rafmacalaba/datause-encoder-data huggingface dataset:
# **9. References**

Altman, M., Gostner, K., Lee, D., Tregenna, F., van der Heijden, T. & Onyango, D. (2005) Review of employment & remuneration trends for selected sectors in the SA economy (Prepared for Office of the Presidency).

Bartelsman, E., Haltiwanger, J, and Scarpetta, S. (2009). Measuring and analysing crosscountry differences in firm dynamics, in Producer Dynamics: New evidence from micro data (Dunne, J., and Roberts, eds), NBER / University of Chicago Press.

Centre for Competition, Regulation and Economic Development. (2016a). Competition, barriers to entry and inclusive growth: Telecommunications Sector Study. [online] Available: http://www.competition.org.za/s/Barrierstoentry_TelecommunicationSectorStudy.pdf [Accessed 23 April 2018].

Centre for Competition, Regulation and Economic Development (2016b). Competition, barriers to entry and inclusive growth: Fruit and Veg City Case Study. [online] Available: http://www.competition.org.za/s/BTEFruitVegChisoroDasNair290216.pdf [Accessed 23 April 2018].

Cutts, M. and Kirsten, J. (2006). Asymmetric price transmission and market concentration: an investigation into four South African agro-food industries. _South African Journal of Economics_ , 74 (2): 323–333.

das Nair, R., and Chisoro, S. (2015). ‘The expansion of regional supermarket chains: changing models of retailing and the implications for local supplier capabilities in South Africa, Botswana, Zambia, and Zimbabwe’. WIDER Working Paper 2015/114. Helsinki: UNU-WIDER.

Edwards, L. and Lawrence, R. (2008). South African Trade Policy Matters: Trade Performance & Trade Policy _. Economics of Transition_ , 16(4): 585-608.

Edwards, l., Cassim, R., and Van Seventer, D. (2009). Trade Policy in South Africa. Conference paper.

~ <u>http://www.tulane.edu/ dnelson/PEBricsConf/Edwards%20SA%20Trade%20Policy.pdf</u> Edwards, L. (2010). “Protectionist Policies and Manufacturing Trade Flows in Africa” in Yifu Lin, J. and Boris Pleskovic (eds.) Annual World Bank Conference on Development Economics 2009, World Bank Publications.

Edwards, L. and Jenkins, R. (2015). The Impact of Chinese Import Penetration on the South African Manufacturing Sector. _The Journal of Development Studies_ , 51(4):447-463.

28
funding for childcare services. Over the same time period, legislation was passed that provided mothers with 65% of their previous salary if they cared for their own child during the first year.

The approach taken by Fong and Lockshin (2000) to understand the effects of these and other policy interventions is to jointly model households‟ decisions about childcare and mothers‟ labor supply. To this end they develop an economic model of household decision making about consumption of childcare quality, of market goods and of leisure. Child care arrangements are classified into six categories defined by combinations of the mother‟s employment status, mode of care (formal and informal), and employment status of other household members. The model assumes that households pay a flat fee for childcare services, where the fees charged by kindergartens are a function of the quality of care provided and the total level of childcare prices within the locality.<sup>20</sup> The empirical model consists of a discrete choice equation for the childcare mode and mother‟s labor supply, an equation for the mother‟s hours at work, and an equation for children‟s hours in paid care. The effects of unobserved variables is incorporated by imposing a factor structure on the error components of the model. The estimation is based on household survey data from the Romanian Child Care and Employment Survey (RCCES) linked with data on childcare providers from the Romania Child Care Facilities Survey (RCCFS). Both datasets were collected by the World Bank in the same geographical areas during the same time period. The surveys were designed to allow matching data on childcare fees and child care quality with the households surveyed in the communities. After the model parameters are estimated, the model is used to simulate responses to changes in the policy environment, for example, to changes in the price of childcare and the level of the mother‟s wage. The model is used to extrapolate to policy variation that is outside the range of that in the data.

