The Quality of Employment in Latin America,
A Multidimensional Perspective

The Quality of Employment (QoE) Index

is a measure of multidimensional deprivation in the labour market. The index expands our knowledge of employment by measuring job quality at the level of individual workers.

It summarises three dimensions of employment (income, job stability and working conditions) to identify which workers experience the most precarious employment situations. The index allows policy makers to compare results across countries, within countries, and across different groups in the population or geographical spaces. Active labour market or social policies can then be focused on those most in need.

“Those attempting to guide the economy and our societies are like pilots trying to steer a course without a reliable compass. The decisions they … make depend on what we measure, how good our measurements are and how well our measures are understood. We are almost blind when the metrics on which action is based are ill-designed or when they are not well understood. For many purposes, we need better metrics.”

Joseph Stiglitz, Amartya Sen and Jean-Paul Fitoussi(2010) in Mismeasuring Our Lives

Kirsten Sehnbruch

London School of Economics and Political Science

Pablo González

Universidad de Chile

Mauricio Apablaza

Universidad del Desarrollo

Rocío Méndez

London School of Economics and Political Science

Verónica Arriagada

Ministry of Social Development and Family Welfare, Chile

Introduction

Recent decades have seen an increasing amount of academic and policy attention focused on the quality of employment (QoE). As the dual processes of globalisation and liberalisation have generated continuous changes in labour markets, employment conditions such as wages, job stability and career prospects have changed. Public policy analysts therefore recognise that for many people, being employed does not guarantee a basic standard of living or well-being (OECD, 2014).

The QoE has thus become an important subject of study in the social sciences, although the literature on the subject is still very diverse and spread across different academic disciplines and international institutions (Burchell et al., 2014). Promoting “Decent Work For All” (SDG 8) without coherent theoretical conceptualisation, data and empirical measurement of what this means is therefore problematic, especially in middle income developing countries where employment must be a policy priority as soon as the most basic needs (e.g. nutrition, shelter and basic education) have been met. Crucially, the quality of employment (QoE) is not only of instrumental importance for improving income levels, inequalities and the coverage of social security systems. It is also intrinsically important to generating individual capabilities and social justice (Sen, 1999).

Sehnbruch et al.(2020) propose a methodology for measuring the quality of employment from a multidimensional and public policy perspective in nine Latin American developing countries (Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, and Uruguay) using household and labour force survey data from 2015. The purpose is to demonstrate that the QoE can be measured by means of a single summary index that can inform policy makers so they can identify

the most vulnerable members of their labour force. This index complements traditional variables such as participation or unemployment rates, which are not always good indicators of labour market performance in developing countries with large informal sectors.

Sehnbruch et al. (2020) use microdata from household and labour market surveys that measure the extent of deprivation both at the individual (micro) and at the national (macro) level. The Alkire/Foster method (2011) was chosen as a method of aggregation for both technical and policy

Measuring the QoE in developing countries: data restrictions and realites

Although the OECD’s indicator of job quality serves as a useful model for an index on the QoE in developed countries where there is greater availability of internationally comparable data, the index is not as easily applicable in developing countries where such data is not readily available. The QoE index presented by Sehnbruch et al. (2020) attempts to address drawbacks related to data availability and comparability found in developing countries. It applies the consensus achieved by the OECD on which dimensions of job quality are important with the idea that it is necessary to construct a synthetic index of the QoE that can meaningfully inform public policy (Sen, Stiglitz and Fitoussi, 2010).

reasons. On the technical side, the method allows for examining the distribution of the QoE across the labour force, as well as the joint distribution of deprivations in the labour force. This helps analyse horizontal inequalities between groups of workers, such as men and women, age groups, rural workers, migrants, or ethnic minorities. Thus, policymakers can focus on the most vulnerable workers with a clear understanding of just how vulnerable they are. Such distributional differences are an even more important consideration in developing countries where labour markets are much more heterogeneous in terms of their composition and where inequalities are more pronounced.

The proposed index assesses the multidimensional QoE (see 1) for individuals in three dimensions and five indicators, summarised in Table 1: occupational status, tenure, social security affiliation, and excessive working hours. For each of these indicators, a deprivation cut-off line is established based on existing studies that demonstrate which dimensions of the QoE are important. Each worker is then categorised according to whether s/he is deprived or not in each indicator, and a deprivation score is constructed based on the nested weight structure specified in Table 1. Equal weights are assigned to each dimension, and equal weights are also assigned to each indicator within the dimension. Finally, a cut-off line of 1/3 is established to determine overall multidimensional deprivation.

