rafmacalaba/data-use-mentions-extended huggingface dataset:
of the Russian Federation after all commitments are implemented is 8.6 percent. But the bound tariffs are only maximum tariffs, as Russia

> 2 See, for example, ATF Bank (2011).

> 3 The previous data available were the annual hard copy reports of the Russian Customs Committee. These reports aggregate information from the tariff line level, so that information is reported on about 1700 aggregated product codes out of about 11,000 tariff lines. Previously, World Bank staff manually entered these data annually in order to perform the calculations that were possible with those data. Earlier work based on these data includes the following. Tarr (1999; 2002) has assessed the average level of the Russian tariff and the degree of tariff escalation by stage of production using 1900 tariff lines.   Afontsev applied the Grossman-Helpman methodology to the Russian tariff structure in two papers. Afontsev (2002) assessed to extent to which industrial lobbying, consumer welfare or government revenue can explain the formation of Russian tariff policy in 1992-1997.  Afontsev (2004) assessed the motivation of the government for the tariff simplification policy of 2000-2001. See the chapters and discussions on Russia in Michalopoulos and Tarr (1994; 1996) for other early calculations.

> 4 The majority of exemptions were eliminated after July 1, 2011.

> 5 Further, the WTO Secretariat dropped specific tariffs when there was no match at the six digit level between the tariff classification and the trade data, which biases down the estimated tariff. Based on our three year base period (one year more recent than the WTO), we calculate this occurred in the case of 22 tariff lines in the years 2012-2020.

3
have relatively large RSEs. Therefore, the RSE is not a good accuracy statistic to use for target variables whose distributions show high levels of dispersion. Second, poverty measurement has traditionally focused on SEs to evaluate the precision of the poverty rates, which aligns more closely with the standard literature on poverty measurement and its practical applications.

# **3.6. Estimation of the sample size**

The HIES 2016/17 uses a stratified, two-stage clustered sampling design. In the first stage, PSUs are equally allocated across the 64 districts in Bangladesh: 36 PSUs are allocated to each of the 64 Bangladesh districts, bringing the total number of PSUs in the sample to 2,304 (36 PSUs*64 districts). An equal number of PSUs are allocated across districts because the HIES 13
# **Figure 1 Employment Rates by Age Cohort of Older Workers and Elderly in Urban and Rural China**

Panel A. Employment Rates by Age Cohort: CHNS and the CHARLS (2008) Pilot <!-- Start of picture text --> Urban Male Urban Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>Age Age<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br><!-- End of picture text --> Panel B. Employment Rates by Age Cohort: CHNS (1991 – 2009) <!-- Start of picture text --> Urban Male Urban Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>age age<br>1991 1997 2000 2009 1991 1997 2000 2009<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br
although they are not robust and only significant in the specification including village fixedeffects.

# _D. Mechanisms: Savings and Tontines_

_Tontines_ are an important vector for investment in rural Niger. As mentioned above, one of the particular features of the cash transfer pilot is that it encouraged beneficiaries to set-up _tontines_ . _Tontines_ represent a possible mechanism used by households to invest in productive activities. We therefore also analyze the impact of the project on participation in _tontines_ 18 months after the end of the program.

Project impacts on participation in _tontines_ is estimated by the SD approach because the relevant variables are only included in the follow-up survey (Table 6). Results show large and significant increases in the participation in _tontines_ , in the number of _tontines_ to which individuals contribute, and in the amount invested in and received from _tontines_ . The percentage of beneficiaries participating in tontines in 2013 is twice the percentage of non-beneficiaries (20.7% vs. 10.2%). The average effect of being a former beneficiary on the amount invested monthly in tontines is 385 FCFA (and the average effect on the amount received when one’s turn comes is 3,566 FCFA). Conditional on tontine participation, the average effect is greater: 1,271 FCFA (13,528 FCFA for amount received). Increased participation in _tontine_ translates into a more prevalent use of tontines to support consumption, but also into investments in productive activities and durable goods. As such, _tontines_ are one of the mechanisms that can explain the observed impacts of transfers on productive assets.

