metadata: authors: 0: author: Islam,Asif Mohammed
count: Middle East and North Africa
docna: 0: docna: Data Transparency in the Middle East and
            North Africa
docty: Policy Research Working Paper
abstracts: cdata!: Data transparency about critical
            economic issues may be key to driving growth and enhancing
            trust in government in the Middle East and North Africa.
            Several knowledge products and technical analyses on the
            region have been greatly constrained by the lack of
            availability of detailed data, and the relatively outdated
            nature of many available datasets. The goal of this study is
            to ascertain the state of data systems in the Middle East
            and North Africa region. Through analysis of several
            indicators, with their limitations in mind, the study uses
            descriptive analyses and uncovers six stylized facts of the
            region: (i) developing economies in the Middle East and
            North Africa have poor data ecosystems, largely due to the
            prevalence of conflict; (ii) developing economies in the
            Middle East and North Africa as a group have experienced the
            largest deterioration in data systems over time; (iii) data
            systems in richer economies in the Middle East and North
            Africa region underperform relative to their income peers;
            (iv) Gulf Cooperation Council economies underperform in data
            openness, especially online access, despite having the
            resources for online features; (v) the regulatory framework
            for data (data infrastructure) is poor throughout the
            region, especially in Gulf Cooperation Council economies;
            and (vi) the dispersion of  source data scores &#x2013;
            a measure of availability and timeliness of micro data
            &#x2013; in the region suggests that national
            statistical offices in the region could learn from each
            other. Furthermore, the study summarizes data availability
            and timeliness for specific macro, micro, and public health
            indicators for countries across the region. The need for
            forging a social contract for data is discussed, as well as
            the role international institutions can play through a
            statistics compact for the region.
display_title: Data Transparency in the Middle East
            and North Africa
listing_relative_url: /research/2023/03/34016771/data-transparency-middle-east-north-africa
topicv3: Digital Government m1325964 678,ICT Statistical Capacity Building m1326175 2356,Open Government and Transparency m1326535 837,Poverty Measurement and Analysis m1326941 707
publishtoextweb_dt: 2023-03-07T00:00:00Z
dois: 10.1596/1813-9450-10346
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 950662
model_extractions: Policy Research Working Paper 10346 Data Transparency in the Middle East and North Africa _Asif M. Islam_ Middle East and North Africa Region Office of the Chief Economist March 2023
Policy Research Working Paper 10346

# **Abstract**

Data transparency about critical economic issues may be key to driving growth and enhancing trust in government in the Middle East and North Africa. Several knowledge products and technical analyses on the region have been greatly constrained by the lack of availability of detailed data, and the relatively outdated nature of many available datasets. The goal of this study is to ascertain the state of data systems in the Middle East and North Africa region. Through analysis of several indicators, with their limitations in mind, the study uses descriptive analyses and uncovers six stylized facts of the region: (i) developing economies in the Middle East and North Africa have poor data ecosystems, largely due to the prevalence of conflict; (ii) developing economies in the Middle East and North Africa as a group have experienced the largest deterioration in data systems over time; (iii) data systems in richer economies in the Middle East and North Africa region underperform relative to their income peers; (iv) Gulf Cooperation Council economies underperform in data openness, especially online access, despite having the resources for online features; (v) the regulatory framework for data (data infrastructure) is poor throughout the region, especially in Gulf Cooperation Council economies; and (vi) the dispersion of source data scores—a measure of availability and timeliness of micro data—in the region suggests that national statistical offices in the region could learn from each other. Furthermore, the study summarizes data availability and timeliness for specific macro, micro, and public health indicators for countries across the region. The need for forging a social contract for data is discussed, as well as the role international institutions can play through a statistics compact for the region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aislam@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Data Transparency in the Middle East and North Africa**

## Asif M. Islam<sup>1</sup>

JEL Codes: C81, C82, D83, EO1 Keywords: Statistical capacity, data openness, data access, transparency, MENA

> 1 Asif M. Islam is a Senior Economist in the Chief Economist Office of the Middle East and North Africa at the World Bank. The analysis benefited greatly from feedback and conversations with Johannes Hoogeveen, Minh C. Nguyen, Umar Serajuddin, and Brian William Stacy.  The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors, or the Governments they represent. All errors and omissions are the author's responsibility.
# **Data Transparency in the Middle East and North Africa**

## **Introduction**

Modern and well-coordinated traditional data collection systems are a feature of advanced economies. Granting access to the research community has been characteristic of these data systems. However, many developing economies have lagged in their statistical capacity to generate data. Furthermore, when data is available, many economies have shied away from sharing it with the wider research community. This is particularly the case for many economies in the Middle East and North Africa (MENA) region. While the fears of data privacy are not unfounded, little attention has been afforded to the costs of inadequate data systems that persist in silos and are unavailable to the best minds to advance the boundaries of knowledge.

