Data Quality in Southeast Asia

Analysis of Official Statistics and Their Institutional Framework as a Basis for Capacity Building and Policy Making in the ASEAN

Manuel Stagars

• Publisher: Palgrave Macmillan
• Year: 2016
• ISBN:
  9781137600639
• Topic: Business
• Subtopic: International Business
• Index: Business

This book explores the reliability of official statisticaldata in the ASEAN (the Association of Southeast Asian Nations), and thebenefits of a better vocabulary to discuss the quality of publicly availabledata to address the needs of all users. It introduces a rigorous method todisaggregate and rate data quality into principal factors containing a total often dimensions, which serves as the basis for a discussion on the opportunitiesand challenges for data quality, capacity building programs and data policy in SoutheastAsia. Tools to standardize and monitor statistical capacity and data qualityare presented, as well as methods and data sources to analyse data quality. Thebook analyses data quality in Indonesia, Malaysia, Singapore, the Philippines, Thailand, Vietnam, Brunei, Laos, Cambodia, and Myanmar, before concluding withthoughts on Open Data and the ASEAN Economic Community (AEC).

© The Editor(s) (if applicable) and The Author(s) 2016

Manuel StagarsData Quality in Southeast Asia10.1057/978-1-137-60063-9_1

Begin Abstract

1. Introduction

Manuel Stagars¹ 

(1)

Singapore, Singapore

 

Abstract

With the emergence of data journalism and Open Data, official statistics have entered the mainstream. Users of official statistics include the government, the private and financial sectors, the scientific community, and the public. However, the enthusiasm concerning the data revolution has leapt ahead of a reasoned consideration of data quality, and discussing the reliability of official statistics can be a political minefield. To better address the needs of data users, a better understanding of the processes behind data and a more robust vocabulary to discuss data quality are needed.

Keywords

National statisticsfactors of data qualityASEAN

End Abstract

Official statistics are the apex of a country’s public data. They are the empirical evidence on which policy-makers act, the private sector develops strategies, and academic research thrives. National statistics offices are often the most data-savvy organizations in a government, and there are few analytical tasks that do not benefit from statistical data. Most governments globally have a good idea of their countries’ economic and social affairs, and agree that a strong and sustainable statistical system is the backbone for solid economic development. Politicians are keenly aware of the disadvantages of poor statistics; if data quality falls short, their policies are grounded on a weak foundation and resulting initiatives may backfire. However, the capacity for data gathering and the willingness to publicly share the data often diverge. High-quality data are hardly the natural product of economies and political frameworks; behind them are complex processes and rules shaping how governments collect, process, and share data. Quantifying these processes and rules, and their influence on data quality is the goal of this analysis.

“Statistics” is a broad term. To someone researching heritage sites, it might mean data on historic building stock, while an economist thinks about national accounts. This study understands statistics as the standard menu underlying key indicators that most developed countries report. These include data on a country’s real sector, fiscal sector, financial sector, external sector, socio-demographic data, and vital statistics including statistics on live births, deaths, marriages and divorces, and other data from civil registration systems. When we speak about data in this study, we mean publicly available statistical data from the websites of national statistics offices. Of course, data includes more than statistics, such as maps, genomes of living organisms, chemical compounds, mathematical formulae, medical data, sensor readings, financial results, and other sources. Nevertheless, “data” in this book refers only to statistical data.

Data quality is a hard enough goal in developed countries. Emerging markets and frontier markets in Southeast Asia have additional complexities to grapple with, such as rapid population growth, urbanization, and nascent institutional frameworks. Data quality is an important topic for all statistics offices globally. This study focuses on countries in the Association of Southeast Asian Nations (ASEAN)—Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. In the present study, the terms Southeast Asia and ASEAN refer to the aforementioned countries with the exclusion of East Timor. For geographical reference, Fig. 1.1 shows the ten member countries in the ASEAN and their immediate neighbors.

Fig. 1.1

ASEAN member states (dark grey) and neighboring countries (dotted) (Adapted from vectorworldmap.com)

1.1 Who Is This Book For?

The study in this book is useful for people working directly or indirectly with publicly available data. At first glance, data quality might seem the domain of statisticians and academics. The reality is that data from official sources is used daily by those in the private sector; data are the core of the global economic and financial system, and they guide decisions about the course of nations. All too often, analysts, decisions makers, and academics take available data at face value. They rarely question the validity of statistics that come from national statistics offices or intergovernmental organizations, such as the United Nations (UN) or the World Bank. Finally, data journalism—news reporting that draws interesting facts from publicly available numerical data—has made inroads in media outlets. ¹ In recent years, official data have exploded into the mainstream, whether or not we are aware of it. Understanding the factors that influence data quality is more important than ever.

1.2 What Is New in This Analysis?

Some data users have a love–hate relationship with official statistics. The consensus is that some data are somewhat accurate, while the interpretation of other data requires more than a grain of salt. However, this raises important questions. If the national statistics of a country are inaccurate, how can we verify or carry out data-driven decisions? If data quality from two countries differs, how can we make meaningful comparisons and reconcile economic policies? The blanket view that statistics are always inaccurate is poor consolation for those in the thick of the debate. We need a better vocabulary to understand, analyze, and discuss data quality to address the needs of all data users, including the government, the private sector, the financial sector, the scientific community, and the public.

