Robust determinants of bilateral trade

What are the policies and country-level conditions which best explain bilateral trade flows between countries? As databases expand, an increasing number of possible explanatory variables are proposed that influence bilateral trade without a clear indication of which variables are robustly important across contexts, time periods, and which are not sensitive to inclusion of other control variables. To shed light on this problem, we apply three model selection methods – Lasso reguarlized regression, Bayesian Model Averaging, and Extreme Bound Analysis -- to candidate variables in a gravity models of trade. Using a panel of 198 countries covering the years 1970 to 2000, we find model selection methods suggest many fewer variables are robust that those suggested by the null hypothesis rejection methodology from ordinary least squares

Sweet diversity: Colonial goods and the rise of European living standards after 1492

When did overseas trade start to matter for living standards? Traditional real-wage indices suggest that living standards in Europe stagnated before 1800. In this paper, we argue that welfare rose substantially, but surreptitiously, because of an influx of new goods as a result of overseas trade. Colonial luxuries such as tea, coffee, and sugar transformed European diets after the discovery of America and the rounding of the Cape of Good Hope. These goods became household items in many countries by the end of the 18th century. We use three different methods to calculate welfare gains based on price data and the rate of adoption of these new colonial goods. Our results suggest that by 1800, the average Englishman would have been willing to forego 10% or more of his income in order to maintain access to sugar and tea alone. These findings are robust to a wide range of alternative assumptions, data series, and valuation methods.Gains from Variety, Columbian Exchange, Trade, Economics of New Goods, Age of Discovery, Living Standards

Applications of econometrics and machine learning to development and international economics

In the first chapter, I explore whether features derived from high resolution satellite images of Sri Lanka are able to predict poverty or income at local areas. I extract from satellite imagery area specific indicators of economic well-being including the number of cars, type and extent of crops, length and type of roads, roof extent and roof type, building height and number of buildings. Estimated models are able to explain between 60 to 65 percent of the village-specific variation in poverty and average level of log income. The second chapter investigates the effects of preferential trade programs such as the U.S. African Growth and Opportunity Act (AGOA) on the direction of African countries’ exports. While these programs intend to promote African exports, textbook models of trade suggest that such asymmetric tariff reductions could divert African exports from other destinations to the tariff reducing economy. I examine the import patterns of 177 countries and estimate the diversion effect using a triple-difference estimation strategy, which exploits time variation in the product and country coverage of AGOA. I find no evidence of systematic trade diversion within Africa, but do find evidence of diversion from other industrialized destinations, particularly for apparel products. In the third chapter I apply three model selection methods – Lasso regularized regression, Bayesian Model Averaging, and Extreme Bound Analysis -- to candidate variables in a gravity models of trade. I use a panel dataset of of 198 countries covering the years 1970 to 2000, and find model selection methods suggest many fewer variables are robust that those suggested by the null hypothesis rejection methodology from ordinary least squares

Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These "poverty maps" are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively, covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896 municipalities in the 2014 MCS-ENIGH. We experiment with several architectures (GoogleNet, VGG) and use GoogleNet as a final architecture where weights are fine-tuned from ImageNet. We find that 1) the best models, which incorporate satellite-estimated land use as a predictor, explain approximately 57% of the variation in poverty in a validation sample of 10 percent of MCS-ENIGH municipalities; 2) Across all MCS-ENIGH municipalities explanatory power reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty from the CNN predictions alone explains 47% of the variation in poverty in the validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas we see slight improvements from using Digital Globe versus Planet imagery, which explain 61% and 54% of poverty variation respectively. We conclude that CNNs can be trained end-to-end on satellite imagery to estimate poverty, although there is much work to be done to understand how the training process influences out of sample validation.Comment: 4 pages, 2 figures, Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl

Big Data in Economics

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data

Explainable AI Helps Bridge the AI Skills Gap: Evidence from a Large Bank

Advances in machine learning have created an “AI skills gap” both across and within firms. As AI becomes embedded in firm processes, it is unknown how this will impact the digital divide between workers with and without AI skills. In this paper we ask whether managers trust AI to predict consequential events, what manager characteristics are associated with increasing trust in AI predictions, and whether explainable AI (XAI) affects users’ trust in AI predictions. Partnering with a large bank, we generated AI predictions for whether a loan will be late in its final disbursement. We embedded these predictions into a dashboard, surveying 685 analysts, managers and other workers before and after viewing the tool to determine what factors affect workers’ trust in AI predictions. We further randomly assigned some managers and analysts to receive an explainable AI treatment that presents Shapely breakdowns explaining why a model classified their loan as delayed and measures of model performance. We find that i) XAI is associated with greater perceived usefulness but less perceived understanding of the machine learning predictions; ii) Certain AI-reluctant groups – in particular senior managers and those with less familiarity with AI – exhibit more reluctant to trust the AI predictions overall; iii) Greater loan complexity is associated with higher degree of trust in the ML predictions; and iv) Some evidence that AI-reluctant groups respond more strongly to XAI. These results suggest that the design of machine learning models will determine who benefits from advances in ML in the workplace

Robust Determinants of Bilateral Trade

What are the policies and country-level conditions which best explain bilateral trade flows between countries? As databases expand, an increasing number of possible explanatory variables are proposed that influence bilateral trade without a clear indication of which variables are robustly important across contexts, time periods, and which are not sensitive to inclusion of other control variables. To shed light on this problem, we apply three model selection methods – Lasso reguarlized regression, Bayesian Model Averaging, and Extreme Bound Analysis -- to candidate variables in a gravity models of trade. Using a panel of 198 countries covering the years 1970 to 2000, we find model selection methods suggest many fewer variables are robust that those suggested by the null hypothesis rejection methodology from ordinary least squares

Sweet Diversity: Colonial Goods and the Welfare Gains from Trade after 1492

When did overseas trade start to matter for living standards? Traditional real-wage indices suggest that living standards in Europe stagnated before 1800. In this paper, we argue that welfare rose substantially, but surreptitiously, because of an influx of new goods as a result of overseas trade. Colonial luxuries such as tea, coffee, and sugar transformed European diets after the discovery of America and the rounding of the Cape of Good Hope. These goods became household items in many countries by the end of the 18th century. We use three different methods to calculate welfare gains based on price data and the rate of adoption of these new colonial goods. Our results suggest that by 1800, the average Englishman would have been willing to forego 10% or more of his income in order to maintain access to sugar and tea alone. These findings are robust to a wide range of alternative assumptions, data series, and valuation methods

Building a Better Model: Variable Selection to Predict Poverty in Pakistan and Sri Lanka

Numerous studies have developed models to predict poverty, but surprisingly few have rigorously examined different approaches to developing prediction models. This paper applies out of sample validation techniques to household data from Pakistan and Sri Lanka, to compare the accuracy of regional poverty predictions from models derived using manual selection, stepwise regression, and Lasso-based procedures. It also examines how much incorporating publically available satellite data into the model improves its accuracy. The five main findings are that: 1) Lasso tends to outperform both discretionary and stepwise models in Pakistan, where the set of potential predictors is large. 2) Lasso and stepwise models give comparable results in Sri Lanka, where the set of predictors is smaller. 3) The accuracy of the prediction model depends considerably on the poverty threshold 4) Including publically available satellite data makes poverty predictions more accurate in Sri Lanka, where predictors are scarce, but slightly less accurate in Pakistan and 5) Including the satellite data increases the benefit of using Lasso in Sri Lanka. We conclude that among the three model selection methods considered, lasso-based models are preferred for generating poverty predictions, especially when the pool of candidate variables is large. Furthermore, when the pool of candidate variables available from household surveys is smaller, incorporating publicly available satellite data can considerably improve the accuracy of regional poverty predictions