Category Archives: Research

The Evidence Mounts: Poverty, Inflation and Rwanda

By Sam Desiere

In a recent blogpost an anonymous researcher on showed that poverty in Rwanda has increased from 2011 to 2014 by 5 percentage points. This contradicts the official poverty statistics and narrative, which claim that poverty decreased by 5.8 percentage points, namely from 44.9% in 2011 to 39.1% in 2014 (NISR, 2015). Importantly, the author published the Stata-files used to analyse the data of the EICV 3 and EICV 4 household surveys, enabling other researchers to verify his claims.

Recently, I also calculated trends in poverty using the same datasets. Although I used a slightly different (and, arguably, less sophisticated) methodology, the results confirm that poverty did not decrease. In addition, I show that the poverty trends are very sensitive to the inflation rate used. With an inflation of 16.7% (as reported by the National Institute of Statistics of Rwanda, NISR), poverty indeed decreased by at least 5 percentage points. With an inflation rate of 30% – which is in my view more in line with the ‘real’ inflation rate – my estimates show that poverty increased by 1.2 percentage points.

The fact that two researchers arrive – independently from each other – at the same conclusion, strengthens my belief that the EICV surveys show that poverty in Rwanda has increased. This has important implications for the current debate about (rural) policies in Rwanda, but I leave a discussion of these implications to researchers and policy makers more familiar with the reality on the ground and focus in this blogpost on the technical aspects of estimating poverty trends.

In this this post, I briefly describe my methodology and key findings and discuss (food) price inflation, which turns out to be a critical parameter. The Stata do-files required to replicate my findings can be found here.


Rwanda’s poverty estimates are based on the Integrated Household Living Conditions Survey (EICV by their French acronym), which are conducted every three years. I used data from EICV 3, conducted in 2010/11 and EICV 4, conducted in 2013/14, which are made publicly available by the NISR. More specifically, I used the modules on food consumption purchased on the market and food consumption from own production. In both waves, the questionnaire of both modules is nearly identical. Food consumption is reported for more than 100 food items.

Unlike the anonymous researcher, I did not use the modules on non-food expenditure. I did so for two reasons. First, the NISR reports that most households spend over 60% of their budget on food. Hence, food expenditure is a good proxy of poverty. Second, non-food expenditure would require some additional data cleaning, which requires additional assumptions. Hence, I simply calculated food expenditure in both waves.

The meta-data of EICV 4 (available on NISR’s website) clearly stipulates that each sampled household in Kigali was visited 11 times over a period of 33 days. The modules on food consumption were administered during every visit. Rural households were visited 8 times over a period of 16 days. The meta-data of EICV 3, however, does not provide information on the number of times a household was visited. I simply assumed that the same methodology, for both rural and urban households, was followed in wave 3 as in wave 4. If this assumption is wrong – something I could not check – the results presented below will be erroneous.

In both waves, households reported how much they had spent on food purchased on the market by food item since the previous visit of the enumerator. I simply added up expenditure on all food items. Households also reported how much they had consumed from own production. Converting the consumption from own production in monetary values was more challenging. Households typically reported consumption from own production in kg. Some households also reported in the same module how much they would have paid on the market for this food item. I used this information to calculate the median, national price for each food item and used this price to convert consumption from own production in its monetary value. Since relatively few households reported prices, I did not attempt to calculate region specific prices nor did I correct for price seasonality. On this point my methodology differs from the anonymous researcher, who calculated a Laspeyres price index to account for spatial and temporal price variation.

To verify my assumptions, I checked whether my estimates of food expenditure are correlated with the household poverty status as reported by the NISR and included as a separate variable in the datasets. In both waves, food expenditure was lower for households classified by the NISR as extremely poor compared to household classified as poor, and the expenditure of this group was in turn lower than the expenditure of non-poor households. These results, available upon request, confirm that my assumptions are at least partially similar to the assumptions of the NISR.

