rafmacalaba/data-use-mentions-extended huggingface dataset:
lead. Europe and Central Asia is doing better than many other regions on its regulatory safeguards.

Across all dimensions measured, no income group demonstrates advanced regulatory frameworks. All country income groups achieve their highest scores on the e-commerce dimension. Enabling access to public intent data as well as safeguarding cybersecurity are two dimensions where countries are performing relatively well, except for low-income countries. On average, countries have adopted about half the recommended regulatory practices for enabling access to public intent data. Although more than 60 percent of the countries surveyed have adopted comprehensive regulatory provisions on cybercrime, significant regulatory gaps persist in the framework for cybersecurity. Meanwhile, all income groups are at evolving or basic levels for enabling the use/reuse of private intent data and facilitation of cross-border data flows. The recent COVID-19 pandemic has provided the impetus for many countries either to adopt missing personal data protection legislation or to strengthen or clarify such legislation already in place.

An important value addition of the diagnostic, compared with other tools, is that it does not merely track the existence of legislation but captures the overall robustness and completeness and completeness of that regulation by recording the presence or absence of key provisions within the legislation. The diagnostic finds that countries are typically far more advanced with the passage of overarching legislation, than they are with the robustness of that legislation, or with administrative measures to support its implementation. A better characterization of the enforcement of data governance frameworks, as well as a deeper understanding of their economic and social impact, remain topics for future research.

29
be a challenging task for central banks in LICs. The survey of International Monetary Fund (IMF) country desk economists reported in IMF (2015) found that most low- and lower-middle-income countries that listed price stability as a central bank objective, but that had not adopted formal inflation targeting, did not have a numerical inflation target, and those that had such a target simply tended to align it with the bank’s inflation forecast.<sup>5</sup> To the extent that central banks in LICs have objectives in addition to low and stable inflation, such as small output or employment gaps, these will also need to be quantified. This too may pose serious challenges for LICs. Estimation of output and employment gaps, and of appropriate objectives for them, is highly problematic in advanced economies, because of instability in the relationship between unemployment and inflation and uncertainty surrounding estimates of potential output. It is likely to be even more so in LICs, for example, because of the higher incidence of supply shocks and the greater prevalence and variability of underemployment.

# 2.4. Weaknesses in the instruments and transmission mechanism of monetary policy

In advanced economies and many of EMDEs, the key (conventional) monetary policy instrument is a very short-term interest rate, most often an interbank rate such as the federal funds rate in the United States. The central bank can exert close control over the interbank rate through its supply of reserves to the banking system and administration of standing facilities. In LICs, however, interbank markets are typically absent, as are liquid secondary markets in government securities, which the central bank could seek to influence through open-market operations. The government securities market in LICs tends to be a primary market in which the counterparties to the central bank are commercial banks that adopt a buy-and-hold strategy for such securities. Thus, the central bank conducts monetary policy by directly lending to and borrowing from the commercial banking system (for example, through repo transactions) or by doing so indirectly through the primary market
allocated budget, the share of the allocated budget that comes from the exploitation of natural resources, total population in 2007, the average number of years of schooling of the population aged 15 and older in 2007, the number of whitecollar personnel as percentage of total population, the poverty rate in 2007, the percentage of urban population in 2007, the percentage of winning votes of the elected mayor in the 2006 elections, a binary variable of value 1 if the elected mayor is an incumbent, and year-specific effects. Spending data cover all 1,834 Peruvian municipalities; however, because of other data missing, the final sample consists of an unbalanced panel of 1,688 municipalities and 3 years of data (rendering 4,858 observations).

The basic estimation methodology is ordinary least squares regression (OLS) with robust standard errors, clustered by province (a political demarcation of adjacent districts/municipalities). The results are robust to the use of weighted least squares regression (WLS) and median regression (MR) that controls for the influence of outliers. In addition, we apply quantile regressions (QR) in order to allow the estimated effects to vary according to the degree of budget execution.

Table 1 presents the basic OLS regression results for total spending, for spending disaggregated by current and capital expenditures, and for defined capital expenditures. In what follows, we organize the discussion of results using the framework introduced above, which separates the determinants of the budget execution rate along the issues of the budget size and allocation process, local capacity, local needs, and political economy constraints.

Let us first discuss the variables related to the budget. A larger allocated budget is significantly related to a lower execution rate; and this is the case for both total and disaggregated expenditures, with a larger effect on capital than current expenditures. Thus, it seems to be harder to spend a larger budget, especially when this requires planning, project preparation, and implementation capacity. The budget share of natural resource revenues has an ambiguous
experience and education are broadly similar.<sup>41</sup>

# **5.3. Economic Transitions and Returns to Work Experience**

40 countries had a communist regime at one point. We have information on the time when their transition out of communism began.<sup>42</sup> Since the transition involved structural or institutional changes that may have made pre-transition experience obsolete, we hypothesize that countries that recently transitioned out of communism should have lower returns than non-communist economies and this difference should

> 40Note that data (source: _World Development Indicators_ of the World Bank) is available for fewer country-years than before since URs and LFPRs were not systematically measured before the 1990s.

