metadata: authors: 0: author: Fazekas,Mihály
1: author: Brenner,Dominik
2: author: Ladegaard,Peter Farup
count: Jordan
docna: 0: docna: Measuring Legislative Predictability : The
            Case of the Kingdom of Jordan and Implications for the MENA Region
docty: Policy Research Working Paper
theme: Public Administration,Public Sector Management,Transparency, Accountability and Good Governance
abstracts: cdata!: Laws and regulations represent a central
            tool for governments to achieve policy objectives, and they
            also represent a fundamental condition for making desirable
            individual and business decisions. While laws and
            regulations regularly have to be adapted to changing
            circumstances, frequent and sudden modifications indicate
            legislative unpredictability and are expected to impose
            considerable costs on citizens and businesses. Legislative
            predictability is assumed to be the consequence of
            high-quality laws, and existing evidence shows that
            regulatory management systems indeed impact legislative
            predictability. This paper proposes and implements an
            innovative legislative big data analytics approach to
            measuring legislative predictability in the Kingdom of
            Jordan and selected global comparator countries. It also
            maps out the feasibility of such an approach for the wider
            Middle East and North Africa region. Legislative data
            gathered from official government sources point to the high
            frequency of modifications in Jordan compared to a wide
            range of countries where data are available Around 10 to 15
            percent of the original laws have been modified within 24
            months of enactment over the past 20 years. In addition to
            prevalent modifications of new laws, even older laws face a
            comparatively high risk of modification in Jordan.
            Additional data collection following the template outlined
            in this paper could deliver a comparative data set, enabling
            a better understanding of the drivers and trends of
            legislative predictability and hence better evidence-based policies.
display_title: Measuring Legislative Predictability
            : The Case of the Kingdom of Jordan and Implications for the
            MENA Region
listing_relative_url: /research/2024/08/34376409/measuring-legislative-predictability-case-kingdom-jordan-implications-mena-region
topicv3: Regulatory Management m1326539 2096,Regulatory Impact Assessment m1503292 2645,Regulatory Governance and Transparancy
            m1504629 3131
publishtoextweb_dt: 2024-08-12T00:00:00Z
dois: 10.1596/1813-9450-10864
no_of_pages: 56
author_keys: 0: author_key: NA
1: author_key: 2297948
2: author_key: 795277
model_extractions: Policy Research Working Paper 10864

# Measuring Legislative Predictability

The Case of the Kingdom of Jordan and Implications for the MENA Region _Mihály Fazekas Dominik Brenner Peter Ladegaard_ Governance Global Practice August 2024
Policy Research Working Paper 10864

# **Abstract**

Laws and regulations represent a central tool for governments to achieve policy objectives, and they also represent a fundamental condition for making desirable individual and business decisions. While laws and regulations regularly have to be adapted to changing circumstances, frequent and sudden modifications indicate legislative unpredictability and are expected to impose considerable costs on citizens and businesses. Legislative predictability is assumed to be the consequence of high-quality laws, and existing evidence shows that regulatory management systems indeed impact legislative predictability. This paper proposes and implements an innovative legislative big data analytics approach to measuring legislative predictability in the Kingdom of Jordan and selected global comparator countries. It also maps out the feasibility of such an approach for the wider Middle East and North Africa region. Legislative data gathered from official government sources point to the high frequency of modifications in Jordan compared to a wide range of countries where data are available Around 10 to 15 percent of the original laws have been modified within 24 months of enactment over the past 20 years. In addition to prevalent modifications of new laws, even older laws face a comparatively high risk of modification in Jordan. Additional data collection following the template outlined in this paper could deliver a comparative data set, enabling a better understanding of the drivers and trends of legislative predictability and hence better evidence-based policies.

This paper is a product of the Governance Global Practice. 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 authors may be contacted at FazekasM@ceu.edu, BrennerD@ceu.edu and pladegaard@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
# Measuring Legislative Predictability:

The Case of the Kingdom of Jordan and Implications for the MENA Region Mihály Fazekas<sup>1,2</sup> , Dominik Brenner<sup>1</sup> , and Peter Ladegaard<sup>3</sup> 1 Department of Public Policy, Central European University 2 Government Transparency Institute 3 World Bank JEL Classification: G28 Key Words: Regulatory Impact Assessment, Regulatory Governance and Transparency, Jordan, Regulatory Management, Legislative Predictability
# ACKNOWLEDGMENTS

The authors would like to thank Cyril Benoit (Researcher at the Centre for European Studies and Comparative Politics at Sciences Po, Paris), Andreja Marušić, and Valentina Saltane (Senior Private Sector Specialists at the World Bank) for reviews and comments on earlier versions of this paper. The authors would also like to thank officials from Jordan’s Legislation and Opinion Bureau for their invaluable support in facilitating access to and understanding of publicly available legislative data for the Kingdom of Jordan. The authors further like to thank Ahmed Al Shaibani and Muhammad Bilal for outstanding research assistance. The research underpinning this paper was made possible by the financial support of the World Bank.

# 1. INTRODUCTION

Legislation is a central tool for governments and policy makers to implement their agendas and manage the behavior of individuals, firms, and the state. Be it laws or regulations, both can directly affect investment, spending, competitiveness, and growth (Voermans 2009). Yet, for positive effects to materialize, high-quality legislation is required (Mousmouti 2012, Maler 2001). High-quality legislation should support competition, ease legislative and regulatory burdens, provide fair market conditions, and increase citizen satisfaction (Mousmouti 2012, Voermans 2009). High-quality legislation guides the behavior of individuals and firms as intended by the policy maker by being clear, precise and unambiguous; it thereby achieves the intended policy goal while being financially prudent (Maler 2001, Xanthaki 2010, Mousmouti 2012). Efficient laws and regulations rely on the ‘use of minimum costs for the achievement of optimum benefits of the legislative action’ (Xanthaki 2010, p.115). One hallmark of high-quality laws and regulations is _legislative predictability._ Legislative predictability refers to a predictable legislative environment that consists of laws and regulations with no to little modifications in the short-run and limited (repeated) modifications in the mid-term. High-quality legislation that is clear, precise and unambiguous, should result in few ex-post modifications shortly after their introduction and thus display higher legislative predictability. Lowquality legislation, in contrast, tends to be complex, imprecise, and ambiguous, increasing confusion among firms and individuals and requiring more short-term changes to correct for mistakes. Thus, legislative predictability will be lower when legislation is of lower quality.

