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221013 ||| eng |
| 100 |
1 |
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|a Islam, Asif Mohammed
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| 245 |
0 |
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|a Data Transparency and Long-Run Growth
|h Elektronische Ressource
|c Asif Mohammed Islam
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| 260 |
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|a Washington, D.C
|b The World Bank
|c 2020
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| 300 |
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|a 29 pages
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| 700 |
1 |
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|a Lederman, Daniel
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WOBA
|a World Bank E-Library Archive
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| 490 |
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|a World Bank E-Library Archive
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| 028 |
5 |
0 |
|a 10.1596/1813-9450-9493
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| 856 |
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|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-9493
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a For centuries states have engaged in collecting data to serve various interests. In modern times, a data gap has emerged between developing and developed economies, with the latter having more advanced data systems. The authors explore the effects of data transparency on long-run growth for a sample of mostly developing economies. Data transparency is defined as the timely production of credible statistics as measured by the Statistical Capacity Index. The paper finds that data transparency has a positive effect on real gross domestic product per capita, implying a statistically significant impact on transitional growth to a higher potential level of gross domestic product per capita. The estimates indicate an elasticity of the magnitude of 0.03 percent per year, which is much larger than the elasticity of trade openness and schooling in the estimation sample. The empirics employ a variety of econometric estimators, including dynamic panel and cross-sectional instrumental variables estimators, with the latter approach yielding a higher estimated elasticity. The findings are robust to the inclusion of several factors in addition to political institutions and exogenous commodity-price and external debt-financing shocks
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