Statistics versus livelihoods: Questioning Rwanda’s pathway out of poverty
An Ansoms, Esther Marijnen, Giuseppe Cioffo and Jude Murison
Published in Review of African Political Economy, 2016
Abstract
Recent statistics indicate that poverty in Rwanda decreased impressively between
2006 and 2014. This seems to confirm Rwanda’s developmental progress. This
paper however argues for a more cautious interpretation of household survey data.
We contrast macro-level statistical analysis with in-depth field research on
livelihood conditions. Macro-economic numbers provide interesting information;
however, differentiated evidence is required to understand how poverty works. On
the basis of the Rwandan case study, we conclude that because the political stakes
of data collection and analysis are high, cross-checking is crucial.
Keywords: Rwanda, poverty, statistics, livelihoods, donor policies, development.
Introduction
Over the past decade, controversy has emerged on the reliability of national development
statistics. Jerven’s book ‘Poor numbers’ (2013A) highlights that many data on African
countries are of poor quality, partial and unreliable. The author warns against the tendency to
rely on such data for decisions on where to allocate aid on the basis of ‘evidence-based
policy’. In some African countries, such as Ghana and Nigeria, GDP has consistently been
underestimated due to miscalculation (Jerven, 2013B). However, given that GDP estimates
rely upon approximations and assumptions, it is also plausible that GDP rates are
overestimated. Wallace for example found evidence of GDP manipulation in the case of
China, particularly in politically sensitive times (Wallace, 2014). Also for Ethiopia, there are
accounts of overestimated GDP figures (IMF, 2013B), interpreted by some as deliberate
manipulation by the Ethiopian government.
In response to the renewed debate on the limited reliability of GDP estimates, a rich collection
of literature has been reflecting upon the usefulness of other statistics. Data compilation
initiatives - such as the Human Development Index or the Multidimensional Poverty Index of
UNDP, and the Happy Planet Index from the New Economics Foundation – explore the
options of generating multidimensional cross-country datasets. New methods explore the
1
reliability of data based on mobile phone communications and airtime credit purchases
(Gutierrez et al., 2013). A 2014 OECD report analysed trends in global well-being on the
basis of life expectancy, education, personal security and gender inequality. It concluded that
these alternative indexes resonate to a more equal world than when considering GDP per
capita figures (OECD, 2014).
At national levels, an important alternative data source is the nationally representative
household living conditions survey. Poverty and inequality statistics calculated from
household surveys are generally considered as robust; and are used to update the reliability of
GDP figures (Jerven, 2013B). On the downside, such large-scale surveys are not available
every year given their considerable cost. An interesting exercise is thus to compare GDP
evolutions with evolutions in poverty and inequality statistics for those years for which survey
data are available. In the case of Rwanda, the latest household living conditions survey
(EICV4) was conducted in 2013/4 after previous surveys in 2010/11 (EICV3), 2005/6
(EICV2) and 2000/1 (EICV1). The results (see table 1) are illuminating.
High GDP growth rates between 2000/1 and 2005/6 were not accompanied by significant
poverty reduction. As a result of high population growth rate, the absolute number of people
living in poverty increased (UNDP 2007). This led to questions about the pro-poor character
of economic growth. However, between 2005/6 and 2010/11, continued average GDP per
capita growth went together with spectacular poverty decrease. In addition, the gini
coefficient, a measure of inequality, decreased. The EICV4 results suggest that these trends
have continued up to 2013/4. It is to be noted, however, that the methodology for calculating
the 2013/14 poverty line differs profoundly from the method used for EICV1, 2 and 3 (a fact
recognised by NISR, 2015). As a result of the changed methodology, it is doubtful that the
poverty percentage is comparable to past poverty rates; although this is done by the Rwandan
government (NISR, 2015).
This paper argues that the sole reliance on large-scale household surveys in order to assess the
level of socio-economic progress can be misleading. In fact, while providing comprehensive
information on general social trends, large-scale surveys can under-represent or even
misrepresent the situation of more marginal groups in society. This is a crucial issue, as the
results of large-scale surveys play a pivotal role in determining the allocation of international
aid money. Moreover, positive outcomes from large-scale surveys may be used in order to
endorse and legitimise government policies, thus minimizing, or at time bluntly overlooking,
2
the effects of such policies on population groups whose situation is glossed over in statistical
data.
Table 1: Growth – poverty – inequality statistics compared
EICV1
2000/1
EICV2
2005/6
EICV3
2010/11
EICV4
2013/14
(3)
2000/12005/6
2005/62010/11
2010/11 –
2013/14 (3)
Annual
Annual
Annual
GDP (mia
1,745
2,503
3,706
4,532
growth of
growth of
growth of
frw constant
7.5%
8.2%
6.9%
2011 prices)
GDP per
Annual
Annual
Annual
capita (Frw
213,343
274,410
355,377
404,229
growth %
growth %
growth %
constant
of 5.2%
of 5.3%
of 4.4%
2011 prices)
58.9%
56.7%
44.9%
39.1%
2.2% ↓
11.8% ↓
5.8% ↓ (3)
% Poor (1)
% Extreme
40.0%
35.8%
24.1%
16.3%
4.2% ↓
11.7% ↓
7.8% ↓ (3)
poor (2)
Gini
0.507
0.522
0.490
0.448
coefficient
Ration of
7.07
7.10
6.36
6.01
90th to 10th
percentile
(1) The percentage of poor is based on a poverty line of 64,000 FRW (2001 prices).
(2) Extreme poverty is calculated on the basis of a poverty line of 45,000 FRW (2001 prices).
