WORKSHOP Linkages of Agriculture, Nutrition and Economic Development Co-Organized by IFPRI, UPNG, INA, Australian National University, and Department of Foreign Affairs and Trade JUN 12, 2019 - 09:00 AM TO 12:30 PM +10

1. Insights on small business income generation and associated
dietary improvements in rural PNG
Gracie Rosenbach and Emily Schmidt
International Food Policy Research Institute
June 12, 2019
Holiday Inn Hotel
Port Moresby, PNG

2. Small businesses/non-farm enterprises in Papua New Guinea
• PNG’s Agenda 2030, following Sustainable
Development Goals 8.3 and 10.2, highlights the
importance of the informal economy
• In PNG, the informal economy used to largely be
comprised of women, but men are becoming more
involved (Audit of the Informal Economy, 2018)
• Income from the informal economy is largely
spent on education expenses, by both men and
women (although more so by women) (Audit of
the Informal Economy, 2018)

3. Importance of non-farm enterprises (NFEs)
• Nonfarm enterprises improve household
wellbeing (as measured by total
expenditures and food security) in Nigeria
(Shehu and Sidique, 2014)
• Nonfarm enterprises mainly serve to
diversify risk throughout Africa (Nagler and
Naudé, 2014)
• These findings are similar to our results in
rural PNG

4. More than one-third of surveyed households have an NFE
NFEs are most common in non-poor households, and households in Middle Ramu
(Madang) and Nuku (West Sepik)
66%
50%
59%
53%
61%
56%
57%
27%
40%
38%
41%
27%
31%
34%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Poor households
Non-poor households
West Sepik
Madang
East Sepik
ARoB
Household
povertystatusProvince
All
HHS
Own farm only Own farm + wage labor Own farm + NFE Own farm, wage labor, & NFE

5. NFEs vary by area, but non-agricultural trade is important in all
areas
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Province
ARoB 44% 3% 40% 3% 4% 6% 120
East Sepik 5% 11% 58% 12% 7% 8% 85
Madang 1% 0% 89% 0% 9% 1% 170
West Sepik 12% 14% 59% 3% 5% 7% 105

6. NFEs vary by area, but non-agricultural trade is important in all
areas
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Province
ARoB 44% 3% 40% 3% 4% 6% 120
East Sepik 5% 11% 58% 12% 7% 8% 85
Madang 1% 0% 89% 0% 9% 1% 170
West Sepik 12% 14% 59% 3% 5% 7% 105
• Non-agricultural trade is especially important in Middle Ramu (Madang)

7. NFEs vary by area, but non-agricultural trade is important in all
areas
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Province
ARoB 44% 3% 40% 3% 4% 6% 120
East Sepik 5% 11% 58% 12% 7% 8% 85
Madang 1% 0% 89% 0% 9% 1% 170
West Sepik 12% 14% 59% 3% 5% 7% 105
• Non-agricultural trade is especially important in Middle Ramu (Madang)
• Buin (ARoB) is the only area where non-agricultural trade is not the dominant type
of enterprise (mainly due to the high prevalence of cocoa trade)

8. NFEs also vary by the gender of the owner
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Ownership
Male 7% 4% 67% 1% 11% 9% 148
Female 13% 10% 68% 9% 0% 1% 120
Joint 22% 4% 62% 2% 7% 3% 212
• Non-agricultural trade remains the most common type of enterprise, and there is a
similar prevalence of non-agricultural trade across all owner genders

9. Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Ownership
Male 7% 4% 67% 1% 11% 9% 148
Female 13% 10% 68% 9% 0% 1% 120
Joint 22% 4% 62% 2% 7% 3% 212
NFEs also vary by the gender of the owner
• Non-agricultural trade remains the most common type of enterprise, and there is a
similar prevalence of non-agricultural trade across all owner genders
• Male owned enterprises (31% of the sample) are the most likely to be transport and
other services

10. NFEs also vary by the gender of the owner
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Ownership
Male 7% 4% 67% 1% 11% 9% 148
Female 13% 10% 68% 9% 0% 1% 120
Joint 22% 4% 62% 2% 7% 3% 212
• Non-agricultural trade remains the most common type of enterprise, and there is a
similar prevalence of non-agricultural trade across all owner genders
• Male owned enterprises (31% of the sample) are the most likely to be transport and
other services
• Female owned (25%) are the most likely to be handicrafts and grain

