rafmacalaba/data-use-mentions-extended huggingface dataset:
in debt markets. In Morocco, public pensions funds play a more active role in public debt markets, particularly Caisse de Dépôt et de Gestion (CDG) holding 9 percent of public debt. It is the most important participant in the primary market and the second largest on the secondary market for government securities. This has raised concerns about its potential to influence public debt market prices.

**The contribution of the insurance sector to public debt markets is very limited with the exception of Morocco, due to the sector’s very small size across the region.** Assets are mainly technical reserves for the property and casualty insurance business which are likely to be invested in very liquid instruments. Life insurance is underdeveloped in the region, and life insurance companies are not expected to make a major contribution to fixed income market development in the short run. Morocco’s insurance industry is the largest with assets amounting in 2008 to 17.3 percent of GDP, and holdings of 12 percent of public debt.  However, there is a high degree of concentration with the three largest companies holding about 50 percent of market share in terms of premiums collected.

# **<u>Foreign investors</u>**

**Foreign investors have been key agents to develop local currency government bond markets in many emerging market countries.** Country cases illustrating their importance can be found in Central and Eastern Europe (Poland, Hungary and Czech Republic), Latin America (Brazil and Mexico), and South East Asia (Malaysia and Indonesia). Statistics measuring foreign investors ́ exposure to public debt are not always available or accurate, and may differ depending on the source (e.g. Central Banks ́ official registers, industry data). Comparisons across countries cannot always be straightforward given that in some countries investments may be indirect through structured off-shore derivatives<sup>47</sup> or that domestic investors may be registered as foreign holders.

**In general, in the most advanced EMEs foreign
.127***|
||(0.158)|(0.0986)|(0.101)|(0.120)|
|Observations|206|200|406|406|

_Note:_ Standard errors in parenthesis.

Column 4 shows results of pooled regression with standard errors clustered at the individual level *** p<0.01, ** p<0.05, * p<0.1 Source: Saraf, 2019; World Bank Group, 2018 Figure 4 below show prevalence trends across the three rounds for all participants.

> 10 The pooled sample comprises of all respondents from both follow‐up panels (i.e. 5 weeks and 3 months). Table 5, Column 3 shows the result of the pooled logistic regression, and Column 4 shows the same result after clustering standard errors at the individual level.

18
the retention of non-white females. Finally, finding larger effects for female rather than male incumbents is also in line with Elvira and Cohen (2001) who find that a higher proportion of women leads to a significant reduction of turnover among women, whereas an increase of men does not cause a decrease in quitting among men.

# **5 Conclusion**

We study the impact of having same-race coworkers on workers’ retention at the firm. Using matched employer-employee data from Brazil, we exploit the deaths of workers from different racial groups as an exogenous shock to the racial composition of peer groups. We find that the death of a non-white worker – relative to the death of a white worker – decreases the likelihood of staying at the same firm for non-white incumbents. In contrast, we find little evidence that the racial coworker composition matters for white incumbents. The death-induced decline in the non-white share of the peer group makes non-white incumbents more likely to quit their current job – while not affecting their likelihood of being laid off – and to move to a new job where the non-white share is again higher than in their old job. Together, these results suggest 21
. Second, whether landlords‟ choice of partners with greater agricultural ability affects the productivity impact of land rental market and the extent to which policy can help to encourage productivity-enhancing outcomes.

Use of within-household fixed effects estimates for owner-cum-tenants to answer the first question suggests that output on sharecropped plots is significantly lower, by between 16% and 25%, than on owned plots cultivated by the same household. If landlords share in provision of inputs, the inefficiency will disappear. Landlords‟ monitoring capacity, i.e., being a farmer or having off-spring live in the village, attenuates but fails to eliminate the inefficiency arising from sharecropping contracts.

