Poverty and growth impacts of high oil prices: Evidence from Sri Lanka

Naranpanawa, Athula; Bandara, Jayatilleke S.

doi:10.1016/j.enpol.2012.01.065

Cited by 33 publications

(25 citation statements)

References 22 publications

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“…However, some studies take a long-term approach, such as Ojha et al (2013), and others apply multi household CGE models to climate-relevant topics (Naranpanawa and Bandara, 2012;Rausch and Mowers, 2012;Rausch et al, 2011;Yusuf and Resosudarmo, 2015). Ojha et al (2013) modeled nine household types within a recursive dynamic CGE framework to analyze the implications of several different economic growth strategies for India.…”

Section: Examplesmentioning

confidence: 99%

Methods for including income distribution in global CGE models for long-term climate change research

2015

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Section: Examplesmentioning

confidence: 99%

Methods for including income distribution in global CGE models for long-term climate change research

2015

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“…As mentioned, we use Easterly's (2007) wheat-sugar ratio as the cross-sectional variable and interact it with the oil price, which introduces variation in inequality over time. A higher oil price arguably leads to higher inequality numbers because higher oil prices have a disproportionately larger adverse effect on the poor, who spend a larger share of their budget on staple food items and transport, the prices of both of which increase with the oil price (empirical evidence on the oil price-poverty-inequality link stems mostly from country case studies; see, e.g., Naranpanawa and Bandara (2012) for Sri Lanka, or Essama-Nssah et al 2007for South Africa). The estimator then compares the difference in growth in years following a high oil price to years following a low oil price in countries that have a high wheat-to-sugar ratio (low inequality) to countries that have a high wheat-to-sugar ratio (high inequality).…”

Section: Data and Empirical Strategymentioning

confidence: 99%

Re-estimating the relationship between inequality and growth

Scholl

Klasen

2018

Oxford Economic Papers

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In this paper, we revisit the inequality–growth relationship using an enhanced panel data set with improved inequality data. We explicitly take into account the special role of transition (post-Soviet) countries and add an instrumental variable (IV) estimation to add a causal interpretation to our findings. Our analysis is based on the specification used by Forbes in her 2000 paper, but we also address functional form concerns raised by Banerjee and Duflo three years later. We arrive at three main findings: First, the significant positive association between inequality and economic growth in the full sample is entirely driven by transition countries. Second, this relationship in transition countries is not robust to the inclusion of separate time effects. Lastly, it appears that this association is not causal but rather driven by the particular timing of the transition. Results from IV estimation confirm our interpretation of the observed positive relationship in the overall sample as non-causal.

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“…In the realm of climate mitigation, many national studies assess the distributional impacts of mitigation using general equilibrium approaches, mostly for the US and Europe [31][32][33][34][35][36][37][38][39][40][41][42] , though increasingly also for developing countries 32,[43][44][45][46][47][48] . Methodologically, the literature reveals a variety of stages towards including distributional impacts on households.…”

Section: State Of the Artmentioning

confidence: 99%