11. NFEs also vary by the gender of the owner
Agriculture
trade (excl.
grain)
Grain
milling/trade
Non-agriculture
trade
Making
handicrafts
Transport
services
Other
services N
All NFEs 15% 6% 65% 4% 6% 5% 480
Ownership
Male 7% 4% 67% 1% 11% 9% 148
Female 13% 10% 68% 9% 0% 1% 120
Joint 22% 4% 62% 2% 7% 3% 212
• Non-agricultural trade remains the most common type of enterprise, and there is a
similar prevalence of non-agricultural trade across all owner genders
• Male owned enterprises (31% of the sample) are the most likely to be transport and
other services
• Female owned (25%) are the most likely to be handicrafts and grain
• Joint owned (44%) are the most likely to be other agricultural trade

12. NFE income varies considerably across households and areas
• NFEs in Buin and
Middle Ramu have
the highest incomes,
while NFEs in Maprik
have the least
• Female owned NFEs
receive notably less
income than male or
joint owned NFEs0
200
400
600
800
1000
1200
All NFEs ARoB East
Sepik
Madang West
Sepik
Male
owned
Female
owned
Joint
owned
PNGKina(PGK)
Median nonfarm enterprise annual income (PGK)

13. How do households with NFEs compare to those without?
• Challenges to evaluating the impact of non-farm
enterprises on overall household welfare
• Probable that having a non-farm enterprise is not
random
• For example, households that have small
businesses may have higher incomes to begin
with

14. Evaluating the impact of non-farm enterprises on household welfare
• Need to identify a suitable comparison group:
What would households be like had they not
invested in a non-farm enterprise?
• Use propensity score matching technique to
compare similar groups

15. Evaluating the impact of non-farm enterprises on household welfare
• Need to identify a suitable comparison group:
What would households be like had they not
invested in a non-farm enterprise?
• Use propensity score matching technique to
compare similar groups
• Match household characteristics based on
observable observations, for example:
• Household size and composition (number
of working individuals)
• Having experienced a climate shock

16. Households with NFEs consume more protein and kilocalories
Difference Significance
Protein (g) per capita per
day 12.4 ***
Kilocalories per capita per
day 266 ***
Household Dietary
Diversity Score 0.40 *
Food expenditure per
capita per year (000s kina) 0.25 **
Total expenditure per
capita per year (000s kina) 0.35 *
Total Livestock Unit -0.004
• Compared to HHs without NFEs,
HHs with NFEs (when holding
other factors constant):
• Consume 12.4g more protein
per person per day
Note: Households were matched on number of working age males, asset and housing indexes,
locations, and whether the household experienced a drought.

17. Households with NFEs consume more protein and kilocalories
Difference Significance
Protein (g) per capita per
day 12.4 ***
Kilocalories per capita per
day 266 ***
Household Dietary
Diversity Score 0.40 *
Food expenditure per
capita per year (000s kina) 0.25 **
Total expenditure per
capita per year (000s kina) 0.35 *
Total Livestock Unit -0.004
Note: Households were matched on number of working age males, asset and housing indexes,
locations, and whether the household experienced a drought.
• Compared to HHs without NFEs,
HHs with NFEs (when holding
other factors constant):
• Consume more protein per
person per day
• Consume more kilocalories per
person per day
• Consume a more diversified
diet

18. Households with NFEs consume more protein and kilocalories
Difference Significance
Protein (g) per capita per
day 12.4 ***
Kilocalories per capita per
day 266 ***
Household Dietary
Diversity Score 0.40 *
Food expenditure per
capita per year (000s kina) 0.25 **
Total expenditure per
capita per year (000s kina) 0.35 *
Total Livestock Unit -0.004
Note: Households were matched on number of working age males, asset and housing indexes,
locations, and whether the household experienced a drought.
• Compared to HHs without NFEs,
HHs with NFEs (when holding
other factors constant):
• Consume more protein per
person per day
• Consume more kilocalories per
person per day
• Consume a more diversified
diet
• Have higher food and total
expenditures