Legislative predictability has come to the fore in the midst of frequent and lasting crises and an ever more complex socio-economic environment. Policy makers, thus, face pressures towards faster lawmaking, while maintaining high-quality laws and regulations. Legislation is increasingly perceived as a “commodity on a time-to-market clock” (Voermans et al 2015, p. 283). Yet, these trends towards increasing legislative speed can compromise legislative quality. In turn, lower-quality legislation can hinder effective and efficient policy implementation, create confusion among citizens and firms, and result in high degrees of uncertainty (Xanthaki 2010).

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Despite legislative predictability’s beneficial impacts and central policy importance, there is a notable lack of an objective, reliable, and replicable methodology for its measurement that could be consistently applied to a wide range of countries in a repeated fashion. Given recent advances in computational methods as well as the academic literature, it is possible to make advances filling this gap. By implication, the objective of this working paper is to adapt and test a novel measurement approach to legislative predictability in the Kingdom of Jordan and to investigate if the approach can be applied in the broader MENA region.

The paper is organized in the following way. Section 2 briefly reviews the causes and effects of legislative predictability based on the available literature. Section 3 introduces the underlying data and indicators needed for estimating and analyzing legislative predictability. Section 4 presents legislative predictability patterns across a range of countries worldwide compared to Jordan. Section 5 provides the main analysis, with detailed descriptive and explanatory findings on Jordanian laws and regulations. Section 6 provides a country mapping of the MENA region to see the extent to which legislative data is available in the region and, thus, if the analysis can be reproduced beyond Jordan. Section 7 concludes by proposing an ambitious yet feasible agenda for deploying the proposed indicators across the MENA region.

# 2. BACKGROUND: CAUSES AND EFFECTS OF LEGISLATIVE PREDICTABILITY

Legislative predictability, as a key dimension of high-quality legislation, impacts a range of actors, potentially improving social welfare or diminishing it. Legislative predictability caters to the needs of businesses and households as the predictability of policy survival is decisive to ensure investment confidence (Aizenman & Marion 1993, Ramey & Ramey 1995, Fatal & Mihov 2003, Maltzman & Shipan, 2008). Frequent changes in the institutional environment in which companies operate - that is, laws and regulations in particular – increase uncertainty and negatively affect investment choices (Henisz 2000). Firms are particularly sensitive to uncertainty that is exogenous to their company, among which laws and regulations are a crucial component (Miliken 1987). Governments need to provide credible commitment to ensure that firms operate in a predicable legislative and regulatory environment. Aizenman and Marion (1993) were among the first that found a negative relationship between the unpredictability or uncertainty of policy making and economic growth. They placed a particular emphasis on businesses, which, in the context of high unpredictability, will withhold investments. Further research has strengthened this claim since then (Fatas & Mihov 2013. Baker et al. 2016, Al-Thaqeb et al. 2019).

There is some evidence that uncertainty can be used strategically by companies to benefit from the ‘first-mover’ advantage (Aragon-Correa & Sharma 2003, Carrera et al. 2003, Doh & Pearce 2004). However, firms mostly take a wait-and-see approach and postpone investments if uncertainty is high to increase planning security and might exit markets with high uncertainty due to risk aversion (Gulen & Ion 2016, Marcus & Kaufman 1986, Yang et al. 2004, Hoffmann et al. 2009). Even if firms continue to invest, investment tends to be limited and mainly focused on reversible and short-term rather than long-term, irreversible investment (Chen et al. 2019, Rugman & Verbeke 1998, Doh & Pearce 3
2004). Unpredictability also has a negative effect on firm value, especially for smaller-scale companies with lower financial leverage and firms with high R&D spending and high marketization (Zhu et al. 2020). In a situation of high uncertainty, the borrowing costs for firms increase and company performance decreases (Iqbal et al. 2020, Colak et al. 2017, Kelly et al. 2016; Pastor & Veronesi 2012).

Legislative (un)predictability also affects households. One impact channel goes via lower company performance. A consequence of the negative effect of legislative uncertainty on companies is lower employment, which directly affects household income and spending. It is therefore not surprising that existing evidence indicates that households also delay investments and spending (Bernanke 1983, Bloom 2009, Pastor & Veronesi 2012). Reduced household spending, in turn, negatively affects production and thus economic growth (Bloom, Bond & Van Reenen 2007, Al Thaqeb et al. 2019). Ultimately, these recent studies confirm what Henisz (2000) already stated more than two decades ago: high unpredictability creates ‘lower and less economically productive investment, and, therefore lower rates of economic growth” (p. 2).

Improving legislative predictability depends on a wide range of social, economic, and political factors, most of which are hardly amenable to short to mid-term policy interventions. Nevertheless, regulatory management systems (or ‘Better regulations’ tools) have emerged as a policy tool to effectively improve legislative quality and support legislative predictability (Mousmouti 2012, Brenner & Fazekas 2023). By combining public consultations with ex-ante impact assessments and ex-post evaluations, these tools follow the principle of transparent, evidence-based policy making and should encourage governments to communicate legislative proposals, provide quantifiable evidence of their economic and societal benefits, conduct public consultations on legislative ideas and modify any policy draft according to feedback and evidence (Baldwin 2010, OECD 2019). Empirical evidence shows that the presence of various regulatory management tools is positively associated with economic growth and productivity, GDP per person employed, as well as a firm’s market access and productivity (Torriti et al. 2022). Regulatory management tools such as ex-ante impact assessments are increasingly established in developing economies, and lessons are emerging on critically important factors associated with such tools’ success (Ladegaard et al, 2018).

