rafmacalaba/data-use-mentions-extended huggingface dataset:
3.8 trillion in June 2014, with US$1.8 trillion in dry powder<sup>5</sup> as of December 2014.<sup>6</sup> Almost 60 percent of PE/VC AUM was accounted for by North America; while Europe comprised almost a quarter and Asia 13 percent of total AUM. In 2014 PE/VC funds raised a total of US$495 billion in capital, down 6 percent from the $528 billion raised in 2013. According to data from Preqin, global fund raising for private equity in 2015 is expected to jump, projected to surpass US$790 billion. Investment deal flow remained active in 2014 with 5,305 completed PE deals totaling US$996 billion of capital invested, which marked an 8-year high, according to Pitchbook.<sup>7</sup> Venture capital comprised US$86 billion in deal value in 2014; while buyout activity was US$332 billion.<sup>8</sup> 2014 saw the recent trend of increasing numbers of firms focusing on minority and growth investments, add-on investments, and liquidity continue, with US$445 billion in capital exited from such investments, up 46 percent from 2013’s US$305 billion.<sup>9</sup>

> 2 This section draws from “Private Equity and Venture Capital in SMEs in Developing Countries: The Role for Technical Assistance,” World Bank Policy Research Working Paper 6827 co-authored by Shanthi Divakaran, Patrick McGinnis, and Masood Shariff.  It also draws from Private Equity: Fund Types, Risk and Returns, and Regulation.  Douglas Cumming, 2010. 3 See IFC job study.  www.ifc.org. 4 "Private equity" or "PE" references encompass the following strategies: buyout, growth, PIPE, mezzanine and venture capital. 5 Capital committed but not yet paid-in and deployed.

> 6 Preqin 2015.

> 7 Source: Pitchbook,  http://pitchbook.com/
revised 19<sup>th</sup> ICLS definitions of work and employment. This has been a particularly challenging area for economies with a large share of smallholder farmers, for whom paid and subsistence work often vary seasonally throughout the year, and where women often have economically productive but hidden roles (Benes and Walsh, 2018a; Koolwal, 2019). Over the last few years, country labor force surveys and the surveys supported under the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative have been introducing individual-level questions on the main intended destination of agricultural output to better elicit employment in agriculture under the new definitions.

Management of, and working in, an NFE and wage employment are also underreported under the IHS4 business-as-usual approach, and the difference widens when the recall period is longer. Although participation rates might differ, the ranking of importance of activities is similar in both scenarios, with cropping as the most common activity, then _ganyu_ and livestock, management of a non-farm enterprise, off-farm wage employment and self-employment as least common activities. Further, Table 2 shows that weekly hours worked among those employed are not statistically different across survey approaches for most activities, with some variations by gender (wage employment for men and _ganyu_ for women). This suggests that if participation is screened correctly, weekly hours tend to be adequately estimated for most activities under the business-asusual approach. For annual hours and earnings, we do find greater differences across survey approaches (among women, for example, conditional annual hours worked in wage employment are significantly higher in the IHS4, and conditional hours and earnings in _ganyu_ for both men and women are also significantly higher in the IHS4 as well). The choice of survey approach may matter more, for example, with longer recall periods as well as seasonality of different activities among this population.

Looking at gender differences, the last two columns of Table 2 show that women are significantly more likely than men under both survey approaches to work in crop agriculture over the last
products’ including also machinery equipment products together with respective parts (e.g., hydraulic motors and parts, electrothermic appliances and parts).

How does Bulgaria score on this count? Two observations can be derived from data reported in Table 33. First, EU-oriented exports of these products have recorded strong and steady growth since 1997. The value of exports of parts not covered by network analysis increased 4.2 times between 1997 and 2003; they grew at an average compound rate of 24 percent per year over this period; and their share in EU external imports increased 3.4 times from 0.08 percent to 0.26 percent. If anything, this indicates the emergence of well-established commercial relations based on a stable participation in outsourcing by EU firms.

