rafmacalaba/data-use-mentions-extended huggingface dataset:
made up of two other sub‐sectors, namely Wholesale trade and Trade and repair of motor vehicles. There are several solutions with this problem. One is that we use the whole distribution sector as representative (excluding Trade and repair of motor vehicles) from where each industry and services sector sources its inputs regarding retail services; or that we chose wholesale services only. Both approaches are imperfect and have their weaknesses, but we have checked both strategies and prefer the ones that includes both Wholesale trade and Retail trade.

Another issue that we are faced with is that some services policy indicators are more disaggregated than that the input‐output tables allow us to compute any input coefficients. In order to tease out any sub‐ sector coefficients from these tables, we use Eurostat’s production data which serve us with an indirect way of carving out any sub‐sectors within an aggregate service sector. Admittedly, this method is not a prefect substitute, but allows us nonetheless to be as consistent and detailed as possible.

Furthermore, as part of our robustness check we also use EU‐wide input‐output coefficients in which all 28 member economies are taken up, including a good coverage of the retail sector, in addition to input‐ output coefficients from one exogenous country only which is the US. The main reason for using these two non‐country specific input‐output tables for computing input sourcing coefficients stems from the fact that there seems to be debate in the economic literature about whether one should use the assumption of equal industry technologies across countries or not. Equal technology coefficients seem reasonable if one thinks that the country selection in the sample are reasonably similar in their economic structures and technology endowments. Practically, this might as well form a convenient assumption if a suspicion exists that input‐output tables at the country level are not very well measured for some

> 13 An overview of this matrix with the number of firms in each of these cells is available upon request.

> 14 Note that for Bulgaria we used 2011
# **Conflict countries and employment growth**

**South Asia is a conflict region, with varying degrees of security issues.** According to the United Nations Security Levels System, all countries in the South Asia Region are facing security and conflict issues. However, the extent of the conflicts varies. The UN Security Levels System objectively classifies regional security into six categories: minimal, low, moderate, substantial, high, and extreme. The system identifies Bangladesh, Bhutan and Nepal as minimal to low. Sri Lanka is classified as minimal or in some regions moderate, India is similar aside of the Indian administered Kashmir region. Two countries have several regions which are heavily affected by security issues - Afghanistan and Pakistan. Following this classification, we grouped Afghanistan and Pakistan as countries with a severe ongoing conflict, and describe India, Bangladesh, Bhutan, Nepal and Sri Lanka as countries with a moderate security environment. This picture is confirmed by qualitative reports such as the Conflict Barometer (2010).

**Differences between severe conflict regions and others are vast, because conflict areas miss out on urban growth.** Countries in regions with moderate conflicts grow at an annual average of 11%, which is significantly faster than the 7% relative growth on an annual basis in conflict countries. While growth differences in non-urban areas are not significant, the overall difference is driven by urban areas, where the difference in the average growth rate is 8%.<sup>11</sup>

**Firms in non-urban conflict regions are smaller and younger.** Companies in rural areas are much smaller and younger in conflict countries than in countries with little or moderate conflict. More generally, firms in the sample that are located in conflict areas are younger than firms in regions with a moderate conflict. Interestingly, the opposite holds for urban firms in conflict areas – these are older than urban firms in non-conflict areas.

**Table 5: Firm growth in urban and rural areas in moderate and severe
<!-- Start of picture text --> 1 _<br>e<br>8-<br>2<br>5© 6- ° e e<br>>e<br>=<br>© ee °<br>e e<br>o 4- e@ @ e<br>ne e e e<br>e e bd 3 __ 0<br>2- e 28 e _<br>° ° o° —<br>eo, 6 ee 4 ¥ te Se, ©<br>0-990 ° °<br>I t I 1 i] I<br>24 25 26 27 28 29<br>Gross County Product (log)<br>@ Employment formal (census) @ Employment licensed<br>@ Employment licensed SMEs+ © Employment in VAT firms<br>R2: employment share in formal sector 0.59; in licensed firms 0.41; in licensed SMEs+ 0.48; in VAT firms 0.46<br><!-- End of picture text -->
*_ES9_**|10.02|9.75|||||
|**_ES16_**|11.91|12.06|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
**Figure 4. TFP growth with natural resources and GDP growth, top 25 countries ranked by GDP growth**