Europe is a good starting point to demonstrate that regulatory management tools are indeed correlated with legislative predictability. More broadly, regulatory management tools - from ex-ante impact assessment to ex-post scrutiny – should increase the overall quality of laws and regulations as they increase legislative predictability. Between 2006 and 2012, the number of countries adopting Impact Assessment systems in Europe doubled while the number of impact assessments increased threefold (Figure 1), even though the depth of these systems varies due to context-specific political and administrative factors (Lianos et al. 2016). Since good regulatory practices are assumed to be essential for improved legislative quality and, as a consequence, legislative predictability,

> **Figure 1. Total number of European countries with IAs and IAs produced (2006-12)       Figure 2. Legislative predictability and IAs in France, Hungary, Italy, (Source: Lianos, Fazekas, & Karliuk 2016) and the UK (206-12) (Source: Brenner & Fazekas 2023).**

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<!-- Start of picture text --> ‘total nr. of EU countries adopting RIA (left axis) — — total nr. of RIAsin EU (weighted and upscaled, right axis)<br>25 6,000<br>.<br>7<br>20 eee<br>a =—----<br>15 -7 - o 4,000<br>-<br>10 o o a 2,000<br>5<br>1D<br>2006 2007 2008 2009 2010 2011 2012*<br><!-- End of picture text --> <!-- Start of picture text --> 015<br>®S<br>B 014<br>S<br>3<br>3<br>=Fl 013<br>3=<br>z<br>go<br>2<br>a<br>one Absence of Impact Assessment<br>Presence of Impact Assessment<br>0 20 40 60 80<br>Analysis time (in months)<br><!-- End of picture text -->
composites combining processes, outputs and outcomes which make it hard to assess the effectiveness of government reforms. Fourth, many indicators which do not rely on expert judgement, instead track the existence of specific regulatory management tools and thus not adequate for assessing how each tool is used in practice and how they interact with their contexts such as changing economic circumstances.

While such measurement has been useful, it clearly has limitations in terms of accuracy and validity, calling for new metrics. The availability of large volumes of administrative data on laws and regulations open up new avenues for better gauging the determinants, extent, and effects of volatile laws and regulations. Such legislative predictability indicators should further contribute to a more encompassing understanding of legislative quality.

# **3.1 Legislative and Regulatory Data**

More and more data has been made publicly available in recent years by political institutions through dedicated websites and open-data portals. Yet, the scope and quality of legislative data that are made available still display a strong cross-country variation in terms of temporal scope and depth. Nonetheless, most countries tend to disclose the title and text of legislation, its date of publication and the ID number of previous versions of legislation. The data collection work underpinning the present analysis follows the data collection methods, database structure, and quality standards established by the research team (Fazekas et al, 2024). The wider data collection effort and process is described in Appendix B. Here, we outline the specific data collection approach in Jordan.

The data collection process consists of four stages: source identification, source annotation, web scraping and parsing, as well as data evaluation. First, source identification was conducted in consultation with Jordan’s Legislation and Opinion Bureau (LOB) which hosts the website and managed legislative data. Second, a detailed source annotation was created that precisely marks each variable in the dataset (table 1) on the LOB website. Such annotations are highly technical, offering guidance for the subsequent programming work. Third, the data collection process includes writing algorithms for scraping the LOB website and then parsing the scraped semi-structured data into standard data tables following our data standard.<sup>2</sup> Fourth, data quality, that is scope and accuracy, is evaluated both algorithmically and manually; and if errors are found then data collection and structuring are updated. Data scope is validated by comparing data record counts against record counts on the LOB website. Data accuracy is algorithmically checked by reviewing missing rates and looking at breaks in trends and distributions. Manual validation is done by comparing a small, random sample of dataset records against the original source data on the web.

# **3.2 Indicators**

Both governmental bodies and academics are beginning to develop methodologies which can measure and compare regulatory predictability across countries based on novel data sources. These studies and

> 2 See: <u>https://docs.google.com/spreadsheets/d/1cCuGZnIyAWJHD0ktk239AHTuWZLp566IItYyJFjDCE/edit?gid=0#gid=0</u>

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reports represent the most relevant antecedents to the novel indicators proposed and tested in this report. First, the World Bank has created a measure of legislative predictability for Romania and Türkiye that focuses on the count of amendments and repeals over time (World Bank 2019, 2021). In the case of Romania, the World Bank relied on the number and frequency of amendments and repeals to show changes per year, trends over time, comparisons between different year intervals, as well as amendment patterns across different sectors. For Türkiye, the World Bank analyzed the number of annual changes and average changes over larger time periods with a further breakdown of these patterns by type of legislation. One of the key insights of these analyses was that they allow for an identification of the sectors where unpredictability was the highest. Second, Brenner & Fazekas (2023) uses legislative data on France, Hungary, Italy and the UK, to develop new indicators of legislative predictability focusing on the timing of first modification and number of total modifications.

While our main innovation is the development of legislative predictability indicators, we also propose a number of variables which are essential for exploring the prevalence, distribution and trends of legislative predictability (Table 1).

**Table 1. Indicators used in the analysis (unit of analysis=individual law)**

|**Name**|**Description**|
|---|---|
|Modification count|Total count of modifications a law/regulation received until 2022|
|First modification date|Date when the law/regulation was first modified|
|Category|Type: law or regulation|
|Date passing|The date a law/regulation was published|
|Title|Title of a law/regulation|
|Text|Full text of a law/regulation|
|Text size|Size of the text (expressed in number of characters)|
|Policy Production|Number of laws/regulations passed in a year (level of country-year)|
|Related laws|Count of laws/regulations related to a given law or regulation|
|Initiator|Name of the initiator of legislation|
|Sector|Classification of legislation into non-economic, multisector, and individual<br>NACE industries (including taxation and budget)|

_Modifications_ . To analyze modifications, we create two variables i) tracking the first modification, and ii) a series of modifications. In both cases, each law is analyzed over time. To this end, the data has to be organized in the following way: Each law is observed for a series of months beginning in the month it was published and ending at the moment of data collection. The first one is initially set to zero and stays zero until the law is first modified. At the time of transition, the law takes on a value of 1 and is dropped from the sample thereafter. The second formulation of the dependent variable equals 1 every month the law is changed, otherwise it is 0. Collecting the first date of modifications and the overall number of changes over time is a tested approach and previous work has applied a similar 7
methodology to a set of countries in Europe, namely France, Hungary, Italy, the United Kingdom (Brenner & Fazekas 2023).

