Education Mobility and Poverty Dynamics in Tanzania1
Christian Kamala Kaghoma
Faculty of Economics and Management
University of Yaounde II – Soa
Email: [email protected]
Tél.:(+237) 96849978
Paper Submitted for the CSAE 2012 Conference on Economic Development in Africa
St Catherine’s College, Oxford, 18-20 March 2012
Abstract
Using data from the agrarian region of Kagera, in Tanzania, this paper investigates (1) the
extent of education mobility and (2) the impact of education accumulation on welfare
(poverty) dynamics. Considering the interconnected nature of education accumulation and
poverty we control for the potential endogeneity between education investment and poverty
changes over time, using a Two Stages Probit Least Square model. Results show that there is
a very low welfare mobility and a very high educational mobility in that regardless of the
parents’ educational background, the probability for a child to attain any educational level
lesser or equal to “Lower Secondary Education” level – the “O-level” – is the highest
compared to the rest of the educational categories. However, educational accumulation
negatively impacts the probability of climbing the social (welfare) ladder up to a higher or to
the highest welfare category, suggesting that individual education is likely not to be a gradient
for social mobility. Rather, the greater the proportion of household members with primary
education, the higher is the probability for offspring to experience a higher welfare standard
than their parents. The latter results suggest the existence of a scale effect in the impact of
education on welfare mobility and plead in favor of more attention on the quality of education
in this era of generalization of Education for All Programs.
Key words: Education, Intergenerational Mobility, Poverty dynamics
JEL classification: I32, J62, P36
1
The author acknowledges and is grateful to the African Economic Research Consortium (AERC) for financial support in
the frame of its CPP program and to the Economic Development Initiatives (EDI – Kagera) together with the World Bank for
having made the data available to him.
1
I. Introduction
The human capital approach regards education as an important instrument of reduction of
poverty. Empirical microeconomic studies emphasizing the positive impact of education on
welfare abound in the literature. They are globally split into two main strands, depending on
whether they deal with the direct or indirect effects of human capital investment on welfare.
Obviously, the most dominant strand of this literature is the one that indirectly echoe the
effects of education through outcomes like better utilization of health facilities, shelter, water
and sanitation, and its effects on women's behavior in decisions relating to fertility and family
health, etc. These in turn enhance the productivity of people and yield higher wages (Thomas
and Strauss, 1997; Tilak 1999,2002; Appleton, 2001; Girma and Kedir, 2003,2005; Schultz,
1999, 2002, 2004).Globally this indirect relationship between education and earnings is done
through the rate of return analysis 2 . The second and relatively marginal strand addresses the
direct impact of education on household’s welfare (Glewwe, 1991; Zuluaga, 2007; Himaz and
Aturupane, 2011). However, in spite of their importance, these two strands of studies provide
little or no information on the dynamic connection between accumulation of education and
welfare dynamics over time. Indeed, in developing countries and in Africa especially, where
societies are extremely hierarchical and marked with very high inequality of income and
welfare, the most important aspect of poverty is intergenerational mobility. This is the aspect
that clearly informs on the extent to which poverty or inequality are transmitted across
generations (Bhardan, 2005).
Intergenerational social mobility refers to the relationship between the socio – economic
status of parents and that of their offspring when they become adults. Although the literature
seems to be more focused on income, economic status can be measured by several
socioeconomic outcomes: wage, social class, health, education or occupation (Bauer and
Riphahn, 2006; Causa et al., 2009) 3 . With the increasing availability of panel data on
individuals or households, it is now becoming common to find papers on intergenerational
mobility. However, they are marked by two peculiarities. First, they largely focus on
developed countries (Becker and Tomes, 1986; Nimubona and Vencatachellum, 2007; Guo
2
However such an approach goes with some drawbacks. For instance, it ignores the direct interaction between education and
poverty whereas education may be demanded for itself or as a means of expanding “human capabilities” other than income
since its accumulation may end up having an effect on poverty regardless of whether it induces or not access to an income
generation activity (Sen, 1990).
3
See Black and Devereux, 2010 for the review of the main available empirical works and their findings and specifically
Classen (2010) for the review of studies on the transmission of health capital across generations.
2
and Min, 2008; Hansen, 2010 among the other). Furthermore, when they consider a given
socioeconomic status less attention is given to both the possible interactions between the
different chosen dimensions and those of their respective mobility patterns, except when
education or occupation is used as an instrument for permanent earnings as in Ng et al.
(2009) 4 . Second, with the exception of the case of South Africa and, to a lesser extent,
Senegal (van der Berg, 2002; Keswell, 2004; Louw et al., 2007; Woolard and Klasen, 2005;
Nimubona and Vencatachellum, 2007; Dumas and Lambert, 2011; Lambert et al., 2011),
empirical research on the topic, regardless of the indicators chosen to measure mobility, is
almost inexistent on Sub – Saharan Africa 5 (African countries, hereafter Africa). In addition,
the few studies dealing with education mobility per se, namely van der Berg (2002), Louw et
al. (2007) and Nimubona and Vencatachellum (2007), are based on cross – section data,
relying on the reports that offspring makes on their parents’ education. They are therefore
broadly biased downward in the measurement of mobility among generations (See Muller,
2010; Ng et al., 2009 for the review of other studies based on the observed one – year
outcome) 6 in spite of the different adjustments made to mitigate the latter bias.
However, in a long term dynamic view of poverty changes, education is likely to be seen as
endogenous to long – term wealth and poverty changes. Both education and poverty are
important measures of personal wellbeing and they are closely related in their evolvement. On
one hand, low socioeconomic status (e.g. income poverty) may cause low accumulation of
education and the latter is also more likely to be found among people with low income than
among those with high income (Villa, 2000; Gustafsson and Li, 2004). On the other hand, low
investment in education may lead to low income and thus poverty because low education
reduces the ability to work and thus access to the opportunities offered by the market
(Behrman, 1990; Appleton, 1997, 2001; Brown and Park, 2002; Shapiro and Tambashe, 2005;
Bourguignon and Rodgers, 2007). Thus, to isolate the real effect of education, one should be
able to account for the accumulation of education over time before estimating its contribution
to the poverty change across generations. However, due to the absence of panel datasets of
individuals or households covering a very long period of time, such analysis are very rare in
4
For instance, very few of the existing studies have related the mobility outcomes analyzed with households or individuals
welfare path or poverty status changes over time.
5
For instance, in their international comparisons of fifty years experience of inheritance of educational inequality, Hertz et al.
(2007) reports the case of South Africa as the only Sub – Saharan country where such an issue is addressed. Also, authors
like Nimubona and Vencatachellum, 2007 and Ng (2007) report a series of countries where different studies of the topic have
been made and among them, only South Africa is cited as the African case.
6
The main reason for this downward bias in the estimation of mobility is based on that given one year’s earnings comprise
both transitory as well as permanent components, while only permanent earnings should be used to correlate earnings
between the generations.
3
Africa, which is rather seriously affected by poverty and where the role of education would be
important for climbing the social ladder.
Using the Kagera Health and Development Survey (KHDS), a panel dataset of household and
individuals over 1991 – 2004, this paper attempts to overcome the above drawbacks by
simultaneously modeling the education accumulation (mobility) and its effect on the
dynamics of welfare across generations. It specifically determines the extent of educational
mobility, and appraise the extent to which accumulation of education allow offspring to
benefit from a higher living standard compared to their parents.
The paper is therefore outlined as follows. Section 2 details the data used, succinctly paint the
Kagera region and present some statistics evidences regarding welfare, education, their
respective dynamics and the relationship between the two. Section 3 discusses the options for
modeling education mobility and welfare dynamics accounting for the potential endogeneity
between the two. Section 4 contains econometric results and their discussion. Section 5
concludes the paper.
II. The Data and descriptive evidence.
In this section we present the descriptive statistics and the main pattern they suggest on the
link between education mobility and poverty dynamics, before emphasizing on the
econometric results.
II.1. The data.
The data for this study are from the Tanzanian region of Kagera, an area far from the capital
and coast, bordering Lake Victoria, Rwanda, Burundi, and Uganda. The region is
overwhelmingly rural and primarily engaged in producing bananas and coffee in the north and
rain-fed annual crops (maize, sorghum, and cotton) in the south. Relatively low – quality
coffee exports and agricultural produce are its main sources of income. It is not one of the
poorest areas of Tanzania and appears to mirror the rest of the country on several aspects like
4
growth and poverty reduction 7 . The region also reflects the typical problem of land – locked,
agriculture – based economies: according to national data, real GDP growth was just over 4%
per year between 1994 and 2004 but poverty in Kagera is estimated to have changed very
slightly, falling from 31% to 29% between 1991 and 2000–2001. Thus as reported by
previous studies 8 which have also used the same database, challenges of poverty reduction in
this region seem to be representative for provincial Tanzania as a whole.