Food expenditure can only be compared between the waves if the food inflation rate between 2010/11 and 2013/14 is known. I used two different inflation rates. First, I used an inflation rate of 16.7%, which is reported by the NISR (NISR, 2016, p. 43). Second, I estimated inflation based on food prices reported by the respondents, which I also used to convert food consumption from own production in monetary values. Inflation is then defined as a weighted average of the price increase of nine important crops. I used the same weights as those used by NISR to construct the 2013/14 adjusted food poverty line (NISR, 2015, table B4, p. 38). These estimates of inflation will be discussed in greater detail below.

Since I did not calculate total expenditure, but only food expenditure, I could not use the poverty lines proposed by the NISR. I therefore followed the ‘inverse’ methodology. First, I assumed that the NISR correctly estimated poverty in 2010/11 (44.9%) and used this information to determine the food expenditure threshold in 2010/11 prices that corresponds with this poverty rate. Second, I deflated food expenditure in 2013/14 using two different inflation rates, namely 16.7% and 30%. The first inflation rate corresponds with the inflation rate used by the NISR and thus allows me to replicate the findings of the NISR. The second inflation rate corresponds with my own estimate of inflation using the price data from EICV 3 and EICV 4. Third, I used the food expenditure threshold as an alternative to a poverty line to estimate the poverty rate in 2013/14. This approach is valid because I am not interested in ‘absolute’ poverty figures, but only in poverty trends.

In all analyses, I used the population weights to make the results nationally representative.


Poverty trends

Using the EICV 3 and EICV 4 datasets, I calculated food expenditure per adult equivalent, respectively in 2010/11 prices and 2013/2014 prices. In order to estimate poverty trends, food expenditure in 2013/14 has to be deflated to express it 2010/11 prices. Poverty trends are very sensitive to the inflation rate used to deflate food expenditure. Results are presented for two inflation rates: (1) an inflation rate of 16.7% as reported by NISR and (2) an inflation rate of 30%, which is at the lower end of my inflation estimates based on ESOKO price data or EICV price data (see below for a discussion of inflation).

Figure 1 shows cumulative frequency distributions of food expenditure for these two situations, while table 1 summarizes poverty trends

With an inflation rate of 16.7% (left panel, figure 1), real food expenditure per adult equivalent increased for all households from 2010/11 to 2013/14 and, as a result, poverty decreased. Assuming a poverty rate of 44.9% in 2010/11 (which corresponds to a food poverty line of 100,232 RWF per adult equivalent), poverty decreased by 7.9 percentage points. This poverty reduction is even more pronounced than reported by official statistics, which states than poverty decreased by 5.8 percentage points.

With an inflation rate of 30% (right panel, figure 1), food expenditure does no longer increase between 2011 and 2014 for all households. Again assuming that poverty is 44.9% in 2010/11, poverty even increased by 1.2 percentage points.

Figure 1: Cumulative distribution of food expenditure per adult equivalent for EICV 3 and EICV 4 for an inflation rate of 16.7% and 30%

Table 1: Poverty trends in function of the inflation rate

  Inflation: 16.7% Inflation: 30%
Poor HH EICV 3 (official statistics) 44.9% 44.9%
Poor HH EICV 4 (own estimates) 37.4% 46.1%
Trends in poverty (percentage points) -7.5 +1.2

In sum, the poverty trends are very sensitive to the inflation rate. With an inflation of 16.7% from 2011-2014, poverty decreased by at least 5 percentage points, which is in line with the official reports. With an inflation rate of 30%, poverty does not decrease. The question thus boils down to an accurate estimation of the inflation rate between 2011 and 2014.

Inflation rate

The EICV survey is not an ideal dataset to estimate inflation, because it does not contain much information on food prices. As explained earlier, some households report prices for those food items consumed from own production. This does not only mean that the number of observations is relatively limited, but also that households report prices of those items they did not buy on the market. I nevertheless used this information to calculate mean and median average prices by food item. I calculated national averages without taking into account price seasonality or regional price differences. In order to estimate ‘average’ inflation, a weighted average is taken over nine crops. The weights are proportional to the weights used for the construction of the 2013/14 adjusted food poverty line (NISR, 2015, table B4, p. 38). These nine crops account for 86% of the total calorific intake of the food basket. Two crops dominate this index: cassava (fermented) (weight: 38%) and dry beans (weight: 25%).

