Field | Before | After |
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Field Trial Title | Before State Paralysis: The Impacts of Procurement Risk on Government Effectiveness | After State Paralysis: The Effect of Compliance Uncertainty on Government Effectiveness |
Field Abstract | Before Public procurement plays a key role in allocating limited budgetary resources to public service delivery in countries with a functional rule of law. This project studies a puzzling phenomenon: in developing countries like Brazil, substantive shares of the federal and sub-national budgets are not spent despite clear needs for additional resources to improve the quality of public services or to fund emergency spending in contexts of crisis. In line with a growing literature that documents the potential unintended effects of the enforcement of rules on bureaucratic performance, we investigate the role of procurement risk - when passive waste is misinterpreted as active waste – as a driver of unspent public funds by Brazilian municipal governments. Randomizing information that decreases the perception of procurement risk, we investigate its effects on budget execution. | After Regulation and enforcement around the use of public funds can reduce corruption, but does it also alter incentives to spend? In this paper, we investigate whether compliance uncertainty around spending rules can stifle valuable-to-the-public spending and distort policy choice. We begin by leveraging administrative data of local government accounts to establish a puzzling phenomenon: substantial shares of local budgets in Brazil are not spent despite clear needs for additional resources for public services. We also investigate how random audits, which might increase the salience of compliance uncertainty, affect public spending. Then, we leverage a collaboration with the Brazilian Council of Municipal Health Secretaries (CONASEMS) to conduct an experiment with municipal health secretaries in order to shed light on the impact of compliance uncertainty on secretaries’ choices. In the experiment, we offer valuable-to-the-public spending plans randomly varying the degree of compliance guarantees from the audit courts. |
Field Trial End Date | Before February 15, 2023 | After February 15, 2024 |
Field Last Published | Before August 05, 2022 07:08 PM | After July 15, 2023 05:26 PM |
Field Intervention (Public) | Before We administer a survey to local bureaucrats in the health sector that seeks to understand their demand for different strategies that facilitate the execution of resources. In the survey, we will introduce exogenous variation in the perception of procurement risk associated with each strategy by randomizing the content of the provided information. In the first round, we randomly assign information on whether the spending plan will be interpreted as compliant with the rules by the control agency. In the second round, the experiment will also randomize the complexity of the budget execution strategy. The survey experiment will be implemented in collaboration with CONASEMS, the Brazilian Council of Municipal Health Secretaries. We will administer it during CONASEMS conferences organized over time, starting the pilot at CONASEMS annual congress in July/2022. These events bring together local bureaucrats in the health sector from all over Brazil. | After We administer a survey experiment to local bureaucrats in the health sector that seeks to understand their demand for spending plans that facilitate the execution of resources. We will introduce exogenous variation in the degree of compliance uncertainty around spending rules that each plan entails by randomizing the content of the provided information. We vary the source of the public funds used by the plans since rules and oversight vary depending on the source of funding in Brazil. We also randomly assign information on whether the spending plan will be interpreted as compliant with the rules by the control agency. With these random variations we will be able to understand whether reducing compliance uncertainty increase the implementation of effective health interventions. And whether this mechanism distorts spending of public funds. The survey experiment will be implemented in collaboration with CONASEMS, the Brazilian Council of Municipal Health Secretaries. We will administer it during CONASEMS conferences organized over time, starting the pilot at CONASEMS annual congress in July/2022. These events bring together local bureaucrats in the health sector from all over Brazil. |
Field Intervention End Date | Before February 15, 2023 | After February 15, 2024 |
Field Primary Outcomes (End Points) | Before Our outcome variable will be participants’ demand for different strategies to execute resources. Following a Becker-DeGroot-Marschak (BDM) procedure, we will elicit participants’ maximum willingness to pay to receive these strategies. This will inform us about local health officials’ demand for guarantees of approval from the control agency. Also, we will capture demand for complex initiatives when they have the guarantee that the control agency will approve. | After Our outcome variable will be participants’ demand for different strategies to execute resources. Following a Becker-DeGroot-Marschak (BDM) procedure, we will elicit participants’ maximum willingness to pay to receive these strategies. This will inform us about the local health officials’ demand for compliance guarantees. |