The Kagera Health and Development Survey (KHDS) was originally conducted by the World
Bank and Muhimbili University College of Health Sciences (MUCHS). Initially designed to
assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a
stratified design to ensure relatively appropriate sampling of households with adult mortality
and consisted of about 915 households that were interviewed up to four times from September
1991 to January 1994, at 6 to 7 – month intervals. This first dataset was complemented, in
2004, by another round of data collection with the objective of re - interviewing all
individuals who were household members in any round of the KHDS 1991-1994 and who
were alive at the last interview. Considerable effort was made to track surviving respondents
to their current locations, be it in the same community (typically a village), a nearby
community, within the region, or even outside the region or abroad. Excluding households in
which all previous members were deceased (17 households with 31 people), the KHDS 2004
survey re – contacted 93% of the baseline households 9 , 835 out of 895 households (Beegle,
De Weerdt and Dercon, 2006). In addition to the household survey, the KHDS included
surveys of communities, prices, and facilities. The household questionnaire was a Living
Standards Measurement Study survey instrument that contained numerous indicators of wellbeing such as consumption, expenditure, asset holdings, morbidity health, nutrition, and
education. To ensure comparability of all main indicators and variables from the earlier
survey, the KHDS 2004 used the original questionnaire as the foundation of the survey
instrument.
7
For instance, its mean per capita consumption was near the mean of mainland Tanzania in 2000. While the decline in
poverty is reported to have been steep in Dar es Salaam (from 28 to 18 percent) and other urban areas, it was minimal in rural
Tanzania, ranging between 41 and 39 percent (Household Budget Survey, 2007; Demombynes and Hoogeveen, 2007).
8
Beegle et al. (2006/2008/2009; De Weerdt, 2010; Demombynes and Hoogeveen, 2007 among the others). More details on
both the other studies that have used the KHDS database and the database itself are available on the EDI-Kagera website:
http://www.edi-africa.com/research/khds/introduction.htm.
9
The KHDS panel has an attrition rate that is much lower than that of other well-known panel surveys summarized in
Alderman et al. (2001), in which the attrition rates ranged from 17.5 to the lowest rate of 1.5 % per year, with most of these
surveys covering considerably shorter time periods (two to five years).
5
Each household in which any of the panel individuals lived would be administered the full
household questionnaire. Because the set of household members at the baseline had
subsequently moved, and usually not as a unit, the 2004 round had more than 2,770 household
interviews (from the baseline sample of 912 households). Indeed, given the various split-offs
and changes to the household in panel data, following a household does not make sense, since
a household changes rapidly over time. Defining what the “same” household is highly
complicated because individuals move in and out. The only feasible solution is to base the
sampling and tracking strategy on individuals. Building on this and given the objective of our
study, we only focus on individuals who were observed in the baseline year being between 6
and 18 years old and who are retrieved in 2004 as household heads. Hence, of the 2774
households observed in 2004, only 903 are considered in the analysis for this paper.
The consumption data come from an extensive consumption module administered in 1991 and
again in 2004 10 . Monetary levels were adjusted to account for spatial and temporal price
differences, using price data collected in the Kagera survey in 1991 and 2004, and, for
households outside Kagera, data from the National Household Budget Survey. Consumption
is expressed in annual per capita terms. The poverty line is set at 251503 Tanzanian shillings
(TSh), calibrated to yield for our sample of respondents who remained in Kagera the same
poverty rate as the 2000/01 National Household Budget Survey’s estimate for Kagera
(38.6% 11 ).
In addition to the enormous variables it contains, this long – term coverage and the follow –
up of the same individuals is one of the peculiarities of the KHDS. The thirteen years
10
The consumption aggregate includes home produced and purchased food and non – food expenditure. The non – food
component includes a range of non-food purchases, as well as utilities, expenditure on clothing/personal items, transfers out,
and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded (See Beegle,
De Weerdt and Dercon, 2006 for any detail about the KHDS).
11
We computed the above poverty line from the Distributive Analysis Stata Package (DASP) routines developed by Arraar
and Duclos (2009). This poverty line is different from the one computed by Beegle et al. (2011) though based on the same
procedure of implementation and the same database. Several elements can explain this difference. First, their sub - sample is
not necessarily the same as the one we use since they focus their analyses on the migrants while ours is constituted of
individuals who were between 6 - 18 years in 1991 and are observed as household heads in 2004. Secondly, Beegle et al.
consider the Kagera region as a part of “Other urban areas” and thus refer to the corresponding incidence of poverty as
computed in the frame of the Household Budget Survey (HBS, 2007), namely 28.7 or 29%, to determine the poverty line they
use in the case of Kagera which is set to 109,663 TShs. However, Kagera is an important component of the mainland
Tanzania. This fact is even recognized by Beegle and al. who present it as a representation of landlocked and agriculturedependent economies of Sub – Saharan Africa. Building on this reality, we compute a poverty line that allows us to get a
poverty incidence of 38.6%, that is, the same poverty incidence of the mainland Tanzania in the HBS (2007) for the year
1991/92. This difference of poverty line does not affect the consistency of the analysis since it depends on the problem that is
at hands. Litchfield and McGregor (2008) for instance, used a poverty line of 194,616TSh for the same very region of
Kagera.
6
separating the two waves considered in this study offers the possibility of observing the same
socioeconomic variables of both the household’s head in the initial period as well as those of
their offspring when part of the latter are already household heads. It thus allows the address
of such an important question as welfare, education mobility or their transmission across
generations as well as their interrelationships over time. In fact, in addition to the
aforementioned module on consumption and other social indicators, the questionnaire also
contained a whole section focused on education – related variables administered to all the
interviewees in each of the waves of the survey.
II.2. Descriptive evidence.
II.2.1. Education level and poverty status.
Throughout this paper, we have used a conventional consumption based definition of poverty,
and defined a poor household as one whose Adult – equivalent consumption fall below the
poverty line in any particular year. We have adopted a consumption – based definition of
poverty because this is the only welfare measure which is consistently tracked over all the two
periods under study. Whenever a household's consumption crosses over the poverty line that
household makes a poverty transition. An increase in consumption that moves a household
over the poverty line is defined as an exit or movement out of poverty, while a decrease in
consumption that moves a household's below the poverty line is defined as an entry or
movement into poverty 12 . The table 1 below gives the some descriptive statistics related
education attainment according to poverty status in the two periods.
Table 1: Distribution of educational attainment by poverty status
Proportions
Poverty status/
dynamics
1991
2004
Poor
Non Poor
Poor - Poor
Non Poor - Poor
Poor – Non Poor
Non Poor – Non Poor
40
60
15
12
25
48
100
26,91
73,09
15,00
11,67
24,49
48,44
100
Total
Household head education
1991
Mean/Std.
2,71(3,209)
3,19(3,620)
2,48(3,029)
2,10(3,084)
2,84(3,304)
3,47(3,715)
3,00(3,479)
Max./Min.
08(0)
12(0)
08(0)
08(0)
08(0)
12(0)
12(0)
2004
Mean/Std.
6,71(2,800)
7,55(2,333)
6,81(2,869)
6,62(2,687)
7,14(2,696)
7,75(2,097)
7,33(2,488)
Max./Min.
12(0)
14(0)
12(0)
12(0)
12(0)
14(0)
14(0)
Household members education
1991
Mean/Std.
5,81(2,679)
6,09(2,393)
5,69(2,587)
5,52(2,074)
5,89(2,700)
6,22(2,447)
5,99(2,534)
Max./Min.
12(0)
13(0)
08(0)
11(0)
12(0)
13(0)
13(0)
2004
Mean/Std.
6,80(2,509)
7,54(1,977)
6,95(2,543)
6,61(2,462)
7,30(2,127)
7,66(1,886)
7,32(2,499)
Max./Min.
12(0)
14(0)
10(0)
10(0)
10(0)
14(0)
14(0)
Source : Author’s computations based on the KHDS
12
One difficulty that arises with such a definition of poverty transitions is that if a household's consumption is close to the
poverty line relatively small changes in consumption may be associated with exits out of and entries into poverty. Similarly,
errors in the way consumption is measured from period to period may result in false transitions being recorded. But given the
long – run dimension of the data we are handling such a risk is very small.
7
Table 1 shows that over the 13 years separating the two waves considered in our analysis, the
level of education increased regardless of the transition observed in the poverty status. While
in the baseline year the maximum level of education was respectively 8 years of schooling for
poor households as well as the first three categories of poverty transition and 12 for the non –
poor and the fourth category of poverty transition, in 2004 it respectively goes up to 12 for the
former group and 14 for the latter. As for the inequality of education among the different
groups related to the different poverty status changes, in contrast to the pattern observed in
1991/1994, in 2004 the differences in educational level among the different groups seem to
be reduced substantially in the descendant population. In fact, the ratio of the maximum level
of education to the minimum is greater in 1991 than in 2004, 1.5>1.16 .The same observation
applies also for the mean education of the household members for which the same ratios give
1.625 in 1991/1994, which is greater than 1.16 for 2004. Thus, the descendent population is
in general highly and less unequally educated than that of parents regardless of the poverty
status.
Regarding the dynamics of poverty, the first column of table 1 displays the different poverty
statuses as well as the different changes of these statuses over time. Globally, poverty
declined as reported in the first two rows of table 1.The incidence of poverty decreased, from
40.09 % in 1991/1994 to 26.91 in 2004 13 . However, different households witnessed different
dynamics in the status of poverty. Over the thirteen years separating the two waves
considered, only 15.00% of these households were poor in both periods. That is, only fifteen
percent of poor household heads in 2004 were also from poor households in 1991. 11.67% of
households that were non – poor in the baseline period slumped into poverty by 2004. The
proportion of the household that were poor in 1991 and have escaped from it in 2004
represent 24.49% of observed households. 48.44% of the households were non poor in both
periods indicating a substantial immobility in the upper group of households. The cumulative
frequency of the last two poverty statuses, namely “Poor – Non Poor” and “Non Poor – Non Poor”,
amounts to 72.93% and suggest that among the households we are dealing with in this paper
there has been a strong increase in welfare over the thirteen years. To have an insight on the
role of education in this trend of poverty, we first look at how education mobility relates to
13
This poverty trend is different from the one based on the entire KHDS dataset and reported in Beegle et al., (2006) or other
researches using the latter dataset (Litchfield and McGregor, 2008; Beegle, De Weerdt and Dercon, 2008/ 2011). As noted
already, the sub – sample considered here rather deals with the mere part of individuals who were between 6 – 18 years in the
baseline year and who, in 2004, are already household heads.
