Spatial Autocorrelation Panel Regression : Agricultural Production and Transport Connectivity
Spatial analysis in economics is becoming increasingly important as more spatial data and innovative data mining technologies are developed. Even in Africa, where data often crucially lack quality analysis, a variety of spatial data have recently b...
| Main Authors: | , , |
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| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/594661496768891454/Spatial-autocorrelation-panel-regression-agricultural-production-and-transport-connectivity http://hdl.handle.net/10986/27289 |
| Summary: | Spatial analysis in economics is
becoming increasingly important as more spatial data and
innovative data mining technologies are developed. Even in
Africa, where data often crucially lack quality analysis, a
variety of spatial data have recently been developed, such
as highly disaggregated crop production maps. Taking
advantage of the historical event that rail operations were
ceased in Ethiopia, this paper examines the relationship
between agricultural production and transport connectivity,
especially port accessibility, which is mainly characterized
by rail transport. To deal with endogeneity of
infrastructure placement and autocorrelation in spatial
data, the spatial autocorrelation panel regression model is
applied. It is found that agricultural production decreases
with transport costs to the port: the elasticity is
estimated at -0.094 to -0.143, depending on model
specification. The estimated autocorrelation parameters also
support the finding that although farmers in close locations
share a certain common production pattern, external shocks,
such as drought and flood, have spillover effects over
neighboring areas. |
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