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!