8
the different poverty status changes and then discuss the contributions of educational
categories to the overall poverty.
II.2.2. Education mobility and Educational relative opportunity.
It is useful to start the sketch of the intergenerational education transmission using the
transition (probability) matrix as a descriptive tool. With parental education captured in rows
and their descendants’ educational outcomes in columns, the transition matrix describes the
probability that a child reaches a given level of education conditional on parental education.
Education is a continuous variable as described in the previous paragraph. But, for the
computation of the transition matrix and thus the analysis of the characteristics of
intergenerational educational mobility in Kagera, it is categorized in the following four
educational groups 14 : “No schooling”, “Some Primary Education”, “Some Lower Secondary
Education” and “Some Upper Secondary or / and Tertiary Education”. With the exception of
the first category, “No schooling”, the other educational categories are constructed such that
they include any person who have completed at least one school year of the level or the
category in consideration. The “No schooling” category includes those who have not attended
or completed at least one year of schooling. The matrix is presented in the table below.
Table 2.:Educational mobility matrix
Education category 1991/94
1
2
3
4
Education category 2004
3
4
Total
0.11
0.13
0.73
0.03
100.00
0.03
0.04
0.91
0.03
100.00
0.03
0.01
0.88
0.08
100.00
0.00
0.00
1.00
0.00
100.00
Total
6.98
7.89
81.24
3.89
100.00
The educational category are as follows: 1: No schooling, 2: Some Primary Education, 3: Some Lower Secondary Education and 4: Some Upper Second./Tertiary cal
1
2
Source : Author’s computations based on the KHDS
The numbers on the main diagonal vary from 0.00 to 88 percent, indicating that there are
different mobility patterns across educational groups. The entry in the fourth column of the
first row, 0.03 percent, indicates the probability that a descendent from a parent with no
schooling attains some “Upper Secondary Education” level. It appears that none of the
descendents of parents with the highest education level has reached the same level as their
parents but form this evidence it is difficult to conclude about the mobility in this educational
14
This categorization rests on the idea that there might exist differences in returns to an incremental educational year through
the different main educational sub – groups.
9
category. Indeed, the probability for a child to have an “Lower Secondary Education” degree
or to have completed a given number of years of secondary education below “Lower
Secondary Education” level given that her parent has “Upper Secondary Education” is one.
This suggests that having a parent of “Upper Secondary” education levels is likely to prevent
a child from having less than a “Lower Secondary” education. All the four entries of the third
column of the matrix are greater than 72 percent. Regardless of the parents’ educational
category, the probability for a child to attain any secondary educational level lesser or equal to
“Lower Secondary Education” level is high (the highest).
However, it is worthwhile noting that despite their insightful dimension, taken alone, the
descriptive analysis above does not provide enough information on the relative opportunities
of attaining different educational level depending on parents’ background across the different
educational categories. Thus, following Heineck and Riphahn (2007), I complement this
transition probability matrix by computing two types of mobility indicators: (a) The Prais–
Shorrocks mobility index, and (b) indicators of relative opportunities.
The Prais–Shorrocks (MP) 15 index of 0.99 indicates a very high independence of offspring’s
educational achievements on those of their parents, i.e there is a very high educational
mobility. Since the latter may imply either an upward or a downward mobility, it also needs to
be complemented by the indicators of relative opportunities. The relative educational
opportunities measures allow appraising whether this improvement in educational attainment
equally enhanced the opportunities of children from all the different parental backgrounds. In
the vein of Heineck and Riphahn (2007), this refers to the probability that someone completes
a given educational level “i” given that her parent have completed the same or a lower
educational level relative to the probability that the same person has completed the same “i”
educational level given that her parents have completed a lower educational level 16 . We
namely find:
Prob(CED4|PED3)/ Prob(CED4|PED2)=0.08/0.02=4.00
15
Derived from the mobility matrix (P), it is based on the computation of the trace of the matrix and expressed as follows:
MP =
m − trace (P )
m −1
, with m the number of educational categories, in our case 4, and trace(P ) the trace of the matrix, i.e. the
sum of the different entries of the main diagonal. When trace(P) = 0, that is the rate of social reproduction is zero, there is no
household whose descendants remain in the same educational category. Therefore, there is total mobility and symmetrically,
when trace(P) = m, the rate of reproduction equals 1 and thus all the descendants remain in the same educational categories
as their parents. In this case, there is perfect immobility.
16
It is calculated as the following conditional probability: ⎡
prob (CEDh|PEDh ) ⎤
⎢⎣ prob(CEDh|PEDi <h ) ⎥⎦ where “i” and “h” are any levels of education,
CED the child level of education and PED the parent’s level of education.
10
Prob(CED4|PED3)/ Prob(CED4|PED1)=0.08/0.03=2.70
Prob(CED3|PED3)/ Prob(CED3|PED2)=0.88/0.90=0.98
Prob(CED3|PED3)/ Prob(CED3|PED1)=0.88/0.73=1.22
While the general level of educational level has increased between the periods considered in
this study, the relative opportunities of attaining the two highest education levels for children
are different according to their parents’ background. The above figures show that a person
from a household whose head attained “Lower Secondary Education ” level of education has
4 times higher probability of attaining an “Upper Secondary Education” level of educational
level than another with a father whose educational level is “primary”.
II.2.3. Education mobility and poverty dynamics
Given the longitudinal design of this study, it is possible to consider the extent to which
households move across the different poverty statuses according to the education of their head
in the baseline period. Such an exercise has the advantage of relating education mobility to
poverty mobility. Thus, the following table reports respectively the proportion of households
with a given educational profile relatively to each of the different poverty status changes over
1991 – 2004.
The upper panel of the table 3 relates to poverty dynamics the proportion of households with
any of the four modalities of variable education as observed in the baseline year.
11
Number
Table.3: Education level/Education mobility and Poverty dynamics
Proportion
Poverty mobility
Poor - Poor
Proportion of households with no schooling
Proportion of households with some Primary Education
Proportion of households with some Secondary Education**
Proportion of HHs. with some Second./Tertiary Education***
440(61)*
254(69)
170(710)
10(34)
50.34(6.98)
29.06(7.89)
19.45(81.24)
01.14(03.89)
14.88
20.93
08.99
00.00
Non Poor Poor
14.00
10.41
07.87
00.00
Poor – Non
Poor
24.73
25.19
25.84
00.00
Non Poor –
Non Poor
46.39
56.59
57.30
100.00
Welfare (Consumption) mobility
Cons. Upward
Cons. Stagnate
34.57
26.27
37.64
20.00
39.82
36.08
26.97
30.00
Total
Cons.
Downward
25.60
37.65
35.39
50.00
100.00
100.00
100.00
100.00
Education mobility
No schooling __ No schooling
46.00
05.48
26.00
10.00
32.00
32.00
30.00
28.00
42.00
100.00
No schooling __ Some Primary Education
56.00
06.67
12.28
38.60
22.81
26.32
28.07
49.12
22.81
100.00
No schooling __ Some Secondary Education
308.0
36.71
13.39
11.01
23.51
52.08
36.01
39.29
24.70
100.00
No schooling __ Some Secondary/Tertiary
13.00
01.55
21.43
00.00
35.71
42.86
42.86
57.14
00.00
100.00
Some Primary Education __ Some Primary Education
08.00
0.95
12.50
50.00
00.00
37.50
50.00
50.00
00.00
100.00
Some Primary Education __ Some Secondary Education
153.0
18.24
16.87
06.63
22.89
53.61
29.45
31.29
39.26
100.00
Some Primary Education __ Some Secondary/Tertiary
05.00
00.60
00.00
00.00
00.00
100.0
00.00
20.00
80.00
100.00
Some Secondary Education__ Some Secondary Education
145.0
17.28
08.92
06.37
24.48
59.24
38.22
26.11
35.67
100.00
Some Secondary Education__ Some Secondary/Tertiary
14.00
01.67
00.00
21.43
21.43
57.14
28.57
21.43
50.00
100.00
Decrease
91.00
10.85
28.13
13.54
31.25
27.08
25.00
41.67
33.33
100.00
Source : Author’s computations based on the KHDS
*Figures in parentheses pertain to 2004 while others pertain to the initial period, 1991; ** Secondary education here is limited to “O” – level; *** “A” – level and tertiary education are merged given their underrepresentation in the sample; **** Having
four educational categories we should have 16 modalities for educational mobility. However, since some of the education transitions did not take place, we only report here those for which the proportion of being experienced by any household is
different from zero. Thus, modalities of education transition for which the proportion of household is zero, for all the poverty status change, are not reported in this table. It is the case of: “Some Secondary/Tertiary __ Some Secondary/Tertiary”.
