Estimating individual employment status using mobile phone network data

📝 Abstract

This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. At a societal level unemployment is an important indicator of the performance of an economy. By deriving a broad set of novel mobile phone network indicators reflecting users financial, social and mobility patterns we show how machine learning models can be used to predict 18 categories of profession in a South-Asian developing country. The model predicts individual unemployment status with 70.4 percent accuracy. We further show how unemployment can be aggregated from individual level and mapped geographically at cell tower resolution, providing a promising approach to map labor market economic indicators, and the distribution of economic productivity and vulnerability between censuses, especially in heterogeneous urban areas. The method also provides a promising approach to support data collection on vulnerable populations, which are frequently under-represented in official surveys.

💡 Analysis

This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. At a societal level unemployment is an important indicator of the performance of an economy. By deriving a broad set of novel mobile phone network indicators reflecting users financial, social and mobility patterns we show how machine learning models can be used to predict 18 categories of profession in a South-Asian developing country. The model predicts individual unemployment status with 70.4 percent accuracy. We further show how unemployment can be aggregated from individual level and mapped geographically at cell tower resolution, providing a promising approach to map labor market economic indicators, and the distribution of economic productivity and vulnerability between censuses, especially in heterogeneous urban areas. The method also provides a promising approach to support data collection on vulnerable populations, which are frequently under-represented in official surveys.

📄 Content

Estimating individual employment status
using mobile phone network data

1Pål Sundsøy, 1Johannes Bjelland, 1Bjørn-Atle Reme, 2Eaman Jahani, 3,4Erik Wetter, 3Linus Bengtsson

1Telenor Group Research, 2MIT Media Lab, 3Flowminder Foundation, 4Stockholm School of Economics

ABSTRACT

This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households’ vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. At a societal level unemployment is an important indicator of the performance of an economy. By deriving a broad set of novel mobile phone network indicators reflecting users’ financial, social and mobility patterns we show how machine learning models can be used to predict 18 categories of profession in a South-Asian developing country. The model predicts individual unemployment status with 70.4% accuracy.
We further show how unemployment can be aggregated from individual level and mapped geographically at cell tower resolution, providing a promising approach to map labor market economic indicators, and the distribution of economic productivity and vulnerability between censuses, especially in heterogeneous urban areas. The method also provides a promising approach to support data collection on vulnerable populations, which are frequently under-represented in official surveys. Keywords Big-Data Development, machine learning, unemployment, socio- economic indicators, mobile phone metadata, profession

1. INTRODUCTION Unemployment is a key indicator of labor market performance [1,2]. When workers are unemployed, their families also get affected, while the nation as a whole loses their contribution to the economy in terms of the goods and services that could have been produced [3]. Unemployed workers also lose their purchasing power, which can lead to the unemployment for other workers, creating a cascading effect that ripples through the economy [4]. Additionally, unemployment has been shown to be a driver of interregional migration patterns [5].
   Counting each and every unemployed person on a monthly basis would be a very expensive, time-consuming and impractical exercise. In many countries, such as US, a monthly population survey is run to measure the extent of unemployment in the nation [6]. In developing countries such surveys often tend to have a low spatial and temporal frequency [7]. Lacking statistics may lead to higher uncertainties in economic outlook, lower purchasing capacity and higher burden of debt. The problems of unemployment and poverty have always been major obstacles to economic development [8], and proper background statistics is important to change this trend.
   The increasing availability and reliability of new data sources, and the growing demand of comprehensive, up-to-date international employment data are therefore of high priority. Specifically, privately held data sources have been shown to hold great promise and opportunity for economic research, due to both high spatial and temporal granularity [9].
   One of the most promising rich new data sources is mobile phone network logs [10], which have the potential to deliver near real- time information of human behavior on individual and societal scale [11]. The prediction from mobile phone metadata are vast given that more than half of the world’s population now own a mobile phone. Several research studies have used large-scale mobile phone metadata, in the form of call detail records (CDR) and airtime purchases (top-up) to quantify various socio-economic dimensions. On aggregated level mobile phone data have shown to provide proxy indicators for assessing regional poverty levels [12,13], illiteracy [14], population estimates [15], human migration [16,17] and epidemic spreading [18]. On individual level mobile phone data have been used to predict, among others, socio-economic status [19,20], demographics [21,22] and personality [23].
   Two previous papers analyze employment trends through cell phone data. The work by [24] argue that unemployment rates may be predicted two-to-eight weeks prior to the release of traditional estimates and predict future rates up to four months ahead of official reports accurately than using historical data alone. The other study [25] shows that mobile phone indicators are associated with unemployment and this relationship is robust when controlling for district area, population and mobile penetration rate. The results of these analyses highlight the importance of investigating the relationship between mobile phone data and employment data further.

Our work separates from [24,25] in several ways:
(1) Bottom-up approach: we focus on predicting employment status on the individual level to be able get a c

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