The objective of this study is to ascertain the state of data systems in the MENA region, while making the case that the long run benefits of good quality data are considerable. Through analysis of several indicators, the study uncovers six stylized facts of the region: (i) developing MENA economies have poor data ecosystems, largely due to the prevalence of conflict; (ii) MENA developing economies as a group have experienced the largest deterioration in data systems over time; (iii) data systems in richer economies in the MENA region underperform relative to their income peers; (iv) Gulf Cooperation Council (GCC) economies underperform in data openness, especially online access, despite having the resources for online features; (v) the regulatory framework for data (data infrastructure) is poor throughout the region, especially in GCC economies; and (vi) dispersion of source data scores – a measure of the availability and timeliness of micro data – in the region suggests that National Statistical Offices (NSOs) in the region could learn from each other.

Data transparency about critical economic issues will be the key to driving growth and enhancing trust in government in the MENA region (Arezki et al., 2020 – April 2020 MEU). The need for greater transparency has been more vivid during the pandemic when the prevalence of real time information has been crucial. Furthermore, the onset of the war in Ukraine gas generated shockwaves throughout the region, and lack of data makes it hard to establish the effects on the poor and vulnerable. Equally important for economic and social outcomes is the sharing of data for private sector stakeholders and citizens alike. The free and uncensored exchange of information can improve governance, citizen trust and accountability within the MENA region, while encouraging investments. Several knowledge products and technical analyses on the MENA region have been greatly constrained by the lack of availability of detailed data about the region, and the relatively outdated nature of many available datasets. MENA’s transformation agenda must include data transparency as one of its main pillars.

A core component of the Sustainable Development Goals is that countries must generate their own indicators with their own capabilities. The costs of such efforts are not trivial, and the trade-offs between investing in data capacity and systems versus other pressing needs are difficult for developing country governments to ascertain. Without significant investments and a change in the mindset or a paradigm shift towards data transparency, the MENA region risks being left behind with accelerating data gaps with the increasing digitization of data. The 2021 World Development Report on Data for Better Lives makes the case that a new social contract for data is needed in the region, and globally. The study summarizes what the conditions would be for such a contract, and how the MENA Statistics Compact can advance the data agenda.

2
# **Why Data Systems Matter**

The long run benefits of good data systems and data transparency are considerable (Islam and Lederman, 2020; World Bank (2021) - WDR 2021: Data for Better Lives). There are several channels through which the quality of the data ecosystem could aid development. First, data serves as the basis for policy and reforms. Policies can only be as good as the data they are based on. At a fundamental level, data is about records. Take the example of a business. A manager has the primary goal of raising profits. To achieve this, performance must be benchmarked historically and compared with competitors. Collateral must be evaluated and leveraged to access financing to pursue new ventures. Risk and reward must be balanced. Investors need to be enticed. Without any record keeping, none of these can be achieved. A similar set of challenges face governments and policy makers in the MENA region. Countries need to grow, and to expand options, data must be harnessed to provide guidance. Countries need to compare with past performance and benchmark against other countries at similar levels of development. Countries with high quality information are better able to make better decisions. Through data and evaluation, existing policies may be reformed and refined, while new policies may be experimentally evaluated.

Second, data that is accessible to a wider group of agents can generate better policies and reforms. A powerful tool confined to the tool shed has limited value. When data is accompanied by a large base of sophisticated users, there are substantial expansions in the frontier of knowledge. As researchers test hypotheses, debate and dispute findings from each other, while establishing robust facts and relationships using the same data, the best ideas and new pathways of addressing challenges can emerge. This is the process where simple data can be turned into a stock of knowledge that could generate and direct further knowledge creation and better policy making. It is not surprising that richer economies are researched more than poorer economies. Publications in top ranking journals in the economics profession are skewed towards wealthier economies, which themselves capitalize on this analysis to better understand and improve their economies. Less wealthy economies, on the other hand, cannot benefit from such knowledge, largely because of the lack of data accessibility.

Third, when data is poor or unavailable, the gap between perceptions and reality may grow. Important reforms may lead to real welfare improvements but may have little impact on public perceptions as there is limited data tracking such improvements. These perceptions may foster a narrative resulting in frustrations that manifest in the form of social protests and unrest. Similarly, if data is of dubious quality, the public may lose confidence in such information and may not alter their perceptions despite positive findings from the data. This may contribute to a lack of trust between citizens and their governments, weakening the social contract. More importantly, once a government treads down the path of unreliable or limited data, it may be challenging to trek back. The public may be less willing to trust information from the government, and thus perceptions are calcified and difficult to change resulting in economies that are more prone to social upheavals.

However, for data to inform policy, availability and accessibility of good quality data is necessary but not enough. Several institutions play an important role in facilitating debate fostered by data to ensure policy is an outcome of the process and the public takes ownership of this debate. The process typically resembles the following path. Data is first generated which is not easily digestible by the public at large. Academics generate knowledge from the data that fosters debate among themselves. Think tanks and policy makers join in. Media institutions communicate the information to the public at large who then participate and take ownership of the debate. The outcome of this process is hopefully an optimal set of policies that promote overall welfare. Each of the institutions is critical in fostering and enveloping the public into meaningful debate. Failure of the media to inform the public, or the absence of think tanks in the process would limit the value of information in guiding policy and diminish the returns of investment in data. All the parts must move together for the data ecosystem to be effective.

