Divining the level of corruption: A Bayesian state-space approach

Standaert, Samuel

doi:10.1016/j.jce.2014.05.007

Cited by 82 publications

(38 citation statements)

References 9 publications

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“…For instance, foreign investors may be more likely to bring money into the country if perceived corruption is low. 3 For more details about the statistical method used to produce the Bayesian Corruption Index (BCI), please refer to Standaert (2015).…”

Section: Key Independent Variables: Measures Of Fiscal Transparencymentioning

confidence: 99%

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The effect of fiscal transparency on corruption: A panel cross‐country analysis

Chen

Neshkova

2019

Public Administration

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Both academicians and practitioners have advocated for increased fiscal transparency in government as a means of promoting budget discipline, improving functioning of the public sector, fostering greater accountability, and fighting the global menace of corruption. Despite worldwide calls for greater disclosure, empirical analyses of whether and how fiscal transparency actually affects governance outcomes are still limited. This study draws on public choice and principal–agent theories to demonstrate how public disclosure of budgetary information helps deter government corruption. The data from 95 countries over the period 2006–14 provide evidence that more fiscally transparent countries are perceived as less corrupt. We also find that fiscal transparency matters most at the final stages of the budget process when information disclosure reflects actual government spending. Data also confirm that a Citizens Budget can serve as a strong anti‐corruption tool.

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Section: Key Independent Variables: Measures Of Fiscal Transparencymentioning

confidence: 99%

“…For more details about the statistical method used to produce the Bayesian Corruption Index (BCI), please refer to Standaert ().…”

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confidence: 99%

The effect of fiscal transparency on corruption: A panel cross‐country analysis

Chen

Neshkova

2019

Public Administration

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“…Firstly, a country will have rank x+1 if it is significantly more open than at least one country with rank x. Secondly, if a country's policy is not significantly different from that of any other country, it cannot be ranked. The advantage of constructing a ranking in this way is that small differences between countries will not lead to a different ranking unless they can be identified as statistically significant (Standaert, 2015). Figure 4 shows the results for the 38 countries whose level can be compared.…”

Section: The Migration Policy Indexesmentioning

confidence: 99%

Measuring and Explaining Cross-Country Immigration Policies

2017

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The intensified international migration pressures of the recent decades prompted many developed countries to revise their immigration regulations and increase border controls. However, the development of these reforms as well as their effectiveness in actually managing new immigration flows remains poorly understood. The main reason is that migration regulations are hard to quantify, which has prevented the construction of a universal measure of migration policy. To fill this gap in the literature, we construct an indicator of the restrictiveness of immigration entry policy across countries as well as a more comprehensive indicator of migration policy that also accounts for staying requirements and regulations to foster integration. These indexes are then used to disentangle the factors determining the toughness of migration regulations. Our empirical framework combines elements from the median voter and interest group approach and accounts for cross-country correlation in migration ... AbstractThe intensified international migration pressures of the recent decades prompted many developed countries to revise their immigration regulations and increase border controls. However, the development of these reforms as well as their effectiveness in actually managing new immigration flows remains poorly understood. The main reason is that migration regulations are hard to quantify, which has prevented the construction of a universal measure of migration policy. To fill this gap in the literature, we construct an indicator of the restrictiveness of immigration entry policy across countries as well as a more comprehensive indicator of migration policy that also accounts for staying requirements and regulations to foster integration. These indexes are then used to disentangle the factors determining the toughness of migration regulations. Our empirical framework combines elements from the median voter and interest group approach and accounts for cross-country correlation in migration policies. We find strong evidence of spatial correlation in particular in entry restrictiveness, while the impact of economic determinants of migration policy remains much more modest.

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“…The variable expressing participation in free trade area --was calculated in the same fashion, with value 1 assigned to years where both countries were members of the European Union. To measure the impact of corruption on international trade two additional variables were calculated using Bayesian Corruption Index (Standaert, 2015): the higher the value of the index the higher is the degree of perceived corruption. One, is calculated as the product, while measures the absolute value of the difference in the values of the indicator for two countries under consideration.…”

Section: Data and Measurementmentioning

confidence: 99%

What drives international trade? Robust analysis for the European Union

Beck¹

2020

Journal of International Studies

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Economic literature is full of theories explaining international trade flows and empirical studies striving to verify them. Most of these attempts focus on the verification of single theory at a time without regard to the problem of model uncertainty. As a consequence, empirical research has brought a bulk of inconsistent results. The aim of the present paper is to validate which theories are correct on a purely empirical basis. To accomplish this, Bayesian model averaging was employed to 71 potential determinants of international trade for a sample of EU countries over the 1995-2015 period. The results show that the gravity model takes the lead in the explanation of trade flows. Membership in the EU has also a profound impact on trade, as each year of membership in the EU is associated with a growth rate of bilateral trade between 0.5 and 1.5. Finally, the analysis provides support to the predictions under Heckscher-Ohlin model of trade.

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Divining the level of corruption: A Bayesian state-space approach

Cited by 82 publications

References 9 publications

The effect of fiscal transparency on corruption: A panel cross‐country analysis

The effect of fiscal transparency on corruption: A panel cross‐country analysis

Measuring and Explaining Cross-Country Immigration Policies

What drives international trade? Robust analysis for the European Union

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