rafmacalaba/datause-encoder-data huggingface dataset:
# **BOXES:**

|Box 1: EU decision-making........................................................................................................ 10|
|---|
|Box 2: The basic regulatory framework for EU fixed income markets........................................ 12|
|Box 3: Overview of EU legal instruments.................................................................................... 13|
|Box 4: European Central Bank cooperation ................................................................................. 19|
|Box 5: The MTS System ............................................................................................................. 36|
|Box 6: Synopsis of regulatory framework for different methods of trade-execution.................. 37|
|Box 7: Basic features of regulation of investment firms in the 1993 ISD................................... 39|
|Box 9: Regulation of Danish MCIs: a historical snapshot........................................................... 82|
|Box 10: Summary of key European regulatory framework......................................................... 93|

# **TABLES:**

|Table 1: Suggestion for a regulatory framework for different methods of trade execution. ....... 40|
|---|
|Table 2: Figures for 2002 including tap issues (sources: dialogic and bondware)...................... 56|
|Table 3: Market value of Danish listed bonds by end 2002 ........................................................ 66|

# **ANNEXES**

|Annex 1: Terms of Reference for the study (in extract)........................................................... 102|
|---|
|Annex 2 : Size of EU fixed income markets............................................................................. 104|
|Annex 3: Illustration of the Lamfalussy-decision procedure.................................................... 106|
|Annex 4: EU regulation of fixed income securities markets.................................................... 107|

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therapy for patients with respiratory failure. Therefore, it is believed that when hospital care cannot be accessed, the case fatality rate (CFR) for a COVID-19 infection is higher. We assume that the COVID-related mortality is elevated by a factor of 3 for those patients who are in need of hospitalization but cannot receive it.<sup>20</sup> We denote by _πb_ ( _m_ ) the probability an individual of group _m_ requires hospitalization, conditional on being infected. The share of individuals in need of hospital beds at time _t_ , _Bt_ , is given by We assume that hospital bed allocation is random among those in need. Denoting by _h_ the number of hospital beds, the probability that an infected individual dies at period _t_ is given by As in Atkeson (2020) and Eichenbaum et al. (2021), we assume that it takes an average of 18 days from infection to either recover or die. To obtain weekly mortality probabilities, we multiply the rates obtained from Walker et al. (2020) by 7/18.<sup>21</sup> The number of hospital beds in each country is obtained from the World Bank’s World Development Indicators. It should be noted that the indicator is not measured frequently,

> 20For example, Yang et al. (2020) find that the CFR by the middle of February 2020 within the city of Wuhan, China, the presumed outbreak location of origin for COVID-19, was substantially higher (5.25 percent) than in regions outside Wuhan but within the same province (1.41 percent) and in regions outside the province (0.15 percent). While the CFRs within and outside Wuhan should vary for several reasons, including availability of testing, a key factor was the initial demand for hospital beds exceeded the supply.

> 21It has been noted that the Eichenbaum et al. (2021) SIR-macro model misses on the timing of COVID19 deaths for the US. Unfortunately, there is no practical way to assess whether our model reproduces the relative timings of the economic downturn vs. COVID-19 deaths in our sample of countries. The first basic issue is that our exercise does not model _actual_ lockdowns that occurred in these 85 countries. Rather, we impose the same lockdown parameters on all countries and compare the cross-country differences in outcomes. Second, a systematic comparison of our model’s predictions to actual lockdown outcomes would be complicated by the large heterogeneity among countries in the timing of the appearance of the virus and the timing of the policy responses. In addition, seasonality in transmission rates might play a role. Unfortunately, there is no parsimonious way to incorporate seasonality into our analysis, because we have 85 countries on all continents and climactic zones, and seasonality in transmission rates likely varies substantially across countries. Heterogeneous seasonality would further complicate any potential comparison between the data and our model’s predicted timing of economic downturn and death rates.