Model simulations indicate that a 10% increase in a mother‟s wage offers increases the rate of the mother‟s labor force participation by 10.9% and increases her use of formal care arrangements by 4.3%. Changes in the price of childcare have a smaller effect on the level of maternal employment and on the use of formal care; an increase in the price of care by 10 percent leads to a 1.2 percent decline in the number of working mothers and a 2 percent decrease in the number of households that use formal care. A policy that fully subsidizes formally

> 20 In actuality, households pay fees that depend on their total household income and the number of children enrolled. However, to avoid complications in estimation, Fong and Lokshin (2000) treat childcare fees as exogenous to the households, using regional variation in fees as the main source of identifying information.

44
Table Al.8 Other Put4ic Sector (TL bitlion)

|||1987|1963|source|
|---|---|---|---|---|
|H Flo|FactorInccoe|2,309.8|3,416.6|(Residml)|
|_N_Tbo|Current Tranfers<br>from Btudet|216.2|426.3|(Fiscal<br>dsta)|
|H iFXoc|Interest<br>Received<br>fromFX Deposits<br>atCB|15.0|12.9|(Calculated)|
|HREVo|Revenues|2,541.0|3,855.9|(Calculated)|
|N TDo|DirectTaxes<br> <br>|741.1|852.9|(Fiscal<br>data)|
||Interest<br>Payments on:||||
|NiCRo|CentralBankCredit|-11.8|2S4.9|(iR*CRo)|
|H iso|Domestic<br>onds|486.2|1069.2|(Calculated)|
|H iBop|To Private<br>Sector|0.0|0.0||
|H iBod|ToBankingSystme|486.2|1,069.2|(Bod*C)|
|N iFGo|Foreign<br>Borrouing|275.5|509.9|(COP data)|
|H ECURo|<br>Current Expenditure|1,491.0|2,686.9|(Calculated)|
|H So|Savings|1,050.0|1,168.9|(kesidual)|
|Hlo|Investment|3,555.6|4,624.6|(Fiscal<br>data)|
|H dFXoc|Foreign Exchange Deposits**at**CB|-83.2|145.1|(CB**data)**|
|HKTop|Capital<br>Transfers<br>to PS|315.3|462.3|(Fiscal<br>data)|
|H ECAPo|Copitat<br>Expenditures|3.72.?|5,252.0|(Calculated)|
|H DEFo|Deficit|2732.7|4083.1|(Calculated)|
|HKTbo|Fifnrcing:<br>CapitatTrunfers<br>fro Budget|289.7|666.2|(Fiscaldata)|
|NdCRo|CentraL Su*<br>Credit|531.5|173.8|(From CS stocks)|
|H_dBo_|Domestic Borrouing|629.3|1543.7|<br>(Calculated)|
|HdBod|<br>FroBanking System|629.3|1543.7|(From ES stocks)|
|HdBqp|From Private<br>Sector|0.0|0.0||
|H dFGo|ForeignBorrowing|1252.2|1699.4|(Fiscal<br>data)|

Source: See exptanation in text and right mst colu n.

sector in either current or capital account and both ic.Bop-i and dBop are equal to zero. Consequently our strategy of selecting the private sector as a residual cannot work in this case.

In the capital account (equation A1.12) all financing variables are already determir.ed in the monetary sector. Investment is given from fiscal data and consistent with national accounts data. Transfers from the budget and purchases of existing real assets from the private sector are given from fiscal data. Consequently, savings of the SEEs is determined as the residual of 27
||Commission of BM&FBOVESPA<br>• Free float of 25%in 7years||
|---|---|---|
|WSE<br>NewConnect|• Audited financial statement for last financial year<br>required (since June 2013)<br>• Minimum capital of issuer increased from Zl 100,000 to<br>Zl 500,000<br>• A minimum of 15% of shares must be held by at least<br>10 shareholders, each of whom holds no more than 5%<br>of the total number of votes|• Same as main market<br>• For offerings of <$3.5<br>million an information<br>memorandum may be<br>considered<br>as<br>an<br>admission document|
|BIST ECM|• No quantitative criteria such as profitability, company<br>age, or capital size required<br>• Year-end financial tables required|• Same as main board<br>for IPO<br>• In private placements,<br>prospectus and circular<br>not<br>required<br>for<br>private<br>placement,<br>instead a simplified<br>Trading<br>Information<br>Form.|

_Source:_ WBG compilation of exchange information.

