metadata: authors: 0: author: Islam,Asif Mohammed
1: author: Lederman,Daniel
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docna: 0: docna: Data Transparency and Growth in Developing
            Economies during and after the Global Financial Crisis
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Industrial Economics,Economic Theory & Research,Economic Growth,Financial Sector Policy,Health Care Services Industry,Legislation,Regulatory Regimes,Judicial System Reform,Social Policy,Legal Products,Legal Reform
teratopic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade
abstracts: cdata!: The study explores the effects of data
            transparency on economic growth for developing economies
            over a unique time period &#x2014; at the onset of the
            2007&#x2013;2009 global financial crisis and thereafter.
            Data transparency is defined as the timely production of
            credible statistics as measured by the Statistical Capacity
            Indicator. The paper finds that data transparency has a
            positive effect on real gross domestic product per capita
            during a period of considerable uncertainty. 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 the estimation sample. The
            empirics employ a variety of econometric estimators,
            including dynamic panel and cross-sectional instrumental
            variables estimators, with the latter approach yielding a
            higher estimated elasticity. The findings are robust to the
            inclusion of several factors in addition to political
            institutions and exogenous commodity-price and external
            debt-financing shocks.
repnme: repnme: Data Transparency and Growth in Developing
            Economies during and after the Global Financial Crisis
display_title: Data Transparency and Growth in
            Developing Economies during and after the Global Financial Crisis
listing_relative_url: /research/2020/12/32654156/data-transparency-growth-developing-economies-during-after-global-financial-crisis
topicv3: Economic Growth m1326241 471,Trade m1326924 698,Civil Law m1327064 1044,Financial Sector Development m1327681 1920,Health m1504661 3480
publishtoextweb_dt: 2020-12-09T00:00:00Z
dois: 10.1596/1813-9450-9493
historic_topic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade
origu: Office of the Chief Economist (MNACE)
no_of_pages: 51
author_keys: 0: author_key: 950662
1: author_key: 125180
model_extractions: Policy Research Working Paper 9493 Data Transparency and Growth in Developing Economies during and after the Global Financial Crisis _Asif Mohammed Islam Daniel Lederman_ Middle East and North Africa Region Office of the Chief Economist June 2024
Policy Research Working Paper 9493

# **Abstract**

The study explores the effects of data transparency on economic growth for developing economies over a unique time period—at the onset of the 2007–2009 global financial crisis and thereafter. Data transparency is defined as the timely production of credible statistics as measured by the Statistical Capacity Indicator. The paper finds that data transparency has a positive effect on real gross domestic product per capita during a period of considerable uncertainty. 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 the estimation sample. The empirics employ a variety of econometric estimators, including dynamic panel and cross-sectional instrumental variables estimators, with the latter approach yielding a higher estimated elasticity. The findings are robust to the inclusion of several factors in addition to political institutions and exogenous commodity-price and external debt-financing shocks.

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 authors 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 and Growth in Developing Economies during and after the Global Financial Crisis**

Asif Mohammed Islam Daniel Lederman* Originally published in the Policy Research Working Paper Series on _December 2020._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL Codes: D83, E01, EO2, O47 Keywords: Data transparency, statistics, economic growth

> * Lederman is the Deputy Chief Economist of the Equitable Growth, Finance, and Institutions Practice Group of the World Bank and Islam is a Senior Economist with Middle East and North Africa Chief Economist Office of the World Bank. 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 authors’ responsibility. We are grateful for feedback from the Economic Research Forum presentation panelists Hadi Esfahani, Khalid Abu-Ismail, Mustapha Nabli, and Noha El-Mikawy. We are also grateful to Todd Eisenstadt, Tamar Gutner and Shadi Mokhtari for feedback during the presentations at the American University School of International Services and Center for Environmental Policy. We would also like to thank Kevin Carey, Bledi Celiku, Talip kilic, Aart Kraay, and Harun Onder for comments. We are especially grateful to Daniel Mahler for feedback and advice throughout the development of the study.
# **Data Transparency and Growth in Developing Economies during and after the Global Financial Crisis**

## **I. Introduction**

The perennial question in the development economics profession is why many developing economies do not develop, and whether there are avenues that could allow them to catch up to the levels of income per capita observed in advanced economies. Recent history has witnessed the explosion of data, laying bare stark disparities in data systems between developing and advanced economies. Advanced economies are characterized by modern and coordinated data collection systems that are widely accessible to the research community. Such transparent data systems have presumably created virtuous information feedback loops whereby ideas are generated and debated based on evidence, thus helping improve the quality of public policies over time. The wealth of data and the underpinning architecture of data ecosystems in advanced economies enable them to have a monopoly in attracting research that expands the frontiers of knowledge (Das et al., 2013). In contrast, many developing economies have either lagged in their capacity to generate data or have prevented the research community, the independent media, and civil society from accessing economic data generated by the public sector. Yet, little attention has been paid to the costs of opaque data systems on economic growth in developing economies.

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This paper seeks to make a first contribution to the literature in this direction by exploring the relationship between data transparency and economic growth for developing economies for a unique time period of considerable uncertainty- during and after the global financial crisis.<sup>1</sup> Countries that have credible and timely data systems are likely to perform better during periods of uncertainty than countries that have poor data systems – being transparent is a good antidote for uncertainty. Our work builds on the study by Hodelin (2022) that explores how data density – availability of country-level data in the World Development Indicators (WDI) increases long term economic growth. We define data transparency as the regular publication of credible statistics by the state. Beyond just data density at the country level, data transparency also encompasses the availability of micro data and the credibility of data through adherence to international standards. Our choice of time period is largely dictated by the availability of the data transparency indicator. At the time of writing, we are not aware of other existing empirical analyses of the link between data transparency and subsequent economic growth in developing economies during and after the period of global financial crisis.

Historical data collection can be traced to the first recorded census undertaken by Babylonians around 3800 BC (Grajalez et al., 2013). The purpose and nature of data collection have evolved over time. 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). The data were kept secret from the public and not meant to improve their lives. 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.

> 1 The World Uncertain Index developed by Ahir, Bloom, and Furceri (2022) has an average score of 19,553 between 2005 and 2018 in contrast to a score of 12,829 between 1990 and 2004.

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The modern responsibility of developing countries to produce statistics is well established. The United Nations’ Sustainable Development Goals (SDGs) emphasize the need for countries to generate socioeconomic indicators within the limits of each country’s capacity. The costs of such efforts are not trivial, and the trade-offs between investing in data capacity and other pressing needs are presumably difficult for developing country governments to ascertain.<sup>2</sup> The long-run benefits of transparency, however, could be considerable.

