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Session Overview
Session
2-HP036 - 1/2: Reducing Modern Slavery - 1/2
Time:
Tuesday, 06/July/2021:
11:00am - 12:15pm

Session Chair: Prof. Wendy Kay Olsen, University of Manchester, United Kingdom
Session Chair: Prof. Jamie Anthony Morgan, Leeds Beckett, United Kingdom

Session Abstract

The papers in this session explain different types of modern slavery, from child labour to forced and bonded labour, trafficked labour, and entrapped labour (ie coercive conditions in the employment relationship). We invite papers quantifying or documenting modern slavery; comparing child labour across countries; explaining the entrapment of labour in South Asia; exploring harmful forms of labour in Africa; and theorizing modern slavery.


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Presentations

What about women? Gender aspects of agricultural bonded labour in South Asia

Elena Samonova

University College Dublin, Ireland

Debt bondage is one of the most widespread forms of unfree labour today. It involves the exploitative interlining between labour and credit agreement and can be found in almost all countries in the world. Millions of people are engaged in bonded labour in export industry, however even more people work for local markets or are engaged in household work that does not bring any economic surplus. This paper focuses on bonded labour in agriculture, which is one of the most widespread types of unfree labour in South Asia.

The paper is based on the results of an extensive qualitative field work. The fieldwork took place in May and November 2016 in India, in the state of Rajasthan. The major method of inquiry was interviewing/ group-discussions with (ex) bonded labourers, their family members and staff of local and international NGOs that work in the field. The fieldwork took place in two villages in the Baran district of Rajasthan. In total, 21 were conducted interviews and group discussions. The analysis of the data with the help of grounded theory allowed to identify main patterns in the local system of bonded labour.

Traditional agricultural bonded labour occurs when a person takes a loan and pledges his/her labour to work off the debt, however, power imbalances, manipulation with debts and low remuneration reduce the abilities of debtors to repay the loan, which leads to a life-long enslavement. There is a growing literature on mechanisms of enslavement and liberation. At the same time most of the existing literature is gender blind either focusing on the group of bonded labourers as a homogenous one or on the gender specific forms of slavery like sexual slavery. Such approach may result in overlooking of the problems of women bonded labourers engaged in such traditional forms of bonded labour as bondage in agriculture. The field work shows that many credit agreements include provision of unpaid labour of family members of a debtor, including his wife and children. This paper examines the role of gender norms in enslavement of women along with their husband, focusing on patriarchal gender orders that justify women’s status of “less-human beings” and their subordinate position within the society.



Long-term Trends In The Use Of Child Farm Labour In Africa: A Comparsion Of Kenya, Malawi And Zimbabwe

Erik Green

Lund University, Sweden

Labour studies in colonial Africa are facing a revival (Belucci and Eckert 2019). Although very insightful, this literature in general neglects of role of child labour. ILO estimated in 2106 that one fifth of all children in sub-Saharan Africa were involved as child labour, making it to the region with highest prevalence of child labour in the world. Why is that so? One possible economic explanation could be the prevailing factor endowments. Low land-labour ratios make labour a scarce resources, which historically has been used to explain why bonded and child labour has been common in Africa (Austin 2010, Green 2014). Factor endowments are not static. Due to rapid population growth the region is gradually changing from being labour scarce to one were land availability is shrinking and labour supplies increasing. This transition can explain why unemployment rates have increased over the last decades. Given this change we would see a decline in child labour. Preliminary findings show this to not be the case. The use of child labour has not declined. To explain this paradox this paper will analyse and compare the long-term trends of child labour (1920 to present) on large-scale farms in Kenya, Malawi and Zimbabwe. The three cases are chosen because they offer different political, socio-economic and factor endowment contexts. Despite this child labour has in all three cases played a significant role. The paper will analyse the shifting trends in the use of child labour in the large-scale farms to tease out the mechanisms that can explain its emergence as well as persistence.



Excessive and Hazardous Labour among Children in Kenya: a Multi-level Analysis of Modern Slavery and Normal Work

Wendy Kay Olsen1, Giuseppe Maio2

1University of Manchester, United Kingdom; 2Impact Cubed & Trilateral Research, United Kingdom

This paper applies an ILO-recommended hours threshold to encompass and measure all cases of harmful labour of children in Kenya via a large national dataset. This project is useful because targeted interventions in certain counties of Kenya might help reduce harmful forms of child labour. These interventions however would need joined-up government due to the socially grounded nature of child labour. It is not just done by poor children, but those with female household heads and those in certain social groups.

Definitions: We clear the ground first by noting which labour is hazardous, which labour is non-harmful in terms of amounts (being parttime work) , and which labour discourages school attendance among the age 5-17 population of Kenya.

