rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 7: Panel Regressions – Robustness of main result to alternative indicators: Volume of all transactions for all crypto-assets**

- - <u>Dependent Variable: Log monthly on chain crypto assets volume (all transaction sizes), US dollars</u>

|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|---|---|---|---|---|---|
|**Change in broad money (yoy)**|0.779|0.028|-0.057|-0.026|-0.061|
|Change exchange rate (mom)|-1.865|-0.597|0.940***|0.972***|0.633|
|Change Bitcoin price (mom)||0.580***|0.406***|0.641***|0.342***|
|Change Ether price (mom)||0.574***|-0.021|0.049|0.054|
|US 10-year Yield (inflation adjusted)|||-0.242***||-0.353***|
|**US 10-year breakeven inflation rate**|||1.911***||2.337***|
|**US BBB Corporate bond spread**||||-0.533***|0.196***|
|Change gold price (mom)||||-1.305***|-0.754**|
|Pandemic dummy||1.494***|1.395***|2.417***
21 Future studies will be challenged to develop feasible and salient incentive strategies for reducing risky FSW behaviors, as well as in recruiting an even larger sample than the already large sample size of over 2,000 FSW that we enrolled. The FSW who remained in the study reported favorable views of the study—even those in the control group. Although these responses could reflect social desirability bias, their decision to remain in the study suggests that they did find value in the services offered them. How best to leverage this interest with other behavior change intervention designs remains a high priority research question for slowing HIV/STI transmission among this key node for disease spread.

# **REFERENCES**

1. Kassanjee R, Welte A, Otwombe K, Jaffer M, Milovanovic M, Hlongwane K, et al. HIV incidence estimation among female sex workers in South Africa: a multiple methods analysis of cross-sectional survey data. Lancet HIV. 2022 Nov 1;9(11):e781–90.

2. Abdella S, Demissie M, Worku A, Dheresa M, Berhane Y. HIV prevalence and associated factors among female sex workers in Ethiopia, east Africa: A cross-sectional study using a respondent-driven sampling technique. EClinicalMedicine. 2022 Sep;51:101540.

3. Shannon K, Strathdee SA, Goldenberg SM, Duff P, Mwangi P, Rusakova M, et al. Global epidemiology of HIV among female sex workers: influence of structural determinants. Lancet Lond Engl. 2015 Jan 3;385(9962):55–71.

4. Baral S, Beyrer C, Muessig K, Poteat T, Wirtz AL, Decker MR, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis. 2012;12(7):538–49.

5. HIV and sex work — Human rights fact sheet series 2021 [Internet]. [cited 2022 Dec 12]. Available from: https://www.
We also found evidence of lagged effects of past incomes on _SAW_ . (Ravallion and Lokshin, 2002, report regressions including two years lags, using an earlier round of the RLMS.) However, given that we lose many observations, and including lagged effects did not change our main results, we confine attention here to the cross-sectional regressions for 2002.

Confirming the results of Figure 2, and consistently with the findings of Cockerham (2000) using the 1995 RLMS, we find no sign of an income gradient in _SAH_ controlling for other socio-economic covariates. However, a strong income effect for _SAW_ (for both men and women) is evident in Table 4.

We find generally more agreement between _SAH_ and _SAW_ in the effects of the “nonincome” variables. Both subjective variables tend to fall with age (though over the range of the data for health, and only up to around 50 years for welfare).<sup>16</sup> Both variables tend to be higher for adults living in larger households, though other demographic effects are in less agreement. Education tends to raise _SAH_ (though more so for women than men), as it does for _SAW_ . The region with the highest _SAH_ (controlling for our other covariates) tends to have the highest _SAW_ , though there are discrepancies in ranking amongst other regions. The similarity in the nonincome covariates generates a strong positive correlation between the predicted values for _SAH_ and _SAW_ ; the correlation coefficient between the estimated conditional expectations is 0.64.

