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~person:"Carletto, Calogero"
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Data collection
9
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4
Agricultural statistics
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data collection
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Carletto, Calogero
Grabka, Markus M.
32
Meyer, Bruce D.
21
Mittag, Nikolas
17
Wagner, Gert G.
17
Burkhauser, Richard V.
16
Brakel, Jan A. van den
15
Dillon, Andrew
15
Schouten, Barry
15
Stark, Tom
15
Croushore, Dean Darrell
13
Sokolin, V. L.
13
Beegle, Kathleen
12
Lee, Sokbae
11
Wilhelm, Daniel
11
Dang, Hai-Anh H.
10
Falck-Zepeda, Jose Benjamin
10
Waal, Ton de
10
Abraham, Katharine G.
9
Albers, Sönke
9
Gruère, Guillaume P.
9
Kapetanios, George
9
Manski, Charles F.
9
McKenzie, David J.
9
Merz, Joachim
9
Sithole-Niang, Idah
9
Blau, Francine D.
8
Carneiro, Pedro
8
D'Haultfœuille, Xavier
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Elbers, Chris
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Feng, Shuaizhang
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Figlio, David N.
8
Haltiwanger, John C.
8
Himelein, Kristen
8
Hurd, Michael D.
8
Kahn, Lawrence M.
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ECONIS (ZBW)
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1
Better data, higher impact : improving agricultural data systems for societal change
Carletto, Calogero
- In:
European review of agricultural economics
48
(
2021
)
4
,
pp. 719-740
Persistent link: https://www.econbiz.de/10012618788
Saved in:
2
Agricultural
Data
Collection
to Minimize Measurement Error and Maximize Coverage
Carletto, Calogero
;
Dillon, Andrew
;
Zezza, Alberto
-
2021
error and coverage bias in agricultural
data
collection
. Second, we provide examples of how agricultural data structure …
Persistent link: https://www.econbiz.de/10013219367
Saved in:
3
Agricultural
data
collection
to minimize measurement error and maximize coverage
Carletto, Calogero
;
Dillon, Andrew
;
Zezza, Alberto
- In:
Handbook of agricultural economics : volume 5
,
(pp. 4407-4480)
.
2021
Persistent link: https://www.econbiz.de/10013459619
Saved in:
4
Data Gaps, Data Incomparability, and Data Imputation : A Review of Poverty Measurement Methods for Data-Scarce Environments
Dang, Hai-Anh
-
2020
This paper reviews methods that have been employed to estimate poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross-sectional household surveys, to missing panel household...
Persistent link: https://www.econbiz.de/10012853232
Saved in:
5
Data Gaps, Data Incomparability, and Data Imputation : A Review of Poverty Measurement Methods for Data-Scarce Environments
Dang, Hai-Anh
-
2019
Questions that often come up in contexts where household consumption data are unavailable or missing include: what are the best existing methods to obtain poverty estimates at a single snapshot in time? and over time? and what are the best available methods to study poverty dynamics? A variety...
Persistent link: https://www.econbiz.de/10012895869
Saved in:
6
Mission Impossible? Exploring the Promise of Multiple Imputation for Predicting Missing Gps-Based Land Area Measures in Household Surveys
Kilic, Talip
-
2020
Methodological research has showcased GPS technology as the new gold-standard in land area measurement in large-scale household surveys. Nonetheless, facing budget constraints, survey agencies continue to measure with GPS only plots within sampled enumeration areas or a given radius of dwelling...
Persistent link: https://www.econbiz.de/10012854005
Saved in:
7
Informing migration policies : a data primer
Carletto, Calogero
;
Larrison, Jennica
;
Özden, Çağlar
-
2014
Researchers in many fields, such as demography, economics, and sociology, have established various
data
collection
… in
data
collection
methods. …
Persistent link: https://www.econbiz.de/10010463440
Saved in:
8
Data gaps, data incomparability, and data imputation : a review of poverty measurement methods for data-scarce environments
Dang, Hai-Anh H.
;
Jolliffe, Dean
;
Carletto, Calogero
-
2018
We offer a review of methods that have been employed to provide poverty estimates of poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross sectional household surveys, to...
Persistent link: https://www.econbiz.de/10012130667
Saved in:
9
Agricultural
Data
Collection
to Minimize Measurement Error and Maximize Coverage
Carletto, Calogero
-
2021
and coverage bias in agricultural
data
collection
. Second, it provides examples of how agricultural data structure affects …
Persistent link: https://www.econbiz.de/10012642741
Saved in:
10
Informing Migration Policies: A Data Primer
Carletto, Calogero
;
Larrison, Jennica
;
Ozden, Caglar
-
Institute for the Study of Labor (IZA)
-
2014
Researchers in many fields, such as demography, economics, and sociology, have established various
data
collection
… in
data
collection
methods. …
Persistent link: https://www.econbiz.de/10011106167
Saved in:
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