**Table 33** _: Exports of parts and other engineering products and their share in EU external imports and Bulgaria’s EU-oriented exports in 1996-2003 (in millions of US dollars and percent)_

||1996|1997|1998|1999|2000|2001|2002|2003|Change 2003<br> over 2001|
|---|---|---|---|---|---|---|---|---|---|
|Parts not covered by networks|26.8|26.0|33.4|44.1|50.7|60.7|79.6|110.2|82%|
|Other engineering products andparts|36.1|35.5|44.4|46.2|47.9|61.5|88.7|118.5|93%|
|TOTAL ABOVE|62.9|61.5|77.8|90.3|98.6|122.2|168.3|228.7|87%|
|Imports of parts as percent of exports|
.82|
|TR82|2.69|4.52|5.35|4.29|3.52|2.91|3.31|4.00|
|TR83|3.93|5.43|6.00|4.90|3.77|3.02|3.64|4.64|
|TR90|2.89|3.27|4.10|4.26|4.14|3.87|4.15|4.75|
|TRA1|7.17|9.80|12.36|8.83|9.31|11.82|12.97|15.13|
|TRA2|11.24|17.40|21.45|17.48|16.06|18.50|19.20|22.83|
|TRB1|8.51|8.73|12.70|11.44|12.70|12.39|14.42|17.19|
|TRB2|19.11|19.84|26.96|24.82|26.60|23.98|25.35|28.22|
|TRC1|20.60|22.92|25.80|21.03|19.55|19.80|22.20|23.02|
|TRC2|24.68|26.17|30.62|27.27|25.39|23.76|26.79|31.19|
|TRC3|23.09|25.82|30.82|25.31|22.53|20.62|24.81|26.87|

_Notes_ : LFS imputed data using OLS, regionally deflated. Poverty estimates have standard errors. _Source_ : LFS, SILC 2006-2013; authors’ calculations.

Table 16.
br>months|Yes|Detailed<br>agricultural<br>module<br>Brief HE module<br>One question on<br>income in<br>kind/cash from<br>each type of<br>income (farm,|Yes|_Wage_<br>_employme_<br>_nt only_<br>Some data<br>collected<br>**f**rom rural<br>households<br>on HE<br>income|Yes|Last payment –<br>cash and in<br>kind value<br>_wage_<br>_employment_<br>_only_.<br>In HE module<br>earnings last 30<br>days – but not|Yes|_Wage_<br>_employment_<br>_only_– main<br>and<br>secondary in<br>last 7 days –<br>earnings and<br>in kind<br>payments last|
|||||HE, wage)||||hours||months<br>Selected|

14 Because of the civil conflict, the 1999/2000 household survey did not cover six districts in the North of the country, and therefore is not nationally representative.

12
022|-0.005|
||(5.518)|(0.014)|(2.966)|(0.419)|(1.352)|-0.093|
|txIV (average<br>neighboring countries T)|1.197***<br>(103.02)||1.183***<br>(71.76)||1.226***<br>(55.16)||
|R-squared|0.983|0.772|0.985|0.77|0.986|0.762|
|Number of observations|3249|3249|1603|1603|882|882|
|Number of countries||13||13||13|
|Number of industries||342||246||137|
|Widstat||10612.605||5148.938||3042.99|

Notes: The dependent variable is the natural log of exports in industry j by country i to the World. txT is instrumented using txIV where the instrument is the average infrastructure of neighboring countries. Reported are the beta standardized coefficients of the second stage IV regression with t-values shown in brackets. * Significant at 10%; ** at 5%; *** at 1%.

As far as potential endogeneity problems of the quality of institutions are concerned, we also estimate equation (1) using an instrumental variable approach. As an instrument for the quality of institution of a country we use the average quality of institution across all countries with its same legal origin. In particular, we distinguish between British, French, German, Social or Scandinavian legal origin. It makes intuitive sense to assume that the different legal origins of countries are reflected in today’s quality of institutions of countries, but that legal origins themselves are not affected by comparative advantage in 2000.

Table 10 reports the results
parameter “alpha”.