Overall, the results suggest that beneficiary households continue to participate in the _tontines_ that were set-up during the project, even 18 months after the project terminated. Still, both the share of beneficiaries participating in the tontines and the amounts invested in 2013 are below levels observed during the project (PPFS-CT, 2011).<sup>22</sup> This is consistent with what is expected after beneficiaries stop receiving regular transfers and encouragement to participate in savings groups.

>
countries that are in need; then helping to allocate finance toward the districts that face the highest risks. The empirical application uses 10 years of sub-annual assessments on district-level food insecurity outcomes in 21 developing countries together with monthly covariates that capture known drivers of food insecurity. The predictions are generated using a Random Forest and a novel approach is deployed to adjust probabilistic predictions to optimize the forecasting objectives of the paper. The forecasts are validated, up to a full year ahead, against holdout data and the accuracy is compared to historical non-model-based outlooks. The results indicate that, particularly at longer forecasting horizons, model-based predictions compare favorably. The paper concludes that statistical models demonstrate good ability to detect future crisis outbreaks for a set level of tolerance to false alarms and that using them may help increase lead time for action.

This research contributes to understanding the ability of statistical models to anticipate future food crisis in the particular context supporting early interventions. The paper builds on several earlier efforts.<sup>2</sup> Mellor (1986) discussed prevention strategies with an emphasis on economic weakness, crop failure, and subsequent price signals as leading indicators of famine. Price signals have been discussed further in particular by Seaman and Holt (1980), and their statistical significance has been investigated empirically in the context of the Ethiopian famine of 1972-1974 (Cutler, 1984) and the famine of 1984-1985 in Niger (Khan, 1994). Seaman (2000) explored a dynamic modeling approach to assess risks based on rapid household survey data and simulations of coping strategies under income and food supply shocks. More recently, researchers have begun to use machine learning techniques for prediction. For example, Mwebaze et al. (2010); Okori and Obua (2011) presented attempts at predicting household famine in Uganda between 2004 and 2005 using household data.

The importance of using systematic approaches to predicting food crises and allocating humanitarian resources effectively is likely to increase in coming years as key drivers of food insecurity are expected to worsen into the 21st century.
*|-7.7|1.8|-18.5*|-41.7***|11.3|207.5***|-4.9|-6.5**|-1.7|<br>6.7*|

<u>Notes:</u> (1) OLS regressions controlling for EA and day of the week Fes; the standard errors are clustered at the household-level. ***p<0.01, **p<0.05, *p<0.10. Grey shading = recall arm yields significantly lower estimates.

(2) Based on a pooled individual-level sample for days 5, 8 and 10 (each individual appeared three times in the sample).

(3) Descriptive statistics on conditional minutes are in Appendix Table A5.

# 4.2 Comparisons with time use data based on stylized questions with a 7-day recall

In this section, we compare average daily minutes based on stylized questions on time use with a 7-day recall (obtained by dividing the total reported weekly hours by 7) with the average daily minutes under the 24-hour recall and smartphone treatments (computed across days 5, 8 and 10). The comparisons are provided for the activity categories that were common across the three survey approaches. Table 7 shows that the reliance on stylized time use questions with a 7-day recall leads to higher average daily time across 17
42 and the last 12 months are used frequently, periods that are probably too long for items such as outpatient care and medicines. Multipurpose surveys are better in that spending data are gathered via a health module that varies recall period by type of service. Third, variations in comprehensiveness probably exist across surveys. A review of 100 survey questionnaires cited in Wagstaff et al. (2018a) found that, in 80% of surveys, questions were asked about spending on pharmaceutical products, hospital services, medical services, and paramedical services.