The second unique feature of our proposed indicator set is that we track additional indicators of legislative and regulatory activities. Using these indicators as covariates allows us for a better understanding of these measures' contribution to enhanced legislative stability (and quality). We introduce each of the most relevant potential covariates below.

_Category of law_ : Laws and regulations might have different modification patterns and the category of legislation should account for that. As a categorical variable, it takes the value of 0 for regulations and 1 for laws.<sup>3</sup> Incorporating the category allows us to understand which type of legislation is prone to legal uncertainty and thus require closer attention in the future.

_Sector of law_ . Given that a standard classification of economic sectors is available across countries, such as NACE in the EU, it is possible to assign laws to economic sectors based on their texts and they matching to sector descriptions. This enables a closer look at those sectors that are particularly exposed to legal and regulatory instabilities and allows for targeted political counter-measures to protect businesses in these sectors. More details on this variable can be found in Section 3.3., below.

_Initiator of law_ . Knowing the initiator of legislation not only allows us to observe which actors are generally more active in drafting laws and regulations but also check for the initiators that are linked to higher levels of regulatory predictability or lack of thereof.

# **3.3 Sectoral Classification**

To analyze legislative predictability by economic sector, we have to assign laws and regulations to economic sectors. As our source legislative dataset does not contain such information, we classify laws into economic sectors in a separate analytic stage after the data collection process. The goal of matching laws and regulations to specific sectors is to identify if some are more exposed to unpredictable legislative environment compared to others.

In order to classify laws and regulations into economic sectors, we rely broadly on _Statistical Classification of Economic Activities in the European Union_ (NACE) industry classifications. NACE is an industry standard classification system and mirrors the United Nations’ _International Standard Industrial Classification of all Economic Activities (ISIC)_ . Both NACE and ISIC follow the same logic . While these categories are widely used in the industry and by international organizations, they come with a few caveats from the perspective of legislative analytics. First, a few categories of laws and regulations are not present in the NACE classification, but they are of high relevance for defining the sample for analysis. For example, not all legislation is relevant for economic industries. Various laws and regulations tackle issues of personal life (e.g. marriage or citizenship). We labeled such legislation as

> 3 We collected all primary laws ( )دستورand regulations ( )نظامon the LOB website while excluding constitutional laws

> ( ,)دستور agreements ( )اتفاقیةand instructions/guidelines ( )تعلیمات due to their less economic and/or more administrative scope.

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“Non-Economic”. Additionally, a large set of legislation regulates the public sector – in particular public administrations and municipalities, as well as courts and judges. We combined these laws and regulations under “Public_Administration_and_Judiciary” and remove them for the sectoral analysis of legislative predictability. Second, legislation can tackle multiple sectors at the same time without any NACE category dominating. To account for such laws and regulations, we added the category “multisector”. Third, taxation and budgetary legislation plays a significant role for businesses and households in a horizontal, cross-cutting manner, e.g. changing VAT rates impacts all sectors similarly from mining to IT. This was considered by creating a dedicated taxation and budget category. The resulting modified NACE classification will be called GCO sectoral categorization which allows us to select those laws which have economic relevance and apportion them into specific sectors where legislative activities are likely to differ. (Table 2). While this task is automatized for the broader GCO dataset, we additionally manually checked and corrected all Jordanian laws since 2000 and a subset of Jordanian regulations since 2000 for assuring the highest quality analysis in this paper.

**Table 2. Sectoral Classification labels. NACE original labels and modified/additional labels (GCO categorization)**

|**GCO**|**NACE**|
|---|---|
|Agriculture, forestry and fishing|Agriculture, forestry and fishing|
|Mining and quarrying|Mining and quarrying|
|Manufacturing|Manufacturing|
|Energy|Electricity, gas steam and air conditioning supply|
|Water and waste|Water supply; sewerage; waste management and remediation<br>activities|
|Construction|Construction|
|Wholesale and retail|Wholesale and retail trade repair of motor vehicles and<br>motorcycles|
|Transportation|Transporting and storage|
|Tourism and hospitality|Accommodation and food service activities|
|Information and communication|Information and communication|
|Finance|Financial and insurance activities|
|Real estate|Real estate activities|
|Professional services|Professional scientific and technical activities|
|Business support activities|Administrative and support service activities|
|Public administration and judiciary|Administration of the State and the economic and social<br>policy of the community|
|Defence and police|Provision of services to the community as a whole|
|International affairs and trade|Provision of services to the community as a whole|
|Social security|Compulsory social security activities|
|Education|Education|
|Health care|Human health and social work activities|

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|Arts, entertainment, and recreation|Arts, entertainment and recreation|
|---|---|
|Voluntary and representative organizations|Other services activities|
|Household services|Activities of households as employers, undifferentiated<br>goods and services, producing activities of households for<br>own use|
|Extraterritorial organizations|Activities of extraterritorial organizations and bodies|
|Taxation and budget|Administration of the State and the economic and social<br>policy of the community|
|Multisector laws|-|
|Non-economic laws|-|

## **3.4 Methods for Exploring Legislative Predictability**

Once all the relevant indicators are calculated, both for legislative predictability and contextual variables like sectors, we carry out a range of explorative and descriptive analyses. Combining variables on modifications with contextual variables allows us to measure regulatory predictability and its underlying drivers.