Figure 2 shows the increase in mean and median prices between 2010/11 and 2013/14 for nine crops, while the horizontal lines indicate the weighted average. The increase in median prices ranges from 10% for sorghum to 50% for cassava (both flour and roots). Median and mean inflation are 33% and 42%, respectively. This corresponds to an annual inflation of 9.5% and 12.5%, respectively.

Figure 2: Price increase for nine crops from 2010/11 to 3013/14 (mean and median prices)


These inflation estimates are substantially higher than the ones reported by NISR, which states that food prices increased by 16.7% between Jan 2011 and Jan 2014 (NISR, 2016, p. 43). Moreover, the estimates based on the EICV surveys are remarkably similar to the estimates based on detailed ESOKO price data, where I estimated inflation at 30.5% over the 2011-2014 period (details not reported here).

In sum, I believe that the ‘real’ food inflation rate is substantially higher than the one used by NISR to estimate poverty trends. This probably explains why I find that poverty increased, while the NISR reported that poverty decreased. These findings raise concerns, not only for Rwanda’s (rural) policies, but also for international donors that have presented Rwanda as a model for development because of the supposedly strong poverty reductions.

Sam Desiere is currently a senior researcher at HIVA, the research institute for work and society of the University of Leuven, Belgium. In 2015 he obtained a PhD in agricultural economics from Ghent University, Belgium, which focused on data quality of household surveys in developing countries.

Featured Photograph: As part of the DFID funded Vision 2020 Umurenge Programme (VUP), Rwanda’s flagship Social Protection Programme, women and men in northern Rwanda work on a public works site in 2012, building terraces to prevent soil erosion

Source: A Review of African Political Economy (ROAPE)

Rwandan Poverty Statistics: Exposing the ‘Donor Darling’

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 EconomyVol. 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.

Source: A Review of African Political Economy (ROAPE)

This is how Gacaca Courts were used to build an authoritarian regime in Rwanda!


Often lauded by international observers, Rwanda’s gacaca courts have long been held up by their proponents as a model for successful, post-conflict reconciliation efforts. Confronted with the nearly impossible challenge of rebuilding a country after genocide, Rwanda needed a mechanism to hold those who committed genocide accountable in an efficient and effective manner. The solution was gacaca: a system of 12,000 community-based courts that sought to try genocide criminals while promoting forgiveness by victims, ownership of guilt by criminals, and reconciliation in communities as a way to move forward. While the organizers and leaders of the genocide were mostly sent for trial at the International Criminal Tribunal for Rwanda in Arusha, Tanzania, gacaca courts tried more than  1 million ordinary people who served as the foot soldiers of the genocide.

Relying on dozens of interviews, quantitative analysis of data on genocide crime prisoners, and firsthand observations of gacaca court proceedings in four regions of Rwanda, Anuradha Chakravarty’s new book suggests that the reality of gacaca is much more complicated. In “Investing in Authoritarian Rule: Punishment and Patronage in Rwanda’s Gacaca Courts for Genocide Crimes,” Chakravarty offers a detailed and nuanced look at the ways that Rwanda’s ruling party used the courts to build its own legitimacy, as well as the ways that participants in the courts viewed their role in punishing the guilty through the gacaca process.

Her findings are unsettling and suggest that the gacaca process was far more political and much less conciliatory than the casual observer might want to believe. Chakravarty’s central argument is that Rwanda’s ruling party, the Tutsi-dominated Rwandan Patriotic Front (RPF), used gacaca courts as a tool of patronage to build the new, post-genocide government’s legitimacy, which in turn allowed the RPF to entrench its rule into the virtually unchallenged authoritarian system in Rwanda today.

