Luis Fernando Serrudo

In recent years, there has been an escalation of concern revolving around the effect that automation will have on the future of work. This anxiety has fueled the public and academic debate, fearing that soon this technology will displace jobs at a large scale. Numerous studies have begun to investigate automation’s impact on labor markets, although all have focused on industrialized nations which consist of more service and skilled occupations. Utilizing the World Bank’s STEP Skills Measurement Program Database, we examine automation’s effect on 10 developing countries throughout Latin America, Africa, and Asia. To address the heterogeneity of occupations across the country, we apply a task-based approach and re-calibrate the effect of automation on labor market while analyzing the task structure across countries. Modelling off previous studies, we created an expectation-maximization algorithm to predict the percentage of tasks which are likely to be automated. Jobs whose task automation output was 70% or higher were then considered to be highly automatable. Our results suggest that these developing countries have higher levels of predicted automation risk. Countries range in their level of highly automatable jobs from the lowest being Yunnan – a Chinese province – with 7.7% to the highest of Ghana with 42.4%. We find that occupations containing relatively more routine tasks are more likely to be automated, while workers with a higher level of education reduce their risks. This is the first paper to estimate automation risk rates for developing nations.

En este documento de trabajo se presenta el cálculo del Índice de Progreso Social (IPS) para los 9 Departamentos de Bolivia en el período 2012–2016. Los indicadores fueron construidos con base en información secundaria de fuentes oficiales. Los resultados muestran que los Departamentos de Santa Cruz y Tarija son los que presentan los niveles de progreso social más alto, mientras que los Departamentos de Potosí y Pando son los más rezagados. Por otra parte, el IPS departamental muestra una alta correlación con el PIB per cápita, el índice de pobreza monetaria y el índice de Necesidades Básicas Insatisfechas, pero muestra una baja correlación con el Índice de Desarrollo Humano y ninguna correlación con la inversión pública per cápita.