A Stata 17 implementation of the local ratio autonomy: Calling Python

Juan S. Morales-Castillo
Additional contact information
Juan S. Morales-Castillo: University of Granada

2021 Stata Conference from Stata Users Group

Abstract: In many countries around the world, the public sector is decentralized to improve efficiency in the provision of public services. Until the publication of the paper by Martínez-Vazquez, Vulovic, and Liu (2011), the level of decentralization was approximated through the local income ratio. It has been shown that this covariate is endogenous and that because of the unobservable heterogeneity, it can generate correlation. The local autonomy ratio proposed by these authors is an indicator weighted by the inverse of the distance between municipalities, which in turn is weighted by the sum of the inverse of the distance between all municipalities in the country. However, we propose a local autonomy ratio, conditioned by the distance and population thresholds between the country's municipalities. It is evident that multiple distance and population restrictions must be tested until the effect of this ratio is found to be significant, as a covariate in an econometric model. To reduce the computational cost-time of the estimation, we automated the calculation of the indicator, programming local ratio autonomy in Stata 16 but calling Python. We use Python version 3.9.

Date: 2021-08-07
New Economics Papers: this item is included in nep-isf
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://fmwww.bc.edu/repec/scon2021/US21_Morales-Castillo.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:scon21:27

Access Statistics for this paper

More papers in 2021 Stata Conference from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().