This study explores the partial correlation between data transparency and economic growth. Data transparency, as measured by the Statistical Capacity Indicator (SCI), entails the production and availability of credible statistics. Figure 1 illustrates the unconditional correlation between SCI and GDP-per-capita growth. Country-year observations are ranked in terms of their SCI scores and grouped by terciles along the horizontal axis. Annual growth rates are measured along the vertical axis. In general, higher terciles of the SCI are correlated with faster economic growth for both the sample of economies that are included in the econometric estimations sample and the larger data set that includes economies excluded from the econometric analyses due to missing data on covariates. It is noteworthy that Figure 1 suggests that the positive correlation between SCI and growth is stronger in the larger sample of country-years than in the estimation sample, thus possibly producing a downward bias in the econometric estimates of the partial correlation between SCI and growth.

# **Figure 1: Data Transparency** **<u>and Growth</u>**

|**Data Transparency (SCI) and Log Difference of GDP per Capita (Panel)**|
|---|
|All Economies<br>SampleEconomies|

> 2 It is worth noting here that it is not obvious that setting up minimum standards of data generation and transparency are relatively costly when compared to the public-sector wage bill in developing countries. Furthermore, as discussed later in the paper, the costs of funding national statistical plans seem to be far less than education.

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<!-- Start of picture text --> 0.04<br>0.035<br>0.035<br>0.03<br>0.03<br>0.025<br>0.025<br>0.02<br>0.02<br>0.015<br>0.015<br>0.01 0.01<br>0.005 0.005<br>0 0<br>Tercile 1 Tercile 2 Tercile 3 Tercile 1 Tercile 2 Tercile 3<br>Statistical Capacity Score (SCI) - Terciles Statistical Capacity Score (SCI) - Terciles<br>Data Transparency (SCI) 2005 and Log Difference of GDP per Capita 2005-2018 (Cross-section)<br>All Economies Sample Economies<br>0.45 0.45<br>0.4 0.4<br>0.35 0.35<br>0.3 0.3<br>0.25 0.25<br>0.2 0.2<br>0.15 0.15<br>0.1 0.1<br>0.05 0.05<br>0 0<br>Tercile 1 Tercile 2 Tercile 3 Tercile 1 Tercile 2 Tercile 3<br>Statistical Capacity (SCI) score (2005) terciles Statistical Capacity (SCI) score (2005) terciles<br>Log Difference GDP per Capita<br>Log Difference GDP per Capita<br>(2005-2018) (2005-2018)<br>Log difference GDP per Capita Log difference GDP per Capita<br><!-- End of picture text --> Note: The top panel of figure 1 presents the average per capita growth rate for each tercile of SCI based on a panel data set of economies over time. The bottom panel provides the average log difference in GDP per capita between 2005 and 2018 for each tercile of the SCI score in 2005.

A variety of econometric estimation models are employed in this study to examine the relationship between data transparency and economic growth. Dynamic panel System GMM estimators are employed using a panel dataset as well as an Instrumental Variable approach on a cross-section of economies to account for endogeneity. The results indicate that a 1 percent increase in SCI score is associated with a 0.03 percent increase in the real GDP per capita per annum across panel and OLS cross-section estimations. The magnitude of the effect increases to 0.04 percent using the Instrumental Variables approach. The magnitude of the elasticities uncovered for SCI are larger than the elasticity of GDP per capita with respect to trade openness and even with respect to school enrollment in our sample of economies. Analyses of the economic 5
magnitude of the estimated elasticities further support the contention that data transparency could be a more powerful driver of growth than either trade or schooling, even without taking into account the costs that would be entailed in raising trade or schooling over time relative to improving data systems.

Conceptually, there are several channels through which data transparency can affect development. First, credible and timely data serve as the basis for policy and reforms. Data are about records. Take the example of a business. A manager has the primary goal of raising profits. To achieve this, performance is benchmarked historically and compared with that of competitors. Collateral must be evaluated and leveraged to obtain financing to pursue new ventures. Risk and reward must be balanced. Investors need to be enticed. Without record-keeping, many of these goals could not be achieved. Indeed, experimental evidence indicates that simple interventions that help manufacturing plants improve inventory record keeping in a developing economy are associated with improved performance (Bloom et al., 2020). A similar set of challenges face governments. Countries need to grow, and, to expand options, data must be reliably transparent to provide guidance. Countries with high quality and broadly accessible information can make better decisions. Through data and evaluation, existing policies may be reformed and refined, while new policies may be experimentally evaluated (Rodrik, 2010). Gatti et al., (2024) find that better data transparency reduces growth forecast errors.

Second, data that are accessible to the broader civil society can generate better policies and reforms. Substantial expansions in the frontier of knowledge occur when data are available to a large base of analysts. Researchers test hypotheses, debate and dispute findings, establish robust facts and relationships to facilitate the emergence of the best ideas for addressing challenges. If the initial stock of information in a country is large, then there would be a better knowledge base to inform research and policy. Improved data access has been found to increase scientific research (Nagaraj et al., 2020). Stacy et al., (2024) finds that by analyzing more than 1 million academic articles, the number of articles on a country is strongly correlated with a country’s national statistical system.

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Third, data transparency is particularly important during periods of turmoil – for instance the global financial crisis – when uncertainty is high and the demands on timely data are likely to be larger. The effects of the financial crisis can last for a long period of time (Reinhart and Rogoff, 2014). During the 2007-2008 global financial and economic crisis, sovereign bond spreads widened considerably for developing economies. Greater data transparency can lower sovereign spreads (Gonzalez-Garcia, 2024). Data transparency can bring more credibility in markets and thereby also lower the cost of financing. There is further evidence that government transparency, measured as government dissemination of credible macroeconomic information, reduces the duration of inflation and currency crisis by reducing information asymmetry, improving effectiveness of monetary policy and bolstering public confidence (Shambaugh and Shen, 2018).

