Suchergebnisse

Die im Rahmen des Projekts Eval-MAP II zwischen Mai und Juni 2020 durchgeführte Umfrage wird als Datensatz veröffentlicht und erweitert das Sozio-Ökologische Panel um eine sechste Welle. Die Befragung gibt Einblicke in die Präferenzen und Einstellungen der Haushalte zum Anpassungsverhalten an den Klimawandel sowie zu dessen Vermeidung in Deutschland. Sie schließt eine bisher nicht spezifisch bearbeitete Forschungslücke.

International climate cooperation needs to be negotiated among sovereign countries. The key to cooperation, and to countervail free riding, is reciprocity. Using game theory and a human subject experiment, we show that reciprocity can be built into the negotiation design. Human players negotiating a reciprocal common commitment are substantially more successful in promoting cooperation than when negotiating individual commitments. Moreover, focusing on a uniform common commitment strongly facilitates agreement, as compared to negotiating a vector of commitments, one for each player. Because a carbon price is a natural candidate for a uniform common commitment, our findings suggest that international climate negotiations should focus on reciprocal carbon pricing. Economists advocate carbon pricing for its cost efficiency, yet the role of carbon pricing for promoting cooperation could be at least as important.

Motivated by growth models based on the variety of capital goods, recent empirical studies have established links between productivity and several trade-based measures of product variety, carrying the implication that these measures may represent technology. French and Gaucaite-Wittich (2009) study this implication by explicitely proposing the variety of capital goods available for production as a direct measure of the state of technology.

Mit einem Anteil von rund 30% am Endenergieverbrauch und etwa 20% an den CO2-Emissionen haben private Haushalte in Deutschland einen großen Einfluss auf die Umwelt. Gleichzeitig sind private Haushalte ein zentraler Adressat für politische Interventionen zur Bekämpfung des Klimawandels. Vor diesem Hintergrund hat die Politik zahlreiche Maßnahmen zur Verringerung des Energiekonsums und zur Förderung regenerativer Energietechnologien ergriffen. Diese politischen Maßnahmen bedürfen einer sorgfältigen Evaluierung ihrer Effektivität und Kosteneffizienz, um kostspielige Redundanzen durch sich überlappende Instrumente zu vermeiden. Eine solche Evaluation umwelt- und energiepolitischer Maßnahmen erfordert eine umfangreiche Datenbasis. Besonders im Bereich der privaten Haushalte waren solche Daten in Deutschland bislang nicht verfügbar. Die Reagibilität deutscher Haushalte auf Maßnahmen zur Bekämpfung des Klimawandels war daher weitgehend unbekannt. Das Sozial-Ökologische Panel stellt zu diesem Zweck umfangreiche, frei verfügbare Informationen zum Energieverbrauch und Umweltverhalten privater Haushalte bereit. Die Befragung wurde in vier Wellen durchgeführt. Es liegen Daten für die Jahre 2012, 2013, 2014 und 2015 vor. Diese Daten können anhand einer ID aneinander gespielt werden. Darauf aufbauend können ökonometrische Schätzungen und Analysen verschiedener Präferenzindikatoren sowie des Anpassungsverhaltens privater Haushalte an den Klimawandel durchgeführt werden. Dieser Datensatz umfasst die Daten der Erhebung im Jahr 2013 und ist in englischer Sprache gelabelt. Der Datensatz ist auf Deutsch und auf Englisch erhältlich.

With a share of 30% in total final energy consumption and around 20% in CO2 emissions, private households in Germany strongly affect the environment. At the same time private households are an important target group for policy interventions to fight climate change. Against this background, numerous policy measures that intend to decrease energy consumption and to support renewable energy technologies have been introduced. These policy measures call for accurate evaluation to avoid expensive redundancies due to overlapping policy instruments.
The evaluation of energy and environmental policy measures requires comprehensive and reliable data. So far such data was unavailable in Germany, especially in the context of private households. Hence, the responsiveness of German households to climate protection policies was unknown.
For this purpose, the Socio-Ecological Panel offers rich information on household's energy consumption and environmental behavior. The data was gathered in four household surveys conducted in 2012, 2013, 2014 and 2015. The survey waves can be merged using the household ID. The data builds the basis for empirical analyses of households' adaptation to climate change and the evaluation of environmental and climate policy measures.
This data set comprises the information gathered in the 2013 survey wave.

Die im Rahmen des Projekts AKZEPTANZ zwischen Dezember 2015 und Februar 2016 durchgeführte Umfrage wird als Datensatz veröffentlicht und erweitert das Sozio-Ökologische Panel um eine fünfte Welle. Die Befragung gibt Einblicke in die Präferenzen und Einstellungen der Haushalte zur Energiewende verbundene Systemveränderungen in Deutschland. Sie schließt eine bisher nicht spezifisch bearbeitete Forschungslücke. Einstellungen zu Fairness, Klimawandel, Umwelt, persönlichen Kosten und Nutzen erklären die Bereitschaft der Befragten, für die Einführung von Energie- und Umweltpolitik zu zahlen und diese zu akzeptieren.

With a share of 30% in total final energy consumption and around 20% in CO2 emissions, private households in Germany strongly affect the environment. At the same time private households are an important target group for policy interventions to fight climate change. Against this background, numerous policy measures that intend to decrease energy consumption and to support renewable energy technologies have been introduced. These policy measures call for accurate evaluation to avoid expensive redundancies due to overlapping policy instruments. The evaluation of energy and environmental policy measures requires comprehensive and reliable data. So far such data was unavailable in Germany, especially in the context of private households. Hence, the responsiveness of German households to climate protection policies was unknown. For this purpose, the Socio-Ecological Panel offers rich information on household's energy consumption and environmental behavior. The data was gathered in four household surveys conducted in 2012, 2013, 2014 and 2015. The survey waves can be merged using the household ID. The data builds the basis for empirical analyses of households' adaptation to climate change and the evaluation of environmental and climate policy measures. This data set comprises the information gathered in the 2014 survey wave.