Chakravarty convincingly demonstrates that the RPF’s post-genocide consolidation of power in Rwanda evolved based on the cooperation of individual Hutus, who constitute the vast majority of Rwanda’s population and many of whom had committed genocide crimes. While the early RPF consolidation of power “depended on the use of blatant force through killings and arbitrary arrests,” as time passed a system of mutual benefit developed between the RPF and the majority Hutu population it sought to rule. Writes Chakravarty:

In denouncing others, submitting self-incriminating confessions, and judging their friends and co-ethnics, thousands upon thousands of individual Hutu acted upon and enforced RPF rules, reinforcing the regime with their cooperation in exchange for reduced sentences, security guarantees, the possibility of private gains in the form of personal vengeance or economic windfalls, and opportunities to access public power and social prestige. The RPF unleashed a stream of individualized benefits and sanctions that made “opportunistic investors” of ordinary Hutu who backed RPF rule in their own interests.

Thus it was that through the strengthening of a form of patronage that provided Hutus with protection from problems and access to opportunities, it was Hutus themselves who built and reinforced the RPF’s authoritarian rule, particularly through participation in and performance at the gacaca courts.

This incentive-based relationship, though, was not without risks. Because the RPF was the only option for any Hutu seeking to gain better status or avoid worse punishment for crimes, those Hutus had no choice but to work within the RPF’s system of patronage, but this did not mean that most Hutus accepted “that the RPF were legitimate rulers with the requisite clean hands.”

Importantly, Chakravarty does not argue that the RPF intended this outcome from the gacaca process; rather, the social processes of clientelism and increasing authoritarian control evolved over time in response to the incentives that  gacaca  and other post-conflict rebuilding processes set in place. She grounds her findings in a deep understanding of the role patronage relationships have played in Rwandan history and argues that clientelism has always driven relationships between powerful and ordinary actors in Rwanda. Thus, the decision to go along with the gacaca proceedings was a case in which “vulnerable individuals implicitly understood that they needed to solicit the protection and good will of this unrivaled patron.”

Unfortunately, these incentives led to negative outcomes for many Rwandans, particularly those who were falsely accused of participation in genocide. Chakravarty shows how Rwandans, faced with competing loyalties to different family and clan members alongside the need to demonstrate commitment to the gacaca courts, made decisions about whom to denounce and at what times to do so. Fortunately, she finds that “gacaca courts had secured some local peace,” preventing further violence and limiting the space for disputes to escalate into more dangerous situations. That limited space is a double-edged sword, however, as authoritarian control is essential to maintaining it.

Chakravarty’s findings suggest the need for much more scholarly work on the “tacit bargains” that govern relations between elites and mass publics in the aftermath of atrocity crimes; as she notes, the bargain expressed in and built through the gacaca process is not an inter-elite legislative or ruling party bargain, but rather “an informal elite-mass social contract that consolidated the new order by tying the new elites to their social constituents, and demonstrating to them (‘clients’) the benefits of cooperating with and advancing within the system.” Of particular interest is a question Chakravarty raises in the context of comparison with Nazi Germany’s postwar accountability and justice processes: the ways that individual citizens having a choice of patrons rather than being forced to rely on a sole patron (as in Rwandan) influences outcomes in modern transitional justice processes.

Chakravarty’s work is an indispensable read for anyone interested in transitional justice, post-conflict reconciliation or Africa’s Great Lakes region. Comments from her subjects on topics ranging from how Hutus and Tutsis perceive the RPF’s dominance to whether the gacaca courts actually provided justice offer invaluable insight into how ordinary Rwandans think about their relationship to their government and whether reconciliation has really happened since the genocide ended. Chakravarty does not evaluate whethergacaca was a success, nor does she claim that gacaca was Rwanda’s only potential path to authoritarian rule, but her findings should compel more scholars to explore comparative cases in which vulnerable populations might respond to incentives that lead to the consolidation of authoritarian rule in the wake of mass atrocities.


