The document discusses the role of agriculture in development processes based on perspectives from development literature and international experiences over the past 25-30 years. It notes that while agriculture traditionally played a passive role, more recent evidence shows it can play an active role in supporting industrialization and structural transformation if agricultural growth induces urban-industrial growth. However, many countries in Africa failed to successfully transform due to various challenges. The global context today faces three major crises - finance, food, and climate change - which impact the role of agriculture. In Mozambique, agriculture's contribution to GDP decreased from 1997-2001 but has since stabilized.

1. OMR Conference, 4 September 2013
Maputo, Mozambique
The Role of Agriculture in the Development
Process

2. Overview
• Perspectives from the development literature
• International experiences: the last 25‐30 years
• Current global context: three crises
• Mozambique – experiences over the past decade
• Concluding remarks

3. Traditional views (from theory and empirics)
• Ricardo and colleagues
• The Lewis two‐sector model
• The linkage literature
• Falling relative share of agriculture
• All this tended to suggest a passive and at best a supportive
role

4. The basis for a positive role
• Classical paradigm of positive role of ag in development (1960s)
• Agricultural growth in support of industrialization through the
agricultural transformation (ADLI) (Asian examples)
• How the structural transformation works:
– Agricultural growth induces urban‐industrial growth through
capital, labor, foreign exchange, and market contributions
• Industry (starting with agribusiness) grows faster than agriculture
• As a consequence, the shares of agriculture in aggregate
employment and GDP decline due to success in triggering GDP
growth, not due to failure to grow.

5. A powerful cross-country regularity (1990-2005
average) (de Janvry and Sadoulet 2008)

6. Successful transformation in Asia

8. But Africa experienced many challenges
• Many implementation failures (1970s)
• Import Substitution Industrialization failed
• Many failures in agriculture‐based projects
• Too complex, insufficient support
• Integrated rural development to meet
broadened development objectives (McNamara
1973) ineffective:
– Underestimate emerging private sector roles
– Overestimate state capacity to coordinate
– Undermine cooperative producer organizations

9. And we also saw
• 20 years of neglect under structural adjustment and Washington
Consensus (1985‐2005)
• Adjust the macro‐fundamentals but no sectoral policy
• Industrialize through open economy not agriculture
• Plus for example:
– Descale the role of the state in agriculture, despite pervasive market
failures
– Reduce rural poverty through transfers instead of autonomous
incomes
– Investment in agriculture discouraged by low international commodity
prices (OECD) & adverse environmental effects
– Sharp decline in public expenditures on agriculture
– Sharp decline in overseas development assistance to agriculture
• So what was the outcome?

10. So failed transformation in Africa in general: Labour displaced
from agriculture without associated growth in GDP per capita

11. Plus another type of failed transformation: Growth
without transformation

12. In effect there are (at least) three types of economies (with associated
roles of agriculture in development)
AZE
BDI
BEN
BFA
BGD
BGR
BLR
BOLBRA
CHL
CHN
CIV
CMR
COL
DOM
ECU
EGY
ETH
GHA
GTM
HND
HUN
IDN
IND
IRN
KEN
KHM
LAO
LKA
MAR
MDG
MEX
MLI
MOZ
MWI
NER
NGA
NPL
PAK
PER
PHL
POL
PRY
ROM
RUS
RWA
SEN
SLV
THA
TJK
TUN
TUR
TZA
UGA
UKRVEN
VNM
YEM
ZAF
ZMB
AGO
ARG
CZE
DZA
GIN
MYS
PNG
SDN
SVK
SYR
TCD
TGO
ZAR
ZWE
-0.2
0.0
0.2
0.4
0.6
0.8
0.0 0.2 0.4 0.6 0.8 1.0
Rural poor/total poor, 2002
Poverty data from Ravallion et al. 2007
Other predicted poverty data
Dynamic analysis
Urbanized countries
Agriculture-based
countries
Transforming
countries
70-75
90-96
Indonesia
(1970-96)
Brazil
(1970-96)
India
(1965-94)
China
(1981-2001)
Source: World Bank, World Development Report 2008.
Y-axis: ag contribution to growth:
1990-2005

