Rwandan Poverty Statistics: Exposing the ‘Donor Darling’

Parc National des Volcans, Rwanda. August 4, 2005. Children on a Rwandan farm. Anywhere you go in Rwanda, as soon as you pull out a camera a group of curious children will form to meet the strangers and shyly pose. These children lived on the mountainside farms we crossed on the first part of our trek to see the gorillas. Credit: by Sarel Kromer.

In his book entitled Poor Numbers, Morten Jerven cautioned against taking African development statics at face value, given the high political and financial stakes attached to these numbers, as well as the lack of institutional mechanisms to prevent political interference in many countries. Few countries illustrate his case more starkly than Rwanda. As An Ansoms et al pointed out in an article in the print issue of ROAPE earlier this year, ‘Statistics versus livelihoods: questioning Rwanda’s pathway out of poverty, the Rwandan government has used its record on poverty reduction and economic growth to legitimize its authoritarian rule and to deflect criticism of its human rights record, just as the previous regime had done up until 1990. Furthermore, Rwanda’s spectacular recovery after the genocide has made it somewhat of a “donor darling”, and has enabled the government to attract significant foreign resources in the form of aid from donors desperate to claim a share in this African success story.

Yet, questions have been mounting in recent years about the reality and sustainability of the “Rwandan miracle”, given the heavy-handed nature of the state-led agricultural transformation project (Dawson et al. 2016), and the government’s propensity for debt-financed investments in unproductive prestige projects, such as the Kigali Convention Centre. These questions came to a head in September 2015, when the National Institute of Statistics of Rwanda (NISR) published a poverty profile (NISR, 2015) based on the most recent household budget survey (EICV4 by its French acronym). The report claimed that the proportion of Rwandans living below the poverty line had fallen from 45% in 2010 to 39% in 2014, after a string of similarly successful decreases in the previous surveys. Two months later, Filip Reyntjens published a critique, claiming that the “decrease” in poverty had been artificially engineered by NISR by changing the type of poverty line used, from an “average” consumption basket based on actual consumption patterns of poor Rwandan households, to a “minimum” or “optimal” consumption basket, containing mostly highly caloric and inexpensive food types.

The change is not in itself problematic, as the choice of a poverty line is always, to some extent, arbitrary and there are many different acceptable ways to define a poverty line. The normative minimum consumption basket adopted by NISR is one such way. However, to make trend comparisons, all experts agree that it is crucial to use consistent methodologies, assumptions and definitions across time. Reyntjens claimed that had they done that, they would have found the proportion of people living below the minimum poverty line to have increased by 6 percentage points between 2010 and 2014. Unfortunately, Reyntjens never published the syntax files he used to compute his estimate. Neither did NISR accept to publish its own syntax files. Without this key piece of evidence, the debate has never been closed from a technical point of view, as it is impossible to show convincingly whether poverty has actually increased or decreased in Rwanda between 2010 and 2014.

We hope to contribute to settling this issue by publishing open, transparent and verifiable syntax files built using a publicly available dataset, which can be downloaded from NISR’s own microdata catalogue on its website (the two syntax files can be opened with .txt notepad or STATA software here). There are many ways to compute these things and there are innumerable adjustments and assumptions that must be made to arrive at an aggregate number. Consequently, it is difficult to replicate exactly the official estimates without access to the original syntax files. However, we hope that by submitting these to public scrutiny, such differences can be ironed out in an open and transparent manner, and any mistakes can be corrected to arrive at an estimate that all parties can accept. In constructing these estimates, our main priority has been to ensure consistency between the two surveys. We therefore try to use exactly the same code and assumptions in both years wherever possible. Below, we provide an overview of the key parameters and assumptions that entered the construction of these indices. Since there are several different poverty lines that have been generated by now, we decided to compute trends for all of them, namely:

  • Average consumption basket: representing the minimum amount required to consume 2,500 kcal per day (adjusted for age and gender), using prevailing culinary habits of poor Rwandan households in 2001. This was the official poverty line used in 2001, 2005, 2010.
  • Updated average basket: representing the minimum amount required to consume 2,500 kcal per day (adjusted for age and gender), using prevailing culinary habits of poor Rwandan households in 2014. This was the new poverty line computed by NISR in 2014, which should have been used in EICV4, but was never used because it was deemed too high.
  • Minimum consumption basket: representing the minimum amount required to consume 2,500 kcal per day (adjusted for age and gender), using optimal (i.e. cheap and highly caloric) food types. This was the official poverty line used in 2014 (EICV4).
  • Reyntjen’s poverty line: Reyntjens argued that since the minimum consumption basket was 19% lower than the updated average basket, trend comparisons with 2010 should have been made using a poverty line that was 19% lower than the one used in 2010.[1] For this poverty line, we did not construct a food basket, but simply calculated 81% of the figure from the total poverty line computed from the average consumption basket.


