Parental Background and Childrens Human Capital Development - - PowerPoint PPT Presentation
Parental Background and Childrens Human Capital Development Throughout Childhood and Adolescence: Evidence from Four Low- and Middle- Income Countries Andreas Georgiadis Young Lives Study Department of International Development University
Parental Budget Parental Preferences Parental Productivity Child’s Human Capital
Table 2: Regressions for HAZ-score Across countries and Age groups
Age 5 Age 8 Ethiopia India Peru Vietnam Ethiopia India Peru Vietnam Wealth index 0.169*** 0.105*** 0.144*** 0.049 0.224*** 0.163*** 0.165*** 0.116*** (0.038) (0.033) (0.031) (0.033) (0.036) (0.035) (0.030) (0.037) Mother’s education 0.022** 0.003 0.018*** 0.028*** 0.008 0.018** 0.010 0.017** (0.009) (0.007) (0.007) (0.007) (0.008) (0.007) (0.006) (0.008) Father’s education 0.011 0.012** 0.008 0.022***
0.004 0.015** 0.018** (0.008) (0.005) (0.007) (0.007) (0.008) (0.006) (0.007) (0.008) Mother’s height 0.047*** 0.042*** 0.063*** 0.050*** 0.039*** 0.041*** 0.059*** 0.047*** (0.004) (0.005) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Mother’s bargaining power 0.010 (0.025) 0.025 (0.023)
(0.020)
(0.019) 0.010 (0.026) 0.022 (0.023)
(0.020)
(0.021) Mother’s noncognitive skills
(0.028) 0.043** (0.022) 0.003 (0.022)
(0.021)
(0.025)
(0.025)
(0.022) 0.031 (0.026) Mother’s subjective well-being 0.074*** (0.028) 0.065*** (0.025) 0.033 (0.021) 0.076*** (0.023) 0.034 (0.026) 0.005 (0.026) 0.023 (0.020) 0.042 (0.024) Mother’s social capital
0.026 0.004
0.029 0.031
(0.033) (0.023) (0.024) (0.021) (0.033) (0.023) (0.054) (0.024) Mother’s aspirations for child’s education
(0.015) 0.012 (0.009) 0.042*** (0.013) 0.003 (0.011) 0.020 (0.012) 0.014** (0.007) 0.062*** (0.016) 0.002 (0.011) R-squared 0.17 0.18 0.40 0.37 0.15 0.21 0.36 0.30 Observations 1908 1937 1950 1956 1881 1923 1937 1943 Age 12 Age 15 Ethiopia India Peru Vietnam Ethiopia India Peru Vietnam Wealth index 0.191*** 0.052 0.242*** 0.093 0.124** 0.043 0.137*** 0.098** (0.052) (0.043) (0.058) (0.053) (0.055) (0.045) (0.041) (0.043) Mother’s education 0.023 0.029*** 0.000
0.015 0.033*** 0.011
(0.014) (0.010) (0.010) (0.011) (0.013) (0.011) (0.009) (0.010) Father’s education 0.001
0.006 0.026** 0.009
0.027*** (0.013) (0.008) (0.012) (0.012) (0.013) (0.009) (0.010) (0.010) Mother’s height 0.034*** 0.032*** 0.062*** 0.050*** 0.040*** 0.038*** 0.059*** 0.059*** (0.007) (0.06) (0.007) (0.006) (0.007) (0.006) (0.006) (0.005) Mother’s bargaining power
(0.040) 0.029 (0.034)
(0.031)
(0.033)
(0.042) 0.010 (0.032) 0.012 (0.029)
(0.029) Mother’s noncognitive skills 0.042 (0.041)
(0.033) 0.053 (0.034) 0.020 (0.032)
(0.043)
(0.030)
(0.034)
(0.027) Mother’s subjective well-being 0.047 (0.040) 0.075** (0.034)
(0.031) 0.062** (0.032) 0.006 (0.041) 0.041 (0.035) 0.045 (0.033) 0.032 (0.030) Mother’s social capital
(0.044)
(0.037)
(0.037)
(0.034) 0.048 (0.075) 0.007 (0.036)
(0.095)
(0.031) Mother’s aspirations for child’s education
(0.016)
(0.012) 0.021 (0.020) 0.058** (0.023)