> 41Kahn (2010) find large, negative wage effects of graduating in a bad economy in the United States whereas we focus on the global effects of recessions on the returns to experience, i.e. to what extent more experienced workers are paid more than less experienced workers. We do not study the effects on wage levels.

> 42We obtain this information from Wikipedia (2019) and the Wikipedia webpage of each country. For example, we use 1975 for China, 1986 for Vietnam, 1989 for Poland, 1991 for Russia, and 1992 for Albania.
regularly offer an assessment of the risks to financial stability in the EU.

Over time, the European arrangements for safeguarding financial stability have been based on the guiding principle that a decentralized institutional setting mostly based on the exercise of national responsibilities would be able to prevent and manage crises affecting the single financial market. The national authorities of home - and host-country authorities would cooperate in the management of a crisis on the basis of Community legislation and non-binding agreements such as Memorandum of Understanding. However, also due to the potential impact on national fiscal responsibilities, national authorities would preserve full responsibility and discretion in the actions to take to manage a crisis situation.<sup>16</sup> Unfortunately, and as a direct result of the principles and initiatives just described, the pre-crisis EU institutional architecture for safeguarding financial stability (including crisis management and resolution) evolved into an institutional framework with three characteristics revealed by the crisis to be weaknesses if not flaws: _decentralization, segmentation, and overreliance on voluntary cooperation_ .<sup>17</sup>

- First, financial stability functions were _decentralized_ and based in large part on the exercise of national responsibilities by banking supervisors, central banks, treasuries, and deposit insurance schemes—despite the significant integration of European finance.

- Second, pre-crisis financial stability functions were _segmented_ across sectors and Member States.  For example, supervision of banking groups and financial conglomerates

> 15 The 2008 MoU is available at www.ecb.europa.eu.

> 16 See Schinasi and Teixeira, 2006.

> 17 See Schinasi and Teixeira (2006).

8
spending through off-budget vehicles. When aggregating these different sources of data some possibility of doublecounting is introduced, across some though not all of the categories. Wherever possible this issue is examined and any double-counting directly addressed through subtraction. However, due to data limitations this is not always possible. Table A2.5 qualitatively identifies the nature of the risks across each pair of categories. Since double-counting cannot be entirely eliminated, the resulting aggregate estimates must be considered as an upper bound. Given how low the resulting spending envelopes are relative to estimates of spending needs, this does not, however, affect the main conclusions of the paper.

The BOOST database does not capture foreign capital spending such as contributions made by multilateral, bilateral or other donor institutions, because these are typically not channeled through the budget. This source of funding plays an important role in infrastructure financing in the developing world and needs to be incorporated to get a complete picture. The OECD DAC database covers donor and multilateral commitments to developing countries across sectors. The database covers disbursements to 155 countries (from 29 donors) over the period 2005 – 2019. However, the OECD DAC database does not cover financing contributions by Non-Paris Club Members, notably China, which has become a significant source of infrastructure finance for some developing countries (Malik et al., 2021). Therefore, this study also draws upon the College of William and Mary’s AidData database that captures over 13,000 officially-financed Chinese projects (by Chinese government institutions and state-owned entities) in five regions (Africa, the

> 4 These considerations led to the exclusion of Argentina and Brazil from the BOOST analysis, due to the fact that they are large federal states known to have substantial infrastructure expenditure at the sub-national level, yet only reported central government expenditure to BOOST.

4
log employment and log capital. The residual from this regression for each plant is taken as its TFP. We then take the average of these residuals across plants for a district.

As our data are repeated cross-sections, rather than panels with unique plant identifiers, there are limits with respect to some of our analyses. Perhaps most notably, we do not have accurate measures of exiting plants. Our data do, however, allow us to measure and study new entrants. Plants are distinguished by whether or not they are less than four years old. We will use the term “young” plant or new entrant to describe the activity of plants that are less than four years old. We aggregate young plant activity at the district level, similar to metrics of total activity.

Our core sample contains 312 districts. This sample is roughly half of the total number of districts in India of 630, but it accounts for over 90% of plants, employment, and output in the manufacturing sector 9
the average firm is able to price above marginal cost. About 20 percent of the firm-year observations in our sample are foreign-owned firms, while 2 percent are state-owned firms.<sup>8</sup> Also, the sector-year statistics show that the average manufacturing sector accounts for 44% of its output in foreign-owned firms and has an export share of turnover of around 27%. However, there is significant dispersion with standard deviations around 25% and 17% for foreign-owned firms’ share of output and the export share of turnover, respectively.