No schooling
Some Primary Education
Some Secondary Education
Some Secondary/Tertiary
Entire population
Table 4: Distribution of Households according to their level of education and contribution to poverty
1991
P(2)
Share
Contr.0
Contr.1 Contr.2
P(0)
P(1)
P(2)
2004
Share
P(0)
P(1)
Contr.0
Contr.1
Contr.2
0.5290
0.1614
0.0717
0.3575
0.2735
0.2561
0.2807
0.8925
0.5829
0.4190
0.0360
0.0707
0.1742
0.3169
0.6467
0.3613
0.2193
0.1526
0.1427
0.2447
0.3665
0.9566
0.2777
0.0824
0.1022
0.2151
0.2357
0.1770
0.8251
0.2300
0.0658
0.4891
0.5837
0.4992
0.3528
0.3767
0.0826
0.0280
0.8590
0.7123
0.5896
0.5060
0.0000
0.0000
0.0000
0.0009
0.0000
0.0000
0.0000
0.3089
0.0185
0.0024
0.0028
0.0019
0.0004
0.0001
0.6913
0.2253
0.0913
100.00
100.00
100.00
100.00
0.4543
0.1204
0.0476
100.00
100.00
100.00
100.00
Source : Author’s computations based on the KHDS
12
Regardless of the educational profile in 1991, the category of households that remained non –
poor over the thirteen years covered by our panel are dominant (more than 46%). It is
followed by the category of households with some primary education. However, focusing on
the last two columns in the poverty mobility component of the table, it appears that the
proportion of households that either maintain their welfare position in the upper class or
improve it increases with the average level of education. For instance, the entire portion of
households with some A – level or tertiary education are protected from the risk of falling into
poverty. It is clear that even if poverty is measured on a consumption basis, any variation in it
does not necessarily imply a transition in the poverty status. However, the components on
consumption mobility indicate that above observation on the relationship between poverty and
education also applies for consumption.
From the lower panel of the table, it appears that the level of descendent who both remained
poor in 2004 and are from poor households is very high (26%) compared to those who where
non poor in the initial period and become poor in the last wave (10%). But, the evidence
suggests that disproportionate numbers of the households experiencing the transition from
poverty to non poverty (remaining non poor in both periods), 28.84 % (57.30%) are headed
by individuals whose parents, as observed in 1991, had some secondary education compared
to those who remain poor in both periods.
All the households that experience mobility from primary education to some secondary or
tertiary education remain non poor in both periods. This could suggest that non poor
households are the one who accumulate education the more but such an inference is mitigated
by the results from the mobility from no – schooling to “some secondary/tertiary” which is
experienced by 26% of households who are poor in both periods. What is remarkable with
this last mobility is that no household that has experienced it is found in the category of those
who fell into poverty after being non poor in the initial period. Also, of the 14 households
who experienced mobility from secondary to tertiary, none is poor in both periods.
II.2.4. Education level and its contribution to poverty.
After this brief description of the way education is related to poverty dynamics, it is important
to complement the analysis with a look at the contribution of different educational (level)
groups to poverty. Thus, table 4 gives the distribution of households according to their level
13
of education and contribution to poverty. Use is made of the Pα class of poverty measures 17 .
This class of poverty measures presents properties of additive decomposability and subgroup
consistency, which allow poverty to be evaluated across population subgroups in a coherent
way (Foster et al., 1984, 2009) 18 .
Table 4 indicates that the share of household heads with no schooling has sharply decreased,
from 36% in 1991/94 to 4% in 2004, an overall decrease of 90%. The contribution of this
educational category to the incidence of poverty ( P0 ) also decreases substantially, falling
from 27% to 7%. This is also the case for the depth of poverty ( P1 ) contrary to severity of
poverty ( P2 ) which rather increases. A similar fall is observed for the category of household
heads with some primary education but to a lesser extent than for the former. In return, the
respective shares of the last two categories, namely households with respectively some lower
secondary education and those with some upper secondary or tertiary education, increase two
– fold for the former and three – fold for the latter. As for their contribution to poverty, it
appears that, in both waves, households with some secondary education not only display the
highest incidence of poverty but they are also the most contributive to the incidence of
poverty. From 58% in the baseline period, its contribution grows up to 71% in 2004 and the
same pattern is observed for its contribution to both the depth and the severity of poverty. The
share of the last category increases and very slightly its contribution to both the incidence and
the depth of poverty.
The descriptive data is therefore suggestive of an association between the original educational
status of the household head or his education mobility and household poverty transition, i.e.
17
measure of sensitivity of the index to poverty,
α
⎛ Gi ⎞ where α is a
∑
⎜ ⎟
N i =1 ⎝ z ⎠
z the poverty line and Gi = z − xi with Gi = 0 when xi > z and xi the level
Following Haughton and Khandker (2009), this family of measures can be written as Pα =
1
N
of the welfare metric for the ith household.
18
Based on this property, poverty can be disaggregated into respective contributions of each of the groups constituting the
general sample of observed units. If the population is divided into m sub–groups which are mutually exclusive, then based on
⎛ Gi ⎞
∑
⎜ ⎟
N i =1 ⎝ z ⎠
1
the Pα index, the poverty index for any sub-group j can be computed as: Pα , j =
N
α
.The level of poverty can
then be computed for the entire considered sample of individuals as a weighted sum of the latter indices for the set of subgroups, where the weight of any sub-group j where is its proportion in the total ( q j
= nj N
m
): Pα =
∑ q j Pα , j . The
j =1
contribution of a sub-group j to the total poverty is thus derived as: Cα , j =
q j Pα , j
Pα
.
14
this is in line with intuitive suggestions that lack or low level of education can limit welfare
enhancing opportunities. It is also the case for the change in the level of education. However,
such associations will be further investigated in the econometrics section. Some of the other
key characteristics are now highlighted, enabling us to establish how education investment
might interact with other variables, and how important educational factors are relative to other
variables of interest.
III. Empirical methodology.
III.1. The empirical model of intergenerational mobility and correlation.
As noted already, intergenerational mobility studies investigate how a measure of children’s
outcome correlates with that of their parents. Given that this paper deals with education and
welfare, we start below with a simple approximation of the education achievement but the
same framework applies for any other social outcome like welfare or poverty status. Thus, in
this point, the paper first attempts to identify the impact of parent’s socio – economic
characteristics (education included) on the education of his/ her offspring. In the vein of
Goldberger (1989), the general form of simplest model to be estimated is as follows:
edijt +1 = β0 + β1ed jt + ε ijt +1
(1)
where edijt +1 is a measure of schooling achievement of child i from household j observed at
t + 1. Similarly, ed jt is the education achievement of the most educated parent in household j .
The stochastic disturbance term ε ijt +1 of equation (1) above encompasses unobservable
variables that affect the child education. This unobserved heterogeneity comprises family and
individual – specific components.
The parameter β1 measures the degree of dependence of educational outcome across
generations.
Considering the logarithm of the chosen measures of education allows
interpreting β1 as the elasticity of a child education with respect to his parent’s education or
the intergeneration elasticity and (1 − β1 ) as a measure of the intergenerational mobility (of
education). Thus, an elasticity of zero indicates no persistence in education across
generations. A higher value of β1 means lower intergenerational education mobility, with a
15
β1 < 1 indicating that educational attainment converges over time. Having the elasticity, one
can determine the intergenerational correlation of education by the following formula:
ρed = β1
σt
σ t +1
where σ t and σ t +1 are the standard deviations of edt and edt +1 , respectively. Obviously, the
transmission of education between generations is a complicated process governed by a myriad
of factors including genetics, culture, family values and consumption choices. To account for
this, it is important to extend the above simplest model and include into it a series of control
variables. However, it is highly recommended that the latter be limited to a small number of
controls in order to avoid lessening the effect of parental education (Behrman, 1997) 19 .
Representing the set of the other determinants of the child’s schooling by Z , the regressions
estimated are then of the form:
edijt +1 = β0 + β1ed jt + Z ij Φ + ε ijt +1
(2)
The above specification is most adopted in the empirical studies on the topic of
intergenerational transmission of socio – economic outcomes. Indeed, the inclusion of the
control variables in the model reduces the effect of the unobservable specificities. However, it
can be reinforced by recourse to other estimation techniques other than OLS. Before
presenting the results, we first present below the econometric foundation for the analysis of
the linkage between welfare dynamics and education accumulation.
III.2 Education and welfare mobility: the econometric specification.
As already noted most of the studies dealing with developing countries are based on static
data and focus only either on education or welfare mobility. Guo and Min (2008) remains so
far one of the fewest that pushes the analysis a step further by relating education and
intergenerational income mobility. Estimating two separated equations, they determine in the
first one the coefficient of mobility and in the second one the children’s income as determined
by their parents as well as their own education. The first equation results into the direct effect
while the second reveals the indirect effect of parent’s education. However, despite the
inspiring underpinning of such an approach it suffers from several weaknesses. First, they
consider the parent’s education in the final year of their analysis, 2004. In doing so, they
19
As suggested by Dumas and Lambert (2011), this would be the case for instance if one is to introduce variables such as
textbooks or educational expenses which are partly driven by parents’ education.
16
sidestep the essence
of the notion of mobility in that they implicitly assume that the
education of the parents can be changing with that of their offspring. In such a situation, it is
difficult to presume which of the two is likely to influence the other. Second, including the
parent’s educational level in the estimation of the offspring’s income does not capture much
of the indirect effect of parent’s education. In fact, the latter first impacts the education
attainment of the children (Al-Samarrai and Peasegood, 1998; Ayalew, 2005; Holmes, 2003;
Glick and Sahn, 2000). On the econometric point of view, estimating the two equations
separately assumes the independence of their respective error terms. Yet, the level of
education of parents not only affects that of their offspring – as well as the type and even the
quality of education – but also their level of income. In such a scenario, the two error terms
are likely to be dependent, suggesting that the two equations would rather be estimated in a
system. This is what we do and the presentation of the model we adopt is given below.
