rafmacalaba/data-use-mentions-extended huggingface dataset:
|-0.001***|-0.001***|
||[0.000]|[0.000]|[0.000]|[0.000]|
||(0.267)|(0.837)|(0.015)|(0.008)|
|Beta*Crisis|-0.594*|-0.754**|-0.105|0.014|
||[0.350]|[0.358]|[0.506]|[0.658]|
||(0.333)|(0.084)|(0.800)|(0.585)|
|Country*Year FE|x|x|x|x|
|Nber. Obser.|4254|3779|887|741|
|R squared|0.23|0.23|0.31|0.32|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors clustered by bank reported in brackets. In parentheses we report the pvalue for the test of equality of effects during crisis and pre-crisis.

21
.78, 0.92, and 665 for the same measures. Considering the _R_<sup>2</sup> and SRC, our model performs equally well compared to the census based model, and better than all “top-down” estimates except Facebook and Worldpop 2015 (panel C).<sup>22</sup> In panel B, we present the same results based on a model using only publicly available satellite imagery. Interestingly, similar out-of-sample prediction accuracies are obtained using only public imagery (0.75, 0.92, and 663, and 0.77, 0.92, and 657 for the three measures using the two types of weights).

We multiplied these density predictions with village-level area to obtain population count predictions, which are reported in columns (4)-(6). The relative performance of the population count predictions, in comparison to the top-down estimates, is similar to those of the population density predictions. The _R_<sup>2</sup> and SRC are generally lower for population counts compared to density predictions, but the MAEs are also lower.

> for non-linear relationships. Further, studies predicting population counts and density have specifically shown mixed results on comparative performances between random forest and LASSO-based predictions (Anderson et al., 2014).

> 22 The out-of-sample _R_ 2, SRC, MAE, in that order, for the “top-down” measures are the following : WorldPop 2015 (0.99, 1.00, and 312) and Facebook’s HRSL (0.91, 0.97, and 417), GHSL (0.84, 0.88, and 639), GPW 2015 (0.68, 0.90, and 1029), GPW 2010 (0.29, 0.28, and 1550), WorldPop 2010 (0.31, 0.81, and 1206), and LandScan (0.04, 0.10, and 2562).

12
losses in each country by workers we designate as “fixed input” (non-production) employees and “variable input” (production) employees using the country-specific pooled (2013 and 2019) WBES data. We estimate a set of sector-specific polynomials relating the number of production to non-production workers for each aggregated sector (See Appendix 2 in the longer version of this paper for details) and then compute the ratio of losses by each group within the appropriate ranges for each sector.<sup>35</sup> Using these estimated polynomials, we find that the estimated ratio between production and non-production workers varies substantially over the range of employment levels and by industry. Utilizing these empirical relationships therefore in principle makes our prediction more accurate than one that implicitly assumes that losses occur in equal measure across tasks and levels of total employment. In practice, without this adjustment to occupational category, we find that we are less able to replicate the demographic distribution of realized job losses, especially by level of education and wage quintile.<sup>36</sup> After assigning the resulting mean percentage job losses to PFPS workers represented in the baseline labor force survey by sector and occupational level, we compute, using LMPS survey weights, the percentage of PFPS workers projected to lose their job. In addition to the total rate of job loss, we compute the approximate percentage of PFPS job losses by sector of employment (relative to baseline), by gender, age, educational attainment, wage level and nationality.<sup>37</sup> 5.2 Validity Checks Having thus estimated PFPS job losses, we next assess the predictive accuracy of the magnitude and allocation by demographic groups of our modeled estimates. To do so, we use two bases for comparison. One is wave 2 of the CFUWBES. The second is labor force survey data collected at around the same time as this wave. In the first instance, we predict job losses using the model estimated using CFUWBES1 (Section 4) using the data on the
, and Stulz, 2004).

However, the bonding hypothesis also implies that controlling shareholders who have a large proportion of voting rights compared to cash flow rights and have a greater ability to extract private benefits are less likely to list in the U.S. Doidge, Karolyi, Lins, Miller, and Stulz (2009) investigate this proposition using ownership and control data on more than 4000 firms from 51 countries. They put together an extensive dataset consisting of cash flow and control rights held by the controlling (largest) blockholder in each company as well as the control rights held by a firm‟s officers, directors, top-level managers, and their family members. They use 45
true for manufacturing – this time with the exception of Spain, where this share is low, and somewhat higher shares in Germany and Slovenia. The share of agriculture among shadow employees exceeds 5% only in Eastern European countries (except the Czech R. and Slovenia), as well as in Spain and Portugal. This finding does not change even if the currently non-employed persons who were engaged in informal work during the year before the survey are included. However, one must keep in mind that much of the seasonal agricultural work is performed by legal or illegal temporary immigrants which are unlikely to be covered by the surveys.