13. Role of agriculture fundamental but it differs …
• Agriculture based economies: growth
– Agriculture essential for growth: large
– Importance for food security and poverty
• Transforming countries: equality
– Rapidly growing non‐ag
– Agriculture key to reduce imbabalance + marginalization
• Urbanized economies: inclusion
– Sub‐sectors with comparative advantage
– Include small holdes as suppliers

14. Losses due to global trade policies

15. Agriculture exports highly taxed

16. Low public spending

17. Subsidies and public investment in
Indian agriculture

18. Disaggregated project aid, 2002-2009

19. Foreign aid and poverty

20. Ag growth and the link to poverty

21. Examples of impressive successes

22. Agricultural productivity: two types of farming
(1) Highly efficient agriculture of developed countries and high output per
worker
(2) Inefficient and low‐productivity agriculture of developing countries
• Between the extremes: developing regions e.g. regions in India, Brazil,
export‐oriented sectors in Latin‐America and Asia : reach higher
agricultural productivity levels and growth!
• Agricultural productivity and productivity growth low especially in Sub‐
Saharan Africa

23. Agriculture value
added
($ per agricultural
worker)
2003-05
Cereal production
Yields
(in kilo per
hectare)
2003-05
Thailand 554 3,044
Vietnam 182 4,641
Indonesia 421 4,278
Ethiopia 64 1,213
Tanzania 167 1,403
Mozambique 83 925
Uganda 101 1,559
Kenya 169 1,682
Denmark 22,260 6,088
Source: World Development Report 2008, selected indicators
Productivity examples
x 348
x 40
labour
productivity
land
productivity

24. The risk averse peasant and technology choice
Not necessarily: uncertainties (e.g.
weather, price), imperfect
information, transactions costs, lack
of access to credit and insurance
The peasant tries to maximise not
income but the chance of the
family’s survival => rational
Min.
desirable
consump.
Minimum
consump.
Production/consum.
Time
Technology A
 Technology A: Low yield, little
variation
Technology B
 Technology B: High yield, big
variation
Yield
Probability
Technology
A
Technology
B
Is resistance to technological
innovation due to lack of rationality
or incompetence?

25. What else causes low output growth in low-
productivity subsistence farming
1) Large amounts of land sometimes available, only small parts can be
cultivated: traditional tools (e.g. hoe, ax, knife, …). Use of animals
sometimes impossible because of tsetse fly, lack of fodder in dry season =>
agriculture depends on applying human labour only to small plots of land.
2) Due to limited amount of land cultivated and tools used, small areas tend to
be cultivated intensely => rapidly diminishing returns to labour. Best farming
method is shifting cultivation (i.e. once minerals drawn from soil, new land
cleared and cultivated while old land can recover and be used again later). If
fallow time long enough, manure and chemical fertiliser would be
unnecessary.
3) Seasonality: scarcity of labour in busy parts of the season (planting,
weeding) while underemployed at other times.
Net result: constant level of agricultural output and labour productivity…as
long as population size stable …

26. The global context: three crises
• Finance
• Food
• Climate change

27. CRISIS 1: FINANCE

28. IMFForecast
-4-202468
RealGDPAnnualGrowth(%)
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
World Advanced Economies
Emerging and Developing Economies
Source:IMF World Economic Outlook Database October (2009)
Real GDP Growth in World and Major Economic Groupings (1970-2014)
Present economic downturn deepest in 60 years, and no region
untouched + a lot speculation as to recovery28

29. IMFForecast
-11.89
-2.66
-0.93
0.24
First Oil Crisis
Second Oil Crisis
Dot Com Bubble
Financial Crisis
-15-10-5051015
TradeVolume(AnnualPercentageChange)
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
World Trade Volume
Source:IMF World Economic Outlook Database October (2009)
World Trade Volume (1970-2014)
World trade has experienced its sharpest decline in decades + uncertain
future