(3) The methodology for calculating the 2013/14 poverty line differs profoundly from the method used for EICV1,
2 and 3 (a fact recognised by NISR, 2015). As a result of the changed methodology, it is therefore doubtful that
the poverty percentage can legitimately be compared to past poverty rates.
Source: for GDP data - World Bank, 2015; for other statistics - NISR, 2012 & 2012A; NISR, 2015.
In the first part of this paper, we analyse how recent statistics in Rwanda have shaped the
public attitude and agenda of the Rwandan government as well as that of its international
donors. In the second part of the paper we complement the results of the EICV surveys with
our own qualitative research in Rwanda. We mainly focus on the 2005/6 (EICV2) - 2010/11
(EICV3) period as economic development shifted towards being ‘pro-poor’ according to the
statistics. We exploit the explicative power of qualitative data (Olivier de Sardan, 2008) in
order to highlight the possible gaps and more questionable results of the EICV surveys.
We base our findings on longitudinal in-depth research. Intensive qualitative data gathering
took place in six locations near the same years as the EICV surveys: in 2006/7 (further
referred to as AA field notes) and in 2011 (BB field notes). At both times, semi-structured
focus groups were conducted with village leaders and with diverse socio-economic categories
(between 14 and 20 focus groups per setting, each time including 4 to 7 persons). Questions
focused on people’s livelihood strategies and on the impact of rural policies. In 2013, we
gathered data in two of the six locations (CC field notes). These six settings in the Southern
province are not representative for the whole of Rwanda, or even for the Southern province.
Indeed, our settings were located in districts where - according to the EICV3 - poverty
3
reduction in 2010/11 was limited.1 However, the settings represent a variety of rural living
environments (better-off versus poorer regions, centrally-located versus extremely remote,
more and less fertile). Despite this variety, our findings were quite similar, and we crosschecked with research in two new locations in the Northern Province in 2013 (CC field
notes).
The political importance of statistics
Standardised households surveys are often presented as apolitical and bound by technical
procedures. However, their results have political significance, particularly in countries that
strongly rely on political and financial support from the international community. Socioeconomic progress is important in enhancing the legitimacy of the recipient government;
while donors need ‘success-stories’ to legitimise their expenditures in development
cooperation. Therefore, statistical data and their interpretations should be analysed in light of
the political stakes involved.
Both the 2005/6 and 2010/11 EICV surveys were undertaken to provide input to the
Economic Development and Poverty Reduction Strategies (EDPRS-I and EDPRS-II). The
Rwandan government launched these strategies to achieve its Vision 2020 objectives.2 As
mentioned above, poverty reduction was limited over the 2000/1–2005/6 period when the first
Poverty Reduction Strategy (PRSP) was implemented. The Rwandan government’s explained
that the first PRSP “was elaborated in a post-conflict environment where the main emphasis
was on managing a transition from emergency relief to rehabilitation and reconstruction”
(GoR, 2012: 2). Donors, however, became increasingly critical of the government's rather
exclusive focus on economic growth, and criticised the deepening of existing gaps3. In 2007,
UNDP launched a critical report, ‘Turning Vision 2020 into Reality: From recovery to
sustainable human development’ (UNDP, 2007), which was received negatively by the
government. The report delved extensively into the problem of inequality, and warned
“extreme inequality can weaken political legitimacy and corrode institutions, leading to
higher political instability caused by popular movements of discontent in countries with large
gaps between the rich and the poor.” (UNDP, 2007:18-21)
As Rwanda was - and continues to be - heavily dependent upon international donors (see table
2), the increased donor focus on inequality was problematic for the Rwandan government.
Rwandan political elites had gained legitimacy within the donor community on the basis of
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Rwanda’s high technocratic governance standards (Reyntjens, 2013). By 2012, it was
therefore crucial for the Rwandan government to prove that their development model was
working.
Table 2: Net official development assistance and aid received by Rwanda
(constant 2012)
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
million US$
585
670
679
754
932
961
1069
1235
879
1075
Source: World Bank (2015) World Bank Development Indicators, World Bank, online at databank.worldbank.org
[date last access: 9 November 2015].
When the EICV3 report came out, the announced poverty figures (cfr. table 1) were close to
the targets the Rwandan government had proposed in its 2007 EDPRS-I strategy4. In the
foreword of the EICV3 report, the Minister of Finance and Economic Planning stated “the[se]
milestones are indeed a testament to the guidance and support of the top leadership in the
country in the fight against poverty” (NISR, 2012: 3). President Kagame, in his foreword of
the EDPRS-II report, lauded the achievements and highlighted that “our progress strengthens
the belief that our development ambitions towards the Vision 2020 can be achieved with our
concerted efforts” (GoR, 2012: viii).
The results of EICV3 were presented in February 2012, but the euphoric news was quickly
overshadowed by the creation of the M23 rebel group in eastern Democratic Republic of the
Congo (DRC) in April 2012. Rwanda faced severe criticism from the international community
for its role. Various donors ‘froze’ part of their aid, resulting in a decrease in overall aid
figures in 2012 (see table 2). By November 2013, however, the ‘M23 problem’ was
contained. Many donors resumed their aid and quickly picked up again on the impressively
improved figures.
In the academic literature that appeared around that time, Rwanda was described as a
‘developmental patrimonial state’ of which the ‘politically inspired economic activism’ might
be a model for other African States (Booth & Golooba-Mutebi, 2012). Reports from the
World Bank and IMF pointed to positive achievements in terms of economic growth, poverty
reduction, improvements in business climate, and in public service delivery (World Bank,
2013; IMF, 2013). DfiD, in its operational plan 2011-2015, referred to the 12 percent point
poverty decrease as proof of Rwanda being on track to meet the MDGs (DFID 2012). SIDA,
the Swedish Development Agency, compared Rwanda’s achievements - as an exceptional
African success story - to Thailand, China and Vietnam5. Participant observation of one of the
5
authors within the European External Action Service (EEAS) in 2012 revealed that the
positive survey results were used internally in the EU to counter expressions of concerns
about the authoritarian nature of the government and military involvement in eastern DRC
(author reference purpose). Overall, the results of the 2010/11 survey were welcomed as the
much-needed scientific proof of a successful developmental path and provided donors with a
political justification to allocate aid while ignoring criticism on Rwanda’s limited space for
political freedom.
