rafmacalaba/datause-encoder-data huggingface dataset:
8 issue shortly after, while continuing to service other MinFins and Eurobonds.<sup>19</sup> In 2000, the London Club principal and interest-in-arrears notes (PRINs and IANs) and MinFin obligations in arrears were restructured, with haircuts estimated around 50%.<sup>20</sup> These restructurings substantially resolved Russia’s default and two of the bonds from that time are extant today: The 12.75% 2028 bond and the 2030 step-up note (currently 7.5%) issued as part of the London Club (PRINs and IANs) restructuring.<sup>21</sup> Figure 2 summarizes these previous debt crises.

Figure 2: Stylized timeline of Russian Debt Crises 1991-2000 <!-- Start of picture text --> Late 1991:<br>Vneshekonombank Aug. 1993-Jun. 1994:<br>Feb. 1995: Russia pays<br>declares insolvency. Russia Preliminary talks with USD500mm into escrow<br>approaches commercial London Club stalled over<br>creditors for talks and sovereign immunity and account upon preliminary deal with London Club.<br>commences series of 90- debt succession concerns.<br>day moratoria.<br>Nov. 1996: First Eurobond<br>Nov. 1995: Agreement in April 1996: Paris Club since Bolshevik<br>principle with London Club restructuring agreement Revolution. Final<br>signed. Later, in June,<br>Creditors signed. Payments Yeltsin wins a contested restructuring agreement<br>into escrow have continued signing delayed due to<br>reelection against the<br>all along. difficulty reconciling<br>communist party leader. claims.<br>October 1997: London Aug. 1998: GKO and OFZ<br>Feb. 2000: Preliminary<br>Club Final Agreement defaults. Foreign<br>agreement with PRINs and<br>Execution signed covering Commercial Debt 90-day<br>98.3% of club debt after moratorium. Defaults on IANs, writing-off over 1/3<br>nearly six years since the London and Paris Club of principal and swapping<br>remaining bonds for longer<br>start of the rolling 90-day debts, as well as on the dated instruments.<br>moratoria. principal of MinFins.<br><!-- End of picture text --> Sources: Tikhomirov (2001); Signyanina-Woodruff (2003); J.M. Boughton (2012) at 314-15 Still, the restructurings in 2000 did not mark the end of Russia’s debt difficulties. Scattered restructurings and litigation with holdouts and other creditors continued until 2009.<sup>22</sup> One recent

> 19 Signyanina-Woodruff, note 6 _supra_ , at 537; Reuters “Russia Defaults on Soviet era Debt” Dec. 30, 1998. MinFins are local USD denominated securities of longer tenor, the first few issues were given to former holders of defaulted Vneshekonombank obligations. The remainder of the London Club restructured debts took the form of bonds called PRINs (Principal notes) and IANs (Interest arrears notes) under the 1997 deal.

> 20 _All friends now_ , The Economist (Feb. 17, 2000); Juan J. Cruces & Christoph Trebesch, _Sovereign Defaults: The Price of Haircuts_ , American Economic Journal (database 2014u).

> 21 Prospectus for the Russian Federation US$ 907,788,786 Bonds due 31 March 2007 to 31 March 2030.

> 22 Prospectus for the Russian Federation EUR 750,000,000 1.125 percent bonds due 2027 and the Russian Federation EUR 1,250,000,000 1.85 percent bonds due 2032 (issued 2020), at 206-07.
# **II. Data**

The main source of firm-level data is the EBRD-EIB-World Bank’s Enterprise Surveys (WBES) conducted around 2018-2019. The choice of this particular set of WBES is because they contain questions on family management that are not available for the regular Enterprise Surveys carried out by the World Bank. This set of surveys were a joint venture between the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank (see EIB-EBRD-WB 2022). Data is available for 41 economies encompassing developing and advanced economies in Europe, the Middle East and North Africa (MENA) region, and Mongolia. The surveys have a section on management practices that was only implemented for medium and large firms (those with at least 20 permanent, full-time employees), where management practices were more likely to matter. The surveys also contain the standard World Bank Enterprise Surveys modules, collecting information on a representative sample of formal (registered) private firms operating in manufacturing or service sectors. The WBES data are fully comparable across countries and are collected via face-to-face interviews with business owners or top managers by using a global methodology. The data have been widely used by several studies to explore the private sector in developing economies (Paunov, 2016; Besley and Mueller, 2018; Chauvet and Ehrhar, 2018; Hjort and Poulsen, 2019). One advantage of this round of surveys is that they consist of a set of economies surveyed around a similar timeframe, employing a consistent survey instrument and methodology. These surveys were largely completed before the COVID-19 pandemic outbreak.

