@article{KozickaWeberKalkuhl2019,
  author    = {Kozicka, Marta and Weber, Regine and Kalkuhl, Matthias},
  title     = {Cash vs. in-kind transfers},
  series = {Food Security},
  volume    = {11},
  journal   = {Food Security},
  number    = {4},
  publisher = {Springer},
  address   = {New York},
  issn      = {1876-4517},
  doi       = {10.1007/s12571-019-00942-x},
  pages     = {915 -- 927},
  year      = {2019},
  abstract  = {Historically, India has relied on subsidizing staple food as a major instrument in improving food security. Recently, however, cash transfers have entered the debate as an alternative, as they are associated with lower market distortions, leakages and fiscal costs. This study contributes to this debate by analyzing India's Targeted Public Distribution System (TPDS). Our main objective was to explain the under-purchase, or low take-up, from the TPDS, which is typically attributed to 'leakage', i.e. the diversion of food grains from eligible consumers. We provide an alternative solution based on self-targeting; while poorer households increase their consumption from the TPDS, wealthier households restrain from consuming subsidized commodities. Using a large household dataset, we estimated that such a voluntary opt-out system, based on income, would save a minimum of 6.5\% of grains released through the TPDS. Besides these demand-driven aspects, our analysis indicates that poor regions perform better at lowering the diversion of grains and that large targeting errors exist among female-led households. Finally, we find substantial regional price differences that would benefit the poor and rural population under a uniform cash-transfer system that does not correct for regional price levels.},
  language  = {en}
}
@article{MaheswaranAgarwalSivakumaretal.2019,
  author    = {Maheswaran, Rathinasamy and Agarwal, Ankit and Sivakumar, Bellie and Marwan, Norbert and Kurths, J{\"u}rgen},
  title     = {Wavelet analysis of precipitation extremes over India and teleconnections to climate indices},
  series = {Stochastic Environmental Research and Risk Assessment},
  volume    = {33},
  journal   = {Stochastic Environmental Research and Risk Assessment},
  number    = {11-12},
  publisher = {Springer},
  address   = {New York},
  issn      = {1436-3240},
  doi       = {10.1007/s00477-019-01738-3},
  pages     = {2053 -- 2069},
  year      = {2019},
  abstract  = {Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Nino 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2-8 years) and interdecadal (8-32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Nino 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region.},
  language  = {en}
}