Quantifying and Explaining Causal Effects of World Bank Aid Projects

In recent years, machine learning methods have enabled us to predict with good precision using large training data, such as deep learning. However, for many problems, we care more about causality than prediction. For example, instead of knowing that smoking is statistically associated with lung cancer, we are more interested in knowing that smoking is the cause of lung cancer. With causality, we can understand how the world progresses and how impacts are made on an outcome by influencing the cause. This thesis explores how to quantify the causal effects of a treatment on an observable outcome in the presence of heterogeneity. We focus on investigating the causal impacts that World Bank projects have on environmental changes. This high dimensional World Bank data set includes covariates from various sources and of different types, including time series data, such as the Normalized Difference Vegetation Index (NDVI) values, temperature and precipitation, spatial data such as longitude and latitude, and many other features such as distance to roads and rivers. We estimate the heterogeneous causal effect of World Bank projects on the change of NDVI values. Based on causal tree and causal forest proposed by Athey, we described the challenges we met and lessons we learned when applying these two methods to an actual World Bank data set. We show our observations of the heterogeneous causal effect of the World Bank projects on the change of environment. as we do not have the ground truth for the World Bank data set, we validate the results using synthetic data for simulation studies. The synthetic data is sampled from distributions fitted with the World Bank data set. We compared the results among various causal inference methods and observed that feature scaling is very important to generating meaningful data and results. in addition, we investigate the performance of the causal forest with various parameters such as leaf size, number of confounders, and data size. Causal forest is a black-box model, and the results from it cannot be easily interpreted. The results are also hard for humans to understand. By taking advantage of the tree structure, the neighbors of the project to be explained are selected. The weights are assigned to the neighbors according to dynamic distance metrics. We can learn a linear regression model with the neighbors and interpret the results with the help of the learned linear regression model. in summary, World Bank projects have small impacts on the change to the environment, and the result of an individual project can be interpreted using a linear regression model learned from closed projects

Impact evaluation of trade interventions : paving the way

The focus of trade policy has shifted in recent years from economy-wide reductions in tariffs and trade restrictions toward targeted interventions to facilitate trade and promote exports. Most of these latter interventions are based on the new mantra of"aid-for-trade"rather than on hard evidence on what works and what does not. On the one hand, rigorous impact-evaluation is needed to justify these interventions and to improve their design. On the other hand, rigorous evaluation is feasible because unlike traditional trade policy, these interventions tend to be targeted and so it is possible to construct treatment and control groups. When interventions are not targeted, such as in the case of customs reforms, some techniques, such as randomized control trials, may not be feasible but meaningful evaluation may still be possible. Theis paper discusses examples of impact evaluations using a range of methods (experimental and non-experimental), highlighting the particular issues and caveats arising in a trade context, and the valuable lessons that are already being learned. The authors argue that systematically building impact evaluation into trade projects could lead to better policy design and a more credible case for"aid-for-trade."Economic Theory&Research,Trade Policy,Transport Economics Policy&Planning,Poverty Monitoring&Analysis,Free Trade

Public expenditure and growth

Given that public spending will have a positive impact on GDP if the benefits exceed the marginal cost of public funds, the present paper deals with measuring costs and benefits of public spending. The paper discusses one cost seldom considered in the literature and in policy debates, namely, the volatility derived from additional public spending. The paper identifies a relationship between public spending volatility and consumption volatility, which implies a direct welfare loss to society. This loss is substantial in developing countries, estimated at 8 percent of consumption. If welfare losses due to volatility are this sizeable, then measuring the benefits of public spending is critical. Gauging benefits based on macro aggregate data requires three caveats: a) considering of the impact of the funding (taxation) required for the additional public spending; b) differentiating between investment and capital formation; c) allowing for heterogeneous response of output to different types of capital and differences in network development. It is essential to go beyond country-specificity to project-level evaluation of the benefits and costs of public projects. From the micro viewpoint, the rate of return of a project must exceed the marginal cost of public funds, determined by tax levels and structure. Credible evaluations require microeconomic evidence and careful specification of counterfactuals. On this, the impact evaluation literature and methods play a critical role. From individual project evaluation, the analyst must contemplate the general equilibrium impacts. In general, the paper advocates for project evaluation as a central piece of any development platform. By increasing the efficiency of public spending, the government can permanently increase the rate of productivity growth and, hence, affect the growth rate of GDP.Public Sector Economics&Finance,,Economic Theory&Research,Debt Markets,Public Sector Expenditure Analysis&Management