(0.017) 0.035*** (0.014) 0.025 (0.016) 0.032 (0.020) R-squared 0.2 0.14 0.41 0.25 0.23 0.16 0.31 0.26 Observations 974 977 680 988 968 970 669 967
Table 3: Regressions for PPVT Across Countries and Age Groups
Age 5 Age 8 Ethiopia India Peru Vietnam Ethiopia India Peru Vietnam Wealth index 1.284*** 1.380** 2.701*** 2.310*** 5.688*** 3.083*** 3.297*** 2.263*** (0.467) (0.701) (0.561) (0.618) (1.046) (0.780) (0.363) (0.689) Mother’s education 0.535*** 1.097*** 0.172 0.370** 0.783*** 0.727*** 0.421*** 0.860*** (0.113) (0.145) (0.125) (0.146) (0.274) (0.197) (0.085) (0.179) Father’s education 0.130 0.361*** 0.276** 0.551*** 0.626** 0.394*** 0.506*** 0.544*** (0.092) (0.121) (0.136) (0.135) (0.243) (0.148) (0.087) (0.166) Mother’s height 0.058
0.089 0.072
0.051 (0.045) (0.071) (0.069) (0.062) (0.109) (0.088) (0.047) (0.080) Mother’s bargaining power 0.090 (0.275)
(0.454)
(0.368)
(0.370)
(0.653)
(0.618) 0.122 (0.248)
(0.479) Mother’s noncognitive skills
(0.274) 0.108 (0.429) 0.180 (0.383) 0.156 (0.384) 2.235*** (0.682) 2.643*** (0.665) 0.504** (0.255) 0.982 (0.585) Mother’s subjective well-being 0.322 (0.260) 1.910*** (0.522) 0.883** (0.352) 0.396 (0.425)
(0.687)
(0.616) 0.369 (0.264)
(0.552) Mother’s social capital 0.585 0.253
0.201
(0.343) (0.473) (0.428) (0.377) (0.915) (0.599) (0.691) (0.551) Mother’s aspirations for child’s education 0.096 (0.104) 0.308* (0.173) 0.408** (0.192) 0.149 (0.168) 0.846*** (0.293) 0.506*** (0.172) 0.957*** (0.206) 0.721*** (0.220) R-squared 0.3 0.26 0.3 0.36 0.47 0.23 0.45 0.32 Observations 1861 1851 1903 1747 1857 1901 1842 1848 Age 12 Age 15 Ethiopia India Peru Vietnam Ethiopia India Peru Vietnam Wealth index 2.495** 1.525 2.324*** 4.543*** 5.154*** 4.834*** 2.149*** 5.147*** (1.133) (0.965) (0.831) (1.090) (1.111) (1.220) (0.753) (1.047) Mother’s education 0.492 0.263 0.466*** 0.541** 0.314 0.924*** 0.257 0.232 (0.253) (0.208) (0.143) (0.228) (0.288) (0.265) (0.143) (0.211) Father’s education 0.274 0.259
1.001*** 0.386 0.178 0.543*** 0.224 (0.250) (0.166) (0.185) (0.258) (0.253) (0.211) (0.188) (0.234) Mother’s height 0.098 0.105
0.254 0.131 0.186 0.010 0.041 (0.127) (0.117) (0.088) (0.133) (0.139) (0.145) (0.091) (0.112) Mother’s bargaining power 0.953 (0.776)
(0.703) 0.712 (0.502)
(0.663)
(0.914) 0.003 (0.902)
(0.471)
(0.632) Mother’s noncognitive skills 3.149*** (0.767) 0.729 (0.763) 0.804 (0.529)
(0.720) 1.435 (0.942) 0.864 (0.924)
(0.490) 0.063 (0.593) Mother’s subjective well-being 0.283 (0.807) 2.852*** (0.778)
(0.540)
(0.694) 0.104 (0.940) 0.379 (0.980)
(0.543)
(0.656) Mother’s social capital
(0.821) 0.767 (0.724)
(0.549) 0.303 (0.633) 0.770 (1.645) 1.963** (0.906)
(1.507) 0.554 (0.646) Mother’s aspirations for child’s education 1.257*** (0.345) 1.961*** (0.292) 0.941** (0.388) 2.422*** (0.676) 1.333*** (0.415) 2.945*** (0.309) 2.146*** (0.416) 2.347*** (0.528) R-squared 0.35 0.27 0.34 0.50 0.3 0.31 0.4 0.41 Observations 953 971 672 945 962 944 652 947
Figure 8 The Size of the Coefficient of Mother’s Aspirations for Child’s Education in Child’s PPVT Score Regressions