_Table 1. Summary statistics_

||**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
||log (Outputi,s,t)|120,029|5.15|1.86|-2.06|15.32|
||log (Employmenti,s,t)|120,029|4.05|1.35|2.30|11.35|
|**Firm**|log(Capitali,s,t)|120,029|5.32|1.66|-2.20|14.28|
|**characteristics**|log(Materialsi,s,t)|120,029|4.65|2.08|-6.13|15.12|
||MNEi,s,t|120,029|0.20|0.40|0.00|1.00|
||SOEi,s,t|120,029|0.02|0.15|0.00|1.00|
|**Markus**|Yi,s,
> The sector’s exports grew 2,363 percent in the last decade (from $4.75B to $117.5B). The Textile sector represented 22 percent of the 2019 export basket, and its exports grew 283 percent in the last decade (from $17.8B to $68.2B). The machinery sector represented 8 percent of the export basket, and its exports grew 529 percent (from $3.85B to $24.2B).

**Table 1** lists the selected occupations.

> 11 Given data availability, employment indicators were constructed using 2018 as the base year.

> 12 See Harvard University Growth Lab’s Atlas of Economic Complexity available at https://atlas.cid.harvard.edu/.

8
Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): Objectives and Design.” _American Journal of Epidemiology_ 175 (4): 315–24. https://doi.org/10.1093/aje/kwr294.

Badillo-Suárez, Pilar Amellali, Maricela Rodríguez-Cruz, and Xóchitl Nieves-Morales. 2017. “Impact of Metabolic Hormones Secreted in Human Breast Milk on Nutritional Programming in Childhood Obesity.” _Journal of Mammary Gland Biology and Neoplasia_ 22 (3): 171–91. https://doi.org/10.1007/S10911-0179382-Y/METRICS.

Bardach, Ariel Esteban, Natalia Espínola, Federico Rodríguez Cairoli, Lucas Perelli, Darío Balan, Alfredo Palacios, Federico Augustovski, Andrés Pichón-Riviere, and Andrea Olga Alcaraz. 2023. “The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages’ Consumption in Argentina: A Modeling Study.” _PLOS ONE_ 18 (2): e0279978. https://doi.org/10.1371/JOURNAL.PONE.0279978.

- Barker, David J.P. 2004. “The Developmental Origins of Adult Disease.” _Journal of the American College of Nutrition_ 23 (sup6): 588S-595S. https://doi.org/10.1080/07315724.2004.10719428.

Beal, Ty, Anna Herforth, Shelly Sundberg, Sonja Y. Hess, and Lynnette M. Neufeld. 2021. “Differences in Modelled Estimates of Global Dietary Intake.” _The Lancet_ . https://doi.org/10.1016/S01406736(21)00714-5.

Beal, Ty, Saul S. Morris, and Alison Tumilowicz. 2019. “Global Patterns of Adolescent Fruit, Vegetable, Carbonated Soft Drink, and Fast-Food Consumption: A Meta-Analysis of Global School-Based Student Health Surveys.” _Food and Nutrition Bulletin_ 40 (4): 444–59. https://doi.org/10.1177/0379572119848287/ASSET/IMAGES/LARGE/10.1177_0379572119848287FIG6.JPEG.

Bertram, Melanie Y, Jeremy A
information, personal recreation and cultural, and finally royalties. Russia ́s export structure is concentrated in traditional services; like South Africa, with respect to modern services Russia is somewhat behind other comparators, except India. In the performance of modern services over time, Russia has consistently underperformed other countries. The BRICS show varying levels of development but similarly low levels of modern services exports.

As in other developed countries, the structure of Russia’s service exports is relatively dispersed. In the exports of richer countries, the shares of services subsectors are more equally distributed. **Error! Reference source not found.** shows the relationship between level of development and diversification for all countries, with Russia’s peers highlighted. However, although developed countries have a more even export basket, they also export more in general, as is shown by the size of the hollow circles. Poorer countries seem generally to have somewhat lower entropy indexes, but as the figure illustrates, these vary a lot. Russia, for instance, is doing well given its level of development—much better than South Africa and Brazil. Yet, as the figure shows, Russia’s services trade is rather small compared with some other countries that have similar export diversification (vertical axis).

