Post on 19-Mar-2020
ESSAYS ON RISK AND SOCIAL VISIBILITY: INSURANCE, ASSET POVERTY
AND INTRAHOUSEHOLD HEALTH INEQUALITY
A Dissertation
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
by
Jacqueline Vanderpuye-Orgle
January 2008
© 2008 Jacqueline Vanderpuye-Orgle
ESSAYS ON RISK AND SOCIAL VISIBILITY: INSURANCE, ASSET POVERTY
AND INTRAHOUSEHOLD HEALTH INEQUALITY
Jacqueline Vanderpuye-Orgle, Ph.D.
Cornell University 2008
Risk management is intrinsic to the livelihood choices of people in developing
countries. The effects of exposure to risk and attendant risk coping mechanisms could
have dire implications for individual well-being and economic development as a
whole. This study examines access to social insurance and the Pareto efficiency of risk
pooling. It also examines the existence of asset poverty traps and intrahousehold
inverse-U shaped Kuznets curves as well as the implications of access to insurance for
asset dynamics and intrahousehold health inequality.
We identify a distinct subpopulation of socially invisible individuals who tend
to be younger, poorer, engaged in farming, recent arrivals into the village who have
been fostered, and not a member of a major clan. While the socially visible enjoy
Pareto efficient insurance both at the network and village levels, risk pooling fails for
the socially invisible subpopulation. In addition, whereas asset accumulation patterns
of the socially visible exhibit multiple stable dynamic equilibria, the socially invisible
converge towards a unique dynamic stable equilibrium at the lower asset level. The
asset levels at the respective equilibria were generally higher for the socially invisible
as compared to the socially visible, particularly for the unstable dynamic equilibrium.
The latter implies that the socially invisible are more likely to fall into poverty traps
since there is a broader asset domain over which people in this subpopulation would
collapse toward the lower level asset equilibrium.
Multivariate analyses show that shocks increase the likelihood of a socially
invisible individual falling into a poverty trap. On the other hand, shocks have a
significant effect on the likelihood of a socially visible individual falling into a poverty
trap; this is consistent with the observation that the socially visible enjoy complete risk
pooling. The analyses of intrahousehold inequality indicate that whilst the inverted U-
shape may not exist when using aggregated data, it may exist for the socially visible.
In addition the composition of inequality varies by social visibility. Patterns in relative
BMI within the household suggest that the socially invisible may overcompensate for
risk exposure by protecting children in the allocation of limited food and nutritional
resources.
BIOGRAPHICAL SKETCH
Jacqueline Vanderpuye-Orgle was born in Accra, Ghana. She graduated from
the University of Ghana in 1999, with a Bachelor of Science (First Class Honors)
Degree in Agricultural Economics and was awarded the Best Undergraduate
Dissertation (Agriculture) for her thesis: “Econometric Modeling of the Demand and
Supply of Agricultural Credit by the Agricultural Development Bank in Ghana, 1970-
97”. She was one of two recipients of the African University Pre-doctoral Fellowship
in Economics award. She spent a year at Yale University taking courses in Economics,
French and Mathematics. Jacqueline then worked as a Summer Intern with the
Macroeconomics and Public Policy Divisions of the Development Research Group at
the World Bank, after which she went to Cornell University to pursue a Masters in
Applied Economics and a Ph.D. in Policy Analysis and Management.
iii
With love to my mother, Comfort and my siblings,
Yacoba and Bertrand
iv
ACKNOWLEDGEMENTS
I thank the Lord Almighty for His grace and faithfulness. I am greatly indebted
to David Sahn, my committee chairperson, Chris Barrett, Ravi Kanbur and Chris
Udry, my committee members for their advice, guidance and most of all, their
patience and unwavering support throughout my graduate studies.
I extend my gratitude to Ernest Aryeetey and the research staff at the Institute
of Statistical, Social and Economic Research for providing me with an enabling work
environment during my field research. I would like to thank Geysa and the rest of the
administrative staff in the Department of Policy Analysis and Management for their
support. I am grateful to my friends Naa Dei, Abena, Naalamle, Dela, Dola and the
rest of the church family for providing me with support, love and prayers through this
journey.
Finally, I thank my mother, Comfort Vanderpuye-Orgle for inspiring me to
reach for the stars and for believing in me. I thank my sister Yacoba for the sacrifices
she made, my brother Bertrand for his support as well as Kpakpo, Kukua, Papa Kofi
and Nii Akwei for urging me on. I also thank Niilante Amissah for his support,
encouragement and strength. You gave me hope in the challenging moments and for
that I am eternally grateful.
v
TABLE OF CONTENTS
Biographical Sketch iii
Dedications iv
Acknowledgements v
List of Figures ix
List of Tables xiii
Chapter 1 Risk Management, Social Visibility, Asset Poverty and
Intrahousehold Health Inequality: An Overview 1 1.1 Introduction 1 1.1.1 Risk Management 1 1.1.2 Social Networks and Social Visibility 2 1.1.3 Poverty Dynamics and Asset Poverty Traps 5 1.1.4 Intrahousehold Health Inequality 8 1.2 The Data 10 1.3 Dissertation Chapters 11 1.3.1 Risk Management and Social Visibility in Ghana 11 1.3.2 Risk, Asset Poverty and Social Visibility in Ghana 12 1.3.3 Risk, Intrahousehold Health Inequality and Social
Visibility in Ghana 13 1.4 Conclusions 14 References 17 Chapter 2 Risk Management and Social Visibility in Ghana 25
2.1 Introduction 25 2.2 Risk Pooling and Social Visibility: Simple Theoretical Predictions 29 2.3 The Data 32 2.3.1 Individuals’ Social Networks 33 2.3.2 Consumption 34 2.3.3 Shocks 35
vi
2.4 Social Visibility 38 2.5 Risk Pooling Conditional on Social Visibility 46 2.6 Conclusions 55 References 57
Chapter 3 Risk, Asset Poverty and Social Visibility in Ghana 60
3.1 Introduction 60 3.2 The Data 65 3.2.1 Individuals’ Assets 66 3.2.2 Individuals’ Social Visibility 66 3.2.3 Shocks 67
3.2.4 Consumption 67
3.3 Asset Index 68 3.4 Asset Poverty Dynamics 71 3.5 Who is Likely to Remain Poor? 85 3.6 Conclusions 88 References 92
Chapter 4 Risk, Intrahousehold Health Inequality and Social Visibility in Ghana 96
4.1 Introduction 96 4.2 Analytical Framework 100 4.2.1 Bivariate Analysis of Intrahousehold Inequality 100 4.2.2 Determinants of Intrahousehold Health Inequality 102 4.3 The Data 104 4.3.1 Anthropometrics 105
4.3.2 Social Visibility 105 4.3.3 Shocks 106 4.3.4 Other Sample Characteristics 107
vii
4.4 Empirical Model and Results 107 4.4.1 Analysis of Intrahousehold Inequality 107 4.4.2 Determinants of Intrahousehold Inequality 124 4.5 Conclusions 129 References 134
Appendix A Field Notes 139 A.1 Field Activities 139 A.2 Preliminary Visit to Survey Area 140 A.3 Data Collection Trip 142 Appendix B Survey Modules and Interview Schedule 146 Appendix C Questionnaires 152
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LIST OF FIGURES
3.1 Nonparametric Estimates of Predicted Asset Index, All Individuals 1997-1998 72 3.2 Nonparametric Estimates of Predicted Asset Index, All Individuals 1997-2004 73 3.3 Nonparametric Estimates of Predicted Asset Index, All Individuals 1998-2004 74 3.4 Nonparametric Estimates of Predicted Asset Index: Social Visibility>0, 1997-1998 75 3.5 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0, 1997-1998 75 3.6 Nonparametric Estimates of Predicted Asset Index: Social Visibility>0, 1997-2004 76 3.7 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0, 1997-2004 77 3.8 Nonparametric Estimates of Predicted Asset Index: Social Visibility>0, 1998-2004 77 3.9 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0, 1998-2004 78 3.10 Asset Index Regression Tree 79 3.11 Nonparametric Estimates of Predicted Asset Index: Social Visibility≥0.793, 1997-1998 81 3.12 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0.793, 1997-1998 81 3.13 Nonparametric Estimates of Predicted Asset Index: Social Visibility≥0.793, 1997-2004 82 3.14 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0.793, 1997-2004 82 3.15 Nonparametric Estimates of Predicted Asset Index: Social Visibility≥0.793, 1998-2004 83
ix
3.16 Nonparametric Estimates of Predicted Asset Index: Social Visibility=0.793, 1998-2004 83 3.17 Nonparametric Estimates of Predicted Asset Index: Social Visibility<0.0955, 1997-1998 84 3.18 Nonparametric Estimates of Predicted Asset Index: 0.0955>Social Visibility≥0.793, 1997-1998 84 4.1 Nonparametric Estimates of Intrahousehold Inequality and Mean
Household BMI, All Individuals 108
4.2 Nonparametric Estimates of Intrahousehold Inequality and Household Expenditure Percentile, All Individuals 108
4.3 Intrahousehold Inequality Regression Tree 109 4.4 Nonparametric Estimates of Intrahousehold Inequality and Mean
Household BMI, Socially Visible Households 110
4.5 Nonparametric Estimates of Intrahousehold Inequality and Household Expenditure Percentile, Socially Visible Households 111
4.6 Nonparametric Estimates of Intrahousehold Inequality and Mean Household BMI, Socially Invisible Households 111
4.7 Nonparametric Estimates of Intrahousehold Inequality and Household
Expenditure Percentile, Socially Invisible Households 112
4.8 Nonparametric Estimates of Male to Child BMI and Mean Household BMI, All Households 115
4.9 Nonparametric Estimates of Male to Child BMI and Household
Expenditure Percentile, All Households 116 4.10 Nonparametric Estimates of Female to Child BMI and Mean Household
BMI, All Households 116 4.11 Nonparametric Estimates of Female to Child BMI and Household
Expenditure Percentile, All Households 117 4.12 Nonparametric Estimates of Male to Female BMI and Mean Household
BMI, All Households 117
x
4.13 Nonparametric Estimates of Male to Female BMI and Household Expenditure Percentile, All Households 118
4.14 Nonparametric Estimates of Male to Child BMI and Mean Household
BMI, Socially Visible Households 118 4.15 Nonparametric Estimates of Male to Child BMI and Household
Expenditure Percentile, Socially Visible Households 119 4.16 Nonparametric Estimates of Female to Child BMI and Mean Household
BMI, Socially Visible Households 119 4.17 Nonparametric Estimates of Female to Child BMI and Household
Expenditure Percentile, Socially Visible Households 120 4.18 Nonparametric Estimates of Male to Female BMI and Mean Household
BMI, Socially Visible Households 120 4.19 Nonparametric Estimates of Male to Female BMI and Household
Expenditure Percentile, Socially Visible Households 121 4.20 Nonparametric Estimates of Male to Child BMI and Mean Household
BMI, Socially Invisible Households 121 4.21 Nonparametric Estimates of Male to Child BMI and Household
Expenditure Percentile, Socially Invisible Households 122 4.22 Nonparametric Estimates of Female to Child BMI and Mean Household
BMI, Socially Invisible Households 122 4.23 Nonparametric Estimates of Female to Child BMI and Household
Expenditure Percentile, Socially Invisible Households 123 4.24 Nonparametric Estimates of Male to Female BMI and Mean Household
BMI, Socially Invisible Households 124 4.25 Nonparametric Estimates of Male to Female BMI and Household
Expenditure Percentile, Socially Invisible Households 125
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LIST OF TABLES
2.1 Individual Consumption Expenditures, June 2004- January 2005
(Nominal Cedis Per Month) 36 2.2 Percentage of Individuals Affected by Shocks and Mean Response
Expenditure/ Imputed Value of Damage (Nominal Cedis) 36 2.3 Summary Statistics on Random Matching 39 2.4 Descriptive Statistics: Estimating Social Visibility 40 2.5 Probit Estimation of Social Visibility 42 2.6 Probit Estimation of the Likelihood of Knowing a Random Match 45 2.7 Regression Tests for Full and No Risk Pooling 49 2.8 Risk Pooling for an Individual in Hid Directly Elicited Social Network 53 2.9 Insurance Among Social Networks within an Village 54 2.10 Summary results for Tests for Risk Pooling 55 3.1 Descriptive Statistics: Estimating Livelihood-Weighted Index 69 3.2 Livelihood-Weighted Index Estimation 70 3.3 Asset Index Regression Tree Estimation 80 3.4 Descriptive Statistics: Estimating Likelihood of Poverty Traps 86 3.5 Poverty Trap Probit Estimation, Social Visibility=0 and Social
Visibility>0 87 3.6 Poverty Trap Probit Estimation, Social Visibility≤0.7929 and Social
Visibility>0.7929 89 4.1 Comparisons of Test Points of Intrahousehold Inequality 112 4.2 Parametric Estimates of Intrahousehold Inequality by Mean Household BMI 113
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4.3 Parametric Estimates of Intrahousehold Inequality by Household Expenditure Percentile 113
4.4 Decomposition of Overall Inequality (%) 124 4.5 Descriptive Statistics: Intrahousehold Health Inequality Model 127 4.6 Intrahousehold Health Inequality Naïve Regressions 128 4.7 Intrahousehold Health Inequality Reduced Form Model 130 4.8 Intrahousehold Health Inequality First Stage Regression 131 4.9 Intrahousehold Health Inequality Second Stage Regression 132
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1
CHAPTER ONE
Risk Management, Social Visibility, Asset Poverty and Intrahousehold
Health Inequality: An Overview
1.1 Introduction
Risk management is intrinsic to the livelihood choices of people in
developing countries. The effects of exposure to risk and attendant risk coping
mechanisms could have dire implications for individual well-being and
economic development as a whole. What is the extent of risk exposure in
developing societies? In other words, who is insured and who is not? How
does this affect poverty dynamics, particularly asset poverty? What are the
implications for intrahousehold distribution of welfare? The study seeks to
address these issues. To do so, we draw on several threads in the literature.
1.1.1 Risk Management
The first is the literature on risk management. Several risk coping
mechanisms have been identified in the literature. Risk mitigation may be
through savings in cash or kind (Deaton 1992, Fafchamps, et al. 1998, Paxson
and Chaudhuri 1994, Paxson 1992, Rosenzweig and Wolpin 1993), crop and
plot diversification (Deaton 1989, McCloskey 1976, Townsend 1994, Walker
and Ryan 1990, Walker, et al. 1983), investment in human capital or migration
(Paulson 1994). Once adverse shocks hit, they engage in sale of productive
assets (Hoddinott 2004), increasing labor supply (Bardhan 1983, Kochar 1993,
Rose 1994, Townsend 1995), reducing nutritional intake, pulling children out
2
of school and neglecting the health needs of children (Alderman and Paxson
1992, Ellis 1998). 1
Studies have shown that these insurance mechanisms are costly in
terms of efficiency losses (Coate and Ravallion 1993, Morduch 1999, Barrett
2005). In addition, these tools may not be available to some of these
households. Risk sharing with other households may be the only option. In the
absence of formal financial institutions and public provisions for risk
management, households often resort to informal risk sharing arrangements.
1.1.2 Social Networks and Social Visibility
Social networks have been identified as loci of risk sharing. These
networks reflect an individual’s level of social connectedness via extant social
ties; they reflect the extent to which an individual is known by others, in other
words the extent to which an individual is socially visible. These networks are
fostered by kinship ties, ethnicity, geographical proximity, religion, and gender
groups, inter alia (Goldstein 1999, Santos and Barrett 2004, Udry and Conley
2005, De Weerdt 2005). However, empirical tests often reject the provision of
full insurance by these social networks (Deaton 1992, Townsend 1994, Gertler
and Gruber 1997).2
There are two caveats to be noted: First, networks are often considered
as exogenous institutions; usually some clearly identifiable group such as the
whole village or clan. One could argue that the hypothesis of full insurance
will necessarily be rejected since these tests are conducted for exogenously
1 Especially those of girls. 2 Issues pertaining to measurement error, asymmetric information and contract enforcement have been identified as possible reasons (Alderman and Paxson 1992, Murgai et al. 2001).
3
given groups such as the entire village, community, clan or household and
hence may not adequately capture the true domain and scope of risk sharing.
Extant studies on risk pooling have focused on exogenously given
groups such as the households and villages or entire community as against
within the organizations/ networks. Townsend (1994) noted that “village
economies satisfy the explicit or implicit conditions of general equilibrium
modeling, namely that individuals in the entire community can arrange their
institutions and allocations in such a way as to achieve Pareto optimum. Many
families have been present for generations; many contemporary residents live,
eat and work in the village; the villages have their own legal systems replete
with contract enforcement mechanisms; and village residents may have
relatively good information about the ability, effort and outputs of one another.
Moreover, residents of poor, high risk villages have a collective incentive to
come up with good arrangements: the absence of these can be life threatening.”
This indeed provides some justification for the use of villages as units of study
and, a fortiori, endogenous social networks. The village (or clan or household,
for that matter) may not adequately capture the true mapping of the respective
endogenous network links.
“Theory suggest that the formation of insurance links depends on a
myriad of factors related to smooth information flows, norms, trusts, the
ability to punish, discount rates, group size and the potential gains of
cooperation” (De Weerdt 2005). It is expected that individuals with disparate
income trajectories as well as those with markedly varied access to
technologies, risk aversion or consumption needs are likely to pool risk.
Examining the geographic location and various network links between
respondents from a sample in Ghana, Udry and Conley (2004) observed that
4
there are significant overlaps between different networks in a given village –
credit and information links were most closely associated. In addition,
individuals who appear connected in one dimension appear more likely to be
connected along others. There were, however, some individuals in each village
for each network who seem isolated in these graphs. This could be a
misleading result of using ‘ego-centric’ data as concluded by Udry or Conley
(2004) or it could very well be a true reflection of the fact that these
individuals, for one reason or another, have been selected out of these
networks and are thus isolated, at least at the intra-village level. Some marginal
groups of individuals may be excluded from these networks. Evidence showed
that wealth and other socioeconomic status variables matter: the poor have less
dense risk sharing networks than the rich (De Weerdt 2005).
Secondly, most of the extant studies were undertaken at the household
level, with the implicit assumption of a unitary household model -- assuming
perfect substitutability among decision-makers as well as pooling of all
resources within the household. There is ample evidence in the literature
rejecting these assumptions.3 For instance, Goldstein (1999) rejects full risk
pooling within rural households in Ghana. In fact, he observes that husbands
and wives may have different risk sharing groups. In a more recent study,
Duflo and Udry (2004) examined resource allocation and insurance within
households in Cote d’Ivoire. They tested the effects on the types of goods
consumed by the household of different rainfall configurations with identical
impacts on total expenditure; given the types of crops they affect the most.
They rejected complete insurance within the household, even with respect to 3 See Alderman, Chiappori, Haddad, Hoddinott and Kanbur (1995) and Goldstein (1999) for detailed discussions.
5
publicly observed exogenous shocks. In addition they showed that the
expenditure patterns were not consistent with Pareto efficiency.
The first step in trying to assess the impact of risk on the rural poor is
to determine the true domain and scope of insurance available through social
networks. Some marginal groups are excluded from these networks. Evidence
shows that wealth and other socioeconomic status variables matter: the poor
have less dense risk sharing networks and may not have the wherewithal to
make the nominal contributions required by some of these mutual insurance
groups (Dercon 2005, De Weerdt 2005, Santos and Barrett 2006). Goldstein et
al. (2005) concluded that social exclusion of some community members may
result in the incomplete insurance observed in rural communities. Indeed,
extant studies have identified factors affecting network formation as well as the
characteristics of those who are known but left out of these networks– the
socially excluded. However, there is an even more vulnerable subpopulation
who have not been given their due in the development economics discourse –
the socially invisible: those who are not known in the community and are left
out by default. Who are the socially invisible? Who is left out of informal risk
management institutions? For whom is risk pooling Pareto efficient?
1.1.3 Poverty Dynamics and Asset Poverty Traps
Some insurance may be available but not to all. Differential access to
credit and insurance markets has distributional implications not just in terms of
consumption and opportunity cost of foregone profits but also long term
structural poverty. The need for immediate survival may lock the poor into
persistent poverty (Dercon 2003).
6
The poor consist of those who are persistently poor and those who
move in and out of poverty, with the latter being a very large group (Gaiha and
Deolalikar, 1993; Baulch and McCulloch, 2000; Dercon and Krishnan, 2000;
Jalan and Ravallion, 2000). Somewhat surprisingly, mobility analysis shows
that more households move out of poverty than into poverty (Scott, 2000).
Generally, households may be said to move in and out of poverty by crossing a
given poverty line based on some welfare metric: defining a threshold above
which households converge to desirable equilibria and below which they
converge to undesirable equilibria.
Several factors have been identified in the literature as determining
movement in and out of poverty. 4 Using data from Chile, Scott (2000)
estimated income functions for respective periods. He demonstrated that
income is positively related to education of household head, land ownership
and certain geographic locations but was negatively related to household size
and the presence of disabled household members. Using censored quantile
regression, Jalan and Ravallion (2000) identify physical capital, education and
variability in wealth as decisive factors, in Southern China. Smaller and better-
educated households had lower chronic poverty but neither household size nor
education affected the likelihood of transient poverty.
Regarding the direct impact of risk, Dercon and Krishnan (2000),
controlling for shocks with fixed effect regressions, also identify household
specific crop failure, rainfall and seasonal incentives such as changing labor
demand and prices, as factors determining fluctuations in welfare. McCulloch
4 See Dercon and Krishnan (1998), Glewwe and Hall (1998), Gunning et al (2000), Grootaert, Kanbur and Oh (1997), Jayaraman and Lanjouw (2000), Lanjouw and Stern (1991), Maluccio, Haddad and May (2000), Scott (2000), and Walker and Ryan (1990).
7
and Baulch (2000) estimated a reduced form income equation to show that, in
Pakistan, the variability in income is higher the smaller the proportion of adults
in the household, the less educated its male members are, and the more rain-fed
land it owns. They demonstrate, using policy simulations that interventions
that enable households to smooth their incomes over time can give rise to
reductions in transitory poverty. Scott (2000) in assessing the effects of
negative and positive shocks over time concluded that protracted droughts, loss
of employment, theft and sheer ill fate results in impoverishment. Seemingly
transitory shocks can have long-term effects. Serial correlation between shocks
may render structural poverty traps empirically indistinguishable from
persistent poverty (Barrett, 2001). It is expected that a sequence of positive
(negative) shocks may propel some households into rising (falling) welfare
trajectories such that their long-term position in the welfare distribution may be
altered. It is also possible that short- lived negative shocks can propel some
households onto permanently lower trajectories.
Generally fluctuations in income might be quite significant. However,
it is difficult to distinguish between stochastic and structural transitions using
standard poverty measures. The asset-based approach is said to distinguish
transitory poverty which eases with time due to systemic growth from
persistent structural poverty. Under this approach there are nonlinear asset
dynamics with multiple equilibria which include a threshold that distinguishes
households who can be expected to grow out of poverty from those who are
likely to remain trapped in poverty, i.e. those caught in a poverty trap (Carter
and Barrett 2006). These dynamics may be caused, to a large extent, by risk
8
exposure and response, attendant activity choice and returns to endowments
given incomplete financial and insurance markets. 5 However, the empirical
evidence regarding the existence of these dynamics is mixed.
A single negative shock in bad times (with no buffers) can put
households on a different trajectory than if it occurred under a favorable
external environment. Most households resort to informal means of insurance.
Social insurance has been documented as being likely to have a significant
effect on the fortunes of some households (Baulch and Hoddinot, 2000). Given
the prevalence of risk, do asset poverty traps exist? If so for whom, do they
exist? Particularly, how do these asset dynamics vary with access to social
insurance? Are these thresholds the same for everyone or do they vary by
social visibility? How can we identify individuals who are likely to get caught
in asset poverty traps for targeting purposes?
1.1.4 Intrahousehold Health Inequality
Development policy aimed at improving the wellbeing of individuals
through the provision of safety nets or the provision of mechanisms/
opportunities to move people out of structural poverty traps, inter alia, would
be largely ineffective if (i) the distribution of welfare within the household is
unequal or (ii) the individuals are unable to take advantage of these
opportunities due to poor health.
Theoretical models have been used in the extant literature to explain
intrahousehold inequality. In addition there is some empirical evidence, albeit
scant, indicating that there may be systemic differences in welfare levels 5 See Azariadis and Stachurski (2005) for a complete review.
9
within the household. However, it is not certain how these differences vary
with overall household well-being.6
Risk exposure may explain some of the proposed patterns in the
distribution of welfare within the household. For instance, the adverse effects
of shocks on health outcomes are borne disproportionately by women and
children within the household (Dercon and Krishnan 2000, Hoddinott and
Kinsey 2000, Dercon and Hoddinott 2003). Some households respond to
shocks by reducing in nutritional intake and neglecting the health needs of
children, especially those of girls (Alderman and Paxson 1992, Ellis
1998).This may have far-reaching effects given the fact that poor child health
has been linked with delays in beginning school, persistent effects on cognitive
capacity, diminished intellectual performance and stature, low work capacity
and lower earnings during adulthood (Glewwe and Jacoby 1995, Glick and
Sahn 1997, Grantham-McGregor et al. 1997, Martorell 1999). Strauss and
Thomas (1998) in discussing nutrition efficiency wages posit that “there may
be some people who are so poor and so unhealthy that they are costly to
employ”. In this case, the offered wage is insufficient to maintain the labor
power of the poor, leading to involuntary unemployment, reduction in
consumption/ nutrient intake with reduction in income (in the absence of
insurance), further loss in physical capacity and productivity, and an even
lesser likelihood of employment. This then has implications for child nutrient
intake and could potentially result in intergenerational persistence in poverty.
Given the effects of risk exposure access to social insurance might
condition intrahousehold health inequality. To what extent do intrahousehold
6 See Haddad and Kanbur (1990), Kanbur and Haddad (1992), Haddad, Kanbur and Bouis (1995) and Sahn and Younger (2007) for complete reviews.
10
inequalities manifest in the data? Are there intrahousehold Kuznets curves?
For whom do they exist? In the event that intrahousehold inequalities exist,
how would social visibility affect the distribution of well-being within the
household and how would the latter vary with overall household well-being?
The study seeks to address the issues raised above, towards a better
understanding of the risk insurance vis-à-vis asset poverty and intrahousehold
heath inequality with the broad objectives of enhancing the welfare of the poor
via improved access to insurance; facilitating movement out of poverty and
improving the odds of policy achieving its desired goal – improving the
wellbeing of individuals. To this end, the following chapters take an in-depth
look at these issues. The next section provides a brief description of the data
used. Section 4 provides a summary of the respective chapters. Section 3
concludes with the major results.
1.2 The Data
The data used in this study are from a rural household survey
undertaken from July 2004 to January 2005. This was the third wave of a panel
data set initiated by Christopher Udry and Markus Goldstein. The research was
conducted in the Akwapim South District (specifically the Nsawam - Aburi
area) in the Eastern Region of Ghana. Farmers in this area have been switching
from the cultivation of maize-cassava intercrop for domestic production to
pineapple cultivation for export since the early 1990s. This transition involves
a significant amount of risk by way of the attendant new agronomic practices
as well as exposure to global price fluctuations, hence the need for insurance.
The transition also provides the potential for marked increase in agricultural
11
incomes and asset accumulation.7 Appendix A provides field notes with details
on the most current wave. Appendix B provides a brief description of the
respective modules and survey schedule. Appendix C provides a copy of the
survey questionnaires.
1.3 Dissertation Chapters
1.3.1 Risk Management and Social Visibility in Ghana
This paper examines access to social insurance and the Pareto
efficiency of risk pooling. We hypothesize that social connectedness is crucial
determinant of access to informal social insurance. The relatively socially
invisible (i.e. those who are not widely known in the community) may get left
out by default, while those who are well known in the community may benefit
from social insurance. We start off by identifying the characteristics of the
socially invisible. We then test for risk pooling within and among social
networks to see if the extent of informal insurance available to individuals in
rural Ghana varies by their social visibility.
The key results are as follows. (i) There exists a small but distinct
subpopulation of socially invisible individuals within the sample villages. The
socially invisible tend to be younger, engaged in farming, recent arrivals into
the village, and not a member of a major clan. (ii) Risk pooling is substantial
for the socially visible; we cannot reject the null hypothesis that individual
shocks do not affect individual consumption and that individual consumption
tracks network and village consumption one-for-one. (iii) On the other hand,
risk pooling fails for the socially invisible subpopulation. We overwhelmingly
7 See Goldstein and Udry (1999) for an in-depth discussion of the historical background of the area and the sampling techniques.
12
reject both the null hypothesis that individual shocks do not affect individual
consumption and the null that individual and network or village consumption
move together one-for-one for those individuals who appear socially invisible.
1.3.2 Risk, Asset Poverty and Social Visibility in Ghana
So far we’ve identified the socially invisible and established that
individuals in this subpopulation are left uninsured. As per the previous
section, residual risk exposure might affect asset dynamics and the existence of
poverty traps. We hypothesize that asset dynamics will vary by social
visibility, with the socially invisible converging to a unique dynamic
equilibrium at a lower asset poverty level whilst the relatively visible
experience multiple stable dynamic asset equilibria. We examine the role of
social visibility in conditioning wealth dynamics by using the theory of
bifurcated asset accumulation strategies to empirically identify poverty traps,
using individual level data to allow for effect of differential social visibility
within the household. We estimate nonparametric regressions for all
individuals in the sample and then disaggregate the sample into socially visible
and socially invisible individuals using a predetermined cut-off as well as an
endogenously identified optimal sample splitting level. We then identify the
respective thresholds in asset dynamics and estimate the likelihood of an
individual falling into poverty traps.
The key results are as follows: (i) Asset trajectories exhibit multiple
dynamic stable equilibria for village-level aggregations of individuals.
(ii) Unpacking the results by social visibility indicates that whereas the assets
of the socially visible exhibit multiple stable dynamic equilibria, the socially
invisible converge towards a unique dynamic stable equilibrium at the lower
13
asset level. In addition, the asset levels at the respective equilibria were
generally higher for the socially invisible as compared to the socially visible,
particularly for the unstable dynamic equilibrium. (iii) Males engaged in non-
farm occupations who have resided in the village for more than one generation
are consistently less likely to fall into poverty traps, conditional on being
socially visible. Socially invisible individuals who belong to a major clan are
less likely to fall into a poverty trap. On the other hand, socially invisible
individuals who suffer a farm shock or theft are more likely to fall into a
poverty trap. It is noteworthy that shocks do not have a significant effect on the
assets of the socially visible. This is consistent with the results described in
Section 1.3.1 which notes the socially visible enjoy full insurance.
1.3.3 Risk, Intrahousehold Health Inequality and Social Visibility in
Ghana
Next, we hypothesize that access to insurance will condition the effect
of shocks on the distribution of health outcomes, specifically intrahousehold
health inequality, and the variation of the latter with overall household well-
being. We assesss whether there is a threshold effect in the existence of
Kuznets curves at the household level. We examine whether Kuznets curves
exist and for whom they exist. We also assess the determinants of
intrahousehold inequality.
The key results are as follows: (i) We find no evidence of the inverted
U-shape when using aggregated data, however there is some indication that the
Kuznets curve may exist for a subsample of the population – in this case the
socially visible.(ii) The composition of total inequality varies markedly by
14
social visibility. Whilst over 75% of overall inequality may be attributed to the
within-group component (i.e. intrahousehold inequality) amongst socially
visible households, a little over 50% of overall inequality may be attributed to
the between-group component amongst socially invisible households. (iii)
Patterns in relative BMI within the household suggest that the socially
invisible may overcompensate for risk exposure by protecting children in the
allocation of limited food and nutritional resources. (iv)Wealth, dependency
ratio, age, social visibility as well experiencing shocks had statistically
significant effects on intrahousehold inequality.