1 2 3 Coefficient 5 8 12 15 Child's age in years Ethiopia India Peru Vietnam
Table 4: Regressions for Child’s Noncognitive Skills Across Countries and Age Groups
Age 8 Age 12 Ethiopia India Peru Vietnam Ethiopia India Peru Vietnam Wealth index 0.150*** 0.056 0.069** 0.039 0.073 0.123*** 0.166*** 0.057 (0.030) (0.030) (0.032) (0.036) (0.040) (0.042) (0.055) (0.056) Mother’s education 0.004 0.011 0.020*** 0.010
0.012 0.030*** 0.017 (0.008) (0.006) (0.008) (0.008) (0.010) (0.009) (0.012) (0.012) Father’s education 0.007 0.005
0.034*** 0.018** 0.010 0.026** (0.007) (0.005) (0.008) (0.007) (0.009) (0.008) (0.014) (0.013) Mother’s height
0.002 0.002
0.002 0.007 0.006 0.001 (0.004) (0.003) (0.004) (0.003) (0.005) (0.005) (0.007) (0.005) Mother’s bargaining power
(0.024)
(0.021) 0.003 (0.023)
(0.022) 0.037 (0.029)
(0.031)
(0.036)
(0.033) Mother’s noncognitive skills 0.145*** (0.025) 0.387*** (0.025) 0.081*** (0.027) 0.196*** (0.028) 0.381*** (0.033) 0.318*** (0.040) 0.129*** (0.043) 0.240*** (0.033) Mother’s subjective well-being 0.007 (0.024) 0.015 (0.023) 0.031 (0.023) 0.029 (0.026) 0.044 (0.029)
(0.035)
(0.041) 0.025 (0.034) Mother’s social capital 0.207*** 0.074*** 0.146** 0.056** 0.074** 0.025
(0.033) (0.021) (0.062) (0.024) (0.035) (0.033) (0.039) (0.034) Mother’s aspirations for child’s education
(0.011) 0.021*** (0.007) 0.040** (0.016) 0.037*** (0.012) 0.018 (0.014) 0.032** (0.012) 0.032 (0.026) 0.058** (0.026) R-squared 0.23 0.30 0.11 0.16 0.31 0.21 0.24 0.14 Observations 1877 1917 1921 1949 979 994 685 990 Age 15 Ethiopia India Peru Vietnam Wealth index 0.135*** 0.040 0.182*** 0.111 (0.046) (0.044) (0.050) (0.057) Mother’s education 0.009 0.012 0.049*** 0.015 (0.010) (0.010) (0.011) (0.012) Father’s education 0.014 0.021**
0.007 (0.010) (0.008) (0.014) (0.013) Mother’s height 0.003 0.008
(0.005) (0.005) (0.007) (0.006) Mother’s bargaining power 0.045 (0.034)
(0.032)
(0.037) 0.024 (0.035) Mother’s noncognitive skills 0.177*** (0.039) 0.000 (0.033) 0.143*** (0.041) 0.071** (0.034) Mother’s subjective well-being 0.016 (0.035) 0.027 (0.033) 0.010 (0.040) 0.030 (0.033) Mother’s social capital 0.259*** (0.069) 0.160*** (0.034) 0.082 (0.121) 0.092** (0.040) Mother’s aspirations for child’s education 0.010 (0.013) 0.069*** (0.012) 0.052** (0.026) 0.058*** (0.022) R-squared 0.24 0.15 0.12 0.11 Observations 973 974 672 970
Figure 8 The Size of the Coefficient of Mother’s Aspirations for Child’s Education in Child’s Noncognitive Skills Regressions
.02 .04 .06 .08 Coefficient 5 8 12 15 Child's age in years Ethiopia India Peru Vietnam
cognitive achievement across countries and at all stages of childhood
measures
noncognitive skills across countries and at all stages of childhood
cognitive and noncognitive skills increases with children’s age
The Making of the Middle Class in Africa
Mthuli Ncube and Abebe Shimeles African Development Bank CSAE Conference 2013: Economic Development in Africa 17-19 March 2013, St Catherine’s College, Oxford
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Why study the middle class in Africa?