<!-- Start of picture text --> GDP Growth and TFP<br>(long‐term averages, 1996‐2014)<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>Azerbaijan<br>China<br>Bosnia and Herzegovina<br>Mozambique<br>Rwanda<br>Chad<br>Iraq<br>Lao PDR<br>India<br>Mongolia<br>Nigeria<br>Armenia<br>Kazakhstan<br>Burkina Faso<br>Panama<br>Belarus<br>Tanzania<br>Georgia<br>Sierra Leone<br>Tajikistan<br>Sri Lanka<br>Dominican Republic<br>Malaysia<br>Botswana<br>Jordan<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>TFP GDP<br><!-- End of picture text --> Note that data on diamonds are not available for Botswana, which biases the TFP estimates

# _Comparing TFP measures by region and income group_

Figures 5 and 6 compare average TFP growth rates with and excluding natural resources for countries grouped by income class and region (where regions include developing countries only). In each income class we see that TFP growth with natural resources exceeds the measure 15
99.5|
|Combustion For Manufacturing|0.7|99.7|
|Power Industry|0.5|99.8|
|Chemical Processes|0.3|99.9|
|Iron And Steel Production|0.2|100.0|
|Fossil Fuel Fires|0.2|100.0|
|Railways Pipelines Off-Road Transport|0.0|100.0|
|Total|375.2||
|CO2 Conversion Factor|25.0||
|C02 Equivalent|9,381.2||
|**Source: EDGAR (2022)**|||

> 6 For detailed sector definitions and descriptions, see: EDGAR Sector-Specific Gridmaps, 1970 – 2021 https://edgar.jrc.ec.europa.eu/dataset_ghg70#sources IPCC 2006 Reporting Guidance and Tables

> https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_8_Ch8_Reporting_Guidance.pdf

5
as well as international flights put in place before the end of the month. Early April also saw movement cessations and overnight curfews put in place in populous counties, which remained in effect for several months.

Given the sudden onset, this repurposed study assesses the immediate impact of the business support interventions of our original study in ameliorating the crisis. The analysis is based on three rounds of phone surveys among a sample of 1,054 youth who were part of the first rollout wave of the original study.<sup>1</sup> These surveys were conducted at three months (June), five months (August), and seven months (October) from the start of the pandemic, and

> 1 The original study comprises a sample of 9,380 youth across 15 counties in Kenya. Owing to the large scale of the program, the intervention rollout was organized by groups of 5 counties, and the sample in this repurposed study represents all youth who were part of the first wave of implementation. Please see section 2.1. for more details on study design and intervention timing.

2
sources of the data used in the baseline model (and extensions). Data for 54 advanced economies and EMDEs are used in the baseline model from 1980Q1 to 2022Q4 (see table A3.1). The main variables are described in table 1 and the baseline model includes government spending, tax revenue, the real interest rate, real GDP, and the GDP deflator (figure 1). Appendix A3 provides details on the definition of the real interest rate.

For government spending, to get the greatest cross section both national accounts data– that is real public consumption expenditure on a seasonally adjusted basis–and data from government financial statements–usually collected as monthly nominal government ex10
Armavia has the right to conduct air passenger transportation from Armenia, while the other air companies do not have such a right. They carry freight from Armenia, for example, "Air of Armenia", which operates flights to Europe, but has no right to carry passengers.