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# **Assessing the Role of International Organizations in the Development of the Social Enterprise Sector**<sup>1</sup>

Angela Hoyos Northwestern University Diego F. Angel-Urdinola World Bank JEL Classification: L30, L31, L33, F53, O35.

**Keywords** : Social enterprise, social innovation, international organizations, entrepreneurship, public-private partnerships

## **Corresponding Author:** Diego F. Angel-Urdinola (dangelurdinola@worldbank.org)

> 1 This study was prepared with support from the Middle East and North Africa Multi-donor trust fund (MDTF) (TF015739 – Promoting Social Entrepreneurship in MENA). The authors benefited from valuable comments, strategic guidance, and support from Professors Dr. Anand Sharma and Stephen Glenn Harding, MPA (Northwestern University).
16 democracies’ ‘penchant for acting belligerently against autocratic governments, even to wage imperialistic wars against them. Flexing one’s muscle against a centralized policy is easily rationalized, and this rationale can become compelling to democracies if the action might convert non-democracies to democratic rule and thus enlarge the zone of peace’. Among the supporters of the democratic peace, Russett (1993: 135–136) has argued that the ‘model of “fight them, beat them, and make them democratic” is irrevocably flawed as a basis for contemporary action’ (see also Russett & Oneal, 2001: 303). Russett (2005: 405) concedes that military interventions ‘have sometimes installed democracy by force, but they have more often failed, and the successes have been immensely expensive in lives and treasure’.

A policy of democratization by force requires that decisionmakers in democracies are aware of the regularities described in this paper. If the peacebuilding effects of democracy, the tendency of democracies to win wars, the democratizing effects on authoritarian countries of losing wars were known only to the research community and disbelieved among policymakers, democratic interventionism would be a tool waiting to be discovered. But there is no question the democratic peace has penetrated the Western political establishment right to the top. James Baker, Secretary of State in the administration of George H. Bush, said in 1992 that ’real democracies do not go to war with one another’. President Clinton said in his State of the Union address in 1994 that ‘Democracies don’t attack each other’. And as early as 1990, Margaret Thatcher said that ‘democracies don’t go to war with one another’.<sup>22</sup> The democratic peace seems to have become part of the conventional wisdom of the West over a decade ago. In the Clinton administration enlargement of the world community of democracies was placed at the core of US foreign policy. The introductory quote from President George W. Bush makes it clear that the link between the democratic peace and military intervention is accepted in the current administration. The victory of the democracies in the two world wars and in Korea have not gone unnoticed in the minds of democratic decisionmakers, and the US defeat in the Vietnam war is generally accounted for as a political defeat on the home front in an unpopular war that the US should either not have entered or conducted with

> 22 All of these quotations are from _The Economist_ (1995: 19).
# **1 Introduction**

In the past two decades, the notable uptick in the integration of developing nations into global trade and value chains (GVCs) has sparked heightened interest among policy makers and researchers regarding the implications for labor markets. Consequently, a substantial body of literature has emerged seeking to unravel the intricate relationship between trade dynamics and localized labor market outcomes.

Standard theory about the benefits of trade assumes perfect mobility of factors across geographical regions and industries within a country. However, recent empirical evidence has shed light on the limitations of these conventional theoretical models. Topalova (2010) [9] uses data from India to indicate that regions more exposed to trade liberalization have experienced slower poverty reduction and muted consumption growth, diverging from the predictions of traditional trade frameworks. Assuming segmented labor markets, and exploiting cross-market variation in import exposure, a key study by Autor, Dorn, and Hanson (2013) [2] confirms that the ”China Shock” led to significant decline in employment and wages in more exposed U.S. regions. This sparked more studies on the repercussions of tariff changes or import competition on local labor markets (Pierre and Schott 2016 [7]; Acemoglu et al. 2016 [1]; Dix-Carneiro and Kovak 2013 [3]).