First, we explore legislative predictability across countries, comparing Jordan to a set of different countries. This offers crucial insights into cross-country variability and how different legislative systems operate. The analysis is based on the smoothed hazard risk of legislative modification. Such smooth hazard curves display how the risk of modification changes over time (i.e. months). Second, we deep dive into Jordanian legislative data, by looking at trends to track legislative predictability over two decades. This in-depth descriptive analysis disaggregates the data by year, sector, and initiator. The in-depth analysis starts with the distribution of laws and regulations passed in a given year which takes the absolute yearly count of laws and regulations. Next, the analysis moves to modifications by displaying modifications as percentage of all laws and regulations passed in a given year, followed by modifications as percentage of all laws and regulations passed within 24 months. We further disaggregate modification within 24 months by the providing the percentage share of economic sectors as well as the policy initiator for all laws and regulations that were modified within 24 months.

# 4. LEGISLATIVE PREDICTABILITY IN AN INTERNATIONAL CONTEXT

Uncertainty in policy making is a global issue and legislative predictability concern should not be seen in isolation. Figure 3 provides a set of global comparators with data drawn from six diverse countries, covering both low- and high-income countries, as well different political systems.<sup>4</sup> The purpose of this section is to situate the overall pattern of legislative predictability in the Kingdom of Jordan in a

> 4 The figures are calculated based on the LEGDAT dataset (mid-2023 version). More information on the data and country scope can be found www.globalcorruptionpbservatory.com.

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global perspective. This allows for some initial cross-country insights into the relevance of legislative predictability as a general measure of good governance and provides us the opportunity to discuss the pattern of legislative predictability in Jordan relative to other countries.

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<!-- Start of picture text --> 7\h WA Wy<br>A V [— ~ \F \ IN V \ _ mesclton<br>, —~S vi \| .<br><!-- End of picture text -->
<!-- Start of picture text --> == Laws == Regulations<br>40<br>ry<br>=<br>2<br>= 30<br>N<br>£<br>r=]<br>=<br>B 20<br>=<br>E<br>o<br>oa<br>§ 10<br>c<br>oO<br>S<br>o<br>a<br>0<br>sSsSJ s97 sSy> s& sWS)Ho oSsS) cSx7 nSN& iS\S $xD os§o<br>Production year of original law<br><!-- End of picture text -->
<!-- Start of picture text --> Laws Regulations<br>Ministry of Higher Education and Scientific Research | Ministry of Health I<br>Higher Education Council I Prime Minister l<br>Prime Minister I ci fiance minister H<br>Ministry of Health I Wil Service Sureau<br>Independent Electoral Commission if Greater AmmanMinistry Municipality of Justice 1I<br>General Budget Department | Food and Drug Administration I<br>Ministry of Social Development | The Ministry of Education I<br>Ministry of Interior Affairs I Social Security Corporation I<br>Ministry of Industry and Trade l Ministry of Foreign Affairs !<br>Ministry Of Agriculture I Jordan Radio and Television Corporation |<br>Judicial_ Council. | Corporate .Control Department: |<br>-<br>Jordan The Ministry of Environment I<br>Naval Authority I Telecommunications Regulatory Authority |<br>The Radiological and Nuclear Regulatory Commission I Tafila Technical University I<br>The Crown Prince Foundation I Prime minister I<br>Petra provincial authority I Judicial Council |<br>Orphans Fund Development Foundation I General Budget Department I<br>National Resources Investment and Development Corporation I University of Jordan !<br>Ministry of Transportation I The Ministry of Tourism and Antiquities I<br>_ - 7<br>Ministryof Foreign Affairs I The Ministry of PublicThe Hashemite Works and HousingUniversity I<br>Ministry of Finance I Mutah University I<br>Ministry of Energy and Mineral Resources | Ministry of Youth I<br>Ministry of Culture I Ministry of Water and Irrigation I<br>Ministry of Communications and Information Technology | Ministry of Public Sector Development i<br>Jordan Radio andJordanian Television Nursing Corporation Council I| Ministry M4inistry Indust o Tees" lustry ane an .Supplyrace }<br>Food . . Ministry Of Agriculture 1<br> and Drug Administration ! Land Transport Regulatory Authority I<br>Atomic Energy Authority | Jordanian Judicial Council |<br>Aqaba Special Economic Zone I Audiovisual Media Authority I<br>0 5 10 15 0 5 10 15<br>Percentage Percentage<br><!-- End of picture text -->
<!-- Start of picture text --> Laws by Sectoral Distribution Regulations by Sectoral Distribution<br>Taxation and Budget ; . Public Administration and Judiciary<br>Public Administration and Juciciary Education<br>Multisector 7 7 | Multisector 7<br>International Affairs and Trade Health Care<br>Transportation Finance<br>Finance Transportation<br>Education Social Security<br>Nonconcric TT soniaa<br>Mining and Quarrying Arts, Evitalnment ood Recreation<br>Defence and Police International Affairs and Trade<br>Information and Communication<br>Ream core IL<br>Information and Communication 7 Voluntaryuaier | inieabie:and Reantative ve Organizatialae<br>Energy Taxation and Budget<br>Penicoinrs,iculture, ir ForestryForesty andand FishiFishing eneray<br>Arts, Entertainment and Recreation - Tourism and Hospitality,<br>. : Non-Economic B<br>Business Support Services RealEstate —|<br>Voluntary and Representative Organizations Professional Services<br>Social Security c Agriculture, Forestry and Fishing<br>Extraterritorial Organizations Wholesale and Retail<br>Wholesale and Retail Construction<br>Water and Waste Water and Waste<br>Tourism and Hospitality Mining and Quarrying<br>Real Estate Business Support Services<br>Professional Services Manufacturing<br>Construction Household Services<br>0.00 0.05 0.10 0.0 0.1 0.2<br>Proportion Proportion<br><!-- End of picture text -->

