This paper investigates how social capital contributes to the pro-social behaviour of individuals in a post-conflict environment. I simultaneously investigate the pro-social behaviours in the periods of crisis (floods) and normality and observe whether (structural and relational) social capital has important influences in these two different times. The main novelty of this approach is that I model individuals' pro-social behaviours jointly for both the periods in focus and treat them as systematic outcomes of observed and unobserved (endogenous) influences. I find that more pro-social activities in the normal times are positively associated with such activities in the crisis period. Additionally, the results reveal the importance of (structural) social capital on pro-social behaviour – namely, group membership, size and ethnic structure of individual networks matter. Of particular interest for this post-conflict society and related literature is that greater ethnic diversity of individual networks is supportive for pro-social engagement of citizens. Finally, among the observed economic influences, I find that the respondents working in the informal economy report more pro-social activities while formal employment works more as financial intermediary for these engagements.
This article uses dynamic panel analysis to investigate the relationship between institutional improvement and economic performance in 29 transition countries. The contribution of this paper is two-fold. First, we find that per capita GDP is determined by the entire history of institutional reform under transition and that, conditional on this history, per capita GDP adjusts to recent institutional changes. Moreover, we find that the time-horizon over which we measure institutional change matters, with five-year changes showing the clearest effects on current levels of per capita GDP. Secondly, we address the pronounced methodological heterogeneity of this literature. To compensate for incomplete theoretical guidance from the institutional literature, we draw upon an institutional meta-regression analysis to inform our model specification. Our analysis covers the period 1992–2007.