With a share of 30% in total final energy consumption and around 20% in CO2 emissions, private households in Germany strongly affect the environment. At the same time private households are an important target group for policy interventions to fight climate change. Against this background, numerous policy measures that intend to decrease energy consumption and to support renewable energy technologies have been introduced. These policy measures call for accurate evaluation to avoid expensive redundancies due to overlapping policy instruments.
The evaluation of energy and environmental policy measures requires comprehensive and reliable data. So far such data was unavailable in Germany, especially in the context of private households. Hence, the responsiveness of German households to climate protection policies was unknown.
For this purpose, the Socio-Ecological Panel offers rich information on household's energy consumption and environmental behavior. The data was gathered in four household surveys conducted in 2012, 2013, 2014 and 2015. The survey waves can be merged using the household ID. The data builds the basis for empirical analyses of households' adaptation to climate change and the evaluation of environmental and climate policy measures.
This data set comprises the information gathered in the 2012 survey wave.

The PHF scientific use file Wave 2 Version 3.0 data set is the second updated version of the wave 2 PHF data set and consists of the following five Stata files: PHF_h_wave2_v3_0.dta, PHF_p_wave2_v3_0.dta, PHF_m_wave2_v3_0.dta, PHF_d_wave2_v3_0.dta and PHF_w_wave2_v3_0.dta.
The major changes in SUF Wave 2 Version 3.0 compared to SUF Wave 2 Version 2.0 are as follows:

Editing and correction of some values.

For more details, see the PHF User Guide on website of the Deutsche Bundesbank.

The PHF scientific use file Wave 1 Version 3.0 data set is the second updated version of the wave 1 PHF data set and consists of the following five Stata files: PHF_h_wave1_v3_0.dta, PHF_p_wave1_v3_0.dta, PHF_m_wave1_v3_0.dta, PHF_d_wave1_v3_0.dta and PHF_w_wave1_v3_0.dta.
The major changes in SUF Wave 1 Version 3.0 compared to SUF Wave 1 Version 2.0 are as follows:

Editing and correction of some values.

For more details, see the PHF User Guide on website of the Deutsche Bundesbank.

The USTAN dataset contains annual financial statements of German non-financial corporations which are sent to the Bundesbank first in the context of refinancing operations and later for credit assessment purposes.
USTAN data for the 1987-2017 accounting years can be used as panel data by researchers, who can access items of the balance sheet, income statement and, where applicable, the statement of changes in tangible fixed assets (property, plant and equipment) as well as other firm variables such as economic sector and legal form.
These data flow into the Financial Statements Data Pool, which is the database used for regular statistical analyses of German firms' profitability and financing situation. The results of these analyses can be found in the Bank's Special Statistical Publications 5 and 6.

The PHF scientific use file Wave 2 Version 2.0 data set is the first updated version of the wave 2 PHF data set and consists of the following five Stata files: PHF_h_wave2_v2_0.dta, PHF_p_wave2_v2_0.dta, PHF_m_wave2_v2_0.dta, PHF_d_wave2_v2_0.dta and PHF_w_wave2_v2_0.dta.
The major changes in SUF Wave 2 Version 2.0 compared to SUF Wave 2 Version 1.0 are as follows:

A set of derived variables has been included (PHF_d_wave2_v2_0.dta)
An improved imputation algorithm (especially with regard to the gross/net income conversion) was used to update the data
The replicate weights (PHF_w_wave2_v2_0.dta) and cross-sectional household weights (in PHF_h_wave2_v2_0.dta) were newly calibrated using updated population statistics
Additional editing and correction of some values.

For more details, see the PHF User Guide on website of the Deutsche Bundesbank.

The PHF scientific use file Wave 1 Version 2.0 data set is the first updated version of the wave 1 PHF data set and consists of the following five Stata files: PHF_h_wave1_v2_0.dta, PHF_p_wave1_v2_0.dta, PHF_m_wave1_v2_0.dta, PHF_d_wave1_v2_0.dta and PHF_w_wave1_v2_0.dta.
The major changes in SUF Wave 1 Version 2.0 compared to the first release of SUF Wave 1 are as follows:

A set of derived variables has been included (PHF_d_wave2_v2_0.dta)
Additional IDs (wave, persid) were introduced to account for the panel structure
To get comparable flags for both waves, the coding of the flags was adjusted to the coding of the flags in wave 2.
Additional filter checks, as well as editing and correction of some values.

For more details, see the PHF User Guide on website of the Deutsche Bundesbank.

The MiDi is a collection of individual (i.e. firm-to-firm or private individuals-to-firms) investment relations originally collected to calculate aggregate measures of German foreign direct investment (FDI). It is based on an annual data collection on foreign direct investment stocks that was established by the Deutsche Bundesbank in 1976 in accordance with the German Foreign Trade and Payments Regulation ("Aussenwirtschaftsverordnung"), with the intention to get a better and more accurate picture of the structure and scope of inward and outward FDI of German enterprises. Since 1996, individual companies can be traced over time, which made it possible to prepare a micro-level panel dataset for research purposes. Due to changes in data protection regulation, the available anonymized research data covers all years from 1999 until the respective last currently processed year.