_Single price change_ . We can start with an illustrative example comparing _LV_ estimates with _EV_ estimates derived from Cobb-Douglas preferences in the case of a price change of only one product. We assume that the “true” utility is defined with Cobb-Douglas preferences with _U_ ( _x_ 1 _, x_ 2) = _x_<sup>_α_</sup> 1<sup>_x_</sup> 2<sup>1</sup><sup>_−α_</sup> and _α_ = 0 _._ 3. Assume also that initially we have income _m_ = 100, and prices _p_ 1 = _p_ 2 = 1. Figures 5 and 6 show respectively the impact on welfare and the error size estimated for price increases between 0 and 50 percent. It is shown that _LV_ provides similar estimates to _EV_ up to a price increase of around 10 percent while estimates diverge by about 12 percent for price increases around 20 percent (Figure 5). We can also estimate the percentage error between the two measures - _|_ ( _LV − EV_ ) _/EV |_ - (Figure 6) and this shows that the error is about 6 percent for price increases of about 10 percent and doubles to around 12 percent for price increases around 20 percent. It is also visible that the error is a linear function of the price increase. As already discussed, the marginal approach _LV_ tends to overestimate the negative impact on welfare as compared to other measures. This is due to the fact that the marginal approach essentially captures the income effect but not the substitution effect. The bias illustrated in Figure 6 is also discussed by Banks, Blundell, and Lewbel (1996). Using European data, they find a bias between 5 and 10 percentage points with a price change ranging from 10 to 20 percent. They also confirm the negligible impact when price changes are below 10 percent.

Using the same setting based on Cobb-Douglas preferences, we can now extend comparisons to the
(Tables B3 and B4).

As shown by Ib ́a ̃nez et al. (2024), the regularization program led to improvements in household consumption, income, and overall health, including mental health for adults who participated. These positive changes in economic and health conditions are inconsistent with any potential increase in domestic violence. Additionally, the program did not result in a disproportionate increase in female income compared to male income. Our analysis found no evidence of genderbased differences in the program’s effects on either income or consumption. Therefore, we can confidently rule out higher domestic violence as a factor underlying the observed effects.

These results reinforce our interpretation that empowerment—especially female empowerment— propels the patterns. Once their fear of deportation and other repercussions drops significantly, these migrants report more crimes to the police. This is particularly true for crimes such as domestic violence and sex crimes that disproportionately affect women. Qualitative interviews with Venezuelan migrants reinforced our sense that having the PEP migratory status empowered them to report human rights abuses without fearing punishment.

# **V. B What do PEP holders say about filing crime reports?**

We also leverage data from the Venezuelan Refugee Panel Study (VenRePs) that interviewed a representative sample of Venezuelan PEP holders and non-holders in 2021 to examine the PEP’s effects on migrant welfare.<sup>18</sup> One survey question asked respondents if they had reported being a victim of a crime to the police or any other institution. The responses are in Figure 4. Results for the full sample are in the left panel and by gender in the right panel. First, the results show strikingly low reporting rates across the board for migrants who were victims of a crime. Second, they confirm that migrants with the PEP reported crimes at higher rates than migrants without the PEP. In fact, 9.9 percent of household heads with PEP reported a crime; this number is lower for those without PEP (8.7 percent).

Third
TIETP process differs completely from the receipt compensation process adopted in 1977. People who wanted to receive receipt compensation had to submit to a cumbersome application process. They had to collect their receipts and take them to a regional NTS office or commercial bank commissioned to deal with receipt compensation, where they submitted the receipts to get compensation. Since compensations was confined to an allotted budget, consumers often could not get compensation due to delayed budget allocations or budget shortfalls. The TIETP application and refund process, in contrast, is easy and simple. The tax deduction is credited to wage earners’ income taxes along with other tax deductions and credits, so refunds due from eligible refund applications are not delayed or declined. In addition, applicants do not need to collect all the credit card receipts, as credit companies issue aggregate annual transaction reports, and now prefilled credit card transaction information is presented to taxpayers by the NTS Home Tax Service. TIETP’s convenience and secure credits distinguish the approach from the failed attempts using receipt compensation.

> 44. Article 121‐2, Presidential Decree of Preferential Tax Control Law, effective as of Oct 30, 1999.

> 45. Article 201, Presidential Decree of Income Tax Law; Article 93, Order of Ministry of Strategy and Finance for Income Tax Law.

> 46 _._ Ibid.

28
**Figure 6: Portion of callable bonds among SLBs and green and conventional corporate and municipal bonds**

_Source: Figures on corporate non-green callable bonds from Dias (2021)._ While the sheer difference in these proportions may suggest that call options are more valuable for issuers in SLBs than other bonds, it is important to note that not all call options are useful in avoiding penalties. In particular, we draw attention to two types of such call options: ‘cleanup’ call options and make-whole call options. The former refers to call options which can only be exercised within three months before maturity. These are typically used by issuers to reduce administrative expenses. These cannot be used to minimize the penalty amount (at least substantially) given they can only be exercised shortly before maturity.