<!-- Start of picture text --> Laws Regulations<br>Edaion ae<br>Taxation and Bucget Multisector —[I<br>International Affairs and Trade Po International AffairsSocialand SecurityTrade fT rt—“—S—OCSCY<br>Transportation<br>Information and Communication PY<br>Transporation Taxation and Budget —[i<br>Finance<br>itso Detence and Poco<br>Finance; {ay WholesaleWater and and Waste Retail (|<br>Energy F TourismProfessional and HospitalityServices ||<br>Defence and Police La Arts, Entertainment and Recreation [I<br>Agriculture, Forestry and Fishingacu La Voluntary andAgriculture, Representative Organizations Forestry and Fishing YY]|<br>Voluntary and Representative Organizations | Real Estate |<br>Mining and Quarrying |<br>Information and Communication B Energy a<br>iatrts, eEntertainment andFnRecreation5 | Business SupportConstruction Services a<br>0 5 10 15 20 [) 5 10<br>Percentage Percentage<br><!-- End of picture text -->
15 percent of regulations modified within 24 months are linked to health care. Interestingly, it is not necessarily the sectors that dominate the production of legislation that show most legislative unpredictability, even though a very broad link between production and unpredictability seem to persist. Understanding the drivers behind this pattern will require further investigations.

# 6. DATA MAPPING IN MENA

Given the demonstrated value of legislative predictability indicators in Jordan, a similar approach could be applied to further countries in the MENA region. To establish the feasibility of such extension, we conducted a detailed mapping of data availability in Algeria, Djibouti, the Arab Republic of Egypt, Iraq, Lebanon, Morocco, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates. The goal of this mapping is to show the potential of our legislative predictability analysis beyond the Kingdom of Jordan and the possibility of a detailed cross-country analysis in the MENA region. Following our tested approach in Jordan, we thereby focused on publicly available data from parliamentary, governmental, or ministerial websites. Table 3 provides an overview of the time span, the number of observations and the source link for each country. The presence of N/A does not indicate missing data but rather difficulties in determining the number of observations on the source website.

**Table 3. Overview of legislative data sources reviewed**

|**Country**|**Coverage**|**Observations**|**Source**|
|---|---|---|---|
|Qatar|1961-2023|802|https://www.almeezan.qa/Default.aspx|
|Lebanon|1900-2023|3,788|http://www.legallaw.ul.edu.lb/LegisltaionSearch.aspx|
|Saudi Arabia|1930-2023|447|https://laws.boe.gov.sa/BoeLaws/Laws/|
|Tunisia|1957-2023|5,629|http://www.iort.gov.tn/|
|United Arab|1971-2023|223|https://elaws.moj.gov.ae/laws/search|
|Emirates||||
|Algeria|1964-2023|N/A|https://droit.mjustice.dz/ar/content/أساس�ة- قوانن -2|
|Egypt, Arab|1857-2023|9,813|https://www.cc.gov.eg/legislations|
|Rep.||||
|Iraq|2012-2023|N/A|https://moj.gov.iq/iraqmag/page_1/|
|Morocco|1964-2023|1,055|https://adala.justice.gov.ma/themes|
|Djibouti|N/A|N/A|https://preprod.ansie.dj/Ancienne.php|

Table 4 shows the structural results of this mapping exercise We focused in particular on variables that were available in Jordan and thus would allow a direct comparison with our findings on legislative predictability for Jordan (a detailed mapping of websites is available in Appendix B).

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<!-- Start of picture text -->
[Description | Qatar | UAE | KSA_| Tunisia | Morocco | Lebanon | Algeria | Egypt_| Djibouti | _iraq_|<br>=<br>a ee eee<br>eeee<br>initiatorEP<br><!-- End of picture text -->
economic sector of legislation is not possible. Yet the website contains a list of modifications with the date which is necessary to provide a more general analysis of legislative predictability.

In the case of Lebanon, the University of Lebanon‘s website hosts a comprehensive collection of laws and regulations that can be filtered by year, date, category, topic and administrative section. The individual page of legislation includes the law title, date of passing, law text as well as the date of first modification. Data is available in a standardized html form and thus relatively easy to collect. Morocco, similar to Lebanon, provides text in a structured html format with information on modifications within the text which would allow an imperfect analysis of legislative predictability.

Qatar, the United Arab Emirates, the Kingdom of Saudi Arabia and Tunisia provide the most suitable ways to collect our core variables. In the case of Tunisia, the main source is the country’s official gazette. The website requires a preselection of the code of laws (e.g., labor code), which then opens a list of all related laws in a standardized format with a hyperlink to the respective individual law page. The individual page itself contains the category, law title, date of passing, and the law text. The website also provides a single count of total modifications and related legislation with a separate column that further differentiates the overall count of related legislation by degrees and judicial decisions.

For the United Arab Emirates, the Ministry of Justice website offers a legislation portal with all previous legislation, differentiated by categories and a separate table on the side of the websites listing related legislation. The individual legislative page contains the title, text, date of passing and the initiator. In terms of modification, previous changes based on the date are available. The initiator of legislation can only be determined inside the law text which requires a predefined list of possible initiators, similar to Jordan. While data is in a standardized html format, collecting the date and count of modifications requires links across different website pages.

Saudi Arabia offers a website with an extensive list of laws and regulations. The individual page contains the law title, date of introduction, date of passing, the law text, as well as the count and date of modifications. If modified, the part of the legislative text being modified will be highlighted in yellow with a pop-up window that specifies the original law text, as well as the modified text with the date of modification - year/month/day. The modification is thereby forward looking which allows us to simply count the number of modifications and pick the first modification as the date of first modifications.

Qatar shows one of the best data availability patterns. The Almeezan website provides a list of legislation based on the subject or date of legislation with a filter to distinguish between legislative categories (laws, regulations, etc.). The page for individual legislation then contains the title, date of passing, law text, and previous versions of the legislation based on the count and date of each modification. Information on the initiator is limited and contains, similar to Jordan, only the name of the ministry, organization or body. All data is available in a standardized html form.

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Overall, the data mapping exercise in the MENA shows promising results for several of the countries and would benefit from an application of the legislative predictability in the future work.