**_Establishment size._** Figure 13 compares, for each country, distribution of informal employees and their formal sector counterparts by establishment size. As expected, in all countries the share of small (under 10 workers) units is much larger in the informal sector (recall that we have not used the firm or establishment size when classifying employees as formal or informal). Moreover, in most countries at least half of informal employees work in units of less than 10 workers, and only in four countries (Denmark, Israel, Belgium and the UK) this share falls below 40%.

<!-- Start of picture text --> Formal and Informal Employees by Establishment Size<br>In European Countries, by Region. 2004-2009<br>PL EELVBGHUUARUROCZ SI SK FI NOSEDK ES IT PTGRCY IL CHFRDEAT IE NLBEUK<br>East North South West<br>PL EELVBGHUUARUROCZ SI SK FI NOSEDK ES IT PTGRCY IL CHFRDEAT IE NLBEUK<br>East North South West<br>1-9 workers 10-24 workers<br>25-99 workers<br>Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded<br>Source: Calculation with ESS data<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25
or more members is a household strategy to increase income through remittances. Let the parameter θ�,� be an unobserved measure of household welfare generated by individual _i_ ’s leisure time (i.e., non-market activities including production in the household). If the household’s decision for a member to migrate, and subsequently send remittances, depends on this function of household welfare, then R� is itself a function of θ�,� (Equation II).

Under this scenario, there are two econometric problems in determining the relationship between Y� and R�: omitted variable bias arising from the unobservable nature of θ�,� and a violation of the ignorability assumption since selection into the group of beneficiaries of remittances is non-random.

To address the impact of θ�,� on the estimates, access to either panel data, which can be used to remove individual and household fixed effects, or a strong instrumental variable are ideal. Yet no household panel data exist for the Northern Triangle with sufficient observations for this type of analysis. At the same time, a lack of geographical disaggregation in the regions’ household surveys limits the extent to which surveybased geographical and temporal variation can be exploited for instrumental variables. Other potential data sources that could be mined for instrumental variables – like municipal or departmental migration rates based on census or administrative data, are not complete or available for the three countries.

Given the lack of exogenous information that can be used as a strong instrumental variable in the three countries, an alternative approach is to maximize the similarity between the treated group (households who receive remittances) and the control group (households that did not receive remittances). This can be accomplished through the use of propensity score matching. This methodology has been widely used to look at the impact of remittances internationally, including Acosta (2011) for El Salvador; Berloti and Marchetta (2014) for Ecuador; Bouoiyour and Miftah (2015) for Morocco; Clement (2011) for Tajikistan; Cox-Edwards and
Table (G10) Impacts of the Program on Savings and Loans

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|At least 30 JD in savings|-0.12*|(0.07)|[0.34]|0.32|(0.47)|1,102|
|Value of loans taken in past year, USD PPP|-144.22|(1,980.97)|[1.00]|4,128.63|(12,492.84)|1,089|
|Value of loans given in past year, USD PPP|-113.90|(635.12)|[1.00]|613.62|(4,147.31)|1,084|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: At least 30 JD in savings is an indicator equal to 1 if the household has at least 30 JD in savings, in a bank or otherwise. Value of loans taken in the past year includes formal and informal loans taken out by the household. Value of loans given includes all formal and informal loans made by the household to other individuals.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults)
<mark>male nonparticipation. In that experiment, wives’ home production times do not change, but, interestingly, males increase their time in all labor markets. This reflects how household optimization processes interact with policies, making spouses reallocate the portfolio activities. In this experiment, as wives go more to the labor market, husbands increase home production time, and some decide to not participate in the labor market.</mark>

**Table 9: Observed and simulated mean accepted wages**

||Benchmark|Arrival|Laid-off|Wages|Pt|Home_P|Fertility|Search|
|---|---|---|---|---|---|---|---|---|
|pt_m|1.3|1.3|1.3|1.5|1.3|1.3|1.3|1.3|
|ft_m|0.7|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|pt_w|1.2|1.2|1.2|1.5|1.1|1.2|1.2|1.2|
|ft_w|0.6|0.6|0.6|0.7|0.6|0.6|0.6|0.6|

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time. This table presents the converted values in 2011 USD PPP dollars.

**Figure 6: Mean time in home production by labor market state (%)** Males without children Males with children

<!-- Start of picture text --> 60 60<br>50 50<br>40 40<br>
br>9.8|
|[9] Inactive|3353.1|3423.1<br>% change (ann|3329.4<br>ual)|2.1|-2.7|
|[7] vs [4]|0.5|-0.9|-1.2|||
|[8] vs [5]|-16.9|-21.2|-14.3|||
|[9] vs [6]|2.8|5.2|3.9|||

Source: Labor Force Survey (EL.STAT.) Notes: Seasonally unadjusted estimates for persons 15-74 years old.