There are some notable gender differences in the socio-economic covariates of health and welfare. Education has a much stronger effect on female _SAH_ than for men, while demographic effects are stronger for men. For men, being in the workforce (either with a job or unemployed) has a strong positive effects on _SAH_ (relative to not being in the workforce) but this effect is not evident for women. This could reflect a reporting bias whereby male leisure is seen to be more

> 16 It might be
4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Livelihoods Coping strategy Index (inverted and standardized)<br>6 months 12 months 18 months<br>* ~ ~<br>‘ vy<br>\ Us<br>“U7 =<br>.<br>“4 Nn “0 if x /<br>\ / XN /<br>a — \ / \\ /<br>Sse SN \ / \ ><br>° s, ° \ / ° ~<br>YS<br>-<br>sy sé<br>n, “' aS N -—o a”‘ 4 enw’—7 SN<br>VD7 / \ ‘<br>\<br>\<br>0 2 4 6 8 1 0 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of children attending school<br>6 months 12 months 18 months<br>nN nN n<br>, 7<br>/ Fa ra 4 vo<br>= a U7 / = 77 L/ = / / y<br>/ / 7 /<br>7 / iar a / / _ a _/ /<br>o =-* / °o / ° —_ —<br>--7 / :<br>yO a /<br>/ / /<br>Fa ‘ U7 / Z /<br>ors v7 os ~ 5 / a<br>/<br>\<br>\re<br>0 2 4
# TABLE 5. Official Criteria for Enrollment

|Household|Normalized|
|---|---|
|characteristics|weight|
|Age of household head (years)|0.11|
|Household head without education|9.84|
|Household head with only primary education|<br>5.21|
|Female household head|-0.50|
|Access to health facilities|12.32|
|Children 0 to 11 years old|6.61|
|Children/working age adults|4.54|
|No vehicle (car or truck)|4.13|
|Unpaved floor|12.32|
|No refrigerator|13.14|
|No gas heating|19.73|
|No toilet connected to running water|5.70|
|No washing machine|3.28|
|Family size/rooms in the house|3.58|

_Source:_ Administrative sources. Regional dummies are excluded.

34
sup>16</sup> These results remain robust when differentiating migrants by region of origin and controlling for occupation, region of origin, and region-of-destination (NUTS2 level) fixed effects, or when excluding occupations in which there were no migrants in 2005 (see Appendix 2).

Network effects as drivers of occupational choices can, in turn, have an impact on the vulnerability of migrants to pandemic-related shocks. Past studies analyze the role of networks using different measures, but in general, either using the share of co-ethnic migrants in the same region of residence (as in Beaman, 2012), or the share of co-ethnic migrants in the region of residence working in the same occupation as the migrant (Patel and Vella, 2012). To assess the role of networks, we cannot use these measures in the multinomial logit from the previous section given that the dependent variable is based on occupations; thus, any explanatory factor that is also based on ISCO occupations would create perfect multicollinearity, while region-level measures would be captured by the fixed effects.

We proceed by using a measure of networks at the individual level. The EU-LFS ad hoc migration module of 2014 includes a question on the method the individual used to find a current job. Options include advertisements; public or private employment agencies; training institutions; direct contact with employers; and contacts through relatives, friends, or acquaintances. As equation (1) shows,

> 15 The confidence interval expands as the share of migrants increases (x-axis) since there are fewer number of such occupations.

> 16 We restrict the sample of migrants in the 2018 EU-LFS to those that had been in the country for at most 10 years, so the newer cohort of migrants do not overlap with the older one in 2005.

19
, & M. Torero (Eds.), Food price volatility and its implications for food security and policy. Cham, Switzerland: Springer. doi:10.1007/978-3-319-28201-5 Kisan, G. (2015). Review of global food price databases: Overlaps, gaps and opportunities to improve harmonization. Food Security Information Network. Retrieved 01 10, 2024, from <u>https://reliefweb.int/report/world/review-global-food-price-databases-overlaps-gaps-and-</u> - <u>opportunities improve</u> Lentz, E., & Maxwell, D. (2022). How do information problems constrain anticipating, mitigating, and responding to crises? International Journal of Disaster Risk Reduction, 81(103242). doi:10.1016/j.ijdrr.2022.103242 Lu, Y., Shen, M., Wang, H., & Wang, X. (2021). Machine Learning for Synthetic Data Generation:. Journal of Latex Class Files, 14(8). Retrieved from <u>https://arxiv.org/pdf/2302.04062.pdf</u> Manners, R., Adewopo, J., Niyibituronsa, M., Remans, R., Ghosh, A., Schut, M., et al. (2022). Leveraging Digital Tools and Crowdsourcing Approaches to Generate High-Frequency Data for Diet Quality Monitoring at Population Scale in Rwanda. Front. Sustain. Food Syst., 5. doi:10.3389/fsufs.2021.804821 Minet, J., Curnel, Y., Gobin, A., Goffart, J., Melard, F., Tychon, B., et al. (2017, November). Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. Computers and Electronics in Agriculture, 142(Part A), 126-138. doi:10.1016/j.compag.2017.08.026
# References:

Aaberge, Rolf, Anders Björkland, Markus Jäntti, Marten Palme, Peder Pedersen, Nina Smith, and Tom Wennemo (2002) “Income Inequality and Income Mobility in the Scandinavian Countries Compared to the United States”, Review of Income and Wealth 48(4): 443-469.

