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We examine the foundations of corruption perception at the microlevel. Using micro and macro data, we focus on the incidence of personal characteristics and country effects. We extend previous researches by estimating sub-models taking into account differences in the countries of residence. Our database comes from the 2004 International Social Survey Program survey that includes more than 35 countries. Ordered probit models were estimated in order to study the impact of independent variables on the perceived level of corruption. This article argues that there are socio-demographic variables that play a relevant role in determining corruption perception (such as: gender, education, etc.). We find that country of residence matters and the model shows some relevant patters of behavior. Finally, we find a strong relationship between our ranking of countries and the Corruption Perception Index computed by Transparency International.

We find that individuals’ opinions concerning protectionist policies match with how their revenue could be affected in the medium or long term by trade liberalisation in line with predictions of the comparative advantage models. An adverse macroeconomic context (large increase in the unemployment rate or inflation rate) increases protectionist attitudes, thus reflecting that people do not trust that free trade will lead to lower prices or create jobs despite trade theory optimism. People share a mercantilist view of trade since more imports increase protectionism support, while people positively value exports, especially in small countries. Regarding policy measures, while protectionist measures do not influence protectionism support in general, easy access to exports reduces people’s support for protectionism.

Abstract1Past research has provided evidence of the role of some personal characteristics (age, gender, religion) as risk factors for depression. However, few researchers have jointly examined the specific impact of each characteristic and whether country characteristics (economic performance and others environmental factors) change the probability of being depressed. In general, this is due to the use of single‐country databases. The aim of this article is to extend previous findings by employing a much larger dataset and including the above‐mentioned country effects. We estimate probit models with country effects (model I) and we also explore linkages between specific environmental factors and depression (model II includes variables such as per capita Gross Domestic Product and the GINI index). The dataset for this research comes from the 2007 GALLUP Public Opinion Poll that allows us to consider a large and widely heterogeneous set of micro‐data. Findings indicate that depression is positively related to being a woman, adulthood, divorce, widowhood, unemployment, and low income. Moreover, we provide evidence of the significant association between economic performance and depression. Inequality raises the probability of being depressed, specially for those living in urban areas. Finally, we find that some population characteristics such as age distribution and religious affiliation facilitate depression.