1.4 Conclusions
Economic growth and poverty reduction and have long been the focus
of many development efforts. The environment in which this occurs is fraught
with risk which might, in the medium to long term, thwart these efforts if left
unchecked. We take steps in this directon by assessing access to informal
insurance vis-à-vis social visibility and the implications for asset poverty and
intrahousehold inequality.
We identify a distinct subpopulation of socially invisible individuals
who tend to be younger, poorer, engaged in farming, recent arrivals into the
village who have been fostered, and not a member of a major clan. The results
show that while we cannot reject the null hypothesis that individual shocks do
not affect individual consumption and that individual consumption tracks
network and village consumption one-for-one among the socially visible, risk
pooling fails for the socially invisible subpopulation. In addition, whereas asset
accumulation patterns of the socially visible exhibit multiple stable dynamic
15
equilibria, the socially invisible converge towards a unique dynamic stable
equilibrium at the lower asset level. The asset levels at the respective equilibria
were generally higher for the socially invisible as compared to the socially
visible, particularly for the unstable dynamic equilibrium. The latter implies
that the socially invisible are more likely to fall into poverty traps since there is
a broader asset domain over which people in this subpopulation would collapse
toward the lower level asset equilibrium.
Multivariate analyses of the likelihood of falling into a poverty trap
indicates that males engaged in non-farm occupations who have resided in the
village for more than one generation are consistently less likely to fall into
poverty traps, conditional on being socially visible. Socially invisible
individuals who belong to a major clan are less likely to fall into a poverty
trap. Shocks increase the likelihood of a socially invisible individual falling
into a poverty trap. On the other hand, they do have a significant on the
likelihood of a socially visible individual falling into a poverty trap; this is
consistent with the observation that the socially visible enjoy complete risk
pooling. The analyses of intrahousehold inequality indicate that whilst the
inverted U-shape may not exist when using aggregated data, it may exist for
the socially visible. In addition the composition of inequality varies by social
visibility. Over 75% of overall inequality may be attributed to the within-group
component (i.e. intrahousehold inequality) amongst socially visible
households. On the other hand, a little over 50% of overall inequality may be
16
attributed to the between-group component amongst socially invisible
households.
Patterns in relative BMI within the household suggest that the socially
invisible may overcompensate for risk exposure by protecting children in the
allocation of limited food and nutritional resources. With regards to factors
determining the level of intrahousehold inequality: wealth, dependency ratio,
age, social visibility as well experiencing shocks had statistically significant
effects on intrahousehold inequality.
These findings suggest that external safety net interventions should
target those likely to be socially invisible and not the socially visible persons
for whom informal insurance seems to offer reasonably complete risk pooling.
They also underscore the need for policymakers to focus on structural
determinants of poverty transitions, or the lack thereof, in order to successfully
alleviate poverty in the long run. Finally, the challenges associated with
identifying individuals within the household for targeting purposes lead policy
makers to resort to the second best strategy – focusing on improving overall
household well-being. The existence of a systematic relationship between
intrahousehold inequality and average well-being on the basis of some
identifiable household characteristic such as social visibility should enable
policymakers better tailor interventions to attain the desired goal of improving
individual well-being.
17
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25
CHAPTER TWO
Risk Management and Social Visibility in Ghana
2.1 Introduction
Risk management is crucial to economic advance, indeed to the very
survival, of people in low-income, agrarian countries. In the ideal Arrow-
Debreu world, complete markets with symmetric information would provide
an array of state contingent contracts and all decision-makers in the economy
could make welfare improving exchanges based on each other’s known
preferences and beliefs over states of the world. In this fictional framework all
risks can be addressed with market-based solutions.
In reality, informational asymmetries and covariate risk impede risk
management in general, especially through formal financial institutions. Rural
populations therefore depend heavily on informal institutions for managing
risk in the absence of well-developed insurance markets. 8 But access to
informal insurance is not necessarily uniform. Certain subpopulations may
have superior access to the desirable intertemporal consumption smoothing
made possible by informal insurance mechanisms (Dercon and Krishnan 2000,
Dercon 2005, De Weerdt 2005, Santos and Barrett 2006).
So who is left out of informal risk management institutions? Since
informal institutions are based on endogenously formed social networks among
and within households, for whom is reasonably complete (i.e., Pareto efficient)
8 See Alderman and Paxson (1992), Besley (1995) and Bardhan and Udry (1999) for complete reviews.
26
risk pooling available? To address these issues we draw on several threads in
the literature.
The first thread is the literature on risk pooling and social insurance.
Empirical tests often reject the null hypothesis of full (Arrow-Debreu) social
insurance within rural communities (Deaton 1992, Townsend 1994, Gertler
and Gruber 1997). Issues pertaining to measurement error, asymmetric
information and contract enforcement have been identified as possible reasons
for incomplete risk pooling (Alderman and Paxson 1992, Murgai et al. 2001).
One could argue that the hypothesis of full insurance will necessarily be
rejected because these tests are typically conducted for exogenously given
groups such as the entire village, community, or ethnic group and such
exogenously defined groups might not accurately reflect the true domain and
scope of risk sharing. 9 In addition, most studies of the extent of social
insurance have been undertaken at the household level, implicitly assuming a
unitary household model and thus perfect substitutability among decision-
makers as well as pooling of all resources within the household. Yet there is
ample evidence in the literature rejecting these assumptions, including within
the context of social insurance (Dercon and Krishnan 2000).10
This leads to the second thread of the literature on which we build.
Social networks have long been identified as key loci of informal risk sharing.
Individuals establish networks based on a wide range of individual and mutual
attributes, including but not limited to kinship ties, ethnicity, geographical
proximity, occupation, wealth, religion, and gender (Goldstein 1999, Santos
9 See Goldstein et al. (2005), Santos and Barrett (2006). 10 See Alderman, Chiappori, Haddad, Hoddinott and Kanbur (1995), Doss (1996), Udry (1996), Goldstein (1999) and Duflo and Udry (2004) for evidence on intrahousehold allocation issues.
27
and Barrett 2004, Udry and Conley 2005, De Weerdt 2005, DeWeerdt and
Dercon, 2006, Fafchamps and Gubert 2007). However, some marginal groups
may be less well connected in social networks and may thereby enjoy less
informal insurance access than do wealthier or more powerful members of a
community (Dercon 2002, De Weerdt 2005, DeWeerdt and Dercon 2006,
Santos and Barrett 2006). If social network formation is commonly
asymmetric, in the sense that an agent’s latent demand for a link with another
is a function not just of the absolute social distance between the two, but also
of their ordinal position – e.g., male-female versus female-male, or poorer or
wealthier (Santos and Barrett 2004, DeWeerdt and Dercon, 2006, Fafchamps
and Gubert 2007) – then what appears in the literature as wealth differentiation
in insurance access could be due to differential social visibility based on
individual characteristics, visibility that is correlated with but distinct from
wealth.
We hypothesize, in particular, that social connectedness is the key issue
in access to informal social insurance. Those who are relatively socially
invisible, meaning they are not widely known in the community, may get left
out by default, while those who are well known in the community enjoy the
sort of social insurance widely hypothesized in the literature. If
geographically-defined populations of the sort used in standard empirical
analysis mix the socially visible and the socially invisible, widespread
evidence of partial risk pooling could well mask reasonably complete risk
pooling for those with extensive social networks and negligible risk pooling for
the socially invisible. The practical implication of such a finding would be
straightforward: target external safety net interventions to those likely to be
socially invisible and try not to waste resources on external safety nets for
28
those socially visible persons for whom informal insurance seems to offer
reasonably complete risk pooling.
In this paper we therefore test for risk pooling within and among social
networks, as well as within villages, to see if the extent of informal insurance
available to individuals in rural Ghana varies by their social visibility. The
remainder of the paper proceeds as follows. The next section lays out the
familiar general equilibrium model of risk pooling and uses limiting conditions
on the extent of an agent’s social network to derive exclusionary restrictions
for econometric hypothesis testing. Section 3 then describes the data. Section 4
explores the concept of social invisibility and identifies the characteristics of
those persons we label socially invisible in this sample. Section 5 tests for risk
pooling within and among social networks. Conclusions and policy
recommendations are presented in Section 6.
The key results are as follows. (i) There exists a small but distinct
subpopulation of socially invisible individuals within the sample villages. The
socially invisible tend to be younger, engaged in farming, recent arrivals into
the village, and not a member of a major clan. (ii) Risk pooling is substantial
for the socially visible; we cannot reject the null hypothesis that individual
shocks do not affect individual consumption and that individual consumption
tracks network and village consumption one-for-one. (iii) On the other hand,
risk pooling fails for the socially invisible subpopulation. We overwhelmingly
reject both the null hypothesis that individual shocks do not affect individual
consumption and the null that individual and network or village consumption
move together one-for-one for those individuals who appear socially invisible.
29
2.2 Risk Pooling and Social Visibility: Simple Theoretical Predictions
Shocks are pervasive in most agrarian societies, the study area in
Ghana being no exception. To what extent are shocks insured so that
individuals can smooth consumption and thereby improve intertemporal
welfare? Is risk fully pooled at the village level or at the network level? Does
the extent of risk pooling depend on an individual’s social visibility? To
address these questions, we use a standard model akin to that of Townsend
(1994).
Suppose there are S possible states of the world, each occurring with
objective, constant and commonly known probability πs. Assume individuals
have preferences that are additive across time and over states of nature and
common rates of time preference βt, where λin is the programming weight
associated with individual i in network n. Suppose there are K social networks
in the economy with N members in each network. 11 Let cinst and yinst be
individual i in network n’s consumption and income in state s at time t,
respectively. Then we can denote the Pareto efficient allocation of risk by the
following problem:
(1) ( )⎥⎦
⎤⎢⎣
⎡∑ ∑∑= ==
inst
T
t
S
sst
N
iin cU
instC 0 11max πβλ
subject to
(2) ∑∑==
=N
iinst
N
iinst yc
11
.
The first order condition allocating consumption among two individual
network members, i and j, is given by
11 For now, we abstract from the fact that networks may be interlinked: an individual may belong to more than one network. We explore this possibility below in examining empirically the extent to which risk is spread among social networks.
30
(3) ( ) ( )jnstjninstin cUcU '' λλ =
Suppose each individual’s preferences can be represented by an exponential
utility function
(4) ( ) [ ]instinst ccU αα
−⎥⎦⎤
⎢⎣⎡−= exp1 .
Then substituting (4) into (3), taking logs and rearranging terms, we get
(5) ⎥⎦
⎤⎢⎣
⎡−=
in
jnjnstinst cc
λλ
log .
Then aggregating across all individuals in a network gives
(6) in
N
jjnstinst Ec
Nc += ∑
=1
1
where
(7) ⎥⎦
⎤⎢⎣
⎡−
+= ∑−
=
1
1log
11log1 N
jjninin N
E λλα
which implies that
(8) innstinst cc Ε+=
where nstc is the network mean consumption excluding i and Ein is a constant
that allows for dispersion in consumption levels among network members that
is fixed over time and states of nature. The strong implication of Pareto
efficient risk pooling, as reflected in equation (8), is that contemporaneous own
income, yinst is irrelevant to the determination of individual consumption. We
can decompose agent i’s contemporaneous income in network n into a
permanent component iny and an idiosyncratic component, insty , where the
latter represents a mean zero, i.i.d transitory income shock.. Own income is
thus:
(9) instininst yyy +=
31
Introducing (9) into (8) implies a testable exclusionary restriction for full
insurance:
(10) ( ) innstinstininst cyyc Ε+++= δγβ
Taking first differences in time yields the estimable equation
(11) nstnstinstinsticyc εγβ +Δ+Δ=Δ
Where ∆ is the first difference operator and instε is a mean zero, i.i.d error
term. A test of the full risk pooling hypothesis is then H0: β=0, γ=1 versus HA:
β>0 or γ<1. An analogous test of the full risk pooling hypothesis at village
level emerges from substituting change in mean consumption in village v
(excluding agent i) for that in network n in equation (11 Similarly, the null
hypothesis that distinct social networks within a village pool risk can be
derived by substituting nvstcΔ as the dependent variable and nvstc −Δ for the
second term in equation (11), respectively. Where nvstcΔ is the mean
consumption for individual i’s network and nvstc −Δ is the residual mean for all
other networks in the village excluding individual i’s.
The implication of the above framework for those who are socially
invisible – i.e., for whom N=1 because they are the entirety of their own social
network – is immediately obvious. The summation operators in equations (1)
and (2) drop away, the social allocative efficiency conditions in (3) and (5)
disappear, and the key exclusionary restrictions in equation (11) are thus
turned on their head. For those without recourse to social insurance networks,
there should be no risk pooling, thus H0: β=1, γ=0 versus HA: β<1 or γ>0. This
basic insight that the extent of the social network fundamentally influences the
relation between individual-level income shocks and individual consumption,
i.e., the risk pooling capacity of the individual, implies a need to first define
32
and measure social visibility before testing standard risk pooling hypotheses
because social visibility conditions the appropriate test specification.
2.3 The Data
The data used in this paper are from a rural household survey
undertaken in the Akwapim South District (specifically, the Nsawam - Aburi
area) in the Eastern Region of Ghana from July 2004 to January 2005. This
was the third wave of a panel data set initiated by Chris Udry and Markus
Goldstein and described in detail in Goldstein and Udry (1999). Since the early
1990s farmers in this area have been switching from the cultivation of maize-
cassava intercrop for domestic production to pineapple cultivation for export.
This transition involves a significant amount of risk associated with
transitioning to new agronomic practices and marketing arrangements, as well
as potential disruption of traditional social arrangements.
The original sample was selected using a two-stage procedure. Four
village clusters were purposively selected based on their participation in fruit
and vegetable production as well as their agronomic, market access and
geographic conditions. Sixty married couples (or triples) were then randomly
selected in each village cluster, except for the smallest village cluster, where
all resident couples were included.12 Each individual selected was interviewed
separately. Male enumerators were assigned to male respondents and female
enumerators to female respondents to preserve gender sensitivity and cultural
norms.
12 We loosely refer to village clusters as villages for ease of notation.
33
Three rounds of data were collected at approximately eight week
intervals, rotating between pairs of villages. 13 The first round of data was
collected in September-October 2004. The second round was conducted
October-November 2004. The third round was conducted from late November
2004 through January 2005. Across the four villages, 372 individuals were
surveyed the first round. The second and third rounds had sample sizes of 371
and 350 individuals, respectively. The sample attrition rate was thus 0.3%
between the first and second rounds and 5.9% between the first and third
rounds.14
The subsections that follow offer brief descriptions of the key modules
relevant to our analyses. Other standard variables associated with household
composition, asset holdings, family background, etc. are likewise employed in
the regression analysis in section 5 and described below.
2.3.1 Individuals’ Social Networks
For each respondent, we randomly selected seven individuals in the
sample from the same village (without replacement).15 We then asked each
13 Given budgetary constraints, each enumerator was assigned to one of two villages in pre-assigned pairs. Based on geographic proximity, the first village pair comprised of villages 1 and 4, whereas the second village pair comprised of villages 2 and 3. Interviews were conducted simultaneously in the two villages in a given pair, after which the enumerators moved to the other pair. We spent approximately four weeks in one pair of villages, moved to the next pair for next four weeks, and continued this pattern of rotation for the duration of the study. 14 A simple attrition probit was estimated using robust standard errors, with the dependent variable ATTRIT=1 if individuals were lost between rounds 1 and 3, = 0 otherwise. The estimation results indicate that individuals lost between rounds 1 and 3 were more likely to be younger males whose parents had held village offices. Neither wealth nor the incidence of any of the shocks used in the subsequent analyses was statistically significant in explaining patterns of attrition. 15 Respondents were also non-randomly matched with three other village-specific “focal” individuals identified from the community-studies approach taken in a preliminary field trip as individuals in the villages from whom advice is commonly sought. We focus on the random matches in this study.
34
respondent about their knowledge of the match i.e., “Do you know__?”,
followed by a series of questions about their relationship with each of these
matched individuals: how often they talked with them, and whether or not
he/she could approach the individual to deal with any of a set of specific issues
related to farming and credit. In administering the questionnaire we made a
clear distinction between knowing of someone (i.e., using the Akan translation
of just “having heard of the person”) and actually knowing the person in the
sense of mutual acquaintance. Knowing a random match in this sense is
indicative of an extant social link. This gives us a random sample not only of
individuals but also of prospective social relations, which is the preferred
method of sampling social networks (Santos and Barrett 2007). By design, the
characteristics of these random matches are representative of people with
whom they have (and do not have) extant social links, i.e., it provides an
unbiased representation of the structure of their social networks.
2.3.2 Consumption
Detailed data were collected on purchased food, general family
expenses and personal expenditures by each respondent in the household.16
Even though these expenditure questionnaires were administered at the
individual level, with the head and spouse(s) of head being interviewed
separately regarding contributions made towards purchasing an item,
individual expenditures were not assigned. Hence, we follow Goldstein (1999)
in assigning particular items as purchases for own-consumption: alcoholic
beverages, non-alcoholic pre-packaged beverages, prepared food (from
16 Recall periods varied by expenditure based on the modal frequency of purchase reported in waves 1 and 2. These essentially intra-annual expenditure were converted to nominal monthly rates.
35
kiosks), personal care products, hair cuts, public transport, petrol, car repairs,
newspapers, entertainment, lottery tickets and kola nuts.
Table 2.1 presents the mean and standard deviation of expenditure on
these goods purchased for own-consumption per village and round as well as
the share of total expenditure incurred by each respondent on purchased food,
general family needs and personal items spent on these assigned individual
items in a typical month. On average, respondents spent 393,383 Cedis;
306,422 Cedis and 379,544 Cedis in rounds 1, 2 and 3, respectively.17 These
values account for 16%, 19% and 18% of total expenditures in rounds 1, 2 and
3, respectively. Village 4 had the least individual expenditures.
2.3.3 Shocks
Respondents were asked about a series of prospective idiosyncratic
shocks. We selected four types of shocks: (i) value of damage caused by
general farm problems; (ii) total curative health care expenses; (iii) value of
personal items stolen; and (iv) funeral expenses upon sudden death of family
member. For each of type of shock we asked about the out-of-pocket expense
incurred as a result of that shock or the imputed value of damage experienced.
Table 2.1 presents the frequency of shocks faced by respondents as well as the
nominal mean response expenditures and imputed values of damage caused by
these shocks by round.
Overall, 92% of respondents reported suffering at least one of these
shocks in round 1. 83% and 79% reported experiencing a shock in rounds 2
and 3, respectively. By way of percentage of individuals affected, health
shocks were the most frequent in rounds 1 and 3, with sudden death within the
17 The mean exchange rate over the survey period was roughly 9000 Cedis/ US$1.00.
36
Table 2.1: Individual consumption expenditures, June 2004- January 2005 (nominal Cedis per month)
Table 2.2: Percentage of individuals affected by shocks and mean response expenditure/ imputed value of damage (nominal Cedis)
Village Round 1 Round 2 Round 3
Mean Standard Deviation
Share of Total Expenditure Mean
Standard Deviation
Share of Total Expenditure Mean
Standard Deviation
Share of Total Expenditure
1 378033 361238 0.190 389513 374807 0.244 607648 1952393 0.235 2 615055 1166220 0.189 363188 438698 0.209 373984 333596 0.220 3 442872 1894519 0.117 258599 375004 0.151 359117 1721446 0.131 4 171599 137713 0.126 228730 415352 0.144 188596 210620 0.134
Full Sample 393383 1097788 0.156 306422 404741 0.185 379544 1334326 0.178
Shocks All
Rounds Round 1 Round 2 Round 3
Frequency Frequency Mean
Share of Total
Expenditure Frequency Mean
Share of Total
Expenditure Frequency Mean
Share of Total
Expenditure Farm problems 31.7 52.0 1107214 0.512 22.8 304984 0.238 20.6 200643 0.133 Total Health Expenses 56.7 72.2 1699291 0.580 50.0 379575 0.513 46.0 307786 0.286 Theft of personal item 14.7 19.1 768218 0.405 13.7 381595 0.203 11.0 365600 0.390 Sudden death 50.7 50.5 880862 0.459 55.9 171809 0.125 45.6 228589 0.109 Any/All Shocks 84.6 91.8 2640621 1.093 83.0 492290 0.494 78.7 415992 0.319
36
37
family being the most frequent shock experienced in round 2. Over all three
rounds, 57% of the respondents reported having suffered health problems and
51% had suffered a sudden death in the family. Morbidity and mortality thus
pose a huge financial burden on families in this area. In addition, 32% of the
respondents suffered from at least one of a variety of farm problems, the most
prevalent being infestation by grasscutters (a common rodent in the area). Only
15% of the respondents experienced theft of a personal item. While the
questions were focused on idiosyncratic shocks, covariate risk associated with
rainfall and price patterns did not appear to be the primary concern in these
communities.
Not only were shocks commonplace, they were also very costly. The
total value of losses due to shocks was 2640621 Cedis in round 1, 492290 in
round 2 and 415992 in round 3. These figures correspond to 109%, 49% and
32% of total expenditures incurred by respondents on purchased food, family
and personal items in rounds 1, 2 and 3, respectively. Even though few people
reported experiencing theft of personal items, this shock was associated with
huge losses. This was the most serious shock in rounds 2 and 3, in terms of the
magnitude of imputed value of loss. Health shocks were the most serious in
round 1. By way of share of total expenditure captured by the value of loss,
health shocks were the most important at 58% and 51% in rounds 1 and 2,
respectively, with theft of personal items accounting for 39% in round 3. The
magnitude of these shocks relative to household expenditure levels underscores
that idiosyncratic shocks can imperil the accumulated assets of households if
they have insufficient (formal or informal) insurance. Hence our desire to
understand who is reasonably well insured.
38
2.4 Social Invisibility
Although much discussion of informal insurance implicitly assumes
that everyone participates equally in the social networks that mediate
interhousehold transfers, recent studies find significant intra-village variation
in social connectedness, with a non-trivial share of individuals relatively
isolated from other residents (Santos and Barrett 2006). Historical accounts
from Africa confirm this pattern, emphasizing the correspondence between
extreme poverty and limited social interactions. For example, Iliffe (1987,
p.42) notes that “[t]o be poor is one thing, but to be destitute is quite another,
since it means the person so judged is outside the normal network of social
relations and is consequently without the possibility of successful membership
in ongoing groups, the members of which can help him if he requires it.” Such
observations motivate our hypothesis that risk pooling through social networks
may vary within villages, with poorer individuals generally being more
socially invisible and therefore enjoying less access to informal insurance than
do wealthier, better connected individuals.
We define social invisibility as a condition in which an individual
resides within a community but is not known by some minimal share of other
members of that community. Toward this end, we define social visibility (SV)
index. Let Ni be the number of times that respondent i’s name was drawn in the
random matching process and presented to some other respondent j. Then let
ni=Σjkij for j=1,…,Ni, be the number of times that i was identified by others
when presented as a random match (kij=1 when j knows i, =0 otherwise). We
then estimate the social visibility index, SV, as:
(12) 10 , ≤≤= ii
ii SV
NnSV
39
The descriptive statistics on Ni, ni and SVi are presented in Table 2.3.
Each individual was presented on average 5 times as a random match, out of
which they were known on average by 3.38 respondents. 26 of the respondents
(i.e., 8.39%) were not known by any of their random matches. Clearly they
qualify as socially invisible by this metric. It turns out that, by this measure,
SV=0 proves the statistically optimal maximum index value for denoting a
respondent “socially invisible”, with those with SV>0 classified as socially
visible.18
Table 2.3: Summary statistics on random matching
Notes: * Based on an optimal cut-off level of 0.80 predicted probability of i being known
Table 2.4 presents summary statistics on the individual characteristics
for the socially visible and the socially invisible. Six key facts emerge from
this table. First, the socially invisible are predominantly female (only 23% of
them are male). Second, they are slightly younger than their socially visible
counterparts. Third, a little more than half of them (54%) have been fostered,
i.e., as a child they lived in the care of persons other than their parents and
outside of their homes for at least a year. Fourth, far more of them were the
first generation to reside in the village, 71 percent versus 39 percent among the
18 More precisely, we estimated all the models reported below for different threshold values of SV to separate the socially visible and socially invisible, defining Socially Invisible=1 if SV≤ 0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35 and 0.40, with Socially Invisible=0 otherwise. The greatest likelihood value was achieved at SV≤0.
Variable Mean Median Min Max Number of times presented as a match (N) 5.00 5.00 1 15 Number of times known as a match (n) 3.38 3.00 0 13 Social visibility (SV) index – direct matches 0.67 0.75 0 1 SV index – imputed matches* 0.38 0.32 0 1
40
Table 2.4: Descriptive statistics: estimating social visibility
Frequency (%) Variable Definition
Entire Sample
Socially Visible
Socially Invisible
Male =1 if male, 0 otherwise 47.6 49.5 22.9* Age Respondent’s age 45.5
(12.9) 45.9
(13.1) 40.8* (9.4)
Level of Schooling No schooling Primary School Middle School Higher School
=1 if respondent has no schooling, 0 otherwise =1 if primary level, 0 otherwise =1 if middle school or junior secondary school, 0 otherwise =1 if any higher, 0 otherwise
25.2 17.0 50.8 5.7
25.1 16.6 51.2 5.7
25.7 22.9 45.7 5.7
Occupation Farmer Other Unemployed
=1 if farmer, 0 otherwise =1 if trader, artisan, teacher, civil servant, office or health worker, agricultural or non-agricultural labor, 0 otherwise =1 if student/ pupil, unemployed or not in the labor force
78.9 17.8
3.3
78.8 18.9
3.1
80.0 14.3
45.7
Major clan =1 if member of a major clan, 0 otherwise 89.8 90.2 84.3 Herelong =1 if not the first generation to reside in
village, 0 otherwise
59.1 61.4 28.6*
Parents held office =1 if parents holds any village office, 0 otherwise
47.1 48.1 31.3*
Fostered =1 if respondent was fostered, 0 otherwise
53.7 53.6 54.3
Value of non-land wealth
Value of esusu, bonds, pension, jewelry, cash being kept with others, chemicals, seeds, crops and goods to be traded in millions of Cedis
6.7 (9.2)
7.0 (9.5)
3.7 (3.8)
Value of inheritance
Total value of any current and expected land and non-land inheritance in hundred millions of Cedis
92.2 (1656.4
)
99.2 (1718.7)
0.2 (0.6)
Location Village 1 Village 2 Village 3 Village 4
=1 if Village cluster 1, 0 otherwise =1 if Village cluster 2, 0 otherwise =1 if Village cluster 3, 0 otherwise =1 if Village cluster 4, 0 otherwise
25.8 22.9 24.3 27.0
26.7 24.3 21.8 27.2
14.3* 4.3* 57.1*
24.3 Notes: * Differences in means statistically significant at the 5% level. The standard deviations of continuous variables are given in parentheses.
41
socially visible. Fifth, fewer of them had parents who have held village
offices. Finally, the socially invisible are poorer than their socially visible
counterparts, with a mean non-land wealth value of 3.7 million Cedis as
compared to 7.0 million Cedis for the socially visible.
These simple cross-tabulations are reinforced by a multivariate
regression analysis. Let itl =1 be an indicator variable that equals one if
individual i is socially invisible at time t. Let Pr{ itl =1} be the probability that
itl =1 conditional on some individual characteristics, itX . We then estimate
(13) Pr{ itl =1}= )0( >+Λ ititX εβ
by probit regression, where Λ is the normal CDF. We estimate a random
effects model using all three rounds of data, with observations clustered on the
respondent’s identity.
The parameter estimates are presented in Table 2.5. The results
reinforce the descriptive statistical results. Social invisibility is declining in
age and wealth, is significantly less for those whose family has resided in the
village for more than one generation and for those who are not farmers. This
latter result is consistent with Santos and Barrett’s (2004) results using these
same data but a different model; they likewise find that teachers and traders are
more likely to be known and the older are less likely to know the younger
members of community. Belonging to a major clan reduces the likelihood of
being socially invisible since one may establish links with members of ones’
matrikin (Goldstein 1999, De Weerdt 2002, Santos and Barrett 2004, Udry and
Conley 2005). On the other hand, having been fostered increases the likelihood
of being socially invisible. In addition, having education beyond the middle
42
Table 2.5: Probit estimation of social invisibility
Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively. Comparison group is a female respondent from village 1 who farms and has a middle school education.
Variables Marginal Effects Standard Error Prob>|z| Dependent Variable: Socially invisible (SV=0) Individual Characteristics Male -0.123 0.226 0.587 Age -0.027* 0.009 0.004 No schooling -0.298 0.252 0.237 Primary school education 0.284 0.215 0.187 Higher than middle school 0.655** 0.329 0.046 Non-farm occupation -0.605*** 0.343 0.077 Unemployed 0.605 0.457 0.185 Assets Value of non-land wealth -0.067** 0.032 0.037 Value of inheritance -0.103 0.106 0.330 Social characteristics Major clan -0.576** 0.230 0.012 Herelong -0.292*** 0.178 0.100 Fostered 0.650** 0.228 0.004 Parents held office -0.077 0.178 0.665 Location Village 2 -0.240 0.344 0.485 Village 3 1.255 0.270 0.000 Village 4 0.532 0.268 0.047 n = 852 Log likelihood = -144.54 Wald χ2(16) = 67.59, p-value = 0.000
43
school level increases the likelihood of being socially invisible. While this
seems counterintuitive, De Weerdt (2002, p.12) found that “households with
educated members tend to lie closer to each other on the network graph.” By
assortative matching, the relatively highly educated are thus more likely to be
linked to each other and since they have fewer peers (i.e., those with higher
than middle school education make up only 7.03% of the population) they
appear less likely to be known by others. Moreover, the relatively highly
educated may have left the village for a number of years in pursuit of
education, thereby interrupting patterns of social interaction that condition
social visibility. Being male, and having parents who held village offices were
consistent in sign with the cross tabulations but not statistically significant,
even at the 10% level.
The definition of social invisibility used thus far relies on reporting by
a very small subsample – from 1 to 15 people – to whom each respondent’s
name was presented as a random match. This may generate small sample
variability in the measure of social visibility. So we repeated the exercise, this
time by estimating each individual’s social connectedness as reflected in the
pattern of kij, the indicator variable reflecting whether j knows i. Now, rather
than using kij directly to estimate SVi as a function of the relatively few
matches to which each individual’s name was presented, Ni, we instead
estimate a probit regression of kij to establish patterns of social visibility and
then use those estimates to predict the probability of i being known by each
sample respondent j.19 Based on an optimally chosen cut-off value for that
probability, we can then estimate SVi over the whole village sample, Nv-1, for
each respondent, substantially increasing the number of prospective social 19 We used the entire roster of individuals in the intra-village samples from wave 2.
44
links over which social visibility is measured for each individual. We follow
Santos and Barrett (2004) in this specification, allowing for asymmetry in who
knows whom by controlling for the direction of prospective differences
between a respondent and her match, not just the algebraic distance between
them. For example, a non-teacher may indicate that she knows a random
match who is a teacher, but the teacher might not remember that respondent.
The probit estimation results are presented in Table 2.6. Respondents
who were both male, had the same level of education, had resided in the
village for more than one generation and belonged to the same clan were more
likely to know each other. Older, poorer respondents who were farmers or
traders were more likely to be known. In addition, females were more likely to
know males, males were more likely to know females and non-farmers were
more likely to know farmers. On the other hand, farmers were less likely to
know non-farmers, respondents who were fostered were less likely to know
those who were not fostered and less likely to know each other.
Based on the results reported in Table 2.6, out-of-sample predictions
were made to determine the likelihood of each individual being known by all
sampled individuals in their village. We then set ijk̂ =1 based on some minimum
threshold probability, using 0.1 point intervals from 0.1 to 0.9. We then
computed iSV∧
≡ΣNv-1 ijk̂ /(Nv-1) for each respondent based on these predicted
probabilities and cut-off value. As before, we used different threshold values to
discretize the resulting ∧
SV continuum into the socially invisible and the
socially visible. For the predicted social visibility index, the maximum
likelihood occurs when we let socially invisible=1 if iSV∧
≤0.25 and 0
otherwise, for ijk̂ =1 if the predicted probability ≥ 0.8. By this definition,
45
Table 2.6: Probit estimation of the likelihood of knowing a random match
Notes:***, **, * Significant at the 1%, 5% and 10% levels, respectively.
