The proposal to adapt Alkire-Foster to measure and aggregate the quality of jobs was first developed by Brummund, Mann, and Rodríguez-Castelán (2018) to benchmark job quality across the Latin America and Caribbean region. The Alkire-Foster method has much to offer, particularly a set of properties derived from an axiomatic framework that are relevant for identifying the poor and measuring poverty, including the ability to combine into a coherent framework cardinal data, such as labor income, and ordinal data, for example, benefits such as the availability of paid annual leave. These properties, although restrictive, have significant value as a desired measure of good jobs. These are decomposability, replication invariance, and symmetry. Decomposability shows that the aggregate JQM is the weighted sum of JQMs of various subpopulations, such as workers in different industries or rural and urban workers, whereby the weights are the relative sizes of these subgroups in the total population. Similarly, important to measuring job quality among different groups of interest are replication invariance and symmetry, which imply that job quality measured for groups of varying sizes are comparable and that individuals in these groups are equally contributing to the measure of job quality.

The proposed approach applies the Alkire-Foster framework to dimensions of achievement rather than dimensions of deprivation. This means that, if any of the indicators for a dimension are satisfied, the dimension overall is considered a success and is set equal to 1. The working conditions dimension is unique in that all indicators need to be satisfied to define success. A higher JQM therefore indicates greater job quality, whereas a higher Alkire-Foster poverty measure indicates greater levels of poverty. The JQM can be formally written as follows: where JQMi is the JQM for individual i ; Id,i is an indicator function set equal to 1 if individual i ’s job is a success in dimension d , and wd are the weights assigned to each dimension.

The proposed JQM sums the successes across dimensions and assumes that dimensions are weighted equally with unity weights to highlight the difference with deprivation indicators. Measuring job quality in this way will reach a job quality score of 0 if a worker has a job that does not meet any of the criteria of success in all dimensions. In contrast, a worker with a job that has successes in all four defined dimensions receives the maximum job quality score of 4.

Whether each dimension should be equally weighted is debatable. In the case of linear aggregation indicators whereby the success within each dimension is based on indicator functions as in the AlkireFoster method, weights take on the sole role of normatively determining the relative importance of each dimension.<sup>17</sup> The crucial issue is how to identify the appropriate weights. There are various weighting options for multidimensional indexes, of which the equal weights approach is the standard practice in the field (Decancq and Lugo 2013; World Bank 2018). Equal weighting can be justified if there are no compelling reasons to consider some dimensions more important than others or if the intention is to minimize intervention (Alkire and Foster 2011; Brandolini and D’Alessio 1998; Mayer and Jencks 1989). In the case of job quality, however, it can be argued that remuneration is the most important dimension. The analysis in this paper therefore conducts sensitivity analysis giving a higher weight to the earnings dimension. Other approaches to the selection of weights are those based on the revealed preferences of the population. Although these data-driven methods would reduce arbitrariness, it would not allow for

> 17 In contrast, other multidimensional indexes such as the Human Development Index do not use indicator functions, and, in addition to weights, the marginal rate of substitution among dimensions may be nonconstant.

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