3
# **A Brief Primer on the Data and Transparency Literature**

Centuries ago, rulers acknowledged the need for systematic data collection. Historical data collection can be traced to the first recorded census undertaken by Babylonians around 3800 BC (Grajalez et al., 2013). Early data collection served rulers with the aim of accounting for wealth and power. Information was gathered for taxation purposes, counting of men for military recruitment and workforce, and ascertaining conquered populations and territories (World Bank 2021). Data was kept secret from the public and not meant to improve their lives but to help rulers pursue their agenda. This raised general distrust of data collection activities among the populace. In contrast, the enlightenment ideals in eighteenth century Europe emphasized objective scientific inquiry. The role of data evolved to a means of examining society and became more public. In the late eighteenth century, statistical agencies were set up in Europe and North America to publish official statistics and inform the public.

Data transparency is defined as the regular publication of credible statistics by the state. This includes the frequency and availability of micro-data, socio-economic indicators, and adherence of data to international standards. The literature has acknowledged the importance of statistics and the benefits of evidence-based policy in terms of generating reforms through experimentation (Rodrik, 2010). However, theoretical arguments have been made that better statistics may not necessarily lead to reforms. Binswanger and Oechslin (2020) theoretically show that under certain political conditions better statistics can inhibit reforms. For instance, with better statistics, voters are less inclined to give politicians the benefit of the doubt when confronted with disappointing economic data, thereby weakening politicians’ incentives to experiment and carry out reforms in the first place. Thus, the benefits of data transparency warrant empirical validation. However, recent research indicates that democracies are more likely to transform talk of reform during economic downturns into self-correcting reforms than less democratic societies (Arezki et al. 2020).

The role of transparency in facilitating good institutions by limiting corruption has been well debated. Press freedom and political institutions that increase accountability have been found to limit corruption across economies (Brunetti and Weder, 2003; Lederman et al., 2005; McMillan and Zoido, 2004). As noted by Djankov et al. (2003), two challenges in institutional design are the control of disorder and dictatorship, and transparent governments are better able to control disorder while incurring few social losses. Cordis and Warren (2014) find that strengthening the Freedom of Information Act (FOIA) in the United States reduces corruption and increases the likelihood that corrupt acts will be uncovered. In Mozambique, Armand et al. (2020) implemented a large-scale field experiment following the dissemination of information about a substantial natural gas discovery to test whether information can limit the political resource curse. The study finds that when information reaches citizens, it increases mobilization and reduces violence, but when information does not spread beyond local leaders, it has negative effects such as elite capture and increase in rent-seeking activities.

However, studies have also shown that transparency is not always desirable, or it is insufficient to limit corruption. Bac (2001) argues that transparency can have adverse effects by providing information to outsiders on who to bribe. Prat (2005) contends that complete transparency is not always beneficial and the type of information matters. This is part of the rationale of executive privilege - an agent may care more about appearances than engage in frank discussions in decision-making if it is understood that the internal discussions will be made public. Furthermore, information alone may not be sufficient to produce positive outcomes if there is little political engagement by citizens (Khemani et al., 2016). More importantly, information has to have a high signal-to-noise ratio to be impactful (Kosec and Wantchekon, 2020).

Studies have empirically explored elements of data transparency on governance, while not explicitly labeling it as such. For instance, Islam (2006) constructs a transparency index based on the frequency and availability of 11 indicators from four sectors (real, fiscal, financial, and external) and examines its effect 4
on measures of governance. Hollyer et al, (2011) create a transparency index based on the availability of country-level data – 172 variables in total categorized under Economic Policy and Debt in the World Bank’s World Development Indicators – to show that democratic countries are more transparent. Similarly, Williams (2009) constructs a transparency index based on the quantity of data released by governments through the availability of socio-economic data contained in the World Development Indicators and the International Finance Statistics databases. Choi and Hashimoto (2018) proxy for data transparency policy reforms using subscriptions to the IMF’s Data Standards Initiatives and find that such reforms lead to falls in sovereign bond spreads. The study finds that the larger quantity of data produced, as measured by the index, is positively correlated to the quality of bureaucracy, investment and financial sector development.

Finally, there is the question of whether data transparency can contribute to long term growth. The role of data transparency in facilitating economic growth is closely tied to institutions in the literature. The quality of institutions is largely dependent on their degree of accountability and corruption. Institutions operating in secrecy – lack of transparency - tend to be poor in quality as public officials can seek personal gain with little accountability. A large body of literature documented the detrimental effects of corruption on economic growth (Mauro 1995; Ehrlich and Lui, 1999). The secretive nature of corruption makes it distortionary, thereby discouraging investment and slowing growth (Shleifer and Vishny, 1993). Corruption can discourage foreign direct investment, entry of new firms, and misallocate public expenditures (De Soto, 1989; Mauro, 1998; Wei, 2000; Brada et al., 2019). Furthermore, institutions or even corruption that is “unpredictable” discourages investment and lowers growth (Campos et al., 1999). Corruption may also reduce parental investments in human capital (Varvarigos and Arsenis, 2015; Brunetti et al., 1998). Islam and Lederman (2020) find a statistically significant impact on transitional growth to a higher potential level of gross domestic product per capita. The estimates indicate an elasticity of the magnitude of 0.03 percent per year, which is much larger than the elasticity of trade openness and schooling in their estimation sample. Thus, there is some evidence that better data ecosystems can have far-reaching economic benefits.