While earlier analyses provided estimates of the magnitude of the „Marshallian inefficiency‟ comparable in magnitude to those obtained here, lack of information on partners in rental markets made it impossible to ascertain whether this was due to the contractual form, or to tenants‟ inherent productivity. Analysis of pair fixed effects for properly chosen plot samples, i.e., plots owned and cultivated by either the landlord 2
**Figure 12. Predicted Trade Estimates with and without Fixed Effects, 2009–11**

<!-- Start of picture text --> 13<br>8 GBRUSADEU<br>DNKB AUE ENLDSW U JKRSPLF P TCHN I FNOLNT ERAA<br>3 G EZNG LUX SVN B OAPHES G PR F MYALVTB RIRL HUNLLR IDN THAGRT CZ RASTAOM U K IENDC TUO RR CHN<br>COL<br>UR Y<br>-2<br>-7<br>-12<br>-10 -5 0 5 10 15<br>log of predicted services exports, NO FE (09-11)<br>log of predicted services expots, FE (09-11)<br><!-- End of picture text --> Source: WDI, Trade In Services database, World Bank (TIS), STRI, and Centre d'Etudes Prospectives et d'Informations Internationales (CEPII). Notes: In the figure, Russia’s bilateral exports with other EU countries are in blue and are labelled according to their 3‐digit ISO‐country code. Also included are other countries with whom Russia trades, shown in red (other middle‐income countries) and yellow (East and South East Asian countries); plus a 45‐degree line is depicted. FE = Fixed Effects, BGR = Bulgaria, AUT = Austria, BEL = Belgium, CZE = Czech Republic, DNK = Denmark, EST = Estonia, FIN = Finland, FRA = France, DEU = Germany, GRC = Greece, HUN = Hungary, IRL = Ireland, ITA = Italy, LVA = Latvia, LTU = Lithuania, LUX = Luxembourg, NLD = Netherlands, POL = Poland, PRT = Portugal, ROM = Romania, SVN = Slovenia, ESP = Spain, SWE = Sweden, GBR = Great Britain, RUS = Russia, GEO = Georgia, UKR = Ukraine, TUR = Turkey, COL = Colombia, NGA = Nigeria, URY =
fairly high site fidelity (e.g., lobster [Acosta 1999]) to those that move extensively as larvae (e.g., shrimp [Calderon-Aguilera et al. 2003]) or adults (e.g., tuna [Schaefer et al. 2007]). This variation allows for an analysis of how cooperatives deal differently with species that vary in their mobility.

Moreover, the region’s terrestrial and marine ecosystems respond dramatically to ENSO (El Niño/Southern Oscillation) events, which occur every several years (Polis et al. 2002; Velarde et al. 2004). During El Niño years, ocean waters warm, upwelling slows, and rainfall increases, with important implications for fisheries species (Velarde et al. 2004; Aburto-Oropeza et al. 2007). While ocean productivity varies temporally—both with ENSO and other sources of climatic variability—and spatially throughout the Gulf region, we find remarkable coherence in the variability of mean concentration of chlorophyll _a_ , a common proxy for marine primary productivity (Mann and Lazier 2005) in the vicinity of the three cooperatives for which we have logbook data (Leslie et al. 2015). ENSO’s significant role enables us to explicitly test the influence of periodic environmental shocks on cooperatives’ decision-making. ENSO may affect species through three channels: recruitment and growth of juveniles, growth of adults, and movement of adults. Here we focus on the recruitment channel.

Fishing cooperatives have had a long history in the Gulf of California—and Mexico more broadly—and continue to be a major factor in the fishing industry today. Under the 1947 Fisheries Law, cooperatives had exclusive concessions to the eight most commercially valuable species and often had rights to bays, estuaries, or lagoons adjacent to their lands (DeWalt 2001; Young 2001). In addition to cooperatives, the fisheries law created two other classes of fishermen: _permisionarios_ , who are private individuals or corporate entities with permits to catch—and sell to the open market—species
their children’s schooling outcomes. It does so by analyzing a household survey from Rwanda, the EICV, that contains, in part because of the 1994 Genocide and the HIV/AIDS epidemic, a very large proportion of children living in households from which their biological parents are absent. The data also contain information about the type of relationship that links the children to their “adoptive” families and about the educational attainment of the absent biological parents. These two features control for non-random placement of the “adoptees,” one of the main sources of bias in using the adoption strategy.