Antman, Francisca and David J. McKenzie (2005) “Poverty Traps and Nonlinear Income Dynamics with Measurement Error and Individual Heterogeneity”, Mimeo. Stanford University.

Arellano, Manuel (1989) “A Note on the Anderson-Hsiao Estimator for Panel Data”, Economics Letters 31: 337-41.

Arellano, M. and S. Bond (1991) “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations”, Review of Economic Studies 58: 277-97.

Atkinson, A.B., F. Bourguignon and C. Morrisson (1992) Empirical Studies of Earnings Mobility, Fundamentals of Pure and Applied Economics 52, Harwood Academic Publishers: Philadelphia.

Barro, Robert J. and Xavier Sala-i-Martin (1999) Economic Growth. The MIT Press: Cambridge, MA.

Baulch, Bob and John Hoddinott (2000) “Economic Mobility and Poverty Dynamics in Developing Countries”, Journal of Development Studies 36(6): 1- 24.

Collado, M. Dolores (1997) “Estimating dynamic models from time series of independent cross-sections”, Journal of Econometrics 82(1): 37-62.

Deaton, Angus (1985) “Panel data from time series of cross-sections”, Journal of Econometrics 30: 109-126. Fields, Gary S., Paul L. Cichello, Samuel Freije, Marta Menéndez and David Newhouse (2003) “For Richer or for Poorer? Evidence from Indonesia, South Africa, Spain and Venezuela”, Journal of Economic Inequality 1: 67-99.

Glewwe, Paul and Phong Nguyen (2002) “Economic Mobility in Vietnam in the 1990s”, World Bank Policy Research Working Paper No. 2838. Gottschalk, Peter (1997) “Inequality, Income
104|82|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

18 Estimates based on a poverty rate of $3.10, instead of $1.90, where the results are shown to be qualitatively similar.

22
food; and (iii) on income earned “by chance”, including lottery gains and disaster/relief payments.

**Summary Statistics.** Fertilizer and non-fertilizer products whose imports were banned accounted for 15%-19% of total imports in 2017-2019 (Appendix Table A4). This share declined to 12% in 2020 and 10% in 2021, suggesting the bans were at least partially effective. Summary statistics for some key variables of interest, shown in Appendix A.L, provide evidence of the ban’s effects: the average value of fertilizer imports per firm-year plummeted from 2 million USD to only 491 thousand USD from the pre-ban to the post-ban period, while the average value of agricultural exports per firm-year fell from 1.62 million USD to 1.53 million USD. Remotely sensed crop yields decrease substantially from the pre-ban (2021) to the post-ban (2022) period: average rice yields across divisional secretariats (DSs) fall from more than 4,000 kg/ha to less than 3,000 kg/ha, while average tea yields fall from 1,644 kg/ha to 1,404 kg/ha.

and water management from space and distinguishes it from other land use types using spectral (color) and temporal information included in the satellite signal. Because water and vegetation indexes are more visible for rice than for other crops, crop type mapping and yield are better predicted for rice than for other crops using remote sensing tools (Dong et al., 2016).

> 23These remote sensing estimates aggregated to the DS level are highly consistent with government-reported measures of rice area (from survey-based crop-cutting experiments) and rice yields (from production statistics).

> 24Since tea is a multiyear permanent crop, planted areas do not change annually. Thus, we fix tea-growing areas to the 2021 tea map.

> 25High fertilizer subsidy rates, in the 40-50% range, are also reported in the World Food Programme’s analyses of food security in Sri Lanka (FAO
Literacy (%)|Intensification<br>|28.9<br>|28.6<br>|22.1<br>|22.1<br>|25.4|
||Post Green Revolution<br>|33.9|32.5|25.1|22.2|28.7|
|Distance from|Green Revolution<br>Intensification|6.0<br>40|6.5<br>44|7.6<br>48|9.2<br>95|7.2<br>54|
|<br>paved road (krn)|<br>Post Green Revolution|.<br>2.1|.<br>2.5|.<br>2.2|.<br>3.8|.<br>2.5|

Green Revolution =1966-74; Intensification = 1975-84; Post Green Revolution, =1985-94.