# **Measuring the Performance of Data Systems**

The ideal data transparency measure covers the capacity of the data system to produce timely and good quality data that is widely accessible. Additional elements include the legal framework surrounding data and the features or services data systems provide. There are three main measures that are widely used: (i) the Statistical Capacity Indicator (SCI) from the World Bank (that is to be discontinued), (ii) the Statistical Performance Indicator (SPI) also from the World Bank released as part of WDR 2021, and (iii) the Open Data Inventory (ODIN) from the Open Data Network.<sup>2</sup> The SCI is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The SCI score is then calculated as the simple average of all three area scores on a scale of 0-100. The SCI only covers developing economies but has the advantage of going back historically to 2004/2005, allowing for tracking the evolution of data systems over time. See Table A2 in the annex for definitions of subcomponents.

The SPI, which is the successor to the SCI, measures the capacity and maturity of national statistical systems by assessing the use of data, the quality of services, the coverage of topics, the sources of information, and the infrastructure and availability of resources. The goal is to improve development outcomes and track progress toward the Sustainable Development Goals. The Statistical Performance

> 2 See Table A1 in the annex for a summary of all three indicators.

5
Indicators (SPI) is a framework of 5 pillars and 22 dimensions to assess the maturity of national statistical systems. The five pillars include variety of data users ( _Data use_ ); the provision of services that connect users and producers such as online access, IMF SDSS subscription, and NADA data catalogue ( _Data services_ ); production of indicators that track the SDGs ( _data products_ ); availability and frequency of micro data ( _data sources_ ); and _data infrastructure_ , which encompasses adherence to international standards, legislation, skills, and partnerships. The SPI is only available from 2016 and is still a work in progress. For instance, the pillar on data use only covers the variety of international organizations as users of data. The pillar on data infrastructure only covers legislation. The data sources pillar is limited in coverage of administrative data. The SPI has built on the SCI in several meaningful ways. It is global in coverage, includes several additional sources of micro data, and includes additional dimensions of openness under the category of _data services_ (see table A5). See Table A3 in the annex for definitions of SPI subcomponents.

The Open Data Inventory (ODIN) measures how complete a country’s statistical offerings are and whether their data meet international standards of openness. Data assessed in ODIN must be official country data that are published on the national statistics office’s (NSO) website or any other official country website that is linked from the NSO website. Coverage scores are based on the availability of key indicators and appropriate disaggregation over time and for geographic subdivisions. These encompass social statistics, economic and financial statistics, and environmental statistics. Openness scores are based on whether data can be downloaded in machine-readable and non-proprietary formats, are accompanied by metadata and whether download options exist such as bulk download and user-selection or APIs, and have an open terms of use or data license. Thus, this openness measure is really one of online access restricted to NSO or NSOlinked websites. Access to microdata is excluded and so is adherence to international standards. See Table A3 in the annex for definitions of SPI subcomponents.

The correlation matrix between the three indicators is presented in table 1. The SCI and SPI are highly correlated (0.86), while the SPI is also highly correlated with ODIN (0.76). The pairwise correlations are statistically significant at the 1% level.

**Table 1: Correlation between SCI, SPI, and ODIN**

||Statistical Capacity<br>Indicator (SCI)|Statistical<br>Performance<br>Indicator (SPI)|Open Data<br>Inventory<br>(ODIN)|
|---|---|---|---|
|StatisticalCapacityIndicator(SCI)|1|||
|Statistical Performance Indicator (SPI)|0.862***|1||
|Open DataInventory (ODIN)|0.672***|0.761***|1|

***statistically significant at the 1% level

# **Six Stylized Facts about Data Systems in MENA**

The stylized facts below describe the state of data systems across the MENA region. Data systems in MENA are characterized on the one hand by underperforming developing economies due to the prevalence of conflict, and on the other hand by rich economies that are severely underperforming relative to their income 6
peers. In the middle are several economies making strides for better data systems but are still far away from the frontier. Do note that the cross-sectional comparison of data systems in MENA is largely based on the year 2018, which is the year when data availability overlaps for the SPI, SCI, and ODIN.

# **Stylized Fact 1: Developing MENA economies have poor data ecosystems, largely due to the prevalence of conflict**

Data systems across the developing MENA economies are poor in comparison to other developing regions. The MENA region scores the lowest for both the SCI and SPI (figure 1). The ODIN overall score places MENA at a virtual tie with East Asia and the Pacific, both being above Sub-Saharan Africa. This is mainly due to a higher ODIN coverage score for the MENA region, indicating a higher prevalence of indicators in NSO websites available at a disaggregated level. Note that ODIN does not consider access to micro data or the adherence of data to international standards.<sup>3</sup>

**Figure 1: Data Systems across Developing Economies by Region**

<!-- Start of picture text --> SCI (2018) SPI (2018)<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br><!-- End of picture text -->

**ODIN, 2018**

> 3 One element not discussed in the study is the openness and coverage of gender statistics as provided by the ODIN indicator. The MENA region has the second lowest score of all regions, but it is quite similar to the other regions except for North America and Europe and Central Asia.

