Parallel session 6b

1. Comments on
Looking at Pro-Poor Growth from an
Agricultural Perspective
Stephan Klasen and Malte Reimers
Thesia Garner/John Ruser
US Bureau of Labor Statistics
Presented at
33rd IARIW General Conference
Rotterdam
August 28, 2014

2. Outline
 Research issue
 Motivation
 What they did (overview, later details)
 Results
 Authors’ caveat and remarks
 Garner/Ruser comments

3. Motivation of this paper
 Pro-poor growth (PPG) analysis investigates
distributional pattern of growth
 Literature has developed various tools to
measure how “pro-poor”, recent growth in
country
 Regularly referred to as the “PPG-toolbox”
 Literature has not directly allowed for importance
of agriculture for poverty reduction
 Important question: What was the distributional
pattern of agricultural productivity growth?

4. This paper
 Assesses how “pro-poor” recent growth has been
in Rwanda
 Analyzes 3 waves of a Rwandan household
living conditions survey data
 Assesses impact of Rwandan growth using the
traditional PPG-toolbox
 In addition: Extends the toolbox to examine the
distributional pattern of agricultural productivity
growth (inverse rel. bet. farm size and land prod.)
 Land productivity (value of production/hectares)
 Labor productivity (value of production/adults in ag.)
4

5. The Agricultural Perspective
5
Question 1: To what extent were the productivity-poor
able to increase their agricultural productivity?
 Monetary productivity growth incidence curve (PGIC)
 Crop-specific productivity growth incidence curve (PGIC)
Question 2: Did the productivity-poor benefit from the
expansion in the provision of social services?
 Productivity opportunity curve (POC), Type 1
Question 3: To what extent were the human
capital-poor
able to increase their agricultural productivity?
 Productivity opportunity curve (POC), Type 2

6. What is Pro-Poor Growth
(PPG)?
 Klasen (2008) provides various definitions of PPG
 Weak-absolute:
– PPG is any growth (e.g., income) where the poor benefit to
some extent
 Relative:
– Growth has to be relatively faster for poor than for non-poor
– Implies falling relative inequality
 Strong-absolute:
– Absolute increases for poor have to be larger than for non-poor
– Implies falling absolute inequality

7. Tools in the hitherto Existing
PPG-Toolbox
1. Growth Incidence Curve (Ravallion and Chen
2003)
 Plots quantile-specific growth rates of income against quantiles of
the population ranked by their p.c. income
2.Non-Income Growth Incidence Curve
(Grosse et al. 2008; Klasen 2008)
Based on the idea of the GIC, but now focusing on non-income
dimensions of poverty
Two versions: Conditional and Unconditional NIGICs
3. Opportunity Curve (Ali and Son 2008)
 Likewise focusing on non-income dimensions of poverty
Plots levels of access to certain social services or
education/health outcomes against the cumulative share of the population
ranked by p.c. income

8. Nexus between Agriculture and
Poverty Reduction
 Recent literature has pointed out the
”extraordinary importance of agricultural
productivity for poverty reduction worldwide….”
 Need to account for this in the PPG-toolbox

9. Different Perspectives on
Pro-Poor Growth
Existing PPG toolbox understands
a) the poor in terms of p.c. income/expenditures
o Ex.: GIC, Conditional NIGIC, Opportunity Curves
o Did the income-poor benefit from recent developments
b) the poor in terms of education or health
o Ex.: Unconditional NIGIC
o Did the education/health-poor benefit from recent
developments?
This paper extends the toolbox to understand the
c) the poor in terms of agricultural productivity
o Did the productivity-poor benefit from recent developments?

