Unidad central de proceso o proceso maestro

This essay sought to address the question of why PNG’s remote villages are disadvantaged and to identify the characteristics of the poorest in the villages, including the links to food insecurity. This has important implications for the estimated one million people who may be living in similar circumstances across PNG.

The geospatial influences on village level assets were examined and it was found that the isolation of the study area is a major determinant of village level poverty. This is consistent with the limited international research on remote rural poverty (Bird et al. 2003) and a range of PNG specific poverty research (Cammack 2007; Allen et al. 2009). In the case of the study area, isolation impedes the development of markets for labour, food, and financial services. Isolation also directly reduces access to and quality of health and education services.

65

The topography of the villages and the surrounding area both contributes to and exacerbates the impact of the isolation. To travel to the nearest road villagers must walk over mountainous terrain for two days. This limits their capacity of villagers to transport their coffee crop to market or to bring in supplies. The altitude of the study area also places it in the malaria zone. This is reflected in the statistics from the health centre records and reports of ill-health from the household survey. Frequent ill-health, in the context of households that engage in subsistence farming, impacts directly on food supply.

The distribution of asset poverty within the study area was examined along with the household qualities that influence asset levels. The asset ownership index calculations showed that coffee garden ownership was generally associated with but is no guarantee of higher asset wealth across the board. This is likely to be a specific finding for the study area that relates directly to its isolation. Even though coffee is the dominant cash income source for households, the distance to markets and irregular access to air transport means that owning large coffee gardens is not a guarantee of commensurate cash income and the financial resources to acquire additional assets.

Livestock was mostly owned by those with higher levels of assets. Chicken ownership, in particular, was skewed towards households with other assets. Pig ownership, on the other hand, was less associated with owning other assets. This reflects the multi-purpose nature of pigs in the study area. It was possible to own several pigs in the village but still be asset poor in other respects. This finding is relevant for future use of asset based poverty measures in Papua New Guinea.

The regression analysis of asset ownership and a range of poverty correlates showed that, in the context of the study area, the households with more assets were located at a lower elevation. It seems likely that land quality decreased as households spread up the mountains onto steeper ground.

In the context of a group of villages where village level assets are low, the ordering of the asset ranking for households could help identify those most vulnerable to food insecurity or malnutrition. The results of the regressions of different food security measures showed that higher asset ownership was not associated with higher food

66

variety. In this context, higher food variety may not be closely related to better welfare, particularly if no meat is consumed.

In a situation where meat-based protein deficiency is reported to be common, higher meat consumption is a significant advantage for a household. The regression analysis showed that households with more assets had greater meat consumption than those with low asset levels. This has significant implications for welfare. Activities such as hunting appear to only partially offset the reduced meat-based protein consumption associated with lower asset levels. This suggests that a traditional lifestyle involving hunting and subsistence agriculture, at least in this location, cannot currently meet nutritional needs. Also, in the absence of markets, even those households with cash income may struggle to meet their requirements for meat-based protein.

This essay adds to poverty research in PNG by being the first village level study to examine geospatial influences and village level influences on poverty together with the link to food insecurity. It finds that geospatial influences disadvantage the study area as a whole, and those households at a higher elevation in particular. It also finds that the study area as a whole is food-insecure, and that those with fewer assets are more food insecure.

67

Appendix: Chapter 2

Table A2.1: Summary of main variables and data sources used in poverty analysis in PNG

Wilson (1975) De Albuquerque and D’Sa (1986) Hanson et al. (2001) Gibson et al. (2004) National Economic and Fiscal Commission (NEFC) District Development Index (2004)

Small holder cash crop production by sub- district

(government estimates for 1969-70 )

Population density

(Census 1980 and Provincial Data System 1978-79)

Land potential

(Mapping Agricultural Systems of PNG Project (MASP), and PNG Resource Information System database (PNGRIS ))

Size of dwelling

(PNG Household Survey 1996)

Income levels (based on World Bank estimates of share of people in district who are poor – see Gibson et al., 2004) Hospital beds per 1000

(Administrative data)

Sex ratios (Census 1980)

Agriculture pressure ( MASP)

Age of household members (PNG Household Survey 1996)

Health standards - life expectancy (Census 2000)

Enrolments in primary and secondary schools per 1000

(Administrative and Mission data)