This study breaks out the opaque term “data quality” into three factors: the institutional framework, statistical processes, and statistical output. Together, these factors comprise ten dimensions, each of which has between two and five underlying indicators. This analysis combines quality guidelines of the ASEAN Community Statistical System (ACSS) with those of the European Statistical System (ESS), which allows a more direct comparison of the countries in the two unions. How this works will become clear in Chap. 3, which describes the method in more detail. Separating quality into enabling factors and components shifts the discussion from a personal opinion to an assessment that relies on factual information on individual indicators. Quality assessments become reproducible for anyone with the indicator and rating scale, and access to documents that underlie the ratings. This indicator system may help facilitate discussions about statistics in an economic community, such as the ASEAN, by inviting national statisticians, policy makers, and data users to debate individual dimensions in detail. In any event, more important than a one-time assessment of statistical quality is a roadmap to make official statistics more robust.

1.3 Factors of Quality in National Statistical Systems

Many factors influence the quality of statistics. Laws—written and unwritten—might prevent us from conducting a census in a certain region. There could also be a lack of knowledge on how to effectively conduct a census or insufficient funds or manpower to carry it out. The institutional framework surrounding the statistical function of a country is therefore an important part of data quality.

When the institutional framework permits a national statistics office to collect data of high quality, its staff need professional training, procedures, and methods to turn these data into useful and accurate statistics. Sound statistical processes are thus another important enabling factor.

Finally, users are interested in the statistical output of national statistics offices. Accuracy is the most important characteristic of useful statistics, but others are equally important. For instance, technological bottlenecks prevent accessing to otherwise accurate data, or publications might have a long time lag and so are only available when the reality has already changed. Accessibility and timeliness are two dimensions that influence the quality of statistical output. Chapter 3 discusses additional dimensions in detail.

1.4 Motivation for this Research

ASEAN is a diverse economic union. In 2014, its population of 624 million ² created a collective gross domestic product (GDP) close to US$2.5 trillion ³ and over US$1.6 trillion in exports of goods and services. ⁴ It includes two nations (Brunei and Singapore) that lead the world in GDP per capita in PPP (Purchasing Power Parity terms), but also Cambodia, Laos, and Myanmar, which are on the UN list of the lowest developed countries (LDCs). ASEAN is a fast-growing economic powerhouse with plans to deepen regional integration and trade. High-quality data lie at the heart of economic interaction between countries, and it is important to understand how countries in ASEAN differ in terms of their statistical framework, methods, and output for anyone working with data in the region.

Official statistics also play a fundamental role in government accountability toward citizens and neighbors. Open Data portals signal goodwill in terms of making data more readily accessible. But, without addressing the underlying factors of data quality, they fail to realize their full potential. Improving and harmonizing data in Southeast Asia will not happen overnight. In the long run, an active dialogue about data quality will benefit ASEAN, its neighbors and investors in economic, social, and environmental initiatives. Overarching challenges, such as rapid urbanization or resilience toward climate change, will require data that are comparable without reservations across the member countries of the union.

Notes

1.

Rogers, Simon (2011) ‘Data journalism at the Guardian: what is it and how do we do it?’ http://​www.​theguardian.​com/​news/​datablog/​2011/​jul/​28/​data-journalism, date accessed 29 September 2015.

 

2.

UNCTADstat (2015a) ‘Total population’, http://​unctadstat.​unctad.​org, date accessed September 29, 2015.

 

3.

UNCTADstat (2015a) ‘Gross domestic product in US dollars at current prices and current exchange rates’, http://​unctadstat.​unctad.​org, date accessed September 29, 2015.

 

4.

UNCTADstat (2015a) ‘Exports of goods and Services in US dollars at current prices and current exchange rates (BPM6)’, http://​unctadstat.​unctad.​org, date accessed September 29, 2015.

 

© The Editor(s) (if applicable) and The Author(s) 2016

Manuel StagarsData Quality in Southeast Asia10.1057/978-1-137-60063-9_2

Begin Abstract

2. Tools to Standardize and Monitor Statistical Capacity and Data Quality

Manuel Stagars¹

(1)

Singapore, Singapore

Abstract

Operation guidelines and best practices exist for national statistics offices. These include the UN Fundamental Principles of Official Statistics, the System of National Accounts, the International Monetary Fund Data Dissemination Standards, the Codes of Practice of ...

Table of contents

• Cover
• Frontmatter
• 1. Introduction
• 2. Tools to Standardize and Monitor Statistical Capacity and Data Quality
• 3. Method and Data Sources to Analyze Data Quality in the ASEAN
• 4. Data Quality Analysis of Group A: Indonesia, Singapore, the Philippines, and Malaysia
• 5. Data Quality Analysis of Group B: Thailand, Vietnam, and Brunei
• 6. Data Quality Analysis of Group C: Laos, Cambodia, and Myanmar
• 7. Conclusion and Outlook: Towards Open Data and the ASEAN Economic Community
• Backmatter