The econometric model used to identify the independent effect of education investment on
poverty status changes over time can be summarized by the following two equations:
edit = γ 1wdit + β1 X1 + δ1Z1 + u1t
(3)
wdit = γ 2edit + β 2 X 2 + u2t
(4)
where edit is the educational investment of individual i measured as completed years of
schooling observed in time t . wdit* is the welfare transition made by individual i between
1991 – 2004. Measured by consumption, welfare is divided into four quartiles. For the
purposes of this paper, welfare dynamics 20 is perceived as a binary indicator of whether an
individual crosses the social ladder up to its highest level. That is, he/she goes from any
welfare quartile or category to the highest one. X i denotes the observed variables that are
thought to affect educational investment and welfare dynamics outcomes. These include basic
individual characteristics, such as sex and age of the individual as well as some household
characteristics. Z i consists of observed factors affecting education but not welfare. The error
term u1 represents the factors causing variation in education investment that are not included
in X i or Z i . Likewise, u 2 is the welfare transition error term. These error terms are allowed to
be correlated with each other and omitted factors, like the rate of time discount, can affect
both education and welfare dynamics through both of these terms. Given this, Ordinary Least
Square (OLS) is no longer appropriate for the estimation of the equation (3) and (4) unless
one uses proxies to control for all the factors that are likely to induce correlation between the
error terms. However, using proxy variables may not capture all factors that are correlated
20
Welfare dynamics is used alternatively with poverty dynamics.
17
with education and poverty. Therefore, the two equations (3) and (4) above should be
estimated as a system where the first step should consist in finding a valid Z i (i.e. an
instrumental variable or variables). Without a suitable Z i for the identification of the model
even the traditional Two – Stage Least Square cannot provide robust estimations. As an
alternative, we use the Two – Stage Probit Least Squares (hereafter 2SPLS) method suggested
by Maddala (1983). It is typically applied to correct for endogeneity between a continuous
dependent variable and binary endogenous regressors on the right-hand side. The 2SPLS
approach is computed in two steps. Adopting the commonly used notation, we describe them
below 21 . In the first stage, the initial model is as follows:
y1 = y1* = γ 1y2* + β1' X1 + u1
(5)
y2* = γ 2y1 + β2' X 2 + u2
(6)
While y1 (continuous variable standing for education) is completely observed ( y1 = y1* ), y 2* is
the dichotomous endogenous variable standing for the welfare mobility and corresponds to
its latent counterpart ( y2* ) such that
⎧1 if y2* > 0,
y2 = ⎨
⎩0 otherwise
Thus, one can only estimate Π
2
σ2
, with σ 22 = Var (v 2 ) .The structural equations can now be
written as
y1 = γ 1σ 2y2** + β1X1 + u1
y2** =
γ2
β'
u
y1 + 2 X 2 + 2
σ2
σ2
σ2
(7)
(8)
The two – stage estimation then proceeds with the estimation of the reduced – form OLS and
probit models for educational investment and welfare dynamics behavior respectively:
y1 = Π1 X1 + ν 1
(9)
y2* = Π 2 X 2 + ν 2
(10)
Where X is the matrix of all exogenous variables and Π1 , Π 2 the vectors of parameters to be
)
estimated. The predicted values from equations (9) and (10), y1 , y)2** are plugged back into the
model for the second stage estimation. Thus the original endogenous variables in (5) and (6)
are replaced by their fitted values from (9) and (10).
21
Keshk (2003) has written a Stata module that allows estimating a model with mixed qualitative and continuous variables as
discussed above.
18
IV. Econometric results.
IV.1. Education and welfare mobility
IV.1.1. Education mobility
Below we comment the extent to which educational mobility impacts on poverty dynamics.
We first present educational mobility separately, followed by welfare mobility before relating
the two. Table 5 shows the results of the simplest specification of education mobility.
Table 5 : Education mobility
Dependant variable :Log Education of the HHH in 2004
Log Education of the HHH in 1991
Constant
Observations
R-squared
(1)
0,126 (6,26)***
1,892 (68,80)***
(2)
0,140 (3,67)***
1,815 (36,72)***
(3)
0,114 (5,20)***
1,951 (63,15)***
(4)
0,249 (5,10)***
1,672 (26,38)***
(4)
0,108 (3,75)***
1,991 (46,41)***
842
0,04
352
0,04
490
0,05
210
0,11
215
0,06
Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%, (1):the full sample; (2):the poor; (3):the non poor; (4):the
lowest welfare group and (5):the highest welfare category
Source: Author’s computations based on the KHDS
Estimates of the intergenerational elasticities and correlations of education between parents
and their children for the entire sample and separately by quartiles of educational level
indicate a weak degree of persistence across generations or a higher intergenerational
education mobility. For the full sample of households, the estimated intergenerational
elasticity of education is significantly different from zero and equal to 0.126 which implies a
correlation of 0.21. To account for the potential effect of difference in socio – economic status
on the importance of education mobility among generations, we compute equation (2)
separately for the households classified as poor and non poor respectively in the initial period
on the one hand, and for different quartiles of consumption – based welfare level on the other
hand.
Intergenerational education correlations range from 0.19 in the category of poor
households to 0.23 in that of non poor households. Comparing households in the highest
consumption quartile relative to those in the lowest reveals that the intergenerational
correlation of education for the latter (0.42) is almost the double of that of the former
category. Such results suggest a very high persistence of education at lower welfare levels and
therefore a very low educational mobility for households belonging to this specific category.
As already noted, the transmission of education between generations is a complex process that
may imply several factors and parents’ education and ability influence the achievement of
19
their offspring through a number of different channels (Haveman and Wolfe,1995). Thus, the
simplest model was extended by the inclusion of other explanatory variables as proposed in
similar studies (Nimubona and Vencatachellum, 2007; Dumas and Lambert, 2011; Heineck
and Riphanhn, 2007; Daouli et al., 2010). In selecting among potential controls of the parent’s
education and the way it determines the achievement of their offspring education, a key
consideration for us has been selecting variables that are arguably exogenous. The selected
determinants can be grouped broadly into individual and household – level variables. They
namely include the age and gender of both the parent and the child, the percentage of adults
with respectively non – formal and primary education and the share of agricultural assets in
the total assets of the household 22 . The results of the extended model of education mobility
are thus given in table 6 below.
Table 6 :Extended model of Education mobility
Dependant variable :Log Education of the HHH in 2004
Log Education of the HHH in 1991
Prop. Adults with Non formal Education
Prop. Adults with some Primary Educ.
Age of HHH in 1991
Age of the individual in 1991
Percent Agri. in Physical Assets.
Sex of HHH , (Male=1)
Sex of the individual
Constant
Observations
R-squared
(1)
0,010 (0,38)
0,206 (3,86)***
0,69 (10,1)***
0,003 (3,05)***
0,025 (4,55)***
-3,974(2,85)***
-0,308 (2,92)***
0,128 (1,84)*
0,876 (7,55)***
842
0,22
(2)
-0,044 (0,90)
0,094 (1,00)
0,95 (7,7)***
0,004 (2,7)***
0,023 (2,38)**
-5,66 (1,83)*
-0,301 (1,92)*
0,184 (1,61)
0,664(3,42)***
352
0,25
(3)
0,023 (0,74)
0,26 (4,2)***
0,5 (6,3)***
0,002 (2,14)**
0,025 (3,9)***
-3,042 (2,11)**
-0,112 (0,69)
0,074 (0,87)
1,074(7,6)***
490
0,20
(4)
0,028 (0,45)
0,115 (1,08)
0,987
0,005 (1,97)**
0,019 (1,61)
-19,91(2,26)**
-0,403 (1,94)*
0,345 (2,05)**
0,441 (1,66)*
210
0,40
(5)
-0,004 (0,08)
0,39 (3,43)***
0,33 (2,94)***
0,001 (0,76)
0,033 (3,5)***
-10,33(2,02)**
0,273 (0,65)
0,063 (0,57)
1,171 (6,26)***
215
0,21
Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%, (1):the full sample; (2):the poor; (3):the non poor; (4):the
lowest welfare group and (5):the highest welfare category
Source: Author’s calculations based the KHDS
Column (1) gives the results of the OLS regression of the extended model of education
mobility for the full sample households. It appears that all the above variables are highly
significant (at least 5%) in the explanation of the level of education. In comparison to the
three precedent specifications, the elasticity decreases with the inclusion of additional
explanatory variables, ranging from 0.162 in the first column or specification to the 0.060.
The latter value for the elasticity implies a correlation of education across generations
amounting to 0.10, a value much lesser than the three precedents. In addition to the parental
22
One could also think to add the birth sequencing as suggested by some of the above reported studies on developing
countries. However, in the case of Tanzania, Al – Samarrai and Reilly (2000), whose study is based on a country – wide
survey, concludes that this aspect has exerted a minimal influence on primary school attendance. Thus, it can be inferred that
on this regard, the other schooling levels are no exception given that as already noted primary education was the most
dominant since Mwalimu Nyerere’s era.