The key outcome variable is the quality of management practices, following the methodology implemented by Bloom et al. (2013b). This consists of eight components: (i) Problem resolution, (ii) Number of performance indicators measured, (iii) Level of ease or difficulty to achieve production or service provision targets, (iv) Knowledge of production or service provision targets, (v) Basis of manager bonuses, (vi) Length of focus of production targets, (vii) Promotion of non-managers, and (viii) Dismissal of underperforming managers. The scoring for each component is provided in table A4. The management practices module is only implemented for medium and large firms. Figure 1 provides the management practices scores for the economies in the sample.

The main variable of interest is the presence of family members in management. The specific question asked in the survey instrument is “ _What percentage of the key management positions of this firm are occupied by members of this family?_ ” We use two variables in the analysis. First, we use this exact variable to capture the intensity of family management. Second, we capture firms where all management is family.

5
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|---|---|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Provincial and Peri-Urban Water and Sanitation Project|Lixin Gu|
|Post Conflict - Economic Rehabilitation Credit|Slaheddine Khenissi|
|Post Conflict - Economic Rehabilitation Credit|Slaheddine Khenissi|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|Renewable EnergyMarket Transformation|Karan Capoor|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|CGAP-Pilot for Fund to Finance MFI Ratings|Patricia Mwangi|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Natural Disaster Risk Management Project|CuongHungPham|
|Decentralized Infrastructure for Rural Transformation|Lucia Spinelli|
|Decentralized Infrastructure for Rural Transformation|Lucia Spinelli|
|Decentralized Infrastructure for Rural Transformation|Lucia Spinelli|
|Decentralized Infrastructure for Rural Transformation|Lucia Spinelli|
<!-- Start of picture text --> S SS n SS a:<br>Data not collected Collected<br>ee2 ee<br>but methodologically weak Methodologically strong<br>he—<br>but not processed & analyzed| Macelt ttt RST ire)<br>but not disseminated & used<br>» * Lackof surveys overall ¢ Inappropriate or * No calculation of * Lack of timely gender<br>E * Lack of special topic incomplete relevant indicators statistical abstracts and<br>i] surveys questionnaire design * Limited tabulation factbooks<br>£ © Insufficient ¢ Flawed data collection * No disaggregation by sex ¢ No communication and<br>administrative data approaches and other characteristics dissemination strategy<br><!-- End of picture text -->
The relatively high levels of trade restrictiveness and the significant differences between Arab countries in the average OTRI level have implications for the potential economic effects of efforts to further reduce trade barriers on a regional basis. The higher are MFN tariffs, the greater the danger that preferential liberalization will generate welfare reducing trade diversion. The same is true for initiatives to lower the incidence of NTMs if this is done in a way that pertains only to intra-Arab trade. As the removal of tariffs on intra-PAFTA trade has already occurred, countries with high MFN tariffs can reduce the potential costs of trade diversion by lowering external tariffs. In the case of NTMs, there is great scope to pursue reductions in trade costs in a way that benefits all traders – both from within the region and those located outside it – thereby avoiding the costs of trade diversion.