Fourth, when data are of questionable quality or unavailable, the gap between perceptions and reality may grow. Important reforms may lead to real welfare improvements yet have little impact on public perceptions because there is limited data tracking such improvements. These perceptions may foster a narrative that results in frustrations that manifest themselves in social protests and unrest (Abi-Nassif et al., 2020). Similarly, if data are of dubious quality, the public may lose confidence in such information and may not alter their perceptions despite positive findings from the data. More important, once a government walks down the path of unreliable or limited data accessibility, it may be difficult to regain credibility. The public may be less willing to trust information from the government, which makes it difficult for a changed government to change public perceptions. The result is economies that are more prone to social upheavals.<sup>3</sup>

> 3 Lack of transparency hurts even more when systems are under stress by potent threats, such as during the Covid19 pandemic. An optimal societal response requires open and direct communication across several actors in society—the government, health care systems, civil society, and various institutions. Information needs to be collected in real time to enable governments and public health officials to take timely, decisive actions. Citizens need to report cases and respond to behavioral changes requested by the government. The flow of data is the oil of the engine of this system of interactions and responses. When data are not made public or are misused, the engine can fail. The ramifications of the lack of trust, forged by limited transparency, come into stark relief when citizens are confused about what to believe.

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Transparency, in general, can be a means to hold governments accountable and improve institutional quality (Khemani et al., 2016; Lederman et al., 2005; Djankov et al., 2004). A large literature has established the consequences of institutions on output per capita and growth (Hall and Jones, 1999; Acemoglu et al., 2005). Corruption has been found to reduce growth while democratic institutions are found to improve development (Mauro 1995; Acemoglu et al., 2001). Transparency is the antithesis of corruption while being a vital characteristic of democratic institutions. This study contributes to the literature by focusing on a specific type of transparency – data transparency - and its effect on economic growth. A few studies have empirically explored the effects of the quantity of data available on governance, financial development and investment (Islam, 2006; Williams, 2009; Hollyer et al., 2011; Binswanger and Oechslin, 2015). Holdein (2022) explores the effects of the quantity data available on economic growth. At the time of writing, we are unaware of any other studies that have empirically explored the relationship between data transparency and economic growth for developing economies, especially during and after the global financial crisis. This study fills a gap in the long literature of determinants of economic growth and policies to improve development at large.

To summarize, the study makes the four contributions to the literature. First, it estimates the empirical relationship between data transparency and economic growth in developing economies during a unique period of considerable macroeconomic uncertainty, namely - during and after the Global Financial Crisis of 2008/9. At the time of writing, there is no other study of this kind. Second, the study employs a comprehensive indicator of data transparency, the Statistical Capacity Indicator (SCI), which captures the regular publication of credible statistics by the state, encompassing the availability of micro data and the credibility of data through adherence to international standards. Third, the study employs a variety of econometric estimation models to examine the relationship between data transparency and economic growth, including two alternative estimators that account for the endogeneity of data transparency. Finally, 8
the study provides insights on the size of the effects of data transparency through comparisons with trade openness and schooling in the estimation sample that are well established in growth estimations.

The rest of the paper is structured as follows. Section II provides an overview of the related literature. Sections III and IV present the estimation strategy and data, respectively. Results are provided in section V, while section VI concludes.

# **II. Related Literature**

The role of data transparency in facilitating economic growth is closely tied to institutions in the literature. A salient point of Coase (1960) was that transaction costs matter and may explain the nature of organizations such as the firm. Positive transaction costs imply institutions can determine the payoffs to political and economic activity in developing economies, which in turn influence economic development (Douglas, 1989; Douglas 1990; Williamson, 2000). “Bad” institutions may lead private agents to allocate resources towards protection, forgoing productive investments. Hall and Jones (1999) show that institutions and government policies (called “social infrastructure”) explain variation in capital accumulation, productivity, and thus output per worker across economies. Countries that inherited weak or “extractive” institutions experience higher macro-economic volatility and lower output per capita today (Acemoglu et al., 2001; Acemoglu et al., 2003; Acemoglu et al., 2005). In contrast, democratic institutions tend to lead to long-run growth (Acemoglu et al., 2019; Rodrik, 2000). Institutions have also been found to have a larger role in explaining income levels than geography and trade (Rodrik et al., 2004).

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 has accounted for 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).

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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).

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) implements 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 10
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).

Transparency in general covers a range of areas including general institutional transparency regarding laws and regulations to specific sectors such as financial transparency. In this study we add to the literature by specifically investigating the effects of data transparency on economic growth. 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 selfcorrecting reforms than less democratic societies (Arezki et al. 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 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 11
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. More recently, Hodelin (2022) find the that the quantity of data produced in the World Bank’s World Development Indicators is positively related to economic growth, especially for middle income countries.

We build on these studies by using a comprehensive and rigorous measure of data transparency, known as the Statistical Capacity Indicator (SCI). The SCI measures both the production of micro and macro data as well as the frequency and quality of such data through adherence to international standards. The SCI has been used to assess the statistical capacity of economies and regions (Devarajan, 2013; Angrist et al., 2021) Kubota and Zeufack (2020) use the SCI to explore how data transparency can reduce the external cost of borrowing, as proxied by sovereign bond spreads, in Sub-Saharan Africa. Furthermore, we explore the effect of data transparency on overall economic growth over a period of considerable uncertainty – during and after the global financial crisis. Transparency is a good antidote for uncertainty. Shambaugh and Shen 2018) find that government transparency reduces the duration of inflation and currency crisis by reducing information asymmetry, improving effectiveness of monetary policy and bolstering public confidence. As far as we are aware at the time of writing, our study is the first to empirically explore the effect of data transparency, using a comprehensive measure, on overall economic growth for developing economies during and after the global financial crisis.

# **III. Estimation Strategy**

The identification strategy involves two strategies. The first is dynamic panel estimation methods to account for endogeneity concerns. The second is cross-sectional data where we lag the predictors to the initial time period of the data set (2005) and the level of income per capita at the end period of the data set (2018). This 12
attenuates concerns of reverse causality. We also implement an Instrumental Variables approach to address endogeneity concerns. We account for as many factors as possible mentioned in the literature to limit omitted variable bias.

The regression model to estimate the effect of data transparency ( _SCI_ ) on economic growth ( ) for country _i_ in year _t_ can be written as follows: ∆YY i =∝0 1 i −1 2S i −1 i −1 i (1) XX ii ii is the vector of control variables; ∆YY + ββ YY + ββis the country fixed or random effects; SS + ββ XX + υυ + tt + εε is the year fixed effects, ii ii XXand i is the error term. Equation (1) can be rewritten as (2) and then estimated in levels as shown in υυ tt equation (3) where the income level (εε is regressed on the lagged income per capita variable ( , the data transparency measure, while accounting for several other covariates. YY) YY) There are certain advantages to estimating equation (3) using this approach as used by other studies (Barro, 1991; Brueckner and Lederman, 2018). Equation (3) elucidates the auto regressive process by providing the coefficient on the lag of income per capita ( i −1). This allows for the exploration of conditional convergence of economies. Furthermore, equation (3) allows us to explore whether the SCI has an effect YY on the speed of convergence. The setup follows endogenous growth models as presented by Romer (1990, 1994). The lagged level of income per capita is endogenous as presented in equation (3). To address this, we employ systems General Methods of Moments (GMM) dynamic panel estimators as developed by Arellano and Bover (1995) and Blundell and Bond (1998) where equation (3) is estimated in levels and differences. For the estimation in levels, lagged income per capita is instrumented by further lags in differences, while in the differences equation, differences of lagged income per capita is instrumented by 13
further lags in levels. The literature has paid considerable attention to the relevance of instruments in dynamic panel estimations (Bazzi and Clemens, 2013; Kraay, 2015).