The middle class is often associated with stability and driver of social and economic reforms (e.g Sridharan, 2004, Loyza et al, 2012 ) A large middle class ushers in possibilities for social mobility and trickling down of wealth or inclusive growth (e.g. Doepke and Zilibotti, 2007; Birdsall, 2010) A large middle class is a source of dynamic economic growth and entrepreneurship (Easterly, 2001; Desgoigts and Jaramillo, 2009);
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Objectives of this study
Building on existing work of the African Development Bank (2011), this study attempts to provide answers to the following questions? * What is the size of the middle class and how has it evolved
characteristics? How path dependent is a middle class status at the household level? * What explains cross-country variation in the size of the middle class? speci…cally we focus on institutions and policy. For the latter governance, education and health are examined in some detail.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Identifying the middle class
Some focus on relative de…nition where the upper and lower bounds are a certain percentage of either the median or mean income (e.g. Birdsall, Graham and Pettinato, 2000) Others use absolute de…nition such as individuals living below 2$ and 10$ per day (Banerjee and Du‡o, 2008; Milanovic and Yitzhaki, 2002; Bhalla, 2008 and others). While each de…nition has some grounding, arbitrariness cannot be avoided. In our case we used the African median weighted by population which is 0.5-0.7. most important however is to study the whole distribution provided in the Kernel density.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
On Data and Method
The main source of data for this paper comes from Demographic Health Surveys (DHS) for 42 countries covering the 1990s and 2000s. A pseudo-panel constructed on the basis of age-sex cohort was also used to look into mobility across classes and also role of education and health as important pathways. In addition we report results from rich panel data set from Ethiopia that covers 10 years in …ve waves to analyze the dynamics of the middle class.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Note on data and methods
A composite asset index was constructed for each household using the Multiple Correspondence Analysis method (MCA). This is a method close to a Principal Components analysis and is appropriate for the type of response in the data (mostly categorical) The MCA helps establish weight for the assets based on optimal variance attributed to each of the categories.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
MCA in brief
Wj = ∑
k i=1 aicij
i represents the k assets that individual j possesses at a point in time to achieve a welfare level Wj , which could be cardinal or unit free (ordinal) depending on how the components enter the welfare measure.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
MCA in brief
asset index =
11
∑
i=1 mi
∑
j=1
wijZij wij are the computed weights A question Qi with mi answer choices is transformed into a set of binary question Zij, j = 1...mi in such a way that choosing modality j
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Results
The average size of the middle class that we estimated from the DHS data is strikingly closer to the …gure reported in AfDB(2011): * in the late 2000 (2006-2009) the size of the middle class in Africa on the average was around 14%. * AfDB(2011) reported 13.4% for 2010 based on consumption expenditure lying between $4-$20 per day per person. Our report from DHS data showed clear sign of increasing middle class from 5% in the 1990s to 15%. AfDB(2011) did not show much improvement.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Trends in the size of middle class
Figure 1: Trend in the size of middle class and Gini coe¢cient in asset index
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Results
Asset-based estimates of middle class are highly correlated with consumption based measures
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Results
Except for a few, most countries recorded growth of the middle class since the 1990s Change in the middle class size(%population)
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Size of middle class, poverty and inequality
It is possible for the size of the middle class to decrease following a dramatic decline in poverty and inequality (see case of Egypt: based
Figure 4: Kernel density of Asset Index for Egypt
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Size of middle class, poverty and inequality
Or for size of middle class to increase followed also by signi…cant decline in poverty and inequality (case of Ghana : based on DHS-Figure 5) Figure 5: Kernel density for Ghana
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Size of middle class, poverty and inequality
Or size of middle class to decline following an increase in poverty and inequality (case of Madagascar) Figure 6: Kernel density for Madagascar
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Mobility into and out of middle class status
Table 1: Transition Matrix
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
(cont’d)
Table 1: Transition Matrix
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
(cont’d)
Table 1: Transition Matrix
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Ethiopian Case
Transition matrix for self-reported wealth status in urban Ethiopia: 1994-2004
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Survival function for middle class status in Ethiopia
Strong state dependence
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Cross-country correlates of size of middle class:
Exogenous factors and initial conditions are important
Higher level of ethnic fractionalization is correlated with low incidence
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Cross country correlates
Size of the middle class is strongly correlated with initial level of development
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
The Role of Institutions:
Random e¤ects estimation of determinants of asset index Governance, ethnicity, education and health remain important correlates with the making of middle class
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
The Role of Social Capital:
Trust is important even after controlling for the e¤ect of inequality in assets
Random e¤ects estimation of determinants of asset index
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Education:
The role of education may slow down with increase in its supply
Regression coe¢cients of education and mean years of education
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Ethnic Division
Growth in the size of middle class is much slower in countries where ethnic division is high suggesting again some structural bottlenecks
Growth rate in asset index and some initial conditions
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Concluding Remarks
Size of middle class is rising in most African countries: which is a good thing The probability of maintaining a middle class status is also fairly high with real possibilities to move up as well as slip back into poverty. Ethnicity, initial per capita income, level of trust among citizens shape the evolution of middle class
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Concluding Remarks
From a policy perspective, it is evident that improving governance conditions and investing on education and health can take countries a long way in improving the size of the middle class. Would nurturing the size of the middle class compatible with short term and long term interests of national governments? We do not know at this point.