In part, the falling unemployment rates reflect higher inactivity, as jobless workers were less likely to search for work. Table 1 confirms that more workers were inactive in February 2020 than one year earlier. The pandemic accentuated this trend: in March inactivity increased by 2.1%. However, labor force participation statistics need to be carefully interpreted while the lockdown is in place since some people who are counted as inactive might still have some attachment to the labor force. This is evident when we consider the “potential additional labor force” (PALF), which includes those “seeking work but who are not immediately available” and those “available for work and wanting to work but not currently seeking work” (Hornstein et al., 2014). According to LFS data, the size of the PALF increased by 40% in 2020Q1 compared to 2019Q1, and by 72% compared to 2019Q4.<sup>16</sup> This suggests that lockdown measures increased the underutilization of labor, with growing numbers awaiting recall, unable to look for jobs because of the lockdown, or discouraged by the lack of new job openings. As economic activity resumes, these marginally attached workers may be more likely to (re)join the labor force.

# **5
, P., & Hutchings, A. (2013). Australasian Consumer Fraud Taskforce: Results of the 2012 online consumer fraud survey (p. 13). Canberra, Australia: Australian Institute of Criminology.

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- Lichtenberg, P. A.,
, one may be concerned, as Acemoglu et al. (2005) were about their own study, that the presence of socialist countries in our sample may largely a¤ect the estimation results. Indeed, most socialist countries had high levels of education in the 1980s and did not experience any particularly increase in educational attainments during or immediately after the transition. In addition, prior to the transition, legal emigration was strongly restricted, while after the transition most socialist countries

> 20In unreported regressions, we also introduce as control variables, the mediam age of the population, and urbanization rate. While human capital loses its signi...cance, probably because of multicollinearity, the total emigration rate remains signi...cant when considering these additional control variables as exogenous. If they are considered as pre-determined, then the emigration rate also loses its signi...cance too, which may be due either to collinearity or instruments proliferation.

21To further assess the robustness of our results, in unreported regressions we considered the total emigration rate divided by a coverage measure in the Defoort (2008) dataset. Recall that the Defoort ...gures are based on the six major destination countries (USA, Canada, Australia, Germany, UK and France). Comparing the emigration stocks in 2000 in the Defoort data set with those in the Docquier and Marfouk (2006) data set (which is based to 30 OECD destination countries) yields a variable indicating the percentage of coverage of the Defoort data set. Dividing the total emigration rate by this coverage measure does not a¤ect the quality of the results.

14
percent— reduced agricultural water use would be necessary (FAO, 2009).<sup>19</sup> The simulations do not address the negative impact that this is likely to have on agricultural production.

Figure 4.1: Government (current and capital) spending under alternative scenarios (Average annual percentage share of GDP, 2004-2015) <!-- Start of picture text --> 25<br>2.7<br>20<br>2.5<br>15<br>2.1<br>13.9<br>12.7<br>10<br>7.0<br>5<br>0.6<br>1.7 5.2 5.6 5.8<br>2.4<br>0<br>base mdg-ftr mdg-tax mdg-db<br>primary education health water and sanitation<br><!-- End of picture text --> Source: Authors’ estimates based on results from simulations with MAMS.

In addition, targeting all MDGs together reveals the importance of cross-MDG complementarities in assessing the costs of reaching multiple MDGs. In fact, progress in the coverage of drinking water and sanitation (MDG 7) exerts a positive influence on health (MDG 4), and thus allows for savings in the production of health services; in turn, a healthier student population more easily achieves completion of educational cycles (MDG 2). For example, improvements in water and sanitation alone have a positive impact on the under-five mortality rate; under a foreign financing scenario (not shown), the MDG 4 in 2015 is 5.1 per 1000 lower than in the base scenario. Overall, the savings in total spending due to cross-MDG synergies is equivalent 2.4 percent of GDP.

> 19 See < _http://www.fao.org/nr/water/aquastat/countries/yemen/index.stm_ >.

30
well as the associated changes in the components and properties of the natural area, including, in this case, the quantity of biodiversity. A quantitative understanding of the structure and function of the ecosystem is required. Market imperfections have to be taken into account, as well as stock effects when changes in ecosystem services are sufficiently large. For insurance services provided by biodiversity, the production function approach requires the estimation of the damages to human wellbeing avoided by having biodiverse natural areas.

If the SNA boundary were extended and existence values were brought into the national balance sheet, many of the difficulties in measuring existence value using stated preference methods would come to the fore. And it is unclear whether the total existence value would fall if a single species became (locally) extinct – for example, people might value the existence of other species more as a result of the extinction. Existence values may simply not lend themselves to marginal valuation.

