{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99138"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99138","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Investigating the heterogeneous effects of temperature on economic growth","abstract":"\"This paper investigates the effect of temperature on economic growth on a panel of 156 countries over a 50-year time period. We use random fluctuations in a country's annual average temperature over time as our strategy for identifying the causal effect of temperature on GDP per capita growth. Previous work has found that temperature has a statistically significant relationship with growth when fitting data from all countries with a nonlinear model. We hypothesize that countries with different observable characteristics have differing responses to changes in temperature and we use recursive model partitioning to find data-driven splits in the dataset. We run the model on a number of country-level characteristics and we find that GDP per capita percentile and historical mean temperature are the variables that best fit the data. We divide the countries into four groups: low income - low temperature (20 countries), low income - high temperature (70 countries), high income - low temperature (36 countries), high income - high temperature (33 countries). We obtain different coefficients for the relationship between temperature and growth for each group. We also use agricultural GDP per capita growth as a dependent variable and obtain coefficients for the relationship between temperature and agricultural GDP per capita growth. We compare our results with results obtained from a model that does not have any splits (Burke et. al., 2015). We find that dividing countries into groups using a data-driven method has a significant impact on future projections of GDP per capita growth under different climate change scenarios and how we interpret the effects that changes in average temperatures might have on countries. A model that does not split countries into groups overestimates the gains that low temperature countries might make from rising temperatures and underestimates the ability of high income - high temperature countries to capitalize on the high temperatures that they regularly experience. Our model takes into account adaptations to historical temperatures that countries may have and we find that low temperature countries have lower \"\"optimum temperatures\"\" that help them achieve maximum growth and likewise, high temperature countries have higher \"\"optimum temperatures.\"\" We predict that low income - low temperature countries and high income - low temperature countries are likely to face low growth (1.73 % and 0.00879 % respectively) in the year 2100; low income - high temperature countries will face negative growth (-3.19 %) and high income - high temperature countries will face positive growth (3.16 %).\"","abstract_html":""This paper investigates the effect of temperature on economic growth on a panel of 156 countries over a 50-year time period. We use random fluctuations in a country's annual average temperature over time as our strategy for identifying the causal effect of temperature on GDP per capita growth. Previous work has found that temperature has a statistically significant relationship with growth when fitting data from all countries with a nonlinear model. We hypothesize that countries with different observable characteristics have differing responses to changes in temperature and we use recursive model partitioning to find data-driven splits in the dataset. We run the model on a number of country-level characteristics and we find that GDP per capita percentile and historical mean temperature are the variables that best fit the data. We divide the countries into four groups: low income - low temperature (20 countries), low income - high temperature (70 countries), high income - low temperature (36 countries), high income - high temperature (33 countries). We obtain different coefficients for the relationship between temperature and growth for each group. We also use agricultural GDP per capita growth as a dependent variable and obtain coefficients for the relationship between temperature and agricultural GDP per capita growth. We compare our results with results obtained from a model that does not have any splits (Burke et. al., 2015). We find that dividing countries into groups using a data-driven method has a significant impact on future projections of GDP per capita growth under different climate change scenarios and how we interpret the effects that changes in average temperatures might have on countries. A model that does not split countries into groups overestimates the gains that low temperature countries might make from rising temperatures and underestimates the ability of high income - high temperature countries to capitalize on the high temperatures that they regularly experience. Our model takes into account adaptations to historical temperatures that countries may have and we find that low temperature countries have lower ""optimum temperatures"" that help them achieve maximum growth and likewise, high temperature countries have higher ""optimum temperatures."" We predict that low income - low temperature countries and high income - low temperature countries are likely to face low growth (1.73 % and 0.00879 % respectively) in the year 2100; low income - high temperature countries will face negative growth (-3.19 %) and high income - high temperature countries will face positive growth (3.16 %)."","abstract_has_math":false,"creators":["Ang, Qi Qi Amanda"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Applied Econ","degree_department":null,"school":null,"contributors":["Crost, Benjamin","Baylis, Katherine R.","Dall'Erba, Sandy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-02T20:02:39Z","date_published":"2018-03-02T20:02:39Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Economics","Climate","Growth","Temperature"],"languages":["eng"],"rights":["Copyright 2017 Qi Qi Amanda Ang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99138","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Crost, Benjamin","Baylis, Katherine R.","Dall'Erba, Sandy"]},{"key":"dc:creator","label":"Author","values":["Ang, Qi Qi Amanda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-02T20:02:39Z","2020-03-03T10:15:35Z","2017-07-21","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Applied Econ"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economics","Climate","Growth","Temperature"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Qi Qi Amanda Ang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99138"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"This paper investigates the effect of temperature on economic growth on a panel of 156 countries over a 50-year time period. We use random fluctuations in a country's annual average temperature over time as our strategy for identifying the causal effect of temperature on GDP per capita growth. Previous work has found that temperature has a statistically significant relationship with growth when fitting data from all countries with a nonlinear model. We hypothesize