It is crucial to note that trade affects not only tradable but also non-tradable sectors within the same local labor markets; while increased import competition or market access directly impacts specific tradable sectors, there are also indirect effects on non-tradable sectors such as retail, healthcare, or hospitality in the same region. With few exceptions, current literature largely ignores estimating these indirect effects propagated through domestic production linkages within a country [10]. While a few studies have examined the indirect effects of import shocks on local labor markets, there has been limited exploration of indirect effects of exports. A central motivation for this empirical inquiry stems from the well-documented fact that as economies undergo structural transformation—that is, as they move from less agriculture to more services and manufacturing—the 1
To summarize: the responsiveness of poverty to growth when measured from the surveys has generally remained the same across the pre- and post-reform periods, while there are signs that the responsiveness to growth measured through the national accounts has declined during the postreform period. This seems to be largely the product of the faster post-reform growth not being fully reflected in the surveys, and the increase in inequalities during the post-reform period. _Urban-rural composition of consumption growth_ Table 5 summarizes the results in testing the poverty impact of the urban-rural composition of consumption growth. Table 6 gives the test statistics on whether the urban-rural composition of growth matters and whether the population shift effect is significant. These results on the relative effects of urban-rural growth are presented for national poverty measures as well as separately for urban and rural areas.

For the pre-1991 period, the hypothesis that it is only the overall rate of growth that matters for poverty reduction is strongly rejected (Table 6). The weaker hypothesis of uniform poverty effects of urban and rural growth is also strongly rejected. This echoes the results from Ravallion and Datt (1996). Thus, we confirm our earlier finding that the growth effects on poverty for pre-1991 are largely attributable to rural consumption growth, with virtually no contribution from urban growth and a only limited contribution from the Kuznets process.

However, there is a significant structural shift between the pre-91 and post-91 periods. The hypothesis of similar growth effects during the two-periods is rejected (at the 8% level of significance or better; see Table 5). In the post-1991 period, the rural growth rate remains significant for poverty reduction (with the possible exception of the squared poverty gap measure) though the growth effects are smaller in absolute terms. Unlike the pre-1991 period, rural growth does not appear to be the prime driver of national poverty reduction. The most notable change is that the (share-weighted) urban growth variable is now highly significant. We can also mostly reject the null that only the overall growth rate matters for poverty reduction in the post-1991 period (Table 6), although the evidence for a Kuznetz effect during this period is weaker and only limited to the headcount index of poverty.

The emergence of a significant effect of urban growth on national poverty is the most striking feature of the post-reform economic growth in India. Table 7 reports the elasticities of national H, PG, and SPG measures with respect to urban and rural growth. The contrast between 21
<!-- Start of picture text --> Figure 3: Median Survival Time based on Fixed Costs by Country<br>Ukraine<br>Kazakhs ...<br>Kenya<br>Greece<br>Portugal<br>Turkey<br>Jordan<br>Russia<br>Italy<br>Morocco<br>Colombia<br>Peru<br>0 4 8 12 16<br><!-- End of picture text -->

**Figure 3: Median Survival Time based on Fixed Costs by Country**

<!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Weeks<br>Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> The median survival time has significant variation across countries within a given sector (Table A3). The median Portuguese firm in the manufacturing of food and beverages, for example, has a survival time of 7.6 weeks, whereas the median firm in the same sector in Colombia can last 26.3 weeks. Variation is even larger in the manufacturing of metals and fabricated metal products. The median Ukrainian firm can survive for just a little over 8 weeks, while the median Turkish firm has sufficiency liquidity for more than 10 months (44.1 weeks). Great variation is also present across sectors within a given country. In Kenya, for example, the median firm in the manufacturing of chemical, plastic and mineral products cannot even last a week, while a firm in the manufacturing of food and beverages can last for 16.8.

The mean survival time is longer, suggesting heterogeneity among firms and the likelihood that some firms can persist even in extreme economic hardship. The construction sector, for example, can survive a total lack of revenues for 12 weeks, while businesses in manufacturing sectors can survive on average for up to 38 weeks (Figure 4).