Variables Definition of variables (i=respondent, j= -i intra-village sample individual) Marginal Effects Prob>|z| Both male =1 if both i and j are male, 0 otherwise 1.030*** 0.000 Female, male =1 if i is female and j is male, 0 otherwise 0.394*** 0.000 Male, female =1 if i is male and j is female, 0 otherwise 0.200** 0.029 Older =1 if i is older than j, 0 otherwise 0.152** 0.024 Same education =1 if both i and j have the same level of education, 0 otherwise 0.213*** 0.000 Same occupation =1 if both i and j have the same occupation, 0 otherwise -0.119 0.230 Trader, non-trader =1 if only i identifies himself as a trader, 0 otherwise -0.375 0.131 Non-trader, trader =1 if only j identifies himself as a trader, 0 otherwise 0.207 0.121 Farmer, non-farmer =1 if only i identifies himself as a farmer, 0 otherwise -0.527*** 0.000 Non-farmer, farmer =1 if only j identifies himself as a farmer, 0 otherwise 0.542*** 0.000 Same clan =1 if both i and j belong to the same clan, 0 otherwise 0.245*** 0.001 Both herelong =1 if both i and j have resided in the village for more than one generation, 0 otherwise 0.327*** 0.001 Herelong, not-herelong =1 if only i has resided in the village for more than one generation, 0 otherwise 0.012 0.913 Not-herelong, herelong =1 if only j has resided in the village for more than one generation, 0 otherwise 0.061 0.562 Both fostered =1 if both i and j have been fostered, 0 otherwise -0.291** 0.004 Fostered, not-fostered =1 if only i has been fostered, 0 otherwise -0.317** 0.004 Not-fostered, fostered =1 if only j has been fostered, 0 otherwise 0.032 0.762 Poorer =1 if i is poorer than j, 0 otherwise 0.111** 0.054 Age Age of i 0.003 0.323 Value of nonland wealth Value of non-land wealth owned by i, millions of Cedis 0.000 0.433 Farmer =1 if i identifies himself as a farmer, 0 otherwise 0.723*** 0.000 Trader =1 if i identifies himself as a trader, 0 otherwise 0.776** 0.003 Village 2 =1 if i lives in village 2, 0 otherwise 0.187* 0.094 Village 3 =1 if i lives in village 3, 0 otherwise -0.754*** 0.000 Village 4 =1 if i lives in village 4, 0 otherwise -0.199* 0.055 n=3724 Log likelihood = -1952.09 Wald chi2(25) = 342.66 Prob>chi2 = 0.000
45
46
22.79% of the sample was socially invisible, nearly three times as many as
under the stricter SVi=0 criterion used with the direct matching data. The
bottom row of Table 2.3 compares the two socially visibility indices computed
by the direct and probabilistic imputation methods. The predicted social
visibility continuum had a lower mean, 0.38 as compared to 0.67 for the index
based on direct matches. On average, respondents were predicted to be less
visible than previously indicated by the index based on random matches with a
small sub-sample. The two social visibility indices were statistically
significantly correlated at the one percent level with a correlation coefficient of
0.47.
But the real point is that there is considerable variation in social
visibility even within relatively small rural villages. Clearly, not all residents
within these rural Ghanaian villages are equally well known by others within
the community. Some people appear sufficiently infrequently known by others
that one might reasonably term them socially invisible. Does this matter to
individual risk management capacity?
2.5 Risk Pooling Conditional on Social Visibility
If social connectedness fundamentally affects how an individual’s
expenditures vary with the shocks she experiences, as suggested by section 2’s
simple general equilibrium model of risk pooling, then conventional tests for
risk pooling should condition on these measures of social (in)visibility. The
relevant hypotheses differ between the socially visible – who are expected to
pool risk – and the socially invisible – who are not.
47
Given the heterogeneity within these Ghanaian villages in social
connectedness, as demonstrated in section 4, we hypothesize that global tests
of risk pooling may mask heterogeneity in access to risk management through
mutual insurance and related mechanisms. We use the agricultural, health, theft
and mortality shocks discussed earlier as proxies for insty . This requires
adaptation of equation (11), substituting change in the vector of shocks for the
scalar instyΔ variable. The joint null hypothesis of full risk pooling remains
unchanged ( FH0 : β=0, γ=1 versus FAH : β>0 or γ<1). We expect this to apply
only to the socially visible subsample. The no risk pooling null hypothesis has
to be adapted for the use of proxy variables, however. Thus we test NH0 : γ=0
versus NAH : γ>0 and expect the null to apply to the socially invisible subsample
only. The coefficient estimates on the shock variables should also be
statistically significantly different from zero, indicating that individual-level
consumption is directly affected by individual-level shocks, but the magnitudes
of the relevant coefficients are indeterminate given the dummy variable nature
of the shock indicators. Rejection of the former null via an F-test is strong
evidence against full risk pooling, while rejection of the latter null via a t-test
suggests at least partial risk pooling.
In order to generate results that are directly comparable to those in the
existing literature, we first assess the extent to which any individual (visible or
invisible) pools risk with other individuals in the village. Following equation
(11), we regress the period-on-period change in individual private consumption
expenditures on the period-on-period change in farm, health, theft and
mortality shocks as well as the period-on-period change in residual village
average consumption (i.e., excluding person i). We use a fixed effects
estimator, clustering observations on the respondent’s identity, with Huber-
48
White robust standard errors. Table 2.7 shows that individual private
consumption is not statistically significantly related to individual shocks and
tracks village average consumption directly, albeit not one-for-one. A unit
change in the village average consumption corresponds to only a 0.48 change
in individual private consumption. This implies that individual consumption
varies in response to covariate shocks that affect village average consumption,
implying some social insurance. While an F-test rejects the null hypothesis of
full risk pooling, a t-test on the village average consumption also rejects the
null of no risk pooling.
As seems the norm in the existing empirical literature on risk pooling in
village economies, these data support a finding of partial risk pooling but reject
the full insurance hypothesis when one pools all households. Such a finding
could certainly be attributable to any of several well-known and quite plausible
insurance contracting problems related to search, transactions costs,
monitoring and enforcement, etc. (Fafchamps 1992, Murgai et al. 2002).
The contribution of this paper is to offer a different, potentially
complementary explanation of this familiar result. In particular, we
hypothesize that global tests applied to all sample respondents may mask
differences between subpopulations with different degrees of social
connectedness, blending socially visible individuals who enjoy reasonably
complete risk pooling with socially invisible individuals who have little or no
access to risk pooling to manage idiosyncratic shocks.
To explore that hypothesis, we now disaggregate the data into two
subsamples: socially visible and socially invisible individuals. We repeat the
49
Table 2.7: Regression tests for full and no risk pooling Dependent variable: Change in individual private consumption expenditure
All Individuals Visible Individuals Invisible Individuals
Coefficient Robust
Standard Error Coefficient Robust
Standard Error Coefficient Robust
Standard Error change in loss due to farm problems -0.056 0.045 -0.012 0.022 -0.872*** 0.110 change in total health expenses 0.002 0.002 0.001 0.001 0.020*** 0.001 change in theft of personal item 0.029 0.039 -0.008 0.015 -0.979*** 0.099 change in expenses due to sudden death 0.004 0.008 0.002 0.007 -0.614*** 0.077 change in residual village average consumption 0.483*** 0.226 0.564*** 0.268 -0.042 0.123 constant -9691.782 56172.990 -10796.500 67423.300 -39829.600*** 42733.190 Joint test for full risk pooling F(5, 301) =2.10, Prob>F=0.07 F(5, 257) =1.42 , Prob>F=0.22 F(5, 12) =223.6, Prob>F= 0.00 n=649 n=597 n=52 R2=0.010 R2=0.007 R2=0.193 Notes: ***, **, * Significant at the 1% , 5% and 10% levels, respectively.
49
50
previous exercise for each subsample, now regressing change in individual
level consumption for visible individuals on the change in their individual level
shocks and the change in residual village average consumption (i.e., excluding
the respondent) for other visible individuals. We then repeat this regression
using only socially invisible respondents. The results based on the direct
elicitation method for determining social visibility are reported in the second
and third columns in Table 2.7.20
The estimated partial correlation between changes in average
consumption of visible individuals within the village and changes in individual
consumption is more than 10 times greater for socially visible respondents than
for socially invisible persons. Furthermore, the various shocks have no
significant effect on individual private consumption even at the 10% level
among the socially visible subpopulation. Their estimated magnitudes are
smaller than in the pooled regression and much smaller than in the same
regression applied to socially invisible individuals, for whom each shock
variable is significant at the one percent level. Most notably, an F-test fails to
reject the full insurance null hypothesis for the socially visible, even at the
20% level, but overwhelmingly rejects it for the socially invisible. Indeed,
among the socially invisible, we cannot reject the no risk pooling null
hypothesis even at the 20% percent level. Visible individuals appear to achieve
something very close to full risk pooling with other visible individuals in the
20 The results based on the predicted social network structure method – classifying individuals as socially visible if SVi>0.25 for kij=1 if the predicted probability ≥ 0.8, which yielded the greatest likelihood value – are available from the authors by request. These generate qualitatively similar results. But because of the imprecision necessarily introduced by using imputed social networks, we favor the estimates based on directly elicited networks.
51
village while socially invisible persons are left out of these arrangements and
must self-insure against idiosyncratic shocks.21
By an order of magnitude, health and mortality shocks have the least
effect on consumption of the socially invisible, followed by farm and theft
shocks. The difference in the effects of the respective shocks may be attributed
to a number of factors. For instance, tradition places more value on life than
inanimate objects hence a theft, although considered unfortunate, is not viewed
as crucial to survival and hence may not garner the same level of support as a
health shock. Moreover, custom requires that one call on the sick and the
bereaved.22 The latter enforces some generalized reciprocity with regards to
health and mortality shocks. Second, whereas a health shock is readily
observable and verifiable, other than in extreme cases thefts and farm shocks
may not be obvious to all others in the village. People do not assist with shocks
they do not know happened. The directions of change as per the signs on the
coefficients of shocks are mixed. Whereas an increase in the period-on-period
change in mortality, farm and theft shocks is associated with a decrease in the
corresponding individual private consumption expenditures, an increase in the
period-on-period change in health shocks is associated with an increase in
individual private consumption expenditures. Curiously, health shocks appear
slightly overcompensated for by private consumption expenditures.
21 We also tested for full risk pooling of visible individuals with all other individuals in the entire village. The results showed that visible individuals only partially pool risk with the entire village comprising both the socially visible and invisible. This is to be expected since by definition the visible do not have any social connections with the socially invisible in the village. 22 It is customary for a sick person to send a message to friends and family informing them about his/her predicament. In addition, one has to go greet the bereaved and offer them drinks and/or cash towards the organization of the funeral. In the Akan tradition the latter is termed “wo ko bo nsawa” (translated as “going to give a donation towards the funeral”).
52
To this point, we have explored risk pooling at the village and within
the village subpopulation that is similarly (in)visible. Since the data include
information on social network structure, however, we can go one step further
and repeat the risk pooling tests, but now assess the extent to which an
individual pools risk with members of his/her social network by using the
change in average consumption within the individual’s social network, nstcΔ ,
as the key regressor in place of the change in village average consumption. In
using the directly elicited network based on random matching within sample,
this analysis is obviously conditional on being visible since the optimal
threshold for distinguishing the socially visible from the socially invisible
using that measure was SVi=0. We use the mean individual consumption for
the known random matches as a proxy for network average consumption.
The point estimates in Table 2.8, with an intermediate estimate for γ of
0.92 and a standard error of 0.06, indicate that we can reject the no risk pooling
null hypothesis at the one percent level. On the other hand we fail to reject the
full insurance null hypothesis at conventional significance levels. The
individual shock variables’ coefficients are statistically insignificantly different
from zero, with low magnitudes similar to those found in comparing socially
visible individuals against all other socially visible individuals within the
village.23
23 The regression results from the predicted social network structure method were similar. We can reject the no risk pooling null hypothesis at the one percent level, with a γ of 0.80 and a standard error of 0.23. However, we fail to reject the full insurance null hypothesis, with F(5,297) =0.81 and Prob>F=0.5422. The individual shock variables were not statistically significantly different from zero.
53
Table 2.8: Risk pooling for an individual in his directly elicited social network Dependent variable: Change in individual private consumption expenditure
Coefficient Robust Standard
Error change in loss due to farm problems -0.009 0.019 change in total health expenses 0.001 0.002 change in theft of personal item 0.015 0.022 change in expenses due to sudden death 0.005 0.009 change in residual network average consumption 0.920*** 0.059 constant -106935.100 102756.000 Joint test for full risk pooling: F(5, 257)=0.47, Prob>F = 0.80 n=597 R2=0.750
Finally, we assess the extent to which these respective networks pool
risk with other networks in the village. The preceding analysis assumes
implicitly that networks are segmented and thus that there is no spillover of
insurance benefits from one network to another through one or more members
common to multiple networks. However, interlinkages may enable networks
to reinsure each other, such that j’s mutual insurance relation with two
individuals, h and i, who do not know or directly interact with one another,
effectively creates indirect (second-order) risk pooling among h and i. We can
crudely test this hypothesis using the same method by taking the social
network as a unit and regressing the change in each individual’s social network
average consumption on the change in network-level mean farm, health, theft
and mortality shocks – thereby capturing the covariate element of shocks
within the social network – as well as the change in residual average
consumption for all networks in the village (i.e., excluding the current
network). The results are given in Table 2.9.
Network average consumption statistically significantly comoves with
village average consumption. Indeed, the point estimate is strikingly close to
54
one, which would suggest perfect reinsurance if network average consumption
were uncorrelated with average (i.e., covariate) shocks within the network.
However, we do find that network average consumption statistically
significantly covaries with expenditures related to health shocks within the
network. We thus reject both the full and no risk pooling null hypotheses,
although reinsurance among networks appears substantial in economic terms
nonetheless. This only further underscores how social visibility is necessary for
individuals to take advantage of informal risk pooling mechanisms available
through social networks, including the reinsurance apparently available
through network interlinkage.24
Table 2.9: Insurance among social networks within a village Dependent variable: Change in network average consumption expenditure
Coefficient Robust Standard
Error change in loss due to farm problems 0.001 0.013 change in total health expenses 0.002*** 0.001 change in theft of personal item 0.004 0.011 change in expenses due to sudden death 0.004 0.005 change in residual village average consumption 1.021*** 0.244 constant -3083.776 32233.200 Joint test for full risk pooling: F(5, 265) = 28.67, Prob>F = 0.00 n=597 R2=0.055
One important limitation of the foregoing analysis is that the data only
permit us to study intra-village networks. Social networks may certainly cross
village lines. In fact, it may be these ‘weak ties’ that serve the very purpose of 24 The predicted social network structure method yields qualitatively similar results. We reject the full risk pooling null hypotheses. However, the coefficient on the village average consumption was much lower, 0.023 as compared to 1.021 in the direct matching regression. In addition, we can only reject the no risk pooling null hypothesis at the 10% level.
55
spreading risk, e.g., social re-insurance, as in models of marriage markets that
consider risk management incentives in the choice of a spouse for a child
(Rosenzweig and Stark 1989). This remains a topic for future study, as the
present data do not permit exploration of social linkages beyond the village.
Subject to that important caveat, our results corroborate previous studies’
findings, using other data, that there is only partial risk pooling within rural
villages. Unpacking this result by disaggregating the data according to the
social visibility of individual respondents, however, shows that full risk
pooling is achieved by visible individuals with other visible individuals both at
village and network levels, with something very close to full risk pooling (i.e.,
reinsurance) among social networks within the village. On the other hand, the
socially invisible fail to achieve risk pooling at any economically or
statistically significant level. Table 2.10 summarizes these results.
Table 2.10: Summary of results for tests for risk pooling Full Risk Pooling No Risk Pooling Inference An individual in a village Rejected Rejected Partial risk pooling
A visible individual in a village Not rejected Rejected Full risk pooling
An invisible individual in a village Rejected Not rejected No risk pooling
An individual in a network Not rejected Rejected Full risk pooling
A network in a village Rejected Rejected Partial reinsurance
2.6 Conclusions
Although risk management is crucial to rural households in low-income
countries and the development studies literatures in economics and cognate
disciplines are rich with descriptions of informal insurance arrangements, risk
pooling through social networks may not be universally available for the
56
simple reason that not everyone is socially well connected. The simple general
equilibrium theory of risk pooling implies that individuals who are socially
visible within networks will enjoy the consumption smoothing benefits of
mutual insurance while those who are socially invisible will not. If villages
include both types of individuals, then tests of the extent of informal insurance
based on regressions that pool both sorts of individuals can lead to biased,
intermediate results suggesting partial risk pooling, as is typical of the
literature.
This paper identified a minority subpopulation of socially invisible
individuals in rural Ghana who are not widely known by other residents within
their villages. In particular, we find that poorer, younger residents who farm,
do not belong to a major clan, have been fostered and have resided in the
village for only one generation are most likely to be socially invisible.
Estimating now-standard Townsend-style regressions, we obtain the usual,
partial risk pooling result when we fail to separate the sample into socially
invisible and socially visible subpopulations. Once we separate the sample,
however, we cannot reject the full risk pooling null hypothesis for socially
visible individuals, nor can we reject the no risk pooling hypothesis for the
socially invisible. Thus in these data, village-level tests for complete mutual
insurance appear to represent a mixture model that generates misleading results
of universal partial risk pooling when the reality seems more socially
variegated, with a socially invisible minority of the population having little
access to social networks-mediated risk management, while most of the
population enjoys something economically and statistically close to complete
pooling of idiosyncratic risk. Moreover, for those in social networks,
interlinkages among social networks appear to provide quite effective
57
reinsurance against network-level covariate risk that is idiosyncratic within the
village.
In summary, this study corroborates a vast literature that finds many
individuals in rural villages use social networks to effectively insure
themselves against idiosyncratic risk, while also accommodating an oft-
overlooked literature on social exclusion and social invisibility within rural
villages that suggests insurance coverage is likely uneven among individuals.
Within-village variation in social connectedness, like within-village variation
in wealth and other attributes, appears to have a profound effect on risk
management capacity.
In policy terms, empirical evidence of Pareto efficient allocation of
idiosyncratic risk among socially visible members of networks suggests that
given binding budget constraints, interventions should target primarily (i)
village-level (or larger-scale) covariate risk that is inherently uninsurable
through social networks and (ii) idiosyncratic shocks faced by those left out of
these networks (i.e., the socially invisible). This implies a need for careful
identification of who is socially well-connected and who is not, paying
particular attention to the latter subpopulation for the purposes of targeting
interventions that might stitch up the holes in extant social safety nets. This
might be done through direct interventions to provide (quasi-)insurance to
socially invisible persons, or through efforts to improve the social integration
of individuals most likely not to be well-connected socially (e.g., recent
migrants into a community). Given our finding that young farmers who are
relatively recent settlers in a community are most likely to be socially invisible,
this might suggest possibilities involving quasi-insurance built into agricultural
credit, product sales or input delivery contracts with certain demographic
58
subgroups as an indirect means of insuring this subpopulation. Greater effort
needs to be made to identify and reach the socially invisible, lest they fall
through the apparent holes in otherwise well-functioning social safety nets.
57
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Alderman, H. and Paxson, C. H. (1992). Do the Poor Insure? A Synthesis of the
Literature on Risk and Consumption in Developing Countries. Washington DC, World Bank.
Alderman, H. Chiappori, P.A., Haddad, L., Hoddinott, J., and Kanbur, R. (1995).
Unitary Versus Collective Models of the Household: Is it Time to Shift the Burden of Proof? World Bank Research Observer,10, 1-9.
Bardhan, P. and Udry C. (1999). Risk and Insurance in an Agricultural Economy.
Development Economics. Oxford: Oxford University Press. Besley, T. (1995). Nonmarket Institutions for Credit and Risk Sharing in Low Income
Countries. Journal of Economic Perspectives, 9(3), 115-127. De Weerdt, J. (2005). Risk-Sharing and Endogenous Network Formation. In S.
Dercon (Ed.), Insurance Against Poverty. New York: Oxford University Press. De Weerdt, J. and Dercon, S. (2006). Risk-Sharing Networks And Insurance Against
Illness. Journal of Development Economics, 81,337–356. Deaton, A. (1992). Savings and Income Smoothing in Cote D'ivoire. Journal of
African Economies, 1, 1-24. Dercon, S. (2005). Risk, Poverty and Public Action. In S. Dercon (Ed.), Insurance
Against Poverty. New York: Oxford University Press. Dercon, S. and P. Krishnan (2000). In Sickness and In Health: Risk Sharing Within
Households In Rural Ethiopia. Journal of Political Economy, 108(4), 688-727. Doss, C. R. (1996). Intrahousehold Resource Allocation in an Uncertain Environment.
American Journal of Agricultural Economics, 78, 1335-1339.
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Duflo, E. and Udry, C. (2004). Intrahousehold Resource Allocation in Cote d'Ivoire: Social Norms, Separate Accounts and Consumption Choices: Yale University.
Fafchamps, M. (1992). Solidarity Networks in Preindustrial Societies: Rational
Peasants with a Moral Economy. Economic Development and Cultural Change, 41 (1), 147-174.
Fafchamps, M. and F. Gubert (2007), The formation of risk sharing networks, Journal
of Development Economics, 83(2), 326-350. Gertler, P. and Gruber J. (1997). Insuring Consumption against Illness. National
Bureau of Economic Research Working Paper Series. 6035,1-57. Goldstein, M. (1999). Chop Time, No Friends: Intrahousehold and Individual
Insurance Mechanisms in Southern Ghana. Yale University. Goldstein, M., De Janvry, A. and Sadoulet, E. (2005). Is a Friend in Need a Friend
Indeed? Inclusion and Exclusion in Mutual Insurance Networks in Southern Ghana. In S. Dercon (Ed.), Insurance Against Poverty. New York: Oxford University Press.
Iliffe, J. (1987), The African poor: A history, Cambridge, Cambridge University Press. Murgai, R., Winters, P., Sadoulet E. and De Janvry A. (2002). Localized and
Incomplete Mutual Insurance. Journal of Development Economics, 67, 245-274.
Santos, P. and Barrett, C. (2004). Interest and Identity in Network Formation: Who do
Smallholders Seek out for Information in Rural Ghana?: Cornell University. Santos, P. and Barrett, C. (2006). Informal Insurance in the Presence of Poverty Traps:
Evidence from Ethiopia: Cornell University. Santos, P. and Barrett, C. (2007). Understanding the Formation of Social Networks.
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Townsend, R. (1994). Risk and Insurance in Village India. Econometrica, 62(3), 539–91.
Udry, C. (1996). Gender, Agricultural Production and the Theory of the Household.
Journal of Political Economy, 104 (5), 1010-1046. Udry, C. and Conley, T. (2005). Social Networks in Ghana. In C. Barrett (Ed.), The
Social Economics of Poverty. London: Routeledge.
60
CHAPTER THREE
Risk, Asset Poverty and Social Visibility in Ghana
3.1 Introduction
Poverty has long been the focus of policy discussions and empirical research.
Several factors have been identified as determining movement in and out of poverty.
However, using standard poverty measures to distinguish between stochastic and
structural transitions in poverty is difficult. The asset-based approach is said to
distinguish transitory poverty which eases with time due to systemic growth from
persistent structural poverty.
Under this approach there are nonlinear asset dynamics with multiple
equilibria which include a threshold that distinguishes households who can be
expected to grow out of poverty from those who are likely to remain trapped in
poverty, i.e. those caught in a poverty trap (Carter and Barrett 2006). These dynamics
may be caused, to a large extent, by risk exposure and response, attendant activity
choice and returns to endowments given incomplete financial and insurance markets.25
However, the empirical evidence regarding the existence of these dynamics is mixed.
The effect of risk pervasive in most developing countries is exacerbated by the
fact that formal financial markets are essentially missing. The poor often resort to
social networks to insure against residual risk exposure but access to these networks is
not uniform. Social visibility has been identified as a key determinant of access to
social insurance with the socially visible enjoying complete insurance whilst their
socially invisible counterparts are left uninsured (Vanderpuye-Orgle and Barrett
2007). Given the prevalence of risk, do asset poverty traps exist? If so for whom, do
they exist? Particularly, how do these asset dynamics vary with access to social
25 See Azariadis and Stachurski (2005) for a complete review.
61
insurance? Are these thresholds the same for everyone or do they vary by social
visibility? How can we identify individuals who are likely to get caught in asset
poverty traps for targeting purposes?
To address these questions, we draw from two main strands of literature. The
first is the literature on asset dynamics and poverty traps. Extant studies have
identified determinants of economic mobility. 26 Most of these studies use
income/expenditure based poverty measures which can not adequately distinguish
between structural trends and transitory movements. In addition, measurement errors
rife in income or expenditure welfare metrics renders transition variation suspect. The
asset-based approach gets at individuals who are structurally poor and who may stay
poor without policy intervention.27 Carter and Barrett (2006) extend static poverty
indices, developing an approach based on nonlinear dynamics that result in a dynamic
asset poverty line and the possibility of poverty traps. They define a dynamic asset
poverty line which is analogous to the statistic poverty line as the threshold that
distinguishes households who can be expected to grow out of poverty conditional on
asset trajectories from those who are likely to remain trapped in poverty. In essence,
the dynamic asset poverty line is the threshold at which accumulation dynamics
bifurcate leading to multiple dynamic stable equilibria. Households with assets above
this given critical asset level will tend to move ahead towards a high asset equilibrium,
whereas those below this level will fall behind towards a stable dynamic low asset
equilibrium termed a poverty trap.
Several studies have focused on identifying the threshold effect attendant with
multiple dynamic equilibria and identifying poverty traps. The empirical results have
been mixed. On one hand, some studies find no evidence of nonlinear wealth
26 See Baulch and Hoddinnot (2000) for further discussions. 27 See Carter and May (2001), Adato et al. (2006), Barrett et al. (2006), Carter and Barrett (2006) for detailed discussions.
62
dynamics associated with poverty traps (Lokshin and Ravallion 2001, Mckenzie and
Woodruff 2003, Jalan and Ravallion 2004). On the other hand, some studies find that
these dynamics exist (Dercon 1998, Lybbert et al 2004, Adato et al 2006, Santos and
Barrett 2006). For instance, Lybbert et al (2004) find herd dynamics that follow an S-
shaped curve with two stable dynamic equilibria and a threshold (i.e. an unstable
equilibrium). However, it must be noted that even within a given economy, these
dynamics may not exist for everyone. Santos and Barrett (2006) find complex wealth
dynamics with distinct convergence groups defined by individual ability. They found
that individuals with lower ability converge to a unique dynamic equilibrium at a
lower asset level whereas those with higher ability have multiple stable dynamic
wealth equilibria.
Generally, the reasons for asset poverty center on incomplete financial markets
and risk. Carter and Barrett (2006) note that poor households’ limited access to credit,
insurance and savings may encumber their ability to accumulate wealth. Poor
households who neither have access to capital to finance the high costs associated with
higher return options nor have access to insurance to cope against downside risk will
follow lower expected returns thus perpetuating poverty. This leads to the second
strand of literature on the extension of risk, social insurance and social visibility. Risk
is integral to the lives of members of agrarian communities and it may cause poverty
traps in one of several ways. Empirical evidence indicates that risk aversion is
decreasing in wealth thus the poor may forgo considerable income to reduce risk thus
trapping them in poverty. The temporal nature of some risk may also cause the poor to
adopt the quasi-option value, incurring huge losses by delaying irreversible
investments till uncertainty is resolved over time (Rosenzweig and Biswinger 1993).
This may also result in nonadoption of new technology with costly efficiency losses
through foregone output (Feder et al 1985). In addition, limited diversification options
63
conditional on wealth may result in differential rates of return with the poor being
trapped in low return opportunities offering limited accumulation potential and
neglible risk reduction (Barrett 1997, Dercon 1998).
In the absence of well-functioning insurance and credit markets, rural poor
have resorted to informal institutions such as social networks for ex ante and ex poste
responses to shocks. However, access to insurance networks is far from uniform
(Dercon and Krishnan 2000, Dercon 2005, De Weerdt 2005, Santos and Barrett 2006).
Social connectedness is central to access to social insurance. Indeed, social visibility --
the extent to which an individual is widely known in the community-- is a good
predictor of the efficiency of social insurance. Vanderpuye-Orgle and Barrett (2007)
show that the socially visible attain full risk pooling both at the village and network
levels whereas the socially invisible are left uninsured.
Social visibility may thus affect asset dynamics in several ways. Firstly, by
ensuring access to social insurance, social visibility may mitigate the need for physical
and human capital asset decumulation ex-poste of shocks. It may also enhance
productivity by providing ex-ante insurance against shocks associated with the
adoption of new technology, eliminating the need to wait for the temporal resolution
of risk. Secondly, social visibility as characterized by social connectedness may
facilitate social learning and technology adoption through social networks. Individuals
who know and interact with others are more likely to gain access to knowledge about a
high return production process and are thus more likely to switch to this technology
(Conley and Udry, 2005). Thirdly, social visibility may enhance the security of
landholding and thus encourage investments in land fertility. Well-known or well-
connected individuals, who are likely to either hold powerful positions in society or
know others who do, have more secure tenure rights and are more likely to make
investments towards improving land productivity and enjoy higher returns (Gavian
64
and Fafchamps 1996, Goldstein and Udry 2005). Finally, the basic tenets of
microcredit –i.e. the use of group lending and peer monitoring– may imply that
individuals who are not socially visible, with limited connections, are by default
rationed out of these auxiliary credit schemes adopted widely by most non-
governmental agencies. This may impose barriers to entry for diversification vis-à-vis
capital requirements or keep the less socially visible below the minimum project size
required to reap higher returns. The positive wealth gradient in social visibility,
compounded by the positive correlation between returns to assets and social visibility
may result in nonlinear asset dynamics differentiated by social visibility.
We hypothesize that asset dynamics will vary by social visibility: those who
are socially invisible will converge to a unique dynamic equilibrium at a lower asset
poverty level while those who are relatively visible will experience multiple stable
dynamic asset equilibria. We examine the role of social visibility in conditioning
wealth dynamics by using the theory of bifurcated asset accumulation strategies to
empirically identify poverty traps. We estimate nonparametric kernel regressions for
all individuals in the sample and then disaggregate the sample into socially visible and
socially invisible individuals, first using the cut-off in the social visibility continuum
identified by Vanderpuye-Orgle and Barrett (2007) and then using regression trees to
endogenously identify the optimal sample splitting level. We then identify the
respective thresholds in asset dynamics and estimate the likelihood of an individual
falling below the lower poverty thresholds for targeting purposes. We use individual
level data to allow for effect of differential social visibility within the household.
The remainder of the paper is organized as follows. The next section describes
the data used in this paper. Section 3 generates an appropriate multidimensional asset
index. Section 4 then analyzes asset dynamics based on this index whilst Section 5
65
identifies the characteristics of individuals who fall below the lower poverty
thresholds. Conclusions and policy recommendations are presented in Section 6.
The key results indicate that asset trajectories exhibit multiple dynamic stable
equilibria for village-level aggregations of individuals. Unpacking the results by social
visibility indicates that whereas the assets of the socially visible exhibit multiple stable
dynamic equilibria, the socially invisible converge towards a unique dynamic stable
equilibrium at the lower asset level. In addition, the asset levels at the respective
equilibria were generally higher for the socially invisible as compared to the socially
visible. These results are supported by a nonparametric regression tree analyses.
3.2 The Data
The data used in this paper are from a rural household survey undertaken from
July 2004 to January 2005. This was the third wave of a panel data set initiated by
Christopher Udry and Markus Goldstein. The research was conducted in the Akwapim
South District (specifically the Nsawam - Aburi area) in the Eastern Region of Ghana.