Companies engaged in cargo transportation are rather big - they have 4-5 large cargo aircraft IL-76.<sup>29</sup>

**Air Armenia** is possessing cargo airplanes and operating cargo flights from Armenia to the rest of the world. This company operates regular flights twice a week from Yerevan to Frankfurt (Hahn) and from Frankfurt to any other destination. Air Armenia is a private airline established in 2003 and based at Yerevan Zvartnots Airport in Armenia (EVN, UGEE).<sup>30</sup>

The most challenging was to obtain data on air transportation in Armenia. Through National Statistical Service of RA we got the total air passenger and cargo traffic data for 2009 in Armenia. (8,4 th. Tons of cargo freight and 1496,3 th. Man). Majority of armenian air companies do not have web-sites and did not provide any private information. Only the national carrier of Armenia Armavia publicly report information on its air traffic. The company flew 700,000 passengers, which is 47,64% out of volume of passenger transport in 2009.

So, We define the share of airline in passenger carrying market by a company share in total passenger turnover, the same for the cargo carrying market. To derive the total market share we put weights to be equal 50% for passenger and 50% for cargo carrying market.

As the only Armavia has the right to conduct air passenger transportation from Armenia, we assume that the rest of market share (52,36%) refers to foreign air companies mentioned above. Unfortunately there is no public available information of passenger allocation among foreign companies. So we allocate equally 33,33% to CIS, EU, and ROW region in
where is total employment in the economy, is informal employment, is value added, are IIddII control variables in sector jjHH _j_ and year _t_ , and and jjHH are sector time fixed effects, respectively. For this jjHH jjHH LL LL YY XX estimation informal employment includes those who are self-employed and non-paid employees. jj HH μμ λλ Capturing non-paid employees is important, especially for low-income countries, as their number can reach more than 60 percent of total employment in countries such as Liberia, as well as in the agriculture sector where labor activities for smallholder farming are spread among household members.<sup>27</sup> Harmonized labor force surveys collected by the World Bank are used to derive a measure of the informal sector for this estimation, substantially expanding the sample size and including countries outside the Latin America and Caribbean region, when compared to other studies. The sample includes 87 countries, where each country has more than one survey (see Appendix 1).<sup>28</sup> Labor productivity is measured as a logarithm of the value added of the sector in constant prices divided by the number of total (formal and informal) workers.

The empirical results in Table 2 are in line with the theoretical model and the stylized facts discussed above, showing that productivity growth is associated with a decrease in informal employment across the three sectors considered (agriculture, manufacturing, and services). These results are consistent with Maloney (2001) and Loayza and Rigolini (2011). Columns (1)-(3) provide estimates for equation (14) without control variables for the three sectors, while columns (4)-(6) show regression results with control variables, such as export and import shares in GDP, human capital, the dependency ratio, the real interest rate, the log of population, and country fixed effects. The regression results indicate that there is a significant negative relationship between labor productivity and share of informal employment across all sectors; and the results hold in both
the true poverty rates. All models use only census-based covariates.<sup>28</sup> Panel (a) of Figure 1 presents these results and we find evidence of considerable downward bias for both the PSU- and municipality-level models. For example, for the PSU-level model, we find that the median “true” _R_<sup>2</sup> is 0.88 whereas the median “direct” _R_<sup>2</sup> is 0.59. Interestingly, the true _R_<sup>2</sup> tends to be higher for the PSU-level model than the municipality-level model, but the opposite is the case for the direct _R_<sup>2</sup> . This suggests that model selection based on the direct _R_<sup>2</sup> can be misleading in that the direct _R_<sup>2</sup> incorrectly suggests that the municipality is the preferred level of estimation. More specifically, we find that the true _R_<sup>2</sup> identifies the PSU as the preferred level of estimation for 437 of the 500 samples, but the direct _R_<sup>2</sup> only selects the PSU level for four of the 500 samples. The direct _R_<sup>2</sup> then identifies the incorrect level of estimation for 433 of the 500 samples.