30. 2.98
-0.71
IMFForecast
-10123
NetPrivateCapitalFlows(%ofGDP)
1990 1995 2000 2005 2010 2015
Year
Net Private Capital Flows
Source: IMF World Economic Outlook Data Base April (2009)
Net Private Capital Flows to Emerging and Developing Economies (1990-2014)
Net private capital flows to the South have fallen
dramatically

31. CRISIS 2: FOOD

32. 050100150200250
IndicesofMarketPrices(2005=100)
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year
Rice Wheat
Maize Soybeans
Source : IMF Primary Commodity Price Data Base
Note: Rice:Thailand(Bangkok); Wheat:US Gulf ; Maize:US ; Soybeans: US
Cereal Prices in Indices of Market Prices (1957-2008)
Food prices soared in 2007‐2008 and then fell back: prospects?

33. Underlying factors
• Food price developments reflect:
– Low priority to agriculture/food production
– Shifting demand patterns
– Biofuels (+ lack of research in alternative energy
sources)
• Underlying structural drivers behind 2007‐2008 spike
remain in place – if growth resumes food prices likely
to increase again
• Global food architecture not geared to deal with
supply shortages – governments may intensify
protection to try to satisfy domestic consumers

34. Responses
• National responses to food crisis have varied
• Africa: Macro‐policies main tool to limit impact of world price
shocks
• Elsewhere: Greater focus on social protection
• But too much social protection ad hoc, stop‐go, high cost – needs
to be systematic
• A double bind:
– If recovery stalls: new trade and financial shocks
– If recovery is sustained: food and energy prices will climb and hit
energy and food importers
• Need for public action – but fiscal space limited in the smaller and
poorer economies

35. CRISIS 3: CLIMATE

36. Climate change (1)
• Present global growth model clearly unsustainable –
the challenges are unprecedented
• To respond, the world must transform existing energy
systems (mitigation) and simultaneously adapt to the
climate change that is already built into global
climate (adaptation)
• Failure in shifting from fossil‐fuel dependence evident
in run‐up in oil price prior to the financial crisis (due
to lack of investment in energy research)

37. 020406080100120140
US$PerBarrel
01jan1998 01jan2000 01jan2002 01jan2004 01jan2006 01jan2008 01jan2010
Daily
Note:Oil prices refer to Brent; US dollars per barrel
Source:US Department of Energy
Oil Prices ( January 1998 to October 2009)
Huge run‐up, then a fall as recession set in – but ...?

38. Climate change (2)
• If growth resumes energy prices will move back up
• Places huge burdens on poor countries – a range of fiscal effects,
which make states more aid‐dependent, not less
• Costs far exceed current level of aid:
– Per annum mitigation in developing countries by 2030: USD
140‐175 billion
– Per annum adaptation costs by 2050: USD 30‐109 billion
– Aid is presently around USD 100 billion in total
• Climate change finance is as fragmented as traditional aid, will
funding be additional, and who takes control of supply (how much
voice for the South?)
• Climate change financing seen as compensation – but aid
processes remain conditional

39. BACK TO MOZAMBIQUE

40. The Role of Agriculture in Mozambique
• The contribution of agriculture to GDP decreased
significantly from 1997 to 2001, before immediately
increasing again. Since recovery from the 2000 floods, the
contribution of all sectors has stayed quite stable.
35 35 35 31 29 24 23 28 28 27 27 28 28 30 31 32 32
15 16 18 22 23 25 26
23 26 27 25 26 26 24 24 23 24
51 48 47 47 49 51 52 49 46 45 48 46 46 46 45 45 44
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Sectoral Contributions to GDP
Agriculture, value added (% of GDP) Industry, value added (% of GDP) Services, etc., value added (% of GDP)
Source: World Bank Mozambique Metadata