Data and Methods: We model the risk of the harmful forms of labour by county, both by personal and household features, and by multilevel modelling of local characteristics. The main data source is the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) for individual- level variables. We took county-level variables from other sources, namely 2005/6 KIHBS, the Basic Education Statistical Booklet (UNICEF 2014), and the Gross County Product report (Kenya National Bureau of Statistics 2019). Poisson regression was used.

Findings: Careful treatment of age allows us to see the rapid expansion of child labour in the age group 10-17 years. 13% of 10-11-year olds were in harmful child labour (16% in rural areas). ILO-based age-specific thresholds were used to define excessive hours. In particular the following was deemed excessive for ages 15-17: 43 hours or more in economic activities, or 28 hours or more of household chores each week. Allowing for household wealth was crucial, as child labour was rare in all the quintiles except the lowest 20%. Overall we found:

-Rural children were much more likely to be in harmful child labour;

-Muslim children in Kenya were less likely than traditionalist religion to be in harmful child labour;

-Children in female-headed households were much more likely to be in harmful child labour;

-Boys were more likely than girls to be in harmful child labour, after taking into account all the preceding factors.

The model adjusted for all the poverty and education factors that would be expected to lie in the background as drivers and protective factors for child labour risks. Next, we did more tests, showing:

-Those in a county with a high percentage of the economy in mining and quarrying were more likely to be in harmful child labour. The county characterised by the highest unadjusted proportion of children doing harmful forms and amounts of child labour is Samburu (34%); the next three highest are Baringo, Narok, and Kitui. Urban areas of Makueni county had the highest levels of urban harmful child labour in Kenya.

-High numbers of secondary school teachers was a protective factor;

It is possible to map the counties whose overall level was high, and those whose adjusted level after all modelled factors was high. The maps are provided.

This paper is one of the first multi-level models of child labour. The original results validate going beyond economics in the analysis of child labour.



Peasant Explanations Versus Human Capital Explanations of Child Labour: Six South Asian Countries Compared

Wendy Kay Olsen

University of Manchester, United Kingdom

Child labour is defined by the International Labour Office as a damaging situation of overwork, hazardous work, or forced labour, when faced by children of school age. This definition is applied here across six countries. Small amounts of work are non-harmful for children. Yet ‘child labour’, and child protection more broadly, is a distinct policy target which requires clarity about what is the defining feature of harmful child labour.

This paper focuses on listing and measuring the various promoting and obstructing factors, including poverty as well as local social norms. Women’s increasing uptake of outside work can push upward the amount of child labour. I add some further explanatory variables. Minority-group status, land, and irrigation are measured as drivers of child labour. I measure “child labour” using recent South Asian survey data from the International Labour Organisation (ILO), India’s National Sample Survey 2017/8, and the United Nations Multiple Indicator Cluster Surveys, harmonising data for Afghanistan, Bangladesh, Bhutan, India, Myanmar, Nepal, and Pakistan. Two countries have only partial coverage (Afghanistan, Pakistan). The prevalence of market child labour was higher in some states of India in 2012 than in Nepal and Bangladesh. In 2017/8 Indian child labour declined. Pockets of child labour are found in two areas: rural children who have access to land; and urban children; and mainly in minority social groups. This paper explores the policy implications. I question the human capital model of labour markets. The model helps support social-inclusion policies.

Statistical modelling shows that high child labour prevalence is found in rural areas where children have access to land, either in their own right or more commonly through tenancy and casual labour. However, where their parents have a job in a preferred occupation, the child is more likely to be in school and not in hazardous child labour. Thus job creation schemes are important for the eradication of child labour.

Another hot spot area in some regions is minority social group children in urban areas. See also our paper Bonded Child Labour in South Asia, Department for International Development, by Shavana Musa and Wendy Olsen, URL https://www.gov.uk/dfid-research-outputs/bonded-child-labour-in-south-asia-building-the-evidence-base-for-dfid-programming-and-policy-engagement . Among dominant groups, cultural capital is higher, than among minority groups. The minority status is a sharper indicator for child labour in urban than in rural areas. In India, this results from intra-national migration. The poorest families migrate to urban areas at which point both children and adults work in the informal sector. The policy dimension of this matter is about targeting programmes to encourage school attendance and that the minority group movement from rural to urban area could be inhibited by other social protection schemes. I encourage a joined-up partnership of government departments: housing, health and child-birth related support would make a big difference.

The paper thus supports integrated rural social development with a clear focus on child working in agriculture and families of minority social-group status.

Some variations across regions on these two themes are spelt out in detail. Gaps in the knowledge base are pointed out, notably partial coverage of key surveys in Pakistan and Afghanistan.

I am grateful to my team of co-authors on previous papers, which include Diego Perez Ruiz and Arkadiusz Wisniowski for a paper on combining data on child labour using information theory to optimise knowledge; Shavana Musa for her joint writing of a paper for DFID reviewing the situation in South Asia for child labourers; and Giuseppe Maio who has worked with me on Kenya.



 
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