Since the early 1990s farmers in this area have been switching from the cultivation of
maize-cassava intercrop for domestic production to pineapple cultivation for export. 28
This transition involves a significant amount of risk by way of the attendant new
agronomic practices as well as exposure to global price fluctuations. It also allows for
marked improvements in asset accumulation given increased incomes associated with
pineapple cultivation.
The original sample was selected in 1996 using a two-stage procedure. Four
village clusters were purposively selected within this area based on their participation
in fruit and vegetable production as well as their array of agronomic, market access
and geographic conditions. Sixty married couples (or triples) were then randomly
28 See Goldstein and Udry (1999) for an in-depth discussion of the historical background of the area and the sampling techniques.
66
selected in each village cluster, except for the smallest village cluster where all
resident couples were interviewed. Male enumerators were assigned to male
respondents and female enumerators to female respondents to preserve gender
sensitivity and cultural norms. In all, 436 individuals were surveyed fifteen times in
the first and second waves. Of these, 372 individuals were surveyed three times in the
third wave. The sample attrition rate from the 1998-2004 interval was thus 14.68%.
Data from all three waves were used. A brief description of the modules relevant to
the analysis in this paper is given below.
3.2.1 Individuals’ Assets
Data were gathered on a variety of assets at the individual level. First we asked
about stores of farm inputs such as chemicals and seeds as well as farm equipment,
machines, motor vehicles and other equipment. We then asked for imputed values or
actual costs of equipment for business outside of farming such as distilling equipment,
commercial vehicles, sewing machines, etc. as well as stocks of trading goods. In
terms of financial assets, we asked about susu savings, expected pot sizes from esusu,
money being held by others, bank deposits, bonds, stocks or other financial assets.29
Information on the approximate current value of jewelry and cloth as well as cash on
hand was gathered.
3.2.2 Individuals’ Social Visibility
For each respondent, we randomly selected seven individuals in the sample
from the same village (without replacement).30 We then asked them whether or no
29 Susu is an informal savings mechanisms where the individual saves money with a local collecting agent to be redeemed at a latter period for an agreed fee. Esusu is similar, in this case the pot of savings is paid out on a rotating schedule amongst a group of savers. 30 Respondents were also non-randomly matched with three other village-specific “focal” individuals identified from the community-studies approach taken in a preliminary field trip as individuals in the villages from whom advice is commonly sought. We focus on the random matches in this study.
67
they knew the match. In administering the questionnaire we were sure to make the
distinction between knowing of someone (i.e. using the Akan translation of just
“having heard of the person”) and actually knowing the person. Knowing a random
match in this sense is indicative of an extant social link. This gives us a random
sample of the individuals with which they have social links. The characteristics of
these random matches, by sampling design would be representative of people with
whom they have extant social links i.e., their social networks. Even though we do not
have the overall shape or size of the network, we know about the type of people they
establish social links with. Vanderpuye-Orgle and Barrett (2007) defined social
visibility as the ratio of the number of times an individual is known when presented as
a random match to the total number of times one is presented as a random match.
3.2.3 Shocks
We use data on the realization of shocks as captured by the incidence of the
following: (i) damage caused by general farm problems; (ii) illnesses (iii) theft of
personal item stolen; (iv) contributions towards organization of funeral upon sudden
death of family member.
3.2.4 Consumption
Detailed data were collected on purchased food, general family expenses and
personal expenditures by each respondent in the household. 31 Even though these
expenditure questionnaires were administered at the individual level, with the head
and spouse(s) of head being interviewed separately regarding contributions made
towards purchasing an item, individual expenditures were not assigned. Hence, we
follow Goldstein (1999) in assigning particular items as purchases for own-
31 Recall periods varied by expenditure based on the modal frequency of purchase reported in waves 1 and 2. These essentially intra-annual expenditure were converted to nominal monthly rates.
68
consumption: alcoholic beverages, non-alcoholic pre-packaged beverages, prepared
food (from kiosks), personal care products, hair cuts, public transport, petrol, car
repairs, newspapers, entertainment, lottery tickets and kola nuts.
3.3 Asset Index
An analysis of asset dynamics requires a measure that appropriately aggregates
the assets of an individual at any given point in time and allows for
multidimensionality. Thus, we follow Adato, Carter and May (2006) in defining a
livelihood-weighted asset index. This index aggregates the respective economics
assets of an individual at time t, given by a vector At, into a uni-dimensional index
L(At).
Carter and May (2001) define an asset poverty line as the level of assets
required to produce an expected living standard equal to the poverty line. The asset
poverty line was derived by regressing livelihood of individual i at time t, lit, on a
vector of k assets for individual i at time t, Aikt.32
lit =βAit + εit (1)
where lit= t
it
PLc (2)
cit is private individual consumption expenditure and PLt is a proxy of the value of the
individual subsistence needs, in this case the universal $1/day poverty line.33 Ait is a
32 The use of the term livelihood in reference to the ratio of some income proxy to the poverty line is simply aimed at shifting the focus from money-metric welfare indicators to a broader notion of income generating strategies, entitlements and vulnerabilities associated with these strategies (Carter and May, 1999) 33 We are unable to appropriately deflate the asset values because we do not have corresponding community level prices for the assets. However, we minimally control for this by including year dummies in the subsequent regression.
69
vector of number of years of schooling, value of aggregate non-land wealth and value
of land inheritance. Table 3.1 presents the descriptive statistics for the respective
waves.
Table 3.1: Descriptive statistics: Estimating livelihood-weighted asset index
Mean Variable Definition Entire
Sample Year 1997 Year 1998 Year 2004
Consumption expenditures
Individual private consumption expenditures
128156 (608106)
19798 (27545)
26166 (24603)
393383 (1097788)
Livelihood metric
Individual consumption expenditures normalized by the poverty line
0.6 (2.1)
0.3 (0.4)
0.3 (0.3)
1.3 (3.7)
Years of schooling
Number of years of schooling
6.8 (4.7)
6.9 (4.8)
7.0 (4.8)
6.4 (4.4)
Value of non-land wealth
Value of crops, chemicals, cash, jewelry, goods to be traded and business equipment in millions of Cedis
0.8 (3.4)
0.1 (0.2)
0.2 (0.5)
2.1 (5.5)
Value of land inheritance
Total value of land inheritance in hundred millions of Cedis
2.5 (5.8)
1.3 (1.0)
1.5 (1.0)
4.5 (9.5)
Notes: Standard deviations given in parentheses
Equation 1 was estimated using a fixed effects regression, with
heteroskedasticity robust standard errors and observations clustered on the individual.
We included a polynomial expansion of the respective assets as well as interaction
terms to allow the marginal returns on these assets to vary with the level of the asset as
well as the magnitudes of other assets. The regression results in Table 3.2 indicate
that an additional year of schooling is associated with a 5.4% increase in the asset
index.
70
Table 3.2: Livelihood-weighted asset index estimation
Variables Coefficient Robust Standard
Error Dependent Variable: Livelihood-weighted asset index Assets Years of schooling, S 0.054** 0.024 Value of non-land wealth, W 0.637*** 0.051 Value of land inheritance, I 0.059** 0.025 Asset Interactions S2 0.000 0.002 W2 0.012*** 0.001 I2 -0.001*** 0.001 SxW -0.102*** 0.005 SxI -0.001 0.002 WxI -0.021*** 0.006 SxWxI 0.003*** 0.001 Other Factors Village 2 0.128 0.121 Village 3 0.000 0.117 Village 4 -0.143 0.115 Year 1998 0.038 0.096 Year 2004 0.750*** 0.120 n = 1038
R2 = 0.636 Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively.
71
A unit increase in the value of land inheritance is associated with a 5.9% increase in
the asset index whereas a unit increase in non-land wealth is associated with 63.7%
increase in the index. The second order polynomial of the non-land wealth as well as
the interaction between education wealth and land inheritance have positive effects on
the asset index while the second order polynomial of land inheritance and the
interactions between schooling and wealth and wealth and inheritance are associated
with a decrease in the asset index. As expected, the year 2004 is associated with an
increase in the asset index.
The significance of the most of the coefficients and the overall goodness of fit
(R2 statistic) of 0.636 indicate that the model adequately estimates the weighted asset
index. It must be noted that the respective coefficients give the marginal contribution
of the respective assets to livelihood. We estimate the fitted value of the regression
function in order to generate an asset index with assets weighted by their marginal
contributions to livelihood. The latter is termed the livelihood-weighted asset index,
Lit, and is expressed in poverty line units (PLUs) such that a value of 1 means the
bundle predicts the poverty level of material wellbeing and 0.5 means the assets
predict livelihood at half the poverty line (Adato, Carter and May 2006). We use this
index as a basis for exploring asset dynamics in the next section.
3.4 Asset Poverty Dynamics
Asset dynamics were estimated using a nonparametric method to allow for
non-linearities in asset trajectories. We used a Nadaraya-Watson regression with an
Epanechnikov kernel to estimate the bivariate relationship between Lit and Lit+1,
estimating the asset dynamics for 1997-1998, 1997-2004 and 1998-2004 respectively.
72
We selected the optimal bandwidth based on Silverman’s rule of thumb as determined
by the Bounds of Stata package (Beresteanu and Manski 2000) 34 . The dynamic
equilibria are identified by the asset levels at which Lit = Lit+1.
We first estimate the regressions for all individuals in the sample. Figure 3.1 shows
the nonparametric estimates of the predicted livelihood-weighted asset index for the
1997-1998 period with the corresponding confidence band shown by the grayed out
area. A 45-degree line was overlaid to identify the points at which Li1997 = Li1998. The
horizontal axis measures initial period assets, Li1997, and the vertical axis measures
later period assets, Li1998. The asset dynamics display the expected S-shaped trajectory.
The critical asset threshold (the Micawber threshold) occurs at 0.76 PLUs. Thus,
individuals at with asset levels at 76% of the poverty line are likely to decumulate
assets, tending towards the lower poverty trap equilibrium of 0.48 PLUs. Whereas
those above this threshold may move ahead towards the upper asset equilibrium of
0.86 PLUs. However, it is uncertain if this equilibrium exists since the lower
confidence band does not cross the equilibrium at this point.
0.2
.4.6
.81
1998
Ass
et In
dex (P
overty Line Units)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.065) Figure 3.1. Nonparametric estimates of predicted asset index: All individuals, 1997-1998 34 See Silverman (1986) for detailed discussions.
0.48 0.73 0.86
Poverty Trap
Micawber Threshold
Expected Asset Dynamics Asset Indext+1= Asset Indext
73
Figure 3.2 replicates the analyses for the 1997-2004 interval. The results are similar
in terms of the general shape of the curve as well as the location of multiple equilibria.
The lower asset poverty equilibrium is again at 0.48 PLUs. However, the unstable
critical threshold is at lower level, 0.61 PLUs and both confidence bands cross the
equilibrium. The upper asset equilibrium is also at a lower level, 0.72 PLUs. Similarly,
the asset dynamics for the 1998-2004 interval display the S-shaped trajectory with
multiple equilibria (Figure 3.3). It is interesting to note that the equilibria are also
lower than that of the 1997-1998 equilibria. The poverty trap occurs at 0.36 PLUs as
compared to 0.48 PLUs. Even though it is not certain if this difference is statistically
significant, these results may indicate a temporal component to the existence of asset
poverty, albeit nonlinear. This may seem counterintuitive to entire concept of a stable
equilibrium, on the face of it. However it should be noted that it does not negate the
existence of equilibria in the limits, yet it is plausible that the given level of equilibria
identified at any point in time may be some nonlinear function of the length of the t to
t+1 interval.
0.2
.4.6
.81
2004
Ass
et In
dex (P
over
ty L
ine
Units)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.053) Figure 3.2. Nonparametric estimates of predicted asset index: All individuals, 1997-2004
Poverty Trap
Micawber Threshold
Expected Asset Dynamics Asset Indext+1= Asset Indext
0.48 0.61 0.72
74
0.2
.4.6
.81
2004
Ass
et In
dex
(Pov
erty
Lin
e Uni
ts)
0 .2 .4 .6 .81998 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.047) Figure 3.3. Nonparametric estimates of predicted asset index: All individuals, 1998-2004
Given variations in social visibility, these single (dominant) trajectories per
given period may distort the equilibrium levels for respective groups of individuals.
We disaggregate the data and estimate the trajectories for socially visible versus
socially invisible individuals. We first use the social visibility cut-off identified by
Vanderpuye-Orgle and Barrett (2007). We identify individuals as being socially
invisible if social visibility is equal to zero and socially visible if social visibility is
greater than zero. Figure 3.4 shows the nonparametric estimates of the predicted
livelihood-weighted asset index for the socially visible for 1997-1998. The graph is
identical to that of the aggregated data since they are the dominant subgroup.
However, the lower poverty equilibrium is slightly less at 0.46 PLUs. In addition, the
lower confidence band crosses the 45-degree line at the upper asset equilibrium. On
the other hand, the socially invisible have a much higher critical asset threshold of
0.81 PLUs as well as a higher poverty trap equilibrium of 0.51 PLUs (Figure 3.5).
Perhaps the most notable difference is the absence of the upper asset equilibrium for
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold Poverty Trap
0.36 0.51 0.62
75
0.2
.4.6
.81
1998
Ass
et In
dex
(Pov
erty
Lin
e Uni
ts)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.063) Figure 3.4. Nonparametric estimates of predicted asset index: Social Visibility>0,
1997-1998
0.2
.4.6
.81
1998
Ass
et In
dex (P
over
ty L
ine
Uni
ts)
0 .2 .4 .6 .81997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.054) Figure 3.5. Nonparametric estimates of predicted asset index: Social Visibility=0,
1997-1998
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold Poverty Trap
0.46 0.73 0.86
Expected Asset Dynamics Asset Indext+1= Asset Indext
Poverty Trap
Micawber Threshold
0.51 0.81
76
the socially invisible. In essence, whereas the assets of the socially visible exhibit
multiple stable dynamic equilibria, the socially invisible converge towards a unique
dynamic stable equilibrium at the lower asset level, with the latter being slightly
higher for the socially invisible. The results for 1997-2004 are similar. Figures 3.6 and
3.7 indicate that the socially invisible have a unique stable equilibrium whilst the
socially visible have multiple equilibria. In addition, both the Micawber threshold and
lower asset poverty traps occur at a slightly higher asset levels for the socially
invisible as compared to the socially visible. Figures 3.8 and 3.9 show similar results
for 1998-2004.
0.2
.4.6
.81
2004
Ass
et In
dex
(Pov
erty
Lin
e U
nits
)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.043) Figure 3.6. Nonparametric estimates of predicted asset index: Social Visibility>0,
1997-2004
Poverty Trap
Micawber Threshold
0.50 0.61 073
Expected Asset Dynamics Asset Indext+1= Asset Indext
77
0.2
.4.6
.820
04 A
sset
Inde
x (P
over
ty L
ine
Uni
ts)
0 .2 .4 .6 .81997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.060) Figure 3.7. Nonparametric estimates of predicted asset index: Social Visibility=0,
1997-2004
0.2
.4.6
.81
2004
Ass
et In
dex
(Pov
erty
Lin
e U
nits
)
0 .2 .4 .6 .8 11998 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.039) Figure 3.8. Nonparametric estimates of predicted asset index: Social Visibility>0,
1998-2004
Poverty Trap Micawber Threshold
0.51 0.68
Expected Asset Dynamics Asset Indext+1= Asset Indext
Poverty Trap
Micawber Threshold
0.38 0.50 0.62
Expected Asset Dynamics Asset Indext+1= Asset Indext
78
0.2
.4.6
.820
04 A
sset
Inde
x (P
over
ty L
ine
Uni
ts)
0 .2 .4 .6 .81998 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.064) Figure 3.9. Nonparametric estimates of predicted asset index: Social Visibility=0,
1998-2004
These results are consistent with our hypothesis -- asset dynamics vary by
social visibility. However the actual levels of the estimated dynamic equilibria do not
seem to differ much in terms of magnitude, particularly at the lower stable
equilibrium. In essence the estimated proportions of people caught in the poverty trap
do not differ much between the subpopulations. On other hand, the unstable
equilibrium is consistently higher for the socially invisible. This indicates a broader
asset domain over which people in this subpopulation would collapse toward the lower
level asset equilibrium, i.e. the poverty trap.
Next, we use regression trees to identify the optimal sample split based on
homogenous subsets, eliminating the exogenous determination of the threshold
variable and cut-off levels. This is nonparametric procedure generates sample splits to
Expected Asset Dynamics Asset Indext+1= Asset Indext
0.39 0.55
79
maximize the fit of piecewise linear regression functions.35 We use the Generalized,
Unbiased Interaction Detection and Estimation (GUIDE) algorithm described in Loh
(2002).
The asset index regression tree is presented in Figure 3.10. The numbered
circles represent terminal nodes that contain different subsamples. The empty circles
show the splitting criteria with the threshold variables and values. At each node, an
observation goes the left if the value is smaller than the threshold level, otherwise it
goes to the right. The first sample splitting variable is social visibility with a threshold
level of 0.7929. Within the subsample of those with social visibility above the 0.7929,
another split occurs on the basis of education. Within the subsample of those with
social visibility below this level, a further split occurs at 0.0955. These splits are
consistent with the existence of multiple dynamic equilibria for the socially visible. It
must be noted that the threshold level of 0.0955 is close to the level identified in
Vanderpuye-Orgle and Barrett (2007) – there are only two individuals with social
visibility between zero and 0.0955. The lack of further splits for the subsample with
social visibility less than or equal to 0.0955 is also consistent with the existence of a
unique dynamic stable equilibrium for the socially invisible.
visib≤ 0.7929
visib≤ 0.0955
4
0.1282
5
1.15
noschool= 0
6
5.45
7
5.66
GUIDE piecewise multiple linear least-squares model. At each intermediate node, acase goes to the left child node if and only if the condition is satisfied. Number initalics beneath a leaf is the sample mean of livelihood.
Figure 3.10. Asset index regression tree
35 See Hardle (1990) for a brief description of regression trees.
80
Table 3.3 presents the asset index regression models associated with the
respective terminal nodes as well as the number of observations in each subsample.
The first sample split at the 0.7929 identifies 995 individuals as socially invisible and
252 individuals as socially visible. We replicate the poverty dynamics analysis using
this threshold. Figure 3.11 shows the social visible experience multiple dynamic stable
equilibria for 1997-1998. In addition, the socially invisible also experience multiple
dynamic stable equilibria (Figure 3.12). This is to be expected since this definition of
the socially invisible lumps more observations towards the bottom of the continuum
and includes fairly well connected individuals.36 Nonetheless, the equilibria for the
socially invisible occur at higher asset levels.
Table 3.3: Asset index regression tree estimation
36 It should be noted that the statistical significance of the splits identified by the regression trees is uncertain.
Variable Coefficient t-
statistic Coefficient t-
statistic Coefficient t-
statistic Coefficient t-
statistic
Terminal Node Visibility≤0.0955 Visibility>0.0955
Visibility>0.7929, No school=0
Visibility>0.7929, No school=1
Social Visibility 12.69***
26.76 0.26*** 4.76 0.34* 1.59 1.00 1.13
Age 0.01** 2.28 0.01 0.62 0.01** 2.28 0.01** 2.05 Value of land inheritance 0.01*** 3.30 0.01** -2.53 0.01 0.80 0.01 0.56 Value of non-land wealth -0.02** -1.96 -0.01 -0.87 0.03** 2.67 0.55*** 13.92 Farmer -0.03** -2.29 0.05** 2.27 0.18*** 3.22 0.55** 2.60 Male 0.01 0.63 0.01 0.49 -0.05 -1.23 0.26* 1.65 Year 1998 0.01 1.42 0.02*** 5.23 0.01 1.20 0.02 0.14 Year 2004 0.01* 1.90 0.06*** 16.08 0.02*** 3.40 0.01 0.21 No school -0.09*** -7.78 -0.07*** -2.81 -- -- -- -- n = 224 n = 771 n = 160 n = 92
R2 = 0.811 R2 = 0.299 R2 = 0.257 R2 =0.731
81
0.2
.4.6
.81
1998
Ass
et In
dex (P
over
ty L
ine
Units)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.064) Figure 3.11. Nonparametric estimates of predicted asset index: Social Visibility≥ 0.793, 1997-1998
0.2
.4.6
.81
1998
Ass
et In
dex
(Pov
erty
Lin
e Uni
ts)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.045) Figure 3.12. Nonparametric estimates of predicted asset index: Social Visibility< 0.793, 1997-1998
Poverty Trap
Poverty Trap Micawber Threshold
0.53 0.69 0.86
Expected Asset Dynamics Asset Indext+1= Asset Indext
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold
0.43 0.68 0.84
82
Figures 3.13 and 3.14 yield similar results for 1997-2004. Figures 3.15 and
3.16 also yield similar results for 1998-2004. Hence, using a high social visibility cut-
off which lumps individuals who are fairly well connected (i.e. those who are known
about 80% of the time) to the bottom results in both socially visible and invisible
experiencing multiple dynamic stable equilibria. However, the socially invisible have
consistently higher asset equilibria.
0.2
.4.6
.8
2004
Ass
et In
dex (P
overty Line Units)
0 .2 .4 .6 .81997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.053) Figure 3.13. Nonparametric estimates of predicted asset index: Social Visibility≥ 0.793, 1997-2004
0.2
.4.6
.81
2004
Ass
et In
dex (P
over
ty Line Units)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.041) Figure 3.14. Nonparametric estimates of predicted asset index: Social Visibility< 0.793, 1997-2004
Micawber Threshold
Poverty Trap
0.48 0.59 0.68
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold
Poverty Trap
0.50 0.61 0.70
Expected Asset Dynamics Asset Indext+1= Asset Indext
83
0.2
.4.6
.820
04 A
sset
Inde
x (P
over
ty L
ine
Uni
ts)
0 .2 .4 .6 .81998 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.053) Figure 3.15. Nonparametric estimates of predicted asset index: Social Visibility≥ 0.793, 1998-2004
0.2
.4.6
.81
2004
Ass
et In
dex
(Pov
erty
Lin
e U
nits
)
0 .2 .4 .6 .8 11998 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.040) Figure 3.16. Nonparametric estimates of predicted asset index: Social Visibility< 0.793, 1998-2004
Micawber Threshold
Poverty Trap
0.38 0.47 0.61
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold Poverty Trap
0.36 0.51 0.62
Expected Asset Dynamics Asset Indext+1= Asset Indext
84
Finally, we split the subsample with social visibility below 0.7929 at the
0.0955 threshold to check the robustness of our original results. Figures 3.17 and 3.18
show those with social visibility below 0.0955 have a unique stable equilibrium whilst
those with socially visibility between 0.0955-0.7929 have multiple dynamic equilibria
for 1997-1998. The results for 1997-2004 and 1998-2004 are similar.37 Thus the initial
results are consistent with the regression tree analysis.
0.2
.4.6
.81
1998
Ass
et In
dex (P
over
ty Line Units)
0 .2 .4 .6 .81997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.063) Figure 3.17. Nonparametric estimates of predicted asset index: Social Visibility< 0.0955, 1997-1998
0.2
.4.6
.81
2004
Ass
et In
dex (P
over
ty Line Units)
0 .2 .4 .6 .8 11997 Asset Index (Poverty Line Units)
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.065) Figure 3.18. Nonparametric estimates of predicted asset index: 0.0955>Social Visibility≥0.793, 1997-1998 37 The graphs are available upon request from authors.
Poverty Trap
0.54
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold
0.73
Poverty Trap
Expected Asset Dynamics Asset Indext+1= Asset Indext
Micawber Threshold
0.72 0.49 0.85
85
3.5 Who is Likely to Remain Poor?
The previous section establishes the existence of multiple dynamic equilibria
and poverty traps. It is evident that the asset thresholds vary by social visibility. Even
within these two subpopulations of socially visible and invisible individuals, some
individuals may be more likely to fall into poverty traps. We now turn to identifying
the characteristics of those who are likely to fall into poverty traps and remain the
structurally poor. It should be noted that our focus here is not on establishing causality
rather we seek to provide benchmarks for targeting purposes.
Based on the nonparametric kernel regression estimates in the previous section
we identify individuals who are at/ below the lower poverty threshold – i.e. those
caught in the poverty trap. We generated a categorical variable, poverty trap using the
respective results for the socially visible and invisible in the 1997-1998, 1997-2004
and 1998-2004 periods.
Let Γit=1 be an indicator variable that equals one if individual i is caught in the
poverty trap equilibrium at time t. Let Pr{ itl =1} be the probability that Γit =1
conditional on some individual characteristics, itX . We then estimate
Pr{ Γit =1}= )0( >+Λ ititX εβ (3)
by probit regression, where Λ is the normal CDF. We estimate a random effects
model, with observations clustered on the respondent’s identity.
The parameter estimates presented in Table 3.4 presents the summary statistics
of the variables used in the analysis. Table 3.5 presents the probit estimation of the
likelihood of falling into a poverty trap using the social visibility cut-off identified by
Vanderpuye-Orgle and Barrett (2007). We’ll focus on the results that are robust across
all three time intervals. Conditional on being socially visible, males engaged in non-
farm occupations who have resided in the village for more than one generation are
86
Table 3.4: Descriptive statistics: Estimating the likelihood of Poverty Traps Frequency (%) Variable Definition
1997-1998
1997-2004
1998-2004
Poverty Trap (visible) =1 if the livelihood-weighted index is at or below the Poverty trap threshold
for socially visible individuals, 0 otherwise
37.5 39.7 37.8
Poverty Trap (invisible) =1 if the livelihood-weighted index is at or below the Poverty trap threshold for socially invisible individuals, 0 otherwise
40.7 40.1 38.9
Poverty Trapregression tree(visible) =1 if the livelihood-weighted index is at or below the Poverty trap threshold for socially visible individuals using regression tree sample split, 0 otherwise
33.1 38.68 37.6
Poverty Trap regression tree(invisible) =1 if the livelihood-weighted index is at or below the Poverty trap threshold or socially invisible individuals using regression tree sample split, 0 otherwise
42.1 39.68 33.64
Male =1 if male, 0 otherwise 73.2 47.4 47.2 Age Respondent’s age 40.8
(14.3) 43.0
(13.2) 42.9
(11.5) Occupation Farmer Other Unemployed
=1 if farmer, 0 otherwise =1 if trader, artisan, teacher, civil servant, office or health worker, agricultural or non-agricultural labor, 0 otherwise =1 if student/ pupil, unemployed or not in the labor force
71.1 17.2
11.6
69.5 19.3
11.2
77.5 14.2
10.3 Major clan =1 if member of a major clan, 0
otherwise 68.1 79.4 79.8
Herelong =1 if not the first generation to reside in village, 0 otherwise
82.9 74.2 72.6
Fostered =1 if respondent was fostered, 0 otherwise
67.4 64.7 62.2
Shocks Farm problems Health shock Sudden death Theft of personal item
=1 if experienced problems on farm, 0 otherwise =1 if experienced a health shock, 0 otherwise =1 if experienced sudden death in the family, 0 otherwise =1 if experienced theft of personal item, 0 otherwise
72.7
52.3 76.5
22.1
69.57
48.17 56.10
15.24
62.3
60.8 65.6
28.1
Location Village 1 Village 2 Village 3 Village 4
=1 if Village cluster 1, 0 otherwise =1 if Village cluster 2, 0 otherwise =1 if Village cluster 3, 0 otherwise =1 if Village cluster 4, 0 otherwise
25.9 21.8 28.2 24.1
26.7 23.4 24.8 25.2
26.7 23.4 24.8 25.2
Notes: The standard deviations of continuous variables are given in parentheses.
Table 3.5: Poverty trap probit estimation, social visibility=0 and social visibility>0
Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively.
Variables 1997-1998 1997-2004 1998-2004 Visible Invisible Visible Invisible Visible Invisible
Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z|
Dependent Variable: Poverty Trap Individual Characteristics
Male -0.267* 0.077 0.519 0.435 -0.270** 0.031 1.845*** 0.009 -0.226* 0.077 1.146** 0.032
Age 0.000 0.944 -0.015 0.511 -0.016*** 0.000 -0.020 0.428 -0.012** 0.011 -0.007 0.704 Non-farm occupation -0.543** 0.012 -1.667** 0.037 -0.521*** 0.001 -0.413 0.598 -0.454* 0.009 -1.591** 0.037 Unemployed -0.139 0.779 -0.209 0.834 -0.237 0.516 -0.246 0.860 -0.379 0.359 3.333** 0.037 Major clan -0.134 0.370 -1.259** 0.050 -0.770*** 0.000 -1.640** 0.013 -0.533*** 0.000 -1.449** 0.022 Herelong -0.408** 0.024 -0.243 0.619 -0.509*** 0.001 0.746 0.244 -0.461*** 0.002 0.323 0.535 Fostered 0.185 0.234 2.229*** 0.001 0.383*** 0.003 1.795** 0.020 0.031 0.813 0.685 0.190 Shocks
Farm problems 0.534 0.385 0.448*** 0.002 0.891 0.561
3.000*** 0.003 0.095 0.497
2.134*** 0.001
Health shock -0.088 0.516 -0.682 0.232 -0.097 0.409 -0.830 0.227 -0.093 0.442 -0.011 0.985
Sudden death 0.196 0.250 -0.494 0.496 -0.188 0.238 1.932*** 0.037 0.069 0.691 0.452 0.472
Theft of personal item 0.125 0.450 0.934*** 0.067 0.070 0.620 1.415* 0.069 0.139 0.346 1.208* 0.060 Location Village 2 -0.495** 0.024 -0.171 0.776 -0.278 0.102 -0.729 0.395 -0.483*** 0.010 -0.711 0.273 Village 3 0.378** 0.036 -1.773 0.115 0.402 0.015 -2.379** 0.028 0.209 0.202 -0.694 0.357 Village 4 0.205 0.324 0.760 0.201 0.249 0.135 1.600 0.150 0.504*** 0.003 0.909 0.151 n = 644 n = 156 n = 612 n = 103 n = 594 n = 133 Log likelihood = -231.85 Log likelihood = -25.64 Log likelihood = -331.74 Log likelihood = -36.67 Log likelihood = 309.22 Log likelihood = -32.72 Wald χ2(14) = 213.08 Wald χ2(14) = 32.67 Wald χ2(14) = 144.12 Wald χ2(14) = 32.21 Wald χ2(14) = 68.16 Wald χ2(14) = 37.78 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000
87
88
consistently less likely to fall into poverty traps, across the 1997-1998, 1997-2004 and
1998-2004 periods. Contemporaneous shocks do not significantly affect asset
dynamics for the socially visible.
Conditional on being socially invisible, individuals who belong to a major clan
are less likely to fall into a poverty trap. Conversely, socially invisible individuals who
experience farm shocks or thefts are more likely to fall into a trap. These results are
robust across the 1997-1998, 1997-2004 and 1998-2004 periods. We replicated the
analysis using the first sample split from the regression tree analysis. Table 3.6 shows
the results are generally mixed for the respective periods. Focusing on the temporally
robust results indicate that being male was associated with a decreased likelihood of
falling into poverty traps, conditional on being socially visible whereas socially
invisible individuals who experience farm shocks are more likely to fall into poverty
traps.
3.6. Conclusions
Poverty alleviation has been central to policy debates for decades. Some
milestones have been reached vis-à-vis poverty favorable indices; however a lot
remains to be done, particularly with regards improving the welfare of those who may
be persistently poor. The asset-based approach allows analysts to differentiate between
stochastic and structural poverty transitions.
