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You will find a complete range of our monographs, muti-authored and edited works including peer-reviewed, original scholarly research across the social sciences and aligned disciplines. We publish long and short form research and you can browse the complete Bristol University Press and Policy Press archive of over 1500 titles.
Policy Press also publishes policy reviews and polemic work which aim to challenge policy and practice in certain fields. These books have a practitioner in mind and are practical, accessible in style, as well as being academically sound and referenced.
This chapter summarizes the uses of surveillance technologies in migration, border management and humanitarian aid, to explain contemporary capitalism’s mainstream approach to migration and the motivations behind the investments of tech companies.
Given the far-reaching effects of these technologies, it is evident they will soon have consequences for humanity as a whole. Migrants, asylum seekers and refugees have much weaker legal status than those of citizens, and states claim the authority to control those who cross their borders by invoking their sovereign rights. The fields of border and national security are more secretive, face less scrutiny and leak less information, and societies take a less sympathetic approach to migrants and foreigners. This allows tech companies, along with military and financial firms and security bureaucracies, to do as they please on the global stage, and implement the most speculative AI or blockchain projects.
Digital identity projects are related to supporting migrants and refugees and contributing to their financial and social integration. Despite mostly positive approaches (with or without reservations), when digital identity initiatives are evaluated from the perspective of the manifestation of surveillance capitalism in migration management, many examples demonstrate corporate agenda-setting and new hegemonic discourses.
Smart border and digital identity applications do not contradict but complement one another. If the main issue in the use of surveillance technologies in border and migration management is data collection and extraction, smart border applications function, through data analysis, to decide who is allowed to cross the border and, after crossing the border, how far they are allowed to go. A digital identity, on the other hand, is where such data is stored, and thus very valuable for both states and private companies for financial inclusion and tracking purposes.
Surveillance technologies developed in migration and border management can spread to other fields and affect an entire society. Suppose the lie detectors piloted on borders are successful and they meet no significant opposition from society, then we should not be surprised to see lie detectors in workplaces or in police stations in the near future. This, in turn, would mean new mechanisms of oppression in society at large.
When it comes to social issues, advanced statistics can be manipulated in favour of the powerful, with probable options being presented as inevitable or actual. This encourages an approach that perceives such situations as neutral and objective, magnifying the algorithm and obscuring the structural factors. This can cause serious problems in migration and border management.
Big data analysis has exciting potential in migration and border management and humanitarian emergencies. Inferences made from data sets allow us to analyse the basic characteristics and dynamics of mass movements and make predictions about the future.
This chapter discusses how private companies’ dominance over big data analysis should be questioned more vigorously. Companies that collect and analyse data have better capacity than states and UN agencies to monitor and direct ‘transient’ people. Strong legal protections do not exist regarding privacy and consent in many countries on migration routes.
Because of active national and international use of smartphones, social media and mobile banking, companies have become the biggest collectors and analysts of data. As most of these companies rely on the business model of surveillance capitalism, this provides invaluable resources to capitalism for the surveillance, manipulation and steering of people in a given direction.
In addition to discussing the consequences of capitalist policies in migration; this chapter demonstrates how surveillance technologies in this field provide valuable data on the trajectory of capitalism.
A large number of companies and states invest in these fields, but further debate is needed on motivations besides profit maximization, such as how alliances are formed, which actors set the agenda, and how capitalists use the outcomes. This is related to the process by which surveillance technologies provide global capitalism with immense power to reshape and reposition the global proletariat.
At a time when large masses of people move quickly, these technologies make it possible to identify migrants before they even arrive at the border, analyse their data, decide on whether to allow them entry, and closely monitor those allowed in. This process of selection and the interests of capitalism in shaping the labour market constitute the essence of the issue.
Many technological solutions that stand out within migration and border management fall within the scope of the tech product package known as ‘smart borders’. These ‘solutions’ make it possible to control activities beyond physical borders and to detect and prevent migration, through such tools as AI algorithms, drones, facial recognition, biometrics, satellite images, sensors and mobile phone and social media data analyses.