10. Rwanda
10
• Since the genocide in 1994 strong growth performance
o avg. growth of per capita income between 2000 and 2011: 4.67%
• Rwanda pursues with the “Vision 2020” very ambitious goals to
increase the population’s access to social services
• Most densely populated country in Sub-Saharan Africa:
o approx. 431 inhabitants per km² compared to an average of SSA
countries of approx. 36 inhabitants per km²
• Major problem: Agricultural land is already scarce (avg. farm size
below 0.7 hectares) and the population keeps growing at a rapid
pace
Crucial to increase the agricultural land productivity

11. Rwanda - 2
11
• Therefore, the government of Rwanda has set up a
wide array of programmes aiming to increase
agricultural productivity
• Some examples:
o Crop intensification programme
o Commercialisation of crop production programme
o Erosion protection programme
o Land consolidation programme
o Regionalization of crops programme
o etc.
 Considerable efforts undertaken to increase agricultural
productivity

12. EICV household data
 Three waves of the Enquête Intégrale sur les
Conditions de Vie des Ménages au Rwanda (EICV)
 Nationally representative repeated cross-sections
covering
 EICV1 (2000/01): 6,420 households (32,000 people)
 EICV2 (2005/06): 6,900 households (35,000 people)
 EICV3 (2010/11): 14,308 households (70,000 people)
 Includes detailed information on agricultural
production, education, health, and household
consumption expenditures

13. Results- Traditional Toolbox
 Rwanda achieved impressive progress in
monetary and non-monetary dimensions
 literacy, sanitation, and health
 Progress in many cases not only pro-poor in the
weak-absolute, but also in the relative and in
various cases even in the strong-absolute sense
13

14. Standard Growth Incidence Curve
(expenditures)
0 20000 40000 60000 80000
Absolute Change (in RWF)
0 1 2 3 4 5
Annual Growth Rate %
Pro-poor:
weak-absolute and
relative
Not-pro-poor: strong
absolute
0 20 40 60 80 100
Expenditure percentiles
GIC (lhs) - entire pop. 95% CI upper b. (lhs) - entire pop. 95% CI lower b. (lhs) - entire pop.
GIC (lhs) - agrar. only 95% CI upper b. abs - agrar. only 95% CI lower b. abs - agrar. only
GIC abs - entire pop. 95% CI upper b. abs - entire pop. 95% CI lower b. abs - entire pop.
GIC abs - agrar. only 95% CI upper b. abs - agrar. only 95% CI lower b. abs - agrar. only
Growth
in
access

15. Opportunity Curves for Adult
Literacy
Share being able to read and write (aged 15+) .3 .4 .5 .6 .7
10 20 30 40 50 60 70 80 90 100
Cum. share of the population (by expenditures)
EICV 1 EICV1 - 95% CI lower b. EICV1 - 95% CI upper b.
EICV 2 EICV2 - 95% CI lower b. EICV2 - 95% CI upper b.
EICV 3 EICV3 - 95% CI lower b. EICV3 - 95% CI upper b.
Level in
access
Pro-poor:
weak-absolute,
relative, and absolute

16. Results – Extended Toolbox
 Extensions to incorporate ag productivity show:
 Productivity-levels of the land productivity-poor
increased relatively and absolutely faster than
those of the land productivity-rich
 However, in the case of the labor productivity-poor
abs. increases were smaller
16

17. Monetary Land Productivity
Growth Incidence Curve
-600000 -400000 -200000 0 200000
Absolute Change (in RWF)
-5 0 5 10
Annual Growth Rate %
Pro-poor:
weak-absolute
and relative
Pro-poor: strong
absolute
0 20 40 60 80 100
Land productivity percentiles
PGIC (lhs) 95% CI upper b. (lhs) 95% CI lower b. (lhs)
PGIC abs 95% CI upper b. abs 95% CI lower b. abs

18. Monetary Labor Productivity
Growth Incidence Curve
0 50000 100000 150000
Absolute Change (in RWF)
2 4 6 8 10
Annual Growth Rate %
Pro-poor:
weak absolute
and relative
Not-pro-poor:
strong absolute
0 20 40 60 80 100
Labor productivity percentiles
PGIC (lhs) 95% CI upper b. (lhs) 95% CI lower b. (lhs)
PGIC abs 95% CI upper b. abs 95% CI lower b. abs

