Government spending, migration, and human capital: impact on economic welfare and growth- theory and evidence

The purpose of this dissertation is to analyze the effects of public policies on rural-urban migration and human capital expansion, and to examine the role of human capital (among other domestic and external factors) in the long-term economic growth of developing countries. Human capital expansion and labor migration from villages to cities are two aspects of the structure of labor markets in poor countries that are continuously influenced by public policies— policies that are often either ineffective or have unintended adverse consequences. For example, while much of human resource policy in developing countries is directed toward increasing the supply of educated labor, inter sectoral in-country migration and unemployment have become more pronounced, requiring new thinking on policy responses. This dissertation analyzes the outcomes of such policies and offers insights into how they might be improved. Chapter 2 extends a two-sector, general-equilibrium model of rural-urban migration to include government spending. Provision of public goods acts as a productivity-enhancing input in private production that results in external economies of scale. This approach is generalized by introducing an unbalanced allocation of public expenditure in rural and urban sectors due to political economy considerations, differential sector output elasticities with respect to government input, and distortionary taxation. The chapter studies the effects of an increase in public spending and taxation on sectoral outputs, factor prices, urban unemployment, and welfare. Of particular concern here is to study the effect of an unbalanced allocation of government spending between rural and urban areas. Chapter 3 studies the effects of selected education policies on the size of the educated labor pool and on economic welfare using the “job ladder” model of education, which is relevant to liberal arts education in developing countries. The policies considered are (1) increasing the teacher student ratio, (2) raising the relative wage of teachers, and (3) increasing the direct subsidy per student. In addition, the chapter analyzes the impact of wage rigidities in the skilled or modern sector on the size of the educated labor force. The analysis consists of five major sections. First, it reformulates the Bhagwati-Srinivasan job ladder model to make it amenable to analyzing the comparative static results of the effects of selected policies. Second, since higher education is mostly publicly financed, the analysis extends the job ladder model to incorporate public financing of the education sector. It then examines that model along with the effects of changes in policy parameters. Third, the analysis develops another extension of the job ladder model to include private tuition practices by teachers that are prevalent in many developing countries. Fourth, to analyze the impact of wage rigidities in a less restrictive framework where individuals can choose education based on ability and cost, the chapter develops an overlapping generations model of education with job ladder assumptions of wage rigidities in the skilled or modern sector. The chapter examines the flexible market and fixed market (with wage rigidities) equilibrium scenarios, and compares the impact on the threshold level of abilities and the size of the educated labor force. Finally, using specific functional forms of human capital production, cost, and ability density functions, the chapter analyzes the equilibrium outcomes. The analysis shows that in an economy with wage rigidities in the skilled sectors (modern and education sectors), the result of quality-enhancing policies under the simple job ladder model is an increase in the total size of the educated labor force. However, under an extended version of the job ladder model, the result depends on the relative size of the effects of an increase in the cost of education and the effects of an increase in the expected wage. The overlapping generations/job ladder model formulation used in the chapter finds that an increase in the present value of the expected wage and/or an increase in the marginal product of education will increase the demand for education. The minimum threshold level of ability falls, and more people are encouraged to acquire educational skills. Chapter 4 estimates the effects of openness, trade orientation, human capital, and other factors on total factor productivity (TFP) and output for a pooled cross section, time-series sample of countries from Africa and Asia, as well as for the two regions separately. The models are estimated for the level and growth of both TFP and output by using panel fixed effects. The generalized method of moments is also applied to address endogeneity issues. Several variables related to political, financial, and economic risks are used as instruments, together with the lagged values of the dependent and endogenous explanatory variables. The data for this study span 40 years (1972–2011) and are grouped into five-year averages. Several sources were used to obtain the most updated data, including the newly released Penn World Table (Version 8.0). The chapter finds that inducing a greater outward orientation generally boosts TFP, per capita output, and growth. Greater accumulation of human capital has a consistently positive effect on output and TFP growth in both Africa and Asia. Its positive influence comes rather independently of trade variables than interactive terms with openness. Furthermore, inflation does not negatively affect growth, although inflation variability is found to adversely affect TFP and output in Africa. Chapter 5 concludes the dissertation by providing conclusions, a summary of major results, and possible directions for future research

