2B Risk and Mistrust

127 Pages·2014·2.21 MB·English

The Transmission of Mistrust:
Institutional Consequences of Early-Life Rainfall
Ariel BenYishay(cid:132)
October 2013
PRELIMINARY DRAFT
Abstract
In many developing countries, early-life socioeconomic shocks have been
shown to affect nutritional status, human capital investments and earning po-
tential in adulthood. To date, there has been no evidence on whether these
childhood shocks also affect pro-social behaviour later in life. I study the im-
pacts of early-life rainfall shocks on trust in adulthood across 17 sub-Saharan
African countries. Controlling for country fixed effects, country-specific age ef-
fects, and individual-level observables, I find that rainfall in the 5 years imme-
diately after birth substantially increases localized trust of one’s relatives and
neighbors in adulthood. These effects vary dramatically across ethnic groups
withdifferenthistoricalexposuretoslaveexporting:Onlyinethnicgroupswith
low historical levels of trust due to slavery does early-life rainfall affect trust
in adulthood. This transmission of mistrust may be due to weaker risk-sharing
norms in these ethnic groups, which differentially affect cohorts exposed to
greater shocks in childhood.
Keywords: trust, rainfall, childhood shocks
(cid:132) University of New South Wales. Email: [email protected]
1 Introduction
Early childhood is a crucial period for nutritional and broader human capital
investments. A growing body of work documents that economic shocks, conflict,
and poor rainfall limit these investments and thus damage the physiological and
cognitive development of affected children (Friedman and Sturdy 2011 provide a
review of this work). Moreover, new work is showing that these effects can persist
well into adulthood, weakening the affected individuals’ productivity, income, and
socioeconomic status.
At the same time, a number of recent psychology studies have found that social
norms may develop much earlier than previously thought. Children begin to exhibit
differences in inequality aversion, anonymous sharing, and trust as early as three
years old, and these differences expand over their childhoods ((Fehr, Bernhard and
Rockenbach, 2008), (Malti et al., 2012), (Evans, Athenstaedt and Krueger, 2013)).
Taken together, these literatures raise the question: Can economic shocks in child-
hood have persistent effects on how a person interacts with others in her society?
This paper explores this question in the context of rural sub-Saharan Africa,
where rainfall represents one of the largest annual shocks to economic production.
I examine the impacts of early-life exposure to these rainfall shocks on trust of
others among a cross-section of adults in 17 sub-Saharan African countries. Using
granular rainfall data available at the 0.5 x 0.5 degree fineness (equivalent to an
areas of roughly 56km x 56km at the equator), I am able to control for a variety of
country-specific and country-time-specific factors, as well as individual-level observ-
ables. Controlling for these factors, I find that abnormally low rainfall in the first five
yearsoflifesignificantlyreducesanindividual’strustofherfriendsorrelatives.Ithen
validate these findings using a separate survey conducted in Malawi, focusing on the
risk-sharing behavior exhibited by adults with differing childhood exposure to rain-
fall shocks. Again, I find that rainfall in early life affects an individual’s propensity
to share food with her neighbors as an adult.
The effects I find are consistent with children learning about the risk-sharing
norms of their communities through exposure to shocks. Although rainfall shocks
may be common across a village, these shocks’ impacts on households and these
households ability to insure against the shocks can vary dramatically (evidence on
theseheterogeneousimpactsofcommonshocksisdiscussedinsection5).Individuals’
behaviors in insuring one another against such risks are closely tied to their trust of
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one another. Across the sample of sub-Saharan communities I study, contemporary
trust levels are related to each ethnic group’s exposure to slave extraction, as Nunn
and Wantchekon (2011) [henceforth NW] document. In communities with greater
exposure to slave extraction, I find that lower trust levels are transmitted to children
who experience rainfall shocks in childhood. These individuals also exhibit less risk-
sharing in response to village rainfall shocks as adults. In ethnic communities with
greater historical trust levels, children exposed to rainfall shocks do not exhibit lower
trust and show greater risk-sharing. This pattern is consistent with the early-life
shocks serving as an opportunity to learn about local norms around risk-sharing,
thereby transmitting these behaviors across generations.
Alternativemechanismsmayalsoproducesimilarresults.Iftrustandrisk-sharing
are also cross-sectionally correlated with socioeconomic status, early life shocks may
affect these social measures through its impacts on health, education, or income.