In all consumption baskets, the quantities and caloric values are kept constant across surveys. Prices for each item are given as the national median price across regions and across months, as reported in the auto-consumption module of the EICV survey (see table 3 below). Consumption aggregates have been adjusted for spatial and temporal price differences using a Laspeyres index (see table 2 below). The Laspeyres index was chosen because it yielded estimates that were closest to official poverty estimates in EICV3 for the average basket. The choice of price index does not affect the conclusions of this blogpost.

The results are reported in table 1 below. All poverty lines yield similar trends when used consistently over time, indicating that poverty increased between 5% and 7% points between 2010 and 2014. All changes are statistically significant at the 5% level.

It should be noted that our results differ from those obtained by simply updating the poverty line for inflation using CPI data, as was done by NISR in their 2016 trend report (NISR, 2016). In principle, if the data are of good quality and sufficiently disaggregated, both methods should be equivalent and should not yield significantly different results. This therefore raises questions about the quality / reliability of official CPI data, and/or the quality of price data collected by the EICV. In either case, this would undermine our ability to correctly estimate poverty levels in Rwanda. The discrepancies found here should invite us to more closely scrutinize official statistics coming out of the Rwandan statistical office. GDP growth figures appear to be incompatible with the findings of the EICV survey, given than agriculture still accounts for about one third of GDP and two thirds of the labour force.


Table 1: Summary of poverty lines and poverty rates

  Average basket Updated basket Minimum basket Reyntjen’s poverty line
  2010 2014 2010 2014 2010 2014 2010 2014
Share of non-food[2] (% of total cons.) 31 34.8
Total caloric intake (kcal/ adult/ day) 1346 1215 1212
Total food cost per pers./year (Rwf) 96,797 121,795 98,069 125,504 77,559 101,116
Non-food component (Rwf/ pers/year) 43,489 54,720 52,344 66,987 41,397 53,899
Total poverty line (Rwf/ pers/ year) 140,286 176,515 150,413 192,491 118,956 155,015 113,632 142,977
Poverty rate (% of pop< tot. pov. Line) 45.2 50.2 49.2 55.8 35.2 42.2 32.5 37.1
Change in poverty rate +5* +6.6* +7* +4.6*
*Change is statistically significant at 5% level


Table 2: Laspeyres price index by quarter and province (computed from price data in auto-consumption file)

2010 Kigali City Southern Western Northern Eastern
First quarter 1.47 0.98 0.89 0.98 1.14
Second quarter 1.31 0.98 0.92 0.96 1.05
Third quarter 1.38 0.98 0.92 0.98 1.13
Fourth quarter 1.31 0.98 0.92 1.00 1.14
2014 Kigali City Southern Western Northern Eastern
First quarter 1.22 0.93 1.01 0.96 1.09
Second quarter 1.20 0.95 0.96 0.91 1.08
Third quarter 1.27 0.98 1.06 1.05 1.04
Fourth quarter 1.14 0.92 1.07 0.99 1.02