**Table 1: Overall and Tariff-only Trade Restrictiveness Indices (2007, percent)**

|**Region **|**Total Trade**|**Agriculture**|**Manufacturing**|
|---|---|---|---|
|East Asia and Pacific|**12.5**|**34.5**|**10.6**|
||_4.9_|_8.3_|_4.7_|
|Europe and Central Asia|**11.3**|**41.1**|**8.5**|
||_5.0_|_13.0_|_4.4_|
|Latin America and Caribbean|**13.9**|**34.1**|**12.1**|
||_8.0_|_13.3_|_7.2_|
|**Middle East and North Africa**|**20.7**|**46.6**|**16.9**|
||_6.7_|_11.1_|_6.2_|
|GCC*|**NA**|**NA**|**NA**|
||_3.9_|_3.7_|_4.0_|
|Morocco, Tunisia|**25.7**|**97.2**|**29.3**|
||_17.1_|_36.0_|_15.0_|
|Egypt, Jordan, Lebanon|**22.5**|**40.4**|**18.8**|
||_5.3_|_6.8_|_5.0_|
|South Asia|**17.5**|**45.5**|**15.3**|
||_13.0_|_20.8_|_12.8_|
|Sub-Saharan Africa|**19.3**|**28.9**|**20.9**|
||_11.8_|_14.9_|_11.4 _|

_Source_ : Kee, Nicita and Olarreaga (2009); UNCTAD and World Bank staff. _Notes:_ OTRI (i.e., including NTMs) in bold; TTRI in italics. Aggregates are unweighted averages of countries within each group.

* Bahrain, Qatar, Saudi Arabia, UAE.

6
has been overdrawn. Britain's ICOR<sup>during the first half of the 19th century</sup> was 3.1 (Panel A, total, 1800-1860),<sup>above the 2.9 figure for Meiji Japan and</sup> not far below the 3.4 figure for low-income<sup>countries in the 1950s (Panel B).</sup> Furthermore, there is no evidence that<sup>Britain's ICOR in manufacturing was any</sup> different from that of the contemporary developing countries. While Britain's ICOR was low, to describe her investment<sup>requirements</sup> as "ashtonishingly small" (Mathias, 1972, p. viii) and<sup>concern with saving and capital "less</sup> relevant" (Landes, 1969, p. 78) compared to<sup>many poor developing countries</sup> today is, to say the least, overstating<sup>the case.</sup> Second, one of the key reasons why investment<sup>requirements during the</sup> First Industrial Revolution were so<sup>modest is that Britain failed to commit</sup> resources to those urban investment activites<sup>which, in Landes' (1969, p. 78)</sup> words, make industrialization such a costly venture today, and which,<sup>in W.</sup> Arthur Lewis' (1978, p. 29) words, make<sup>contemporary</sup> Third World cities so capital-intensive. Investment in housing and public works<sup>simply failed to</sup> keep pace with the rest of Britain's<sup>economy in the first half of the 19th</sup> century. One can see .his very clearly<sup>in Panel A of Table 22. While the total</sup> ACOR drops precipitously between<sup>1800 and 1860, the ACOR for the total economny</sup> less agriculture, housing and public works actually<sup>rises over the same</sup> period. While the ACOR outside of<sup>agriculture does fall, implying low ICORs</sup> and modest investment requirements at<sup>the margin, the reason for it is that</sup> capital-intensive housing and public works were given<sup>short shrift.</sup> Another way of illustrating this point is to examine the behavior<sup>of</sup> capital stock growth in social overhead<sup>--residential housing plus public</sup> works and public buildings. Table 23<sup>documents per annum growth rates in</sup> capital stocks per capita (1851-1860 prices).<sup>The table's message _s clear.</sup> Investment requirements durinlg the late 18th century were kept modest<sup>simply</sup> by allowing the stock of social overhead<sup>per capita to fall, contributing,</sup>

-72-
# **B Main Results Without Double-Lasso Selected Controls**

Table B1: Effect of Extended Provider Training on Learning: Provider Post-Training and Clinic Endline Surveys - No Additional Controls