Alternatively, low rainfall may induce civil conflict across groups, leading individuals
to distrust others from outside their own groups. I assess the evidence on these
channels by controlling for adult socioeconomic characteristics and examining inter-
group trust, finding that neither of these mechanisms drive the main results.
This paper relates to several literatures in development economics, psychology,
and history. A recent stream of work examines the long-lasting effects of childhood
shocks on socioeconomic outcomes in adulthood. Maccini and Yang (2009) find that
Indonesiangirlswhosehomedistrictsreceivedlittlerainfallduringtheirchildhoodare
shorter, less educated, and poorer as adults. Alderman, Hoddinott and Kinsey (2006)
find similar effects from civil war and rainfall shocks among young adults in in rural
Zimbabwe. Studying a cross-Africa sample of more mature adult women, Moradi
(2010) finds that economic shocks in early childhood (ages 0-4) and at puberty (10-
14) significantly reduce these women’s height-for-weight. Almond et al. (2007) and
Neelsen and Stratmann (2011) similarly identify the long-term economic, health, and
marital outcomes from early exposure to famine in China and Greece. Conversely,
improvements in health conditions that prevent traumatic events in childhood–such
as reduced exposure to malaria–have also been shown to generate large socioeco-
nomic gains in adulthood, as Bleakley (2010), Cutler et al. (2010), and Lucas (2010)
document. Interestingly, in the Indian context, Shah and Steinberg (2013) find hu-
man capital investments respond positively to rainfall before age 4 but negatively
during school years, as the opportunity cost of school attendance rises.
A separate literature examines how shocks during childhood directly affect social
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behavior and may explain some of the divergence in these behaviors even at early
ages. For example, Bauer, Cassar and Chytilova (2011) find that exposure to warfare
and direct trauma alters egalitarian motives and social preferences in children be-
tween the ages of 7 and 11. Other studies find large differences in inequality aversion,
trust, and sharing among children emeging between the ages of 3 and 9 ((Fehr, Bern-
hard and Rockenbach, 2008), (Malti et al., 2012), (Evans, Athenstaedt and Krueger,
2013)).
Related to the historical transmission mechanism, Tabellini (2008) and Guiso,
Sapienza and Zingales (2008) offer theoretical models of the intergenerational trans-
mission of trust and other social capital. These models are consistent with evidence
provided by NW, Grosjean (2011), Voigtla¨nder and Voth (2012) and others on the
persistence of cultural values around social capital over hundreds of years. Most
directly related to this paper, Durante (2010) studies the effects of pre-industrial
rainfall variability on contemporary patterns of trust across European communities.
He finds that early variability induced trust among communities who co-insured one
another, and this variation in trust has persisted even after rainfall variability de-
clined in economic importance after the Industrial Revolution. The present paper
builds on these historical investigations by examining potential mechanisms through
which trust is transmitted across generations.
The paper is laid out as follows: In section 2, I lay out the empirical methodology
and describe the data in section 3. I present results in section 4 and discuss causal
channels in section 5. In section 6, I conduct a series of robustness checks. I then
validate these results using a separate survey conducted in Malawi, discussed in
section 7. I discuss future work and conclude in section 8.
2 Empirical Strategy
I identify the effects of exposure to rainfall shocks throughout one’s lifecycle by
comparing age-specific exposure at a given location and year. My variation in rainfall
is sufficiently fine to control for countrywide factors that affect all individuals in a
given five year age bin. My primary specification is:
Trust = α+ΣT [β Rainfall ]+D +D +D ∗D +ΓX +(cid:15) (1)
ijcb a=0 a aj b c b c ijcb ijb
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where Trust is the level of trust of individual i born in year b living in location
ijct
j in country c, Rainfall is the measure of rainfall at age a, and ΣT sums over
aj a=0
all ages between 0 and adulthood (at age T). Each β can thus be interpreted as an
a
age-specific effect of rainfall. In this specification, D is a vector of dummy variables
b
capturing 5-year bins for year-of-birth, D is a vector of country fixed effects, and
c
D ∗D captures year-of-birth and country-specific fixed effects. X is a vector of
b c ijcb
individual and ethnicity-specific control variables, including educational attainment,
occupation, religion, current living conditions, historical slave extraction, and a vari-
ety of other characteristics. I estimate this specification using ordinary least squares
and use two-way clustering of standard errors to account for correlation within each
age bin and each subnational location.