7
<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> The low performance in the SCI score for the developing MENA region is largely, but not completely, due to the prevalence of economies in conflict or fragile situations. The low scoring economies include the Syrian Arab Republic, the Republic of Yemen, Iraq and Libya. Figure 2 shows a positive correlation between SCI and the log of real GDP per capita for 129 developing economies. Countries such as the Arab Republic of Egypt, Jordan, and the Islamic Republic of Iran tend to perform better than their income peers. However, they remain far from the frontier, even for developing economies.

**Figure 2: SCI - MENA Developing Economies and Income Peers**

8
# **Stylized Fact 2: MENA developing economies as a group have experienced the largest deterioration in data systems over time**

The statistical capacity indicator (SCI) is the only indicator of the three examined that can trace the evolution of data systems over a long period of time. The SCI is available only for developing economies. Although data is available from 2004, data for the bulk of countries are largely available from 2005. Therefore, figure 3 provides the SCI scores by region for 2005 and 2020. The MENA region shows the largest fall in SCI scores within this timeframe, despite starting at a low base. This drop scores it below Sub-Saharan Africa to be the lowest scoring region among all the regional groupings of developing economies. The reasons for the fall vary across countries, with some aging out of standards based on the recency of data, or the absence of micro and macro indicator data. Conflict also plays a big role with Libya and Yemen experiencing large declines in data systems over this period.

**Figure 3: SCI – 2005-2020**

<!-- Start of picture text --> 90<br>78 78<br>80<br>70<br>68 68<br>70 64<br>63 61<br>60 54 56 57 57<br>50<br>40<br>30<br>20<br>2005 2020 2005 2020 2005 2020 2005 2020 2005 2020 2005 2020<br>Middle East & North Africa Sub-Saharan East Asia & Pacific Latin America & South Asia Europe & Central<br>Africa Caribbean Asia<br><!-- End of picture text -->

# **Stylized Fact 3: Data systems in richer economies in the MENA region underperform relative to their income peers**

A consistent pattern across MENA economies is the underperformance of data systems in the GCC economies relative to their income peers. Figure 4 presents the correlation between the ODIN score and log of GDP per capita, and the correlation between the SPI score and log of GDP per capita. Both indicators, unlike the SCI, cover both developing and advanced economies. Figure 4 shows that all GCC economies underperform relative to their income peers in the SPI index. This pattern is also replicated for the ODIN score, although Oman is the only exception, outperforming its income peers. The ODIN score however is more limited in scope by only focusing on NSO websites, and the online access features in these websites. A striking finding is that there are developing economies in the MENA region that overperform with respect to their income peers, in many cases even better than GCC economies, although they remain far from the frontier countries.

## **Figure 4: SPI and ODIN Scores**

9
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**Table 2. Status of survey data (with year collected) on public facing website of MENA NSOs**

|**Economy/data**<br>**category**|**Establish**<br>**ment**<br>**survey**|**Price**<br>**survey**|**Consumption**<br>**survey**|**Labor**<br>**force**<br>**survey**|**Health survey**|**Population**<br>**census**|**Economic**<br>**census**|**Total data**<br>**categories**<br>**with recently**<br>**collected data**<br>**per economy**|
|---|---|---|---|---|---|---|---|---|
|Algeria|No|Yes (2021)|No|No|No|No|Yes (2011)|2/7|
|Bahrain|No|Yes (2021)|Yes (2014/15)|No|Yes (2018)|Yes (2020)|No|4/7|
|Djibouti|No|Yes (2021)|Yes (2017/18)|No|No|Yes (2011)|No|3/7|
|Egypt, Arab Rep.|No|Yes (2021)|Yes (2017/18)|Yes<br>(2020)|Yes (2014) *|Yes (2017)|Yes (2017)|6/7|
|Iran, Islamic Rep.|No|Yes (2020)|Yes (2019/20)|Yes<br>(2018)|No|Yes (2016)|No|4/7|
|Iraq|No|Yes (2019)|Yes<br>(2017/2018) *|No|Yes (2018) *|No|No|3/7|
|Jordan|No|Yes (2021)|Yes (2017)|Yes<br>(2020)|Yes (2017/18)|Yes (2015)|Yes (2018)|6/7|
|Kuwait|Yes (2018)|Yes (2020)|Yes (2019/21)|Yes<br>(2015)|No|No|No|4/7|
|Lebanon|No|Yes (2020)|No|Yes<br>(2018/19)|No|No|No|2/7|
|Libya|No|Yes (2020)|No|No|Yes (2014)|No|No|2/7|
|Malta|Yes (2016)|Yes (2021)|Yes (2015)|Yes<br>(2020)|No|Yes (2011)|No|5/7|
|Morocco|Yes (2019)|Yes (2021)|Yes (2014)|Yes<br>(2019)|No|Yes (2014)|No|5/7|
|Oman|No|Yes (2020)|No|No|Yes (2014) *|Yes (2020)|Yes (2020)|4/7|
|Qatar|No|Yes (2020)|No|Yes<br>(2019)|No|Yes (2015)|Yes (2015)|4/7|
|Saudi Arabia|Yes (2019)|Yes (2021)|Yes (2018)|Yes<br>(2020)|Yes (2017/18)|Yes (2010)|Yes (2010)|7/7|
|Syrian Arab<br>Republic|No|Yes (2019)|No|No|No|No|Yes (2019)|2/7|
|Tunisia|No|Yes (2021)|Yes (2015)|Yes<br>(2017)|Yes (2018) *|Yes (2014)|No|5/7|
|United Arab<br>Emirates|No|Yes (2020)|No|No|No|No|No|1/7|
|West Bank and Gaza|Yes (2018)|Yes (2021)|Yes (2017)|Yes<br>(2019)|Yes (2019/20) *|Yes (2017)|Yes (2017)|7/7|
|Yemen, Rep.|No|No|No|No|No|Yes (2014)|Yes (2014)|2/7|
|Total economies<br>indicatingcollection|5/20|19/20|12/20|11/20|9/20|13/20|9/20||