# 7. CONCLUSION

The analysis introduced a range of indicators and background legislative variables (e.g., sector) both for the Kingdom of Jordan and selected global comparators. This analysis demonstrated the fruitfulness of bringing data analytics to the domain of legislative analysis, for better regulation and improved law making.

According to the presented empirical evidence, Jordan experienced an unpredictable legislative environment in the last two decades as highlighted by cross-country comparison. Based on a robust empirical strategy that relies on the full legislative corpus from the official government website, this working paper has focused on the modification of laws and regulations by conducting a range of explorative and descriptive analyses. To determine the drivers of unpredictability, we combined variables on the modifications of laws and regulations with contextual variables linked to sectors and initiators.

The results showed that Jordan experienced several periods of high legislative unpredictability over time with more between 30 percent of legislation being modified within 24 months after passing. We further showed that legislative unpredictability is most prevalent in the education sector for laws and the health care sector for regulations with a broader pattern indicating a possible link between production and unpredictability. This environment poses issues for the investment choices of businesses and households who tend to delay investments under high uncertainty and thus negatively affect the general growth trajectory.

Based on a detailed website mapping, it is established that legislative analytics can be extended to the broader MENA region which would allow for a more encompassing regional perspective on legislative unpredictability to detect common trends and drivers. Understanding legislative unpredictability within the national context and across the MENA region would then also benefit the tailoring of regulatory management systems, such as Regulatory Impact Assessment (RIA), to increase legislative predictability and thus certainty among businesses and households.

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<!-- Start of picture text --> Laws Regulations<br>Ministry of Higher Education and Scientific Research I Ministry of Health l<br>Higher Education Council | Prime Minister |<br>Ministry of Health I Finance Minister |<br>General Budget Department ! Food and Drug Administration l<br>Ministry of Industry and Trade I The Ministry of Education l<br>Ministry Of Agriculture | . . .<br>Jordan Naval Authority I Social Security Corporation l<br>The Radiological and Nuclear Regulatory Commission | Corporate Control Department I<br>The Crown Prince Foundation I Tafila Technical University I<br>Prime Minister ] Jordan Radio and Television Corporation |<br>National Resources Investment and Development Corporation I University of Jordan |<br>Ministry of Transportation I The Ministry of Tourism and Antiquities |<br>Ministry of Social Development I The Ministry of Environment |<br>Ministry of Interior Affairs | The Hashemite University |<br>Ministry of Foreign Affairs I Mutah University l<br>Ministry of Finance ! Ministry of Water and Irrigation l<br>Ministry of Energy and Mineral Resources I Ministry of Industry and Trade I<br>Ministry of Culture I _ .<br>Ministry of Communications and Information Technology I Ministry of Foreign Affairs |<br>Jordanian Nursing Council I Ministry Of Agriculture |<br>Jordan Radio and Television Corporation I Land Transport Regulatory Authority I<br>Food and Drug Administration I Greater Amman Municipality I<br>Atomic Energy Authority I Civil Service Bureau |<br>Aqaba Special Economic Zone i} Audiovisual Media Authority |<br>0 5 10 15 0 5 10 15<br>Percentage Percentage<br><!-- End of picture text -->
source (i.e. the respective website). After all the preparatory steps, the data collection starts, using robust web scraping and parsing algorithms which are replicable and scalable allowing for continuous data collection at a comparatively low cost (assuming source data format and structure remain the same). The last stage is the data evaluation. Even the best source annotation and data collection effort can result in data scraping or parsing errors. Evaluating the collected data is therefore critical for assuring quality data which is well structured and accurately mirrors the official government data. Once the first version of the collected data is ready, our research team verifies that the data structure corresponds to the GCO legislative data template (variable names and contents are permissible). Moreover, data content is checked on a sample to verify if information is correctly parsed from the web into our database and that there are no obvious errors such as variables are completely missing or stored in the wrong table. We check if the missing rates or rates of non-sensical values (e.g. zeros) for each variable are low. These checks typically reveal data quality and scope errors which then are fed back into improving the data collection and parsing algorithms. We cycle through data collection, checking and updates until data quality is sufficiently high.

**Table B1. Overview of GCO legislative datasets**

|**Country**|**Coverage**<br>**(Years)**|**n (laws)**|**n (total)**|
|---|---|---|---|
|Bulgaria|2006-2022|1,659|3,061|
|Chile|1900-2022|(2,504)|13,971|
|Colombia|1998-2022|1,598|10,657|
|France|2012-2022|473|1,700|
|Hungary|1998-2022|4,365|5,743|
|Jordan*|1900-2022|2,065|2,065|
|India|1952-2022|3,433|9,022|
|Portugal|1976-2022|4,810|12,974|
|Russian Federation|1995-2022|9,616|31,438|
|United Kingdom|2006-2022|534|3,116|
|United States|1995-2022|4,425|135,052|
|**Total**||**48,087**|**315,256**|

_Notes: *The total number of observations for Jordan includes laws and regulations. For all other countries the total includes non-enacted (or ongoing) bills and enacted laws._ 28
# **Appendix C. MENA Data Mapping with extended variable list**

This appendix provides the data mapping table with the full list of variables that were checked on national parliamentary and government websites across the MENA region.