# _ii. Private Placement_

Many exchanges allow or have plans to introduce the ability for SMEs to list through a private placement (termed “listing by introduction”) as well as public offering (see Table 10). This reduces requirements such as to produce a prospectus if the placement is made to a restricted number of investors. Private placement not only reduces costs by removing the more expensive and onerous public offer requirements, but also often reduces the time it takes to come to market.

As is common with private placements, given the reduced information requirements, shareholders are restricted by type, for instance, to high-net-worth individuals or institutional investors who are assumed to be better able to evaluate risk, and/or by imposing a maximum number of shareholders.

- WSE NewConnect limits the number of investors in a private placement to 149, although the actual number is usually closer to 10 to 20 investors. Most NewConnect listings (70%) were issued through a private placement, attesting to its more attractive structure.

- NSE Emerge and JSE’s AltX also have a private placement mechanism. Bovespa Mais is likely to introduce one in late 2014.

It is important to note that once listed by private placement, SMEs have to comply with the ongoing disclosure and governance requirements of the SME exchange, because once their shares are traded on the secondary market they are open to all investors. In some cases, shares bought in a private placement cannot be sold during a lock-up period that allows the company time to meet the disclosure requirements of the exchange.

Experiences with private placement have not been hassle free. NewConnect faced an unanticipated problem regarding minority investor rights as companies decided not to list after organizing a private placement in which investors were locked in and had no market through which to exit. Polish investors approached the exchange for recourse, but the exchange was unable to intervene because the listing had not occurred.

24
# Panel B. Mean regression

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>ALB BGR BRA COL CRI ECU IDN MEX MKD PER PHL ROU TUR ZAF<br>Bias using 2006-2009 Bias using 2010-2018<br><!-- End of picture text --> <mark>Notes: Actual 2020 employments levels come from National Statistical Offices. The exact data of actual 2020</mark> employment data is shown in Table A1 in the Appendix.

# **<mark>3. Microsimulations</mark>**

This section turns to the microsimulations themselves. We use actual employment changes taken from the ILO as an input into the microsimulation models. We use the elasticity-based estimates as a robustness check. Whenever possible, these models should use actual available employment data, but when that data is not yet available, analysts would have to use employment projections. The macro-micro simulation model applies country-specific macroeconomic projections to a behavioral model built on household survey microdata. The microsimulations are based on a household income generation model (Bourguignon and Ferreira 2005), while the macro data come from a variety of macroeconomic projections in combination with observed data for 2020 obtained from different data sources for each country. The macro-micro model allows for three main channels of transmission of the 2020 shock: through job losses, labor income changes, and nonlabor (remittance) income changes. Having estimated (or used actual) labor and remittances income changes, we predict the household per capita income or consumption value for 2020 and we calculate the shares of poor, vulnerable, and middle class in each country.

<mark>We apply this model to five countries in five regions: Brazil, the Philippines, South Africa, Sri Lanka, and Türkiye.</mark><sup>13</sup> <mark>The model requires five main inputs. Country-specific household survey microdata for each country is based on the 2019 Pesquisa Nacional por Amostra de DomiciliosContinua for Brazil; the 2018 Household Income and Consumption Expenditures Survey for Türkiye; the 2018 Family Income and Expenditure Survey for Philippines; the 2016 Household Income and Expenditure Survey for Sri Lanka; and the 2019 General Household Survey for South</mark>

> 13 We select the countries according to the availability of labor force surveys (for the measurement of employment level in 2020) and a household survey (for poverty measurement in 2019) with data on income and remittances.