This paper complements the extant literature in exploring the existence of
multiple dynamic asset equilibria and poverty traps. We contribute to this body of
work by examining the role of social visibility in conditioning asset dynamics. Our
results indicate that asset trajectories exhibit multiple dynamic stable equilibria for
village-level aggregations of individuals. The general shape of the graph vis-à-vis the
Table 3.6: Poverty Trap regression tree probit estimation, social visibility≤0.7929 and social visibility>0.7929
Variables 1997-1998 1997-2004 1998-2004 Visible Invisible Visible Invisible Visible Invisible
Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z| Marginal Effects
Prob>|z|
Dependent Variable: Poverty Trap regression tree Individual Characteristics Male -0.852*** 0.007 0.022 0.889 -1.380*** 0.000 0.007 0.964 -1.224*** 0.000 0.073 0.670 Age 0.008 0.538 0.001 0.919 -0.011 0.378 -0.021*** 0.000 -0.006 0.623 -0.006 0.279 Non-farm occupation -1.582** 0.047 -0.592*** 0.005 -0.434 0.342 -0.315** 0.066 -0.554 0.352 -0.395* 0.071 Unemployed 0.219 0.536 -0.159 0.712 2.430*** 0.001 0.473 0.211 1.622** 0.025 0.609 0.177 Major clan 0.553 0.343 -0.101 0.528 -0.808* 0.062 -0.822*** 0.000 -0.963* 0.075 -0.598*** 0.001 Herelong -0.095 0.850 -0.054 0.729 -0.879* 0.089 0.163 0.269 -0.271 0.533 -0.037 0.835 Fostered 0.088 0.827 0.602*** 0.000 0.276 0.397 0.358** 0.015 0.232 0.503 0.434** 0.013 Shocks
Farm problems 0.102 0.075 0.662*** 0.001 0.722 0.821 1.111*** 0.005 0.049 0.879 1.892*** 0.001
Health shock -0.070 0.822 -0.263** 0.082 -0.520 0.121 -0.679 0.732 -0.457 0.178 -0.404** 0.013 Sudden death 0.092 0.785 -0.017 0.935 -0.008 0.979 0.690*** 0.000 0.030 0.933 0.630 0.734 Theft of personal item 0.269 0.510 0.030 0.861 0.276 0.464 -0.315** 0.049 0.564 0.155 -0.331** 0.097 Location Village 2 1.169 0.303 0.164 0.413 0.533 0.361 -0.334* 0.069 0.560 0.456 -0.857*** 0.001 Village 3 0.068 0.952 0.346* 0.098 1.665** 0.006 0.024 0.898 1.328* 0.080 -0.068 0.759 Village 4 0.431 0.696 0.187 0.377 0.231 0.706 0.368* 0.050 0.899 0.236 0.298 0.157 n = 167 n = 633 n = 180 n = 535 n = 165 n =562 Log likelihood = -51.23 Log likelihood = -200.76 Log likelihood = -56.64 Log likelihood = -249.81 Log likelihood = -48.94 Log likelihood = -172.48 Wald χ2(14) = 42.99 Wald χ2(14) = 106.92 Wald χ2(14) = 50.37 Wald χ2(14) = 150.81 Wald χ2(14) = 38.87 Wald χ2(14) = 161.80 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000 p-value = 0.000
89
90
presence of multiple equilibria was robust across respective time intervals. However,
the actual asset levels identified as equilibria varied non-linearly with the length of the
time interval.
Unpacking the results by social visibility indicates that whereas the assets of
the socially visible exhibit multiple stable dynamic equilibria, the socially invisible
converge towards a unique dynamic stable equilibrium at the lower asset level. In
addition, the asset levels at the respective equilibria were generally higher for the
socially invisible as compared to the socially visible, particularly for the unstable
dynamic equilibrium. The latter implies that the socially invisible are more likely to
fall into poverty traps since there is a broader asset domain over which people in this
subpopulation would collapse toward the lower level asset equilibrium. However the
estimated proportions of people caught in the poverty trap do not differ much between
the subpopulations. These results are supported by a nonparametric regression tree
analyses.
We also undertake a multivariate regression analyses to identify the
characteristics of the socially visible and socially invisible individuals who are likely
to fall into poverty traps. Focusing on results that are robust across the three time
intervals, males engaged in non-farm occupations who have resided in the village for
more than one generation are consistently less likely to fall into poverty traps,
conditional on being socially visible. On the other hand, socially invisible individuals
who belong to a major clan are less likely to fall into a poverty trap. On the other
hand, socially invisible individuals who suffer a farm shock or theft are more likely to
fall into a poverty trap.
These results underscore the need for policymakers to focus on structural
determinants of poverty transitions, or the lack thereof, in order to successfully
alleviate poverty in the long run. Given limited resources there is a need to tailor
91
social interventions to individual characteristics, in this case social visibility.
Specifically there is a need to target younger, female residents who farm, do not
belong to a major clan and have resided in the village for only one generation with
interventions to hoist them out of structural poverty.
92
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Carter, M. and May, J. (2001). One Kind of Freedom: Poverty Dynamics in Post-
Apartheid South Africa. World Development, 29, 1987-2006. Conley, T. G. and Udry, C. R. (2005). Learning about a New Technology: Pineapple
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Indeed? Inclusion and Exclusion in Mutual Insurance Networks in Southern
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Udry, C. and Conley, T. (2005). Social Networks in Ghana. In C. Barrett (Ed.), The
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96
CHAPTER FOUR
Risk, Intrahousehold Health Inequality and Social Visibility in Ghana
4.1 Introduction
If the goal of development policy is to improve the well-being of individuals
then potential intrahousehold disparities in the welfare of individuals should be taken
into account when targeting households for interventions. To this extent, policy
discussions are rife with debates on the implications of different patterns of
intrahousehold resource allocation and intrahousehold inequality as well as questions
regarding whether or not the household is the appropriate unit of empirical analyses
and policy design. Theoretical models have been used in the extant literature to
explain intrahousehold inequality. In addition there is some empirical evidence, albeit
scant, indicating that there may be systemic differences in welfare levels within the
household. However, it is not certain how these differences vary with overall
household well-being.38 To what extent do intrahousehold inequalities manifest in the
data? Are there intrahousehold Kuznets curves? For whom do they exist?
Risk exposure may explain some of the proposed patterns in the distribution of
welfare within the household. For instance, the adverse effects of shocks on health
outcomes are borne disproportionately by women and children within the household
(Dercon and Krishnan 2000, Hoddinott and Kinsey 2000, Dercon and Hoddinott
2003). Some households respond to shocks by reducing in nutritional intake and
neglecting the health needs of children, especially those of girls (Alderman and
Paxson 1992, Ellis 1998). The effects of risk may be exacerbated by the fact that
formal insurance and credit markets are essentially missing in most parts of
38 See Haddad and Kanbur (1990), Kanbur and Haddad (1992), Haddad, Kanbur and Bouis (1995) and Sahn and Younger (2007) for complete reviews.
97
developing nations. The rural poor often resort to social networks for coping with risk
but access to these networks is disparate (Goldstein 1999, Santos and Barrett 2004,
Udry and Conley 2004, De Weerdt 2005, Goldstein et al 2005). Social connectedness
as proxied by social visibility has been found to be a key factor in determining access
to social insurance (Vanderpuye-Orgle and Barrett 2007). Nonetheless it is not certain
if access to social insurance would condition intrahousehold inequality. In the event
that intrahousehold inequalities exist, how would social visibility affect the
distribution of well-being within the household and how would the latter vary with
overall household well-being?
In addressing these issues, we draw on several strands in the literature. The
first focuses on the extensions of inequality, intrahousehold resource allocation and
intrahousehold inequality. Kuznets (1955) hypothesized that the relationship between
inequality and average well-being is characterized by an inverted U-shaped curve;
with inequality increasing as welfare increases and then declining at some higher
level. This inverse U-shaped curve is often referred to as the Kuznets curve. There is
ample empirical evidence to support this hypothesis using time series and cross-
sectional data at the national level.39 Analogous theoretical relationships have been put
forward using models of intrahousehold allocation. Using the unitary household
model, intrahousehold inequality could reflect an efficient arrangement designed to
improve aggregate welfare. It may reflect different distributions of energy-intensive
activities such that households maximizing a common objective function allocate
marginal calories to men in order to enhance productivity (Pitt, Rosenzweig and
Hassan 1990). Using cooperative and noncooperative bargaining models, Kanbur and
Haddad (1990) showed that bargaining models predict Kuznets’ inverse-U relationship
as a result of interactions between the effects of increases in the total resources being
39 See Anand and Kanbur for a complete review.
98
bargained over and changes in bargaining positions. However the empirical evidence
supporting the intrahousehold Kuznets curve has been mixed.
Variations in these results may be attributed in part to challenges in identifying
a suitable econometric procedure and welfare metric. Haddad and Kanbur (1990) find
tentative empirical support for an inverse-U relationship using caloric intake data
standardized by age, sex and physiological need requirements however they did not
fully account for gender and age differentiation in energy intensity and activities.
Kanbur and Haddad (1992) using activity-pattern information to construct energy
expenditures for each household member concluded that the continuity requirements
of the spline model may be driving the exhibited continuous inverse-U shape. Haddad,
Kanbur and Bouis (1995) improving both variable construction and variable choice by
adjusting for energy expenditures and using total household expenditures per capita as
a measure of overall household welfare found no evidence of the inverse-U shape.
Similarly, Sahn and Younger (2007) using the body mass index (BMI) as a summary
measure of caloric consumption net of needs found no evidence of the intrahousehold
Kuznets curve.
These studies generally estimate a single (dominant) curve which aggregates
all households within a given sample (or country). One could argue that there might be
an endogenous threshold within a given sample, above which households may exhibit
a Kuznets curve and below which no evidence would be found. For instance upon
disaggregating the data, Sahn and Younger (2007) note that there is some indication
that these curves might exist for better off countries – there was a hint of down-turn in
inequality at very high levels of mean BMI.40 It may be that these curves exist only for
a subset of the population. The key issue may be to empirically identify these subsets.
40 The small number of observations and the large standard errors at the tails did not allow for the validation of this hypothesis.
99
In this paper, we explore the effect of differences in risk exposure and the attending
compensating behavior.
This leads to the second strand literature on the effects of shocks directly on
health outcomes and indirectly through the budget constraint and consumption or
nutrient intake. Principal results that emerge indicate that (i) uninsured shocks
adversely affect household health outcomes and (ii) these effects are not equitably
distributed within the household (Behrman and Deolalikar 1990, Alderman and
Paxson 1992, Ellis 1998, Gertler and Gruber 2002, Dercon and Krishnan 2000,
Hoddinott and Kinsey 2000, Dercon and Hoddinott 2003). For instance, some families
use the body as ‘a store of wealth’– feasting when prices are low in anticipation of a
shock and fasting when prices are high (Dercon and Hoddinott 2003). The effects of
shocks have been associated with significant fluctuations in body mass with
individuals within poorer household, especially women, being unable to smooth their
consumption over time and within the household (Dercon and Krishnan 2000,
Hoddinott and Kinsey 2000, Dercon and Hoddinott 2003). Access to social insurance
may moderate the effect of shocks on the distribution of health, particularly for the
liquidity constrained with no little or no private insurance.
The third strand of literature is the extension of risk management and social
insurance. Social networks have been identified as a locus of insurance in lieu of
formal financial and insurance markets. These networks are fostered by kinship ties,
ethnicity, geographical proximity, religion, and gender groups, inter alia (Goldstein
1999, Santos and Barrett 2004, Udry and Conley 2004, De Weerdt 2005). However,
some marginal groups are excluded from these networks (Dercon 2002, De Weerdt
2005, Santos and Barrett, 2006). Social connectedness has been found to be a crucial
element to social insurance. Conceptualizing exclusion on the basis of social visibility,
Vanderpuye-Orgle and Barrett (2007) showed that wealthier, older residents who
100
belong to a major clan, have resided in the village for more than one generation and
are not engaged in farming are more likely to be socially visible. In addition, the
socially visible attained full risk pooling both at the social network and village levels
whereas the socially invisible were left uninsured. In essence, social visibility is an
indicator of the extent to which an individual may be insured from risk through social
networks and this may affect the nature of intrahousehold inequality.
This paper complements existing literature that use non-income measures to
examine intrahousehold inequality. It builds on the extant analyses by assessing
whether there is a threshold effect in the existence of Kuznets curves at the household
level. The goal of the paper is twofold: (i) to assess whether Kuznets curves exist and
for whom they exist (ii) to assess the determinants of intrahousehold inequality. We
hypothesize that access to insurance as proxied by social visibility will condition the
effect of shocks on the distribution of health outcomes and the variation of the latter
with overall household well-being.
The remainder of the paper is organized as follows. The next section presents
the analytical framework. Section 4.3 describes the data used in this paper. Section 4.4
presents the specification of the empirical model and results and Section 4.5 concludes
with a summary of the results and policy implications.
4.2 Analytical Framework
4.2.1 Bivariate Analysis of Intrahousehold Inequality
First, we identify an appropriate measure of inequality. We follow Haddad,
Kanbur and Bouis (1995) and Sahn and Younger (2007) in adapting the Generalized
Entropy (GE) class of measures to estimate the level of intrahousehold health
inequality. Where GE is given as follows:
101
yi is the value of the welfare index for individual i, μ is the mean value for the
household, f (yi) represents the population share of the individual i in the household, in
this case the inverse of the household size, and K is the number of households.
We use the mean logarithm deviation (i.e. GE with c=0). This is particularly
sensitive to changes at the bottom of the distribution. It must be noted that the GE
class of entropy measures is additively decomposable into within-group and between-
group components.
For K exogenously given households the GE can be decomposed as following:
The first term on the right-hand-side of the above equation is the within-group
inequality and second term is the between-group inequality.
Where
( )GE w I I e egg
K
g k k= +=∑
11 1μ μ,...,
w
f c
f c
f c
g
gg
c
gg
g
=
⎧
⎨
⎪⎪⎪⎪⎪⎪⎪⎪
⎩
⎪⎪⎪⎪⎪⎪⎪⎪
⎛
⎝⎜
⎞
⎠⎟ ≠
⎛
⎝⎜
⎞
⎠⎟ =
=
μμ
μμ
0 1
1
0
,
( )
( )
( )
GE
f yy
c
f yy y
c
f yy
c
ii
c
i
K
ii i
i
K
ii
i
K
=
⎛⎝⎜
⎞⎠⎟ −
⎡
⎣⎢⎢
⎤
⎦⎥⎥
≠
⎛⎝⎜
⎞⎠⎟
⎛⎝⎜
⎞⎠⎟ =
⎛⎝⎜
⎞⎠⎟ =
⎧
⎨
⎪⎪⎪⎪
⎩
⎪⎪⎪⎪
=
=
=
∑
∑
∑
μ
μ μ
μ
1 0 1
1
0
1
1
1
,
log
log
102
Ig is the inequality in the gth household, μg is the mean of the gth household, eg is a
vector of 1's of the length ng, and ng is the population of the gth household.
Next, we pick the Body Mass Index (BMI), Quetelet Index, as the health
outcome to be analyzed. The BMI is measured as the individual’s weight in kilograms
divided by squared height in meters and is a reliable measure of nutrient intake and
health which also captures chronic energy deficiency (James, Ferro-Luzzi and
Waterlow 1988). Although the BMI captures only consumption relating to food and
health, it adequately reflects intrahousehold allocation of resources relative to need. It
is also easy to measure fairly accurately at the individual level as compared to
consumption and income.41 We standardize the BMI for age and gender using the Epi
info software made available by the Center for Disease Control. Using a 20-year old
female as the reference group we generate the following:
BMI=F-1a*,g* (Fa,g(bmi)) (4)
where BMI is the standardized BMI, F is the distribution function of BMI in the WHO
reference population for age a and gender g, bmi is the individual’s body mass index,
a* is 20years and g* is female. We use this as a basis for estimating the Kuznets
curve.
4.2.2 Determinants of Intrahousehold Health Inequality
Our analytical approach focuses on the determinants of health outcomes. We
draw from the theory on health demand functions using a model akin to that of
Thomas, Lavy and Strauss (1996) and Foster (1995).
Suppose households maximize intertemporally additive expected utility:
Ui=v(Ui1,Ui2,...,UiT) for t=1,…, T (5)
41 See Sahn and Younger (2007) for a comparison of the BMI to other welfare metrics.
103
Where utility at time t which is a concave function dependent on the
household’s consumption of goods (cit), health outcome (hit) and some preference
shifters based on household characteristics such as life cycle position, education,
occupation, gender (Xit) and is given as follows:
Uit = uit (cit, hit; Xit) (6)
Suppose households maximize expected utility subject to constraints by which
inputs such as nutrients generate health outcomes via a health production function Hit.
Where health in period t is a function of previous health outcome (Hit-1), physical
inputs such as nutrients (git), energy expenditure (Lit), environmental factors such as
sanitation (Eit) which may have a direct impact on health outcomes conditional on
inputs, unobserved household characteristics such as inherent health or immunities (εi)
as well as observed household characteristics such as life cycle position, household
size and dependency ratio (Xit) The latter would also include social visibility which
would affect health outcomes by mitigating the need for either ex ante responses to
shocks such as using the body as a store of wealth or ex poste responses such as
reducing nutritional intake of some household members. The health production
function is thus given as follows:
Hit=h(Hit-1, git, Lit, Xit, Eit, εi) (7)
In addition, the household’s budget constraint at time t is given as follows:
Wit+1=Wit (1+rt) + yit(Hit-1, wit, Lit ,sit) - ptcit (8)
Where Wit and Wit+1 is wealth in period t and t+1, respectively, yit is the income
level in period t, wit and sit are levels of income-generating assets and transitory
income shocks faced in period t, ptcit is the expenditure on consumption bundle ci at
prices pt and rt is the interest rate. The constrained maximization problem yields a set
of necessary conditions which when solved lead to reduced form goods and adult
health demand functions. The latter is given as follows:
104
Hit = ft (Hit-1, Wit, Xit, Eit, εi) (9)
Thus health status in period t is function of previous period’s health outcome
(Hit-1), wealth (Wit), a vector of household characteristics including social visibility
(Xit), environmental factors (Eit) and unobserved household characteristics (εi). The
main implication of this framework is that social visibility should have a statistically
significant effect in determining health outcomes.
4.3 The Data
The data used in this paper are from a rural household survey undertaken from
July 2004 to January 2005. This was the third wave of a panel data set initiated by
Christopher Udry and Markus Goldstein. The research was conducted in the Akwapim
South District (specifically the Nsawam - Aburi area) in the Eastern Region of Ghana.
Since the early 1990s farmers in this area have been switching from the cultivation of
maize-cassava intercrop for domestic production to pineapple cultivation for export.
This transition involves a significant amount of risk by way of the attendant new
agronomic practices as well as exposure to global price fluctuations, hence the need
for insurance.42
The original sample was selected using a two-stage procedure. Four village
clusters were purposively selected within this area based on their participation in fruit
and vegetable production as well as their array of agronomic, market access and
geographic conditions. Sixty married couples (or triples) were then randomly selected
in each village cluster, except for the smallest village cluster where all resident
couples were interviewed. Male enumerators were assigned to male respondents and
42 See Goldstein and Udry (1999) for an in-depth discussion of the historical background of the area and the sampling techniques.
105
female enumerators to female respondents to preserve gender sensitivity and cultural
norms.
Three rounds of data were collected at approximately eight week intervals with
372 individuals in the first. 371 and 350 individuals were surveyed in the second and
third rounds, respectively. We use data from the first and third rounds for during
which anthropometric information was collected. The sample attrition rate between the
first and third rounds was 5.9%. A brief description of the modules relevant to the
analysis in this paper is given below.
4.3.1 Anthropometrics
Anthropometric data was collected on all members of the households in rounds
one and three, respectively. We collected height and weights of each individual using
standard procedures uniform across all enumerators in the study. The ideal would have
been to use stadiometers and medical scales however we didn’t have enough of them,
for as long as we needed and they were too heavy to be carried to each household.
Instead we used regular tape measures for measuring heights: the individuals were
made to stand against a wall, we then marked the top of their heads on the wall and
measured the distance from the base of the wall to the mark using the tape. We used
bathroom scales for the weights: we first set the scale on a flat concrete surface, reset
it to zero using no parallax as the standard and had the individuals stand upright on it.
4.3.2 Social Visibility
For each respondent, we randomly selected seven individuals in the sample
from the same village (without replacement). 43 We then asked them a series of
43 Respondents were also non-randomly matched with three other village-specific “focal” individuals identified from the community-studies approach taken in a preliminary field trip as individuals in the villages from whom advice is commonly sought. We focus on the random matches in this study.
106
questions about each match, preceded by their knowledge of the match i.e., “Do you
know__?”. In administering the questionnaire we were sure to make the distinction
between knowing of someone (i.e. using the Akan translation of just “having heard of
the person”) and actually knowing the person. Knowing a random match in this sense
is indicative of an extant social link. This gives us a random sample of the individuals
with which they have social links. The characteristics of these random matches, by
sampling design would be representative of people with whom they have extant social
links i.e., their social networks. Even though we do not have the overall shape or size
of the network, we know about the type of people they establish social links with.
Using this data, Vanderpuye-Orgle and Barrett (2007) estimate a continuum of
social visibility as the ratio of the number of times the respondent is known by others
when presented as a random match to the number of times the respondent’s name was
drawn in the random matching process. There were some differences, albeit highly
correlated, in social visibility within the household. We use the mean value to
represent the household’s level of social visibility.
4.3.3 Shocks
To measure shocks to income we focus on the causes of income shocks as
against the actual changes in income. Respondents were asked about a series of
morbidity, mortality, on-farm problems and theft of personal items. It must be noted, it
is likely that these self-reported shocks are likely to be correlated with wealth. For
instance, the wealthier are more likely to have items that may be stolen. In addition,
morbidity may be endogenous to BMI. In this paper we use data on the following: (i)
whether or not they experience farm problems, (ii) whether or not they made a
contribution towards organizing the funeral of a close relative who died suddenly.
107
These shocks were highly correlated within the household thus we use data on shocks
faced by the household head.
4.3.4 Other Sample Characteristics
This above was supplemented with data from the household roster, assets and
family background questionnaires. We used data from the first and third round of this
wave for which there is corresponding anthropometric information.
4. 4 Empirical Model and Results
4.4.1 Analysis of Intrahousehold Inequality
To assess the existence of the Kuznets curves, we estimate a bivariate
nonparametric relationship between the mean household BMI (i.e. our measure of
household well-being) and the mean log deviation of the standardized BMI using a
Nadaraya-Watson regression with an Epanechnikov kernel overlaid by a quadratic fit
of the regression equation. We selected the optimal bandwidth based on Silverman’s
rule of thumb as determined by the Bounds of Stata package (Beresteanu and Manski
2000)44. We also replicated the analysis using mean per capita household expenditure
for purposes of comparison.
Figure 4.1 shows the graph for all households in the sample. The pattern
indicates that intrahousehold inequality increases as the mean household BMI
increases. This pattern is not consistent with the inverted U-shaped Kuznets curve
however it is consistent withthe results from Sahn and Younger(2007) which indicates
an increasing relationship between intrahousehold inequality and mean household
well-being. On the other hand the graph using the household expenditure percentile
shows some curvature, albeit very slight (Figure 4.2).
44 See Silverman (1986) for detailed discussions.
108
0.0
2.0
4.0
6M
ean
Log
Dev
iatio
n
16 18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5311784864565047) Figure 4.1. Nonparametric estimates of intrahousehold inequality and mean household
BMI, all households
-.4-.2
0.2
.4M
ean
Log
Dev
iatio
n
.4 .45 .5 .55 .6Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0023179840992232) Figure 4.2. Nonparametric estimates of intrahousehold inequality and household
expenditure percentile, all households
109
To determine whether the Kuznets curve exists for some and not for others, we
disaggregated the data into socially visible and socially invisible households. We used
the mean social visibility index of the household instead of the head of the
household’s to allow for the fact that the heads and respective spouses may have
different levels of social connectedness. We used regression trees to identify the
optimal sample split based on homogenous subsets thus eliminating the exogenous
determination of the threshold variable and cut-off levels. This nonparametric
procedure generates sample splits to maximize the fit of piecewise linear regression
functions.45 We used the Generalized, Unbiased Interaction Detection and Estimation
(GUIDE) algorithm described in Loh (2002) to estimate the regression trees.
The regression tree for the mean log deviation is presented in Figure 4.3. The
numbered circles represent terminal nodes that contain different subsamples. The
empty circles show the splitting criteria with the threshold variables and values. At
each node the, an observation goes the left if the value is smaller than the threshold
level, otherwise it goes to the right. The only sample splitting variable is social
visibility with a threshold level of 0.4875.
visib≤ 0.4875
2
2.97E-03
3
0.0315
GUIDE piecewise multiple linear least-squares model. At each intermediate node, acase goes to the left child node if and only if the condition is satisfied. Number initalics beneath a leaf is the sample mean of theilmeasu.
Figure 4.3: Intrahousehold inequality regression tree 45 See Hardle (1990) for a brief description of regression trees.
110
The graph showing the bivariate nonparametric relationship of mean household
BMI against the mean log deviation of the standardized BMI for socially visible
households in Figure 4.4 maps a delicate inverted-U shape, indicative of Kuznets
curve. This is consistent with our hypothesis. Given access to social insurance, the
socially visible manage to reduce disparities in health outcomes as the mean
household BMI, our non-income welfare metric, increases beyond a certain level.
However, this result is not robust to alternative measures of welfare. The pattern of
intrahousehold inequality with increasing per capita expenditure is essentially flat
(Figure 4.5).On the other hand, intrahouseholds inequality increases with mean
household BMI for the socially invisible households (Figure 4.6). Figure 4.7 shows
the pattern of intrahousehold inequality is essentially flat relationship with increasing
per capita expenditure.
.01
.02
.03
.04
.05
Mea
n Lo
g D
evia
tion
16 18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5566200442016269) Figure 4.4. Nonparametric estimates of intrahousehold inequality and mean household
BMI, socially visible households
111
-.1-.0
50
.05
.1M
ean
Log
Dev
iatio
n
.35 .4 .45 .5 .55 .6Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0045625218129007) Figure 4.5. Nonparametric estimates of intrahousehold inequality and household
expenditure percentile, socially visible households
-.02
-.01
0.0
1.0
2.0
3M
ean
Log
Dev
iatio
n
18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.7109861203703327) Figure 4.6. Nonparametric estimates of intrahousehold inequality and mean household
BMI, socially invisible households
112
-.2-.1
0.1
.2
Mea
n Lo
g Dev
iatio
n
.38 .4 .42 .44 .46Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0020687822185208) Figure 4.7. Nonparametric estimates of intrahousehold inequality and household
expenditure percentile, socially invisible households
Pairwise statistical comparisons of intrahousehold inequality at the 10th, 50th
and 90th percentile of mean household BMI and per capita expenditure are consistent
with these results (Table 4.1). The parametric estimates presented in Table 4.2 and
4.3 also provide qualitative support of these results vis-à-vis the signs of the
coefficients however the latter are not statistically significant.
Table 4.1: Comparisons of test points of intrahousehold inequality
Mean BMI Expenditure per capita
10th vs. 50th percentile
50th vs. 90th percentile
10th vs. 50th percentile
50th vs. 90th percentile
All Individuals I I I* D* Visible Individuals I D* I* D* Invisible Individuals I* I* I D
113
Table 4.2: Parametric estimates of intrahousehold inequality by mean household BMI
Notes: ***, **, * statistically significant at the 1%, 5% and 10% levels Coefficients multiplied by 100
Table 4.3: Parametric estimates of intrahousehold inequality by per capita expenditure
Notes: ***, **, * statistically significant at the 1%, 5% and 10% levels Coefficients multiplied by 100
Coefficient
Robust Standard Error Coefficient
Robust Standard Error Coefficient
Robust Standard Error
Dependent Variable: Log Mean Deviation All Individuals Socially Visible Socially Invisible Mean BMI 0.605 0.017 0.566 0.008 0.595 0.013 Mean BMI squared 0.016 0.000 -0.017 0.001 0.014 0.001 Constant -2.915 0.173 -1.127 0.082 -5.832 0.135 n =238 n =196 n =42 R2 = 0.009 R2 = 0.042 R2 = 0.041
Coefficient
Robust Standard Error Coefficient
Robust Standard Error Coefficient
Robust Standard Error
Dependent Variable: Log Mean Deviation All Individuals Socially Visible Socially Invisible Household Expenditure Percentile -0.161 1.806 -14.986 1.326 -31.469 2.247 Household Expenditure Percentile squared 0.036 1.843 -4.312 1.48 26.8388 2.629 Constant 0.088 0.437 8.296 0.302 8.792 0.483 n =238 n =196 n =42 R2 =0.028 R2 =0.072 R2 =0.062
114
The extant literature highlights the differential effect of shocks on women and
children household. To explore the extent to which this manifests in relative BMI
ratios over different ranges of welfare, we generate the ratios of male to child BMI,
female to child BMI and male to female BMI. First we estimate the kernel regression
for all households. The graphs for the male to child and female to child BMI ratios are
striking similar. Figures 4.8 and 4.10 show a convex relationship between the
respective ratios and mean household BMI. The high ratio at low mean household
BMI could be indicative of an allocation based on labor productivity in times of
relative scarcity, the need for this strategy diminishes as welfare increases. The high
ratio at low mean household BMI could reflect cultural tendency for people in the
survey areas to put on some weight to reflect their wealth and status in society, with
adults gaining weight much faster than children. The latter is consistent with the
increasing ratios with per capita expenditures in Figures 4.9 and 4.11. Similarly, the
male to female ratio displays a convex pattern with increasing mean household BMI
(Figure 4.12), although the relationship with expenditure is flat (Figure 4.13).
Disaggregating the sample, the socially visible households have identical patterns of
male to child BMI, female to child BMI and male to female BMI as the entire sample
(Figures 4.14-4.19). The male to child BMI and female to child BMI ratios remain
essentially flat with increasing mean household BMI and per capita expenditure for
the socially invisible households (Figures 4.20-4.23). Even though, they are face
significant uninsured risk there is no indication that socially invisible households
discriminate against children. It may be that they overcompensate for risk exposure by
protecting children in the allocation of limited nutritional and health inputs that affect
body mass. This overcompensating behavior may also be gleaned from Sahn and
Younger’s (2007) observation that at the children’s body mass is highest relative to
both male and female adults among the most resource constrained. They note that
115
“among poor households where command over resources, including food is most
limited, there is an attempt to protect the most vulnerable members of the household,
the young children, from food and related stresses that contribute to low BMI”( Sahn
and Younger 2007, p 13). On the other hand, the male to female ratio increases with
increasing welfare and then declines (Figures 4.24 and 4.25).
The different patterns of intrahousehold inequality for socially visible and
socially invisible households suggest that the level of inequality in the respective
households might differ systematically. We decompose overall inequality into within
and between-group components to assess the share of overall inequality explained by
intrahousehold inequality. Table 4.4 shows that over 75% of total inequality in the
entire sample and among socially visible households may be attributed to
intrahousehold inequality, whereas a little over 50% of total inequality among socially
invisible households may be attributed to inequality between households.
11.5
22.5
3
Male
to C
hild B
MI R
atio
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5411384864562047) Figure 4.8. Nonparametric estimates of male to child BMI and mean household BMI, all households
116
1.2
1.4
1.6
1.8
22.
2M
ale
to C
hild
BM
I Rat
io
.35 .36 .37 .38 .39 .4Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0064632280128579) Figure 4.9. Nonparametric estimates of male to child BMI and household expenditure percentile, all households
11.
52
2.5
3Fe
mal
e to
Chi
ld B
MI R
atio
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5411384864562047) Figure 4.10. Nonparametric estimates of female to child BMI and mean household BMI, all households
117
1.2
1.4
1.6
1.8
22.
2Fe
mal
e to
Chi
ld B
MI R
atio
.35 .36 .37 .38 .39 .4Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0064632280128579) Figure 4.11. Nonparametric estimates of female to child BMI and household expenditure percentile, all households
.98
.99
11.
011.
02M
ale
to F
emal
e BM
I Rat
io
16 18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5411384864562047) Figure 4.12. Nonparametric estimates of male to female BMI and mean household BMI, all households
118
.9.9
51
1.05
Mal
e to
Fem
ale
BM
I Rat
io
.3 .35 .4 .45 .5Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0064632280128579) Figure 4.13. Nonparametric estimates of male to female BMI and household
expenditure percentile, all households
.51
1.5
22.