19. Productivity Opportunity
Curves
 Labor productivity-poor households exhibit:
 Considerably lower levels of education
 Relatively similar health levels
 NEW - Human capital poor households:
 Labor and land productivity-levels
considerably for human capital-poor then
of the human capital-rich households
 Productivity growth is significantly slower
19

20. Productivity Opportunity Curve
(Type 2) for Human Capital
Value of agricultural production per worker 80000 100000 120000 140000
10 20 30 40 50 60 70 80 90 100
Endowment with human capital
Cum. share of the population (by human capital)
EICV 1 EICV1 - 95% CI lower b. EICV1 - 95% CI upper b.
EICV 2 EICV2 - 95% CI lower b. EICV2 - 95% CI upper b.
EICV 3 EICV3 - 95% CI lower b. EICV3 - 95% CI upper b.

21. Authors caveat and
remarks
 PPG analysis is only descriptive and the graphs do
not imply causality
 PPG analysis should be seen as monitoring tool that
allows policy makers to assess from an ex-post
perspective who has benefited from recent
developments in a country
 Results of such a PPG analysis can then – as a
second step – help to improve the targeting of
governmental and non-governmental efforts in the
field of education, health, or agricultural policy

22. Garner/Ruser comments
 Very interesting analysis that extends the PPG
toolbox to incorporate agricultural productivity
 Paper shows value of the new tools and the
important distinction between labor and land
productivity
 Hard to see relationship between variables in the
opportunity curves. We prefer the incidence
curves
 e.g., can better see access to improved sanitation
in incidence curves
22

23. Garner/Ruser comments
 Changes in some variable values between
waves seem remarkable. What is driving them?
 Public policies/interventions
– But, paper does describe some government
programs that seek to increase ag prod
– It would be helpful to see some information on
government interventions and their timing to infer
connections
– Provide table showing interventions with dates
23

24. -600000 -400000 -200000 0 200000
Absolute Change (in RWF)
-5 0 5 10
Annual Growth Rate %
Agricultural Programs
0 20 40 60 80 100
Land productivity percentiles
PGIC (lhs) 95% CI upper b. (lhs) 95% CI lower b. (lhs)
PGIC abs 95% CI upper b. abs 95% CI lower b. abs

25. Garner/Ruser comments
 Changes in some variable values between
waves seem remarkable. What is driving them?
 Changes in demographics
– e.g. literacy increased because young are going to
school and proportion of young has increased
– Role of the genocide, mortality larger for men vs women
Provide some data on evolution of demographics
 Would like to see decomposition of growth and
opportunity
 Government policies: introduction and intensiveness
 Change in demographics
25

26. Garner/Ruser comments
 Benefit of these household data: link between
income and non-income variables with ag data
 But, an Ag Census would presumably get more
info about ag productivity
 For example, information about agricultural capital
 Current study looks at two one-factor productivity
measures: land and labor
 But, how does use of capital affect the pro-poorness
of growth?
 It does not seem that this question can be answered
with these data
26

27. Garner/Ruser comments
 New variables
 Capital
 Alternative uses of land
27

28. Garner/Ruser comments
 Construction of variables
 Consumption expenditure vs. “full income” -
impact
 Adjust for price differences, Jan. 2001 (CPI?)
– Impact time period?
– Appropriate for your consumption expenditure measure?
 Equivalence scale adjustment
 Monetary value of amount harvested – theoretical
28

29. Garner/Ruser comments
 Measurement issues such as changes in
survey questions between waves
 It would be helpful to know more about the data
sets and questions asked
 In appendix: Show key questions and how they
have changed (if they have)

30. Garner/Ruser comments
 Data collection methods
 Reference period: urban ~ 30 days; Rural~14 days
 Interaction of interviewer and interviewee
– Male vs. female interviewers
– Male vs. female primary agriculture head of household
 E.g. males more likely to boast?
30

31. Thank you for your attention!

32. Contact Information
Thesia Garner, Ph.D.
garner.thesia@bls.gov
+1 202-691-6576