41. Public expenditure in agriculture
• State support to agriculture has not achieved the 10% Maputo Declaration target
– the average empirical value attained by ‘transforming’ economies over the last
30 years.
• Actual expenditure of budget increases have tended to remain unfulfilled
• The large budgets in 2003 and 2004 can be attributed to the rehabilitation of the
Massingir Dam, and the Chokwe Irrigation Scheme in 2006 and 2007 – accounting
for a large part of the budget
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
2001 2002 2003 2004 2005 2006 2007 2008 2009
Share of Agriculture in the Total Budget Share of Actual Agricultural Expenditure in Total
Maputo Declaration Target ‐ 10%
Source: Public expenditure review ‐ MozSAKSS
(2011)

42. Levels of staple crop production
• Maize continues to be most produced staple crop
• Production levels have shown no signs of increase over the last 10 years
• Rainfall and weather patterns continue to heavily influence staple crop production
(2005 and 2006)
0
20
40
60
80
100
120
140
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2002 2003 2005 2006 2007 2008 2012
Kg Per Capita
'000 Tonnes
Millet
Sorghum
Rice
Maize
Staple Production p/c
Staple Production p/c (Rural)
Source: TIA/IAI

43. Staple crop productivity (kg/ha)
• Maize productivity levels still fluctuating and much below potential
• Rice productivity stagnation
• Sorghum and millet productivity levels fluctuate with no trend of
improvement
0
100
200
300
400
500
600
700
800
900
2002 2005 2006 2007 2008 2012
Crop Yield (Kg/Ha)
Maize
Rice
Sorghum
Millet
Source: TIA/IAI

44. Input use
• Extremely low utilisation of agricultural inputs
• No evidence of improvement
• Not uniformly distributed throughout the country...
0%
2%
4%
6%
8%
10%
12%
14%
2002 2003 2005 2006 2007 2008 2012
% of Input Use
Improved Seed
Fertilisers
Pesticides
Animal traction
Irrigation
Source: TIA/IAI

45. Who uses inputs (%)?
IMPROVED SEEDS FERTILISER PESTICIDE ANIMAL TRACTION IRRIGATION
2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12
Niassa 8.8 5.5 13.1 8.5 7.5 6.0 0.0 0.0 5.5 6.3
Cabo Delgado 1.6 5.0 1.8 1.4 11.7 14.4 0.0 0.1 2.0 2.4
Nampula 6.4 4.9 2.3 2.3 9.5 5.6 0.1 0.1 3.7 5.4
Zambézia 7.3 7.2 0.8 0.5 1.2 0.8 0.1 0.1 2.4 3.2
Tete 16.5 23.4 15.4 15.2 7.3 6.9 30.7 21.2 18.0 16.3
Manica 17.4 22.1 2.2 2.5 1.9 2.7 12.4 14.6 9.9 17.2
Sofala 8.4 11.4 1.1 1.3 7.0 3.4 1.8 3.2 4.8 9.0
Inhambane 7.9 6.1 1.7 3.4 1.8 2.6 47.4 42.8 18.7 20.5
Gaza 8.6 11.2 3.4 2.4 2.8 2.5 45.4 44.9 19.9 12.9
Maputo 13.7 10.1 4.7 6.2 4.1 5.0 13.5 14.0 22.8 22.4
Total 8.4 9.7 3.7 3.6 5.7 4.7 11.0 9.8 7.8 8.9
• Large use of improved seeds, fertilisers, pesticides and animal
traction in Tete
• Evidence of increased adoption of improved seeds varieties in
Tete, Manica, Sofala and Cabo Delgado
• Fertiliser, pesticide and animal traction has generally not
changed much
• Extremely low use of animal traction above the Zambezi River
Increase > 5 %
Increase 3 ‐ 5%
Decrease 3 ‐ 5 %
Decrease > 5 %
BOLD values show
above 10% usage