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Beliefs A|bout RDTs||Be|liefs About M|icroscopy Te|sts|
||RDT<br>Beliefs<br>Index|Share RDT<br>Positive<br>Simple<br>Malaria|Share RDT<br>Positive<br>Severe<br>Malaria|Share RDT<br>Negative<br>No Malaria|Microscopy<br>Beliefs<br>Index|Share<br>Microscopy<br>Positive:<br>Simple<br>Malaria|Share<br>Microscopy<br>Positive:<br>Severe<br>Malaria|Share<br>Microscopy<br>Negative:<br>No Malaria|
|_A. Data from Post-Train_|_ing Survey_||||||||
|Extended Training|0.347<sup>_∗∗∗_</sup>|0.221<sup>_∗∗∗_</sup>|0.071<sup>_∗∗∗_</sup>|0.016|0.027|-0.002|-0.005|0.037|
|B0|(0.075)|(0.046)|(0.019)|(0.045)|(0.118)|(0.036)|(0.029)|(0.049)|
|Mean (Basic Training)|0.000|0.599|0.888|0.863|0.000|0.846|0.921|0.829|
|N|205|198|196|176|201|197|195|173|
|_B. Data from Provider F_|_ollow-Up S_|_urvey_|||||||
|Extended Training|0.380<sup>_∗∗∗_</sup>|0.186<sup>_∗∗_</sup>|0.056<sup>_∗_</sup>|0.039|0.188|0.038|0.008|0.074|
||(0.132)|(0.084)|(0.033)|(0.026)|(0.166)|(0.046)|(0.022)|(0.048)|
|_B. Provider Attendance_|||||||||
|Mean (Basic Training)|0.000|0.560|0.889|0.938|0.000|0.840|0.954|0.868|
|N|85|85|85|85|85|85|85|85|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata fixed effects. Sample in both panels limited to providers who attended the training. Index components are standardized relative to the basic provider training mean. In cases where some (but not all) index components for a respondent are missing, missing components are imputed to the treatment group mean prior to standardization. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.
Table A18: Correlates of Transportation and Identification

|_Dep. Var.:_|_Transpor_|_tation Index_|_Identifcat_|_ion Index_|
|---|---|---|---|---|
||_Coef._|_Std. Err._|_Coef._|_Std. Err._|
|Female|-0.010|(0.088)|-0.262***|(0.098)|
|Age|0.016|(0.015)|0.018|(0.015)|
|Currently attending school|0.065|(0.077)|-0.048|(0.072)|
|Years of education|-0.060|(0.040)|-0.072*|(0.040)|
|English spoken at home|0.237|(0.162)|0.374**|(0.151)|
|Single|-0.036|(0.083)|0.060|(0.085)|
|Does not live with the family|-0.055|(0.105)|0.048|(0.120)|
|Household size|-0.006|(0.017)|-0.016|(0.020)|
|Wealth index|0.116*|(0.063)|0.157**|(0.074)|
|Home owner|-0.087|(0.104)|-0.126|(0.111)|
|Father obtained education higher then sec.|-0.029|(0.087)|0.051|(0.087)|
|Mother obtained education higher then sec.|-0.084|(0.100)|-0.123|(0.102)|
|Muslim|0.015|(0.086)|-0.100|(0.084)|
|Yoruba|0.043|(0.128)|0.126|(0.146)|
|Constant|0.009|(0.520)|0.026|(0.651)|
|Observations|3,753||3,753||
|R-squared|0.019||0.030||
|P-value F-test of joint signifcance|0.391||0.001||

Notes: Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga.

xxv
1

# **1. Introduction**

Ninety percent of the OECD countries finance the majority of their health expenditures publicly (the exceptions are Mexico and the United States). The OECD is split roughly equally into tax-financed systems and social health insurance (SHI) systems: in half of the 30 countries, SHI contributions make up a majority of general government spending on health. In the rest of the world, the fraction of countries financing the majority of their health spending publicly is smaller (56% compared to 70% in the OECD), and only 21% finance the majority of their government spending through SHI.<sup>1</sup> The relative merits of SHI and tax finance is an old debate, but one where the evidence is surprisingly thin. There is fairly clear evidence that payments for health care are more progressive or less regressive in tax-financed systems than in SHI systems.<sup>2</sup> It is also clear that tax-financed systems are more successful at ensuring universal coverage within a single health system; SHI systems, by contrast, typically struggle to cover the informal sector and the poor.<sup>3</sup> But the presumption that those not covered by a SHI scheme fare less well—in terms of the quantity and quality of care they receive, the amount they pay for it out-of-pocket if they get it, or both—is not always borne out by the limited evidence available.<sup>4</sup>

> 1  The figures in this paragraph are calculated from data in the World Health Report annexes. 2  Wagstaff et al. (1992; 1999) find that SHI is less progressive than tax-financed systems (in fact, is mostly regressive) in the OECD countries. O’Donnell et al. (2007) find the same in Asia.