Russia still trades mainly with countries with which it has close historical ties. **Error! Reference source not found.** looks at how Russia’s share of exports to selected markets compares to the shares the rest of the world exports to the same countries; it seems to be disproportionately linked to Ukraine. Russia’s presence in developed countries like Japan and Indonesia is relatively small. Russia could also increase its trade in services with the EU market—its trade relationships seem to be dependent on small‐country markets like Bulgaria and not connected enough with larger and higher‐income markets. For example, although trade complementarity with Germany is rising, Russia’s services trade is not as intense as the intensity of all other
# 5 Measuring the “pours” component

Kaminsky et al. (2004) coined the expression “when it rains, it pours”to capture the amplification effect of procyclical fiscal policy on output. Consider, for example, a fall in commodity prices. Such a shock will have a direct negative effect on output through standard channels, which is the “rain”component.<sup>35</sup> The fall in output and consumption will reduce the tax base, thus increasing the primary fiscal deficit and forcing the fiscal authority to reduce government spending and/or increase taxes to restore fiscal balance. This, in turn, will have an additional negative output effect (the “pours”component). The total effect of the fall in commodity prices can be thus decomposed into a rain and a pours component: The purpose of this section is to quantify the “pours” component. This will be done in four steps:

- Panel regression (with country fixed-effects) to quantify the effects of changes in commodity prices on output. (This will be the total output effect)

- Panel regression (with country fixed-effects) to quantify the effects of changes in commodity prices on government spending. This is the fiscal response.

- Computation of the average fiscal multiplier.

- Computation of the pours component.

## 5.1 Data

Our sample consists of 15 commodity-exporting countries: 11 EMDE and 4 AE. The EMDE countries are Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Indonesia, Russian Federation, South Africa, and Ukraine, and the 4 advanced economies are Australia, Canada, New Zealand, and Norway.<sup>36</sup> 37 The data is an-

> 35On the output effects of terms of trade shocks, see, for example, Mendoza (1995).

> 36The number of countries in our sample was limited by the need to have quarterly data for the purposes of computing an average fiscal multiplier using the Blanchard-Perotti identification.

>
<sup>rd</sup> ), Romania (ranked 55<sup>th</sup> ) and Croatia (ranked 56<sup>th</sup> ) tax evasion was seen as most problematic (Table 2). It is unclear, however, if the survey participants in the various countries used the same unit of measurement when assessing tax evasion. For example, it is possible that in one country people are more sensitive to the problem of tax evasion than in another country, even if the actual level of tax evasion is lower. Some caution is therefore needed when comparing the country ranking within the NM-EU countries and also in comparison with other countries.

11. The EU Commission in the European Employment Observatory has published estimates of informal sectors, which are based on National Accounts statistics (EC 2007). These estimates confirm that informal sectors are relatively large in most of the new EU countries. According to these estimates, informal sectors are smaller than estimated by Schneider and the ranking across the NM-EU countries is also different. These estimates reveal that Estonia has the smallest informal sector among the new EU member countries and it is also lower than in some old EU member countries, including Sweden. Estonia is followed by the Czech Republic, Slovakia, Poland, Lithuania, Slovenia, Hungary, Latvia, and Bulgaria (Table 3).

**12.** Applying a regression model, Albu (2007) estimated for Romania the average share of informal income in total household income at between 17 and 18 percent in 2005, down from around 22 to 23 percent in 2000, which is also lower than the estimate by Schneider and close to the estimate of the EU study. It has also been estimated that in the Czech Republic, Hungary, and Poland around one tenth of the workforce are typically not reported for tax purposes and in Slovakia and Lithuania this share is 6 percent (OECD, 2008, EC 2007).

4
# I. **INTRODUCTION**

A centerpiece of Russia's real sector reforms-the privatization of 15,000 enterprises through vouchers-was to make the transition to a market credible, irreversible, and rapid. The objective of the mass privatization program (MPP) was to establish a critical mass of profit-seeking corporations, no longer dependent upon state support for their survival, and a class of owners willing to invest in their enterprises and manage their restructuring.' The Russian MPP, however, has brought few tangible benefits to enterprises privatized in voucher auctions. Ineffective corporate governance, little new investment, and the distortions of continuing government intervention have limited the amount of restructuring these firms could undertake in practice. A diverse constellation of indicators now suggests that, among firms in other transition countries of comparable wealth, Russian firms are ranked at the bottom in terms of output, factor productivity, profitability, and cost efficiency. Recent surveys of Russian industrial sectors, moreover, conclude that the policies of regional governments tend to favor lowproductivity incumbents, protecting them from takeovers and more productive new entrants.<sup>2</sup> In this paper we suggest that the absence of restructuring and investment are the consequences of interactions between insiders (manager-owners), and regional governments within a particular property rights system. Two inter-related features of this system are consequential to the Russian real-sector problem. First, regional governments still use enterprises to protect local employment. In the Russian regions, govermments face a tradeoff between enforcing cash-based tax collections and maximizing tax revenues, on the one hand, and extracting "social" benefits in the form of excess employment on the other. If the managers-owners were to maximize the value of the firm, the firm would be left vulnerable to expropriatory policies that alter the net return on investment-e.g., restrictions on layoffs, costly regulations or tariffs, etc. Anticipating that, managers choose instead to maximize the private benefits of control, and no restructuring is accomplished. The regional governments, knowing that the taxable revenue of