14
<!-- Start of picture text --> .<br>CDD IBRD 43660 | MAY 2018<br> Global Portfolio a<br>Central Asia eins<br>10 projects<br>$500 million<br>9 6 countries 9 9’ 9<br>Middle East and v : )<br>North Africa , Z 99 ¢ 9 EastAsia<br>v q $2004 projects million g 5 \]t ) ——27 projects<br>v i g $3.7 billion<br>7 4 countries 9 i} ]<br>9 8 yr v 9 9 A] 9 8 countries<br>v - ' : gy South Asia 9 2 9<br>v 9 g g 7] 42 projects<br>Latin America v y $7 billion<br>and the Caribbean 9 Africa @! 7 countries<br>26 projects 9 78 Projects<br>$1.9billion | $5.8bilion<br>15 countries 32 countries<br>Source: World Bank CDD Database as of July 2017.<br><!-- End of picture text -->
especially on larger scales, and often not methodologically recorded. Despite this weakness, deductive modeling offers a viable option to risk indexing in many contexts and is helpful, especially for risk comparisons on larger scales.

In this study, we use deductive methods to determine two dimensions of vulnerability. Vulnerability to mortality is calculated based on historical disaster mortality in precedent hazard events and vulnerability to economic losses is determined through past economic losses in disasters. We extract the loss data on number of deaths and amount of economic losses from the Emergency Events Database (EM-DAT) (http://www.em-dat.net) for the period from 1980 to 2007 (Table 3). EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) which classifies an event as a disaster and includes it into EM-DAT if at least one of the following criteria applies: Ten or more people were killed, 100 or more people were affected, a declaration of a state of emergency was made, or an appeal for international assistance was made. EM-DAT records more than 600 disasters globally each year. For each event, the database lists the type of disaster, the country, the date, death tolls, estimated damage, and the number of affected people. Aggregating over more than 8,000 entries in EM-DAT helps compensate for missing data and reporting inaccuracies.

**Table 3: Data sources for the vulnerability component**

|Dataset|Description|Unit|Source|
|---|---|---|---|
|EM-DAT|International disaster database for major|Number of|Centre for Research on|
|(Emergency|hazards across the world, listing country,|fatalities/economic|the|
|Events<br>Database)|date, death tolls, estimated damage,<br>number of homeless and affected people.<br>The database contains over 14,000<br>disasters and is compiled from various<br>sources, including UN|losses per disaster|Epidemiology of<br>Disasters<br>(CRED)<br>http://www.em-dat.net/|
||agencies, NGOs, insurance companies,<br>research institutes, andpress agencies.|||

We calculate different vulnerability coefficients, or loss weights, for the two vulnerability categories of population and GDP. Weights are obtained for all of the four hazard types for each of the 25 World Bank 14
expect that PCs signed with firms facing more competition benefit from less information asymmetry and hence should be associated with higher productivity. Performance measures will be more meaningful, and shirking will be more evident in competitive firms relative to firms with more market power, and it will be easier to discern the firms' true efficiency level by comparing them with other similar firms. In the empirical analysis we proxied for competition with markup ratios (based on Li 1997; see appendix).