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Ncube & Shimeles (AfDB) The Making of the Middle Class in Africa CSAE Conference 2013: Economic Developme / 29
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Violence against women in Sub-Saharan Africa
Andreas Kotsadam1 Sara Cools2
1University of Oslo 2BI Norwegian Business School
19 March 2013
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Motivation
Domestic violence is prevalent in all societies but to different degrees. It entails large costs in terms of women’s health, productivity, shame, and fear. Fear of violence affect more women than those actually beaten. Other family members suffer, in particular children.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
What we do
Using microdata for over 540 000 women and almost 200 000 men we:
1 Examine the variation in acceptance and actual wife beating
across time and space.
2 Explore explanations at both the individual and contextual
level.
3 Explore hypotheses regarding conflict, religion, and education
using spatial data, historical exposure and reforms.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Preview of Results
Female employment at the individual as well as at the societal level is associated with more wife beating. So is living in a community with more wealth inequality. Individual attitudes toward beating predicts actual violence, also living in communities that accept wife beating. We find no heightened risk of exposure to wife beating during conflicts. Having more education, or a partner with more education is correlated with less risk of wife beating but we find no effects
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Outline
1
Previous literature and testable hypotheses
2
Data
3
Results from basic regressions
4
A closer look at religion, conflicts, and education.
5
Summary
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Previous literature
There are many different explanations for why men abuse their partners. Evolutionary psychology and radical feminism both claim that control of women’s sexuality is central. The large variation across time and space suggests social factors are important. We focus on resources, religion, inequality, and conflict.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Resources
Individual level resources (wealth, employment, education) lead to autonomy and autonomy is argued to reduce violence. On the other hand, increased resources may lead to a backlash:
1 A threat to male dominance as resources carry symbolic value. 2 Violence may be used to reinstate men’s bargaining power. Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Religion
Religious traditions shape attitudes, both at the individual and societal level. Whether this influence is in a conservative and patriarchal direction is unclear and contested. Nunn (2011) finds that Protestantism in Africa is associated with higher gender equality in education while Catholicism is associated with less.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Inequality
Inequality among men and households is claimed to be a risk factor for wife beating (Jewkes 2002). So is inequality between men and women (True 2012).
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Conflict
Increased violence against women in times of conflict may not be solely driven by military strategies. Several studies find domestic violence to increase during conflicts (see True 2012 for an overview). Mechanisms are thought to be hypermasculinity and a celebration of armed masculinity. La Mattina (2013), however, finds no evidence of increased generalized wife beating after the conflict in Rwanda.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Demographic and Health Surveys
DHS data with standardized surveys has been collected in developing countries since the 1980s. Since the 1990s DHS surveys include questions on attitudes toward wife beating. Data on actual experience of domestic violence has been collected since the late 1990s in a special module. Women of fertile age (15-49) are always interviewed and recently a smaller subset of men are also included.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Attitudes toward wife beating
For women there are 50 surveys with attitudes toward wife beating including 540 842 persons. 21 517 clusters, in 242 regions, in 29 countries for the years 1992-2011. 195 188 men from 22 countries between 1999-2011.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Measures (1)
Respondents are asked if a husband is justified in beating his wife if she:
1 goes out without telling him, 2 neglects the children, 3 argues with him, 4 refuses to have sex with him, 5 or burns the food. Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Measures (2)
We create two different variables from these questions:
1 Beat=1 if the person agrees with at least one of the
2 Nrbeat= number of statements the respondent agrees with.
1.6 on average.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Actual wife beating
Only women who have ever had a parther are asked. We have data from 21 surveys including 108 087 women. 9 426 clusters, in 154 regions, in 15 countries for the years 2003-2011.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Measures (1)
A special domestic violence module is used with: Specifically trained staff. Strict protocol to ensure privacy. A modified Conflict Tactics Scale with many different questions. 12 different questions ranging from pushing, shaking and slapping to attacking with gun, knife or other weapon.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Measures (2)
We create two different variables from these questions:
1 Physical violence=1 if the woman has ever experienced any
type of abuse: 32 %.
2 Violence last year= If the respondent has been abused last
year: 27 %.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Results from basic regressions
All these regressions: Include year and region fixed effects. Include region specific time trends. Cluster the standard errors at the DHS cluster level.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Attitudes toward violence (Table 2 column 4)
VARIABLES beat urban
(0.004) age
(0.001) age2 0.009*** (0.001) working 0.008*** (0.003) schoolyears
(0.000) husband_schoolyears
(0.000)
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Continued (Table 2 column 4)
VARIABLES beat number_children 0.007*** (0.000) wealth_quintile
(0.001) christian
(0.005) muslim 0.017*** (0.006)
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Actual violence (Table 4 column 4)
VARIABLES physical urban 0.017*** (0.005) age 0.006*** (0.001) age2
(0.002) working 0.050*** (0.003) schoolyears
(0.001) husband_schoolyears
(0.000)
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Continued (Table 4 column 4)
VARIABLES physical number_children 0.010*** (0.001) wealth_quintile 0.003 (0.002) muslim
(0.009) christian
(0.008)
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Nonlinear relationship with education
.2 .4 .6 .8 S hare of w om en 1 2 3
Source: Own calculations based on DHS databy level of educational attainment
Attitudes and experience of violence
beat physical_violence
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Household wealth is only correlated with attitudes
.2 .4 .6 S hare of w om en 1 2 3 4 5
Source: Own calculations based on DHS databy wealth quintile
Attitudes and experience of violence
beat physical_violence
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Contextual level variables
We aggregate our variables of main interest to the cluster and regional level using the Jacknife method. We also create a gini coefficient of wealth inequality. We include country fixed effects and country specific time trends. Standard errors are clustered at the regional level when regional variables are included.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Results of contextual variables (1)
The cluster level seems to be more important than the regional level. This is also confirmed by multilevel regressions. More education for women seems unrelated to risk of wife beating. While living in areas where men are more educated is more dangerous.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Results of contextual variables (2)
Having more women working in the cluster is correlated with more violence. So is living in a cluster with more Christians. But living in a context with more Muslims is correlated with less violence. Living in a more wealth unequal area is strongly correlated with more violence.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
The relationship between attitudes and actual beating
At the individual level, thinking wife beating is ok is correlated with an 8 ppt higher actual experience. Contextual acceptability of wife beating is a strong predictor of being a victim, also when controlling for individual attitudes.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
A closer look at Christianity
Following Nunn (2010, 2011) we use data on historical missionary influence from a map by Roome (1924). We know the position of all Catholic and Protestant missionary stations in Africa at the time. Nunn (2010) shows that these missions explain Christianity today and Nunn (2011) shows that women with ancestors exposed to Protestant missions have higher education.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Missionary stations and wife beating
Reduced form results show that living close to a historical location of a Protestant (but not a Catholic) mission is correlated with less acceptance toward wife beating. Living close to where any type of mission was situated is correlated with more actual wife beating. As we believe the link is via more religiosity we instrument Christianity with historical exposure to missions and find that the second stage effects of being Christian is strongly correlated with less acceptance of albeit more actual wife beating.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
No causal interpretation
Even though the first stages are strong and plausible and we are unable to reject that the instruments valid we do not give the results a causal interpretation. Missionary stations were not allocated randomly. Nunn (2010, 2011) controls for historic railway lines, explorer routes, soil quality, and water resources among other things.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Conflicts
We use the UCDP GED dataset with areas exposed to deadly conflicts in Africa since 1989. The dataset includes the starting and end dates of conflicts as well as the number of deaths. We merge the conflict polygons with our DHS clusters and calculate the distance in time to exposure to conflicts.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Education
Following Fenske (2012) we exploit a number of educational reforms to see the effects on wife beating (he looks at polygamy). In particular, we exploit the primary school expansion in Nigeria in 1976 (Osili and Long 2008), the expansion of secondary school in Zimbabwe in 1980 (Aguero and Bharadway 2012, Aguero and Ramachandran 2012), and the extension of primary school by one year in Kenya in 1985 (Chicoine 2012) as sources of exogenous variation. We find no effects of education on violence using these reforms, but neither can we reject quite large effects.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Summary (1)
Wife beating is widely accepted in SSA and the levels of exposure are high. Female employment is associated with more violence as is living in places where more women work. Being Muslim or living in a Muslim community is correlated with less risk of wife beating. Christianity seems correlated with less acceptance, albeit more actual wife beating.