Alternatively, we employ a cross-sectional estimation that regresses the level of income per capita in 2018 on the 2005 levels of the SCI score as well as other covariates as shown in equation 4.

=∝0 1 2S (4) ii,2018 ii,2005 ii,2005 XX ii,2005 ii YY + (1 + ββ )YY + ββ SS + ββ XX + εε We also employ an Instrumental Variables (IV) approach where we instrument the SCI score with legal origins and perceptions of government effectiveness. Details are provided in the Instrumental Variables subsection of the results.

The sample of the study largely consists of developing economies between 2005 and 2018. The panel data set consists of 124 economies while the cross-sectional data set consists of 91 economies.<sup>4</sup> The beginning of the sample is close to the onset of the financial crisis in 2007. Thus, the sample largely covers a period of considerable volatility in growth across the world. While the year effects included in our panel estimations does capture global shocks, there is considerable heterogeneity in how the financial crisis unfolder across economies. Tables A2 and A3 provide a list of economies in the panel and cross-sectional sample, respectively.

The outcome variable - income per capita - is measured using the log of Real GDP per Capita (constant 2010 USD) obtained from the World Bank’s World Development Indicators. Tables 3 and 4 provide summary statistics for Real GDP per capita for both the panel and cross-section data sets, respectively. Tables 5 and 6 present the correlation between the log of real GDP per capita with SCI and other covariates

> 4 Several economies have gaps in the SCI data, with some of them missing data for 2005 or 2018. Thus, the sample of economies covered for the cross-sectional data is less than the panel data.

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for both cross-sectional and panel data, respectively. Both tables 5 and 6 show that the log of GDP per capita is positively correlated with the SCI score, statistically significant at the 1 percent level.

The proxy variable used for data transparency is the World Bank’s Statistical Capacity Indicator (SCI). The overall SCI indicator is based on a diagnostic framework to assess the capacity of national statistical systems over time. This indicator is ideal as it covers both the quantity (frequency) and to some extent the quality aspects of data, building on studies that have mostly focused on the former (Williams, 2009; Hollyer et al, 2011). The framework has three dimensions: (i) Source data, (ii) Methodology, and (iii) Periodicity and timeliness of socioeconomic indicators. Each dimension is evaluated on criteria based on metadata information from the World Bank, IMF, UN, UNESCO, and WHO. The overall SCI score is the average of the three sub-indicators calculated for each dimension.<sup>5</sup> The source data dimension reflects whether a country conducts data collection activity in line with internationally recommended periodicity, and whether data from administrative systems are available and reliable for statistical estimation purposes. This dimension covers the micro-data aspect of data transparency that is essential given microdata are foundational for a country’s data system. Specifically, the criteria used are the periodicity of population and agricultural censuses, the periodicity of poverty and health-related surveys, and completeness of vital registration system coverage. A country can achieve a perfect score if it has conducted 1 or more population census in the last 10 years, 1 or more agricultural census in the last 10 years, 3 or more health surveys in the last 10 years, 3 or more health surveys in the last 10 years, and has a complete vital registration system.

The statistical methodology dimension measures a country’s ability to adhere to internationally recommended standards and methods. This aspect is captured by assessing guidelines and procedures used

> 5 The composite score ranges from 0 to 100. For our analysis, we rescale this score to be between 0 and 1.

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to compile macroeconomic statistics and social data reporting and estimation practices. This dimension measures the quality aspect of the data system. Under the assumption that international guidelines provide the benchmark of ideal data systems, adherence to such standards may imply that the quality of data systems meets well-established standards. Countries are evaluated against a set of criteria such as use of an updated national accounts base year, use of the latest balance of payments manual, the external debt reporting status, updated consumer price index, updated industrial production index, updated import/export prices, accounting basis for reporting government financial data, vaccine reporting to WHO (discrepancy between WHO and government estimates), subscription to IMF’s Special Data Dissemination Standard, and enrollment data reporting to the United Nations Educational, Scientific, and Cultural Organization (UNESCO).<sup>6</sup> Each criterion has equal weight.

The periodicity and timeliness dimension measures the availability and periodicity of key socioeconomic indicators, of which nine are Millennium Development Goals (MDG) indicators. This dimension attempts to measure the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The periodicity of the main indicators considered, each receiving equal weight, includes: (i) Income poverty indicator, (ii) Child malnutrition indicator, (iii) Child mortality indicator, (iv) Immunization indicator, (v) HIV/AIDS indicator, (vi) Maternal health indicator, (vii) Gender equality in education indicator, (viii) Primary completion indicator, (ix) Access to water indicator, (x) GDP growth indicator.

Several studies have used the SCI to evaluate the quality of data systems and investigate its relationship with other macroeconomic variables (Devarajan, 2013; Kubota and Zeufack, 2020). Tables 3 and 4 provide summary statistics for the SCI indicator for both the panel and cross-section data sets, respectively. Tables 5 and 6 present the correlation between the SCI and other variables for both cross-sectional and panel data,

> 6 The discrepancy between vaccination rates from government administrative records and household surveys has been documented by Sandefur and Glassman (2015) as an indicator of bad government data.

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respectively. The SCI indicator is positively correlated with income per capita as well as most of the political institution variables.

