo e ngaahi malaʻe ʻi he ngaahi ʻata fakasatelaite ʻe 42% ʻi hono fua ʻaki ʻa e GPS pea fakaleleiʻi ʻi he ngaahi fakafuofua naʻe lipooti pe ʻe kita, neongo naʻe siʻisiʻi ange ʻene tonu ʻi he GPS.
Abstract
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.
Faiako
Ko e taha moʻoni ia ʻo e kau palofesa lelei taha kuo u maʻu ʻi he ʻUnivesiti ʻo ʻAmelika. ʻOku ne feinga ke fakaʻaongaʻi ʻe he kalasi ʻa e ngaahi sipinga moʻoni ʻo e fakamatala kae ʻikai ko e ʻSally has 4 apple and Dave has 15 orangesʻ style problems... Ko e meʻa kotoa pe ia ʻoku totonu ke aʻusia ʻe ha palofesa ʻunivesiti pea mo ha niʻihi kehe.
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2021 – 2024
Palofesa Tokoni · ʻUnivesiti ʻAmelika
Fakalakalaka fakaʻekonomika, fakaʻaongaʻi ʻo e econometrics, mo e talateu micro- mo macroeconomics, akoʻi ʻaki ʻa e fakamatala moʻoni mo e ngaahi sipinga moʻoni ʻo e mamani.
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2015 – 2018
ʻInisititiuti Maʻolunga ʻi Siniva
Economics for International Affairs (2015–2016) mo e toe vakaiʻi ʻo e Macroeconomics A (2017–2018).
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Kolo
Economics Discord
Ko ha 501(c)(3) ʻoku ʻikai fakatupu paʻanga mo fengaueʻaki ʻa e Discord server ʻa ia ʻoku fetokoniʻaki ai ʻa e fanau ako ʻekonomika ʻe 3,000 tupu, kau fakatotolo mo e kau ʻekonomika mei he ngaahi fonua ʻe 30 ke nau ako.
Dollar Scholars
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Tokotaha Fokotuʻu
Dollar Scholars
Ko ha fakavaʻe ki he ako fakapaʻanga ʻa e kakai lalahi, ʻoku akoʻi fakahangatonu pea fakavaʻe ʻi he fakatotolo, koeʻuhi he ʻikai lava ʻe he fakamoʻua ʻo tokoniʻi ʻa e kakai ʻoku ʻikai ke nau ʻilo e founga hono fakaʻaongaʻi.
Polokalama
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Fakalakalaka ʻo e Uepisaiti
dollarscholars.org
Naʻa ku fokotuʻutuʻu, fakakouti mo liliu ʻa e uepisaiti ʻa e fakavaʻe: ko ha saiti Django ʻoku fokotuʻutuʻu ʻaki ʻa e Tailwind CSS pea ʻoku fakalele pe ʻe ia ʻaki ʻa e nginx ʻi ha server Ubuntu, ʻa ia naʻe toe langa foʻou mo e maluʻi ʻi he fakakaukau hili ha ʻohofi fakaʻilekitulonika. ʻOku haʻu ia ʻi ha lea fakafonua ʻe 44, kau ai ʻa e ngaahi lea fakafonua ʻe fa mei he toʻomataʻu ki he toʻohema (ʻAlepea, Hepeluu, Peasia mo e ʻUrdu), pea ʻe lava ke vakaiʻi mo fakatonutonu ʻe he kau liliu lea tokoni ha faʻahinga tatau pe ʻo fakafou ʻi ha founga ngaue faingofua ʻa e CSV.
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Financial Knowledge Study
Ko ha savea mo ha sivi ʻo e ʻilo fakapaʻanga ʻi ha ngaahi lea fakafonua kehekehe ʻi ha lea fakafonua ʻe 31, fakataha mo ha ngaahi fehuʻi fakafiefia ʻe nima hange ko e Pirate Speak mo e 1930s Mobster. ʻOku toʻo ʻa e ngaahi fehuʻi mei he Lusardi & Mitchell (2014) mo e FINRA Foundationʻs National Financial Capability Study; naʻe faʻu ʻa e ngaahi liliu ʻaki ʻa e Microsoft Copilot mo e Deep Translator pea vakaiʻi ʻe he kau ngaue tokoni ʻi he feituʻu ʻoku maʻu ai.
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Fakatotolo fakakomipiuta
Fakamatala mo e ʻanalaiso ʻo e geospatial
Python (pandas) mo e Stata ki he ngaahi fakamatala lalahi hange ko e savea ʻa e ngaahi ʻapi, mo e GIS (ArcGIS) ki he fakamatala fakasatelaite, kau ai ʻa e mape ʻo e ngaahi maama poʻuli ʻo e VIIRS ʻi mui ʻi he uepisaiti ko ʻeni.
fakamatala ʻi he eSports
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2004 – 2011
Tokotaha Fakamatala ʻo e eSports
Naʻe ui ko e Counter-Strike 1.6 mo e Warcraft III maʻa e ngaahi kautaha fakamafola kau ai ʻa e DirecTV, MTV mo e MTV Korea, fakataha mo e kau fakamafola hange ko e Alex “Vansilli” Nguyen, Glenn “Boomerman” Lane mo e Zach “Hoo_man” Godfrey.
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Press · MTV.com (ʻakaivi)
Video game play-by-play comes with a deadly challenge