nep-ict New Economics Papers
on Information and Communication Technologies
Issue of 2019‒06‒10
six papers chosen by
Marek Giebel
Universität Dortmund

  1. The Role of Information Technology to Enhance Property Tax Revenue in Kenya, Tanzania and Zambia By McCluskey, William; Franzsen, Riël; Kabinga, Mundia; Kasese, Chabala
  2. How to Design a Financial Management Information System; A Modular Approach By Gerardo Uña; Richard I Allen; Nicolas M Botton
  3. Security Analysis of Machine Learning Systems for the Financial Sector By Shiori Inoue; Masashi Une
  4. News-driven infl ation expectations and information rigidities By Vegard H. Larsen; Leif Anders Thorsrud; Julia Zhulanova
  5. Adoption of e-commerce by individuals and digital divide: Evidence from Spain By Ángel Valarezo; Rafael López; Teodosio Pérez-Amaral
  6. The transition to the knowledge economy, labor market institutions, and income inequality in advanced democracies By Hope, David; Martelli, Angelo

  1. By: McCluskey, William; Franzsen, Riël; Kabinga, Mundia; Kasese, Chabala
    Abstract: Public finance theory suggests that property tax is an ideal local tax. But it’s also a ‘data-hungry’ tax, making it difficult and costly to administer properly– especially at the local government level where capacity, skills and resources are often lacking. Given its high data demands, property tax administration lends itself to the application of modern information and communication technology (ICT) systems. Over the last 40 to 50 years, however, studies have shown that weak administration is the core reason for poor revenue performance, particularly issues of data compilation and management, lack of transparency, poor billing and collection practices and weak enforcement. Summary of Working Paper 88 by William McCluskey, Riël Franzsen, Mundia Kabinga and Chabala Kasese
    Keywords: Economic Development, Finance, Governance,
    Date: 2019
  2. By: Gerardo Uña; Richard I Allen; Nicolas M Botton
    Abstract: A well-functioning financial management information system (FMIS) provides timely, reliable, and comprehensive reports that support implementation of the government’s fiscal policies and fiscal rules, and the formulating, controlling, monitoring, and executing of the budget. The architecture of FMISs has undergone a transformation since these systems were first developed in the 1980s. Rather than attempting to cover all or most public financial management (PFM) functions, many FMISs now focus on a few core functions such as accounting and reporting, budget execution, and cash management. Yet a survey of 46 countries shows that many face severe challenges in transforming their FMIS into an effective tool of fiscal governance. These challenges relate to weaknesses in the system’s core functions, its institutional coverage, the information technology platforms it uses, and the ease of sharing data with other IT systems. This How to Note discusses how to address these chal-lenges. Replacing an FMIS with an entirely new system may not be an optimal strategy. By utilizing the latest technology, a better approach may be to update or replace one or more core modules of the system: the so-called modular approach. Implementation of an effective FMIS, however, depends on two critical preconditions: strong political motivation and commitment, and the system’s ability to meet ongoing and anticipated PFM needs.
    Keywords: Financial management information systems;Financial Management Information Systems, fiscal policy, fiscal rules, accounting, reporting, budget execution, cash management
    Date: 2019–05–15
  3. By: Shiori Inoue (Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Masashi Une (Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail:
    Abstract: The use of artificial intelligence, particularly machine learning (ML), is being extensively discussed in the financial sector. ML systems, however, tend to have specific vulnerabilities as well as those common to all information technology systems. To effectively deploy secure ML systems, it is critical to consider in advance how to address potential attacks targeting the vulnerabilities. In this paper, we classify ML systems into 12 types on the basis of the relationships among entities involved in the system and discuss the vulnerabilities and threats, as well as the corresponding countermeasures for each type. We then focus on typical use cases of ML systems in the financial sector, and discuss possible attacks and security measures.
    Keywords: Artificial Intelligence, Machine Learning System, Security, Threat, Vulnerability
    JEL: L86 L96 Z00
    Date: 2019–05
  4. By: Vegard H. Larsen; Leif Anders Thorsrud; Julia Zhulanova
    Abstract: We investigate the role played by the media in the expectations formation process of households. Using a news-topic-based approach we show that news types the media choose to report on, e.g., (Internet) technology, health, and politics, are good predictors of households' stated in ation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we document that the underlying time series properties of relevant news topics explain the timevarying information rigidity among households. As such, we not only provide a novel estimate showing the degree to which information rigidities among households vary across time, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding infl ation expectations and information rigidities.
    Keywords: Expectations, Media, Machine Learning, Inflation
    Date: 2019–04
  5. By: Ángel Valarezo (Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).); Rafael López (Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).); Teodosio Pérez-Amaral (Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).)
    Abstract: E-commerce penetration rates are distant among those groups of individuals with the lowest and the highest levels of online shopping adoption. This is an indicator of digital divide, having negative effects in terms of untapped opportunities for people, companies and the whole economy. Key socioeconomic and demographic determinants of adoption of ecommerce are explored, analyzing a dataset of 174,776 observations for the period 2008-2017 in Spain. The empirical analysis is based on a standard neoclassical utility maximization framework. Linear probability model, logistic regression, and Heckman’s sample selection correction model have been used. The results suggest that e-commerce adoption is positively related with being male, having higher levels of education, income and digital skills, being Spanish, and being employed; while being female, older and belonging to a household of two or more members have negative effects. An interaction between digital skills and age has been introduced in the model, where high digital skills seem to have a positive influence, partly counteracting the lower odds for some age groups. Policy recommendations related to demand and supply measures are suggested to foster the adoption of e-commerce.
    Keywords: E-commerce; Digital divide; Linear probability model; Logistic regression; Heckman’s sample selection correction; Polychoric correlation; Digital skills; Time and regional dummies; Pool data; Utility maximization framework.
    JEL: C25 D11 O33
    Date: 2019–03
  6. By: Hope, David; Martelli, Angelo
    Abstract: The transition from Fordism to the knowledge economy in the world's advanced democracies was underpinned by the revolution in information and communications technology (ICT). The introduction and rapid diffusion of ICT pushed up wages for college-educated workers with complementary skills and allowed top managers and CEOs to reap greater rewards for their own talents. Despite these common pressures, income inequality did not rise to the same extent everywhere; income in the Anglo-Saxon countries remains particularly unequally distributed. To shed new light on this puzzle, the authors carry out a panel data analysis of eighteen OECD countries between 1970 and 2007. Their analysis stands apart from the existing empirical literature by taking a comparative perspective. The article examines the extent to which the relationship between the knowledge economy and income inequality is influenced by national labor market institutions. The authors find that the expansion of knowledge employment is positively associated with both the 90/10 wage ratio and the income share of the top 1 percent, but that these effects are mitigated by the presence of strong labor market institutions, such as coordinated wage bargaining, strict employment protection legislation, high union density, and high collective bargaining coverage. The authors provide robust evidence against the argument that industrial relations systems are no longer important safeguards of wage solidarity in the knowledge economy.
    Keywords: ICT; income inequality; industrial relations; information and communications technology; knowledge economy; labor market institutions; technological change; wage inequality
    JEL: N0 R14 J01
    Date: 2019–04–01

This nep-ict issue is ©2019 by Marek Giebel. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.