Andreas Kotsadam, Sara Cools Violence against women
Introduction Previous literature and testable hypotheses Data Results from basic regressions A closer look at religion, conflicts, and education. Summary
Summary (2)
Living in a community with more wealth inequality is associated with more violence against women. Attitudes are important predictors of actual wife beating, both at the individual and contextual level. We find no heightened risk of exposure to domestic violence during conflicts. Having more education, or a partner with more education is correlated with less risk of wife beating but we find no effects
reject effects either.
Andreas Kotsadam, Sara Cools Violence against women
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Intensity
Share
Inequality (Poor) Total Within group Between Group
caste A_caste poor_shr var_depr_caste_p within_group_p between_group_AST 57.0% 12.6% 2.75 SC 55.0% 22.1% 2.67 OBC 52.1% 33.3% 2.38 General 50.6% 32.0% 2.22 India 52.9% 100% 2.49 2.44 0.05
13.9% 23.7% 31.7% 28.6% 2.0% 0% 20% 40% 60% 80% 100% Contribution Between Group General OBC SC ST
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1999 Intensity (MPI) Share
Inequality (Poor) Total Within group Between Group
caste A_caste poor_shr var_depr_caste_p within_group_p between_group_AST 57.0% 12.6% 2.75 SC 55.0% 22.1% 2.67 OBC 52.1% 33.3% 2.38 General 50.6% 32.0% 2.22 India 52.9% 100% 2.49 2.44 0.05 2006 ST 56.3% 12.9% 2.86 SC 52.6% 22.9% 2.44 OBC 50.8% 42.1% 2.26 General 49.7% 22.0% 2.30 India 51.7% 100% 2.43 2.39 0.04
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1999 Intensity (MPI) Share
Inequality (Poor) Total Within group Between Group
caste A_caste poor_shr var_depr_caste_p within_group_p between_group_AST 57.0% 12.6% 2.75 SC 55.0% 22.1% 2.67 OBC 52.1% 33.3% 2.38 General 50.6% 32.0% 2.22 India 52.9% 100% 2.49 2.44 0.05 2006 ST 56.3% 12.9% 2.86 SC 52.6% 22.9% 2.44 OBC 50.8% 42.1% 2.26 General 49.7% 22.0% 2.30 India 51.7% 100% 2.43 2.39 0.04
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1999 Intensity (MPI) Share
Inequality (Poor) Total Within group Between Group
caste A_caste poor_shr var_depr_caste_p within_group_p between_group_AST 57.0% 12.6% 2.75 SC 55.0% 22.1% 2.67 OBC 52.1% 33.3% 2.38 General 50.6% 32.0% 2.22 India 52.9% 100% 2.49 2.44 0.05 2006 ST 56.3% 12.9% 2.86 SC 52.6% 22.9% 2.44 OBC 50.8% 42.1% 2.26 General 49.7% 22.0% 2.30 India 51.7% 100% 2.43 2.39 0.04
13.9% 23.7% 31.7% 28.6% 2.0% 0% 20% 40% 60% 80% 100% Between Group General OBC SC ST 15.3% 23.0% 39.2% 20.8% 1.7% 0% 20% 40% 60% 80% 100% Between Group General OBC SC ST
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α + ... + cn α)/n]1/α
α + ... + cn α)/n
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