Both the USA and EU have hired private companies to militarize their borders with advanced surveillance technologies. Public funding is not being used to ensure the rights of migrants or to resolve economic and political problems, but to support companies in the development of new and deadly technologies.
The most significant producers of smart border applications are military companies. Those who produce the weapons and bombs that force refugees to flee their countries are the same companies that produce the tools for detecting those people in border areas.
In recent years, UN agencies, global tech corporations, states and humanitarian NGOs have invested in advanced technologies from smart borders to digital identities to manage migratory movements. These are surveillance technologies that have intensified the militarization of borders and became a testing ground for surveillance capitalism.
This book shows how these technologies reproduce structural inequalities and discriminative policies. Korkmaz reveals the way in which they grant extensive powers to states and big tech corporations to control communities.
Unpacking the effects of surveillance capitalism on vulnerable populations, this is a much-needed intervention that will be of interest to readers in a range of fields.
First, I describe and innovatively interpret the most important APM and explain how to use them best. Next is a critique of APM sensitive to distribution among the poor, based on two questions: a) Why should the relevant inequality for an APM be the inequality among the poor excluding the non-poor? and b) Why should decreasing marginal well-being start from the very bottom, from the second soup spoon? Nutritional evidence is shown to prove that this assumption is false; in food intake there is a stage of increasing marginal well-being to food additions. Dasgupta has called this the high fixed cost of living. APM sensitive to distribution among the poor are thus demolished, and I propose two new APM sensitive to total social inequality. One focuses on the inequality between the poor and the non-poor, which follows the logic of Sen’s poverty index but instead of the Gini coefficient (G) among the poor introduces what I call the relative gap between the average achievement score (A) of the poor (AP) and that of the non-poor (AR): which is GPR = (AR − AP) / AR. The second replaces G among the poor by G among the whole population in Sen’s APM. Illustrative results for Mexico are presented.
The chapter includes analysis of the seven combined poverty measurement methods (PMM), identifying a central difference between Latin American and European PMM. In the latter, direct measurement aims at identifying deprivation due to income restrictions. In sharp contrast, in both the Integrated PMM (IPMM) and Social Progress Index (SPI), the point of departure is that direct and indirect methods are complementary, as they consider different well-being sources (WBS) and identify deprivation in different dimensions. This difference explains the divergent P criteria which are applied in both groups of PMM methods (in both regions). In this chapter, a typology of such criteria is built. Whereas ‘truly poor’ PMM identify as poor only those who are poor both in the direct and indirect dimensions (the intersection of both sets), the IPMM and SPI do not restrict P to this intersection, as they use a weighted average of each dimension’s scores to obtain the overall P index. The conclusion arrived at is that combined methods which can be grouped under the heading ‘truly poor’ end up reducing their field of study to the consequences of a low level of current income, reducing the six WBS to one, leaving the hope for an integrated approach only to the IPMM and SPI.
This book integrates my best texts on poverty conceptualisation/measurement over 35 years, presenting an innovative approach practically unknown in English. The core breakthrough is the holistic view that culminates in the Integrated Poverty Measurement Method (IPMM). The book provides the critique to support this approach, reflected in conceptual discussions and critical appraisals that constitute the Critique of the Political Economy of Poverty (CPEP), including systematised criticism of salient poverty measurement methods. I appropriated positive ideas from many authors and intertwined them with the concepts I developed to build the narrative, which becomes transparent in principles and good practices. Three distinctive features delineate. a) It includes free time (FT) – going beyond combined methods that include only income/consumption and unsatisfied basic needs (UBN) – thus broadening the range of phenomena considered, including domestic chores, caring, and leisure activities. b) It avoids dichotomic indicators by cardinalising ordinal variables, allowing the calculation of a synthetic final metric indicator and the more elaborated aggregate poverty measures (APM). Chapter 9 discusses existing APM, interprets them innovatively, shows the conceptual flaws of those that are sensitive to distribution among the poor, and proposes an APM that integrates social inequality. c) Regarding income, IPMM introduces innovations and calculates a specific poverty line for each household.