> 3  Carrin and James (2004) document the time it took several of the SHI countries to achieve universal coverage. Baeza and Packard (2006) document coverage in several Latin American countries. Wagstaff (2007) includes coverage statistics for several countries, as well as for different groups.

> 4  Being in a SHI scheme is typically found to be associated with higher rates of utilization, but not always lower levels of out-of-pocket spending or a lower risk of especially large out-of-pocket payments—see e.g. Gertler  and Solon (2000), Waters, Anderson and Mays (2004), and Wagstaff and Lindelow (2005).
estimates of _Pr_ [ _Y > τ, district d_ ] for _d_ = _Cairo, Alexandria_ . We estimate these by: _Pr_ [ _Y > τ, district d_ ] = _Pr_ [ _Y > τ |district d_ ] _Pr_ [ _district d_ ], where _Pr_ [ _district d_ ] (the share of the urban population residing in district _d_ ) is obtained from the most recent population census and where _Pr_ [ _Y > τ |district d_ ] is estimated using Proposition 7. For comparison the latter is also estimated using data from the HIECS only. The two different estimators are denoted by _λ_<sup>ˆ</sup> _prop_ 7 and _λ_<sup>ˆ</sup> _svy_ , respectively. _Pr_ [ _Y > τ, district d_ ] and _Pr_ [ _district d_ ] are denoted by _P_ and _π_ , respectively, such that _P_<sup>ˆ</sup> _prop_ 7 = _πλ_<sup>ˆ</sup> _prop_ 7 and _P_<sup>ˆ</sup> _svy_ = _πλ_<sup>ˆ</sup> _svy_ (where we suppressed the subscript _d_ for ease of notation). The estimates are presented in Table 4.

|sub-group|_π_|ˆ_λprop_7|ˆ_λsvy_|ˆ_Pprop_7|ˆ_Psvy_|
|---|---|---|---|---|---|
|Cairo|0.251|0.116|0.101|0.029|0.025|
|Alexandria|0.130|0.079|0.048|0.010|0.006|
|Other urban|0.619|0.028|0.028|0.017|0.017|

Table 4: Estimates of _π_ , _λ_ and _P_ Notice that our estimate of _λ_ finds that the percentage of households residing in Cairo and Alexandria with incomes exceeding _τ_ is larger than what the HIECS alone would have us believe. This combined with the earlier observation that _θ_ ˆ _mix < θ_ ˆ _svy_ leads us to believe that relying on survey data alone will arguably under-estimate both the number of households with high incomes as well as the size of their incomes (either because top income earners are missing in the survey or because they under-report their incomes, or both). Table 5 compares estimates of top income shares _S_ obtained using the HIECS to those obtained using both the HIECS and the house price data. The additional columns compare estimates of inequality among top income households (i.e. only including households whose income exceeds _τ_ ) for three different measures of inequality.

|sub-group|_Smix_|_Ssvy_|_Ginimix_|_Ginisvy_|_MLDmix_|_MLDsvy_|_Theilmix_|_Theilsvy_|
|---|---|---|---|---|---|---|---|---|
|Cairo|0.159|0.118|0.414|0.227|0.295|0.087|0.532|0.107|
|Alexandria|0.042|0.036|0.283|0.156|0.141|0.038|0.208|0.041|
|Other|0.063|0.066|0.223|0.223|0.082|0.082|0.097|0.097|

Table 5: Estimates of _S_ , _Gini_ , _MLD_ and _Theil_ (for the top tail) Estimates of total inequality for (urban) Egypt are obtained by adding estimates of bottom- and between inequality to the estimates of top inequality reported in Table 5. Bottom inequality (i.e. inequality among households with income below _τ_ ) is estimated using the HIECS only. The between inequality component is estimated using data from both sources as it is a function of average income among top earners (which is a function of _θ_ ; see eq. 15) as well as a 23