Recently, the release of the 2013/14 EICV4 results led to controversy on the methodology
used for recalculating the poverty line – and thus, on the comparability of the data between
EICV3 (2010/11) and EICV4 (2013/14). Whereas the Rwandan government’s official report
announced a poverty reduction of 6% (based on a recalculated poverty line), Reyntjens came
to an estimate of a 6% poverty increase between 2010/11 and 2013/14 (Reyntjens, 2015). The
story was picked up by France 24 (Germain, 2015). The National Institute of Statistics of
Rwanda refuted the allegations of manipulating its poverty statistics, claiming that the
‘changes to the ratio of products in the food basket [on the basis of which the poverty line is
calculated] are made following a rigorous methodological process’ (NISR, 2015). However,
they did not respond to the technical aspects of Reyntjens’ arguments in relation to the
comparability of the poverty lines. Yet, the Rwandan government was backed up by some of
its major donors. A DFID spokesperson said, ‘we believe the revision of the methodology
used to estimate poverty levels for the EICV4 poverty survey was justified’ (Germain, 2015).
The NewTimes cites IMF Mission Chief Redifer saying, ‘we have no reason to doubt the
numbers’ (Agutamba, 2015).
Overall, it is clear that the EICV surveys have a major importance to both Rwandan policy
makers as well as international donors in evaluating and justifying policy implementation and
aid effectiveness. And indeed, these large-scale statistical surveys provide interesting
information. However, they tend to ignore the diverse accounts of people’s livelihood
strategies, and turn a blind eye to life experiences regarding public policies. Although the
controversy around the 2013/14 EICV4 dataset is much stronger, we have decided to focus on
enriching statistical material from 2005/6 (EICV2) and 2010/11 (EICV3) with in-depth
qualitative data gathered around that same period.
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Confronting macro-economic data with every-day poverty: Sampling problems
In a critical analysis of household survey data, Carr-Hill notices that surveys typically underrepresent six vulnerable subgroups: (1) the homeless, (2) those in institutions, (3) mobile,
nomadic or pastoralist populations, (4) those in fragile disjointed households, (5) slum
populations, and (6) areas posing security risks. According to Carr-Hill (2014:136), “those six
subgroups constitute a large fraction of the ‘poorest of the poor’”, and their omission in the
‘denominator’ is likely to insert substantial biases in poverty assessments. This argument
might also be relevant when considering the Rwandan context. It is quite likely that homeless,
mobile populations, or those illegitimately living in slums at the borders of Kigali, are
underrepresented in the overall dataset. However, if this statistical problem would explain part
of the spectacular poverty decrease over the 2005/6-2010/11 period, then the denominator
problem should be more outspoken for EICV3 then for EICV2.
We have reason to believe that this is the case. In fact, we found a strange anomaly in the
distribution of population by age group in EICV3 (2010/11), compared to EICV2 (2006/7).
The samples of both EICVs should be representative for the total population. And given that
this period was not characterised by major societal upheaval, we would not expect significant
changes in the age structure of the population, except maybe in the youngest and eldest
groups. Furthermore, it would be logical that a particular age group in 2010/11 would contain
approximately the same proportion of people as the lower age group in 2005/6.
Table 3: Distribution of population by age group on basis of EICV sample extrapolations
EICV2
EICV3
Age groups
2005/6
Total
People
2005/6
9.491.000
Change
2005/6–2010/11
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-…
1,561,000
1,331,000
1,232,000
1,203,000
1,002,000
687,000
492,000
390,000
400,000
342,000
266,000
170,000
123,000
292,000
0.70%
5.48%
-2.19%
-14.71%
-11.68%
-5.68%
2.44%
6.15%
-7.75%
-2.05%
-6.39%
-5.88%
People
2010/11
10,762,000
1,630,000
1,572,000
1,404,000
1,205,000
1,026,000
885,000
648,000
504,000
414,000
369,000
335,000
249,000
160,000
361,000
Age groups 2010/11
Total
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-…
7
Source: Compiled from data in National Institute of Statistics of Rwanda (2012) The Third Integrated Household
Living Conditions Survey (EICV3): Main Indicators Report, Kigali, NISR, p.30.
However, when we compare youth age groups on the basis of EICV sample extrapolations
(NISR, 2012A), we notice some strange anomalies (see table 3). The 20-24 age group in
2010/11 contains almost 15% less people than the 15-19 age group in 2005/6. Similarly, the
25-30 age group by 2010/11 contains almost 12% less people than the 20-24 age group in
2005/6. In total, we are talking about 294,000 ‘missing youth’ not taken into account in the
2010/11 EICV sample6. It is as if 15.6% of male youth and 11.3% of female youth have
disappeared from the 2010/11 sample in comparison to 2005/6. Three questions then arise.
First, what could be the poverty profile of these ‘missing youth’? Second, which factors could
explain their omission in the survey sample? And third, what impact could this statistical
anomaly have upon the reported 2006-2011 poverty decrease?