46. Land use
• Average farm size falling in the South – labour shortages, rural to urban
migration
• Increasing average farm size in Zambézia and Nampula
• Low levels of land registration nationally. Improved vastly in Maputo
Province (25% in 2012) time, greater competition over land?
0
0.5
1
1.5
2
2.5
2002 2003 2005 2006 2007 2008 2012
Average size of area cultivated (Ha)
Average Cultivated Area per Household
North
Centre
South
National
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
2002 2005 2008 2012
% of Farmers with Registered Land
DUAT Registration
Source: TIA/IAI Source: TIA/IAI

47. Services (%)
• Large fall in farmers receiving extension advice throughout the country
• Farmers receiving price information decreased in the north, but rose in
the centre and south
• Association membership increased slightly nationally, yet fell drastically
in Maputo province
• Emergency seed reception fell in the centre and south, and was already
initially very low in all other areas of the country
EXTENSION ADVICE RECEIVED PRICE INFO ASSOCIATION MEMBER RECEIVED CREDIT EMERGENCY SEEDS
2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12
Niassa 14.5 9.4 40.7 34.3 6.7 5.9 7.5 3.3 3.0 3.7
Cabo Delgado 14.9 6.3 43.4 35.8 4.5 4.7 2.9 2.5 2.2 2.0
Nampula 15.2 9.1 62.7 43.5 6.4 7.8 3.6 3.8 2.4 1.2
Zambézia 9.5 7.4 26.1 37.7 3.8 7.6 0.9 1.6 4.1 3.2
Tete 16.3 11.9 37.5 46.3 5.0 4.9 7.7 6.6 4.8 5.5
Manica 12.6 7.2 43.3 46.1 4.7 5.8 1.3 3.4 5.4 2.3
Sofala 20.4 11.2 46.2 50.4 3.2 4.8 4.5 3.1 9.8 6.3
Inhambane 7.2 6.5 23.3 29.9 2.6 6.2 1.1 3.1 3.7 2.4
Gaza 16.7 6.5 24.8 31.3 9.3 8.0 2.5 2.4 12.0 5.7
Maputo 11.6 7.2 21.5 32.8 15.2 8.0 2.9 2.0 18.5 2.7
Total 13.4 8.3 39.6 39.6 5.3 6.6 3.1 3.1 4.8 3.2
Increase > 5 %
Increase 3 ‐ 5%
Decrease 3 ‐ 5 %
Decrease > 5 %
BOLD values show usage
above national average
Source: TIA/IAI

48. Cash crop producers
• Overall fall in farmers cultivating tobacco, whilst soybean is emerging as a
viable cash crop
• No major identifiable trends
• National level statistics disguise important provincial level trends...
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
2002 2003 2005 2006 2007 2008 2012
% of Farmers Cultivating Crop
Cotton
Tobacco
Sunflower
Sesame
Soybean
Source: TIA/IAI

49. Cash crop producers (%)
• Increase in cotton farmers in Niassa and Cabo
Delgado, large fall in Nampula
• Tobacco slightly falling throughout the country
COTTON TOBACCO SUNFLOWER SESAME SOYBEAN
2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12
Niassa 0.6 3.9 15.8 12.4 7.3 2.2 5.7 3.4 0.3 0.2
Cabo Delgado 13.7 17.1 1.1 0.3 0.5 0.2 14.5 15.6 0.0 0.0
Nampula 12.0 6.3 1.8 0.8 0.6 0.1 11.3 10.4 0.1 0.1
Zambezia 3.2 2.0 2.5 2.0 1.8 1.6 2.6 3.8 0.1 0.4
Tete 8.5 7.0 11.7 8.1 0.8 1.1 3.7 5.0 5.7 7.7
Manica 2.2 3.5 3.8 0.6 3.5 3.9 12.9 10.5 0.0 0.2
Sofala 9.4 7.7 1.5 0.4 0.5 0.1 15.5 20.9 0.0 0.0
Inhambane 0.1 0.3 0.4 0.1 0.0 0.2 0.3 0.3 0.0 0.0
Gaza 0.0 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.0 0.0
Maputo 0.0 0.0 1.3 0.2 0.1 0.0 0.3 0.4 0.0 0.0
Total 6.3 5.1 3.4 2.2 1.4 0.9 7.1 7.4 0.5 0.9
Increase > 5 %
Increase 3 ‐ 5%
Decrease 3 ‐ 5 %
Decrease > 5 %
BOLD values show above
10%
Source: TIA/IAI