By controlling for country-age fixed effects, I am using only within-country dif-
ferences in age-specific exposure to rainfall at a specific location. Moreover, even
within-country, cross-sectional rainfall variation may be correlated with other fixed
factors that affect trust levels. I therefore normalize rainfall at a given location by
converting rainfall levels to percentiles of the long-run distribution of rainfall at
that location (I describe this normalization in the following section). This effectively
eliminates cross-sectional variation in the mean and variability of rainfall at the sub-
national location level, and ensures that I identify the causal impact of rainfall in a
given year on subsequent trust outcomes.
Iestimatethisspecificationusingordinaryleastsquares,usingtwo-wayclustering
of standard errors across both individual locations and 5-year age bins (Cameron,
Gelbach and Miller, 2011). I use this specification to compare the impacts of rainfall
on trust of one’s relatives and neighbors, as well as those within and outside of one’s
own ethnic group.
3 Data
3.1 Afrobarometer Data
The individual-level data I utilize are from the 2005 Afrobarometer survey round.
Thesurveyscover17sub-SaharanAfricancountries:Benin,Botswana,Ghana,Kenya,
Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South
Africa, Tanzania, Uganda, Zambia, and Zimbabwe. These nationally representative
surveys are based on interviews conducted in the local languages of a random sample
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of individuals of voting age in each country. In order to compare the effects of early-
life rainfall to that in early adulthood, I limit my sample to those respondents aged
30 or over. I further limit my analysis to respondents in rural areas, as these house-
holds are most likely to be engaged in agriculture and thus to be directly affected by
rainfall shocks. There are 8,350 respondents in this sample.
Myprimaryoutcomemeasuresareeachindividual’sresponsetothequestion:how
much do you trust each of the following types of people: Your relatives, your neighbors,
people from your own ethnicgroup, and people from other ethnic groups?Respondents
answered that they trust these individuals not at all, just a little, somewhat, or a
lot. I follow the Afrobarometer coding and convert these responses into a [1-4] scale
of trust for each of these questions.
The Afrobarometer data also include each respondent’s age, ethnicity, religion,
educational level, occupational category, frequency of deprivation of basic necessities,
andself-ratedlivingconditions,physicalandmentalhealth.Thedataalsoincludethe
respondents’ current town or village of residence, which NW geo-code to latitudinal
and longitudinal coordinates.
An important concern is that the Afrobarometer data contains neither the loca-
tion of one’s birth nor subsequent migration choices. As I describe below, I therefore
merge the rainfall data for each respondent’s current location. If individuals with dif-
ferent preexisting trust levels differentially migrate out of low-rainfall locations and
into high-rainfall locations, this could bias my estimates in either direction. Although
existing work shows that internal migration in Africa does respond to rainfall shocks
both in departing from one’s birthplace and in the selection of one’s destination (c.f.
Lewin, Fisher and Weber 2012), this literature also typically finds that migrants
have lower social capital in their destinations (c.f. La Ferrara 2003). Such negative
selection on trust at one’s destination would imply that migration may dampen the
responsetorainfallshocksIobserve(migrantswouldexhibitbothlower-than-average
trust and higher-than-average rainfall in early life, as I mis-assign them the early-life
rainfall at their destination). Nonetheless, in section 6, I test whether my primary
results vary across other indicators of likely migration.
An additional limitation of this data is that it naturally omits individuals who
die in their childhood as a result of rainfall shocks. If this mortality is selective (i.e.,
correlated with trust in adulthood), it may induce survivor bias. As in the case of
migration, it is likely that mortality would be negatively correlated with trust; thus,
correcting this bias would likely amplify the effects found herein.
