nep-big New Economics Papers
on Big Data
Issue of 2017‒06‒18
four papers chosen by
Tom Coupe'
University of Canterbury
  1. Components of Uncertainty By Vegard Høghaug Larsen
  2. Growth, Nighttime Lights and Power Infrastructure Investment: Evidence from Angola Abstract: An increasing number of papers in the literature use satellite data on nighttime lights as a proxy for economic activities, such as GDP or GDP growth. They implicitly assume that the relationship between GDP and nighttime lights works through the demand side, and there is no constraint on the supply of electricity. This paper first points out a paradox in using this method: the countries for which the method is needed the most, i.e. the countries with poor statistical capacity, are just the countries, for which the assumption of the method is satisfied the least, i.e. the countries with a large power infrastructure deficit. Motivated by this, we collected the data on power infrastructure investment in Angola, a country with a large power infrastructure funding gap. Indeed, we find that in the case of Angola the stable relationship between GDP growth and lights growth assumed in the literature is broken. Instead,increase in lights strongly co-moved with increase in power infrastructure investment. The strong link between lights and investment enables us to develop a new method of quantitatively evaluating value-for-money for infrastructure investments, which directly estimates the cost-effectiveness of transforming investment to welfare, as measured by lights. We estimate the overall cost-effectiveness, and the cost-effectiveness of different financing methods in the case of Angola. By Qi Zhang; James Cust
  3. Estimating the Impact of Crop Diversity on Agricultural Productivity in South Africa By Cecilia Bellora; Élodie Blanc; Jean-Marc Bourgeon; Eric Strobl
  4. The R package emdi for estimating and mapping regionally disaggregated indicators By Kreutzmann, Ann-Kristin; Pannier, Sören; Rojas-Perilla, Natalia; Schmid, Timo; Templ, Matthias; Tzavidis, Nikos
  1. By: Vegard Høghaug Larsen
    Abstract: Uncertainty is acknowledged to be a source of economic fluctuations. But, does the type of uncertainty matter for the economy’s response to an uncertainty shock? This paper offers a novel identification strategy to disentangle different types of uncertainty. It uses machine learning techniques to classify different types of news instead of specifying a set of keywords. It is found that, depending on its source, the effects of uncertainty on macroeconomic variable may differ. I find that both good (expansionary effect) and bad (contractionary effect) types of uncertainty exist
    Keywords: Newspaper, Topic model, Uncertainty, Business cycles, Machine learning
    Date: 2017–04
    URL: http://d.repec.org/n?u=RePEc:bny:wpaper:0053&r=big
  2. By: Qi Zhang; James Cust
    Keywords: procurement, growth accounting, nighttime lights, investment, electricity, infrastructure, value-for-money
    JEL: Q4 O1 H4
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:oxf:oxcrwp:185&r=big
  3. By: Cecilia Bellora; Élodie Blanc; Jean-Marc Bourgeon; Eric Strobl
    Abstract: Crop biodiversity has the potential to enhance resistance to strains due to biotic and abiotic factors and to improve crop production and farm revenues. To investigate the effect of crop biodiversity on crop productivity, we build a probabilistic model based on ecological mechanisms to describe crop survival and productivity according to diversity. From this analytic model, we derive reduced forms that are empirically estimated using detailed field data of South African agriculture combined with satellite derived data. Our results confirm that diversity has a positive and significant impact on crop survival odds. We show the consistency of these results with the underlying ecologic and agricultural mechanisms.
    JEL: Q15 Q57
    Date: 2017–06
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:23496&r=big
  4. By: Kreutzmann, Ann-Kristin; Pannier, Sören; Rojas-Perilla, Natalia; Schmid, Timo; Templ, Matthias; Tzavidis, Nikos
    Abstract: The R package emdi offers a methodological and computational framework for the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for assessing, processing and presenting the results. A range of indicators that includes the mean of the target variable, the quantiles of its distribution and complex, non-linear indicators or customized indicators can be estimated simultaneously using direct estimation and the empirical best predictor (EBP) approach (Molina and Rao 2010). In the application presented in this paper package emdi is used for estimating inequality indicators and the median of the income distributions for small areas in Austria. Because the EBP approach relies on the normality of the mixed model error terms, the user is further assisted by an automatic selection of data-driven transformation parameters. Estimating the uncertainty of small area estimates (using a mean squared error - MSE measure) is achieved by using both parametric bootstrap and semi-parametric wild bootstrap. The additional uncertainty due to the estimation of the transformation parameter is also captured in MSE estimation. The semi-parametric wild bootstrap further protects the user against departures from the assumptions of the mixed model in particular, those of the unit-level error term. The bootstrap schemes are facilitated by computationally effcient code that uses parallel computing. The package supports the users beyond the production of small area estimates. Firstly, tools are provided for exploring the structure of the data and for diagnostic analysis of the model assumptions. Secondly, tools that allow the spatial mapping of the estimates enable the user to create high quality visualizations. Thirdly, results and model summaries can be exported to Excel spreadsheets for further reporting purposes.
    Keywords: offcial statistics,parallel computation,small area estimation,visualization
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:zbw:fubsbe:201715&r=big
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