The Robust Reading Competition Annotation and Evaluation Platform

The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become a de-facto evaluation standard for robust reading systems and algorithms. Concurrent with its second incarnation in 2011, a continuous effort started to develop an on-line framework to facilitate the hosting and management of competitions. This paper outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the competitions. The RRC Annotation and Evaluation Platform is a modular framework, fully accessible through on-line interfaces. It comprises a collection of tools and services for managing all processes involved with defining and evaluating a research task, from dataset definition to annotation management, evaluation specification and results analysis. Although the framework has been designed with robust reading research in mind, many of the provided tools are generic by design. All aspects of the RRC Annotation and Evaluation Framework are available for research use.Comment: 6 pages, accepted to DAS 201

READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.Comment: Submitted to DAS201

Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

Curriculum Learning for Handwritten Text Line Recognition

Recurrent Neural Networks (RNN) have recently achieved the best performance in off-line Handwriting Text Recognition. At the same time, learning RNN by gradient descent leads to slow convergence, and training times are particularly long when the training database consists of full lines of text. In this paper, we propose an easy way to accelerate stochastic gradient descent in this set-up, and in the general context of learning to recognize sequences. The principle is called Curriculum Learning, or shaping. The idea is to first learn to recognize short sequences before training on all available training sequences. Experiments on three different handwritten text databases (Rimes, IAM, OpenHaRT) show that a simple implementation of this strategy can significantly speed up the training of RNN for Text Recognition, and even significantly improve performance in some cases

CNN training with graph-based sample preselection: application to handwritten character recognition

In this paper, we present a study on sample preselection in large training data set for CNN-based classification. To do so, we structure the input data set in a network representation, namely the Relative Neighbourhood Graph, and then extract some vectors of interest. The proposed preselection method is evaluated in the context of handwritten character recognition, by using two data sets, up to several hundred thousands of images. It is shown that the graph-based preselection can reduce the training data set without degrading the recognition accuracy of a non pretrained CNN shallow model.Comment: Paper of 10 pages. Minor spelling corrections brought regarding the v2. Accepted as an oral paper in the 13th IAPR Internationale Workshop on Document Analysis Systems (DAS 2018

Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.Comment: Submitted to DAS-201

Handwriting Recognition of Historical Documents with few labeled data

Historical documents present many challenges for offline handwriting recognition systems, among them, the segmentation and labeling steps. Carefully annotated textlines are needed to train an HTR system. In some scenarios, transcripts are only available at the paragraph level with no text-line information. In this work, we demonstrate how to train an HTR system with few labeled data. Specifically, we train a deep convolutional recurrent neural network (CRNN) system on only 10% of manually labeled text-line data from a dataset and propose an incremental training procedure that covers the rest of the data. Performance is further increased by augmenting the training set with specially crafted multiscale data. We also propose a model-based normalization scheme which considers the variability in the writing scale at the recognition phase. We apply this approach to the publicly available READ dataset. Our system achieved the second best result during the ICDAR2017 competition

Baseline Detection in Historical Documents using Convolutional U-Nets

Baseline detection is still a challenging task for heterogeneous collections of historical documents. We present a novel approach to baseline extraction in such settings, turning out the winning entry to the ICDAR 2017 Competition on Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both, the actual extraction of baselines, as well as for a simple form of layout analysis in a pre-processing step. To the best of our knowledge it is the first CNN-based system for baseline extraction applying a U-net architecture and sliding window detection, profiting from a high local accuracy of the candidate lines extracted. Final baseline post-processing complements our approach, compensating for inaccuracies mainly due to missing context information during sliding window detection. We experimentally evaluate the components of our system individually on the cBAD dataset. Moreover, we investigate how it generalizes to different data by means of the dataset used for the baseline extraction task of the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts (HisDoc). A comparison with the results reported for HisDoc shows that it also outperforms the contestants of the latter.Comment: 6 pages, accepted to DAS 201

A fine-grained approach to scene text script identification

This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online