Thomas Pave Sohnesen
Biographic Data
| ID | 6455192 |
|---|---|
| NAME | Thomas Pave Sohnesen |
| GIVEN NAMES | Thomas Pave |
| FAMILY NAME | Sohnesen |
| SIGNATURE | SOHNESEN T P |
| AFFILIATIONS | University of Copenhagen |
| ORCID | 0000-0003-3942-8568 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 10 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 2 |
Educational expansion and shifting private returns to education
We examine how returns to education have evolved in the context of post‐conflict reconstruction and economic growth in Mozambique over the period 1996–2015. We show that private rates of return to education have declined at lower levels of schooling, but remained stable and possibly even increased at the highest levels. Returns are increasingly convex in non‐agricultural jobs but almost flat in agriculture. Using consumption expenditure data, as …
Impact of Ethiopia’s 2015 drought on child undernutrition
In 2015, Ethiopia experienced one of its worst droughts in decades. Using nationally representative data from before and after the event, we do not find evidence that this drought led to widespread increases in chronic or acute child undernutrition. However, further analysis indicates that chronic undernutrition rates did increase in drought-exposed areas that had a limited road network. This finding suggests that the recent investments in road i…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty…
Impact of Ethiopia’s 2015 drought on child undernutrition
In 2015, Ethiopia experienced one of its worst droughts in decades. Using nationally representative data from before and after the event, we do not find evidence that this drought led to widespread increases in chronic or acute child undernutrition. However, further analysis indicates that chronic undernutrition rates did increase in drought-exposed areas that had a limited road network. This finding suggests that the recent investments in road i…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Impact of Ethiopia’s 2015 drought on child undernutrition
In 2015, Ethiopia experienced one of its worst droughts in decades. Using nationally representative data from before and after the event, we do not find evidence that this drought led to widespread increases in chronic or acute child undernutrition. However, further analysis indicates that chronic undernutrition rates did increase in drought-exposed areas that had a limited road network. This finding suggests that the recent investments in road i…
Educational expansion and shifting private returns to education
We examine how returns to education have evolved in the context of post‐conflict reconstruction and economic growth in Mozambique over the period 1996–2015. We show that private rates of return to education have declined at lower levels of schooling, but remained stable and possibly even increased at the highest levels. Returns are increasingly convex in non‐agricultural jobs but almost flat in agriculture. Using consumption expenditure data, as …
Economic growth (4 works) · Economics (4 works) · Agricultural risk and resilience (2 works) · Computer Science (2 works) · Development economics (2 works) · Econometrics (2 works) · Income, Poverty, and Inequality (2 works) · Mathematics (2 works) · Poverty (2 works) · Random forest (2 works)