Dependency ratios (Census 1980)

Access to services (MASP)

Schooling of household head. (PNG Household Survey 1996)

Education standards - literacy and school attendance

(Census 2000) Administration staff per 1000

(Administrative data)

Urbanisation

(Census 1980, paper by Boutaua)

Income from agriculture (MASP)

Main source of income (PNG Household Survey 1996) Accessibility to district headquarters

(Index constructed)

Internal Migration (Census 1980)

Child Malnutrition

(based on 1982-83 National Nutrition Survey (NNS))

Geographic and climatic features (PNGRIS database)

Level of local services (Administrative data)

Employment (Census 1980)

Population (estimates for 2000 based on 1980 and 1990 census data)

Agriculture system remote from services (MASP)

Education status (Census 1980)

% of wages as main income source in Local Level Government (LLG)

(Census 2000) Health

(Townsend and Nou Taboro b₎

% of betelnut as income source in LLG (Census 2000)

Accessibility

(Provincial Data System)

Notes: a _{Boutau, G et al. 1983 ‘1980 national population census: district level analysis’ Department of Geography and Demography, University of Papua New Guinea.} b_{Townsend, P and Nou-Taboro, O 1984 ‘Mapping materal and child health coverage in Papua New Guinea’ PNG Institute of Applied Social and Economic Research.}

68

Table A 2.2: Most disadvantaged districts

District Wilson (1974) De Albuquerque and D’Sa (1986) Hanson et al. (2001) Gibson et al. (2004) NEFC District Development Index (2004) Middle Ramu (Madang) X X X X X Rai Coast (Madang) X X X X X Telefomin (Sandaun) X X X X X Vanimo-Green River (Sandaun) X X X X X Aitape-Lumi (Sandaun) X X X X Koroba-Lake Kopiago (Southern Highlands) X X X X Jimi (Western Highlands) X X X X X Oburu- Wonenara (Eastern Highlands) X X X Goilala (Central) X X X X X Ambunti- Dreikikir (East Sepik) X X X X

Sources: de Albuquerque and D’Sa (1986) (pp29-32), Gibson et al. 2004 (p.43), Hanson et al. 2001 (p.310)

69

Table A2.3: Descriptive statistics model of low asset ownership

Descriptive statistics: probit model of low asset ownership

Poor Less Poor

Independent Variables Mean S.D Mean S.D

Elevation (above 1131m) 0.63 0.49 0.43 0.50

Membership of organisation/s 0.22 0.42 0.40 0.50

Household head literate 0.59 0.49 0.85 0.36

Female ownership of assets 0.09 0.30 0.27 0.45

n=79

70

Table A2.4: Probit model for food variety - using variables from asset poverty model

Probit model for food variety (using variables from asset model)

Coefficient p-value 90 per cent confidence interval

Elevation (above 1131m) 0.238 0.395 -0.22 0.7 (0.24)a Membership of organisation/s 0.109 0.743 -0.44 0.66 (0.33)

Household head literate 0.193 0.572 -0.37 0.75

(0.34)

Female ownership of assets -0.317 0.357 -0.88 0.25

(0.34) Constant -0.055 0.856 -0.55 0.44 (0.39) LR chi2 (4) =2.20 Prob>chi2=0.699 Log likelihood=-55.70

Standard errors in brackets.*, **, *** indicates 10, 5 and 1 per cent levels of significance respectively. Source: Author’s calculations.

71

Table A2.5: Probit model for low meat consumption - using variables from asset poverty model

Probit model for low meat (using variables from asset model)

Coefficient p-value 90 per cent confidence interval

Elevation (above 1131m) 0.118 0.675 -0.34 0.58 (0.28) Membership of organisation/s -0.024 0.943 -0.57 0.52 (0.33)

Household head literate -0.212 0.541 -0.78 0.35

(0.35)

Female ownership of assets -0.696 0.047** -1.27 -0.12

(0.35) Constant 0.413 0.179 -0.09 0.92 (0.31) LR chi2 (4) =4.65 Prob>chi2=0.325 Log likelihood=-54.47

Standard errors in brackets.*, **, *** indicates 10, 5 and 1 per cent levels of significance respectively. Source: Author’s calculations.

72

Chapter 3 Poverty, risks and coping strategies in