Table C1.

|**Name**|**Description**|**Qatar**|**UAE**|**Algeria**|**Djibouti**|<br>**Egypt,**<br>**Arab**<br>**Rep. **|**Iraq**|**Lebanon**|**Morocco**|**KSA**|**Tunisia**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Law_title**|Title of legislation|||||||||||
|**Law_text**|Text of legislation|||||||||||
|**Category**|Type of legislation:<br>law or regulation|||||||||||
|**Initiator**|Initiator of legislation<br>(government, a<br>minister,MP,etc|||||||||||
|**date_**<br>**introduction**|Date when a<br>legislation was first<br>introduced|||||||||||
|**date_**<br>**passing**|Date when a<br>legislation<br>passed/became law|||||||||||
|**affecting_**<br>**law_**<br>**first_date**|Date a legislation was<br>modified for the first<br>time|||||||||||
|**affecting_**<br>**law_**<br>**count**|Number of<br>modification a<br>legislation received<br>afterpassing|||||||||||
|**bill_id**|Unique source ID for<br>the passed law/bill<br>(on the source)-|||||||||||
|**originator_**<br>**name**|Who actually<br>initiated the<br>legislation; list all the<br>names of the MPs or<br>names government<br>body/bodies|||||||||||
|**originator_**<br>**affiliation**|Institutional/party<br>affiliation of the<br>originator. For<br>government-<br>sponsored bills: the<br>respective<br>department/ministry.<br>For MP-sponsored<br>bills: the respective<br>partyaffiliation|||||||||||

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|**procedure_**|Expectional or|
|---|---|
|**type_**<br>**standard**|ordinary procedure<br>[English terms across<br>all countries]|
|**bill_type**|Categories available<br>on the source (e.g.<br>general laws,<br>omnibus laws,<br>budgetarylaws)|
|**bill_status**|Whether a bill was<br>passed/rejected by<br>the parliament otr<br>neither (under<br>consideration)|
|**modified_**<br>**law_id**|Reference bill_id(s)<br>of laws modified|
|**modified_**<br>**laws_count**|Count of laws<br>modified by the<br>enacted law|
|**date_**<br>**stage_1 **|Date of start of first<br>legislative step|
|**date_**<br>**stage_2**|Date of the start of<br>the second legislative<br>step|
|**date_**<br>**stage_n **|Date of the last<br>legislative step|
|**name_**<br>**stage_1 **|Name of the first<br>legislative step|
|**name_**<br>**stage_2 **|Name of the second<br>legislative step|
|**name_stage_n**|<br>Name of the nth<br>legislative step|
|**number_**<br>**stage_1**|Consecutive<br>numbering of the<br>stage|
|**number_**<br>**stage_2**|Consecutive<br>numbering of the<br>stage|
|**number_**<br>**stage_n**|Consecutive<br>numbering of the<br>stage|
|**size_**<br>**stage_1**|Cumulative<br>depth/size of all<br>debates during the<br>given stage; number<br>of characters of<br>speeches held or the<br>length of the report|

30
|**size_**|Cumulative|
|---|---|
|**stage_2**|depth/size of all<br>debates during the<br>given stage; number<br>of characters of<br>speeches held or the<br>length of the report|
|**size_s**<br>**tage_n**|Cumulative<br>depth/size of all<br>debates during the<br>given stage; number<br>of characters of<br>speeches held or the<br>length of the report|
|**stages_**<br>**count**|Total number of<br>legislative stages|
|**committee_**<br>**count**|Count of committees<br>from the committees<br>data table|
|**committee_**<br>**name**|Involved<br>Committees; list the<br>names of all of them|
|**committee_**<br>**date**|Date of start of<br>committee stage|
|**committee_**<br>**role**|Committee role:<br>national term (e.g. in<br>Hungary kiijelölt<br>bizottság)|
|**committee_**<br>**hearing_**<br>**count**|Total number of<br>hearings (sessions)<br>for the committees<br>stage. Number can<br>be larger than<br>committee_count<br>(row 32)|
|**ia_dummy**|Coded 1 if an impact<br>assessment was<br>conducted as part of<br>the legislative<br>process,otherwise 0|
|**ia_title**|Short title of the<br>impact assessment<br>document|
|**ia_text**|Full text in machine<br>processible format of<br>the impact<br>assessment|
|**ia_date**|Date of publication<br>of the impact<br>assessment|

31
|**ia_size**|Number of<br>characters in the full<br>text|
|---|---|
|**bill_size**|Size of the bill before<br>its 1st reading by the<br>lower chamber,<br>expressed in number<br>of characters|
|**bill_text**|Full text of the bill<br>before its first<br>reading|
|**bill_**<br>**text_**<br>**url**|Full url of the bill (as<br>collected from the<br>source)|
|**law_size**|Size of the enacted<br>law, expressed in<br>number of<br>characters|
|**law_text_**<br>**url**|Full url of the final<br>law (as collected<br>from)|
|**amendment_**<br>**count**|Count of the number<br>of tabled<br>amendments during<br>the entire legislative<br>process|
|**amendment_**<br>**id**|Unique source ID for<br>the tabled<br>amendment|
|**amendment_**<br>**text**|Full text of the<br>amendment|
|**amendment_**<br>**text_**<br>**url**|URL of full text of the<br>amendment<br>(html/pdf/etc.)|
|**amendments_**<br>**stage_**<br>**name**|<br>Name of the stage in<br>which an<br>amendment<br>occurred (should<br>mirror the name<br>from "Name of the<br>nth legislative step"|
|**amendments_**<br>**stage_**<br>**number**|<br>Number of the stage<br>in which an<br>amendment<br>occurred (should<br>mirror the name<br>from<br>"number_stage_np"|
|**amendment_**<br>**committee_**<br>**name**||

32
|**amendment_**<br>**plenary**|Lower or upper<br>house|
|---|---|
|**amendment_**<br>**originator**|Names of MPs that<br>submitted the<br>amendment|
|**Amendment_**<br>**originator_aff**|Parties of the MPs<br>submitting the<br>amendment or<br>department if it is a<br>governnment<br>amendment|
|**amendment_**<br>**outcome**|Amendment was<br>approved or rejected|
|**amendment_**<br>**vote_for**|Number of votes in<br>favour|
|**amendment_**<br>**vote_against**|Number of votes<br>against|
|**amendment_**<br>**vote_abst**|Number of absentee<br>votes|
|**plenary_size**|Count of the size of<br>all plenary debates,<br>measured as the<br>number of characters<br>of plenary debates<br>during the legislative<br>process, sum of all<br>(recorded) debates,<br>except committee<br>stages|
|**final_vote_**<br>**for**|Number of final<br>votes in favour|
|**final_vote_**<br>**against**|Number of final<br>votes against|
|**final_vote_**<br>**abst**|Number of final<br>absentee votes|

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