53
Mal
e to
Chi
ld B
MI R
atio
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5566200442016269) Figure 4.14. Nonparametric estimates of male to child BMI and mean household BMI, socially visible households
119
.51
1.5
22.
53
Mal
e to
Chi
ld B
MI R
atio
.35 .36 .37 .38 .39 .4Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0040055326519716) Figure 4.15. Nonparametric estimates of male to child BMI and household expenditure percentile, socially visible households
.51
1.5
22.
53
Fem
ale
to C
hild
BM
I Rat
io
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5566200442016269) Figure 4.16. Nonparametric estimates of female to child BMI and mean household BMI, socially visible households
120
.51
1.5
22.
53
Fem
ale
to C
hild
BM
I Rat
io
.35 .36 .37 .38 .39 .4Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0040055326519716) Figure 4.17. Nonparametric estimates of female to child BMI and household expenditure percentile, socially visible households
.98
.99
11.
011.
021.
03M
ale
to F
emal
e BM
I Rat
io
16 18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.5566200442016269) Figure 4.18. Nonparametric estimates of male to female BMI and mean household BMI, socially visible households
121
.85
.9.9
51
1.05
Mal
e to
Fem
ale
BM
I Rat
io
.3 .35 .4 .45 .5Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0040055326519716) Figure 4.19. Nonparametric estimates of male to female BMI and household expenditure percentile, socially visible households
-50
510
Mal
e to
Chi
ld B
MI R
atio
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.6129779981451228) Figure 4.20. Nonparametric estimates of male to child BMI and mean household BMI, socially invisible households
122
02
46
Mal
e to
Chi
ld B
MI R
atio
.36 .37 .38 .39Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0057232964924509) Figure 4.21. Nonparametric estimates of male to child BMI and household expenditure percentile, socially visible households
-50
510
Fem
ale
to C
hild
BM
I Rat
io
18 20 22 24Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.6129779981451228) Figure 4.22. Nonparametric estimates of female to child BMI and mean household BMI, socially invisible households
123
02
46
Fem
ale
to C
hild
BM
I Rat
io
.36 .37 .38 .39Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0057232964924509) Figure 4.23. Nonparametric estimates of female to child BMI and household expenditure percentile, socially visible households
.98
.99
11.
011.
02M
ale
to F
emal
e B
MI R
atio
18 20 22 24 26Mean Household BMI
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.6129779981451228) Figure 4.24. Nonparametric estimates of male to female BMI and mean household BMI, socially invisible households
124
.97
.98
.99
11.
01M
ale
to F
emal
e BM
I Rat
io
.34 .36 .38 .4Household Expenditure Percentile
Nadaraya-Watson estimates using Epanechnikov kernel with Silverman’s optimal bandwidth (h=0.0057232964924509) Figure 4.25. Nonparametric estimates of male to female BMI and household expenditure percentile, socially visible households
Table 4.4: Decomposition of overall inequality (%)
4.4.2 Determinants of Intrahousehold Inequality
So far we have examined bivariate relationships between intrahousehold
inequality and welfare, using mean household BMI and per capita expenditure. This
allows us to assess how intrahousehold inequality varies with welfare. However, it
doesn’t illuminate on the underlying factors driving the observed levels of
intrahousehold inequality. We use the theory on household health demand functions to
GE(-1) GE(0) GE(1) GE(2) Within Between Within Between Within Between Within Between All Individuals 85.311 14.689 80.895 19.137 79.060 20.940 78.421 21.509 Socially Visible 85.067 14.955 80.116 19.884 78.097 21.903 77.387 22.509 Socially Invisible 48.043 51.957 45.321 54.679 43.011 56.989 40.941 58.554
125
examine these factors. Recall that we seek to estimate the following health demand
function:
Hit = ft (Hit-1, Wit, Xit, Eit, εi) (10)
We write an estimable version of equation (5) as
Hit =βH Ht-1 + βw Wit +βX Xit +βissit + +βiVVi+ εi (11)
Suppose that the household derives utility from reducing intrahousehold
inequality in health, ceteris paribus. The we can estimate an analogous model where
the health outcome at the household level is intrahousehold inequality represented by
the log mean deviation. Where Wit is a vector of the household’s stock of income-
generating assets, Xit is a vector of household characteristics including social visibility,
and sit are transitory shocks that affect intrahousehold inequality through the budget
constraint and Vi is the village dummy for household i.
It must be noted that social visibility is itself a function of the individual’s
characteristics. 46 We use the instrumental variables approach to deal with the
endogeneity of social visibility. In order to avoid a finite sample bias which is a
function of the number of instruments, we select two variables which are strong
predictors of the endogenous variable. We use data on whether or not an belong to a
major clan and the whether or not the respondent’s parents held a village office as
instruments for social visibility. These variables affect social visibility but are not
correlated with BMI. We estimate the following system of structural equations.
Socially Visibility = α0 + α1age + α2 gender +α3 education + α4occupation +
α 5wealth + α6major clan + α7parent’s office +ε (12)
Mean Log Deviation BMI = γ0 + γ1age + γ2 gender+ γ3 education + γ4occupation +
γ5wealth + γ6dependency ratio+ γ7household size + γ8shock +
γ9 Social Visibility + γ9village+ v (13)
46 See Vanderpuye-Orgle and Barrett (2007) for a detailed description.
126
The Necessary (Order) Condition for identification states that “For an equation to
be identified, the total number of variables excluded from it but included in the other
equations must be at least as great as the number of equations in the system less than
one” (Koutsoyiannis 1977). Let K be the total number of exogenous and endogenous
variables in the model. Suppose M is the total number of exogenous and endogenous
variables in a particular equation and G is the total number of endogenous variables.
Then the Order Condition is satisfied if (K-M) ≥ (G-1), where inequality denotes
overidentification. From equations 12 and 13, K=17, G=2, M=8 for equations 12and
M=9 for equation 13. Thus the Order Condition is satisfied for equations 12 and 13,
with both equations being overidentified.
We start off by estimating a naive regression which ignores the endogeneity of
social visibility. Table 4.5 presents the summary statistics of variables estimated in
this model. We estimate two equations, i.e. with and without shocks, using a random
effect generalized least squares estimation procedure and a panel household-level data,
with observations clustered within the household. The results are presented in Table
4.6.
First, overlooking the endogeneity of social invisibility, the results indicate that
wealthy households have lower mean log deviation BMI indices. Conversely socially
invisible households with high dependency ratios have higher intrahousehold
inequality. Controlling for current period shocks yields similar results; in addition
households with older heads have higher intrahousehold inequality. Suffering either a
farm or mortality shock is associated with an increase in mean log deviation BMI.
127
Table 4.5: Descriptive statistics: intrahousehold health inequality model
Variable Definition Frequency (%) or Mean
Log mean deviation BMI
Household log mean deviation using BMI as a welfare metric 0.02 (0.05)
Socially Invisible =1 if mean social visibility is less than or equal to 0.48750, 0 otherwise
11.54
Age Age of household head 47.24 (13.34)
Head’s wealth Household head’s non-land wealth in millions of Cedis 8.01 (17.9)
Spouse’s wealth Spouse’s non-land wealth in millions of Cedis 1.04 (3.02)
No education =1 if household head has no formal education, 0 otherwise
16.53
Farmer =1 if household head farmer, 0 otherwise
88.84
Dependency Ratio Ratio of number of individuals under age 18 and above age 64 to those between ages 18-64
1.24 (2.05)
Household size Number of individuals in household 9.14 (4.20)
Major clan =1 if household head belongs to a major clan, 0 otherwise
79.17
Parent’s office =1 if household head parents held village offices, 0 otherwise
73.72
Farm shock =1 if suffered farm shock in round 0 otherwise
39.10
Mortality shock =1 if suffered mortality shock 0 otherwise
62.50
Location Village 1 Village 2 Village 3 Village 4
=1 if Village cluster 1, 0 otherwise =1 if Village cluster 2, 0 otherwise =1 if Village cluster 3, 0 otherwise =1 if Village cluster 4, 0 otherwise
24.42 20.16 28.42 27.00
128
Table 4.6: Intrahousehold health inequality naïve regressions
Coefficients Standard
error Coefficients Standard
error Dependent Variable: Household’s log mean deviation Household characteristics Head: age 0.004 0.003 0.004* 0.002 Head: no education -0.014 0.010 -0.011 0.009 Head: farmer -0.011 0.009 -0.012 0.009 Head's Wealth -0.003*** 0.001 -0.003** 0.001 Spouse's Wealth -0.002 0.007 -0.003 0.007 Socially Invisible 0.033*** 0.012 0.023* 0.012 Shocks Farm shock -- -- 0.009* 0.005 Mortalitity shock -- -- 0.026*** 0.008 Location Village 2 0.018* 0.010 0.012 0.010 Village 3 0.027*** 0.010 0.041*** 0.011 Village 4 0.000 0.009 0.016 0.010 Other factors Dependency ratio 0.009* 0.002 0.008* 0.002 Household size -0.001 0.001 -0.001 0.001
n =238 n =238 Wald χ2(11) = 61.89 Wald χ2(13) = 79.61
p-value = 0.00 p-value = 0.00 Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively.
Comparison group is a household with a head from village 1 who is not a farmer has some education and is socially visible.
129
Next, we estimate the reduced form model with parent’s office and major clan
as instruments for social visibility. The results presented in Table 4.7 indicate that
wealthy households with uneducated heads who belong to a major clan and have
parents who held village offices have lower mean log deviation BMI indices.
Households with older heads high and dependency ratios have higher intrahousehold
inequality. Controlling for current period shocks, yields similar results. In this case,
suffering a mortality shock increases intrahousehold inequality as well.
The first stage regression of the structural model presented in Table 4.8
indicates that wealthy households with younger heads who belong to a major clan and
have parents who held village offices are less likely to be in social invisible. This is
consistent with the results of Vanderpuye-Orgle and Barrett (2007). The second stage
regression presented in Table 4.9 shows that being socially invisible with a high
dependency ratio increases intrahousehold inequality. This is robust to the
specification with shocks. In addition, experiencing a mortality shock is associated
with an increase in mean log deviation BMI .
4.5 Conclusions
This paper assesses the effects of social visibility on the existence of Kuznets
curves and the determinants of intrahousehold inequality. The results indicate that
whilst the inverted U-shape may not exist when using aggregated data, it may exist for
a subsample of the population – in this case the socially visible. In addition the
composition of inequality varies by social visibility. Over 75% of overall inequality
may be attributed to the within-group component (i.e. intrahousehold inequality)
amongst socially visible households. On the other hand, a little over 50% of overall
inequality may be attributed to the between-group component amongst socially
invisible households.
130
Table 4.7: Intrahousehold health inequality reduced form model
Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively. Comparison group is a household with a head from village 1 who is not a farmer has some education , does not from a major clan, has some education and has parents who did not hold village offices.
Coefficients Standard
error Coefficients Standard
error Dependent Variable: Household’s log mean deviation Household Characteristics Head: age 0.005* 0.003 0.005** 0.002 Head: no education -0.018* 0.009 -0.014 0.009 Head: farmer -0.006 0.009 -0.007 0.009 Head's Wealth -0.003** 0.002 -0.003** 0.002 Spouse's Wealth 0.000 0.001 0.000 0.001 Parent’s office -0.026*** 0.008 -0.027*** 0.008 Major clan -0.029** 0.012 -0.026** 0.012 Shocks Farm shock -- -- 0.006 0.005 Mortality shock -- -- 0.029*** 0.007 Location Village 2 0.020* 0.010 0.013 0.010 Village 3 0.023** 0.010 0.039*** 0.011 Village 4 0.002 0.010 0.019* 0.010 Other factors Dependency ratio 0.010*** 0.001 0.008*** 0.001 Household size -0.001 0.001 -0.001 0.001
n =238 n =238
Wald χ2(12) = 76.33 Wald χ2(14) = 98.07
p-value = 0.000 p-value = 0.000
131
Table 4.8: Intrahousehold health inequality first stage regression
Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively. F-test of instruments=249.68, p-value = 0.00 Comparison group is a household with a head from village 1 who is not a farmer has some education, does not from a major clan, has some education and has parents who did not hold village offices.
Coefficient Standard error Dependent Variable: Socially Invisible Individual Characteristics Head: age -0.0030*** 0.001Head: no education 0.022 0.032Head: farmer 0.023 0.031Head's Wealth 0.008* 0.005Spouse's Wealth -0.001 0.002Location Village 2 0.004 0.034Village 3 -0.003 0.035Village 4 0.057* 0.048Instrumental Variables Parent’s office -0.106*** 0.029Major clan -0.482*** 0.035n =238 Wald χ2(10) = 363.21 p-value = 0.00
132
Table 4.9: Intrahousehold health inequality second stage regression
Coefficients Standard
error Coefficients Standard
error Dependent Variable: Household’s log mean deviation Individual characteristics Head: age 0.002 0.003 0.002 0.003Head: no education -0.011 0.009 -0.007 0.009Head: farmer -0.006 0.009 -0.007 0.009Head's Wealth -0.004 0.001 -0.004 0.001Spouse's Wealth 0.000 0.000 0.007 0.007Socially Invisible 0.271*** 0.063 0.263*** 0.064Shocks Farm shock -- -- 0.005 0.005Mortalitity shock -- -- 0.029*** 0.007Location Village 2 0.028* 0.011 0.021 0.010Village 3 0.016 0.010 0.032*** 0.011Village 4 -0.002 0.010 0.015 0.010Other factors Dependency ratio 0.010*** 0.001 0.008*** 0.001Household size -0.001 0.001 -0.001 0.001
n =238 n =238
Wald χ2(11) = 68.74 Wald χ2(13) = 85.63 p-value = 0.00 p-value = 0.00
Notes: ***, **, * Significant at the 1%, 5% and 10% levels, respectively. Comparison group is a household with a head from village 1 who is not a farmer has some education and is socially visible.
133
Patterns in relative BMI within the household suggest that the socially invisible
may overcompensate for risk exposure by protecting children in the allocation of
limited food and nutritional resources. With regards to factors determining the level of
intrahousehold inequality: wealth, dependency ratio, age, social visibility as well
experiencing shocks had statistically significant effects on intrahousehold inequality.
By way of policy implications, the challenges associated with identifying
individuals within the household for targeting purposes lead policy makers to resort to
the second best strategy – focusing on improving overall household well-being. The
existence of a systematic relationship between intrahousehold inequality and average
well-being on the basis of some identifiable household characteristic such as social
visibility should enable policymakers better tailor interventions to attain the desired
goal of improving individual well-being.
134
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Unitary Versus Collective Models of the Household: Is it Time to Shift the Burden of Proof? World Bank Research Observer. 10: 1-9.
Alderman, H. and C. H. Paxson (1992). Do the Poor Insure? A Synthesis of the
Literature on Risk and Consumption in Developing Countries, The World Bank.
Anand, S. and Kanbur, R. (1993). The Kuznets Process and the Inequality-
Development Relationship, Journal of Development Economics, 40, 25-52. Beresteanu, A. and Manski, C. F. (2000). Bounds for STATA: Draft Version 1.0.
Northwestern University. Behrman, J. R. and Deolalikar, A. (1988). Health and Nutrition. In H. Chenery and
T.N. Srinivasan (Eds.) Handbook of Development Economics, 1. Amsterdam: North-Holland Press, 631-711.
De Weerdt, J. (2005). Risk-Sharing and Endogenous Network Formation. In S.
Dercon (Ed.) Insurance against Poverty. New York: Oxford University Press, 197-216.
Deaton, A. (1992). Household Saving in LDCs: Credit Markets, Insurance and
Welfare, Scandinavian Journal of Economics, 94, 253-73. Dercon, S. and Hoddinott, J. (2003). Health, Shocks and Poverty Persistence. In S.
Dercon (Ed.) Insurance against Poverty. New York: Oxford University Press. Dercon, S. and Krishnan, P. (2000). In Sickness and in Health: Risk Sharing within
Households in Ethiopia, Journal of Political Economy, 108, 688-727. Doss, C. R. (1996). Intrahousehold Resource Allocation in an Uncertain Environment.
American Journal of Agricultural Economics, 78, 1335-1339.
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Duflo, E. and Udry, C. (2004). Intrahousehold Resource Allocation in Cote d'Ivoire: Social Norms, Separate Accounts and Consumption Choices.
Ellis, F. (1998). Household Strategies and Rural Livelihood Diversification. Journal of
Development Studies, 35(1), 1-38. Gertler, P. and Gruber, J. (1997). Insuring Consumption against Illness, National
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Insurance Mechanisms in Southern Ghana. Yale University. Goldstein, M., De Janvry, A. and Sadoulet, E. (2005). Is a Friend in Need a Friend
Indeed? Inclusion and Exclusion in Mutual Insurance Networks in Southern Ghana. In S. Dercon (Ed.) Insurance against Poverty. New York: Oxford University Press.
Goldstein, M. and Udry, C. (1999). Agricultural Innovation and Resource
Management in Ghana, Washington D.C.: International Food Policy Research Institute.
Grantham-McGregor, S., Walker, C., Chang, S. and Powell, C. (1997). Effects of
Early Childhood Supplementation with and without Stimulation on Later Development in Stunted Jamaican Children, American Journal of Clinical Nutrition, 66, 247-53.
Haddad, L. and Kanbur, R. (1990). How Serious Is the Neglect of Intrahousehold
Inequality?, Economic Journal, 11, 866-81. Haddad, L., Kanbur, R. and Bouis, H. (1993). Intrahousehold Inequality at Different
Welfare Levels: Energy Intake and Energy Expenditure Data from the Philippines. Oxford Bulletin of Economic and Statistics, 57 (3), 389-409.
Hardle, W. (1990). Applied Nonparametric Regression. Cambridge: Cambridge
University Press.
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Hoddinott, J. and Kinsey, B. (1999). Adult Health in Time of Drought, Washington
D.C.: International Food and Policy Research Institute. Hoddinott, J. and Kinsey, B. (2001). Child Growth in Time of Drought, Oxford
Bulletin of Economics and Statistics, 64, 409-36. Kanbur, R. and Haddad, L.(1992). Is There an Intrahousehold Kuznets Curve? Some
Evidence from the Philippines, Public Finance, 47, 77-93. Kuznets, S. (1955). Economic Growth and Income Inequality. American Economic
Review, 45, 1-28.
Loh, W. (2002). Regression Trees with Unbiased Variable Selection and Interaction Detection. Statistica Sinica, 12, 361-386
Sahn, D. E. and Younger, S. (2007). Measuring Intra-Household Health Inequality:
Explorations using the Body Mass Index. Cornell University. Santos, P. and Barrett, C. (2004). Interest and Identity in Network Formation: Who Do
Smallholders Seek out for Information in Rural Ghana?, Cornell University. Silverman, B. (1986). Density Estimation for Statistics and Data Analysis, London:
Chapman & Hall. Strauss, J. and Thomas, D. (1998). Health, Nutrition, and Economic Development,
Journal of Economic Literature, 36 (2) 766-817. Townsend, R. M. (1994). Risk and Insurance in Village India, Econometrica, 62, 539-
91. Udry, C. and Conley, T. (2005). Social Networks in Ghana, in C. Barrett (Ed.)The
Social Economics of Poverty. London: Routeledge. Vanderpuye-Orgle, J. and Barrett, C. (2007). Risk Management and Social Visibility
in Ghana. Cornell University.
139
APPENDIX A
Field Notes
A.1 Field Activities
A study of credit transactions in response to inter-temporal risk requires the
use of panel data. We build on the data set generated by Christopher Udry47 and
Markus Goldstein48 in the Eastern Region of Ghana, from November 1996 to October
1998, to create a three-period panel. The research was conducted in the Akuapim
South District (specifically the Nsawam - Aburi area) in the Eastern Region of Ghana.
This dynamic agricultural region lying in the forest savanna transition zone of Ghana
is particularly interesting because of the evolving farming systems. Since the early
1990s farmers in this area have been switching from the cultivation of maize-cassava
intercrop for domestic production to pineapple cultivation for export. This involves
not only a significant amount of technological innovation and investment but also risk-
taking.
The sample was generated using a two-stage sampling procedure. First, four
village clusters were purposively selected within this area based on their participation
in fruit and vegetable production as well as their array of agronomic, market access
and geographic conditions. Sixty married couples (or triples) were then randomly
selected in each village cluster, except for the smallest village cluster where all
resident couples were interviewed.
The field research was undertaken in two phases: (i) a preliminary trip to the
survey area to establish protocol for another round of surveys with the same sample of
47 Professor, Department of Economics, Yale University. 48 Lecturer, Department of Development Studies, London School of Economics.
140
respondents; assess the level of attrition and undertake a quasi-qualitative study using
the community studies approach to gather information towards formulating/revising
survey instruments, and (ii) an extended trip for data-collection.
A.2 Preliminary Visit to Survey Area
Duration: 12/16/2003 - 01/22/2004
Upon arrival in Ghana, I set up meetings with Professor Ernest Aryeetey, the
Director of the Institute of Statistical, Social and Economic Research (ISSER) and Mr.
Ernest Appiah, a research fellow at ISSER who led the data collection team in the first
two panels. My first meeting was with Mr. Appiah, during which we discussed the
requirements for the project: customary token gifts for the chiefs (bottles of imported
Schnapps), contact persons, transportation, lodging, the research schedule,
enumerators, etc. Two factors weighed on the commencement of the field trips: (i)
establishing contact with Bob, a seasoned enumerator from the previous rounds who
would introduce me to the chiefs and people in the respective villages and (ii) the
break in formal activities due to the Yuletide season. We thus set the date for
beginning the actual field trips for Monday, 01/05/04.
In the mean time, I met with Professor Aryeetey to discuss my appointment as
a Research Associate at ISSER. Upon my return to Ghana, I would be provided with
office space at ISSER as well as three national service personnel who I could employ
as part of
my data collection team. I would also be expected to participate in the weekly seminar
series.
Starting on 01/05/04, Bob and I visited the villages in the previous sample,
namely Pokrom, Konkonuru, Oboadaka and Ashweriase - Daman. As custom
demands, we presented drinks to the chiefs and elders (or those acting in their
141
absence) in the respective villages and explained our intention of undertaking survey
in 06/04. We received a hearty welcome in each of the villages as they fondly
recollected personal experiences from the previous rounds.
We also talked to some of the people in the villages. This gave me some
insights into some changes in the sources of credit, among other things. For instance,
most of the villages now had farming cooperatives. However, the loans disbursed were
meager and could not be used for meaningful investments. The farmers talked
extensively about the types of risk they faced and how they tried to deal with it. A
discussion with a staff of Malinas Ghana Limited, a pineapple export firm which uses
farmers in Konkoronu and Oboadaka as outgrowers, revealed that they paid 30% of
expected sales prior to harvest if the farmer "forced" the plants49. This changes the
extent of risk borne by the farmers. In addition, recent developments in infrastructure,
by way of feeder roads, coupled with the aforementioned cooperatives may
significantly change the dynamics of social networks and access.
Information gathered from these interactions served as a basis for the
development of a survey questionnaire, which I later revised - rephrasing some of the
questions to be consistent with the previous rounds. I set in motion plans to pretest the
survey instrument using 20 households in Konkonuru. However, I was infected with a
serious case of malaria at the beginning of my fourth week. This slowed down the
pace of events. In the end, the ailment, along with a last minute hitch in logistics and
the fact that most of the questions in the survey instrument had been adapted from the
previous surveys and have thus been amply tested, resulted in a decision to postpone
the survey pretest till 06/04 and/ or adopt the grounded theory approach used by Udry
and Goldstein
49 That is, if the plants were induced to flower prematurely; thus reducing the time from sowing to harvest.
142
in the previous rounds.
On the whole, this preliminary trip was invaluable. It afforded me a better
understanding of the issues relating to risk management and social insurance in the
respective villages. It also made it possible for me to put in place logistics required for
the actual data collection thus paving the way for successful doctoral dissertation
research.
A.3 Data Collection trip
Duration: 07/02/2004 - 01/29/2005
I arrived in Ghana on Saturday, 07/03/2004. In the first two weeks, I met with
Professor Ernest Aryeetey to go over the field research schedule, the personnel
requirements and equipment to be used. I was assigned an office at ISSER and met
with the three national service personnel designated to work with me on the project.
After various discussions about time availability, readiness to relocate and language
issues only one of them was selected. We also retained Bob on the project as the lead
enumerator. I started a series of meetings with Mr. Ernest Appiah to recruit and
interview additional research assistants. I also met with the director of the Center for
Remote Sensing and Geographic Information Services (CERSGIS), University of
Ghana, Legon, Dr Amamoo Otchere, to explore the possibility of hiring personnel
from that office to map the respondents' farming plots. The latter proved to be quite
expensive so it was aborted in favor of hiring and training a research assistant from the
Geography department to map the plots and to administer the questionnaires relating
to the acquisition and use of plots as well.
I met with Markus Goldstein and Ernest Appiah on 07/16/04 to discuss
questionnaires and logistics. I had another meeting with Markus Goldstein and Chris
Udry on 07/19/04 to finalize the questionnaires, research schedule and logistics. It was
143
decided that due to budgetary constraints and concerns for respondent fatigue, I would
increase the survey interval to 8 weeks: administering the entire set of modules at a
sitting instead of staggering the questionnaires over a 4-weeek interval schedule. We
also reduced the number of enumerators from eight to four, with two of them being
assigned to two villages, respectively, and moving back and forth from one village to
the other every four weeks. As with the previous rounds, male enumerators were
assigned to male respondents and female enumerators to female respondents,
respectively, to preserve gender sensitivity and cultural norms. On 07/20/04, I
presented a paper at the ISSER and Cornell University International Conference,
Ghana at Half Century (held from 07/18/04- 07/20/04).
We hired two additional enumerators. In the meantime, we went to the villages
to present another round of drinks to the chiefs, informing them of my safe return and
a tentative date for the start of the project as well as to ask for help in securing
accommodation in the area. We were asked to pay fees to the respective gong-gong
beaters (town announcers) to make announcements and create awareness for the
project so we won't be viewed as strangers in the town. The process of securing
accommodation required several trips during which we identified our local ‘resource
persons’, introduced our enumerators to the respondents and identified the logistics on
the ground vis-à-vis lodging needs (such as bedding, food and water), transportation
and coordination. We initially rented two places in each of the villages: one for the
male enumerator and one for the female enumerator -- I would share with the latter
when the need arose to stay for extended periods of time. I was to be based in Accra
and would commute to the villages daily, supplying water and stationary and
questionnaires when needed. We later rented a third place for the plot mapping
personnel.
144
We held an all-day training session on 08/02/04 for the research assistants to
go over the wording of the questionnaires in twi (the local dialect in the survey area);
complying with Cornell’s University Committee on Human Subjects (UCHS)
regulations such as getting verbal consent from the respondents; as well as
comportment and dealing with sensitive questions. We also had mock interviews with
some role playing to clarify concepts. We moved to the villages on 08/08/04 and
started administering the questionnaires the next day. As mentioned earlier, there was
a male and female enumerator in each of two villages, at any point in time. Each
enumerator was assigned to two villages (either Ashweriase-Daman and Pokrom or
Konkonuru and Oboadaka). We started out in Ashweriase-Daman and Konkonuru (i.e.
1a in the Appendix table) and then moved to Pokrom and Oboadaka (i.e. 1b in the
Appendix table), the cycle was repeated two more times. There were a total of six
rounds; each respondent was interviewed three times.
In all, 436 individuals were surveyed fifteen times in the first and second
panels. Of which, 332 individuals were surveyed three times in the third panel. The
sample attrition rate over the 1998-2004 interval was 23.85%. Several modules were
administered. A brief description of each module and the survey schedule are given in
the appendix. I returned to the United States on 01/29/05. I went back to Ghana for a
workshop organized by the Consortium of Development Partnerships and ISSER on
04/14/05-04/15/05 (viz., CDP Workshop on Entrepreneurship and Poverty Alleviation
in Agribusiness) to give a presentation on Data Availability and Research Issues
relating to the study of pineapple as a crop system in Ghana.
Field work was quite an experience — a good one, on the whole. It was
challenging, though, for the most part. We had to deal with issues of respondent
fatigue; a peak tomato farming season in one of the villages and hence very busy
respondents; constant demands for 'big' gifts and occasionally disgruntled respondents;
145
as well as illnesses (we had on average two weeks of slowed activity for all four
enumerators at different points in time): one enumerator was eventually given a whole
week off by the doctor and the plot mapping personnel was also admitted in hospital
for a while. The list goes on. However, it was a excellent research experience and I'll
do it all over again, given the chance.
146
APPENDIX B
Survey Modules and Interview Schedule
Household Roster and Anthropometrics - This data was collected at the start of the
survey and again in the final round. A short update covering additions to and
departures from the households since the last survey in 1998 was administered in the
first round. Whether or not the household was still in the same community and
reasons for moving, if they had, was also documented. This information was updated
in the final round. Data on age, gender, education, occupation, migration and marital
status were collected on all children ever borne to each wife, as well as on all current
household members. Anthropometric data (i.e. weight and height) of the respondents
was also collected.
Birth History – This adopts questions from the Demographic and Health Surveys. It
asks detailed questions on all children ever born to the respondents in the last six years
including birth weights and heights, antenatal care, who assisted in delivery and where
delivery occurred. This questionnaire, in addition to the household roster, would allow
for the use of a concept of poverty which goes beyond the unidimensional
income/expenditure measure to capture a fuller multidimensional notion.
Assets and Trading Stocks - This gathered data on stocks of food, farm inputs,
livestock, financial assets and participation in ROSCAs. Information on trading stocks
and business assets was also collected.
Credit Practices - This refers respondents to lenders and borrowers identified in 1998
and asks if there are still transacting with them and if not why. It also asks if they
147
would lend/ borrow to individuals in three specified locations, each farther apart from
the present location. It is aimed at capturing penalties for default as well as the spatial
gradient in credit activities.
Lending, Lending Continuation, Borrowing, and Borrowing Continuation – These
questionnaires are based on tracking each loan from the point at which it is extended
until it is either repaid or written-off. Data is collected on both contractual terms and
actual payments, and on the use of any enforcement mechanisms.
Non-Farm Income - Non-farm income provides about half of the income of the sample
respondents. Costs and revenues of the respondents’ non-farm enterprises are
collected. In addition, this questionnaire records income from employment, from
farms outside of the study area (many own, or have some rights over, land in other
areas), and from miscellaneous sources such as inheritances or pensions. Respondents
are also asked to estimate their spouses’ income from non-farm sources which,
combined with a similar question on the sale of farm output, will allow us to see what
spouses know about each others income.
Gifts - Requests data on gifts received or given from individuals other than the spouse
as well as gifts received by or given to the spouse.
Expenditure - The purchased food, food from own farm, and family expenses
questionnaires constitute the expenditure survey. This requests standard data on
expenditures, but at the level of the individual rather than the household. In addition,
respondents are asked about the expenditures of their spouse(s), and about the transfer
of “chop money” - the main means of spouse to spouse transfer.
148
Family Background - This provides information on the wealth of the respondents’
families, the respondents’ parents’ education and occupation, migration histories, the
origins of the family, inheritances as well village offices held and the privileges
thereof.
Learning – This questionnaire is designed based on matching the respondent with a
random sample of six other respondents, and with two other village-specific “focal”
individuals identified from community-studies approach taken in the preliminary field
trip as individuals in the villages from whom advice is commonly sought. Each
respondent is asked about his/her relationship with each of these individuals, and
whether or not he/she could approach the individual to deal with one of a set of
specific issues.
Shocks - An account of significant unexpected events which occurred on the plots of
the respondent and his/her spouse. It also asked about illness and other unexpected
events, including death of livestock and theft.
Plot Mapping - All of the plots were mapped using GPS equipment and GIS software.