# _Measuring income_

Because, as mentioned above, total consumption increases after a health shock and pushes sick households up the consumption distribution while income does not, catastrophic (and impoverishing) payments computed with reference to income are easier to interpret, especially when looking at inequalities in catastrophic payments (Wagstaff et al. 2018a). It is customary to distinguish four main components in the measurement of income: (i) wage income from labor services; (ii) rental income from the supply of land, capital, or other assets; (iii) self-employment income; and (iv) current transfers from government or nongovernment agencies or other households. It is sometimes claimed (cf. e.g. Deaton and Zaidi 2002) that developing-country surveys (including the LSMS surveys) do well on (i) but less well on the other components of income. Recent initiatives such as the FAO RIGA project (Quiñones et al. 2009) are changing this. Sometimes income is not measured at all in developing-country surveys, so using it is not an option. The 2018 HEFPI database uses income rather than consumption for all high-income countries, and for certain upper middle-income countries. For most developing countries, we use consumption. Many of the highincome country datapoints come from ex-post data harmonization efforts like the LIS – see below.

# _Measuring consumption_

Surveys have differed a great deal in the level of detail of their consumption modules. The LSMS surveys, which have been designed and implemented with the explicit objective
The Basel II IRB framework suggests a minimum number of 9 loan classifications or buckets, leaving banks free to define the default probabilities assigned to each bucket. The IRB curve then defines the level of capital required given the default probability and other parameters. The IRB approach in fact simply validates some of what large, sophisticated G10 banks were already doing for their internal risk management purposes. The large banks have developed their own rating methodologies and have tested how their own ratings map into default probabilities and value at risk, both on an individual claim and on a portfolio basis. In fact many such banks are concerned that the approach does not give them sufficient freedom to use their own portfolio models of credit risk and must use the IRB formula to approximate the risk of a loan portfolio.<sup>17</sup> Supervisors across G10 have been catching up in their understanding of the internal risk management models and have maintained this formula approach to ease with monitoring and because as yet they are concerned precisely with the parameter estimation risk and model risk discussed above.

With respect to developing countries the starting point may be very different. First, banks are in general less advanced in terms of developing internal rating methodologies, mapping those ratings into default probabilities and developing portfolio models. Indeed in some countries the motivation on the part of supervisors to move toward Basel II’s IRB may be to get their banks to improve their own internal risk management, rather than catching up with what their banks are already doing. Second, supervisors have significantly less resources and in many countries supervisory skills and legal powers are sadly lacking. Indeed the statistics on compliance with the Basel Core Principals for

> 17 See the comments by several large banks on the proposals on www.bis.org

15
18.88|19.54|<br>21.02|<br>21.02|<br>100.00|
|Percent of total population in quintile|20.00|20.00|20.00|20.00|<br>20.00|<br>100.00|

# Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,278. Rural population is divided into quintile groups on the basis of total per capita fitted expenditures.

a In 1997, 1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. b Includes expenditures on fully and partially subsidized sugar.
SWFs tend to invest more in Africa. Using the Linaburg-Maduell transparency index for SWF transparency as control in column (2), we find our coefficient of interest, _α_ 2, remains positive and significant at the 1 percent level, while risk enters negatively and statistically significantly different from zero at the 1 percent level. In terms of networking variables, we find that the dummy variable political fund remains positive and significant at the 10 percent level, while the share of christians has a positive and significant effect on the investment share of SWFs in Africa at the 5 percent level. In column (3), we add the capitalization of SWFs in $ billion as a control. Our coefficient of interest, _α_ 2, remains positive and significant at the 5 percent level, while _α_ 3 remains negative but insignificant. This suggests that return rate increases SWF investments, while risk does not affect these investments. The coefficients associated with the dummy variable political fund, the share of christians and muslims, enter positively and statistically significantly different from zero at the 1 percent and 10 percent levels, respectively. These findings confirm our theoretical model and empirical strategy, and show that there are political and cultural biases for SWF investments in Africa. In column (4), we find an interesting result after controlling for the dummy variable called Islamic fund. The rate of return has a positive and significant effect on SWF investments, while risk exerts a negative and insignificant effect. We also find that SWF investments are politically biased since the coefficient of political fund remains positive and significant at the 1 percent level. Also, the coefficient associated with the share of christians enters positively and significantly different from zero at the 10 percent level. But the dummy variable Islamic fund enters negatively and insignificantly.