There are other alternative indicators of statistical capacity and data access. Examples include the Statistical Performance Indicator recently released in the 2021 World Development Report (World Bank 2021) and the Open Data Inventory (ODIN). Our choice of SCI is largely due to the fact that the data is available since 2004, while the SPI is available from 2016 and ODIN is available from 2015, excluding data from 2019. The correlation between the SCI and SPI is about 0.77 for the few years where this overlap. The correlation between SCI and ODIN is 0.56 for the years where there is overlap. The estimations in this study were replicated using the SPI and ODIN instead of the SCI, and the results are largely consistent despite the short time span.<sup>7</sup>

# **IV. Data – Other Variables**

We control for several factors that are typically accounted for in growth regressions. These broadly include human capital, sectoral composition, trade, financial development, commodity-price shocks, external debtfinancing shocks, and quality of political institutions. Given that the sample of analysis is largely based on developing economies, we employ variables that tend to have broader coverage across developing economies. We use exports plus imports over GDP for the measure of trade openness. Sectoral composition is captured through the share of manufacturing value added and the share agriculture, forestry and fishing value added over GDP. We also account for human capital using gross primary school enrollment rates. Financial development is measured as the domestic credit to the private sector as a percentage of GDP, following Levine et al., (2000). These variables are compiled by the World Bank’s World Development Indicators.

> 7 Results are available upon request from the authors.

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We measure commodity shocks using a commodity terms of trade index following Gruss and Kebhaj (2019). Commodity-price shocks are calculated by taking the first differences of the log of the commodity terms of trade index, using historical fixed weights. The commodity terms of trade index is based on international prices of up to 45 individual commodities, constituting broad categories of energy, metals, food and beverages, and agricultural raw materials. External debt-financing shocks are calculated as the log difference of the US T-bill (10-year, long term) multiplied by the lagged external debt. The debt shock variable is included in a few specifications as it entails a drop in the number of observations due to availability of debt data.

We account for political institutions using data from the Worldwide Governance Indicators - WGI (Kauffman et al., 2010) following other studies in the literature (Antras and Chor, 2013; Grembi et al., 2016). In our estimations we include Voice and Accountability, Political Instability, Rule of Law, and Control of Corruption. Voice and Accountability is a measure of perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Political Instability measures perceptions of the likelihood of political instability and/or politically motivated violence. Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.

We control for internal armed conflict using the UCDP/PRIO Armed Conflict Database updated by Pettersson and Öberg (2020). We follow a definition, similar to Abu Bader and Ianchovichina (2019) where armed internal conflict is a contested incompatibility where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths per year. For the 18
panel dataset the variable takes a value of 1 if internal armed conflict, zero otherwise. The exception is economies such as Iraq and Afghanistan that were in conflict for all years in the sample and thus attain values of zero. For the cross-section estimations the variable is the number proportion of years that an economy experienced internal conflict between 2005 and 2018.<sup>8</sup> We include data for legal origins that are used as instruments in our Instrumental Variables approach. Data on legal origins capture whether the historical origin of a country’s laws is German, French or from the United Kingdom. These data are obtained from La Porta et al., (2008), who provide an overview of studies using legal origins. In an additional specification, we also use the 1996 to 2003 sample average of the Government Effectiveness measure from the WGI. Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Tables 3 and 4 provide summary statistics for all the variables for the crosssection and panel data sets, respectively. Tables 5 and 6 present the correlation between these variables. Table A1 provides the definitions and sources of all the variables.

# **V. Results**

Table 1 presents the main results. Columns 1, 2 and 3 present the cross-sectional OLS estimation results corresponding to equation (4) with no controls, standard controls, and standard controls plus external debtfinancing shocks, respectively. The specification with the debt shocks is presented separately as it entails a drop in observations. The SCI variable is positive and statistically significant at the 1 percent level for all three specifications presented in the first three columns. Using the specification with the standard set of controls, the magnitude of the SCI coefficient indicates an elasticity of 0.4 percent. However, to be

> 8 All the results presented in this study are largely unchanged if the internal armed conflict variable is excluded from the estimations

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comparable to the panel estimations, it has to be annualized which then translates to an elasticity of 0.03 percent (0.417/14).

Columns 4 through 10 of table 1 present the panel data results corresponding to equation (3). Column 4 presents the findings for year fixed effects with the standard set of controls and column 5 presents the results for Random Country Effects and Year Fixed Effects. The coefficient for SCI is statistically significant at the 1 percent level for the Year Fixed Effects estimations (column 4) and at the 5 percent level for the Random Country Effects with Year Fixed Effects estimations (column 5). The estimated elasticity is 0.03 percent (similar to the cross-sectional OLS estimates) for the Year Fixed Effects. However, this elasticity drops to 0.02 percent for the Random Country Effects with Year Fixed Effects estimations.

To account for the endogeneity of the lagged level of GDP per capita, we employ Two Step System GMM estimators. The findings are presented in columns 6 through 10 in table 1. In columns 6 through 9, only the lagged level of GDP per capita is instrumented by further lags in both levels and differences. In column 10 we instrument both the both the lagged level of GDP and the lagged level of SCI. The system GMM results using all the lags possible for instruments are presented without any controls (column 6) and with the standard controls (column 7). The reported Hansen J statistic does not reject the null that the instruments are valid lending credibility to the instruments. However, first order autocorrelation AR(1) and second order autocorrelation AR(2) are detected and thus in column 8 we replicate the results of column 7 using only lags of 3 or more as instruments given that AR(3) is not detected. In column 9, we replicate the specification in column 8 with the inclusion of the external debt- financing shock variable. The magnitude of the effect of SCI on the log of GDP per capita is largely the same for all the system GMM estimations showing an elasticity of 0.028 to 0.030 percent, mostly consistent with cross-sectional OLS estimates. Thus, a 1 percent increase in the SCI score translates to a 0.03 percent increase in the level of GDP per capita. The positive effect of SCI on growth appears to be driven primarily by the cross‐country variation captured in the “levels” equation. In column 10 the coefficient for SCI more than doubles when we also instrument 20
SCI with further lags in both levels and differences. The Hansen J- statistic shows a p-value of 1, and thus does not reject the null that the instruments are valid, however the high p-value raises concerns of instrument proliferation. We also cannot rule out the possibility that the instruments are weak.<sup>9</sup> The coefficient of the lagged log of GDP per capita is statistically significant at the 1 percent level. Its magnitude appears to be close to unity, implying that shocks to the level of (log) GDP per capita are highly persistent. The magnitude of the coefficient can also reveal information on the degree of conditional convergence. Using the cross-sectional OLS estimates including the standard controls (Table 1, column 2) and the System GMM estimates (Table 1, column 8), we obtain the convergence coefficient of -0.002 (annualized) and -0.01, respectively. The negative coefficient indicates conditional convergence within the set of largely developing economies in the sample. We also interacted SCI with the lagged log of GDP per capita to explore whether the SCI may affect the speed of convergence.<sup>10</sup> The coefficient of the interaction term is statistically insignificant across all estimations. The similar magnitude of the effect of SCI in the panel estimates and the cumulative effect between 2005 and 2018 as captured by the cross-sectional OLS estimates may suggest that the effect of the SCI on growth may be permanent.