that countries with different observable characteristics have differing responses to changes in temperature and we use recursive model partitioning to find data-driven splits in the dataset. We run the model on a number of country-level characteristics and we find that GDP per capita percentile and historical mean temperature are the variables that best fit the data. We divide the countries into four groups: low income - low temperature (20 countries), low income - high temperature (70 countries), high income - low temperature (36 countries), high income - high temperature (33 countries). We obtain different coefficients for the relationship between temperature and growth for each group. We also use agricultural GDP per capita growth as a dependent variable and obtain coefficients for the relationship between temperature and agricultural GDP per capita growth. We compare our results with results obtained from a model that does not have any splits (Burke et. al., 2015). We find that dividing countries into groups using a data-driven method has a significant impact on future projections of GDP per capita growth under different climate change scenarios and how we interpret the effects that changes in average temperatures might have on countries. A model that does not split countries into groups overestimates the gains that low temperature countries might make from rising temperatures and underestimates the ability of high income - high temperature countries to capitalize on the high temperatures that they regularly experience. Our model takes into account adaptations to historical temperatures that countries may have and we find that low temperature countries have lower \"\"optimum temperatures\"\" that help them achieve maximum growth and likewise, high temperature countries have higher \"\"optimum temperatures.\"\" We predict that low income - low temperature countries and high income - low temperature countries are likely to face low growth (1.73 % and 0.00879 % respectively) in the year 2100; low income - high temperature countries will face negative growth (-3.19 %) and high income - high temperature countries will face positive growth (3.16 %).\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01","The student, Qi Qi Amanda Ang, accepted the attached license on 2017-07-20 at 16:39.","The student, Qi Qi Amanda Ang, submitted this Thesis for approval on 2017-07-20 at 16:45.","This Thesis was approved for publication on 2017-07-21 at 10:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11564 on 2018-03-02 at 13:03:08","Made available in DSpace on 2018-03-02T20:02:39Z (GMT). No. of bitstreams: 2 ANG-THESIS-2017.pdf: 560466 bytes, checksum: 4bd7841814b766fc9f26eb2ef9ab6bfc (MD5) LICENSE.txt: 4213 bytes, checksum: df266d9195baf6e707d9b252d2e4ff4e (MD5) Previous issue date: 2017-07-21","Embargo set by: Seth Robbins for item 105093 Lift date: 2020-03-02T20:02:46Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 105093 on 2020-03-03T10:15:35Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigating the heterogeneous effects of temperature on economic growth"]}]}],"canonical_facts":{"dc:contributor":["Crost, Benjamin","Baylis, Katherine R.","Dall'Erba, Sandy"],"dc:creator":["Ang, Qi Qi Amanda"],"dc:date":["2018-03-02T20:02:39Z","2020-03-03T10:15:35Z","2017-07-21","2017-08"],"dc:description":["\"This paper investigates the effect of temperature on economic growth on a panel of 156 countries over a 50-year time period. We use random fluctuations in a country's annual average temperature over time as our strategy for identifying the causal effect of temperature on GDP per capita growth. Previous work has found that temperature has a statistically significant relationship with growth when fitting data from all countries with a nonlinear model. We hypothesize that countries with different observable characteristics have differing responses to changes in temperature and we use recursive model partitioning to find data-driven splits in the dataset. We run the model on a number of country-level characteristics and we find that GDP per capita percentile and historical mean temperature are the variables that best fit the data. We divide the countries into four groups: low income - low temperature (20 countries), low income - high temperature (70 countries), high income - low temperature (36 countries), high income - high temperature (33 countries). We obtain different coefficients for the relationship between temperature and growth for each group. We also use agricultural GDP per capita growth as a dependent variable and obtain coefficients for the relationship between temperature and agricultural GDP per capita growth. We compare our results with results obtained from a model that does not have any splits (Burke et. al., 2015). We find that dividing countries into groups using a data-driven method has a significant impact on future projections of GDP per capita growth under different climate change scenarios and how we interpret the effects that changes in average temperatures might have on countries. A model that does not split countries into groups overestimates the gains that low temperature countries might make from rising temperatures and underestimates the ability of high income - high temperature countries to capitalize on the high temperatures that they regularly experience. Our model takes into account adaptations to historical temperatures that countries may have and we find that low temperature countries have lower \"\"optimum temperatures\"\" that help them achieve maximum growth and likewise, high temperature countries have higher \"\"optimum temperatures.\"\" We predict that low income - low temperature countries and high income - low temperature countries are likely to face low growth (1.73 % and 0.00879 % respectively) in the year 2100; low income - high temperature countries will face negative growth (-3.19 %) and high income - high temperature countries will face positive growth (3.16 %).\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01","The student, Qi Qi Amanda Ang, accepted the attached license on 2017-07-20 at 16:39.","The student, Qi Qi Amanda Ang, submitted this Thesis for approval on 2017-07-20 at 16:45.","This Thesis was approved for publication on 2017-07-21 at 10:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11564 on 2018-03-02 at 13:03:08","Made available in DSpace on 2018-03-02T20:02:39Z (GMT). No. of bitstreams: 2 ANG-THESIS-2017.pdf: 560466 bytes, checksum: 4bd7841814b766fc9f26eb2ef9ab6bfc (MD5) LICENSE.txt: 4213 bytes, checksum: df266d9195baf6e707d9b252d2e4ff4e (MD5) Previous issue date: 2017-07-21","Embargo set by: Seth Robbins for item 105093 Lift date: 2020-03-02T20:02:46Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 105093 on 2020-03-03T10:15:35Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/99138"],"dc:language":["eng"],"dc:rights":["Copyright 2017 Qi Qi Amanda Ang"],"dc:subject":["Economics","Climate","Growth","Temperature"],"dc:title":["Investigating the heterogeneous effects of temperature on economic growth"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural & Applied Econ"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:37Z"}