School or Work? The Role of Weather Shocks in Madagascar · School or Work? The Role of Weather...
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Transcript of School or Work? The Role of Weather Shocks in Madagascar · School or Work? The Role of Weather...
Francesca Marchetta (CERDI, Université Clermont Auvergne)
David E. Sahn (Cornell University, IZA)
Luca Tiberti (PEP, Université Laval)
School or Work? The Role of Weather
Shocks in Madagascar
3rd IZA/DFID GLM-LIC Research Conference: New Research on Labor Markets in Low-Income Countries
19-20 October 2017, Washington DC, USA
Motivation
2
« A rapid growing body of research applies panel methods to examine how
temperature, precipitation and windstorms influence economic outcomes »
(Dell et al., 2014, pg. 740)
Weather shocks have a significant impact on human capital
formation and long-lasting effects on individual well-being and the
overall macroeconomy through:
income (Levine and Yang, 2014),
wages (Mahajan, 2017),
nutrition and health (Maccini and Yang, 2009; Tiwari, Jakoby and
Skoufias, 2017)
consumption and caloric intake (Asfaw and Maggio, 2017)
Education (see later)
Our contribution
3
The paper contributes to the literature on the economic
effects of weather events on human capital.
We study the impact of weather events on schooling and
working decisions in Madagascar, using individual panel data
on a cohort of young man and woman from 2004 through
2011.
Outline of the presentation
4
Why Madagascar?
Relevant Literature
Data
Estimation Approach
Results
Conclusion
Why Madagascar?
5
Why Madagascar?
6
Madagascar is one of the 10 countries in the world with the highest Climate Risk Index (Germanwatch): hurricanes, floods and drought heavily affect the country.
Things will not be better: according to USAID, climate scientists expect flooding and erosion to increase in some regions of the country, as rainfall increases in intensity, while in the south, rainfall will be less predictable, leading to greater extremes, including intermittent drought.
75% of the population rely on agriculture (most of them are rainfed farms); widespread poverty and absent or incompletecredit/insurance markets
Relevant literature: schooling
7
Students in more humid and warmer villages of Costa Rica are at a
higher risk of school failures (Villalobos, 2016).
Students living in Mongolian districts affected by severe winters are
less likely to complete mandatory school (Groppo and Kraehnert,
2017). But the impact was significant only for students from herding
households.
Adverse rainfall conditions in Ivory Coast decrease children’s school
enrollment (Jensen, 2010).
Favorable rainfall conditions in the year of birth has a positive effect on
education outcomes for adult Indonesian women (Maccini and Yang,
2009).
Relevant literature: labor
8
Skoufias et al. (2016) show that rainfall variability in India is associated to more diversification of rural households.
Bandyopadhyay and Skoufias (2015) find that ex ante rainfall variability risk in Bangladesh would push non-head adult members away from the agricultural sectors, also at a cost of a lower total household welfare.
Shah and Steinberg (2017) found that positive rainfall conditions would increase average wages in the Indian rural sector. This would encourage parents to increase their children’s on-farm labor supply and to decrease schooling participation (“productivity wage shifter”).
Dumas (2015) shows that child labor increases with increases in rainfall in Tanzania in absence of efficient labor markets (“price effect”).
Expected effects
9
Overall effect of a positive deviation in rainfall on schooling(labour) is ambigous:
Income effect: Positive(negative)
Price/productivity effect: Negative(positive)?
Infrastructure: negative
Also, distinction between:
Contemporaneous and
Lagged effects
Heterogenous ability of buffering shocks (or vulnerability) acrosshouseholds
Madagascar Young Adults Survey
10
We use data from the Madagascar Life Course Transition of Young
Adults Survey (2011-2012) and the Progression Through School
and Academic Performance in Madagascar Study (EPSPAM, 2004).
These are the two latest rounds of a survey that follows a cohort of
(now) young adults born in the late 1980s.
The first wave was a PASEC survey conducted in 1998.
Using 2011 and 2004 data, we build a 8-year individual-level panel
dataset, covering the period 2004-2011.
Madagascar Young Adults survey
11
The Madagascar Young Adult Survey collects a large set of information on cohort members, but also basic information on all family members, on dwelling and family assets and community infrastructures.
We have information on 1,499 cohort members, aged 21 to 23 at the time of the survey.
436 cohort members have left their community of origin between 2004 and 2012: we define them as (internal) migrants.
Main variables’ definition
12
School
Work
Raifall
School-to-work transition
13
0
20
40
60
80
100
indiv
idu
al sta
tus
14 15 16 17 18 19 20 21 22 23
in school school and work work no school, no work
Rainfall data: satellite
14
Gridded daily 30 years precipitation estimation dataset centered over Africa at 0.1 degree spatial resolution (from 1983 to 2012).
Source: African Rainfall Climatology (v2), National Oceanic and Atmospheric Administration.
We calculated the total amount of precipitation that falls fromNovember to April (rainy season) every year.
We then standardized using the long term (1991-2012) averageand standard deviation. Too many missing for the period 1983-1990.
