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Annex 4
Household panel data – capturing poverty dynamics and determinants
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Lessons learned
The analyses suggest that three sets of factors are related to chronic poverty across the board. The first
is demographics – in most countries, larger households and households with a higher proportion of
dependants tend to be chronically poor more often than others. This factor is common to most surveys,
except for Albania, where the demographic transition has already happened. Second, low levels of land
and/or livestock ownership are associated with chronic poverty in most countries, except Albania,
South Africa and Viet Nam. Low levels of assets are clearly associated with high vulnerability and low ability
to seize economic opportunities. For households with low education levels or few possibilities for off-farm
employment, low farm assets can sometimes mean no income options. This may not hold true, however,
in countries where rural economies are more diversified (which includes, in this sample, South Africa,
Albania and Viet Nam). Third, low education level of the household head is associated with chronic poverty
almost everywhere. Both primary and secondary education make a difference.
In terms of livelihood characteristics, households engaged in the non-farm labour market are often less
likely to be chronically poor. This is the case in Nicaragua, the United Republic of Tanzania and Viet Nam,
and the same trend exists in Albania, Indonesia and Uganda, although it is not significant. All in all, the
results support the idea that involvement in non-agriculture labour, and specifically a high or growing share
of income from non-agriculture labour, is associated with lower incidence of chronic poverty among
households. Similar trends were illustrated in a few countries for non-agriculture self-employment: in three
case (Indonesia, Nicaragua, Viet Nam), in particular high non-agriculture self employment incidence
or income shares are associated with lower chronic poverty incidence. Engagement in agricultural
self-employment was very variably associated with chronic poverty across countries – in Nicaragua,
households engaged in agriculture labour were more likely to be chronically poor, while it was the opposite
in Indonesia and the United Republic of Tanzania.
If low levels of education, high numbers of dependants, and low farm assets are clearly associated
with chronic poverty, would improvement on these three variables be associated with mobility out of
poverty? In general, the analysis shows that these factors tend to be associated with exit from poverty in
the same way as their lack is associated with chronic poverty. However, in many cases the association is
even more significant when it comes to reduced vulnerability to mobility into poverty.
Concerning livelihoods, trends show a frequent association of non-farm rural employment with higher
prevalence of mobility out of poverty (a particularly significant association in Ethiopia and Nicaragua). In
Viet Nam, on the other hand, it is non-farm self-employment that is more significantly correlated with
mobility out of poverty, and this is also true in Indonesia and Nicaragua. Conversely, participation in
agriculture wage employment is significantly correlated with vulnerability to move (back) into poverty in
some countries.