The results of the analysis suggest that the nurture component of the intergenerational transmission of human capital is important, in particular for mothers, contrary to recent evidence proposed by Behrman and Rosenzweig (2002) and Plug (2004). In the preferred specification, even after controlling for non-random placement by including the schooling of the biological parents and the type of relationship linking the child and the head of his new household, the education of the most educated female adult in the new household has a positive and significant effect on the schooling of the child welcomed in the household. The magnitude of the effect is similar to the effect in a biological mother-child relationship.<sup>16</sup> The effect of the education of the most educated male in the relationship is smaller than in a biological father-child relationship, but remains positive and significant. When boys and girls are analyzed separately, it appears that, when the nurture component is isolated, the mother’s education matters more for girls, while the father’s education has a stronger effect on boys’ educational achievement. The analysis of interaction terms indicates that the positive effects of the education of the “adoptive” parents are only present for children related to the head of their new household (grandchildren and other relatives).

The external validity of this study might be questioned since it is difficult to argue that children being “adopted” into recomposed households in the years following the
to reducing inequality. Extra years of education translate in general into higher salaries and therefore, inequality in education attainment can contribute to inequality in income. Second, a high quality tertiary education system can build the strong human capital basis for economies to compete globally and increase growth, laying the ground for both higher productivity and earnings, and more fiscal space that can create room for redistribution. With respect to the first issue, the possible “equalizing” role of education depends to a great extent on whether students from poor backgrounds are able to reach tertiary education. Moreover, even if they do reach this level, they may not benefit from higher earnings if the labor market does not value their knowledge, skills and competencies to the same extent as of richer students. This could happen either due to the poor quality of education institutions attended by the poor or the better access of the rich to quality jobs through family and social connections (or a combination of both). These factors can significantly attenuate the potentially “equalizing” role of education and even generate negative incentives for investing in higher levels of education among the poor.

In this paper, we analyze the inequality in access to and completion of tertiary education in Central America, as it is an important means to reduce income inequality in the long run. Because access to tertiary education is contingent on successfully completing lower levels of education, the paper also examines the inequality in progression through the education system.

> 3 The Central American countries analyzed in this paper includes Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua and Panama. 4 OECD.StatExtracts. Income distribution- Inequality- GINI coefficient after taxes and transfers for latest year.  Available online: <u>http://stats.oecd.org/Index.aspx?QueryId=26068.  Date accessed: June 14, 2012.</u> 5 These three countries are the only available in World Bank, 2011.

2
Inequality, Economic Growth and Nonrenewable Resources countries, in which an increase in resource dependence is associated with higher rates of income inequality and lower GDP per capita.

Table 12: Estimated Results from the Non-Recursive Model Specification (resource-rich subsample)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.175***|0.385**|-0.194***|0.355**|
|Lower income|dropped|dropped|-12.765***|51.841***|
|Lower income* nonrenewable resource|0.121**|-0.960***|0.167***|-0.925***|
|dependence|||||
|High-low education ratio|0.337|-|1.754***|-|
|High-low education ratio square|-0.001|-|-0.030***|-|
|Consumer price index (2005)|-0.001|-|-0.009|-|
|Services value added share|0.114**|-|0.132***|-|
|Manufacturing value added share|0.002|-|-0.078|-|
|Agriculture value added share|-0.157**|-|-0.093|-|
|GDPc|-0.085|-|-0.195***|-|
|Log labor
,315|7.5|8.09|0.14|
|Informal lenders|2,931|0.7|0.2|0.07|1,616|0.83|0.26|0.19|1,315|0.51|0.13|0.23|
|**Pct. Offered Business Training**|3,451|52.47|51.82|0.83|1,880|51.29|52.03|0.93|1,571|54.1|51.6|0.48|
|<br>**Member is eligible for loan lottery (Yes=1)**|3,451|63.72|69.00|0.54|1,880|64.63|59.69|0.82|1,571|62.45|62.21|0.66|
|P-val of F-test that all baseline characteristics are<br>jointly insignificant||||0.65||||0.58||||0.65|

