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Bitte um Gnade für den BundeshaushaltReint GroppDer Spiegel, 13. November 2024
Promoting green product consumption is one important element in building a sustainable society. Yet green products are usually more costly. In times of high inflation, not only budget constraints but also the fear that prices will continue to rise might dampen green product consumption and, hence, limit the effectiveness of exerted efforts to promote sustainable behaviors. To test this suggestion, we conducted a Germany-wide survey with almost 1,200 respondents, followed by a framed field experiment (N=500) to confirm causality. In the survey, respondents’ stated “green” purchasing behavior is, as to be expected, positively correlated with concerns about climate change. It is also negatively correlated with concerns about future inflation and energy costs, but after controlling for observable characteristics such as income and educational level only the correlation with concerns about future prices remains significant. This result is driven by individuals with below-median environmental attitude. In the framed field experiment, we use the priming method to manipulate the saliency of inflation concerns. Whereas sizably relaxing the budget constraint (i.e., by 50 percent) has no impact on the share of organic products in participants’ baskets, the priming significantly decreases the share of organic products for individuals with below-median environmental attitude, similar to the survey data.
We use project-level information for the largest regional economic development program in German history to study how government subsidies to firms affect credit markets. We identify credit market responses by considering both, bank lending and firm borrowing during 1998-2019. We find that subsidies lead to larger lending volumes without crowding out credit to non-subsidized firms. Banks that are more exposed to subsidized firms exhibit moderately higher credit risk though. Firm subsidies support lending especially when credit constraints are elevated during the years of the financial crisis.
Syndicated loan data provided by DealScan is an essential input in banking research. This data is rich enough to answer urging questions on bank lending, e.g., in the presence of financial shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice and we provide guidelines for applied research.