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3.2 Rainfall Data
I merge the Afrobarometer data with historical rainfall data available from the
Terrestrial Precipitation: 1900-2010 Gridded Monthly Time Series, Version 3.01
(Matsuura and Willmott 2012). The same dataset has been used by Jayachandran
(2006) and Dell, Jones and Olken (2012). This data provides worldwide (terrestrial)
monthly mean precipitation data at 0.5 x 0.5 degree resolution (approximately 56km
x 56km at the equator). Values are interpolated for each grid node from an average
of 20 different weather stations, with corrections for elevation. I construct annual
total precipitation values at each node. I match each town or village of residence in
the Afrobarometer survey sample to a quadrant in this dataset, with four nodes at
the corners of each quadrant. To interpolate the precipitation value at the town or
residence, I construct a weighted average of the precipitation at each of these four
nodes, with the inverse of the distance to these nodes as their weights. In Section 6, I
document that this weighting scheme does not significantly affect my results. I thus
obtain a panel of annual precipitation values at each location. To isolate the effects of
time-varying shocks from time-invariant differences across locations, I convert each
location’s time series into percentiles.
This data is then merged into the Afrobarometer dataset based on each respon-
dent’s year of birth and location. Each location-birth-year cohort thus obtains a
unique rainfall history. I then combine these annual levels into 5-year bins: from
birth to age 4, ages 5-9, 10-14, 15-19, 20-24, and 25-29. Each respondent’s rainfall
history thus reflects the average percentile of rainfall in each 5-year bin.
3.3 Historical Data
I rely on NW for secondary historical data on the slave exports and historical
boundaries of each ethnicity in the sample, as well as for historical controls for
each ethnicity’s contact with European explorers, exposure to Christian missions,
urbanization in 1400, and malaria ecology.
I present summary statistics on the merged data in Table 1.
4 Results on age-specific rainfall
In Table 2, I present the baseline results on the effects of age-specific rainfall
on trust of one’s neighbors in adulthood. I begin by including only the age-specific
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rainfall shocks, and fixed effects for both age bins and countries. These results are
shown in column 1. They indicate that the average percentile of rainfall between
birth and age 4 and between ages 20 and 29 have significant impacts on trust of
neighbors among those aged 30 or over. Experiencing rainfall at the 75th rather than
25th percentile in each of these age bins raises one’s trust level by 0.1, roughly 10%
of the standard deviation of trust in the sample. In column 2 of Table 2, I estimate
the same specification among those aged 45 or over, adding rainfall at age 30-34,
35-39, and 40-44 to the estimation. I do not find that rainfall at these older age bins
has a significant effect on trust. I next return to my sample of 30+ year olds and add
individual-level baseline controls to the specification. These controls capture gender,
education level, occupation, ethnicity, religion, and self-rated living conditions. The
results, shown in column 3, indicate that rainfall between birth and age 4 remains
significant at the 95% confidence level and quantitatively similar. Rainfall in the
later 20’s (ages 25-29) is also significant and similar in magnitude, but none of the
other rainfall measures are significant at the 95% confidence level. In column 4, I add
country-age bin interactions as shown in Eq. 1. The significance and magnitude of
rainfallatages0-4and24-29arebothlargerandnowsignificantatthe1%confidence
level.
I next consider whether the effects of rainfall vary across the distribution at each
location. I now run the same regression instead using quintiles of average rainfall at
each age bin (with the lowest rainfall bin further disaggregated into the 0-10 and
11-20 percentiles). Figure 1 plots the coefficients of this regression for the rainfall
quintiles at age 0-4. The results show that trust levels drop off substantially at the
bottom of the rainfall distribution: whereas trust of neighbors is approximately 1.9
in the median bin, it drops by 0.05 in the 10-20th percentile and a further 0.05
in the bottom 10%. (Omitted for brevity, none of the coefficients for these lowest
quintiles are significant at any of the other age bins). These results indicate that
early childhoods with the worst rainfall are associated with significantly lower trust
of neighbors.
InTable3,Icomparetheeffectsofexperiencingthebottom10%ofrainfallduring
earlychildhoodfortrustofdifferentindividuals.Incolumn1,Iconsidertrustofone’s
relatives as the dependent variable. Again, I control for country-age bin fixed effects
and the full set of individual covariates. The results are largely similar to those for
trust of one’s neighbors; the worst rainfall at age 0-4 leads to a 0.1 reduction in trust
of one’s neighbors and a similar response at age 25-29. In columns 2 and 3, I separate
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the sample by gender. The early-childhood effects for males (col 2) and females (col
3) are quite similar, with the effect among men slightly smaller and more precisely
estimated than that among women. At the same time, experiences at ages 25-29 are
concentrated among men, as the effect of low rainfall at these ages among women is
not different from zero.