Notes: * denotes variable measured at follow-up, conducted in December 2008. Pct. Offered Business Training and Member eligibility come from administrative data from NRSP.
# Annex 4.  National-Level Indicators of Empowerment

Many asset indicators have already been generated and tested by such survey instruments as the Living Standards Measurement Survey and social capital assessment tools. Table 1 identifies a series of asset indicators that have been identified by these instruments. The draft module for measuring empowerment at the national level (annex 5) makes use of many of the indicators presented in this table.

Opportunity structure comprises the presence and operations of institutions, defined as rules. Tables 2-4 of this annex lists key indicators for measuring institutions in different domains and sub-domains. Column one of these tables identifies indicators that refer to the presence of rules; column two those that gauge the operation of rules. A series of data sources exist that can provide information on the proposed opportunity structure indicators. Existing data sources are listed and numbered in column three of the tables. Where a data source exists for a suggested indicator, the source is identified in parentheses. Where no prior source is available, new data need to be collected from either the survey.

Direct indicators of empowerment capture the extent to which a person is able to transform a choice into a desired outcome. Specifically, they distinguish between three different degrees of empowerment, namely (1) whether a person has the option to make a choice, (2) whether the person decides to make use of the option to choose, and (3) whether the person achieves the desired result after making a choice. Table 5 identifies a list of indicators that can be used to ascertain respondents’ degrees of empowerment for each domain and level they operate in. The module for measuring empowerment at the national level (presented in annex 5) makes use of the direct empowerment indicators presented in table 5.

62
|458|458|458|

_Source_ : International Monetary Fund World Economic Outlook, International Monetary Fund International Financial Statistics, World Trade Organization, World Bank World Development Indicators, www.PolicyUncertainty.com, Baker et al. (2016), and authors’ calculations. See Table 1 for details on sources and definitions. Notes: Heteroscedasticity-robust standard errors corrected for clustering at country level are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01. Panel covers period 19952018. Growth indicators are computed as log-differences. A lag effect is allowed in specifications 2a, 2b and 2c. Specifically, the EPU aggregate for year _t_ is based on monthly indexes from January through December of year _t_ , in specifications 1a, 1b and 1c, and from November of year _t-1_ through October of year _t_ , in specifications 2a, 2b and 2c. Results for goods import volume specifications are robust to using data on goods import volume from World Trade Organization.

14
antitrust investigations** Our analysis begins with the universe of 261 investigations into suspected monopolistic practices as of the end of 2018, since the founding of the Commission in 1993. The Commission’s website provides a list of all cases and access to the disclosures of each investigation and final resolutions. Unlike in other jurisdictions including the United States, the Commission discloses the relevant market for each investigation, not just those that go to trial.

A task in antitrust investigation is the definition of the relevant market, or the set of products and geography in which the agent or agents under investigation compete (see Davis and Garc ́es, 2009). To analyze outcomes in investigated markets, we map the relevant market for each investigation to economic activities classified in the Economic Census. In doing so, we address two challenges highlighted by Shapiro (2018) and Benkard, Yurikoglu and Zhang (2021) that arise when mapping relevant markets in antitrust investigations to industrial 11
providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure.

