2013
DOI: 10.1111/j.1435-5957.2011.00408.x
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Substitution bias and the construction of a spatial cost of living index

Abstract: We estimate the spatial substitution bias based on the difference between a price index (PI) and the true cost of living (COL). This bias is computed at three geographical scales, using several fixed baskets and across different expenditures quartiles. Our results show a significant substitution bias for small geographical units. The choice of the base basket is also relevant for the bias estimation. Finally, the spatial substitution bias is larger for upper side of the expenditure distribution due to the hete… Show more

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Cited by 18 publications
(10 citation statements)
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“…Additionally, we found a positive correlation between wages and the housing price index ratio between the residence and the work‐region. A potential explanation of this strong correlation lies in that probably FIFO/DIDO commuters that face high differences in the cost of living between the residence and work‐region, are also workers who do not have to take on the high cost of living detected in mining regions due to increasing housing demand as well as the high production costs faced by realtors in those areas, but they can negotiate their salary according to the cost of living of their residence‐region (See Paredes and Iturra for a discussion of regional housing prices). Given the objectives of our research, hereafter our attention is mainly focused on the coefficient for long‐distance commuters.…”
Section: Resultsmentioning
confidence: 99%
“…Additionally, we found a positive correlation between wages and the housing price index ratio between the residence and the work‐region. A potential explanation of this strong correlation lies in that probably FIFO/DIDO commuters that face high differences in the cost of living between the residence and work‐region, are also workers who do not have to take on the high cost of living detected in mining regions due to increasing housing demand as well as the high production costs faced by realtors in those areas, but they can negotiate their salary according to the cost of living of their residence‐region (See Paredes and Iturra for a discussion of regional housing prices). Given the objectives of our research, hereafter our attention is mainly focused on the coefficient for long‐distance commuters.…”
Section: Resultsmentioning
confidence: 99%
“…While previous literature only performed estimation exercises at country or city level, our work is different in that we argue that considering a single market is unsustainable in light of Chile's economic geography. This issue was also pointed out by Paredes and Iturra Rivera (2013), who estimate regional housing price indices for Chile using an AIDS. Even when the authors do not report elasticities, they show how economic geography can affect the stability of the demand system parameters.…”
Section: Literature Reviewmentioning
confidence: 88%
“…The figures show how HS income elasticities differ only marginally across regions with the exception of the Antofagasta Region (II). However, a feasible explanation about this difference is related to the markedly higher housing prices in this region which is found in Paredes (2011) and Paredes and Iturra Rivera (2013). We suspect that the particularly high housing demand in the Antofagasta region is pushing prices up resulting in a significant reduction in the income elasticity for this region.…”
Section: Empirical Analysismentioning
confidence: 93%
“…Data for Chile, however, are only available from the 1990s. Second, Chile does not report a spatial price index and there is no official indicator regarding how expensive a particular county (or region) is with respect to the rest of the country (see Paredes and Iturra, , or Paredes, , for a deeper discussion). We thus have to build a proxy to control for the spatial price differentials.…”
Section: Methodsmentioning
confidence: 99%