How Can Trust Be Measured? an Alternative Approach Using Retailers’ Refund Policies

Fernando Arteaga (University of Pennsylvania)

Abstract : What is trust and how can we measure it? Social scientists widely accept as an intuitive truth that trust impacts positively on the economic success of societies. By now there is a large empirical literature studying and quantitatively assessing such a relationship. Yet, there is no consensus on how trust can be properly measured. In this paper, I survey the literature and present an alternative to the common approaches on measuring trust. Traditionally, trust has been identified by relying on surveys—directly asking people if, and how much, they trust their fellow countrymen—and/or on experiments—creating a perfectly controlled environment where the role of trust can be properly isolated and identified. Both approaches have important limitations: the former is prone to misidentification, while the latter is limited by scale issues. I argue that it is possible to capture trust-levels in a real-world context by locating proxies: refund policies implicitly account for the level of trust that retailers posit in their customers and represent a tacit measure of their client’s overall trustworthiness. By constructing an index of refund policies of stores that sell a similar set of homogenous goods across different regions/countries, we can get a reliable estimate of trust-differences across these regions/countries. I use Ikea as a study case of how such a proxy can be built.

Machine Learning Approaches to Testing Institutional Hypotheses: the Case of Acemoglu, Johnson, and Robinson (2001)

Boubacar Diallo (Qatar University)

Abstract : In their seminal 2001 work, Acemoglu, Johnson, and Robinson (AJR) argued that institutions influence economic development, using the logarithm of settler mortality as an instrument to establish a causal effect. A number of economists and other social scientists have challenged this work in terms of both data and identification strategy. One of these criticisms concerned the IV estimated coefficients and standard errors, which were nearly twice as large as the OLS coefficients and standard errors. My research uses machine learning to test the robustness of AJR's findings. Using the AJR dataset, which I randomly divide into training data and testing data, I am able to predict the average protection against expropriation risk from settler mortality. These predicted values of property rights protection are then regressed on per capita GDP growth. My results indicate a strong and positive effect of property rights protection on growth, consistent with AJR's earlier results. Moreover, the use of machine learning to obtain institutional values yields estimates close to the OLS estimates, unlike AJR. Removing African countries and neo-European countries such as Canada, Australia, USA, and New Zealand does not alter the sign and significance of the coefficient of interest. These results suggest that machine learning can be helpful to economists facing data issues.

Labor Market Outcomes, Cognitive and Noncognitive Skills in Rural China

Paul Glewwe (University of Minnesota)
Yang Song (Colgate University)
Xianqiang Zou (Renmin University of China)

Abstract : A growing literature studies how cognitive and noncognitive skills influence labor market outcomes beyond their effects via years of schooling. This paper uses a rich longitudinal data set from rural China to study the relationship between childhood cognitive and noncognitive skills and labor market outcomes. Results show that cognitive skills have strong explanatory power for wages when young adults are in their late 20s, even after controlling for years of education. We also find gender differences in the returns to various noncognitive skills, including internalizing behavior, externalizing behavior, and educational aspirations.

Bureaucratic Nepotism

Juan Felipe Riano (University of British Columbia)

Abstract : Nepotism is one of the most chronic pathologies within public administrations around the world and one especially endemic to developing countries. Yet, empirical evidence on the impact of this behavior on the functioning of the state is scarce. In this paper, I present empirical evidence on how family connections within the public administrations could distort the process of hiring, promotion, and compensation of civil servants, and how these strategically respond to the enforcement of anti-nepotism legislation. I also investigate how the presence of nepotistic career paths ultimately relates to the performance of governmental agencies and individual bureaucrats. My analysis focuses on the Colombian public administration and its entire bureaucratic system. I use un-anonymized administrative data on the universe of civil servants and their family members in the first degree of consanguinity. Based on this, I reconstruct bureaucratic family networks and full career paths of public servants. My empirical strategy exploits discontinuities in anti-nepotism legislation and the political turnover of top bureaucrats to evaluate the impact of kinship ties on civil servants' outcomes. As opposed to most of the literature on patronage and political quid-pro-quo exchange, I emphasize the role of kinship networks within the complete hierarchical structure of the state, from top managers to low tier bureaucrats, regardless of the political affiliation of individuals and their inherent jurisdictional power.