Evaluation of road access impacts on poverty and other social welfare indicators in rural Highlands communities of Papua New Guinea

Transcription

1 September 2014 Evaluation of road access impacts on poverty and other social welfare indicators in rural Highlands communities of Papua New Guinea Aaron Batten Economist, Papua New Guinea Resident Mission, Pacific Department, Asian Development Bank (ADB) Christopher Edmonds Senior Economist and Co-Chair ADB Interdepartmental Impact Evaluation Committee, Office of the Director General, Pacific Department, Manila, ADB John Gibson Professor, Department of Economics, University of Waikato, Waikato, New Zealand

2 Presentation outline Overview ADB projects being studied Explain why the evaluation is important & appears feasible using the proposed approach Review earlier work that provides the foundation for the proposed evaluation Guides methodology and provides confidence about prospects that proposed evaluation can obtain useful findings Explain plans for the follow up study Discuss alternative approaches to evaluating road socioeconomic impacts

3 Highlands Region Road Improvement and Investment Program The ADB financed Highlands Region Road Improvement and Investment Program upgrades the core road network in the PNG Highlands. By completion, the Program plans to develop 2,500 kilometers of major national and provincial roads carrying the bulk of the traffic in the highlands region of PNG.

4 Highlands Region Road Improvement and Investment Program The investment program includes: (i) projects to improve about 1,400 km of the road network funded through four tranches under a multi-tranche financing facility and (ii) design and supervise road improvement works, preparation and administration of long-term road maintenance contracts for the entire 2,500 km of the road network, and while building capacity of road involved agencies.

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6 Rationale for the study The Highlands region is a major contributor to the PNG economy through its agricultural and mineral exports. It is home to 40% of the country's population and more than 50% of the country s poor. This population relies extensively on the road network for movement of people and goods.

7 Rationale for the study An earlier study by Gibson and Rozelle (2003) found that communities facing travel times of more than 60 minutes to the nearest roads have double the poverty incidence of those immediately adjacent to roads after adjusting for other income determinants. On average, the poorest highland communities in PNG must spend 75% more time than non-poor communities for travel to the closest mode of motorized transportation.

8 Transport times from the 2003 study Source: Gibson, J. and Rozelle, S. (2003). Poverty and Access to Roads in Papua New Guinea, Economic Development and Cultural Change, Vol. 52, No. 1 (October), pp

9 and poor access appears to be strongly related to poverty Source: Gibson, J., Datt, G., Allen, B., Hwang, V., Bourke, R.M., & Parajuli, D. (2005). Mapping Poverty in Rural Papua New Guinea, Pacific Economic Bulletin, 20(1),

10 and the number of poor in the Highlands is rising. Estimated number of people below upper poverty line, ,400,000 1,200,000 Up by 100,000 since ,000, , , , ,000 0 NCD Papuan Highlands Momase Islands Source: Gibson, J., 2014, Initial poverty estimates from the PNG 2009/10 Household Income and Expenditure Survey, Unpublished.

11 Gibson and Rozelle (2003) Estimation model -- Consumption and Poverty Estimates used data from the 1995/96 PNG Household Survey OLS or IV regression models of consumption need fewer assumptions than probit models of poverty status Can predict probability that each household is poor using the equations: ln ci z xib ui prob ln cˆ ˆ i z 0 x i b ˆ Estimates also yield predicted poverty gap and severity Required steps 1. Run the regression model 2. Use the estimated parameters and apply them to data with simulated interventions (e.g. changing access to infrastructure) 3. Mean of predicted poverty rates by household gives average headcount rate

12 Regression results Table 7. Estimates of the Model of Log Welfare Ratio for Rural Households (N=830). Roads Exogenous Roads Endogenous (1) (2) (3) (4) Demographics Household size (3.45)** (3.40)** (3.08)** (0.58) Household size, squared (3.30)** (3.32)** (2.65)* (0.95) Number below age (2.45)* (2.57)* (2.41)* (0.47) Number above age (0.24) (0.15) (0.37) (0.27) Age of household head (years) (0.87) (0.92) (0.93) (0.33) Squared age of household head (0.77) (0.75) (0.69) (0.58) Dummy: Female-headed household (0.94) (0.96) (1.50) (0.71) Education, employment & occupation Dummy: Household head is literate (3.66)** (3.53)** (3.58)** (1.14) Average years of schooling of adults (1.68)+ (1.65) (0.58) (0.72) Dummy: Head s income from minor sources (2.99)** (3.10)** (3.12)** (0.41) Dummy: Head is tree crop farmer (1.26) (1.24) (0.81) (0.76) Dummy: Head is in formal sector (3.61)** (3.60)** (4.32)** (1.05) % of adults with no cash income sources (0.80) (0.45) (0.83) (1.51)