Laura Seay is an Assistant Professor of Government at Colby College. She studies African politics, conflict, and development, with a focus on central Africa. She has also written for Foreign Policy, The Atlantic, Guernica, and Al Jazeera English


Revealed: how the wealth gap holds back economic growth

OECD report rejects trickle-down economics, noting ‘sizeable and statistically negative impact’ of income inequality

Organisation for Economic Co-operation a

OECD secretary-general Angel Gurría said that ‘addressing high and growing inequality is critical to promote strong and sustained growth’. Photograph: Eric Piermont/AFP/Getty Images

The west’s leading economic thinktank on Tuesday dismissed the concept of trickle-down economics as it found that the UK economy would have been more than 20% bigger had the gap between rich and poor not widened since the 1980s.

Publishing its first clear evidence of the strong link between inequality and growth, the Paris-based Organisation for Economic Cooperation and Development proposed higher taxes on the rich and policies aimed at improving the lot of the bottom 40% of the population, identified by Ed Miliband as the “squeezed middle”.

Trickle-down economics was a central policy for Margaret Thatcher and Ronald Reagan in the 1980s, with the Conservatives in the UK and the Republicans in the US confident that all groups would benefit from policies designed to weaken trade unions and encourage wealth creation.

The OECD said that the richest 10% of the population now earned 9.5 times the income of the poorest 10%, up from seven times in the 1980s. However, the result had been slower, not faster, growth.

It concluded that “income inequality has a sizeable and statistically negative impact on growth, and that redistributive policies achieving greater equality in disposable income has no adverse growth consequences.

“Moreover, it [the data collected from the thinktank’s 34 rich country members] suggests it is inequality at the bottom of the distribution that hampers growth.”

According to the OECD, rising inequality in the two decades after 1985 shaved nine percentage points off UK growth between 1990 and 2000. The economy expanded by 40% during the 1990s and 2000s but would have grown by almost 50% had inequality not risen. Reducing income inequality in Britain to the level of France would increase growth by nearly 0.3 percentage points over a 25-year period, with a cumulated gain in GDP at the end of the period in excess of 7%.

“These findings have relevant implications for policymakers concerned about slow growth and rising inequality,” the paper said.

“On the one hand it points to the importance of carefully assessing the potential consequences of pro-growth policies on inequality: focusing exclusively on growth and assuming that its benefits will automatically trickle down to the different segments of the population may undermine growth in the long run, in as much as inequality actually increases.

“On the other hand, it indicates that policies that help limiting or – ideally – reversing the long-run rise in inequality would not only make societies less unfair, but also richer.”

Rising inequality is estimated to have knocked more than 10 percentage points off growth in Mexico and New Zealand, nearly nine points in the UK, Finland and Norway, and between six and seven points in the United States, Italy and Sweden.

The thinktank said governments should consider rejigging tax systems to make sure wealthier individuals pay their fair share. It suggested higher top rates of income tax, scrapping tax breaks that tend to benefit higher earners and reassessing the role of all forms of taxes on property and wealth.

However, the OECD said, its research showed “it is even more important to focus on inequality at the bottom of the income distribution. Government transfers have an important role to play in guaranteeing that low-income households do not fall further back in the income distribution”.

The authors said: “It is not just poverty (ie the incomes of the lowest 10% of the population) that inhibits growth … policymakers need to be concerned about the bottom 40% more generally – including the vulnerable lower-middle classes at risk of failing to benefit from the recovery and future growth. Anti-poverty programmes will not be enough.”

Angel Gurría, the OECD’s secretary general, said: “This compelling evidence proves that addressing high and growing inequality is critical to promote strong and sustained growth and needs to be at the centre of the policy debate. Countries that promote equal opportunity for all from an early age are those that will grow and prosper.”

Source: The Guardian, December 9, 2014.

World population to hit 11bn in 2100 – with 70% chance of continuous rise.

