Parallel session IARIW 2014

1. The Dynamics of Non-Monotonic Poverty:
Theory and Application to Time Poverty in
Mexico
Florent Bresson (Universite d’Orleans)
Jose V. Gallegos (Research Center of Universidad del Pacifico)
Gaston Yalonetzky (University of Leeds)
Discussant: KK Tang (University of Queensland)
1

2. Colour Scheme
• Material drawn from the paper is in black.
• Comments and questions are in blue.
2

3. Objective
• Propose new poverty measures that consider
– Dynamics of poverty
– Non-monotonicity of poverty
• Apply them to study time poverty in Mexico
during 2005-2010
3

4. Motivation
• Why dynamics of poverty?
• Static measures of poverty provides only a snap
shoot for a particular time
• Some people may be in poverty for only a short
period of time, while some others (e.g. least
educated) may stay for much longer
• So, in order to identify the most vulnerable
groups, it is important to consider poverty over
time and to distinguish those in chronic poverty
from those in transient poverty
• Why non-monotonicity of poverty?
4

5. Monotonic Income Poverty
5
1.25 Income per day ($)
(Wold Bank
poverty line)
Poverty intensity
0

6. Non-Monotonic Time Poverty
0 24
leisure/day (hour)
lower
Shortfall poverty
(too little leisure)
threshold

7. Non-Monotonic Time Poverty
0 24
leisure/day (hour)
lower
Shortfall poverty
(too little leisure)
threshold
But too much leisure could be
due to some negative situations:
• Unable to do domestic or paid
work due to poor health
• Being unemployed due to poor
labour market environment

8. Non-Monotonic Time Poverty
Excess poverty
(too much leisure)
0 upper 24
leisure/day (hour)
lower
Shortfall poverty
(too little leisure)
threshold
threshold

9. Data
• Mexican state ENOE panel dataset
• Year coverage: 2005-2010
– Overlap with the Global Financial Crisis
– GDP growth fell from 5% in 2006 to –4.7% in 2009
• Balanced panel
– Full sample: age 18-65
– Subsample: age 18-65, paid work in non-agriculture
sectors throughout the period
– No dropping in and out of the subsample due to
changes in employment status
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10. Data
• Leisure = total time minus time used for the
following activities:
– work, domestic chores, study, child care, house
building and maintenance, and community
services
• Poverty thresholds:
– 20 hrs/day (upper)
– 11.5 hrs/day (lower)
– Why these numbers are chosen is not clear
12

11. 13
• Year-by-year static measures
• Female is more likely in either type of poverty than male
• Excess poverty contributes far more than shortfall to total
poverty, justifying its inclusion in the analysis
• % of female in excess poverty increased rapidly even before
GFC (explanation?)

12. • More shortfall poverty and less excess poverty for both
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genders due to work as expected
• Strong dynamics even at the aggregate in both
samples: need to consider chronic and transient
poverty

13. Leisure (x, hrs)
Time (t)
Excess poverty
threshold
Shortfall poverty
threshold
xt
In and out of excess poverty only

14. No in chronic excess or shortfall poverty, only in transient
excess poverty
Leisure (x, hrs)
Time (t)
xt
Excess poverty
threshold
Shortfall poverty
threshold

15. No in chronic excess or shortfall poverty, only in transient
excess poverty
Leisure (x, hrs)
Time (t)
xt
Excess poverty
threshold
Shortfall poverty
threshold
Overstate the utility of {xt}
if smoothing of leisure
consumption is impossible

16. Leisure (x, hrs)
Time (t)
xt
No in chronic shortfall poverty, however…
Excess poverty
threshold
Shortfall poverty
threshold
Stability premium when
more leisure is better

17. Leisure (x, hrs)
Time (t)
xt
In chronic excess poverty
Excess poverty
threshold
Shortfall poverty
threshold
Stability premium when
less leisure is better

18. Empirical Setting
• Excess poverty threshold = 20 hrs/day
• Shortfall poverty threshold = 11.5 hrs/day
• Higher permanent leisure (less is better) = μβ=1.1
• Lower permanent leisure (more is better) = μβ=0.9
• If μβ=1.1 > 20 => chronic excess poverty
• If μβ=0.9 < 11.5 => chronic shortfall poverty
• (μβ=1.1 > 20) & (μβ=0.9 < 11.5) at the same time is
theoretically possible because μβ=1.1 > μβ=0.9
• Gap of β’s (0.9-1.1) is narrow enough & gap of thresholds
(11.5-20) is wide enough to rule that out in practice
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19. • Excess poverty dominates almost completely especially for the full sample
• Paid workers in non-agriculture have less excess & more shortfall poverty
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than the fall sample as seen in static analysis
• Paid female workers are much more likely in shortfall poverty than male,
probably because they still bear most of domestic duties (confirmed with
the data?)

20. 22
• The definitions of “none-5” and “superior” are
not clear
• Excess poverty dominates completely
• Poverty falls with education level

21. 23
• Spike of shortfall & excess poverty with bachelor
degree?
• Employed post-graduates have the highest % of
excess poverty (93.6%)?

22. Poverty Intensity Weight
• The paper accounts for severity of poverty by
weighting each head count with various
measures of poverty intensity
• Intensity function: utility gap from the
threshold as % of maximum possible utility
gap (based on the Clark-Hemming-Ulph family
used by Foster and Santos (2013))
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gL =
11.50.9 -m0.9
b =0.9
11.50.9 - 0
gH =
1.1 - 201.1
241.1 - 201.1
mb =1.1

23. • Excess poverty is even more dominating
• Differences between paid workers and full sample are even
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greater
• Intensity measure is bounded between 0 and 1, why
weighted head count for female is greater than 1?

24. 26
• Poverty falls with education in general as expected
• Poverty for none-5 and primary is greater than 1?

25. 27
• Much lower excess poverty as expected due to
work
• Excess poverty decreased till bachelor but
increased after?

26. Spell Counting Approach
• If an individual spends a proportion of time in
poverty below/above a threshold then he/she
is deemed chronically time poor
• In the empirics
– leisure < 11.5 hrs/day for at least 60% of time =>
chronic shortfall poverty
– leisure > 20 hrs/day for at least 60% of time =>
chronic excess poverty
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27. 29
• Results are very similar between the two
approaches except for female paid workers

28. Other “Treasury” of the Paper
• Intensity weighted head count poverty using the
spell counting approach
• Other head county poverty measures
• Other intensity functions
• Measures of transient poverty
• Detail examination of properties of various
poverty measures
• Poverty transition probability over 2005 and 2010
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29. Other Comments
• An argument of considering excess time poverty
is that it may be due to unemployment or poor
health
• Given typically employment and health data are
more widely available (and more accurate?),
what is the value-added of measuring excess time
poverty from the policy perspective?
• The main contribution of the paper is on the
methodology rather than the application
• Are there other applications of the non-monotonic
poverty measures besides leisure?
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30. Conclusion
• Objective: To propose measures of chronic and
transient non-monotonic poverty; apply them to
study leisure time poverty in Mexico
• Motivation: Either too much or too little leisure is
bad, so need to distinguish between excess and
shortfall poverty
• Methodology: A number of poverty head count
measures with and without intensity weight are
proposed; their properties are carefully examined
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31. Conclusion
• Finding:
– Various measures give largely consistent findings
– 36% of 18-65 years old population is time poor
– Excess poverty dominates shortfall poverty in all
cases, and mostly by an extreme large margin
– Those with paid work in non-agriculture sectors
are much more likely to be in shortfall poverty and
much less likely in excess poverty
– Amongst paid workers, female are much more
likely to be in shortfall poverty than male
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