The coefficients of the other control variables (not presented) are not statistically significant across most specifications apart from trade, ranging from 10 to 1 percent level, depending on the specification.

9 The system GMM estimates may have the issue that the instruments are weak. We investigate this issue further. We collapsed the data into 2 year averages leading to 7 time periods in order to lower the time dimension (T) with respect to the number of countries. We then follow the literature (Bazzi and Clemens, 2013; Kraay, 2015) and produce confidence sets by “inverting” test statistics that are valid regardless of whether instruments are strong and when they are weak). These include the Anderson-Rubin (AR) test statistic, Conditional likelihood ratio (CLR) test statistic, and the K and J overidentification test statistic (K-J). For tractability, we use only two lags for the system GMM estimations. Specifications with and without controls are employed. We also unbundle the system GMM into “difference” and “levels” equations. We find the following. The positive effect of SCI on growth appear to be driven primarily by the cross-country variation captured in the “levels” equation. The confidence sets (95% level) show that the range of possible values for the coefficient of lagged SCI are largely positive. However, the confidence sets are unbounded. Thus, we cannot disregard the possibility of weak instruments. These findings are available upon request.

> 10 Not reported, but available from the authors upon request.

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However, the magnitude of the effect of SCI on real GDP per capita is much larger than trade openness. Using the cross-section OLS model (column 2) and the System GMM model (column 8) with the standard controls, the elasticity of trade (at the sample mean) with regards to GDP per capita is 0.01, regardless of the model used. In contrast the elasticity of SCI with respect to GDP per capita is 0.03, about three times larger. This is a striking finding given that trade has been afforded far more attention than data transparency in the literature. Furthermore, the fact that other governance indicators are not significant predictors of the level of GDP per capita, conditional on the lagged level of GDP per capita, suggests that data transparency might be a key conduit through which institutions affect growth.

# **Instrumental Variables (IV)**

To try and establish the robustness of our findings, we use the Instrumental Variables approach with the cross-sectional data. Note that in the cross-sectional estimations, the predictors are lagged to 2005 and the explanatory variable takes the value of the year 2018. In this sense, reverse causality may be less likely. The results provided later on in the study show that they pass the Oster tests, possibly indicating that omitted variable bias is less likely an issue. However, we further employ an Instrumental Variables approach to limit endogeneity even further. The ideal instrumental variable would be one that is correlated with data transparency but has no direct effect on GDP per capita. Data systems may be affected by their economic and cultural distance from other economies. We capture this through legal origins. Legal origins may proxy for the initial distance or similarity between a country’s legal system and the colonial power. These legal systems may then influence the evolution of data systems. This is our main instrument for data transparency. As an additional robustness, we consider measures of economic and linguistic distance to Germany, Great Britain, France, Spain, and USA. Economic distance is captured by trade flows. Linguistic distance is captured by common spoken language and an index of linguistic proximity.

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For the legal origins instruments, in our sample of analysis all the economies have adopted three types of legal systems – French, German, and English. Broadly, French and German legal origins fall under civil law while English legal origins pertain to Common law. We find that French and German legal systems are positively correlated with the SCI while English legal origins are negatively correlated with SCI. We posit a number of plausible explanations with the acknowledgment that this is speculative rather than based on a specific literature on the evolution of data ecosystems. Civil law systems are generally based on extensive codification. They tend to be comprehensive with frequently updated legal codes that try and account for every situation including detailing procedures and punishment. In contrast, Common law is uncodified and is based on precedent. Thus, one possible explanation is that the extensive nature of Civil law (German and French) requires a greater undertaking of data collection, and thus a higher requirement of statistical capacity, especially in developing economies that may be far from the minimum standard of data requirements. In contrast, given Common law (English) is based on precedent, the urgency to develop data systems in developing economies may be less. Another possible explanation is that economies with Civil law legal origins tend to have a more interventionist state with heavier regulations than Common law legal origins (La Porta et al., 2008). Heavier regulations may accompany extensive data collection efforts to monitor compliance. From the correlation observed in the data, better data systems may be side-effects of the features of economies with Civil law legal origins. The instruments are likely to satisfy the exclusion restriction criterion given their similar use by Levine et al., (2000) to instrument for the effect of financial development on economic growth. However, the implication is that our estimations must account for the other channels through which legal origins may affect growth. La Porta et al., (2008) document the economic consequences of legal origins. The two main channels through which legal origins may affect growth are through financial development and governance. To account for the financial development channel, domestic credit to the private sector as a percentage of GDP is a control variable in all the estimations. Furthermore, several governance variables from the Worldwide Governance Indicators are included in our estimations.

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We also entertain an additional instrumental variable in the form of government effectiveness obtained from the Worldwide Governance Indicators. This variable specifically captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Better public and civil service as well as the quality of policy formulation are likely to be positively correlated with data transparency. We take the average score for the years available predating the sample - 1996-2003 – to aim for some level of exogeneity. While government effectiveness alone may be a harder sell in terms of satisfying the exclusion restriction, the fact that we account for other governance measures such as Voice and Accountability, Political Stability, Rule of Law and Control of Corruption, may limit the direct effects of government effectiveness as defined on economic growth.

The results for the Instrumental Variables estimations using legal origins as instruments are presented in Table 2. Columns 1 and 2 provide the second stage and first stage results respectively for the estimations with legal origins as instrumental variables. The first stage results show that French and German legal origins are positively correlated with the SCI, statistically significant at the 1 percent level. The second stage shows that SCI has a positive coefficient, statistically significant at the 5 percent level. The magnitude is somewhat larger than all the other panel and OLS cross-section estimates, indicating an elasticity of 0.04 percent (annualized). The Hansen test of overidentifying restrictions does not reject the joint null that the instruments are valid. The underidentification test rejects the null that the instruments are underidentified, indicating that they are relevant. The weak identification test shows that the F-stat is greater than most of the Stock and Yogo thresholds (with the exception of the 10% maximal IV size) implying the instruments are not weak.<sup>11</sup> Do note however that the weak identification tests assume homoskedasticity and thus may

> 11 With regards to the Stock and Yogo thresholds, the maximal relative bias thresholds are based on the ratio of the bias of the estimator to the bias of OLS. The relative bias thresholds are based on the performance of the Wald test statistic given that the Wald test rejects too often in the case of weak identification .

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be unreliable (Baum et al., 2007). The Shea Partial R square indicates that 17.8 percent of the variation in the endogenous variable is explained by the instruments.