Table 3: Food baskets used to compute poverty lines

  both years 2010 2014 both years both years both years
Sweet potato 92 80 100 0.4033 0.3114 0.0915
Irish Potato 67 120 150 0.1763 0.1257 0.0242
Banana – cooking (Inyamunyo) 75 120 150 0.0573 0.0783 0.0227
Dry beans 341 300 400 0.1130 0.0758 0.0758
Cassava (root) 109 100 150 0.0410 0.0694 0.0694
Cassava (flour) 338 200 300 0.0134 0.0391 0.0063
Sorghum juice(Ubushera) 173 150 180 0.0000 0.0000 0.0000
Tomato 17 200 200 0.0106 0.0146 0.0146
Corn (flour) 363 300 350 0.0100 0.0184 0.0012
Cabbages 19 100 100 0.0207 0.0172 0.0172
Local Banana beer 48 300 300 0.0096 0.0000 0.0000
Avocado 119 90 100 0.0036 0.0143 0.0494
Amarante (small leafed green) 22 100 150 0.0124 0.0150 0.0150
Local sorghum beer(ikigage) 173 150 180 0.0150 0.0000 0.0000
Cassava (fermented) 362 150 200 0.0056 0.0113 0.1097
Dry maize (grain) 356 180 240 0.0103 0.0138 0.0225
Eggplant 21 150 200 0.0070 0.0082 0.0082
Cassava leaves 53 150 200 0.0068 0.0093 0.0093
Local rice 280 500 600 0.0027 0.0092 0.0035
Tarot/amateke 86 100 150 0.0098 0.0189 0.0476
Maize (fresh) 36 100 150 0.0065 0.0000 0.0000
Fresh milk 61 150 200 0.0010 0.0062 0.0062
Fresh bean 53 200 250 0.0002 0.0000 0.0000
Banana fruit (Imineke) 60 150 200 0.0038 0.0056 0.0028
Sorghum (flour) 343 300 350 0.0031 0.0051 0.0075
Onion 24 250 325 0.0017 0.0024 0.0024
Curdled Milk 75 200 200 0.0007 0.0053 0.0053
Local banana juice 48 200 200 0.0000 0.0035 0.0020
Groundnut flour 387 900 1000 0.0004 0.0000 0.0000
Sorghum 343 250 250 0.0253 0.0028 0.0143
Amarante (large leafed green) 22 100 170 0.0039 0.0028 0.0028
Pumpkin 19 100 100 0.0068 0.0058 0.0058
Pineapple 26 100 125 0.0002 0.0013 0.0013
Carrot 38 200 250 0.0003 0.0011 0.0011
Papayas 26 100 150 0.0006 0.0014 0.0014
Mangos 45 100 125 0.0000 0.0022 0.0074
Beef meat 150 1400 1400 0.0006 0.0016 0.0000
Green pea (fresh) 37 400 500 0.0006 0.0000 0.0000
Fish (fresh / frozen) 49 1000 1020 0.0005 0.0000 0.0000
Eggs 139 70 240 0.0007 0.0009 0.0009
Guava 17 70 100 0.0002 0.0000 0.0000
Soya (dry) 335 300 400 0.0000 0.0004 0.0004
Yams/Ibikoro 109 130 160 0.0000 0.0104 0.0104
Pepper 17 250 300 0.0002 0.0000 0.0000
Plums 24 425 600 0.0001 0.0000 0.0000
Pork meat 220 1150 1400 0.0000 0.0003 0.0000
Wheat (flour) 364 350 450 0.0001 0.0000 0.0000
Goat meat 164 1500 1800 0.0002 0.0000 0.0000
Orange (local) 34 200 200 0.0000 0.0002 0.0002
String bean 32 200 200 0.0068 0.0000 0.0000
Soya (fresh) 405 200 250 0.0023 0.0000 0.0000
Green pea (dry) 339 500 700 0.0010 0.0000 0.0000
Ground nuts (peanuts) 567 800 1000 0.0009 0.0001 0.0001
Fish (dry / smoked) 199 500 500 0.0000 0.0127 0.0127
Other Meats 126 550 800 0.0000 0.0000 0.0005
Bread 261 239 303 0.0011 0.0000 0.0000
Imported rice 363 460 583 0.0014 0.0000 0.0000
Palm oil 884 668 846 0.0036 0.0000 0.0000
Sugar (local) 380 500 634 0.0027 0.0000 0.0000

The authors of this article have asked for anonymity.  

Featured Photograph: Parc National des Volcans, Rwanda. August 4, 2005


Reyntjens, F. 2015. “Lies, Damned Lies and Statistics: Poverty Reduction Rwandan-style and How the Aid Community Loves It.” Blog of 3 November 2015 posted on

NISR. 2015. Rwanda Poverty Profile Report 2013/2014: Results of Integrated Household Living Conditions Survey. Kigali: NISR.

An Ansoms, Esther Marijnen, Giuseppe Cioffo, and Jude Murison, “Statistics versus livelihoods: questioning Rwanda’s pathway out of poverty”, Review Of African Political Economy Vol. 44 , Iss. 151, 2017.

National Institute of Statistics of Rwanda (NISR), Poverty Trend Analysis Report, June 2016.

Jerven, Morten. Poor numbers: how we are misled by African development statistics and what to do about it. Cornell University Press, 2013.

Dawson, Neil, Adrian Martin, and Thomas Sikor. ‘Green revolution in sub-saharan Africa: Implications of imposed innovation for the wellbeing of rural smallholders.’ World Development 78 (2016): 204-218.


[1] Note that Reyntjens argument is not strictly speaking correct, since it would still require us to compare two different consumption baskets. To be methodologically sound, the 19% reduction would thus need to be applied to the same basket in both years, as we are doing here.

[2] In the average consumption basket, the non-food component is computed based on the average food share for households in the 7th decile in 2001. In the updated and minimum baskets, the non-food components are computed based on the average food share for households in the 5th decile in 2014.