20
education, other households or parental backgrounds affect the offspring education
achievement. The percentage of adults living in the households and having a certain primary
school education or any non – formal education appears to be very crucial in the attainment of
a given level of education. It is the same for both the age of the parent and that of the child
himself. Their coefficients are positive and express the direct and positive link between all
these household background elements and the logarithm of the educational achievement of the
child. This result accords with the existing empirical findings indicating that the average
human capital in the household and/ or in the community matters for children’s educational
attainment (Borjas, 1995; Basu and Foster, 1998; Jolliffe, 2002).
While Kagera is predominantly an agrarian economy, the share of agricultural assets in the
bulk of the household’s assets affects negatively the level of education achievement. That is,
the more a household is endowed in agricultural assets the less its offspring acquire education.
Such a result named “wealth paradox” by Bhalotra and Heady (2003) has already been
obtained by these authors in the case of Ghana and Pakistan, and in India by Rosenzweig and
Wolpin (1985), three developing countries whose rural economies share many common
patterns with those of Tanzania. In the previous literature this wealth paradox is mainly seen
as an outcome of the labor market imperfections which are reinforced by an ill – functioning
of the land market. However, in the case of Tanzania and based on our results, it seems to be
more anchored in the educational system with its low quality as already pointed in previous
researches, especially at the primary level (Wedgewood, 2007; Kaliba and Ghebreyesus,
2011). Under Mwalimu Julius Nyerere’s 23 era and many years later, education at the primary
level essentially focused on imparting technical skills for agriculture and businesses to
prepare students for living and working in rural areas. As shown by Kaliba and Ghebreyesus
(2011), this primary education policy has been and still is at odds with the revealed
preferences of parents who, concerning the primary school curriculum, rather highly value
elements like teaching mathematics and science skills, or good written and spoken English.
As a consequence, their willingness to pay for primary education is very low, only 37% of the
parents surveyed being ready to pay for the educational curriculum in its current state.
Logically, such a perception of formal education may result in that households which are
better endowed in agricultural assets decide to work with their offspring given that what is
supposed to be gained from the school can be also acquired, and maybe in a better may,
23
The first presented of the United Republic of Tanzania and initiator of the “Ujamaa policy”, the African socialism.
21
through experience on the farm. This is for instance the explanation
given by Rosenzweig
and Wolpin (1985) to the phenomenon in the case of India.
The results also indicate that compared to female – headed households, education
achievement is generally lesser when the household head is male. This is contrary to number
of studies on developing countries and especially to Tansel (1997) and Dumas and Lambert
(2011) who find, in the respective cases Ghana and Cote d’Ivoire on the one hand and Senegal
in the other, that father’s education influences more that of the child. The decrease of the
intergenerational education elasticity and (correlation) due to the introduction of controls in
the model as shown above is suggestive of a weak robustness of the relationship. Therefore, it
is necessary to make a sensitivity test and ascertain to what extent there exists an
intergenerational transmission of education across generations. In fact, there may be genetic
differences in ability that are transmitted from parent to child and that lead to
intergenerational persistence in both preference and education across generations. Of course,
to the extent that this is the underlying cause of the intergenerational correlation of education,
it may suggest a more limited role for policy 24 . Based on this, we compute the elasticity of
education between generations for different quartiles parent’s welfare distribution. As
indicated by the results in table 10(Appendix No.1), at the lowest quartiles the elasticity
substantially decreases but remains non significant. It is statistically significant from zero only
for the highest quartile of the welfare. Amounting to 0.110, it implies a correlation of 0.184.
Having the above results on educational mobility it is important to have a look on the patterns
of welfare mobility before presenting the linkages between the two.
IV.1.2. Welfare mobility
The descriptive evidence presented in the previous section suggests a clear transition of
welfare across generations for either measure of the latter, namely consumption or poverty.
However, knowing the proportion of individuals experiencing any type of welfare change
over time is neither informative on the degree of dependence of welfare across generations
nor on the variables from which the observed change can be based.
The econometric
regression of welfare mobility suggests the results in table 7 below.
24
As shown by Emran and Shilpi (2011), in one of the most recent and the extremely rare studies dealing with developing
countries, when this aspect is not taken into account there is a risk an overestimation of the intergenerational linkage of the
outcomes that is being studies.
22
Table 7 : Model of welfare mobility
Dependant variable :Log of the HHAE Consumption in 2004
(1)
(2)
(3)
(4)
0.308(8.0)***
0.397(3.6)***
0.306(4.1)***
0.489(3.3)***
Log of HHAE consumption in 1991
Welfare Category 1991: Highest
Welfare Category 1991: Upper Middle
Welfare Category 1991: Lower Middle
8.92(18.39)***
7.850(5.91)*** 8.945(9.38)***
6.782(3.79)***
Constant
842
352
490
210
Observations
0.07
0.04
0.03
0.05
R-squared
Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%
Source: Author’s calculations based the KHDS
(5)
(6)
0.38(2.53)**
7.974(4.05)***
215
0.03
0.363(6.2)***
0.229(3.9)***
0.138(2.33)**
12.6(301.72)***
842
0.05
From above the table, we note that based on the full sample of households, the
intergenerational elasticity of welfare (consumption) is statistically very significant and
amounts to 0.308, which implies a correlation of 0.27. Splitting the households into poor and
non – poor allows noticing that while remaining very statistically significant, the elasticity of
intergenerational transmission of welfare is different for the two categories, 0.397 for the poor
and 0.306 for the non – poor. This implies a correlation between the parent’s and offspring
welfare ranging from 0.34 to 0.26. This result is suggestive of the fact that welfare correlation
across generations is more correlated for poor households than for the non – poor. Contrary to
what is observed in the case of education transmission, the latter pattern is corroborated even
after the disaggregation of the analysis at the level of the different welfare quartiles.
To make the results clearer we further disaggregate the effect of the parent’s welfare by
categorizing it into four different groups associated to each of the quartiles of the parent’s
welfare distribution. Considering the lowest quartile of the welfare distribution as the
baseline, such an exercise allows us to determine whether there is a difference in the linkage
of welfare across generations and its importance according to the group to which the parents
belong in the distribution of welfare. This is a relevant step of analysis given that even if
poverty and welfare are measured based on consumption any mobility in the welfare
(consumption) does not necessarily imply a transition into the poverty status. The result
(column (6)) shows that compared to the lowest welfare group, the value of the elasticity and
thus that of correlation increases as we go from the lowest to the highest group, namely 0.204
for the lower – middle group, 0.244 for the upper – middle group and 0.401 for the highest
group and the induce respective correlation,
0.176, 0.21 and 0.345. It thus suggest that
mobility decreases
23
Having measured the degree of mobility across generations in education and welfare, it is
important to appraise to the extent to which the two are interconnected. In fact, as noted
already, beyond the importance of education by its own, its accumulation is more motivated
by the presumption that it may serve as an avenue for social escalation. It is therefore of high
relevance to determine to what extent education and welfare mobility are related. This is what
is depicted in the following point.
IV.2. Linkage between education and welfare mobility
Table 8 presents results of the Two Stages Probit Least Squares which provide some evidence
on the gradients of both education accumulation and social mobility across welfare groups.
Equation (1) describes the extended version of education accumulation and (2) the probit
estimation for whether the individual has shifted from any welfare category to the highest.
With exception of the instruments for the welfare dynamics and education accumulation
respectively, the rest of the variables are the same as those in the previous analysis. Like in
the extended version aforementioned the impact of parental education on their offspring’s
decrease after controlling for other variables. The instrument for the household welfare
dynamics is highly significant, validating the results presented in the previous point and
suggesting the relevance of the choice of the estimation strategy, the Two Stages Probit Least
Square.
Of great importance to be noted given the aim of this paper is the direct effect of education
accumulation on the probability of mobility towards the highest welfare level. The marginal
effect of the instrumental for accumulated education is negative and significant at 5%. Thus,
an increase of one year of education is likely to reduce the probability of moving towards the
highest welfare group by 0.203 and improvement of children’s education does not help
elevate them into the highest welfare group. β value of children’s education is (–0.733). Its
opposition is 0.481, meaning that for every 1 year of increase in children’s length of
education, the weighted opportunity for them to move up into the highest welfare group
decreases by 2.08 percent. That is, after family factors such as parent’s sex and their welfare
category, the increase in the length of children’s education does not help improve their
chances of getting into the highest welfare group. Such a result can be explained by the
peculiarity of Tanzania as economy.
24
Table 8 : Results of the TSPLS - Education and mobility to a higher welfare category.
INSTRUMENTS
Highest Welfare Category in 2004, Yes=1 and No=0
Accumulation of Education 1991/94 – 2004
Equation (1):OLS
Equation (2) : Probit
Coefficient
0.944 (7.41)***
Coefficient
Exp(Coefficient)
Effet marginal
-0.733 (2.30)**
0.482** (0.153)
-0.203** (0.0884)
0.006 (0.24)
1.006 (0.0246)
0.955 (0.646)
2.295** (0.894)
1.005 (0.00381)
0.00164 (0.00681)
EXPLANATORY VARIABLES
Education of the HHH in 1991
Prop. Adults with Non formal Education
Prop. Adults with Primary Education
Age of the HHH in 1991
Age of the individual in 1991
Share of Agricultural Assets in the total Physical Assets
Land area detained, in Acre
Sex of HHH, (Male=1)
Sex of the individual
Welfare Category in 1991: Highest
Welfare Category in 1991: Upper Middle
Welfare Category in 1991: Lower Middle
Prop. HH Adults working off-farm, in 1991
Distance Daily Market, in Km
HH size in 1991
Nomber of Old people: >65 ans
Share of Non-Agric. In the HH income
Prop. Old people in the HH
Activity sector 2004: General Commerce
Activity sector 2004: Techniques & Others
Activity sector 2004: Global Administration
Activity sector of Parent * Educ. Descendant : Non-Agr.