To measure household welfare, different metrics have been proposed and can generally be classified either into monetary or non-monetary metrics. Monetary metrics equate levels of well-being to a monetary indicator of utility (Samuelson, 1974), usually income, consumption or expenditure. Non-monetary metrics define normatively dimensions of deprivations, e.g., access to education and clean water, and aggregate them either by using dimension-specific thresholds or an aggregated threshold. The most commonly used approach in this class is the Multidimensional Poverty Index by UNDP and Oxford University (MPI; OPHI, 2018). While it is not uncommon to find experts with a strong preference for one measure over the other, both classes of poverty measures are in most cases complementing each other. While the monetary metric has the advantage of its theory-grounded definition without normative choices, it is complex to measure 13
# **References:**

Bennear, L., and S. Olmstead. 2008. The impacts of the ‘right to know’: Information disclosure and the violation of drinking water standards. _Journal of Environmental Economics and Management_ .

Bennear, L.S. 2008, What Do We Really Know: The Effect Of Reporting Thresholds On Inference Using Environmental Right-To-Know Data, _Regulation and Governance_ , 2(3): 293-315.

- Blacconiere, W.G., and W.D. Northcut. 1997. Environmental information and market reactions to environmental legislation. _Journal of Accounting, Auditing and Finance_ , 12, 2, 149-178.

.

- Blackman, A., S. Afsah and D. Ratunanda.  2004.  How do public disclosure pollution control programs work? Evidence from Indonesia. _Human Ecology Review_ 11(3): 235-246.

- Bougherara, D., G. Grolleau and N. Mzoughi.  2005.  Can information-based policies do more harm than good? INRA-ENESAD UMR CESAER Working Paper No. 2005/5.

- Bui, L. _Public Disclosure of Private Information as a Tool for Regulating Emissions: Firm-Level Responses by Petroleum Refineries to the Toxics Release Inventory_ , 2005. Working paper 05-13, US Census Bureau

- Dasgupta, S., B. Laplante and N. Mamingi.  2001.  Pollution and capital markets in developing countries. _Journal of Environmental Economics and Management_ 42: 310-335.

- Dasgupta, S., J.H. Hong, B. Laplante and N. Namingi.  2004.  Disclosure of environmental violations and the stock market in the Republic of Korea.

- Dasgupta, S., H. Wang, D. Wheeler, 2006, “Disclosure Strategies for Pollution Control,” in T. Tietenberg and H. Folmer (eds.), _International Yearbook of Environmental and Resource Economics_ , Vol. X ( _Edward Elgar_ : Cheltenham, U.K), 2006.

- Delmas, M., Montes-Sancho, M., and J. Shimshack.  2007
# **(ii) Tabulated statistics**

For eight countries, we rely on tabulated or grouped data acquired from these countries’ NSOs to create the 2020 distribution. This information is typically published in the form of quantile growth rates. In several cases, we need to make important assumptions to match this data to our 2019 distributions. For example, the tabulations might use a different income concept, use an equivalence scale, cover different parts of the year or rank households instead of individuals. See Appendix B for further details.

# **(iii) High-frequency phone surveys**

Our next preferred source of data is the HFPSs. At the time of writing, the HFPSs were conducted in 85 countries resulting in 155 harmonized indicators. We use this method for 37 countries covering 13% of the global population. Note that more than half of these countries are in SubSaharan Africa (SSA). For 59% of the population residing in this region, the HFPSs is the only source of information on the income growth of households in 2020. HFPSs are also available for 7 countries in ECA (amounting to 14% of the population in the region), 5 countries in EAP (covering 7% of the regions’ population), and 5 countries in LAC and the Middle East and North Africa (MENA) covering 5% and 4% of the respective regional populations.

While the phone surveys provide information on whether households gained or lost income or consumption since the beginning of the pandemic, they do not contain information on households’ level of income or consumption, nor do they report the size of the change in income or consumption. To utilize the information in the HFPSs, we need to (a) map the changes in income or consumption from the HFPSs to the 2019 welfare distributions discussed above and (b) estimate the size of the change for each household. We need to make several strong assumptions to use the HFPSs, which in many countries is the only data that were (and