Let us start with the first question. According to the household survey data, the 2010/11
poverty rate among youth (age 14-35) of 38.5% is significantly lower than the overall poverty
rate of 44.9% (NISR, 2012B). Our qualitative research, however, suggested the opposite:
increasingly problematic living conditions for a majority of Rwandan youth. First of all, land
scarcity hugely reduced rural youth’s chances to generate an income within the agricultural
sector. According to the EICV surveys, cultivated land per household decreased from 0.75
hectares in 2005/6 to 0.59 hectares in 2010/11. By 2010/11, over 83% of households disposed
of less than one hectare, in comparison to about 75% in 2005/6 (NISR, 2012C). Moreover,
land is highly unequally distributed between socio-economic categories and age groups.
Whereas older farmers still hold on to their historic property, many young farmers are not
capable of inheriting or buying enough land to sustain their family’s needs (Musahara and
Huggins, 2004, AA field notes, 2006/7). They have to look for other kinds of jobs on the daily
labour market where employment opportunities for young unskilled labour force are limited.
We will come back to this point later in the paper.
Another important problem for youth relates to the Rwandan government’s villagisation
policy. Customarily, people do not live in clearly identifiable villages but live scattered on the
hills (De Lame, 2005). Traditionally, young men would ask their father for ‘their’ part of the
family’s land in order to build their house and cultivate their own plot(s). Owning a house
allowed them to make the transition to ‘adulthood’ (Sommers, 2012). However, the Rwandan
government envisions a modern spatial organisation. Since 1994, Rwandan policy makers
have attempted to resettle households in grouped settlements (for a critique, see Newbury,
8
2011; Leegwater, 2011). When in the early 2000s, civil society, scholars, and later
international donors objected to the negative impact of this policy, it was partially abandoned.
However, in the 2010s a ‘mild’ version of the centralisation approach has reappeared: newly
established households are obliged to settle at specific sites within centralised communities
(Ansoms and Rostagno, 2012). Next to culturally based objections, there are two main
economic problems for young households. First, the cost of land in these ‘centres’ is often
very high. Second, houses have to be build according to costly standards (with a separate
kitchen, stable and toilet and with proper roofing) (BB field notes, 2011). As a result many
young men lack the means to build their own house. They cannot marry, and as a
consequence, they cannot start their ‘adult’ life. This phenomenon is closely linked with the
increasing incidence of unmarried young mothers, resulting in growing social exclusion and
marginalisation (BB field notes, 2011; Sommers, 2012; Ansoms and Rostagno, 2012). A
significant portion of these young people, especially those from poorer families, are ‘stuck’ in
their status as ‘youth’ because they do not have the necessary means to start their adult lives
(Sommers, 2012).
So our qualitative analysis suggests that poverty among youth could be more prevalent than
what EICV surveys suggest. But this brings us to our second question: why would youth –
and particularly poor rural youth - have been more under-represented in the 2010/11 survey
than in the 2005/6 survey? There are two plausible and complementary explanations. First,
interviewed household heads might not have mentioned their (near-to-) adult sons and
daughters as part of the household because they had migrated. Indeed, migration is definitely
a strategy for young people to search for income-generating opportunities, but also a way-out
to escape from the social stigma of lacking the means to build a house (Sommers, 2012).
Normally, those absent at the time of the 2010/11 survey should still have been included in
the sample, given that the 2002 Household Census was updated for the EICV3 locations.
However, from our qualitative research, we noticed that local authorities often do not consider
migrants as part of the local community. For example, migrants were very often not included
in the participatory mapping exercises that were undertaken as part of the Ubudehe project in
all Rwandan villages at several points in time (AA and BB field notes, 2006/7, 2011). Young
poor migrants might thus have been underrepresented in the EICV3 sample.
The second possibility is that household heads did not report the presence of their (near-to)
adult sons and daughters because they were officially no longer supposed to be part of the
parents’ households. In our own research, we frequently came across young adults
9
illegitimately ‘occupying’ a side building of their parents’ house, with or without their
permission (BB field notes, 2011). Such – generally poor - households could not be officially
registered because they had ignored grouped settlement regulations, and were thus not
included in the EICV3 sample.
Overall, the problem of ‘missing youth’ seems an important issue. However, the impact of
this statistical anomaly on poverty reduction estimates is likely limited. Even if we would
assume that all 294,000 missing youth were poor – which is highly unlikely – the impact on
the 2010/11 poverty rate (46.4% instead of 44.9%) would have been minor. At the same time,
the issue of ‘missing youth’ might reflect more fundamental problems with underrepresentation of vulnerable subgroups in the 2010/11 survey. However, this cannot be
verified on the basis of the available information.
Erroneous answers and misreporting
Although the sample anomaly described above had only a minor impact on overall poverty
estimates, other factors might have led to an overestimation of poverty reduction. Scholarly
research on the effect of non-response7 in household surveys (see e.g. Bethlehem et al., 2011)
is abundant, but research on response effects and strategic answering in household surveys is
scarcer. Nevertheless, interviewees’ answers may significantly divert from reality for several
reasons.
When considering the cognitive aspects of survey methods, Schwartz (2007) distinguishes
several steps. Respondents first have to interpret the question. They then have to recall the
relevant information with regards to a particular reference period and measurement unit. Nondeliberate distortions at each of these steps may take place. In addition, respondents may edit
their answer for reasons of ‘social desirability’ and ‘situational adequacy’. These ‘response
effects’ insert significant bias in the survey data (Schwartz, 2007).
A well-known phenomenon in nearly every household survey is the discrepancy between
consumption and income estimates. Households generally underestimate their income
(Deaton, 1997), and sometimes overestimate their consumption – particularly for food items
(for more details, see FAO, 2008). Under reporting of income may be the result of fallacious
memory, but it may also be a deliberate strategy to avoid taxes (see e.g. Hurst et al., 2014).