19. Households with NFEs consume more protein and kilocalories
Difference Significance
Protein (g) per capita per
day 12.4 ***
Kilocalories per capita per
day 266 ***
Household Dietary
Diversity Score 0.40 *
Food expenditure per
capita per year (000s kina) 0.25 **
Total expenditure per
capita per year (000s kina) 0.35 *
Total Livestock Unit -0.004
Note: Households were matched on number of working age males, asset and housing indexes,
locations, and whether the household experienced a drought.
• Compared to HHs without NFEs,
HHs with NFEs (when holding
other factors constant):
• Consume more protein per
person per day
• Consume more kilocalories per
person per day
• Consume a more diversified
diet
• Have higher food and total
expenditures
• There is no difference in livestock
ownership

20. What factors are associated with women or men starting an NFE?
• Availability of income – households with more
income have the ability to start an NFE
• Asset index
• Housing quality index

21. What factors are associated with women or men starting an NFE?
• Availability of income – households with more
income have the ability to start an NFE
• Asset index
• Housing quality index
• Availability of labor – households with more
working aged members have surplus labor to
devote to an NFE in addition to the farm
• Working age males and females

22. What factors are associated with women or men starting an NFE?
• Availability of income – households with more
income have the ability to start an NFE
• Asset index
• Housing quality index
• Availability of labor – households with more
working aged members have surplus labor to
devote to an NFE in addition to the farm
• Working age males and females
• Risk diversification strategy – households that
experienced a shock or perceive that a shock is
likely to occur may diversify their income sources
to protect against risks
• Experienced a drought in the past 5 years

23. Female-owned NFEs are likely started for risk diversification
Male owned Female owned Joint owned
Asset Index + +
Housing Quality Index + +
Working aged male household members + -- +
Working aged female household members + +
Experienced a drought in past 5 years +
Matrilineal community + +
Note: The plus and minus signs represent increased or decreased probability of having that type of enterprise,
compared to having no enterprise

24. Female-owned NFEs are likely started for risk diversification
Male owned Female owned Joint owned
Asset Index + +
Housing Quality Index + +
Working aged male household members + -- +
Working aged female household members + +
Experienced a drought in past 5 years +
Matrilineal community + +
Note: The plus and minus signs represent increased or decreased probability of having that type of enterprise,
compared to having no enterprise
• Having a higher income increases the probability of having a male or joint owned NFE

25. Female-owned NFEs are likely started for risk diversification
Male owned Female owned Joint owned
Asset Index + +
Housing Quality Index + +
Working aged male household members + -- +
Working aged female household members + +
Experienced a drought in past 5 years +
Matrilineal community + +
Note: The plus and minus signs represent increased or decreased probability of having that type of enterprise,
compared to having no enterprise
• Having a higher income increases the probability of having a male or joint owned NFE
• Additional labor increases the probability of having an NFE

26. Female-owned NFEs are likely started for risk diversification
Male owned Female owned Joint owned
Asset Index + +
Housing Quality Index + +
Working aged male household members + -- +
Working aged female household members + +
Experienced a drought in past 5 years +
Matrilineal community + +
Note: The plus and minus signs represent increased or decreased probability of having that type of enterprise,
compared to having no enterprise
• Having a higher income increases the probability of having a male or joint owned NFE
• Additional labor increases the probability of having an NFE
• Experiencing a shock or perceiving the possibility of a shock occurring increases the
probability of having a female owned enterprise

27. Conclusions and takeaways
• Households with NFEs:
• Have improved wellbeing – increased incomes and better consumption
outcomes
• Have additional household labor to divide between garden and NFE
• Utilize the NFEs as an income and risk diversification strategy for climate
resilience

28. Conclusions and takeaways
• Households with NFEs:
• Have improved wellbeing – increased incomes and better consumption
outcomes
• Have additional household labor to divide between garden and NFE
• Utilize the NFEs as an income and risk diversification strategy for climate
resilience
• Key takeaways:
• Female owned enterprises are crucial for food security, but are less profitable
• Promoting small businesses through credit schemes could benefit the rural
population

29. Thank You
Contact information:
Gracie Rosenbach
Research Analyst, IFPRI
g.rosenbach@cgiar.org
Emily Schmidt
Research Fellow, IFPRI
e.schmidt@cgiar.org

30. References
• Nagler, P, and W. Naudé. 2014. Non-farm entrepreneurship in rural Africa: Patterns and
determinants. IZA DP No. 8008.
• National Audit of the Informal Economy. 2018.
• Shehu, A., and S. F. Sidique. 2014. A propensity score matching analysis of the impact of
participation in non-farm enterprise activities on household wellbeing in rural Nigeria. UMK Procedia
1(2014): 26-32.