The Impact of Road Development on Household Welfare in Rural Papua New Guinea

Abstract

In this paper we evaluate the impact of road development on household welfare in rural Papua New Guinea (PNG) between 1996 and 2010, using two geocoded cross-sectional national household surveys and corresponding road maps. We make use of time-variation in road surface type and condition as recorded in PNG’s National Road Asset Management System, focusing on routes that connect rural households to urban areas. To tackle endogenous placement of road infrastructure programs, we employ a correlated random effects model that controls for the location-specific average road quality over the period of analysis. We also use a newly developed generalised quantile regression method to investigate whether road works favour the poor. Our estimates show that better roads to nearest towns lead to higher consumption levels and housing quality, and to less reliance on subsistence farming. The effects are stronger among poor and remote households.

Keywords:

• Rural roads
• infrastructure
• household welfare
• social issues
• poverty
• Papua New Guinea

Acknowledgements

The authors wish to thank John Gibson for his assistance with the survey data and his useful comments during early phases of the research. In addition, we thank Bryant Allen for providing access to the PNGRIS database.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors commit to make the data and do-files available on request.

Notes

1 Since the PNGHS 96 used sampling based on census units from the census of 1990, on which data is unavailable, we first had to recode the 1990 census units. For this, we relied on the census unit names listed in Gibson and Rozelle (1998) as well as the generous help of staff at the National Statistical Office of PNG. The HIES 09/10 was sampled from the census of 2000, which made locating the census units straightforward.

2 The two road datasets offer slightly different spatial representations of common road segments, with positional differences ranging up to several hundred metres. To ensure that our analysis is not influenced by differences in the coverage and spatial representation of the road network across the two years, we use the representation of the 2009 map for both maps. That means that we have to include the information on surface type and road condition of 2000 in the 2009 road map. We match roads based on road section IDs, and where those are missing, on spatial proximity. It should also be mentioned that we cannot make use of the most detailed survey of the national road network to date, the Comprehensive Visual Road Condition Survey, which was collected in 2014 by the Papua New Guinea-Australia Transport Sector Support Program (TSSP) together with the DoW. Due to the sudden heavy rise in national road investments starting in 2011, we believe that the conditions in this new survey do not adequately reflect the conditions around the time the HIES 09/10 was conducted.

3 Details on the construction of this variable, as well as a discussion of remote sensing as an alternative to identify road quality and surface type, can be found in the appendix of Edmonds, Wiegand, Koomen, Pradhan, and Andrée (2018).

4 The latter criterion leads to the exclusion of three settlements that can only be reached by water. All towns with more than 1000 inhabitants in 2011 were already towns in 2000.

5 We construct per capita expenditure as well as regional poverty lines as explained in Gibson and Rozelle (1998) and Gibson (2012). Particularly, we use the revised consumption figures, poverty lines, and sampling weights for the PNGHS 96 explained in Gibson (2012) to make expenditure and poverty comparable between the two surveys. For the HIES 09/10, Gibson (2012) suggests three different consumption figures. Due to evidence of diary fatigue, we use the figure based on the shortest time horizon (7 days). The poverty lines we use take the cost of a locally consumed food basket and add the non-food spending of households whose food expenditures exactly meet this cost (Lanjouw & Lanjouw, 2001).

6 Like Gibson and Rozelle (2003), we assign children aged between 0 and 6 years a weight of 0.5, while children older than 6 years as well as adults are assigned a weight of 1.

7 The definitions vary slightly between survey rounds. For the PNGHS 96, the variable indicates that in the two weeks prior to the survey, at least one household member engaged in the production of sago, bananas, corn, sweet potato, cassava, taro, or other fresh fruits or vegetables without selling them. For the HIES 09/10, the variable indicates that in the week prior to the survey, at least one household member engaged in agricultural production for own consumption. The means of both variables are very close, as shown in Table 1.

8 Measures of housing quality were left out in the construction of the consumption figures (Gibson, 2012), so are not part of the first two outcome variables.

9 The sampling weights used in the regressions of consumption and poverty status are person-specific, those in the regressions of schooling are children-specific, and those in the other regression are household-specific.

10 We take the population size for 1996 from the 2000 census, and the population size for 2009/10 from the 2011 census.

Additional information

Funding

This work was supported by the Asian Development Bank [Regional project 46185 ‘Developing Impact Evaluation Methodologies, Approaches, and Capacities in Selected Developing Member Countries’, subproject 2].