Activity sector of Parent * Educ. Descendant : Unemployed
Activity sector* Educ. 2004: General Commerce
Activity sector* Educ. 2004:Administration
Welfare Category in 1991* Educ. Descendant.: Upper Middle
Welfare Category in 1991* Educ. Descendant.: Lowest
0.044 (1.71)*
0.656 (3.22)***
1.757 (6.47)***
0.010 (2.81)***
0.161 (7.57)***
-20.51 (3.16)***
0.084 (1.74)*
-0.813 (1.75)*
0.403 (1.46)
Constant
3.371(7.26)***
Chi2
Observations
R-squared ou Pseudo R-squared
840
0.69
0.834 (2.14)**
0.005 (1.33)
-9.792 (1.48)
-0.859 (1.87)*
6.124
-0.162
5.830
0.702
-0.026
-0.051
-0.415
-0.300
3.925
-3.380
0.499
-3.142
0.110
0.068
0.572
0.440
0.763
0.726
-1.567
142.7
840
0.153
(2.78)***
(0.20)
(2.73)***
(1.13)
(3.97)***
(1.58)
(1.00)
(0.50)
(1.30)
(1.58)
(1.66)*
(1.27)
(2.02)**
(2.14)**
(2.02)**
(1.41)
(2.56)**
(2.49)**
(2.96)***
0.0000561
0.942
0.423*
0.481*
446.9***
0.853
333.3***
2.013
0.974***
0.950
0.662
0.743
49.39
0.0347
1.643*
0.0441
1.116**
1.070**
1.768**
1.548
2.138**
2.061**
840
(0.000372)
(0.0414)
(0.194)
(0.209)
(985.3)
(0.696)
(711.3)
(1.254)
(0.00641)
(0.0307)
(0.275)
(0.444)
(148.7)
(0.0740)
(0.493)
(0.109)
(0.0611)
(0.0337)
(0.501)
(0.483)
(0.637)
(0.600)
0.00139 (0.00105)
-2.721
(1.839)
-0.160***(0.0482)
0.992*** (0.0135)
-0.0428 (0.211)
0.989*** (0.0170)
0.194
(0.173)
-0.0073***(0.002)
-0.0142 (0.00899)
-0.115
(0.115)
-0.0824 (0.166)
1.084
(0.837)
-0.316***(0.087)
0.159
(0.107)
-0.269***(0.0653)
0.0306** (0.0152)
0.0187** (0.0088)
0.158** (0.0790)
0.121
(0.0868)
0.211** (0.0824)
0.201** (0.0809)
840
Absolute value of t statistics in parentheses for coefficients and Standard errors in parentheses for odds ratios and marginal effects ; * significant at 10%; **
significant at 5%; *** significant at 1%
Source : Author’s calculations based on the KHDS
As already noted from the descriptive statistics in the precedent section, although globally the
mobility of the education is proven between the two periods, there was no big change in the
educational level. It is true that the foremost modality of educational mobility is "No.
schooling - Some Secondary education" which represents 37 % of the whole observed
mobility. But set globally, the average years of schooling changed respectively from 3 to 7
years of studies between 1991/1994 and 2004 for the heads of the household, and from 6 to 7
years for all the members of the household. In Tanzania, primary school education spends 7
years. Thus, while translating an increase of the number of years of schooling, a variation of
25
this extent certainly expresses a change but within the same level of education, namely the
primary level. This is in accordance with Solon’s theoretical model (Solon, 2004) insofar as
the amount of investment in human capital is supposed to be positively related to its returns.
Further, according to the results of the previous studies on Tanzania (Wedgewood, 2007;
Kaliba and Ghebreyesus, 2011), it is completely plausible that such a change does not
correspond to a substantial acquisition of knowledge that is likely to induce an increase of
skills (Rosenzweig, 1996; Tilak, 2007) and consequently an effect on the households’ welfare.
Two reasons can be invoked on this regard. First, Wedgewood (2007) 25 shows that given the
low quality of the Tanzanian educational system, the latter can itself impede education
accumulation to translate in poverty reduction. In fact, the system can induce so a poor quality
of education that the potential benefit it is supposed to produce might not be realized26 .This
seems to be confirmed by the significance of the proportion of household adults with primary
education which positively impact on the dependant variable 27 . Van der Berg (2002) found
similar results in the case of South Africa. He shows that due to its low quality, especially in
black areas, the school system contributes little to supporting the upward mobility of poor
children in the labor market 28 . Second, Kagera is predominantly an agrarian economy while
as noted by Sandefur et al (2006) the growth of employment, which was seen as the basis for
poverty reduction, has been dominated by non – rural self – employment. Even studies based
on developed countries lend support on the importance of the quality of the education
outcome as a nexus in the relationship between social mobility and education accumulation.
For instance, comparing France and Japan, LeFranc et al. (2010) find that more than the
former, the latter country experiences income mobility as a result of education mobility. They
25
Tilak (2007) elaborates on this idea and confirms, based on the case the India, that while primary education gives the
basics on education, rarely does it provide skills necessary for employment—self-employment or otherwise, that can ensure a
reasonable level of wages and economic living. Moreover, most of the literacy and primary education programs are also
found to be not imparting literacy that is sustainable in such a way that children do not relapse into illiteracy. Secondly,
primary education rarely serves as a meaningful terminal level of education. Thirdly, even if primary education imparts some
valuable attributes, in terms of attitudes and skills, they are not sufficient; and if primary education is able to take the people
from below the poverty line to above the poverty line, it is possible that this could be just above the poverty line, but not
much above; and more importantly the danger of their falling below poverty line at any time could be high.
26
In such a context, spending a long time for schooling corresponds to a renunciation to exert an income generating activity
without increasing however one’s chances of insertion on the labor market. See Caucutt and Kumar (2007) for an elaborated
discussion on the costs that the time spent for education induces to African countries in terms of growth.
27
More recently, a serious questioning has been raised in the literature upon the generalization of the Universal Primary
Education (UPE) programs which are being implemented in several developing countries. While they are inducing a clear
increase of the attendance of primary school they go with a serious decline of the quality of education. See for instance
Kadzamira and Rose (2003) in the case of Malawi or Deininger (2003) for Uganda. The latter author suggests that the
government put a special attention on the quality of the education program in order to ensure that it induces higher levels of
human capital.
28
The importance of the quality of education as a basis of its private and social returns has largely been analyzed in the
literature (Behrman and Birdsall, 1983; Henaff, 2005).
26
attribute such differences to quality differences between the educational systems across the
two countries.
For the control variables, the same regression results show that chances of children in various
welfare groups getting into the highest welfare group vary greatly. In terms of parents
belonging to the highest welfare group, the exponent of the coefficient (Exp (β)) is 2,232
29
,
meaning that the chances of children in the highest welfare group remaining in the highest
welfare group are 2,2 times higher than those of children in the lowest welfare group getting
into the highest welfare group. When parent belong to the upper middle welfare group, the
Exp (β) is 1,436, indicating that the chances of children in the upper middle welfare group
getting into the highest welfare group are 1,43 times those of children in the lowest welfare
group getting into the highest welfare group. In case of parent belonging to the lower middle
welfare group, the Exp (β) is 1.875, meaning that the chances of children in the lower middle
welfare group getting into the highest welfare group are 1.9 times those of children in the
lowest welfare group getting into the highest welfare group. In contrast, it is hard for children
of the lowest welfare group, as the most disadvantaged in social welfare distribution, to be
freed from the impact of the parents and to move up into the highest welfare group.
Regarding the sex of the parent – household head – there is no gender bias in favour of male
headed households but rather. The Exp (β) associated to the variable pertaining to gender is
0.424, meaning that the chances for children from female headed households getting in the
highest welfare group are 2.27 times those of children from a male headed household. This
result is in the line of the existing literature since the effect of the sex of the household head
on the offspring is not conclusive (Dearden et al., 1997; Behrman, 1997; Chadwick and
Solon, 2002; Black et al., 2005).
The impact of the sector of activity in which the individual evolves in elevating him from any
welfare group to the highest, compared to his parents, is very significant. In terms of a person
belonging to the group of persons evolving in “Technical and other sectors”, the Exp (β) is
29
e
β j xt
is the relative risk ratio for a unit change in the variable x: a relative risk ratio (rrr) of less than one means that an
increase in variable x increases the probability that the household is in the base category, i.e. the category identified in the
denominator, whereas an rrr of more than one implies an increase in the probability of the individual being in the alternative
state, i.e. that identified in the numerator.
27
1,65 meaning that the chances of that person reaching the highest welfare group are 1,7 times
those of the persons evolving in the agricultural sector.
Regression results of the different interaction items of the dummy variable of the parent’s
activity group and children’s length of education show that children’s education plays a
different role in getting them into the highest welfare group when the parent’s are different.
The regression coefficient of the interactive variable of the dummy variable of parent being
without a job and children’s length of education is 0.11 and its opposed value is 1.12. Thus, as
compared to individual coming from a family with a parent evolving in farming activities, the
chance of reaching the highest welfare group is 1.12 times.