Make-whole call options require the issuer to pay the bondholders not only the call price upon exercise but also the present value of all remaining coupon payments from call date to maturity. In essence, the issuer is responsible for its original liability, so the bondholder does not suffer a financial loss if the bond is redeemed early. Make-whole call options are rarely exercised in practice due to the high cost of doing so for the issuer. The high cost makes make-whole call options an inefficient tool to minimize penalty payouts. This is especially true as the makewhole premium the issuer has to pay the bondholder is typically higher if targets are unmet (as the higher coupon rate is used to determine future coupon payments), causing make-whole options for SLBs to be even costlier.

Hence, our analysis excludes both clean-up call options and make-whole call options when examining if SLB issuers exploit call option provisions to minimize potential penalties. This leaves only 40 (19 percent) of SLBs that have a call option that could be useful to potentially minimize step-up penalties; our subsequent analysis in this subsection focuses on these ‘penalty-minimizing’ call options. SLBs with such penalty-minimizing call options tend to be more likely to be speculative grade, shorter in maturity and have a smaller
.737|
|�<br>**B. Upper Estima**<br>|**te**<br>|||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.070|0.070|
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.090|0.090|
|Labor force participation rate, growth,g|_(4)_|0.007|0.006|
|ρ<br>_Capital‐to‐Output (K/Y) Ratios_<br>|<br>_(with D_|_amage)_||
|SimulatedK�<sup>�</sup>/Y||1.133|1.127|
|SimulatedK<sup>�</sup>/Y||1.139|0.784|
|�<br>*Multiply by 100 to obtain parameter/variable v<br>growth terms (%).|alues in p|ercentage sh|are or|

- _(1)_ Based on a further downward adjustment to the UN population statistics to reflect higher migration and fatalities than the central projection. See Section 2.2. Population changes over 2011‐2018 are calibrated such that they follow the trend in the UN population statistics but with steeper slopes.

- _(2)_ Human capital growth is calculated based on the adjusted population data. See Section 3.1.3. As in the central projection, we keep the index of human capital unchanged from 2010 to 2012, such that the contraction mainly occurs over 2013‐2015 with average growth of ‐4.37% (average growth over 2016‐2018: 0.57%).

- _(3)_ Investment‐to‐output ratios are based
yearly data are scarce and of dubious quality. Although some low income countries have annual data dating back to 1971, time series with less than 40 observations are insucient to estimate the process (2.2) in a reliable way. For example, even with 40 observations per equation, a vector autoregression with six endogenous variables, a constant, one lag on the endogenous variables, and no exogenous variables has 63 parameters to estimate with 240 observations, less than 4 observations per parameter.

To cope with this problem, I pool data from several low income countries allowing for partial heterogeneity in the form of country xed-eects and country-specic exogenous variables, but assuming common slope coecients and covariance matrix across countries. Thus, I replace the specication (2.2) with a vector autoregression applied to a panel of cross-country and time series data (Panel VARX) represented by Here, countries are indexed by _i_ = 1 _,_ 2 _, ..., N_ ; the time index is _t_ = 1 _,_ 2 _, ..., Ti_ , where _Ti_ denotes the number of usable observations per country; and _p_ and _q_ denote the number of lags on the endogenous and exogenous variables respectively. Thus, the total number of observations is _T_ =<sup>�</sup><sup>_N_</sup> _i_ =1<sup>_Ti_.</sup> In addition, _αi_ is a vector of country xed-eects and the residuals _εi,t_ are i.i.d. shocks satisfying _E_ ( _εi,t_ ) = **0** , _E_ � _εi,tε_<sup>_′_</sup> _i,t_ � = Ω for all _i_ and _t_ , _E_ � _εi,tε_<sup>_′_</sup> _i,s_ � = **0** for all _i_ and _t_ = _s_ , and _E_ � _εi,tε_<sup
|---|
|**Export trade**||||||
|Export value|243,526|553,417|5,570,730|25|458,000,000|
|Lockdown dummy|243,526|0.43|0.50|0|1|
|Stringency index|243,526|0.53|0.50|0|1|
|**Import trade**||||||
|Import value|848,983|232,370|2,402,721|25|294,000,000|
|lockdown|848,983|0.43|0.50|0|1|
|Stringency index|848,983|0.50|0.50|0|1|