_Source:_ Pakistan Agriculture Census reports data on these parameters for the period 1971, 1981, and 1991. The values for other ye extrapolated using a constant growth rate in between the two periods. The values reported here are the average for the study period.

8
|**Table 1: Descriptive statistics across surveys and samples**|**IHPS**|**IHS4**|**IHS4**|
|---|---|---|---|
||||**restricted**|
|**Panel A: Household level information for owners of agricultural land**||||
|Household size|5.010|4.457|4.409|
|Head’s age (years)|44.382|44.824|44.971|
|Head female†|0.264|0.311|0.342|
|Head reads & writes Chichewa†|0.685|0.672|0.661|
|<br>Head’s education (years)|5.560|5.298|5.230|
|Somebody reads & writes Chichewa†|0.911|0.874|0.868|
|<br>Highest educ.by any member (years)|7.881|7.332|7.244|
|Christian†|0.785|0.827|0.829|
|<br>Household asset and dwelling wealth index|0.247|0.184|0.183|
|Household agricultural asset access index|0.208|0.195|0.195|
|<br>Area of agricultural land (ha)|0.647|0.569|0.577|
|No. of parcels|1.537|1.399|1.420|
|Uses org. manure†|0.297|0.257|0.263|
|Cultivates cash crop†|0.097|0.073|0.075|
|Has formal document to agric. land†|0.023|0.016|0.016|
|
***|-0.295***|0|
|Not employed in the<br>agricultural sector|-0.01|-0.130***|0|-0.217***|-0.338***|0|
|Access to electricity|-0.008***|-0.091***|0|-0.314***|-0.397***|0|
|Piped drinking water|-0.024***|-0.064***|0.0021|-0.218***|-0.258***|0.0021|
|Improved sanitation|-0.006**|-0.064***|0|-0.210***|-0.267***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A6: Identifying the $3.20 poor based on covariates of poverty**

|||_Nigeria 201_<br>|_8/2019_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)*
sions, the survey separately asked the household head and the head’s spouse about their satisfaction. Each respondent could choose “very satisfied,” “fairly satisfied,” “not very satisfied,” or “not at all satisfied,” with each welfare dimensions. Out of the 13 dimensions reported, we focus on the five dimensions that correspond to the consumption and employment-based metrics that significantly declined following the capture of the citysatisfaction with food consumption, income, work, life, and health.<sup>14</sup> Additionally, there was another subjective question on welfare in a separate food security module that was very similar to the question on satisfaction with food consumption. Specifically, the most senior woman in the household was asked whether there was a time in the past week that the household “did not have enough food or money to have food.”<sup>15</sup> If the respondent answered affirmatively, then the respondent was asked if the household engaged in each of five common food coping strategies in the past week, and the number of days in which those coping strategies were employed.<sup>16</sup> Table 1 presents summary statistics of the share of households that reported each level of satisfaction separately for the head and spouse. The vast majority of respondents had low levels of satisfaction- this was especially true for satisfaction with food, life, and health. These satisfaction levels likely reflect the decline in living standards over the decade prior to the survey. Poverty significantly increased in the Republic of Yemen since 2005, in part driven by the political uncertainty of 2012 and the sharp increase in food prices beginning in 2011 (e.g., WFP 2012; Dang and Ianchovichina 2016; World Bank 2017a).

Somewhat surprisingly, subjective welfare was similar in the capital city to the rest of the Republic ofYemen despite the fact that the capital city had significantly lower poverty

> 14Although the focus is on the five dimensions described in the text, the appendix
provisions has been made on the basis of 279 RTAs included in the World Bank’s database.<sup>4</sup> Among them, 100 RTAs include some visa-related provisions.<sup>5</sup> On such a sub-sample of RTAs, a set of 30 binary-choice questions (i.e. categorical variables) have been used to code the specific type of visa-prevision included.<sup>6</sup> The ’Visa’ dummy used in our econometric analysis is therefore equal to one if a trade agreement contains at least one visa related provision, and zero otherwise. Based on the 30 binary-choice questions, we also test the effect of specific types of visa provisions (see next section).

> 4 Database available at: `https://datacatalog.worldbank.org/dataset/content-deep-trade-agreements` .

> 5Among the 279 RTAs covered by the DTA database, there might be some agreements, other than the 100 DTAs identified for visa-specific mapping, that contain visa-related provisions. However _"(a) those that contained irrelevant key term hits; (b) PTAs no longer in force (such as the US-Canada PTA, superseded by NAFTA); and, to ensure consistency with other chapters of this Handbook, (c) PTAs that did not appear in the World Bank’s Deep Integration database_ " are not included in the list of 100 RTAs coded for visa-provisions.

