Poverty is prevalent in mountain areas, agricultural productivity is

Poverty in general refers to an
inability to fulfill basic needs and low standards of living. Income is most
often used as an indicator of poverty that people with higher income are less
vulnerable to poverty than people with lower income, all else being equal. For
given income, poverty depends on access to social services such as healthcare,
education and social safety nets, and access to basic facilities such as
electricity, safe drinking water, sanitation and markets (Iceland & Bauman 2007). Sen (1988) describes poverty as an
absence of entitlement, and ‘… not having some basic opportunities of material
wellbeing, the failure to have certain minimum capabilities” (Sen, 1985, P 669). He recognizes the
multidimensional nature of poverty that it is not just a matter of income, it
is one of a failure of achieving certain minimum capabilities. There is now
consensus that poverty is a complex issue and can be manifested in variety of
ways including absence of economic opportunities, vulnerability, social
exclusion and discrimination, social insecurity and violence, lack of education
and basic services, and poor health and unhealthy living conditions. Hunger and
food insecurity are the most serious forms of extreme poverty, which causes
more than 8 million deaths annually (Sachs 2005).

 

Poverty in mountain
context is distinct from the poverty in other areas. This is because
communities in mountain areas are extremely vulnerable to poverty due to
fragile and harsh environment, marginalization, inaccessibility, diversity, and
limited livelihood options (Jodha 2000) (Jodha 1990), Ellis-Jone
1999). Climatic change and climate-induced hazards, such as extended
droughts, intense rainfall in short duration and resulting landslides, are
adding extra burden to the mountain communities (McDowell et al. 2013).
Since weather-dependent rain-fed agriculture is prevalent in mountain
areas, agricultural productivity is declining. As a result, migration of adults
from the mountain region to more accessible areas is becoming a common
phenomenon; and people left behind in the mountain communities are getting
poorer. They lack enabling environment, appropriate technologies, productive
assets, relevant information, and basic facilities; constraining their
productive and adaptive capacities for fighting against poverty.  

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The global extreme poverty was
projected to be below 10% in 2015 (Ferreira et al. 2016). However, the distribution of
the poor is skewed against the developing and middle-income countries. For the
people just above the poverty line, there is a high risk of falling them back
into poverty. The  poor  and people just above a poverty line are not
only living in highly fragile mountain community and conflict-affected regions (Collier 2007) but also in middle-income
countries where inequality has been increasing with average income (Kanbur & Sumner 2012). Kanbur & Sumner (2012) claim that the nature of
poverty is changing as countries make progress. In 1990, almost 90% of the poor
were living in low-income countries, but in 2010, almost 72% of the global poor
were living in middle-income countries. This changing geography of poverty is
due to ever-increasing inequality coupled with economic progress. Even if
countries average income has been rising over time in several countries,
marginalized, deprived, excluded and poor people living in inaccessible
mountain communities are not getting the benefits of the economic growth (Ravallion 2001).

 

In order to address the global
poverty with topmost importance, the UN general assembly in September 2015
agreed to adopt a set of Sustainable Development Goals (SDGs), where
eradicating extreme poverty by 2030 being the first goal (Resolution 2015). These SDGs are widely
accepted and nobody now doubts that the progress in poverty reduction would be
the main touchstone of the inclusive development and future progress of any
country or a region in coming years. The SDG 1 aims to “end poverty in all its
form everywhere”. The SDG 1 has seven different targets and 14 indicators,
which intends to eradicate extreme poverty, defined as proportion of population
below the international poverty line (USD 1.25 a day at 2005 price), and also
halved the proportion of the population living in different forms of poverty
defined nationally by 2030. The SDGs, also known as the Agenda 2030, has 17
goals, where first seven goals are not only closely linked to the SDG 1, but
the achievements of these goals are primarily linked with the progress made in
achieving the ‘no poverty’ goal, highlighting the importance of attaining the
‘no poverty’ goal.

 

Economic growth directly helps to
reduce the risk of being in the poverty trap (Collier, 2007). This happens, as
growth, under certain conditions, tends to enhance both private income and
government revenue, and diversifies exports from primary natural resources
based trade to more manufacturing based trade. Establishment of manufacturing
industries enhances employment opportunities. In the past several decades, the
world has made remarkable progress in reducing the global poverty through
industrialization (Chen & Ravallion 2008). However, many developing and
middle-income countries’ extreme poverty has been deeply rooted, and in the
absence of proper redistribution of the accumulated wealth from the sustained
economic growth, inequality has been worsening globally – creating pockets of
poverty in excluded and mountain areas in several low and middle-income
countries. When countries develop, it naturally creates opportunities in
accessible areas endowed with natural resources or favorable climatic
conditions. Such geographical locations generally have more economic activities
and attract more people, resulting into economics of scales. This virtuous
cycle of development ultimately leads to skewed distribution of wealth and
income in accessible areas making it difficult for poor and fragile mountain
regions to catch up (Nguyen & Dizon 2017).