In columns 3 and 4 we present the findings with the inclusion of government effectiveness (1996-2003 average) as an additional instrument. The findings are largely the same with the statistical significance of the coefficient of SCI strengthening to the 1 percent level. The magnitude of the elasticity is unchanged. The validity and relevance of the instruments are retained as indicated by the overidentification and underidentification tests. In the first stage, the coefficient of the Government Effectiveness variable is positive and statistically significant at the 1 percent level. This result further supports the argument that data transparency might be a key channel through which governance institutions affect development. The Shea Partial R square increases to 26.5 percent. In the 5<sup>th</sup> and 6<sup>th</sup> columns of table 2, we include the external debt-financing shock variable as a covariate, reducing the sample to 81 economies. The results are largely unchanged. The slightly larger coefficient of SCI in the IV estimates than the OLS estimates may indicate that the instruments may have reduced measurement error and provided cleaner estimates of the effects of data transparency on growth. However, we cannot rule out the possibility that some simultaneity bias may have also been addressed by the instruments, and the increase in the coefficient of SCI in the IV estimates could be because overall the measurement error was larger than the bias due to reverse causality.

As a robustness, we consider additional indicators of economic distance and linguistic distance to Germany, Great Britain, France, Spain and USA. Economic distance is captured by trade flows (following the extensive literature on the gravity model of trade). Linguistic distance is captured by common spoken language and an index of linguistic proximity of different native languages. The common spoken language indicator is essentially the sums of the products of the population shares that speak identical languages by country pair with some adjustments due to double counting (see Melitz and Touba 2014 for details). The key underlying idea is that Germany, Great Britain, France, Spain and the United States are close to the frontier of data systems and are not included in the sample. Thus, countries that have closer relationships 25
may be more likely to adopt similarly good data systems. The results are provided in table 10. Column 1 and 2 present the results for economic distance measured by trade flows. The magnitude of the coefficient for SCI is almost double the estimates where legal origins are the instruments. The coefficient is statistically significant at the 1% level. However, the instruments do not pass the underidentification test. Shea Partial R square is only 0.093. In columns 3 and 4 of table 10 we provide the results for linguistic proximity, and the results are promising. The magnitude of the coefficient of SCI is close to the estimates where legal origins are the instruments. The coefficient of SCI is statistically significant at the 1% level. The Shea Partial R square is 0.393. The instruments pass the underidentification and overidentification tests.

# **Magnitude of the Effects**

The elasticity of the SCI with respect to log of real GDP per capita per year is 0.03 for the OLS crosssection estimation. This estimate is unchanged whether the base OLS estimation with year effects in the panel data set is used or the dynamic panel estimators that account for the endogeneity of the lagged level of real GDP per capita. However, the IV estimations yield a higher elasticity of 0.04 (annualized). To put this in context, the estimates of elasticity of schooling as well as trade openness with respect to the log of real GDP per capita are much lower. The system GMM estimations accounting for the endogeneity of lagged real GDP per capita yield an elasticity of 0.01 for trade (at the mean) and 0.009 for primary school enrollment (at the mean). In both cases the magnitude of the elasticities is much lower than the elasticity of SCI. The elasticity for trade is largely unchanged at 0.01 with the IV estimations, while the elasticity for primary school enrollment falls considerably to 0.001. Do note that the coefficient for primary school enrollment is statistically insignificant and unstable across the estimations. The IV estimate of the elasticity of SCI is about four times larger than the elasticity of trade openness (0.04 vs 0.01). Using these same estimates, if Burundi were to achieve Chile’s SCI score in 2018 (from 0.57 to 0.9) – an increase of 58.8 percent - then Burundi would increase its real GDP per capita by 2.3 percent.

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We explore the economic magnitude of effects of increasing SCI, trade openness, and primary school enrollment by one standard deviation on the log of real GDP per capita. For panel estimations, we use the within-country standard deviation of the variables. For all the estimations, the magnitude of the effect of increasing SCI by 1 standard deviation (ranging from 0.002 to 0.009) is larger than the magnitude for primary school enrollment (0.0001 to 0.001). Comparing SCI with trade openness, the magnitude of the effect of increasing SCI by 1 standard deviation is slightly larger than the magnitude of trade openness in the system GMM estimations (0.0023 vs. 0.0016) but more than twice as large using the IV estimations (0.009 vs. 0.004). The caveat with comparisons of the effects of data transparency, trade openness, and education is that they may entail different costs. Unfortunately, there is little data available on the amount invested by countries on data transparency (World Bank, 2021). However, there is some indication that data transparency is less costly than for example improving education systems.<sup>12</sup> The developing Middle East and North Africa (MENA) region is the only region in the sample that experienced a decrease in the SCI score between 2005 and 2018, thus offering another assessment of the economic magnitude of the impact of SCI on the level of GDP per capita. Within this period the average SCI score for the region fell by approximately 4.4 percent, while trade decreased by 2.7 percent (using 2018 as the base). Between 2005 and 2017, primary school enrollment rates increased by 1.5 percent.<sup>13</sup> The base OLS estimates with the cross-section data show a loss of -1.8 percent in GDP per capita between 2005 and 2018 due to the fall in SCI. This is larger than the -0.4 percent decrease in GDP per capita due to the decline in trade openness (between 2005 and 2018), and the 0.01 percent increase due to the increase primary school enrollment (between 2005 and 2017). Using the OLS with year fixed effects estimates (panel data), the loss in GDP due to the fall in SCI is slightly higher at -1.9 percent. This is still larger than the loss of -0.4 percent and gain of 0.4 percent due to the decrease in trade openness and the increase in schooling respectively.

> 12 Source: How should governments spend on data <u>https://blogs.worldbank.org/opendata/how-much-shouldgovernments-spend-data</u> (accessed July, 25<sup>th</sup> 2021, 10:45 pm).

> 13 We use the 2017 value for primary school enrollment rate due to the sparse data in MENA for 2018. All trends are for developing economies in the MENA region (i.e. high-income economies in the region are excluded).

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The system GMM estimations indicate a loss of -1.8 percent in the GDP per capita between 2005 and 2018 due to the fall in SCI. The corresponding loss for the decrease in trade is -0.4 percent and the gain due to the increase in schooling is 0.2 percent. Finally, using the IV estimates, the loss in GDP per capita due to the fall in SCI rises to -2.4 percent. The increase in GDP per capita due to schooling is 0.02 percent while the loss in GDP per capital due to the decrease in trade is -0.4 percent. Using these estimates, the loss in GDP per capita in the MENA region due to the fall in SCI is much larger than that of trade and negates the gains from schooling within the same time period. Again, these comparisons do not take into account the costs of raising school enrollment or trade, but since the SCI score declined in absolute terms no such costs would come into an accounting of the gains versus the costs. In other words, if MENA had maintained the level of SCI it had in 2005, at no extra costs, the benefits in terms of GDP per capita would far outstrip the gains from school enrollment once the costs of achieving gains in schooling are taken into account.