Climatic zones
15
Köppen–Geiger
climate classification
system
Rainfall deviation by climatic zone
16
-2-1
01
23
2004 2006 2008 2010year
national
-2-1
01
23
2004 2006 2008 2010year
zone 1
-2-1
01
23
2004 2006 2008 2010year
zone 2-2
-10
12
3
2004 2006 2008 2010year
zone 3
-2-1
01
23
2004 2006 2008 2010year
zone 4
-2-1
01
23
2004 2006 2008 2010year
zone 5
-2-1
01
23
2004 2006 2008 2010year
zone 6
-2-1
01
23
2004 2006 2008 2010year
zone 7
-2-1
01
23
2004 2006 2008 2010year
zone 8
Estimation approach (basic model) –
on rural population only
17
We assume that schooling and working decisions are interdependent, so we use a bivariate probit.
The latent variables 𝑆∗ and 𝑊∗:
𝑆𝑖𝑐𝑡∗ = 𝜷1
𝑆𝑋𝑖𝑐𝑡 + 𝜷2𝑆𝑟𝑎𝑖𝑛𝑙𝑡 + 𝜷3
𝑆𝑟𝑎𝑖𝑛𝑙𝑡 ∗ 𝑎𝑠𝑠𝑒𝑡𝑖2004 + 𝜃𝑖𝑡𝑆 +𝜇𝑧
𝑆 +𝜃𝑡𝑆 + 𝜺𝑖𝑐𝑡
𝑆
𝑊𝑖𝑐𝑡∗ = 𝜷1
𝑊𝑋𝑖𝑐𝑡 + 𝜷2𝑊𝑟𝑎𝑖𝑛𝑙𝑡 + 𝜷3
𝑆𝑟𝑎𝑖𝑛𝑙𝑡 ∗ 𝑎𝑠𝑠𝑒𝑡𝑖2004 + 𝜃𝑖𝑡𝑊 + 𝜇𝑧
𝑊 + 𝜃𝑡𝑊 +
𝜺𝑖𝑐𝑡𝑊
where:
𝑆 =1 𝑖𝑓 𝑆∗ > 0
0 𝑖𝑓 𝑆∗ ≤ 0
𝑊 =1 𝑖𝑓 𝑊∗ > 0
0 𝑖𝑓 𝑊∗ ≤ 0
𝜀𝑆 and 𝜀𝑊 are nomally distributed error terms, with mean 0, variance 1 and 𝑐𝑜𝑣 𝜺𝑖𝑡
𝑆 , 𝜺𝑖𝑡𝑊 = 𝜌. Errors are clustered at the climatic station level.
Main specifications (school)
18
Equation: school 1 2 3 4
Rainfall (6 months) 0.057* 0.098** 0.108*** 0.110***
(0.031) (0.040) (0.039) (0.041)
Assets 0.009*** 0.010*** 0.010*** 0.010***
(0.003) (0.003) (0.003) (0.003)
Rainfall x Assets -0.002* -0.002* -0.002*
(0.001) (0.001) (0.001)
Cyclones -0.237*** -0.388***
0.096 (0.097)
Lagged rainfall 0.053
(0.044)
Lagged rainfall x Assets -0.001
(0.001)
Main specifications (work)
19
Equation: work (1) (2) (3) (4)
Rainfall (6 months) -0.101** -0.146*** -0.153*** -0.163***
(0.040) (0.047) (0.047) (0.045)
Assets -0.010*** -0.010*** -0.010*** -0.010***
(0.003) (0.003) (0.003) (0.003)
Rainfall x Assets 0.002* 0.002* 0.003**
(0.001) (0.001) (0.001)
Cyclones 0.237* 0.269**
(-0.130) (0.132)
Lagged rainfall -0.175***
(0.046)
Lagged rainfall x Assets 0.002**
(0.001)
Observations 8,600 8,600 8,600 8,600
Marginal effects on school
20
-.02
-.01
0.0
1.0
2.0
3.0
4.0
5M
arg
inal E
ffects
of
1 u
nit c
hange in r
ain
fall
z-s
core
s
p10 p25 p50 p75 p90
percentiles on assets
Marginal effects on work
21
-.08
-.07
-.06
-.05
-.04
-.03
-.02
-.01
0.0
1.0
2M
arg
inal E
ffects
of
1 u
nit c
hange in r
ain
fall
z-s
core
s
p10 p25 p50 p75 p90
percentiles on assets
Other specifications
22
Interaction of rainfall with women dummy
by excluding migrants
By excluding non-agricultural households
With station fixed effects
With different definitions of the rainfall (over 12 months; 5
categories, drought, seasonality index)
Conclusion
23
We studied whether rainfall shocks cause young people to drop out of school and enter the labor market to mitigate the impact of drought, floods, and cyclones.
While a priori the overall effect is ambiguous, empirically wefound that:
o negative rainfall deviations and cyclones reduce the probability of attending school and push young men and women into working
o Hardest hit are the less wealthy households
o both contemporaneous and lagged effects of the weather shocks, and that they are of a similar magnitude
o poor young women are even more susceptible to being pushed into the labor market with negative rainfall deviations
o Results are robust to various specifications and rainfall’s definitions
Our study highlights the importance of mitigation efforts to prevent negative human capital effects due to rainfall shocks
Thanks!