The Afrobarometer surveys also include questions of trust of others in one’s own
ethnic group and in other groups. In columns 4 and 5 of Table 3, I return to the full
sample of all adults and estimate the effects of rainfall on these trust measures. In
both cases, the effects of early-life childhood rainfall shocks are negative and larger
than those at older ages, although none of the rainfall measures is significant. The
effects on inter-group trust are also smaller in magnitude than those on both intra-
group trust and trust of one’s neighbors and relatives, suggesting that they are more
closely linked to localized trust (even in ethnically diverse localities).
5 Causal channels
5.1 Transmission of trust
How are shocks in early childhood transmitted to social perceptions in adult-
hood? One possibility may be that individuals learn not to trust their relatives
and neighbors to honor risk-sharing arrangements when such shocks occur. Such
arrangements for risk-sharing are quite common. Cox and Fafchamps (2007) provide
an overview of the literature on kinship-based mutual insurance, highlighting how
even for informal agreements, kinship and social ties can be used to enforce con-
tracts through reputation, threat of exclusion, group identification and emotional
mechanisms. Community-based risk management mechanisms among neighbors are
reviewed in Bhattamishra and Barrett (2010). Empirically,Goldstein, de Janvry and
Sadoulet (2002) and Fafchamps and Lund (2003) document how Ghanaian and Filip-
pino households insure one another specifically via networks of relatives and friends.
Naturally, communities respond differently to shocks, in some cases coping more
collaboratively and in others less so, based in part on the pre-existing norms and
trust among their members. Many studies on the degree of this collaboration focus
on mutual insurance against idiosyncratic rather aggregate risk (a long literature
follows Townsend’s 1994 work on village-level risk-sharing). Even for aggregate risks
such as rainfall, however, exposure to this risk and ability to cope with it may well be
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heterogeneous within a community. Christiaensen and Subbarao (2005) and Dercon
and Christiaensen (2011) show heterogeneous vulnerability to local rainfall shocks
among farmers in Kenya and Ethiopia. Mobarak and Rosenzweig (2013) demonstrate
that informal rainfall insurance varies within Indian villages by social network po-
sition. Clearly, norms around local risk-sharing may still be quite important when
facing rainfall shocks.
Thesenormsareoftentransmittedtoensuinggenerations:KatzandRotter(1969)
and Dohmen et al. (2012) document strong correlations in the propensity to trust
others among parents and that of their children. I highlight the possibility that the
transmission of risk-sharing norms may take place through experience with these
shocks: Children exposed observe the community’s norms around risk-sharing and
trust and, in effect, inherit them. Children who are not directly exposed to these
shocks may observe the community’s response more noisily or not at all. In other
words, individuals have only imperfect information on the quality of informal insti-
tutions in their community, such as whether risk sharing mechanisms will be honored
when needed. Experiencing a shock reveals the “true” quality of these institutions. In
locations with low quality institutions, these shocks reveal this information and thus
worsen perceptions of these institutions (in high-quality locations, the symmetric ef-
fect in improving perceptions could also occur). Informal institutional quality–in this
case, the trust necessary for mutual insurance–evolves endogenously and persistently.
Communities with little trust and risk-sharing may be “stuck” in these equilibria.
To understand whether there is evidence of such persistence, I study the effect
of pre-existing trust in a community on the response to early childhood rainfall
shocks.Todoso,oneneedseitherhistoricalmeasuresoftrustacrosscommunitiesthat
precede the earliest birth in the sample, or exogenous historical factors that lead to
variationincontemporarytrust.Itakethesecondapproach.NWhighlighttheroleof
slavery extraction in determining historical levels of trust that have been transmitted
to present day populations. Because subnational slave extraction may have been
correlated with preexisting trust levels and other factors that directly affect current
trustlevels,IfollowNWandproxyforslaveextractionexposureusingeachlocation’s
distance to the coast. At the same time, I control for other local features that may
be correlated with both slavery extraction and trust levels, including district-level
ethnic fractionalization, the share of one’s own ethnicity in the district, and a set of
ethnicity fixed effects.1 These covariates are derived from and discussed at length in
1Whereas NW control for ethnicitiy-specific European missions per square km in the district in
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