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**<u>Table 2: Wage Bill Growth and Cyclicality</u>**

|Dependent Variable:<br>|Wage_Bill_<br>|Growthit(Gro<br>|wth Rate of R<br>|eal Wage Bill<br>|, %)<br>|
|---|---|---|---|---|---|
|Model|OLS|FE|D-GMM-1|D-GMM-2|D-GMM-3|
||(1)|(2)|(3)|(4)|(5)|
|Wage_Bill_Growthit-1|0.220***|0.030|0.131|0.124|0.185|
||(0.070)|(0.075)|(0.082)|(0.081)|(0.271)|
|IMF_Programit|-1.479|-2.596*|-2.838|-2.828|-13.037*|
||(1.156)|(1.419)|(2.119)|(2.386)|(7.402)|
|Revenue_GDPit|-0.259**|0.200|0.397|0.470|0.154|
||(0.120)|(0.256)|(0.630)|(0.392)|(0.224)|
|Output_Gapit|1.274***|1.379***|1.463***|1.506***|0.618*|
||(0.300)|(0.368)|(0.374)|(0.422)|(0.325)|
|Electionsit|3.088*|3.180|3.273*|2.721*|0.735|
||(1.788)|(2.002)|(1.636)|(1.379)|(0.494)|
|Year Effects|Yes|Yes|Yes|Yes|Yes|
|Internal Instruments|No|No|Yes|Yes|Yes|
|# of Instruments|--|--|22|22|22|
|AR(1) Test p-val.|--|--|0.003|0.009|0.065|
|AR(2) Test p-val.|--|--|0.194|0.268|0.792|
|Hansen J Test p-val.|--|--|0.740|0.740|0.462|
|Sample Period|2000-2011|2000-2011|2000-2011|2000-2011|2000-2011|
|Countries|26|26|26|26|19|
|Observations|236|236|210|210|171|

Notes: The unit of observation is a country-year from the sample described in the Data Appendix. Columns (1) and (2) report standard errors clustered at the country level. Column (3) reports one-step difference GMM with clustered standard errors, while column (4) reports two-step GMM whose standard errors have had the Windmeijer correction. Column (5) reports only Western European countries. The internal instrument is the second lag of Wage_Bill_Growthit, and internal instruments are collapsed. ***, **, * indicate statistical significance at the 1%, 5%, 10% levels, respectively.

The results are reported in Table 2. The table presents the baseline results for OLS and Fixed Effects, one-step D-GMM (D-GMM-1), two-step D-GMM with Windmeijer corrected standard errors (D-GMM-2), and D-GMM-2 for only Western European countries (D-GMM-3). The results show that the real wage bill growth rate is significantly higher in election years and in periods when the output gap is positive. In election years, the real wage bill growth increases by 2.7% percentage points. This effect, however, is not present in Western European countries. The latter is consistent with previous research that found that political cycles are effectively mitigated in higher income countries with stronger checks and balances to prevent manipulation of fiscal policies during the electoral cycle. OutputGap is positive and significant across all specifications, though the effect is less in Western European countries than in transition economies (with the coefficient for transition economies being about twice the coefficient in Western European countries) . This result is consistent with earlier findings of more pronounced pro-cyclicality of fiscal policies in developing and transition economies. While the coefficient on 9
<!-- Start of picture text --> Household expenditure share, by treatment status<br>Untreated Treated − Bolsa Família<br>2.0% 4.4% 1.4% 1.5% 3.4%<br>3.2% 18.8% 5.4%<br>7.3% 3.5%<br>32.3%<br>2.4%<br>11.7%<br>20.2%<br>7.4%<br>36.2%<br>5.5%<br>33.4%<br>Food Housing Clothing Food Housing Clothing<br>Transport Hygiene Health Transport Hygiene Health<br>Education Recreation Others Education Recreation Others<br><!-- End of picture text --> Figure 3: Household expenditure shares, by treatment status Tables 4 and 5 show the number of observations (n), the mean, the standard deviation (sd), the minimum, the maximum (max) and the median (med) for some critical variables used in the model, including expenditures, amount of transfer and size of families according to treatment status (Bolsa Fam ́ılia= 1 for treated and Bolsa Fam ́ılia= 0 for untreated)<sup>58</sup> .