[3] National median price of product as reported in the auto-consumption module.


  1. The Continuing Saga of Rwandan Poverty Data
    via Ken Opalo, there is new analysis out of the 2014 Rwanda poverty numbers that contradicts official Government reports, finding that poverty actually rose between 2010 and 2014. Professor Filip Reyntjens made a similar argument at the time, which I disagreed with.

    This new (anonymous) analysis in the Review of African Political Economy supports the conclusion of Reyntjens, based on new analysis of the survey microdata (with commendably published stata code). The key difference seems to be that their analysis updates the poverty line based on prices reported in the survey microdata rather than using the official Consumer Price Index (CPI) measure of inflation.

    What I took away from this at the time was the apparent fragility of trend data on poverty that depends on consumption aggregates and price data. I also drafting a follow-up blogpost that for whatever reason never got posted, so here it is.

    What else do we know about welfare in Rwanda?

    Given the disagreement about the right price indices to use for calculating the poverty line, it might be informative to look at other indicators of welfare that we might care about, and even better to look at other indicators from a different survey. In fact, it was looking at the first table in the EICV4 Report that made me doubt Filip’s claim that poverty had actually increased. This table suggests that between 2010 and 2014,

    – inequality was down,
    – school attendance and literacy up,
    – housing and access to electricity and clean water improved,
    – health services improved, and
    – household savings were up.

    Read the full response here

  2. Our blogger responds to Lee Crawfurd (above):

    Response to

    1. Price index: The original study used a different (unspecified) source for its price index: “prices were sourced from the National Institute of Statistics of Rwanda/ Price Statistics Division” (NISR 2015, p.16). The fact that two different studies reached almost identical conclusions, using two different data sources, suggests that the findings are very robust indeed. This does not point to a “fragility of trend analysis”, but in fact strengthens the main conclusion of the paper, which is the need to more closely scrutinize CPI data coming out of NISR.

    2. Inequality: The poverty rate in Rwanda is affect by what happens in the 5th decile, not what happens in the first and 10th, so the statistics presented by rovingbandit are misleading. In fact, it is quite common for inequality to decrease during an economic downturn, due to the simple fact that the incomes of the middle class have further to fall than those of the very poor. If anything, these findings reinforce the need to scrutinise Rwanda growth data, which appear to be incompatible with the findings of the EICV. Growt data are much easier to “fudge” than poverty data.

    3. Maternal health, etc.: None of the other indicators presented by rovingbandit have anything to do with the poverty rate. The presented indicators are affected by service delivery in health, education, housing, etc., which are to a large extent funded and supported by donors. Progress in these sectors is well documented and has never been questioned in the poverty debate. The poverty rate in Rwanda is primarily influenced by what happens in the agricultural sector. In this sector, there are well-documented problems related to forced mono-culture, land consolidation, etc. Unfortunately, open discussion of these issues is not allowed in Rwanda, so it is difficult to know the true extent and impact of these problems. Our results suggest that the issue might be severe.

    Conclusion: The idea of the “Rwandan miracle” is so entrenched that it sometimes easier to question the facts, than to revise our assumptions and beliefs – even when presented with clear and objective empirical evidence, as in this case. What this study tells us, is that we must look behind the shiny facade of Rwanda’s success story:
    Many of the skyscrapers in Kigali are in fact half-empty and have been financed with public money, or by companies closely affiliated with the RPF (
    The Kigali Convention Centre, prime symbol of Rwanda’s success, was financed by a $400 million public debt bond ( To put thing in perspective, this is as if the UK government had spent £400 billion on the Millennium Dome. The KCC occupancy rate is reported to be less than 20% (
    RwandAir opens a direct flight to London, but runs a deficit of $50 million per year, financed by Rwandan tax payers (

    Rwanda scores highly on the World Bank’s Doing Business, but Rwandan businessmen, like Tribert Rujugiro or Asinapol Rwigara, get exiled or killed. This might explain why private investment levels and Foreign Direct Investment are well below the regional average.

    In short, nothing is quite what it seems in the land of a thousand hills, and on closer inspection, Rwanda appears less like an African exception and symbol of renewal, than a perpetuation of the worst forms of neo-colonial clienelism and extractive economy. One that is, for now, being sustained by a steady, but unsustainable, stream of funds from foreign aid and public debt, as well as the illicit trade of minerals from Congo.

  3. Im really sorry but this debate is not constructive, poverty indexes that are overly dependent on highly fluctuating prices might not be the best indicator of poverty. The whole under representation of inflation debate is a global one and does not warrant this title. Go get some prozac.


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