To take into account the level of financial development and bank efficiency of countries in Africa, we add private credit, overhead costs and cost to income ratio as controls in columns (5)-(8). The coefficients associated with the return rate and
(low) paying firms or due to firms paying higher (lower) wages to seemingly similar workers. Aghion et al. (2019) are the first to use matched employer-employee data for larger firms in the United Kingdom to investigate the innovation wage premium. The authors find a positive innovation premium that is larger for low-skilled workers, which in further theoretical and empirical analysis, the authors associate with increasing complementarity between workers in high-skilled occupations and those in some low-skilled occupations in innovative Overall, the literature documents several wage premiums associated with firm characteristics. The few existing studies looking at innovative firms suggest a positive wage premium associated with innovation. However, what is less clear from the evidence is what part of this premium is associated to sorting of workers into more innovative firms, what part is explained by innovative firms paying higher wages before turning innovative and what is the change in the wage premium once firms become innovative. These questions are explored empirically in the next sections.

6
ninis not searching for work)**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 29.2 58.7 58.2<br>27.4 54.1<br>21.0 21.8 23.8 19.7 20.5 50.644.5 51.946.2 46.3 50.9 49.3<br>16.6 17.5<br>15.4<br>7.6 5.8 6.2 8.7 7.9<br>0.4 0.5 1.3 1.2 1.6<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>30.0 60.0<br>20.0 40.0<br>10.0 20.0<br>& not searching for work not searching for work) & not searching for work not searching for work)<br>Ninis starting own household (% Ninis in relevant population Ninis starting own household (% Ninis in relevant population<br>0.0 0.0<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 22
seekers from Haiti, 38,232 migrants from Colombia, 9,063 asylum seekers from Cuba and 3,986 refugees from the Syrian Arab Republic.

Figure 1: Migration Trend in Brazil <!-- Start of picture text --> 2016 2017 2018 2019 2020<br>World Venezuela<br>Haiti Bolivia<br>Colombia Argentina<br>China<br>1,500<br>1,000<br>Total(’000)<br>500<br>0<br><!-- End of picture text --> Source: Author’s calculation from SISMIGRA and STIMAR.

Figure 2 shows the trend of Venezuelans migrants and asylum seekers in Brazil. Although the number of Venezuelans increased quickly from about 1100 in 2017 to 118,000 by the end of 2018 to about 265,000 in the first quarter of 2020, the stock started dwindling down after the onset of the COVID-19 pandemic. As of October 31, 2020, there were about a total of 261,441 Venezuelan refugees and migrants in Brazil. The figure shows that half of the Venezuelans, who entered Brazil, exited Brazil either to go back to the Rep ́ublica Bolivariana de Venezuela or to remigrate to other countries. When the pandemic hit, the movement across borders was restricted and more Venezuelans were leaving than coming in and as a result the total number started falling. The Federal Police database reveals that half of the Venezuelan migrants and asylum seekers entered Brazil with regular visa, while the other half registered as asylum seekers. As of October 2020, there were 145,462 Venezuelan migrants, 96,556 Venezuelan asylum seekers and 46,647 Venezuelans refugees, who had migrated to Brazil since July 2017. Most Venezuelan migrants and asylum seekers enter through the municipality of Pacaraima in Roraima, which is not surprising given its proximity to the Rep ́ublica Bolivariana de Venezuela at the North (FGVDAPP, 2020). Brazil went through a recession in 2015 and 2016, which
value on uncultivated,||||||||
|forested and non protected land (log mn USD)|7.2|7.7|5.9|8.1|***|8.8|**|
|Yield gap (Percent)|0.60|0.47|0.27|0.66|***|0.62|*|
|**Institutional quality**|||||***|||
|Regulatory Quality Rank (Percent)|49.7|62.5|80.1|38.9|***|40.4||
|Rule of Law Rank (Percent)|49.4|59.8|80.0|35.9|***|37.8||
|Control of Corruption Rank (Percent)|49.5|58.9|80.5|37.5|***|37.2||
|Political Stability & No Violence Rank (Percent)|49.2|49.5|67.5|34.2|***|35.4||
|Voice & Accountability Rank (Percent)|49.4|52.2|64.2|36.7|***|39.0||
|Weak Investor Protection|85.7|70.6|60|90.4|**|87.8||
|Land Tenure SecurityIndex|-0.02|0.61|2.15|-0.98|***|-0.95||
|No. of observations(countries)|215|56|23|84||43||