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of recent data for each data category Total recent data indicated to have been collected across all economies 78/140 Source: Ekhator Mobayode and Hoogeveen (2021) Note: Evidence that a survey was collected can be explicit like in a “survey section” of the website or “implicit” like in a report, summary table and/or any mention or reference to the data on the website. * indicates instances where collection of recent microdata was not indicated on NSOs website, but the research team discovered it on an external website. These include Iraq: Rapid welfare monitoring survey SWIFT 2017/2018 downloadable from <u>https://microdata.worldbank.org/,</u> Egypt (2014) downloadable from http://www.dhsprogram.com/ and Iraq MICS 2018, Oman MICS 2014, Tunisia: MICS 2018, West Bank and Gaza (Palestine) MICS 2019/20 downloadable from <u>https://mics.unicef.org/surveys.</u>

# **(ii) Frequency of Macro Economic Data in MENA**

While the SCI, SPI, and ODIN indicators capture the quality of the data ecosystem to a large extent, they do not necessarily encompass the extent and availability of high-frequency data. Table 3 presents the availability of high-frequency data on GDP, industrial production, and unemployment for 19 economies in the MENA region. The date of the most recent data available is also indicated. This information is compiled from the websites of various statistical offices or data portal initiatives, central banks and ministries of planning, economy, or finance across the MENA region. The findings are benchmarked against Mexico, which serves as a good comparator for the MENA region given its upper-middle-income economy status and a well-functioning data ecosystem. Of the 19 economies in the MENA region, 15 report quarterly data on GDP. Some economies lack information for the year 2020 entirely. Economies in conflict such as Libya (2014) and Yemen (2017) have outdated data. Only 10 of the 19 MENA economies have monthly or quarterly information on industrial production—for the remaining nine economies, information is not readily available. Only eight economies report quarterly unemployment data, while none have monthly data. The benchmark country, Mexico, reports unemployment data monthly. A caveat is that the table does not consider quality. For example, definitions of unemployment may be inconsistent with international standards (Arezki et al., 2020).

**Table 3 MENA Macro Data Availability (as of January 2022)**

|**Economy**|**GDP Data**|**GDP Data**<br>**latest**<br>**(calendar**<br>**year)**|**Industrial**<br>**Production**<br>**Index Data**|**Industrial**<br>**Production**<br>**Index latest**<br>**(calendar year)**|**Unemployment**<br>**Data**|**Unemployment**<br>**Data latest**<br>**(calendar year)**|
|---|---|---|---|---|---|---|
|Algeria|Quarterly|Q2 2021|Quarterly|Q3 2021|Semi-Annually|May 2019|
|Bahrain|Quarterly|Q3 2021|n/a|n/a|Only Census|2020|
|Djibouti|Annually|2018|n/a|n/a|n/a|n/a|
|Egypt, Arab Rep.|Quarterly|Q3 2021|Monthly|September 2021|Quarterly|Q3 2021|
|Iran, Islamic Rep.|Quarterly|Q3 2021|Quarterly|Q3 2021|Quarterly|Q3 2021|
|Iraq|Quarterly|Q2 2021|Quarterly|Q2 2021|Only 2 years|2016|
|Jordan|Quarterly|Q3 2021|Monthly|October 2021|Quarterly|Q3 2021|
|Kuwait|Quarterly|Q4 2020|n/a|n/a|Annually|2021|