In Section 4, we discussed two other ways that the _R_<sup>2</sup> can be misleading for assessing model performance: it is invariant to locational shifts and influenced by the variance of the true poverty rates. Using the Mexican data, we can assess the relevance of the former issue by comparing the

> 27For examples, see Jean et al. (2016), Pokhriyal and Jacques (2017), Steele et al. (2017), Lee and Braithwaite (2020), Yeh et al. (2020), or Chi et al. (2022).

>
# **1. Introduction**

Governance is crucial for central banks. According to Bossu and Rossi (2019), the concept of central bank governance is multifaceted. They contend that a clearly defined central bank mandate and decision-making structure are essential to support accountability and legitimacy. Such a mandate should elaborate on objectives, functions, and powers, specifying the legal tools available for a central bank to implement its functions. Clear decision-making structures are also necessary to ensure adequate implementation of the mandate. Given the complexity of central banks and the multiple responsibilities they assume, it is crucial to define what to decide, who must decide, and how decisions must be made (Bossu and Rossi 2019).

Our empirical analysis focuses on the governance of reserve management, a key part of central bank governance overall. As foreign currency reserves have significantly increased and represent an essential part of central bank assets, the process for making investment decisions and overseeing results becomes more relevant. We aim to contribute to the reserve management governance discussion with data-driven analysis using a unique survey data set on central bank reserve management practices. Most previous publications on the topic are prescriptive and qualitative. Notably, our research shows that governance arrangements do matter for the investment policy of foreign reserve portfolios.

As the first step in this analysis, we briefly review the literature on central bank governance, focusing on reserve management operations. We then describe the data that we use for empirical research. We use a unique data set collected by the World Bank's Reserve Advisory and Management Partnership (RAMP) using two surveys conducted across 99 central banks in 2018 and 105 central banks in 2019. The surveys collected data on central banks' reserves management activities, including their governance structures, components of their investment policies and asset allocations, and their accounting and profit-sharing methodologies. We also deploy data that describes the broader institutional and macroenvironment in which these central banks operate and proxies for reserve adequacies. We use this data set to address novel research questions to find links between specific
instance, the recent passing of the Services in the Internal Market Directive (2006/123/EC) in 2006 did not happen without fierce public opposition and was eventually significantly watered down by the European Parliament with many sectors such as broadcasting, audio‐visuals and postal services being excluded.

Although the EU strives for reforms of services markets to make them explicitly more tradable across countries, the reform indexes taken up in this study are closely linked to the domestic regulatory setting within each European economy.<sup>8</sup> Our index proxies for services reform are taken from the OECD’s PMR database which are known as the Non‐Manufacturing Regulations (NMR) in services as explained in Koske _et al_ . (2013) and which have been used in previous industry‐level studies such as Barone and Cingano (2011). The database covers 12 services sector‐specific reform barriers such as in electricity, post and telecommunications, transportation and professional services. The main reason for using these indicators is because of their multiple year coverage. In fact, the PMR database provides good time‐ 8 Hence, these measures take up not only the foreign entry of firms into the domestic economy, as otherwise trade restrictiveness indicators would do, but also domestic entry _in addition to_ the potential of foreign entry. Moreover, it should be stressed that our reform indicators are really measuring regulations at the (unilateral) domestic level and therefore reflect within country scenarios as opposed to indicators that measure the cross‐border regulations. Partly, our indicators will pick up some elements of the latter, but as to date no precise reform indexes exist at the EU‐level which takes up the pure trade‐related aspects for reform in a longer time‐series.