This procedure yields much more accurate measures of plot size than are available in
most surveys in developing countries. Moreover, by mapping the relative locations of
all plots (and associated roads, paths and villages) it may be possible to distinguish
between information transmission that is associated with social connections from that
associated with geographical proximity. The mapping procedure opens the opportunity
to investigate the importance of unobserved (and commonly unconsidered) spatial
autocorrelation in production shocks. Finally, by accurately determining the location
of each plot, it will be possible to match the agronomic and fertility data to medium-
149
term historical information available from satellite photography and also to collect
follow-up information indefinitely in the future. In association with the mapping, the
Plot questionnaire was administered. This questionnaire is concerned with the history
of each plot. The questionnaire also requests information on the current contractual
status of the land, focusing in particular on the details of sharecropping contracts,
which are very frequently used in the study area.
Plot Wrap-Up - This and other administrative questionnaires are used to keep track of
new plots as well as dropped and fallowed plots. This final questionnaire requests data
on the value of crops still standing as the survey ends and the property rights the
spouse and children of the respondent would have if the respondent were to die.
1
Schedule of Surveys
2004 - 2005
Round50
1a 1b 2a 2b 3a 3b
date started 9-Aug 6-Sept 4-Oct 1-Nov 29-Nov 27-Dec
Questionnaire
Household roster
Anthropometrics
Credit practices
Lending
Lending continuation
Borrowing
Borrowing continuation
Gifts and marital transfers
Events / Shocks
50 Grayed out cells indicate periods over which the questionnaires were administered.
150
2
Schedule of Surveys Continued
Assets and Trading stocks
Family Background
Learning
Expenditure
Other income
Plot questionnaire and Plot
mapping
Plot wrap up
Birth history 151
APPENDIX C
Questionnaires
152
Risk Management and Social Insurance Study
Section 1: Household Roster
VILLAGE HHN RESPONDENT RESPOND. # DATE ROUND
IS THIS HOUSEHOLD IN THE SAME COMMUNITY AS 1998? (Circle one) Yes……………………………………1 No…………………………………….2 IF NO, WHERE DID THEY MOVE TO? (See location codes on page 1)
Write any reasons given for relocation here:
Good (morning/ afternoon/ evening). I’m and we are conducting a survey of households in this area. From 1996-1998 we interviewed people living in this compound. This earlier study has been very helpful in learning about people’s living conditions and in planning government policies. As a follow- up to this earlier work we would again like to ask questions about the people who lived here in 1996-1998. The information you give us will be kept confidential. You and your household members will not be identified by name in any of the documents we plan to write.
HOUSEHOLD ROSTER PAGE 1
HOUSEHOLD HEAD, FIRST SPOUSE ALL OF HER CHILDREN EVER BORN (INLUDING CHILDREN LIVING ELSEWHERE) AS LISTED IN 1996-98 (Complete Questions for all Pre-printed Persons)
Schooling Name Sex Is […] alive? Yes…1 No…WRITE YEAR OF DEATH THEN GO TO NEXT PERSON
Approximate Current Age in Years in 2004
HH member? Yes…..1 No…...2
If no, where does s/he stay?
Highest level attended
Highest grade in that level
Occup-ation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
0 Head
1 Spouse
2 Child
3 Child
4 Child
Marital Status Location 8 Eastern region town 1 Primary Occupation 8 Non- agric labor 1 Married 1Daman/Ahweriase 9 Accra 2 Middle/ JSS 1 Farming 9 Trading 2 Consensual union 2 Pokrom/Nsabaa 10 Other Urban 3 SSS/ Tech/ Comm/ 2 Teaching 10 Student/pupil 3 Divorced 3 Oboadaka/ Kwamekrom 11 Other Rural training college 3 Artisan 11 Unemployed 4 Separated 4 Konkonuru 12 Not in Ghana 4 Post sec/ Nursing/ Poly 4 Office Worker 12 Not in the labor force 5 Widow 5 Aburi 5 Higher 5 Civil Servant 6 Never Married 6 Nsawam Level of School 6 Agric labor 7 NA (under 15 yrs) 7 Eastern region rural 0 None 7 Health worker
5 Child
6 Child
7 Child
8 Child
9 Child
10 Child
11 Child
HOUSEHOLD ROSTER PAGE 2 ADDITIONAL SPOUSE ALL OF HER CHILDREN EVER BORN (INLUDING CHILDREN LIVING ELSEWHERE) AS LISTED IN 1996-98 (Complete Questions for all Pre-printed Persons)
Schooling Wife #2
Name Sex Is […] alive? Yes…1 No…WRITE YEAR OF DEATH THEN GO TO NEXT PERSON
Approximate Current Age in Years in 2004
HH member?
If no, where does s/he stay?
Highest level attended
Highest grade in that level
Occup-ation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
12 Spouse
Marital Status Location 8 Eastern region town 1 Primary Occupation 8 Non- agric labor 1 Married 1Daman/Ahweriase 9 Accra 2 Middle/ JSS 1 Farming 9 Trading
13 Child
14 Child
15 Child
16 Child
17 Child
18 Child
19 Child
20 Child
21 Child
2 Consensual union 2 Pokrom/Nsabaa 10 Other Urban 3 SSS/ Tech/ Comm/ 2 Teaching 10 Student/pupil 3 Divorced 3 Oboadaka/ Kwamekrom 11 Other Rural training college 3 Artisan 11 Unemployed 4 Separated 4 Konkonuru 12 Not in Ghana 4 Post sec/ Nursing/ Poly 4 Office Worker 12 Not in the labor force 5 Widow 5 Aburi 5 Higher 5 Civil Servant 6 Never Married 6 Nsawam Level of School 6 Agric labor 7 NA (under 15 yrs) 7 Eastern region rura l 0 None 7 Health worker
22 Child
23 Child
24 Child
25 Child
HOUSEHOLD ROSTER PAGE 3 ALL OTHER HOUSEHOLD MEMBERS AS LISTED IN 1996-98
(Complete Questions for all Pre-printed Persons)
Please specify Other family member / Other (id code):_________________________________________________________________________________________
Relationship 12. Wofa 4 Separated Occupation 10 Student/pupil 1 Head 13 Uncle 5 Widow 1 Farming 11 Unemployed 2 Wife 14 Aunt 6 Never Married 2 Teaching 12 Not in the labor force 3 Child 15 Other family member 7 NA (under 15 yrs) 3 Artisan 4 Grandson 16 Househelp Level of School 4 Office Worker 5 Grand daughter 17 Permanent worker 0 None 5 Civil Servant 6 Mother 18 Friend 1 Primary 6 Agric labor 7 Father 19 Other 2 Middle/ JSS 7 Health worker 8 Brother Marital Status 3 SSS/ Tech/ Comm/ 8 Non- agric labor 9 Sister 1 Married training college 9 Trading 10 Nephew 2 Consensual union 4 Post sec/ Nursing/ Poly 11 Niece 3 Divorced 5 Higher
Schooling ID code
Name Sex Is […] alive? Yes…1 No…WRITE YEAR OF DEATH THEN GO TO NEXT PERSON
ApproximateCurrent Age in Years in 2004
Relation -ship to head
Relation -ship to
head’s spouse
HH member?
If no, where does s/he stay?
Highest level attended
Highest grade in that level
Occup-ation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
26
27
28
29
30
31
32
33
34
35
HOUSEHOLD ROSTER PAGE 4 NEW HOUSEHOLD HEAD, FIRST SPOUSE AND ALL OF HER CHILDREN EVER BORN (INCLUDING CHILDREN LIVING ELSEWHERE)
Schooling
ID
Name
Sex
Age
Child of head?
HH member?
If no, where does s/he stay?
Highest level attended
Highest grade in that level
Occup ation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
Correction by head
02 Head
36 Spouse
37 Child
38 Child
39 Child
40 Child
41 Child
42 Child
43 Child
44 Child
45 Child
46 Child
47 Child
Marital Status1 Married 2 Consensual union 3 Divorced 4 Separated 5 Widow 6 Never Married 7 NA (under 15 yrs) Location 1Daman/Ahweriase
2 Pokrom/Nsabaa 3 Oboadaka/Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural 12 Not in Ghana
Level of School0 None 1 Primary 2 Middle/JSS 3 SSS/Tech/Comm/ training college 4 Post sec/ Nursing/Poly 5 Higher Occupation1 Farming 2 Teaching
3 Artisan 4 Office worker 5 Civil Servant 6 Agric labor 7 Health worker 8 Non-agric labor 9 Trading 10 Student/pupil 11 Unemployed 12 Not in labor force
HOUSEHOLD ROSTER PAGE 5
NEW ADDITIONAL SPOUSE AND ALL OF HER CHILDREN EVER BORN (INCLUDING CHILDREN LIVING ELSEWHERE) Schooling
Name
Sex
Age
Child of head?
HH member?
If no, where does s/he stay?
Highest level attended
Highest grade in that level
Occu pation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
Correction by head
48 Spouse
49 Child
50 Child
51 Child
52 Child
53 Child
54 Child
55 Child
56 Child
57 Child
58 Child
59 Child
60 Child
61 Child
Marital Status1 Married 2 Consensual union 3 Divorced 4 Separated 5 Widow 6 Never Married 7 NA (under 15 yrs) Location 1Daman/Ahweriase
2 Pokrom/Nsabaa 3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural
12 Not in Ghana Level of School0 None 1 Primary 2 Middle/JSS 3 SSS/Tech/Comm/ training college 4 Post sec/ Nursing/Poly 5 Higher Occupation
1 Farming 2 Teaching 3 Artisan 4 Office worker 5 Civil Servant 6 Agric labor 7 Health worker 8 Non-agric labor 9 Trading 10 Student/pupil 11 Unemployed
12 Not in Labor Force
HOUSEHOLD ROSTER PAGE 6 ALL OTHER NEW HOUSEHOLD MEMBERS
Schooling
ID code
Name
Sex
Age
Relation-ship to head
Relat-ionship to head’s spouse
Years in house-hold
Highest level attended
Highest grade in that level
Occu- pation
Marital Status
Weight
Height
Page and ID of spouse
Months Away in Last 12
Correction by head
62
63
64
65
66
67
68
69
70
71
72
Please specify Other family member / Other (id code):_________________________________________________________________________________________ Relationship 12. Wofa 4 Separated Occupation 10 Student/pupil 1 Head 13 Uncle 5 Widow 1 Farming 11 Unemployed 2 Wife 14 Aunt 6 Never Married 2 Teaching 12 Not in the labor force 3 Child 15 Other family member 7 NA (under 15 yrs) 3 Artisan 4 Grandson 16 Househelp Level of School 4 Office Worker 5 Grand daughter 17 Permanent worker 0 None 5 Civil Servant 6 Mother 18 Friend 1 Primary 6 Agric labor 7 Father 19 Other 2 Middle/ JSS 7 Health worker 8 Brother Marital Status 3 SSS/ Tech/ Comm/ 8 Non- agric labor 9 Sister 1 Married training college 9 Trading 10 Nephew 2 Consensual union 4 Post sec/ Nursing/ Poly 11 Niece 3 Divorced 5 Higher
PAGE 7
Section 2a: Credit Practices
1. Have you ever refused to give someone a loan? Y / N If yes, why___________________________________ 2. Would you lend to someone from the next village? Y / N If no, why ________________________________ 3. Would you lend to someone from Aburi/ Nsawam? Y / N If no, why ________________________________ 4. Would you lend to someone from Accra? Y / N If no, why ________________________________________ 5. Do you have a bank account? Y / N If no, why ____________________________________________ 7. Would you borrow from a bank? Y / N If no, why ____________________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE
ROUND
In the last survey you gave loans to [….] Please fill in from 1998
Do you still give [….] loans?
Y / N
If NO, WHY………
1.
2.
3.
4.
5.
6.
7.
In the last survey you got loans from [….] Please fill in from 1998
Do you still get loans from [….] ? Y / N
If NO, WHY………
1.
2.
3.
4.
5.
6.
PAGE 8
Section 2b: Lending
(One form to be filled for EACH loan)
Ask question 1 ONLY ONCE, for each respondent 1. In the last two months, have you given anyone any loans that have NOT YET BEEN REPAID? This could include
money, food, or goods, but not land. Y / N [If no, END] 2. What did you lend? Cash (enter amount) ____________________________________________________
Goods (enter items) ____________________________ Value __________________________________ 3. When did you make the loan (date/ how many days ago)? ___________________________________ 4. Who did you make the loan to? Name (the name is optional/use IRID)______________________________
residence_________________ relationship _________________________________________________ 5. Was there a witness to the loan? Y / N If yes, relationship to witness (record IRID)____________________ 6. What will he use the loan for? Purpose _____________________________________________________ 7. When do you expect the loan to be repaid? __________________________________________________ 8. How much will he repay? Cash____________ or Interest rate___________________________________ or Goods (enter items) ____________________________ Value _____________________________ 9. Did the borrower provide any collateral? Y/N If Yes, write details here:
Approximate value of collateral______________________________________________________________ Who is using the collateral now? Lender or Borrower ___________________________________
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11Brother 12 Sister 13Wofa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend
19 Non-resident friend 20 Extension Agent 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader Residence 1 Daman/Ahweriase 2 Pokrom/Nsabaa 3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam
7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural 12 Not in Ghana Purpose 1 Farming 2 Medical 3 Consumption 4 Trading 5 Other business 6 Travel 7 Funeral 8 Other Ceremony 9 School fees
VILLAGE
HHN
RESPONDENT RESPOND.
# DATE
ROUND
PAGE 9
Section 2c: Lending Continuation
(One form to be filled for EACH outstanding loan) Bring out the loan form completed in earlier round. Remind the respondent of the loan in question
Name of Lender
Cash / Goods Enter amount/ items
Value
Ask questions 1-4 of all respondents 1. Since our last interview, did the […..] give you any repayments, or any gifts in thanks for this loan? Y / N
If the answer to 1 is No, ask questions 6 and 7 2. If Yes, what did he give you? Cash Goods (enter items) ____________________________ Value ______________________________ Date/ how many days ago_______________________________________ 3. Since our last interview, did you give him any further installments of money or goods that are part of this loan? Y / N If
yes, what did you give him? Cash Goods (enter items) ____________________________ Value
Date/ how many days ago_______________________________________
4. Do you expect to receive anything from the borrower in the future on account of this loan? Y / N If yes, what do you expect to receive? When do you expect to receive it?
5. Do you consider this loan to be fully repaid? Y / N If no, have you taken any action to attempt to be repaid? Describe the action:
6. Would you lend to this borrower again, if he requests a loan? Y / N 7. Has the borrower experienced any unexpected problems on his farm or in his family since you gave him this loan?Y/N
If yes, what was the problem and when did it occur?
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11Brother 12 Sister 13Wofa
14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend 19 Non-resident friend 20 Extension Agent 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest
28 Trader Residence 1 Daman/Ahweriase 2 Pokrom/Nsabaa 3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural
12 Not in Ghana Purpose 1 Farming 2 Medical 3 Consumption 4 Trading 5 Other business 6 Travel 7 Funeral 8 Other Ceremony 9 School fees
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE
ROUND
PAGE 10
Section 3a: Borrowing
(One form to be filled for each loan) Ask questions 1 only once, for each respondent 1. In the last two months, have you received any loans that you have NOT YET REPAID? This could include money,
food, or goods, but not land. Y/N [If no, END] 2. What did you borrow? Cash (enter amount)________________________________________________________
Goods (enter items) ____________________________ Value _______________________________________ 3. When did you get the loan (date/ how many days ago)?____________________________________________ 4. Who did you borrow from? Name(the name is optional/use IRID) _______________________________________
Residence_________________ Relationship ________________________________________________________ 5. Was there a witness to the loan? Y/N If yes, relationship to witness(record IRID)___________________________ 6. What will you use the loan for? Purpose ___________________________________________________________ 7. When do you expect to repay the loan(date/ how many days ago)?__________________________________ 8. How much will you repay? Cash_____________ or Interest rate _______________________________________ or Goods (enter items) ____________________________ Value_____________________________________ 9. Did you provide any collateral? Y / N If Yes, write details here:
Approximate value of collateral______________________________________________________________ Who is using the collateral now? Lender or Borrower
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11Brother 12 Sister 13WBfa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin
18 Local Friend 19 Non-resident friend 20 Extension Agent 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader Residence 1 Daman/Ahweriase 2 Pokrom/Nsabaa 3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi
6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural 12 Not in Ghana Purpose 1 Farming 2 Medical 3 Consumption 4 Trading 5 Other business 6 Travel 7 Funeral 8 Other Ceremony 9 School fees
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
PAGE 11
Section 3b: Borrowing Continuation
(One form to be filled for each outstanding loan) Bring out the loan form completed in earlier round. Remind the respondent of the loan in question
Name of Borrower
Cash / Goods Enter amount/ items
Value
Ask questions 1-5 on all respondents 3. Since our last interview, did you give the lender any repayments, or any gifts in thanks for the loan? Y / N
If yes, what did you give him? Cash____________________________________________________ Goods (enter items) ____________________________ Value_________________________________
Date/ how many days ago_______________________________________
If the answer to 3 is no, ask question 6 4. Since our last interview did he give you any further installments of money/goods that are part of this loan? Y / N
If yes, what did he give you? Cash _________________________________________________________ Goods (enter items) ___________________________ Value_____________________________________
Date/ how many days ago_______________________________________ 5. Do you expect to give any further repayment of this loan to the lender? Y / N
If yes, what do you expect to give? _________________________________________________________ When do you expect to give it? ____________________________________________________________
6. Do you consider the loan fully repaid? Y/ N
If No: Has the lender taken any action to attempt to be repaid? Describe the action: 7. Has the lender asked for repayment? Y/N
Why (ask for both Y and N. If answer is needs the money, probe why) 8. Would you approach this lender to borrow again, if you needed a loan? Y / N Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11Brother 12 Sister 13Wbfa 14 Uncle 15 Aunt
16 Co-wife 17 Cousin 18 Local Friend 19 Non-resident friend 20 Extension Agent 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader Residence 1 Daman/Ahweriase 2 Pokrom/Nsabaa
3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban 11 Other Rural 12 Not in Ghana Action 1 Requested payment 2 Discussed the issue with your relatives 3 Discussed the issue with (relationship code) 4 Kept collateral 5 Taken collateral
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
PAGE 12 _________________________________________________________________________________________________
Section 4: Gifts and Transfers _________________________________________________________________________________________________
1. How much chop money did your spouse give directly to you for this past two months? ____________ 2. How much chop money did you give directly to your spouse for this past two months? ____________ 3. How much did your spouse get/keep from selling crops from your farm for this past two months? ___________________ 4. How much did you get/keep from selling output from your spouse’s farms for this past two months? _________________ 5. In the last two months have you receive a gift from anyone?
6. In the last two months did you give any gifts?
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Name/ I. Roster #
Residence of giver
Item
Value
I. Roster #
Residence of giver
Item
Value
PAGE 13 _______________________________________________________________________________________________
Section 5: EVENTS QUESTIONNAIRE _______________________________________________________________________________________________ I would like to ask you about recent events that have happened to you and your household. In the last 6 months have you lost any of the crop you have to pests? Y / N Date noticed
Crop
Units
Quantity
Value
Have you lost a job or not gotten work you were expecting any time during the last 2 months (Note: this includes all work except that on the respondents own farms)? Y / N
If yes, date _________________________ Have you not been paid an amount you were expecting for work in the last 2 months? Y / N
Date
Amount
Date
Amount
Date
Amount
Have you been paid an amount you were not expecting for work in the last 2 months? Y/N
Date
Amount
Date
Amount
Date
Amount
In the last 2 months, have you received any loan repayment you were not expecting?
Date
Amount
Date
Amount
Date
Amount
In the last 2 months, have you not received a loan repayment you were expecting?
Date
Amount
Date
Amount
Date
Amount
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
PAGE 14
Now I would like to ask you about your health for the last year. Please tell me about significant events that have happened regarding your health in the last year (including illness, pregnancies, etc)
Event
Date started
Duration # of days
Days of work/farming missed
Resulting expenses
Who paid?
Has anything been stolen from you in the last 6 months? Y / N
If yes, value ____________ Has anything been stolen from the household since last 6 months? Y / N
If yes, value ____________ Have you had a sudden death in your family in the last 6 months? Y/N
If yes, did you make any contributions towards the organization of the funeral? Y/N If yes, how much?_______________________ date_______________________
In the last 2 months have you had any other unexpected expenses? Y / N
event
date
amount
event
date
amount
In the last 2 months has your spouse had any unexpected expenses? Y / N
event
date
amount
event
date
amount
PAGE 15 If you farm pineapple, did you sell some in the last 6 months? Y / N sale date
did the exporter reject some?
was this more than expected?
Amount received
Amount that you expected to receive
Y / N
Y / N
Y / N
Y / N
Y / N
Y / N
Y / N
Y / N
Y / N
Y / N
Did you receive any payments in advance for the output? If Yes, how much? _________________________________ Did you have to return any of that amount? Y / N Why?______________________________________ Crops: 1 Cassava 2 Maize 3 Plantain 4 Cocoyam 5 Yam 6 Pineapple 7 Tomato 8 Garden egg 9 Okro 10 Pepper 11 Oranges 12 Banana 13 Avocado 14 Oil Palm 15 Cocoa 16 Sugar Cane 17 Bean
18 Groundnut Unit: 1 Pounds 2 Kilos 3 Tons 4 Minibag 5 Maxibag 6 Liter 7 Gallon 8 Beer bottle 9 Hundred Fruits 10 Cobs 11 American tin (olonka) 12 Basket 13 Bowl
Units continued 14 Bunch 15 Sucker 16 Kerosine tin 17 Rope 18 Headload 19 Tomato tin
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11 Brother 12 Sister 13 Wofa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin
18 Local Friend m/f 19 Non-resident m/f friend 20 Extension Agent m/f 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader m/f
Events 1 termites 2 other insects 3 rodents 4 grasscutter 5 other animal 6 wilt 7 leaves fall off 8 mushroom/fungus 9 rotten roots 10 rotten crops 11 not enough water 12 flood 13 chemical damage 14 planting error 15 unidentified disease 16 no market for crop
Severity: 1 Minor 2 Noticable loss 3 Significant loss 4 Major loss 5 Total Crop Failure Response: 1 None 2 Replant 3 Specific chemicals 4 Hand picking 5 Stop cultivating 6 Uproot affected area 7 Uproot entire farm
8 Destroy affected crop 9 Make traps 10 Change seed or variety 11 Change method of planting 12 Treat seeds before planting 13 Treat suckers 14 Can watering 15 Pump irrigation
PAGE 16 Tell me about the problems on your farm in the last six months (such as wilt, termites, pilfered crops, crop loss for some other reason)?
Dates
Plot
Event (use codes, if possible)
Crops
% of plot
severity
response
discussed with
value of damage
Have you had any sudden livestock deaths in the last 6 months? Y / N If yes, what type of livestock? ________________________ How many died?__________________________________ What did you do with the dead livestock? _______________________________________________________________
How many do you have left? _________________________
PAGE 17 _________________________________________________________________________________________________
Section 6: Asset Holdings and Trading Stocks _________________________________________________________________________________________________
I. A. I would like to ask you about your own stores of food and farm output: Food/crop
Unit
Quantity
Value
When stored?
How Treated?
Is this yours, your spouses’, or joint?
B. I would like to ask you about your own stores of seeds and planting material: Seed
Unit
Quantity
Value
When stored?
How Treated?
Crops/Food: 1 Cassava 2 Maize 3 Plantain 4 Cocoyam 5 Yam 6 Pineapple 7 Tomato 8 Garden egg 9 Okro 10 Pepper 11 Oranges 12 Banana
13 Avocado 14 Oil Palm 15 Cocoa 16 Sugar Cane 17 Bean 18 Groundnut Unit: 1 Pounds 2 Kilos 3 Metric Tonnes 4 Minibag 5 Maxibag
6 Liter 7 Gallon 8 Beer bottle 9 Hundred 10 Fruits 11Olonka 12 Basket 13 Bowl 14 Bunch 15 Sucker 16 Kerosine tin 17 Rope 18 Headload
19 Tomato tin 20 Marg. Tin 21 Cobs How Treated 1 None/Dried 2 Smoke 3 Dusban 4 Other Chemical (Specify)
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
PAGE 18 C. I would like to ask about your own stores of farm chemicals: Chemical
Unit
Quantity
Current Value
D. I would like to ask you about your own farm equipment, machines, motor vehicles or other equipment (e.g., chain saw, gun):
Equipment
Quantity
Age
Condition
Total current Value
E. I would like to ask you about any equipment you own for your businesses outside of farming (for example distilling equipment, a car, sewing machine, cooking utensils, grinding mill, carpentry tools)
Equipment
Quantity
Age
Condition
Total Current Value
PAGE 19
F. I would like to ask you about any goods you have to trade at this moment: Goods
Quantity
Units
Value
Chemicals: 1 Karate 2 Acellic 3 Dusban 4 Cymbush 5 Cymethoate 6 Sumithion 7 Furadan 8 Diazinon 11 Biobit 12 Dipel 2x 13 Of-nak 21 Atrazine 22 Hyvar X 23 Diuron 24 Gramoxone 25 Fusilad 26 Roundup 27 Basta
31 Cocide 32 Champion 33 Topsin M 34 Bavistin 35 Alielte 41 NPK 15-15-15 42 NPK 23-15-5 43 NPKMg 11-5-27-5 44 Ammonia 45 Urea 46 Lobi 44 47 Grofol (NPK) 48 K Fol (Potash) 49 Wuxal 51 Carbide
52 Biozyome 53 Almephon Unit: 1 Pounds 2 Kilos 3 Tons 4 Minibag 5 Maxibag 6 Liter 7 Gallon 8 Beer bottle 9 Hundred 10 Fruits 11 American tin 12 Basket 13 Bowl 14 Bunch 15 Sucker
16 Kerosine tin 17 Rope 18 Headload 19 Tomato tin Condition: 1 Like new 2 Good 3 Average 4 Poor 5 Not working
PAGE 20 II. Livestock: Please tell me about the animals that you own:
Number
Price of each if sold today
Shared? (If yes, enter Roster #)
How Shared?
Male
Female
Calves-Male
Goats
Calves-Female
Male
Female
Calves-Male
Sheep
Calves-Female
Male
Female
Pig
Piglets
Layers
Chickens
Broilers
Guinea Fowl
Other Poultry
Rabbits
Other:
Relationship: 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather
10Grandmother 11Brother 12 Sister 13 Wofa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin (m/f) 18 Local friend (m/f) 19 Non-residident
friend (m/f) 20 Extension Agent (m/f) 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader (m/f)
PAGE 21 III. Financial Saving: A. Do you use a susu collector? Y/N [If no, go to B] Frequency
Deposit Amount
Accumulated Savings
Collection Date
Relationship to collector
B. Are you a member of an esusu group? Y/N [If no, go to C] Frequency
Deposit Amount
Pot size
Collection date
Relationship with group leader
C. Are you holding money for anyone? Y/N [If no, go to D] Relationship
Location
Amount
Since when
D. Is anyone holding any money for you? Y/N [If no, go to E] Relationship
Location
Amount
Since when
Relationship: 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter
9 Grandfather 10Grandmother 11Brother 12 Sister 13 Wofa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin (m/f)
18 Local friend (m/f) 19 Non-residident friend (m/f) 20 Extension Agent (m/f) 21 Market 22 Cooperative 23 NGO 24 Exporter
25 Village head 26 Lineage head 27 Priest 28 Trader (m/f)
PAGE 22 E. Do you have any deposits in a bank? Y/N [If no, go to F] Location
Type of Account
Balance
F. Do you have any bonds, stock or other financial asset? Y/N [If no, go to G] Use this space to record details (cost of bond, maturity date, face value, or company and number of shares):
G. Do you currently receive, or do you anticipate receiving, a pension? Y/N
Source:____________________________________________________________
Year started receiving/expect to start receiving:____________________________
Current monthly amount:___________ or anticipated monthly amount: _________
H. Could you tell us the approximate current value of your jewelry, if any? ___________
I. Could you tell us the approximate current value of your cloth, if any? ______________
At this point, reiterate the confidential nature of the interview!
J. Could you tell us how much cash you have now? ______________________________
In addition to these cedis, do you have any foreign currency? _________________
If yes, how much ___________________________________________________
PAGE 23 Now I would like to ask you about the stocks of goods that you own for trading. Could you please list for me the goods that you own that you are planning to sell? This includes raw materials to be used in any business (such as food for cooking for sale), but it does not include items harvested from your own plots.
Item Quantity
Total Value
PAGE 24
_________________________________________________________________________________________________________ Section 7: Family Background
_________________________________________________________________________________________________________
1. Family structure and wealth Family or village office you hold _________________________________________________________ Please describe any privilege this office gives you:
Family home town__________ and region__________ Your first language_______________________ Are you the first of your family to reside here? Y/N
If no, for approximately how many years has your family lived here?_______________________ If yes, for approximately how many years have you lived here?____________________________
Which abusua/clan are you a member of: (1) Oyoko (2) Asone (3) Agona Other __________________________________________________ Your income this year from family land rented out ____________ Size of family land sold or leased (long-term) in last 12 months ___________ Your income from sale _________
2. Parents’ Economic Background
If dead, Year of death
Years of school
First occupation
Second occupation
birthplace
residence
wife # or # of wives
# of children
mother
father
Family or village office your parent’s hold/held _________________________________________________________ Please describe any privilege this office gives you:
Land Size Unit 1 Ropes 2 Poles 3 Acres 4 Hectares
5 Square miles 6 Farms Period
1 Daily 2 Past week 3 Past two weeks 4 Previous full month 5 Since January 1
6 Annual 7 Last major season 8 Last minor season 9 School term
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE
ROUND
PAGE 25 Age at start of independent farming (or first marriage, if not a farmer)_______________ Please give us the following information about your parents. If they are already death, please state at year of death…
3. Fosterage Did you live with anyone away from your parents for at least a year while growing up?
4. Land Inheritances, received or anticipated
Do you have someone you might expect to inherit/ have inherited land from? Y/N If yes, what is your relationship to the person?______________________________
What is the size of the land?______________________________________ What is the current value of the land?_______________________________
Do you expect to inherit anything else? Y/N If yes, what is your relationship to the person?______________________________
What is the current value of the item?_______________________________ Residence/Place Roof Floor 7 Grandson 18 Local Friend Purpose 1 Daman/Ahweriase 12 Not in Ghana 1 Roofing Sheets 1 Concrete 8 Granddaughter 19 Non-resident friend 1 Househelp 2 Pokrom/Nsabaa 13 Family home town 2 Bamboo 2 Dirt 9 Grandfather 20 Extension Agent 2 Schooling 3 Oboadaka/ Kwamekrom 3 Grass/palm thatch 10 Grandmother 21 Market 3 Adopted 4 Konkonuru Land Area Unit Relationship 11Brother 22 Cooperative 4 Farming 5 Aburi 1 Ropes Walls 1 Self 12 Sister 23 NGO 5 Apprentice 6 Nsawam 2 Poles 1 Brick 2 Spouse 13Wbfa 24 Exporter Occupation 7 Eastern region rural 3 Acres 2 Plaster over brick 3 Son 14 Uncle 25 Village head 1 Farmer 8 Eastern region town 4 Hectares 3 Concrete block 4 Daughter 15 Aunt 26 Lineage head 2 Trader 9 Accra 5 Farms 4 Mud 5 Father 16 Co-wife 27 Priest 3 Artisan
10 Other Urban 5 Bamboo 6 Mother 17 Cousin 28 Trader 4 Civil Servant
5 Labourer
Size of farms cultivated by mother and father own
house? Y/N
material for
unit
total
cocoa
oil palm
food
other
roof
walls
floor
mother
father
ages
relationship
place purpose foster parent’s
occupation
number of farms
main farming activity
PAGE 26 Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11Brother 12 Sister 13WBfa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend 19 Non-resident friend 20 Extension Agent 21 Market 22 Cooperative 23 NGO 24 Exporter 25 Village head 26 Lineage head 27 Priest 28 Trader Residence/Location 1 Daman/Ahweriase 2 Pokrom/Nsabaa 3 Oboadaka/ Kwamekrom 4 Konkonuru 5 Aburi 6 Nsawam 7 Eastern region rural 8 Eastern region town 9 Accra 10 Other Urban
11 Other Rural 12 Not in Ghana Land Area Unit 1 Ropes 2 Poles 3 Acres 4 Hectares 5 Farms
PAGE 27 _______________________________________________________________________________________________________
Section 8: Learning (Village1) _______________________________________________________________________________________________________
Use y=yes, n=no, 99=yes but he wouldn’t know/have any Name --> xxxxx Traditional
Leader Extension officer
HHN/ID ---> 31 0 100 9 200 9 Do you know ---? Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N When did you last talk with ---?