Regression results of the different interaction terms of the dummy variable of the parent’s
welfare category and children’s length of education show that children’s education plays a
different role in getting them into the highest welfare according to their social background,
captured through parent’s membership to different welfare group. The regression coefficient
of the interactive variable of the dummy variable of parents belonging to the upper middle
welfare group and children’s length of education is 0.763 and its exponentiated value is 2.15.
Thus, as compared to individual coming from a family belonging to the lowest quartile of
welfare, the chance of reaching the highest welfare group given their education is 2.15 times.
For children coming from the lower - middle welfare group, it is 2.01 times that of their
fellow coming from the lowest welfare category.
This result corroborates the existing
empirical finding (Haveman and Wolfe, 1995; Checci et al., 2008) as well as the previous
statistic analysis that points out the importance of the parents’ education background for the
explanation of differences of opportunities of schooling attainment among their offspring.
IV.3. Robustness test
To test the robustness of the above results, our strategy consists of changing the dependant
variable and considering any change from a lower welfare group to an upper one. This stems
from the fact that on a strictly empirical point of view, mobility does not necessarily means
escalating the entire social ladder up to the highest sub – category but rather a change from
any position to another one on the social scale. Results are in the right hand side panel of the
table 9 below.
28
With this new specification, all the variables that explain the accumulation of education
remain significant as in the previous specifications, with the exception of the degree of
significance which increases in general.
Table 9: Results of the TSPLS - Education and mobility to a higher welfare category.
INSTRUMENTS
Higher welfare Category in 2004, Yes=1 and No=0
Accumulation of Education 1991/94 – 2004
Equation (1):OLS
Equation (2) : Probit
Coefficient
0.686 (6.66)***
Coefficient
Exp(Coefficient):Odds R.
Effet marginal
-0.229 (1.76)*
0.799* (0.104)
-0.0732* (0.0425)
0.018 (0.93)
1.018
0.00583 (0.00634)
0.300 (0.88)
-0.007 (2.56)**
1.336
(0.455)
0.993** (0.00274)
0.0946 (0.111)
-0.0023** (0.0009)
-6.379 (1.34)
0.00177 (0.00842)
-2.068
-0.002 (0.01)
1.000
-0.0000207(0.0817)
-1.457 (1.60)
1.685 (9.41)***
-0.854 (1.40)
-0.016(4.00)***
0.019 (0.87)
-0.302 (1.56)
-1.515(2.70)***
3.090 (2.20)**
0.735 (4.28)***
0.878 (4.26)***
0.479 (2.08)**
0.127 (3.13)***
0.032 (1.39)
0.239
(0.218)
5.375***(0.963)
0.420
(0.257)
0.984***(0.00400)
1.019
(0.0228)
0.741
(0.144)
0.222***(0.124)
21.42** (30.10)
2.080***(0.357)
2.395***(0.494)
1.608** (0.370)
1.135***(0.0459)
1.032
(0.0239)
-0.347** (0.150)
0.589***(0.0526)
-0.283
(0.199)
-0.005***(0.0013)
0.00628 (0.00730)
-0.0977 (0.0636)
-0.492***(0.181)
1.000** (0.459)
0.270***(0.0668)
0.325***(0.0794)
0.171* (0.0887)
0.0414***(0.013)
0.0104 (0.00754)
0.297 (2.35)**
0.29(10.09)***
-0.256(0.43)
293.0
840
0.276
1.341** (0.170)
1.339***(0.0389)
0.0958** (0.0410)
0.0954***(0.009)
840
840
EXPLANATORY VARIABLES
Education of the HHH in 1991
Prop. Adults with Non formal Education
Prop. Adults with Primary Education
Age of the HHH in 1991
Age of the individual in 1991
Share of Agricultural Assets in the total Physical Assets
Land area detained, in Acre
Sex of HHH, (Male=1)
Sex of the individual
Welfare Category in 1991: Highest
Welfare Category in 1991: Upper Middle
Welfare Category in 1991: Lower Middle
Prop. HH Adults working off-farm, in 1991
Distance Daily Market, in Km
HH size in 1991
Nomber of Old people: >65 ans
Share of Non-Agric. In the HH income
Prop. Old people in the HH
Activity sector 2004: General Commerce
Activity sector 2004: Techniques & Others
Activity sector 2004: Global Administration
Activity sector of Parent * Educ. Descendant : Non-Agr.
Activity sector of Parent * Educ. Descendant : Unemployed
Activity sector* Educ. 2004: General Commerce
Activity sector* Educ. 2004:Administration
Welfare Category in 1991* Educ. Descendant.: Upper Middle
Welfare Category in 1991* Educ. Descendant.: Lowest
0.073 (2.18)**
0.706 (2.73)***
2.146 (6.59)***
0.012 (2.88)***
0.147 (5.54)***
-18.216 (2.58)**
0.112 (1.97)**
-1.966 (3.89)***
0.037 (0.11)
Constant
3.084(5.37)***
Chi2
Observations
R-squared ou Pseudo R-squared
840
0.44
(0.0198)
(0.250)
(1.551)
Absolute value of t statistics in parentheses for coefficients and Standard errors in parentheses for odds ratios and marginal effects ; * significant at 10%; **
significant at 5%; *** significant at 1%
Source : Author’s calculations based on the KHDS
The impact of education accumulation on the mobility towards an upper welfare category also
remains negative and highly significant, comforting the results of the previous specification
that the increase of children’s years of education does not increase their probability of moving
to a higher welfare category. Rather, for the control variables, there is both a change in the
degree of significance as well as in the significance itself. On the one hand, two groups of
29
variables become significant: the proportion of old persons in the household and a set of
dummies of the professional category of the observed individual. While in the first estimation
only the professional category “Technical and other sectors” is likely to increase the
probability of climbing up to the highest welfare category, the results of this alternative
estimation resort that compared to their peer evolving agriculture, people in all other
professional sectors have more chance of being in a welfare category which is higher than that
of their parents. Taken together with the interaction variable of the descendent education with
his/her parent’s sector of activity, this variable confirms that the effect of education on the
mobility to both the highest welfare level as well as for any higher welfare group indirectly
passes through its capacity of diverting from agriculture. On the other hand, the age of the
parent (household responsible) in the base year and the share of non – farm income in the bulk
of the household income become significant; their increase reducing the probability that
someone experiences a mobility towards a higher welfare group. Remarkably, the sex of
parent and the percentage of adults of the household with some primary education which are
highly significant in the probability of reaching the highest welfare category become no
significant for the probability of moving high across the welfare scale and regardless of the
level. For the specific case of education, such a pattern is in accordance with the
aforementioned idea of the quality of primary education.
V. Conclusion
Despite the abundance of empirical microeconomic studies emphasizing the positive impact
of education on welfare, when it comes to Africa little is known on the dynamic connection
between accumulation of education and welfare dynamics over generations. The contribution
of this paper is to fill this gap by appraising the extent to which education would serve as an
instrument for climbing the social ladder. Based on the Kagera Health and Development
Survey (KHDS) from the north – west Tanzania, the paper specifically determines the extent
of educational mobility (1), and appraise the extent to which the observed education mobility
allows offspring to benefit from a higher living standard compared to their parents (2).
The main results are that there exists a clear educational mobility in Kagera insofar that
descendants’ level of education weakly depends on their parents’ education, with the
intergenerational education elasticity and correlation respectively ranging from 0.108 to 0.14
30
and from 0.19 to 0.23. Educational mobility is marked by a “wealth paradox” since the
accumulation of education of the descendant appears to be inversely proportional to volume
of agricultural assets of the family, a result seen as a consequence of the low quality of
education prevailing in the area. Regarding welfare mobility, it is as weak as the observed
household in the final period is from the highest welfare category in the baseline period.
The linkage between education mobility and social mobility captured through poverty
transition over generations reveals that an additional year of education reduces rather than
augmenting the likelihood of escalating the welfare scale. However, the number of household
members with a certain primary education background positively affects the probability for a
descendant to either shift to the highest welfare quartile or to the higher one than that of his
parents. This result not only is robust to the control of other variables that can affect the
probability of experiencing a poverty dynamics over generations but also holds after
controlling for the potential endogeneity between education mobility and welfare change over
time.
VI.
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AAPENDIX 1: Table 10 :Education mobility for different quartiles of HH Consumption
Dependent variable: log HH education in 2004
(1)
(2)
Log HH Education in 1991
0,015 (0,93)
Prop. Adults Non – formal Educ.
0,040 (1,74)*
Prop. Adults Primary Educ.
0,439 (1,52)
Age of HH in 1991
0,000 (0,43)
Age of the Individual 1991
0,009 (2,02)**
Share of Agri. Assets in phys. Assets
-5,42 (3,91)***
Sex of HH : Mâle, Yes=1
-0,087 (0,15)
Sexe of the individual
0,069 (0,25)
Constant
1,508 (3,98)***
Observations
842
t statistics in parentheses;* significant at 10%; ** significant at 5%; *** significant at 1%
0,000 (0,64)
0,000 (1,60)
0,000 (2,90)***
0,000 (2,26)**
-0,000 (0,21)
-2,982 (4,17)***
0,000 (0,88)
0,000 (1,01)
2,197 (29,66)***
842
(3)
0,109 (2,24)**
-0,018 (0,35)
-0,005 (0,08)
0,013
-3,827
-0,343
-0,062
2,169
842
(1,41)
(2,51)**
(3,72)***
(0,41)
(12,95)***
36