6
<mark>simulated dynamic stochastic general equilibrium model and show that up to 100,000 jobs could disappear in the short‐run and more than 300,000 in the long‐run. Another study by Bauer</mark> _<mark>et al</mark>_ <mark>. (2013) uses a computable general equilibrium GTAP model to estimate the economic impact of the GDPR. It finds that this law could lead to losses up to 1.3 percent of the EU’s GDP as a result of a reduction of trade between the EU and the rest of the world.</mark> Goldfarb and Tucker (2012) empirically prove the adverse link between restrictive data policies and innovation and point out that stricter privacy regulations may harm innovative activities by presenting the results of previous studies undertaken with respect to two sectors, namely health services and online advertising. Both studies show that there are strong linkages between the effective sourcing and use of data and innovation based on open markets. Recent work by Goldfarb and Trefler (2018) discusses the potential theoretical implications of restrictive data policies such as data localization and strict privacy regulations on innovation and trade, albeit from the perspective of AI. The authors make clear that an expanded innovative AI industry in which data flows are an important factor would be distorted by restrictive data policies such as data localization.

This paper combines the two strands of the literature by developing a specific yet expanded data policy restrictiveness index based on Ferracane _et al._ (2018a). It then relates the index to firms’ digital innovation activities for a set of East Asian countries for which we specifically have developed the data policy index. The index covers various measures related to data activities and is much broader in scope than equivalent indexes used in the papers described above. For instance, in addition to restrictions on the cross‐border flow and domestic use of data, we now also include restrictions related to IPR for digital sectors, intermediate liability and content access for online
; and the Geothermal Development Company (GDC), also in 2008. The main institutions of Kenya’s power sector are described further in box 1.

In the wake of these reforms, Kenya Vision 2030 was launched in 2008. A 22-year development plan, it sets out a path to Kenya becoming an industrializing, middle-income country by 2030. Much of the plan depends on the continued development of the energy sector, and power subsector, in order to support industrialization, the advancement of modern infrastructure, and socioeconomic development through increasing the access, availability and quality of supply. Some notable advances have been made in this regard, including increasing electricity access to around 75 percent,<sup>3</sup> moving to a position of ‘surplus’ capacity through a more resilient technology mix<sup>4</sup> with total generation now at 2,370MW, improved operational efficiency of the sector, and the successful attraction of private sector investment into independent power projects (of which there were 12 in 2017). While some of these outcomes can be linked to sector reforms, others—including in particular electrification and geothermal power development—depended critically on concerted action and significant funding from the government.

> 3 Based on the World Bank’s Multi-Tier Framework Survey.

> 4 In the 1990s, hydroelectric power plants accounted for almost 70 percent of installed capacity which had severe implications in the drought prone region, especially in the critical drought of 1999-2001 where the reserve margin had been reduced to a point that regular load shedding was necessary.

3
assistance programs. We explored the role of the following seven community level resources or programs in mitigating negative welfare impacts of shocks in rural areas of Java: (1) presence of technical irrigation in the community<sup>7</sup> , (2) Kampung Improvement Program (an informal housing area upgrading program that provided basic services and infrastructure through community based organizations), (3) Infrastructure Development Program (a community-based infrastructure development program), and (4) availability of credit through the INPRES Poor Villages Program, (5) the village has a _Padat Karya_ program, a loose collection of laborintensive programs sponsored by various government departments (Sumarto et al. 2002), (6) the village had a PDM-DKE (Regional Empowerment to Overcome the Impact of Economic Crisis) program, a block grant program for villages to support public works or revolving funds for credit (Sumarto et al. 2002), and (7) the _Inpres Desa Tertinggal_ (IDT) (Program for Underdeveloped Villages), another block grant program targeting extremely poor villages (Sumarto et al. 2002). Data on the first four community level programs above were available in both the 1997 and 2000 IFLS surveys, so we pooled the data to increase the number of observations. However, data on the last three community-based programs above were only available in the 2000 IFLS survey, so we could only use the single year of observations in evaluating those programs.

As discussed earlier, recognizing that government assistance programs are often targeted to poor areas, we use propensity score matching to infer the moderating impact of some community level interventions on the impact of the shock. For each of the community-based programs, we estimate the average treatment effect of the intervention on per capita household expenditures components (total, nonfood, and food) among households exposed to the shock and located in communities with the program of interest (i.e. SATT, or the sample average treatment effect for the treated). The results in Table 3 are shown as