Field Experimental Design (Public) | Before The survey experiment will first describe some of the main problems that municipalities are dealing with in the health sector. We will present to health officials several initiatives that aim to overcome those challenges. These solutions will be presented along with a strategy to execute resources. We will use the Becker-DeGroot-Marschak (BDM) method to elicit participants’ demand for the proposed strategies. In the first round, the sample will be randomized into treatment and control group. The treatment group will receive information increasing the guarantee that the control agency will approve the expenses once the strategy to execute resources is implemented. This treatment reduces the perception of procurement risk associated with budget execution. Health officials in the control group will not be given any guarantee that the spending plan will be interpreted as compliant with the rules. In the second round, the experiment will randomize the complexity of the proposed policy. Participants in the treatment group will be presented a more complex health intervention, increasing the perceived risk of incurring procedural mistakes that could be framed as wrongdoing. We will also randomize in the second round the information on whether the spending plan will be interpreted as compliant with the rules by the control agency. Then, a fourth of the sample is expected to be assigned to each treatment arm. | After The survey experiment will first describe some of the main problems that municipalities are dealing with in the health sector. We will present to health officials several initiatives that aim to overcome those challenges. These solutions will be presented along with a strategy to execute resources. We will use the Becker-DeGroot-Marschak (BDM) method to elicit participants’ demand for the proposed strategies. In the first round, the sample will be randomized into treatment and control group. The treatment group will receive information increasing the guarantee that the control agency will approve the expenses once the strategy to execute resources is implemented. This treatment reduces the compliance uncertainty associated with budget execution. This variation will let us test whether reducing uncertainty over compliance with spending regulation increase the implementation of effective health interventions by local bureaucrats in the health sector. In the second round, we will introduce variation in both the effectiveness of the proposed policy and the source of the public funds. We will test whether the compliance uncertainty raised by the execution of non-discretionary and more heavily regulated funds distorts public policy decisions as it makes local secretaries switch to safer but less effective public policies. We will also randomize in the second round the information on whether the spending plan will be interpreted as compliant with the rules by the control agency. With this variation, we will test whether the distortion can be explained by regulation risk. Finally, to be able to interpret the magnitude of the willingness to pay by local bureaucrats, we will introduce variation in another relevant dimension for policy implementation, as a benchmark. |
Field Planned Number of Observations | Before 300 individuals | After 400 individuals per event |
Field Sample size (or number of clusters) by treatment arms | Before In the first round, half of the sample will be assigned to treatment and half to control group. In the second round, one fourth will receive T1+T2, one fourth T1+C2, one fourth C1+T2, and one fourth C1+C2. Being T1 the compliance treatment and T2 the complexity treatment. | After We will randomize the sample into four groups. It will vary the information they receive associated with the spending plan. 1. Non-discretionary funds and low regulation risk 2. Non-discretionary funds and high regulation risk 3. Discretionary funds and high regulation risk 4. Non-discretionary funds and high regulation risk + benchmark constraint At the same time, each group will be randomized into an effective and less effective public policy. |
Field Did you obtain IRB approval for this study? | Before No | After Yes |
Field | Before | After |
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Field Document | Before |
After
PaP_July2023_AEA.pdf
MD5:
ac08dab21cd8968ff15aaa1334a84ef9
SHA1:
bc05fb8ae9fa8ea56e9b1cdb6a337a68545f2da9
|
Field | Before | After |
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Field IRB Name | Before | After UC Davis IRB |
Field IRB Approval Date | Before | After May 05, 2023 |
Field IRB Approval Number | Before | After 1936881-2 |
Field | Before | After |
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Field Affiliation | Before | After CONASEMS |
Field | Before | After |
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Field Affiliation | Before | After CONASEMS |
Field | Value |
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Field Document |
Value
PaP_July2022_AEA.pdf
MD5:
f8922bcb5238b083a0e623beee69737f
SHA1:
a35850f0efc43600c714d6edcd7cefb8e09b6362
|