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Poverty Dynamics
Mother and girl in Ethiopia – will the next generation get out of poverty?

In the SAT regions, 644 million people live below the poverty level (less than 1.25 $ a day), the majority being subsistence smallholder farmers. How can we describe an average poor smallholder farmer? What is his or her social background, livelihood options, physical and economic environment? How did he or she become poor and are they trapped in poverty? 
Understanding poverty dynamics is key for formulating appropriate development strategies to help the poor in SAT regions sustainably escape from poverty.

644 million - People living on  less than $1.25 a day in the semi-arid tropics

75% - Percentage of poor living in rural areas, a majority live on agriculture

90 - Out of 100 poor in developing countries live in Asia 

In the semi-arid tropics, 644 million people are poor, and the majority of them depend on agriculture. Who are they and can they move out of poverty?

Poverty concepts that shape poverty reduction policies

There are several definitions of poverty, according to various indicators of well-being, incomes but also access to basic services like education and safe water. Recently, experts measured poverty on a range of indicators known as the multidimensional index.

Governments and development experts use cursors (poverty lines) below which a person or a household is considered poor, like UN's "earning below 1.25$ a day".

Research on poverty dynamics is about understanding if poverty is persistent (poverty trap) or a temporary condition, and which population is more likely to move in or out of poverty (economic mobility). [source: Evanson]

How do we measure poverty and economic mobility over time and what factors can help an individual escape poverty or trigger poverty over a life cycle? Answers to these questions shape poverty reduction policies and institutions, as they help identify drivers of change and define possible development pathways .

Village Dynamics Studies: a rare and valuable knowledge base to measure poverty dynamics

In developing countries, most poverty data come from cross-sectional panels (population census or other one- time collection of certain indicators from a large population share). Following up the same households over the long term is rare. Such time-series panel (longitudinal) data are costly and require building local capacity in social and economics sciences.

The Village Dynamics Studies in South Asia (VDSA) aims at improving the quantity and quality of time-series data from 42 villages in India and Bangladesh, on farming activities and household economy at district, village and household level.  

Revisiting the same households and villages over cropping seasons and years allows identifying what drives the major social and economic changes in semi-arid tropics villages. It also helps assess the impact of development initiatives, like the Self-Help Group-Bank Linkage Programme in India.

How do we track change in rural poverty? A permanent investigator collects detailed information over time (5 years using the experience of the 30 years data collection effort of Village Level Studies in 6 villages of Maharashtra and Andhra Pradesh since 1975) at village and household level (micro level).

Data at national and district level are also collected,  like crop area, livestock numbers, farm harvest prices, agricultural wages and monthly rainfall, to determine the different agro ecologies and priority poverty areas. Trends analysis of these meso level data highlights emerging issues like climate change, urbanization and water scarcity.

Village data include demographic changes, socio-economic issues, land use and cropping patterns, investments  in infrastructures / basic services and their maintenance, the management of common property resources and the local governanceHousehold data  help define the poor economics, like family assets, incomes from farm and non farm activities, consumption patterns (eg level of self-consumption), education, health and nutrition situation, gender status, what government welfare programmes the household benefits from ; and whether the crop and livestock activities are profitable.

A recent study shows there is no bias (treatment effect) induced by the permanent presence of village investigators that would make the villagers more progressive and knowledgeable.

Well-thought indicators are measured such as land ownership, characteristics of the farming systems, education rate across gender, non-farm employment and nutrition status. This rich time-series data allow us to compare population segments more susceptible to poverty. For example, whether certain minorities more vulnerable to persistent poverty or if there a "geographical trap" reflecting the role of environment degradation.

Drivers of change in rural semi-arid tropics

Tackling low agricultural productivity is important but not enough to reduce poverty. Introduction of a new farming technology like an improved sorghum variety or water and soil conservation practices may greatly improve agriculture productivity and growth, but large-scale uptake depends on different factors including social learning.

Access to learning facilities to acquire new skills is a key driver of change within a community.

Some social factors may drastically change poverty dynamics. For instance, changes in social networks or family structure like the slow disappearance of joint families [ref changes in agriculture and village economies], the gender status, or health situation [eg impact of HIV on livelihoods], can change the way individuals and households cope with poverty, or acquire new skills and knowledge to improve their situation.

Even though agricultural growth plays a strong role in reducing rural poverty, diversification of village economies is very important too. A comparison between 6 VLS villages  [link "Drivers of change: dynamics of rural livelihoods and poverty in SAT India"] shows the importance of non-farm and urban economy to drive smallholder farms out of poverty in rural India, demonstrating that rural development programmes have to look beyond agriculture.

IMPACT of poverty dynamics studies

Poverty dynamics studies influence research and policy decision-makers.

Defining research priorities: In the 1970s ICRISAT decided not to invest in herbicide and weed science as VLS research found out that the use of herbicide was not profitable for farmers, that crop value was more dependent on erratic rainfall and soil fertility; and that hand weeding was an important share of women incomes.

Influencing development policy-making: Demonstration that common property resources were important in the incomes and nutrition of the poor led to a decision by the Indian government not to hand over wastelands to private industry for afforestation.

Markets (here in Maharashtra) if inclusive, can lift small farmers out of poverty.
Farmers harvesting chickpea in Ethiopia – The majority of poor in SAT are smallholder farmers.
Tribal family in rural Maharashtra – Poverty is measured through a range of welfare indicators such as housing and family assets.
Village consultation (Village Dynamics Studies in South Asia).
Village mapping on the ground, India (VDSA).
Women pilgrims in India – Gender has to be considered in poverty dynamics.
Mali community water harvesting tank - Water access, key for the development of the semi-arid tropics.
Men sewing in Bamako market, Mali - Rural economy is also about non-farm livelihoods.