10
Or, it may result from under-representation of higher income groups in household survey
samples (see e.g. Wang and Woo, 2011), leading to lower average incomes.
Similar distortions occur with regards to other variables. Our own experience in Rwanda
suggests that respondents are reluctant to provide answers on questions regarding productive
resources (i.e. ownership of land, variable capital, labour productivity and agricultural output)
(AA and CC field notes, 2006/7 and 2013). Such information is sensitive, and moreover the
respondent may be suspicious of the researcher's motivations (Ansoms, 2012). However,
whereas standardised, large-scale surveys rely on a 'one-time' approach (all information is
collected at one moment in time), qualitative and mixed research is more iterative in nature.
This allows for comparisons of respondents’ answers over different moments in time, and
allows the respondent to re-evaluate and re-consider his or her answers.
Question is now whether such ‘response effects’ in the case of Rwanda’s EICV surveys partly
explain the reported 2005/6-2010/11 poverty decrease. Did respondents in 2010/11 have
reasons to overestimate certain achievements, and more so than in 2005/6? Our micro-level
field research suggests that this is the case. Over the last couple of years, Rwanda has been
transformed into a target-oriented society. Since 2006, authorities at district level have to
commit themselves to a system of ‘performance contracts’ (imihigo). These contracts between
the president, line ministries and local authorities bind the district authorities to reaching
particular targets set in line with national development priorities (Versailles, 2012; Ingelaere,
2010; Thomson, 2013). The contracts generally leave little room for local authorities to set
their own policy objectives (Chemouni, 2014; Gaynor, 2014). The goals can be multiple:
reaching production targets for particular crops, making sure that the local population
participates in health insurance schemes, reinforcing particular settlement schemes, imposing
decent housing standards, etc. (BB field notes, 2011).
This target-orientation seems to translate into tangible results. Through qualitative data
gathered in 2011, Ingelaere (2014) reports how the population experienced an improvement in
the delivery of basic services. DHS data indicate that improved service delivery led to a sharp
improvement in health statistics between 2005 and 2010 (NIRS, 2012E, for a discussion, see
McKay and Verpoorten, forthcoming). However, the follow-up of local imihigo performance
contracts is very strict. An evaluation team composed of representatives from several line
ministries score each district every semester on the basis of targets reached. Repeated underperformance may lead to firing the district mayor (Versailles, 2012). In fear of these
11
sanctions, it occurs regularly that local officials rigidly and blindly implement the set targets,
irrespectively of the possible negative consequences for the local population (BB and CC field
notes, 2011 and 2013). Ingelaere highlights how in the imihigo system, “the chain of
accountability goes upwards towards higher authorities and not downwards towards the
population” (Ingelaere, 2010: 288). Moreover, over the years, pressure to meet these targets
has increased, so that “local officials often cut corners to meet the development
commitments” (Thomson, 2013). Also the New Times reported on local authorities who
manipulated data in order to show to the national government that they reached incredible
levels of progress (Rugira, 2014).
This awareness-raising about the importance of reaching targets definitely reached the
ground. Our micro-level interviews in 2011 and 2013 showed that local farmers were very
aware of authorities’ expectations. Households are supposed to shift from subsistence to
market-led commercially oriented agriculture in line with the Crop Intensification Program,
launched in 2006/2007. They are supposed to adopt mono-cropping techniques on
consolidated land, and to cultivate particular market-oriented crops such as maize, rice, beans
and cassava. And most of all, they are supposed to produce more (BB and CC field notes,
2011 and 2013; Huggins, 2014; Huggins, 2013, Cioffo and Ansoms, forthcoming). In fact, the
2005 land law gives district authorities the responsibility to safeguard that all land is well
managed and productively exploited, if not the farmer may lose access (GoR, 2005).
Households are even actively inserted into the accountability chain (cfr. Ingelaere, 2010). In
2011, we already noted that in certain settings, individual households had been obliged to sign
household-level performance contracts (BB field notes, 2011). This is part of a broader
strategy – officially launched by the Minister of Local Affairs in February 2012 - to involve
all households in setting up a performance contract notebook. In this notebook, households
are to commit themselves to their own development targets, in line with local and national
development priorities.
However, our research material also revealed that authorities’ expectations often did not
match local realities on the ground. Many of our interviewees strongly resented the imposition
of preferential market-oriented crops per region, and attempted to circumvent these
obligations by secretly cultivating their preferred crops (BB field notes, 2011; see also Cioffo
and Ansoms, forthcoming with regards to enforced maize and wheat production, Van Damme
et al., 2014 with regards to beer versus cooking and dessert banana type preferences; Huggins,
2013 on forced pyrethrum production). Farmers from various settings indicated that crop
12
harvest in marshland cooperatives8 had been disappointing for several years, and that incomes
from crop sales though such cooperatives were often problematically low (BB, field notes,
2011; CC field notes, 2013, see also author reference). Smallholder farmers reported lower
food security as a result of a loss of ownership over their productive process. Land use
consolidation ties farmers into dynamics of commercial agriculture that regularly result in
food security failures for the poorest households (CC field notes, 2013, see also author
reference). Farmers reported their frustration having been obliged to sell part of their assets
(mostly goats) in order to pay their health insurance (BB field note, 2011).
At the same time, interviews with Rwandan farmers suggest that reticence to discuss issues of
inequality with local-level authorities is widespread. Farmers highlighted the political weight
of imihigo contracts on local authorities, and pointed to the way in which imihigo tie the
whole population to the development targets. When discussing the forceful sale of household
cattle for the payment of a health insurance, one focus group participant stated: “it is because
of imihigo, it is because of these objectives they have to reach… if people do not have a
health insurance, they do not respect government plan and, they would not reach their
objectives. That’s why they push us” (CC, focus group September 2013, Southern Province).