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|Lebanon|Quarterly|Q4 2019|n/a|n/a|only census|2018-2019|
|---|---|---|---|---|---|---|
|Libya|Annually|2014|n/a|n/a|Labor force<br>survey|2013|
|Morocco|Quarterly|Q3 2021|Quarterly|Q3 2021|Quarterly|Q3 2021|
|Oman|Quarterly|Q3 2021|n/a|n/a|Annually|2020|
|Qatar|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q2 2021|
|Saudi Arabia|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Syrian Arab Republic|Annually|2019|n/a|n/a|Annually|2019|
|Tunisia|Quarterly|Q3 2021|Monthly|September 2021|Quarterly|Q3 2021|
|United Arab Emirates|Quarterly|Q2 2020|n/a|n/a|Annually|2020|
|West Bank and Gaza|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Yemen, Rep.|Annually|2017|n/a|n/a|only census|2013|
|**MENA Total: 19**|**Quarterly:**<br>**15/19**||**Monthly or**<br>**Quarterly:**<br>**10/19**||**Quarterly:**<br>**8/19**||
|**Mexico**|**Quarterly**|**Q3 2021**|**Monthly**|**October 2021**|**Monthly**|**November**<br>**2021**|

Source: Gatti et al., (2022). Information obtained from country statistical office websites; government data portal websites; central bank websites and Ministry of Planning/Economy/Finance websites Notes: N/a means information is not readily available. All data accessed as of January 2022. This table is not exhaustive, it has only surveyed country Statistical Office Websites, Government Data Portal Websites, Central Bank Websites and Ministry of Planning/Economy/Finance websites. Third-party websites were excluded from this survey. The table has been updated and verified by World Bank country economists. Mexico was selected as a comparator because it is an uppermiddle-income economy with a well-functioning data ecosystem.

On top of the lack of published monthly unemployment data in the MENA region, there are also challenges regarding the definitions employed. For examples, countries around the world usually follow the ILO’s definitions of employment and unemployment, which are consistent with definitions adopted by other developed countries, such as the United States (see Table 4).

**Table 4 Definitions of Employment and Unemployment from the U.S.  BLS, the French INSEE and the ILO**

|**_Definitions_**|**Employment**|**Unemployment**|**Source**|
|---|---|---|---|
|**United States**<br>**-Bureau of**<br>**Labor**<br>**Statistics**<br>**(BLS)**|Someone, aged 16 or over, who has either (1) worked at<br>least 1 hour as a paid employee or (2) in their own<br>business, profession, trade, or farm, or (3) was not<br>temporarily absent from their job, business, or farm,<br>whether or not they were paid for the time off, or (4)<br>worked without pay for a minimum of 15 hours in a<br>business or farmowned by amemberoftheir family.|Someone, aged 16 or over, who (1)<br>does not have a job, (2) has actively<br>looked for one in the past 4 weeks, and<br>(3) is available for work.|https://www.b<br>ls.gov/cps/def<br>initions.htm|

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|**France-**<br>**Institut**<br>**Nationale de**<br>**la statistique**<br>**et des etudes**<br>**economiques**<br>**(INSEE)**|Individuals who worked for any amount of time, even if<br>only for an hour in the course of the reference week. Only<br>individuals in the working-age population (between 15 and<br>64 years of age) are considered.|All people aged 15 and older who do<br>not have a job and are looking for one.|https://www.i<br>nsee.fr/en/met<br>adonnees/defi<br>nitions|
|---|---|---|---|
|**International**<br>**Labor**<br>**Organization**<br>**(ILO)**|All those of working age (15 years and over) who, during a<br>short reference period, were engaged in any activity to<br>produce goods or provide services for pay or profit. They<br>comprise employed persons "at work", that is, who worked<br>in a job for at least one hour; and employed persons "not at<br>work" due to temporary absence from a job, or to working-<br>time arrangements (such as shift work, flextime and<br>compensatory leave for overtime).|All those of working age (15 years and<br>over) who were not in employment,<br>carried out activities to seek<br>employment during a specified recent<br>period, and were available to take up<br>employment given a job opportunity.|https://www.il<br>o.org/ilostat-<br>files/Docume<br>nts/Statistical<br>%20Glossary.<br>pdf|

The definition of official employment and unemployment rates in MENA appear to be inconsistently reported across countries. Many MENA countries do not comply with the ILO’s definition of employment or unemployment—or do not provide sufficient information to indicate whether the definitions are followed (see Table 5). Oman, which has one of the highest ODIN scores in the region, does not specify the definition of employment and unemployment. Countries in conflict, such as the Syrian Arab Republic and Yemen, do not provide any information on unemployment. Changes in definitions may entail shifts in the employment and unemployment numbers reported (Arezki et al., 2020).