13 Assets Dummy: Owns agricultural capital goods (4.23)** (4.31)** (4.21)** (0.45) Number of pigs owned (1.55) (1.48) (1.26) (1.24) Community characteristics Traveling time to nearest road (hours) a (3.20)** (3.58)** (2.19)* (2.18)* Index of market development (0.02) (0.06) (0.84) (0.28) Traveling time (Govt. Station minus road ROAD-STATION) (1.34) Geo-climatic variables Elevation ( 000 metres) (1.71)+ (1.55) (1.90)+ (2.27)* Dummy: Slope > 10 degrees (5.58)** (5.73)** (5.55)** (3.14)** Dummy: land inundation occurs (1.79)+ (1.96)+ (1.94)+ (1.32) Dummy: Rainfall deficit is rare (3.35)** (3.35)** (3.29)** (1.29) Annual rainfall ( 000 mm) (3.44)** (3.45)** (3.43)** (2.10)* Intercept (5.06)** (5.25)** (5.62)** (3.32)** Poverty line used to deflate consumption: Regional Regional Regional Community R Standard error of disturbances ( ) b Zero-slopes F-test c 10.90** 10.77** 11.77** 1.73+

14 Issues in Regression Modelling the Effect of Interventions on Poverty Are the variables used causes or results of poverty Better access to infrastructure might reduce poverty but infrastructure could be endogenous Perhaps roads are built in rich areas not poor areas because rich areas have more commerce to make the road economically viable Estimated relationship between roads and poverty could be biased Are there excluded variables that cause both poverty and the independent variables in the model bad environments might cause poverty and also cause poor infrastructure Roads are hard to build in wet/steep areas and such environments also may have lots of poverty but because of the bad environment rather than the lack of roads Modelling poverty requires careful attention to exogeneity and excluded heterogeneity Look for instrumental variables for the effects of interest

15 Instrumental variable used To control for that fact that poverty could influence road construction, an IV needed Need a variable that explains roads, but does not explain consumption (poverty) except through its impact on roads. Variable used is the year in which the PNG national highway system penetrated into each of PNG s districts. Having a national highway enter a district should stimulate the building of feeder roads, thus reduce the traveling time to the nearest road. Those districts that have had a national highway for a longer time are likely to have a larger network of feeder roads and households in those areas should face shorter travel times

16 Simulation results from 2003 study Table 8. Simulated Effect of Certain Changes on Rural Poverty in Papua New Guinea in Headcount Poverty gap Poverty severity (Percent change from baseline predicted values) Baseline: Actual values Baseline: Predicted values Increase literacy rate of household heads to 100 percent (-16.52) 8.85 (-21.48) 4.05 (-24.79) Increase household average school years per adult by one year (-3.04) (-3.89) 5.15 (-4.48) Decrease traveling time to road to 3 hours for communities where currently > 3 hours (-5.36) (-8.19) 4.83 (-10.27) Decrease traveling time to road to 2 hours for communities where currently > 2 hours (-5.77) (-8.74) 4.80 (-10.90) Note: Each simulated change is considered in isolation of the other changes. The model used to predict poverty is reported in Table 7, column (1). The percent change from base is calculated from the predicted baseline values.

17 Planned study The study will update the results of the 2003 study, using data from a the recently released national Household Income and Expenditure Survey (HIES) conducted in 2009/10. This latest HIES will be combined with data from the 1996 HIES to estimate changes in the incidence of poverty across PNG over time.

18 Planned study Complementing the information from the HIES, newly available geo-referencing techniques to more accurately identify proximity to roads along with a database of luminosity in rural PNG between 1992 and 2012 that will be used to proxy changes in rural wealth. Many details remain to be worked out, but confident that research will yield useful findings for several reasons.

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20 At first glance, night time vision of PNG does not reveal much PNG

21 but a closer look reveals some important growth centers across the country Port Moresby

22 as noted earlier focus of study will be on Highlands region as this is where majority of poverty is and where ADB has worked for more than 10 years

23 aiming to track luminosity across treatment vs. nontreatment roads between 2003 Luminosity of area covered by ADB road projects in 2003

24 and Luminosity of area covered by ADB road projects in 2012

25 Learning from evaluation experience Reliance on secondary data with regional coverage Characteristics of the ADB project facilitate this Challenges of attempting to conduct original survey data collection for a quasi-experimental study of road impacts Use of instrumental variables to control of possible causality of poverty on roads Extensive set of other factors controlled for in the multivariate regression

26 References Gibson, J. and Rozelle, S. (2003). Poverty and Access to Roads in Papua New Guinea, Economic Development and Cultural Change, Vol. 52, No. 1 (October), pp Gibson, J., Datt, G., Allen, B., Hwang, V., Bourke, R.M., & Parajuli, D. (2005). Mapping Poverty in Rural Papua New Guinea, Pacific Economic Bulletin, 20(1),

27 Thank you Pacific Department Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines Tel