The situation appeared equally clear to another participant: “There are often meetings at the
district office, and it is the executive secretary in the sector that goes there. They decide the
imihigo there: ‘we are going to do this, we will have that many health insurances’. Then they
talk to local authorities and they say: ‘you should have that many health insurances, that much
this and that”. And if we don’t have the money, they will even sell our bean seeds to buy a
health insurance. And that’s the way it is, like it or not” (ib.). Similar accounts were gathered
with regards to the necessity to reach certain production targets in line with regional and
national priorities.
It is in such context that interviewees are confronted with a government-related surveyor who
questions the interviewee on the achievements of its household. Those same interviewees
have been massively sensitised and pushed by local authorities to reach certain targets.
“[W]hen people are sensitized”, Purdekova (2012: 16) writes, “they are handed ‘indisputably’
positive guidelines; these are not to be discussed”. Such ‘guidelines’ may concern the
obligation to join a marshland cooperative that aims for particular production targets, the
importance of growing maize or wheat instead of sorghum for food security or to use
industrial fertilizers, often regardless of households economic capacity to adopt such
guidelines. “Ultimately, the attempt is not to make people ‘believe’ all the messages as
13
sensitisation cannot make this possible. Rather, the aim is for people to possess key
information and to know what is expected of them” (ib.). Efforts of sensitisation in relation to
the central developmental objectives have clearly intensified over the last decade. For this
reason, we consider it likely that interviewees’ considerations of social desirability and
situational adequacy – leading to an exaggeration of their performance – played a role in their
responses to the EICV3 survey.
Strategic interpretation of data
A final problem with EICV3 is the way in which particular data have been interpreted.
According to the EICV3 report, the increased agricultural production and the increased
commercialisation of agriculture were two among three factors explaining the spectacular
decrease in poverty figures (NISR, 2012). Indeed, it goes beyond doubt that the agricultural
yields in 2010/11 were higher than in 2005/6. However, part of the explanation lies in the fact
that agro-climatic conditions were better in 2010/11 (McKay and Verpoorten, forthcoming).
The agricultural performance in 2005/6 was severely affected by draught (see e.g. FEWS,
2005 and 2006); whereas 2010/11 was a good agricultural season. However, the EICV3 report
does little to explain the importance of this factor in the increase in agricultural output, thus
overlooking possibly important nuances (McKay and Verpoorten, forthcoming).
Another factor highlighted in the EICV3 report to explain poverty reduction, is the substantial
creation of off-farm jobs. According to the surveys, more than half a million additional jobs
were created in the off-farm sector and over 130,000 jobs in the farm sector over the 2005/6 –
2010/11 period (see table 4). According to EICV3 data, over three quarters of people
employed in the off-farm sector are non-poor, whereas this is only slightly over half in the
farm sector (see table 5). Moreover, off-farm jobs tend to (almost) full-time employment,
whereas farm jobs are only part-time employment9. The EICV3 report notes that “there has
been substantial creation of jobs, predominantly in non-farm activities, over the past five
years. This was almost certainly an important factor contributing to poverty reduction”
(NISR, 2012: 11).
Where do such relatively attractive off-farm jobs come from? At first sight, one might say that
this labour force was absorbed through an increase in formal enterprise registration. Between
2007/8 and 2010/11, the total number of registered enterprises rose by almost 70 (cfr. table 6;
Gökgür, 2012). The Rwandan Private Sector Federation has intensely invested in facilitating
14
‘doing business’ in Rwanda. These efforts are reflected in the improvement of the Rwandan
performance in the World Bank’s Doing Business Report, in which Rwanda stands out as top
reformer in Sub-Saharan Africa. Rwanda performs particularly well in terms of easiness to
start a business, to register property, and to access credit (World Bank, 2016).
Table 4: Change in Farm and Non-Farm Employment between 2001/2 and 2010/11
EICV1
EICV2
EICV3
2001/2002
2005/6
2010/11
people
%
people
%
people
%
3,421,000
88.6%
3,417,000
79.5%
3,553,000
71.6%
Farm employment
-independent farmers
3,278,000
84.9%
3,065,000
71.3%
3,063,000
61.8%
-wage earners from farming
143,000
3.7%
352,000
8.2%
490,000
9.9%
442,000
11.4%
883,000
20.5%
1,406,000
28.4%
Non-farm employment
-independent non-farmers
134,000
3.5%
347,000
8.1%
479,000
9.7%
-wage earners outside farming
284,000
7.4%
468,000
10.9%
838,000
16.9%
-un-paid non-farming
24,000
0.6%
68,000
1.6%
89,000
1.8%
3,863,000
100.0% 4,300,000
100.0% 4,959,000
100.0%
TOTAL EMPLOYMENT
Source: Compiled from data in National Institute of Statistics of Rwanda (2012) The Third Integrated Household
Living Conditions Survey (EICV3): Main Indicators Report, Kigali, NISR, p. 93.
Table 5: Poverty status by main job in 2010/11
Extremely
Total
Poor
Non-poor
poor
people
3,553,000
Farm employment
25.0%
23.0%
52.1%
-independent farmers
22.9%
22.9%
54.3%
3,063,000
-wage earners from farming
38.1%
23.7%
38.2%
490,000
Non-farm employment
11.3%
11.7%
77.1%
1.406.000
-independent non-farmers
10.4%
13.3%
76.3%
479,000
-wage earners outside farming
11.4%
10.9%
77.8%
838,000
-un-paid non-farming
15.3%
10.4%
74.3%
89,000
4,959,000
TOTAL EMPLOYMENT
21.1%
19.8%
59,1%
Source: Compiled from data in National Institute of Statistics of Rwanda (2012) EICV3 Thematic Report –
Economic activity, Kigali, NISR, p. 38.
