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Between 2018 and 2022, Montenegro introduced a series of significant policy reforms. The reforms affected economic, educational, and social policies, ranging from the introduction of a universal child allowance to major changes in its labour market regulations and tax rules. From an economic policy perspective, the most significant reform package was implemented in January 2022.
It was composed of a huge increase in Montenegro’s statutory minimum wage, alongside a new income tax regime and the abolishment of mandatory health insurance contributions. According to the Government, the reform package aimed at increasing the living standards of citizens and promoting a more sustainable and inclusive growth model.
This report evaluates the conjoint impact of this reform package.
This paper studies how the swiftness and delay of punishment affect behavior. Using rich administrative data from automated speed cameras, we exploit two (quasi-)experimental sources of variation in the time between a speeding offense and the sending of a ticket. At the launch of the speed camera system, administrative challenges caused delays of up to three months. Later, we implemented a protocol that randomly assigned tickets to swift or delayed processing. We identify two different results. First, delays have a negative effect on payment compliance: the rate of timely paid fines diminishes by 7 to 9% when a ticket is sent with a delay of four or more weeks. We also find some evidence that very swift tickets – sent on the first or second day following the offense – increase timely payments. These results align with the predictions of expert scholars that we elicited in a survey. Second, speeding tickets cause a strong, immediate, and persistent decline in speeding. However, we do not detect any robust, differential effects of swiftness or delay on speeding. This challenges widely held beliefs, as reflected in our survey. Yet, we document large mechanical benefits of swift punishment and provide a theoretical framework of learning and updating that explains our findings.
We introduce political salience into a canonical model of attacks against political regimes, as scaling agents’ expressive payoffs from taking sides. Equilibrium balances heterogeneous expressive concerns with material bandwagoning incentives, and we show that comparative statics in salience characterize stability. As main insight, when regime sanctions are weak, increases from low to middling salience can pose the greatest threat to regimes – ever smaller shocks suffice to drastically escalate attacks. Our results speak to the charged debates about democracy, by identifying conditions under which heightened interest in political decision-making can pose a threat to democracy in and of itself.
Cities increasingly address climate change, e.g. by pledging city-level emission reduction targets. This is puzzling for the provision of a global public good: what are city governments’ reasons for doing so, and do pledges actually translate into emission reductions? Empirical studies have found a set of common factors which relate to these questions, but also mixed evidence. What is still pending is a theoretical framework to explain those findings and gaps. This paper thus develops an abstract public choice model. The model features economies of scale and distinguishes urban reduction targets from actual emission reductions. It is able to support some stylized facts from the empirical literature and to resolve some mixed evidence as special cases. Two city types result. One type does not achieve its target, but reduces more emissions than a free-riding city. These relations reverse for the other type. The type determines whether cities with lower abatement costs more likely set targets. A third type does not exist. For both types, cities which set targets and have higher private costs of carbon are more ambitious. If marginal net benefits of mitigation rise with city size, then larger cities gain more from setting climate targets. Findings are contrasted with an alternative model where targets reduce abatement costs. Some effects remain qualitatively the same, while others clearly differ. The model can thus guide further empirical and theoretical work.
Individuals vary considerably in how much they earn during their lifetimes. This study examines the role of the tax-and-transfer system in mitigating such inequalities, which could otherwise lead to disparities in living standards. Utilizing a life-cycle model, we determine that taxes and transfers offset 45% of lifetime earnings inequality attributed to differences in productive abilities and education. Additionally, the system insures against 48% of lifetime earnings risk. Implementing a lifetime tax reform linking annual taxes to previous employment could improve the system’s insurance capabilities, albeit at the cost of a lower employment rate.
We analyze how machine learning predictions may improve antibiotic prescribing in the context of the global health policy challenge of increasing antibiotic resistance. Estimating a binary antibiotic treatment choice model, we find variation in the skill to diagnose bacterial urinary tract infections and in how general practitioners trade off the expected cost of resistance against antibiotic curative benefits. In counterfactual analyses we find that providing machine learning predictions of bacterial infections to physicians increases prescribing efficiency. However, to achieve the policy objective of reducing antibiotic prescribing, physicians must also be incentivized. Our results highlight the potential misalignment of social and heterogeneous individual objectives in utilizing machine learning for prediction policy problems.
This paper studies the spread of compliance behavior in neighborhood networks in Austria. We exploit a field experiment that varied the content of mailings sent to potential evaders of TV license fees. The data reveal a strong treatment spillover: untreated households are more likely to switch from evasion to compliance in response to mailings received by their network neighbors. Digging deeper into the properties of the spillover, we find that it is concentrated among close neighbors of the targets and increases with the treated households' diffusion centrality. Local concentration of equally treated households implies a lower spillover.
Cities are becoming increasingly important in combatting climate change, but their overall role in global solution pathways remains unclear. Here we suggest structuring urban climate solutions along the use of existing and newly built infrastructures, providing estimates of the mitigation potential.