_Notes:_ The table shows unweighted averages of country characteristics. It includes 215 countries of which 107 are either investors or host countries in the period October 1
it mostly is in poor countries), then the wage adjustment required to equilibrate labor demands across sectors will mostly need to come from the manufacturing sector and will need to be relatively large to extract sufficient labor out of that sector. Hence, the model predicts that a standard economic structure (large agricultural and services sectors) will result in an elasticity of wages with respect to food prices that is large and positive in the long run, typically close to unity (Jacoby 2013; Ivanic and Martin 2014). Much less clear, theoretically and empirically, is the speed of wage adjustments to higher food prices, though econometric estimates suggest that short-run wage elasticities vary between 0.25 and 0.69 (Ravallion 1990; Palmer‐Jones 1993; Rashid 2002; Lasco et al. 2008).

In summary, the effects of higher food prices on the incomes or expenditures of the poor are complex, dynamic, and ambiguous _a priori_ . Undoubtedly, higher food prices create both winners and losers. But given that the bulk of the world’s poor are still predominantly rural (Ravallion et al. 2007), any sizeable agricultural supply and wage responses to higher food prices could well result in net poverty reduction over the medium term.

# III. DATA AND METHODS

This section describes in detail the data and methods used in the analysis with particular attention to some of the limitations involved.

# _Poverty Indicators_

In this paper, we first explore these medium-term impact of higher prices on changes in national poverty rates in a sample of around 300 observations from the World Bank’s (2014) POVCAL 4
**_<u>________________________________________________________</u>_**

measured in terms of the weight of goods shipped. Tramp services are defined as bulk and tanker services. Data is annual, and can be purchased from USDOT.

# _-_ _<u>International Monetary Fund (IMF) International Finance Statistics (IFS)</u>_

International Financial Statistics is a standard source of international statistics on all aspects of international and domestic finance. It reports, for most countries of the world, current data needed in the analysis of problems of international payments and of inflation and deflation, i.e., data on exchange rates, international liquidity, international banking, money and banking, interest rates, prices, production, international transactions, government accounts, and national accounts.

The IFS database contains time series data from 1948. The browser software provides an interface for browsing the database, selecting series of interest, displaying the selected series in a spreadsheet format, and saving the selected series for transfer to other software systems, such as Microsoft Excel.

The IFS database contains approximately 32,000 time series covering more than 200 countries and areas and includes all series appearing on the IFS Country Pages; exchange rate series for all Fund member countries, plus Aruba and the Netherlands Antilles; major Fund accounts series; and most other world, area, and country series from the IFS World Tables. The Country, World, and Commodity Prices Tables, as presented in the monthly printed copy of IFS, are available as selection options. The Economic Concept View provides a cross-country view of the concepts in the IFS by individual or groups of countries.

IFS includes the ratio of CIF to FOB, but no breakdown is available for individual commodities – the measure is an aggregate over all commodity types imported. The CIF/FOB comparison is a simple representation of transaction costs, as it covers total imports of each reporting country. The CIF (cost, insurance, freight) price measures the cost of the imported good