A spatial cost of living index for Colombia using a microeconomic approach and censored data

Abstract

SUMMARYThis paper describes a methodology to calculate a spatial cost of living index using Colombian data for 2006 that takes into consideration the microeconomic behavior of households. Using the Almost Ideal Demand System and recovering the expenditure functions for the 23 main Colombian cities, the index proposed is compared to the traditional methodologies used to calculate the regional basket of goods in the country and to an alternative methodology proposed by Romero (2005). This comparison suggests that when the substitution effects are not considered, and the same basket of goods is evaluated in every city, the index is biased, and this bias increases when the difference between cities increases. For reducing the bias, we use a microeconomic approach that keeps the households' level of utility constant and allows substitution among different baskets of goods. According to our calculations, Bogota is still the most expensive city in the country followed by Armenia, Cali, Bucaramanga and Ibague.

Spatial Cost of Living and Welfare Indices for India with Application to Policies Implementation

“…However, the commodities that can be covered are restricted to the data that has been captured in the national survey and may not reflect true basket of non-tradable commodities that households spend, like transportation and housing. Similar issues have been faced by other attempts in various emerging economies like Brazil (Ferreira et al, 2003) and Columbia (Atuesta & Paredes, 2011). This study also uses secondary level aggregate data and, hence, suffers from the same limitation.…”

Happy but Unequal: Differences in Subjective Well-Being across Individuals and Space in Colombia

“…On the one hand, the relative importance of the subjective economic variables can be explained by considerable differences in the cost of living across Colombian provinces and cities that are not fully captured by income and employment data. For example, Atuesta and Paredes Araya ( 2012 ) show that although income levels in Bogotá are generally higher than in the rest of the country, the cost of living there is also highest. On the other hand, it is also well-known that people’s aspirations, expectations, social comparisons, and tolerance for inequality drive people’s SWB levels (Clark et al, 2008 ; Ferrer-i-Carbonell & Ramos, 2014 ).…”

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“…In addition, household expenditure on goods and services is often used as a cost-of-living measurement (Kurre, 2003). According to Navamuel et al (2019); Brahmbhatt & Christiansen (2008); Atuesta & Araya (2012) rising cost of living is caused by rising price of goods. Increasing income and standard of living are factors that pull inflation to grow (AlAzzawi, 2020;Combes & Gobillon, 2015).…”