> 6Additional details of the coding methodology, as well as the questionnaire are available at: `https://pubdocs. worldbank.org/en/229401607632831170/Chapter-8-Visa-and-Asylum.pdf` .

3
# **Figure 23: Labor Market Indicators**

<!-- Start of picture text --> A. Employment and Wage Groups (%), 1991 to 2004<br><!-- End of picture text --> B. Educational Attainment of the Labor Force by Age <u>(1=None; 7=University)</u> <!-- Start of picture text --> Employment and Wage Groups (%)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>25 35 45 55 59<br>0 Years of Age<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>Monthly Wage Earners as % of Wage Earners Monthly Wage Earners as % of Workers Feb 1991 Feb 1996<br>Wage Earners as % of Workers Employment as % of 15 to 59 Year-Old Feb 2001 Feb 2004<br>Unemployment as % of 15 to 59 Year-Old<br>C. Educational Attainment of the Labor Force (%) D. Education Attainment of Wage Workers (%),<br>1991 to 2004<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>None Less than Primary Primary Lower Secondary None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University Upper Secondary Vocational University<br>E. Education Attainment of Monthly Wage Workers F. Educational Attainment of the Labor Force
0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ PPI Project Database. (http://ppi.worldbank.org), in current $ millions.

Adopting lower-cost technologies could substantially reduce the cost of meeting the posited infrastructure targets, and help make the funding gap manageable. Meeting the MDGs for water supply and sanitation with lower-cost technologies than previously used (such as stand posts, boreholes, and improved latrines), could reduce the associated price tag from $1,701 million to $889 million each year. Similarly, meeting transport connectivity standards using lower-cost road-surfacing technologies (such as single-surface treatment) together with a rural policy that narrows its focus exclusively to provide 100 percent accessibility currently active (as opposed to potential) agricultural land postponing, could reduce

> <sup>45</sup>
sup>th</sup> to23<sup>rd</sup>,2021|1,146|1,032|1,316|1,020|1,009|1,007|
|**Panel B: Glob**<br>|**al Monitoring Data base**|||||||
|Global<br>Monitoring<br>Database<br>**Panel C: Com**|**parative data from country**|Overall:<br>350,749<br>Internet:<br>272,598<br>Year: 2019<br>**labor force s**|Overall:<br>34,733<br>Internet:<br>11,043<br>Year:<br>2015<br>**urveys**|Overall:<br>804,698<br>Internet:<br>80,840<br>Year: 2018|Overall:<br>52,841<br>Internet:<br>12,355<br>Year: 2015|Overall:<br>61,768<br>Internet:<br>Not<br>availabl<br>e<br>Year:<br>2016|<br>Overall:<br>30,731<br>Internet:<br>20,256<br>Year: 2018|
|Sample size||193,000<br>HH/month||793,542<br>HHs and<br>203,464<br>individuals|11,997<br>individuals|25,750<br>HH|58,560 HH|
|Survey name<br>and<br>timeframe||PNAD:<br> Dec 2020;<br>May and,<br>June 2021||National<br
Szekely (2000:38) note in a cross- country study which includes Latin American countries that “a common feature across countries is that we do not find strong evidence of negative saving or even declining saving in the last part of the life cycle in any country”.

**Figure 1a. Fewer People  age 15-65, and More Age 65+ in the Future Brazilian Population**

**Figure 1b. Average Saving Rates Virtually Unchanged after Age 40 — Except if Public** **<u>Pensions are Excluded</u>**

<!-- Start of picture text --> 60%<br>23% 71%<br>40%<br>21% 70%<br>20%<br>69%<br>19%<br>68% 0%<br>17%<br>0 10 20 30 40 50 60 70 80 90<br>67% -20%<br>15%<br>66% -40%<br>13%<br>65% -60%<br>11%<br>64% -80%<br>9% 63% -100%<br>7% 62%<br>-120%<br>2010 2015 2020 2025 2030 2035 2040 2045 2050<br>-140% Saving rate, excl. public pensions<br>Share of retirees in the popualtion Saving rate, incl. public pensions<br>Share of workers in the population (right axis)<br><!-- End of picture text --> Source: UN Population Division. Source: Author’s estimations based on POF 2008.<sup>5</sup> There are several possible explanations for why people save in old age. First, elderly may desire to leave bequests for their working aged children or their grandchildren. The literature on this subject is well explored theoretically by,