Evidence and analysis

DASHBOARD INDICATOR

Table 2 shows how deprived the labour force of each country is in each dimension and indicator. It illustrates that headcount levels of deprivation in some indicators, especially occupational status and social security affiliation are significantly higher than deprivations in other indicators, including the dimension of income. Mexico, Paraguay, Bolivia, Peru and Colombia systematically perform worse across all dimensions than Brazil, Uruguay and Chile. As one would intuitively expect, the two most developed countries in Latin America (included in this study) perform best across all dimensions. However, between them there are significant differences: for example, Uruguay performs worse in the dimension of income, but better when it comes to occupational status.

Another interesting result presented in Table 3 is that the variation of deprivations in the indicator tenure is much lower than in other

indicators, with less developed countries such as Paraguay and Bolivia showing less deprivation than could be expected. This result suggests a high incidence of informal employment in these countries as these jobs, despite being precarious in their lack of affiliation to a pension system, are often more stable and long-term than the jobs of salaried workers, particularly those with fixed-term contracts. However, Colombia and Peru also have high levels of deprivation in the indicator occupational status and are highly deprived in terms of tenure. It is this kind of result which can be derived from country comparisons that are particularly useful to policymakers in each country when it comes to identifying how their QoE compares to other countries in the region, and when considering labour policy options.

The headcount ratio (H) shows the proportion of deprived individuals in each country, which confirm that Chile, Uruguay and Brazil perform better in terms of their QoE than Mexico, Colombia, Ecuador, Peru, Bolivia and Paraguay.

The average intensity share (A), which averages simultaneous deprivations experienced by workers in each country, i.e. the intensity of their QoE, reflects the same grouping of countries although the range of intensity scores is much narrower.

On average workers in Latin America are deprived in terms of at least half of their QoE indicators. M0 (following Alkire and Foster, 2011) represents the adjustment of the Headcount Ratio by the Average Intensity Score.

The adjusted headcount ratio M0 again shows the same grouping of countries when it comes to deprivations, with Chile, Uruguay and Brazil showing lower levels of deprivation.

The QoE index presents new insights on the subject of job quality. It should foster the development of further national and international indicators of this type. National indicators should be adapted according to local circumstances and data availability.

It should also help refocus policy debates about employment on key dimensions such as job stability that are an important part of worker vulnerability.

Finally, it should provide policymakers and international institutions with a motivation for gathering more and better data on employment conditions in developing countries, for example, by establishing homogeneous survey modules on employment conditions.

These findings also suggest that there is no clear association between quality of employment and unemployment rates:

Countries such as Bolivia and Mexico have low unemployment rates (around 4%), while their QoE Headcount ratio is very high.

No Latin American country achieves low unemployment and a low rate of QoE deprivations.

Notes: Employment and unemployment rates obtained from CEPALSTAT: Economic Commission for Latin America and the Caribbean (ECLAC). Sizes of the circles represent the population in the workforce (economically active population).

As the governments of developing countries consider the impact of future technological changes on their labour markets, this kind of data becomes a crucial input into policy-making as Active Labour Market Policies will have to be targeted not only at vulnerable workers, but also at those in the middle of the QoE spectrum, who will need to adapt their skill sets to work with new technologies.

Further reading

Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics. https://doi.org/10.1016/j.jpubeco.2010.11.006
Burchell, B., Sehnbruch, K., Piasna, A., & Agloni, N. (2014). The quality of employment and decent work: definitions, methodologies, and ongoing debates. Cambridge Journal of Economics, 38, 459–477. https://doi.org/10.1093/cje/bet067 OECD. (2014). OECD Employment Outlook 2014. https://doi.org/10.1787/empl_outlook-2014-en

Sehnbruch, K., González, P., Apablaza, M., Méndez, R., & Arriagada, V. (2020). The Quality of Employment (QoE) in nine Latin American countries: A multidimensional perspective. World Development, 127, 104738. https://doi.org/10.1016/j. worlddev.2019.104738
Sen, A. (1999). Development as Freedom. Oxford University Press.

Joseph Stiglitz, Amartya Sen and Jean-paul Fitoussi (2010) Report by the Commission on the Measurement of Economic Performance and Social Progress. Mismeasuring our Lives : Why GDP doesn’t add up. New York: New Press