General Equilibrium Models: An Overview

This article reviews the literature on general equilibrium models, relevant to the Chilean economy, and revised versions of the papers presented at the Conference of General Equilibrium Models for the Chilean Economy organized by the Central Bank of Chile, that will be published in a book by the same name (edited by Rómulo Chumacero and Klaus Schmidt-Hebbel, 2005). This introductory chapter provides a brief overview of the development and application of three families of GEMs: macroeconomic GEMs, computable general equilibrium models, and overlapping generations models. We also summarize the scope and main results of the twelve GEMs that comprise the volume.

Consumer credit information systems: A critical review of the literature. Too little attention paid by lawyers?

This paper reviews the existing literature on consumer credit reporting, the most extensively used instrument to overcome information asymmetry and adverse selection problems in credit markets. Despite the copious literature in economics and some research in regulatory policy, the legal community has paid almost no attention to the legal framework of consumer credit information systems, especially within the context of the European Union. Studies on the topic, however, seem particularly relevant in view of the establishment of a single market for consumer credit. This article ultimately calls for further legal research to address consumer protection concerns and inform future legislation

The Effectiveness of Community-Based Development in Poverty Reduction : A Descriptive Analysis of a Women-Managed NGO in Rural Pakistan

To assess the targeting performance of community-based development activities and deduce the impact of such activities on poverty reduction, we implemented a survey of a non-governmental organization (NGO) in northwestern Pakistan. A distinct characteristic of this NGO is that it is managed mostly by women and its interventions are conducted through community-based organizations (COs), most of whose members are also female. This characteristic is rather unusual for a male-dominated society like Pakistan. Descriptive analyses of village, CO, and household level data shows that the NGO was able to target poorer villages. Villages with COs are characterized by lower adult literacy rates, lower availability of basic amenities, and higher susceptibility to natural disasters. With regard to household-level welfare indicators -- such as consumption, women’s empowerment, children’s school enrolment, and the weight-for-age of infants -- we found that the consumption levels of CO member households tended to be lower than that of households in non-CO villages. However, the difference between CO member households and non-member households in CO villages was insignificant, possibly owing to the mixing of the selection effect (i.e., poorer households are served by the NGO) and the causal effect of interventions on poverty reduction. On women’s empowerment and child schooling, CO member households tend to perform better than other households, suggesting the favorable impact of the interventions and/or the self-selection of such households vis-à-vis program participation.

Finance and growth in the EU: new evidence from the liberalisation and harmonisation of the banking industry

JEL Classification: G21, G28, O5Banking Deregulation, Economic Growth, European Union, Financial Development

Discriminatory Lending:Evidence from Bankers in the Lab

We implement a lab-in-the-field experiment with 334 Turkish loan officers to document gender discrimination in small business lending and to unpack the mechanisms at play. Each officer reviews multiple real-life loan applications in which we randomize the applicant's gender. While unconditional approval rates are the same for male and female applicants, loan officers are 26 percent more likely to require a guarantor when we present the same application as coming from a female instead of a male entrepreneur. A causal forest algorithm to estimate heterogeneous treatment effects reveals that this discrimination is strongly concentrated among young, inexperienced, and gender-biased loan officers. Discrimination mainly affects female loan applicants in male-dominated industries, indicating how financial frictions can perpetuate entrepreneurial gender segregation across sectors

Measuring Risk: Political Risk Insurance Premiums and Domestic Political Institutions.

There is a renewed interest in political science on how political risk affects multinational corporations operating in emerging markets. Most existing studies suffer from data problems where researchers can only offer indirect evidence of the relationship between political institutions and political risk. In this paper I utilize a new data resource to explore how domestic institutions affect political risks for multinationals. Utilizing price data from political risk insurance agencies I test how domestic political institutions affect the premiums multinationals pay for coverage against 1) expropriations and contract disputes and 2) government restrictions on capital transactions. I find that constraints on politicians lead to marginally lower expropriation and transfer risks. Democracy, on the other hand, greatly reduces expropriation risk but has no impact on transfer risk.FDI, political risk, expropriation, insurance