Laboratoire d’Économie d’Orléans

The prevailing literature on International Environmental Agreements (IEAs) commonly posits the existence of a Depth-Participation Dilemma (DPD), suggesting that while precise agreements with well-defined obligations promote deeper cooperation, their specificity often deters participation. This paper revisits this assumption and introduces the differing institutional capacities of the negotiating countries. It thus provides a theoretical framework for understanding the interplay between vagueness, institutional capacities, and, most importantly, the dynamics of the DPD. Our analysis suggests that the DPD is only relevant when institutional capacities are highly uneven across negotiating states, or when a significant number of participants have limited capacities. In contrast, we find that greater precision enhances participation when most negotiating countries possess moderate to high institutional capacities. To test these predictions, we analyze ratification behavior for five climate-related IEAs from 1995 to 2018, constructing a precision index. Our empirical findings reveal (i) a bandwagon effect, where countries are more likely to ratify agreements when others do, (ii) that greater institutional differences weaken this bandwagon effect, thereby reducing ratification rates, (iii) that vagueness in agreements generally undermines incentives to ratify, and (iv) that vague agreements, once ratified, are less binding and correlate with higher CO 2 emissions. This framework and evidence provide new insights into how agreement design influences participation and effectiveness in IEAs. • We examine the Depth-Participation Dilemma (DPD) in environmental agreements. • A theoretical framework with the agreements vagueness is developed. • The DPD is relevant when institutions are uneven across negotiating states. • A precision index is constructed for five climate-related agreements. • We analyze ratification behavior for these agreements from 1995 to 2018. • We find a bandwagon effect where countries ratify agreements when others do. • Greater institutional differences weaken this bandwagon effect. • This framework provides insights on the participation in environmental agreements.

Abstract Building on Robinson & Acemoglu’s (Economic origins of dictatorship and democracy, Cambridge University Press, 2006) framework, this paper theoretically examines the interplay between tax evasion and bribery, focusing on the trade-off between tax evasion penalties and the deterrent effects of bribery sanctions in democratic societies with redistributive politics. The paper introduces the concept of the "tax evasion-bribery paradox" as a novelty. In this paradox, low detection probabilities of tax evasion caused by an under-resourced government make bribery less attractive but also prevent effective tax collection. Bribing tax officials is a high-stakes action that provides no added benefit if tax evasion is already going undetected. Otherwise, a similar mitigating effect on bribery is observed when the tax authority is well-resourced to enforce compliance. The high probability of detection discourages bribery as strong monitoring and auditing make it more likely that illegal activities will be uncovered. The study also emphasises that a greater likelihood of detecting bribery considerably decreases the occurrence of bribery. In this case, the tax evasion rate is limited to the tax rate level, while the tax penalty conditions the bribe. Moreover, wealthier individuals are more likely to engage in bribery due to their ability to absorb the associated costs. At the same time, lump-sum transfers can reduce bribery incentives compared to non-lump-sum transfers.

This paper investigates the effects of natural disasters on bank liquidity creation in sub-Saharan African during the period 1988-2018. Using bank-level data from more than 30 countries, we find that natural disasters affect negatively the liquidity creation in the region. The cumulative effect over the three years following a disaster is economically significant, amounting to a total reduction of 4% in the average liquidity generated. This impact is mainly channeled through the asset-side activities of banks. We also find heterogeneous impact of natural disasters on bank liquidity creation based on the size of banks, the magnitude of disasters and the income level of countries. Moreover, these effects are mainly observed when disasters strike on a large-scale. On the contrary, there is no significant difference depending on whether or not the disaster is climatic in origin. Additional tests show that foreign ownership of banks as well as monetary policy change do not qualitatively alter our primary findings. These results support bank regulation policies taking into the specificities of banks operating in environments prone to frequent natural disasters. Specifically, we recommend that central banks implement targeted regulatory measures such as stress-testing or resilience programs. As natural disasters are likely to increase in the coming years due to climate change, we suggest that microprudential policies be further strengthened and adapted to incorporate climate change considerations. • Analysis of the effects of natural disasters on bank liquidity creation in sub-Saharan Africa. • Sub-Saharan Africa is prone to climate shocks and is the most vulnerable in the world • Natural disasters negatively affect bank liquidity creation • The effect is mainly driven by the asset-side activities of banks. • The impact of natural disasters on bank liquidity creation is heterogeneous

This paper examines whether traditional monetary policy affects firms differently based on their carbon emissions—a previously unexplored driver of monetary policy non-neutrality. We develop a theoretical model predicting that carbon-intensive (”brown”) firms are more sensitive to monetary policy shocks due to investor preferences for ”green” assets. We test this prediction using stock price data from U.S. firms between 2010 and 2019. Our findings confirm that brown firms are more sensitive to monetary policy shocks than green firms. This heightened sensitivity persists even after controlling for financial constraints and intensifies with rising climate awareness among investors. Our results suggest that traditional monetary policy is not carbon-neutral and may unintentionally amplify biases related to carbon emissions, highlighting important considerations for policymakers. • We show that monetary policy transmission is not carbon-neutral. • Brown firms are more sensitive to monetary policy (MP) than green firms. • This effect persists even after controlling for firms’ fundamental characteristics. • Investor preference for green assets partly explains this discrepancy. • Our findings suggest central banks may need to adjust policies to avoid carbon bias.

This paper investigates the effect of fiscal decentralization on public finance performance for two levels of government on a panel of 33 developing economies from 2000 to 2020. Using the bias-corrected Least Square Dummy Variable estimator (LSDVC), we demonstrate that fiscal decentralization could enhance fiscal performance. The main findings are as follows: First, a larger share of decentralized expenditure is associated with a stronger central fiscal balance, but this effect diminishes with increased vertical fiscal imbalances (transfer dependency of subnational governments). The findings also show that vertical fiscal imbalances and revenue decentralization undermine fiscal positions at the central government level. At the sub-national level, we find a U-shaped relation between revenue autonomy (measured as the sub-national governments’ share of tax revenues) and sub-national budget deficits. Nonetheless, deficits of sub-national governments can be avoided through increased local accountability, for example, by having regional governments’ executive and legislative officials locally elected.

ABSTRACTThis paper proposes a novel hybrid multiscale decomposition and bootstrap approach for carbon price interval forecasting, aiming to overcome the limitations of traditional carbon price point forecasting. The original carbon price is decomposed into simple modes using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and various bootstrap methods are applied to perform a random sampling with a replacement on each mode, generating pseudo datasets forecasted using extreme gradient boosting (XGB). The forecasting values of all modes are then integrated into the original carbon price interval forecasting values. The empirical results, based on samples from China's Guangdong and Hubei carbon markets, provide compelling evidence of the effectiveness of our model. It achieves higher forecasting accuracy, higher interval coverage, and narrower forecasting intervals than currently popular prediction models, instilling confidence in its potential to enhance carbon price forecasting.