Conversely, if values of the benefits from the use of natural areas, based on surveys of willingness to pay, were brought into imputed measures of total wealth and national income, it 9
|130|93.6|
|1993|85|86.3|47|113.1|71.1|

_Source:_ CSO quarterly.

Also real manufacturing wages fell in the 1980s and 1990s. As earlier mentioned, the number of employees in manufacturing rose during the 1980s, whereas it was roughly

> 4 The 1980 Employment Act requires explicit permission from the Ministry of Labor's Retrenchment Committee before any wage or salary employee can be laid-off. Furthermore, contract workers are protected during the contract period. However, employees employed up to six weeks, the so-called casual workers, are totally excluded from the permission clause.

10
Selected Selected Regions<br>8.00% 50.3<br>800% 35.9%<br>5.00% 30.%<br>4.00% 25.30<br>3.00% 20.%<br>200%;; ee 15.%10.%10.%<br>1.00% ee eww ww ww 8 ow www en 59%<br>eeso8so88 2 ge 8 8 eats —————2s2s a g fm ~ an ms<br>8 8 8 & 8 & RRR RR RR RA 5s 56 6s 6865s 8 &B S288 85 8 42 8<br>aoofo&#ofo&# FR &A A A FR A A A A RR A A<br>ma ASEAN me CEMAC ee EAC ee ECOWAS mee VIERCOSUR me WAEMU WAEMU = == 3 regions regions meee Seo) meme CEM AC AC ee FAC ee ECOWAS «© eee WAEML<br>és. 4550 i : Oil exporters<br>2 40 I = Non-oil resource-intensive counties<br>Ez 35 I e @ Rest of Sub-Saharan Affica<br>3 8 30 I © ‘“ TM Comparator countries<br>CS 25 i . e © 1991-95 average t<br>ees 20 I q e é ee<br>S38 15 I 1 P J<br>‘GeS 40 1 aan | : ec<br>no eeeeewoo Qe2RgxessgegasoeoSS SESE SRE GS SESS° SBSaARSLSESCASSSESSSOSLSHYESSEVNLGRDSS ESSE ESERIES SSSRERESESZ ESBS?<br>ReEesges-sezxs&SSSS5 2E8< = Su SS 7TMS.65 FOS2ZLO2 HES CHEE<br>sc 33S gugz=%as Op SES 838 P§°S g 85s g°> Pses<br>DNs“© HK °o Ss& a=N 8 ==Es as = ero2.3 a2 baoS Presetas<br>5 Fs 2 = 388<br>ea32 8 8 SEs32<br>Sources: Eora database; and
the regulator (Sender) wants vessels (Receivers) to catch fewer juveniles, but the signal conveyed by closure announcements directs fishing to the places with the most juveniles. Instead, the regulator could announce where vessels catch high percentages of adults. Because fishers are paid by the ton, they are not fishing more near closures because they specifically want to catch juveniles. They fish more near closures because they want to reduce their search costs and increase the tons of anchoveta they catch per set. The regulator could use the electronic logbook data to calculate locations with high percentages of adult catch. Vessels might react to this information in the same way they 45
*Figure 16. Nominal exchange rate of zloty (monthly data)**

<!-- Start of picture text --> USD/zl EUR/zl CHF/zl<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Notes: monthly average exchange rate; January 2002 = 100; the growth means depreciation of the zloty. Source: NBP.

**Figure 18. Gross capital inflows (quarterly data)**

**Figure 17. Structure of loans to households**

<!-- Start of picture text --> Consumer loans Housing loans Other 25 billion USD % of GDP (right - hand scale) % 20<br>%<br>100<br>90 20<br>15<br>80<br>70 15<br>60 10<br>50 10<br>40 5<br>30 5<br>20<br>0<br>10 0<br>0 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 -5 -5<br>Source: NBP.<br><!-- End of picture text --> Notes: gross capital inflows are: total FDI in Poland, portfolio investments liabilities and other investment liabilities. Source: authors’ calculation.

32
of adopting digital business solutions, namely email to communicate with clients and suppliers and launching a business website, on firm-level revenue-based total factor productivity (TFPR) and demand for labor and capital. We rely on publicly available data from the World Bank’s Enterprise Survey database (WBES), which collects information on sales, factor and input usage, exporting status, managerial experience, and digital-technology adoption at the manufacturing

> 1. https://www.worldbank.org/en/topic/digitaldevelopment/overview.

2
only one elderly were lost due to the death of the person. Some households moved out of the village, and some households were not surveyed because they were working far away on their farms. 12 The community survey collects information about the village governance structure and the demographic, geographical, agro-ecological and socio-economic conditions in the village. The household survey provides information on household demographic characteristics, including educational status and health, agricultural production and sales, non-farm activities and income, consumption, shocks, assets, access to credit and perceptions on trust and empowerment.