**Table 5 Definitions of Employment and Unemployment**

||Follow ILO<br>unemployment definition|Follow ILO employment<br>definition|Age of working<br>population|
|---|---|---|---|
|Morocco|YES|unspecified|15 and above|
|Algeria|YES|YES|unspecified|
|Tunisia|YES|unspecified|unspecified|
|Libya|YES|unspecified|15 and above|
|Egypt, Arab Rep.|YES|YES|15 and above|
|Lebanon|YES|unspecified|YES|
|West Bank and Gaza|YES|unspecified|15 and above|
|Jordan|YES|unspecified|15 and above|
|Saudi Arabia|NO|unspecified|15 and above|
|Oman|Unspecified|unspecified|unspecified|
|United Arab Emirates|YES|unspecified|YES|
|Qatar|YES|unspecified|15 and above|
|Bahrain|YES|YES|unspecified|
|Kuwait|YES|unspecified|YES|
|Iran, Islamic Rep.|YES|YES|NO|
|Djibouti|NO|NO|unspecified|
|Iraq|unspecified|unspecified|unspecified|

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# **Forging a Social Contract for Data**

The note has detailed the data challenge rampant in the region. Bridging the MENA gap requires a multipronged approach to develop sustainable data ecosystems. Where governments lack capacity to generate data, investments are needed to build capacity. Where governments are unwilling to share data, agreements and understanding need to be developed in concert with a clear agenda highlighting the benchmarks of good data ecosystems and the crucial role of data in generating good policies and social harmony. And when important topics are under-researched in the region and require specific data, investments could be made for one-off data collection activities to set up a baseline of knowledge in the region. There is also the promise of digital technologies. If data and digital technologies are combined effectively, there is scope to improve vital services and allow for policies that consider feedback from citizens and businesses. These are key aspects of strengthening the social contract between the state and its citizens.

The 2021 World Development Report on Data for Better Lives calls for a new social contract on data around the world. This is much needed in the MENA region. This is only possible through the existence of an enabling environment. Such an enabling environment has several components. It consists of a mix of legal “safeguards” - ensuring protection of personal data and cybersecurity; and “enablers” that include e- transactions, open data and access to information. Independent, capable, and well-resourced institutions are needed to enforce laws effectively. Part of the process is building the right infrastructure and harmonizing technical standards. The trust engendered could incentivize people and firms to participate in the data economy. To ensure effective and active participation, data literacy and the capacity of civil servants and the broader population will have to be scaled. The evolving social contract also includes the responsible use and reuse of data for development purposes, built on trust that encourages individuals and firms to contribute that essential data. When used in an effective and trustworthy way, data and digital technologies effectively can be crucial enablers for improving service delivery, the accuracy of decisionmaking, fighting corruption, and increasing transparency.

There are important roles for governments, the private sector and civil society to enable data to create value. Governments play a key role in developing strategies and rules to govern data, with input from civil society and the private sector. Governments also play a key role in producing the data and statistics to inform about developmental progress. The private sector’s role as a producer and user of data is also vital to enable the region to compete within the global digital economy. For that to happen, bottlenecks must be removed to enable access to quality and freely available traditional data sources such as surveys and censuses with open licenses for reuse. Investments in the production of statistics are much needed. There needs to be strong commitment to traditional data (which the WDR 2021 terms “public intent data”) – the kind of data that is used for public good, that improves service delivery and that measures progress specifically for the most vulnerable. This kind of data holds governments accountable and has the potential to empower individuals and groups in fragile and conflict-affected settings where traditional micro data has been sorely lacking. Empowering civil society organizations and academia to research and advocate for the better use of data and the protection of data rights may increase trust in the data economy.

International institutions such as the World Bank can play an important role in developing a social contract around data. One way forward for the World Bank, especially with improving traditional sources of data, would be to create a Statistics Compact for the region.<sup>6</sup> The goal of the Statistics Compact is twofold: (i) close the microdata gap (data collection and sharing) and (ii) invest in statistical modernization. The Statistics Compact would support investments in a minimum set of microdata sets comprising of population

> 6 <u>https://blogs.worldbank.org/opendata/scaling-support-official-statistics-middle-east-and-north-africa-mena-datacompact</u>

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and economic censuses (collected every decade), establishment (formal & informal sector), consumption/income, health and labor force surveys (collected every 3 years) and price surveys (collected monthly) along with their public dissemination at the lowest level of disaggregation allowed for while maintaining data anonymization.<sup>7</sup> These investments are not limited to supporting data collection, but also include capacity building to assure data quality, harmonization of data collection methods and tools across surveys and countries, data anonymization and dissemination and technical assistance (TA) aimed at improving data integration and interoperability.

The microdata component is complemented with investments in statistical modernization aimed at increasing the capacity to combine survey and non-survey (administrative; transaction, big) data for the production of statistics. In a world that is digitizing rapidly, NSOs need to start to exploit the opportunities for statistics production by utilizing available data from administrative processes (such as population and business registries), transactions (e.g. scanning data from supermarkets; financial transactions; VAT and tax records) or other big data sources (satellite imagery; social media etc.) and combining these with available microdata sets. Utilizing more secondary data sources requires improvements in data governance. Data governance includes establishing the infrastructure and technology, setting up and maintaining the processes and policies, and identifying the individuals and organizations that have both the authority and responsibility for handling and safeguarding specific types of data. At the national level, data governance also covers data interoperability, standards and quality control. A MENA Statistics Compact could support statistical modernization by offering capacity building and technical assistance aimed at reshaping data production processes and improving data governance systems.

.

> 7 This is just a minimalistic number of microdata sets that would have to be collected regularly. A strong case can also be made to include income surveys (particularly in countries with a large formal sector), perception surveys and wealth surveys.

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