50. Cash crop production
• Soybean growth enormous between 2002 and 2012 ( >700%); 59% of the total production was in
Tete in 2012, 24% in Zambézia
• Sesame production has more than doubled; 25% of production in Sofala in 2012, 23% in Nampula
• Large fall in tobacco production from 2006 peak; 49% of production in Tete in 2012, 38% in Niassa
• Cotton has remained relatively stable; 27% of production in both Tete and Nampula in 2012
0
100
200
300
400
500
600
700
2002 2003 2005 2006 2007 2008 2012
Index = 100
Cotton
Tobacco
Sunflower
Sesame
Soybean
Index = 100
Source: TIA/IAI

51. Characteristics of productive maize
farmers (2002-12)
• The top 20% productive maize farmers are much more productive than other
maize farmers
• Inverse relationship between maize productivity and the area cultivated (maize
area and total area)
• Farmers who use fertiliser and pesticides are more productive
• Hired workers and cash crops are associated with higher maize productivity
Maize
Productivity
Quintiles
Maize Yield
(Kg/Ha)
Maize Area
(Ha)
Total
Cultivated
Area (Ha)
Fertiliser Use
(%)
Pesticide Use
(%)
Hire Worker?
(%)
Cultivate Cash
Crop? (%)
1 – Lowest
Yield
73 0.8 1.8 1.8 3.3 16.1 42.2
2 272 0.8 1.8 3.1 4.6 19.2 45.6
3 514 0.7 1.7 3.9 6.7 20.6 48.8
4 912 0.6 1.6 5.9 7.0 24.5 51.4
5 – Highest
Yield
2981 0.4 1.4 7.5 7.9 30.3 57.9
Source: TIA/IAI

52. Looking closer at the most productive 20%
• The most productive farmers have generally become less concentrated in Niassa and Nampula,
with Tete and Manica possessing a higher proportion. The share in Maputo province has
increased dramatically.
• The most productive farmers in Tete and Manica use more improved seeds and sell increasing
amounts of maize.
• The total area farmed by the most productive farmers decreased significantly nationwide,
except in Nampula, Zambézia and Sofala.
• The percentage of the most productive farmers selling maize has fallen in most parts of the
country – significantly in Manica, yet increased in Nampula and Inhambane. This implies that
those who do sell maize are selling much more of it.
PROVINCIAL DISTRIBUTION
OF TOP 20% MAIZE FARMERS (%)
IMPROVED SEEDS (%) TOTAL AREA (ha)
FARMER SELLS MAIZE
(%)
MAIZE SOLD (KGS)
2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12
Niassa 32 24 11 7 1.70 1.61 35 34 175 447
Cabo Delgado 19 17 3 8 1.51 1.26 32 30 158 259
Nampula 25 17 10 10 1.17 1.33 34 40 150 474
Zambézia 20 22 11 11 1.39 1.43 52 49 227 442
Tete 22 27 22 26 2.07 1.64 27 21 471 1050
Manica 25 26 19 26 1.68 1.46 59 36 433 1119
Sofala 20 19 11 11 1.54 1.67 39 33 279 504
Inhambane 5 7 9 9 1.02 0.92 9 18 50 311
Gaza 9 13 12 16 1.17 0.93 13 5 85 169
Maputo 14 25 25 14 0.80 0.42 16 11 116 395
Total 20 20 13 15 1.48 1.39 38 33 239 586
Increase > 5 %
Increase 3 ‐ 5%
Decrease 3 ‐ 5 %
Decrease > 5 %
BOLD values show above
average
Source: TIA/IAI