Not surprisingly, we expect incentives to raise productivity. We also expect that the strength of the incentive is likely to be constrained by information asymmetry. Since incentives to SO]Es have a cost to society, governments will try to assure that the incentive payment does not exceed the social gain from increased firm efficiency. Hence, when there is information asymmetry, government will tend to set the incentive too low to motivate much improvement in performance. Alternatively, government may opt for a high powered incentive despite information asymmetry, in which case the firm may use its private information to extract a bonus without improving efficiency (for example, by convincing the government that the firm is less efficient than it really is, so that targets are set too low). Thus, without solving the information problem, introducing incentives can bring about only limited change. (Laffont and Tirole, chapter 1, 1994). In our analysis, the inceintive we investigate is the elasticity of the total wage bill to profits. This incentive device has drawbacks. First, it may be distributed equally among employees in a context where there are limited ways to punish shirking (for example, there are often restrictions on firing SOE workers), which may reduce its incentive effects. Second, it reduces funds available for investment, which may reduce the long-term growth rate. Finally, since it is aimed only at workers, it will not have a sustained impact unless the 11
activities. This shows: (i) the majority of clubs provided life skills training; (ii) vocational skills training took off after travel quarantines were lifted in January 2015 (these trainings are delivered by professionals, not club mentors). Microfinance began to be disbursed from April 2015. Panel C shows club membership: the median club has 30 members, implying membership rates of around 31% given the number of women aged 12 to 25 identified in the baseline census. Finally, Panel D shows the ratio of club attendance, based on an unannounced spot check in May 2015, to club membership. On any given day, ELA clubs can have many non-members present and participating in activities. All women can take advantage of the public good benefit ELA clubs provide in terms of a space in which they can socialize with other young women, and spend time away from men.

# **4.3 Pre-Crisis Characteristics**

## **4.3.1 Villages**

Panel A of Table 1 shows that treatment and control villages are well balanced on their size, mean poverty score across households, exposure to NGOs, religion, location and proximity to primary health units (PHUs) and secondary schools. Beyond the effective implementation of the program described above, this evidence suggests there was high fidelity with treatment-control randomization protocols.<sup>25</sup> The remainder of the Table shows data from the village leader survey, conducted during the crisis. Panel B highlights the stigma associated with pregnant girls: there is near universal agreement among elders with the statement that “girls who are visibly pregnant have a bad influence on their non-pregnant peers.” In control villages, only 12% of elders agree with statements that pregnant girls should be allowed to continue their education, or to take formal exams.

Panel C shows further details on village impacts and policy responses during the crisis. Few villages were quarantined (6%), and nearly all were visited by contact tracer teams. There are no differences between treatment and control villages in the receipt of assistance from NGOs in terms of food aid or school supplies (excluding BRAC). Control villages are more likely to have received some form of government relief. This mitigates against finding beneficial impacts of ELA.

## **4.3.2 Young Women**

Table 2 shows pre-crisis characteristics of young women. On nearly all dimensions, the samples are balanced between those in treatment and control villages.

> 25There are some differences in means across treatment and control villages, even if these are not statistically significant. Most notably, control villages are 55% larger than treatment villages (as measured by the number of dwellings), and are 17% closer to the nearest secondary school. To mitigate biases from any such imbalances, all the village level covariates in Panel A are controlled for in our regression specifications.

15
patents, payments for licenses and royalties, stocks of capital equipment, and so forth - cover more than a part of this.

Another reason is that technology is diffused in many different ways. MNC technology can spread to new users through formal market transactions - transfers - or through informal, nonmarket mediated channels that may be voluntary or involuntary. For each alternative, the role of MNCs can be either active or passive. Table 1 shows some of the possible modes of international technology diffusion, grouped according to the type of transaction and role of the MNC. Foreign' direct investment is another potential channel of technology transfer, but we have not included it explicitly in the table: what distinguishes FDI from sales of equipment or licenses to outsiders (or even joint ventures) is that the MNC has chosen to retain the control and ownership of its proprietary technologies within the corporation.

# _TABLE 1 International Diffusion of Technology: Type of Transaction and Role of MNCs_

||_Role o_|_f MNCs_|
|---|---|---|
|_Type of Transaction_|**ACTIVE**|**_PASSE_**|
|FORMAL|joint ventures,<br>licensing|goods trade|
|INFORMAL|linkages|trade journals,<br>scientific exchange|

Source: Adapted from Fransman (1985).

It is an open question which of the transfer modes mentioned in the table is the most important, because it is difficult to compare the technology content of the different transactions, and because there are no comprehensive data available to measure the magnitude of the informal transfers. However, there are some old data provided by the UNCTC (1985) on formal transactions that are useful in outlining the quantitative dimensions, although the figures are neither complete nor 4
29

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