The losses faced by the MENA region thus far only account for the drop in SCI between 2005 and 2018. A simple correlation between log GDP per capita and the SCI in 2005 suggests that the MENA region was underperforming in terms of the SCI with regards to its level of development. If the MENA region had achieved the average level of SCI in 2005 given its GDP per capita, as opposed to starting below and declining further through 2018, the estimated total losses due to the MENA region’s SCI deficit (using the IV estimations) would be approximately 5.9-7.5% of GDP per capita.<sup>14</sup>

# **Robustness: Oster tests**

Under certain assumptions, the selection on observables can be used to identify the likely bias from selection on unobservable omitted variables. The test can be used to infer the degree of unobserved selection that would need to exist to reduce the impact of data transparency on economic growth - essentially, what

> 14 The 5.9-7.5 % range is due to the choice of sample. The relationship between GDP per capita and the SCI is stronger for the cross-section 2005 sample of analysis then the full sample that includes any country with GDP per capita and SCI data available in 2005. The total loss using the full sample is 5.9 (3.5 +2.4) percent. The total loss based on the estimation sample is 7.5 (5.1+2.4) percent.

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is the magnitude of omitted variable bias needed to overturn the results. The δ value is a ratio that suggests how large the relationship between unobserved omitted variables and data transparency would need to be relative to the relationship between covariates and data transparency for the effect of data transparency on growth to be zero, assuming proportionality. The Oster test also provides bound of β (coefficient of data transparency) under the assumption that the selection on unobservables is equal to the selection on observables. Note that the R2 for our base estimations in the cross-section is 0.97 and 0.99 in the panel estimations.

The Oster test results are provided in in table 7. As shown, for the log of statistical capacity score in 2005, using R2 value of 1, the δ value is 4.9 for the cross-section. This means that the selection on unobservables needs to be close to almost 5 times the selection on the observables to explain away the effect of the log of statistical score in 2005 on GDP per capita in 2018. Similarly, the δ value is 11.2 for the lagged statistical score for the panel estimates. This means that the selection on unobservables needs to be close to almost 11 times the selection on the observables to explain away the effect of the lagged log of statistical score on GDP per capita. Furthermore, table 7 shows that the coefficient of the log of statistical score is positive and statistically significant at the 5 percent level assuming the selection on unobservables is equal to the selection on observables for the cross-section and statistically significant at the 1 percent level for the panel estimates.

# **Robustness: Increase Sample Size using Initial Values of Variables**

As a robustness check, we consider the initial values of all the independent variables including the main variable of interest. Initial is defined as the value for the year 2005, or the earliest available after 2005. We repeat the cross-sectional estimates in table 8 and 9. This increases the coverage to 141 in the base specification for the OLS estimates. The IV estimate contain about 119 economies. This represents about 96 to 98 percent of the population in developing economies. The coefficients for the initial statistical capacity score indicator for the OLS estimates do not change much from the base estimates. For the IV 29
estimates using legal origins as instruments, the coefficient of the initial statistical capacity score indicator is about 1.8 times larger than the base estimates, although the instruments are weaker. IV estimates where both legal origins and government effectiveness are instruments are consistent with the base IV estimates.

# **VI. Conclusion**

This study finds that on average around the years before and after the global financial crisis, developing economies that were more data transparent with better statistical systems (SCI) performed better in terms of growth of GDP per capita. We find an elasticity of 0.03 per year between SCI in 2005 with respect to GDP per capita in 2018 conditional on the 2005 GDP per capita. Although modest, this elasticity is about three times larger than the elasticity of trade with respect to GDP per capita. We believe that this could be because good statistical systems allow governments to deploy better policies. Better statistical systems also enable them to effectively adjust their policies during periods of uncertainty, which was certainly the case in the in the sample period under study in this paper. The results stand after accounting for a host of factors including political institutions, economy structure, trade openness, school enrollment rates, internal armed conflict, and commodity and debt shocks. The findings are robust to a myriad of specifications and estimating models ranging from GMM system estimations that uses time series to identify the effects and Instrumental Variables (IV) approach using legal origins as instruments.

The main channels are that data transparency leads to better policies, and more certainty and therefore lower forecast errors. Ultimately data transparency enables credible analyses and informed public discourse, that could lead to better policymaking, societal trust and long run economic development. This is more important in periods of uncertainty such as the global financial crisis. Investments in data can enlarge the economic pie. This may matter for more autocratic governments balancing acts of political suppression and expanding the economic pie. Data opacity can be used as a tool to keep citizens in the dark, but it might 30
come at the cost of foregoing opportunities to increase the economic pie. Although we are not able to ascertain for sure for sure that all of these channels were operational, the suggestive evidence indicates that these are worth taking seriously.

The implications for development of these findings are that data transparency is a policy instrument that perhaps has been ignored. This is important because in many developing countries reforms that are expected to be pro-growth are often difficult to implement for political economy reasons or lack of fiscal resources. This is one policy that makes little demands of both. The findings established are of importance to the profession given that the role of data transparency in promoting growth has received little empirical attention in the literature. The use of a comprehensive statistical index is a key contribution given the literature has largely focused on the density of available macro-data instead of the adherence to international standards and the availability of micro-data. While the role of transparency in improving governance and limiting corruption is well understood, the specific role of data transparency has been much debated with theoretical arguments made both for and against better statistics, indicating the topic is ripe for empirical verification. Our study thus fills a gap in the long literature on the determinants of economic growth.

We acknowledge some limitations in our work, highlighting vast opportunities for future research in this area. Our sample is a selection of developing economies, and a few economies that may have transitioned into high-income status. This is a limitation of the SCI. The estimated effects may be larger when a global sample is included. Furthermore, empirically teasing out the specific mechanisms of the effects is beyond the scope of this study and provides room for important contributions to the literature for future research. Finally, this study is timely given the recent explosion of data all across the globe. Our study is mostly limited to traditional sources of data, and thus the effect of new types of data such as big data is a promising area for future research. This would also bring in the issue of disinformation that has been a rising concern with the spread of digital technologies.

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