In a normal month, how often do you talk with ----?
Have you ever gone to --- for advice about your farm?
Could you go to --- if you had a problem with unhealthy crops?
Could you go to --- if you wanted to find a buyer for any of your crops?
Could you ever go to---if you had financial problems?
Could you ever go to---to borrow money?
Could you ever go to – for advice on how to access loans?
y – yes Codes for Last spoke n – no 1 today 99 - yes, but he wouldn’t know/have any 2 yesterday
3 past week 4 past two weeks 5 past month 6 past six months 7 past year 8 more than a year now
HHN
RESPONDENT RESPOND. #
DATE
ROUND
VILLAGE 1
PAGE 28
Organizations Are you still a member of _________________________ Y /N ________________________ Y / N ________________________________ Y / N ______________________________________ Y / N (Fill in from 1996 groups identified) If not, why did you leave the organization? Which of the groups is no longer functioning?____________________________________________ Ask if respondent is currently a member of the following organizations 1. Onipa Hiamoa Kuw (community improvement group) (0) not a member (1) active member (2) inactive/past member (Circle one) 2. Sie wo ho (funeral organization) (0) not a member (1) active member (2) inactive/past member (Circle one) 3. Biakoy kuo (farming organization) (0) not a member (1) active member (2) inactive/past member (Circle one) 4. Youth group (0) not a member (1) active member (2) inactive/past member (Circle one) 5. Community works group (0) not a member (1) active member (2) inactive/past member (Circle one) 6. Any other organization? Name: _____________ (0) not a member (1) active member (2) inactive/past member (Circle one)
PAGE 27 ________________________________________________________________________________________________________
Section 8: Learning (Village2) ________________________________________________________________________________________________________
Use y=yes, n=no, 99=yes but he wouldn’t know/have any Name --> xxxxx Traditional
Leader Extension officer
HHN/ID ---> 61 0 100 9 200 9 Do you know ---? Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N When did you last talk with ---?
In a normal month, how often do you talk with ----?
Have you ever gone to --- for advice about your farm?
Could you go to --- if you had a problem with unhealthy crops?
Could you go to --- if you wanted to find a buyer for any of your crops?
Could you ever go to---if you had financial problems?
Could you ever go to---to borrow money?
Could you ever go to – for advice on how to access loans?
y – yes Codes for Last spoke n – no 1 today 99 - yes, but he wouldn’t know/have any 2 yesterday
3 past week 4 past two weeks 5 past month 6 past six months 7 past year 8 more than a year now
HHN
RESPONDENT RESPOND. #
DATE
ROUND
VILLAGE 2
PAGE 28
Organizations Are you still a member of ____________________________________________________________________ (Fill in from 1996 groups identified) If not, why you leave the organization? Which of the groups is no longer functioning?____________________________________________ Ask if respondent is currently a member of the following organizations 1. Town Development Committee (0) not a member (1) active member (2) inactive/past member (Circle one) 2. School Management Committee (0) not a member (1) active member (2) inactive/past member (Circle one) 3. Assembly Committee (0) not a member (1) active member (2) inactive/past member (Circle one) 4. Funeral Committee (0) not a member (1) active member (2) inactive/past member (Circle one) 5. Xxx Pineapple Farmers Association (0) not a member (1) active member (2) inactive/past member (Circle one) 6. New (unnamed) Pineapple Farmers Association (0) not a member (1) active member (2) inactive/past member (Circle one) 7. Clinic Expansion Project (0) not a member (1) active member (2) inactive/past member (Circle one) 8. Any other organization?_______________________________ (name) (0) not a member (1) active member (2) inactive/past member (Circle one)
PAGE 27 _______________________________________________________________________________________________________
Section 8: Learning (Village3) ______________________________________________________________________________________________________
Use y=yes, n=no, 99=yes but he wouldn’t know/have any Name --> xxxxx Traditional
Leader Extension officer
HHN/ID ---> 44 0 51 or 68 0 200 9 Do you know ---? Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N When did you last talk with ---?
In a normal month, how often do you talk with ----?
Have you ever gone to --- for advice about your farm?
Could you go to --- if you had a problem with unhealthy crops?
Could you go to --- if you wanted to find a buyer for any of your crops?
Could you ever go to---if you had financial problems?
Could you ever go to---to borrow money?
Could you ever go to – for advice on how to access loans?
y – yes Codes for Last spoke n – no 1 today 99 - yes, but he wouldn’t know/have any 2 yesterday
3 past week 4 past two weeks 5 past month 6 past six months 7 past year 8 more than a year now
HHN
RESPONDENT RESPOND. #
DATE
ROUND
VILLAGE 3
PAGE 28
Organizations Are you still a member of ____________________________________________________________________ (Fill in from 1996 groups identified) If not, why you leave the organization? Which of the groups is no longer functioning?____________________________________________ Ask if respondent is currently a member of the following organizations 1. Xxx Farmers Association (0) not a member (1) active member (2) inactive/past member (Circle one) 2. Xxx Pineapple Growers and Cooperative Society (0) not a member (1) active member (2) inactive/past member (Circle one) 3. Road Improvement Project (headed by Xxx) (0) not a member (1) active member (2) inactive/past member (Circle one) 4. Community Improvement Group (headed by Xxxx) (0) not a member (1) active member (2) inactive/past member (Circle one)
5. Town Development Committee (0) not a member (1) active member (2) inactive/past member (Circle one) 6. Any other organization?_______________________________ (name) (0) not a member (1) active member (2) inactive/past member (Circle one)
PAGE 27 _______________________________________________________________________________________________________
Section 8: Learning (Village4) _______________________________________________________________________________________________________
Use y=yes, n=no, 99=yes but he wouldn’t know/have any Name --> xxxxx Traditional
Leader Extension officer
HHN/ID ---> 81 0 100 0 200 9 Do you know ---? Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N When did you last talk with ---?
In a normal month, how often do you talk with ----?
Have you ever gone to --- for advice about your farm?
Could you go to --- if you had a problem with unhealthy crops?
Could you go to --- if you wanted to find a buyer for any of your crops?
Could you ever go to---if you had financial problems?
Could you ever go to---to borrow money?
Could you ever go to – for advice on how to access loans?
y – yes Codes for Last spoke n – no 1 today 99 - yes, but he wouldn’t know/have any 2 yesterday
3 past week 4 past two weeks 5 past month 6 past six months 7 past year 8 more than a year now
HHN
RESPONDENT RESPOND. #
DATE
ROUND
VILLAGE 4
PAGE 28
Organizations Are you still a member of ____________________________________________________________________ (Fill in from 1996 groups identified) If not, why you leave the organization? Which of the groups is no longer functioning?____________________________________________ Ask if respondent is currently a member of the following organizations 1. Wo Yε Na Yε Yε Ma Wo (funeral organization) (0) not a member (1) active member (2) inactive/past member (Circle one) 2. Any other organization? _______________________________ (name) 0) not a member (1) active member (2) inactive/past member (Circle one)
Page 29 ____________________________________________________________________________________________________________________
Section 9a: Family Expenses ________________________________________________________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Yourself Spouse All others Has anyone in your household spent anything on ... Y/N
Period
Value Value Value Relation School fees, parent assoc. School term School uniform and shoes Annual Other school expenses including books and stationary
School term
Medicine Previous full month Other medical expenses Previous full month Adult shoes Annual Children shoes Annual Cloth Annual Material for adult clothes Annual Material for child clothes Annual Adult clothes Previous full month Child clothes Previous full month Soap Past two weeks Personal care products (pomade, combs, shampoo...)
Previous full month
Brooms, home maint. products Previous full month Home repairs, painting Annual Building of new house in this town Annual Building of new house in another town Annual Public transportation Previous full month Petrol, motor oil Previous full month Repairs, other vehicle exp. Previous full month Newspapers Previous full month Charcoal, firewood Past two weeks Matches, candles Previous full month Other fuel for cooking, light Previous full month Stove, coal pot Annual Kitchen equipment (pots, dishes) Annual Lanterns Previous full month Furniture Last 3 months Sheets, towels Previous full month Hairdressing, haircuts Past two weeks Domestic servants Previous full month Jewelry and watches Last 3 months Entertainment (cinema, sports, tapes, toys)
Previous full month
Taxes Previous full month
Page 30
Yourself Spouse All others Has anyone in your household spent anything on ... in the past 12 months? Y/N
Period
Value Value Value Relation
Weddings, dowries Annual Contributions for organizing a family funeral
Last 3 months
Attending funerals Previous full month Lottery tickets Past two weeks Cigarettes, tobacco, cola Daily Sewing machine Annual Radio, tape player Annual Other major appliance Annual Bicycle Annual Motorbike Annual Car, other vehicle Annual Camera Annual
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11 Brother 12 Sister 13 W]fa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend m/f 19 Non-resident friend m/f 20 Extension Agent m/f 21 Market 22 Cooperative 23 NGO 24 Exporter m/f 25 Village head 26 Lineage head 27 Priest 28 Trader m/f 29 employed by resp (employee) 30 employer of resp. 31 landlord 32 tenant/renter 33 niece 34 nephew
Page 31 ____________________________________________________________________________________________________________________
Section 9b: Food From Family Farms ________________________________________________________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
From your farms
From spouse’s farm
From other farms
Have people in your household eaten … from your own farms in the past 12 months? Y/N
Period
Value Value Value RelationMaize Past week Fresh Cassava Past week Gari, kokonte, other Previous full
month
Yams Annual Cocoyam Past week Plantain Previous full
month
Potato, sweet potato Previous full month
Oil Palm Fruit Past two weeks Groundnuts Annual Fish, crabs, snails, shellfish Past week Chicken Annual Pigeon, dove, duck, turkey, other fowl
Annual
Goat, sheep Annual Bushmeat Previous full
month
Eggs Past two weeks Palm oil Previous full
month
Other oil Previous full month
Pineapples Past week Oranges, mangoes, pawpaws, bannanas, coconut, other fruit
Previous full month
Sugar cane, honey Previous full month
Palm wine, akpeteshie Last 3 months Other drinks Last 3 months Tomatoes Annual Onions Annual Garden egg, Okro Annual Beans, peas Annual Pepper Past week Kontomle Past week Other vegetables Past two weeks Other foods Past week Firewood Past week
Page 32 Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11 Brother 12 Sister 13 W]fa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend m/f 19 Non-resident friend m/f 20 Extension Agent m/f 21 Market 22 Cooperative 23 NGO 24 Exporter m/f 25 Village head 26 Lineage head 27 Priest 28 Trader m/f 29 employed by resp (employee) 30 employer of resp. 31 landlord 32 tenant/renter 33 niece 34 nephew
Page 33
____________________________________________________________________________________________________________________
Section 9c: Purchased Food ________________________________________________________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Yourself Spouse All Others Has anyone in your household purchased… for consumption in the past 12 months? Y/N
Period Value Value Value Relation
Maize Past week Corn dough Past week Rice Previous full
month
Bread, macaroni or flour Past week Cassava Past week Gari, kokonte, other Previous full
month
Yams Previous full month
Cocoyam Previous full month
Plantain Previous full month
Potato, sweet potato, millet, guinea corn, sorghum
Previous full month
Oil Palm Fruit Past week Groundnuts Past week Fish, crabs, snails, shellfish Daily Chicken Annual Pigeon, dove, duck, turkey, other fowl
Annual
Goat, sheep Previous full month
Beef Previous full month
Eggs Previous full month
Palm oil Past week Other oil Previous full
month
Margarine, butter Previous full month
Pineapples Previous full month
Oranges, mangoes, pawpaws, bannanas, coconut, other fruit
Previous full month
Sugar cane, toffee, honey Previous full month
Milk, milk powder, baby food
Previous full month
Page 34
Yourself Spouse All Others
Has anyone in your household purchased… for consumption in the past 12 months? Y/N
Period
Value Value Value Relation Sugar, milo, tea, coffee Previous full
month
Non-alchoholic beverages (home or away from home)
Previous full month
Alchoholic beverages (home or away from home)
Previous full month
Salt Previous full month
Tomatoes Past week Onions Past week Garden egg, Okro Past week Beans, peas Previous full
month
Pepper Past week Kontomle Past two weeks Other vegetables Past two weeks Fufu, kenkey, banku, other prepared foods, chop bars
Daily
Other food Past week
Page 35 ____________________________________________________________________________________________________________________
Section 10: Other Income ________________________________________________________________________________ 1. Please tell me about any business other than farming you did in the last two months: 2.
Please tell me about the work for pay for the past two weeks: Period: 1 Daily 7 Last major season 2 Past week 8 Last minor season 3 Past two weeks 4 Previous full month 5 Since January 1 6 Annual
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Type of business Value of initial stock of raw materials or goods for sale
A. Expenses Purchased raw materials or goods for sale Raw materials or goods for sale from farm Own labor days:
daily hours: daily value:
days: daily hours: daily value:
days: daily hours: daily value:
Spouse labor days: daily hours: daily value:
days: daily hours: daily value:
days: daily hours: daily value:
Other unpaid labor days: daily hours: daily value:
days: daily hours: daily value:
days: daily hours: daily value:
Wages and payments for services Transport/fuel Building costs or equipment purchase Equipment or building repairs, or rental Taxes or fees paid Other B. Revenue: Sales/Other Payments Value of own and household consumption Value of gifts given from business Value of final stock of raw materials or goods for sale
1 2 3 Occupation Casual or permanent work? Days worked over the past two weeks Cash earnings the past two weeks Value of kind earnings the past two weeks Average hours per day Have you been paid in full
Page 36 3. Please tell me about your income from farms for the past two months. 4. In the past two months have you had any other source of income, beyond those that we have
discussed so far? For example, there might be a pension, or you might have received money from bride payments or inheritances. Also includes income from sales of land.
5. Does your spouse get any income from a business other than farming? Y / N
Does your spouse work for pay? Y / N Does your spouse get income from farms outside of this town? Y / N Does your spouse get income from any other source other than farming? Y / N
If the answer to any of these questions is yes, please estimate for me the net income earned by your spouse from her business or employment:
Period: 1 Daily 2 Past week 3 Past two weeks 4 Previous full month 5 Since January 1 6 Annual 7 Last major season 8 Last minor season Weather: 1 Better than usual 2 Same as usual 3 Worse than usual
Location Expenses Income from sale of output
Weather in that location over that period
1 2 3 4 5 6 7 8 9
Type of Income Amount of income since our last interview
Spouse’s business, job or other income source
Spouse ID
Period Net Income
Page 37 ____________________________________________________________________________________________________________________
Section 11: Plot Questionnaire ________________________________________________________________________________ I. Current Contracts
A. Is this your own land. Or your family land? Y / N [If yes, go to B]
1. Are you renting this plot? Y / N [If no, skip to 2]
Contract type: Abusa Abunu Cash Rent Other:_____________________
Year contract began:_____________ Month:_______________________
Crop/Input Share of tenant Is the rent dependent on the type of crop you plant? Y / N
If yes, how so?___________________________________________________________
Any planned division of land? Y / N If yes, map.
Rent is for how long?________________ Outstanding rent:_____________________
Value of payments made for this rental period:__________________________________
Residence of owner:_________________ Relationship to owner:_________________
Name of owner:__________________________________________________________
Do you do other business with the owner? (Y / N) If Yes, explain_________________
_______________________________________________________________________
Have there been any disputes over the rent during this period (Y / N) If Yes, explain
_______________________________________________________________________
2. Other (i.e. neither own/rent: explain):_________________________________________
Month/ Year when you began farming this plot: Have you given anything to the owner on account of the land? Y/ N
Cash:________ Kind:_________________ Other:__________________
Do you plan to give anything to the owner on account of the land? Y/ N
Cash:________ Kind:_________________ Other:__________________
Value of payments made for this rental period:__________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Page 38
Residence of owner:_________________ Relationship to owner:_________________
Name of owner:__________________________________________________________
Do you do other business with the owner? (Y / N) If Yes, explain________________
_______________________________________________________________________
Have there been any disputes over the rent during this period? (Y / N) If Yes, explain
_______________________________________________________________________
B. Is someone else using this land? Y/N [If no go to II]
1. Are you renting this plot out to someone else? Y / N [If no, skip to 2]
Contract type: Abusa Abunu Cash Rent Other:_____________________
Year contract began:_____________ Month:_______________________
Crop/Input Share of landlord Any planned division of land? Y / N If yes, map.
Amount of fixed rent:________________ Date paid:___________________________
Rent is for how long?________________ Outstanding rent:_____________________
Value of payments made for this rental period:__________________________________
Residence of tenant:_________________ Relationship to tenant:_________________
Name of tenant:__________________________________________________________
Do you do other business with the owner? (Y / N) If Yes, explain _________________
_______________________________________________________________________
[Go to II]
2. Other (explain):__________________________________________________________ Month/ Year when he/she began cultivating this plot: Has he/she given you anything on account of the land? Y / N
Cash:________ Kind:_________________ Other:__________________
Do you expect to receive anything on account of the land? Y/ N
Cash:________ Kind:_________________ Other:__________________
Residence of other person:_______ Relationship to other person :_________________
Name of other person:_____________________________________________________
Page 39
Do you do other business with the owner? (Y/N, explain)_________________________
_______________________________________________________________________
[Go to II]
II. Plot History
How did you obtain this land?_____________________________________________________________
From whom did you obtain the land?_______________________________________________________
When did you obtain this land?____________________________________________________________
Did they pay anything on account of this land? Y/N
Amount____________________ Period__________________ To whom_______________
If respondent purchased, full price:__________________________________________________________
If respondent mortgaged, terms:_____________________________________________________________
Have you paid anything this year on account of the land to the person from whom you obtained it? Y/ N
Cash:________ Kind:____________ Other:_______________ Abunu/Abusa____________
Crop/Input Share of landlord
Do you expect to pay anything this year on account of the land? Y/ N
Cash:________ Kind:____________ Other:_______________ Abunu/Abusa____________
Crop/Input Share of landlord
Tenancy Disputes: Have you had a dispute with your tenant/ borrower? Y/N
Land Disputes: Have there been any disputes over the control of this plot in the family?
If Yes, explain______________________________________________________
Has the village head intervened in any dispute? Y/N
Have there been any court cases regarding this plot? Y/N
Any written documentation regarding the plot? Y/N
Page 40 Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11 Brother 12 Sister 13 W]fa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend m/f 19 Non-resident friend m/f 20 Extension Agent m/f 21 Market 22 Cooperative 23 NGO 24 Exporter m/f 25 Village head 26 Lineage head 27 Priest 28 Trader m/f 29 employed by resp (employee) 30 employer of resp. 31 landlord 32 tenant/renter 33 niece 34 nephew Abusua 1 agona 2 asona 3 bretuo 4 asakyiri 5 abrade 6 oyoko 7 weme 8 twidan 9 anlo 10 nkonya 11 kona 12 aduama/aduana 13 atsegeme Divorce 1. Self 2. Spouse 3. Share Crops 1 Cassava 2 Maize 3 Plantain 4 Cocoyam 5 Yam 6 Pineapple 7 Tomato 8 Garden egg
Page 41 9 Okro 10 Pepper 11 Oranges 12 Banana 13 Avocado 14 Oil Palm 15 Cocoa 16 Sugar Cane 17 Bean 18 Groundnut 19 Cabbage 20 Cocoa 21 Pawpaw 22 Firewood 23 Watermelon 24 Coconut 25 Cashew 26 Ginger 27 Sweet pepper 28 Potato 29 Sun flower Events 1 termites 2 other insects 3 rodents 4 grasscutter 5 other animal 6 wilt 7 leaves fall off 8 mushroom/fungus 9 rotten roots 10 rotten crops 11 not enough water 12 flood 13 chemical damage 14 planting error 15 unidentified disease 16 no market forcrop 17 theft 18 bush fire (unintended) Severity 1 Minor 2 Noticable loss 3 Significant loss 4 Major loss 5 Total Crop Failure Response 1 None 2 Replant 3 Specific chemicals 4 Hand picking 5 Stop cultivating 6 Uproot affected area 7 Uproot entire farm 8 Destroy affected crop 9 Make traps 10 Change seed or variety 11 Change method of planting 12 Treat seeds before planting 13 Treat suckers 14 Can watering 15 Pump irrigation 16 weeding
Page 42 ____________________________________________________________________________________________________________________
Section 13: Plot Wrap up ________________________________________________________________________________ One form to be filled for each of the plots on the respondent’s plot list. 1. If someone wanted to buy all of the crops on this land now (even if they are not ready), what
would they be worth?_________________________________________________________
2. Please tell me about each crop you are growing, when you will harvest it, and the total value all of that crop will have when you harvest it.
if the respondent is growing the same crop at different stages enter them as different crops (for example, 2a, 2b, 2c) 3. Who did you obtain this plot from? _______ (add to I. roster if not there yet) 4. This plot belongs to which abusua?
(1) agona (2) asona (3) bretuo (4) asakyiri (5) abrade (6)oyoko (Other) ___________ 5. Would your spouse have access to farm this plot if you (god forbid) died? Y/N
If Y for how many years? ______ (99 = as long as they want) 6. Would your children have access to farm this plot if you (god forbid) died? Y/N
If Y for how many years ? _______ (99 = as long as they want) 7. Will you still be able to farm this plot if you and your spouse were to be divorced? Y/N
If Y for how many years? ______ (99= as long as they want) 8. Who would get the crops growing on this plot if you were divorced? (1) me (2) spouse (3) share _____% resp _______% spouse 9. Comments:
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
Crop Date to harvest Final Value of all the crop growing now
Page 43
Relationship 1 Self 2 Spouse 3 Son 4 Daughter 5 Father 6 Mother 7 Grandson 8 Granddaughter 9 Grandfather 10 Grandmother 11 Brother 12 Sister 13 W]fa 14 Uncle 15 Aunt 16 Co-wife 17 Cousin 18 Local Friend m/f 19 Non-resident friend m/f 20 Extension Agent m/f 21 Market 22 Cooperative 23 NGO 24 Exporter m/f 25 Village head 26 Lineage head 27 Priest 28 Trader m/f 29 employed by resp (employee) 30 employer of resp. 31 landlord 32 tenant/renter 33 niece 34 nephew Abusua 1 agona 2 asona 3 bretuo 4 asakyiri 5 abrade 6 oyoko 7 weme 8 twidan 9 anlo 10 nkonya 11 kona 12 aduama/aduana 13 atsegeme Di vorce 1. Self 2. Spouse 3. Share Crops 1 Cassava 2 Maize 3 Plantain 4 Cocoyam 5 Yam 6 Pineapple 7 Tomato 8 Garden egg 9 Okro
Page 44 10 Pepper 11 Oranges 12 Banana 13 Avocado 14 Oil Palm 15 Cocoa 16 Sugar Cane 17 Bean 18 Groundnut 19 Cabbage 20 Cocoa 21 Pawpaw 22 Firewood 23 Watermelon 24 Coconut 25 Cashew 26 Ginger 27 Sweet pepper 28 Potato 29 Sun flower Events 1 termites 2 other insects 3 rodents 4 grasscutter 5 other animal 6 wilt 7 leaves fall off 8 mushroom/fungus 9 rotten roots 10 rotten crops 11 not enough water 12 flood 13 chemical damage 14 planting error 15 unidentified disease 16 no market forcrop 17 theft 18 bush fire (unintended) Severity 1 Minor 2 Noticable loss 3 Significant loss 4 Major loss 5 Total Crop Failure Response 1 None 2 Replant 3 Specific chemicals 4 Hand picking 5 Stop cultivating 6 Uproot affected area 7 Uproot entire farm 8 Destroy affected crop 9 Make traps 10 Change seed or variety 11 Change method of planting 12 Treat seeds before planting 13 Treat suckers 14 Can watering 15 Pump irrigation 16 weeding
Page 45 _____________________________________________________________________________________________________________________________
Section 14: Birth History ______________________________________________________________________________________
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND
NO.
QUESTIONS AND FILTERS CODING CATEGORIES SKIP
201
Now I would like to ask about all the births you have had during your life. Have you ever given birth?
YES ...........................................................1NO .............................................................2
──<206
202
Do you have any sons or daughters to whom you have given birth who are now living with you?
YES ...........................................................1NO .............................................................2
──<204
203
How many sons live with you? And how many daughters live with you? IF NONE, RECORD ‘00’.
┌──┬──┐SONS AT HOME .................│░░│░░│ ├──┼──┤DAUGHTERS AT HOME .......│░░│░░│ └──┴──┘
204
Do you have any sons or daughters to whom you have given birth who are alive but do not live with you?
YES ...........................................................1NO .............................................................2
──<206
205
How many sons are alive but do not live with you? And how many daughters are alive but do not live with you? IF NONE, RECORD ‘00’.
┌──┬──┐SONS ELSEWHERE ............│░░│░░│ ├──┼──┤DAUGHTERS ELSEWHERE ..│░░│░░│ └──┴──┘
206
Have you ever given birth to a boy or girl who was born alive but later died? IF NO, PROBE: Any baby who cried or showed signs of life but did
not survive?
YES ...........................................................1NO .............................................................2
──<208
207
How many boys have died? And how many girls have died? IF NONE, RECORD ‘00’.
┌──┬──┐BOYS DEAD.......................│░░│░░│ ├──┼──┤GIRLS DEAD ......................│░░│░░│ └──┴──┘
208
SUM ANSWERS TO 203, 205, AND 207, AND ENTER TOTAL.
IF NONE, RECORD ‘00’.
┌──┬──┐TOTAL ..............................│░░│░░│ └──┴──┘
209
CHECK 208: Just to make sure that I have this right: you have had in TOTAL _____ births during your life. Is that correct? ┌──┐ ┌──┐ PROBE AND YES ├──┘ NO └──┴──< CORRECT │ 201-208 AS ? NECESSARY.
210
CHECK 208: ONE OR MORE ┌──┐ NO BIRTHS ┌──┐ BIRTHS ├──┘ └──┴────────────────────────────────────── ?
──<END
Page 46
211
Now I would like to record the names of all your births, whether still alive or not, starting with the first one you had. RECORD NAMES OF ALL THE BIRTHS IN 212. RECORD TWINS AND TRIPLETS ON SEPARATE LINES.
212
213
214
215
216
217 IF ALIVE:
218 IF ALIVE
219 IF ALIVE:
220 IF DEAD:
221
What name was given to your (first/next) baby? (NAME)
Were any of these births twins?
Is (NAME) a boy or a girl?
In what month and year was (NAME) born? PROBE: What is his/her birthday?
Is (NAME) still alive?
How old was (NAME) at his/her last birthday? RECORD AGE IN COM-PLETED YEARS.
Is (NAME) living with you?
RECORD HOUSEHOLD LINE NUMBER OF CHILD (RECORD ‘00’ IF CHILD NOT LISTED IN HOUSEHOLD)
How old was (NAME) when he/she died? IF ‘1 YR’, PROBE: How many months old was (NAME)? RECORD DAYS IF LESS THAN 1 MONTH; MONTHS IF LESS THAN TWO YEARS; OR YEARS.
Were there any other live births between (NAME OF PREVIOUS BIRTH) and (NAME)?
01
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
02
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
YES......... 1 NO........... 2
03
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
YES......... 1 NO........... 2
04
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
YES......... 1 NO........... 2
05
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
YES......... 1 NO........... 2
06
SING...1 MULT..2
BOY.. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES ...... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS.......1 │░░│░░│ ├──┼──┤MONTHS .2 │░░│░░│ ├──┼──┤YEARS ....3 │░░│░░│ └──┴──┘
YES......... 1 NO........... 2
Page 47
212
213
214
215 216 217
IF ALIVE: 218 IF ALIVE
219 IF ALIVE:
220 IF DEAD:
221
What name was given to your next baby? (NAME)
Were any of these births twins?
Is (NAME) a boy or a girl?
In what month and year was (NAME) born? PROBE: What is his/her birthday?
Is (NAME) still alive?
How old was (NAME) at his/her last birthday? RECORD AGE IN COM-PLETED YEARS.
Is (NAME) living with you?
RECORD HOUSEHOLD LINE NUMBER OF CHILD (RECORD �00' IF CHILD NOT LISTED IN HOUSEHOLD)
How old was (NAME) when he/she died? IF ‘1 YR’, PROBE: How many months old was (NAME)? RECORD DAYS IF LESS THAN 1 MONTH; MONTHS IF LESS THAN TWO YEARS; OR YEARS.
Were there any other live births between (NAME OF PREVIOUS BIRTH) and (NAME)?
07
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
08
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
09
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
10
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
11
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
12
SING ...1 MULT ..2
BOY .. 1 GIRL . 2
┌──┬──┐ MONTH│░░│░░│ └──┴──┘ YEAR ┌──┬──┬──┬──┐ │░░│░░│░░│░░│ └──┴──┴──┴──┘
YES .... 1 NO ...... 2 │ ? 220
AGE IN YEARS ┌──┬──┐ │░░│░░│ └──┴──┘
YES....... 1 NO ........ 2
LINE NUMBER
┌──┬──┐ │░░│░░│ └──┴──┘
│ ?
(NEXT BIRTH)
┌──┬──┐DAYS ...... 1 │░░│░░│ ├──┼──┤MONTHS 2 │░░│░░│ ├──┼──┤YEARS.... 3 │░░│░░│ └──┴──┘
YES .........1 NO...........2
222
Have you had any live births since the birth of (NAME OF LAST BIRTH)? YES
NO
223
COMPARE 208 WITH NUMBER OF BIRTHS IN HISTORY ABOVE AND MARK:
NUMBERS ┌──┐ NUMBERS ARE ┌──┐ ARE SAME ├──┘ DIFFERENT └──┴──< (PROBE AND RECONCILE) │ ?
CHECK: FOR EACH BIRTH: YEAR OF BIRTH IS RECORDED.
FOR EACH LIVING CHILD: CURRENT AGE IS RECORDED.
FOR EACH DEAD CHILD: AGE AT DEATH IS RECORDED.
FOR AGE AT DEATH 12 MONTHS OR 1 YR.: PROBE TO DETERMINE EXACT NUMBER OF MONTHS.
┌──┐ │░░│ ├──┤ │░░│ ├──┤ │░░│ ├──┤ │░░│ └──┘
Page 48 Now I would like to ask you about all your children born in the last 6 years (/ since 1998)
[Use names from 212-216, please fill out form for each respective child]
Name of child, explanation for absence of health card
Type of Antenatal Care / Delivery Place of Delivery Size of baby 1. Doctor 1. Your home 1. Very large 2. Nurse 2. Other home 2. Larger than average 3. Midwife 3. Government Hospital/ Clinic 3. Average 4. Traditional birth attendant 4. Private Hospital/ Clinic 4. Smaller than average 5. Relative/friend 5. Other 5. Very small 6. No one 6. Don’t know/remember 7. Other
Antenatal Care Delivery Name of Child (Please fill in from 212)
Antenatal care for preg-nancy? Y/N
Type Months into preg-nancy
Number of times
Who assisted with the delivery
Where did you give birth to […..]
Size of baby
Was baby measured at birth? Y / N
Were you given a health card? Y / N
If yes, did you lose it? Y / N If yes, skip next ques.
Do you still have it? Y / N If no, explain below
Birth weight in grams from health card
Height at birth from health card
Page 49 ________________________________________________________________________________________________________________________
Section 11b: Plot Mapping __________________________________________________________________________________
One form to be filled for each of the plots on the respondent’s plot list
North West
Comments:
VILLAGE
HHN
RESPONDENT RESPOND. #
DATE ROUND