However, the spectacular increase in the total number of formally registered establishments
did not result in a high job increase in the formal sector (only 84,130 additional jobs). 10 By
2010/11, formal off-farm employment represented less than 20% of all off-farm jobs and only
5% of all employment (see also Gökgür, 2012). The informal sector however employed an
impressive 1,146,791 people, either as wage earners or as independents. Did for some reason
the Rwandan government’s business incentives for the formal sector result in a boost of
activity in the informal sector? Whereas the measures for business facilitation have impressed
international donors, these policies were tailor-made for large-scale, capital-intensive projects
inserted into the formal economy. Our micro-level evidence indicates that the climate became
much more difficult for small-scale investment from the side of local entrepreneurs inserted in
the informal economy (author reference). Let us consider a couple of examples.
15
Table 6: Employment generated by establishments/enterprises (2008-2011)
(number of people)
2007/8
2010/11
Change
72,994
123,526
+ 50,532
Number of establishments/enterprises
2.7 persons
2.3 persons
Average worker by establishment/enterprise
197,816
281,946
+ 84,130
Number of persons employed in enterprises
of which agriculture, forestry, fishing
*
22,737
of which off-farm
*
259,209
1,092,200 °
1,406,000 °
Total Non-Farm Employment
formal off-farm employment
*
259,209
informal off-farm employment
*
1,146,791
Note: The establishment/enterprises include private enterprises, party-statals, cooperatives, non-profit
organizations, and public sector or mixed enterprises.
* The original data for 2007/8 were no longer available since the Enterprise Survey Report has been taken off the
Private Sector Federation’s website shortly after the publication of a critical discussion paper (Gökgür, 2012).
° These data were already presented in the previous table. Total informal off-farm employment is then the result
of total non-farm employment (reported in the EICV3 report) minus formal off-farm employment (reported in the
2011 establishment census).
Source: Compiled from data provided in Enterprise Survey 2008 and Establishment Census 2011, Private Sector
Federation, Rwanda; published in Gökgür, G. (2012) ‘Rwanda’s ruling party-owned enterprises: Do they
enhance or impede development?’, IOB Discussion Papers 2012.03, Antwerp: Institute of Development Policy
and Management.
One of the sectors in which jobs could have been created, is in the manufacturing of rural
products into higher value products. This was in any case one of the objectives of the 2007
EDPRS-I. Over the last years, older transformation units have indeed been upgraded (e.g.
factories for coffee and tea), while other facilities have been developed. However, the net
gains of the creation or rehabilitation of such facilities are often limited. In 2013, we
conducted an in-depth study of three of such facilities in both Southern and Northern province
(a tea factory, a coffee factory, and a cassava flour factory). In all three cases, local farmers
had been forced into explicit or implicit contract farming schemes, obligatorily selling their
production to the factory. This gave the factory’s management significant power to reduce the
prices paid to local farmers. Moreover, employment opportunities within the processing
facilities were limited, and wages paid were relatively low in comparison to other off-farm
jobs in the informal sector (CC field notes, 2013; also see author reference).
Within the sector of transport and trade, policy initiatives inserted complications for informal
businesses. Street vending in Kigali has been prohibited (Sommers, 2012). Petty markets have
been relocated at significant distance from the centre. But also in rural areas, petty trade
became highly regulated as a result of policy makers’ attempt to formalise the supply chains
of local markets. Traders need an official licence to operate on the market. We had several
accounts of farmers being fined highly for selling produce informally along the roadside.
Farmers are increasingly dependent upon fewer traders operating at a larger scale, and are
obliged to accept lower prices because they lack alternative options to bring their produce to
16
the market. A lot of people previously active in trading goods have been obliged to cease their
activity because they lack the necessary means to formalise their enterprise (BB, field notes,
2011). Another important informal labour-absorbing activity, artisanal brick and tile-baking,
has been prohibited. Modern ovens are operated by officially registered entrepreneurs or
cooperatives, but absorb much less labour and pay lower salaries (author reference).
When bringing all together, we are left with a confusing puzzle. According to EICV3, there
was spectacular job creation mainly in the off-farm sector, and these jobs resulted in higher
living standards (given the lower poverty figures of people employed in off-farm jobs). At the
same time, we were able to demonstrate that formal job creation was limited (on the basis of
the government’s establishment census figures). Moreover, our own in-depth qualitative
research suggests that the policy measures complicated the functioning of the informal sector.
Hence, the question of how substantial job creation was realised over the 2005/6-2010/11
period and how this could have been a major factor in poverty reduction, remains unclear.
Conclusion
Standardised, large-scale surveys have become the norm when evaluating the performance of
countries’ policies and of development aid allocation. The process that leads to the production
of such surveys is presented as technically-bound, apolitical, and objective. However, this
paper has demonstrated that a complicated reality exists behind the neutral façade of largescale samples.
This is clear in the case of Rwanda. Recent messages coming from Rwanda are euphoric. The
statistics indicate steady economic growth rates and decreasing levels of poverty and
inequality. However, McKay and Verpoorten (forthcoming: 16) found for Rwanda that
“subjective measures of well-being do not necessarily align well with objective measures of
well-being; and that the mismatch may be considerable in Rwanda as a result of rapid and
profound economic and social transformations”. Indeed, quantitative surveys and qualitative
assessments measure a different concept of poverty and well-being. In this article, we also
argue for a more cautious analysis of the available statistical data, because when nuancing
nationally representative statistics with the results from longitudinal in-depth field research
we identify three main problems in the 2005/6 – 2010/11 poverty assessment.