53. Comparing the top 20% with the rest
• In general, the most productive maize farmers produce and sell significantly more maize,
using less land.
• This is particularly apparent in Tete (where higher levels of fertiliser use is found) and in
Manica.
• The difference in the quantity of maize produced did not change much over time, yet the
quantity of maize sold more than doubled, especially in Tete and Manica.
• More farmers with higher maize yields sell their produce and cash crops than others
DIFFERENCES BETWEEN THE TOP 20% PRODUCTIVE MAIZE FARMERS AND THE BOTTOM 80%, OVER TIME
AVERAGE MAIZE PRODUCTION (KGS) AV. MAIZE SOLD (KGS)
FARMER SOLD MAIZE
(%)
SELLS CASH CROP (%) USE FERTILISER (%)
2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12
Niassa + 1233 + 828 + 73 + 238 + 11 + 14 + 6 + 7 + 4 + 3
Cabo Delgado + 489 + 479 + 104 + 124 + 12 + 16 + 1 + 11 0 + 1
Nampula + 327 + 421 + 85 + 316 + 12 + 20 + 6 + 5 + 1 + 4
Zambézia + 511 + 515 + 136 + 278 + 17 + 15 + 9 + 4 + 2 + 1
Tete + 1340 + 1055 + 385 + 730 + 12 + 13 + 14 + 8 + 13 + 11
Manica + 1154 + 1195 + 299 + 851 + 32 + 19 + 7 + 5 + 1 ‐ 1
Sofala + 704 + 813 + 221 + 327 + 21 + 18 + 10 + 12 ‐ 1 + 2
Inhambane + 255 + 404 + 43 + 227 + 6 + 12 + 2 + 1 ‐ 1 + 3
Gaza + 976 + 673 + 73 + 16 + 9 + 2 0 + 1 + 4 + 6
Maputo + 910 + 319 + 97 + 125 + 12 + 8 + 3 + 7 0 + 4
Total + 777 + 744 + 167 + 406 + 18 + 16 + 9 + 7 + 3 + 4
BOLD values
show above
average
differences
Source: TIA/IAI

54. CONCLUDING REMARKS

55. What can we say about jobs?
• Diagnosis and analysis based on existing
quantitative evidence
• Four nationally representative micro-surveys:
 1996/97, 2002/03, 2008/09 living standards household
surveys
 2004/05 labour force survey
• National accounts: value added by sector

56. Informal jobs dominate
25
4
45
25
21
11
46
22
25
7
49
19
28
5
46
21
3
1
51
46
3
2
52
43
4
1
57
37
4
1
53
42
020406080100
96/97 02/03 04/05 08/09 96/97 02/03 04/05 08/09
Urban Rural
Regular wage Irregular wage
Informal / self employed Family worker
%workingpop.

57. Why is the jobs and ag. agendas important?
• Demographic dynamics unavoidable
 c. 300,000 new entrants/year to labour market, many unskilled
• Growth of higher productivity firms (e.g., mining) good
for aggregate macro/fiscal indicators
 BUT will have minimal direct positive effect on poverty reduction
without employment growth
• Raising productivity in household agriculture a powerful
lever for reducing poverty
 BUT not so far transformative in itself on aggregate

58. Strategic policy priorities
• Leverage natural resources to stimulate pro-jobs
structural transformation (an opportunity) -> time to
scale up
• Specifically aim for:
 Employment growth in higher value secondary
and tertiary industries
 Step-increase in agricultural productivity
BOTH commercial and smallholder
agriculture encouraging links between
them
 Export push (outside of natural resources)
• NOT an agenda of neutral or marginal improvements –
decisive transformation required

59. www.wider.unu.edu
Helsinki, Finland