Source: Authors’ compilation using PSA data 7
, tobacco and consumption from own-production. A rate of 17.4 percent is applied to alcohol and tobacco consumption as an approximation to the excise taxes to these items. The VAT rate approximates the effective VAT rate defined as the actual VAT revenue collection divided by household consumption expenditures in years 2016. The excise tax is also an approximation, based on the ratio of excise taxes on tobacco and their market price.

No simulations about excise taxes on dual-fuel vehicles are included because no information on type of vehicle owned is available in the household survey. Moreover, no distributional impact of price controls and excise taxes on diesel and gasoline has been estimated because these have a more complex and ever-changing form of policy intervention.<sup>14</sup>

> 14 The Mongolian government exerts different types of intervention on the gasoline market. It changes excise taxes (including temporary exemptions), controls prices and sometimes even grants soft loans for importers, all with the objective to avoid wide oscillations of gasoline prices. Excise taxes to gasoline amount to 321 billion MNT in 2016 (about 1.3 percent of GDP). In any case, it does not seem to be the case that gasoline prices in Mongolia are far from <u>regional international prices.</u>

8 MONGOLIA Distributional impact of taxes and transfers / May 2018
<!-- Start of picture text --> Informality Rate<br>Egypt, 2009<br>Pp .<br>19 23 \<br>| aee F \<br>[_] t2.221685, 44.5059] Be<br>Own elaboration with LFS data<br><!-- End of picture text -->

<!-- Start of picture text --> Female Labor Force Participation<br>Egypt, 2009<br>Pe ad TS PRs =<br>me<br>ey | Ye]<br>Own elaboration with LFS data<br><!-- End of picture text -->

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equivalences may be overestimated. Using a national sample of the United States, Goodman and Ittner (1992) explore the accuracy of owner estimates of house values relative to the sales prices of the same properties. They find that the median homeowner in the mid-1980s overvalued the home by about 6 percent. Garner and Short (2001) find that housing costs based on self-reported rental equivalence resulted in higher estimates (almost 15 percent) than those based on a hedonic model. Garner, Short, and Kogan (2006) state that the median reported rental equivalence is higher in the U.S. Consumer Expenditure Survey than in estimates based on the same data using the hedonic model, the rent-to-value model, or the payment approach. The way the information on selfassessed rental values is elicited in surveys may also matter in explaining the inaccuracy of homeowners. Phrasing the question to homeowners as “How much would you receive as a rent if you were to lend your apartment?” as opposed to “How much would you pay if you were to rent the dwelling you are currently living in?” might be one way to mitigate self-assessment bias because of misreporting. In fact, the theoretical and experimental literature on the difference between willingness to accept and willingness to pay for the same good concludes that measures of the former are usually superior to measures of the latter (Fehr, Hakimov, and Kübler 2015; Hanemann 1991; Tunçel and Hammit 2014).

Even if homeowners were able to provide accurate estimates of the rental market values of their dwellings, a comparison of average values, either unconditional or conditional on a set of observable characteristics of dwellings, might lead to incorrect inferences. In countries in which rental markets are developed and thick, and information about market rental values is available, the characteristics of the dwellings inhabited by homeowners and by tenants might nonetheless differ considerably. Attention should therefore be paid to the selection among comparison groups to avoid comparing the incomparable. This matters both in the case of national accounts and country-level CPIs where obtaining correct
.g. ultra-processed foods, sugar-sweetened beverages), as well as combinations of food groups that are recommended either for increased or limited consumption. The strengths and limitations of these indicators for this work are summarized in Table 3.

The Global Diet Quality Project provides new nationally representative data on prevalence of consumption of food groups for 37 LMIC (and up to 54 countries by 2023) (Global Diet Quality Project, 2022), using self-reported consumption of foods from different groups based on non-quantitative 24-hour recalls (Herforth et al. 2020). These data can be used to construct several metrics at the population level. The **Global Dietary Recommendations (GDR) Score** has two components, NCD-Protect and NCD-Risk.

6