17
First, we noted that about 294,000 ‘missing youth’ were not taken into account in the 2010/11
EICV sample. They were likely left out of the sample after migrating or due to their
transformation into ‘illegitimate’ non-registered families. We also pointed to youth’s
problematic living conditions as a result of land scarcity, limited job availability outside the
agricultural sector, and new housing regulations. Despite the fact that the omission of 294,000
young people – likely the poorest among them – only had a limited impact upon overall
poverty estimates, these missing youth may represent a larger problem of ‘invisible’
vulnerable categories in household surveys.
Two other problems are more fundamental. First, there is the mismatch between authorities’
pressures to reach performance targets at all cost, and the realities on the ground. Smallholder
farmers face difficulties inserting themselves into forced production and commercialisation
schemes, or to align to target-oriented policy objectives. They feel the pressure of authorities’
targets through explicit and implicit threats when falling short of expectations. At the same
time, the public space to call into question certain policies is extremely limited. Interviewees’
considerations of ‘social desirability’ and ‘situational adequacy’ may have influenced the
answers given to a government-related surveyor, which could have resulted in overestimated
production figures.
A final issue lies in the interpretation of the EICV statistics. The EICV report does not take
into account how agro-ecological variations might explain part of the increased agricultural
performance in 2010/11. Another problem is the mismatch between EICV statistics indicating
massive high-value off-farm job creation, and the observations from in-depth longitudinal
research indicating a deteriorating climate for small-scale off-farm investment in the informal
economy.
Indeed, Rwanda is not the only country in which a conflict between qualitative and
quantitative analysis of poverty change occurs. Appleton and Booth (2001) for example have
come to a similar conclusion when confronting participatory and survey-based approaches to
poverty monitoring in Uganda. We do however highlight the importance of considering the
political value of statistics to obtain a more in-depth understanding of Rwanda’s pathway out
of poverty. Given the shortcomings of national household datasets, it is thus crucial to bring
the findings of in-depth qualitative research back into the picture in order to improve the
poverty-reduction capacity of public policies.
18
Acknowledging the shortcomings of standardised large-scale surveys is not the equivalent of
throwing away the baby with the bathwater. Rather, it is an acknowledgement of the
complicated nature of social life, and of surveying as a social activity that is influenced by
power relations as well as by existing inequalities and biases both on the side of the
researchers and the respondents. Because of these reasons, a more complex approach by
government surveyors is needed, combining the explanatory power of different research
techniques. At the same time, donors and policy makers should move beyond accepting largescale surveys at face-value, as a more critical outlook may benefit both the effectiveness of
aid and the interests of those whose voice is often ignored by large-scale statistics. These
conclusions seem of crucial value for continued work on the controversy raised in relation to
the EICV4 2013/14 results.
Endnotes
1
McKay and Verpoorten (forthcoming) have calculated changes in poverty between 2005/6 and 2010/11 on the
basis of the EICV surveys. They differentiate between 5 categories from low to very high poverty reduction. Our
six settings in the Southern Province are located in districts in the lowest 3 categories (so with low to medium
poverty reduction). However, the two locations in the Northern Province, in which we cross-checked certain
findings in 2013, are located in a district with very high poverty reduction.
2
In the document, ‘Vision 2020’ the Republic of Rwanda outlines its vision for the future of Rwanda,see GoR,
2002.
3
See for an example interview with IMFs chief mission in Rwanda Kristina Kostial by Natalie Hairfield,
‘Rwanda's Task: Manage More Aid’, July 17, 2007, see
http://www.imf.org/external/pubs/ft/survey/so/2007/INT0718A.htm.
4
For the EDPRS-I policy, launched in 2007, the aim was to translate continued economic growth into
considerable poverty reduction. The policy set targets for reducing overall poverty rates from 57 to 46%, and
extreme poverty from 37 to 24% (GoR, 2007: 34).
5
See the website of SIDA, http://www.sida.se/English/where-we-work/Africa/Rwanda/Developments-inRwanda [date last access, 30 October 2014].
6
In-depth analysis of district data illustrates that the problem of missing youth appears nationwide except for
Kigali’s districts. The phenomenon is most prominent in Nyamagabe and Kamonyi districts in the Southern
province, in Rulindo district in the western province, in Burera district in the northern province, and in
Nyagatare district in the Eastern province (own calculations based on GoR (2013), EICV District profiles, online
at: http://www.statistics.gov.rw/survey-period/integrated-household-living-conditions-survey-3-eicv-3.
7
Nonresponse occurs when persons included in the sample refuse to participate or forget to respond to certain
questions.
8
The organisation of agricultural activities in marshlands had by 2011 undergone radical change. Around 20052007, the national government mandated the local administration to allocate wetland plots to farmers’
associations. These associations have recently (2009-2011) been urged to group themselves into officially
recognized cooperatives. Moreover, cultivation practices have also changed. Crop diversification (combination
of different crop types on the same plot of land) is no longer permitted; instead cooperatives concentrate on
market-oriented ‘high-value’ crops such as rice, sugar cane, maize, etc. Crops are planted in monocropping
arrangements, and cooperative members cultivate and harvest together (Ansoms et al., 2014).
9
The average number of hours spent per week in off-farm jobs is 39.7 hours for wage earners outside farming
(median 40.0 hours), and 25.8 hours for independent non-farmers (median 18 hours). This is significantly higher
than the average number of hours worked by wage earners from farming (18.8 hours; median: 15 hours), and by
independent farmers (17.0 hours; median: 15.0 hours) (NISR, 2012D: 13).
10
Formal non-farm employment as a share of total non-farm employment increased mildly from 18 to 20%
between 2007/8 and 2010/11. The average number of workers per enterprise decreased from 2.7 to 2.3 persons per
establishment (see table 5).
19
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