The world’s population is now odds-on to swell ever-higher for the rest of the century, posing grave challenges for food supplies, healthcare and social cohesion. A ground-breaking analysis released on Thursday shows there is a 70% chance that the number of people on the planet will rise continuously from 7bn today to 11bn in 2100.

The work overturns 20 years of consensus that global population, and the stresses it brings, will peak by 2050 at about 9bn people. “The previous projections said this problem was going to go away so it took the focus off the population issue,” said Prof Adrian Raftery, at the University of Washington, who led the international research team. “There is now a strong argument that population should return to the top of the international agenda. Population is the driver of just about everything else and rapid population growth can exacerbate all kinds of challenges.” Lack of healthcare, poverty, pollution and rising unrest and crime are all problems linked to booming populations, he said.

“Population policy has been abandoned in recent decades. It is barely mentioned in discussions on sustainability or development such as the UN-led sustainable development goals,” said Simon Ross, chief executive of Population Matters, a thinktank supported by naturalist Sir David Attenborough and scientist James Lovelock. “The significance of the new work is that it provides greater certainty. Specifically, it is highly likely that, given current policies, the world population will be between 40-75% larger than today in the lifetime of many of today’s children and will still be growing at that point,” Ross said.

Many widely-accepted analyses of global problems, such as the Intergovernmental Panel on Climate Change’s assessment of global warming, assume a population peak by 2050.

Sub-saharan Africa is set to be by far the fastest growing region, with population rocketing from 1bn today to between 3.5bn and 5bn in 2100. Previously, the fall in fertility rates that began in the 1980s in many African countries was expected to continue but the most recent data shows this has not happened. In countries like Nigeria, the continent’s most populous nation, the decline has stalled completely with the average woman bearing six children. Nigeria’s population is expected to soar from 200m today to 900m by 2100.

The cause of the stalled fertility rate is two-fold, said Raftery: a failure to meet the need for contraception and a continued preference for large families. “The unmet need for contraception – at 25% of women – has not changed in for 20 years,” he said. The preference for large families is linked to lack of female education which limits women’s life choices, said Raftery. In Nigeria, 28% of girls still do not complete primary education.

population graph
Global population trend. Photograph: Guardian

Another key factor included for the first time was new data on the HIV/AIDS epidemic showing it is not claiming as many lives as once anticipated. “Twenty years ago the impact on population was absolutely gigantic,” Raftery said. “Now the accessibility of antiretroviral drugs is much greater and the epidemic appeared to have passed its peak and was not quite as bad as was feared.”

The research, conducted by an international team including UN experts, is published in the journal Science and for the first time uses advanced statistics to place convincing upper and lower limits on future population growth. Previous estimates were based on judgments of future trends made by researchers, a “somewhat vague and subjective” approach, said Raftery. This predicted the world’s population would range somewhere between 7bn and 16bn by 2100. “This interval was so huge to be essentially meaningless and therefore it was ignored,” he said.

But the new research narrows the future range to between 9.6bn and 12.3bn by 2100. This greatly increased certainty – 80% – allowed the researchers to be confident that global population would not peak any time during in the 21st century.

Another population concern is the ageing populations currently seen in Europe and Japan, which raises questions about how working populations will support large numbers of elderly people. But the new research shows the same issue will affect countries whose populations are very young today. Brazil, for example, currently has 8.6 people of working age for every person over 65, but that will fall to 1.5 by 2100, well below the current level in Japan. China and India will face the same issue as Brazil, said Raftery: “The problem of ageing societies will be on them, in population terms, before they know it and their governments should be making plans.”

In separate work, published on Monday, Wolfgang Lutz, director of the Vienna Institute of Demography, highlighted education as crucial in not only reducing birth rates but also enabling people to prosper even while populations are growing fast. In Ghana, for example, women without education have an average of 5.7 children, while women with secondary education have 3.2 and women with tertiary education only 1.5. But he said: “It is not primarily the number of people that’s important in population policy, it’s what they are capable of, their level of education, and their health.”

SOURCE: The Guardian