The distribution of all variables is skewed. This is especially pronounced for the expenditure and income variables. Most interviewed persons report very low or no expenditures while few report high expenditures. Also, according to Table 4, which considers the absolute values of expenditure, the mean and the median for untreated households are larger than BFP’s beneficiaries for all groups of goods.

The median amount of transfer from BFP is R$ 83.6 (mean R$ 84), which represents almost 20% of median labor income for these families. In per capita terms, they received about R$ 20.00 a month. As for the number of members, treated households are significantly larger than untreated (4.65 members on average against 3.09).

When we consider ethnicity, we notice that the number of self-declared white members are much larger among untreated households, while the number of self-declared blacks and ‘pardos’ are almost double among BFP’s beneficiaries.

> 58The information about eligibility criteria and conditionalities of Bolsa Fam ́ılia Program is available in section 8.1 of the appendix

24
to the MoLSA PMT models, in that they both use per capita consumption as a dependent variable and they both have regional models. The current work compares the potential overlap between MoLSA PMT and both old (VM) and new (SEVAT) MCNA models. The next section describes how this is done.

# 3.3 Pseudo-Proxy Means Test (PPMT) Methodology

The 2016 Vulnerability Assessment data<sup>8</sup> and the 2018 Multi Cluster Needs Assessment (MCNA) survey that formed the basis for CLCI’s VM and the SEVAT models do not have the full set of SSN PMT variables. To fill this assessment gap, we propose a probabilistic pseudo-PMT (PPMT) formula that includes only indicators that are common to the national PMT and the vulnerability models (VM and SEVAT) and provides its performance criteria. Using the data from the nationally representative SWIFT survey, we run an ordinary least squares (OLS) regression of household consumption on the common set of proxy indicators, _X_ s. For each common indicator, we assign a pseudo-weight based on its estimated association to household consumption as below: where, is a per capita monthly expenditure of household in natural log, which is a function of a vector of covariates/proxies, , and a random idiosyncratic error term, . and s are the estimated weights yy ii for a constant term and pseudo-proxy variables respectively. jj jj XX εε αα� ββ̂ As discussed in Box 1, we acknowledge the possibility of alternative approaches than the one presented here. There are, however, genuine concerns over data privacy and sharing, divisions in terms of concepts, and philosophy. Given the circumstances and scope of preliminary focus to be purely technical, we believe the tool could prove useful and is technically robust to allow the initial work to begin and momentum and discussion to ensue.

_Box 1: Possible alternative approaches? – A discussion_ The method employed in the paper may not be the only approach possible in this context. Creating a PPMT that is limited to the overlapping proxies between the two PMTs may be restrictive in terms of number of available variables for model prediction. An alternative could be, rather than focusing the comparison between the two targeting models, comparing available information in the underlaying data sets of the two models may be more flexible. Potentially more variables will overlap between the data sets than between models. Then following the procedure described in the paper, one would estimate weights using the household survey that was used to devise the national PMT and create the probabilistic referral thresholds. One will even have an option to select an optimal set of variables and strike the balance between parsimony and goodness of fit when there are too may overlapping variables. With potentially more and/or better correlated covariates, the new pseudo-PMT may have more accurate referrals than the proposed PPMT when applied to the humanitarian database.

> 8 Data collected across several CCI and other CWG partners were harmonized for the 2016 “Beneficiary Vulnerability Analysis” report. The harmonized data formed a basis for the 2016 VM model. See Cash Consortium of Iraq (2016).

7