 

Based on the progress made in the
past in reducing geographically linked poverty, it is quite challenging to meet
the SDG 1 by 2030 in several of these countries. It would be even more
difficult to fulfil the SDG 1 in mountain context as mountain environment is
naturally harsh with extreme slops and cold weather, living condition is hard,
access to market and resources is inadequate, and infrastructures are absent (Jodha 2005; Epprecht et al. 2008). Mountain people are deprived
of wellbeing due to isolated and inaccessible landscapes with limited
resources. Thus, the mountain area is less suitable for mechanizing the
agriculture using the existing technology, which is mostly developed for
flatter areas, and there is not much research and development in agriculture
technology for addressing mountain specific needs.    

 

Addressing mountain poverty,
requires proper targeting with appropriate instruments (Jalan & Ravallion 2002) (Minot et al. 2006). Targeting can be done by
selecting activities (e.g., education, healthcare), indicators (e.g., land
holding size, hype of house), locations (rural, away from the markets), or
self-selection (e.g., participating in employment guarantee or food-for-work
program where wage rate is fixed below the market rate so that people who are
employed elsewhere have no incentive to participate in such program), or some
combination of these approaches (Weiss 2005). While targeting poverty,
there is a possibility of under-coverage (failure to reach some of the targeted
groups) or leakage (benefits received outside the target group). This mainly
happens when we lack the knowledge of indicators and determinants of poverty.
To address the mountain poverty we may need geographical targeting combined
with mountain specific activities and indicators.

 

In order to better targeting the
mountain poverty, one approach would be to understand the determinants of
mountain poverty using cross-country analysis. However, cross-country analysis
becomes irrelevant in the face of missing comparable cross-country data. Due to
data problem, cross-country analysis masks welfare impacts of economic growth
irrelevant. Ravallion (2001) recommends a deeper
micro empirical work to understand the effect of growth on poverty. This
research, therefore, aims to: 1) examine the current state of mountain poverty,
and 2) identify its major determinants using country specific microdata.

 

We mainly analyze the poverty
status and its determinants in eight countries from the Hindu-Kush Himalaya
(HKH), namely Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal and
Pakistan. The HKH region is known for the mountain ranges as the water tower of
Asia or the ‘Third Pole,’ as it holds the largest amount of ice outside the
Polar Regions (Bahadur 1993) (REF ICIMOD work). Almost 1.9
billion (est. July 20171) people live in this HKH region and downstream,
whose livelihoods depend on the water flowing down from the HKH region. The
disaggregated knowledge and better understanding of the causes of poverty in
the mountain context helps policy makers, civil society organization and the
donors to: i) identify the poor that need help, ii) develop appropriate
development agenda, iii) better targeting of aid and other social supports for
helping the poor based on location, ethnicity, occupational groups and
education levels, and iv) monitor and evaluate the effectiveness of targeted
policy interventions undertaken to reduce poverty for fulfilling the SDGs.

 

Using nationally representative
large-scale household survey data, we examine the mountain poverty in HKH
countries. Our sub-national level analysis suggests that in the HKH countries,
mountain poverty is higher than the poverty in other regions in almost all HKH
countries. The major determinants of poverty are household size, dependency
ratio, access to electricity, safe drinking water and sanitation, cleaner
cooking fuel, access to information and market, education level of household
head, and ownership of cattle. In HKH countries, where society is divided into
different castes and ethnic groups, the lower caste and socially disadvantaged
ethnic groups are poorer than the higher castes/ethnic groups. Surprisingly,
gender of household heads, however, is not a significant determinant of poverty
when education level of household head and other characteristics of the
households are taken into account. Our quantitative estimates on the effects of
different determinants on poverty provide much needed information on identifying
the effective instruments for addressing mountain poverty using limited
resources.

 

In what follows, we discuss the
methods used for analyzing poverty incidence in HKH countries. We then discuss
spatial and group level poverty incidence in each county. In the final section,
we highlight the main observations and draw some practical conclusion.

 

1 https://www.cia.gov/library/publications/the-world-factbook/rankorder/2119rank.html  access Dec 12, 2017

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